stat

Ousmane Saine
Soumaila Dembélé
Gane Samb Lo
Mohamed Cheikh Haidara

"Overview description of the Gambian GABECE Educational Data and associated algorithms and unsupervized learning process" by Ousmane Saine, Soumaila Dembélé, Gane Samb Lo, and Mohamed Cheikh Haidara, arXiv:2012.08488v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The results of Heston model calibration for GBP-EUR and EUR-GBP.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
parameter & GBP-EUR/EUR-GBP \\ \hline\hline
$N_{MC}$ & $10^9$ \\ \hline
$h$ & $0.05$ \\ \hline
$\upsilon_0$ & $0.0086$ \\ \hline
$\kappa$ & $1.500$ \\ \hline
$\delta$ & $0.71020580946071$ \\ \hline
$\theta$ & $0.02949445852250$ \\ \hline
$\rho$ & $-0.40966532579627$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Pricing FX Options under Intermediate Currency" by S. Maurer, T. E. Sharp, and M. V. Tretyakov, arXiv:1912.01387v2, 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.


math

"Runge-Kutta-Nyström symplectic splitting methods of order 8" by F. Casas, S. Blanes, and A. Escorihuela-Tomàs, arXiv:2202.01541v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccccc|}
\hline
 Bins ($M$) & $512$ & $1024$ & $2048$ & $4096$ & $8192$ & $16384$ \\
 \hline
\hline
 & \multicolumn{6}{|c|}{$a = M/4$} \\
 \hline
 MPTK & $3.96$ & $8.40$ & $17.0$ & $36.4$ & $75.7$ & -- \\
 Proposed & $0.92$ & $1.00$ & $1.02$ & $1.03$ & $1.03$ & $1.08$ \\
 \hline
 & \multicolumn{6}{|c|}{$a = M/8$} \\
 \hline
 MPTK & $6.73$ & $15.1$ & $30.2$ & $61.3$ & $147$ & -- \\
 Proposed & $1.70$ & $1.90$ & $1.95$ & $2.10$ & $2.20$ & $2.21$ \\
 \hline
 & \multicolumn{6}{|c|}{$a = M/16$} \\
 \hline
 MPTK & $13.2$ & $28.3$ & $56.8$ & $119$ & $274$ & -- \\
 Proposed & $3.20$ & $3.50$ & $4.00$ & $4.50$ & $4.60$ & $5.08$ \\
 \hline
 & \multicolumn{6}{|c|}{$a = M/32$} \\
 \hline
 MPTK & $23.2$ & $52.6$ & $110$ & $233$ & $530$ & -- \\
 Proposed & $6.35$ & $7.40$ & $7.90$ & $8.20$ & $9.60$ & $10.7$ \\
 \hline
 \hline
 Multi-Gabor & \multicolumn{3}{|c|}{$a = M/4$} & \multicolumn{3}{|c|}{$a = M/8$} \\
 \hline
 MPTK & \multicolumn{3}{|c|}{$142$} & \multicolumn{3}{|c|}{$285$} \\
 Proposed & \multicolumn{3}{|c|}{$10.6$} & \multicolumn{3}{|c|}{$20.9$} \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Execution time in seconds for MPTK and the proposed method on Gabor and Multi-Gabor dictionaries ($180$k selection steps). The Multi-Gabor dictionaries are a concatenation of the dictionaries with $M = 512,\ldots,8192$ at redundancies $a = M/4$ and $a = M/8$, respectively.}
\end{table}
\end{document}

math

Zdeněk Průša
Nicki Holighaus
Peter Balazs

"Fast Matching Pursuit with Multi-Gabor Dictionaries" by Zdeněk Průša, Nicki Holighaus, and Peter Balazs, arXiv:2202.12380v1, 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}{l|r|r|cccc}
			\toprule
			\textbf{Data} &
			\textbf{Stocks} &
			\textbf{Days} &
			\textbf{Dates} \\
			\midrule
			U.S.\textsuperscript{1} &
			178 &
			5035 &
			2003-01-02 to 2022-12-30 \\
			China\textsuperscript{1} &
			171 &
			2430 &
			2011-01-06 to 2020-12-31 \\
			U.K.\textsuperscript{1} &
			21 &
			2383 &
			2014-01-06 to 2024-01-10 \\
			Korea\textsuperscript{1} &
			220 &
			2170 &
			2014-01-02 to 2022-11-01 \\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

q-fin

Hyunwoo Lee
Jihyeong Jeon
Jaemin Hong
U Kang

"Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction" by Hyunwoo Lee, Jihyeong Jeon, Jaemin Hong, and U Kang, arXiv:2508.20108v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Product Centered Dirichlet Processes for Bayesian Multiview Clustering" by Alexander Dombowsky and David B. Dunson, arXiv:2312.05365v4, 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{Average dice score performance (\%) of the GCN refinement at different uncertainty thresholds $\tau$ for the \textbf{spleen} segmentation problem. The table compares the results for the 6-surrounding + 16 random connectivity (ours) vs. a 26 surrounding connectivity (n26). cnn9 indicates results obtained with a CNN trained on nine samples.}
\begin{tabular}{l|c|c|c|c|c}
			\hline
			Connectivity & GCN & GCN & GCN & GCN & GCN \\
			 & $\tau = 1e-3$ & $\tau = 0.3$ & $\tau = 0.5$ & $\tau = 0.8 $ & $\tau = 0.999$ \\
			\hline
			Ours & $95.01 \pm 1.5$ & $94.92 \pm 1.4$ & $94.98 \pm 1.4$ & $94.97 \pm 1.4$ & $95.07 \pm 1.3$ \\
			\hline
			n26 & $94.26 \pm 1.6$ & $94.11 \pm 1.8$ & $95.09 \pm 1.9$ & $94.14 \pm 1.8$ & $94.43 \pm 1.8$ \\
			\hline
			\hline
			Ours-cnn9 & $80.91 \pm 28.8$ & $80.94 \pm 28.9$ & $80.94 \pm 28.8$ & $80.98 \pm 28.9$ & $81.15 \pm 28.9$\\
			\hline
			n26-cnn9 & $79.63 \pm 28.6$ & $79.65 \pm 28.5$ & $79.68 \pm 28.5$ & $79.90 \pm 28.5$ & $80.38 \pm 28.7$\\
			\hline
			\end{tabular}
\end{table}
\end{document}

eess

Roger D. Soberanis-Mukul
Nassir Navab
Shadi Albarqouni

"An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation" by Roger D. Soberanis-Mukul, Nassir Navab, and Shadi Albarqouni, arXiv:2012.03352v1, 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|cc}
Range & Rank-1 & Rank-5 \\
\hline
40-60\% & 28.38 (0.51) & 45.43 (0.63) \\
30-50\% & 33.62 (0.61) & 51.33 (0.71) \\
20-40\% & 40.25 (0.73) & 57.68 (0.80) \\
10-30\% & 45.35 (0.82) & 61.85 (0.86)
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"MimicGait: A Model Agnostic approach for Occluded Gait Recognition using Correlational Knowledge Distillation" by Ayush Gupta and Rama Chellappa, arXiv:2501.15666v1, 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}{lcc}
\toprule
\textbf{Dataset} & \textbf{Purity (\%)} & \textbf{NMI (\%)} \\ \midrule
LFR2 & 93.73 & 94.89 \\
LFR4 & 89.84 & 93.25 \\
LFR6 & 78.17 & 86.36 \\
LFR8 & 24.03 & 52.31 \\
PS & 92.68 & 90.10 \\
CSMN & 86.64 & 67.45 \\
Brain & 46.91 & 41.56 \\
DBLP & 48.11 & 37.60 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Purity and NMI in the synthetic and real datasets of the proposed GTENN alogrithm}
\end{table}
\end{document}

cs

"GTENN: A Spatiotemporal Graph Neural Framework for Community Discovery in Dynamic Social Networks" by Shuangshuang Chu and Yingnan Xu, arXiv:2501.12208v2, 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}{|l|c|ccc|c|}
 Region & N & \multicolumn{3}{c}{Accuracy (\%)} \\
 \hline
 ~ & N & $\lambda = 0$ & $\lambda = \lambda^*$ & $\lambda = 1$ \\ \hline
 Cingulum Frontal Parahippocampal L & 513 & 65.69 & 65.88 & 59.84 \\ %\hline
 Cingulum Frontal Parahippocampal R & 476 & 65.33& 62.81 & 56.51 \\ %\hline
 Cingulum Frontal Parietal L & 786 & 66.66& 65.39 & 57.88 \\ %\hline
 Cingulum Frontal Parietal R & 785 & 65.98& 66.11 & 59.61 \\ %\hline
 Cingulum Parahippocampal L & 786 & 71.37& 73.28 & 61.06 \\ %\hline
 Cingulum Parahippocampal R & 786 & 70.48 & 71.75 & 61.06 \\ %\hline
 Cingulum Parahippocampal Parietal L & 770 & 66.10 & 66.88 & 59.87 \\ %\hline
 Cingulum Parahippocampal Parietal R & 779 & 66.23& 64.31 & 59.30 \\ %\hline
 Cingulum Parolfactory L & 786 & 65.52 & 65.77 & 54.32 \\ %\hline
 Cingulum Parolfactory R & 786 & 63.23 & 65.13 & 56.23 \\ %\hline
 Fornix L & 771 & 69.90 & 70.55 & 55.25 \\ %\hline
 Fornix R & 768 & 69.66 & 68.09 & 55.46 \\ %\hline
 Uncinate Fasciculus L & 786 & 65.26 & 62.84 & 60.68 \\ %\hline
 Uncinate Fasciculus R & 786 & 61.57 & 60.55 & 61.70 \\ \hline
 All* Regions Combined & 739 & 74.96 & 74.01 & 69.01\\ \hline
 \end{tabular}
\caption{Leave One Out Cross Validation Accuracy for SVM built LOT embedding with respect to a 200 support point barycenter, for each region, for different values of $\lambda$. N denotes sample size - There is missingness due to the fact that DSI studio does not return results for some regions of some subjects.}
\end{table}
\end{document}

stat

Michael Wilson
Tom Needham
Anuj Srivastava

"Fused Gromov-Wasserstein Variance Decomposition with Linear Optimal Transport" by Michael Wilson, Tom Needham, and Anuj Srivastava, arXiv:2411.10204v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small Effect of valid inequalities on six small-size instances}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrrrrr}
 \toprule
 & \multicolumn{5}{c}{without valid inequalities} & \multicolumn{5}{c}{with valid inequalities} \\
 mathematical model & $z_{lb}$ & $z_{ub}$ & $\%$gap & t(s) & opt & $z_{lb}$ & $z_{ub}$ & $\%$gap & t(s) & opt \\
 \cmidrule(lr){1-1} \cmidrule(lr){2-6} \cmidrule(lr){7-11}
 time-based & 1036.25 & 1077.49 & 3.24 & 2484.51 & 2 & 1040.67 & 1076.85 & 2.81 & 2288.63 & 3 \\
 flow-based & 1062.79 & 1076.85 & 1.00 & 953.69 & 5 & 1064.43 & 1076.85 & 0.88 & 751.28 & 5 \\
 node-based & 1033.41 & 1084.99 & 3.86 & 1912.63 & 3 & 1037.93 & 1068.09 & 2.38 & 1745.36 & 4 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Reza Atefi
Manuel Iori
Majid Salari
Dario Vezzali

