\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation results by varying iterations number.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
Iter. &	MC Price & BSDE Price	& Err.\% & Time$^*$ \\
\midrule\midrule
20,000 &	13.645	&	12.751	&	7.01\%	&	10231		\\
40,000 &	13.528	&	13.524	&	0.03\%	&	20274		\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

A deep solver for BSDEs with jumps

Kristoffer Andersson
Alessandro Gnoatto
Marco Patacca
Athena Picarelli

"A deep solver for BSDEs with jumps" by Kristoffer Andersson, Alessandro Gnoatto, Marco Patacca, and Athena Picarelli, arXiv:2211.04349v3, 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}{lcccc}
				\hline
				Dataset &Noun	&Verb	&Adjective	&Adverb	\\
				\hline
				SE2		&77.5	&43.3	&73.0	&78.0	\\
				SE15	&69.5	&57.4	&80.6	&85.0	\\
				\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mohannad AlMousa
Rachid Benlamri
Richard Khoury

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

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

"Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach" by Federico D'Amario and Milos Ciganovic, arXiv:2210.00883v1, 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{Energy expended by rate-encoded and direct-encoded SNNs on an \textit{Eyeriss} platform normalized with respect to the energy of 1 MAC operation. We use 10 timesteps for both architectures.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{} & \multicolumn{2}{c|}{Normalized energy/image} \\ \hline
Scheme & MLP/MNIST & VGG5/CIFAR10 \\ \hline
Rate coding & 8.58E+06 & 2.73E+07 \\ \hline
Direct coding & 2.26E+07 & 4.22E+07 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Youngeun Kim
Hyoungseob Park
Abhishek Moitra
Abhiroop Bhattacharjee
Yeshwanth Venkatesha
Priyadarshini Panda

"Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?" by Youngeun Kim, Hyoungseob Park, Abhishek Moitra, Abhiroop Bhattacharjee, Yeshwanth Venkatesha, and Priyadarshini Panda, arXiv:2202.03133v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Yingqi Liu
Tianlu Pan
Jingjun Tan
Renxin Zhong
Can Chen

"Integrated Take-off Management and Trajectory Optimization for Merging Control in Urban Air Mobility Corridors" by Yingqi Liu, Tianlu Pan, Jingjun Tan, Renxin Zhong, and Can Chen, arXiv:2508.15395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The Results After XE and CMAL Training When Using Different Numbers of Unlabeled Images on MSCOCO Image Captioning Dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|rrrrrr}
			\toprule
			\#unlabel & stage & B1 & B4 & M & R & S & C \\
			\midrule
			\midrule
			\multirow{2}[0]{*}{0} & XE & 78.5 &	35.3 &	27.3 &	56.9 	&20.8& 	115.5 \\
			& CMAL & 80.3 	&37.3 &	28.1 &	58.0& 	21.8 &	124.0 \\
			\midrule
			\multirow{2}[0]{*}{50k} & XE & 78.8 	&36.2 &	27.6 &	57.2 &	21.1 	&118.1 \\
			& CMAL & 80.2 & 	37.6 & 	28.1 & 	58.1 & 	21.9 & 	124.8 \\
			\midrule
			\multirow{2}[0]{*}{100k} & XE & 79.0 &	36.2 &	27.7 &	57.3 &	21.2& 	118.3 \\
			& CMAL & \bf80.5 &	\bf38.0 &	\bf28.3 &	\bf58.2 &	\bf22.0 &	\bf125.5 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Longteng Guo
Jing Liu
Xinxin Zhu
Hanqing Lu

"Fast Sequence Generation with Multi-Agent Reinforcement Learning" by Longteng Guo, Jing Liu, Xinxin Zhu, and Hanqing Lu, arXiv:2101.09698v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
			\hline
			\textbf{Algorithm} & \textbf{Computational complexity} \\
			\hline
 \hline
			MAD-OMP & $\mathcal{O}(MQT_{\theta} + (MT_{\theta})^3)$\\
			PD-OMP 	& $\mathcal{O}(MQT_{\theta}T_{r} + (MT_{\theta}T_{r})^3)$	 \\
			Oracle LS	& $\mathcal{O}(2MQL^2+L^3+MT_{\theta}T_{r}L)$\\
 PD-ZALMS (proposed) & $\mathcal{O}(2MQT_{\theta}T_{r})$\\
			\hline
		\end{tabular}
\end{adjustbox}
\caption{Computational complexity of the proposed and baseline algorithms.}
\end{table}
\end{document}

eess

"Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering" by Vidya Bhasker Shukla and Italo Atzeni, arXiv:2508.08663v1, 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}{rr}
 \hline
 & value \\
 \hline
Accuracy & 0.78 \\
 Kappa & 0.44 \\
 Sensitivity (No burn accuracy) & 0.50 \\
 Specificity (Burn accuracy) & 0.90 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Kendra Walker
Ben Moscona
Kelsey Jack
Seema Jayachandran
Namrata Kala
Rohini Pande
Jiani Xue
Marshall Burke

"Detecting Crop Burning in India using Satellite Data" by Kendra Walker, Ben Moscona, Kelsey Jack, Seema Jayachandran, Namrata Kala, Rohini Pande, Jiani Xue, and Marshall Burke, arXiv:2209.10148v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Luca De Benedictis
Vania Licio
Anna Pinna

"From the historical Roman road network to modern infrastructure in Italy" by Luca De Benedictis, Vania Licio, and Anna Pinna, arXiv:2208.06675v1, 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{Italian Serie A Time Trends}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 & \multicolumn{2}{c}{Ln(suspense)} & \multicolumn{2}{c}{Ln(surprise)} \\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & (1) & (2) & (3) & (4) \\
 \midrule
 Season & -0.002 & -0.001 & 0.002 & 0.004* \\
 & (0.002) & (0.002) & (0.002) & (0.002) \\
 Juventus × season & & 0.015** & & 0.006 \\
 & & (0.007) & & (0.007) \\
 Napoli × season & & -0.001 & & -0.005 \\
 & & (0.006) & & (0.007) \\
 Internazionale × season & & -0.021*** & & -0.024*** \\
 & & (0.006) & & (0.007) \\
 \midrule
 Main effects & & Yes & & Yes\\
 Observations & 5,320 & 5,320 & 5,320 & 5,320 \\
 R\textsuperscript{2} & 0.000 & 0.018 & 0.000 & 0.010\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Raphael Flepp
Tim Pawlowski
Travis Richardson

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation analysis of the number of heads of the cross-attention blocks in self-cascade architecture.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
 & \multicolumn{3}{c}{Number of head} \\\midrule
Models & 8 & 12 & 16 \\
Reuse & 24.47/10.82 & 24.21/9.81 & 23.78/10.12 \\
Progressive & 24.12/8.99 & 23.92/8.87 & 23.71/8.91 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yue Shi
Liangxiu Han
Darren Dancy
Lianghao Han

"WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images" by Yue Shi, Liangxiu Han, Darren Dancy, and Lianghao Han, arXiv:2412.18996v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Metadata of the processed datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
			\toprule
			\textbf{Collection} & \# records & $|F^S|$ & \# subgroups & $|DOM(F)|$\\
			\midrule
			$SD1$ & 20,000 & 10 & 4 & 4 \\
			$SD2$ & 40,000 & 15 & 4 & 4 \\
			\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Bar Genossar
Thinh On
Md. Mouinul Islam
Ben Eliav
Senjuti Basu Roy
Avigdor Gal

"MISFEAT: Feature Selection for Subgroups with Systematic Missing Data" by Bar Genossar, Thinh On, Md. Mouinul Islam, Ben Eliav, Senjuti Basu Roy, and Avigdor Gal, arXiv:2412.06711v1, 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}{cccc}
 \hline
 size & excluded region & \multicolumn{2}{c}{confidence interval} \\
 & & uncorrected & corrected \\
 \hline
 3 & $(-7.2, -4.8)\cup (-0.4, 1.5)\cup (12.4, 14.2)$ & $(9.38, 10.77)$ & $(9.38, 10.77)$ \\
 4 & $(-9.7,-6.8)\cup (1.4, 8.6)\cup (11.2,13.2)$ & $(9.11,10.51)$ & $(9.10, 10.51)$\\
 5 & $(-17.8, -13.8)\cup (-1.9, 0.8)\cup (9.8,13.0)\cup (14.2, 16.6)$ & $(8.60,10.18)$ & $(8.61,10.99)$\\
 6 & $(-19.5,-15.6)\cup (-2.5,-0.2)\cup (9.4, 14.6)$ & $(8.36,9.95)$ & $(8.41,11.74)$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Examples of the excluded region of the conditional truncated normal estimator and the confidence intervals before and after correction, for different selected model sizes by AIC. }
\end{table}
\end{document}

stat

"Valid confidence intervals for regression with best subset selection" by Huiming Lin and Meng Li, arXiv:2311.13768v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model" by Edoardo Berton and Lorenzo Mercuri, arXiv:2112.11968v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results on COVID-19 Dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\multicolumn{4}{|c|}{COVID-19 vs Suspected} \\ \hline
Method & Accuracy & Sensitivity & Specificity \\ \hline
SVM & $87 \pm 5$ & $89 \pm 8$ & $86 \pm 9$ \\ \hline
RamanNet & $93 \pm 3$ & $97 \pm 4$ & $90 \pm 6$ \\ \hline
\multicolumn{4}{|c|}{COVID-19 vs Healthy} \\ \hline
Method & Accuracy & Sensitivity & Specificity \\ \hline
SVM & $91 \pm 4$ & $89 \pm 7$ & $93 \pm 6$ \\ \hline
RamanNet & $95$ & $95 \pm 4$ & $96 \pm 3$ \\ \hline
\multicolumn{4}{|c|}{Suspected vs Healthy} \\ \hline
Method & Accuracy & Sensitivity & Specificity \\ \hline
SVM & $69 \pm 5$ & $70 \pm 9$ & $66 \pm 9$ \\ \hline
RamanNet & $82 \pm 6$ & $77 \pm 15$ & $87 \pm 11$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Nabil Ibtehaz
Muhammad E. H. Chowdhury
Amith Khandakar
Susu M. Zughaier
Serkan Kiranyaz
M. Sohel Rahman

"RamanNet: A generalized neural network architecture for Raman Spectrum Analysis" by Nabil Ibtehaz, Muhammad E. H. Chowdhury, Amith Khandakar, Susu M. Zughaier, Serkan Kiranyaz, and M. Sohel Rahman, arXiv:2201.09737v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
& AFEW & FPHA & NTU RGB+D\ & HDM05\\
\midrule
Euclidean & $30.08$\tiny$\pm 1.36$& $30.72 $\tiny$\pm 1.03$ & $34.63$\tiny$\pm 3.10$ & $0.80 $\tiny$\pm 0.10$\\
Aff-Inv & $35.17$ \tiny $\pm 1.78$&$\mathbf{66.53} $\tiny$\mathbf{\pm 1.64}$ & $41.00$ \tiny $\pm 0.50$& $67.91$ \tiny${\pm 1.27}$\\
Log-Euc&$\mathbf{36.38}$\tiny $\mathbf{\pm 0.24}$& $64.58 $\tiny$\pm 0.98$& $\mathbf{42.99 }$\tiny$\mathbf{\pm 0.23}$& $\mathbf{69.80}$\tiny$\mathbf{ \pm 1.51}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{We show that the Euclidean ResNet performs worse across all datasets, and fails for HDM05.}
\end{table}
\end{document}

stat

Riemannian Residual Neural Networks

Isay Katsman
Eric Ming Chen
Sidhanth Holalkere
Anna Asch
Aaron Lou
Ser-Nam Lim
Christopher De Sa

