\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Structure of encoder. Regularisation layers are not shown.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Type} & \textbf{Parameters} & \textbf{Input size} \\ \hline
conv & 100x1/30 & 512x1 \\ \hline
pool & 2 & 512x30 \\ \hline
conv & 100x1/15 & 256x30 \\ \hline
pool & 2 & 256x15 \\ \hline
conv & 30x1/15 & 128x15 \\ \hline
pool & 2 & 128x15 \\ \hline
conv & 20x1/5 & 64x15 \\ \hline
pool & 2 & 64x5 \\ \hline
dense & 30 & 32x5 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Iana Sereda
Sergey Alekseev
Aleksandra Koneva
Alexey Khorkin
Grigory Osipov

"Problems of representation of electrocardiograms in convolutional neural networks" by Iana Sereda, Sergey Alekseev, Aleksandra Koneva, Alexey Khorkin, and Grigory Osipov, arXiv:2012.00493v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Filipe Costa de Souza
Wilton Bernardino
Silvio Cabral Patricio

"How life-table right-censoring affected the Brazilian Social Security Factor: an application of the gamma-Gompertz-Makeham model" by Filipe Costa de Souza, Wilton Bernardino, and Silvio Cabral Patricio, arXiv:2309.03498v3, 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{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
 \bfseries Model & \bfseries Concept Accuracy & \bfseries Task Accuracy & \bfseries Task F1 \\
 \midrule
 HuBERT & - & \textbf{0.9133} \textnormal{\scriptsize(±0.0414)} & \textbf{0.9085} \textnormal{\scriptsize(±0.0476)}\\
 CBM & 0.8443 \textnormal{\scriptsize(±0.0346)} & 0.8776 \textnormal{\scriptsize(±0.0247)} & 0.8566 \textnormal{\scriptsize(±0.0175)}\\
 CEM & \textbf{0.8450} \textnormal{\scriptsize(±0.0205)} & 0.8730 \textnormal{\scriptsize(±0.0385)} & 0.8599 \textnormal{\scriptsize(±0.0168)}\\
 \midrule
 Ideal CBM & - & 0.9052 \scriptsize(±0.0259) & 0.8953 \scriptsize(±0.0309)\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Davide Ghia
Gabriele Ciravegna
Alkis Koudounas
Marco Fantini
Erika Crosetti
Giovanni Succo
Tania Cerquitelli

"A Concept-based approach to Voice Disorder Detection" by Davide Ghia, Gabriele Ciravegna, Alkis Koudounas, Marco Fantini, Erika Crosetti, Giovanni Succo, and Tania Cerquitelli, arXiv:2507.17799v1, 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{Evaluation measures for inter-rater variability}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|c|c}
\toprule
 & & \multicolumn{2}{c}{Inter-Observer} \\
 & & PROMISE12 (n=30) & ProstateX (n=20) \\
\hline
\multirow{4}{*}{DSC} & Whole & 0.90 & 0.93 \\
 & Apex & 0.80 & 0.90 \\
 & Mid & n.a. & 0.96 \\
 & Base & 0.86 & 0.89 \\
\hline
\multirow{4}{*}{ABD} & Whole & 1.82 & 0.66 \\
 & Apex & 2.55 & 0.63 \\
 & Mid & n.a & 0.49 \\
 & Base & 2.21 & 0.86 \\
\hline
\multirow{4}{*}{95-HD} & Whole & 5.64 & 3.15 \\
 & Apex & 6.36 & 2.84 \\
 & Mid & n.a & 2.02 \\
 & Base & 6.28 & 3.56 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Anneke Meyer
Grzegorz Chlebus
Marko Rak
Daniel Schindele
Martin Schostak
Bram van Ginneken
Andrea Schenk
Hans Meine
Horst K. Hahn
Andreas Schreiber
Christian Hansen

"Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI" by Anneke Meyer, Grzegorz Chlebus, Marko Rak, Daniel Schindele, Martin Schostak, Bram van Ginneken, Andrea Schenk, Hans Meine, Horst K. Hahn, Andreas Schreiber, and Christian Hansen, arXiv:2009.11120v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c||c|c|c|c|c||c|c|c|c|c|}
		\hline
		\rule[-1ex]{0pt}{2.5ex} Kodaira type & $\text{I}_0$ & $\text{I}_{n \ge 1}$ & $\text{II}$ & $\text{III}$ & $\text{IV}$ & $\text{I}_0^*$ & $\text{I}_{n \ge 1}^*$ & $\text{II}^*$ & $\text{III}^*$ & $\text{IV}^*$ \\\hline
		\rule[-1ex]{0pt}{2.5ex} Euler characteristic & $0$ & $n$ & $2$ & $3$ & $4$ & $6$ & $n+6$ & $10$ & $9$ & $8$ \\\hline
		\rule[-1ex]{0pt}{2.5ex} Monodromy order $m$ & $1$ & $\infty$ & $6$ & $4$ & $3$ & $2$ & $\infty$ & $6$ & $4$ & $3$ \\\hline
		\rule[-1ex]{0pt}{2.5ex} $d$ for $z^m = t^d$ & $1$ & - & $1$ & $1$ & $1$ & $1$ & - & $5$ & $3$ & $2$ \\\hline
		\rule[-1ex]{0pt}{2.5ex} Log canonical threshold $c$ & $1$ & $1$ & $\frac{5}{6}$ & $\frac{3}{4}$ & $\frac{2}{3}$ & $\frac{1}{2}$ & - & $\frac{1}{6}$ & $\frac{1}{4}$ & $\frac{1}{3}$ \\\hline
	\end{tabular}
\caption{Euler characteristic contributions, monodromy, local models, and log canonical thresholds for singular fibers in the Kodaira classification.}
\end{table}
\end{document}

