\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{This table shows the median improvement of our approach compared to a stratified dummy classifier for stocks grouped per market sector and implied volatility regime. The best and worst regime for each sector are respectively indicated by bold and underlined text styles.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule
 & Low & Medium & High & Very High \\
\midrule
XLB & 1.87 &	\textbf{3.06} &	1.02 &	\underline{0.77} \\
XLC & 3.57 &	4.76 &	\textbf{5.27} &	\underline{0.26} \\
XLE & 2.55 &	2.46 &	\textbf{4.0} &	\underline{-2.55} \\
XLF & 3.06 &	\textbf{4.08} &	2.64 &	\underline{-1.7} \\
XLI & 0.05 &	\textbf{1.53} &	0.17 &	\underline{-1.5} \\
XLK & \underline{0.60} &	2.38 &	3.91 &	\textbf{5.70} \\
XLP & 2.72 &	\textbf{3.83} &	3.23 &	\underline{1.53} \\
XLRE & \textbf{8.5} &	6.38 &	4.93 &	\underline{4.25} \\
XLU & \textbf{6.63} &	5.95 &	5.36 &	\underline{1.79} \\
XLV & \textbf{4.25} &	3.23 &	\underline{1.62} &	2.13 \\
XLY & 2.04 &	3.66 &	\textbf{5.95} & \underline{0.85} \\
\midrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Thomas Dierckx
Jesse Davis
Wim Schoutens

"Nowcasting Stock Implied Volatility with Twitter" by Thomas Dierckx, Jesse Davis, and Wim Schoutens, arXiv:2301.00248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{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}{lccc}
 \toprule
 \textbf{asset} & \textbf{Our approach}& & \\ \midrule
 1 & 0.0& 0.0& 0.0 \\
 2 & 0.0 & 0.0 & 0.0\\
 3 & 0.0680& 0.0680 & 0.0680 \\
 4 & 0.1880 & 0.1880 & 0.1880\\
 5 & 0.0& 0.0& 0.0\\
 6 & 0.3913 & 0.3913 & 0.3913 \\
 7 & 0.2309 & 0.2309 & 0.2309\\
 8 & 0.1216 & 0.1216 & 0.1216\\ \midrule
 \multicolumn{1}{c}\textbf{objective}&11.00 $\%$&11.00$\%$ & 11.00$\%$ \\ \midrule
 \multicolumn{1}{c}\textbf{number of SSD constraints}&\textbf{3}& 506 & 550 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Second order stochastic dominance (case $p=1$): a comparison with prominent approaches}
\end{table}
\end{document}

math

Rajmadan Lakshmanan
Alois Pichler
Miloš Kopa

"Higher-Order Stochastic Dominance Constraints in Optimization" by Rajmadan Lakshmanan, Alois Pichler, and Miloš Kopa, arXiv:2501.14565v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Decision rule at month 4, mean of the blip estimates over 25 imputed datasets, SPRINT trial, 2010-2013. }
\begin{tabular}{|l|c|c|c|}
 \hline
 Variable &QLOMA & WOMA & No. of times stat. significant$^*$ \\ \hline
 Intercept visit & 8.8 & 9.8 & 2 \\ \hline
 Intercept add-on & -10.3 & -7.0 & 2 \\ \hline
 \multicolumn{4}{|c|}{Visit interaction with:} \\ \hline
 Intensive group & -0.5 & -0.0 & 1 \\ \hline
 Age & -0.0 & -0.1 & 1 \\ \hline
 Female sex & 0.6 & 0.0 & 0 \\ \hline
 Race Black & \multicolumn{3}{|c|}{Reference} \\ \hline
 Race Hispanic & -0.7 & 0.0 & 0 \\ \hline
 Race White & 0.0 & -0.1 & 0 \\ \hline
 \hspace{0.2cm} Other & -0.8 & 0.4 & 0 \\ \hline
 Smoking (ever) & -0.2 & -0.7 & 0 \\ \hline
 BMI & -0.0 & -0.1 & 0 \\ \hline
 HDL & -0.1 & -0.0 & 17 \\ \hline
 SBP Baseline & 0.0 & -0.0 & 0 \\ \hline
 SBP Current month & -0.0 & -0.0 & 0 \\ \hline
 CVD & -0.7 & -1.2 & 0 \\ \hline
 Aspirin use & 0.4 & -0.1 & 0 \\ \hline
 Statin use & 0.6 & 0.7 & 0 \\ \hline
 \end{tabular}
\end{table}
\end{document}

stat

Janie Coulombe
Dany El-Riachi
Fanxing Du
Tianze Jiao

"Constructing optimal dynamic monitoring and treatment regimes: An application to hypertension care" by Janie Coulombe, Dany El-Riachi, Fanxing Du, and Tianze Jiao, arXiv:2501.08274v1, 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
 function type & $v$ & $a$ & $b$ \\
 \hline
 $S_I$ & $0.65$ & $10^{-2}$ & $1$ \\
 \hline

\end{tabular}
\end{adjustbox}
\caption{Specification of the security breach function.}
\end{table}
\end{document}

q-fin

Giorgia Callegaro
Claudio Fontana
Caroline Hillairet
Beatrice Ongarato

"A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks" by Giorgia Callegaro, Claudio Fontana, Caroline Hillairet, and Beatrice Ongarato, arXiv:2505.01221v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|}\hline
Category & $\tau$-att & $\lambda$-sim & qasim \\ \hline
Entertainment (47) & 0.446& 0.382 & 0.297\\
Health (62) &0.483 & 0.419 & 0.354\\
Politics 	(143) & 0.45 & 0.300 &	0.272 \\
Society 	(38) & 	0.28 & 0.157 & 0.236 \\
Family (20) & 0.6 & 0.350 & 0.40 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Precision$@$1 of models across categories}
\end{table}
\end{document}

cs

Manisha Verma
Kapil Thadani
Shaunak Mishra

"Powering COVID-19 community Q&A with Curated Side Information" by Manisha Verma, Kapil Thadani, and Shaunak Mishra, arXiv:2101.11556v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method" by Yang Yang, Tong Zhang, Jian Wu, and Lijie Su, arXiv:2504.08743v1, 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}{cccccccccc}
\toprule
Metric & \multicolumn{4}{c}{Top-1 Accuracy (\%)} & & \multicolumn{4}{c}{Seen-Unseen AUC (\%)} \\ \cmidrule(lr){2-5} \cmidrule(l){7-10}
Dataset & \multicolumn{2}{c}{CUB} & \multicolumn{2}{c}{NAB} & & \multicolumn{2}{c}{CUB} & \multicolumn{2}{c}{NAB} \\
Split-Mode & Easy & Hard & Easy & Hard & & Easy & Hard & Easy & Hard \\ \midrule
CIZSL-v2 &41.9 & 15.5 &35.0 &9.6 & & 39.2 & 12.6 & 23.6 &6.9\\
CIZSL-v2+SeGC & \textbf{42.4} & \textbf{16.4} & \textbf{35.2} & \textbf{10.6} & & \textbf{39.3} & \textbf{14.9} & \textbf{23.8} & \textbf{7.5}\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Semantic guided categorizer.}% The chosen hyperparameters for CUB\&NAB easy\&hard are 1, 0.0001, 0.1, 0.1, respectively. Then we test the performance with best hyperparameters.}
\end{table}
\end{document}

cs

"CIZSL++: Creativity Inspired Generative Zero-Shot Learning" by Mohamed Elhoseiny, Kai Yi, and Mohamed Elfeki, arXiv:2101.00173v2, 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}{lllllllll}
meta-estimator & prop\_score\_model & spearman & kendall & pearson & prob\_better\_better & prob\_better\_or\_equal & class\_score \\
ipw & DecisionTree & 0.88 & 0.70 & 0.81 & 0.83 & 0.86 & mean\_test\_f1 \\
ipw & DecisionTree & -0.90 & -0.74 & -0.81 & 0.11 & 0.17 & mean\_test\_average\_precision \\
ipw & DecisionTree & 0.84 & 0.67 & 0.80 & 0.78 & 0.86 & mean\_test\_balanced\_accuracy \\
ipw & LogisticRegression\_l1\_liblinear & -0.86 & -0.73 & -0.72 & 0.04 & 0.36 & mean\_test\_f1 \\
ipw & LogisticRegression\_l1\_liblinear & -0.22 & -0.11 & -0.47 & 0.27 & 0.64 & mean\_test\_average\_precision \\
ipw & LogisticRegression\_l1\_liblinear & -0.87 & -0.75 & -0.73 & 0.00 & 0.44 & mean\_test\_balanced\_accuracy \\
ipw & LogisticRegression\_l1\_saga & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw & LogisticRegression\_l1\_saga & -0.86 & -0.81 & -0.74 & 0.00 & 0.49 & mean\_test\_average\_precision \\
ipw & LogisticRegression\_l1\_saga & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw & LogisticRegression\_l2 & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw & LogisticRegression\_l2 & 0.24 & 0.21 & 0.34 & 0.27 & 0.87 & mean\_test\_average\_precision \\
ipw & LogisticRegression\_l2 & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw & LogisticRegression\_l2\_liblinear & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw & LogisticRegression\_l2\_liblinear & 0.83 & 0.77 & 0.93 & 0.51 & 1.00 & mean\_test\_average\_precision \\
ipw & LogisticRegression\_l2\_liblinear & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw & SVM\_sigmoid & 0.37 & 0.37 & 0.10 & 0.51 & 0.80 & mean\_test\_f1 \\
ipw & SVM\_sigmoid & -0.49 & -0.42 & -0.34 & 0.09 & 0.62 & mean\_test\_average\_precision \\
ipw & SVM\_sigmoid & 0.43 & 0.37 & 0.22 & 0.40 & 0.87 & mean\_test\_balanced\_accuracy \\
ipw & Standardized\_SVM\_rbf & 0.68 & 0.53 & 0.63 & 0.62 & 0.84 & mean\_test\_f1 \\
ipw & Standardized\_SVM\_rbf & 0.36 & 0.26 & 0.86 & 0.58 & 0.67 & mean\_test\_average\_precision \\
ipw & Standardized\_SVM\_rbf & 0.67 & 0.51 & 0.62 & 0.60 & 0.84 & mean\_test\_balanced\_accuracy \\
ipw & Standardized\_SVM\_sigmoid & 0.06 & 0.08 & 0.41 & 0.42 & 0.64 & mean\_test\_f1 \\
ipw & Standardized\_SVM\_sigmoid & -0.52 & -0.42 & -0.54 & 0.18 & 0.47 & mean\_test\_average\_precision \\
ipw & Standardized\_SVM\_sigmoid & 0.16 & 0.16 & 0.41 & 0.42 & 0.71 & mean\_test\_balanced\_accuracy \\
ipw & kNN & 0.90 & 0.76 & 0.91 & 0.86 & 0.89 & mean\_test\_f1 \\
ipw & kNN & -0.84 & -0.74 & -0.78 & 0.11 & 0.18 & mean\_test\_average\_precision \\
ipw & kNN & 0.87 & 0.74 & 0.88 & 0.82 & 0.89 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & DecisionTree & 0.81 & 0.65 & 0.61 & 0.81 & 0.83 & mean\_test\_f1 \\
ipw\_trimeps.01 & DecisionTree & -0.87 & -0.69 & -0.68 & 0.14 & 0.19 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & DecisionTree & 0.77 & 0.61 & 0.61 & 0.75 & 0.83 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & LogisticRegression\_l1\_liblinear & -0.89 & -0.79 & -0.80 & 0.02 & 0.33 & mean\_test\_f1 \\
ipw\_trimeps.01 & LogisticRegression\_l1\_liblinear & -0.22 & -0.11 & -0.44 & 0.27 & 0.64 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & LogisticRegression\_l1\_liblinear & -0.87 & -0.75 & -0.81 & 0.00 & 0.44 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & LogisticRegression\_l1\_saga & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw\_trimeps.01 & LogisticRegression\_l1\_saga & -0.91 & -0.88 & -0.74 & 0.00 & 0.49 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & LogisticRegression\_l1\_saga & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & LogisticRegression\_l2 & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw\_trimeps.01 & LogisticRegression\_l2 & 0.24 & 0.21 & 0.33 & 0.27 & 0.87 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & LogisticRegression\_l2 & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & LogisticRegression\_l2\_liblinear & & & & 0.00 & 1.00 & mean\_test\_f1 \\
ipw\_trimeps.01 & LogisticRegression\_l2\_liblinear & 0.86 & 0.81 & 0.92 & 0.51 & 1.00 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & LogisticRegression\_l2\_liblinear & & & & 0.00 & 1.00 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & SVM\_sigmoid & 0.20 & 0.19 & -0.30 & 0.42 & 0.73 & mean\_test\_f1 \\
ipw\_trimeps.01 & SVM\_sigmoid & -0.19 & -0.16 & 0.09 & 0.18 & 0.71 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & SVM\_sigmoid & 0.29 & 0.25 & -0.20 & 0.36 & 0.82 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & Standardized\_SVM\_rbf & 0.68 & 0.53 & 0.59 & 0.62 & 0.84 & mean\_test\_f1 \\
ipw\_trimeps.01 & Standardized\_SVM\_rbf & 0.36 & 0.26 & 0.88 & 0.58 & 0.67 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & Standardized\_SVM\_rbf & 0.67 & 0.51 & 0.59 & 0.60 & 0.84 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & Standardized\_SVM\_sigmoid & -0.06 & -0.03 & 0.40 & 0.38 & 0.60 & mean\_test\_f1 \\
ipw\_trimeps.01 & Standardized\_SVM\_sigmoid & -0.22 & -0.16 & -0.54 & 0.29 & 0.58 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & Standardized\_SVM\_sigmoid & 0.00 & 0.05 & 0.40 & 0.38 & 0.67 & mean\_test\_balanced\_accuracy \\
ipw\_trimeps.01 & kNN & 0.66 & 0.52 & 0.74 & 0.73 & 0.78 & mean\_test\_f1 \\
ipw\_trimeps.01 & kNN & -0.65 & -0.52 & -0.55 & 0.22 & 0.27 & mean\_test\_average\_precision \\
ipw\_trimeps.01 & kNN & 0.59 & 0.46 & 0.63 & 0.69 & 0.76 & mean\_test\_balanced\_accuracy
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Brady Neal
Chin-Wei Huang
Sunand Raghupathi

