\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}{lrrrrrr}
 & & & $K = 2$ & & & \\
 & N & t & C & L & gh & vg \\
\midrule
Mean & 0.9989 & 0.9851 & 0.943 & 0.9909 & 0.9999 & 0.9833 \\
Std. Err. & 0.0022 & 0.0076 & 0.0157 & 0.0064 & 0.0005 & 0.0081 \\
 & & & $K = 3$ & & & \\
Mean & 0.9322 & 0.9228 & 0.7709 & 0.9059 & 0.9786 & 0.8506 \\
Std. Err. & 0.0171 & 0.0165 & 0.0746 & 0.0186 & 0.0087 & 0.0249 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Mean and standard error (Std. Err.) values of ARI for the posterior probabilities for Gaussian (N), t, Cauchy (C), Laplace (L), generalized hyperbolic (gh) and variance gamma (vg) with $T=1000$ for $K = 2$ and $K = 3$.}
\end{table}
\end{document}

stat

"Hidden Markov graphical models with state-dependent generalized hyperbolic distributions" by Beatrice Foroni, Luca Merlo, and Lea Petrella, arXiv:2412.03668v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"A Statistical Inquiry into Gender-Based Income Inequality in Canada" by Ali R. Kaazempur-Mofrad, arXiv:2212.13622v1, 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

"Beyond the heat: The mental health toll of temperature and humidity in India" by Manuela Fritz, arXiv:2503.08761v1, 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{List of covariates used to generate objective benchmarks}
\begin{tabular}{ll}
					\hline\hline
		\\
		Type & Covariate \\
		\\
		\multicolumn{2}{l}{\textbf{Socio-demographic characteristics}}\\
			Continuous 	& Age \\
			Categorical & Female \\
			& School degree (4 categories: none, lower sec. degree, middle sec. degree, upper sec. degree) \\
			& Further education (3 categories: none, vocational certificate, university degree) \\
			& German citizen \\
			& Married \\
			& \# of children (3 categories: 0, 1, $\geq$ 2) \\
			\multicolumn{2}{l}{\textbf{Last job}}\\
			Continuous 	& Wage in last job (gross, per month) \\
			Categorical & Last job was quit by individual \\
			\multicolumn{2}{l}{\textbf{Labor market history}}\\
			Continuous 	& Duration of last UE spell in months \\
			& \# of months employed in year t-1 \\
			& \# of months employed in year t-2 \\
			& \# of months employed in year t-3 \\
			& \# of months employed in years t-4 to t-10 \\
			& \# of months unemployed in year t-1 \\
			& \# of months unemployed in year t-2 \\
			& \# of months unemployed in year t-3 \\
			& \# of months unemployed in years t-4 to t-10 \\
			& \# of months in ALMP in year t-1 \\
			& \# of months in ALMP in year t-2 \\
			& \# of months in ALMP in year t-3 \\
			& \# of months in ALMP in years t-4 to t-10 \\
			Categorical & \# of employers in last 2 years (5 categories) \\
			& \# of employers in last 10 years (5 categories) \\
			& \# of UE spells in last 2 years (5 categories) \\
			& \# of ALMP programs in last 2 years (5 categories) \\
			& Average wage in year t-1 (5 categories) \\
			& Average wage in year t-2 (5 categories) \\
			& Average wage in year t-3 (5 categories) \\
			& Average wage in years t-4 to t-10 (5 categories) \\
			& All months regularly employed in year t-1 \\
			& Zero months regularly employed in year t-2 \\
			& All months regularly employed in year t-2 \\
			& Zero months regularly employed in year t-3 \\
			& All months regularly employed in year t-3 \\
			& Zero months regularly employed in years t-4 to t-10 \\
			& All months regularly employed in years t-4 to t-10 \\
			& Zero months unemployed in year t-1 \\
			& Zero months unemployed in year t-2 \\
			& Zero months unemployed in year t-3 \\
			& Zero months unemployed in years t-4 to t-10 \\
			& Zero months in ALMP in year t-1 \\
			& Zero months in ALMP in year t-2 \\
			& Zero months in ALMP in year t-3 \\
			& Zero months in ALMP in years t-4 to t-10 \\
			\multicolumn{2}{l}{\textbf{Local labor market}}\\
			Categorical & Unemployment rate in employment agency district at time of UE entry \\
			& (8 categories: West $<$3\%, 3-6\%, 6-9\%, $>$9\%; East $<$12\%, 12-14\%, 14-16\%, $>$16\%)		\\
			\multicolumn{2}{l}{\textbf{Timing of unemployment spell}}\\
			Categorical & Calendar month of entry into unemployment (12 categories) \\
	\\
\hline \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Marco Caliendo
Robert Mahlstedt
Aiko Schmeißer
Sophie Wagner

"The Accuracy of Job Seekers' Wage Expectations" by Marco Caliendo, Robert Mahlstedt, Aiko Schmeißer, and Sophie Wagner, arXiv:2309.14044v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Architecture Metrics for HSI+LiDAR, HSI, and LiDAR Based On Training Model (100Epochs)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|c|c|c|}
\hline
Architecture & Metrics & HSI+LiDAR & HSI & LiDAR \\
\hline
\multirow{3}{*}{{Model 1}} & OA & \underline{\textbf{0.9668}} & 0.9613 & 0.2738 \\
 & AA & \underline{\textbf{0.9722}} & 0.9685 & 0.2833 \\
 & Kappa & \underline{\textbf{0.9639}} & 0.9583 & 0.2208 \\
\hline
\multirow{3}{*}{Model 2} & OA & 0.9538 & 0.9430 & 0.2523 \\
 & AA & 0.9609 & 0.9535 & 0.2657 \\
 & Kappa & 0.9498 & 0.9381 & 0.2208 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Judy X Yang
Jing Wang
Chen Hong Sui
Zekun Long
Jun Zhou

"HSLiNets: Hyperspectral Image and LiDAR Data Fusion Using Efficient Dual Non-Linear Feature Learning Networks" by Judy X Yang, Jing Wang, Chen Hong Sui, Zekun Long, and Jun Zhou, arXiv:2412.00302v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{amsfonts}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Bardet-Biedl syndrome gene expression study. Posterior summary statistics for the five best models $(\mathcal{M}_1,\dots,\mathcal{M}_5)$, as estimated by the RJ algorithm. For each probe the reported summary statistics are the mean, the standard deviation (in italics), and the marginal inclusion probability (MIP). The summary statistics are calculated using only the post burn-in draws corresponding to the $j$-th model.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccccc}\\
\toprule
& &\multicolumn{6}{c}{\textit{Summary statistics}}\\
\cmidrule(lr){3-8}
Probe & gene & $\mathcal{M}_1$ & $\mathcal{M}_2$ & $\mathcal{M}_3$ & $\mathcal{M}_4$ & $\mathcal{M}_5$ & MIP \\
\cmidrule(lr){1-1}\cmidrule(lr){2-2}\cmidrule(lr){3-8}
\multirow{2}{*}{1369660\_at} & \multirow{2}{*}{Defb1} & -0.35 & -0.31 & -0.31 & -0.37 & -0.27&\multirow{2}{*}{0.76} \\
 & & {\it (0.08)} & {\it (0.06)} & {\it (0.08)} & {\it (0.08)} &{\it (0.08)} \\
\multirow{2}{*}{1376429\_at}& \multirow{2}{*}{Actl7b} & -0.20 & -0.19 & -0.16 & -0.18 & -0.18 & \multirow{2}{*}{0.99}\\
 && {\it (0.07)} & {\it (0.07)} & {\it (0.07)} & {\it (0.08)} & {\it (0.09)} \\
\multirow{2}{*}{1389910\_at}&\multirow{2}{*}{Tmem230}& 0.64 & 0.64 & 0.59 & 0.57 & 0.55 & \multirow{2}{*}{0.91}\\
 && {\it (0.11)} & {\it (0.12)}& {\it (0.11)} &{\it (0.09)} & {\it (0.11)} \\
\midrule
\multirow{2}{*}{1368967\_at} &\multirow{2}{*}{Eif2b3}& -0.10 & -0.04 & -0.02 & -0.01 &&\multirow{2}{*}{0.52}\\
&& {\it (0.10)} & {\it (0.10)} & {\it (0.10)} &{\it ( 0.09)} \\
\multirow{2}{*}{1369028\_at }&\multirow{2}{*}{Six3}& -0.18 & -0.11 & -0.15 & -0.18 &&\multirow{2}{*}{0.82}\\
&& {\it (0.11)} & {\it (0.11)} &{\it (0.10)} & {\it (0.10)} \\
\multirow{2}{*}{1370570\_at} & \multirow{2}{*}{Nrp1}& -0.07 & -0.05 & -0.03 & -0.04&&\multirow{2}{*}{0.67} \\
&& {\it (0.10)} & {\it (0.11)} & {\it (0.09)} & {\it (0.08)} \\
\multirow{2}{*}{1376386\_at} && -0.05 & -0.02 & -0.01 & -0.02&&\multirow{2}{*}{0.93} \\
&& {\it (0.09)} & {\it (0.09)} & {\it (0.08)} & {\it (0.08)} \\
\multirow{2}{*}{1382674\_a\_at} &\multirow{2}{*}{Tnrc6c}& -0.07 & -0.11 & -0.07 & -0.07&&\multirow{2}{*}{0.90} \\
 && {\it (0.09)} & {\it (0.09)} & {\it (0.09)} & {\it (0.08)} \\
\multirow{2}{*}{1382904\_at} & \multirow{2}{*}{Nsrp1} & 0.18 & 0.14 & 0.11 & 0.18 &&\multirow{2}{*}{0.58}\\
 && {\it (0.09)} & {\it (0.07)} & {\it (0.08)} & {\it (0.08)} \\
\multirow{2}{*}{1385539\_at}&& -0.22 & -0.14 && -0.15 & -0.18 &\multirow{2}{*}{0.73} \\
 && {\it (0.10)} & {\it (0.10)} && {\it (0.11)} & {\it (0.07)} \\
\midrule
\multirow{2}{*}{1367915\_at} &\multirow{2}{*}{Dgat1}& 0.06 & 0.12 & 0.11 &&&\multirow{2}{*}{0.30}\\
 && {\it (0.10)} & {\it (0.10)} & {\it (0.10)} \\
\multirow{2}{*}{1379541\_at}&\multirow{2}{*}{Tmtc4}& -0.01 & -0.10 & -0.06 &&&\multirow{2}{*}{0.26}\\
&& {\it (0.14)} & {\it (0.14)} & {\it (0.14)} \\
\midrule
\multirow{2}{*}{1375354\_at}&& &&& -0.17 & -0.16 &\multirow{2}{*}{0.64}\\
 && &&& {\it (0.07)} & {\it (0.07)} \\
\multirow{2}{*}{1379495\_at} && & 0.28 & 0.23 &&&\multirow{2}{*}{0.28}\\
&& &{\it (0.11)} & {\it (0.13)} &&\\
\midrule
\multirow{2}{*}{1371045\_at} & \multirow{2}{*}{Asic1} & &&&& -0.10& \multirow{2}{*}{0.16}\\
 && &&&& {\it (0.08)} \\
1371452\_at &\multirow{2}{*}{Ubl7}& &&&& -0.12& \multirow{2}{*}{0.50}\\
&& &&&& {\it (0.09)} \\
\multirow{2}{*}{1374479\_at} &\multirow{2}{*}{Ino80c}& &&&& 0.14 &\multirow{2}{*}{0.19}\\
 && &&&& {\it (0.10)} \\
\multirow{2}{*}{1376445\_at} &\multirow{2}{*}{Il17b}& &&& -0.10&&\multirow{2}{*}{0.20} \\
&& &&& {\it (0.08)}& \\
\multirow{2}{*}{1376728\_at} &\multirow{2}{*}{Rbm47}& &&& 0.03&&\multirow{2}{*}{0.19} \\
 && &&& {\it (0.07)} \\
\multirow{2}{*}{1379029\_at} &\multirow{2}{*}{Zfp62}& && 0.14 &&&\multirow{2}{*}{0.28}\\
&& && {\it (0.10)} \\
\multirow{2}{*}{1386770\_x\_at} &\multirow{2}{*}{Kcne2}&&&& & -0.03 &\multirow{2}{*}{0.17}\\
 && &&& & {\it (0.08)} \\
\multirow{2}{*}{1386969\_at} &\multirow{2}{*}{Nrn1}& &&&& 0.07 &\multirow{2}{*}{0.46}\\
&& &&&& {\it (0.08)} \\
\multirow{2}{*}{1387290\_at} &\multirow{2}{*}{Galnt10}& &&&& -0.00&\multirow{2}{*}{0.48} \\
&& &&&& {\it (0.09)} \\
\multirow{2}{*}{1390394\_at}&& &&&& 0.16&\multirow{2}{*}{0.34} \\
 && &&&& {\it (0.10)} \\
\multirow{2}{*}{1399050\_at} &\multirow{2}{*}{Adss}&&&& & -0.12&\multirow{2}{*}{0.26} \\
&& &&&& {\it (0.11)} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mauro Bernardi
Claudio Busatto
Manuela Cattelan

