\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of Rolling Window (RW) Setup and Market Characteristics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
\toprule
{Economy} & {Daily Returns} & {RW Size} & {No. of RW (Daily)} & {No. of RW (Weekly)} & {Avg Volatility (SD, \%)} \\
\midrule
United States & 1,758 & 250 & 1,508 & 301 & 2.01 \\
India & 1,728 & 245 & 1,483 & 296 & 1.80 \\
Hong Kong & 1,719 & 244 & 1,475 & 295 & 1.72\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Semiparametric Dynamic Copula Models for Portfolio Optimization" by Savita Pareek and Sujit K. Ghosh, arXiv:2504.12266v1, 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{Success rates of the CBO method on the non-convex function with heart-shaped constraint.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|r|r|r|r|}
 \hline
 \multirow{2}{*}{$K$} & \multicolumn{4}{c|}{${N}$} \\
 \cline{2-5}
 & \textbf{10} & \textbf{20} & \textbf{50} & \textbf{100} \\
 \hline
 5 & 0.29 & 0.54 & 0.90 & 0.95 \\
 10 & 0.46 & 0.69 & 0.88 & 0.99 \\
 20 & 0.54 & 0.81 & 0.95 & 1.00 \\
 50 & 0.59 & 0.78 & 0.98 & 0.99 \\
 100 & 0.57 & 0.81 & 0.97 & 1.00 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Well-posedness and approximation of reflected McKean-Vlasov SDEs with applications" by P. D. Hinds, A. Sharma, and M. V. Tretyakov, arXiv:2412.20247v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}
			\toprule
 & $n_p$ & $\gamma$ & $V$ & $r_p$ & $r_s$ & $r_o$ & $r_m$\\
 \hline
 \emph{3DMatch} & 256 & 24 & 0.025 & 0.0375 & 0.1 & 0.0375 & 0.05\\
 \emph{ModelNet} & 384 & 64 & 0.06 & 0.018 & 0.06 & 0.04 & 0.04 \\
 \emph{odometryKITTI} & 512 & 48 & 0.3 & 0.21 & 0.75 & 0.45 & 0.3 \\
			\bottomrule

	\end{tabular}
\end{adjustbox}
\caption{Hyper-parameters configurations for different datasets.}
\end{table}
\end{document}

eess

Shengyu Huang
Zan Gojcic
Mikhail Usvyatsov
Andreas Wieser
Konrad Schindler

"PREDATOR: Registration of 3D Point Clouds with Low Overlap" by Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler, arXiv:2011.13005v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Extended GEE Mean Parameter Estimates and Robust Standard Errors (BC0) for CARDIA Study}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
\hline
\textbf{} & \multicolumn{8}{c}{\textit{Exchangeable}} \\ \cline{2-9}
\textbf{} & \multicolumn{2}{c}{\textit{Black Females}} & \multicolumn{2}{c}{\textit{Black Males}} & \multicolumn{2}{c}{\textit{White Females}} & \multicolumn{2}{c}{\textit{White Males}} \\
 & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 \\
 \hline
Intercept & -0.021 & 0.111 & 0.168 & 0.110 & 0.165 & 0.147 & 0.300 & 0.155 \\
Age/10 & \textbf{0.343} & 0.157 & \textbf{0.654} & 0.177 & -0.245 & 0.187 & -0.099 & 0.197 \\
Age-Square & -0.597 & 0.437 & 0.293 & 0.469 & -0.309 & 0.514 & -0.757 & 0.576 \\
Some College & \textbf{-0.680} & 0.117 & \textbf{-0.949} & 0.125 & \textbf{-0.593} & 0.161 & \textbf{-0.960} & 0.169 \\
College Degree & \textbf{-1.744} & 0.171 & \textbf{-1.795} & 0.177 & \textbf{-1.864} & 0.161 & \textbf{-1.994} & 0.168 \\
Year(Yr/10) & \textbf{0.463} & 0.214 & 0.385 & 0.241 & \textbf{-0.707} & 0.265 & -0.004 & 0.248 \\
Year-square & \textbf{-0.934} & 0.373 & -0.334 & 0.415 & 0.510 & 0.474 & -0.388 & 0.433 \\
Year-cubic & \textbf{0.358} & 0.168 & 0.037 & 0.186 & -0.200 & 0.215 & 0.132 & 0.193 \\
\hline
\textbf{} & \multicolumn{8}{c}{\textit{Modified AR(1)}} \\ \cline{2-9}
\textbf{} & \multicolumn{2}{c}{\textit{Black Females}} & \multicolumn{2}{c}{\textit{Black Males}} & \multicolumn{2}{c}{\textit{White Females}} & \multicolumn{2}{c}{\textit{White Males}} \\
 & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 \\
 \hline
Intercept & -0.014 & 0.111 & 0.164 & 0.110 & 0.146 & 0.147 & 0.294 & 0.156 \\
Age/10 & \textbf{0.318} & 0.157 & \textbf{0.648} & 0.176 & -0.207 & 0.187 & -0.046 & 0.193 \\
Age-Square & -0.597 & 0.431 & 0.314 & 0.468 & -0.185 & 0.506 & -0.641 & 0.577 \\
Some College & \textbf{-0.681} & 0.117 & \textbf{-0.971} & 0.125 & \textbf{-0.566} & 0.162 & \textbf{-1.006} & 0.169 \\
College Degree & \textbf{-1.775} & 0.171 & \textbf{-1.784} & 0.177 & \textbf{-1.902} & 0.163 & \textbf{-1.986} & 0.167 \\
Year(Yr/10) & \textbf{0.492} & 0.228 & 0.469 & 0.258 & \textbf{-0.695} & 0.280 & -0.037 & 0.258 \\
Year-square & \textbf{-1.009} & 0.392 & -0.467 & 0.439 & 0.496 & 0.496 & -0.286 & 0.447 \\
Year-cubic & \textbf{0.390} & 0.174 & 0.094 & 0.194 & -0.202 & 0.222 & 0.071 & 0.197 \\
\hline
 & \multicolumn{8}{c}{\textit{5-Dependent}} \\ \cline{2-9}
 & \multicolumn{2}{c}{\textit{Black Females}} & \multicolumn{2}{c}{\textit{Black Males}} & \multicolumn{2}{c}{\textit{White Females}} & \multicolumn{2}{c}{\textit{White Males}} \\
\textbf{} & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 \\
\hline
Intercept & -0.020 & 0.110 & 0.171 & 0.109 & 0.157 & 0.146 & 0.295 & 0.155 \\
Age/10 & \textbf{0.332} & 0.156 & \textbf{0.651} & 0.175 & -0.231 & 0.185 & -0.079 & 0.193 \\
Age-Square & -0.597 & 0.432 & 0.310 & 0.464 & -0.260 & 0.505 & -0.702 & 0.570 \\
Some College & \textbf{-0.679} & 0.117 & \textbf{-0.963} & 0.124 & \textbf{-0.582} & 0.160 & \textbf{-0.981} & 0.168 \\
College Degree & \textbf{-1.758} & 0.169 & \textbf{-1.795} & 0.176 & \textbf{-1.885} & 0.160 & \textbf{-1.995} & 0.166 \\
Year(Yr/10) & \textbf{0.480} & 0.214 & 0.386 & 0.241 & \textbf{-0.704} & 0.266 & -0.014 & 0.244 \\
Year-square & \textbf{-0.963} & 0.371 & -0.327 & 0.416 & 0.512 & 0.475 & -0.348 & 0.429 \\
Year-cubic & \textbf{0.369} & 0.166 & 0.035 & 0.186 & -0.204 & 0.215 & 0.107 & 0.191 \\
\hline
 & \multicolumn{8}{c}{\textit{Unequal Time}} \\ \cline{2-9}
 & \multicolumn{2}{c}{\textit{Black Females}} & \multicolumn{2}{c}{\textit{Black Males}} & \multicolumn{2}{c}{\textit{White Females}} & \multicolumn{2}{c}{\textit{White Males}} \\
\textbf{} & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 & Estimate & BC0 \\
\hline
Intercept & -0.020 & 0.110 & 0.167 & 0.110 & 0.152 & 0.146 & 0.290 & 0.154 \\
Age/10 & \textbf{0.332} & 0.156 & \textbf{0.656} & 0.175 & -0.239 & 0.184 & -0.086 & 0.193 \\
Age-Square & -0.597 & 0.432 & 0.325 & 0.465 & -0.257 & 0.503 & -0.688 & 0.569 \\
Some College & \textbf{-0.679} & 0.117 & \textbf{-0.960} & 0.124 & \textbf{-0.574} & 0.160 & \textbf{-0.980} & 0.168 \\
College Degree & \textbf{-1.758} & 0.169 & \textbf{-1.794} & 0.176 & \textbf{-1.882} & 0.160 & \textbf{-1.984} & 0.166 \\
Year(Yr/10) & \textbf{0.480} & 0.214 & 0.401 & 0.241 & \textbf{-0.701} & 0.266 & -0.012 & 0.245 \\
Year-square & \textbf{-0.963} & 0.371 & -0.356 & 0.414 & 0.508 & 0.475 & -0.353 & 0.428 \\
Year-cubic & \textbf{0.369} & 0.166 & 0.046 & 0.185 & -0.205 & 0.215 & 0.107 & 0.191 \\
\hline
\multicolumn{9}{l}{*Bold estimates indicate p-value\textless{}0.05.}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Tracie L. Shing
John S. Preisser
Richard C. Zink

"GEECORR: A SAS macro for regression models of correlated binary responses and within-cluster correlation using generalized estimating equations" by Tracie L. Shing, John S. Preisser, and Richard C. Zink, arXiv:2011.11592v1, 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|c|c|c|}
\hline
Features & \textbf{Next}(mAP) & Our Model(mAP) & Improve \\
\hline\hline
Full Features & 0.192 & \textbf{0.213} & 10.9$\%$ \\
No P-Objects & 0.198 & \textbf{0.213} & 7.6$\%$ \\
No P-Scene & 0.206 & \textbf{0.211} & 2.4$\%$ \\
No P-Keypoint & 0.190 & \textbf{0.212} & 11.6$\%$ \\
No P-Appearence & 0.154 & \textbf{0.179} & 16.2$\%$ \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Comparison to baseline method \textbf{Next} on the ActEV/VIRAT dataset}
\end{table}
\end{document}

eess

Bo Chen
Decai Li
Yuqing He
Chunsheng Hua

"SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction" by Bo Chen, Decai Li, Yuqing He, and Chunsheng Hua, arXiv:1912.05003v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|c|c|c|c|c|}
\cline{3-7}
& & \multicolumn{5}{c|}{\textit{\textbf{Predicted class}}} \\
\cline{3-7}
& & \textbf{Snow} & \textbf{Sunset} & \textbf{Trees} & \textbf{Rocks} & \textbf{Night} \\
\hline
\multirow{5}{*}{\textit{\textbf{Actual class}}} & \textbf{Snow} & 99.5 & 0 & 0 & 0 & 0.5 \\ \cline{2-7}
 & \textbf{Sunset} & 0 & 99.5 & 0.5 & 0 & 0 \\
\cline{2-7}
 & \textbf{Trees} & 1 & 0.5 & 56.5 & 42 & 0 \\
\cline{2-7}
 & \textbf{Rocks} & 2 & 0.5 & 7 & 91 & 0 \\ \cline{2-7}
 & \textbf{Night} & 1 & 8 & 9 & 2.5 & 79.5
\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Manel Mateos
Alejandro González
Xavier Sevillano

"Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation" by Manel Mateos, Alejandro González, and Xavier Sevillano, arXiv:2101.00990v1, 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{Thresholds of Staked ETH Required for Various Ethereum Attack Scenarios }
\begin{tabular}{|c|l|}
\hline
\textbf{Staked ETH} & \textbf{Attack Type} \\ \hline
33\% & Delay finality \\
34\% & Cause double finality \\
51\% & Censorship and control over blockchain's future \\
66\% & Censorship and control over blockchain's future and past \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Krzysztof Gogol
Yaron Velner
Benjamin Kraner
Claudio Tessone

