\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Safety annotations data distribution}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \toprule
 Utterance label & Total collected \\
 \midrule
 All & 48,348 \\
 Unsafe - Harm & 5,570 \\
 Unsafe - Bias & 2,560 \\
 Unsafe - Misinformation & 1,260 \\
 Safe & 41,810 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Romal Thoppilan
Daniel De Freitas
Jamie Hall
Noam Shazeer
Apoorv Kulshreshtha
Heng-Tze Cheng
Alicia Jin
Taylor Bos
Leslie Baker
Yu Du
YaGuang Li
Hongrae Lee
Huaixiu Steven Zheng
Amin Ghafouri
Marcelo Menegali
Yanping Huang
Maxim Krikun
Dmitry Lepikhin
James Qin
Dehao Chen
Yuanzhong Xu
Zhifeng Chen
Adam Roberts
Maarten Bosma
Vincent Zhao
Yanqi Zhou
Chung-Ching Chang
Igor Krivokon
Will Rusch
Marc Pickett
Pranesh Srinivasan
Laichee Man
Kathleen Meier-Hellstern
Meredith Ringel Morris
Tulsee Doshi
Renelito Delos Santos
Toju Duke
Johnny Soraker
Ben Zevenbergen
Vinodkumar Prabhakaran
Mark Diaz
Ben Hutchinson
Kristen Olson
Alejandra Molina
Erin Hoffman-John
Josh Lee
Lora Aroyo
Ravi Rajakumar
Alena Butryna
Matthew Lamm
Viktoriya Kuzmina
Joe Fenton
Aaron Cohen
Rachel Bernstein
Ray Kurzweil
Blaise Aguera-Arcas
Claire Cui
Marian Croak
Ed Chi
Quoc Le

"LaMDA: Language Models for Dialog Applications" by Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Vincent Zhao, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Pranesh Srinivasan, Laichee Man, Kathleen Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, and Quoc Le, arXiv:2201.08239v3, 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
 Elastic PCA & $\Delta= 0$ & $\Delta = 0.04$ & $\Delta = 0.08$ & $\Delta = 0.12$ & $\Delta = 0.16$ \\ \hline \hline
 $n=15$ & $0.01$ & $0.07$ & $0.58$ & $0.77$ & $0.86$ \\ \hline
 $n=30$ & $0.02$ & $0.22$ & $0.92$ & $0.99$ & $1.00$ \\ \hline
 $n=50$ & $0.03$ & $0.25$ & $0.95$ & $1.00$ & $1.00$ \\ \hline
 $n=75$ & $0.01$ & $0.39$ & $0.98$ & $1.00$ & $1.00$ \\ \hline\hline
 Cross-sectional PCA & $\Delta= 0$ & $\Delta = 0.04$ & $\Delta = 0.08$ & $\Delta = 0.12$ & $\Delta = 0.16$ \\ \hline \hline
 $n=15$ & $0.01$ & $0.02$ & $0.03$ & $0.05$ & $0.07$ \\ \hline
 $n=30$ & $0.02$ & $0.09$ & $0.17$ & $0.18$ & $0.30$ \\ \hline
 $n=50$ & $0.02$ & $0.08$ & $0.13$ & $0.34$ & $0.36$ \\ \hline
 $n=75$ & $0.05$ & $0.04$ & $0.24$ & $0.24$ & $0.40$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Proportion of simulations with estimated changepoint at the $\alpha = 0.05$ level for the amplitude change. (Top) Using the elastic PCA test statistic (Bottom) Using the cross-sectional PCA test statistic of . }
\end{table}
\end{document}

stat

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

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 Experiment & epochs: & epochs: & epochs: & epochs: \\
 & 30+200 & 30+0 & 100+0 & 0+230 \\
 \hline
 1 & 490 & None & 446 & None \\% AC
 2 & 396 & 658 & 516 & None \\% AC
 3 & 382 & None & None & None \\% AC
 4 & 408 & 650 & 436 & None \\ % AC
 5 & 428 & 706 & 1046 & None \\ % Firnov
 6 & 396 & None & None & 508 \\ % Fironov
 7 & 426 & 890 & 614 & None \\ % Fironov
 8 & None& 598 & None & None \\ %Rustem
 9 & 392 & 464 & 524 & None \\ % Shishina
 10 & None& 626 & 452 & 434 \\ %Liuda
 Median & 402& 650 & 516 & 455.5 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

A. Chervov
A. Soibelman
S. Lytkin
I. Kiselev
S. Fironov
A. Lukyanenko
A. Dolgorukova
A. Ogurtsov
F. Petrov
S. Krymskii
M. Evseev
L. Grunvald
D. Gorodkov
G. Antiufeev
G. Verbii
V. Zamkovoy
L. Cheldieva
I. Koltsov
A. Sychev
M. Obozov
A. Eliseev
S. Nikolenko
N. Narynbaev
R. Turtayev
N. Rokotyan
S. Kovalev
A. Rozanov
V. Nelin
S. Ermilov
L. Shishina
D. Mamayeva
A. Korolkova
K. Khoruzhii
A. Romanov

"CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs" by A. Chervov, A. Soibelman, S. Lytkin, I. Kiselev, S. Fironov, A. Lukyanenko, A. Dolgorukova, A. Ogurtsov, F. Petrov, S. Krymskii, M. Evseev, L. Grunvald, D. Gorodkov, G. Antiufeev, G. Verbii, V. Zamkovoy, L. Cheldieva, I. Koltsov, A. Sychev, M. Obozov, A. Eliseev, S. Nikolenko, N. Narynbaev, R. Turtayev, N. Rokotyan, S. Kovalev, A. Rozanov, V. Nelin, S. Ermilov, L. Shishina, D. Mamayeva, A. Korolkova, K. Khoruzhii, and A. Romanov, arXiv:2502.18663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Method} & \multicolumn{2}{c}{\textbf{STL-10 (in distribution)}} & \multicolumn{2}{c}{\textbf{Oxford-IIIT Pet (out of distribution)}} \\ \cline{2-5}
 & Linear Probing & KNN & Logistic Regression & KNN \\ \midrule

 SimCLR & 77.71 & 74.92 & 74.80 & 71.48 \\

 DCL & 78.32 & 75.03 & 74.41 & 70.22 \\
\midrule
\multicolumn{5}{c}{$q_\phi$ is a Student's t-distribution} \\ \midrule
t-SimCLR & 85.11 & 83.05 & 83.40 & 81.41 \\
\textbf{Our Debiasing $\alpha = 0.2$} & 85.94 & 83.15 & 84.11 & 81.15 \\
\textbf{Our Debiasing $\alpha = 0.4$} & 86.13 & \textbf{84.14} & 84.07 & \textbf{84.13} \\
\textbf{Our Debiasing $\alpha = 0.6$} & \textbf{87.18} & 83.58 & \textbf{84.51} & 83.04 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Shaden Alshammari
John Hershey
Axel Feldmann
William T. Freeman
Mark Hamilton

"I-Con: A Unifying Framework for Representation Learning" by Shaden Alshammari, John Hershey, Axel Feldmann, William T. Freeman, and Mark Hamilton, arXiv:2504.16929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Intrinsic unconditional stability in space-time isogeometric approximation of the acoustic wave equation in second-order formulation" by Matteo Ferrari and Ilaria Perugia, arXiv:2503.11166v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation" by Peilin Rao and Randall R. Rojas, arXiv:2509.05922v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\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|c|c|c|c|c|}
\hline
 & (1) & (2) & (3) & (4) & (5) \\ \hline
Variables & Model 1 & Model 2 & Model 3 & Model 4 & Model 5 \\ \hline
 & & & & & \\ \hline
positive & 0.19* & 0.19* & 0.21* & 0.11 & -0.07 \\ \hline
 & (0.08) & (0.08) & (0.09) & (0.09) & (0.15) \\ \hline
negative & -0.83* & -0.82* & -0.93* & -0.82 & 0.36 \\ \hline
 & (0.40) & (0.40) & (0.41) & (0.42) & (0.73) \\ \hline
anxiety & 1.06 & 1.10 & 1.07 & 0.93 & -2.40 \\ \hline
 & (0.72) & (0.73) & (0.77) & (0.78) & (1.40) \\ \hline
anger & 0.52 & 0.56 & 0.98 & 0.91 & 0.16 \\ \hline
 & (0.71) & (0.71) & (0.73) & (0.74) & (1.44) \\ \hline
sad & -0.23 & -0.33 & -0.06 & -0.25 & -1.50 \\ \hline
 & (0.59) & (0.59) & (0.62) & (0.63) & (1.10) \\ \hline
certain & -0.02 & 0.01 & 0.04 & -0.01 & -0.27 \\ \hline
 & (0.23) & (0.23) & (0.23) & (0.24) & (0.40) \\ \hline
 & & & & & \\ \hline
MAR(3 m) & & YES & YES & YES & YES \\ \hline
Industry & & & YES & YES & YES \\ \hline
Year & & & & YES & YES \\ \hline
MAR(5 d) & & & & & YES \\ \hline
N & 1,742 & 1,742 & 1,739 & 1,739 & 651 \\ \hline
BIC & 2426 & 2450 & 2484 & 2521 & 1047 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Logistic Regression of Value Based Label Function ($y_I(T^c_d)(5)$) on Analysts' Recommendations within 3 months ($MAR(3\,m)$) and Sentiment of Earnings Calls Transcripts. Standard errors in parentheses. Significance levels marked with stars: *** p<0.001, ** p<0.01, * p<0.05}
\end{table}
\end{document}

cs

Sourav Medya
Mohammad Rasoolinejad
Yang Yang
Brian Uzzi

"An Exploratory Study of Stock Price Movements from Earnings Calls" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, 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{Quaternionic Errors and Syndrome Measurements for the Five-Qubit Code in~}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccl}
\hline
\textbf{Error} & \textbf{Syndrome} $(S_1, S_2, S_3, S_4)$ & \textbf{Quaternionic Error} & \textbf{Error Description for each q-qubit} \\
\hline
$X_1$ & $(-1, -1, +1, +1)$ & $iX_1$ & Bit-flip with quaternionic phase on q-qubit 1 \\
$Y_1$ & $(-1, -1, +1, +1)$ & $jY_1$ & Bit and phase-flip with quaternionic phase \\
$Z_1$ & $(+1, -1, +1, +1)$ & $kZ_1$ & Phase-flip with quaternionic phase \\
$X_2$ & $(-1, -1, -1, +1)$ & $iX_2$ & Bit-flip on q-qubit 2 \\
$Y_2$ & $(-1, -1, -1, +1)$ & $jY_2$ & Y-error on q-qubit 2 \\
$Z_2$ & $(+1, -1, -1, +1)$ & $kZ_2$ & Z-error on q-qubit 2 \\
$X_3$ & $(-1, +1, -1, -1)$ & $iX_3$ & Bit-flip on q-qubit 3 \\
$Y_3$ & $(-1, +1, -1, -1)$ & $jY_3$ & Y-error on q-qubit 3 \\
$Z_3$ & $(+1, +1, -1, -1)$ & $kZ_3$ & Z-error on q-qubit 3 \\
$X_4$ & $(-1, +1, +1, -1)$ & $iX_4$ & Bit-flip on q-qubit 4 \\
$Y_4$ & $(-1, +1, +1, -1)$ & $jY_4$ & Y-error on q-qubit 4 \\
$Z_4$ & $(+1, +1, +1, -1)$ & $kZ_4$ & Z-error on q-qubit 4 \\
$X_5$ & $(+1, +1, +1, -1)$ & $iX_5$ & Bit-flip on q-qubit 5 \\
$Y_5$ & $(+1, +1, +1, -1)$ & $jY_5$ & Y-error on q-qubit 5 \\
$Z_5$ & $(+1, +1, +1, -1)$ & $kZ_5$ & Z-error on q-qubit 5 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Valentine Nyirahafashimana
Nurisya Mohd Shah
Umair Abdul Halim
Mohamed Othman
Sharifah Kartini Said Husain

