\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Classification performance of VGD and RGD on chest CT images}
\begin{tabular}{c|ccc}
 \toprule
 Method & Precision & Recall & F1 Score\\
 \midrule
 VGD & 0.898 & 0.829 & 0.862 \\
 RGD & \textbf{0.954} & \textbf{0.880} & \textbf{0.916} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Xiaoyu Zhang
Di Wang
Guodong Li
Defeng Sun

"Robust Gradient Descent Estimation for Tensor Models under Heavy-Tailed Distributions" by Xiaoyu Zhang, Di Wang, Guodong Li, and Defeng Sun, arXiv:2412.04773v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DEFault's Performance on Fault Categorization}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrcccc}
\toprule
\textbf{Category} & \textbf{Testset} & \textbf{Accuracy} & \textbf{Precision} & \textbf{Recall} & \textbf{F1-Score} \\
\midrule
\midrule
Loss & 555 & 0.97 & 0.97 & 0.96 & 0.96 \\
Optimization & 330 & 0.94 & 0.93 & 0.95 & 0.94 \\
Activation & 322 & 0.91 & 0.92 & 0.91 & 0.91 \\
Hyperparameter & 658 & 0.93 & 0.93 & 0.92 & 0.92 \\
Layer & 520 & 0.86 & 0.89 & 0.84 & 0.86 \\
Regularization & 176 & 0.95 & 0.94 & 0.95 & 0.94 \\
Weights & 312 & 0.90 & 0.90 & 0.89 & 0.89 \\
\midrule
\textbf{Overall} & & \textbf{0.92} & \textbf{0.93} & \textbf{0.92} & \textbf{0.92} \\
\bottomrule
\multicolumn{6}{l}{\scriptsize Precision, Recall and F1-Score are weighted averages}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sigma Jahan
Mehil B Shah
Parvez Mahbub
Mohammad Masudur Rahman

"Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification" by Sigma Jahan, Mehil B Shah, Parvez Mahbub, and Mohammad Masudur Rahman, arXiv:2501.12560v1, 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{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rr}
 \hline
 species & $n_i$ \\
 \hline
Reed warbler & 409 \\
 Blyth's reed warbler & 41 \\
 Paddyfield warbler & 18 \\
 Marsh warbler & 414 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{The number of observations of each species.}
\end{table}
\end{document}

math

"Classification Under Partial Reject Options" by Måns Karlsson and Ola Hössjer, arXiv:2202.14011v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning" by Fred Shone and Tim Hillel, arXiv:2501.10221v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Determination the Solution of a Stochastic Parabolic Equation by the Terminal Value" by Fangfang Dou and Wanli Du, arXiv:2202.05439v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of 3-Diophantine triples in $\mathbb{F}_p$ for various primes $p$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|c|c|}
 \hline
 $p$ & $p\equiv 1, 2 \pmod 3$ & $N_3(p)$ & $a$ (if $p\equiv 1 \pmod 3$) & Error term $(a+1)/3$\\ \hline
 5 & 2 & 2 & - & -\\ \hline
 7 & 1 & 11 & 2 & 1\\ \hline
 11 & 2 & 60 & - & -\\ \hline
 13 & 1 & 110 & -1 & 0\\ \hline
 17 & 2 & 280 & - & -\\ \hline
 19 & 1 & 407 & -4 & -1\\ \hline
 23 & 2 & 770 & - & -\\ \hline
 29 & 2 & 1638 & - & -\\ \hline
 31 & 1 & 2031 & 2 & 1\\ \hline
 37 & 1 & 3572 & 5 & 2\\ \hline
 41 & 2 & 4940 & - & -\\ \hline
 43 & 1 & 5739 & -4 & -1\\ \hline
 101 & 2 & 80850 & - & -\\ \hline
 229 & 1 & 97472 & 11 & 4\\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Trajan Hammonds
Seoyoung Kim
Steven J. Miller
Arjun Nigam
Kyle Onghai
Dishant Saikia
Lalit M. Sharma

"$k$-Diophantine $m$-tuples in Finite Fields" by Trajan Hammonds, Seoyoung Kim, Steven J. Miller, Arjun Nigam, Kyle Onghai, Dishant Saikia, and Lalit M. Sharma, arXiv:2201.06232v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of different control flow and logical operation frequencies.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccc}
\toprule
\textbf{Dataset} & \textbf{if} & \textbf{while} & \textbf{for} & \textbf{and} & \textbf{or} & \textbf{except} & \textbf{return} & \textbf{break} & \textbf{continue} & \textbf{bool\_op} \\
\midrule
Code Alpaca & 0.42 & 0.06 & 0.43 & 0.03 & 0.01 & 0.01 & 0.66 & 0.01 & 0.00 & 0.05 \\
Evol CodeAlpaca & 1.35 & 0.15 & 0.68 & 0.15 & 0.14 & 0.12 & 1.59 & 0.03 & 0.02 & 0.29 \\
CodeFeedBack & 1.59 & 0.14 & 0.76 & \underline{0.19} & 0.14 & 0.08 & 1.62 & \underline{0.05} & 0.02 & 0.33 \\
OSS Instruct & 1.38 & 0.07 & 0.59 & 0.16 & 0.08 & 0.07 & 1.58 & 0.04 & 0.01 & 0.24 \\
Ours (func-level) & \underline{2.29} & \underline{0.16} & \underline{1.14} & 0.16 & \underline{0.20} & \underline{0.70} & \underline{3.06} & \underline{0.05} & \underline{0.10} & \underline{0.35} \\
Ours (file-level) & \textbf{3.60} & \textbf{0.38} & \textbf{1.77} & \textbf{0.25} & \textbf{0.21} & \textbf{1.11} & \textbf{5.24} & \textbf{0.08} & \textbf{0.10} & \textbf{0.45} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yaoxiang Wang
Haoling Li
Xin Zhang
Jie Wu
Xiao Liu
Wenxiang Hu
Zhongxin Guo
Yangyu Huang
Ying Xin
Yujiu Yang
Jinsong Su
Qi Chen
Scarlett Li

"EpiCoder: Encompassing Diversity and Complexity in Code Generation" by Yaoxiang Wang, Haoling Li, Xin Zhang, Jie Wu, Xiao Liu, Wenxiang Hu, Zhongxin Guo, Yangyu Huang, Ying Xin, Yujiu Yang, Jinsong Su, Qi Chen, and Scarlett Li, arXiv:2501.04694v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Simos G. Meintanis
Bojana Milošević
Marko Obradović

"Bahadur efficiency for certain goodness--of--fit tests based on the empirical characteristic function" by Simos G. Meintanis, Bojana Milošević, and Marko Obradović, arXiv:2201.08604v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The sizes of the matrices in the training dataset. The total number of samples in the training dataset is 15000. The number or rows and columns in the different matrices are sampled uniformly as $n,m\sim \mathcal{U}[low,high]$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccr}
\toprule
Nbr of samples & Row range & Column range\\
\midrule
10000& $[10, 35]$& $(10, 35)$ \\
2000&[30, 70]&[30, 70]\\
1500&[30,100]&[10,35] \\
1500&[10,35]&[30,100]\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Graph-based Neural Acceleration for Nonnegative Matrix Factorization" by Jens Sjölund and Maria Bånkestad, arXiv:2202.00264v1, 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}
(n,m)&$ (2000,10)$& $(5000,20)$ & $(10000,50)$\\ \hline
Benchmark & 90.32s (17.51s) & 895.1s (86.5s) & 4767s (611s) \\
 Random & 6.31s (1.95s) & 14.4s (3.3s) & 22.5s (4.0s)\\
Spatial & 6.36s (2.49s) & 13.8s (3.0s) & 21.2s (4.9s)\\
\end{tabular}
\caption{\scriptsize Empirical (MMLE) Bayes rescaling of the Mat\'ern Gaussian process prior. Average run time for computing the posterior. Benchmark: Non-distributed method. Method 1: Random partitioning, Method 2: Spatial partitioning.}
\end{table}
\end{document}

stat

Botond Szabo
Amine Hadji
Aad van der Vaart

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{stmaryrd}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
		\toprule
		\textbf{Symbol} & \textbf{Meaning} \\
		\midrule
		$\mathcal{X}$ & Finite state space \\
		$\mathcal{L} = \mathcal{L}(\mathcal{X})$ & Set of transition matrices on $\mathcal{X}$ \\
		$\mathcal{P}(\mathcal{X})$ & Set of probability masses with full support on $\mathcal{X}$ \\
		$\llbracket m,n \rrbracket$ & $\{m, m+1, \dots, n\}$ for $m \leq n$ and $m,n \in \mathbb{Z}$\\
		$\llbracket n \rrbracket$ & $\llbracket 1,n \rrbracket$ for $n \in \mathbb{N}$\\
		$\ell^2(\pi)$ & $\pi$-weighted $\ell^2$ Hilbert space with inner product $\langle f,g \rangle_\pi = \sum_x f(x)g(x)\pi(x)$ \\
		$\ell^2_0(\pi)$ & $\{f \in \ell^2(\pi);~ \pi(f) = 0\}$ \\
		$\mathcal{S}(\pi)$ & Set of $\pi$-stationary transition matrices on $\mathcal{X}$ \\
		$\mathcal{L}(\pi)$ & Set of $\pi$-reversible transition matrices on $\mathcal{X}$ \\
		$P^*$ & $\ell^2(\pi)$-adjoint of $P$ \\
		$\Pi$ & Transition matrix where each row equals to $\pi \in \mathcal{P}(\mathcal{X})$ \\
		$\mathcal{I}(\pi)$ & Set of isometric involution matrices with respect to $\pi$ on $\mathcal{X}$ \\
		$\mathcal{L}(\pi, Q)$ & Set of $(\pi, Q)$-self-adjoint transition matrices: $L^* = QLQ$ for $L \in \mathcal{L}(\pi,Q)$ \\
		$\mathbf{P}$ & Set of permutations on $\mathcal{X}$ \\
		$\Psi(\pi)$ & Set of involution and equi-probability permutations with respect to $\pi$ \\
		$Q_\psi$ & Permutation matrix induced by $\psi \in \mathbf{P}$: $Q_\psi(x,y) = \delta_{y = \psi(x)}$ for all $x,y \in \mathcal{X}$ \\
		$D^\pi_{KL}(P \| L)$ & $\pi$-weighted KL divergence from $L$ to $P$ \\
		${}^Q D_{KL}(P \| L)$ & $Q$-left-deformed KL divergence from $L$ to $P$: $D_{KL}^\pi(QP \| QL)$ \\
		$D_{KL}^Q(P \| L)$ & $Q$-right-deformed KL divergence from $L$ to $P$: $D_{KL}^\pi(PQ \| LQ)$ \\
		$\overline{P}(Q)$ & $(\pi, Q)$-self-adjoint projection of $P$: $\frac{1}{2}(P + QP^*Q)$ \\
		$(R_l)_{l \in \mathbb{N}}$ & Sequence of alternating projections \\
		$R_\infty$ & Limit of alternating projections \\
		$P_\beta = P_\beta(N,H)$ & {\footnotesize Metropolis-Hastings with proposal $N$ and target Hamiltonian $H$ at inverse temperature $\beta$} \\
		$h(P_\beta)$ & Critical height associated with $P_\beta$ \\
		\bottomrule
	\end{tabular}
\caption{Summary of notations}
\end{table}
\end{document}

stat

"Improving the convergence of Markov chains via permutations and projections" by Michael C. H. Choi, Max Hird, and Youjia Wang, arXiv:2411.08295v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ll}
		& ChestX-ray8 dataset & CheXpert dataset \\
		\hline
		\rowcolor{lightgray}
		Number of patient radiographs & 112,120 & 224,316 \\
		Number of patients & 30,805 & 65,240 \\
		\rowcolor{lightgray}
		Age, mean (SD), years & 46.9 (16.6) & 60.7 (18.4) \\
		Percentage of females (\%) & 43.5\% 		 & 40.6\% \\
		\rowcolor{lightgray}
		Number of pathology labels & 8 & 14 \\
		Cardiomegaly & 2,776 & 30,092 \\
		\rowcolor{lightgray}
		Edema & 2,303 & 61,493 \\
		Effusion & 13,317 & 86,477 \\
		\rowcolor{lightgray}
		Pneumothorax & 5,302 & 20,401 \\
		Atelectasis & 11,559 & 59,583 \\
		\rowcolor{lightgray}
		Consolidation & 4,667 & 12,983 \\
		Pneumonia & 1,431 & 20,656
	\end{tabular}
\end{table}
\end{document}

eess

Tianyu Han
Sven Nebelung
Federico Pedersoli
Markus Zimmermann
Maximilian Schulze-Hagen
Michael Ho
Christoph Haarburger
Fabian Kiessling
Christiane Kuhl
Volkmar Schulz
Daniel Truhn

"Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization" by Tianyu Han, Sven Nebelung, Federico Pedersoli, Markus Zimmermann, Maximilian Schulze-Hagen, Michael Ho, Christoph Haarburger, Fabian Kiessling, Christiane Kuhl, Volkmar Schulz, and Daniel Truhn, arXiv:2011.13011v1, 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}{lrrrrrr}
 Parameters & Gaussian & t & Cauchy & Laplace & generalized hyperbolic & variance gamma \\
 \midrule
 $\lambda$ & -20 & -1 & -0.5 & 1 & (d+1)/2 & 1.5 \\
 $\chi$ & 40 & 2 & 2 & 0.001 & 2 & 0.001 \\
 $\psi$ & 0.001 & 0.001 & 0.001 & 0.5 & 3 & 0.5 \\
 \bottomrule
 \end{tabular}
\caption{Values of the parameters $\lambda,\ \chi, \ \psi$ used in the simulation study.}
\end{table}
\end{document}

stat

"Hidden Markov graphical models with state-dependent generalized hyperbolic distributions" by Beatrice Foroni, Luca Merlo, and Lea Petrella, arXiv:2412.03668v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
			\hline
			\multirow{2}{*}{Algorithm} & \multicolumn{2}{c|}{Outlier noise model} & \multicolumn{2}{c|}{Gaussian noise model} \\ \cline{2-5} &$r'=r$ &$r'\geq r$ &$r'=r$ &$r'\geq r$\\ \hline
			\texttt{GD}+$\ell_2$-loss & N/A & N/A & $\nu^2\kappa^2 dr$ & $\nu^2\kappa^2dr'$ \\ \hline
			\texttt{SubGM}+$\ell_1$-loss & ${\kappa^2dr^2}$ $^*$ & $\kappa^{12} d{r'}^3$~$^*$ & N/A & N/A \\ \hline
			\bf Our results & \multicolumn{2}{c|}{$\frac{\kappa^4dr^{2}}{(1-p)^2}$ (see Corollary~)} & \multicolumn{2}{c|}{$\nu^2 \kappa^4dr^{2}$ (see Corollary~)} \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization" by Jianhao Ma and Salar Fattahi, arXiv:2202.08788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{move\_base parameters for SagarBot}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\toprule
\multicolumn{2}{|c|}{move\_base Parameters} \\ \midrule
Parameter & Value \\ \midrule
yaw\_goal\_tolerance & 0.1 \\ \midrule
xy\_goal\_tolerance & 0.2 \\ \midrule
obstacle\_range & 5.0 \\ \midrule
raytrace\_range & 8.0 \\ \midrule
inflation\_radius & 0.55 \\ \midrule
robot\_radius & 0.4 \\ \midrule
update\_frequency & 10.0 \\ \midrule
publish\_frequency & 10.0 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Robot localization in a mapped environment using Adaptive Monte Carlo algorithm" by Sagarnil Das, arXiv:2501.01153v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Real-world example - input data $X$ for 10 flights and 5 data features}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \toprule
 &price[Euro]&time[h]&stops&luggages&rating\\
 \midrule
 flight A & $300$ & $10$ & $2$ & $0$ & $2.5$\\
 flight B & $600$ & $5$ & $2$ & $1$ & $3.0$\\
 flight C & $1500$ & $4$ & $1$ & $2$ & $4.0$\\
 flight D & $400$ & $8$ & $2$ & $0$ & $3.5$\\
 flight E & $500$ & $8$ & $2$ & $1$ & $3.0$\\
 flight F & $700$ & $5$ & $2$ & $1$ & $4.5$\\
 flight G & $900$ & $6$ & $1$ & $1$ & $4.0$\\
 flight H & $1100$ & $6$ & $1$& $2$& $3.5$\\
 flight I & $1300$ & $5$ & $2$& $2$& $5.0$\\
 flight J & $1700$ & $4$ & $1$& $2$& $5.0$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization" by Philipp Wissgott, arXiv:2501.19113v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule[1.5pt]
\ \ \ \ \ \ \ \ \qquad {\bf Hyper-parameter} \ \ \ \ \ \ \ \ \qquad & \ \ \ \ \ \ \ \ \qquad Value \ \ \ \ \ \ \ \ \qquad \cr
\midrule[1.0pt]
 Layers & 6 \\
 Hidden layers & 16 \\
 Learning rate & .01 \\
 Epochs & 100 \\
\bottomrule[1.5pt]
\end{tabular}
\end{adjustbox}
\caption{The hyperparameters of NODE and NODE for the learning ROMs of Euler equations.}
\end{table}
\end{document}

math

Justin Baker
Elena Cherkaev
Akil Narayan
Bao Wang

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{AUROC for toxity prediction. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|cc|cc|}
 \hline
 &
 \multicolumn{2}{|c|}{Neurotoxicity}
 &
 \multicolumn{2}{|c|}{Cardiotoxicity}
 \\
 & Classical & Mixup & Classical & Mixup \\
 \hline
 Smoothed hinge loss & $ 0.881 $ & $ 0.909 $
 & $0.818$ & $ 0.857 $ \\
 BCE loss & $0.888$ & $\mathbf{0.923}$
 & $0.832$ & $\mathbf{0.902}$ \\
 Quadratic hinge loss & $ 0.685 $ & $ 0.797 $
 & $0.643$ & $ 0.862 $ \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Linearly Convergent Mixup Learning

Gakuto Obi
Ayato Saito
Yuto Sasaki
Tsuyoshi Kato

"Linearly Convergent Mixup Learning" by Gakuto Obi, Ayato Saito, Yuto Sasaki, and Tsuyoshi Kato, arXiv:2501.07794v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Beam parameters ranges}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
			\toprule
			Length (m) & Width (m) & Height (m) & Young's modulus (Pa) & Density (kg/$\text{m}^3$) \\
			\midrule
			$[3.0 , 6.0]$ & $[0.2, 0.4]$ & $[0.4, 0.6]$ & $[2.5 \cdot 10^{10}, 4.3 \cdot 10^{10}]$ & $[2.0 \cdot 10^3, 2.6 \cdot 10^3]$ \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"StAnD: A Dataset of Linear Static Analysis Problems" by Luca Grementieri and Francesco Finelli, arXiv:2201.05356v1, 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{Baseline hyperparameters.}
\begin{tabular}{llllll}
\toprule
\textbf{Hyperparameter} & \textbf{mPCG Model} \\
\midrule
Learning rate & 0.001 \\
Weight decay & 1e-5 \\
Momentum & 0.9 \\
Gamma & 0.1 \\
Step size & 3 \\
Batch size & 64 \\
Number of hidden layers & 1\\
Hidden layer size & 512 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Milan Marocchi
Matthew Fynn
Kayapanda Mandana
Yue Rong

"Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals" by Milan Marocchi, Matthew Fynn, Kayapanda Mandana, and Yue Rong, arXiv:2509.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Ablation study on interleaved data scaling and pre-training data composition.} `S': speech input and output. `S$\rightarrow$T': speech input and text output. `T$\rightarrow$S': text input and speech output. ``Web Q." stands for Web Questions. ``Llama Q." stands for Llama Questions.}
\begin{tabular}{lcccccc|cccccc}
\toprule
\multirow{3}{*}{Model} & \multicolumn{6}{c|}{\textbf{Speech Language Modeling}} & \multicolumn{6}{c}{\textbf{Spoken Question Anwsering}} \\
 & \multicolumn{3}{c}{sTopic-StoryCloze} & \multicolumn{3}{c|}{sStoryCloze} & \multicolumn{2}{c}{Web Q.} & \multicolumn{2}{c}{Llama Q.} & \multicolumn{2}{c}{Trivia QA} \\
 & S & T$\rightarrow$S & S$\rightarrow$T & S & T$\rightarrow$S & S$\rightarrow$T & S & S$\rightarrow$T & S & S$\rightarrow$T & S & S$\rightarrow$T \\
 \midrule
9B (600B Interleave) & \textbf{82.9} & \textbf{85.0} & \textbf{93.6} & \textbf{62.4} & \textbf{63.2} & \textbf{76.3} & \textbf{15.9} & \textbf{32.2} & \textbf{50.7} & \textbf{64.7} & \textbf{26.5} & \textbf{39.1} \\
- No Interleaving & 72.8 & 53.3 & 53.3 & 51.7 & 51.4 & 53.7 & 0.1 & 0.3 & 2.3 & 2.3 & 0.2 & 0.5 \\
- 100B Interleave & 80.9 & 83.6 & 93.3 & 59.4 & 61.3 & 73.4 & 9.3 & 25.4 & 37.0 & 60.0 & 11.7 & 26.9 \\
- 200B Interleave & 82.1 & 84.7 & 93.2 & 61.5 & 62.6 & 76.0 & 13.3 & 29.7 & 44.0 & 63.0 & 18.7 & 31.3 \\ \midrule
1.5B & 77.5 & 81.4 & \textbf{90.1} & 55.4 & 58.6 & 64.0 & 5.4 & \textbf{17.6} & 18.3 & \textbf{42.7} & 4.6 & 15.6 \\
- No Interleaving & 74.6 & 55.3 & 51.3 & 51.7 & 53.4 & 52.8 & 0.0 & 0.1 & 1.3 & 4.3 & 0.0 & 0.2 \\
- No Speech & 78.0 & 82.1 & 89.5 & 55.9 & 59.3 & 64.4 & 4.9 & 17.3 & 17.7 & 38.3 & 4.6 & 15.5 \\
- No Text & 78.5 & \textbf{83.2} & 89.0 & 54.8 & 58.3 & 63.4 & \textbf{6.3} & 17.4 & 20.3 & 42.3 & \textbf{7.0} & \textbf{16.3} \\
- No ASR \& TTS & \textbf{78.7} & 81.4 & 89.5 & \textbf{56.7} & \textbf{59.5} & \textbf{68.5} & 6.1 & 17.0 & \textbf{23.0} & 41.0 & 5.4 & 14.0 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Aohan Zeng
Zhengxiao Du
Mingdao Liu
Lei Zhang
Shengmin Jiang
Yuxiao Dong
Jie Tang

"Scaling Speech-Text Pre-training with Synthetic Interleaved Data" by Aohan Zeng, Zhengxiao Du, Mingdao Liu, Lei Zhang, Shengmin Jiang, Yuxiao Dong, and Jie Tang, arXiv:2411.17607v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Xianyi Zeng
Giovanni Stabile
Efthymios N. Karatzas
Guglielmo Scovazzi
Gianluigi Rozza

"Embedded domain Reduced Basis Models for the shallow water hyperbolic equations with the Shifted Boundary Method" by Xianyi Zeng, Giovanni Stabile, Efthymios N. Karatzas, Guglielmo Scovazzi, and Gianluigi Rozza, arXiv:2201.09546v1, 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{Instance segmentation performance on manual annotations.}
\begin{tabular}{cc|ccccc}
IoU Threshold & Class & Precision & Recall & TP & FP & FN \\
 \hline
\multirow{2}{*}{0.50} & Car & 66.8 & 79.3 & 544 & 270 & 142 \\
 & Pedestrian & 51.4 & 68.4 & 128 & 121 & 59 \\
\hline
\multirow{2}{*}{0.70} & Car & 57.7 & 68.5 & 470 & 344 & 216 \\
 & Pedestrian & 48.6 & 64.7 & 121 & 128 & 66 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Brian H. Wang
Wei-Lun Chao
Yan Wang
Bharath Hariharan
Kilian Q. Weinberger
Mark Campbell

"LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images" by Brian H. Wang, Wei-Lun Chao, Yan Wang, Bharath Hariharan, Kilian Q. Weinberger, and Mark Campbell, arXiv:1910.13955v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Ivory Yang
Weicheng Ma
Chunhui Zhang
Soroush Vosoughi

