\documentclass{article}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Classification Accuracy(\%) under Netattack over 5 random seeds}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc|cccc}
\hline
\multirow{2}{*}{} & \multirow{2}{*}{\textbf{Model}} & \multirow{2}{*}{\textbf{Clean}} & \multicolumn{4}{c}{\textbf{Evasive}} & \multicolumn{4}{c}{\textbf{Poisoning}} \\ \cline{4-11}
 & & \textbf{} & \textbf{1} & \textbf{2} & \textbf{3} & \textbf{4} & \textbf{1} & \textbf{2} & \textbf{3} & \textbf{4} \\ \hline
\multirow{5}{*}{\rotatebox{90}{\textbf{Cora}}} & \textbf{GCN} & 80.0\scriptsize{$\pm$7.87} & 51.5\scriptsize{$\pm$4.87} & 38.0\scriptsize{$\pm$6.22} & 31.0\scriptsize{$\pm$2.24} & 26.0\scriptsize{$\pm$3.79} & 47.5\scriptsize{$\pm$7.07} & 39.5\scriptsize{$\pm$2.74} & 30.0\scriptsize{$\pm$5.00} & 26.5\scriptsize{$\pm$3.79} \\
 & \textbf{RGCN} & 80.0\scriptsize{$\pm$4.67} & 49.5\scriptsize{$\pm$6.47} & 36.0\scriptsize{$\pm$5.18} & 30.5\scriptsize{$\pm$3.25} & 25.5\scriptsize{$\pm$2.09} & 46.5\scriptsize{$\pm$5.75} & 35.5\scriptsize{$\pm$3.70} & 29.0\scriptsize{$\pm$3.79} & 25.5\scriptsize{$\pm$2.73} \\
 & \textbf{GAT} & 77.8\scriptsize{$\pm$3.97} & 48.0\scriptsize{$\pm$8.73} & 39.5\scriptsize{$\pm$5.70} & 36.5\scriptsize{$\pm$5.48} & 32.5\scriptsize{$\pm$5.30} & 50.5\scriptsize{$\pm$5.70} & 38.0\scriptsize{$\pm$5.97} & 33.5\scriptsize{$\pm$2.85} & 26.0\scriptsize{$\pm$3.79} \\
 & \textbf{DGI} & 82.5\scriptsize{$\pm$4.33} & 62.0\scriptsize{$\pm$4.81} & 46.0\scriptsize{$\pm$3.79} & 34.0\scriptsize{$\pm$5.18} & {27.5}\scriptsize{$\pm$3.06} & 62.5\scriptsize{$\pm$3.54} & 43.5\scriptsize{$\pm$3.79} & {31.5}\scriptsize{$\pm$6.75} & 26.5\scriptsize{$\pm$4.18} \\
 & \textbf{GCL} & 64.4\scriptsize{$\pm$4.27} & 53.8\scriptsize{$\pm$5.20} & 38.8\scriptsize{$\pm$8.54} & 25.6\scriptsize{$\pm$3.15} & 18.8\scriptsize{$\pm$3.23} & 41.9\scriptsize{$\pm$6.25} & 33.1\scriptsize{$\pm$8.00} & 28.8\scriptsize{$\pm$7.77} & 23.1\scriptsize{$\pm$5.54} \\
 & \textbf{GACL} & 82.0\scriptsize{$\pm$3.26} & \textbf{67.5}\scriptsize{$\pm$5.00} & \textbf{46.0}\scriptsize{$\pm$5.76} & \textbf{35.5}\scriptsize{$\pm$4.81} & \textbf{29.0}\scriptsize{$\pm$6.75} & \textbf{64.5}\scriptsize{$\pm$5.70} & \textbf{44.0}\scriptsize{$\pm$7.42} & \textbf{33.5}\scriptsize{$\pm$6.02} & \textbf{27.5}\scriptsize{$\pm$5.30} \\ \hline
\multirow{5}{*}{\rotatebox{90}{\textbf{Citeseer}}} & \textbf{GCN} & 71.8\scriptsize{$\pm$6.94} & 42.5\scriptsize{$\pm$7.07} & 27.5\scriptsize{$\pm$6.37} & 18.0\scriptsize{$\pm$3.26} & 15.0\scriptsize{$\pm$2.50} & 29.0\scriptsize{$\pm$7.20} & 20.5\scriptsize{$\pm$1.12} & 17.5\scriptsize{$\pm$1.77} & 13.0\scriptsize{$\pm$2.09} \\
 & \textbf{RGCN} & 73.5\scriptsize{$\pm$8.40} & 41.5\scriptsize{$\pm$7.42} & 24.5\scriptsize{$\pm$6.47} & 18.5\scriptsize{$\pm$6.52} & 13.0\scriptsize{$\pm$1.11} & 31.0\scriptsize{$\pm$5.48} & 19.5\scriptsize{$\pm$2.09} & 13.5\scriptsize{$\pm$2.85} & 5.00\scriptsize{$\pm$1.77} \\
 & \textbf{GAT} & 72.3\scriptsize{$\pm$8.38} & {49.0}\scriptsize{$\pm$9.12} & 33.0\scriptsize{$\pm$5.97} & 22.0\scriptsize{$\pm$4.81} & 18.0\scriptsize{$\pm$3.26} & 38.0\scriptsize{$\pm$5.12} & 23.5\scriptsize{$\pm$4.87} & 16.5\scriptsize{$\pm$4.54} & 12.0\scriptsize{$\pm$2.09} \\
 & \textbf{DGI} & 78.5\scriptsize{$\pm$5.76} & 64.0\scriptsize{$\pm$4.18} & 49.5\scriptsize{$\pm$4.47} & 36.5\scriptsize{$\pm$5.18} & 30.5\scriptsize{$\pm$5.97} & 57.5\scriptsize{$\pm$4.68} & 40.0\scriptsize{$\pm$7.70} & 31.0\scriptsize{$\pm$2.24} & {25.5}\scriptsize{$\pm$5.70} \\
 & \textbf{GCL} & 70.0\scriptsize{$\pm$7.36} & 59.4\scriptsize{$\pm$6.57} & 47.5\scriptsize{$\pm$5.40} & 36.3\scriptsize{$\pm$6.29} & 32.5\scriptsize{$\pm$4.08} & 50.6\scriptsize{$\pm$5.54} & 38.3\scriptsize{$\pm$6.77} & 38.1\scriptsize{$\pm$4.27} & 26.9\scriptsize{$\pm$8.00}\\
 & \textbf{GACL} & 77.5\scriptsize{$\pm$3.06} & \textbf{66.0}\scriptsize{$\pm$3.79} & \textbf{53.0}\scriptsize{$\pm$5.70} & \textbf{46.0}\scriptsize{$\pm$4.87} & \textbf{37.0}\scriptsize{$\pm$1.12} & \textbf{63.5}\scriptsize{$\pm$4.18} & \textbf{41.0}\scriptsize{$\pm$3.79} & \textbf{40.0}\scriptsize{$\pm$9.19} & \textbf{30.5}\scriptsize{$\pm$5.97} \\ \hline
\multirow{5}{*}{\rotatebox{90}{\textbf{Pubmed}}} & \textbf{GCN} & 82.6\scriptsize{$\pm$6.98} & 39.5\scriptsize{$\pm$4.81} & 32.0\scriptsize{$\pm$4.81} & 31.0\scriptsize{$\pm$5.76} & 31.0\scriptsize{$\pm$5.76} & 36.0\scriptsize{$\pm$4.18} & 32.5\scriptsize{$\pm$6.37} & 31.0\scriptsize{$\pm$5.76} & 28.5\scriptsize{$\pm$5.18} \\
 & \textbf{RGCN} & 79.0\scriptsize{$\pm$5.18} & 39.5\scriptsize{$\pm$5.70} & 33.0\scriptsize{$\pm$4.80} & 31.5\scriptsize{$\pm$4.18} & 30.0\scriptsize{$\pm$5.00} & 38.5\scriptsize{$\pm$4.18} & 31.5\scriptsize{$\pm$2.85} & 29.5\scriptsize{$\pm$3.70} & 27.0\scriptsize{$\pm$3.70} \\
 & \textbf{GAT} & 78.6\scriptsize{$\pm$6.70} & 41.0\scriptsize{$\pm$8.40} & 33.5\scriptsize{$\pm$4.18} & 30.5\scriptsize{$\pm$4.47} & 31.0\scriptsize{$\pm$4.18} & 39.5\scriptsize{$\pm$3.26} & 31.0\scriptsize{$\pm$4.18} & 30.0\scriptsize{$\pm$3.06} & 35.5\scriptsize{$\pm$5.97} \\
 & \textbf{DGI} & 79.0\scriptsize{$\pm$7.20} & 40.5\scriptsize{$\pm$5.86} & 31.0\scriptsize{$\pm$4.54} & 29.5\scriptsize{$\pm$3.71} & 28.0\scriptsize{$\pm$2.74} & 40.0\scriptsize{$\pm$4.81} & 31.0\scriptsize{$\pm$3.79} & 28.5\scriptsize{$\pm$4.18} & 28.0\scriptsize{$\pm$4.68} \\
 & \textbf{GCL} & 67.5\scriptsize{$\pm$7.07} & \textbf{45.3}\scriptsize{$\pm$1.77} & \textbf{35.8}\scriptsize{$\pm$6.29} & 28.3\scriptsize{$\pm$5.10} & 28.1\scriptsize{$\pm$4.26} & 40.25\scriptsize{$\pm$1.77} & 33.25\scriptsize{$\pm$6.61} & \textbf{30.3}\scriptsize{$\pm$1.44} & 19.4\scriptsize{$\pm$3.75}\\
 & \textbf{GACL} & 83.0\scriptsize{$\pm$5.42} & 43.0\scriptsize{$\pm$5.42} & 34.0\scriptsize{$\pm$5.18} & \textbf{30.0}\scriptsize{$\pm$3.06} & \textbf{28.5}\scriptsize{$\pm$4.18} & \textbf{41.0}\scriptsize{$\pm$3.79} & \textbf{34.0}\scriptsize{$\pm$2.85} & 29.5\scriptsize{$\pm$3.71} & \textbf{28.5}\scriptsize{$\pm$4.18} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jiayan Guo
Shangyang Li
Yue Zhao
Yan Zhang

"Learning Robust Representation through Graph Adversarial Contrastive Learning" by Jiayan Guo, Shangyang Li, Yue Zhao, and Yan Zhang, arXiv:2201.13025v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{marvosym}
\usepackage{pifont}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll|cccccc}
			\toprule
			\multicolumn{3}{c}{overlap attention} & \multicolumn{3}{c}{\textit{3DMatch}} & \multicolumn{3}{c}{\textit{3DLoMatch}} \\
 \cline{4-9}
 \textit{ov.} & \textit{$\times$ov.} & \textit{cond.} & FMR & IR & RR & FMR & IR & RR \\
 \hline
 & & & \underline{96.4} & 39.6 & 82.6 & 72.2 & 14.5 & 38.9\\
 \ding{51} & & & 94.6 & 38.3 & 84.1 & 67.1 & 14.3 & 42.8 \\
 \ding{51} & \ding{51} & & \underline{96.4} & 50.8 & 87.7 & \underline{73.8} & 20.9 & 56.5 \\
 \ding{51} & & \ding{51} & 95.7 & \underline{52.1} & \underline{88.0} & 72.5 & \underline{21.2} & \underline{57.5} \\
 \ding{51} & \ding{51} & \ding{51} & \textbf{96.7} & \textbf{58.0} & \textbf{89.0} & \textbf{78.6} & \textbf{26.7}& \textbf{59.8} \\
			\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Ablation of the network architecture. \textit{ov.} denotes upsampling the overlap scores; \textit{cond.} denotes conditioning the bottleneck features on the respective other point cloud; \textit{$\times$ov.} denotes upsampling the cross overlap scores.}
\end{table}
\end{document}

eess

Shengyu Huang
Zan Gojcic
Mikhail Usvyatsov
Andreas Wieser
Konrad Schindler

"PREDATOR: Registration of 3D Point Clouds with Low Overlap" by Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler, arXiv:2011.13005v3, 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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The number of solutions for the ladder $N(2n)$ with $2n$ players for the case when the payoff parameters obey $x>y>x/2$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 $2n$ & $N_{ladd}(2n)$ \\ \hline
 4 & 2 \\
 6 & 3 \\
 8 & 4 \\
 10 & 7 \\
 12 & 10 \\
 14 & 17 \\
 16 & 26 \\
 18 & 43 \\
 20 & 68 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Victoria Sánchez Muñoz
Michael Mc Gettrick