"Solution of a Practical Vehicle Routing Problem for Monitoring Water Distribution Networks" by Reza Atefi, Manuel Iori, Majid Salari, and Dario Vezzali, arXiv:2202.02549v2, 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}{llr}
\hline\hline
&3-digit SIC Industry&Fraction\tabularnewline
\hline
1&Local Passenger Transit&$0.0\%$\tabularnewline
2&Automative Dealers \& Service Stations&$1.8\%$\tabularnewline
3&Tobacco Products&$1.8\%$\tabularnewline
4&Petroleum \& Coal&$2.0\%$\tabularnewline
5&Auto Services&$2.2\%$\tabularnewline
6&General Building Contractors&$2.5\%$\tabularnewline
7&Water Transportation&$2.7\%$\tabularnewline
8&Paper \& Allied Products&$2.9\%$\tabularnewline
9&Transportation by Air&$3.8\%$\tabularnewline
10&Social Services&$3.9\%$\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Zero-Leverage Puzzle

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Execution success improvement by replacing predicted dependency metadata with oracle metadata.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcr}
 \toprule
 \textbf{Language} & \textbf{Exec} & \textbf{Exec (with Orac.)} & $\Delta $ \\
 \midrule
 Python & 42.9 & 55.1 & +28.4\% \\
 Rust & 11.2 & 38.8 & +246.4\% \\
 C\# & 13.5 & 15.6 & +15.6\% \\
 JavaScript & 43.2 & 67.4 & +50.5\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Linghao Zhang
Junhao Wang
Shilin He
Chaoyun Zhang
Yu Kang
Bowen Li
Jiaheng Wen
Chengxing Xie
Maoquan Wang
Yufan Huang
Elsie Nallipogu
Qingwei Lin
Yingnong Dang
Saravan Rajmohan
Dongmei Zhang
Qi Zhang

"DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale" by Linghao Zhang, Junhao Wang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Jiaheng Wen, Chengxing Xie, Maoquan Wang, Yufan Huang, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, and Qi Zhang, arXiv:2501.13699v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
			\hline
			$L$ & 2 & 4 & 6 & 8 \\ \hline
			ratio & $8\times 10^{-9}$ & $10^{-9}$ & $2\times 10^{-10}$ & $8\times 10^{-11}$ \\ \hline
		\end{tabular}
\end{adjustbox}
\caption{\textit{In this table we define {\rm ratio} := $ {\int_{L+1}^\infty U(\xi) d\xi}/{ \int_{0}^\infty U(\xi) d\xi }$, for different values of $L$, and $\phi = 6$. Here we show that we obtain a good approximation of the integral of the potential $U(\xi)$ with a few units of $L$.}}
\end{table}
\end{document}

math

Clarissa Astuto
Antonio Raudino
Giovanni Russo

"Multiscale Modeling of Sorption Kinetics" by Clarissa Astuto, Antonio Raudino, and Giovanni Russo, arXiv:2202.02552v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Classification performance table in ablation experiments (\%~$\uparrow$)}
\begin{tabular}{|l|l|l|l|l|l|l|}
\hline
Baseline & SECA & Non-Local & CBAM & CA & F1 & Accuracy \\
\hline
$\checkmark$ & & & & & $94.35 \pm 6.69$ & $94.20 \pm 0.99$ \\
\hline
$\checkmark$ & $\checkmark$ & & & & $94.78 \pm 5.52$ & $\underline{94.55} \pm 0.86$ \\
\hline
$\checkmark$ & & $\checkmark$ & & & $93.82 \pm 4.95$ & $94.12 \pm 0.56$ \\
\hline
$\checkmark$ & & $\checkmark$ & $\checkmark$ & & $91.29 \pm 9.36$ & $91.29 \pm 2.15$ \\
\hline
$\checkmark$ & & $\checkmark$ & & $\checkmark$ & $94.50 \pm 5.51$ & $94.39 \pm 0.65$ \\
\hline
$\checkmark$ & $\checkmark$ & $\checkmark$ & & & $\mathbf{96.26} \pm \mathbf{4.18}$ & $\mathbf{96.31} \pm \mathbf{0.43}$ \\
\hline
\end{tabular}
\end{table}
\end{document}

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{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the datasets, for which $B \in \mathbb{R}^{m\times n}$ and $A \in \mathbb{R}^{m \times r}$.}
\begin{tabular}{l|l|l|l|l}
Dataset & Type & $m$ & $n$ & $r$ \\ \hline
CBCL & Faces & 2429 & $19 \times 19 = 361$ & 49 \\
Frey & Faces & 1965 & $20 \times 28 = 560$ & 36 \\
Kuls & Faces & 20 & $64 \times 64 = 4096$ & 5 \\
Jasper & Hyperspectral & 198 & $100 \times 100 = 10000$ & 4 \\
Samson & Hyperspectral & 156 & $95 \times 95 = 9025$ & 3 \\
Urban & Hyperspectral & 162 & $307 \times 307 = 94249$ & 6 \\
Cuprite & Hyperspectral & 188 & $250 \times 191 = 47750$ & 12
\end{tabular}
\end{table}
\end{document}

stat

Nicolas Nadisic
Jeremy E Cohen
Arnaud Vandaele
Nicolas Gillis

"Matrix-wise $\ell_0$-constrained Sparse Nonnegative Least Squares" by Nicolas Nadisic, Jeremy E Cohen, Arnaud Vandaele, and Nicolas Gillis, arXiv:2011.11066v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Comparison of algorithms performance based on the RPD-metric.}}
\begin{tabular}{ccccccccccc}
 \toprule
 \multirow{2}{*}{$N$} & \multicolumn{2}{c}{EMA} & \multicolumn{2}{c}{MOEA-D} & \multicolumn{2}{c}{MVO}& \multicolumn{2}{c}{NSGAII}
 & \multicolumn{2}{c}{DPEIA}\\
 \cmidrule(r){2-3}\cmidrule(r){4-5}\cmidrule(r){6-7} \cmidrule(r){8-9} \cmidrule(r){10-11}
 & $C_{max}$ & $C^{m}$ & $C_{max}$ & $C^{m}$ & $C_{max}$ & $C^{m}$ & $C_{max}$ & $C^{m}$ &
 $C_{max}$ & $C^{m}$\\
 \midrule
 100 & 98.18 & 258.71 & 88.48 & 156.45 & 55.37 & 198.04 & 63.32 & 211.83 & 4.43 & 45.72\\
 200 & 96.17 & 99.62 & 70.96 & 85.56 & 68.67 & 102.77 & 70.65 & 100.38 & 3.39 & 4.63\\
 300 & 121.93 & 73.10 & 92.01 & 69.03 & 81.45 & 75.23 & 67.39 & 83.06 & 3.24 & 1.84\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"Joint optimization for production operations considering reworking" by Yilan Shen, Boyang Li, and Xi Zhang, arXiv:2411.01772v1, 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{Table of parameter values}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl} \hline
 Parameter & Unit & Value \\\hline
 $q$ & $L/min$ & $100.0$ \\
 $V$ & $L$ & $100.0$ \\
 $c_{Af}$ & $mol/L$ & $1.0$ \\
 $T_f$ & $K$ & 350.0 \\
 $E/R$ & $K$ & $8750.0$ \\
 $k_0$ & $min^{-1}$ & $7.2 \times 10^{10}$ \\
 $-\Delta H$ & $J/mol$ & $5.0 \times 10^4$ \\
 $UA$ & $J/min\cdot K$ & $5.0 \times 10^4$ \\
 $c_p$ & $J/g\cdot K$ & $0.239$ \\
 $\rho$ & $g/L$ & $1000.0$ \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"An efficient implementation of graph-based invariant set algorithm for constrained nonlinear dynamical systems" by Benjamin Decardi-Nelsona and Jinfeng Liu, arXiv:2202.06111v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline
 \textbf{Timestep} & \textbf{Exponential} & \textbf{Discrete Weibull} & \textbf{Power law} \\ % inserts table %heading
 \hline
 Daily Limit Buy	& 2.674 +- 1.346 & 1.098 +- 0.198 & 1.154 +- 0.222 \\
 Daily Limit Sell & 2.018 +- 1.071 & 1.059 +- 0.104 & 1.128 +- 0.167\\
 Weekly Limit Buy & 2.515 +- 0.651 & 1.165 +- 0.191 & 1.051 +- 0.066\\
 Weekly Limit Sell &	1.771 +- 0.471 & 1.004 +- 0.014 & 1.076 +- 0.060 \\
 Monthly Limit &	2.038 +- 0.394 & 1.063 +- 0.127 & 1.048 +- 0.062 \\
 Hourly Limit Buy & 2.624 +- 1.279 & 1.094 +- 0.126 & 1.163 +- 0.268 \\
 Hourly Limit Sell &	1.918 +- 0.753 & 1.056 +- 0.074 & 1.128 +- 0.168 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Mean $NPS_d(Daily)$ of Proposed Distributions on Different Timesteps}
\end{table}
\end{document}

q-fin

Can Yilmaz Altinigne
Harun Ozkan
Veli Can Kupeli
Zehra Cataltepe

"An Empirical Study on Arrival Rates of Limit Orders and Order Cancellation Rates in Borsa Istanbul" by Can Yilmaz Altinigne, Harun Ozkan, Veli Can Kupeli, and Zehra Cataltepe, arXiv:1909.08308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
Outputof $k$th Layer& OL~($\uparrow$) & NR-CL~($\uparrow$) & R-CL~($\uparrow$) \\
\midrule
$k$=1& 0.8521 & 0.5651 & 0.6112 \\
$k$=2& 0.8588 & 0.6350 & 0.6504 \\
$k$=3& 0.8606 & 0.6767 & 0.6638 \\
$k$=6& \textbf{0.8673} & \textbf{0.6971} & \textbf{0.6784} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Effects of iteration strategy. The output of $k$th decoder layer is used as final output.}
\end{table}
\end{document}

cs

Xiaolei Chen
Junchi Yan
Wenlong Liao
Tao He
Pai Peng

"Int2Planner: An Intention-based Multi-modal Motion Planner for Integrated Prediction and Planning" by Xiaolei Chen, Junchi Yan, Wenlong Liao, Tao He, and Pai Peng, arXiv:2501.12799v1, 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{diagbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Example~:} Relative {errors} for the model problem on the L-shaped domain. $h=2^{-10}$, $H=2^{-5}$, $\gamma_{0}$=10.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|} \hline
 {{\diagbox{\qquad Method}{Error}}} & { Energy norm}
& {$L^{2}$} & {$L^{\infty}$} \\ \hline
{ LODM } & 0.2834e-01 & 0.1553e-02 & 0.6536e-02 \\ \hline { FE-LODM} & 0.2628e-01 & 0.1449e-02 & 0.6526e-02 \\ \hline
{ FE-OMsPGM} & 0.1045e-00 & 0.7596e-02 & 0.2424e-01 \\ \hline
{ LODM (error in $\Omega_{1}$) } & 0.1507e-02 & 0.1695e-02 & 0.5583e-02 \\ \hline
{ FE-LODM (error in $\Omega_{1}$ ) } & 0.4257e-03 & 0.4043e-03 & 0.5018e-03 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A combined multiscale finite element method based on the LOD technique for the multiscale elliptic problems with singularities" by Kuokuo Zhang, Weibing Deng, and Haijun Wu, arXiv:2202.13044v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{Standardised Residuals Tests for IAG 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
		 & Residuals & Tests & Statistics & p-value \\ % inserts table
		\hline
		Jarque-Bera Test & $R$ & $\chi^2$ & 126041.2 & 0 \\ %inserting body of the table
		Shapiro-Wilk Test & $R$ & W & 0.842114 & 0 \\
	 Ljung-Box Test & $R$ & Q(10) & 12.66334 & 0.2431 \\
		Ljung-Box Test & $R$ & Q(15) & 15.02200 & 0.4498 \\
		Ljung-Box Test & $R$ & Q(20) & 21.73719 & 0.3549 \\
		Ljung-Box Test & $R^2$& Q(10) & 0.146992 & 1 \\
		Ljung-Box Test & $R^2$ & Q(15) & 0.333308 & 1 \\
		Ljung-Box Test & $R^2$ & Q(20) & 0.706543 & 1\\
		LM-Arch Test & R & T$R^2$ & 0.243845 & 1 \\
		\hline \hline%inserts single line
	\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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\hline
\textbf{Measure} & \textbf{Statistics} & \textbf{Value} \\ \hline
\textbf{Familiar with Diabetes} & Min & 1 \\
 & Max & 7 \\
 & Mean & 4.09 \\
 & Median & 4.0 \\
 & Std & 1.86 \\ \hline
\textbf{Age} & Min & 16 \\
 & Max & 67 \\
 & Mean & 36.99 \\
 & Median & 35.5 \\
 & Std & 12.01 \\ \hline
\textbf{Education} & High school degree & 53.78\% \\
 & College degree & 38.89\% \\
 & Master's degree & 4.63\% \\
 & PhD degree & 3.70\% \\ \hline
\textbf{Gender} & Woman & 51.85\% \\
 & Man & 45.37\% \\
 & Nonbinary & 1.85\% \\
 & Other & 0.93\% \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Summary of participant demographic information and familiarity with diabetes.}
\end{table}
\end{document}