math

"Isotrivial Lagrangian fibrations of compact hyper-Kähler manifolds" by Yoon-Joo Kim, Radu Laza, and Olivier Martin, arXiv:2312.15147v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Classification accuracy for all the attacks on the MNIST dataset (with $U=2$, wherever applicable).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
				\toprule
				Model & Method & Benign & PGD & CW & BSA & RAND\\\hline
				\multirow{5}{*}{Softmax} & Baseline & .992 & .082 & .540 & .180 & .270\\
				& LWTA\textsuperscript{max} & .993 & .302 & .800 & .390 & .543\\
				& LWTA\textsuperscript{max} \& IBP & \textbf{.994} & .890 & .920 & .840 & .361\\
				& LWTA & .992 & .900 & \textbf{.990} & \textbf{1.0} & .810\\
				& LWTA \& IBP & .992 & \textbf{.935} & \textbf{.990} & \textbf{1.0} & \textbf{.961}\\\bottomrule
				\multirow{5}{*}{Logistic} & Baseline & .993 & .093 & .660 & .210 & .684\\
				& LWTA\textsuperscript{max} & .993 & .388 & .780 & .420 & .700\\
				& LWTA\textsuperscript{max} \& IBP & \textbf{.993} & .894 & .960 & .950 & .230\\
				& LWTA & .991 & .856 & \textbf{.990} & \textbf{.990} & \textbf{.982}\\
				& LWTA \& IBP & .991 & \textbf{.901} & \textbf{.990} & \textbf{.990} & .981\\\hline
				\multirow{5}{*}{LogisticEns10} & Baseline & .993 & .382 & .880 & .480 & .905\\
				& LWTA\textsuperscript{max} & .993 & .303 & .920 & .520 & .900\\
				& LWTA\textsuperscript{max} \& IBP& .\textbf{994} & .860 & .940 & .910 & .400\\
				& LWTA & .992 & .603 & .900 & .550 & .809\\
				& LWTA \& IBP& .993 & \textbf{.889} & \textbf{.980} & \textbf{.970} & \textbf{1.0}\\\hline
				\multirow{5}{*}{Tanh16} & Baseline & .993 & .421 & .790 & .320 & .673\\
				& LWTA\textsuperscript{max} & .992 & .462 & .910 & .420 & .573\\
				& LWTA\textsuperscript{max} \& IBP& .\textbf{994} & .898 & .960 & .940 & .363\\
				& LWTA & .992 & .862 & \textbf{.990} & \textbf{.980} & \textbf{.785}\\
				& LWTA \& IBP& .990 & \textbf{.900} & \textbf{.990} & \textbf{.980} & \textbf{.785}\\\hline

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

cs

Konstantinos P. Panousis
Sotirios Chatzis
Antonios Alexos
Sergios Theodoridis

"Local Competition and Stochasticity for Adversarial Robustness in Deep Learning" by Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos, and Sergios Theodoridis, arXiv:2101.01121v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Bonus-Malus scale premiums for tweedie's compound poisson models" by Jean-Philippe Boucher and Raïssa Coulibaly, arXiv:2311.03222v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Similarity measures for the different XAI methods using the DT model and the \textit{recommended} set of features.}
\begin{tabular}{ccccccc}
\toprule
 & MDI-DT & MDA-DT & SHAP-DT & LIME-DT & Guidelines & Experts \\
\cmidrule(lr){1-1}\cmidrule(lr){2-5}\cmidrule(lr){6-7}
MDI-DT & \cellcolor[gray]
 {0.8}1.00 & 0.53 & 0.80 & 0.53 & 0.24 & 0.34 \\
MDA-DT & 0.53 & \cellcolor[gray]
 {0.8}1.00 & 0.60 & 0.54 & 0.23 & 0.26 \\
SHAP-DT & 0.80 & 0.60 & \cellcolor[gray]
 {0.8}1.00 & 0.60 & 0.27 & 0.33 \\
LIME-DT & 0.53 & 0.54 & 0.60 & \cellcolor[gray]
 {0.8}1.00 & 0.27 & 0.29 \\
\midrule
Guidelines & 0.24 & 0.23 & 0.27 & 0.27 & \cellcolor[gray]
 {0.8}1.00 & 0.82 \\
Experts & 0.34 & 0.26 & 0.33 & 0.29 & 0.82 & \cellcolor[gray]
 {0.8}1.00 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

José Bobes-Bascarán
Eduardo Mosqueira-Rey
Ángel Fernández-Leal
Elena Hernández-Pereira
David Alonso-Ríos
Vicente Moret-Bonillo
Israel Figueirido-Arnoso
Yolanda Vidal-Ínsua

"Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach" by José Bobes-Bascarán, Eduardo Mosqueira-Rey, Ángel Fernández-Leal, Elena Hernández-Pereira, David Alonso-Ríos, Vicente Moret-Bonillo, Israel Figueirido-Arnoso, and Yolanda Vidal-Ínsua, arXiv:2403.19820v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c||c|c|c|c|c|c|}
\hline
 \textbackslash & $z_0=3$ & $z_1=3$ & $z_2=6$ & $z_3=1$ &
$z_3=2$ & $z_3=3$ & $ z_3=4 $ & $z_3=5 $ & $ z_3=6 $\\
\hline \hline
CM & 4.49 & 5.17 & 6.69 & 4.04 & 4.43 & 4.63 & 4.88 & 5.18 & 6.96 \\ \hline
OT & 11.12 & 12.97 & 8.70 & 14.81 & 12.71 & 11.81 & 10.84 & 9.92 & 8.40 \\ \hline
NT & 11.53 & 14.33 & 9.44 & 17.71 & 14.48 & 13.21 & 11.91 & 10.68 & 8.85 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Constrained optimal abatement}
\end{table}
\end{document}

q-fin

"Climate uncertainty, financial frictions and constrained efficient carbon taxation" by Felix Kübler, arXiv:2210.09066v1, 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{Hyperparameters and parameters of the BARK model. For fixed parameters, their value is given. Parameters that are sampled are given the label \textit{Sampled}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{clc}
 \toprule
 Symbol & Description & Value \\
 \midrule
 $m$ & Number of trees in ensemble & 50 \\
 $(\alpha, \beta)$ & Parameters of node depth prior & $(0.95, 2)$ \\
 $(\nu, q)$ & Parameters of noise prior & $(3, 0.9)$ \\
 \midrule %
 $\sigma_0^2$ & Signal variance & 1 \\
 $\sigma_y^2$ & Noise variance & \textit{Sampled} \\
 $T_i$ & The $i$th tree structure & \textit{Sampled} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Toby Boyne
Jose Pablo Folch
Robert M Lee
Behrang Shafei
Ruth Misener

"BARK: A Fully Bayesian Tree Kernel for Black-box Optimization" by Toby Boyne, Jose Pablo Folch, Robert M Lee, Behrang Shafei, and Ruth Misener, arXiv:2503.05574v2, 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{Outage detection performance with noisy data, $\alpha=1\%$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
 \toprule
 Noise level & Average Detection Delay & False Alarm Rate \\
 \midrule
 noiseless & $2.51$ & $1.26\%$\\
 0.1\% & $2.76$ & $1.36\%$\\
 0.5\% & $3.64$ & $2.88\%$ \\
 \midrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee" by Chenhan Xiao, Yizheng Liao, and Yang Weng, arXiv:2309.07157v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Out of Domain Performance Comparison evaluation on (f) of Models Across Different Music Types}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{Music Type}} & \multicolumn{2}{c|}{\textbf{Q-SVM}} & \multicolumn{2}{c|}{\textbf{ResNet18}} & \multicolumn{2}{c|}{\textbf{ViT}} \\ \cline{2-7}
 & \textbf{Acc} & \textbf{F1} & \textbf{Acc} & \textbf{F1} & \textbf{Acc} & \textbf{F1} \\ \hline
a & 0.24 & 0.02 & 0.47 & 0.45 & 0.14 & 0.13 \\ \hline
bc & 0.72 & 0.79 & 0.57 & 0.55 & 0.76 & 0.77 \\ \hline
d & 0.61 & 0.71 & 0.68 & 0.68 & 0.44 & 0.43 \\ \hline
e & 0.58 & 0.70 & 0.29 & 0.31 & 0.09 & 0.11 \\ \hline
abcde & 0.54 & 0.64 & 0.52 & 0.50 & 0.39 & 0.39 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yupei Li
Hanqian Li
Lucia Specia
Björn W. Schuller

