\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{System parameters for the simulations}
\begin{tabular}{|c|c|}
			\hline
			Parameter & value \\
			\hline
			 Carrier frequency & 2\,GHz \\
			\hline
			 Bandwidth & 20\,MHz \\
			\hline
			 Noise figure & 10\,dB \\
			\hline
		 	Noise variance & -91\,dBm \\
			\hline
			$D$,\,$d_0$ & 300\,m, 1\,m \\
			\hline
		 	$\sigma_{sh}^2$ & 8\,dB \\
			\hline
		 	$\tau_c$, $\tau_p = \tau_{p,d}$ & 196, $K$ \\
			\hline
			$\nu$ & 3.4 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Diluka Loku Galappaththige
Rajan Shrestha
Gayan Amarasuriya Aruma Baduge

"Exploiting Cell-Free Massive MIMO for Enabling Simultaneous Wireless Information and Power Transfer" by Diluka Loku Galappaththige, Rajan Shrestha, and Gayan Amarasuriya Aruma Baduge, arXiv:2010.13545v2, licensed under CC0 1.0. 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{The characteristics of the benchmark dataset}
\begin{tabular}{lllrr}\toprule
 \textbf{Dataset} & \textbf{Type} & \textbf{Task} & \textbf{\#observations} & \textbf{\#variables} \\\midrule
 \texttt{SEA} & Synthetic & Classification & 50000 & 4\\
 \texttt{Hyperplane} & Synthetic & Classification & 20000 & 3\\
 \texttt{NOAA} & Real & Classification & 18159 & 9\\
 \texttt{Ozone} & Real & Classification & 2534 & 73\\
 \texttt{Elec2} & Real & Classification & 45312 & 9\\
 \texttt{Friedman} & Synthetic & Regression & 20000 & 11\\\bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"datadriftR: An R Package for Concept Drift Detection in Predictive Models" by Ugur Dar and Mustafa Cavus, arXiv:2412.11308v1, 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

"An SFP--FCC Method for Pricing and Hedging Early-exercise Options under Lévy Processes" by Tat Lung and Chan, arXiv:1909.07319v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{GNN evaluation loss}
\begin{tabular}{lrrr}
		\toprule
		\bfseries Topology & \bfseries GNN $T=6$ & \bfseries Baseline & \bfseries GNN \bfseries $T=1$\\
		\midrule
		train & -1.37 & -- & -1.34\\
		test & -1.43 & -- & -1.38\\
		validation & -1.42 & 1.10 & -1.39\\
		Average SNDLib & -0.88 & 1.62 & -0.74\\
		dfn-bwin & 0.73 & 4.09 & 1.19\\
		abilene & -1.67 & 0.87 & -1.62\\
		nobel-germany & -1.37 & 0.88 &-1.32\\
		cost266 & -1.32 & 1.00& -1.25 \\
		geant & -1.38 & 1.01 &-1.33\\
		nobel-eu & -1.32 & 0.97 & -1.20\\
		janos-us & -1.14 & 1.11 & -1.04\\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

cs

Krzysztof Rusek
Piotr Boryło
Piotr Jaglarz
Fabien Geyer
Albert Cabellos
Piotr Chołda

"RiskNet: Neural Risk Assessment in Networks of Unreliable Resources" by Krzysztof Rusek, Piotr Boryło, Piotr Jaglarz, Fabien Geyer, Albert Cabellos, and Piotr Chołda, arXiv:2201.12263v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example table}
\begin{tabular}{|c|c|c|} \hline
 Species & \bf Mean & \bf Std.~Dev. \\ \hline
 1 & 3.4 & 1.2 \\
 2 & 5.4 & 0.6 \\ \hline
 \end{tabular}
\end{table}
\end{document}

stat

"Asymptotic consistency of the WSINDy algorithm in the limit of continuum data" by Daniel A. Messenger and David M. Bortz, arXiv:2211.16000v1, 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{Scaled Close Prices data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|}
\hline
\textbf{Close Price} & 411.15 & 414.05 & 410.20 & 410.25 & 410.00\\ \hline
\textbf{Scaled Close} & 0.1840 & 0.1874 & 0.1828& 0.1829& 0.1826\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction" by Shangeth Rajaa and Jajati Keshari Sahoo, arXiv:1905.07581v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Bank distress in the news: Describing events through deep learning" by Samuel Rönnqvist and Peter Sarlin, arXiv:1603.05670v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Successful anomaly detection rate}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
 Index & $0$ & $1$ & $2$ & $3$ & $4$ & $5$ & $6$ & $7$ & $8$ & $9$ & $10$ & \\
\hline\hline
\emph{$R$ ($\%$)} & $74.3$ & $66.1$ & $53.6$ & $63.3$ & $74.2$ & $75.3$ & $67.3$ & $68.0$ & $66.4$ & $67.6$ & $68.9$ & $76.9$ \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Signal processing with a distribution of graph operators" by Feng Ji and Wee Peng Tay, arXiv:2012.06296v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Reliability and Economic performance of Different Models and Probability Level}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule
$\gamma$ & Indices & M1 & M2 & M3 \\
 \midrule
 \multirow{2}[1]{*}{0.05} & \textit{LORP} / \textit{ERNS}& & 0.3 / 12.0 & 0.0 / 0.0 \\\specialrule{0em}{0.1em}{0em}
 &$\text{Cost}^{\text{RT}}$ / $\text{Cost}^{\text{TC}}$ & \textit{LORP} 0.6 & 365.6 / 3039.1 & 0.0 / 2799.7 \\\specialrule{0em}{0.1em}{0em}
 \multirow{2}[1]{*}{0.25} & \textit{LORP} / \textit{ERNS} & \textit{ERNS} 30.8 & 0.4 / 14.0 & 0.1 / 3.0 \\\specialrule{0em}{0.1em}{0em}
 & $\text{Cost}^{\text{RT}}$ / $\text{Cost}^{\text{TC}}$ & $\text{Cost}^{\text{RT}}$ 1057.9 & 440.0 / 2909.0 & 0.0 / 2799.7 \\\specialrule{0em}{0.1em}{0em}
 \multirow{2}[1]{*}{0.45} & \textit{LORP} / \textit{ERNS} & $\text{Cost}^{\text{TC}}$ 3088.3 & 0.4 / 15.2 & 0.2 / 3.6 \\\specialrule{0em}{0.1em}{0em}
 &$\text{Cost}^{\text{RT}}$ / $\text{Cost}^{\text{TC}}$ & & 487.4 / 2810.4 & 0.0 / 2407.7
 \\\specialrule{0em}{0.1em}{0em}
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Ning Qi
Pierre Pinson
Mads R. Almassalkhi
Lin Cheng
Yingrui Zhuang

"Chance-Constrained Generic Energy Storage Operations under Decision-Dependent Uncertainty" by Ning Qi, Pierre Pinson, Mads R. Almassalkhi, Lin Cheng, and Yingrui Zhuang, arXiv:2201.06407v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The first $c_1,$ second $c_2,$ and fourth $c_4$ cumulants of various models.} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|} % centered columns (4 columns)
\hline
\multicolumn{2}{|l|}{L\'evy models}\\
\hline %inserts double horizontal lines
BS&$c_1=(r-q+\omega)t$\quad$c_2=\sigma^2 t,$\quad $c_4=0,$ $\omega=-0.5\sigma^2$\\
\hline
NIG&$c_1=(r-q+\omega)t+\delta t\beta/\sqrt{\alpha^2-\beta^2}$\\
\,&$c_2=\delta t \alpha^2 (\alpha^2-\beta^2)^{-3/2}$\\
\,&$c_4=\delta t \alpha^2 (\alpha^2+4\beta^2)^{-3/2}(\alpha^2-\beta^2)^{-7/2}$\\
\,&$\omega=-0.5\sigma^2-\delta(\sqrt{\alpha^2-\beta^2}-\sqrt{\alpha^2-(\beta+1)^2}) $\\
\hline %inserts single line
VG&$c_1=(r-q+\theta+\omega)t$\\
\,&$c_2=(\sigma^2+\upsilon\theta^2)t$\\
\,&$c_4=3(\sigma^4\upsilon+2\theta^4\upsilon^3+4\sigma^2\theta^2\upsilon^2)t$\\
\,&$\omega={1 / \upsilon}\log(1-\theta\upsilon-\sigma^2\upsilon/2)$\\
\hline %inserts single line
$\rm CGMY$&$c_1=(r-q+\omega)t$\\
\,&$c_2=(C\Gamma(2-Y)(M^{Y-2} + G^{Y-2})t$\\
\,&$c_4=(C\Gamma(4-Y)(M^{Y-4} + G^{Y-4})t$\\
\,&$\omega=\left(C\Gamma(-Y)G^Y\left(\left(1+\frac{1}{G}\right)^Y-1-\frac{Y}{G}\right)+C\Gamma(-Y)M^Y\left(\left(1-\frac{1}{M}\right)^Y-1+\frac{Y}{M}\right)\right)$\\
\hline %inserts single line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Rank score results for different algorithm classes}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \hline
 Algorithm & Score \\
 \hline
 SVM & 0.672 \\
 ANN\{R\} & 0.643 \\
 ANN & 0.579 \\
 FUZZ & 0.528 \\
 GARCH & 0.508 \\
 ARIMA & 0.471 \\
 RW & 0.333 \\
 LRM & 0.298 \\
 AR & 0.227 \\
 BH & 0.167\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey" by Lukas Ryll and Sebastian Seidens, arXiv:1906.07786v2, 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 effect of pre-training, numerical properties, and residual connection. The top two are highlighted by \textbf{first} and \underline{second}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Model} & \textbf{Accuracy}
& \textbf{Recall}
& \textbf{Precision}
& {$F_1$}
\\
\midrule
w/o pre-training
&0.9171
&0.8804
&0.9043
&0.8921
\\
w/o numerical attributes
&\underline{0.9471}
&\underline{0.9136}
&\underline{0.9555}
&\underline{0.9341}
\\
w/o residual connection
&0.9108
&0.9036
&0.8958
&0.8996
\\
HaCKG (Ours)
&\textbf{0.9657}
&\textbf{0.9573}
&\textbf{0.9794}
&\textbf{0.9682}
\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Van Thuy Hoang
Tien-Bach-Thanh Do
Jinho Seo
Seung Charlie Kim
Luong Vuong Nguyen
Duong Nguyen Minh Huy
Hyeon-Ju Jeon
O-Joun Lee

"Halal or Not: Knowledge Graph Completion for Predicting Cultural Appropriateness of Daily Products" by Van Thuy Hoang, Tien-Bach-Thanh Do, Jinho Seo, Seung Charlie Kim, Luong Vuong Nguyen, Duong Nguyen Minh Huy, Hyeon-Ju Jeon, and O-Joun Lee, arXiv:2501.05768v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Integrating Spatial and Semantic Embeddings for Stereo Sound Event Localization in Videos" by Davide Berghi and Philip J. B. Jackson, arXiv:2509.06598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Mixed Precision GMRES-based Iterative Refinement with Recycling" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fitting parameters \( a \), \( b \), and RMSE for the function in Equation~().}
\begin{tabular}{lrrr}
 \toprule
 City & $a$ & $b$ & RMSE\\
 \midrule
 New York & $4.38 \times 10^4$ & $38.7$ & $76.7$ \\
 London & $1.70 \times 10^3$ & $49.1$ & $92.1$ \\
 Tokyo & $8.72 \times 10^4$ & $19.1$ & $70.6$ \\
 Boston & $1.08 \times 10^3$ & $4.5$ & $27.3$\\
 Chicago & $1.43 \times 10^3$ & $14.6$ & $36.1$ \\
 Washington & $1.20 \times 10^3$ & $10.1$ & $33.4$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Shu-ichi Kinoshita
Yuya Bando
Hiroki Sayama

