\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
			\hline
			\small\textbf{Case}& 	\small\textbf{Subcase}& \small\textbf{$\boldsymbol{O_1}$}& \small\textbf{$\boldsymbol{O_2}$} & \small\textbf{Global} \\
			\hline
			\hline

			\multirow{2}{1cm}{\centering{1.1}}
			&
			$b_1>0$
			&
			\phantom{h} $L^1_2$ \phantom{h}
			&
			\phantom{h} $L^2_1$ \phantom{h}
			&
			G1 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_4$
			&
			$L^2_3$
			&
			G2\\
			\hline

			\multirow{2}{1cm}{\centering{1.2}}
			&
			$b_1>0$
			&
			$L^1_2$
			&
			$L^2_1$
			&
			G3, G4 or G5 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_4$
			&
			$L^2_3$
			&
			G6\\
			\hline

			\multirow{2}{1cm}{\centering{1.3}}
			&
			$b_1>0$
			&
			$L^1_1$
			&
			$L^2_2$
			&
			G7, G8 or G9 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_3$
			&
			$L^2_4$
			&
			G10\\
			\hline

			\multirow{2}{1cm}{\centering{1.4}}
			&
			$b_1>0$
			&
			$L^1_2$
			&
			$L^2_1$
			&
			G11 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_4$
			&
			$L^2_3$
			&
			G12\\
			\hline

			\multirow{2}{1cm}{\centering{1.5}}
			&
			$b_1>0$
			&
			$L^1_1$
			&
			$L^2_2$
			&
			G13 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_3$
			&
			$L^2_4$
			&
			G14\\
			\hline

			\multirow{2}{1cm}{\centering{2.1}}
			&
			$b_1>0$
			&
			$L^1_7$
			&
			$L^2_1$
			&
			G15 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_8$
			&
			$L^2_3$
			&
			G16\\
			\hline

			\multirow{2}{1cm}{\centering{2.2}}
			&
			$b_1>0$
			&
			$L^1_7$
			&
			$L^2_1$
			&
			G17 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_8$
			&
			$L^2_3$
			&
			G18\\
			\hline

			\multirow{2}{1cm}{\centering{2.3}}
			&
			$b_1>0$
			&
			$L^1_5$
			&
			$L^2_2$
			&
			G19 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_6$
			&
			$L^2_4$
			&
			G20\\
			\hline

			\multirow{2}{1cm}{\centering{2.4}}
			&
			$b_1>0$
			&
			$L^1_7$
			&
			$L^2_1$
			&
			G21 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_8$
			&
			$L^2_3$
			&
			G22\\
			\hline

			\multirow{2}{1cm}{\centering{3.1}}
			&
			$b_1>0$
			&
			$L^1_2$
			&
			$L^2_6$
			&
			G23 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_4$
			&
			$L^2_{8}$
			&
			G24\\
			\hline

			\multirow{2}{1cm}{\centering{3.2}}
			&
			$b_1>0$
			&
			$L^1_1$
			&
			$L^2_5$
			&
			G25 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_3$
			&
			$L^2_7$
			&
			G26\\
			\hline

			\multirow{2}{1cm}{\centering{3.3}}
			&
			$b_1>0$
			&
			$L^1_2$
			&
			$L^2_9$
			&
			G27 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_4$
			&
			$L^2_{10}$
			&
			G28\\
			\hline

			\multirow{2}{1cm}{\centering{3.4}}
			&
			$b_1>0$
			&
			$L^1_1$
			&
			$L^2_5$
			&
			G29 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_3$
			&
			$L^2_7$
			&
			G30\\
			\hline

			\multirow{2}{1cm}{\centering{4.1}}
			&
			$b_1>0$
			&
			$L^1_7$
			&
			$L^2_6$
			&
			G31\\
			\cline{2-5}

			\multirow{2}{*}
			&
			$b_1<0$
			&
			$L^1_8$
			&
			$L^2_{8}$
			&
			G32\\
			\hline

			\multirow{4}{1cm}{\centering{4.2}}
			&
			$b_1>0$, $c_0-b_0>0$
			&
			$L^1_5$
			&
			$L^2_5$
			&
			G33\\
			\cline{2-5}

			\multirow{4}{*}
			&
			$b_1>0$, $c_0-b_0<0$
			&
			$L^1_7$
			&
			$L^2_9$
			&
			G34\\
			\cline{2-5}

			\multirow{4}{*}
			&
			$b_1<0$, $c_0-b_0>0$
			&
			$L^1_6$
			&
			$L^2_7$
			&
			G35\\
			\cline{2-5}

			\multirow{4}{*}
			&
			$b_1<0$, $c_0-b_0<0$
			&
			$L^1_8$
			&
			$L^2_{10}$
			&
			G36\\
			\hline
		\end{tabular}
\end{adjustbox}
\caption{Classification of the global phase portraits of systems .}
\end{table}
\end{document}

math

Érika Diz-Pita
Jaume Llibre
M. Victoria Otero-Espinar

"Planar Kolmogorov systems with infinitely many singular points at infinity" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.14416v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{SIMULATION SETUP PARAMETER}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
			\hline
			\hline
			\textbf{Parameters} & \textbf{Value}\\
			\hline
			Classifier
			Training examples & 1000\\
			Weight parameters & $\alpha$=0.4;$\beta$=0.6\\
			Epoch & 10\\
			IP & localhost\\
			Shards & 8\\
			FL devices & 10 \\
			PoW difficulty & 4\\
			Predefined parameters & $w_1$=$w_2$=$a$=0.5; te=60min; u=1\\
			\hline
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hajar Moudoud
Soumaya Cherkaoui
Lyes Khoukhi

"Towards a Secure and Reliable Federated Learning using Blockchain" by Hajar Moudoud, Soumaya Cherkaoui, and Lyes Khoukhi, arXiv:2201.11311v1, 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.


q-fin

"Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies" by Qizhao Chen, arXiv:2508.16378v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

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


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{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccc}
 \hline\hline

 & Method & C2ST & MMD & MED-DIST & MEAN-ERR \\
 \hline

 \multirow{2}{*}{\textit{\shortstack{Bernoulli \\ GLM}}} & SNPE-C & 0.749 $\pm$ 0.017 & 0.210 $\pm$ 0.024 & \textbf{11.454} $\pm$ 0.255 & 0.188 $\pm$ 0.117 \\
 & ASNPE & \textbf{0.725} $\pm$ 0.012 & \textbf{0.146} $\pm$ 0.057 & 11.993 $\pm$ 0.172 & \textbf{0.150} $\pm$ 0.085 \\
 \hline

 \multirow{2}{*}{\textit{\shortstack{SLCP \\ distractors}}} & SNPE-C & 0.987 $\pm$ 0.001 & 0.172 $\pm$ 0.001 & 16.716 $\pm$ 1.014 & \textbf{0.899} $\pm$ 0.065 \\
 & ASNPE & \textbf{0.985} $\pm$ 0.002 & \textbf{0.148} $\pm$ 0.022 & \textbf{16.547} $\pm$ 0.499 & 0.906 $\pm$ 0.220 \\
 \hline

 \multirow{2}{*}{\textit{\shortstack{Gaussian \\ mixture}}} & SNPE-C & 0.773 $\pm$ 0.009 & 0.167 $\pm$ 0.006 & 1.051 $\pm$ 0.037 & 0.532 $\pm$ 0.074 \\
 & ASNPE & \textbf{0.771} $\pm$ 0.006 & \textbf{0.150} $\pm$ 0.025 & \textbf{1.010} $\pm$ 0.066 & \textbf{0.440} $\pm$ 0.164 \\
 \hline\hline
 \end{tabular}
\end{adjustbox}
\caption{Results comparing SNPE-C and ASNPE for various metrics on the \textit{Bernoulli GLM}, \textit{SLCP distractors}, and \textit{Gaussian mixture} tasks from . Experimentation details, metrics, and associated plots can be found in Appendix~.}
\end{table}
\end{document}

stat

Sam Griesemer
Defu Cao
Zijun Cui
Carolina Osorio
Yan Liu

"Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference" by Sam Griesemer, Defu Cao, Zijun Cui, Carolina Osorio, and Yan Liu, arXiv:2412.05590v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|} \hline
$z$ & $a$ & supersingular primes \\ \hline
$\left[15, 15, 15, 1\right]$ & $\left(1, 13, 1\right)$ & $p \equiv 14 \bmod{15}$ \\ \hline
$\left[16, 16, 8, 1\right]$ & $\left(1, 13, 2\right), \left(1, 1, 14\right)$ & $p \equiv 15 \bmod{16}$ \\
 & $\left(1, 9, 6\right)$ & $p \equiv 7, 15 \bmod{16}$ \\ \hline
$\left[18, 9, 6, 1\right]$ & $\left(1, 14, 3\right), \left(1, 2, 15\right)$ & $p \equiv 2 \bmod{3}$ \\ \hline
$\left[20, 10, 4, 1\right]$ & $\left(1, 14, 5\right)$ & $p \equiv 19 \bmod{20}$ \\ \hline
$\left[21, 21, 3, 1\right]$ & $\left(1, 13, 7\right)$ & $p \equiv 2 \bmod{3}$ \\ \hline
$\left[24, 8, 3, 1\right]$ & $\left(1, 15, 8\right)$ & $p \equiv 23 \bmod{24}$ \\ \hline
$\left[28, 28, 2, 1\right]$ & $\left(1, 13, 14\right)$ & $p \equiv 3 \bmod{4}$ \\ \hline
$\left[30, 15, 2, 1\right]$ & $\left(1, 14, 15\right)$ & $p \equiv 29 \bmod{30}$ \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Cyclic genus $7$}
\end{table}
\end{document}

math

"Supersingular curves via the Shimura--Taniyama method" by Jeremy Booher and Rachel Pries, arXiv:2503.04878v2, 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 on the Radiology Model. (Sym-- Model using embeddings from the $S$-model. PT-- VGG16 uses pre-trained weights from the end-to-end model)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Model & Accuracy & AUC \\
\midrule
VGG-16 & 0.83 & 0.96 \\
VGG-16 + Sym & 0.86 & 0.97 \\
VGG-16 + Sym (PT) & \textbf{0.88} & \textbf{0.99} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Rishab Khincha
Soundarya Krishnan
Tirtharaj Dash
Lovekesh Vig
Ashwin Srinivasan

"Constructing and Evaluating an Explainable Model for COVID-19 Diagnosis from Chest X-rays" by Rishab Khincha, Soundarya Krishnan, Tirtharaj Dash, Lovekesh Vig, and Ashwin Srinivasan, arXiv:2012.10787v2, 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}{lccccc}
\toprule
\textbf{Measure} & \textbf{Expert 1} & \textbf{Expert 2} & \textbf{Expert 3} & \textbf{Gold} & \textbf{Model} \\
\midrule
Average & 2.22 & 2.36 & 2.29 & 2.91 & 2.32 \\
Standard deviation & 1.95 & 2.12 & 2.04 & 2.35 & 1.15 \\\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Narrative counts per document for human experts and \texttt{GPT-4o}.}
\end{table}
\end{document}

q-fin

Tobias Schmidt
Kai-Robin Lange
Matthias Reccius
Henrik Müller
Michael Roos
Carsten Jentsch

"Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models" by Tobias Schmidt, Kai-Robin Lange, Matthias Reccius, Henrik Müller, Michael Roos, and Carsten Jentsch, arXiv:2506.15041v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of coverage probabilities and average of confidence interval lengths for two methods. The results for the lengths are in the parentheses.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
\hline
Scenario I & Truth & method & $90\%$ & $95\%$ & $99\%$ \\ \hline\hline
$(\theta=0.9, \phi=-0.5)$ & $T/2$ & NSP & 0.370 & 0.370 & 0.329 \\
& & & (279.130) & (275.920) & (191.210)\\
&& our method & 0.920 & 0.960 & 0.970 \\
& & & (50.220) & (60.420) & (85.850) \\
\hline
$(\theta=0.9, \phi=0.5)$ & $T/2$ & NSP & 0.273 & 0.343 & 0.271 \\
& & & (273.790) & (243.440) & (189.990) \\
&& our method & 0.940 & 0.970 & 0.970 \\
& & & (50.860) & (61.200) & (86.750) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Sepideh Mosaferi
Abolfazl Safikhani
Peiliang Bai

