\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textsc{Number of variables required by Algorithm 1 and 2 for different setups for $N = 8$.}}
\begin{tabular}{|l||l|l|l|l|}
		\hline
		\textbf{$M$} & $2$ & $3$ & $4$ & $5$ \\
		\hline
		Algorithm 1 & 267 & 400 & 533 & 666 \\
		Algorithm 2 & 55 & 94 & 141 & 196 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

cs

Thang X. Vu
Symeon Chatzinotas
Van-Dinh Nguyen
Dinh Thai Hoang
Diep N. Nguyen
Marco Di Renzo
Bjorn Ottersten

"Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance" by Thang X. Vu, Symeon Chatzinotas, Van-Dinh Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Marco Di Renzo, and Bjorn Ottersten, arXiv:2101.07004v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
			\toprule
			\textbf{Model} & \textbf{Lung segmentation} & \textbf{Lobe segmentation} \\
			\midrule
			Baseline Tiramisu & $89.41 \pm 0.45$ & 77.97 $\pm$ 0.31\\
			Baseline + Res-SE 			& $91.78 \pm 0.52$ & 80.12 $\pm$ 0.28 \\
			Baseline + C-LSTM 				& $91.49 \pm 0.57$ & 79.47 $\pm$ 0.38\\
			Baseline + Res-SE + C-LSTM 	& \textbf{94.01 $\pm$ 0.52} &\textbf{ 83.05 $\pm$ 0.27}\\
			\bottomrule
		\end{tabular}
\caption{Ablation studies of our segmentation network in terms of dice score. Best results are shown in bold. Note: we did not compute confidence intervals on these scores as they are obtained from a very large set of CT pixels.}
\end{table}
\end{document}

cs

Matteo Pennisi
Isaak Kavasidis
Concetto Spampinato
Vincenzo Schininà
Simone Palazzo
Francesco Rundo
Massimo Cristofaro
Paolo Campioni
Elisa Pianura
Federica Di Stefano
Ada Petrone
Fabrizio Albarello
Giuseppe Ippolito
Salvatore Cuzzocrea
Sabrina Conoci

"An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans" by Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato, Vincenzo Schininà, Simone Palazzo, Francesco Rundo, Massimo Cristofaro, Paolo Campioni, Elisa Pianura, Federica Di Stefano, Ada Petrone, Fabrizio Albarello, Giuseppe Ippolito, Salvatore Cuzzocrea, and Sabrina Conoci, arXiv:2101.11943v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


math

I. Cherkaoui
S. Belabssir
J. Horgan
I. Dey

"Quasi-cyclic Linear Error-Block Code-based Post-quantum Signature" by I. Cherkaoui, S. Belabssir, J. Horgan, and I. Dey, arXiv:2503.23405v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Tâm Le Minh
Julyan Arbel
Thomas Möllenhoff
Mohammad Emtiyaz Khan
Florence Forbes

"Natural Variational Annealing for Multimodal Optimization" by Tâm Le Minh, Julyan Arbel, Thomas Möllenhoff, Mohammad Emtiyaz Khan, and Florence Forbes, arXiv:2501.04667v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Hankun He
Takuya Boehringer
Benjamin Schäfer
Kate Heppell
Christian Beck

"Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning" by Hankun He, Takuya Boehringer, Benjamin Schäfer, Kate Heppell, and Christian Beck, arXiv:2501.07599v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||l|l|l|l|l|l||}
 \hline	 & MACD & MAMA & MAPS & MAPS2 & RFOR \\
\hline Return (\%) &0.060&-0.014&-0.014&-99.44& 6478.3\\
\hline Sharpe Ratio&0.030&-0.030&-0.030&0.040&0.01\\
\hline PSR(0)&0.943&0.146&0.146&0.932&.753\\
\hline mTRL(0)&0.490&2.100&2.100&1.100&117.0\\
\hline PSR(0.1)&0.000&0.000&0.000&0.003&NaN\\
\hline mTRL(0.1)&0.200&0.100&0.100&0.300&NaN\\
 \hline
		\end{tabular}
\caption{Results for the five strategies for asset BTCUSD in three years. A black cell indicates a situation where the strategy surpasses the proposed Sharpe Ratio threshold: 0 or 0.1}
\end{table}
\end{document}