"Fast QR updating methods for statistical applications" by Mauro Bernardi, Claudio Busatto, and Manuela Cattelan, arXiv:2412.05905v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{siunitx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean performance in \textbf{ratio} for player selection methods across diverse scenarios}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
 \toprule
 4-Player Scenarios & Parameter & $\text{traj}_{S}\downarrow$ & $\text{traj}_L\downarrow$ & $\text{Dist}_m\uparrow$ & Consistency $\uparrow$ & Player Num. & Runtime \\
 \midrule
 \midrule
 \textbf{PSN-Full} & $m_\text{th}=0.5$ & 0.781 & 0.970 & 0.968 & 0.986 & 1.619 & 0.164 \\
 \textbf{PSN-Partial} & $m_\text{th}=0.5$ & \textbf{0.750} & 0.971 & 0.973 & 0.986 & 1.806 & 0.233\\
 \texttt{Distance} & $r_\text{th}=\SI{2.5}{\meter}$ & 1.219 & 1.011 & 0.984 & 0.987 & 2.506 & 0.368\\
 \midrule
 \textbf{PSN-Full} & \multirow{8}[0]{*}{$|U^i|=1$} & 1.094 & 0.990 & 0.978 & \textbf{0.989} & \multirow{8}[0]{*}{2.000} & 0.134 \\
 \textbf{PSN-Partial} & & 1.000 & \textbf{0.966} & 0.977 & 0.987 & & 0.113 \\
 \texttt{kNNs} & & 1.438 & 1.021 & 0.993 & \textbf{0.989} & & 0.128 \\
 \texttt{Cost Evolution} & & 1.813 & 0.976 & 0.997 & 0.951 & & 0.120 \\
 \texttt{Gradient} & & 1.781 & 1.012 & 0.987 & 0.953 & & 0.117 \\
 \texttt{Hessian} & & 1.500 & 1.014 & 0.995 & 0.980 & & 0.134 \\
 \texttt{BF} & & 1.406 & 1.017 & 0.992 & 0.983 & & 0.115 \\
 \texttt{CBF} & & 1.344 & 1.004 & \textbf{1.000} & 0.971 & & 0.121 \\
 \toprule
 10-Player Scenarios & Parameter & $\text{traj}_{S}\downarrow$ & $\text{traj}_L\downarrow$ & $\text{Dist}_m\uparrow$ & Consistency $\uparrow$ & Num.P & Runtime\\
 \midrule
 \midrule
 \textbf{PSN-Full} & $m_\text{th}=0.5$ & \textbf{0.857} & 0.994 & 0.958 & 0.994 & 4.473 & 0.124\\
 \textbf{PSN-Partial} & $m_\text{th}=0.5$ & \textbf{0.857} & 0.991 & 0.970 & 0.994 & 4.033 & 0.095\\
 \texttt{Distance} & $r_\text{th}=\SI{2.5}{\meter}$ & 1.190 & 0.998 & 0.980 & 0.989 & 2.277 & 0.022\\
 \midrule
 \textbf{PSN-Full} & \multirow{8}[0]{*}{$|U^i|=4$} & \textbf{0.857} & 0.991 & 0.972 & \textbf{0.995} & \multirow{8}[0]{*}{5.000} & 0.102\\
 \textbf{PSN-Partial} & & \textbf{0.857} & 0.988 & 0.957 & \textbf{0.995} & & 0.124 \\
 \texttt{kNNs} & & 0.952 & 1.000 & 0.965 & 0.984 & & 0.123 \\
 \texttt{Cost Evolution} & & 1.048 & \textbf{0.980} & 0.974 & 0.954 & & 0.121 \\
 \texttt{Gradient} & & 0.952 & 1.000 & 0.967 & 0.984 & & 0.115 \\
 \texttt{Hessian} & & 0.952 & 0.999 & 0.979 & 0.987 & & 0.128 \\
 \texttt{BF} & & 1.000 & 1.003 & 1.015 & 0.984 & & 0.145 \\
 \texttt{CBF} & & 0.952 & 1.002 & 1.013 & 0.984 & & 0.135 \\
 \toprule
 CITR & Parameter & $\text{traj}_{S}\downarrow$ & $\text{traj}_L\downarrow$ & $\text{Dist}_m\uparrow$ & Consistency $\uparrow$ & Num.P & Runtime\\
 \midrule
 \midrule
 \textbf{PSN-Full} & $m_\text{th}=0.5$ & 1.222 & 0.996 & 0.938 & 0.994 & 5.523 & 0.193\\
 \textbf{PSN-Partial} & $m_\text{th}=0.5$ & 1.222 & 0.996 & 0.918 & \textbf{0.996} & 4.633 & 0.112\\
 \texttt{Distance} & $r_\text{th}=\SI{2.5}{\meter}$ & 1.333 & 0.997 & 0.825 & 0.993 & 1.829 & 0.010\\
 \midrule
 \textbf{PSN-Full} & \multirow{5}[0]{*}{$|U^i|=4$} & 1.222 & 0.996 & 0.922 & \textbf{0.996} & \multirow{5}[0]{*}{5.000} & 0.119\\
 \textbf{PSN-Partial} & & 1.222 & 0.996 & 0.917 & 0.993 & & 0.098 \\
 \texttt{kNNs} & & \textbf{1.111} & 0.998 & 0.926 & 0.991 & & 0.109\\
 \texttt{Cost Evolution} & & 1.222 & \textbf{0.994} & \textbf{0.944} & 0.969 & & 0.126\\
 \texttt{BF} & & \textbf{1.111} & 0.999 & 0.943 & 0.989 & & 0.121\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|}
 epochs & m \\
 \hline
 $\theta_t - \theta$
 &
 $
 - m \alpha + c \sum_{i=1}^m \nabla L(\theta_i)
 $
 \\
 $\nabla L(\theta_t) - \nabla L$
 &
 $
 (- \alpha)^{i-1} \sum_{h=1}^{i} \frac{1}{h!}
 \left(1 + \sum_{l=2}^{m} (l-1)^{i-h}l^{h-1} \right)
 $
 \\
 $\nabla^j L(\theta_t) - \nabla^j L$
 &
 $
 (- \alpha)^{i-j} \sum_{h=j}^{i} \frac{\nabla^i L}{h!}
 \left(1 + \sum_{l=2}^{m} (l-1)^{i-h}l^{h-j} \right)
 $
 \\
 $L(\theta_t) - L$
 &
 $
 (- \alpha)^{i} \sum_{h=0}^{i} \frac{\nabla^i L}{h!}
 \left(1 + \sum_{l=2}^{m} (l-1)^{i-h}l^{h-j} \right)
 $
 \end{tabular}
\end{table}
\end{document}

math

"On the Trajectories of SGD Without Replacement" by Pierfrancesco Beneventano, arXiv:2312.16143v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Cobol2Vec: Learning Representations of Cobol code" by Ankit Kulshrestha and Vishwas Lele, arXiv:2201.09448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrrrrr}
 \toprule
 & $\ $ $\ $ i2t@1 &	$\ $ i2t@5 &	$\ $ i2t@10 &	$\ $ t2i@1	 & $\ $ t2i@5 & $\ $ t2i@10 & $\ $	$\ $ Mean \\ \midrule
 ViT & 53.88 &	73.17 &	78.49 &	53.98 &	73.83 &	79.29 &	68.77 \\
 RoPE & 55.27 &	74.27 &	79.52 &	55.22 &	74.61 &	80.25 &	69.86 \\
 RoPE\text{-}M & 55.30 &	74.08 &	79.47 &	55.36 &	74.73 &	80.18 &	69.85 \\
 \midrule
 Circulant\text{-}S & \underline{55.52} &	\underline{74.69} &	\underline{79.91} &	\underline{55.68} &	\underline{75.03} &	\underline{80.45} &	\underline{70.21} \\
 Cayley\text{-}S & \textbf{55.70} &	\textbf{75.08} &	\textbf{80.24} &	\textbf{55.82} &	\textbf{75.40} &	\textbf{80.65} &	\textbf{70.48} \\
 \bottomrule
\end{tabular}
\caption{Image-to-text (i2t) and text-to-image (t2i) WebLI recall @ rank (best numbers: in \textbf{bold}, second-best: \underline{underlined}.)}
\end{table}
\end{document}

stat

Connor Schenck
Isaac Reid
Mithun George Jacob
Alex Bewley
Joshua Ainslie
David Rendleman
Deepali Jain
Mohit Sharma
Avinava Dubey
Ayzaan Wahid
Sumeet Singh
René Wagner
Tianli Ding
Chuyuan Fu
Arunkumar Byravan
Jake Varley
Alexey Gritsenko
Matthias Minderer
Dmitry Kalashnikov
Jonathan Tompson
Vikas Sindhwani
Krzysztof Choromanski

"Learning the RoPEs: Better 2D and 3D Position Encodings with STRING" by Connor Schenck, Isaac Reid, Mithun George Jacob, Alex Bewley, Joshua Ainslie, David Rendleman, Deepali Jain, Mohit Sharma, Avinava Dubey, Ayzaan Wahid, Sumeet Singh, René Wagner, Tianli Ding, Chuyuan Fu, Arunkumar Byravan, Jake Varley, Alexey Gritsenko, Matthias Minderer, Dmitry Kalashnikov, Jonathan Tompson, Vikas Sindhwani, and Krzysztof Choromanski, arXiv:2502.02562v1, 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}{r|cc}
\toprule
 & \textbf{CSQA} & \textbf{RS} \\ \midrule
\# All Examples & 12,102 & 5,715 \\
\# Train Examples & 9,741 & 3,510 \\
\# Validation Examples & 1,221 & 1,021 \\
\# Test Examples & 1,140 & 1,184 \\ \midrule
Average Question Length & 15.06 & 24.04 \\
\% Long Qs ($>$20 tokens) & 16.5\% & 47.3\% \\
Distinct Question Words & 6,822 & 7,110 \\
Distinct Choice Words & 7,044 & 9,912 \\
Avg PLL of Qs & -34.41 & -53.98 \\
\midrule
QA-NLI Conflict & 12.7\% & 39.6\% \\
QA-NLI Neutral & 71.6\% & 44.9\% \\
QA-NLI Entailment & 15.7\% & 15.5\% \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Key statistics of the \textsc{RiddleSense} dataset (v1.1) vs the CommonsenseQA (CSQA) dataset.}
\end{table}
\end{document}

cs

Bill Yuchen Lin
Ziyi Wu
Yichi Yang
Dong-Ho Lee
Xiang Ren

"RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge" by Bill Yuchen Lin, Ziyi Wu, Yichi Yang, Dong-Ho Lee, and Xiang Ren, arXiv:2101.00376v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Maciej K Wozniak
Mattias Hansson
Marko Thiel
Patric Jensfelt

"UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps" by Maciej K Wozniak, Mattias Hansson, Marko Thiel, and Patric Jensfelt, arXiv:2403.17633v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yawen Guan
Garritt L. Page
Brian J Reich
Massimo Ventrucci
Shu Yang

"A spectral adjustment for spatial confounding" by Yawen Guan, Garritt L. Page, Brian J Reich, Massimo Ventrucci, and Shu Yang, arXiv:2012.11767v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis" by Md Talha Mohsin, arXiv:2507.22936v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{array}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The ranges of markups and quantities satisfying $q \ge 0$, $u' > 0$, $u'' < 0$, and $0<\mu \le 1$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|cc}
\multicolumn{5}{c}{Case 1: $\alpha >0$.} \\
\hline
 & \multicolumn{2}{c|}{Markups} & \multicolumn{2}{c}{Quantities} \\ \cline{2-5}
 & $\sigma \le 1$ & $\sigma > 1 $ & $\sigma \le 1$ & $\sigma > 1 $ \\ \hline
\multirow{2}{*}{Case 1a. $1-\beta \sigma>0$ and $\beta \neq 1$} &
\multirow{2}{*}{$\frac{1}{1-\beta} \le \frac{1}{\mu} < \infty$} &
\multirow{2}{*}{$\frac{1}{1-\beta} \le \frac{1}{\mu} < \frac{\sigma}{\sigma-1}$} &
\multirow{2}{*}{$0 \le q < -\frac{1-\beta}{\alpha (\sigma-1)}$$^\dagger$} &
\multirow{2}{*}{$0 \le q < \infty$} \\
&
&
&
&
\\ \hline
&
&
&
&
\\
\multirow{2}{*}{Case 1b.} \hskip .05cm $1-\beta \sigma<0$ and $\beta\in(0,1)$ &
\multirow{2}{*}{n.a.} &
$\frac{\sigma}{\sigma-1} < \frac{1}{\mu } \le \frac{1}{1-\beta}$ &
\multirow{2}{*}{n.a.} & $0 \le q < \infty$ \\
\hskip 1.73cm
$1-\beta \sigma<0$ and $\beta=1$
&
& $\frac{\sigma}{\sigma-1} < \frac{1}{\mu} < \infty$
&
& $0 < q < \infty$
\\ \hline
\multicolumn{5}{c}{} \\
 \multicolumn{5}{c}{Case 2: $\alpha <0$.} \\ \hline
 & \multicolumn{2}{c|}{Markups} & \multicolumn{2}{c}{Quantities} \\ \cline{2-5}
 & $\sigma \le 1$ & $\sigma > 1 $ & $\sigma \le 1$ & $\sigma > 1 $ \\ \hline
 &
&
&
&
\\
\multirow{2}{*}{Case 2a.} \hskip .05cm $1-\beta \sigma>0$ and $\beta\in(0,1)$ &
$1 < \frac{1}{\mu } \le \frac{1}{1-\beta}$ &
$1 < \frac{1}{\mu } \le \frac{1}{1-\beta}$ &
$0 \le q < -\frac{\beta}{\alpha} $ &
$0 \le q < -\frac{\beta}{\alpha} $ \\
\hskip 1.73cm
$1-\beta \sigma>0$ and $\beta=1$
& $1 < \frac{1}{\mu } <\infty^{\ddagger}$
& n.a.
& $0 < q < {-\frac{1}{\alpha}}^{\ddagger}$
& n.a.
\\ \hline
\multirow{2}{*}{Case 2b. $1-\beta \sigma<0$ and $\beta \in (0,1)$} &
\multirow{2}{*}{n.a.} &
\multirow{2}{*}{$\frac{1}{1-\beta} \le \frac{1}{\mu } < \infty$} &
\multirow{2}{*}{n.a.} &
\multirow{2}{*}{$0 \le q < -\frac{1-\beta}{\alpha (\sigma-1)}$} \\
&
&
&
&
\\ \hline
\multicolumn{5}{p{18.8cm}}{\scriptsize {\it Notes}:
$^{\dagger}$ means that when $\sigma=1$, $0 \le q < -\frac{1-\beta}{\alpha (\sigma-1)}$ must be replaced with $0 \le q < \infty$.
$^{\ddagger}$ means that $\sigma=1$ must be excluded.
The case with $\alpha=0$ corresponds to the CES and requires that $\beta \in (0,1)$.
The case with $1-\beta \sigma=0$ also corresponds to the CES and requires that $\sigma > 1$.
If $\alpha=0$ or $\beta =\frac{1}{\sigma}$, then the markup is given by $\frac{1}{\mu} =\frac{1}{1-\beta} \in (1, \infty)$ or $\frac{1}{\mu} =\frac{\sigma}{\sigma-1} \in (1, \infty)$, respectively, and the range of quantities is given by $0\le q<\infty$.
Since these two cases are well known, we omit them from the table.}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Linear fractional relative risk aversion" by Kristian Behrens and Yasusada Murata, arXiv:2509.09865v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrr|rrrr}
\multicolumn{4}{c}{problem sizes} & \multicolumn{3}{c}{timing (s)} &
\multirow{2}{*}{iterations} \\
$n$ & $q$ & $m$ & $nm$ & MOSEK & PDMCF (CPU) & PDMCF (GPU) \\
\hline
$3000$ & $10$ & $34424$ & $1\times 10^8$ & OOM & $7056$ & $96$ & $4140$ \\
$5000$ & $10$ & $57338$ & $3\times 10^8$ & OOM & $19152$ & $395$ & $3970$ \\
$10000$ & $10$ & $114054$ & $1\times 10^9$ & OOM & $87490$ & $1908$& $4380$
\end{tabular}
\end{adjustbox}
\caption{Runtime table for large size problems.}
\end{table}
\end{document}

math

"Solving Large Multicommodity Network Flow Problems on GPUs" by Fangzhao Zhang and Stephen Boyd, arXiv:2501.17996v2, 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{Selected ESG issues by }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}
\hline
\multicolumn{9}{l}{\textbf{Environmental Issues}} \\ \hline
\multicolumn{9}{l}{\textit{- Climate change and related risks}} \\
\multicolumn{9}{l}{\textit{- The need to reduce toxic releases and waste}} \\
\multicolumn{9}{l}{\textit{- New regulation expanding the boundaries of environmental liability with regard to products and services}} \\
\multicolumn{9}{l}{\textit{- Increasing pressure by civil society to improve performance, transparency and accountability,}} \\
\multicolumn{9}{l}{\textit{leading to reputational risks if not managed properly}} \\
\multicolumn{9}{l}{\textit{- Emerging markets for environmental services and environment-friendly products}} \\
\multicolumn{9}{l}{} \\ \hline
\multicolumn{9}{l}{\textbf{Social Issues}} \\ \hline
\multicolumn{9}{l}{\textit{- Workplace health and safety}} \\
\multicolumn{9}{l}{\textit{- Community relations}} \\
\multicolumn{9}{l}{\textit{- Human rights issues at company and suppliers’ / contractors’ premises}} \\
\multicolumn{9}{l}{\textit{- Government and community relations in the context of operations in developing countries}} \\
\multicolumn{9}{l}{\textit{- Increasing pressure by civil society to improve performance,}} \\
\multicolumn{9}{l}{\textit{ transparency and accountability, leading to reputational risks}} \\
\multicolumn{9}{l}{} \\ \hline
\multicolumn{9}{l}{\textbf{Corporate Governance Issues}} \\ \hline
\multicolumn{9}{l}{\textit{- Board structure and accountability}} \\
\multicolumn{9}{l}{\textit{- Accounting and disclosure practices}} \\
\multicolumn{9}{l}{\textit{- Audit committee structure and independence of auditors}} \\
\multicolumn{9}{l}{\textit{- Executive compensation}} \\
\multicolumn{9}{l}{\textit{- Management of corruption and bribery issues}}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
\hline
 \emph{HGM} & \emph{RRWGM} & \emph{TM} & \textbf{ARSRG}$_{1^{st}}$ & \textbf{ARSRG}$_{2^{nd}}$\\ \hline
 0.1000 & 0.0545 & 0.0545 & 0.20961 & 0.39803\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Error analysis for activator-inhibitor example using NLS 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
$x_1$&0.1150&0.2125&0.1275&0.1723&0.6963\\
$x_2$&-0.2376&0.3971&0.2435&0.2927&0.7120\\
$f_1(\cdot)$&-5.7007e-04&7.0178&0.0173&0.0202&0.2692\\
$f_2(\cdot)$&-0.0101&8.5119&0.0266&0.0369&0.6857\\
\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{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Confusion matrix for the decision tree algorithm on the metrics dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|ccc|}
 \cline{3-5}
 \multicolumn{2}{c|}{\multirow{2}{*}{Diagnosis}} & \multicolumn{3}{|c|}{Actual} \\
 \cline{3-5}
 \multicolumn{2}{c|}{} & P & C & H \\
 \hline
 & P & 75.5\,\% & 45.0\,\% & 5.0\,\% \\
 & C & 19.6\,\% & 42.5\,\% & 5.0\,\% \\
 \multirow{-3}{*}{Predicted} & H & 6.7\,\% & 12.5\,\% & 90.0\,\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

"Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features" by C. V. Vletter, H. L. Burger, H. Alers, N. Sourlos, and Z. Al-Ars, arXiv:2201.11852v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The most important variables in the batches}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}\toprule
 & & \multicolumn{3}{c}{\textbf{\#batch}}\\\cmidrule(lr){3-5}
 \textbf{Dataset} & \textbf{Model} & \textbf{10} & \textbf{20} & \textbf{30} \\\midrule
 \texttt{SEA} & LR & \texttt{V2} & \texttt{V2} & \texttt{V2} \\
 & DT & \texttt{V1} & \texttt{V1} & \texttt{V1} \\
 & RF & \texttt{V2} & \texttt{V2} & \texttt{V2} \\\midrule
 \texttt{Hyperplane} & LR & \texttt{variable\_1} & \texttt{variable\_1} & \texttt{variable\_1} \\
 & DT & \texttt{variable\_1} & \texttt{variable\_1} & \texttt{variable\_2} \\
 & RF & \texttt{variable\_1} & \texttt{variable\_1} & \texttt{variable\_1} \\\midrule
 \texttt{NOAA} & LR & \texttt{attribute1} & \texttt{attribute4} & \texttt{attribute1} \\
 & DT & \texttt{attribute2} & \texttt{attribute2} & \texttt{attribute2} \\
 & RF & \texttt{attribute2} & \texttt{attribute4} & \texttt{attribute2} \\\midrule
 \texttt{Ozone} & LR & \texttt{V26} & \texttt{V52} & \texttt{V52} \\
 & DT & \texttt{V31} & \texttt{V36} & \texttt{V36} \\
 & RF & \texttt{V42} & \texttt{V61} & \texttt{V61} \\\midrule
 \texttt{Elec2} & LR & \texttt{nswprice} & \texttt{nswprice} & \texttt{nswprice} \\
 & DT & \texttt{nswprice} & \texttt{nswprice} & \texttt{nswprice} \\
 & RF & \texttt{nswprice} & \texttt{nswprice} & \texttt{nswprice} \\\midrule
 \texttt{Friedman} & LR & \texttt{variable\_4} & \texttt{variable\_4} & \texttt{variable\_4} \\
 & DT & \texttt{variable\_4} & \texttt{variable\_4} & \texttt{variable\_4} \\
 & RF & \texttt{variable\_4} & \texttt{variable\_4} & \texttt{variable\_4} \\\bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"datadriftR: An R Package for Concept Drift Detection in Predictive Models" by Ugur Dar and Mustafa Cavus, arXiv:2412.11308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


q-fin

Markus Brueckner
Tomoo Kikuchi
George Vachadze

"Transitional Dynamics of the Saving Rate and Economic Growth" by Markus Brueckner, Tomoo Kikuchi, and George Vachadze, arXiv:2012.15435v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
Age & 18-29 & 30-49 & 50-64 & 65+ \\ \hline
18-29 & 1 & 0.9 & 0.8 & 0.7 \\ \hline
30-49 & 0.9 & 0.9 & 0.8 & 0.8 \\ \hline
50-64 & 0.8 & 0.8 & 0.9 & 0.8 \\ \hline
65+ & 0.7 & 0.8 & 0.8 & 0.8 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{\small Experiment 1 Graphon}
\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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The results of method of Baroughi-Bonab et al. on instance of Example .}
\begin{tabular}{cccccc}
			\hline
			 $\mathbf{x}_{B}=(x_{B},y_{B})$& $\mathbf{P}_{1B}$& $\mathbf{P}_{2B}$&$\mathbf{P}_{3B}$&$\mathbf{P}_{4B}$&$||\bar{\mathbf{x}}-\mathbf{x}_{B}||$\\
			\hline
			( 0.0000, 1.0000)&
		(1.3333,0.0000)&(-5.0000,3.0000)&(7.0000,2.0000)&
		(0.0000,0.5000)&
		0.00000\\
			\hline
		$F_B$&154.1667&&&&\\
			\hline
		CPU(in s)&0.0426&&&&\\

