stat

Taban Baghfalaki
Reza Hashemi
Christophe Tzourio
Catherine Helmer
Helene Jacqmin-Gadda

"A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes" by Taban Baghfalaki, Reza Hashemi, Christophe Tzourio, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.03797v1, 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{Targeted Moments, Excess Mobility Model}
\begin{tabular}{lcclcc}
\toprule
\toprule
Moment & Model & Data & Moment & Model & Data \\ \cmidrule(lr){1-3} \cmidrule(lr){4-6}
Agg. output per worker mean & 1.005 & 1.000 & Rel. separation rate young/prime-aged & 2.134 & 1.994 \\
Agg. output per worker persistence, $\rho_{outpw}$ & 0.761 & 0.753 & Rel. separation rate recent hire/all & 5.230 & 4.944\\
Agg. output per worker st. dev., $\sigma_{outpw}$ & 0.0094 & 0.0094 & Prob (unemp. within 3 yr for empl.) & 0.148 & 0.122 \\
Mean unemployment & 0.0355 & 0.0355 & Empirical elasticity matching function & 0.522 & 0.500 \\
Average u. duration movers/stayers & 1.211 & 1.139 & 5-year OLS return to occ. tenure & 0.150 & 0.154 \\
Repeat mobility: occ. stay after stay & 0.600 & 0.634 & 10-year OLS return to occ. tenure & 0.230 & 0.232 \\
Occ. mobility young/prime-aged & 1.162 & 1.163 \\
Occ. mobility-duration profiles & \multicolumn{2}{c}{Fig 5a,b,c} & U. survival profiles & \multicolumn{2}{c}{Fig 5d,e} \\%\hline
\bottomrule
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

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

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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Early Positioning of Sandwich Attacks}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\hline
Category & Mempool Position Prob.	& Block Position Prob.	& T-statistic	& P-value \\
\hline \hline
Front Run	& 0.4995	& 0.0569	& 16.9385	& (0.0000) \\
Sandwich Middle	& 0.4952	& 0.0576	& 7.8495	 & (0.0000) \\
Back Run	& 0.4979	& 0.0749	& 16.0773	& (0.0000) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering" by Bruce Mizrach and Nathaniel Yoshida, arXiv:2508.04003v1, 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|}
\hline
 Motion & Precision & Recall & F1 \\
\hline
Turn left & 0.9757 & 0.9331 & 0.9539 \\
\hline
Turn right & 0.9483 & 0.9670 & 0.9576 \\
\hline
Pick up object & 0.9861 & 0.9658 & 0.9758 \\
\hline
Backwards & 0.9723 & 0.9565 & 0.9643 \\
\hline
Forwards & 0.9398 & 0.9969 & 0.9675 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{\textbf{CNN performance metrics.}}
\end{table}
\end{document}

eess

Silas Ruhrberg Estévez
Josée Mallah
Dominika Kazieczko
Chenyu Tang
Luigi G. Occhipinti

"Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals" by Silas Ruhrberg Estévez, Josée Mallah, Dominika Kazieczko, Chenyu Tang, and Luigi G. Occhipinti, arXiv:2411.16273v1, 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{Average results of the models with the focal-R-MSE loss with $\beta=2$ and $\theta=1$ for two, three, and six lead months SSTA and MHW forecasts.}
\begin{tabular}{llllll}
\textbf{Across All Locations} & \textbf{MSE↓} & \textbf{CSI↑} & \textbf{CSI 80↑} & \textbf{Training Time↓} & \textbf{PUR↓} \\ \hline
Average (two lead months) & \textbf{0.5240} & 0.0 & 0.2202 & 26.128 & 0\% \\
Average (three lead months) & \textbf{0.6484} & 0.0 & 0.0151 & 26.3625 & 48.3\% \\
Average (six lead months) & \textbf{0.7209} & 0.0 & 0.0 & 26.1644 & 99.2\%
\end{tabular}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Weighted-F1 scores of the four models in the final ensemble evaluated on the local validation set. 'Efficient Net' abbreviate as 'EffNet' for readability.}
\begin{tabular}{|l|l|l|}
\hline
\textbf{Model} & \textbf{Accuracy} & \textbf{Weighted-F1 score} \\
\hline
EffNet-B3 (\textit{Baseline}) & 0.95970 & 0.95911 \\
EffNet-B4 (\textit{Baseline}) & 0.96147 & 0.96104 \\
EffNet-B3 VAE (mixup $\alpha$=0.5) & 0.96767 & 0.96731 \\
EffNet-B4 VAE (mixup $\alpha$=0.5) & 0.96988 & 0.96909 \\
EffNet-B3 VAE (mixup $\alpha$=1.0) & 0.96944 & 0.96953 \\
EffNet-B4 VAE (mixup $\alpha$=1.0) & 0.96767 & 0.96738 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Robust Pollen Imagery Classification with Generative Modeling and Mixup Training" by Jaideep Murkute, arXiv:2102.13143v1, 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}{r|lll|lllll}
&\multicolumn{3}{c}{New Scheme} &\multicolumn{5}{c}{ILUTP($\tau$)} \\
\# & $\tau_{large}$ & $\tau_{mix}$ & $\tau_{small}$ & $10^{-5}$ & $10^{-4}$ & $10^{-3}$ &$10^{-2}$ & $10^{-1}$ \\ \hline
1 & 3 & 2 & 2& 3 &$\dagger$ &$\dagger$ &$\dagger$ &$\dagger$ \\
2 & 2 & 2 & 2 & 2 & $\dagger$ &$\dagger$ &$\dagger$ &$\dagger$ \\
3 & 2 & 1 & 1 & 2 & 4 & 6 &12 &13 \\
4 & 2 & 2 & 2&$1^\star$ &$2^\star$ & 3 & 6 & $32^\star$ \\
5 & 2 & 3 & 2 & $1^\star$ &$1^\star$ & 3 &7 &$44^\star$ \\
6 & 3 & 3 & 1 & $\dagger$ & 2 & 3 & 7 &$\ddagger$ \\
7 & 3 & 3 & 2 &$\dagger$ & $\dagger$ & $\ddagger$ & $9^\star$ &$\ddagger$ \\
8 & 4 & 3 & 2 & $\dagger$ & $\dagger$ & $\ddagger$ & 10 &$\dagger$ \\
\end{tabular}
\end{adjustbox}
\caption{Number of outer fGMRES/GMRES iterations for the generalized case}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training and test RMSE for the random forest and gam fits to the bike data. The gam has better test set performance than the random forest.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrr}
 \hline
 & train & test \\
 \hline
RF & 438.1 & 748.1 \\
 GAM & 573.1 & 670.2 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Catherine B. Hurley
Mark O'Connell
Katarina Domijan

"Interactive slice visualization for exploring machine learning models" by Catherine B. Hurley, Mark O'Connell, and Katarina Domijan, arXiv:2101.06986v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average PSNR, LPIPS, and CD values with a scaling factor of 4. The best ones are highlighted in bold.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
		\hline
		\textbf{Dataset} & \textbf{Method} & \textbf{PSNR} $\uparrow$ & \textbf{LPIPS} $\downarrow$ & \textbf{CD} $\downarrow$ \\
		\hline
		\multirow{5}{*}{Tangaroa}
		& TI & 36.6797 & 0.1439 & 2.1923 \\
		& SRGAN & 39.8904 & 0.1115 & 2.1292 \\
		& SSR-VFD & 38.9527 & 0.1445 & 2.5566 \\
		& PSRFlow & 41.7391 & 0.0970 & 1.9757 \\
		& \textbf{CD-TVD} & \textbf{45.1457} & \textbf{0.0425} & \textbf{1.9320} \\
		\hline
		\multirow{5}{*}{Half Cylinder}
		& TI & 40.0438 & 0.0411 & 1.6275 \\
		& SRGAN & 41.8104 & 0.0340 & 1.7277 \\
		& SSR-VFD & 43.3012 & 0.0660 & 1.5289 \\
		& PSRFlow & 43.3228 & 0.0514 & 1.6310 \\
		& \textbf{CD-TVD} & \textbf{46.4046} & \textbf{0.0246} & \textbf{1.1318} \\
		\hline
		\multirow{5}{*}{Shock Vortex}
		& TI & 29.4103 & 0.0305 & 1.2840 \\
		& SRGAN & 32.2311 & 0.0102 & 2.0138 \\
		& SSR-VFD & 31.8848 & 0.0126 & 1.4101 \\
		& PSRFlow & 37.4574 &0.0089 & 1.1755 \\
		& \textbf{CD-TVD} & \textbf{43.8938} & \textbf{0.0016} & \textbf{1.0405} \\
		\hline
		\multirow{5}{*}{Hurricane}
		& TI & 38.6966 & 0.0802 & 3.1279 \\
		& SRGAN & 43.6950 & 0.0696 & 2.2285 \\
		& SSR-VFD & 44.8119 & 0.0584 & 2.6773 \\
		& PSRFlow & 45.6459 & 0.0632 & 1.7936 \\
		& \textbf{CD-TVD} & \textbf{48.0591} & \textbf{0.0494} & \textbf{1.4345} \\
		\hline

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

eess

Chongke Bi
Xin Gao
Jiangkang Deng
Guan Li
Jun Han

"CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data" by Chongke Bi, Xin Gao, Jiangkang Deng, Guan Li, and Jun Han, arXiv:2508.08173v2, 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{Objective results in CoVoC Challenge 2024}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \textbf{System} & \textbf{CER(\%)} & \textbf{SECS} \\
 \midrule
 Proposed & 10.29 & 0.797 \\

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

eess

Shuoyi Zhou
Yixuan Zhou
Weiqin Li
Jun Chen
Runchuan Ye
Weihao Wu
Zijian Lin
Shun Lei
Zhiyong Wu

"The Codec Language Model-based Zero-Shot Spontaneous Style TTS System for CoVoC Challenge 2024" by Shuoyi Zhou, Yixuan Zhou, Weiqin Li, Jun Chen, Runchuan Ye, Weihao Wu, Zijian Lin, Shun Lei, and Zhiyong Wu, arXiv:2412.01100v2, 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}{l|c}
\toprule
Team Name & Valence \\
\hline
biic & $\textbf{0.6953}^{\star}$ \\
Voinosis & 0.6941 \\
SRPOL & 0.6932 \\
SRPOL & 0.6906 \\
biic & 0.6880 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Top-5 rank in emotional attributes (valence) among the \textit{Test3} testing set.}
\end{table}
\end{document}

eess

Jing-Tong Tzeng
Bo-Hao Su
Ya-Tse Wu
Hsing-Hang Chou
Chi-Chun Lee

"Lessons Learnt: Revisit Key Training Strategies for Effective Speech Emotion Recognition in the Wild" by Jing-Tong Tzeng, Bo-Hao Su, Ya-Tse Wu, Hsing-Hang Chou, and Chi-Chun Lee, arXiv:2508.07282v1, 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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{\textsc{RELand}} performance using \textbf{blockV} validation on Bolívar department.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|cccc}
\toprule
Municipality & Imbalance\% & ROC (↑) & PR (↑) & Height (↓) & rHeight (↓) \\ \midrule
Cartagena de Indias & 0.04 & 0.94 & 0.00 & 0.06 & 140.00 \\
Córdoba & 0.08 & 0.95 & 0.27 & 0.10 & 116.50 \\
San Juan Nepomuceno & 0.04 & 0.94 & 0.00 & 0.06 & 151.00 \\
Santa Rosa del Sur & 0.04 & 0.92 & 0.00 & 0.30 & 731.00 \\
Zambrano & 1.94 & 0.92 & 0.24 & 1.84 & 95.21 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mateo Dulce Rubio
Siqi Zeng
Qi Wang
Didier Alvarado
Francisco Moreno
Hoda Heidari
Fei Fang

"RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization" by Mateo Dulce Rubio, Siqi Zeng, Qi Wang, Didier Alvarado, Francisco Moreno, Hoda Heidari, and Fei Fang, arXiv:2311.03115v1, 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}{lccccc}
 \toprule
 & \multicolumn{5}{c}{Equivalent HF function evaluations $N$} \\
 \cmidrule(l){2-6}
 Method & $N=100$ & $N=1000$ & $N=10000$ & $N=20000$ & $N=30000$ \\
 \midrule
 GD & $2.48 \pm 0.00$ & $2.48 \pm 0.00$ & $0.62 \pm 0.00$ & $0.43 \pm 0.00$ & $0.34 \pm 0.00$ \\ %\hline
 CD & $\mathbf{1.48 \pm 0.00}$ & $1.48 \pm 0.00$ & $0.70 \pm 0.00$ & $0.49 \pm 0.00$ & $0.40 \pm 0.00$ \\ %\hline
 FS-SSD & $2.47 \pm 0.00$ & $2.45 \pm 0.00$ & $2.26 \pm 0.00$ & $2.08 \pm 0.00$ & $1.92 \pm 0.00$ \\ %\hline
 SPSA & $1.95 \pm 0.05$ & $\mathbf{0.61 \pm 0.04}$ & $0.19 \pm 0.00$ & $0.14 \pm 0.00$ & $0.12 \pm 0.00$ \\ %\hline
 GS & $2.47 \pm 0.00$ & $2.46 \pm 0.00$ & $2.36 \pm 0.00$ & $2.25 \pm 0.00$ & $2.15 \pm 0.00$ \\ %\hline
 HF-SSD & $2.07 \pm 0.15$ & $0.77 \pm 0.06$ & $0.22 \pm 0.02$ & $0.17 \pm 0.02$ & $0.14 \pm 0.02$ \\ %\hline
 BF-SSD & $2.00 \pm 0.05$ & $0.66 \pm 0.03$ & $\mathbf{0.17 \pm 0.00}$ & $\mathbf{0.11 \pm 0.00}$ & $\mathbf{0.09 \pm 0.00}$ \\ %\hline
 VR-SSD & $2.47 \pm 0.00$ & $2.10 \pm 0.02$ & $0.63 \pm 0.01$ & $0.43 \pm 0.00$ & $0.35 \pm 0.00$ \\ %\hline
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Performance values (mean $\pm$ std over 10 runs) showing the objective function for different optimization methods at various HF function evaluations $N$ with $\ell=20$ and $c=0.99$. The minimum values in each \emph{column} are highlighted in bold.}
\end{table}
\end{document}

math

Nuojin Cheng
Alireza Doostan
Stephen Becker

"Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search" by Nuojin Cheng, Alireza Doostan, and Stephen Becker, arXiv:2505.00162v1, 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{Application Model Runtime}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
\toprule
Subgroup & Sample Size & Runtime$^a$\\
\midrule
Hispanic & 11959 & 6.28\\
Non-Hispanic White & 46019 & 23.67\\
Non-Hispanic Black & 5635 & 3.69\\
Non-Hispanic Asian and Pacific Islander & 5521 & 3.72\\
Non-Hispanic Other & 1886 & 1.26\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jacob Englert
Stefanie Ebelt
Howard Chang

"Estimating Heterogeneous Exposure Effects in the Case-Crossover Design using BART" by Jacob Englert, Stefanie Ebelt, and Howard Chang, arXiv:2311.12016v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning" by Yahel Jacobs, Reut Dayan, and Uri Shaham, arXiv:2411.01532v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of participants' domain, expertise and interview length}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllc}
		\toprule
		\textbf{Code} & \textbf{Perspective} & \textbf{Role} & \textbf{Sector} & \textbf{Interview length} \\
		 & & & & (minutes) \\
		\midrule
		P1 & Practitioner & Customer manager & Software vendor & 61 \\
		P2 & Practitioner & Data Scientist & Retail & 55 \\
		P3 & Academic & Professor & Computer Science & 55 \\
		P4 & Academic & Research Fellow & Population and Public health & 53 \\
		P5 & Practitioner & Emerging Technologies lead & Goverment & 60 \\
		P6 & Academic & Professor & Business & 54 \\
		P7 & Practitioner & Marketing manager & Software vendor & 64 \\
		P8 & Practitioner & Project Manager & Public health & 62 \\
		P9 & Practitioner & Senior analyst & public health supply chain & 47 \\
		P10 & Practitioner & Data Scientist & Retail & 57 \\
		P11 & Practitioner & CEO & Supply chain software & 57 \\
		P12 & Practitioner & Director & Business & 44 \\
		P13 & Practitioner & Analyst & Global public health & 58 \\
		P14 & Practitioner & CEO & Software company & 40 \\
		P15 & Practitioner & Demand planner & Pharmaceutical & 65 \\
		P16 & Practitioner & Scientific lead & Humanitarian & 54 \\
		P17 & Practitioner & Director for data science and service innovation & Software & 57 \\
		P18 & Practitioner & Product manager & Technology & 56 \\
		P19 & Academic & Professor & Business & 50 \\
		P20 & Practitioner & Data Scientist & Retail & 55 \\
		P21 & Academic & Professor & Infectious disease & 43 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Bahman Rostami-Tabar
Travis Greene
Galit Shmueli
Rob J. Hyndman

"Good intentions, unintended consequences: exploring forecasting harms" by Bahman Rostami-Tabar, Travis Greene, Galit Shmueli, and Rob J. Hyndman, arXiv:2411.16531v3, 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{Method signatures from Panorama's aidl file}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 Generic APIs & Bluetooth, Wifi-Direct \& Cloudlet APIs \\
 \hline
 \texttt{List<Device> discover\_nw(service\_name)} & \texttt{List<Device> get\_bt\_devices} \\
 \texttt{Device get\_local(service\_name)} & \texttt{create\_piconet} \\
 \texttt{Plan perform\_optimization(task)} & \texttt{start\_wd\_discovery} \\
 \texttt{Result execute\_optimization} & \texttt{List<Device> get\_wd\_devices} \\
 \texttt{Result process\_local(task)} & \texttt{ connect\_wd(device\_name)} \\
 & \texttt{connect\_to\_cloudlet} \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Khaled Alanezi
Xinyang Zhou
Lijun Chen
Shivakant Mishra

"Panorama: A Framework to Support Collaborative Context Monitoring on Co-Located Mobile Devices" by Khaled Alanezi, Xinyang Zhou, Lijun Chen, and Shivakant Mishra, arXiv:2101.04335v1, 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
\caption{Summary Statistics of the Beirut Meta-data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
 & \textbf{Number of apartments} & \textbf{Mean DSM} & \textbf{Mean building height} & \textbf{Area} & \textbf{Perimeter} & \textbf{Era} & \textbf{Built year} & \textbf{Floors} \\ \hline
\textbf{Count} & 535 & 1023 & 1023 & 1257 & 1257 & 711 & 789 & 870 \\ \hline
\textbf{Number of nulls} & 831 & 343 & 343 & 109 & 109 & 655 & 577 & 496 \\ \hline
\textbf{Mean} & 6.72 & 41.24 & 18.47 & 568.03 & 86.95 & 2.74 & 1954.41 & 4.17 \\ \hline
\textbf{Std} & 6.95 & 18.39 & 10.88 & 1484.34 & 76.44 & 0.97 & 36.93 & 2.63 \\ \hline
\textbf{Min} & 1 & 3.78 & 1.66 & 17.58 & 17.81 & 1 & 1219 & 1 \\ \hline
\textbf{Max} & 60 & 113.70 & 95.65 & 16574.86 & 1062.72 & 5 & 2021 & 26 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge" by Ali Ismail and Mariette Awad, arXiv:2201.10389v1, 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{Conditional video generation FVD results on HyperKvasir with different pathological findings, where lower values are better. Bold denotes best performance. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccccc|c}
\toprule
{Method} & {Bar.} & {Cancer} & {Eso.} & {Ecta.} & {Perf.} & {Polyps} & {Ulcer} & {Varices} & {{Avg.}} \\
\midrule
 SimDA & 3479.1 & 5065.1 & 2041.4 & 3643.6 & 1641.7 & 3656.2 & 3688.0 & 3919.3 & 3391.8 \\
 VDM & 1758.8 & 4635.1 & 1366.9 & 2057.6 & 897.0 & 2348.3 & 2172.6 & 1766.5 & 2125.4 \\
 VidGPT & 1433.1 & 2965.7 & 955.7 & 1649.4 & 636.3 & 1705.1 & 1616.1 & 1427.7 & 1548.6 \\
 \midrule
 \textbf{EndoGen} & 402.1	&908.0	&286.3	&628.1	&300.6	&423.2	&496.6	&612.9&\textbf{507.2}
\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Xinyu Liu
Hengyu Liu
Cheng Wang
Tianming Liu
Yixuan Yuan

"EndoGen: Conditional Autoregressive Endoscopic Video Generation" by Xinyu Liu, Hengyu Liu, Cheng Wang, Tianming Liu, and Yixuan Yuan, arXiv:2507.17388v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Emma Gerard
Sarah Zohar
Hoai-Thu Thai
Christelle Lorenzato
Marie-Karelle Riviere
Moreno Ursino

"Bayesian dose-regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics" by Emma Gerard, Sarah Zohar, Hoai-Thu Thai, Christelle Lorenzato, Marie-Karelle Riviere, and Moreno Ursino, arXiv:2011.11480v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Modeling Count Data via Copulas

Hadi Safari-Katesari
S. Yaser Samadi
Samira Zaroudi

"Modeling Count Data via Copulas" by Hadi Safari-Katesari, S. Yaser Samadi, and Samira Zaroudi, arXiv:2012.02021v1, 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|cccc}
 \hline