"Is It Navajo? Accurate Language Detection in Endangered Athabaskan Languages" by Ivory Yang, Weicheng Ma, Chunhui Zhang, and Soroush Vosoughi, arXiv:2501.15773v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of classification accuracy on MNIST dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
		\toprule
		\multicolumn{5}{c}{Accuracy (\%)} \\
		\midrule
		\multicolumn{5}{c}{Gaussian Noise $N(0.5,2)$} \\
		\midrule
		$\beta$ & Noise proportion & Truncation & & Non-truncation \\
		2.0 & 20\% & 86.85 (0.01) & & 75.71 (0.01) \\
		2.0 & 50\% & 84.39 (0.02) & & 74.27 (0.03) \\
		2.0 & 80\% & 76.06 (0.03) & & 72.96 (0.03) \\
		\midrule
		\multicolumn{5}{c}{Uniform Noise $U(2,5)$} \\
		\midrule
		$\beta$ & Noise proportion & Truncation & & Non-truncation \\
		1.5 & 20\% & 96.56 (0.01) & & 95.91 (0.09) \\
		1.5 & 50\% & 93.87 (0.01) & & 92.46 (0.01) \\
		1.5 & 80\% & 93.45 (0.03) & & 91.65 (0.01) \\
		\midrule
		\multicolumn{5}{c}{Pareto Noise $\beta = 2.01$} \\
		\midrule
		$\beta$ & Noise proportion & Truncation & & Non-truncation \\
		2.0 & 20\% & 88.75 (0.02) & & 87.85 (0.01) \\
		2.0 & 50\% & 76.03 (0.03) & & 74.74 (0.01) \\
		2.0 & 80\% & 65.12 (0.02) & & 60.96 (0.04) \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lihu Xu
Fang Yao
Qiuran Yao
Huiming Zhang

"Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption" by Lihu Xu, Fang Yao, Qiuran Yao, and Huiming Zhang, arXiv:2201.03182v2, 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{Dataset Information}
\begin{tabular}{ccccc}
			\hline\hline
			\textbf{Dataset} & \textbf{Sampling Rate} &\textbf{ Modality} & \textbf{Training Samples} & \textbf{Test Samples} \\
			\hline\hline
			\multirow{3}*{UTD MHAD}
			& \multirow{2}*{50}
			& Depth & 46639 & 11660 \\
			\cline{3-5}
			& & \multirow{1}*{Inertial}
			& 11031 & 2745 \\\hline

			\multirow{3}*{Berkeley MHAD}
			& \multirow{2}*{30}
			& Depth & 26400 & 6500 \\
			\cline{3-5}
			& & \multirow{1}*{Inertial}
			& 2612 & 653 \\\hline

			\multirow{3}*{UTD Kinect V2}
			& \multirow{2}*{50}
			& Depth & 14098 & 3524 \\
			\cline{3-5}
			& & \multirow{1}*{Inertial}
			& 3532 & 884 \\
			\hline\hline

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

eess

"CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors" by Zeeshan Ahmad and Naimul khan, arXiv:2010.16073v1, 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}{llc}
 \toprule
 \textbf{Fusion Module} & \textbf{Visual Feature} & \textbf{PPL} $\downarrow$ \\
 \midrule
 \multicolumn{3}{l}{\textit{Baseline}} \\
 \quad \textsc{Speech-only} & N/A & 5.6 \\
 \midrule
 \multicolumn{3}{l}{\textit{Fusion: Direct Concatenation}} \\
 \quad \textsc{Direct Concat} & SMIRK & 132.0 \\
 \midrule
 \multicolumn{3}{l}{\textit{Fusion: Qformer Variants}} \\
 \quad \textsc{Q-Former Infill} & SMIRK & 5.8 \\
 \quad \textsc{Q-Former Prefix} & SMIRK & \textbf{5.5} \\
 \quad \textsc{Q-Former Prefix} & VGG-Face2 & 8.2 \\
 \quad \textsc{Q-Former Prefix} & MAGVIT2 & 8.6 \\
 \bottomrule
 \end{tabular}
\caption{Perplexity (PPL) Comparison across Fusion Modules and Visual Features.}
\end{table}
\end{document}

eess

Weiting Tan
Jiachen Lian
Hirofumi Inaguma
Paden Tomasello
Philipp Koehn
Xutai Ma

"Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation" by Weiting Tan, Jiachen Lian, Hirofumi Inaguma, Paden Tomasello, Philipp Koehn, and Xutai Ma, arXiv:2508.16188v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|c|c|c|c|c|}
			 & $K$, m/day & $\alpha$ & $n$ & $\theta_r$ & $\theta_s$ \\\hline
			Clay & 0.048 & 0.8 & 1.09 & 0.068 & 0.38 \\
			Soil & 0.1 & 5.9 & 3 & 0.1 & 0.35 \\
			Gravel & 100 & 6 & 3 & 0.04 & 0.3 \\
			Clay loam & 0.2496 & 3.6 & 1.56 & 0.078 & 0.43 \\
			Sand & 7.128 & 14.5 & 2.68 & 0.045 & 0.43 \\
			Repository & 0.1 & 14.5 & 3 & 0.045 & 0.4
		\end{tabular}
\end{adjustbox}
\caption{Media properties for the realistic case}
\end{table}
\end{document}

math

"First-order continuation method for steady-state variably saturated groundwater flow modeling" by Denis Anuprienko, arXiv:2201.05202v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrr}
\toprule
\bf LLM & \bf T1 & \bf T2 & \bf T3 & \bf T4 & \bf T5 & \bf en \\
\midrule
\texttt{Phi-3.5-mini-instruct} & 18.9 & 32.6 & 30.7 & 28.8 & 34.4 & 52.6 \\
\texttt{gemma-2-9b-it} & 29.2 & 40.9 & 36.4 & 33.5 & 35.3 & 52.8 \\
\texttt{Meta-Llama-3-8B-Instruct} & \textbf{31.1} & \underline{43.0} & 39.4 & 35.9 & 36.4 & 56.6 \\
\texttt{Qwen2.5-7B-Instruct} & \underline{30.7} & \textbf{43.7} & \underline{42.0} & \underline{38.1} & \underline{40.5} & \textbf{62.7} \\
\texttt{aya-expanse-8b} & 28.3 & 42.5 & \textbf{43.0} & \textbf{39.8} & \textbf{40.9} & \underline{59.9} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Comparison between different LLM backbones all trained with the instruct tuning data with L100 languages and 50\% English (as in \S).}
\end{table}
\end{document}

cs

Gregor Geigle
Florian Schneider
Carolin Holtermann
Chris Biemann
Radu Timofte
Anne Lauscher
Goran Glavaš

"Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model" by Gregor Geigle, Florian Schneider, Carolin Holtermann, Chris Biemann, Radu Timofte, Anne Lauscher, and Goran Glavaš, arXiv:2501.05122v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Benchmark results for WideResNet. We plot means obtained over the 10 runs and 95\% confidence intervals computed using the t-distribution. Stopping metrics are evaluated on the validation set (10\% of training data). All metrics are reported for the best model as selected by the corresponding stopping metric, after TS (fitted using the validation set).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccc}
\toprule
& & Logloss & Brier & Accuracy & ECE & smECE \\
Dataset & Stopping metric & & & \\
\midrule
\multirow{4}{*}{\footnotesize CIFAR-10} & Logloss & $0.123$ \footnotesize{$\pm 0.004$} & $0.061$ \footnotesize{$\pm 0.002$} & $95.9\%$ \footnotesize{$\pm 0.2$} & $\bf 0.006$ \footnotesize{$\pm 0.001$}& $\bf 0.008$ \footnotesize{$\pm 0.000$} \\
 & Brier score & $0.121$ \footnotesize{$\pm 0.002$} & $\bf 0.056$ \footnotesize{$\pm 0.001$} & $\bf 96.4\%$ \footnotesize{$\pm 0.1$} & $\bf 0.006$ \footnotesize{$\pm 0.001$}& $\bf 0.008$ \footnotesize{$\pm 0.001$} \\
 & Accuracy & $0.122$ \footnotesize{$\pm 0.002$} & $\bf 0.056$ \footnotesize{$\pm 0.001$} & $\bf 96.4\%$ \footnotesize{$\pm 0.1$} & $\bf 0.006$ \footnotesize{$\pm 0.001$}& $\bf 0.008$ \footnotesize{$\pm 0.001$} \\
 & TS-refinement & $\bf 0.120$ \footnotesize{$\pm 0.002$} & $\bf 0.056$ \footnotesize{$\pm 0.001$} & $96.3\%$ \footnotesize{$\pm 0.1$} & $0.007$ \footnotesize{$\pm 0.001$}& $\bf 0.008$ \footnotesize{$\pm 0.001$} \\
\midrule
\multirow{4}{*}{\footnotesize CIFAR-100} & Logloss & $0.798$ \footnotesize{$\pm 0.007$} & $0.300$ \footnotesize{$\pm 0.002$} & $78.4\%$ \footnotesize{$\pm 0.3$} & $0.020$ \footnotesize{$\pm 0.002$}& $0.020$ \footnotesize{$\pm 0.002$} \\
 & Brier score & $0.803$ \footnotesize{$\pm 0.003$} & $\bf 0.298$ \footnotesize{$\pm 0.001$} & $\bf 78.8\%$ \footnotesize{$\pm 0.1$} & $0.021$ \footnotesize{$\pm 0.002$}& $0.020$ \footnotesize{$\pm 0.001$} \\
 & Accuracy & $0.791$ \footnotesize{$\pm 0.011$} & $0.300$ \footnotesize{$\pm 0.002$} & $78.6\%$ \footnotesize{$\pm 0.2$} & $0.020$ \footnotesize{$\pm 0.003$}& $0.019$ \footnotesize{$\pm 0.002$} \\
 & TS-refinement & $\bf 0.771$ \footnotesize{$\pm 0.004$} & $\bf 0.298$ \footnotesize{$\pm 0.002$} & $78.5\%$ \footnotesize{$\pm 0.2$} & $\bf 0.016$ \footnotesize{$\pm 0.002$}& $\bf 0.016$ \footnotesize{$\pm 0.001$} \\
\midrule
\multirow{4}{*}{\footnotesize SVHN} & Logloss & $\bf 0.109$ \footnotesize{$\pm 0.002$} & $0.043$ \footnotesize{$\pm 0.001$} & $\bf 97.3\%$ \footnotesize{$\pm 0.1$} & $\bf 0.007$ \footnotesize{$\pm 0.001$}& $\bf 0.008$ \footnotesize{$\pm 0.001$} \\
 & Brier score & $0.110$ \footnotesize{$\pm 0.003$} & $\bf 0.042$ \footnotesize{$\pm 0.001$} & $\bf 97.3\%$ \footnotesize{$\pm 0.1$} & $\bf 0.007$ \footnotesize{$\pm 0.001$}& $\bf 0.008$ \footnotesize{$\pm 0.001$} \\
 & Accuracy & $0.112$ \footnotesize{$\pm 0.002$} & $\bf 0.042$ \footnotesize{$\pm 0.001$} & $\bf 97.3\%$ \footnotesize{$\pm 0.1$} & $0.008$ \footnotesize{$\pm 0.001$}& $0.009$ \footnotesize{$\pm 0.001$} \\
 & TS-refinement & $\bf 0.109$ \footnotesize{$\pm 0.001$} & $\bf 0.042$ \footnotesize{$\pm 0.000$} & $\bf 97.3\%$ \footnotesize{$\pm 0.1$} & $\bf 0.007$ \footnotesize{$\pm 0.001$}& $\bf 0.008$ \footnotesize{$\pm 0.001$} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Eugène Berta
David Holzmüller
Michael I. Jordan
Francis Bach

"Rethinking Early Stopping: Refine, Then Calibrate" by Eugène Berta, David Holzmüller, Michael I. Jordan, and Francis Bach, arXiv:2501.19195v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|lllll}
 & Overall & Ul & Co & Mu & Nn \\ \hline
M\textsubscript{0} & 14.1 & 18.5 & 14.0 & 10.8 & 13.2 \\ \hline
M\textsubscript{1} & 12.8 & 15.3 & 13.1 & 10.2 & 12.6 \\
+LM & 10.9 & 12.7 & 11.6 & 8.8 & 10.4
\end{tabular}
\caption{ASR performance breakdown by speaker variety of MíleGlór test set.}
\end{table}
\end{document}

eess

Liam Lonergan
Ibon Saratxaga
John Sloan
Oscar Maharog
Mengjie Qian
Neasa Ní Chiaráin
Christer Gobl
Ailbhe Ní Chasaide