"Quantum Error Correction in Quaternionic Hilbert Spaces" by Valentine Nyirahafashimana, Nurisya Mohd Shah, Umair Abdul Halim, Mohamed Othman, and Sharifah Kartini Said Husain, arXiv:2504.19833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|rrrr}
 \hline
$\alpha$ & 0.05 & 0.01 & 0.005 & 0.001 \\ \hline
 $\textrm{Pr}(V\geq 1)$ & \bf{0.2296} & 0.0678 & 0.0360 & 0.0074 \\
 $\textrm{E}(V)$ & 4.0825 & 0.7680 & 0.3598 & 0.0527 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{ Observed false alarm probability for repeated testing on overlapping samples $20,\dots,100$, based on 10,000 simulations.}
\end{table}
\end{document}

stat

Samuel Ackerman
Eitan Farchi
Orna Raz
Marcel Zalmanovici
Parijat Dube

"Detection of data drift and outliers affecting machine learning model performance over time" by Samuel Ackerman, Eitan Farchi, Orna Raz, Marcel Zalmanovici, and Parijat Dube, arXiv:2012.09258v3, 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}
			\hline \hline
			Sentiment Variable & Description\\\hline
			$I_{i,t}$ & \ Indicator for whether there is an article.\\
			$Pos_{i,t}$ (BL) & \ The average proportion of positive words using the BL lexicon.\\
			$Neg_{i,t}$ (BL) & \ The average proportion of negative words using the BL lexicon.\\
			$Pos_{i,t}$ (LM)& \ The average proportion of positive words using the LM lexicon.\\
			$Neg_{i,t}$ (LM)& \ The average proportion of negative words using the LM lexicon.\\
			$Pos_{i,t}$ (MPQA)& \ The average proportion of positive words using the MPQA lexicon.\\
			$Neg_{i,t}$ (MPQA)& \ The average proportion of negative words using the MPQA lexicon.\\\hline\hline
		\end{tabular}
\end{adjustbox}
\caption{Sentiment variables for articles published on or after trading day $t$ and before trading day $t+1$.}
\end{table}
\end{document}

q-fin

Junni L. Zhang
Wolfgang Karl Härdle
Cathy Y. Chen
Elisabeth Bommes

"Distillation of News Flow into Analysis of Stock Reactions" by Junni L. Zhang, Wolfgang Karl Härdle, Cathy Y. Chen, and Elisabeth Bommes, arXiv:2009.10392v1, 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}
(n,m)&$ (1000,10)$& $(3000,20)$ & $(5000,20)$\\ \hline
 BM& 0.91 & 1.00 & 1.00\\
 M1& 0.05 & 0.00 & 0.00 \\
 M2& 1.00 & 1.00 & 1.00\\
 M3& 1.00 & 1.00 & 1.00\\
 M4& 1.00 & 1.00 & 1.00\\
\end{tabular}
\end{adjustbox}
\caption{\scriptsize Hierarchical Bayes rescaling of the GP with squared exponential covariance kernel. Proportion of runs when the $L_2$-credible ball contains $f_0$.}
\end{table}
\end{document}

stat

Botond Szabo
Amine Hadji
Aad van der Vaart

"Adaptation using spatially distributed Gaussian Processes" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, 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{Summary of the analyzed marks data of Table }
\begin{tabular}{lrrrrrrr}
			\hline
			Data set &$n$ &max.&min. &median &mean&CS\\\hline
			$X_1$	& 570 & 20.00 & 0.00&11.35 &7.61 & $-0.0603$&\\
			$X_2$ & 570 & 20.00 &0.00& 5.00 &5.495 & 0.2972 \\
			$X_3$&570 &	20.00 &0.00 &8.90&7.147& $-0.0310$ \\
			\hline
		\end{tabular}
\end{table}
\end{document}

stat

Sobom M. Somé
Célestin C. Kokonendji
Francial G. B. Libengué Dobélé-Kpoka

"An effective estimation of multivariate density functions using extended-beta kernels with Bayesian adaptive bandwidths" by Sobom M. Somé, Célestin C. Kokonendji, and Francial G. B. Libengué Dobélé-Kpoka, arXiv:2502.05366v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model Architecture Comparison}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
Model & Parameters & Layers & Key Innovation & Training Time (s/epoch) \\
\midrule
Simple LSTM & 714,499 & 4 & Bidirectional processing & 1--2 \\
Advanced LSTM & 1,424,387 & 5 & Stacked BiLSTM & 2--3 \\
Custom Attention & 3,143,783 & 8 & Attention mechanism & 3--4 \\
Transformer & 3,917,187 & 7 & Self-attention & 18--19 \\
CNN & 1,397,347 & 9 & Multi-scale convolutions & 1--2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Sayem Hossen
Monalisa Moon Joti
Md. Golam Rashed

"Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach" by Sayem Hossen, Monalisa Moon Joti, and Md. Golam Rashed, arXiv:2508.09935v1, 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{Multinomial Logit Regression Summary}
\begin{tabular}{|l|c|c|c|}
 \hline
 & AVG & Prediction = C1 & Prediction = C2 \\
 \hline coef & & -22.8763 (0.0000) & 14.9750 (0.000)\\
 \hline yield & 1.003 & 22.4499 (0.0000) & -16.0069 (0.000)\\
 \hline LL & \multicolumn{3}{r|}{-3151.802} \\
 \hline LLR test: $\chi^2$ & \multicolumn{3}{r|}{214.596 (0.0000)} \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Predicting Stock Price Movement as an Image Classification Problem" by Matej Steinbacher, arXiv:2303.01111v1, 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{ISBI 2017 dataset classification results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
\textbf{Method} & \textbf{AUC} & \textbf{SE} & \textbf{SP} & \textbf{AC} & \textbf{BAC} \\ \hline
\multicolumn{6}{c}{Melanoma vs Rest} \\ \hline
ISBI 2017 Winner & \textbf{0.868} & \textbf{0.735} & \textbf{0.851} & \textbf{0.828} & \textbf{0.793} \\
\textbf{Proposed} & 0.806 & 0.695 & 0.780 & 0.765 & 0.737 \\ \hline
 \multicolumn{6}{c}{Seborrheic keratosis vs Rest} \\ \hline
ISBI 2017 Winner & \textbf{0.953} & \textbf{0.978} & 0.773 & 0.803 & \textbf{0.876} \\
\textbf{Proposed} & 0.883 & 0.795 & \textbf{0.813} & \textbf{0.810} & 0.804 \\ \hline
\multicolumn{6}{c}{Average} \\ \hline
ISBI 2017 Winner & \textbf{0.911} & \textbf{0.856} & \textbf{0.812} & \textbf{0.816} & \textbf{0.834} \\
\textbf{Proposed} & 0.845 & 0.745 & 0.797 & 0.788 & 0.771 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data" by José G. M. Esgario and Renato A. Krohling, arXiv:2201.10650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The contingency Chi-square results for Year (2015-2019) Vs Type (Rear-end/Sideswipe).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll} \toprule
 & Statistic & P-value \\\midrule
Year vs type & 5.605 & 0.231 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|r|r|r|r|r}
 \hline
 \multicolumn{2}{c}{}&\multicolumn{2}{|c}{PH ($\lambda=1$, avg)}&\multicolumn{2}{|c|}{Partial model}& \\
 Util. & External demand & Runtime & Costs & Runtime & Costs & Gap (\%)\\
 \hline
 \multirow{2}{*}{50\%}
 & end item demand & 1168 & 35268.0 & 456 & 35268.0 & \\
 & incl. component demand & & & 1018 & 37176.7 & \\
 \hline
 \multirow{2}{*}{90\%}
 & end item demand & 996 & 66433.9 & 6086 & 66433.9 & \\
 & incl. component demand & 3678 & 72255.0 & 10800 & 71939 (gap 2.2\%) & \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of the partial implicit model considering 256 scenario paths sampled from the full scenario tree with $|\Omega| = 3$ and therefore $|\Phi|=2187$ to the PH algorithm with $\rho = 1$ and averaging as consensus calculation considering the same 256 scenario paths. Solutions of the PH and partial model are evaluated on the full scenario tree and the respective costs are reported alongside runtime in seconds.}
\end{table}
\end{document}

math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

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


q-fin

Pablo Brañas-Garza
Antonio Cabrales
María Paz Espinosa
Diego Jorrat

"The effect of ambiguity in strategic environments: an experiment" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model performance across benchmarks with varying novel class ratios.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccc}
 \toprule
 \textbf{Novel class ratio} & \multicolumn{3}{c}{\textbf{CIFAR10}} & \multicolumn{3}{c}{\textbf{CIFAR100}} & \multicolumn{3}{c}{\textbf{Tiny-ImageNet}} \\
 \cmidrule(lr){2-4} \cmidrule(l){5-7} \cmidrule(l){8-10}
 & Seen & Novel & All & Seen & Novel & All & Seen & Novel & All \\
 \midrule
 50\% & 96.5 & 97.1 & 96.8 & 80.1 & 63.9 & 71.9 & 68.8 & 42.4 & 55.0\\
 60\% & 96.5 & 92.1 & 93.9 & 80.3 & 60.4 & 68.1 & 68.5 & 41.1 & 51.7 \\
 70\% & 97.5 & 91.0 & 93.0 & 81.3 & 58.6 & 65.3 & 71.3 & 34.7 & 45.6 \\
 80\% & 98.4 & 92.9 & 94.0 & 78.9 & 58.0 & 61.8 & 69.5 & 32.9 & 40.0 \\
 90\% & 97.8 & 93.6 & 94.0 & 82.0 & 50.7 & 53.5 & 69.4 & 26.4 & 30.3 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Shengjie Niu
Lifan Lin
Jian Huang
Chao Wang

