\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Optimal number of hidden layer neurons and relative test error (the minimum of the test error curve) for synthetic datasets with sample size $T=300$ and input dimension $n_0=80$. For the traditional ELM, the minimum is evaluated on the mean test error curve across 20 realizations and its standard deviation is reported.}
\begin{tabular}{l|cc|cc|cc}
\toprule
\multirow{2}{*}{\textbf{dataset}} & \multicolumn{2}{c|}{\textbf{A-ENR-ELM}} & \multicolumn{2}{c|}{\textbf{I-ENR-ELM}} & \multicolumn{2}{c}{\textbf{ELM}} \\
 & \textbf{n} & \textbf{value} & \textbf{n} & \textbf{value} & \textbf{n} & \textbf{value} \\
\midrule
Dataset 13 & 78 & 0.6902 & 59 & 0.6844 & 83 & 0.6969 $\pm$ 0.0136 \\
Dataset 14 & 66 & 0.3728 & 58 & 0.3659 & 90 & 0.4068 $\pm$ 0.0118 \\
Dataset 15 & 68 & 0.7294 & 54 & 0.7125 & 79 & 0.7704 $\pm$ 0.0164 \\
Dataset 16 & 52 & 0.4669 & 57 & 0.4527 & 80 & 0.4778 $\pm$ 0.0123 \\
Dataset 17 & 13 & 0.6360 & 13 & 0.6594 & 52 & 0.6649 $\pm$ 0.0273 \\
Dataset 18 & 28 & 0.3448 & 69 & 0.3433 & 80 & 0.3491 $\pm$ 0.0123 \\
Dataset 19 & 87 & 0.8214 & 65 & 0.7903 & 82 & 0.8306 $\pm$ 0.0214 \\
Dataset 20 & 90 & 0.5840 & 52 & 0.5906 & 83 & 0.6466 $\pm$ 0.0114 \\
Dataset 21 & 106 & 0.7112 & 54 & 0.7005 & 76 & 0.7160 $\pm$ 0.0303 \\
Dataset 22 & 111 & 0.3656 & 75 & 0.3661 & 83 & 0.3619 $\pm$ 0.0130 \\
Dataset 23 & 24 & 0.6508 & 72 & 0.6238 & 81 & 0.6419 $\pm$ 0.0131 \\
Dataset 24 & 99 & 0.3770 & 59 & 0.3649 & 83 & 0.3829 $\pm$ 0.0111 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Effective Non-Random Extreme Learning Machine" by Daniela De Canditiis and Fabiano Veglianti, arXiv:2411.16229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sound Classification of Four Insect Classes" by Yinxuan Wang and Sudip Vhaduri, arXiv:2412.12395v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
 layer& GRU Model\\
 \hline
 1& Embedding(2000, 200)\\
 2& Dropout(p=0.3)\\
 3& GRU(hidden\_size= 24, num\_layers=2, dropout=0.3, bidirectional=True)\\
 4& Maxpool() \& Avgpool()\\
 5& Concat(last,max, avg)\\
 6& Linear(in\_features=72, out\_features=1, bias=True)
 \end{tabular}
\caption{Model structure for GRU}
\end{table}
\end{document}

stat

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

"Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning" by Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, and Tong Zhang, arXiv:2309.02476v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llccccll}
 \toprule
 \textbf{NAME1} & \textbf{NAME2} & \textbf{SEX} & \textbf{D} & \textbf{M} & \textbf{Y} & \textbf{ENUMCAP} & \textbf{ENUMPC} \\
 \midrule
 COUIE & PRICE & M & 1 & 6 & 1960 & 1 WINDSOR ROAD & DE03US \\
 ABBIE & PVICE & F & 9 & 11 & 1961 & 1 WINDSOR ROAD & DE03US \\
 LACEY & PRICE & F & 7 & 2 & 1999 & 1 WINDSOR ROAD & DE03US \\
 SAMUEL & PRICE & M & 13 & 4 & 1990 & 1 WINDSOR ROAD & DE03US \\
 JOSEPH & PRICE & M & 20 & 4 & 1986 & 1 WINDSOR ROAD & DE03US \\
 \bottomrule
 \end{tabular}
\caption{Example records from \texttt{census} dataset with variables used for \texttt{txt} column. Variables (changed here for better printing): \textbf{NAME1} (first name), \textbf{NAME2} (surname), \textbf{SEX} (gender), \textbf{D}, \textbf{M}, \textbf{Y} (birth date), \textbf{ENUMCAP} (address), \textbf{ENUMPC} (postal code).}
\end{table}
\end{document}

q-fin

"BlockingPy: approximate nearest neighbours for blocking of records for entity resolution" by Tymoteusz Strojny and Maciej Beręsewicz, arXiv:2504.04266v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison across metrics and question complexities for ICEWS Actor and CronQuestions KG datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|c|c|c|c|c|c|c|c|c|c|c|c|}
 \hline
 Dataset & Model & \multicolumn{4}{c|}{MRR $\uparrow$} & \multicolumn{4}{c|}{Hits@1 $\uparrow$} & \multicolumn{4}{c|}{Hits@3 $\uparrow$} \\
 \cline{3-14}
 && Overall & Simple & Medium & Complex & Overall & Simple & Medium & Complex & Overall & Simple & Medium & Complex \\
 \hline\hline
 ICEWS Actor & RAG & 0.365 & 0.726 & 0.274 & 0.106 & 0.265 & 0.660 & 0.128 & 0.011 & 0.391 & 0.776 & 0.331 & 0.086 \\
 & RAG\textsubscript{semantic} & 0.427 & 0.794 & 0.337 & 0.162 & 0.301 & 0.723 & 0.164 & 0.022 & 0.484 & 0.852 & 0.424 & 0.195 \\
 & TimelineKGQA & \textbf{0.660} & \textbf{0.861} & \textbf{0.632} & \textbf{0.497} & \textbf{0.486} & \textbf{0.782} & \textbf{0.435} & \textbf{0.257} & \textbf{0.858} & \textbf{0.929} & \textbf{0.845} & \textbf{0.805} \\
 \hline
 CronQuestions KG & RAG & 0.331 & 0.771 & 0.218 & 0.101 & 0.235 & 0.704 & 0.092 & 0.009 & 0.348 & 0.824 & 0.249 & 0.077 \\
 & RAG\textsubscript{semantic} & 0.344 & 0.775 & 0.229 & 0.122 & 0.237 & \textbf{0.707} & 0.094 & 0.010 & 0.371 & \textbf{0.828} & 0.267 & 0.122 \\
 & TimelineKGQA & \textbf{0.522} & \textbf{0.788} & \textbf{0.510} & \textbf{0.347} & \textbf{0.319} & 0.676 & \textbf{0.283} & \textbf{0.103} & \textbf{0.758} & 0.759 & \textbf{0.667} & \textbf{0.834} \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Qiang Sun
Sirui Li
Du Huynh
Mark Reynolds
Wei Liu

"TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs" by Qiang Sun, Sirui Li, Du Huynh, Mark Reynolds, and Wei Liu, arXiv:2501.04343v1, 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}{lccccccc}
& \multicolumn{3}{c}{MIPLIB} && \multicolumn{3}{c}{NN-Verification} \\
\cline{2-4} \cline{6-8}
Parameter & Mean & Median & Std Deviation && Mean & Median & Std Deviation \\
\hline
$\lambda_{1}$ (\texttt{dcd}) & 0.253 & 0.253 & 0.006 && 0.253 & 0.254 & 0.005\\
$\lambda_{2}$ (\texttt{eff}) & 0.294 & 0.296 & 0.012 && 0.215 & 0.214 & 0.006 \\
$\lambda_{3}$ (\texttt{isp}) & 0.306 & 0.300 & 0.018 && 0.279 & 0.279 & 0.002 \\
$\lambda_{4}$ (\texttt{obp}) & 0.147 & 0.149 & 0.011 && 0.253 & 0.253 & 0.007 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Statistics of random initialised parameters per instance (train + test) in Experiment .}
\end{table}
\end{document}

math

Mark Turner
Thorsten Koch
Felipe Serrano
Michael Winkler

"Adaptive Cut Selection in Mixed-Integer Linear Programming" by Mark Turner, Thorsten Koch, Felipe Serrano, and Michael Winkler, arXiv:2202.10962v3, 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{Relative errors of estimating mean and standard deviation with random initial values using 5,000 Monte Carlo simulations. The relative error is computed by dividing the absolute error by the reference.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 \hline\hline
 Algorithms & Error of mean & Error of std & Average time (s)\\
 \midrule
 ASK & 6.1403e-03 $\pm$ 4.5974e-03 & 7.4006e-03 $\pm$ 5.5312e-03 & 0.0031 \\
 RK4 & 6.1403e-03 $\pm$ 4.5977e-03 & 7.4014e-03 $\pm$ 5.5306e-03 & 1.0555 \\
 \hline\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Bian Li
Yi-An Ma
J. Nathan Kutz
Xiu Yang

"The Adaptive Spectral Koopman Method for Dynamical Systems" by Bian Li, Yi-An Ma, J. Nathan Kutz, and Xiu Yang, arXiv:2202.09501v4, 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{\small Results for the 2D PDE where $\kappa_{true} = 1$. \emph{Abbrev:} $\Delta u _b$ = mean model error evaluated along domain's boundary}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
		\toprule
$\,$ & \textbf{E-PINN} & E-PINN(V) & B-PINN & DE \\
		\midrule
$\kappa$ & \textbf{1.00} & 1.02
& 1.01 & 0.99 \\
$\sigma_\kappa$ & \textbf{0.01} & 0.01
& 0.04 & 0.09 \\
$\rho_e$ & \textbf{0.7} & 0.7 & 0.1 &
0.5 \\
$\rho_n$ & \textbf{0.8} & 0.6 & -0.2 &
0.5\\
ECP & \textbf{0.93} & 0.89 & 1.00 &
0.63 \\
$\Delta u_b$ & \textbf{0.002} & 0.006 & 0.01 & 0.001 \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hai Siong Tan
Kuancheng Wang
Rafe McBeth

"Evidential Physics-Informed Neural Networks" by Hai Siong Tan, Kuancheng Wang, and Rafe McBeth, arXiv:2501.15908v1, 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{$\tau^r$-torsion for $\langle \gamma,h_0e_0w_2,h_1w_2^2\rangle$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|}
\hline
$g,w_1$-multiples & $\gamma g^2w_1^{m+1}$ & $(\gamma g^2+h_0e_0w_2)w_1^{m+1}$ & $\gamma g^3w_1^{m+1}$ & $\gamma g^4w_1^{m+1}$ \\ \cline{1-1}
$\tau^r$-torsion & $\tau^2$ & $\tau$ & $\tau$ & $\tau$ \\ \hline
$g,w_1$-multiples & $\gamma g^5w_1^{m+1}$ & $(\gamma g^5+h_1gw_1w_2^2)w_1^m$ & $\gamma g^{n+6}$ & $\gamma g^{n+6}w_1^{m+1}$ \\ \cline{1-1}
$\tau^r$-torsion & $\tau$ & $\tau^2$ & $\tau^2$ & $\tau$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"$\mathrm{H}\mathbb{F}_2$-synthetic homotopy groups of topological modular forms" by Peter Marek, arXiv:2202.11305v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Google is all you need: Semi-Supervised Transfer Learning Strategy For Light Multimodal Multi-Task Classification Model" by Haixu Liu, Penghao Jiang, and Zerui Tao, arXiv:2501.01611v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Key experiment parameters.}
\begin{tabular}{c|c|c}
\toprule%\hline
\textbf{Parameter} & \textbf{Value} & \textbf{Dimension} \\
 \midrule%\hline
 Central carrier frequency & 10.0 & GHz \\
 Transmission distance $R$ & 8.5 & m \\
 Target rotational speed & 1.0 & rad/min \\
 Tx transmitting antenna gain ${{G_{\rm{t}}}}$ & 16.0 & dB \\
 Rx receiving antenna gain ${{G_{\rm{r}}}}$ & 16.0 & dB \\
 Tx transmitting power ${{P_{\rm{t}}}}$ & 28.0 & dBm \\
 Rx receiving power ${{P_{\rm{r}}}}$ & 20.0 & dBm \\
 Same height of Tx/Rx and target & 1.5 & m \\
 Radius of the two metal spheres & 20.0 & cm \\
 Spacing between the metal spheres & 40.0 & cm \\
 \bottomrule%\hline
\end{tabular}
\end{table}
\end{document}

eess

Yufei Zhao
Yong Liang Guan
Dong Chen
Afkar Mohamed Ismail
Xiaoyan Ma
Xiaobei Liu
Chau Yuen