cs

"Engaging with AI: How Interface Design Shapes Human-AI Collaboration in High-Stakes Decision-Making" by Zichen Chen, Yunhao Luo, and Misha Sra, arXiv:2501.16627v1, 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|cc|cc|cc}
 N & $L^2$-err in $h_h$ & rate
 & $L^2$-err in $u_h$ & rate
& $L^2$-err in $m_h$ & rate\\
\hline
 25& 1.420e-03 & -- & 1.670e-03 & -- & 1.379e-02 &-- \\
 50& 1.567e-04 & 3.18 & 2.083e-04 & 3.00 & 1.683e-03 & 3.03 \\
100& 1.917e-05 & 3.03 & 2.820e-05 & 2.89 & 2.371e-04 & 2.83 \\
200& 2.363e-06 & 3.02 & 3.880e-06 & 2.86 & 3.403e-05 & 2.80 \\
\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A high-order velocity-based discontinuous Galerkin scheme for the shallow water equations: local conservation, entropy stability, well-balanced property, and positivity preservation" by Guosheng Fu, arXiv:2201.13040v1, 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{Training Loss of Models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
\hline
\textbf{Model} & \textbf{Training Loss} \\ \hline
Artificial Neural Network(ANN) & 8.04649e-06 \\ \hline
Convolutional Neural Network(CNN) & 5.58822e-06 \\ \hline
Neural Arithmetic Logic Units Network(NALU) & 1.91356e-06 \\ \hline
NALU CNN Network(NALU-CNN) & 5.58499e-07 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction" by Shangeth Rajaa and Jajati Keshari Sahoo, arXiv:1905.07581v1, 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
Model Type & (mAP) \\
\hline\hline
Human nodes only & 0.194 \\
Human and Objects nodes & 0.208 \\
Human, Objects and Shadow nodes & \textbf{0.213} \\
\hline
\end{tabular}
\caption{Comparison results with different model types.}
\end{table}
\end{document}

eess

Bo Chen
Decai Li
Yuqing He
Chunsheng Hua

"SCR-Graph: Spatial-Causal Relationships based Graph Reasoning Network for Human Action Prediction" by Bo Chen, Decai Li, Yuqing He, and Chunsheng Hua, arXiv:1912.05003v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluated cloud workloads. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \textbf{Workload} & \textbf{Description} & \textbf{Key metric}\\
 \midrule
 Cache & Memcached read/writes~. & Tail Latency \\
 Database & Queries on a SQL database~. & Tail Latency\\
 Big Data & Sorting with TeraSort~. & Run Time\\
 Web & 3-tier web application~. & Throughput\\
 KV-Store & Querying a KV-store~. & Tail Latency\\
 Graph & Computing pagerank~. & Run Time\\
 Microservices & Social network~. & Tail Latency \\
 LLM-FT & BERT LLM fine-tuning~. & Run Time\\
 Video Conf & Video conference application~. & Throughput \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Benjamin Reidys
Pantea Zardoshti
Íñigo Goiri
Celine Irvene
Daniel S. Berger
Haoran Ma
Kapil Arya
Eli Cortez
Taylor Stark
Eugene Bak
Mehmet Iyigun
Stanko Novaković
Lisa Hsu
Karel Trueba
Abhisek Pan
Chetan Bansal
Saravan Rajmohan
Jian Huang
Ricardo Bianchini

"Coach: Exploiting Temporal Patterns for All-Resource Oversubscription in Cloud Platforms" by Benjamin Reidys, Pantea Zardoshti, Íñigo Goiri, Celine Irvene, Daniel S. Berger, Haoran Ma, Kapil Arya, Eli Cortez, Taylor Stark, Eugene Bak, Mehmet Iyigun, Stanko Novaković, Lisa Hsu, Karel Trueba, Abhisek Pan, Chetan Bansal, Saravan Rajmohan, Jian Huang, and Ricardo Bianchini, arXiv:2501.11179v2, 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|}
\hline
Method&Train&Test& $\#$ params\\
\hline
EqnDis Poly (2,1) & 0.9203 & 0.9155 & 14\\
\textbf{EqnDis Fourier (2,1)} & \textbf{0.9582 }& \textbf{0.9570 }& 26\\
EqnDis Fourier (2,2) & 0.9523 & 0.9515 &58\\
\hline
RNN (3,10) & 0.9550 & 0.9570 & 146 \\
LSTM (1,5) & \textbf{0.9805} & \textbf{0.9799} & 146\\
CFN (2,3) & 0.9440 & 0.9309 & 88 \\
NCDE (32,32-1) & 0.9785 & 0.9750 & 1,221\\
\hline
 \end{tabular}
\caption{A comparison of the accuracy and number of parameters for various methods on the synthetic dataset based on the stochastic gated diffusion equation. See Section .}
\end{table}
\end{document}

stat

Ryeongkyung Yoon
Harish S. Bhat
Braxton Osting

"A non-autonomous equation discovery method for time signal classification" by Ryeongkyung Yoon, Harish S. Bhat, and Braxton Osting, arXiv:2011.11096v1, 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{Part of the table with word counts describing data science by different web sources as collected by .}
\begin{tabular}{lccccccc} \hline
 & dsc.test & dsctechniques & datadiversity & dsc.tips & $\ldots$ & wikipedia \\ \hline
statistics & 6 & 2 & 2 & 0 & $\ldots$ & 2 \\
big data & 2 & 0 & 3 & 5 & $\ldots$ & 0 \\
analytics & 1 & 0 & 2 & 0 & $\ldots$ & 2 \\
database & 2 & 0 & 0 & 2 & $\ldots$ & 1 \\
insight & 0 & 0 & 4 & 0 & $\ldots$ & 2 \\
$\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ & $\vdots$ &$ \ddots$ & $\vdots$ \\
programming & 7 & 0 & 0 & 0 & $\ldots$ & 0 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Casting Multiple Shadows: High-Dimensional Interactive Data Visualisation with Tours and Embeddings" by Stuart Lee, Ursula Laa, and Dianne Cook, arXiv:2012.06077v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Testing loss of Models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
\hline
\textbf{Model} & \textbf{Testing Loss} \\ \hline
Artificial Neural Network(ANN) & 1.30709e-06 \\ \hline
Convolutional Neural Network(CNN) & 5.99638e-07 \\ \hline
Neural Arithmetic Logic Units Network(NALU) & 4.31875e-07 \\ \hline
NALU CNN Network(NALU-CNN) & 3.05196e-07 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \toprule
 biLSTM-CRF & F-1 & Test NLL\\
 \midrule
 \midrule
 Standard & $93.22 \pm 0.09$ & $1.07 \pm 0.03$
 \\ \midrule
 Sequence Input Mixup ($\alpha=0.1$) & $93.29 \pm 0.09$ & $\mathbf{0.59 \pm 0.01}$
 \\ \midrule
 Pre-Output Mixup ($\alpha=0.7$) & $\mathbf{93.30 \pm 0.09} $ & $0.72 \pm 0.01$
 \\ \midrule
 Through-Time Mixup ($\alpha=0.1$) & $93.17 \pm 0.06$ & $0.67 \pm 0.004$
 \end{tabular}
\caption{Evaluation of biLSTM-CRF model using conventional and Seuquence Mixup training methods on CoNLL-2003 NER task.}
\end{table}
\end{document}

stat

Armin Karamzade
Amir Najafi
Seyed Abolfazl Motahari

"Regularizing Recurrent Neural Networks via Sequence Mixup" by Armin Karamzade, Amir Najafi, and Seyed Abolfazl Motahari, arXiv:2012.07527v1, 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{Descriptive Analysis -- Updating Subsample}
\begin{tabular}{lrrrrr}
		\hline
		& Mean & Std.dev & Min & Max & Missing \\
		\hline
		Prior & 39.55 & 28.43 & 1.00 & 90.00 & 0 \\
		Change & 1 & 0 & 1 & 1 & 0 \\
		Post & 2.64 & 2.70 & 0.10 & 15.00 & 0 \\
		Sex & 0.54 & 0.50 & 0 & 1 & 0 \\
		Age & 33.95 & 14.63 & 16 & 78 & 6 \\
		Police & 2.99 & 0.86 & 1 & 5 & 0 \\
		Educ\_Int & 0.91 & 0.29 & 0 & 1 & 0 \\
		Matching\_Sex & 0.93 & 0.26 & 0 & 1 & 0 \\
		\hline
		\multicolumn{6}{l}{{\footnotesize Total of 127 observations.}}\\
		\multicolumn{6}{l}{{\footnotesize Source: Elaborated by the author.}}
	\end{tabular}
\end{table}
\end{document}

stat

José Raimundo Carvalho
Diego de Maria André
Yuri Costa

"Individual Updating of Subjective Probability of Homicide Victimization: a "Natural Experiment'' on Risk Communication" by José Raimundo Carvalho, Diego de Maria André, and Yuri Costa, arXiv:2312.08171v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
 \toprule
 & \multicolumn{2}{c}{\textbf{Time}}&\multicolumn{2}{c}{\textbf{Location}}\\
 \textbf{Reasoner} & \textbf{ Acc(\%)} & \textbf{X-F1$^\beta$} &\textbf{Std. Acc(\%)} & \textbf{X-F1$^\beta$} \\
 \midrule
 ChatGPT &\textbf{0.30} &\textbf{43.72} & \textbf{22.99} &\textbf{56.11} \\
 Llama 3.1 8B &0.15& 27.09& 7.53 & 20.18\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hammad Ayyubi
Xuande Feng
Junzhang Liu
Xudong Lin
Zhecan Wang
Shih-Fu Chang

"PuzzleGPT: Emulating Human Puzzle-Solving Ability for Time and Location Prediction" by Hammad Ayyubi, Xuande Feng, Junzhang Liu, Xudong Lin, Zhecan Wang, and Shih-Fu Chang, arXiv:2501.14210v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A weak Galerkin method and its two-grid algorithm for the quasi-linear elliptic problems of non-monotone type" by Peng Zhu and Shenglan Xie, arXiv:2202.11164v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PDE parameters setup. The discretization in time and space is indicated by $n_t$ and $n_x$ respectively, $t_{\rm{max}}$ represents the simulation physical time, and $L$ is the domain length.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \toprule
 & PDE & $n_t$ & $n_x$ & $t_{\rm{max}}$& $L$ \\
 \midrule
 \multirow{3}{*}{Train }&\emph{Burgers} & 0.1 & 0.25 & 14 & $2\pi$ \\
 &\emph{KS} & 0.1 & 0.25 & 14 & 64 \\
 &\emph{KdV} & 0.1 & 0.25 & 14 & 64 \\
 \midrule
 \multirow{3}{*}{Test }&\emph{Burgers} & 0.1 & 0.25 & 32 & $2\pi$ \\
 &\emph{KS} & 0.1 & 0.25 & 32 & 64 \\
 &\emph{KdV} & 0.1 & 0.25 & 32 & 64 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Dario Coscia
Max Welling
Nicola Demo
Gianluigi Rozza