"Nash Equilibria in certain two-choice multi-player games played on the ladder graph" by Victoria Sánchez Muñoz and Michael Mc Gettrick, arXiv:2101.09103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks" by Jozef Barunik and Matej Nevrla, arXiv:2208.14267v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter search space of XGBoost.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule
\textbf{Hyperparameter} & \textbf{Distribution} \\ \midrule
max\_depth & $\text{uniform\_int}[1,11]$\\
num\_estimators & $1000$\\
min\_child\_weight & $\text{log\_uniform\_int}[1,1\mathrm{e}2]$\\
subsample & $\text{unifrom}[0.5,1]$\\
learning\_rate & $\text{log\_unifrom}[1\mathrm{e}{-5},0.7]$\\
col\_sample\_by\_level & $\text{uniform}[0.5,1]$\\
col\_sample\_by\_tree & $\text{uniform}[0.5,1]$\\
gamma & $\text{log\_uniform}[1\mathrm{e}{-8},7]$\\
lambda & $\text{log\_uniform}[1,4]$\\
alpha & $\text{log\_uniform}[1\mathrm{e}{-8},1\mathrm{e}2]$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccc}
\hline
minute & $1$ & $2$ & $3$ & $4$ & $5$ & $6$ & $\ldots$\\
\hline
fight (armed) & $0.300$ & $0.276$ & $0.249$ & $0.213$ & $0.162$ & $0.162$ &$\ldots$ \\
fight (unarmed) & $0.050$ & $0.046$ & $0.041$ & $0.035$ & $0.027$ & $0.027$ & $\ldots$ & \\
hide & $0.919$ & $0.824$ & $0.706$ & $0.543$ & $0.217$ & $0.217$& $\ldots$\\
run & $0.100$ & $0.092$ & $0.083$ & $0.071$ & $0.054$ & $0.054$ & $\ldots$\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Survival probabilities of a civilian of focus in an open arena when $N=210$, $m=15$, $e=25$, $p_r=0.1$, $p_1=0.05$, $p_2=0.3$, and $T_2=4\leq T_1$ when there is only one possibly armed civilian.}
\end{table}
\end{document}

stat

"Analytic models for active shooter incidents with civilian resistance" by Liang Hong, arXiv:2310.17111v2, 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|c|c|c}
$n$&4 &5&6 &7&8&9&10&11&12&13&14\\ \hline
$\left\lfloor\frac{2n}{5}\right\rfloor$&1 &2&2 &2&3&3&4&4&4&5&5\\ \hline
$\gamma_h(C_n) $ &2&2&2&3&4&3&4&4&4&5&6\\
\end{tabular}
\end{adjustbox}
\caption{$\gamma_h(C_n)$ for small $n$}
\end{table}
\end{document}

math

"Tight upper bounds on the hop domination number of triangle-free graphs" by Shinya Fujita and Boram Park, arXiv:2503.04124v1, licensed under CC0 1.0. 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|r|r|r}\hline
		Level& Taxon& MW &$R^2$ & \textbf{CAT} \\
 		&& $p$-value&difference& $p$-value\\
		\hline
 	Phylum &Bacteroidetes &$\mathbf{<0.01}$ &0.0502	&$0.072$\\
		Phylum &Firmicutes&$\mathbf{<0.01}$&0.0337	&$\mathbf{0.035}$\\
		Class &Bacteroidia&$\mathbf{<0.01}$	&0.0503&$0.072$\\
 	Class &Clostridia&$\mathbf{<0.01}$&0.0304	&$\mathbf{0.050}$\\
 	Class &Mollicutes&$\mathbf{0.01}$&0.0001	& $0.084$\\
		Order &Bacteroidales&$\mathbf{<0.01}$&0.0503	&$0.072$\\
		Order &Clostridiales&$\mathbf{<0.01}$&0.0303	&$\mathbf{0.050}$\\
 	Family &Micrococcaceae&$\mathbf{0.01}$&$<0.0001$	&$0.071$\\
		Family & Ruminococcaceae&$\mathbf{0.03}$ & 0.0365 &$\mathbf{<0.001}$\\
 	Genus &\textit{Faecalibacterium}&$\mathbf{0.01}$&0.0151	&$\mathbf{0.005}$\\
 Genus &\textit{Gardnerella}&$\mathbf{0.03}$&	$<0.0001$&$0.879$ \\
 Genus &\textit{Peptoniphilus} &0.12&$<0.0001$&$0.148$ \\
 Genus &\textit{Phascolarctobacterium}&$\mathbf{0.01}$&0.0010&$\mathbf{0.029}$\\
 Genus &\textit{Rothia}	&$\mathbf{0.01}$ & $<0.0001$&$0.071$\\
 Genus &\textit{Ruminococcus}&$\mathbf{0.03}$ & 0.0096	&$\mathbf{<0.001}$\\
 Species &\textit{B.\ stercoris}&$\mathbf{0.03}$	&$<0.0001$&$0.866$\\
		Species &\textit{F.\ prausnitzii}&$\mathbf{0.01}$	&0.0151&$\mathbf{0.005}$\\
		Species &\textit{M.\ hungatei}&0.18	&$<0.0001$&$\mathbf{0.038}$\\
		Species &\textit{R.\ bromii}&0.08	&0.0043&$\mathbf{0.002}$\\\hline
	\end{tabular}
\end{adjustbox}
\caption{Level in the taxonomic tree, taxon, Mann-Whitney $p$-value, $R^2$ difference before and after removing candidate taxon, and $p$-value from \textbf{CAT} when applied to features identified by LEfSe in .}
\end{table}
\end{document}

stat

Yushu Shi
Liangliang Zhang
Kim-Anh Do
Robert R. Jenq
Christine B. Peterson

"CAT: a conditional association test for microbiome data using a leave-out approach" by Yushu Shi, Liangliang Zhang, Kim-Anh Do, Robert R. Jenq, and Christine B. Peterson, arXiv:2309.08109v1, 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{Total number of articles obtained per source before and after NER filtering, with percentage reduction shown in brackets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\hline
\textbf{News Source} & \textbf{No. of articles pre processing} & \textbf{No. of articles post NER filtering} \\ \hline
MarketWatch & 309,187 & 236,214 (23.6\%)\\
Reuters & 38,141 & 35,741 (6.3\%)\\
The Motley Fool & 205,270 & 147,413 (28.2\%) \\
\hline
\textbf{Total} & \textbf{552,598} & \textbf{419,368} (\textbf{24.1\%}) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Giorgos Iacovides
Wuyang Zhou
Danilo Mandic

"FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs" by Giorgos Iacovides, Wuyang Zhou, and Danilo Mandic, arXiv:2507.18417v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jane She
Matthew Egberg
Michael R. Kosorok

"An optimal dynamic treatment regime estimator for indefinite-horizon survival outcomes" by Jane She, Matthew Egberg, and Michael R. Kosorok, arXiv:2501.18070v1, 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|l|l|l}
\hline
\textbf{Regions} & \textbf{1} & \textbf{2} & \textbf{3} & \textbf{4} &
\textbf{5} & \textbf{6} \\
\hline \hline
\textbf{OLr} & 23.4750 & 18.4683 & 16.9887 & 21.2629 & 14.9777 & 23.1817 \\
\hline
\textbf{ORr} & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
\textbf{OLrf} & 24.5746 & 20.3071 & 18.5780 & 9.7478 & 18.0010 & 13.9349 \\
\hline
\textbf{ORrf} & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
\textbf{OLrm} & 22.5746 & 16.7094 & 15.4745 & 17.1072 & 24.5693 & 19.3186 \\
\hline
\textbf{ORrm} & 0 & 0 & 0 & 0 & 0 & 0 \\
\hline \hline
\end{tabular}
\end{adjustbox}
\caption{Outliers in Mathematics grades by region and year}
\end{table}
\end{document}

stat

Ousmane Saine
Soumaila Dembélé
Gane Samb Lo
Mohamed Cheikh Haidara

"Overview description of the Gambian GABECE Educational Data and associated algorithms and unsupervized learning process" by Ousmane Saine, Soumaila Dembélé, Gane Samb Lo, and Mohamed Cheikh Haidara, arXiv:2012.08488v1, 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{Baseline and Remediation Performance Metrics for Models (Simple Prompt)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccccc}
\hline
\textbf{Model} & \multicolumn{2}{c}{\textbf{Baseline}} & & \multicolumn{3}{c}{\textbf{Remediation}} \\
\cline{3-4} \cline{6-8}
& & \textbf{\hspace{1em}MAE\hspace{1em}} & \textbf{\hspace{1em}Freq\hspace{1em}} & & \textbf{\hspace{1em}MAE\hspace{1em}} & \textbf{\hspace{1em}Freq\hspace{1em}} & \boldmath{$\hat{\alpha}^*$} \\
\hline
\multicolumn{8}{c}{Direct Race Indicator} \\
\hline
\texttt{Command R} & & 110.5 & 130 & & 100.4 & 122 & 0.2 \\
\texttt{Command R+} & & 27.8 & 97 & & 21.3 & 94 & 0.2 \\
\texttt{Llama 3.1 (8b)} & & 58.9 & 63 & & 37.6 & 35 & 0.16 \\
\texttt{Llama 3.1 (70b)} & & 12.7 & 14 & & 5.2 & 7 & -0.04 \\
\texttt{Mistral v0.3} & & 29.2 & 100 & & 16.8 & 45 & -0.04 \\
\hline
\multicolumn{8}{c}{Indirect Race Indicator} \\
\hline
\texttt{Command R} & & 20.1 & 130 & & 15.2 & 101 & -0.2 \\
\texttt{Command R+} & & 27.1 & 74 & & 21.6 & 71 & -0.1 \\
\texttt{Llama 3.1 (8b)} & & 42.1 & 42 & & 26.4 & 26 & 0.08 \\
\texttt{Llama 3.1 (70b)} & & 6.1 & 12 & & 3.6 & 7 & 0.02 \\
\texttt{Mistral v0.3} & & 29.3 & 58 & & 16.1 & 72 & 0.04 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Social Group Bias in AI Finance

"Social Group Bias in AI Finance" by Thomas R. Cook and Sophia Kazinnik, arXiv:2506.17490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of experiments performed on $TPN$\textit{.}}\centering%
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Examples & Tools & TINA & ROMEO & ORIS & \multicolumn{2}{|c|}{ITPNAnalyzer}
\\ \hline
& & DBM & DBM & DBM & DBM & Tdis \\ \hline
& Classes & 2 & 2 & 2 & 2 & 2 \\ \cline{2-7}\cline{5-7}
Proc 1 & Edges & 2 & 2 & $NA$ & 2 & 2 \\ \cline{2-7}\cline{5-7}
& Times (ms) & 0 & $NA$ & 0 & 0 & 0 \\ \hline
& Classes & 186 & 186 & 188 & 186 & 186 \\ \cline{2-7}\cline{5-7}
Proc 1 2 & Edges & 262 & 262 & $NA$ & 262 & 262 \\ \cline{2-7}\cline{5-7}
& Times (ms) & 0 & $NA$ & 16 & 0 & 0 \\ \hline
& Classes & 958 & 958 & 1.038 & 958 & 958 \\ \cline{2-7}\cline{5-7}
Proc 1 2 3 & Edges & 1506 & 1506 & $NA$ & 1506 & 1506 \\
\cline{2-7}\cline{5-7}
& Times (ms) & 16 & $NA$ & 391 & 5 & 30 \\ \hline
& Classes & 5.219 & 5.219 & 6.029 & 5.219 & 5.219 \\ \cline{2-7}\cline{5-7}
Proc 1 2 3 4 & Edges & 8.580 & 8.580 & $NA$ & 8.580 & 8.580 \\
\cline{2-7}\cline{5-7}
& Times (ms) & 140 & $NA$ & 2.719 & 38 & 80 \\ \hline
& Classes & 42.909 & 42.909 & 52.452 & 42.909 & 42.909 \\
\cline{2-7}\cline{5-7}
Proc 1 2 3 4 5 & Edges & 73.842 & 73.842 & $NA$ & 73.842 & 73.842 \\
\cline{2-7}\cline{5-7}
& Times (ms) & 6.739 & $NA$ & 30.000 & 670 & 1.310 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Time distance based computation of the state space of preemptive real time systems" by Abdelkrim Abdelli, arXiv:2201.09784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

