\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}
 \bottomrule[0.9pt]
 Method & C-$\mathcal{L}_1$ & PSNR & LPIPS & SSIM \\
 \hline
 \emph{static $\beta$ (Tight coupling}) & 1.95 & 33.02 & 0.0385 & 0.966 \\
 \emph{Poisson (Tight coupling}) & 2.03 & - & - & - \\
 \emph{Ours (Tight coupling}) & {1.50} &33.23& {0.0340} & {0.968}\\
 \emph{Ours (Loose coupling)} & \textbf{1.37} &\textbf{33.86}& \textbf{0.0330} & \textbf{0.971}\\ \hline
 \textit{w/o depth,normal} & 3.63 & 32.83 & 0.0380 & 0.963 \\
 \textit{w/o normal} & 1.74 & 33.18 & 0.0383 & 0.967 \\
 \toprule[0.9pt]
 \end{tabular}
\caption{Ablation study of the surface and GS alignment on the NeRF Synthetic dataset and the volumetric rendering depth (normal) loss, where we evaluate the effect of each proposed component. C-$\mathcal{L}_1$ is an abbreviation for the Chamfer-$\mathcal{L}_1$ distance.}
\end{table}
\end{document}

cs

Xiaoyang Lyu
Yang-Tian Sun
Yi-Hua Huang
Xiuzhe Wu
Ziyi Yang
Yilun Chen
Jiangmiao Pang
Xiaojuan Qi

"3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting" by Xiaoyang Lyu, Yang-Tian Sun, Yi-Hua Huang, Xiuzhe Wu, Ziyi Yang, Yilun Chen, Jiangmiao Pang, and Xiaojuan Qi, arXiv:2404.00409v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Condition numbers of $\mathcal{A}_h\mathcal{Q}_h$, based on the 1-dimensional heat equation. For the discretization, $n_x = 64$, with $\Delta t$ chosen according to the Courant number $C = 2 \tau \Delta t/\Delta x^2$, $\theta = 0.5$.}
\begin{tabular}{|l|c|ccc|c|} \hline
 & \multirow{2}{*}{$C = \frac{2\tau \Delta t}{\Delta x^2}$} & \multicolumn{3}{|c|}{$\kappa (\mathcal{A}_h \mathcal{Q}_h)$} & $\kappa(\mathcal{A}_h)$\\
 & & $\nu = 2$ & $\nu = 4$ & $\nu = 8$ & \\ \hline
 \multirow{3}{*}{T-coarsening } & 0.16 & 2.62 & 4.05 & 8.69 & 82.95 \\
 & 0.32 & 2.62 & 4.05 & 8.69 & 42.39 \\
 & 0.64 & 2.62 & 4.05 & 8.67 & 22.11 \\ \hline
 \multirow{3}{*}{TS-coarsening } & 0.16 & 17.20 & 19.13 & 24.05 & 82.95 \\
 & 0.32 & 8.82 & 9.85 & 12.87 & 42.39 \\
 & 0.64 & 4.84 & 5.53 & 8.87& 22.11 \\ \hline
 \multirow{3}{*}{ MGRIT-based intergrid operators } & 0.16 & 2.64 & 4.05 & 10.76 & 82.95 \\
 & 0.32 & 2.69 & 5.52 & 10.73 & 42.39 \\
 & 0.64 & 2.85 & 5.59 & 10.74 & 22.11 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

"Parallel-in-time Multilevel Krylov Methods: A Prototype" by Yogi A. Erlangga, arXiv:2401.00228v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Spearman's coefficients between distance measures and the average coverage gap}
\begin{tabular}{c|c|c|c|c|c|c}
\toprule\toprule
Dataset & Airfoil& PeMSD4 & PeMSD8 & Seattle & U.S. & Japan\\ \midrule
$W$ & \textbf{0.59} (0.24) & \underline{0.84} (0.03) & \textbf{0.90} (0.03) & \textbf{0.84} (0.05) & \textbf{0.77} (0.06) & \textbf{0.57} (0.05) \\
TV & 0.45 (0.16) & \textbf{0.88} (0.03) & \underline{0.86} (0.06) & \underline{0.75} (0.09) & 0.67 (0.10) & 0.37 (0.06) \\
KL & 0.40 (0.21) & 0.49 (0.17) & 0.51 (0.09) & 0.45 (0.17) & 0.60 (0.11) & \underline{0.53} (0.05) \\
$\Delta\mathbb{E}$ & \underline{0.55} (0.19) & 0.78 (0.05) & 0.85 (0.04) & 0.71 (0.06) & \underline{0.68} (0.08) & 0.37 (0.09) \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Rui Xu
Chao Chen
Yue Sun
Parvathinathan Venkitasubramaniam
Sihong Xie

"Wasserstein-regularized Conformal Prediction under General Distribution Shift" by Rui Xu, Chao Chen, Yue Sun, Parvathinathan Venkitasubramaniam, and Sihong Xie, arXiv:2501.13430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c|c|c}
 			Market &	Abbrev. & \# assets & Country & From-To \\ \hline
 			Dow Jones Industrial Average &	DJIA & 30 & USA & \multirow{6}{*}{09/04/2019-23/03/2020}\\
Euro Stoxx 50 &	STOXX50 & 47 & Eurozone & \\
DAX 30 	&	DAX & 28 & Germany & \\
 CAC 40		&	CAC & 40 & France & \\
 FTSE 100	&	FTSE & 98 & UK & \\
EuroNext 100	&	N100 & 98 & Eurozone & \\
 				\hline
 			\end{tabular}
\caption{List of the daily datasets analyzed.}
\end{table}
\end{document}

q-fin

Francesco Cesarone
Rosella Giacometti
Jacopo Maria Ricci

"Non-parametric cumulants approach for outlier detection of multivariate financial data" by Francesco Cesarone, Rosella Giacometti, and Jacopo Maria Ricci, arXiv:2305.10911v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Same as Table ~ but for \textbf{evaluation set}.}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \multirow{2}{*}{Method} & \multicolumn{2}{|c|}{Full} & \multicolumn{2}{|c|}{Core}\\
 \cline{2-5}
 & DER & JER & DER & JER \\
 \hline
 Baseline 	& 19.19	&43.28&	20.39&	48.61\\
 Proposed 	& 17.20	&37.30&	18.66&	42.23\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

A Kishore Kumar
Shefali Waldekar
Goutam Saha
Md Sahidullah

"ABSP System for The Third DIHARD Challenge" by A Kishore Kumar, Shefali Waldekar, Goutam Saha, and Md Sahidullah, arXiv:2102.09939v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
		\hline
		Porosity & 20\%\\
		\hline
		Total Compressibility & $5\times 10^{-8}$ Pa$^{-1}$\\
		\hline
		Dynamic Viscosity & 0.002 Pa*s\\
		\hline
		Production Index & 3.891$\times$10$^{-4}$ m$^3$Sec$^{-1}$MPa$^{-1}$\\
		\hline
		Bottom-hole Pressure & 26 MPa \\
		\hline
	\end{tabular}
\caption{A summary of constant physical and well parameters for test case 3.}
\end{table}
\end{document}

math

Zhao Zhang
Xinpeng Li
Menghan Li
Jiayu Zhai
Piyang Liu
Xia Yan
Kai Zhang

"Nonlinear Expectation Inference for Efficient Uncertainty Quantification and History Matching of Transient Darcy Flows in Porous Media with Random Parameters Under Distribution Uncertainty" by Zhao Zhang, Xinpeng Li, Menghan Li, Jiayu Zhai, Piyang Liu, Xia Yan, and Kai Zhang, arXiv:2312.06923v3, 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{Constants for the estimates of solutions, Example~2}
\begin{tabular}{cccccccccc}
\hline
\multicolumn{10}{c}{Lyapunov--Krasovskii approach}\\
\hline
$\delta$ & $H_1$ & $H_2$ & $\Delta$ & $\hat{c}_1$ & $\hat{c}_2$ & $\chi$ & $\mathrm{w}_0$ & $\mathrm{w}_1$ & $\mathrm{w}_2$\\
\hline
$0.001$ & $0.0111$ & $0.0058$ & $6.789\cdot 10^{-4}$ & $1.4728$ & $0.002$ & $0.39$ & $0.0022$ & $0.0092$ & $0.0022$\\
\hline
\multicolumn{10}{c}{Lyapunov--Razumikhin approach}\\
\hline
\multicolumn{2}{c}{$\delta$} & $H$ & \multicolumn{2}{c}{$\Delta$} & $\tilde{c}_1$ & \multicolumn{2}{c}{$\tilde{c}_2$} & $\alpha$ & $\rho$\\
\hline
\multicolumn{2}{c}{$0.001$} & $0.0066$ & \multicolumn{2}{c}{$6.8\cdot 10^{-4}$} & $1.4698$ & \multicolumn{2}{c}{$0.019$} & $2$ & $0.0643$\\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

Gerson Portilla
Irina Alexandrova
Sabine Mondié
Alexey Zhabko

"Estimates for solutions of homogeneous time-delay systems: Comparison of Lyapunov-Krasovskii and Lyapunov-Razumikhin techniques" by Gerson Portilla, Irina Alexandrova, Sabine Mondié, and Alexey Zhabko, arXiv:2101.10139v3, 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|}
\hline
 & Median & Mean \\
\hline
Block & 0 & 0.0011 \\
\hline
Date & 2 & 153.06 \\
\hline
\end{tabular}
\caption{Median and mean number of transactions for all days}
\end{table}
\end{document}

q-fin

Quantifying MEV On Layer 2 Networks

"Quantifying MEV On Layer 2 Networks" by Arthur Bagourd and Luca Georges Francois, arXiv:2309.00629v1, 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{Post Quantum Cryptography V/S Classical Cryptography}
\begin{tabular}{|c|c|c|}
		\hline
		\textbf{Features} & \textbf{Post Quantum Cryptography} & \textbf{Classical Cryptography}\\
		\hline
		Basis & Quantum mechanics & Mathematical computation\\
		\hline
		Development & Infantile \& not tested fully & Deployed and tested\\
		\hline
		Existing Infrastructure & Sophisticated & Widely used\\
		\hline
		Digital Signature & Not present & present\\
		\hline
		Bit rate & 1Mbit/s avg & Depend on Computing power\\
		\hline
		Cost & Crypto chip $\texteuro100,000$ & Almost zero\\
		\hline
		Register storage (n-bit) at any moment & One n-bit string & Million of miles\\
		\hline
		Communication Range & 10 miles max & Million of miles\\
		\hline
		Requirements & Devoted h/w \& communication lines & S/w and portable\\
		\hline
		Life expectancy & No change based on physics laws & Require changes as computing power increases\\
		\hline
		Medium & Dependent & Independent\\
		\hline
	\end{tabular}
\end{table}
\end{document}

cs

Tanmay Tripathi
Abhinav Awasthi
Shaurya Pratap Singh
Atul Chaturvedi

"Post Quantum Cryptography and its Comparison with Classical Cryptography" by Tanmay Tripathi, Abhinav Awasthi, Shaurya Pratap Singh, and Atul Chaturvedi, arXiv:2403.19299v1, 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{Distribution of workers across occupations - February 1986 to April 1987}
\begin{tabular}{lcccc}
 \hline
\multicolumn{5}{c}{Distribution across source occupations (occupation code) (\%)} \\ \hline
 & SIPP 1985 & SIPP 1986 & SIPP 1987 & p-value (no difference) \\ \hline \hline
managing occupations& 12.31 & 13.11 & 13.99 &	0.064 \\
professional speciality& 13.40 & 12.92 & 13.18 &	0.380 \\
technicians and related support& 3.85 & 3.89 & 4.08 &	0.829 \\
sales occ.& 9.74 & 9.98 & 9.86 &	0.599\\
admin support& 18.67 &	 18.17 &	18.26 &	0.366 \\
services& 10.96 &	11.66 & 11.19 & 0.166 \\
farming/fish/logging& 1.09 & 1.08 & 1.03 & 0.940\\
mechanics and repairers& 4.87 &	4.47 & 4.69 & 0.231 \\
construction and extractive& 3.47 & 3.65 & 3.60 &	 0.503 \\
precision production& 4.01 &	 4.19 & 3.75 &	0.643 \\
machine operators/assemblers& 9.27 & 8.89 & 8.63 &	0.355 \\
transportation and materials moving& 4.63 &	 4.34 & 4.31 & 0.340 \\
laborers& 3.73 &	3.66 & 3.42 &	0.714 \\ \hline
\multicolumn{5}{c}{Distribution across destination occupations (occupation code) (\%)} \\ \hline
managing occupations& 12.58 & 13.27 & 14.18 &	0.103 \\
professional speciality& 13.31 & 12.90 & 13.10 &	0.451 \\
technicians and related support& 3.82 & 3.92 & 4.01 &	0.693 \\
sales occ.& 9.76 & 9.89 & 9.77 &	0.797 \\
admin support& 18.53 &	 18.20 & 18.20 & 0.546 \\
services& 10.96 & 11.57 & 11.19 &	0.229 \\
farming/fish/logging& 1.08 &	1.07 & 1.03 &	0.924 \\
mechanics and repairers& 4.87 &	4.46 & 4.73 &	0.222 \\
construction and extractive& 3.59 & 3.60 & 3.56 &	0.954 \\
precision production& 4.01 & 4.26 & 3.77 &	0.480 \\
machine operators/assemblers& 9.24 &	8.93 & 8.77 &	0.455 \\
transportation and materials moving& 4.62 &	4.31 & 4.33 &	0.310 \\
laborers& 3.62 &	3.62 & 3.36 &	0.906 \\ \hline
\multicolumn{5}{p{0.8\textwidth}}{\tiny{Workers aged 19-66, not enrolled in school, in two adjacent waves, measured in the first month of the current wave, with employment in one firm only in the previous wave, and employment in one (but possibly different) firm only in the wave that follows, without any self-employment, with un-imputed occupations reported in both waves. Person weights are used to scale observations per month within panel group (1985 versus 1986+1987).}}
 \end{tabular}
\end{table}
\end{document}

q-fin

"Unemployment and Endogenous Reallocation over the Business Cycle" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The table for some important material properties parameters}
\begin{tabular}{lcc} %
 \toprule
 \textbf{Parameter} & \textbf{Symbol} & \textbf{Value} \\
 \midrule
 Elastic modulus for isotropic material & $E_{\text{iso}}$ & 1 \\
 Possion ratio for isotropic materials & $v_{\text{iso}}$ & 0.3 \\
 Yield strength for isotropic material & $\sigma_\text{S}$ & 2 \\
 x/y-direction Elastic modulus for orthotropic material & $E_{11}$, $E_{22}$ & 0.3 \\
 xy-direction Elastic modulus for orthotropic material & $E_{12}$ & 0.2 \\
 xy-Possion ratio for orthotropic materials & $v_{12}$ & 0.24 \\
 x/y-direction Yield strength for isotropic material & $\sigma_\text{X}$, $\sigma_\text{Y}$ & 1 \\
 xy-direction Yield strength for isotropic material & $\sigma_\text{XY}$ & 0.5 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