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

math

"Inverse single facility location problem in the plane with variable coordinates" by Nazanin Tour-Savadkoohi and Jafar Fathali, arXiv:2503.07016v1, 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{activation functions of DNN.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
		\hline
		Name & $\sigma(v_i)$ & Range \\ \hline
		ReLU & $max(0,v_i)$ & $[0,\infty)$ \\ \hline
		tanh & $\frac{e^{v_i}-e^{-v_i}}{e^{v_i}+e^{-v_i}}$ & $(-1,1)$ \\ \hline
		sigmoid & $\frac{1}{1+e^{-v_i}}$ & $(0,1)$ \\ \hline
	 softmax & $\frac{e^{v_i}}{\sum_je^{-v_i}}$ & $(0,1)$ \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\it User Evaluation of Visualizing Visual Appearance Patterns}			\centering
\begin{tabular}{l|c|c} \hline%\noalign{\smallskip}
\hspace{50pt} \textbf{Questions} & \textbf{Mean} & \textbf{Std. } \\	\hspace{30pt} 0(poor) - 10(excellent) &\textbf{Value} & \textbf{Dev.} \\
\hline%\noalign{\smallskip}
Q1: Do you often use map-based navigation services &9.2 & 1.2\\
 ~~(e.g. Google map, Apple map, etc.) when planning a new route? &&\\
 \hline

Q2: Is the new visualization of the streetview information &7.4& 1.5 \\
~~ potentially useful for you or others? && \\
 \hline
Q3: How are you familiar with the geo-environment in this example? &7.6& 1.9 \\
\hline
Q4: Do you think the four patterns can represent the visual &8.0& 1.3 \\
~~ appearance of this area? &&\\
\hline
Q5: Do you agree that the pattern distribution shown on the three &7.9&1.4\\
~~routes is reasonable, based on your own experience? &&\\
\hline
Q6: Can you quickly get the information from the visualizations? &7.9 &1.6 \\
\hline
Q7: Does the map view convey useful information? &8.1 &1.4\\
\hline
Q8: Does the RouteViewLine convey useful information? &7.4 &1.7\\
\hline
Q9: Does the coordinated raw street-view images convey &8.4 &1.4 \\
~~useful information? &&\\
\hline
\hline%\noalign{\smallskip}
\end{tabular}
\end{table}
\end{document}

cs

Tsung Heng Wu
Md Amiruzzaman
Ye Zhao
Deepshikha Bhati
Jing Yang

"Visualizing Routes with AI-Discovered Street-View Patterns" by Tsung Heng Wu, Md Amiruzzaman, Ye Zhao, Deepshikha Bhati, and Jing Yang, arXiv:2404.00431v1, 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}{lccccccccc}
\toprule
Feature & Method & \multicolumn{6}{c}{CIFAR-10N} & \multicolumn{2}{c}{CIFAR-100N} \\
\cmidrule(lr){3-8} \cmidrule(l){9-10}
 & & Clean & Aggre & Rand1 & Rand2 & Rand3 & Worst & Clean & Noisy \\ \midrule
\multirow{4}{*}{DINOv2 SSL} & CE (ours) & 99.25 & 98.69 & 98.8 & 98.65 & 98.67 & 95.71 & \bf{92.85} & \bf{83.17} \\
& MAE & \bf{99.27} & \bf{99.04} & \bf{99.01} & \bf{99.09} & \bf{99.11} & 95.55 & 90.68 & 82.55 \\
& Sigmoid & 99.26 & 98.86 & 98.91 & 98.87 & 98.96 & \bf{96.66} & 92.82 & 82.03 \\
& ELR & 99.09 & 98.49 & 98.62 & 98.53 & 98.56 & 95.60 & 89.99 & 82.75 \\
& SAM & 99.09 & 97.66 & 98.47 & 98.53 & 98.47 & 95.47 & 89.97 & 82.85 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Label Noise: Ignorance Is Bliss

Yilun Zhu
Jianxin Zhang
Aditya Gangrade
Clayton Scott

"Label Noise: Ignorance Is Bliss" by Yilun Zhu, Jianxin Zhang, Aditya Gangrade, and Clayton Scott, arXiv:2411.00079v1, 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{Equivalent Circuit Parameters of Varactor Diode }
\begin{tabular}{|cccc|}
		\hline\hline
		\textbf{VR (V)} & \textbf{C (pF)} & \textbf{R (ohm)} & \textbf{L (nH)} \\ \hline
		 0 & 2.31 & 4.51 & 0.70 \\ \hline
		 -4 & 0.84 & 4.04 & 0.70 \\ \hline
		 -7 & 0.55 & 3.66 & 0.70 \\ \hline
		 -11 & 0.38 & 3.18 & 0.70 \\ \hline
		 -14 & 0.31 & 2.86 & 0.70 \\ \hline
		 -16 & 0.27 & 2.65 & 0.70 \\ \hline
		 -19 & 0.24 & 2.38 & 0.70 \\ \hline\hline
	\end{tabular}
\end{table}
\end{document}

eess

Xilong Pei
Haifan Yin
Li Tan
Lin Cao
Zhanpeng Li
Kai Wang
Kun Zhang
Emil Björnson

"RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials" by Xilong Pei, Haifan Yin, Li Tan, Lin Cao, Zhanpeng Li, Kai Wang, Kun Zhang, and Emil Björnson, arXiv:2103.00534v2, 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{{\bf Quantitative evaluation.} The MSCOCO validation set with 18 Instagram filters is used for evaluation. Our method supports a few variations. Our method can have (R): regularization, (AU): Aleatoric uncertainty, (U): combined uncertainty, (CC): color correlation. Note that for WCT2, we gave option that uses features from decoder and skip-connection since it performs the best. Note that lower $\Delta E_{00}^*$ (a.k.a., Delta-E 2000) is better.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\hline\noalign{\smallskip}
Methods & PSNR & $\Delta E_{00}^*$\\
\noalign{\smallskip}
\hline
\noalign{\smallskip}
{\bf Ours } & {\bf 25.226 } & {\bf 6.660 } \\
{\bf Ours (w. U, CC) } & {\bf 24.931 } & {\bf 6.725 } \\
{\bf Ours (w. AU) } & {\bf 25.438 } & {\bf 6.427 } \\
{\bf Ours (w. U) } & {\bf 25.495 } & {\bf 6.394 } \\
{\bf Ours (w. R, U) } & {\bf 26.093 } & {\bf 6.148 }\\
WCT2 & 16.473 & 17.516 \\
Color Transfer & 7.325 & 34.914 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Filter Style Transfer between Photos

Jonghwa Yim
Jisung Yoo
Won-joon Do
Beomsu Kim
Jihwan Choe

"Filter Style Transfer between Photos" by Jonghwa Yim, Jisung Yoo, Won-joon Do, Beomsu Kim, and Jihwan Choe, arXiv:2007.07925v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{No. Days to Achieve $90\%$ of Infection} & \textbf{No Pre-release Mitigation} & \textbf{With Pre-release Mitigation} \\
\textbf{} & \textbf{} & \textbf{(Thermal Fogging $35\%$)} \\ \hline
Single Release & 5 years & 185 days \\ \hline
2-Batch Release & 174 days & 146 days \\ \hline
3-Batch Release & 189 days & 145 days \\ \hline
4-Batch Release & 131 days & 120 days \\ \hline
5-Batch Release & 123 days & 113 days \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Daniela Florez
Ricardo Cortez
James M. Hyman
Zhuolin Qu

"Improving Wolbachia-Based Control Programs in Urban Settings: Insights from Spatial Modeling" by Daniela Florez, Ricardo Cortez, James M. Hyman, and Zhuolin Qu, arXiv:2503.12262v1, 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{Interpretation of Ratings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|r|c|c|c|}
\hline
 Ratings difference & Win & Draw & Loss \\
\hline
0 & 35.7 & 28.6 & 35.7 \\
10 & 37.1 & 28.5 & 34.5 \\
20 & 38.5 & 28.2 & 33.3 \\
50 & 42.5 & 27.8 & 29.7 \\
100 & 49.9 & 25.7 & 24.3 \\
200 & 63.8 & 20.5 & 15.7 \\
500 & 90.5 & 6.0 & 3.5 \\
800 & 98.1 & 1.2 & 0.7 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Ratings of European and South American Football Leagues Based on Glicko-2 with Modifications" by Andrei Shelopugin and Alexander Sirotkin, arXiv:2310.11459v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training data breakdown of ASR task for 12.5Hz speech tokenizer. Tokens measured in billion.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule
Language & Speech Hours & Speech Tokens & Text Tokens & Total Tokens \\ \midrule
Chinese & 21,624 & 0.97B & 0.42B & 1.39B \\
English & 68,733 & 3.09B & 1.06B & 4.16B \\ \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{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The approximation errors $\varepsilon$ ($\varepsilon_1$)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 & $L = 4$ & $L = 8$ & $L = 16$ & $L = 32$ & $L = 64$ & $L = 128$ & $L = 256$ \\
 \hline
 \hline
 $x_4(t)$ & 0.035217 & 0.004319 & 0.000524 & 0.000065 & $8.21 \cdot 10^{-6}$ & $1.06 \cdot 10^{-6}$ & $1.39 \cdot 10^{-7}$ \\
 & (0.005314) & (0.000531) & (0.000060) & ($7.14 \cdot 10^{-6}$) & ($8.71 \cdot 10^{-7}$) & ($1.08 \cdot 10^{-7}$) & ($1.34 \cdot 10^{-8}$) \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On Spectral Approach to the Synthesis of Shaping Filters" by Konstantin A. Rybakov, arXiv:2501.15174v1, 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{Final rank of the models for the heteroscedastic noise cases. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccl}
\cline{1-4}
\textbf{Model} & \textbf{low5\_high50} & \textbf{low5\_high100} &\textbf{low5\_high500} & \\
\cline{1-4}
LM	&			3	&		4	&		4	&		\\
GP	&			1	&		1	&		3	&	\\
SVM	&			1	&		2	&		1	&		\\
RF	&			5	&		5	&		2	&	\\
ANN\_sh	&		4	&		3	&		6	&		\\
ANN\_dp	&		6	&		7	&		6	&		\\
aml	&			6	&		6	&		5	&		\\
\cline{1-4}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Rosa Arboretti
Riccardo Ceccato
Luca Pegoraro
Luigi Salmaso