"M6: Multi-generator, Multi-domain, Multi-lingual and cultural, Multi-genres, Multi-instrument Machine-Generated Music Detection Databases" by Yupei Li, Hanqian Li, Lucia Specia, and Björn W. Schuller, arXiv:2412.06001v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
		\hline
		Algorithm & Setup & Single iteration \\
		\hline
		Standard MHE & $N=30,N_{T_s}=1$ s & 0.28 s \\
		Standard MHE & $N=30,N_{T_s}=20$ & 4.5 s \\
		Multi-rate MHE & $N=30,N_{T_s}=$Var & 4.2 s \\

		\hline
	\end{tabular}
\caption{ \textit{Standard} and \textit{multi-rate} MHE computation times for different configurations.}
\end{table}
\end{document}

eess

Tushar Desai
Federico Oliva
Riccardo M. G. Ferrari
Daniele Carnevale

"Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator" by Tushar Desai, Federico Oliva, Riccardo M. G. Ferrari, and Daniele Carnevale, arXiv:2310.14332v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Pooya Aavani
Alexandre Trindade
Fangyuan Zhang

"Detecting Imprinting and Maternal Effects Using Monte Carlo Expectation Maximization Algorithm" by Pooya Aavani, Alexandre Trindade, and Fangyuan Zhang, arXiv:2401.00517v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Risk in Network Economies

"Risk in Network Economies" by Victor Sellemi, arXiv:2208.01467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Add caption}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 \textbf{Customer compositions} & \textbf{Case 1} & \textbf{Case 2} & \textbf{Case 3} \\
 \midrule
 Type 1 & 0.2 & 0.4 & 0.1 \\
 Type 2 & 0.2 & 0.2 & 0.2 \\
 Type 3 & 0.2 & 0.15 & 0.4 \\
 Type 4 & 0.2 & 0.1 & 0.2 \\
 Type 5 & 0.2 & 0.05 & 0.1 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Yajing Chen
Yanrong Li
Xiao-Lin Wang
Zhi-Sheng Ye

"Joint Design and Pricing of Extended Warranties for Multiple Automobiles with Different Price Bands" by Yajing Chen, Yanrong Li, Xiao-Lin Wang, and Zhi-Sheng Ye, arXiv:2501.18203v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccc}
 \toprule
 \multicolumn{2}{c}{\bf{Transformation}}& $\boldsymbol{\hat G_2}$ \bf{(Ours)} & \bf{SE} & \bf{KL} & \bf{TV} \\
 \midrule
 \emph{Isometric} \\
 \cmidrule{1-2}
 A) Identity & $t(x) = x$ & $0.43$ & $2.09$ & $29.4$ & $0.71$\\
 B) Reflection & $t(x) = -x$& $0.43$ & $2.09$ & $\textcolor{red}{\boldsymbol{10.0}}$ & $0.71$\\
 C) Translation & $t(x) = x+12$& $0.43$ & $2.09$ & $29.5$ & $0.71$\\
 \\
 \emph{Non-Isometric} \\
 \cmidrule{1-2}
 D) Absolute Value & $t(x) = |x|$& $0.60$ & $\textcolor{red}{\boldsymbol{2.09}}$ & \bf{\textcolor{red}{DNE}} & $\textcolor{red}{\boldsymbol{0.71}}$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Davin Hill
Josh Bone
Aria Masoomi
Max Torop
Jennifer Dy

"Axiomatic Explainer Globalness via Optimal Transport" by Davin Hill, Josh Bone, Aria Masoomi, Max Torop, and Jennifer Dy, arXiv:2411.01126v2, 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}
& \multicolumn{5}{c}{sample size $N$}\\
& 100 & 300 & 1000 & 3000 & 10000 \\
\midrule
SBTM (ours) & \textbf{0.022} & 0.018 & \textbf{0.0082} & 0.0082 & \textbf{0.0036} \\
SDE & 0.029 & \textbf{0.013} & 0.014 & \textbf{0.0068} & 0.0043 \\
SVGD & 2.8 & 1.4 & 2.4 & 2.1 & 2.0 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Score-Based Deterministic Density Sampling" by Vasily Ilin, Peter Sushko, and Jingwei Hu, arXiv:2504.18130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{siunitx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Summary of the acquisition parameters of the 2DeteCT dataset, adapted from . *$^1$ (Thoraeus = Sn 0.1mm, Cu 0.2mm, Al 0.5mm), *$^2$ these quantities are based on the motor readings of the FleX-ray scanner which get translated into physical quantities and are subject to alignment errors.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Acquisition parameter} & \textbf{Mode 1} & \textbf{Mode 2} & \textbf{Mode 3} \\
\hline
Tube voltage & $\SI{90.0}{\kilo\volt}$ & $\SI{90.0}{\kilo\volt}$ & $\SI{60.0}{\kilo\volt}$ \\
\hline
Tube power & $\SI{3.0}{\watt}$ & $\SI{90.0}{\watt}$ & $\SI{60.0}{\watt}$ \\
\hline
Filters used & Thoraeus*$^1$ & Thoraeus & No Filter \\
\hline
Exposure time & \multicolumn{3}{c|}{$\SI{50.0}{\milli\second}$} \\
\hline
Binned detector pixel size & \multicolumn{3}{c|}{$\SI{149.6}{\micro\metre}$} \\
\hline
Number of binned detector pixels & \multicolumn{3}{c|}{956} \\
\hline
Source to object distance *$^2$ & \multicolumn{3}{c|}{$\SI{431.020}{\milli\metre}$} \\
\hline
Source to detector distance *$^2$ & \multicolumn{3}{c|}{$\SI{529.000}{\milli\metre}$} \\
\hline
Number of projections & \multicolumn{3}{c|}{3601} \\
\hline
Angular increment & \multicolumn{3}{c|}{$\SI{0.1}{\deg}$} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Maximilian B. Kiss
Ander Biguri
Zakhar Shumaylov
Ferdia Sherry
K. Joost Batenburg
Carola-Bibiane Schönlieb
Felix Lucka