"Spatio-Temporal Differences in Bike Sharing Usage: A Tale of Six Cities" by Shu-ichi Kinoshita, Yuya Bando, and Hiroki Sayama, arXiv:2412.19294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Detect & Describe: Deep learning of bank stress in the news" by Samuel Rönnqvist and Peter Sarlin, arXiv:1507.07870v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of classification performance in CNP detection.}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Method} & \textbf{Accuracy} & \textbf{Specificity} & \textbf{Sensitivity} \\
\hline
K-NN (raw) & 35.00 & 35.00 & 35.00 \\
SVM (raw) & 32.50 & 31.57 & 33.33 \\
LDA (raw) & 42.50 & 42.85 & 42.10 \\
\hline
K-NN (NCA) & 55.00 & 54.54 & 55.55 \\
SVM (NCA) & 55.00 & 60.00 & 54.17 \\
LDA (NCA) & 55.00 & 56.25 & 55.00 \\
\hline
NN-Baseline (raw) & 57.50 & 55.00 & 60.00 \\
FIN-ENN (raw) & \textbf{62.50} & \textbf{65.00} & \textbf{60.00} \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Reza Khanmohammadi
Tuka Alhanai
Mohammad M. Ghassemi

"The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains" by Reza Khanmohammadi, Tuka Alhanai, and Mohammad M. Ghassemi, arXiv:2309.12279v1, 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{adjustbox}
\usepackage{hhline}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|c|c|c}
			& $T_{comp}$, s & \# of successful (failed) steps & \# of Newton iterations \\ \hhline{=|=|=|=}
TPFA, 0 & 258.9 & 1 (0) & 14 \\
	 1 & 245.1 & 1 (0) & 12 \\ \hline
MPFA-O, 0 & 842.9 & 1 (0) & 14 \\
		1 & 842.2 & 1 (0) & 13 \\ \hline
NTPFA-B, 0 & 1044.1 & 1 (0) & 40 \\
		 1 & 1061.3 & 1 (0) & 41 \\\hline
NMPFA-B, 0 & 1245.1 & 1 (0) & 36 \\
		 1 & 1241.4 & 1 (0) & 36 \\\hline
MFD, 0 & & 1 (0) & \\
	 1 & & 1 (0) &
		\end{tabular}
\end{adjustbox}
\caption{Comparison of zero- and first-order predictor for the real-life object test}
\end{table}
\end{document}

math

"First-order continuation method for steady-state variably saturated groundwater flow modeling" by Denis Anuprienko, arXiv:2201.05202v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experiment results on the diffferent $\lambda$.}
\begin{tabular}{c|cc}
		\toprule
		$\lambda$	& ACC(\%) & WF1(\%) \\
		\midrule
		0 & 67.89 & 67.66 \\
		0.01 & 68.63 & 68.39 \\
		0.1 & 69.11 & 68.96 \\
		1 & 70.53 & 70.6 \\
		10 & 64.43 & 64.19 \\
		\bottomrule
	\end{tabular}
\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.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \hline
 SIPP Panels & Qtr from & Qtr to (inclusive) \\ \hline \hline
 1984,85,86,87,1988 & 1985q1 & 1989q3 \\
 1990,91,92,1993 & 1991q2 & 1995q3 \\
 1996 & 1997q3 & 1999q4 \\
 2001 & 2002q2 & 2003q4 \\
 2004 & 2005q1 & 2007q3 \\
 2008 & 2009q3 & 2013q3 \\
 \hline
\end{tabular}
\caption{Quarters included in time series for overall occupational mobility rate of all unemployed (with unemployment duration between 1-14 months }
\end{table}
\end{document}

q-fin

"Unemployment and Endogenous Reallocation over the Business Cycle" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, 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{Performance comparison on the SBD val set: The '+' symbol denotes the utilization of HGNetv2 B3 as the backbone.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 Method & Venue & $\mathrm{AP}_{vol}$ & $\mathrm{AP}_{50}$ & $\mathrm{AP}_{70}$ \\
 \midrule
 DeepSnake & $CVPR'2020$ & 54.4 & 62.1 & 48.3 \\
 DANCE & $WACV'2021$ & 56.2 & 63.6 & 50.4 \\
 EigenContours & $CVPR'2022$ & - & 56.5 & - \\
 E2EC & $CVPR'2022$ & 59.2 & 65.8 & 54.1 \\
 PolySnake & $TCSVT'2024$ & 60.0 & 66.8 & 55.3 \\
 TSnake & $APSIPA'2024$ & 60.5 & 67.2 & 56.1 \\
 Contourformer & - & 67.6 & 75.5 & 63.6 \\
 $\mathrm{Contourformer}^{+}$ & - & \textbf{69.2} & \textbf{76.9} & \textbf{65.8} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Weiwei Yao
Chen Li
Minjun Xiong
Wenbo Dong
Hao Chen
Xiong Xiao

"ContourFormer: Real-Time Contour-Based End-to-End Instance Segmentation Transformer" by Weiwei Yao, Chen Li, Minjun Xiong, Wenbo Dong, Hao Chen, and Xiong Xiao, arXiv:2501.17688v3, 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{{\bf Comparison of NAR db status entries in 2023 and 2025 and miRNAverse}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|r|r|r|}
 \hline
 & \textbf{2023} & \textbf{2025} & \textbf{miRNAverse} \\ \hline
 Unique databases & 2,025 & 2,082 & 194 \\ \hline\hline
 Unavailable & 466 & 587 & 76 \\ \hline
 Recheck & 100 & 107 & 8 \\ \hline
 Available & 1,416 & 1,349 & 107 \\ \hline\hline
 Unknown updated & 834 & 770 & 114 \\ \hline
 Downloadable & 1,011 & 1,078 & 80 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"NAR db status Version 2 and miRNAverse: Over Two Years of Manual Meta-Registry Curation and Updates" by Marcel Friedrichs and Cassandra Königs, arXiv:2501.18660v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Inflation and deflation factors for Monte Carlo (MC) estimated posterior standard deviation with $\alpha = 0.05$. When $M = 100$, by inflating the MC estimated posterior standard deviation by a factor of $1.1607$ (inflating by $16.07\%$), the extra uncertainty resulting from the MC procedure is accounted for with $97.5\%$ confidence. Similarly, when $M = 100$, deflating the MC estimated posterior standard deviation by a factor of $0.8785$ provides a lower bound on the true underlying Bayesian uncertainty with $97.5\%$ confidence. When considered simultaneously, the inflation and deflation factors bracket the true uncertainty with $95\%$ confidence.}
\begin{tabular}{lll}
 \hline
 \# Monte Carlo samples, $M$ & Deflation: $L = \sqrt{\frac{M - 1}{\chi^2_{M - 1, 1 - \alpha / 2}}}$ & Inflation: $R = \sqrt{\frac{M - 1}{\chi^2_{M - 1, \alpha / 2}}}$ \\ \hline
 10 & 0.6987 & 1.7549 \\
 100 & 0.8785 & 1.1607 \\
 1,000 & 0.9580 & 1.0458 \\
 10,000 & 0.9863 & 1.0141 \\
 100,000 & 0.9956 & 1.0044 \\
 1,000,000 & 0.9986 & 1.0014 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Michael Stanley
Mikael Kuusela
Brendan Byrne
Junjie Liu

"Posterior Uncertainty Estimation via a Monte Carlo Procedure Specialized for Data Assimilation" by Michael Stanley, Mikael Kuusela, Brendan Byrne, and Junjie Liu, arXiv:2310.01397v2, 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
\begin{tabular}{lrrr}
\toprule
{} & snflw. seed & snflw. oil & snflw. cake \\
\midrule
Africa N & 1.8 & 48.1 & 42.4 \\
Africa S & 0.0 & 4.5 & 2.1 \\
America S & 0.0 & 1.1 & 1.2 \\
America N & 0.6 & 6.3 & 0.6 \\
Asia C & 0.1 & 5.0 & 0.1 \\
Asia E & 0.0 & 48.7 & 0.0 \\
Asia S & 0.5 & 67.7 & 0.8 \\
Asia SE & 0.0 & 7.1 & 0.4 \\
Asia W & 5.2 & 24.8 & 15.9 \\
Australia & 0.0 & 8.5 & 2.5 \\
Europe E & 0.2 & 2.1 & 15.8 \\
Europe N & 1.8 & 38.1 & 39.1 \\
Eurpe S & 0.3 & 12.3 & 15.2 \\
Europe W & 0.6 & 9.9 & 27.9 \\
\bottomrule
\end{tabular}
\caption{Relative loss of different food products in different world regions after a shock to the respective product. These numbers correspond to the right half of each cell in the lower part of fig. 5 in the main text.}
\end{table}
\end{document}

q-fin

Moritz Laber
Peter Klimek
Martin Bruckner
Liuhuaying Yang
Stefan Thurner

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc} \hline\hline \\
Data / Model & AMH & CLA & FRA & GUM & JOE & BSA \\ \hline \\
AMH & 0.34 & -0.08 & 0.02 & -1.07 & \textbf{2.48} & -4.32 \\
CLA & 14.75 & \textbf{147.42} & 74.91 & 78.37 & 53.23 & 7.4\\
FRA & 0.13 & 7.11 & \textbf{10.00} & 1.62 & 3.09 & -3.01 \\
GUM & 77.85 & 199.23 & 238.08 & \textbf{276.27} & 270.27 & 110.14\\
JOE & 37.76 & 75.85 & 98.33 & 118.10 & \textbf{120.91} & 55.33\\
\hline\hline
\end{tabular}
\caption{Simulation study 1: Bivariate data from a mixture of Archimedean copulas. LPML statistics when taking a sample of size $n=200$ and fitting the five models. Bayesian semiparametric Archimedean copula competing model in the last column.}
\end{table}
\end{document}

stat

Ruyi Pan
Luis E. Nieto-Barajas
Radu V. Craiu

"Bayesian nonparametric mixtures of Archimedean copulas" by Ruyi Pan, Luis E. Nieto-Barajas, and Radu V. Craiu, arXiv:2412.09539v3, 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 MSE in Scenario 1.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
\hline
$n$ &RPackage & CVTF &SURE & Traditional CV \\
\hline
300 & 0.081 (0.030) & 0.073 (0.020) & 0.076 (0.030) & 0.074 (0.020)\\
600 & 0.042 (0.010) & 0.040 (0.010) & 0.040 (0.010) & 0.039 (0.010)\\
1200 & 0.022 (0.008) & 0.022 (0.009) & 0.023 (0.007) & 0.022 (0.008)\\
2400 & 0.013 (0.003) & 0.012 (0.003) & 0.014 (0.004) & 0.012 (0.004)\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Anamitra Chaudhuri
Sabyasachi Chatterjee

"A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond" by Anamitra Chaudhuri and Sabyasachi Chatterjee, arXiv:2201.02654v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Notes on BIM and BFM Optimal Power Flow With Parallel Lines and Total Current Limits" by Frederik Geth and Bin Liu, arXiv:2202.06449v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework" by Chao Wang, Richard Gerlach, and Qian Chen, arXiv:1807.02422v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
		\toprule
		\textbf{Property} & \multicolumn{2}{c}{\textbf{Value}}\\
		\midrule
		Blood density, $\rho$ & \multicolumn{2}{c}{1.060 g/cm$^3$} \\
		Blood viscosity, $\mu$ & \multicolumn{2}{c}{0.04 dyne$\cdot$s/cm$^2$} \\
		Velocity profile order, $\zeta$ & \multicolumn{2}{c}{9} \\
		Diastolic pressure, $P_d$ & \multicolumn{2}{c}{9.4$\hat{6}$ $\cdot$10$^4$ dyne/cm$^2$} \\
		External pressure, $p_{ext}$ & \multicolumn{2}{c}{0}\\
		\midrule
		& \textbf{Aorta} & \textbf{Iliac}\\
		\midrule
		Length, $l$ & 8.6 cm & 8.5 cm\\
		Radius at diastolic pressure, $r_d$ & 0.86 cm & 0.60 cm\\
		Area at diastolic pressure, $A_d$ & 2.3235 cm$^2$ & 1.1310 cm$^2$\\
		Wall thickness, $h$ & 1.032 mm & 0.72 mm\\
		Young's modulus, $E$ & 5.0$\cdot$10$^6$ dyne/cm$^2$ & 7.0$\cdot$10$^6$ dyne/cm$^2$\\
		WK resistance, $R_1$ & - & 6.8123$\cdot$10$^2$ dyne$\cdot$s/cm$^5$\\
		WK compliance, $C$ & - & 3.6664$\cdot$10$^{-5}$ cm$^5$/dyne\\
		WK resistance, $R_2$ & - & 3.1013$\cdot$10$^4$ dyne$\cdot$s/cm$^5$\\
		Outflow pressure, $P_{out}$ & - & 0\\
		\midrule
		Initial cross-sectional area, $A(x, 0)/\widehat{A}(0)$ & 1.8062 cm$^2$ & 0.94789 cm$^2$\\
		Initial velocity, $u(x, 0)/U(0)$ & 0 & 0\\
		Initial pressure, $p(x, 0)/P(0)$ & 0 & 0\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Geometrical and mechanical properties of the aortic bifurcation model.}
\end{table}
\end{document}

math

Beatrice Ghitti
Eleuterio F. Toro
Lucas O. Müller

"Nonlinear lumped-parameter models for blood flow simulations in networks of vessels" by Beatrice Ghitti, Eleuterio F. Toro, and Lucas O. Müller, arXiv:2201.01617v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Van Quang Nguyen
Quoc Chuong Nguyen
Thu Huong Dang
Truong-Son Hy

"Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing Problems" by Van Quang Nguyen, Quoc Chuong Nguyen, Thu Huong Dang, and Truong-Son Hy, arXiv:2501.00852v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Lihu Xu
Fang Yao
Qiuran Yao
Huiming Zhang

"Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption" by Lihu Xu, Fang Yao, Qiuran Yao, and Huiming Zhang, arXiv:2201.03182v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Assessment study for the preliminary implementation of parallelization on a CPU and a GPU}
\begin{tabular}{|l|l|l|l|}
 \hline
 Processor & Language & Time(sec) & Note \\ \hline
 Intel Xeon & NA & 158.08 & Sequential code \\ \hline
 Intel Xeon & OpenMP & 6.36 & Double checked locking \\ \hline
 Nvidia Tesla K20c & CUDA & 9.34 & Global memory only\\
 \hline
 \end{tabular}
\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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The SV performance comparison. The results for these baseline models are sourced from~ and~.}
\begin{tabular}{lccc}
\hline
Model & Params. & Data & EER (\%) \\
\hline
FBANK & - & - & 9.56 \\
\hline
Modified CPC~ & 1.84M & 60k hr & 12.86 \\
wav2vec~ & 32.54M & 960 hr & 7.99 \\
vq-wav2vec~ & 34.15M & 960 hr & 10.38 \\
wav2vec 2.0 Base~ & 95.04M & 960 hr & 6.02 \\
HuBERT Base~ & 94.68M & 960 hr & 5.11 \\
WavLM Base~ & 94.70M & 960 hr & 4.69 \\
WavLM Base+~ & 94.70M & 94k hr & 4.07 \\
\hline
wav2vec 2.0 Large~ & 317.38M & 60k hr & 5.65 \\
HuBERT Large~ & 316.61M & 60k hr & 5.98 \\
WavLM Large~ & 316.62M & 94k hr & \textbf{3.77} \\
\hline
VC-SPK2VEC & 72.4M & 60k hr & 5.32 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Haorui He
Yuchen Song
Yuancheng Wang
Haoyang Li
Xueyao Zhang
Li Wang
Gongping Huang
Eng Siong Chng
Zhizheng Wu

"Noro: Noise-Robust One-shot Voice Conversion with Hidden Speaker Representation Learning" by Haorui He, Yuchen Song, Yuancheng Wang, Haoyang Li, Xueyao Zhang, Li Wang, Gongping Huang, Eng Siong Chng, and Zhizheng Wu, arXiv:2411.19770v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Jakob Nolte
Maureen M. J. Guichelaar
Donald E. Bouman
Stephanie M. van den Berg
Maryam Amir Haeri

"A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma Prediction" by Jakob Nolte, Maureen M. J. Guichelaar, Donald E. Bouman, Stephanie M. van den Berg, and Maryam Amir Haeri, arXiv:2501.10733v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\\SSB-FSK optimum minimum normalized squared Euclidean distance $d_{\text{min}-\text{opt}}^2$ and optimum bandwidth occupancy $BW_{\text{opt}}$ for $99\%$ bandwidth BW occupancy, for different modulation levels $M$.}
\begin{tabular}{cccccccc}
 \toprule
 \multicolumn{8}{c}{\boldmath$d_{\text{min}-\text{opt}}^2$ - Config.~A} \\ \midrule \midrule
 $M$ & $L$ & $w$ & $h$ & $d_{\text{min}}^2$ & $BW$ & SSB-LOSS (\%) & $N$ \\ \midrule
 2 & 5 & 1.3 & 0.78 & 2.4 & 0.906 & 1.764 & 15 \\ \midrule
 4 & 2 & 0.7 & 0.49 & 3.53 & 0.906 & 2.561 & 25 \\ \midrule
 8 & 2 & 0.6 & 0.36 & 3.25 & 0.904 & 1 & 8 \\ \bottomrule
 \multicolumn{8}{c}{\boldmath$BW_{\text{opt}}$ - Config.~B} \\ \midrule \midrule
 $M$ & $L$ & $w$ & $h$ & $d_{\text{min}}^2$ & $BW$ & SSB-LOSS (\%) & $N$ \\ \midrule
 2 & 5 & 1.2 & 0.65 & 1.774 & 0.785 & 2 & 14 \\ \midrule
 4 & 2 & 0.8 & 0.33 & 1.773 & 0.65 & 2.732 & 10 \\ \midrule
 8 & 2 & 0.6 & 0.26 & 1.8 & 0.677 & 1.21 & 8 \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Karim Kassan
Haïfa Farès
D. Christian Glattli
Yves Louët

"Performance vs. Spectral Properties For Single-Sideband Continuous Phase Modulation" by Karim Kassan, Haïfa Farès, D. Christian Glattli, and Yves Louët, arXiv:2011.10541v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{MADDPG hyperparameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \toprule
 Hyperparameter & Value \\
 \midrule
 Policy learning rate & 0.01 \\
 Critic learning rate & 0.01 \\
 Batch size & 1024 \\
 Discount factor ($\gamma$) & 0.95 \\
 Target smoothing coefficient ($\tau$) & 0.01 \\
 \multirow{2}{*}{Exploration noise} & Ornstein-Uhlenbeck \\
 & $\theta = 0.15$ $\sigma = 0.2$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Enrique Adrian Villarrubia-Martin
Luis Rodriguez-Benitez
David Muñoz-Valero
Giovanni Montana
Luis Jimenez-Linares

"Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement Learning" by Enrique Adrian Villarrubia-Martin, Luis Rodriguez-Benitez, David Muñoz-Valero, Giovanni Montana, and Luis Jimenez-Linares, arXiv:2501.08234v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparisons of the errors and the CPU times of the WG method and the two-grid WG method for Example , $\tau=h^{1/2}$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc}
\toprule[1.5pt]
\multirow{2}*{$h$}&\multicolumn{2}{c|}{WG method}&\multicolumn{2}{c}{Two-grid WG method}\\ \cline{2-5}
 & $\|u-u_h\|_{1,h}$&CPU time (sec.)&$\|u-u^h\|_{1,h}$&CPU time (sec.) \\
\hline
 $1/4$&1.58E+00 &0.8125&1.57E+00&0.8594\\
 $1/16$&4.30E-01&7.8438&4.79E-01&2.9063\\
 $1/36$&1.92E-01&44.6563&2.25E-01&10.0000\\
 $1/64$&1.08E-01&111.9688&1.28E-01&23.9063\\
 $1/100$&6.93E-02&283.5781&8.26E-02&71.6719\\
 \hline
 Rate&0.97&&0.91&\\
 \bottomrule[1.5pt]
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A weak Galerkin method and its two-grid algorithm for the quasi-linear elliptic problems of non-monotone type" by Peng Zhu and Shenglan Xie, arXiv:2202.11164v2, 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{Network Parameters.}
\begin{tabular}{lllll}
\toprule
\textbf{Parameter} & \textbf{CIFAR10-32} & \textbf{AFHQV2-64} & \textbf{FFHQ-64} & \textbf{CelebA-64} \\
\textbf{normalization} & "GroupNorm" & "GroupNorm" & "GroupNorm" & "GroupNorm" \\
\textbf{nonlinearity } & "swish" & "swish" & "swish" & "swish" \\
\textbf{nf } & 128 & 128 & 128 & 128 \\
\textbf{ch\_mult } & {[} 2, 2, 2{]} & {[}1, 2, 2, 2{]} & {[}1, 2, 2, 2{]} & {[}1, 2, 2, 2{]} \\
\textbf{num\_res\_blocks } & 4 & 4 & 4 & 4 \\
\textbf{attn\_resolutions )} & (16,) & (16,) & (16,) & (16,) \\
\textbf{resamp\_with\_conv } & True & True & True & True \\
\textbf{fir } & True & True & True & True \\
\textbf{fir\_kernel } & {[}1, 3, 3, 1{]} & {[}1, 3, 3, 1{]} & {[}1, 3, 3, 1{]} & {[}1, 3, 3, 1{]} \\
\textbf{skip\_rescale } & True & True & True & True \\
\textbf{resblock\_type } & "biggan" & "biggan" & "biggan" & "biggan" \\
\textbf{progressive } & "none" & "none" & "none" & "none" \\
\textbf{progressive\_input } & "residual" & "residual" & "residual" & "residual" \\
\textbf{progressive\_combine } & "sum" & "sum" & "sum" & "sum" \\
\textbf{attention\_type } & "ddpm" & "ddpm" & "ddpm" & "ddpm" \\
\textbf{embedding\_type } & "fourier" & "fourier" & "fourier" & "fourier" \\
\textbf{init\_scale } & 0.0 & 0.0 & 0.0 & 0.0 \\
\textbf{fourier\_scale } & 16 & 16 & 16 & 16 \\
\textbf{conv\_size } & 3 & 3 & 3 & 3 \\
\textbf{num\_scales } & 18 & 18 & 18 & 18 \\
\textbf{dropout } & 0.13 & 0.25 & 0.05 & 0.1 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

James Thornton
Louis Bethune
Ruixiang Zhang
Arwen Bradley
Preetum Nakkiran
Shuangfei Zhai

"Composition and Control with Distilled Energy Diffusion Models and Sequential Monte Carlo" by James Thornton, Louis Bethune, Ruixiang Zhang, Arwen Bradley, Preetum Nakkiran, and Shuangfei Zhai, arXiv:2502.12786v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Main operator conversion from 2D CNNs into ACS CNNs. $(\{1,K\})\times K\times K$ and $(1\times)1\times 1$ denote the kernel sizes.}
\begin{tabular}{cc}
		\toprule
		2D CNNs & ACS CNNs\\
		\midrule
		Conv2D $K\times K$ & ACSConv $K\times K$\\
		Conv2D $1\times 1$ & Conv3D $1\times 1\times 1$\\
		\{Batch,Group\}Norm2D & \{Batch,Group\}Norm3D \\
		\{Max,Avg\}Pool2D $K\times K$ & \{Max,Avg\}Pool3D $\{1,K\}\times K \times K$ \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Jiancheng Yang
Xiaoyang Huang
Yi He
Jingwei Xu
Canqian Yang
Guozheng Xu
Bingbing Ni

