\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}\hline
 Elastic fully-functional & $\Delta= 0$ & $\Delta = 0.04$ & $\Delta = 0.08$ & $\Delta = 0.12$ & $\Delta = 0.16$ \\ \hline \hline
 $n=15$ & $0.21$ & $0.15$ & $0.04$ & $0.05$ & $0.05$ \\ \hline
 $n=30$ & $0.21$ & $0.12$ & $0.05$ & $0.04$ & $0.04$ \\ \hline
 $n=50$ & $0.19$ & $0.11$ & $0.04$ & $0.04$ & $0.01$ \\ \hline
 $n=75$ & $0.21$ & $0.04$ & $0.05$ & $0.04$ & $0.00$ \\ \hline\hline
 Cross-sectional fully-functional & $\Delta= 0$ & $\Delta = 0.04$ & $\Delta = 0.08$ & $\Delta = 0.12$ & $\Delta = 0.16$ \\ \hline \hline
 $n=15$ & $0.22$ & $0.15$ & $0.07$ & $0.07$ & $0.03$ \\ \hline
 $n=30$ & $0.22$ & $0.12$ & $0.06$ & $0.04$ & $0.02$ \\ \hline
 $n=50$ & $0.22$ & $0.11$ & $0.05$ & $0.04$ & $0.01$ \\ \hline
 $n=75$ & $0.22$ & $0.05$ & $0.05$ & $0.04$ & $0.01$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Sample-size-normalized RMSE of estimated change time for the phase change. (Top) Using the elastic fully-functional test statistic (Bottom) Using the cross-sectional fully-functional test statistic of .}
\end{table}
\end{document}

stat

"Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources" by J. Derek Tucker and Drew Yarger, arXiv:2211.12687v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
		\textbf{} &
		\multirow{2}{*}{Centralized planning } &
		\multicolumn{2}{c}{Decentralized planning with CA} \\ \cline{3-4}
		&
		&
		Independent planning &
		{CA with LNF} \\ \hline
		Comput. time (mean$\pm$ std)(ms)
		&345.8$\pm$ 87.2
		& 138.6$\pm$ 62.4 &
		9.97 $\pm$ 0.4
	\end{tabular}
\end{adjustbox}
\caption{Reach-avoid mission. Computation times (mean and standard deviation) comparison between centralized planning following~ and decentralized planning (independent planning with LNF) over 100 runs of the scenario.}
\end{table}
\end{document}

cs

Alëna Rodionova
Yash Vardhan Pant
Connor Kurtz
Kuk Jang
Houssam Abbas
Rahul Mangharam

"Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme" by Alëna Rodionova, Yash Vardhan Pant, Connor Kurtz, Kuk Jang, Houssam Abbas, and Rahul Mangharam, arXiv:2101.10404v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amssymb}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c}
		\toprule
		Backbone 				 										& OS 	&AA & C & mIOU (\%) \\
		\midrule[0.5pt]
		\midrule[0.5pt]
		ResNet-50
		& 16 	&\checkmark		 & 	& 49.73 \\
		~& 16 	 	&\checkmark		 & \checkmark 	& \textbf{50.23} \\
		\midrule
		Xception65
		& 16 	&\checkmark		 & 	& 52.42 \\
		~& 16 	 	&\checkmark		 & \checkmark 	& \textbf{52.65} \\
		\midrule
		EfficientNetB7
		& 16 	&\checkmark		 & 	& 57.24 \\
		~& 16 	 	&\checkmark		 & \checkmark 	& \textbf{57.93} \\
		& 8 	 	&\checkmark		 & \checkmark 	& \textbf{58.40} \\

		\bottomrule[0.5pt]

	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ye Huang
Di Kang
Wenjing Jia
Xiangjian He
Liu Liu

"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Electrical absolute calibrated devices}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
		\hline
		 \textbf{Device name} & \textbf{abbreviation} & \textbf{reference} \\
		\hline
 Simple PCIe FMC carrier & SPEC & ~ \\
 Simple PCIe FMC carrier 7 & SPEC7 & ~ \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

P. P. M. Jansweijer
N. A. D. Boukadida
K. Hanhijärvi
A. Wallin
B. Eglin
E. Laier English

"First electrical White Rabbit absolute calibration inter-comparison" by P. P. M. Jansweijer, N. A. D. Boukadida, K. Hanhijärvi, A. Wallin, B. Eglin, and E. Laier English, arXiv:2201.08640v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
 \toprule
 \textbf{Release Cycle} & \textbf{4.2.0} & \textbf{4.4.0} \\
 \midrule
 Total Tickets & 273 & 393 \\
 Resolved Tickets & 252 & 346 \\
 Percent Resolved & 92.3\% & 88.0\% \\
 Resolved by Release & 205 & 330 \\
 Percent Resolved by Release & 75.1\% & 84.0\% \\
 Release Duration & 412 & 352 \\
 Tickets per Day & 0.66 & 1.12 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Statistics on performance related JIRA tickets over the previous two release cycles.}
\end{table}
\end{document}

cs

"Creating a Virtuous Cycle in Performance Testing at MongoDB" by David Daly, arXiv:2101.10231v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{A quantitative comparison of model performance in terms of PSNR and SRE metrics when different SR backbone models are integrated into the WaveDiffUS SDE solver for the UR tasks (scaling from $\times 4$ to $\times 128$) using the independent datasets DOTA, DIOR, and DDISR. Outputs marked with an asterisk (*) indicate evaluations are performed with pseudo ground truth images, where the UR image resolution exceeds the ground truth resolution.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\toprule
 & \multicolumn{6}{c}{DOTA} \\\midrule
 \multirow{2}{*}{Method} & \multicolumn{6}{c}{Scale} \\
 & $\times 4$ & $\times 8$ &$\times 16$ &$\times 32$* &$\times 64$* & $\times 128$* \\\midrule
 LE-GAN & 19.15/11.41 & 17.11/12.88 & 15.91/13.32 & 11.53/15.42 & 9.21/18.89 & 7.91/23.33 \\
 LE-GAN-WaveDiffUR & 19.74/11.39 & 19.18/11.12 & 18.21/12.86 & 16.74/13.40 & 12.58/13.63 & 11.21/15.87 \\
 RefVAE & 22.14/14.22 & 22.01/15.98 & 20.34/16.23 & 16.34/18.23 & 12.01/23.99 & 10.34/30.24 \\
 RefVAE-WaveDiffUR & 22.38/13.13 & 22.82/14.25 & 21.38/14.14 & 19.38/15.14 & 18.32/16.26 & 17.38/19.15 \\
 EDiffSR & 20.15/16.31 & 18.92/17.31 & 15.15/18.32 & 10.15/22.32 & 8.92/27.32 & 7.15/38.33 \\
 EDiffSR-WaveDiffUR & 20.02/16.51 & 19.29/17.02 & 18.02/17.52 & 17.25/18.21 & 15.99/18.35 & 12.02/20.53 \\
 CSP-WaveDiffUR(one-step) & 28.21/7.82 & 28.18/7.88 & 26.21/9.83 & 21.21/12.83 & 19.18/17.89 & 18.21/19.84 \\
 CSP-WaveDiffUR & 28.32/7.22 & 28.31/7.37 & 27.32/8.03 & 25.52/9.23 & 24.31/11.38 & 24.28/13.24 \\\midrule
 & \multicolumn{6}{c}{DIOR} \\\midrule
 \multirow{2}{*}{Method} & \multicolumn{6}{c}{Scale} \\
 &$\times 4$ & $\times 8$ &$\times 16$* &$\times 32$* &$\times 64$* & $\times 128$* \\\midrule
 LE-GAN & 19.41/12.69 & 19.19/13.18 & 18.31/14.82 & 14.35/17.34 & 10.21/19.99 & 6.92/27.53 \\
 LE-GAN-WaveDiffUR & 19.98/12.46 & 19.33/13.08 & 18.42/14.66 & 17.21/16.89 & 15.99/17.72 & 14.25/20.55 \\
 RefVAE & 18.35/15.92 & 17.32/16.18 & 15.52/16.74 & 12.21/19.98 & 8.81/25.93 & 5.89/28.83 \\
 RefVAE-WaveDiffUR & 19.01/15.28 & 18.99/15.82 & 17.59/16.11 & 14.49/17.52 & 13.21/19.14 & 10.15/21.71 \\
 EDiffSR & 18.78/13.51 & 17.98/14.23 & 14.88/15.66 & 12.62/20.22 & 9.21/24.41 & 5.32/26.93 \\
 EDiffSR-WaveDiffUR & 18.11/12.82 & 17.23/13.31 & 16.32/14.83 & 15.12/16.11 & 13.11/18.21 & 10.31/19.68 \\
 CSP-WaveDiffUR(one-step) & 24.63/12.71 & 23.21/13.81 & 21.92/15.32 & 18.84/19.32 & 14.22/22.12 & 10.36/24.96 \\
 CSP-WaveDiffUR & 25.12/8.99 & 24.92/9.87 & 22.31/9.72 & 21.03/10.21 & 18.79/12.42 & 16.32/14.85 \\\midrule
 & \multicolumn{6}{c}{DDISR} \\\midrule
 \multirow{2}{*}{Method} & \multicolumn{6}{c}{Scale} \\
 & $\times 4$ & $\times 8$ &$\times 16$ &$\times 32$ &$\times 64$ & $\times 128$ \\\midrule
LE-GAN & 24.14/16.89 & 21.91/18.87 & 19.19/21.82 & 16.59/24.34 & 12.21/28.99 & 8.21/32.31 \\
LE-GAN-WaveDiffUR & 24.38/15.08 & 23.21/16.16 & 23.09/16.98 & 19.99/19.72 & 16.55/22.55 & 14.32/26.37 \\
RefVAE & 28.12/18.18 & 25.52/19.74 & 22.11/21.81 & 18.81/25.93 & 12.89/28.23 & 9.82/36.20 \\
RefVAE-WaveDiffUR & 28.29/17.21 & 27.59/18.91 & 26.39/19.12 & 22.99/20.41 & 19.15/23.71 & 16.15/27.71 \\
EDiffSR & 28.98/23.13 & 27.88/26.66 & 24.92/29.12 & 19.21/34.19 & 13.28/39.93 & 12.08/38.82 \\
EDiffSR-WaveDiffUR & 29.23/21.11 & 28.32/22.83 & 27.17/23.11 & 25.91/24.29 & 21.18/25.68 & 18.11/28.85 \\
CSP-WaveDiffUR(one-step) & 31.21/12.19 & 30.92/13.39 & 28.84/16.32 & 24.22/18.12 & 20.36/21.62 & 16.13/28.21 \\
CSP-WaveDiffUR & 34.92/8.27 & 34.19/8.75 & 32.33/9.26 & 30.79/11.95 & 28.32/14.85 & 23.48/16.85 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yue Shi
Liangxiu Han
Darren Dancy
Lianghao Han

"WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images" by Yue Shi, Liangxiu Han, Darren Dancy, and Lianghao Han, arXiv:2412.18996v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
\hline
Method & Recovery\% \\ \hline
DenseCPD & 50.7 \\ \hline
ProDCoNN & {\color[HTML]{000000} 40.7} \\ \hline
SBROF & 39.2 \\ \hline
SPIN2 & 33.6 \\ \hline
Wang's model & 33.0 \\ \hline
ProteinSolver & 30.8 \\ \hline
SPIN & 30.3 \\ \hline
Rosetta & 30.3 \\ \hline
GVP-GNN & 44.9 \\ \hline
DWNN & \textbf{51.5} \\ \hline
\end{tabular}
\end{adjustbox}
\caption{We test some variants of attack location search methods on AG-news dataset.POS means only using pos\_tags to find the locations to be attacked and PR is the search method we use.Result shows that PR is more efficient than others.}
\end{table}
\end{document}

cs

Jiahan Li
Shitong Luo
Congyue Deng
Chaoran Cheng
Jiaqi Guan
Leonidas Guibas
Jian Peng
Jianzhu Ma

"Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning" by Jiahan Li, Shitong Luo, Congyue Deng, Chaoran Cheng, Jiaqi Guan, Leonidas Guibas, Jian Peng, and Jianzhu Ma, arXiv:2201.13299v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Julia Herbinger
Susanne Dandl
Fiona K. Ewald
Sofia Loibl
Giuseppe Casalicchio

"Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation" by Julia Herbinger, Susanne Dandl, Fiona K. Ewald, Sofia Loibl, and Giuseppe Casalicchio, arXiv:2310.03112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Baseline comparison of the MMSE score regression. Our test results are corresponding to the regression ensemble.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccc}
 \toprule
 & Model & RMSE\\
 \hline
 LOSO & Luz et al. & 4.38\\
 & Ensemble (\textit{ours}) & \textbf{0.82}\\
 \hline
 TEST & Luz et al. & 5.20\\
 & Ensemble (\textit{ours}) & \textbf{4.60}\\

 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Utkarsh Sarawgi
Wazeer Zulfikar
Nouran Soliman
Pattie Maes

"Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity" by Utkarsh Sarawgi, Wazeer Zulfikar, Nouran Soliman, and Pattie Maes, arXiv:2009.00700v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
 & Criminal & Non-Criminal \\
\hline
Train dataset & 8336 & 8336 \\
\hline
Test dataset & 2000 & 2000 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Train-test data information.}
\end{table}
\end{document}

cs

Rahul Goel
Modar Sulaiman
Kimia Noorbakhsh
Mahdi Sharifi
Rajesh Sharma
Pooyan Jamshidi
Kallol Roy

"Pre-Trained Language Transformers are Universal Image Classifiers" by Rahul Goel, Modar Sulaiman, Kimia Noorbakhsh, Mahdi Sharifi, Rajesh Sharma, Pooyan Jamshidi, and Kallol Roy, arXiv:2201.10182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small Certified robust accuracy of TPC on CurveNet}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcc}
 \toprule
 \multirow{2}{*}[-2pt]{Transformation} & \multirow{2}{*}[-2pt]{Attack radius} & \multicolumn{2}{c}{Certified Accuracy ($\%$)} \\ \cline{3-4}\\
 & & PointNet& CurveNet \\
 \midrule
 Z-rotation & 180$^\circ$ & 81.3& \textbf{85.4}\\
 Z-shear & 0.2 & 77.7 & \textbf{87.8}\\
 Z-twist & 180$^\circ$ & 64.3 & \textbf{86.2}\\
 Z-taper & 0.2 & 76.5 & \textbf{88.6}\\
 Linear & 0.2 & 59.9 & \textbf{77.7} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"TPC: Transformation-Specific Smoothing for Point Cloud Models" by Wenda Chu, Linyi Li, and Bo Li, arXiv:2201.12733v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{the values of $x_{\pi,i}^*$ and $x_{\mu,i}^*, i=1,2$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
 \hline
 $\!\!\!$& $\!\!\!\!\!\beta_{i,1}\geq \overline{\pi}\!\!$& $\!\!1\leq \beta_{i,1}\leq \overline{\pi}\!\!$ &$\!\!\beta_{i,1}\leq1\leq\beta_{i,2}\!\!$ & $\!\!0\leq\beta_{i,2}\leq1\!\!$
 & $\!\!\beta_{i,2}\leq 0\leq \beta_{i,3}\!\!$& $\!\!\underline{\pi}\leq\beta_{i,3}\leq 0\!\!$& $\!\!\beta_{i,3}\leq \underline{\pi} $\\
 \hline
 $\!\!\!x_{\pi,i}^*$ &$\;\;\overline{\pi}$ &$\beta_{i,1}$ & $1$ & $\beta_{i,2}$ & $0$ & $\beta_{i,3}$& $\underline{\pi}$\\
 \hline
 $\!\!\!x_{\mu,i}^*$ &$\;\;\underline{\mu}_i$ &$\underline{\mu}_i$ & $\underline{\mu}_i$ & $\underline{\mu}_i$ & $r$ & $\overline{\mu}_i$& $\overline{\mu}_i$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Pricing Model for Data Assets in Investment-Consumption Framework with Ambiguity" by Xiaoshan Chen, Chen Yang, and Zhou Yang, arXiv:2505.16106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|}
\hline
 & \textbf{Recall} & \textbf{Precision} \\ \hline
SCL & 0.37 & 0.96\\ \hline
App. No. & 0.73 & 0.88 \\ \hline
Our Method & 0.89 & 0.86 \\ \hline
\end{tabular}
\caption{Results of Manual Quality Assessment of sampled 120 cases against various approaches, for citation extraction and mapping.}
\end{table}
\end{document}

cs

T. Y. S. S Santosh
Rashid Gustav Haddad
Matthias Grabmair

"ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights" by T. Y. S. S Santosh, Rashid Gustav Haddad, and Matthias Grabmair, arXiv:2404.00596v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Paragraph mAP@IoU[.50:.95] score and F1$_{var}$ score comparisons. All models are tested on the PubLayNet development set. Numbers for mAP in the first 2 rows are from~.}
\begin{tabular}{lccc}
\noalign{\smallskip}\noalign{\smallskip}
\hline \hline\noalign{\smallskip}
Model & Training Set & mAP & F1$_{var}$ \\
\noalign{\smallskip}\hline\noalign{\smallskip}
F-RCNN~ & PubLayNet training & 0.910 & - \\
M-RCNN~ & PubLayNet training & 0.916 & - \\
F-RCNN-Q & PubLayNet training & 0.914 & 0.945 \\
Tesseract~ & - & 0.571 & 0.707 \\
OCR + Heuristic & - & 0.302 & 0.364 \\
OCR + GCNs & Augmented web synthetic & 0.748 & 0.867 \\
OCR + GCNs & PubLayNet training & 0.842 & 0.959 \\
OCR + Ground Truth \hspace{-2mm} & - & 0.892 & 0.997 \\
\noalign{\smallskip}\hline
\end{tabular}
\end{table}
\end{document}

cs

Renshen Wang
Yasuhisa Fujii
Ashok C. Popat

"Post-OCR Paragraph Recognition by Graph Convolutional Networks" by Renshen Wang, Yasuhisa Fujii, and Ashok C. Popat, arXiv:2101.12741v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Analysis of model performance against noise. Here, we evaluate the perceptual performance and downstream task performance on the Cityscapes dataset with channel PER = 10\%. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|c|c|}
\hline
\textbf{Steps} & \textbf{FID}$\downarrow$ & LPIPS$\downarrow$ & Depth$\downarrow$ \\
\hline
28/32& 30.55 & 0.1560 & 5.99 \\
\hline
50/62& 30.05 & 0.1524&6.08 \\
\hline
94/100& 28.42& 0.1470& 5.97\\
\hline
194/200& \textbf{27.23} & \textbf{0.1438} & \textbf{5.88}\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Achintha Wijesinghe
Suchinthaka Wanninayaka
Weiwei Wang
Yu-Chieh Chao
Songyang Zhang
Zhi Ding

"LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency" by Achintha Wijesinghe, Suchinthaka Wanninayaka, Weiwei Wang, Yu-Chieh Chao, Songyang Zhang, and Zhi Ding, arXiv:2412.17839v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \hline
	Current paper		& 	& 	\\
 \hline
	$\alpha t$			& $r\mu$			& $\alpha$ 			\\
	$\alpha s$			& $r\mu(1 - s_{\rm HJR})$		& $\alpha(1 - r)$ 		\\
	$\dfrac{\beta(s)}{\beta(t)} = \dfrac{e^{\alpha s} - 1}{e^{\alpha t} - 1}$ & $\dfrac{e^{r\mu(1 - s_{\rm HJR})} - 1}{e^{r\mu} - 1}$
							& $q_r = \dfrac{e^{-\alpha r} - e^{-\alpha}}{1 - e^{-\alpha}}$	\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"The Feller diffusion conditioned on a single ancestral founder" by Conrad J. Burden and Robert C. Griffiths, arXiv:2504.19428v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|r|r|r|r||r|r|}
\hline
Model & & Mean & Volatility & Max Draw & Sharpe & Calmar \\ \hline
\multirow{2}{*}{
LightGBM-dart without FE} & Mean & 0.0254 & 0.0266 & 0.1567 & 0.9593 & 0.1639 \\ \cline{2-7}
 & S.D. & 0.0006 & 0.0007 & 0.0158 & 0.0365 & 0.0175 \\ \hline
\multirow{2}{*}{LightGBM-gbdt without FE} & Mean & 0.0253 & 0.0312 & 0.2338 & 0.8104 & 0.1100 \\ \cline{2-7}
& S.D. & 0.0006 & 0.0006 & 0.0296 & 0.0278 & 0.0153 \\ \hline
\multirow{2}{*}{
MLP without FE} & Mean & 0.0233 & 0.0271 & 0.1643 & 0.8600 & 0.1446 \\ \cline{2-7}
& S.D. & 0.0009 & 0.0011 & 0.0248 & 0.0365 & 0.0219 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Variability of the performance of ML models in the test period (2015-05-15 to 2022-09-23). The mean and standard deviation of each portfolio metrics are calculated over models with 10 different random seeds for each method }
\end{table}
\end{document}

q-fin

"Online learning techniques for prediction of temporal tabular datasets with regime changes" by Thomas Wong and Mauricio Barahona, arXiv:2301.00790v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Robust iterative methods for linear systems with saddle point structure" by Murat Manguoğlu and Volker Mehrmann, arXiv:2502.21174v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccccc|}
\cline{2-9}
 & \multicolumn{8}{c|}{Satellite image (2) data set} \\ \cline{2-9}
 & \multicolumn{2}{c|}{PCA} & \multicolumn{2}{c|}{robPCA} & \multicolumn{2}{c|}{ICA} & \multicolumn{2}{c|}{ACA} \\ \cline{2-9}
 & PC1 & PC2 & robPC1 & robPC2 & IC1 & IC2 & AC1 & AC2 \\ \hline
Var1 & 19 (3\%) & 17 (5\%) & 24 (7\%) & 25 (6\%) & 19 (4\%) & 13 (4\%) & 18 (8\%) & 12 (9\%) \\
Var2 & 15 (3\%) & 13 (5\%) & 35 (7\%) & 13 (6\%) & 23 (4\%) & 17 (4\%) & 2 (8\%) & 10 (8\%) \\
Var3 & 18 (3\%) & 29 (5\%) & 15 (6\%) & 18 (6\%) & 31 (4\%) & 5 (4\%) & 26 (6\%) & 8 (7\%) \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Contributions of three most important (for each component) variables for PCA, robPCA, ICA, and ACA for Satellite image (2) data set.}
\end{table}
\end{document}

stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

"Abnormal component analysis" by Romain Valla, Pavlo Mozharovskyi, and Florence d'Alché-Buc, arXiv:2312.16139v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrrr}
\toprule
\multirow{2}{*}{\textbf{Classifier}} & \multicolumn{4}{r}{\textbf{By-Class Balanced Accuracy}} & \multirow{2}{*}{\textbf{Average}} \\
 & \textit{PV} & \textit{DB} & \textit{FE} & \textit{ITE} & \\ \midrule
Decision tree & 0.8373 & 0.7827 & 0.6369 & 0.6357 & 0.7231 \\
Random forest & 0.8506 & 0.7976 & 0.7151 & 0.7193 & 0.7707 \\
Gradient boosted trees & 0.8534 & 0.7993 & 0.7270 & 0.6991 & 0.7697 \\
Linear discriminant analysis & 0.7005 & 0.6777 & 0.5244 & 0.5746 & 0.6193 \\
Multinomial logistic regression & 0.7054 & 0.6817 & 0.4744 & 0.4864 & 0.5870 \\
Multi-layer perceptron & 0.7515 & 0.7138 & 0.8571 & 0.4864 & 0.7022 \\
Stack (Trees) & 0.8592 & 0.8021 & 0.7823 & \textbf{0.7799} & \textbf{0.8059} \\
Stack (Linear classifiers excluded) & \textbf{0.8622} & 0.8030 & \textbf{0.7865} & 0.7698 & 0.8054 \\
Stack (All) & 0.8616 & \textbf{0.8032} & 0.7760 & 0.7789 & 0.8049 \\ \bottomrule
\end{tabular}
\caption{Comparison of classification models.}
\end{table}
\end{document}

q-fin

Incident-Specific Cyber Insurance

Wing Fung Chong
Daniel Linders
Zhiyu Quan
Linfeng Zhang

"Incident-Specific Cyber Insurance" by Wing Fung Chong, Daniel Linders, Zhiyu Quan, and Linfeng Zhang, arXiv:2308.00921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Spectral properties of the Bloch-Torrey operator in three dimensions" by Denis S. Grebenkov, arXiv:2312.04200v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The effect of different weights in the feature extractor CNN. Measures are averaged across 7 runs of the game for each model on the CIFAR-10 validation set. Communication rate values in brackets are standard deviations across games, which show the sensitivity to different model initialisations and training runs. The message length standard deviation is measured across each game and averaged across the 7 runs, and show how much variance there is in transmitted message length.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
 \toprule
 Feature extractor & Comm. & Message & Top-5 & \#Target & Target \\
 & rate & length & comm. & class & class \\
 & & & rate & in top-5 & avg. rank \\
 \midrule
 Pretrained \& fixed &0.90 ($\pm$0.02) & 4.93 ($\pm$0.34) & 1 & 1.86 & 46.25\\

 Random \& frozen & 0.93 ($\pm$0.03) &4.90 ($\pm$0.39) & 1 & 1.69 &51.65\\

 Learned end-end & 0.94 ($\pm$0.02) &4.90 ($\pm$0.39) & 1 & 1.5 &57.14\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"The emergence of visual semantics through communication games" by Daniela Mihai and Jonathon Hare, arXiv:2101.10253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 & Hyperparameter & Value \\
 \midrule
 DQN HP & Optimizer & Adam~ \\
 & Critic architecture & MLP \\
 & Critic learning rate & 1{e}-3 \\
 & Critic hidden layers & 1 \\
 & Critic hidden dim & 24 \\
 & Critic activation function &
 ReLU \\
 & Mini-batch size & 1024 \\
 & Number of gradient steps & 50K \\
 & Discount factor & 0.99 \\
 & Target update rate & 1 \\
 & Target update period & 8 \\
 & Loss Function & Huber Bellman Error \\
 \end{tabular}
\end{adjustbox}
\caption{Hyperparameters for the DQN algorithm used in Section~.}
\end{table}
\end{document}

stat

Lauren H. Cooke
Harvey Klyne
Edwin Zhang
Cassidy Laidlaw
Milind Tambe
Finale Doshi-Velez

"Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping" by Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, and Finale Doshi-Velez, arXiv:2312.09983v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\toprule
\bf Tag & \bf Description & \bf Tag & \bf Description \\
\hline
ADV & adverb & ADJ & adjective \\
CONJ & conjunction & DET & determiner \\
NOUN & noun & NSUFF & noun suffix \\
NUM & number & PART & particle \\
PUNC & punctuation & PRON & pronoun \\
PREP & preposition & V & verb \\
ABBREV & abbreviation & VSUFF & verb suffix \\
FOREIGN & non-Arabic & FUT\_PART & future particle \\
PROG\_PART & progressive particle & EMOT & Emoticon/Emoji\\
MENTION & twitter mention & HASH & Hashtag \\
URL & URL & ~~~ -- & ~~~ --\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{ The POS tag set in~.}
\end{table}
\end{document}

cs

Muhammad Khalifa
Muhammad Abdul-Mageed
Khaled Shaalan

"Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling" by Muhammad Khalifa, Muhammad Abdul-Mageed, and Khaled Shaalan, arXiv:2101.04758v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 \textbf{Parameter} & \textbf{Value} \\
 \hline
 \hline
 \textit{Cell radius} & $250$ \textit{m} \\
 \hline
 \textit{Max D2D separation} & $20$ \textit{m} \\
 \hline
 \textit{Max UE transmission power:} $P_{UE-max}$ & $25$ \textit{dBm} \\
 \hline
 \textit{Total system bandwidth} & $5$ \textit{MHz} \\
 \hline
 \textit{Carrier frequency} & $2$ \textit{GHz} \\
 \hline
 \textit{Shadowing standard deviation:} $\sigma$ & $8$ \textit{dB}\\
 \hline
 \textit{Noise power spectral density:} $N_0$ & $-174$ \textit{dBm/Hz}\\
 \hline
 \textit{Antenna separation at UE} & $0.3$ \textit{m}\\
 \hline
 \textit{Path loss exponent} & $3$ \\
 \hline
 \textit{SI cancellation factor:} $\zeta$ & $80$ \textit{dB}\\
 \hline
 \textit{$R_{min-j}$ for all data files } & $5$ \textit{Mbps}\\
 \hline

 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Optimal Power Allocation in Uplink NOMA with Simultaneous Cache-Enabled D2D Communications" by Aditya Powari and Daniel K. C. So, arXiv:2412.00977v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Implementation details of PGD-VAE. GIN represents the layer of Graph Isomorphism Network; ReLU represents the Rectified Linear Unit activation function; FC is the fully connected layer; Sigmoid is the sigmoid activation function.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline Layer & Local-pattern encoder&Global-pattern encoder&Local decoder & Neighborhood decoder & Global decoder & Assembler\\\cline{2-7}
 \hline
 Input & A & A & $z_l$ & $z_l$ & $z_g$ & $A^{(l)}$, $A^{(g)}$, $A^{(n)}$\\
 \hline
 Layer1 & GIN+ReLU & GIN+ReLU & FC+ReLU & FC+ReLU & FC+ReLU & -\\
 \hline
 Layer2 & GIN+ReLU & GIN+ReLU & FC+ReLU & FC+ReLU & FC+ReLU & - \\
 \hline
 Layer3 & GIN+ReLU & GIN+ReLU & Sigmoid & Sigmoid & Sigmoid & -\\
 \hline
 Layer4 & Node clustering & Sum pooling &- &- & Replace diagonal with zero & - \\
 \hline
 Layer5 & Concatenate representative node embedding & - &- &- & - & -\\
 \hline
 Output & $z_l$ & $z_g$ & $A^{(l)}$ & $A^{(n)}$ & $A^{(g)}$ & A \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Deep Generative Model for Periodic Graphs" by Shiyu Wang, Xiaojie Guo, and Liang Zhao, arXiv:2201.11932v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrr}
 \hline
 & mCube & Bettonville & Crevecoeur \\
 \hline
mean\_CRPS & $\mathbf{11,148.82}$ & 14,166.51 & 15,405.99 \\
 median\_CRPS & 4,077.85 & $\mathbf{2,377.34}$ & 4,251.77 \\
 PICP\_99 & $\mathbf{0.60}$ & 0.37 & 0.12 \\
 IS\_99 & {101,642.41} & $\mathbf{385,729.23}$ & 71,808.31 \\
 PICP\_95 & $\mathbf{0.57}$ & 0.29 & 0.11 \\
 IS\_95 & {66,168.42} & $\mathbf{66,284.85}$ & 61,955.80 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{CRPS, IS, PICP for the RBNS claims of the 3 competing micro-reserving methods on the subset of Allianz bodily injury claims.}
\end{table}
\end{document}

stat

Emmanuel Jordy Menvouta
Jolien Ponnet
Robin Van Oirbeek
Tim Verdonck

"mCube: Multinomial Micro-level reserving Model" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{4-MNIST-Bags model results. The mean performance was calculated over ten repeat trainings of each model, and the standard error of the mean is given.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
		\toprule
		Model & Train Accuracy & Val Accuracy & Test Accuracy \\
		\midrule
		MI-Net & \textbf{0.995 $\pm$ 0.001} & 0.972 $\pm$ 0.002 & 0.971 $\pm$ 0.002 \\
		mi-Net & 0.993 $\pm$ 0.001 & \textbf{0.973 $\pm$ 0.002} & \textbf{0.974 $\pm$ 0.002} \\
		MI-Attn & 0.991 $\pm$ 0.002 & 0.967 $\pm$ 0.003 & 0.967 $\pm$ 0.003 \\
		MI-GNN & 0.984 $\pm$ 0.002 & 0.968 $\pm$ 0.002 & 0.966 $\pm$ 0.002 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Joseph Early
Christine Evers
Sarvapali Ramchurn

"Model Agnostic Interpretability for Multiple Instance Learning" by Joseph Early, Christine Evers, and Sarvapali Ramchurn, arXiv:2201.11701v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Difference in motivation pre- to post-selection.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|llll|}
		\multicolumn{4}{c}{Least Squares Means} \\ \hline
		Condition & MAB Assign. & $\Delta$ Motivation & Std. Err. \\ \hline
		Control & Arm-Downward & 0.0071 & 0.1268 \\
		Control & Arm-Mixed & -0.1189 & 0.1315 \\
		Control & Arm-Upward & -0.1117 & 0.1258 \\
		Experimental & Arm-Downward & 0.1013 & 0.1267 \\
		Experimental & Arm-Mixed & 0.3099 & 0.2216 \\
		Experimental & Arm-Upward & 0.0395 & 0.1134 \\ \hline
		\multicolumn{4}{c}{Overall Means} \\
		\hline
		Control & - & 0.0194 & 0.0201 \\
		Experimental & - & 0.1456 & 0.0400 \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jichen Zhu
Diane H. Dallal
Robert C. Gray
Jennifer Villareale
Santiago Ontañón
Evan M. Forman
Danielle Arigo

"Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity" by Jichen Zhu, Diane H. Dallal, Robert C. Gray, Jennifer Villareale, Santiago Ontañón, Evan M. Forman, and Danielle Arigo, arXiv:2101.10020v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
\hline Parameter & Values \\
\hline minibatch size & 5,10,20,50,100,200 \\
\hline components & 50 \\
\hline epochs & 100 \\
\hline lr & 0.01, 0.001, 0.0001, 0.00001 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

James Chapman
Lennie Wells
Ana Lawry Aguila

"Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning" by James Chapman, Lennie Wells, and Ana Lawry Aguila, arXiv:2310.01012v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Linear evaluation accuracy (\%) of contrastive learning representations learned by the objectives with/without regularizations on the mutual information between encoder and projector features.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
 \toprule
 Dataset & Framework & Baseline & Bottleneck Regularizer & Gains \\ \midrule
 \multirow{2}{*}{CIFAR-10} & SimCLR & 87.47 & 87.73 & +0.26 \\
 & Barlow Twins & 89.00 & 89.32 & +0.32 \\ \midrule
 \multirow{2}{*}{CIFAR-100} & SimCLR & 58.12 & 59.38 & +1.26 \\
 & Barlow Twins & 64.16 & 66.31 & +2.15 \\ \midrule
 \multirow{2}{*}{ImageNet-100} & SimCLR & 67.36 & 68.42 & +1.06 \\
 & Barlow Twins & 69.96 & 71.3 & +1.34 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Zhuo Ouyang
Kaiwen Hu
Qi Zhang
Yifei Wang
Yisen Wang

"Projection Head is Secretly an Information Bottleneck" by Zhuo Ouyang, Kaiwen Hu, Qi Zhang, Yifei Wang, and Yisen Wang, arXiv:2503.00507v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Robustness Check: Alternative Model Specification}
\begin{tabular}{lc}
 \midrule
 Dependent Variable: & (1) \\
 NumOfPurchases & \\
 \midrule
 Recommended & 0.401** \\
 & (0.061) \\
 Fund Type Dummies & Yes \\
 Month Dummies & Yes \\
 Day of Week Dummies & Yes \\
 Control Variables & Yes \\
 Number of Observations & 21,834 \\
 \textcolor[rgb]{ .133, .133, .133}{Pseudo $R^2$} & 0.302 \\
 \midrule
 \multicolumn{2}{p{26em}}{\scriptsize Notes: {***} $p<0.01$, {**} $p<0.05$, {*} $p<0.1$. NumberOfPurchases is the dependent variable in Column (1). Column (1) displays the result using a Poisson regression model. Control variables include management fees, purchase fees, custodial fees, asset size, dummy for valuable fund brand, fund past returns, fund risk level, and fund tenure. Robust standard errors are in parentheses.} \\
 \end{tabular}
\end{table}
\end{document}

q-fin

"The Effect of Product Recommendations on Online Investor Behaviors" by Ruiqi Rich Zhu, Cheng He, and Yu Jeffrey Hu, arXiv:2303.14263v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

D. Martínez-Tibaduiza
Vladimir Vargas-Calderón
J. G. Dueñas
J. Flórez-Jiménez
A. Z. Khoury

"$\texttt{Symdyn}$: an automated algebraic solution for high-order quantum systems" by D. Martínez-Tibaduiza, Vladimir Vargas-Calderón, J. G. Dueñas, J. Flórez-Jiménez, and A. Z. Khoury, arXiv:2503.22061v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Occupational mobility and unemployment duration}
\begin{tabular}{lccccc}
 \hline
	&Health prof, 	& Account, archit, legal	& Managers, officials, &	Clerical 	& Retail and sales \\
	&School/college teachers &	tech., others &proprietors	&workers	&workers \\
	\hline
Break &	-0.0002 &	0.0040	&-0.0014 &	0.0034	&0.0044	 \\
	&{\footnotesize{(0.0027)}}	&{\footnotesize{(0.0039)}}	&{\footnotesize{(0.0043)}}	&{\footnotesize{(0.0033)}}	&{\footnotesize{( 0.0030)}}	 \\
Constant &	0.0458$^{***}$	& 0.0774$^{***}$	&0.1459$^{***}$	&0.0966$^{***}$	&0.0510$^{***}$	\\
	&{\footnotesize{(0.0040)}}	&{\footnotesize{(0.0100)}}	&{\footnotesize{(0.0064)}}	&{\footnotesize{(0.0134)}}&{\footnotesize{(0.0121)}}	\\
Time trend &	X	&X	&X	&X	&X \\
\hline
$R^{2}$	& 0.8482	&0.9619	&0.8552	&0.8294	&0.8793 \\
\hline
\hline
	& Foremen	& Craftsmen	 &Operatives 	& Laborer	& Other service \\
	&	workers	&kindred workers	&kindred workers	&workers	& workers \\
	\hline
Break &	-0.0004 	&0.0012	&0.0031	&-0.0085$^{***}$ &	-0.0031 \\
	&{\footnotesize{(0.0023)}}	&{\footnotesize{(0.0029)}}	&{\footnotesize{(0.0044)}}	&{\footnotesize{(0.0017)}}	& {\footnotesize{(0.0023)}}\\
Constant &	0.0179$^{***}$&	0.0494$^{***}$&	0.1717$^{***}$	&0.0524$^{***}$	& 0.0907$^{***}$\\
	&{\footnotesize{(0.0034)}}	& {\footnotesize{(0.0043)}} &	{\footnotesize{(0.0066)}}	&{\footnotesize{(0.0015)}}	&{\footnotesize{(0.0027)}} \\
Time trend &	X	&X	&X	&X	&X \\
\hline
$R^{2}$	& 0.6108	&0.7911	&0.9110&	0.8437	&0.4639 \\
\hline
\hline
\multicolumn{6}{l}{{\footnotesize{Levels of significance: $^{*} p<0.1$, $^{**} p<0.05$, $^{***} p<0.01$}}}
 \end{tabular}
\end{table}
\end{document}

q-fin

"Unemployment and Endogenous Reallocation over the Business Cycle" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{(Scenario III) Averages of the 5 smallest of SP/KS-GSP/EO/KS-GEOs whose MAEs are less than the thresholds. Those scores are selected in the Pareto solutions appearing in Figure~. Standard deviations are in the parentheses next to the averages.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc}
\toprule
& \multicolumn{4}{c}{\textbf{Community and Crime} (MAE $\leq$ 0.12)} \\
\hline
Model & SP ($\downarrow$) & KS-GSP ($\downarrow$) & EO ($\downarrow$) & KS-GEO ($\downarrow$) \\
\hline
\hline
SBP & 0.225 (0.007) & 0.141 (0.004) & 0.083 (0.003) & 0.060 (0.002) \\
CON & - & - & 0.094 (0.004) & 0.062 (0.002) \\
HGR & 0.247 (0.002) & 0.155 (0.001) & 0.082 (0.004) & 0.069 (0.002) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes" by Jinwon Sohn, Qifan Song, and Guang Lin, arXiv:2311.05866v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c||cc|cc}
 & \multicolumn{2}{c|}{\textbf{ViT-H}} & \multicolumn{2}{c}{\textbf{ViT-B}} \\
\toprule
$R$ & $\tau$ & $\rho$ & $\tau$ & $\rho$ \\
\midrule
$1$ & $-0.82$ & $-0.96$ & $-0.79$ & $-0.94$ \\
$3$ & $-0.80$ & $-0.95$ & $-0.76$ & $-0.94$ \\
$5$ & $-0.77$ & $-0.93$ & $-0.75$ & $-0.93$ \\
$7$ & $-0.74$ & $-0.92$ & $-0.73$ & $-0.91$ \\
$9$ & $-0.72$ & $-0.91$ & $-0.70$ & $-0.90$ \\
$11$ & $-0.71$ & $-0.90$ & $-0.70$ & $-0.89$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Rank correlation (Kendall $\tau$ and Spearman $\rho$) between SAM prediction IoU and object tree-likeness as measured by \textbf{CPR} with different hyperparameter values $R$ on the \textbf{synthetic dataset}, to supplement Table .}
\end{table}
\end{document}

eess

Yixin Zhang
Nicholas Konz
Kevin Kramer
Maciej A. Mazurowski

"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects" by Yixin Zhang, Nicholas Konz, Kevin Kramer, and Maciej A. Mazurowski, arXiv:2412.04243v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc} \hline
 $p_k$ & $0< r_0 < 1/2$ & $1/2< r_0 < 1$ \\ \hline
 $p_3$ & $-2r_0^3+3r_0^2$ & $-2r_0^3+3r_0^2$ \\
 $p_2$ & $ - \frac{4}{3}r_0^3+r_0^2$ & $\frac{4}{3}r_0^3 - \frac{1}{3}({3}r_0-1)^2$ \\
 $p_1$ & $\frac{14}{3}r_0^3-6r_0^2+2r_0$ & $\frac{2}{3}(1-r_0)^{3}$ \\
 $p_0$ & $(1-2r_0)^{3}$ & $0$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Individual graph probabilities for the 3 node hard RGG in $[0,1]$}
\end{table}
\end{document}

math

"Entropy of Random Geometric Graphs in High and Low Dimensions" by Oliver Baker and Carl P. Dettmann, arXiv:2503.11418v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
			\toprule
			 & 0~10 & 0~15 & 0~20 & 0 & 10 & 15 \\
			\midrule
			\midrule
			\multirow{1}{*}{SIDD} & 39.31/0.9570 & 39.12/0.9562 & 29.73/0.7780 & 29.99/0.7689 & 38.77/0.9530 & 37.73/0.93521 \\
			\midrule
			\multirow{1}{*}{OOD avg} & 39.78/0.9823 & 39.83/0.9815 & 27.71/0.7426 & 29.90/0.8201 & 39.57/0.9770 & 14.32/0.2985 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Inju Ha
Donghun Ryou
Seonguk Seo
Bohyung Han

"Learning to Translate Noise for Robust Image Denoising" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Kengo Nagashima
Hisashi Noma
Yasunori Sato
Masahiko Gosho