"PSN Game: Game-theoretic Planning via a Player Selection Network" by Tianyu Qiu, Eric Ouano, Fernando Palafox, Christian Ellis, and David Fridovich-Keil, arXiv:2505.00213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model error for the population dynamics example. The first three rows show the error of the N-CTMC model as we increase the number of trajectories in the training set. For the largest training set, we also show GLM model errors. N-CTMC training is halted when $\Delta\text{loss} < 10^{-10}$. W denotes that the error metric is weighted. For the GLM model, training is halted when $\| \nabla \theta \| < 0.01$.} %table updated
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l|l|l}
Trajectories & MAE & W-MAE & MSE & W-MSE & Epochs \\ \hline
100 & 0.304 & $2.45 \cdot 10^{-1}$ & 0.244 & $1.53 \cdot 10^{-1}$ & 12,900 \\
1000 & 0.157 & $1.01 \cdot 10^{-1}$ & 0.093 & $2.84 \cdot 10^{-2}$ & 33,500 \\
10000 & 0.099 & $3.82 \cdot 10^{-2}$ & 0.051 & $5.13 \cdot 10^{-3}$ & 89,800 \\ \hline
10000 (GLM) & 1.028 & $7.06 \cdot 10^{-1}$ & 3.208 & $1.39 \cdot 10^{ 0}$ & 2,300 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Neural Continuous-Time Markov Models

"Neural Continuous-Time Markov Models" by Majerle Reeves and Harish S. Bhat, arXiv:2212.05378v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Anna Pandolfi
Ignacio Romero
Michael Ortiz

"An optimal-transport finite-particle method for driven mass diffusion" by Anna Pandolfi, Ignacio Romero, and Michael Ortiz, arXiv:2503.02813v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A High Order IMEX Method for Generalized Korteweg de-Vries Equations" by Seth Gerberding, arXiv:2503.15397v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccccccc}
 & PV & ProbI & PredI & RI & PI & S\&P & \textcolor{blue}{ETF} \\ \hline
 $R$ & 6.45\% & 7.21\% & 7.22\% & -0.00\% & -0.01\% & 16.43 \% & \textcolor{blue}{-25.46\%} \\
 $SR$ & 0.6195 & 0.7115 & 0.6852 & 0.0363 & -0.0299 & 0.8868 & \textcolor{blue}{-0.6299}
 \end{tabular}
\caption{Annualized performances with $c = 20$bps over the test sample of Section including the Brent, the Dubai and the WTI futures contract.}
\end{table}
\end{document}

q-fin

Viviana Fanelli
Claudio Fontana
Francesco Rotondi

"A hidden Markov model for statistical arbitrage in international crude oil futures markets" by Viviana Fanelli, Claudio Fontana, and Francesco Rotondi, arXiv:2309.00875v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textsf{ Poisson brackets} & $\begin{matrix}
 \textsf{ No. of polynomials without} \\
 \textsf{ using the grading method }
 \end{matrix}$ & $\begin{matrix}
 \textsf{Maximum No. of polynomials after applying} \\
 \textsf{ the grading method}
 \end{matrix}$ & $\Delta$ \\
 \hline
 $\{\textbf{q}_2,\textbf{q}_2\}$ & $ 31 $ & $8 $ & $23$ \\
 \hline
 $\{\textbf{q}_2,\textbf{q}_3\}$ & $102 $ & $39 $ & $63$ \\
 \hline
 $\{\textbf{q}_2,\textbf{q}_4\}$ & $261$ & $129 $ & $133 $ \\
 \hline
 $\{\textbf{q}_3,\textbf{q}_4\}$ & $478 $ & $236 $ & $242 $ \\
 \hline
 $\{\textbf{q}_4,\textbf{q}_4\}$ & $ 990 $ & $492 $ & $498$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of the number of polynomials }
\end{table}
\end{document}

math

Rutwig Campoamor-Stursberg
Danilo Latini
Ian Marquette
Junze Zhang
Yao-Zhong Zhang

"On the construction of polynomial Poisson algebras: a novel grading approach" by Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang, and Yao-Zhong Zhang, arXiv:2503.03490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Self-sustained price bubbles driven by Bitcoin innovations and adaptive behavior" by Misha Perepelitsa and Ilya Timofeyev, arXiv:2012.14860v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 Model & AUC & ECE \\
 \midrule
 Baseline & 84.7 $\pm$ 0.8 & 8.9 $\pm$ 1.4\\
 Vanilla Label Smoothing & 85.6 $\pm$ 0.6 & 3.2 $\pm$ 0.6\\
 Agreement-Aware (Linear) & 86.1 $\pm$ 0.9 & 5.3 $\pm$ 0.7\\
 Agreement-Aware (Piecewise) & \textbf{86.4} $\pm$ 0.4 & 2.9 $\pm$ 0.4\\
 Agreement-Aware (Nonlinear) & 86.3 $\pm$ 0.7 & \textbf{2.8} $\pm$ 0.6\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Agreement-aware label smoothing improves performance (higher AUC) and model calibration (lower ECE). Means and standard deviations are reported across 10 seeds.}
\end{table}
\end{document}

cs

Jerry Wei
Lorenzo Torresani
Jason Wei
Saeed Hassanpour

"Calibrating Histopathology Image Classifiers using Label Smoothing" by Jerry Wei, Lorenzo Torresani, Jason Wei, and Saeed Hassanpour, arXiv:2201.11866v1, 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}{cc}
			\hline\hline
			Series & $p$ values \\
			\hline
			FRM&$0.28$\\
			VIX &0.01\\
			DFRM&$0.01$\\
			\hline\hline
		\end{tabular}
\end{adjustbox}
\caption{$p$ values of ADF test for stationarity}
\end{table}
\end{document}

q-fin

Lining Yu
Wolfgang Karl Härdle
Lukas Borke
Thijs Benschop

"An AI approach to measuring financial risk" by Lining Yu, Wolfgang Karl Härdle, Lukas Borke, and Thijs Benschop, arXiv:2009.13222v1, 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{Name of hyperparameters and their correspoding domain for the computer vision model.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
Hyperparameter & Domain\\
\midrule
Number of base filters & \{4, 8, 16, 32, 64\}\\
Dropout rate for convolutional blocks & {}[0.1, 0.6]\\
Batch normalization for convolutional blocks & \{false, true\}\\
Type of global pooling & \{max, average\}\\
Ignore additional inputs & \{false, true\}\\
\addlinespace
Number of units for the fully-connected layer & \{128, 256, 512, 1024, 2048\}\\
Batch normalization for the fully-connected layer & \{false, true\}\\
Dropout rate for the fully-connected layer & {}[0.1, 0.6]\\
Learning rate & {}[$10^{-8}$, $10^{-1}$]\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Weihao Li
Dianne Cook
Emi Tanaka
Susan VanderPlas
Klaus Ackermann

"Automated Assessment of Residual Plots with Computer Vision Models" by Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas, and Klaus Ackermann, arXiv:2411.01001v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Tristan Cazenave
Jean-Baptiste Sevestre
Matthieu Toulemont

"Stabilized Nested Rollout Policy Adaptation" by Tristan Cazenave, Jean-Baptiste Sevestre, and Matthieu Toulemont, arXiv:2101.03563v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Nuojin Cheng
Alireza Doostan
Stephen Becker

"Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search" by Nuojin Cheng, Alireza Doostan, and Stephen Becker, arXiv:2505.00162v1, 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
 \textbf{Variable} & \textbf{Symbol} & \textbf{Asian Call} & \textbf{Down-Out Call} \\
 \hline
 Maturity & $T$ & $0.25y$ & $0.25y$\\
 \hline
 Spot Price & $S_0$ & $100$ & $100$\\
 \hline
Time steps & $D$ & 32 & 32\\
 \hline
Interest rate & $r$ & $3\%$ & $3\%$\\
 \hline
Volatility & $\sigma$ & $30\%$ & $30\%$ \\
 \hline
Strike & $K$ & 100 & 100 \\
 \hline
Barrier & $B$ & - & 90/80/70\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Pricing parameters for the financial instruments under analysis.}
\end{table}
\end{document}

q-fin

Luca Albieri
Sergei Kucherenko
Stefano Scoleri
Marco Bianchetti

"Effective dimensionality reduction for Greeks computation using Randomized QMC" by Luca Albieri, Sergei Kucherenko, Stefano Scoleri, and Marco Bianchetti, arXiv:2504.11576v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Prashant Kumar Choudhary
Nouhaila Innan
Muhammad Shafique
Rajeev Singh

"HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction" by Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique, and Rajeev Singh, arXiv:2503.15403v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}
\toprule
 Parameter: & $V_0$ & $\kappa$ & $\theta$ & $\sigma_v$ & $\rho$ & $\lambda$ & $\mu_J$ & $\sigma_J$ \\
\midrule
 Initial value $(\Theta_0)$ & 0.0576 & 2.03 & 0.04 & 0.38 & -0.7 & 0.59 & -0.05 & 0.07 \\
\hline Uniform distribution lower bound (a) & 0.01 & 0.01 & 1 & 0.1 & -1 & 0 & -0.1 & 0.0\\
 Uniform distribution upper bound (b) & 0.1 & 0.1 & 5 & 0.5 & 1 & 5 & 0.1 & 0.1 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Initial parameters $\Theta_0$ used by in their simulation study of the Bates model. Parameters are based on the analysis by of calibrated Bates models fitted to S\&P 500 Index options. Lower and upper bounds of uniform noise in equation . }
\end{table}
\end{document}

q-fin

"Joint calibration of the volatility surface and variance term structure" by Jiwook Yoo, arXiv:2509.08096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Will the last be the first? School closures and educational outcomes" by Michele Battisti and Giuseppe Maggio, arXiv:2208.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
Model & Number of Parameters & Model &F1 \\
\hline
 & $1.1\times10^7$ & CNN & 86.9\% \\ \hline
 & $8.7\times10^7$ & CNN & 86.7\% \\ \hline
 & $2.5\times 10^7$ & CNN & 85.7\% \\ \hline
 & 1,115 & CNN & 87.9\% \\ \hline
 \textbf{Ours} & \textbf{24} & RF & \textbf{90.1\%} \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Comparison with related works for the C-NMC data. The best approaches for the smallest number of parameters and highest F1 score are boldened.}
\end{table}
\end{document}

cs

"Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features" by William Franz Lamberti, arXiv:2201.11864v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Component & Level $P_1$ & Slope $P_2$ & Curvature $P_3$ \\
\hline
Skewness of $Z_k$ & 0.12 & -0.8 & -0.14 \\
\hline
Skewness of $Z_k/V$ & 0.24 & -0.37 & 0.08 \\
\hline
Kurtosis of $Z_k$ & 3.59 & 6.49 & 4.62 \\
\hline
Kurtosis of $Z_k/V$ & 3.92 & 4.01 & 4.58 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Skewness and kurtosis for autoregression innovations from~. We see that division by $V$ makes $Z_2$ (innovations for slope) but not $Z_1$ and $Z_3$ (innovations for level and intercept) closer to Gaussian. Recall that skewness and kurtosis for the Gaussian law are 0 and 3.}
\end{table}
\end{document}

stat

"Zero-Coupon Treasury Rates and Returns using the Volatility Index" by Jihyun Park and Andrey Sarantsev, arXiv:2411.03699v4, 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{Model Parameters for Reproducibility}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \hline
 \textbf{Parameter} & \textbf{Value} \\
 \hline
 Notional value ($V$) & 18\$ \\
 Time step ($dt$) & 1 year \\
 Number of time steps ($T$) & 3 \\
 Annual volatility ($\sigma$) & $0.2382$ \\
 Annual average log-return ($\mu$) & $0.1274$\\
 Put barrier ($b$) & 0.7 \\
 Put strike return ($K$)& 1 \\
 Risk-free rate ($r$) & $4\%$\\
 First binary option strike ($k_1$) & 1.1 \\
 Second binary option strike ($k_2$) & 1.1 \\
 First binary option payoff ($f_1$)& $2$ \\
 Second binary option payoff ($f_2$)& $5$ \\
 Standard Gaussian truncation ($s_{min}$) & $3$\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Francesca Cibrario
Ron Cohen
Emanuele Dri
Christian Mattia
Or Samimi Golan
Tamuz Danzig
Giacomo Ranieri
Hanan Rosemarin
Davide Corbelletto
Amir Naveh
Bartolomeo Montrucchio

