\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{AI Teaching Impact and Workplace Skill Disruption, OLS}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
& \textbf{(1)} & \textbf{(2)} \\
& \textit{Dep. Var.:} & \textit{Dep. Var.:} \\
& \textbf{AI Skill Disruption Index} & \textbf{AI Teaching Impact} \\
\midrule
AI Teaching Impact & 0.758*** & \\
& (0.179) & \\
\addlinespace
AI Workplace Skill Disruption & & 0.242*** \\
& & (0.055) \\
\addlinespace
Skill Type FE & Yes & Yes \\
\addlinespace
Intercept & -0.828 & 0.155 \\
& (0.528) & (0.189) \\
\midrule
Observations & 90 & 90 \\
Adj. $R^2$ & 0.470 & 0.273 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Regression Estimates of Risk vs Mispricing Effects on Predictability Decay}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc} \toprule
 & \multicolumn{5}{c}{LHS: Long-Short Strategy Return (bps pm, scaled) } \\
 \hline
RHS Variables & (1) & (2) & (3) & (4) & (5) \\
 \hline
Intercept & 71.4 & 71.4 & 71.4 & 71.4 & 73.0 \\
 & (3.7) & (3.7) & (3.7) & (3.7) & (3.9) \\
 Post-Sample & -28.9 & -25.4 & -25.5 & -22.9 & -5.6 \\
 & (5.6) & (7.1) & (6.6) & (10.7) & (8.2) \\
 Post-Pub & & -4.2 & & -3.0 & \\
 & & (7.8) & & (12.5) & \\
 Post-Sample x Risk & -19.1 & -7.6 & -22.5 & -10.1 & -15.6 \\
 & (7.7) & (10.2) & (8.5) & (12.9) & (7.6) \\
 Post-Pub x Risk & & -15.2 & & -16.4 & \\
 & & (13.3) & & (15.9) & \\
 Post-Sample x Mispricing & & & -4.5 & -3.2 & \\
 & & & (5.8) & (11.0) & \\
 Post-Pub x Mispricing & & & & -1.8 & \\
 & & & & (12.0) & \\
 Post-2004 & & & & & -33.7 \\
 & & & & & (9.9) \\
 \hline
Null: Risk No Decay & $<$ 0.1\% & $<$ 0.1\% & $<$ 0.1\% & $<$ 0.1\% & $<$ 0.1\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Andrew Y. Chen
Alejandro Lopez-Lira
Tom Zimmermann

"Does Peer-Reviewed Research Help Predict Stock Returns?" by Andrew Y. Chen, Alejandro Lopez-Lira, and Tom Zimmermann, arXiv:2212.10317v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Algorithm for Void Identification}
\begin{tabular}{rl}
\textbf{Step} & \textbf{Action} \\
1: & Identify Convex Hull \(P^{CH}\) of \(P\) \\
2: & Select a starting point \(P^{CH}_i\) from \(P^{CH}\) \\
3: & For each \(P^{CH}_i\), create and analyze segments \(L_{ij}\) \\
4: & Calculate MDS(\(L_{ij}\)) for each segment \\
5: & Select \(L^{best}_{ij}\) with minimum MDS value \\
6: & Apply Delaunay Triangulation and Voronoi Diagrams to select DV point on \(L^{best}_{ij}\) \\
\end{tabular}
\end{table}
\end{document}

math

"Void Shape Identification in a 2D Point Distribution" by Netzer Moriya, arXiv:2312.17533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \hline
 \textbf{Method} & \textbf{Error control} & \textbf{Speed} \\
 \hline
 AdaPT (2018) & Finite FDR & Slow \\
 AdaPT\textsubscript{g} (2021) & Finite FDR & Slow \\
 AdaPT-GMM\textsubscript{g} (2021) & Finite FDR & Slow \\
 ZAP-asymp (2022) & Asymptotic FDR & Fast \\
 AdaFDR (2019) & Asymptotic FDP & Fast \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Summary of some popular and recent p-value based multiple testing procedures that use side information, indicating each method's type-1 error control and speed.}
\end{table}
\end{document}

stat

Jack Freestone
William Stafford Noble
Uri Keich

"A semi-supervised framework for diverse multiple hypothesis testing scenarios" by Jack Freestone, William Stafford Noble, and Uri Keich, arXiv:2411.15771v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Hanhan Zhou
Tian Lan
Guru Venkataramani
Wenbo Ding

"On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning" by Hanhan Zhou, Tian Lan, Guru Venkataramani, and Wenbo Ding, arXiv:2201.11803v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean and standard error across the 200 scenarios in the training dataset, \textbf{controlling all vehicles in every scenario} (Self-play). The reported HR-PPO performance is with $\lambda = 0.06$ (green square in the Figures above).}
\begin{tabular}{lrrrrr}
\toprule
& \textbf{GC-ADE} & \textbf{Accel MAE} & \textbf{Action Acc.} (\%) & \textbf{Speed MAE} & \textbf{Steer MAE} \\
Agent & & & & & \\ \midrule
BC & 0.31 ± 0.01 & 1.71 ± 0.02 & 5.61 ± 0.02 & 0.84 ± 0.02 & 0.02 ± 0.00 \\
HR-PPO & 0.54 ± 0.01 & 2.09 ± 0.02& 3.25 ± 0.01 & 1.82 ± 0.03 & 0.02 ± 0.00 \\
PPO & 1.32 ± 0.03 & 3.93 ± 0.02 & 0.20 ± 0.00 & 5.07 ± 0.08 & 0.08 ± 0.00 \\\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"Human-compatible driving partners through data-regularized self-play reinforcement learning" by Daphne Cornelisse and Eugene Vinitsky, arXiv:2403.19648v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Confusion matrix of classification for three methods (Naive Bayes, Class-weighted SVM and LDA)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc|cc|c}
\hline
\hline
\multirow{2}{*}{True\textbackslash Pred} & \multicolumn{2}{c|}{NB} & \multicolumn{2}{c|}{SVM$_{cw}$} & \multicolumn{2}{c|}{LDA} & \multirow{2}{*}{Total} \\
 & 1 & 0 & 1 & 0 & 1 & 0 & \\
 \hline
1 & 0 & 582 & 0 & 582 & 361 & 221 & 582 \\
0 & 0 & 4004 & 0 & 4004 & 188 & 3816 & 4004 \\
Total & 0 & 4586 & 0 & 4586 & 549 & 4037 & 4586 \\
\hline
Accuracy & \multicolumn{2}{c|}{0.873} & \multicolumn{2}{c|}{0.873} & \multicolumn{2}{c|}{0.907} & \\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Xinwen Ni
Wolfgang Karl Härdle
Taojun Xie

"A Machine Learning Based Regulatory Risk Index for Cryptocurrencies" by Xinwen Ni, Wolfgang Karl Härdle, and Taojun Xie, arXiv:2009.12121v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Clean test accuracy of RN-18 models trained on unlearnable CIFAR-10 by either small patch noise applied at random location or a mixture of two type of noises.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc|cc}
\hline
\multirow{2}{*}{\textbf{Methods}} & \multicolumn{4}{c|}{\textbf{Patch Size}} & \multicolumn{2}{c}{\textbf{Noise Mixture}} \\
 & \textbf{$8\times8$} & \textbf{$16\times16$} & \textbf{$24\times24$} & \textbf{$32\times32$} & \textbf{$\Delta_{c1}\lor\Delta_{c2}$} & \textbf{$\Delta_{c1}+\Delta_u$} \\ \hline
Standard Training & 87.23 & 19.19 & 26.42 & 16.42 & 25.41 & 14.94 \\
Fast Autoaugment & 90.66 & 36.69 & 50.60 & 22.89 & 56.20 & 30.40 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hanxun Huang
Xingjun Ma
Sarah Monazam Erfani
James Bailey
Yisen Wang

"Unlearnable Examples: Making Personal Data Unexploitable" by Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang, arXiv:2101.04898v2, 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{Metadata of the processed datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
 			\hline
 			\textbf{Datasets} & \# records & $|F^S|$ & \# subgroups \\
 \hline
 Attrition & 59,598 & 19 & 8 \\
 			\hline
 			Mobile & 2,000 & 15 & 2 \\
 			\hline
 			Loan & 67,463 & 15 & 3 \\
 			\hline
 			$SD1$ & 50,000 & 15 & 4 \\
 			\hline
 			$SD2$ & 50,000 & 20 & 4 \\
 			\hline
 	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Bar Genossar
Thinh On
Md. Mouinul Islam
Ben Eliav
Senjuti Basu Roy
Avigdor Gal

"MISFEAT: Feature Selection for Subgroups with Systematic Missing Data" by Bar Genossar, Thinh On, Md. Mouinul Islam, Ben Eliav, Senjuti Basu Roy, and Avigdor Gal, arXiv:2412.06711v1, 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|}
\hline
Heading level & Example & Font size and style\\
\hline
Title (centered) & {\Large\bfseries Lecture Notes} & 14 point, bold\\
1st-level heading & {\large\bfseries 1 Introduction} & 12 point, bold\\
2nd-level heading & {\bfseries 2.1 Printing Area} & 10 point, bold\\
3rd-level heading & {\bfseries Run-in Heading in Bold.} Text follows & 10 point, bold\\
4th-level heading & {\itshape Lowest Level Heading.} Text follows & 10 point, italic\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Alex Dixon
Ranko Lazić
Andrzej S. Murawski
Igor Walukiewicz

