\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|rrrrrrrrrrrr}
\toprule
Covariance & \multicolumn{4}{|c|}{$\alpha$} &
\multicolumn{4}{|c|}{$\beta_{PDR}$} & \multicolumn{4}{|c}{$\gamma$}\\
Estimation & CC & FL & CRE & CI & CC & FL & CRE & CI & CC & FL & CRE & CI \\
\midrule
bank clustered & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & & & & \\
time clustered & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & & & & \\ \hline
bank clustered & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\
time clustered & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Should Bank Stress Tests Be Fair?

"Should Bank Stress Tests Be Fair?" by Paul Glasserman and Mike Li, arXiv:2207.13319v2, 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{Performance comparison of DeepWORD and logic model method.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\hline\noalign{\smallskip}
Methods & Easy & Hard & Mixed \\
\noalign{\smallskip}\hline\noalign{\smallskip}
Logic model & 92.91 & 71.83 & 79.90 \\
DeepWORD & \textbf{99.17} & \textbf{94.35} & \textbf{95.14} \\
\noalign{\smallskip}\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zizhang Wu
Jason Wang
Tianhao Xu
Fan Wang

"Detecting Owner-member Relationship with Graph Convolution Network in Fisheye Camera System" by Zizhang Wu, Jason Wang, Tianhao Xu, and Fan Wang, arXiv:2201.12099v1, 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{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison on \textbf{FakeParaEgg}. (A) single domain tests, (B) \textcolor{blue}{out-of-domain generalizability tests} }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|c|c|c|c|c|}
 \hline
 & Method & mF1 & mIoU & Prec. & Recall & Spec. \\
 \hline
 & U-Net & .749 & .616 & .770 & .855 & .9934 \\
 (A) & RRU-Net & .777 & .652 & .788 & .898 & .9938\\
 & UCM-Net & .713 & .586 & .782 & .867 & .9937\\

 & Ours & \textcolor{blue}{\textbf{.922}} & \textcolor{blue}{\textbf{.885}} & .878 & .970 & .9972\\ \hline \hline

 & {ManTra-Net} & .099 & .060 & .094 & .280 & .9302\\
 & {DOA-GAN} & .390 & .315 & .792 & .603 & .9959\\
 (B) & {UCM-Net} & .478 & .396 & .714 & .742 & .9923\\
 & Ours & \textcolor{blue}{\textbf{.739}} & \textcolor{blue}{\textbf{.582}} & .828 & .667 & .9962\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Hao-Chiang Shao
Yuan-Rong Liao
Tse-Yu Tseng
Yen-Liang Chuo
Fong-Yi Lin

"Copy-Move Detection in Optical Microscopy: A Segmentation Network and A Dataset" by Hao-Chiang Shao, Yuan-Rong Liao, Tse-Yu Tseng, Yen-Liang Chuo, and Fong-Yi Lin, arXiv:2412.10258v1, 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{Dataset Sample}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
 \hline
 Model & Year & Battery & Price & Miles \\
 \hline
 Model S & 2013 & Base & 34200 & 36800 \\
 \hline
 Model 3 & 2018 & 75 & 46995 & 2193 \\
 \hline
 Model S & 2018 & 75D & 64900 & 1095 \\
 \hline
 Model X & 2016 & P90D & 84984 & 20680 \\
 \hline
 Model S & 2016 & 75D & 58989 & 20303 \\
 \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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics analysis for model trained by one frame only.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|cccccccc}
 \hline
 \textbf{Frame} & \textbf{Criterion(cm)} & \textbf{Mean$\pm$ std} & \textbf{min} & \textbf{Medium}
 \\
 \hline
 ED & LDE & 1.59$\pm$1.85 & 0.04 & 0.95 \\
 & LE & 1.04$\pm$1.20 & 0.02 & 0.68 \\
 \hline
 ES & LDE & 1.76$\pm$1.49 & 0.10 & 1.34 \\
 & LE & 1.77$\pm$1.39 & 0.01 & 1.49\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\MakeUppercase{Spatial characterization for test scenarios}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|llll|}
			\hline
			& m & M & \(\nu\) \\
			\hline
			Sensors & \(1.0\) & \(10.0\) & \(4.0\) \\
			\hline
			Actuators (nominal) & \(1.0\) & \(0.0\) & \(4.0\) \\
			Actuators (realistic) & \(1.0\) & \(30.0\) & \(4.0\) \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Modeling of a multiple source heating plate" by Stephan Scholz and Lothar Berger, arXiv:2011.14939v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Prediction/Truth & Healthy (Negative) & Malignant (Positive) \\
\hline
 Healthy (Negative) & 26& 0 \\ \hline
 Malignant (Positive)& 0 & 26 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{The ALL-IDB2 confusion matrix for the validation data.}
\end{table}
\end{document}

cs

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{HopSkipJump} & \textbf{1} & \textbf{2} & \textbf{3} & \textbf{4} & \textbf{5}\\
\hline
Success Rate (\%) & 0 & 100 & 100 & 100 & 100\\
\hline
\% of Unrealistic Values & - & 87.0 & 0 & 0 & 0\\
\hline
\# Checked Fields & - & - & 554 & 465 & 418\\
\hline
\# Non-Editable Fields & - & - & - & 159 & 0 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Results obtained with each experiment configuration for HopSkipJump attack}
\end{table}
\end{document}

cs

Francesco Cartella
Orlando Anunciacao
Yuki Funabiki
Daisuke Yamaguchi
Toru Akishita
Olivier Elshocht

"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data" by Francesco Cartella, Orlando Anunciacao, Yuki Funabiki, Daisuke Yamaguchi, Toru Akishita, and Olivier Elshocht, arXiv:2101.08030v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Experimental performance on 196 intersections in New York}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 \toprule
 \textbf{Model} & \textbf{real} & \textbf{mixed$_l$} & \textbf{mixed$_h$} & mean \\
 \midrule
 Random & 1869.00 & 1748.76 & 1892.73 & 1836.83 \\
 MaxPressure & 845.72 & 589.24 & 920.31 & 785.09 \\
 Fixedtime & 1831.65 & 1742.71 & 1205.97 & 1593.44 \\
 FixedtimeOffset & 1702.82 & 1732.37 & 1869.66 & 1768.28 \\
	SlidingFormula & 1185.64 & 921.28 & 1299.19 & 1135.37 \\
 SOTL & 1862.34 & 1793.37 & 1939.53 & 1865.08 \\
 \midrule
 Individual RL & 1877.46 & 1369.64 & 1906.43 & 1717.84 \\
 MetaLight & 1285.74 & 1005.36 & 1368.96 & 1220.02 \\
 PressLight & 1586.75 & 1547.74 & 1685.74 & 1606.74 \\
 CoLight & 637.79 & 479.31 & 761.10 & 626.07 \\
	GeneraLight & 710.23 & 485.84 & 862.47 & 686.18 \\
 \midrule
 MetaVIM & \textbf{586.36} & \textbf{396.57} & \textbf{748.65} & \textbf{577.19} \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Liwen Zhu
Peixi Peng
Zongqing Lu
Xiangqian Wang
Yonghong Tian

"MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control" by Liwen Zhu, Peixi Peng, Zongqing Lu, Xiangqian Wang, and Yonghong Tian, arXiv:2101.00746v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{NN Architectures used in our approach (CNN-based).}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Name} & \textbf{Encoder NN} & \textbf{Decoder NN} & \textbf{Generative Network} \\ \hline
\multirow{4}{*}{\textbf{Layer}} & \textit{FC+eLU} & \textit{Conv2d+ReLU} & \textit{Conv1d+ReLU+BN} \\ \cline{2-4}
 & \textit{Conv1d+ReLU+Flatten} & \textit{Conv2d+ReLU+Flatten} & \textit{Cov1d+ReLU+BN+Flatten} \\ \cline{2-4}
 & \textit{FC+Linear} & \textit{FC+ReLU} & \textit{FC+Linear} \\ \cline{2-4}
 & \textit{Normalization ($\ell_{2}$)} & \textit{FC+Softmax} & \textit{Normalization ($\ell_{2}$)} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attacks" by Yudi Dong, Huaxia Wang, and Yu-Dong Yao, arXiv:2103.02654v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ The parameters of the considered instance in Example }.
\begin{tabular}{ccccccc}
\hline
i& $A_i=(a_i,b_i)$& $w_i$& $c_{i1}^{+}$&$c_{i2}^{+}$&$c_{i1}^{-}$&$c_{i2}^{-}$\\
\hline
1& $(1,0)$&6&$\sqrt{2}$&5&$1$&$6$\\
2& $(-5,3)$&3&7&3&4&2\\ % \\
3& $ (7,2)$&1&1&2&4&4\\
4& $(0,-0.5)$&2&2&1&4&1\\
\hline
\end{tabular}
\end{table}
\end{document}

math

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


cs

Sourav Medya
Mohammad Rasoolinejad
Yang Yang
Brian Uzzi

"An Exploratory Study of Stock Price Movements from Earnings Calls" by Sourav Medya, Mohammad Rasoolinejad, Yang Yang, and Brian Uzzi, arXiv:2203.12460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 & Normal volumes & Normal slices & Abnormal volumes & Abnormal slices\\
 \hline
 Training & 119 & 16506 & - & - \\
 \hline
 Validation & 30 & 4126 &- &- \\
 \hline
 Test & 30 & 4314 & 44 & 4805 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Distribution of the number of CT volumes and slices in this study.}
\end{table}
\end{document}

eess

Abinav Ravi Venkatakrishnan
Seong Tae Kim
Rami Eisawy
Franz Pfister
Nassir Navab

"Self-Supervised Out-of-Distribution Detection in Brain CT Scans" by Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy, Franz Pfister, and Nassir Navab, arXiv:2011.05428v1, 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{Pearson’s correlation test between MaFI scores and IWERs. ** ($p < 0.01$); ***($p < 0.001$). }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc||ccc}
\toprule
\textbf{Model} & \multicolumn{3}{c||}{\textbf{Auto-AVSR}} & \multicolumn{3}{c}{\textbf{AVEC}} \\
\midrule
\textbf{SNR} & \textbf{-5} & \textbf{0} & \textbf{5} & \textbf{-5} & \textbf{0} & \textbf{5} \\
\midrule
\textbf{\textbf{AO}} & 0.002 & -0.021 & 0.039 & -0.019 & -0.020 & -0.052 \\
\textbf{VO} & \multicolumn{3}{c||}{-0.097**} & \multicolumn{3}{c}{-0.173***} \\
\textbf{\textbf{AV}} & -0.040 & 0.055 & 0.035 & -0.151** & -0.150** & -0.080 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Uncovering the Visual Contribution in Audio-Visual Speech Recognition" by Zhaofeng Lin and Naomi Harte, arXiv:2412.17129v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 $\lambda_{\text{min}}$
 & $\lambda_{\text{max}}$ & $\Delta \lambda$ & $h_{\text{min}}$ & $h_{\text{max}}$ & $\Delta h$ \\
 \hline
 $27$ & $216.6$ & $1$ & $0$ & $50$ & $0.5$ \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Meta-parameters for Algorithm .}
\end{table}
\end{document}

q-fin

Giorgia Callegaro
Claudio Fontana
Caroline Hillairet
Beatrice Ongarato

"A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks" by Giorgia Callegaro, Claudio Fontana, Caroline Hillairet, and Beatrice Ongarato, arXiv:2505.01221v1, 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{Median and IQR of the latent multi-ranks estimated through the eigenvalue ratio (ER) and the Growth Ratio (GR) for the 20 different simulations in the two scenarios for $30\%$ and $50\%$ of total entries missing.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|ccc|ccc}