"Autocallable Options Pricing with Integration-Based Exponential Amplitude Loading" by Francesca Cibrario, Ron Cohen, Emanuele Dri, Christian Mattia, Or Samimi Golan, Tamuz Danzig, Giacomo Ranieri, Hanan Rosemarin, Davide Corbelletto, Amir Naveh, and Bartolomeo Montrucchio, arXiv:2507.19039v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Means of measures grouped by placement and labeling methods. Bold p-values indicate when the difference of mean measures between placement methods is significant.\newline DR = placement by dimensionality reduction.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \toprule
 \textbf{Measure}& \textbf{Label}& \multicolumn{2}{c}{\textbf{Placement}} & \textbf{p-value}\\
 \cmidrule(r){3-4}
 &&\textbf{DR} &\textbf{Random} & \\
 \midrule
 Time (s) &&&&\\
 & Baseline & 12.3 & 12.7 & 0.54\\
 & Shape & 10.7 & 14.3 & \textbf{0.0008}\\
 & Color & 11.6 & 13.7 & 0.051\\
 & Texture & 11.6 & 15.7 &\textbf{0.0005}\\
 Hovered&&&&\\
 samples& Baseline & 14.9 & 17.9 & \textbf{0.04}\\
 & Shape & 8.7 & 12.5 & \textbf{0.004}\\
 & Color & 13.8 & 15.7 & 0.16\\
 & Texture & 13.5 & 15.3 &0.30\\
 Distance &&&&\\
 (pixels)& Baseline & 2807 & 3094 & 0.12\\
 & Shape & 2313 & 3123 & \textbf{0.0005}\\
 & Color & 2765 & 3588 & \textbf{0.002}\\
 & Texture & 2681 & 3403 & \textbf{0.009}\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"A proposal and evaluation of new timbre visualisation methods for audio sample browsers" by Etienne Richan and Jean Rouat, arXiv:2011.15096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of BSS Data Across Six Cities}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrl}
 \toprule
 City & \multicolumn{2}{c}{\# of Rentals/Returns} & \multicolumn{2}{c}{\# of Stations Used} & Data Collection \\
 & Weekdays & Weekends & Weekdays & Weekends & Period \\
 \midrule
 New York & 5036538 & 1759118 & 2664 & 2594 & 2023-10-13 to 11-11 \\
 London & 1210884 & 373178 & 803 & 802 & 2023-09-28 to 10-27 \\
 Tokyo & 1563721 & 464659 & 1300 & 777 & 2023-11-01 to 11-30 \\
 Boston & 528142 & 193625 & 468 & 468 & 2023-10-15 to 11-13 \\
 Chicago & 464355 & 147714 & 1151 & 989 & 2023-11-01 to 11-30 \\
 Washington D.C. & 549643 & 252463 & 750 & 747 & 2023-10-15 to 11-13 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Shu-ichi Kinoshita
Yuya Bando
Hiroki Sayama

"Spatio-Temporal Differences in Bike Sharing Usage: A Tale of Six Cities" by Shu-ichi Kinoshita, Yuya Bando, and Hiroki Sayama, arXiv:2412.19294v1, 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{Percentage of rejections to the null hypothesis of Bayesian projection posterior procure (BP), linear regression procedure (LR), and piecewise linear fitting (PL) when the true regression functions are not coordinatewise increasing.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccc|ccc|ccc|}
			\hline
			& \multicolumn{3}{c|}{$n=100$} & \multicolumn{3}{c|}{$n=200$} & \multicolumn{3}{c|}{$n=500$}\\
			& BP & LR & PL & BP & LR & PL & BP & LR & PL\\
			\hline
			$f_7$ & 64.5 & 8.5 & 73 & 93.5 & 10 & 99.5 & 100 & 10.5& 100\\
			$f_8$ & 100 & 84.5&83 & 100 & 98.5 & 100 & 100& 100 & 100\\
			$f_9$ & 35.5& 7.6& 28.5 & 96& 94.5 & 66.5 & 100 & 100 & 100\\
			$f_{10}$ & 100 & 100 & 100 & 100& 100 & 100& 100 & 100 & 100\\
			$f_{11}$ & 100 & 100 & 98.5 & 100 & 100 & 100& 100 & 100 & 100\\
			$f_{12}$ & 35 & 0& 55&89.5 & 0 &94.5& 100 & 0&100\\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Posterior Contraction and Testing for Multivariate Isotonic Regression" by Kang Wang and Subhashis Ghosal, arXiv:2211.12595v1, 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{Mean values of the estimated parameter values with 95$\%$ confidence intervals for model .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\hline
Parameter & Mean Value & 95$\%$ CI\\
\hline
$\sigma_e$&0.0788 & (0.055, 0.082) \\
$\sigma_h$& 1.36$e^{-5}$&(1.08$e^{-5}$, 1.7$e^{-5}$)\\
$\omega$&0.87&(0.83, 0.89)\\
$\delta$&4&(3.2, 5.5)\\
$\phi_1$&0.091&(0.078, 0.098)\\
$a$&14&(10, 20)\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Adrita Ghosh
Parthasakha Das
Tanujit Chakraborty
Pritha Das
Dibakar Ghosh

"Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study" by Adrita Ghosh, Parthasakha Das, Tanujit Chakraborty, Pritha Das, and Dibakar Ghosh, arXiv:2503.14009v1, 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{ResNet architecture.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
		\toprule
		\multicolumn{4}{c}{Generator $G(z)$} \\
		\midrule
		Operation &Kernel size& Resample& Output Dims\\
		\midrule
		Noise & N/A & N/A & $128$ \\
		Linear & N/A & N/A & $128\times4\times4$ \\
		Residual block & $[3\times3]\times2$ & Up & $128\times8\times8$ \\
		Residual block & $[3\times3]\times2$ & Up & $128\times16\times16$ \\
		Residual block & $[3\times3]\times2$ & Up & $128\times32\times32$ \\
		Conv, tanh & $3\times3$ & N/A & $3\times32\times32$ \\
		\bottomrule
		\toprule
		\multicolumn{4}{c}{Critic $D(x)$} \\
		\midrule
		Operation &Kernel size& Resample& Output Dims\\
		\midrule
		Residual block & $[3\times3]\times2$ & Down & $128\times16\times16$ \\
		Residual block & $[3\times3]\times2$ & Down & $128\times8\times8$ \\
		Residual block & $[3\times3]\times2$ & N/A & $128\times8\times8$ \\
		Residual block & $[3\times3]\times2$ & N/A & $128\times8\times8$ \\
		ReLU, mean pool & N/A & N/A & $128$ \\
		Linear & N/A & N/A & $1$ \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Minkai Xu
Zhiming Zhou
Guansong Lu
Jian Tang
Weinan Zhang
Yong Yu

"Towards Generalized Implementation of Wasserstein Distance in GANs" by Minkai Xu, Zhiming Zhou, Guansong Lu, Jian Tang, Weinan Zhang, and Yong Yu, arXiv:2012.03420v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{graphicx}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Normalized confusion matrix}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\cline{3-6}
\multirow{4}{*}{\rotatebox{90}{True label}} & Trees & 0.54 & 0.18 & 0.28 & 0 \\
 & Grass & 0.32 & 0.65 & 0.14 & 0 \\
 & Impervious & 0.17 & 0.09 & 0.73 & 0 \\
 & Water & 0.01 & 0 & 0 & 0.99 \\ \cline{3-6}
\multicolumn{2}{c}{\multirow{2}{*}{}} & Trees\hspace{1mm} & Grass\hspace{1mm} & Impervious\hspace{0mm} & Water\hspace{1mm} \\
\multicolumn{2}{c}{} & \multicolumn{4}{c}{Predicted label}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Juan Carrillo
Daniel Garijo
Mark Crowley
Rober Carrillo
Yolanda Gil
Katherine Borda

"Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees" by Juan Carrillo, Daniel Garijo, Mark Crowley, Rober Carrillo, Yolanda Gil, and Katherine Borda, arXiv:2009.10263v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization" by Chenhao Jin, Yinhua Xia, and Yan Xu, arXiv:2503.19379v1, 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{ASR performance at 15 Hz token rate under different entropy-based segmentation criteria: M1 global threshold, M2 relative change, M3 combined.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
Method & WER (\%) & CER (\%) \\
\midrule
M1 & \textbf{5.8} & \textbf{3.1} \\
M2 & 6.3 & 3.4 \\
M3 & 5.9 & 3.2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jialong Zuo
Guangyan Zhang
Minghui Fang
Shengpeng Ji
Xiaoqi Jiao
Jingyu Li
Yiwen Guo
Zhou Zhao

"Entropy-based Coarse and Compressed Semantic Speech Representation Learning" by Jialong Zuo, Guangyan Zhang, Minghui Fang, Shengpeng Ji, Xiaoqi Jiao, Jingyu Li, Yiwen Guo, and Zhou Zhao, arXiv:2509.00503v1, 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}{ccccrr}
\toprule
 & & & & &\\
Site & AgeGroup & Gender & Diagnosis & Axis 1 & Depression \\
\midrule
Site 1 & adult & F & 0.0 & 35 & 35\\ % FSU\_WAVE1
 & & & 1.0 & 59 & 59\\
 & & M & 0.0 & 11 & 11\\
 & & & 1.0 & 12 & 12\\
Site 2 & child & F & 0.0 & 72 & 95 \\ % SDSU\_D2018
 & & & 1.0 & 31 & 8 \\
 & & M & 0.0 & 101 & 119 \\
 & & & 1.0 & 19 & 1 \\
Site 3 & child & F & 0.0 & 82 & 128\\ % fsu\_d2018b50
 & & & 1.0 & 57 & 11\\
 & & M & 0.0 & 98 & 150\\
 & & & 1.0 & 54 & 2\\
Site 4 & child & F & 0.0 & 41 & 58\\ % fsu\_d2018w8
 & & & 1.0 & 21 & 4\\
 & & M & 0.0 & 45 & 63\\
 & & & 1.0 & 20 & 2\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Participant label distribution}
\end{table}
\end{document}

eess

Garrett Honke
Irina Higgins
Nina Thigpen
Vladimir Miskovic
Katie Link
Sunny Duan
Pramod Gupta
Julia Klawohn
Greg Hajcak

"Representation learning for improved interpretability and classification accuracy of clinical factors from EEG" by Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, and Greg Hajcak, arXiv:2010.15274v3, licensed under CC0 1.0. 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{Coherent communication gain under Channel Model 2 with $D=10000$ and $r=100$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|r|r|r|r|r|r|r}
$(N,M)$ & RB & RT & BT & SF & IO & ES & $G_{\text{UB}}$ \\ \hline \hline
$(3,10)$ & 29.40 & 38.36 & 49.47 & 50.51 & \bf{50.63} & 50.63 & 90 \\ \hline
$(10,10)$ & 101.38 & 228.66 & 295.63 & 295.63 & \bf{321.06} & - & 1000
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Coherent Communications in Self-Organizing Networks with Distributed Beamforming" by Yi Shi and Yalin E. Sagduyu, arXiv:1912.10112v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A PINN-enriched finite element method for linear elliptic problems" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Correlation coefficient of each annotator with the 10 click simulated user}
\begin{tabular}{|l|l|}
 \hline
 \textbf{Annotator} & \textbf{Correlation Coefficient} \\ \hline
 Annotator 1 & 0.9251 \\ \hline
 Annotator 2 & 0.7357 \\ \hline
 Annotator 3 & 0.7819 \\ \hline
 Annotator 4 & 0.4040 \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Matthias Hadlich
Zdravko Marinov
Moon Kim
Enrico Nasca
Jens Kleesiek
Rainer Stiefelhagen

"Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images" by Matthias Hadlich, Zdravko Marinov, Moon Kim, Enrico Nasca, Jens Kleesiek, and Rainer Stiefelhagen, arXiv:2311.14482v1, 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{Performance Comparison: Bandit Algorithms}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 \multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c}{\textbf{Accuracy, \%}} \\
 & \textbf{Train} & \textbf{Valid} & \textbf{Test} \\
 \midrule
 No Bandit & 80.2 & 77.3 & 77.3 \\
 TS & 81.1 & 80.3 & 79.3 \\
 UCB & 81.6 & 80.8 & 80.2 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Aleksey A. Kocherzhenko
Nirmal Sobha Kartha
Tengfei Li
Hsin-Yi
Shih
Marco Mandic
Mike Fuller
Arshak Navruzyan

"Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations" by Aleksey A. Kocherzhenko, Nirmal Sobha Kartha, Tengfei Li, Hsin-Yi, Shih, Marco Mandic, Mike Fuller, and Arshak Navruzyan, arXiv:2202.00146v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{QKD system parameters.}
\begin{tabular}{cccc}
\hline
$p_{pl}$ & $\alpha_{sift}$ & $\eta_{pd}$ & $\alpha_{err}$ \\ \hline
0.5 & 0.2 & 0.3 & 0.2 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

"Quantum-Key Distribution using Decoy Pulses to Combat Photon-Number Splitting by Eavesdropper: An Event-by-Event Impairment Enumeration Approach for Performance Evaluation and Design" by Debasish Datta, arXiv:2501.18394v1, 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{The table communicates the numbers of reconstructed ticks per contract, numbers of positive labels, as well as total numbers of entries per contract. 'Reb.' corresponds to the rebound - the required number of ticks for the positive labelling. Rebound columns show numbers of positively labelled entries.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|r|rrr|r}
 Contract & No. Ticks &Reb. 7 & Reb. 11 & Reb. 15 & Total \\
 \hline
 ESH2017 & 1271810 & 896 & 804 & 703 & 4911 \\
 ESM2017 & 1407792 & 951 & 881 & 756 & 4181 \\
 ESU2017 & 1243120 & 858 & 812 & 693 & 3446 \\
 ESZ2017 & 1137427 & 689 & 640 & 518 & 12317 \\
 ESH2018 & 2946336 & 2014 & 1983 & 1953 & 11537 \\
 ESM2018 & 2919757 & 1965 & 1936 & 1868 & 6534 \\
 ESU2018 & 1825417 & 1271 & 1226 & 1095 & 16331 \\
 ESZ2018 & 3633969 & 2677 & 2620 & 2565 & 11718 \\
 ESH2019 & 3066530 & 1990 & 1949 & 1883 & 9743 \\
 ESM2019 & 2591000 & 1711 & 1692 & 1630 & 10389 \\
 ESU2019 & 2537197 & 1761 & 1735 & 1704 & 6191 \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components" by Artur Sokolovsky and Luca Arnaboldi, arXiv:2009.09993v4, 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}{ccc}
		\toprule
		Graph 1 & Graph 2 \\
		\midrule
		$\begin{bmatrix}
			1 & -1 & 0 & 1 & 0 & 0\\
			0 & 1 & -1 & 0 & 1 & 0\\
			-1 & 0 & 1 & 0 & 0 & 1\\
		\end{bmatrix}$ & $\begin{bmatrix}
			1 & 1 & 0 & 0 & 0 & 0\\
			0 & -1 & 1 & 0 & 1 & 1\\
			0 & 0 & 0 & 1 & -1 & 0\\
		\end{bmatrix}$ \\
		\bottomrule
	\end{tabular}
\caption{Choices of $\eta$ for the two possible genus $3$ smoothed graphs.}
\end{table}
\end{document}

math

"Constructions of superabundant tropical curves in higher genus" by Sae Koyama, arXiv:2312.12538v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|ccc|}
\hline
$n_0$ & $T_{\mathrm{hor}}$ & $T$\\
\hline
\hline
$0.25$ & $750$ days & $20.25$\\
\hline
$0.50$ & $1000$ days & $27.00$\\
\hline
$0.75$ & $1500$ days & $40.50$\\
\hline
\end{tabular}
\end{adjustbox}
\caption{\footnotesize Values of $T_{\mathrm{hor}}$ used for the optimal control problems.}
\end{table}
\end{document}

math

Alessandro Scagliotti
Federico Scagliotti
Laura Deborah Locati
Federico Sottotetti

"Ensemble optimal control for managing drug resistance in cancer therapies" by Alessandro Scagliotti, Federico Scagliotti, Laura Deborah Locati, and Federico Sottotetti, arXiv:2503.08927v2, 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{Computational time for each method}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr} \hline
\multirow{2}{*}{Situation} & \multirow{2}{*}{$\Delta$} & \multirow{2}{*}{MAF} & \multicolumn{2}{c}{Sum of computational time (s)} \\ \cline{4-5}
 & & & \phantom{00}pMMCM\phantom{00} & MMCM \\ \hline
overall null hypothesis & 0.00 & 0.33 & 298.25 & 0.92 \\
(i) additive & 0.25 & 0.12 & 94.77 & 0.90 \\
(ii) dominant & 1.00 & 0.50 & 16.78 & 0.92 \\
(iii) recessive & 0.50 & 0.25 & 71.85 & 0.90 \\
(iv) valley & 0.25 & 0.33 & 254.31 & 0.92 \\ \hline
\multicolumn{5}{l}{\scriptsize \shortstack[l]{\\Abbreviations: MAF, minor allele frequency; pMMCM, permuted modified maximum contrast method;\\ MMCM, modified maximum contrast method.}}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Kengo Nagashima
Yasunori Sato
Chikuma Hamada

"A modified maximum contrast method for unequal sample sizes in pharmacogenomic studies" by Kengo Nagashima, Yasunori Sato, and Chikuma Hamada, arXiv:2012.03475v1, 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{Composition of SISAR database}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\toprule
 & Animals	& Buildings	 & Humans	 & Sports & Plants & Scenery \\ \midrule
Source Images & 20&20&20&20	&11 &9 \\
HR Images & 1680&1680&1680&1680	&924 &756 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Tiesong Zhao
Yuting Lin
Yiwen Xu
Weiling Chen
Zhou Wang

"Learning-Based Quality Assessment for Image Super-Resolution" by Tiesong Zhao, Yuting Lin, Yiwen Xu, Weiling Chen, and Zhou Wang, arXiv:2012.08732v1, 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|cccccccccc|c}
 & \multicolumn{11}{c}{Uncertainty-Weighted Portfolios}\\
 \hline
&	$D1$ &$D2$&$D3$&$D4$&$D5$&	$D6$ &$D7$&$D8$&$D9$&$D10$&	$All$\\
\hline
E-GPR ($\gamma$-exp) & 6.39 & 1.90 & 1.92 & 4.56 & 6.89 & 10.54 & 14.10 & 17.62 & 22.83 & 31.85 & 12.80 \\
E-GPR (affine) & -0.21 & -0.46 & 0.35 & 0.67 & 0.61 & 3.02 & 1.23 & 2.66 & 6.72 & -0.45 & 1.49 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{In this table, we report the out-of-sample predictive performance, measured by $R^2_{pool}$ in percentage points, across the sorted portfolios obtained using uncertainty-weighted strategy. We compare the performance of our ensemble GPR model with $\gamma$-exponential kernel to ensemble GPR model with affine kernel. The first ten columns ($D1$ to $D10$) report $R^2_{pool}$ for each decile while the last column (All) reports $R^2_{pool}$ calculated over the grand panel of all deciles.}
\end{table}
\end{document}

q-fin

"Empirical Asset Pricing via Ensemble Gaussian Process Regression" by Damir Filipović and Puneet Pasricha, arXiv:2212.01048v2, 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}{bad controls in 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 &$.35\pm .12$ & $.17\pm .02$ & $.30 \pm .04$ & $.24 \pm .04$\\
& NICE &$.08 \pm .04$ & $.05\pm .01$ & $.07 \pm .02$ & $.07 \pm .01$\\
\hline
\textbf{med}& Adjust All &$.26 \pm .02$ & $.27\pm .05$ & $.39 \pm .07$ & $.13\pm .01$\\
& NICE &$.04\pm .01$ & $.05\pm .02$ & $.06 \pm .01$ & $.04 \pm .01$\\
\hline
\textbf{high} & Adjust All&$.31 \pm .04$ & $.23\pm .06$ & $.31 \pm .07$ & $.12\pm .01$\\
& NICE &$.07\pm .02$ & $.07 \pm .03$ & $.14\pm .07$ & $.06\pm .03$\\
\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{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Loss comparison}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|cccc}
 \toprule
 \multirow{2}[2]{*}{Model} & \multirow{2}[2]{*}{Training Loss} & \multicolumn{4}{c}{SP500} \\
 & & IC & ICIR & ARR & Sharpe \\
 \midrule
 \multirow{3}[2]{*}{LSTM} & IC loss & 0.14 & 1.37 & 0.11 & 0.43 \\
 & MSE Loss & 0.12 & 1.20 & 0.17 & 0.61 \\
 & Pairwise ranking loss & 0.05 & 0.87 & 0.09 & 0.34 \\
 \midrule
 \multirow{3}[2]{*}{RSR} & IC loss & 0.09 & 1.21 & 0.12 & 0.51 \\
 & MSE Loss & 0.06 & 1.11 & 0.14 & 0.76 \\
 & Pairwise ranking loss & 0.03 & 0.47 & 0.02 & 0.19 \\
 \midrule
 \multirow{3}[2]{*}{Mixer} & IC loss & 0.09 & 1.21 & 0.14 & 0.64 \\
 & MSE Loss & 0.11 & 1.23 & 0.17 & 0.70 \\
 & Pairwise ranking loss & 0.06 & 0.93 & 0.11 & 0.59 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Saizhuo Wang
Hao Kong
Jiadong Guo
Fengrui Hua
Yiyan Qi
Wanyun Zhou
Jiahao Zheng
Xinyu Wang
Lionel M. Ni
Jian Guo