 Experts & Solar Panels & $T_1$ &$T_2$&$T_3$&$T_4$ \\ \hline
 & $P_1$ &EH&MH&EH & M\\
 & $P_2$ &VH &M&VH&M\\
 $E_1$ & $P_3$ &MH&L&ML&ML\\
 & $P_4$ &VH&MH&H&M\\
 & $P_5$ &H&MH&VH&MH\\ \hline
 & $P_1$ &VH&H&VH & MH\\
 & $P_2$ &MH&M&H&MH\\
 $E_2$ & $P_3$ &M&M&M&ML\\
 & $P_4$ &VH&MH&VH&M\\
 & $P_5$ &VH&H&VH&M
 \\\hline
 & $P_1$ &EH&VH&H & ML\\
 & $P_2$ &H&ML&MH&M\\
 $E_3$ & $P_3$ &ML&VL&ML&VL\\
 & $P_4$ &H&H&MH&ML\\
 & $P_5$ &H&VH&H&H
 \\\hline
 \end{tabular}
\end{adjustbox}
\caption{ Group decision matrix with linguistic term}
\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}
\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
 $n$ & Error $\|\cdot\|_1$ & Order $\|\cdot\|_1$ & Error
 $\|\cdot\|_{\infty}$ & Order $\|\cdot\|_{\infty}$ & $\%$
 Success \\
 \hline
 40 & 2.60E$-5$ & $-$ & 1.72E$-4$ & $-$ & 99.73 $\%$ \\
 \hline
 80 & 3.25E$-7$ & 6.32 & 1.60E$-6$ & 6.75 & 99.92 $\%$ \\
 \hline
 160 & 8.45E$-9$ & 5.27 & 2.31E$-8$ & 6.11 & 99.97 $\%$ \\
 \hline
 320 & 2.64E$-10$ & 5.00 & 6.95E$-10$ & 5.06 & 99.99 $\%$ \\
 \hline
 640 & 8.26E$-12$ & 5.00 & 2.13E$-11$ & 5.03 & 99.99 $\%$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Filter without $D(x_i)$ terms, $\delta=0.2,$ $\delta'=0.1$.}
\end{table}
\end{document}

math

"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, 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{Model performance summary of AUC for \textit{TalkingPoint} and \textit{ALZConnected}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \textbf{Model} & \textbf{\textit{TalkingPoint}} & \textbf{\textit{ALZConnected}} \\
 \midrule
 XGBoost & 0.850 $_{\pm0.026}$ & 0.867 $_{\pm0.014}$ \\
 Gradient Boosting & 0.847 $_{\pm0.027}$ & 0.867 $_{\pm0.021}$ \\
 Random Forest & 0.842 $_{\pm0.025}$ & 0.858 $_{\pm0.014}$ \\
 Extra Trees & 0.837 $_{\pm0.024}$ & 0.851 $_{\pm0.021}$ \\
 AdaBoost & 0.830 $_{\pm0.021}$ & 0.848 $_{\pm0.016}$ \\
 Decision Tree & 0.793 $_{\pm0.026}$ & 0.795 $_{\pm0.023}$ \\
 LR & 0.593 $_{\pm0.018}$ & 0.601 $_{\pm0.012}$ \\
 SVC & 0.552 $_{\pm0.017}$ & 0.567 $_{\pm0.011}$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Congning Ni
Qingxia Chen
Lijun Song
Patricia Commiskey
Qingyuan Song
Bradley A. Malin
Zhijun Yin

"Catalysts of Conversation: Examining Interaction Dynamics Between Topic Initiators and Commentors in Alzheimer's Disease Online Communities" by Congning Ni, Qingxia Chen, Lijun Song, Patricia Commiskey, Qingyuan Song, Bradley A. Malin, and Zhijun Yin, arXiv:2412.13388v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Taban Baghfalaki
Reza Hashemi
Catherine Helmer
Helene Jacqmin-Gadda

"A Two-stage Joint Modeling Approach for Multiple Longitudinal Markers and Time-to-event Data" by Taban Baghfalaki, Reza Hashemi, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.05765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Fei Wu
Danning Sui
Thomas Thiery
Mallesh Pai

"Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest" by Fei Wu, Danning Sui, Thomas Thiery, and Mallesh Pai, arXiv:2507.13023v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{Index} & \textbf{Test 1 Eigenvalues} & \textbf{Test 2 Eigenvalues} \\ \hline
 $\lambda_1$ & \(-5.75892737 \times 10^{-5}\) & \(-4.93801632 \times 10^{-5}\) \\ \hline
 $\lambda_2$ & \(-1.03753149\) & \(-1.15851652\) \\ \hline
 $\lambda_3$ & \(-2.23424145\) & \(-2.61595495\) \\ \hline
 $\lambda_4$ & \(-3.88774292\) & \(-3.59187336\) \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{OU process: First four eigenvalues of the Koopman generator estimated using SDMD.}
\end{table}
\end{document}

math

Yuanchao Xu
Kaidi Shao
Isao Ishikawa
Yuka Hashimoto
Nikos Logothetis
Zhongwei Shen

"A Data-Driven Framework for Koopman Semigroup Estimation in Stochastic Dynamical Systems" by Yuanchao Xu, Kaidi Shao, Isao Ishikawa, Yuka Hashimoto, Nikos Logothetis, and Zhongwei Shen, arXiv:2501.13301v4, 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{Data description and sources}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\hline \hline
\textbf{Variable} & \textbf{Description} & \textbf{Source} \\ \hline
\textit{Aggregate Analysis:} \\
GPBII and NGPBII & Green and Non-Green Patent Based Innovation index & Patents view + \\
TFP & Total Factor Productivity & \\
Output & U.S. Real Gross Domestic Product & FRED \\
Consumption & U.S. Real Consumption & FRED \\
Investment & U.S. Real Investment & FRED \\
Hours & Non-farm Business Sector: Hours Worked for All Workers & FRED \\
Consumer Price Index & Consumer Price Index for All Urban Consumers: All Items & FRED \\
Federal Funds Rate & Effective federal funds rate & FRED \\
Consumer Confidence & Consumer Confidence Index & Michigan Survey of Consumers \\
Stock Price Index & Standard and Poor’s 500 Composite Stock Price Index & Robert Shiller’s website\\
CPI Core & U.S. CPI for all urban consumers: all items less food
and energy & FRED \\
CPI Energy & U.S. CPI for all urban consumers: energy & FRED \\
CPI Durables & U.S. CPI for all urban consumers: durables & FRED \\
CPI Non-durables & U.S. CPI for all urban consumers: non-durables & FRED \\
CPI Services & U.S. CPI for all urban consumers: services & FRED \\
Oil and Gas stock price & Average oil and gas sectoral stock price index & Datastream \\
Electricity Stock Price & Average electricity sectoral stock price index & Datastream \\
Automobiles Stock Price & Average automobiles sectoral stock price index & Datastream \\
Mining Stock Price & Average mining sectoral stock price index & Datastream \\
Retail Stock Price & Average retail sectoral stock price index & Datastream \\
Travel and Leisure Stock Price & Average travel and leisure sectoral stock price index & Datastream \\
\hline \hline\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Becoming Green: Decomposing the Macroeconomic Effects of Green Technology News Shocks" by Oscar Jaulin and Andrey Ramos, arXiv:2507.18386v1, 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{Ambiguous terms statistics for all gold standard datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
			\hline
			&Noun	&Verb	&Adjective	&Adverb	\\
			\hline
			\# of terms	&4300	&1652&	955	&346\\
			\# of ambiguous	&3442&	1555&	732	&208\\
			Average granularity&5.7	&11.0&	4.7	&4.4\\
			Max \#senses&	33&	59&	21	&13\\
			Mode	&	2&	4&	2&	2\\
			Median	&	5&	7&	4&	3\\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mohannad AlMousa
Rachid Benlamri
Richard Khoury

"A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph" by Mohannad AlMousa, Rachid Benlamri, and Richard Khoury, arXiv:2101.02875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math


\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}{cccc}
\hline
\hline
\textbf{\#$H$ - Heads} & \textbf{Precision} & \textbf{Recall} & \textbf{F1}\\
\hline
\hline
1 Head & 74.9 & 74.5 & 74.4 \\
4 Heads & 76.9 & 76.8 & 76.8 \\
8 Heads & 77.4 & 77.2 & 77.2 \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\caption{Ablation study with varying number of Heads \#$H$ and fixed Layers \textbf{\#$L$ = 3} on the Sarcasm Corpus V2 Dialogues dataset .}
\end{table}
\end{document}

cs

"Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media" by Ramya Akula and Ivan Garibay, arXiv:2101.05875v1, 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{Runtime and hyperparameters for fitting 25 different runs of each model, with a datasize of $15,\!000$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc} % creating eight columns
 \toprule %inserting double-line
 Model & Total Runtime & RQS Knots & Flow Layers & Learning Rate & NN Width & NN Depth \\
 \toprule % inserts single-line
 $M_{1}$ & 45.4 mins & 8 & 5 & 5e-3 & 50 & 4\\
 $M_{2}$ & 44.1 mins & 8 & 5 & 5e-3 & 50 & 4\\
 $M_{3}$ & 66.3 mins & 8 & 5 & 5e-3 & 50 & 4\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Daniel de Vassimon Manela
Laura Battaglia
Robin J. Evans

"Marginal Causal Flows for Validation and Inference" by Daniel de Vassimon Manela, Laura Battaglia, and Robin J. Evans, arXiv:2411.01295v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Datasets used in HRTF individualization experiments}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc} \toprule
 \multirow{2}{*}{Dataset} & \# Source & Source & \multicolumn{2}{c}{\# Subjects} \\
 & pos. $B$ & dist. $r$ (m) & Train & Test \\ \midrule
 CIPIC & 1250 & 1.00 & 30 & 5 \\
 HUTUBS & 440 & 1.47 & 85 & 6 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ryan Niu
Shoichi Koyama
Tomohiko Nakamura

"Head-Related Transfer Function Individualization Using Anthropometric Features and Spatially Independent Latent Representation" by Ryan Niu, Shoichi Koyama, and Tomohiko Nakamura, arXiv:2508.16176v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The comparison between PBAN and spatial/channel attention on the CVIU database, with the best performance values highlighted in bold. The “spatial atten" and “channel atten" pool the channel and spatial dimensions to 1, respectively. The Flops is calculated with input tensor shape $3\times32\times32$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|cccc}
\toprule
Model & Params/M & Flops/G & Speed/ms & SRCC $\uparrow$ & KRCC $\uparrow$ & PLCC $\uparrow$ & RMSE $\downarrow$ \\
\midrule
Channel Atten & 2.222 & 0.181 & 11.284 & 0.963 & 0.836 & 0.964 & 0.716 \\
Spatial Atten & 2.222 & 0.181 & 11.099 & 0.913 & 0.751 & 0.917 & 1.062 \\
PBAN & 2.222 & 0.181 & 11.292 & \textbf{0.978} & \textbf{0.872} & \textbf{0.981} & \textbf{0.397} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yixiao Li
Xiaoyuan Yang
Guanghui Yue
Jun Fu
Qiuping Jiang
Xu Jia
Paul L. Rosin
Hantao Liu
Wei Zhou