 \multicolumn{2}{}{} &
 \multicolumn{3}{c}{ER} &
 \multicolumn{3}{c}{GR} \\

 $(R_1^0, R_2^0, R_3^0)$ & NA (\%) & $R_1$ & $R_2$ & $R_3$ &$R_1$ & $R_2$ &$R_3$ \\
 &&& median(IQR) &&& median(IQR)&\\
 \hline
 (5,5,5) & 30 & 1(1) & 1(1) & 1(0.25)& 1.5(2)& 1(1) & 1(1)\\
 & 50 & 1(0) & 1(0) & 1(1)& 1(0.25)& 1(0) & 1(0.25)\\
 (10,7,3) & 30 & 1(1) & 1(1) & 1(0)& 1(1)& 1.5(1) & 1(0)\\
 & 50 & 1(1) & 1(1) & 1(0.25) & 1(1)& 1(1) & 1(0)\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Bayesian Adaptive Tucker Decompositions for Tensor Factorization" by Federica Stolf and Antonio Canale, arXiv:2411.10218v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Sepideh Mosaferi
Abolfazl Safikhani
Peiliang Bai

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


\documentclass{article}
\usepackage{amsmath}
\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}{lccccc}
\toprule
\multirow{2}[2]{*}{Model} &
\multirow{2}[2]{*}{Method} &
\multicolumn{4}{c}{\text{$\epsilon=3$}} \\
\cmidrule(lr){3-6}
& & $E=3$ & $E=10$ & $E=20$ & $E=30$ \\
\midrule
\multirow{2}{*}{RoBERTa-base} %
& Flat clipping (tuned) & $88.70 (0.52)$ & $90.17 (1.10)$ & $91.47 (1.07)$ & $91.60 (0.95)$\\
& Adaptive per-layer & $90.50 (1.21)$ & $91.90 (0.72)$ & $92.33 (0.42)$ & $92.23 (0.06)$ \\
\midrule
\multirow{2}{*}{RoBERTa-large} & Flat clipping (tuned) & $92.20 (0.26)$ & $93.07 (0.75)$ & $93.67 (0.40)$ & $94.23 (0.67)$ \\
& Adaptive per-layer & $91.73 (0.59)$ & $93.27 (0.45)$ & $93.90 (0.26)$ & $94.13 (0.38)$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jiyan He
Xuechen Li
Da Yu
Huishuai Zhang
Janardhan Kulkarni
Yin Tat Lee
Arturs Backurs
Nenghai Yu
Jiang Bian

"Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping" by Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, and Jiang Bian, arXiv:2212.01539v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{}
\begin{tabular}{lll}
 \toprule[1.5pt]
 Location & \parbox[t]{2cm}{DE Iteration \\ Number} & \parbox[t]{2.5cm}{One-time\\ Initialization Time}\\
 \midrule
 Conference Room & 1 & 10.2 sec \\
 Dining Hall & 2 & 41.8 sec \\
 Gas Station & 2 & 18.4 sec \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Yangyong Zhang
Maliheh Shirvanian
Sunpreet S. Arora
Jianwei Huang
Guofei Gu

"Practical Speech Re-use Prevention in Voice-driven Services" by Yangyong Zhang, Maliheh Shirvanian, Sunpreet S. Arora, Jianwei Huang, and Guofei Gu, arXiv:2101.04773v1, 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{Clothing Data sets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
 \toprule
 Name & Citation \\
 \midrule
 Fashionista data set & \\
 Clothing Attributes Data set & \\
 Paper Doll & \\
 Color-Fashion Data set & \\
 MVC Dataset & \\
 Deep Fashion Data set & \\
 Fashion-MNIST & \\
 imat fashion & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

M. J. A. van Wezel
L. J. Hamburger
Y. Napolean

"Fine-grained Classification of Rowing teams" by M. J. A. van Wezel, L. J. Hamburger, and Y. Napolean, arXiv:1912.05393v1, 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}{|cc|}
 \hline
 Models & Put Price MAPE (\%) \\
 \hline\hline
 PINN & 10.8237 \\
 \hline
 ConvLSTM & 4.0159 \\
 \hline
 SA-ConvLSTM & 4.0352 \\
 \hline
 ConvTF & 4.8325 \\
 \hline
 PI-ConvTF & 2.9297 \\
 \hline
 VAR & 6.7802 \\
 \hline
 ARIMA & 23.4107 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Average put option price MAPE values across test dates for all models trained without outlier data. These inferred put option prices are derived from the call option prices previously estimated by our models. The MAPE values are computed after excluding outlier values of put options that fall below the $20^{th}$ percentile.}
\end{table}
\end{document}

q-fin

Soohan Kim
Seok-Bae Yun
Hyeong-Ohk Bae
Muhyun Lee
Youngjoon Hong

"Physics-Informed Convolutional Transformer for Predicting Volatility Surface" by Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae, Muhyun Lee, and Youngjoon Hong, arXiv:2209.10771v3, 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|ccccccccccccccccccccccccccccccccccc}
$j$ & 1& 2& 3& 4& 5& 6& 7& 8& 9&10&11&12&13&14&15&16&17&18&19\\
\hline
$a(j)$ & 1& 3& 4& 5& 7& 9&10&12&13&14&16&17&18&20&22&23&24&26&28\\
$b(j)$ & 2& 6& 8&11&15&19&21&25&27&30&34&36&39&43&47&49&52&56&60
\end{tabular}
\end{adjustbox}
\caption{The sequences $a(j)$ and $b(j)$.}
\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{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \hline
 $M_a$ & Estimate & SE\\
 \hline
 1000& 0.3047400& 0.09359366\\
 2000 &0.2962185& 0.12384585\\
 5000& 0.2859627& 0.26879603\\
 10000& 0.2915771& 0.21386340\\
 20000& 0.2859134 &0.38467096\\
 50000 &0.1124109 &2.40895732\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Change the number of null IVs from $\mathbb{G}_a$ from 1000 to 50000 where the number of each other three groups is fixed to be 1000.}
\end{table}
\end{document}

stat

"A Random Effects Model-based Method of Moments Estimation of Causal Effect in Mendelian Randomization Studies" by Wenhao Cao and Saonli Basu, arXiv:2312.10926v1, 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{The MSD, MAD, and ABWD for S\&P $500$ stock index dataset. The best bootstrapping technique with the least MSD, MAD, and ABWD is marked in bold.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
			\hline
			& Bootstrap Methods & MSD & MAD & ABWD\\
			\hline

			\multirow{3}{*}{Open Price}&
			$\quad$NOBB$\quad$ & $6946.36$ & $65.72$ & $18181.5$ \\
			& $\quad$MBB$\quad$ & $4118.35$ & $48.47$ & $12993.9$ \\
			& $\quad$LBB$\quad$ & $\textbf{2209.72}$ & $\textbf{35.88}$ & $\textbf{11545.4}$ \\

			\hline \multirow{3}{*}{High Price} & $\quad$NOBB$\quad$ & $6752.75$ & $65.76$ & $17270.2$ \\
			& $\quad$MBB$\quad$ & $4073.18$ & $50.24$ & $12771.3$ \\
			& $\quad$LBB$\quad$ & $\textbf{2788.50}$ & $\textbf{42.34}$ & $\textbf{11857.1}$ \\

			\hline \multirow{3}{*}{Low Price}&
			$\quad$NOBB$\quad$ & $8383.30$ & $73.70$ & $16886.9$ \\
			& $\quad$MBB$\quad$ & $5736.40$ & $58.16$ & $15095.6$ \\
			& $\quad$LBB$\quad$ & $\textbf{4027.22}$ & $\textbf{50.35}$ & $\textbf{11601.9}$ \\

			\hline \multirow{3}{*}{Closing Price} & $\quad$NOBB$\quad$ & $10198.64$ & $80.64$ & $15953.9$ \\
			& $\quad$MBB$\quad$ & $8053.63$ & $72.07$ & $12183.8$ \\
			& $\quad$LBB$\quad$ & $\textbf{6342.93}$ & $\textbf{65.40}$ & $\textbf{10855.8}$ \\

			\hline \multirow{3}{*}{Volume} &
			$\quad$NOBB$\quad$ & $4.33e+17\quad$ & $\textbf{4.36e+08}\quad$ & $6.82e+10$ \\
			& $\quad$MBB$\quad$ & $4.41e+17\quad$ & $4.47e+08\quad$ & $6.75e+10$ \\
			& $\quad$LBB$\quad$ & $\textbf{6.50e+10}\quad$ & $3.83e+17\quad$ & $\textbf{4.25e+08}$ \\

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

q-fin

"Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network" by Aryan Bhambu and Arabin Kumar Dey, arXiv:2211.13915v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Maciej K Wozniak
Mattias Hansson
Marko Thiel
Patric Jensfelt