"Experimental Study of RCS Diversity with Novel No-divergent OAM Beams" by Yufei Zhao, Yong Liang Guan, Dong Chen, Afkar Mohamed Ismail, Xiaoyan Ma, Xiaobei Liu, and Chau Yuen, arXiv:2412.18762v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Adrian Celaya
David Fuentes
Beatrice Riviere

"Adaptive Collocation Point Strategies For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method" by Adrian Celaya, David Fuentes, and Beatrice Riviere, arXiv:2501.07700v4, 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{CVA Swaps Portfolio}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
Issued & Notional & Maturity & Rec / Pay Fixed & Coupon & Freq \\
 \hline
4/20/20 & 10 & 4/20/21 & Rec & 0.51\% & quarterly \\
\hline
4/20/20 & 10 & 4/20/22 & Pay & 0.41\% & quarterly \\
\hline
4/20/20 & 10 & 4/20/23 & Pay & 0.40\% & quarterly \\
\hline
4/20/20 & 10 & 4/20/25 & Rec & 0.47\% & quarterly \\
\hline
4/20/20 & 10 & 4/20/27 & Pay & 0.57\% & quarterly \\
 \hline
4/20/20 & 10 & 4/20/30 & Rec & 0.69\% & quarterly \\
\hline
4/20/20 & 10 & 4/20/35 & Rec & 0.74\% & quarterly \\
\hline
4/20/20 & 10 & 4/20/40 & Rec & 0.83\% & quarterly \\
\hline
4/20/20 & 10 & 4/20/45 & Pay & 0.83\% & quarterly \\
\hline
4/20/20 & 10 & 4/20/50 & Pay & 0.85\% & quarterly \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.01781v2, 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{Five most important variables in boosting of binomial regression models with selection probabilities in brackets.}
\begin{tabular}{lll}
\\ \hline
\hline \\
\textbf{50NB} & \textbf{100NB} & \textbf{250NB} \\
\hline \\
References (0.70) & References (0.38) & Intercept (0.184) \\
Intercept (0.18) & Intercept (0.22) & Language3 (0.172) \\
Length (0.10) & Language3 (0.22) & References (0.156) \\
Language3 (0.02) & Length (0.11) & Length (0.056) \\
 & MeSH (0.04) & Journal Article (0.056) \\
\hline \\
\end{tabular}
\end{table}
\end{document}

stat

"Mitigating Consequences of Prestige in Citations of Publications" by Michael Balzer and Adhen Benlahlou, arXiv:2411.05584v2, 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{Comparison of the COS, filter-COS and SFP methods in terms of absolute error for pricing a call option under the Heston model with $T=1$. The reference values are $50.070539139715081$ ($K= 50$) and $3.181555642433310$ ($K=105.453$). The SFP method is more accurate than others with fewer summation terms required. The parameters are taken from \textbf{Heston--Para1} .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccc||cccc|} % centered columns (4 columns)
\hline
$K= 50$ &\textbf{COS}&\textbf{filter-COS}&\textbf{SFP}&$K=105.453$&\textbf{COS}&\textbf{filter-COS}&\textbf{SFP}\\
\hline
$N/U$& Error & Error & Error &$N/U$& Error &Error &Error \\
\hline
8&5.031&8.546&3.514e-01&8&19.71&24.37&2.608\\
16&8.763e-01&1.202&1.047e-02&16&9.163&13.71&12.81\\
32&4.861e-01&1.197&6.327e-06&32&3.201&5.902&4.681\\
64&3.715e-02&9.661e-02&1.861e-08&64&7.801e-01&1.888&3.649e-02\\
128&6.003e-03&2.361e-02&2.467e-11&128&1.003e-01&3.941e-01&1.001e-03\\
256&3.922e-04&2.363e-04&8.527e-14&256&3.722e-03&3.801e-02&1.898e-04\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Singular Fourier-Padé Series Expansion of European Option Prices" by Tat Lung Chan, arXiv:1706.06709v3, 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{Classification result for the HMDB51 dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
\hline
Selected Class Levels & Precision & Recall & F1-Score & Accuracy \\ \hline
drink & 89.00 & 93.00 & 91.00 & - \\ \hline
eat & 89.00 & 95.00 & 92.00 & - \\ \hline
fall\_floor & 84.00 & 69.00 & 76.00 & - \\ \hline
sit & 85.00 & 86.00 & 86.00 & - \\ \hline
stand & 91.00 & 79.00 & 85.00 & - \\ \hline
walk & 91.00 & 96.00 & 93.00 & - \\ \hline
Average & 89.00 & 89.00 & 89.00 & 89.22 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Subrata Kumer Paul
Abu Saleh Musa Miah
Rakhi Rani Paul
Md. Ekramul Hamid
Jungpil Shin
Md Abdur Rahim

"IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare" by Subrata Kumer Paul, Abu Saleh Musa Miah, Rakhi Rani Paul, Md. Ekramul Hamid, Jungpil Shin, and Md Abdur Rahim, arXiv:2501.07039v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Narayanaswamy Balakrishnan
Maria Jaenada
Leandro Pardo

"Robust inference for an interval-monitored step-stress experiment with competing risks for failure" by Narayanaswamy Balakrishnan, Maria Jaenada, and Leandro Pardo, arXiv:2311.04300v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
 \toprule
 Fr Datasets & E5-Mistral & LUSIFER \\
 \midrule
 AmazonReviewsClassification & 43.36 & 49.96 \\
 MTOPIntentClassification & 70.39 & 79.14 \\
 MassiveIntentClassification & 71.12 & 70.88 \\
 MassiveScenarioClassification & 74.68 & 73.96 \\
 TweetSentimentClassification & 50.23 & 62.62 \\
 SIB200Classification & 72.45 & 79.51 \\
 FrenchBookReviews & 46.77 & 48.07 \\
 PawsXPairClassification & 62.15 & 65.93 \\
 RTE3 & 88.45 & 87.62 \\
 XNLI & 76.60 & 62.75 \\
 MasakhaNEWSClusteringP2P & 50.96 & 48.59 \\
 MasakhaNEWSClusteringS2S & 52.08 & 63.12 \\
 MLSUMClusteringP2P & 42.69 & 42.70 \\
 MLSUMClusteringS2S & 42.60 & 41.51 \\
 HALClusteringS2S & 24.21 & 24.16 \\
 SIB200ClusteringS2S & 29.94 & 43.30 \\
 MultiEURLEXMultilabelClassification & 5.00 & 3.51 \\
 BelebeleRetrieval & 84.66 & 83.76 \\
 MintakaRetrieval & 52.60 & 18.88 \\
 OpusparcusPC & 94.58 & 90.63 \\
 STS17 & 84.66 & 82.19 \\
 SICKFr & 79.12 & 74.22 \\
 STS22 & 76.50 & 73.77 \\
 STSBenchmarkMultilingualSTS & 83.98 & 78.42 \\
 SummEvalFr & 31.38 & 31.91 \\
 \midrule
 Avg. & 59.65 & 59.24 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Detailed results of E5-Mistral and LUSIFER on the French benchmark datasets.}
\end{table}
\end{document}

cs

Hieu Man
Nghia Trung Ngo
Viet Dac Lai
Ryan A. Rossi
Franck Dernoncourt
Thien Huu Nguyen

"LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models" by Hieu Man, Nghia Trung Ngo, Viet Dac Lai, Ryan A. Rossi, Franck Dernoncourt, and Thien Huu Nguyen, arXiv:2501.00874v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|}
\hline
Missing component & PSNR \\
\hline\hline
Exposure learning & 28.72 \\
Flow network & 30.62 \\
Exposure normalization & 30.96 \\
Annealed loss term & 31.18 \\
None & \bf{31.42} \\
\hline
\end{tabular}
\caption{Average PSNR of ablated models.}
\end{table}
\end{document}

eess

Omer Dahary
Matan Jacoby
Alex M. Bronstein

"Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times" by Omer Dahary, Matan Jacoby, and Alex M. Bronstein, arXiv:2012.04515v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Qinyang Wang
Hoileong Lee
Xiaodi Pu
Yuanming Lai
Yiming Ma

"SLENet: A Novel Multiscale CNN-Based Network for Detecting the Rats Estrous Cycle" by Qinyang Wang, Hoileong Lee, Xiaodi Pu, Yuanming Lai, and Yiming Ma, arXiv:2507.19566v1, 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{Markets most frequently analyzed by geography and count}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
 \hline
 Country & Count \\
 \hline
 UNITED STATES & 75 \\
 TAIWAN & 19 \\
 INDIA & 12 \\
 JAPAN & 10 \\
 SOUTH KOREA & 10 \\
 CHINA & 9 \\
 BRAZIL & 6 \\
 TURKEY & 6 \\
 GERMANY & 5 \\
 SINGAPORE & 5 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey" by Lukas Ryll and Sebastian Seidens, arXiv:1906.07786v2, 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}{rrrrr}
		\hline
		& b & c & d & e \\
		\hline
		a & -0.5 & -0.333 & -0.25 & -0.2 \\
		b & 0.5 & -0.333 & -0.25 & -0.2 \\
		c & 0.0 & 0.667 & -0.25 & -0.2 \\
		d & 0.0 & 0.000 & 0.75 & -0.2 \\
		e & 0.0 & 0.000 & 0.00 & 0.8 \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Efficient Representations for High-Cardinality Categorical Variables in Machine Learning" by Zixuan Liang, arXiv:2501.05646v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Panagiotis Papastamoulis
Fotios Milienos

"Bayesian inference and cure rate modeling for event history data" by Panagiotis Papastamoulis and Fotios Milienos, arXiv:2310.06926v2, 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{Error metrics for various models ordered by SMAPE (Residence 1 -- Fall).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \toprule
 \textbf{Model} & \textbf{MAE} & \textbf{RMSE} & \textbf{SMAPE} \\
 \midrule
 Transformer & 22.00 & 31.56 & 8.59 \\
 AttentionLSTM & 24.04 & 33.74 & 9.07 \\
 GRU & 25.48 & 33.28 & 9.71 \\
 HyperNetLSTM & 26.30 & 34.56 & 9.86 \\
 TempConv & 24.50 & 31.80 & 9.93 \\
 LSTM & 28.95 & 38.15 & 11.01 \\
 RNN & 32.95 & 44.94 & 11.89 \\
 MiniAutoEncXGBoost & 32.40 & 38.88 & 12.30 \\
 MiniWXGBoost & 32.51 & 40.19 & 12.32 \\
 ARFFNN & 34.49 & 43.91 & 13.05 \\
 Nbeats & 35.17 & 45.56 & 13.41 \\
 MLP & 45.05 & 54.24 & 17.05 \\
 MiniWSGD & 54.97 & 74.19 & 21.91 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Muhammad Umair Danish
Mathumitha Sureshkumar
Tehara Fonseka
Umeshika Uthayakumar
Vinura Galwaduge

"An Investigation into Seasonal Variations in Energy Forecasting for Student Residences" by Muhammad Umair Danish, Mathumitha Sureshkumar, Tehara Fonseka, Umeshika Uthayakumar, and Vinura Galwaduge, arXiv:2501.07423v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework" by Chao Wang, Richard Gerlach, and Qian Chen, arXiv:1807.02422v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

María Jaenada
Elena Castilla
Nirian Martín
Leandro Pardo

"Robust approach for comparing two dependent normal populations through Wald-type tests based on Rényi's pseudodistance estimators" by María Jaenada, Elena Castilla, Nirian Martín, and Leandro Pardo, arXiv:2202.00982v2, 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{Test of Auto Regressive Conditional Heteroscedasticity (ARCH) effect }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}		\hline
		& Ljung box test & LM test & ADF test \\
		\hline
		P-value & $7.835e-05$ & $< 2.2e-16$ & 0.01
		\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Estimating the volatility of Bitcoin using GARCH models" by Samuel Asante Gyamerah, arXiv:1909.04903v2, 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{Case study 2: definition of design space. }
\begin{tabular}{ccc}
 \hline
 Design Variable & Min & Max \\
 \hline
 $F_1 \text{(mL/min)}$ & 0.3 & 3.0 \\
 $F_2 \text{(mL/min)}$ & 0.3 & 3.0 \\
 $F_3 \text{(mL/min)}$ & 0.3 & 3.0 \\
 $T(^oC)$ & 60.0 & 120.\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Panagiotis Petsagkourakis
Federico Galvanin