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
 Variable & Source & Resolution\\
 \hline
 Evapotranspiration (ET) & ECOSTRESS & 70m \\
 Evaportaive Stress Index (ESI) & ECOSTRESS & 70m \\
 Water Use Efficiency (WUE) & ECOSTRESS & 70m \\
 Land Surface Temperature (LST) & ECOSTRESS & 70m \\
 Normalized Differened Vegetation Index (NDVI) & Harmonized Landsat Sentinel &30m \\
 Leaf Area Index (LAI) & MODIS & 500m \\
 AM Soil Moisture (AM SM) & SMAP & 9km \\
 PM Soil Moisture (PM SM) & SMAP & 9km \\
 Daily Average Dew Point Temperature (TDMEAN) & PRISM & 4km \\
 Daily Average Air Temperature (TMEAN) & PRISM & 4km \\
 Vapor Pressure Deficit Maximum (VPDMAX) & PRISM & 4km\\
 Vapor Pressure Deficit Minimum (VPDMIN) & PRISM & 4km\\
 Elevation & Digital Elevation Model & 30m\\
 Aspect &Digital Elevation Model &30m\\
 Slope& Digital Elevation Model & 30m
\end{tabular}
\end{adjustbox}
\caption{Variables used as covariates in the Super Learner model. With the exception of the final three variables, all variable were included as both their current state and temporal trend.}
\end{table}
\end{document}

stat

Nicholas Simafranca
Bryant Willoughby
Erin O'Neil
Sophie Farr
Brian J Reich
Naomi Giertych
Margaret Johnson
Madeleine Pascolini-Campbell

"Modelling wildland fire burn severity in California using a spatial Super Learner approach" by Nicholas Simafranca, Bryant Willoughby, Erin O'Neil, Sophie Farr, Brian J Reich, Naomi Giertych, Margaret Johnson, and Madeleine Pascolini-Campbell, arXiv:2311.16187v1, 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{\small{Statistics for the case of crosshole GPR data in a multi-Gaussian domain when considering the 1000 independent test models. The comparison is made between the predicted model (Predicted \#1) and the closest training model in data space (Closest TR) within the training set of 20,000 examples using the RMSE in data space ( RMSE$_{\rm data}$), the ${\rm l}_1$-norm (${\rm l}_1$) and the structural similarity index (SSIM). For each metric, the minimum (Min), median (Median) and maximum (Max) values are reported together with the 10$^{\rm th}$, 25$^{\rm th}$, 75$^{\rm th}$ and 90$^{\rm th}$ percentiles (P10, P25, P75, P90), respectively. The TR size variable signifies the number of training examples used to train \textit{vec2pix}.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccc}%
			\hline
			Model & TR size & Min & P10 & P25 & Median & P75 & P90 & Max\\
			\hline
			& & & & & & & & \\
			\multicolumn{9}{c}{RMSE$_{\rm data}$ (ns)} \\
			Closest TR & 20,000 & 0.77 & 0.92 & 0.97 & 1.03 & 1.10 & 1.16 & 1.73\\
			Predicted & 20,000 & 0.50 & 0.55 & 0.56 & 0.58 & 0.61 & 0.63 & 0.92 \\
			Predicted & 10,000 & 0.53 & 0.59 & 0.61 & 0.64 & 0.68 & 0.73 & 1.12 \\
			Predicted & 5,000 & 0.56 & 0.63 & 0.67 & 0.81 & 1.08 & 1.39 & 2.24 \\
			\multicolumn{9}{c}{${\rm l}_1$ (m/ns)} \\
			Closest TR & 20,000 & 6.92 & 10.57 & 11.41 & 12.36 & 13.56 & 14.56 & 19.27 \\
			Predicted & 20,000 & 3.68 & 5.19 & 5.80 & 6.50 & 7.35 & 8.28 & 12.16 \\
			Predicted & 10,000 & 4.29 & 5.77 & 6.47 & 7.24 & 8.16 & 9.05 & 13.48 \\
			Predicted & 5,000 & 4.20 & 6.48 & 7.11 & 7.97 & 8.83 & 10.00 & 13.14 \\
			\multicolumn{9}{c}{SSIM (-)}\\
			Closest TR & 20,000 & 0.66 & 0.75 & 0.78 & 0.80 & 0.83 & 0.84 & 0.91 \\
			Predicted & 20,000 & 0.87 & 0.91 & 0.92 & 0.93 & 0.94 & 0.95 & 0.96 \\
			Predicted & 10,000 & 0.84 & 0.89 & 0.91 & 0.92 & 0.93 & 0.94 & 0.96 \\
			Predicted & 5,000 & 0.84 & 0.88 & 0.89 & 0.91 & 0.92 & 0.93 & 0.95 \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Eric Laloy
Niklas Linde
Diederik Jacques

"Approaching geoscientific inverse problems with vector-to-image domain transfer networks" by Eric Laloy, Niklas Linde, and Diederik Jacques, arXiv:1912.09954v3, 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{MNIST: The comparison of accuracy between standard models (Std., Std.+L1 and Std.+L2) and Min-Max model.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
		\toprule
		& Std. & Std.+L1 & Std.+L2 & Min-Max \\ \midrule
		No attack & \textbf{0.9861} & 0.9747 & 0.9576 & 0.9844 \\
		FGSM & 0.8152 & 0.7610 & 0.7631 & \textbf{0.9475} \\
		PGD & 0.5928 & 0.6198 & 0.7130 & \textbf{0.8369} \\
		CW & 0.5697 & 0.4961 & 0.5403 & \textbf{0.6892} \\
		MIA & 0.6268 & 0.6190 & \textbf{0.7181} & 0.6980 \\
		L2BIA & 0.9747 & 0.9659 & 0.9501 & \textbf{0.9765} \\
		LinfBIA & 0.6046 & 0.6276 & 0.7126 & \textbf{0.8599} \\ \bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks" by Haojing Shen, Sihong Chen, and Ran Wang, arXiv:2011.13719v1, 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{Hyperparameter space used for PPO grid search optimization (108 total combinations)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\hline
\textbf{Hyperparameter} & \textbf{Values} & \textbf{Description} \\ \hline
learning\_rate & 0.0001, 0.0003, 0.001 & Adjusts the step size for model updates. \\
gamma & 0.9, 0.99, 0.999 & Discount factor for future rewards. \\
batch\_size & 64, 128 & Number of samples per training step. \\
max\_grad\_norm & 0.5, 0.9 & Clips gradients to stabilize training. \\
ent\_coef & 0, 0.001, 0.01 & Controls the balance between exploration and exploitation. \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Alejandro Campoy-Nieves
Antonio Manjavacas
Javier Jiménez-Raboso
Miguel Molina-Solana
Juan Gómez-Romero

"SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning" by Alejandro Campoy-Nieves, Antonio Manjavacas, Javier Jiménez-Raboso, Miguel Molina-Solana, and Juan Gómez-Romero, arXiv:2412.08293v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{List of Notations}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|} \hline % 其中，|c|表示文本居中，文本两边有竖直表线。
			\bf{Notations} & \bf{Definition} \\ \hline
			$N$ & Number of transmit antennas \\ \hline
			$M$ & Number of reflection/refraction elements \\ \hline
			$M_s$ & Number of sensing elements \\ \hline
			$\mathcal{D}$& Set of spaces \\ \hline
			$\mathcal{K}_d$& Set of users in space $d$\\ \hline
			$\mathcal{J}_d$ &Set of targets in space $d$ \\ \hline
			$K_d$ & Number of users in space $d$\\ \hline
			$J_d$ & Number of targets in space $d$\\ \hline
			$L$ & Number of time indexes\\ \hline
			$\kappa$ & Rician factor\\ \hline
			$\lambda$ & Carrier wavelength \\\hline
			$\zeta$ & Antenna or element separation \\ \hline
			$\mathbf{H}$ & Channel between the BS and the MF-RIS \\ \hline
			$\mathbf{g}_{k}$ &Channel between the MF-RIS and the $k$-th user \\ \hline
			$\mathbf{G}_d$ & Multi-target response matrix in space $d$ \\ \hline
			$\sigma_k^2$ & Noise power for the $k$-th user\\ \hline
			$\sigma_r^2$ & Noise power for reflection/refraction elements \\ \hline
			$\sigma_s^2$ & Noise power for sensing elements \\ \hline
			$\gamma_k$ & Receiving SINR of the $k$-th user \\ \hline
			$\gamma_d$ & Echo SINR received by the MF-RIS in space $d$ \\ \hline
			$\gamma_{\rm{th}}$ &QoS requirement of each user \\ \hline
			$P^{\rm{max}}_{\rm{total}}$& Total power budget of the system \\ \hline
			$P^{\rm{max}}_{\rm{b}}$ &Maximum transmit power of the BS\\ \hline
			$P^{\rm{max}}_{\rm{r}}$& Power budget of the MF-RIS \\ \hline
			$\mathcal{R}_{\rm{MF}}$ & Feasible set of the MF-RIS coefficients \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Dongsheng Han
Peng Wang
Wanli Ni
Wen Wang
Ailing Zheng
Dusit Niyato
Naofal Al-Dhahir

"Multi-Functional RIS Integrated Sensing and Communications for 6G Networks" by Dongsheng Han, Peng Wang, Wanli Ni, Wen Wang, Ailing Zheng, Dusit Niyato, and Naofal Al-Dhahir, arXiv:2412.01251v3, 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{Training time in hours}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \toprule
 & SVGP & DKLGP & TSDiff & RS$^3$GP & VRS$^3$GP\\
 \midrule
 Solar & 1.90 & 2.41 & 2.75 & 0.27 & 0.45\\
 Electricity & 2.23 & 1.91 & 4.09 & 0.62 & 0.98 \\
 Traffic & - & - & 4.44 & 2.31 & 2.70 \\
 Exchange & 0.75 & 0.65 & 2.00 & 0.23 & 0.29 \\
 M4 & 1.68 & 1.94 & 1.96 & 0.86 & 1.02 \\
 UberTLC & 1.89 & 2.19 & 3.19 & 0.53 & 0.71 \\
 KDDCup & 2.03 & 1.55 & 2.10 & 0.65 & 0.93 \\
 Wikipedia & - & - & 6.17 & 3.52 & 6.10 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Csaba Tóth
Masaki Adachi
Michael A. Osborne
Harald Oberhauser

"Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes" by Csaba Tóth, Masaki Adachi, Michael A. Osborne, and Harald Oberhauser, arXiv:2412.19727v1, 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{Different performance with different block and grid settings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \hline
 Grid size / block size & run time(ms) on Tesla K40c & Tasks per thread \\ \hline
 32 / 32 & 3714.5 & $2^6$ \\ \hline
 128 / 128 & 335.5 & $2^2$ \\ \hline
 256 / 256 & 388.1 & $2^0$ \\ \hline
 512 / 512 & 474.9 & \textless$2^0$\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"GPU acceleration of a patient-specific airway image segmentation and its assessment" by Yu-Wei Chang and Tony W. H. Sheu, arXiv:2012.10684v1, 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{Aggregated Descriptive Statistics of Daily Log Returns (\%) for the Top 250 U.K. Equities.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline
 Statistic & Minimum & Average & Maximum \\
 \hline
 Mean Return (\%) & -0.068 & 0.000 & 0.045 \\
 Standard Deviation (\%) & 0.509 & 1.937 & 4.881 \\
 Minimum Return (\%) & -83.974 & -18.412 & -4.292 \\
 Maximum Return (\%) & 3.897 & 16.081 & 87.385 \\
 Skewness (log returns) & -4.899 & -0.380 & 3.856 \\
 Kurtosis (log returns) & 2.465 & 19.151 & 552.298 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Haoyuan Wang
Chen Liu
Minh-Ngoc Tran
Chao Wang