"Benchmarking learned algorithms for computed tomography image reconstruction tasks" by Maximilian B. Kiss, Ander Biguri, Zakhar Shumaylov, Ferdia Sherry, K. Joost Batenburg, Carola-Bibiane Schönlieb, and Felix Lucka, arXiv:2412.08350v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{diagbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The performance comparison of different algorithms. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l}
		\hline	\hline
		\diagbox{Algorithms}{Parameters} & $ \kappa $ & c & T (sec) \\
		\hline
		The algorithm in & 1 & 0 & 21 \\
		\hline
		The algorithm in Corollary 1 & 10 & 0 & 13 \\
		\hline
		The proposed algorithm in & 2 & 20 & 1.2 \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Prescribed-Time Fully Distributed Nash Equilibrium Seeking in Noncooperative Games" by Zhi Feng and Guoqiang Hu, arXiv:2009.11649v1, 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{Rainbow DQN hyperparameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 \hline
 Parameter & Value \\
 \hline
 Discounting factor $\gamma$ & $0.9$ \\
 Learning rate $\alpha$ & $5\cdot10^{-4}$ \\
 Batch size & $32$ \\
 Online network update period & $4$ steps \\
 Learning start & $1,000$ steps \\
 Target network sync period & $500$ steps \\
 Distributional atoms & 51 \\
 Distributional bounds $v_{min}$, $v_{max}$ & [-1,10] \\
 Replay buffer capacity & $50,000$ \\
 Replay buffer prioritisation exponent $\omega$ & $0.6$ \\
 Replay buffer importance sampling $\beta$ & $0.6 \rightarrow 0.4$ \\
 Importance sampling annealing & 20,000 steps \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform" by David Cotton and Zenon Chaczko, arXiv:2101.11188v1, 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{DimeNet log-log regressions of training time per epoch on number of GPUs under different power caps but max clock speed (1380 MHz).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
\textbf{Power (W)}&\textbf{$\beta_{\text{\# GPUs}}$}&\textbf{Goodness-of-fit $R^2$} %&\textbf{Standard Error ($\beta$)} \\
\\
\midrule
100 & 0.93 & 0.99 \\ %& 0.02\\
200 & 0.84 & 0.99 \\ %& 0.03\\
250 & 0.82 & 0.99 \\ %& 0.03\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Nathan C. Frey
Baolin Li
Joseph McDonald
Dan Zhao
Michael Jones
David Bestor
Devesh Tiwari
Vijay Gadepally
Siddharth Samsi

"Benchmarking Resource Usage for Efficient Distributed Deep Learning" by Nathan C. Frey, Baolin Li, Joseph McDonald, Dan Zhao, Michael Jones, David Bestor, Devesh Tiwari, Vijay Gadepally, and Siddharth Samsi, arXiv:2201.12423v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Ya-nan Han
Jian-wei Liu
Bing-biao Xiao
Xin-Tan Wang
Xiong-lin Luo

"Bilevel Online Deep Learning in Non-stationary Environment" by Ya-nan Han, Jian-wei Liu, Bing-biao Xiao, Xin-Tan Wang, and Xiong-lin Luo, arXiv:2201.10236v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Leandro Aparecido Passos
João Paulo Papa

"A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines" by Leandro Aparecido Passos and João Paulo Papa, arXiv:2101.05795v1, 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{Impact Analysis - Percentage of Category Revenue Contributed by Customer Samples Defined in the Study}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
 & Dog Food & Cat Food & Dog Hygiene & Cat Hygiene \\
\midrule
Sales Revenue (\%) & 37.4 & 35.6 & 27.8 & 29.2 \\ \midrule
Adopters & 765 & 372 & 838 & 450 \\
Non-Adopters & 2879 & 870 & 1400 & 1165 \\ \midrule
Total & 3644 & 1242 & 2238 & 1615 \\ \midrule
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"How does online shopping affect offline price sensitivity?" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Enlarging a connected graph while keeping entropy and spectral radius: self-similarity techniques" by Alberto Seeger and David Sossa, arXiv:2501.13701v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Method & {FPS (2080Ti)}$\uparrow$ & {FPS (A6000)}$\uparrow$ & \# Gaussians \\
 \toprule
 Faceformer~ \textcolor{brown}{\textbf{+ G.A.}} & 25.38 & 42.23 & 65-100K \\
 CodeTalker~ \textcolor{brown}{\textbf{+ G.A.}} & 23.92 & 38.98 & 65-100K \\
 Imitator~ \textcolor{brown}{\textbf{+ G.A.}} & 23.32 & 39.71 & 65-100K \\
 SyncTalk~ & 10.14 & 21.51 & N/A \\
 ER-NeRF~ & 16.88 & 17.98 & N/A \\
 RAD-NeRF~ & 17.18 & 21.41 & N/A \\
 GaussianTalker~ & 57.82 & 59.01 & 41K-44K \\
 TalkingGaussian~ & OOM & 73.34 & 31K-98K \\
 Ours & \textbf{74.29} & \textbf{123.48} & 30-35K \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Shivangi Aneja
Artem Sevastopolsky
Tobias Kirschstein
Justus Thies
Angela Dai
Matthias Nießner

"GaussianSpeech: Audio-Driven Gaussian Avatars" by Shivangi Aneja, Artem Sevastopolsky, Tobias Kirschstein, Justus Thies, Angela Dai, and Matthias Nießner, arXiv:2411.18675v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study of the model adopting DenseNet-169 as the baseline on APTOS dataset. Accuracy, F1 Score, and number of parameters (\# Parameters) are reported.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
\hline
\textbf{Method} & \textbf{Accuracy (\%)} & \textbf{F1 Score (\%)} & \textbf{\# Parameters (M)} \\
\hline
Baseline (DenseNet-169) & 79.0 & 80.0 & 16.8 \\
+ GAB only & 82.2 & 81.5 & 16.8 \\
+ CAB only & 82.1 & 81.2 & 16.8 \\
+ GAB + CAB (Ours) & \textbf{83.6} & \textbf{82.0} & 17.0 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Abdul Hannan
Zahid Mahmood
Rizwan Qureshi
Hazrat Ali

"Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning" by Abdul Hannan, Zahid Mahmood, Rizwan Qureshi, and Hazrat Ali, arXiv:2507.19199v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Anna Freni Sterrantino
Denis Rustand
Janet van Niekerk
Elias Teixeira Krainski
Håvard Rue

"A graphical framework for interpretable correlation matrix models" by Anna Freni Sterrantino, Denis Rustand, Janet van Niekerk, Elias Teixeira Krainski, and Håvard Rue, arXiv:2312.06289v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of reconstruction and generative quality among models using both of our datasets. Reconstruction Error (Rec. Err.) and 1-NNA scores using CD and EMD are calculated for every model. Lower scores are better ($\downarrow$) for each model.}
\begin{tabular}{ccccc}
 \hline
 \\