"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, 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}{lcccccc}
 \hline
 Dataset & \# Samples & \# Features & \# Categorical & \# Classes \\
 \hline
 statlog & 6,435 & 36 & 0 & 6 \\
 spambase & 4,601 & 57 & 0 & 2 \\
 electrical & 10,000 & 13 & 0 & 2 \\
 occupancy~ & 20,560 & 5 & 0 & 2 \\
 gas~ & 13,910 & 128 & 0 & 6 \\
 drybean~ & 13,611 & 16 & 0 & 7 \\
 \hline
 energy~ & 19,735 & 27 & 0 & - \\
 bike~ & 17,379 & 10 & 5 & - \\
 metro & 48,204 & 6 & 1 & - \\
 sgemm~ & 241,600 & 14 & 0 & - \\
 ovctt & 68,784 & 20 & 2 & - \\
 californiahousing & 20,640 & 8 & 0 & - \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Main characteristics of the datasets used in experiments, including number of samples, number of features, number of categorical features and number of classes.}
\end{table}
\end{document}

cs

"Robust supervised learning with coordinate gradient descent" by Stéphane Gaïffas and Ibrahim Merad, arXiv:2201.13372v1, 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|}
 \hline
 $\bar{L}$ & $\bar{R}$ & $\nu$ & $\bar{p}$ \\
 \hline
 10 & 3 or 10 & 0.3 & 1 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Geometric and material parameters.}
\end{table}
\end{document}

math

"Asymptotically accurate and geometric locking-free finite element implementation of a refined shell theory" by Khanh Chau Le and Hoang-Giang Bui, arXiv:2503.23369v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Junhui Mi
Rahul D. Tendulkar
Sarah M. C. Sittenfeld
Sujata Patil
Emily C. Zabor

"Combining missing data imputation and internal validation in clinical risk prediction models" by Junhui Mi, Rahul D. Tendulkar, Sarah M. C. Sittenfeld, Sujata Patil, and Emily C. Zabor, arXiv:2411.14542v1, 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{AWOISV Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 \textbf{Parameter} & \textbf{Symbol} & \textbf{Unit} & \textbf{Value} \\
 \midrule
 Vehicle Mass & $m$ & kg & 10,000 \\
 Vehicle Inertia & $I_Z$ & $\text{kg} \cdot \text{m}^2$ & 34,823 \\
 Wheel Steering Range & $[\delta_{\min}, \delta_{\max}]$ & rad & $[-\pi/2, \pi/2]$ \\
 Tire Cornering Stiffness & $C_\alpha$ & N/rad & 40,000 \\
 Friction Coefficient & $\mu$ & - & 0.85 \\
 Tire Rolling Radius & $R_{eff}$ & m & 0.35 \\
 Axle-to-CG X Distance & $X_{A1} \sim X_{A4}$ & m & \parbox{2cm}{$[3.55, 1.75,$\\ $-1.75, -3.55]$} \\
 Wheel Track & $M$ & m & 2.72 \\
 Height of CG & $H_{CG}$ & m & 1.21 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Xu Yang
Jun Ni
Hengyang Feng
Feiyu Wang
Tiezhen Wang

"Modeling and Control of AWOISV: A Filtered Tube-Based MPC Approach for Simultaneous Tracking of Lateral Position and Heading Angle" by Xu Yang, Jun Ni, Hengyang Feng, Feiyu Wang, and Tiezhen Wang, arXiv:2508.13457v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 Layout & Type & $\epsilon(1)$ & $\vert \mathcal{V} \vert$ & $\vert \mathcal{E} \vert$ & $d_{avg}$\\
 \hline
 Campus A & City Block & 0.197 & 87 & 232 & 2.667 \\
 Campus B & University & 0.178 & 116 & 336 & 2.897\\
 Campus C & University & 0.192 & 113 & 288 & 2.549\\
 Campus D & University & 0.171 & 97 & 266 & 2.742\\
 Campus E & Military Base & 0.189 & 36 & 80 & 2.222\\
 Campus F & University & 0.185 & 50 & 128 & 2.56\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Attributes of the Campus Environments.}
\end{table}
\end{document}

eess

Deepak Mallya
Arpita Sinha
Leena Vachhani

"$ε$-Optimal Multi-Agent Patrol using Recurrent Strategy" by Deepak Mallya, Arpita Sinha, and Leena Vachhani, arXiv:2509.11640v1, 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{Panel D: Pre-pandemic transition (Jan 2016–Dec 2020).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrr}
 \toprule
 Metric & LTT & MKT & STT+LTT & STT & MKT+STT+LTT & MKT+STT & SGCTAT \\
 \midrule
 Cumulative Return (\%) & 26.7 & 9.3 & 34.7 & 25.1 & 40.3 & 27.8 & 2.3 \\
 Annual Return (\%) & 4.8 & 1.8 & 6.1 & 4.6 & 7.0 & 5.0 & 0.5 \\
 Volatility (\%) & 9.2 & 11.3 & 8.5 & 8.1 & 9.3 & 9.1 & 11.0 \\
 Sharpe Ratio & 0.40 & 0.05 & 0.58 & 0.42 & 0.62 & 0.42 & -0.06 \\
 Max Drawdown (\%) & 12.2 & 20.3 & 8.3 & 8.4 & 9.9 & 10.8 & 22.4 \\
 Return/Max-DD & 0.40 & 0.09 & 0.74 & 0.54 & 0.70 & 0.47 & 0.02 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Eric Benhamou
Jean-Jacques Ohana
Alban Etienne
Béatrice Guez
Ethan Setrouk
Thomas Jacquot

"Re-evaluating Short- and Long-Term Trend Factors in CTA Replication: A Bayesian Graphical Approach" by Eric Benhamou, Jean-Jacques Ohana, Alban Etienne, Béatrice Guez, Ethan Setrouk, and Thomas Jacquot, arXiv:2507.15876v1, 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{HEV system model design and references}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c||}
 \hline
 {\textbf{System Modelling}} & {\textbf{List of References}} \\
 \hline\hline
 Drive-line Dynamics & ,, \\
 \hline
 Internal Combustion Engine (ICE) & , \\
 \hline
 Electric Machine & , \\
 \hline
 Energy Storage Elements & ,, \\
 \hline
 Battery Life Estimation Models & ,, \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements" by Satish Vedula, arXiv:2503.09028v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Surface Hausdorff distance(mm) on CT and MRI.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|l|l|l|}
\hline
\multirow{2}{*}{Method} & \multicolumn{4}{c|}{CT} &\multicolumn{4}{c|}{MRI} \\
\cline{2-9}
 ~ & LVM & LAC& LVC & AA & LVM & LAC& LVC & AA \\
\hline
Single& 5.00 & 10.63 & 5.00 & 13.38 & 8.50 & 15.03 & 4.47 & 6.00\\
UMMKD & 5.83 & 14.40 & 6.01 & 13.97 & 10.05 & 8.94 & 4.47 & 3.61 \\
Joint & 5.39 & 14.32 & 4.24 & 13.30& 6.40 & 40.17 & 9.84 & 4.47 \\
MSBN & 5.00 & 10.63 & 4.24 & 13.93 & 4.12 & 9.06 & 4.47 & 3.00 \\
Affinity & 6.00 & 10.63 & 4.89 & 15.13 & 10.05 & 12.08 & 5.00 & 67.09\\
Ours & 5.39 & \textbf{10.30} & \textbf{4.24} & 15.80 & \textbf{4.12} & 9.49 & \textbf{4.12} & \textbf{2.83} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jingkun Chen
Wenqi Li
Hongwei Li
Jianguo Zhang

"Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation" by Jingkun Chen, Wenqi Li, Hongwei Li, and Jianguo Zhang, arXiv:2101.01513v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
 \hline
 Ticker & Keywords\\\hline
 MTLR & mechel, mining, ore, raw materials, energy, ferroalloys, coal \\\hline
 SNGS & gas, geological exploration, oil, Surgutneftegas, petroleum products, electricity, drilling \\\hline
 SMLT & rent, development, developer, real estate, construction, Moscow region, residential areas\\\hline
 \end{tabular}
\end{adjustbox}
\caption{Keywords by companies extracted from their descriptions}
\end{table}
\end{document}

q-fin

"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Mirco Ciallella
Stephane Clain
Elena Gaburro
Mario Ricchiuto

"Very high order treatment of embedded curved boundaries in compressible flows: ADER discontinuous Galerkin with a space-time Reconstruction for Off-site data" by Mirco Ciallella, Stephane Clain, Elena Gaburro, and Mario Ricchiuto, arXiv:2312.07170v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Covariate adjustment and estimation of difference in proportions in randomized clinical trials" by Jialuo Liu and Dong Xi, arXiv:2308.15688v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Information-theoretic subset selection of multivariate Markov chains via submodular optimization" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, 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|}
\hline
Method & Frobnability \\
\hline\hline
Theirs & Frumpy \\
Yours & Frobbly \\
Ours & Makes one's heart Frob\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Results. Ours is better.}
\end{table}
\end{document}

cs

Ashar Ali
Upal Mahbub
Gokce Dane
Gerhard Reitmayr

"DIREG3D: DIrectly REGress 3D Hands from Multiple Cameras" by Ashar Ali, Upal Mahbub, Gokce Dane, and Gerhard Reitmayr, arXiv:2201.11187v1, 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|}
 \hline
 $n$ & Error $\|\cdot\|_1$ & Order $\|\cdot\|_1$ & Error
 $\|\cdot\|_{\infty}$ & Order $\|\cdot\|_{\infty}$ \\
 \hline
 40 & 1.88E$-3$ & $-$ & 3.65E$-2$ & $-$ \\
 \hline
 80 & 6.62E$-5$ & 4.82 & 3.94E$-3$ & 3.21 \\
 \hline
 160 & 1.72E$-7$ & 8.52 & 9.45E$-6$ & 8.70 \\
 \hline
 320 & 2.50E$-9$ & 6.10 & 9.45E$-6$ & 5.37 \\
 \hline
 640 & 4.49E$-11$ & 5.80 & 6.73E$-9$ & 5.08 \\
 \hline
 1280 & 9.61E$-13$ & 5.55 & 1.92E$-10$ & 5.13 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for Burgers equation, $t=0.3$. WLS-UW.}
\end{table}
\end{document}

math

"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, 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}{rrrrrrr} \toprule
Instance & Pilots & Airports & Flights & Pairings & Long pairings (\%) & Flight time (min) \\ \midrule
I5 & 239 & 34 & 5743 & 1222 & 64.5 & 1037333 \\
I7 & 322 & 54 & 7765 & 1473 & 57.0 & 1289546 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Description of the instances available}
\end{table}
\end{document}

math

Philippe Racette
Frédéric Quesnel
Andrea Lodi
François Soumis

"Accelerated windowing for the crew rostering problem with machine learning" by Philippe Racette, Frédéric Quesnel, Andrea Lodi, and François Soumis, arXiv:2503.00160v1, 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{The mean square error of feature maps with different compression ratios using pixel-level and feature-level consistency as the loss function.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
		\toprule[1pt]
		compression ratio (BPP) & 0.125 bpp & 0.25 bpp & 0.5 bpp \\
		\hline
		pixel-level consistency & 2.7865 & 1.6392 & 1.4698 \\
		\hline
		feature-level consistency & 3.7512 & 2.0676 & 1.8129 \\
		\toprule[1pt]
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Semantic-assisted image compression