"SoK: Liquid Staking Tokens (LSTs) and Emerging Trends in Restaking" by Krzysztof Gogol, Yaron Velner, Benjamin Kraner, and Claudio Tessone, arXiv:2404.00644v3, 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{Factors and Levels for Blood Glucose Experiment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cllcc} % Defines table with left and center columns
\toprule
\multicolumn{2}{c}{\text{Factor}} & \multicolumn{3}{c}{\text{Level}} \\
\cmidrule(lr){3-5}
 & & \text{1} & \text{2} & \text{3} \\
\midrule
\text{A},& Wash & No & Yes & \\
\text{B},& Microvial volume (ml) & 2.0 & 2.5 & 3.0 \\
\text{C},& Caras H$_2$O level (ml) & 20 & 28 & 35 \\
\text{D},& Centrifuge rpm & 2,100 & 2,300 & 2,500 \\
\text{E},& Centrifuge time (min) & 1.75 & 3 & 4.5 \\
\text{F},& Sensitivity, absorption & (.10, 2.5) & (.25, 2) & (.50, 1.5) \\
\text{G},& Temperature ($^\circ$C) & 25 & 30 & 37 \\
\text{H},& Dilution ratio & 1:51 & 1:101 & 1:151 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Automated Analysis of Experiments using Hierarchical Garrote" by Wei-Yang Yu and V. Roshan Joseph, arXiv:2411.01383v1, 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}{rrrrr}
 \toprule
 \textit{property} & $p=0.25$ & $p=0.20$ & $p=0.15$ & $p=0.10$ \\
 \midrule
 generated 		& 180,000,000 & 180,000,000 & 180,000,000 & 180,000,000 \\
 connected 		& 174,497,428 & 167,659,141 & 152,927,782 & 118,197,366 \\
 total found 		& 32,328 & 58,121 & 59,666 & 23,900 \\
 distinct found 	& 19,077 & 35,286 & 41,866 & 21,004 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\small General results about graphs generated with the $G(n,p)$ model.}
\end{table}
\end{document}

math

Serafino Cicerone
Gabriele Di Stefano
Sandi Klavžar
Ismael G. Yero

"Burning some myths on privacy properties of social networks against active attacks" by Serafino Cicerone, Gabriele Di Stefano, Sandi Klavžar, and Ismael G. Yero, arXiv:2504.16944v1, 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{Unit-task data for the batch process problem}%标题
\begin{tabular}{lccc}%四个c代表该表一共四列，内容全部居中
 \toprule%第一道横线
 Unit & Capacity& Suitability&Mean processing time\\
 \midrule%第二道横线
 Heater 1 & 100 & Heating & 4.0 \\
 Heater 2 & 120 & Heating & 4.0 \\
 Heactor 1~~ & ~70~ & ~~~~Reaction 1, 2, 3~~~~& 4.0 \\
 Reactor 2 & 80 & Reaction 1, 2, 3 & 3.0 \\
 Reactor 3 & 70 & Reaction 1, 2, 3 & 4.0 \\
 Reactor 4 & 120 & Reaction 1, 2, 3 & 5.0 \\
 Still 1 & 200 & Separation & 5.0 \\
 Still 2 & 150 & Separation & 5.0 \\
 \bottomrule%第三道横线
 \end{tabular}
\end{table}
\end{document}

math

Shiyuan Qu
Fenglian Dong
Zhiwei Wei
Chao Shang

"Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs" by Shiyuan Qu, Fenglian Dong, Zhiwei Wei, and Chao Shang, arXiv:2412.17623v2, 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}{cccccccc}
\hline\hline
State & Min & Median & Mean & Max & IQR & Skewness & Kurtosis\\
\hline
$S_{1}$ & 1.00 & 1.00 & 1.67 & 62.00 & 0 & 8.99 & 116.50 \\
 $S_{2}$& 1.00 & 1.00 & 1.31 & 117.00 & 0 & 29.27 & 1,609.20 \\
 $S_{3}$ & 1.00 & 1.00 & 1.21 & 57.00 & 0 & 21.55 & 668.24 \\
 $S_{4}$& 1.00 & 1.00 & 1.29 & 70.00 & 0 & 22.09 & 734.76 \\
 $S_{5}$ & 1.00 & 1.00 & 1.21 & 16.00 & 0 & 8.21 & 96.75 \\
 $S_{6}$& 1.00 & 1.00 & 1.33 & 28.00 & 0 & 8.79 & 101.93 \\
 \hline\hline
\end{tabular}
\end{adjustbox}
\caption{Summary statistics for the reporting delay of claims in each state, with a time discretization of 30 days.}
\end{table}
\end{document}

stat

Emmanuel Jordy Menvouta
Jolien Ponnet
Robin Van Oirbeek
Tim Verdonck

"mCube: Multinomial Micro-level reserving Model" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, 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{Example of expanding walk forward validation without where $t_{i}$ represents the feature vector of trading day $i$. In this example, a training window with an initial size $s=3$ is taken together with a testing window of size $k=1$. We therefore consistently use the feature vectors of past trading days to train a model (underlined) and subsequently test said model on trading day $t+k$ (bold). }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \toprule
 Iteration & Variable roles\\
 \midrule
 1 & $ \underline{t_{1} \hspace{1.5mm} t_{2} \hspace{1.5mm} t_{3}} \hspace{1.5mm} \mathbf{ t_{4} } \hspace{1.5mm} t_{5} \hspace{1.5mm} \dotsi \hspace{1.5mm} t_{n} $ \\
 2 & $ \underline{ t_{1} \hspace{1.5mm} t_{2} \hspace{1.5mm} t_{3} \hspace{1.5mm} t_{4}} \hspace{1.5mm} \mathbf{t_{5}} \hspace{1.5mm} \dotsi \hspace{1.5mm} t_{n} $ \\
 \vdots & \vdots \\
 m & $ \underline{ t_{1} \hspace{1.5mm} \dotsi \hspace{1.5mm} t_{n-3} \hspace{1.5mm} t_{n-2} \hspace{1.5mm} t_{n-1}} \hspace{1.5mm} \mathbf{t_{n}} $ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Thomas Dierckx
Jesse Davis
Wim Schoutens

"Nowcasting Stock Implied Volatility with Twitter" by Thomas Dierckx, Jesse Davis, and Wim Schoutens, arXiv:2301.00248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \textbf{Number of Cameras} & \textbf{ADAE Study (Table )} & \textbf{CoV} \\
 \midrule
 Single smart camera trap & 5 & 1.59 \\
 Smart camera trap network & 7 & 7.33\\
 Single quadcopter & 3 & 3.01 \\
 \bottomrule \\
 \end{tabular}
\end{adjustbox}
\caption{Quantifying Burstiness of ADAE Studies}
\end{table}
\end{document}

eess

Jenna Kline
Austin O'Quinn
Tanya Berger-Wolf
Christopher Stewart

"Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge" by Jenna Kline, Austin O'Quinn, Tanya Berger-Wolf, and Christopher Stewart, arXiv:2412.01000v1, 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{Global structure preservation quality during dimensionality reduction of real-life datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 & \multicolumn{4}{c}{Quality measure} \\
 \cmidrule(r){2-5}
 Method & L. C. & W. D. $H_{0}$ & T. A. & RTD \\
 \midrule
 \multicolumn{5}{c}{\textbf{F-MNIST into 16D space}} \\
 UMAP & 0.602 & 592.0 $\pm$ 3.9 & 0.741 $\pm$ 0.02 & 12.31 $\pm$ 0.4\\
 AE & 0.879 & 320.5 $\pm$ 1.9 & 0.850 $\pm$ 0.00 & 5.52 $\pm$ 0.2 \\
 TopoAE & 0.905 & 190.7 $\pm$ 1.2 & 0.867 $\pm$ 0.01 & 3.69 $\pm$ 0.2 \\
 RTD & \underline{0.960} &\underline {181.2 $\pm$ 0.8} & \underline{0.907 $\pm$ 0.00} & \underline{3.01 $\pm$ 0.1}\\
 RTD-L & {0.933} & \underline{181.7 $\pm$ 1.4} & \underline{0.910 $\pm$ 0.01} & {3.75 $\pm$ 0.2}\\
 \midrule
 \multicolumn{5}{c}{\textbf{COIL-20 into 16D space}} \\
 UMAP & 0.301 & 274.7 $\pm$ 0.0 & 0.574 $\pm$ 0.01 & 15.99 $\pm$ 0.5\\
 AE & 0.834 & 183.6 $\pm$ 0.0 & 0.809 $\pm$ 0.01 & 8.35 $\pm$ 0.2\\
 TopoAE & 0.910 & 148.0 $\pm$ 0.0 & 0.822 $\pm$ 0.02 & 6.90 $\pm$ 0.2\\
 RTD & {0.944} & {88.9 $\pm$ 0.0} & {0.892 $\pm$ 0.01} & {5.78 $\pm$ 0.1} \\
 RTD-L & \underline{0.949} & \underline{77.1 $\pm$ 0.0} & \underline{0.898 $\pm$ 0.01} & \underline{5.35 $\pm$ 0.2}\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Eduard Tulchinskii
Daria Voronkova
Ilya Trofimov
Evgeny Burnaev
Serguei Barannikov

"RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks" by Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, and Serguei Barannikov, arXiv:2503.11910v1, 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|c||c|c|}
		\hline
		\multirow{2}{*}{}
		Dataset & \multicolumn{2}{c||}{MARS*}
		& \multicolumn{2}{c|}{Duke-Raw-TKL} \\
		\hline %\cline{2-5}
		$\epsilon$& Rank-1 & mAP
		& Rank-1 & mAP \\
		\hline \hline
		0.9
		& 48.7 & 30.4 & 48.3 & 33.3 \\ \hline
		0.8
		& 54.2 & 36.2 & 55.3 & 41.3 \\ \hline
		0.7
		& \bf 54.5 & \bf 37.2 &\bf 56.1 &\bf 41.5 \\ \hline
		0.6
		& 50.7 & 34.1 & 47.7 & 35.2 \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Unsupervised Noisy Tracklet Person Re-identification" by Minxian Li, Xiatian Zhu, and Shaogang Gong, arXiv:2101.06391v1, 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{Inference time for different values of the number of iterations/samples.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Num Samples $L$ & 0 Iter & 1 Iter & 2 Iter \\
\midrule
5 & $0.26 \pm 0.52 $ & $1.99 \pm 0.54 $& $16.40\pm 2.04 $\\
10 & $0.20 \pm 0.41 $ & $3.24 \pm 0.47 $& $58.28\pm 1.15 $\\
20 & $0.22 \pm 0.44 $ & $5.86 \pm 0.53 $& $224.86\pm 0.71 $\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

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

"Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields" by Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Fragkiskos D. Malliaros, and Michalis Vazirgiannis, arXiv:2411.05399v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\it $p$-values for the Augmented Dickey-Fuller (ADF) test for a unit root and the Engle-Granger test for cointegration between $G_t$ and $E_t$ on the GCP data (1959--2022), see Section for details. First six rows are ADF tests and the last row is the Engle-Granger test. ``AR'': The null model is a unit root model while the alternative model is an AR model. ``ARD'': The null model is a unit root model while the alternative model is an AR model with drift. ``TS'': The null model is a unit root model with a constant (i.e. a drift in levels) while the alternative model is an AR model with a time trend. ``Engle-Granger'': The null hypothesis is that the error term $u_t$ in $G_t = \alpha E_t + u_t$ has a unit root, i.e. that $G_t$ and $E_t$ are not cointegrated. The value ``$L$'' denotes the number of lags included in both the null and alternative models, see equations --.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc} \hline%\hline
 & $L=0$ & $L=1$ & $L=2$ & $L=3$ & $L=4$ & $L=5$ \\ \hline
$y_t = G_t$ (AR) & 0.2349 & 0.4867 &0.6597 & 0.7982 & 0.8403& 0.8470 \\
$y_t = G_t$ (ARD) & 0.0039 & 0.0778 & 0.3105 & 0.4384 &0.4564 & 0.3895 \\
$y_t = G_t$ (TS) & 0.0010 & 0.0010& 0.0035 & 0.0192& 0.0231 & 0.0056 \\
$y_t = E_t$ (AR) & 0.9990 & 0.9990 & 0.9990 & 0.9990 & 0.9953 & 0.9984 \\
$y_t = E_t$ (ARD) & 0.9488 & 0.9329 & 0.9038 & 0.8533 & 0.8201 & 0.8871 \\
$y_t = E_t$ (TS) & 0.0933 & 0.2108 & 0.3108 & 0.3339 & 0.2250 & 0.4775 \\
Engle-Granger & 0.0010 & 0.0010 & 0.0019 & 0.0135 & 0.0173 & 0.0038 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mikkel Bennedsen
Eric Hillebrand
Siem Jan Koopman