"Riemannian Residual Neural Networks" by Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, and Christopher De Sa, arXiv:2310.10013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 & \multicolumn{2}{c}{\textbf{Cross-Validation}} & \multicolumn{2}{c}{\textbf{Test Set Cohort}}\tabularnewline
 & AUC & Brier score & AUC & Brier score\tabularnewline
\hline
\hline
flexible BART & 0.68 & 0.173 & 0.557 & 0.162\tabularnewline
cv-BART & 0.68 & 0.168 & 0.557 & 0.162\tabularnewline
flexible EB-coBART 1 & 0.72 & 0.155 & 0.697 & 0.154\tabularnewline
flexible EB-coBART 2 & 0.71 & 0.156 & \textbf{0.714} & \textbf{0.153}\tabularnewline
IPI-BART & 0.70 & \textbf{0.153} & 0.669 & 0.154\tabularnewline
Random Forest & 0.69 & 0.183 & 0.63 & 0.158\tabularnewline
CoRF & 0.71 & 0.157 & 0.65 & 0.159\tabularnewline
Ecpc & \textbf{0.73} & 0.159 & 0.705 & 0.154\tabularnewline
Ridge & 0.72 & 0.161 & 0.69 & 0.154\tabularnewline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jeroen M. Goedhart
Thomas Klausch
Jurriaan Janssen
Mark A. van de Wiel

"Co-data Learning for Bayesian Additive Regression Trees" by Jeroen M. Goedhart, Thomas Klausch, Jurriaan Janssen, and Mark A. van de Wiel, arXiv:2311.09997v2, 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 key parameters of PMSM}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Parameters& Units &Values\\
\hline
Rated power $P$& W & 200\\
Rated speed $N_r$& rpm & 1500\\
Rated load $T_{L}$& N$\cdot$m & 1.2\\
Rated voltage $U_r$& V & 36\\
Rated current $I_r$& A & 7.5\\
Pole pairs $N_p$& * & 5\\
Rotational inertia $J$&Kg$\cdot$m$^2$& 6.8$\times$ 10$^{-5}$\\
PM flux linkage $\psi_{f}$&Wb & 0.012\\
Stator resistance $R_s$ &$\Omega$& 0.4\\
Winding inductance $L_s$ &mH& 0.7\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Wenjie Mei
Xiaorui Wang
Yanrong Lu
Ke Yu
Shihua Li

"Learning and Current Prediction of PMSM Drive via Differential Neural Networks" by Wenjie Mei, Xiaorui Wang, Yanrong Lu, Ke Yu, and Shihua Li, arXiv:2412.09028v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Forecasting realized volatility in the stock market: a path-dependent perspective" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|}
 \hline
 & $\underline{\hat{g}}_{1}$ & $\underline{\hat{g}}_{2}$ & $\underline{\hat{g}}_{3}$ \\
 \hline
$\underline{\hat{g}}_{1}$ & $0$ & $- \underline{\hat{g}}_{3}$ & $ \underline{\hat{g}}_{2}$ \\
\hline
$\underline{\hat{g}}_{2}$ & $ \underline{\hat{g}}_{3}$ & $0$ & $- \underline{\hat{g}}_{1}$ \\
\hline
$\underline{\hat{g}}_{3}$ & $- \underline{\hat{g}}_{2}$ & $\underline{\hat{g}}_{1}$ & $0$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Structure table for the Lie algebra $\mathfrak{su}(2)$ in the Pauli basis. }
\end{table}
\end{document}

math

D. Martínez-Tibaduiza
Vladimir Vargas-Calderón
J. G. Dueñas
J. Flórez-Jiménez
A. Z. Khoury

"$\texttt{Symdyn}$: an automated algebraic solution for high-order quantum systems" by D. Martínez-Tibaduiza, Vladimir Vargas-Calderón, J. G. Dueñas, J. Flórez-Jiménez, and A. Z. Khoury, arXiv:2503.22061v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 Model name & Depth & Channels & Parameter count \\
 \hline
 Utility & 5 & $2^6$ & 34M \\
 U-Noise Small & 2 & $2^4$ & 10K \\
 U-Noise Medium & 3 & $2^4$ & 130K \\
 U-Noise Large & 4 & $2^4$ & 537K \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Details of the Utility and U-Noise models used in experiments. \textit{Depth} denotes the number of downsample and upsample layers; \textit{Channels} denotes the number of output channels of the first layer.}
\end{table}
\end{document}

cs

Teddy Koker
Fatemehsadat Mireshghallah
Tom Titcombe
Georgios Kaissis

"U-Noise: Learnable Noise Masks for Interpretable Image Segmentation" by Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe, and Georgios Kaissis, arXiv:2101.05791v3, 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}{l|c|cc}
\toprule
\textbf{Architecture} & \textbf{Backbone} & \textbf{IoU} & \textbf{F1-Score} \\
\hline
Unet & EfficientNetb-3 & 0.817 & 0.831\\
\hline
LinkNet & EfficientNetb-3 & 0.813 & 0.828\\
\hline
FPN & EfficientNetb-3 & 0.882 & 0.91 \\
 & Resnet18 & 0.862 & 0.885 \\
 & Resnet50 & 0.841 & 0.863 \\
\bottomrule
\end{tabular}
\caption{Quantitative results for the keypoints masks generations. More details about the results for other backbones can be found Figure }
\end{table}
\end{document}

cs

"Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning" by Paul Garnier and Théophane Gregoir, arXiv:2101.05388v1, 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}{lcc}
\toprule
Sample Size & t-test (ETH) & p-value \\
\midrule
Merkle 92 & -0.764 & 0.223 \\
Flashbots 98 & -1.309 & 0.097 \\
Blink 97 & -0.851 & 0.198 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Net Average Price Difference vs MEV Blocker.}
\end{table}
\end{document}

q-fin

"Private MEV Protection RPCs: Benchmark Stud" by Paul Janicot and Alex Vinyas, arXiv:2505.19708v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{crr}
 \hline
 \thead{\textbf{Split Ratio} \\ \textbf{Labeled / Unlabeled}} & \thead{\textbf{CER (\%)} \\ \textbf{LJ Original}} & \thead{\textbf{CER (\%)}\\ \textbf{LJ Noisy}}\\
 \hline
 30 / 70 & 11.1 & 15.5 \\
 50 / 50 & 4.8 & 11.5 \\
 70 / 30 & 4.0 & 10.9 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Results for the \textbf{ASR${_\text{SpeechChain}}$} with updates of different ratio of labeled and unlabeled data during base task learning in the first stage and second stage of the framework.}
\end{table}
\end{document}

eess

Geoffrey Tyndall
Kurniawati Azizah
Dipta Tanaya
Ayu Purwarianti
Dessi Puji Lestari
Sakriani Sakti

"Continual Learning in Machine Speech Chain Using Gradient Episodic Memory" by Geoffrey Tyndall, Kurniawati Azizah, Dipta Tanaya, Ayu Purwarianti, Dessi Puji Lestari, and Sakriani Sakti, arXiv:2411.18320v1, 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}{l|l|l}
 \toprule \midrule
 \textbf{Model} & \textbf{WER} & \textbf{WERR} \\
 \midrule
 USM baseline & 28.8 & - \\
 USM SI-ASR & \textbf{17.5} & 39\% \\
 \midrule
 RNN-T baseline & 38.2 & - \\
 RNN-T SI-ASR & 26.4 & 31\% \\
 RNN-T Personalized & \textbf{11.3} & 71\%\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Results comparing unadapted, speaker independent and personalized models, evaluated on a subset of prompted speech test set.}
\end{table}
\end{document}

eess

Jimmy Tobin
Katrin Tomanek
Subhashini Venugopalan

"Towards a Single ASR Model That Generalizes to Disordered Speech" by Jimmy Tobin, Katrin Tomanek, and Subhashini Venugopalan, arXiv:2412.19315v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"The causal effect of a timeout at stopping an opposing run in the NBA" by Connor Gibbs, Ryan Elmore, and Bailey Fosdick, arXiv:2011.11691v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Matteo Straccamore
Matteo Bruno
Andrea Tacchella

"Comparative Analysis of Technological Fitness and Coherence at different geographical scales" by Matteo Straccamore, Matteo Bruno, and Andrea Tacchella, arXiv:2503.22666v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary Statistics for SPY Minute-Level Data (January-June 2025)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
\textbf{Variable} & \textbf{Mean} & \textbf{Std Dev} & \textbf{Min} & \textbf{25\%} & \textbf{75\%} & \textbf{Max} \\
\midrule
Close Price (\$) & 578.50 & 32.84 & 482.77 & 560.32 & 602.19 & 613.14 \\
Volume & 111,706 & 168,052 & 0 & 24,576 & 134,720 & 12,103,574 \\
Log Returns & 0.000001 & 0.000774 & -0.035 & -0.000214 & 0.000221 & 0.034 \\
Spread Proxy & 0.000672 & 0.000704 & 0.000000 & 0.000308 & 0.000800 & 0.032 \\
Number Ticks & 261.8 & 458.7 & 0 & 44 & 282 & 22,798 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example table.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\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{adjustbox}
\end{table}
\end{document}

stat

Shiheng Zhang
Jiahao Zhang
Jie Shen
Guang Lin

"An Element-wise RSAV Algorithm for Unconstrained Optimization Problems" by Shiheng Zhang, Jiahao Zhang, Jie Shen, and Guang Lin, arXiv:2309.04013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Pariyakorn Maneekul
Zelda B. Zabinsky
Giulia Pedrielli

"Branching Adaptive Surrogate Search Optimization (BASSO)" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textbf{Name} & \textbf{Symbol} & \textbf{Seed} & \textbf{Calibrated Value} \\
 \hline
 Mean reversion speed & $a$ & $0.1$ & $0.267419$\\
 \hline
 Asset Correlation & $\rho$ & $0.0$ & $0.86381$\\
 \hline
 Vol-of-vol & $\chi$ & $1.0$ & $0.0287296$\\
 \hline
 Stochastic Volatility correlation & $\rho_v$ & $1.0$ & $-0.18058$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Calibrated values of $\hat{\mathbf{p}}$ against the S\&P GSCI Crude Oil ER index 1 Year Call Options quoted on 30 November 2019 and the S\&P GSCI Crude Oil ER index 1 Year Call Options quoted on 31 December 2019, both on CME market. For this calibration the hybrid global-local procedure described in Section has been used.}
\end{table}
\end{document}

q-fin

Pricing commodity index options

Alberto Manzano
Emanuele Nastasi
Andrea Pallavicini
Carlos Vázquez

"Pricing commodity index options" by Alberto Manzano, Emanuele Nastasi, Andrea Pallavicini, and Carlos Vázquez, arXiv:2208.01289v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Risk in Network Economies

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Input signal for Example~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccccc}
 $t$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\ \hline
 \multirow{2}{*}{\!$u(t)$\!} & -0.46 & -1.09 & 0.9 & 1.38 & 1.24 & 1.54 & 0.22 & -1.68 \\
 & 1.86 & -0.87 & -1.42 & 1.06 & 1.6 & -1.94 & 1.11 & -1.03
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Amir Shakouri
Henk J. van Waarde
M. Kanat Camlibel