"BARNN: A Bayesian Autoregressive and Recurrent Neural Network" by Dario Coscia, Max Welling, Nicola Demo, and Gianluigi Rozza, arXiv:2501.18665v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Composition of Wage Differentials between Migrants and Natives" by Panagiotis Nanos and Christian Schluter, arXiv:1306.1781v3, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Caihua Chen
Xindan Li
Caleb Tolman
Suyang Wang
Yinyu Ye

"Sparse Portfolio Selection via Quasi-Norm Regularization" by Caihua Chen, Xindan Li, Caleb Tolman, Suyang Wang, and Yinyu Ye, arXiv:1312.6350v1, licensed under CC BY 3.0. 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}{cccccc}
\toprule
Parameter & Region 1 & Region 2 & Region 3 \\ \midrule
scale & 208.34 [208.18] & 227.62 [227.54] & 228.68 [228.73] \\
shape & 0.2199 [0.2205] & 0.1776 [0.1771] & 0.1109 [0.1121] \\
rate & 0.0559 [0.0559] & 0.0554 [0.0554] & 0.0557 [0.0557] \\
\bottomrule
\end{tabular}
\caption{Comparison of parameter estimates from kriging to a fine grid and averaging the gridded values versus using the shortcut in the {\tt gstat} package to krige to the polygons of our regions. The latter method's estimates are in brackets. Note that the estimates are very close, so faster computation times lead us to use the built-in functionality of {\tt gstat}.}
\end{table}
\end{document}

stat

Carlynn Fagnant
Julia C. Schedler
Katherine B. Ensor

"Spatial-Temporal Extreme Modeling for Point-to-Area Random Effects (PARE)" by Carlynn Fagnant, Julia C. Schedler, and Katherine B. Ensor, arXiv:2311.17271v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Loss of Models in the whole data set}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
\hline
\textbf{Model} & \textbf{Total Loss} \\ \hline
Artificial Neural Network(ANN) & 1.29998e-06 \\ \hline
Convolutional Neural Network(CNN) & 1.07971e-06 \\ \hline
Neural Arithmetic Logic Units Network(NALU) & 3.97540e-07 \\ \hline
NALU CNN Network(NALU-CNN) & 3.30627e-07 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{booktabs}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|r|r|r|r|}
 \toprule
 \multicolumn{2}{|c|}{\multirow{2}[4]{*}{}} & \multicolumn{4}{c|}{Data} \\
 \cmidrule{3-6} \multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{Definition 1} & Definition 2 & Definition 3 \\
 \midrule
 \multicolumn{2}{|c|}{Total number of variables in the definition} & \multicolumn{2}{c|}{30} & 5000 & 5 \\
 \midrule
 \multicolumn{2}{|c|}{} & Group 1 & Group 2 & Group 3 & Group 4 \\
 \midrule
 \multicolumn{2}{|c|}{Number of variables used in the group} & 20 & 5 & 20 (17) & 5 \\
 \midrule
 \multirow{4}[8]{*}{Test AUC} & logit & 70.87\% & 74.42\% & 74.64\% & 74.09\% \\
 \cmidrule{2-6} & imbalanced random forest & 78.41\% & 71.96\% & 75.31\% & 46.81\% \\
 \cmidrule{2-6} & balanced random forest & 80.02\% & 76.35\% & 76.67\% & 72.46\% \\
 \cmidrule{2-6} & boosting & 79.66\% & 77.46\% & 78.13\% & 74.24\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Test AUCs of four groups of account data (3 definitions) in logit, random forest and boosting. The 20 variables in Group 1 and Group 3 are selected respectively by AIC and by variable importance in boosting. The 5 best variables in Group 2 are chosen according to variable importance in boosting. All the variables in Group 2 are included in Group 1. Among the 20 variables in Group 3, three variables are not available for most of the observations ($>50\%$) and are eliminated for random forest and for logistic regression.}
\end{table}
\end{document}

q-fin

Jinglun Yao
Maxime Levy-Chapira
Mamikon Margaryan

"Checking account activity and credit default risk of enterprises: An application of statistical learning methods" by Jinglun Yao, Maxime Levy-Chapira, and Mamikon Margaryan, arXiv:1707.00757v1, 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{Theory--Data Crosswalk}
\begin{tabular}{ll}
\toprule
Model object & Empirical moment \\
\midrule
Short-sale cap $s$ (Miller) & Sign asymmetry: $\kappa_{+} > \kappa_{-}$; High-VIX $\times$ Low-breadth triple \\
Clientele mix $(n_R, n_I)$ & Larger $\kappa$ in low-breadth / non-optionable segments \\
Risk-bearing $\psi$ & State dependence in $\rho$ (fast decay in high VIX) \\
Belief shift $\theta$ & $\kappa$ level (impact) for a $1\sigma$ shock \\
Persistence driver & $\rho$ (half-life $\ln(0.5)/\ln(\rho)$) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Sentiment Feedback in Equity Markets: Asymmetries, Retail Heterogeneity, and Structural Calibration" by Lucas Marques Sneller, arXiv:2509.11970v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ARCH Test results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\toprule
Probability of F & 0.000 \\
\hline
Probability of LM Statistic (Chi-Square(1)) & 0.000 \\
\hline
\multicolumn{2}{c}{Variables} \\
\hline
Probability of C & 0.000 \\
\hline
value of alpha1(squared residuals) & 0.284 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Sai Srikar Nimmagadda
Pawan Sasanka Ammanamanchi

"BitMEX Funding Correlation with Bitcoin Exchange Rate" by Sai Srikar Nimmagadda and Pawan Sasanka Ammanamanchi, arXiv:1912.03270v1, 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|l|l|}
\hline
Ticker&Peak & Trough\\\hline
S\&PCOMP&0.125485&0.023136\\
TOKYOSE&0.087145&0.053484\\
HNGKNGI&0.140173&0.013434\\
FRUSS1L&0.117058&0.021920\\
FTSE100&0.084874&0.019177\\
DJSTOXX&0.065708&0.016427\\
DAXINDX&0.127369&0.029723\\
FRCAC40&0.050226&0.033363\\
BRBOVES&0.074050&0.015495\\
TRKISTB&0.092147&0.016112\\
MXIPC35&0.101478&0.011147\\
BNGKSET&0.123534&0.060996\\\hline
\end{tabular}
\end{adjustbox}
\caption{P-values of peak/trough predictions for all the tested stock indices.}
\end{table}
\end{document}

q-fin

Wanfeng Yan
Edgar van Tuyll van Serooskerken

"Forecasting Financial Extremes: A Network Degree Measure of Super-exponential Growth" by Wanfeng Yan and Edgar van Tuyll van Serooskerken, arXiv:1505.04060v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{F-measures and accuracies of the drowsiness detection using for the evaluation dataset in NTHU-DDD dataset. The listed values below the drowsiness and non-drowsiness attributes represent the results of F-measures.}
\begin{tabular}{lclclclcl}

			\hline\noalign{\smallskip}

			Scenario & Drowsiness (F) & Non-drowsiness (F) & Accuracy\\

			\noalign{\smallskip}

			\hline

			\noalign{\smallskip}

			{Day bare face} & 0.809 & 0.784 & 0.796\\

			{Day glasses} & 0.789 & 0.774 & 0.781\\

			{Day sunglasses} & 0.758 & 0.718 & 0.738\\

			{Night bare face} & 0.753 & 0.777 & 0.765\\

			{Night glasses} & 0.718 & 0.750 & 0.734\\

			\hline

			{Average} & 0.765 & 0.760 & 0.762\\

			\hline

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

eess

Jongmin Yu
Sangwoo Park
Sangwook Lee
Moongu Jeon

"Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework" by Jongmin Yu, Sangwoo Park, Sangwook Lee, and Moongu Jeon, arXiv:1910.09722v1, 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{FX market data for 1 year maturity options, Bloomberg 03/06/2016.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
& GBP-EUR & USD-EUR & GBP-USD \\ \hline\hline
$\sigma_{25Put\Delta}$ & $12.435\%$ & $9.005\%$ & $11.000\%$ \\ \hline
$\sigma_{ATM}$ & $10.945\%$ & $9.250\%$ & $13.072\%$ \\ \hline
$\sigma_{25Call\Delta}$ & $10.345\%$ & $10.265\%$ & $9.972\%$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Pricing FX Options under Intermediate Currency" by S. Maurer, T. E. Sharp, and M. V. Tretyakov, arXiv:1912.01387v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{Zero-shot diagnostic performance under different summarizers (a) and under report perturbations (b).}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
\toprule
\multicolumn{8}{c}{\textbf{(a) Summarizer Comparison on Original Reports}} \\
\midrule
Summarizer & AUC & ACC & F1 & Prec & mAP & Rec@1 & Prec@3 \\
\midrule
\textcolor{lightgray}{\textit{Smaller-scale Language Models}} \\
Qwen-2.5-7B~ & 69.1 & 64.6 & 68.5 & 38.1 & 41.8 & \textbf{15.3} & \textbf{45.3} \\
Lingshu-7B~ & 69.2 & 64.9 & 68.7 & 38.3 & 42.4 & 9.9 & 43.0 \\
Llama-3-8B~ & 68.6 & 65.1 & 68.9 & 38.4 & 42.0 & 12.3 & 44.3 \\
Medgemma-4b~ & 68.6 & 64.7 & 68.5 & 37.8 & 41.7 & 10.8 & 43.7 \\
\textcolor{lightgray}{\textit{Larger-scale Language Models}} \\
Medgemma-27b~ & 69.2 & 64.8 & 68.6 & 38.1 & 42.5 & 14.8 & 43.2 \\
Deepseek-V3~ & 68.6 & 65.9 & 69.5 & 38.5 & 42.6 & 13.5 & 42.9 \\
GPT-4 Turbo & \textbf{70.0} & \textbf{65.5} & \textbf{69.3} & \textbf{39.1} & \textbf{43.0} & 14.3 & 44.0 \\
\midrule
\multicolumn{8}{c}{\textbf{(b) Robustness Under Report Perturbations}} \\
\midrule
Perturbation & AUC & ACC & F1 & Prec & mAP & Rec@1 & Prec@3 \\
\midrule
Baseline & \textbf{70.0} & 65.5 & 69.3 & \textbf{39.1} & 43.0 & 14.3 & 44.0 \\
Shortened expressions & 69.9 & \textbf{65.8} & \textbf{69.5} & 38.9 & \textbf{43.3} & 14.0 & 43.7 \\
Lengthened expressions & 68.3 & 64.6 & 68.5 & 37.8 & 41.9 & \textbf{15.4} & \textbf{45.8} \\
Grammatical errors & 67.6 & 63.6 & 67.5 & 37.5 & 41.1 & 13.5 & 43.0 \\
Missing sentences (30\%) & 67.4 & 64.5 & 68.3 & 37.6 & 41.1 & 10.3 & 40.4 \\
Missing sentences (40\%) & 66.9 & 64.4 & 68.2 & 37.7 & 41.4 & 9.6 & 39.2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Haoran Lai
Zihang Jiang
Qingsong Yao
Rongsheng Wang
Zhiyang He
Xiaodong Tao
Weifu Lv
Wei Wei
S. Kevin Zhou