"A Regression-Based Approach to the CO2 Airborne Fraction: Enhancing Statistical Precision and Tackling Zero Emissions" by Mikkel Bennedsen, Eric Hillebrand, and Siem Jan Koopman, arXiv:2311.01053v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Narayanaswamy Balakrishnan
Ritwik Bhattacharya

"D-optimal joint best linear unbiased prediction of order statistics" by Narayanaswamy Balakrishnan and Ritwik Bhattacharya, arXiv:2012.11836v1, 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{\footnotesize Artificial neural network and optimization details.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllllllll}
\hline\hline
\multicolumn{6}{c}{\multirow{1}{*}{ANN architecture}} & & \multicolumn{6}{c}{\multirow{1}{*}{ANN optimization}}\\\cline{1-6}\cline{8-13}
 & FxA & FxL & FxFlA & HidL & 3 & & E & 3000 & InitLR & $10^{-3}$ & Opt & \texttt{Adam} \\
InS & 7 & 6 & 9 & HidS & 200 & & B & $1024$ & DecR & 0.1 & LossFunc & MSE \\
OutS & 21 & 21 & 14 & ActUn & \texttt{ReLU} & & & & DecS & 1000 & & \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"On Pricing of Discrete Asian and Lookback Options under the Heston Model" by Leonardo Perotti and Lech A. Grzelak, arXiv:2211.03638v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 & Transition data $p_{ij}$ of $V$ \\
 \hline
 $U_{i} \cap D = U_{j} \cap D = \emptyset$ & $E_{ij}$\\
 \hline
 $U_{i} \cap D = \emptyset, \, U_{j} \cap D \neq \emptyset$ & $E_{ij} \mathrm{diag}\{s_{j}^{\alpha_{1}},\cdots,s_{j}^{\alpha_{r}}\}$\\
 \hline
 $U_{i} \cap D \neq \emptyset, \, U_{j} \cap D = \emptyset$ & $\mathrm{diag}\{s_{i}^{-\alpha_{1}},\cdots,s_{i}^{-\alpha_{r}}\}E_{ij}$\\
 \hline
 $U_{i} \cap U_{j} \cap D \neq \emptyset$ & $\mathrm{diag}\{s_{i}^{-\alpha_{1}},\cdots,s_{i}^{-\alpha_{r}}\}E_{ij} \mathrm{diag}\{s_{j}^{\alpha_{1}},\cdots,s_{j}^{\alpha_{r}}\}$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{The transition data for higher rank.}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Propagated $P(OBE)$ and $P(OBE_{10})$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrrrrr}
 \noalign{\smallskip}\hline\noalign{\smallskip}
 $OBE$ & 0.946 & 0.0538 & 0.0000215 & 0.00000332 & 0.00000299\\
 $OBE_{10}$ & 0.942 & 0.0582 & 0.0000673 & 0.0000192 & 0.0000174\\
 \noalign{\smallskip}\hline\noalign{\smallskip}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Gurcan Comert
Mashrur Chowdhury
David M. Nicol

"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, 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{Linear Hypothesis Tests (Table )}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrr}
 \hline
Specification & Hypothesis & Chisq & Pr($>$Chisq) \\
 \hline
(1) & $\hat{\beta}_{Democrat}-\hat{\beta}_{Repuclican}=0$ & 0.04 & 0.84 \\
 (2) & $\hat{\beta}_{Democrat}-\hat{\beta}_{Repuclican}=0$ & 3.04 & 0.08 \\
 (3) & $\hat{\beta}_{Democrat}-\hat{\beta}_{Repuclican}=0$ & 0.13 & 0.72 \\
 (4) & $\hat{\beta}_{Democrat}-\hat{\beta}_{Repuclican}=0$ & 0.26 & 0.61 \\
 (1) & $\hat{\beta}_{Dem. city}-\hat{\beta}_{Rep. city}=0$ & 2.94 & 0.09 \\
 (2) & $\hat{\beta}_{Dem. city}-\hat{\beta}_{Rep. city}=0$ & 7.51 & 0.01 \\
 (3) & $\hat{\beta}_{Dem. city}-\hat{\beta}_{Rep. city}=0$ & 2.19 & 0.14 \\
 (4) & $\hat{\beta}_{Dem. city}-\hat{\beta}_{Rep. city}=0$ & 0.10 & 0.76 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Ulrich Matter
Roland Hodler
Johannes Ladwig

"Personalization of Web Search During the 2020 US Elections" by Ulrich Matter, Roland Hodler, and Johannes Ladwig, arXiv:2209.14000v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Number of iterations to convergence on the ItP and ItC cases with a finer mesh. A value of $\infty$ shows non-convergence due to divergence, while * indicates convergence but not to the required precision.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|r|r}
Method & Iterations (ItP) & Iterations (ItC) \\\hline
Dogleg & 7* & >100\\
Steihaug & >100 & >100 \\
Newton & 6* & $\infty$\\
L-BFGS & >100 & >100\\
ADAM & >100 & >100 \\
ADAGRAD & >100 & >100\\
Picard &$\infty$ & $\infty$ \\
Broyden & 18* & 31\\
Broyden (inv) & 15* & 30*\\
BFGS & $\infty$ & $\infty$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Alberto Cattaneo
Varun Shankar
M. Keith Ballard

"Rootfinding and Optimization Techniques for Solving Nonlinear Systems of Equations Arising from Cohesive Zone Models" by Alberto Cattaneo, Varun Shankar, and M. Keith Ballard, arXiv:2502.19583v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Christian Chan
Xiaotian Dai
Thierry Chekouo
Quan Long
Xuewen Lu

"Broken Adaptive Ridge Method for Variable Selection in Generalized Partly Linear Models with Application to the Coronary Artery Disease Data" by Christian Chan, Xiaotian Dai, Thierry Chekouo, Quan Long, and Xuewen Lu, arXiv:2311.00210v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{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
 Ativos & BRKM5 & ITUB4 & KLBN4 & VALE3 \tabularnewline
\hline
\hline
BRKM5 & 1.00 & 0.37 & 0.28 & 0.33\tabularnewline
\hline
ITUB4 & 0.37 & 1.00 & 0.18 & 0.36\tabularnewline
\hline
KLBN4 & 0.28 & 0.18 & 1.00 & 0.24\tabularnewline
\hline
VALE3 & 0.33 & 0.36 & 0.24 & 1.00\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\caption{Matriz de covariância dos preços de fechamento das ações da Braskem (BRKM5), Itaú (ITUB4), Vale (VALE3) e Klabin (KLBN4) referentes ao período entre 01 de janeiro de 2016 até 20 de setembro de 2021.}
\end{table}
\end{document}

q-fin

Askery Canabarro
Taysa M. Mendonça
Ranieri Nery
George Moreno
Anton S. Albino
Gleydson F. de Jesus
Rafael Chaves

"Quantum Finance: a tutorial on quantum computing applied to the financial market" by Askery Canabarro, Taysa M. Mendonça, Ranieri Nery, George Moreno, Anton S. Albino, Gleydson F. de Jesus, and Rafael Chaves, arXiv:2208.04382v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Stable cohomology of moduli spaces of hyperelliptic curves on Hirzebruch surfaces" by Jonas Bergström and Angelina Zheng, arXiv:2312.14800v2, 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}{llll}
\hline
Parameter name & Description & Value & Unit \\
\hline
\texttt{NFIXORB} & Switch to fix orbital parameters & 1 & \\
\texttt{ECCEN} & Eccentricity & 1.67$\times 10^{-2}$ & \\
\texttt{MVELP} & Longitude of perihelion & 102.9 & \\
\texttt{OBLIQ} & Obliquity & 23.44 & \\
\texttt{GSOL0} & Solar constant & 1367.0 & W\,m$^{-2}$ \\
\texttt{CO2} & Atm. CO$_2$ concentration & is varied & ppm \\
\texttt{zstar} & & 2500 & m \\
\texttt{lambda} & & 4.5$\times 10^{-3}$ & m$^{-1}$ \\
\texttt{astar} & & 0.8714$\times 10^{-4}$ & \\
\texttt{arange} & & 0.2843$\times 10^{-4}$ & \\
\hline
\end{tabular}
\end{adjustbox}
\caption{PlaSim-LSG model settings for selected namelist parameters.}
\end{table}
\end{document}

math

Reyk Börner
Oliver Mehling
Jost von Hardenberg
Valerio Lucarini

"Global stability of the Atlantic overturning circulation: Edge states, long transients and a boundary crisis under CO$_2$ forcing" by Reyk Börner, Oliver Mehling, Jost von Hardenberg, and Valerio Lucarini, arXiv:2504.20002v2, 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{Breast cancer data: performance of the logistic regression model for different feature selection approaches.}
\begin{tabular}{|c|c|c|c|}
	\hline
	& AUROC & Accuracy & Number of features \\
	\hline
	All features & 0.9844 & 0.9297 & 10\\
	\hline
	Best choice of features & {\bf 0.9863} & 0.9297 & 5 \\
	\hline
	Selection with RFE & 0.9839 & 0.9297 & 6 \\
	\hline
	Selection with LASSO & 0.9845 & 0.9262 & 3\\
	\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Gerhard Hellstern
Vanessa Dehn
Martin Zaefferer

"Quantum computer based Feature Selection in Machine Learning" by Gerhard Hellstern, Vanessa Dehn, and Martin Zaefferer, arXiv:2306.10591v1, 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{Forecasting accuracy under different dataset}
\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|}{Wind Speed}			& \multicolumn{2}{c|}{Wind Direction} \\
\hline
Method & MAE & RMSE & MAE & RMSE \\
\hline
Remote Sensing 	& $0.0403$ & $ 0.0573 $ 		& $0.0596$ & $ 0.1151 $ \\
\hline
Hour Ahead 	& $0.0719$ & $ 0.0972 $ & $0.1051$ & $ 0.1687 $ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Hwei-Ming Chung
Sabita Maharjan
Yan Zhang
Frank Eliassen
Tingting Yuan

"Edge Intelligence Empowered UAVs for Automated Wind Farm Monitoring in Smart Grids" by Hwei-Ming Chung, Sabita Maharjan, Yan Zhang, Frank Eliassen, and Tingting Yuan, arXiv:2009.11256v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Selected variables on which natural regression splines are fit, along with the number of knots.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llc}
 \toprule
 \textbf{Variable} & \textbf{MLR model} $kl$ \textbf{Knots} & \\
 \midrule
 \texttt{BalanceToPrincipal} & P$l$ & 3 \\
 \texttt{CreditLeverage} & P$l$ & 3 \\
 \texttt{g0\_Delinq\_Num} & P$l$ & 5 \\
 \multirow[t]{2}{*}{\texttt{InterestRate\_Margin} } & P$l$ & 3 \\
 & D$l$ & 3 \\
 \texttt{Prepaid\_Pc} & P$l$ & 4 \\
 \texttt{Repo\_rate} & P$l$ & 3 \\
 \multirow[t]{2}{*}{\texttt{RollEver\_24}} & P$l$ & 5 \\
 & D$l$ & 6 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework" by Arno Botha, Tanja Verster, and Roland Breedt, arXiv:2502.14479v3, 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{SNR and CNR of strain estimates from our unsupervised method and OVERWIND, and local NCC scores for the three image pair results from the \textit{in vivo} dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline
{} & \multicolumn{3}{c}{Unsupervised} & \multicolumn{3}{c}{OVERWIND} \\ %\cline{2-7}
 & Case 1 & Case 2 & Case 3 & Case 1 & Case 2 & Case 3 \\ \hline
SNR & 0.14 & 0.07 & 0.1 & 0.12 & 0.35 & 0.23 \\
CNR & 6.22 & 5.2 & 4.5 & 4.9 & 3.15 & 2.23 \\
LNCC & 0.87 & 0.81 & 0.94 & 0.90 & 0.81 & 0.93 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation" by Rémi Delaunay, Yipeng Hu, and Tom Vercauteren, arXiv:2008.09572v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Hybrid deep additive neural networks