"Deep Learning Enhanced Multivariate GARCH" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, licensed under CC BY-SA 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.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{xcolor}
\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|}
\hline
Stimulus & \textcolor{red}{$K$} & $K_{min}$& $K_{max}$ & \textcolor{red}{$C$} & $C_{min}$ & $C_{max}$ & \textcolor{red}{$\mu_{P}$} & $\sigma_{P}$ & $\mu_{R}$ & $\sigma_{R}$ \\
\hline \hline
Natural Movie & \textcolor{red}{1} & 0 & 4 & \textcolor{red}{6} & 2 & 6 & \textcolor{red}{0.3} & 0.1 & 0.04 & 0.02 \\
\hline
White Noise & \textcolor{red}{2} & 0 & 4 & \textcolor{red}{2} & 2 & 6 & \textcolor{red}{0.55} & 0.05 & 0.04 & 0.02 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Hyper-parameters fitted to Off-Brisk Transient RGC responses to white noise and natural movie stimuli. Key differences highlighted in red.}
\end{table}
\end{document}

cs

Christopher Warner
Kiersten Ruda
Friedrich T. Sommer

"A probabilistic latent variable model for detecting structure in binary data" by Christopher Warner, Kiersten Ruda, and Friedrich T. Sommer, arXiv:2201.11108v1, 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{Parameters of fitted Gamma distributions using Chave dataset .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Variable & Shape & Location & Scale \\ %\hline \\
 Diameter [cm] & 0.68 & 5.00 & 30.18\\
 Height [m] & 1.92 & 1.18 & 7.75 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yuanyuan Wang
Qian Song
Dawood Wasif
Muhammad Shahzad
Christoph Koller
Jonathan Bamber
Xiao Xiang Zhu

"How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning" by Yuanyuan Wang, Qian Song, Dawood Wasif, Muhammad Shahzad, Christoph Koller, Jonathan Bamber, and Xiao Xiang Zhu, arXiv:2412.06451v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The optimal RobustCRF's hyperparameters for the feature-based attacks.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
Hyperparameter & Cora & CiteSeer & PubMed & CS \\
\midrule
$r$ & 0.1 & 0.9 & 0.3 &0.3 \\
$\sigma$ & 0.9 & 0.8 & 0.9 &0.5 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yassine Abbahaddou
Sofiane Ennadir
Johannes F. Lutzeyer
Fragkiskos D. Malliaros
Michalis Vazirgiannis

"Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields" by Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Fragkiskos D. Malliaros, and Michalis Vazirgiannis, arXiv:2411.05399v1, 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}{lc}
\toprule
 Parameter & Value \\
\midrule
Kite mass $m$ & 1 kg \\
Characteristic area $A$ & 10 m$^2$\\
Air density $\rho$ & 1.2 kg/m$^3$\\
Engine drum mass $M$ & 10 kg\\
Engine drum radius $R$ & 0.2 m\\
Wind speed $W^w$ (constant flow) & 10 m/s\\
Speed threshold $\dot{r}_{\mathrm{thr}}$($T \rightarrow R$ phase) & 0.2 m/s\\
Distance threshold ${r}_{\mathrm{thr}}$ ($R \rightarrow T$ phase) & 27 m\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Parameters for the AWE system under consideration.}
\end{table}
\end{document}

eess

Lorenzo Basile
Maria Grazia Berni
Antonio Celani

"Harvesting energy from turbulent winds with Reinforcement Learning" by Lorenzo Basile, Maria Grazia Berni, and Antonio Celani, arXiv:2412.13961v2, 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{Averaged non-binary dice coefficient and SSIM at epoch $210$. The models \textbf{orig} and \textbf{gen5} perform best.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
model & non-binary dice & SSIM \\ \hline
\textbf{orig} & \textbf{0.2638} & 0.8409 \\
\textbf{gen5} & 0.2266 & \textbf{0.8492} \\
\textbf{dis3} & 0.1566 & 0.8448 \\
\textbf{l11,gan100} & 0.1578 & 0.7926 \\
\textbf{l150,gan50} & 0.1484 & 0.7379 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Rosanna Dietrich-Sussner
Amirabbas Davari
Thorsten Seehaus
Matthias Braun
Vincent Christlein
Andreas Maier
Christian Riess

"Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm" by Rosanna Dietrich-Sussner, Amirabbas Davari, Thorsten Seehaus, Matthias Braun, Vincent Christlein, Andreas Maier, and Christian Riess, arXiv:2101.03252v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
 & \textbf{Masc} & \textbf{Fem} & \textbf{Bias} & \textbf{Overall} \\
\midrule
\midrule
c2f + SpanBERT-Large & 90.5 & \bf 86.3 & \bf 0.95 & \bf 88.4 \\
c2f + Longformer-Base & 87.6 & 82.3 & 0.94 & 84.9 \\
c2f + Longformer-Large & 90.1 & 85.4 & \bf 0.95 & 87.8 \\
\midrule
s2e + Longformer-Large & \bf 90.6 & 85.8 & \bf 0.95 & 88.3 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Performance on the test set of the GAP coreference dataset. The reported metrics are F1 scores.}
\end{table}
\end{document}

cs

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|cccc|} \hline
 & uni & bi & uni-VF & bi-VF \\ \hline
Line & 2.71 & 0.75 & 0 & 0 \\ \hline
Path & 3.33 & 1.14 & 0.28 & 0 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Average number of loss of contact with the surface during the line /path following tasks}
\end{table}
\end{document}

cs

Laura Petrich
Jun Jin
Masood Dehghan
Martin Jagersand

"Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?" by Laura Petrich, Jun Jin, Masood Dehghan, and Martin Jagersand, arXiv:2101.02750v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
 \hline
 & \textbf{Half NN -- Half MC} & \textbf{Fully Neural} \\
 \hline
 \texttt{calibration time (avg)} & 12 m & 5 s \\
 \texttt{device} & GPU & CPU \\
 \texttt{\# cores} & & 1 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Average calibration times under our approach (right) as opposed to the state of the art (left).}
\end{table}
\end{document}

q-fin

Fabio Baschetti
Giacomo Bormetti
Pietro Rossi

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Validity for the point-wise convolution of GMDC on CVIU database. Note that the SubEC is not involved in these variants. The proposed GMDC uses grouped kernels denoted as $[K_1, K_2, \dots, K_N]$. The best performance is highlighted in bold.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc|cccc}
\toprule
Model & \multicolumn{4}{c|}{w/o Point-wise Conv} & \multicolumn{4}{c}{with Point-wise Conv} \\
\midrule
GMDC Groups & SRCC $\uparrow$ & KRCC $\uparrow$ & PLCC $\uparrow$& RMSE $\downarrow$& SRCC $\uparrow$& KRCC $\uparrow$& PLCC $\uparrow$& RMSE $\downarrow$\\
\midrule
$[3,7]$ & 0.945 & 0.800 & 0.951 & 0.884 & 0.974 & 0.866 & 0.977 & 0.605 \\
$[3,5,7]$ & 0.967 & 0.850 & 0.968 & 0.713 & 0.972 & 0.857 & 0.972 & 0.642 \\
$[3,5,7,9]$ & 0.973 & 0.864 & 0.976 & \textbf{0.601} & \textbf{0.975} & \textbf{0.868} & \textbf{0.978} & 0.632\\
\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.


math

"Multivariate Rényi inaccuracy measures based on copulas: properties and application" by Shital Saha and Suchandan Kayal, arXiv:2502.17215v1, 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{ Descriptive statistics of daily log returns across the six markets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllll}
\toprule
Market&Mean&Median&Min&Max&Std&Skewness&Kurtosis\\ \midrule
AORD&0.0155&0.0691&-7.261&4.533&0.946&-0.730&9.118\\
DAX&0.0134&0.0763&-11.863&12.027&1.456&-0.165&9.637\\
FTSE 100&0.0024&0.0343&-10.137&9.485&1.162&-0.308&10.471\\
Hang Seng&0.0048&0.0369&-13.582&13.407&1.449&-0.018&10.618\\
Nikkei&0.0054&0.0438&-12.111&13.235&1.481&-0.381&9.140\\
S\&P 500&0.0186&0.0636&-12.670&10.642&1.233&-0.405&13.829\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall" by James W. Taylor and Chao Wang, arXiv:2508.16919v1, 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{ Error profile for computing .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|rr|rr}
 \hline
$G_i$ & % \multicolumn{2}{c|}{$\|\b u-\b u_h\|_{0}$ $O(h^r)$} &
 $\|\b u-\b u_h\|_{0}$&$O(h^r)$ & $\|\nabla_w(\b u-\b u_h)\|_0$ & $O(h^r)$ \\ \hline
 & \multicolumn{4}{c}{By the $P_1$ WG element on Figure grids} \\
 6& 0.362E-03 & 2.5& 0.127E+00 & 1.5 \\
 7& 0.766E-04 & 2.2& 0.561E-01 & 1.2 \\ \hline
 & \multicolumn{4}{c}{By the $P_1$ WG element on Figure grids} \\
 6& 0.308E-02 & 2.0& 0.470E+00 & 1.0 \\
 7& 0.767E-03 & 2.0& 0.236E+00 & 1.0 \\
 \hline
 & \multicolumn{4}{c}{By the $P_2$ WG element on Figure grids} \\
 5& 0.267E-03 & 4.0& 0.775E-01 & 3.0 \\
 6& 0.171E-04 & 4.0& 0.998E-02 & 3.0 \\
 \hline
 & \multicolumn{4}{c}{By the $P_2$ WG element on Figure grids} \\
 5& 0.714E-03 & 2.9& 0.931E-01 & 2.1 \\
 6& 0.900E-04 & 3.0& 0.229E-01 & 2.0 \\
 \hline
 & \multicolumn{4}{c}{By the $P_3$ WG element on Figure grids} \\
 5& 0.176E-01 & 5.0& 0.797E+01 & 4.0 \\
 6& 0.548E-03 & 5.0& 0.499E+00 & 4.0 \\
 \hline
 & \multicolumn{4}{c}{By the $P_3$ WG element on Figure grids} \\
 4& 0.797E-03 & 4.4& 0.633E-01 & 3.6 \\
 5& 0.470E-04 & 4.1& 0.590E-02 & 3.4 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


q-fin

Shiang-Hung Hu
Po-Hsuan Lin
Thomas R. Palfrey
Joseph Tao-yi Wang
Yu-Hsiang Wang

"Strategy Method Effects in Centipede Games: An Optimal Design Approach" by Shiang-Hung Hu, Po-Hsuan Lin, Thomas R. Palfrey, Joseph Tao-yi Wang, and Yu-Hsiang Wang, arXiv:2508.06425v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Pretraining on IsarStep for the MetaMathStep task.}
\begin{tabular}{lllllll}
 \toprule
 Model & Top-1 Acc. & Top-10 Acc. \\
 \midrule
 No pretrain & 67.7 & 76.5 \\
 LIME \texttt{Mix} &69.1 &77.9 \\
 Pretrain on IsarStep &67.0 &76.1 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Yuhuai Wu
Markus Rabe
Wenda Li
Jimmy Ba
Roger Grosse
Christian Szegedy

"LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning" by Yuhuai Wu, Markus Rabe, Wenda Li, Jimmy Ba, Roger Grosse, and Christian Szegedy, arXiv:2101.06223v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcr|rrr}
		variable & stat. & current & lag 1 & lag 2 & lag 3 \\
		\hline
		Markit NA credit index
			& correl. & \color{blue}{\textbf{0.380}} & \textbf{0.323} & \textbf{0.266} & \textbf{0.209} \\
			& p-value & 0.000 & 0.000 & 0.000 & 0.000 \\
		Bloomberg FRN index, OAS
			& correl. & \color{blue}{\textbf{0.714}} & \textbf{0.512} & \textbf{0.310} & \textbf{0.107}\\
			& p-value & 0.000 & 0.000 & 0.000 & 0.000\\
		Bloomberg FRN index
			& correl. & \color{blue}{\textbf{-0.623}} & \textbf{-0.445} & \textbf{-0.267} & \textbf{-0.088}\\
			& p-value & 0.000 & 0.000 & 0.000 & 0.058 \\
		Bloomberg US corporate index, OAS
			& correl. & \color{blue}{\textbf{0.670}} & \textbf{0.498} & \textbf{0.327} & \textbf{0.155}\\
			& p-value & 0.000 & 0.000 & 0.000 & 0.001 \\
		Bloomberg US corporate index
			& correl. & \color{blue}{\textbf{-0.276}} & \textbf{-0.233} & \textbf{-0.190} & \textbf{-0.147}\\
			& p-value & 0.000 & 0.000 & 0.000 & 0.002 \\
		National financial conditions index
		 & correl. & \textbf{0.595} & \color{blue}{\textbf{0.622}} & \textbf{0.534} & \textbf{0.368}\\
		 & p-value & 0.000 & 0.000 & 0.000 & 0.000 \\
		Credit market distress index
		 & correl. & \color{blue}{\textbf{0.309}} & \textbf{0.173} & \textbf{0.150} & \textbf{-0.025} \\
		 & p-value & 0.000 & 0.000 & 0.001 & 0.593 \\
 LIBOR-OIS spread
		 & correl. & \color{blue}{\textbf{0.617}} & \textbf{0.443} & \textbf{0.269} & \textbf{0.095} \\
		 & p-value & 0.000 & 0.000 & 0.000 & 0.042 \\
 TED spread
		 & correl. & \color{blue}{\textbf{0.611}} & \textbf{0.449} & \textbf{0.288} & \textbf{0.126} \\
		 & p-value & 0.000 & 0.000 & 0.000 & 0.007 \\
		\hline
		SPDR financial sector ETF
			& correl. & \color{blue}{\textbf{-0.426}} & \textbf{-0.344} & \textbf{-0.262} & \textbf{-0.180}\\
			& p-value & 0.000 & 0.000 & 0.000 & 0.000 \\
		\hline
		Economic policy uncertainty
		 & correl. & \color{blue}{\textbf{0.244}} & \textbf{0.184} & \textbf{0.123} & 0.062 \\
		 & p-val. & 0.000 & 0.000 & 0.009 & 0.184 \\
		Volatility index
			& correl. & \textbf{0.103} & \color{blue}{\textbf{0.146}} & \textbf{0.188} & \textbf{0.230}\\
			& p-value & 0.026 & 0.002 & 0.005 & 0.000 \\
	\end{tabular}
\end{adjustbox}
\caption{Monthly correlations of AXI with selected financial indicators}
\end{table}
\end{document}