"Evolutionary de-homogenization using a generative model for optimizing solid-porous infill structures considering the stress concentration issue" by Shuzhi Xu, Hiroki Kawabe, and Kentaro Yaji, arXiv:2412.19154v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An Outer Approximation Method for Solving Mixed-Integer Convex Quadratic Programs with Indicators" by Linchuan Wei and Simge Küçükyavuz, arXiv:2312.04812v1, 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{Total wall time required to perform $150$ epochs (passes over the full set of agents) of Alg.~ for various choices of the sub-sample size $S$ and the total number of agents $N$.}
\begin{tabular}{ccc}
\toprule
$S$ & $N$ & wall-time in secs.\\
\midrule
$500$ & $100$k & $498$ \\
$1000$ & $100$k & $712$ \\
$2000$ & $100$k & $1382$ \\
$5000$ & $100$k & $5720$ \\
\midrule
$500$ & $1000$k & $4260$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"A Polarization Opinion Model Inspired by Bounded Confidence Communications" by Jacek Cyranka and Piotr B. Mucha, arXiv:2312.14599v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Robust Trading in a Generalized Lattice Market" by Chung-Han Hsieh and Xin-Yu Wang, arXiv:2310.11023v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
\toprule
\textbf{Object} & \textbf{Set Representation} & \textbf{Notation} \\
\midrule
\textbf{Pure Question} $q(A)$
 & $\{A, \neg A\}$ & $q(A) = \{A, \neg A\}$ \\
\textbf{Askable Question} $A?$
 & $(A, \neg A)$ & $A? = (A, \neg A)$ \\
\textbf{Subject} (Rank-2 e.g.)
 & $\{\,AB,\;A\neg B,\;\neg A\,B,\;\neg A\,\neg B\}$ & $ab = q(A)\circ q(B)$ \\
\textbf{Answers}
 & $\mathrm{Yes}((x,y))=x,\;\mathrm{No}((x,y))=y$ & $\mathrm{Yes}(A?)=A$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

The Mathematics of Questions

"The Mathematics of Questions" by R. O'Flanagan, arXiv:2503.18992v2, 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{Initial Improvements (in \%) over test functions (Dimensions=$n$) for choices of $\alpha$ and $\lambda$ with a budget $T=40$ and stopping threshold $b=T/4$.}
\begin{tabular}{lcccrrr}
 \toprule
\textbf{Test Function} & $n$ & $\lambda$ & $\alpha$ & \textbf{GD} & \textbf{CGD-FD} \\
 \midrule
Quadratic function & $10$ & 0.4 & 0.01 &
 18.89 & \textbf{97.91}\\
 Rotated hyper-ellipsoid function & $5$ & 0.5 & 0.01 &
 15.94 & \textbf{82.76}\\
 Levy function & $2$ & \texttt{L}(0.01, 0.1) & 0.05 &
 23.73 & \textbf{63.21}\\
 Branin function & $2$ & 0.07 & 0.01 &
 37.53 & \textbf{87.07}\\
 Griewank function & $2$ & 40.0 & 0.01 &
 0.01 & \textbf{0.08}\\
 Matyas function & $2$ & 10.0 & 0.01 &
 1.83 & \textbf{34.40}\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

"CGD: Modifying the Loss Landscape by Gradient Regularization" by Shikhar Saxena, Tejas Bodas, and Arti Yardi, arXiv:2504.16182v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Peng Ding
Jiading Fang
Peng Li
Kangrui Wang
Xiaochen Zhou
Mo Yu
Jing Li
Matthew R. Walter
Hongyuan Mei

"MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models" by Peng Ding, Jiading Fang, Peng Li, Kangrui Wang, Xiaochen Zhou, Mo Yu, Jing Li, Matthew R. Walter, and Hongyuan Mei, arXiv:2403.19913v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparision different method in terms of ECE~ and accuracy of the discriminator on the real-world scenario.}
\begin{tabular}{c|c|lc}
\toprule
\hline
Dataset & Method & ECE $\downarrow$ (\%) & ACC $\uparrow$ (\%) \\ \hline
\multirow{4}{*}{DIV2K} & RealESRGAN & 8.8 & 97.0 \\
 & +\textbf{DifAugGAN} & 7.4 (\textbf{-1.4}) & 98.8 \\ \cline{2-4}
 & RealESRGAN + LDL & 9.6 & 95.2 \\
 & +\textbf{DifAugGAN} & 5.6 (\textbf{-4.0}) & 97.7 \\ \hline
\multirow{4}{*}{Urban100} & RealESRGAN & 6.9 & 97.5 \\
 & +\textbf{DifAugGAN} & 2.8 (\textbf{-4.2}) & 97.5 \\ \cline{2-4}
 & RealESRGAN + LDL & 8.1 & 95.3 \\
 & +\textbf{DifAugGAN} & 3.1 (\textbf{-5.0}) & 97.4 \\ \hline
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Axi Niu
Kang Zhang
Joshua Tian Jin Tee
Trung X. Pham
Jinqiu Sun
Chang D. Yoo
In So Kweon
Yanning Zhang

"DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution" by Axi Niu, Kang Zhang, Joshua Tian Jin Tee, Trung X. Pham, Jinqiu Sun, Chang D. Yoo, In So Kweon, and Yanning Zhang, arXiv:2311.18508v1, 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{Fluid properties in wheat leaf simulation in Figure .}
\begin{tabular}{ll}
 \toprule
 Property & Value(s) \\ \midrule
 Density, $\rho_0$ & $1000 \,\mathrm{kg/m^3}$ \\
 Viscosity, $\mu$ & $0.89 \,\mathrm{mPa\cdot s}$ \\
 Speed of sound, $c_0$ & $100\,\mathrm{m/s}$ \\
 Volume, $V$ & $0.27 \,\mathrm{\mu L}$ \\
 $s_\mathrm{ff}$ & $2 \,\mathrm{mN/m}$ \\
 $s_\mathrm{fs}$ & $5.6 \text{ (leaf)}, \; 7.875 \text{ (hairs)} \,\mathrm{mN/m}$ \\
 Resolution, $H$ & $4 \cdot 10^{-5} \,\mathrm{m}$ \\
 \# Particles & $267\,731$ fluid particles \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Riley M Whebell
Timothy J Moroney
Ian W Turner
Ravindra Pethiyagoda
Scott W McCue

"Computing sessile droplet shapes on arbitrary surfaces with a new pairwise force smoothed particle hydrodynamics model" by Riley M Whebell, Timothy J Moroney, Ian W Turner, Ravindra Pethiyagoda, and Scott W McCue, arXiv:2412.03810v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Superpixel-based Refinement for Object Proposal Generation" by Christian Wilms and Simone Frintrop, arXiv:2101.04574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
		\hline
		Marginal Distributions $F_i$&Minimum Coskewness&Maximum Coskewness\\
		\hline
		$N(\mu_i, \sigma_i^2)$&$-\frac{2\sqrt{2\pi}}{\pi}$&$\frac{2\sqrt{2\pi}}{\pi}$\\
		\hline
		$Student(\nu),\ \nu>3$&$-\frac{4(\nu-2)\sqrt{(\nu-2)\pi}\Gamma(\frac{\nu+1}{2})}{(3-4\nu+\nu^2)\pi\Gamma(\frac{\nu}{2})}$&$\frac{4(\nu-2)\sqrt{(\nu-2)\pi}\Gamma(\frac{\nu+1}{2})}{(3-4\nu+\nu^2)\pi\Gamma(\frac{\nu}{2})}$\\
		\hline
		$Laplace(\mu_i, b_i)$&$-\frac{3\sqrt{2}}{2}$&$\frac{3\sqrt{2}}{2}$\\
		\hline
		$U[a_i, b_i]$&$-\frac{3\sqrt{3}}{4}$&$\frac{3\sqrt{3}}{4}$\\
		\hline
	\end{tabular}
\caption{Maximum and minimum coskewness for various choices of the marginal distributions. $\Gamma(x)$ denotes the gamma function.}
\end{table}
\end{document}

q-fin

Coskewness under dependence uncertainty

Carole Bernard
Jinghui Chen
Ludger Ruschendorf
Steven Vanduffel

"Coskewness under dependence uncertainty" by Carole Bernard, Jinghui Chen, Ludger Ruschendorf, and Steven Vanduffel, arXiv:2303.17266v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{} & \textbf{Overall Parameters} & \textbf{Trainable Parameters} & \textbf{Train Time Reduction} \\ \hline
\textbf{BERT-base} & $110$M & $110$M & $\times 1$ \\ \hline
\textbf{DistilBERT} & $66$M & $66$M & $\times 1.83$ \\ \hline
\multirow{2}{*}{\textbf{AMoC}} & \multirow{2}{*}{$110$M - $7$M $\cdot$ L} & $7$M $\cdot$ $\min \{L, 12-L\}$ & \multirow{2}{*}{$\times 11$} \\
 & & $+ 17M \cdot \mathbb{1}_{\{1 \in c\}}$ & \\ \hline
\end{tabular}
\caption{Comparison of number of parameters and training time between BERT-base, \textbf{DistilBERT} and \textbf{AMoC} when removing $L$ layers. \textbf{AMoC}'s number of trainable parameters is an upper bound.}
\end{table}
\end{document}

cs

"Model Compression for Domain Adaptation through Causal Effect Estimation" by Guy Rotman, Amir Feder, and Roi Reichart, arXiv:2101.07086v2, 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{Comparison of per-organ mean F1 scores on the OCELOT test set for cell detection. The CTM trained with the soft IS ground truth is denoted as SoftCTM. The tissue-label leaking model is denoted as TLLM. The highest score for the three ground truth formats is \underline{underlined}, the overall highest score is marked in \textbf{bold}.}
\begin{tabular}{lccccccc}
 \midrule
 & all & kidney & endometrium & bladder & prostate & stomach & head-neck \\
 & n=130 & n=41 & n=25 & n=26 & n=16 & n=12 & n=10 \\
 \midrule
 Circle & .6599 & .6457 & .6791 & .6317 & \underline{.6276} & .6573 & .6547\\
 Hard IS & .6516 & .6551 & .6486 & .6276 & .6228 & .6593 & .6624\\
 \textbf{Soft IS} & \underline{.6777} & \underline{.6608} & \underline{.7087} & \underline{\textbf{.6515}} & .6220 & \underline{.6911} & \underline{.6966} \\
 \hline
 SoftCTM & .7090 & .7208 & .7527 & .6240 & .6416 & .7126 & .7457 \\
 SoftCTM + TTA & \textbf{.7172} & \textbf{.7323} & \textbf{.7560} & .6291 & \textbf{.6525} & \textbf{.7360} & \textbf{.7474} \\
 \hline
 TLLM & .7269 & .7409 & .7225 & .7259 & .6921 & .7092 & .7314 \\
 TLLM + TTA & .7315 & .7469 & .7210 & .7300 & .7063 & .7219 & .7338 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction" by Lydia A. Schoenpflug and Viktor H. Koelzer, arXiv:2312.12151v1, 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{Global Robustness: percentage robustness (PR) and total verification runtime (sumRT in seconds) for all correctly-classified test Japanese Vowel audio sequences}
\begin{tabular}{ccccccccc}
 \toprule
 \centering
 $noise$ & $PR_{SFSI}$ & $PR_{SFAI}$ & $PR_{MFSI}$ & $PR_{MFAI}$ & $sumRT_{SFSI}$ & $sumRT_{SFAI}$ & $sumRT_{MFSI}$ & $sumRT_{MFAI}$ \\
 \hline
 50 & 96.82 & 49.13 & 97.39 & 65.89 & 5.5019 & 4.2007 & 4.2197 & 4.1148 \\
 60 & 96.82 & 40.75 & 97.39 & 43.64 & 4.5148 & 3.9238 & 3.9123 & 3.9200 \\
 70 & 96.82 & 34.68 & 97.10 & 18.78 & 4.6223 & 4.0966 & 4.0778 & 4.0626 \\
 80 & 96.82 & 30.63 & 97.10 & 3.17 & 4.5658 & 4.0998 & 4.0550 & 4.0714 \\
 90 & 96.82 & 26.58 & 97.10 & 0 & 4.5773 & 4.0785 & 4.0715 & 4.0605 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approach" by Neelanjana Pal and Taylor T Johnson, arXiv:2311.12130v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Risk-Adaptive Local Decision Rules