"Fotheidil: an Automatic Transcription System for the Irish Language" by Liam Lonergan, Ibon Saratxaga, John Sloan, Oscar Maharog, Mengjie Qian, Neasa Ní Chiaráin, Christer Gobl, and Ailbhe Ní Chasaide, arXiv:2501.00509v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Collision-based Dynamics for Multi-Marginal Optimal Transport" by Mohsen Sadr and Hossein Gorji, arXiv:2412.16385v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Advanced Statistical Metrics for AI-Generated Surveys}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\hline
\textbf{Survey Type} & \textbf{MAE} & \textbf{RMSE} & \textbf{JS Distance} & \textbf{Entropy} & \textbf{|Conditional Entropy|} & \textbf{Cramér's V} \\
\hline
Naive AI Survey & 10.53 & 11.35 & 0.19 & 3.54 & 0.24 & 0.35 \\
Structured AI Survey & 6.69 & 7.39 & 0.13 & 3.48 & 0.18 & 0.74 \\
Guided AI Survey & 3.37 & 4.65 & 0.067 & 3.28 & 0.014 & 0.68 \\
Naive Persona-based AI Survey & 9.02 & 10.28 & 0.157 & 3.48 & 0.19 & 0.73 \\
Structured Persona-based AI Survey & 8.53 & 9.73 & 0.152 & 3.48 & 0.18 & 0.73 \\
Guided Persona-based AI Survey & 0.03 & 0.17 & 0.0016 & 3.30 & 0.0005 & 1.00 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ioannis Tzachristas
Santhanakrishnan Narayanan
Constantinos Antoniou

"Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs?" by Ioannis Tzachristas, Santhanakrishnan Narayanan, and Constantinos Antoniou, arXiv:2501.13955v1, 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{Averaged (over 200 replicates) mean squared error (MSE, $\times 10^4$), standard deviation (SD, $\times 10^4$) with $\hat x$ computed using SMCOms-BR, \texttt{nlm} and \texttt{asm} in Example 2.}
\begin{tabular}{ccccccccccc}
\cline{1-11} % Modified to 1-11
\multicolumn{2}{c}{Method} && \multicolumn{2}{c}{ $\beta$} && \multicolumn{2}{c}{MSE} && \multicolumn{2}{c}{SD}\\
\cline{1-11} % Modified to 1-11
\multicolumn{2}{c}{SMCOms-BR} && \multicolumn{2}{c}{1} && \multicolumn{2}{c}{\textbf{0.565}} && \multicolumn{2}{c}{\textbf{0.116}}\\
\multicolumn{2}{c}{} && \multicolumn{2}{c}{2} && \multicolumn{2}{c}{\textbf{0.368}} && \multicolumn{2}{c}{\textbf{0.905}}\\
\cline{1-11} % Modified to 1-11
\multicolumn{2}{c}{\texttt{nlm}} && \multicolumn{2}{c}{1} && \multicolumn{2}{c}{0.573} && \multicolumn{2}{c}{0.117}\\
\multicolumn{2}{c}{} && \multicolumn{2}{c}{2} && \multicolumn{2}{c}{0.374} && \multicolumn{2}{c}{0.916}\\
\cline{1-11} % Modified to 1-11
\multicolumn{2}{c}{\texttt{asm}} && \multicolumn{2}{c}{1} && \multicolumn{2}{c}{0.629} && \multicolumn{2}{c}{0.133}\\
\multicolumn{2}{c}{} && \multicolumn{2}{c}{2} && \multicolumn{2}{c}{0.403} && \multicolumn{2}{c}{1.018}\\
\cline{1-11} % Modify to 1-11
\end{tabular}
\end{table}
\end{document}

stat

Xiaohong Chen
Zengjing Chen
Wayne Yuan Gao
Xiaodong Yan
Guodong Zhang

"Optimization via Strategic Law of Large Numbers" by Xiaohong Chen, Zengjing Chen, Wayne Yuan Gao, Xiaodong Yan, and Guodong Zhang, arXiv:2412.05604v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Moment-based multi-resolution HWENO scheme for hyperbolic conservation laws" by Jiayin Li, Chi-Wang Shu, and Jianxian Qiu, arXiv:2201.12193v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\hline
\textbf{Speaker} & \textbf{Female} & \textbf{Male}\\
\hline
Caller & 72.51 & 27.49 \\
Advisor & 81.78 & 18.22 \\
\end{tabular}
\end{adjustbox}
\caption{Percentage of genders in the SESHA dataset.}
\end{table}
\end{document}

cs

Melissa Torgbi
Andrew Clayman
Jordan J. Speight
Harish Tayyar Madabushi

"Adapting Whisper for Regional Dialects: Enhancing Public Services for Vulnerable Populations in the United Kingdom" by Melissa Torgbi, Andrew Clayman, Jordan J. Speight, and Harish Tayyar Madabushi, arXiv:2501.08502v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparisons for spacecraft rendezvous missions}
\begin{tabular}{c|ccc|ccc}
\hline \hline
Method & \multicolumn{3}{c|}{LEO} & \multicolumn{3}{c}{Molniya Orbit} \\
 & Avg. Time (s) & Worst-case (s) & $\Delta V$ (km/s) & Avg. Time (s) & Worst-case (s) & $\Delta V$ (km/s) \\
\hline
TSG & 0.1476 & 0.2057 & 1.2939 & 0.0852 & 0.1665 & 0.3558 \\
L-TSG & 0.0233 & 0.0328 & 2.2477 & 0.0256 & 0.0323 & 0.9889 \\
\hline \hline
\end{tabular}
\end{table}
\end{document}

eess

Taehyeun Kim
Robin Inho Kee
Ilya Kolmanovsky
Anouck Girard

"Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor" by Taehyeun Kim, Robin Inho Kee, Ilya Kolmanovsky, and Anouck Girard, arXiv:2412.05748v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Dataset Summary}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\toprule
Data Type & Source & Time Range & Sample Size \\
\midrule
Stock Price Data & CSMAR & 2005-2022 & 3.2M rows \\
Financial Text & Listed Company Announcements & 2005-2022 & 5.2M documents \\
Macro Data & National Bureau of Statistics & 2005-2022 & 18 indicators \\
Event Data & GDELT & 2005-2022 & 127K events \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Lumen AI
Tengzhou No. 1 Middle School
Shihao Ji
Zihui Song
Fucheng Zhong
Jisen Jia
Zhaobo Wu
Zheyi Cao
Xu Tianhao

"Chinese Stock Prediction Based on a Multi-Modal Transformer Framework: Macro-Micro Information Fusion" by Lumen AI, Tengzhou No. 1 Middle School, Shihao Ji, Zihui Song, Fucheng Zhong, Jisen Jia, Zhaobo Wu, Zheyi Cao, and Xu Tianhao, arXiv:2501.16621v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \hline
 Region & (1) & (2) & ARIMA \\
 \hline
 Matara& 9.33& \bf{8.62} & 9.54\\
 Anuradhapura&10.25&\bf{9.88}&12.2\\
 Polonnaruwa & 7.20&\bf{6.08}&7.57\\
 Kurunegala & 17.50 &\bf{16.80} & 17.50 \\
 Hambantota & 41.28& \bf{39.77}& 41.28\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{MAE errors of different models}
\end{table}
\end{document}

math

"An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics" by Zhijian Li, Jack Xin, and Guofa Zhou, arXiv:2201.09394v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Example~:} Relative errors for the model problem with {the coefficient} given by Figure \,.\,$h=2^{-10}$\,,\,\,$H=2^{-5}$\,,\,\,$\gamma_{0}=10$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|} \hline
{ Relative error} & { Energy norm}
& $L^{2}$ & {$L^{\infty}$} \\ \hline
{ LODM } & 0.4938e-01 & 0.4882e-02 & 0.1889e-01 \\ \hline
{ FE-LODM } & 0.2169e-01 & 0.7238e-03 & 0.1248e-02\\ \hline
{ FE-OMsPGM} & 0.1063e-00 & 0.5564e-02 & 0.2580e-00 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
		\hline
		{Test} & {Method} & {$T = 0.01$} & {$T = 0.03$} & {$T = 0.05$} \\
		\hline
		{Test I (a)}
		& {SG} & {0.5696} & {1.3974} & {2.3244} \\
		& {SC} & {10.9827} & {30.1244} & {48.1486} \\
		{Test I (b)}
		& {SG} & {6.3211} & {17.9350} & {29.6033} \\
		& {SC} & {18.4569} & {54.1962} & {89.4438} \\
		\hline
	\end{tabular}
\caption{{CPU time (units in seconds) of SG and SC at different times for Test I.}}
\end{table}
\end{document}

math

"Efficient stochastic asymptotic-preserving scheme for tumor growth models with uncertain parameters" by Ning Jiang, Liu Liu, and Huimin Yu, arXiv:2503.11468v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small{Statistics for the case of transient subsurface pressure data in a binary channelized domain when considering the 1000 independent test models. The comparison is made between the predicted model (Predicted \#1) and the closest training model in data space (Closest TR) within the training set of 20,000 examples using the RMSE in data space ( RMSE$_{\rm data}$), the ${\rm l}_1$-norm (${\rm l}_1$) and the structural similarity index (SSIM). For each metric, the minimum (Min), median (Median) and maximum (Max) values are reported together with the 10$^{\rm th}$, 25$^{\rm th}$, 75$^{\rm th}$ and 90$^{\rm th}$ percentiles (P10, P25, P75, P90), respectively. The TR size variable signifies the number of training examples used to train \textit{vec2pix}.}}
\begin{tabular}{ccccccccc}%
			\hline
			Model & TR size & Min & P10 & P25 & Median & P75 & P90 & Max\\
			\hline
			& & & & & & & & \\
			\multicolumn{9}{c}{RMSE$_{\rm data}$ (m)} \\
			Closest TR & 20,000 & 0.011 & 0.024 & 0.030 & 0.043 & 0.064 & 0.092 & 0.385\\
			Predicted & 20,000 & 0.011 & 0.016 & 0.020 & 0.031 & 0.083 & 0.248 & 0.713\\
			Predicted & 10,000 & 0.011 & 0.018 & 0.023 & 0.039 & 0.105 & 0.276 & 8.981 \\
			Predicted & 5000 & 0.011 & 0.020 & 0.028 & 0.051 & 0.128 & 0.308 & 8.981 \\
			\multicolumn{9}{c}{${\rm l}_1$ (m)}\\
			Closest TR & 20,000 & 548 & 2335 & 3251 & 4359 & 5665 & 7049 & 11160 \\
			Predicted & 20,000 & 316 & 1064 & 1436 & 1990 & 2559 & 3222 & 6634\\
			Predicted & 10,000 & 348 & 1174 & 1608 & 2154 & 2838 & 3504 & 6692 \\
			Predicted & 5,000 & 456 & 1316 & 1796 & 2415 & 3171 & 4015 & 6750 \\
			\multicolumn{9}{c}{SSIM (-)}\\
			Closest TR & 20,000 & 0.11 & 0.37 & 0.47 & 0.55 & 0.64 & 0.72 & 0.89 \\
			Predicted & 20,000 & 0.36 & 0.62 & 0.67 & 0.73 & 0.79 & 0.84 & 0.93 \\
			Predicted & 10,000 & 0.34 & 0.59 & 0.66 & 0.71 & 0.77 & 0.82 & 0.93 \\
			Predicted & 5,000 & 0.34 & 0.56 & 0.62 & 0.70 & 0.76 & 0.81 & 0.91 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Eric Laloy
Niklas Linde
Diederik Jacques

"Approaching geoscientific inverse problems with vector-to-image domain transfer networks" by Eric Laloy, Niklas Linde, and Diederik Jacques, arXiv:1912.09954v3, 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}{crlll}
 Coefficients & Isolated & Band(3) &TIGER & GLASSO\\
${\beta}_1$ & -0$\cdot $78 (1$\cdot$59) & 0$\cdot$64 (0$\cdot$73) & 1$\cdot $58 (1$\cdot$07) & 0$\cdot$56 (0$\cdot$47) \\
${\beta}_2$ & -0$\cdot $68 (1$\cdot$30) & 1$\cdot$01 (0$\cdot$80) & 1$\cdot $91 (1$\cdot$00) & 1$\cdot$99 (0$\cdot$47)* \\
${\beta}_3$ & -0$\cdot $85 (1$\cdot$47) & 1$\cdot$91 (0$\cdot$66)* & 2$\cdot $80 (1$\cdot$04)* & 1$\cdot$34 (0$\cdot$62)* \\