"RealCause: Realistic Causal Inference Benchmarking" by Brady Neal, Chin-Wei Huang, and Sunand Raghupathi, arXiv:2011.15007v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Confusion matrix for the SVM algorithm on the metrics dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|ccc|}
 \cline{3-5}
 \multicolumn{2}{c|}{\multirow{2}{*}{Diagnosis}} & \multicolumn{3}{|c|}{Actual} \\
 \cline{3-5}
 \multicolumn{2}{c|}{} & P & C & H \\
 \hline
 & P & 49.0\,\% & 15.0\,\% & 0\,\% \\
 & C & 37.3\,\% & 57.5\,\% & 13.3\,\% \\
 \multirow{-3}{*}{Predicted} & H & 13.7\,\% & 27.5\,\% & 86.7\,\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

C. V. Vletter
H. L. Burger
H. Alers
N. Sourlos
Z. Al-Ars

"Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features" by C. V. Vletter, H. L. Burger, H. Alers, N. Sourlos, and Z. Al-Ars, arXiv:2201.11852v1, 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{Additional hyperparameters for UCI experiments. Hidden Units, Residual Blocks, and Layer Normalization refer to the VAE encoder/decoder networks.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
 & \textsc{Power} & \textsc{Gas} & \textsc{Hepmass} & \textsc{Miniboone} & BSDS \\ \midrule
Batch Size & 512 & 512 & 512 & 1024 & 1024 \\
Latent Dimension & 16 & 16 & 16 & 32 & 64 \\
Hidden Units & 256 & 256 & 256 & 256 & 256 \\
Residual Blocks & 2 & 2 & 2 & 5 & 5 \\
Layer Normalization & No & No & No & Yes & Yes \\
Training Steps & 200000 & 200000 & 200000 & 22000 & 200000 \\
$\beta$ Schedule & Cyclical & Cyclical & Cyclical & Cyclical & Monotonic \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Posterior Matching for Arbitrary Conditioning" by Ryan R. Strauss and Junier B. Oliva, arXiv:2201.12414v4, 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
Age & $\beta_{\textbf{S}}$ & $\beta_{\textbf{K}}$ & $\beta_{\textbf{I}}$ & $\mu_{\textbf{K}}$ & $\mu_{\textbf{I}}$ \\ \hline
18-29 & 0.4 & 0.5 & 0.75 & 0.1 & 0.1 \\ \hline
30-49 & 0.3 & 0.42 & 0.62 & 0.05 & 0.05 \\ \hline
50-64 & 0.3 & 0.32 & 0.48 & 0.05 & 0.05 \\ \hline
65+ & 0.3 & 0.2 & 0.3 & 0.15 & 0.15 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{\small Experiment 1 Coefficients}
\end{table}
\end{document}

math

"Modeling of Rumor Propagation in Large Populations with Network via Graphon Games" by Huaning Liu and Gokce Dayanikli, arXiv:2503.09107v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|lll}
&\multicolumn{3}{c}{Tolerances} \\
 & $\tau_{large}$ & $\tau_{mix}$ & $\tau_{small}$ \\ \hline
$\tau_{SAROC}$ & $10^{-3}$ & $10^{-2}$ & $10^{-5}$ \\
$\rho_{SAROC}$ & $10^{-3}$ & $10^{-2}$ & $10^{-5}$ \\
$\tau_{FSAI}$ & $10^{-3}$ & $10^{-3}$ & $10^{-5}$ \\
$\rho_{FSAI}$ & $10^{-3}$ & $10^{-3}$ & $10^{-5}$\\ \hdashline
$\tau_{MGS}$ & $10^{-3}$ & $10^{-2}$ & $10^{-5}$ \\
$w_{MGS}$ & $5$ & $5$ & $15$ \\\hdashline
$\epsilon_{in}$ & $10^{-3}$ & $10^{-4}$ & $10^{-5}$\\
$\epsilon_{innermost}$ & $10^{-3}$ & $10^{-5}$ & $10^{-5}$\\
\end{tabular}
\end{adjustbox}
\caption{Parameters of the new schemes}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Optimal hyperparameter settings for each dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
\hline
Dataset & Learning Rate & Dropout Rate & Weight Decay & $\rho$ \\ \toprule
PACS & \(3 \times 10^{-5}\) & 0.5 & \(10^{-4}\) & 0.03 \\
VLCS & \(10^{-5}\) & 0.5 & \(10^{-4}\) & 0.03 \\
OfficeHome & \(10^{-5}\) & 0.5 & \(10^{-6}\) & 0.1 \\
TerraIncognita & \(10^{-5}\) & 0.2 & \(10^{-6}\) & 0.05 \\
DomainNet & \(2 \times 10^{-5}\) & 0.5 & \(10^{-4}\) & 0.1 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Youngjun Song
Youngsik Hwang
Jonghun Lee
Heechang Lee
Dong-Young Lim

"DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization" by Youngjun Song, Youngsik Hwang, Jonghun Lee, Heechang Lee, and Dong-Young Lim, arXiv:2503.23430v2, 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|lll|lll|}
\hline
 & \multicolumn{3}{c|}{Model-based greedy} & \multicolumn{3}{c|}{Model-based without interaction} \\ \hline
TC & Mean & CI width & \# runs & Mean & CI width & \# runs \\ \hline
Aa & 2390.12 & 3.96 & 1200 & 2388.44 & 4.5 & 650 \\ \hline
Ab & 2375.02 & 2.82 & 900 & 2374.1 & 3.6 & 500 \\ \hline
Ac & 2359.9 & 2.4 & 900 & 2359.52 & 3.2 & 500 \\ \hline
Ad & 2344.82 & 3.01 & 900 & 2344.95 & 3.4 & 500 \\ \hline
Ae & 2329.72 & 4.22 & 1550 & 2330.37 & 4.1 & 500 \\ \hline
Ba & 2384.63 & 2.58 & 900 & 2383.25 & 3.3 & 500 \\ \hline
Bb & 2369.89 & 1.83 & 900 & 2368.68 & 2.4 & 500 \\ \hline
Bc & 2354.78 & 1.53 & 900 & 2354.1 & 2.1 & 500 \\ \hline
Bd & 2339.67 & 1.9 & 900 & 2339.53 & 2.7 & 1000
\\ \hline
Be & 2323.71 & 2.3 & 900 & 2324.95 & 2.7 & 500
\\ \hline
 & & Total= & 9950 & & Total= & 5650 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Model-based greedy and Model-based greedy without interaction mean results for the first ten treatment conditions effects and corresponding CI width where the third column shows the number of runs required to reach that estimate. }
\end{table}
\end{document}

stat

Abdulrahman A. Ahmed
M. Amin Rahimian
Mark S. Roberts

"Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder" by Abdulrahman A. Ahmed, M. Amin Rahimian, and Mark S. Roberts, arXiv:2308.13040v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|l|l|l|l|l|l|}
\hline
Meta Model & & 0.0207 & 1.0073 & 0.3492 \\ \hline
\multirow{2}{*}{Small} & original & 0.0232 $\pm$ 0.0005 & 1.1238 $\pm$ 0.0167 & 0.2251 $\pm$ 0.0072 \\ \cline{2-5}
 & ensemble & 0.0251 $\pm$ 0.0002 & 1.1658 $\pm$ 0.0121 & 0.3528 $\pm$ 0.0098 \\ \hline
\multirow{2}{*}{Standard} & original & 0.0237 $\pm$ 0.0002 & 1.1434 $\pm$ 0.0165 & 0.3454 $\pm$ 0.0410 \\ \cline{2-5}
 & ensemble & 0.0257 $\pm$ 0.0001 & 1.1891 $\pm$ 0.0099 & 0.4318 $\pm$ 0.0095 \\ \hline
\multirow{2}{*}{Large} & original & 0.0235 $\pm$ 0.0003 & 1.1505 $\pm$ 0.0248 & 0.4147 $\pm$ 0.0350 \\ \cline{2-5}
 & ensemble & 0.0260 $\pm$ 0.0001 & 1.2114 $\pm$ 0.0050 & 0.4838 $\pm$ 0.0062 \\ \hline
\end{tabular}
\caption{Performance of (a) small, (b) standard and (c) large XGBoost Models with different feature sampling schemes on test period (Eras 901-1050), compared with Meta Model along with equal weighted ensemble of Meta Model and itself.}
\end{table}
\end{document}

q-fin

"Deep incremental learning models for financial temporal tabular datasets with distribution shifts" by Thomas Wong and Mauricio Barahona, arXiv:2303.07925v10, 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|r|rrrrr|}
 \hline
 & & \multicolumn{5}{c|}{Number of trees} \\
Number of & Total number & \multicolumn{5}{c|}{with a fixed number of galls ($E_{n,g}$)} \\
leaves ($n$) & of trees ($A_n$) & $g=0$ & $g=1$ & $g=2$ & $g=3$ & $g=4$ \\ \hline
 1 & 1 & 1 & - & - & - & - \\
 2 & 1 & 1 & - & - & - & - \\
 3 & 2 & 1 & 1 & - & - & - \\
 4 & 6 & 2 & 4 & - & - & - \\
 5 & 72 & 3 & 15 & 2 & - & - \\
 6 & 272 & 6 & 48 & 18 & - & - \\
 7 & 1064 & 11 & 148 & 107 & 6 & - \\
 8 & 4271 & 23 & 435 & 528 & 78 & - \\
 9 & 17,497 & 46 & 1250 & 2295 & 661 & 19 \\
10 & 72,483 & 98 & 3512 & 9185 & 4356 & 346 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Numbers of unlabeled time-consistent galled trees with specified numbers of leaves and galls. Entries $E_{n,g}$ are computed recursively from () and are copied from Table 3 of .}
\end{table}
\end{document}