"Risk-Adaptive Local Decision Rules" by Johannes O. Royset and Miguel A. Lejeune, arXiv:2310.09844v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
 \toprule
 $\lambda$ & 0.0001 & 0.001 & 0.01 & 0.1 \\
 \midrule
 Last & 78.16 & 78.21 & 77.99 & 77.4 \\
 \bottomrule
 \end{tabular}
\caption{Accuracy vs. weight ``$\lambda$" for $\mathbb{D}_{\text{KL}}$}
\end{table}
\end{document}

cs

"CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models" by Saurav Jha, Dong Gong, and Lina Yao, arXiv:2403.19137v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training and test dataset description.}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
 & Vendor & Centre & \# studies & Annotations & used for training \\
\hline
\multirow{1}{*}{Training} & A & 1 & 75 & yes & yes \\
& \multirow{1}{*}{B} & 2 & 50 & yes & yes \\
& & 3 & 25 & yes & yes \\
& C & 4 & 25 & no & no \\
\hline
\multirow{1}{*}{Testing} & \multirow{1}{*}{A} & 1 & 21 & no & no \\
& & 6 & 29 & no & no \\
& \multirow{1}{*}{B} & 2 & 24 & no & no \\
& & 3 & 26 & no & no \\
& C & 4 & 50 & no & no \\
& D & 5 & 50 & no & no \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Peter M. Full
Fabian Isensee
Paul F. Jäger
Klaus Maier-Hein

"Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI" by Peter M. Full, Fabian Isensee, Paul F. Jäger, and Klaus Maier-Hein, arXiv:2011.07592v1, 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{\bf WER for Isolated spoken digit recognition task for training and testing phase.}
\begin{tabular}{ccc}
\hline
& \textbf{Training WER} & \textbf{Testing WER} \\
\hline
\textbf{Mean} & $0.05\%$ & $0.34\%$ \\
\textbf{Std} & $0.13\%$ & $0.51\%$\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Prajnesh Kumar
Mingwei Jin
Ting Bu
Santosh Kumar
Yu-Ping Huang

"Efficient Reservoir Computing using Field Programmable Gate Array and Electro-optic Modulation" by Prajnesh Kumar, Mingwei Jin, Ting Bu, Santosh Kumar, and Yu-Ping Huang, arXiv:2102.09049v1, 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|}
\hline
Method & Frobnability \\
\hline\hline
Theirs & Frumpy \\
Yours & Frobbly \\
Ours & Makes one's heart Frob\\
\hline
\end{tabular}
\caption{Results. Ours is better.}
\end{table}
\end{document}

eess

Sheng Li
Mingxing Tan
Ruoming Pang
Andrew Li
Liqun Cheng
Quoc Le
Norman P. Jouppi

"Searching for Fast Model Families on Datacenter Accelerators" by Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc Le, and Norman P. Jouppi, arXiv:2102.05610v1, 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{Hypothetical Excess Return Comparison Between Recommended and Non-Recommended Funds}
\begin{tabular}{cccc}
 \midrule
 Investor Group & Non-Recommended Funds & Recommended Funds & Difference \\
 \midrule
 All Investors & -0.033 & -0.016 & 0.017** \\
 SES: Low & -0.036 & -0.010 & 0.026*** \\
 SES: Medium & -0.026 & -0.031 & -0.005 \\
 SES: High & -0.030 & -0.027 & -0.003 \\
 \midrule
 \multicolumn{4}{p{40em}}{\scriptsize Notes: {***} $p < 0.01$, {**} $p < 0.05$, {*} $p < 0.1$. The significance level is based on an independent sample t-test.} \\
 \end{tabular}
\end{table}
\end{document}

q-fin

"The Effect of Product Recommendations on Online Investor Behaviors" by Ruiqi Rich Zhu, Cheng He, and Yu Jeffrey Hu, arXiv:2303.14263v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Robust Trading in a Generalized Lattice Market" by Chung-Han Hsieh and Xin-Yu Wang, arXiv:2310.11023v1, 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{Hyperparameters of hallucination mitigation experiments.}
\begin{tabular}{lc}
\toprule
Hyperparameter Name & Hyperparameter Value \\ \midrule
Learning rate (all methods) & 1e-4 \\
Training batch size per device (all methods) & 4 \\
N\_epoch training (SFT) & 8 \\
N\_epoch training (MHM) & 8 \\
N\_epoch training (MEND) & 4 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Lei Yu
Meng Cao
Jackie Chi Kit Cheung
Yue Dong

"Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations" by Lei Yu, Meng Cao, Jackie Chi Kit Cheung, and Yue Dong, arXiv:2403.18167v2, 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{Employee sets for the reduction}
\begin{tabular}{llll}
\hline
\textbf{Sets}& & &\\
\hline
$\mathcal{E}$ & & set of all employees in order of seniority, = $\{1,...M\}$ & $|\mathcal{E}| = M$\\
$\mathcal{E}^C $ & & set of seniority indices of critical employees, = $\{i_k: i_k = k + \sum_{j = 1}^{k - 1}a_j, k\in A \}$ &$|\mathcal{E}^C| = N$ \\
$\mathcal{E}^S_{k} $ & & $k^{th}$ block of stable employees, = $\{i: i_k < i \leq i_k + a_k\}$ & $|\mathcal{E}^S_{k}| = a_k$ \\
$\mathcal{E}^S $ & & set of seniority indices of stable employees, = $\cup_{k\in A} \mathcal{E}^S_{k}$ & $|\mathcal{E}^S| = \sum_{k \in A}a_k$ \\
$\mathcal{E}^M $ & & singleton set for the last employee, = $\{M\}$ &$|\mathcal{E}^M| = 1$\\
\hline
\end{tabular}
\end{table}
\end{document}

math

Prakash Gawas
Antoine Legrain
Louis-Martin Rousseau

"Notification Timing for On-Demand Personnel Scheduling" by Prakash Gawas, Antoine Legrain, and Louis-Martin Rousseau, arXiv:2312.06139v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Local Eviction Moratoria and the Spread of COVID-19" by Julia Hatamyar and Christopher F. Parmeter, arXiv:2307.00251v1, 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}
 performance index & value & MPC setting \\\hline
 $J_{\rm exact,2}$ & 567.8602 & $n=2$, exact solution\\
 $J_{\rm exact,3}$ & 320.1679 & $n=3$, exact solution\\
 $J_{\rm exact,4}$ & 319.9293 & $n=4$, exact solution\\
 $J_{\rm exact,10}$ & 319.6873& $n=10$, exact solution\\
 $J_{\rm exact,20}$ & 319.6846& $n=20=T$, exact solution\\\hline
 $J_{\rm svd}$ & 328.3266 & $n=3$, $m=2$ (single SVD)\\
 $J_{\rm ksvd}$ & 320.1677 & $n=3$, $m=2$ (K-SVD)\\\hline
 \end{tabular}
\end{table}
\end{document}

eess

"Reduction of the Number of Variables in Parametric Constrained Least-Squares Problems" by Alberto Bemporad and Gionata Cimini, arXiv:2012.10423v1, 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}{lrrr}
 \toprule
 \textbf{Dataset} & \textbf{Tokens (B)} & \textbf{Vocab (M)} & \textbf{Size (GB)}\\
 \midrule
 PhilArchive & 0.510 & 2.530 & 3.04 \\
 FreeLaw Opinions & 8.722 & 4.524 & 52.28\\
 PUBMED title abstracts (2019) & 3.076 & 2.814 & 20.14 \\
 Ubuntu IRC (until 2020) & 1.360 & 3.363 & 6.38 \\
 Youtube Subs & 1.268 & 4.039 & 5.31 \\
 EuroParliament Proceedings (96-2011) & 0.709 & 2.753 & 4.60 \\
 NIH ExPORTER awarded grant & 0.304 & 0.535 & 1.93 \\
 USPTO & 7.478 & 3.156 & 54.51 \\
 Open Web Text & 14.036 & 19.026 & 77.88 \\
 Stack Exchange & 3.734 & 13.099 & 24.97 \\
 Books3 & 18.497 & 13.073 & 100.94 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Taishi Nakamura
Mayank Mishra
Simone Tedeschi
Yekun Chai
Jason T Stillerman
Felix Friedrich
Prateek Yadav
Tanmay Laud
Vu Minh Chien
Terry Yue Zhuo
Diganta Misra
Ben Bogin
Xuan-Son Vu
Marzena Karpinska
Arnav Varma Dantuluri
Wojciech Kusa
Tommaso Furlanello
Rio Yokota
Niklas Muennighoff
Suhas Pai
Tosin Adewumi
Veronika Laippala
Xiaozhe Yao
Adalberto Junior
Alpay Ariyak
Aleksandr Drozd
Jordan Clive
Kshitij Gupta
Liangyu Chen
Qi Sun
Ken Tsui
Noah Persaud
Nour Fahmy
Tianlong Chen
Mohit Bansal
Nicolo Monti
Tai Dang
Ziyang Luo
Tien-Tung Bui
Roberto Navigli
Virendra Mehta
Matthew Blumberg
Victor May
Huu Nguyen
Sampo Pyysalo

"Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code" by Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Vu Minh Chien, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Junior, Alpay Ariyak, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Noah Persaud, Nour Fahmy, Tianlong Chen, Mohit Bansal, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Huu Nguyen, and Sampo Pyysalo, arXiv:2404.00399v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 \multirow{2}{*}{\textbf{UQ algorithm}} & \multicolumn{3}{c|}{\textbf{ResNet-50}} & \multicolumn{3}{c|}{\textbf{VGG-11}} \\
 \cline{2-7}
 & \textbf{Accuracy} & $\mathbf{U_{\text{correct}} \pm \text{std}}$ &
 $\mathbf{U_{\text{wrong}} \pm \text{std}}$ & \textbf{Accuracy} & $\mathbf{U_{\text{correct}} \pm \text{std}}$ &
 $\mathbf{U_{\text{wrong}} \pm \text{std}}$ \\
 \hline
 MCD & 88.1\% & $0.01 \pm 0.03$ & $0.09 \pm 0.05$ & 83.7\% & $0.01 \pm 0.02$ & $0.05 \pm 0.04$ \\
 \hline
 EDL & 87.4\% & $0.21 \pm 0.22$ & $0.73 \pm 0.29$ & 80.2\% & $0.30 \pm 0.36$ & $0.87 \pm 0.27$ \\
 \hline
 CP & 88.0\% & $0.46 \pm 0.28$ & $0.75 \pm 0.18$ & 81.0\% & $0.35 \pm 0.28$ & $0.61 \pm 0.23$ \\
 \hline
 \end{tabular}
\caption{Comparison of UQ Algorithms: Accuracy, average Uncertainty Estimates for ResNet-50 and VGG-11. The table presents model accuracy along with average uncertainty values for both correctly classified samples and wrongly classified samples, as determined by the core network and per their respective ground truth labels. Algorithms include Monte Carlo Dropout (MCD), Evidential Deep Learning (EDL), and Conformal Prediction (CP).}
\end{table}
\end{document}

eess

Jamil Fayyad
Shadi Alijani
Homayoun Najjaran

"Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification" by Jamil Fayyad, Shadi Alijani, and Homayoun Najjaran, arXiv:2312.07460v2, 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|}
 \hline
 Parameter & Value \\\hline
 Embedding size $d$ & $1024$\\
 Vocabulary size $q$ & $103$ \\
 Position embedding type & None\\
 \# Attention heads $h$ & $16$\\
 MLP inner dimensionality $d'$ & 2048\\
 Sequence length & $512$\\
 Base learning rate & 1e-5\\
 Optimizer & Adam\\
 LR warmup & Linear for $10$ epochs\\
 LR decay schedule & Cosine, one cycle with default parameters\\
 Dropout & None\\
 Activation & GELU\\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

"Improving Length-Generalization in Transformers via Task Hinting" by Pranjal Awasthi and Anupam Gupta, arXiv:2310.00726v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
\hline
\textbf{Brand} & \textbf{CA} & \textbf{NPMI} & \textbf{UCI} \\ \hline
\multicolumn{4}{c}{Sentiment Topics} \\ \hline
JST & 0.1317 & -0.0470 & \textbf{-1.7636} \\
TBIP & \textbf{0.1525} & \textbf{-0.0434} & -1.8521 \\
BTM & 0.1407 & -0.0556 & -1.9784 \\ \hline
\multicolumn{4}{c}{Objective Topics} \\ \hline
JST & 0.1239 & \textbf{-0.0718} & -2.2614 \\
Scholar & 0.1196 & -0.0980 & -2.9508 \\
TBIP & 0.1244 & -0.0770 & -2.3726 \\
BTM & \textbf{0.1299} & -0.0735 & \textbf{-2.2542} \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Runcong Zhao
Lin Gui
Gabriele Pergola
Yulan He

"Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews" by Runcong Zhao, Lin Gui, Gabriele Pergola, and Yulan He, arXiv:2101.10150v1, licensed under CC0 1.0. 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}{lcccc}
\toprule
\textbf{CV} & \textbf{Gradient} & \textbf{Random} & \textbf{XGBoost} & \textbf{LightGBM} \\
 & \textbf{Boosting} & \textbf{Forest} & & \\
\midrule
Random & 0.758 & 0.741 & 0.769 & 0.773 \\
SP 200 & 0.754 & 0.741 & 0.769 & 0.783 \\
SP 422 & \underline{0.773} & \textbf{0.772} & \underline{0.786} & \textbf{0.783} \\
SP 600 & \textbf{0.790} & \underline{0.770} & \textbf{0.799} & \textbf{0.783} \\
envBlock & 0.755 & 0.746 & 0.762 & 0.769 \\
SPT 200 & 0.717 & 0.710 & 0.706 & 0.705 \\
SPT 422 & 0.726 & 0.704 & 0.711 & 0.708 \\
SPT 600 & 0.735 & 0.714 & 0.725 & 0.721 \\
\midrule
Best score & 0.790 & 0.772 & \textbf{0.799} & 0.783 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling" by Diana Koldasbayeva and Alexey Zaytsev, arXiv:2502.03480v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ding Ning
Varvara Vetrova
Sébastien Delaux
Rachael Tappenden
Karin R. Bryan
Yun Sing Koh

