\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average and standard deviation (Std) of 10 random runs of F1 score (\%) on Mammal, WordNet noun, MCG, and Hearst.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{clccccccccc}
 \toprule

 \multicolumn{2}{c}{\multirow{2}{*}{\textbf{RegD}}}& \multicolumn{2}{c}{\textbf{Mammal}} & \multicolumn{2}{c}{\textbf{Noun}} & \multicolumn{2}{c}{\textbf{MCG}} & \multicolumn{2}{c}{\textbf{Hearst}} \\
 \cmidrule(lr){3-4} \cmidrule(lr){5-6} \cmidrule(lr){7-8} \cmidrule(lr){9-10}
 && d=2 & d=5 & d=5 & d=10 & d=5 & d=10 & d=5 & d=10 \\
 \midrule
 \multirow{2}{*}{fixed seed} & box & \textbf{64.9} & 71.6 & 53.8 & 51.3 & \textbf{50.7 }& \textbf{58.5 }&\textbf{ 42.8} & \textbf{49.6 }\\
& ball & 62.7&\textbf{71.8}& \textbf{58.4} & \textbf{59.1} & 44.9& 46.8 & 37.7 & 37.7\\
\midrule
average &\multirow{2}{*}{box}& \textbf{63.00} & \textbf{69.65} & \textbf{52.98} & 49.33 & \textbf{50.49} & \textbf{57.71} & \textbf{42.70} & \textbf{48.72} \\
($\pm$std) & & ($\pm$2.01) & ($\pm$3.14) & ($\pm$1.00) & ($\pm$1.12) & ($\pm$0.44) & ($\pm$0.41) & ($\pm$0.50) & ($\pm$0.12) \\
\hline
average &\multirow{2}{*}{ball} & 56.70 & 66.76 & 51.63 & \textbf{59.31} & 44.09 & 46.35 & 37.24 & 37.11 \\
($\pm$std) & & ($\pm$4.91) & ($\pm$2.91) & ($\pm$1.52) & ($\pm$2.29) & ($\pm$0.55) & ($\pm$0.37) & ($\pm$0.37) & ($\pm$0.47) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Achieving Hyperbolic-Like Expressiveness with Arbitrary Euclidean Regions: A New Approach to Hierarchical Embeddings" by Hui Yang and Jiaoyan Chen, arXiv:2501.17518v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Percentage difference in AUROC between PSN and LR, fit to the Sepsis data using each state representation in the column ``State''. G1, G3, G4, and G6 denote different patient groups based on the rate of change of the NEWS2 score; G1 and G6 have higher variability than G3 and G4.}%
\begin{tabular}{lcccc}
\toprule
State & G1 & G3 & G4 & G6 \\
\midrule
$X_t$ & $-12.5$ & $-13.7$ & $-14.0$ & $-11.8$ \\
$A_{t-1}$ & $-10.5$ & $-7.1$ & $-6.7$ & $-9.2$ \\
$H_{(0)}$ & $-3.6$ & $-3.9$ & $-4.0$ & $-3.2$ \\
$H_{(0)}, \bar{H}_t$ & $-2.9$ & $-3.3$ & $-3.3$ & $-2.6$ \\
$H_{(1)}, \bar{H}_t$ & $-2.4$ & $-3.0$ & $-3.0$ & $-2.2$ \\
$H_{(2)}, \bar{H}_t$ & $-2.3$ & $-2.9$ & $-2.9$ & $-2.1$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Anton Matsson
Lena Stempfle
Yaochen Rao
Zachary R. Margolin
Heather J. Litman
Fredrik D. Johansson

"How Should We Represent History in Interpretable Models of Clinical Policies?" by Anton Matsson, Lena Stempfle, Yaochen Rao, Zachary R. Margolin, Heather J. Litman, and Fredrik D. Johansson, arXiv:2412.07895v1, 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{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Attacked classification accuracy ($\pm$ standard deviation) on the OGBN-Arxiv dataset.}
\begin{tabular}{lccc}
\toprule
Dataset & GCN & NoisyGCN & RobustCRF \\
\midrule
Clean & \textbf{60.41 (0.15)} & 59.97 (0.11) & 60.28 (0.15) \\
Random & 58.97 (0.24) & 58.71 (0.10) & \textbf{59.03 (0.26)}\\
PGD &50.24 (0.42) & \textbf{50.26 (0.37)} & 50.10 (0.56) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Yassine Abbahaddou
Sofiane Ennadir
Johannes F. Lutzeyer
Fragkiskos D. Malliaros
Michalis Vazirgiannis

"Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields" by Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Fragkiskos D. Malliaros, and Michalis Vazirgiannis, arXiv:2411.05399v1, 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}{|lllllll|}
\hline
- & - & - & Method 1 & Method 1 & Method 2 & Method 2\\
\hline
Approx. Exch. RGN & Feature & Brute Force & Mean & Variance &Mean & Variance \\
\hline
$ER$ &Degree& 7.65817e-7 & 7.65828e-7 & 9.1742e-17 & 7.657237-7 & 9.27162e-17 \\
\hline
$EG_1$ &Degree & 7.65817e-7 & 7.65831e-7 & 9.1765e-17& 7.652716e-7 & 9.12736e-17 \\
\hline
$EG_2$ &Degree & 0.000929188 &0.000929213 & 8.9826e-8 &0.00092982711 & 8.9726e-8\\
\hline
$ER$ & Blocks & 50.2342& 50.2748 & 0.00281 & 50.26839 & 0.00281\\
\hline
$EG_1$ & Blocks & 48.9342 & 48.8281& 0.00301&48.88271& 0.003018\\
\hline
$EG_2$ & Blocks & 1826.2342 & 1827.1977& 1.28392& 1828.1723& 1.282887\\
\hline
\end{tabular}
\caption{Expected loss of worst case scenario with brute force (ground truth) compared with the mean of expected loss of the worst case scenario for the density and blocks of the three different models providing the variance of Expected loss of worst case scenario}
\end{table}
\end{document}

stat

Robustness on Networks

Marios Papamichalis
Simon Lunagomez
Patrick J. Wolfe

"Robustness on Networks" by Marios Papamichalis, Simon Lunagomez, and Patrick J. Wolfe, arXiv:2012.02914v1, 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
\caption{Description of the data sets}
\begin{tabular}{|c|c|c|c|}
\hline
 & \# instances & \# features & \# classes \\ \hline
MNIST Digits & 70,000 & 784 & 10 \\ \hline
USPS Digits & 11,000 & 256 & 10 \\ \hline
Caltech7 & 441 & ~60,000 & 7 \\ \hline
MSRC-V1 & 210 & 68160 & 7 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Amir Aradnia
Maryam Amir Haeri
Mohammad Mehdi Ebadzadeh

"Adaptive Explicit Kernel Minkowski Weighted K-means" by Amir Aradnia, Maryam Amir Haeri, and Mohammad Mehdi Ebadzadeh, arXiv:2012.02805v1, 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}{l|ll}
\toprule
\textbf{Prompt Style} & \textbf{GSM8K} & \textbf{CSQA}\\
\hline
\multicolumn{3}{l}{\textbf{SOLAR 11B Instruct}}\\
\hline
COT STEP + SG + SC & \textbf{83.78} & 79.28\\
COT STEP + SB + SC & 78.92 & \textbf{80.75}\\
\hline
\multicolumn{3}{l}{\textbf{Phi3 Mini 128K Instruct}}\\
\hline
COT STEP + SG + SC & \textbf{90.30} & \textbf{77.56}\\
COT STEP + SB + SC & 87.42 & 76.17\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparison of accuracy from Step-wise Greedy (SG) search and Step-wise Beam (SB) search - both with Self-Consistency (SC) on GSM8K and CSQA (CommonsenseQA). SC represents Self-consistency with majority voting of 20 sample generations. We bold the best result per block per dataset.}
\end{table}
\end{document}

cs

"Zero-Shot Verification-guided Chain of Thoughts" by Jishnu Ray Chowdhury and Cornelia Caragea, arXiv:2501.13122v1, 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{Number of GMRES-IR and RGMRES-IR refinement steps with the number of GMRES iterations for each refinement step for real matrices, using precisions ($u_f,u,u_r$) = (half,single,double) and ($m,k$) = (40,6).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
		\hline
		Matrix & GMRES-IR (40) & RGMRES-IR(40,6) \\ \hline
		orsirr\_1 & 12 (6,6) & 12 (6,6) \\
		comsol & 46 (22,24) & 30 (22,8) \\
		circuit204 & 40 (12,14,14) & 29 (12,9,8) \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Mixed Precision GMRES-based Iterative Refinement with Recycling" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Relative performance rankings between the tested models in the TiEBe dataset. For example, Qwen2-70B has the third best result in french questions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccc|l}
Model & France & Portugal & China & Brazil & USA & Best-performing country \\ \hline
GPT-4o & 1 & 1 & 1 & 1 & 1 & All \\
Qwen2-70B & 3 & 5 & 2 & 5 & 3 & China \\
Sabiá-3 & 4 & 4 & 4 & 3 & 5 & Brazil \\
Llama3-70B & 5 & 3 & 5 & 4 & 4 & Portugal \\
Mistral-large & 2 & 2 & 3 & 2 & 2 & All, except China
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Thales Sales Almeida
Giovana Kerche Bonás
João Guilherme Alves Santos
Hugo Abonizio
Rodrigo Nogueira

"TiEBe: Tracking Language Model Recall of Notable Worldwide Events Through Time" by Thales Sales Almeida, Giovana Kerche Bonás, João Guilherme Alves Santos, Hugo Abonizio, and Rodrigo Nogueira, arXiv:2501.07482v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small LLMs' performance to detect Pleural Effusion}
\begin{tabular}{lc}
 \toprule
 LLM & Decision \\
 \midrule
 Grok 3 & right-sided, large. \\
 GPT 4o & bilateral, moderate to large \\
 Gemini 2.5 Flash & bilateral, moderate to large \\
 Claude Sonnet 4 & No \\
 Copilot & left-sided, large \\
 Perplexity & left-sided, large \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Anindya Bijoy Das
Shahnewaz Karim Sakib
Shibbir Ahmed

"Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities" by Anindya Bijoy Das, Shahnewaz Karim Sakib, and Shibbir Ahmed, arXiv:2508.07031v1, 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|c|}
\hline
\textbf{ID} & \textbf{Hypothesis} & \textbf{Estimator} & \textbf{Formula} & \textbf{\#Sites} & \textbf{$N$} \\ \hline
1 & Allowedforbidden & $t$-test & \texttt{dv $\sim$ iv} & 36 & 6292 \\ \hline
2 & Anchoring1 & $t$-test & \texttt{dv $\sim$ iv} & 36 & 5362 \\ \hline
3 & Anchoring2 & $t$-test & \texttt{dv $\sim$ iv} & 36 & 5284 \\ \hline
4 & Anchoring3 & $t$-test & \texttt{dv $\sim$ iv} & 36 & 5627 \\ \hline
5 & Anchoring4 & $t$-test & \texttt{dv $\sim$ iv} & 36 & 5609 \\ \hline
6 & Contact & $t$-test & \texttt{dv $\sim$ iv} & 36 & 6336 \\ \hline
7 & Flag & $t$-test & \texttt{dv $\sim$ iv} & 36 & 6251 \\ \hline
8 & Gainloss & $t$-test & \texttt{dv $\sim$ iv} & 36 & 6271 \\ \hline
9 & Gambfal & $t$-test & \texttt{dv $\sim$ iv} & 36 & 5942 \\ \hline
10 & Iat & $t$-test & \texttt{dv $\sim$ iv} & 36 & 5851 \\ \hline
11 & Money & $t$-test & \texttt{dv $\sim$ iv} & 36 & 6333 \\ \hline
12 & Quote & $t$-test & \texttt{dv $\sim$ iv} & 36 & 6325 \\ \hline
13 & Reciprocity & $t$-test & \texttt{dv $\sim$ iv} & 36 & 6276 \\ \hline
14 & Scales & $t$-test & \texttt{dv $\sim$ iv} & 36 & 5899 \\ \hline
15 & Sunk & $t$-test & \texttt{dv $\sim$ iv} & 36 & 6330 \\ \hline
\end{tabular}
\caption{Estimator, number of sites and total sample size $N$ for each study (indices and variable names in cleaned data and this paper) for ManyLabs 1 data.}
\end{table}
\end{document}