"Reinventing 2D Convolutions for 3D Images" by Jiancheng Yang, Xiaoyang Huang, Yi He, Jingwei Xu, Canqian Yang, Guozheng Xu, and Bingbing Ni, arXiv:1911.10477v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Bootstrapped AUROC using varying explanations on subsets of the dataset with increasingly divergent MIL risk logits}
\begin{tabular}{l|ccc}
\toprule
& \multicolumn{3}{c}{Data Subset} \\
Explanation & $X$ & $X_1$ & $X_3$ \\
\hline
cow camel & 0.55$\pm$0.05 & 0.55$\pm$0.06 & 0.60$\pm$0.11 \\
tumor lymphocyte inverse & 0.38$\pm$0.06 & 0.31$\pm$0.07 & 0.11$\pm$0.07 \\
\hline
tumor lymphocyte & 0.62$\pm$0.05 & 0.67$\pm$0.07 & 0.77$\pm$0.11 \\
detailed analysis & 0.62$\pm$0.05 & 0.68$\pm$0.06 & 0.79$\pm$0.09 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Yoni Schirris
Eric Marcus
Jonas Teuwen
Hugo Horlings
Efstratios Gavves

"From Explainable to Explained AI: Ideas for Falsifying and Quantifying Explanations" by Yoni Schirris, Eric Marcus, Jonas Teuwen, Hugo Horlings, and Efstratios Gavves, arXiv:2508.09205v2, 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{Hyperparameters used for different SUPERB tasks.}
\begin{tabular}{lcc}
 \toprule
 Task	& learning rate &	batch size	\\
 \midrule
PR	& 2e-4 &	128\\
ASR	&1e-4	&32\\
KS	& 1e-5	& 512	\\
IC	& 1e-4	& 12\\
SF	& 1e-4	& 128\\
SID	& 1e-4 &	32\\
ASV	&5e-5	&512\\
SD	& 5e-3	&256	\\
ER	& 1e-5	& 32\\
\bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Muhammad Huzaifah
Geyu Lin
Tianchi Liu
Hardik B. Sailor
Kye Min Tan
Tarun K. Vangani
Qiongqiong Wang
Jeremy H. M. Wong
Jinyang Wu
Nancy F. Chen
Ai Ti Aw

"MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond" by Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor, Kye Min Tan, Tarun K. Vangani, Qiongqiong Wang, Jeremy H. M. Wong, Jinyang Wu, Nancy F. Chen, and Ai Ti Aw, arXiv:2412.11538v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 Method &$\Delta$Acc of $r_0$& $\Delta$Acc of $ r_{N-1} $&Overall Acc\\
 \midrule
 VAR~ &\cellcolor{red!15}-3.5 &\cellcolor{red!15}-3.7 &57.3\\
 $\text{VGCM (w/o F)}$ &\cellcolor{red!15}-3.3 &\cellcolor{red!15}-3.2 &66.9\\
 \textbf{$\text{VGCM}$} & \cellcolor{green!5}\textbf{\underline{-0.7}} &\cellcolor{green!5}\textbf{\underline{-0.3}} &68.7\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Tieyuan Chen
Huabin Liu
Yi Wang
Yihang Chen
Tianyao He
Chaofan Gan
Huanyu He
Weiyao Lin

"MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning" by Tieyuan Chen, Huabin Liu, Yi Wang, Yihang Chen, Tianyao He, Chaofan Gan, Huanyu He, and Weiyao Lin, arXiv:2501.07227v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{soul}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule
 \textbf{Class} & \textbf{Case Precision} & \textbf{Case Recall} & \textbf{Instance Precision} & \textbf{Instance Recall} \\
 \midrule
PHI Presence & 1.0000 ± 0.0000 & 0.9923 ± 0.0000 & 0.9869 ± 0.0008 & 0.9808 ± 0.0012 \\ \hline
Date & 1.0000 ± 0.0000 & 0.9963 ± 0.0000 & 0.9963 ± 0.0000 & 0.9963 ± 0.0000 \\
Patient Name & 0.9778 ± 0.0032 & 0.9815 ± 0.0017 & 0.9642 ± 0.0033 & 0.9731 ± 0.0000\\
Address & 0.9952 ± 0.0018 & 1.0000 ± 0.0000 & 0.9857 ± 0.0045 & 1.0000 ± 0.0000\\
Email & 1.0000 ± 0.0000 & 0.9496 ± 0.0000 & 1.0000 ± 0.0000 & 0.9496 ± 0.0000\\
Phone Nr & 1.0000 ± 0.0000 & 0.9908 ± 0.0021 & 1.0000 ± 0.0000 & 0.9908 ± 0.0021\\
Identifier & 0.9916 ± 0.0000 & 0.9949 ± 0.0035 & 0.9815 ± 0.0023 & 0.9848 ± 0.0023\\
 \bottomrule

 \end{tabular}
\end{adjustbox}
\caption{\hl{PHI detection performance of setup 1 (YOLOv11 + EasyOCR + GPT-4o) on RadPHI-test.}}
\end{table}
\end{document}

cs

Tuan Truong
Ivo M. Baltruschat
Mark Klemens
Grit Werner
Matthias Lenga

"Exploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images" by Tuan Truong, Ivo M. Baltruschat, Mark Klemens, Grit Werner, and Matthias Lenga, arXiv:2501.09552v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Neuron Type} & \textbf{$a$} & \textbf{$b$} & \textbf{$v_0$} & \textbf{$\Delta u$} \\
 \midrule
 Regular spiking (RS) & 0.02 & 0.2 & -65 & 8 \\
 Low-threshold spiking (LTS) & 0.02 & 0.25 & -65 & 2 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Parameters for different neuron types.}
\end{table}
\end{document}

cs

Berta Ros
Mireia Olives-Verger
Caterina Fuses
Josep M Canals
Jordi Soriano
Jordi Abante

"Integration of Calcium Imaging Traces via Deep Generative Modeling" by Berta Ros, Mireia Olives-Verger, Caterina Fuses, Josep M Canals, Jordi Soriano, and Jordi Abante, arXiv:2501.14615v3, 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}{ll|cccc}
\multicolumn{3}{l}{Valid Adjustment} & \multicolumn{3}{c}{$\epsilon_{att}$} \\
\hline
& & {\textsc \textbf{Mod1}} & {\textsc\textbf{Mod2} } & {\textsc \textbf{Mod3}} & {\textsc \textbf{Mod4}}\\
\hline
\textbf{Low} &Adjust All &$.04 \pm .08$ & $.05\pm .09$ & $.07 \pm .09$ & $.01 \pm .01$\\
& NICE &$.07 \pm .03$ & $.02 \pm .01$ & $.09\pm .03$ & $.04 \pm .02$ \\
\hline
\textbf{Med} & Adjust All &$.07 \pm .10$ & $.05\pm .05$ & $.04 \pm .04$ & $.07 \pm .08$\\
& NICE &$.05\pm .02$ & $.04 \pm .03$ & $.05 \pm .03$ & $.03 \pm .02$\\
\hline
\textbf{High} & Adjust All &$.07\pm .07$ & $.06\pm .05$ & $.06 \pm .07$ & $.04 \pm .04$\\
& NICE &$.02\pm .01$ & $.06 \pm .03$ & $.04\pm .02$ & $.07 \pm .04$\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Invariant Representation Learning for Treatment Effect Estimation" by Claudia Shi, Victor Veitch, and David Blei, arXiv:2011.12379v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ \small Out-of-sample validation of age-specific population projection generated from 2011 to 2020. The deterministic and Bayesian methods were validated for 2020, over 39 counties and 21 five-year age groups, i.e., 819 values. MAE is mean absolute error. RMSE is root mean squared error. The cov80 and cov95 columns refer to the percentage of the observations that fell within their probability interval.}
\begin{tabular}{l|l|rrrrr}\hline
\bf Scale & \bf Method & \bf MAE & \bf RMSE & \bf Bias & \bf cov80 & \bf cov95\\\hline
Counts & Basic Rogers-Castro & 847 & 3948 & \bf 95 & 34.1 & 45.3 \\
& Deterministic FDM & 693 & 3219 & 117 & 43.1 & 57.3 \\
& Bayesian FDM & \bf 651 & \bf 2902 & 114 & \bf 62.6 & \bf 75.5 \\\hline
Shares & Basic Rogers-Castro & 0.63 & 0.98 & 0.0011 & 31.7 & 46.5 \\
& Deterministic FDM & 0.46 & 0.82 & \bf 0.0008 & 37.4 & 56.0 \\
& Bayesian FDM & \bf 0.44 & \bf 0.71 & 0.0097 & \bf 60.1 & \bf 76.7 \\\hline
\end{tabular}
\end{table}
\end{document}

stat

Hana Ševčíková
James Raymer
Adrian E. Raftery

"Forecasting Net Migration By Age: The Flow-Difference Approach" by Hana Ševčíková, James Raymer, and Adrian E. Raftery, arXiv:2411.09878v1, 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{Values in \%. Performance of Xception and VGG11 on deepfake detection. A plus represents VIBA injection and a minus means standard model without VIBA.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Model} & \textbf{Acc.} & \textbf{Precision} & \textbf{Recall} & \textbf{ECE} \\
 \midrule
 Xception - & 81.51 & 77.49 & 89.34 & 0.1216 \\
 Xception + & 81.47 & 77.43 & 89.34 & 0.1210 \\
 VGG - & 79.96 & 75.42 & 88.95 & 0.1244 \\
 VGG + & 79.87 & 75.31 & 88.95 & 0.1246 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Veronika Solopova
Lucas Schmidt
Dorothea Kolossa

"Extending Information Bottleneck Attribution to Video Sequences" by Veronika Solopova, Lucas Schmidt, and Dorothea Kolossa, arXiv:2501.16889v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Antonio Di Crescenzo
Barbara Martinucci
Serena Spina

"Continuous-time multi-type Ehrenfest model and related Ornstein-Uhlenbeck diffusion on a star graph" by Antonio Di Crescenzo, Barbara Martinucci, and Serena Spina, arXiv:2201.03977v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Archchana Sindhujan
Diptesh Kanojia
Constantin Orasan
Shenbin Qian

"When LLMs Struggle: Reference-less Translation Evaluation for Low-resource Languages" by Archchana Sindhujan, Diptesh Kanojia, Constantin Orasan, and Shenbin Qian, arXiv:2501.04473v1, 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{Percent-delta from source language human translation with no vocabulary masking}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
Source & French & Swedish & Turkish & English \\
\midrule
English & 0.05 & -0.29 & -0.22 & \\
French & & -0.01 & 0.06 & -0.10 \\
Swedish & 0.10 & & 0.30 & 0.13 \\
Turkish & 0.81 & 0.46 & & -0.00 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Characterizing the Effects of Translation on Intertextuality using Multilingual Embedding Spaces" by Hope McGovern, Hale Sirin, and Tom Lippincott, arXiv:2501.10731v1, 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
\begin{tabular}{lrrr}
\toprule
Intervention & Referrals In & Referrals Out & Total \\
\midrule
\multicolumn{4}{l}{\textbf{Community: 1}} \\
\midrule
T: Small Molecule & 174 & 188 & 362 \\
\midrule
\multicolumn{4}{l}{\textbf{Community: 2}} \\
\midrule
I: MAbs Checkpoint & 92 & 147 & 239 \\
\midrule
\multicolumn{4}{l}{\textbf{Community: 3}} \\
\midrule
Chemotherapy & 4 & 10 & 14 \\
I: Adoptive Cell Transfer & 10 & 3 & 13 \\
I: Anti Cancer Vaccine & 4 & 7 & 11 \\
I: MAbs Co-Stimulatory & 31 & 22 & 53 \\
I: Oncolytic Virus & 4 & 5 & 9 \\
T: Antibody-Drug Conjugate & 18 & 10 & 28 \\
T: Combined & 9 & 8 & 17 \\
\midrule
\multicolumn{4}{l}{\textbf{Community: 4}} \\
\midrule
I: Combined & 54 & 22 & 76 \\
\midrule
\multicolumn{4}{l}{\textbf{Community: 5}} \\
\midrule
I: MAbs Targeting & 31 & 22 & 53 \\
\midrule
\multicolumn{4}{l}{\textbf{Community: 6}} \\
\midrule
Drug Repurposing & 7 & 3 & 10 \\
I: Other & 25 & 6 & 31 \\
T: Monoclonal Antibody & 6 & 16 & 22 \\
\midrule
\multicolumn{4}{l}{\textbf{Community: 7}} \\
\midrule
Combined Modality & 0 & 1 & 1 \\
\midrule
\multicolumn{4}{l}{\textbf{Community: 8}} \\
\midrule
Radioconjugate & 1 & 0 & 1 \\
\bottomrule
\end{tabular}
\caption{Smith-Pittman communities and identified and grouped interventions}
\end{table}
\end{document}

stat

"Centrality in Collaboration: A Novel Algorithm for Social Partitioning Gradients in Community Detection for Multiple Oncology Clinical Trial Enrollments" by Benjamin Smith, Tyler Pittman, and Wei Xu, arXiv:2411.01394v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Taban Baghfalaki
Reza Hashemi
Christophe Tzourio
Catherine Helmer
Helene Jacqmin-Gadda