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, 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}{lccccc}
\hline \hline
\textbf{Base model} & \textbf{AUC} & \textbf{ACC} & \textbf{SEN} & \textbf{SPC} & \textbf{PPV} \\
\hline
NASNet-PI & 96.24 & 91.00 & 86.67 & 92.86 & 83.87 \\
NASNet-WM & 93.81 & 88.00 & 80.00 & 91.43 & 80.00 \\
NASNet-OI & 84.62 & 77.00 & 73.33 & 78.57 & 59.46 \\
\hline\hline
\end{tabular}
\caption{Classification evaluation metrics reported for SST data set resulted from NASNet base model in the proposed recognition pipeline, using three different input scenarios.}
\end{table}
\end{document}

eess

Mostafa Jahanifar
Neda Zamani Tajeddin
Meisam Hasani
Babak Shekarchi
Kamran Azema

"Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks" by Mostafa Jahanifar, Neda Zamani Tajeddin, Meisam Hasani, Babak Shekarchi, and Kamran Azema, arXiv:2011.11777v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Advances in Bayesian Modeling: Applications and Methods" by Yifei Yan, Juan Sosa, and Carlos A. Martínez, arXiv:2502.11321v1, 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}{llll}
		\textbf{Abbreviations} & \\
		CNNs & Convolutional Neural Networks &
		RQCs & Random Quantum Circuits \\
		QC & Quantum Computing &
		COVID-19 & Coronavirus Disease 2019 \\
		ReLU & Rectified Linear Unit &
		DL & Deep Learning \\
		Conv & Convolutional &
		CXR & Chest X-Ray \\
		CT & Computed Tomography
	\end{tabular}
\end{table}
\end{document}

eess

Essam H. Houssein
Zainab Abohashima
Mohamed Elhoseny
Waleed M. Mohamed

"Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images" by Essam H. Houssein, Zainab Abohashima, Mohamed Elhoseny, and Waleed M. Mohamed, arXiv:2102.06535v1, 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}{cccccc}
\hline
 & & & \multicolumn{3}{c}{Posterior Means} \\
Parameter & TRUE & MLE & Vague & Logistic & Wted \\
\hline
$\beta_0$ & 1.5 & 1.6905 & 1.9587 & 1.3887 & 1.4235 \\
$\beta_1$ & 0.3 & 0.4563 & 0.5580 & 0.3324 & 0.1793 \\
$\beta_2$ & -0.6 & -0.6801 & 0.7931 & -0.5125 & -0.2997 \\
$\beta_3$ & 0.02 & 0.0441 & 0.1010 & 0.0115 & 0.0013 \\
\hline
\end{tabular}
\caption{Average maximum likelihood estimates, and average posterior means for the $n=50$ simulation study.}
\end{table}
\end{document}

stat

"Logistic regression models: practical induced prior specification" by Ken B. Newman, Cristiano Villa, and Ruth King, arXiv:2501.18106v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sobom M. Somé
Célestin C. Kokonendji
Francial G. B. Libengué Dobélé-Kpoka

"An effective estimation of multivariate density functions using extended-beta kernels with Bayesian adaptive bandwidths" by Sobom M. Somé, Célestin C. Kokonendji, and Francial G. B. Libengué Dobélé-Kpoka, arXiv:2502.05366v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{EFA achieves better performance than FM across all four settings. Here, the mean predicted ratings correspond to actual ratings of 1, 2 and 3, respectively.}
\begin{tabular}{cccccc}
\hline
\multicolumn{2}{c}{\multirow{2}{*}{Setting}} & \multicolumn{2}{c}{Test cross-entropy} & \multicolumn{2}{c}{Mean predicted ratings} \\ \cline{3-6}
\multicolumn{2}{c}{} & FM & EFA & FM & EFA \\ \hline
\multirow{2}{*}{v1} & Bidirectional & 0.971 & \textbf{0.945} & (2.06, 2.10, 2.21) & (1.93, 2.06, 2.30) \\ \cline{2-6}
 & Unidirectional & 0.972 & \textbf{0.953} & (2.06, 2.09, 2.20) & (1.98, 2.09, 2.29) \\ \hline
\multirow{2}{*}{v2} & Bidirectional & 0.507 & \textbf{0.494} & (2.06, 2.10, 2.21) & (1.94, 2.08, 2.31) \\ \cline{2-6}
 & Unidirectional & 0.508 & \textbf{0.499} & (2.06, 2.10, 2.20) & (1.98, 2.09, 2.29) \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Exponential Family Attention

"Exponential Family Attention" by Kevin Christian Wibisono and Yixin Wang, arXiv:2501.16790v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Multi-kernel property in high-frequency price dynamics under Hawkes model" by Kyungsub Lee, arXiv:2302.11822v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Federico A. Bugni
Ivan A. Canay
Deborah Kim

"Testing Conditional Stochastic Dominance at Target Points" by Federico A. Bugni, Ivan A. Canay, and Deborah Kim, arXiv:2503.14747v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c||} \hline
 $\Psi(L)$ & $L$\\ %\# of colorings & Links\\
 \hline
 $(75, 50)$ & $1_{1}^2$ \\
 \hline
 $(14,49)$ & $5_3^2$ \\
 \hline
 $(10, 150)$ & $6_1^2$ \\
 \hline
 $(50, 30)$ & $3_1^2$, $6_4^2$, $6_{10}^2$ \\
 \hline
 $(10, 30)$ & $4_1^2$, $5_1^2$, $5_2^2$, $6_2^2$, $6_3^2$, $6_5^2$, $6_6^2$, $6_7^2$, $6_8^2$, $6_9^2$, $6_{11}^2$ \\
 \hline
 $(10, 0)$ & $6_{12}^2$ \\
 \hline

\end{tabular}
\caption{Table of pairs of colorings of the link diagrams of Figure~ by the two oriented disingquandles $\mathbb{Z}_{10}$ and $\mathbb{Z}_{30}$, respectively.}
\end{table}
\end{document}

math

Mohd Ibrahim Sheikh
Mohamed Elhamdadi
Danish Ali

"Oriented Disingquandles and Invariants of Oriented Dichromatic Singular links" by Mohd Ibrahim Sheikh, Mohamed Elhamdadi, and Danish Ali, arXiv:2401.05359v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc|cccc}
 \toprule
 Epochs & Width & TTA & Mean accuracy & Test-set stddev & Dist-wise stddev & CACE \\
 \midrule
 $1\times$ & $1\times$ & No & 93.25\% & 0.157\% & 0.037\% & 0.0312 \\
 $2\times$ & $1\times$ & No & 93.86\% & 0.152\% & 0.025\% & 0.0233 \\
 $1.5\times$ & $1.5\times$ & No & 94.32\% & 0.142\% & 0.020\% & 0.0269 \\
 $1\times$ & $1\times$ & Yes & 94.01\% & 0.128\% & 0.029\% & 0.0533 \\
 $2\times$ & $1\times$ & Yes & 94.65\% & 0.124\% & 0.022\% & 0.0433 \\
 $1.5\times$ & $1.5\times$ & Yes & 94.97\% & 0.116\% & 0.018\% & 0.0444 \\
 \bottomrule
 \end{tabular}
\caption{Statistical metrics for \texttt{airbench94} trainings (n=10,000 runs each).}
\end{table}
\end{document}

cs

"94% on CIFAR-10 in 3.29 Seconds on a Single GPU" by Keller Jordan, arXiv:2404.00498v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
\toprule
\textbf{Task} & \textbf{Metric} & \textbf{Test Value} \\
\midrule
IC & Test Accuracy (100h) & 15.11 \\
 & Test Accuracy (960h) & 8.46 \\
\midrule
\multirow{8}{*}{SF} & Test Loss & 1.4780 \\
& Test Slot\_type\_f1 & 43.25 \\
& Test Slot\_value\_cer & 82.68 \\
& Test Slot\_value\_wer & 92.52 \\
& Test Slot\_edit\_f1\_full & 23.2851 \\
& Test Slot\_edit\_f1\_part & 23.9784 \\
& Test WER & 63.44 \\
& Test CER & 43.70 \\
\midrule
KS & Test Accuracy & 25.41 \\
\hline
\end{tabular}
\caption{SUPERB Downstream Task Results}
\end{table}
\end{document}

eess

"An Experimental Study: Assessing the Combined Framework of WavLM and BEST-RQ for Text-to-Speech Synthesis" by Via Nielson and Steven Hillis, arXiv:2312.05415v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Marc Chen
Mohammad Shirazi
Peter A. Forsyth
Yuying Li

"Machine Learning and Hamilton-Jacobi-Bellman Equation for Optimal Decumulation: a Comparison Study" by Marc Chen, Mohammad Shirazi, Peter A. Forsyth, and Yuying Li, arXiv:2306.10582v1, 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{The MSE values of the proposed model corresponding to different CNN channel numbers.}
\begin{tabular}{|c|c|c|c|}
\hline
 CNN Channel Number & 3 & 5 & 7 \\ \hline
MSE (Training) & 0.0511 & 0.0311 & 0.019 \\ \hline
MSE (Validation) & 0.0482 & 0.0282 & 0.017 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Praveen Damacharla
Hamid Rajabalipanah
Mohammad Hosein Fakheri

"LSTM-CNN Network for Audio Signature Analysis in Noisy Environments" by Praveen Damacharla, Hamid Rajabalipanah, and Mohammad Hosein Fakheri, arXiv:2312.07059v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Workforce pDEI: Productivity Coupled with DEI" by Lanqing Du and Jinwook Lee, arXiv:2311.11231v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{siunitx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llllllll} \hline
Cases&$\mu_1$&$\mu_2$&$\mu_3$&$\alpha_1$&$\alpha_2$&$\alpha_3$&description\\\hline
Case $1$&$5\mu_0$&$5\mu_0$&$5\mu_0$&$\SI{0.010}{\meter}$&$\SI{0.010}{\meter}$&$\SI{0.010}{\meter}$&same size and permittivity\\
Case $2$&$3\mu_0$&$5\mu_0$&$7\mu_0$&$\SI{0.010}{\meter}$&$\SI{0.010}{\meter}$&$\SI{0.010}{\meter}$&same size but different permeability\\
Case $3$&$5\mu_0$&$5\mu_0$&$5\mu_0$&$\SI{0.015}{\meter}$&$\SI{0.010}{\meter}$&$\SI{0.005}{\meter}$&same permeability but different size\\
Case $4$&$3\mu_0$&$5\mu_0$&$7\mu_0$&$\SI{0.015}{\meter}$&$\SI{0.010}{\meter}$&$\SI{0.005}{\meter}$&different size and permeability\\\hline
\end{tabular}
\caption{Permeabilities and sizes of objects in synthetic data experiment.}
\end{table}
\end{document}

math

Junyong Eom
Sangwoo Kang
Minyeob Lee
Won-Kwang Park

"Real-time inversion of two-dimensional Fresnel experimental database using orthogonality sampling method with single and multiple sources: the case of transverse electric polarized waves" by Junyong Eom, Sangwoo Kang, Minyeob Lee, and Won-Kwang Park, arXiv:2504.18033v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