q-fin

A Case for AXI

"A Case for AXI" by Viktor Tsyrennikov, arXiv:2509.03035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 Model & Active Parameters & One-Step SPE \\
\hline
GNAR(1, [1]) & 2 & 10.286 \\
 VAR(1) & $140^2$ & 12.52 \\
 Res. VAR(1) & 3730 & 11.605 \\
 Sparse VAR(1) & 2785 & 12.986 \\
 AR(1) & $140$ & 82.571 \\
 GNAR(1, [1])* & 141 & 10.173
\end{tabular}
\caption{One-step prediction error, $\hat{\boldsymbol{X}}_{445}$ is predicted using the previous 444 observations.}
\end{table}
\end{document}

stat

Guy Nason
Daniel Salnikov
Mario Cortina-Borja

"New tools for network time series with an application to COVID-19 hospitalisations" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Histogram-based Preprocessing Techniques.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
Reference & Year & Team & Details\\
\hline
 & 2010 & S Samsi & Histogram equalization\\
 & 2011 & H Kong & Histogram equalization\\
 & 2012 & M Oger & Adaptive equalization of histogram\\
 & 2012 & HC Akakin & Normalizing co-occurrence histograms\\
 & 2014 & E Michail & Histogram equalization\\
 & 2014 & K Dimitropoulos & Histogram equalization\\
 & 2016 & N Codella & Histogram equalization\\
 & 2017 & TAA Tosta & Histogram equalization\\
 & 2017 & TAA Tosta & Histogram equalization\\
 &2017 & BB Cheikh & Histogram stretching\\
 & 2018 & TAA Tosta & Histogram equalization\\
 & 2019 & UV Somaratne & Histogram equalization\\
 & 2020 & F Bianconi & Histogram equalization\\
 & 2021 & TA Azevedo Tosta & Histogram equalization\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

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


math

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 & Gap & Hurdle & Stairs & Steps \\
 \hline
 Without Setup Policies & 74 & \textbf{41} & 64 & 142 \\
 With Setup Policies & \textbf{44} & 51 & \textbf{49} & \textbf{90} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Number of failures by terrain type from 1000 episodes of 4 terrain types \textbf{(without the jump terrain)}, randomly shuffled each episode.}
\end{table}
\end{document}

cs

Brendan Tidd
Nicolas Hudson
Akansel Cosgun
Jurgen Leitner

"Learning Setup Policies: Reliable Transition Between Locomotion Behaviours" by Brendan Tidd, Nicolas Hudson, Akansel Cosgun, and Jurgen Leitner, arXiv:2101.09391v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \toprule
 \textsc{Term} & \textsc{Coefficient} & \textsc{Std. Error} & \textsc{t-value} \\
 \midrule
 \textsc{(intercept)} & 0.15 & 0.07 & 2.22 \\
 \textsc{spatial} & 0.02 & 0.08 & 0.26 \\
 \textsc{spatial+} & 0.00 & 0.08 & 0.06 \\
 \textsc{gcnn} & 0.10 & 0.09 & 1.17 \\
 \textsc{dapsm} & 0.78 & 0.19 & 4.13 \\
 \textsc{gmerror} & 0.00 & 0.08 & 0.01 \\
 \textsc{s2sls} & -0.01 & 0.08 & -0.08 \\
 \textsc{ols:smoothness} & -0.15 & 0.10 & -1.51 \\
 \textsc{spatial:smoothness} & -0.19 & 0.10 & -1.88 \\
 \textsc{spatial+:smoothness} & -0.09 & 0.10 & -0.94 \\
 \textsc{gcnn:smoothness} & -0.21 & 0.13 & -1.66 \\
 \textsc{dapsm:smoothness} & -0.80 & 0.15 & -5.29 \\
 \textsc{gmerror:smoothness} & -0.15 & 0.10 & -1.53 \\
 \textsc{s2sls:smoothness} & -0.14 & 0.10 & -1.46 \\
 \textsc{ols:confounding} & 0.61 & 0.34 & 1.77 \\
 \textsc{spatial:confounding} & 0.98 & 0.34 & 2.86 \\
 \textsc{spatial+:confounding} & 1.09 & 0.34 & 3.17 \\
 \textsc{gcnn:confounding} & 0.73 & 0.37 & 1.96 \\
 \textsc{dapsm:confounding} & -2.94 & 2.38 & -1.23 \\
 \textsc{gmerror:confounding} & 0.61 & 0.34 & 1.78 \\
 \textsc{s2sls:confounding} & 0.63 & 0.34 & 1.83 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Coefficients of a statistical linear model for the error in ERF estimation with random effects per dataset and environment and fixed effects by algorithm and environment complexity (smoothness and confounding).}
\end{table}
\end{document}

stat

Mauricio Tec
Ana Trisovic
Michelle Audirac
Sophie Woodward
Jie Kate Hu
Naeem Khoshnevis
Francesca Dominici

"SpaCE: The Spatial Confounding Environment" by Mauricio Tec, Ana Trisovic, Michelle Audirac, Sophie Woodward, Jie Kate Hu, Naeem Khoshnevis, and Francesca Dominici, arXiv:2312.00710v3, 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{Levels Available in \texttt{maritalstatus}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cl}
	\toprule
	\textbf{Final Level} & \textbf{Description} \\
	\midrule
	\textit{married} & Married or in registered civil partnership \\
	\textit{single} & Single \\
	\textit{divorced} & Divorced \\
	\textit{widowed} & Widowed \\
	\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jakob Schwerter
Andrés Romero
Florian Dumpert
Markus Pauly

"Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study" by Jakob Schwerter, Andrés Romero, Florian Dumpert, and Markus Pauly, arXiv:2412.13570v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Sofiane Tanji
Yassine Kamri
François Glineur
Mehdi Madani

"Dual first-order methods for efficient computation of convex hull prices" by Sofiane Tanji, Yassine Kamri, François Glineur, and Mehdi Madani, arXiv:2504.01474v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{makecell}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detection accuracy on the HRSC2016 dataset, 07 means using the 2007 evaluation metric.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
 \Xhline{1pt}
 Method & Backbone & mAP(07) \\
 \Xhline{1pt}
 R$^{2}$CNN & Resnet101 & 73.07 \\
 RRPN & Resnet101 & 79.08 \\
 R$^{2}$PN & VGG16 & 79.6 \\
 ROI-trans & Resnet101 & 86.20 \\
 Gliding Vertex & Resnet101 & 88.20 \\
 BBAVectors & Resnet101 & 88.6 \\
 R$^3$Det & Resnet101 & 89.26 \\
 FPN-CSL & Resnet101 & 89.62\\
 R$^3$Det-DCL & Resnet101 & 89.46\\
 DAL & Resnet101 & 89.77 \\
 R$^3$Det-GWD & Resnet101 & 89.85 \\
 RSDet & ResNet152 & 86.5 \\
 FR-Est & Resnet101 & 89.7 \\
 S$^{2}$A-Net & Resnet101 & 90.2 \\
 Oriented RepPoints& Resnet50 & 90.38 \\
 ReDet & ReResnet50 & 90.46 \\
 Oriented R-CNN & Resnet101 & \textcolor{blue}{90.50} \\
 DARDet & Resnet50 & 90.37 \\
 \hline
 CHPDet(ours) & DLA34 & 88.81 \\
 CHPDet(ours) & Hourglass104 &\textcolor{red}{90.55}\\
 \Xhline{1pt}
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Feng Zhang
Xueying Wang
Shilin Zhou
Yingqian Wang
Yi Hou

"Arbitrary-Oriented Ship Detection through Center-Head Point Extraction" by Feng Zhang, Xueying Wang, Shilin Zhou, Yingqian Wang, and Yi Hou, arXiv:2101.11189v3, 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{Performance of NER systems for Mutation with pre-adjudicated annotations over held-out test data (test1).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|lllll|}
 \hline
 \textbf{Training}&\textbf{System} & \textbf{Precision} & \textbf{Recall}&\textbf{F-score (Std Dev)}\\ \hline
Pre-adjudicated&BioBERT & 78.1 & 63.6 & 70.1 (2.4)\\
&bilstm-crf&72.2&70.7&71.4\\
\hline
Adjudicated&BioBERT& 81.0 & 74.7 & 77.7 (2.4)\\
&bilstm-crf&85.7&72.7&78.7\\
\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

David Martinez Iraola
Antonio Jimeno Yepes

"Single versus Multiple Annotation for Named Entity Recognition of Mutations" by David Martinez Iraola and Antonio Jimeno Yepes, arXiv:2101.07450v1, 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{Coverage Summary of Two Graph Construction Methods: almost full coverage after adopting ANN Neighbors}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc|cc}
 \toprule
 & \multicolumn{2}{c|}{Click Only}&\multicolumn{2}{c}{ANN}\\
 \cline{2-3}\cline{4-5}
 &Q&K&Q&K\\
 \midrule
 1 Neighbor & 4\% & 7\% & 5\% & 7\%\\
 2 Neighbors & 3\% & 4\% & 3\% & 4\%\\
 3 Neighbors & 30\% & 76\% & 92\% & 88\%\\
 \midrule
 \textbf{Coverage} & \textbf{37\%} & \textbf{87\%} & \textbf{100\%} & \textbf{99\%} \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

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


q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{White-box attack robust accuracy for ResNet-18 on CIFAR-100. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
\hline
 Defense & Natural & $PGD-20$ &$CW$ & AA \\
\hline
 \textit{Standard-AT} & 56.59\tiny{ $\pm$ 0.22}
 & 28.18\tiny{ $\pm$ 0.19} & 25.64\tiny{ $\pm$ 0.18} &24.07\tiny{ $\pm$ 0.15} \\
\textbf{AT - SDI \tiny{(Ours)}}
 & \textbf{57.96}\tiny{ $\pm$ 0.14}
 & \textbf{30.78}\tiny{ $\pm$ 0.12}
 & \textbf{29.37}\tiny{ $\pm$ 0.15}
 & \textbf{26.38}\tiny{ $\pm$ 0.10} &\\
\hline
 \textit{TRADES} & 56.96\tiny{ $\pm$ 0.16} & 29.21\tiny{ $\pm$ 0.12} & 25.57\tiny{ $\pm$ 0.08} &24.65\tiny{ $\pm$ 0.06} \\
\textbf{TRADES - SDI \tiny{(Ours)}}& \textbf{60.68}\tiny{ $\pm$ 0.11} & \textbf{31.21}\tiny{ $\pm$ 0.04} & \textbf{28.73}\tiny{ $\pm$ 0.04} &\textbf{26.45}\tiny{ $\pm$ 0.04} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Olukorede Fakorede
Modeste Atsague
Jin Tian

"Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness" by Olukorede Fakorede, Modeste Atsague, and Jin Tian, arXiv:2412.19947v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