"Design choice and machine learning model performances" by Rosa Arboretti, Riccardo Ceccato, Luca Pegoraro, and Luigi Salmaso, arXiv:2201.10239v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c||c|c||c|c||c|c}
 \multicolumn{6}{c}{\textbf{ODE Terms:}}\\
Eqn.~ & \texttt{DDF.} & Eqn.~ & \texttt{DDF.} & Eqn.~ & \texttt{DDF.}& Eqn.~ & \texttt{DDF.}\\
\hline
$A_0$ & \texttt{A0} & $B_{1}$ & \texttt{B1} &$B_{2}$&\texttt{B2}&$B_v$&\texttt{Bv}\\
$C_{1}$ & \texttt{C1} & $D_{11}$ &\texttt{D11}&$D_{12}$&\texttt{D12}&$D_{1v}$&\texttt{D1v}\\
$C_{2}$ & \texttt{C2} & $D_{21}$ &\texttt{D21}&$D_{22}$&\texttt{D22}&$D_{2v}$&\texttt{D2v} \\
$C_{ri}$ & \texttt{Cri\{i\}} & $B_{r1i}$ &\texttt{Br1i\{i\}}&$B_{r2i}$&\texttt{Br2i\{i\}}&$D_{rvi}$&\texttt{Drvi\{i\}} \\
\hline \\
 \multicolumn{6}{c}{ \textbf{Discrete Delay Terms:}}\\
Eqn.~ & \texttt{DDF.} & & & & \\
\hline
$C_{vi}$ & \texttt{Cvi\{i\}} & & &&\\
\hline \\ \multicolumn{6}{c}{\textbf{Distributed Delay Terms: May be functions of \texttt{pvar s}}}\\
Eqn.~ & \texttt{DDF.} & & & & \\
\hline
$C_{vdi}(s) $ & \texttt{Cvdi\{i\}} & & &&\\
\end{tabular}
\end{adjustbox}
\caption{ Equivalent names of Matlab elements of the \texttt{DDF} structure terms for terms in Eqn.~. For example, to set term \texttt{XX} to \texttt{YY}, we use \texttt{DDF.XX=YY}. In addition, the delay $\tau_i$ is specified using the vector element \texttt{DDF.tau(i)} so that if $\tau_1=1, \tau_2=2, \tau_3=3$, then \texttt{DDF.tau=[1 2 3]}. }
\end{table}
\end{document}

math

PIETOOLS 2024: User Manual

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{SIVAL model results. The mean performance was calculated over ten repeat trainings of each model, and the standard error of the mean is given.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
		\toprule
		Model & Train Accuracy & Val Accuracy & Test Accuracy \\
		\midrule
		MI-Net & \textbf{0.984 $\pm$ 0.004} & \textbf{0.850 $\pm$ 0.009} & \textbf{0.819 $\pm$ 0.012} \\
		mi-Net & 0.967 $\pm$ 0.005 & 0.835 $\pm$ 0.010 & 0.808 $\pm$ 0.011 \\
		MI-Attn & 0.972 $\pm$ 0.007 & 0.830 $\pm$ 0.011 & 0.813 $\pm$ 0.012 \\
		MI-GNN & 0.932 $\pm$ 0.014 & 0.803 $\pm$ 0.014 & 0.781 $\pm$ 0.019 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Joseph Early
Christine Evers
Sarvapali Ramchurn

"Model Agnostic Interpretability for Multiple Instance Learning" by Joseph Early, Christine Evers, and Sarvapali Ramchurn, arXiv:2201.11701v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
& MSMARCO & NQ & TriviaQA \\
\Xhline{2.5\arrayrulewidth}
Fine-tuned SOTA & 44.3 & 84.8, 89.8 & 84.1, 87.8 \\
\Xhline{2.5\arrayrulewidth} \\
\multicolumn{4}{c}{Unsupervised} \\
\Xhline{1\arrayrulewidth} \\
BM25 & 18.4 & 62.9, 78.3 & 76.4, 83.2 \\
ICT & - & 50.9, 66.8 & 57.5, 73.6 \\
MSS & - & 59.8, 74.9 & 68.2, 79.4 \\
Contriever & - & 67.2, 81.3 & 74.2, 83.2 \\
\Xhline{1\arrayrulewidth} \\
\texttt{cpt-text} S & 19.9 & 65.5, 77.2 & 75.1, 81.7 \\
\texttt{cpt-text} M & 20.6 & 68.7, 79.6 & 78.0, 83.8 \\
\texttt{cpt-text} L & 21.5 & 73.0, 83.4 & 80.0, 86.8 \\
\texttt{cpt-text} XL & \textbf{22.7} & \textbf{78.8, 86.8} & \textbf{82.1, 86.9} \\
\Xhline{2.5\arrayrulewidth}
\end{tabular}
\end{adjustbox}
\caption{Evaluation of unsupervised \texttt{cpt-text} models of different sizes on several large-scale text search benchmarks. We report MRR@10 on MSMARCO and Recall@20, Recall@100 for NQ and TriviaQA as done in prior work. Results for training with Inverse Cloze Task (ICT) and masked salient spans (MSS) objectives are taken from . \texttt{cpt-text} achieves the best results among unsupervised methods, surpassing keyword search methods on MSMARCO and embedding based methods on NQ and TriviaQA.}
\end{table}
\end{document}

cs

Arvind Neelakantan
Tao Xu
Raul Puri
Alec Radford
Jesse Michael Han
Jerry Tworek
Qiming Yuan
Nikolas Tezak
Jong Wook Kim
Chris Hallacy
Johannes Heidecke
Pranav Shyam
Boris Power
Tyna Eloundou Nekoul
Girish Sastry
Gretchen Krueger
David Schnurr
Felipe Petroski Such
Kenny Hsu
Madeleine Thompson
Tabarak Khan
Toki Sherbakov
Joanne Jang
Peter Welinder
Lilian Weng

"Text and Code Embeddings by Contrastive Pre-Training" by Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, Johannes Heidecke, Pranav Shyam, Boris Power, Tyna Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David Schnurr, Felipe Petroski Such, Kenny Hsu, Madeleine Thompson, Tabarak Khan, Toki Sherbakov, Joanne Jang, Peter Welinder, and Lilian Weng, arXiv:2201.10005v1, 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{Objective metrics comparison of zero-shot style transfer between StableVC and baseline systems.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\hline
Model & Corr $\uparrow$ & RMSE $\downarrow$ & UTMOS $\uparrow$ & WER $\downarrow$ & SECS $\uparrow$ \\ \hline
StyleVC & 0.71 & 14.98 & 3.63 & 3.54 & 0.21 \\
NS2VC & 0.67 & 17.19 & 3.57 & 5.31 & 0.47 \\
DDDM-VC & 0.69 & 15.68 & 3.71 & 3.56 & 0.50 \\
StableVC & \textbf{0.75} & \textbf{12.87} & \textbf{4.06} & \textbf{2.12} & \textbf{0.64} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jixun Yao
Yuguang Yang
Yu Pan
Ziqian Ning
Jiaohao Ye
Hongbin Zhou
Lei Xie

"StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching" by Jixun Yao, Yuguang Yang, Yu Pan, Ziqian Ning, Jiaohao Ye, Hongbin Zhou, and Lei Xie, arXiv:2412.04724v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jiajie Kong
Tony Pourmohamad
Herbert K. H. Lee

"Understanding an Acquisition Function Family for Bayesian Optimization" by Jiajie Kong, Tony Pourmohamad, and Herbert K. H. Lee, arXiv:2310.10614v1, 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{Analysis for the sparsity of reciprocal loss.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \hline
 \textbf{Loss rate} & 2 & 3 & 4 & 5
 \\
 \hline
 Average LDE (cm)/ sequence & 0.46 & \textbf{0.25} & 0.29 & 0.38\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jianzhe Lin
Ghazal Sahebzamani
Christina Luong
Fatemeh Taheri Dezaki
Mohammad Jafari
Purang Abolmaesumi
Teresa Tsang

"Reciprocal Landmark Detection and Tracking with Extremely Few Annotations" by Jianzhe Lin, Ghazal Sahebzamani, Christina Luong, Fatemeh Taheri Dezaki, Mohammad Jafari, Purang Abolmaesumi, and Teresa Tsang, arXiv:2101.11224v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Jefrin Sharmitha Prabhu
Abhinav Vaishya
Shobhit Bhatnagar
Aryaman Manish Kolhe
V. Lalitha
P. Vijay Kumar

"On the Efficacy of the Peeling Decoder for the Quantum Expander Code" by Jefrin Sharmitha Prabhu, Abhinav Vaishya, Shobhit Bhatnagar, Aryaman Manish Kolhe, V. Lalitha, and P. Vijay Kumar, arXiv:2504.21845v2, 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{Adaptive CTC Fusion in Mismatch Conditions. (\% WER)}
\begin{tabular}{lccc}
 	 \toprule
 	 \toprule
 Decoding Strategy& \multicolumn{3}{c}{Test Data}\\
 	 \midrule
 {\bf DIRHA (BLA-L2L)}& {\it BLA-NoMic}& {\it BLA-KA6}& {\it L3L-L4L}\\
 \midrule
 	 Pre-defined CTC [0.5; 0.5] & \bf{26.9}&21&20.3\\
 	 Adaptive CTC Fusion & 27.1&\bf{20.7}&\bf{20} \\
 	 \midrule
 {\bf AMI (MDM-SMDM)}& {\it MDM-NoMic} & {\it MDM-IHM0} & -- \\
 	 \midrule
 	 Pre-defined CTC [0.5; 0.5] &46.1&44&--\\
 	 Adaptive CTC Fusion &\bf{43.1}&\bf{41.9}&--\\
 \bottomrule
 \bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

"Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR" by Ruizhi Li, Gregory Sell, and Hynek Hermansky, arXiv:2102.03055v1, 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
	& \textbf{IEPS} & \textbf{WES} \\
	\hline
	\textbf{Data Loss} & $10^{8}$ & $10^{12}$\\
	\textbf{PDE Loss} & $10^{-14}$ & $10^{-14}$\\
	\textbf{BC Loss} & $10^{-12}$ & $10^{-12}$\\
	\hline
\end{tabular}
\end{adjustbox}
\caption{Weights of the loss functions}
\end{table}
\end{document}

eess

A. Tollardo
F. Cadini
M. Giglio
L. Lomazzi

"DeepF-fNet: a physics-informed neural network for vibration isolation optimization" by A. Tollardo, F. Cadini, M. Giglio, and L. Lomazzi, arXiv:2412.21132v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach" by Xuan He, Danny H. K. Tsang, and Yize Chen, arXiv:2312.06144v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|r|r|r|r|r|r}
 \hline
 \multicolumn{2}{c}{} & \multicolumn{3}{|c}{End item demand only} & \multicolumn{3}{|c}{Component demand} \\
 \hline
 Utilization & $\lambda$ & Runtime (s) & Total costs PH & $\Delta$ (\%) & Runtime (s) & Total costs PH & $\Delta$ (\%)\\
 \hline
 \multirow{4}{*}{50\%}
 & 0.1 & 801.8 & 31689.2 & 5.11 & 1651.4 & 29543.8 & 7.83 \\
 & 1 & 307.9 & 30360.0 & \textbf{1.26} & 533.4 & 27957.7 & \textbf{2.69} \\
 & 10 & 246.9 & 30626.4 & 2.08 & 354.4 & 28052.1 & 3.03 \\
 & 100 & 274.1 & 30670.6 & 2.23 & 377.3 & 28077.2 & 3.06 \\
 \hline
 \multirow{4}{*}{90\%}
 & 0.1 & 841.8 & 79067.8 & 8.82 & 2023.4 & 65682.4 & 5.12\\
 & 1 & 379.5 & 74022.6 & \textbf{4.31} & 1070.7 & 63633.3 & 2.46\\
 & 10 & 281.7 & 74096.6 & 4.32 & 644.6 & 63548.1 & 2.46\\
 & 100 & 329.3 & 74820.8 & 5.88 & 851.0 & 63103.2 & \textbf{2.39} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Majority voting consensus calculation: Evaluation of the $\lambda$-multiple for the penalty parameter $\rho$ in terms of total costs and runtime on a scenario tree with $|\Omega| = 2$ and therefore $|\Phi| = 128$}
\end{table}
\end{document}

math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Error metrics of DNN on ANF and EOG stock \textit{Close} price prediction (validation set)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\multicolumn{6}{c}{\textbf{DNN}}\\\hline
 & \textbf{MSE} & \textbf{RMSE} & \textbf{MAE} & \textbf{MAPE} & \textbf{EVS}\\ \hline
\textit{ANF} & 1.75 & 1.32 & 1.07 & 0.02 & 0.91\\ \hline
\textit{EOG} & 2.39 & 1.55 & 1.23 & 0.01 & 0.7\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Ivan Letteri
Giuseppe Della Penna
Giovanni De Gasperis
Abeer Dyoub

"DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks" by Ivan Letteri, Giuseppe Della Penna, Giovanni De Gasperis, and Abeer Dyoub, arXiv:2210.11532v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"DeFi Liquidation Risk Modeling Using the Reflection Principle for Zero-Drift Brownian Motion" by Timofei Belenko and Georgii Vosorov, arXiv:2505.08100v1, 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{Impact of \textit{3DRF} Gain ($\Delta 3DRF$) over Accuracy.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Network & $\Delta 3DRF$ & Accuracy (\%) & $\Delta$Accuracy~(\%)\\
 \midrule
 \midrule
 VGG-11 & 0 & 92.68 & 0 \\
 \midrule
 Variant-1 & 1.73 & 93.56 & 0.88\\
 Variant-2 & 1.60 & 93.46 & 0.78\\
 \midrule
 Variant-3 & 0.29 & 92.75 & 0.07\\
 \midrule
 Variant-4 & 0.0 & 92.58 & -0.10\\
 Variant-5 & 0.0 & 92.41 & -0.27\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yuke Wang
Boyuan Feng
Xueqiao Peng
Yufei Ding

"An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks" by Yuke Wang, Boyuan Feng, Xueqiao Peng, and Yufei Ding, arXiv:2009.05236v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of results for various BERT models for the second case study (physical activity).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \hline
 & Precision &Recall & F1\\ \hline
 BERT Base & 0.88 & 0.86 & 0.87\\
 PubmedBERT(Abs+Ft) & 0.92 & 0.90 & 0.91\\

 PubmedBERT(Abs) & \textbf{0.96} & \textbf{0.96} & \textbf{0.96} \\
 Bio BERT & 0.88 & 0.90 & 0.88\\

 UMLS BERT& 0.93 & 0.92 & 0.93\\
 Bio-clinical BERT& 0.89 & 0.88 & 0.89\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zitao Shen
Yoonkwon Yi
Anusha Bompelli
Fang Yu
Yanshan Wang
Rui Zhang

"Extracting Lifestyle Factors for Alzheimer's Disease from Clinical Notes Using Deep Learning with Weak Supervision" by Zitao Shen, Yoonkwon Yi, Anusha Bompelli, Fang Yu, Yanshan Wang, and Rui Zhang, arXiv:2101.09244v2, 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{Cognitive tests and primary references.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}\hline
 Test Name & References \\\hline
 Adult Decision Making Competence & \\
 Bayesian Updating & \\
 Berlin Numeracy & \\
 Cognitive Reflection & \\
 Coherence Forecasting & \\
 Denominator Neglect & \\
 & \\
 Graph Literacy & \\
 Impossible Question & \\
 Leapfrog & \\
 Raven Matrices & \\
 Number Series & \\
 & \\
 Shipley General Intelligence & \\
 Time Series & \\
 & \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Edgar C. Merkle
Nikolay Petrov
Sophie Ma Zhu
Ezra Karger
Philip E. Tetlock
Mark Himmelstein

"Identifying good forecasters via adaptive cognitive tests" by Edgar C. Merkle, Nikolay Petrov, Sophie Ma Zhu, Ezra Karger, Philip E. Tetlock, and Mark Himmelstein, arXiv:2411.11126v2, 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}{lcccc}
 \hline
 \multicolumn{5}{c}{Dependent variable: Self reported measure of attention to environment}\\
 \hline
 \hline
 Avg. variability & -6.291***& -7.095***& -9.194***& -9.877***\\
 & (1.587) & (1.902) & (2.087) & (2.003) \\
 Avg. variability sq.& 71.038***& 78.984***& 100.939***& 111.767***\\
 & (18.330) & (21.756) & (22.608) & (21.346) \\
 Income & & -0.001* & -0.001* & -0.001* \\
 & & (0.001) & (0.001) & (0.001) \\
 Male & & -0.013***& -0.013***& -0.013***\\
 & & (0.003) & (0.003) & (0.003) \\
 Age & & 0.002***& 0.002***& 0.002***\\
 & & (0.000) & (0.000) & (0.000) \\
 Education level & & 0.006***& 0.006***& 0.006***\\
 & & (0.001) & (0.001) & (0.001) \\
 Occupation category Controls & N & Y & Y & Y \\
 Historical ethnic group characteristics & N & N & Y & Y \\
 Historical topographic characteristics & N & N & N & Y \\
 Country-year Fixed effects & Y & Y & Y & Y \\
 \hline
 Mean of dep var & 0.702 & 0.704 & 0.704 & 0.704 \\
 St. Dev. of dep var & 0.252 & 0.250 & 0.250 & 0.250 \\
 Min value Avg. Variability& 0.015 & 0.015 & 0.015 & 0.015 \\
 Max value Avg. Variability& 0.093 & 0.093 & 0.093 & 0.093 \\
 R-sq & 0.097 & 0.113 & 0.113 & 0.113 \\
 Adj. R-sq & 0.097 & 0.112 & 0.112 & 0.113 \\
 N & 157142 & 138067 & 138067 & 138067 \\
 \hline
 \multicolumn{5}{c}{ {*}{*}{*} p$<$0.01, {*}{*}p$<$0.05, {*} p$<$0.1, {+} p$<$0.15}\\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Coefficients for the impact of average variability of ancestral climatic conditions on individual's self-reported attention to the environment }
\end{table}
\end{document}

q-fin

"Ancestral origins of attention to environmental issues" by César Barilla and Palaash Bhargava, arXiv:2509.09598v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

I. Seidu
E. Nyarko
S. Iddi
E. Ranganai
K. Doku-Amponsah

"Distribution of the elemental regression weights with t-distributed co-variate measurement errors" by I. Seidu, E. Nyarko, S. Iddi, E. Ranganai, and K. Doku-Amponsah, arXiv:2312.07704v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c||c|c|c|c||c|c}
\textbf{Step} & \textsc{cnot} & $R_z(\theta)$ & $\sqrt{\textsc{not}}$ & \textsc{not} & \textbf{Depth} & \textbf{Cost} \\
\hline
\textbf{1} & 1 & 4 & 4 & 0 & 9 & 13 \\ \hdashline
\textbf{2.1} & 47 & 47 & 26 & 2 & 122 & 310 \\
\textbf{2.2} & 485 & 467 & 133 & 1 & 1086 & 3026 \\
\textbf{2.3} & 488 & 471 & 134 & 1 & 1094 & 3046 \\ \hdashline
\textbf{3.1} & 490 & 470 & 133 & 1 & 1094 & 3054 \\
\textbf{3.2} & 494 & 475 & 134 & 1 & 1104 & 3080 \\
\textbf{3.3} & 498 & 480 & 135 & 1 & 1114 & 3106 \\
\end{tabular}
\end{adjustbox}
\caption{Cumulative counts and costs for the longest path until and including step $N$. We can see that the distribution loading is relatively cheap while the stopping time is the most expensive part.}
\end{table}
\end{document}

q-fin

"Potential Applications of Quantum Computing for the Insurance Industry" by Michael Adam, arXiv:2210.06172v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline
 \multicolumn{2}{|c|}{}& \multicolumn{3}{|c|}{Frequentist}& \multicolumn{2}{|c|}{Bayesian} & \\
 \hline
Quantities & $\theta_*$ & NAIVE & WLP & LSW & BMA & CB & ORACLE \\
 \hline
Coverage & -0.25 & 0.925 & 0.488 & 0.959 & 0.000 & 0.959 & 0.945 \\
 & -0.4 & 0.924 & 0.480 & 0.952 & 0.000 & 0.955 & 0.953 \\
 \hline
\hline
Interval & -0.25 & 1.054 & 1.013 & 1.702 & 0.004 & 1.237 & 0.990 \\
 Length & -0.4 & 1.054 & 1.589 & 1.705 & 0.001 & 1.226 & 0.992 \\
 \hline
\hline
Bias & -0.25 & 0.054 & 0.337 & 0.039 & 0.250 & 0.088 & 0.012 \\
 & -0.4 & 0.088 & 0.339 & 0.065 & 0.400 & 0.093 & 0.011 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Coverage, interval length, and bias corresponding to sample-based standard errors for each method considered for $n=300, d=400$ under signal strengths $\theta_* \in \{-0.25,-0.4\}$.}
\end{table}
\end{document}

stat

"Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates" by Abhishek Ojha and Naveen N. Narisetty, arXiv:2411.17618v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c||c|c|c|c|c|c|c}
	\ & $C_1$ & $C_2$ & $C_3$ & $C_4$ & $C_5$ & $C_6$ & $C_7$ \\
	\hline
	Element & () & $(1,2)(3,4)$ & (1,2,3) & $(1,2,3)(4,5,6)$ & $(1,2,3,4)(5,6)$ & $(1,2,3,4,5)$ & $(1,2,3,4,6)$ \\
	$|C_i|$ &1 & 45& 40 & 40 & 90 & 72 &72 \\
 \end{tabular}
\end{adjustbox}
\caption{Conjugacy classes of $A_6$ that corresponds to the character table.}
\end{table}
\end{document}

math

Po-Shen Hsin
Ryohei Kobayashi
Carolyn Zhang

"Anomalies of Coset Non-Invertible Symmetries" by Po-Shen Hsin, Ryohei Kobayashi, and Carolyn Zhang, arXiv:2503.00105v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components" by Artur Sokolovsky and Luca Arnaboldi, arXiv:2009.09993v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Matteo Gianella
Mario Beraha
Alessandra Guglielmi

"Bayesian nonparametric boundary detection for income areal data" by Matteo Gianella, Mario Beraha, and Alessandra Guglielmi, arXiv:2312.13992v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The experiment result of text classification. The test dataset is the 40,000 data. The accuracy reported by human is 5000 data that sampled from the 500 million data. Model-A, Model-B and Model-C are corresponding to Fig 1.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
Model In Fig 1 & Test Dataset Accuracy & Human Evaluate Accuracy \\
\hline
Model-A & 83.3\% & 88.0\% \\
\hline
Model-B & 91.7\% & 97.2\% \\
\hline
Model-C & 92.5\% & 97.7\% \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Learning From How Humans Correct

"Learning From How Humans Correct" by Tong Guo, arXiv:2102.00225v20, 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{Standardized RMSPE of the OTL-Circuit Example. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Type of mean & EVIGP & mlegp \\ \hline
 Constant & 0.01608 & 0.04882 \\
 Linear & 0.01399 & 0.03584 \\
 Quadratic & 0.01792 & 0.03006\\
 Quadratic, after selection & 0.01625 & N/A \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Lulu Kang
Yuanxing Cheng
Yiwei Wang
Chun Liu

"Energetic Variational Gaussian Process Regression for Computer Experiments" by Lulu Kang, Yuanxing Cheng, Yiwei Wang, and Chun Liu, arXiv:2401.00395v2, 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{Pre-processing steps and parameters for each team}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
Team & Clipping & Cropping & Resize & Interpolation & Other \\ \midrule
SN & $-1000, 4000$ & $448\times448$ & $321\times244\times244$ & Linear & 3D \\
MH & $-1000, 3000$ & $300\times300$ & No & Linear & 2.5D, Scaling \\
EK & $500, 2000$ & $515\times515$ & No & No & 2.5D, Scaling\\
CW & $0, 1900$ & $322\times307$ & No & No & No\\
SV & No & No & $96\times96\times1$ & Linear & Scaling \\
BM & $-100, 3171$ & $480\times480$ & $240\times240\times1$ & Area & Scaling \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Nikolay Burlutskiy
Marija Kekic
Jordi de la Torre
Philipp Plewa
Mehdi Boroumand
Julia Jurkowska
Borjan Venovski
Maria Chiara Biagi
Yeman Brhane Hagos
Roksana Malinowska-Traczyk
Yibo Wang
Jacek Zalewski
Paula Sawczuk
Karlo Pintarić
Fariba Yousefi
Leif Hultin

"MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans" by Nikolay Burlutskiy, Marija Kekic, Jordi de la Torre, Philipp Plewa, Mehdi Boroumand, Julia Jurkowska, Borjan Venovski, Maria Chiara Biagi, Yeman Brhane Hagos, Roksana Malinowska-Traczyk, Yibo Wang, Jacek Zalewski, Paula Sawczuk, Karlo Pintarić, Fariba Yousefi, and Leif Hultin, arXiv:2411.17260v1, 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{System parameters' values}\centering%
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline\hline
\textbf{Parameter} & \textbf{Value/Range} \\ \hline\hline
$\lambda$ & $8.5$ cm \\ \hline
$G_{R,U}$, $G_{R,E}$ & $12$ dBi \\ \hline
$G_{T,B}$, $G_{R,B}$ & $25$ dBi \\ \hline
$G_{T,V}$ & $17$ dBi \\ \hline
$d_{BR}$ & $22$ m \\ \hline
$d_{BU_{j}}(\forall j)$ & $30$ m \\ \hline
$d_{BE_{l}},d_{BV_{k}}(\forall l,k)$ & $20$ m \\ \hline
$\Delta _{r},\Delta _{a}$ & $\lambda /2$ \\ \hline
$\varphi _{BU}$ & $[-15,5]$ deg \\ \hline
$\varphi _{VB}$ & $[20,30]$ deg \\ \hline
$\varphi _{BR}$ & $-40$ deg \\ \hline
$\varphi _{BE}$ & $[-35,-15]$ deg \\ \hline
$\theta _{XZ}$ $\left(\forall X,Z\right)$ & $0$ \\ \hline
$N$ & $25$ ($5\times 5$ REs) \\ \hline
$N_{F}$ & $5$ dB \\ \hline
$P_{\max }$ & $25$ dB \\ \hline
$N_{t}$, $N_{r}$ & $8$ \\ \hline
$B$ & $50$ MHz \\ \hline
$T$ & $298$ Kelvins \\ \hline
$J,L$ & $2$ \\ \hline
$K$ & $3$ \\ \hline
$\gamma _{E,\mathrm{DL}}^{(\max )}$, $\gamma _{E,\mathrm{DL}}^{(\max )}$ & $%
5 $ dB \\ \hline
$\left( \gamma _{B}^{(\min )},\gamma _{U,\mathrm{DL}}^{(\min )}\right) $ & $%
(5,10)$ dB \\ \hline
$\kappa _{XZ}$ $\left(\forall XZ \in \left\{BR, RU_j, V_kR \right\}\right)$ & $20$ dB \\ \hline
$\kappa _{RE_l}$ $\left(\forall l \right)$ & $\infty$ (Pure LoS) \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Elmehdi Illi
Ahmad Bazzi
Marwa Qaraqe
Ali Ghrayeb

"On the Secrecy-Sensing Optimization of RIS-assisted Full-Duplex Integrated Sensing and Communication Network" by Elmehdi Illi, Ahmad Bazzi, Marwa Qaraqe, and Ali Ghrayeb, arXiv:2504.20912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean $T_1, T_2, \text{CBV}, R$ values and standard deviations, in white (WM) and grey (GM) matters ROIs. Values significantly departing from literature reference (last column) are in red.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccc}
 \toprule
 Parameter & ROI & Full matching & HD-GMM & HD-STM & Literature \\
 \midrule
 \multirow{2}[0]{*}{$T_1$(ms)} & WM & $891 \pm 6$ & $650 \pm 23 $ & $869 \pm 19$ & $690$-$1100$ \\
 & GM & $1566 \pm 12$ & $1650 \pm 57$& $1631 \pm 56$ & 1286-1393\\
 \midrule
 \multirow{2}[0]{*}{$T_2$(ms)} & WM &
 $52 \pm 0.0$ & \textcolor{red}{$400 \pm 15$} & $48 \pm 2$ & 56-80 \\
 & GM & $95 \pm 4$ & \textcolor{red}{$370 \pm 43$} & $96 \pm 12$ & 78-117\\
 \midrule
 \multirow{2}[0]{*}{$\text{CBV}$($\%$)} & WM & $5.4 \pm 0.6$ & $2.0 \pm 0.1$ & $4.8 \pm 0.8$ & $1.7$ - $3.6$ \\
 & GM & $9.2 \pm 1.1$ & $4.2 \pm 0.8$ & $7.6 \pm 1$ & $3.0$ - $8.0$ \\
 \midrule
 \multirow{2}[0]{*}{$R$ ($\mu s$)} & WM & $5.7 \pm 0.1$ & $5.6 \pm 0.1$ & $5.7 \pm 0.1$ & $6.8 \pm 0.3$ \\
 & GM & $6.2 \pm 0.1$ & $5.4 \pm 0.4$ & $6.1 \pm 0.1$ & $7.3 \pm 0.3$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Geoffroy Oudoumanessah
Thomas Coudert
Carole Lartizien
Michel Dojat
Thomas Christen
Florence Forbes

"Scalable magnetic resonance fingerprinting: Incremental inference of high dimensional elliptical mixtures from large data volumes" by Geoffroy Oudoumanessah, Thomas Coudert, Carole Lartizien, Michel Dojat, Thomas Christen, and Florence Forbes, arXiv:2412.10173v1, 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{Unsupervised multiple domain adaptation with adversarial training setup}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\toprule
\textbf{~Adaptation Methods~} & \textbf{~ ~ $N$~ ~~} & \textbf{~ ~ $M$~ ~~} \\
\hline\hline
DAT~ & 1 & 1 \\
\hline
~ MS-DAT (based on LDC code)~~ & 6 & 1 \\
MT-DAT (based on LDC code) & 1 & 4 \\
MDAT (based on LDC code) & 6 & 4 \\
MS-DAT (based on k-means) & 3 & 1 \\
MT-DAT (based on k-means) & 1 & 2 \\
MDAT (based on k-means) & 3 & 2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Adversarial Training for Multi-domain Speaker Recognition" by Qing Wang, Wei Rao, Pengcheng Guo, and Lei Xie, arXiv:2011.08623v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Collision-based Dynamics for Multi-Marginal Optimal Transport" by Mohsen Sadr and Hossein Gorji, arXiv:2412.16385v2, 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{Feedforward Module, $\pi^{\{H, L\}}_{(0)}$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 hidden layers & $(512, 512)$ \\
 hidden layer activation & relu \\
 output activation & linear\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sasha Salter
Kristian Hartikainen
Walter Goodwin
Ingmar Posner

"Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning" by Sasha Salter, Kristian Hartikainen, Walter Goodwin, and Ingmar Posner, arXiv:2201.08115v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zhenyu Cui
Anne MacKay
Marie-Claude Vachon

"Analysis of VIX-linked fee incentives in variable annuities via continuous-time Markov chain approximation" by Zhenyu Cui, Anne MacKay, and Marie-Claude Vachon, arXiv:2207.14793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{amsfonts}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
 \toprule
 \multicolumn{2}{c}{} & 2 Agents & 4 Agents & 6 Agents \\
 \midrule
 \multirow{2}{*}{ShapeNet} & $-\mathcal{L}_{task}$ & 6.10 & 20.07 & 29.00 \\
 & $\mathcal{L}_{adv}$ & \textbf{0.37} & \textbf{4.45} & \textbf{13.77} \\
 \midrule
 \multirow{2}{*}{V2V} & $-\mathcal{L}_{task}$ & 20.8 & 63.82 & 79.11 \\
 & $\mathcal{L}_{adv}$ & \textbf{7.55} & \textbf{52.31} & \textbf{76.18} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

James Tu
Tsunhsuan Wang
Jingkang Wang
Sivabalan Manivasagam
Mengye Ren
Raquel Urtasun

"Adversarial Attacks On Multi-Agent Communication" by James Tu, Tsunhsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun, arXiv:2101.06560v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
\hline
\textbf{Experiments} & \textbf{2019-11-23} & \textbf{2019-11-25} & \textbf{2019-12-17} & \textbf{2019-12-18} \\ \hline
B.S & 0.49919 & 0.085 & 0.3669 & 0.2720 \\
\hline
C1. Horizontal mixing exp 1 & 0.3953 & 0.1185 & 0.2065 & 0.2802 \\
\hline
C1. Horizontal mixing exp 2 & 0.4654 & 0.3188 & 0.4875 & 0.3815 \\
\hline
C2. Bottom drag exp 1 & 0.4224 & 0.1096 & 0.3618 & 0.4835 \\
\hline
C2. Bottom drag exp 2 & 0.4375 & 0.3165 & 0.2282 & 0.3676 \\
\hline
C3. Wind stress exp 1 & 0.4462 & 0.4343 & 0.3164 & 0.3224 \\
\hline
C3. Wind stress exp 2 & 0.4883 & 0.087 & 0.3793 & 0.2848 \\
\hline
C4. Modified bathymetry exp 1 & 0.4491 & 0.005& 0.1305 & 0.2363 \\
\hline
C5. Sponge exp 1 & 0.3778 & 0.0278 & 0.29965 & 0.2528 \\
\hline
C6. Mixed radiation-nudging exp 1 & 0.5315 & 0.3682 & 0.2337 &
0.1660 \\
\hline
C6. Mixed radiation-nudging exp 2 & 0.5555 & 0.7476 & 0.5192 & 0.2551 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Results of measured index ${\cal A}$ obtained for the sensitivity experiments. they include the first period (2019-11-23 and 2019-11-25), and the second period (2019-12-17 and 2019-12-18).}
\end{table}
\end{document}

math

G. Garcia-Sanchez
A. M. Mancho
A. G. Ramos
J. Coca
J. A. Jimenez-Madrid

"Dynamical systems for remote validation of very high-resolution ocean models" by G. Garcia-Sanchez, A. M. Mancho, A. G. Ramos, J. Coca, and J. A. Jimenez-Madrid, arXiv:2501.16026v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
\toprule
n\_epochs & acc. & top-$3$. \\
\midrule
$1$ & $74.84$ & $95.76$ \\
$2$ & $76.96$ & $96.00$ \\
$3$ & $75.88$ & $95.88$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jingtan Wang
Xinyang Lu
Zitong Zhao
Zhongxiang Dai
Chuan-Sheng Foo
See-Kiong Ng
Bryan Kian Hsiang Low

"Source Attribution for Large Language Model-Generated Data" by Jingtan Wang, Xinyang Lu, Zitong Zhao, Zhongxiang Dai, Chuan-Sheng Foo, See-Kiong Ng, and Bryan Kian Hsiang Low, arXiv:2310.00646v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{List of notations.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rl}
\toprule
{\bf Notation} & {\bf Description} \\
\midrule
$F$, $F_*$, $F_s$ & Clean/Watermarked/Shadow encoder \\
$\mathcal{D}_{t}$, $\mathcal{D}_{s}$ & Target/Shadow dataset \\
$\mathcal{D}_{p}$, $\mathcal{D}_{v}$ & Private/Verification dataset \\
$T$, $M$ & Trigger, Mask \\
$\kappa$, $G$ &Key-tuple, Decoder \\
 $sk$, $sk_x'$ & Secret vector, Decoded vector \\
DA & Downstream accuracy \\
WR & Watermark rate \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders" by Tianshuo Cong, Xinlei He, and Yang Zhang, arXiv:2201.11692v4, 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 obtained by instance level training on Criteo}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
\hline
\textbf{Experiment} & \textbf{Model} & \textbf{Optimizer} & \textbf{Loss Function} & \textbf{AUC Score} \\ \hline
\textit{E1} & 1-Layer MLP & Adam & BCE & 79.23 \\ \hline
\textit{E2} & 1-Layer MLP & Adam & MSE & 79.2 \\ \hline
\textit{E3} & 2-Layer MLP & Adam & BCE & 80.1 \\ \hline
\textit{E4} & 2-Layer MLP & Adam & MSE & 79.94 \\ \hline
\textit{E5} & 1-Layer MLP & SGD & BCE & 80.7 \\ \hline
\textit{E6} & 1-Layer MLP & SGD & MSE & 80.54 \\ \hline
\textit{E7} & 2-Layer MLP & SGD & BCE & 79.17 \\ \hline
\textit{E8} & 2-Layer MLP & SGD & MSE & 80.56 \\ \hline
\textit{E9} & AutoInt & Adam & BCE & 80.66 \\ \hline
\textit{E10} & AutoInt & Adam & MSE & 80.7 \\ \hline
\textit{E11} & AutoInt & Adam & MSE & 79.02 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Anand Brahmbhatt
Mohith Pokala
Rishi Saket
Aravindan Raghuveer

"LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions" by Anand Brahmbhatt, Mohith Pokala, Rishi Saket, and Aravindan Raghuveer, arXiv:2310.10096v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zero-Leverage Puzzle

"Zero-Leverage Puzzle" by Mykola Pinchuk, arXiv:2302.00761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccccc}
\toprule
\multicolumn{7}{c}{\textbf{Out-of-Class Generalization}} \\
\toprule
& \textbf{Dataset} &
Abs.Rel &
RMSE &
RMSE$_{log}$ &
SILog &
$\delta<1.05$
\\
\toprule
\parbox[t]{2mm}{\multirow{4}{*}{\rotatebox[origin=c]{90}{\textit{ResNet}}}}
& \textit{Mug} & 0.036 & 0.013 & 0.046 & 4.317 & 0.735 \\
& \textit{Wine Glass} & 0.040 & 0.015 & 0.051 & 4.889 & 0.692 \\
& \textit{Fingers} & 0.040 & 0.018 & 0.056 & 5.395 & 0.715 \\
& \textit{Box} & 0.051 & 0.019 & 0.063 & 6.102 & 0.618 \\
\midrule
\parbox[t]{2mm}{\multirow{4}{*}{\rotatebox[origin=c]{90}{\textit{BTS}}}}
& \textit{Mug} & 0.038 & 0.015 & 0.050 & 4.629 & 0.744 \\
& \textit{Wine Glass} & 0.038 & 0.015 & 0.050 & 4.579 & 0.730 \\
& \textit{Fingers} & 0.039 & 0.018 & 0.056 & 5.504 & 0.741 \\
& \textit{Box} & 0.048 & 0.018 & 0.062 & 5.899 & 0.673 \\
\midrule
\parbox[t]{2mm}{\multirow{4}{*}{\rotatebox[origin=c]{90}{\textit{PackNet}}}}
& \textit{Mug} & 0.034 & 0.013 & 0.042 & 3.458 & 0.765 \\
& \textit{Wine Glass} & 0.034 & 0.014 & 0.047 & 4.338 & 0.769 \\
& \textit{Fingers} & 0.030 & 0.014 & 0.047 & 4.479 & 0.832 \\
& \textit{Box} & 0.047 & 0.019 & 0.061 & 5.687 & 0.652 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Monocular depth estimation results for cross-dataset generalization using different networks. Independent models trained on 3/4 object datasets, evaluated on the remaining one.}
\end{table}
\end{document}

cs

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

"Monocular Depth Estimation for Soft Visuotactile Sensors" by Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy, Andrew Beaulieu, Adrien Gaidon, and Alex Alspach, arXiv:2101.01677v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\hline
Chatbot use cases & \multicolumn{2}{c}{Yes} & \multicolumn{2}{l}{No} \\ \cline{2-5}
 & Frequency & \% & Frequency & \% \\ \hline
Answering to students' FAQs & 148 & 52.5 & 134 & 47.5 \\
Assigning student grades & 113 & 40.1 & 169 & 59.9 \\
Facilitating agenda information & 171 & 60.6 & 111 & 39.4 \\
Sharing class materials & 136 & 48.2 & 146 & 51.8 \\
Others & 25 & 8.9 & 257 & 91.1 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Perceived useful chatbot use cases}
\end{table}
\end{document}

cs

J. J. Merelo
P. A. Castillo
Antonio M. Mora
Francisco Barranco
Noorhan Abbas
Alberto Guillen
Olia Tsivitanidou

"Chatbots and messaging platforms in the classroom: an analysis from the teacher's perspective" by J. J. Merelo, P. A. Castillo, Antonio M. Mora, Francisco Barranco, Noorhan Abbas, Alberto Guillen, and Olia Tsivitanidou, arXiv:2201.10289v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}\hline
& $\psi_{1}\left( x,y\right) $ & $\psi_{2}\left( x,y\right) $ & $\psi
_{3}\left( x,y\right) $ & $\psi_{4}\left( x,y\right) $ & $\dot{V}\left(
x,y\right) $\\\hline
$T_{1}^{I}$ & $\leq0$ & $\geq0$ & $\leq0$ & $\leq0$ & $\psi_{1}-\psi_{2}-\psi
_{3}-\psi_{4}$\\\hline
$T_{1}^{II}$ & $\leq0$ & $\leq0$ & $\geq0$ & $\leq0$ & $\psi_{1}+\psi_{2}%
+\psi_{3}-\psi_{4}$\\\hline
$T_{1}^{III}$ & $\geq0$ & $\leq0$ & $\geq0$ & $\geq0$ & $-\psi_{1}+\psi_{2}%
+\psi_{3}+\psi_{4}$\\\hline
\end{tabular}
\end{adjustbox}
\caption{Table of signs of the $\psi_j$ ($j=1,2,3,4$) and corresponding expression for $\dot{V}$.}
\end{table}
\end{document}

math

"Lyapunov functions for Morse-Smale synchronisation diffeomorphisms" by Jorge Buescu and Henrique M. Oliveira, arXiv:2503.13230v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Antonio Cabrales
Manu García
David Ramos Muñoz
Angel Sánchez

"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, 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}{lccc}
			status & number & $\max|y''-f|$ & $\max(|y_{opt}-y_{b}|)$ \\ \hline\hline
		$\to y_b$ & 13	 & 1.1E-06 & 2.9E-10 \\
			$\to y_s$ & 0 & NA & NA \\
			$diverge$ & 12 & diverge & diverge \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


\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}{lcc}
\hline
{Benchmark}
& {Bound}
& {Timing (ms)}
\\
\hline
{PythagoreanSum}$^{\text{b}}$ &{8.88e-16} & 2
\\
{HammarlingDistance}$^{\text{b}}$ &{1.11e-15} & 2
\\
{squareRoot3}$^{*}$ &{4.44e-16} & 2
\\
 {{squareRoot3Invalid}}$^{*}$ &{4.44e-16} & 2
\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Type-Based Approaches to Rounding Error Analysis" by Ariel Eileen Kellison, arXiv:2501.14598v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Benjamin Boys
Mark Girolami
Jakiw Pidstrigach
Sebastian Reich
Alan Mosca
O. Deniz Akyildiz

"Tweedie Moment Projected Diffusions For Inverse Problems" by Benjamin Boys, Mark Girolami, Jakiw Pidstrigach, Sebastian Reich, Alan Mosca, and O. Deniz Akyildiz, arXiv:2310.06721v3, 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{Baseline settings for the simulation of the synthetic dataset in Blender.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\hline
\textbf{Parameter} & \textbf{Setting} \\
\hline
Camera Height & 150 m \\
Camera Angle & 90\textdegree \\
Camera Sensor Width & 36 mm \\
Output Resolution & 8192 x 8192 pixels \\
Rendering Engine & Cycles \\
Adaptive Sampling Noise Threshold & 0.01 \\
Maximum Samples & 2048 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison with state-of-the-art methods}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
Parameter & Relative error$\left(\%\right)$ & RMSE $\left(\%\right)$\\\hline
\hspace{1cm} & $5\%$ & $1\%$ \\
\hspace{1cm} & $30-50\%$ & $1.18-8\%$\\
\hspace{1cm}& $4-8\%$ & $2.31-5.89\%$\\
\hspace{1cm}& - & $2.27\%$\\
\hspace{1cm}& $2\%$ & $6.3\%$\\
Distributed Averaging & $2.49\%$ & $4.8685\%$\\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Thermostatic control for demand response using distributed averaging and deep neural networks" by Kshitij Singh and Pratik K. Bajaria, arXiv:1912.11692v1, 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|cccc|}
\hline
Material properties& Young's modulus $E$ & Poisson ratio $\nu$ & Density $\rho$& Fracture energy $G_0$\\
\hline
Value&$200 GPa$&$0.3$&$7800 kg/m^3$& $1.125 \times 10^5 J/m^2$\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Mechanical properties for the cylinder fragmentation under internal pressure example, following .}
\end{table}
\end{document}

cs

"An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture" by Yue Yu, Huaiqian You, and Nathaniel Trask, arXiv:2101.01434v1, 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{Test set SDR (in dB) for 20 epochs early stopping.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|c|c|c|c|}
 \hline
 & Value of $\lambda_h$ & Basel. & 1/(2L) & 1/L & 1 \\
 \hline
 \multirow{4}[2]{*}{Voc.} & Med & 3.65 & 3.50 & \textbf{3.69} & 3.64 \\
 & MAD & 3.04 & 2.82 & 2.98 & 2.96 \\
 & Mean & -0.56 & -0.39 & \textbf{0.01} & -0.10 \\
 & SD & 14.23 & 13.53 & 13.31 & 13.30 \\
 \hline
 \multirow{4}[2]{*}{Acc.} & Med & 7.37 & 7.32 & \textbf{7.44} & 7.42 \\
 & MAD & 2.10 & 2.11 & 2.14 & 2.14 \\
 & Mean & 7.53 & 7.44 & \textbf{7.56} & 7.54 \\
 & SD & 3.74 & 3.92 & 3.89 & 3.89 \\
 \hline
 \multicolumn{2}{|c|}{Epochs} & 120 & 76 & 167 & 120 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Joaquin Perez-Lapillo
Oleksandr Galkin
Tillman Weyde

"Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy" by Joaquin Perez-Lapillo, Oleksandr Galkin, and Tillman Weyde, arXiv:1910.10071v1, 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}{ccccccc}
			\toprule
			Dataset & Noise2Sim & Clip-denoising & Restormer & GAdap & GAdap-ldct-batch4-5k & GAdap-ldct-batch16-5k \\
			\midrule
			\midrule
			\multirow{1}{*}{LDCT} & 45.98/0.978 & 45.88/0.976 & 41.18/0.968 & 42.32/0.964 & 45.47/0.974 & 46.06/0.977 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Inju Ha
Donghun Ryou
Seonguk Seo
Bohyung Han

"Learning to Translate Noise for Robust Image Denoising" by Inju Ha, Donghun Ryou, Seonguk Seo, and Bohyung Han, arXiv:2412.04727v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Descriptive Statistics for Contributions by Workers and Managers}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
		\toprule[1.5pt]
		& All employees & Workers & Managers \\
 \midrule
		Amount (in million 2010 USD) & 105.82 & 13.31 & 56.84 \\
		Number of contributions & 357,436 & 71,782 & 214,593 \\
 Average amount per contribution (in 2010 USD) & 296.04 & 185.53 & 264.89\\
		Number of donors & 46,719 & 13,335 & 19,830 \\
		Number of recipients & 9,942 & 3,977 & 6,108 \\
		\hline\hline
		\multicolumn{4}{l}{
		\begin{minipage}{0.95\linewidth} \smallskip \scriptsize
		\textbf{Notes:} The table reports descriptive statistics on all matched employee contributions that were donated three cycles before to three cycles after the union election. The difference in the amounts from all employees and the total from workers and managers is driven by contributions for which we were unable to classify the occupation.
		\end{minipage}} \\
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Do Unions Shape Political Ideologies at Work?" by Johannes Matzat and Aiko Schmeißer, arXiv:2209.02637v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.