"Bridged Semantic Alignment for Zero-shot 3D Medical Image Diagnosis" by Haoran Lai, Zihang Jiang, Qingsong Yao, Rongsheng Wang, Zhiyang He, Xiaodong Tao, Weifu Lv, Wei Wei, and S. Kevin Zhou, arXiv:2501.03565v2, 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}{|l|l|l|}

 \hline
 \textbf{Method} & \textbf{Spatial accuracy} & \textbf{Temporal accuracy}\\
 \hline
 \hline
 TWNMF & \textbf{0.81 $\pm$ 0.07} & \textbf{0.62 $\pm$ 0.33}\\
 \hline
 WNMF & 0.72 $\pm$ 0.13 & 0.39 $\pm$ 0.21\\
 \hline
 NMF & 0.27 $\pm$ 0.37 & 0.05 $\pm$ 0.30\\
 \hline

 \end{tabular}
\caption{Quantitative evaluation of the proposed method, WNMF, and NMF on the simultaneous motion segmentation and voltage demixing in the simulated multielectrode drift data. Proposed TWNMF recovers the spatial footprints of drifting neuron signal with high accuracy while at the same time, the temporal voltage signal is accurately deconvolved.}
\end{table}
\end{document}

eess

Erdem Varol
Amin Nejatbakhsh
Conor McGrory

"Temporal Wasserstein non-negative matrix factorization for non-rigid motion segmentation and spatiotemporal deconvolution" by Erdem Varol, Amin Nejatbakhsh, and Conor McGrory, arXiv:1912.03463v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Swap Rates}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
Swap Tenor & 1y & 2y & 3y & 5y & 7y & 10y & 30y \\
 \hline
Swap Rate & 2.13\% & 1.95\% & 1.89\% & 1.88\% & 1.94\% & 2.05\% & 2.27\% \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.03993v1, 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{Average run-time for the three basic operations that may be applied to the LOB for different sizes $N$. The tests are conducted on an Nvidia 2080 Ti and averaged across 1000 serial trials. The book is initially filled to one-third capacity with random orders.}
\begin{tabular}{|c|c|c|c|}
 \hline
 Capacity $N$& Add (ms) & Cancel (ms) & Match (ms)\\
 \hline
 10 & 0.115 & 0.081 & 0.184\\
 100 & 0.117 & 0.095 & 0.206\\
 1000 & 0.162 & 0.093 &0.243\\
 \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Sascha Frey
Kang Li
Peer Nagy
Silvia Sapora
Chris Lu
Stefan Zohren
Jakob Foerster
Anisoara Calinescu

"JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading" by Sascha Frey, Kang Li, Peer Nagy, Silvia Sapora, Chris Lu, Stefan Zohren, Jakob Foerster, and Anisoara Calinescu, arXiv:2308.13289v1, 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|cccc}
 \toprule
 \textbf{Metric} & \textbf{Q1} & \textbf{Q2} & \textbf{Q3} & \textbf{Q4} \\
 \midrule
 GPT Similarity & 71.76 & 72.60 & 69.71 & 67.62 \\
 Llama Similarity & 63.40 & 61.99 & 60.85 & 55.81 \\
 Fluency & 4.85 & 4.70 & 4.58 & 4.67 \\
 Accuracy & 4.78 & 4.86 & 4.80 & 4.69 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{EPIE-ME Results by Quartile of Search Results}
\end{table}
\end{document}

cs

Aaron Gluck
Katharina von der Wense
Maria Leonor Pacheco

"CLIX: Cross-Lingual Explanations of Idiomatic Expressions" by Aaron Gluck, Katharina von der Wense, and Maria Leonor Pacheco, arXiv:2501.03191v3, 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{Parameter values.}
\begin{tabular}{c|c|c|c|c|c|c|c}
			\hline
			$\phi$ & $\kappa$ & $\mu_C$ & $\sigma_C$ & $\lambda$ & $\overline{\omega} $ & $\rho$&$\nu$\\
			\hline
			0.0984 & 0.2 & 0.0231 & 0.0286 & 0.2232 & 0.0662 & 0.4637&0.1546\\
			\hline
		\end{tabular}
\end{table}
\end{document}

q-fin

Dynamic Asset Pricing with α-MEU Model

"Dynamic Asset Pricing with α-MEU Model" by Jiacheng Fan, Xue Dong He, and Ruocheng Wu, arXiv:2507.04093v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|c|c|c}
\hline
 & & SZ & TW & HK\tabularnewline
\cline{1-5}
$\lambda_{1}$ & + & Ener & Null & Null\tabularnewline
 & - & IG & EI & RE\&Fin\tabularnewline
\hline
$\lambda_{2}$ & + & RE & RE & IG\tabularnewline
 & - & Heal & CI & RE\&Fin\tabularnewline
\hline
$\lambda_{3}$ & + & RE & DG & Serv\tabularnewline
 & - & Ener & SI & RE\&Fin\tabularnewline
\hline
$\lambda_{4}$ & + & Null & SI & Null\tabularnewline
 & - & RE & Null & RE\&Fin\tabularnewline
\hline
$\lambda_{5}$ & + & RE & DG & RE\&Fin\tabularnewline
 & - & DG & CI & Null\tabularnewline
\hline
$\lambda_{6}$ & + & Null & CI & RE\&Fin\tabularnewline
 & - & Ener & EI & Null\tabularnewline
\hline
$\lambda_{7}$ & + & Ener & DG & Null\tabularnewline
 & - & Null & CI & RE\&Fin\tabularnewline
\hline
$\lambda_{8}$ & + & EI & CI & Null\tabularnewline
 & - & DG & DG & RE\&Fin\tabularnewline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Spatial and temporal structures of four financial markets in Greater China" by F. Y. Ouyang, B. Zheng, and X. F. Jiang, arXiv:1402.1046v1, licensed under CC BY 3.0. 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{Results obtained by using DMoN--DPR pooling using the best epsilon value of 10, distance weight of 1, and entropy weight of 0.1 on the Coauthor Physics dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
\textbf{Seed} & \textbf{Method} & \textbf{Conductance} & \textbf{Modularity} & \textbf{NMI} & \textbf{F1} \\ \midrule
993 & DMoN--DPR (DE) & 13.94 & 61.00 & 47.60 & 46.37 \\
550 & DMoN--DPR (DE) & 14.11 & 56.74 & 54.67 & 55.98 \\
243 & DMoN--DPR (DE) & 13.89 & 57.00 & 60.05 & 58.60 \\
16 & DMoN--DPR (DE) & 13.90 & 54.44 & 53.68 & 57.90 \\
716 & DMoN--DPR (DE) & 11.35 & 57.47 & 63.18 & 61.39 \\
383 & DMoN--DPR (DE) & 15.41 & 54.93 & 51.58 & 53.92 \\
277 & DMoN--DPR (DE) & 15.84 & 54.43 & 51.48 & 57.57 \\
274 & DMoN--DPR (DE) & 14.14 & 57.42 & 53.66 & 56.28 \\
188 & DMoN--DPR (DE) & 15.33 & 56.20 & 56.80 & 55.88 \\
796 & DMoN--DPR (DE) & 13.83 & 59.24 & 47.47 & 46.26 \\
\midrule
\multicolumn{2}{c}{\textbf{Mean $\pm$ Std}} & 14.17 $\pm$ 1.24 & 56.89 $\pm$ 2.09 & 54.02 $\pm$ 5.00 & 55.02 $\pm$ 4.99 \\
\bottomrule
\multicolumn{2}{c}{\textbf{Mean}} & 14.22 & 56.10 & 54.71 & 56.94 \\
\multicolumn{2}{c}{\textbf{Variance}} & 0.01 & 0.01 & 0.16 & 0.10 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Deep Modularity Networks with Diversity-Preserving Regularization" by Yasmin Salehi and Dennis Giannacopoulos, arXiv:2501.13451v2, 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}{cccccccccc}
		\hline
		 Parameter & ${m}$ & ${N}$ & ${v_1}$ & ${v_m}$ & $\tilde{\alpha}_v$& $x_1$ & $x_N$ & $\tilde{\alpha}_X$ & $M$\\
		Value &$50$ & $2,000$ & $V_0/100$ & $7 v_0$ & $0.6571$ & $X_0/10^6$ & $1.95 X_0$ &$2/100$ &$5,000$ \\
		\hline
	\end{tabular}
\caption{CTMC Parameters}
\end{table}
\end{document}

q-fin

Zhenyu Cui
Anne MacKay
Marie-Claude Vachon

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule[1.5pt]
\ \ \ \ \ \ \ \ \qquad {\bf Hyper-parameter} \ \ \ \ \ \ \ \ \qquad & \ \ \ \ \ \ \ \ \qquad Value \ \ \ \ \ \ \ \ \qquad \cr
\midrule[1.0pt]
 Layers & 2 \cr
 Hidden layers & 64 \cr
 Sequence length & 4 \cr
 Learning rate & .01 \cr
 Epochs & 500 \cr
\bottomrule[1.5pt]
\end{tabular}
\end{adjustbox}
\caption{The hyperparameters of NODE and NODE for the learning ROMs of KPP model.}
\end{table}
\end{document}

math

Justin Baker
Elena Cherkaev
Akil Narayan
Bao Wang

"Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs" by Justin Baker, Elena Cherkaev, Akil Narayan, and Bao Wang, arXiv:2202.12373v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments" by James Enouen, Tianshu Sun, and Yan Liu, arXiv:2309.01780v1, 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}{lc}
 \hline
 \textbf{system} & \textbf{startup time (ms)} \\
 \hline
 Docker & 177.4 \\
 Docker (bare metal) & 203.8 \\
 gVisor & 281.8\\
 Edera-PV & 765.8\\
 Kata Containers & 1934.2\\
 Edera-PVH & 968.6\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{The startup time for each system.}
\end{table}
\end{document}

cs

"Goldilocks Isolation: High Performance VMs with Edera" by Marina Moore and Alex Zenla, arXiv:2501.04580v1, 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}{lrrrr}
\toprule
Split & Train & Dev & Test & Total \\
\midrule
In-Passage & 35,215 & 2,025 & 20,45 & 39,285 \\
In-Table & 26,803 & 1,349 & 1,346 & 29,498 \\
Computed & 664 & 92 & 72 & 828 \\
\midrule
Total & 62,682 & 3,466 & 3,463 & 69,611 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Data Split: In-Table means the answer comes from plain text in the table, and In-Passage means the answer comes from certain passage.}
\end{table}
\end{document}

cs

Ankush Agarwal
Ganesh S
Chaitanya Devaguptapu

"Hybrid Graphs for Table-and-Text based Question Answering using LLMs" by Ankush Agarwal, Ganesh S, and Chaitanya Devaguptapu, arXiv:2501.17767v1, 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}{lcc}
\toprule
Graph & Nodes & Adj. Matrix NNZ\\
\midrule
DHFR 1DRF & 1.8K & 56K \\
COVID spike 6VXX & 23K & 136K \\
Carbon lattice & 1K & 158K \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Vivek Bharadwaj
Austin Glover
Aydin Buluc
James Demmel