CRIX an index for cryptocurrencies

"CRIX an index for cryptocurrencies" by Simon Trimborn and Wolfgang Karl Härdle, arXiv:2009.09782v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Formulas or descriptions of different stepsize schedules.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|l}
\hline
Name & Formula/Description \\ \hline
constant & $\alpha(t)=\alpha_0$ \\
linear & $\alpha(t)=\frac{T-t}{T}(\alpha_0-\alpha_1)+\alpha_1$ \\
exponential & $\alpha(t)=\alpha_0\cdot\left(\alpha_1/\alpha_0\right)^{t/T}$ \\
step & Remain the same stepsize within one ``step", and decay between ``steps". \\
cyclical & Follow Eq.~1 in . \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Entropy-MCMC: Sampling from Flat Basins with Ease" by Bolian Li and Ruqi Zhang, arXiv:2310.05401v5, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{24Hz token rate for voice conversion experiment.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Method} & \textbf{Q-MOS} & \textbf{S-MOS} & \textbf{WER (\%)} \\
\midrule
boundary + max pooling (w/o CALE) & 4.02 & 3.88 & 7.1 \\
boundary + average pooling (w/o CALE) & 3.97 & 3.85 & 7.4 \\
boundary + attention pooling (w/o CALE) & 4.06 & 3.92 & 6.9 \\
\textbf{Ours (with CALE)} & \textbf{4.10} & \textbf{3.95} & \textbf{6.7} \\
\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{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average runtime in seconds and number of cluster features after BETULA initialization for different algorithms and data generators with $N$\,=\,50\,000 and $d$\,=\,5 dimensions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|rr|rr}
 \toprule
 \multirow{2}{*}{Algorithm} & \multirow{2}{*}{Input} & \multicolumn{2}{c|}{Uniform} & \multicolumn{2}{c}{Gaussian} \\
 & & Runtime & |CF| & Runtime & |CF| \\
 \midrule
 Anderberg & full data & 142.96 & - & 147.05 & - \\
 Anderberg & CF centers & 12.99 & 17482.4 & 4.88 & 9496.7 \\
 Anderberg & CF linkage & 12.85 & 17482.4 & 4.71 & 9496.7 \\
 NN-Chain & full data & 49.64 & - & 49.44 & - \\
 NN-Chain & CF aggregation & 11.39 & 17482.4 & 4.36 & 9496.7 \\
 NN-Chain & CF linkage & 7.18 & 17482.4 & 3.15 & 9496.7 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Data Aggregation for Hierarchical Clustering" by Erich Schubert and Andreas Lang, arXiv:2309.02552v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|ccccccccccc|}
 \hline
 & 0 & 0.1& 0.2& 0.3& 0.4& 0.5& 0.6& 0.7 & 0.8& 0.9&1\cr\cline{1-12}
 Iron ores & -0.374 & -1.651& -2.246& \cellcolor{yellow}-3.139& \cellcolor{yellow}-4.309& \cellcolor{yellow}-6.040& \cellcolor{yellow} -8.691& \cellcolor{yellow} -11.774 & \cellcolor{yellow} -18.417& \cellcolor{yellow}-27.614& \cellcolor{yellow}-78.621\cr
 Rebar & -2.079 & -2.403& \cellcolor{yellow}-3.049& \cellcolor{yellow}-4.156& \cellcolor{yellow}-5.632& \cellcolor{yellow} -7.860& \cellcolor{yellow} -11.267& \cellcolor{yellow}-15.182 & \cellcolor{yellow}-23.465& \cellcolor{yellow}-34.486& \cellcolor{yellow}-78.921\cr
Hot rolled coils & -1.690 & -2.060& -2.771&\cellcolor{yellow} -3.889& \cellcolor{yellow} -5.361& \cellcolor{yellow} -7.540& \cellcolor{yellow}-10.852& \cellcolor{yellow} -14.656 & \cellcolor{yellow}-22.709& \cellcolor{yellow}-33.473& \cellcolor{yellow}-78.978\cr
Stainless steel & -1.830 & -2.071& -2.638&\cellcolor{yellow} -3.495& \cellcolor{yellow}-4.699& \cellcolor{yellow} -6.501& \cellcolor{yellow}-9.281& \cellcolor{yellow} -12.537 & \cellcolor{yellow}-19.680& \cellcolor{yellow}-29.717& \cellcolor{yellow}-93.928\cr
Ferro Silicon & -1.966 & -2.168& -2.672& \cellcolor{yellow}-3.488& \cellcolor{yellow}-4.623& \cellcolor{yellow}-6.331& \cellcolor{yellow}-8.979& \cellcolor{yellow}-12.024 &\cellcolor{yellow} -18.528& \cellcolor{yellow}-27.166&\cellcolor{yellow}-62.703\cr
Manganese silicon & -2.240 & -2.558& \cellcolor{yellow}-3.257& \cellcolor{yellow}-4.305& \cellcolor{yellow}-5.767& \cellcolor{yellow}-7.911& \cellcolor{yellow}-11.203& \cellcolor{yellow}-14.944 & \cellcolor{yellow}-22.643& \cellcolor{yellow}-32.366&\cellcolor{yellow}-62.569\cr
Shanghai Copper & -2.354 & -2.592&\cellcolor{yellow} -3.158& \cellcolor{yellow}-4.099&\cellcolor{yellow} -5.417& \cellcolor{yellow}-7.428& \cellcolor{yellow}-10.519& \cellcolor{yellow}-14.148 &\cellcolor{yellow} -21.988& \cellcolor{yellow}-32.780&\cellcolor{yellow}-89.515\cr
Shanghai Aluminium & -1.744 & -1.939& -2.368& \cellcolor{yellow}-3.073& \cellcolor{yellow}-4.070& \cellcolor{yellow}-5.584&\cellcolor{yellow} -7.928& \cellcolor{yellow}-10.714 & \cellcolor{yellow}-16.764& \cellcolor{yellow}-25.439&\cellcolor{yellow}-90.908\cr
Shanghai Zinc & -1.906 & -2.233& \cellcolor{yellow}-2.919&\cellcolor{yellow} -3.962& \cellcolor{yellow}-5.374& \cellcolor{yellow}-7.461& \cellcolor{yellow}-10.657& \cellcolor{yellow}-14.355 & \cellcolor{yellow}-22.326& \cellcolor{yellow}-33.259&\cellcolor{yellow}-89.992\cr
Shanghai Lead &\cellcolor{yellow} -4.584 &\cellcolor{yellow} -5.550& \cellcolor{yellow}-7.381&\cellcolor{yellow} -10.119&\cellcolor{yellow} -13.667&\cellcolor{yellow} -18.750&\cellcolor{yellow} -26.154& \cellcolor{yellow}-34.291 & \cellcolor{yellow}-48.889&\cellcolor{yellow} -64.613&\cellcolor{yellow}-92.167\cr
Shanghai Nickel & -1.487 & -1.759& -2.327&\cellcolor{yellow} -3.196& \cellcolor{yellow}-4.363& \cellcolor{yellow}-6.101&\cellcolor{yellow} -8.768&\cellcolor{yellow} -11.897 &\cellcolor{yellow} -18.720& \cellcolor{yellow}-28.326&\cellcolor{yellow}-93.281\cr
Shanghai Tin & -1.564 & -1.586& -1.722& -2.055& -2.572&\cellcolor{yellow} -3.415& \cellcolor{yellow}-4.769& \cellcolor{yellow}-6.387 &\cellcolor{yellow} -10.017& \cellcolor{yellow}-15.297&\cellcolor{yellow}-86.878\cr
International Copper & -2.266 & -2.505& \cellcolor{yellow}-3.070& \cellcolor{yellow}-3.995& \cellcolor{yellow}-5.297& \cellcolor{yellow}-7.274&\cellcolor{yellow} -10.315& \cellcolor{yellow}-13.888 & \cellcolor{yellow}-21.606& \cellcolor{yellow}-32.258&\cellcolor{yellow}-89.528\cr
Shanghai Gold & -2.458 & \cellcolor{yellow}-2.966& \cellcolor{yellow}-4.002&\cellcolor{yellow} -5.573&\cellcolor{yellow} -7.638&\cellcolor{yellow} -10.680&\cellcolor{yellow} -15.31& \cellcolor{yellow}-20.631 & \cellcolor{yellow}-31.752& \cellcolor{yellow}-46.377&\cellcolor{yellow}-101.248\cr
Bean One &\cellcolor{yellow} -3.791 &\cellcolor{yellow} -4.311&\cellcolor{yellow} -5.351&\cellcolor{yellow} -7.042& \cellcolor{yellow}-9.362& \cellcolor{yellow}-12.810& \cellcolor{yellow}-18.013&\cellcolor{yellow} -23.870 & \cellcolor{yellow}-35.332&\cellcolor{yellow} -48.882&\cellcolor{yellow}-80.202\cr
Bean Two & -2.107 & -2.314& -2.852& \cellcolor{yellow}-3.708&\cellcolor{yellow} -4.895& \cellcolor{yellow}-6.694& \cellcolor{yellow}-9.497& \cellcolor{yellow}-12.740 &\cellcolor{yellow} -19.811&\cellcolor{yellow} -29.479&\cellcolor{yellow}-77.838\cr
Soybean oil & -1.827 & -2.019& -2.511&\cellcolor{yellow} -3.279& \cellcolor{yellow}-4.351&\cellcolor{yellow} -5.986& \cellcolor{yellow}-8.480&\cellcolor{yellow} -11.395 & \cellcolor{yellow}-17.791& \cellcolor{yellow}-26.665&\cellcolor{yellow}-77.745\cr
Soybean meal & -1.937 & -2.203& -2.800&\cellcolor{yellow} -3.760&\cellcolor{yellow} -5.017& \cellcolor{yellow}-6.932& \cellcolor{yellow}-9.885& \cellcolor{yellow}-13.310 & \cellcolor{yellow}-20.677& \cellcolor{yellow}-30.741&\cellcolor{yellow}-79.534\cr
Vegetable oil & -1.858 & -2.030& -2.430& \cellcolor{yellow}-3.118&\cellcolor{yellow} -4.092&\cellcolor{yellow} -5.589& \cellcolor{yellow}-7.922& \cellcolor{yellow}-10.653 & \cellcolor{yellow}-16.647&\cellcolor{yellow} -25.071&\cellcolor{yellow}-78.483\cr
Vegetable meal & -2.092 & -2.345& \cellcolor{yellow}-2.897& \cellcolor{yellow}-3.826&\cellcolor{yellow} -5.059& \cellcolor{yellow}-6.958&\cellcolor{yellow} -9.893& \cellcolor{yellow}-13.292 & \cellcolor{yellow}-20.630& \cellcolor{yellow}-30.646&\cellcolor{yellow}-78.871\cr
Palm oil & -1.623 & -1.771& -2.155& -2.791& \cellcolor{yellow}-3.685&\cellcolor{yellow} -5.056&\cellcolor{yellow} -7.173&\cellcolor{yellow} -9.644 &\cellcolor{yellow} -15.128& \cellcolor{yellow}-22.870&\cellcolor{yellow}-77.838\cr
Corn & -1.765 & -2.046& -2.640&\cellcolor{yellow} -3.570&\cellcolor{yellow} -4.824& \cellcolor{yellow}-6.695& \cellcolor{yellow}-9.579&\cellcolor{yellow} -12.925 & \cellcolor{yellow}-20.128&\cellcolor{yellow} -30.000&\cellcolor{yellow}-80.550\cr
Corn Starch & -2.273 & -2.572&\cellcolor{yellow} -3.234&\cellcolor{yellow} -4.312& \cellcolor{yellow}-5.765& \cellcolor{yellow}-7.969&\cellcolor{yellow} -11.349&\cellcolor{yellow} -15.259 &\cellcolor{yellow} -23.566& \cellcolor{yellow}-34.661&\cellcolor{yellow}-80.752\cr
Eggs & -2.652 & \cellcolor{yellow}-2.947&\cellcolor{yellow} -3.682& \cellcolor{yellow}-4.894& \cellcolor{yellow}-6.521& \cellcolor{yellow}-8.947& \cellcolor{yellow}-12.566&\cellcolor{yellow} -16.684 & \cellcolor{yellow}-24.942& \cellcolor{yellow}-34.955&\cellcolor{yellow}-61.599\cr
No.1 Cotton & -1.459 & -1.544& -1.820& -2.295&\cellcolor{yellow} -2.991&\cellcolor{yellow} -4.050& \cellcolor{yellow}-5.728&\cellcolor{yellow} -7.712 &\cellcolor{yellow} -12.121&\cellcolor{yellow} -18.468&\cellcolor{yellow}-77.492\cr