math

Lily Agranat-Tamir
Michael Fuchs
Bernhard Gittenberger
Noah A. Rosenberg

"Enumerative combinatorics of unlabeled and labeled time-consistent galled trees" by Lily Agranat-Tamir, Michael Fuchs, Bernhard Gittenberger, and Noah A. Rosenberg, arXiv:2504.16302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{This table shows the median number of days a stock resided in one out of four implied volatility regimes, together with each regime's mean implied volatility, stratified dummy performance and the improvement of our approach. In total, 165 stocks were considered over a period spanning January 1st, 2013 till March 1st, 2019. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
 & Frequency & Implied Volatility & Dummy AUC & Improvement \\
\midrule
Low & 392 & 18.6 & 50.75 & +3.15 & \\
Medium & 452 & 22.3 & 50.65 & +3.83 & \\
High & 411 & 26.7 & 52.12 & +3.84 & \\
Very High & 231 & 35.3 & 54.92 & +1.47 & \\
\midrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c||c|c|c|c|c|}\hline
 Training days & IAD & IOD & DR & MSC \\\hline
 1 & $4.77$ & $27.61$ & $\textbf{0.1730}$ & $\textbf{0.2475}$ \\
 2 & $\textbf{4.61}$ & $26.62 $ & $0.1732 $ & $0.2326$ \\
 3 & $4.85$ & $\textbf{27.88}$ & $0.1742$ & $0.2358$ \\\hline
 \end{tabular}
\caption{Validation results for different training duration, one day gives the best results amd more time does not affect the behaviour of the network. }
\end{table}
\end{document}

eess

Emmanuel Maqueda
Javier Alvarez-Jimenez
Carlos Mena
Ivan Meza

"Triplet loss based embeddings for forensic speaker identification in Spanish" by Emmanuel Maqueda, Javier Alvarez-Jimenez, Carlos Mena, and Ivan Meza, arXiv:2102.12564v2, 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{Sample table title}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \multicolumn{2}{c}{Part} \\
 \cmidrule(r){1-2}
 Name & Description & Size ($\mu$m) \\
 \midrule
 Dendrite & Input terminal & $\sim$100 \\
 Axon & Output terminal & $\sim$10 \\
 Soma & Cell body & up to $10^6$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Steffen Schotthöfer
Tianbai Xiao
Martin Frank
Cory D. Hauck

"Neural network-based, structure-preserving entropy closures for the Boltzmann moment system" by Steffen Schotthöfer, Tianbai Xiao, Martin Frank, and Cory D. Hauck, arXiv:2201.10364v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l}
 authblk & babel & cjk & dvips \\
 epsf & epsfig & euler & float \\
 fullpage & geometry & graphics & hyperref \\
 layout & linespread & lmodern & maltepaper \\
 navigator & pdfcomment & pgfplots & psfig \\
 pstricks & t1enc & titlesec & tocbind \\
 ulem
\end{tabular}
\end{adjustbox}
\caption{LaTeX style packages that must not be used.}
\end{table}
\end{document}

cs

Zhuang Li
Lizhen Qu
Shuo Huang
Gholamreza Haffari

"Few-Shot Semantic Parsing for New Predicates" by Zhuang Li, Lizhen Qu, Shuo Huang, and Gholamreza Haffari, arXiv:2101.10708v1, 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{List of Components}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Components & Manufacturer & Product type/Specification \\ \hline
Solenoid valve & Festo & MFH-2-M5 \\ \hline
12V coil & Festo & MSFG-12-OD \\ \hline
Regulator & Festo & MS2-LR-QS6-D6-AR-BAR-B \\ \hline
Airsoft regulator & Polarstar & MRS \\ \hline
Tank regulator & Ninja & HP UL Reg 4500psi \\ \hline
Air tank & DYE & UL \\ \hline
Relay module & Seeed & Groove -2-Channel SPDT Relay \\ \hline
Upboard Computer & Arduino & Micro-controller\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Avijit Banerjee
Jakub Haluska
Sumeet G. Satpute
Dariusz Kominiak
George Nikolakopoulos

"Slider: On the Design and Modeling of a 2D Floating Satellite Platform" by Avijit Banerjee, Jakub Haluska, Sumeet G. Satpute, Dariusz Kominiak, and George Nikolakopoulos, arXiv:2101.06335v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"On the improved estimation of ordered parameters based on doubly type-II censored sample" by Shrajal Bajpai and Lakshmi Kanta Patra, arXiv:2411.06888v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccc}
 \multicolumn{10}{l}{Panel A: Input prompts} \\
 \toprule
 Prompt year & \multicolumn{6}{c}{Input prompt} & & \multicolumn{2}{c}{Correct output}\\
 \midrule
 2001 & \multicolumn{6}{l}{The Sarbanes-Oxley Act was introduced in response to the 2001 Enron } & & \multicolumn{2}{c}{scandal} \\
 2003 & \multicolumn{6}{l}{In 2003, a major public health crisis was the outbreak of the virus known as } & & \multicolumn{2}{c}{SARS} \\
 2008 & \multicolumn{6}{l}{In 2008, the global economy was dominated by the subprime mortgage } & & \multicolumn{2}{c}{crisis} \\
 2016 & \multicolumn{6}{l}{In 2016, market volatility increased due to the uncertainty surrounding the vote in the UK, known as the } & & \multicolumn{2}{c}{Brexit referendum/vote} \\
 2020 & \multicolumn{6}{l}{In 2020, the global economy was severely impacted by the pandemic known as } & & \multicolumn{2}{c}{COVID} \\
 2022 & \multicolumn{6}{l}{In 2022, a major milestone for generative AI was marked by the public release of the AI assistant known as } & & \multicolumn{2}{c}{ChatGPT} \\
 \bottomrule
 \\
 \multicolumn{10}{l}{Panel B: Output predictions} \\
 \toprule
 & \multicolumn{6}{c}{Prompt year} & & \multicolumn{2}{c}{Accuracy}\\
 \cmidrule{2-7} \cmidrule{9-10}
 & 2001 & 2003 & 2008 & 2016 & 2020 & 2022 & & Pre-cutoff & Post-cutoff \\
 \midrule
 Correct output & scandal & SARS & crisis & Brexit referendum/vote & COVID & ChatGPT\\
 \midrule
 GPT-2 & \textcolor{blue}{\textbf{scandal}.\textbackslash n} & chikung & \textcolor{blue}{\textbf{crisis}, which} & \textcolor{blue}{\textbf{Brexit vote}.} &\cellcolor{lightgray}the Ebola virus &\cellcolor{lightgray} the ``AI & & $3/4$ & \cellcolor{lightgray}$0/2$\\
 GPT-2 XL & \textcolor{blue}{\textbf{scandal}, in} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}. The} & \textcolor{blue}{\textbf{Brexit}, and} &\cellcolor{lightgray}the ``Great &\cellcolor{lightgray} Alexa. Alexa & & $4/4$ & \cellcolor{lightgray}$0/2$\\
 \midrule
 $\text{ChronoGPT}_{1999}$ & \cellcolor{lightgray}ous Claims Act & \cellcolor{lightgray}the Ebola virus & \cellcolor{lightgray}market. The & \cellcolor{lightgray}``Budget & \cellcolor{lightgray} the ``green & \cellcolor{lightgray} the AI assistant & & --- & \cellcolor{lightgray}$0/6$\\
 $\text{ChronoGPT}_{2000}$ & \cellcolor{lightgray}a-H & \cellcolor{lightgray}Ebola virus in & \cellcolor{lightgray}rate, which & \cellcolor{lightgray}\textquotedblright B & \cellcolor{lightgray} the Ebola virus & \cellcolor{lightgray} AI-R & & --- & \cellcolor{lightgray}$0/6$\\
 $\text{ChronoGPT}_{2001}$ & Agreement, which & \cellcolor{lightgray}Ebola (E & \cellcolor{lightgray}market & \cellcolor{lightgray}\textquotedblright V & \cellcolor{lightgray}the Ebola virus & \cellcolor{lightgray} AI Magazine. & & $0/1$ & \cellcolor{lightgray}$0/5$\\
 $\text{ChronoGPT}_{2002}$ & \textcolor{blue}{\textbf{scandal}. The} & \cellcolor{lightgray}hantav & \cellcolor{lightgray}market, which & \cellcolor{lightgray}\textquotedblright V & \cellcolor{lightgray} the ``Asian & \cellcolor{lightgray} the AI-R & & $1/1$ & \cellcolor{lightgray}$0/5$\\
 $\text{ChronoGPT}_{2003}$ & \textcolor{blue}{\textbf{scandal}. It} & West Nile virus& \cellcolor{lightgray}market, which & \cellcolor{lightgray}``Voting & \cellcolor{lightgray} the Ebola virus & \cellcolor{lightgray} AIBO. & & $1/2$ & \cellcolor{lightgray}$0/4$\\
 $\text{ChronoGPT}_{2004}$ & \textcolor{blue}{\textbf{scandal}. It} & \textcolor{blue}{\textbf{SARS}.} & \cellcolor{lightgray}market, which & \cellcolor{lightgray} ``Voting & \cellcolor{lightgray} the SARS & \cellcolor{lightgray} AIBO. & & $2/2$ & \cellcolor{lightgray}$0/4$\\
 $\text{ChronoGPT}_{2005}$ & \textcolor{blue}{\textbf{scandal}. It} & \textcolor{blue}{\textbf{SARS} (} & \cellcolor{lightgray}market, which & \cellcolor{lightgray} ``London effect & \cellcolor{lightgray} SARS. & \cellcolor{lightgray} AIBO. & & $2/2$ & \cellcolor{lightgray}$0/4$\\
 $\text{ChronoGPT}_{2006}$ & \textcolor{blue}{\textbf{scandal}. It} & \textcolor{blue}{\textbf{SARS},} & \cellcolor{lightgray}market, which & \cellcolor{lightgray} ``London effect & \cellcolor{lightgray} the SARS & \cellcolor{lightgray} the AIBO & & $2/2$ & \cellcolor{lightgray}$0/4$\\
 $\text{ChronoGPT}_{2007}$ & \textcolor{blue}{\textbf{scandal}. It} & \textcolor{blue}{\textbf{SARS},} & \cellcolor{lightgray}market, which & \cellcolor{lightgray} ``Budget & \cellcolor{lightgray} the Black Death & \cellcolor{lightgray} the AIBO & & $2/2$ & \cellcolor{lightgray}$0/4$\\
 $\text{ChronoGPT}_{2008}$ & \textcolor{blue}{\textbf{scandal}. It} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}, which} & \cellcolor{lightgray} ``Voting & \cellcolor{lightgray} the Black Death & \cellcolor{lightgray} the AIBO & & $3/3$ & \cellcolor{lightgray}$0/3$\\
 $\text{ChronoGPT}_{2009}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}, which} & \cellcolor{lightgray} ``UK vote & \cellcolor{lightgray} the Black Death & \cellcolor{lightgray} the ``C & & $3/3$ & \cellcolor{lightgray}$0/3$\\
 $\text{ChronoGPT}_{2010}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}, which} & \cellcolor{lightgray} ``UK vote & \cellcolor{lightgray} the SARS & \cellcolor{lightgray} the AIBO & & $3/3$ & \cellcolor{lightgray}$0/3$\\
 $\text{ChronoGPT}_{2011}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}, which} & \cellcolor{lightgray} ``Budget & \cellcolor{lightgray} the H1 & \cellcolor{lightgray} the AIBO & & $3/3$ & \cellcolor{lightgray}$0/3$\\
 $\text{ChronoGPT}_{2012}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}, which} & \cellcolor{lightgray} ``Budget & \cellcolor{lightgray} the Black Death & \cellcolor{lightgray} the AI- & & $3/3$ & \cellcolor{lightgray}$0/3$\\
 $\text{ChronoGPT}_{2013}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}, which} & \cellcolor{lightgray} ``Budget & \cellcolor{lightgray} the "Asian & \cellcolor{lightgray} the AIBO & & $3/3$ & \cellcolor{lightgray}$0/3$\\
 $\text{ChronoGPT}_{2014}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}, which} & \cellcolor{lightgray} ``Voting & \cellcolor{lightgray} the "Asian & \cellcolor{lightgray} the ``C & & $3/3$ & \cellcolor{lightgray}$0/3$\\
 $\text{ChronoGPT}_{2015}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}, which} & \cellcolor{lightgray} ``UK vote & \cellcolor{lightgray} the Ebola virus & \cellcolor{lightgray} the AIBO & & $3/3$ & \cellcolor{lightgray}$0/3$\\
 $\text{ChronoGPT}_{2016}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}, which} & ``UK vote & \cellcolor{lightgray} the Ebola virus & \cellcolor{lightgray} the ``C & & $3/4$ & \cellcolor{lightgray}$0/2$\\
 $\text{ChronoGPT}_{2017}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS},} & \textcolor{blue}{\textbf{crisis}, which} & \textcolor{blue}{\textbf{Brexit referendum}.} & \cellcolor{lightgray} the Ebola virus & \cellcolor{lightgray} the ``C & & $4/4$ & \cellcolor{lightgray}$0/2$\\
 $\text{ChronoGPT}_{2018}$ & \textcolor{blue}{\textbf{scandal}. The} & Ebola in West & \textcolor{blue}{\textbf{crisis}, which} & \textcolor{blue}{\textbf{Brexit vote}.} & \cellcolor{lightgray} the Ebola virus & \cellcolor{lightgray} DeepMind's & & $3/4$ & \cellcolor{lightgray}$0/2$\\
 $\text{ChronoGPT}_{2019}$ & \textcolor{blue}{\textbf{scandal}. The} & Ebola in West & \textcolor{blue}{\textbf{crisis}, which} & \textcolor{blue}{\textbf{Brexit referendum}.} & \cellcolor{lightgray} the Ebola virus & \cellcolor{lightgray} Siri. The & & $3/4$ & \cellcolor{lightgray}$0/2$\\
 $\text{ChronoGPT}_{2020}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS}-} & \textcolor{blue}{\textbf{crisis}, which} & \textcolor{blue}{\textbf{Brexit referendum}.} & \textcolor{blue}{\textbf{COVID-}} & \cellcolor{lightgray} Siri. The & & $5/5$ & \cellcolor{lightgray}$0/1$\\
 $\text{ChronoGPT}_{2021}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS}-} & \textcolor{blue}{\textbf{crisis}, which} & \textcolor{blue}{\textbf{Brexit referendum}.} & \textcolor{blue}{\textbf{COVID-}} & \cellcolor{lightgray} Alexa. The & & $5/5$ & \cellcolor{lightgray}$0/1$\\
 $\text{ChronoGPT}_{2022}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS}-} & \textcolor{blue}{\textbf{crisis}, which} & \textcolor{blue}{\textbf{Brexit referendum}.} & \textcolor{blue}{\textbf{COVID-}} & Alexa. The & & $5/6$ & \cellcolor{lightgray}---\\
 $\text{ChronoGPT}_{2023}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS}-} & \textcolor{blue}{\textbf{crisis}, which} & \textcolor{blue}{\textbf{Brexit referendum}.} & \textcolor{blue}{\textbf{COVID-}} & \textcolor{blue}{\textbf{ChatGPT}} & & $6/6$ & \cellcolor{lightgray}---\\
 $\text{ChronoGPT}_{2024}$ & \textcolor{blue}{\textbf{scandal}. The} & \textcolor{blue}{\textbf{SARS}-} & \textcolor{blue}{\textbf{crisis}, which} & \textcolor{blue}{\textbf{Brexit referendum}.} & \textcolor{blue}{\textbf{COVID-}} & \textcolor{blue}{\textbf{ChatGPT}} & & $6/6$ & \cellcolor{lightgray}---\\
 \hline
 \multicolumn{7}{l}{$\text{ChronoGPT}_{1999}$ through $\text{ChronoGPT}_{2024}$} & & $74/80$ & \cellcolor{lightgray}$0/76$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Next-Token Predictions of Major Events using ChronoGPT}
\end{table}
\end{document}