"Hybrid deep additive neural networks" by Gyu Min Kim and Jeong Min Jeon, arXiv:2411.09175v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of CHSZ and TUSZ datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
				\toprule
				Dataset& ABSZ & FSZ & TNSZ & TCSZ \\
				\midrule
				CHSZ & 81 & 87 & 15 & 16 \\
				TUSZ & 76 & 418 & 62 & 48 \\
				\bottomrule
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ruimin Peng
Zhenbang Du
Changming Zhao
Jingwei Luo
Wenzhong Liu
Xinxing Chen
Dongrui Wu

"Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification" by Ruimin Peng, Zhenbang Du, Changming Zhao, Jingwei Luo, Wenzhong Liu, Xinxing Chen, and Dongrui Wu, arXiv:2412.15224v1, 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{Parameters of the Plates Used in Experiments}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll} \toprule
 \textit{Plate Name} & \textit{h, l, w (mm)} & \textit{m (kg)} & $\boldsymbol{r}_g$ & $\mu_0$ & $\mu_1$ \\ \midrule
 Acrylic Board & 300, 300, 40 & 4.0 & Geom.Cent. & 0.5 & 0.4 \\
 Stainless Box & 300, 400, 150 & 6.0 & Geom.Cent. & 0.4 & 0.1 \\
 Plywood Board & 500, 400, 44 & 6.4 & Geom.Cent. & 0.6 & 0.3 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Shogo Hayakawa
Weiwei Wan
Keisuke Koyama
Kensuke Harada

"A Dual-arm Robot that Autonomously Lifts Up and Tumbles Heavy Plates Using Crane Pulley Blocks" by Shogo Hayakawa, Weiwei Wan, Keisuke Koyama, and Kensuke Harada, arXiv:2101.09526v1, 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{Experimental scale factor values of buyer compared with theoretical scale factor values for case-1}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c||}\toprule
 \textit{} & \textit{} & \textit{$\alpha_{B1}$} \\ \midrule
 Theoretical Equilibrium & & 0.666667 \\
 \hline
 DDPG Empirical Equilibrium & mean & 0.577733 \\
 \hline
 & std & 0.023132 \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sanjay Chandlekar
Easwar Subramanian
Sanjay Bhat
Praveen Paruchuri
Sujit Gujar

"Multi-unit Double Auctions: Equilibrium Analysis and Bidding Strategy using DDPG in Smart-grids" by Sanjay Chandlekar, Easwar Subramanian, Sanjay Bhat, Praveen Paruchuri, and Sujit Gujar, arXiv:2201.10127v2, 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}
SPX & Eurostoxx & Footsie & SPX - Eurostoxx & SPX - Footsie \\
\hline
138.89 & 26.84 & 38.15 & 2.61 & 2.16
\end{tabular}
\end{adjustbox}
\caption{Half lives in days of $12$ months At The Money Volatilities and their spreads}
\end{table}
\end{document}

q-fin

"Estimation of Historical volatility and Allocation strategies using Variance Swaps" by Lucio Fiorin, arXiv:2208.03164v1, 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{The two-layer model. Relative Model Error and Relative Misfit Function of $W_2$ with the operators $\mathcal{P}_{2}$ and $\mathcal{P}_{3}$ in $20$, $40$ and $80$ iteration steps, respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
	\toprule
	\multirow{2}{*}{Iteration Steps} & \multicolumn{2}{c}{Relative Model Error} & \multicolumn{2}{c}{Relative Misfit Function} \\
	\cline{2-5}
	 & $W_2$ with $P_2$ & $W_2$ with $P_3$ & $W_2$ with $P_2$ & $W_2$ with $P_3$ \\
 \midrule
	 $20$ & $3.69 \times 10^{-1}$ & $2.15 \times 10^{-1}$ & $4.99 \times 10^{-3}$ & $6.90 \times 10^{-4}$ \\
	 $40$ & $2.23 \times 10^{-1}$ & $1.04 \times 10^{-1}$ & $3.61 \times 10^{-4}$ & $9.41 \times 10^{-5}$ \\

	 $80$ & $ 8.35 \times 10^{-2}$ & $3.04 \times 10^{-2}$ & $1.25 \times 10^{-5}$ & $2.75 \times 10^{-6}$ \\
	 \bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zhengyang Li
Yijia Tang
Jing Chen
Hao Wu

"The Quadratic Wasserstein Metric With Squaring Scaling For Seismic Velocity Inversion" by Zhengyang Li, Yijia Tang, Jing Chen, and Hao Wu, arXiv:2201.11305v1, 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}{lllll}
 \hline
 & \multicolumn{2}{c}{$C_{\text{g}}$ [\texteuro]} & \multicolumn{2}{c}{$Q_{\text{loss}}$ [mAh]}\\
 Planner& summer & winter & summer & winter \\
 \hline
 \textbf{BNoDeg} & 16.01& 77.33& 206.9& 249.2\\
 \textbf{CEmpDeg} & 15.22& 77.45& 216.4& 236.7\\
 \textbf{CPBDeg} & 20.28& 78.25& 206.6&234.3\\
 \end{tabular}
\end{adjustbox}
\caption{Planner comparison cost summary.}
\end{table}
\end{document}

math

Darío Slaifstein
Gautham Ram Chandra Mouli
Laura Ramirez-Elizondo
Pavol Bauer

"Aging-aware Energy Management for Residential Multi-Carrier Energy Systems" by Darío Slaifstein, Gautham Ram Chandra Mouli, Laura Ramirez-Elizondo, and Pavol Bauer, arXiv:2503.16139v3, 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{Quantitative comparison of SSSD-ECG and FlowECG performance metrics using 200 neural function evaluations}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 & MMD $\downarrow$ & DTW $\downarrow$ & Wasserstein $\downarrow$ & SimScore $\uparrow$\\
 \midrule
 SSSD-ECG & 80.70 & \textbf{73.49} & 1.03 & 0.30 \\
 FlowECG (ours) & \textbf{21.09} & 83.96 & \textbf{0.62} & \textbf{0.37} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Vitalii Bondar
Serhii Semenov
Vira Babenko
Dmytro Holovniak

"FlowECG: Using Flow Matching to Create a More Efficient ECG Signal Generator" by Vitalii Bondar, Serhii Semenov, Vira Babenko, and Dmytro Holovniak, arXiv:2509.10491v1, 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{Problem (), number of mesh-points required by the codes for increasing values of $T$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|r|r|r|}
\hline
$T$ & {\tt fhbvm} & {\tt fhbvm2}\\
\hline
$10$ & 95& 30\\
$50$ & 483 & 70\\
$100$ & 968 & 120 \\
$500$ & 4850 &520 \\
$1000$ & 9702& 1020\\
$5000$ & 48521 & 5020\\
$10000$ &97044 & 10020\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Luigi Brugnano
Gianmarco Gurioli
Felice Iavernaro
Mikk Vikerpuur

"Analysis and implementation of collocation methods for fractional differential equations" by Luigi Brugnano, Gianmarco Gurioli, Felice Iavernaro, and Mikk Vikerpuur, arXiv:2503.17719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for various $\gamma$ values}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Threshold $\gamma$} &
\textbf {$| \mathcal{\zeta} |$} & \textbf{Number of samples} &
\textbf{Mean} &
\textbf{Variance}\\
\hline
2 dB & 9 & 20 & 1.060 & 0.561\\
3 dB & 7 & 100 & 1.576 & 0.888\\
5 dB & 3 & 20 & 2.306 & 3.419\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Bora Bozkurt
Ahmet Muaz Aktas
Hasan Atalay Gunel
Mohaned Chraiti
Ali Gorcin
Ibrahim Hokelek

"Beam Codebook Refinement for mmWave Devices with Random Orientations: Concept and Experimental Validation" by Bora Bozkurt, Ahmet Muaz Aktas, Hasan Atalay Gunel, Mohaned Chraiti, Ali Gorcin, and Ibrahim Hokelek, arXiv:2412.20726v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \toprule [0.2em]
 \multirow{1}{*}{\textbf{\textsc{Model}}} %
 & \textbf{Param.} & \textbf{BLEU}
 \\ \midrule[0.1em]
 \textit{All-Shared} & 24M & 14.3 \\
 \textit{All-Shared, $d_m= 768$} & 41M & 22.0 \\
 	\textit{All-Shared (Independent FFN)} & 27M & 22.4 \\
 	\textit{All-Shared (except last)} & 31M & 23.2 \\
 	\textit{Every 2 layers shared} & 38M & 27.2 \\
 	\textsc{Sandwich} & 38M & 27.3 \\
 	\textsc{Sandwich, $L=8$} & 38M & \textbf{27.7} \\
 	\midrule[0.07em]
 	 & 65M & 27.3 \\
 	\textsc{Transformer-base} (Our reimpl.) & 61M & \textbf{27.7} \\
 \bottomrule[0.15em]
 \end{tabular}
\caption{Experiments performed on WMT'14 EN-DE using different parameter sharing techniques.}
\end{table}
\end{document}

cs

Machel Reid
Edison Marrese-Taylor
Yutaka Matsuo

"Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers" by Machel Reid, Edison Marrese-Taylor, and Yutaka Matsuo, arXiv:2101.00234v3, 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{\large Coefficient estimates of 1-factor models of each county employment/economy indicator}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\emph{Variables} & $\Delta$ Employed Persons & $\Delta$ Unemployed Persons & $\Delta$ Labour Force& $\Delta$ Population \\
~& (log)& (log)& (log)& (log)\\
\cmidrule(l{3pt}r{3pt}){1-1} \cmidrule(l{3pt}r{3pt}){2-2} \cmidrule(l{3pt}r{3pt}){3-3} \cmidrule(l{3pt}r{3pt}){4-4} \cmidrule(l{3pt}r{3pt}){5-5}
$\Delta$ Active Mines\textsubscript{t} & 0.001*** & -0.006*** & 0.000. & 0.000\\
~ & (0.000) & (0.001) & (0.000) & (0.000)\\
$\Delta$ Active Mines\textsubscript{t-1} & 0.002*** & 0.000 & 0.002*** & 0.000\\
~ & (0.000) & (0.001) & (0.000) & (0.000)\\
$\Delta$ Active Mines\textsubscript{t-2} & 0.000 & 0.004*** & 0.001*** & 0.000\\
~ & (0.000) & (0.001) & (0.000) & (0.000)\\
$\Delta$ (log) Real GDP & 0.049*** & -0.100*** & 0.038*** & 0.004***\\
~ & (0.002) & (0.006) & (0.002) & (0.001)\\
$\Delta$(log) Population & 0.495*** & 0.775*** & 0.508*** & ~\\
~ & (0.016) & (0.048) & (0.014) & ~\\
\bottomrule
\addlinespace
R$^2$ & 0.246 & 0.323 & 0.217 & 0.501 \\
Additive Effects Type & two-ways & two-ways & two-ways & two-ways\\
\# Unobserved Factors Used & 1 & 1 & 1 & 1\\
\bottomrule
\multicolumn{4}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 $n$ & Error $\|\cdot\|_1$ & Order $\|\cdot\|_1$ & Error
 $\|\cdot\|_{\infty}$ & Order $\|\cdot\|_{\infty}$ \\
 \hline
 20 & 3.78E$-3$ & $-$ & 3.06E$-2$ & $-$ \\
 \hline
 40 & 8.04E$-5$ & 5.56 & 1.41E$-3$ & 4.44 \\
 \hline
 80 & 1.81E$-6$ & 5.48 & 2.39E$-5$ & 5.88 \\
 \hline
 160 & 6.81E$-8$ & 4.73 & 2.42E$-6$ & 3.31 \\
 \hline
 320 & 2.13E$-9$ & 5.00 & 9.96E$-8$ & 4.60 \\
 \hline
 640 & 6.58E$-11$ & 5.01 & 3.57E$-9$ & 4.80 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for problem - , Complex domain, WLS-GAW.}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
 \textbackslash & $z_3=1$ &
$z_3=2$ & $z_3=3$ & $ z_3=4 $ & $z_3=5 $ & $ z_3=6 $\\
\hline \hline
costs & 1 & 0.73 & 0.63 & 0.52 & 0.43 & 0.30 \\ \hline
share & (8, 4, 88) & (11, 5, 84) & (13,5,82) & (15,6,79) & (17, 7, 75) & (29, 12, 59)\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Optimal abatement cost and cost-sharing}
\end{table}
\end{document}