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


\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
Model & Complexity$\uparrow$ & Structure$\uparrow$ & Musicality$\uparrow$ \\ \midrule
Original & 3.22 & 3.47 & 3.56 \\ \midrule
InpaintNet & 2.98 & 3.01 & 3.09 \\ \midrule
SketchNet & 3.04 & 3.29 & 3.26 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Results of the subjective listening test.}
\end{table}
\end{document}

eess

Ke Chen
Cheng-i Wang
Taylor Berg-Kirkpatrick
Shlomo Dubnov

"Music SketchNet: Controllable Music Generation via Factorized Representations of Pitch and Rhythm" by Ke Chen, Cheng-i Wang, Taylor Berg-Kirkpatrick, and Shlomo Dubnov, arXiv:2008.01291v1, 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{DNN architecture} % title name of the table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccrrrrrrr} % creating 10 columns
\hline\hline % inserting double-line
 Layer &Type&Output Shape & Number of Parameters
\\
\hline % inserts single-line
1 & Dense(ReLu)& (None,1024) & 308224 \\
2 & Dropout&(None,1024) & 0 \\
3 & Dense(ReLu)& (None,1024) & 1049600 \\
4 & Dropout&(None,1024) & 0 \\
5 & dense(ReLu)&(None,512) & 524800 \\
6 & Dense(ReLu)&(None,128) & 65664 \\
7 & Dense(ReLu)&(None,64) & 8256 \\
7 & Dense(Sigmoid)&(None,2) & 130 \\
\hline % inserts single-line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Prateek Singh
Ambalika Sharma
Shreesha Maiya

"Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network" by Prateek Singh, Ambalika Sharma, and Shreesha Maiya, arXiv:2202.05177v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Features and Outputs Description}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}%@{}ll@{}
\hline
\rowcolor{lightgray}
Feature & Description \\
\hline
 & Apple, Facebook, Facebook Messages, \\
OTT Traffics & Facebook Video, HTTPS, Instagram, \\
 & Netflix, QUIC, Whatsapp, Youtube \\
\\
CQI & Channel quality indicator \\
\\
MIMO-FI & MIMO rank usage (\%) \\
\hline
\rowcolor{lightgray}
Output & Description \\
\hline
CPU Load & CPU resource consumption (\%) \\ \hline\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Martino Chiarani
Swastika Roy
Christos Verikoukis
Fabrizio Granelli

"XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing" by Martino Chiarani, Swastika Roy, Christos Verikoukis, and Fabrizio Granelli, arXiv:2503.12435v1, licensed under CC0 1.0. 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}{llllll}
 \toprule \hline
 Epoch & 0-4 &5-9 &10-14 &15-19 \\
 \midrule
 Time(s), PMLP &748.4 &747.3 &747.1 &747.0 \\
 Time(s), KDE &3733.0 &4244.0 &4166.6 &4189.0 \\
 \hline
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Running time for using KDE and our PMLP to compute the density. }
\end{table}
\end{document}

stat

Shuyang Liu
Ruiqiu Zheng
Yunhang Shen
Ke Li
Xing Sun
Zhou Yu
Shaohui Lin

"Probability-density-aware Semi-supervised Learning" by Shuyang Liu, Ruiqiu Zheng, Yunhang Shen, Ke Li, Xing Sun, Zhou Yu, and Shaohui Lin, arXiv:2412.17547v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccccccc}
\hline
\textbf{} & \multicolumn{3}{c|}{Diffusion+SVR} & \multicolumn{3}{c|}{HGLET+SVR} & \multicolumn{3}{c|}{GHWT+SVR} & \multirow{2}{*}{SchNet} & \multirow{2}{*}{PaiNN} & \multirow{2}{*}{SO3Net I} & \multirow{2}{*}{SO3Net II} \\ \cline{1-10}
Feature Type & Node & Edge & Both & Node & Edge & Both & Node & Edge & Both & & & & \\ \hline
\multicolumn{14}{c}{Aspirin} \\ \hline
MAE & 4.856 & 3.132 & 3.267 & 4.884 & 3.135 & 3.285 & 4.928 & \textbf{3.075} & 3.225 & 13.5 & 3.8 & 3.8 & \textbf{2.6} \\
RMSE & 6.181 & 4.144 & 4.314 & 6.215 & 4.129 & 4.407 & 6.213 & \textbf{4.123} & 4.316 & 18.3 & 5.9 & 5.7 & \textbf{3.8} \\
\# Parameters & 924 & 3784 & 4708 & 924 & 3784 & 4708 & 924 & 3784 & 4708 & $\sim$ 432k & $\sim$ 341k & $\sim$ 283k & $\sim$ 341k \\ \hline
\multicolumn{14}{c}{Paracetamol} \\ \hline
MAE & 4.609 & 2.715 & 2.795 & 4.723 & 2.643 & 2.710 & 4.748 & \textbf{2.624} & 2.699 & 8.4 & 2.1 & 2.2 & \textbf{1.4} \\
RMSE & 5.860 & 3.418 & 4.116 & 5.964 & 3.338 & 3.424 & 5.961 & \textbf{3.299} & 3.408 & 11.2 & 2.9 & 3.0 & \textbf{1.9} \\
\# Parameters & 924 & 3784 & 4444 & 924 & 3784 & 4444 & 924 & 3784 & 4444 & $\sim$432k & $\sim$ 341k & $\sim$283k & $\sim$341k \\ \hline
 & & & & & & & & & & & & &
\end{tabular}
\end{adjustbox}
\caption{Comparison of the performance of our MHSNs and the other state-of-the-art GNNs for potential energy prediction. We report the accuracy via MAE and RMSE as well as the number of trainable parameters in each network.}
\end{table}
\end{document}

stat

Naoki Saito
Stefan C. Schonsheck
Eugene Shvarts

"Multiscale Hodge Scattering Networks for Data Analysis" by Naoki Saito, Stefan C. Schonsheck, and Eugene Shvarts, arXiv:2311.10270v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Special cases of utility functions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|lll}
\hline
& \multicolumn{3}{c}{} \\
& HARA & Hypergeometric & CREMR \\
& $\beta = 0$ & $\beta \in (0,1)$ & $\beta = 1$ \\
\hline
& \multicolumn{3}{c}{} \\
$\sigma=0$ & \ $u(q)= \frac{K}{\alpha} (1-{\rm e}^{-\alpha q})$ & \ \ $u(q)= \frac{K}{\alpha^{1-\beta}} \gamma(1-\beta, \alpha q)$ \quad & \ \ $u(q)= K\, {\rm Ei}(-\alpha q) $ \quad \\
$\sigma=1$ & \ $u(q)= \frac{K}{\alpha} \ln (\alpha q+1)$ & \ \ $u(q)= \frac{K}{(-\alpha)^{1-\beta}} B (-\alpha q,1-\beta,\beta) $ \quad & \ \ $u(q)= K \ln q $ \quad \\
$\sigma=-1$ & \ $u(q)= - \frac{K}{2} (\alpha q^2- 2q) $ & & \\
\hline
\multicolumn{4}{p{16.5cm}}{\scriptsize{{\it Notes}: {\rm We suppress the additive constant $C$ in each expression.
}}}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{array}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of Algorithm Performance}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
			\hline
			 &
			& \multicolumn{1}{p{5em}<{\centering}|}{{Time cost(s)}}
			& \multicolumn{1}{p{5em}<{\centering}|}{{Number of nodes}}
			& \multicolumn{1}{p{4.78em}<{\centering}|}{{Length of Ini. path}}
			& \multicolumn{1}{p{5em}<{\centering}}{{Length of Opt. path}} \\
			\hline
			\multirow{2}[2]{*}{\text{Map1}} & \text{CGAN-RRT*} & $\textbf{136}$ & $\textbf{7272}$ & $\textbf{552}$ & \multirow{2}[2]{*}{$527$} \\
			& \text{RRT*} & $708$ & $17924$ & $589$ & \\
			\hline
			\multirow{2}[2]{*}{\text{Map2}} & \text{CGAN-RRT*} & $\textbf{192}$ & $\textbf{9312}$ & $\textbf{504}$ & \multirow{2}[2]{*}{$435$} \\
			& \text{RRT*} & $419$ & $13680$ & $815$ & \\
			\hline
			\multirow{2}[2]{*}{\text{Map3}} & \text{CGAN-RRT*} & $\textbf{206}$ & $\textbf{8876}$ & $\textbf{573}$ & \multirow{2}[2]{*}{$535$} \\
			& \text{RRT*} & $1229$ & $23006$ & $608$ & \\
			\hline
			\multirow{2}[2]{*}{\text{Map4}} & \text{CGAN-RRT*} & $\textbf{75}$ & $\textbf{5170}$ & $\textbf{444}$ & \multirow{2}[2]{*}{$420$} \\
			& \text{RRT*} & $710$ & $18164$ & $513$ & \\
			\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Conditional Generative Adversarial Networks for Optimal Path Planning" by Nachuan Ma, Jiankun Wang, and Max Q. -H. Meng, arXiv:2012.03166v1, 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}{|lc|c|}
\hline
Parameter & & Value \\
\hline
Young's modulus (GPa) & $E$ & 240 \\
Poisson's ratio & $v$ & 0.27 \\
Yield strength (MPa) & $Y$ & 600 \\
Initial void size ($\mu$m$^3$) & $\nu_0$ & 0.1 \\
Initial void density ($\mu$m$^{-3}$) & $\eta_0$ & 0.001 \\
Flow exponent & $n$ & 10 \\
Damage exponent & $m$ & 2 \\
Flow coefficient & $f$ & 10 \\
Isotropic dynamic recovery & $R$ & 4\\
Isotropic hardening (GPa) & $H$ & 5\\
Initial hardening (MPa) & $\kappa_0$ & 460\\
Initial damage & $\phi_0$ & 0.08\\
Shear nucleation & $N_1$ & 10 \\
Triaxiality nucleation & $N_3$ & 13\\
Maximum damage & $\phi_\text{max}$ & 0.5 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Wyatt Bridgman
Xiaoxuan Zhang
Greg Teichert
Mohammad Khalil
Krishna Garikipati
Reese Jones

