\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Within-Customer Analysis of Offline Price Elasticity - Main Results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \tabularnewline \midrule \midrule
 Dependent Variable: & \multicolumn{6}{c}{Offline Price Elasticity}\\
 & \multicolumn{2}{c}{Dry Dog Food} & \multicolumn{2}{c}{Dog Hygiene} & \multicolumn{2}{c}{Cat Hygiene} \\
 Model: & (1) & (2) & (3) & (4) & (5) & (6) \\
 \midrule
 \emph{Variables}\\
 Constant & -2.935$^{***}$ & & -2.464$^{***}$ & & -2.125$^{***}$ & \\
 & (0.2142) & & (0.2429) & & (0.1998) & \\
 $\mathcal{I}\{t \geq T_{i}^{\textrm{adopt}}\}$ & -0.3870$^{***}$ & -0.1173$^{*}$ & -1.916$^{***}$ & -0.7239$^{**}$ & -0.8860$^{***}$ & -0.3347$^{**}$\\
 & (0.0759) & (0.0543) & (0.3563) & (0.2361) & (0.1727) & (0.1250)\\
 \midrule
 \emph{Fixed-effects}\\
 Customer & & Yes & & Yes & & Yes\\
 YearMonth & & Yes & & Yes & & Yes\\
 \midrule
 \emph{Fit statistics}\\
 Observations & 29,416 & 29,416 & 28,375 & 28,375 & 15,081 & 15,081\\
 R$^2$ & 0.31325 & 0.86657 & 0.34866 & 0.72137 & 0.37526 & 0.75980\\
 Within R$^2$ & & 0.04011 & & 0.03352 & & 0.03960\\
 \midrule \midrule
 \multicolumn{5}{l}{\emph{Bootstrapped standard-errors in parentheses via 500 replications}}\\
 \multicolumn{7}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"How does online shopping affect offline price sensitivity?" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, 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{Analysis of performance and efficiency across varied settings of $d$ on medium-sized datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
\toprule
& $d$ & \textbf{2} & \textbf{3} & \textbf{4 (default)} & \textbf{6} & \textbf{8} \\ \midrule
\multirow{2}{*}{Classification} & Performance (Accuracy) & 0.7402 & 0.7588 & \underline{0.7602} & 0.7583 & 0.7545 \\
& Parameters (M) & \underline{0.0058} & 0.0064 & 0.0070 & 0.0087 & 0.0108 \\
& FLOPs (M) & \underline{0.1801} & 0.1807 & 0.1815 & 0.1834 & 0.1857 \\
\midrule
\multirow{2}{*}{Regression} & Performance (R2 score) & 0.5961 & 0.6699 & 0.6796 & 0.6111 & \underline{0.6914} \\
& Parameters (M) & \underline{0.0047} & 0.0051 & 0.0056 & 0.0069 & 0.0087 \\
& FLOPs (M) & \underline{0.1786} & 0.1791 & 0.1797 & 0.1812 & 0.1831 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

Kuan-Yu Chen
Ping-Han Chiang
Hsin-Rung Chou
Chih-Sheng Chen
Tien-Hao Chang

"DOFEN: Deep Oblivious Forest ENsemble" by Kuan-Yu Chen, Ping-Han Chiang, Hsin-Rung Chou, Chih-Sheng Chen, and Tien-Hao Chang, arXiv:2412.16534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccc}
\toprule
Metric & \multicolumn{4}{c}{Top-1 Accuracy (\%)} & & \multicolumn{4}{c}{Seen-Unseen AUC (\%)} \\ \cmidrule(lr){2-5} \cmidrule(l){7-10}
Dataset & \multicolumn{2}{c}{CUB} & \multicolumn{2}{c}{NAB} & & \multicolumn{2}{c}{CUB} & \multicolumn{2}{c}{NAB} \\
Split-Mode & Easy & Hard & Easy & Hard & & Easy & Hard & Easy & Hard \\ \midrule
GAZSL~& 43.7 & 10.3 & 35.6 & 8.6 & & 35.4 & 8.7 & 20.4 & 5.8 \\ \midrule
GAZSL~ + \textbf{CIZSL}\\ \cmidrule{1-1}
Interpolate & 41.87 & \textbf{15.48} & 35.01 & \textbf{9.61} & & 39.19 & 12.58 & \textbf{23.59} & \textbf{6.92}\\ \hline
Negative Extrapolate & 43.37 & 14.93 & \textbf{35.32} & 9.28 & & 38.88 & 12.74 & 23.22 & 6.73\\ \hline
Positive Extrapolate & 43.50 & 14.84 & 35.08 & 9.49 & & 39.22 & 12.73 & 23.44& 6.70\\ \hline
Neg\&Pos Extrapolate & \textbf{43.85} & 15.35 & 35.30 & 9.47 & & 39.04 & \textbf{12.95} & 23.44 & 6.81\\ \hline
Interpolate \& Extrapolate & 42.89 & 14.97 & \textbf{35.36} & 9.51 & & \textbf{39.42} & 12.38 & 23.44 & 6.77\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Interpolate and extrapolate of hallucinated text. }
\end{table}
\end{document}

cs

"CIZSL++: Creativity Inspired Generative Zero-Shot Learning" by Mohamed Elhoseiny, Kai Yi, and Mohamed Elfeki, arXiv:2101.00173v2, 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}
 \hline
 Algorithm & Global Acc & Local Acc \\
 \hline
 FedAvg & 20.77 & 66.64 \\
 \hline
 FedProx & 18.70 & 75.60 \\
 \hline
 FedPAQ & 18.56 & 76.57 \\
 \hline
 Fed-SimCLR & 39.12 & 77.43 \\
 \hline
 Fed-SimSiam & 36.44 & 75.21 \\
 \hline
 \textbf{Fed-QSSL} & \textbf{62.26} & \textbf{83.03} \\
 \hline \hline
 Fed-SimCLR (Full) & 40.76 & 80.56 \\
 \hline
 \textbf{Fed-QSSL} (Full) & \textbf{72.26} & \textbf{90.01} \\
 \hline
\end{tabular}
\caption{Experiments on CIFAR-10 with model bitwidth configuration $50\% 6$-bit and $50\% 12$-bit.}
\end{table}
\end{document}

eess

"Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity" by Yiyue Chen, Haris Vikalo, and Chianing Wang, arXiv:2312.13380v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution" by Guillermo Navas-Palencia, arXiv:2502.16015v1, 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{ Summary of notations.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
 Notation& Meaning \\
\hline
 $\mathcal{G}(\mathcal{V},\mathcal{E})$& Graph (or Network), vertex set $\mathcal{V}$, edge set $\mathcal{E}$ \\
\hline
$\mathcal{T}$& Tree \\
\hline
 $\mathcal{P}_{uv}$& A path connecting vertex $u$ to vertex $v$\\
\hline
$e_{uv}$(or $uv$)& An edge connecting vertices $u$ and $v$ \\
\hline
 $d_{uv}$& Distance between vertices $u$ and $v$\\
\hline
$D$& Diameter of network \\
\hline
 $d_{f}$& Fractal dimension of fractal network\\
\hline
$r$& Pearson correlation coefficient\\
\hline
$\mathcal{N}_{k}$& The number of vertices with degree $k$\\
\hline
$\Omega_{u}$& The neighboring set $\Omega_{u}$ of vertex $u$ in graph\\
\hline
$A_{v}(t)$& Set of new vertices inserted on all the existing edges incident with vertex $v$ in $\mathcal{T}_{t}$ \\
\hline
$B_{v}(t)$& Set of new vertices as leaves directly connected to vertex $v$ in tree $\mathcal{T}_{t}$\\
\hline
$k_{v}(t)$& Degree of vertex $v$ in tree $\mathcal{T}_{t}$\\
\hline
$P(k)$& Degree distribution of network\\
\hline
$P_{cum}(k)$& Cumulative degree distribution of network \\
\hline
$\mathcal{H}_{u\rightarrow v}$& Hitting time from vertex $u$ to $v$ for random walks on network\\
\hline
$\langle\mathcal{H}_{\mathcal{G}}\rangle$& Mean hitting time for random walks on network $\mathcal{G}(\mathcal{V},\mathcal{E})$\\
\hline
$\langle\mathcal{W}_{\mathcal{G}}\rangle$& Average shortest path length of network $\mathcal{G}(\mathcal{V},\mathcal{E})$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Scale-free tree network with an ultra-large diameter" by Fei Ma and Ping Wang, arXiv:2101.02320v2, 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}{l|c|cccc}
\toprule
& Main & \multicolumn{4}{c}{Alternative Specifications} \\
\midrule
Post Service & 0.137*** & 0.365*** & 0.367*** & 0.366*** & 0.300*** \\
& (0.0103) & (0.0102) & (0.0102) & (0.0102) & (0.105) \\
Post Bill & -0.074*** & -0.066*** & -0.069*** & -0.067*** & -0.062*** \\
& (0.0099) & (0.0101) & (0.0100) & (0.0100) & (0.0010) \\
\midrule
$\overline{\text{spend}_{it}}$ & \$93.82 & \$93.82 & \$93.82 & \$93.82 & \$93.82 \\
Household FEs & X & X & X & X & X \\
Year FEs & X & & X & X & X \\
Week of Year FEs & X & & & X & X \\
Provider FEs & X & & & & X\\
Event Type FEs & X & & & & \\
Observations & 126,747,805 & 126,747,805 & 126,747,805 & 126,747,805 & 126,747,805 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{ Robustness of Effects to Expanded Services: Injuries and Appendectomies}
\end{table}
\end{document}

q-fin

"Medical Bill Shock and Imperfect Moral Hazard" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, 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{Components of $\phi_1$ in a three-feature model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
				\hline
				$S$ & $\omega_{S}$ & $\mathbb{E}_{y,x_1,\mathbf{x}^{S}}\mathbb{E}_{\mathbf{x}^{\overline{S}}}\left(
				G\left(y;\mathbf{x};\delta_0\right)\right)- \mathbb{E}_{y,\mathbf{x}^{S}}\mathbb{E}_{x_1,\mathbf{x}^{\overline{S}}}\left(
				G\left(y;\mathbf{x};\delta_0\right)\right)$ \\
				\hline
				$\left\{\emptyset\right\}$ & $1/3$ &
				$\mathbb{E}_{y,x_1}\mathbb{E}_{x_2,x_3}\left(
				G\left(y;\mathbf{x};\delta_0\right)\right)-\mathbb{E}_{y}\mathbb{E}_{x_1,x_2,x_3}\left(
				G\left(y;\mathbf{x};\delta_0\right)\right)$\\
				$\left\{x_{2}\right\}$ & $1/6$ &
				$\mathbb{E}_{y,x_1,x_2}\mathbb{E}_{x_3}\left(
				G\left(y;\mathbf{x};\delta_0\right)\right)-\mathbb{E}_{y,x_2}\mathbb{E}_{x_1,x_3}\left(
				G\left(y;\mathbf{x};\delta_0\right)\right)$ \\
				$\left\{x_{3}\right\}$ & $1/6$ & $\mathbb{E}_{y,x_1,x_3}\mathbb{E}_{x_2}\left(
				G\left(y;\mathbf{x};\delta_0\right)\right)-\mathbb{E}_{y,x_3}\mathbb{E}_{x_1,x_2}\left(
				G\left(y;\mathbf{x};\delta_0\right)\right)$ \\
				$\left\{x_{2},x_{3}\right\}$ & $1/3$ & $\mathbb{E}_{y,x_1,x_2,x_3}\left(
				G\left(y;\mathbf{x};\delta_0\right)\right)-\mathbb{E}_{y,x_2,x_3}\mathbb{E}_{x_1}\left(
				G\left(y;\mathbf{x};\delta_0\right)\right)$\\
				\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Hué Sullivan
Hurlin Christophe
Pérignon Christophe
Saurin Sébastien

"Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring" by Hué Sullivan, Hurlin Christophe, Pérignon Christophe, and Saurin Sébastien, arXiv:2212.05866v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \rowcolor[HTML]{b1457c} \textcolor{white}{Algorithm} & \textcolor{white}{type} & \textcolor{white}{$\epsilon$} & \textcolor{white}{$T$} \\
 {Non-smooth} & - & 0.00 & $18.08 \pm 0.85$ \\
 \hline
 {Smooth} & - & 0.00 & $18.15 \pm 0.75$ \\
 \hline \hline
 \multirow{2}{*}{Non-smooth} & \multirow{2}{*}{random} & 0.025& $18.22 \pm 1.28$\\
 & & 0.050& $18.68 \pm 1.97$\\
 \hline
 \multirow{2}{*}{Smooth} & \multirow{2}{*}{random}& 0.025 & $18.17 \pm 0.84$\\
 & & 0.050 & $18.33 \pm 2.23$\\
 \hline \hline
 \multirow{2}{*}{Non-smooth} & \multirow{2}{*}{adv}& 0.025 & $19.33 \pm 1.76$ \\
 & & 0.050& $19.76 \pm 2.85$\\
 \hline
 \multirow{2}{*}{Smooth} & \multirow{2}{*}{adv}& 0.025 & $18.51 \pm 0.65$ \\
 & & 0.050 & $18.84 \pm 1.12$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of smooth and non-smooth policies under with no (-), random, or adversarial (Adv) noise at noise level $\epsilon$. $T$ is the time required to complete the task.}
\end{table}
\end{document}

eess

Shili Wu
Yancheng Zhu
Aniruddha Datta
Sean B. Andersson

"Multi-agent Robust and Optimal Policy Learning for Data Harvesting" by Shili Wu, Yancheng Zhu, Aniruddha Datta, and Sean B. Andersson, arXiv:2508.16490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
	\hline
	& Sample Mean & Sample Variance & Sharp Ratio \\
	\hline
	Monthly Rebalancing, 5 years, $d = 8$ & 8.07335 & 1.47720 & 6.60138 \\
	\hline
	Monthly Rebalancing, 1 year, $d = 1.4$ & 1.40067 & 0.06056 & 5.48819 \\
	\hline
	Daily Rebalancing, half year, $d = 1.2$& 1.19658 & 0.03186 & 6.42324 \\
	\hline
	Daily Rebalancing, 1 year, $d = 1.4$ & 1.39368 & 0.04920 & 6.05735 \\
	\hline
\end{tabular}
\end{adjustbox}
\caption{\small Sample Mean, Sample Variance and Sample Sharp Ratio of terminal wealth in different investing \\ horizon and expected return}
\end{table}
\end{document}

stat

"Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application" by Lucky Li, arXiv:2412.05906v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sayar Karmakar
Stefan Richter
Wei Biao Wu

"Simultaneous inference for time-varying models" by Sayar Karmakar, Stefan Richter, and Wei Biao Wu, arXiv:2011.13157v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
 \toprule
 LMM & \textit{Naive} & \textit{+\textbf{TAMA}} \\
 \midrule
 GPT-4o & 41.8 & 80.2 (\textbf{+38.4})\\
 GPT-4o-mini & 11.8 & 51.1 (\textbf{+39.3})\\
 Gemini-1.5-pro & 25.4 & 87.8 (\textbf{+62.4})\\
 Gemini-1.5-flash& 17.9 & 36.4 (\textbf{+18.5})\\
 qwen-vl-max-0809& 61.7 & 80.5 (\textbf{+18.8})\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jiaxin Zhuang
Leon Yan
Zhenwei Zhang
Ruiqi Wang
Jiawei Zhang
Yuantao Gu

"See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers" by Jiaxin Zhuang, Leon Yan, Zhenwei Zhang, Ruiqi Wang, Jiawei Zhang, and Yuantao Gu, arXiv:2411.02465v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Efficient and Accurate Calibration to FX Market Skew with Fully Parameterized Local Volatility Model" by Dongli Wu, Bufan Zhang, and Xiao Lin, arXiv:2211.14431v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|cccc}
 & \multicolumn{2}{c}{Construction} & \multicolumn{2}{c}{Evaluation} \\
 & Tuning & Constants & Mean & Covariance \\
 \hline
 Unstructured GP & $\mathcal{O}(n^3)$ & $\mathcal{O}(n^3)$ & $\mathcal{O}(n)$ & $\mathcal{O}(n^2)$ \\
 Structured GP & $\mathcal{O}(n \log n)$ & $\mathcal{O}(n \log n)$ & $\mathcal{O}(n)$ & $\mathcal{O}(n \log n)$
 \end{tabular}
\end{adjustbox}
\caption{Comparison of construction and evaluation costs between the unstructured and structured Gaussian processes in terms of the number of samples $n$ used to fit the Gaussian process. Construction costs include tuning parameters and computing constants for evaluating the posterior mean and covariance.}
\end{table}
\end{document}

stat

Aleksei G. Sorokin
Aleksandra Pachalieva
Daniel O'Malley
James M. Hyman
Fred J. Hickernell
Nicolas W. Hengartner

"Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media" by Aleksei G. Sorokin, Aleksandra Pachalieva, Daniel O'Malley, James M. Hyman, Fred J. Hickernell, and Nicolas W. Hengartner, arXiv:2310.13765v1, 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}
 \multicolumn{6}{c}{\textbf{ODE Terms:}}\\
Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.}\\
\hline
$A_0$ & \texttt{A0} & $B_{1}$ & \texttt{B1} &$B_{2}$&\texttt{B2}\\
$C_{1}$ & \texttt{C1} & $D_{11}$ &\texttt{D11}&$D_{12}$&\texttt{D12}\\
$C_{2}$ & \texttt{C2} & $D_{21}$ &\texttt{D21}&$D_{22}$&\texttt{D22} \\ \hline \\
 \multicolumn{6}{c}{ \textbf{Discrete Delay Terms:}}\\
Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.}\\
\hline
$A_i$ & \texttt{Ai\{i\}} & $B_{1i}$ & \texttt{B1i\{i\}} &$B_{2i}$&\texttt{B2i\{i\}}\\
$C_{1i}$ & \texttt{C1i\{i\}} & $D_{11i}$ &\texttt{D11i\{i\}}&$D_{12i}$&\texttt{D12i\{i\}}\\
$C_{2i}$ & \texttt{C2i\{i\}} & $D_{21i}$ &\texttt{D21i\{i\}}&$D_{22i}$&\texttt{D22i\{i\}}\\
\hline \\ \multicolumn{6}{c}{\textbf{Distributed Delay Terms: May be functions of \texttt{pvar s}}}\\
Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.} & Eqn.~ & \texttt{DDE.}\\
\hline
$A_{di} $ & \texttt{Adi\{i\}} & $B_{1di}$ & \texttt{B1di\{i\}} &$B_{2di}$&\texttt{B2di\{i\}}\\
$C_{1di} $& \texttt{C1di\{i\}} &$ D_{11di} $&\texttt{D11di\{i\}}&$D_{12di}$&\texttt{D12di\{i\}}\\
$C_{2di}$ & \texttt{C2di\{i\}} & $D_{21di} $&\texttt{D21di\{i\}}&$D_{22di}$&\texttt{D22di\{i\}}\\
\end{tabular}
\end{adjustbox}
\caption{ Equivalent names of Matlab elements of the \texttt{DDE} structure terms for terms in Eqn.~. For example, to set term \texttt{XX} to \texttt{YY}, we use \texttt{DDE.XX=YY}. In addition, the delay $\tau_i$ is specified using the vector element \texttt{DDE.tau(i)} so that if $\tau_1=1, \tau_2=2, \tau_3=3$, then \texttt{DDE.tau=[1 2 3]}. }
\end{table}
\end{document}

math

PIETOOLS 2024: User Manual

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

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Augmented dataset hyperparameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
\toprule
\textbf{Hyperparameter} & \textbf{mPCG Model} \\
\hline
\midrule
Learning rate & 1e-5\\
Weight decay & 6.1148e-05\\
Momentum & 0.17562 \\
Gamma & 0.02444 \\
Step size & 4\\
Batch size & 32\\
Number of hidden layers & 3\\
Hidden layer size & 512\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Milan Marocchi
Matthew Fynn
Kayapanda Mandana
Yue Rong

"Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals" by Milan Marocchi, Matthew Fynn, Kayapanda Mandana, and Yue Rong, arXiv:2509.11606v1, 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}{*}{Sampling Method} & \multirow{2}{*}{Reweighting} & \multicolumn{2}{c}{Ten Class}\tabularnewline
\cline{3-5} \cline{4-5} \cline{5-5}
 & & 300&1000 & 2000\tabularnewline
\hline
Random with $Y$ & Equal & 58.35 (0.39)&71.79 (0.54) &78.24 (0.41)\tabularnewline
\hline
Uncertainty with $Y$(Clip) & Equal& 58.28 (0.73)&72.19 (0.36) &	78.96 (0.24) \tabularnewline
\hline
Uncertainty with y(Clip) & Inverse Uncertainty (Clip) &60.88 (0.39) & 73.24 (0.6) &	79.49 (0.06) \tabularnewline
\hline
Uncertainty without y(Clip) & Equal & 58.13 (1.02)& 72.69 (0.57) &78.94 (0.21)	\tabularnewline
\hline
Uncertainty without y(Clip) & Inverse Uncertainty (Clip) &58.88 (0.53) &72.21 (0.31) & 78.96 (0.28) \tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\caption{ This table illustrates the performance of different sampling methods on ten-class classification tasks for ResNet20 on CIFAR. 1000/2000/5000 means 1000/2000/5000 samples for each class. We run each method 5 times and report the mean and std for them. Here we use dropout to estimate the uncertainty. (Figure dropout VS 10 models, bootstrap)}
\end{table}
\end{document}

stat

Yong Lin
Chen Liu
Chenlu Ye
Qing Lian
Yuan Yao
Tong Zhang

"Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning" by Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, and Tong Zhang, arXiv:2309.02476v1, 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{Experiment Data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
Experiment & Task's Euclidean Distance (m) & Time (sec) & Exploit Nodes\\
\hline
{Obstacle avoidance}
& 7 & 22.2319 & 13000 \\
\hline
{Long Distance}
& 45.44 & 98.6619 & 91000 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Wenjian Hao
Rongyao Wang
Alexander Krolicki
Yiqiang Han