"Safe model-based design of experiments using Gaussian processes" by Panagiotis Petsagkourakis and Federico Galvanin, arXiv:2011.10009v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Information-theoretic subset selection of multivariate Markov chains via submodular optimization" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, 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{Quantitative results with $I_0=10^4$ and $K=1024$ with parallel beam CT geometry.}
\begin{tabular}{l|ll}
 \hline
 Methods & PSNR & SSIM \\ \hline \hline
 FBP & $18.05\pm 0.67$ & $0.32\pm 0.01$ \\
 N2I & $37.97\pm 0.95$ & $0.93\pm 0.01$ \\
 Score-based & $17.80\pm 0.53$ & $0.43\pm 0.03$ \\
 FBPConvNet (Supervised) & $39.00\pm 0.93$ & $0.94\pm 0.01$
 \\
 \textbf{RAN2I} & $\textbf{38.37}\pm 1.06$ & $\textbf{0.94}\pm 0.01$ \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction" by Hang Xu and Alessandro Perelli, arXiv:2312.12644v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
		\hline
		& Scale 1.1 & Scale 1.2 & Scale 1.3 \\
		\hline
		Frequency & 0.5hz - 0.625hz & 0.625hz - 0.75hz & 0.75hz - 0.875hz \\
		\hline
		& Scale 1.4 & Scale 2 & Scale 3 \\
		\hline
		Frequency & 0.875hz - 1hz & 0.25-0.50hz & 0.125hz - 0.25hz\\
		\hline
		& Scale 4 & Scale 5& Scale 6 \\
		\hline
		Frequency& 0.0625hz - 0.125hz & 0.0312hz - 0.0625hz & 0.0156hz - 0.0312hz\\
		\hline
		& Scale 7 & & \\
		\hline
		Frequency& 0.0078hz - 0.0156hz & & \\
		\hline
	\end{tabular}
\caption{The frequency domain from scale 1.1 to scale 7 by using DWPT and DWT. }
\end{table}
\end{document}

eess

Xi Chen
Yu Guan
Jian Qing Shi
Xiu-Li Du
Janet Eyre

"Designing Compact Features for Remote Stroke Rehabilitation Monitoring using Wearable Accelerometers" by Xi Chen, Yu Guan, Jian Qing Shi, Xiu-Li Du, and Janet Eyre, arXiv:2009.08798v3, 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}{ll}
		\textbf{Hyperparameter}	& \textbf{Value} \\
		Optimiser & Adam \\
		Batch size & $2$ \\
		Learning rate & $0.001$ \\
		Epochs & $20$ \\
		Learning rate schedule & Constant
	\end{tabular}
\end{adjustbox}
\caption{Hyperparameters for the convolutional Bouncing balls experiment.}
\end{table}
\end{document}

math

Imbedding Deep Neural Networks

"Imbedding Deep Neural Networks" by Andrew Corbett and Dmitry Kangin, arXiv:2202.00113v2, 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{Notation description}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cl}
 \toprule
 \textbf{\emph{Symbols}} & \textbf{\emph{Meaning}} \\
 \toprule
 $N$ & \textit{number of pedestrians in a scene}\\
 $i$ & \textit{the pedestrian $i$}\\
 $T_o$ & \textit{period of observed trajectory}\\
 $T_p$ & \textit{period of future trajectory}\\
 $X_i$ & \textit{observed trajectory of pedestrian $i$}\\
 $Y_i$ & \textit{ground truth future trajectory of pedestrian $i$}\\
 $\hat{Y}_i$ & \textit{predicted future trajectory of pedestrian $i$}\\
 $V^t$ & \textit{graph nodes which represents a set of pedestrians at time $t$}\\
 $A^t$ & \textit{adjacency matrix which represents social interactions} \\
 & \textit{between pedestrians at time $t$}\\
 $G^t$ & \textit{graph constructed from 2D pedestrian coordinates} \\
 & \textit{at time $t$, equal to $(V^t,A^t)$}\\
 $(x_i^t,y_i^t)$ & \textit{coordinate of the pedestrian $i$ at time $t$}\\
 $\tau$ & \textit{the current moment of a trajectory}\\
 $\hat{\alpha}_{ij}$ & \textit{attention coefficient with a random weight}\\

 \bottomrule

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

cs

Jiajia Xie
Sheng Zhang
Beihao Xia
Zhu Xiao
Hongbo Jiang
Siwang Zhou
Zheng Qin
Hongyang Chen

"Pedestrian Trajectory Prediction Based on Social Interactions Learning With Random Weights" by Jiajia Xie, Sheng Zhang, Beihao Xia, Zhu Xiao, Hongbo Jiang, Siwang Zhou, Zheng Qin, and Hongyang Chen, arXiv:2501.07711v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Michael Hellstern
Byol Kim
Zaid Harchaoui
Ali Shojaie

"Spectral Differential Network Analysis for High-Dimensional Time Series" by Michael Hellstern, Byol Kim, Zaid Harchaoui, and Ali Shojaie, arXiv:2412.07905v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yusuke Narita
Kyohei Okumura
Akihiro Shimizu
Kohei Yata

"Counterfactual Learning with General Data-generating Policies" by Yusuke Narita, Kyohei Okumura, Akihiro Shimizu, and Kohei Yata, arXiv:2212.01925v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of audio datasets}
\begin{tabular}{l|llll}
\toprule
Dataset & Total duration & Classes & Length & Channels \\\midrule
Audioset & 241 d & 527 & $\leq$10s & Mono\\
SONYC-UST & 51.4 h & 8 / 23 & 10s & Mono\\
UrbanSound8k & 8.75 h & 10 & $\leq$4s & Stereo\\
ESC-50 & 2.8h & 50 & 10s & Mono\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Augustin Arnault
Baptiste Hanssens
Nicolas Riche

"Urban Sound Classification : striving towards a fair comparison" by Augustin Arnault, Baptiste Hanssens, and Nicolas Riche, arXiv:2010.11805v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l||r|r|r|l|}
		\hline
		\multicolumn{5}{|l|}{\cellcolor[gray]{0.95} \vphantom{\Big|} Comparison using width parameter $w=0.05$.}\\ \hline\hline
\textbf{Method} & Value & \textbf{Bias} & \textbf{Std.~Dev.} & \textbf{Improve} \\ \hline
\hline
Finite Difference & 0.7359 & -0.0002 & 0.0110 & \\ \hline
Stoch.~AD & 0.7359 & -0.0001 & 0.0112 & 0.98 \\ \hline
Stoch.~AD with Regression & 0.7355 & -0.0006 & 0.0036 & 3.12 \\ \hline
Likelihood Ratio & 0.7361 & 0.0000 & 0.0025 & 1.42 \\ \hline
Analytic & 0.7361 & 0.0000 & 0.0000 & $\infty$ \\ \hline
	\end{tabular}
\end{adjustbox}
\caption{Results from 10000 Monte-Carlo experiments, each using 200000 paths and width parameter $w=0.05$: The stochastic AAD with regression brings a 3.1 times variance reduction. The use of a likelihood ratio method just gives an additional 42\% improvement.}
\end{table}
\end{document}

q-fin

"Stochastic Algorithmic Differentiation of (Expectations of) Discontinuous Functions (Indicator Functions)" by Christian P. Fries, arXiv:1811.05741v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Node classification accuracy (\%) under binary noise settings. ART (ms): Average Running Time per epoch in ms. The best and second-best learning accuracy are in bold and underlined. We also note that for the p-Laplacian framelet model, we fixed $p =2.5$ so that the model is more suitable for citation networks . We also highlight that our results on pL-UFG is different compared to due to the difference in data split.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|cccccc|}
\hline
Datasets & Noise Level (\%) & Clean & 5 & 15 & 25 & 50 &ART (ms)\\
\hline
\multirow{6}{*}{\textbf{Cora}} & Spectral CNN
& 73.3 & 26.0$\pm$5.1 & 19.5$\pm$4.1 & 15.2$\pm$4.7 & 19.1$\pm$6.9 & 12.3\\
& ChebyNet
& 81.2 & 30.6$\pm$4.4 & 30.9$\pm$2.6 & 21.5$\pm$7.6 & 18.1$\pm$4.1 & 16.2\\
& GCN
& 81.5 & 76.7$\pm$0.8 & 67.8$\pm$1.0 & 61.0$\pm$0.9 & 54.5$\pm$2.4 & 9.25 \\
& GWNN
& 82.8 & 43.5$\pm$1.6 & 30.2$\pm$1.0 & 23.6$\pm$1.3 & 20.1$\pm$3.2 &15.2\\
& GPRGNN
& 81.6\(\pm\)0.7 &65.9\(\pm\)7.2 &24.1\(\pm\)5.3 &22.4\(\pm\)7.0 &21.5\(\pm\)6.1 &14.2 \\
& H2GCN
& 82.9\(\pm\)0.5 &70.3\(\pm\)1.3 &34.6\(\pm\)5.6 &26.1\(\pm\)7.5 &24.1\(\pm\)8.2 &26.5\\
& UFG
& 83.6$\pm$0.6 & 75.6$\pm$0.8 & \underline{70.6$\pm$0.7} & {66.9$\pm$1.7} & {60.6$\pm$2.7} & 29.4\\
& pL-UFG$^{2.5}$ &\underline{83.9$\pm$1.7} &\underline{77.8\(\pm\)2.4} & \underline{68.8\(\pm\)0.3} &\textbf{68.4\(\pm\)0.9} &\underline{66.5\(\pm\)1.6} &40.8 \\
& QUFG & \textbf{83.9$\pm$0.8} & \textbf{79.1$\pm$0.9} & \textbf{71.6$\pm$1.2} & \underline{68.2$\pm$2.1}& \textbf{62.7$\pm$2.1} & 32.7\\
\hline
\multirow{6}{*}{\textbf{Citeseer}} & Spectral CNN
& 58.9 & 19.0$\pm$1.4 & 17.6$\pm$3.9 & 18.0$\pm$2.6 & 20.3$\pm$4.5 &9.3\\
& ChebyNet
& 69.8 & 19.4$\pm$1.8 & 17.3$\pm$4.1 & 18.2$\pm$2.7 & 17.2$\pm$3.3 & 31.4\\
& GCN
& 70.3 & \underline{62.6$\pm$0.8} & {47.3$\pm$1.2} & {39.4$\pm$1.1} & {31.9$\pm$2.5} & 10.1\\
& GWNN
& 71.7 & 49.4$\pm$1.4 & 21.1$\pm$0.3 & 21.3$\pm$0.5 & 15.8$\pm$0.9 &16.5\\
& GPRGNN
 & 70.9\(\pm\)1.5 & 19.3\(\pm\)2.1 & 18.6\(\pm\)1.7 & 18.4\(\pm\)2.0 & 18.1\(\pm\)2.3 & 13.6\\
& H2GCN & \underline{ 73.1\(\pm\)0.4 } & 47.8\(\pm\)2.2 & 24.2\(\pm\)1.3 &19.5\(\pm\)1.5 & 19.2\(\pm\)0.9 & 26.3\\
& UFG
& 72.7$\pm$0.6 & {61.6$\pm$1.3} & 40.8$\pm$7.0 & 26.0$\pm$3.2 & 23.5$\pm$2.7 & 49.1 \\
& pL-UFG$^{2.5}$ &72.2\(\pm\)1.4 &\textbf{64.8\(\pm\)3.1} & \textbf{50.3\(\pm\)3.3} &\underline{42.8\(\pm\)2.8} &\underline{39.7\(\pm\)1.4} & 50.1 \\
& QUFG & \textbf{73.4$\pm$0.6} & 60.9$\pm$1.8 & \underline{47.4$\pm$1.8} & \textbf{44.9$\pm$2.3} & \textbf{42.4$\pm$1.6} & 45.6\\
\hline
\multirow{6}{*}{\textbf{Coauthor-CS}} & Spectral CNN
& 54.3$\pm$0.5 & 12.3$\pm$4.0 & 8.5$\pm$4.4 & 6.1$\pm$2.7 & 6.1$\pm$2.3 &33.6\\
& ChebyNet
& 61.4$\pm$0.9 & 14.5$\pm$4.5 & 10.1$\pm$2.3 & 8.3$\pm$1.3& 5.0$\pm$1.5 &190.6\\
& GCN
& 91.3$\pm$0.2 & 89.3$\pm$0.6 & \underline{84.5$\pm$1.1} & \underline{64.9$\pm$0.7} & 8.3$\pm$3.0 & 32.4\\
& GWNN
& 89.5$\pm$2.3 & 80.8$\pm$0.2 & 71.5$\pm$1.3 & 46.3 $\pm$1.1 & 9.2$\pm$5.4 & 49.1\\
& GPRGNN & 92.4\(\pm\)0.2 & 88.1\(\pm\)1.0 & 32.6\(\pm\)12.4 & 20.4\(\pm\)6.2 & 10.7\(\pm\)2.9 & 30.2\\
& H2GCN & \underline{ 93.0\(\pm\)0.1 } & 71.3\(\pm\)0.7 & 12.7\(\pm\)5.6 & 11.9\(\pm\)6.4 & 10.2\(\pm\)4.8 & 147.2\\
& UFG
& 91.9$\pm$0.3 & {90.2$\pm$0.5} & 80.8$\pm$1.5 & 60.8$\pm$0.4 & \underline{9.4$\pm$1.5} &411.7\\
& pL-UFG$^{2.5}$ &92.4\(\pm\)0.1 &\textbf{91.1\(\pm\)0.8} &81.4\(\pm\)2.3 &66.0\(\pm\)5.3 &\textbf{14.8\(\pm\)1.1} & 474 \\
& QUFG & \textbf{93.7$\pm$0.6} & \underline{90.2$\pm$0.3} & \textbf{85.5$\pm$0.6} & \textbf{66.0$\pm$1.1} & \underline{10.3$\pm$2.8} & 329.4 \\
\hline
\multirow{6}{*}{\textbf{Coauthor-Physics}} & Spectral CNN
& 51.2$\pm$2.0 & 16.5$\pm$0.3 & 11.2$\pm$1.8 & 7.9$\pm$0.7 & 8.1$\pm$4.5 & 84.7\\
& ChebyNet
& 65.8$\pm$3.5 & 16.8$\pm$2.3 & 12.1$\pm$2.9 & 10.9$\pm$2.4 & 9.0$\pm$3.6 & 791.4\\
& GCN
& 94.1$\pm$0.5 & 92.7$\pm$0.4 & 84.5$\pm$1.1 & \textbf{66.4$\pm$7.0} & 30.3$\pm$18.5 & 92.7\\
& GWNN
& 79.2$\pm$1.3 & 76.9$\pm$0.3 & 68.3$\pm$2.1 & 50.0$\pm$4.5 & 19.2$\pm$3.4 & 201.3\\
& GPRGNN & 93.5\(\pm\)1.8 & 81.4\(\pm\)2.6 & 39.1\(\pm\)4.9 & 28.8\(\pm\)6.4 & 20.1\(\pm\)2.5 & 129.4\\
& H2GCN & 94.2\(\pm\)1.5 & 60.5\(\pm\)2.7 & 38.4\(\pm\)6.5 & 22.1\(\pm\)3.8 & 18.2\(\pm\)2.5 & 511.2\\
& UFG
& \underline{94.3$\pm$0.4} & \textbf{93.0$\pm$0.6} & {84.7$\pm$1.7} & 43.6$\pm$12.5 & {40.4$\pm$0.5} & 1490.5 \\
& pL-UFG$^{2.5}$ &91.7\(\pm\)0.1 &88.9\(\pm\)1.5 &\underline{86.1\(\pm\)2.4} &{51.4\(\pm\)4.5} &\textbf{49.8\(\pm\)3.6} &1630 \\
& QUFG & \textbf{94.4$\pm$0.4} & \underline{92.8$\pm$0.4} & \textbf{87.0$\pm$0.4} & \underline{56.0$\pm$8.1} & \underline{43.8$\pm$2.3} & 1380.4\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mengxi Yang
Dai Shi
Xuebin Zheng
Jie Yin
Junbin Gao

"Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution" by Mengxi Yang, Dai Shi, Xuebin Zheng, Jie Yin, and Junbin Gao, arXiv:2201.04728v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The experimental results for subjective evaluation.}
\begin{tabular}{cccccccc}
 \toprule
 \textbf{Model} & \multicolumn{2}{c}{\textbf{Clean}} & \multicolumn{2}{c}{\textbf{Noisy (0--5 dB)}}\\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & \textbf{CMOS $\uparrow$} & \textbf{SMOS $\uparrow$} & \textbf{CMOS $\uparrow$} & \textbf{SMOS $\uparrow$}\\
 \midrule
 Ground Truth & 4.04 & 3.90 & 4.04 & 3.90\\
 \midrule
 Our Baseline & \textbf{3.29} & \textbf{3.02} & 2.09 & 2.75\\
 Noro & 3.16 & 2.90 & \textbf{2.95} & \textbf{2.97}\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Haorui He
Yuchen Song
Yuancheng Wang
Haoyang Li
Xueyao Zhang
Li Wang
Gongping Huang
Eng Siong Chng
Zhizheng Wu

"Noro: Noise-Robust One-shot Voice Conversion with Hidden Speaker Representation Learning" by Haorui He, Yuchen Song, Yuancheng Wang, Haoyang Li, Xueyao Zhang, Li Wang, Gongping Huang, Eng Siong Chng, and Zhizheng Wu, arXiv:2411.19770v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{Parameter} & \textbf{Value} & \textbf{Units} \\
 \hline \hline
 \multicolumn{3}{|c|}{\textit{Physical Parameters}} \\
 \hline
 Mass ($m$) & 0.8652 & kg \\
 Inertia ($I_{yy}) $ & 9.77E-03 & kg-m$^2$ \\
 Arm Length ($l$) & 0.244 & m \\
 Wing Chord ($\bar{c}$) & 0.087 & m \\
 Wing Span ($b$) & 1.016 & m \\
 Rotor Diameter ($2R$) & 0.229 & m \\
 Min Thrust ($T_{min}$) & 0 & N \\
 Max Thrust - 2 Motors ($T_{max}$) & 5.886 & N \\
 Thrust-Weight Ratio ($TW$) & 1.387 & - \\
 \hline
 \multicolumn{3}{|c|}{\textit{Controller Gains}} \\
 \hline
 Proportional - Position ($K_p$) & \textit{diag}(11.6,17.4) & m$^{-1}$ \\
 Derivative - Position ($K_d$) & \textit{diag}(6.82,6.82) & s-m$^{-1}$ \\
 Proportional - Attitude ($K_R$) & 74.73 & rad$^{-1}$ \\
 Derivative - Attitude ($K_{\omega}$) & 17.29 & s-rad$^{-1}$ \\
 \hline
 \end{tabular}
\caption{\small A summary of the physical parameters and controller gains used in the simulation of transition maneuvers}
\end{table}
\end{document}

eess

"Modeling, Planning, and Control for Hybrid UAV Transition Maneuvers" by Spencer Folk, arXiv:2412.06197v1, 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}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l}
 \toprule
 \textbf{Dataset Source:} Helpsteer2 (Train) \\
 \textbf{Label Accuracy:} $72.96\pm1.04$ \\
 \textbf{Size (Train):} 8218 \\
 \textbf{Size (Validation):} 432 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Details of the dataset used in the labeling strategy oversight experiment. Label Accuracy denotes the proportion of cases where Bob’s preference labels match those of Alice in $\mathcal{D}_{\tt B}$.}
\end{table}
\end{document}

cs

Taywon Min
Haeone Lee
Yongchan Kwon
Kimin Lee

"Understanding Impact of Human Feedback via Influence Functions" by Taywon Min, Haeone Lee, Yongchan Kwon, and Kimin Lee, arXiv:2501.05790v3, 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{CER, WER, and MOS for each method described in Section~.}
\begin{tabular}{l|ccc}
\hline
Method & CER & WER & MOS \\ \hline
\textit{Ground-truth} & 5.1 \% & 17.9 \% & $4.28 \pm 0.13$\\
\textit{Full-sentence} & 5.5 \% & 18.2 \% & $3.82 \pm 0.12$ \\
\textit{Bicontext~(truth)} & 8.2 \% & 24.2 \% & $3.36 \pm 0.16$ \\ \hline
\textit{Independent} & 38.9 \% & 96.9 \% & $2.69 \pm 0.20$ \\
\textit{Unicontext} & 22.8 \% & 53.9 \% & $2.99 \pm 0.18$ \\
\textit{Bicontext} & \textbf{11.9} \% & \textbf{29.8} \% & $3.38 \pm 0.14$ \\
\textit{Bicontext (fine-tuned)} & \textbf{8.0} \% & \textbf{22.5} \% & $3.44 \pm 0.16$ \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Takaaki Saeki
Shinnosuke Takamichi
Hiroshi Saruwatari

"Incremental Text-to-Speech Synthesis Using Pseudo Lookahead with Large Pretrained Language Model" by Takaaki Saeki, Shinnosuke Takamichi, and Hiroshi Saruwatari, arXiv:2012.12612v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
\textbf{Parameter} & \textbf{Description} \\
\midrule
Model & Bayesian hierarchical model \\
Setup Parameters & alpha, beta, lambda, lower limit, upper limit \\
Observations & Length of dugong at a given age \\
Goals & Predicting the length of dugongs at different ages \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Dugongs Environment}
\end{table}
\end{document}

cs

Kanishk Gandhi
Michael Y. Li
Lyle Goodyear
Agam Bhatia
Louise Li
Aditi Bhaskar
Mohammed Zaman
Noah D. Goodman

"BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery" by Kanishk Gandhi, Michael Y. Li, Lyle Goodyear, Agam Bhatia, Louise Li, Aditi Bhaskar, Mohammed Zaman, and Noah D. Goodman, arXiv:2501.01540v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{ADF t-statistics test for geophysical data}}% title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 	\hline \hline
 \multirow{2}{*}{Events} &
 \multicolumn{2}{c}{TUC station} & \multicolumn{2}{c}{ANMO station} \\ \cline{2-5}
 &
 Test statistics & p-value & Test statistics & p-value \\
 \hline
 		Earthquake & -42.018 & 0.01 & -40.509 & 0.01\\ % inserting body of the table
 		Explosion & -40.831 & 0.01 & -38.954 & 0.01\\
\hline \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maria C Mariani
Md Al Masum Bhuiyan
Osei K Tweneboah
Hector Gonzalez-Huizar
Ionut Florescu

"Volatility Models Applied to Geophysics and High Frequency Financial Market Data" by Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, and Ionut Florescu, arXiv:1901.09145v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|c|c||}
 \hline
 & $\mu_{e}$ & $\sigma_{e}$ \\\hline
 $Z_{\rm H}=[0.1 \ 0.1]^{\rm T} $ & 0.2327
& 0.1949 \\
 $Z_{\rm H}=[0.3 \ 0.5]^{\rm T} $ & 0.0777
& 0.0651 \\
 $Z_{\rm H}=[0.5 \ 1]^{\rm T} $ & 0.0426
& 0.0358
\\\hline
 \end{tabular}
\caption{The mean and stand deviation of the driver's steering performance error for different values of $Z_{\rm H}$}
\end{table}
\end{document}