stat

Ying Jin
Naoki Egami
Dominik Rothenhäusler

"Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization" by Ying Jin, Naoki Egami, and Dominik Rothenhäusler, arXiv:2412.08869v1, 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{Performance comparison of reasoning with and without the full table on WikiTQ and TabFact.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \textbf{Method} & \textbf{WikiTQ} & \textbf{TabFact} \\
 \midrule
 PoTable (Previous SOTA) & 64.73 & 88.93 \\
 TableMaster w/ Full Table in Reasoning & 78.13 & 90.12 \\
 TableMaster w/o Full Table in Reasoning & 77.23 (-0.90) & 89.58 (-0.54) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"TableMaster: A Recipe to Advance Table Understanding with Language Models" by Lang Cao and Hanbing Liu, arXiv:2501.19378v3, 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
\caption{Comparison between VAD and the proposed \textit{PadAug(HT)} on the 'chunk3s+head1s+tail1s' test sets.}
\begin{tabular}{lccccccc}
\toprule
\multirow{2}{*}{Model} & \multirow{2}{*}{Approch} & \multicolumn{2}{c}{Voxceleb1-O} & \multicolumn{2}{c}{Voxceleb1-E} & \multicolumn{2}{c}{Voxceleb1-H} \\
\cmidrule(lr){3-4}\cmidrule(lr){5-6}\cmidrule(lr){7-8}
 & & EER & minDCF & EER & minDCF & EER & minDCF \\
\midrule
\multirow{2}{*}{ResNet34}
 & VAD & 1.292 & 0.135 & 1.300 & 0.148 & 2.358 & 0.234 \\
 & PadAug(HT) & 1.170 & 0.122 & 1.261 & 0.144 & 2.311 & 0.229 \\
\midrule
\midrule
\multirow{2}{*}{ECAPA-TDNN-512}
 & VAD & 1.431 & 0.169 & 1.577 & 0.178 & 2.926 & 0.285 \\
 & PadAug(HT) & 1.435 & 0.162 & 1.569 & 0.174 & 2.831 & 0.278 \\
\midrule
\midrule
\multirow{2}{*}{ECAPA-TDNN-1024} & VAD & 1.223 & 0.142 & 1.345 & 0.155 & 2.515 & 0.249 \\
 & PadAug(HT) & 1.154 & 0.144 & 1.327 & 0.152 & 2.411 & 0.241 \\
\midrule
\midrule
\multirow{2}{*}{CAM++} & VAD & 1.026 & 0.149 & 1.219 & 0.151 & 2.444 & 0.250 \\
 & PadAug(HT) & 1.000 & 0.150 & 1.172 & 0.138 & 2.281 & 0.232 \\
\toprule
\end{tabular}
\end{table}
\end{document}

eess

Zijun Huang
Chengdong Liang
Jiadi Yao
Xiao-Lei Zhang

"PadAug: Robust Speaker Verification with Simple Waveform-Level Silence Padding" by Zijun Huang, Chengdong Liang, Jiadi Yao, and Xiao-Lei Zhang, arXiv:2508.14732v1, 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|c|c|c|}
 \hline
 \textbf{Algorithm} & \textbf{Recall} & \textbf{TNR} & \textbf{Balanced accuracy} \\
 \hline
 Logistic regression & 64\% & 81\% & 72\%\\
 \hline
 Random forest & 76\% & 81\% & 78\%\\
 \hline
 Neural network & 78\% & 80\% & 79\%\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{VCSP metrics of three different algorithms.}
\end{table}
\end{document}

math

Mouad Morabit
Guy Desaulniers
Andrea Lodi

"Machine-learning-based arc selection for constrained shortest path problems in column generation" by Mouad Morabit, Guy Desaulniers, and Andrea Lodi, arXiv:2201.02535v1, 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|cccc|}
 \hline
 & $T_1$ & $T_2$ & $T_3$ & $T_4$ \\\hline
 $\theta^*$ & $\langle (0.87,0.13);0.86 \rangle$ & $\langle (0.66,0.34);0.34 \rangle$& $\langle (0.84,0.16);0.82 \rangle$ & $\langle (0.65,0.35);0.35 \rangle$ \\ \hline
 $\theta^-$ & $\langle (0.53,0.47);0.51 \rangle$ & $\langle (0.32,0.68);0.68 \rangle$& $\langle (0.44,0.56);0.44 \rangle$ & $\langle (0.42,0.58);0.58 \rangle$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{C-IF positive and negative ideal solutions}
\end{table}
\end{document}

cs

"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Hao-Chiang Shao
Yuan-Rong Liao
Tse-Yu Tseng
Yen-Liang Chuo
Fong-Yi Lin

"Copy-Move Detection in Optical Microscopy: A Segmentation Network and A Dataset" by Hao-Chiang Shao, Yuan-Rong Liao, Tse-Yu Tseng, Yen-Liang Chuo, and Fong-Yi Lin, arXiv:2412.10258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{graphicx}
\usepackage{rotating}
\usepackage{booktabs}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrr}
 Method & \rotatebox[origin=c]{90}{Preprocessing} & \rotatebox[origin=c]{90}{Existence} & \rotatebox[origin=c]{90}{Validity} & \rotatebox[origin=c]{90}{Optimality} & \rotatebox[origin=c]{90}{Objective} & \rotatebox[origin=c]{90}{Solution} & \rotatebox[origin=c]{90}{Total}\\
 \midrule
 Naïve solve
 & /
 & /
 & /
 & /
 & $\approx n^{\omega}$
 & $\approx n^{\omega}$
 & $\approx pn^{\omega}$\\
 Basis system solving
 & $m^{\omega}$
 & $m$
 & $m^{\omega}$
 & $m^{\omega}$
 & $m^{\omega}$
 & $m^{\omega}$
 & $pm^{\omega}$\\
 Zuidwijk
 & $m^{\omega+1}$
 & $m$
 & $m^2$
 & $m^2$
 & $m$
 & N/A
 & $m^{\omega+1} + pm^2$\\
 Eigendecomposition
 & $m^\omega$
 & $m$
 & $m^2$
 & $m^2$
 & $m$
 & $m^2$
 & $m^\omega + pm^2$\\
 Schur
 & $m^\omega$
 & $m$
 & $m^2$
 & $m^2$
 & $m^2$
 & $m^2$
 & $m^\omega + pm^2$\\
 Eigendecomposition ($\alpha\beta$)
 & $m^\omega$
 & $m$
 & $m^2$
 & $m^2$
 & $m^2$
 & $m^2$
 & $m^\omega + pm^2$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Complexity of various methods to compute $o^*(\lambda)$ and $x_B(\lambda)$ for all $\lambda \in \Lambda$, $|\Lambda|=p$, in big-Oh ($\mathcal{O}(\cdot)$) asymptotic complexity. In the table, $n$ is the number of variables in the original problem and $m$ the number of constraints, with $m < n$ in general. $\mathcal{O}(n^\omega)$ is the complexity of multiplying two $n \times n$ matrices.}
\end{table}
\end{document}

cs

Guillaume Derval
Bardhyl Miftari
Damien Ernst
Quentin Louveaux

"Efficient LP warmstarting for linear modifications of the constraint matrix" by Guillaume Derval, Bardhyl Miftari, Damien Ernst, and Quentin Louveaux, arXiv:2501.04151v1, licensed under CC BY-SA 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{Comparison of cross validated mean absolute error (MAE) errors in percentage for each channel selection method, attention-based selection, reduction by PCA and random channel selection when $n$ number of channels are selected for both T1 and T2 values.}
\begin{tabular}{l|cc}
\hline
Method / $n$ & T1 - MAE(\%) & T2 - MAE(\%) \\ \hline
Attention / 100 & \textbf{2.07} & \textbf{0.79} \\
PCA / 100 & 6.22 & 1.48 \\
Random / 100 & 2.26 & 0.84 \\ \hline
Attention / 200 & \textbf{1.88} & \textbf{0.68} \\
PCA / 200 & 4.49 & 1.71 \\
Random / 200 & 2.08 & 0.69 \\ \hline
Attention / 300 & \textbf{1.93} & \textbf{0.77} \\
PCA / 300 & 4.85 & 1.65 \\
Random / 300 & 2.08 & 0.78 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Refik Soyak
Ebru Navruz
Eda Ozgu Ersoy
Gastao Cruz
Claudia Prieto
Andrew P. King
Devrim Unay
Ilkay Oksuz

"Channel Attention Networks for Robust MR Fingerprinting Matching" by Refik Soyak, Ebru Navruz, Eda Ozgu Ersoy, Gastao Cruz, Claudia Prieto, Andrew P. King, Devrim Unay, and Ilkay Oksuz, arXiv:2012.01241v1, 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}{ccccc}
 & \multicolumn{2}{c}{Boundary condition (i)} &\multicolumn{2}{c}{Boundary condition (i)}\\
\cline{2-5}
$\Delta\xi$ & $E^n$ & $p$ & $E^n$ & $p$ \\
\hline
 $1/10$ & $2.21\times 10^{-3}$ & & $7.03\times 10^{-4}$ & \\
 $1/20$ & $5.35\times 10^{-4}$ & $2.050$ & $1.72\times 10^{-4}$ & $2.024$ \\
 $1/40$ & $1.31\times 10^{-4}$ & $2.029$ & $4.29\times 10^{-5}$ & $2.010$ \\
 $1/80$ & $3.22\times 10^{-5}$ & $2.021$ & $1.06\times 10^{-5}$ & $2.004$ \\
 $1/160$ & $7.84\times 10^{-6}$ & $2.041$ & $2.66\times 10^{-6}$ & $2.002$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{The error of the numerical solution of $F$, fixed at time $t^n=1$ and the order of accuracy for boundary condition $(i)$ and $(ii)$. }
\end{table}
\end{document}

math

Evangelos F. Magirou
Paraskevas Vassalos
Nikolaos Barakitis

"On a Boundary Updating Method for the Scalar Stefan Problem" by Evangelos F. Magirou, Paraskevas Vassalos, and Nikolaos Barakitis, arXiv:2202.06418v1, 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{Computational Complexity}
\begin{tabular}{|c||c|}
\hline\hline
\textbf{Technique}
& \textbf{Number of CMs}
\\ \hline \hline
Estimation Technique for SU-OTFS
& $\frac{QN^2 L_{\rm{ch}}}{2} + MN(2L_{\rm{ch}}+Q-1)$
\\ \hline
Estimation Technique for MU-OTFS
& $\frac{Q N^2 (L_{\rm{ch}} \!+\! \kappa_{\rm{max}})}{2} \!\!+\!\! MN(2L_{\rm{ch}} \!+\! 2\kappa_{\rm{max}} \!+\! \log_2{\!MN} \!\!+\! Q \!-\! 1)$
\\ \hline
TO and Channel Estimation (Absorbed CFO)
&$MN(2L_{\rm{ch}}+Q-1)$
\\ \hline\hline
\end{tabular}
\end{table}
\end{document}

eess

Mohsen Bayat
Sanoopkumar P. S.
Arman Farhang

"Time and Frequency Synchronization for Multiuser OTFS in Uplink" by Mohsen Bayat, Sanoopkumar P. S., and Arman Farhang, arXiv:2507.17966v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
\toprule
\textbf{Category} & \textbf{Average Accuracy} \\
\midrule
Countries & 91.2\% \\
Professions & 84.2\% \\
Body Parts & 82.7\% \\
Academic Subjects & 73.0\% \\
Clothing & 72.6\% \\
\bottomrule
\end{tabular}
\caption{Average Category Performance (Layer 31)}
\end{table}
\end{document}

eess

Neta Glazer
Yael Segal-Feldman
Hilit Segev
Aviv Shamsian
Asaf Buchnick
Gill Hetz
Ethan Fetaya
Joseph Keshet
Aviv Navon