"An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks" by Vivek Bharadwaj, Austin Glover, Aydin Buluc, and James Demmel, arXiv:2501.13986v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Adversarial Resilience on ImageNet64}
\begin{tabular}{llrrr}
Model & Attack & Base {[}-\%{]} & SVBI {[}-\%{]} & DVBI {[}-\%{]} \\ \hline
 & FGSM & 37.76 & 32.72 & 28.59 \\
 & \cellcolor[HTML]{EFEFEF}EAD & \cellcolor[HTML]{EFEFEF}35.43 & \cellcolor[HTML]{EFEFEF}1.96 & \cellcolor[HTML]{EFEFEF}1.13 \\
 & C\&W & 37.85 & 5.48 & 1.196 \\
\multirow{-4}{*}{ResNet-18} & \cellcolor[HTML]{EFEFEF}JSMA & \cellcolor[HTML]{EFEFEF}34.66 & \cellcolor[HTML]{EFEFEF}17.07 & \cellcolor[HTML]{EFEFEF}9.00 \\ \hline
 & FGSM & 41.70 & 32.22 & 32.18 \\
 & \cellcolor[HTML]{EFEFEF}EAD & \cellcolor[HTML]{EFEFEF}37.73 & \cellcolor[HTML]{EFEFEF}1.81 & \cellcolor[HTML]{EFEFEF}1.15 \\
 & C\&W & 41.73 & 5.52 & 2.56 \\
\multirow{-4}{*}{ResNet-50} & \cellcolor[HTML]{EFEFEF}JSMA & \cellcolor[HTML]{EFEFEF}39.32 & \cellcolor[HTML]{EFEFEF}19.18 & \cellcolor[HTML]{EFEFEF}10.25 \\ \hline
 & FGSM & 41.71 & 31.13 & 30.12 \\
 & \cellcolor[HTML]{EFEFEF}EAD & \cellcolor[HTML]{EFEFEF}41.66 & \cellcolor[HTML]{EFEFEF}1.84 & \cellcolor[HTML]{EFEFEF}1.04 \\
 & C\&W & 41.94 & 5.53 & 2.53 \\
\multirow{-4}{*}{ResNet-101} & \cellcolor[HTML]{EFEFEF}JSMA & \cellcolor[HTML]{EFEFEF}38.97 & \cellcolor[HTML]{EFEFEF}15.39 & \cellcolor[HTML]{EFEFEF}11,12 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Alireza Furutanpey
Pantelis A. Frangoudis
Patrik Szabo
Schahram Dustdar

"Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication" by Alireza Furutanpey, Pantelis A. Frangoudis, Patrik Szabo, and Schahram Dustdar, arXiv:2412.10265v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Online learning techniques for prediction of temporal tabular datasets with regime changes" by Thomas Wong and Mauricio Barahona, arXiv:2301.00790v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{True Positive, False Positive, and False Negative Comparison for FTG (775300 timesteps) and Pure Pursuit (2647570 timesteps).}
\begin{tabular}{lcccccc}
		\toprule
		\textbf{Method} & \multicolumn{3}{c}{\textbf{FTGAgent}} & \multicolumn{3}{c}{\textbf{Pure Pursuit}} \\
		\cmidrule(lr){2-4} \cmidrule(lr){5-7}
		& \textbf{TP} & \textbf{FP} & \textbf{FN} & \textbf{TP} & \textbf{FP} & \textbf{FN} \\
		\midrule
	Static & 106281 & 36329 & 2889 & 255376 & 33049 & 628 \\
	Ttc & 80187 & 12371 & 28983 & 236694 & 100915 & 19310 \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Thomas A. Henzinger
Fabian Kresse
Kaushik Mallik
Emily Yu
Đorđe Žikelić

"Predictive Monitoring of Black-Box Dynamical Systems" by Thomas A. Henzinger, Fabian Kresse, Kaushik Mallik, Emily Yu, and Đorđe Žikelić, arXiv:2412.16564v1, 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{Optimal upgrade policy and minimal costs in case of overhaul moments $M_i=H/(m+1)$, $i=1,\ldots,m$. The number in bold correspond to moments that coincide with overhauls.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
			\hline
			$m$ & Optimal upgrade policy $\Pi^*$ & $K^*(H)$ \\
			\hline
			0 & \{7.5,15,22.5\} & 52.3884 \\
			1 & \{7.5,\textbf{15},22.5\} & 47.3884 \\
			2 & \{\textbf{10},\textbf{20}\} & 42.6101 \\
			3 & \{\textbf{7.5},\textbf{15},\textbf{22.5}\} & 37.3884 \\
			4 & \{\textbf{6},\textbf{12},\textbf{18},\textbf{24}\} & 37.0887 \\
			5 & \{\textbf{5},\textbf{10},\textbf{15},\textbf{20},\textbf{25}\} & 38.7322 \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Fiona Sloothaak
Alp Akçay
Matthieu van der Heijden
Geert-Jan van Houtum

"After-sales services during an asset's lifetime: collaborative planning of system upgrades" by Fiona Sloothaak, Alp Akçay, Matthieu van der Heijden, and Geert-Jan van Houtum, arXiv:2202.12128v2, 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{Latency--performance trade-offs for \textsc{LLaMA3-8B} on \textsc{SQuAD}. We report AUROC and average runtime per example (in seconds).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \textbf{Method} & \textbf{AUROC} & \textbf{Avg. Runtime (s)} \\
 \midrule
 Post-Conf & 0.515 & 0.08 \\
 QueRE (5 follow-ups) & 0.868 & 0.17 \\
 QueRE (10 follow-ups) & 0.897 & 0.17 \\
 QueRE (20 follow-ups) & 0.916 & 0.36 \\
 QueRE (30 follow-ups) & 0.928 & 0.55 \\
 QueRE (40 follow-ups) & 0.933 & 0.74 \\
 QueRE (50 follow-ups) & 0.949 & 0.89 \\
 Self-Consistency & 0.534 & 0.19 \\
 Semantic Entropy & 0.521 & 2.44 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Predicting the Performance of Black-box LLMs through Self-Queries" by Dylan Sam, Marc Finzi, and J. Zico Kolter, arXiv:2501.01558v3, 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
\caption{Empirical SDF estimation error $\mathcal{E}$ and dropout-network SDF estimation error averaged across $8$ object instances under different LiDAR measurement noise standard deviation $\sigma$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
LiDAR Noise $\sigma$ & SDF Empirical Error & SDF Dropout Error\\
\hline
$0.01$ & $0.0173$ & $0.0132$ \\
$0.02$ & $0.0288$ & $0.0184$ \\
$0.05$ & $0.0463$ & $0.0242$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Kehan Long
Vikas Dhiman
Melvin Leok
Jorge Cortés
Nikolay Atanasov

"Safe Control Synthesis with Uncertain Dynamics and Constraints" by Kehan Long, Vikas Dhiman, Melvin Leok, Jorge Cortés, and Nikolay Atanasov, arXiv:2202.09557v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Sparse grid implementation of a fixed-point fast sweeping WENO scheme for Eikonal equations" by Zachary M. Miksis and Yong-Tao Zhang, arXiv:2201.08912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training and Testing Times for Different Algorithms}
\begin{tabular}{ccccc}
\toprule
	& & & & \\
	&MUSIC&DeepMUSIC&CNN&MoD-DNN\\
	\hline
	& & & & \\
	training [h]&-& 30.8 & 1.6 & 15.6 \\
	& & & & \\
	testing [ms]&9.4 &31.6 &1.8 &14.9\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Shengheng Liu
Zihuan Mao
Xingkang Li
Mengguan Pan
Peng Liu
Yongming Huang
Xiaohu You

"Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB" by Shengheng Liu, Zihuan Mao, Xingkang Li, Mengguan Pan, Peng Liu, Yongming Huang, and Xiaohu You, arXiv:2412.10644v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Shuhua Gao
Yunyi Zhao
Cheng Xiang
Yu Ming
Tan Kuan Tak
Tong Heng Lee

"On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method" by Shuhua Gao, Yunyi Zhao, Cheng Xiang, Yu Ming, Tan Kuan Tak, and Tong Heng Lee, arXiv:2011.12114v4, 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{DCI Format 1\_0 for C-RNTI in NR}
\begin{tabular}{||c|c||}
		\hline
		Field Names& Occupied Bits \\
		\hline\hline
		Identifier for DCI formats & 1 bits \\
	 Frequency domain resource assignments & Variable \\
	 Time domain resource assignments & X bits \\
	 VRB-to-PRB mapping & 1 bits \\
	 Modulation and Coding scheme & 5 bits \\
	 New data indicator & 1 bits \\
	 Redundancy version & 2 bits \\
	 HARQ process number & 4 bits \\
	 Downlink assignment index & 2 bits \\
	 TPC command assignment for scheduled PUCCH & 2 bits \\
	 PUCCH resource indicator & 3 bits \\
	 PDSCH-to-HARQ feedback timing indicator & 3 bits \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Hongzhi Chen
De Mi
Manuel Fuentes
Eduardo Garro
Jose Luis Carcel
Belkacem Mouhouche
Pei Xiao
Rahim Tafazolli

"On the Performance of PDCCH in LTE and 5G New Radio" by Hongzhi Chen, De Mi, Manuel Fuentes, Eduardo Garro, Jose Luis Carcel, Belkacem Mouhouche, Pei Xiao, and Rahim Tafazolli, arXiv:2001.02757v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{rccr||rccr}
 {\bf n} & {\bf code}& {\bf name} & {\bf claims} & {\bf n} & {\bf code}& {\bf name} & {\bf claims} \\
1 & 12011 &Broward, FL & 31,059& 7 & 34029 &Ocean, NJ & 52,436 \\
2 & 12086 & Miami-Dade, FL & 61,197& 8 & 36059 &Nassau, NY & 50,067 \\
3 & 17031 &Cook, IL & 15,180& 9 & 36103 &Suffolk, NY & 33,130 \\
4 & 22051 &Jefferson Parish, LA & 133,162 & 10 & 48167 &Galveston, TX & 60,224 \\
5 & 22071 &Orlean Parish, LA & 126,405 & 11 & 48201 &Harris, TX & 171,202\\
6 & 22103 &St Tammany Parish, LA & 37,514 \\
 \end{tabular}
\end{table}
\end{document}

stat

"Learning Inter-Annual Flood Loss Risk Models From Historical Flood Insurance Claims and Extreme Rainfall Data" by Joaquin Salas, Anamitra Saha, and Sai Ravela, arXiv:2212.08660v1, 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{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{AUC score across different datasets with the varying number of generated data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule
 \multirow{2}*{AUC} & \multicolumn{4}{c}{Number of Generated Samples}\\\cline{2-5}
 & $\textcolor{green}{50}/150/\textcolor{orange}{200}$ & $\textcolor{green}{100}/200/\textcolor{orange}{300}$ & $\textcolor{green}{150}/250/\textcolor{orange}{400}$ & $\textcolor{green}{200}/300/\textcolor{orange}{500}$ \\
 \midrule
 MNIST & $0.50$ & $0.52$ & $0.51$ & $0.51$ \\
 CIFAR10 & $0.53$ & $0.53$ & $0.54$ & $0.55$ \\
 \textcolor{orange}{SkinCancer} & $0.52$ & $0.54$ & $0.56$ & $0.56$ \\%\hline
BBCNews & $0.54$ & $0.55$ & $0.55$ & $0.55$ \\%\hline
\textcolor{green}{IMDB} & $0.53$ & $0.56$ & $0.57$ & $0.56$ \\%\hline
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Dayong Ye
Tianqing Zhu
Shang Wang
Bo Liu
Leo Yu Zhang
Wanlei Zhou
Yang Zhang

"Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI" by Dayong Ye, Tianqing Zhu, Shang Wang, Bo Liu, Leo Yu Zhang, Wanlei Zhou, and Yang Zhang, arXiv:2501.16671v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
Base $\beta$&$\Upsilon(\beta)$&Searched to $\beta^k$'s place\\
\hline
5&0&Proven normal\\
\hline
13&1&$13^4$'s place\\
\hline
17&1&$17^4$'s place\\
\hline
37&1&$37^3$'s place\\
\hline
53&1&$53^2$'s place\\
\hline
61&1&$61^2$'s place\\
\end{tabular}
\end{adjustbox}
\caption{The first few prime bases with four zeros in their Pisano period.}
\end{table}
\end{document}

math

"The Fibonacci Sequence is Normal Base 10" by Brennan Benfield and Michelle Manes, arXiv:2202.08986v1, 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{Shallow CVNN computational complexities.}
\begin{tabular}{lcc}
\hline
\textbf{CVNN}& \textbf{Training} & \textbf{Inference}\\
\hline
CVFNN & $N(8P+12R+8)+8R$ & $4N(P+R)$\\
SCFNN & $N(8P+12R+8)+6R$ & $4N(P+R)$\\
MLMVN & $N(8P+12R+16)+12R$ & $4N(P+R+1)+4R$\\
C-RBF & $N(4P+6R+5)+4R$ & $N(2P+2R+1)$\\
FC-RBF & $N(12P+12R+12)+4R$ & $4N(P+R)$\\
PT-RBF & $N(4P+12R+12)+4R$ & $2N(P+2R+1)$\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Kayol Soares Mayer
Jonathan Aguiar Soares
Ariadne Arrais Cruz
Dalton Soares Arantes

"On the Computational Complexities of Complex-valued Neural Networks" by Kayol Soares Mayer, Jonathan Aguiar Soares, Ariadne Arrais Cruz, and Dalton Soares Arantes, arXiv:2310.13075v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The first $c_1,$ second $c_2,$ and fourth $c_4$ cumulants of various models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|} % centered columns (4 columns)
\hline
\multicolumn{2}{|l|}{L\'evy Models}\\
\hline %inserts double horizontal lines
BSM&$c_1=(r-q+\omega)t$\quad$c_2=\sigma^2 t,$\quad $c_4=0,$ $\omega=-0.5\sigma^2$\\
\hline %inserts single line
VG&$c_1=(r-q+\theta+\omega)t$\\
\,&$c_2=(\sigma^2+\upsilon\theta^2)t$\\
\,&$c_4=3(\sigma^4\upsilon+2\theta^4\upsilon^3+4\sigma^2\theta^2\upsilon^2)t$\\
\,&$\omega={1 \over \upsilon}\log(1-\theta\upsilon-\sigma^2\upsilon/2)$\\
\hline %inserts single line
$\rm CGMY$&$c_1=(r-q+\omega)t$\\
\,&$c_2=( C\Gamma(2-Y)(M^{Y-2} + G^{Y-2})t$\\
\,&$c_4=(C\Gamma(4-Y)(M^{Y-4} + G^{Y-4})t$\\
\,&$\omega=\left(C\Gamma(-Y)G^Y\left(\left(1+\frac{1}{G}\right)^Y-1-\frac{Y}{G}\right)+C\Gamma(-Y)M^Y\left(\left(1-\frac{1}{M}\right)^Y-1+\frac{Y}{M}\right)\right)$\\
\hline %inserts single line
\multicolumn{2}{|l|}{Affine Processes}\\
\hline
Heston&$c_1=(r-q)t+(1-e^{-\lambda t}){\bar{y}-y_0\over 2\lambda }-0.5\bar{y}t$\\
\,&$c_2={1 \over 8\lambda^3}\Big(\eta t \lambda\exp(-\lambda t)(y_0-\bar{y})(8\lambda\rho -4\eta)+$\\
\,&\quad\quad$\lambda\rho\eta(1-e^{-\lambda t})(16\bar{y}-8y_0)+2\bar{y}\lambda t(-4\rho\eta+\eta^2+4\lambda^2)+$\\
\,&\quad\quad$\eta^2((\bar{u}-2u_0)e^{-2\lambda t}+\bar{y}(6e^{-\lambda t}-7)+2y_0)+8\lambda^2(y_0-\bar{y})(1-e^{-\lambda t})\Big)$\\
\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{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|r|r|r}
 \toprule
 Instances &
 $e_3$ &
 Clock Cycles &
 Seconds &
 Oracle calls \\
 \midrule
 SIDHp434 & 137 & 4249 & 4.62 & 226 \\
 SIDHp503 & 159 & 7257 & 7.76 & 263 \\
 SIDHp610 & 192 & 17424 & 18.64 & 318 \\
 SIDHp751 & 239 & 40638 & 42.77 & 396 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Faulty isogenies: a new kind of leakage

Gora Adj
Jesús-Javier Chi-Domínguez
Víctor Mateu
Francisco Rodríguez-Henríquez

"Faulty isogenies: a new kind of leakage" by Gora Adj, Jesús-Javier Chi-Domínguez, Víctor Mateu, and Francisco Rodríguez-Henríquez, arXiv:2202.04896v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
		\toprule
		& {\bf Bole} & {\bf Tamale} & {\bf Yendi}
		\\
		\midrule
		{\bf Accuracy} & 0.8319 & 0.8207 & 0.8895
		\\
		\midrule
		{\bf AUC} & 0.8104 & 0.7991 & 0.8334
		\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Performance of evaluation metrics of the proposed stacking ensemble classification.}
\end{table}
\end{document}

q-fin

Samuel Asante Gyamerah
Philip Ngare
Dennis Ikpe

"Hedging crop yields against weather uncertainties -- a weather derivative perspective" by Samuel Asante Gyamerah, Philip Ngare, and Dennis Ikpe, arXiv:1905.07546v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{Summary statistics for INTEL stock exchange}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Parameter & Estimate & Standard Error \\ % inserts table
		\hline
$\alpha_0$ & -0.1650 & 0.4123
\\
$\alpha_1$ & 0.8629 & 0.0682
\\
$\sigma_{\omega}$ & 0.5978 & 0.1871
\\
$\lambda$ &-14.166 & 2.9146
\\
$\sigma_0$ & 0.9478 & 0.1311
\\
$\phi_1$ &-3.3405 & 0.1904
\\
$\sigma_1$ & 3.2604 & 0.1158
\\
\hline \hline %inserts single line
\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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{BCVA Wasserstein Radii}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
Percentage of $\delta^u$ & 50\% & 60\% & 70\% & 80\% & 90\% & 100\% \\
 \hline
W Radius delta & 14.45 & 17.34 & 20.23 & 23.12 & 26.01 & 28.90 \\
 \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{PSNR results of training our model with and without RefGAN dataset.}
\begin{tabular}{ccc}
\hline
 & w/o RefGAN & ours \\
\hline
wild (101) & 25.39 & \textbf{25.80} \\
postcard (199) & 22.57 & \textbf{23.65} \\
object (200) & 24.70 & \textbf{25.76} \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Utilizing Multi-step Loss for Single Image Reflection Removal" by Abdelrahman Elnenaey and Marwan Torki, arXiv:2412.08582v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"The Maximum Number of Sets for 12 Cards is 14" by Justin Stevens and Duncan Wilson, arXiv:2501.12565v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|}\hline
$\lambda$ & $100$ & $50$ & $20$ & $10$ & $5$ & $3$\\\hline
$N(0,1)$ & $0.556$ & $0.612$ & $0.766$ & $0.928$ & $0.998$ & $1.000$\\
$t_{\lambda}$ & $0.444$ & $0.388$ & $0.234$ & $0.072$ & $0.002$ &
$0.000$\\\hline
\end{tabular}
\end{adjustbox}
\caption{Weights for normal and $t_\lambda$ errors in Example 6.}%
\end{table}
\end{document}

math

Combining Evidence

"Combining Evidence" by Michael Evans and Yang Jian Guo, arXiv:2202.02922v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}%
 \hline%
 & $\frac{1}{1+(r/\sigma)^2}$
 &$e^{-(r/\sigma)^2}$
 \\
 \hline
 Dense & 1.72 [1.62, 1.77]e-2 & 1.76 [1.71, 1.80]e-2 \\ %
 HDF & 1.71 [1.62, 1.77]e-2 & 1.76 [1.70, 1.80]e-2 \\ %
 Nyst. & 1.78 [1.71, 2.08]e-2 & 1.79 [1.71, 1.83]e-2 \\ %
 \hline%
 \hline%
 & Matérn $\nu=1.5$
 & Matérn $\nu=2.5$
 \\
 \hline
 Dense & 1.58 [1.54,1.62]e-2 & 1.69 [1.66,1.73]e-2 \\ %
 HDF & 1.58 [1.55,1.62]e-2 & 1.69 [1.65,1.73]e-2 \\ %
 Nyst. & 2.52 [1.70,7.94]e-2 & 1.69 [1.68,1.72]e-2 \\ %
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Mean squared error (MSE) of predictions compared to ground truth values in the test split of our regression experiment. Entries are the median, minimum, and maximum MSEs observed. In all cases, the higher accuracy of our approximation resulted in better MSE. For the Matérn kernel with $\nu=1.5$ the Nystr\"{o}m approximation yielded conditioning issues, and the iterative method sometimes failed to converge in 1024 iterations.}
\end{table}
\end{document}

math

"Linear Time Kernel Matrix Approximation via Hyperspherical Harmonics" by John Paul Ryan and Anil Damle, arXiv:2202.03655v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The average wage gap in the transition data by labour market segment.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|rr|rr|rr} \hline \hline
& &\multicolumn{2}{|c|}{Services} &\multicolumn{2}{|c}{Unskilled} &\multicolumn{2}{|c}{Skilled}\\
Age & Wages & Native&Migrant & Native & Migrant & Native &Migrant \\ \hline
25-30 & mean & 122.36 & 88.94 & 107.77 & 92.54 & 124.74 & 111.07 \\
& sd & 41.86 & 44.15 & 37.68 & 36.09 & 29.94 & 35.21 \\
& $\Delta \log(w)$ & \multicolumn{2}{|c}{.32} &
\multicolumn{2}{|c}{.15} & \multicolumn{2}{|c}{.11} \\ \hline
30-40 & mean & 156.35 & 99.38 & 120.94 & 97.99 & 135.79 & 116.65 \\
& sd & 51.22 & 55.02 & 38.24 & 36.61 &32.04 & 36.22 \\
& $\Delta \log(w)$ & \multicolumn{2}{|c}{.45} &
\multicolumn{2}{|c}{.21} & \multicolumn{2}{|c}{.15} \\ \hline
40-55 & mean & 158.17 & 112.74 & 125.05 & 107.49 & 138.29 & 126.20 \\
& sd & 48.09 & 56.81 & 36.71 & 36.89 & 33.29 & 33.50 \\
& $\Delta \log(w)$ & \multicolumn{2}{|c}{.33} & \multicolumn{2}{|c}{.15} & \multicolumn{2}{|c}{.09} \\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Composition of Wage Differentials between Migrants and Natives" by Panagiotis Nanos and Christian Schluter, arXiv:1306.1781v3, licensed under CC BY 3.0. 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{Elastic media parameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Parameters & Isotropic medium & Anisotropic medium (AM1)
 \\ \hline
 $c_{11}$ [GPa] & 97.20 & 20
 \\ \hline
 $c_{12}$ [GPa]& 36.85 & 3.8
 \\ \hline
 $c_{22}$ [GPa]& 97.20 & 4
 \\ \hline
 $c_{33}$ [GPa]& 30.17 & 2
 \\ \hline
 $\rho $ [gm/cm$^3$]& $2.7$ & $20/36$
 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"The perfectly matched layer (PML) for hyperbolic wave propagation problems: A review" by Kenneth Duru and Gunilla Kreiss, arXiv:2201.03733v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Metric-scale scene reconstruction results}. We evaluate on the EMDB~, SLOPER4D~, and RICH~ datasets and compare with the other scene reconstruction baselines. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|cccc|c}
\toprule
 & \textbf{EMDB} & \multicolumn{4}{c|}{\textbf{SLOPER4D}} & \textbf{RICH} \\
\textbf{Methods} & \textbf{ATE $\downarrow$} & \textbf{ATE $\downarrow$} & \textbf{Abs Rel $\downarrow$} & \textbf{$\delta$<1.25 $\uparrow$} & \textbf{CD $\downarrow$} & \textbf{CD $\downarrow$} \\ \midrule