eess

"Modulation of Control Authority in Adaptive HapticShared Control Paradigms" by Vahid Izadi and Amir H. Ghasemi, arXiv:2007.07436v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{Standardised Residuals Tests for DISCOVER stock exchange}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} % centered columns (4 columns)
		\hline\hline %inserts double horizontal lines
		 & & & Statistics & P-value \\ % inserts table
		\hline
		Jarque-Bera Test & R & $\chi^2$ & 1688779 & 0 \\ %inserting body of the table
		Shapiro-Wilk Test & R & W & 0.722888 & 0 \\
	 Ljung-Box Test & R & Q(10) & 10.93070 & 0.3629 \\
		Ljung-Box Test & R & Q(15) & 13.58973 & 0.5568 \\
		Ljung-Box Test & R & Q(20) & 16.81071 & 0.6652 \\
		Ljung-Box Test & $R^2$ & Q(10) & 0.198119 & 0.9999 \\
		Ljung-Box Test & $R^2$ & Q(15) & 0.258743 & 1 \\
		Ljung-Box Test & $R^2$ & Q(20) & 0.369189 & 1 \\
		LM Arch Test & R & $TR^2$ & 0.208586 & 1 \\
		\hline \hline%inserts single line
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Out-of-sample prediction accuracy in the density correction of TMAX and PRCP based on a spatiotemporal SPCDE model, compared with canonical correlation analysis (CCA) and quantile mapping (QM). Other than the Wasserstein distance metric, the remaining values correspond to the RMSE between observed and calibrated data for 2001--2014. Metrics are averaged across spatial locations (pairs of locations for spatial correlation). Best (smallest) values for each combination of region, variable, and metric are highlighted.}
\begin{tabular}{clcccccc}
 & & \multicolumn{3}{c}{\textbf{Southwest}} & \multicolumn{3}{c}{\textbf{Southeast}} \\
 & & SPCDE & CCA & QM & SPCDE & CCA & QM \\\toprule
\multirow{4}{*}{\textbf{TMAX}} & Wasserstein distance & \textbf{0.3001} & 0.9270 & 0.3073 & \textbf{1.0377} & 1.3454 & 1.6735 \\
& 0.95 quantile & 1.2225 & \textbf{0.9202} & 1.1136 & \textbf{1.4198} & 1.7515 & 2.2220 \\
 & Lag-1 autocorrelation & \textbf{0.0878} & 0.6873 & 0.6340 & \textbf{0.0617} & 0.6768 & 0.6261 \\
 & Spatial correlation & \textbf{0.0567} & 0.0737 & 0.0670 & \textbf{0.0231} & 0.0653 & 0.0639 \\\midrule
\multirow{4}{*}{\textbf{PRCP}} & Wasserstein distance & \textbf{0.1049} & 0.5427 & 0.1256 & \textbf{0.3057} & 0.8573 & 0.4780 \\
& 0.95 quantile & 1.7358 & 2.3285 & \textbf{1.6520} & \textbf{4.9077} & 4.9544 & 5.8247 \\
 & Proportion of zeros & \textbf{0.1222} & 0.2111 & 0.2227 & \textbf{0.0818} & 0.1056 & 0.1978 \\
 & Lag-1 autocorrelation & \textbf{0.1441} & 0.4102 & 0.3539 & \textbf{0.0970} & 0.2978 & 0.2362 \\
 & Spatial correlation & \textbf{0.1205} & 0.1281 & 0.1283 & \textbf{0.0528} & 0.1401 & 0.1382 \\\midrule
 & Cross correlation & \textbf{0.0017} & 0.3209 & 0.3209 & \textbf{0.0006} & 0.1039 & 0.1039\\\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Reetam Majumder
Shiqi Fang
A. Sankarasubramanian
Emily C. Hector
Brian J. Reich

"Spatiotemporal Density Correction of Multivariate Global Climate Model Projections using Deep Learning" by Reetam Majumder, Shiqi Fang, A. Sankarasubramanian, Emily C. Hector, and Brian J. Reich, arXiv:2411.18799v2, 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
 Total Assets & Cash \& Equivalents & Receivables \\
 \hline
 Inventories & Sales & Cost of Goods Sold \\
 \hline
 Gross Profit & Operating Cash Flow & Operating Income \\
 \hline
 Depreciation, Depletion \& Amortization & Interest Expense & Non-Operating Income/Expense \\
 \hline
 Income Taxes & Advertising Expense & Research \& Development Expense \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Selected model input data features from Form 10-K for each company.}
\end{table}
\end{document}

q-fin

"Learned Sectors: A fundamentals-driven sector reclassification project" by Rukmal Weerawarana, Yiyi Zhu, and Yuzhen He, arXiv:1906.03935v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Steven H. Wang
Maksim Zubkov
Kexin Fan
Sarah Harrell
Yuyang Sun
Wei Chen
Andreas Plesner
Roger Wattenhofer

"ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting" by Steven H. Wang, Maksim Zubkov, Kexin Fan, Sarah Harrell, Yuyang Sun, Wei Chen, Andreas Plesner, and Roger Wattenhofer, arXiv:2501.06582v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ The IKLD between original and synthetic data, averaged over 200 Monte Carlo runs and standardised between 0 and 1. Higher values indicate similar datasets in terms of underlying distribution.}
\begin{tabular}{lccccc}
 \toprule
 \textbf{Generator} & $n=50$ & $n=160$ & $n=500$ & $n=1600$ & $n=5000$ \\
 \midrule
 \textbf{Synthpop} & 0.939 & 0.976 & 0.994 & 0.995 & 0.996 \\
 \textbf{BN} & 0.934 & 0.984 & 0.997 & 0.998 & 0.999 \\
 \textbf{BN DAG} & 0.936 & 0.986 & 0.996 & 0.998 & 0.999 \\
 \textbf{Default CTGAN} & 0.853 & 0.905 & 0.861 & 0.903 & 0.933 \\
 \textbf{CTGAN} & 0.918 & 0.952 & 0.975 & 0.984 & 0.988 \\
 \textbf{Default TVAE} & 0.822 & 0.861 & 0.915 & 0.959 & 0.983 \\
 \textbf{TVAE} & 0.838 & 0.898 & 0.979 & 0.996 & 0.998 \\
 \bottomrule
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

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

"The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Data" by Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet, Johan Decruyenaere, Stijn Vansteelandt, and Thomas Demeester, arXiv:2312.07837v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llll}
 & Financials & Telecom. & Energy \\ \hline
LPA estimate & 52.99 & 3.43 & 4.23 \\
12 month MA estimate & 55.58 & 2.42 & 3.08 \\
36 month MA estimate & 55.50 & 2.94 & 4.17 \\
Most recurring window(w) & 160 & 184 & 309 \\
w month MA estimate & 55.25 & 2.90 & 4.62 \\
LPA MSE & 1008.24 & 3.11 & 8.61 \\
12 month MSE & 1159.29 & 7.34 & 3.29 \\
36 month MSE & 1154.25 & 4.89 & 8.25 \\
w month MSE & 1139.23 & 5.06 & 11.03\\ \hline
\end{tabular}
\caption{Forecast results for 02-2020 to 04-2020 based on the adaptively selected MLE, fixed windows and most recurring window proportionally \((w)\) in the distribution plots in figure ()}
\end{table}
\end{document}

stat

Kainat Khowaja
Danial Saef
Sergej Sizov
Wolfgang Karl Härdle

"Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition" by Kainat Khowaja, Danial Saef, Sergej Sizov, and Wolfgang Karl Härdle, arXiv:2012.06577v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c||c|c|c}
\hline
\multicolumn{4}{c}{\cellcolor{lightgray}\bf Regression task} \\ \hline
Datasets & \# of features & \# of data & feature type \\
\hline
Weather & 21 & 7750 & real \\
\hline
Conductivity & 81 & 11000 & real \\
\hline
Air quality & 13 & 7322 & real \\
\hline
Wave energy & 48 & 9500 & real \\
\hline
\multicolumn{4}{c}{\cellcolor{lightgray}\bf Time series prediction task} \\ \hline
Datasets & \# of features & \# of data & feature type \\
\hline
Traffic & 5,10 & 6500 & real \\
\hline
Temperature & 5,10 & 5500 & real \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Distributed Online Learning with Multiple Kernels" by Jeongmin Chae and Songnam Hong, arXiv:2011.08930v1, 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{Test accuracy (\%) of on various neural networks trained on CIFAR100.}
\begin{tabular}{c|ccccc}
 \textbf{Model} & $\l=0.0$ & $\l=0.5$ & $\l=1.0$ & $\l=1/t$ & $\l=1-1/t$ \\
 \hline
 ResNet-18 & $79.82_{\pm 0.15}$ & $\boldsymbol{80.01}_{\pm 0.07}$ & $79.76_{\pm 0.03}$ & $79.93_{\pm 0.13}$ & $79.86_{\pm 0.04}$ \\
 WideResNet-28-10 & $83.07_{\pm 0.25}$ & $83.29_{\pm 0.33}$ & $83.51_{\pm 0.09}$ & $82.83_{\pm 0.18}$ & $\boldsymbol{83.66}_{\pm 0.19}$ \\
 \end{tabular}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{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
 Agent & (radius, acceleration) of agent \\
 \hline
 0 & (0.3, 1) \\
 1 & (0.2, 2) \\
 2 & (0.1, 3) \\
 3 & (0.1, 3) \\
 4 & (0.2, 2) \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{\texttt{Firefighting} training robots.}
\end{table}
\end{document}

cs

Kevin Fu
Shalin Anand Jain
Pierce Howell
Harish Ravichandar

"Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination" by Kevin Fu, Shalin Anand Jain, Pierce Howell, and Harish Ravichandar, arXiv:2501.06058v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for the proposed architectures. Combined is the accuracy of a logistic regression based ensemble.}
\begin{tabular}{lllrr}
 \toprule
 \multirow{2}{*}{Task} & \multirow{2}{*}{Backbone} & \multirow{2}{*}{Plane} & \multirow{2}{*}{Validation AUC} & \multirow{2}{*}{Combined} \\
 & & & & \\
 \midrule
 \multicolumn{5}{l}{\textbf{\textcolor{blue}{Configuration from 4.1}}} \\
 \midrule
 \multirow{3}{*}{ACL} & \multirow{3}{*}{ResNet18} & Axial & 0.9341 & \multirow{3}{*}{0.9557} \\
 & & Coronal & 0.9259 & \\
 & & Sagittal & 0.8967 &
 \\
 \multirow{3}{*}{Meniscus} & \multirow{3}{*}{ResNet18} & Axial & 0.8815 & \multirow{3}{*}{0.9081} \\
 & & Coronal & 0.8521 & \\
 & & Sagittal & 0.7805 & \
 \\
 \multirow{3}{*}{Abnormal} & \multirow{3}{*}{ResNet18} & Axial & 0.9486 & \multirow{3}{*}{0.9381} \\
 & & Coronal & 0.9128 & \\
 & & Sagittal & 0.9503 & \\
 \midrule
 \multicolumn{4}{l}{Average} & \textbf{0.9340} \\
 \midrule
 \multicolumn{5}{l}{\textbf{\textcolor{blue}{Configuration from 4.2}}} \\
 \midrule
 \multirow{3}{*}{ACL} & \multirow{3}{*}{ResNet18} & Axial & 0.9007 & \multirow{3}{*}{0.9386} \\
 & & Coronal & 0.8681 & \\
 & & Sagittal & 0.7851 & \
 \\
 \multirow{3}{*}{Meniscus} & \multirow{3}{*}{ResNet18} & Axial & 0.7873 & \multirow{3}{*}{0.8213} \\
 & & Coronal & 0.8012 & \\
 & & Sagittal & 0.7517 & \
 \\
 \multirow{3}{*}{Abnormal} & \multirow{3}{*}{ResNet18} & Axial & 0.9061 & \multirow{3}{*}{0.9326} \\
 & & Coronal & 0.7288 & \\
 & & Sagittal & 0.9112 & \\
 \midrule
 \multicolumn{4}{l}{Average} & 0.8975 \\
 \midrule
 \multicolumn{5}{l}{\textbf{\textcolor{blue}{Configuration from 4.3}}} \\
 \midrule
 ACL & ResNet18 & 3 Planes & 0.8883 & N/A \\
 Meniscus & ResNet18 & 3 Planes & 0.7868 & N/A \\
 Abnormal & ResNet18 & 3 Planes & 0.8981 & N/A \\
 \midrule
 \multicolumn{3}{l}{Average} & 0.8577 & \\
 \midrule
 \multicolumn{5}{l}{\textbf{\textcolor{blue}{Configuration from 4.4}}} \\
 \midrule
 ACL & \multirow{3}{*}{ResNet18} & \multirow{3}{*}{3 Planes} & 0.8434 & N/A \\
 Meniscus & & & 0.7876 & N/A \\
 Abnormal & & & 0.8522 & N/A \\
 \midrule
 \multicolumn{3}{l}{Average} & 0.8278 & \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

David Azcona
Kevin McGuinness
Alan F. Smeaton

"A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet Dataset" by David Azcona, Kevin McGuinness, and Alan F. Smeaton, arXiv:2010.01947v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter search space of SAINT.}
\begin{tabular}{cc}
\toprule
\textbf{Hyperparameter} & \textbf{Distribution}\\
\midrule
num\_layers & $\text{uniform\_int}[1,2,3,6,12]$\\
num\_heads & $[2,4,8]$\\
layer\_size & $\text{uniform\_int}[32,64,128]$\\
dropout & $[0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8]$\\
learning\_rate & $\text{log\_uniform}[1\mathrm{e}{-5},1\mathrm{e}{-3}]$\\
batch\_size & $[128,256]$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