"Leafy Automata for Higher-Order Concurrency" by Alex Dixon, Ranko Lazić, Andrzej S. Murawski, and Igor Walukiewicz, arXiv:2101.08720v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Estimates of the Production Function and Productivity Parameters}
\begin{tabular}{ccccc}
\toprule[1pt]
\multicolumn{5}{c}{\bf Panel A: Elasticities and Returns to Scale} \\
 & Mean & 1st Qu. & Median & 3rd Qu. \\ \cline{2-5}
Capital elasticity & 0.0361 & 0.0213 & 0.0368 & 0.0507 \\
 & (0.0080) & (0.0094) & (0.0081) & (0.0099) \\
Labor elasticity & 0.0950 & 0.0458 & 0.0864 & 0.1287 \\
 & (0.0003) & (0.0001) & (0.0002) & (0.0004) \\
Material elasticity & 0.7377 & 0.7041 & 0.7463 & 0.7869 \\
 & (0.0020) & (0.0019) & (0.0021) & (0.0022) \\
RTS & 0.8688 & 0.8540 & 0.8695 & 0.8834 \\
 & (0.0081) & (0.0095) & (0.0082) & (0.0100) \\
\midrule
\multicolumn{5}{c}{\bf Panel B: Productivity Parameters} \\
\multicolumn{2}{c}{\sl Labor-Augmenting} & & \multicolumn{2}{c}{\sl Factor-Neutral} \\ \cline{1-2} \cline{4-5}
$\rho_{\varphi,0}$& 0.0000 &&$\rho_{\omega,0}$ & 0.6525 \\
 & --- & & & (0.1026) \\
$\rho_{\varphi,1}$ & 0.5723 & & $\rho_{\omega,1}$ & 0.7562 \\
 & (0.0244) & & & (0.0307) \\
$\rho_{\varphi,2}$ & 0.0726 & & $\rho_{\omega,2}$ & 0.0020 \\
 & (0.0183) & & & (0.0084) \\
\midrule
\multicolumn{5}{p{9cm}}{\footnotesize {\sc Notes:} The productivity processes are parameterized as follows: $\varphi_{it}=\rho_{\varphi,1}\varphi_{it-1}+\rho_{\varphi,2}Z_{it-1}+\zeta_{\varphi,it}$ with $\rho_{\varphi,0}$ normalized to 0, and $\omega_{it}=\rho_{\omega,0}+\rho_{\omega,1}\omega_{it-1}+\rho_{\omega,2}Z_{it-1}+\zeta_{\omega,it}$. Bootstrap standard errors are in parentheses.} \\
\bottomrule[1pt]
\end{tabular}
\end{table}
\end{document}

q-fin

"A System Approach to Structural Identification of Production Functions with Multi-Dimensional Productivity" by Emir Malikov, Shunan Zhao, and Jingfang Zhang, arXiv:2302.13429v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{siunitx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Scenario Details}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 & 4-Player Scenario & 10-Player Scenario & CITR \\
 \midrule
 \midrule
 Scenario size (position limit) & $\SI{5}{\meter}\times\SI{5}{\meter}$ & $\SI{7}{\meter}\times\SI{7}{\meter}$ & $\SI{7.5}{\meter}\times\SI{25.5}{\meter}$ \\
 \midrule
 Observation interval & \multicolumn{3}{c}{10 Steps $=\SI{1}{\second}$} \\
 \midrule
 Receding horizon steps & \multicolumn{2}{c}{50 Steps $=\SI{5}{\second}$} & Vary by data \\
 \midrule
 Velocity limit & \multicolumn{2}{c}{$v_{x,k}^i, v_{y,k}^i\in[-2,2]$} & $v_{x,k}^i\in[-1, 1.5],\ v_{y,k}^i\in[-2.5,2.5]$ \\
 \midrule
 Control limit & \multicolumn{2}{c}{$a_{x,k}^i, a_{y,k}^i\in[-1,1]$} & $a_{x,k}^i, a_{y,k}^i\in[-0.5,0.5]$ \\
 \midrule
 Game weight & \multicolumn{2}{c}{$\textbf{w}^i=[1, 1, 0.1, 2]$} & $\textbf{w}^i=[0.3, 1, 0.1, 1]$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Tianyu Qiu
Eric Ouano
Fernando Palafox
Christian Ellis
David Fridovich-Keil

"PSN Game: Game-theoretic Planning via a Player Selection Network" by Tianyu Qiu, Eric Ouano, Fernando Palafox, Christian Ellis, and David Fridovich-Keil, arXiv:2505.00213v1, 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
$f$~[Hz] & 5 & 10 & 20 & 100 \\
\midrule
ARE [$^\circ$] & 0.662 & 0.635 & 0.449 & 0.505 \\
PhE [$\cdot 10^5$] & 1.336 & 1.284 & 1.123 & 1.124 \\
\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.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
		\toprule
		& \textbf{Low} & \textbf{Medium}&\textbf{High}&\textbf{Extreme}\\
		\midrule
		\rowcolor{gray!15}
		Low & 1.51 & 3.75 & 6.62 & 11.57 \\
		\rowcolor{gray!15}
		& (1.69) & (4.42) & (7.76) & (13.00) \\
		Medium & 6.72 & 9.34 & 15.37 & 27.17 \\
		& (5.56) & (12.19) & (18.57) & (29.98) \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Michele Azzone
Matteo Ghesini
Davide Stocco
Lorenzo Viola

"Climate Physical Risk Assessment in Asset Management" by Michele Azzone, Matteo Ghesini, Davide Stocco, and Lorenzo Viola, arXiv:2504.19307v1, licensed under CC0 1.0. 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}{lll}
 \toprule
 $P_1$ & instruction & Given the possible intents: \{$L$\} \\
 \cmidrule{2-3}
 & input & What is the user's intent in '\texttt{\bf x}'? Intent: \\
 \toprule
 $P_2$ & instruction & Given the following options: \{$L$\} \\
 \cmidrule{2-3}
 & input & What did the user want when the user said, '\texttt{\bf x}'? Answer:\\
 \toprule
 $P_3$ & instruction & Classify the USER's utterances into one of the following\\
 & & intent options: \{$L$\} \\
 \cmidrule{2-3}
 & input & USER: '\texttt{\bf x}' Intent: \\
 \toprule
 $P_4$ & instruction & Given a USER's utterance, choose one of the following\\
 & & intents: \{$L$\} \\
 \cmidrule{2-3}
 & input & USER: '\texttt{\bf x}' Intent: \\
 \bottomrule
 \end{tabular}
\caption{Prompt template variations for IC.}
\end{table}
\end{document}

cs

Paramita Mirza
Viju Sudhi
Soumya Ranjan Sahoo
Sinchana Ramakanth Bhat

"ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler" by Paramita Mirza, Viju Sudhi, Soumya Ranjan Sahoo, and Sinchana Ramakanth Bhat, arXiv:2403.17536v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multicolumn{3}{|c|}{\textbf{ISA}} & \multicolumn{3}{|c|}{\textbf{Collisional OT}}\\
\hline
No. Coll. & Time [s] & Rel. Error [-] & No. Coll. & Time [s] & Rel. Error [-] \\
\hline
$N_p^2/2$ &
\textbf{0.146 ± 0.021}&
\textbf{9.164e-03 ±3.611e-04}&
500$(N_p/2)$ &
{0.037 ± 0.003}&
{3.706e-02 ± 4.738e-04}
\\ \hline
2$(N_p^2/2)$ &
0.287 ± 0.020 &
9.972e-04 ± 1.017e-04 &
1000$(N_p/2)$ &
{0.066 ± 0.001} &
{1.927e-02 ± 8.256e-05}
\\ \hline
3$(N_p^2/2)$ &
0.423 ± 0.020&
5.659e-04 ± 6.927e-05&
1500$(N_p/2)$ &
\textbf{0.114 ± 0.010} &
\textbf{1.239e-02 ± 7.734e-04}
\\ \hline
4$(N_p^2/2)$ &
0.529 ± 0.006 &
5.366e-04 ± 2.189e-05 &
2000$(N_p/2)$&
{0.149 ± 0.006} &
{9.247e-03 ± 3.580e-04}
\\ \hline
5$(N_p^2/2)$ &
0.651 ± 0.017&
5.109e-04 ± 9.676e-05 &
10000$(N_p/2)$&
0.699 ± 0.010&
1.820e-03 ± 1.494e-04
\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Evolution of relative error and the execution time in finding the Wasserstein distance between Banana and Normal distribution given $N_p=8000$ samples repeated $20$ times using ISA and collisional OT.}
\end{table}
\end{document}

stat

"Collision-based Dynamics for Multi-Marginal Optimal Transport" by Mohsen Sadr and Hossein Gorji, arXiv:2412.16385v2, 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}{ccccc}
 \hline
 $z $& $(3,4)$& $(3,3 )$&$(2,4)$ &otherwise\\
\hline
$P^z_{(1,3),(2,1)}$& $\frac{2}{7}$ & $\frac{3}{7}$ & $\frac{2}{7}$ & 0\\
\hline
 \end{tabular}
\caption{Results of aggregation $(1,3)+(2,1)$}
\end{table}
\end{document}

math

"Lattice aggregations of boxes and symmetric functions" by Natasha Rozhkovskaya, arXiv:2312.03168v1, 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{SNPs and its associated gene selected by the GPLM-BAR method.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
\hline
SNP & Gene \\
\hline
 rs1407961 & ZMYM2 \\
 rs8037353 & GABRG3 \\
 rs7107322 & PRCP \\
 rs2387952 & ADARB2 \\
 rs6680365 & CAMTA1 \\
 rs3136558 & IL1B \\
 rs4131888 & SLC7A11 \\
 rs3845439 & CACNA1E\\
 rs769449 & APOE \\
 rs9549675 & F10 \\
 rs2805543 & ADARB2 \\
 rs821292 & GFOD1 \\
 rs12612481 & PDE11A \\
 rs7188981 & RBFOX1 \\
 rs9932172 & RBFOX1 \\
 rs9282537 & ABCA1 \\
 rs244072 & ADA \\
 rs11859718 & CDH13 \\
 rs17585580 & CNTN5 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Christian Chan
Xiaotian Dai
Thierry Chekouo
Quan Long
Xuewen Lu

"Broken Adaptive Ridge Method for Variable Selection in Generalized Partly Linear Models with Application to the Coronary Artery Disease Data" by Christian Chan, Xiaotian Dai, Thierry Chekouo, Quan Long, and Xuewen Lu, arXiv:2311.00210v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Relative errors and the number of breaking points for Burgers' equation with sinusoidal initial data}
\begin{tabular}{|c|c|c|c|}
\hline
Time & $\frac{\|\tilde{u}(\cdot, t_k)- u^{(k)}_{N}\|_{L^2(\Omega)}}{\|\tilde{u}(\cdot, t_k)\|_{L^2(\Omega)}}$ & $n_k$ \\ \hline
0.0 & $6.6923\times 10^{-4}$ & 78\\ \hline
0.1 & $7.8352\times 10^{-4}$ & 78\\ \hline
0.2 & $4.0166\times 10^{-2}$ & 56\\ \hline
0.3 & $5.1491\times 10^{-2}$ & 38 \\ \hline
0.4 & $5.3515\times 10^{-2}$ & 30\\ \hline
0.5 & $5.4162\times 10^{-2}$ & 25\\ \hline
\end{tabular}
\end{table}
\end{document}

math

"Evolving Neural Network (ENN) Method for One-Dimensional Scalar Hyperbolic Conservation Laws: I Linear and Quadratic Fluxes" by Zhiqiang Cai and Brooke Hejnal, arXiv:2312.06919v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Optimal variable acceptance sampling plan for exponential distribution using Bayesian estimate under Type I hybrid censoring" by Ashlyn Maria Mathai and Mahesh Kumar, arXiv:2311.16693v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Credit risk for large portfolios of green and brown loans: extending the ASRF model" by Alessandro Ramponi and Sergio Scarlatti, arXiv:2506.12510v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Anindya Goswami
Kuldip Singh Patel
Pradeep Kumar Sahu