q-fin

"Climate uncertainty, financial frictions and constrained efficient carbon taxation" by Felix Kübler, arXiv:2210.09066v1, 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}{cl}
\hline
parameter/variables & biological meaning \\
\hline
$v$ & membrane potential \\
$\omega$ & recovery (adaptation) variable \\
$I$ & input current\\
$\mu$ & time course for membrane potential \\
$\gamma$ & depletion strength of recovery variable \\
$a$ & threshold between electrical silence and
electrical firing \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A comprehensive numerical investigation of a coupled mathematical model of neuronal excitability" by Burcu Gürbüz, Aytül Gökçe, and Mahmut Modanlı, arXiv:2501.18013v1, 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{CNN Model Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 & \textbf{Basic CNN} & \multicolumn{3}{c}{\textbf{Deep CNN}} \\ \cline{2-5}
\textbf{Parameter} & \textbf{Block 1} & \textbf{Block 1} & \textbf{Block 2} & \textbf{Block 3} \\ \hline
Convolutional 1D layer & & & & \\
Filter Size & 64 & 32 & 64 & 128 \\
Kernel Size & 2 & 3 & 2 & 1 \\
Padding & same & same & same & same \\
Dense Layer: Relu Activation & 265 & - & - & - \\ \hline
\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{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccccccc}
\hline
\textbf{} & \multicolumn{3}{c|}{Diff+SVR} & \multicolumn{3}{c|}{HGLET+SVR} & \multicolumn{3}{c|}{GHWT+SVR} & \multirow{2}{*}{SchNet} & \multirow{2}{*}{PaiNN} & \multirow{2}{*}{SO3Net I} & \multirow{2}{*}{SO3Net II} \\ \cline{1-10}
Feature Type & Node & Edge & Both & Node & Edge & Both & Node & Edge & Both & & & & \\ \hline
\multicolumn{14}{c}{Aspirin} \\ \hline
MAE & 4.856 & 3.132 & 3.267 & 4.884 & 3.135 & 3.285 & 4.928 & \textbf{3.075} & 3.225 & 13.5 & 3.8 & 3.8 & \textbf{2.6} \\
RMSE & 6.181 & 4.144 & 4.314 & 6.215 & 4.129 & 4.407 & 6.213 & \textbf{4.123} & 4.316 & 18.3 & 5.9 & 5.7 & \textbf{3.8} \\
\# Parameters & 924 & 3784 & 4708 & 924 & 3784 & 4708 & 924 & 3784 & 4708 & $\sim$ 432k & $\sim$ 341k & $\sim$ 283k & $\sim$ 341k \\ \hline
\multicolumn{14}{c}{Paracetamol} \\ \hline
MAE & 4.609 & 2.715 & 2.795 & 4.723 & 2.643 & 2.71 & 4.748 & \textbf{2.624} & 2.699 & 8.4 & 2.1 & 2.2 & \textbf{1.4} \\
RMSE & 5.860 & 3.418 & 4.116 & 5.964 & 3.338 & 3.424 & 5.961 & \textbf{3.299} & 3.408 & 11.2 & 2.9 & 3.0 & \textbf{1.9} \\
\# Parameters & 924 & 3784 & 4444 & 924 & 3784 & 4444 & 924 & 3784 & 4444 & $\sim$432k & $\sim$ 341k & $\sim$283k & $\sim$341k \\ \hline
 & & & & & & & & & & & & &
\end{tabular}
\end{adjustbox}
\caption{Comparison of the performance of our MHSNs and the other state-of-the-art GNNs for nuclear energy prediction. We report the accuracy via MAE and RMSE as-well as the number of trainable parameters in each network}
\end{table}
\end{document}

stat

Naoki Saito
Stefan C. Schonsheck
Eugene Shvarts

"Multiscale Hodge Scattering Networks for Data Analysis" by Naoki Saito, Stefan C. Schonsheck, and Eugene Shvarts, arXiv:2311.10270v5, 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{SSIM scores for different SR loss functions (LF) across multiple datasets. The highest SSIM values for each dataset and the overall average are highlighted in bold, with second-best values underlined.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Dataset / LF} & \textbf{Baseline} & \textbf{L1} & \textbf{Combo} & \textbf{Hybrid} \\
 \midrule
 FUSAR & 0.930 & 0.929 & \underline{0.932} & \textbf{0.934} \\
 MSTAR & 0.730 & 0.758 & \textbf{0.761} & \underline{0.760} \\
 HRSID & 0.800 & \textbf{0.935} & \underline{0.933} & \underline{0.933} \\
 \midrule
 \textbf{Average} & 0.820 & 0.874 & \underline{0.875} & \textbf{0.876} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ch Muhammad Awais
Marco Reggiannini
Davide Moroni
Oktay Karakus

"A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery" by Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, and Oktay Karakus, arXiv:2508.06407v1, 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
\caption{Feasibility response of hybrid precoders.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccccccccccc}
		\toprule
		\multirow{3}{*}{\rotatebox{90}{\kern-2.25em Case}} &
		\multirow{1}{*}{Parameters} &
		\multirow{2}{*}{\texttt{FALCON}} &
		\multicolumn{15}{c}{\texttt{WMMSE} (\%)} \\
		\cmidrule{4-18}
		& configuration & & \multicolumn{3}{c}{\scalebox{.65}{$ P^{(0)}_\mathrm{m} = 0.70 P_\mathrm{tx} $}} & \multicolumn{3}{c}{\scalebox{.65}{$ P^{(0)}_\mathrm{m} = 0.80 P_\mathrm{tx} $}} & \multicolumn{3}{c}{\scalebox{.65}{$ P^{(0)}_\mathrm{m} = 0.90 P_\mathrm{tx} $}} & \multicolumn{3}{c}{\scalebox{.65}{$ P^{(0)}_\mathrm{m} = 0.95 P_\mathrm{tx} $}} & \multicolumn{3}{c}{\scalebox{.65}{$ P^{(0)}_\mathrm{m} = 0.99 P_\mathrm{tx} $}} \\
		\cmidrule(lr){3-3}
		\cmidrule(lr){4-6}
		\cmidrule(lr){7-9}
		\cmidrule(lr){10-12}
		\cmidrule(lr){13-15}
		\cmidrule(lr){16-18}
		& \scalebox{.65}{$ { \left[ N_\mathrm{tx}, K, C^\mathrm{th}_0 \right]} $} & (\%) & \texttt{MRT} & \texttt{ZF} & \texttt{SLNR} & \texttt{MRT} & \texttt{ZF} & \texttt{SLNR} & \texttt{MRT} & \texttt{ZF} & \texttt{SLNR} & \texttt{MRT} & \texttt{ZF} & \texttt{SLNR} & \texttt{MRT} & \texttt{ZF} & \texttt{SLNR} \\
		\midrule

		A & $ {\scriptscriptstyle \left[ 4, 2, 2.5 \right]} $ & \textbf{100} & 54 & 58 & 69 & 82 & 77 & 68 & 82 & 82 & 78 & 87 & 87 & 84 & \textbf{\underline{87}} & 87 & 87 \\
		\midrule

		B & $ {\scriptscriptstyle \left[ 6, 3, 2.0 \right]} $ & \textbf{100} & 63 & 54 & 62 & 70 & 72 & 67 & 79 & 79 & 76 & 84 & 83 & 81 & \textbf{\underline{88}} & 87 & 85 \\
		\midrule

		C & $ {\scriptscriptstyle \left[ 8, 4, 1.5 \right]} $ & \textbf{96} & 74 & 81 & 79 & 85 & 85 & 86 & 93 & 94 & 91 & 93 & 95 & 94 & \textbf{\underline{96}} & 96 & 96 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Luis F. Abanto-Leon
Matthias Hollick
Bruno Clerckx
Gek Hong Sim

"Sequential Parametric Optimization for Rate-Splitting Precoding in Non-Orthogonal Unicast and Multicast Transmissions" by Luis F. Abanto-Leon, Matthias Hollick, Bruno Clerckx, and Gek Hong Sim, arXiv:2201.10426v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test accuracy (\%) of node classification benchmarks using DGI and MVGRL methods. We compare the performances of models trained with JSD loss (Contrast), loss part only involving negative pairs (NO Pos), loss only involving positive pairs (NO Neg), and no optimization objective (NO Training). Mean accuracy with standard derivation is reported after 10 runs. We conduct significance testing using Wilcoxon Signed Rank Test , comparing the contrastive loss with other loss types. The p-value is averaged across datasets. A value below 0.05 denotes significant accuracy difference (\colorbox{red!10}{red}), while a value above 0.05 indicates insignificance (\colorbox{green!10}{green}).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccccccc}
 \toprule
 \textbf{Method} & \textbf{Loss} & Cora & CiteSeer & PubMed & Photo & Computers & Chameleon & Squirrel & \textbf{Avg} &\textbf{Avg p-value} \\
 \midrule
 \multirow{5}*{DGI} & Contrast & 83.38 $\pm$ 2.67 & 72.07 $\pm$ 2.37 & 84.77 $\pm$ 0.71 & 88.10 $\pm$ 1.81 & 83.35 $\pm$ 0.71 & 39.56 $\pm$ 2.86 & 34.55 $\pm$ 0.88 & 69.40 & -\\
 \cmidrule(r){2-11}
 ~ & NO Training & 69.78 $\pm$ 3.39 & 55.15 $\pm$ 2.09 & 79.56 $\pm$ 1.35 & 69.08 $\pm$ 3.30 & 56.03 $\pm$ 1.97 & 31.44 $\pm$ 1.70 & 24.57 $\pm$ 1.22 & 55.09 & \cellcolor{red!10}0.0020 \\
 \cmidrule(r){2-11}
 ~ & NO Pos & 66.84 $\pm$ 3.54 & 54.79 $\pm$ 3.33 & 78.25 $\pm$ 0.99 & 58.34 $\pm$ 3.92 & 71.98 $\pm$ 1.38 & 35.81 $\pm$ 2.34	& 26.99 $\pm$ 0.20 & 56.14 & \cellcolor{red!10}0.0020 \\
 \cmidrule(r){2-11}
 ~ & NO Neg & 67.35 $\pm$ 4.61 & 58.17 $\pm$ 2.57 & 77.23 $\pm$ 1.05 & 62.75 $\pm$ 3.75 & 72.66 $\pm$ 1.48 & 31.62 $\pm$ 4.06 & 27.75 $\pm$ 1.85 & 56.79 & \cellcolor{red!10}0.0022 \\
 \midrule
 \multirow{5}*{MVGRL} & Contrast & 84.41 $\pm$ 1.44 & 75.27 $\pm$ 0.79 & 85.62 $\pm$ 0.63 & 89.23 $\pm$ 1.52 & 79.58 $\pm$ 0.15 & 42.45 $\pm$ 2.43 & 33.97 $\pm$ 2.54 & 70.08 & - \\
 \cmidrule(r){2-11}
 ~ & NO Training & 77.94 $\pm$ 2.23 & 58.92 $\pm$ 2.88 & 82.13 $\pm$ 0.63 & 81.15 $\pm$ 3.25 & 69.07 $\pm$ 0.40 & 32.23 $\pm$ 1.94 & 24.41 $\pm$ 1.10 & 60.84 & \cellcolor{red!10}0.0022\\
 \cmidrule(r){2-11}
 ~ & NO Pos & 75.44 $\pm$ 1.42 & 61.08 $\pm$ 2.48 & 81.26 $\pm$ 1.30 & 36.03 $\pm$ 1.57 & 38.36 $\pm$ 0.55 & 36.86 $\pm$ 2.56 & 29.98 $\pm$ 1.52 & 51.29 & \cellcolor{red!10}0.0020 \\
 \cmidrule(r){2-11}
 ~ & NO Neg & 54.93 $\pm$ 4.67 & 35.03 $\pm$ 5.20 & 56.26 $\pm$ 1.91 & 36.47 $\pm$ 2.37 & 38.36 $\pm$ 0.56 & 29.34 $\pm$ 2.04 & 28.06 $\pm$ 1.66 & 39.78 & \cellcolor{red!10}0.0020 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Xiaojun Guo
Yifei Wang
Zeming Wei
Yisen Wang

"Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning" by Xiaojun Guo, Yifei Wang, Zeming Wei, and Yisen Wang, arXiv:2311.02687v1, 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{Average European league ratings of top 5 teams}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|}
 \hline
 \# & league & rating & \# & league & rating \\ \hline
 1 & England. First & 2118.8 & 50 & Lithuania. First & 1661.7 \\ \hline
 2 & Germany. First & 2069.9 & 51 & North Macedonia. First & 1651.8 \\ \hline
 3 & Italy. First & 2064.0 & 52 & Portugal. Second & 1646.9 \\ \hline
 4 & Spain. First & 2039.4 & 53 & Kosovo. First & 1642.1 \\ \hline
 5 & Portugal. First & 2000.7 & 54 & Luxembourg. First & 1640.6 \\ \hline
 6 & France. First & 1981.4 & 55 & Estonia. First & 1637.8 \\ \hline
 7 & Netherlands. First & 1965.8 & 56 & Moldova. First & 1636.9 \\ \hline
 8 & Belgium. First & 1923.3 & 57 & Malta. First & 1622.7 \\ \hline
 9 & Turkey. First & 1911.9 & 58 & Austria. Second & 1619.6 \\ \hline
 10 & Russia. First & 1906.5 & 59 & Denmark. Second & 1616.9 \\ \hline
 11 & Austria. First & 1899.3 & 60 & Faroe Islands. First & 1615.1 \\ \hline
 12 & Switzerland. First & 1893.4 & 61 & Albania. First & 1614.2 \\ \hline
 13 & Poland. First & 1890.7 & 62 & Gibraltar. First & 1612.3 \\ \hline
 14 & Sweden. First & 1883.9 & 63 & Greece. Second & 1611.2 \\ \hline
 15 & Ukraine. First & 1878.1 & 64 & Netherlands. Second & 1602.1 \\ \hline
 16 & Greece. First & 1868.8 & 65 & Sweden. Second & 1587.9 \\ \hline
 17 & Denmark. First & 1852.3 & 66 & Belgium. Second & 1581.6 \\ \hline
 18 & Czech Republic. First & 1832.4 & 67 & Romania. Second & 1577.1 \\ \hline
 19 & Scotland. First & 1826.2 & 68 & Montenegro. First & 1562.7 \\ \hline
 20 & Israel. First & 1817.1 & 69 & Hungary. Second & 1557.3 \\ \hline
 21 & Serbia. First & 1816.6 & 70 & Wales. First & 1556.8 \\ \hline
 22 & Bulgaria. First & 1809.7 & 71 & Israel. Second & 1553.7 \\ \hline
 23 & Norway. First & 1808.1 & 72 & Scotland. Second & 1532.3 \\ \hline
 24 & Romania. First & 1807.6 & 73 & Croatia. Second & 1525.0 \\ \hline
 25 & Croatia. First & 1807.2 & 74 & Ukraine. Second & 1519.7 \\ \hline
 26 & Cyprus. First & 1795.4 & 75 & Slovenia. Second & 1517.7 \\ \hline
 27 & Germany. Second & 1785.3 & 76 & Norway. Second & 1516.7 \\ \hline
 28 & Latvia. First & 1767.9 & 77 & Slovakia. Second & 1490.9 \\ \hline
 29 & Italy. Second & 1767.6 & 78 & Kazakhstan. Second & 1477.3 \\ \hline
 30 & England. Second & 1766.0 & 79 & Serbia. Second & 1473.3 \\ \hline
 31 & Spain. Second & 1763.3 & 80 & North Macedonia. Second & 1466.5 \\ \hline
 32 & Slovenia. First & 1749.0 & 81 & Bulgaria. Second & 1460.6 \\ \hline
 33 & Slovakia. First & 1748.5 & 82 & Georgia. Second & 1456.8 \\ \hline
 34 & Belarus. First & 1735.6 & 83 & Bosnia And Herzegovina. Second & 1456.6 \\ \hline
 35 & Armenia. First & 1732.3 & 84 & Czech Republic. Second & 1453.2 \\ \hline
 36 & Hungary. First & 1727.1 & 85 & Andorra. First & 1443.9 \\ \hline
 37 & Georgia. First & 1726.1 & 86 & Armenia. Second & 1443.3 \\ \hline
 38 & Bosnia And Herzegovina. First & 1724.8 & 87 & Cyprus. Second & 1436.2 \\ \hline
 39 & Finland. First & 1721.9 & 88 & Finland. Second & 1430.4 \\ \hline
 40 & France. Second & 1721.9 & 89 & Ireland. Second & 1427.2 \\ \hline
 41 & Northern Ireland. First & 1706.5 & 90 & San Marino. First & 1412.1 \\ \hline
 42 & Azerbaijan. First & 1704.0 & 91 & Belarus. Second & 1409.0 \\ \hline
 43 & Switzerland. Second & 1700.7 & 92 & Albania. Second & 1374.1 \\ \hline
 44 & Ireland. First & 1698.4 & 93 & Latvia. Second & 1365.0 \\ \hline
 45 & Turkey. Second & 1689.6 & 94 & Iceland. Second & 1362.8 \\ \hline
 46 & Russia. Second & 1684.8 & 95 & Malta. Second & 1327.4 \\ \hline
 47 & Kazakhstan. First & 1683.2 & 96 & Lithuania. Second & 1316.7 \\ \hline
 48 & Poland. Second & 1671.4 & 97 & Estonia. Second & 1304.2 \\ \hline
 49 & Iceland. First & 1669.8 & 98 & Northern Ireland. Second & 1276.8 \\ \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.


math

Madeleine Bonsma-Fisher
Kent Bonsma-Fisher

"How big a table do you need for your jigsaw puzzle?" by Madeleine Bonsma-Fisher and Kent Bonsma-Fisher, arXiv:2312.04588v1, 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{Run time and Hardware used for our method (SITD) and the state-of-the-art baseline Rhino.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Category\textbackslash Method} & \textbf{Rhino} & \textbf{SITD (ours)} \\
\midrule
Runtime & 18 mins 40 sec $\pm$ 30 sec & 57.12 sec $\pm$ 1.6 sec \\
\midrule
Hardware & 1 NVIDIA A100 GPU + AMD EPYC 7402 24-Core CPU & 11th Gen Core i5-1140F CPU \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Amir Mohammad Karimi Mamaghan
Andrea Dittadi
Stefan Bauer
Karl Henrik Johansson
Francesco Quinzan

"Diffusion Based Causal Representation Learning" by Amir Mohammad Karimi Mamaghan, Andrea Dittadi, Stefan Bauer, Karl Henrik Johansson, and Francesco Quinzan, arXiv:2311.05421v1, 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{Recovered viscosity constants for the MFG in with $20$ points of observations (both $m$ and $V$ are observed), and $M=400$ collocation points. $\nu$ stands for the true viscosity and $\nu^\dagger$ represents the recovered viscosity. }
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline
 $\nu$ & 1 & 0.5 & 0.1 & 0.05 & 0.01\\
 \hline
 $\nu^{\dagger}$& 1.000804 & 0.497811 & 0.099312 & 0.050905 & 0.015023 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Jinyan Guo
Chenchen Mou
Xianjin Yang
Chao Zhou

"Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes" by Jinyan Guo, Chenchen Mou, Xianjin Yang, and Chao Zhou, arXiv:2312.06625v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \toprule
 \textbf{Codebook Size} & $\mathbf{1024}$ & $\mathbf{2048}$ & $\mathbf{4096}$ & $\mathbf{8192}$& $\mathbf{16384}$ \\
 \midrule
 Classic VQ & $99$\% & $95$\% & $90$\% & $56$\% & $42$\% \\
 Single SLM-VQ & $99$\% & $98$\% & $95$\% & $92$\% & $87$\%\\
 SLM-VQ & $99\%$ & $98$\% & $98$\% & $98$\% & $94$\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Codebook utilization of different quantizers under varying codebook sizes. SLM-VQ works well in all settings.}
\end{table}
\end{document}

eess

Rongkun Xue
Yazhe Niu
Shuai Hu
Zixin Yin
Yongqiang Yao
Jing Yang

"HH-Codec: High Compression High-fidelity Discrete Neural Codec for Spoken Language Modeling" by Rongkun Xue, Yazhe Niu, Shuai Hu, Zixin Yin, Yongqiang Yao, and Jing Yang, arXiv:2507.18897v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Percentage variation of the average of volatility (5 days before and after the announcement date) with respect to the volatility of the announcement day for four volatility time series.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
		&France&Germany&Italy&Spain\\ \hline
		before&-4,40&	-5,92&	-9,79&	-8,56\\
		after&-5,52&	-5,39&	-7,39&	-5,02\\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Demetrio Lacava
Giampiero M. Gallo
Edoardo Otranto

"Unconventional Policies Effects on Stock Market Volatility: A MAP Approach" by Demetrio Lacava, Giampiero M. Gallo, and Edoardo Otranto, arXiv:2010.08259v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c}
 Theoretical $p_g$& Observed $p_g$& Perfect& EKF & $T_{avg}$\\
 \hline
 0.50 & 1.00 & x & & 0.17\\
 \hline
 0.50 & 1.00 & & x & 0.20\\
 \end{tabular}
\caption{S-FxT-CLF results from the 2D nonlinear system goal-reaching study over 100 trials.}
\end{table}
\end{document}

cs

Mitchell Black
Georgios Fainekos
Bardh Hoxha
Dimitra Panagou

"Risk-Aware Fixed-Time Stabilization of Stochastic Systems under Measurement Uncertainty" by Mitchell Black, Georgios Fainekos, Bardh Hoxha, and Dimitra Panagou, arXiv:2403.20258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Sepideh Mosaferi
Abolfazl Safikhani
Peiliang Bai

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccc}
		\toprule & \multicolumn{3}{c}{Proportion of short videos} &
		\multicolumn{3}{c}{Stay durations} & \multicolumn{3}{c}{Finishing rates} \\
		\midrule & Bias & STD & SE & Bias & STD & SE & Bias & STD & SE \\

		Weighted & 0.005 & 0.007 & 0.001 & -0.010 & 0.021 & 0.009 & 0.001 & 0.002 & 0.001 \\
		Data splitting & -0.008 & 0.015 & 0.001 & 0.018 & 0.040 & 0.009 & -0.003 & 0.004 & 0.001 \\
		Data pooling & 0.039 & 0.002 & 0.001 & -0.124 & 0.009 & 0.009 & 0.014 & 0.001 & 0.001 \\
		Snapshot & 0.031 & 0.001 & 0.001 & -0.107 & 0.009 & 0.009 & 0.013 & 0.001 & 0.001 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach" by Nian Si, arXiv:2310.17496v5, 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{Blitzes characteristics in Fortaleza-CE, 2012-13}
\begin{tabular}{lccc}
					\\\hline
					\hline \\
					& Mean & Maximum & Minimum \\ %& Alagoas
					\hline \\

					Duration (hours) & 5.57 & 8 & 0.5 \\ %&

					Number of policemen & 5.84 & 75 & 2 \\ %&

					Police vehicles allocated & 1.8 & 21 & 0 \\ %&
					\\\hline
					\hline \\
				\end{tabular}
\end{table}
\end{document}

q-fin

"Is Crime Displacement Inevitable? Evidence from Police Crackdowns in Fortaleza, Brazil" by José Raimundo Carvalho and Marcelino Guerra, arXiv:2503.13571v1, 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{NLL, ES, and Spatial Quality ($\mathcal{Q}_S$) results of the three predictive distributions from our toy example.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}

 Case $\#$& NLL $\downarrow$ & ES $\downarrow$ & $\mathcal{Q}_S$ $\uparrow$ ($\%$) \\
 \midrule
1& 20.49 &23.10 & \textbf{51.88} \\
2 & 20.49 & 23.10 & 45.98 \\
3 & \textbf{19.33} & \textbf{21.21} & 50.02 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors" by Ali Harakeh and Steven L. Waslander, arXiv:2101.05036v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Federico A. Bugni
Ivan A. Canay
Deborah Kim