A. Felipe
M. Jaenada
P. Miranda
L. Pardo

"Robust parameter estimation of the log-logistic distribution based on density power divergence estimators" by A. Felipe, M. Jaenada, P. Miranda, and L. Pardo, arXiv:2312.02662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{rrrr}
 \toprule\multicolumn{4}{c}{Complete results using the 9 state simulation model}\\
 \cmidrule(lr){1-4} & \textbf{Bias} & \textbf{Covg.} & \textbf{CIW} \\
 \cmidrule(lr){2-4}Pr(Infec. $\mid$ Plac.) & 0.01 & 0.96 & 0.07 \\
 Pr(Infec. $\mid$ mAb) & 0.0 & 0.95 & 0.17 \\
 \cmidrule(lr){1-4}Pr(Sympt. $\mid$ Plac.) & 0.01 & 0.96 & 0.16 \\
 Pr(Sympt. $\mid$ mAb) & 0.0 & 0.96 & 0.37 \\
 \cmidrule(lr){1-4}Pr(Asympt. $\mid$ Plac.) & 0.0 & 0.96 & 0.18 \\
 Pr(Asympt. $\mid$ mAb) & 0.0 & 0.95 & 0.22 \\
 \cmidrule(lr){1-4}Pr(Sympt. $\mid$ Infec., Plac.) & 0.0 & 0.96 & 0.14 \\
 Pr(Sympt. $\mid$ Infec., mAb) & 0.0 & 0.96 & 0.32 \\
 Pr(Asympt. $\mid$ Infec., Plac.) & 0.0 & 0.96 & 0.16 \\
 Pr(Asympt. $\mid$ Infec., mAb) & 0.0 & 0.96 & 0.14 \\
 \cmidrule(lr){1-4}Pr(N+ $\mid$ Infec., Plac.) & 0.0 & 0.96 & 0.08 \\
 Pr(N+ $\mid$ Infec., mAb) & 0.0 & 0.96 & 0.26 \\
 Pr(N+ $\mid$ sympt., Plac.) & 0.0 & 0.95 & 0.07 \\
 Pr(N+ $\mid$ sympt., mAb) & -0.01 & 0.96 & 0.3 \\
 Pr(N+ $\mid$ asympt., Plac.) & 0.0 & 0.95 & 0.18 \\
 Pr(N+ $\mid$ asympt., mAb) & 0.0 & 0.96 & 0.4 \\
 \cmidrule(lr){1-4}RM TI $\mid$ Plac. & 0.0 & 0.95 & 0.12 \\
 RM TI $\mid$ mAb & 0.0 & 0.95 & 0.07 \\
 \cmidrule(lr){1-4}RM PCR+ $\mid$ Plac. & 0.0 & 0.95 & 0.11 \\
 RM PCR+ $\mid$ mAb & -0.01 & 0.96 & 0.18 \\
 \cmidrule(lr){1-4}Pr(Detected $\mid$ Plac.) & 0.0 & 0.95 & 0.07 \\
 Pr(Detected $\mid$ mAb) & 0.0 & 0.96 & 0.11 \\
 \cmidrule(lr){1-4}PE for infec. & 0.0 & 0.96 & 0.19 \\
 PE for sympt. infec. & 0.0 & 0.96 & 0.16 \\
 PE for asympt. infec. & 0.01 & 0.95 & 0.97 \\
 \cmidrule(lr){1-4}RR for sympt. $\mid$ infec. & 0.0 & 0.96 & 0.35 \\
 RR for N+ $\mid$ infec. & 0.0 & 0.96 & 0.28 \\
 RR for N+ $\mid$ sympt. & 0.0 & 0.96 & 0.3 \\
 RR for N+ $\mid$ asympt. & 0.0 & 0.96 & 0.44 \\
 \bottomrule
 \end{tabular}
\caption{Bias, coverage, and 95\% confidence interval width of estimates of key quantities obtained by fitting the 9 state simulation model described in Supplementary Appendix to hypothetical panel data where PCR and serology were assessed weekly. RM TI is the restricted mean time to infection, RM PCR+ is the restricted mean duration of PCR positivity, RR is relative risk, and PE is protective efficacy, calculated as $PE = 1 - RR$.}
\end{table}
\end{document}

stat

Raphael Morsomme
C. Jason Liang
Allyson Mateja
Dean A. Follmann
Meagan P. O'Brien
Chenguang Wang
Jonathan Fintzi

"Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams" by Raphael Morsomme, C. Jason Liang, Allyson Mateja, Dean A. Follmann, Meagan P. O'Brien, Chenguang Wang, and Jonathan Fintzi, arXiv:2501.14097v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Dynamic spectral co-clustering of directed networks to unveil latent community paths in VAR-type models" by Younghoon Kim and Changryong Baek, arXiv:2502.10849v1, 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{Comparison of control effort for continuous action space algorithms for batch transesterification process}
\begin{tabular}{lllll}
\hline
Algorithm & Control Effort
\\
\hline
TATD3 & 118515.80 \\
TD3 & 118597.47\\
DDPG & 118553.28 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Tanuja Joshi
Shikhar Makker
Hariprasad Kodamana
Harikumar Kandath

"Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control" by Tanuja Joshi, Shikhar Makker, Hariprasad Kodamana, and Harikumar Kandath, arXiv:2102.13012v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Do Productivity Shocks Cause Inputs Misallocation?" by Davide Luparello, arXiv:2306.08760v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zhi Su
Danni Wu
Zhenkun Zhou
Junran Wu
Libo Yin

"The Impact of Customer Online Satisfaction on Stock Returns: Evidence from the E-commerce Reviews in China" by Zhi Su, Danni Wu, Zhenkun Zhou, Junran Wu, and Libo Yin, arXiv:2306.12119v1, 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}
				\hline\hline
				$J$&$NAQE_J$\\
				\hline
				1&$3.5108$ $10^{-12}$\\
				2&$4.3631$ $10^{-12}$\\
				3&$4.9646$ $10^{-12}$\\
				4&$5.2140$ $10^{-12}$\\
				5&$5.380$ $10^{-12}$\\
				6&$4.3631$ $10^{-12}$\\
				\hline\hline
			\end{tabular}
\caption{NAQE estimates for the coronavirus signal using Haar-Schauder multiwavelet.}
\end{table}
\end{document}

eess

Malika Jallouli
Makerem Zemni
Anouar Ben Mabrouk
Mohamed Ali Mahjoub

"Towards New Multiwavelets: Associated Filters and Algorithms. Part I: Theoretical Framework and Investigation of Biomedical Signals, ECG and Coronavirus Cases" by Malika Jallouli, Makerem Zemni, Anouar Ben Mabrouk, and Mohamed Ali Mahjoub, arXiv:2103.08657v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the experimental setup.}
\begin{tabular}{lccc}
\toprule[2pt]
 Problems & Depth & Width & Dimension (d) \\
\midrule[1pt]
 \textcolor{black}{Laplace Equation} & 3 & 20 & 2 \\
 \textcolor{black}{Burgers' Equation} & 3 & 64 & 2 \\
 Allen-Cahn Equation & 3 & 64 & 2 \\
 Elliptic Equation & 3 & 2d & 10, 20, 100 \\
 Reaction-Diffusion Equation & 3 & 2d & 20, 30, 50 \\
 \bottomrule[2pt]
\end{tabular}
\end{table}
\end{document}

stat

Jiaqi Luo
Yahong Yang
Yuan Yuan
Shixin Xu
Wenrui Hao

"An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks" by Jiaqi Luo, Yahong Yang, Yuan Yuan, Shixin Xu, and Wenrui Hao, arXiv:2501.11222v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sum rate (bps/Hz) results for different $d_R$ values} % title of Table
\begin{tabular}{cccccc} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
\multirow{2}{*}{Method} & \multicolumn{4}{c}{$d_R$} \\
 \cline{2-6}
 & 50 & 100 & 150 & 200 & 250 \\ % inserts table
\hline % inserts single horizontal line
Proposed Matching & 37.0 & 38.3 & 38.6 & 38 & 35.8\\ % inserting body of the table
Distance Matching & 29.2 & 29.9 & 29.6 & 29.1 & 27.0\\ % [1ex] adds vertical space
\hline %inserts single line
\end{tabular}
\end{table}
\end{document}

cs

Jawad Mirza
Bakhtiar Ali
Muhammad Awais Javed

"Stable Matching for Selection of Intelligent Reflecting Surfaces in Multiuser MISO Systems" by Jawad Mirza, Bakhtiar Ali, and Muhammad Awais Javed, arXiv:2101.06502v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Patrick Ebel
Andrea Meraner
Michael Schmitt
Xiaoxiang Zhu

"Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery" by Patrick Ebel, Andrea Meraner, Michael Schmitt, and Xiaoxiang Zhu, arXiv:2009.07683v1, 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{Clean }& \textbf{Distractors}& \textbf{Diff. BG}& \textbf{Both}\\ \hline
 \textbf{KAT (Ours)}& 0.65 & 0.6& 0.62& 0.58\\ \hline
 \end{tabular}
\caption{Success rates for our method showing the effect of distractors and different background (BG).}
\end{table}
\end{document}

cs

"Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics" by Norman Di Palo and Edward Johns, arXiv:2403.19578v3, 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{\textbf{Test Accuracy on CIFAR-10} It represents the mean accuracy and standard deviation for five seeds across each of the five models. The content in parentheses indicates the number of last layers used for the reset; if not specified, one layer is assumed. The meanings of each abbreviation in the table's headline are as follows: KL refers to self-distillation. K represents the number of teachers, where a value of zero indicates that self-distillation is not utilized. Regarding the reset, R indicates whether it is used; thus, when R is zero, reset is not utilized. $T_{\text{cycle}}$ signifies the duration of the cycle used for the teacher update and the student reset, expressed in epochs. M indicates the re-initialization method used for reset, where a value of one means that re-initialization was done using the mean of the teachers' weights. A value of zero signifies that random weights were utilized for re-initialization.}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\toprule
 KL & \multicolumn{3}{|c|}{RESET} & \multicolumn{6}{|c|}{Model} \\
\cmidrule(lr){1-1} \cmidrule(lr){2-4} \cmidrule(lr){5-10}
 K & R & $T_{\text{cycle}}$ & M & googlenet & mobilenetv2 & shufflenet & squeezenet & vgg(3) & vgg(1) \\
\midrule
0 & 0 & 0 & 0 & 94.62 ± 0.05 & 91.81 ± 0.25 & 92.21 ± 0.35 & 92.35 ± 0.13 & 93.68 ± 0.13 & 93.59 ± 0.13 \\
 & 1 & 1 & 0 & 94.27 ± 0.19 & 91.52 ± 0.21 & 91.75 ± 0.26 & 91.96 ± 0.19 & 93.52 ± 0.20 & 93.66 ± 0.19 \\
 & & & 1 & 94.59 ± 0.14 & 91.74 ± 0.09 & 92.30 ± 0.30 & 92.41 ± 0.15 & 93.65 ± 0.21 & 93.61 ± 0.21 \\
 & & 20 & 0 & 94.35 ± 0.24 & 91.81 ± 0.09 & 92.11 ± 0.16 & 92.34 ± 0.21 & 93.59 ± 0.06 & 93.51 ± 0.09 \\
 & & & 1 & 94.66 ± 0.16 & 91.95 ± 0.15 & 92.26 ± 0.16 & 92.17 ± 0.18 & 93.64 ± 0.21 & 93.57 ± 0.09 \\
 & & 50 & 0 & 94.54 ± 0.15 & 91.85 ± 0.18 & 92.38 ± 0.17 & 92.59 ± 0.17 & 93.63 ± 0.13 & 93.54 ± 0.27 \\
 & & & 1 & 94.58 ± 0.11 & 91.86 ± 0.20 & 92.49 ± 0.43 & 92.18 ± 0.11 & 93.68 ± 0.29 & 93.49 ± 0.18 \\
 & & 100 & 0 & 94.62 ± 0.23 & 91.68 ± 0.13 & 92.33 ± 0.17 & 92.28 ± 0.13 & 93.73 ± 0.08 & 93.59 ± 0.12 \\
 & & & 1 & 94.66 ± 0.20 & 91.86 ± 0.14 & 92.29 ± 0.25 & 92.25 ± 0.23 & 93.68 ± 0.18 & 93.59 ± 0.12 \\
1 & 0 & 0 & 0 & 94.42 ± 0.19 & 91.63 ± 0.11 & 92.34 ± 0.27 & 92.26 ± 0.19 & 93.53 ± 0.08 & 93.45 ± 0.20 \\
 & 1 & 1 & 0 & 94.19 ± 0.33 & 91.56 ± 0.21 & 91.97 ± 0.07 & 91.92 ± 0.18 & 93.33 ± 0.13 & 93.71 ± 0.18 \\
 & & & 1 & 94.50 ± 0.18 & 91.85 ± 0.29 & 92.29 ± 0.20 & 92.62 ± 0.27 & 93.55 ± 0.12 & 93.71 ± 0.18 \\
 & & 20 & 0 & 94.36 ± 0.14 & 91.54 ± 0.14 & 91.98 ± 0.25 & 92.20 ± 0.16 & 93.80 ± 0.11 & 93.68 ± 0.21 \\
 & & & 1 & 94.68 ± 0.12 & 91.71 ± 0.14 & 92.30 ± 0.32 & 92.30 ± 0.15 & 93.68 ± 0.10 & 93.70 ± 0.18 \\
 & & 50 & 0 & 94.40 ± 0.10 & 91.63 ± 0.15 & 92.31 ± 0.17 & 92.41 ± 0.19 & 93.64 ± 0.20 & 93.67 ± 0.13 \\
 & & & 1 & 94.25 ± 0.37 & 91.76 ± 0.11 & 92.20 ± 0.17 & 92.43 ± 0.19 & 93.71 ± 0.12 & 93.67 ± 0.13 \\
 & & 100 & 0 & 94.45 ± 0.15 & 91.55 ± 0.12 & 92.58 ± 0.25 & 92.36 ± 0.11 & 93.63 ± 0.18 & 93.65 ± 0.06 \\
 & & & 1 & 94.48 ± 0.22 & 91.80 ± 0.05 & 92.32 ± 0.33 & 92.43 ± 0.14 & 93.71 ± 0.12 & 93.65 ± 0.06 \\
2 & 0 & 0 & 0 & 94.52 ± 0.13 & 91.83 ± 0.18 & 92.22 ± 0.22 & 92.23 ± 0.27 & 93.53 ± 0.12 & 93.42 ± 0.16 \\
 & 1 & 1 & 0 & 94.26 ± 0.18 & 91.85 ± 0.19 & 91.78 ± 0.23 & 91.77 ± 0.30 & 93.30 ± 0.23 & 93.70 ± 0.16 \\
 & & & 1 & 94.46 ± 0.15 & 91.79 ± 0.16 & 92.03 ± 0.26 & 92.69 ± 0.12 & 93.65 ± 0.20 & 93.59 ± 0.21 \\
 & & 20 & 0 & 94.17 ± 0.43 & 91.65 ± 0.19 & 92.32 ± 0.12 & 92.34 ± 0.10 & 93.65 ± 0.18 & 93.47 ± 0.16 \\
 & & & 1 & 94.60 ± 0.08 & 91.86 ± 0.17 & 92.27 ± 0.15 & 92.42 ± 0.22 & 93.62 ± 0.25 & 93.63 ± 0.15 \\
 & & 50 & 0 & 94.33 ± 0.16 & 91.65 ± 0.31 & 92.31 ± 0.19 & 92.48 ± 0.33 & 93.52 ± 0.21 & 93.56 ± 0.12 \\
 & & & 1 & 94.36 ± 0.18 & 91.92 ± 0.23 & 92.32 ± 0.26 & 92.47 ± 0.23 & 93.64 ± 0.24 & 93.56 ± 0.07 \\
 & & 100 & 0 & 94.23 ± 0.41 & 91.62 ± 0.21 & 92.58 ± 0.09 & 92.32 ± 0.19 & 93.70 ± 0.13 & 93.44 ± 0.23 \\
 & & & 1 & 94.45 ± 0.11 & 91.82 ± 0.20 & 92.19 ± 0.24 & 92.50 ± 0.21 & 93.77 ± 0.18 & 93.47 ± 0.18 \\
4 & 0 & 0 & 0 & 94.39 ± 0.17 & 91.92 ± 0.15 & 92.32 ± 0.24 & 92.34 ± 0.22 & 93.45 ± 0.11 & 93.50 ± 0.31 \\
 & 1 & 1 & 0 & 94.22 ± 0.08 & 91.70 ± 0.19 & 91.99 ± 0.22 & 91.97 ± 0.21 & 93.43 ± 0.14 & 93.55 ± 0.08 \\
 & & & 1 & 94.46 ± 0.24 & 91.93 ± 0.29 & 91.96 ± 0.19 & 92.36 ± 0.16 & 93.53 ± 0.12 & 93.55 ± 0.12 \\
 & & 20 & 0 & 94.32 ± 0.17 & 91.53 ± 0.15 & 92.23 ± 0.24 & 92.32 ± 0.22 & 93.70 ± 0.30 & 93.73 ± 0.07 \\
 & & & 1 & 94.43 ± 0.13 & 91.67 ± 0.25 & 92.15 ± 0.19 & 92.64 ± 0.16 & 93.48 ± 0.15 & 93.59 ± 0.18 \\
 & & 50 & 0 & 94.35 ± 0.17 & 91.73 ± 0.27 & 92.30 ± 0.16 & 92.53 ± 0.13 & 93.63 ± 0.24 & 93.61 ± 0.14 \\
 & & & 1 & 94.51 ± 0.11 & 91.86 ± 0.16 & 92.46 ± 0.24 & 92.44 ± 0.08 & 93.61 ± 0.06 & 93.54 ± 0.10 \\
 & & 100 & 0 & 94.42 ± 0.22 & 91.58 ± 0.29 & 92.43 ± 0.20 & 92.48 ± 0.14 & 93.64 ± 0.14 & 93.59 ± 0.20 \\
 & & & 1 & 94.48 ± 0.14 & 91.77 ± 0.21 & 92.60 ± 0.14 & 92.50 ± 0.10 & 93.78 ± 0.10 & 93.49 ± 0.31 \\
8 & 0 & 0 & 0 & 94.57 ± 0.12 & 91.85 ± 0.26 & 92.25 ± 0.15 & 92.42 ± 0.12 & 93.48 ± 0.18 & 93.52 ± 0.14 \\
 & 1 & 1 & 0 & 94.20 ± 0.13 & 91.78 ± 0.13 & 92.03 ± 0.22 & 91.75 ± 0.29 & 93.27 ± 0.16 & 93.78 ± 0.16 \\
 & & & 1 & 94.39 ± 0.30 & 91.82 ± 0.17 & 91.66 ± 0.27 & 92.43 ± 0.02 & 93.62 ± 0.18 & 93.74 ± 0.16 \\
 & & 20 & 0 & 94.27 ± 0.34 & 91.68 ± 0.11 & 92.41 ± 0.20 & 92.44 ± 0.19 & 93.57 ± 0.06 & 93.69 ± 0.09 \\
 & & & 1 & 94.50 ± 0.08 & 91.70 ± 0.15 & 92.20 ± 0.20 & 92.49 ± 0.30 & 93.14 ± 0.23 & 93.77 ± 0.19 \\
 & & 50 & 0 & 94.29 ± 0.13 & 91.78 ± 0.21 & 92.27 ± 0.27 & 92.46 ± 0.09 & 93.67 ± 0.12 & 93.58 ± 0.13 \\
 & & & 1 & 94.42 ± 0.19 & 91.83 ± 0.11 & 92.29 ± 0.14 & 92.45 ± 0.27 & 93.08 ± 0.57 & 93.66 ± 0.15 \\
 & & 100 & 0 & 94.37 ± 0.22 & 91.51 ± 0.19 & 92.35 ± 0.21 & 92.34 ± 0.18 & 93.62 ± 0.19 & 93.57 ± 0.10 \\
 & & & 1 & 94.37 ± 0.17 & 91.55 ± 0.27 & 92.41 ± 0.28 & 92.52 ± 0.12 & 93.22 ± 0.13 & 93.59 ± 0.11 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"Diverse Feature Learning by Self-distillation and Reset" by Sejik Park, arXiv:2403.19941v1, 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{AUC score comparison with Yin et al. state-of-the-art detector for $L_{\infty}$ PGD white-box attack.}
\begin{tabular}{lllll}
 \\
 \cmidrule(r){1-3}
 $n$, $\alpha$, $\varepsilon$ & 40, $0.5$, 8 & 200, $0.5$, 8 \\
 \midrule
 State-of-the-art (Yin et al., 2020) & 0.955 & 0.950 \\
 Ours (ResNet18 detector) & 1 & 1 \\
 \bottomrule
 %
 \end{tabular}
\end{table}
\end{document}