"A new perspective on Willems' fundamental lemma: Universality of persistently exciting inputs" by Amir Shakouri, Henk J. van Waarde, and M. Kanat Camlibel, arXiv:2503.12489v2, 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{Features of datasets used for this experiment.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|r|c|r|r|r|r|r}
\hline
dataset &$N$ & $V$ & $C$ & \multicolumn{4}{|c}{dimensions of each view}\\
\cline{5-8}
&&&& $d_1$ & $d_2$ & $d_3$ & $d_4$ \\
\hline
\hline
BBC & 685 & 4 & 5 & 4659 & 4633 & 4665 & 4684 \\
NGs & 500 & 3 & 5 & 2000 & 2000 & 2000 &--\\
WebKB & 203 & 3 & 4 & 1703 & 230 & 230 & -- \\
100leaves & 1600 & 3 & 100 & 64 & 64 & 64 & -- \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering" by Mitsuhiko Horie and Hiroyuki Kasai, arXiv:2011.12532v1, 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{Various IEEE precisions and their units roundoff. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
		\hline
		Precision & Unit Roundoff \\ \hline
		fp16 (half) & $4.88\cdot 10^{-4}$ \\ \hline
		fp32 (single) & $5.96\cdot 10^{-8}$ \\ \hline
		fp64 (double) & $1.11\cdot 10^{-16}$ \\ \hline
		fp128 (quad) & $9.63\cdot 10^{-35}$ \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"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{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
projective algebraic geometry & combinatorics \\ \hline \hline
projective varieties $X\subseteq\mathbb P^r$ & simplicial complexes $\Delta$ on $\{0,\ldots,r\}$ \\ \hline
homogeneous ideals $I(X)$ & Stanley-Reisner ideals $I(\Delta)$ \\ \hline
varieties of minimal degree & chordal graphs \\ \hline
 & Fr{\"o}berg's theorem \\ \hline \hline
$q$-secant varieties & $q$-secant complexes \\ \hline
varieties of minimal $q$-secant degree & $q$-secant chordal graphs \\ \hline
(cf.\ ) & \\ \hline \hline
general inner projections & edge contractions
\end{tabular}
\end{adjustbox}
\caption{Analogues between the two areas}
\end{table}
\end{document}

math

"A Fröberg type theorem for higher secant complexes" by Junho Choe and Jaewoo Jung, arXiv:2502.20987v2, 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{Loss function values.}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
Stackelberg strategy&Leader&Follower 1&Follower 2&Follower 3&Follower 4\\
&-2210,94& -2818,06 & -1978,79 & -2688,34& -2506,05\\
\hline
non-cooperative strategy&Player 1&Player 2&Player 3&Player 4&Player 5\\
&-2192,96 & -2910,93 & -2010,04 & -2767,10 & -2573,26\\
\hline
\end{tabular}
\end{table}
\end{document}

math

Helmut Gfrerer
Michal Kočvara
Jiří V. Outrata

"On the role of semismoothness in the implicit programming approach to selected nonsmooth optimization problems" by Helmut Gfrerer, Michal Kočvara, and Jiří V. Outrata, arXiv:2412.05953v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Team specific median posterior Excitement Trend Indices summarized across all their matches in the 2019--2020 NBA season and ordered by the season average. The group column shows the clustering induced by our optimization procedure.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr|rrr|r}
\toprule
 & Average & SD & 2.5\% & 50\% & 97.5\% & Group\\
\midrule
New Orleans Pelicans & 11.67 & 4.91 & 3.10 & 11.59 & 23.54 & A\\
Washington Wizards & 11.36 & 4.46 & 2.98 & 11.63 & 18.28 & A\\
San Antonio Spurs & 11.31 & 5.31 & 3.52 & 9.88 & 23.44 & A\\
Oklahoma City Thunder & 11.26 & 5.32 & 1.68 & 11.55 & 23.74 & A\\
Portland Trail Blazers & 11.21 & 4.46 & 3.31 & 10.82 & 18.48 & A\\
Los Angeles Lakers & 11.16 & 4.43 & 3.61 & 10.52 & 19.79 & A\\
Philadelphia 76ers & 11.09 & 4.70 & 2.73 & 10.28 & 21.53 & A\\
Minnesota Timberwolves & 11.09 & 4.28 & 4.01 & 10.69 & 19.46 & A\\
Utah Jazz & 10.96 & 5.44 & 3.09 & 9.76 & 23.54 & A\\
Memphis Grizzlies & 10.94 & 4.59 & 3.04 & 10.36 & 20.84 & A\\
Cleveland Cavaliers & 10.90 & 4.50 & 4.93 & 10.39 & 22.36 & A\\
\hline
Toronto Raptors & 10.69 & 4.44 & 3.48 & 10.33 & 21.79 & B\\
Houston Rockets & 10.67 & 4.12 & 3.52 & 10.74 & 19.17 & B\\
Chicago Bulls & 10.66 & 3.98 & 2.95 & 10.65 & 17.49 & B\\
Boston Celtics & 10.61 & 4.66 & 2.81 & 10.19 & 20.92 & B\\
Orlando Magic & 10.56 & 4.89 & 2.72 & 10.41 & 21.01 & B\\
Milwaukee Bucks & 10.55 & 4.36 & 2.77 & 9.59 & 18.13 & B\\
Los Angeles Clippers & 10.54 & 4.14 & 4.05 & 10.72 & 20.56 & B\\
Miami Heat & 10.44 & 4.29 & 3.28 & 9.91 & 18.54 & B\\
Golden State Warriors & 10.40 & 4.58 & 3.37 & 10.24 & 18.72 & B\\
Indiana Pacers & 10.37 & 3.99 & 3.31 & 10.22 & 18.54 & B\\
\hline
Brooklyn Nets & 10.33 & 4.57 & 3.07 & 10.06 & 18.89 & C\\
Dallas Mavericks & 10.27 & 4.99 & 3.00 & 9.04 & 22.09 & C\\
Atlanta Hawks & 10.26 & 4.35 & 2.50 & 9.75 & 18.31 & C\\
Phoenix Suns & 10.25 & 4.32 & 3.78 & 9.29 & 19.81 & C\\
Denver Nuggets & 10.16 & 4.39 & 3.42 & 9.59 & 19.61 & C\\
New York Knicks & 10.11 & 4.05 & 2.84 & 9.87 & 18.47 & C\\
Sacramento Kings & 9.94 & 4.19 & 3.66 & 9.30 & 18.93 & C\\
Detroit Pistons & 9.86 & 4.00 & 3.73 & 9.44 & 17.08 & C\\
\hline
Charlotte Hornets & 9.26 & 3.74 & 1.96 & 9.22 & 15.95 & D\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Claus Thorn Ekstrøm
Andreas Kryger Jensen

"Having a Ball: evaluating scoring streaks and game excitement using in-match trend estimation" by Claus Thorn Ekstrøm and Andreas Kryger Jensen, arXiv:2012.11915v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{MISE per method on dry bean dataset} for $\beta = \frac{1}{2}$ and varying levels of $\alpha$. We compare CBRNet against several benchmarking methods over varying levels of confounding by cluster. The best results are printed in \textbf{bold}, second-best results are in \textit{italics}. Results for different values of $\alpha$ can be found in Appendix~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 $\beta = \frac{1}{2}$\\
 \hline \\
 \hline \\
 & \multicolumn{4}{c}{$\alpha$}\\
 \cdashline{2-5}\\
 Model & 1.0 & 2.0 & 3.0 & 4.0 \\
 \hline \\
 Linear Regression & 3.32 \scriptsize{± 0.03} & 3.40 \scriptsize{± 0.06} & 3.30 \scriptsize{± 0.02} & 3.32 \scriptsize{± 0.03} \\
 CART & 1.31 \scriptsize{± 0.06} & 1.40 \scriptsize{± 0.09} & 1.56 \scriptsize{± 0.15} & 1.53 \scriptsize{± 0.13} \\
 xgboost & 0.91 \scriptsize{± 0.07} & 1.16 \scriptsize{± 0.09} & 1.15 \scriptsize{± 0.07} & 1.28 \scriptsize{± 0.04} \\
 MLP & 0.54 \scriptsize{± 0.04} & 0.67 \scriptsize{± 0.08} & 0.83 \scriptsize{± 0.09} & 1.06 \scriptsize{± 0.08} \\
 \hdashline\\
 DRNet & 0.62 \scriptsize{± 0.03} & 0.78 \scriptsize{± 0.13} & 0.82 \scriptsize{± 0.07} & 0.98 \scriptsize{± 0.06} \\
 VCNet & 0.75 \scriptsize{± 0.11} & 1.07 \scriptsize{± 0.10} & 1.18 \scriptsize{± 0.11} & 1.35 \scriptsize{± 0.07} \\
 \hdashline\\
 CBRNet(MMD$_{lin}$) & \textit{0.44} \scriptsize{± 0.09} & \textit{0.47} \scriptsize{± 0.12} & \textbf{0.44} \scriptsize{± 0.07} & \textit{0.63} \scriptsize{± 0.15} \\
 CBRNet(MMD$_{rbf}$) & 0.45 \scriptsize{± 0.10} & 0.53 \scriptsize{± 0.14} & 0.50 \scriptsize{± 0.10} & 0.71 \scriptsize{± 0.17} \\
 CBRNet(Wass) & \textbf{0.43} \scriptsize{± 0.09} & \textbf{0.46} \scriptsize{± 0.10} & \textit{0.45} \scriptsize{± 0.08} & \textbf{0.55} \scriptsize{± 0.11} \\
 \hline \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Christopher Bockel-Rickermann
Toon Vanderschueren
Jeroen Berrevoets
Tim Verdonck
Wouter Verbeke

"Using representation balancing to learn conditional-average dose responses from clustered data" by Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, and Wouter Verbeke, arXiv:2309.03731v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The competing methods used in the simulation study}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\cline{1-3}
 & & \\
Method notation & Reference & \textsf{R} package\\
\hline
ECP & & \textbf{ecp}\\
NMCD & & \textbf{changepoint.np}\\
NBS & & --\\
NWBS & & --\\
CPM & & \textbf{cpm}\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Non-parametric multiple change-point detection" by Andreas Anastasiou and Piotr Fryzlewicz, arXiv:2504.21379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\small Platform & $\beta_{\textbf{S}}$ & $\beta_{\textbf{K}}$ & $\beta_{\textbf{I}}$ & $\mu_{\textbf{K}}$ & $\mu_{\textbf{I}}$ \\ \hline
\small Instagram & 0.4 & 0.35 & 0.5 & 0.1 & 0.1 \\ \hline
\small Facebook & 0.4 & 0.5 & 0.75 & 0.1 & 0.1 \\ \hline
\small Tiktok & 0.4 & 0.25 & 0.35 & 0.1 & 0.1 \\ \hline
\small Twitter & 0.4 & 0.2 & 0.3 & 0.1 & 0.1 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{\small Experiment 2 Coefficients}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
 \hline
 & RGB & + Object\\
 \hline
 HAA500 & 33.53\% & 33.73\% \\
 \:\: Sport & 38.52\% & 38.68\% \\
 \:\: Instrument & 30.72\% & 30.07\% \\
 \:\: HAA-COCO & 34.26\% & 34.26\% \\
 \hline
 UCF101 & 57.65\% & 60.19\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Accuracy of I3D when trained with object heatmap. HAA-COCO denotes 147 classes of HAA500 expected to have objects that were detected.}
\end{table}
\end{document}

eess

Jihoon Chung
Cheng-hsin Wuu
Hsuan-ru Yang
Yu-Wing Tai
Chi-Keung Tang

"HAA500: Human-Centric Atomic Action Dataset with Curated Videos" by Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, and Chi-Keung Tang, arXiv:2009.05224v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Prakrati Kushwah
Anupama Ghorai
Jitraj Saha