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

stat

Thien-Minh Le
Ping-Shou Zhong
Chenlei Leng

"Testing the Graph of a Gaussian Graphical Model" by Thien-Minh Le, Ping-Shou Zhong, and Chenlei Leng, arXiv:2212.06931v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sound Classification of Four Insect Classes" by Yinxuan Wang and Sudip Vhaduri, arXiv:2412.12395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lr}
\hline
\textbf{Statistic} & \textbf{Value} \\
\hline
Count & 3656.00 \\
Mean & 29.22 \\
Standard Deviation (std) & 12.84 \\
Minimum (min) & 0 \\
25th Percentile & 18.25 \\
Median (50\%) & 33.25 \\
75th Percentile & 40.25 \\
Maximum (max) & 48.50 \\
\hline
\end{tabular}
\caption{Descriptive Statistics for production - Zarco Use case 1}
\end{table}
\end{document}

stat

Dimitrios P. Panagoulias
Elissaios Sarmas
Vangelis Marinakis
Maria Virvou
George A. Tsihrintzis

"Integrating Dynamic Correlation Shifts and Weighted Benchmarking in Extreme Value Analysis" by Dimitrios P. Panagoulias, Elissaios Sarmas, Vangelis Marinakis, Maria Virvou, and George A. Tsihrintzis, arXiv:2411.13608v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
Method & Rel. $L_2$ error in $\rho$& Norms & Theoretical Bounds\\ \hline
PINN using AA & 6.5783e-03 &100.00\% & 100.00\% \\ \hline
XPINN-1 & \multirow{2}{*}{3.3864e-03} & 125.31\% & \multirow{2}{*}{83.23\%} \\ \cline{1-1} \cline{3-3}
XPINN-2 & & 32.59\% & \\ \hline
\end{tabular}
\end{adjustbox}
\caption{The Rademacher complexity and the theoretical bounds for the Expansion wave problem. These are computed using the relative $L_2$ error in density.}
\end{table}
\end{document}

math

Ameya D. Jagtap
Zhiping Mao
Nikolaus Adams
George Em Karniadakis

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


math

Xianyi Zeng
Giovanni Stabile
Efthymios N. Karatzas
Guglielmo Scovazzi
Gianluigi Rozza

"Embedded domain Reduced Basis Models for the shallow water hyperbolic equations with the Shifted Boundary Method" by Xianyi Zeng, Giovanni Stabile, Efthymios N. Karatzas, Guglielmo Scovazzi, and Gianluigi Rozza, arXiv:2201.09546v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccc|}
 \hline
 & Samples & Embeddable samples & Proportion of embeddable\\
 \hline
$V_4^{Markov}$ & $10^7$ & $173455$ & $0.0173455$ \\
 $V_{\rm{DLC}}$ & $1021195$ & $172380$ & $0.1688022$\\
 $V_{\rm{DD}}$ & $156637 $ & $49471$ & $0.3158321$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{ Embeddable matrices within {\color{black} $4\times 4$ CS Markov matrices and its intersection with DLC matrices and DD matrices.}}
\end{table}
\end{document}

math

Muhammad Ardiyansyah
Dimitra Kosta
Jordi Roca-Lacostena

"Embeddability of centrosymmetric matrices capturing the double-helix structure in natural and synthetic DNA" by Muhammad Ardiyansyah, Dimitra Kosta, and Jordi Roca-Lacostena, arXiv:2202.05889v2, 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 Sparsity of Three Widely Adopted Public Datasets}
\begin{tabular}{l|l}
\hline
Dataset & Sparsity \\ \hline
UK-DALE & 99.86\% \\ \hline
TEALD & 99.29\% \\ \hline
REDD & 98.97\% \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Haoxiang Wang
Jiasheng Zhang
Chenbei Lu
Chenye Wu

"Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective" by Haoxiang Wang, Jiasheng Zhang, Chenbei Lu, and Chenye Wu, arXiv:2011.06205v1, 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 experiment results regarding the models trained using only Cross Entropy Loss (CEL) and Triplet Entropy Loss (TEL). The results in the table show the accuracy ($\%$) of the models on unseen NCHLT speaker data.}
\begin{tabular}{ccccc}
\hline
\textbf{\textit{Model}} & \textbf{\textit{CEL Baseline}} & \textbf{\textit{CEL}} & \textbf{\textit{TEL Baseline}} & \textbf{\textit{TEL}} \\ \hline
CRNN & 71 & 70 & \textbf{75} & 74 \\
Densenet121 (Imagenet) & 78 & 78 & 79 & \textbf{81} \\
Densenet121 & 75 & 74 & 75 & \textbf{77} \\
Resnet50 (Imagenet) & 76 & \textbf{78} & 77 & \textbf{78}\\
Resnet50 & 74 & 72 & 76 & \textbf{78}\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Triplet Entropy Loss: Improving The Generalisation of Short Speech Language Identification Systems" by Ruan van der Merwe, arXiv:2012.03775v1, 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|ccc}
& & & $\sigma$ &\\
\hline
& &1& 2& 4\\
\hline
& 0 & & S1 & S2\\
$\theta$ & 0.4& S3 & & \\
&0.8 & S4 & & \\
\end{tabular}
\caption{Parameter settings for the noise.}
\end{table}
\end{document}

stat

"Preprocessing noisy functional data: a multivariate perspective" by Siegfried Hörmann and Fatima Jammoul, arXiv:2012.05824v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
\toprule
Statistic & \\ \midrule
\# Data scale & 21,342 \\
\# Average tool invocation rounds per data & 2.46 \\
\# Average APIs required per data & 3.48 \\
\% Percentage of parallel tool invocation data & 99.1\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Several important characteristics of DTA-Tools are presented in the table.}
\end{table}
\end{document}

cs

Dongsheng Zhu
Weixian Shi
Zhengliang Shi
Zhaochun Ren
Shuaiqiang Wang
Lingyong Yan
Dawei Yin

"Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation" by Dongsheng Zhu, Weixian Shi, Zhengliang Shi, Zhaochun Ren, Shuaiqiang Wang, Lingyong Yan, and Dawei Yin, arXiv:2501.12432v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Shi Bo
AmirEmad Ghassami
Debarghya Mukherjee

"A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects" by Shi Bo, AmirEmad Ghassami, and Debarghya Mukherjee, arXiv:2412.08827v3, 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{Means of measures grouped by placement and labeling methods. Bold p-values indicate when the difference of mean measures between placement methods is significant.\newline DR = placement by dimensionality reduction.}
\begin{tabular}{lllll}
 \toprule
 \textbf{Measure}& \textbf{Label}& \multicolumn{2}{c}{\textbf{Placement}} & \textbf{p-value}\\
 \cmidrule(r){3-4}
 &&\textbf{DR} &\textbf{Random} & \\
 \midrule
 Time (s) &&&&\\
 & Baseline & 12.3 & 12.7 & 0.54\\
 & Shape & 10.7 & 14.3 & \textbf{0.0008}\\
 & Color & 11.6 & 13.7 & 0.051\\
 & Texture & 11.6 & 15.7 &\textbf{0.0005}\\
 Hovered&&&&\\
 samples& Baseline & 14.9 & 17.9 & \textbf{0.04}\\
 & Shape & 8.7 & 12.5 & \textbf{0.004}\\
 & Color & 13.8 & 15.7 & 0.16\\
 & Texture & 13.5 & 15.3 &0.30\\
 Distance &&&&\\
 (pixels)& Baseline & 2807 & 3094 & 0.12\\
 & Shape & 2313 & 3123 & \textbf{0.0005}\\
 & Color & 2765 & 3588 & \textbf{0.002}\\
 & Texture & 2681 & 3403 & \textbf{0.009}\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"A proposal and evaluation of new timbre visualisation methods for audio sample browsers" by Etienne Richan and Jean Rouat, arXiv:2011.15096v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|r}
 & Learning Rate \\
 \midrule
 Color & $2.5 \times 10^{-3}$ \\
 SH Coefficients & $1.25 \times 10^{-4}$ \\
 Opacity & $2.5 \times 10^{-2}$ \\
 Rotation & $1 \times 10^{-3}$ \\
 Distance \dag & $2.6^{-1} \times 10^{-4}$
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"LinPrim: Linear Primitives for Differentiable Volumetric Rendering" by Nicolas von Lützow and Matthias Nießner, arXiv:2501.16312v3, 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}{lllll}
 \toprule
 Dataset & Scaling factor & EPE & Acc 01 & Acc 005 \\ \midrule
 Multi ShapeNet & Zero & 0.4043 & 5.60\% & 0.76\% \\
 Multi ShapeNet & $\frac{1}{\sqrt{l + 1}}$ & \textbf{0.3497} & \textbf{15.27}\% & \textbf{1.76}\% \\
 Multi ShapeNet & $\frac{1}{l + 1}$ & 0.3850 & 7.57\% & 1.12\% \\
 Multi ShapeNet & $\frac{1}{(l + 1)^2}$ & 0.4137 & 7.33\% & 1.23\% \\ \midrule
 FlyingThings3D & Zero & 0.6500 & 4.41\% & 0.5\% \\
 FlyingThings3D & $\frac{1}{\sqrt{l + 1}}$ & 0.6514 & 5.81\% & 0.9\% \\
 FlyingThings3D & $\frac{1}{l + 1}$ & \textbf{0.5629} & 5.52\% & 0.89\% \\
 FlyingThings3D & $\frac{1}{(l + 1)^2}$ & 0.6395 & \textbf{6.54}\% & \textbf{1.12}\% \\ \bottomrule
 \end{tabular}
\caption{FlowNet3D trained on Adversarial Metric using different scaling factors for the Multi-Scale Triplet loss. The scaling factors are functions that use the level $l$ of the activations. The activations are organized from last to first. Zero only used the last activation.}
\end{table}
\end{document}

eess

"Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation" by Victor Zuanazzi, arXiv:2011.07945v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Reward Components for AMP Control}
\begin{tabular}{cc}
\hline
\textbf{Components} & \textbf{Formula} \\
\hline
 \multicolumn{2}{c}{\textbf{Imitation}} \\
\hline
AMP Reward & $-\log D_\phi(s)$ \\
\hline
 \multicolumn{2}{c}{\textbf{Motion Quality}} \\
\hline
Joint rate & $\exp(- (\dot{q} - \dot{q}_{\text{target}})^2 \sigma^2)$ \\
\hline
 \multicolumn{2}{c}{\textbf{Safety}} \\
\hline
Foot stumble & $\exp(- (F_{\text{max}} - F)^2 / \sigma^2)$ \\
Foot orientation & $r = \exp(- \|\vec{n}_{\text{feet}} - \vec{n}_{\text{ref}}\|^2 / \sigma^2)$ \\
Foot height & $r = \exp(- (h_{\text{feet}} - h_{\text{ref}})^2 / \sigma^2)$ \\
\hline
 \multicolumn{2}{c}{\textbf{Task}} \\
\hline
Linear velocity & $\exp(- (v_{\text{cmd}} - v_{\text{base}})^2 / \sigma^2)$ \\
Angular velocity & $\exp(- (\omega_{\text{cmd}} - \omega_{\text{base}})^2 / \sigma^2)$ \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Arturo Flores Alvarez
Fatemeh Zargarbashi
Havel Liu
Shiqi Wang
Liam Edwards
Jessica Anz
Alex Xu
Fan Shi
Stelian Coros
Dennis W. Hong

"Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids" by Arturo Flores Alvarez, Fatemeh Zargarbashi, Havel Liu, Shiqi Wang, Liam Edwards, Jessica Anz, Alex Xu, Fan Shi, Stelian Coros, and Dennis W. Hong, arXiv:2509.05581v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{hhline}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fusion techniques considered and their corresponding f-measures. The variance-based is preferable. We use as $S_1$ and $S_2$ the static and temporal scores respectively.}
\begin{tabular}{ccc}
\hline
Fusion & Form & f-measure \\ \hhline{===}
Linear & $\sum_i w_i S_i$ & 0.807 \\
Min & min$(S_i)$ & 0.798 \\
Max & max$(S_i)$ & 0.798 \\
Exponential & $\sum_i (1-w_t)S_i, \ \ w_t = d e^{1-d}, \ \ d = \text{max}(\text{frame}_1) - \text{min}(\text{frame}_2))$ & 0.803 \\
Logarithmin & $\sum_i \text{min}(S_i - w_i) + \text{max}(w_i), \ \ w_i =\log(1/\text{var}(S_i))$ & 0.821 \\
Complex & $\sqrt{S^2}, \ \ S = S_1 + i S_2$ & 0.826 \\
Harmonic Mean & $2 \times \prod_i S_i / \sum_i S_i$ & 0.810 \\ \hhline{===}
Variance-based & $\sum_i S_i/\text{var}(S_i)$ & \textbf{0.835} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

George Pantazis
George Dimas
Dimitris K. Iakovidis

"SalSum: Saliency-based Video Summarization using Generative Adversarial Networks" by George Pantazis, George Dimas, and Dimitris K. Iakovidis, arXiv:2011.10432v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Cascade Attentive Dropout for Weakly Supervised Object Detection" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{System Performance Analysis for Case I, II, and III}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 & Case I & Case II & Case III \\
 \hline
Normalized PI & 1 & 1.82 & 1.34 (Mean)
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mehdi Hosseinzadeh
Bruno Sinopoli
Ilya Kolmanovsky
Sanjoy Baruah

"ROTEC: Robust to Early Termination Command Governor for Systems with Limited Computing Capacity" by Mehdi Hosseinzadeh, Bruno Sinopoli, Ilya Kolmanovsky, and Sanjoy Baruah, arXiv:2201.02856v1, 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|c|c|c|c|c|}
 \hline
 Dataset & Model & \multicolumn{4}{c|}{F1} & \multicolumn{4}{c|}{Iou}\\
 & & Material & Pore & Inclusion & All & Material & Pore & Inclusion & All \\\hline
 Synthetic & SAM & & & & & & & & \\
 Ind & UNet2.5 & & & & & & & & \\\hline
 Real & SAM & & & & & & & & \\
 InD & UNet2.5 & & & & & & & & \\\hline
 Real OoD, & SAM & & & & & & & & \\
 more pore, few-Incl. & UNeT2.5D & & & & & & & & \\\hline
 Synthetic OoD, & SAM & & & & & & & & \\
 No-Incl. & UNeT2.5 & & & & & & & & \\\hline
 Real OoD, & SAM & & & & & & & & \\
 less pore, more-Incl. & UNeT2.5D & & & & & & & & \\\hline
 Real OoD, & SAM & & & & & & & & \\
 No-Incl & UNeT2.5 & & & & & & & & \\\hline

 \end{tabular}
\caption{Performance of Finetuning Step 1 on for InD, weak and strong OoD in terms of F1 and IoU for each class (material, pore, inclusion) and combined classes. The higher score is the better.}
\end{table}
\end{document}

eess

"Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing" by Anika Tabassum and Amirkoushyar Ziabari, arXiv:2412.11381v1, 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{Impact of reduced communication and link failure on controller performance }
\begin{tabular}{ccccc}
\hline
 cases & $|f-60|$ & MPSI & $V_error$ & MQSI \\
\hline
Full communication & 0.00 & 0.00 & 0.002 & 0.04\\
Reduced communication & 0.00 & 0.00 & 0.002 & 0.14\\
Link failure & 0.00 & 0.00 & 0.002 & 0.05\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

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


stat

"On the improved estimation of ordered parameters based on doubly type-II censored sample" by Shrajal Bajpai and Lakshmi Kanta Patra, arXiv:2411.06888v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Statistical Learning for Individualized Asset Allocation" by Yi Ding, Yingying Li, and Rui Song, arXiv:2201.07998v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 Grid & Timestep & $L_{1}$-error & order & $L_{\infty}$-error & order\\\hline
 32$^2$ & 1.e-2 & 1.97e-03 & -- & 4.86e-03 & -- \\
 64$^2$ & 2.5e-3 & 3.80e-04 & 2.37 & 6.40e-04 & 2.93\\
 128$^2$ & 6.25e-4 & 8.31e-05 & 2.19 & 1.41e-04 & 2.17\\
 256$^2$ & 1.56e-4 & 2.00e-05 & 2.05 & 6.06e-05 & 1.23\\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Convergence of the temperature field, melting solid}
\end{table}
\end{document}

math

A. Limare
S. Popinet
C. Josserand
Z. Xue
A. Ghigo

"A hybrid level-set / embedded boundary method applied to solidification-melt problems" by A. Limare, S. Popinet, C. Josserand, Z. Xue, and A. Ghigo, arXiv:2202.08300v1, 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{Effect of the initialization in Federated Distillation on the maximum Accuracy achieved after 20 communication rounds. Display are mean and standard deviation over 10 runs of Federated Distillation with a client participation rate of 0.4. Our proposed dual distillation approach closes the gap between random initialization and initialization from previous. }
\begin{tabular}{llllll}
\toprule
 & & & Init Random & Init Prev. & Dual Distill \\
& & $\alpha$ & & & \\
\midrule
LeNet & & 100.0 & 0.977(0.001) & \textbf{0.985(0.001)} & 0.981(0.001) \\
& & 1.0 & 0.974(0.001) & \textbf{0.982(0.001)} & 0.980(0.000) \\
& & 0.1 & 0.920(0.005) & \textbf{0.944(0.008)} & 0.915(0.005) \\
\midrule
ResNet & & 100.0 & 0.763(0.002) & 0.704(0.003) & \textbf{0.776(0.003)} \\
& & 1.0 & 0.738(0.002) & 0.691(0.001) & \textbf{0.752(0.003)} \\
& & 0.1 & 0.473(0.027) & \textbf{0.481(0.007)} & 0.474(0.021) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Felix Sattler
Arturo Marban
Roman Rischke
Wojciech Samek

"Communication-Efficient Federated Distillation" by Felix Sattler, Arturo Marban, Roman Rischke, and Wojciech Samek, arXiv:2012.00632v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Beatrice Ghitti
Eleuterio F. Toro
Lucas O. Müller

"Nonlinear lumped-parameter models for blood flow simulations in networks of vessels" by Beatrice Ghitti, Eleuterio F. Toro, and Lucas O. Müller, arXiv:2201.01617v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|llll|llll|llll}
 \toprule
 Method & \multicolumn{4}{c|}{$G_{pop33}$} & \multicolumn{4}{c|}{$G_{pop2060}$} & \multicolumn{4}{c}{$G_{pop1080}$} \\
 & Niche & Diverse & Popular & Overall & Niche & Diverse & Popular & Overall & Niche & Diverse & Popular & Overall \\
 \midrule
 ERM & 0.214 & 0.210 & 0.240 & 0.225 & 0.216 & 0.206 & 0.262 & 0.224 & 0.232 & 0.218 & \underline{0.268} & 0.227 \\
 CB & 0.208\textsuperscript{*} & 0.205 & 0.229\textsuperscript{*} & 0.217\textsuperscript{*} & 0.211\textsuperscript{*} & 0.202\textsuperscript{*} & 0.243\textsuperscript{*} & 0.216\textsuperscript{*} & 0.231 & 0.211\textsuperscript{*} & 0.242\textsuperscript{*} & 0.218\textsuperscript{*} \\
 CBlog & 0.213 & 0.211 & 0.242 & 0.225 & 0.218 & 0.209 & 0.263 & 0.226 & 0.233 & 0.219 & 0.265 & 0.228 \\
 IPW & 0.214 & 0.210 & 0.244\textsuperscript{*} & 0.226 & \underline{0.220}\textsuperscript{*} & 0.201\textsuperscript{*} & 0.266 & 0.224 & \underline{0.238}\textsuperscript{*} & 0.217 & 0.264 & 0.227 \\
 IPWlog & 0.214 & \underline{0.216}\textsuperscript{*} & 0.245\textsuperscript{*} & 0.228\textsuperscript{*} & 0.217 & 0.204 & 0.260 & 0.223 & 0.233 & 0.215 & 0.255\textsuperscript{*} & 0.224\textsuperscript{*} \\
 GDRO & \underline{0.215} & 0.213 & 0.248\textsuperscript{*} & 0.230\textsuperscript{*} & 0.217 & 0.210\textsuperscript{*} & \underline{0.267}\textsuperscript{*} & \underline{0.228}\textsuperscript{*} & 0.234 & \underline{0.220}\textsuperscript{*} & 0.264 & \underline{0.229} \\
 SDRO & 0.214 & 0.215\textsuperscript{*} & \underline{0.250}\textsuperscript{*} & \underline{0.230}\textsuperscript{*} & 0.219 & \underline{0.211}\textsuperscript{*} & 0.259 & 0.227\textsuperscript{*} & 0.233 & 0.220 & 0.266 & 0.228 \\
 CVaR & \textbf{0.225}\textsuperscript{*} & \textbf{0.228}\textsuperscript{*} & \textbf{0.256}\textsuperscript{*} & \textbf{0.239}\textsuperscript{*} & \textbf{0.232}\textsuperscript{*} & \textbf{0.225}\textsuperscript{*} & \textbf{0.269}\textsuperscript{*} & \textbf{0.239}\textsuperscript{*} & \textbf{0.244}\textsuperscript{*} & \textbf{0.229}\textsuperscript{*} & \textbf{0.268} & \textbf{0.237}\textsuperscript{*} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Group and overall NDCG@20 across popularity-based groups on the Retailrocket dataset. $*$ denotes statistically significant difference to ERM ($p< 0.05$, paired T-test). Best in bold, second best is underlined.}
\end{table}
\end{document}

cs

Krishna Acharya
David Wardrope
Timos Korres
Aleksandr Petrov
Anders Uhrenholt

"Improving Minimax Group Fairness in Sequential Recommendation" by Krishna Acharya, David Wardrope, Timos Korres, Aleksandr Petrov, and Anders Uhrenholt, arXiv:2501.18117v1, 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{Simulation Parameters}
\begin{tabular}{||c|c||}
		\hline
		Samulation Parameters & Values \\
		\hline\hline
		Carrier Frequency & 700 MHz \\
		System bandwidth & 5 MHz \\
		FFT size & 512 \\
		\multirow{2}{*}{CP types}&Extended for LTE-eMBMS\\& Normal for NR-PTP\\
	 DCI length & 12 bits \\
	 Aggregation Level & 1,2,4,8 \\
	 Subcarrier spacing & 15 kHz\\
	 Channel models & AWGN, TDL-A, TDL-C\\
	 \multirow{2}{*}{Channel estimation}&Perfect or 2-dimensional pilot based \\& estimation with linear interpolation\\
	 Equalizer & Minimum mean square error \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
exceptional pair & complex normal form & Unfolding monomials $< m,$
 $m=2$ & $r$ & $\sigma^{1}_{m}, \sigma^{2}_{m}$\\
\hline\hline
 \multirow{4}{*}{$(10,7)$} & $f_{\lambda_{1}\lambda_{2}}=(p(x),q(x))$ &$\{x_{2},x_{3},x_{4},x_{5}\}e_{1}$
 &5 & $\{x_{3}^{2},x_{4}^{2}\}e_{2}$ \\
 &$p(x)=\sum_{i=1}^{4}x_{i}^{2}$ & $\{x_{1}\}e_{2}$ & & \\
 & $q(x)=x_{2}^{2}+\lambda_{1}x_{3}^{2}+\lambda_{2}x_{4}^{2}+x_{5}^{2}$ & & & \\
 & $ \lambda_{i}\neq 0,1 \, i=1,2$ & & & \\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Bimodular strata}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Configurable design parameters of \mbox{BinArray}.}
\begin{tabular}{lll}
		\hline
		Parameter & Meaning & Effect\\
		\hline
		$N_{SA}$ & \# of parallel SA & throughput\\
		$D_{arch}$ & \# of output channels & throughput\\
		$M_{arch}$ & \# of binary tensors & throughput/accuracy\\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