DOFEN: Deep Oblivious Forest ENsemble

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \hline
 Model & SSIM & PSNR & MSE \\
 \hline
 Pix2pix & 0.766$\pm$0.089 & 23.45$\pm$1.86 & 328.01$\pm$227.53 \\
 Pix2pix+image & 0.683$\pm$0.074 & 22.11$\pm$1.58 & 433.39$\pm$254.33 \\
 Pix2pix+add & 0.465$\pm$0.126 & \textbf{24.60$\pm$1.87} &
\textbf{259.09.01$\pm$271.76} \\
 \textbf{Pix2pix+concat} & \textbf{0.782$\pm$0.090} & 23.79$\pm$1.97 & 309.29$\pm$262.89 \\
 \hline
 CycleGAN & 0.808$\pm$0.112 & 24.65$\pm$1.90 & 250.33$\pm$174.61 \\
 CycleGAN+image & 0.822$\pm$0.131 & 24.18$\pm$1.64 & 273.70$\pm$217.10 \\
 CycleGAN+add & 0.815$\pm$0.126 & 24.08$\pm$1.96 & 288.26$\pm$237.60 \\
 \textbf{CycleGAN+concat} & \textbf{0.822$\pm$0.113} & \textbf{25.07$\pm$1.82} &
\textbf{227.00$\pm$182.45} \\
 \hline
 ShareGAN & 0.704$\pm$0.067 & 23.58$\pm$1.48 & 306.18$\pm$162.35 \\
 ShareGAN+image & 0.556$\pm$0.034 & 22.42$\pm$1.33 & 393.77$\pm$177.27 \\
 ShareGAN+add & 0.710$\pm$0.094 & 22.98$\pm$1.23 & 344.14$\pm$155.68 \\
 \textbf{ShareGAN+concat} & \textbf{0.710$\pm$0.065} & \textbf{23.63$\pm$1.41} & \textbf{299.73$\pm$137.62} \\

 \hline
\end{tabular}
\end{adjustbox}
\caption{SSIM, PSNR, and MSE of baseline model and integration method combinations. All models achieved improved performance after incorporating BBBMs in all metrics. Plasma-CycleGAN achieved the highest SSIM, PSNR, and lowest MSE.}
\end{table}
\end{document}

cs

Yanxi Chen
Yi Su
Celine Dumitrascu
Kewei Chen
David Weidman
Richard J Caselli
Nicholas Ashton
Eric M Reiman
Yalin Wang

"Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN" by Yanxi Chen, Yi Su, Celine Dumitrascu, Kewei Chen, David Weidman, Richard J Caselli, Nicholas Ashton, Eric M Reiman, and Yalin Wang, arXiv:2501.02146v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c||c||c||c||c||}
			\hline \hline
			Events	& $\zeta$ & $\phi$ & $\sigma$ & $\psi$ \\
			\hline \hline
			Non-Seizure & 0.14 & 0.99 & 0.99 & 0.30 \\
			Seizure 	& 0.11 & 1.00 & 1.00 & 0.25 \\
			\hline \hline
		\end{tabular}
\caption{Means of the statistical parameters with values normalized between 0 and 1 for all each 1-second time segments. A value closer to zero in the weighted sum of squared residuals ($\zeta$) and Root mean squared error ($\psi$) and values closer to one in R-square ($\phi$) and adjusted R-squared ($\sigma$) suggest a good fit quality and therefore can be used as a predictor.}
\end{table}
\end{document}

eess

Antonio Quintero-Rincon
Carlos D'Giano
Hadj Batatia

"A quadratic linear-parabolic model-based classification to detect epileptic EEG seizures" by Antonio Quintero-Rincon, Carlos D'Giano, and Hadj Batatia, arXiv:1912.08359v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{clcccc}
 \toprule
 &\textbf{Model} & \textbf{N. Params} & \textbf{Optimizer}& \textbf{Time (ms/it)} & \textbf{Rel. $L^2$ Error} \\
 \midrule
 A&Baseline & 21318 &Adam& 4.01 & $1.77 \times 10^{-3}$ \\
 &$vRBA:\Phi(r)=r^2$ & 21318&Adam & 4.03 & $\mathbf{4.08 \times 10^{-4}}$ \\
 &$vRBA:\Phi(r)=e^r$ & 21318&Adam & 4.47 & $4.14 \times 10^{-4}$ \\
 \midrule
 B&Baseline& 2011&SSBroyden & 22.4 & $4.34 \times 10^{-5}$ \\
 &$v RBA:\Phi(r)=r^2$ &2011&SSBroyden & 24.3&$2.42 \times 10^{-6}$ \\
 &$v RBA:\Phi(r)=e^r$ & 2011&SSBroyden & 23.7 & $\mathbf{2.14 \times 10^{-6}}$ \\
 \bottomrule
 \end{tabular}
\caption{Comparison of models for the Allen-Cahn equation, where baseline models using uniform sampling are benchmarked against the proposed vRBA method. The table presents two distinct scenarios: (a) vRBA applied as an importance weighting strategy for a large network using a first-order optimizer (Adam), and (b) vRBA applied as importance sampling for a compact network using a second-order optimizer (SSBroyden). Notably, using an adaptive method with either an exponential ($\Phi(r)=e^r$) or quadratic ($\Phi(r)=r^2$) potential reduces the relative $L^2$ error by approximately an order of magnitude, an improvement that holds true even when using a highly optimized second-order method.}
\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.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of HPT and Score-HPT on the MAESTRO test set. \(\uparrow\) and \(\downarrow\) indicate whether higher or lower values are better.}
\begin{tabular}{cll|ccc}
 \toprule
 \multicolumn{3}{c|}{\textbf{Model \& Inputs}} & \multicolumn{3}{c}{\textbf{MAESTRO test set}} \\
 \cmidrule{1-6}
 x1 & x2 & x3 & \(\space\) MAE \(\downarrow\) & STD \(\downarrow\) & Recall \(\uparrow\) \\
 \midrule
 \multicolumn{2}{l}{\textbf{HPT}} & & & & (79.8\%) \\
 audio & \multicolumn{2}{l|}{} & 5.05 & 4.85 & 90.6\% \\
 \midrule
 \multicolumn{2}{l}{\textbf{Score-HPT}} & & & & \\
 audio & + onset & {} & \textbf{3.48} & \textbf{3.43} & \textbf{95.6\%} \\
 audio & + frame & {} & 3.56 & 3.52 & 95.3\% \\
 audio & + frame$_{\text{ex}}$ & {} & 3.49 & 3.49 & 95.3\% \\
 audio & + onset & + frame & 3.51 & 3.46 & 95.5\% \\
 audio & + onset & + frame$_{\text{ex}}$ & 3.50 & \textbf{3.43} & \textbf{95.6\%} \\
 audio & + frame & + frame$_{\text{ex}}$ & 3.52 & 3.51 & 95.3\% \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Zhanhong He
Roberto Togneri
Defeng
Huang

"Score-Informed BiLSTM Correction for Refining MIDI Velocity in Automatic Piano Transcription" by Zhanhong He, Roberto Togneri, Defeng, and Huang, arXiv:2508.07757v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Performance evaluation of DMUs using hybrid fuzzy multi-objective data envelopment analysis" by Awadh Pratap Singh and Shiv Prasad Yadav, arXiv:2202.01585v1, 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{Comparison of the COS, SWIFT and SFP methods in terms of absolute error for pricing a call option under the BSM model. The reference values have been computed using the Black-Scholes analytical formulae: 99.2025928525532000 ($T = 50$) and 99.9945609694213000 ($T=100$). The SFP method is more accurate than the others with fewer summation terms required. The parameters are taken from \textbf{BSM--Para3}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|ccc|} % centered columns (4 columns)
\hline
Method&Error $(T=50)$& Error $(T=100)$\\
\hline
SWIFT $(m = 0)$ &$1.91e-01$&$2.50e-05$\\
COS $(N = 35)$ &$4.98e-01$&$2.05e+02$\\
SFP $(U=32)$&$2.653e-07$&$7.067e-08$\\
SWIFT $(m = 1)$& $7.78e-09$& $3.20e-06$\\
COS $(N = 70)$& $2.79e-08$ &$2.02e-05$\\
SFP $(U=64)$&$2.251e-10$&$7.037e-11$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Singular Fourier-Padé Series Expansion of European Option Prices" by Tat Lung Chan, arXiv:1706.06709v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
 \hline
Corpus & Language\\\hline
TIMIT & English \\\hline
WSJ & English \\\hline
 CUSENT & Cantonese \\\hline
 863 & Mandarin \\\hline
distant-speech database & German \\\hline
 \end{tabular}
\caption{Information of the corpora used}
\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{amsmath}
\usepackage{multirow}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\color{blue}Impact of intermittent events of different magnitude on the frequency deviation and equal power sharing indices}
\begin{tabular}{cccccc}
\hline
 \multirow{2}{*}{$P^{max}$} & No intermittency & \multicolumn{4}{c}{Intermittency (kW)}\\
 & 350 kW (rated) & 275 & 250 & 225 & 200\\
\hline
MPSI & 0.00 & 0.07 & 0.11 & 0.15 & 0.20\\
$|f-60|$ & 0.00 & 0.00 & 0.00 & 0.00 & 0.00\\
$V_{error}$ & 0.002 & 0.002 & 0.002 & 0.002 & 0.002\\
MQSI & 0.004 & 0.004 & 0.004 & 0.004 & 0.004\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters" by Ankit Singhal, Thanh Long Vu, and Wei Du, arXiv:2012.06685v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The Wilcoxon results $p_W$ comparing the F$_1$-scores of multiple test iterations on the Train$_\mathbf{\text{ext}}$ and Train$_\mathbf{\text{syn}}$ sets. In all cases, the $n$-driven improvements reported in were found to be statistically significant ($p_W < 0.1$), marked with *.}
\begin{tabular}{rrrrrrr}
 \toprule
 & \multicolumn{3}{c}{\textbf{Train$_\mathbf{\text{ext}}$}} & \multicolumn{3}{c}{\textbf{Train$_\mathbf{\text{syn}}$}} \\
 \cmidrule(lr){2-4} \cmidrule(lr){5-7}
 \textbf{$n$} & \textbf{150} & \textbf{300} & \textbf{1,000} & \textbf{150} & \textbf{300} & \textbf{1,000} \\
 \midrule\midrule
 \textbf{150} & 1.00\:\: & 0.00* & 0.02* & 1.00\:\: & 0.00* & 0.00* \\
 \textbf{300} & 0.00* & 1.00\:\: & 0.00* & 0.00* & 1.00\:\: & 0.09* \\
 \textbf{1,000} & 0.02* & 0.00* & 1.00\:\: & 0.00* & 0.09* & 1.00\:\: \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Conformalised data synthesis

"Conformalised data synthesis" by Julia A. Meister and Khuong An Nguyen, arXiv:2312.08999v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Table Type Styles}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Table}&\multicolumn{3}{|c|}{\textbf{Table Column Head}} \\
\cline{2-4}
\textbf{Head} & \textbf{\textit{Table column subhead}}& \textbf{\textit{Subhead}}& \textbf{\textit{Subhead}} \\
\hline
copy& More table copy$^{\mathrm{a}}$& & \\
\hline
\multicolumn{4}{l}{$^{\mathrm{a}}$Sample of a Table footnote.}
\end{tabular}
\end{table}
\end{document}

eess

Juan Cervino
Juan Andres Bazerque
Miguel Calvo-Fullana
Alejandro Ribeiro

"Multi-task Supervised Learning via Cross-learning" by Juan Cervino, Juan Andres Bazerque, Miguel Calvo-Fullana, and Alejandro Ribeiro, arXiv:2010.12993v3, 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|ccc}
\hline \hline
\textbf{VLM Model} & \textbf{Best Latency (ms)} & \textbf{Worst Latency (ms)} & \textbf{Average Latency (ms)} \\ \hline \hline
ChatGPT VLM & \textbf{2982} & \textbf{7120} & \textbf{4756} \\
Claude VLM & 6129 & 7409 & 9373 \\
LLaMA VLM 3 & 7139 & 9664 & 11317 \\
\hline \hline
\end{tabular}
\end{adjustbox}
\caption{Latency comparison for Visual Language Models (VLMs).}
\end{table}
\end{document}

cs

Alejandro Carrasco
Marco Nedungadi
Enrico M. Zucchelli
Amit Jain
Victor Rodriguez-Fernandez
Richard Linares