"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning" by Shengjie Niu, Lifan Lin, Jian Huang, and Chao Wang, arXiv:2411.01833v1, 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
Layer & Layer Type & kernel & num. parameters & num. activations \\
\hline
1 & Convolutional & 3 $\times$ 3 $\times$ 1 $\times$ $d$ & 480 & 37632 \\
2 & Convolutional & 3 $\times$ 3 $\times$ $d$ $\times$ $d$ & 20784 & 37632 \\
3 & MaxPool & 2 $\times$ 2 & - & - \\
4 & Convolutional & 3 $\times$ 3 $\times$ $d$ $\times$ 2$d$ & 41568 & 18816 \\
5 & Convolutional & 3 $\times$ 3 $\times$ 2$d$ $\times$ 2$d$ & 83040 & 18816 \\
6 & MaxPool & 2 $\times$ 2 & - & - \\
7 & Convolutional & 3 $\times$ 3 $\times$ 2$d$ $\times$ 4$d$ & 166080 & 9408 \\
8 & Convolutional & 3 $\times$ 3 $\times$ 4$d$ $\times$ 4$d$ & 331968 & 9408 \\
9 & MaxPool & 2 $\times$ 2 & - & - \\
10 & Dense & 36$d$ $\times$ 4$d$ & 331968 & 192 \\
11 & Dense & 4$d$ $\times$ 4$d$ & 37056 & 192 \\
12 & Dense & 4$d$ $\times$ 10 & 1930 & 10 \\
\hline
Total & & & 1014874 & 132106 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Number of parameters (weights) and activations (nodes) for each layer to be trained in a medium sized network evaluated in this work. In this specific example the depth of the input convolutional layer is $d=48$.}
\end{table}
\end{document}

math

Bakhrom G. Oripov
Andrew Dienstfrey
Adam N. McCaughan
Sonia M. Buckley

"Scaling of hardware-compatible perturbative training algorithms" by Bakhrom G. Oripov, Andrew Dienstfrey, Adam N. McCaughan, and Sonia M. Buckley, arXiv:2501.15403v1, 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|ccc}
 \hline
 \bf Method & $P$ & $R$ & $F_{0.5}$ \\
 \hline
 \bf Ours & \bf 73.86 & 60.74 & \bf 70.8 \\
 ~ - [MASK] replacement & 71.17 & 55.07 & 67.24 \\
 ~ - [MASK] insert & 72.52 & 59.00 & 69.34 \\
 ~ - post edit & 73.00 & \bf 61.36 & 70.34 \\
 ~ - bilingual constraint & 71.17 & 55.89 & 67.48 \\
 ~ w/ an autoregressive translator & 66.99 & 55.44 & 64.31 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Performance of our approach with different schemes on the German dataset. - denotes removing the component or replacing it with the rule-based operation.}
\end{table}
\end{document}

cs

Xin Sun
Tao Ge
Shuming Ma
Jingjing Li
Furu Wei
Houfeng Wang

"A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model" by Xin Sun, Tao Ge, Shuming Ma, Jingjing Li, Furu Wei, and Houfeng Wang, arXiv:2201.10707v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Network Scenario Settings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 \thead{System Parameters} & \thead{Values} \\
 \hline
 Air Technology & 4G LTE \\
 \hline
 Cellular Layout & 10 Macrocell sites \\
 \hline
 Sectors & 3 sectors per eNB\\
 \hline
 Simulation Location & Brussels, Belgium\\
 \hline
 Simulation Area & 3.80 km$^2$\\
 \hline
 User Distribution & Poisson Distribution\\
 \hline
 Propagation Model & Aster (Ray Tracing)\\
 \hline
 Path Loss Matrix Resolution & 10m\\
 \hline
 Geographic Information & \vtop{\hbox{\strut (\textit{1-m Resolution GeoData})}\hbox{\strut Ground Heights (DTM) + }\hbox{\strut Building Heights (DHM) + }\hbox{\strut Land Use Map (DLU)}}\\
 \hline
 Land Cover (Clutter) Types & 15 different classes\\
 \hline
 eNB Transmit Power (max) & 43 dBm\\
 \hline
 eNB Noise Figure & 5 dB\\
 \hline
 eNB Antenna Height & Actual site heights\\
 \hline
 eNB Antenna Model & \vtop{\hbox{\strut Kathrein Directional Antenna}\hbox{\strut (Model 742 265)}}\\
 \hline
 eNB Antenna Gain & 18.3 dBi\\
 \hline
 \vtop{\hbox{\strut eNB Antenna Horizontal}\hbox{\strut Half Power Beamwidth}} & 63$^o$\\
 \hline
 \vtop{\hbox{\strut eNB Antenna Vertical}\hbox{\strut Half Power Beamwidth}} & 4.7$^o$\\
 \hline
 Frequency Band & 2110 FDD (E-UTRA Band 1)\\
 \hline
 Channel Bandwidth (CBW) & 5 MHz\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Usama Masood
Hasan Farooq
Ali Imran
Adnan Abu-Dayya

"Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks" by Usama Masood, Hasan Farooq, Ali Imran, and Adnan Abu-Dayya, arXiv:2201.12899v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{EDM baseline and the off-the-shelf ResNet guided EDM sampling. Sampled for 256 SDE steps. Generating 50000 ImageNet 64x64 samples for evaluation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
ImageNet 64x64 & Classifier & FID \\
\midrule
EDM baseline & - & 1.41 \\
EDM Res101 guided & Off-the-Shelf & \bf{1.33} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jiajun Ma
Tianyang Hu
Wenjia Wang
Jiacheng Sun

"Elucidating The Design Space of Classifier-Guided Diffusion Generation" by Jiajun Ma, Tianyang Hu, Wenjia Wang, and Jiacheng Sun, arXiv:2310.11311v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"A stabilizer-free $C^0$ weak Galerkin method for the biharmonic equations" by Peng Zhu, Shenglan Xie, and Xiaoshen Wang, arXiv:2201.08062v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
& Go & Ruby & Python & Java & JS & PHP & Avg. \\
\Xhline{2.5\arrayrulewidth} \\
\small{CodeBERT} & 69.3 & 70.6 & 84.0 & 86.8 & 74.8 & 70.6 & 76.0 \\
\small{GraphCodeBERT} & 84.1 & 73.2 & 87.9 & 75.7 & 71.1 & 72.5 & 77.4 \\
\Xhline{1\arrayrulewidth} \\
\texttt{cpt-code} S & \textbf{97.7} & \textbf{86.3} & 99.8 & 94.0 & 86.0 & 96.7 & 93.4 \\
\texttt{cpt-code} M & 97.5 & 85.5 & \textbf{99.9} & \textbf{94.4} & \textbf{86.5} & \textbf{97.2} & \textbf{93.5} \\
\Xhline{2.5\arrayrulewidth}
\end{tabular}
\end{adjustbox}
\caption{Comparison of $\texttt{cpt-code}$ on code search across 6 programming languages with CodeBERT and GraphCodeBERT . The task requires finding the relevant code block among 1K candidates for a given natural language query. $\texttt{cpt-code}$ performs substantially better than previous methods on all the languages.}
\end{table}
\end{document}

cs

Arvind Neelakantan
Tao Xu
Raul Puri
Alec Radford
Jesse Michael Han
Jerry Tworek
Qiming Yuan
Nikolas Tezak
Jong Wook Kim
Chris Hallacy
Johannes Heidecke
Pranav Shyam
Boris Power
Tyna Eloundou Nekoul
Girish Sastry
Gretchen Krueger
David Schnurr
Felipe Petroski Such
Kenny Hsu
Madeleine Thompson
Tabarak Khan
Toki Sherbakov
Joanne Jang
Peter Welinder
Lilian Weng

"Text and Code Embeddings by Contrastive Pre-Training" by Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, Johannes Heidecke, Pranav Shyam, Boris Power, Tyna Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David Schnurr, Felipe Petroski Such, Kenny Hsu, Madeleine Thompson, Tabarak Khan, Toki Sherbakov, Joanne Jang, Peter Welinder, and Lilian Weng, arXiv:2201.10005v1, 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
Candidate & voter-1 & voter-2 & P-cN & N/P & $W_{b}^{c}$ \\
 \hline
\hline
A & 10 & -X & 10-cX & $X/10$ & $ 10 \left( \frac{10-cX}{10+bX} \right) $ \\
\hline
B & 0 & 10-X & 10-X & 0 & 10-X \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Negative votes to depolarize politics

"Negative votes to depolarize politics" by Karthik H. Shankar, arXiv:2012.13657v1, licensed under CC0 1.0. 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{BEBs convergence orders}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
\toprule
BEB & Proposition & Proposition & First bound in Theorem & Second bound in Theorem \\
 \midrule
convergence order & $1/K^{1/2}$ & $K^{1/2}/n^{1/4}$ & $1/ n^{1/4}$ & $1/n^{1/2}$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Stratified Permutational Berry--Esseen Bounds and Their Applications to Statistics" by Pengfei Tian, Fan Yang, and Peng Ding, arXiv:2503.13986v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|c|c|}
\hline
\textbf{Metric} & \textbf{} & \textbf{Test Dataset 1} & \textbf{Test Dataset 2} \\ \hline
\multirow{3}{*}{\textbf{Classification}} & \textbf{Accuracy} & 0.4898 & 0.8175 \\ \cline{2-4}
 & \textbf{Recall} & 0.4898 & 0.7448 \\ \cline{2-4}
 & \textbf{F1-score} & 0.8056 & 0.8213 \\ \hline
\multirow{2}{*}{\textbf{Detection}} & \textbf{Avg. Precision} & 0.5000 & 0.8250 \\ \cline{2-4}
 & \textbf{IoU Score} & 0.1254 & 0.6315 \\ \hline
\multirow{2}{*}{\textbf{Segmentation}} & \textbf{Dice-Coefficient} & 0.1767 & 0.6833 \\ \cline{2-4}
 & \textbf{IoU Score} & 0.2029 & 0.6128 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Performance Statistics}
\end{table}
\end{document}

eess

Ayushman Singh
Sharad Prakash
Aniket Das
Nidhi Kushwaha

"ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding" by Ayushman Singh, Sharad Prakash, Aniket Das, and Nidhi Kushwaha, arXiv:2412.05216v1, 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}{ccc}
 \toprule
 IC-based & Cumulative IC-based & Other\\
 \midrule
 30\% & 70\% & IC\textunderscore diff\textunderscore 90\%\\
 40\% & 80\% & IC\textunderscore thp\\
 50\% & 90\% & IC\textunderscore thp*pos(mean)\\
 70\% & mean & \\
 80\% & median & \\
 90\% & std\textunderscore dev & \\
 std\textunderscore dev & var & \\
 var & kurtosis & \\
 & max & \\
 & range & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Critical IC features}
\end{table}
\end{document}

eess

Yuanhao Cheng
Hanyu Bai
Yichen Liang
Xiaofan Cui
Weiren Jiang
Ziyou Song

"Data-Driven Quantification of Battery Degradation Modes via Critical Features from Charging" by Yuanhao Cheng, Hanyu Bai, Yichen Liang, Xiaofan Cui, Weiren Jiang, and Ziyou Song, arXiv:2412.10044v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Jordan Peper
Yan Miao
Sayan Mitra
Ivan Ruchkin

"Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy" by Jordan Peper, Yan Miao, Sayan Mitra, and Ivan Ruchkin, arXiv:2508.14181v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Luis A. Fernández
Isabel Lasheras
Cecilia Pola

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
& AFEW & FPHA & NTU RGB+D\ & HDM05\\
\midrule
Aff-Inv w/o Nonlinearity & $35.17$\tiny$\pm 1.78$& $65.03$\tiny$ \pm 2.22$ & $41.27$\tiny$ \pm 0.22$ & $65.92 $\tiny$\pm 1.27$\\
Aff-Inv & $35.17$ \tiny $\pm 1.78$&$\mathbf{66.53} $\tiny$\mathbf{\pm 1.64}$ & $41.00$ \tiny $\pm 0.50$& $67.91$ \tiny${\pm 0.66}$\\
Log-Euc w/o Nonlinearity & $36.38$\tiny$\pm 1.29$& $65.25 $\tiny$\pm 2.14$ & $42.87$\tiny$\pm 0.83$ & $68.73 $\tiny$\pm 1.75$\\
Log-Euc&$\mathbf{36.38}$\tiny $\mathbf{\pm 1.29}$& $64.58 $\tiny$\pm 0.98$& $\mathbf{42.99 }$\tiny$\mathbf{\pm 0.23}$& $\mathbf{69.80}$\tiny$\mathbf{ \pm 1.51}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{We show that classification accuracy either improves or remains the same with the nonlinearity. For AFEW, we use one layer in the vector field, which is why the reported accuracies are the same.}
\end{table}
\end{document}

stat

Riemannian Residual Neural Networks

Isay Katsman
Eric Ming Chen
Sidhanth Holalkere
Anna Asch
Aaron Lou
Ser-Nam Lim
Christopher De Sa