Qizheng Sun
Caili Guo
Yang Yang
Jiujiu Chen
Xijun Xue

"Semantic-assisted image compression" by Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen, and Xijun Xue, arXiv:2201.12599v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of V-LSPM and MCMC-LSPM for different truncation levels $p$, assessed through accuracy, $F_1$ score, Hamming distance, and posterior predictive network density and transitivity statistics. Standard deviations are given in brackets. The observed network density for the simulated networks is 0.21 (0.02) while the observed network transitivity is 0.50 (0.02). }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|cc|cc|cc|cc|}
\hline
{ } & \multicolumn{2}{c|}{{ Accuracy}} & \multicolumn{2}{c|}{{ $F_1$ score}} & \multicolumn{2}{c|}{{ Hamming distance}} & \multicolumn{2}{c|}{{ Network density}} & \multicolumn{2}{c|}{{ Network transitivity}} \\ \cline{2-11}
\multirow{-2}{*}{{ $p$}} & { VI} & { MCMC} & { VI} & { MCMC} & { VI} & { MCMC} & { VI} & { MCMC} & { VI} & { MCMC} \\ \hline
{ 2} & { 0.63 (0.04)} & { 0.85 (0.03)} & { 0.44 (0.03)} & { 0.63 (0.08)} & { 0.37 (0.04)} & { 0.16 (0.03)} & { 0.45 (0.05)} & { 0.21 (0.02)} & { 0.56 (0.04)} & { 0.46 (0.04)} \\ \hline
{ 4} & { 0.68 (0.03)} & { 0.89 (0.02)} & { 0.52 (0.02)} & { 0.75 (0.04)} & { 0.33 (0.03)} & { 0.11 (0.02)} & { 0.46 (0.05)} & { 0.21 (0.02)} & { 0.60 (0.05)} & { 0.50 (0.02)} \\ \hline
{ 10} & { 0.63 (0.02)} & { 0.89 (0.02)} & { 0.51 (0.02)} & { 0.74 (0.04)} & { 0.38 (0.03)} & { 0.11 (0.02)} & { 0.56 (0.04)} & { 0.21 (0.02)} & { 0.72 (0.03)} & { 0.50 (0.02)} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Xian Yao Gwee
Isobel Claire Gormley
Michael Fop

"Variational Inference for the Latent Shrinkage Position Model" by Xian Yao Gwee, Isobel Claire Gormley, and Michael Fop, arXiv:2311.16451v2, 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{EfficientDet-D0 Angle experts on VisDrone validation set}
\begin{tabular}{c|cccc}

				& B & A & mAP$_{50}$ & mAP$_{50}^\text{avg}$\\
				\hline
				EfficientDet-D0 & 21.5 & 24.9 & 26.3 & 23.2 \\

				Angle@backbone & \bf 22.1 & \bf 26.2 & \bf 27.6 & \bf 24.2

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

cs

Benjamin Kiefer
Martin Messmer
Andreas Zell

"Diminishing Domain Bias by Leveraging Domain Labels in Object Detection on UAVs" by Benjamin Kiefer, Martin Messmer, and Andreas Zell, arXiv:2101.12677v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The Truth Table of Radix-4 Code and Proposed M-RD4 Code}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 \multicolumn{4}{|c|}{Binary Bits} & Radix-4 Bit & M-RD4 Bit \\
 \hline
 $t_{i+3}$&$t_{i+2}$ & $t_{i+1}$ & $t_{i}$ & & $z_j$ \\
 \hline
 0/1&0&0&0 &0 &0 \\
 \hline
 0/1&0&0&1 &1 &1 \\
 \hline
 0/1&0&1&0 &1 &1 \\
 \hline
 0&\multirow{2}{*}{0}&\multirow{2}{*}{1}&\multirow{2}{*}{1} &\multirow{2}{*}{2} &2\\
 \cline{1-1}
 \cline{6-6}
 1& & & & & -2\\
 \hline
 0&\multirow{2}{*}{1}&\multirow{2}{*}{0}&\multirow{2}{*}{0} &\multirow{2}{*}{-2} &2\\
 \cline{1-1}
 \cline{6-6}
 1& & & & & -2\\
 \hline
 0/1&1&0&1 &-1 &-1 \\
 \hline
 0/1&1&1&0 &-1 &-1 \\
 \hline
 0/1&1&1&1 &0 &0 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Rui Xiao
Kejie Huang
Yewei Zhang
Haibin Shen

"A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights" by Rui Xiao, Kejie Huang, Yewei Zhang, and Haibin Shen, arXiv:2101.02419v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
 \toprule
 \multirow{2}{*}{Model} & \multirow{2}{*}{Dense} & \multicolumn{2}{c|}{2:4} \\
 & & AP & \textbf{+gAP} \\
 \midrule
 YOLOv5n & 46.20 & 13.40 & \textbf{37.10} \\
 YOLOv5s & 56.40 & 40.70 & \textbf{51.60} \\
 YOLOv5m & 64.20 & 54.00 & \textbf{61.20} \\
 YOLOv5l & 67.40 & 59.80 & \textbf{65.40} \\
 \midrule
 BERT SQuAD & 88.54 & 84.75 & 87.41 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"SPDY: Accurate Pruning with Speedup Guarantees" by Elias Frantar and Dan Alistarh, arXiv:2201.13096v2, 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|l}
 Characteristic polynomial of $\phi(T)$ & Order of $\phi(T)$\\\hline
 $ x^7 + x^6 - 3x^5 - 3x^4 + 3x^3 + 3x^2 - x - 1$& $2$\\
 $x^7 - x^6 - 2x^4 + 2x^3 + x - 1$& $3$\\
 $x^7 + x^6 - x - 1$& $6$\\
 $x^7-1$ & $7$\\
 $ x^7 + (1/2)(\pm \sqrt{13}-1)x^6 + (1/2)(3\mp \sqrt{13})x^5 + (1/2)(\pm\sqrt{13}-5)x^4$ & $13$\\
 $- (1/2)(\pm \sqrt{13}-5)x^3 - (1/2)(3\mp \sqrt{13})x^2 - (1/2)(\pm \sqrt{13}-1)x - 1$ &
 \end{tabular}
\caption{Characteristic polynomials for $L_2(13)$.}
\end{table}
\end{document}

math

Angelica Babei
Andrew Fiori
Cameron Franc

"The moduli space of representations of the modular group into $G_2$" by Angelica Babei, Andrew Fiori, and Cameron Franc, arXiv:2501.17276v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|c|c|c|c||}
\hline
$\kappa$ & 0.01 & 0.1 & 1 & 2\\
\hline
rate & -0.352 & -0.470 & -0.456 & -0.446\\
\hline
\end{tabular}
\caption{Convergence rate for difference choices of hyperparameter $\kappa$.}
\end{table}
\end{document}

math

Xuhui Zhang
Jose Blanchet
Youssef Marzouk
Viet Anh Nguyen
Sven Wang

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{SemRoDe's robustness to white-box attacks on BERT-MR}
\begin{tabular}{lccc}
\hline
\textbf{Approach} & \textbf{CA (↑)} & \textbf{AUA (↑)} & \textbf{ASR (↓)} \\
\hline
Baseline & 86.6 & 55.6 & 35.8 \\
TextFooler + AT & 85.8 & 58.6 & 31.7 \\
TextFooler + MMD & 86.0 & 67.6 & 21.4 \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Brian Formento
Wenjie Feng
Chuan Sheng Foo
Luu Anh Tuan
See-Kiong Ng

"SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks" by Brian Formento, Wenjie Feng, Chuan Sheng Foo, Luu Anh Tuan, and See-Kiong Ng, arXiv:2403.18423v1, 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{Rating-Level OLS with Clustered Standard Errors}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
& \textbf{(1)} & \textbf{(2)} \\
& \multicolumn{2}{c}{\textit{Dependent Variable: AI Teaching Impact}} \\
\midrule
AI Workplace Skill Disruption & 0.231*** & 0.231*** \\
& (0.045) & (0.055) \\
\addlinespace
Skill Type FE & Yes & Yes \\
Language FE & Yes & Yes \\
\addlinespace
Intercept & -0.042 & -0.042 \\
& (0.252) & (0.188) \\
\midrule
Observations & 152 & 152 \\
Adj. $R^2$ & 0.302 & 0.302 \\
\addlinespace
Clustering & Respondent & Respondent \& Skill \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Training for Obsolescence? The AI-Driven Education Trap" by Andrew J. Peterson, arXiv:2508.19625v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|l|}
 \hline
 \textbf{ETF Basket} & \textbf{Sharpe Ratio} & \textbf{Type} \\ \hline
 india, tw, can, israel, uk, spore, aus & 3.904 & Mixed \\ \hline
 india, tw, can, israel, uk, spore, aus & 3.733 & Mixed \\ \hline
 israel, tw, japan, spore, sk, india & 3.424 & Mixed \\ \hline
 spore, india, usa, aus, israel, uk & 3.354 & Mixed \\ \hline
 uk, india, sk, spore, china, eu & 3.311 & Mixed \\ \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Using Internal Bar Strength as a Key Indicator for Trading Country ETFs" by Aditya Pandey and Kunal Joshi, arXiv:2306.12434v1, 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}{lrrrrr}
\toprule
 & NN-RR & NN-VN & MAVE & SIR & SAVE \\ \midrule
mean & 0.602 & 0.774 & 0.743 & 1.324 & 0.772 \\
std & 0.043 & 0.116 & 0.159 & 0.190 & 0.161 \\
$\bar{d}$ & 19.6 & --- & 19.25 & 19.6 & 20.6 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{The results of testing errors on 20 replicates across different methods. We report the average and standard deviation of testing errors, along with averaged optimal $d$ determined through cross validation. NN-RR, NN-VN, MAVE, SIR, SAVE represent the neural network-based method with rank regularization, vanilla neural network regression, MAVE-based regression, SIR-based regression, and SAVE-based regression, respectively.}
\end{table}
\end{document}