"QuantBench: Benchmarking AI Methods for Quantitative Investment" by Saizhuo Wang, Hao Kong, Jiadong Guo, Fengrui Hua, Yiyan Qi, Wanyun Zhou, Jiahao Zheng, Xinyu Wang, Lionel M. Ni, and Jian Guo, arXiv:2504.18600v1, 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{Topics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}\hline
Topic & \hspace{1em}Frequency\hspace{1em} & \hspace{1em}Percentage\hspace{1em} \\\hline
Artificial intelligence & 103 & 12.61 \\
US politics & 50 & 6.12 \\
Macroeconomics & 31 & 3.79 \\
Israel and the Palestinian territories & 30 & 3.67 \\
YouTube and its streamers & 27 & 3.3 \\
Video games & 24 & 2.94 \\
Manifold Markets & 22 & 2.69 \\
The Russo-Ukrainian War & 21 & 2.57 \\
Football & 19 & 2.33 \\
American football & 17 & 2.08 \\
Legal rulings & 16 & 1.96 \\
Television & 14 & 1.71 \\
Basketball & 12 & 1.47 \\
X (formerly Twitter) & 11 & 1.35 \\
Taylor Swift & 10 & 1.22 \\
UK politics & 10 & 1.22 \\
Cryptocurrency & 9 & 1.1 \\
Elon Musk & 9 & 1.1 \\
The 2024 Paris Olympics & 9 & 1.1 \\
The Oscars & 9 & 1.1 \\
Russian politics & 9 & 1.1 \\
Apple Inc. & 8 & 0.98 \\
Science & 8 & 0.98 \\
Nuclear energy and weapons & 7 & 0.86 \\
Satellites and space & 7 & 0.86 \\
Amazon.com, Inc. & 6 & 0.73 \\
Canadian politics & 6 & 0.73 \\
Effective altruism & 6 & 0.73 \\
Tesla, Inc. & 6 & 0.73 \\
Other topics & 203 & 36.87 \\
\hline
Total & 719 & 100 \\\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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effects of Level 2 Subsidies on Level 2 Stations \\ and Sales Measures: Staggered Difference-in-Differences}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\toprule
 & Log(Lvl2) & Log(Tesla Destination Stations) & Log(PHEV Sales) & Log(BEV Sales)\\
\midrule
Baseline & 0.025 (0.012) ** & -0.009 (0.019) & 0.018 (0.009) ** & 0.028 (0.009) ***\\
Baseline + Controls & 0.028 (0.009) *** & 0.003 (0.020) & 0.022 (0.009) ** & 0.024 (0.009) **\\
High Pop & 0.022 (0.010) ** & 0.009 (0.025) & 0.010 (0.009) & 0.009 (0.007)\\
Disadvantaged & 0.016 (0.012) & 0.014 (0.029) & 0.011 (0.009) & -0.003 (0.008)\\
Urban & 0.021 (0.011) * & 0.002 (0.025) & 0.008 (0.008) & 0.010 (0.007)\\
\addlinespace
High GDP & 0.021 (0.011) * & 0.003 (0.026) & 0.010 (0.009) & 0.011 (0.007)\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Powering Up a Slow Charging Market: How Do Government Subsidies Affect Charging Station Supply?" by Zunian Luo, arXiv:2210.14908v6, 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{Same setup as Table~, but using \textbf{geometric} annealing.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
& \multicolumn{5}{c}{sample size $N$}\\
& 100 & 300 & 1000 & 3000 & 10000 \\
\midrule
SBTM (ours) & \textbf{0.058} & \textbf{0.032} & 0.037 & \textbf{0.025} & 0.031 \\
SDE & 0.060 & 0.032 & \textbf{0.029} & 0.029 & \textbf{0.028} \\
SVGD & 0.47 & 0.69 & 0.99 & 1.1 & 1.0 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Cafeteer noise; STOI(\%) and PESQ comparison}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
 & \multicolumn{2}{c}{SNR = -5} & \multicolumn{2}{c}{SNR = 0} & \multicolumn{2}{c}{SNR = 5} \\
 & STOI & PESQ & STOI & PESQ & STOI & PESQ \\
 \hline
Noisy & 50.5 & 1.54 & 68.6 & 1.87 & 83.0 & 2.22\\
2D-RFCNN & 52.3 & 1.58 & 70.1 & 1.93 & 84.5 & 2.31 \\
LSTM & 53.9 & 1.65 & 71.3 & 2.00 & 84.7 & 2.37\\
No weight & 54.1 & 1.75 & 72.7 & \textbf{2.11} & 86.0 & 2.47 \\
Temporal & 54.6 & 1.72 & 72.8 & 2.08 & 85.8 & 2.44 \\
Local & \textbf{55.3} & 1.74 & \textbf{73.5} & 2.10 & \textbf{86.4} & 2.46 \\
Freq.-wise & 54.2 & \textbf{1.76} & 72.6 & \textbf{2.11} & 86.1 & \textbf{2.49} \\
Freq.-wise (MSE) & 54.6 & 1.74 & 73.1 & 2.10 & 86.1 & 2.47
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Frequency Gating: Improved Convolutional Neural Networks for Speech Enhancement in the Time-Frequency Domain" by Koen Oostermeijer, Qing Wang, and Jun Du, arXiv:2011.04092v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Nomenclature}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \toprule
 \multicolumn{1}{p{2.585em}}{Symbol} & Description \\
 \midrule
$f_i^{rgb}$ & The i-th level RGB feature \\
$f_i^d $ & The i-th level depth feature \\
$e_i $ & The i-th output feature of coarse refinement branch \\
$R_c^i$ & The i-th coarse refinement block \\
$S_i^c $ & The i-th output saliency map of coarse refinement branch \\
${\cal F}_i $
 & The i-th level encoding feature map of fine-grained refinement branch \\
${\cal O}_i $
 & The i-th level decoded feature map of fine-grained refinement branch \\
$G$
 & The ground truth saliency map \\
$S^f $
 & The predicted saliency map of fine-grained refinement branch \\
$U\left( \cdot \right)$
 & The upsample operation \\
$ \otimes $
 & Element-wise multiplication broadcasted along feature planes \\
$Cat\left( \cdot \right)$
 & Channel-wise concatenation operation \\
$GP\left( \cdot \right)$
 & Global average pooling function \\
$\mathcal{L}\left( \cdot \right)$
 & The binary cross-entropy loss \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ze-yu Liu
Jian-wei Liu
Xin Zuo
Ming-fei Hu

"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Junghyun Lee
Hanseul Cho
Se-Young Yun
Chulhee Yun

"Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint" by Junghyun Lee, Hanseul Cho, Se-Young Yun, and Chulhee Yun, arXiv:2310.18593v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yunshi Wen
Tengfei Ma
Tsui-Wei Weng
Lam M. Nguyen
Anak Agung Julius

"Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification" by Yunshi Wen, Tengfei Ma, Tsui-Wei Weng, Lam M. Nguyen, and Anak Agung Julius, arXiv:2411.01006v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccccc}
\toprule
\multicolumn{7}{c}{\textbf{Cross-camera generalization (PackNet)}} \\
\toprule
& \textbf{Dataset} &
Abs.Rel$\downarrow$ &
RMSE$\downarrow$ &
RMSE$_{log}$$\downarrow$ &
SILog$\downarrow$ &
$\delta_{1.05}$$\uparrow$ \\
\toprule
\parbox[t]{2mm}{\multirow{4}{*}{\rotatebox[origin=c]{90}{\textit{Left}}}}
& \textit{Mug}
& 0.032 & 0.015 & 0.051 & 4.944 & 0.820 \\
& \textit{Wine Glass}
& 0.034 & 0.015 & 0.053 & 5.177 & 0.804 \\
& \textit{Fingers}
& 0.047 & 0.020 & 0.068 & 6.392 & 0.628 \\
& \textit{Box}
& 0.052 & 0.020 & 0.072 & 6.442 & 0.606 \\
\midrule
\parbox[t]{2mm}{\multirow{4}{*}{\rotatebox[origin=c]{90}{\textit{Right}}}}
& \textit{Mug}
& 0.031 & 0.012 & 0.041 & 3.871 & 0.803 \\
& \textit{Wine Glass}
& 0.046 & 0.020 & 0.058 & 5.230 & 0.595 \\
& \textit{Fingers}
& 0.062 & 0.026 & 0.077 & 4.484 & 0.389 \\
& \textit{Box}
& 0.058 & 0.023 & 0.071 & 6.180 & 0.459 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Dataset transfer results using PackNet . Independent models were trained using input-output pairs from each of the available cameras, and evaluated on the remaining one.}
\end{table}
\end{document}

cs

Rares Ambrus
Vitor Guizilini
Naveen Kuppuswamy
Andrew Beaulieu
Adrien Gaidon
Alex Alspach

"Monocular Depth Estimation for Soft Visuotactile Sensors" by Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy, Andrew Beaulieu, Adrien Gaidon, and Alex Alspach, arXiv:2101.01677v1, 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{Number of iterations for various methods; setting as in Table~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|ccccc}
& & \multicolumn{5}{c}{$\delta$} \\
Method & Stopping & $10^{-5}$ & $10^{-4}$ & $10^{-3}$ & $10^{-2}$ & $10^{-1}$ \\ \hline
Nesterov & $k_{opt}$ & 371 & 163 & 65 & 26 & 15 \\
Landweber & $k_{opt}$ & 11000 & 2193 & 512 & 145 & 36 \\
$\nu$-Method & $k_{opt}$ & 190 & 82 & 33 & 22 & 9 \\
CGNE & $k_{opt}$ & 10 & 6 & 4 & 3 & 2 \\
Nesterov & Discrepancy & 260 & 111 & 39 & 13 & 1 \\
Landweber & Discrepancy & 5106 & 1080 & 220 & 37 & 1 \\
$\nu$-Method & Discrepancy & 190 & 96 & 33 & 10 & 1 \\
CGNE & Discrepancy & 8 & 5 & 4 & 2 & 1 \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Optimal-order convergence of Nesterov acceleration for linear ill-posed problems" by Stefan Kindermann, arXiv:2101.08168v1, 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{Tower detection performance for \textit{GridTracer} and human annotators using the $DmAP$ metric (higher better). Parenthesis indicates the subset of testing data over which each method was scored. We train \textit{GridTracer} using the "conventional" data handling scheme in Fig. (a)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l||c|c|c|c}
 \hline \hline
 & Arizona & Kansas & New Zealand & Average \\
 \hline \hline
 \textit{GridTracer} (100\%) & 0.73 & 0.54 & 0.55 & 0.61 \\
 \hline \hline
 Human (20\%) & 0.92 & 0.88 & 0.79 & 0.86 \\
 \hline
 \textit{GridTracer} (20\% ) & 0.72 & 0.55 & 0.49 & 0.59 \\
 \hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Bohao Huang
Jichen Yang
Artem Streltsov
Kyle Bradbury
Leslie M. Collins
Jordan Malof

"GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery" by Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, and Jordan Malof, arXiv:2101.06390v1, 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}{lcr}%% Table column specifiers
 1 & 2 & 3 \\ %% A tabular row ends with \\
 4 & 5 & 6 \\
 7 & 8 & 9 \\
\end{tabular}
\end{adjustbox}
\caption{Table Caption}
\end{table}
\end{document}

math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The performance for cross-dataset evaluation, where the best and suboptimal performance values are in \textcolor{red}{Red} and \textcolor{blue}{Blue}, respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|ccc}
 \toprule
 Train & \multicolumn{3}{c|}{CVIU} & \multicolumn{3}{c}{QADS} \\
 \midrule
 Test & \multicolumn{3}{c|}{QADS} & \multicolumn{3}{c}{CVIU} \\
 \midrule
 Model & SRCC $\uparrow$ & KRCC $\uparrow$ & PLCC $\uparrow$ & SRCC $\uparrow$ & KRCC $\uparrow$ & PLCC $\uparrow$ \\
 \midrule
 DeepSRQ & 0.723 &\makebox[1em][c]{\textemdash} & 0.749 & 0.648 &\makebox[1em][c]{\textemdash} &\makebox[1em][c]{\textemdash} \\
 HLSRIQA & \textcolor{blue}{0.786} &\makebox[1em][c]{\textemdash} & \textcolor{blue}{0.773} & $\;$\textcolor{red}{0.785 } &\makebox[1em][c]{\textemdash} & \textcolor{blue}{0.776} \\
 EK-SR-IQA & 0.645 &\makebox[1em][c]{\textemdash} &\makebox[1em][c]{\textemdash} & 0.682 &\makebox[1em][c]{\textemdash} &\makebox[1em][c]{\textemdash} \\
 JCSAN & 0.702 & 0.530 & 0.710 & 0.631 & 0.463 & 0.729 \\
 TADSRNet & 0.739 & 0.547 & 0.736 & 0.657 & 0.489 & 0.729 \\
 PBAN & \textcolor[rgb]{ 1, 0, 0}{$\;$0.890 } & $\;$\textcolor{red}{0.732 } & $\;$\textcolor{red}{0.892 } & \textcolor{blue}{0.730} & $\;$\textcolor{red}{0.550 } & $\;$\textcolor{red}{0.794 } \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yixiao Li
Xiaoyuan Yang
Guanghui Yue
Jun Fu
Qiuping Jiang
Xu Jia
Paul L. Rosin
Hantao Liu
Wei Zhou