"Riemannian Residual Neural Networks" by Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, and Christopher De Sa, arXiv:2310.10013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Tomoki Toyabe
Yasuhiro Hasegawa
Takahiro Hoshino

"Positive-Unlabelled Survival Data Analysis" by Tomoki Toyabe, Yasuhiro Hasegawa, and Takahiro Hoshino, arXiv:2011.13161v1, 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
{\bf Test} & {\bf Mean} & {\bf STD} & {\bf Normalized Distance} & {\bf Max
Distance} \\
\hline\hline
Baseline $T=0$ & 14.02 & 1.47 & 0 & 0 \\
\hline
Sample $T=0$ & 14.02 & 1.47 & 2.27E-04 & 1.45E-03 \\
\hline
Capitalization/Punctuation & 13.85 & 1.48 & 5.82E-02 & 5.12E-02\\
\hline
Spelling & 14.07 & 1.51 & 1.78E-02 & 5.28E-02\\
\hline
Grammar + Spelling & 13.95 & 1.52 & 4.32E-02 & 1.36E-01\\
\hline
\end{tabular}
\end{adjustbox}
\caption{ {\bf Negligible Effect of Paraphrasing:} With GPT-3 set at zero temperature, repeated samples generate nearly identical responses (first two rows). Variability in the query obtained by changing punctuation and capitalization, or introducing spelling errors and grammatical errors, causes changes in the distributions but no change in the highest ranking response or in the median. Two different distances between sample distributions are chosen: the normalized distance (absolute difference between the means, normalized by the sum of the standard deviations) and the maximum absolute value of the difference between corresponding histogram bins. In all cases, the distances are insignificant to the outcome of my experiments.}
\end{table}
\end{document}

q-fin

"The Minimum Wage as an Anchor: Effects on Determinations of Fairness by Humans and AI" by Dario G. Soatto, arXiv:2210.10585v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


q-fin

"Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty" by Marius Protte and Behnud Mir Djawadi, arXiv:2507.15439v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of instances (number of patients) used during training. These represent sample sizes for all 12 leads.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
\toprule
Dataset & Train & Validation & Test\\
\midrule
\multirow{1}{*}{Chapman}&76,614 (6,387)&25,524 (2,129)&25,558 (2,130)\\
\multirow{1}{*}{PTB-XL}& 22,670 (11,335) &3,284 (1,642)&3,304 (1,152)\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Dani Kiyasseh
Tingting Zhu
David A. Clifton

"CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age" by Dani Kiyasseh, Tingting Zhu, and David A. Clifton, arXiv:2011.14230v2, 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}{lr}
\toprule
Model & Memory (GB) \\
\midrule
c2f + SpanBERT-Large & 16.2 \\
c2f + Longformer-Base & 12.0 \\
c2f + Longformer-Large & 15.7 \\
\midrule
s2e + Longformer-Large & ~~\textbf{4.3} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Peak GPU memory usage during inference on OntoNotes, when processing one document at a time.}
\end{table}
\end{document}

cs

"Coreference Resolution without Span Representations" by Yuval Kirstain, Ori Ram, and Omer Levy, arXiv:2101.00434v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Common hyperparameters for PhyloGFN-Bayesian and PhyloGFN-Parsimony.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \multicolumn{2}{c}{Transformer encoder} \\
 \midrule
 hidden size & 128 \\
 number of layers & 6 \\
 number of heads & 4 \\
 learning rate (model) & 5e-5 \\
 learning rate (Z) & 5e-3 \\
 \midrule
 \multicolumn{2}{c}{tree MLP} \\
 \midrule
 hidden size & 256 \\
 number of layers & 3 \\
 \midrule
 \multicolumn{2}{c}{edge MLP} \\
 \midrule
 hidden size & 256 \\
 number of layers & 3 \\
 \midrule
 \multicolumn{2}{c}{Z MLP (in temperature-conditioned PhyloGFN)} \\
 \midrule
 hidden size & 128 \\
 number of layers & 1 \\
 \midrule
 \multicolumn{2}{c}{temp MLP (in temperature-conditioned PhyloGFN)} \\
 \midrule
 hidden size & 256 \\
 number of layers & 3 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

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

"PhyloGFN: Phylogenetic inference with generative flow networks" by Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, and Yoshua Bengio, arXiv:2310.08774v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Correlation coefficients of the plain and cipher image.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
Direction & \multicolumn{6}{c}{Correlation coefficients} \\ \midrule
 & \multicolumn{3}{c}{Plain image} & \multicolumn{3}{c}{Cipher Image} \\ \cmidrule(l){2-7}
 & Red & Green & Blue & Red & Green & Blue \\ \midrule
Horizontal & 09424 & 0.9559 & 0.9319 & 0.0034 & -0.0013 & -0.007 \\
Vertical & 0.9464 & 0.9617 & 0.9406 & 0.0032 & -0.0063 & -0.0002 \\
Diagonal & 0.9094 & 0.9285 & 0.8981 & 0.0002 & -0.0006 & 0.0009 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yehia Lalili
Toufik Bouden
Morad Grimes
Abderrazek Lachouri

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


\documentclass{article}
\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}{cccc}
 \toprule
 Relative $L_2$ error & PINNs & IR-PINNs1 & IR-PINNs2 \\
 \midrule
 $u$ & 1.1278e-01 & 6.9356e-03 & 3.9945e-03 \\
 \midrule
 Running time (hours) & 46.91 & 49.36 & 48.23 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\textit{Kuramoto-Sivashinsky equation:} Relative $L_2$ errors and running time of different methods.}
\end{table}
\end{document}

math

Xiaodong Feng
Haojiong Shangguan
Tao Tang
Xiaoliang Wan

"Integral regularization PINNs for evolution equations" by Xiaodong Feng, Haojiong Shangguan, Tao Tang, and Xiaoliang Wan, arXiv:2503.23729v1, 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}{lccc}
\toprule
\textbf{Borough} & \textbf{EMS Stations} & \textbf{Hospitals} & \textbf{Total} \\ \midrule
Bronx & 19 & 12 & 31 \\
Brooklyn & 76 & 15 & 91 \\
Manhattan & 28 & 23 & 51 \\
Queens & 15 & 10 & 25 \\
Staten Island & 23 & 6 & 29 \\ \midrule
\textbf{Total NYC} & \textbf{161} & \textbf{66} & \textbf{227} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Number of EMS Stations and Hospitals by boroughs in NYC}
\end{table}
\end{document}

eess

"Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach" by Haoran Su and Joseph Y. J. Chow, arXiv:2412.04369v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of Bilateral Geopolitical Events by Era, 1950--2024}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
& \multicolumn{4}{c}{Event Counts} & \multicolumn{2}{c}{Goldstein Score} \\
\cmidrule(lr){2-5} \cmidrule(lr){6-7}
Period & Cooperation & Conflict & Total & Conflict (\%) & Mean & SD \\
\midrule
Cold War (1950--1990) & 206,857 & 58,770 & 265,627 & 22.1 & 2.33 & 5.04 \\
Globalization (1991--2009) & 220,840 & 37,958 & 258,798 & 14.7 & 3.92 & 4.11 \\
Fragmentation (2010--2024) & 251,060 & 58,000 & 309,060 & 18.8 & 3.70 & 3.68 \\
\midrule
Full Period & 678,757 & 154,728 & 833,485 & 18.6 & 3.38 & 4.33 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Geopolitical Barriers to Globalization

"Geopolitical Barriers to Globalization" by Tianyu Fan, Mai Wo, and Wei Xiang, arXiv:2509.12084v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Confusion Matrix for EoE Diagnosis}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|}
\multicolumn{2}{c}{}&\multicolumn{2}{c}{\textbf{Predicted}}\\
\cline{3-4}
\multicolumn{2}{c|}{}& EoE & Normal \\
\cline{2-4}
\multirow{2}{*}{\textbf{True Diagnosis}}& EoE & 44 & 0 \\
\cline{2-4}
& Normal & 1 & 56 \\
\cline{2-4}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

William Adorno
Alexis Catalano
Lubaina Ehsan
Hans Vitzhum von Eckstaedt
Barrett Barnes
Emily McGowan
Sana Syed
Donald E. Brown

"Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision" by William Adorno, Alexis Catalano, Lubaina Ehsan, Hans Vitzhum von Eckstaedt, Barrett Barnes, Emily McGowan, Sana Syed, and Donald E. Brown, arXiv:2101.05326v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
\hline
\toprule
& \multicolumn{4}{|c}{VQA v2.0 val} \\
\hline
& {\footnotesize All} & {\footnotesize Yes/No} & {\footnotesize Num} & {\footnotesize Other} \\
\hline
\hline
UpDn & 63.15 & 80.38 & 42.84 & 55.86 \\
UpDn + caption & 63.85 & 81.10 & 43.63 & 55.90 \\
UpDn + category & 63.51 & 81.62 & 42.17 & 55.38 \\
\midrule
Ours w/o caption & \bf 64.09 & 81.82 & 44.37 & 55.74 \\
Ours w/ caption & \underline{64.24} & 82.36 & 44.52 & 56.02 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Ablation study to investigate the effect of each component: caption, and answer category. ``Ours w/o caption" indicates our final model in which only image and question pairs are needed at test time; while ``Ours w/ caption" represents the model using caption during evaluation. The result of our best model are in bold; while the best performance with captions as inputs during testing is underlined for comparison.}
\end{table}
\end{document}

cs

"Latent Variable Models for Visual Question Answering" by Zixu Wang, Yishu Miao, and Lucia Specia, arXiv:2101.06399v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Modeling animal movement with directional persistence and attractive points" by Gianluca Mastrantonio, arXiv:2012.03248v2, 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|ccc}
& \textbf{2006-2010} & \textbf{2011-2015} & \textbf{2016-2020}\\
\hline
 \textbf{Number of nodes} & 318 & 1005 & 1064 \\
 \textbf{Number of links} & 1152 & 3937& 3520\\
 \textbf{Volume of citations} & 7935 & 63411 & 63059\\
 \textbf{Density} & 0.01 & 0.004 & 0.003 \\
\textbf{Average in-degree (std)}& 3.6 (3.9) & 3.9 (6.2) & 3.3 (5.1)\\
\textbf{Average out-degree (std)} & 3.6 (13.7) & 3.9 (27.9) & 3.3 (26.7)\\
\textbf{Max in-degree} & 22 & 50&41\\
\textbf{Max out-degree}& 212 & 782 &783\\
\textbf{Average in-strength (std)}& 25 (78) & 63 (221) & 59 (254)\\
\textbf{Average out-strength (std)}& 25 (85) & 63 (1334) & 59 (1445)\\
\textbf{Max in-strength}& 719 & 3500 & 4982\\
 \textbf{Max out-strength}& 2954 & 42138 & 46951\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Network binary properties}
\end{table}
\end{document}

q-fin

Rossana Mastrandrea
Fabio Montobbio
Gabriele Pellegrino
Massimo Riccaboni
Valerio Sterzi

"Leveraging Knowledge Networks: Rethinking Technological Value Distribution in mRNA Vaccine Innovations" by Rossana Mastrandrea, Fabio Montobbio, Gabriele Pellegrino, Massimo Riccaboni, and Valerio Sterzi, arXiv:2503.14072v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Nadja van 't Hoff
Giovanni Mellace
Seetha Menon