stat

"Neural Networks Perform Sufficient Dimension Reduction" by Shuntuo Xu and Zhou Yu, arXiv:2412.19033v1, 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{Level 1 Crypto TVL Portfolio Descriptive Statistics. Values are portfolio returns in percentage points and are at the weekly frequency. TVL is total TVL. There are 105 weekly observations. \(^{***}\), \(^{**}\), and \(^{*}\), denote statistical significance at the 1\%, 5\%, and 10\% levels respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
\hline
 & HML & Q1 & Q2 & Q3 & Q4\\
\hline
mean & -0.27 & 1.59 & 2.36\(^{**}\) & 1.70\(^{**}\) & 1.32\(^{*}\)\\
std & 7.79 & 12.19 & 12.49 & 8.99 & 7.90\\
min & -40.92 & -23.27 & -26.04 & -25.07 & -17.73\\
25\% & -2.90 & -5.13 & -4.35 & -2.30 & -3.29\\
50\% & 0.85 & -0.59 & 0.73 & 0.81 & 0.27\\
75\% & 3.63 & 6.62 & 7.60 & 7.77 & 5.98\\
max & 15.72 & 58.49 & 47.24 & 29.04 & 28.99\\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns" by Matt Brigida, arXiv:2506.03287v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{UCI tabular datasets experiment, . Hyperparameter search space.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l||c||c|}
			\hline
			Hyperparameters & PCP-Map & COT-Flow\\
			\hline\hline
			Batch size & \{32, 64\} & \{32, 64\}\\
			Learning rate & \{0.01, 0.005, 0.001\} & \{0.01, 0.005, 0.001\} \\
			Feature Layer width, $w$ & \{32, 64, 128, 256, 512\} & \{32, 64, 128, 256, 512\}\\
 Context Layer width, $u$ & \{$\frac{w}{2^i}| \frac{w}{2^i} > m, i=0,1,...$\}$\cup$\{$m$\} & \\
			Number of layers, $K$ & \{2, 3, 4, 5, 6\} & \{2\} \\
			Number of time steps, $n_t$ & & \{8, 16\}\\
			$[\log(\alpha_1), \log(\alpha_2)]$ & & [$\mathcal{U}$(-1, 3), $\mathcal{U}$(-1, 3)]\\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Zheyu Oliver Wang
Ricardo Baptista
Youssef Marzouk
Lars Ruthotto
Deepanshu Verma

"Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference" by Zheyu Oliver Wang, Ricardo Baptista, Youssef Marzouk, Lars Ruthotto, and Deepanshu Verma, arXiv:2310.16975v3, 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 the base and finetuned models across three retrieval metrics.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
 & Base (Snowflake Arctic Embed-Medium) & Finetuned \\
\midrule
MRR@10 & 38\% & 84\% \\
MAP@100 & 39\% & 84\% \\
NDCG@10 & 43\% & 86\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Amin Haeri
Jonathan Vitrano
Mahdi Ghelichi

"Generative AI Enhanced Financial Risk Management Information Retrieval" by Amin Haeri, Jonathan Vitrano, and Mahdi Ghelichi, arXiv:2504.06293v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Summary of different approaches}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c||c|c|c|c|c|c|c}
 \toprule
 Method & Co-distillation & Diversity & I.B. & Merging & Others & Branch/Net & Consistently better than Ind. \\
 \midrule
 DML & Pred. pairwise & & & & & Net &\\
 CL-ILR & Pred. & & & & & Branch &\\
 ONE & Preds & & & Gate & & Branch&\\
 FFL & Pred. & & & Feat. Fus. & & Both&\\
 OKDDip & Pred. asymetric & & & & & Both & $\approx$ \\
 KDCL & Pred. & Data Augmentation & & Weights on val & & Net &\\
 PCL & Pred. & Data Augmentation & & Feat. Fus. & Mean teacher & Branch & $\approx$ \\
 AFD & Features & & & & & Net & $\approx$ \\
 \midrule
 GAL & & Gradients & & & & Net &\\
 GPMR & & Gradients & & & Grads. Magnitude & Net &\\
 ADP & & Non maximum pred. & & & Entropy Pred. & Both & $\checkmark$\\
 DIBS & & JSD Features & VIB & & Custom sampling & Both & $?$ \\
 \midrule
 \midrule
 IB & & & VIB & & & Both & $\checkmark$ \\
 CEB & & & VCEB & & & Both & $\checkmark$\\
 \midrule
 IBR (Ours ) & & R & VIB & & & Both & $\checkmark$\\
 CEBR (Ours ) & & R & VCEB & & & Both & $\checkmark$\\
 \midrule
 DICE (Ours ) & & CR & VCEB & & & Both & $\checkmark$\\%
 \bottomrule%
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation" by Alexandre Rame and Matthieu Cord, arXiv:2101.05544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Francesco Bilotta
Alberto Binetti
Giacomo Manferdini

"Blameocracy: Causal Attribution in Political Communication" by Francesco Bilotta, Alberto Binetti, and Giacomo Manferdini, arXiv:2504.06550v2, 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|}
			\hline
			$m$ & $k$ & Talbot-Gaver-Stehfest & Euler-Gaver-Stehfest \\ \hline \hline
			30 & 15 & $0.016420$ & $0.016413$ \\ \hline
			30 & 18& $0.016420$ & $0.016413$\\ \hline
		\end{tabular}
\end{adjustbox}
\caption{Joint density $p(1,2,2)$.}
\end{table}
\end{document}

q-fin

Runhuan Feng
Pingping Jiang
Hans Volkmer

"Geometric Brownian motion with affine drift and its time-integral" by Runhuan Feng, Pingping Jiang, and Hans Volkmer, arXiv:2012.09661v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The four testing boundary conditions }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrrrl}
	 Case	& $d_{11}$ & $d_{12}$ & $d_{13}$ & $d_{14}$ & $d_{21}$ & $d_{22}$ & $d_{23}$ & $d_{24}$ & condition on \\ \hline\hline
	 $Neumann$ & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & $v_s,u_s$ \\
		$Dirichlet$ & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & $v_s, v_e$ \\
		$Mix$ & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 1 & $v_s+u_s, v_e+u_e$ \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions" by Xiaorong Zou, arXiv:2504.19280v1, 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{Wilcoxon Signed-Rank Test Results (CMG vs. Other Models)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
\textbf{Comparison} & \textbf{p-value} \\
\hline
CMG vs. BiLSTM & 0.00399 \\
CMG vs. Gaussian Naive Bayes & 0.00012 \\
CMG vs. GRU & 0.02520 \\
CMG vs. Logistic Regression & 0.00106 \\
CMG vs. LSTM & 0.01004 \\
CMG vs. Multi Logistic Reg. & $2.93 \times 10^{-7}$ \\
CMG vs. Random Forest & 0.00027 \\
CMG vs. XGBoost & 0.00160 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data" by Arif Pathan, arXiv:2506.17244v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Classification of torsion of elliptic curves over quartic fields" by Maarten Derickx and Filip Najman, arXiv:2412.16016v2, 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{The effect of interleaving self-supervision and multi-agent game-play. The game setup has two tasks: Sender Predicting Rotation as per Table~, while using various augmentations (original and SimCLR same or individual).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
 \toprule
 Feature Extractor & Comm. & Top-5 & \#Target & Target & Rot.\\
 & rate & comm. & class & class & acc.\\
 & & rate & in top-5 & avg. rank & \\
 \midrule
 \multicolumn{6}{l}{\textbf{Sender \& Receiver images augmented with the original transforms:}}\\
 Learned end-end & 0.72 & 0.98 & 2.05 & 42.89 & 0.83\\
 Pretrained SS end-end & 0.84 & 0.99 & 2.19 & 40.19 & 0.79\\
 Pretrained SS \& fixed & 0.80 & 0.99 & 2.23 & 39.72 & 0.7\\
 \midrule
 \multicolumn{6}{l}{\textbf{Sender \& Receiver images augmented with SimCLR transforms:}}\\
 Learned end-end & 0.53 & 0.92 & 2.22 & 37.16 & 0.80\\% this hasnowd
 Pretrained SS end-end & 0.49 & 0.89 & 2.18 & 38.74 & 0.79\\
 Pretrained SS \& fixed & 0.42 & 0.85 & 2.14 & 39.57 & 0.78\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{This table presents the hyper-parameter configuration used for building hidden Markov models using the hmmlearn package. The default value is used for hyper-parameters not listed. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule
\textbf{Hyper-parameter} & \textbf{Values} \\
\midrule
n\_components & 4 \\
n\_iter & 100 \\
random\_state & 42 \\
emissions & Gaussian \\
algorithm & Viterbi \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Thomas Dierckx
Jesse Davis
Wim Schoutens

"Nowcasting Stock Implied Volatility with Twitter" by Thomas Dierckx, Jesse Davis, and Wim Schoutens, arXiv:2301.00248v1, 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|cc|cr|cc}
\hline
 & \multicolumn{2}{c|}{Hodge Scattering + SVM} & \multicolumn{2}{c|}{UGT} & \multicolumn{2}{c}{GFN} \\ \hline
Graph & Accuracy & \# Param & Accuracy & \# Param & Accuracy & \# Param \\ \hline
Collab & 80.38 & 256 & 77.84 & 866,746 & 81.50 & 68,754 \\
DD & 72.71 & 256 & 80.23 & 76,928 & 79.37 & 68,754 \\
IMDB-B & 73.10 & 256 & 77.04 & 55,508 & 73.40 & 68,754 \\
IMDB-M & 49.67 & 256 & 53.60 & 48,698 & 51.80 & 68,818 \\
MUTAG & 85.78 & 256 & 80.23 & 4,178 & 85.83 & 68,818 \\
PROTEINS & 75.35 & 256 & 78.53 & 1,878 & 76.46 & 68,818 \\
PTC & 68.28 & 256 & 69.63 & 12,038 & 66.60 & 68,818 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

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.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Truth table of NAND.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \hline
 \multicolumn{2}{c}{input}&\multicolumn{2}{c}{output}&\multicolumn{2}{c}{buffer}\\ \hline
 f & b & f&b & f&b\\ \hline
 0 & 0 & -&1&-&discharge\\
 0 & 1 & -&1&-&-\\
 1 & 1 & -&0&store&store\\
 1 & 0 & -&1&store&discharge\\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shota Inagaki
Shiu Mochiyama
Takashi Hikihara