q-fin

Songrun He
Linying Lv
Asaf Manela
Jimmy Wu

"Chronologically Consistent Large Language Models" by Songrun He, Linying Lv, Asaf Manela, and Jimmy Wu, arXiv:2502.21206v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
Test & MI (Treat.) & MI (Control) & D (Treat.) & D (Control) \\
\midrule
Asymptotic Cochran's $\chi^2$ & 0 & 0 & 0.002 & 0.479 \\
mod. Cochran's $\chi^2$ & 0 & 0.003 & 0.005 & 0.492 \\
debiased Cochran's $\chi^2$ I & 0.062 & 0.285 & 0.049 & 0.286 \\
debiased Cochran's $\chi^2$ II & 0.062 & 0.285 & 0.048 & 0.285 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{P-values for homogeneity testing with a composite null. }
\end{table}
\end{document}

math

Lucas Kania
Larry Wasserman
Sivaraman Balakrishnan

"Testing Random Effects for Binomial Data" by Lucas Kania, Larry Wasserman, and Sivaraman Balakrishnan, arXiv:2504.13977v1, 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{DenseNet statistic and pvalue (ISIC-2016)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
\textbf{Model} & \textbf{DenseNet} & \textbf{DenseNet+Wavelet} & \textbf{DenseNet+Wavelet+IGWO} & \textbf{DenseNet+Wavelet+Fox} & \textbf{DenseNet+Wavelet+MGTO} \\
\midrule
DenseNet & 0.0, 1.0 & -0.0705, 0.9454 & -2.3359, 0.0477 & -1.0857, 0.3092 & -0.6534, 0.5318 \\
DenseNet+Wavelet & 0.0705, 0.9454 & 0.0, 1.0 & -1.1801, 0.2718 & -0.4831, 0.6419 & -0.3026, 0.7698 \\
DenseNet+Wavelet+IGWO & 2.3359, 0.0477 & 1.1801, 0.2718 & 0.0, 1.0 & 1.0702, 0.3157 & 1.2137, 0.2594 \\
DenseNet+Wavelet+Fox & 1.0857, 0.3092 & 0.4831, 0.6419 & -1.0702, 0.3157 & 0.0, 1.0 & 0.2389, 0.8171 \\
DenseNet+Wavelet+MGTO & 0.6534, 0.5318 & 0.3026, 0.7698 & -1.2137, 0.2594 & -0.2389, 0.8171 & 0.0, 1.0 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ramin Mousa
Saeed Chamani
Mohammad Morsali
Mohammad Kazzazi
Parsa Hatami
Soroush Sarabi

"Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers" by Ramin Mousa, Saeed Chamani, Mohammad Morsali, Mohammad Kazzazi, Parsa Hatami, and Soroush Sarabi, arXiv:2412.00472v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Higher-Order Stochastic Dominance Constraints in Optimization" by Rajmadan Lakshmanan, Alois Pichler, and Miloš Kopa, arXiv:2501.14565v3, 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}{lll}
\hline
 & Unweighted & Weighted \\ \hline
NSDUH analysis \\
$\,\,\,\,$Sensitivity & 0.71 ($\pm$ 0.003) & 0.63 ($\pm$ 0.004) \\
$\,\,\,\,$Specificity & 0.76 ($\pm$ 0.001) & 0.81 ($\pm$ 0.001) \\
$\,\,\,\,$AUROC & 0.80 ($\pm$ 0.001) & 0.80 ($\pm$ 0.001) \\ \hline
NCS-R analysis\\
$\,\,\,\,$Sensitivity & 0.66 ($\pm$ 0.02) & 0.67 ($\pm$ 0.02) \\
$\,\,\,\,$Specificity & 0.72 ($\pm$ 0.01) & 0.73 ($\pm$ 0.01) \\
$\,\,\,\,$AUROC & 0.73 ($\pm$ 0.003) & 0.74 ($\pm$ 0.004) \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Weighted and unweighted performance metrics for the NSDUH and NCS-R analyses. Monte Carlo standard deviations are in parentheses.}
\end{table}
\end{document}

stat

"Evaluating Binary Outcome Classifiers Estimated from Survey Data" by Adway S. Wadekar and Jerome P. Reiter, arXiv:2311.00596v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c|c}
	Stage & Operator & Resolution & \#OC & \#L & SE & AF(TPU/GPU)\\
	\midrule
	1 & Conv3x3 & $ 224 \times 224 $ & 32 & 1 & N/A & swish/ReLU \\
	2 & Conv2x2 for reshaping$^{\dagger}$ & $ 112 \times 112 $ & 128 & 1 & N/A & ReLU/ReLU\\
	3 & MBConv1, k3x3 & $56 \times 56 $ & 64 &1 & 1 & ReLU/ReLU\\
	4 & Fused MBConv6, k3x3 & $56 \times 56$ & 24 & 2 & 0.5 & swish/ReLU\\
	5 & Fused MBConv6, k5x5 & $56 \times 56$ & 40 & 2 & 0.25 & swish/ReLU\\
	6 & MBConv6, k3x3 & $28 \times 28 $ & 80 & 3 & 0.25 & ReLU/ReLU\\
	7 & MBConv6, k5x5 & $14 \times 14$ & 112 & 3 & 0.25 & ReLU/ReLU\\
	8 & MBConv6, k5x5 & $14 \times 14$ & 192 & 4 & 0.25 & ReLU/ReLU\\
	9 & MBConv6, k3x3 & $7 \times 7$ & 320 & 1 & 0.25 & ReLU/ReLU\\
	10 & Conv1x1 \& Pooling \& FC & $7 \times 7$ & 1280 & 1 & N/A & ReLU/ReLU\\
\end{tabular}
\end{table}
\end{document}

eess

Sheng Li
Mingxing Tan
Ruoming Pang
Andrew Li
Liqun Cheng
Quoc Le
Norman P. Jouppi

"Searching for Fast Model Families on Datacenter Accelerators" by Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc Le, and Norman P. Jouppi, arXiv:2102.05610v1, 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|cccc|cccc}
\hline
 & \multicolumn{4}{c|}{\textbf{URMP}} & \multicolumn{4}{c}{\textbf{Bach10}} \\
\textbf{Model} & P & R & F1 & F1\textsubscript{no} & P & R & F1 & F1\textsubscript{no} \\
\hline
MUSC & \textbf{86.5} & 83.1 & \textbf{84.6} & 93.0 & 65.0 & 64.8 & 64.8 & 77.0 \\
Ours w/o aug & 83.4 & 81.2 & 82.2 & 92.8 & 66.7 & 71.3 & 68.9 & 79.0 \\
Ours w/ aug & 86.1 & \textbf{83.6} & 84.5 & \textbf{93.1} & 68.1 & 71.8 & 69.9 & 79.5 \\
Ours + FT w/o aug & 84.4 & 79.0 & 81.3 & 91.3 & \textbf{69.5} & \textbf{73.7} & \textbf{71.5} & \textbf{80.2} \\
Ours + FT w/ aug & 85.0 & 82.1 & 83.3 & 92.9 & 63.3 & 68.4 & 65.7 & 77.8 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Transcription performance on the URMP and Bach10 violin tracks. "FT" denotes models fine-tuned from a piano-pretrained checkpoint; "aug" denotes augmentation. Bold indicates best result per column.}
\end{table}
\end{document}

eess

Yueh-Po Peng
Ting-Kang Wang
Li Su
Vincent K. M. Cheung

"Is Transfer Learning Necessary for Violin Transcription?" by Yueh-Po Peng, Ting-Kang Wang, Li Su, and Vincent K. M. Cheung, arXiv:2508.13516v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Xiangnan Feng
Johannes Wachs
Simone Daniotti
Frank Neffke