 \textbf{Model} &
 \textbf{Dataset} &
 \textbf{Rec. Err.} &
 \multicolumn{2}{c}{\textbf{1-NNA(\%, $\downarrow$)}}
 \\
 & & ($\downarrow$) & \textbf{CD} & \textbf{EMD} \\

 \\
 \hline
 \\
 \multirow{2}{*}{\textbf{$\mathbf{\beta}$-VAE}} & Torus & \textbf{0.25} & \textbf{51.37} & 56.25\\
 & Hippocampus & \textbf{0.85} & 56.58 & 55.96 \\
 \\
 \hline
 \\
 \multirow{2}{*}{\textbf{$\mathbf{\beta}$-TCVAE}} & Torus & 0.28 & 52.35 & 54.71\\
 & Hippocampus & 0.86 & \textbf{56.35} & \textbf{55.43}\\
 \\
 \hline
 \\
 \multirow{2}{*}{\textbf{SG VAE}} & Torus & 0.33 & 52.78 & 56.33\\
 & Hippocampus & 1.08 & 61.79 & 60.05 \\
 \\
 \hline
 \\
 \multirow{2}{*}{\textbf{Attribute VAE}} & Torus & 0.36 & 59.38 & 57.81 \\
 & Hippocampus & 1.09 & 59.38 & 58.37 \\
 \\
 \hline
 \\
 \multirow{2}{*}{\textbf{SC VAE (Ours)}} & Torus & 0.38 & 51.56 & \textbf{53.12} \\
 & Hippocampus & 1.07 & 58.69 & 59.76\\
 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Jakaria Rabbi
Johannes Kiechle
Christian Beaulieu
Nilanjan Ray
Dana Cobzas

"Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning" by Jakaria Rabbi, Johannes Kiechle, Christian Beaulieu, Nilanjan Ray, and Dana Cobzas, arXiv:2404.00785v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Mohannad AlMousa
Rachid Benlamri
Richard Khoury

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


math

"Smooth Integer Encoding via Integral Balance" by Stanislav Semenov, arXiv:2505.02259v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc} \hline
							 	& 		& X (3) 	& Y (2) & Z (1)	\\ \hline
										& Budget	& 		& 		&		\\ \hline
							 Agent 1 	& 1 		& 	& + 	& +		\\ \hline
							 Agent 2 	& 1	 	& 	& + 	& +		\\ \hline
							 Agent 3 	& 3		& + 		& +		& 		\\ \hline
							\end{tabular}
\caption{Example instance where EGAL rules are not strategyproof.}
\end{table}
\end{document}

cs

"Participatory Funding Coordination: Model, Axioms and Rules" by Haris Aziz and Aditya Ganguly, arXiv:2101.11722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Wei Xie
Barry L. Nelson
Russell R. Barton

"Statistical Uncertainty Analysis for Stochastic Simulation" by Wei Xie, Barry L. Nelson, and Russell R. Barton, arXiv:2011.04207v1, 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}{cc}
 \toprule
 \bfseries Section & \bfseries Average Timing Deviation $\pm$\\
 & Standard Deviation\\
 \midrule
 Soprano & $0.134 \pm 0.039$ sec \\ \midrule
 Alto & $0.093 \pm 0.0024$ sec \\ \midrule
 Tenor & $0.100 \pm 0.021$ sec \\ \midrule
 Bass & $0.124 \pm 0.021$ sec\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Timing deviations averaged across the CSD. These values measure the time span in which all singers in the unison transition from voiced to unvoiced, and vice-versa, averaged across all transitions in each song.}
\end{table}
\end{document}

eess

Pritish Chandna
Helena Cuesta
Emilia Gómez

"A Deep Learning Based Analysis-Synthesis Framework For Unison Singing" by Pritish Chandna, Helena Cuesta, and Emilia Gómez, arXiv:2009.09875v1, 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{LSQ Execution Time Comparisons}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c|c||}
		\hline
		Number of & Points per & Parallelized & NumPy\\
		Clusters & Cluster & pseudoinverse & pseudoinverse\\
		\hline
		\hline
		8 & $100$ & $1.81ms$ & $2.08ms$ \\
		\hline
		8 & $150$ & $1.83ms$ & $2.10ms$ \\
		\hline
		16 & $100$ & $1.81ms$ & $4.18ms$ \\
		\hline
		16 & $150$ & $1.83ms$ & $4.21ms$ \\
		\hline
		32 & $100$ & $1.81ms$ & $8.74ms$\\
		\hline
		32 & $150$ & $1.83ms$ & $8.77ms$\\
		\hline
		64 & $100$ & $1.81ms$ & $16.84ms$\\
		\hline
		64 & $150$ & $1.83ms$ & $17.19ms$\\
		\hline
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Parallelized Instantaneous Velocity and Heading Estimation of Objects using Single Imaging Radar" by Nihal Singh, Dibakar Sil, and Ankit Sharma, arXiv:2012.12618v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Maja J. Hjuler
Harald V. Skat-Rørdam
Line H. Clemmensen
Sneha Das

"EmoTale: An Enacted Speech-emotion Dataset in Danish" by Maja J. Hjuler, Harald V. Skat-Rørdam, Line H. Clemmensen, and Sneha Das, arXiv:2508.14548v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Daniel Jenson
Jhonathan Navott
Mengyan Zhang
Makkunda Sharma
Elizaveta Semenova
Seth Flaxman

"Transformer Neural Processes - Kernel Regression" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Bases for the subspaces $T_{2,k}$, $k\in\{0,\ldots,d+2\}$, and $T_{2,k}^\ast$, $k\in\{0,\ldots,d\}$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c||c|c|c|c|c}
 \toprule
& $k=0$ & $k=1$ & $k\in\{2,\ldots,d-1\}$ & $k=d$ & $k\in\{d+1,d+2\}$\\
 \midrule
$T_{2,k}$ & $\{Q\Phi_{0}^{0,0}\}$ & $\{\Phi_{1}^{0,2}, Q\Phi_{1}^{0,0}\}$ & $\{\Phi_{k}^{0,2},Q\Phi_{k}^{0,0},\Phi_{k-2}^{2,0}\}$ & $\{\Phi_{d-2}^{2,0},Q\Phi_{d}^{0,0}\}$ & $\{Q\Phi_{k-2}^{2,0}\}$\\
$T_{2,k}^\ast$ & $\{Q\Phi_{0}^{0,0}\}$ & $\{\Phi_{1}^{0,2},Q\Phi_{1}^{0,0}\}$ & $\{\Phi_{k}^{0,2},Q\Phi_{k}^{0,0}\}$ & $\{Q\Phi_{d}^{0,0}\}$ & -- \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Daniel Hug
Michael A. Klatt
Dominik Pabst