"Electric Power Processing Using Logic Operation and Error Correction" by Shota Inagaki, Shiu Mochiyama, and Takashi Hikihara, arXiv:2010.12900v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 Resolution & Batch size & $m$ & $H$ & $K$ & $p_{d}$ & $s$ & $\lambda$ \\ \hline
 $64\times64$ & 50 & 2 & \multirow{3}{*}{256} & \multirow{3}{*}{512} & 15 $mm$ & \multirow{3}{*}{0.95} & \multirow{3}{*}{(638 $nm$, 520 $nm$, 450 $nm$)} \\ \cline{1-3} \cline{6-6}
 $128\times128$ & 20 & 3 & & & 21.5 $mm$ & & \\ \cline{1-3} \cline{6-6}
 $256\times256$ & 5 & 4 & & & 25 $mm$ & & \\ \hline
 \end{tabular}
\caption{Holo-VQVAE configurations for various input resolutions.}
\end{table}
\end{document}

cs

"Holo-VQVAE: VQ-VAE for phase-only holograms" by Joohyun Park and Hyeongyeop Kang, arXiv:2404.01330v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison on Market1501 dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
		\hline
		\multirow{2}{*}{Methods} & \multicolumn{2}{c}{Market1501} \\ \cline{2-3}
		& Rank-1(\%) & mAP(\%) \\ \hline
		IANet & 94.4 & 83.1 \\
		DGNet & 94.8 & 86.0 \\
		SCAL & 95.8 & 89.3 \\
		Circle Loss & 96.1 & 87.4 \\
		SB & 94.5 & 85.9 \\ \hline
		SB + RK & 95.4 & 94.2 \\
		SB + GGT(ours) & \textbf{94.6} & 85.7 \\
		SB + GGT+ RK(ours) & \textbf{96.2} & \textbf{94.7} \\
		SB + LGT(ours) & \textbf{95.1} & \textbf{87.2} \\
		SB + LGT + RK(ours) & \textbf{95.9} & \textbf{94.4} \\ \hline
		FastReID & 96. 3 & 90.3 \\
		FastReID + RK & 96.8 & 95.3 \\
		FastReID + GGT(ours) & \textbf{96.5} & \textbf{91.2} \\
		FastReID + GGT + RK(ours) & \textbf{96.9} & \textbf{95.6} \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method" by Yunpeng Gong, Liqing Huang, and Lifei Chen, arXiv:2101.08533v5, 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{Comparison of running times for GCN, GAT-jmlr and GAT*.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc}
\toprule
 Method & GCN & GAT-jmlr & GAT* \\ \hline
 Runtime (/s) & 8.63 & 10.03 & 8.93 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Zhongtian Ma
Qiaosheng Zhang
Bocheng Zhou
Yexin Zhang
Shuyue Hu
Zhen Wang

"Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models" by Zhongtian Ma, Qiaosheng Zhang, Bocheng Zhou, Yexin Zhang, Shuyue Hu, and Zhen Wang, arXiv:2412.15496v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Universal Inference for Incomplete Discrete Choice Models" by Hiroaki Kaido and Yi Zhang, arXiv:2501.17973v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrcc}
\hline\hline
 & $d$ & MAP & MR1\\
\hline\hline
2DFTM~ & 50 & 0.275 & 155 \\
Dmax~ & $5.5\,\text{k}$ & 0.322 & 132 \\
SiMPle~ & $2.2\,\text{k}$ & 0.332 & 142\\
Qmax~ & $5.5\,\text{k}$ & 0.365 & 113 \\
Qmax*~ & $5.5\,\text{k}$ & 0.373 & 104 \\
EarlyFusion~ & $8.5\,\text{k}$ & 0.426 & 116 \\
LateFusion~ & $5.5\,\text{k}$ & 0.454 & 177 \\
MOVE~ & $4\,\text{k}$ & 0.495 & 42 \\ % 0.489 & 43 \\
MOVE~ & $16\,\text{k}$ & 0.507 & 40 \\ % \textbf{0.506} & \textbf{42} \\
\textbf{Re-MOVE} & \textbf{256} & \textbf{0.524} & \textbf{38} \\
\hline\hline
\end{tabular}
\end{adjustbox}
\caption{Comparison with existing VI systems using Da-TACOS (taken from~). When not explicit, embedding sizes $d$ are estimated for a song duration of 3.5\,min (see text). Results for the proposed methodology are highlighted in bold.}
\end{table}
\end{document}

eess

"Less is more: Faster and better music version identification with embedding distillation" by Furkan Yesiler, Joan Serrà, and Emilia Gómez, arXiv:2010.03284v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Gaurab Aryal
Anirban Chattopadhyaya
Federico Ciliberto

"Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers" by Gaurab Aryal, Anirban Chattopadhyaya, and Federico Ciliberto, arXiv:2503.15785v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Semi-supervised Keypoint Localization

Olga Moskvyak
Frederic Maire
Feras Dayoub
Mahsa Baktashmotlagh

"Semi-supervised Keypoint Localization" by Olga Moskvyak, Frederic Maire, Feras Dayoub, and Mahsa Baktashmotlagh, arXiv:2101.07988v1, 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{Comparison of Power and FDP metrics for Detectron and SHSD across different OOD sizes.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
\textbf{OOD Size} & \textbf{Power Detectron} & \textbf{FDP Detectron} & \textbf{Power SHSD} & \textbf{FDP SHSD} \\ \midrule
100 & N/A & 1.00 & N/A & 0.00 \\
1000 & N/A & 1.00 & N/A & 0.00 \\
2000 & 0.96 & 0.61 & 0.40 & 0.02 \\
3000 & 0.98 & 0.60 & 0.63 & 0.02 \\
3500 & 0.98 & 0.60 & 0.67 & 0.02 \\
8593 & 0.98 & 0.60 & 0.74 & 0.04 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Salim I. Amoukou
Tom Bewley
Saumitra Mishra
Freddy Lecue
Daniele Magazzeni
Manuela Veloso

"Sequential Harmful Shift Detection Without Labels" by Salim I. Amoukou, Tom Bewley, Saumitra Mishra, Freddy Lecue, Daniele Magazzeni, and Manuela Veloso, arXiv:2412.12910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean and standard deviation (across samples) results of the proposed DDSP vocoder for speech enhancement on a low-resource and causal CRN model.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 Metrics & CRN & (\textbf{Ours}) CRN + DDSP \\
 \midrule
 \multicolumn{3}{c}{Small version ($\approx$300K parameters)} \\
 \midrule
 PESQ ($\uparrow$) & 1.83 \textcolor{gray}{$\pm$ 0.29} & \textbf{1.88} \textcolor{gray}{$\pm$ 0.29} \\
 STOI ($\uparrow$) & 71.95 \textcolor{gray}{$\pm$ 8.50} & \textbf{72.96} \textcolor{gray}{$\pm$ 8.42} \\
 DNSMOS {\scriptsize (OVRL)} ($\uparrow$) & 2.79 \textcolor{gray}{$\pm$ 0.23} & \textbf{3.15} \textcolor{gray}{$\pm$ 0.25} \\
 MCD ($\downarrow$) & 6.91 \textcolor{gray}{$\pm$ 1.27} & \textbf{5.92} \textcolor{gray}{$\pm$ 0.95} \\
 SSIM ($\uparrow$) & 0.81 \textcolor{gray}{$\pm$ 0.05} & \textbf{0.83} \textcolor{gray}{$\pm$ 0.04}\\
 \midrule
 \multicolumn{3}{c}{Large version ($\approx$600K parameters)} \\
 \midrule
 PESQ ($\uparrow$) & \textbf{1.85} \textcolor{gray}{$\pm$ 0.30} & 1.81 \textcolor{gray}{$\pm$ 0.28} \\
 STOI ($\uparrow$) & \textbf{72.75} \textcolor{gray}{$\pm$ 8.59} & 72.36 \textcolor{gray}{$\pm$ 8.26} \\
 DNSMOS {\scriptsize (OVRL)} ($\uparrow$) & 2.77 \textcolor{gray}{$\pm$ 0.22} & \textbf{3.12} \textcolor{gray}{$\pm$ 0.23} \\
 MCD ($\downarrow$) & 6.86 \textcolor{gray}{$\pm$ 1.28} & \textbf{6.09} \textcolor{gray}{$\pm$ 1.03}\\
 SSIM ($\uparrow$) & 0.81 \textcolor{gray}{$\pm$ 0.05} & \textbf{0.82} \textcolor{gray}{$\pm$ 0.04}\\
 \midrule[\heavyrulewidth]
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Heitor R. Guimarães
Ke Tan
Juan Azcarreta
Jesus Alvarez
Prabhav Agrawal
Ashutosh Pandey
Buye Xu

"Improving Resource-Efficient Speech Enhancement via Neural Differentiable DSP Vocoder Refinement" by Heitor R. Guimarães, Ke Tan, Juan Azcarreta, Jesus Alvarez, Prabhav Agrawal, Ashutosh Pandey, and Buye Xu, arXiv:2508.14709v1, 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{F1 Scores across IT data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\toprule
Model & Graph type & F1 Reg1 & F1 Reg 2 \\ \midrule
PCMCI+ &W& 12.1 & \textbf{29.6} \\
DYNOTEARS &W& 18.2 & \textbf{28.5} \\
Rhino &W& 28.6 & 25.8 \\
CASTOR &W& 18.2 & \textbf{28.5} \\
CASTOR non-lin &W& \textbf{40.0} & 24.5 \\ \midrule
CD-NOD & S& \multicolumn{2}{c}{23.5}\\
CASTOR non-lin & S& \multicolumn{2}{c}{\textbf{36.8}}\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Causal Temporal Regime Structure Learning" by Abdellah Rahmani and Pascal Frossard, arXiv:2311.01412v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sohrab Andaz
Carson Eisenach
Dhruv Madeka
Kari Torkkola
Randy Jia
Dean Foster
Sham Kakade