"A heteroencoder architecture for prediction of failure locations in porous metals using variational inference" by Wyatt Bridgman, Xiaoxuan Zhang, Greg Teichert, Mohammad Khalil, Krishna Garikipati, and Reese Jones, arXiv:2202.00078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Bosch dataset}
\begin{tabular}{cc|cccc}
		\toprule
		\multicolumn{2}{c |}{\textbf{Sample Num (1184687)}} & \multicolumn{4}{c}{\textbf{Features Num (968)}} \\
		\textbf{Positive} & \textbf{Negative} & \textbf{L0} & \textbf{L1} & \textbf{L2} & \textbf{L3}\\
		\midrule
		1177808 & 6879 & 168 & 513 & 42 & 245 \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

cs

Ning Ge
Guanghao Li
Li Zhang
Yi Liu Yi Liu

"Failure Prediction in Production Line Based on Federated Learning: An Empirical Study" by Ning Ge, Guanghao Li, Li Zhang, and Yi Liu Yi Liu, arXiv:2101.11715v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule
 \textbf{S.No.} & $\alpha$ & $\varepsilon$ & $\delta$ & \textbf{NEs} \\
 \midrule
 1 & 2 & 0.9 & 4 & 49 \\
 2 & 2 & 0.9 & 0.4 & 32 \\
 3 & 2 & 0.54 & 4 & 6 \\
 4 & 2 & 0.54 & 0.4 & 12 \\
 5 & 0.2 & 0.9 & 4 & 557 \\
 6 & 0.2 & 0.9 & 0.4 & 262 \\
 7 & 0.2 & 0.54 & 4 & 142 \\
 8 & 0.2 & 0.54 & 0.4 & 31 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{The number of symmetric NEs }
\end{table}
\end{document}

q-fin

Gurkirat Wadhwa
Akansh Verma
Veeraruna Kavitha
Priyank Sinha

"Price equilibria with positive margins in loyal-strategic markets with discrete prices" by Gurkirat Wadhwa, Akansh Verma, Veeraruna Kavitha, and Priyank Sinha, arXiv:2506.17239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Jason A. Platt
Stephen G. Penny
Timothy A. Smith
Tse-Chun Chen
Henry D. I. Abarbanel

"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, 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{MSE and MAE for mortality evaluation at various $\tau$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \hline
 \textbf{Threshold} & \multicolumn{2}{c}{\textbf{MSE} $\downarrow$} & \multicolumn{2}{c}{\textbf{MAE} $\downarrow$} \\
 & MC & PEMC & MC & PEMC \\
 \hline
 $n=0$ & 10.974 & 4.142 & 2.765 & 1.587 \\
 $n=20$ & 9.535 & 3.266 & 2.497 & 1.416 \\
 $n=40$ & 9.174 & 3.122 & 2.428 & 1.376 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Fengpei Li
Haoxian Chen
Jiahe Lin
Arkin Gupta
Xiaowei Tan
Honglei Zhao
Gang Xu
Yuriy Nevmyvaka
Agostino Capponi
Henry Lam

"Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate" by Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Honglei Zhao, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, and Henry Lam, arXiv:2412.11257v3, 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}
& \multicolumn{5}{c}{sample size $N$}\\
& 100 & 300 & 1000 & 3000 & 10000 \\
\midrule
SBTM (ours) & \textbf{0.24} & 0.19 & \textbf{0.11} & \textbf{0.089} & \textbf{0.084} \\
SDE & 0.24 & \textbf{0.18} & 0.14 & 0.094 & 0.084 \\
SVGD & 4.8 & 3.3 & 4.7 & 5.3 & 5.5 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Score-Based Deterministic Density Sampling" by Vasily Ilin, Peter Sushko, and Jingwei Hu, arXiv:2504.18130v2, 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{Performance comparison on MSMT17 dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
		\hline
		\multirow{2}{*}{Methods} & \multicolumn{2}{c}{MSMT17} \\ \cline{2-3}
		& Rank-1(\%) & mAP(\%) \\ \hline
		IANet & 75.5 & 46.8 \\
		DGNet & 77.2 & 52.3 \\
		RGA-SC & 80.3 & 57.5 \\
		SCSN & 83.8 & 58.5 \\
		AdaptiveReID & 81.7 & 62.2 \\ \hline
		FastReID & 85.1 & 63.3 \\
		FastReID + GGT(ours) & \textbf{86.2} & \textbf{65.3} \\
		FastReID + GGT\&LGT(ours) & \textbf{86.2} & \textbf{65.9} \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method" by Yunpeng Gong, Liqing Huang, and Lifei Chen, arXiv:2101.08533v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l}
 \toprule
 \textbf{\em \textcolor{blue}{Easy} questions} \\
 Q: What city in america has the highest population / Gold: New York City \\
 {\em New York City}: New York City is the most populous city in the United States ... \\
 \midrule
 Q: Who plays 11 in stranger things on netflix / Gold: Millie Bobby Brown \\
 {\em Millie Bobby Brown}: She gained notability for her role as Eleven in the first season of Netflix science \\ fiction series Stranger Things. \\
 \midrule
 Q: Where did leave it to beaver take place / Gold: Mayfield \\
 {\em Leave It to Beaver}: Leave It to Beaver is set in the fictitious community of Mayfield and its environs. \\
 \midrule
 Q: The substance that is dissolved in the solution / Gold: solute \\
 {\em Solution}: A solute is a substance dissolved in another substance, known as a solvent. \\
 \midrule
 Q: This means that in dna cytosine is always paired with / Gold: guanine \\
 {\em Guanine}: In DNA, guanine is paired with cytosine. \\
 \toprule
 \textbf{\em \textcolor{red}{Hard} questions} \\
 Q: What is the area code for colombo sri lanka / Gold: 036 \\
 Telephone numbers in Sri Lanka: (Answer in the table) \\
 \midrule
 Q: Which country is highest oil producer in the world / Gold: United States / Predictions: Russia \\
 {\em History of the petroleum industry in the United States}: For much of the 19th and 20th centuries, the \\ US was the largest oil producing country in the world. \\
 {\em List of countries by oil production}: (Answer in the table) \\
 {\em Russia}: Russia is the world's leading natural gas exporter and second largest natural gas producer, \\ while also the second largest oil exporter and the third largest oil producer. \\
 \midrule
 Q: Who did gibbs shoot in season 10 finale / Gold: Mendez \\
 Predictions: fbi agent tobias fornell (from all systems in unrestricted/6Gb track) %, pedro hernandez, aquaria, magnussen
 \\
 {\em Past, Present and Future (NCIS)} Gibbs realizes that Mendez is .. The show then returns to the scene \\ depicted in a scene from season 10 finale's ... shoots and kills Mendez. \\
 \midrule
 Q: The girl next door film based on a true story / Gold: loosely / Prediction: girl next door	\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sewon Min
Jordan Boyd-Graber
Chris Alberti
Danqi Chen
Eunsol Choi
Michael Collins
Kelvin Guu
Hannaneh Hajishirzi
Kenton Lee
Jennimaria Palomaki
Colin Raffel
Adam Roberts
Tom Kwiatkowski
Patrick Lewis
Yuxiang Wu
Heinrich Küttler
Linqing Liu
Pasquale Minervini
Pontus Stenetorp
Sebastian Riedel
Sohee Yang
Minjoon Seo
Gautier Izacard
Fabio Petroni
Lucas Hosseini
Nicola De Cao
Edouard Grave
Ikuya Yamada
Sonse Shimaoka
Masatoshi Suzuki
Shumpei Miyawaki
Shun Sato
Ryo Takahashi
Jun Suzuki
Martin Fajcik
Martin Docekal
Karel Ondrej
Pavel Smrz
Hao Cheng
Yelong Shen
Xiaodong Liu
Pengcheng He
Weizhu Chen
Jianfeng Gao
Barlas Oguz
Xilun Chen
Vladimir Karpukhin
Stan Peshterliev
Dmytro Okhonko
Michael Schlichtkrull
Sonal Gupta
Yashar Mehdad
Wen-tau Yih

"NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned" by Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, and Wen-tau Yih, arXiv:2101.00133v2, 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
\textbf{AI lab} & \textbf{Year} & \textbf{Human capital share} & \textbf{Compute share} \\ \hline
C3.ai & 2020 & 0.275 & 0.725 \\
OpenAI & 2018 & 0.335 & 0.665 \\
\textbf{MS COCO 2017 (B1+trend)} & \textbf{--} & \textbf{0.581} & \textbf{0.419} \\
\textbf{ImageNet (A3+trend)} & \textbf{--} & \textbf{0.675} & \textbf{0.325} \\
Splunk & 2021 & 0.808 & 0.192 \\
Alteryx & 2021 & 0.900 & 0.100 \\
DocuSign & 2021 & 0.911 & 0.089 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Economic impacts of AI-augmented R&D

Tamay Besiroglu
Nicholas Emery-Xu
Neil Thompson

"Economic impacts of AI-augmented R&D" by Tamay Besiroglu, Nicholas Emery-Xu, and Neil Thompson, arXiv:2212.08198v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of results using various schemes and boundary conditions.}
\begin{tabular}{lcc}
 \toprule
 Scheme & Re = 5000 \\ \midrule
 Ghia et al. & $x = 0.5117; \ y = 0.5352$ \\
 Medic et al. & $x = 0.53; \ y = 0.53$ \\
 Stabilized FE (with slip bc) $\mathbb{P}_1^2 \times \mathbb{P}_1$ (adapted mesh) & $x = 0.5018; \ y = 0.5000$ \\
 Stabilized FE (with no-slip bc) $\mathbb{P}_1^2 \times \mathbb{P}_1$ (adapted mesh) & $x = 0.5172; \ y = 0.5351$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Aparna Bansal
Nicolás Barnafi
Rodolfo Araya
Dwijendra Narain Pandey

"Equal order stabilized finite elements with Nitsche for stationary Navier-Stokes problem with slip boundary conditions : a priori and a posteriori error analysis" by Aparna Bansal, Nicolás Barnafi, Rodolfo Araya, and Dwijendra Narain Pandey, arXiv:2501.14715v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Characteristics by ESG Quintile}
\begin{tabular}{lccccc}
\toprule
ESG Quintile & Avg Return & Log Size & Leverage & Profitability & N \\
\midrule
Q1 (Brown) & 0.027\% & 22.04 & 0.386 & 0.122 & 13,017 \\
Q2 & 0.038\% & 22.56 & 0.419 & 0.164 & 13,008 \\
Q3 & 0.038\% & 22.92 & 0.439 & 0.189 & 13,034 \\
Q4 & 0.004\% & 23.31 & 0.512 & 0.133 & 13,033 \\
Q5 (Green) & -0.002\% & 23.65 & 0.510 & 0.196 & 13,010 \\
\midrule
No ESG Data & 0.030\% & 21.40 & 0.430 & -0.000 & 18,459 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Green Shields: The Role of ESG in Uncertain Time" by Fatih Kansoy and Dominykas Stasiulaitis, arXiv:2506.02143v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\hline
Strategy & $(1/K)\sum_{k=1}^{K}\log(V_{T_{k}})$ \\
\hline
FGP & 0.078694 \\
EWP & 0.0092019 \\
Market & -9.79007e-07 \\
DWP $p=0.3$ & 0.00660325 \\
DWP $p=0.5$ & 0.00478323 \\
DWP $p=0.8$ & 0.0019446 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Neural Functionally Generated Portfolios" by Michael Monoyios and Olivia Pricilia, arXiv:2506.19715v1, 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{Controllers from the Literature}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 Authors & Citation & Legend Code \\ \hline
 Taylor et al. & & TAY \\ \hline
 Black et al. & & BLA \\ \hline
 Lopez et al. & (w/o SMID) & LOP \\ \hline
 Lopez et al. & (w/ SMID) & LSM \\ \hline
 Zhao et al. & & ZHA \\ \hline
 Proposed Method & & PRO \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mitchell Black
Ehsan Arabi
Dimitra Panagou