Cotton Yarn & -1.813 & -1.913& -2.278&\cellcolor{yellow} -2.876&\cellcolor{yellow} -3.755& \cellcolor{yellow}-5.084& \cellcolor{yellow}-7.199& \cellcolor{yellow}-9.701 & \cellcolor{yellow}-15.203& \cellcolor{yellow}-23.017&\cellcolor{yellow}-77.862\cr
White sugar & -2.850 & \cellcolor{yellow}-3.024& \cellcolor{yellow}-3.550& \cellcolor{yellow}-4.526&\cellcolor{yellow} -5.885& \cellcolor{yellow}-8.001&\cellcolor{yellow} -11.299&\cellcolor{yellow} -15.096 & \cellcolor{yellow}-23.256&\cellcolor{yellow} -34.188&\cellcolor{yellow}-79.838\cr
Apple & -0.491 & -0.733& -1.177& -1.803& -2.579&\cellcolor{yellow} -3.697& \cellcolor{yellow}-5.378& \cellcolor{yellow}-7.307 & \cellcolor{yellow}-11.504& \cellcolor{yellow}-17.424&\cellcolor{yellow}-61.124\cr
Red dates & -1.229 & -1.393& -1.758& -2.353&\cellcolor{yellow} -3.151& \cellcolor{yellow}-4.355&\cellcolor{yellow} -6.222& \cellcolor{yellow}-8.409 &\cellcolor{yellow} -13.178&\cellcolor{yellow} -19.858&\cellcolor{yellow}-62.742\cr
Peanuts & -1.395 & -1.646& -2.128&\cellcolor{yellow} -2.870& \cellcolor{yellow}-3.859&\cellcolor{yellow} -5.344& \cellcolor{yellow}-7.615&\cellcolor{yellow} -10.254 &\cellcolor{yellow} -15.930& \cellcolor{yellow}-23.642&\cellcolor{yellow}-60.799\cr
Stalked rice & -0.942 & -1.222& -1.775& -2.597& \cellcolor{yellow}-3.632&\cellcolor{yellow} -5.161& \cellcolor{yellow}-7.499& \cellcolor{yellow}-10.210 &\cellcolor{yellow} -16.114& \cellcolor{yellow}-24.446&\cellcolor{yellow}-81.155\cr
Pig & -1.966 & -1.760& -1.702& -1.784& -2.242& \cellcolor{yellow}-2.868& \cellcolor{yellow}-3.975& \cellcolor{yellow}-5.281 &\cellcolor{yellow} -8.243&\cellcolor{yellow} -12.613&\cellcolor{yellow}-60.838\cr
Fuel & -1.428 & -1.615& -2.019& -2.704& \cellcolor{yellow}-3.618& \cellcolor{yellow}-5.016& \cellcolor{yellow}-7.184&\cellcolor{yellow} -9.724 &\cellcolor{yellow} -15.279& \cellcolor{yellow}-23.158&\cellcolor{yellow}-79.110\cr
Liquefied Petroleum Gas & -1.353 & -1.570& -2.036& -2.769&\cellcolor{yellow} -3.750& \cellcolor{yellow}-5.213& \cellcolor{yellow}-7.478& \cellcolor{yellow}-10.109 & \cellcolor{yellow}-15.865& \cellcolor{yellow}-23.974&\cellcolor{yellow}-78.700\cr
Ethylene glycol & -2.809 &\cellcolor{yellow} -3.234&\cellcolor{yellow} -4.156&\cellcolor{yellow} -5.650& \cellcolor{yellow}-7.603&\cellcolor{yellow} -10.516& \cellcolor{yellow}-14.920& \cellcolor{yellow}-19.927 & \cellcolor{yellow}-30.077&\cellcolor{yellow} -42.728&\cellcolor{yellow}-79.186\cr
Polyethylene & -2.615 &\cellcolor{yellow} -3.069& \cellcolor{yellow}-4.063&\cellcolor{yellow} -5.545& \cellcolor{yellow}-7.515& \cellcolor{yellow}-10.414&\cellcolor{yellow} -14.779& \cellcolor{yellow}-19.733 & \cellcolor{yellow}-29.808&\cellcolor{yellow} -42.343&\cellcolor{yellow}-78.727\cr
PTA & -1.728 & -1.870& -2.268&\cellcolor{yellow} -2.961& \cellcolor{yellow}-3.392& \cellcolor{yellow}-5.391& \cellcolor{yellow}-7.680&\cellcolor{yellow} -10.369 & \cellcolor{yellow}-16.236&\cellcolor{yellow} -24.498&\cellcolor{yellow}-79.117\cr
Polyvinyl chloride & -1.502 & -1.905& -2.792& \cellcolor{yellow}-4.053&\cellcolor{yellow} -5.670& \cellcolor{yellow}-8.015& \cellcolor{yellow}-11.542&\cellcolor{yellow} -15.581 & \cellcolor{yellow}-24.040& \cellcolor{yellow}-35.189&\cellcolor{yellow}-79.259\cr
Polypropylene &\cellcolor{yellow} -3.028 & \cellcolor{yellow}-3.635& \cellcolor{yellow}-4.872& \cellcolor{yellow}-6.724& \cellcolor{yellow}-9.145&\cellcolor{yellow} -12.659& \cellcolor{yellow}-17.878& \cellcolor{yellow}-23.733 &\cellcolor{yellow} -35.080& \cellcolor{yellow}-48.368&\cellcolor{yellow}-78.921\cr
Styrene & -2.438 & -2.555& \cellcolor{yellow}-3.124&\cellcolor{yellow} -4.137&\cellcolor{yellow} -5.491&\cellcolor{yellow} -7.554& \cellcolor{yellow}-10.763& \cellcolor{yellow}-14.514 & \cellcolor{yellow}-22.418& \cellcolor{yellow}-33.132&\cellcolor{yellow}-79.563\cr
Pure soda & -2.377 & -2.384& -2.543&\cellcolor{yellow} -2.966& \cellcolor{yellow}-3.692& \cellcolor{yellow}-4.866& \cellcolor{yellow}-6.771& \cellcolor{yellow}-9.041 & \cellcolor{yellow}-14.088& \cellcolor{yellow}-21.360&\cellcolor{yellow}-77.330\cr
Urea & -2.068 & -2.300& -2.748&\cellcolor{yellow} -3.541& \cellcolor{yellow}-4.648&\cellcolor{yellow} -6.345& \cellcolor{yellow}-8.960& \cellcolor{yellow}-11.991 & \cellcolor{yellow}-18.450& \cellcolor{yellow}-27.080&\cellcolor{yellow}-62.846\cr
Rubber & -2.720 & \cellcolor{yellow}-3.325& \cellcolor{yellow}-4.504& \cellcolor{yellow}-6.262& \cellcolor{yellow}-8.566& \cellcolor{yellow}-11.913&\cellcolor{yellow} -16.925& \cellcolor{yellow}-22.584 & \cellcolor{yellow}-33.727& \cellcolor{yellow}-47.110&\cellcolor{yellow}-80.432\cr
No.20 rubber &\cellcolor{yellow} -4.229 & \cellcolor{yellow}-4.997& \cellcolor{yellow}-6.617& \cellcolor{yellow}-9.088& \cellcolor{yellow}-12.283& \cellcolor{yellow}-16.876& \cellcolor{yellow}-22.508& \cellcolor{yellow}-30.773 &\cellcolor{yellow} -43.653& \cellcolor{yellow}-57.311&\cellcolor{yellow}-80.153\cr
Paper pulp & -1.998 & -2.178& -2.657&\cellcolor{yellow} -3.442& \cellcolor{yellow}-4.548& \cellcolor{yellow}-6.236& \cellcolor{yellow}-8.869& \cellcolor{yellow}-11.921 & \cellcolor{yellow}-18.590& \cellcolor{yellow}-27.822&\cellcolor{yellow}-78.333\cr
Staple Fiber & -2.565 &\cellcolor{yellow} -2.876& \cellcolor{yellow}-3.644& \cellcolor{yellow}-4.885& \cellcolor{yellow}-6.526& \cellcolor{yellow}-8.997&\cellcolor{yellow} -12.783& \cellcolor{yellow}-17.152 &\cellcolor{yellow} -26.248& \cellcolor{yellow}-38.022&\cellcolor{yellow}-78.730\cr
Coking coal & -1.676 & -1.840& -2.240& \cellcolor{yellow}-2.919&\cellcolor{yellow} -3.860&\cellcolor{yellow} -5.297& \cellcolor{yellow}-7.543& \cellcolor{yellow}-10.172 & \cellcolor{yellow}-15.957& \cellcolor{yellow}-24.110&\cellcolor{yellow}-78.567\cr
Coke & -1.866 & -2.188& -2.858& \cellcolor{yellow}-3.906& \cellcolor{yellow}-5.304&\cellcolor{yellow} -7.382& \cellcolor{yellow}-10.562&\cellcolor{yellow} -14.237 &\cellcolor{yellow} -22.057& \cellcolor{yellow}-32.601&\cellcolor{yellow}-79.257\cr
CSI & -1.706 & -2.020& -2.642&\cellcolor{yellow} -3.608& \cellcolor{yellow}-4.899&\cellcolor{yellow} -6.806& \cellcolor{yellow}-9.714&\cellcolor{yellow} -13.073 & \cellcolor{yellow}-20.109&\cellcolor{yellow} -29.431&\cellcolor{yellow}-66.374\cr
Shanghai and Shenzhen & -1.142 & -1.420& -1.971& -2.791&\cellcolor{yellow} -3.854& \cellcolor{yellow}-5.415& \cellcolor{yellow}-7.795& \cellcolor{yellow}-10.552 &\cellcolor{yellow} -16.467&\cellcolor{yellow} -24.594&\cellcolor{yellow}-67.359\cr
SSE & -1.239 & -1.511& -2.051& \cellcolor{yellow}-2.865&\cellcolor{yellow} -3.931&\cellcolor{yellow} -5.503& \cellcolor{yellow}-7.908&\cellcolor{yellow} -10.693 &\cellcolor{yellow} -16.678& \cellcolor{yellow}-24.879&\cellcolor{yellow}-67.436\cr
Ten debts & -1.654 & -1.836& -2.302&\cellcolor{yellow} -3.054&\cellcolor{yellow} -4.101&\cellcolor{yellow} -5.657& \cellcolor{yellow}-8.075&\cellcolor{yellow} -10.890 & \cellcolor{yellow}-16.922&\cellcolor{yellow} -25.262&\cellcolor{yellow}-67.843\cr
Five debts & -1.687 & -1.858& -2.328& \cellcolor{yellow}-3.080&\cellcolor{yellow} -4.141& \cellcolor{yellow}-5.704& \cellcolor{yellow}-8.136&\cellcolor{yellow} -10.976 & \cellcolor{yellow}-17.042& \cellcolor{yellow}-25.401&\cellcolor{yellow}-67.324\cr
Second Debt & -1.550 & -1.769& -2.293&\cellcolor{yellow} -3.127& \cellcolor{yellow}-4.248& \cellcolor{yellow}-5.911& \cellcolor{yellow}-8.479& \cellcolor{yellow}-11.462 & \cellcolor{yellow}-17.814&\cellcolor{yellow} -26.502&\cellcolor{yellow}-68.633\cr
Shanghai Silver & -1.724 & -2.337&\cellcolor{yellow} -3.494& \cellcolor{yellow}-5.141& \cellcolor{yellow}-7.226& \cellcolor{yellow}-10.234& \cellcolor{yellow}-14.766&\cellcolor{yellow} -19.955 &\cellcolor{yellow} -30.795& \cellcolor{yellow}-45.103&\cellcolor{yellow}-101.618\cr
Crude Oil & -1.398 & -1.521& -1.833& -2.389& \cellcolor{yellow}-3.161& \cellcolor{yellow}-4.350&\cellcolor{yellow} -6.203& \cellcolor{yellow}-8.392 & \cellcolor{yellow}-13.252& \cellcolor{yellow}-20.301&\cellcolor{yellow}-100.094\cr
Low sulphur crude & -0.759 & -1.015& -1.490& -2.197& \cellcolor{yellow}-3.083& \cellcolor{yellow}-4.385&\cellcolor{yellow} -6.367& \cellcolor{yellow}-8.658 &\cellcolor{yellow} -13.660& \cellcolor{yellow}-20.799&\cellcolor{yellow}-78.810\cr
Petroleum bitumen & -1.114 & -1.440& -2.068&\cellcolor{yellow} -2.987& \cellcolor{yellow}-4.164& \cellcolor{yellow}-5.885& \cellcolor{yellow}-8.497& \cellcolor{yellow}-11.516 & \cellcolor{yellow}-18.017& \cellcolor{yellow}-27.019&\cellcolor{yellow}-78.242\cr
Methanol & -2.150 & -2.532&\cellcolor{yellow} -3.357& \cellcolor{yellow}-4.626&\cellcolor{yellow} -6.293& \cellcolor{yellow}-8.753& \cellcolor{yellow}-12.482& \cellcolor{yellow}-16.762 & \cellcolor{yellow}-25.667& \cellcolor{yellow}-37.210&\cellcolor{yellow}-78.386\cr
Glass & -0.802 & -1.117& -1.701& -2.545& \cellcolor{yellow}-3.612&\cellcolor{yellow} -5.167&\cellcolor{yellow} -7.524& \cellcolor{yellow}-10.256 &\cellcolor{yellow} -16.172&\cellcolor{yellow} -24.481&\cellcolor{yellow}-79.247\cr\cline{1-12}
 \end{tabular}
\caption{ Optimal FradDiff stationarity of contract indices for china futures trading varieties.}
\end{table}
\end{document}