"Beyond Transcription: Mechanistic Interpretability in ASR" by Neta Glazer, Yael Segal-Feldman, Hilit Segev, Aviv Shamsian, Asaf Buchnick, Gill Hetz, Ethan Fetaya, Joseph Keshet, and Aviv Navon, arXiv:2508.15882v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Mark Turner
Thorsten Koch
Felipe Serrano
Michael Winkler

"Adaptive Cut Selection in Mixed-Integer Linear Programming" by Mark Turner, Thorsten Koch, Felipe Serrano, and Michael Winkler, arXiv:2202.10962v3, 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
\caption{Performance of proposed AuthNet model w.r.t different datasets}
\begin{tabular}{lcc}
\hline
\textbf{Metric} & \textbf{MIRACL-VC1} & \textbf{Collated Dataset} \\ \hline
\textbf{Sensitivity} & 0.998 & 0.987 \\ %\hline
\textbf{Specificity} & 0.969 & 0.976 \\ %\hline
\textbf{Accuracy} & 0.980 & 0.981 \\ %\hline
\textbf{AUC Score} & 0.990 & 0.989 \\ %\hline
\textbf{Equal Error Rate}& 0.023 & 0.037 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Mohit Raghavendra
Pravan Omprakash
B R Mukesh
Sowmya Kamath

"AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements" by Mohit Raghavendra, Pravan Omprakash, B R Mukesh, and Sowmya Kamath, arXiv:2012.02515v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

DOFEN: Deep Oblivious Forest ENsemble

Kuan-Yu Chen
Ping-Han Chiang
Hsin-Rung Chou
Chih-Sheng Chen
Tien-Hao Chang

"DOFEN: Deep Oblivious Forest ENsemble" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccc} \toprule \toprule
 Building no. & Solar capacity (kWp) & Battery capacity (kWh) & Grid con. capacity (kW) \\
 \midrule \midrule
 0 & 617 & 1,119 & -- \\
 1 & 538 & 973 & -- \\
 2 & 497 & 844 & -- \\
 3 & 583 & 983 & -- \\
 4 & 554 & 989 & -- \\ \midrule
 Total & 2,789 & 4,908 & 1,227 \\
 \bottomrule \bottomrule
 \end{tabular}
\caption{Installed asset capacities for prior system design.}
\end{table}
\end{document}

eess

"Quantifying the benefit of load uncertainty reduction for the design of district energy systems under grid constraints using the Value of Information" by Max Langtry and Ruchi Choudhary, arXiv:2412.16105v2, 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|c|c|c|}
			\hline
			$k$ & 0 & 1 & 2 & 3 & 4 \\ \hline
			$M_k$ & 2.9065e+04 & -7.0352e+09 & 1.9534e+15 & -5.7704e+20 & 1.7634e+26 \\ \hline
		\end{tabular}
\end{adjustbox}
\caption{\textit{The first few coefficients $M_k$ (Eq.~) of the power series . $\varepsilon = 10^{-4}$.}}
\end{table}
\end{document}

math

Clarissa Astuto
Antonio Raudino
Giovanni Russo

"Multiscale Modeling of Sorption Kinetics" by Clarissa Astuto, Antonio Raudino, and Giovanni Russo, arXiv:2202.02552v2, 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}{|c|c|}
\hline
\textbf{Number of Layers} & \textbf{Number of Units} \\ \hline
1 & 128 or 256 \\
2 & 128, 64 \\ \hline
\multicolumn{2}{|c|}{Dropout (0.5)} \\ \hline
\multicolumn{2}{|c|}{Output Dense} \\ \hline
\end{tabular}
\caption{BiGRU Architecture Configurations.}
\end{table}
\end{document}

eess

Kleanthis Avramidis
Agelos Kratimenos
Christos Garoufis
Athanasia Zlatintsi
Petros Maragos

"Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms" by Kleanthis Avramidis, Agelos Kratimenos, Christos Garoufis, Athanasia Zlatintsi, and Petros Maragos, arXiv:2102.06930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Heterogeneous Effects by CEO Age}
\begin{tabular}{lcccc}
\toprule
 & Young CEOs & Middle-aged & Older CEOs & Difference \\
 & (Under 50) & (50-59) & (60+) & (Young-Old) \\
\midrule
AI Investment Effect & 0.031*** & 0.024*** & 0.018** & 0.013** \\
 & (0.009) & (0.008) & (0.009) & (0.006) \\
\midrule
Sample Share of Adopters & 38.2\% & 28.7\% & 15.3\% & --- \\
Average Investment (¥M) & 145.2 & 98.4 & 67.1 & --- \\
\midrule
Observations & 892 & 1,146 & 697 & 2,735 \\
First-stage F & 21.4 & 18.7 & 12.3 & --- \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises" by Tatsuru Kikuchi, arXiv:2508.03757v1, 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{Computation times in [s] for scenario .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccccc|} \hline
		$n$ & 5 & 6 & 7 & 8 & 9 \\
		\hline
		DFS & 0.03 & 0.41 & 318 & $>$600 & - \\
		Alg. & 0.05 & 0.48 & 6.3 & 88 & 2462 \\
		$N$ & 125 & 1296 & 16807 & 262144 & 4782969\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"How to find all connections in the Pantelides algorithm for delay differential-algebraic equations" by Daniel Collin, arXiv:2202.01772v1, 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{Numerical comparisons of four methods in testing across 1,560 slice pairs. The best and the second best results are \textbf{bold} and \underline{underlined}, respectively.}
\begin{tabular}{c|c|c|c|cccc|ccc}
\hline
Methods& L1~$\downarrow$SSIM~$\uparrow$ &	PSNR~$\uparrow$ 	& IS~$\uparrow$\\\hline\hline
VAEonly 	&\textbf{148.8}$\pm$0.1&	0.9486$\pm$0.0014&	32.68$\pm$0.06&	11.13$\pm$0.11\\
VAE-GAN 	&151.4$\pm$0.2&	0.9507$\pm$0.0013&	34.14$\pm$0.05&	11.68$\pm$0.17\\
Pix2Pix 	&150.2$\pm$0.2&	\underline{0.9612}$\pm$0.0011&	\underline{36.81}$\pm$0.07&	\underline{13.77}$\pm$0.15\\\hline
Proposed &\underline{149.6}$\pm$0.2&	\textbf{0.9746}$\pm$0.0015& \textbf{38.72}$\pm$0.07& \textbf{15.58}$\pm$0.12\\\hline
\end{tabular}
\end{table}
\end{document}

eess

Xiaofeng Liu
Fangxu Xing
Jerry L. Prince
Aaron Carass
Maureen Stone
Georges El Fakhri
Jonghye Woo

"Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis" by Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Aaron Carass, Maureen Stone, Georges El Fakhri, and Jonghye Woo, arXiv:2101.05439v1, 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|c|c|}
 \hline
 & $h=|\cdot|^2$ & $h=|\cdot|^3$ & $h=|\cdot|^4$ & $h=|\cdot|^6$ & $h = |\cdot|^8$ & $h = |\cdot|^{10}$\\
 \hline
 \hline
 $\alpha(h)$ & 1 &$\frac{1 - \sqrt{2}\sqrt[4]{3} + \sqrt{3}}{2}$ & $2-\sqrt{3}$ & $\frac{7-3\sqrt{5}}{2}$ & & \\
 \hline
 \end{tabular}
\caption{Special cases of the symmetry coefficient for Legendre functions $h$. For a derivation of the computation of $\alpha(|\cdot|^p)$ when $p \in \{3, 6, 8, 10\}$, see .}
\end{table}
\end{document}

math

"The Symmetry Coefficient of Positively Homogeneous Functions" by Max Nilsson and Pontus Giselsson, arXiv:2504.18296v1, 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{Metrics Benchmark. Baseline: Dice + CrossEntropy. All "Dice" scores are in $\uparrow$\%, Hausdorff in mm($\downarrow$)}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \textbf{Model} & \textbf{Dice } & \textbf{CLDice } & \textbf{SurfaceDice} & \textbf{Hausdorff}\\
 \hline
 Baseline IRCAD & 72.28 (7.2) & 72.97 (7.8) & 79.06 (7.7) & 68.83 (53.36) \\
 CLDice IRCAD & 73.29 (6.4) & 75.24 (6.9) & 80.57 (6.6) & 65.05 (52.09)\\
 Baseline \textbf{L}IRCAD & \textbf{76.22 (5.9)} & 77.29 (5.4) & 83.57 (5) & 68 (54.7) \\
 CLDice \textbf{L}IRCAD & 76.05 (5.2) & \textbf{77.95 (5.1)} & \textbf{83.62 (5.1)} & \textbf{61 (51.7)} \\
 NeuralSkel \textbf{L}IRCAD & 75.5 (6.0) & 76.75 (5.8) & 82.4 (6.0) & 70 (53.6) \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Yassine Machta
Omar Ali
Kevin Hakkakian
Ana Vlasceanu
Amaury Facque
Nicolas Golse
Irene Vignon-Clementel

"Enhancing the automatic segmentation and analysis of 3D liver vasculature models" by Yassine Machta, Omar Ali, Kevin Hakkakian, Ana Vlasceanu, Amaury Facque, Nicolas Golse, and Irene Vignon-Clementel, arXiv:2411.15778v4, 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{Sample size of total study ($n_{total}$), expected size of treatment arm $(n_{treated})$, and covariate dimension $(d)$ for the semi-synthetic datasets of second experiment. For the ACIC-2017 synthetic dataset, we employed simulation setting 24, characterized by additive errors, a large signal, large noise, and strong confounding.}
\begin{tabular}{|c|c|c|c|c|}
\hline
Dataset & $n_{total}$ & $n_{treated}$ & $d$ & source\\
\hline
\hline
ACIC-2017 & 4302 & $2233$ & 58 & \\
\hline
ACIC-2018 & varied & varied & varied & \\
\hline
IHDP & 672 & 123 & 25 & \\
\hline
Lalonde CPS & 16177 & $167$ & 9 & \\
\hline
Lalonde PSID & 2675 & $175$ & 9 & \\
\hline
Twins & 11984 & 8935 & 76 & \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Doubly robust inference via calibration

Lars van der Laan
Alex Luedtke
Marco Carone

"Doubly robust inference via calibration" by Lars van der Laan, Alex Luedtke, and Marco Carone, arXiv:2411.02771v2, 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{Experimental data}
\begin{tabular}{lccc}
 & Myocarditis ($R=1$) & No myocarditis ($R=0$) & Total\\
 Vaccinated ($E=1$) & 30 & 70 & 100\\
 Unvaccinated ($E=0$) & 12 & 88 & 100
 \end{tabular}
\end{table}
\end{document}

stat

Individual Causation with Biased Data

"Individual Causation with Biased Data" by Monica Musio and Philip Dawid, arXiv:2311.08242v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c}
\toprule
\hline
Bandwidth & {$I(p_{i,j})$} & \textit{K}=1 & \textit{K}=3 & \textit{K}=5 \\
\hline
\multirow{4}{*}{h=5} & $\min(p_{k}(i,j))$ & 94.75 & \textbf{88.82} & \textbf{93.70} \\
& $\max(p_{k}(i,j))$ & 95.42 & 95.19 & \textbf{87.56} \\
& $\operatorname{avg}(p_{k}(i,j))$ & 95.61 & 95.35 & 95.19 \\
& $Q_t(p_{k}(i,j))$ & 94.97 & 95.62 & 95.45 \\
\hline
\multirow{1}{*}{$h\rightarrow +\infty$, LASSL} & None & \multicolumn{3}{c}{95.30} \\
\hline
\bottomrule
\end{tabular}
\caption{Comparison of different $K$ and $I(p_{i,j})$ on CIFAR10 dataset with 40 labeled data.}
\end{table}
\end{document}