"The building blocks of software work explain coding careers and language popularity" by Xiangnan Feng, Johannes Wachs, Simone Daniotti, and Frank Neffke, arXiv:2504.03581v1, 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}{lll}
 \hline
 Scenario & $\delta$ & Runtime \\
 \hline
 Paris & 0.1s & 13.65ms \\
 Paris & 0.2s & 0.01ms \\
 New York & 0.1s & 92.50ms \\
 Singapore & 0.1s & 33.33ms \\
 Singapore & 0.2s & 23.01ms \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Latex default table}
\end{table}
\end{document}

stat

"Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference" by Kaiwen Hou, arXiv:2311.18826v4, 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{Quantitative Dice Summary: Reinforcement Agent Performance for Each Specific Dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Prompt Number} & \multicolumn{4}{|c|}{\textbf{Dice Coefficient (Mean ± Standard Deviations)}} \\
\cline{2-5}
& \textbf{CT Lung} & \textbf{MRI Ovarian} & \textbf{MRI Renal} & \textbf{US Breast} \\
\hline
0 & 0.0791 ± 0.028 & 0.119 ± 0.032 & 0.056 ± 0.027 & 0.055 ± 0.032 \\
\hline
1 & 0.218 ± 0.024 & 0.339 ± 0.025 & 0.351 ± 0.017 & 0.123 ± 0.028 \\
\hline
2 & 0.310 ± 0.019 & 0.481 ± 0.017 & 0.458 ± 0.016 & 0.334 ± 0.022 \\
\hline
4 & 0.449 ± 0.019 & 0.549 ± 0.015 & 0.502 ± 0.017 & 0.411 ± 0.021 \\
\hline
5 & 0.485 ± 0.021 & 0.588 ± 0.013 & 0.505 ± 0.016 & 0.465 ± 0.017 \\
\hline
7 & 0.525 ± 0.018 & 0.594 ± 0.013 & 0.505 ± 0.012 & 0.515 ± 0.014 \\
\hline
9 & 0.534 ± 0.019 & 0.595 ± 0.012 & 0.502 ± 0.014 & 0.513 ± 0.013 \\
\hline
10 & 0.534 ± 0.016 & 0.595 ± 0.011 & 0.504 ± 0.016 & 0.514 ± 0.012 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yuli Wang
Victoria Shi
Wen-Chi Hsu
Yuwei Dai
Sophie Yao
Zhusi Zhong
Zishu Zhang
Jing Wu
Aaron Maxwell
Scott Collins
Zhicheng Jiao
Harrison X. Bai

"Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities" by Yuli Wang, Victoria Shi, Wen-Chi Hsu, Yuwei Dai, Sophie Yao, Zhusi Zhong, Zishu Zhang, Jing Wu, Aaron Maxwell, Scott Collins, Zhicheng Jiao, and Harrison X. Bai, arXiv:2412.17943v2, 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{Supervised evaluation performance for different feature types and evaluation algorithms. See text for details.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc}
\bf \small Model &\bf \small Proj &\bf \small Pre-feature &\bf \small Pool &\bf \small Proj (16 avg) &\bf \small Pre-feature (16 avg) &\bf \small Pool (16 avg)
\\ \hline \\
SVM &0.911 &0.889 &0.895 &0.922 &0.905 &{\bf 0.929} \\
kNN &0.904 &0.851 &0.105 &{\bf 0.910} &0.800 &0.103 \\
NearSub &0.898 &0.902 &0.903 &0.903 &0.909 &{\bf 0.911} \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zengyi Li
Yubei Chen
Yann LeCun
Friedrich T. Sommer

"Neural Manifold Clustering and Embedding" by Zengyi Li, Yubei Chen, Yann LeCun, and Friedrich T. Sommer, arXiv:2201.10000v1, 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|c|c|c|}
 \hline
 &&&\\
 \textbf{Method} & \textbf{Gates} & \textbf{Depth} & \textbf{Preprocessing} \\
 &&&\\
 \hline
 &&&\\
 Pyramid Circuit & $O(nd)$ & $n+d$ & $O(nd^{2})$ \\
 &&&\\
 \hline
 &&&\\
 Sine Cosine Decomposition & $O(nd)$ & $d \log n$ & $O(nd^{2})$ \\
 &&&\\
 \hline
 &&&\\
 Clifford Loaders & $O(nd)$ & $d \log n$ & $O(nd)$ \\
 &&&\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Quantum machine learning with subspace states" by Iordanis Kerenidis and Anupam Prakash, arXiv:2202.00054v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative comparison of different methods on the Mayo2016 and MCTD datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc|cccc}
 \hline
 \multirow{2}{*}{Methods} & \multicolumn{4}{c|}{Mayo2016 dataset} & \multicolumn{4}{c}{MCTD dataset} \\ \cline{2-9}
 & PSNR$\uparrow$ & SSIM$\uparrow$ & RMSE$\downarrow$ & LPIPS$\downarrow$ & PSNR$\uparrow$ & SSIM$\uparrow$ & RMSE$\downarrow$ & LPIPS$\downarrow$ \\ \hline
 RED-CNN & 32.32 & 0.9227 & 9.87 & 0.0235 & 37.81 & 0.9069 & 50.11 & 0.0535 \\
 DU-GAN & 32.64 & 0.9278 & 9.56 & 0.0198 & 38.52 & 0.9075 & 43.33 & 0.0291 \\
 SeD & 32.87 & 0.9283 & 9.32 & 0.0201 & 39.43 & 0.9157 & 40.78 & 0.0304 \\
 CTformer & 33.16 & 0.9287 & 8.98 & 0.0235 & 38.97 & 0.9134 & 40.54 & 0.0458 \\
 CoreDiff & 33.61 & 0.9342 & 8.58 & 0.0227 & 40.46 & 0.9290 & 34.40 & 0.0474 \\
 ASCON & 33.60 & 0.9318 & 8.57 & 0.0242 & \textbf{40.58} & 0.9278 & 34.43 & 0.0657 \\ \hline
 ALDEN-MedSAM & 33.59 & \textbf{0.9343} & 8.56 & 0.0192 & 40.51 & 0.9281 & 34.86 & 0.0273 \\
 ALDEN-DINOv2 & \textbf{33.71} & 0.9341 & \textbf{8.44} & \textbf{0.0176} & 40.57 & \textbf{0.9296} & \textbf{34.37} & \textbf{0.0265} \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Runze Wang
Zeli Chen
Zhiyun Song
Wei Fang
Jiajin Zhang
Danyang Tu
Yuxing Tang
Minfeng Xu
Xianghua Ye
Le Lu
Dakai Jin

"Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning" by Runze Wang, Zeli Chen, Zhiyun Song, Wei Fang, Jiajin Zhang, Danyang Tu, Yuxing Tang, Minfeng Xu, Xianghua Ye, Le Lu, and Dakai Jin, arXiv:2508.07788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography" by Chunmei Wang and Shangyou Zhang, arXiv:2503.18200v1, 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}{ccccccc}
 \toprule
 Within-regime Markov order $k_{i,g}$ ($i=1,\dots,4,g=1,2$) & 0 & 1 & 2 & 3 & 4 & 5 \\
 \midrule
 AIC & 3884 & 3804 & 3789 & 3768 & 3771 & 3776 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Arbitrage swap proportion comparing swap amount with first mover optimal swap amount and gas fee with first mover swap return}
\begin{tabular}{cc}
 \toprule
 \textbf{Swap amount $<$ first mover optimal swap amount} & 74.813\% \\ \midrule
 \textbf{Gas fee $<$ first mover swap return(not consider gas fee)} & 82.971\% \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

Arbitrage on Decentralized Exchanges

"Arbitrage on Decentralized Exchanges" by Xue Dong He, Chen Yang, and Yutian Zhou, arXiv:2507.08302v1, 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{}
\begin{tabular}{c|ccc|ccc}
 & \multicolumn{3}{c|}{w/o Static Obstacles} & \multicolumn{3}{c}{w/ Static Obstacles} \\ \hline
Method & Success & Collision & Time {[}s{]} & Success & Collision & Time {[}s{]} \\ \hline
ORCA & 0.77 & 0.23 & \textbf{31.52} & 0.50 & 0.40 & 41.91 \\
CADRL & 0.33 & 0.27 & 66.43 & 0.43 & \textbf{0.13} & 80.15 \\
SARL & 0.37 & 0.10 & 45.57 & 0.30 & 0.50 & 47.52 \\
MORP-RB & \textbf{0.97} & \textbf{0.03} & 34.20 & 0.73 & 0.27 & 40.50 \\
MORP-IL & \textbf{0.97} & \textbf{0.03} & 39.49 & \textbf{0.75} & 0.25 & \textbf{38.70} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Taekyung Kim
Seunghyun Lim
Gwanjun Shin
Geonhee Sim
Dongwon Yun

"An Open-Source Low-Cost Mobile Robot System with an RGB-D Camera and Efficient Real-Time Navigation Algorithm" by Taekyung Kim, Seunghyun Lim, Gwanjun Shin, Geonhee Sim, and Dongwon Yun, arXiv:2103.03054v4, 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{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{\color{black}Comparison of ENApp D-OPF and ADMM Based Method}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{System}& \multicolumn{2}{c|}{Total Loss (kW)} & \multicolumn{2}{c|}{Time (sec)}& \multicolumn{2}{c|}{\#Iterations}\\\cline{2-7}
 &{ENApp}&{ADMM}&{ENApp}&{ADMM}&{ENApp}&{ADMM}\\
\hline
{TS-1}&{21.9}&{21.8}&{3}&{145}&{4}&{272}\\
\hline
{TS-3}&{249.1}&{249.1}&{180}&{6120}&{17}&{756}\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Distributed Optimization using Reduced Network Equivalents for Radial Power Distribution Systems" by Rabayet Sadnan and Anamika Dubey, arXiv:2012.02473v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \multirow{2}{*}{Language location} & \multirow{2}{*}{\#} & Percentage \\
 & & of all entries\\
 \midrule
 Africa & 18 & 9.38\%\\
 Americas* & 3 & 1.56\%\\
 Asia & 61 & 31.77\%\\
 Europe & 46 & 23.96\%\\
 Latin America and the Caribbean & 17 & 8.85\%\\
 Middle East and North Africa & 4 & 2.08\%\\
 North Africa & 2 & 1.04\%\\
 North America & 11 & 5.73\%\\
 Oceania & 5 & 2.60\%\\
 World-wide & 21 & 10.94\%\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Distribution of data creators' locations over geographic regions (only first location for each entry).}
\end{table}
\end{document}

cs

Angelina McMillan-Major
Zaid Alyafeai
Stella Biderman
Kimbo Chen
Francesco De Toni
Gérard Dupont
Hady Elsahar
Chris Emezue
Alham Fikri Aji
Suzana Ilić
Nurulaqilla Khamis
Colin Leong
Maraim Masoud
Aitor Soroa
Pedro Ortiz Suarez
Zeerak Talat
Daniel van Strien
Yacine Jernite

"Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources" by Angelina McMillan-Major, Zaid Alyafeai, Stella Biderman, Kimbo Chen, Francesco De Toni, Gérard Dupont, Hady Elsahar, Chris Emezue, Alham Fikri Aji, Suzana Ilić, Nurulaqilla Khamis, Colin Leong, Maraim Masoud, Aitor Soroa, Pedro Ortiz Suarez, Zeerak Talat, Daniel van Strien, and Yacine Jernite, arXiv:2201.10066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The numbers of spanning trees of $H_0,\cdots,H_2$ and $H^2_0,\cdots,H^2_2$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 G & $\tau(G)$ & G & $\tau(G)$ & G & $\tau(G)$ \\
 \hline
 $H_0$ & 6 & $H_1$ & 241943 & $H_2$ & 6.71512031151729$\times 10^{32}$ \\
 $H^2_0$ & 6 & $H^2_1$ & 8426691368 & $H^2_2$ & 8.04003508846179$\times 10^{109}$
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Hexagonal and k-hexagonal graph's normalized Laplacian spectrum and applications" by Hao Li, Xinyi Chen, and Hao Liu, arXiv:2504.12781v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A numerical Fourier cosine expansion method with higher order Taylor schemes for fully coupled FBSDEs" by Balint Negyesi and Cornelis W. Oosterlee, arXiv:2501.10988v1, 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{Simulation results: non-constant conditional mean reward differences}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
		\toprule \\
		& \multicolumn{4}{c}{Our Proposed Method with APS Controls} & \multicolumn{2}{c}{Method with Mean Differences} & Direct \\
		\cline{2-5} \cline{6-7} \\
		 & $\delta = 0.1$ & $\delta=0.5$ & $\delta = 1$ & $\delta = 2.5$ & A/B Test Sample & Full Sample & Method \\
		 & (1) & (2) & (3) & (4) & (5) & (6) & (7) \\
		\hline \\
		\multicolumn{8}{c}{Experiment 1: Mix of A/B Test and Deterministic Policy} \\ \\
		\hspace{3mm} Bias & $-.012$ & .015 & .022 & .018 & $-.026$ & $-.006$ &---\\ \\
		 \hspace{3mm} S.D. & .045 & .038 & .033 & .026 & .045 & .018 &---\\\\
		 \hspace{3mm} RMSE & .046 & .041 & .040 & .031 & .052 & .019 &---\\\\
		 \hspace{3mm} Avg. $N$ & 1806 & 6009 & 11627 & 30136 & 500 & 50000 &--- \\ \\
		\multicolumn{8}{c}{Experiment 2: Upper Confidence Bound} \\ \\
		\hspace{3mm} Bias & $-.118$ & $-.119$ & $-.121$ & $-.119$ &--- &--- & $-.117$ \\ \\
		 \hspace{3mm} S.D. & .027 & .012 & .009 & .006 &---&---& .006 \\\\
		 \hspace{3mm} RMSE & .121 & .120 & .121 & .119 &---&---& .117 \\\\
		 \hspace{3mm} Avg. $N$ & 3397 & 17343 & 31107 & 47601 &---&---& 50000 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yusuke Narita
Kyohei Okumura
Akihiro Shimizu
Kohei Yata