cs

Rachel Sterneck
Abhishek Moitra
Priyadarshini Panda

"Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks" by Rachel Sterneck, Abhishek Moitra, and Priyadarshini Panda, arXiv:2101.01543v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
 \hline
Hypothesis & test stat & Twice median & Max AFUs & Crime disp.\ \\
 \hline
$H_{01}$ & \multirow{4}{*}{DiM} & 0.148 & \textbf{0.011} & 0.989 \\
 $H_{02}$ & & 0.856 & 0.538 & 0.462 \\
 $H_{03}$ & & 0.406 & 0.200 & 0.800 \\
 $H_0$ by FCT & & 0.179 & \textbf{0.036} & 0.919 \\
 \hline
 $H_{01}$ & \multirow{4}{*}{rs5} & 0.074 & \textbf{0.008} & 0.992 \\
 $H_{02}$ & & 0.128 & 0.055 & 0.945 \\
 $H_{03}$ & & 0.404 & 0.182 & 0.818 \\
 $H_0$ by FCT & & \textbf{0.029} & \textbf{0.004} & 0.997 \\
 \hline
 $H_{01}$ & \multirow{4}{*}{rs20} & 0.159 & \textbf{0.011} & 0.989 \\
 $H_{02}$ & & 0.220 & 0.164 & 0.836 \\
 $H_{03}$ & & 0.513 & 0.280 & 0.720 \\
 $H_0$ by FCT & & 0.095 & \textbf{0.019} & 0.984 \\
 \hline
\end{tabular}
\caption{Third column: Twice the median of $p$-values for testing~ across $2,000$ module set constructions. Fourth column: $p$-values for testing~ using the module sets that maximize the expected number of active focal units. Fifth column: $p$-values for testing the crime displacement hypothesis~ using the module sets that maximize the expected number of active focal units. \textbf{Bold} denotes value below $0.05$.}
\end{table}
\end{document}

stat

"Finite-Sample Valid Randomization Tests for Monotone Spillover Effects" by Shunzhuang Huang, Xinran Li, and Panos Toulis, arXiv:2501.02454v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Luigi Brugnano
Gianmarco Gurioli
Felice Iavernaro

"A shooting-Newton procedure for solving fractional terminal value problems" by Luigi Brugnano, Gianmarco Gurioli, and Felice Iavernaro, arXiv:2312.08516v5, 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{Summary of the red core from Fig. .}
\begin{tabular}{ll}
\hline
\textbf{Category} & \textbf{Details} \\ \hline
\textbf{Source Set} & pep\_c, h\_e \\ \hline
\textbf{Sink Set} & pyr\_c \\ \hline
\textbf{Non-autocatalytic Set} & adp\_c, h2o\_c \\ \hline
\textbf{Autocatalytic Set} & atp\_c, h\_c, pi\_c \\ \hline
\textbf{Amplification Factor} & 1.8932605011330286 \\ \hline
\textbf{Reactions} & \\ \hline
R13 & atp\_c + h2o\_c $\to$ adp\_c + h\_c + pi\_c \\
R18 & adp\_c + pi\_c + 4h\_e $\to$ atp\_c + 3h\_c + h2o\_c \\
R20 & adp\_c + h\_c + pep\_c $\to$ atp\_c + pyr\_c \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Víctor Blanco
Gabriel González
Praful Gagrani

"Identifying Self-Amplifying Hypergraph Structures through Mathematical Optimization" by Víctor Blanco, Gabriel González, and Praful Gagrani, arXiv:2412.15776v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math


math

"A Bayesian decision-theoretic approach to sparse estimation" by Aihua Li, Surya T. Tokdar, and Jason Xu, arXiv:2502.00126v1, 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{Stationary points in the matter scalar -field normalization.}%
\begin{tabular}{ccccc}
\hline\hline
\textbf{Point} & $\mathbf{\ }\left( \mathbf{\omega }_{m},\mathbf{\chi ,\zeta
,r,\Phi }\right) $ & \textbf{Existence} & $w_{eff}$ & \textbf{Acceleration}
\\
$Q_{1}^{-}$ & $\left( 0,\sqrt{2},0,-\sqrt{\frac{5}{3}},-\frac{3}{20}\sqrt{%
\frac{3}{10}}\right) $ & Always & $-1$ & de Sitter \\
$Q_{1}^{+}$ & $\left( 0,\sqrt{2},0,\sqrt{\frac{5}{3}},-\frac{3}{20}\sqrt{%
\frac{3}{10}}\right) $ & Always & $-1$ & de Sitter \\
$Q_{2}^{-}$ & $\left( 0,-\sqrt{2},0,-\sqrt{\frac{5}{3}},\frac{3}{20}\sqrt{%
\frac{3}{10}}\right) $ & Always & $-1$ & de Sitter \\
$Q_{2}^{+}$ & $\left( 0,-\sqrt{2},0,\sqrt{\frac{5}{3}},-\frac{3}{20}\sqrt{%
\frac{3}{10}}\right) $ & Always & $-1$ & de Sitter \\
$Q_{3}^{-}$ & $\left( 1+\frac{6}{8\beta ^{2}-21},-\frac{2}{3\sqrt{8\beta
^{2}-21}},0,-\frac{4\beta }{\sqrt{3\left( 8\beta ^{2}-21\right) }},-\frac{%
3\left( 63+64\beta ^{2}\left( \beta ^{2}-3\right) \right) }{1024\beta ^{2}}%
\right) $ & Always & $-1$ & de Sitter \\
$Q_{3}^{+}$ & $\left( 1+\frac{6}{8\beta ^{2}-21},\frac{2}{3\sqrt{8\beta
^{2}-21}},0,\frac{4\beta }{\sqrt{3\left( 8\beta ^{2}-21\right) }},-\frac{%
3\left( 63+64\beta ^{2}\left( \beta ^{2}-3\right) \right) }{1024\beta ^{2}}%
\right) $ & Always & $-1$ & de Sitter \\
$Q_{4}^{-}$ & $\left( 0,0,1,-\frac{1}{\sqrt{3}},-\frac{3}{8}\lambda \right) $
& Always & $-1$ & de Sitter \\
$Q_{4}^{+}$ & $\left( 0,0,1,\frac{1}{\sqrt{3}},-\frac{3}{8}\lambda \right) $
& Always & $-1$ & de Sitter \\ \hline\hline
\end{tabular}
\end{table}
\end{document}

math

Giannis Papagiannopoulos
Genly Leon
Andronikos Paliathanasis

"Avoiding Big Rip Singularities in Phantom Scalar Field theory with Gauss-Bonnet term" by Giannis Papagiannopoulos, Genly Leon, and Andronikos Paliathanasis, arXiv:2501.18392v2, 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{Statistical parameters of the received signal in the SDJD receiver.}
\begin{tabular}{c|c||c|c}
		\hline\hline
		Mean & Value & Variance & Value\\
		\hline\hline
		$\mu _{1}$ & $h\sqrt \rho {E_p}+{h^2}(1 - \rho ){E_p} +\left( {1 - \rho } \right)c{N_0}$ & $\sigma _{1}^2$ & $\frac{{\rho {E_p}{N_0}}}{2} + 2{h^2}{{(1 - \rho )}^2}{E_p}{N_0} + {{{(1 - \rho )}^2}N_0^2c}$\\
		\hline
		$\mu _{2}$ & $\left( {1 - \rho } \right)c{N_0}$ & $\sigma _{2}^2$ & $\frac{{\rho {E_p}{N_0}}}{2} + {{{(1 - \rho )}^2}N_0^2c}$ \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Qianqian Wang
Quansheng Guan
Julian Cheng
Fei Ji

"A Splitting-Detection Joint-Decision Receiver for Ultrasonic Intra-Body Communications" by Qianqian Wang, Quansheng Guan, Julian Cheng, and Fei Ji, arXiv:2101.01369v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative results for AIM 2020 Video Extreme Super--Resolution Challenge ($16\times$).}
\begin{tabular}{clrrrc} \hline
 Fidelity & \multirow{2}{*}{Team} & \multirow{2}{*}{PSNR} & \multirow{2}{*}{SSIM} & \multirow{2}{*}{Runtime} & Perceptual \\
 Rank & & & & & Rank \\ \hline
 1 & KirinUK & \textbf{22.83} & \textbf{0.6450} & 6.1s & 1 \\
\rowcolor{lightgray} 2 & MGBP--3D & 22.48 & 0.6304 & 4.83s & 4 \\
 2 & Team-WVU & 22.48 & 0.6378 & 4.90s & 2 \\
 3 & sr\_xxx & 22.43 & 0.6353 & 4s & 5 \\
 4 & ZZX & 22.28 & 0.6321 & 4s & 3 \\
 5 & lyl & 22.08 & 0.6256 & 13s & 6 \\
 6 & TTI & 21.91 & 0.6165 & 0.249s & -- \\
 7 & CET\_CVLab & 21.77 & 0.6112 & 0.04s & 7 \\
 & Bicubic & 20.69 & 0.5770 & & \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Multi-Grid Back-Projection Networks