"Convergence-rate and error analysis of sectional-volume average method for the collisional breakage equation with multi-dimensional modelling" by Prakrati Kushwah, Anupama Ghorai, and Jitraj Saha, arXiv:2504.15365v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Efficient and Accurate Calibration to FX Market Skew with Fully Parameterized Local Volatility Model" by Dongli Wu, Bufan Zhang, and Xiao Lin, arXiv:2211.14431v2, 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{AUC scores of different layout novelty detection methods on \textbf{Dataset-2}, where the best and second-best results are respectively highlighted in bold and underline}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
 \hline
 \footnotesize Method &\footnotesize AUC Score \\
 \hline
 \footnotesize LSA~ & \footnotesize 0.690 \\
 \hline
 \footnotesize GEOM~ & \footnotesize 0.752 \\
 \hline
 \footnotesize GOAD~ & \footnotesize \underline{0.768} \\
 \hline
 \footnotesize Autoencoder (Global) & \footnotesize 0.675 \\
 \hline
 \footnotesize LithoNet (Local) & \footnotesize {0.744} \\
 \hline
 \footnotesize \textbf{SA-Glocal (AE + SA-LithoNet)} & \footnotesize \textbf{0.932} \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hao-Chiang Shao
Hsing-Lei Ping
Kuo-shiuan Chen
Weng-Tai Su
Chia-Wen Lin
Shao-Yun Fang
Pin-Yian Tsai
Yan-Hsiu Liu

"Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning" by Hao-Chiang Shao, Hsing-Lei Ping, Kuo-shiuan Chen, Weng-Tai Su, Chia-Wen Lin, Shao-Yun Fang, Pin-Yian Tsai, and Yan-Hsiu Liu, arXiv:2201.09717v1, 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|c|}\hline
map & max(t)\\\hline
\texttt{Csigma\_\_CW\_sigma\_nu} & 190\\\hline
\texttt{Csigma\_\_Q\_CW\_nu\_sigma} & 200\\\hline
\texttt{CW\_sigma\_nu\_\_CW\_sigma\_nu\_eta} & 200\\\hline
\texttt{Csigma\_\_Csigmasq} & 193\\\hline
\texttt{Csigmasq\_\_S0} & 200\\\hline
\texttt{C2h4\_\_Csigmasq} & 200\\\hline
\texttt{Csigmasq\_\_DC2h4} & 196\\\hline
\texttt{Csigmasq\_\_Csigma} & 200\\\hline
\texttt{Csigmasq\_\_C2\_by\_sigmasq} & 184\\\hline
\texttt{Csigmasq\_\_Q\_C2h4} & 200\\\hline
\texttt{Csigmasq\_\_S0\_by\_sigmasq} & 200\\\hline
\texttt{C2\_\_C2h4} & 200\\\hline
\texttt{DC2h4\_\_C2} & 193\\\hline
\texttt{C2h4\_\_S0} & 200\\\hline
\texttt{C2\_\_Q\_DC2h4} & 200\\\hline
\texttt{C2h4\_\_S0\_by\_sigmasq} & 199\\\hline
\texttt{C2\_\_C2h5} & 200\\\hline
\texttt{C2h5\_\_Ctheta4} & 200\\\hline
\texttt{Ctheta4\_\_DC2h5} & 200\\\hline
\texttt{DC2h5\_\_C2} & 200\\\hline
\texttt{C2h5\_\_S0} & 200\\\hline
\texttt{C2\_\_Q\_DC2h5} & 200\\\hline
\texttt{C2h5\_\_S0\_by\_theta4} & 200\\\hline
\texttt{Ctheta4\_\_Q\_C2h5} & 200\\\hline
\texttt{Ctheta4\_\_S0} & 200\\\hline
\texttt{Ctheta4\_\_C2\_by\_theta4} & 168\\\hline
\texttt{Ctheta4\_\_S0\_by\_theta4} & 200\\\hline
\texttt{C2\_\_C2h6} & 200\\\hline
\texttt{C2h6\_\_Ctheta5} & 200\\\hline
\texttt{Ctheta5\_\_DC2h6} & 200\\\hline
\texttt{DC2h6\_\_C2} & 200\\\hline
\texttt{C2h6\_\_S0} & 200\\\hline
\texttt{C2\_\_Q\_DC2h6} & 198\\\hline
\texttt{C2h6\_\_S0\_by\_theta5} & 198\\\hline
\texttt{Ctheta5\_\_Q\_C2h6} & 200\\\hline
\texttt{Ctheta5\_\_S0} & 200\\\hline
\texttt{C2\_\_Q\_CW\_2\_theta5\_2\_Eq\_eta\_theta5} & 136\\\hline
\texttt{Ctheta5\_\_Q\_CW\_2\_theta5\_2\_Eq\_eta\_theta5} & 198\\\hline
\texttt{DC2h6\_\_Q\_CW\_2\_theta5\_2\_Eq\_eta\_theta5} & 191\\\hline
\texttt{C2\_C2\_\_C2} & 180\\\hline
\texttt{C2\_\_C2\_C2} & 180\\\hline
\texttt{CW\_2\_eta\_\_Q\_RP1\_6} & 180\\\hline
\texttt{Ceta\_Ceta\_\_Ceta} & 157\\\hline
\texttt{Ceta\_\_Ceta\_Ceta} & 160\\\hline
\texttt{Cnu\_Cnu\_\_Cnu} & 180\\\hline
\end{tabular}
\caption{Maps between CW spectra}
\end{table}
\end{document}

math

"Machine Proofs for Adams Differentials and Extension Problems among CW Spectra" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10876v2, 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}
\textbf{Set} & \textbf{\#Videos} & \textbf{Total Duration} & \textbf{Avg Duration} \\ \hline
Trainval & 4,004 & 30 hr & 3.42 sec \\ \hline
Test & 412 & 51 min & 2.32 sec \\
\end{tabular}
\end{adjustbox}
\caption{LRS3 dataset statistics.}
\end{table}
\end{document}

eess

Lucas Goncalves
Prashant Mathur
Xing Niu
Brady Houston
Chandrashekhar Lavania
Srikanth Vishnubhotla
Lijia Sun
Anthony Ferritto

"Improving Lip-synchrony in Direct Audio-Visual Speech-to-Speech Translation" by Lucas Goncalves, Prashant Mathur, Xing Niu, Brady Houston, Chandrashekhar Lavania, Srikanth Vishnubhotla, Lijia Sun, and Anthony Ferritto, arXiv:2412.16530v1, 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{Productivity Processes Estimates}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
& & $\omega^H$ & $\omega^L$ \\
 \midrule
 Persistency (H) & $\rho_H$ & 0.880 & \\
 & & (0.053) & (-) \\
 Persistency (L) & $\rho_L$ & & 0.885 \\
 & & (-) & (0.015) \\
 $IDA$ control & $\beta_1$ & -0.031 & 0.010 \\
 & & (0.015) & (0.011) \\
 $Imp_{-1}$ control & $\beta_2$ & -0.071 & -0.044 \\
 & & (0.095) & (0.063) \\
 \midrule
 Year FE& & YES & YES \\
 \bottomrule

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

q-fin

"Do Temporary Workers Face Higher Wage Markdowns? Evidence from India's Automotive Sector" by Davide Luparello, arXiv:2507.08222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}\hline
 s & $R_2(s)$ &Classic Bound & Our Bound \\\hline\hline
 $5$ & $R_2(5) \leq 46$ & $m_2(5)\leq 2,677$ & $m_2(5)\leq 392$ \\\hline
 $6$ & $R_2(6)\leq 160$ & $m_2(6)\leq 1,293,533$ & $m_2(6)\leq 152,000$ \\\hline
 $7$ & $R_2(7)\leq 492$ & $m_2(7)\leq 1,265,045,472$ & $m_2(7)\leq 282,475,249$ \\\hline
 $8$ & $R_2(8)\leq 1,518$ & $m_2(8)\leq 5,114,696,152,715$ & $m_2(8)\leq 4,065,272,127,504$ \\\hline
 $9$ & $R_2(9)\leq 4,956$ & $m_2(9)\leq 1.437\times 10^{17}$ & $m_2(9)\leq 6.947\times 10^{17}$ \\\hline
 $10$& $R_2(10)\leq 16,064$ & $m_2(10)\leq 1.788\times 10^{22}$ & $m_2(10)\leq 1.032 \times 10^{24}$ \\\hline
 \end{tabular}
\end{adjustbox}
\caption{Values of the bound given by , v.s. the classical probabilistic bound . The bound is initially an improvement on , but becomes worse for $s \geq 9$.}
\end{table}
\end{document}

math

"Bounds on the Critical Multiplicity of Ramsey Numbers with Many Colors" by Bryce Christopherson and Casia Steinhaus, arXiv:2501.18869v2, 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{99\% confidence intervals for the moments, standard deviation and median of the jump part of the logarithmic returns of Bitcoin price}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
& 1st moment & 2d moment & 3d moment & SD & Median \\ \hline
Lower CI & \(-0.0074\) & 0.0044 & \(-0.0991\) & 0.0658 & \(-0.0001\)\\
True value & 0.0028 & 0.0044 & \(-0.0003\) & 0.0662 & 0.0062 \\
Upper CI & 0.0119 & 0.0381 & 0.0442 & 0.1951 & 0.0111 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes" by Ekaterina Morozova and Vladimir Panov, arXiv:2210.13824v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Anna Little
Daniel McKenzie
James Murphy

"Balancing Geometry and Density: Path Distances on High-Dimensional Data" by Anna Little, Daniel McKenzie, and James Murphy, arXiv:2012.09385v2, 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 distribution of non-MC integers $n<1100$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
$\lfloor n/100 \rfloor$ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 &10 \\
\hline
$\sharp\{n|$ non-MC$\}$ & 21 & 16 & 20 & 19 & 13 & 17& 17 & 14 & 23 & 13 & 16 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"The Largest Circle Enclosing $n$ Lattice Points" by Jianqiang Zhao, arXiv:2505.06234v2, 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||ccc|}
		\hline
		& Elementary Step & Rate Expression & Linear Form \\
		\hline\hline
		1 & $A + * \to A^*$ & $r = k^+ C_A C_*$ & $r_A = \beta_{(Nk^+)} C_A - \beta_{(k^+)} C_A U_A$ \\
		2 & $A + * \rightleftharpoons A^*$ & $r=k^+ C_A C_* - k^-C_{A^*}$ & $r_A = \beta_{(Nk^+)}C_A -\beta_{(k^+)} C_AU_{A^*} - \beta_{(k^-)} U_{A^*}$ \\
		3 & $A+2* \to 2A^*$ & $r = k^+ C_A C_*^2$ & $r_A = \beta_{(Nk^+)} C_A - \beta_{(2Nk^+)} C_A U_{A^*} + \beta_{(k^+)} C_A U_{A^*}^2$ \\
		4 & $A^* + B^* \to 2* + C$ & $r = k^+C_{A^*}C_{B^*}$ & $r_{A^*} = \beta_{(k^+)}U_{A^*}U_{B^*}$ \\
		\hline
	\end{tabular}
\end{adjustbox}
\caption{Comparison of elementary steps that correspond to the traditional rate expression and linear model coefficients.}
\end{table}
\end{document}