stat

Shuyang Liu
Ruiqiu Zheng
Yunhang Shen
Ke Li
Xing Sun
Zhou Yu
Shaohui Lin

"Probability-density-aware Semi-supervised Learning" by Shuyang Liu, Ruiqiu Zheng, Yunhang Shen, Ke Li, Xing Sun, Zhou Yu, and Shaohui Lin, arXiv:2412.17547v2, 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}{rcc}
		\toprule
		& $\alpha = 0.5$ & $\alpha = 0.75$ \\
		\midrule
		ex. () & 0.05 (0.00) & 0.04 (0.00) \\
		ex. () & 0.17 (1.13) & 0.19 (0.19) \\
		ex. () & 0.25 (0.25) & 0.33 (0.35) \\
		ex. () & 0.32 (0.37) & 0.56 (0.56) \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Estimated exponent $s$ in the fitted error function $a t^{-s}$ as in () for examples (-). Here, the temporal grid was taken according to () with $\gamma = 10^{-1}$ while the spatial mesh $h = 10^2$. The number in parenthesis is the exact value $\alpha(2-p)/2$ rounded to two decimal places.}
\end{table}
\end{document}

math

"Error of the Galerkin scheme for a semilinear subdiffusion equation with time-dependent coefficients and nonsmooth data" by Łukasz Płociniczak, arXiv:2202.13728v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of VLSTMs at various levels}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccccccc|c|c|}
\hline
\multicolumn{8}{|c|}{\textbf{VLSTM Levels}} & \textbf{Actionable} & \textbf{Weighted} \\
1 & 2 & 5 & 20 & 100 & 500 & 2000 & 5000 & \textbf{Accuracy} (\%) & \textbf{F-score}\\
\hline
\checkmark & & & & & & & & 55.33 & 0.52\\
\hline
 & \checkmark & & & & & & & 54.12 & 0.53\\
\hline
 & & \checkmark & & & & & & 60.36 & 0.58\\
\hline
 & & & \checkmark & & & & & \textbf{61.37} & \textbf{0.59} \\
\hline
 & & & & \checkmark & & & & 56.97 & 0.57 \\
\hline
 & & & & & \checkmark & & & 53.63 & 0.54\\
\hline
 & & & & & & \checkmark & & 48.38 & 0.49\\
\hline
 & & & & & & & \checkmark & 50.96 & 0.50 \\
\hline
\hline
\checkmark & \checkmark & & & & & & & 58.98 & 0.57 \\
\hline
\checkmark & \checkmark & \checkmark & & & & & & 66.19 & 0.65\\
\hline
\checkmark & \checkmark & \checkmark & \checkmark & & & & & 71.41 & 0.68\\
\hline
\checkmark & \checkmark & \checkmark & \checkmark & \checkmark & & & & 75.54 & 0.75\\
\hline
\checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & & & 78.45 & 0.76 \\
\hline
\checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & & 79.82 & 0.78 \\
\hline
\checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \textbf{80.70} & \textbf{0.79} \\
\hline
 & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & 80.29 & \textbf{0.79} \\
 \hline
 & & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & 79.68 & 0.78\\
 \hline
 & & & \checkmark & \checkmark & \checkmark & \checkmark & \checkmark & 76.52 & 0.73 \\
\hline
 & & & & \checkmark & \checkmark & \checkmark & \checkmark & 70.69 & 0.71 \\
\hline
 & & & & & \checkmark & \checkmark & \checkmark & 57.46 & 0.54 \\
\hline
 & & & & & & \checkmark & \checkmark & 52.01 & 0.52\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading" by Prakhar Ganesh and Puneet Rakheja, arXiv:1809.01506v3, 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{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Konstantin Burlachenko
Samuel Horváth
Peter Richtárik

"FL_PyTorch: optimization research simulator for federated learning" by Konstantin Burlachenko, Samuel Horváth, and Peter Richtárik, arXiv:2202.03099v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

María Jaenada
Elena Castilla
Nirian Martín
Leandro Pardo

"Robust approach for comparing two dependent normal populations through Wald-type tests based on Rényi's pseudodistance estimators" by María Jaenada, Elena Castilla, Nirian Martín, and Leandro Pardo, arXiv:2202.00982v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The Impact of the Number of Sub-Contours($N_{c}$) on Model Accuracy and Resource Consumption.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \toprule
 Method & $\mathrm{AP}_{vol}$ & $\mathrm{AP}_{50}$ & $\mathrm{AP}_{70}$ & FPS & Memory Usage (MB) \\
 \midrule
 $N_{c}$=0 & 64.0 & 74.2 & 58.3 & 49.6 & 1632 \\
 $N_{c}$=4 & 65.1 \color{green}({+1.1}) & 74.3 \color{green}({+0.1}) & 60.3 \color{green}({+2.0}) & 39.8 \color{red}({-9.8}) & 2072 \color{red}({+440}) \\
 $N_{c}$=8 & 65.8 \color{green}({+0.7}) & 75.0 \color{green}({+0.7}) & 61.4 \color{green}({+1.1}) & 38.8 \color{red}({-1.0}) & 2640 \color{red}({+568}) \\
 $N_{c}$=16 & 66.5 \color{green}({+0.7}) & 75.6 \color{green}({+0.6}) & 62.1 \color{green}({+0.7}) & 28.3 \color{red}({-10.5}) & 3734 \color{red}({+1094}) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Weiwei Yao
Chen Li
Minjun Xiong
Wenbo Dong
Hao Chen
Xiong Xiao

"ContourFormer: Real-Time Contour-Based End-to-End Instance Segmentation Transformer" by Weiwei Yao, Chen Li, Minjun Xiong, Wenbo Dong, Hao Chen, and Xiong Xiao, arXiv:2501.17688v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \toprule
 Category & definitely (\%) & possibly (\%) \\
 \toprule
 \textbf{\em As valid as gold} \\
 Semantically the same & 60 & 22 \\
 Open-ended question & 6 & 4 \\
 Ambiguous entity/event references & 20 & 20 \\
 Answer has different granularity & 14 & 6 \\
 Gold is incorrect & 2 & 6 \\
 \midrule
 \textbf{\em Valid but not the best} \\
 Ambiguous answer type & 0 & 20 \\
 Answer is time-dependent & 0 & 18 \\
 \midrule
 \textbf{\em Less plausible} \\
 Conflicting info in Wikipedia & 0 & 4 \\
 Plausible only in certain condition & 0 & 4 \\
 Mismatch with question intent & 0 & 8 \\
 Incorrect prediction & 0 & 2 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Sewon Min
Jordan Boyd-Graber
Chris Alberti
Danqi Chen
Eunsol Choi
Michael Collins
Kelvin Guu
Hannaneh Hajishirzi
Kenton Lee
Jennimaria Palomaki
Colin Raffel
Adam Roberts
Tom Kwiatkowski
Patrick Lewis
Yuxiang Wu
Heinrich Küttler
Linqing Liu
Pasquale Minervini
Pontus Stenetorp
Sebastian Riedel
Sohee Yang
Minjoon Seo
Gautier Izacard
Fabio Petroni
Lucas Hosseini
Nicola De Cao
Edouard Grave
Ikuya Yamada
Sonse Shimaoka
Masatoshi Suzuki
Shumpei Miyawaki
Shun Sato
Ryo Takahashi
Jun Suzuki
Martin Fajcik
Martin Docekal
Karel Ondrej
Pavel Smrz
Hao Cheng
Yelong Shen
Xiaodong Liu
Pengcheng He
Weizhu Chen
Jianfeng Gao
Barlas Oguz
Xilun Chen
Vladimir Karpukhin
Stan Peshterliev
Dmytro Okhonko
Michael Schlichtkrull
Sonal Gupta
Yashar Mehdad
Wen-tau Yih

"NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned" by Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, and Wen-tau Yih, arXiv:2101.00133v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Reza Atefi
Manuel Iori
Majid Salari
Dario Vezzali

"Solution of a Practical Vehicle Routing Problem for Monitoring Water Distribution Networks" by Reza Atefi, Manuel Iori, Majid Salari, and Dario Vezzali, arXiv:2202.02549v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{\color{black}Number of samples in the sets } $V_+, V_{em+}, V_{\rm{DLC}}\cap V_+,V_{\rm{DLC}}\cap V_{em+},V_{\rm{DD}}\cap V_+\mbox{ and }V_{\rm{DD}}\cap V_{em+}$ {\color{black}using hit-and-miss methods} and Theorem~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 $n$ &$10^4$ & $10^5$ & $10^6$ & $10^7$\\
 \hline
 {\color{black}Samples in $V_4^{Markov}$} & {\color{black}280} & {\color{black}2767}& {\color{black}27829} & {\color{black}277628}\\
 \hline
 {\color{black}Samples $V_+$} & {\color{black}23}&{\color{black}192}&{\color{black}1999}&{\color{black}20601}\\
 \hline
 {\color{black}Samples in $V_{em+}$} & {\color{black}3}&{\color{black}34}&{\color{black}359}&{\color{black}3511}\\
 \hline
 {\color{black}Samples in $V_{\rm{DLC}}\cap V_+$}& {\color{black}19}& {\color{black}154} & {\color{black}1541}& {\color{black}15830}\\\hline
 {\color{black}Samples in $V_{\rm{DD}}\cap V_+$}&{\color{black}3}&{\color{black}31}&{\color{black}262}&{\color{black}2889}\\\hline
 {\color{black}Samples in $V_{\rm{DLC}}\cap V_{em+}$}& {\color{black}3}&{\color{black}34}& {\color{black}357}& {\color{black}3503}\\\hline
 {\color{black}Samples in $V_{\rm{DD}}\cap V_{em+}$}&{\color{black}1}&{\color{black}15}&{\color{black}105}&{\color{black}1011}\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Muhammad Ardiyansyah
Dimitra Kosta
Jordi Roca-Lacostena

"Embeddability of centrosymmetric matrices capturing the double-helix structure in natural and synthetic DNA" by Muhammad Ardiyansyah, Dimitra Kosta, and Jordi Roca-Lacostena, arXiv:2202.05889v2, 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{Coverage rates of the bootstrap 95\% confidence intervals when employing the WQ-SA method in Simulation 3}
\begin{tabular}{lcccccc}
 \hline
 & $m$& $\psi_{10}$ & $\psi_{11}$ & $\psi_{20}$ & $\psi_{2A}$ & $\psi_{22}$ \\
 \hline
 Sample size 500 &413 & 0.961 & 0.958 & 0.950 & 0.960 & 0.961 \\
 Sample size 2000 &1864 & 0.946 & 0.944 & 0.947 & 0.948 & 0.939 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

"Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates" by Jian Sun, Bo Fu, and Li Su, arXiv:2312.01735v5, 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|cccc|}
 \hline
 Engineers & $T_1$ & $T_2$ & $T_3$ &$T_4$ \\ \hline
 $E_1$ & M & L & L &ML \\ \hline
 $E_2$ & ML & M & ML &M \\ \hline
 $E_3$ & L & EL & L &VL \\ \hline