"Minkowski tensors for voxelized data: robust, asymptotically unbiased estimators" by Daniel Hug, Michael A. Klatt, and Dominik Pabst, arXiv:2502.00092v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
Coin & Symbol & Dates\\
\hline
Bitcoin	 & BTC & 28/04/2013 - 21/11/2020\\
Litecoin & LTC & 28/04/2013 - 21/11/2020\\
Ethereum & ETH & 07/08/2015 - 21/11/2020\\
XRP & XRP & 04/08/2013 - 21/11/2020\\
Monero & XMR & 21/05/2014 - 21/11/2020\\
Tether & USDT & 25/02/2015 - 21/11/2020\\
Dash & DASH & 14/02/2014 - 21/11/2020\\
Stellar & XLM & 05/08/2014 - 21/11/2020\\
Dogecoin & DOGE & 15/12/2013 - 21/11/2020\\
DigiByte & DGB & 06/02/2014 - 21/11/2020\\
NEM & XEM & 01/04/2015 - 21/11/2020\\
Siacoin & SC & 26/08/2015 - 21/11/2020\\
\end{tabular}
\end{adjustbox}
\caption{Cryptocurrencies used to detect shock events in market.}
\end{table}
\end{document}

stat

Apostolos Chalkis
Emmanouil Christoforou
Ioannis Z. Emiris
Theodore Dalamagas

"Modeling asset allocation strategies and a new portfolio performance score" by Apostolos Chalkis, Emmanouil Christoforou, Ioannis Z. Emiris, and Theodore Dalamagas, arXiv:2012.05088v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Asmik Nalmpatian
Christian Heumann
Stefan Pilz

"Advanced Techniques in Mortality Trend Estimation: Integrating Generalized Additive Models and Machine Learning to Evaluate the COVID-19 Impact" by Asmik Nalmpatian, Christian Heumann, and Stefan Pilz, arXiv:2311.15401v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study : CorLoc performance(\%) of each category on the PASCAL VOC 2007 trainval set}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccc}
		\hline\noalign{\smallskip}
		Methods 						 & aero & bike & bird & boat & bottle & bus & car & cat & chair & cow \\
		\noalign{\smallskip}\hline\noalign{\smallskip}
		Baseline & \textbf{84.6} & 78.4 & 59.2 & 49.5 & 44.7 & 77.7 & 85.0 & 61.0 & 34.8 & 75.3 \\
		 + CADM & \textbf{84.6} & 77.6 & 70.6 & 56.4 & \textbf{50.4} & 78.7 & 84.4 & 39.0 & \textbf{47.9} & 82.9	 \\
		 + GCM & 84.2 & \textbf{82.4} & 70.3 & \textbf{64.4} & 44.7 & \textbf{79.7} & \textbf{86.6} & 47.1 & 46.9 & \textbf{84.2} \\
		 + Both & 84.2 & 75.7 & \textbf{75.1} & 50.0 & 40.8 & 77.7 & 82.3 & \textbf{68.6} & 46.3 & 82.2 \\

		\hline\noalign{\smallskip}
		Methods								 & table & dog & horse & mbike & person & plant & sheep & sofa & train & tv \\
		\noalign{\smallskip}\hline\noalign{\smallskip}
		Baseline & 44.1 &\textbf{ 70.5} & 65.0 & 88.8 & \textbf{11.4} & \textbf{57.1} & 73.2 & 51.9 & 66.2 & \textbf{82.4} \\
		 + CADM & 52.1 & 38.4 & 70.4 & \textbf{90.4} & 10.8 & 55.7 & 81.4 & \textbf{69.4} & \textbf{78.7} & 82.1 \\
 		 + GCM & 48.7 & 40.0 & \textbf{73.5} & 88.8 & 11.0 & 55.7 & \textbf{82.5} & 66.4 & 70.3 & \textbf{82.4} \\
		 + Both & \textbf{52.9} & 63.5 & 73.1 & 89.6 & 10.6 & 52.7 & 79.4 & 55.4 & \textbf{78.7} & 82.1 \\

		\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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PAD-UFES dataset classification results between melanoma and non-melanoma.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline
\textbf{Experiment} & $\textbf{\textit{n}}_f$ & \textbf{AUC} & \textbf{SE} & \textbf{SP} & \textbf{AC} & \textbf{BAC} \\ \hline
Image features & 59 & 0.838 & 0.652 & 0.844 & 0.823 & 0.748 \\
Image features* & 9 & 0.862 & 0.671 & 0.850 & 0.831 & 0.761 \\
Context information & 7 & 0.853 & 0.667 & 0.803 & 0.788 & 0.735 \\
Combined & 59+7 & 0.904 & 0.726 & \textbf{0.902} & \textbf{0.883} & 0.814 \\
Combined* & 9+7 & \textbf{0.928} & \textbf{0.777} & 0.894 & 0.881 & \textbf{0.835} \\ \hline
\multicolumn{6}{l}{* Experiments with the best image features.}
\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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluation metrics on the test data set.}
\begin{tabular}{c|c|c|c|c}
		\hline
		\textbf{Class} & \textbf{Accuracy} & \textbf{Recall} & \textbf{$\boldsymbol{F_1}$ score} & \textbf{Support} \\
		\hline
		0 & 0.99 & 0.99 & 0.99 & 3302 \\
		1 & 0.96 & 0.97 & 0.97 & 1305 \\
		\hline
		\textbf{Macro average} & \textbf{0.98} & \textbf{0.98} & \textbf{0.98} & \textbf{4607} \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Rafael Viana-Cámara
Mario Campos-Soberanis
Diego Campos-Sobrino

"Hybrid phonetic-neural model for correction in speech recognition systems" by Rafael Viana-Cámara, Mario Campos-Soberanis, and Diego Campos-Sobrino, arXiv:2102.06744v1, 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{Comparisons of proposed model with a variety of available techniques where errors are calculated between ground truth images and background-masked reconstructed images as shown in Figure by using Mean Absolute Error (MAE) in percentage. *SVD is used for channel reduction from 2000 to 5 in pre-processing for Dictionary Matching.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
\hline
Models & T1 - MAE(\%) & T2 - MAE(\%) \\ \hline
Dictionary Matching & 4 & 1.01 \\
Dictionary Matching* & 4.7 & 1.96 \\
Hoppe et al.~(RNN) & 5.61 & 1.79 \\
Chen et al. & 3.38 & 1.3 \\
Hoppe et al.~(CNN) & 2.07 & 1.03 \\
Balsiger et al. & 1.82 & 1 \\
Fang et al. & 1.72 & 1.14 \\
Proposed & \textbf{1.58} & \textbf{0.57} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