"Gender Differences in Healthcare Utilisation -- Evidence from Unexpected Adverse Health Shocks" by Nadja van 't Hoff, Giovanni Mellace, and Seetha Menon, arXiv:2509.01310v1, 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{Extended imputation results. For each dataset, values were removed completely at random in the amounts of 5\%, 10\%, 25\%, 50\%, and 75\%. The two numbers directly below each dataset designate the number of observations and number of variables, respectively, for the listed dataset. The average mean-squared errors (MSE) between the original and imputed values were recorded along with the standard deviations. RF-GAP generally outperforms the other definitions. Average rankings can be found in Table~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|cc|cc|cc|cc|cr|}
 \hline
Dataset & Proximity & 5\% & & 10\% & & 25\% & & 50\% & & 75\% & \\
 \hline
Arrhythmia & RF-GAP & \textbf{0.637} & (0.05) & \textbf{0.913} & (0.06) & \textbf{1.463} & (0.06) & \textbf{2.142} & (0.06) & \textbf{2.783} & (0.06) \\
 452 & Original & 0.665 & (0.05) & 0.950 & (0.06) & 1.512 & (0.06) & 2.188 & (0.06) & 2.792 & (0.05) \\
 279 & OOB & 0.674 & (0.05) & 0.964 & (0.06) & 1.532 & (0.06) & 2.205 & (0.06) & 2.766 & (0.05) \\
 & RFProxIH & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \\
 \hline
Auto-Mpg & RF-GAP & 0.230 & (0.05) & 0.338 & (0.06) & 0.587 & (0.06) & 0.941 & (0.06) & 1.484 & (0.20) \\
 392 & Original & \textbf{0.226} & (0.05) & \textbf{0.334} & (0.05) & \textbf{0.584} & (0.06) & \textbf{0.938} & (0.06) & \textbf{1.400} & (0.10) \\
 8 & OOB & 0.232 & (0.05) & 0.342 & (0.06) & 0.595 & (0.06) & 0.955 & (0.06) & 1.425 & (0.10) \\
 & RFProxIH & NA & NA & NA & NA & NA & NA & NA& NA & NA & NA \\
 \hline
Balance Scale & RF-GAP & \textbf{3.240} & (0.22) & \textbf{4.581 }& (0.22) & \textbf{7.288} & (0.23) & \textbf{10.278} & (0.24) & \textbf{12.553} & (0.26) \\
 645 & Original & 3.287 & (0.22) & 4.649 & (0.22) & 7.385 & (0.23) & 10.336 & (0.24) & 12.561 & (0.27) \\
 4 & OOB & 3.318 & (0.22) & 4.686 & (0.22) & 7.432 & (0.23) & 10.382 & (0.24) & 12.609 & (0.27) \\
 & RFProxIH & 3.305 & (0.23) & 4.674 & (0.23) & 7.414 & (0.23) & 10.351 & (0.25) & 12.571 & (0.27) \\
 \hline
Banknote & RF-GAP & \textbf{1.372} & (0.12) & \textbf{1.965} & (0.12) & \textbf{3.211} & (0.13) & \textbf{4.798} & (0.12) & \textbf{ 6.176} & (0.12) \\
 1372 & Original & 1.427 & (0.12) & 2.048 & (0.12) & 3.361 & (0.12) & 4.986 & (0.11) & 6.313 & (0.12) \\
 5 & OOB & 1.443 & (0.12) & 2.065 & (0.12) & 3.379 & (0.12) & 4.994 & (0.12) & 6.319 & (0.12) \\
 & RFProxIH & 1.420 & (0.12) & 2.042 & (0.12) & 3.357 & (0.12) & 4.985 & (0.12) & 6.314 & (0.12) \\
 \hline
Breast Cancer & RF-GAP & \textbf{2.816} & (0.20) & \textbf{4.027} & (0.22) & \textbf{6.479} & (0.20) & \textbf{9.362} & (0.20) & \textbf{11.737} & (0.23) \\
 699 & Original & 2.903 & (0.20) & 4.154 & (0.23) & 6.670 & (0.21) & 9.563 & (0.22) & 11.848 & (0.23) \\
 16 & OOB & 2.891 & (0.20) & 4.125 & (0.23) & 6.604 & (0.21) & 9.483 & (0.21) & 11.800 & (0.23) \\
 & RFProxIH & 2.921 & (0.20) & 4.183 & (0.23) & 6.713 & (0.22) & 9.601 & (0.22) & 11.869 & (0.23) \\
 \hline
Diabetes & RF-GAP & \textbf{0.710 }& (0.20) & \textbf{0.960} & (0.16) & \textbf{1.591} & (0.17) & \textbf{2.328} & (0.15) & \textbf{2.932} & (0.14) \\
 678 & Original & 0.713 & (0.19) & 0.973 & (0.15) & 1.622 & (0.17) & 2.364 & (0.16) & 2.941 & (0.14) \\
 8 & OOB & 0.713 & (0.19) & 0.971 & (0.15) & 1.616 & (0.17) & 2.360 & (0.15) & 2.941 & (0.14) \\
 & RFProxIH & 0.715 & (0.19) & 0.977 & (0.15) & 1.628 & (0.17) & 2.367 & (0.16) & 2.942 & (0.14) \\
 \hline
E. Coli & RF-GAP & 0.862 & (0.14) & 1.235 & (0.12) & 1.991 & (0.11) & 2.873 & (0.13) & 3.673 & (0.14) \\
 336 & Original & 0.886 & (0.14) & 1.267 & (0.12) & 2.031 & (0.12) & 2.909 & (0.14) & 3.706 & (0.17) \\
 8 & OOB & 0.898 & (0.14) & 1.286 & (0.12) & 2.059 & (0.12) & 2.955 & (0.14) & 3.782 & (0.23) \\
 & RFProxIH & \textbf{0.831} & (0.23) & \textbf{1.156} & (0.36) & \textbf{1.774} & (0.67) & \textbf{2.170} & (1.26) & \textbf{2.146} & (1.80) \\
 \hline
Glass & RF-GAP & \textbf{0.064} & (0.02) & \textbf{0.093} & (0.02) & \textbf{0.161} & (0.02) & 0.251 & (0.12) & 0.479 & (0.49) \\
 214 & Original & 0.067 & (0.02) & 0.098 & (0.02) & 0.169 & (0.03) & 0.248 & (0.03) & 0.325 & (0.02) \\
 10 & OOB & 0.068 & (0.02) & 0.099 & (0.02) & 0.169 & (0.03) &\textbf{ 0.246} & (0.03) & \textbf{0.323} & (0.02) \\
 & RFProxIH & 0.067 & (0.02) & 0.099 & (0.02) & 0.171 & (0.03) & 0.250 & (0.03) & 0.326 & (0.02) \\
 \hline
Heart Disease & RF-GAP & \textbf{0.314} & (0.07) & \textbf{0.487} & (0.11) & \textbf{0.757} & (0.09) & \textbf{1.116} & (0.08) & 1.411 & (0.09) \\
 303 & Original & 0.317 & (0.07) & 0.491 & (0.11) & 0.764 & (0.09) & 1.121 & (0.08) & 1.405 & (0.08) \\
 13 & OOB & 0.316 & (0.07) & 0.489 & (0.11) & 0.761 & (0.09) & \textbf{1.116} & (0.08) & \textbf{1.399 }& (0.08) \\
 & RFProxIH & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \\
 \hline
Hill Valley & RF-GAP & \textbf{1.418} & (0.20) & \textbf{4.093 }& (0.43) & \textbf{9.971} & (0.65) & \textbf{15.307} & (0.75) & \textbf{19.586} & (0.88) \\
 606 & Original & 2.052 & (0.29) & 5.304 & (0.39) & 10.656 & (0.60) & 16.134 & (0.76) & 20.568 & (0.92) \\
 101 & OOB & 1.673 & (0.26) & 4.996 & (0.42) & 10.526 & (0.59) & 15.984 & (0.75) & 20.489 & (0.92) \\
 & RFProxIH & 2.232 & (0.30) & 5.512 & (0.38) & 10.716 & (0.59) & 16.161 & (0.76) & 20.578 & (0.92) \\
 \hline
Ionosphere & RF-GAP & \textbf{4.472} & (0.23) & \textbf{6.522} & (0.23) & \textbf{10.560} & (0.29) & \textbf{15.636} & (0.34) & \textbf{20.092} & (0.39) \\
 351 & Original & 4.782 & (0.22) & 6.991 & (0.23) & 11.291 & (0.27) & 16.406 & (0.30) & 20.501 & (0.35) \\
 34 & OOB & 4.745 & (0.22) & 6.933 & (0.23) & 11.184 & (0.27) & 16.299 & (0.31) & 20.446 & (0.35) \\
 & RFProxIH & 4.800 & (0.22) & 7.021 & (0.23) & 11.344 & (0.27) & 16.449 & (0.30) & 20.519 & (0.35) \\
 \hline
Iris & RF-GAP & \textbf{0.211} & (0.04) & \textbf{0.300} & (0.04) & \textbf{0.494} & (0.04) & \textbf{0.716} & (0.04) & 0.935 & (0.05) \\
 150 & Original & 0.219 & (0.04) & 0.310 & (0.04) & 0.509 & (0.04) & 0.727 & (0.04) & \textbf{0.934} & (0.05) \\
 4 & OOB & 0.217 & (0.04) & 0.307 & (0.04) & 0.503 & (0.04) & 0.722 & (0.04) & 0.937 & (0.05) \\
 & RFProxIH & 0.221 & (0.04) & 0.312 & (0.05) & 0.512 & (0.04) & 0.730 & (0.04) & 0.936 & (0.05) \\
 \hline
Liver & RF-GAP & \textbf{0.315} & (0.16) & \textbf{0.501} & (0.23) & \textbf{0.828} & (0.24) & \textbf{1.168} & (0.23) & \textbf{1.491} & (0.20) \\
 345 & Original & 0.322 & (0.16) & 0.512 & (0.22) & 0.847 & (0.23) & 1.192 & (0.22) & 1.503 & (0.19) \\
 7 & OOB & 0.321 & (0.16) & 0.510 & (0.22) & 0.844 & (0.24) & 1.190 & (0.22) & 1.503 & (0.19) \\
 & RFProxIH & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \\
 \hline
Lymphography & RF-GAP & \textbf{1.046} & (0.14) &\textbf{ 1.521} & (0.13) &\textbf{ 2.480} & (0.13) & \textbf{3.599} & (0.12) & 4.569 & (0.12) \\
 148 & Original & 1.065 & (0.15) & 1.551 & (0.14) & 2.529 & (0.14) & 3.650 & (0.13) & \textbf{4.568} & (0.12) \\
 18 & OOB & 1.065 & (0.14) & 1.549 & (0.13) & 2.524 & (0.13) & 3.644 & (0.13) & \textbf{4.568} & (0.13) \\
 & RFProxIH & 1.076 & (0.15) & 1.567 & (0.14) & 2.551 & (0.14) & 3.670 & (0.13) & 4.579 & (0.12) \\
 \hline
Optical Digits & RF-GAP & \textbf{15.774} & (0.17) & \textbf{22.572} & (0.17) & \textbf{37.003} & (0.19) & \textbf{55.369} & (0.21) & \textbf{71.456} & (0.26) \\
 3823 & Original & 17.222 & (0.20) & 24.612 & (0.19) & 40.138 & (0.20) & 58.897 & (0.22) & 73.616 & (0.27) \\
 64 & OOB & 17.199 & (0.19) & 24.549 & (0.18) & 39.904 & (0.20) & 58.569 & (0.21) & 73.442 & (0.27) \\
 & RFProxIH & 17.303 & (0.20) & 24.732 & (0.19) & 40.336 & (0.20) & 59.062 & (0.22) & 73.681 & (0.27) \\
 \hline
Parkinsons & RF-GAP & \textbf{0.400} & (0.18) &\textbf{ 0.60}0 & (0.20) &\textbf{ 0.968} & (0.19) & \textbf{1.471} & (0.16) & 1.841 & (0.15) \\
 197 & Original & 0.411 & (0.18) & 0.613 & (0.20) & 0.988 & (0.18) & 1.484 & (0.16) & 1.846 & (0.15) \\
 23 & OOB & 0.408 & (0.18) & 0.610 & (0.20) & 0.979 & (0.19) & 1.475 & (0.16) & \textbf{1.836} & (0.15) \\
 & RFProxIH & 0.412 & (0.18) & 0.616 & (0.20) & 0.994 & (0.18) & 1.489 & (0.16) & 1.851 & (0.15) \\
 \hline
Seeds & RF-GAP & \textbf{0.204} & (0.04) & \textbf{0.283} & (0.04) &\textbf{ 0.476} & (0.04) & \textbf{0.703} & (0.04) & \textbf{0.895} & (0.04) \\
 210 & Original & 0.223 & (0.05) & 0.308 & (0.04) & 0.506 & (0.04) & 0.730 & (0.04) & 0.911 & (0.04) \\
 7 & OOB & 0.218 & (0.05) & 0.300 & (0.04) & 0.494 & (0.04) & 0.715 & (0.04) & 0.897 & (0.04) \\
 & RFProxIH & 0.226 & (0.05) & 0.313 & (0.04) & 0.512 & (0.04) & 0.737 & (0.04) & 0.917 & (0.04) \\
 \hline
Sonar & RF-GAP & \textbf{3.044 }& (0.13) & \textbf{4.551} & (0.13) &\textbf{ 7.278 }& (0.14) & \textbf{10.879} & (0.16) &\textbf{ 13.923} & (0.18) \\
 208 & Original & 3.246 & (0.13) & 4.834 & (0.14) & 7.631 & (0.15) & 11.174 & (0.16) & 14.055 & (0.18) \\
 60 & OOB & 3.211 & (0.13) & 4.778 & (0.13) & 7.544 & (0.15) & 11.101 & (0.16) & 14.011 & (0.18) \\
 & RFProxIH & 3.264 & (0.14) & 4.864 & (0.14) & 7.673 & (0.15) & 11.200 & (0.16) & 14.069 & (0.18) \\
 \hline
Titanic & RF-GAP & \textbf{0.409} & (0.22) & \textbf{0.609} & (0.23) & \textbf{1.067} & (0.26) & \textbf{1.600} & (0.22) & \textbf{1.991} & (0.16) \\
 712 & Original & 0.416 & (0.21) & 0.621 & (0.23) & 1.096 & (0.26) & 1.639 & (0.22) & 2.010 & (0.16) \\
 7 & OOB & 0.415 & (0.21) & 0.620 & (0.23) & 1.093 & (0.26) & 1.637 & (0.22) & 2.010 & (0.16) \\
 & RFProxIH & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \\
\hline
Wine & RF-GAP & \textbf{0.236} & (0.08) & 0.345 & (0.07) & 0.579 & (0.07) & 0.825 & (0.07) & 1.044 & (0.08) \\
 178 & Original & \textbf{0.236} & (0.08) & 0.344 & (0.08) & \textbf{0.577} & (0.07) & \textbf{0.822} & (0.07) & \textbf{1.038} & (0.08) \\
 13 & OOB & 0.240 & (0.08) & 0.353 & (0.07) & 0.585 & (0.07) & 0.829 & (0.07) & 1.043 & (0.08) \\
 & RFProxIH & 0.237 & (0.08) & \textbf{0.343} & (0.08) &\textbf{ 0.577} & (0.07) & 0.823 & (0.07) & 1.040 & (0.07) \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jake S. Rhodes
Adele Cutler
Kevin R. Moon