stat

M. Ross Kunz
Adam Yonge
Zongtang Fang
Andrew J. Medford
Denis Constales
Gregory Yablonsky
Rebecca Fushimi

"Data Driven Reaction Mechanism Estimation via Transient Kinetics and Machine Learning" by M. Ross Kunz, Adam Yonge, Zongtang Fang, Andrew J. Medford, Denis Constales, Gregory Yablonsky, and Rebecca Fushimi, arXiv:2011.08810v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \hline
 Algorithm & MNIST $\rightarrow$ USPS & SVHN $\rightarrow$ MNIST \\
 \hline
 Source only & 75.2\% & 60.1\% \\
 Gradient Reversal & 77.1\% & 73.9\% \\
 Domain Confusion & 79.1\% & 68.1\% \\
 CoDAN & 91.2\% & did not converge \\
 ADDA & 89.4\% & 76.0\% \\
 Associative & 94.1\% & 93.6\% \\
 DANN & 60.8\% & 76.3\% \\
 Deep Coral & 69.5\% & 76.3\% \\
 VADA & 90.6\% & 92.6\% \\
 \hline
 E-ADDA & \textbf{95.4\%} & \textbf{95.4\%} \\
 \hline
 \\
 \\
 \end{tabular}
\end{adjustbox}
\caption{We compare our technique, E-ADDA, with nine other state-of-the-art deep domain adaptation techniques on two tasks (the performance is measured in accuracy, per the evaluation standard of the computer vision community).}
\end{table}
\end{document}

cs

Ye Gao
Brian Baucom
Karen Rose
Kristina Gordon
Hongning Wang
John Stankovic

"E-ADDA: Unsupervised Adversarial Domain Adaptation Enhanced by a New Mahalanobis Distance Loss for Smart Computing" by Ye Gao, Brian Baucom, Karen Rose, Kristina Gordon, Hongning Wang, and John Stankovic, arXiv:2201.10001v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
Architecture & +PDCR & +UAFS & JA & DI & AC \\
\midrule
UNet & & & 69.34 & 79.33 & 90.96 \\
UNet &\checkmark& & 70.50 & 80.51 & 92.02 \\
UNet &&\checkmark& 70.28 & 80.41 & 91.94 \\
UNet &\checkmark&\checkmark& \bf 71.35 & \bf 81.52 & \bf 93.01 \\
\midrule
FCN & & &72.31 & 81.20 & 92.22 \\
FCN &\checkmark& & 74.00 & 82.95 & 93.09\\
FCN & &\checkmark& 74.67 & 83.39 & 93.02 \\
FCN &\checkmark&\checkmark& \bf 75.57 & \bf 84.25 & \bf 93.95\\
\midrule
DeepLabv3+ &&& 77.15 & 85.13 & 92.88 \\
DeepLabv3+ &\checkmark& & 79.00& 86.91 & 94.11\\
DeepLabv3+ &&\checkmark& 78.96 & 86.19 & 94.28 \\
DeepLabv3+ &\checkmark&\checkmark& \bf 80.86 & \bf 88.74 & \bf 95.18 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Results of ablation studies for different architectures. A strong and consistent improvement is demonstrated.}
\end{table}
\end{document}

cs

Zhuowei Li
Zihao Liu
Zhiqiang Hu
Qing Xia
Ruiqin Xiong
Shaoting Zhang
Dimitris Metaxas
Tingting Jiang

"Contrastive and Selective Hidden Embeddings for Medical Image Segmentation" by Zhuowei Li, Zihao Liu, Zhiqiang Hu, Qing Xia, Ruiqin Xiong, Shaoting Zhang, Dimitris Metaxas, and Tingting Jiang, arXiv:2201.08779v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The results of ablation study on DKD metric when adapting Med-VFMs to AMOS2022-CT and AMOS2022-MRI domains. The performance was evaluated using Dice scores, and the results were reported as Mean$\pm$SD.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
\toprule
\multirow{2}{*}{Methods} & \multicolumn{2}{c|}{AMOS2022-CT} & \multicolumn{2}{c}{AMOS2022-MR} \\
\cline{2-5}
 & $5\%$ & $10\%$ & $5\%$ & $10\%$ \\
\midrule
RAND & 73.31$\pm$15.50 & 77.57$\pm$14.65 & 35.84$\pm$27.14 & 48.06$\pm$25.24 \\
High DKD & 76.14$\pm$13.67 & 79.48$\pm$12.56 & 45.23$\pm$20.89 & 64.46$\pm$19.56 \\
Low DKD & 64.48$\pm$14.72 & 68.20$\pm$14.83 & 26.86$\pm$22.67 & 42.59$\pm$20.75 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jin Yang
Daniel S. Marcus
Aristeidis Sotiras

"Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning" by Jin Yang, Daniel S. Marcus, and Aristeidis Sotiras, arXiv:2509.10784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative results on Kvasir-SEG and unseen (Challenge) dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l|l|l|l}
\hline
\textbf{Dataset}&\textbf{Method} & \textbf{DSC} & \textbf{mIOU} & \textbf{Recall} & \textbf{Precision} & \textbf{FPS} \\ \hline
Kvasir-SEG& DDANet & 0.8576 & 0.7800 & 0.8880 & 0.8643 & 69.59\\ \hline
Unseen (\textbf{Challenge}) & DDANet & 0.7874 & 0.7010 & 0.7987 & 0.8577 & 70.23 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Nikhil Kumar Tomar
Debesh Jha
Sharib Ali
Håvard D. Johansen
Dag Johansen
Michael A. Riegler
Pål Halvorsen

"DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation" by Nikhil Kumar Tomar, Debesh Jha, Sharib Ali, Håvard D. Johansen, Dag Johansen, Michael A. Riegler, and Pål Halvorsen, arXiv:2012.15245v1, 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}{|l|l|}
		\hline
		Porosity & 10\%\\
		\hline
		Total Compressibility & $5\times 10^{-8}$ Pa$^{-1}$\\
		\hline
		Dynamic Viscosity & 0.002 Pa*s\\
		\hline
		Production Index & 1.175$\times$10$^{-5}$ m$^3$Sec$^{-1}$MPa$^{-1}$\\
		\hline
		Bottom-hole Pressure & 20 MPa \\
		\hline
	\end{tabular}
\caption{A summary of constant physical and well parameters for test case 1.}
\end{table}
\end{document}

math

Zhao Zhang
Xinpeng Li
Menghan Li
Jiayu Zhai
Piyang Liu
Xia Yan
Kai Zhang

"Nonlinear Expectation Inference for Efficient Uncertainty Quantification and History Matching of Transient Darcy Flows in Porous Media with Random Parameters Under Distribution Uncertainty" by Zhao Zhang, Xinpeng Li, Menghan Li, Jiayu Zhai, Piyang Liu, Xia Yan, and Kai Zhang, arXiv:2312.06923v3, 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{Comparative Analysis of Machine Learning Model Performance}
\begin{tabular}{lllll}
 \toprule
 \textbf{Model name} & \textbf{Precision} & \textbf{Recall} & \textbf{F1} & \textbf{Accuracy} \\
 \midrule

 NB & 0.778 & 0.989 & 0.772 & 0.740 \\
 GLM & 0.797 & 0.989 & 0.830 & 0.809 \\
 GBM & \textbf{0.815} & \textbf{0.998} & \textbf{0.843} & \textbf{0.822} \\
 MLP& 0.792 & 0.998 & 0.839 & 0.819 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Zainab Al-Zanbouri
Gauri Sharma
Shaina Raza

"Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions" by Zainab Al-Zanbouri, Gauri Sharma, and Shaina Raza, arXiv:2403.19057v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Pre-training Results.} `S': speech input and output. `S$\rightarrow$T': speech input and text output. `T$\rightarrow$S': text input and speech output. Results for Spirit-LM are taken from and other results are from . We use $\emptyset$ to indicate tasks and modalities not supported by the model, and - to indicate scores that are not publicly available.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc|cccccc}
\toprule
 \multirow{3}{*}{Model} & \multicolumn{6}{c|}{\textbf{Speech Language Modeling}} & \multicolumn{6}{c}{\textbf{Spoken Question Anwsering}} \\
 & \multicolumn{3}{c}{sTopic-StoryCloze} & \multicolumn{3}{c|}{sStoryCloze} & \multicolumn{2}{c}{Web Questions} & \multicolumn{2}{c}{Llama Questions} & \multicolumn{2}{c}{TriviaQA} \\
 & S & T$\rightarrow$S & S$\rightarrow$T & S & T$\rightarrow$S & S$\rightarrow$T & S & S$\rightarrow$T & S & S$\rightarrow$T & S & S$\rightarrow$T \\
\midrule
GSLM & 66.6 & $\emptyset$ & $\emptyset$ & 53.3 & $\emptyset$ & $\emptyset$ & 1.5 & $\emptyset$ & 4.0
& $\emptyset$ & - & - \\
AudioLM & - & $\emptyset$ & $\emptyset$ & - & $\emptyset$ & $\emptyset$ & 2.3 & $\emptyset$ & 7.0 & $\emptyset$ & - & - \\
TWIST & 76.4 & $\emptyset$ & $\emptyset$ & 55.4 & $\emptyset$ & $\emptyset$ & 1.1 & $\emptyset$ & 0.5 & $\emptyset$ & - & - \\
Spirit-LM & 82.9 & 72.7 & 88.6 & 61.0 & 59.6 & 64.6 & - & - & - & - & - & - \\
SpeechGPT & $\emptyset$ & $\emptyset$ & $\emptyset$ & $\emptyset$ & $\emptyset$ & $\emptyset$ & $\emptyset$ & 6.5 & $\emptyset$ & 21.6 & $\emptyset$ & 14.8 \\
Spectron & - & - & - & - & - & - & $\emptyset$ & 6.1 & $\emptyset$ & 21.9 & $\emptyset$ & - \\
Moshi & \textbf{83.0} & $\emptyset$ & $\emptyset$ & 60.8 & $\emptyset$ & $\emptyset$ & 9.2 & 26.6 & 21.0 & 62.3 & 7.3 & 22.8 \\ \midrule
Ours (9B) & 82.9 & \textbf{85.0} & \textbf{93.6} & \textbf{62.4} & \textbf{63.2} & \textbf{76.3} & \textbf{15.9} & \textbf{32.2} & \textbf{50.7} & \textbf{64.7} & \textbf{26.5} & \textbf{39.1} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Aohan Zeng
Zhengxiao Du
Mingdao Liu
Lei Zhang
Shengmin Jiang
Yuxiao Dong
Jie Tang