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|cc|cc}
\toprule
\textbf{Model} & \textbf{Linear} & \multicolumn{2}{c|}{\textbf{Larger noisy set}} & \multicolumn{2}{c}{\textbf{Small clean set}} \\
\midrule
(weights in M) & - & random* & p-t & random & p-t \\
\midrule
ResNet-18 (11) & \textbf{74.3} & 65.4 & \textbf{78.2} & 56.5 & \textbf{77.9} \\
VGG-like (0.3) & 70.0 & 70.6 & \textbf{72.8} & 61.1 & \textbf{72.3} \\
CRNN (1) & 64.4 & 72.0 & \textbf{74.2} & 58.7 & \textbf{69.1} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Eduardo Fonseca
Diego Ortego
Kevin McGuinness
Noel E. O'Connor
Xavier Serra

"Unsupervised Contrastive Learning of Sound Event Representations" by Eduardo Fonseca, Diego Ortego, Kevin McGuinness, Noel E. O'Connor, and Xavier Serra, arXiv:2011.07616v1, 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{True regression parameters used for data generation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
		\toprule
		& $q_{1}$ & $q_{2}$ & $q_{3}$ & $q_{4}$ \\
		\midrule
		$p_{0}$ (Intercept) & 0.000 & 0.433 & 0.433 & 0.000 \\
		$p_{1}$ ($T_{ji}$) & 0.000 & 0.000 & 0.000 & 0.000 \\
		$p_{2}$ ($w_{ji}$) & 1.027 & 0.601 & 0.427 & 0.000 \\
		$p_{3}$ ($w_{ji}T_{ji}$)& -2.055 & -1.201 & -0.854 & 0.000 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Xynthia Kavelaars
Joris Mulder
Maurits Kaptein

"Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity" by Xynthia Kavelaars, Joris Mulder, and Maurits Kaptein, arXiv:2212.03473v4, 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{A summary of hyperparameters by datasets. Logistic denotes a logistic model and NN denotes a neural network one hidden layer.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccccc}
 \toprule
 \multirow{2}{*}{Dataset} & \multirow{2}{*}{Seed data $n_{\mathrm{s}}$} & \multicolumn{4}{c}{ML predictor $f$} & \\ \cmidrule{3-7}
 & & Model & \# of hidden nodes & Epoch & Learning rate & Retrain period \\
 \midrule
 \texttt{Insurance} & 100 & Logistic & - & 10 & $5\times 10^{-3}$ & 50 \\
 \texttt{Adult} & 100 & Logistic & - & 10 & $10^{-2}$ & 50 \\
 \texttt{Postures} & 200 & Logistic & - & 10 & $3\times10^{-2}$ & 50 \\
 \texttt{Skin-nonskin} & 50 & Logistic & - & 10 & $3\times10^{-2}$ & 50 \\
 \texttt{Fashion-MNIST} & 50 & NN & 400 & 30 & $10^{-4}$ & 150 \\
 \texttt{MNIST} & 50 & NN & 400 & 30 & $10^{-4}$ & 150 \\
 \texttt{CIFAR10} & 100 & NN & 400 & 30 & $10^{-4}$ & 150 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Competition over data: how does data purchase affect users?" by Yongchan Kwon, Antonio Ginart, and James Zou, arXiv:2201.10774v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Fanwei Kong
Nathan Wilson
Shawn C. Shadden

"A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction" by Fanwei Kong, Nathan Wilson, and Shawn C. Shadden, arXiv:2102.07899v2, 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
Level & Pixel Erasure Rate & Rotation $^{\circ}$ & Image Scale & Translations\\
\hline
Light & $5 \%$ & $[-5,5]$ & $[0.9,1.1]$ & $(0\%,0\%) $ \\
\hline
Medium & $10 \%$& $[-7.5,7.5]$ & $[0.8,1.2]$ & $(0\%,0\%) $ \\
\hline
Heavy & $20 \%$ & $[-10,10]$ & $[0.6,1.4]$ & $(20\%,20\%)$ \\
\hline
Ultimate & $40 \%$& $[-20,20]$ & $[0.5,1.5]$ & $(40\%,40\%)$ \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Milad Sefidgaran
Abdellatif Zaidi
Piotr Krasnowski

"Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior" by Milad Sefidgaran, Abdellatif Zaidi, and Piotr Krasnowski, arXiv:2504.18455v1, 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{Simulated RBNS reserves (in M~CAD) using the activation patterns with development periods of 6 months (columns 3 and 4) and 1 year (columns 5 and 6).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrr}
\toprule
\multirow{2}{*}{Coverage} & \multirow{2}{*}{True reserves} & \multicolumn{2}{c}{Act. Patterns (6 mo.)} & \multicolumn{2}{c}{Act. Patterns (1 y.)} \\
\cline{3-6}
 & & Mean & $\text{VaR}_{0.95}$ & Mean & $\text{VaR}_{0.95}$ \\
\toprule
Accident Benefits & $>$158.90 & 146.61 & 170.46 & 162.15 & 196.64\\
Bodily Injury & $>$288.17 & 283.27 & 295.02 & 300.02 & 312.58 \\
Vehicle Damage & $>$73.63 & 70.77 & 76.43 & 70.80 & 72.95 \\
Loss of Use & $>$4.20 & 4.17 & 4.24 & 3.41 & 4.01 \\
\midrule
Global reserve & $>$524.91 & 504.82 & 546.15 & 536.86 & 574.58 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Marie Michaelides
Mathieu Pigeon
Hélène Cossette

"Individual Claims Reserving using Activation Patterns" by Marie Michaelides, Mathieu Pigeon, and Hélène Cossette, arXiv:2208.08430v3, 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{Effect of distillation loss weights on performance.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc|cc}
\toprule
$\lambda_{task}$ & $\lambda_{CE}$ & $\lambda_{KL}$ & ACC (\%) & F1 (\%) \\
\midrule
1.0 & 1.0 & 1.0 & 87.35 & 87.38 \\
1.0 & 0.0 & 1.0 & 86.36 & 86.53 \\
1.0 & 1.0 & 0.0 & 87.27 & 87.26 \\
1.5 & 1.0 & 0.0 & 87.59 & 87.58 \\
\textbf{1.5} & \textbf{1.0} & \textbf{0.3} & \textbf{87.99} & \textbf{87.96} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Postman headers }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
 \hline
 Key & Value \\ \hline
 Content-Type & application/json \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Second Hand Price Prediction for Tesla Vehicles" by Sayed Erfan Arefin, arXiv:2101.03788v1, 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{(\( \ell_2 \)) norm Localization Error (Meters)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccc}
 \toprule
 Setup & Method & Mean & Median & 90th \% \\
 \midrule
 2 Arrays (MLE) & GCC-PHAT & 3.62 & 1.74 & 8.48 \\
 & \textbf{GCC+} & \textbf{1.83} & \textbf{1.25} & \textbf{4.13} \\
 3 Arrays (IRLS) & GCC-PHAT & 2.84 & 1.45 & 8.40 \\
 & \textbf{GCC+} & \textbf{1.58} & \textbf{1.25} & \textbf{3.32} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Localization using Angle-of-Arrival Triangulation" by Amod K. Agrawal, arXiv:2508.16908v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\textbf{Years $y$} & 0 & 1 & 2 & 3 & 4 & 5\\
\hline
\hline
\textbf{Milestone year $m$} & \textcolor{blue}{0} & - & \textcolor{blue}{2} & - & - & \textcolor{blue}{5}\\
\hline
\textbf{Milestone year weight $W_{m}^{I}$} & \textcolor{blue}{2} & - & \textcolor{blue}{3} & - & - & \textcolor{blue}{1} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Milestone years}
\end{table}
\end{document}

math

"Discounting Approaches in Multi-Year Investment Modelling for Energy Systems" by Ni Wang and Diego A. Tejada-Arango, arXiv:2504.21709v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

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


q-fin

"Beyond the heat: The mental health toll of temperature and humidity in India" by Manuela Fritz, arXiv:2503.08761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PI sets for correlation.}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline
 & $\textup{LL}_{\textup{ridge}}$ & $\textup{LL}_{\textup{knn}}$ & $\eta = 0$ & $\eta = 10$ & $\eta = 20$ & $\eta = 30$ & $\eta = 40$ & $\eta = 50$ \\
 \hline
 \textup{upper bound} & 1.005 & 1.187 & 0.997 & 0.932 & 0.911 & 0.900 & 0.895 & 0.887\\
 \hline
 \textup{lower bound} & -0.707 & -1.036 & -0.997 & -0.646 & -0.623 & -0.607 & -0.597 & -0.592\\
 \hline
 \textup{PI interval length} & 1.712 & 2.223 & 1.994 & 1.578 & 1.534 & 1.507 & 1.492 & 1.479\\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Sirui Lin
Zijun Gao
Jose Blanchet
Peter Glynn

"Tightening Causal Bounds via Covariate-Aware Optimal Transport" by Sirui Lin, Zijun Gao, Jose Blanchet, and Peter Glynn, arXiv:2502.01164v1, 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{Ablation study on class token position and Mamba type. Mid-position tokens and bidirectional Mamba configurations (BiMamba-Bi-Bi) provide the best accuracy, highlighting the importance of token placement and directional flow.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \textbf{Ablation Objective} & \textbf{Type} & \textbf{V1-12} \\
 \midrule
 \multirow{3}{*}{class token position}
 & Mid & \cellcolor{blue!20}{\textbf{98.01}} \\
 & Head & 97.75 \\
 & End & 97.92 \\
 \midrule
 \multirow{3}{*}{mamba type}
 & BiMamba-Bi-Bi & \cellcolor{blue!20}{\textbf{98.01}} \\
 & BiMamba-Fo-Bi & 97.69\\
 & Mamba-Fo-Fo & 78.29\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Keyword Mamba: Spoken Keyword Spotting with State Space Models" by Hanyu Ding, Wenlong Dong, and Qirong Mao, arXiv:2508.07363v1, 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{ The IKLD between original and synthetic data, averaged over 250 Monte Carlo runs and standardised between 0 and 1. Higher values indicate similar datasets in terms of underlying distribution.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 \textbf{Generator} & \textbf{Debiased} & $n=50$ & $n=160$ & $n=500$ & $n=1600$ & $n=5000$ \\
 \midrule
 \textbf{CTGAN (SDV)} & \textbf{no} & 0.849 & 0.924 & 0.939 & 0.963 & 0.980 \\
 \textbf{CTGAN (SDV)} & \textbf{yes} & 0.860 & 0.935 & 0.957 & 0.966 & 0.981 \\
 \textbf{CTGAN (Synthcity)} & \textbf{no} & 0.838 & 0.894 & 0.867 & 0.890 & 0.929 \\
 \textbf{CTGAN (Synthcity)} & \textbf{yes} & 0.849 & 0.906 & 0.885 & 0.897 & 0.933 \\
 \textbf{TVAE (SDV)} & \textbf{no} & 0.704 & 0.819 & 0.908 & 0.985 & 0.980 \\
 \textbf{TVAE (SDV)} & \textbf{yes} & 0.734 & 0.832 & 0.913 & 0.987 & 0.983 \\
 \textbf{TVAE (Synthcity)} & \textbf{no} & 0.791 & 0.830 & 0.887 & 0.929 & 0.977 \\
 \textbf{TVAE (SDV)} & \textbf{yes} & 0.813 & 0.860 & 0.913 & 0.943 & 0.980 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Alexander Decruyenaere
Heidelinde Dehaene
Paloma Rabaey
Christiaan Polet
Johan Decruyenaere
Thomas Demeester
Stijn Vansteelandt