cs

Murilo Sibrao Bernardini
Paulo Andre Lima de Castro

"Is it a great Autonomous FX Trading Strategy or you are just fooling yourself" by Murilo Sibrao Bernardini and Paulo Andre Lima de Castro, arXiv:2101.07217v2, 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{Univariate polynomial logistic regression models of age and $\log$(BMI) fitted separately, with their respective higher order terms.}
\begin{tabular}{l|llll}
\hline
Variable & Estimate & Std. Error & z-value & p-value \\
\hline
Intercept & -3.575 & 1.168 & -3.061 & 2E-03\\
Age & 0.107 & 4E-02 & 2.645 & 8E-03 \\
$\text{Age}^2$ & -8E-04 & 4E-04 & -2.220 & 2.6E-02 \\
\hline
Intercept & -38.952 & 8.684 &-4.486 & 7.3E-06 \\
$\log(\text{BMI})$ & 22.561 & 5.035 & 4.480 & 7.45E-06 \\
$\log(\text{BMI})^2$ & -3.255 &0.729 & -4.468 & 7.88E-06 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Christian Chan
Xiaotian Dai
Thierry Chekouo
Quan Long
Xuewen Lu

"Broken Adaptive Ridge Method for Variable Selection in Generalized Partly Linear Models with Application to the Coronary Artery Disease Data" by Christian Chan, Xiaotian Dai, Thierry Chekouo, Quan Long, and Xuewen Lu, arXiv:2311.00210v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets" by Shuo Sun, Molei Qin, Xinrun Wang, and Bo An, arXiv:2302.00586v2, 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}{lrrr}
\hline
& Full tuning & Solve and Gradient & Gradient \\ \hline
CVXPYlayers & 1.684 sec & 1.570 sec & 0.033 sec \\
CVXPYgen & 0.696 sec & 0.490 sec & 0.007 sec \\
\hline
\end{tabular}
\caption{Computation times with CVXPY and CVXPYgen for the elastic net example.}
\end{table}
\end{document}