q-fin

"Application of supervised learning models in the Chinese futures market" by Fuquan Tang, arXiv:2303.04581v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 \toprule
 \textbf{Event ID} & \textbf{Timestamp (sec)} & \textbf{Event Type} & \textbf{Order ID} & \textbf{Size (shares)} & \textbf{Price} & \textbf{Direction} \\
 \midrule
 \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots \\
 1312 & 34714.133632201 & deletion & 206833312 & 2516 & \$11.86 & sell \\
 \vdots & \vdots & \vdots & \vdots & \vdots & \vdots \\
 \midrule
 \end{tabular}
\end{adjustbox}
\caption{Example message from a LOBSTER message file. This example corresponds to the order book dynamics shown in Figure .}
\end{table}
\end{document}

q-fin

Lorenzo Lucchese
Mikko Pakkanen
Almut Veraart

"The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective" by Lorenzo Lucchese, Mikko Pakkanen, and Almut Veraart, arXiv:2211.13777v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{FD Filter results for the simulated data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|ll|}
\cline{3-4}
 & & \multicolumn{2}{c|}{FD filter} \\ \cline{3-4}
 & & Healthy & Faulty \\ \hline
\multirow{2}{*}{Ground truth} & Healthy & 2474 & 1 \\ \cline{2-4}
 & Faulty & 92 & 1258 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Casper van Peijpe
Farhad Ghanipoor
Youri de Loore
Pim Hacking
Nathan van de Wouw
Peyman Mohajerin Esfahani

"Fault Isolation for the Ink Deposition Process in High-End Industrial Printers" by Casper van Peijpe, Farhad Ghanipoor, Youri de Loore, Pim Hacking, Nathan van de Wouw, and Peyman Mohajerin Esfahani, arXiv:2412.07545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Audit exemption thresholds in Norway, Nordic EU Member States, and the UK. Obtained from the Federation of European Accountants.}
\begin{tabular}{lccc}
\toprule
Country &Total assets (€) &	operating revenue (€) &	Number of employees\\
\midrule
Norway&		2,000,000&	500,000 &	10 \\
Sweden &	150,000 &	300,000&	3	\\
Denmark&	4,837,000&	9,674,000&	50	\\
Finland&	100,000&	200,000&	3	\\
Iceland&	1,400,000&	2,800,000&	50	\\
UK&		6,541,000&	13,082,000 &	50 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Adults in the room? The auditor and dividends in small firms: Evidence from a natural experiment" by Hakim Lyngstadås and Johannes Mauritzen, arXiv:2301.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of Convergence Rates for Errors}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
				\hline
				& Coupling Error & Modeling Error & Total Error \\
				\hline
				B-QCE & $\varepsilon^2$ & $\varepsilon^2$ & $\varepsilon^2$ \\
				B-QHOCE & $\varepsilon^2$ & $\varepsilon^4$ & $\varepsilon^2$ \\
				B-QCF & - & $\varepsilon^2$ & $\varepsilon^2$ \\
				B-QHOCF & - & $\varepsilon^4$ & $\varepsilon^4$ \\
				\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Formulation and Analysis of Blended Atomistic to Higher-Order Continuum Coupling Methods for Crystalline Defects" by Junfeng Lu, Hao Wang, and Yangshuai Wang, arXiv:2502.18854v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experimental datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllll}
			\hline
			Dataset & Abbrevation & Type & Train & Test & Dimensions & Length & Classes \\ \hline
			ArticularyWordRecognition & AWR & Motion & 275 & 300 & 9 & 144 & 25 \\
			AtrialFibrillation & AF & ECG & 15 & 15 & 2 & 640 & 3 \\
			BasicMotions & BM & HAR & 40 & 40 & 6 & 100 & 4 \\
			CharacterTrajectories & CT & Motion & 1422 & 1436 & 3 & 182 & 20 \\
			Cricket & CR & HAR & 108 & 72 & 6 & 1197 & 12 \\
			EthanolConcentration & EC & HAR & 261 & 263 & 3 & 1751 & 4 \\
			FaceDetection & FD & EEG/MEG & 5890 & 3524 & 144 & 62 & 2 \\
			HandMovementDirection & HMD & EEG/MEG & 160 & 74 & 10 & 400 & 4 \\
			Heartbeat & HB & AS & 204 & 205 & 61 & 405 & 2 \\
			JapaneseVowels & JV & AS & 270 & 370 & 12 & 29 & 9 \\
			Libras & LIB & HAR & 180 & 180 & 2 & 45 & 15 \\
			LSST & LSST & Other & 2459 & 2466 & 6 & 36 & 14 \\
			MotorImagery & MI & EEG/MEG & 278 & 100 & 64 & 3000 & 2 \\
			NATOPS & NA & HAR & 180 & 180 & 24 & 51 & 6 \\
			PEMS-SF & PEMS & Other & 267 & 173 & 963 & 144 & 7 \\
			PenDigits & PD & Motion & 7494 & 3498 & 2 & 8 & 10 \\
			SelfRegulationSCP1 & SR1 & EEG/MEG & 268 & 293 & 6 & 896 & 2 \\
			SelfRegulationSCP2 & SR2 & EEG/MEG & 200 & 180 & 7 & 1152 & 2 \\
			SpokenArabicDigits & SAD & AS & 6599 & 2199 & 13 & 93 & 10 \\
			StandWalkJump & SWJ & ECG & 12 & 15 & 4 & 2500 & 3 \\
			UWaveGestureLibrary & UW & HAR & 120 & 320 & 3 & 315 & 8 \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mingsen Du
Yanxuan Wei
Xiangwei Zheng
Cun Ji

"Causal and Local Correlations Based Network for Multivariate Time Series Classification" by Mingsen Du, Yanxuan Wei, Xiangwei Zheng, and Cun Ji, arXiv:2411.18008v1, 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|cccccccccccccccccccccccccc}
$j$ & 0 & 1& 2& 3& 4& 5& 6& 7& 8& 9&10&11&12&13&14&15&16&17&18&19\\
\hline
$h(j)$ & 0& 1& 1& 2& 3& 4& 4& 5& 5& 6& 7& 7& 8& 9&10&10&11&12&13&13
\end{tabular}
\end{adjustbox}
\caption{First few values of the $h$ sequence.}
\end{table}
\end{document}

math

The Narayana Morphism and Related Words

"The Narayana Morphism and Related Words" by Jeffrey Shallit, arXiv:2503.01026v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"A New Perspective on Impartial and Unbiased Apportionment" by Ross Hyman and Nicolaus Tideman, arXiv:2201.11787v2, 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{Experiment results on the transformed layers in wav2vec 2.0.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
		\toprule
		\multirow{2}[2]{*}{method} & \multirow{2}[2]{*}{Shallow transformer} & \multirow{2}[2]{*}{Deep transformer} & \multirow{2}[2]{*}{ACC(\%)} & \multirow{2}[2]{*}{WF1(\%)} \\
		& & & & \\
		\midrule
		concat & 12 & 0 & 66.67 & 66.78 \\
		\midrule
		\multirow{4}[2]{*}{Attention} & 11 & 1 & 68.61 & 68.55 \\
		& 10 & 2 & 68.54 & 68.32 \\
		& 9 & 3 & 70.53 & 70.6 \\
		& 8 & 4 & 69.29 & 69.06 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition" by Feng Li, Jiusong Luo, and Wanjun Xia, arXiv:2412.05558v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Descriptive Statistics of Suspense and Surprise in Women’s Matches}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccccccc}
 \toprule
 League & Metric & N & Mean & Median & SD & Min & Max& BM & $<$ BM \\
 \midrule
 \multirow{2}{*}{Top 5 Leagues}
 & Suspense & 725 & 4.93 &5.45 & 2.47 & 0.16&9.08 &5.96& *** \\
 & Surprise & 725 &1.24 & 1.01 & 0.84 &0.10 & 4.96&1.17& \\
 \midrule
 \multirow{2}{*}{England}
 & Suspense & 132 & 4.94 & 5.27 & 2.36 & 0.27 & 8.33 &5.96&*** \\
 & Surprise & 132 & 1.33 & 1.06 & 0.91 & 0.16 & 4.70 &1.17& \\
 \midrule
 \multirow{2}{*}{Germany}
 & Suspense & 132 & 4.65 & 5.10 & 2.47 & 0.16 & 8.35 &5.96& ***\\
 & Surprise & 132 & 1.11 & 0.88 & 0.77 & 0.15 & 3.56 &1.17& \\
 \midrule
 \multirow{2}{*}{Spain}
 & Suspense & 240 & 5.03 & 5.63 & 2.43 & 0.26 & 9.08 &5.96&*** \\
 & Surprise & 240 & 1.21 & 1.01 & 0.77 & 0.12 & 4.03 &1.17& \\
 \midrule
 \multirow{2}{*}{Italy}
 & Suspense & 90 & 5.10& 5.80& 2.53 &0.27 & 8.35&5.96&*** \\
 & Surprise & 90 & 1.21 & 1.13 & 0.78& 0.10 &4.46 &1.17& \\
 \midrule
 \multirow{2}{*}{France}
 & Suspense & 131 & 4.88 & 5.48 & 2.58 & 0.36 & 8.58&5.96&*** \\
 & Surprise & 131 & 1.34 & 1.13 & 0.95 & 0.17 & 4.96&1.17& \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Raphael Flepp
Tim Pawlowski
Travis Richardson

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


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{CHARACTERISTICS OF THE LARGE SCALE DATASETS}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
		\hline
		\multirow{2}{*}{Dataset} & \multicolumn{6}{c}{Characteristics}\\
		\cline{2-7} & N & 	D & 	covariance & 	ross-validation & 	Number of samples in class 1 & 	Number of samples in lass 2\\
		\hline
		Synth1 & 	15000 &	200 &	 250 & 	5 & 	7592 & 	7408\\
		Synth2 &	15000 &	200 & 	0 & 	5 & 	6619 & 	8381\\
		Synth3 & 	15000 & 	200 & 	250 & 	10 & 	8434 & 	6566\\
		Synth4 & 	15000 & 	200 & 	0 & 	10 & 	6619 & 	8381\\
		Synth5 & 	15000 & 	200 & 	250 & 	3 & 	7707 & 	7293\\
		Synth6 & 	15000 & 	200 & 	0 & 	3 & 	7796 & 	7204\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jian-wei Liu
Zheng-ping Ren
Run-kun Lu
Xiong-lin Luo

"GMM Discriminant Analysis with Noisy Label for Each Class" by Jian-wei Liu, Zheng-ping Ren, Run-kun Lu, and Xiong-lin Luo, arXiv:2201.10242v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \toprule
 \textbf{Variable} & \textbf{Value} \\
 \midrule
 Mean riskless rate & 1.03\\
 Mean deposits over assets & 0.65\\

 Prob. of being in disaster, $\zeta=\underline{\zeta}$ & 1.45\%\\

 Prob. of financial crisis & 0.50\%\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{ Characteristics of the equilibrium.}
\end{table}
\end{document}

q-fin

Bailouts and Redistribution

"Bailouts and Redistribution" by Mikayel Sukiasyan, arXiv:2509.10933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sooyong Lee
Adam C Sales
Hyeon-Ah Kang
Tiffany A. Whittaker