"Debiasing Synthetic Data Generated by Deep Generative Models" by Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Thomas Demeester, and Stijn Vansteelandt, arXiv:2411.04216v2, 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}
(n,m)&$ (1000,10)$& $(3000,20)$ & $(5000,20)$\\ \hline
 BM& 0.94 & 1.00 & 1.00\\
 M1& 0.22 & 0.02 & 0.00 \\
 M2& 1.00 & 1.00 & 1.00\\
 M3& 1.00 & 1.00 & 0.05\\
 M4& 1.00 & 1.00 & 1.00\\
\end{tabular}
\end{adjustbox}
\caption{\scriptsize Empirical Bayes (MMLE) squared exponential Gaussian process prior. Proportion of runs when the $L_2$-credible ball contains $f_0$.}
\end{table}
\end{document}

stat

Botond Szabo
Amine Hadji
Aad van der Vaart

"Adaptation using spatially distributed Gaussian Processes" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
Config & Setting \\
\hline
\hline
Embedding size & 256 \\
Number of layers & 3 \\
Number of heads & 16 \\
MLPs & False \\
LayerNorms & False \\
\hline
Input dim & 5 \\
Sequence length & 20 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Linear attention architecture details.}
\end{table}
\end{document}

math

Jerry Liu
Jessica Grogan
Owen Dugan
Ashish Rao
Simran Arora
Atri Rudra
Christopher Ré

"Towards Learning High-Precision Least Squares Algorithms with Sequence Models" by Jerry Liu, Jessica Grogan, Owen Dugan, Ashish Rao, Simran Arora, Atri Rudra, and Christopher Ré, arXiv:2503.12295v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|r|}
\hline
\textbf{Aggregation} & \textbf{MAPE, \%} \\\hline
stacked & 0.060703 \\\hline
merged & 0.361174\\\hline
\end{tabular}
\end{adjustbox}
\caption{Aggregation method average performance.}
\end{table}
\end{document}

q-fin

"Deep Learning Models Meet Financial Data Modalities" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2504.13521v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|c|ccc|c}
 Dataset & Pool & bps & \multicolumn{3}{c|}{Time Range} & Spacing \\
 & & && Start & End & \\ \hline
 \multirow{2}{*}{ARB5} & \multirow{2}{*}{ARB--USDC} & \multirow{2}{*}{5}
 & Date--time: & Aug 23, 2023 & Nov 26, 2024 & 8.39 h \\
 & &
 & Block Number: &\texttt{124163200} & \texttt{278646400} & \texttt{115200} \\ \hline
 \multirow{2}{*}{ETH5, ETH30} & \multirow{2}{*}{ETH--USDC} & \multirow{2}{*}{5, 30}
 & Date-time: & Aug 1, 2021 & Nov 26, 2024 & 8.26 h \\
 & &
 & Block Number: &\texttt{12940529} & \texttt{21274529} & \texttt{2400} \\
 \end{tabular}
\end{adjustbox}
\caption{Full dataset ranges. Dates are rounded to calendar days (UTC). The Spacing column reports time spacing (hours) on the date row and block step on the block row. ETH5 and ETH30 share the same blocks and times. Spacing in hours is computed as the average hourly time between the clock times at the exact block-number intervals.}
\end{table}
\end{document}

q-fin

"Dynamics of Liquidity Surfaces in Uniswap v3" by Jimmy Risk, Shen-Ning Tung, and Tai-Ho Wang, arXiv:2509.05013v1, 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}{cccc}
 \toprule
 Clip version & CC & BSS & CSS \\
 \midrule
 Inter (low) & $0.78$ & $0.38$ & $0.51$ \\
 Inter (medium) & $0.85$ & $0.49$ & $0.67$ \\
 Inter (high) & $0.89$ & $0.52$ & $0.74$ \\
 Intra () & $0.90$ & $0.48$ & $0.72$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Semantic conservation comparisons for Clip-based compression schemes. Different parametrizations of our inter-item model are compared to their intra version .}
\end{table}
\end{document}

eess

"SMIC: Semantic Multi-Item Compression based on CLIP dictionary" by Tom Bachard and Thomas Maugey, arXiv:2412.05035v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline\hline
 &Min & $q_{.25}$& Median & Mean & $q_{.75}$ & Max \\
\hline
 Relative differences&0.65& 0.86& 1.00& 1.03& 1.15& 1.54
\\
 Absolute differences&-1,485,636& -532,913& 4,910& 20,462& 481,347& 1,459,625\\
 \hline\hline
\end{tabular}
\end{adjustbox}
\caption{Summary statistics for the relative and absolute differences between the predicted and observed reserves in the calibration set. }
\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{\textbf{ IND Generalization}: Top-1 accuracy, ECE and NLL averaged over in-distribution on test samples of the \textbf{ImageNet} dataset over three random seeds. The best score for each metric is shown in \textbf{bold}, and the second-best is \underline{underlined}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
 \toprule
 Method & Top-1 Acc (\%) ($\uparrow$) & ECE ($\downarrow$) &
 NLL ($\downarrow$) \\
 \midrule
 Baseline & 73.8 $\pm$ 0.3 & 0.013 $\pm$ 0.015 & 1.05 $\pm$ 0.01 \\
 SSL-Ensemble & \textbf{75.1 $\pm$ 0.1} & \underline{0.014 $\pm$ 0.000} & \textbf{0.98 $\pm$ 0.01} \\
 Our method & \underline{74.0 $\pm$ 0.0} & \textbf{0.010 $\pm$ 0.000} & \underline{1.03 $\pm$ 0.01} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Amirhossein Vahidi
Lisa Wimmer
Hüseyin Anil Gündüz
Bernd Bischl
Eyke Hüllermeier
Mina Rezaei

"Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning" by Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, and Mina Rezaei, arXiv:2308.14705v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{diagbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 \diagbox{~}{$r_{\delta,t}$}{~~~~~~} & CT & DT \\
 \hline
 non-Contractive & Theorem & Theorem \\
 \hline
 Contractive & Theorem & Theorem \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{}
\end{table}
\end{document}

math

"Safety Verification of Nonlinear Stochastic Systems via Probabilistic Tube" by Zishun Liu, Saber Jafarpour, and Yongxin Chen, arXiv:2503.03328v1, 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
 & Point mass & Mean & $\ell_{Z|_{\{Z<1\}}}$ & $\ell_Z$ & AIC \\
 \midrule
 Empirical density (Blue) & 0.034 & 0.339 & - & - & - \\
 MLE of the standard problem (Green) & 0.040 & 0.380 &26 907 &8 164 & -16 323 \\
 MLE of the extended problem (Red) & 0.034 & 0.362 &30 453 &11 783 & -23 558 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Sine logarithmic linked exposure example: results.}
\end{table}
\end{document}

stat

"Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class" by Selim Gatti and Mario V. Wüthrich, arXiv:2310.11471v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c}
 \toprule
 \multirow{2}{*}{Click Generation Strategy (Training)} & \multicolumn{2}{c}{Inference Strategy (Validation)} \\ \cline{2-3}
 {} & Global & Local Patch-wise \\ \hline
 Non-corrective (10 clicks) & $47.00\%$ & $47.00\%$ \\
 Corrective: $N_\text{max}=10$ (DeepEdit ) & $84.90\%$ & $82.02\%$ \\
 Corrective: $N_\text{max}=10$, $p=50\%$ & $81.07\%$ & $81.23\%$ \\
 Corrective: $N_\text{max}=10$, $\text{Dice}_\text{max}=90\%$ & \textbf{85.34}$\%$ & \textbf{85.50$\%$} \\
 Corrective: $N_\text{max}=10$, $p=50\%$, $\text{Dice}_\text{max}=90\%$ & $82.54\%$ & $82.93\%$ \\
 \bottomrule
 \end{tabular}
\caption{Results (Dice score) for \textsc{SW-FastEdit} when using different click generation strategies during training and various inference strategies during validation. During validation, we always simulate exactly 10 clicks for each sample.}
\end{table}
\end{document}

eess

Matthias Hadlich
Zdravko Marinov
Moon Kim
Enrico Nasca
Jens Kleesiek
Rainer Stiefelhagen

"Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images" by Matthias Hadlich, Zdravko Marinov, Moon Kim, Enrico Nasca, Jens Kleesiek, and Rainer Stiefelhagen, arXiv:2311.14482v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The performance of different network components.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
	Components & NSS & CC & AUC \\ \hline
 S-SPCNN & 0.6301 & 0.4058 & 0.5471 \\ \hline
 S-CNN & 0.5819 & 0.3837 & 0.5173 \\ \hline
 T-SPCNN & 0.782 & 0.4384 & 0.5762 \\ \hline
 T-CNN & 0.617 & 0.4137 & 0.5335 \\ \hline
 ST-SPCNN- & 0.9653 & 0.5587 & 0.6069 \\ \hline
 ST-CNN & 0.8487 & 0.4634 & 0.5589 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jie Li
Ling Han
Cong Zhang
Qiyue Li
Zhi Liu

"Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback" by Jie Li, Ling Han, Cong Zhang, Qiyue Li, and Zhi Liu, arXiv:2201.12525v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Arthur Brack
Daniel Uwe Müller
Anett Hoppe
Ralph Ewerth

"Coreference Resolution in Research Papers from Multiple Domains" by Arthur Brack, Daniel Uwe Müller, Anett Hoppe, and Ralph Ewerth, arXiv:2101.00884v1, 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{Hyperparameters of QMIX and VDN}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llc}
 \toprule
 Method & Name & Value \\
 \midrule
 QMIX \& VDN & learning rate & $5 \times 10^{-4}$\\
 & RMSprop $\alpha$ & 0.99 \\
 & replay buffer size & 5000 episodes \\
 & target network update interval & 200 episodes \\
 & $\gamma$ & 0.99 \\
 & double DQN target & True \\
 & initial $\epsilon$ & 1 \\
 & final $\epsilon$ & 0.05 \\
 & $\epsilon$ anneal period & 50000 steps \\
 & $\epsilon$ anneal rule & linear \\
 \midrule
 QMIX & mixing network hidden layers & 1 \\
 & mixing network hidden layer units & 32 \\
 & mixing network non-linearity & ELU \\
 & hypernetwork hidden layers & 2 \\
 & hypernetwork hidden layer units & 64 \\
 & hypernetwork non-linearity & ReLU \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Anuj Mahajan
Mikayel Samvelyan
Tarun Gupta
Benjamin Ellis
Mingfei Sun
Tim Rocktäschel
Shimon Whiteson