"A novel difference equation approach for the stability and robustness of compact schemes for variable coefficient PDEs" by Anindya Goswami, Kuldip Singh Patel, and Pradeep Kumar Sahu, arXiv:2209.02873v3, 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{Overview of the News Article Dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
& \textbf{2018} & \textbf{2019} & \textbf{2020}
\\
\hline
\textbf{\# of companies} & 2,665 & 2,940 & 2,879 \\
\textbf{\# of articles} & 1,165,840 & 1,399,277 & 1,174,754 \\
\hline
\textbf{\# of articles per company} & & & \\
\textit{\textbf{average}} & 435 & 473 & 406 \\
\textit{\textbf{max}} & 1,984 & 2,154 & 2,005 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, 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{Unlabelled PPG corpora used for pipeline development.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Corpus} & \textbf{Site} & \textbf{Native $f_s$} & \textbf{Hours} & \textbf{LED}\\
 \midrule
 WildPPG~ & wrist & 128 Hz & 216 & green \\
 We-Be~ & wrist & 25 Hz & 60 & green \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Wei Shao
Ruoyu Zhang
Zequan Liang
Ehsan Kourkchi
Setareh Rafatirad
Houman Homayoun

"Self-Supervised and Topological Signal-Quality Assessment for Any PPG Device" by Wei Shao, Ruoyu Zhang, Zequan Liang, Ehsan Kourkchi, Setareh Rafatirad, and Houman Homayoun, arXiv:2509.12510v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c||c|c|}
		\hline
		Parameter & $B_1$ & $B_2$ & $C_1$ & $C_2$ \\
\hline
		$\beta$ & $8$ & $6$ & $8$ & $8$ \\
		$b$ & $0.4$ & $0.4$ & $0.8$ & $0.4$ \\
		$\mu$ & $16\%$ & $10 \%$ & $-5 \%$ & $16 \%$ \\
		$\eta$ & $2$ & $4$ & $4$ & $4$ \\
		\hline
	\end{tabular}
\caption{Parameter sets $B_1,B_2,C_1$ and $C_2.$}
\end{table}
\end{document}

q-fin

"Optimal investment with insurable background risk and nonlinear portfolio allocation frictions" by Hugo E. Ramirez and Rafael Serrano, arXiv:2303.04236v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Risk in Network Economies

"Risk in Network Economies" by Victor Sellemi, arXiv:2208.01467v1, 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|rr}
 \hline
\hline
 & MSE & MDA \\
 \hline
FIPC vs. TMI & 24.97 & 0.97 \\
 IPC vs. TMI & 4743.50 & 0.91 \\
 \hline
\hline
\end{tabular}
\end{adjustbox}
\caption{Comparison of IPC with CRIX methodology (FIPC) and rescaled IPC against TMI}
\end{table}
\end{document}

q-fin

CRIX an index for cryptocurrencies

"CRIX an index for cryptocurrencies" by Simon Trimborn and Wolfgang Karl Härdle, arXiv:2009.09782v1, 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{Dataset statistics after preprocessing: number of users, items, interactions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
		\toprule
		Dataset & Users & Items & Interactions \\
		\midrule
		MovieLens-10M & 71{,}567 & 10{,}677 & 7{,}972{,}582 \\
		EachMovie & 74{,}425 & 1{,}649 & 2{,}216{,}887 \\
		Jester & 54{,}906 & 151 & 1{,}450{,}010 \\
		Amazon-book & 52{,}643 & 91{,}599 & 2{,}984{,}108 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Julien Nicolas
César Sabater
Mohamed Maouche
Sonia Ben Mokhtar
Mark Coates

"Differentially private and decentralized randomized power method" by Julien Nicolas, César Sabater, Mohamed Maouche, Sonia Ben Mokhtar, and Mark Coates, arXiv:2411.01931v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c|c||}
 \hline
 Name & How many different digits in the batch & batch size and hyper-parameters\\
 \hline
 \hline
 single & 1 & $\cdot$ \\
 \hline
 pair & 2 & $\cdot$ \\
 \hline
 5nums & 5 & $\cdot$ \\
 \hline
 original & random & $\cdot$ \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Experiment details. $\cdot$ indicates the baseline setting.}
\end{table}
\end{document}

cs

"Representations learnt by SGD and Adaptive learning rules: Conditions that vary sparsity and selectivity in neural networks" by Jin Hyun Park, arXiv:2201.11653v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example of a query from with Semantic ANN: proxy neighbor are quite relevant to the original query}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
 \toprule
 &ANN&Clicked Neigh&Neigh&Neigh\\
 Query&Query&Keyword&\# Impress&\# Click\\
 \midrule
 video& & free games & 58&1\\
 \cline{3-5}
 games & no & online games & 260 & 4\\
 \cline{3-5}
 computers & internet &online & & \\
 free & games & computer games & 67 & 1\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jason Yue Zhu
Yanling Cui
Yuming Liu
Hao Sun
Xue Li
Markus Pelger
Tianqi Yang
Liangjie Zhang
Ruofei Zhang
Huasha Zhao

"TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search" by Jason Yue Zhu, Yanling Cui, Yuming Liu, Hao Sun, Xue Li, Markus Pelger, Tianqi Yang, Liangjie Zhang, Ruofei Zhang, and Huasha Zhao, arXiv:2101.06323v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"A unified approach to study the existence and numerical solution of functional differential equation" by Dang Quang A and Dang Quang Long, arXiv:2101.09519v1, 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}{lrrrr}
 \hline
Model & AS & MWIS & IAS centered at 15k & IAS uniform \\
 \hline
\texttt{CU-select} & 669 & 133 & 774 & 326 \\
 \texttt{per-capita} & 865 & - & 1029 & 366 \\
 \texttt{COVIDhub-ensemble} & 873 & 159 & 1067 & 438 \\
 \texttt{USC-SI\_kJalpha} & 995 & 91 & 1216 & 1097 \\
 \texttt{JHUAPL-Gecko} & 1034 & 164 & 1141 & 418 \\
 \texttt{MUNI-ARIMA} & 1084 & 169 & 1248 & 440 \\
 \texttt{COVIDhub-trained\_ensemble} & 1089 & 169 & 1271 & 823 \\
 \texttt{COVIDhub-baseline} & 1175 & 170 & 1317 & 535 \\
 \texttt{JHUAPL-Bucky} & 1358 & 102 & 1566 & 1214 \\
 \texttt{JHUAPL-SLPHospEns} & 1540 & 129 & 1604 & 1102 \\
 \texttt{UVA-Ensemble} & 2469 & 213 & 2635 & 2494 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{For one illustrative week, a comparison of allocation scores (AS), mean weighted interval scores (MWIS), and two varieties of Integrated Allocation Scores (IAS), (see Section ). All metrics are shown for 10 models that made forecasts of hospital admissions for 2022-01-03. Results are sorted by AS. For all metrics, lower scores indicate better accuracy.}
\end{table}
\end{document}

stat

Aaron Gerding
Nicholas G. Reich
Benjamin Rogers
Evan L. Ray

"Evaluating infectious disease forecasts with allocation scoring rules" by Aaron Gerding, Nicholas G. Reich, Benjamin Rogers, and Evan L. Ray, arXiv:2312.16201v3, 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{\small Inference time of TPC for different transformations}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llc}
\toprule
 Transformation & Attack radius & Inference time ($\mathrm {ms}$)\\
 \midrule
 General rotation & 15$^\circ$ & 9.56\\
 Z-rotation & 180$^\circ$ & 3.12\\
 Z-shear & 0.2 & 3.46\\
 Z-twist & 180$^\circ$ & 3.76\\
 Z-taper & 0.5 & 10.61 \\
 Linear & 0.2 & 3.94 \\
 Z-twist $\circ$ Z-rotation & 50$^\circ$, 5$^\circ$& 5.34\\
 Z-taper $\circ$ Z-rotation & 0.2, 1$^\circ$ & 8.72\\
 Z-twist $\circ$ Z-taper $\circ$ Z-rotation & 20$^\circ$, 0.2, 1$^\circ$ & 9.40\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{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.


stat

"Distributed Estimation and Gap-Free Analysis of Canonical Correlations" by Canyi Chen and Liping Zhu, arXiv:2412.17792v1, 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}{ccccc}
 \toprule
 \textbf{S.No.} & $\alpha$ & $\varepsilon$ & $\delta$ & \textbf{NEs} \\
 \midrule
 1 & 2 & 0.9 & 4 & 49 \\
 2 & 2 & 0.9 & 0.4 & 32 \\
 3 & 2 & 0.54 & 4 & 6 \\
 4 & 2 & 0.54 & 0.4 & 12 \\
 5 & 0.2 & 0.9 & 4 & 557 \\
 6 & 0.2 & 0.9 & 0.4 & 262 \\
 7 & 0.2 & 0.54 & 4 & 142 \\
 8 & 0.2 & 0.54 & 0.4 & 31 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Number of NEs.}
\end{table}
\end{document}