"A Two-stage Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers and Competing Risk Outcomes" by Taban Baghfalaki, Reza Hashemi, Christophe Tzourio, Catherine Helmer, and Helene Jacqmin-Gadda, arXiv:2412.03797v1, 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{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
			\hline
			$n$ & $\mathcal{F}_n$ & $\mathcal{K}_n$ \\
			\hline\hline
			$4$ & $998395$ & $28750$ \\
			$6$ & $605497$ & $20320$ \\
			$8$ & $26361$ & $1264$ \\
			$10$ & $895218$ & $51527$ \\
			$12$ & $67466$ & $750$ \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On Ternary Diophantine Equations of Signature $(p,p,3)$ over Number Fields" by Erman Isik, Yasemin Kara, and Ekin Ozman, arXiv:2201.13270v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Cell A* vs. Local hybrid A*}
\begin{tabular}{|c|c|c|c|c|}
\hline
{Algorithm} & {Goal state} & {Time (sec)} & {Path Length (m)} & {Average Nodes}\\
\hline
{} & (6, 60) & 40.53 & 80.01 & 30.5 \\
{Cell A*} & (15, 50) & 46.33 & 91.93 & 27.79 \\
{} & (30, 43) & 43.67 & 87.08 & 27.96 \\
\hline
{} & (6, 60) & 47.43 & 86.5 & 31.1 \\
{Local Hybrid A*} & (15, 50) & Failed & Failed & Failed \\
{} & (30, 43) & 53.293 & 91.5 & 29.56 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Wenjian Hao
Rongyao Wang
Alexander Krolicki
Yiqiang Han

"Cell A* for Navigation of Unmanned Aerial Vehicles in Partially-known Environments" by Wenjian Hao, Rongyao Wang, Alexander Krolicki, and Yiqiang Han, arXiv:2009.07404v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Alessandro Brusaferri
Matteo Matteucci
Stefano Spinelli
Andrea Vitali

"Probabilistic electric load forecasting through Bayesian Mixture Density Networks" by Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli, and Andrea Vitali, arXiv:2012.14389v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Algorithm 1. BCD-based Algorithm for JDPEW}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \# & \textbf{Procedures} \\
 \midrule
 1: & \textbf{Input:} Coefficients $\lambda_j$, $\mathbb{I}_{ik}$, $P^{0}_{kj}$, $d_h$, $f_{kj}$, $c_{jk}$, $\theta$ \\
 2: & \textbf{Initialization:} Initial price discount of EW products $z_{ih}^0$\\
 3: & \textbf{Repeat:} \\
 4: & $\space$ $\space$ Calculate $x_{ij}^*$ and $y_{i}^*$ based on EW design problem given $p_{ij}^{[k-1]}$ and $z_{ih}^{[k-1]}$ $\hspace{1.65em}$ (STEP-1)\\
 5: & $\space$ $\space$ Set $x_{ij}^{[k]} := x_{ij}^*$ and $y_{i}^{[k]} := y_{i}^*$\\
 6: & $\space$ $\space$ Calculate $z_{ih}^*$ and $p_{ij}^*$ based on EW pricing problem given $x_{ij}^{[k]}$ and $y_{i}^{[k]}$ $\hspace{3em}$ (STEP-2)\\
 7: & $\space$ $\space$ Set $z_{ih}^{[k]} = z_{ih}^*$, $p_{ij}^{[k]} = p_{ij}^*$, and $k:=k+1$\\
 8: & \textbf{Until: }$x_{ij}^*$, $y_{i}^*$, $p_{ij}^*$ and $z_{ih}^*$ are all convergent \\
 9: & \textbf{Output:} Solutions $x_{ij}^*$, $y_{i}^*$, $p_{ij}^*$ and $z_{ih}^*$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 \toprule
 \textbf{Statistic} & \textbf{Value} \\
 \midrule
 Std of cross-attention & 0.826 \\
 Std of self-attention & 0.334 \\
 Mean ratio of each layer's attention std & 3.210 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\textbf{Statistics for attention score distribution.}}
\end{table}
\end{document}

cs

"DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models" by Hyogon Ryu, NaHyeon Park, and Hyunjung Shim, arXiv:2501.04304v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mohammad Kaviul Anam Khan
Olli Saarela
Rafal Kustra

"Marginal and Conditional Importance Measures from Machine Learning Models and Their Relationship with Conditional Average Treatment Effect" by Mohammad Kaviul Anam Khan, Olli Saarela, and Rafal Kustra, arXiv:2501.16988v2, 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{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
			\hline\hline

			$n$ & $\mathcal{F}_n$ & $\mathcal{G}_n$ & $\mathcal{K}_n$ \\
			\hline\hline
			$3$ & $8600$ & $3488$ & $3046$ \\
			$4$ & $1243$ & $1$ & $1$ \\
			$5$ & $23$ & $13$ & $13$ \\
			\hline\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comprehensive Performance Comparison of Different Algorithms}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccr}
\toprule
\multirow{2}{*}{Algorithm} & \multirow{2}{*}{Dataset} & \multirow{2}{*}{Purity} & \multirow{2}{*}{NMI} & Training Time & Memory/Params \\
 & & & & (min) & \\
\midrule
\multirow{2}{*}{GTENN} & LFR2 & \textbf{0.937} & \textbf{0.949} & 12.3 & 2.1GB/48K \\
 & PS & \textbf{0.927} & \textbf{0.901} & 8.5 & 1.8GB/48K \\
\multirow{2}{*}{NWFCM} & LFR2 & 0.721 & 0.698 & 6.2 & 0.8GB/12K \\
 & PS & 0.704 & 0.681 & 4.1 & 0.6GB/12K \\
\multirow{2}{*}{ASTGCN} & LFR2 & 0.834 & 0.821 & 28.6 & 4.5GB/156K \\
 & PS & 0.798 & 0.785 & 18.7 & 3.8GB/156K \\
\multirow{2}{*}{MSTGCN} & LFR2 & 0.856 & 0.843 & 45.3 & 6.8GB/243K \\
 & PS & 0.841 & 0.827 & 32.1 & 5.9GB/243K \\
\multirow{2}{*}{ST-GAT} & LFR2 & 0.812 & 0.795 & 22.4 & 3.7GB/128K \\
 & PS & 0.789 & 0.773 & 16.2 & 3.1GB/128K \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"GTENN: A Spatiotemporal Graph Neural Framework for Community Discovery in Dynamic Social Networks" by Shuangshuang Chu and Yingnan Xu, arXiv:2501.12208v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 \hline
 \bf{Token Allocation} & {\bf{VLM AVG}} \ \\
 \hline
 Random & 53.83 \\
 GBPR & 54.37 \\
 Img-GBPR & \bf{54.92} \\

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

cs

Miao Rang
Zhenni Bi
Chuanjian Liu
Yehui Tang
Kai Han
Yunhe Wang

"Eve: Efficient Multimodal Vision Language Models with Elastic Visual Experts" by Miao Rang, Zhenni Bi, Chuanjian Liu, Yehui Tang, Kai Han, and Yunhe Wang, arXiv:2501.04322v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
 \hline Method & Acc \\
\hline Our & 95.08 \\
\hline Cloudnet & \ 90.00 \\
\hline & 80.00\\
\hline MMI & 75.42\\
\hline M\textunderscore DF & 78.21\\
\hline M\textunderscore JFCNN & 84.55\\
\hline V\textunderscore DF & 85.10\\
\hline V\textunderscore JFCNN & 86.79\\
\hline V\textunderscore DF + MMI & 86.33 \\
\hline V\textunderscore JFCNN + MMI & 89.40\\
\hline V\textunderscore DF + M\textunderscore DF & 90.21 \\
\hline J\textunderscore JFCNN & 78.82\\
\hline JFCNN & 93.37 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Voting in Transfer Learning System for Ground-Based Cloud Classification" by Mario Manzo and Simone Pellino, arXiv:2103.04667v1, 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{Datasets and models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule
 Datasets & Length & Dimensions & Models\\
 \midrule
 MNIST & 60,000 & 28×28 & 2-layer-CNN \\
 CIFAR-10 & 50,000 & 32×32×3 & ResNet-18 \\
 CIFAR-100 & 50,000 & 32×32×3 & ResNet-18 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning" by Zitong Li, Qingqing Ye, and Haibo Hu, arXiv:2501.16614v1, 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{Statistics of the TAG datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule[1.5pt]
 Dataset & \#Nodes & \#Edges & \#Classes \\ \hline
 Cora & 2,708 & 5,429 & 7 \\
 PubMed & 19,717 & 44,338 & 3 \\
 OGB-Arxiv & 169,343 & 1,166,245 & 40 \\
 OGB-Products (subset) & 54,025 & 74,420 & 47 \\
 Amazon-Sports & 173,055 & 1,946,555 & 13 \\
 Amazon-Computers & 87,229 & 721,107 & 10 \\
 Amazon-Photo & 48,362 & 500,939 & 12 \\
 Amazon-Children & 76,875 & 1,631,453 & 24 \\
 Amazon-History & 41,551 & 358,574 & 13 \\
 \bottomrule[1.5pt]

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

cs

Yuanfu Sun
Zhengnan Ma
Yi Fang
Jing Ma
Qiaoyu Tan

"GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design" by Yuanfu Sun, Zhengnan Ma, Yi Fang, Jing Ma, and Qiaoyu Tan, arXiv:2501.15755v1, 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{\textbf{Process of converting SHA in iFormer towards SHViT.} Intermediate models are only measured by latency.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
			\toprule
			Modification & Params(M) & GMACs & Latency (ms) & Top-1(\%) \\
			\toprule
			SHA Baseline without Modulation & 9.9M & 1758M & 1.12ms & 79.4 \\
			+ split & 9.9M & 1758M & 1.18ms & - \\
			+ attention on 1/4 channels & 8.3M & 1547M & 1.02ms & - \\
			+ concat & 8.7M & 1579M & 1.11ms & 79.5 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"iFormer: Integrating ConvNet and Transformer for Mobile Application" by Chuanyang Zheng, arXiv:2501.15369v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean Dice Coefficients and 95th Percentile Hausdorff Distance (HD95) across all datasets for various point number strategies }
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Number of Points} & \multicolumn{4}{|c|}{\textbf{Dice Coefficient (Mean ± Standard Deviations)}} \\
\cline{2-5}
& \textbf{Ovarian Tumor} & \textbf{Renal Tumor} & \textbf{Lung Tumor} & \textbf{Breast Tumor} \\
\hline
1 & 0.272 ± 0.075 & 0.300 ± 0.057 & 0.389 ± 0.098 & 0.512 ± 0.070 \\
\hline
2--4 & 0.597 ± 0.129 & 0.687 ± 0.113 & 0.736 ± 0.045 & 0.780 ± 0.061 \\
\hline
5 or more & 0.806 ± 0.084 & 0.708 ± 0.108 & 0.761 ± 0.040 & 0.790 ± 0.062 \\
\hline
\multicolumn{5}{|c|}{\textbf{HD95 (mm, Mean ± Standard Deviations)}} \\
\hline
1 & 32.751 ± 6.179 & 23.616 ± 5.850 & 14.596 ± 3.004 & 26.992 ± 5.157 \\
\hline
2--4 & 17.412 ± 6.327 & 4.703 ± 2.415 & 6.930 ± 2.262 & 12.662 ± 4.460 \\
\hline
5 or more & 8.450 ± 4.622 & 4.740 ± 2.630 & 5.815 ± 1.823 & 10.859 ± 3.862 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