"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, 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{$P(\text{reversal}|\text{condition})$, assuming $OR_{xy}>1$, $OR_{wy}>1$, and conditional on Table .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
&&Strong Simpson&Adjusted Risk Difference&Weak Simpson\\
\hline
Cornfield&& 			$.0544\pm .0005$ 	&$.2064\pm .0009$ 		&$.9586\pm .0004$\\
Risk Ratio&&			$.0543\pm .0005$ 	&$.2096\pm .0009$		&$.9400\pm .0005$\\
Risk Difference&&		$.0506\pm .0005$ 	&$.1890\pm .0008$		&$.9415\pm .0005$\\
Pearson Correlation&& 	$.0801\pm .0005$ 	&$.2572\pm .0008$ 		&$1\pm 0$ \\
Mixed &&				$.0716\pm .0005$ 	&$.2380\pm .0008$ 		&$.9872\pm .0002$\\
Odds Ratio&&			$.0430\pm .0003$	&$.1659\pm .0005$		&$.9089\pm .0004$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Odds are the sign is right

"Odds are the sign is right" by Brian Knaeble and Julian Chan, arXiv:2310.06808v1, 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
 $\nu$ & $32\times 32$ & $64\times 64$ & $128\times 128$\\
 \midrule
 $1\cdot 10^{-2}$ & 4 & 5 & 8 \\
 $5\cdot 10^{-3}$ & 4 & 5 & 7\\
 $2.5\cdot 10^{-3}$ & 5 & 5 & 7 \\
 $1.25\cdot 10^{-3}$ & 7 & 7 & 7 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lorenzo Lazzarino
Yuji Nakatsukasa
Umberto Zerbinati

"Preconditioned normal equations for solving discretised partial differential equations" by Lorenzo Lazzarino, Yuji Nakatsukasa, and Umberto Zerbinati, arXiv:2502.17626v2, 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{Table of State and Events: Machine-1 First Task Robot}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 States & Events \\
 \hline
 Robot Idle:0 & First goal started:1 \\
 Walking:1 & First goal reached:3, Robot failed:0\\
 Docking:2 & Docking finished:5, Robot failed:0\\
 Fail:3 & Error flag:17 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"The Analysis of Discrete-Event System in Autonomous Package Delivery using Legged Robot and Conveyor Belt" by Garen Haddeler, arXiv:2101.12347v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc||ccc}
 & LSTM(2, 80) & LSTM(3, 80) & Simple Avg & LSTM(2, 80) & LSTM(3, 80) & Simple Avg \\ \hline
MAE & \textbf{0.548} & 0.759 & 0.624 & \textbf{0.725} & 0.754 & 0.825 \\
MSE & \textbf{0.682} & 0.911 & 0.757 & \textbf{0.952} & 0.992 & 1.062
\end{tabular}
\end{adjustbox}
\caption{Comparison of 2019 prediction results for Site 0 (left) and Site 1 (right), comparing LSTM Models and the simple averaging model. For LSTM(x, y) in the header, x is the number of layers of LSTM, and y is the number of epochs.}
\end{table}
\end{document}

math

Mingshi Cui
Kunting Qi
Byran Smucker
Durai Sundarmoorthi

"A Multi-Objective Capacity-Constrained Optimization of Corn Planting Scheduling" by Mingshi Cui, Kunting Qi, Byran Smucker, and Durai Sundarmoorthi, arXiv:2503.20517v1, 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{Abbreviations for model-related terms}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
	\hline
		\textbf{Abbreviation} & \textbf{Full name} & \textbf{Location} \\
	\hline
		bcMLM & Bias-corrected multilevel model & Section~ \\
	\hline
		bcRegFE & Bias-corrected regularized fixed effects model & Section~ \\
 \hline
		CRSE & Cluster-robust standard error & Section~ \\
	\hline
		FE & Fixed effects model & Section~ \\
	\hline
		Group-FE & Group fixed effects model & Section~ \\
	\hline
		GLM & Generalized linear model & Section~ \\
	\hline
		MLE & Maximum likelihood estimation & Section~ \\
	\hline
		MLM & Multilevel model & Section~ \\
	\hline
		RegFE & Regularized fixed effects model & Section~ \\
	\hline
		RI & Random intercepts model & Section~ \\
	\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

He Bai
Asa Ferguson
Leonard Wainstein
Jonathan Wells

"Comparing multilevel and fixed effect approaches in the generalized linear model setting" by He Bai, Asa Ferguson, Leonard Wainstein, and Jonathan Wells, arXiv:2411.01723v2, 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{Country List}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c}
 Brazil \\
 Chile \\
 Indonesia \\
 Mexico \\
 Peru \\
 South Africa \\
 Turkey \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Santiago Camara
Sebastian Ramirez Venegas

"The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity" by Santiago Camara and Sebastian Ramirez Venegas, arXiv:2209.11150v1, licensed under CC0 1.0. 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
$M$ &
$\langle \overline{K^{}}_{\rm Bayes}\rangle$ &
$\langle\overline{K^{}}_{1}\rangle$ &
$\langle\overline{K^{}}_{1+2}\rangle$ \\ \hline
55 & 0.233 & 1.756 & 1.685 \\
45 & 0.276 & 1.700 & 1.643 \\
35 & 0.338 & 1.723 & 1.680 \\
25 & 0.428 & 1.753 & 1.717 \\
15 & 0.606 & 1.770 & 2.164 \\
11 & 0.730 & 1.762 & 4.946 \\
9 & 0.818 & 1.774 & 11.63 \\
7 & 0.916 & 1.848 & 32.24 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Maximum Entropy competes with Maximum Likelihood" by A. E. Allahverdyan and N. H. Martirosyan, arXiv:2012.09430v1, 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{Kernel-level design (\textbf{CIFAR-10}) on VGG16-opt.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Network & MFLOPs & Param. & Acc.(\%)\\
 \midrule
 \midrule
 Baseline & 370 & 9.64M & 92.95\\
 \midrule
 DW+PW & 50 & 1.11M & 92.12\\
 \midrule
 DW+GPW-g2 & 30 & 0.67M & 92.35\\
 \midrule
 DW+GPW-g4 & 20 & 0.36M & 88.05\\
 \midrule
 DW+GPW-g8 & 10 & 0.20M & 86.41\\
 \midrule
 \textbf{DW+RPW-g2-o33\%} & \textbf{30} & \textbf{0.66M} & \textbf{92.52}\\
 \midrule
 \textbf{DW+RPW-g2-o50\%}& \textbf{30} & \textbf{0.66M} &
 \textbf{92.70}\\
 \midrule
 \textbf{DW+RPW-g4-o33\%} & \textbf{20} & \textbf{0.36M} & \textbf{91.61}\\
 \midrule
 \textbf{DW+RPW-g4-o50\%} & \textbf{20} & \textbf{0.36M} &
 \textbf{91.59}\\
 \midrule
 \textbf{DW+RPW-g8-o33\%} & \textbf{10} & \textbf{0.20M} & \textbf{89.86}\\
 \midrule
 \textbf{DW+RPW-g8-o50\%} & \textbf{10} & \textbf{0.20M} & \textbf{90.19}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yuke Wang
Boyuan Feng
Xueqiao Peng
Yufei Ding

"An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks" by Yuke Wang, Boyuan Feng, Xueqiao Peng, and Yufei Ding, arXiv:2009.05236v4, 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{Model Wind Power Accuracy.}
\begin{tabular}{lccc}
 \hline
 & Training & Validation & Test \\
 \hline
 Single Linear & 0.8800 & 0.8469 & 0.8454 \\
 Single ResNet & 0.9025 & 0.8426 & 0.8433 \\
 Stacked Linear & 0.8360 & 0.8103 & 0.8189 \\
 Stacked ResNet & \textbf{0.9037} & \textbf{0.8668} & \textbf{0.8713} \\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

"Estimating Solar and Wind Power Production using Computer Vision Deep Learning Techniques on Weather Maps" by Sebastian Bosma and Negar Nazari, arXiv:2103.08727v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for different quality levels of JPEG 2000.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 \toprule
 JPEG 2000 & 100 & 250 & 500 & 750 & 900 \\
 \midrule
 Ours & $35.29$ & $4.16$ & $00.31$ & $ 00.04 $ & $00.03$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Chaoning Zhang
Adil Karjauv
Philipp Benz
In So Kweon

"Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach" by Chaoning Zhang, Adil Karjauv, Philipp Benz, and In So Kweon, arXiv:2101.00973v1, 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}{ccccccc}
 \toprule
 &\multicolumn{3}{c}{Fashion-MNIST (200 labels)} & \multicolumn{3}{c}{Fashion-MNIST (400 labels)} \\
 \cmidrule(r){2-7}
 $\lambda$ & $\sigma=0.05$ & $\sigma=0.1$ & $\sigma=0.5$ & $\sigma=0.05$ & $\sigma=0.1$ & $\sigma=0.5$ \\
 \midrule
 $0.1$ & $75.39\pm1.12$ & $74.96\pm0.96$ & $75.24\pm1.20$ & $75.39\pm1.12$ & $74.96\pm0.96$ & $75.24\pm1.20$ \\
 $0.05$ & $76.35\pm1.09$ & $76.25\pm1.30$&$76.81\pm0.88$ & $76.35\pm1.09$ & $76.25\pm1.30$&$76.81\pm0.88$\\
 $0.025$ & $76.42\pm0.85$ & $76.77\pm1.29$ & $76.84\pm1.23$ & $76.42\pm0.85$ & $76.77\pm1.29$ & $76.84\pm1.23$ \\
 \bottomrule
 \end{tabular}
\caption{Ablation study on Fashion-MNIST dataset with 200/400 labels. Test-set classification accuracy ($\%$) is shown averaged over 10 runs.}
\end{table}
\end{document}