"Sample size calculations for single-arm survival studies using transformations of the Kaplan-Meier estimator" by Kengo Nagashima, Hisashi Noma, Yasunori Sato, and Masahiko Gosho, arXiv:2012.03355v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Exploring different variations of DFM. The best results are highlighted in \textcolor{red}{red}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
\toprule
\toprule
Variation & A ($\uparrow$) & R ($\downarrow$) & EAO ($\uparrow$) \\
\toprule
Tir-\textgreater{}Rgb \& Rgb-\textgreater{}Tir & 0.6408 & 0.2938& 0.4063 \\
\midrule
Rgb-\textgreater{}Tir & {\color[HTML]{FE0000} 0.6475} & 0.2767& 0.4058\\
\midrule
Tir-\textgreater{}Rgb(DFM) & 0.6426 & {\color[HTML]{FE0000} 0.2647} & {\color[HTML]{FE0000} 0.4164} \\
\bottomrule
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Temporal Aggregation for Adaptive RGBT Tracking" by Zhangyong Tang, Tianyang Xu, and Xiao-Jun Wu, arXiv:2201.08949v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary Statistics of overall sample aged 16-65 years}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
& STEM degree & Non-STEM degree \\
\midrule
Employment Participation & 0.510 & 0.331 \\
Monthly wage (INR) & 19725.58 & 8387.115 \\
Avg. age (years) & 31.62 & 35.53 \\
Formal education (years) & 16.31 & 15.56 \\
\midrule
\multicolumn{3}{l}{\textbf{Gender (\%)}} \\
\midrule
Male (\%) & 69.68 & 55.57 \\
Female (\%) & 30.32 & 44.43 \\
\midrule
\multicolumn{3}{l}{\textbf{Marital Status (\%)}} \\
\midrule
Unmarried (\%) & 49.25 & 34.33 \\
Currently married (\%) & 49.88 & 63.44 \\
Widow & 0.49 & 1.68 \\
Divorced & 0.37 & 0.55 \\
\midrule
\multicolumn{3}{l}{\textbf{Religion (\%)}} \\
\midrule
Hinduism & 82.71 & 78.12 \\
Islam & 8.54 & 9.63 \\
Christianity & 5.52 & 8.03 \\
Others & 3.23 & 4.21 \\
\midrule
\multicolumn{3}{l}{\textbf{Social Group (\%)}} \\
\midrule
Minority (Low caste) & 14.02 & 22.21 \\
Majority (High caste) & 85.98 & 77.88 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Do STEM graduates fare better at times of Crises? Evidence from COVID 19 pandemic in India" by Jheelum Sarkar, arXiv:2508.12471v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Win Rates of Models Across Companies}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
\toprule
\textbf{Company} & \textbf{GPT} & \textbf{Perplexity} & \textbf{Claude} & \textbf{Gemini} & \textbf{DeepSeek} \\
\midrule
Amazon & 58.60\% & 37.20\% & 25.10\% & 21.80\% & 19.20\% \\
Apple & 59.10\% & 38.90\% & 24.20\% & 22.30\% & 19.60\% \\
Google & 64.10\% & 40.10\% & 26.90\% & 20.90\% & 16.30\% \\
Meta & 61.00\% & 36.30\% & 22.30\% & 18.40\% & 16.80\% \\
Microsoft & 66.40\% & 41.20\% & 30.50\% & 18.60\% & 15.90\% \\
Nvidia & 62.50\% & 38.10\% & 23.90\% & 18.90\% & 14.70\% \\
Tesla & 62.30\% & 36.60\% & 23.20\% & 20.50\% & 19.10\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis" by Md Talha Mohsin, arXiv:2507.22936v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
 & $W_1 = 0$ & $W_2 = 1$ \\
\hline
$X = 0$ & 0.01 & 0.5 \\
$X = 1$ & 0.5 & 0.01
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Does Regression Produce Representative Causal Rankings?" by Apoorva Lal, arXiv:2411.02675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ding Ning
Varvara Vetrova
Sébastien Delaux
Rachael Tappenden
Karin R. Bryan
Yun Sing Koh

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

How manipulable are prediction markets?

"How manipulable are prediction markets?" by Itzhak Rasooly and Roberto Rozzi, arXiv:2503.03312v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
\hline
\textbf{Statistic} & \textbf{Value} \\
\hline
Count & 3652.00 \\
Mean & 23.80 \\
Standard Deviation (std) & 11.51 \\
Minimum (min) & 0.25 \\
25th Percentile & 13.43 \\
Median (50\%) & 28.75 \\
75th Percentile & 33.75 \\
Maximum (max) & 40.00 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Descriptive Statistics for production - ``joao" Use case 2 `}
\end{table}
\end{document}

stat

Dimitrios P. Panagoulias
Elissaios Sarmas
Vangelis Marinakis
Maria Virvou
George A. Tsihrintzis

"Integrating Dynamic Correlation Shifts and Weighted Benchmarking in Extreme Value Analysis" by Dimitrios P. Panagoulias, Elissaios Sarmas, Vangelis Marinakis, Maria Virvou, and George A. Tsihrintzis, arXiv:2411.13608v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Combined path loss characteristics for the DeepMIMO scenarios; provided .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
\hline
\textbf{Scenario} & \textbf{Max (dB)} & \textbf{Min (dB)} & \textbf{Mean \(\pm\) Std (dB)} \\
\hline
\emph{O1} & \(151.41\) & \(58.93\) & \(86.51 \pm 8.88\) \\
\emph{B5} & \(133.03\) & \(60.61\) & \(93.02 \pm 13.77\) \\
\emph{I3} & \(79.25\) & \(45.18\) & \(53.89 \pm 3.21\) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Rajesh Shrestha
Mingjie Shao
Mingyi Hong
Wing-Kin Ma
Xiao Fu

"Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm" by Rajesh Shrestha, Mingjie Shao, Mingyi Hong, Wing-Kin Ma, and Xiao Fu, arXiv:2411.16043v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of treaty generation features in the simulated reinsurance environment.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
\textbf{Feature} & \textbf{Description} \\
\midrule
Exposure Size & Total insured value subject to reinsurance \\
Loss Distribution & Empirical distribution calibrated from historical claims \\
Line of Business & Categorical indicator (e.g., property, casualty) \\
Attachment Point & Threshold at which reinsurance coverage begins \\
Coverage Limit & Maximum payout by the reinsurer \\
Retention Level & Portion of risk retained by the primary insurer \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning" by Stella C. Dong and James R. Finlay, arXiv:2506.13113v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
 & \textbf{Baseline} & \textbf{x\_add} & \textbf{s\_add} & \textbf{x\_cat} & \textbf{s\_cat} \\ \hline \hline
\textbf{dev\_clean} & 6.8 & 6.3 & 6.4 & 6.2 & 6.7 \\ %\hline
\textbf{test\_clean} & 6.8 & 6.1 & 6.2 & 6.3 & 6.5 \\ \hline %\hline
\textbf{dev\_other} & 13.8 & 14.8 & 14.1 & 15.1 & 15.5 \\ %\hline
\textbf{test\_other} & 14 & 14.5 & 14.3 & 15.3 & 15.6 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Librispeech: Average error rate (\%WER)}
\end{table}
\end{document}

eess

Vishwas M. Shetty
Metilda Sagaya Mary N J
S. Umesh

"Investigation of Speaker-adaptation methods in Transformer based ASR" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Consistency of Predictions and Voting Behavior}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline
 \hline
 & \multirow{2}{*}{Test} & \multirow{2}{*}{Lottery} & \multirow{2}{*}{Total} \\
 Prediction & & & \\
 \hline
 Own $>$ Group & 152 & 22 & 174 \\
 Own $\leq$ Group & 40 & 58 & 98 \\
 \hline
 Total & 192 & 80 & 272 \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
 \toprule
 Flow Extractor & Dataset & EPE & zEPE & Acc 01 & Acc 005 \\ \midrule
 Segmenter & Single ShapeNet & 0.3786 & 0.9455 & 2.98\% & 0.43\% \\
 Segmenter & Multi ShapeNet & 0.8444 & 0.9595 & 0.34\% & 0.03\% \\
 Segmenter & FlyingThings3D & 0.7559 & 0.9953 & 4.82\% & 0.98\% \\ \midrule
 FlowNet3D & Single ShapeNet & 0.3911 & 0.9768 & 2.76\% & 0.3\% \\
 FlowNet3D & Multi ShapeNet & 0.8242 & 0.9337 & 0.44\% & 0.03\% \\
 FlowNet3D & FlyingThings3D & 0.7538 & 0.9925 & 5.1\% & 1.3\% \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Flow Extractors trained with nearest neighbor loss and cycle consistency loss. }
\end{table}
\end{document}

eess

"Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation" by Victor Zuanazzi, arXiv:2011.07945v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \hline
 Method & CIFAR-10 Top-1 & CIFAR-10 Top-5 & CIFAR-100 Top-1 & CIFAR-100 Top-5 \\
 \hline
 Barlow twins & \textbf{92.1} & 99.73 & \textbf{71.38} & \textbf{92.32} \\
 VICReg & 91.68 & 99.66 & 68.56 & 90.76 \\
 \textbf{SSL-EY} & 91.43 & \textbf{99.75} & 67.52 & 90.17 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Performance comparison of SSL methods on CIFAR-10 and CIFAR-100.}
\end{table}
\end{document}

stat

"Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning" by James Chapman, Lennie Wells, and Ana Lawry Aguila, arXiv:2310.01012v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation Framework Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
\toprule
\textbf{Parameter} & \textbf{Value} \\
\midrule
Total simulation period & 30 days \\
Traditional operations sample & 5,000 trips \\
Weather-aware AI sample & 5,000 trips \\
Weather prediction accuracy & 87\% (3-hour forecast) \\
Base revenue per minute & ¥52.3 \\
Working hours per day & 10 hours \\
Working days per year & 300 days \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity" by Tatsuru Kikuchi, arXiv:2507.17099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Relationship with market expectations}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rcccccccc}
			\textbf{(Tenor)} & \multicolumn{2}{c}{\textbf{1-month}} & \multicolumn{2}{c}{\textbf{3-month}} & \multicolumn{2}{c}{\textbf{6-month}} & \multicolumn{2}{c}{\textbf{12-month}} \\ \hline
			 & \textbf{Baseline} & \textbf{Comms} & \textbf{Baseline} & \textbf{Comms} & \textbf{Baseline} & \textbf{Comms} & \textbf{Baseline} & \textbf{Comms} \\ \hline
			CPI F'cast & \textbf{0.08} & -0.08 & \textbf{0.14} & \textbf{-0.15} & \textbf{0.2} & -0.16 & 0.13 & -0.11 \\
			Activity F'cast & -0.06 & -0.05 & -0.09 & -0.08 & -0.1 & -0.09 & -0.06 & -0.02 \\
			Unemp F'cast & \textbf{-0.01} & -0.01 & \textbf{-0.02} & -0.01 & \textbf{-0.02} & -0.01 & \textbf{-0.02} & 0.0 \\
			$HDS_{mins,t-1}$ & & \textbf{0.36} & & \textbf{0.67} & & \textbf{1.0} & & \textbf{1.27} \\
			$HDS_{mins,t-2}$ & & \textbf{0.21} & & \textbf{0.39} & & \textbf{0.54} & & \textbf{0.41} \\
			$HDS_{statement,t-1}$ & & \textbf{0.11} & & \textbf{0.17} & & 0.17 & & 0.08 \\
			$HDS_{statement,t-2}$ & & \textbf{-0.14} & & \textbf{-0.21} & & \textbf{-0.35} & & \textbf{-0.49} \\ \hline
			AIC & 66.58 & 7.96 & 271.09 & 184.34 & 395.31 & 302.67 & 457.6 & 385.32
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Ornithologist: Towards Trustworthy "Reasoning" about Central Bank Communications" by Dominic Zaun Eu Jones, arXiv:2505.09083v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{General case studies results in optimized KF-PLS, compared to the regular PLS. The performance was improved in all cases. *The number of iterations until convergence. $\uparrow$ indicates if the metric is optimal if maximized, whereas $\downarrow$ indicates that indicator is to be minimized. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c|c}
 \textbf{Case} & \multicolumn{3}{c|}{\textbf{Original PLS}} & \multicolumn{4}{c}{\textbf{Kernel-Flows Optimized (KF-PLS)}} \\
 & $Q^2_{test} \uparrow $ & NRMSE $\downarrow$ & Accuracy $\uparrow$ & $Q^2_{test} \uparrow $ & NRMSE $\downarrow$ & Accuracy $\uparrow$ & Iterations*\\
 \hline
 1 & 0.85 & 5.2 & - & 0.99 & 1.21 & - & 213 \\
 2 & - & - & 0.7 & - & - & 1 & 70 \\
 3 & 0.9 & 0.82 & - & 0.96 & 0.53 & - & 30 \\
 4 & 0.94 & 2.36 & - & 0.97 & 1.68 & - & 65\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Zina-Sabrina Duma
Jouni Susiluoto
Otto Lamminpää
Tuomas Sihvonen
Satu-Pia Reinikainen
Heikki Haario

"KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows" by Zina-Sabrina Duma, Jouni Susiluoto, Otto Lamminpää, Tuomas Sihvonen, Satu-Pia Reinikainen, and Heikki Haario, arXiv:2312.06547v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study results. \texttt{P2P} refers to the point-to-plane method from Open3D, \texttt{DR} refers to Doppler residuals, \texttt{DOR} indicates the usage of dynamic outlier rejection, and \texttt{DICP} refers to Doppler ICP that uses point-to-plane method, Doppler residuals, and outlier rejection.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrrrrrrr}
 \toprule
 Sequence &
 Method &
 \shortstack{RPE\\Trans (m)} & \shortstack{RPE\\Rot (deg)} & \shortstack{Path\\Error (m)} & \shortstack{\# Iters\\(mean)} \\
 \midrule
\multirow{4}{*}{\shortstack{Robin\\Williams\\Tunnel}}
& P2P
& {1.9343} %
& {0.1533} %
& {219.10} %
& {20.3} \\ %
& P2P+DOR
& {2.6035} %
& {0.6699} %
& {298.04} %
& \textbf{6.0} \\ %
& P2P+DR
& {0.0758} %
& {0.1416} %
& {0.05} %
& {9.9} \\ %
& DICP
& \textbf{0.0758} %
& \textbf{0.1415} %
& \textbf{0.05} %
& {9.8} \\ %
\midrule
\multirow{4}{*}{\shortstack{Brisbane\\Lagoon\\Freeway}}
& P2P
& {0.3270} %
& {0.0915} %
& {45.27} %
& {10.6} \\ %
& P2P+DOR
& {0.1148} %
& {0.0883} %
& {9.67} %
& {9.9} \\ %
& P2P+DR
& \textbf{0.1112} %
& {0.0869} %
& \textbf{4.38} %
& {10.4} \\ %
& DICP
& {0.1301} %
& \textbf{0.0869} %
& {7.20} %
& \textbf{9.7} \\ %
\midrule
\multirow{4}{*}{\shortstack{San\\Francisco\\City}}
& P2P
& {0.0323} %
& \textbf{0.0479} %
& {23.78} %
& \textbf{5.9} \\ %
& P2P+DOR
& {0.0322} %
& {0.0481} %
& \textbf{4.55} %
& {7.1} \\ %
& P2P+DR
& \textbf{0.0316} %
& {0.0482} %
& {10.18} %
& {7.1} \\ %
& DICP
& {0.0317} %
& {0.0482} %
& {8.43} %
& {6.0} \\ %
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"DICP: Doppler Iterative Closest Point Algorithm" by Bruno Hexsel, Heethesh Vhavle, and Yi Chen, arXiv:2201.11944v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
			\hline

			\multicolumn{4}{c}{$\Delta\ln(\text{Gross Corporate saving rate}_{it})$} \\

			\hline
			& (1) & (2) & (3) \\

			$\Delta\ln(y_{it}) $ & -0.621*** & -0.021 & -0.029 \\

			& (0.166)&(0.195) & (0.079)\\

			$\Delta\ln(y_{it})*y_i$ & & -0.030*** & -0.028*** \\

			& & (0.005)& (0.004)\\

			$\Delta\ln(y_{it})*\lambda_{i}$ & & & -0.025** \\

			& & &(0.010)\\

			$\Delta\ln(\lambda_{it})$ & & & -0.015 \\

			& & &(0.009)\\

			Country FE & Yes & Yes & Yes \\

			Year FE & Yes & Yes & Yes \\

			Observations & 887 & 887 & 706 \\

			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Markus Brueckner
Tomoo Kikuchi
George Vachadze

"Transitional Dynamics of the Saving Rate and Economic Growth" by Markus Brueckner, Tomoo Kikuchi, and George Vachadze, arXiv:2012.15435v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of average \textit{NPCR} and \textit{UACI} values.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
\multicolumn{3}{c|}{$NPCR$} & \multicolumn{3}{c}{$UACI$} \\ \midrule
Red & Green & Blue & Red & Green & Blue \\ \midrule
$99.5911\%$ & $99.6277\%$ & $99.5209\%$ & $33.3946$ & $33.4467\%$ & $33.4536\%$ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yehia Lalili
Toufik Bouden
Morad Grimes
Abderrazek Lachouri

"A robust image encryption scheme based on new 4-D hyperchaotic system and elliptic curve" by Yehia Lalili, Toufik Bouden, Morad Grimes, and Abderrazek Lachouri, arXiv:2411.17643v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Correlation coefficient of each annotator with the 10 click simulated user}
\begin{tabular}{|l|l|l|l|l|}
 \hline
 \textbf{Annotator 1} & \textbf{Annotator 2} & \textbf{Annotator 3} & \textbf{Annotator 4} \\ \hline
 0.9251 & 0.7357 & 0.7819 & 0.4040 \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Matthias Hadlich
Zdravko Marinov
Moon Kim
Enrico Nasca
Jens Kleesiek
Rainer Stiefelhagen

"Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images" by Matthias Hadlich, Zdravko Marinov, Moon Kim, Enrico Nasca, Jens Kleesiek, and Rainer Stiefelhagen, arXiv:2311.14482v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{makecell}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detection accuracy on the UCAS-AOD dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
 \Xhline{1pt}
 Method & Backbone & car & airplane &mAP(07) \\
 \Xhline{1pt}
 YOLOv3 & Darknet53 &74.63 &89.52 &82.08 \\
 RetinaNet & Resnet101 &84.64 &90.51 &87.57 \\
 FR-O & Resnet101 &86.87 &89.86 &88.36 \\
 ROI-trans & Resnet101 &87.99 &89.90 &88.95 \\
 FPN-CSL & Resnet101 &88.09 &90.38 &89.23 \\
 R$^3$Det-DCL & Resnet101 &88.15 &90.57 &89.36 \\
 DAL & Resnet101 &\textcolor{red}{89.25} &90.49 &\textcolor{blue}{89.87} \\
 \hline
 CHPDet(ours) & DLA34 &88.58 &\textcolor{blue}{90.64} &89.61 \\
 CHPDet(ours) & Hourglass104 &\textcolor{blue}{89.18} &\textcolor{red}{90.81} &\textcolor{red}{90.00} \\
 \Xhline{1pt}
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Feng Zhang
Xueying Wang
Shilin Zhou
Yingqian Wang
Yi Hou

"Arbitrary-Oriented Ship Detection through Center-Head Point Extraction" by Feng Zhang, Xueying Wang, Shilin Zhou, Yingqian Wang, and Yi Hou, arXiv:2101.11189v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 \toprule
 LLMs & Acc & F1 & Prec & Recall \\
 \midrule
 LLAMA-2-7B & 47.1\% & 54.3\% & 48.9\% & 61.1\% \\
 Mistral-7B & 62.9\% & 66.7\% & 61.9\% & \cellcolor{cyan!20}\textbf{72.2\%} \\
 GPT-3.5 & \cellcolor{cyan!20}\textbf{68.6\%} & \cellcolor{cyan!20}\textbf{68.6\%} & \cellcolor{cyan!20}\textbf{70.6\%} & 66.7\% \\
 GPT-3.5 (temp=1) & 52.4\% & 46.4\% & 55.9\% & 39.8\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Performance comparison of different LLMs for predicting the interestingness of flight log benchmarks.}
\end{table}
\end{document}

eess

Taohong Zhu
Adrians Skapars
Fardeen Mackenzie
Declan Kehoe
William Newton
Suzanne Embury
Youcheng Sun

"SAFLITE: Fuzzing Autonomous Systems via Large Language Models" by Taohong Zhu, Adrians Skapars, Fardeen Mackenzie, Declan Kehoe, William Newton, Suzanne Embury, and Youcheng Sun, arXiv:2412.18727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \textbf{Parameter} & \textbf{Values} \\
 \midrule
 Pattern & 28 patterns \\
 Tempo & 60 BPM, 80 BPM, 100 BPM \\
 Movement & little, normal, large \\
 Volume & quiet, medium, loud \\
 Technique & finger, pick \\
 Chords & major and minor chord progressions \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Results on microphone audio.}
\end{table}
\end{document}

eess

Sebastian Murgul
Johannes Schimper
Michael Heizmann

"Joint Transcription of Acoustic Guitar Strumming Directions and Chords" by Sebastian Murgul, Johannes Schimper, and Michael Heizmann, arXiv:2508.07973v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|c|c|c|c||}
\hline
$\tilde\kappa$ & 0.1 & \textbf{0.3} & 0.5 & 1\\
\hline
rate & -0.257 & \textbf{-0.408} & -0.456 & -0.433\\
\hline
\end{tabular}
\caption{Convergence rate for the proposed estimator for difference choice of hyperparameter $\tilde\kappa$.}
\end{table}
\end{document}