math

"Code generation for solving and differentiating through convex optimization problems" by Maximilian Schaller and Stephen Boyd, arXiv:2504.14099v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The number of true positive change points detected by all three change point detection methods}
\begin{tabular}{lccc}
\toprule
True Positive & PELT & HP & $l_1$ (95\% percentile) \\ \midrule
Change Point 20 & 581 & 485 & 604 \\
Change Point 50 & 597 & 490 & 641 \\ \midrule
Average FDR & 0.94 & 0.72 & 0.72 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Multiple change point detection based on Hodrick-Prescott and $l_1$ filtering method for random walk time series data" by Xiyuan Liu, arXiv:2501.11805v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A tutorial on optimal dynamic treatment regimes" by Chunyu Wang and Brian DM Tom, arXiv:2502.16988v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Covariate adjustment and estimation of difference in proportions in randomized clinical trials" by Jialuo Liu and Dong Xi, arXiv:2308.15688v1, 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{Synthetic DiD Analysis: Extensive Margin, Events in Over 60\% of Years}
\begin{tabular}{l|ccccccccc}
\toprule
{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)}&{(7)}&{(8)}&{(9)} \tabularnewline
\midrule
\textbf{Panel A. Violence}&KLK Index&Anderson Index&&&&&&& \tabularnewline
\midrule Ceasefire \( \times \) FARC&--0.2329***&--0.3741***&&&&&&& \tabularnewline
&(0.0338)&(0.0840)&&&&&&& \tabularnewline
&&&&&&&&& \tabularnewline
Treated Municipalities&216&216&&&&&&& \tabularnewline
Potential Controls&727&727&&&&&&& \tabularnewline
Control Municipalities&590&627&&&&&&& \tabularnewline
&&&&&&&&& \tabularnewline
\midrule \textbf{Panel B. Economic}&Light Int.&Value Added (pc)&Built Up&Agr. Productivity&Share Urban&Firm Entry&Formal Empl.&KLK Index&Anderson Index \tabularnewline
\midrule Ceasefire \( \times \) FARC&--0.0071&--1.0999&--0.0020&0.1048&0.0002&--0.2330&--0.0111***&--0.0346***&--0.0258 \tabularnewline
&(0.0084)&(1.0281)&(0.0014)&(0.1853)&(0.0021)&(0.1818)&(0.0018)&(0.0117)&(0.0202) \tabularnewline
&&&&&&&&& \tabularnewline
Treated Municipalities&216&216&216&216&216&216&216&216&216 \tabularnewline
Potential Controls&711&711&711&711&711&711&711&727&727 \tabularnewline
Control Municipalities&610&633&576&632&638&629&625&631&624 \tabularnewline
&&&&&&&&& \tabularnewline
\midrule \textbf{Panel C. State Capacity}&Tax Revenue&Oper. Costs&Gov. Trans./Rev.&Fiscal Perf.&Admin. Perf.&KLK Index&Anderson Index&& \tabularnewline
\midrule Ceasefire \( \times \) FARC&--0.0324***&--0.0060**&0.5295&--0.0883**&--0.0690&--0.0987***&--0.0552*&& \tabularnewline
&(0.0073)&(0.0025)&(0.4474)&(0.0377)&(0.0524)&(0.0291)&(0.0291)&& \tabularnewline
&&&&&&&&& \tabularnewline
Treated Municipalities&207&207&207&207&207&216&216&& \tabularnewline
Potential Controls&704&704&704&704&704&727&727&& \tabularnewline
Control Municipalities&616&568&619&599&592&598&590&& \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Peace Dividends: The Economic Effects of Colombia's Peace Agreement" by Miguel Fajardo-Steinhäuser, arXiv:2301.01843v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
 \toprule
 Hyperparameter & Value \\
 \cmidrule(lr){1-1}\cmidrule(lr){2-2}
 Initial LR & 0.1\\
 Weight decay & 5e-4\\
 Momentum & 0.9\\
 \cmidrule(lr){1-2}
 Spectral Norm (if used) & 3.0\\
 \bottomrule
 \end{tabular}
\caption{\textbf{Standard hyperparameters were used during training} and spectral norm was set to 3.0 if it was used.}
\end{table}
\end{document}

stat

"Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks" by Felix Jimenez and Matthias Katzfuss, arXiv:2501.04816v1, 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{A Simple Table Example.}
\begin{tabular}{|c|c|c|}
\hline
Order & Arbitrary coefficients & coefficients\\
of filter & $e_m$ & $b_{ij}$ \\
\hline
1& $b_{ij}=\hat{e}.\hat{\beta_{ij}}$, & $b_{00}=0$\\
\hline
2&$\beta_{22}=(~1,-1,-1,~~1,~~1,~~1)$ &\\
\hline
3& $b_{ij}=\hat{e}.\hat{\beta_{ij}}$, & $b_{00}=0$,\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Mohammad Reza Kavianinia
Mohammad Mehdi Setoode
Mohammad Javad Emadi

"On Age of Information and Energy-Transfer in a STAR-RIS-assisted System" by Mohammad Reza Kavianinia, Mohammad Mehdi Setoode, and Mohammad Javad Emadi, arXiv:2312.02776v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c} % <-- Alignments: 1st column left, 2nd middle and 3rd right, with vertical lines in between
			$\boldsymbol{t^{on}$ $(t^{off})}$ & \textbf{VPP value (EUR)} & \textbf{Computational Time (s)} \\
			\hline
			1 (1) & $88.02 \cdot 10^{6}$ & 0.21 \\
			4 (4) & $86.95 \cdot 10^{6}$ & 14.93\\
			16 (16) & $85.57 \cdot 10^{6}$ & 22.15 \\
			24 (24) & $84.11 \cdot 10^{6}$ & 33.67 \\
			54 (54) & $81.03 \cdot 10^{6}$ & 51.71 \\
			96 (96) & $75.79 \cdot 10^{6}$ & 96.12 \\
			124 (124) & $72.45 \cdot 10^{6}$ & 132.17 \\
			160 (160) & $68.16 \cdot 10^{6}$ & 186.42 \\
		\end{tabular}
\caption{VPP evaluation varying $t^{on}$ and $t^{off}$.}
\end{table}
\end{document}