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

"Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities" by Yuli Wang, Victoria Shi, Wen-Chi Hsu, Yuwei Dai, Sophie Yao, Zhusi Zhong, Zishu Zhang, Jing Wu, Aaron Maxwell, Scott Collins, Zhicheng Jiao, and Harrison X. Bai, arXiv:2412.17943v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Estimates of ACE obtained using missing data methods in the case study ($n=961$)}
\begin{tabular}{cccc}
\hline
Approach & Estimate & Standard error & 95\% Confidence interval \\ \hline
\multicolumn{4}{l}{Missingness assumption (i) null} \\ \hline
naïve NARFCS & 0.22 & 0.13 & (-0.03, 0.47) \\
NAR-SMCFCS & 0.19 & 0.14 & (-0.09, 0.47) \\
NAR-SMC-stack & 0.18 & 0.12 & (-0.05, 0.42) \\ \hline
\multicolumn{4}{l}{Missingness assumption (ii) $0.5M_Y$} \\ \hline
naïve NARFCS & 0.26 & 0.13 & (0.01, 0.52) \\
NAR-SMCFCS & 0.24 & 0.13 & (-0.03, 0.50) \\
NAR-SMC-stack & 0.24 & 0.12 & (0.00, 0.47) \\ \hline
\multicolumn{4}{l}{Missingness assumption (iii) $0.3M_Y+0.2M_YX$} \\ \hline
naïve NARFCS & 0.29 & 0.12 & (0.04, 0.53) \\
NAR-SMCFCS & 0.26 & 0.14 & (-0.01, 0.54) \\
NAR-SMC-stack & 0.26 & 0.12 & (0.02, 0.49) \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Jiaxin Zhang
S. Ghazaleh Dashti
John B. Carlin
Katherine J. Lee
Jonathan W. Bartlett
Margarita Moreno-Betancur

"Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference" by Jiaxin Zhang, S. Ghazaleh Dashti, John B. Carlin, Katherine J. Lee, Jonathan W. Bartlett, and Margarita Moreno-Betancur, arXiv:2411.13829v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
 \toprule
 \multirow{2}{*}{Problem Classification} & \multicolumn{2}{c}{Parallelization Strategy} \\
 & Over Data & Over Particles \\
 \midrule
 Dataset size & Large & Small \\
 Number of particles & Small & Large \\
 Seed Distribution & Sparse & Dense \\
 Vector Field Complexity & No critical & No circular \\
 & points & field \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Abhishek Yenpure
Sudhanshu Sane
Roba Binyahib
David Pugmire
Christoph Garth
Hank Childs

"A Guide to Particle Advection Performance" by Abhishek Yenpure, Sudhanshu Sane, Roba Binyahib, David Pugmire, Christoph Garth, and Hank Childs, arXiv:2201.08440v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Combining Evidence

"Combining Evidence" by Michael Evans and Yang Jian Guo, arXiv:2202.02922v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Manuel Sommer
Nora Fenske
Christian Heumann
Peter Scholz-Kreisel
Felix Heinzl

"Methods to derive uncertainty intervals for lifetime risks for lung cancer related to occupational radon exposure" by Manuel Sommer, Nora Fenske, Christian Heumann, Peter Scholz-Kreisel, and Felix Heinzl, arXiv:2412.06054v1, 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{Values of the BIC statistic for models under consideration, where the Poisson and negative-binomial distributions model the full data and the binomial distribution models the truncated data. Preferred models are indicated in bold text. }
\begin{tabular}{lrrr}
\toprule
 Distribution & Linear predictor & Log-likelihood & BIC \\ \hline
 & \textbf{1} & \textbf{-23.7} & \textbf{50.7} \\
 & 2 & -23.4 & 53.4 \\
Poisson & 3 & -23.0 & 52.6 \\
 & 4 & -23.0 & 55.9 \\
 \textit{(Full data)} & 5 & -22.7 & 58.6 \\\hline
 & 1 & -23.7 & 54.0 \\
 & 2 & -23.4 & 56.7 \\
Negative-binomial & 3 & -23.0 & 55.9 \\
 & 4 & -23.0 & 59.2 \\
 \textit{(Full data)} & 5 & -23.7 & 61.9 \\\hline \hline
 & \textbf{1} & \textbf{-7.8} & \textbf{18.6} \\
 & 2 & -7.0 & 20.2 \\
Binomial & 3 & -7.8 & 21.6 \\
 & 4 & -7.0 & 23.2 \\
 \textit{(Truncated data)} & 5 & -5.7 & 23.5 \\\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Performance of capture-recapture population size estimators under covariate information" by Layna Charlie Dennett and Dankmar Böhning, arXiv:2312.08391v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Use of algebraic dual representations in domain decomposition methods for Darcy flow in 3D domain" by V. Jain, A. Palha, and M. Gerritsma, arXiv:2202.10073v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A Doubly Adaptive Penalty Method for the Navier Stokes Equations" by Kiera Kean, Xihui Xie, and Shuxian Xu, arXiv:2201.03978v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Signal parameters were drawn from random distributions for each simulated event.}
\begin{tabular}{ccc}
\hline\hline
& FM Sweep & Pulse train \\
\hline
Duration/Width (s) & $T\in \mathcal{U}(0.2,0.4)$ & $\tau=0.005$ \\
Delay (s) & $t_0\in\mathcal{U}(-0.1,0.1)$ & $t_0\in\mathcal{U}(-0.1,0.1)$ \\
Frequency (Hz) & $f_0 \in\mathcal{U}(100,400)$ & $f_c=200$\\
& $\Delta f\in\mathcal{U}(-150,150)$ & \\
Peak spacing (s) & & $\Delta t \in 0.47$*beta$(4,23)$\\
Number of peaks & & $N_p\in\lfloor 13$*beta$(3.5,8)\rfloor$\\
SNR (dB) & $SNR\in\mathcal{U}(15,30)$ & $SNR\in\mathcal{U}(0,30)$ \\
\hline\hline
\end{tabular}
\end{table}
\end{document}

eess

Emma Ozanich
Aaron Thode
Peter Gerstoft
Lauren A. Freeman
Simon Freeman

"Deep embedded clustering of coral reef bioacoustics" by Emma Ozanich, Aaron Thode, Peter Gerstoft, Lauren A. Freeman, and Simon Freeman, arXiv:2012.09982v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Xinzhi Zhang
Leslie A Hulvershorn
Todd Constable
Yize Zhao
Selena Wang

"Cost efficiency of fMRI studies using resting-state vs task-based functional connectivity" by Xinzhi Zhang, Leslie A Hulvershorn, Todd Constable, Yize Zhao, and Selena Wang, arXiv:2411.01092v1, 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{Case A: The values of control effort consumed by different guidance laws with different impact times.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
 & $J_{ITCG}$~($m^2/s^3$) & $J_{FTNOG}$~($m^2/s^3$) & $J^*$($m^2/s^3$) \\ %& Relative Error
\hline
$t_g = 25~s$ & $2.1434\times10^4$ & $ 2.1371\times10^4$ & $ 2.1350\times10^4$ \\ % & $8.1192\%$ \\
\hline
$t_g = 30~s$ & $3.1057\times10^4$ &$3.0624\times10^4$ & $3.0563\times10^4$ \\ % & $0.2536\%$
\hline
$t_g = 40~s$ & $1.3487\times10^5$ & $ 3.8912\times10^4$ & $3.8738\times10^4$ \\% & $0.3604\%$
\hline
$t_g = 50~s$ &NA & $3.9678\times10^4$ & $3.9625\times10^4$ \\% & $0.4804\%$
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Nonlinear Optimal Guidance for Fixed-Time Impact on a Stationary Target" by Kun Wang, Zheng Chen, Han Wang, and Jun Li, arXiv:2201.02021v1, 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}{lcccccccc}
 \toprule
 & $i=1$ & $i=2$ & $i=3$ & $i=4$ & $i=5$ & $i=6$ & $i=7$ & $i=8$ \\
 \midrule
 $\widehat{ep}_{i1}$ & 0.30 & 0.21 & 0.25 & 0.42 & 0.27 & 0.23 & 0.18 & 0.17 \\
 $\widehat{ep}_{i2}$ & 0.30 & 0.21 & 0.25 & 0.42 & 0.27 & 0.23 & 0.18 & 0.17 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On Carbon Taxes Effectiveness to Induce a Clean Technology Transition: An Evaluation Framework Based on Optimal Strategic Capacity Planning" by N. Wolf, P. Escalona, A. Angulo, and J. Weston, arXiv:2202.11507v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Recognition accuracy on the COIL-$100$ database.}
\begin{tabular}{|c|c|}
\hline
\textbf{Method} & \textbf{Accuracy}\\
\hline
$LLP$+\textbf{ARSRG}$emb$ & \textbf{99.55}\%\\
\hline
LLP+BoW & 51.71\%\\
\hline
gdFil & 32.61\%\\
\hline
VFSR& 91.60\%\\
\hline
APGM & 99.11\%\\
\hline
VEAM & 99.44\%\\
\hline
DTROD-AdaBoost & 84.50\%\\
\hline
RSW+Boosting & 89.20\%\\
\hline
Sequential Patterns & 89.80\%\\
\hline
LAF & 99.40\%\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Attributed Relational SIFT-based Regions Graph (ARSRG): concepts and applications" by Mario Manzo, arXiv:1912.09972v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
 & Sheep & Rabbit & Devil \\
 \hline
Sheep & X & X & X \\
Rabbit & X & - & - \\
Devil & X & - & X
\end{tabular}
\end{adjustbox}
\caption{Class Matrix}
\end{table}
\end{document}

cs

Ewnetu Bayuh Lakew
Petter Svärd
Erik Elmroth
Johan Tordsson

"Optimising Virtual Resource Mapping in Multi-Level NUMA Disaggregated Systems" by Ewnetu Bayuh Lakew, Petter Svärd, Erik Elmroth, and Johan Tordsson, arXiv:2501.01356v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Regression predictor training}
\begin{tabular}{llll}
\toprule
Model & Total Epochs & Best Validation Loss & Best Validation MAPE (\%) \\ \midrule
(a2) & \cellcolor[HTML]{C0C0C0}123 & \cellcolor[HTML]{C0C0C0}0.0317 & \cellcolor[HTML]{C0C0C0}5.277 \\
(b2) & 126 & 0.0277 & 4.082 \\
(c2) & \cellcolor[HTML]{C0C0C0}84 & \cellcolor[HTML]{C0C0C0}0.0497 & \cellcolor[HTML]{C0C0C0}9.591 \\
(d2) & 65 & 0.0420 & 7.354 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

James Daniell
Kazuma Kobayashi
Ayodeji Alajo
Syed Bahauddin Alam

"Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction" by James Daniell, Kazuma Kobayashi, Ayodeji Alajo, and Syed Bahauddin Alam, arXiv:2211.13157v4, 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{Computation costs (second) $|B|=2$ KB}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
	\hline
		Building Block & \textit{CP} & $\textit{CS}_1$ & $\textit{CS}_2$ & $E_{r=8}$ \& $S_{r=8}$ \\
	\hline
		\textsf{BlockRangeRet} & $0.1$ & $0.031$ & $0.028$ & $1.5$\\
	\hline
		\textsf{PerReFunc} & $8.5$ & $2.2$ & $2.7$ & $0.2$ \\
	\hline
		\textsf{PathRangeRet} & $6.7$ & $1.07$ & $1.3$ & $1.7$ \\
	\hline
		\textsf{PriRangeEvict} & $5.3$ & $368.5$ & $369.5$ & $0.2$ \\
	\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Viet Vo
Shangqi Lai
Xingliang Yuan
Surya Nepal
Qi Li