"Visual Language Models as Operator Agents in the Space Domain" by Alejandro Carrasco, Marco Nedungadi, Enrico M. Zucchelli, Amit Jain, Victor Rodriguez-Fernandez, and Richard Linares, arXiv:2501.07802v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Survival causal rule ensemble method considering the main effect for estimating heterogeneous treatment effects" by Ke Wan, Kensuke Tanioka, and Toshio Shimokawa, arXiv:2309.11914v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline &\multicolumn{2}{c|}{ACAT2}&\multicolumn{2}{c|}{WBACAT2}& \multicolumn{2}{c|}{ACAT4} & \multicolumn{2}{c|}{WBACAT4} \\
 Points & Order & Error & Order & Error & Order & Error & Order& Error \\
 \hline
 200 & - & 8.75E-4 & - & 8.74E-4 & - & 2.67E-5 & - & 2.67E-5 \\
 \hline
 400 & 2.01 & 2.17E-4 & 2.01 & 2.17E-4 & 3.98 & 1.69E-6 & 3.98 & 1.69E-6 \\
 \hline
 800 & 2.00 & 5.42E-5 & 2.00 & 5.42E-5 & 3.99 & 1.06E-7 & 3.99 & 1.06E-7 \\
 \hline
 1600 & 2.00 & 1.35E-5 & 2.00 & 1.35E-5 & 4.00 & 6.61E-9 & 4.00 & 6.61E-9 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Test : Errors in $L^1$ norm and convergence rates related to $q$ for ACAT2, ACAT4, WBACAT2 and WBACAT4 at time $t=0.2$ and CFL$=0.9.$}
\end{table}
\end{document}

math

"Well-balanced adaptive compact approximate Taylor methods for systems of balance laws" by H. Carrillo, E. Macca, C. Pares, and G. Russo, arXiv:2202.02068v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l||r|r|r|l|}
		\hline
		\multicolumn{5}{|l|}{\cellcolor[gray]{0.95} \vphantom{\Big|} Comparison using width parameter $w=0.025$.}\\ \hline\hline
\textbf{Method} & Value & \textbf{Bias} & \textbf{Std.~Dev.} & \textbf{Improve} \\ \hline
\hline
Finite Difference & 0.7362 & 0.0001 & 0.0156 & \\ \hline
Stoch.~AD & 0.7362 & 0.0002 & 0.0161 & 0.97 \\ \hline
Stoch.~AD with Regression & 0.7359 & -0.0002 & 0.0054 & 3.01 \\ \hline
Likelihood Ratio & 0.7361 & 0.0000 & 0.0025 & 2.11 \\ \hline
Analytic & 0.7361 & 0.0000 & 0.0000 & $\infty$ \\ \hline
	\end{tabular}
\end{adjustbox}
\caption{Results from 10000 Monte-Carlo experiments, each using 200000 paths. The width parameter for the conditional expectation was $w=0.025$. The regression is performed on the distribution with a width parameter of $w=0.25$. Both parameters are half the size as those used for Table~. The bias on the AAD with regression vanishes, while the improvement of the variance reduction stays similar.}
\end{table}
\end{document}

q-fin

"Stochastic Algorithmic Differentiation of (Expectations of) Discontinuous Functions (Indicator Functions)" by Christian P. Fries, arXiv:1811.05741v5, licensed under CC BY-SA 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{Questions available for each dataset (number of dataset). Class, All and Any are short for ``what is the class of $x$?'', ``are all of $x_1,\dots,x_m$ from class $c$?'' and ``is any of $x_1,\dots,x_m$ from class $c$?'', respectively.}
\begin{tabular}{|l|c|c|c|c|c|}
\hline
Question & $m$, $cost$ (1) & $m$, $cost$ (2) & $m$, $cost$ (3) & $m$, $cost$ (4) & $m$, $cost$ (5) \\ \hline
Class & 1, 1 & 1, 1 & 1, 1 & 1, 1 & 1, 1 \\ \hline
All & 1, 0.18 & 1, 0.25 & 1, 0.23 & 1, 0.23 & 1, 0.21 \\ \hline
All & 2, 0.18 & 2, 0.25 & 2, 0.235 & 2, 0.235 & 2, 0.215 \\ \hline
All & & & 5, 0.24 & 5, 0.24 & 3, 0.22 \\ \hline
Any & 2, 0.2 & 2, 0.25 & 2, 0.235 & 2, 0.235 & 2, 0.215 \\ \hline
Any & & & 5, 0.24 & 5, 0.24 & 3, 0.22 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems" by Yiran Huang, Jian-Feng Yang, and Haoda Fu, arXiv:2501.00277v1, 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{Statistics of the Created Graph}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
User Nodes & Image Nodes & User-User Edges & User-Image Edges \\
\hline\hline
108899 & 85157 & 1649058 & 197561 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sree Bhattacharyya
Shuhua Yang
James Z. Wang

"A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks" by Sree Bhattacharyya, Shuhua Yang, and James Z. Wang, arXiv:2501.07746v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Fatih Ayten
Mehmet C. Ilter
Akshay Jain
Ossi Kaltiokallio
Jukka Talvitie
Elena Simona Lohan
Henk Wymeersch
Mikko Valkama

"Failure Tolerant Phase-Only Indoor Positioning via Deep Learning" by Fatih Ayten, Mehmet C. Ilter, Akshay Jain, Ossi Kaltiokallio, Jukka Talvitie, Elena Simona Lohan, Henk Wymeersch, and Mikko Valkama, arXiv:2508.14739v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for \textit{Vanilla Formal} Verification using SEVIN}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multicolumn{2}{|c|}{\multirow{3}{*}{Specification}} & \multicolumn{4}{c|}{NNC Architecture} \\ \cline{3-6}
\multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{\textbf{NvidiaNet}} & \multicolumn{2}{c|}{\textbf{ResNet18}} \\ \cline{3-6}
\multicolumn{2}{|c|}{} & Results & Time(s) & Results & Time(s) \\ \hline
\multirow{2}{*}{Safety} & $\varphi_1$ & SAT & 0.417 & SAT & 0.6343 \\ \cline{2-6}
 & $\varphi_2$ & SAT & 0.4430 & SAT & 0.7023 \\ \hline
\multirow{3}{*}{Performance} & $\varphi_3$ & SAT & 0.412 & SAT & 0.6799 \\ \cline{2-6}
 & $\varphi_4$ & SAT & 0.3667 & SAT & 0.5746 \\ \cline{2-6}
 & $\varphi_5$ & SAT & 0.6383 & SAT & 0.8215 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Scalable and Interpretable Verification of Image-based Neural Network Controllers for Autonomous Vehicles" by Aditya Parameshwaran and Yue Wang, arXiv:2501.14009v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Configuration spaces on a wedge of spheres and Hochschild-Pirashvili homology" by Nir Gadish and Louis Hainaut, arXiv:2202.12494v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Adaptive Noise-Tolerant Network for Image Segmentation" by Weizhi Li, arXiv:2501.07163v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of our method with other JPEG approximation techniques. The results are reported with the metric of BER ($\%$).}
\begin{tabular}{cccccc}
 \toprule
 JPEG Quality & JPEG-Drop & JPEG-Mask & JPEG-Drop + JPEG-Mask & JPEG~ & Ours \\
 \midrule
 JPEG-10 & $49.33$ & $46.23$ & $46.38$ & $35.68$ & $\mathbf{31.22}$ \\
 JPEG-25 & $48.50$ & $39.48$ & $41.38$ & $22.00$ & $\mathbf{16.75}$ \\
 JPEG-50 & $47.62$ & $32.77$ & $38.48$ & $15.85$ & $\mathbf{12.63}$ \\
 JPEG-75 & $46.75$ & $27.22$ & $37.10$ & $14.03$ & $\mathbf{12.41}$ \\
 JPEG-90 & $46.08$ & $18.57$ & $34.93$ & $13.81$ & $\mathbf{12.00}$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Chaoning Zhang
Adil Karjauv
Philipp Benz
In So Kweon

"Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach" by Chaoning Zhang, Adil Karjauv, Philipp Benz, and In So Kweon, arXiv:2101.00973v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\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
 Type & Name & Normal form & Conditions &
 $\mathcal{K}$-$\textrm{cod}
 \leq n$\\
 \hline \hline
$\Sigma^{1}$ &$A_{j}$ &$(x^{j+1})$ & $ 1\leq j\leq n$ & $j$ \\\hline
$\Sigma^{2,0}$ &$B^{\pm}_{p,q}$ &$(xy, x^{p}\pm y^{q})$ & $2\leq p,q \leq n-2$ &
 $p+q $ \\\hline
$\Sigma^{2,0}$ & $B^{*}_{p,p}$ & $(x^{2}+y^{2}, x^{p})$ & $3 \leq p\leq 4$
 & $2p$ \\\hline
$\Sigma^{2,1}$ &$C_{2k-1}$ &$(x^{2}+y^{3},y^{3})$ & &
 $7$\\\hline
$\Sigma^{2,1}$ & $C_{2k}$ &$(x^{2}+y^{3}, xy^{2})$ &
 &$8$ \\
\hline \hline
 \end{tabular}
\end{adjustbox}
\caption{${\cal K}$-orbits of stable germs $n=p \leq 8$}
\end{table}
\end{document}

math

"Old and new results on density of stable mappings" by Maria Aparecida Soares Ruas, arXiv:2201.03888v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Taketo Akama
Zhuohao Zhang
Pengcheng Li
Kotaro Hongo
Hiroaki Kitano
Shun Minamikawa
Natalia Polouliakh

"Predicting Artificial Neural Network Representations to Learn Recognition Model for Music Identification from Brain Recordings" by Taketo Akama, Zhuohao Zhang, Pengcheng Li, Kotaro Hongo, Hiroaki Kitano, Shun Minamikawa, and Natalia Polouliakh, arXiv:2412.15560v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Validation Accuracy of MNIST on TDFN}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
Fixation Point Number & Random Selected & Generated by FPG & Coverage\tabularnewline
\midrule
\midrule
0 & 68.03\% & 68.03\% & 0\%\tabularnewline
\midrule
2 & 74.00\% & 84.20\% & 12.5\%\tabularnewline
\midrule
4 & 86.41\% & 92.89\% & 25\%\tabularnewline
\midrule
8 & 93.20\% & 96.11\% & 50\%\tabularnewline
\midrule
12 & 95.29\% & 97.02\% & 75\%\tabularnewline
\midrule
16 & 96.28\% & 97.79\% & 100\%\tabularnewline
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Task-Driven Fixation Network: An Efficient Architecture with Fixation Selection" by Shuguang Wang and Yuanjing Wang, arXiv:2501.01548v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|}
\hline
\textbf{Layer (type)} & \textbf{Output Shape} & \textbf{Param \#} \\
\hline
Linear & [-1, 1, 100] & 300 \\
\textit{ReLU} + Residual & [-1, 1, 100] & 0 \\
Linear & [-1, 1, 100] & 10,100 \\
\textit{ReLU} + Residual & [-1, 1, 100] & 0 \\
Linear & [-1, 1, 100] & 10,100 \\
\textit{ReLU} + Residual & [-1, 1, 100] & 0 \\
Linear & [-1, 1, 100] & 10,100 \\
\textit{ReLU} + Residual & [-1, 1, 100] & 0 \\
Linear & [-1, 1, 100] & 10,100 \\
\textit{ReLU} + Residual & [-1, 1, 100] & 0 \\
Linear & [-1, 1, 100] & 10,100 \\
\textit{ReLU} + Residual & [-1, 1, 100] & 0 \\
Linear & [-1, 1, 1] & 101 \\
\hline
\textbf{Total params:} & \multicolumn{2}{c|}{\textbf{50,901}} \\
\hline
\end{tabular}
\caption{Summary of the ratio copula architecture used for the 2D image data. Here, a simple ratio copula model is used.}
\end{table}
\end{document}

stat

"Your copula is a classifier in disguise: classification-based copula density estimation" by David Huk, Mark Steel, and Ritabrata Dutta, arXiv:2411.03014v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 Instance & EGM & Broyden & J-symm & J-symm-LS & J-symm-Tr \\ \hline
 \texttt{22433} & - & 940 & 220 & 67 & 51 \\ \hline
 \texttt{23588} & - & 773 & 220 & 162 & - \\
 \hline
 \texttt{assign1-5-8} & - & 1157 & 272 & 169 & 311 \\
 \hline
 \texttt{b-ball} & - & 231 & 87 & 24 & 65 \\
 \hline
 \texttt{enlight8} & - & 277 & 225 & 68 & 63 \\
 \hline
 \texttt{enlight9} & - & 327 & 178 & 77 & 58 \\
 \hline
 \texttt{enlight\_hard} & - & 536 & 206 & 67 & 51 \\
 \hline
 \texttt{gr4x6d} & - & 150 & 119 & 60 & 837 \\
 \hline
 \texttt{neos5} & - & 262 & 88 & 14 & 33\\
 \hline
 \texttt{prod1} & - & 1346 & 590 & 588 & 1356 \\
 \hline
 \texttt{prod2} & - & 1083 & 512 & 373 & - \\ \hline
 \texttt{ran13x13} & - & 736 & 402 & 241 & 390 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Number of iterations for different methods to find an approximate solution with $\|F(z)\|\le 10^{-4}$ for the bilinear zero sum game. ``-'' refers to the algorithm fails to terminate within $2000$ iterations.}
\end{table}
\end{document}

math

"A $J$-Symmetric Quasi-Newton Method for Minimax Problems" by Azam Asl, Haihao Lu, and Jinwen Yang, arXiv:2202.02279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparing supervised performance of the state-of-the-art models on PTB-XL that incorporate patient-specific metadata as compared to models operating on signals only. The notation follows .}
\begin{tabular}{lll}
		\toprule
		&\multicolumn{2}{c}{\textit{PTB-XL}}\\
		& signal & signal \& meta\\