 \end{tabular}
\caption{The evaluation of $P'_3$ based on the linguistic terms}
\end{table}
\end{document}

math

"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 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}
 Predicates & Expression \\
 \hline
 $\psi_{g_1}$ & $d^{g_1}_{\tau} < 0.05$ \\
 $\psi_{g_2}$ & $d^{g_2}_{\tau} < 0.05$ \\
 $\psi_{g_3}$ & $d^{g_3}_{\tau} < 0.05$ \\
 $\psi_{o_1}$ & $d^{o_1}_{\tau} > r_{obs}$ \\
 $\psi_{o_2}$ & $d^{o_2}_{\tau} > r_{obs}$ \\
 $\psi_{coll}$ & $d^{rob}_{\tau} > 2\,r_{rob}$
 \end{tabular}
\caption{Predicates used in the TLTL formulation.}
\end{table}
\end{document}

eess

Clara Lucía Galimberti
Luca Furieri
Giancarlo Ferrari-Trecate

"Parametrizations of All Stable Closed-loop Responses: From Theory to Neural Network Control Design" by Clara Lucía Galimberti, Luca Furieri, and Giancarlo Ferrari-Trecate, arXiv:2412.19280v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
\toprule
& \multicolumn{2}{c}{miniImageNet} & \multicolumn{2}{c}{tieredImageNet} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & 1-shot & 5-shot & 1-shot & 5-shot \\
\midrule
No pruning & 52.8 $\pm$ 0.8 & 67.4 $\pm$ 0.4 & 54.5 $\pm$ 0.8 & 71.3 $\pm$ 0.4 \\
\midrule
Top-1 & 52.8 $\pm$ 0.8 & \textbf{67.6} $\pm$ 0.4 & \textbf{55.1} $\pm$ 0.8 & \textbf{72.7} $\pm$ 0.4 \\
Top-2 & \textbf{52.9} $\pm$ 0.8 & \textbf{67.6} $\pm$ 0.4 & 54.1 $\pm$ 0.8 & \textbf{72.7} $\pm$ 0.4 \\
Top-3 & 52.6 $\pm$ 0.8 & 67.4 $\pm$ 0.4 & 55.0 $\pm$ 0.8 & 72.4 $\pm$ 0.4 \\
\bottomrule
\end{tabular}
\caption{Mean meta-test accuracy scores on 5-way miniImageNet and tieredImageNet classification with 95\% confidence intervals computed over 5 different runs. We used a Conv-4 backbone with 64 channels for these results.}
\end{table}
\end{document}

stat

"Subspace Adaptation Prior for Few-Shot Learning" by Mike Huisman, Aske Plaat, and Jan N. van Rijn, arXiv:2310.09028v1, 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{Number} & \textbf{Description} \\
 & \\
 \hline
 & \\
 RG-1 & Utilize data-driven algorithms to derive a truly objective classification heuristic. \\
 & \\
 \hline
 & \\
 RG-2 & Rank candidate sector universes against each other using entirely objective criteria. \\
 & \\
 \hline
 & \\
 RG-3 & Evaluate our risk-adjusted return optimal sector universe against the benchmark. \\
 & \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Specific, itemized research goals of the Learned Sectors project.}
\end{table}
\end{document}

q-fin

"Learned Sectors: A fundamentals-driven sector reclassification project" by Rukmal Weerawarana, Yiyi Zhu, and Yuzhen He, arXiv:1906.03935v1, licensed under CC BY-SA 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|r|r|}
\hline
\multirow{2}{*}{\textbf{Community}} & \multicolumn{4}{c|}{\textbf{Reddit}} & \multicolumn{4}{c|}{\textbf{Voat}} \\
\cline{2-9}
& \textbf{Posts} & \textbf{Comments} & \textbf{Users} & \textbf{Avg. Sent.} & \textbf{Posts} & \textbf{Comments} & \textbf{Users} & \textbf{Avg. Sent.} \\
\hline
funny & 4.2M & 57.6M & 5.5M & 0.056 & 47K & 97K & 20K & 0.001 \\
\hline
gaming & 2.9M & 41.4M & 4.1M & 0.114 & 29K & 41K & 12K & 0.093 \\
\hline
pics & 2.4M & 49.5M & 4.9M & 0.075 & 15K & 24K & 9K & 0.037 \\
\hline
videos & 3.1M & 33.7M & 3.2M & 0.054 & 45K & 41K & 14K & -0.037 \\
\hline
gifs & 449K & 20.6M & 2.8M & 0.051 & 8K & 14K & 6K & 0.029 \\
\hline
technology & 848K & 10.9M & 1.3M & 0.055 & 35K & 49K & 19K & 0.029 \\
\hline
fatpeoplehate & 0 & 1.4M & 79K & 0.011 & 75K & 279K & 22K & 0.002 \\
\hline
GreatAwakening & 67K & 848K & 26K & 0.079 & 103K & 222K & 12K & 0.016 \\
\hline
MillionDollarExtreme & 69K & 1.2M & 28K & 0.008 & 7K & 14K & 3K & 0.000 \\
\hline
CringeAnarchy & 195K & 6.3M & 342K & -0.031 & 1K & 2K & 1K & -0.038 \\
\hline
KotakuInAction & 128K & 6.7M & 146K & -0.029 & 3K & 5K & 2K & -0.028 \\
\hline
MensRights & 148K & 3.0M & 185K & -0.076 & 2K & 2K & 1K & -0.162 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Community-level statistics for Reddit and Voat. The first six rows show general-interest communities, while the last six show controversial communities. Average sentiment scores range from -1 (negative) to 1 (positive).}
\end{table}
\end{document}

cs

Marija Mitrović Dankulov
Aleksandar Tomašević
Slobodan Maletić
Miroslav Anđelković
Ana Vranić
Darja Cvetković
Boris Stupovski
Dušan Vudragović
Sara Major
Aleksandar Bogojević

"Multi-Platform Aggregated Dataset of Online Communities (MADOC)" by Marija Mitrović Dankulov, Aleksandar Tomašević, Slobodan Maletić, Miroslav Anđelković, Ana Vranić, Darja Cvetković, Boris Stupovski, Dušan Vudragović, Sara Major, and Aleksandar Bogojević, arXiv:2501.12886v1, 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}{|l|c|c|c|c|}
\hline
\textbf{Step Size Schedule} & \textbf{FID$\downarrow$} & \textbf{LPIPS$\downarrow$} & \textbf{PSNR$\uparrow$} & \textbf{SSIM$\uparrow$} \\
\hline
Cosine Annealing & \textbf{49.66} & \textbf{0.1254} &\textbf{ 25.53} & \textbf{0.724} \\
Fixed Lower Bound & 70.73 & 0.1790& 24.22 & 0.677 \\
Fixed Upper Bound & 59.94 & 0.1559 & 24.22 & 0.686\\
\hline
\end{tabular}
\caption{Performance comparison of different scale schedules on the FFHQ dataset. Metrics include FID, LPIPS, PSNR, and SSIM.}
\end{table}
\end{document}

stat

Darshan Thaker
Abhishek Goyal
René Vidal

"Frequency-Guided Posterior Sampling for Diffusion-Based Image Restoration" by Darshan Thaker, Abhishek Goyal, and René Vidal, arXiv:2411.15295v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Naoki Saito
Stefan C. Schonsheck
Eugene Shvarts

"Multiscale Hodge Scattering Networks for Data Analysis" by Naoki Saito, Stefan C. Schonsheck, and Eugene Shvarts, arXiv:2311.10270v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mouad El Bouchattaoui
Myriam Tami
Benoit Lepetit
Paul-Henry Cournède

"Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data" by Mouad El Bouchattaoui, Myriam Tami, Benoit Lepetit, and Paul-Henry Cournède, arXiv:2310.10559v2, 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{ABT variables, part 1}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \hline
 & & \\
 Nr & Name & Description \\
 & & \\
 \hline
1 & cid & Id of application \\
2 & aid & Id of Cust. \\
3 & period & Year, month in format YYYYMM \\
4 & act\_age & Actual Cust. age \\
5 & act\_cc & Actual credit capacity (installment plus spendings) over income \\
6 & act\_loaninc & Loan amount over income \\
7 & app\_income & Cust. income \\
8 & app\_loan\_amount & Loan amount \\
9 & app\_n\_installments & Number of installments \\
10 & app\_number\_of\_children & Number of children \\
11 & app\_spendings & Spendings \\
12 & app\_installment & Installment amount \\
13 & app\_char\_branch & Branch \\
14 & app\_char\_gender & Gender \\
15 & app\_char\_job\_code & Job code \\
16 & app\_char\_marital\_status & Marital status \\
17 & app\_char\_city & City type \\
18 & app\_char\_home\_status & Home status \\
19 & app\_char\_cars & Cars \\
20 & act\_call\_n\_loan & Actual Cust. loan number \\
21 & act\_ccss\_n\_loan & Actual Cust. loan number of Css product \\
22 & act\_cins\_n\_loan & Actual Cust. loan number of Ins product \\
23 & act\_ccss\_maxdue & Cust. actual maximal due installments on product css \\
24 & act\_cins\_maxdue & Cust. actual maximal due installments on product ins \\
25 & act\_ccss\_n\_loans\_act & Cust. actual number of loans on product css \\
26 & act\_cins\_n\_loans\_act & Cust. actual number of loans on product ins \\
27 & act\_ccss\_utl & Cust. actual utilization rate on product css \\
28 & act\_cins\_utl & Cust. actual utilization rate on product ins \\
29 & act\_call\_cc & Cust. credit capacity (all installments plus spendings) over income \\
30 & act\_ccss\_cc & Cust. credit capacity (installment plus spendings) over income on product css \\
31 & act\_cins\_cc & Cust. credit capacity (installment plus spendings) over income on product ins \\
32 & act\_ccss\_dueutl & Cust. due installments over all installments rate on product css \\
33 & act\_cins\_dueutl & Cust. due installments over all installments rate on product ins \\
34 & act\_cus\_active & Cust. had active (status=A) loans one month before \\
35 & act\_ccss\_n\_statB & Cust. historical number of finished loans with status B on product css \\
36 & act\_cins\_n\_statB & Cust. historical number of finished loans with status B on product ins \\
37 & act\_ccss\_n\_statC & Cust. historical number of finished loans with status C on product css \\
38 & act\_cins\_n\_statC & Cust. historical number of finished loans with status C on product ins \\
39 & act\_ccss\_n\_loans\_hist & Cust. historical number of loans on product css \\
40 & act\_cins\_n\_loans\_hist & Cust. historical number of loans on product ins \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Credit acceptance process strategy case studies - the power of Credit Scoring" by Karol Przanowski, arXiv:1403.6531v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Taveena Lotey
Aman Verma
Partha Pratim Roy

"EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters" by Taveena Lotey, Aman Verma, and Partha Pratim Roy, arXiv:2412.17818v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Caihua Chen
Xindan Li
Caleb Tolman
Suyang Wang
Yinyu Ye

"Sparse Portfolio Selection via Quasi-Norm Regularization" by Caihua Chen, Xindan Li, Caleb Tolman, Suyang Wang, and Yinyu Ye, arXiv:1312.6350v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Tuan Truong
Ivo M. Baltruschat
Mark Klemens
Grit Werner
Matthias Lenga

"Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images" by Tuan Truong, Ivo M. Baltruschat, Mark Klemens, Grit Werner, and Matthias Lenga, arXiv:2501.09552v4, 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}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training hours for each experiment across the different LLMs. +DPO and +KTO columns indicate the incremental time taken on top of SFT.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrr}
 \toprule
 & \textbf{SFT} & \textbf{+ DPO} & \textbf{+ KTO} \\
 \midrule
 \rowcolor[rgb]{ .906, .902, .902} \textbf{Mistral 7B Instruct v0.2} & 2 & 0.5 & 1.5 \\
 \textbf{Mixtral 8x7B Instruct v0.1} & 3.5 & 1.2 & 4.5 \\
 \rowcolor[rgb]{ .906, .902, .902} \textbf{Llama2 13B Chat} & 3.5 & 1.5 & 3.5 \\
 \textbf{Llama3 8B Instruct} & 2.5 & 0.5 & 2\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Melissa Kazemi Rad
Huy Nghiem
Andy Luo
Sahil Wadhwa
Mohammad Sorower
Stephen Rawls

"Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment" by Melissa Kazemi Rad, Huy Nghiem, Andy Luo, Sahil Wadhwa, Mohammad Sorower, and Stephen Rawls, arXiv:2501.13080v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcclc}
\toprule
\textbf{Event (example)} & \textbf{Valence} & \textbf{Sym.} & \textbf{Topic} & \textbf{Count} \\
\midrule
higher inflation & rising & ↑ & Inflation & 58 \\
economic stability in the us & positive & ↑ & Economy & 29 \\
higher interest rates & rising & ↑ & Interest rates & 27 \\
the Fed's aggressiveness & loose & ↑ & Monetary policy & 25 \\
the ECB tightening its m.p. & tight & ↓ & Monetary policy & 23 \\
economy plunges toward hard landing & falling & ↓ & Economy & 22 \\
lower inflation rates & falling & ↓ & Inflation & 21 \\
higher gas prices & rising & ↑ & Energy prices & 19 \\
stocks are getting attractive again & rising & ↑ & Stock Market & 15 \\
\bottomrule
\end{tabular}
\caption{Most frequent valence-topic combinations in extracted narratives. The presented \textit{Events} (left column) are sampled from all events that translate to the respective valence-topic combination.}
\end{table}
\end{document}

q-fin

Tobias Schmidt
Kai-Robin Lange
Matthias Reccius
Henrik Müller
Michael Roos
Carsten Jentsch

"Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models" by Tobias Schmidt, Kai-Robin Lange, Matthias Reccius, Henrik Müller, Michael Roos, and Carsten Jentsch, arXiv:2506.15041v1, 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}{|l|c|c|}
		\hline \hline
		\begin{minipage}[c]{18mm} FS \\ algorithm \end{minipage} 	& Features & Accuracy results (\%) \\
		\hline
		\hline
		\begin{minipage}[c]{18mm} QoV + BSS ($N=5$)
		\end{minipage} 	&
		\begin{minipage}[c]{61mm}
			$\mathrm{H3}_a^{\sigma}$,
			$\mathrm{H5}_i^{\mu}$,
			$\mathrm{H6}_i^{\mu}$,
			$\mathrm{RelH6}_i$,
			$\mathrm{MFCC}_i(6)$
		\end{minipage}
		& \begin{minipage}{30mm}\centering $Acc=84.4\pm5.4$ \\ $Sens=75.0\pm6.5$ \\ $Spec=87.9\pm4.9$ \end{minipage}\\

		\begin{minipage}[c]{18mm} RelieFF ($N=5$) \end{minipage} &
		\begin{minipage}[c]{61mm}
			$\mathrm{MFCC}_a(8)$,
			$\mathrm{MFCC}_a(11)$,
			$\mathrm{MFCC}_i(2)$,
			$\mathrm{MFCC}_i(6)$,
			$\mathrm{PFR}_a$

		\end{minipage}
		& \begin{minipage}{30mm}\centering $Acc = 93.3\pm3.7$ \\ $Sens = 83.3\pm5.6$ \\ $Spec = \mathbf{97.0\pm2.6}$ \end{minipage} \\

		\begin{minipage}[c]{18mm} LASSO ($N=12$) \end{minipage} &
		\begin{minipage}[c]{61mm}
			$d_1$,
			$\mathrm{PFR}_a$,
			$\mathrm{H7}_i^{\sigma}$,
			$\mathrm{RelH6}_a$,
			$\mathrm{MFCC}_a(6)$,
			$\mathrm{MFCC}_a(8)$,
			$\mathrm{MFCC}_i(2)$,
			$\mathrm{MFCC}_i(3)$,
			$\mathrm{MFCC}_i(6)$,
			$\mathrm{MFCC}_i(9)$,
			$\Delta\mathrm{MFCC}_i(6)$,
			$\Delta\mathrm{MFCC}_i(12)$,

		\end{minipage}
		& \begin{minipage}{30mm}\centering $Acc = \mathbf{95.6\pm3.1}$ \\ $Sens = \mathbf{91.7\pm4.1}$ \\ $Spec = \mathbf{97.0\pm2.6}$ \end{minipage} \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

"Classification of ALS patients based on acoustic analysis of sustained vowel phonations" by Maxim Vashkevich and Yulia Rushkevich, arXiv:2012.07347v2, 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
 Constraint & Follows from \\
 \hline
 \hline
 & \\
 \hline
 , & Lemma~ and (i) \\
 \hline
 & Lemma~: and Lemma~ \\
 \hline
 , & Lemma~: and Lemma~ \\
 \hline
 , & Lemma~ \\
 \hline
 & \\
 \hline
 & \\
 \hline
 & (ii) and (iii) \\
 \hline
 & Lemma~: , Lemma~, (ii) and (iii) \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Constraint feasibility}
\end{table}
\end{document}

math

Erdős' minimum overlap problem

"Erdős' minimum overlap problem" by Ethan Patrick White, arXiv:2201.05704v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \Xhline{2pt}
 \textbf{Model} & \textbf{\#Failure} & \textbf{LLM-Score} & \textbf{Rouge-L} & \textbf{FII} \\
 \hline
 Qwen-7B & 219 & 80.9 & 0.247 & 44.58 \\
 Qwen1.5-7B & 94 & 85.2 & 0.240 & 25.29 \\
 Yi1.5-9B & 1481 & 84.1 & 0.248 & 41.42 \\
 glm4-9b & 51 & 82.6 & 0.237 & 37.91 \\
 \Xhline{2pt}
 \end{tabular}
\end{adjustbox}
\caption{Average Results for a single simulation in each MIRAGE scenario w/o E, with E indicating cases of forced self-exposure.}
\end{table}
\end{document}

cs

Yin Cai
Zhouhong Gu
Zhaohan Du
Zheyu Ye
Shaosheng Cao
Yiqian Xu
Hongwei Feng
Ping Chen

"MIRAGE: Exploring How Large Language Models Perform in Complex Social Interactive Environments" by Yin Cai, Zhouhong Gu, Zhaohan Du, Zheyu Ye, Shaosheng Cao, Yiqian Xu, Hongwei Feng, and Ping Chen, arXiv:2501.01652v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll|ccc|c}
 & & EKF & UKF & \textbf{SIF} & SIF Improvement \\
\hline
\multirow{4}{*}{RMSE} & $x_1$ & 0.7774 & 0.8344 & \textbf{0.7398} & \multirow{4}{*}{5\%} \\
 & $x_2$ & 0.3867 & 0.3987 & \textbf{0.3881} & \\
 & $x_3$ & 0.6884 & 0.7343 & \textbf{0.6781} &\\
 & $x_4$ & 0.3758 & 0.3808 & \textbf{0.3732} & \\
\hline
 ANEES & & 10.9559 & 5.3510 & \textbf{4.0810} & 40\%\\
\hline
\end{tabular}
\caption{EKF, UKF, and SIF performance summary.}
\end{table}
\end{document}

eess

Jindrich Dunik
Jakub Matousek
Ondrej Straka
Erik Blasch
John Hiles
Ruixin Niu

"Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation" by Jindrich Dunik, Jakub Matousek, Ondrej Straka, Erik Blasch, John Hiles, and Ruixin Niu, arXiv:2412.07239v2, 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}{|c|c|}
 \hline
 & \\
 RG-1 & Utilize data-driven algorithms to derive a truly objective classification heuristic. \\
 & \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\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}
 \hline
 \textbf{Model Name} & \textbf{\# Params} & \textbf{FFN} & \textbf{CRAM} & \textbf{ASCENT-ViT} \\
 \hline
 ViT-Base & 86M & $<$1\% & 2\% & 2.4\% \\
 ViT-Large & 307M & $<$1\% & $\leq$1\% & $\leq$1\% \\
 DeIT-Base & 86M & $<$1\% & 2\% & 2.5\% \\
 SWIN & 88M & $<$1\% & 2\% & 2.2\% \\
 ViT-Base (Dino) & 86M & $<$1\% & 2\% & 2.4\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Percentage of additional parameters introduced by specific classifier heads of various ViT architectures. CRAM and ASCENT-ViT offer improved explainability at a fraction of the additional parameter cost.}
\end{table}
\end{document}

cs

Sanchit Sinha
Guangzhi Xiong
Aidong Zhang

"ASCENT-ViT: Attention-based Scale-aware Concept Learning Framework for Enhanced Alignment in Vision Transformers" by Sanchit Sinha, Guangzhi Xiong, and Aidong Zhang, arXiv:2501.09221v2, 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}{lccccc}
& MIPLIB &&&& NN-Verification \\
\cline{2-3} \cline {5-6}
Parameter & Constant &&&& Constant \\
\hline
$\lambda_{1}$ (\texttt{dcd}) & 0.600 &&&& 0.065 \\
$\lambda_{2}$ (\texttt{eff}) & 0.124 &&&& 0.633 \\
$\lambda_{3}$ (\texttt{isp}) & 0.175 &&&& 0.301 \\
$\lambda_{4}$ (\texttt{obp}) & 0.100 &&&& 0.000 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Statistics of generated constant parameters in Experiment }
\end{table}
\end{document}

math

Mark Turner
Thorsten Koch
Felipe Serrano
Michael Winkler

"Adaptive Cut Selection in Mixed-Integer Linear Programming" by Mark Turner, Thorsten Koch, Felipe Serrano, and Michael Winkler, arXiv:2202.10962v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model architecture of downstream GNN layers as well as MLP baseline for TadPole.}
\begin{tabular}{lcccc}
 \toprule
 Layer size& &MLP & GCN-dDGM \\
 \midrule
 & & N/A & dDGM\\ \hline
 (No. features, 32) & & Linear & GCN Conv\\
 N/A & & ELU &ELU\\
 (32, 16) & & Linear & GCN Conv\\
 N/A & & ELU &ELU\\
 (16, 8) & & Linear & GCN Conv\\
 N/A & & ELU &ELU\\
 (8, 8) & & Linear & Linear\\
 N/A & & BatchNorm &BatchNorm\\
 N/A & & ELU &ELU\\
 (8, 8) & & Linear & Linear\\
 N/A & & BatchNorm &BatchNorm\\
 N/A & & ELU &ELU\\
 (8, No.classes) & & Linear & Linear\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Yuan Lu
Haitz Sáez de Ocáriz Borde
Pietro Liò

"AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference" by Yuan Lu, Haitz Sáez de Ocáriz Borde, and Pietro Liò, arXiv:2311.11891v1, 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{Relative error of approxiamtion with $k$-truncated SVD}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}\hline
$k$ & 5 & 15 & 25 & 35 & 45 \\\hline
$\frac{\|A_k - A\|_F}{\|A\|_F}$ & 0.2546-0.2304$\epsilon$ & 0.1446-0.1345$\epsilon$ & 0.1052-0.0932$\epsilon$ & 0.0838-0.0752$\epsilon$ & 0.0696-0.0619$\epsilon$ \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Liqun Qi
David M. Alexander
Zhongming Chen
Chen Ling
Ziyan Luo

"Low Rank Approximation of Dual Complex Matrices" by Liqun Qi, David M. Alexander, Zhongming Chen, Chen Ling, and Ziyan Luo, arXiv:2201.12781v1, 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}{lllll}
 \toprule
 &\sc min &\sc max &\sc avg &\sc size (\#) \\
 \midrule
 & 34 & 1,484 & 172.9 & 18,615 \\
 & 36 & 67,963 & 2,430 & 101,857 \\
 & 1 & 291,949 & 900 & 1,280,927 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Greek FAIR datasets appropriate for pre-training tasks with min, max, average length in characters and size in documents.}
\end{table}
\end{document}

cs

John Pavlopoulos
Juli Bakagianni
Kanella Pouli
Maria Gavriilidou

"Open or Closed LLM for Lesser-Resourced Languages? Lessons from Greek" by John Pavlopoulos, Juli Bakagianni, Kanella Pouli, and Maria Gavriilidou, arXiv:2501.12826v1, 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|l|l|l|}
\hline
Mesh & \# coeffs & Spline degree & MSE value & Training \\
 & & & & time [s] \\ \hline
10 x 10 & 121 & linear & 1.98e-07 & 9 \\ \hline
10 x 10 & 169 & quadratic & 5.29e-07 & 10 \\ \hline
10 x 10 & 225 & cubic & 2.18e-07 & 11 \\ \hline
20 x 20 & 441 & linear & 6.99e-07 & 14 \\ \hline
20 x 20 & 529 & quadratic & 6.96e-07 & 18 \\ \hline
20 x 20 & 625 & cubic & 9.14e-08 & 19 \\ \hline
40 x 40 & 1681 & linear & 1.46e-06 & 15 \\ \hline
40 x 40 & 1849 & quadratic & 1.37e-06 & 16 \\ \hline
40 x 40 & 2025 & cubic & 7.58e-06 & 18 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Summary of obtained training results for a coefficient-approximating DNN. The training time rises with the number of coefficients, as expected. The training times are only an indication of the time required to train the model, because they vary significantly with batch size and the number of iterations. The error tends to increase with the number of coefficients, however the actual output is still perfectly usable.}
\end{table}
\end{document}