"A Fixed-Time Stable Adaptation Law for Safety-Critical Control under Parametric Uncertainty" by Mitchell Black, Ehsan Arabi, and Dimitra Panagou, arXiv:2011.12362v1, 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{Backnode vector for Exercise~. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 Node & Backnode \\
 \hline
 1 & 4 \\
 2 & 6 \\
 3 & $-1$ \\
 4 & 7 \\
 5 & 4 \\
 6 & 5 \\
 7 & 3 \\
 8 & 10 \\
 9 & 5 \\
 10 & 6 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, 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{Main Optimization Parameters with $N_d=10$ and $N_{APs}=2$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|}
\cline{2-3}
 & RF & RF-OC \\ \hline
Avg. Number of Variables & 1572 & 3521 \\ \hline
Avg. Number of Constraints & 3823 & 6246 \\ \hline
Avg. Running time (s) for 3\% Duality Gap & 11 & 40 \\ \hline
Avg. Duality Gap for 60 (s) & 1.2\% & 1.3\% \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Aymen Hamrouni
Sofie Pollin
Hazem Sallouha

"AoI in Context-Aware Hybrid Radio-Optical IoT Networks" by Aymen Hamrouni, Sofie Pollin, and Hazem Sallouha, arXiv:2412.12914v3, 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
		& size & \multicolumn{2}{c}{speed} \\
 \cmidrule(lr){2-2} \cmidrule(lr){3-4}
		& params & inference & training \\
		\midrule
		 & 330\,k & 10\,ms & 10\,h \\ % 328'339
		DeepSphere (Jiang architecture) & 590\,k & 5\,ms & 3\,h \\ % 590k
 DeepSphere & 13\,M & 33\,ms & 13\,h \\ % 12'926'432
		DeepSphere (wider architecture) & 52\,M & 50\,ms & 20\,h \\
		\bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

DeepSphere: a graph-based spherical CNN

Michaël Defferrard
Martino Milani
Frédérick Gusset
Nathanaël Perraudin

"DeepSphere: a graph-based spherical CNN" by Michaël Defferrard, Martino Milani, Frédérick Gusset, and Nathanaël Perraudin, arXiv:2012.15000v1, 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}{lllllll}
\toprule
Type & $\zeta(x)$ & Exact & Source term & Kernel & PINN MAE & RISN MAE \\ \midrule
Fredholm & $x$ & $\sin(x t)$ & $x \cos(y x) + \frac{-1 + \cos(x)}{x}$ & $1$ & $3.00 \times 10^{-3}$ & $2.51 \times 10^{-4}$ \\
Fredholm & $x$ & $\sin(x t)$ & $x \cos(y x) - x \sin(y) + x \sin(y) \cos(x)$ & $x^2 \sin(y)$ & $1.24 \times 10^{-3}$& $1.95 \times 10^{-4}$ \\
Fredholm & $x^2$ & $\sin(x t)$ & $x \cos(y x) + \frac{\cos(x) \sin(x) - x}{2x}$ & $1$ & $1.89 \times 10^{-3}$ & $1.05 \times 10^{-4}$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Numerical results for solving partial integro-differential equations using the proposed RISN and traditional PINN, comparing the MAE to showcase RISN's superior performance.}
\end{table}
\end{document}

math

Mahdi Movahedian Moghaddam
Kourosh Parand
Saeed Reza Kheradpisheh

"Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations" by Mahdi Movahedian Moghaddam, Kourosh Parand, and Saeed Reza Kheradpisheh, arXiv:2501.16370v2, 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{Details of benchmark datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 ID & Name & m & n \\
 \midrule
 (a) & Cleveland & 173 & 13 \\
 (b) & Ionosphere & 351 & 34 \\
 (c) & New-thyroid & 215 & 4 \\
 (d) & Parkinsons & 195 & 22 \\
 (e) & Sonar & 208 & 60 \\
 (f) & TicTacToe & 958 & 27 \\
 (g) & Vowel & 988 & 13 \\
 (h) & Wisconsin & 683 & 9 \\
 (i) & German & 1000 & 20 \\
 (j) & Shuttle & 1829 & 9 \\
 (k) & Segment & 2308 & 19 \\
 (l) & Waveform & 5000 & 21 \\
 (m) & TwoNorm & 7400 & 20 \\
 (n) & IJCNN01 & 49990 & 22 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Least Squares Maximum and Weighted Generalization-Memorization Machines" by Shuai Wang, Zhen Wang, and Yuan-Hai Shao, arXiv:2308.16456v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l|l|l}
 Char. & t-stat & p-value & average-1 & average-2 \\
 \hline
 $Z^I$ & -100.40 & 0.00 & 0.16 & 0.78 \\
 $\bar{Q}$ & 84.83 & 0.00 & 0.17 & 0.02 \\
 $Y_{tot}^\alpha$ & 81.17 & 0.00 & 1.08 & 0.35 \\
 $Y_{tot}^I$ & 4.82 & $1.44\times 10^{-6}$ & 1.42 & 1.35
 \end{tabular}
\end{adjustbox}
\caption{Two sample t-test for characteristics in Scenario 1 ($\phi_1=2$) and Scenario 2 ($\phi_1=20$). The other parameters are the same as in Figure .}
\end{table}
\end{document}

q-fin

"Liquidity Competition Between Brokers and an Informed Trader" by Ryan Donnelly and Zi Li, arXiv:2503.08287v1, 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{Online results. $\uparrow$/$\downarrow$ means that higher/lower is better. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Method & CVR ($\uparrow$) & CTCVR ($\uparrow$) & CPA ($\downarrow$) \\
 \midrule
 UKD & +3.4\% & +5.0\% & -4.3\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zixuan Xu
Penghui Wei
Weimin Zhang
Shaoguo Liu
Liang Wang
Bo Zheng

"UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation" by Zixuan Xu, Penghui Wei, Weimin Zhang, Shaoguo Liu, Liang Wang, and Bo Zheng, arXiv:2201.08024v1, 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{Graph datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
Graphs & \#vertices & \#edges & Description \\ \hline\hline
CiteSeer (CI)~ & 3264 & 4536 & Publication citation \\ \hline
MiCo (MI)~ & 100K & 1.1M & Co-authorship \\ \hline
Orkut (OK)~ & 3.1M & 117.2M & Social network \\ \hline
UK-2005 (UK)~ & 39M & 936M & Social network \\ \hline
Friendster (FR)~ & 65M & 1.8B & Social network \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Efficient Mining of Frequent Subgraphs with Two-Vertex Exploration" by Peng Jiang, Rujia Wang, and Bo Wu, arXiv:2101.07690v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data provided by SdSv for speaker verification.}
\begin{tabular}{c|c}
 \toprule
 enrollment & test\\
 \hline
 1 to 29 utterances (average of 7) & 1 utterance\\
 giving a net speech duration & (95\% less than\\
 from 3 to 120 seconds & 5 seconds)\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"Asymmetric and trial-dependent modeling: the contribution of LIA to SdSV Challenge Task 2" by Pierre-Michel Bousquet and Mickael Rouvier, arXiv:2403.19634v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
		Credit line & \$1 million \\
		Rate & SOFR 30-day compounded average + AXI \\
		Spread & 1.00\% \\
		Day count & 360 \\
		Amortized & No \\
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

A Case for AXI

"A Case for AXI" by Viktor Tsyrennikov, arXiv:2509.03035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data summary of the collected coverage}
\begin{tabular}{ccccc}
\hline
Media & Tot. collected files & Total hours & Avg. time per file \\
\hline
{BLW} & {744} & {171.16} & 14~minutes \\
{BSM} & {3885} & {398.15} & 6~minutes \\
{YFM} & {318} & {2467} & 8~hours \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Jean Marie Tshimula
D'Jeff K. Nkashama
Patrick Owusu
Marc Frappier
Pierre-Martin Tardif
Froduald Kabanza
Armelle Brun
Jean-Marc Patenaude
Shengrui Wang
Belkacem Chikhaoui

"Characterizing Financial Market Coverage using Artificial Intelligence" by Jean Marie Tshimula, D'Jeff K. Nkashama, Patrick Owusu, Marc Frappier, Pierre-Martin Tardif, Froduald Kabanza, Armelle Brun, Jean-Marc Patenaude, Shengrui Wang, and Belkacem Chikhaoui, arXiv:2302.03694v1, 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}{ccc}
 \hline
 \textbf{Model} & \textbf{HumanAccept-zh} & \textbf{HumanChitchat-zh} \\ \hline
 GLM-4-Voice & 92.55 & 59.50 \\
 MiniCPM-o-2.6 & 87.60 & 58.29 \\
 Baichuan-Omni-1.5 & 95.00 & 26.26 \\
 LLaMA-Omni & 49.16 & 9.21 \\
 SpeechGPT-2.0-preview & 76.41 & 41.22 \\
 Freeze-Omni & 87.57 & 30.9 \\
 Qwen2.5-Omni & 82.93 & 80.89 \\
 Kimi-Audio & 86.67 & 47.95 \\
 GPT4o-Audio (API) & 96.29 & 34.45 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Model scores (\%) on Safety\&Values and Chitchat\&Human-likeness Tasks.}
\end{table}
\end{document}

eess

Zehan Li
Hongjie Chen
Yuxin Zhang
Jing Zhou
Xuening Wang
Hang Lv
Mengjie Du
Yaodong Song
Jie Lian
Jian Kang
Jie Li
Yongxiang Li
Zhongjiang He
Xuelong Li