"Counterfactual Learning with General Data-generating Policies" by Yusuke Narita, Kyohei Okumura, Akihiro Shimizu, and Kohei Yata, arXiv:2212.01925v1, 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{Impact on conditioning on lower boundary of solution with $\theta=\pi/2$. ${y_s}$ denote the second solution identified in Table . None approaches to $y_b$, twenty one scenarios (1-2,4-14, 16-21,23-24) go to the other solution $y_s$, four scenarios (3,15,22,25) diverges. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
		status & number & $\max|y''-f|$ & $\max|y_{opt}-{y_s}|$ \\\hline\hline
		 $\to y_b$ & 0 & NA & NA \\
		$\to y_s$ & 21 & 1.2E-07 & 3.1E-10 \\
		$diverge$ & 4 & diverge & diverge \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Parallelly Tempered Generative Adversarial Nets: Toward Stabilized Gradients" by Jinwon Sohn and Qifan Song, arXiv:2411.11786v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Functions & Classical $3$-symbol Code & function-correcting $3-$symbol Codes \\
 \hline
 $2t-b-$locally binary functions & $\lfloor \frac{t}{3} \rfloor log_q(k + 2t - 2)$ & $2t - 2$ \\
 \hline
 $b-$symbol weight functions & $\lfloor \frac{t}{3} \rfloor log_q(k + 2t - 2)$ & $\frac{q(2t-3)}{q\left( 1 - \sqrt{\frac{\ln(2t-2)}{2t - 2}}\right) - 1}$ \\
 \hline
 $$b-$$symbol weight distribution functions & $\lfloor \frac{t}{3} \rfloor log_q(k + 2t - 2)$ & $2t$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{A Comparison of Redundancy in Classical 3-Symbol Codes and Function-Correcting 3-Symbol Codes}
\end{table}
\end{document}

math

Anamika Singh
Abhay Kumar Singh
Eitan Yaakobi

"Function-Correcting Codes for b-Symbol Read Channels" by Anamika Singh, Abhay Kumar Singh, and Eitan Yaakobi, arXiv:2503.12894v1, 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{Setting 2: comparison of average Sharpe ratios and expected utilities for Bayesian with correct degenerate prior ($\text{BCPD}$); DRC and DRBC ($\delta = 10^{-3}/10^{-2}$).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccr}%{p{2.65cm}p{2cm}p{2.7cm}}
\toprule
Method & Sharpe ratio & Expected Utility\\
\midrule
$\text{BCPD}$ & 2.44595 & $6.07575$\\% \pm 0.72128$ \\
$\text{DRC}$($10^{-3}$) & 1.04900 & $3.46948$\\% \pm 0.41257$ \\
$\text{DRC}$($10^{-2}$) & 0.91977 & $3.38634 $\\%\pm 0.40271$ \\
DRBC ($10^{-3}$) & 1.48139 & $4.43138$\\% \pm 0.70654$\\
DRBC ($10^{-2}$) & 1.48136 & $4.43145$\\% \pm 0.70654$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Jose Blanchet
Jiayi Cheng
Hao Liu
Yang Liu

"Duality and Policy Evaluation in Distributionally Robust Bayesian Diffusion Control" by Jose Blanchet, Jiayi Cheng, Hao Liu, and Yang Liu, arXiv:2506.19294v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Joseph Wilson
Chris van der Heide
Liam Hodgkinson
Fred Roosta

"Uncertainty Quantification with the Empirical Neural Tangent Kernel" by Joseph Wilson, Chris van der Heide, Liam Hodgkinson, and Fred Roosta, arXiv:2502.02870v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Do Productivity Shocks Cause Inputs Misallocation?" by Davide Luparello, arXiv:2306.08760v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Expected initial state of the Deputy relative to the Chief, e.g., \( \mathbb{E}\big[X_d(t_{0}) - X_c(t_{0})\big] \).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
\( \delta x_0 \) [km] & \( \delta y_0 \) [km] & \( \delta z_0 \) [km] & \( \delta{\dot x_0} \) [km/s] & \( \delta{\dot y_0} \) [km/s] & \( \delta{\dot z_0} \) [km/s] \\
\hline
-9.7168 & -0.3110 & 0.5869 & 0.0014 & -0.0035 & -0.0068 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Taehyeun Kim
Robin Inho Kee
Ilya Kolmanovsky
Anouck Girard

"Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor" by Taehyeun Kim, Robin Inho Kee, Ilya Kolmanovsky, and Anouck Girard, arXiv:2412.05748v1, 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{change detection results of different methods on the Rome dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
\hline\hline
Methods & FP & FN & OE & PCC (\%) & KC(\%) & F1(\%)\\ \hline
PCAKM & 649 & 1864 & 2513 & 96.17 & 82.93 & 85.12\\
NR-ELM & 1726 & \textbf{101} & 1827 & 97.21 & 89.12 & 90.74\\
GaborPCANet & 785 & 1348 & 2133 & 96.75 & 85.96 & 87.84 \\
LR-CNN & 582 & 1057 & 1639 & 97.50 & 89.26 & 90.70\\
MLFN & 952 & 242 & 1194 & 98.18 & 92.59 & 93.65\\
DBN & 682 & 758 & 1440 & 97.80 & 90.74 & 92.01\\
DCNet & \textbf{339} & 888 & 1227 & 98.12 & 91.93 & 93.01\\
GKSNet & 706 & 304 & \textbf{1010} & \textbf{98.46} & \textbf{93.64} & \textbf{94.54}\\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Junjie Wang
Feng Gao
Junyu Dong
Shan Zhang
Qian Du

"Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network" by Junjie Wang, Feng Gao, Junyu Dong, Shan Zhang, and Qian Du, arXiv:2201.08954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|cccc}
\multicolumn{3}{l}{valid adjustment} & \multicolumn{3}{c}{$\epsilon_{pehe}$} \\
\hline
&TARnet & {\textsc \textbf{Mod1}} & {\textsc\textbf{Mod2} } & {\textsc \textbf{Mod3}} & {\textsc \textbf{Mod4}}\\
\hline
\textbf{low} &Adjust All &$.16\pm .06$ & $.13\pm .05$ & $.22 \pm .18$ & $.05 \pm .01$\\
& NICE &$.06\pm .02$ & $.05\pm .02$ & $.06 \pm .02$ & $.05 \pm .01$\\
\hline
\textbf{med}& Adjust All &$.14 \pm .06$ & $.15\pm .02$ & $.12 \pm .02$ & $.06\pm .02$\\
& NICE &$.05\pm .01$ & $.05 \pm .01$ & $.07 \pm .03$ & $.04 \pm .01$\\
\hline
\textbf{high} & Adjust All&$.13\pm .07$ & $.12\pm .02$ & $.16 \pm .09$ & $.06\pm .01$\\
& NICE &$.05\pm .01$ & $.05 \pm .01$ & $.07\pm .02$ & $.04\pm .01$\\
\hline
\end{tabular}
\end{adjustbox}
\caption{}
\end{table}
\end{document}

stat

"Invariant Representation Learning for Treatment Effect Estimation" by Claudia Shi, Victor Veitch, and David Blei, arXiv:2011.12379v2, 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
 $\beta_0$ & $\beta_1$ & $\beta_2$ & $\beta_{1,2}$ & $\lambda_{1,0}$ & $\lambda_{1,1}$ & $\theta_1$ & $\lambda_{2,0}$ & $\lambda_{2,1}$ & $\theta_2$ \\ \hline
 0.0840 & -0.2568 & 0.7415 & 0.2078 & 35.57 & 6.99 & 0.8142 & 10.15 & 0.21 & 0.9691 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Fabio Baschetti
Giacomo Bormetti
Pietro Rossi

"Joint deep calibration of the 4-factor PDV model" by Fabio Baschetti, Giacomo Bormetti, and Pietro Rossi, arXiv:2507.09412v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The average number of modes found over $10$ nested sampling runs, sampling the distribution shown in~, with and without mode collapse mitigation, for varying number of live points.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 Method& $n_{\rm live} = 20$ & $n_{\rm live} = 50$ & $n_{\rm live} = 100$ & $n_{\rm live} = 200$\\\midrule
 Without Mode Collapse Mitigation & $3.6$ & $6.4$ & $8.2$ & $8.9$\\
 With Mode Collapse Mitigation & $4.1$ & $6.4$ & $8.4$ & $9$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Pablo Lemos
Nikolay Malkin
Will Handley
Yoshua Bengio
Yashar Hezaveh
Laurence Perreault-Levasseur

"Improving Gradient-guided Nested Sampling for Posterior Inference" by Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, and Laurence Perreault-Levasseur, arXiv:2312.03911v1, 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{\textbf{Robustness of R-FID against latent perturbations.} This table will be removed as it has only 4 new numbers. We will insert them somehow into Figure 5.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
\toprule
\midrule
 & $\alpha=0.7$ & $\alpha=1.0$ \\
\midrule
 $\kappa=64$ & 98.3 $\rightarrow$ 128.1 & 88.1 $\rightarrow$ 126.6 \\
$\kappa=128$ & 119.9 $\rightarrow$ 157.8 & 113.8 $\rightarrow$ 162.5\\
\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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
		\hline
		Parameter & Value\\ \hline \hline
		$\kappa_{1}$ & 10 \\
		$\kappa_{2}$ & 1 \\
		$\kappa_{3}$ & 500\\
		$\kappa_{4}$ & 1\\
		$\kappa_{5}$ & 1.8 \\ \hline
	\end{tabular}
\caption{Baseline values for the controllers used in the simulation.}
\end{table}
\end{document}

cs

Felipe Morales
Rafael Cisneros
Romeo Ortega
Antonio Sanchez-Squella

"Nonlinear Voltage Regulation of an Auxiliary Energy Storage of a Multiport Interconnection" by Felipe Morales, Rafael Cisneros, Romeo Ortega, and Antonio Sanchez-Squella, arXiv:2403.19901v1, 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{ Location of estimated change points in the EEG data set using some selected alternative methods. Note that the true value is around 85 seconds.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
\hline
Method & Channel P3 & Channel T3 & Channel T5 \\ \hline\hline
PELT & 92 & 90 & $\{ 18, 20, 33, 35, 85\}$ \\
SDE & 92 & 90 & 85 \\
DP-UNIVAR & $\{92, 104 \}$ & $\{93, 95, 98, 100, 102 \}$ & $\{96, 98, 100, 103\}$ \\
LOCAL & 104 & 94 & 104 \\
STD & $\{104, 92, 97 \}$ & $\{ 94, 87, 100\}$ & $\{ 104, 90, 82, 97\}$ \\
WBS UNI-NONPAR & $\{ 104, 95\}$ & missing & $\{90, 82, 98 \}$ \\
ROB-WBS & $\{ 104, 100\}$ & missing & $\{104, 90, 85, 93 \}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Sepideh Mosaferi
Abolfazl Safikhani
Peiliang Bai

"Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models" by Sepideh Mosaferi, Abolfazl Safikhani, and Peiliang Bai, arXiv:2503.23211v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Tim Tsz-Kit Lau
Weijian Li
Chenwei Xu
Han Liu
Mladen Kolar

"Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism" by Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, and Mladen Kolar, arXiv:2412.21124v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

C. Biliotti
F. J. Bargagli-Stoffi
N. Fraccaroli
M. Puliga
M. Riccaboni

"Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic" by C. Biliotti, F. J. Bargagli-Stoffi, N. Fraccaroli, M. Puliga, and M. Riccaboni, arXiv:2212.01705v3, 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{ \small \textbf{OOD detection}. Results reported using AUROC show our method enhances the baseline up to 6\%.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 IND & OOD & Baseline & SSL-Ensemble & Our method \\
 \midrule
 \multirow{4}{6em}{CIFAR-100} & SVHN & 84.22 & 84.95 & \textbf{88.00} \\
 & Uniform & 91.65 & 90.53 & \textbf{97.57} \\
 & Gaussian & 90.00 & 89.42 & \textbf{94.10} \\
 & CIFAR-10 & 74.71 & 74.80 & \textbf{75.18} \\
 \midrule
 \multirow{4}{6em}{CIFAR-10} & SVHN & 95.03 & 96.68 & \textbf{97.07}\\
 & Uniform & 96.73 & 91.64 & \textbf{99.05}\\
 & Gaussian & 96.39 & 93.24 & \textbf{99.24} \\
 & CIFAR-100 & 91.79 & 91.59 & \textbf{91.87} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Amirhossein Vahidi
Lisa Wimmer
Hüseyin Anil Gündüz
Bernd Bischl
Eyke Hüllermeier
Mina Rezaei

"Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning" by Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, and Mina Rezaei, arXiv:2308.14705v2, 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{Simulation Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|} \hline
			\textbf{Parameter} & \textbf{Value} \\\hline
			Number of MEC servers $M$ & $5$ \\ \hline
			Plateau factors of MZipf $q_m$ & $[100, 200, 90, 40, 80]$\\ \hline
			Zipf factors of MZipf $k_m$ & $[0.60, 0.60, 0.75, 0.90, 0.90]$\\ \hline
			Data size of contents $\eta_c$ & $1\sim8$ GB \\ \hline
			Downloading payment of contents $\phi_c$ & $0.05\sim0.5$ HKD \\ \hline

			Number of DNN layers & $6$ \\ \hline
			Number of neurons per hidden layer & $128$ \\ \hline
			Learning rate $\xi$ & $0.002$ \\ \hline
			Update coefficient $\tau$ & $0.005$ \\ \hline
			Discounted factor $\gamma$ & $0.99$ \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Personalized Federated Deep Reinforcement Learning for Heterogeneous Edge Content Caching Networks" by Zhen Li, Tan Li, Hai Liu, and Tse-Tin Chan, arXiv:2412.12543v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ The mean (for the absolute values of the elements), standard deviation and kurtosis of the signal components of correlation tensors for three different weeks. The values within the brackets indicate standard deviations of the quantities. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
 \hline
 & 2017, November 06 - November 12 & 2018, January 01- January 07 & 2018, February 12 - February 18 \\
 \hline
 $\langle{|M_{ij}^{\alpha\beta}(\rm signal)|}\rangle$ & $0.343 (0.006)$ &$ 0.324 (0.005)$ & $0.334 (0.005)$ \\
 \hline
 standard deviation & $0.419 (0.006)$ & $0.401 (0.005)$ & $0.409 (0.005)$ \\
 \hline
 kurtosis & $2.447 (0.035) $ & $2.538 (0.032)$ & $2.500 (0.029) $ \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Abhijit Chakraborty
Tetsuo Hatsuda
Yuichi Ikeda

"Projecting XRP price burst by correlation tensor spectra of transaction networks" by Abhijit Chakraborty, Tetsuo Hatsuda, and Yuichi Ikeda, arXiv:2211.03002v2, 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{Household Variables}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
\textbf{Variable} & \textbf{Meaning} \\
\midrule
$N_t$ & Total population size at time $t$ \\
$x_t$ & Share of young individuals in the population at time $t$ \\
$n_t^b$ & Birth rate at time $t$ \\
$n_t^d$ & Death rate at time $t$ \\
$\text{age}_t^i$ & Age of individual $i$ at time $t$ \\
$a_t^i$ & Total assets held by individual $i$ (savings + investment) \\
$s_t^i$ & Savings held by individual $i$ \\
$v_t^i$ & Risky investment held by individual $i$ \\
$c_t^i$ & Consumption of individual $i$ \\
$h_t^i$ & Labor supply (work effort) of individual $i$ \\
$e_t^i$ & Education level of individual $i$ (affecting productivity) \\
$W_t$ & Wage rate at time $t$ \\
$p_t$ & Price level at time $t$ \\
$r_t$, $\rho_t$ & Returns on savings and risky investments, respectively \\
$m_t^i$ & Disposable income of individual $i$ after tax (and pension contribution if young) \\
$\alpha_t^i$ & Allocation ratio: share of $m_t^i$ allocated to savings/investment \\
$\theta_t^i$ & Investment ratio: share of $a_{t+1}^i$ allocated to risky investment \\
$a_{t+1}^i$ & Next-period asset holdings after financial allocation \\
$f_t^y$ & Mandatory pension contribution for young individuals \\
$f_t^o$ & Pension benefit received by old individuals \\
$T_t(\cdot)$ & Tax function applied to income and capital \\
$\beta$ & Time discount factor \\
$\sigma$ & Coefficient of relative risk aversion \\
$\gamma$ & Inverse Frisch elasticity of labor supply \\
$o_t^i$ & Personal observation of individual $i$ at time $t$ \\
$o_t^{\text{global}}$ & Global observation vector shared by all agents \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Qirui Mi
Qipeng Yang
Zijun Fan
Wentian Fan
Heyang Ma
Chengdong Ma
Siyu Xia
Bo An
Jun Wang
Haifeng Zhang

"EconGym: A Scalable AI Testbed with Diverse Economic Tasks" by Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, and Haifeng Zhang, arXiv:2506.12110v1, 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{Reversion coefficients under different market conditions}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
\hline
$\lambda$ & 0 & 0.2 & 0.4 & 0.6 & 0.8 & 1 \\ \hline
SR reversion & \hspace{0.5em}14.5\%\hspace{0.5em} & \hspace{0.5em}13.3\%\hspace{0.5em} & \hspace{0.5em}11.2\%\hspace{0.5em} & \hspace{0.5em}8.3\%\hspace{0.5em} & \hspace{0.5em}4.5\%\hspace{0.5em} & \hspace{0.5em}0.0\%\hspace{0.5em} \\
LR reversion & 39.8\% & 37.3\% & 33.0\% & 26.2\% & 15.9\% & 0.0\% \\ \hline
$\alpha$ & 0 & 0.2 & 0.4 & 0.6 & 0.8 & 1 \\ \hline
SR reversion & 22.7\% & 22.4\% & 21.5\% & 19.9\% & 17.6\% & 14.5\% \\
LR reversion & 52.3\% & 51.8\% & 50.6\% & 48.4\% & 44.9\% & 39.8\% \\ \hline
$m$ & 10 & 20 & 30 & 40 & 50 & 60 \\ \hline
SR reversion & 14.5\% & 16.0\% & 16.7\% & 16.9\% & 18.5\% & 18.6\% \\
LR reversion & 39.8\% & 57.1\% & 67.1\% & 74.4\% & 79.3\% & 82.0\% \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

How manipulable are prediction markets?

"How manipulable are prediction markets?" by Itzhak Rasooly and Roberto Rozzi, arXiv:2503.03312v1, 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
 $A_0$&$A_1$ &$A_2$ &$A_3$ &$A_4$ \\ \hline
 49.6219&-210.6617 & 341.7304 &-249.1275 &68.4388 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Fitting coefficients.}
\end{table}
\end{document}

math

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

"Spinning boson stars in nonlinear sigma models and Universal Relations" by Christoph Adam, Jorge Castelo Mourelle, Alberto García Martín-Caro, and Andrzej Wereszczynski, arXiv:2502.20923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{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|c}
		\toprule[1pt]
		Layer Counts &\# of Channels & mIOU (\%) & FLOPs \\
		\midrule[0.5pt]
		\midrule[0.5pt]
		-& -& 50.27($\pm$0.2)& 68.7G\\
		\midrule
		1&128& 50.75($\pm$0.2)& +0.00024G\\
		3&128& 50.85($\pm$0.2) & +0.00027G\\
		5&128& \textbf{51.06($\pm$0.2)} & +0.00030G \\
		7&128& 50.40($\pm$0.3) & +0.00043G \\
		\midrule
		5&64& 50.12($\pm$0.2)& +0.00015G\\
		5&256 & 50.35($\pm$0.4) & +0.00098G \\
		\bottomrule[1pt]
	\end{tabular}
\end{adjustbox}
\caption{ Results without using channelization (Row 1) and using channelization with different layer counts and channel numbers. Numbers in parentheses indicate standard deviations (see Sect.~).}
\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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters mean values obtained via MLPMMH for TASI data (with 249 values of daily returns.)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c|c}
 Parameter & $V_0$ & $H$ & $\rho$ & $\kappa$ & $\lambda$ & $\nu$ & r \\
 \hline
 Estimated Values & 0.16447 & 0.4557 & -0.43407 & 0.0017 & 11.0022 & 0.0445 &-5.7093e-04
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Bayesian Parameter Inference for Partially Observed Stochastic Volterra Equations" by Ajay Jasra, Hamza Ruzayqat, and Amin Wu, arXiv:2310.03114v2, 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{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Zeyi Lu
Xiaoxiao Ma
Yujun Huang
Minxiao Chen
Bin Chen
Baoyi An
Shu-Tao Xia

"EDPC: Accelerating Lossless Compression via Lightweight Probability Models and Decoupled Parallel Dataflow" by Zeyi Lu, Xiaoxiao Ma, Yujun Huang, Minxiao Chen, Bin Chen, Baoyi An, and Shu-Tao Xia, arXiv:2507.18969v1, 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{Comparision of DAE statistics} % title name of the table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccrrrrrrr} % creating 10 columns
\hline\hline % inserting double-line
 DAE &SNR improvement & PSNR &PRD(\%)
\\
\hline % inserts single-line
DNN Based DAE &9.82$\pm {2.69}$dB &0.98 &5.8 \\
CNN Based DAE& 13.43 $\pm {1.51}$dB&0.95 & 3.6 \\
RNN(LSTM) Based DAE& -5.11 $\pm {2.66}$dB&-6.17 & 18.3 \\
\hline % inserts single-line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Prateek Singh
Ambalika Sharma
Shreesha Maiya

"Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network" by Prateek Singh, Ambalika Sharma, and Shreesha Maiya, arXiv:2202.05177v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{A quantitative comparison of model performance in terms of PSNR and SRE metrics when different SR backbone models are integrated into the WaveDiffUS SDE solver for the UR tasks (scaling from $\times 4$ to $\times 128$) using the independent datasets DOTA, DIOR, and DDISR. Outputs marked with an asterisk (*) indicate evaluations are performed with pseudo ground truth images, where the UR image resolution exceeds the ground truth resolution.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\toprule
 & \multicolumn{6}{c}{DOTA} \\\midrule
 \multirow{2}{*}{Method} & \multicolumn{6}{c}{Scale} \\
 & $\times 4$ & $\times 8$ &$\times 16$ &$\times 32$* &$\times 64$* & $\times 128$* \\\midrule
 LE-GAN & 19.15/11.41 & 17.11/12.88 & 15.91/13.32 & 11.53/15.42 & 9.21/18.89 & 7.91/23.33 \\
 LE-GAN-WaveDiffUR & 19.74/11.39 & 19.18/11.12 & 18.21/12.86 & 16.74/13.40 & 12.58/13.63 & 11.21/15.87 \\
 RefVAE & 22.14/14.22 & 22.01/15.98 & 20.34/16.23 & 16.34/18.23 & 12.01/23.99 & 10.34/30.24 \\
 RefVAE-WaveDiffUR & 22.38/13.13 & 22.82/14.25 & 21.38/14.14 & 19.38/15.14 & 18.32/16.26 & 17.38/19.15 \\
 EDiffSR & 20.15/16.31 & 18.92/17.31 & 15.15/18.32 & 10.15/22.32 & 8.92/27.32 & 7.15/38.33 \\
 EDiffSR-WaveDiffUR & 20.02/16.51 & 19.29/17.02 & 18.02/17.52 & 17.25/18.21 & 15.99/18.35 & 12.02/20.53 \\
 CSP-WaveDiffUR(one-step) & 28.21/7.82 & 28.18/7.88 & 26.21/9.83 & 21.21/12.83 & 19.18/17.89 & 18.21/19.84 \\
 CSP-WaveDiffUR & 28.32/7.22 & 28.31/7.37 & 27.32/8.03 & 25.52/9.23 & 24.31/11.38 & 24.28/13.24 \\\midrule
 & \multicolumn{6}{c}{DIOR} \\\midrule
 \multirow{2}{*}{Method} & \multicolumn{6}{c}{Scale} \\
 &$\times 4$ & $\times 8$ &$\times 16$* &$\times 32$* &$\times 64$* & $\times 128$* \\\midrule
 LE-GAN & 19.41/12.69 & 19.19/13.18 & 18.31/14.82 & 14.35/17.34 & 10.21/19.99 & 6.92/27.53 \\
 LE-GAN-WaveDiffUR & 19.98/12.46 & 19.33/13.08 & 18.42/14.66 & 17.21/16.89 & 15.99/17.72 & 14.25/20.55 \\
 RefVAE & 18.35/15.92 & 17.32/16.18 & 15.52/16.74 & 12.21/19.98 & 8.81/25.93 & 5.89/28.83 \\
 RefVAE-WaveDiffUR & 19.01/15.28 & 18.99/15.82 & 17.59/16.11 & 14.49/17.52 & 13.21/19.14 & 10.15/21.71 \\
 EDiffSR & 18.78/13.51 & 17.98/14.23 & 14.88/15.66 & 12.62/20.22 & 9.21/24.41 & 5.32/26.93 \\
 EDiffSR-WaveDiffUR & 18.11/12.82 & 17.23/13.31 & 16.32/14.83 & 15.12/16.11 & 13.11/18.21 & 10.31/19.68 \\
 CSP-WaveDiffUR(one-step) & 24.63/12.71 & 23.21/13.81 & 21.92/15.32 & 18.84/19.32 & 14.22/22.12 & 10.36/24.96 \\
 CSP-WaveDiffUR & 25.12/8.99 & 24.92/9.87 & 22.31/9.72 & 21.03/10.21 & 18.79/12.42 & 16.32/14.85 \\\midrule
 & \multicolumn{6}{c}{DDISR} \\\midrule
 \multirow{2}{*}{Method} & \multicolumn{6}{c}{Scale} \\
 & $\times 4$ & $\times 8$ &$\times 16$ &$\times 32$ &$\times 64$ & $\times 128$ \\\midrule
LE-GAN & 24.14/16.89 & 21.91/18.87 & 19.19/21.82 & 16.59/24.34 & 12.21/28.99 & 8.21/32.31 \\
LE-GAN-WaveDiffUR & 24.38/15.08 & 23.21/16.16 & 23.09/16.98 & 19.99/19.72 & 16.55/22.55 & 14.32/26.37 \\
RefVAE & 28.12/18.18 & 25.52/19.74 & 22.11/21.81 & 18.81/25.93 & 12.89/28.23 & 9.82/36.20 \\
RefVAE-WaveDiffUR & 28.29/17.21 & 27.59/18.91 & 26.39/19.12 & 22.99/20.41 & 19.15/23.71 & 16.15/27.71 \\
EDiffSR & 28.98/23.13 & 27.88/26.66 & 24.92/29.12 & 19.21/34.19 & 13.28/39.93 & 12.08/38.82 \\
EDiffSR-WaveDiffUR & 29.23/21.11 & 28.32/22.83 & 27.17/23.11 & 25.91/24.29 & 21.18/25.68 & 18.11/28.85 \\
CSP-WaveDiffUR(one-step) & 31.21/12.19 & 30.92/13.39 & 28.84/16.32 & 24.22/18.12 & 20.36/21.62 & 16.13/28.21 \\
CSP-WaveDiffUR & 34.92/8.27 & 34.19/8.75 & 32.33/9.26 & 30.79/11.95 & 28.32/14.85 & 23.48/16.85 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yue Shi
Liangxiu Han
Darren Dancy
Lianghao Han

"WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images" by Yue Shi, Liangxiu Han, Darren Dancy, and Lianghao Han, arXiv:2412.18996v1, licensed under CC0 1.0. 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 performances among the conventional methods across different paradigms}
\begin{tabular}{c|cc|cc}
\hline
\multirow{2}{*}{Method} & \multicolumn{2}{c|}{Normal paradigm} & \multicolumn{2}{c}{AI paradigm} \\ \cline{2-5}
 & $F_{\beta}$ & std. & $F_{\beta}$ & std. \\ \hline
ShallowConvNet & 0.5314 & 0.1314 & 0.6481 & 0.1421 \\
DeepConvNet & 0.6328 & 0.1753 & 0.6899 & 0.1641 \\
EEGNet & 0.6425 & \textbf{0.0928} & 0.6841 & \textbf{0.0881} \\
\textbf{Proposed} & \textbf{0.6875} & 0.1308 & \textbf{0.7266} & 0.1481 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Sung-Jin Kim
Heon-Gyu Kwak
Hyeon-Taek Han
Dae-Hyeok Lee
Ji-Hoon Jeong
Seong-Whan Lee

"A Distributed Inference System for Detecting Task-wise Single Trial Event-Related Potential in Stream of Satellite Images" by Sung-Jin Kim, Heon-Gyu Kwak, Hyeon-Taek Han, Dae-Hyeok Lee, Ji-Hoon Jeong, and Seong-Whan Lee, arXiv:2312.09446v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \toprule
 \multirow{1}{*}{Custodian type} & \multirow{1}{*}{\#}\\
 \midrule
 University or research institution & 45 \\
 Commercial entity & 41 \\
 Nonprofit / NGO & 26 \\
 Private individual & 20 \\
 Government organization & 17 \\
 Library, museum or archival institute & 16\\
 Community (incl. online) & 2 \\
 Startup & 1 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Distribution of custodian types.}
\end{table}
\end{document}

cs

Angelina McMillan-Major
Zaid Alyafeai
Stella Biderman
Kimbo Chen
Francesco De Toni
Gérard Dupont
Hady Elsahar
Chris Emezue
Alham Fikri Aji
Suzana Ilić
Nurulaqilla Khamis
Colin Leong
Maraim Masoud
Aitor Soroa
Pedro Ortiz Suarez
Zeerak Talat
Daniel van Strien
Yacine Jernite

"Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources" by Angelina McMillan-Major, Zaid Alyafeai, Stella Biderman, Kimbo Chen, Francesco De Toni, Gérard Dupont, Hady Elsahar, Chris Emezue, Alham Fikri Aji, Suzana Ilić, Nurulaqilla Khamis, Colin Leong, Maraim Masoud, Aitor Soroa, Pedro Ortiz Suarez, Zeerak Talat, Daniel van Strien, and Yacine Jernite, arXiv:2201.10066v1, 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}{cccccccccccc}
 \hline
 & \multicolumn{3}{c}{Component 1} & & \multicolumn{3}{c}{Component 2} & & \multicolumn{3}{c}{Component 3} \\
 \cline{2-4} \cline{6-8} \cline{10-12}
 Density & $\pi_1$ & $\mu_1$ & $\sigma_1^2$
 & & $\pi_2$ & $\mu_2$ & $\sigma_2^2$
 & & $\pi_3$ & $\mu_3$ & $\sigma_3^2$ \\
 \cline{2-4} \cline{6-8} \cline{10-12}
 1 & 0.75 & 0.00 & 0.83 & & 0.25 & 1.37 & 0.09 & & -- & -- & -- \\
 2 & 0.45 & -0.93 & 0.22 & & 0.45 & 0.93 & 0.22 & & 0.10 & 0.00 & 0.04 \\
 3 & 0.50 & -0.74 & 0.14 & & 0.30 & 0.37 & 0.55 & & 0.20 & 1.47 & 0.14 \\
 4 & 0.50 & 0.00 & 0.14 & & 0.35 & 1.28 & 0.14 & & 0.15 & 2.56 & 0.11 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Top panel: univariate density functions used in the simulation study. Bottom panel: corresponding parameter settings for the mixture components.}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablations on Initial alignment comparisons of our method compared to TransFill without the CST Module.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
Model &PSNR$\uparrow$&SSIM$\uparrow$&LPIPS$\downarrow$\\ \hline\hline
GeoFill & \bf{31.47} & \bf{0.9748} & \bf{0.0525} \\\hline
GeoFill (no CST) & 29.35 & 0.9688 & 0.0579 \\\hline
TransFill & 30.64 & 0.9732 & 0.0540 \\\hline
TransFill (no CST) & 26.03 & 0.9598 & 0.0742 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yunhan Zhao
Connelly Barnes
Yuqian Zhou
Eli Shechtman
Sohrab Amirghodsi
Charless Fowlkes

"GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding" by Yunhan Zhao, Connelly Barnes, Yuqian Zhou, Eli Shechtman, Sohrab Amirghodsi, and Charless Fowlkes, arXiv:2201.08131v2, 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}
 \multicolumn{6}{c}{\textbf{ODE Terms:}}\\
Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.}\\
\hline
$A_0$ & \texttt{A0} & $B_{1}$ & \texttt{B1} &$B_{2}$&\texttt{B2}\\
$C_{1}$ & \texttt{C1} & $D_{11}$ &\texttt{D11}&$D_{12}$&\texttt{D12}\\
$C_{2}$ & \texttt{C2} & $D_{21}$ &\texttt{D21}&$D_{22}$&\texttt{D22} \\ \hline \\
 \multicolumn{6}{c}{ \textbf{Discrete Delay Terms:}}\\
Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.}\\
\hline
$A_i$ & \texttt{Ai\{i\}} & $B_{1i}$ & \texttt{B1i\{i\}} &$B_{2i}$&\texttt{B2i\{i\}}\\
$C_{1i}$ & \texttt{C1i\{i\}} & $D_{11i}$ &\texttt{D11i\{i\}}&$D_{12i}$&\texttt{D12i\{i\}}\\
$C_{2i}$ & \texttt{C2i\{i\}} & $D_{21i}$ &\texttt{D21i\{i\}}&$D_{22i}$&\texttt{D22i\{i\}}\\
\hline \\ \multicolumn{6}{c}{\textbf{Distributed Delay Terms: May be functions of \texttt{pvar s}}}\\
Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.}\\
\hline
$A_{di} $ & \texttt{Adi\{i\}} & $B_{1di}$ & \texttt{B1di\{i\}} &$B_{2di}$&\texttt{B2di\{i\}}\\
$C_{1di} $& \texttt{C1di\{i\}} &$ D_{11di} $&\texttt{D11di\{i\}}&$D_{12di}$&\texttt{D12di\{i\}}\\
$C_{2di}$ & \texttt{C2di\{i\}} & $D_{21di} $&\texttt{D21di\{i\}}&$D_{22di}$&\texttt{D22di\{i\}}\\
\end{tabular}
\end{adjustbox}
\caption{ Equivalent names of Matlab elements of the \texttt{DDE} structure terms for terms in Eqn.~. For example, to set term \texttt{XX} to \texttt{YY}, we use \texttt{DDE.XX=YY}. In addition, the delay $\tau_i$ is specified using the vector element \texttt{DDE.tau(i)} so that if $\tau_1=1, \tau_2=2, \tau_3=3$, then \texttt{DDE.tau=[1 2 3]}. }
\end{table}
\end{document}

math

PIETOOLS 2024: User Manual

Sachin Shivakumar
Declan Jagt
Danilo Braghini
Amritam Das
Yulia Peet
Matthew Peet

"PIETOOLS 2024: User Manual" by Sachin Shivakumar, Declan Jagt, Danilo Braghini, Amritam Das, Yulia Peet, and Matthew Peet, arXiv:2501.17854v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.