"Learning an Inventory Control Policy with General Inventory Arrival Dynamics" by Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, and Sham Kakade, arXiv:2310.17168v2, 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}{|l|r|r|r|}
\hline
Acc. Factor & Spiral & Gaussian\\
\hline
$R1$ & \multicolumn{2}{c|}{$0.0001$} \\
$R8$ & $0.1$ & $0.01$\\
$R16$ & $1.0$ & $0.01$\\
$R32$ & $10.0$ & $1.0$\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Regularization values $\lambda$ used for the reconstruction with BART.}
\end{table}
\end{document}

math

Miriam Löcke
Pim van Ooij
Cristóbal Bertoglio

"Parameter estimation in fluid flow models from undersampled frequency space data" by Miriam Löcke, Pim van Ooij, and Cristóbal Bertoglio, arXiv:2503.04092v1, 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}{r|lll|lllll}
&\multicolumn{3}{c}{New Scheme} &\multicolumn{5}{c}{ILUTP($\tau$)} \\
\# & $\tau_{large}$ & $\tau_{mix}$ & $\tau_{small}$ & $10^{-5}$ & $10^{-4}$ & $10^{-3}$ &$10^{-2}$ & $10^{-1}$ \\ \hline
1 & 17 & $\ddagger$ & 2& $\dagger$ & $\dagger$ &$\dagger$ &$\dagger$ &$\dagger$ \\
2 & $\ddagger$ & $\ddagger$ & 2&$1^{\star}$ & $\dagger$ &$\dagger$ &$\dagger$ &$\dagger$ \\
3 & 718 & $\ddagger$ & 2& $1^{\star}$ & $\dagger$ &$\dagger$ &$\dagger$ &$\dagger$ \\
4 & 14 & 23 & 2& 2 & 3 & 5 &$\dagger$ &$\dagger$ \\
5 & 99 & $\ddagger$ & 4 & 3 & $6^{\star}$ & $30^{\star}$ &$\dagger$ &$\dagger$ \\
6 & $\ddagger$ & $\ddagger$ & 12 & 4 & 7 & $\ddagger$ &$\dagger$ &$\dagger$ \\
7 & 3 & 6 & 2 & 3 & 4 & 6 & 9 &$\dagger$ \\
8 & $\ddagger$ & $\ddagger$ & 1 & $3^{\star}$& $3^{\star}$ & $7^{\star}$ &$\dagger$ &$\dagger$ \\
\end{tabular}
\end{adjustbox}
\caption{Number of outer fGMRES/GMRES iterations for the symmetric case}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
\textbf{Method} & \textbf{Train Accuracy} & \textbf{Test Accuracy} \\ \hline
MLP & 70.37\% & 50.69\% \\
CNN & 61.68\% & 50.29\% \\
ResNet & 95.67\% & 51.71\% \\
RNN & 61.47\% & 50.76\% \\
GRU & 53.98\% & 50.92\% \\
LSTM & 80.32\% & 50.21\% \\
Autoencoder & 50.10\% & 50.50\% \\
t-CNN & 58.75\% & 50.71\% \\
m-CNN & 50.32\% & 50.00\% \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Average results of the complementary experiment using cryptocurrency price time series. The average test accuracy in all machine learning models are approximate 50\%.}
\end{table}
\end{document}

q-fin

"Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks" by Yu Zhang, Zelin Wu, and Claudio Tessone, arXiv:2504.12771v1, 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{Ablation results for conversion model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 System & WER ($\% \downarrow$) & $\Delta\text{PPG}$ ($\downarrow$) & FD ($\downarrow$) & F0 corr. ($\uparrow$) \\
 \midrule
 Source & 15.86 & 0.51 & 67.29 & 0.74 \\
 \midrule
 Ours & {\bf 16.25} & {\bf 0.30} & 35.24 & {\bf 0.77} \\
 \ - accent embeddings & 16.82 & 0.31 & 35.75 & 0.76 \\
 \quad - phone supervision & 19.55 & 0.33 & 34.97 & 0.77 \\
 \ - extra data & 17.13 & 0.31 & 39.75 & 0.77 \\
 \ - pre-training & 27.59 & 0.40 & {\bf 30.83} & 0.76 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Qibing Bai
Sho Inoue
Shuai Wang
Zhongjie Jiang
Yannan Wang
Haizhou Li

"Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data" by Qibing Bai, Sho Inoue, Shuai Wang, Zhongjie Jiang, Yannan Wang, and Haizhou Li, arXiv:2507.17735v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{U.S.: Parameter Estimates and Persistence for DCC, Scalar BEKK, and LSTM-BEKK Models (4 Assets).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccc}
 \toprule
 Portfolio Size & Model & $a$ & $b$ & $a + b$ \\
 \midrule
 \multirow{3}{*}{4} & DCC & 0.042 & 0.871 & 0.913 \\
 & Scalar BEKK & 0.033 & 0.952 & 0.984 \\
 & LSTM-BEKK & 0.038 & 0.930 & {0.968} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Haoyuan Wang
Chen Liu
Minh-Ngoc Tran
Chao Wang

"Deep Learning Enhanced Multivariate GARCH" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, 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}{|c|c|c|c|c|c|c|}
 \cline{1-7}
 \textbf{embeding method} &
 \textbf{Classifier} &
 \textbf{Accuracy} &
 \textbf{AUC Score} &
 \textbf{Precision} &
 \textbf{Recall} &
 \textbf{F1 Score} \\
\cline{1-7}
 \multirow{3}{*}{Without pre-trained word embedding} & CNN & 85.0\% & 50 \% & 0 & 0 & 0 \\
 & RNN & 84.3\% & 50 \% & 0 & 0 & 0\\
 & CRNN & 85.3\% & 50 \% & 0 & 0 & 0\\
 \cline{1-7}
 \multirow{3}{*}{\textsc{FastText}} & CNN & 70\% & 68.6 \% & 0.75 & 0.43 & 0.54 \\
 & RNN & 83.6\% & 64.3 \% & 1 & 0.28 & 0.44\\
 & CRNN & 85.0\% & 50 \% & 0 & 0 & 0\\
 \cline{1-7}
 \multirow{3}{*}{word2vec} & CNN & 85.7\% & 57.1 \% & 1 & 0.14 & 0.25 \\
 & RNN & 82.9\% & 57.1 \% & 1 & 0.14 & 0.25\\
 & CRNN & 85.3\% & 64.3 \% &1 & 0.29 & 0.44\\
 \cline{1-7}
\end{tabular}
\end{adjustbox}
\caption{Deep learning Classifier Overall Performance }
\end{table}
\end{document}

cs

Sarah Alqurashi
Btool Hamoui
Abdulaziz Alashaikh
Ahmad Alhindi
Eisa Alanazi

"Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter" by Sarah Alqurashi, Btool Hamoui, Abdulaziz Alashaikh, Ahmad Alhindi, and Eisa Alanazi, arXiv:2101.05626v1, 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{We observe the impact of incorporating sequence of strategies on conversation outcome prediction in terms of Macro-F1 and Weighted-F1 score. For P4G, we observe strategies of the persuader (ER), persuadee (EE) and both. For CB, we observe strategies of the buyer (BU), seller (SE) and both.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcclcc}
\toprule
 \multicolumn{3}{c}{Persuasion (P4G)} & \multicolumn{3}{c}{Negotiation (CB)} \\
User & Macro-F1 & W-F1 & User & Macro-F1 & W-F1\\
\midrule
ER & 0.588& 0.620 & BU & \textbf{0.618} &\textbf{0.640}\\
EE & 0.618& 0.640 & SE & 0.462& 0.508\\
Both &\textbf{ 0.646}& \textbf{0.671} & Both & 0.605& 0.626\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ritam Dutt
Sayan Sinha
Rishabh Joshi
Surya Shekhar Chakraborty
Meredith Riggs
Xinru Yan
Haogang Bao
Carolyn Penstein Rosé

"RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations" by Ritam Dutt, Sayan Sinha, Rishabh Joshi, Surya Shekhar Chakraborty, Meredith Riggs, Xinru Yan, Haogang Bao, and Carolyn Penstein Rosé, arXiv:2101.10545v1, 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{Constant parameters for the $\rm UO_{2}$ creep model }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
Parameter & $A_{1}$ & $A_{2}$ & $A_{3}$ & $A_{4}$ & $A_{6}$ & $A_{7}$ \\
Value & 0.3919 & $1.3100 \times 10^{-19}$ & -87.7 & $2.0391\times 10^{-25}$ & -90.5 & $3.7226 \times 10^{-35}$\\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Kazuma Kobayashi
Dinesh Kumar
Syed Bahauddin Alam

"AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology" by Kazuma Kobayashi, Dinesh Kumar, and Syed Bahauddin Alam, arXiv:2211.13687v3, 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{Performance analysis of the Point-GR Classification Model Vs Total Number of Points.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Total Points} & \textbf{Overall Accuracy} & \textbf{Mean Accuracy} \\
\hline 2048 & 89.87 & 86.02 \\
\hline 1024 & 92.7 & 89.09 \\
\hline 921 & 92.26 & 89.92 \\
\hline 819 & 92.42 & 89.3 \\
\hline 716 & 91.65 & 87.68 \\
\hline 614 & 90.11 & 85.95 \\
\hline 512 & 88.08 & 83.5 \\
\hline 204 & 47.97 & 40.16 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Md Meraz
Md Afzal Ansari
Mohammed Javed
Pavan Chakraborty

"Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation" by Md Meraz, Md Afzal Ansari, Mohammed Javed, and Pavan Chakraborty, arXiv:2412.03052v1, 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}
\hline
Approaches & Weights & Relative Performance \\ \hline
Rule I & [0.15, 0.25, 0.25, 0.35] & 0.758 \\\hline
Rule II & [0.1, 0.2, 0.3, 0.4] & 0.279\\ \hline
Rule III & [0.1, 0.2, 0.2, 0.5]& 0.076\\\hline
Rule IV & [0.05, 0.2, 0.35, 0.45] &0.120\\\hline
Proposed method & Learned &1.152
\end{tabular}
\end{adjustbox}
\caption{Performance comparison to heuristic rules}
\end{table}
\end{document}

cs

Yuan Tian
Minghao Han
Chetan Kulkarni
Olga Fink

"A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning" by Yuan Tian, Minghao Han, Chetan Kulkarni, and Olga Fink, arXiv:2201.08445v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{booktabs}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrr}
\toprule
 &
 \textbf{HILT} &
 \cellcolor[HTML]{FFFFFF}\textbf{Synthesized} &
 \cellcolor[HTML]{FFFFFF}\textbf{HILT} &
 \cellcolor[HTML]{FFFFFF}\textbf{Synthesized} \\ \midrule
\textbf{Hallucination Type} &
 \multicolumn{2}{c}{\textbf{\# Positive Pairs}} &
 \multicolumn{2}{c}{\cellcolor[HTML]{FFFFFF}\textbf{\# Negative Pairs}} \\
\textbf{Imaginary Figure} & 120800 & 507360 & 14800 & 62160 \\
\textbf{Place} & 116770 & 513788 & 13050 & 56115 \\
\textbf{Bothersome Number} & 68570 & 281137 & 7275 & 40740 \\
\textbf{Temporal Issue} & 57860 & 271942 & 6600 & 29700 \\
\textbf{Total} & \multicolumn{2}{c}{1938227} & \multicolumn{2}{c}{230440} \\ \bottomrule
\end{tabular}
\caption{$\mathcal{FACTOID}$ dataset statistics.}
\end{table}
\end{document}

cs

Vipula Rawte
S. M Towhidul Islam Tonmoy
Krishnav Rajbangshi
Shravani Nag
Aman Chadha
Amit P. Sheth
Amitava Das

"FACTOID: FACtual enTailment fOr hallucInation Detection" by Vipula Rawte, S. M Towhidul Islam Tonmoy, Krishnav Rajbangshi, Shravani Nag, Aman Chadha, Amit P. Sheth, and Amitava Das, arXiv:2403.19113v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.