"Generalization in Cooperative Multi-Agent Systems" by Anuj Mahajan, Mikayel Samvelyan, Tarun Gupta, Benjamin Ellis, Mingfei Sun, Tim Rocktäschel, and Shimon Whiteson, arXiv:2202.00104v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{diagbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
\toprule [1.0pt]
\hline
\diagbox{$i$ (trustor)}{$j$ (trustee)} & R & \quad B\\
\midrule [0.5pt]
T & ${1+(g\omega-1)x, (1-\omega)gx}$ & \quad ${1-x, gx}$ \\
N & ${1, 0}$ & \quad ${1, 0}$ \\
\bottomrule[1.0pt]
\end{tabular}
\end{adjustbox}
\caption{The payoff matrix in the trust game. Within each item, the first payoff is for the trustor (row player) and the second is for the trustee (column player).}
\end{table}
\end{document}

stat

Guozhong Zheng
Jiqiang Zhang
Jing Zhang
Weiran Cai
Li Chen

"Decoding trust: A reinforcement learning perspective" by Guozhong Zheng, Jiqiang Zhang, Jing Zhang, Weiran Cai, and Li Chen, arXiv:2309.14598v2, 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}{ll}
 \toprule
 \bfseries Dataset & \bfseries Result\\
 \midrule
 Data1 & 0.12345\\
 Data2 & 0.67890\\
 Data3 & 0.54321\\
 Data4 & 0.09876\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mehak Aggarwal
Nishanth Arun
Sharut Gupta
Ashwin Vaswani
Bryan Chen
Matthew Li
Ken Chang
Jay Patel
Katherine Hoebel
Mishka Gidwani
Jayashree Kalpathy-Cramer
Praveer Singh

"Towards Trainable Saliency Maps in Medical Imaging" by Mehak Aggarwal, Nishanth Arun, Sharut Gupta, Ashwin Vaswani, Bryan Chen, Matthew Li, Ken Chang, Jay Patel, Katherine Hoebel, Mishka Gidwani, Jayashree Kalpathy-Cramer, and Praveer Singh, arXiv:2011.07482v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \toprule
 $\nu$ & P (GMRES) & $R^TR$ (CGNE) & $P^TP$ (CGNE) \\
 \midrule
 $1 \cdot 10^{-2}$ &1435& 2118 & 2117 \\
 $5\cdot 10^{-3}$ & 699& 1073 & 1056 \\
 $1\cdot 10^{-3}$ & 136& 218 & 217\\
 $5\cdot 10^{-4}$ & 69 & 112 &113 \\
 $1\cdot 10^{-4}$ & 16 & 27 &27 \\
 $5\cdot 10^{-5}$ & 2 & 7 &7 \\
 $1\cdot 10^{-5}$ & 5 & 8 &8 \\
 $5\cdot 10^{-6}$ & 4 & 7 &7 \\
 $1\cdot 10^{-6}$ & 3 & 4 &4 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lorenzo Lazzarino
Yuji Nakatsukasa
Umberto Zerbinati

"Preconditioned normal equations for solving discretised partial differential equations" by Lorenzo Lazzarino, Yuji Nakatsukasa, and Umberto Zerbinati, arXiv:2502.17626v2, 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{Memory savings for offload strategies that minimizes communication volume compared to the baseline.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
 FWD-BWD&p16&g16&Update&Memory&Reduction \\
 \hline
 gpu&gpu&gpu&gpu&16M&1x (baseline)\\
 gpu&gpu&cpu&gpu&14M&1.14x\\
 gpu&gpu&gpu&cpu&4M&4x\\
 gpu&gpu&cpu&cpu&4M&8x\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jie Ren
Samyam Rajbhandari
Reza Yazdani Aminabadi
Olatunji Ruwase
Shuangyan Yang
Minjia Zhang
Dong Li
Yuxiong He

"ZeRO-Offload: Democratizing Billion-Scale Model Training" by Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, and Yuxiong He, arXiv:2101.06840v1, 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{Treatment distribution}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
			\hline\hline
			treatment & observations \\
			\hline
			 randomized out of JC & 1698 \\
		 controls (no training) & 200 \\
			academic training & 830 \\
			 vocational training & 843 \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Double machine learning for sample selection models" by Michela Bia, Martin Huber, and Lukáš Lafférs, arXiv:2012.00745v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Development set results with a ratio of the leading characters correctly recognized.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
		\toprule
		Model & Ratio & CER & SER \\
		\midrule
		\multirow{3}{*}[-1pt]{Conv1d resnet 3} & 0 & 54.8 & 49.6 \\
		& 1/3 & 61.9 & 55.3\\
		& 1/2 & 57.3 & 51.4\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Wen-Chin Huang
Chia-Hua Wu
Shang-Bao Luo
Kuan-Yu Chen
Hsin-Min Wang
Tomoki Toda

"Speech Recognition by Simply Fine-tuning BERT" by Wen-Chin Huang, Chia-Hua Wu, Shang-Bao Luo, Kuan-Yu Chen, Hsin-Min Wang, and Tomoki Toda, arXiv:2102.00291v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
\hline
 & \textbf{Corruption Types} \\ \hline
\textbf{Noise} & Gaussian Noise, Impulse noise, Shot noise \\
\textbf{Blur} & Motion blur, Zoom blur, Defocus blur, Glass blur \\
\textbf{Weather} & Snow, Frost, Fog, Brightness \\
\textbf{Digital} & Contrast, Jpeg compression, Pixelate, Elastic transform \\ \hline
\end{tabular}
\caption{Detail of four corruption groups with 15 corruption types}
\end{table}
\end{document}

cs

Yun-Yun Tsai
Fu-Chen Chen
Albert Y. C. Chen
Junfeng Yang
Che-Chun Su
Min Sun
Cheng-Hao Kuo

"GDA: Generalized Diffusion for Robust Test-time Adaptation" by Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen, Junfeng Yang, Che-Chun Su, Min Sun, and Cheng-Hao Kuo, arXiv:2404.00095v2, 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
Old Scheme&New Scheme&Ratio\\
\hline
139.85&121.54&1.151\\
\hline
\end{tabular}
\end{adjustbox}
\caption{\sf Example 3: The CPU times (in seconds) consumed by the studied schemes.}
\end{table}
\end{document}

math

New More Efficient A-WENO Schemes

Shaoshuai Chu
Alexander Kurganov
Ruixiao Xin

"New More Efficient A-WENO Schemes" by Shaoshuai Chu, Alexander Kurganov, and Ruixiao Xin, arXiv:2503.10221v2, 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}{lcccccc}
 \toprule
 Filter & CatBoost & DeepLOB & CNN+LSTM & CNN+XGB & XGBoost & Logistic Reg. \\
 \midrule
 \multicolumn{7}{c}{\textbf{Next 100 ms, 5‐level LOB}} \\
 Raw & 0.3922 & 0.4080 & 0.3987 & 0.4029 & 0.3939 & 0.3777 \\
 Kalman & 0.3651 & 0.3838 & 0.3594 & 0.3609 & 0.3875 & 0.3732 \\
 Savitzky–Golay & 0.4135 & \textbf{0.4281} & 0.4207 & 0.4179 & 0.4173 & 0.4101 \\
 \midrule
 \multicolumn{7}{c}{\textbf{Next 500 ms, 40‐level LOB}} \\
 Raw & 0.4066 & 0.4286 & 0.4160 & 0.4159 & 0.4457 & 0.4356 \\
 Kalman & 0.3216 & 0.3044 & 0.3255 & 0.3211 & 0.4114 & 0.2993 \\
 Savitzky–Golay & 0.5189 & 0.5237 & 0.5252 & 0.5282 & 0.5393 & \textbf{0.5434} \\
 \midrule
 \multicolumn{7}{c}{\textbf{Next 1000 ms, 40‐level LOB}} \\
 Raw & 0.4046 & 0.4099 & 0.4142 & 0.4118 & 0.4357 & 0.4901 \\
 Kalman & 0.3191 & 0.3618 & 0.3603 & 0.3397 & 0.4084 & 0.3421 \\
 Savitzky–Golay & 0.4611 & 0.4762 & 0.4936 & 0.4604 & 0.4999 & \textbf{0.5382} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Ternary Classification Across Horizons and Filters (\(T=1\))}
\end{table}
\end{document}

q-fin

"Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer" by Haochuan Wang, arXiv:2506.05764v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llrrr}
\toprule
& & \multicolumn{3}{c}{Token corruption [\%]} \\
\cmidrule{3-5}
& & $10$ & $15$ & $20$ \\
\midrule
\multirow{4}{*}{\rotatebox[origin=c]{0}{BERT}}
& Base & $.721$ & $.703$ & $.671$ \\
& Jacobian & $.729$ & $.710$ & $.680$ \\
& H\"{o}lder & $.734$ & $.717$ & $.684$ \\
& \cellcolor{lightgray} \textsc{JacHess} & $\mathbf{.751}$ & $\mathbf{.742}$ & $\mathbf{.729}$ \\
\midrule
\multirow{4}{*}{\rotatebox[origin=c]{0}{OPT-125m}}
& Base & $.681$ & $.654$ & $.613$ \\
& Jacobian & $.694$ & $.667$ & $.642$ \\
& H\"{o}lder & $.693$ & $.679$ & $.648$ \\
& \cellcolor{lightgray} \textsc{JacHess} & $\mathbf{.731}$ & $\mathbf{.723}$ & $\mathbf{.704}$ \\
\midrule
\multirow{4}{*}{\rotatebox[origin=c]{0}{OPT-1.3b}}
& Base & $.814$ & $.802$ & $.743$ \\
& Jacobian & $.820$ & $.807$ & $.759$ \\
& H\"{o}lder & $.823$ & $.814$ & $.770$ \\
& \cellcolor{lightgray} \textsc{JacHess} & $\mathbf{.840}$ & $\mathbf{.831}$ & $\mathbf{.802}$ \\
\midrule
\multirow{4}{*}{\rotatebox[origin=c]{0}{OPT-6.7b*}}
& Base & $.831$ & $.809$ & $.787$ \\
& Jacobian & $.834$ & $.819$ & $.799$ \\
& H\"{o}lder & $.834$ & $.817$ & $.796$ \\
& \cellcolor{lightgray} \textsc{JacHess} & $\mathbf{.851}$ & $\mathbf{.839}$ & $\mathbf{.813}$ \\
\midrule
\multirow{4}{*}{\rotatebox[origin=c]{0}{LLaMA-2-7b*}}
& Base & $.831$ & $.813$ & $.786$ \\
& Jacobian & $.837$ & $.822$ & $.804$ \\
& H\"{o}lder & $.832$ & $.811$ & $.796$ \\
& \cellcolor{lightgray} \textsc{JacHess} & $\mathbf{.869}$ & $\mathbf{.853}$ & $\mathbf{.829}$ \\
\bottomrule
\end{tabular}
\caption{Average predictive accuracy with token corruption. We adjust the percentage of token corruption to $10$\%, $15$\%, and $20$\%. For each dataset, we conduct experiments five times using different seeds and report the average score on the GLUE benchmark. Best scores within the same model and token corruption setup are shown in \textbf{bold}.}
\end{table}
\end{document}

cs

"From Robustness to Improved Generalization and Calibration in Pre-trained Language Models" by Josip Jukić and Jan Šnajder, arXiv:2404.00758v1, 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{Average prevalence rates (unit: \%).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
			\hline
			Dataset $\backslash$ Number of seeds & 1 & 2 & 3 & 4 & 5 \\ \hline
			small-world & 1.26 & 2.51 & 3.75 & 4.95 & 6.13 \\ \hline
			email-Eu-core & 2.63 & 5.15 & 7.42 & 9.62 & 11.68 \\ \hline
			political blogs & 1.91 & 3.77 & 5.56 & 7.21 & 8.78 \\ \hline
			ego-Facebook & 1.26 & 2.44 & 3.59 & 4.72 & 5.79 \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yi-Jheng Lin
Che-Hao Yu
Tzu-Hsuan Liu
Cheng-Shang Chang
Wen-Tsuen Chen