math

Xuhui Zhang
Jose Blanchet
Youssef Marzouk
Viet Anh Nguyen
Sven Wang

"Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes" by Xuhui Zhang, Jose Blanchet, Youssef Marzouk, Viet Anh Nguyen, and Sven Wang, arXiv:2312.09862v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcr}
Left&Centered&Right\\
\hline
1 & 2 & 3\\
10 & 20 & 30\\
100 & 200 & 300\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On the Reasoning Capacity of AI Models and How to Quantify It" by Santosh Kumar Radha and Oktay Goktas, arXiv:2501.13833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Limit values of vector $\Psi(x)=(\psi_{\alpha}(x),\psi_{\beta}(x))$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc} \hline
		$x\to 0$	& $q >1$ & 		$0<q<1$ \\ \hline
		$	\alpha\geq1$		& 	$(+\infty,-\infty)$ & $(0,0)$ \\
		$	0<\alpha<1$	& $(0,0)$ & $(+\infty,-\infty)$
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Maximum $\log_q$ Likelihood Estimation for Parameters of Weibull Distribution and Properties: Monte Carlo Simulation" by Mehmet Niyazi Çankaya and Roberto Vila, arXiv:2012.08294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
			\toprule
			& \textbf{Test P} & \textbf{Test R} & \textbf{F1}\\
			\hline
			\textbf{Sequential fine-tune with RW} & \textbf{73.1} & 76.2 & \textbf{74.6} \\
			Zero-transfer & 67.6 & \textbf{77.4} & 72.1 \\
			Sequential fine-tune with CE & 71.2 & 74.3 & 72.7 \\
			Skip fine-tune on English & 71.0 & 73.6 & 72.3 \\
			Fine-tune on En/De mixed (CE) & \textbf{73.1} & 75.9 & 74.5 \\
			Fine-tune on En/De mixed (RW) & 65.5 & 42.6 & 51.6 \\
			\toprule
		\end{tabular}
\end{adjustbox}
\caption{Ablation study results evaluated on CoNLL2002 German NER data. All experiments used the mBERT-base model. RW denotes the re-weighted loss we proposed in the paper; CE denotes the regular cross entropy loss.}
\end{table}
\end{document}

cs

"Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment" by Bing Li, Yujie He, and Wenjin Xu, arXiv:2101.11112v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Margin-closed regime-switching multivariate time series models" by Lin Zhang, Harry Joe, and Natalia Nolde, arXiv:2312.10706v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Problem}&\textbf{Sampling} & \textbf{N. Params} & \textbf{Time (ms/it)} & \textbf{Rel. $L^2$ Error} \\
 \midrule
 BGD (DeepONet)&Baseline & 101100 & 220 & $7.41 \times 10^{-2}$ \\
 &$vRBA:\Phi(x)=r^2$ & 101100 & 230 & $6.24 \times 10^{-3}$ \\
 &$vRBA:\Phi(x)=e^r$ & 101100& 230 & $\mathbf{4.97 \times 10^{-3}}$ \\
 \midrule
 NS (FNO)&Baseline & 1622849 & 445 & $5.13 \times 10^{-2}$ \\
 &$vRBA:\Phi(x)=r^2$ & 1622849 & 459 & $2.37 \times 10^{-2}$ \\
 &$vRBA:\Phi(x)=e^r$ & 1622849 & 460 & $\mathbf{2.25\times 10^{-2}}$ \\
 \midrule
 WE (TC-UNet)&Baseline & 2432001 & 810 & $3.69 \times 10^{-2}$ \\
 &$vRBA:\Phi(x)=r^2$ & 2432001 & 866 & $\mathbf{1.00 \times 10^{-2}}$ \\
 &$vRBA:\Phi(x)=e^r$ & 2432001 & 867 & $1.05\times 10^{-2}$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Performance of the vRBA framework on three operator learning benchmarks: bubble growth dynamics (BGD) solved with a DeepONet, the Navier-Stokes (NS) equations for Kolmogorov flow with an FNO, and the wave equation (WE) with a TC-UNet. In all cases, the vRBA method significantly outperforms the baseline model, which uses uniform sampling. The performance gain is most pronounced for the DeepONet architecture, where vRBA reduces the relative $L^2$ error by an order of magnitude, with significant improvements also observed for the FNO and TC-UNet models. For these operator learning tasks, all models were trained with the Adam optimizer, and the vRBA method was applied using a hybrid strategy of importance weighting in the spatial domain and importance sampling over the function space.}
\end{table}
\end{document}

math

Juan Diego Toscano
Daniel T. Chen
Vivek Oommen
George Em Karniadakis

"A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning" by Juan Diego Toscano, Daniel T. Chen, Vivek Oommen, and George Em Karniadakis, arXiv:2509.14198v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fault parameters used for testing.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\hline
Parameters & Symbol & Value & Unit \\ \hline
Trajectory setpoint $\theta_a$ & $s$ & $\begin{bmatrix}
4 + 2 \sin\left(\frac{1}{2}t\right) \\
1.5 + 2 \sin\left(\frac{1}{2}t\right)
\end{bmatrix}$ & rad \\
Noise amplitude & $\nu$ & $\mathcal{U}(-1,1)\cdot10^{-6}$ & - \\ \hline
\multicolumn{4}{l}{Fault Parameters Broken Belt} \\ \hline
Time of fault occurrence & - & $27, 31.5, 36, \ldots, 58.5$ & $s$ \\ \hline
\multicolumn{4}{l}{Fault Parameters Tilt} \\ \hline
Upper arm tilt & $\alpha$ & $1.8, 4.5$ & deg \\
Lower arm tilt & $\beta$ & $1.8, 4.5$ & deg \\
End-effector tilt & $\gamma$ & $1.8, 4.5$ & deg \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Tim van Esch
Farhad Ghanipoor
Carlos Murguia
Nathan van de Wouw

"Hybrid Model-Data Fault Diagnosis for Wafer Handler Robots: Tilt and Broken Belt Cases" by Tim van Esch, Farhad Ghanipoor, Carlos Murguia, and Nathan van de Wouw, arXiv:2412.09114v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"M$^3$PC: Test-time Model Predictive Control for Pretrained Masked Trajectory Model" by Kehan Wen, Yutong Hu, Yao Mu, and Lei Ke, arXiv:2412.05675v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Estimates of the Productivity Effects: Endogeneity of Lagged FDI}
\begin{tabular}{lc|ccc}
			\toprule
			& Exogenous	& IPW & IV-External & IV-Lewbel \\
			\midrule
			\multicolumn{5}{c}{\textbf{---Median Estimates---}} \\
			$SP$	& 0.327 & 0.477 & 0.466 & 0.414 \\
			& (0.211, 0.399) & (0.413, 0.512) & (0.411, 0.585) & (0.276, 0.515) \\
			$DL$	& 0.138 & 0.128 & 0.171 & 0.191 \\
			& (0.118, 0.164) & (0.105, 0.158) & (0.138, 0.241) & (0.053, 0.341) \\
			$TIL$	& 0.037 & 0.055 & 0.067 & 0.066 \\
			& (0.022, 0.050) & (0.044, 0.069) & (0.056, 0.100) & (0.019, 0.126) \\
			\midrule
			\multicolumn{5}{c}{\textbf{---Statistically $>0$ (\% Obs.)---}} \\
			$SP$	& 83.84 & 98.55 & 97.17 & 79.56 \\
			$DL$	& 89.13 & 91.40 & 78.48 & 82.30 \\
			$TIL$	& 86.71 & 98.99 & 97.64 & 83.48 \\
			\midrule
			\multicolumn{5}{p{11cm}}{\footnotesize {\sc Notes:} Reported are the results for the productivity process in under baseline specification. The two-sided 95\% bootstrap percentile confidence intervals for the median point estimates are in parentheses. Statistical positiveness is at the 5\% significance level, using one-sided bootstrap confidence intervals. The ``Exogenous'' column corresponds to our proposed estimation procedure under the structural assumption of weak exogeneity of $G_{i,t-1}$, with the second-stage eq.~ estimated via least squares. The three models on the right allow for the violation of $\mathbb{E}[\zeta_{it}+\eta_{it}|G_{i,t-1}]=0$ and address the potential endogeneity of lagged FDI in via (i) inverse probability weighting [IPW], (ii) instrumentation using external IVs including coastal province dummy, province-level openness measure and their interactions with predetermined $L_{i,t-1}$ [IV-External], (iii) instrumentation with the heteroskedasticity-based internal IV \`a la [IV-Lewbel]. } \\
			\bottomrule[1pt]
		\end{tabular}
\end{table}
\end{document}

q-fin

"On the Estimation of Cross-Firm Productivity Spillovers with an Application to FDI" by Emir Malikov and Shunan Zhao, arXiv:2302.14602v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Local scores for the orientation of a single directed or bidirected edge, {\color{black}see Appendix~D}.}} %
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 {\footnotesize Edge} & {\footnotesize Information} & {\footnotesize Symmetrized fNML complexity (Markov equivalent)} \medskip\\
{\footnotesize $\!\!\!X\to Y$} & {\footnotesize $\!\!-I(X;Y|\mathbf{Pa'}_{\!_{Y\!\setminus\! X}})$} & {\footnotesize $\!\!\!{1\over 2N}\Big(\sum_j^{q_{_{x\!\setminus\! y}}\!r_y}\log \mathcal{C}_{n_j}^{r_x}-\sum_j^{q_{_{x\!\setminus\! y}}}\log \mathcal{C}_{n_j}^{r_x}+\sum_j^{q_{_{y\!\setminus\! x}}\!r_x}\log \mathcal{C}_{n_j}^{r_y}-\sum_j^{q_{_{y\!\setminus\! x}}}\log \mathcal{C}_{n_j}^{r_y}\Big)$}\medskip \\
{\footnotesize $\!\!\!X\leftarrow Y$} & {\footnotesize $\!\!-I(X;Y|\mathbf{Pa'}_{\!_{X\!\setminus\! Y}})$} & {\footnotesize $\!\!\!{1\over 2N}\Big(\sum_j^{q_{_{x\!\setminus\! y}}\!r_y}\log \mathcal{C}_{n_j}^{r_x}-\sum_j^{q_{_{x\!\setminus\! y}}}\log \mathcal{C}_{n_j}^{r_x}+\sum_j^{q_{_{y\!\setminus\! x}}\!r_x}\log \mathcal{C}_{n_j}^{r_y}-\sum_j^{q_{_{y\!\setminus\! x}}}\log \mathcal{C}_{n_j}^{r_y}\Big)$} \medskip \\
{\footnotesize $\!\!\!X\leftrightarrow Y$} & {\footnotesize $\!\!-I(X;Y|\mathbf{Pa'}_{\!_{XY}})$} & {\footnotesize $\!\!\!{1\over 2N}\Big(\sum_j^{q_{_{xy}}r_y}\log \mathcal{C}_{n_j}^{r_x}-\sum_j^{q_{_{xy}}}\log \mathcal{C}_{n_j}^{r_x}\,+\sum_j^{q_{_{yx}}r_x}\log \mathcal{C}_{n_j}^{r_y}-\sum_j^{q_{_{yx}}}\log \mathcal{C}_{n_j}^{r_y}\Big)$}\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"An efficient search-and-score algorithm for ancestral graphs using multivariate information scores" by Nikita Lagrange and Herve Isambert, arXiv:2412.17508v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Heterogeneous Response of Investment to $\epsilon^{X}_t$ \\ \footnotesize Bank Leverage \& Alt. Specification}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 & \multicolumn{4}{c}{Firm investment - $\Delta \log k_{i,t}$ } \\
 & (1) & (2) & (3) & (4) \\ \hline \hline
 & & & & \\
$\left(l_{i,t-1} - \mathbb{E}_{i} \left[ l_{t}\right] \right) \epsilon^{X}_t$ & -0.0959 & -0.106 & -0.0958 & -0.0619 \\
 & (0.0728) & (0.0753) & (0.0859) & (0.0874) \\
 & & & & \\
Firm FE & yes & yes & yes & yes \\
Sector-Time FE & yes & yes & yes & yes \\
Control for Lagged Inv. & no & yes & yes & yes \\
Firm-Controls & no & no & yes & yes \\
Aggregate-Controls & no & no & no & yes \\
 & & & & \\
Observations & 11,042 & 10,937 & 10,349 & 10,346 \\ \hline
$R^{2}$ & 0.043 & 0.046 & 0.055 & 0.064 \\ \hline \hline
\multicolumn{5}{c}{ Clustered standard errors in parentheses} \\
\multicolumn{5}{c}{ *** p$<$0.01, ** p$<$0.05, * p$<$0.1} \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Santiago Camara
Sebastian Ramirez Venegas