q-fin

"A Heath-Jarrow-Morton framework for energy markets: a pragmatic approach" by Matteo Gardini and Edoardo Santilli, arXiv:2305.01485v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Value of the objective function under different designs}
\begin{tabular}{ccccccccc}
 \hline
 $N$ & $T$ & Obj. Func. & $\theta^*$ & $\mathbb T^*$ & $\mathbb T^*_1$ & $\mathbb T^*_2$ & $\mathbb T^1$ & $\mathbb T^2$\\
 \hline
 \multirow{3}{*}{10} & \multirow{3}{*}{244} & $\mathcal L(1,0)$ & 1.763 & $\mathbb T^*_2$ & 0.265 & \textbf{0.225} & 0.981 & 0.239\\
 && $\mathcal L(0,1)$ & 0.902 & $\mathbb T^*_2$ & 0.528 & \textbf{0.510} & 1.230 & 0.537\\
 && $\mathcal L(0.5,0.5)$ & 1.221 & $\mathbb T^*_2$ & 0.396 & \textbf{0.368} & 1.106 & 0.388\\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

"Minimax Optimal Design with Spillover and Carryover Effects" by Haoyang Yu, Wei Ma, and Hanzhong Liu, arXiv:2501.14602v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Scaling and shape of financial returns distributions modeled as conditionally independent random variables" by Hernán Larralde and Roberto Mota Navarro, arXiv:2504.20488v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Short Circuit Current Constrained UC in High IBG-Penetrated Power Systems" by Zhongda Chu and Fei Teng, arXiv:2101.04918v1, 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{sbiLSTM layers Parameters}
\begin{tabular}{|c|c|c|}
		\hline
			\textbf{Layer (type)} & \textbf{Output Shape} & \textbf{Param} \\
			\hline
			LSTM & [(None, None, 256)] & 394240 \\
			\hline
			LSTM & [(None, 256)] & 394240 \\
			\hline
 Dense & [(None, 1024)] & 263168 \\
			\hline
 Dropout & [(None, 1024)] & 0 \\
			\hline
 Dense & [(None, 512)] & 524800 \\
			\hline
 Dropout & [(None, 512)] & 0 \\
			\hline
 Dense & [(None, 128)] & 65664 \\
			\hline
 Dropout & [(None, 128)] & 0 \\
			\hline
 Dense & [(None, 64)] & 8256 \\
			\hline
 Dense & [(None, 4)] & 260 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Maria Frasca
Davide La Torre
Gabriella Pravettoni
Ilaria Cutica

"Predicting Parkinson's disease evolution using deep learning" by Maria Frasca, Davide La Torre, Gabriella Pravettoni, and Ilaria Cutica, arXiv:2312.17290v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
\toprule
Variable & Type & Definition \\
\midrule
time & Float & The time in seconds since the start of the simulation\\
distance & Float & Distance between both objects\\
angle1 & Angle & Angle of the first object\\
pos1x & Float & X position of the first object\\
pos1y & Float & Y position of the first object\\
angle2 & Angle & Angle of the second object\\
pos2x & Float & X position of the second object\\
pos2y & Float & Y position of the second object\\
\bottomrule
\end{tabular}
\caption{Description of variables present in the dataset.}
\end{table}
\end{document}

cs

"A Multi-Branched Radial Basis Network Approach to Predicting Complex Chaotic Behaviours" by Aarush Sinha, arXiv:2404.00618v2, 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{Average lateral position error}
\begin{tabular}{cccc}
\toprule
& LVM & Dreamer & SLAC \\ \midrule
 Error [m] &0.196 & 0.316 & 0.872 \\\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Yuhang Zhang
Yao Mu
Yujie Yang
Yang Guan
Shengbo Eben Li
Qi Sun
Jianyu Chen

"Steadily Learn to Drive with Virtual Memory" by Yuhang Zhang, Yao Mu, Yujie Yang, Yang Guan, Shengbo Eben Li, Qi Sun, and Jianyu Chen, arXiv:2102.08072v1, 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 result of vocal classification with two audio signal}
\begin{tabular}{|l|l|l|l|}
\hline
 & Mean precision & Mean recall & Mean F1 score \\ \hline
Raw signal & 85.4 & 82.6 & 83.96 \\ \hline
Separated signal & \textbf{93.94} & \textbf{91.78} & \textbf{92.84} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Toan Pham Van
Ngoc N. Tran
Ta Minh Thanh