"Positively Correlated Samples Save Pooled Testing Costs" by Yi-Jheng Lin, Che-Hao Yu, Tzu-Hsuan Liu, Cheng-Shang Chang, and Wen-Tsuen Chen, arXiv:2011.09794v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

CRIX an index for cryptocurrencies

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary Statistics: Inflation Measures}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \hline \hline
 & \multicolumn{4}{c}{Inflation Measures} \\
 & \multirow{2}{*}{Headline} & \multirow{2}{*}{Core} & \multirow{2}{*}{Median} & Trimmed \\
 & & & & Mean \\
 \cline{2-5}
 & \multicolumn{4}{c}{Full Sample (748 months)}\\
 \cline{2-5}
 Mean & 3.27 & 3.21 & 3.33 & 2.96 \\
 Std. Dev. & 2.42 & 2.13 & 2.01 & 1.86 \\
 Coeff. Var. & 0.74 & 0.66 & 0.60 & 0.63 \\
 \cline{2-5}
 & \multicolumn{4}{c}{$\pi<2.5\%$ (373 months)}\\
 \cline{2-5}
 Mean & 1.55 & 1.73 & 2.01 & 1.72 \\
 Std. Dev. & 0.67 & 0.53 & 0.95 & 0.70 \\
 Coeff. Var. & 0.43 & 0.31 & 0.47 & 0.41 \\
 \cline{2-5}
 & \multicolumn{4}{c}{$2.5\%\leq\pi<5\%$ (252 months)}\\
 \cline{2-5}
 Mean & 3.61 & 3.51 & 3.55 & 3.17 \\
 Std. Dev. & 0.71 & 1.06 & 0.84 & 0.77 \\
 Coeff. Var. & 0.20 & 0.30 & 0.24 & 0.24 \\
 \cline{2-5}
 & \multicolumn{4}{c}{$5\%\leq\pi$ (123 months)}\\
 \cline{2-5}
 Mean & 7.76 & 7.09 & 6.85 & 6.31 \\
 Std. Dev. & 2.00 & 1.59 & 1.60 & 1.57 \\
 Coeff. Var. & 0.26 & 0.22 & 0.23 & 0.25 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Sergio Ocampo
Raphael Schoenle
Dominic A. Smith

"Extending the Range of Robust PCE Inflation Measures" by Sergio Ocampo, Raphael Schoenle, and Dominic A. Smith, arXiv:2207.12494v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Survey calibration for causal inference: a simple method to balance covariate distributions" by Maciej Beręsewicz, arXiv:2310.11969v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test accuracy (\%) of on various neural networks trained on CIFAR10.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
 \textbf{Model} & $\l=0.0$ & $\l=0.5$ & $\l=1.0$ & $\l=1/t$ & $\l=1-1/t$ \\
 \hline
 ResNet-18 & $96.13_{\pm 0.05}$ & $96.16_{\pm 0.03}$ & $\boldsymbol{96.33}_{\pm 0.03}$ & $96.20_{\pm 0.05}$ & $96.22_{\pm 0.09}$ \\
 WideResNet-28-10 & $\boldsymbol{97.26}_{\pm 0.44}$ & $96.98_{\pm 0.08}$ & $97.05_{\pm 0.05}$ & $96.73_{\pm 0.04}$ & $96.63_{\pm 0.35}$ \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Sharpness-Aware Minimization: General Analysis and Improved Rates" by Dimitris Oikonomou and Nicolas Loizou, arXiv:2503.02225v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results showing the speaker diarization performance using baseline (B) and proposed methods (M1 and M2) on \textbf{development set} of third DIHARD challenge. M1: domain-dependent threshold, M2: domain-dependent threshold and domain-dependent parameter for PCA.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \multirow{2}{*}{Method} & \multicolumn{2}{|c|}{Full} & \multicolumn{2}{|c|}{Core}\\
 \cline{2-5}
 & DER & JER & DER & JER \\
 \hline
 B	& 19.59	&43.01	&20.17&	47.28\\
 M1	& 17.97	&40.33	&18.73	&44.77\\
 M2	& 17.40&	38.08&	17.95&	42.12\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

A Kishore Kumar
Shefali Waldekar
Goutam Saha
Md Sahidullah

"Domain-Dependent Speaker Diarization for the Third DIHARD Challenge" by A Kishore Kumar, Shefali Waldekar, Goutam Saha, and Md Sahidullah, arXiv:2101.09884v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\toprule
\textbf{Scenario} & \textbf{Survival Changes} & \textbf{Health Changes} & \textbf{Total Value} \\
\midrule
CR0 & 274.68 & 916.92 & 1,191.59 \\
CR1 & 986.78 & 3,628.88 & 4,615.66 \\
CR2 & 1,216.19 & 5,515.61 & 6,731.80 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Willingness-to-pay at age 50 for changes in survival, health, and their combination over remaining lifespan (thousands of CHF).}
\end{table}
\end{document}

q-fin

"Economic impact of biomarker-based aging interventions on healthcare costs and individual value" by Federico Felizzi, arXiv:2503.20357v1, 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{Performance of the MAISI models and the WDM model for both the MS and ADNI dataset, across image quality, subject preservation, and text alignment accuracy.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc|c}
 \hline
 & \multicolumn{2}{c|}{\textbf{Quality and Diversity}}
 & \multicolumn{2}{c|}{\textbf{Subject Preservation}}
 & \textbf{Text Alignment} \\
 \cline{2-6}
 \textbf{Model} & FID $\downarrow$ & MS-SSIM $\downarrow$
 & MS-SSIM $\uparrow$ & PSNR (dB) $\uparrow$
 & Accuracy (\%) $\uparrow$ \\
 \hline
 \multicolumn{6}{c}{\textbf{MS Dataset}} \\
 \hline
 MAISI Linear & 0.1734 & \textbf{0.8626} & 0.8614 & 21.18 & 92.64 \\
 MAISI Rflow & 0.1625 & 0.8715 & 0.9638 & 27.92 & 89.19 \\
 WDM & \textbf{0.1622} & 0.8684 & \textbf{0.9680} & \textbf{28.47} & \textbf{94.58} \\
 \hline
 \multicolumn{6}{c}{\textbf{ADNI}} \\
 \hline
 MAISI Linear & 0.1752 & 0.8362 & 0.8635 & 22.16 & 62.26 \\
 MAISI Rflow & 0.1690 & 0.8357 & 0.9864 &\textbf{ 34.86} & \textbf{72.01}\\
 WDM & \textbf{0.1647} & \textbf{0.7485} & \textbf{0.9895} & 34.77 & 71.17\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mohamed Mohamed
Brennan Nichyporuk
Douglas L. Arnold
Tal Arbel

"Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance" by Mohamed Mohamed, Brennan Nichyporuk, Douglas L. Arnold, and Tal Arbel, arXiv:2509.05978v1, 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{The performance (\%) of different VOT models under BOBA on OTB100 dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc|cc}
\toprule
Attack Mode$\rightarrow$ & \multicolumn{2}{c|}{No Attack} & \multicolumn{2}{c|}{One-Shot} & \multicolumn{2}{c}{Few-Shot} \\ \hline
Model$\downarrow$, Metric$\rightarrow$ & Pr-B & AUC-B & Pr-A & AUC-A & Pr-A & AUC-A \\ \hline
SiamFC & 72.70 & 53.78 & 11.44 & 9.51 & 9.37 & 7.64 \\
SiamRPN++ & 76.89 & 54.85 & 35.71 & 21.02 & 23.79 & 15.84 \\
SiamFC++ & 79.71 & 60.81 & 77.51 & 57.79 & 75.67 & 57.04 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yiming Li
Haoxiang Zhong
Xingjun Ma
Yong Jiang
Shu-Tao Xia

"Few-Shot Backdoor Attacks on Visual Object Tracking" by Yiming Li, Haoxiang Zhong, Xingjun Ma, Yong Jiang, and Shu-Tao Xia, arXiv:2201.13178v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Mohammad Rasoul Narimani
Katherine R. Davis
Daniel K. Molzahn

"Certifying the Nonexistence of Feasible Path Between Power System Operating Points" by Mohammad Rasoul Narimani, Katherine R. Davis, and Daniel K. Molzahn, arXiv:2509.05935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llcccc}
\toprule
\multirow{7}*{\rotatebox[origin=c]{90}{\emph{Epsilon ($\epsilon$ = 0.02)}}} & \emph{Pseudo-Reference} & de-en & en-de & ru-en & en-ru \\
\cmidrule(lr){2-6}
 & Ancestral & 58.77 & 64.21 & \textbf{47.92} & 71.19 \\
 & Beam & \underline{57.24} & \underline{63.49} & 46.56 & \underline{68.26} \\
 & Epsilon ($\epsilon$ = 0.02) & 59.07 & 65.32 & 46.56 & 71.85 \\
 & Epsilon ($\epsilon$ = 0.02)$^*$ & 59.19 & 65.25 & 46.59 & 71.79 \\
 & Nucleus ($p$ = 0.6) & 57.82 & 64.31 & \underline{45.42} & 71.08 \\
 & Nucleus ($p$ = 0.9) & \textbf{59.65} & \textbf{65.44} & 47.77 & \textbf{72.33} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Atsumoto Ohashi
Ukyo Honda
Tetsuro Morimura
Yuu Jinnai