"BinArray: A Scalable Hardware Accelerator for Binary Approximated CNNs" by Mario Fischer and Juergen Wassner, arXiv:2012.03481v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}\hline
		Nb& Dim&$T_\text{basis}$&$T_\text{ass}$&$T_\text{solve}$& $e_{L^2}$& $e_{H^1}$ \tabularnewline\hline
		/&274,625&/& 112.0 &2429.0&/&/ \tabularnewline\hline
		4+0 & 2916 & 10.2 & 121.5 & 65.2 & 1.97e-04 & 1.11e-01 \tabularnewline\hline
		6+0 & 4374 & 12.2 & 123.3 & 120.7 & 8.65e-05 & 6.12e-02	\tabularnewline\hline
		8+0 & 5832 & 14.2 & 121.0 & 190.8 & 5.98e-05 & 4.83e-02\tabularnewline\hline
		2+1 & 2187 & 12.0 & 123.6 & 40.8 & 8.60e-04 & 2.90e-01 \tabularnewline\hline
		3+1 & 2916 & 13.6 & 123.1 & 65.4 & 1.86e-04 & 1.06e-01 \tabularnewline\hline
		3+2 & 3645 & 19.3 & 123.2 & 100.0 & 1.31e-04 & 7.30e-02 \tabularnewline\hline
		4+2 & 4374 & 22.4 & 121.9 & 127.7 & 8.27e-05 & 5.39e-02 \tabularnewline\hline
		5+1 & 4374 & 17.7 & 124.0 & 128.4 & 7.35e-05 & 5.29e-02 \tabularnewline\hline
		4+1 & 3645 & 15.7 & 123.5 & 95.9 & 1.04e-04 & 6.88e-02 \tabularnewline\hline
		4+1(1 update) & 3645 & 21.4 & 123.2 & 96.6 & 7.82e-05 & 4.93e-02 \tabularnewline\hline
		4+1(3 updates) & 3645 & 32.2 & 123.9 & 97.2 & 5.67e-05 & 3.21e-02 \tabularnewline\hline
		5+1(1 update) & 4374 & 24.4 & 123.5 & 127.9 & 5.51e-05 & 3.76e-02 \tabularnewline\hline
		5+1(3 updates) & 4374 & 37.2 & 125.2 & 128.5 & 4.12e-05 & 2.44e-02 \tabularnewline\hline
	\end{tabular}
\end{adjustbox}
\caption{Computational performance comparison between the reference solution and GMsFEM solution, $K_2$, mixed boundary condition.}
\end{table}
\end{document}

math

"Generalized multiscale finite element method for highly heterogeneous compressible flow" by Shubin Fu, Eric Chung, and Lina Zhao, arXiv:2201.07371v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A comparison of learning rate selection methods in generalized Bayesian inference" by Pei-Shien Wu and Ryan Martin, arXiv:2012.11349v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

María Jaenada
Elena Castilla
Nirian Martín
Leandro Pardo

"Robust approach for comparing two dependent normal populations through Wald-type tests based on Rényi's pseudodistance estimators" by María Jaenada, Elena Castilla, Nirian Martín, and Leandro Pardo, arXiv:2202.00982v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Human evaluation on LRS3.} Mean opinion scores (MOS) (1-5) with 95\% confidence intervals for our VoxCeleb2-trained models evaluated on LRS3. VTTS (TV-ordered) achieves better performance in Intelligibility (3.62) and Synchronization (3.12), while both methods perform equally well in Naturalness (3.01). GT is an upper bound, while GT (discrete) shows degradation due to speech quantization. These results demonstrate our model maintains good perceptual quality even on out-of-distribution data. Numbers or prior methods are taken directly from their respective papers.}
\begin{tabular}{lcccccc}\toprule
 \textbf{Method} & \textbf{Intelligibility} ($\uparrow$) & \textbf{Naturalness} ($\uparrow$) & \textbf{Synchronization} ($\uparrow$) \\\midrule
 GT & 4.79 \textpm 0.05 & 4.79 \textpm 0.05 & 4.73 \textpm 0.06 \\
 GT (discrete) & 4.32 \textpm 0.11 & 3.80 \textpm 0.11 & 4.59 \textpm 0.07 \\
 \midrule
 \multicolumn{4}{@{}l}{\textbf{\textit{Lip to Speech Synthesis}}} \\
 Lip2Speech & 2.21 \textpm 0.08 & 2.20 \textpm 0.09 & 2.69 \textpm 0.08 \\
 SVTS & 2.17 \textpm 0.08 & 2.15 \textpm 0.09 & 2.71 \textpm 0.09 \\
 VCA-GAN & 2.19 \textpm 0.08 & 2.20 \textpm 0.09 & 2.71 \textpm 0.08 \\ \midrule
 \multicolumn{4}{@{}l}{\textbf{\textit{Dubbing Methods}}} \\
 HPMDubbing & - & 2.37 \textpm 0.14 & - \\
 StyleDubber & - & 1.79 \textpm 0.12 & - \\ \midrule
 VTTS (TV-ordered) & \textbf{3.62 \textpm 0.20} & \textbf{3.01 \textpm 0.22} & \textbf{3.12 \textpm 0.27 } \\
 VTTS (VT-ordered) & 3.30 \textpm 0.21 & \textbf{3.01 \textpm 0.17} & 2.35 \textpm 0.22 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Akshita Gupta
Tatiana Likhomanenko
Karren Dai Yang
Richard He Bai
Zakaria Aldeneh
Navdeep Jaitly

"Visatronic: A Multimodal Decoder-Only Model for Speech Synthesis" by Akshita Gupta, Tatiana Likhomanenko, Karren Dai Yang, Richard He Bai, Zakaria Aldeneh, and Navdeep Jaitly, arXiv:2411.17690v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of different quantization settings (4-bit, 16-bit) against full 32-bit precision for LLaMA3-3B and LLaMA3-8B models. We report AUROC scores for various uncertainty and representation-based detectors. \textbf{QueRE remains consistently strong across quantization settings.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccc}
 \toprule
 \textbf{Model (Quantization)} & \textbf{Pre-conf} & \textbf{Post-conf} & \textbf{Logprobs} & \textbf{Semantic Ent.} & \textbf{QueRE} \\
 \midrule
 \textbf{LLaMA3-3B (4bit vs 32bit)} & 0.61 & 0.59 & 0.57 & 0.71 & \textbf{0.99} \\
 \textbf{LLaMA3-3B (16bit vs 32bit)} & 0.50 & 0.51 & 0.51 & 0.58 & \textbf{0.99} \\
 \textbf{LLaMA3-8B (4bit vs 32bit)} & 0.67 & 0.50 & 0.61 & 0.63 & \textbf{0.98} \\
 \textbf{LLaMA3-8B (16bit vs 32bit)} & 0.51 & 0.54 & 0.55 & 0.56 & \textbf{0.97} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Predicting the Performance of Black-box LLMs through Self-Queries" by Dylan Sam, Marc Finzi, and J. Zico Kolter, arXiv:2501.01558v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|ccc|ccc|ccc|ccc|}
\hline
\multirow{2}{*}{Model Architecture} & \multicolumn{3}{c|}{Dice [\%]} & \multicolumn{3}{c|}{HD95 [mm]} & \multicolumn{3}{c|}{LW Dice [\%]} & \multicolumn{3}{c|}{LW HD95 [mm]} \\
\cline{2-13}
& WT & ET & TC & WT & ET & TC & WT & ET & TC & WT & ET & TC \\
\hline
RhizoNet & 83.46& \textbf{81.76}& \textbf{83.27}& 33.16& 25.78& 27.58& 49.07& 66.72& 67.25& 172.24& 87.11& 90.25\\
Combined Dataset &{ 94.46} & 80.87 & 80.39 & 4.23 & 14.55 & \textbf{9.10} & \textbf{93.96} & 77.86 & \textbf{76.24} & 4.30 & 26.29 & \textbf{25.58} \\
DAEnsemble & 94.42 & 75.78 & 73.86 & 4.14 & 15.85 & 20.12 & 93.76 & 72.95 & 70.20 & 4.24 & 27.60 & 36.54 \\
DANN & 94.47 & 81.68 & 77.30 & 3.97 & \textbf{14.47} & 25.10 & 93.86 & \textbf{78.86} & 71.26 & 4.06 & \textbf{26.23} & 31.90 \\
DANN w/o Grad. Reverse& \textbf{94.50} & 80.64 & 76.04 & \textbf{3.41} & 15.09 & 25.08 & 93.39 & 77.77 & 69.96 & \textbf{3.54} & 26.84 & 31.93 \\
\hline
\end{tabular}
\caption{Test performance metrics for the different training pipelines considered. Metrics include Dice, HD95, lesion-wise Dice (LW Dice), and lesion-wise HD95 for whole tumor (WT), tumor core (TC) and ET.}
\end{table}
\end{document}

eess

Juampablo E. Heras Rivera
Agamdeep S. Chopra
Tianyi Ren
Hitender Oswal
Yutong Pan
Zineb Sordo
Sophie Walters
William Henry
Hooman Mohammadi
Riley Olson
Fargol Rezayaraghi
Tyson Lam
Akshay Jaikanth
Pavan Kancharla
Jacob Ruzevick
Daniela Ushizima
Mehmet Kurt

"An Ensemble Approach for Brain Tumor Segmentation and Synthesis" by Juampablo E. Heras Rivera, Agamdeep S. Chopra, Tianyi Ren, Hitender Oswal, Yutong Pan, Zineb Sordo, Sophie Walters, William Henry, Hooman Mohammadi, Riley Olson, Fargol Rezayaraghi, Tyson Lam, Akshay Jaikanth, Pavan Kancharla, Jacob Ruzevick, Daniela Ushizima, and Mehmet Kurt, arXiv:2411.17617v1, 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{Optimized parameter values of Patch Antenna}
\begin{tabular}{|c|c|}
\hline
Parameter & Value($\mu$m) \\
\hline
Patch Length ($L_p$)	&36 \\
\hline
Patch Width ($W_p$)	&60\\
\hline
Ring Thickness	&5\\
\hline
Inset Feed Width ($W_1$)	&1\\
\hline
Inset Gap ($W_2$)	&5\\
\hline
Patch cut Width ($W_3$)&4\\
\hline
Patch cut Length ($l_4$)	&10\\
\hline
patch slot Length ($L_c$)	&20\\
\hline
Patch Slot Width ($W_c$) & 5\\
\hline
Substrate Length ($L_s$) & 130\\
\hline
Substrate Width ($W_s$) & 100\\
\hline
Substrate Thickness ($h$) & 45\\
\hline
Microstrip Length ($l_1$) & 40\\
\hline
Inset feed line Length ($l_2$) & 25\\
\hline
Inset cut Length ($l_3$) & 15 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Omar Osman
Abdullah Qayyum
Maziar Nekovee

"Partially Reflected Surface (PRS)-Loaded Graphene-Based Patch Antenna for 6G" by Omar Osman, Abdullah Qayyum, and Maziar Nekovee, arXiv:2507.17393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Values of $t_{n,p}$ such that Inequality holds for $t\geq t_{n,p}$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|lllllll|}
				\hline
				$t_{n,p}$&$3797$&$223$&$46$&$42,19$&$9$&$24,15$&$6$\\
				\hline
				$(n,p)$&$(7,7)$&$(8,2)$&$(9,3)$&$(10,2), (10,5)$&$(11,11)$&$(12,2), (12,3)$&$(13,13)$\\
				\hline
				$\nu$&15.7&10.3&9&8&7.5&7.5&7\\
				\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mamta Rani
Avnish K. Sharma
Sharwan K. Tiwari
Anupama Panigrahi

"Inverses of $r$-primitive $k$-normal elements over finite fields" by Mamta Rani, Avnish K. Sharma, Sharwan K. Tiwari, and Anupama Panigrahi, arXiv:2201.11334v1, 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{Contingency Table for McNemar's test for multiple choice questions without use of an image as part of the question.}
\begin{tabular}{lcc}
\toprule
 & \textbf{ChatGPT4 Correct} & \textbf{ChatGPT4 Incorrect} \\
\midrule
\textbf{ChatGPT3.5 Correct} & 39 & 6 \\
\textbf{ChatGPT3.5 Incorrect} & 17 & 1 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Equity in the Use of ChatGPT for the Classroom: A Comparison of the Accuracy and Precision of ChatGPT 3.5 vs. ChatGPT4 with Respect to Statistics and Data Science Exams" by Monnie McGee and Bivin Sadler, arXiv:2412.13116v1, 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{Sample table title}
\begin{tabular}{lll}
 \toprule
 \multicolumn{2}{c}{Part} \\
 \cmidrule(r){1-2}
 Name & Description & Size ($\mu$m) \\
 \midrule
 Dendrite & Input terminal & $\sim$100 \\
 Axon & Output terminal & $\sim$10 \\
 Soma & Cell body & up to $10^6$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