"TELEVAL: A Dynamic Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios" by Zehan Li, Hongjie Chen, Yuxin Zhang, Jing Zhou, Xuening Wang, Hang Lv, Mengjie Du, Yaodong Song, Jie Lian, Jian Kang, Jie Li, Yongxiang Li, Zhongjiang He, and Xuelong Li, arXiv:2507.18061v1, 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
111.97&104.06&1.076\\
\hline
\end{tabular}
\end{adjustbox}
\caption{\sf Example 2: 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cll|}
			\hline
			\textbf{Case} & \textbf{Conditions} & \textbf{Finite singular points} \\
			\hline
			\hline
			1& $b\delta >c-\delta$. & $P_0$ saddle, $P_1$ stable node. \\	\hline
			2& $b\delta =c-\delta$. & $P_0$ saddle, $P_1$ saddle-node.\\	\hline
			3& $0<b\delta <c-\delta$, $B\geq0$, $A>0$. & $P_0$ saddle, $P_1$ saddle, $P_2$ unstable node. \\	\hline
			4& $0<b\delta <c-\delta$, $B\geq0$, $A<0$. & $P_0$ saddle, $P_1$ saddle, $P_2$ stable node. \\	\hline
			5& $0<b\delta <c-\delta$, $B<0$, $A>0$. & $P_0$ saddle, $P_1$ saddle, $P_2$ unstable focus. \\	\hline
			6& $0<b\delta <c-\delta$, $B<0$, $A<0$. & $P_0$ saddle, $P_1$ saddle, $P_2$ stable focus. \\	\hline
			7& $0<b\delta <c-\delta$, $B<0$, $A=0$. & $P_0$ saddle, $P_1$ saddle, $P_2$ weak stable focus. \\	\hline
		\end{tabular}
\end{adjustbox}
\caption{The finite singular points in the closed positive quadrant.}
\end{table}
\end{document}

math

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

"Global phase portraits of a predator-prey system" by Érika Diz-Pita, Jaume Llibre, and M. V. Otero-Espinar, arXiv:2501.13728v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|c|}
 \hline
 Dataset & Start (s) & Length (s) & Metric & Best & Model config. \\ \hline \hline
 MMI & 0 & 6 & BAC & 86.7 & Linear (2.) \\ \hline
 BCIC & -2 & 6 & Accuracy & 42.6 & Linear (2.) \\ \hline
 ERN & -0.7 & 2 & AUROC & 0.65 & Linear (2.) \\ \hline
 SSC & 0 & 30 & BAC & 0.72 & Linear (2.) \\ \hline
 P300 & -0.7 & 2 & AUROC & 0.72 & BENDR (1.) \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Performances of downstream datasets. Start and length refer to length of trials and start with respect to event markers in seconds. Best performance specifies average performance across all subjects (and therefore folds) for best performing model configuration. BAC: class balanced accuracy; AUROC: area under the receiver operating characteristic curve. Model configurations are numbered in accordance with the list presented in section .}
\end{table}
\end{document}

cs

Demetres Kostas
Stephane Aroca-Ouellette
Frank Rudzicz

"BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data" by Demetres Kostas, Stephane Aroca-Ouellette, and Frank Rudzicz, arXiv:2101.12037v1, 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.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c}
 & $k = 2$ & $k = 3$ & $k = 4$ & $k \geq 5$ \\ \hline
 Conjectured bound & $f_k(\mathcal{F})\geq\frac13$ & $f_k(\mathcal{F})\geq\frac15$ & $f_k(\mathcal{F})\geq\frac19$ & $f_k(\mathcal{F})\geq\frac1{2^{k-1}+1}$\\ \hline
 & $m\leq 5$ & $m\leq 9$ & $m\leq 17$ & $ m \le 2^k + 1$ \\
 & & & & $2^k + 2 \leq m \leq 2^{3(k-1)}$ \\
 & $m\geq 4$ & $m\geq 6$ & $m\geq 14$ & $m \geq 2^{2.71(k-1)}$ \\
 \end{tabular}
\caption{Effective range of three propositions, with $|\mathcal{F}|=m$}
\end{table}
\end{document}

math

"Frequent elements in union-closed set families" by Shagnik Das and Saintan Wu, arXiv:2412.03862v3, 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
$p$ & Anti-corr Gaussian & NUTS & Comp-wise slice & EPC slice \\ \hline
10 & \textbf{0.41} & 26.18 & 3.61 & 3.04 \\ \hline
50 & \textbf{0.93} & 108.33 & 31.44 & 4.16 \\ \hline
500 & \textbf{5.60} & 22875.74 & 1121.47 & 16.14 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Gibbs Sampling using Anti-correlation Gaussian Data Augmentation, with Applications to L1-ball-type Models" by Yu Zheng and Leo L. Duan, arXiv:2309.09371v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective" by Mustafa Cavus and Przemyslaw Biecek, arXiv:2412.09712v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A comprehensive numerical investigation of a coupled mathematical model of neuronal excitability" by Burcu Gürbüz, Aytül Gökçe, and Mahmut Modanlı, arXiv:2501.18013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c}
 \hline
 $h_{\text{min}}$ & $h_{\text{max}}$ & $\Delta h$ & $\lambda_{\text{min}}$ & $\lambda_{\text{max}}$ & $\Delta \lambda$ & $t_{\text{init}}$ & $H_{\text{init}}$ & $\lambda_t(\omega)$\\
 \hline
 $0$ & $50$ & $0.5$ & $27$ & $216.6$ & $1$ & $t$ & $h$ & $\lambda^P$ \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Meta-parameters for Algorithm .}
\end{table}
\end{document}

q-fin

Giorgia Callegaro
Claudio Fontana
Caroline Hillairet
Beatrice Ongarato

"A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks" by Giorgia Callegaro, Claudio Fontana, Caroline Hillairet, and Beatrice Ongarato, arXiv:2505.01221v1, 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{Experiment results on the diffferent modalities.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
		\toprule
		Modality & ACC(\%) & WF1(\%) \\
		\midrule
		A & 66.06 & 65.59 \\
		T & 58.74 & 58.63 \\
		V & 29.88 & 26.31 \\
		A+T & 67.75 & 67.45 \\
		A+V & 66.33 & 64.14 \\
		A+V+T & 70.53 & 70.6 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"WavFusion: Towards wav2vec 2.0 Multimodal Speech Emotion Recognition" by Feng Li, Jiusong Luo, and Wanjun Xia, arXiv:2412.05558v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Weighted F1 scores of the evaluated sentiment-based methods on the FPB, TFNS, and NWGI financial sentiment datasets. HIV-4, VADER, and LMD are lexicon-based baselines. FinBERT and FinLlama are SFT classification LLMs, while Instruct-FinGPT, FinGPT v3.3 and FinSFT are instruction-tuned causal LLMs. FinDPO is our proposed DPO-aligned causal LLM. FinBERT is excluded from evaluation on FPB due to data leakage, as it was originally trained on the full FPB dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\hline
\textbf{Model} & \textbf{FPB} & \textbf{TFNS} & \textbf{NWGI} & \textbf{Average} \\ \hline
HIV-4 & 0.357 & 0.401& 0.384 & 0.385 \\
VADER & 0.536 & 0.518 & 0.462 & 0.491 \\
LMD & 0.546 & 0.572 & 0.440 & 0.498 \\
FinBERT & --- & 0.733 & 0.538 & 0.611\\
FinLlama & 0.707 & 0.904 & 0.538 & 0.679\\
Instruct-FinGPT & 0.777 & 0.828 & 0.583 & 0.690\\
FinGPT v3.3 & 0.879 & 0.903 & 0.643 & 0.762 \\
\textit{FinSFT} & 0.829 & 0.850 & 0.708 & 0.771 \\
\textbf{FinDPO (Ours)} & 0.865 & 0.872 & 0.833 & \textbf{0.846} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Giorgos Iacovides
Wuyang Zhou
Danilo Mandic

"FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs" by Giorgos Iacovides, Wuyang Zhou, and Danilo Mandic, arXiv:2507.18417v1, 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{\small{Utilitarian and egalitarian HC for the 4 feeders with the Gini coefficient evaluating the inequality of the utilitarian solution and the PoF assessing the efficiency of the egalitarian solution.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 RF & $HC_{uti}$ [$kW$] & $Gini_{uti}$ & $HC_{ega}$ [$kW$] & $PoF_{ega}$ \\
 \hline
 $1$ & 658 & 0.3062 & 520 & 0.21 \\
 $2$ & 1575 & 0.9876 & 937 & 0.41 \\
 $3$ & 922 & 0.8148 & 325 & 0.65 \\
 $4$ & 699 & 0.7247 & 442 & 0.37 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Olivia Rubbers
Sari Kerckhove
Md Umar Hashmi
Dirk Van Hertem

"Fairness for distribution network hosting capacity" by Olivia Rubbers, Sari Kerckhove, Md Umar Hashmi, and Dirk Van Hertem, arXiv:2508.16834v1, 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}
\toprule
\textbf{Top 15 selected unigrams and bigrams}\\
\midrule
waiver (0.26), \_UNK\_ million (0.21), restructuring (0.21), \\severance (0.20), subordinated (0.20), financial covenant (0.15), \\ indenture (0.14), lender (0.14), interest payment (0.14), \\senior secured (0.14), asset sale (0.12), senior (0.09), \\ cross default (0.09), indebtedness (0.07), \\event default (0.05), credit facility (0.05) \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{ Top 15 tokens with largest logistic regression coefficients (shown in parentheses) of the Binary bag-of-words model with single year history.}
\end{table}
\end{document}

q-fin

Henri Arno
Klaas Mulier
Joke Baeck
Thomas Demeester

"Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines" by Henri Arno, Klaas Mulier, Joke Baeck, and Thomas Demeester, arXiv:2208.11334v1, 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
\caption{Evaluation on the fluctuated effective bandwidth}
\begin{tabular}{l|c|cc}
 \toprule
 Setting & method & LSD $\downarrow$ & POLQA $\uparrow$ \\
 \midrule
 \multirow{3}*{10-48 kHz} &unprocessed & 2.55 &3.96 \\
 \cline{2-4}
 & AERO~{} & 0.82 & 4.26\\
 \cline{2-4}
 & BAE-Net & \textbf{0.69} & \textbf{4.38}\\
 \cline{1-4}
 \multirow{3}*{14-48 kHz} &unprocessed & 2.03 &4.40\\
 \cline{2-4}
 & AERO~{} & 0.69 & 4.42\\
 \cline{2-4}
 & BAE-Net & \textbf{0.56} & \textbf{4.54}\\
 \cline{1-4}
 \multirow{3}*{20-48 kHz} &unprocessed & 1.05 &4.58\\
 \cline{2-4}
 & AERO~{} & 0.51 & 4.57\\
 \cline{2-4}
 & BAE-Net & \textbf{0.42} & \textbf{4.64}\\
 \cline{1-4}
 \multirow{3}*{24-48 kHz} &unprocessed & 0.08 &4.61\\
 \cline{2-4}
 & AERO~{} & \textbf{0.03} & 4.65\\
 \cline{2-4}
 & BAE-Net & 0.04 & \textbf{4.68}\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Guochen Yu
Xiguang Zheng
Nan Li
Runqiang Han
Chengshi Zheng
Chen Zhang
Chao Zhou
Qi Huang
Bing Yu