math

Kamil Doległo
Anna Paszyńska
Maciej Paszyński
Leszek Demkowicz

"Deep neural networks for smooth approximation of physics with higher order and continuity B-spline base functions" by Kamil Doległo, Anna Paszyńska, Maciej Paszyński, and Leszek Demkowicz, arXiv:2201.00904v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\large Coefficient estimates of 1-and 2- factor models incorporating Renewable Energy Investments}
\begin{tabular}{lcc}
\toprule
~& (1)& (2)\\
 & \multicolumn{2}{c}{$\Delta$Unemployment Rate}\\
\midrule
$\Delta$ Active Mines\textsubscript{t} & -0.069*** & -0.069***\\
~ & (0.007) & (0.007)\\
$\Delta$ Active Mines\textsubscript{t-1} & -0.007 & -0.007\\
~ & (0.007) & (0.007)\\
$\Delta$ Active Mines\textsubscript{t-2} & 0.040*** & 0.040***\\
~ & (0.006) & (0.007)\\
REE $\geq 0.1$\% of GDP\textsubscript{t} & -0.057*** & -0.057***\\
~ & (0.013) & (0.014)\\
REE $\geq 0.1$\% of GDP\textsubscript{t-1} & -0.016 & -0.016\\
~ & (0.014) & (0.014)\\
REE $\geq 0.1$\% of GDP\textsubscript{t-2} & 0.007 & 0.007\\
~ & (0.015) & (0.015)\\
$\Delta$ Active Mines\textsubscript{t} $\times$ REE & 0.073* & 0.073*\\
~ & (0.029) & (0.030)\\
$\Delta$ Active Mines\textsubscript{t-1} $\times$ REE & 0.030 & 0.030\\
~ & (0.032) & (0.033)\\
$\Delta$ Active Mines\textsubscript{t-2} $\times$ REE & -0.031 & -0.031\\
~ & (0.031) & (0.032)\\
$\Delta$ (log) Real GDPPC\textsubscript{t} & -0.962*** & -0.962***\\
~ & (0.044) & (0.045)\\
\bottomrule
\addlinespace
R$^2$ & 0.434 & 0.434\\
Additive Effects Type & two-ways & two-ways\\
\# Unobserved Factors Used & 1 & 2\\
\bottomrule
\addlinespace
\multicolumn{3}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\
\end{tabular}
\end{table}
\end{document}

q-fin

"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Junan Zhang
Xueyao Zhang
Jing Yang
Yuancheng Wang
Fan Fan
Zhizheng Wu

"Multi-Metric Preference Alignment for Generative Speech Restoration" by Junan Zhang, Xueyao Zhang, Jing Yang, Yuancheng Wang, Fan Fan, and Zhizheng Wu, arXiv:2508.17229v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Global Bias-Corrected Divide-and-Conquer by Quantile-Matched Composite for General Nonparametric Regressions" by Yan Chen and Lu Lin, arXiv:2201.12597v1, 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{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of notations.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cl}
\toprule
\textbf{Notations} & \textbf{Meaning} \\ \midrule
\textbf{$\mathcal{G}$} & the hypergraph \\
\textbf{$\mathcal{V}$} & the node set \\
\textbf{$N$} & the number of nodes \\
\textbf{$\mathcal{E}$} & the hyperedge set \\
\textbf{$K$} & the number of hyperedges \\
\textbf{$\mathcal{N}_i$} & the neighbor set of node $v_i$ \\
\textbf{$\mathbf{X}$} & the node features matrix \\
\textbf{$\mathbf{Z}$} & the hyperedge features matrix \\
\textbf{$d$} & the feature size of node and hyperedges \\
\textbf{$B$} & the $l_2$ norm bound of node and hyperedge features \\
\textbf{$M$} & the maximum hyperedge size \\
\textbf{$R$} & the maximum incident hyperedge set size \\
\textbf{$D$} & the maximum node degree \\
\textbf{$\mathbf{X}[i,:]$} & the $i^{th}$ row of matrix $\mathbf{X}$ \\
\textbf{$d_e$} & the degree of hyperedge $e$ \\
\textbf{$\epsilon$} & the hyperparameter \\
\textbf{$e^M$} & the $M^{th}$-order hyperedge \\
\textbf{$\mathcal{E}^M(v_i)$} & the $M^{th}$-order incident hyperedge of node $v_i$ \\
\textbf{$\mathcal{N}^M(v_i)$} & the $M^{th}$-order neighborhood of node $v_i$ \\
\textbf{$a_e$} & the adjacency value of hyperedge $e$ \\
\textbf{$\mathbf{T}$} & the matrix of the hyperedge weight \\
\textbf{$\mathbf{J}$} & the incident matrix \\
\textbf{$S$} & the set of training data \\
\textbf{$m$} & the size of training data \\
\textbf{$C$} & the number of classes \\
\textbf{$y$} & the hypergraph label \\
\textbf{$\mathcal{D}$} & the distribution over sample space \\
\textbf{$A$} & the input $A=(\mathcal{G}, \mathbf{X}, \mathbf{Z})$ \\
\textbf{$F$} & the hypothesis space \\
\textbf{$\gamma$} & the margin \\
\textbf{$\mathcal{L}_{\mathcal{D}}$} & the multiclass margin generalization loss \\
\textbf{$\mathcal{L}_{S, \gamma}$} & the multiclass margin empirical loss \\
\textbf{$P$} & the prior distribution over the learned parameters \\
\textbf{$Q$} & the posterior distribution over the perturbed parameters \\
\textbf{$h$} & the maximum hidden dimension \\
\textbf{$L$} & the number of propagation steps \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Generalization Performance of Hypergraph Neural Networks" by Yifan Wang, Gonzalo R. Arce, and Guangmo Tong, arXiv:2501.12554v2, 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|ll|lll|l|lll|l}
\hline
 & \multicolumn{2}{l|}{Metropolis} & \multicolumn{3}{l|}{Small city} & Village & \multicolumn{3}{l|}{Mountain} & \\
 & S1 & S2 & S3 & S4 & S5 & S6 & S7 & S8 & S9 & mean \\ \hline
BEV-MAE (Scratch) & 75.0 & \textbf{81.7} & \textbf{97.3} & \textbf{93.4} & \textbf{100.0} & 89.3 & 98.7 & 77.3 & 80.6 & 88.2 \\
BEV-MAE (Ours) & \textbf{77.8} & 81.2 & \textbf{97.3} & 90.8 & \textbf{100.0} & \textbf{90.4} & \textbf{100.0} & \textbf{81.6} & \textbf{85.2} & \textbf{89.4} \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Detailed results of terrain scene recognition. The scores represent the IoUs from the best model among three runs. }
\end{table}
\end{document}

cs

Haoyi Xiu
Xin Liu
Taehoon Kim
Kyoung-Sook Kim

"Advancing ALS Applications with Large-Scale Pre-training: Dataset Development and Downstream Assessment" by Haoyi Xiu, Xin Liu, Taehoon Kim, and Kyoung-Sook Kim, arXiv:2501.05095v1, 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{The improvement of SOFA at 24 hours post-admission for sepsis patients in the selected cohort of the MIMIC-III database under the estimated optimal fluid strategies with different values of the sensitivity parameter $\gamma_1'$ in the WQ-SA method}
\begin{tabular}{rcccc}
 \hline
 $\gamma'_1$ & Mean & $m$ & Bootstrap std & Bootstrap 95\% CI \\
 \hline
 0 & 1.184 & 823 & 0.178 & (0.837, 1.534) \\
 0.25 & 1.288 & 823 & 0.181 & (0.930, 1.642) \\
 0.5 & 1.369 & 823 & 0.185 & (1.003, 1.726) \\
 0.75 & 1.436 & 823 & 0.198 & (1.044, 1.821) \\
 1 & 1.475 & 823 & 0.201 & (1.082, 1.873) \\
 1.25 & 1.486 & 823 & 0.204 & (1.085, 1.887) \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

"Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates" by Jian Sun, Bo Fu, and Li Su, arXiv:2312.01735v5, 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}{lll|cccc}
Fire & Mean & Covariance & RMSE & CA & CA-High \\\hline
KNP & LR & Independent & 213 & 28.0 & 0.03 \\
 & LR & Spatial & 115 & 57.5 & 57.9 \\
 & SL & Independent & 121 & 55.7 & 53.6 \\
 & SL & Spatial & 114 & 58.0 & 55.4 \\
&\\
Windy & LR & Independent & 215 & 32.1 & 9.8\\
 & LR & Spatial & 98 & 66.0 & 78.1 \\
 & SL & Independent & 112 & 61.6 & 73.2 \\
 & SL & Spatial & 98 & 66.5 & 77.8 \\
&\\
CZU & LR & Independent & 207 & 37.3 & 39.0 \\
 & LR & Spatial & 89 & 70.1 & 84.8 \\
 & SL & Independent & 95 & 69.3 & 84.8 \\
 & SL & Spatial & 91 & 70.7 & 86.3 \\
&\\
Kincade & LR & Independent & 164 & 43.0 & 17.5 \\
 & LR & Spatial & 88 & 66.8 & 67.8\\
 & SL & Independent & 94 & 65.3 & 69.1 \\
 & SL & Spatial & 82 & 69.3 & 73.8 \\
\end{tabular}
\caption{Within-fire cross-validation root mean squared prediction error (RMSE), classification accuracy (CA) percent for fire severity level (high enhanced growth (dNBR from -500 - -251), low enhanced regrowth (-250 - -101), unburned (-100 - 99), low (100 - 269), moderate-low (270 - 439), moderate-high (440 - 659), and high (660 - 1300)) and CA for the high categories for Linear Regression (LR) and Super Learner (SL) models with independent and spatial error structure. }
\end{table}
\end{document}

stat

Nicholas Simafranca
Bryant Willoughby
Erin O'Neil
Sophie Farr
Brian J Reich
Naomi Giertych
Margaret Johnson
Madeleine Pascolini-Campbell

"Modelling wildland fire burn severity in California using a spatial Super Learner approach" by Nicholas Simafranca, Bryant Willoughby, Erin O'Neil, Sophie Farr, Brian J Reich, Naomi Giertych, Margaret Johnson, and Madeleine Pascolini-Campbell, arXiv:2311.16187v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Rough Bergomi turns grey

Antoine Jacquier
Adriano Oliveri Orioles
Zan Zuric

"Rough Bergomi turns grey" by Antoine Jacquier, Adriano Oliveri Orioles, and Zan Zuric, arXiv:2505.08623v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{(Scenario I) Averages of the 5 smallest of EO/KS-GEOs whose AUCs are greater 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{tabular}{c|cc|cc|cc}
\toprule
& \multicolumn{2}{c|}{\textbf{Adult} (AUC $\geq$ 0.80)} & \multicolumn{2}{|c|}{\textbf{Cred. Card.} (AUC $\geq$ 0.70)} & \multicolumn{2}{|c}{\textbf{Law School.} (AUC $\geq$ 0.80)} \\
\hline
 Model & EO ($\downarrow$) & KS-GEO ($\downarrow$) & EO ($\downarrow$) & KS-GEO ($\downarrow$) & EO ($\downarrow$) & KS-GEO ($\downarrow$) \\
\hline
\hline
SBP & 0.009 (0.005) & 0.070 (0.003) & 0.003 (0.001) & 0.040 (0.001) & 0.020 (0.010) & 0.078 (0.001) \\
CON & 0.016 (0.007) & 0.066 (0.002) & 0.001 (0.001) & 0.038 (0.002) & 0.014 (0.003) & 0.074 (0.002) \\
HGR & 0.127 (0.025) & 0.167 (0.007) & 0.002 ($\approx 0$) & 0.047 (0.002) & 0.587 (0.152) & 0.365 (0.027) \\
KDE & 0.282 (0.064) & 0.155 (0.009) & 0.001 (0.001) & 0.049 (0.001) & 0.194 (0.016) & 0.216 (0.007) \\
NEU & 0.364 (0.013) & 0.182 (0.003) & 0.016 (0.003) & 0.046 (0.001) & 0.380 (0.033) & 0.245 (0.012) \\
\bottomrule
\end{tabular}
\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.


math

Mamta Rani
Avnish K. Sharma
Sharwan K. Tiwari
Anupama Panigrahi

"Inverses of $r$-primitive $k$-normal elements over finite fields" by Mamta Rani, Avnish K. Sharma, Sharwan K. Tiwari, and Anupama Panigrahi, arXiv:2201.11334v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zhenyu Cui
Anne MacKay
Marie-Claude Vachon