"On the True Distribution Approximation of Minimum Bayes-Risk Decoding" by Atsumoto Ohashi, Ukyo Honda, Tetsuro Morimura, and Yuu Jinnai, arXiv:2404.00752v1, 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{ Comparative evaluation of Classification network on varied values of Nearest Neighbors($k$=5,10,20...40) points on ModelNet-40 dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{$\textbf{k}$} & \textbf{Overall Accuracy} & \textbf{Mean Accuracy}\\
 \hline 5 & 29.53 & 24.79 \\
 \hline 10 & 85.21 & 79.26 \\
 \hline 15 & 91.73 & 88.28 \\
 \hline 20 & 92.7 & 89.09 \\
 \hline 30 & 91.12 & 87.23 \\
 \hline 40 & 87.72 & 83.23 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Md Meraz
Md Afzal Ansari
Mohammed Javed
Pavan Chakraborty

"Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation" by Md Meraz, Md Afzal Ansari, Mohammed Javed, and Pavan Chakraborty, arXiv:2412.03052v1, 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}{llll}
\hline
\# & Column & Non-null count \\
\hline
 0 & campaign & 907 non-null \\
 1 & cf\_total\_days & 907 non-null \\
 2 & cf\_total\_seconds & 907 non-null \\
 3 & cf\_type & 907 non-null \\
 4 & giveaways\_categories & 646 non-null \\
 5 & givaways\_totals & 646 non-null \\
 6 & pt\_categories & 907 non-null \\
 7 & pt\_totals & 907 non-null \\
 8 & fm\_categories & 729 non-null \\
 9 & fm\_total & 729 non-null \\
 10 & sch\_categories & 907 non-null \\
 11 & sch\_total & 907 non-null \\
 12 & ad\_categories & 591 non-null \\
 13 & ad\_total & 591 non-null \\
 14 & schm\_total & 907 non-null \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Moisés Ramírez
Raziel Ruíz
Nathan Klarer

"The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis" by Moisés Ramírez, Raziel Ruíz, and Nathan Klarer, arXiv:2212.12285v1, 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}{cccc}
\toprule
\textbf{Model} & \textbf{Hit} (\%) & \textbf{UnRel} (\%) & \textbf{F1} (\%) \\ \midrule
MLP-AD & 9.62 & 83.37 & 18.75 \\
MLP-ASD & 63.26 & 81.88 & 31.35 \\
GMemNN & \textbf{67.30} & \textbf{81.05} & \textbf{32.59} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{The experimental results of the conversational evaluation. MLP-AD stands for the pretrained state-of-the-art MLP model for automatic diagnosis (AD) provided by the authors of . MLP-ASD stands for the MLP model for automatic symptom detection (ASD) in this work. Hit stands for average hit rate $R_h$, UnRel stands for average unrelated rate $R_u$.}
\end{table}
\end{document}

cs

"Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks" by Hongyin Luo, Shang-Wen Li, and James Glass, arXiv:2101.09773v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|} \hline
 dimensions& 1d& 2d& 3d\\ \hline
 layers-MLP& \{1,20,1\} & \{2,20,40,20,1\} & \{3,20,40,20,1\} \\ \hline
 parameters& 61 & 1741 & 1761 \\ \hline
 activate function& tanh & tanh & tanh \\ \hline
 lr& 2e-03&1e-03&3e-03 \\ \hline
 loss function& $J_{r}(\theta)$& $J_{r}(\theta)$ or $J_{R}(\theta)$&$J_{R}(\theta)$ and $J_{r}(\theta)$ \\ \hline
 epochs& 10000 & 10000 & 15000+10000 \\ \hline
 collection points& 1000 & 400 &1000 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Network structure and training parameters for different dimensions.}
\end{table}
\end{document}

math

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


math

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Technology adoption and bribery across countries.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline
 \hline
 & {Low-income} & {Middle-income} & {High-income} \\
 & {countries} & {countries} & {countries} \\
 \hline
 {Share of modern firms (\% of all firms) } & 36.8\% & 55.4\% & 75.6\% \\
 {Bribe of traditional firms (\% of annual sales)}& 1.96\% & 0.99\% & 1.01\% \\
 {Probability of bribe payment of traditional firms} & 28\% & 15\% & 13\% \\
 {Bribe of modern firms (\% of annual sales)} & 2.60\% & 1.11\% & 0.60\% \\
 {Probability of bribe payment of modern firms} & 34\% & 16\% & 11\% \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Jafar M. Olimov
Yi-Chan Tsai
Hao-Yu Yang

"Heterogeneous Bribery, Technology Choice, and Capital Accumulation" by Jafar M. Olimov, Yi-Chan Tsai, and Hao-Yu Yang, arXiv:2507.13124v2, 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}{lll}
\hline
Covariate & Levels & Available from target?\\
\hline
Age group & 18-24, 25-34, 35-39, 40+ & Yes\\
Gender & Female, non-female & Yes \\
Party& Democratic, Republican, Other/Unknown & Yes\\
Race& White, Black, Latinx, other & No\\
Voted in 2000 & 0, 1 & No\\
Voted in 2002 & 0, 1 & No\\
Voted in 2004 & 0, 1 & Available in some counties\\
Voted in 2006 & 0, 1 & Available in some counties\\
Voted in 2008 & 0, 1 & Available in some counties\\
Voted in 2010 & 0, 1 & Yes\\
Voted in 2012 & 0, 1 & Yes\\
Voted in 2014 & 0, 1 & Yes\\
Voted in 2016 & 0, 1 & Yes\\
Voted in 2018 & 0, 1 & Yes\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Descriptions on covariates in pooled data.}
\end{table}
\end{document}

stat

"Transfer Learning Between U.S. Presidential Elections: How Should We Learn From A 2020 Ad Campaign To Inform 2024 Ad Campaigns?" by Xinran Miao, Jiwei Zhao, and Hyunseung Kang, arXiv:2411.01100v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Pablo Brañas-Garza
Antonio Cabrales
María Paz Espinosa
Diego Jorrat

"The effect of ambiguity in strategic environments: an experiment" by Pablo Brañas-Garza, Antonio Cabrales, María Paz Espinosa, and Diego Jorrat, arXiv:2209.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of Double Linear Policy with MA Indicator}
\begin{tabular}{ccccc}
 \hline
 & $w_{{\rm MA}_5}$ & $w_{{\rm MA}_{10}}$ & $w_{{\rm MA}_{20}}$ & $w_{{\rm MA}_{30}}$\\
 \hline
 \hline
 {\rm Gain-Loss} & 0.0061 & 0.0076 & 0.0117 & 0.0042\\
 \hline
 Variance & 9.7e-06 & 1.1e-05 & 1.4e-05 & 2.2e-06\\
 \hline
 Sharpe Ratio & 0.7097 & 0.8806 & 1.6343 & 0.8651\\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"On Robustness of Double Linear Policy with Time-Varying Weights" by Xin-Yu Wang and Chung-Han Hsieh, arXiv:2303.10806v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{Classifier} & \multicolumn{2}{c|}{Surface} & \multicolumn{2}{c|}{Section} & \multicolumn{2}{c|}{Mixed} \\ \cline{2-7}
 & P & R & P & R & P & R \\ \hline
R. Forest & 0.87 & 0.82 & 0.82 & 0.82 & \textbf{0.91} & \textbf{0.91} \\ \hline
XGboost & 0.93 & 0.93 & 0.89 & 0.89 & \textbf{0.96} & \textbf{0.96} \\ \hline
AlexNet & 0.93 & 0.95 & 0.83 & 0.82 & 0.92 & 0.92 \\ \hline
VGG19 & 0.95 & 0.96 & 0.91 & 0.92 & 0.94 & 0.92 \\ \hline
Inception & \textbf{0.98} & \textbf{0.97} & \textbf{0.94} & \textbf{0.96} & \textbf{0.97} & \textbf{0.98} \\ \hline
\end{tabular}
\caption{Weighted average metrics comparison for section and surface patches, as well as mixed patches.}
\end{table}
\end{document}

eess

Francisco Lopez
Andres Varela
Oscar Hinojosa
Mauricio Mendez
Dinh-Hoan Trinh
Jonathan ElBeze
Jacques Hubert
Vincent Estrade
Miguel Gonzalez
Gilberto Ochoa
Christian Daul

"Assessing deep learning methods for the identification of kidney stones in endoscopic images" by Francisco Lopez, Andres Varela, Oscar Hinojosa, Mauricio Mendez, Dinh-Hoan Trinh, Jonathan ElBeze, Jacques Hubert, Vincent Estrade, Miguel Gonzalez, Gilberto Ochoa, and Christian Daul, arXiv:2103.01146v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccccccccccccccccccccccc}
$j$ & 0 & 1& 2& 3& 4& 5& 6& 7& 8& 9&10&11&12&13&14&15&16&17&18&19\\
\hline
{\tt SP0}[j] & 0&2&1&0&1&0&0&2&1&0&0&2&1&0&2&1&0&1&0&0&\\
{\tt SP1}[j] & 1&0&0&2&1&0&2&1&0&1&0&0&2&1&0&1&0&0&2&1&\\
\end{tabular}
\end{adjustbox}
\caption{Infinite words coding the right-special factors of ${\bf n}$.}
\end{table}
\end{document}

math

The Narayana Morphism and Related Words

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


\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}{lcccr}
\toprule
Data set & Naive & Flexible & Better? \\
\midrule
Breast & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
Glass2 & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
Credit & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6& \\
Horse & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
Meta & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
Pima & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5& \\
Vehicle & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Simplifying deflation for non-convex optimization with applications in Bayesian inference and topology optimization" by Mohamed Tarek and Yijiang Huang, arXiv:2201.11926v1, 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{Maximal subgroups of $\mathrm{Sp}_4(q)$, for $q=2^e, e>1$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|ccc|}
\hline
Group & Order& Conditions\\
\hline
$\vphantom{E^{3^2}}E_q^3\colon\mathrm{GL}_2(q)$ & $q^3\cdot (q^2+q)(q-1)^2$ & \\%
$\vphantom{E^{3^2}}E_q^3\colon\mathrm{GL}_2(q)$ & $q^3\cdot (q^2+q)(q-1)^2$ & \\%
$\mathrm{Sp}_2(q)\wr 2$ & $2q^2(q^2-1)^2$ & \\%
$\mathrm{Sp}_2(q^2)\colon 2$ & $2q^2(q^4-1)$ & \\
$\mathrm{Sp}_4(q_0)$ & $q_0^4(q_0^2-1)(q_0^4-1)$ & $q=q_0^r, r$ is prime\\%
$\mathrm{SO}_4^+(q)$ & $2q^2(q^2-1)^2$ & \\
$\mathrm{SO}_4^-(q)$ & $2q^2(q^4-1)$ & \\
$\mathrm{Sz}(q)$ & $q^2(q^2+1)(q-1)$ & $e$ odd\\%
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Strong Gelfand Pairs of the Symplectic Group $\boldsymbol{\rm Sp}_4(q)$ where $q$ is even" by Stephen P. Humphries and Joseph E. Marrow, arXiv:2504.20279v2, 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{Table summarizing the objective speech quality evaluation on the DNS $no\_reverb$ ($with\_reverb$) test set}
\begin{tabular}{l|l|l|l}
Method & PESQ & SI-SDR & STOI \\
 & (MOS) & (dB) & (\%) \\
\hline
Noisy & 1.58 (1.82) & 9.1 (9.0) & 91.5 (86.6) \\
DNS Baseline & 1.83 (1.52) & 12.5 (9.2) & 90.6 (82.1) \\
GRU(TFE) & 2.27 (2.36) & 14.9 (13.2) & 94.2 (89.4) \\
GRU(STME) & 2.59 (2.64) & 12.4 (12.0) & 94.2 (90.1) \\
GRU(TFE+STME$^{R}$) & 2.57 (2.63) & \textbf{15.9 (14.5)} & 95.2 (90.6) \\
GRU(TFE+STME) & \textbf{2.71 (2.75)} & \textbf{15.9 (14.5)} & \textbf{95.5 (91.2)}
\end{tabular}
\end{table}
\end{document}