q-fin

Gurkirat Wadhwa
Akansh Verma
Veeraruna Kavitha
Priyank Sinha

"Price equilibria with positive margins in loyal-strategic markets with discrete prices" by Gurkirat Wadhwa, Akansh Verma, Veeraruna Kavitha, and Priyank Sinha, arXiv:2506.17239v1, 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{Two most common codes per category. To give examples of the codes in each categories, we list the two most common codes per category. A full list of codes, their rate of occurrence, and their category can be found in the appendix.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrl}
 \toprule
 Code & BMC & BMC+K & SymEx & CEGAR & Category \\ \midrule
 Assert Condition & 0.115 & 0.182 & 0.155 & 0.118 & Assertion Satisfiability \\
 Branch Condition & 0.019 & 0.027 & 0.100 & 0.064 & Branching (No Loops) \\
 Conditional Statement & 0.010 & 0.036 & 0.009 & 0.018 & Branching (No Loops) \\
 Directly Dependent Function Return & 0.096 & 0.000 & 0.100 & 0.082 & Error Reachability \\
 Dead Code & 0.019 & 0.018 & 0.009 & 0.018 & Misc. \\
 Error Call & 0.019 & 0.055 & 0.045 & 0.027 & Error Reachability \\
 Input Fun & 0.000 & 0.000 & 0.018 & 0.018 & Misc. \\
 Loop Condition (Large Bound) & 0.010 & 0.027 & 0.000 & 0.045 & Loop (Large Bound) \\
 Loop Condition (Small Bound) & 0.038 & 0.027 & 0.027 & 0.000 & Loop (Small Bound) \\
 Update Of Directly Dependent Var & 0.087 & 0.182 & 0.136 & 0.073 & Assertion Satisfiability \\
 Update Of Loop Condition Var (Large Bound) & 0.019 & 0.064 & 0.000 & 0.009 & Loop (Large Bound) \\
 Update Of Recursion Condition Var (Small Bound) & 0.000 & 0.000 & 0.055 & 0.036 & Loop (Small Bound) \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Algorithm Selection for Software Verification using Graph Neural Networks" by Will Leeson and Matthew B Dwyer, arXiv:2201.11711v3, 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{Comparison of topic modeling results (iOS Data)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \hline
 n-gram & S$^1$ & $p^2$ & $c^3$ & $|T|$ & score\\
 \hline
 uni & neg & $-9.536$ & $0.389$ & $15$ & --- \\
 bi & neg & $-23.903$ & $0.703$ & $15$ & $12.80$ \\
 tri & neg & $-21.000$ & $0.779$ & $7$ & $11.39$ \\
 \hline
 uni & neu & $-9.076$ & $0.355$ & $15$ & --- \\
 bi & neu & $-20.940$ & $0.730$ & $15$ & $11.34$ \\
 tri & neu & $-29.120$ & $0.569$ & $15$ & $15.34$ \\
 \hline
 uni & pos & $-9.379$ & $0.371$ & $15$ & --- \\
 bi & pos & $-23.302$ & $0.724$ & $15$ & $12.51$ \\
 tri & pos & $-30.647$ & $0.686$ & $13$ & $16.17$\\
 \hline
 \emph{Notes:} & \multicolumn{5}{l}{1: Sentiment \ \ \ 2: Perplexity \ \ \ 3: Coherence}\\
 \multicolumn{6}{l}{Score is not calculated for the worst performing topic model.}
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews" by Yekta Amirkhalili and Ho Yi Wong, arXiv:2503.11861v1, 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{Results for the control task for $200$ s.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 Training dataset & Model (A) & Model (B) & Model (C) \\
 \hline
 Control error Eq.~
 & 5.05E-03	& 1.36E-03 & 4.19E-04 \\
 Mean Control input Eq.~
 & 3.40E-05 & 4.01E-05 & 1.18E-05 \\
 Constraint error Eq.~
 & 0.0 & 0.0 & 4.3E-04 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems" by Adrian Lepp, Jörn Tebbe, and Andreas Besginow, arXiv:2504.21377v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Ornstein-Uhlenbeck Process for Horse Race Betting: A Micro-Macro Analysis of Herding and Informed Bettors" by Tomoya Sugawara and Shintaro Mori, arXiv:2503.16470v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
RPC & Inclusion Delay & Price Improvement & Average Rebate & Success Rate \\
\midrule
MEV Blocker & 1.34 blocks & -- & 0.0035 ETH & 85\% \\
Flashbots & 1.49 blocks & 21bps less & 0.0016 ETH & 84\% \\
Merkle & 1.86 blocks & 9bps less & 0.0009 ETH & 70\% \\
Blink & 1.56 blocks & 9bps less & 0.0000 ETH & 84\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Net Average Price Difference vs MEV Blocker.}
\end{table}
\end{document}

q-fin

"Private MEV Protection RPCs: Benchmark Stud" by Paul Janicot and Alex Vinyas, arXiv:2505.19708v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Antonio Cabrales
Manu García
David Ramos Muñoz
Angel Sánchez

"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Austin Stone
Oscar Ramirez
Kurt Konolige
Rico Jonschkowski

"The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels" by Austin Stone, Oscar Ramirez, Kurt Konolige, and Rico Jonschkowski, arXiv:2101.02722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market" by Penggan Xu, arXiv:2505.20608v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Robust statistical inference for the matched net benefit and the matched win ratio using prioritized composite endpoints" by Roland A. Matsouaka and Adrian Coles, arXiv:2011.10720v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Xiandong Zou
Xiangyu Zhao
Pietro Liò
Yiren Zhao

"Will More Expressive Graph Neural Networks do Better on Generative Tasks?" by Xiandong Zou, Xiangyu Zhao, Pietro Liò, and Yiren Zhao, arXiv:2308.11978v4, 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{Testing Results. Last row reflects overall mean performance.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
 \toprule
 \multicolumn{4}{c}{RMSE} & \multicolumn{4}{c}{$R^2$}\\
 \cmidrule(r){1-4}
 \cmidrule(r){5-8}
 \multicolumn{2}{c}{Victoria Coast} & \multicolumn{2}{c}{Fraser River Mouth} & \multicolumn{2}{c}{Victoria Coast} & \multicolumn{2}{c}{Fraser River Mouth}\\
 \cmidrule(r){1-4}
 \cmidrule(r){5-8}
 DINEOF & VConstruct & DINEOF & VConstruct & DINEOF & VConstruct & DINEOF & VConstruct\\
 \cmidrule(r){1-4}
 \cmidrule(r){5-8}
 .104 & .125 & .183 & .152 & .247 & -.089 & .759 & .834\\
 .093 & .096 & .209 & .234 & .667 & .646 & .788 & .736\\
 .078 & .08 & .131 & .119 & .569 & .552 & .797 & .833\\
 .071 & .086 & .154 & .193 & .736 & .614 & .789 & .688\\
 .067 & .068 & .164 & .176 & .499 & .472 & .898 & .883\\
 \cmidrule(r){1-4}
 \cmidrule(r){5-8}
 .0826 & .091 & .1684 & .1748 & .544 & .439 & .806 & .791\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder" by Matthew Ehrler and Neil Ernst, arXiv:2101.10260v1, 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 & 3.66E$-5$ & $-$ & 7.45E$-4$ & $-$ \\
 \hline
 80 & 6.96E$-7$ & 5.72 & 1.73E$-5$ & 5.43 \\
 \hline
 160 & 1.33E$-8$ & 5.70 & 3.58E$-7$ & 5.59 \\
 \hline
 320 & 3.34E$-10$ & 5.32 & 1.15E$-8$ & 4.96 \\
 \hline
 640 & 1.02E$-11$ & 5.04 & 3.43E$-10$ & 5.06 \\
 \hline
 1280 & 3.19E$-13$ & 4.99 & 1.03E$-11$ & 5.06 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for Burgers equation, $t=0.3$.}
\end{table}
\end{document}

math

"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of {\tt Ex-Greedy} and {\tt IMM} over 10 datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
Dataset 	& k & {\tt IMM} &	{\tt Ex-Greedy} \\ \hline
{\tt breast\_cancer} & 2 & 1,00 & 1,00 \\
{\tt iris} & 3 & 1,04 & 1,04 \\
{\tt wine} & 3 & 1,00 & 1,00 \\
{\tt covtype} & 7 & 1,03 & 1,03 \\
{\tt mice} & 8 & 1,12 & {\bf 1,08} \\
{\tt digits } & 10 & {\bf 1,23 } & 1,24 \\
{\tt anuran} & 10 & 1,30 & {\bf 1,15} \\
{\tt CIFAR-10} & 10 & 1,23 & {\bf 1,17} \\
{\tt avila} & 12 & 1,10 & 1,10 \\
{\tt newgroup} & 20 & 1,01 & 1,01 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"On the price of explainability for some clustering problems" by Eduardo Laber and Lucas Murtinho, arXiv:2101.01576v2, 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{Hall sensitivity values for different materials}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
Material & $H_c$ ($\Omega$/T) \\
\hline
InAs & 370 \\
InSb & 370 \\
Graphene & 800 \\
Graphene & 1200 \\
Graphene & 2093 \\
Graphene & 2745 \\
MoS$_2$/h-BN & 2996 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Reconsideration of feasibility of Hall amplifier" by Abhimanyu Kumar and Om Prakash Pandey, arXiv:2012.00304v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\multirow{2}{*}{} & \multicolumn{3}{c}{Part 1 violent crimes}\\
\cmidrule(rl){2-4}
&\% Change& \# of Crimes & \shortstack{Est. Costs\\Avoided*}\\
\midrule
Chronic Hot Spots & -23.8\% & -19 &\$2,701,891 \\%\hline
Temporary Hot Spots& -33.3\% & -5 &\$709,437 \\\hline
All Hot Spots& -25.3\% & -24 &\$3,411,328 \\%\hline
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Dylan J. Fitzpatrick
Wilpen L. Gorr
Daniel B. Neill

"Policing Chronic and Temporary Hot Spots of Violent Crime: A Controlled Field Experiment" by Dylan J. Fitzpatrick, Wilpen L. Gorr, and Daniel B. Neill, arXiv:2011.06019v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Newton's method applied to rational functions: Fixed points and Julia sets" by Tarakanta Nayak, Soumen Pal, and Pooja Phogat, arXiv:2503.08498v1, 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{SPICEsat Component Overview} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}% centered columns (3 columns)
 \hline\hline %inserts double horizontal lines
 \textbf{Component} & \textbf{Description} & \textbf{Vendor} \\ % inserts table
 \hline % inserts a single horizontal line
 ADCS (MSS) & Satellite attitude and control & Blue Canyon XACT-50\\
 Camera (VSS) & Sloshing video recording & ArduCam OV5647 \\
 Tank & Primary experiment with fluid & Polycarbonate \\
 Pressure Sensors (LSS) & Pressure sensor mapping & Sensitronics \\
 Chassis \& Panel & Satellite main chassis & SpaceMind 6U \\
 Electrical Power & Power for satellite & SpaceMind \\
 Payload Computer & Payload computer / control & RasberryPi 4B+ \\
 Onboard Computer & Flight computer & GOMSpace NanoMind A3200 \\
 UHF Transceiver & Omni-directional comms & Ultra High Frequency (UHF) Transceiver \\
 S-band Transceiver & High data rate comms & GOMSpace Software Defined Radio (SDR) \\
 \hline %inserts a single line
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Michael fogel
Snigdha Sushil Mishra
Laurent Burlion