"Testing Conditional Stochastic Dominance at Target Points" by Federico A. Bugni, Ivan A. Canay, and Deborah Kim, arXiv:2503.14747v2, 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|c|c|c|}
 \hline
 \textbf{Controller} & \textbf{$x_{error}$ (cm)} & \textbf{$y_{error}$ (cm)} & \textbf{$z_{error}$ (cm)} \\ \hline
 HL Commander & 2.7112 & 3.0767 & 0.3595 \\ \hline
 No Dynamics & 4.9282 & 5.8952 & 0.812 \\ \hline
 Magnet Dynamics & 4.2868 & 5.7235 & 0.4739 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Controller average error comparison for figure-8, showing the least error from HL Commander, followed by LL Commander with magnet dynamics, and lastly LL Commander without magnet dynamics.}
\end{table}
\end{document}

eess

Magnisketch Drone Control

Ashley Kline
Abirami Elangovan
Dominique Escandon
Scott Wade
Aatish Gupta

"Magnisketch Drone Control" by Ashley Kline, Abirami Elangovan, Dominique Escandon, Scott Wade, and Aatish Gupta, arXiv:2412.10670v1, 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|c|}
 \hline
 Beam & Solution Length & Solution Length \\
 Width & Nonbacktracking RW & Simple RW \\
 \hline
 2**10 & None & None \\
 \hline
 2**12 & 252 & None \\
 \hline
 2**14 & 202 & None \\
 \hline
 2**16 & 194 & None \\
 \hline
 2**18 & 194 & None \\
 \hline
 2**20 & 190 (Ideal) & None \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Without non-backtracking system cannot perform well on single epoch training. Effect of beam size is clear, but not so strong. }
\end{table}
\end{document}

math

A. Chervov
A. Soibelman
S. Lytkin
I. Kiselev
S. Fironov
A. Lukyanenko
A. Dolgorukova
A. Ogurtsov
F. Petrov
S. Krymskii
M. Evseev
L. Grunvald
D. Gorodkov
G. Antiufeev
G. Verbii
V. Zamkovoy
L. Cheldieva
I. Koltsov
A. Sychev
M. Obozov
A. Eliseev
S. Nikolenko
N. Narynbaev
R. Turtayev
N. Rokotyan
S. Kovalev
A. Rozanov
V. Nelin
S. Ermilov
L. Shishina
D. Mamayeva
A. Korolkova
K. Khoruzhii
A. Romanov

"CayleyPy RL: Pathfinding and Reinforcement Learning on Cayley Graphs" by A. Chervov, A. Soibelman, S. Lytkin, I. Kiselev, S. Fironov, A. Lukyanenko, A. Dolgorukova, A. Ogurtsov, F. Petrov, S. Krymskii, M. Evseev, L. Grunvald, D. Gorodkov, G. Antiufeev, G. Verbii, V. Zamkovoy, L. Cheldieva, I. Koltsov, A. Sychev, M. Obozov, A. Eliseev, S. Nikolenko, N. Narynbaev, R. Turtayev, N. Rokotyan, S. Kovalev, A. Rozanov, V. Nelin, S. Ermilov, L. Shishina, D. Mamayeva, A. Korolkova, K. Khoruzhii, and A. Romanov, arXiv:2502.18663v1, 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 Training statistics of the 5 homophilic, heterophilic and real-world benchmark datasets used in this work for AMES in this work.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc} \hline
 & Training Epochs & Learning Rate & Weight Decay \\ \hline
 \textbf{Cora} & 1,500 & $5\times10^{-3}$& $1\times10^{-5}$\\
 \textbf{CiteSeer} & 1,500 & $4\times10^{-3}$& $1\times10^{-5}$\\
 \textbf{Squirrel} &1,000 &$1\times10^{-2}$ & $1\times10^{-5}$\\
\textbf{Chameleon} & 1,000& $1\times10^{-2}$& $1\times10^{-5}$\\
 \textbf{TadPole} & 800& $1\times10^{-3}$& $2\times10^{-4}$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yuan Lu
Haitz Sáez de Ocáriz Borde
Pietro Liò

"AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference" by Yuan Lu, Haitz Sáez de Ocáriz Borde, and Pietro Liò, arXiv:2311.11891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Nikki L. B. Freeman
Sydney E. Browder
Katharine L. McGinigle
Michael R. Kosorok

"Dynamic treatment regime characterization via value function surrogate with an application to partial compliance" by Nikki L. B. Freeman, Sydney E. Browder, Katharine L. McGinigle, and Michael R. Kosorok, arXiv:2212.00650v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
Method & Metric & 50D & 100D & 150D & 200D \\ \hline\hline
\multirow{3}{*}{PINN } & Speed & 19.82it/s & 6.56it/s & 3.50it/s & N.A.\\ \cline{2-6}
 & Memory & 6199MB & 21453MB & 44631MB & $>$80GB \\ \cline{2-6}
 & Error & 2.34E-2$\pm$1.91E-2 & 6.01E-3$\pm$2.32E-3 & 3.11E-3$\pm$1.79E-3 & N.A. \\ \hline\hline
 \multirow{3}{*}{HTE ($V=16$)} & Speed & 438.79it/s	&109.57it/s	&54.86it/s&	25.89it/s
\\ \cline{2-6}
& Memory & 991MB	&2085MB	&3589MB&	7225MB
\\ \cline{2-6}
 & Error & 2.36E-2$\pm$1.97E-2 & 6.83E-3$\pm$1.63E-3 & 3.91E-3$\pm$1.47E-3 & 4.40E-3$\pm$5.65E-3\\ \hline\hline
\multirow{3}{*}{HTE ($V=512$)} & Speed & 123.98it/s & 54.07it/s& 42.44it/s & 23.50it/s\\ \cline{2-6}
& Memory & 1861MB & 2089MB & 3593MB & 7229MB\\ \cline{2-6}
 & Error & 2.34E-2$\pm$1.96E-2 & 6.08E-3$\pm$2.28E-3 & 3.39E-3$\pm$1.99E-3 & 4.03E-3$\pm$4.85E-3\\ \hline\hline
 \multirow{3}{*}{HTE ($V=1024$)} & Speed &78.50it/s	&34.72it/s&	33.77it/s&	20.36it/s\\ \cline{2-6}
& Memory & 2535MB&	3037MB	&3595MB&	7231MB
\\ \cline{2-6}
 & Error & 2.34E-2$\pm$1.96E-2 & 6.01E-3$\pm$2.35E-3 & 3.13E-3$\pm$1.82E-3 & 3.97E-3$\pm$4.91E-3\\ \hline
\end{tabular}
\caption{Computational results for the Biharmonic equation. In all-dimensional biharmonic PDEs, HTE consistently achieves results similar to those of full PINN while significantly outpacing full PINN in terms of speed and notably reducing memory consumption. In the case of 200 dimensions, where full PINN exceeds memory limits, HTE still converges rapidly.}
\end{table}
\end{document}

math

Zheyuan Hu
Zekun Shi
George Em Karniadakis
Kenji Kawaguchi

"Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks" by Zheyuan Hu, Zekun Shi, George Em Karniadakis, and Kenji Kawaguchi, arXiv:2312.14499v2, 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{Values of the calculated errors for pricing results for ETH, rounded to three significant figures.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}

		\hline
		& RMSE& MAE &MAPE&MSLE \\
		\hline
		BS & 64.4 &48.2 & 0.105 & 0.0413\\
 Heston & 46.7 & 29.1 & 0.0682 & 0.0139 \\
 Kou &30.8 & 18.2 & 0.0412 & 0.00534\\
 MJD &25.9& 16.6 & 0.0423 & 0.00662 \\
 SVJ &39.1 &15.1 & 0.019 & 0.00284 \\
		VG & 48.1 & 29 & 0.0662 & 0.0128 \\
 \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Pricing options on the cryptocurrency futures contracts" by Julia Kończal, arXiv:2506.14614v1, 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{\textbf{Comparison of Performances (Mean$\pm$Standard Deviation) on Multi-Class Semantic Segmentation Task of Dataset-3}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{Network}} & \multicolumn{5}{c|}{\textbf{Dataset-3}} \\ \cline{2-6}
 & \textbf{Sen.(\%)} & \textbf{Spec.(\%)} & \textbf{Dice(\%)} & \textbf{IoU(\%)} & \textbf{p-Value} \\ \hline
\textbf{Unet~} & 56.9$\pm$0.19 & 68.6$\pm$0.23 & 42.2$\pm$0.35 & 37.7$\pm$0.28 & - \\ \hline
\textbf{Unet++~} & 57.3$\pm$0.25 & 70.4$\pm$0.31 & 44.8$\pm$0.29 & 40.1$\pm$0.33 & - \\ \hline
\textbf{Attention-Unet-2D~} & 58.7$\pm$0.22 & 71.8$\pm$0.29 & 48.5+0.42 & 43.9$\pm$0.25 & - \\ \hline
\textbf{CPF-Net~} & 61.5$\pm$0.28 & 73.1$\pm$0.17 & 51.4$\pm$0.38 & 47.7$\pm$0.34 & - \\ \hline
\textbf{Semi-Inf-Net~} & 64.9$\pm$0.31 & 76.3$\pm$0.27 & 50.9$\pm$0.27 & 46.4$\pm$0.26 & - \\ \hline
\textbf{CovSegNet(ours)} & \textbf{76.4$\pm$0.18} & \textbf{87.7$\pm$0.16} & \textbf{64.6$\pm$0.21} & \textbf{59.5$\pm$0.29} & \textbf{6e-5} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Tanvir Mahmud
Md Awsafur Rahman
Shaikh Anowarul Fattah
Sun-Yuan Kung

"CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans" by Tanvir Mahmud, Md Awsafur Rahman, Shaikh Anowarul Fattah, and Sun-Yuan Kung, arXiv:2012.01473v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison of MobileNet-V1 on ImageNet.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
Method & Sparsity & Params & FLOPs & Top-1 Acc. \\ \midrule
MobileNet-V1 & 0.00 & 4.12M & 569M & 71.95 \\
\midrule
GMP & 74.11 & 1.09M & 163M & 67.70 \\
STR & 75.28 & 1.04M & 101M & 68.35 \\
STR & 79.07 & 0.88M & 81M & 66.52 \\
\rowcolor[gray]{0.9} OptG & 80.00 & 0.82M & 124M & \textbf{70.27} \\
\midrule
GMP & 89.03 & 0.46M & 82M & 61.80 \\
STR & 85.80 & 0.60M & 55M & 64.83 \\
STR & 89.01 & 0.46M & 42M & 62.10 \\
STR & 89.62 & 0.44M & 40M & 61.51 \\
\rowcolor[gray]{0.9} OptG & 90.00 & 0.41M & 80M & \textbf{66.80} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yuxin Zhang
Mingbao Lin
Mengzhao Chen
Fei Chao
Rongrong Ji

"OptG: Optimizing Gradient-driven Criteria in Network Sparsity" by Yuxin Zhang, Mingbao Lin, Mengzhao Chen, Fei Chao, and Rongrong Ji, arXiv:2201.12826v4, 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|}
 \hline
 \textbf{Field} & \textbf{Value}\\
 \hline
 \hline
 File format & PDF \\
 \hline
 Page limit & 11 pages, {\bf not }\\
 & {\bf including references}\\
 \hline
 Paper size & US Letter 8.5in $\times$ 11in\\
 \hline
 Top margin & 1in\\
 \hline
 Bottom margin & 1in\\
 \hline
 Left margin & 0.75in\\
 \hline
 Right margin & 0.75in\\
 \hline
 Body & 2-column, single-spaced\\
 \hline
 Separation b/t columns & 0.25in\\
 \hline
 Body font & 10pt\\
 \hline
 Abstract font & 10pt, italicized\\
 \hline
 Section heading font & 12pt, bold\\
 \hline
 Subsection heading font & 10pt, bold\\
 \hline
 Caption font & 9pt, bold\\
 \hline
 References & 8pt, no page limit, list \\
 & all authors' names\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Formatting guidelines for submission. }
\end{table}
\end{document}

eess

Rongxiang Wang
Zhiming Xu
Felix Xiaozhu Lin

"WhisperFlow: speech foundation models in real time" by Rongxiang Wang, Zhiming Xu, and Felix Xiaozhu Lin, arXiv:2412.11272v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Ibon Gracia
Luca Laurenti
Manuel Mazo
Alessandro Abate
Morteza Lahijanian