		\midrule
		\textit{xresnet1d50}${}^\dagger$ (this work) & 0.9286(28)& 0.9388(26)\\
		\textit{DLTB-ECG} & 0.934 &0.942 \\
		\textit{S4 model}${}^\dagger$ (supervised, this work)& 0.9417(16)& 0.9434(33)\\
		\textit{S4 with FCE}${}^\dagger$ (pretrained, this work)& \textbf{0.9445(19)}& \textbf{0.9463(08)} \\

		\bottomrule

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

stat

"Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata" by Temesgen Mehari and Nils Strodthoff, arXiv:2308.15291v1, 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{Mean absolute error (MAE) and standard deviation of error for bias-correction frameworks across all MIMIC dataset subjects, comparing $N=1$ and $N=5$ measurements.}
\begin{tabular}{lcccc}
\hline
\textbf{Method} & $\mathbf{N}$ & \textbf{SBP MAE} & \textbf{DBP MAE} \\
\hline
LS & $1$ & $9.29 \pm 7.39$ & $6.29 \pm 4.70$ \\
LS & $5$ & $9.29 \pm 4.92$ & $6.29 \pm 3.26$ \\
\hline
ML & $1$ & $4.96 \pm 7.39$ & $3.51 \pm 4.70$ \\
ML & $5$ & $4.96 \pm 4.92$ & $3.51 \pm 3.26$ \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Estimation-Theoretic Bias Reduction for Oscillometric Blood Pressure Readings" by Masoud Nateghi and Reza Sameni, arXiv:2508.15687v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\toprule
 & AS & SAV & Indirect GARCH \\ \midrule
$\beta_2, GM$ & 0.9160 & 0.9250 & 0.858 \\
p-value & 0.0252 & 0.0186 & 0.0260 \\
$\beta_2, IBM$ & 0.9178 & 0.9023 & 0.9068 \\
p-value & 0.0665 & 0.0582 & 0.0531 \\
$\beta_2, SPX$ & 0.9164 & 0.9252 & 0.8578 \\
p-value & 0.0252 & 0.0186 & 0.026 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{The table above shows the regressive parameter, $\beta_2$ for each asset under each of the three CAViaR regimes. With the exception of IBM by a slight margin, all three assets suggest significant autoregressive VaR at the $95 \%$ level for (a) Asymmetric Slope and (b) Symmetric Absolute Value, and (c) Indirect GARCH}
\end{table}
\end{document}

q-fin

"Value-at-Risk: The Effect of Autoregression in a Quantile Process" by Khizar Qureshi, arXiv:1605.04940v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{rrr}
\toprule
\textbf{Model} & \textbf{RCT} & \textbf{Non-RCT} \\
\midrule
TARNet & 0\% & 0\% \\
CausalForest & 12\% & 6\%\\
S-BART & 12\% & 8\% \\
T-BART & 12\% & 6\% \\
S-engression & 18\% & 6\%\\
T-engression & 24\% & 8\%\\
\bottomrule
\end{tabular}
\caption{Percentage of $p > 0.05$ across 50 Trials.}
\end{table}
\end{document}

stat

Daniel de Vassimon Manela
Linying Yang
Robin J. Evans

"Testing Generalizability in Causal Inference" by Daniel de Vassimon Manela, Linying Yang, and Robin J. Evans, arXiv:2411.03021v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Garyfallos Konstantinoudis
Anthony Hauser
Julien Riou

"Bayesian ensemble modelling to monitor excess deaths during summer 2022 in Switzerland" by Garyfallos Konstantinoudis, Anthony Hauser, and Julien Riou, arXiv:2308.15251v1, 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{Attack surfaces for equipped vehicles and pedestrians}
\begin{tabular}{llllll}
 \hline
 Target & Type & Feasibility & Metric & Detection & Metric \\
 \hline
 SA & LTC & low & $[h\ l\ l]$ & med & $[l\ h\ l]^T \times [h\ l\ l]$ \\
 & CRL & med & $[l\ h\ l]$ & med & $[l\ h\ l]^T \times[l\ h\ l]$ \\
 & PC & med & $[l\ h\ l]$ & med & $[l\ h\ l]^T \times[l\ h\ l]$ \\
 RSE & WSA & high & $[l\ l\ h]$ & low & $[h\ l\ l]^T \times[l\ l\ h]$ \\
 & DB & high & $[l\ l\ h]$ & med & $[l\ h\ l]^T \times[l\ l\ h]$ \\
 & DOSR & high & $[l\ l\ h]$ & high & $[l\ l\ h]^T \times[l\ l\ h]$ \\
 & SD & low & $[h\ l\ l]$ & high & $[l\ l\ h]^T \times[h\ l\ l]$ \\
 VEHs & CB & low & $[h\ l\ l]$ & low & $[h\ l\ l]^T \times[l\ h\ l]$ \\
 & BLK & med & $[l\ h\ l]$ & low & $[h\ l\ l]^T \times[l\ h\ l]$ \\
 & RB & low & $[h\ l\ l]$ & low & $[h\ l\ l]^T \times[h\ l\ l]$ \\
 & BSM & high & $[l\ l\ h]$ & low & $[h\ l\ l]^T \times[l\ l\ h]$ \\
 & DOS & high & $[l\ l\ h]$ & low & $[h\ l\ l]^T \times[l\ l\ h]$ \\
 & MP & high & $[l\ l\ h]$ & med-high & $[l\ m\ h]^T \times[l\ l\ h]$ \\
 & DCC & med & $[l\ h\ l]$ & med & $[h\ l\ l]^T \times[l\ h\ l]$ \\
 & LC & med & $[l\ h\ l]$ & low & $[h\ l\ l]^T \times[h\ l\ l]$ \\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

Gurcan Comert
Mashrur Chowdhury
David M. Nicol

"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Measures of Model Fit: Data}
\begin{tabular}{c|ccc}
 \hline \hline
\multirow{2}{*}{Criterion} & \multicolumn{3}{c}{\# of components} \\ \cline{2-4}
 & 1 & 2 & 3 \\
\hline
AIC & 26,525.61 & 24,914.09 & \textbf{24,572.43} \\
BIC & 26,787.67 & 25,457.00 & \textbf{25,396.19} \\
DIC & 26,449.99 & 24,767.12 & \textbf{24,358.25} \\
WAIC & 26,530.46 & 24,916.78 & \textbf{24,564.57} \\
LOO & 26,530.79 & 24,916.68 & \textbf{24,568.12} \\
 \hline \hline
 \end{tabular}
\end{table}
\end{document}

stat

"Bayesian estimation of finite mixtures of Tobit models" by Caio Waisman, arXiv:2411.09771v1, 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}{llll}
		\toprule
			Sketch & Sketching time & Required sketch size $k$ \\
			\midrule
			Gaussian & $O(ndk) $ & $O((d+\log(1/\delta))/\epsilon^2) $\\
			Hadamard & $O(nd \log k)$ & $O((\sqrt{d}+\sqrt{\log n})^2(\log (d/\delta))/ \epsilon^2)$\\
			Clarkson-Woodruff & $O(nd)$& $O(d^2/(\delta\epsilon^2))$ \\
			Uniform & $O(k)$ & $-$ & \\
			\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Daniel Ahfock
William J. Astle
Sylvia Richardson

"On randomized sketching algorithms and the Tracy-Widom law" by Daniel Ahfock, William J. Astle, and Sylvia Richardson, arXiv:2201.00450v1, 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{Default experimental settings for MNIST}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 Dataset & MNIST \\
 Architecture & CONV-CONV-DROPOUT-FC-DROPOUT-FC \\
 Training objective & Negative log likelihood loss \\
 Evaluation objective & Top-1 accuracy \\
 \midrule
 Batch size per worker & $32$ \\
 Momentum & 0.9 \\
 Learning rate & 0.01 \\
 LR decay & No \\
 LR warmup & No \\
 Weight decay & No \\
 \midrule
 Repetitions & 1 \\%, with varying seeds \\
 Reported metric & Mean test accuracy over the last 150 iterations \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lie He
Sai Praneeth Karimireddy
Martin Jaggi

"Byzantine-Robust Decentralized Learning via ClippedGossip" by Lie He, Sai Praneeth Karimireddy, and Martin Jaggi, arXiv:2202.01545v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Bank distress in the news: Describing events through deep learning" by Samuel Rönnqvist and Peter Sarlin, arXiv:1603.05670v2, 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{FlipFlop Problem Results for RHC, SA, GA, and MIMIC with Different Hyperparameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textbf{Algorithm} & \textbf{Param} & \textbf{Average Fitness} & \textbf{Feval} \\ \hline

 \textbf{RHC} & Restarts = 0 & 33.0 & 22 \\ \hline
 \textbf{RHC} & Restarts = 5 & 37.6 & 37 \\ \hline
 \textbf{RHC} & Restarts = 10 & 36.6 & 10 \\ \hline

 \textbf{SA} & ExpConst = 0.001 & 41.8 & 587 \\ \hline
 \textbf{SA} & ExpConst = 0.005 & 41.2 & 277 \\ \hline
 \textbf{SA} & ExpConst = 0.01 & 43.0 & 130 \\ \hline

 \textbf{GA} & PopSize = 100 & 43.8 & 46 \\ \hline
 \textbf{GA} & PopSize = 200 & 43.0 & 19 \\ \hline
 \textbf{GA} & PopSize = 300 & 43.0 & 62 \\ \hline

 \textbf{MIMIC} & PopSize = 100 & 42.8 & 16 \\ \hline
 \textbf{MIMIC} & PopSize = 200 & 45.6 & 18 \\ \hline
 \textbf{MIMIC} & PopSize = 300 & 46.8 & 18 \\ \hline

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

cs

"Benchmarking Randomized Optimization Algorithms on Binary, Permutation, and Combinatorial Problem Landscapes" by Jethro Odeyemi and Wenjun Zhang, arXiv:2501.17170v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Simos G. Meintanis
Bojana Milošević
Marko Obradović

"Bahadur efficiency for certain goodness--of--fit tests based on the empirical characteristic function" by Simos G. Meintanis, Bojana Milošević, and Marko Obradović, arXiv:2201.08604v1, 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{Comparison of FLOPs and parameters across different models}
\begin{tabular}{lcc}%% Table column specifiers
 Network & FLOPs & Params \\ \hline
 EEGNet & 103.6M & 19.9K \\
 EEG-ARNN & 260.0M & 69.8K \\
 Deep4Conv & 185.3M & 0.2M \\
 FusionCNN & 4.5G & 3.4G \\ \hline
 Single-head transformer$^*$ & 11.9G & 1.3G \\ \hline
 Proposed & 202.0M & 2.9M \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection" by Youshen Zhao and Keiji Iramina, arXiv:2412.14522v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components" by Ladislav Kristoufek, arXiv:1502.00225v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.