"BAE-Net: A Low complexity and high fidelity Bandwidth-Adaptive neural network for speech super-resolution" by Guochen Yu, Xiguang Zheng, Nan Li, Runqiang Han, Chengshi Zheng, Chen Zhang, Chao Zhou, Qi Huang, and Bing Yu, arXiv:2312.13722v1, 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|ccc}
 Model & P & R & F1 \\ \hline
 MF & 89.03 & 42.2 & 57.26 \\
 ADE & 86.74 & 43.25 & 57.72 \\
 \hline \hline
 NOA & 78.14 & 35.06 & 48.40 \\
 UAD & 89.01 & 70.08 & 78.37 \\
 BEM & 86.75 & 35.94 & 50.82 \\
 DECBAE & 88.67 & 74.32 & 80.86 \\
 GAD & 89.27 & 76.66 & 81.90 \\ \hline \hline
 MadDog & 92.27 & 85.01 & 88.49
 \end{tabular}
\end{adjustbox}
\caption{Performance of models for acronym disambiguation (AD)}
\end{table}
\end{document}

cs

Amir Pouran Ben Veyseh
Franck Dernoncourt
Walter Chang
Thien Huu Nguyen

"MadDog: A Web-based System for Acronym Identification and Disambiguation" by Amir Pouran Ben Veyseh, Franck Dernoncourt, Walter Chang, and Thien Huu Nguyen, arXiv:2101.09893v1, 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{Permutation prediction accuracy (\%) on validation data for different segment counts and training strategies.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\toprule
\multirow{2}{*}{Training Strategy} & \multicolumn{6}{c}{Number of Segments ($N$)} \\
\cmidrule{2-7}
 & 3 & 4 & 5 & 6 & 7 & 8 \\
\midrule
Direct (Epoch 30) & 100.0 & 94.8 & 67.4 & 35.7 & 9.2 & 0.3 \\
Direct (Epoch 100) & 100.0 & 100.0 & 99.5 & 70.2 & 32.5 & 1.7 \\
Direct (Epoch 200) & 100.0 & 100.0 & 100.0 & 87.6 & 68.3 & 4.9 \\
\midrule
Curriculum (Epoch 30) & 100.0 & - & - & - & - & - \\
Curriculum (Epoch 50) & - & 100.0 & - & - & - & - \\
Curriculum (Epoch 90) & - & - & 100.0 & - & - & - \\
Curriculum (Epoch 140) & - & - & - & 100.0 & - & - \\
Curriculum (Epoch 170) & - & - & - & - & 100.0 & - \\
Curriculum (Epoch 200) & - & - & - & - & - & 84.2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Daniel La'ah Ayuba
Jean-Yves Guillemaut
Belen Marti-Cardona
Oscar Mendez Maldonado

"SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation" by Daniel La'ah Ayuba, Jean-Yves Guillemaut, Belen Marti-Cardona, and Oscar Mendez Maldonado, arXiv:2507.19781v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results on the Caltech 101 dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\textbf{Method} & \textbf{Accuracy}\\
\hline
BoAW & 74.00\% \\
\hline
 BoVW & \textbf{83.00\%} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of trading strategies, net of fees.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 \toprule
 \textbf{Strategy} & \textbf{Trades} & \textbf{Mean Return} & \textbf{Std Dev Return} & \textbf{Avg Holding Time (sec)} \\
 \midrule
 Imbalance taker & 18927 & $-1.9621$ & 2.1653 & 15.04 \\
 Naive maker & 12623 & $-0.4307$ & 2.6486 & 22.56 \\
 Imbalance maker w/o cancel & 8851 & $-0.4705$ & 2.8529 & 32.17 \\
 Imbalance maker w/ cancel & 7333 & $-0.4921$ & 3.1143 & 38.83 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Jakob Albers
Mihai Cucuringu
Sam Howison
Alexander Y. Shestopaloff

"To Make, or to Take, That Is the Question: Impact of LOB Mechanics on Natural Trading Strategies" by Jakob Albers, Mihai Cucuringu, Sam Howison, and Alexander Y. Shestopaloff, arXiv:2502.18625v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
\toprule
region & country \\ \hline
America S & Antigua and Barbuda \\
America S & Argentina \\
America S & Bahamas \\
America S & Barbados \\
America S & Belize \\
America S & Bolivia (Plurinational State of) \\
America S & Brazil \\
America S & Chile \\
America S & Colombia \\
America S & Costa Rica \\
America S & Cuba \\
America S & Dominica \\
America S & Dominican Republic \\
America S & Ecuador \\
America S & El Salvador \\
America S & Grenada \\
America S & Guatemala \\
America S & Guyana \\
America S & Haiti \\
America S & Honduras \\
America S & Jamaica \\
America S & Mexico \\
America S & Nicaragua \\
America S & Panama \\
America S & Paraguay \\
America S & Peru \\
America S & Puerto Rico \\
America S & Saint Kitts and Nevis \\
America S & Saint Lucia \\
America S & Saint Vincent and the Grenadines \\
America S & Suriname \\
America S & Trinidad and Tobago \\
America S & Uruguay \\
America S & Venezuela (Bolivarian Republic of) \\
America N & Canada \\
America N & United States of America \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Countries and world regions in the Americas}
\end{table}
\end{document}

q-fin

Moritz Laber
Peter Klimek
Martin Bruckner
Liuhuaying Yang
Stefan Thurner

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


math

"Additive and Multiplicative Coinvariant Spaces of Weyl Groups in the Light of Harmonics and Graded Transfer" by Sebastian Debus and Tobias Metzlaff, arXiv:2412.17099v1, 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
\caption{\small\emph{Names and wavelengths of spectral bands of Sentinel2 images. SWIR: Short Wave Infrared, NIR: Near Infrared. The bands are ordered in increasing wavelength.}}
\begin{tabular}{c|c|c|c}
 \multirow[c]{2}{*}{Band Name} & Central & \multirow[c]{2}{*}{Bandwidth (mm)} & \multirow[c]{2}{*}{Description}\\
 & Wavelength (nm) & & \\
 \hline
 B01 & 442.7 & 21 & Coastal aerosol\\
 B02 & 492.4 & 66 & Blue\\
 B03 & 559.8 & 36 & Green\\
 B04 & 664.6 & 31 & Red\\
 B05 & 704.1 & 15 & Vegetation Red Edge 1\\
 B06 & 740.5 & 15 & Vegetation Red Edge 2\\
 B07 & 782.8 & 20 & Vegetation Red Edge 3\\
 B08 & 832.8 & 106 & NIR\\
 B08A & 864.7 & 21 & Narrow NIR\\
 B09 & 945.1 & 20 & Water Vapor\\
 B11 & 1613.7 & 91 & SWIR 1\\
 B12 & 2202.4 & 175 & SWIR 2\\
 \end{tabular}
\end{table}
\end{document}

math

"Incremental Hierarchical Tucker Decomposition" by Doruk Aksoy and Alex A. Gorodetsky, arXiv:2412.16544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Active-power losses and optimality gaps.}
\begin{tabular}{lrrrrrrrrr}
 \toprule
 & \multicolumn{2}{c}{AC} & \multicolumn{2}{c}{Jabr SOC} & & & \\
 \cmidrule(l{0.5em}r{0.40em}){2-3} \cmidrule(l{0.5em}r{0.40em}){4-5}
 Case & Objective & Losses & Objective & Losses & Opt Gap & Loss Gap \\
 \midrule
 1354pegase & 74069.35 & 10.09 & 74008.58 & 9.48 & 0.08\% & 6.04\% & \\ %i2 SOC 74017.66/9.57
 ACTIVSg10k & 2485898.75 & 23.01 & 2466666.10 & 13.88 & 0.77\% & 39.68\% & \\
 ACTIVSg70k & 16439499.83 & 156.83 & 16217263.66 & 121.31 & 1.35\% & 22.65\% & \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

"Accurate Linear Cutting-Plane Relaxations for ACOPF" by Daniel Bienstock and Matias Villagra, arXiv:2312.04251v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A note on Erdős matrices and Marcus\unicode{x2013}Ree inequality" by Aman Kushwaha and Raghavendra Tripathi, arXiv:2503.09542v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The results of different methods on the MELD database.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
		\toprule
		Method & ACC(\%) & WF1(\%) & Year \\
		\midrule
		DialogueTRM & 65.66 & 63.55 & 2020 \\
		MMGCN & - & 58.65 & 2021 \\
		MM-DFN & 62.49 & 59.46 & 2022 \\
		UniMSE & 65.09 & 65.51 & 2022 \\
		HAAN-ERC & 66.5 & 65.66 & 2023 \\
		\textbf{Ours} & \textbf{66.93} & \textbf{66.1} & \textbf{2024} \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{GNN test accuracy on Amazon Computer Dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \toprule
 Architecture & Accuracy \\
 \midrule
 GCN & $0.8677 \pm 0.0004$\\
 SAGE & $0.9124 \pm 0.0007$\\
 GAT & $0.8920 \pm 0.0010$\\
 CGCN & $0.8689 \pm 0.0011$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Eduard Tulchinskii
Daria Voronkova
Ilya Trofimov
Evgeny Burnaev
Serguei Barannikov

"RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks" by Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, and Serguei Barannikov, arXiv:2503.11910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc} \hline
 &
 \multicolumn{3}{c}{\% (Not rejected by \textit{Complete} $\cap$ rejected by \textit{method})} &
 \multicolumn{3}{c}{\% (Rejected by \textit{Complete} $\cap$ Rejected by \textit{method})}
 \\ \hline
 $n_r$ &
 Naive &
 Variability corrected&
 Full &
 Naive &
 Variability corrected&
 Full
 \\ \hline
500 & 0.169 & 0.013 & 0.036 & 0.598 & 0.086 & 0.512 \\
1500 & 0.085 & 0.014 & 0.033 & 0.540 & 0.206 & 0.513 \\
5000 & 0.052 & 0.024 & 0.035 & 0.517 & 0.366 & 0.509 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison of methods on FTO gene analysis containing 33 SNPs. The \textit{Naive} method reject a higher percentage of SNPs not rejected by the complete method (using all of the observations). Note, the parameter $n_r$ has no effect on the \textit{Full} method, and the differences between rows are only due to different samples taken from the original study.}
\end{table}
\end{document}

stat

"Inferring on joint associations from marginal associations and a reference sample" by Tzviel Frostig and Ruth Heller, arXiv:2212.01809v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\cline{1-3}\cline{5-7}
\textbf{Trigram} & \textbf{Count} & \textbf{Match?} & & \textbf{Unigram} & \textbf{Count} & \textbf{Match?} \\\cline{1-3}\cline{5-7}
greatest common divisor & 44 & partly & & jug & 80 & \checkmark \\
the greatest common & 39 & \checkmark & & times & 74 & \checkmark \\
a and b & 31 & \checkmark & & zero & 63 & \checkmark \\
is equal to & 26 & \checkmark & & equal/equals & 63 & \checkmark \\
x plus y & 22 & \checkmark & & y & 60 & \checkmark \\
linear combination of & 20 & \checkmark & & divides & 59 & \checkmark \\
common divisor of & 18 & partly & & gallon & 56 & \checkmark \\
a linear combination & 16 & \checkmark & & m & 54 & \checkmark \\
the b jug & 12 & \checkmark & & right & 54 & \\
three gallon jug & 11 & partly & & x & 53 & \checkmark \\\cline{1-3}
\textbf{Bigram} & \textbf{Count} & \textbf{Match?} & & gallons & 50 & \checkmark \\\cline{1-3}
common divisor & 44 & partly & & plus & 48 & \checkmark \\
greatest common & 44 & \checkmark & & divisor & 45 & \checkmark \\
the greatest & 39 & \checkmark & & one & 45 & \checkmark \\
gallon jug & 39 & \checkmark & & three & 44 & \\
equal to & 39 & \checkmark & & greatest & 44 & \checkmark \\
linear combination & 38 & \checkmark & & common & 44 & \checkmark \\
a and & 38 & \checkmark & & linear & 43 & \checkmark \\
and b & 32 & \checkmark & & minus & 43 & \checkmark \\
is equal & 26 & \checkmark & & combination & 38 & \checkmark \\
to prove & 25 & & & number & 37 & \checkmark \\
plus y & 22 & \checkmark & & prime & 37 & \\
x plus & 22 & \checkmark & & prove & 30 & \\
m divides & 21 & \checkmark & & theorem & 27 & \\
combination of & 20 & \checkmark & & five & 26 & \checkmark \\
s prime & 20 & & & jugs & 24 & \checkmark \\
times a & 19 & & & example & 23 & \\
divisor of & 19 & \checkmark & & algorithm & 23 & \checkmark \\
the remainder & 17 & \checkmark & & state & 22 & \\
that m & 17 & & & remainder & 20 & \checkmark \\\cline{1-3}\cline{5-7}
\end{tabular}
\end{adjustbox}
\caption{Matching results for lecture ``Number Theory I''}
\end{table}
\end{document}

eess

"Unsupervised Spoken Term Discovery on Untranscribed Speech" by Man-Ling Sung, arXiv:2011.14060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{makecell}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Latency setups with corresponding parameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc|c}
\Xhline{2\arrayrulewidth}
\toprule
\multirow{2}{*}{\textbf{Latency}} & Chunk & Right & FIFO & Update & Speaker & \multirow{2}{*}{RTF} \\
 & Size & Context & Queue & Period & Cache & \\
\big[sec\big] & \multicolumn{5}{c|}{\big[frame count\big]} & \\ \midrule
10.0 & 124 & 1 & 124 & 124 & 188 & 0.005 \\
1.04 & 6 & 7 & 188 & 144 & 188 & 0.093 \\
0.32 & 3 & 1 & 188 & 144 & 188 & 0.180 \\
\bottomrule
\Xhline{2\arrayrulewidth}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ivan Medennikov
Taejin Park
Weiqing Wang
He Huang
Kunal Dhawan
Jinhan Wang
Jagadeesh Balam
Boris Ginsburg

"Streaming Sortformer: Speaker Cache-Based Online Speaker Diarization with Arrival-Time Ordering" by Ivan Medennikov, Taejin Park, Weiqing Wang, He Huang, Kunal Dhawan, Jinhan Wang, Jagadeesh Balam, and Boris Ginsburg, arXiv:2507.18446v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
		status & number & $\max|y''-f|$ & $\max|y_{opt}-y_{b}|$ \\ \hline\hline
 $\to y_b$ & 12	 & 1.0E-07 & 1.3E-09 \\
 $\to y_s$ & 11	 & 1.6E-07 & 2.7E+00 \\
 $diverge$ & 2 & diverge & diverge \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c|c}
 Size & Color & Blocks & $n_B$ & $t_B$ & Time $\sigma$ & Time $\sigma^{-1}$ & Time $\mathsf{eval}_h$\\
 \hline\hline
 $224 \times 224$	& RGB & 1000 &	150	& 384 & $0.0235$ &	$0.0963$ & $0.0128$ \\
 $128 \times 128$ & RGB	& 100 & 491	& 1256	& $0.1238$ & $0.3712$ &	$0.0455$ \\
 $64	\times 64$ & RGB &	100	& 122	& 312	& $0.0185$ & $0.0777$ &	$0.0101$ \\
 $28 \times 28$ & RGB & 10 & 235	& 601 &	$0.0436$ &	$0.1584$ & $0.0204$ \\
 $28	\times 28$ & Gray &	1 &	784	& 2007 & $0.2596$ &	$0.6667$ & $0.0784$
 \end{tabular}
\end{adjustbox}
\caption{Total time in seconds taken by our scheme to produce the $\sigma$-polynomial of an image, the $\sigma$-polynomial and its inverse of an image, and the solution using the $\mathsf{eval}_h$ function, where the images are divided into blocks with $t = 0.01qn$.}
\end{table}
\end{document}

math

"Property-Preserving Hashing for $\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks" by Hassan Asghar, Chenhan Zhang, and Dali Kaafar, arXiv:2504.16355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Asymptotic Properties of the Maximum Likelihood Estimator for Markov-switching Observation-driven Models" by Frederik Krabbe, arXiv:2412.19555v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Hyperparameter} & \textbf{Allen-Cahn} & \textbf{Burgers} \\
\hline
N. of Adam training iterations & 5e3 & 5e3 \\
N. of SSBroyden training iterations & 6e4 & 6e4 \\
Number of hidden layers $N$ & 3 & 3 \\
Hidden layer dimension $H$ & 30 & 30 \\
Activation function & $\tanh(\cdot)$ & $\tanh(\cdot)$ \\
Periodicity Encoding & $\sin(\cdot), \cos(\cdot)$ & $\sin(\cdot), \cos(\cdot)$ \\
\hline
\multicolumn{3}{|c|}{\textbf{vRBA Parameters (Sampling)}} \\
\hline
Resampling Frequency & 100 iter. & 100 iter. \\
$\gamma$ (EMA memory) & 0.9 & 0.9 \\
$\eta$ (EMA learning rate) & 0.1 & 0.1 \\
$\phi$ (Smoothing) & 0.9 & 1.0 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Implementation details for the Allen-Cahn and Burgers' equations using a second-order SSBroyden optimizer, following the methodology in .}
\end{table}
\end{document}

math

Juan Diego Toscano
Daniel T. Chen
Vivek Oommen
George Em Karniadakis

"A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning" by Juan Diego Toscano, Daniel T. Chen, Vivek Oommen, and George Em Karniadakis, arXiv:2509.14198v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation" by Peilin Rao and Randall R. Rojas, arXiv:2509.05922v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Pascal François
Geneviève Gauthier
Frédéric Godin
Carlos O. Pérez-Mendoza

"Deep Hedging with Options Using the Implied Volatility Surface" by Pascal François, Geneviève Gauthier, Frédéric Godin, and Carlos O. Pérez-Mendoza, arXiv:2504.06208v3, 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{Down--and--out call: finite–difference vs.\ Monte–Carlo}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}\hline
Method & Price & Rel.\ error (\%) & CPU (s)\\\hline
Grünwald--Letnikov ($\Delta x=0.012$) & 131.42 & 0.21 & 1.7\\
Grünwald--Letnikov ($\Delta x=0.008$) & 131.09 & 0.00 & 3.9\\
Monte--Carlo ($10^{6}$) & 131.10 & -- & 82.0\\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Pricing Fractal Derivatives under Sub-Mixed Fractional Brownian Motion with Jumps" by Nader Karimi, arXiv:2506.24111v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}\hline
{Dataset} & {MOM} & {NAG} & \texttt{Grad-Avg} & {SGD} \\ \hline
 \textbf{Boston Housing} & 14.31 & 2.31 & \textbf{3.35} & 3.36 \\
 \textbf{CBM} & 0.76 & 0.48 & \textbf{0.32} & 0.31 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{MSE on the test set for both the regression datasets using normal initialization.`MOM' stands for `SGD with Momentum'}
\end{table}
\end{document}

stat

Saugata Purkayastha
Sukannya Purkayastha

"A Variant of Gradient Descent Algorithm Based on Gradient Averaging" by Saugata Purkayastha and Sukannya Purkayastha, arXiv:2012.02387v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Aida Abiad
Ángeles Carmona
Andrés M. Encinas
Maria José Jiménez
Álvaro Samperio

"A Spectral Approach to Kemeny's Constant" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, 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{Modeling and sampling time in the \textbf{CelebA} and \textbf{MNIST} dataset of 2000 artificial samples based on a latent space of size $n=2000$ in [s]. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 & \textbf{CelebA} & \textbf{CelebA} & \textbf{MNIST} &\textbf{MNIST} \\
 Method &Learn &Sample&Learn &Sample\\\hline
 Gauss &$ <$0.01 & 0.01 & 0.002 & 0.002 \\
 Indep. & 4.10 &0.07 & 0.393 &0.003 \\
 KDE & 75.25 & 0.01 & 13.958 & 0.001 \\
 GMM & 1.35 & 0.03 & 0.115 & 0.004 \\
 VCAE & 306.97 & 148.48 & 10.345 & 4.590 \\
 EBCAE & 3.41 & 59.36 & 0.328 & 5.738 \\
 Real NVP & 2541.19 &3.69 &341.608 & 0.477 \\ \hline

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

stat

Maximilian Coblenz
Oliver Grothe
Fabian Kächele

"Learning Nonparametric High-Dimensional Generative Models: The Empirical-Beta-Copula Autoencoder" by Maximilian Coblenz, Oliver Grothe, and Fabian Kächele, arXiv:2309.09916v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Hanhan Zhou
Tian Lan
Guru Venkataramani
Wenbo Ding

"On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning" by Hanhan Zhou, Tian Lan, Guru Venkataramani, and Wenbo Ding, arXiv:2201.11803v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.