"OblivCDN: A Practical Privacy-preserving CDN with Oblivious Content Access" by Viet Vo, Shangqi Lai, Xingliang Yuan, Surya Nepal, and Qi Li, arXiv:2501.07262v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Maxyn Leitner
Rebecca Dorn
Fred Morstatter
Kristina Lerman

"Characterizing Network Structure of Anti-Trans Actors on TikTok" by Maxyn Leitner, Rebecca Dorn, Fred Morstatter, and Kristina Lerman, arXiv:2501.16507v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of sizes of quantities in the assumptions - for various solvers as given in . In the table, $f$ denotes a generic function, $n$ is the problem dimension, $k$ is the maximum number of GMRES iterations in any refinement step, $\rho_n$ is the growth factor in LU, and $\tilde{A}=\widehat{U}^{-1}\widehat{L}^{-1}A$ for the computed factors $\widehat{L}$ and $\widehat{U}$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
 & SIR & GMRES-IR ($u_g=u, u_p=u^2$) & GMRES-IR ($u_g=u_p=u$) \\ \hline
$u_s$ & $u_f$ & $u$ & $u_g+u_p\kappa_\infty(A)$ \\
$\Vert E_i\Vert_\infty$ & $f(n,\rho_n)$ & $f(n,k,\rho_n)\kappa_\infty(\tilde{A})$ & $f(n,k,\rho_n)\kappa_\infty(\tilde{A})$ \\
$c_1$ & $f(n,\rho_n)$ & $f(n,k,\rho_n)\Vert \tilde{A}\Vert_\infty$ & $f(n,k,\rho_n)\Vert \tilde{A}\Vert_\infty$ \\
$c_2$ & $f(n,\rho_n)$ & $f(n,k,\rho_n)\kappa_\infty(A)$ & $f(n,k,\rho_n)\kappa_\infty(A)$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Mixed Precision Iterative Refinement with Sparse Approximate Inverse Preconditioning" by Erin Carson and Noaman Khan, arXiv:2202.10204v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Models Prediction and Compression Performance}
\begin{tabular}{lrrr}
Dataset & Acc@1 {[}\%{]} & Bpp (SVBI) & Bpp (DVIB) \\ \hline
MNIST & 97.36 $\pm$ 1.77 & 0.0829 & 0.0161 \\
\rowcolor[HTML]{EFEFEF}
CIFAR-10 & 85.25 $\pm$ 1.40 & 0.5677 & 0.0308 \\
SVHN & 94.04 $\pm$ 0.69 & 0.4321 & 0.0086 \\
\rowcolor[HTML]{EFEFEF}
ImageNet64 & 49.36 $\pm$ 1.12 & 1.2673 & 0.0115 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Alireza Furutanpey
Pantelis A. Frangoudis
Patrik Szabo
Schahram Dustdar

"Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication" by Alireza Furutanpey, Pantelis A. Frangoudis, Patrik Szabo, and Schahram Dustdar, arXiv:2412.10265v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Closed-loop comparison $N = 12$.}
\begin{tabular}{c|ccccc}
		\hline
		 & mean & median & min & max & std \\
		$\sum_k \Delta J$ & 0.371 & 0 & 0 & 35.970 & 1.808 \\
		Time (s) & 0.0323 & 0.0152 & 0.00361 & 0.315 & 0.0362 \\
		Time (s) & 0.00143 & 0.00125 & 0.000605 & 0.0517& 0.00231
	\end{tabular}
\end{table}
\end{document}

eess

Samuel Mallick
Azita Dabiri
Bart De Schutter

"Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees" by Samuel Mallick, Azita Dabiri, and Bart De Schutter, arXiv:2412.00490v2, 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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Confusion matrices (in percentages) in the test samples of binary classifications. Here $0$ is normal and $1$ and is not normal.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccc|ccc|ccc|ccc|}
\hline
 & \multicolumn{12}{|c|}{ Labels} \\ \cline{2-13}
 & \multicolumn{3}{|c|}{ Random Forest} &\multicolumn{3}{|c|}{ SVM }& \multicolumn{3}{|c|}{N-S-W} & \multicolumn{3}{|c|}{$\textrm{RP}_{10000}$} \\ \cline{2-13}
 & & \textbf{0} & \textbf{1} & & \textbf{0} & \textbf{1} & & \textbf{0} & \textbf{1} & & \textbf{0} & \textbf{1} \\ \hline
\multirow{2}{*}{Pred.} & \textbf{0} & 5.9 & 2.1& \textbf{0}& 6.4 & 1.6& \textbf{0}& 5.3 & 2.7 & \textbf{0} & 5.1 & 2.9 \\
& \textbf{1} & 20.9 & 71.1 & \textbf{1}& 22.5 & 69.5 & \textbf{1} & 16.6 & 75.4 & \textbf{1} & 15.5 & 76.5 \\ \cline{1-13}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Ricardo Fraiman
Leonardo Moreno
Thomas Ransford

"A quantitative Heppes Theorem and multivariate Bernoulli distributions" by Ricardo Fraiman, Leonardo Moreno, and Thomas Ransford, arXiv:2201.07628v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The comparison between PBAN and spatial/channel attention on the CVIU database, with the best performance values highlighted in bold. The “spatial atten" and “channel atten" pool the channel and spatial dimensions to 1, respectively. The Flops is calculated with input tensor shape $3\times32\times32$.}
\begin{tabular}{c|ccc|cccc}
\toprule
Model & Params/M & Flops/G & Speed/ms & SRCC $\uparrow$ & KRCC $\uparrow$ & PLCC $\uparrow$ & RMSE $\downarrow$ \\
\midrule
Channel Atten & 2.222 & 0.181 & 11.284 & 0.963 & 0.836 & 0.964 & 0.716 \\
Spatial Atten & 2.222 & 0.181 & 11.099 & 0.913 & 0.751 & 0.917 & 1.062 \\
PBAN & 2.222 & 0.181 & 11.292 & \textbf{0.978} & \textbf{0.872} & \textbf{0.981} & \textbf{0.397} \\
\bottomrule
\end{tabular}
\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.


stat

"IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation" by Taejong Joo and Diego Klabjan, arXiv:2310.10611v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
Method & $A$ & $B$ & $W$\\
\hline
PCA & $\Sigma_{XX}$ & $\mathbf{I}$ & $\begin{pmatrix}W\end{pmatrix}$\\
\hline
LDA & $\mathbf{S}_B$ & $\mathbf{S}_W$ & $\begin{pmatrix}W\end{pmatrix}$\\
\hline
CCA & $\begin{pmatrix} \Sigma_{XX} & \Sigma_{XY} \\ \Sigma_{YX} & \Sigma_{YY} \end{pmatrix}$ & $\begin{pmatrix} \Sigma_{XX} & 0 \\ 0 & \Sigma_{YY} \end{pmatrix}$ & $\begin{pmatrix} U \\ V \end{pmatrix}$\\
\hline
PLS & $\begin{pmatrix} 0 & \Sigma_{XY} \\ \Sigma_{YX} & 0 \end{pmatrix}$ & $\mathbf{I}$ & $\begin{pmatrix} U \\ V \end{pmatrix}$ \\
\hline
\end{tabular}
\caption{Definitions of $A$ and $B$ for different subspace learning methods. $\mathbf{S}_B$ and $\mathbf{S}_W$ are the between and within class scatter matrices. With the exception of PLS, $\textbf{A}$ is always positive semi-definite.}
\end{table}
\end{document}

stat

James Chapman
Lennie Wells
Ana Lawry Aguila

"Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning" by James Chapman, Lennie Wells, and Ana Lawry Aguila, arXiv:2310.01012v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{List of input parameters and binary variables of ILP program.}
\begin{tabular}{ll}
 \toprule
 \textbf{Input} & \textbf{Meaning}\\
 \midrule
 \footnotesize
 $R,N$ & Set of robots and time slots, respectively. \\
 $B,I$ & Set of BSs and RISs, respectively.\\
 $M_{i,n}$ & Set of subsets $m$ with robots conflicted \\
 & at RIS $i$ at time $n$.\\
 $U$ & Robots simultaneously allocated at RIS.\\
 $\Psi_r$ & SINR threshold for robot $r$.\\
 $\xi$ & Interference on robot $r$ at $n$ caused by\\
 & comm. between BS $b$ / RIS $i$ and robot $r^\prime$, \\
 & $\xi=\{\xi_{b,r\prime r,n},\xi_{i,r\prime r,n}\}$.\\
 $P$ & Power with no gain obtained at robot $r$\\
 & via BS $b$ / RIS $i$ at $n$, $P=\{P_{b,r,n},P_{i,r,n}\}$.\\
 $G$ & Antenna gain of BS $b$ / robot $r$, $G=\{ G_b,G_r\}$.\\
 $P_o$ & Noise power.\\
 $D$ & RIS configuration time.\\
 $K_r$ & Outage time slot threshold for robot $r$.\\
 $\mu$ & Large constant, e.g., $\mu=10^9$.\\
\toprule
 \textbf{Variables} & \textbf{Meaning}\\
 \midrule
 $X$ & Allocation of BS $b$ / RIS $i$ to robot $r$ at $n$, \\
 & $X=0/1$: unallocated/allocated, \\
 & $X=\{X_{b,r,n},X_{i,r,n}\}$.\\
$W_{i,r,n}$ & Allocation of RIS $i$ to robot $r$ at time $n$\\
 & as well as availability of RIS $i$ at time $n$, \\
 & $W_{i,r,n}=0/1$: unallocated/allocated.\\
 $O_{r,n}$ & Connection status of robot $r$ at time $n$,\\
 & $O_{r,n}=0/1$: connection/outage.\\
$Y_{i,r,n}$ & Allocation history of RIS $i$ to robot $r$ at $n$,\\
 & $Y_{i,r,n}=0/1$: unallocated/allocated \\
 & at least once during $[n-D+1,n]$.\\
$C_{i,n}$ & Availability status of RIS $i$ at time $n$,\\
 & $C_{i,n}=0/1$ : available/unavailable.\\
$Z$ & Dummy variable: $Z=\{Z_{b,r,n},Z_{i,r,n}\}=0/1$.\\
\bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Cao Vien Phung
Max Franke
Ehsan Tohidi
June Heinemann
Andre Drummond
Stefan Schmid
Slawomir Stanczak
Admela Jukan