"Scaling Speech-Text Pre-training with Synthetic Interleaved Data" by Aohan Zeng, Zhengxiao Du, Mingdao Liu, Lei Zhang, Shengmin Jiang, Yuxiao Dong, and Jie Tang, arXiv:2411.17607v2, 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{Recognition accuracy (\%) of competing approaches on the AR database when two neutral face images (from Sessions 1 and 2) per subject are used as training samples, the dimension that leads to the best result for IGO and SOIGO based approaches is given in parentheses.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
Methods & \multicolumn{2}{c}{Sunglasses} & \multicolumn{2}{c}{Scarf} & Overall \\ \hline
 & Session1 & Session2 & Session1 & Session2 & \\
HQPAMI & 61.33 & 59.33 & 44.67 & 48.00 & 53.33 \\
NR & 34.00 & 33.33 & 33.00 & 35.67 & 34.00 \\
ProCRC & 53.00 & 54.67 & 18.00 & 17.67 & 35.84 \\
F-LR-IRNNLS & 90.33 & 87.67 & 78.67 & 76.00 & 83.17 \\
EGSNR & 88.00 & 89.33 & 80.00 & 73.00 & 82.58 \\
LDMR & 71.00 & 63.67 & 64.00 & 61.00 & 64.92 \\
GD-HASLR & 93.00 & 93.33 & 82.67 & 84.00 & 88.25 \\
IGO-PCA-NNC & 93.00 (182) & 91.67 (191) & 78.00 (199) & 74.00 (193) & 84.17 \\
IGO-PCA-CRC & 96.00 (128) & 95.33 (116) & 85.00 (190) & 84.00 (160) & 90.08 \\
SOIGO-PCA-NNC & 96.33 (187) & 92.67 (197) & \textbf{86.33 (166)} & 83.67 (189) & 89.75 \\
CSOIGO & \textbf{97.33 (144)} & \textbf{95.67 (124)} & 86.00 (119) & \textbf{85.67 (198)} & \textbf{91.17} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Face recognition via compact second order image gradient orientations" by He-Feng Yin, Xiao-Jun Wu, and Xiaoning Song, arXiv:2201.09246v2, 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{CORDIC Hyperbolic Rotational Mode ($\cosh$ \& $\sinh$)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccr}
 \hline
 \textbf{i (clk)} & \textbf{Ei = tanh\textsuperscript{-1}(2\textsuperscript{-i})} & \textbf{Xi+1 $\rightarrow$ $\cosh$(0.5)} & \textbf{Yi+1 $\rightarrow$ sinh(0.5)} & \textbf{Zi+1 $\rightarrow$ 0} & \textbf{di} \\ \hline
 & & \textbf{Initial} Xi = 1/k' & \textbf{Initial} Yi = 0 & Zinput = 0.5 & \\ \hline
 \textbf{1} & 0.5493 & 1.2075 & \textbf{0.6037} & -0.0493 & 1 \\ \hline
 \textbf{2} & 0.2554 & 1.0566 & 0.3019 & 0.2061 & -1 \\ \hline
 \textbf{3} & 0.1257 & 1.0943 & 0.4339 & 0.0804 & 1 \\ \hline
 \textbf{4} & \textbf{0.0626} & \textbf{1.1214} & \textbf{0.5023} & \textbf{0.0179} & \textbf{1} \\ \hline
 \textbf{5} & 0.0313 & 1.1371 & 0.5374 & -0.0134 & 1 \\ \hline
 \textbf{6} & 0.0156 & 1.1287 & 0.5196 & 0.0022 & -1 \\ \hline
 \textbf{7} & 0.0068 & 1.1328 & 0.5284 & -0.0046 & 1 \\ \hline
 \textbf{8} & 0.0039 & 1.1307 & 0.5240 & -0.0007 & -1 \\ \hline
 \textbf{9} & 0.0020 & \textbf{1.1297} & \textbf{0.5218} & \textbf{0.0013} & -1 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mukul Lokhande
Gopal Raut
Santosh Kumar Vishvakarma

"Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads" by Mukul Lokhande, Gopal Raut, and Santosh Kumar Vishvakarma, arXiv:2412.11702v1, 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}{||ccc||}
 \hline
 Algorithm 1 & Algorithm 2 & Reject Null Hypothesis? \\
 \hline\hline
 Logistic Regression & Decision Tree & Yes \\
 \hline
 Decision Tree & Neural Network & Yes \\
 \hline
 Logistic Regression & Neural Network & No \\
 \hline
\end{tabular}
\caption{Hypothesis decision results}
\end{table}
\end{document}

cs

"Comparing Classification Models on Kepler Data" by Rohan Saha, arXiv:2101.01904v2, 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{Hypothetical capacitor optimization options}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{part \#} & \textbf{Description} & \textbf{$\mu F$ at 3.3V} & \textbf{Cost} & \textbf{Area mm$^2$} \\
\hline
A & 1$\mu F$ 0201 6.3V & 0.35 & \$0.002 & 0.7 \\
\hline
B & 2.2$\mu F$ 0201 6.3V & 0.85 & \$0.003 & 0.7 \\
\hline
C & 1$\mu F$ 0201 10V & 0.45 & \$0.003 & 0.7 \\
\hline
D & 2.2$\mu F$ 0201 10V & 0.95 & \$0.004 & 0.7 \\
\hline
E & 2.2$\mu F$ 0402 6.3V & 0.90 & \$0.003 & 1.3 \\
\hline
F & 4.7$\mu F$ 0402 6.3V & 1.70 & \$0.007 & 1.3 \\
\hline
G & 2.2$\mu F$ 0402 10V & 1.00 & \$0.005 & 1.3 \\
\hline
H & 4.7$\mu F$ 0402 10V & 1.95 & \$0.008 & 1.3 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Automating Capacitor Part Selection with Dual-Objective Optimization" by Luke Brantingham and Jason Grover, arXiv:2507.16071v1, 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{Coherent beamforming protocols.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
RB & Random beamforming protocol \\ \hline
RT & Random target protocol \\ \hline
BT & Best target protocol \\ \hline
SF & Sequential fixing protocol \\ \hline
IO & Iterative optimization protocol \\ \hline
ES & Exhaustive search protocol
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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


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

			 1.1
			&

			&
			L12
			&
			Unstable node
			&
			G1 \\
			\hline

			1.2
			&

			&
			L3
			&
			Stable node
			&
			G2 \\
		\hline

			\multirow{2}{1cm}{\centering{1.3}}
			&
			$\mu<-1$
			&
			L8
			&
			Saddle
			&
			G3 \\
			\cline{2-5}

			\multirow{2}{*}
			&
			$\mu\in(-1,0)$
			&
			L13
			&
			Unstable node
			&
			G4 \\
			\hline

			\multirow{4}{1cm}{\centering{1.4}}
			&
			$\mu<-2$
			&
			L1
			&
			Saddle
			&
			G5 \\
			\cline{2-5}

			\multirow{4}{*}
			&
			$\mu\in(-1,0)$
			&
			L4
			&
			Saddle
			&
			G7 \\
			\cline{2-5}

			\multirow{4}{*}
			&
			$\mu\in(-2,-1)$
			&
			L15
			&
			Saddle
			&
			G7\\
			\cline{2-5}

			\multirow{4}{*}
			&
			$\mu=-2$
			&
			L17
			&
			Saddle
			&
			G8 \\
			\hline

			1.5
			&

			&
			L12
			&
			Unstable node
			&
			G9 \\
			\hline

			1.6
			&

			&
			L3
			&
			Stable node
			&
			G10 \\
			\hline

		\multirow{2}{1cm}{\centering{1.7}}
		&
		$\mu\in(-1,0)$
		&
		L10
		&
		Stable node
		&
		G11 \\
		\cline{2-5}

		\multirow{2}{*}
		&
		$\mu<-1$
		&
		L11
		&
		Saddle
		&
		G12 \\
		\hline

				\multirow{4}{1cm}{\centering{1.8}}
				&
				$\mu<-2$
				&
				L2
				&
				Saddle
				&
				G13 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu\in(-1,0)$
				&
				L7
				&
				Unstable node
				&
				G14 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu\in(-2,-1)$
				&
				L16
				&
				Saddle
				&
				G15 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu=-2$
				&
				L18
				&
				Saddle
				&
				G16 \\
				\hline

				1.9
				&

				&
				L9
				&
				Stable node
				&
				G19 \\
				\hline

				1.10
				&

				&
				L6
				&
				Unstable node
				&
				G20 \\
				\hline

				1.11
				&

				&
				L12
				&
				Unstable node
				&
				G17 \\
				\hline

				\multirow{2}{1cm}{\centering{1.12}}
				&
				$\mu<-1$
				&
				L8
				&
				Saddle
				&
				G21 \\
				\cline{2-5}

				\multirow{2}{*}
				&
				$\mu\in(-1,0)$
				&
				L13
				&
				Unstable node
				&
				G22 \\
				\hline

				1.13
				&

				&
				L3
				&
				Stable node
				&
				G18 \\
				\hline
				\multirow{4}{1cm}{\centering{1.14}}
				&
				$\mu<-2$
				&
				L1
				&
				Saddle
				&
				G23 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu\in(-1,0)$
				&
				L4
				&
				Stable node
				&
				G24 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu\in(-2,-1)$
				&
				L15
				&
				Saddle
				&
				G25 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu=-2$
				&
				L17
				&
				Saddle
				&
				G26 \\
				\hline

				1.15
				&

				&
				L12
				&
				Unstable node
				&
				G27 \\
				\hline

				\multirow{2}{1cm}{\centering{1.16}}
				&
				$\mu<-1$
				&
				L8
				&
				Saddle
				&
				G35 \\
				\cline{2-5}

				\multirow{2}{*}
				&
				$\mu\in(-1,0)$
				&
				L13
				&
				Unstable node
				&
				G36 \\
				\hline

				1.17
				&

				&
				L3
				&
				Stable node
				&
				G28 \\
				\hline

				\multirow{4}{1cm}{\centering{1.18}}
				&
				$\mu<-2$
				&
				L1
				&
				Saddle
				&
				G37 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu\in(-1,0)$
				&
				L4
				&
				Stable node
				&
				G38 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu\in(-2,-1)$
				&
				L15
				&
				Saddle
				&
				G39 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu=-2$
				&
				L17
				&
				Saddle
				&
				G40 \\
				\hline

				\multirow{2}{1cm}{\centering{1.19}}
				&
				$\mu\in(-1,0)$
				&
				L10
				&
				Saddle
				&
				G29 \\
				\cline{2-5}

				\multirow{2}{*}
				&
				$\mu<-1$
				&
				L11
				&
				Saddle
				&
				G30 \\
				\hline

				\multirow{4}{1cm}{\centering{1.20}}
				&
				$\mu<-2$
				&
				L2
				&
				Saddle
				&
				G31 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu\in(-1,0)$
				&
				L7
				&
				Unstable node
				&
				G32 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu\in(-2,-1)$
				&
				L16
				&
				Saddle
				&
				G33 \\
				\cline{2-5}

				\multirow{4}{*}
				&
				$\mu=-2$
				&
				L18
				&
				Saddle
				&
				G34 \\
				\hline

				2.1
				&

				&
				L46
				&
				Stable node
				&
				G41 \\
				\hline

				2.2
				&

				&
				L47
				&
				Unstable node
				&
				G42 \\
				\hline

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

math

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

"Phase portraits of a family of Kolmogorov systems depending on six parameters" by Érika Diz-Pita, Jaume Llibre, and M. Victoria Otero-Espinar, arXiv:2501.13714v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Gaurab Aryal
Anirban Chattopadhyaya
Federico Ciliberto

"Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers" by Gaurab Aryal, Anirban Chattopadhyaya, and Federico Ciliberto, arXiv:2503.15785v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \toprule
 \textbf{Model} & \textbf{Ogbn-mag} \\ \midrule
 GraphSAGE & 46.32 $\pm$ 0.73 \\
 \textbf{+ MGM} & \textbf{47.94 $\pm$ 0.65} \\
 \hdashline\noalign{\vskip 0.5ex}
 GAT & 44.54 $\pm$ 0.63 \\
 \textbf{+ MGM} & \textbf{46.28 $\pm$ 0.25} \\
 \hdashline\noalign{\vskip 0.5ex}
 FiLM & 41.72 $\pm$ 0.22 \\
 \textbf{+ MGM} & \textbf{43.32 $\pm$ 0.27} \\
 \hdashline\noalign{\vskip 0.5ex}
 GATv2 & 45.41 $\pm$ 0.42 \\
 \textbf{+ MGM} & \textbf{46.74 $\pm$ 0.36} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Performance comparison on Ogbn-mag.}
\end{table}
\end{document}

stat

Muhammad Arslan Manzoor
Ruihong Zeng
Dilshod Azizov
Preslav Nakov
Shangsong Liang

"MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media" by Muhammad Arslan Manzoor, Ruihong Zeng, Dilshod Azizov, Preslav Nakov, and Shangsong Liang, arXiv:2412.10467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrr}
\hline
& Full tuning & Solve and gradient & Gradient \\ \hline
CVXPYlayers & 61 sec & 57 sec & 23 sec \\
CVXPYgen & 21 sec & 17 sec & 2 sec \\
\hline
\end{tabular}
\caption{Computation times with CVXPY and CVXPYgen for the portfolio optimization example.}
\end{table}
\end{document}

math

"Code generation for solving and differentiating through convex optimization problems" by Maximilian Schaller and Stephen Boyd, arXiv:2504.14099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Prediction performance comparison among different models for different $g_{true}$'s}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|cc|c||c|c|c|c|}
 \hline
 \multirow{5}{*}{\rotatebox[origin=c]{90}{$h_{true}(x)=$}} & \multirow{5}{*}{\rotatebox[origin=c]{90}{$x^2-3x+2$}} & \multirow{5}{*}{\rotatebox[origin=c]{90}{$\beta_{true}=\sum\limits_{i=1,3}2b_i(t)+$}} & \multirow{5}{*}{\rotatebox[origin=c]{90}{$\sum\limits_{i=2,4}\sqrt{2}b_i(t)$}} &$g_{true}(x)$ & \multicolumn{2}{|c|}{$x^3-3x+4$} & \multicolumn{2}{|c|}{$5x^2-4$}\\
 \cline{5-9}
 &&&& Prediction & $R^2$ & $\frac{||\widehat{g}-g_{true}||}{||g_{true}||}$ & $R^2$ & $\frac{||\widehat{g}-g_{true}||}{||g_{true}||}$\\
 \cline{5-9}\cline{5-9}
 &&&& SI-ScoSh (Poly) & 0.99 & 0.33 & 0.98 & 0.36 \\
 &&&& SI-ScoSh (SVM) & 0.98 & 0.28 & 0.98 & 0.29\\
 &&&& SI-ScoF(\textit{FR}) & 0.54 & 0.59 & 0.12 & 0.97 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Scalar-on-Shape Regression Models for Functional Data Analysis" by Sayan Bhadra and Anuj Srivastava, arXiv:2411.15326v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{RQ$_1$.} Detection Capability and Detected Warnings in the 47 projects.}
\begin{tabular}{l|l|l|l|l|l|l|l} \hline
\multirow{2}{*}{\textbf{Tool}} & \multicolumn{3}{c|}{\textbf{Detection Capability}} & \multicolumn{4}{c}{\textbf{Detected warning}} \\ \cline{2-8}
& \textbf{\# rule } & \textbf{type} & \textbf{severity} & \textbf{\# rule }& \textbf{\# occurrences }& \textbf{type} & \textbf{severity}\\ \hline
Better Code Hub & 10 & 3 & 3 & 8 & 27,888 & 3 & 3\\
Checkstyle & 173 & 14 & 4 & 88 & 9,686,813 & 12 & 2\\
Coverity & & & & 130 & 7,431 & 26 & 3\\
Findbugs & 424 & 9 & 4 & 255 & 33,704 & 9 & 3\\
PMD & 305 & 8 & 5 & 275 & 3,380,493 & 7 & 5\\
SonarQube & 413 & 3 & 5 & 180 & 418,433 & 3 & 5\\\hline
\end{tabular}
\end{table}
\end{document}

cs

Valentina Lenarduzzi
Savanna Lujan
Nyyti Saarimaki
Fabio Palomba

"A Critical Comparison on Six Static Analysis Tools: Detection, Agreement, and Precision" by Valentina Lenarduzzi, Savanna Lujan, Nyyti Saarimaki, and Fabio Palomba, arXiv:2101.08832v1, 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{Comparison of BD method with other defense methods against targeted hard-label attacks in terms of ASR (\%).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccc}
		\toprule
		Dataset & Defense & HSJA & BA & SimBA-DCT \\
		\midrule
		 & DD & 98 & 80 & - \\
		 & AT & 100 & 50 & 4 \\
		 & Region-based & 100 & 85 & - \\
		 MNIST & BD ($\theta = 0.5, \sigma = 0.1$)
		 & \textbf{38}& \textbf{17} & \textbf{3} \\
		 & BD ($\theta = 0.7, \sigma = 0.1$)& \textbf{7} & \textbf{9}& \textbf{0} \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Boundary Defense Against Black-box Adversarial Attacks" by Manjushree B. Aithal and Xiaohua Li, arXiv:2201.13444v1, 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{List of hyperparameters for CIFAR-10/100, ImageNet-100, and Tiny-ImageNet.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
 \toprule
 \textbf{Hyper-parameter} & CIFAR-10 & CIFAR-100 & ImageNet-100 & Tiny-ImageNet \\
 \midrule
 SGD-momentum & \multicolumn{4}{c}{0.9} \\
 SGD-learning rate & \multicolumn{4}{c}{0.1} \\
 SK-$\epsilon$ & \multicolumn{4}{c}{10} \\
 SK-iteration & \multicolumn{4}{c}{10} \\
 \# of local views & \multicolumn{4}{c}{4} \\
 \midrule
 SGD-weight decay & 0.0005 & 0.0005 & 0.0001 & 0.0001 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Shengjie Niu
Lifan Lin
Jian Huang
Chao Wang

"OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning" by Shengjie Niu, Lifan Lin, Jian Huang, and Chao Wang, arXiv:2411.01833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Pariyakorn Maneekul
Zelda B. Zabinsky
Giulia Pedrielli

"Branching Adaptive Surrogate Search Optimization (BASSO)" by Pariyakorn Maneekul, Zelda B. Zabinsky, and Giulia Pedrielli, arXiv:2504.18002v1, 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{Ablation study of normalization methods for multi-domain CTR prediction. STAR FCN is trained BN, LN and PN respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
	\toprule
	 &STAR FCN (BN) & STAR FCN (LN) & STAR FCN (PN) \\
	\midrule
	Overall AUC &0.6415 & 0.6463 & \textbf{0.6506} \\
	\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiang-Rong Sheng
Liqin Zhao
Guorui Zhou
Xinyao Ding
Binding Dai
Qiang Luo
Siran Yang
Jingshan Lv
Chi Zhang
Hongbo Deng
Xiaoqiang Zhu

"One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction" by Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, and Xiaoqiang Zhu, arXiv:2101.11427v5, 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}{lccc}
\toprule
\multirow{2}{*}{Embeddings} & Word2Vec 		& GloVe 						& fastText \\
 			 & Org/Deb 		& Org/Deb 					& Org/Deb \\ \midrule
definition 	 			& 83.0/\textbf{83.9} 	& 83.0/\textbf{83.4} 			& 92.0/\textbf{93.2}\\
stereotype 			& 13.4/\textbf{12.3} 	& 12.0/\textbf{11.4} 			& 5.5/\textbf{4.3} \\
none 			& \textbf{3.6}/3.9 	& \textbf{5.0}/5.2 			& \textbf{2.5}/\textbf{2.5} \\ \midrule
sub-definition 			& 50.0/\textbf{57.5} 	& \textbf{67.5}/\textbf{67.5} 	& 82.5/\textbf{85.0} \\
sub-stereotype 			& 40.0/\textbf{32.5} 	& \textbf{27.5}/\textbf{27.5} 	& 12.5/\textbf{10.0} \\
sub-none 			& \textbf{10.0}/\textbf{10.0} & \textbf{5.0}/\textbf{5.0} & \textbf{5.0}/\textbf{5.0} \\ \bottomrule
\end{tabular}
\caption{Prediction accuracies for gender relational analogies on SemBias.}
\end{table}
\end{document}

cs

"Dictionary-based Debiasing of Pre-trained Word Embeddings" by Masahiro Kaneko and Danushka Bollegala, arXiv:2101.09525v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
Model & Number of Parameters & Model &Accuracy \\
\hline
 & 256 & SVM & 89.72\% \\ \hline
 & 4096 & Knn & 93.84\% \\ \hline
 & \textbf{8} & Knn & 95.24\% \\ \hline
 & 45 & Knn & 95.67\% \\ \hline
 & 1087 & CNN \& SVM & 96.11\% \\ \hline
 & 142 & Ensemble & 99.2\% \\ \hline
 & 6.1$\times10^7$ & CNN & 99.5\% \\ \hline
 \textbf{Ours} & 24 & RF & \textbf{100.00\%} \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Comparison with related works for the ALL-IDB2 data. The best approaches for the smallest number of parameters and highest accuracy are boldened.}
\end{table}
\end{document}

cs

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overall performance comparison on IEMOCAP (4-class).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
Model & Upstream(A) & Upstream(T) & ACC (\%) & F1 (\%) \\
\midrule
GatedxLSTM~ & CLAP & CLAP & -- & 75.97±1.38 \\
MER-HAN~ & Bi-LSTM & BERT & 74.20 & 73.66 \\
MMI-MMER~ & Wav2Vec2 & BERT &77.02 & -- \\
MGCMA~ & Wav2Vec2 & BERT &78.87 & -- \\
MEP~ & openSMILE & BERT & 80.18 & 80.01 \\
Bi-GRU~ & Acoustic model & GloVe & 80.63 & -- \\
MemoCMT & HuBERT & BERT & 81.85 & -- \\
Linear & emotion2vec & BERT & 81.68 & 80.75 \\
CFIA & emotion2vec & BERT & 83.37 & -- \\
\midrule
\textbf{LPGNet(Frame)} & emotion2vec & BERT & \textbf{83.87} & \textbf{83.68} \\
\textbf{LPGNet(Utterance)} & emotion2vec & BERT & \textbf{87.99} & \textbf{87.96} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition" by Zhining He and Yang Xiao, arXiv:2508.08925v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
		\hline
 Method & avg-min-CDR $\downarrow$ & JDR $\downarrow$ & Recall $\uparrow$ & FPS $\uparrow$\\
 \hline
 Video SOD~& 14.49\%& 4.939& 22.91\%& 6.3\\
 Video FP~& 14.47\%& 0.878&45.73\%& 15.6\\
 Video Cropping~& 15.25\%& 5.382&20.09\%& 24.9\\
 \hline
 Our Framework& \textbf{10.94\%}& \textbf{0.865}& \textbf{69.53\%} &\textbf{28.6}\\
 \hline
		\end{tabular}
\end{adjustbox}
\caption{Comparison of our {\it Rank Sub-Select} module based {\it H2V} framework with the state-of-the-art Video Salient Object Detection, Video Fixation Prediction and Video Cropping methods in {\it H2V-142K} Video Subset.}
\end{table}
\end{document}

cs

Horizontal-to-Vertical Video Conversion

Tun Zhu
Daoxin Zhang
Yao Hu
Tianran Wang
Xiaolong Jiang
Jianke Zhu
Jiawei Li

"Horizontal-to-Vertical Video Conversion" by Tun Zhu, Daoxin Zhang, Yao Hu, Tianran Wang, Xiaolong Jiang, Jianke Zhu, and Jiawei Li, arXiv:2101.04051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \cline{2-5}
 & daily & six-hourly & three-hourly & 10min \\
 \hline
 daily&1&$\mathbf{2.14\cdot10^{-1}}$&$\mathbf{2.18\cdot10^{-1}}$& $\mathbf{1.29\cdot10^{-1}}$ \\ \hline
 six-hourly&&1&$\mathbf{1.00}$&$\mathbf{9.78\cdot10^{-1}}$ \\ \hline
 three-hourly&&&1& $\mathbf{9.77\cdot10^{-1}}$ \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{$p$-values of Kolmogorov-Smirnov test for instantaneous Owez wind speed distributions. All values where $p \leq 0.05$ are marked \textbf{bold} and the corresponding distributions are not considered to be significantly different.}
\end{table}
\end{document}

stat

Nina Effenberger
Nicole Ludwig
Rachel H. White

"Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts" by Nina Effenberger, Nicole Ludwig, and Rachel H. White, arXiv:2309.09540v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Error analysis for Lorenz's system example using proposed method}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
variable&bias&MAPE&MAE&RMSE&R$^2$\\ \hline
$x_1$&-0.5613&0.2924&1.8619&2.2451&0.8609\\
$x_2$&-0.3759&0.3940&2.3256&2.8221&0.8260\\
$x_3$&-2.1314&0.1638&3.5015&4.2435&0.6758\\
$f_1(\cdot)$&1.5081&2.1949&19.6961&25.3391&0.5510\\
$f_2(\cdot)$&1.8151&1.2384&26.3919&32.8529&0.6511\\
$f_3(\cdot)$&-0.9230&1.3472&30.2180&39.2456&0.6421\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