"The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity" by Santiago Camara and Sebastian Ramirez Venegas, arXiv:2209.11150v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the comparison of decisions made by computer vision model with decisions made by conventional tests and visual tests conducted by human.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\toprule
Violations & \#Samples & \#Agreements & Agreement rate\\
\midrule
\multicolumn{4}{l}{\textbf{Compared with conventional tests}}\\
\hspace{1em}heteroskedasticity & 540 & 464 & 0.8593\\
\hspace{1em}non-linearity & 576 & 459 & 0.7969\\
\multicolumn{4}{l}{\textbf{Compared with visual tests conducted by human}}\\
\hspace{1em}heteroskedasticity & 540 & 367 & 0.6796\\
\hspace{1em}non-linearity & 576 & 385 & 0.6684\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Weihao Li
Dianne Cook
Emi Tanaka
Susan VanderPlas
Klaus Ackermann

"Automated Assessment of Residual Plots with Computer Vision Models" by Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas, and Klaus Ackermann, arXiv:2411.01001v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule\toprule
\textbf{Market} & \textbf{Share ($k=1$)} & \textbf{Share ($k=2$)} \\
\midrule
Control Group & {0.14\%} & {44.84\%} \\
Partial market test ($k=1$, 30\%) & {67.02\%} & {8.69\%} \\
Total market test (30\%) & {2.96\%} & {81.30\%} \\
\bottomrule\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Demand patterns representing scenario 1}
\end{table}
\end{document}

q-fin

Soheil Ghili
K. Sudhir
Nitish Jain
Ankur Garg

"Second-degree Price Discrimination: Theoretical Analysis, Experiment Design, and Empirical Estimation" by Soheil Ghili, K. Sudhir, Nitish Jain, and Ankur Garg, arXiv:2507.13426v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test outcomes for Models 1--4 in Section .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 {\bf Model A} & {\bf Model B} & {\bf S-W statistic} & {\bf In favor}\\
 \midrule
1 & 2 & 0.987 & $-$\\
1 & 3 & 0.791 & $-$\\
1 & 4 & 0.680 & $-$\\
2 & 3 & -0.706 & $-$\\
2 & 4 & -0.706 & $-$\\
3 & 4 & 0.727 & $-$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Itai Dattner
Shota Gugushvili
Oleksandr Laskorunskyi

"Model Selection for Ordinary Differential Equations: a Statistical Testing Approach" by Itai Dattner, Shota Gugushvili, and Oleksandr Laskorunskyi, arXiv:2308.16438v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Exploring Gender and Race Biases in the NFT Market" by Howard Zhong and Mark Hamilton, arXiv:2304.06484v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Makoto Araya
Masaaki Harada
Hadi Kharaghani
Sho Suda
Wei-Hsuan Yu

"Unbiased weighing matrices of weight $9$" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Models} & \textbf{\textsc{per}} & \textbf{\textsc{loc}} & \textbf{\textsc{org}} & \textbf{Overall} \\
 \midrule
 \texttt{SpaCy} & 0.64 & \textbf{0.54} & \textbf{0.16} & 0.95\\
 \texttt{Stanza} & 0.69 & 0.4 & 0.11 & 0.94 \\
 \texttt{Natasha} & \textbf{0.77} & \textbf{0.54} & 0.14 & \textbf{0.96} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{The performance of off-the-shelf tools}
\end{table}
\end{document}

cs

Timofey Atnashev
Veronika Ganeeva
Roman Kazakov
Daria Matyash
Michael Sonkin
Ekaterina Voloshina
Oleg Serikov
Ekaterina Artemova

"Razmecheno: Named Entity Recognition from Digital Archive of Diaries "Prozhito"" by Timofey Atnashev, Veronika Ganeeva, Roman Kazakov, Daria Matyash, Michael Sonkin, Ekaterina Voloshina, Oleg Serikov, and Ekaterina Artemova, arXiv:2201.09997v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Forecasting realized volatility in the stock market: a path-dependent perspective" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Components and specifications for the mmW receiver}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Component} & \textbf{Gain} & \textbf{NF} & \textbf{OIP3} \\
 & (dB) & (dB) & (dBm) \\ \hline
LNA ({MAAL-01111}) & 19 & 2.5 & 20 \\ \hline
Down converter ({HMC1065LP4E}) & 9 & 3 & 14 \\ \hline
Low-pass filter (LPF) ({LFCN-900+}) & $-1$ & 1 & N/A \\ \hline
IF amplifier ({RAM-8A+}) & [31.5,24] & 2.6 & 24.4 \\ \hline
VGA ({ADL5331}) & [$-15$,15] & 9 & 39 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sravan Pulipati
Viduneth Ariyarathna
Udara De Silva
Najath Akram
Elias Alwan
Arjuna Madanayake
Soumyajit Mandal
Theodore S. Rappaport

"A Direct- Conversion Digital Beamforming Array Receiver with 800 MHz Channel Bandwidth at 28 GHz using Xilinx RF SoC" by Sravan Pulipati, Viduneth Ariyarathna, Udara De Silva, Najath Akram, Elias Alwan, Arjuna Madanayake, Soumyajit Mandal, and Theodore S. Rappaport, arXiv:1911.09180v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Coefficients of Linear Regression Analysis (****p<0.001, ***p<0.01, **p<0.05, *p<0.1)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Predictors} & \textbf{F-stat} & \textbf{$R^2$} & \textbf{RSE} \\ \hline
Panel & 1.861 & 0.02783 & 565.20 \\ \hline
DTA & 33.85 & 0.3425 & 464.80 \\ \hline
ROE & 14.59 & 0.1834 & 518.00 \\ \hline
ROA & 2.122 & 0.03027 & 594.00 \\ \hline
TATO & 67.9 & 0.5109 & 400 \\ \hline
CR & 5.272 & 0.7506 & 551.30 \\ \hline
Term & 0.7566 & 0.01151 & 569.00 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Syeda Tasnim Fabiha
Rubaiyat Jahan Mumu
Farzana Aktar
B M Mainul Hossain

"A Regression-Based Share Market Prediction Model for Bangladesh" by Syeda Tasnim Fabiha, Rubaiyat Jahan Mumu, Farzana Aktar, and B M Mainul Hossain, arXiv:2507.18643v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Pariyakorn Maneekul
Zelda B. Zabinsky
Giulia Pedrielli

"Branching Adaptive Surrogate Search Optimization (BASSO)" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Huyen Giang Thi Thu
Thang Viet Doan
Tai Le Quy

"An experimental study on fairness-aware machine learning for credit scoring problem" by Huyen Giang Thi Thu, Thang Viet Doan, and Tai Le Quy, arXiv:2412.20298v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Revenue Impact by AI Component}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
\toprule
\textbf{AI Component} & \textbf{Revenue Increase (\%)} \\
\midrule
Weather Prediction & +61.8 \\
Positioning Optimization & +23.7 \\
Route Optimization & +12.4 \\
Dynamic Pricing Integration & +8.7 \\
System Integration Effects & +0.7 \\
\midrule
\textbf{Total Weather-Aware AI Impact} & \textbf{+107.3} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity" by Tatsuru Kikuchi, arXiv:2507.17099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
&$f_{M1}$&$f_{M2}$\\
\hline
$n$&0.8037121822811545&4.764615814550553\\
\hline
$u_x$&-0.3403147128006618&0.05740548475117823\\
\hline
$u_y$&0&0\\
\hline
$u_z$&0&0\\
\hline
$T$&0.1033754314349305&3.442950196134546\\
\hline
\end{tabular}
\end{adjustbox}
\caption{$n=\pi^{3/2}$, $\overline{\mathbf{v}}=\mathbf{0}$, and $T=3$.}
\end{table}
\end{document}

math

Joseph Nakao
Gianluca Ceruti
Lukas Einkemmer

"A low-rank, high-order implicit-explicit integrator for three-dimensional convection-diffusion equations" by Joseph Nakao, Gianluca Ceruti, and Lukas Einkemmer, arXiv:2503.04932v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Luis A. Fernández
Isabel Lasheras
Cecilia Pola

"A mathematical perspective on the paradox that chemotherapy sometimes works backwards" by Luis A. Fernández, Isabel Lasheras, and Cecilia Pola, arXiv:2503.10670v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}%{@{} l*{5}{S} @{}}
 \toprule
 {$\epsilon$} & {0.1} & {$10^{-1/2}$} & {1} & {$10^{1/2}$} & {10} \\
 \midrule
 Laplace & 1.828 & 2.095 & 2.269 & 2.311 & 1.973 \\
 TuLap & 2.405 & 3.324 & 3.877 & 4.06 & 4.076 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Variance ratio on population mean}
\end{table}
\end{document}

math

Yu-Wei Chen
Raghu Pasupathy
Jordan A. Awan

"Optimal Survey Design for Private Mean Estimation" by Yu-Wei Chen, Raghu Pasupathy, and Jordan A. Awan, arXiv:2501.18121v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\hline
 Method & Input & Hidden layer 1 & Hidden layer 2 & Output\\
\hline
\hline
 LSTM & (6,1) & 32 & 32 & (1,4)\\

 BD-LSTM & (6,1) & 32 & 16 & (1,4) \\

 ED-LSTM & (6,1) & 32 & - & (1,4) \\

\hline &
\end{tabular}
\end{adjustbox}
\caption{Respective LSTM model topologies for the univariate case.}
\end{table}
\end{document}

cs

Rohitash Chandra
Ayush Jain
Divyanshu Singh Chauhan

"Deep learning via LSTM models for COVID-19 infection forecasting in India" by Rohitash Chandra, Ayush Jain, and Divyanshu Singh Chauhan, arXiv:2101.11881v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
\textbf{Task} & \textbf{Accuracy} \\ \hline
Trained on 2881 twits, & 49.5\% \\
Tested on all Alana dataset & \\ \hline
Increasing the number of the trees & 70.2\% \\
from 25 to 2000 & \\ \hline
Combining Vader dataset & \\
(600 sentiments words) &\textbf{ 71.1\%} \\
with Alana dataset, 25 trees & \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Random Forest Model Experiments}
\end{table}
\end{document}

cs

Mohamad Alissa
Issa Haddad
Jonathan Meyer
Jade Obeid
Nicolas Wiecek
Sukrit Wongariyakavee

"Sentiment Analysis for Open Domain Conversational Agent" by Mohamad Alissa, Issa Haddad, Jonathan Meyer, Jade Obeid, Nicolas Wiecek, and Sukrit Wongariyakavee, arXiv:2101.00675v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.