"Channel Attention Networks for Robust MR Fingerprinting Matching" by Refik Soyak, Ebru Navruz, Eda Ozgu Ersoy, Gastao Cruz, Claudia Prieto, Andrew P. King, Devrim Unay, and Ilkay Oksuz, arXiv:2012.01241v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Jason A. Platt
Stephen G. Penny
Timothy A. Smith
Tse-Chun Chen
Henry D. I. Abarbanel

"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Spanish La Liga Time Trends}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 & \multicolumn{2}{c}{Ln(suspense)} & \multicolumn{2}{c}{Ln(surprise)} \\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & (1) & (2) & (3) & (4) \\
 \midrule
 Season & 0.015*** & 0.006*** & 0.013*** & 0.006*** \\
 & (0.002) & (0.002) & (0.002) & (0.002) \\
 Real Madrid × season & & 0.039*** & & 0.022*** \\
 & & (0.009) & & (0.008) \\
 Barcelona × season & & 0.050*** & & 0.039*** \\
 & & (0.011) & & (0.009) \\
 Atlético Madrid × season & & 0.004 & & 0.010 \\
 & & (0.007) & & (0.008) \\
 \midrule
 Main effects & & Yes & & Yes \\
 Observations & 5,320 & 5,320 & 5,320 & 5,320 \\
 R\textsuperscript{2} & 0.011 & 0.125 & 0.007 & 0.062 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Raphael Flepp
Tim Pawlowski
Travis Richardson

"Suspense and Surprise in European Football" by Raphael Flepp, Tim Pawlowski, and Travis Richardson, arXiv:2506.21253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
			\hline
		 & DL: Train L2/Valid. L2 & DT: Train L2/Valid. L2 \\
			\hline
			2D-kFNO & 0.0021/0.0015 & 0.0025/0.0025 \\
		 2D-FNO & 0.0041/0.0030 & 0.0053/0.0053 \\
		 \hline
		\end{tabular}
\end{table}
\end{document}

math

Rixin Yu
Marco Herbert
Markus Klein
Erdzan Hodzic

"Koopman Theory-Inspired Method for Learning Time Advancement Operators in Unstable Flame Front Evolution" by Rixin Yu, Marco Herbert, Markus Klein, and Erdzan Hodzic, arXiv:2412.08426v1, 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}{cccccccc}
 \toprule
 $a_1$ & $a_2$ & $a_3$ & $a_4$ & $b_1$ & $b_2$ & $\tau_c$ & $\tau_h$ \\ \midrule
 $4\cdot10^{-3}$ & $5.1 \cdot 10^{-11}$ & $7.3\cdot10^{-3}$ & $ 10^{-11}$ & $0.011$ & $0.006$ & $18.3$ & $2$\\
 \bottomrule\\
 \end{tabular}
\end{adjustbox}
\caption{Lumped-parameter coefficients of system .}
\end{table}
\end{document}

math

Ioannis Dimanidis
Tolga Ok
Peyman Mohajerin Esfahani

"Offline Reinforcement Learning via Inverse Optimization" by Ioannis Dimanidis, Tolga Ok, and Peyman Mohajerin Esfahani, arXiv:2502.20030v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Median, Maximum and Minimum test loss of the optimizers for the Allen-Cahn equation.}
\begin{tabular}{lrrr}
\toprule
 & Median & Minimum & Maximum \\
\midrule
ANaGRAM & \textbf{6.22e-05} & \textbf{1.45e-05} & 1.38e-03 \\
Adam & 7.10e-04 & 6.29e-04 & \textbf{1.18e-03} \\
E-NGD & 2.74e+00 & 2.58e+00 & 3.43e+00 \\
GD & 2.41e-03 & 1.70e-03 & 1.93e-02 \\
L-BFGS & 1.48e-03 & 9.08e-04 & 5.09e-03 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning" by Nilo Schwencke and Cyril Furtlehner, arXiv:2412.10782v2, 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{Option Pricing Results: Microstructure-Enhanced vs Black-Scholes}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Parameter/Model} & \textbf{Value} & \textbf{Notes} \\
\midrule
\textbf{Option Specifications} & & \\
Underlying Asset & SPY & S\&P 500 ETF \\
Current Spot Price & \$600.00 & At-the-money \\
Strike Price & \$600.00 & At-the-money \\
Time to Expiration & 30 days & 0.0822 years \\
Risk-free Rate & 5.0\% & Annualized \\
& & \\
\textbf{Pricing Results} & & \\
Microstructure-Enhanced Price & \$15.41 & Our methodology \\
Black-Scholes Price & \$17.87 & Traditional benchmark \\
Absolute Difference & \$2.46 & 13.79\% lower \\
Relative Difference & -13.79\% & Economically significant \\
& & \\
\textbf{Volatility Analysis} & & \\
Historical Volatility & 24.3\% & Annualized from data \\
Model Implied Volatility & 21.0\% & From state factors \\
Volatility Difference & 3.2pp & Model efficiency gain \\
& & \\
\textbf{Computational Performance} & & \\
Tree Nodes Generated & 2,047 & 10 time steps \\
Tree Construction Time & 87.31 sec & One-time cost \\
Pricing Time & 0.004 sec & Per option \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

"Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach" by Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.16701v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Robustness of FID against latent perturbations.} \textcolor{red}{I think we can do only images for this section.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
\toprule
\midrule
learning rate & 0.1 & 0.2 & 0.3 \\
\midrule
 0.7 & xxx & xxx & xxx \\
 1.0 & xxx & xxx & xxx \\
 1.3 & xxx & xxx & xxx \\
\midrule
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Motasem Alfarra
Juan C. Pérez
Anna Frühstück
Philip H. S. Torr
Peter Wonka
Bernard Ghanem

"On the Robustness of Quality Measures for GANs" by Motasem Alfarra, Juan C. Pérez, Anna Frühstück, Philip H. S. Torr, Peter Wonka, and Bernard Ghanem, arXiv:2201.13019v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{System} & \textbf{MMAU} & \textbf{MMAR} & \textbf{MMSU} & \textbf{MMAU-Pro} \\
\midrule
Qwen2.5-3B-Instruct & 28.90 & 29.50 & 27.92 & \textbf{27.64} \\
Qwen2.5-7B-Instruct & 34.80 & {36.39} & 37.82 & \textbf{16.27} \\
Llama-3.1-8B-Instruct & 37.50 & 34.35 & 38.40 & \textbf{32.91} \\
Gemma-3-4b-it & {{40.40}} & 35.03 & 39.62 & \textbf{28.94} \\
Gemma-3-12b-it & 38.14 & 35.03 & {{41.34}} & \textbf{30.94} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparison of text-only LLMs' performance across four audio benchmarks (accuracy). The lowest score in each column is in bold.}
\end{table}
\end{document}

eess

Sonal Kumar
Šimon Sedláček
Vaibhavi Lokegaonkar
Fernando López
Wenyi Yu
Nishit Anand
Hyeonggon Ryu
Lichang Chen
Maxim Plička
Miroslav Hlaváček
William Fineas Ellingwood
Sathvik Udupa
Siyuan Hou
Allison Ferner
Sara Barahona
Cecilia Bolaños
Satish Rahi
Laura Herrera-Alarcón
Satvik Dixit
Siddhi Patil
Soham Deshmukh
Lasha Koroshinadze
Yao Liu
Leibny Paola Garcia Perera
Eleni Zanou
Themos Stafylakis
Joon Son Chung
David Harwath
Chao Zhang
Dinesh Manocha
Alicia Lozano-Diez
Santosh Kesiraju
Sreyan Ghosh
Ramani Duraiswami

"MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence" by Sonal Kumar, Šimon Sedláček, Vaibhavi Lokegaonkar, Fernando López, Wenyi Yu, Nishit Anand, Hyeonggon Ryu, Lichang Chen, Maxim Plička, Miroslav Hlaváček, William Fineas Ellingwood, Sathvik Udupa, Siyuan Hou, Allison Ferner, Sara Barahona, Cecilia Bolaños, Satish Rahi, Laura Herrera-Alarcón, Satvik Dixit, Siddhi Patil, Soham Deshmukh, Lasha Koroshinadze, Yao Liu, Leibny Paola Garcia Perera, Eleni Zanou, Themos Stafylakis, Joon Son Chung, David Harwath, Chao Zhang, Dinesh Manocha, Alicia Lozano-Diez, Santosh Kesiraju, Sreyan Ghosh, and Ramani Duraiswami, arXiv:2508.13992v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Effectiveness of Network Modules in SPA.} Ablation study evaluating the impact of network components on segmentation performance for the REFUGE2 and Kidney datasets after three iterations. The table compares random sampling (Random Gaussian), updating the mean and variance (Gaussian), and updating distribution weights (Mixture Gaussian). Combining all modules achieves the highest Dice Scores, highlighting their complementary benefits in optimizing segmentation performance.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline\hline
Random Gaussian & Gaussian & Mixture Gaussian & REFUGE2 & Kidney \\ \hline
\checkmark & & & 80.29 & 90.05 \\
\checkmark & \checkmark & & 84.12 & 92.06 \\
\checkmark & & \checkmark & 82.87 & 92.29\\
\rowcolor{cyan!40!white!20}
\checkmark & \checkmark & \checkmark & \textbf{85.42} & \textbf{94.26} \\ \hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jiayuan Zhu
Junde Wu
Cheng Ouyang
Konstantinos Kamnitsas
J. Alison Noble

"SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation" by Jiayuan Zhu, Junde Wu, Cheng Ouyang, Konstantinos Kamnitsas, and J. Alison Noble, arXiv:2411.15513v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Mequanent Argaw Muluneh
Yan-Tsung Peng
Li Su

"Computational Analysis of Yaredawi YeZema Silt in Ethiopian Orthodox Tewahedo Church Chants" by Mequanent Argaw Muluneh, Yan-Tsung Peng, and Li Su, arXiv:2412.18788v1, 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{Team formations in \texttt{Terran} tasks}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
\texttt{10\_Terran} & \texttt{10\_Terran\_Hard} \\
 \midrule
\textbf{Training} & \textbf{Training} \\
1 marine \& 9 marauders & 1 marine \& 9 marauders \\
3 marines \& 7 marauders & 2 marines \& 8 marauders \\
4 marines \& 6 marauders & 3 marines \& 7 marauders \\
5 marines \& 5 marauders & 4 marines \& 6 marauders \\
6 marines \& 4 marauders & 5 marines \& 5 marauders \\
8 marines \& 2 marauders & 6 marines \& 4 marauders \\
9 marines \& 1 marauder & 7 marines \& 3 marauders \\
5 marauders \& 5 medivacs & 8 marines \& 2 marauders \\
7 marauders \& 3 medivacs & 9 marines \& 1 marauder \\
9 marauders \& 1 medivac & 5 marauders \& 5 medivacs \\
7 marines \& 3 medivacs & 6 marauders \& 4 medivacs \\
8 marines \& 2 medivacs & 7 marauders \& 3 medivacs \\
9 marines \& 1 medivac & 8 marauders \& 2 medivacs \\
10 marines & 9 marauders \& 1 medivac \\
10 marauders & 7 marines \& 3 medivacs \\
8 marines \& 1 marauder \& 1 medivac & 8 marines \& 2 medivacs \\
1 marine \& 8 marauders \& 1 medivac & 9 marines \& 1 medivac \\
5 marines \& 3 marauders \& 2 medivacs & \textbf{Testing} \\
2 marines \& 7 marauders \& 1 medivac & 10 marines \\
6 marines \& 2 marauders \& 2 medivacs & 10 marauders \\
2 marines \& 6 marauders \& 2 medivacs & 8 marines \& 1 marauder \& 1 medivac \\
4 marines \& 4 marauders \& 2 medivacs & 1 marine \& 8 marauders \& 1 medivac \\
\textbf{Testing} & 5 marines \& 3 marauders \& 2 medivacs \\
2 marines \& 8 marauders & 3 marines \& 5 marauders \& 2 medivacs \\
7 marines \& 3 marauders & 4 marines \& 3 marauders \& 3 medivacs \\
6 marauders \& 4 medivacs & 3 marines \& 4 marauders \& 3 medivacs \\
8 marauders \& 2 medivacs & 7 marines \& 2 marauders \& 1 medivac \\
3 marines \& 5 marauders \& 2 medivacs & 2 marines \& 7 marauders \& 1 medivac \\
4 marines \& 3 marauders \& 3 medivacs & 6 marines \& 2 marauders \& 2 medivacs \\
3 marines \& 4 marauders \& 3 medivacs & 2 marines \& 6 marauders \& 2 medivacs \\
7 marines \& 2 marauders \& 1 medivac & 4 marines \& 4 marauders \& 2 medivacs \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Anuj Mahajan
Mikayel Samvelyan
Tarun Gupta
Benjamin Ellis
Mingfei Sun
Tim Rocktäschel
Shimon Whiteson

"Generalization in Cooperative Multi-Agent Systems" by Anuj Mahajan, Mikayel Samvelyan, Tarun Gupta, Benjamin Ellis, Mingfei Sun, Tim Rocktäschel, and Shimon Whiteson, arXiv:2202.00104v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Mobility and Mobility Measures

"Mobility and Mobility Measures" by Frank A. Cowell and Emmanuel Flachaire, arXiv:2502.19608v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.