Pablo Navarrete Michelini
Wenbin Chen
Hanwen Liu
Dan Zhu
Xingqun Jiang

"Multi-Grid Back-Projection Networks" by Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu, Dan Zhu, and Xingqun Jiang, arXiv:2101.00150v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Bohr-Rogosinski inequalities involving Schwarz functions" by Sabir Ahammed and Molla Basir Ahamed, arXiv:2312.05635v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Qazi J. Azhad
Abdul Nasir Khan
Bhagwati Devi
Jahangir Sabbir Khan
Ayush Tripathi

"Bayesian estimation of Unit-Weibull distribution based on dual generalized order statistics with application to the Cotton Production Data" by Qazi J. Azhad, Abdul Nasir Khan, Bhagwati Devi, Jahangir Sabbir Khan, and Ayush Tripathi, arXiv:2502.02927v1, 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{Technical parameters used to model conversion nodes.}
\begin{tabular}{|c|ccccc|}
	\hline
	& $\phi_1$ & $\phi_2$ & $\phi_3$ & $\mu$ & $\Delta_{+,-}$ \\
	\hline
	HVDC Interconnection & \small{0.9499} & & & &\\
	 & \small{-} & & & &\\
	\hline
	Electrolysis & \small{50.6} & \small{9.0} & \small{8.0} & \small{0.05} & \small{1.0}\\
	 & \small{GWh$_{el}$/kt$_{H_2}$} & \small{kt$_{H_2O}$/kt$_{H_2}$} & \small{kt$_{O_2}$/kt$_{H_2}$} & \small{-} & \small{-/h} \\
	\hline
	Methanation & \small{0.5} & \small{2.75} & \small{2.25} & \small{1.0} & \small{0.0} \\
	 & \small{kt$_{H_2}$/kt$_{CH_4}$} & \small{kt$_{CO_2}$/kt$_{CH_4}$} & \small{kt$_{H_2O}$/kt$_{CH_4}$} & \small{-} & \small{-/h}\\
	\hline
	Desalination & \small{0.004} & & & \small{1.0} & 0.0\\
	 & \small{GWh$_{el}$/kt$_{H_2O}$} & & & \small{-} & \small{-/h}\\
	\hline
	Direct Air Capture & \small{0.1091} & \small{0.0438} & \small{5.0} & \small{1.0} & \small{0.0} \\
	 &\small{GWh$_{el}$/kt$_{CO_2}$}&\small{kt$_{H_2}$/kt$_{CO_2}$} &\small{kt$_{H_2O}$/kt$_{CO_2}$}& \small{-} & \small{-/h} \\
	\hline
	CH$_4$ Liquefaction & \small{0.616} & & & \small{0.0} & 1.0\\
	 & \small{GWh$_{el}$/kt$_{LCH_4}$} & & & \small{-} & \small{-/h}\\
	\hline
	LCH$_4$ Carriers & \small{0.994} & & & &\\
	 & \small{-} & & & & \\
	\hline
	LCH$_4$ Regasification & \small{0.98} & & & &\\
	 & \small{-} & & & &\\
	\hline
	\end{tabular}
\end{table}
\end{document}

eess

Mathias Berger
David Radu
Ghislain Detienne
Thierry Deschuyteneer
Aurore Richel
Damien Ernst

"Remote Renewable Hubs For Carbon-Neutral Synthetic Fuel Production" by Mathias Berger, David Radu, Ghislain Detienne, Thierry Deschuyteneer, Aurore Richel, and Damien Ernst, arXiv:2102.11375v2, 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}{|ccc|} \hline
\textbf{Traffic class} & \textbf{Our accuracy(\%)} & \textbf{Flowpic's accuracy(\%)}\\\hline
Unencrypted & 		99.7 & 		97.0\\\hline
Tor & 	 	99.4 & 		85.7\\\hline
VPN & 	 	99.9 & 		99.7\\\hline
\end{tabular}
\caption{Average accuracies for on one vs all categorization of different traffic classes for each encryption technique. The reported results are the averages of results in Table for each encryption technique. The corresponding Flowpic accuracies, from Table 3 (rows 4-6) in , are always lower.}
\end{table}
\end{document}

cs

Ali Rasteh
Florian Delpech
Carlos Aguilar-Melchor
Romain Zimmer
Saeed Bagheri Shouraki
Timothée Masquelier

"Encrypted Internet traffic classification using a supervised Spiking Neural Network" by Ali Rasteh, Florian Delpech, Carlos Aguilar-Melchor, Romain Zimmer, Saeed Bagheri Shouraki, and Timothée Masquelier, arXiv:2101.09818v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Antonino Castelli
Paolo Giudici
Alessandro Piergallini

"Building crypto portfolios with agentic AI" by Antonino Castelli, Paolo Giudici, and Alessandro Piergallini, arXiv:2507.20468v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
$V$ & 1 & 5 & 10 & 15 & 16 \\ \hline
Speed & 435.63it/s & 402.10it/s & 382.91it/s & 350.18it/s & 345.10it/s \\ \hline
Memory & 1073MB & 1075MB & 1080MB & 1085MB & 1089MB \\ \hline
Error\_1 & 3.02E-03$\pm$2.16E-03 & 2.70E-03$\pm$1.35E-03 & 2.55E-03$\pm$1.08E-03 & 2.41E-03$\pm$8.10E-04 & \textbf{2.38E-3$\pm$1.72E-3} \\ \hline
Error\_2 & 2.23E-05$\pm$5.00E-05 & 1.96E-05$\pm$2.91E-05 & 1.75E-05$\pm$3.28E-05 & 1.62E-05$\pm$1.25E-05 & \textbf{1.59E-5$\pm$9.29E-6} \\ \hline
\end{tabular}
\caption{Effects of the number of random vectors $V$ on the convergence for 100,000-dimensional Sine-Gordon equations with the two-body (Error\_1) and three-body (Error\_2) exact solutions.}
\end{table}
\end{document}

math

Zheyuan Hu
Zekun Shi
George Em Karniadakis
Kenji Kawaguchi

"Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks" by Zheyuan Hu, Zekun Shi, George Em Karniadakis, and Kenji Kawaguchi, arXiv:2312.14499v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
 \hline
 $A_1$ & $A_2$ &$\textnormal{gcd}_{(A_1,A_2)}(m,n)$ &$C_{(A_1,A_2)}(m,n)$ & $\Phi_{(A_1,A_2)}(m,n)$\\
 \hline
 \hline
 $D$& $D$& $(m,n)$&$c_n(m)$ & $\frac{\varphi(n)\mu(n/(m,n))}{\varphi(n/(m,n))}$
 \\
 \hline
 $D$ & $U$ & $(m,n)_*$& $c^*(m,n)$ & $\frac{\varphi^*(n)\mu^*(n/(m,n)_*)}{\varphi^*(n/(m,n)_*)}$ \\
 \hline
 $D$ & $A$ & $(m,n)_A$ & $c_A(m,n)$ & $\frac{\varphi_A(n)\mu_A(n/(m,n)_A)}{\varphi_A(n/(m,n)_A)}$ \\
 \hline
 $U$ & $U$ & $(m,n)_{**}$ & $\Tilde{c}(m,n)$ & $\frac{\varphi^*(n)\mu^*(n/(m,n)_{**})}{\varphi^*(n/(m,n)_{**})}$ \\
 \hline
 \end{tabular}
\caption{Some Ramanujan sums in literature that fit within our framework.}
\end{table}
\end{document}

math

"Further Generalization of Ramanujan Sums with Regular A-Functions" by Udvas Acharjee and N. Uday Kiran, arXiv:2503.11671v2, 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{ Decision rule at month 9, mean of the blip estimates over 25 imputed datasets, SPRINT trial, 2010-2013. }
\begin{tabular}{|l|c|c|c|}
 \hline
 Variable & QLOMA & WOMA & No. of times stat. significant$^*$ \\ \hline
 Intercept visit & -0.7 & 0.8 & 0 \\ \hline
 Intercept add-on & -9.0 & -9.0 & 0 \\ \hline
 \multicolumn{4}{|c|}{Visit interaction with:} \\ \hline
 Intensive group & 1.4 & 0.8 & 6 \\ \hline
 Age & -0.0 & -0.1 & 3 \\ \hline
 Female sex & -0.6 & -0.5 & 2 \\ \hline
 Race Black & \multicolumn{3}{|c|}{Reference} \\ \hline
 Race Hispanic & -0.6 & -0.6 & 1 \\ \hline
 Race White & -1.0 & -1.4 & 1 \\ \hline
 \hspace{0.2cm} Other & -1.2 & -1.7 & 1 \\ \hline
 Smoking (ever) & -0.2 & -0.3 & 0 \\ \hline
 BMI & 0.0 & -0.0 & 1 \\ \hline
 HDL & 0.0 & 0.0 & 2 \\ \hline
 SBP Baseline & 0.0 & 0.0 & 3 \\ \hline
 SBP Current month & -0.0 & -0.0 & 1 \\ \hline
 CVD & 0.6 & 0.6 & 0 \\ \hline
 Aspirin use & 0.6 & 0.9 & 1 \\ \hline
 Statin use & 0.3 & 0.2 & 0 \\ \hline
 \end{tabular}
\end{table}
\end{document}

stat

Janie Coulombe
Dany El-Riachi
Fanxing Du
Tianze Jiao

"Constructing optimal dynamic monitoring and treatment regimes: An application to hypertension care" by Janie Coulombe, Dany El-Riachi, Fanxing Du, and Tianze Jiao, arXiv:2501.08274v1, 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|}
 \hline
 Algorithm & Complexity \\
 \hline
 ULA & $O(e^{32LR^2}\kappa^2\frac{d}{\epsilon^{2}})$ \\
 \hline
 RD + ULA & $e^{O(L^3 \log^3(L^2(R^2\kappa^2 + d/\alpha)/\epsilon))}$ \\
 \hline
 RD + Rejection Sampling & $O(L^{d/2} \epsilon^{-d} e^{L\| x^*\|^2 +L^2(\kappa^2R^2 +d/\alpha)})$ \\
 \hline
 RD + Self-normalized IS (ours) & $O(d^7 L^{d+8}\epsilon^{-2(d+3)}(R^2\kappa^2 + \frac{d}{\alpha})^{2(d+4)})$\\
 \hline
\end{tabular}
\caption{Complexity of sampling algorithms in the smooth, strongly dissipative case. We denoted $x^*$ the global minimum of $V$.}
\end{table}
\end{document}

math

"Polynomial time sampling from log-smooth distributions in fixed dimension under semi-log-concavity of the forward diffusion with application to strongly dissipative distributions" by Adrien Vacher, Omar Chehab, and Anna Korba, arXiv:2501.00565v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detection Results of $AVTENet_{ff}$ (Feature fusion).}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\textbf {Test-Set type} & \textbf {Class} & \textbf {Precision} & \textbf {Recall} & \textbf {F1-Score} & \textbf {Accuracy} \\
\hline \hline
\multirow{2}{*}{Testset-I} & Real & 0.96 & 0.97 & 0.96 & \multirow{2}{*}{0.96} \\
\cline{2-5}
& Fake & 0.97 & 0.96 & 0.96 & \\
\hline
\multirow{2}{*}{Testset-II} & Real & 1.00 & 0.97 & 0.99 & \multirow{2}{*}{0.99} \\
\cline{2-5}
& Fake & 0.97 & 1.00 & 0.99 & \\
\hline
\multirow{2}{*}{faceswap} & Real & 0.93 & 0.97 & 0.95 & \multirow{2}{*}{0.95} \\
\cline{2-5}
& Fake & 0.97 & 0.93 & 0.95 & \\
\hline
\multirow{2}{*}{faceswap-wav2lip} & Real & 1.00 & 0.97 & 0.99 & \multirow{2}{*}{0.99} \\
\cline{2-5}
& Fake & 0.97 & 1.00 & 0.99 & \\
\hline
\multirow{2}{*}{fsgan} & Real & 0.93 & 0.97 & 0.95 & \multirow{2}{*}{0.95} \\
\cline{2-5}
& Fake & 0.97 & 0.93 & 0.95 & \\
\hline
\multirow{2}{*}{fsgan-wav2lip} & Real & 1.00 & 0.97 & 0.99 & \multirow{2}{*}{ 0.99} \\
\cline{2-5}
& Fake & 0.97 & 1.00 & 0.99 & \\
\hline
\multirow{2}{*}{RTVC} & Real & 0.96 & 0.97 & 0.96 & \multirow{2}{*}{0.96} \\
\cline{2-5}
& Fake & 0.97 & 0.96 & 0.96 & \\
\hline
\multirow{2}{*}{wav2lip} & Real & 1.00 & 0.97 & 0.99 & \multirow{2}{*}{0.99} \\
\cline{2-5}
& Fake & 0.97 & 1.00 & 0.99 & \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Ammarah Hashmi
Sahibzada Adil Shahzad
Chia-Wen Lin
Yu Tsao
Hsin-Min Wang

"AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection" by Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao, and Hsin-Min Wang, arXiv:2310.13103v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Unemployment and Endogenous Reallocation over the Business Cycle" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Ao Zhou
Bin Liu
Jin Wang
Grigorios Tsoumakas

"Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples" by Ao Zhou, Bin Liu, Jin Wang, and Grigorios Tsoumakas, arXiv:2403.18192v1, 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{\footnotesize EPOR value and cost of the different hedging strategies (values are reported in bps).}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 & \textit{EPOR} & \textit{OpR-MiM} & \textit{FxR-OpM} & \textit{FxR-MiM} \\
 \hline
 {Bullet} & 48.53 & 49.96 & 49.74 & 50.21 \\
 \hline
 {Linear} & 15.66 & 15.95 & 15.91 & 14.06 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Leonardo Perotti
Lech A. Grzelak
Cornelis W. Oosterlee

"Pricing and hedging the prepayment option of mortgages under stochastic housing market activity" by Leonardo Perotti, Lech A. Grzelak, and Cornelis W. Oosterlee, arXiv:2507.08641v1, 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{Vehicle suepnsion parameters}
\begin{tabular}{cc|cc}
\toprule
Parameter & Value & Parameter & Value \\ \hline
 $t_0$ & 0 s & $t_f$ & 3 s \\
 $b$ & $100.8$ Ns/m & $k$ & $2.15*10^4$ N/m \\
 $r_{\mathrm{max}}$ & $0.04$ m & $s_{\mathrm{max}}$ & $0.04$ m \\
 $m_{\mathrm{U}}$ & $65$ kg & $m_{\mathrm{S}}$ & $325$ kg \\
 $w_1$ & $10^5\, s^{-1}m^{-2}$ & $k_t$ & $232.5\times10^3$ N/m \\
 $w_2$ & $0.5\, s^{3}m^{-2}$ & $b_t$ & $0$ Ns/m \\
 $w_3$ & $10^{-5}\, s^{-1}N^{-2}$ & & \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"LGR-MPC: A user-friendly software based on Legendre-Gauss-Radau pseudo spectral method for solving Model Predictive Control problems" by Saeid Bayat and James T. Allison, arXiv:2310.15960v1, 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{amsfonts}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average optimality gaps after $1800$ seconds. Optimality gaps are computed as $\vert$\texttt{best\_bound}-\texttt{objective\_value}$\vert$/$\vert$\texttt{objective\_value}$\vert$. The column ``Reduction'' reports the difference in optimality gap between BD and the solver, as a percentage. ``Instances solved'' reports the number of instances solved to an optimality gap smaller than the target $0.5\%$ tolerance.}
\begin{tabular}{ccccccccc}
 \toprule
 && & &\multicolumn{3}{c}{Gap (\%)} & \multicolumn{2}{c}{Instances solved}\\
 \cmidrule{5-9}
 $|\mathcal{K}|$ & $|\mathcal{I}|$ & $S$ & & Solver & BD & Reduction (\%) & Solver& BD\\
 \midrule
 400 & 20 & 3 & & 29.28 & 1.37 & 95.32 & 0/3 & 2/3\\
 600 & 20 & 3 & & 38.03 & 3.25 & 91.45 & 0/3 & 2/3\\
 800 & 20 & 3 & & 38.56 & 4.30 & 88.84 & 0/3 & 1/3\\
 \cmidrule{4-9}
 & & & \textbf{Average} & \textbf{35.29} & \textbf{2.97} & \textbf{91.58} & \textbf{0/9} & \textbf{5/9}\\
 \midrule
 400 & 20 & 4 & & 21.81 & 6.81 & 68.78 & 0/3 & 1/3\\
 600 & 20 & 4 & & 30.70 & 10.22 & 66.71 & 0/3 & 0/3\\
 800 & 20 & 4 & & 32.31 & 7.54 & 76.66 & 0/3 & 0/3\\
 \cmidrule{4-9}
 & & & \textbf{Average} & \textbf{28.27} &
 \textbf{8.19} & \textbf{70.03} & \textbf{0/9}& \textbf{1/9}\\
 \midrule
 400 & 20 & 5 & & 17.36 & 8.05 & 53.63 &0/3&0/3\\
 600 & 20 & 5 & & 25.38 & 12.36 & 51.30 &0/3&0/3\\
 800 & 20 & 5 & & 28.24 & 13.32 & 52.83 &0/3 &0/3\\
 \cmidrule{4-9}
 & & & \textbf{Average} & \textbf{23.66} & \textbf{11.25} & \textbf{52.45} & \textbf{0/9} & \textbf{0/9}\\
 \midrule
 & \multicolumn{3}{c}{} & \textbf{29.07} & \textbf{7.47} & \textbf{74.30} & \textbf{0/27} & \textbf{6/27}\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Zonification and Pricing in Carsharing

"Zonification and Pricing in Carsharing" by Jiali Deng and Giovanni Pantuso, arXiv:2312.01489v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of the peg-in-hole task for different variations of the grasps.}
\begin{tabular}{cccc}
			\toprule[0.12em]
			\centering \multirow{2}{*}{\textbf{Strategy}} & \multicolumn{2}{c}{\textbf{Success rate}} & \multirow{2}{*}{\textbf{Time taken}}\\
			\cmidrule{2-3}
			& \# Trials & Successful Trials & \textbf{(seconds)} \\
			\midrule
			Closed loop & $20$ & $20$ & $9.9 \pm 8.5 $ \\
			Open loop & $10$ & $2$ & - \\
			GMM-sampling & $5$ & $0$ & - \\
			\bottomrule[0.12em]
		\end{tabular}
\end{table}
\end{document}

eess

Suhan Shetty
João Silvério
Sylvain Calinon

"Ergodic Exploration using Tensor Train: Applications in Insertion Tasks" by Suhan Shetty, João Silvério, and Sylvain Calinon, arXiv:2101.04428v2, 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{Possible Pulses and Their Analytical Expressions for GPIM.}
\begin{tabular}{|c|c|}\hline
			Possible Pulses & Analytical Expressions\\ \hline
			$\pm \psi_0(t) \pm \psi_1(t)$ & $2^ {1/4} (1 \pm 2 \sqrt{\pi}t) e^{-\pi t^2}$ \\ \hline
			$\pm \psi_0(t) \pm \psi_2(t)$ & $2^ {1/4} (1 \pm \frac {4\pi t^2 - 1}{\sqrt{2}}) e^{-\pi t^2}$\\ \hline
			$\pm \psi_1(t) \pm \psi_2(t)$ & $2^ {1/4} (2 \sqrt{\pi} t \pm \frac {4\pi t^2 - 1}{\sqrt{2}} ) e^{-\pi t^2}$\\ \hline
			$\pm \psi_1(t) \pm \psi_3(t)$ & $2^ {1/4} (2 \sqrt{\pi}t \pm \frac {4\pi \sqrt{2 \pi} t^3 - 3\sqrt(2 \pi) t}{\sqrt{3}}) e^{-\pi t^2}$\\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

Pulse Index Modulation

Sultan Aldirmaz-Colak
Erdogan Aydin
Yasin Celik
Yusuf Acar
Ertugrul Basar

"Pulse Index Modulation" by Sultan Aldirmaz-Colak, Erdogan Aydin, Yasin Celik, Yusuf Acar, and Ertugrul Basar, arXiv:2101.09340v1, 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{Results for the data with 27 features, with QC optimization approaches.}
\begin{tabular}{|c|c|c|c|c|}
	\hline
	& Objective & AUROC & Accuracy & Number of features \\
	\hline

	QUASM-Simulator, p=1& -0.6821 & 0.7412 & 0.731 & 7 \\
	\hline
	QUASM-Simulator, p=2& -0.6974 & 0.718 & 0.707 & 7 \\
	\hline
	QUASM-Simulator, p=3 & -0.70421 & 0.7435 & 0.707& 7 \\
	\hline
	Real Device (Auckland), p=1 & -0.6097 & 0.7323 & 0.722 & 5 \\
	\hline
	Real Device (Auckland), p=2 & -0.6218 & 0.7444 & 0.709 & 8 \\
	\hline
	Real Device (Auckland), p=3 & -0.6348 & 0.7469 & 0.7225 & 6 \\
	\hline
	Real Device (Kolkata), p=4 & -0.6199 & 0.7482 & 0.72 & 9\\
	\hline
	Real Device (Ehningen), p=1 & -0.6073 & 0.7374 & 0.702 & 7 \\
	\hline
	Real Device (Ehningen), p=2 & -0.5982 & 0.7304 & 0.695 & 7 \\
	\hline
	Real Device (Ehningen), p=3 & -0.3543 & 0.7452 & 0.712 & 7 \\
	\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Gerhard Hellstern
Vanessa Dehn
Martin Zaefferer

"Quantum computer based Feature Selection in Machine Learning" by Gerhard Hellstern, Vanessa Dehn, and Martin Zaefferer, arXiv:2306.10591v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrl}
\toprule
 \textbf{Dataset} & \textbf{Size} & \textbf{Lang.} & \textbf{Format}\\
\midrule
CARE-MI & $344$ & ZH & MCQ/LFGQ/TFQ \\
M3KE & $299$ &ZH & MCQ \\
ARC & $291$ & EN & MCQ\\
MATH & $300$ & EN & MCQ \\
\midrule
Total & $1,234$ & - & - \\
\bottomrule
\end{tabular}
\caption{Summary of the evaluation datasets. \textbf{Lang.} stands for the language of the datasets.}
\end{table}
\end{document}

cs

Wangyue Li
Liangzhi Li
Tong Xiang
Xiao Liu
Wei Deng
Noa Garcia

"Can multiple-choice questions really be useful in detecting the abilities of LLMs?" by Wangyue Li, Liangzhi Li, Tong Xiang, Xiao Liu, Wei Deng, and Noa Garcia, arXiv:2403.17752v3, 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{The effect of feedback on performance -- sensitivity to different specifications}
\begin{tabular}{lrrrrrrr}
			\toprule
			\textbf{Performance} & (1) & ~~~~ & (2) & ~~~~ & (3) & ~~~~ & (4) \\
			\cline{2-2} \cline{4-4} \cline{6-6} \cline{8-8}\\
			\multicolumn{8}{l}{\textit{Panel A: Diving (N=13075)}} \\
			Positive Feedback ~~~~ & 0.242*** & ~~& 0.208*** & ~~& 0.115*** & ~~& 0.100*** \\
			 & (0.036) & ~~& (0.034) & ~~& (0.032) & ~~& (0.035) \\
			Negative Feedback & 0.018 & ~~& 0.024 & ~~& 0.001 & ~~& 0.007 \\
			 & (0.030) & ~~& (0.030) & ~~& (0.029) & ~~& (0.030) \\
			\\
			\multicolumn{8}{l}{\textit{Panel B: Ski jumping (N=4529)}} \\
			Positive Feedback & 0.201*** & ~~& 0.180*** & ~~& 0.145*** & ~~& 0.107*** \\
			 & (0.035) & ~~& (0.036) & ~~& (0.034) & ~~& (0.034) \\
			Negative Feedback & -0.063 & ~~& -0.055 & ~~& -0.049 & ~~& -0.026 \\
			 & (0.043) & ~~& (0.041) & ~~& (0.037) & ~~& (0.041) \\
		 \\
			\midrule
			Base Covariates & x & & x & & x & & x \\
			All Covariates & & & x & & x & & x \\
			Individual Fixed Effect & & & & & x & & \\
			Individual x Season FE & & & & & & & x \\

			\bottomrule
			\multicolumn{8}{l}{\footnotesize Notes: Linear regression. Full regressions in Tables and . All regressions contain previous'}\\
			\multicolumn{8}{l}{\footnotesize ~~~~~~~~~ jumps jury assessment (\textit{Base Covariates}). \textit{All Covariates} include prev. jumps wind and }\\
			\multicolumn{8}{l}{\footnotesize ~~~~~~~~~ gate points and distance (ski jumping) or difficulty (diving). Also, points behind, }\\
			\multicolumn{8}{l}{\footnotesize ~~~~~~~~~ compatriot judge, home event, current ranking, SD of previous performance, and start }\\
			\multicolumn{8}{l}{\footnotesize ~~~~~~~~~ order. Standard errors are clustered on the individual level. *, **, and *** represents }\\
			\multicolumn{8}{l}{\footnotesize ~~~~~~~~~ statistical significance at the 10 \%, 5 \%, and 1 \% level, respectively.}

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

q-fin

"'Good job!' The impact of positive and negative feedback on performance" by Daniel Goller and Maximilian Späth, arXiv:2301.11776v1, 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|c|c|c|c|c|c|c|c}
$C$ & linear & dt & gbdt & rf & mlp-sos & mlp-bigm\\
\hline
$D$ & - & depth & \# estimators, depth & \# estimators, depth & \# layers, layer size & \# layers, layer size
\end{tabular}
\caption{ML predictor parameters for SurrogateLIB}
\end{table}
\end{document}

math

Mark Turner
Antonia Chmiela
Thorsten Koch
Michael Winkler

"PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs" by Mark Turner, Antonia Chmiela, Thorsten Koch, and Michael Winkler, arXiv:2312.08074v2, 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{Equilibrium rates in Case A with term debt maturity of 5 years.}
\begin{tabular}{|l|c|c|}
\hline
 $r_{\min}=0.0786$ & $r_{\mathrm{fix}}=0.8852$ & $r_{\max} = 0.3500$ \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Gianmarco Bet
Francesco Dainelli
Eugenio Fabrizi

"The financial health of a company and the risk of its default: Back to the future" by Gianmarco Bet, Francesco Dainelli, and Eugenio Fabrizi, arXiv:2302.10140v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.