"Analysis of VIX-linked fee incentives in variable annuities via continuous-time Markov chain approximation" by Zhenyu Cui, Anne MacKay, and Marie-Claude Vachon, arXiv:2207.14793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Point processes with event time uncertainty" by Xiuyuan Cheng, Tingnan Gong, and Yao Xie, arXiv:2411.02694v1, 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
\caption{ \textbf{RIS vs. Relays}}
\begin{tabular}{l|c|c|c|c}
\hline
 & \textbf{RIS}& \textbf{AF Relay} & \textbf{DF Relay} & \textbf{FD Relay} \\
 \hline
 With RF Chains? &No& Yes&Yes&Yes\\
 \hline
 SP Capability? &No& No&Yes&Yes\\
 \hline
 Noise?&No & Yes&Yes&Yes\\
 \hline
 Duplex &Full& Half&Half& Full\\
 \hline
 Hardware cost& Low&Median& High& Very high\\
\hline
Power Consumption& Low &Median& High& Very high\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Cunhua Pan
Hong Ren
Kezhi Wang
Jonas Florentin Kolb
Maged Elkashlan
Ming Chen
Marco Di Renzo
Yang Hao
Jiangzhou Wang
A. Lee Swindlehurst
Xiaohu You
Lajos Hanzo

"Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions" by Cunhua Pan, Hong Ren, Kezhi Wang, Jonas Florentin Kolb, Maged Elkashlan, Ming Chen, Marco Di Renzo, Yang Hao, Jiangzhou Wang, A. Lee Swindlehurst, Xiaohu You, and Lajos Hanzo, arXiv:2011.04300v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experiment results on LVC BLOCK.}
\begin{tabular}{c|cc}
		\toprule
		Models & ACC(\%) & WF1(\%) \\
		\midrule
		w/o LVC block & 69.90 & 69.84 \\
		w/ LVC block & 70.53 & 70.60 \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

"WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition" by Feng Li, Jiusong Luo, and Wanjun Xia, arXiv:2412.05558v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Environment encoding thresholds for 8D BipedalWalker.}
\begin{tabular}{cccc}
\toprule
\textbf{Stump Height (High)} & \textbf{Pit Gap (High)} & \textbf{Ground Roughness} & \textbf{Stairs Height (High)} \\
\midrule
$\geq 2.4$ & $\geq 6$ & $\geq 4.5$ & $\geq 5$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Jayden Teoh
Wenjun Li
Pradeep Varakantham

"Improving Environment Novelty Quantification for Effective Unsupervised Environment Design" by Jayden Teoh, Wenjun Li, and Pradeep Varakantham, arXiv:2502.05726v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Portfolio Alpha Analysis with Newey-West Standard Errors}
\begin{tabular}{lcc}
\toprule
Factor & Coefficient & Newey-West SE \\
\midrule
Alpha (Constant) & -34.7 & 11.2 \\
Market (MKT) & 100.3 & 4.3 \\
Size (SMB) & -110.9 & 23.7 \\
Value (HML) & -68.9 & 59.6 \\
Profitability (RMW) & 215.1 & 164.3 \\
Investment (CMA) & -356.0 & 123.1 \\
Momentum (MOM) & 149.2 & 33.2 \\
Betting Against Beta (BAB) & -79.7 & 99.9 \\
Quality (QMJ) & 104.2 & 115.1 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Sentiment Feedback in Equity Markets: Asymmetries, Retail Heterogeneity, and Structural Calibration" by Lucas Marques Sneller, arXiv:2509.11970v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Invidious Comparisons: Ranking and Selection as Compound Decisions" by Jiaying Gu and Roger Koenker, arXiv:2012.12550v3, 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[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison of YOLOv11 and Proposed G-YOLOv11 detectors on the GRAZPEDWRI-DX test set. Speed represents the total time for preprocessing, inference, and post-processing a single image.}
\begin{tabular}{lccccc}
 \toprule
 Model & Precision & Recall & mAP@0.5 & mAP@0.5:0.95 & Speed (ms) \\
 \hline
 YOLOv11n & $\mathbf{0.812}$ & $\mathbf{0.509}$ & $\mathbf{0.545}$ & $\mathbf{0.339}$ & $1.8$ \\
 \rowcolor{gray!30}
 \textbf{G-YOLOv11n (ours)} & $0.769$ & $0.5$ & $0.522$ & $0.319$ & $\mathbf{1.6}$ \\
 YOLOv11s & $\mathbf{0.79}$ & $\mathbf{0.55}$ & $\mathbf{0.554}$ & $\mathbf{0.351}$ & $2.4$ \\
 \rowcolor{gray!30}
 \textbf{G-YOLOv11s (ours)} & $0.784$ & $0.54$ & $0.545$ & $0.334$ & $\mathbf{1.9}$ \\
 YOLOv11m & $\mathbf{0.827}$ & $\mathbf{0.537}$ & $\mathbf{0.577}$ & $\mathbf{0.361}$ & $4.9$ \\
 \rowcolor{gray!30}
 \textbf{G-YOLOv11m (ours)} & $0.768$ & $0.534$ & $0.541$ & $0.339$ & $\mathbf{2.8}$\\
 YOLOv11l & $0.646$ & $\mathbf{0.702}$ & $\mathbf{0.619}$ & $\mathbf{0.385}$ & $6$ \\
 \rowcolor{gray!30}
 \textbf{G-YOLOv11l (ours)} & $\mathbf{0.806}$ & $0.493$ & $0.535$ & $0.341$ & $\mathbf{3}$\\
 YOLOv11x & $\mathbf{0.766}$ & $\mathbf{0.571}$ & $\mathbf{0.614}$ & $\mathbf{0.381}$ & $10$\\
 \rowcolor{gray!30}
 \textbf{G-YOLOv11x (ours)} & $0.67$ & $0.548$ & $0.562$ & $0.352$ & $\mathbf{4.5}$\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays" by Abdesselam Ferdi, arXiv:2501.00647v1, 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}{|c|c|c|}
 \hline
 \textbf{Clustering method} & \textbf{SWER} \\\hline
 K-means Clustering & 59.10\% \\\hline
 AHC with ward method & 37.86\% \\\hline
 Gaussian Mixture Model & 60.51\% \\\hline
 Bayesian Gaussian Mixture Model & 41.51\% \\\hline
 HDBSCAN soft clustering & 34.78\% \\\hline
 \end{tabular}
\caption{Subword mismatch rate of the first DNN-HMM iterative training using pseudo transcript from different clustering methods}
\end{table}
\end{document}

eess

"Unsupervised Spoken Term Discovery on Untranscribed Speech" by Man-Ling Sung, arXiv:2011.14060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rllcc} \hline \hline
& Counterfactually & Remaining & Wage & Migrant\\
& equalised para. & differing para.
& differential & effect\\
 \hline
(1)& & $\underline{p},\alpha,\mu,\sigma,\lambda,\delta$ & 32.022& 6.825	\\
(2)& $\mu,\sigma$ & $\underline{p},\alpha,\lambda,\delta$& 30.096& 3.747	\\
(3)& $\delta$ &$\underline{p},\alpha,\mu,\sigma,\lambda$ & 28.973& 1.954	\\
(4)& $\lambda$ & $\underline{p},\alpha,\mu,\sigma,\delta$ & 34.029&10.032	\\
(5)& $\mu,\sigma,\delta$ & $\underline{p},\alpha,\lambda$ & 27.423&-0.524	\\
(6)& $\alpha,\mu,\lambda$ & $\underline{p},\alpha,\delta$ & 31.694& 6.300	\\
(7)& $\lambda,\delta$ & $\underline{p},\alpha,\mu,\sigma$ & 30.459& 4.328	\\
(8)& $\mu,\sigma,\lambda,\delta$ & $\underline{p},\alpha$ & 28.758 & 1.610	\\
(9)& $\underline{p},\alpha$ & $\mu,\sigma,\lambda,\delta$ &	& 4.904	\\
(10)& $\underline{p},\alpha,\mu,\sigma$ & $\lambda,\delta$ &	& 1.932	\\
(11)& $\underline{p},\alpha,\delta$ & $\mu,\sigma,\lambda$ &	& 0.750	\\
(12)& $\underline{p},\alpha,\lambda$ & $\mu,\sigma,\delta$ &	& 7.814	\\
(13)& $\underline{p},\alpha,\mu,\sigma,\delta$ & $\lambda$ &	&-1.842	\\
(14)& $\underline{p},\alpha,\mu,\sigma,\lambda$ & $\delta$ &	& 4.400	\\
(15)& $\underline{p},\alpha, \lambda,\delta$ & $\mu, \sigma$ &	& 2.741	\\
\hline\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Composition of Wage Differentials between Migrants and Natives" by Panagiotis Nanos and Christian Schluter, arXiv:1306.1781v3, licensed under CC BY 3.0. 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|c|c|c|c|}
 \hline
 & \multicolumn{3}{|c|}{Rosenzweig-MacArthur} & \multicolumn{3}{|c|}{Leslie-Gower-May} \\
 \hline
 & Holling & Ivlev & Trigonometric & Holling & Ivlev & Trigonometric \\
 \hline
 $a_i$ & 3.05 & 0.6282 & 0.3707 & 1683.333 & 399.8009 & 384.1465\\
 $b_i$ & 2.68 & 4.8204 & 7.6281 & 3.333 & 3.1656 & 2.4028\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Values of the parameters obtained from the fitted functional response where the Holling functional response is fixed and the other functional responses are fit to the Holling response via the parameters $a_i$ and $b_i$.}
\end{table}
\end{document}

math

Sarah K. Wyse
Maria M. Martignoni
May Anne Mata
Eric Foxall
Rebecca C. Tyson

"Structural sensitivity in the functional responses of predator-prey models" by Sarah K. Wyse, Maria M. Martignoni, May Anne Mata, Eric Foxall, and Rebecca C. Tyson, arXiv:2201.11256v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sound Classification of Four Insect Classes" by Yinxuan Wang and Sudip Vhaduri, arXiv:2412.12395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Ramin Mousa
Saeed Chamani
Mohammad Morsali
Mohammad Kazzazi
Parsa Hatami
Soroush Sarabi

"Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers" by Ramin Mousa, Saeed Chamani, Mohammad Morsali, Mohammad Kazzazi, Parsa Hatami, and Soroush Sarabi, arXiv:2412.00472v1, 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{Parameters of the three wave components, superimposed according to linear wave theory in Eqs. -, to generate the analytic ground truth for elevation $\eta_\mathrm{true}(x,t)$ and potential $\Phi_\mathrm{true}(x,t,z)$. This data is used to extract synthetic surface measurements $\eta_\mathrm{m}(x=x_{\mathrm{wb},j},t)$ for PINN training and to validate the PFT-PINN solutions $\tilde{\eta}(x,t)$ and $\tilde{\Phi}(x,t,z)$ after training.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccccccc}
\toprule
\toprule
& \multicolumn{4}{c}{base parameters} & \multicolumn{4}{c}{resulting parameters} \\
\cmidrule(rl){2-5} \cmidrule(rl){6-9}
 component $i$ & $\omega_i\, [\frac{\mathrm{rad}}{\mathrm{s}}]$ & $a_i \, [\mathrm{m}]$ & $d_i\, [\mathrm{m}]$ & $\phi_i \, [\mathrm{rad}]$& $T_i \, [\mathrm{s}]$ & $k_i \, [\frac{\mathrm{rad}}{\mathrm{m}}]$ & $L_i \, [\mathrm{m}]$& $\epsilon_i \, [\mathrm{rad}]$ \\
\midrule
 1 &0.418 & 1.117 & 200 & $+0.50 \pi$ & 15.0 & 0.018 & 350.8 & 0.02 \\
2& 0.838 & 0.280 & 200 & $-0.20 \pi$ & 7.5 & 0.072 & 87.8 & 0.02 \\
3& 1.047 & 0.358 & 200 & $+0.75 \pi$ & 6.0 & 0.112 & 56.2 & 0.04\\
\bottomrule
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Svenja Ehlers
Norbert Hoffmann
Tianning Tang
Adrian H. Callaghan
Rui Cao
Enrique M. Padilla
Yuxin Fang
Merten Stender

"Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves" by Svenja Ehlers, Norbert Hoffmann, Tianning Tang, Adrian H. Callaghan, Rui Cao, Enrique M. Padilla, Yuxin Fang, and Merten Stender, arXiv:2501.08430v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.