"Nanosatellite Design Considerations for a Mission to Explore the Propellant Sloshing Problem" by Michael fogel, Snigdha Sushil Mishra, and Laurent Burlion, arXiv:2412.20659v1, 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{Resulting RMSE and MAPE after personalization over 180 clients}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 		\hline
 			 &\multicolumn{3}{c|}{RMSE} & \multicolumn{3}{c|}{MAPE}\\
 			\hline
 			Scenario & Min & Max & Mean & Min & Max & Mean \\
 			\hline
 			1 & ~0.0 & 2.47 & 0.550 & 8.13\% & 99.16\% & 36.33\% \\ \hline
 			2 & ~0.0 & 2.47 & 0.551 & 7.89\% & 91.23\% & 36.39\% \\ \hline
 			3 & ~0.0 & 2.371 & 0.536 & 7.64\% & 88.76\% & 34.27\% \\ \hline
 	 	4 & ~0.0 & 2.375 & 0.536 & 8.00\% & 82.14\% & 34.14\% \\ \hline
 		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Electrical Load Forecasting Using Edge Computing and Federated Learning" by Afaf Taik and Soumaya Cherkaoui, arXiv:2201.11248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The first two columns report the mean and the standard deviation (in brackets) of the ARI for the two groups over the $50$ simulated datasets under Experiment 1. The last column shows the percentage of times that each method gathers all observations of the second group in a single cluster, hence, lower values are better.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
& Group 1 & Group 2 & \% 1 cluster \\ \hline
MFM & 0.992 (0.011) & 0.002 (0.037) & 98\% \\
HDP & 0.992 (0.011) & 0.122 (0.183) & 68\% \\
HMFM - Marginal & 0.990 (0.012) & 0.216 (0.191) & 42\% \\
HMFM - Conditional & 0.991 (0.012) & 0.177 (0.190) & 52\% \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Hierarchical Mixture of Finite Mixtures

Alessandro Colombi
Raffaele Argiento
Federico Camerlenghi
Lucia Paci

"Hierarchical Mixture of Finite Mixtures" by Alessandro Colombi, Raffaele Argiento, Federico Camerlenghi, and Lucia Paci, arXiv:2310.20376v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"datadriftR: An R Package for Concept Drift Detection in Predictive Models" by Ugur Dar and Mustafa Cavus, arXiv:2412.11308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Hué Sullivan
Hurlin Christophe
Pérignon Christophe
Saurin Sébastien

"Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring" by Hué Sullivan, Hurlin Christophe, Pérignon Christophe, and Saurin Sébastien, arXiv:2212.05866v4, 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{Line characteristics of 5 bus network}
\begin{tabular}{cccc}
\hline
from bus	&	to bus	&	X (PU)	&	Limit (MW)	\\\hline
1	&	2	&	0.030	&	240	\\
1	&	4	&	0.050	&	270	\\
1	&	5	&	0.060	&	250	\\
2	&	3	&	0.025	&	270	\\
3	&	4	&	0.030	&	270	\\
4	&	5	&	0.020	&	270	\\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Controllable Transmission Networks under Demand Uncertainty with Modular FACTS" by Alireza Soroudi, arXiv:2105.10402v1, 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{Ablation study of partitioned normalization (PN) and star topology fully-connected neural networks (STAR FCN). All models are trained with the proposed auxiliary network.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
	\toprule
	 & Base (BN) & Base (PN) & STAR FCN (BN) & STAR FCN (PN) \\
	\midrule
	Overall AUC & 0.6364 &0.6485 & 0.6455 & \textbf{0.6506} \\
	\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiang-Rong Sheng
Liqin Zhao
Guorui Zhou
Xinyao Ding
Binding Dai
Qiang Luo
Siran Yang
Jingshan Lv
Chi Zhang
Hongbo Deng
Xiaoqiang Zhu

"One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction" by Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, and Xiaoqiang Zhu, arXiv:2101.11427v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter settings for Experiment~2 (Affiliated Valuations + Q-learning).}
\begin{tabular}{ll}
\toprule
\textbf{Parameter} & \textbf{Possible Values}\\
\midrule
Number of bidders ($n$) & $\{2,4,6\}$\\
Reserve price ($r$) & $\{0.0,0.1,0.2,0.3\}$\\
Affiliation ($\eta$) & e.g.\ $\{0.0,0.25,0.5,0.75,1.0\}$\\
Learning rate ($\alpha$) & e.g.\ $\{0.001,0.005,0.01,0.05,0.1\}$\\
Discount factor ($\gamma$) & e.g.\ $\{0.0,0.5,0.9,0.99\}$\\
Exploration & $\varepsilon$-greedy or Boltzmann\\
Q-update mode & Asynchronous or synchronous\\
State features & e.g.\ $s_i,\,\text{median-of-others},\,\text{winner-bid}$\\
Number of episodes & e.g.\ $10^5$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments" by Pranjal Rawat, arXiv:2306.09437v2, 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{Summary statistics for PL- and ML- portfolios over the full and downturn periods}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
& $n$ & $\bar{L}$ & $\hat{\sigma}$ & Max workout period & Mean workout period & $r_f$ \\
\midrule
\multicolumn{7}{c}{Personal Loans} \\
\midrule
Full & $121,151$ & $0.749$ & $0.290$ & $87$ months & $4.67$ months & $6.37\%$\\
Downturn & $60,032$ & $0.760$ & $0.273$ & $80$ months & $4.57$ months & $8.93\%$\\
\midrule
\multicolumn{7}{c}{Mortgage Loans}\\
\midrule
Full & $126,580$ & $0.256$ & $0.366$ & $113$ months & $25.64$ months & $6.88\%$\\
Downturn & $60,779$ & $0.293$ & $0.372$ & $90$ months & $33.59$ months & $8.49\%$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Janette Larney
Arno Botha
Gerrit Lodewicus Grobler
Helgard Raubenheimer

"A cost of capital approach to determining the LGD discount rate" by Janette Larney, Arno Botha, Gerrit Lodewicus Grobler, and Helgard Raubenheimer, arXiv:2503.23992v1, 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{Cardiologists' assessments of the quality of synthetic cardiac MRI compared to original cardiac MRI. The table shows the percentage of the responses where the synthetic cardiac MRI was evaluated as better, similar, or worse than the original.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
\textbf{Better} & \textbf{Similar} & \textbf{Worse} \\ \hline
\textbf{14.58\% } & \textbf{30.21\%} & \textbf{55.21\%}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ilke Adalioglu
Serkan Kiranyaz
Mete Ahishali
Aysen Degerli
Tahir Hamid
Rahmat Ghaffar
Ridha Hamila
Moncef Gabbouj

"Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration" by Ilke Adalioglu, Serkan Kiranyaz, Mete Ahishali, Aysen Degerli, Tahir Hamid, Rahmat Ghaffar, Ridha Hamila, and Moncef Gabbouj, arXiv:2412.06445v1, 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}{ccccr} \hline \hline \\
 & $\theta_1$ & $\theta_2$ & $\theta_1$ \\ \hline \\
 Model & \multicolumn{2}{c}{FRA} & GUM \\
 mean & 8.60 & -13.92 & 2.05\\
 $q_{2.5}$ & 7.50 & -17.80 & 1.86\\
 $q_{97.5}$ & 9.78 & -10.38 & 2.24\\
 weight & 0.86 & 0.14 & 1 \\ \hline \hline
\end{tabular}
\end{adjustbox}
\caption{Occupancy data. Post MCMC summaries given chosen cluster configuration.}
\end{table}
\end{document}

stat

Ruyi Pan
Luis E. Nieto-Barajas
Radu V. Craiu

"Bayesian nonparametric mixtures of Archimedean copulas" by Ruyi Pan, Luis E. Nieto-Barajas, and Radu V. Craiu, arXiv:2412.09539v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 & \multicolumn{2}{c}{Log rents} \\ \cmidrule(l){2-3}
 & \shortstack{Levels\\(1)}
 & \shortstack{First Differences\\(2)} \\ \midrule
 Residence MW & -0.0432 & -0.0199 \\
 & (0.1751) & (0.0195) \\
 Workplace MW & 0.0376 & 0.0687 \\
 & (0.2033) & (0.0298) \\ \midrule
 Economic controls & Yes & Yes \\
 P-value autocorrelation test & & $<0.0001$ \\
 R-squared & 0.9924 & 0.0216 \\
 Observations & 80,340 & 78,912 \\ \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.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example of visiting order}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 			\hline
 			Patients & 5 (2) & 1 (3) & 3 (1) &1 (2)& 4 (3) & 2 (1) & 6 (2) \\
 			\hline
 			Caregivers & & & & & & & \\
 			\hline
 		\end{tabular}
\end{table}
\end{document}

math

"Mathematical models and heuristics for the home health care routing and scheduling problem" by Mohammed Bazirha, arXiv:2312.08032v1, 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{Structure of encoder. Regularisation layers are not shown.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Type} & \textbf{Parameters} & \textbf{Input size} \\ \hline
conv & 100x1/30 & 512x1 \\ \hline
pool & 2 & 512x30 \\ \hline
conv & 100x1/15 & 256x30 \\ \hline
pool & 2 & 256x15 \\ \hline
conv & 30x1/15 & 128x15 \\ \hline
pool & 2 & 128x15 \\ \hline
conv & 20x1/5 & 64x15 \\ \hline
pool & 2 & 64x5 \\ \hline
dense & 30 & 32x5 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Iana Sereda
Sergey Alekseev
Aleksandra Koneva
Alexey Khorkin
Grigory Osipov

"Problems of representation of electrocardiograms in convolutional neural networks" by Iana Sereda, Sergey Alekseev, Aleksandra Koneva, Alexey Khorkin, and Grigory Osipov, arXiv:2012.00493v1, 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}{lllll}
\hline
Methods & Highest RankIC & Randomized ID & BID with GBT & BID with IID \\
\hline
Mean RankIC & \textbf{0.1035} & 0.0651 & 0.0553 & \textbf{0.0752} \\
Mean Correlation & 0.2276$\downarrow$ & 0.5741$\downarrow$ & \textbf{0.1132} & \textbf{0.1497} \\
\hline
Sharpe Ratio (OS) & 1.0276 & 1.0544 & 0.5045 & \textbf{1.5721} \\
Sharpe Ratio (IS) & \textbf{2.6511} & 1.3019 & 1.4965 & 2.3231 \\
\hline
Annual Return (OS) & 0.1043 & 0.0932 & 0.0484 & \textbf{0.1633} \\
Annual Return (IS) & \textbf{0.4390} & 0.2281 & 0.2425 & 0.3805 \\
\hline
Max Drawdown (OS) & 0.0632 & \textbf{0.0373} & \textbf{0.0484} & \textbf{0.0552} \\
Max Drawdown (IS) & \textbf{0.0892} & 0.1548 & 0.1232 & \textbf{0.0975} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies" by Jun Lu and Joerg Osterrieder, arXiv:2209.14532v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{MDP formulation of the capacity-distortion trade-off}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 state $\delta_{i-1}$& $P_{S_{i-1}|Z^{i-1}}(\cdot|y^{i-1})$ \\
 \hline
 action $a_i$ & $P_{X_i|S_{i-1} Z^{i-1}} (\cdot|\cdot, y^{i-1})$ \\
 \hline
 reward $r_i$ & $I(X_i, S_{i-1};Y_i|Z^{i-1}) - \beta \mathbb E[ d(S_i, \hat S_i)]$.\\
 \hline
 disturbance & $z_i$ \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Homa Nikbakht
Yonina C. Eldar
H. Vincent Poor