S. Syafiie
Aries Subiantoro
Vivi Andasari
Fernando Tadeo

"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 $n$ & Error $\|\cdot\|_1$ & Order $\|\cdot\|_1$ & Error
 $\|\cdot\|_{\infty}$ & Order $\|\cdot\|_{\infty}$ \\
 \hline
 20 & 1.14E$-2$ & $-$ & 1.12E$-1$ & $-$ \\
 \hline
 40 & 4.82E$-3$ & 1.56 & 4.54E$-2$ & 1.40 \\
 \hline
 80 & 8.33E$-6$ & 9.18 & 2.25E$-4$ & 7.66 \\
 \hline
 160 & 7.53E$-8$ & 6.79 & 3.00E$-6$ & 6.23 \\
 \hline
 320 & 2.14E$-9$ & 5.14 & 1.01E$-7$ & 4.89 \\
 \hline
 640 & 6.59E$-11$ & 5.02 & 3.57E$-9$ & 4.82 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for problem - , Complex domain, WLS-UW.}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|} \hline
 \text{type} & $A_n$ & $D_n$ & $E_6$ & $E_7$ & $E_8$ \\ \hline
 ${h}$ & $n+1$ & $2n-2$ & $12$ & $18$ & $30$ \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Yuki Kanakubo
Gleb Koshevoy
Toshiki Nakashima

"Products of Kirillov-Reshetikhin modules and maximal green sequences" by Yuki Kanakubo, Gleb Koshevoy, and Toshiki Nakashima, arXiv:2503.23833v1, 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{Shapiro-Wilk statistics value for all the models . Top 5 values highlighted.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|l|l} \toprule
 & \multicolumn{3}{c}{\textbf{Normalization method}} \\ \cmidrule{2-4}
\textbf{Model} & $\frac{X-\min(X)}{\max(X)-\min(X)}$ & \textbf{$log_{10}()$} & $\gamma()$ \\ \midrule
$\frac{wind\ *\ rain}{distance}$ & $0.07$ & $\boldsymbol{0.99}$ & $0.99$ \\ \hline
$\frac{rain}{distance}$ & $0.06$ & $0.92$ & $0.93$ \\ \hline
$\frac{wind}{distance}$ & $0.13$ & $0.95$ & $0.97$ \\ \hline
$\frac{wind\ *\ rain}{\sqrt[]{distance}}$ & $0.53$ & $\boldsymbol{0.98}$ & $\boldsymbol{0.98}$ \\ \hline
$\frac{rain}{\sqrt[]{distance}}$ & $0.51$ & $0.88$ & $0.89$ \\ \hline
$\frac{wind}{\sqrt[]{distance}}$ & $0.88$ & $0.88$ & $\boldsymbol{0.98}$ \\ \hline
$\frac{wind\ *\ rain}{\sqrt[3]{distance}}$ & $0.65$ & $\boldsymbol{0.98}$& $\boldsymbol{0.98}$ \\ \hline
$\frac{rain}{\sqrt[3]{distance}}$ & $0.65$ & $0.85$& $0.86$ \\ \hline
$\frac{wind}{\sqrt[3]{distance}}$ & $0.96$ & $0.85$& $\boldsymbol{0.99}$ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Somya D. Mohanty
Brown Biggers
Saed Sayedahmed
Nastaran Pourebrahim
Evan B. Goldstein
Rick Bunch
Guangqing Chi
Fereidoon Sadri
Tom P. McCoy
Arthur Cosby

"A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma" by Somya D. Mohanty, Brown Biggers, Saed Sayedahmed, Nastaran Pourebrahim, Evan B. Goldstein, Rick Bunch, Guangqing Chi, Fereidoon Sadri, Tom P. McCoy, and Arthur Cosby, arXiv:2101.00480v1, 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{Performance drop of each method compared to the best one, reported from Fig.~.}
\begin{tabular}{l|c|c|c}
 \hline
 \textbf{Method} & \textbf{$0.25$-quantile} & \textbf{Median} & \textbf{$0.75$-quantile} \\
 \hline
 SIMBa-1		&	\textbf{1.01}	&	\textbf{1.08}	&	\textbf{1.18}	\\
 SIMBa-4		&	1.02	&	1.10	&	1.30	\\
 CVA		&	1.12	&	1.20	&	1.54	\\
 MOESP		&	1.20	&	1.30	&	1.64	\\
 N4SID		&	1.26	&	1.37	&	1.67	\\ \hline
 PARSIM-K	&	\textbf{1.00}	&	\textbf{1.00}	&	\textbf{1.09}	\\
 PARSIM-S	&	1.47	&	4.75	&	30.91	\\
 PARSIM-P	&	2.40	&	7.00	&	223.76	\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Loris Di Natale
Muhammad Zakwan
Philipp Heer
Giancarlo Ferrari-Trecate
Colin N. Jones

"SIMBa: System Identification Methods leveraging Backpropagation" by Loris Di Natale, Muhammad Zakwan, Philipp Heer, Giancarlo Ferrari-Trecate, and Colin N. Jones, arXiv:2311.13889v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing" by Cong William Lin and Wu Zhu, arXiv:2504.13629v1, 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{Results of SLM-Llama on the StyleTalk dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccccc|}
\hline
\textbf{Methods} &
 \multicolumn{2}{c|}{\textbf{CS Score}} &
 \multicolumn{4}{c|}{\textbf{EGS Score}} \\ \hline
\textbf{} &
 \textbf{Content} &
 \textbf{Style} &
 \textbf{C1} &
 \textbf{C2} &
 \textbf{C3} &
 \textbf{C4} \\ \hline
 &
 \multicolumn{6}{c|}{\textbf{Judge: Qwen2.5-72B-Instruction}} \\ \hline
\textbf{Qwen2-Audio} &
 3.86 &
 3.94 &
 8.12 &
 9.73 &
 7.70 &
 8.16 \\ \hline
\textbf{Llama-Omni} &
 3.76 &
 4.00 &
 8.10 &
 9.64 &
 7.55 &
 8.09 \\ \hline
\textbf{SLM-Qwen (ours)} &
 \textbf{4.28} &
 \textbf{4.40} &
 \textbf{8.98} &
 \textbf{9.95} &
 \textbf{8.44} &
 \textbf{8.91} \\ \hline
\textbf{SLM-Llama (ours)} &
 4.12 &
 4.22 &
 8.69 &
 9.74 &
 8.19 &
 8.60 \\ \hline
 &
 \multicolumn{6}{c|}{\textbf{Judge: Llama-3.1-70B-Instruction}} \\ \hline
\textbf{Qwen2-Audio} &
 3.95 &
 4.03 &
 8.71 &
 9.97 &
 7.33 &
 7.97 \\ \hline
\textbf{Llama-Omni} &
 3.80 &
 4.06 &
 8.51 &
 9.91 &
 7.04 &
 7.63 \\ \hline
\textbf{SLM-Qwen (ours)} &
 \textbf{4.41} &
 \textbf{4.44} &
 \textbf{9.59} &
 \textbf{10.00} &
 \textbf{8.07} &
 \textbf{8.73} \\ \hline
\textbf{SLM-Llama (ours)} &
 4.28 &
 4.34 &
 9.23 &
 9.92 &
 7.93 &
 8.46 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Chun Wang
Chenyang Liu
Wenze Xu
Weihong Deng

"Dual Information Speech Language Models for Emotional Conversations" by Chun Wang, Chenyang Liu, Wenze Xu, and Weihong Deng, arXiv:2508.08095v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Makoto Araya
Masaaki Harada
Hadi Kharaghani
Sho Suda
Wei-Hsuan Yu

"Unbiased weighing matrices of weight $9$" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quaternionic Extensions of Pauli Operators}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
\hline
\textbf{Operator} & \textbf{Quaternionic Form} & \textbf{Interpretation} \\
\hline
\( X \) & \( \mathcal{Q}_X = iX \) & Bit-flip with quaternionic phase \\
\( Y \) & \( \mathcal{Q}_Y = jY \) & Bit and phase-flip with quaternionic phase \\
\( Z \) & \( \mathcal{Q}_Z = kZ \) & Phase-flip with quaternionic twist \\
\( I \) & \(\mathcal{Q}_I = I \) & Identity operator \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Valentine Nyirahafashimana
Nurisya Mohd Shah
Umair Abdul Halim
Mohamed Othman
Sharifah Kartini Said Husain

"Quantum Error Correction in Quaternionic Hilbert Spaces" by Valentine Nyirahafashimana, Nurisya Mohd Shah, Umair Abdul Halim, Mohamed Othman, and Sharifah Kartini Said Husain, arXiv:2504.19833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}\toprule
\multirow{2}{*}{\textbf{Strategy}} %&\multirow{2}{*}{\textbf{Persuasion}}
&\textbf{Persuasion (P4G)}
&\multicolumn{2}{c}{\textbf{Negotiation (CB)}} \\\cmidrule{2-4}
& \textbf{EE}&\textbf{BU} &\textbf{SE} \\\midrule
Source Derogation &2.16 &7.61 &0.44 \\
Counter Argument &2.28 &3.74 &6.06 \\
Personal Choice &2.52 &9.43 &8.49 \\
Information Inquiry &7.19 &18.27 &0.38 \\
Self Pity &1.58 &4.66 &0.34 \\
Hesitance &1.76 &15.78 &9.14 \\
Self-assertion &0.94 &2.20 &5.05 \\
Not a strategy &81.56 &38.30 &70.09 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ritam Dutt
Sayan Sinha
Rishabh Joshi
Surya Shekhar Chakraborty
Meredith Riggs
Xinru Yan
Haogang Bao
Carolyn Penstein Rosé

"RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations" by Ritam Dutt, Sayan Sinha, Rishabh Joshi, Surya Shekhar Chakraborty, Meredith Riggs, Xinru Yan, Haogang Bao, and Carolyn Penstein Rosé, arXiv:2101.10545v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 & Transition data $p_{ij}$ of $(L,\alpha)$ \\
 \hline
 $U_{i} \cap D = U_{j} \cap D = \emptyset$ & $g_{ij}$ \\
 \hline
 $U_{i} \cap D = \emptyset, \, U_{j} \cap D \neq \emptyset$ & $g_{ij} s_{j}^{\alpha}$\\
 \hline
 $U_{i} \cap D \neq \emptyset, \, U_{j} \cap D = \emptyset$ & $g_{ij}/s_{i}^{\alpha}$\\
 \hline
 $U_{i} \cap U_{j} \cap D \neq \emptyset$ & $g_{ij}(s_{j}/s_{i})^{\alpha}$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{The transition data of $(L,\alpha)$.}
\end{table}
\end{document}

math

"Constructing Parabolic Non-Abelian Hodge Correspondence in Positive Characteristic Using Parabolic Bases" by Xiaojin Lin, arXiv:2501.13775v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.