"Geometry- and Accuracy-Preserving Random Forest Proximities" by Jake S. Rhodes, Adele Cutler, and Kevin R. Moon, arXiv:2201.12682v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study : different dimension of dropout in CADM on PASCAL VOC 2007. $\checkmark$ indicates that the component is used.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
		\hline\noalign{\smallskip}
		channel-dropout & spatial-dropout & channel + spatial dropout & mAP \\
		\noalign{\smallskip}\hline\noalign{\smallskip}
		& & & 48.1 \\
		$\checkmark$ & & & 47.6 \\
		& $\checkmark$ & & 48.8 \\
		& & $\checkmark$ & \textbf{49.8} \\
		\noalign{\smallskip}\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Cascade Attentive Dropout for Weakly Supervised Object Detection" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, 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{Demographics for the PND data set. FU: follow-up time }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule
 & AD & SCD & MCIc & MCInc \\
\cmidrule(lr){2-3}
\cmidrule(lr){4-5}
\emph{\# participants} & 199 & 138 & 48 & 91 \\
\emph{male / female} & 94 / 105 & 92 / 46 & 33 / 15 & 56 / 35 \\
\emph{age (y; mean$\pm$std)} & $73.1\pm9.6$ & $63.2\pm10.3$ & $70.4\pm7.9$ & $68.8\pm12.6$ \\
\emph{FU (y; mean$\pm$std)} & N.A. & N.A. & $2.7 \pm 1.2$ & $2.2 \pm 0.8$\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Esther E. Bron
Stefan Klein
Janne M. Papma
Lize C. Jiskoot
Vikram Venkatraghavan
Jara Linders
Pauline Aalten
Peter Paul De Deyn
Geert Jan Biessels
Jurgen A. H. R. Claassen
Huub A. M. Middelkoop
Marion Smits
Wiro J. Niessen
John C. van Swieten
Wiesje M. van der Flier
Inez H. G. B. Ramakers
Aad van der Lugt

"Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease" by Esther E. Bron, Stefan Klein, Janne M. Papma, Lize C. Jiskoot, Vikram Venkatraghavan, Jara Linders, Pauline Aalten, Peter Paul De Deyn, Geert Jan Biessels, Jurgen A. H. R. Claassen, Huub A. M. Middelkoop, Marion Smits, Wiro J. Niessen, John C. van Swieten, Wiesje M. van der Flier, Inez H. G. B. Ramakers, and Aad van der Lugt, arXiv:2012.08769v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc|c}
 \toprule
 Ours-NFE & \textbf{FID} $\downarrow$ & \textbf{FVD} $\downarrow$ & \textbf{E-FID} $\downarrow$ & \textbf{LSE-D} $\downarrow$ & \textbf{FPS} $\uparrow$\\
 \cline{2-6}
 \hline
 Ours-2 & 21.785 & 178.831 & 1.542 & 7.559 & 45.22 \\
 Ours-5 & 21.440 & 164.463 & 1.331 & 7.155 & 44.74 \\
 Ours-10 \textbf{(default)} & 21.100 & 162.052 & 1.229 & 7.290 & 41.37 \\
 Ours-20 & 21.158 & 164.392 & 1.293 & 7.343 & 38.20 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait" by Taekyung Ki, Dongchan Min, and Gyeongsu Chae, arXiv:2412.01064v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Jin Yang
Daniel S. Marcus
Aristeidis Sotiras

"Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning" by Jin Yang, Daniel S. Marcus, and Aristeidis Sotiras, arXiv:2509.10784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Amol Salunkhe
Georgios Georgalis
Abani Patra
Varun Chandola

"An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds" by Amol Salunkhe, Georgios Georgalis, Abani Patra, and Varun Chandola, arXiv:2211.14098v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
		\toprule
		& \textbf{Available Companies} & \textbf{Average Intensity}&\textbf{Standard Deviation}\\
		\midrule
		\rowcolor{gray!15}
		Low & 1417 & 0.032 & 0.062\\
		Medium & 4733 & 0.071 & 0.104 \\
		\rowcolor{gray!15}
		High & 797 & 0.202 & 0.224 \\
		Extreme & 1476 & 0.345 & 0.338 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{\raggedright Descriptive analysis of asset intensity clustered in Low, Medium, High, and Extreme Risk. We report the number of available companies, the average vulnerability score, and the standard deviation. Descriptive statistics are computed at the company level.}
\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{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters of the studied models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c||c|c|c|c|c|c|c}
\thead{Arrival \\ process} & \thead{Service \\ process} & \thead{Number \\ of phases \\ Arrival} & \thead{Number \\ of phases \\ Service} & \thead{$v_A$} & \thead{$v_S$} & \thead{Number \\ of waiting slots \\ $m$} & \thead{Number \\ of states} & \thead{Number \\ of transitions} \\ \hline \hline
M & M & 1 & 1 & 1 & 1 & 5 & 10 368 & 63 688 \\ \hline
Ph & M & 2 & 1 & 0.8 & 1 & 5 & 141 108 & 829 825 \\ \hline
M & Ph & 1 & 12 & 1 & 0.3 & 5 & 623 376 & 3 664 703 \\ \hline
Ph & Ph & 2 & 12 & 0.8 & 0.3 & 5 & 8 192 448 & 46 447 056
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Utilizing phase-type distributions for queueing-based railway junction performance determination" by Tamme Emunds and Nils Nießen, arXiv:2412.04188v1, 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{DQN-CQL vs DecQN-CQL - performance and computation comparison for cheetah-run task. Performance figures are mean normalised scores $\pm$ one standard error, with 0 and 100 representing random and expert policies, respectively. Computation figures are training time and GPU usage. Actions figures are total number of actions requiring value estimation based on atomic/factorised representation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
Method & $n_i$ & Actions & Score & Training time (mins) & GPU usage (MB) \\
\midrule
DQN-CQL & 3 & 729 & 79.7 $\pm$ 6.0 & 19 & 266 \\
DQN-CQL & 4 & 4096 & 30.2 $\pm$ 1.7 & 30 & 412 \\
DQN-CQL & 5 & 15625 & 32.2 $\pm$ 1.4 & 85 & 958 \\
DQN-CQL & 6 & 46656 & 32.3 $\pm$ 4.0 & 227 & 2388 \\
DecQN-CQL & 3 & 18 & 92.7 $\pm$ 3.7 & 19 & 244 \\
DecQN-CQL & 4 & 24 & 85.4 $\pm$ 3.8 & 19 & 246 \\
DecQN-CQL & 5 & 30 & 79.5 $\pm$ 1.8 & 20 & 246 \\
DecQN-CQL & 6 & 36 & 84.3 $\pm$ 2.5 & 20 & 246 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Alex Beeson
David Ireland
Giovanni Montana