"A MIMO ISAC System for Ultra-Reliable and Low-Latency Communications" by Homa Nikbakht, Yonina C. Eldar, and H. Vincent Poor, arXiv:2501.13025v2, 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{Dynamic analysis data of the SWATH style ship ‘wasp’~)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}\toprule
 & Freeboard&	Cycles& \multicolumn{2}{c}{Displacement}	&	Time&	Period\\\midrule
 &in&	n&	B1&	 B(n+1)& $\Delta$ t(s)&	P\\\midrule
 \multirow{3}{*}{Roll}&	16&	6&	 7&	3.5&	37.90&	6.32\\
	&12&	5&	10.5&	4.5&	36.33&	7.27\\
	&8	&6	& 13	&4.5	&41.60	&6.93\\
\multirow{3}{*}{Pitch
(Black)}	&16&	2&	1.5&	0.5&	12.61&	6.31\\
	&12&	4&	3.5&	0.75&	27.66&	6.91\\
	&8&	4&	3.25&	0.75&	25.74&	6.44\\
\multirow{3}{*}{Pitch
(Front)}&	16&	2&1.5&	0.5&	12.26&	6.13\\
	&12&	5&	2.5&	1&	34.46&	6.89\\
	&8&	6&	2.75&	1&	38.48&	6.41\\\bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

MM Rashid
Rupal Roy
Md Manjurul Ahsan
Zahed Siddique

"Design and Development of an Autonomous Surface Vehicle for Water Quality Monitoring" by MM Rashid, Rupal Roy, Md Manjurul Ahsan, and Zahed Siddique, arXiv:2201.10685v1, 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{Ratings of the best South American clubs}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|}
 \hline
 \# & team & rating & league \\ \hline
 1 & Palmeiras & 1961.0 & Brazil. First \\ \hline
 2 & Flamengo RJ & 1883.1 & Brazil. First \\ \hline
 3 & Atletico-MG & 1849.4 & Brazil. First \\ \hline
 4 & River Plate Argentina & 1842.7 & Argentina. First \\ \hline
 5 & Athletico-PR & 1826.8 & Brazil. First \\ \hline
 6 & Ind. del Valle & 1823.2 & Ecuador. First \\ \hline
 7 & Fluminense & 1821.5 & Brazil. First \\ \hline
 8 & Libertad Asuncion & 1815.7 & Paraguay. First \\ \hline
 9 & Internacional & 1810.3 & Brazil. First \\ \hline
 10 & Sao Paulo & 1809.9 & Brazil. First \\ \hline
 11 & Cerro Porteno & 1793.6 & Paraguay. First \\ \hline
 12 & Defensa y Justicia & 1787.2 & Argentina. First \\ \hline
 13 & Fortaleza Brazil & 1782.6 & Brazil. First \\ \hline
 14 & Botafogo RJ & 1780.7 & Brazil. First \\ \hline
 15 & Talleres Cordoba & 1775.5 & Argentina. First \\ \hline
 16 & Nacional Uruguay & 1772.8 & Uruguay. First \\ \hline
 17 & LDU Quito & 1749.1 & Ecuador. First \\ \hline
 18 & Boca Juniors & 1748.4 & Argentina. First \\ \hline
 19 & Olimpia Asuncion & 1747.6 & Paraguay. First \\ \hline
 20 & Estudiantes L.P. & 1745.8 & Argentina. First \\ \hline
 21 & Bragantino & 1742.5 & Brazil. First \\ \hline
 22 & Gremio & 1740.8 & Brazil. First \\ \hline
 23 & Corinthians & 1737.5 & Brazil. First \\ \hline
 24 & San Lorenzo Argentina & 1736.6 & Argentina. First \\ \hline
 25 & Millonarios & 1732.7 & Colombia. First \\ \hline
 26 & Newells Old Boys & 1728.4 & Argentina. First \\ \hline
 27 & Racing Club & 1723.9 & Argentina. First \\ \hline
 28 & Atl. Nacional & 1718.7 & Colombia. First \\ \hline
 29 & Belgrano & 1717.7 & Argentina. First \\ \hline
 30 & Argentinos Jrs & 1712.4 & Argentina. First \\ \hline
 31 & Rosario Central & 1704.5 & Argentina. First \\ \hline
 32 & America MG & 1704.4 & Brazil. First \\ \hline
 33 & Cruzeiro & 1701.2 & Brazil. First \\ \hline
 34 & Atletico GO & 1699.1 & Brazil. Second \\ \hline
 35 & Sport Recife & 1697.7 & Brazil. Second \\ \hline
 36 & Barcelona SC & 1694.3 & Ecuador. First \\ \hline
 37 & Ceara & 1688.8 & Brazil. Second \\ \hline
 38 & Penarol & 1688.2 & Uruguay. First \\ \hline
 39 & Godoy Cruz & 1685.2 & Argentina. First \\ \hline
 40 & Goias & 1681.7 & Brazil. First \\ \hline
 41 & Nacional Asuncion & 1681.1 & Paraguay. First \\ \hline
 42 & Guarani Paraguay & 1681.0 & Paraguay. First \\ \hline
 43 & Cuiaba & 1680.3 & Brazil. First \\ \hline
 44 & Lanus & 1680.2 & Argentina. First \\ \hline
 45 & Tigre & 1677.0 & Argentina. First \\ \hline
 46 & Sportivo Trinidense & 1676.8 & Paraguay. First \\\hline
 47 & Santos & 1674.6 & Brazil. First \\ \hline
 48 & U. Catolica Ecuador & 1667.5 & Ecuador. First \\ \hline
 49 & Aucas & 1667.0 & Ecuador. First \\ \hline
 50 & Bahia & 1664.5 & Brazil. First \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications" by Andrei Shelopugin and Alexander Sirotkin, arXiv:2310.11459v1, 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{Classification Report}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rcccc}
\hline
\textit{Max Length} & \textit{512} & \textit{Training Epochs} & \textit{4} & \textit{2} \\
\textit{Batch Size} & \textit{32} & \textit{Learning Rate} & \textit{1e-4} & \textit{2e-5} \\ \hline
\textbf{} & \textbf{precision} & \textbf{recall} & \textbf{f1-score} & \textbf{support} \\ \hline
\textbf{noise} & 0.80 & 0.79 & 0.80 & 23,938 \\
\textbf{ESG relevant} & 0.85 & 0.86 & 0.85 & 33,150 \\ \hline
\textbf{accuracy} & & & 0.83 & 57,088 \\
\textbf{macro avg} & 0.83 & 0.83 & 0.83 & 57,088 \\
\textbf{weighted avg} & 0.83 & 0.83 & 0.83 & 57,088 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c||c|c}
		\hline
		& & &\\
		Symbol&Value&Symbol&Value\\
		\hline
		& & &\\
		$f_{\text{c}}$&$4.8498\;\mathrm{GHz}$&$R$&$10\;\mathrm{m}$\\
		& & &\\
		$\Delta f$&$60\;\text{KHz}$&$P_{UE}$&$23\;\mathrm{dBm}$\\
		& & &\\
		$B$&$100\;\text{MHz}$&$\Delta \theta$&$0.1^{\circ}$\\
		& & &\\
		$M$&4&$\boldsymbol{\theta}$&$[-60^{\circ},60^{\circ}]$\\
		\hline
	\end{tabular}
\end{adjustbox}
\caption{Simulation settings.}
\end{table}
\end{document}

eess

Shengheng Liu
Xingkang Li
Zihuan Mao
Peng Liu
Yongming Huang

"Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB" by Shengheng Liu, Xingkang Li, Zihuan Mao, Peng Liu, and Yongming Huang, arXiv:2501.00009v1, 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|}
\hline
\textbf{Code $\backslash$ Erasure Rate} & $\mathbf{0.2}$ & $\mathbf{0.225}$ & $\mathbf{0.25}$ & $\mathbf{0.275}$ & $\mathbf{0.3}$ & $\mathbf{0.325}$ \\
\hline
$[[1525,25]]$ & $20$ & $19$ & $30$ & $42$ & $250$ & $289$ \\
\hline
$[[3904,64]]$ & $15$ & $16$ & $33$ & $38$ & $534$ & $663$ \\
\hline
$[[6100,100]]$ & $15$ & $20$ & $21$ & $41$ & $738$ & $994$ \\
\hline
$[[8784,144]]$ & $13$ & $21$ & $21$ & $24$ & $38$ & $1378$ \\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Maximum weights of residual error after peeling for the $[[1525,25]]$, $[[3904,64]]$, $[[6100,100]]$, and $[[8784,144]]$ quantum expander codes.}
\end{table}
\end{document}

math

Jefrin Sharmitha Prabhu
Abhinav Vaishya
Shobhit Bhatnagar
Aryaman Manish Kolhe
V. Lalitha
P. Vijay Kumar

"On the Efficacy of the Peeling Decoder for the Quantum Expander Code" by Jefrin Sharmitha Prabhu, Abhinav Vaishya, Shobhit Bhatnagar, Aryaman Manish Kolhe, V. Lalitha, and P. Vijay Kumar, arXiv:2504.21845v2, 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 Learning Rates and Convergence Times from Figure~}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
 & \textbf{optimal learning rate} & \textbf{optimal convergence time} \\
\hline
$\eta$ coordinates & $\alpha_{\eta}=0.0025$ & \hspace{0.2cm}$k=41$ \\
natural gradient & $\alpha_{ng}\in [0.541,0.695]$ & $k=3$\\
$\theta$ coordinates & $\alpha_{\theta}\in 6.93$ & \hspace{0.2cm}$k=20$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence" by Adwait Datar and Nihat Ay, arXiv:2504.19259v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Víctor Blanco
José-Fernando Camacho-Vallejo
Carlos Corpus