"Deep Learning Approach for Singer Voice Classification of Vietnamese Popular Music" by Toan Pham Van, Ngoc N. Tran, and Ta Minh Thanh, arXiv:2102.12111v1, licensed under CC BY-SA 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{GOS scoring criteria represent the degree of traumatic brain injury, with scores ranging from 1, indicating the worst outcome, to 5, signifying the best outcome. A GOS score above 4 is considered indicative of a good prognosis.}
\begin{tabular}{|c|c|}
\hline
Score & Categories\\
\hline
1 & Dead\\
 2 & Vegetative state: unconscious and unaware of their surroundings\\
 3 & Severe disability: conscious but dependent on others for daily living activities\\
 4 & Moderate disability: independent but has some physical or cognitive deficits\\
 5 & Good recovery: pre-injury level of the function\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Xuhao Shan
Xinyang Li
Ruiquan Ge
Shibin Wu
Ahmed Elazab
Jichao Zhu
Lingyan Zhang
Gangyong Jia
Qingying Xiao
Xiang Wan
Changmiao Wang

"GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration" by Xuhao Shan, Xinyang Li, Ruiquan Ge, Shibin Wu, Ahmed Elazab, Jichao Zhu, Lingyan Zhang, Gangyong Jia, Qingying Xiao, Xiang Wan, and Changmiao Wang, arXiv:2311.04772v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Deformation of Marchenko-Pastur distribution for the correlated time series" by Masato Hisakado and Takuya Kaneko, arXiv:2305.12632v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of different alignment loss, measured by the performance of TriDet on THUMOS14-C. All losses improve robustness while only our TRC loss enhances the performance on clean data.}
\begin{tabular}{l|c|c}
 \hline
 \multirow{2}{*}{Loss Function} & \multirow{2}{*}{\shortstack{Clean \\ mAP}} & \multirow{2}{*}{\shortstack{Corrupted \\ mAP}} \\
 & & \\
 \cline{1-3}
 Without Alignment & 75.16 & 61.10 \\
 Mean Square Error & 74.59 (0.57 $\downarrow$) & {62.79 (1.69 $\uparrow$)} \\
 KL divergence & 73.32 (1.84 $\downarrow$) & {67.81 (6.71 $\uparrow$)} \\
 TRC (Ours) & \textbf{75.60 (0.44 $\uparrow$)} & {\textbf{68.28 (7.18 $\uparrow$)}} \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Runhao Zeng
Xiaoyong Chen
Jiaming Liang
Huisi Wu
Guangzhong Cao
Yong Guo

"Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions" by Runhao Zeng, Xiaoyong Chen, Jiaming Liang, Huisi Wu, Guangzhong Cao, and Yong Guo, arXiv:2403.20254v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c|c}
 \toprule
 400 epochs w/o clicks & {} & \checkmark & \checkmark \\
 200 epochs with clicks & \checkmark & {} & \checkmark \\
 \hline
 Dice@0 & $24.47\%$ & $73.04\%$ & $68.03\%$\\
 Dice@10 & $85.50\%$ & - & $84.79\%$ \\
 \bottomrule
 \end{tabular}
\caption{Results from non-interactive pre-training with local inference. Dice@X denotes the Dice score after X clicks.}
\end{table}
\end{document}

eess

Matthias Hadlich
Zdravko Marinov
Moon Kim
Enrico Nasca
Jens Kleesiek
Rainer Stiefelhagen

"Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images" by Matthias Hadlich, Zdravko Marinov, Moon Kim, Enrico Nasca, Jens Kleesiek, and Rainer Stiefelhagen, arXiv:2311.14482v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of area, delay, and power scaling (\% reduction) factors for 10~nm to 7~nm scaling. The reference scaling data~ corresponds to TSMC's technology scaling.}
\begin{tabular}{l|l|c|c}
					\hline
					 \textbf{Source} & \textbf{Area}&\textbf{Delay} & \textbf{Power}\\
\hline
Cai~ & 30 -- 35 & 10 & 35\\
\hline
Stillmaker~ \, \, & 59 & 19.1 & 9.1\\
\hline
This work & \textbf{36.7} & \textbf{7.5} & \textbf{30}\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"DeepScaleTool : A Tool for the Accurate Estimation of Technology Scaling in the Deep-Submicron Era" by Satyabrata Sarangi and Bevan Baas, arXiv:2102.10195v1, 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{ Trace results for $24^4$ lattices with error tolerance $0.0005*24^4$. Compare Hutchinson with the Multipolynomial Method. We average with 10 configurations.}
\begin{tabular}{|c|c|c|c|c|c|} \hline\hline
Method & Noise vectors & Time & MVP's \\ \hline \hline
Hutch., defl. PP(70)-GMRES & 188 & 19.6 hours & $2.02*10^5$ \\ \hline
3 Poly's, $10^{-12}$ for deflation & 2, 17.1, 101 & 6.84 hours & $6.68*10^4$ \\ \hline
3 Poly's, extra deflation & 2, 3, 24.9 & 3.07 hours & $2.80*10^4$ \\ \hline \hline
\end{tabular}
\end{table}
\end{document}

math

Paul Lashomb
Ronald B. Morgan
Travis Whyte
Walter Wilcox

"Multipolynomial Monte Carlo Trace Estimation" by Paul Lashomb, Ronald B. Morgan, Travis Whyte, and Walter Wilcox, arXiv:2402.00016v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Lie Symmetries for the Shallow Water Magnetohydrodynamics Equations in a Rotating Reference Frame" by Andronikos Paliathanasis and Amlan Halder, arXiv:2412.14578v1, 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{Atari-10 DQN hyperparameters}
\begin{tabular}{rl}
 \toprule
 \textbf{Hyperparameter} & \textbf{Value} \\
 \midrule
 Learning Rate & 0.0001 \\
 Buffer Size & $1\times 10^6$ \\
 $\gamma$ & $0.99$ \\
 GAE $\lambda$ & $0.9$ \\
 Target Network Update Steps & 1000 \\
 Batch Size & 32 \\
 Start $\epsilon$ & 1 \\
 End $\epsilon$ & $0.01$ \\
 Exploration Fraction & $0.1$ \\
 Number of Steps without Training & $80000$ \\
 Train Frequency & $4$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Benjamin Ellis
Matthew T. Jackson
Andrei Lupu
Alexander D. Goldie
Mattie Fellows
Shimon Whiteson
Jakob Foerster

"Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps" by Benjamin Ellis, Matthew T. Jackson, Andrei Lupu, Alexander D. Goldie, Mattie Fellows, Shimon Whiteson, and Jakob Foerster, arXiv:2412.17113v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Min-max ranges for different CarRacing F1 tracks that are used for IQM and Optimality Gap plotting.}
\begin{tabular}{lrr}
 \toprule
 Track & Episode Steps & Min-Max Reward Range \\
 \midrule
 Australia & \multirow{5}{*}{1500} & \multirow{5}{*}{{[}-150, 850{]}} \\
 Austria & & \\
 Belgium & & \\
 Italy & & \\
 Monaco & & \\
 \midrule
 Brazil & \multirow{11}{*}{2000} & \multirow{11}{*}{{[}-200, 800{]}} \\
 China & & \\
 France & & \\
 Germany & & \\
 Hungary & & \\
 Netherlands & & \\
 Russia & & \\
 Singapore & & \\
 Spain & & \\
 UK & & \\
 USA & & \\
 \midrule
 Bahrain & \multirow{3}{*}{2500} & \multirow{3}{*}{{[}-250, 750{]}} \\
 Malaysia & & \\
 Portugal & & \\
 \midrule
 Mexico & 3000 & {[}-300, 700{]} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Jayden Teoh
Wenjun Li
Pradeep Varakantham