"Fully Latent Principal Stratification With Measurement Models" by Sooyong Lee, Adam C Sales, Hyeon-Ah Kang, and Tiffany A. Whittaker, arXiv:2309.04047v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Impact of Macroeconomic Inputs on Deposits}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\toprule
\textbf{} & \textbf{Reserves} & \textbf{Total Loans} & \textbf{Retail Loans} \\ \hline
Retail & -6\% & 53\% & -23\% \\
Wholesale & 61\% & 64\% & -23\% \\
Total & 54\% & 116\% & -45\% \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits" by Ni Zhan, arXiv:2101.09230v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l}
\hline
\textbf{Dataset} & \textbf{\# contests} & \textbf{avg. \# participants / contest} \\ \hline
Codeforces & 1087 & 2999 \\ %\hline
TopCoder & 2023 & 403 \\ %\hline
Reddit & 1000 & 20 \\
Synthetic & 50 & 2500 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Summary of test datasets.}
\end{table}
\end{document}

cs

"An Elo-like System for Massive Multiplayer Competitions" by Aram Ebtekar and Paul Liu, arXiv:2101.00400v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 \hline
 Criterion & 1 & 2 & 3 & 4 \\
 \hline
 AIC & 12781.58 & 10559.51 & \textbf{10454.77} & 10457.10 \\
 BIC & 12832.06 & 10631.62 & \textbf{10548.51} & 10572.47 \\
 DIC & 12781.36 & 10559.41 & \textbf{10453.53} & 10459.58 \\
 Neg log marginal likelihood & 6405.793 & 5299.607 & \textbf{5246.078} & 5249.384 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Information criteria and marginal likelihoods for results from models with 1--4 transitions. Bold indicates the smallest value for each criterion.}
\end{table}
\end{document}

stat

Themistoklis Botsas
Jonathan A. Cumming
Ian H. Jermyn

"A Bayesian approach to deconvolution in well test analysis" by Themistoklis Botsas, Jonathan A. Cumming, and Ian H. Jermyn, arXiv:2012.03217v1, 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}{llll}
 \toprule
 \textbf{Model} & \textbf{60\% labels} & \textbf{80\% labels} & \textbf{100\% labels} \\ \midrule
 GAT & 37.90 $\pm$ 0.41 & 39.22 $\pm$ 0.71 & 33.75 $\pm$ 3.12 \\
 \textbf{+MGM} & \textbf{40.80 $\pm$ 3.83} & \textbf{41.99 $\pm$ 1.44} & \textbf{43.63 $\pm$ 2.80} \\
 FiLM & 39.89 $\pm$ 1.69 & 38.59 $\pm$ 4.30 & 43.32 $\pm$ 2.25 \\
 \textbf{+MGM} & \textbf{42.93 $\pm$ 4.00} & \textbf{38.62 $\pm$ 2.73} & \textbf{49.68 $\pm$ 1.62} \\
 FAGCN & 24.32 $\pm$ 3.18 & 22.73 $\pm$ 0.00 & 24.77 $\pm$ 7.52 \\
 \textbf{+MGM} & \textbf{34.10 $\pm$ 7.36} & \textbf{39.89 $\pm$ 4.04} & \textbf{48.77 $\pm$ 0.00} \\
 GATv2 & 40.54 $\pm$ 1.47 & 42.14 $\pm$ 2.76 & 51.42 $\pm$ 2.32 \\
 \textbf{+MGM} & \textbf{42.98 $\pm$ 2.51} & \textbf{44.35 $\pm$ 2.14} & \textbf{54.50 $\pm$ 2.55} \\
 \bottomrule

 \end{tabular}
\end{adjustbox}
\caption{The impact of different proportions of training labeled data on the performance (Macro-F1) of MGM for the Fact-2020 task.}
\end{table}
\end{document}

stat

Muhammad Arslan Manzoor
Ruihong Zeng
Dilshod Azizov
Preslav Nakov
Shangsong Liang

"MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media" by Muhammad Arslan Manzoor, Ruihong Zeng, Dilshod Azizov, Preslav Nakov, and Shangsong Liang, arXiv:2412.10467v1, 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{This table highlights the eigenvalues of the Jacobian and their multiplicities for the static phase wave state for different system sizes.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 $N$ value \hspace{1 cm} & (Eigenvalue, Multiplicity)\\ \hline

 2 \hspace{1 cm} &$(0, 4)$\\

 3 \hspace{1 cm} &$(0, 4), (-\dfrac{J}{4}, 2)$ \\

 4 \hspace{1 cm} &$(0, 6), (-\dfrac{J}{2}, 2)$ \\

 $N\ge 5$ \hspace{1 cm} &$(0, N+1), (-\dfrac{J}{2}, N-3), (-\dfrac{J}{4}, 2)$ \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Samali Ghosh
Kevin O'Keeffe
Gourab Kumar Sar
Dibakar Ghosh

"Dynamics of pulsating swarmalators on a ring" by Samali Ghosh, Kevin O'Keeffe, Gourab Kumar Sar, and Dibakar Ghosh, arXiv:2504.14912v1, 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.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{soul}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccc}
\toprule
\cline{4-7}
\textbf{} & & & \multicolumn{4}{c}{\textbf{VMI (ours)}} \\ \cline{4-7}
 & General MI & Generative MI & \multicolumn{2}{c|}{\textbf{DCGAN}} & \multicolumn{2}{c}{\textbf{StyleGAN}} \\ \cline{4-7}
 & & & \textbf{Gaussian} & \textbf{Flow} & \textbf{Gaussian} & \textbf{Flow} \\ \cline{4-7}
 \midrule
\textbf{Accuracy} & 0.23{\tiny$\pm$ 0.29 } & 0.28{\tiny$\pm$ 0.24 } & 0.36{\tiny$\pm$ 0.25 } & \ul{ 0.42}{\tiny$\pm$ 0.28 } & \ul{ 0.54}{\tiny$\pm$ 0.24 } & {\bf 0.69}{\tiny$\pm$ 0.23 } \\
\textbf{Precision} & 0.00{\tiny$\pm$ 0.00 } & 0.15{\tiny$\pm$ 0.09 } & 0.20{\tiny$\pm$ 0.05 } & 0.08{\tiny$\pm$ 0.13 } & {\bf 0.30}{\tiny$\pm$ 0.09 } & 0.15{\tiny$\pm$ 0.12 } \\
\textbf{Density} & 0.00{\tiny$\pm$ 0.00 } & 0.06{\tiny$\pm$ 0.03 } & 0.08{\tiny$\pm$ 0.03 } & 0.02{\tiny$\pm$ 0.03 } & {\bf 0.18}{\tiny$\pm$ 0.06 } & 0.08{\tiny$\pm$ 0.06 } \\
\midrule
\textbf{Recall} & 0.00{\tiny$\pm$ 0.00 } & 0.04{\tiny$\pm$ 0.04 } & 0.07{\tiny$\pm$ 0.06 } & 0.00{\tiny$\pm$ 0.00 } & {\bf 0.32}{\tiny$\pm$ 0.10 } & 0.05{\tiny$\pm$ 0.04 } \\
\textbf{Coverage} & 0.00{\tiny$\pm$ 0.00 } & 0.14{\tiny$\pm$ 0.07 } & 0.17{\tiny$\pm$ 0.04 } & 0.00{\tiny$\pm$ 0.01 } & {\bf 0.43}{\tiny$\pm$ 0.09 } & 0.12{\tiny$\pm$ 0.08 } \\
\textbf{Diversity} & 0.00{\tiny$\pm$ 0.00 } & 0.09{\tiny$\pm$ 0.08 } & 0.12{\tiny$\pm$ 0.07 } & 0.00{\tiny$\pm$ 0.01 } & {\bf 0.38}{\tiny$\pm$ 0.13 } & 0.09{\tiny$\pm$ 0.09 } \\
\midrule
\textbf{FID} & 499.54 & 142.66 & 104.23 & 265.14 & \textbf{63.78 } & 123.17 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{ChestX-ray8: comparing baseline and our attacks. }
\end{table}
\end{document}

cs

Variational Model Inversion Attacks

Kuan-Chieh Wang
Yan Fu
Ke Li
Ashish Khisti
Richard Zemel
Alireza Makhzani

"Variational Model Inversion Attacks" by Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, and Alireza Makhzani, arXiv:2201.10787v1, 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{Summary of corresponding notations and quantities.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \toprule
 Proof & Notation & Original Notation & Previous Works & Short Description \\
 \midrule
 General & & & & \\
 \midrule
 & $C_a$ & $n$ & VOO & Number of action samples \\
 & $C_s$ & $C$ & Sparse Sampling & Number of state samples \\
 & & & POWSS & Number of state/observation samples\\
 & $D$ & $H$ & VOO & Horizon of the problem \\
 & & & Sparse Sampling & \\
 & $d$ & $h$ & VOO & Given depth of the tree \\
 & $d$ & $h, n$ & Sparse Sampling & Given depth of the tree/estimator \\
 \midrule
 VOWSS & & & & \\
 \midrule
 & $\mathcal{R}_{C_a}$ & $\mathcal{R}_n$ & VOO & VOO agent regret bound\\
 & $\epsilon$ & $\frac{\lambda}{1-\gamma}$ & POWSS & POWSS concentration bound\\
 & $p$ & $\delta$ & POWSS & POWSS bound tail probability \\
 \midrule
 VOSS & & & & \\
 \midrule
 & $\mathcal{R}_{C_a}$ & $\mathcal{R}_n$ & VOO & VOO agent regret bound\\
 & $\epsilon$ & $\lambda$ & Sparse Sampling & Chernoff concentration bound\\
 & $p$ & $e^{-\lambda^2C/V_{\max}^2}$ & Sparse Sampling & Chernoff bound tail probability \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Michael H. Lim
Claire J. Tomlin
Zachary N. Sunberg

"Voronoi Progressive Widening: Efficient Online Solvers for Continuous State, Action, and Observation POMDPs" by Michael H. Lim, Claire J. Tomlin, and Zachary N. Sunberg, arXiv:2012.10140v3, 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{Values of GA hyperparameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \cmidrule(r){1-2}
 parameter name & value \\
 \midrule
 mutation rate & $10\%$\\
 survivor rate range & $[0.0, 0.6]$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jagdeep Singh Bhatia
Holly Jackson
Yunsheng Tian
Jie Xu
Wojciech Matusik

"Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots" by Jagdeep Singh Bhatia, Holly Jackson, Yunsheng Tian, Jie Xu, and Wojciech Matusik, arXiv:2201.09863v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ loss functions of DNN}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
		\hline
	 Name & $\mathcal{L}(\boldsymbol{p},\boldsymbol{r})$ \\ \hline
 	MSE & $||\boldsymbol{p}-\boldsymbol{r}||_2^2$ \\ \hline
	Categorical cross-entropy & $-\sum_jp_j\log(r_j)$ \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Feibo Jiang
Kezhi Wang
Li Dong
Cunhua Pan
Wei Xu
Kun Yang

"Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks" by Feibo Jiang, Kezhi Wang, Li Dong, Cunhua Pan, Wei Xu, and Kun Yang, arXiv:1911.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small{Memory of {\color{black}LU solver} and fast solver in Example . (total memory: 256GB) }}
\begin{tabular}{c|ccccccccc}
					\hline
					$n$ & $2^7$ & $2^8$ &$2^9$ & $2^{10}$ & $2^{11}$ & $2^{12}$ &6000 \\
					\hline
					{\color{black}mem(Fast Solver)} (MB)&1.5& 3.3&9.7&34 &131& 515 & 554\\
					{\color{black}mem(LU Solver)} (MB) &-& 262 &1049& 4719&20709&88343& 204997 \\
 {\color{black}mem(Fast Solver)/mem(LU Solver)} (\%)&-&1.26&0.92&0.72&0.63&0.58&0.27\\
 \hline
		\end{tabular}
\end{table}
\end{document}

math

Lixiu Wang
Lueling Jia
Zijian Cao
Huiyuan Li
Zhimin Zhang

"Fast Maxwell Solvers Based on Exact Discrete Eigen-Decompositions I. Two-Dimensional Case" by Lixiu Wang, Lueling Jia, Zijian Cao, Huiyuan Li, and Zhimin Zhang, arXiv:2503.09933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.