math

"Towards Scalable Topological Regularizers" by Hiu-Tung Wong, Darrick Lee, and Hong Yan, arXiv:2501.14641v2, 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{OpenML Task ID mappings for \textbf{regression} datasets with \textbf{heterogeneous features}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule
\textbf{OpenML ID} & \textbf{Dataset} \\ \midrule
361093 & analcatdata\_supreme \\
361094 & visualizing\_soil \\
361095 & black\_friday \\
361096 & diamonds \\
361097 & Mercedes\_Benz\_Greener\_Manufacturing \\
361098 & Brazilian\_houses \\
361099 & Bike\_Sharing\_Demand \\
361101 & nyc-taxi-green-dec-2016 \\
361102 & house\_sales \\
361103 & particulate-matter-ukair-2017 \\
361104 & SGEMM\_GPU\_kernel\_performance \\
361287 & topo\_2\_1 \\
361288 & abalone \\
361289 & seattlecrime6 \\
361291 & delays\_zurich\_transport \\
361292 & Allstate\_Claims\_Severity \\
361293 & Airlines\_DepDelay\_1M \\
361294 & medical\_charges \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Individual performance comparison for dog-trot $n=\{3, 10, 30, 50, 75, 100\}$. Figures are mean normalised scores $\pm$ one standard error, with 0 and 100 representing random and expert policies, respectively. At the bottom of the table we also provide totals, split by data quality and the entire set. We see across all datasets that DecQN-IQL/OneStep perform best, followed by DecQN-CQL and DecQN-BCQ. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
Environment -dataset & BC & DecQN & DecQN-BCQ & DecQN-CQL & DecQN-IQL & DecQN-OneStep \\
\midrule
DogTrot ($n=3$) & & & & & & \\
-expert & 98 $\pm$ 0.7 & 0.1 $\pm$ 0.1 & 94.4 $\pm$ 0.5 & 99.5 $\pm$ 0.7 & 98.9 $\pm$ 2.2 & 101.2 $\pm$ 1.6 \\
-medium-expert & 62 $\pm$ 3.7 & 0 $\pm$ 0.3 & 74.9 $\pm$ 3.5 & 84.8 $\pm$ 3.7 & 89.3 $\pm$ 1.4 & 93.9 $\pm$ 1.4 \\
-medium & 43.8 $\pm$ 0.5 & 0.1 $\pm$ 0.1 & 49.1 $\pm$ 0.6 & 46.5 $\pm$ 0.5 & 52 $\pm$ 0.3 & 50.2 $\pm$ 0.2 \\
-random-medium-expert & 37.2 $\pm$ 3.6 & 0.1 $\pm$ 0.2 & 0.1 $\pm$ 0.1 & 43.4 $\pm$ 0.5 & 44.1 $\pm$ 1.2 & 44.9 $\pm$ 0.7 \\
\midrule
DogTrot ($n=10$) & & & & & & \\
-expert & 97.3 $\pm$ 1.7 & -0.3 $\pm$ 0 & 96.8 $\pm$ 1.8 & 99.2 $\pm$ 1.3 & 106.5 $\pm$ 1.1 & 113.4 $\pm$ 0.9 \\
-medium-expert & 58.1 $\pm$ 5.5 & 0.5 $\pm$ 0.1 & 82.3 $\pm$ 4.6 & 83.4 $\pm$ 2.1 & 105.1 $\pm$ 2.1 & 109.8 $\pm$ 1.2 \\
-medium & 34.7 $\pm$ 0.4 & 0.5 $\pm$ 0.2 & 36.9 $\pm$ 0.3 & 39.1 $\pm$ 0.6 & 41 $\pm$ 0.8 & 47.2 $\pm$ 0.4 \\
-random-medium-expert & 33.6 $\pm$ 4.4 & 0.1 $\pm$ 0.1 & 6.1 $\pm$ 2.8 & 33.4 $\pm$ 1.5 & 52.1 $\pm$ 2.3 & 45.4 $\pm$ 4.1 \\
\midrule
DogTrot ($n=30$) & & & & & & \\
-expert & 99.7 $\pm$ 0.4 & -0.3 $\pm$ 0 & 100.3 $\pm$ 2.3 & 99.8 $\pm$ 0.8 & 102.8 $\pm$ 0.4 & 106.4 $\pm$ 0.8 \\
-medium-expert & 70.3 $\pm$ 4.9 & 0.6 $\pm$ 0.2 & 74.1 $\pm$ 8 & 90.7 $\pm$ 3.7 & 98.3 $\pm$ 1.6 & 100.7 $\pm$ 3 \\
-medium & 33.1 $\pm$ 0.2 & 0.7 $\pm$ 0.2 & 33.6 $\pm$ 0.4 & 33.4 $\pm$ 0.4 & 39.9 $\pm$ 0.1 & 40.5 $\pm$ 0.5 \\
-random-medium-expert & 22.4 $\pm$ 0.9 & 0.2 $\pm$ 0.1 & 5.2 $\pm$ 2.2 & 22.9 $\pm$ 2.5 & 31.6 $\pm$ 1.3 & 30.4 $\pm$ 1.8 \\
\midrule
DogTrot ($n=50$) & & & & & & \\
-expert & 98.2 $\pm$ 0.5 & 0.5 $\pm$ 0.1 & 98.6 $\pm$ 0.6 & 97.7 $\pm$ 0.6 & 99.2 $\pm$ 0.9 & 100.4 $\pm$ 1.4 \\
-medium-expert & 67.7 $\pm$ 5 & -0.3 $\pm$ 0 & 68.7 $\pm$ 4.2 & 88.9 $\pm$ 2.1 & 95 $\pm$ 2.1 & 97.1 $\pm$ 1.4 \\
-medium & 34.9 $\pm$ 0.4 & -0.3 $\pm$ 0 & 36.5 $\pm$ 0.7 & 36.7 $\pm$ 0.2 & 45.1 $\pm$ 0.6 & 43.1 $\pm$ 0.7 \\
-random-medium-expert & 21 $\pm$ 2 & 0.7 $\pm$ 0.1 & 9.3 $\pm$ 1.5 & 27.6 $\pm$ 1.3 & 27.9 $\pm$ 2.4 & 36.4 $\pm$ 1.8 \\
\midrule
DogTrot ($n=75$) & & & & & & \\
-expert & 98.4 $\pm$ 0.8 & 0.6 $\pm$ 0.2 & 99.6 $\pm$ 1.1 & 98 $\pm$ 1 & 101.4 $\pm$ 1.4 & 102.9 $\pm$ 0.9 \\
-medium-expert & 75.3 $\pm$ 2.6 & 0 $\pm$ 0 & 74.9 $\pm$ 16.7 & 85 $\pm$ 2 & 89.9 $\pm$ 0.7 & 97.9 $\pm$ 2.5 \\
-medium & 41.4 $\pm$ 0.2 & 0.5 $\pm$ 0.1 & 41.3 $\pm$ 0.8 & 42.5 $\pm$ 0.1 & 48.5 $\pm$ 0.4 & 47.8 $\pm$ 0.5 \\
-random-medium-expert & 27.7 $\pm$ 0.7 & 0.7 $\pm$ 0.2 & 0 $\pm$ 0.1 & 33.4 $\pm$ 2 & 45.3 $\pm$ 2.9 & 43.6 $\pm$ 2.6 \\
\midrule
DogTrot ($n=100$) & & & & & & \\
-expert & 96.4 $\pm$ 1.1 & 0.1 $\pm$ 0 & 95 $\pm$ 1.8 & 100.4 $\pm$ 0.7 & 103.2 $\pm$ 0.4 & 105.5 $\pm$ 0.8 \\
-medium-expert & 70.9 $\pm$ 6.5 & 0.4 $\pm$ 0.1 & 74.6 $\pm$ 5.8 & 74.4 $\pm$ 5.1 & 93.9 $\pm$ 1 & 101.5 $\pm$ 2.5 \\
-medium & 34.7 $\pm$ 0.8 & 0.1 $\pm$ 0.1 & 34.5 $\pm$ 0.5 & 34.8 $\pm$ 0.7 & 40.9 $\pm$ 0.2 & 41.5 $\pm$ 0.5 \\
-random-medium-expert & 18.9 $\pm$ 2.7 & 0.3 $\pm$ 0.1 & 0 $\pm$ 0 & 12.7 $\pm$ 2.4 & 24 $\pm$ 2.8 & 26.2 $\pm$ 1 \\
\midrule
Sum & & & & & & \\
-expert & 588 $\pm$ 5.2 & 0.7 $\pm$ 0.4 & 584.7 $\pm$ 14 & 594.6 $\pm$ 5.1 & 612 $\pm$ 6.4 & 629.8 $\pm$ 6.4 \\
-medium-expert & 404.3 $\pm$ 28.2 & 1.2 $\pm$ 0.7 & 449.5 $\pm$ 64.9 & 507.2 $\pm$ 18.7 & 571.5 $\pm$ 8.9 & 600.9 $\pm$ 12 \\
-medium & 222.6 $\pm$ 2.5 & 1.6 $\pm$ 0.7 & 231.9 $\pm$ 5.8 & 233 $\pm$ 2.5 & 267.4 $\pm$ 2.4 & 270.3 $\pm$ 2.8 \\
-random-medium-expert & 160.8 $\pm$ 14.3 & 2.1 $\pm$ 0.8 & 30.2 $\pm$ 17.6 & 173.4 $\pm$ 10.2 & 225 $\pm$ 12.9 & 226.9 $\pm$ 12 \\
-all & 1375.7 $\pm$ 50.2 & 5.6 $\pm$ 2.6 & 1296.3 $\pm$ 102.3& 1508.2 $\pm$ 36.5 & 1675.9 $\pm$ 30.6 & 1727.9 $\pm$ 33.2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Alex Beeson
David Ireland
Giovanni Montana

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


math

Mengqi Hu
Bian Li
Yi-An Ma
Yifei Lou
Xiu Yang

"A Gradient-Based Optimization Method Using the Koopman Operator" by Mengqi Hu, Bian Li, Yi-An Ma, Yifei Lou, and Xiu Yang, arXiv:2312.14361v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Carlos Misael Madrid Padilla
Zhi Zhang
Xiaokai Luo
Daren Wang
Oscar Hernan Madrid Padilla

"Dense ReLU Neural Networks for Temporal-spatial Model" by Carlos Misael Madrid Padilla, Zhi Zhang, Xiaokai Luo, Daren Wang, and Oscar Hernan Madrid Padilla, arXiv:2411.09961v8, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Non-parametric multiple change-point detection" by Andreas Anastasiou and Piotr Fryzlewicz, arXiv:2504.21379v1, 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
 Resolution & Error & Order \\
 \hline
 $h_0$ & 5.72E$-$2 & $-$ \\
 \hline
 $h_1$ & 1.18E$-$2 & $1.48$ \\
 \hline
 $h_2$ & 7.40E$-$3 & $1.32$ \\
 \hline
 $h_3$ & 3.24E$-$3 & $1.19$ \\
 \hline
 $h_4$ & 1.51E$-$3 & $1.10$ \\
 \hline
 $h_5$ & 7.26E$-$4 & $1.06$ \\
 \hline
 $h_6$ & 3.56E$-$4 & $1.03$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Simple weights (), example 1.}
\end{table}
\end{document}

math

"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Leandro Aparecido Passos
João Paulo Papa

"A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines" by Leandro Aparecido Passos and João Paulo Papa, arXiv:2101.05795v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Initial Prefetcher Options -} We show the regular and irregular prefetcher options we used at each cache level to construct our set of 300 PSCs. We reduce the number of PSCs down to 5 PSCs before training.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l}
\hline
\hline
\textbf{L1I\$} & \textbf{L1D\$} & \textbf{L2\$} & \textbf{LLC} \\
\hline
No Prefetcher & No Prefetcher & No Prefetcher & No Prefetcher \\
\hline
Next-line & Next-line & Next-line & Next-line \\
\hline
FNL+MMA & IPCP & IPCP & \\
\hline
DJOLT & MLOP & SPP & \\
\hline
EIP & Bingo & KPCP & \\
\hline
 & & Ip-Stride & \\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Furkan Eris
Marcia S. Louis
Kubra Eris
Jose L. Abellan
Ajay Joshi

"Puppeteer: A Random Forest-based Manager for Hardware Prefetchers across the Memory Hierarchy" by Furkan Eris, Marcia S. Louis, Kubra Eris, Jose L. Abellan, and Ajay Joshi, arXiv:2201.12027v1, 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{Computational setup of the test cases}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
		\hline
		&1D M1 & 1D M2 & 2D M1 \\
		\hline
		$T$ & $(0,0.7]$ & $(0,0.7]$ & $(0,10]$ \\	\hline
		Time steps & $8750$ & $8750$ & $33333$\\ 	\hline
		$\mathbf X$ & $[0,1]$ & $[0,1]$& $[-1.5,1.5]\times[-1.5,1.5]$ \\	\hline
		Grid cells $n_x$&$5000$ & $5000$ & $2000^2$ \\	\hline
		Quadrature & Gauss-Legendre & Gauss-Legendre & Tensorized Gauss-Legendre\\	\hline
		$\mathbf{V}$ & $[-1,1]$ & $[-1,1]$ & $[-1,1]\times[0,2\pi)$ \\ 	\hline
		Quadrature points & $28$ & $28$ & $400$\\	\hline
		Basis & Monomial & Monomial & Monomial\\ 	\hline
		CFL number & $0.4$ & $0.4$ & $0.4$ \\ 	\hline
		$\sigma$ & $1.0$ & $1.0$ & $0.0$\\ 	\hline
		$\tau$ & $0.5$ & $0.5$ & $0.0$\\ 	\hline
	\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.


math

"A PINN-enriched finite element method for linear elliptic problems" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The performance of our framework trained with image tags and subitizing, respectively. The better ones are marked in \textbf{bold}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 Dataset & Metric & w/ image-tag & w/ subitizing \\\hline
 \multirow{2}{*}{ECSSD} & $F_{\beta}\uparrow$ & 0.825 & \textbf{0.858} \\
 \cline{2-4}
 ~ & MAE$\downarrow$ & 0.110 & \textbf{0.108} \\\hline
 \multirow{2}{*}{DUT-OMRON} & $F_{\beta}\uparrow$ & 0.745 & \textbf{0.778} \\
 \cline{2-4}
 ~ & MAE$\downarrow$ & 0.103 & \textbf{0.083} \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiaoyang Zheng
Xin Tan
Jie Zhou
Lizhuang Ma
Rynson W. H. Lau

"Weakly-Supervised Saliency Detection via Salient Object Subitizing" by Xiaoyang Zheng, Xin Tan, Jie Zhou, Lizhuang Ma, and Rynson W. H. Lau, arXiv:2101.00932v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.