"Improving Environment Novelty Quantification for Effective Unsupervised Environment Design" by Jayden Teoh, Wenjun Li, and Pradeep Varakantham, arXiv:2502.05726v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average dice score performance (\%) of the GCN refinement compared with the initial CNN predictions. Statistical significance is indicated by (*) for a p-value $<0.05$, and (**) for a p-value $< 0.01$ with respect to the 2D-CNNs predictions.}
\begin{tabular}{l|c||c|c||c|c}
			\hline
			Task & 3D U-Net & 2D U-Net & Ours & 2D QuickNat & Ours \\
			 & & Initial & GCN-Refinement & Initial & GCN-Refinement \\
			\hline
			Pancreas & $60.14 \pm 10.07 $ & $76.89 \pm 6.6 $ & $78.20 \pm 6.1 $* & $61.31 \pm 13.1$ & $61.57 \pm 13.0$ \\
			\hline
			\hline
			Spleen & $82.37 \pm 16.8 $ & $93.17 \pm 2.5 $ & $95.20 \pm 1.4$** & $91.83 \pm 6.0$ & $92.97 \pm 2.3$ \\
			\hline
			\end{tabular}
\end{table}
\end{document}

eess

Roger D. Soberanis-Mukul
Nassir Navab
Shadi Albarqouni

"An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation" by Roger D. Soberanis-Mukul, Nassir Navab, and Shadi Albarqouni, arXiv:2012.03352v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Peeler's detection accuracy.}
\begin{tabular}{rrrrrrr}
 \toprule
 \textbf{Acc. (\%)} & \textbf{TPR (\%)} &\textbf{FPR (\%)} &\textbf{FNR (\%)} & \textbf{Prec. (\%)} & \textbf{Rec. (\%)} & \textbf{F1 (\%)}\\ \midrule
 99.52 & 99.63 & 0.58 & 0.37 & 99.41 & 99.63 & 99.52 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Muhammad Ejaz Ahmed
Hyoungshick Kim
Seyit Camtepe
Surya Nepal

"Peeler: Profiling Kernel-Level Events to Detect Ransomware" by Muhammad Ejaz Ahmed, Hyoungshick Kim, Seyit Camtepe, and Surya Nepal, arXiv:2101.12434v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
	\hline
	& Sample Mean & Sample Variance & Sharp Ratio \\
	\hline
	Monthly Rebalancing, 5 years, $d = 8$ & 8.07335 & 1.47720 & 6.60138 \\
	\hline
	Monthly Rebalancing, 1 year, $d = 1.4$ & 1.40067 & 0.06056 & 5.48819 \\
	\hline
	Daily Rebalancing, half year, $d = 1.2$& 1.19658 & 0.03186 & 6.42324 \\
	\hline
	Daily Rebalancing, 1 year, $d = 1.4$ & 1.39368 & 0.04920 & 6.05735 \\
	\hline
\end{tabular}
\caption{\small Sample Mean, Sample Variance and Sample Sharp Ratio of terminal wealth in different investing \\ horizon and expected return}
\end{table}
\end{document}

math

"Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application" by Lucky Li, arXiv:2412.05906v2, 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{Wash trading detection results by methodology}
\begin{tabular}{lccc}
\toprule
\textbf{Analysis Method} & \textbf{Events Detected} & \textbf{Tokens Affected} \\
\midrule
Zero-Risk Position & 958 (94.48\%) & 271 (96.10\%) \\
Circular Volume & 219 (21.60\%) & 128 (45.39\%) \\
Persistent Manipulators & 37 (3.65\%) & 14 (4.96\%) \\
\midrule
\textbf{Total Detected Cases} & \textbf{1,014} & \textbf{282} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"A Midsummer Meme's Dream: Investigating Market Manipulations in the Meme Coin Ecosystem" by Alberto Maria Mongardini and Alessandro Mei, arXiv:2507.01963v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccccc}
\toprule
\multirow{2.5}{*}{\textbf{Method}} &
\multicolumn{3}{c}{\textbf{Sample-Level}} &
\multicolumn{3}{c}{\textbf{Dataset-Level}} \\
\cmidrule(r){2-4} \cmidrule(r){5-7} & $\rho$ & $r$ & $\tau$ & $\rho$ & $r$ & $\tau$ \\
\midrule
Generative Augmentor & 30.2 & 33.5 & 22.5 & 26.7 & 25.4 & 18.6 \\ \midrule
+ C.T. & 51.4 & 51.9 & 38.9 & 53.8 & 51.9 & 38.2 \\
+ C.T. + filter $\delta=0.2$ & 53.7 & 51.3 & 41.5 & 55.9 & 47.8 & 40.3 \\
+ C.T. + filter $\delta=0.4$ & 52.8 & 54.0 & 40.4 & 56.8 & 53.7 & 40.1 \\
+ C.T. + filter $\delta=0.6$ & \textbf{55.7} & \textbf{55.3} & \textbf{42.8} & \textbf{58.0} & \textbf{55.9} & \textbf{42.0} \\
+ C.T. + filter $\delta=0.8$ & 46.3 & 42.7 & 35.9 & 49.5 & 41.5 & 35.7 \\
\bottomrule
\end{tabular}
\caption{Analysis of our controlling mechanisms for local augmentation. \textit{C.T.} stands for context truncation. $\delta$ is the threshold for confidence filtering.}
\end{table}
\end{document}

