\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{diagbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|l|l|}
\hline
\diagbox{$w$}{ASE$\sim$}& $\nu_0$ & $\nu_2$ & $\nu_4$ &$\omega_1$ & $\omega_3$ & $B_0$ & $B_2$ \\ \hline
 $0.95$& $1e-7$ &$1e-7$ &$5e-11$ & $2e-8$ & $7e-11$ & $3e-12$ & $7e-12$\\ \hline
$0.89$ & $1e-7$ & $2e-7$ & $3e-11$& $4e-8$ & $4e-11$ & $2e-12$ & $1e-9$ \\ \hline
 $0.79$& $3e-7$ & $2e-7$ & $2e-11$& $5e-8$ & $2e-11$ & $1e-12$ & $1e-9$ \\ \hline
\end{tabular}
\end{adjustbox}
\caption{ASE for the multipolar coefficients and three different stars. We see how the magnitudes can vary depending on the field frequency.}
\end{table}
\end{document}

math

Christoph Adam
Jorge Castelo Mourelle
Alberto García Martín-Caro
Andrzej Wereszczynski

"Spinning boson stars in nonlinear sigma models and Universal Relations" by Christoph Adam, Jorge Castelo Mourelle, Alberto García Martín-Caro, and Andrzej Wereszczynski, arXiv:2502.20923v3, 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
 $\mathtt{has\_loan(C,L)}$ & $\mathtt{status(L)}$ & $\mathtt{credit\_score(C)}$ \\
 \hline
 \hline
 $\mathtt{true}$ & $\mathtt{appr}$ & $\mathtt{gaussian(700, 10.9)}$ \\
 $\mathtt{true}$ & $\mathtt{decl}$ & $\mathtt{gaussian(600, 20.5)}$ \\
 $\mathtt{false}$ & $\mathtt{appr}$ & $\mathtt{gaussian(650, 15.4)}$ \\
 $\mathtt{false}$ & $\mathtt{decl}$ & $\mathtt{gaussian(650, 15.4)}$ \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Nitesh Kumar
Ondrej Kuzelka
Luc De Raedt

"First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs" by Nitesh Kumar, Ondrej Kuzelka, and Luc De Raedt, arXiv:2201.11165v2, 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{Information on video and audio files that must accompany a manuscript submission.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
\rule[-1ex]{0pt}{3.5ex} Item & Video & Audio \\
\hline
\rule[-1ex]{0pt}{3.5ex} File name & Video1, video2... & Audio1, audio2... \\
\hline
\rule[-1ex]{0pt}{3.5ex} Number of files & 0-10 & 0-10 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Size of each file & 5 MB & 5 MB \\
\hline
\rule[-1ex]{0pt}{3.5ex} File types accepted & .mpeg, .mov (Quicktime), .wmv (Windows Media Player) & .wav, .mp3 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Han Liu
Kathryn L. Holloway
Dario J. Englot
Benoit M. Dawant

"A Multi-rater Comparative Study of Automatic Target Localization Methods for Epilepsy Deep Brain Stimulation Procedures" by Han Liu, Kathryn L. Holloway, Dario J. Englot, and Benoit M. Dawant, arXiv:2201.11002v1, 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{Distribution of State-Dependent Factors Across Market States}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
\textbf{Parameter} & \textbf{Mean} & \textbf{Std Dev} & \textbf{Min} & \textbf{Max} & \textbf{Range} \\
\midrule
\textbf{Movement Factors} & & & & & \\
Up Factor $u(s)$ & 1.000676 & 0.000261 & 1.000410 & 1.001577 & 0.001167 \\
Down Factor $d(s)$ & 0.999335 & 0.000220 & 0.998683 & 0.999576 & 0.000893 \\
& & & & & \\
\textbf{Probabilities} & & & & & \\
Physical $p_{RF}(s)$ & 0.498 & 0.277 & 0.059 & 0.939 & 0.881 \\
Risk-Neutral $p_{MMM}(s)$ & 0.499 & 0.054 & 0.419 & 0.572 & 0.153 \\
Absolute Difference & 0.217 & 0.106 & 0.063 & 0.369 & 0.306 \\
& & & & & \\
\textbf{Calibration Metrics} & & & & & \\
Implied Volatility & 0.000551 & 0.000353 & 0.000322 & 0.001405 & 0.001083 \\
KL Divergence & 0.177 & 0.142 & 0.001 & 0.560 & 0.559 \\
Sample Size & 465 & 219 & 43 & 689 & 646 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Akash Deep
Chris Monico
W. Brent Lindquist
Svetlozar T. Rachev
Frank J. Fabozzi

"Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach" by Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.16701v1, 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|llll|}
\hline
 & \# Annotations & A1 & A2 & A3 \\ \hline
A1 & 10,685 & - & 0.492 & 0.600 \\
A2 & 3,051 & 0.492 & \textbf{-} & 0.500 \\
A3 & 9,847 & 0.600 & 0.500 & - \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Number of Annotations and Pairwise Cohen's Kappa scores $\kappa_{\text{cohen}}$ between annotators.}
\end{table}
\end{document}

cs

James Ravenscroft
Arie Cattan
Amanda Clare
Ido Dagan
Maria Liakata

"CD2CR: Co-reference Resolution Across Documents and Domains" by James Ravenscroft, Arie Cattan, Amanda Clare, Ido Dagan, and Maria Liakata, arXiv:2101.12637v1, 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{Mechanism Decomposition by CEO Gender}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
 & \multicolumn{2}{c}{Male CEOs} & \multicolumn{2}{c}{Female CEOs} & \\
 & Effect & \% of Total & Effect & \% of Total & Difference \\
\midrule
Cost Reduction & 0.009*** & 39\% & 0.012** & 36\% & -0.003 \\
 & (0.003) & & (0.005) & & (0.006) \\
Revenue Enhancement & 0.008** & 35\% & 0.015*** & 45\% & 0.007* \\
 & (0.003) & & (0.005) & & (0.004) \\
Innovation Output & 0.006** & 26\% & 0.006* & 18\% & 0.000 \\
 & (0.003) & & (0.003) & & (0.004) \\
\midrule
Total Effect & 0.023*** & 100\% & 0.033*** & 100\% & 0.010* \\
 & (0.008) & & (0.012) & & (0.006) \\
\midrule
Observations & 2,535 & & 200 & & 2,735 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises" by Tatsuru Kikuchi, arXiv:2508.03757v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
District & Governor & Treasurer & Sec. of State\\
\hline
CO 1 & 0.755025 & 0.732848 & 0.733744\\
CO 2 & 0.644199 & 0.608735 & 0.621864\\
CO 3 & 0.480772 & 0.464260 & 0.471826\\
CO 4 & 0.397891 & 0.394212 & 0.398713\\
CO 5 & 0.407602 & 0.392769 & 0.384713\\
CO 6 & 0.561725 & 0.547696 & 0.550642\\
CO 7 & 0.597754 & 0.586725 & 0.588472
\end{tabular}
\end{adjustbox}
\caption{Democratic vote shares for enacted districts in each of the three 2018 focus races.}
\end{table}
\end{document}

stat

Jeanne Clelland
Haley Colgate
Daryl DeFord
Beth Malmskog
Flavia Sancier-Barbosa

"Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado" by Jeanne Clelland, Haley Colgate, Daryl DeFord, Beth Malmskog, and Flavia Sancier-Barbosa, arXiv:2011.06049v2, 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|}
\hline
Equation \\
\hline
$x \cdot y = a \oplus b$ \\
$x\cdot y = \overline{a} \oplus b$ \\
$x\cdot \overline{y} = a \oplus b$ \\
$x \cdot \overline{y} = \overline{a} \oplus b$ \\
$\overline{x} \cdot y = a \oplus b$ \\
$\overline{x} \cdot y = \overline{a} \oplus b$ \\
$\overline{x} \cdot \overline{y} = a \oplus b$ \\
$\overline{x} \cdot \overline{y} = \overline{a} \oplus b$ \\
$x + y = a \oplus b$ \\
$x + y = \overline{a} \oplus b$ \\
$x + \overline{y} = a \oplus b$ \\
$x + \overline{y} = \overline{a} \oplus b$ \\
$\overline{x} + y = a \oplus b$ \\
$\overline{x} + y = \overline{a} \oplus b$ \\
$\overline{x} + \overline{y} = a \oplus b$ \\
$\overline{x} + \overline{y} = \overline{a} \oplus b$ \\
\hline
 \end{tabular}
\caption{List of all equations leading to a winning probability of $|\cos(\frac{\pi}{8})|^2$, for a classical maximum at $\frac{3}{4}$.}
\end{table}
\end{document}

math

"Two new non-equivalent three-qubit CHSH games" by Hamza Jaffali and Frédéric Holweck, arXiv:2312.00578v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Oriana Presacan
Alexandru Dorobantiu
Vajira Thambawita
Michael A. Riegler
Mette H. Stensen
Mario Iliceto
Alexandru C. Aldea
Akriti Sharma

"Merging synthetic and real embryo data for advanced AI predictions" by Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler, Mette H. Stensen, Mario Iliceto, Alexandru C. Aldea, and Akriti Sharma, arXiv:2412.01255v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Multiple change point detection in functional data with applications to biomechanical fatigue data" by Patrick Bastian, Rupsa Basu, and Holger Dette, arXiv:2312.11108v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Md Umar Hashmi
Jonathan Cavaleiro
Lucas Pereira
Ana Bušić

"Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal" by Md Umar Hashmi, Jonathan Cavaleiro, Lucas Pereira, and Ana Bušić, arXiv:1911.10458v1, 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}{ccccccccccc}
 \toprule
 \textbf{Event ID} & \textbf{Timestamp (sec)} & $p^{(1)}_a$ & $v^{(1)}_a$ & $p^{(1)}_b$ & $v^{(1)}_b$ & $\cdots$ & $p^{(10)}_a$ & $v^{(10)}_a$ & $p^{(10)}_b$ & $v^{(10)}_b$ \\
 \midrule
 \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & $\cdots$ & \vdots & \vdots & \vdots & \vdots\\
 1311 & 34713.685155243 & \$11.86 & 12000 & \$11.85 & 8800 & $\cdots$ & \$11.95 & 500 & \$11.73 & 5500 \\
 1312 & 34714.133632201 & \$11.86 & 9484 & \$11.85 & 8800 & $\cdots$ & \$11.95 & 500 & \$11.73 & 5500 \\
 \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & $\cdots$ & \vdots & \vdots & \vdots & \vdots\\
 \midrule
 \end{tabular}
\end{adjustbox}
\caption{Example order book update from a LOBSTER order book file. This example corresponds to the order book dynamics shown in Figure .}
\end{table}
\end{document}

q-fin

Lorenzo Lucchese
Mikko Pakkanen
Almut Veraart

"The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective" by Lorenzo Lucchese, Mikko Pakkanen, and Almut Veraart, arXiv:2211.13777v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllllll|}
 \hline
\textbf{Approach} & \textbf{Accuracy} & \textbf{Recall} & \textbf{Specificity} & \textbf{$F_1$} \\ \hline
 \textbf{Cho et al.} & 0.6746 & & & \\
 \textbf{Kumar et al.} & 0.6724 & & &\\
 \textbf{Sadiq et al.} & 0.8502 & & & \\
 \textbf{Sadiq et al.} & 0.8769 & 0.8762 & 0.8775 & 0.8770\\
	\cellcolor{green!30}\textbf{Ours} & \cellcolor{green!30}0.995 & \cellcolor{green!30}0.9986 & \cellcolor{green!30}0.9983 & \cellcolor{green!30}0.9984\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Table summarizing the findings from four studies that analyzed the same dataset. Our results, which outperform the others, are highlighted in green.}
\end{table}
\end{document}

eess

Dario Sanalitro
Marco Finocchiaro
Pasquale Memmolo
Emanuela Cutuli
Maide Bucolo

"A Single Subject Machine Learning Based Classification of Motor Imagery EEGs" by Dario Sanalitro, Marco Finocchiaro, Pasquale Memmolo, Emanuela Cutuli, and Maide Bucolo, arXiv:2508.21724v1, licensed under CC BY-SA 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{change detection results of different methods on the Bern dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc}
\hline\hline
Methods & FP & FN & OE & PCC (\%) & KC(\%) & F1(\%)\\ \hline
PCAKM & \textbf{95} & 258 & 353 & 99.61 & 83.36 & 83.56\\
NR-ELM & 104 & 228 & 332 & 99.63 & 84.63 & 84.81\\
GaborPCANet & 131 & 193 & 324 & 99.64 & 85.41 & 85.59\\
LR-CNN & 97 & 216 & 313 & 99.65 & 85.54 & 85.71\\
MLFN & 97 & 225 & 322 & 99.64 & 85.06 & 85.24\\
DBN & 145 & \textbf{159} & 304 & 99.66 & \textbf{86.59} & \textbf{86.76}\\
DCNet & 146 & 161 & 307 & 99.66 & 86.45 & 86.62\\
GKSNet & 111 & 190 & \textbf{301} & \textbf{99.67} & 86.34 & 86.51 \\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Junjie Wang
Feng Gao
Junyu Dong
Shan Zhang
Qian Du

"Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network" by Junjie Wang, Feng Gao, Junyu Dong, Shan Zhang, and Qian Du, arXiv:2201.08954v2, 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{Optimal threshold values after optimization.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \hline
 $N$ &$\mathbf{\lambda_{opt}}$ &$\mathbf{\alpha(\lambda_{opt})}$ &$\mathbf{BTR(\lambda_{opt})}$\\
 \hline
 \textbf{256} & 0.72 & 0.81 & 0.17 \\
 \textbf{512} & 0.75 & 0.81 & 0.18 \\
 \textbf{1024} & 0.76 & 0.78 & 0.18 \\
 \textbf{2048} & 0.78 & 0.76 & 0.2 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Federico Francesco Luigi Mariani
Michele Zhu
Maurizio Magarini

"Semantic Communications via Features Identification" by Federico Francesco Luigi Mariani, Michele Zhu, and Maurizio Magarini, arXiv:2503.20720v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
			\hline
 \multirow{2}{*}{Algorithm} & \multicolumn{2}{c}{\textsf{Pendulum-v0}} & \multicolumn{2}{c}{\textsf{BipedalWalkerHardcore-v3}} \\

			 & Mean & Variance & Mean & Variance \\
 \hline
 TD3 & -122 & 6903 & 234 & 9348 \\
 VARAC & -126 & \textbf{4826} & 221 & \textbf{6090} \\
			\hline
	\end{tabular}
\end{adjustbox}
\caption{The mean and variance of the policy learned by TD3 and VARAC algorithms under two gym environments.}
\end{table}
\end{document}

stat

Han Zhong
Xun Deng
Ethan X. Fang
Zhuoran Yang
Zhaoran Wang
Runze Li

"Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy" by Han Zhong, Xun Deng, Ethan X. Fang, Zhuoran Yang, Zhaoran Wang, and Runze Li, arXiv:2012.14098v2, 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{Ablation study: the AUC scores of the proposed method and its variants on \textbf{Dataset-2}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
 \hline
 \footnotesize Method & \footnotesize AUC Score \\
 \hline
 \footnotesize Autoencoder & \footnotesize {0.675} \\
 \hline
 \footnotesize LithoNet (OC-SVM) & \footnotesize {0.720} \\
 \hline
 \footnotesize LithoNet (MC-SVM) & \footnotesize {0.744} \\
 \hline
 \footnotesize Glocal (AE + LithoNet) & \footnotesize {0.846} \\
 \hline
 \footnotesize SA-LithoNet (OC-SVM) & \footnotesize {0.857} \\
 \hline
 \footnotesize SA-LithoNet (MC-SVM) & \footnotesize {0.864} \\
 \hline
 \footnotesize \textbf{SA-Glocal (AE + SA-LithoNet)} & \footnotesize \textbf{0.932} \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hao-Chiang Shao
Hsing-Lei Ping
Kuo-shiuan Chen
Weng-Tai Su
Chia-Wen Lin
Shao-Yun Fang
Pin-Yian Tsai
Yan-Hsiu Liu

"Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning" by Hao-Chiang Shao, Hsing-Lei Ping, Kuo-shiuan Chen, Weng-Tai Su, Chia-Wen Lin, Shao-Yun Fang, Pin-Yian Tsai, and Yan-Hsiu Liu, arXiv:2201.09717v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Edson Pindza
Jules Clement Mba
Sutene Mwambi
Nneka Umeorah

"Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model" by Edson Pindza, Jules Clement Mba, Sutene Mwambi, and Nneka Umeorah, arXiv:2310.09622v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Pablo Brañas-Garza
Antonio Cabrales
María Paz Espinosa
Diego Jorrat

"The effect of ambiguity in strategic environments: an experiment" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, 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}{cccccc}
 \toprule
 Within-regime Markov order $k_{i,g}$ ($i=1,\dots,4,g=1,2,3$) & 0 & 1 & 2 & 3 & 4 \\
 \midrule
 AIC of models with two regimes & 3897 & 3789 & 3768 & 3753 & 3759 \\
 AIC of models with three regimes & 3812 & 3692 & 3679 & 3676 & 3689 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{The AIC values of models fitted by multi-stage approach, without external business cycle information. Markov order of 0 in the table corresponds to the hidden Markov model.}
\end{table}
\end{document}

stat

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Physical parameters of the DFIM~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
	Parameter & Value & Parameter & Value \\
	\hline
	$L_1$ & $0.02645$ & $R_1$ [$\Omega$]& $0.036$ \\
	$L_2$ & $0.0264$& $R_2$ [$\Omega$]& $0.038$ \\
	$L_m$ & $0.0257$& $\omega_0$ [rad/s]& $2\pi70.8$\\
	$p$ & $3$ & $\omega_r$ [rad/s]& $2\pi62$ \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Guido Carnevale
Nicola Mimmo
Giuseppe Notarstefano

"Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration" by Guido Carnevale, Nicola Mimmo, and Giuseppe Notarstefano, arXiv:2412.02758v3, 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{$\text{RMST}_{\text{diff}}$ for LUME-LUNG Treatment delay}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
 \hline
Model & $\text{RMST}_{\text{diff}}$ & WAIC \\
 \hline
 Change-point & 2.72 & 3933.05 \\
 Log-Normal & 1.83 & 3935.17 \\
 Generalized-Gamma & 1.95 & 3936.63 \\
 Royston Parmar (PH) & 2.63 & 3937.92 \\
 Royston Parmar (non-PH) & 2.69 & 3939.61 \\
 Log-Logistic & 2.09 & 3945.81 \\
 Gamma & 2.56 & 3958.73 \\
 Weibull & 2.60 & 3969.97 \\
 Gompertz & 2.53 & 3995.38 \\
 Exponential & 2.51 & 4001.31 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Extrapolation of Relative Treatment Effects using Change-point Survival Models" by Philip Cooney and Arthur White, arXiv:2401.00568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Electricity Market Predictability: Virtues of Machine Learning and Links to the Macroeconomy" by Jinbo Cai, Wenze Li, and Wenjie Wang, arXiv:2507.07477v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Luca De Benedictis
Vania Licio
Anna Pinna

"From the historical Roman road network to modern infrastructure in Italy" by Luca De Benedictis, Vania Licio, and Anna Pinna, arXiv:2208.06675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 $a$ & $b$ & $\varepsilon$ & $c$ & $d$ & $K$ & $\tau_1$ & $\tau_2$ \\ \hline
 $0.0428$ & $[1.2,1.5]$ & $0.1$ & $0.16$ & $0.0522$ & $0.0435$ & $[0,500]$ & ${[0,1000]}$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Parameter values set { as given in~, and a wide range for delay parameters $\tau_{1,2}$}.}
\end{table}
\end{document}

math

A. Flores--Pérez
M. A. González--Olvera
V. F. Breña--Medina

"Transitions to Intermittent Chaos in Quorum Sensing Dynamics" by A. Flores--Pérez, M. A. González--Olvera, and V. F. Breña--Medina, arXiv:2503.14363v3, 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{Average F1 Scores with Different Values of $\beta$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
			\toprule
			 $\beta$ & 0.01 & 0.05 & 0.1 & 0.6 & 1 \\
			\midrule
			F1 score & 0.903 & 0.925 & 0.926 & 0.907 & 0.705 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Power System Event Identification based on Deep Neural Network with Information Loading" by Jie Shi, Brandon Foggo, and Nanpeng Yu, arXiv:2011.06718v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
\multirow{2}{*}{\textbf{Location}} & \multicolumn{2}{c|}{\textbf{VAR (\%)}} \\ \cline{2-3}
 & \textit{\textbf{MRE}} & \textit{\textbf{MAE}} \\ \hline
New York & 11.28, 95\% CI {[}10.9, 11.6{]} & 0.15 \\ \hline
California & 13.48, 95\% CI {[}13.4, 13.5{]} & 0.23 \\ \hline
Florida & 17.49, 95\% CI {[}17.5, 17.5{]} & 0.38 \\ \hline
New Jersey & 17.93, 95\% CI {[}17.9, 18{]} & 0.26 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{The errors of forecasting the outbreak of COVID-19 (\% of people tested and positive) for the next 30 days using VAR model.}
\end{table}
\end{document}

stat

Rohan Sukumaran
Parth Patwa
T V Sethuraman
Sheshank Shankar
Rishank Kanaparti
Joseph Bae
Yash Mathur
Abhishek Singh
Ayush Chopra
Myungsun Kang
Priya Ramaswamy
Ramesh Raskar

"COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms" by Rohan Sukumaran, Parth Patwa, T V Sethuraman, Sheshank Shankar, Rishank Kanaparti, Joseph Bae, Yash Mathur, Abhishek Singh, Ayush Chopra, Myungsun Kang, Priya Ramaswamy, and Ramesh Raskar, arXiv:2101.10266v2, 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{Location Accuracy}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \toprule
 Model & Backbone & $\rm AP_{50:95}$ & $\rm AP_{50}$ & $\rm AP_{75}$ & $\rm Recall$\\
 \midrule
 RFMask(4) & ResNet-18 & 0.586 & 0.966 & 0.678 & 0.662 \\
 RFMask(4) & ResNet-34 & 0.590 & 0.966 & 0.689 & 0.665 \\
 RFMask(4) & ResNet-50 & 0.581 & 0.966 & 0.671 & 0.656 \\
 RFMask(12) & ResNet-18 & 0.621 & 0.967 & 0.783 & 0.691 \\
 RFMask(12) & ResNet-34 & 0.631 & 0.967 & 0.817 & 0.699 \\
 RFMask(12) & ResNet-50 & \textbf{0.632} & \textbf{0.967} & \textbf{0.824} & \textbf{0.701} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zhi Wu
Dongheng Zhang
Chunyang Xie
Cong Yu
Jinbo Chen
Yang Hu
Yan Chen

"RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals" by Zhi Wu, Dongheng Zhang, Chunyang Xie, Cong Yu, Jinbo Chen, Yang Hu, and Yan Chen, arXiv:2201.10175v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
Parameter & Default Value \\
\midrule
Replay Buffer Size & 1000000 \\
Minibatch Size & 256 \\
Discount factor $(\gamma)$ & 0.99 \\
Learning Rate & $3 \times 10^{-4}$ \\
Update Actor Every & 1 \\
Update Critic Every & 1 \\
Update Critic Target Every & 1 \\
Learning Starts & 100 \\
Target Smoothing Coefficient $(\tau)$ & 0.005 \\
Target Entropy & $\vert \mathcal{A} \vert$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Default parameters for SAC implementation.}
\end{table}
\end{document}

eess

Gautham Vasan
Mohamed Elsayed
Alireza Azimi
Jiamin He
Fahim Shariar
Colin Bellinger
Martha White
A. Rupam Mahmood

"Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers" by Gautham Vasan, Mohamed Elsayed, Alireza Azimi, Jiamin He, Fahim Shariar, Colin Bellinger, Martha White, and A. Rupam Mahmood, arXiv:2411.15370v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zero-Leverage Puzzle

"Zero-Leverage Puzzle" by Mykola Pinchuk, arXiv:2302.00761v1, 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{UAV physics model parameters }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c||}
 \hline
 Parameter & Value \\
 \hline
 Timestep & 0.01 sec \\
 Motor lag & 0.05 sec \\
 Mass $m$ & 1.5 kg \\
 Arm length $l$ & 0.13 m \\
 Tilt angle range $[\theta_{min}, \theta_{max}]$ & $[-\frac{\pi}{3}, \frac{\pi}{3}]$ rad \\
 Motor thrust range $[F_{min}, F_{max}]$ & $[0, 15.0]$ N \\ %[-0.5ex]
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Aditya M. Deshpande
Rumit Kumar
Ali A. Minai
Manish Kumar

"Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors" by Aditya M. Deshpande, Rumit Kumar, Ali A. Minai, and Manish Kumar, arXiv:2007.07793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Baseline results (PSNR) for $\times2$ SR. We train RCAN on DF2K with the original protocol (1st row) and our large-batch training strategies (2nd and 3rd rows). The {\setlength{\fboxsep}{2pt}\colorbox{green!25}{highlighted}} setting matches the original strategy and reduces the training time by $77\%$, which is used as the baseline for further improvements.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
BS, $\eta$ & Time & Set5 & Set14 & B100 & Urban100
\\
\midrule
16, ~~0.0001 & 7 Days & 38.35 & 34.33 & 32.48 & 33.59 \\
256, 0.0016 & 1.6 Days & 38.34 & 34.35 & 32.45 & 33.52 \\
\rowcolor{green!25}
256, 0.0032 & 1.6 Days & 38.35 & 34.42 & 32.46 & 33.61 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zudi Lin
Prateek Garg
Atmadeep Banerjee
Salma Abdel Magid
Deqing Sun
Yulun Zhang
Luc Van Gool
Donglai Wei
Hanspeter Pfister

"Revisiting RCAN: Improved Training for Image Super-Resolution" by Zudi Lin, Prateek Garg, Atmadeep Banerjee, Salma Abdel Magid, Deqing Sun, Yulun Zhang, Luc Van Gool, Donglai Wei, and Hanspeter Pfister, arXiv:2201.11279v1, 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 position optimization of the 2 holes case. The position is represented by the coordinate of the hole center.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\toprule
Hole No. & Position before optimization & Position after optimization \\
\midrule
1th hole & (64, 64) & (54, 54) \\
2th hole & (64, 64) & (54, 74)			\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks" by Hao Ma, Yang Sun, and Mario Chiarelli, arXiv:2201.10002v1, 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{AD Test Statistics and Critical Values}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
 \toprule
 \textbf{Parameters}& \textbf{SU 1} \\
 \midrule
 AD Test Statistic & 46.89 \\
 Critical Value at 15\% &0.57 \\
 Critical Value at 10\% &0.65 \\
 Critical Value at 5\% &0.78 \\
 Critical Value at 2.5\% &0.90 \\
 Critical Value at 1\% &1.08 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Hardware-in-the-Loop Evaluation of Goodness of Fit (GoF) Testing for Dynamic Spectrum Sharing" by Mir Lodro, Simon Armour, and Mark A. Beach, arXiv:2501.12693v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Confidence set for mixture order selection" by Alessandro Casa and Davide Ferrari, arXiv:2503.18790v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|r|r|r}
 \hline
 \multicolumn{5}{c}{End item demand only - no adjustments} \\
 \hline
 Utilization & $\lambda$ & Converged (\%) & Runtime (s) & $\Delta$ (\%)\\
 \hline
 \multirow{4}{*}{50\%}
 & 0.1 & 8.3 & 2662.9 & 0.00 \\
 & 1 & 29.1 & 3992.3 & 0.47 \\
 & 10 & 91.6 & 225.1 & 3.41 \\
 & 100 & 100.0 & 232.4 & 5.35\\
 \hline
 \multirow{4}{*}{90\%}
 & 0.1 & 20.8 & 2041.1 & 0.00 \\
 & 1 & 25.0 & 1138.7 & 0.83\\
 & 10 & 75.0 & 401.7 & 3.24\\
 & 100 & 79.2 & 636.5 & 4.46 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Averaging consensus calculation: Evaluation of the $\lambda$-multiple for the penalty parameter $\rho$ in terms of total costs and runtime for end item demands only on a scenario tree with $|\Omega| = 2$ and therefore $|\Phi| = 128$. Reporting percentage of converged PH runs and respective runtimes and cost deltas to optimal costs over the converged runs.}
\end{table}
\end{document}

math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, 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{Abnormality classification performance on patches of size $32\times32$. Metrics computed on fully-sampled images, (A) 4$\times$ and (B) $8\times$ undersampled images, which were reconstructed using the various methods studied in this paper. The reported metrics are Sensitivity (Sn), Specificity (Sp), F1 score, Kappa and Area Under the ROC curve (AUROC).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
\hline\hline
\multicolumn{6}{c}{AF=4$\times$}\\
Input & Sn & Sp & F1 score & Kappa & AUROC\\
\hline
Fully-sampled & 86.12 & 65.90 & 78.25& 52.04& 86.24\\
\hline
U-Net & 89.74 & 62.93 & 79.17 & 52.69& 86.24\\
FNAF U-Net & 90.23 & 63.51 & 79.64 & 53.76 & 86.37\\
B-Box U-Net & 91.13 & 62.11 & 79.63 & 53.27 & 86.71\\
\hline
I-RIM & 94.50 & 52.31 & 78.09 & 46.84 & 85.70\\
FNAF I-RIM & 94.09 & 54.20 & 78.49 & 48.32 & 85.79\\
B-Box I-RIM & 91.38 & 55.77 & 77.62 & 47.17 & 84.89\\
\hline\hline
 \\
\multicolumn{6}{c}{(A)} \\
\\
\hline\hline
\multicolumn{6}{c}{AF=8$\times$}\\
Input & Sn & Sp & F1 score & Kappa & AUROC\\
\hline
Fully-sampled & 86.12 & 65.90 & 78.25& 52.04& 86.24\\
\hline
U-Net & 85.30 & 68.95 & 78.89 & 54.26 & 86.04\\
FNAF U-Net & 85.71 & 69.19 & 79.21 & 54.92 & 86.28\\
B-Box U-Net & 87.11 & 68.78 & 79.83 & 55.91 & 87.04\\
\hline
I-RIM & 92.28 & 55.93 & 78.14 & 48.24 & 85.43\\
FNAF I-RIM & 91.71 & 56.67 & 78.08 & 48.41 & 85.34\\
B-Box I-RIM & 87.03 & 59.06 & 76.40 & 46.11 & 84.20 \\
\hline\hline
 \\
\multicolumn{6}{c}{(B)} \\
\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Francesco Calivá
Kaiyang Cheng
Rutwik Shah
Valentina Pedoia

"Adversarial Robust Training of Deep Learning MRI Reconstruction Models" by Francesco Calivá, Kaiyang Cheng, Rutwik Shah, and Valentina Pedoia, arXiv:2011.00070v3, 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 Number of Trainable Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \toprule
 ~ & Number of Parameters \\
 \midrule
 Vanilla Transformer & 1.68M \\
 BNT & 11.2M \\
 BolT & 1.68M \\
 QuantumTSTransformer & 22K \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Junghoon Justin Park
Jungwoo Seo
Sangyoon Bae
Samuel Yen-Chi Chen
Huan-Hsin Tseng
Jiook Cha
Shinjae Yoo