eess

Tyler Vuong
Yangyang Xia
Richard M. Stern

"A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement" by Tyler Vuong, Yangyang Xia, and Richard M. Stern, arXiv:2102.07330v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization" by Chenhao Jin, Yinhua Xia, and Yan Xu, arXiv:2503.19379v1, 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{Wall-clock time of finite volume (FV) model and attention-GMUS to simulate a trajectory for three flow cases.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
 \hline
 Dataset & GMUS & Transformer & GT Model & Speed-up \\
 \hline
 Cylinder flow (for 400 time steps) & $2$sec & $0.4758$sec & $1688.59$sec & $\boldsymbol{682}$ \\
 \hline
 Sonic flow (for 40 time steps)& $0.2$sec & $0.047$sec & $25.026$sec & $\boldsymbol{100}$ \\
 \hline
 Vascular flow (for 250 time steps)& $2.63$sec & $0.31$sec & $2451$sec & $\boldsymbol{800}$ \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xu Han
Han Gao
Tobias Pfaff
Jian-Xun Wang
Li-Ping Liu

"Predicting Physics in Mesh-reduced Space with Temporal Attention" by Xu Han, Han Gao, Tobias Pfaff, Jian-Xun Wang, and Li-Ping Liu, arXiv:2201.09113v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A Simple Weak Galerkin Finite Element Method for a Class of Fourth-Order Problems in Fluorescence Tomography" by Chunmei Wang and Shangyou Zhang, arXiv:2503.18200v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The results under ($2^1$, $10^{0}$) in terms of node classification accuracy, AUC score, F1 score, $\Delta_{SP}$, and $\Delta_{EO}$ . All numerical numbers are in percentage.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
\textbf{($2^1$, $10^{0}$)} & \textbf{Accuracy} & \textbf{AUC} & \textbf{F1 Score} & \textbf{$\Delta_{SP}$} & \textbf{$\Delta_{EO}$} \\
\midrule
\textbf{GCN} & 58.4\% & 66.4\% & 63.9\% & 41.4\% & 33.4\% \\
\textbf{NIFTY} & 61.2\% & 68.2\% & 66.2\% & 36.1\% & 13.3\% \\
\textbf{FairGNN} & 55.2\% & 62.2\% & 61.4\% & 16.8\% & 7.77\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yushun Dong
Binchi Zhang
Hanghang Tong
Jundong Li

"Certified Defense on the Fairness of Graph Neural Networks" by Yushun Dong, Binchi Zhang, Hanghang Tong, and Jundong Li, arXiv:2311.02757v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|c|c}
 \toprule
 \multicolumn{2}{l|}{Category} & Valid & Test \\
 \midrule
 \multirow{2}{*}{Gender (\%)} & Female & 51.7 & 51.7 \\
 & Male & 48.3 & 48.3 \\\hline
 \multirow{4}{*}{Age (\%)} & 18-27 & 37.9 & 34.5 \\
 & 28-37 & 34.5 & 31.0 \\
 & 38-47 & 10.4 & 13.8 \\
 & 48 and above & 17.2 & 20.7 \\\hline
 \multirow{5}{*}{Region (\%)} & East & 13.8 & 13.8 \\
 & West & 20.7 & 17.2 \\
 & North & 13.8 & 20.7 \\
 & South & 37.9 & 41.4 \\
 & Center & 13.8 & 6.9 \\\hline
 \multirow{2}{*}{Device (\%)} & Phone & 62.1 & 79.3 \\
 & Computer & 37.9 & 20.7 \\\hline
 \multirow{2}{*}{Headphone (\%)} & Yes & 20.7 & 17.2 \\
 & No & 79.3 & 82.8 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{The validation and test sets speaker details.}
\end{table}
\end{document}

eess

Yerbolat Khassanov
Saida Mussakhojayeva
Almas Mirzakhmetov
Alen Adiyev
Mukhamet Nurpeiissov
Huseyin Atakan Varol

"A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline" by Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, and Huseyin Atakan Varol, arXiv:2009.10334v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Inverse single facility location problem in the plane with variable coordinates" by Nazanin Tour-Savadkoohi and Jafar Fathali, arXiv:2503.07016v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \cline{2-4}
 & \multicolumn{3}{c|}{\textbf{Confidence $1-\gamma$}} \\
 \hline
 \textbf{Method} & 0.5 & 0.8 & 0.9 \\
 \hline
 RESET Ensemble & 82987 & 82729 & 82558 \\
 \hline
 FDP-SD & 75977 & 75464 & 75254 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{\textbf{The average number of discoveries at 1\% FDP threshold at varying confidence parameters using the HEK293 data.} The average number of discoveries reported for RESET Ensemble and FDP-SD on the combined HEK293 spectrum files at different confidence values $1 - \gamma = 0.5, 0.8, 0.9$. The averages are taken over 5 applications of each method, once for each of the 5 combined target-decoy databases.}
\end{table}
\end{document}

stat

Jack Freestone
William Stafford Noble
Uri Keich

"A semi-supervised framework for diverse multiple hypothesis testing scenarios" by Jack Freestone, William Stafford Noble, and Uri Keich, arXiv:2411.15771v2, 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{Criterion values for solutions $\mathbf{p}^*$ (final single optimal solution), $\mathbf{p}_{challenge}$ (solution submitted to the Challenge), $\mathbf{p}_{initial}$ (initial solution given by the Challenge), under Scenario 1, Site 0}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 & \textbf{$\mathbf{p}^*$} & \textbf{$\mathbf{p}_{challenge}$} & \textbf{$\mathbf{p}_{initial}$} \\ \hline
\textbf{Median Absolute Difference ($\mathbf{f}_{11}$)} & 53 & 823 & 4{,}012 \\
\textbf{Max Absolute Difference ($\mathbf{f}_{12}$)} & 2{,}537 & 4{,}320 & 19{,}222 \\
\textbf{\# of Non-zero Harvest Week ($\mathbf{f}_{13}$)} & 50 & 53 & 54 \\
\textbf{Total Amount of Wasted Product ($\mathbf{f}_{14}$)} & 16{,}514 & 34{,}320 & 102{,}204 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mingshi Cui
Kunting Qi
Byran Smucker
Durai Sundarmoorthi

"A Multi-Objective Capacity-Constrained Optimization of Corn Planting Scheduling" by Mingshi Cui, Kunting Qi, Byran Smucker, and Durai Sundarmoorthi, arXiv:2503.20517v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Eigen Portfolios: From Single Component Models to Ensemble Approaches" by ZhengXiang Zhou and Yuqi Luan, arXiv:2508.15586v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hard environment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
 \hline
 Card & Cores & Peak single precision flops \\ \hline
 Nvidia Tesla K40c & 2880 & 4.29Tflops \\ \hline
 Nvidia Tesla K20c & 2496 & 3.52 Tflops \\ \hline
 Intel Xeon E5-2630 & 6 & 0.134 Tflops \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"GPU acceleration of a patient-specific airway image segmentation and its assessment" by Yu-Wei Chang and Tony W. H. Sheu, arXiv:2012.10684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccc|}
\hline
Infant & $F_{s}$ (Hz) & Base & Gain \\
 \hline
1 & 250 & 16 & 800.6597\\
2 & 500 & 16 & 1220.7707\\
3 & 250 & 16 & 1140.7954\\
4 & 500 & 16 & 834.3036\\
5 & 500 & 16 & 800.6597\\
6 & 500 & 16 & 800.6141\\
7 & 500 & 16 & 1283.8528\\
8 & 500 & 16 & 1420.7631\\
9 & 500 & 16 & 800.6597\\
10 & 500 & 16 & 800.4159\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Information obtained from the header file(.hea) for each infant from the database. }
\end{table}
\end{document}

eess

"Computational Challenges in Non-parametric Prediction of Bradycardia in Preterm Infants" by Sinjini Mitra, arXiv:2011.09307v1, 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}{cccll}
 $K$ & $\gamma_K$ & $\alpha_K$ & & $\sigma^2_K$\\
 \hline
 1 &1 &2 & $=2$ & 0.420\\
2& $\frac{3}{2}$ &$\frac{9}{5}$ &$=1.8$ & 0.242 \\
3 &$\frac{133}{72}$ &$\frac{133}{78}$ &$\approx1.7051$ & 0.135\\
4 & $\frac{2384}{1125}$ &$\frac{9536}{5775}$ &$\approx1.6513$ & 0.083\\
5 &$\frac{36469}{15552}$& $\frac{182345}{112752}$& $\approx1.6172$ & 0.056 \\
6 & $\frac{31796145419183}{12522149640000}$ &$\frac{31796145419183}{19945995498000}$ & $\approx1.5941$
& 0.040
\end{tabular}
\end{adjustbox}
\caption{Mean and variance for small $K$. The variance is approximated using a Monte-Carlo method on with 1 million samples of $D$.}
\end{table}
\end{document}

math

Cecilia Holmgren
Jasper Ischebeck
Daniel Krenn
Florian Lesny
Ralph Neininger

"Probabilistic analysis of optimal multi-pivot QuickSort" by Cecilia Holmgren, Jasper Ischebeck, Daniel Krenn, Florian Lesny, and Ralph Neininger, arXiv:2503.22334v1, 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|ccc}
 & Euler-Maruyama & Milstein & simplified order 2.0 weak Taylor\\
 \hline
 weak & $1$ & $1$ & $2$\\
 strong & $1/2$ & $1$ & $1$
 \end{tabular}
\end{adjustbox}
\caption{Weak and strong convergence rates of given different discretization schemes}
\end{table}
\end{document}

math

"A numerical Fourier cosine expansion method with higher order Taylor schemes for fully coupled FBSDEs" by Balint Negyesi and Cornelis W. Oosterlee, arXiv:2501.10988v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Bo Wang
Wei Xie
Tugce Martagan
Alp Akcay
Bram van Ravenstein

"Biomanufacturing Harvest Optimization with Small Data" by Bo Wang, Wei Xie, Tugce Martagan, Alp Akcay, and Bram van Ravenstein, arXiv:2101.03735v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.