TRAM$^\star$~ & 1.13 & 124.8& 0.33 & 0.38 &59.70 & 4.77\\
MASt3R$^\star$~ & 1.51 & 28.0& 0.48 & 0.07 & 29.64 & 3.96 \\
MonST3R$^\star$~ & 2.51 & 20.8 & 0.25 & 0.70& 10.21 &5.43 \\ \midrule
JOSH (ours) & \textbf{1.13} & \textbf{7.4}& \textbf{0.18} & \textbf{0.86} & \textbf{7.38} & \textbf{2.81} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zhizheng Liu
Joe Lin
Wayne Wu
Bolei Zhou

"Joint Optimization for 4D Human-Scene Reconstruction in the Wild" by Zhizheng Liu, Joe Lin, Wayne Wu, and Bolei Zhou, arXiv:2501.02158v1, 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
\caption{{{Estimated value differences and the empirical coverage of confidence intervals for the value difference between the optimal decision and pre-specified decision rules. }}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\hline \hline
($n,N$)&(2000,5000) & (2000, 15,000)&(3000,5000) & (3000, 15,000)\\\hline
 Estimates&\multicolumn{4}{c}{$E(Q^*_{n})-E(Q_{n,\pi_1})=1.037$} \\
$\mathbb{P}_N\widehat{Q}^*_{n}-\mathbb{P}_N\widehat{Q}_{n,\pi_1}$ &1.034&1.030& 1.039&1.040 \\
$\widehat{\sigma}(\mathbb{P}_N\widehat{Q}^*_{n}-\mathbb{P}_N\widehat{Q}_{n,\pi_1})$ &0.032&0.030&0.026&0.025 \\
\multicolumn{4}{l}{Coverage}\\
90\% CI &0.830&0.816&0.856&0.844\\
95\% CI&0.896&0.888&0.914&0.902\\
99\% CI&0.958&0.954&0.978&0.978\\\\
 Estimates&\multicolumn{4}{c}{$E(Q^*_{n})-E(Q_{n,\pi_2})=1.734$} \\
$\mathbb{P}_N\widehat{Q}^*_{n}-\mathbb{P}_N\widehat{Q}_{n,\pi_2}$ &1.736&1.732&1.730&1.733 \\
$\widehat{\sigma}(\mathbb{P}_N\widehat{Q}^*_{n}-\mathbb{P}_N\widehat{Q}_{n,\pi_2})$ &0.036&0.036&0.030&0.029 \\
\multicolumn{4}{l}{Coverage}\\
90\% CI &0.846&0.846&0.866&0.862 \\
95\% CI&0.902&0.908&0.928&0.920\\
99\% CI&0.962&0.958&0.988&0.988 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Statistical Learning for Individualized Asset Allocation" by Yi Ding, Yingying Li, and Rui Song, arXiv:2201.07998v3, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Toy example 1}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
Mean & Variance & $x^* (\phi=0.01)$ & $L_i$ & OK to drop & RSC \\
$ \begin{bmatrix}
1 \\ 2 \\ 3 \\
\end{bmatrix} $
&
$ \begin{bmatrix}
2 & 1 & 1 \\ 1 & 2 & 1 \\ 1 & 1 & 2 \\
\end{bmatrix} $
&
$\begin{bmatrix}
0.3233 \\ 0.3333 \\ 0.3433 \\
\end{bmatrix}$
&
$\begin{bmatrix}
0.6667 \\ 0.6667 \\ 0.6667 \\
\end{bmatrix}$
&
$\begin{bmatrix}
Yes \\ No \\ No \\
\end{bmatrix}$
&
$\begin{bmatrix}
0.2640 \\ 0.2722 \\ 0.2803 \\
\end{bmatrix}$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Caihua Chen
Xindan Li
Caleb Tolman
Suyang Wang
Yinyu Ye

"Sparse Portfolio Selection via Quasi-Norm Regularization" by Caihua Chen, Xindan Li, Caleb Tolman, Suyang Wang, and Yinyu Ye, arXiv:1312.6350v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{A list of representative works of training-free test-time reinforcing.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
 Method & Category & Representative literature \\ \midrule
\multirow{6}{*}{Verbal Reinforcement Search}
 & Individual Agent &Romera et al., Shojaee et al., \\& & Mysocki et al.,Ma et al. &\\ \cmidrule(l){2-4}
 & Multi-Agent &Chen et al.,Zhou et al.,\\ & &Le et al. ,Yu et al. \\ \cmidrule(l){2-4}
 & Embodied Agent & Boiko et al.
 \\ \bottomrule
\multirow{6}{*}{Memory-based Reinforcement}
 & Experiential Learning &Zhang et al.,Gao et al. \\& & Qian et al. &\\ \cmidrule(l){2-4}
 & Reflective Learning &Shinn et al.,Sun et al.,\\&&Sun et al. \\ \cmidrule(l){2-4}
 & Concept Learning &Zhang et al.,Gao et al. ,\\ &&Guan et al. \\ \bottomrule
\multirow{4}{*}{Agentic System Search}
 & Prompt Level &Madaan et al., Fernando et al.\\ & & Yang et al. &\\ \cmidrule(l){2-4}
 &Module Level & Shang et al., Zhang et al. \\ \cmidrule(l){2-4}
 & Agent Level &Huot et al.,Zhuge et al. \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Fengli Xu
Qianyue Hao
Zefang Zong
Jingwei Wang
Yunke Zhang
Jingyi Wang
Xiaochong Lan
Jiahui Gong
Tianjian Ouyang
Fanjin Meng
Chenyang Shao
Yuwei Yan
Qinglong Yang
Yiwen Song
Sijian Ren
Xinyuan Hu
Yu Li
Jie Feng
Chen Gao
Yong Li

"Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models" by Fengli Xu, Qianyue Hao, Zefang Zong, Jingwei Wang, Yunke Zhang, Jingyi Wang, Xiaochong Lan, Jiahui Gong, Tianjian Ouyang, Fanjin Meng, Chenyang Shao, Yuwei Yan, Qinglong Yang, Yiwen Song, Sijian Ren, Xinyuan Hu, Yu Li, Jie Feng, Chen Gao, and Yong Li, arXiv:2501.09686v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Robust penalized spline estimation with difference penalties" by Ioannis Kalogridis and Stefan Van Aelst, arXiv:2012.13295v2, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
Method & Rel. $L_2$ error in $\rho$ & Norms & Theoretical Bounds\\ \hline
PINN using AA & 6.5533e-03& 100.00\% & 100.00\% \\ \hline
XPINN-1 & \multirow{3}{*}{5.3864e-03} & 41.34\% & \multirow{3}{*}{89.43\%} \\ \cline{1-1} \cline{3-3}
XPINN-2 & & 121.69\% & \\ \cline{1-1} \cline{3-3}
XPINN-3 & & 49.72\% & \\ \hline
\end{tabular}
\end{adjustbox}
\caption{The Rademacher complexity and the theoretical bounds for the oblique shock wave problems. These are computed using the relative $L_2$ error in density.}
\end{table}
\end{document}

math

Ameya D. Jagtap
Zhiping Mao
Nikolaus Adams
George Em Karniadakis

"Physics-informed neural networks for inverse problems in supersonic flows" by Ameya D. Jagtap, Zhiping Mao, Nikolaus Adams, and George Em Karniadakis, arXiv:2202.11821v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Spatiotemporal Graph Ablation Experiment}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 \toprule[1.5pt]
 \multirow{2}{*}{Models} & \multicolumn{3}{c}{PEMS03} & \multicolumn{3}{c}{PEMS08} \\
 \cmidrule[0.75pt]{2-4} \cmidrule[0.75pt]{5-7}
 & MAE & RMSE & MAPE(\%) & MAE & RMSE & MAPE(\%) \\
 \midrule[0.75pt]
 use pg &15.22 &26.67 &15.91 &14.14 &23.46 &9.17 \\
 use tg &15.89 &27.22 &16.14 &13.99 &23.46 &9.16 \\
 use sg &15.20 &26.16 &15.61 &13.97 &23.12 &9.35 \\
 SFADNet &14.66 &24.50 &15.04 &13.65 &23.23 &8.98 \\
 \bottomrule[1.5pt]
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mei Wu
Wenchao Weng
Jun Li
Yiqian Lin
Jing Chen
Dewen Seng

"SFADNet: Spatio-temporal Fused Graph based on Attention Decoupling Network for Traffic Prediction" by Mei Wu, Wenchao Weng, Jun Li, Yiqian Lin, Jing Chen, and Dewen Seng, arXiv:2501.04060v1, 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}{lccccccc}
& \multicolumn{3}{c}{MIPLIB} && \multicolumn{3}{c}{NN-Verification} \\
\cline{2-4} \cline{6-8}
 & Min & Median & Max && Min & Median & Max \\
\hline
Time (s) & 0.001 & 0.037 & 0.944 && 0.032 & 0.087 & 1.378 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Inference time statistics of trained GCNN from Experiment per instance-seed pair.}
\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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The number and proportion of four different scenarios when Agent 4 and Agent 5 discussed different misinformation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \hline
 & \shortstack{Agent 4 \\convinces Agent 5 }
 & \shortstack{Agent 5 \\convinces Agent 4 }
 & \shortstack{mutual\\resistance}
 &\shortstack{bilateral\\influence} \\
 \hline
 HIV& \textbf{38(59.4\%)}& 14(21.9\%)& 10(15.6\%)& 2(3.1\%)\\
 QAnon& \textbf{32(56.1\%)}& 9(15.8\%)& 13(22.8\%)& 3(5.3\%)\\
 5G& \textbf{22(36.7\%)}& 17(28.3\%)& 20(33.3\%)& 1(1.7\%)\\
 MMR& \textbf{25(45.5\%)}& 15(27.3\%)& 13(23.6\%)& 2(3.6\%)\\
 Chloride& \textbf{25(55.6\%)}& 16(35.6\%)& 3(6.7\%)& 1(2.2\%)\\
 Superfood& \textbf{23(51.1\%)}& 17(37.8\%)& 4(8.9\%)& 1(2.2\%)\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Personality Modeling for Persuasion of Misinformation using AI Agent" by Qianmin Lou and Wentao Xu, arXiv:2501.08985v1, 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|c|}
		\hline
		& \textbf{Nasdaq} & \textbf{Crude-oil} & \textbf{Gold} & \textbf{US-dollar} \\
		\hline
		\textbf{Nasdaq} & 1 & 0.19 & 0.026 & -0.091 \\\hline
		\textbf{Crude-oil} & 0.19 & 1 & 0.099 & -0.07 \\\hline
		\textbf{Gold} & 0.026 & 0.099 & 1 & -0.4 \\\hline
		\textbf{US-dollar} & -0.091 & -0.07 & -0.4 &1 \\
		\hline
	\end{tabular}
\caption{Effective correlation coefficient results.}
\end{table}
\end{document}

q-fin

Pouriya Khalilian
Amirhossein N. Golestani
Mohammad Eslamifar
Mostafa T. Firouzjaee
Javad T. Firouzjaee

"Mapping Crisis-Driven Market Dynamics: A Transfer Entropy and Kramers-Moyal Approach to Financial Networks" by Pouriya Khalilian, Amirhossein N. Golestani, Mohammad Eslamifar, Mostafa T. Firouzjaee, and Javad T. Firouzjaee, arXiv:2507.09554v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 & \\
 RG-3 & Evaluate our risk-adjusted return optimal sector universe against the benchmark. \\
 & \\
 \hline
 \end{tabular}
\end{adjustbox}
\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.