David Tran
Alex Valtchanov
Keshav Ganapathy
Raymond Feng
Eric Slud
Micah Goldblum
Tom Goldstein

"Analyzing the Machine Learning Conference Review Process" by David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, and Tom Goldstein, arXiv:2011.12919v2, 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
\textbf{Optical Configuration} & Cassegrain \\ \hline
\textbf{Mount configuration} & Alt-Azimuthal \\ \hline
\textbf{Primary aperture} & 9.0 m \\ \hline
\textbf{Primary depth} & 1.53 \\ \hline
\textbf{Secondary aperture} & 1.17 \\ \hline
\textbf{F/D} & 0.368 \\ \hline
\textbf{Surface rms (static)} & 0.5 mm \\ \hline
\textbf{Azimuth Travel} & 0º to 360º \\ \hline
\textbf{Azimuth Travel Rate} & 4 rpm \\ \hline
\textbf{Elevation Travel} & 35º to 90º \\ \hline
\textbf{Elevation Travel Rate} & 2º/s \\ \hline
\textbf{Maximum Operational Wind (5GHz)} & 25 km/h \\ \hline
\textbf{Survival Winds} & 150 km/h \\ \hline
\textbf{Reflector Weight} & 1900 kg \\ \hline
\textbf{Pedestal Weight} & 3500 Kg \\ \hline
\textbf{Foundation Size} & 4.5 x 4.5 x 0.75 m \\ \hline
\textbf{Concrete Volume} & 12 ms \\ \hline
\textbf{Beamwidth @ 5 GHz} & 44 arcmin \\ \hline
\textbf{Beamwidth @ 10 GHz} & 22 arcmin \\ \hline
\textbf{Pointing Accuracy (wind limited)} & \textless{}1/20 beamwidth \\ \hline
\textbf{Gain @ 5 GHz ($G$)} & 46 dB \\ \hline
\textbf{G/T (5 GHz)} & 39 dB/K \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

João Pandeirada
Miguel Bergano
João Neves
Paulo Marques
Domingos Barbosa
Bruno Coelho
Valério Ribeiro

"Development of the first Portuguese radar tracking sensor for Space Debris" by João Pandeirada, Miguel Bergano, João Neves, Paulo Marques, Domingos Barbosa, Bruno Coelho, and Valério Ribeiro, arXiv:2102.10457v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Descriptions of datasets used in the experiments}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
		\hline
		Dataset & \# Classes & \# Features & \# Samples \\
		\hline
		Heart & $2$ & $44$ & $267$ \\\hline
		Ionosphere & $2$ & $32$ & $351$\\\hline
 Parkinson & $2$ & $754$ & $756$ \\\hline
		Micromass & $10$ & $1087$ & $360$ \\\hline
 HTAD & $7$& $54$& $1386$\\\hline
 MNIST & $10$ & $784$ & $60000$ \\\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Nhan Phan
Thu Nguyen
Pål Halvorsen
Michael A. Riegler

"Principal Components for Neural Network Initialization" by Nhan Phan, Thu Nguyen, Pål Halvorsen, and Michael A. Riegler, arXiv:2501.19114v2, 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{multirow}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Calibrated Parameters in the Newell Model}
\begin{tabular}{cccc|ccc}\Xhline{1pt}
 Type & Car Model & Engine & Speed Level & $\tau$ (s) & $\delta$ (m) & $w$ (m/s)\\ \Xhline{1pt}
 	 \multirow{2}*{HDV} & HDV-1&& Low speed&1&9&-9\\
 		 & HDV-2& &Median and high speed&1&6&-6\\
 			\Xhline{0.5pt}
			 \multirow{5}*{ACC}& X & & Median and high &1.1 &10 &-9.09 \\
				 & Y & Normal &Median and high& 1.1 & 6 & -5.45 \\
				 & Y & Sport &Median and high& 1 & 8 & -8 \\
			 & Z & Normal &Low, median and high& 1 & 12 & -12 \\
				 & Z & Power &Low, median and high& 1 & 12&-12 \\\Xhline{1pt}
		\end{tabular}
\end{table}
\end{document}

stat

Xinzhi Zhong
Yang Zhou
Soyoung Ahn
Danjue Chen

"Understanding Heterogeneity of Automated Vehicles and Its Traffic-level Impact: A Stochastic Behavioral Perspective" by Xinzhi Zhong, Yang Zhou, Soyoung Ahn, and Danjue Chen, arXiv:2401.00355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Simos G. Meintanis
Bojana Milošević
Marko Obradović

"Bahadur efficiency for certain goodness--of--fit tests based on the empirical characteristic function" by Simos G. Meintanis, Bojana Milošević, and Marko Obradović, arXiv:2201.08604v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition" by Hamid Nasiri and Peter Garraghan, arXiv:2501.12067v1, 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|l|l|l|l|} \hline
\multirow{2}{*}{} &\multicolumn{4}{|c|}{Number of Valid Manoeuvres}\\ \cline{2-5}
&MJ test bench& P80 test bench& MJ field&EbiSwitch field\\ \hline
nominal & 356
& 263
& \multirow{5}{*}{No labelling} &\multirow{5}{*}{No labelling} \\ \cline{1-3}
 obstacle & 274
& 503
& &\\ \cline{1-3}
 friction & 355
& NA
& &\\ \cline{1-3}
power supply issue & 125
& 164
& &\\ \cline{1-3}
misalignment of movable parts & NA
& 203
& &\\ \hline
total & 1110
& 1133
& 58407&13119\\ \hline
 \end{tabular}
\caption{Summary of manoeuvres of the proprietary datasets}
\end{table}
\end{document}

eess

Eduardo Di Santi
Ruixiang Ci
Clément Lefebvre
Nenad Mijatovic
Michele Pugnaloni
Jonathan Brown
Victor Martín
Kenza Saiah

"Scalable, Technology-Agnostic Diagnosis and Predictive Maintenance for Point Machine using Deep Learning" by Eduardo Di Santi, Ruixiang Ci, Clément Lefebvre, Nenad Mijatovic, Michele Pugnaloni, Jonathan Brown, Victor Martín, and Kenza Saiah, arXiv:2508.11692v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Thi Kim Hue Nguyen
Koen Van den Berge
Monica Chiogna
Davide Risso

"Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data" by Thi Kim Hue Nguyen, Koen Van den Berge, Monica Chiogna, and Davide Risso, arXiv:2011.12044v3, 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
\begin{tabular}{c|c|c|c}
 & \multirow{2}{*}{RMS $\left\Vert e\right\Vert $} & \multirow{2}{*}{RMS $\left\Vert f-\Phi\left(x,\hat{\theta}\right)\right\Vert $} & Off-trajectory RMS\tabularnewline
 & & & $\left\Vert f-\Phi\left(x,\hat{\theta}\right)\right\Vert $\tabularnewline
\hline
S1 & $0.0734$ & $7.2575$ & $5.7432$\tabularnewline
\hline
S2 & $0.0586$ & $5.7656$ & $5.0960$\tabularnewline
\hline
S3 & $0.0587$ & $5.7743$ & $5.0937$\tabularnewline
\hline
S4 & $0.0582$ & $5.7412$ & $5.3619$\tabularnewline
\end{tabular}
\end{table}
\end{document}

eess

Saiedeh Akbari
Omkar Sudhir Patil
Warren E. Dixon

"LyLA-Therm: Lyapunov-based Langevin Adaptive Thermodynamic Neural Network Controller" by Saiedeh Akbari, Omkar Sudhir Patil, and Warren E. Dixon, arXiv:2508.14989v1, 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{Coverage of nominal 95\% pointwise confidence intervals and 95\% simultaneous confidence band (SCB) around Nelson-Aalen component plots for Gaussian random fields. Results from 1000 simulations on $60 \times 60$ lattices, with Mat\'{e}rn correlation function with parameters $\eta$ and $\nu$ }
\begin{tabular}{clrrrrrr}
 & & \multicolumn{5}{c}{Percentile}\\
$(\eta, \nu)$ & Method & 0.9 & 0.7 & 0.5 & 0.3 & 0.1 & SCB \\
\\
(5,1) & Standard & 84.0& 92.2& 93.5& 88.9& 82.2 & 73.9\\
&Par. bootstrap& 95.3 &94.4 & 95.8& 95.3& 96.2& 94.1\\
&Replications & 94.2 &94.7 &95.7 &93.9 &94.7 &94.4\\
\\
(10,1) & Standard & 67.8 & 82.7 & 83.3 & 73.7 & 65.5 & 39.1\\
&Par. bootstrap& 95.3 &95.1 & 95.6 & 95.7 & 94.7 & 93.6 \\
&Replications & 95.1 &94.0& 94.9 & 95.9 & 94.0 & 96.4\\
 \\
\\
(5,2) & Standard & 93.4 & 94.6 & 95.0 & 93.2 & 90.8 & 82.9\\
&Par. bootstrap & 95.4 &95.2& 95.7& 95.5 &95.8& 95.9\\
&Replications & 94.0 & 94.6 &95.0& 94.3& 93.7 & 93.4\\
\end{tabular}
\end{table}
\end{document}

stat

Kathryn Garside
Aida Gjoka
Robin Henderson
Hollie Johnson
Irina Makarenko

"Event History and Topological Data Analysis" by Kathryn Garside, Aida Gjoka, Robin Henderson, Hollie Johnson, and Irina Makarenko, arXiv:2012.08810v1, 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|c|c|c|c|c|}
 \hline
 $t$&
 $\frac{-(p+m+1)u}{p|\xi|^2}$&&
 $\frac{-p\varphi(u)-(m+1)u}{p|\xi|^2}$&&
 $0$&&
 $\frac{p\varphi(u)-(m+1)u}{p|\xi|^2}$&&
 $\frac{(p-m-1)u}{p|\xi|^2}$ \\
 \hline
 $\frac{d\,\Re{G_m}}{d\,t}$&&$-$&$0$&$+$&$+$&$+$&$0$&$-$& \\
 \hline
 $\Re{G_m}$&
 &$\searrow$&&$\nearrow$&$\nearrow$&$\nearrow$&$\Re{F(\sigma_0)}$&$\searrow$& \\
 \hline
 location&on $K_u$&&on $K_{\varphi(u)}$&&$\frac{m+1}{p}$&&$\sigma_m$&&on $K_{-u}$ \\
 \hline
\end{tabular}
\caption{The derivative sign chart of $\Re{G_m}\bigl((m+1)/p-\overline{\xi}t\bigr)$ when $p\varphi(u)-(m+1)u>0$.}
\end{table}
\end{document}

math

"The colored Jones polynomial of the figure-eight knot and an $\operatorname{SL}(2;\mathbb{R})$-representation" by Hitoshi Murakami, arXiv:2312.00350v1, 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}{lcccccc}
\toprule
Dataset & \# Patients & Age (mean $\pm$ std) & \% Male & Hours & ICU (\%) & Intubated (\%) \\
\midrule
MGH ImP Train & 17441 & 62 $\pm$ 16 & 62\% & 239099 & 96\% & 23\% \\
MGH ImP Tune & 2219 & 63 $\pm$ 16 & 63\% & 31249 & 96\% & 21\% \\
MGH ImP Validation & 2182 & 63 $\pm$ 16 & 62\% & 29828 & 95\% & 21\% \\
MGH Capnography & 4832 & 64 $\pm$ 13 & 71\% & 45697 & 93\% & 89\% \\
MIMIC & 4617 & 63 $\pm$ 16 & 56\% & 266702 & 100\% & -- \\
\bottomrule
\end{tabular}
\caption{Patient and segment level characteristics of the MGH datasets used for training and validation, as well as those of the MIMIC dataset used for external validation. ImP indicates impedance pneumography; ICU, intensive care unit.}
\end{table}
\end{document}

eess

Thomas Kite
Brian Ayers
Nicholas Houstis
Asishana A. Osho
Thoralf M. Sundt
Aaron D Aguirre

"Continuous Determination of Respiratory Rate in Hospitalized Patients using Machine Learning Applied to Electrocardiogram Telemetry" by Thomas Kite, Brian Ayers, Nicholas Houstis, Asishana A. Osho, Thoralf M. Sundt, and Aaron D Aguirre, arXiv:2508.15947v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.