"Resting-state fMRI Analysis using Quantum Time-series Transformer" by Junghoon Justin Park, Jungwoo Seo, Sangyoon Bae, Samuel Yen-Chi Chen, Huan-Hsin Tseng, Jiook Cha, and Shinjae Yoo, arXiv:2509.00711v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Localization using Angle-of-Arrival Triangulation" by Amod K. Agrawal, arXiv:2508.16908v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Validation results}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rcccccc}
			 & \multicolumn{3}{c}{\textbf{Three-class}} & \multicolumn{3}{c}{\textbf{Five-class}} \\
			 & \% (Pts) & \% (Pts) & \% & \% (Pts) & \% (Pts) & \% \\
			Level & Acc/Mean Err. (Llama) & Acc/Mean Err. (Phi) & Baseline & Acc/Mean Err. (Llama) & Acc/Mean Err. (Phi) & Baseline \\
			Paragraph & 65 (0.06) & 54 (0.14) & 38 & 58 (0.19) & 43.4 (0.18) & 31 \\
			Sentence & 67.4 (0.06) & 53 (0.08) & 37.3 & 51 (0.16) & 39 (0.15) & 34
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of DAC 2013 i-vector dataset without domain adaptation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
		\toprule
		\textbf{Systems\#} & \textbf{PLDA} & \textbf{EER (\%)} & \textbf{DCF10} & \textbf{DCF08} \\
		\hline\hline
		1 & SRE & 2.33 & 0.402 & 0.235\\
		2 & SWB & 5.65 & 0.632 & 0.427\\
		3 & SRE-1phn & 9.35 & 0.724 & 0.520\\
		4 & SWB & 5.66 & 0.633 & 0.427\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Adversarial Training for Multi-domain Speaker Recognition" by Qing Wang, Wei Rao, Pengcheng Guo, and Lei Xie, arXiv:2011.08623v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results From Industry Classification Task Using $k$-Fold Cross Validation With $k=4$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule
 \textbf{Model} & \textbf{Precision} & \textbf{Recall} & \textbf{F1} & \textbf{Accuracy} \\
 \midrule
 Returns Embedding & 0.74 & 0.72 & 0.72 & 72\% \\
 News Embedding & 0.89 & 0.85 & 0.84 & 85\% \\
 Multimodal Embedding & \textbf{0.91} & \textbf{0.90} & \textbf{0.90} & \textbf{90\% } \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets" by Rian Dolphin, Barry Smyth, and Ruihai Dong, arXiv:2211.06378v1, 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{The Area Under the ROC Curve for The Vessel Segmentation in Pathological Images of STARE Dataset}
\begin{tabular}{|l|c|}
 \hline
 \textbf{Method} & \textbf{Accuracy} \\
 \hline
 \multicolumn{2}{|c|}{Unsupervised Methods} \\
 \hline
 Lam et al. & 0.9707 \\
 \hline
 Ricci and Perfetti (Implemented in & 0.9343 \\
 \hline
 Jiang and Mojon & 0.8906 \\
 \hline
 Method 1 & 0.9446\\
 \hline
 Method 2 & 0.9432\\
 \hline
 \multicolumn{2}{|c|}{Supervised Methods} \\
 \hline
 Soares et al. (DRIVE) & 0.9455\\
 \hline
 Soares et al. (STARE) & 0.9571\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Automated Localization of Blood Vessels in Retinal Images" by Vahid Mohammadi Safarzadeh, arXiv:2401.02962v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test case 1 -- inputs.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
		\toprule
		\textbf{Metering period} & \textbf{{\color{red}User1}} & \textbf{{\color{red}User2}} & \textbf{{\color{green}User3}} & \textbf{{\color{orange}User4}} \\
		\midrule
		\midrule
		\multicolumn{5}{c}{Consumption} \\
		\midrule
		2017-03-01 00:00 & 0.17 & 0.21 & -0.50 & 0.08 \\
		2017-03-01 00:15 & 0.21 & 0.23 & -0.30 & -0.02 \\
		\midrule
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Miguel Manuel de Villena
Samy Aittahar
Sebastien Mathieu
Ioannis Boukas
Eric Vermeulen
Damien Ernst

"Allocation of locally generated electricity in renewable energy communities" by Miguel Manuel de Villena, Samy Aittahar, Sebastien Mathieu, Ioannis Boukas, Eric Vermeulen, and Damien Ernst, arXiv:2009.05411v2, 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{Economic Significance: Holding Period Sensitivity Analysis}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrrrr}
\toprule
\textbf{Holding Period} & \textbf{Strategy} & \textbf{Total Net P\&L} & \textbf{Sharpe Ratio (Ann.)} & \textbf{Profit Factor} & \textbf{Max Drawdown} & \textbf{Max Drawdown (\%)} \\
\midrule
\multirow{2}{*}{5 Days (Baseline)} & Fixed-Size & \$31,247.50 & 0.38 & 1.22 & (\$52,682.50) & 55.66\% \\
& Pyramiding & \$797,222.50 & 1.62 & 2.77 & (\$176,712.50) & \textbf{186.71\%} \\
\midrule
\multirow{2}{*}{7 Days} & Fixed-Size & \$112,385.00 & 1.23 & 1.93 & (\$39,287.50) & 41.00\% \\
& Pyramiding & \$1,180,760.00 & 2.00 & 3.95 & (\$135,325.00) & \textbf{141.21\%} \\
\midrule
\multirow{2}{*}{10 Days} & Fixed-Size & \$200,985.00 & 2.01 & 3.00 & (\$25,000.00) & 10.76\% \\
& Pyramiding & \$1,404,622.50 & 2.18 & 4.42 & (\$239,230.00) & 15.74\% \\
\midrule
\multirow{2}{*}{12 Days} & Fixed-Size & \$235,210.00 & 2.03 & 3.34 & (\$56,052.50) & 18.37\% \\
& Pyramiding & \$1,165,810.00 & 1.21 & 2.50 & (\$694,205.00) & 40.40\% \\
\midrule
\multirow{2}{*}{20 Days} & Fixed-Size & \$217,385.00 & 1.23 & 1.95 & (\$229,240.00) & 57.59\% \\
& Pyramiding & \$735,522.50 & 0.63 & 1.45 & (\$1,634,867.50) & \textbf{80.06\%} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation" by Peilin Rao and Randall R. Rojas, arXiv:2509.05922v1, 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{Statistics of the LastFM, Delicious, MovieLens and Yahoo! datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 \toprule
 & LastFM & Delicious & MovieLens & Yahoo! \\ \hline
number of users & $1,892$ & $1,867$ & $2,113$ & $290$ \\
number of items & $17,632$ & $69,226$ & $10,197$ & $20$ \\
number of logs & $186,480$ & $437,594$ & $47,957$ & $128,960$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints" by Hanchi Huang, Li Shen, Deheng Ye, and Wei Liu, arXiv:2308.12680v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Resampling Methods that Generate Time Series Data to Enable Sensitivity and Model Analysis in Energy Modeling" by Kelly Wang and Steven O. Kimbrough, arXiv:2502.08102v1, 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{Parameters of the four-area test system}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|llll|}
		\hline
		\multicolumn{4}{|c|}{\bf Main parameters of the VSC-HVDC link (per-unit values)}				\\
		\hline
		\multicolumn{2}{|l}{Converter-side inductors: $L = 0.05$}
		&\multicolumn{2}{l|}{\textit{LCL} capacitors:	$C_{\rm F} = 0.05$}								\\
		\multicolumn{2}{|l}{Grid-side inductors: $L_{\rm g} = 0.05$}
		&\multicolumn{2}{l|}{Grid-side resistors: $R_{\rm g} = 0.01$}									\\
		\multicolumn{2}{|l}{DC-side capacitors: $C_{\rm dc} = 0.06$}
		&\multicolumn{2}{l|}{DC-link resistors: $R_{\rm dc} = 0.015$}									\\
		\multicolumn{4}{|l|}{PI gains of the PLL: $103.1({\rm{rad/s}}),5311.5({\rm{rad/s}})$}			\\
		\multicolumn{4}{|l|}{PI gains of the current control loop: $0.3({\rm{p.u.}}),10({\rm{p.u.}})$}	\\
		\multicolumn{4}{|l|}{PI gains of the voltage control loop: $4({\rm{p.u.}}),40({\rm{p.u.}})$}	\\
		\multicolumn{4}{|l|}{PI gains of the power control loop: $0.2({\rm{p.u.}}),2({\rm{p.u.}})$}		\\
		\multicolumn{4}{|l|}{PI gains of the dc voltage control loop: $5({\rm{p.u.}}),50({\rm{p.u.}})$}	\\
		\hline
		\multicolumn{4}{|c|}{\bf Main parameters of the SGs (per-unit values)}							\\
		\hline
		$X_d = 2.065$		&	$X_q = 1.974$		&	$X'_d = 0.4879$			&	$X'_q = 1.19$		\\
		$X''_d = 0.35$		&	$X''_q = 0.35$		&	$T'_{d0} = 6.56$		&	$T'_{q0} = 1.5$		\\
		$T''_{d0} = 0.05$	&	$T''_{q0} = 0.035$	&	$J_{\rm SG} = 8.658$	&	$R_a = 0.0025$		\\
		\hline
		\multicolumn{4}{|c|}{Fast exciters (IEEET1 Model)}												\\
		\hline
		$K_A = 50$			&	$T_A = 0.05$		&	$K_F = 0.0057$			&	$T_F = 0.5$			\\
		$T_R = 0.1$			&						&							&						\\
		\hline
		\multicolumn{4}{|c|}{Steam Turbine and Governor (IEEEG1 Model)}									\\
		\hline
		$T_1 = 0.5$			&	$T_2 = 1$			&	$T_3 = 0.6$				&	$T_4 = 0.6$			\\
		$T_5 = 0.5$			&	$T_6 = 0.8$			&	$T_7 = 1$				&	$K = 5$				\\
		$K_1 = 0.3$			&	$K_2 = 0$			&	$K_3 = 0.25$			&	$K_4 = 0$			\\
		$K_5 = 0.3$			&	$K_6 = 0$			&	$K_7 = 0.15$			&	$K_8 = 0$			\\
		\hline
		\multicolumn{4}{|c|}{\bf Impedance of lines and power consumption of loads (per-unit values)}	\\
		\hline
		\multicolumn{2}{|l}{Line 1-5 \& 11-15: $0.005+j0.05$}
		&\multicolumn{2}{l|}{Line 2-5 \& 12-15: $0.02+j0.2$}											\\
		\multicolumn{2}{|l}{Line 5-6 \& 15-16: $0.002+j0.02$}
		&\multicolumn{2}{l|}{Line 6-10 \& 12-20: $0.004+j0.04$}											\\
		\multicolumn{2}{|l}{Line 6-7 \& 16-17: $0.01+j0.2$}
		&\multicolumn{2}{l|}{Line 7-8 \& 17-18: $0.014+j0.28$}											\\
		\multicolumn{2}{|l}{Line 8-9 \& 18-19: $0.004+j0.08$}
		&\multicolumn{2}{l|}{Line 8-18: $0.012+j0.12$}													\\
		\multicolumn{2}{|l}{Line 9-3 \& 19-13: $0.05+j0.02$}
		&\multicolumn{2}{l|}{Line 9-4 \& 19-14: $0.05+j0.15$}											\\
		\multicolumn{2}{|l}{$P_{\rm Load1} = 0.9493$ (IM: 0.5)}
		&\multicolumn{2}{l|}{$P_{\rm Load2} = 1.3$ (IM: 1.2)}											\\
		\multicolumn{2}{|l}{$P_{\rm Load3} = 0.7$ (IM: 0.2)}
		&\multicolumn{2}{l|}{$P_{\rm Load4} = 1.7$ (IM: 1.4)}											\\
		\multicolumn{2}{|l}{$C_1$ \& $C_3$: $0.25$}
		&\multicolumn{2}{l|}{$C_2$ \& $C_4$: $0.15$}													\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Linbin Huang
Jeremy Coulson
John Lygeros
Florian Dörfler

"Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping" by Linbin Huang, Jeremy Coulson, John Lygeros, and Florian Dörfler, arXiv:1911.12151v4, 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{Computed average MSPE, RMSPE, MAPE, $R^2$, and $R^2_{pred}$ values of the LQ and PLS methods for the North Dakota weather data. The subscripts $_{\text{main}}$, $_{\text{full}}$, and $_{\text{selected}}$ respectively correspond to the case where the model is estimated using the main effect, full, and selected models. The values given in brackets are the estimated standard errors for the calculated performance metrics.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllrr}
\toprule
{Method} & {MSPE} & {RMSPE} & {MAPE} & {$R^2$} & {$R^2_{pred}$} \\
\midrule
LQ$_{\text{main}}$ 			& 1.445 (0.080)	& 0.094	(0.003)	& 0.069	(0.002)	& 0.988 (9.8 $\times 10^{-4}$)	& 0.986 (7.8 $\times 10^{-4}$) \\
LQ$_{\text{full}}$			& 1.422 (0.080)	& 0.093	(0.003)	& 0.068	(0.002)	& 0.989 (8.1 $\times 10^{-4}$)	& 0.986 (7.5 $\times 10^{-4}$) \\
LQ$_{\text{selected}}$		& 1.389 (0.103)	& 0.092	(0.004)	& 0.067	(0.002)	& 0.990 (8.6 $\times 10^{-4}$)	& 0.987 (9.7 $\times 10^{-4}$) \\
PLS$_{\text{main}}$ 		& 1.343 (0.078)	& 0.090	(0.003)	& 0.066	(0.002)	& 0.989 (1.1 $\times 10^{-3}$)	& 0.987 (7.6 $\times 10^{-4}$) \\
PLS$_{\text{full}}$			& 1.352 (0.078)	& 0.090	(0.003)	& 0.066	(0.002)	& 0.989 (1.1 $\times 10^{-3}$)	& 0.987 (7.8 $\times 10^{-4}$) \\
PLS$_{\text{selected}}$		& 1.300 (0.093)	& 0.087	(0.003)	& 0.064	(0.002)	& 0.990 (9.0 $\times 10^{-4}$)	& 0.989 (8.9 $\times 10^{-4}$) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"A partial least squares approach for function-on-function interaction regression" by Ufuk Beyaztas and Han Lin Shang, arXiv:2012.05313v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Shivam Pal
Aishwarya Gupta
Saqib Sarwar
Piyush Rai

"Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization" by Shivam Pal, Aishwarya Gupta, Saqib Sarwar, and Piyush Rai, arXiv:2411.18385v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jiyu Luo
Denise Rava
Jelena Bradic
Ronghui Xu

"Doubly Robust Estimation under Possibly Misspecified Marginal Structural Cox Model" by Jiyu Luo, Denise Rava, Jelena Bradic, and Ronghui Xu, arXiv:2311.07752v1, 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
 & \textbf{Hybrid QNN} & \textbf{Classical NN} & \textbf{Photonic QNN} \\ \hline
\textbf{Features used} & 9 & 9 & 3 \\ \hline
\textbf{Epochs} & 25 & 25 & 25 \\ \hline
\textbf{Learning Rate} & 0.08 & 0.08 & 0.08 \\ \hline
\textbf{Batch Size} & 5 & 5 & 5 \\ \hline
\textbf{Shots} & 1024 & NA & 1024 \\ \hline
\textbf{NN layers} & 3 & 3 & 3 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Quantum classical hybrid neural networks for continuous variable prediction" by Prateek Jain and Alberto Garcia Garcia, arXiv:2212.04209v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{graphicx}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The performance of the optimised 2D U-Net for MRI prostate segmentation on four datasets using Dice Similarity Coefficient (DSC).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}

 \multicolumn{6}{c}{Training Datasets}\\
 &Dataset & Promise 12 & Prostate X & Decathlon & NIC ISBI \\ \hline\hline
 \multirow{5}{*}{\rotatebox[origin=c]{90}{Testing Datasets}}
 &Promise12 & \textit{0.9069} & 0.8869 & 0.9239 &0.828 \\
 &Prostate X & 0.5891 & \textit{0.9054} & 0.9107 & 0.8011 \\
 &Decathlon&0.6055 & 0.6479 & \textit{0.9148} & 0.7395 \\
 &NIC ISBI & 0.6931 & 0.7463 & 0.7628 & \textit{0.8909} \\
 &Combined&\textbf{0.9171} & \textbf{0.9061} & \textbf{0.9427} & \textbf{0.8960} \\

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

eess

David Gillespie
Connah Kendrick
Ian Boon
Cheng Boon
Tim Rattay
Moi Hoon Yap

"Deep learning in magnetic resonance prostate segmentation: A review and a new perspective" by David Gillespie, Connah Kendrick, Ian Boon, Cheng Boon, Tim Rattay, and Moi Hoon Yap, arXiv:2011.07795v1, 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}{lrrrr}
\toprule
$C$ & 0.05 & 0.1 & 0.2 & 0.5 \\
\midrule
ARE [$^\circ$] & 0.871 & 0.379 & 0.449 & 0.747 \\
Equiv. PhE [$\cdot 10^5$] & 1.200 & 1.130 & 1.123 & 1.171 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Event-based Photometric Bundle Adjustment" by Shuang Guo and Guillermo Gallego, arXiv:2412.14111v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Short-time behavior of the At-The-Money implied volatility for the jump-diffusion stochastic volatility Bachelier model" by Elisa Alòs, Òscar Burés, and Josep Vives, arXiv:2503.22282v1, 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}{cccc}
\hline\hline
Sensor & Description & Resolution & Spatial Coverage \\ \hline
Radar & Hybrid surface rainfall & 500 m & 1,000 km $\times$ 1,000 km \\
IR 10.5 $\mu m$ & Infrared radiation & 2 km &1,000 km $\times$ 1,000 km \\
WV 6.3 $\mu m$ & Water vapor channels (upper-level) & 2 km & 1,000 km $\times$ 1,000 km \\
WV 7.3 $\mu m$ & Water vapor channels (lower-level) & 2 km & 1,000 km $\times$ 1,000 km \\ \hline
DEM & Digital elevation model & 2 km& 1,000 km $\times$ 1,000 km \\
\hline\hline
\end{tabular}
\end{adjustbox}
\caption{Description of sensor types in our Sat2Rdr dataset}
\end{table}
\end{document}

eess

Young-Jae Park
Doyi Kim
Minseok Seo
Hae-Gon Jeon
Yeji Choi

"Data-driven Precipitation Nowcasting Using Satellite Imagery" by Young-Jae Park, Doyi Kim, Minseok Seo, Hae-Gon Jeon, and Yeji Choi, arXiv:2412.11480v1, 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{Comparison of w/o self-attention for the GCN on two datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline
 \textbf{Method} & \textbf{DHG-14} & \textbf{DHG-28}& \textbf{FPHA}
 \\
 \hline
 GCN & 90.83\% & 87.74\%& 83.48\% \\
 SAGCN & 93.33\% & 91.54\%& 87.83\% \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Chuankun Li
Shuai Li
Yanbo Gao
Xiang Zhang
Wanqing Li

"A Two-stream Neural Network for Pose-based Hand Gesture Recognition" by Chuankun Li, Shuai Li, Yanbo Gao, Xiang Zhang, and Wanqing Li, arXiv:2101.08926v1, 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{RNN(Bidirectional LSTM) architecture} % title name of the table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccrrrrrrr} % creating 10 columns
\hline\hline % inserting double-line
 Layer &Type&Output Shape & Number of Parameters
\\
\hline % inserts single-line
 1 & Input& (None,300) & - \\
2 & Bidirectional(LSTM)&(None,300,64) & 8960 \\
3 & Bidirectional(LSTM)&(None,64) & 25088 \\
4 & Dropout&(None,64) & 0 \\
5 & $dense_1$(ReLu)&(None,64) & 4160 \\
6 & $dense_2$(Sigmoid)&(None,64) & 130 \\
\hline % inserts single-line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Prateek Singh
Ambalika Sharma
Shreesha Maiya

"Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network" by Prateek Singh, Ambalika Sharma, and Shreesha Maiya, arXiv:2202.05177v1, 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{Hyperparameter search space of Trompt.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule
\textbf{Hyperparameter} & \textbf{Distribution} \\ \midrule
hidden\_dimension & $[18,128]$\\
feature\_importances\_type & $[\text{concat},\text{add}]$\\
feature\_importances\_dense & $[\text{true},\text{false}]$\\
feature\_importances\_residual\_connection & $[\text{true},\text{false}]$\\
feature\_importances\_sharing\_dense & $[\text{true},\text{false}]$\\
feature\_embeddings\_residual\_connection & $[\text{true},\text{false}]$\\
minimal\_batch\_ratio & $[0.1,0.01]$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Block Builder Market Shares}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
\hline
Builder & MEV Value (ETH) & Block Count \\
\hline \hline
beaverbuild.org & 111907.12 & 1,390,451 \\
Titan Builder & 77,989.67 & 1,070,749 \\
rsync-builder.xyz & 31,392.580577 & 271,284 \\
NotMEV & 0.000000 & 265,409 \\
Ultrasound Money & 2,439.68 & 3,7530 \\
Flashbots & 5,186.19 & 34,316 \\
jetbldr.xyz & 916.93 & 23,614 \\
@penguinbuild.org & 695.03 & 9,457 \\
Builder+ www.btcs.com/builder & 1,106.77 & 7,742 \\
f1b.io & 591.957 & 7,050 \\
\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{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of an ablation experiment in which TAFM-Net was trained on each one of the ISIC datasets and tested on the other two. Numbers are means of a 3-fold cross-validation. Best score per metric and per training dataset in \textbf{bold}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccc}
\toprule
\multirow{2}[4]{*}{\textbf{Training}} & \multirow{2}[4]{*}{\textbf{Testing}} & \multicolumn{5}{c}{\textbf{Performance (\%)}} \\
\cmidrule{3-7} & & $\mathbf{A}$ & $\mathbf{Sn}$ & $\mathbf{Sp}$ & $\mathbf{J}$ & $\mathbf{D}$ \\
\toprule
& ISIC 2016 & \textbf{98.44} & \textbf{96.65} & \textbf{97.66} & \textbf{93.64} & \textbf{96.66} \\
ISIC 2016 & ISIC 2017 & 94.95 & 87.95 & 93.14 & 84.88 & 88.17 \\
& ISIC 2018 & 95.87 & 91.39 & 95.92 & 89.88 & 93.53 \\
\midrule
& ISIC 2016 & 96.44 & 94.65 & 94.66 & 90.64 & 94.66 \\
ISIC 2017 & ISIC 2017 & \textbf{96.95} & \textbf{92.95} & \textbf{95.14} & \textbf{86.88} & \textbf{92.17} \\
& ISIC 2018 & 94.87 & 93.39 & 94.92 & 88.88 & 94.53 \\
\midrule
& ISIC 2016 & 95.44 & 92.65 & 93.66 & 90.64 & 91.66 \\
ISIC 2018 & ISIC 2017 & 91.95 & 89.95 & 93.14 & 83.88 & 90.17 \\
& ISIC 2018 & \textbf{97.87} & \textbf{96.39} & \textbf{97.92} & \textbf{92.88} & \textbf{96.53} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Tariq M Khan
Dawn Lin
Shahzaib Iqbal
Erik Meijering

"TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer Attention and Focal Modulation" by Tariq M Khan, Dawn Lin, Shahzaib Iqbal, and Erik Meijering, arXiv:2411.17556v1, 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{\small{Statistics of the ensemble-based uncertainty quantification results obtained for transient subsurface pressure data in a binary channelized domain. The True and Mean models are those depicted in Figure . The mean model is obtained from the deep ensemble models \#1 to \#5. RMSE$_{\rm data}$ denotes the RMSE in data space, ${\rm l}_1$ refers to the ${\rm l}_1$-norm and SSIM to the structural similarity index. The ${\rm l}_1$ is calculated in terms of $\log_{10}K_s$ (-) while the SSIM is computed in the rescaled $\left[0,1\right]$ domain.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}%
			\hline
			True model & RMSE$_{\rm data}$ (m) & ${\rm l}_1$ (m) & SSIM (-)\\
			\hline
			(a) True & 0.010 & 0 & 1 \\
			(b) Mean & 0.577 & 2492 & 0.67\\
			(d) Model \#1 & 0.590 & 2594 & 0.67 \\
			(e) Model \#2 & 0.095 & 2908 & 0.66 \\
			(f) Model \#3 & 0.267 & 2254 & 0.71 \\
			(g) Model \#4 & 0.523 & 2912 & 0.64 \\
			(h) Model \#5 & 0.523 & 1782 & 0.73 \\

			(i) True & 0.010 & 0 & 1 \\
			(j) Mean & 0.449 & 2321 & 0.62\\
			(l) Model \#1 & 0.216 & 2246 & 0.65 \\
			(m) Model \#2 & 0.426 & 2558 & 0.63 \\
			(n) Model \#3 & 0.183 & 2486 & 0.63 \\
			(o) Model \#4 & 0.187 & 1988 & 0.67 \\
			(p) Model \#5 & 0.233 & 2356 & 0.63 \\

			(q) True & 0.010 & 0 & 1 \\
			(r) Mean & 0.377 & 3046 & 0.60\\
			(t) Model \#1 & 0.416 & 3334 & 0.60 \\
			(u) Model \#2 & 0.252 & 3422 & 0.58 \\
			(v) Model \#3 & 0.271 & 3226 & 0.59 \\
			(w) Model \#4 & 0.049 & 2520 & 0.64 \\
			(x) Model \#5 & 0.397 & 2728 & 0.63 \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Eric Laloy
Niklas Linde
Diederik Jacques

"Approaching geoscientific inverse problems with vector-to-image domain transfer networks" by Eric Laloy, Niklas Linde, and Diederik Jacques, arXiv:1912.09954v3, 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}{cccccc} \hline
 \textbf{Congested} & \textbf{Link}& \multicolumn{2}{c}{ \textbf{PG}} &
\multicolumn{2}{c}{ \textbf{PIG}} \\
 \textbf{link}& \textbf{type}
 &
$E(V / C)$& $P(V / C > 0.95)$& $E(V / C)$&
$P(V / C > 0.95)$ \\
\hline
 16841& Small regional road& 1.384& 1& 1.383&
1 \\
 17493& Local road& 1.152& 1& 1.151&
1 \\
 22149& Local road& 0.935& 0.046& 0.934&
0.022 \\
 28980& Highway& 2.114& 1& 2.114&
1 \\
29060& Local road& 0.935& 0.046& 0.934&
0.022 \\
 83662& Local road& 0.941& 0.236& 0.941&
0.208 \\
 83928& Highway& 1.260& 1& 1.259&
1 \\
 84514& Main regional road& 1.144& 1& 1.144&
1 \\
 92846& Local road& 0.941& 0.236& 0.941&
0.208 \\
 92849& Local road& 1.098& 1& 1.097&
1 \\
106252& Local road& 1.024& 1& 1.024&
1 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Konstantinos Perrakis
Dimitris Karlis
Mario Cools
Davy Janssens

"Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures" by Konstantinos Perrakis, Dimitris Karlis, Mario Cools, and Davy Janssens, arXiv:2011.06045v1, 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{Comparison of the mesh refinement history}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
 \hline \multicolumn{5}{c}{DC}\\
		\hline \textbf{MR} & \textbf{Mesh} &\textbf{Max. AL } & \textbf{Max. CV} & \textbf{NLP}\\
 \textbf{Iteration} & \textbf{Size} & \textbf{Error} & \textbf{Error} & \textbf{Time}\\
		\hline 1 & 100 & 3.6e-01 & 24.1 & 1.5 s\\
		\hline 2 & 250 & 3.0e-01 & 24.1 & 3.9 s\\
		\hline 3 & 1171 & 9.3e-03 & 24.1 & 26.7 s\\
 \hline 4 & 2336 & 5.1e-03 & 24.1 & 44.9 s\\
 \hline 5 & 2555 & 1.5e-04 & 24.1 & 26.7 s\\
 \hline 6 & 2586 & 6.7e-05 & 24.1 & 41.5 s\\
 \hline 7 & 2624 & 6.7e-05 & 24.1 & 42.1 s\\
 \hline 8 & 2649 & 6.7e-05 & 24.1 & 44.4 s\\
		\hline
 \hline \multicolumn{5}{c}{IRR-DC}\\
		\hline \textbf{MR} & \textbf{Mesh} &\textbf{Max. AL} & \textbf{Max. CV} & \textbf{NLP}\\
 \textbf{Iteration} & \textbf{Size} & \textbf{Error} & \textbf{Error} & \textbf{Time}\\
		\hline 1 & 100 & 2.6e-03 & 3.6e-02 & 2.7 s\\
		\hline 2 & 138 & 5.6e-05 & 3.3e-03 & 4.5 s\\
		\hline 3 & 139 & 5.6e-05 & 6.4e-04 & 7.1 s\\
 \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Reliable Solution to Dynamic Optimization Problems using Integrated Residual Regularized Direct Collocation" by Yuanbo Nie and Eric C. Kerrigan, arXiv:2503.09123v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Buddha Nath Sharma
Anish Rai
SR Luwang
Md. Nurujjaman
Sushovan Majhi

"Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective" by Buddha Nath Sharma, Anish Rai, SR Luwang, Md. Nurujjaman, and Sushovan Majhi, arXiv:2502.14431v1, 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{Lowest MLS value achieved for each of the ten cases and the corresponding registration configuration.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
Case \# & MLS (mm) & Regularizer ($\mu$) & Grid Spacing (mm) \\ \midrule
1 & 1.15 & Diffusion ($10^{-2}$) & 30 \\
2 & 1.28 & Diffusion ($10^{-3}$) & 10 \\
3 & 1.52 & Linear Elastic ($10^{-8}$) & 10 \\
4 & 1.56 & Curvature ($10^{-1}$) & 20 \\
5 & 1.80 & Curvature ($10^{-2}$) & 10 \\
6 & 1.56 & Curvature ($10^{-1}$) & 10 \\
7 & 1.74 & Curvature ($10^{-2}$) & 10 \\
8 & 1.49 & Curvature ($10^{-2}$) & 10 \\
9 & 1.50 & Third-order ($1$) & 10 \\
10 & 1.42 & Curvature ($10^{-2}$) & 10 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Keyur D. Shah
James A. Shackleford
Nagarajan Kandasamy
Gregory C. Sharp

"A Generalized Framework for Analytic Regularization of Uniform Cubic B-spline Displacement Fields" by Keyur D. Shah, James A. Shackleford, Nagarajan Kandasamy, and Gregory C. Sharp, arXiv:2010.02400v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of the ASVspoof 2019 database.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|cc|cc}
		\toprule
		\multirow{3}{*}{Subset}&\multicolumn{2}{c}{\#Speaker}&\multicolumn{4}{c}{\#Utterances}\\
		\cmidrule(lr){2-3} \cmidrule(lr){4-7}
		&\multirow{2}{*}{M}&\multirow{2}{*}{F}&\multicolumn{2}{c}{LA}&\multicolumn{2}{c}{PA}\\
		\cmidrule(lr){4-5} \cmidrule(lr){6-7}
		&&&Bona fide&Spoof&Bona fide&Spoof\\
		\midrule
		Training & 8 & 12 & 2580 & 22800 & 5400 & 48600 \\
		Dev. & 8 & 12 & 2548 & 22296 & 5400 & 24300 \\
		Eval. & 21 & 27 & 7355 & 63882 & 18090 & 116640 \\
		\hline
		Total & 37 & 51 & 12483& 108978& 28890 & 189540 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yuanjun Zhao
Roberto Togneri
Victor Sreeram

"Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System" by Yuanjun Zhao, Roberto Togneri, and Victor Sreeram, arXiv:2012.03154v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The final classification architectures found for the GLUE datasets.}
\begin{tabular}{ccccccc}
 \toprule
method & sst2 & cola & mrpc & mnli & rte & qqp \\
 \cmidrule(r){1-1} \cmidrule(l){2-7}
pooling & mean & [CLS] & [CLS] & max & [CLS] & max \\
number linear layers & 5 & 1 & 4 & 3 & 1 & 1 \\
hidden dim linear layers & 50 & - & 74 & 117 & - & - \\
number conv layers & 0 & 0 & 4 & 3 & 0 & 2 \\
number heads conv & - & - & 107 & 9 & - & 159 \\
kernel size & - & - & 7 & 11 & - & 7 \\
skip connection & - & - & True & True & - & True \\
number attention layers & 1 & 0 & 4 & 0 & 0 & 0 \\
number attention heads & 4 & - & 16 & - & - & - \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Philip Kenneweg
Sarah Schröder
Barbara Hammer