"A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology" by Víctor Blanco, José-Fernando Camacho-Vallejo, and Carlos Corpus, arXiv:2503.00561v1, 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{RTF benchmark}
\begin{tabular}{lllll}
\toprule
model/decoding\_chunk & full & 16 & 8 & 4 \\ \midrule
server (x86) float32 & 0.079 & 0.095 & 0.128 & 0.186 \\
server (x86) int8 & 0.072 & 0.081 & 0.098 & 0.134 \\
on-device (Android) float32 & 0.164 & 0.251 & 0.350 & 0.505 \\
on-device (Android) int8 & 0.082 & 0.114 & 0.130 & 0.201 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Zhuoyuan Yao
Di Wu
Xiong Wang
Binbin Zhang
Fan Yu
Chao Yang
Zhendong Peng
Xiaoyu Chen
Lei Xie
Xin Lei

"WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit" by Zhuoyuan Yao, Di Wu, Xiong Wang, Binbin Zhang, Fan Yu, Chao Yang, Zhendong Peng, Xiaoyu Chen, Lei Xie, and Xin Lei, arXiv:2102.01547v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|} \hline
\textbf{Separation}	&	\textbf{Approx. \#Hops}	&	\textbf{Latency Gain} \\ \hline
1	&	1	&	14.8\% \\
2	&	1	&	27.9\% \\
4	&	2	&	30.25\% \\
6	&	3	&	8.1\% \\
8	&	4	&	-152\%\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Scalability of latency gain}
\end{table}
\end{document}

cs

"Harvest: A Reliable and Energy Efficient Bulk Data Collection Service for Large Scale Wireless Sensor Networks" by Vinayak Naik and Anish Arora, arXiv:2101.11211v1, 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{Sensitivity and Specificity of BraTS-Africa Without Fine-Tuning, With Full Fine-Tuning, and With PEFT Fine-Tuning}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
 \hline
 & \multicolumn{3}{c|}{Without Fine-Tuning} & \multicolumn{3}{c|}{With Full Fine-Tuning} & \multicolumn{3}{c|}{With PEFT Fine-Tuning} \\ \hline
 & ET & TC & WT & ET & TC & WT & ET & TC & WT \\ \hline
 Sensitivity $\uparrow$ &
 0.40 & 0.27 & 0.36 & 0.64 & 0.59 & 0.68 & \textbf{0.67} & \textbf{0.66} & \textbf{0.75} \\
 \textit{Std. Dev.} &
 (0.37) & (0.34) & (0.41) & (0.31) & (0.38) & (0.38) & \textbf(0.27) & \textbf(0.33) & \textbf(0.35) \\ \hline
 Specificity $\uparrow$ &
 0.99 & 0.98 & 0.96 & \textbf{0.99} & \textbf{0.99} & \textbf{0.99} & \textbf{0.99} & \textbf{0.99} & \textbf{0.99} \\
 \textit{Std. Dev.} &
 (0.01) & (0.03) & (0.06) & \textbf(0.002) & \textbf(0.004) & (0.01) & (0.001) & \textbf(0.004) & \textbf(0.007) \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Bijay Adhikari
Pratibha Kulung
Jakesh Bohaju
Laxmi Kanta Poudel
Confidence Raymond
Dong Zhang
Udunna C Anazodo
Bishesh Khanal
Mahesh Shakya

"Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset" by Bijay Adhikari, Pratibha Kulung, Jakesh Bohaju, Laxmi Kanta Poudel, Confidence Raymond, Dong Zhang, Udunna C Anazodo, Bishesh Khanal, and Mahesh Shakya, arXiv:2412.14100v1, 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}{c|c|c}
\toprule
\textbf{Object Type} & \textbf{Numerical Independence Condition} & \textbf{Implication} \\
\midrule
\textbf{Propositions} & $i(AB)=i(A)+i(B)$ & $P(AB)=P(A)P(B)$ \\
\textbf{Questions} & $d(a*b)=d(a)+d(b)$ & $P(AB)=P(A)P(B)$ \\
\textbf{Subjects} & $i(ab)=i(a)+i(b)$ & $P(AB)=P(A)P(B)\;\text{or}\;A\sim B$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

The Mathematics of Questions

"The Mathematics of Questions" by R. O'Flanagan, arXiv:2503.18992v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
				\hline
				\textbf{Methods} & \textbf{Within Group} & \textbf{Between Groups}\\
				\hline
				SY2010 & Accept & Accept \\ \hline
				LC2012 & Accept & Accept \\ \hline
				CLX2013 & Reject & Reject \\ \hline
				HC2018 & Accept & Accept \\ \hline
				Proposed & Accept & Reject \\ \hline
			\end{tabular}
\end{adjustbox}
\caption{Comparison of results for the ALL data.}
\end{table}
\end{document}

stat

"Two sample test for covariance matrices in ultra-high dimension" by Xiucai Ding, Yichen Hu, and Zhenggang Wang, arXiv:2312.10796v1, 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{Default configuration for all experiments.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cl}
 \toprule
 \textbf{Category} & \textbf{Value} \\
 \midrule
 Input resolution & 256$\times$256 px \\
 Slices per sweep & 100 \\
 Number of Gaussians & 120 per slice ($\approx$ 12k) \\
 Loss & Hybrid L1 (80\%) + SSIM (20\%) \\
 Learning rates & \parbox{3cm}{Means:0.20, Opacity:0.03, Scale:0.01, Intensity:0.008} \\
 Batch size & 32 slices \\
 Epochs & 60 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Max Krähenmann
Sergio Tascon-Morales
Fabian Laumer
Julia E. Vogt
Ece Ozkan

"From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound" by Max Krähenmann, Sergio Tascon-Morales, Fabian Laumer, Julia E. Vogt, and Ece Ozkan, arXiv:2508.14552v1, 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{BESS utilization and losses in Approaches 1 and 2}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
			\hline
			 	 & \multicolumn{2}{|c|}{Number of cycles} & \multicolumn{2}{|c|}{BESS losses (kWh)}\\
			 \hline
			Year 	& Approach 1	& Approach 2 & Approach 1 & Approach 2 \\
			\hline
			\hline
	 2027 & 4.8 & 2.9	& 7,479.4	& 3,838.3 \\
	 2032	& 6.4	& 2.8	& 7,536.3	& 2,318.1 \\
	 2037	& 7.5	& 3.3	& 11,293.7	& 4,108.0\\
	 2042 & 8.4	& 5.2	& 16,661.3	& 9,709.9 \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Quan Nguyen
Christine Holland
Siddharth Sridhar

"Design and Optimization of EV Charging Infrastructure with Battery in Commercial Buildings" by Quan Nguyen, Christine Holland, and Siddharth Sridhar, arXiv:2509.12160v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
Audio Feature&Variability&Inertia\\
\hline
Danceability&-0.03&0.03\\
Loudness&-0.01&0.05\\
Speechiness&0.05&0.09*\\
Acousticness&0.02&0.05\\
Instrumentalness&-0.11**&0.02\\
Liveness&-0.05&0.05\\
Tempo&-0.05&0.03\\
Mode&-0.05&0.07\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Spearman correlations between users' dynamic measures represented by Variability and Inertia of audio features and K10 scores. (*p\textless0.05, **p\textless0.01)}
\end{table}
\end{document}

eess

"Static and Dynamic Measures of Active Music Listening as Indicators of Depression Risk" by Aayush Surana, Yash Goyal, and Vinoo Alluri, arXiv:2009.13685v1, 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|l|l|l|l|l|l|l|}
\hline
 $\rho$ & \emph{SIFT} & \emph{SURF} & \emph{ORB} & \emph{FREAK} & \emph{BRIEF} & \textbf{ARSRG}$_{1^{st}}$ & \textbf{ARSRG}$_{2^{nd}}$\\ \hline
 $0.6$ & 0.0674 & 0.0820 & 0.2051 & 0.05584& 0.10689 & 1.0 & 1.0\\ \hline
 $0.7$ & 0.0401 & 0.0441 & 0.0742 & 0.04671& 0.05664 & 1.0 & 1.0\\ \hline %old values 0.6571 & 1.0
 $0.8$ & 0.0312 & 0.0338 & 0.0348 & 0.04072& 0.03452 & 1.0 & 1.0\\ \hline %old values 0.1428 & 0.6666
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications" by Mario Manzo, arXiv:1912.09972v1, 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|c}
Window side length & 1 & 2 & 4 & 8 \\ \hline
Poisson (assuming estm.\ intensity) & 0.053 & 0.053 & 0.052 & 0.051\\
Poisson (assuming known intensity) & 0.0015 & 0.0011 & 0.0008 & 0.0008 \\\hline
Mat{\'e}rn (assuming estm.\ intensity) & 0.63 & 1.00 & 1.00 & 1.00\\
Mat{\'e}rn (assuming known intensity) & 0.31 & 0.96 & 1.00 & 1.00
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Anne Marie Svane
Christophe Biscio
Rasmus Waagepetersen

"A functional central limit theorem for the K-function with an estimated intensity function" by Anne Marie Svane, Christophe Biscio, and Rasmus Waagepetersen, arXiv:2309.12834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{Kernel } & $\mathbf{h}$ & $\mathbf{\mathcal{C}_{k}}$ & \textbf{Error1} & \textbf{Error2} & \textbf{Error3}\\
\hline
Gaussian & 4.981171 & 0.002179295 & 7.90000005 & 7.6266667 & 7.95026705\\
Epanechnikov & 10.52929 & 1.02x$10^{-05}$ & 6.9500001 & 6.5284985 & 6.8285046\\
Cosine & 10.23305 & 1.26x$10^{-07}$ & 7.4 & 7.2599999 & 7.4782377\\
Uniform & 2.675163 & 0.004355385 & 8.4401727 & 8.19666675 & 8.45300395\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Average test error for each kernel as calculated for infant 5 through Monte Carlo simulation}
\end{table}
\end{document}

eess

"Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants" by Sinjini Mitra, arXiv:2011.09307v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\centering The comparison of precision, recall and F1-score for our model and Double-net in percentage from three different views for xVertSeg dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline
\textbf{Plane} & \textbf{Model}& \textbf{Phase }& \textbf{Precision(\%)} &\textbf{Recall(\%)}&\textbf{F1-score(\%)} \\\hline
\multirow{4}{*}{Sagittal} & \multirow{2}{*}{DoubleU-Net } &Valid& 93.14 & 93.49 & 93.31 \\ & & Test & 92.23 & 92.94 & 92.58 \\\cline{2-6}
& \multirow{2}{*}{\textbf{DoubleU-Net ++} } &Valid& \textbf{98.49} & \textbf{98.23} & \textbf{98.35} \\ & & Test & \textbf{97.95} & \textbf{97.54} & \textbf{97.74} \\
\hline
\multirow{4}{*}{Coronal } & \multirow{2}{*}{DoubleU-Net } &Valid& 91.48 & 91.34 & 91.40 \\ & & Test & 90.74 & 90.01 & 90.37 \\\cline{2-6}
& \multirow{2}{*}{\textbf{DoubleU-Net ++ }} &Valid&\textbf{94.48} &\textbf{94.83} & \textbf{94.65} \\ & & Test & \textbf{93.79} & \textbf{93.64} &\textbf{93.71} \\ \hline
\multirow{4}{*}{Axial } & \multirow{2}{*}{DoubleU-Net } &Valid& 92.81 & 92.94 & 92.87 \\ & & Test & 92.16 & 92.11 & 92.13 \\\cline{2-6}
& \multirow{2}{*}{\textbf{DoubleU-Net ++}} &Valid& \textbf{96.49} & \textbf{96.83} & \textbf{96.65} \\ & & Test & \textbf{96.02 } & \textbf{96.17}& \textbf{96.09} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Simindokht Jahangard
Mahdi Bonyani
Abbas Khosravi

"DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation" by Simindokht Jahangard, Mahdi Bonyani, and Abbas Khosravi, arXiv:2201.12389v1, 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{Clustering-based Methods for Segmentation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
Reference & Year & Team & Segmentation Method\\
\hline
 & 2008 & O Sertel & $k$-means clustering\\
 & 2008 & O Sertel & $k$-means clustering, eccentricity\\
 & 2009 & O Sertel & $k$-means clustering\\
 & 2010 & S Samsi & $k$-means clustering\\
 & 2010 & J Han & $k$-means clustering\\
 & 2012 & S Samsi & $k$-means clustering\\
 & 2012 & B Oztan & $k$-means clustering\\
 & 2016 & P Shi & Hierarchical $k$-means clustering\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiaoqi Li
Haoyuan Chen
Chen Li
Md Mamunur Rahaman
Xintong Li
Jian Wu
Xiaoyan Li
Hongzan Sun
Marcin Grzegorzek

"What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review" by Xiaoqi Li, Haoyuan Chen, Chen Li, Md Mamunur Rahaman, Xintong Li, Jian Wu, Xiaoyan Li, Hongzan Sun, and Marcin Grzegorzek, arXiv:2201.08550v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An Auto-Stabilized Weak Galerkin Method for Elasticity Interface Problems on Nonconvex Meshes" by Chunmei Wang and Shangyou Zhang, arXiv:2501.13822v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experimental testbed}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|}
\hline
CPU & Intel(R) Core(TM) i7-6700K @ 4.00GHz\\
\hline
GPU & NVIDIA GeForce GTX Titan X \\
\hline
CUDA & 10.2 \\
\hline
RAM & 64 GB\\
\hline
Oprating system & Ubuntu 18.04 LTS\\
\hline
Language & Python 3.5.6\\
\hline
Tensorflow & 2.2.0 \\
\hline
Keras & 2.3.1 \\
\hline
OpenCV & 3.1.0 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Latent-Space Inpainting for Packet Loss Concealment in Collaborative Object Detection" by Ivan V. Bajić, arXiv:2102.00142v1, 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{\small Inference time of TPC for different transformations}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llc}
\toprule
 Transformation & Attack radius & Inference time ($\mathrm {ms}$)\\
 \midrule
 General rotation & 15$^\circ$ & 9.56\\
 Z-rotation & 180$^\circ$ & 3.12\\
 Z-shear & 0.2 & 3.46\\
 Z-twist & 180$^\circ$ & 3.76\\
 Z-taper & 0.5 & 10.61 \\
 Linear & 0.2 & 3.94 \\
 Z-twist $\circ$ Z-rotation & 50$^\circ$, 5$^\circ$& 5.34\\
 Z-taper $\circ$ Z-rotation & 0.2, 1$^\circ$ & 8.72\\
 Z-twist $\circ$ Z-taper $\circ$ Z-rotation & 20$^\circ$, 0.2, 1$^\circ$ & 9.40\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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


q-fin

Youngbin Lee
Yejin Kim
Suin Kim
Yongjae Lee

"Integrating LLM-Generated Views into Mean-Variance Optimization Using the Black-Litterman Model" by Youngbin Lee, Yejin Kim, Suin Kim, and Yongjae Lee, arXiv:2504.14345v1, 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|cccccc}
 & $0$ & $1$ & $2$ & $\cdots$ & $c$ \\
 \hline
 $0$ & $1$ & . & . & $\cdots$ & . \\
 $q$ & . & $\beta_{1,q}$ & $\beta_{2,q}$ & $\cdots$ & $\beta_{c,q}$ \\
 \end{tabular}
\end{adjustbox}
\caption{Betti tables of $\sigma_q\Delta(\overline{K}_{r+1})$ and $\sigma_q\Delta(P_{r+1})$}
\end{table}
\end{document}

math

"A Fröberg type theorem for higher secant complexes" by Junho Choe and Jaewoo Jung, arXiv:2502.20987v2, 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 & 2.07E$-3$ & $-$ & 3.87E$-2$ & $-$ \\
 \hline
 80 & 5.32E$-4$ & 1.96 & 1.96E$-2$ & 0.98 \\
 \hline
 160 & 1.34E$-4$ & 1.99 & 9.81E$-3$ & 1.00 \\
 \hline
 320 & 3.38E$-5$ & 1.99 & 4.91E$-3$ & 1.00 \\
 \hline
 640 & 8.48E$-6$ & 1.99 & 2.45E$-3$ & 1.00 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Constant extrapolation (first order).}
\end{table}
\end{document}

math

"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Complexity in number of multiplications or divisions}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|cc|}
\hline
\multirow{2}{*}{Stage} & \multicolumn{2}{c|}{General} & \multicolumn{2}{c|}{Simulated$^*$}
\\ %\cline{2-5}
 & OPS & OPA & OPS & OPA
 \\ \hline
Observer & $\frac{3}{T_{\textsc{m}}}$ & $\frac{6}{T_{\textsc{m}}}$ & $30$ & $60$
\\ \hline
Predictor & $\frac{5}{T}$ & $\frac{10}{T}$ & $5\times10^{3}$ & $10\times10^{3}$
\\ \hline
 & \multicolumn{2}{c|}{$2\frac{MN}{T}$} & \multicolumn{2}{c|}{$1.8\times10^{6}$}
\\ \hline
LUT & \multicolumn{2}{c|}{$\frac{MN}{T}$} & \multicolumn{2}{c|}{$0.9\times10^{6}$}
\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Luis C. Mathias
Atefeh Termehchi
Taufik Abrão
Ekram Hossain

"Beamforming Control in RIS-Aided Wireless Communications: A Predictive Physics-Based Approach" by Luis C. Mathias, Atefeh Termehchi, Taufik Abrão, and Ekram Hossain, arXiv:2508.16980v1, 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|c|c|}
 \hline
 \textit{Parameter} & $t_0$ & $t_f$ & $k_1$ & $k_{-1}$ & $k_2$ & $E_1^a / R$ & $E_{-1}^a / R$ & $E_2^a / R$ & $e_{\text{tot}}$ \\
 \hline
 \textit{Value} & 0 & 30 & 0.04 & 0.03 & 0.035 & 200 & 220 & 190 & 0.3 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Parameter values used for the numerical simulation and control of the CPDS .}
\end{table}
\end{document}

math

Simone Cacace
Alessio Oliviero
Mario Pezzella

"Modified Patankar Semi-Lagrangian Scheme for the Optimal Control of Production-Destruction systems" by Simone Cacace, Alessio Oliviero, and Mario Pezzella, arXiv:2501.13085v1, 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{Overview of assigned Sentiment Labels}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccllll}
 & \textbf{year} & \textbf{positive} & \textbf{negative} & \textbf{pos/neg} & \textbf{pos-neg} \\ \hline
\textbf{ESG} & \textbf{2018} & 78.1\% & 21.9\% & 3.570 & 0.562 \\
 & \textbf{2019} & 78.7\% & 21.3\% & 3.687 & 0.573 \\
 & \textbf{2020} & 75.3\% & 24.7\% & 3.057 & 0.507 \\ \hline
\textbf{Noise} & \textbf{2018} & 77.9\% & 22.1\% & 3.523 & 0.558 \\
 & \textbf{2019} & 77.5\% & 22.5\% & 3.445 & 0.550 \\
 & \textbf{2020} & 74.9\% & 25.1\% & 2.987 & 0.498 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Confusion Matrix obtained during cross-validation of our final detector on our Bell's Palsy Video dataset.} %\tabcolsep{4}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|}
			\hline
			\multirow{3}{*}{Bell's Palsy}&&Predicted No&Predicted Yes\\\cline{2-4}
			&Actual No& 32&2\\
			&Actual Yes& 2&39\\
			\hline

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

cs

Sharik Ali Ansari
Koteswar Rao Jerripothula
Pragya Nagpal
Ankush Mittal

"Eye-focused Detection of Bell's Palsy in Videos" by Sharik Ali Ansari, Koteswar Rao Jerripothula, Pragya Nagpal, and Ankush Mittal, arXiv:2201.11479v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data set details: the number of crops per subset, their classes and the percentage of the total number of image crops in each set. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
	\hline
	Set & Non-malignant & Malignant & Total crops & Percentage\\
	\hline
	Train & 561 & 397& 958 & 70.3\\
	Validation & 104 & 95& 199 & 14.6\\
	Test & 116& 89& 205 & 15.1\\ \hline
	Total & 781& 581&1362 & 100\\
	\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ana C. Perre
Luís A. Alexandre
Luís C. Freire

"Are Virtual DES Images a Valid Alternative to the Real Ones?" by Ana C. Perre, Luís A. Alexandre, and Luís C. Freire, arXiv:2508.15594v1, 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{Surrogate Model Accuracy (accuracy is measured as the percentage of correct predictions)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
 & {GP} & {MoNNE} & {BMNN (VI)}\\
\midrule
DTLZ2 & $0.9$ & $0.92$& $0.9$ \\
VLMOP3 & $0.86$ & $0.92$ & $0.84$\\
VehicleSafety & $0.82$ & $0.86$ & $0.86$\\
OSY & $0.82$ & $0.88$ & $0.88$\\
CarCabDesign & $0.84$ & $0.96$ & $0.94$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Hanyang Wang
Juergen Branke
Matthias Poloczek

"Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble" by Hanyang Wang, Juergen Branke, and Matthias Poloczek, arXiv:2501.18792v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.