"An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces" by Alex Beeson, David Ireland, and Giovanni Montana, arXiv:2411.11088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A PINN-enriched finite element method for linear elliptic problems" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, 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|cc|cc||} % centered columns (4 columns)
			\hline\hline %inserts double horizontal lines
			Algorithms & bad data & bad data & data attack & data attack \\
& (EPFL) & (Texas) & (EPFL) & (Texas) \\			\hline\hline
			ICA-GAN	& 97\%	 & 95\% 	 & 94\% & 80\%	\\
			\hline
			OC-SVM	 & 94\% 	& 35\%	 & 61\% & 71\% \\
			\hline
			F-AnoGAN	& 78\% & 5\% 	 & 53\% & 18\% \\
			\hline
			NRT	 & 50\%		& 88\%	 & 5\% & 5\% \\
			\hline\hline
	\end{tabular}
\end{adjustbox}
\caption{TPR values of different algorithms at FPR=0.05 constraint under bad data and data attack anomalies.}
\end{table}
\end{document}

eess

Kursat Rasim Mestav
Xinyi Wang
Lang Tong

"A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems" by Kursat Rasim Mestav, Xinyi Wang, and Lang Tong, arXiv:2012.05163v2, 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}{ccccc}
 \toprule
 & \multicolumn{3}{c}{\textbf{Center E (Unseen)}} \\
 \cmidrule{2-5}
 \textbf{Method} & \textbf{MAE [HU]} & \textbf{SSIM} & \textbf{PSNR [dB]} & \textbf{Time} \\
 \midrule
 2D+ & 104.0 (99.5-115.6) & 0.89 (0.86-0.89) & 26.49 (25.43-27.35) & 16h 00min \\
 2D Patches & 162.4 (155.3-173.0) & 0.84 (0.82-0.85) & 25.52 (24.65-26.12) & \textbf{8h 39min} \\
 Multi-2D & 111.7 (107.7-122.3) & 0.88 (0.85-0.89) & 26.33 (25.32-27.20) & 9h 35min \\
 \midrule
 \textbf{Random}\\\textbf{Multi-2D (Our)} & \textbf{102.0 (96.7-110.5)} & \textbf{0.89 (0.86-0.89)} & \textbf{26.58 (25.52-27.42)} & 12h 34min \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{The median and interquartile range (IR) for the mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) obtained for each training paradigm. The total training time required to complete 25 rounds is also reported.}
\end{table}
\end{document}

eess

Ciro Benito Raggio
Mathias Krohmer Zabaleta
Nils Skupien
Oliver Blanck
Francesco Cicone
Giuseppe Lucio Cascini
Paolo Zaffino
Lucia Migliorelli
Maria Francesca Spadea

"FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis" by Ciro Benito Raggio, Mathias Krohmer Zabaleta, Nils Skupien, Oliver Blanck, Francesco Cicone, Giuseppe Lucio Cascini, Paolo Zaffino, Lucia Migliorelli, and Maria Francesca Spadea, arXiv:2412.06690v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{Average running times (seconds) of one realisation of estimators for the FHN model, from partial observations.}}
\begin{tabular}{cc}
\toprule \\
 $ \hat{\theta}_{2,n}$ & $\hat{\theta}_{3,n} $
 \\ \midrule
 \\
100.976 & 138.428
 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Parameter Inference for Hypo-Elliptic Diffusions under a Weak Design Condition" by Yuga Iguchi and Alexandros Beskos, arXiv:2312.04444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
 \toprule
 Context Size & Inference Time (ms) & GPU Memory \\
 \midrule
 1 & $25.28 \pm 0.16$ & 28 MB \\
 16 & $57.05 \pm 0.20$ & 1.89 GB \\
 32 & $86.57 \pm 0.06$ & 3.64 GB\\
 64 & $146.04 \pm 0.16$ & 7.15 GB \\
 128 & $267.42 \pm 0.24$ & 12.16 GB \\
 256 & $604.15 \pm 0.36$ & 24.17 GB \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Hallee E. Wong
Jose Javier Gonzalez Ortiz
John Guttag
Adrian V. Dalca

"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance" by Hallee E. Wong, Jose Javier Gonzalez Ortiz, John Guttag, and Adrian V. Dalca, arXiv:2412.15058v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Pablo Brañas-Garza
Antonio Cabrales
María Paz Espinosa
Diego Jorrat

"The effect of ambiguity in strategic environments: an experiment" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Atanu Bhattacharjee
Gajendra K. Vishwakarma
Souvik Banerjee

"A modified risk detection approach of biomarkers by frailty effect on multiple time to event data" by Atanu Bhattacharjee, Gajendra K. Vishwakarma, and Souvik Banerjee, arXiv:2012.02102v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
		\toprule[1pt]
		 Methods & Backbone & mIOU (\%) \\
		\midrule[0.5pt]
		\midrule[0.5pt]
		DSSPN~ & ResNet-101 & 38.9 \\
		SVCNet~ & ResNet-101 & 39.6 \\
		EMANet~ & ResNet-101 & 39.9 \\
		SPYGR~ & ResNet-101 & 39.9 \\
		OCR~ & ResNet-101 & 39.5 \\
		\midrule[0.5pt]
		DANet~ & ResNet-101 & 39.7\\
		\midrule[0.5pt]
		\textbf{Our CAA} & ResNet-101 & \textbf{41.2} \\
		\bottomrule[0.5pt]
	\end{tabular}
\end{adjustbox}
\caption{Comparisons with other state-of-the-art approaches on the COCO-Stuff 10K test set. For a fair comparison, all compared methods use ResNet-101 and naive upsampling.}
\end{table}
\end{document}

cs

Ye Huang
Di Kang
Wenjing Jia
Xiangjian He
Liu Liu

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|r|r|r}
 \hline
 \multicolumn{5}{c}{Including component demand - no adjustments} \\
 \hline
 Utilization & $\lambda$ & Converged (\%) & Runtime (s) & $\Delta$ (\%)\\
 \hline
 \multirow{4}{*}{50\%}
 & 0.1 & 0.00 & - & - \\
 & 1 & 29.2 & 7816.1 & 0.56 \\
 & 10 & 100.0 & 322.1 & 3.26 \\
 & 100 & 100.0 & 208.8 & 4.62 \\
 \hline
 \multirow{4}{*}{90\%}
 & 0.1 & 0.00 & - & - \\
 & 1 & 8.3 & 4264.0 & 0.09 \\
 & 10 & 79.2 & 1390.2 & 2.10 \\
 & 100 & 66.6 & 1262.3 & 3.53 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Averaging consensus calculation: Evaluation of the $\lambda$-multiple for the penalty parameter $\rho$ in terms of total costs and runtime for the case including component demands on a scenario tree with $|\Omega| = 2$ and therefore $|\Phi| = 128$. Reporting percentage of converged PH runs and respective runtimes and cost deltas to optimal costs over the converged runs.}
\end{table}
\end{document}

math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Types of heat transfer}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}\toprule[1.25pt]
 \textbf{Type} & \textbf{Model} \\
 \hline
 Advection & $\dot{m}c_pT$ \\
 Diffusion & $\frac{kA}{L}T$ \\
 Convection & $hAT$ \\
 \bottomrule[1.25pt]
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Demetrius Gulewicz
Uduak Inyang-Udoh
Trevor Bird
Neera Jain

"Nonlinear Model Predictive Control of a Hybrid Thermal Management System" by Demetrius Gulewicz, Uduak Inyang-Udoh, Trevor Bird, and Neera Jain, arXiv:2411.15929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Information-theoretic subset selection of multivariate Markov chains via submodular optimization" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of different baselines on the dataset without confounder. The numbers in ``( )'' indicate relative performance compared to ERM: \textcolor{green}{green} for improvement, \textcolor{red}{red} for inferiority.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
			\hline

			Method & noise=0 & noise=1 & noise=2 \\
			\hline
			\text{ERM} & 48.33$\pm$0.70 & 35.16$\pm$1.42 & 24.91$\pm$1.21 \\
 			\text{ASAP} & 48.94$\pm$0.63 (\textcolor{green}{+0.61}) & 33.52$\pm$1.34 (\textcolor{red}{-1.64}) & 26.35$\pm$0.88 (\textcolor{green}{+1.44}) \\
 \hline
 \hline
			\text{DIR } & 46.80 $\pm$0.92(\textcolor{red}{-1.53}) & 36.37$\pm$1.18(\textcolor{green}{+1.21}) & 27.53$\pm$1.02(\textcolor{green}{+2.62}) \\
			\text{CIGA } & 43.18$\pm$1.24(\textcolor{red}{-5.15}) & 26.42$\pm$1.38(\textcolor{red}{-8.74}) & 24.47$\pm$1.29(\textcolor{red}{-0.44}) \\
			\text{RCGRL } & 52.72$\pm$1.60(\textcolor{green}{+4.39}) & 30.50$\pm$0.52(\textcolor{red}{-4.66}) & 26.44$\pm$1.26(\textcolor{green}{+1.53}) \\
 			\text{DISC } & 45.60$\pm$0.79(\textcolor{red}{-2.73}) & 38.35$\pm$1.31(\textcolor{green}{+3.19}) & 26.80$\pm$0.98(\textcolor{green}{+1.89}) \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Hang Gao
Chengyu Yao
Jiangmeng Li
Lingyu Si
Yifan Jin
Fengge Wu
Changwen Zheng
Huaping Liu

"Rethinking Causal Relationships Learning in Graph Neural Networks" by Hang Gao, Chengyu Yao, Jiangmeng Li, Lingyu Si, Yifan Jin, Fengge Wu, Changwen Zheng, and Huaping Liu, arXiv:2312.09613v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Projected Langevin Monte Carlo algorithms in non-convex and super-linear setting" by Chenxu Pang, Xiaojie Wang, and Yue Wu, arXiv:2312.17077v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Unlocking Retrospective Prevalent Information in EHRs -- a Pairwise Pseudolikelihood Approach" by Nir Keret and Malka Gorfine, arXiv:2309.01128v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 Model & Active Parameters & One-Step SPE \\
\hline
GNAR(1, [1]) & 2 & 3.061 \\
 VAR(1) & $140^2$ & 7.196 \\
 Res. VAR(1) & 3759 & 5.888 \\
 Sparse VAR(1) & 3017 & 6.842 \\
 AR(1) & $140$ & 88.066 \\
 GNAR(1, [1])* & 141 & 3.089
\end{tabular}
\end{adjustbox}
\caption{One-step prediction error, $\hat{\boldsymbol{X}}_{450}$ is predicted using the previous 449 observations.}
\end{table}
\end{document}

stat

Guy Nason
Daniel Salnikov
Mario Cortina-Borja

"New tools for network time series with an application to COVID-19 hospitalisations" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, 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{Average over 1000 trials of the number of iterations required to obtain an optimal solution starting from a guess within +/- 50\% of the known optimum}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \cline{2-4}
 & SQP & LSQP & SLCP \\ \hline
 Floudas & 30.17 & 14.30 (-52.60\%) & 12.92 (-57.18\%) \\
 Kirschen-Ozturk & 72.55 & 18.89 (-73.96\%) & 16.38 (-77.42\%) \\
 Hoburg-0 & - & 17.79 & 16.29 (-8.44\%) \\
 Hoburg-1 & - & 17.82 & 16.66 (-6.47\%) \\
 Hoburg-3 & - & 17.66 & 16.87 (-4.49\%) \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"A Method of Sequential Log-Convex Programming for Engineering Design" by Cody Karcher and Robert Haimes, arXiv:2201.08436v1, 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{WordNet sense ranking based on SemCor frequencies}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llr}
				\hline
				\textbf{Sense} & \textbf{Definition} & \textbf{Frequency} \\
				\hline
				plant-1 & Buildings for carrying on industrial labor & 338 \\
				plant-2 & A living organism lacking the power of locomotion & 207 \\
				plant-3 & Something planted secretly for discovery by another & 2\\
				plant-4 & An actor situated in the audience whose acting is & 0 \\
				& rehearsed but seems spontaneous to the audience &\\
				\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mohannad AlMousa
Rachid Benlamri
Richard Khoury