"Neural Architecture Search for Sentence Classification with BERT" by Philip Kenneweg, Sarah Schröder, and Barbara Hammer, arXiv:2403.18547v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Impact of the \textit{T} values on accuracy }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c||c|c|c|c|c}
 Clauses & T & Training & Testing & Validation & Better Classification \\
 \hline
 \hline
 30 & 2 & 83.5 \% & 80.5 \% & 83.8 \% & \checkmark\\
 30 & 23 & 74.9 \% & 71.1 \% & 76.1 \% & \\
 \hline
 450 & 2 & 89.7 \% & 86.1 \% & 84.9 \% & \\
 450 & 23 & 96.8 \% & 92.5 \% & 92.7 \% & \checkmark\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jie Lei
Tousif Rahman
Rishad Shafik
Adrian Wheeldon
Alex Yakovlev
Ole-Christoffer Granmo
Fahim Kawsar
Akhil Mathur

"Low-Power Audio Keyword Spotting using Tsetlin Machines" by Jie Lei, Tousif Rahman, Rishad Shafik, Adrian Wheeldon, Alex Yakovlev, Ole-Christoffer Granmo, Fahim Kawsar, and Akhil Mathur, arXiv:2101.11336v1, 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{Classification accuracy ($\%$) on Cora and CiteSeer, with $\epsilon_x=1$. The performance are insignificant in various edge privacy budgets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
\toprule
 $\epsilon_{a}$& 7.0 & 7.3 & 7.5& 7.7 & 7.9 \\
\midrule
 Cora&$50.9\pm3.8$ & $54.9\pm3.7$ & $57.2\pm2.5$ & $61.4\pm2.6$& $63.7\pm2.4$ \\
 CiteSeer&$35.0\pm1.8$&$39.1\pm1.3$&$42.3\pm1.5$&$44.5\pm1.2$&$45.7\pm1.8$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Towards Private Learning on Decentralized Graphs with Local Differential Privacy" by Wanyu Lin, Baochun Li, and Cong Wang, arXiv:2201.09398v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\infty$) processes and applications" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Entropy and distribution of the flood model input variables}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccllc}
		\toprule
		 & Variable & Description & Distribution Function & Exponential Entropy $e^{H(X_i)}$ \\
		\cmidrule(lr){1-5}
	 $x_1$ & $Q$ & Maximal annual flowrate [m$^3$/s]	 & Truncated Gumbel $\mathcal{G}$(1013,558) on [500,3000] & 2051 \\
	 $x_2$ & $K_s$ & Strickler coefficient [ - ]	 & Truncated Normal $\mathcal{N}$(30,$8^2$) on [15, $\infty$] & 30 \\
	 $x_3$ & $Z_{\nu}$ & River downstream level [m]	 & Triangular $\mathcal{T}$(49,50,51) & 1.65 \\
	 $x_3$ & $Z_m$ & River upstream level [m]	 & Triangular $\mathcal{T}$(54,55,56) & 1.65 \\
	 $x_5$ & $D_d$ & Dyke height [m]	 & Uniform $\mathcal{U}$[7,9] & 2 \\
	 $x_6$ & $C_b$ & Bank level [m]	 & Triangular $\mathcal{T}$(55,55.5,56) & 0.825 \\
	 $x_7$ & $L$ & Length of the river stretch [m]	 & Triangular $\mathcal{T}$(4990,5000,5010) & 16.5\\
	 $x_8$ & $B$ & River width [m]	 & Triangular $\mathcal{T}$(295,300,305) & 8.24 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Derivative based global sensitivity analysis and its entropic link" by Jiannan Yang, arXiv:2310.00551v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Anisotropic local constant smoothing for change-point regression function estimation" by John R. J. Thompson and W. John Braun, arXiv:2012.00180v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Kewin Pączek
Damian Jelito
Marcin Pitera
Agnieszka Wyłomańska

"Statistical applications of the 20/60/20 rule in risk management and portfolio optimization" by Kewin Pączek, Damian Jelito, Marcin Pitera, and Agnieszka Wyłomańska, arXiv:2504.02840v1, 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||}
\hline
$U$ & $U_T$ & $MV$ & $MV_T$ \\
\hline
0.1041 & 0.1064 & 0.1032 & 0.1018\\
\hline
0.1040 & 0.1065 & 0.1021 & 0.1034\\
\hline
0.1042 & 0.1067 & 0.1019 & 0.1018\\
\hline
0.1038 & 0.1064 & 0.1019 & 0.1031\\
\hline
0.1029 & 0.1062 & 0.1017 & 0.1033\\
\hline
0.1044 & 0.1065 & 0.1018 & 0.1027\\
\hline
0.1043 & 0.1068 & 0.1028 & 0.1036\\
\hline
0.1048 & 0.1065 & 0.1024 & 0.1029\\
\hline
0.1044 & 0.1064 & 0.1017 & 0.1039\\
\hline
0.1056 & 0.1064 & 0.1038 & 0.1021\\
\hline
\end{tabular}
\end{adjustbox}
\caption{\textbf{GAN quality-based metrics' values across various training datasets from the same problem instance.} The second and last columns display the values for the training set, defined as the $U$ and the $MV$ computed for the samples in $\mathcal{D}_{\text{Train}}$. We see that the GAN's $U$ is always less than the training threshold; however, this is not always true for $MV$, as the GAN has a lower $MV$ value only $70\%$ of the time.}
\end{table}
\end{document}

cs

Kaitlin Gili
Marta Mauri
Alejandro Perdomo-Ortiz

"Generalization Metrics for Practical Quantum Advantage in Generative Models" by Kaitlin Gili, Marta Mauri, and Alejandro Perdomo-Ortiz, arXiv:2201.08770v3, 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
 Method & Mean KPE (mm) & AUC (0-50 mm)\\
 \hline
 \textit{DIREG3D}-Mono & 12.37 & 0.755\\
 \hline
 \textit{DIREG3D}-Stereo & 11.39 & 0.774\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Results on the in-house synthetic dataset}
\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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc} % 只有两列
 \toprule
 \textbf{Step} & \textbf{Predicted Values} \\
 \midrule
 Max(Window 1, Window 2, Window 3) & [3.7487e-01, 1.0177e-05, 1.7565e-02, 2.0318e-02, 1.2092e-01, 1.7365e-04, 3.3524e-03, \textbf{9.9258e-01}, 8.1936e-01, 1.2045e-02] \\
 \cmidrule(lr){1-2}
 \textbf{Final Prediction} & 7 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Max method}
\end{table}
\end{document}

eess

Taketo Akama
Zhuohao Zhang
Pengcheng Li
Kotaro Hongo
Hiroaki Kitano
Shun Minamikawa
Natalia Polouliakh

"Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings" by Taketo Akama, Zhuohao Zhang, Pengcheng Li, Kotaro Hongo, Hiroaki Kitano, Shun Minamikawa, and Natalia Polouliakh, arXiv:2412.15560v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{One-way Chi-square test for hours }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll} \toprule
 & All Type & Rear-end & Sideswipe \\ \midrule
Statstic & 5350.39& 5028.855 & 691.545 \\
P-value & 0.0& 0.0 & 2.679e-131 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiwen Chen
Hao Wang
Abolfazl Razi
Brendan Russo
Jason Pacheco
John Roberts
Jeffrey Wishart
Larry Head
Alonso Granados Baca

"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, 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}{cccccc}
\toprule
\textbf{Model} & \textbf{2-cell} & \textbf{4-cell} & \textbf{8-cell} & \textbf{blastocyst} & \textbf{morula} \\ \midrule
\textbf{LDM} & 24 & 28 & 31 & 10 & 14 \\ \midrule
\textbf{StyleGAN} & 24 & 24 & 34 & 41 & 36 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{FID scores for the synthetic data generated by the best generative models trained for the different classes.}
\end{table}
\end{document}

eess

Oriana Presacan
Alexandru Dorobantiu
Vajira Thambawita
Michael A. Riegler
Mette H. Stensen
Mario Iliceto
Alexandru C. Aldea
Akriti Sharma

"Merging synthetic and real embryo data for advanced AI predictions" by Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler, Mette H. Stensen, Mario Iliceto, Alexandru C. Aldea, and Akriti Sharma, arXiv:2412.01255v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrrr}
\toprule
Model & Training Accuracy & Test Accuracy & LogLik & AIC & BIC & AUC & Deviance & Parameters\\
\midrule
LIWC\_LM\_Stress & 0.78 & 0.80 & -366.95 & 787.89 & 914.38 & 0.87 & 733.89 & 26\\
LIWC\_Stress & 0.77 & 0.79 & -374.89 & 797.78 & 910.21 & 0.86 & 749.78 & 23\\
LM\_Stress & 0.74 & 0.80 & -410.58 & 837.17 & 874.64 & 0.87 & 821.17 & 7\\
Stress\_Diction & 0.72 & 0.79 & -428.09 & 866.18 & 889.61 & 0.86 & 856.18 & 4\\
LIWC & 0.69 & 0.63 & -470.61 & 981.22 & 1074.92 & 0.72 & 941.22 & 19\\
\addlinespace
LM & 0.68 & 0.74 & -489.88 & 987.75 & 1006.49 & 0.78 & 979.75 & 3\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Bankruptcy prediction using disclosure text features" by Sridhar Ravula, arXiv:2101.00719v1, 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 study on varying number of merges $\texttt{num\_merges}$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \bfseries $\texttt{num\_merges}$ & \bfseries BLEU-4 & \bfseries Rouge-L & \bfseries Meteor & \bfseries BertScore F1 \\
 \midrule
 500 & 13.61 ± 0.53 & 46.50 ± 0.28 & 28.49 ± 0.49 & 92.33 ± 0.02 \\
 1750 & 14.50 ± 0.25 & 46.74 ± 0.48 & 30.03 ± 0.25 & \textbf{92.55} ± 0.01 \\
 2500 & \textbf{15.10} ± 0.39 & 46.37 ± 0.28 & \textbf{30.12} ± 0.23 & 92.53 ± 0.05 \\
 3500 & 13.93 ± 0.21 & \textbf{47.08} ± 0.56 & 29.17 ± 0.31 & 92.53 ± 0.07 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

William Han
Chaojing Duan
Michael A. Rosenberg
Emerson Liu
Ding Zhao

"ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling" by William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, and Ding Zhao, arXiv:2412.14373v3, 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{Measurement noise values as a fraction of trajectory range.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 Measurement noise value & 1e-6 & 1e-5 & 5e-5 & 1e-4 & 1e-3\\
 \hline
 $2\sigma$ to range percent ratio & 0.25 & 0.78 & 1.75 & 2.47 & 7.81 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Artem Mustaev
Nicholas Galioto
Matt Boler
John D. Jakeman
Cosmin Safta
Alex Gorodetsky