"Temporal Logic Control for Nonlinear Stochastic Systems Under Unknown Disturbances" by Ibon Gracia, Luca Laurenti, Manuel Mazo, Alessandro Abate, and Morteza Lahijanian, arXiv:2412.11343v3, 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|}
\hline
 &\textbf{Inter-Sentential} & \textbf{Intra-Sentential} \\
\hline
English-Sanskrit & 2,356 & 2,338 \\
Sanskrit-English & 2366 & 851 \\
English-Bengali & 339 & 124 \\
Bengali-English & 339 & 0 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Code-Mixing type for our dataset}
\end{table}
\end{document}

cs

Jivnesh Sandhan
Ayush Daksh
Om Adideva Paranjay
Laxmidhar Behera
Pawan Goyal

"Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages" by Jivnesh Sandhan, Ayush Daksh, Om Adideva Paranjay, Laxmidhar Behera, and Pawan Goyal, arXiv:2201.11391v2, 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}{|cll|}
\hline
\textbf{Case} & \textbf{Conditions} & \textbf{Finite singular points} \\
\hline
\hline
1& $c_3^2>4c_0c_2$, $c_1\mu\neq0$. & $P_0$, $P_1$, $P_2$, $P_4$. \\	\hline
2 & $c_3^2>4c_0c_2$, $c_1\mu=0$, $a_0\neq0$. & $P_0$, $P_1$, $P_2$. \\
\hline
3 & $c_3^2=4c_0c_2$, $c_1\mu\neq0$. & $P_0$, $P_3$, $P_4$. \\
\hline
4 & $c_3^2=4c_0c_2$, $c_1\mu=0$, $a_0\neq0$. & $P_0$, $P_3$. \\
\hline
5 & $c_3^2<4c_0c_2$, $c_1\mu\neq0$. & $P_0$, $P_4$. \\
\hline
6 & $c_3^2<4c_0c_2$, $c_1\mu=0$, $a_0\neq0$. & $P_0$. \\
\hline
\end{tabular}
\end{adjustbox}
\caption{The different cases for the finite singular points.}
\end{table}
\end{document}

math

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

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


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effect of Learning Rate Sweep on Benchmark Scores. Cells highlighted in green indicate better scores.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
\multirow{2}{*}{\textbf{Benchmark}} & \multirow{2}{*}{\textbf{Granite Base Pretrained}} & \multicolumn{4}{c}{\textbf{Learning Rates}} \\ \cmidrule(lr){3-6}
 & & \textbf{2e-5} & \textbf{4e-5} & \textbf{8e-5} & \textbf{1e-4} \\ \midrule
\textbf{MMLU} & 0.48 & \cellcolor{green!30}0.52 & \cellcolor{green!30}0.52 & \cellcolor{green!30}0.52 & \cellcolor{green!30}0.52 \\
\textbf{MTBench} & - & \cellcolor{green!30}6.76 & 6.64 & 6.53 & 6.47 \\
\textbf{Leaderboard (BBH)} & 0.09 & \cellcolor{green!30}0.10 & 0.09 & 0.09 & 0.08 \\
\textbf{Leaderboard (MuSR)} & 0.01 & \cellcolor{green!30}0.08 & 0.07 & \cellcolor{green!30}0.08 & 0.06 \\
\textbf{ARC} & 0.78 & 0.74 & \cellcolor{green!30}0.75 & \cellcolor{green!30}0.75 & 0.73 \\
\textbf{GSM8K} & 0.11 & \cellcolor{green!30}0.38 & 0.36 & 0.37 & 0.30 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Aldo Pareja
Nikhil Shivakumar Nayak
Hao Wang
Krishnateja Killamsetty
Shivchander Sudalairaj
Wenlong Zhao
Seungwook Han
Abhishek Bhandwaldar
Guangxuan Xu
Kai Xu
Ligong Han
Luke Inglis
Akash Srivastava

"Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs" by Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, and Akash Srivastava, arXiv:2412.13337v1, 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{Experts classification accuracy.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|c|}
\hline
Dataset & Average & Best expert & Voting\\
\hline
\hline
3 classes & 60.88 & 62.67 & 62.00\\
\hline
0 vs 0.1 & 88.17 & 90.50 & 89.00\\
\hline
0 vs 1 & 89.34 & 93.00 & 90.50\\
\hline
0.1 vs 1 & 50.17 & 52.00 & 51.00\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Aleksei Shpilman
Dmitry Boikiy
Marina Polyakova
Daniel Kudenko
Anton Burakov
Elena Nadezhdina

"Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks" by Aleksei Shpilman, Dmitry Boikiy, Marina Polyakova, Daniel Kudenko, Anton Burakov, and Elena Nadezhdina, arXiv:2012.12125v1, 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{amsfonts}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\toprule
 & \multicolumn{3}{c }{\textbf{ MDID }}&
 \multicolumn{3}{c }{\textbf{ CSIQ }}\\% &
\cline{2-7}
 & \textbf{PLCC} & \textbf{SROCC} & \textbf{KROCC}& \textbf{PLCC} & \textbf{SROCC} & \textbf{KROCC} \\
\hline
SSIM &\colorbox{green!30}{0.581}&\colorbox{green!30}{0.576}&\colorbox{green!30}{0.411}&\colorbox{green!30}{0.812}&\colorbox{green!30}{0.812}&\colorbox{green!30}{0.606} \\
MS-SSIM &\colorbox{green!30}{0.836}&\colorbox{green!30}{0.841}&\colorbox{green!30}{0.654}&\colorbox{green!30}{0.913}&\colorbox{green!30}{0.917}&\colorbox{green!30}{0.743} \\
MAD &\colorbox{green!30}{0.742} &\colorbox{green!30}{0.725} &\colorbox{green!30}{0.533}&\colorbox{green!30}{0.950}&\colorbox{green!30}{0.947}&\colorbox{green!30}{0.796} \\
GSM &\colorbox{green!30}{0.825} &\colorbox{green!30}{0.827} &\colorbox{green!30}{0.636}&\colorbox{green!30}{0.906}&\colorbox{green!30}{0.910}&\colorbox{green!30}{0.729} \\
HaarPSI &\colorbox{green!30}{0.904}&\colorbox{green!30}{0.903}&\colorbox{green!30}{0.734}&\colorbox{green!30}{0.946}&\colorbox{green!30}{0.960}&\colorbox{green!30}{0.823}\\
MDSI &\colorbox{green!30}{0.829}&\colorbox{green!30}{0.836}&\colorbox{green!30}{0.653}&\colorbox{green!30}{0.953}&\colorbox{green!30}{0.957}&\colorbox{green!30}{0.812} \\
CSV &\colorbox{green!30}{0.879}&\colorbox{green!30}{0.881}&\colorbox{green!30}{0.700}&\colorbox{green!30}{0.933}&\colorbox{green!30}{0.933}&\colorbox{green!30}{0.766}\\
GMSD &\colorbox{green!30}{0.864}&\colorbox{green!30}{0.862}&\colorbox{green!30}{0.680}&\colorbox{green!30}{0.954}&\colorbox{green!30}{0.957}&\colorbox{green!30}{0.812}\\
DSS &\colorbox{green!30}{0.870}&\colorbox{green!30}{0.866}&\colorbox{green!30}{0.679}&\colorbox{green!30}{0.953}&\colorbox{green!30}{0.955}&\colorbox{green!30}{0.811} \\
VSI &\colorbox{green!30}{0.855}&\colorbox{green!30}{0.857}&\colorbox{green!30}{0.671}&\colorbox{green!30}{0.928}&\colorbox{green!30}{0.942}&\colorbox{green!30}{0.785}\\
PerSIM &\colorbox{green!30}{0.823}&\colorbox{green!30}{0.820}&\colorbox{green!30}{0.630}&\colorbox{green!30}{0.924}&\colorbox{green!30}{0.929}&\colorbox{green!30}{0.768}\\
BLeSS-SR-SIM &\colorbox{green!30}{0.805}&\colorbox{green!30}{0.815}&\colorbox{green!30}{0.626}&\colorbox{green!30}{0.892}&\colorbox{green!30}{0.893}&\colorbox{green!30}{0.718}\\
BLeSS-FSIM &\colorbox{green!30}{0.848}&\colorbox{green!30}{0.847}&\colorbox{green!30}{0.658}&\colorbox{green!30}{0.882}&\colorbox{green!30}{0.885}&\colorbox{green!30}{0.701}\\
BLeSS-FSIMc &\colorbox{green!30}{0.878}&\colorbox{green!30}{0.883}&\colorbox{green!30}{0.702}&\colorbox{green!30}{0.913}&\colorbox{green!30}{0.917}&\colorbox{green!30}{0.743}\\
LCSIM1 &-&-&-&\colorbox{green!30}{0.897}&\colorbox{green!30}{0.949}&\colorbox{green!30}{0.799}\\
ReSIFT &\colorbox{green!30}{0.905}&\colorbox{green!30}{0.895}&\colorbox{green!30}{0.716}&\colorbox{green!30}{0.884}&\colorbox{green!30}{0.868}&\colorbox{green!30}{0.695}\\
IQ($\mathcal{L}_T$) &\colorbox{green!30}{0.867}&\colorbox{green!30}{0.865}&\colorbox{green!30}{0.708} & \colorbox{green!30}{0.915} & \colorbox{green!30}{0.912} & \colorbox{green!30}{0.720} \\
MS-UNIQUE &\colorbox{green!30}{0.863}&\colorbox{green!30}{0.871}&\colorbox{green!30}{0.689}&\colorbox{green!30}{0.918}&\colorbox{green!30}{0.929}&\colorbox{green!30}{0.759}\\
SSIM CNN &\colorbox{green!30}{0.904}&\colorbox{green!30}{0.907}&\colorbox{green!30}{0.732}&\colorbox{green!30}{0.952}&\colorbox{green!30}{0.946}&\colorbox{green!30}{0.794}\\
RVSIM &\colorbox{green!30}{0.884}&\colorbox{green!30}{0.884}&\colorbox{green!30}{0.709}&\colorbox{green!30}{0.923}&\colorbox{green!30}{0.903}&\colorbox{green!30}{0.728}\\
2stepQA & \colorbox{green!30}{0.753}& \colorbox{green!30}{0.759}& \colorbox{green!30}{0.562}&\colorbox{green!30}{0.841}&\colorbox{green!30}{0.849}&\colorbox{green!30}{0.655}\\
SUMMER &\colorbox{green!30}{0.742}&\colorbox{green!30}{0.734}&\colorbox{green!30}{0.543}&\colorbox{green!30}{0.826}&\colorbox{green!30}{0.830}&\colorbox{green!30}{0.658}\\
CEQI &\colorbox{green!30}{0.863}&\colorbox{green!30}{0.864}&\colorbox{green!30}{0.685}&\colorbox{green!30}{0.956}&\colorbox{green!30}{0.956}&\colorbox{green!30}{0.814} \\
CEQIc &\colorbox{green!30}{0.864}&\colorbox{green!30}{0.863}&\colorbox{green!30}{0.684}&\colorbox{green!30}{0.956}&\colorbox{green!30}{0.955}&\colorbox{green!30}{0.810}\\
VCGS &\colorbox{green!30}{0.867}&\colorbox{green!30}{0.869}&\colorbox{green!30}{0.687}&\colorbox{green!30}{0.931}&\colorbox{green!30}{0.944}&\colorbox{green!30}{0.790}\\
DISTS &\colorbox{green!30}{0.862}&\colorbox{green!30}{0.860}&\colorbox{green!30}{0.669}&\colorbox{green!30}{0.930}&\colorbox{green!30}{0.930}&\colorbox{green!30}{0.764}\\
\hline
DeepFL-IQA &- &- &- &0.946 &0.930 &- \\
BLINDER &- &- &- &0.968 &0.961 &- \\
RankIQA &- &- &- &0.960 &0.947 &- \\
BPSOM-MD &- &- &- &0.860 &0.904 &- \\
NSSADNN &- &- &- &0.927 &0.893 &- \\
\midrule
\textit{ ActMapFeat}(ours)&\textbf{0.930}&\textbf{0.927}&\textbf{0.769}&\textbf{0.971}&\textbf{0.970}&\textbf{0.850}\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"A combined full-reference image quality assessment approach based on convolutional activation maps" by Domonkos Varga, arXiv:2010.09361v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.