"An Optimization Driven Link SINR Assurance in RIS-assisted Indoor Networks" by Cao Vien Phung, Max Franke, Ehsan Tohidi, June Heinemann, Andre Drummond, Stefan Schmid, Slawomir Stanczak, and Admela Jukan, arXiv:2412.20254v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Errors of delta family approach with number of time steps $N = 2000$ (for Example ).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}\\
\hline
 \multirow{2}{*}{$M$} & \multicolumn{2}{c}{Errors} &\multirow{2}{*}{CPU time (Seconds)}\\
\cline{2-3}
 & Prime value & Optimal strategy \\
\cline{1-4}
6 &2.95e-4	&6.08e-2 	&2.729 \\
\cline{1-4}
8 &8.50e-5 	&9.46e-3 	&3.152 \\
\cline{1-4}
10 &2.97e-5 	&2.93e-3 	&3.956 \\
\cline{1-4}
12 &7.48e-6	&1.94e-3 	&7.356 \\
\cline{1-4}
14 &2.73e-6 	&1.12e-3 	&9.107 \\
\cline{1-4}
16 &1.31e-6 	&4.84e-4 	&14.294 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Delta family approach for the stochastic control problems of utility maximization" by Jingtang Ma, Zhengyang Lu, and Zhenyu Cui, arXiv:2202.12745v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{xcolor}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}%{p{1.3cm}p{0.9cm}p{1.3cm}p{1.3cm}p{1.3cm}p{1.3cm}p{1.3cm}p{1.3cm}p{1.3cm}} %
\toprule
 & & \multicolumn{7}{r}{} \\
 & Method & FP (ours) & RF & LS & FF & PC & DTP & BP (ref.)\\
Dataset & Metric & & & & & & & \\
\midrule
\multirow[t]{2}{*}{FMNIST MLP}& AUC & 98.3 ± 0.0 & 98.0 ± 0.0 & 98.7 ± 0.1 & 98.5 ± 0.1 & 98.2 ± 0.1 & 97.8 ± 0.2 & 99.0 ± 0.0 \\
 & Acc & 86.3 ± 0.1 & 84.0 ± 0.2 & 83.1 ± 2.0 & 85.6 ± 0.8 & 83.0 ± 0.4 & 79.3 ± 2.0 & 88.4 ± 0.3 \\
\multirow[t]{2}{*}{Promoters 1d-CNN}& AUC & 88.7 ± 0.5 & 80.0 ± 1.1 & 86.6 ± 1.1 & 83.5 ± 0.7 & 79.7 ± 1.7 & 78.8 ± 0.8 & 94.1 ± 0.3 \\
 & Acc & 81.8 ± 0.5 & 72.2 ± 1.4 & 79.7 ± 1.2 & 75.3 ± 3.6 & 70.5 ± 3.6 & 69.3 ± 3.3 & 87.4 ± 0.7 \\
\multirow[t]{2}{*}{PTBXL-MI 1d-CNN}& AUC & 95.5 ± 0.5 & 94.7 ± 1.8 & 97.3 ± 0.3 & 89.3 ± 3.8 & 86.0 ± 0.9 & 86.4 ± 2.2 & 99.3 ± 0.0 \\
 & Acc & 86.5 ± 1.1 & 83.4 ± 2.1 & 86.1 ± 4.0 & 69.4 ± 8.2 & 64.2 ± 5.0 & 70.1 ± 5.8 & 94.9 ± 0.6 \\
\multirow[t]{2}{*}{CIFAR 2d-ViT}& AUC & 91.5 ± 0.5 & 83.1 ± 0.5 & & & & & 96.1 ± 0.4 \\
 & Acc & 83.5 ± 0.5 & 75.9 ± 0.6 & & & & & 89.2 ± 0.7 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{\textcolor{black}{Test performance of various learning methods across different datasets. The FMNIST dataset was modelled with a multi-layer perception; Promoters and PTBXL-MI datasets were modelled using 1D convolutional neural networks (1D-CNNs); and CIFAR was modelled using vision transformers. BP: bacpropagation; DTP: Difference Target Propagation; FF: Forward Forward; FP: Forward Projection; LS: Local Supervision; PC: Predictive Coding; RF: random features}}
\end{table}
\end{document}

cs

"Closed-Form Feedback-Free Learning with Forward Projection" by Robert O'Shea and Bipin Rajendran, arXiv:2501.16476v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
 Model & MSE & Mean Active Parameters \\
 \hline
 GNAR(1, 1) & 7.847(2.908) & 2 \\
 Sparse VAR(1) & 7.686(2.940) & 451 \\
 Res. VAR(1) & 9.149(3.068) & 1840 \\
 VAR(1) & 11.371(3.525) & 19600 \\
 AR(1) & 7.816(2.931) & 140 \\
 \end{tabular}
\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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average results for both architectures of G1 on the generated IR images.}
\begin{tabular}{lllll}
\hline\noalign{\smallskip}
Model & EN & CC & PSNR & SSIM \\
\noalign{\smallskip}\hline\noalign{\smallskip}
Original & 9.9158 & 0.8593 & 17.9341 & 0.5506 \\
U-Net & 9.9474 & 0.9203 & 19.9473 & 0.7541 \\
\noalign{\smallskip}\hline
\end{tabular}
\end{table}
\end{document}

eess

J. F. Ciprián-Sánchez
G. Ochoa-Ruiz
M. Gonzalez-Mendoza
L. Rossi

"FIRe-GAN: A novel Deep Learning-based infrared-visible fusion method for wildfire imagery" by J. F. Ciprián-Sánchez, G. Ochoa-Ruiz, M. Gonzalez-Mendoza, and L. Rossi, arXiv:2101.11745v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Coloring distance graphs on the plane

Joanna Chybowska-Sokół
Konstanty Junosza-Szaniawski
Krzysztof Węsek

"Coloring distance graphs on the plane" by Joanna Chybowska-Sokół, Konstanty Junosza-Szaniawski, and Krzysztof Węsek, arXiv:2201.04499v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Spatially Adaptive Variable Screening in Presurgical fMRI Data Analysis" by Yifei Hu and Xinge Jessie Jeng, arXiv:2312.13482v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluation metrics for binary classification on the NSL-KDD dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\hline
\textbf{Model} & \textbf{Accuracy (\%)} & \textbf{Precision (\%)} & \textbf{Recall (\%)} & \textbf{F1-Score (\%)} \\
\midrule
\hline
\rowcolor{green!20} \textbf{LENS-XAI Teacher} & \textbf{91.62} & \textbf{91.64} & \textbf{91.62} & \textbf{91.61} \\
\rowcolor{green!20} \textbf{LENS-XAI Student} & \textbf{90.89} & \textbf{91.01} & \textbf{90.89} & \textbf{90.87} \\
\hline
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity" by Muhammet Anil Yagiz and Polat Goktas, arXiv:2501.00790v2, 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[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Type-specific accuracy (ACC) and time F1-score}
\begin{tabular}{lc|ccc|c} % Added vertical lines as specified
 \toprule
 Methods & Dataset & \multicolumn{3}{c|}{Accuracy ($\%$, $\uparrow$)} & Time F1 ($\uparrow$)\\
 \rowcolor{lightgray}
 & & \textit{Rep} & \textit{Prolong} & \textit{Block} &\\ % Subtitles
 \midrule
 Kourkounakis et al. ~& UCLASS & 84.46 & 94.89 & - & 0\\
 Jouaiti et al. ~ & UCLASS & 89.60 & 99.40 & - & 0\\
 H-UDM ~ & UCLASS & 75.18&- & 50.09 & 0.700\\
 YOLO-Stutter~ & UCLASS & 92.00 & 91.43 & 56.00 & {0.893}\\
 SSDM~ & UCLASS & 92.00 & 91.70 & 60.08 & 0.898\\
 SSDM 2.0 (Ours) & UCLASS & \textbf{92.60} & \textbf{92.00} & \textbf{64.78} & \textbf{0.904}\\
 \midrule
 Jouaiti et al. ~ &SEP-28K& 78.70& 93.00 & - & 0 \\
 H-UDM ~ &SEP-28K & 70.99& -&66.44&0.699 \\
 YOLO-Stutter~ & SEP-28K& 82.01& 89.19& 68.09 & 0.813\\
 SSDM~ & SEP-28K& 84.08& 92.33& 69.99 & 0.818\\
 SSDM 2.0 (Ours) & SEP-28K& \textbf{86.77}& \textbf{93.44}& \textbf{70.02} & \textbf{0.830}\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Jiachen Lian
Xuanru Zhou
Zoe Ezzes
Jet Vonk
Brittany Morin
David Baquirin
Zachary Mille
Maria Luisa Gorno Tempini
Gopala Krishna Anumanchipalli

"SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies" by Jiachen Lian, Xuanru Zhou, Zoe Ezzes, Jet Vonk, Brittany Morin, David Baquirin, Zachary Mille, Maria Luisa Gorno Tempini, and Gopala Krishna Anumanchipalli, arXiv:2412.00265v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Dictionaries for classification into \emph{Topics} in Italian }
\begin{tabular}{|c|}
\hline
\\
\textbf{Economics}\\
\hline
\\
economia, economic*, soldi, investiment*, banc*, finanz*, disoccupa*, bancarott*, imprenditor*, \\
impres*, lavoro, bonus, commerci*, gestione, piano, sostegno, crisi, iva, dipendent*, fiscal*, \\
coldiretti, agevolazioni, contribut*, reddito, salari*, confedilizia, confindustria, cgil,\\
professionist*, negoz*, euro, mutuo, mutui, tasse, tassa, tassazione, tassat*, evasione, \\
fisco, sindacat*, inps, credit*, prestit*, stipendio, deficit, lavorator*, produzion*,\\
produttiv*, aziend*, client*, soci, salone, ristorant*, smart-working, smart working, \\
commercial*, supermercat*, spes*, mercat*, turist*, turismo, licenzia* fiera, fiere, \\
cassa integrazione, lavorare, lavorare a casa. \\
\\
\hline
\\
\textbf{Health}\\
\hline
\\
coronavirus, virus, contag*, covid, tampon*, mascher*, casi, quaranten*, \\
mort*, ospedal*,
malat*, malatti*, sanità, sanitari*, medic*, medicina, infermier*,
\\positiv*, farmaci*, kn95, terapi*, terapia intensiva, terapie intensive, sars,
\\ sars-cov-2, paziente zero, pazient*, infett*, salute,decess*, influenza,
\\peste, sanita, guarit*, guarigion*,
ammarlarsi, ammalarci, ammalat*,\\
ammalare, covid19, croce rossa, epidemiolog*, oms, febbre, asintomatic*, \\ rianimazione, epidemia, respiratori, ricoverat*, portatore sano. \\
\\
\hline
\\
\textbf{Politics}\\
\hline
\\
politic*, govern*, italiaviva, salvini, renzi, conte, meloni, presidente, lega, ministro, \\
sindaco, decreto, legge, movimento5stelle, mattarella, segretari*, legislativo, parlament*,\\
giunta, assessor*, ue, politic*, profughi, pd, ong, sinistra, migranti, democrazia,\\
democratic*, partito, partiti, sardine, dimettiti, dimission*, fascismo, fascist*, \\ 5s, protesta, contedimettiti, nazismo, nazist*, destra, casta, m5s.\\
\\
\hline
\\
\textbf{Policy}\\
\hline
\\
chius*, sospes*, cancellat*, limitazion*, annullat*, chiud*, sospension*, isolat*, isolamento, \\
rinviat*, scorte, viveri necessari, zona rossa, zona arancione, luoghi di aggregazione, distanza,
\\ restrizion*, controlli, posto di blocco, posti di blocco, spostamenti, autocertificazione.\\
\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

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

"Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic" by C. Biliotti, F. J. Bargagli-Stoffi, N. Fraccaroli, M. Puliga, and M. Riccaboni, arXiv:2212.01705v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccccc}\hline
\textbf{$p$}&$6$&$7$&$8$&$9$&$10$\\\hline
\textbf{$|\Delta_N(3^p,t_0)|$}&$0.0001$&$0.00004$&$0.00001$
 &$0.000004$&$0.000001$\\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Evaluation of the Gauss integral

"Evaluation of the Gauss integral" by Dmitri Martila and Stefan Groote, arXiv:2202.12394v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Timm Faulwasser
Arne-Jens Hempel
Stefan Streif

"On the Turnpike to Design of Deep Neural Nets: Explicit Depth Bounds" by Timm Faulwasser, Arne-Jens Hempel, and Stefan Streif, arXiv:2101.03000v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Mohamed BenSalah
Salih Tatar
Suleyman Ulusoy

"Bayesian Inference for a Time-Fractional HIV Model with Nonlinear Diffusion" by Mohamed BenSalah, Salih Tatar, and Suleyman Ulusoy, arXiv:2503.23638v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Daan B. Zult
Peter G. M. van der Heijden
Bart F. M. Bakker

"Bias correction in multiple-systems estimation" by Daan B. Zult, Peter G. M. van der Heijden, and Bart F. M. Bakker, arXiv:2311.01297v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Erdős' minimum overlap problem

"Erdős' minimum overlap problem" by Ethan Patrick White, arXiv:2201.05704v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c||c|c|c|c|c|c|}\hline
			Insured & \multicolumn{12}{c|}{Years ($t$) } \\
			$i$ & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11& 12\\ \hline
			1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
			2 & 2 & 0 & 1 & 2 & 0 & 1 & 2 & 0 & 1 & 2 & 0 & 1 \\
			3 & 4 & 1 & 3 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 2 & 0 \\
			4 & 0 & 2 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 3 & 1 & 4 \\ \hline
		\end{tabular}
\caption{Insureds with claims experience}
\end{table}
\end{document}

stat

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