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\large Impacts of Spatial Lag Model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
\toprule
\multicolumn{9}{c}{Outcome Variable: $\Delta$Unemployment Rate}\\
\cmidrule{3-7}
 & \multicolumn{2}{c}{$\Delta$ Active Mines\textsubscript{t}} & \multicolumn{2}{c}{$\Delta$ Active Mines\textsubscript{t-1}} & \multicolumn{2}{c}{$\Delta$ Active Mines\textsubscript{t-2}} & \multicolumn{2}{c}{$\Delta$ (log) Real GDPPC\textsubscript{t}} \\
\cmidrule(l{3pt}r{3pt}){2-3} \cmidrule(l{3pt}r{3pt}){4-5} \cmidrule(l{3pt}r{3pt}){6-7} \cmidrule(l{3pt}r{3pt}){8-9}
 & Estimate & Sim. SE & Estimate & Sim. SE & Estimate & Sim. SE & Estimate & Sim. SE\\
\midrule
Direct & -0.036*** & 0.005 & -0.009. & 0.005 & 0.028*** & 0.005 & -0.651*** & 0.032\\
Indirect & -0.082*** & 0.012 & -0.020. & 0.011 & 0.064*** & 0.010 & -1.490*** & 0.080\\
Total & -0.118*** & 0.017 & -0.029. & 0.016 & 0.091*** & 0.015 & -2.141*** & 0.111\\
\bottomrule
\multicolumn{9}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\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|}\hline
 Lang. pair & Train & Test & Dev \\\hline
 EN-FR/FR-EN & 1,467,489 & 499,487 & 7,723\\%\hline
 EN-ES/ES-EN & 1,472,203 & 459,633 & 5,734\\\hline
 \end{tabular}
\end{adjustbox}
\caption{Number of parallel sentences for the training, testing and development sets.}
\end{table}
\end{document}

cs

Eva Vanmassenhove
Dimitar Shterionov
Matthew Gwilliam

"Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation" by Eva Vanmassenhove, Dimitar Shterionov, and Matthew Gwilliam, arXiv:2102.00287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
\toprule
region & country \\ \hline
Australia & Australia \\
Australia & New Zealand \\
Melanesia & Fiji \\
Melanesia & New Caledonia \\
Melanesia & Papua New Guinea \\
Melanesia & Solomon Islands \\
Melanesia & Vanuatu \\
Micronesia & Kiribati \\
Polynesia & French Polynesia \\
Polynesia & Samoa \\
not classified & Belgium-Luxembourg \\
not classified & Czechoslovakia \\
not classified & Netherlands Antilles \\
not classified & RoW \\
not classified & Serbia and Montenegro \\
not classified & USSR \\
not classified & Yugoslav SFR \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Regions of Australia, New Zealand and Oceania and not classified}
\end{table}
\end{document}

q-fin

Moritz Laber
Peter Klimek
Martin Bruckner
Liuhuaying Yang
Stefan Thurner

"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average lap times on the six corners of different control methods. The minimum speed (in [km/h]) and maximum slip angle (in [$^{\circ}$]) at each corner are also analyzed with our method. The success rates (SR) are also displayed.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc|c}
Lap time [s] & \#1 & \#2 & \#3 & \#4 & \#5 & \#6 & SR \\\hline
No Feedback & 7.8 & 9.4 & 12.2 & 8.7 & 7.0 & 11.9 & 1/10 \\
Low Gain & 7.9 & 8.9 & 12.2 & 8.9 & 6.9 & 12.2 & 3/10 \\
High Gain & 8.3 & 8.7 & 12.2 & 10.0 & 7.6 & 13.6 & 10/10 \\
\rowcolor{gray!50} TOAST & 7.7 & 7.8 & 11.8 & 9.5 & 8.3 & 9.4 & 10/10 \\ \hline \hline
\rowcolor{gray!50} Min. Speed & 33.2 & 24.4 & 22.6 & 33.5 & 26.7 & 26.7 & \\
\rowcolor{gray!50} Max. Slip & 3.4 & 7.2 & 6.8 & 2.8 & 7.4 & 4.2 & \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Taekyung Kim
Hojin Lee
Seongil Hong
Wonsuk Lee

"TOAST: Trajectory Optimization and Simultaneous Tracking using Shared Neural Network Dynamics" by Taekyung Kim, Hojin Lee, Seongil Hong, and Wonsuk Lee, arXiv:2201.08321v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Shi Bo
AmirEmad Ghassami
Debarghya Mukherjee

"A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects" by Shi Bo, AmirEmad Ghassami, and Debarghya Mukherjee, arXiv:2412.08827v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\toprule
{} & snflw. seed & snflw. oil & snflw. cake \\
\midrule
Africa N & 1.8 & 48.2 & 42.9 \\
Africa S & 0.0 & 4.5 & 2.1 \\
America S & 0.0 & 1.1 & 1.2 \\
America N & 0.6 & 6.7 & 1.1 \\
Asia C & 0.1 & 5.1 & 0.2 \\
Asia E & 0.0 & 48.7 & 0.0 \\
Asia S & 0.5 & 67.7 & 1.2 \\
Asia SE & 0.0 & 7.1 & 0.4 \\
Asia W & 5.2 & 27.0 & 18.8 \\
Australia & 0.0 & 8.5 & 2.5 \\
Europe E & 0.2 & 2.3 & 15.9 \\
Europe N & 1.8 & 38.2 & 39.1 \\
Eurpe S & 0.3 & 12.5 & 15.3 \\
Europe W & 0.6 & 10.3 & 28.1 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Relative loss in \% of different food products in different world regions after a shock to the sunflower seed production of Ukraine. We use the abbreviation snflw. for sunflower. These numbers correspond to the left half of each cell in the lower part of fig. 5 in the main text.}
\end{table}
\end{document}

q-fin


\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|}
\hline
Magnification & Benign & Malignant & Total\\
\hline
40X		&625		&1370	&1995\\
100X	&644		&1437	&2081\\
200X	&623		&1390	&2013\\
400X	&588		&1232	&1820\\
Total	&2480	 &5429	&7909\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Details about the whole dataset.}
\end{table}
\end{document}

eess

"Performance Comparison of Balanced and Unbalanced Cancer Datasets using Pre-Trained Convolutional Neural Network" by Ali Narin, arXiv:2012.05585v1, 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{Effects on Listing Outcomes}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllll}
\toprule
 & Log Nights
 (3 Months) & Log Revenue
 (3 Months) & Log Platform Revenue
 (3 Months) & Log Nights
 (12 Months) & Log Revenue
 (12 Months) & Log Platform Revenue
 (12 Months)\\
\midrule
Treatment & 0.002 & 0.006 & 0.004 & 0.002 & 0.003 & -1.267\\
 & (0.004) & (0.008) & (0.006) & (0.004) & (0.008) & (2.423)\\
\midrule
Num.Obs. & 651947 & 651947 & 651947 & 651947 & 651947 & 651947\\
R2 & 0.125 & 0.110 & 0.119 & 0.116 & 0.111 & 0.146\\
Controls for User and Listing Char. & Yes & Yes & Yes & Yes & Yes & Yes\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"More Reviews May Not Help: Evidence from Incentivized First Reviews on Airbnb" by Andrey Fradkin and David Holtz, arXiv:2112.09783v1, 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{Summary of filtered results for all instances (i.e., only feasible solutions)}
\begin{tabular}{llrrr}
 \hline\noalign{\smallskip}
 Model & Cut & OptimalTimes & GapWithoutInf & \#Inf \\
 \noalign{\smallskip}\hline\noalign{\smallskip}
 DD-JS & IIS & 350.0 & 0.5 & 1 \\
 DD-JS & No-Good & 345.5 & 0.6 & 0 \\
 DD-LJ & IIS & 430.7 & 0.7 & 1 \\
 DD-LJ & No-Good & 420.1 & 0.7 & 0 \\
 IP & IIS & 384.0 & 1.8 & 229 \\
 IP & No-Good & 468.3 & 1.2 & 2 \\
 \noalign{\smallskip}\hline
 \end{tabular}
\end{table}
\end{document}

math

Nicolás Casassus
Margarita Castro
Gustavo Angulo

"A Decision Diagram Approach for the Parallel Machine Scheduling Problem with Chance Constraints" by Nicolás Casassus, Margarita Castro, and Gustavo Angulo, arXiv:2504.20889v1, 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{OvA configuration for the dataset in~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 \textbf{Problem} & \textbf{Classification Problem} & \textbf{(\%min,\%maj)} & \textbf{IR}\\ %\midrule
 \hline
 1 & Artemisia vs. all & (3.00,97.00) & 32.33\\
 \hline
 2 & Bathsheba vs. all & (3.00,97.00) & 32.33\\
\hline
3 & Danae vs. all & (12.00,88.00) & 7.33\\
\hline
4 &
Doctor\_Nicolaes vs. all & (3.00,97.00) & 32.33\\
\hline
5 & HollyFamilly vs. all & (2.00,98.00) & 49.00\\
\hline
6 & PortraitOfMariaTrip vs. all & (3.00,97.00) &
32.33\\
\hline
 7 & PortraitOfSaskia vs. all &
(1.00,99.00) & 99.00\\
\hline
 8 & RembrandtXXPortrai vs. all & (2.00,98.00) & 49.00\\
 \hline
 9 & SaskiaAsFlora vs. all & (3.00,97.00) & 32.33\\
 \hline
 10 & SelfportraitAsStPaul vs. all & (8.00,92.00) & 11.50\\
 \hline
 11 & TheJewishBride vs. all & (4.00,96.00) & 24.00\\
 \hline
 12 & TheNightWatch vs. all & (9.00,91.00) & 10.11\\
 \hline
 13 & TheProphetJeremiah vs
all & (7.00,93.00) & 13.28\\
\hline
 14 &
TheReturnOfTheProdigalSon vs. all & (9.00,91.00) &
10.11\\
\hline
 15 & TheSyndicsoftheClothmakersGuild vs. all
& (5.00,95.00) & 19.00\\
 \hline
 16 & Other vs. all &
(26.00,74.00) & 2.84\\%\bottomrule
\hline
\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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Audit Basic Error Classification}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 \toprule
 Task Name & Error Category \\
 \midrule
 Transaction TYPE Record Error & Record Error \\
 Transaction DATE Record Error & Record Error\\
 Transaction PAYMENT/RECEIPT\_STATUS Record Error & Record Error \\
 Transaction PAYMENT\_METHOD Record Error & Record Error\\
 Transaction QUANTITY Record Error & Record Error\\
 Transaction UNIT\_PRICE Record Error & Record Error\\
 Transaction RECEIVE\_METHOD Record Error & Record Error\\
 Transaction AMOUNT Calculation Error & Calculation Error\\
 Transaction TAX\_AMOUNT Calculation Error & Calculation Error\\
 Transaction PROFIT Calculation Error & Calculation Error\\
 Transaction Without PREPARER Error & Transaction Approval Mismatch\\
 Transaction Without APPROVER Error& Transaction Approval Mismatch\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Junzhe Jiang
Chang Yang
Aixin Cui
Sihan Jin
Ruiyu Wang
Bo Li
Xiao Huang
Dongning Sun
Xinrun Wang

"FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs" by Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Ruiyu Wang, Bo Li, Xiao Huang, Dongning Sun, and Xinrun Wang, arXiv:2505.13533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.