"A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation" by Artem Mustaev, Nicholas Galioto, Matt Boler, John D. Jakeman, Cosmin Safta, and Alex Gorodetsky, arXiv:2412.06601v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An Exact SIR Series Solution and an Exploration of the Related Parameter Space" by Daniel P Hobbs, arXiv:2504.17026v1, 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
\caption{Simulation parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 & Symbol & Description & Value \\\hline
 & $K$ & controller gain & 0.2 \\
 Controller & $c$ & controller frequency [rad/s] & 2\\
 parameters & $a$ & pertubation amplitude & 0.2\\
 & $\omega$ & pertubation frequency [rad/s] & 8\\

 \hline
 & $\theta^{*}$ & optimizer static map & 8 \\
 System & $y^{*}$ & optimal value static map & 64 \\
 parameters & $H$ & Hessian & -1 \\
 & $D(\theta(t))$ & state-dependent delay & $D(\theta(t))=\frac{1}{2}\sin(5\theta(t))^2$ \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jaqueline G. Mesquita
Tiago Roux Oliveira
Henrique C. dos Reis

"Slow and fast dynamics in measure functional differential equations with state-dependent delays through averaging principles and applications to extremum seeking" by Jaqueline G. Mesquita, Tiago Roux Oliveira, and Henrique C. dos Reis, arXiv:2412.20362v1, 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|}
\hline
\textbf{Network type} & \textbf{Real multiplications} \\
\hline
MLP & $N_N N_I$ \\
\hline
1D-CNN & $N_F N_I N_K N_O$ \\
\hline
RNN & $N_S N_H (N_I + N_H)$ \\
\hline
LSTM & $N_S N_H (4N_I + 4N_H + 3)$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Murat Temiz
Yongwei Zhang
Yanwei Fu
Chi Zhang
Chenfeng Meng
Orhan Kaplan
Christos Masouros

"Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities" by Murat Temiz, Yongwei Zhang, Yanwei Fu, Chi Zhang, Chenfeng Meng, Orhan Kaplan, and Christos Masouros, arXiv:2509.06968v1, 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}{ccc}
 \toprule
 Trait & ICC [95\% CI] & \textit{p}-value \\
 \midrule
 ``warm'' & $0.68$ $[0.64$, $0.71]$ & $< 0.001$ \\
 ``well-intentioned'' & $0.77$ $[0.66$, $0.73]$ & $< 0.001$ \\
 ``competent'' & $0.57$ $[0.52$, $0.61]$ & $< 0.001$ \\
 ``intelligent'' & $0.56$ $[0.51$, $0.60]$ & $< 0.001$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Participants' evaluations of their co-players were highly consistent, as assessed by intraclass correlation coefficient (ICC)~. ICC ranges from 0 to 1, with higher values indicating greater consistency.}
\end{table}
\end{document}

cs

Kevin R. McKee
Xuechunzi Bai
Susan T. Fiske

"Warmth and competence in human-agent cooperation" by Kevin R. McKee, Xuechunzi Bai, and Susan T. Fiske, arXiv:2201.13448v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, 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{Hasbrouck's Information Share}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Market} & & \\ \hline
Centralized Market & 0.986 & 0.981 \\ \hline
Decentralized Market & 0.019 & 0.014 \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Juan Plazuelo Pascual
Carlos Tardon Rubio
Juan Toro Cebada
Angel Hernando Veciana

"Price Discovery in Cryptocurrency Markets" by Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, and Angel Hernando Veciana, arXiv:2506.08718v1, 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{Classification accuracy (\%) on Digit-Five dataset~ with different federated DA methods. Baseline results repeated from . ``$\to$ mt'' means ``mm,sv,sy,up$\to$mt.'' Settings (\uppercase\expandafter{\romannumeral1}), (\uppercase\expandafter{\romannumeral2}), and (\uppercase\expandafter{\romannumeral3}) as in . }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 \toprule
 Methods & $\to$mt & $\to$mm & $\to$up & $\to$sv & $\to$sy & Avg \\ \midrule
 Source Only & 75.4 & 49.6 & 75.5 & 22.7 & 44.3 & 53.5 \\
 f-DANN[4]~ & 86.1 & 59.5 & 89.7 & 44.3 & 53.4 & 66.6 \\
 f-DAN[3]~ & 86.4 & 57.5 & \underline{90.8} & \underline{45.3} & \underline{58.4} & \underline{67.7} \\
 FADA~ & 91.4 & 62.5 & \textbf{91.7} & \textbf{50.5} & \textbf{71.8} & \textbf{73.6} \\ \midrule
 FedRF-TCA (\uppercase\expandafter{\romannumeral1}) & 97.3 & \underline{64.6} & 89.2 & 41.5 & 43.9 & $67.3$ \\
 FedRF-TCA (\uppercase\expandafter{\romannumeral2}) & \textbf{97.5} & \textbf{65.3} & 89.2 & 41.9 & 43.8 & $67.5$ \\
 FedRF-TCA (\uppercase\expandafter{\romannumeral3}) & \underline{97.4} & 64.3 & 89.5 & 41.9 & 44.4 & $67.5$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Zhanbo Feng
Yuanjie Wang
Jie Li
Fan Yang
Jiong Lou
Tiebin Mi
Robert. C. Qiu
Zhenyu Liao

"Robust and Communication-Efficient Federated Domain Adaptation via Random Features" by Zhanbo Feng, Yuanjie Wang, Jie Li, Fan Yang, Jiong Lou, Tiebin Mi, Robert. C. Qiu, and Zhenyu Liao, arXiv:2311.04686v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Estimation of Gender Wage Gap in the University of North Carolina System" by Zihan Zhang and Jan Hannig, arXiv:2505.24078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Achim Ahrens
Alessandra Stampi-Bombelli
Selina Kurer
Dominik Hangartner

"Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization" by Achim Ahrens, Alessandra Stampi-Bombelli, Selina Kurer, and Dominik Hangartner, arXiv:2305.00545v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mingyang Zhou
Zichao Yan
Elliot Layne
Nikolay Malkin
Dinghuai Zhang
Moksh Jain
Mathieu Blanchette
Yoshua Bengio

"PhyloGFN: Phylogenetic inference with generative flow networks" by Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, and Yoshua Bengio, arXiv:2310.08774v2, 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{Design of the penalty mechanism.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
\hline
\toprule[1.3pt]
\textbf{Parameter} & \textbf{Value} \\
\midrule
Contract Capacity ($C_{contract}$) & 700~kW \\
Basic Capacity Rate ($R_{base}$) & \$15/kW/month \\
\midrule[1.3pt]
\textbf{Exceed Electricity Amount} & \textbf{Penalty Charge} \\ \hline
Up to 10\% over $C_{contract}$ & \( 2\cdot R_{base} \) \\
More than 10\% over $C_{contract}$ & \( 3\cdot R_{base} \) \\
\bottomrule[1.3pt]
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Lo Pang-Yun Ting
Ali Şenol
Huan-Yang Wang
Hsu-Chao Lai
Kun-Ta Chuang
Huan Liu

"Uncertainty-Aware Critic Augmentation for Hierarchical Multi-Agent EV Charging Control" by Lo Pang-Yun Ting, Ali Şenol, Huan-Yang Wang, Hsu-Chao Lai, Kun-Ta Chuang, and Huan Liu, arXiv:2412.18047v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 $s$ & $2000$ & $500$ & $200$ & $50$\\
 \midrule
 Time (s) & $119.81$ & $25.16$ & $9.86$ & $2.72$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Time (seconds) required to construct the calibration bands on the mean of independent normal responses $(Y_i)_{i=1}^{n}$ using the set $\mathcal{J}^{nbh}_{s}$ for various sizes $s$.}
\end{table}
\end{document}

math

Łukasz Delong
Selim Gatti
Mario V. Wüthrich

"Calibration Bands for Mean Estimates within the Exponential Dispersion Family" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llll}
 \toprule
 Regression & Predictors & Coefficients & P-value \\
 \midrule
 $\beta_1$ $\sim$ Category & Cancer & -0.185 & 0.419 \\
 & Immune & -0.650 & 0.0172* \\
 \bottomrule
 \end{tabular}
\caption{Regression results between $\beta_1$ and the three features}
\end{table}
\end{document}

q-fin

Yongzhuo Chen
Yixuan Liang
Yiran Liu
Brian Hobbs
Michael Kane

"Valuing Post-Revenue Biopharmaceutical Assets with Pfizer's Current Portfolio as a Case Study" by Yongzhuo Chen, Yixuan Liang, Yiran Liu, Brian Hobbs, and Michael Kane, arXiv:2312.02250v1, 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 & 8.73E$-6$ & $-$ & 2.44E$-5$ & $-$ \\
 \hline
 80 & 2.70E$-7$ & 5.01 & 7.35E$-7$ & 5.05 \\
 \hline
 160 & 8.45E$-9$ & 5.00 & 2.31E$-8$ & 4.99 \\
 \hline
 320 & 2.64E$-10$ & 5.00 & 6.95E$-10$ & 5.06 \\
 \hline
 640 & 8.26E$-12$ & 5.00 & 2.13E$-11$ & 5.03 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for problem , $t=1$, IW.}
\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{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 & \multicolumn{4}{c}{Log rents} \\ \cmidrule(lr){2-5}
 & \multicolumn{2}{c}{Levels} & \multicolumn{2}{c}{First Differences} \\ \cmidrule(lr){2-3}\cmidrule(lr){4-5}
 & \shortstack{Baseline\\(1)}
 & \shortstack{Arellano\\Bond (2)}
 & \shortstack{Baseline\\(3)}
 & \shortstack{Arellano\\Bond (4)} \\ \midrule
 Residence MW & -0.0432 & -0.0055 & -0.0219 & -0.0221 \\
 & (0.1751) & (0.0298) & (0.0175) & (0.0234) \\
 Workplace MW & 0.0376 & 0.0065 & 0.0685 & 0.0702 \\
 & (0.2033) & (0.0346) & (0.0288) & (0.0390) \\
 Lagged log rents & & 0.8421 & & 0.3299 \\
 & & (0.0179) & & (0.0177) \\ \midrule
 Economic controls & Yes & Yes & Yes & Yes \\
 P-value equality & 0.8264 & 0.8481 & 0.0514 & 0.1378 \\
 Observations & 80,340 & 80,321 & 80,241 & 80,217 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Gabriele Borg
Diego Gentile Passaro
Santiago Hermo

"From Workplace to Residence: The Spillover Effects of Minimum Wage Policies on Local Housing Markets" by Gabriele Borg, Diego Gentile Passaro, and Santiago Hermo, arXiv:2208.01791v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.