cs

Dawei Zhu
Wenhao Wu
Yifan Song
Fangwei Zhu
Ziqiang Cao
Sujian Li

"CoUDA: Coherence Evaluation via Unified Data Augmentation" by Dawei Zhu, Wenhao Wu, Yifan Song, Fangwei Zhu, Ziqiang Cao, and Sujian Li, arXiv:2404.00681v1, 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{Age-gender mortality rates for insurance population in Italy}
\begin{tabular}{ccc}
\hline
\textbf{Age} & \textbf{Gender} & \textbf{Rates} \\
\hline
20 & M & 0.000532 \\
21 & M & 0.000526 \\
22 & M & 0.000518 \\
23 & M & 0.000508 \\
24 & M & 0.000492 \\
25 & M & 0.000506 \\
26 & M & 0.000528 \\
27 & M & 0.000572 \\
28 & M & 0.000634 \\
29 & M & 0.000705 \\
... & ... & ... \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Mortality simulations for insured and general populations" by Asmik Nalmpatian and Christian Heumann, arXiv:2502.08814v1, 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{Solution of instance $A5$ with single time window and deterministic parameters}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c}
		\hline
		Patients & 5(6)&	2(6)&	9(5)&	8(5)&	10(2)&	6(6)&	3(5)&	1(2)&	7(5)&	4(5)	 \\
		\hline
		Caregivers & 1&3&	3&	1&	2&	3&	1&	2&	1&	3 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

math

"Mathematical models and heuristics for the home health care routing and scheduling problem" by Mohammed Bazirha, arXiv:2312.08032v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Introduction to probability and statistics: a computational framework of randomness" by Lakshman Mahto, arXiv:2401.08622v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{Average running times (seconds) of one realisation of estimators for the $q$-GLE model.}}
\begin{tabular}{lcc}
\toprule \\
 Experiment & $ \hat{\theta}_{2,n}$ & $\hat{\theta}_{3,n} $
 \\ \midrule
 \\
 \textbf{Design A} - \textbf{Case I}
 & 38.25 & 44.33
 \\
 \textbf{Design A} - \textbf{Case II}
 & 24.46 & 28.34
 \\
 \textbf{Design B} - \textbf{Case I}
 & 27.48 & 28.71
 \\
 \textbf{Design B} - \textbf{Case II}
 & 90.26 & 104.12
 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Parameter Inference for Hypo-Elliptic Diffusions under a Weak Design Condition" by Yuga Iguchi and Alexandros Beskos, arXiv:2312.04444v2, 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{Weight percentages of each element in the optimal alloy.}
\begin{tabular}{cccccc}
\hline
Element & Mo & Nb & Ti & V & W \\ \hline
Weight \% & 33.99 & 2.05 & 0.16 & 63.80 & 0.00 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Danial Khatamsaz
Joseph Wagner
Brent Vela
Raymundo Arroyave
Douglas L. Allaire

"Towards Autonomous Experimentation: Bayesian Optimization over Problem Formulation Space for Accelerated Alloy Development" by Danial Khatamsaz, Joseph Wagner, Brent Vela, Raymundo Arroyave, and Douglas L. Allaire, arXiv:2502.05735v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccrrrr}
 \toprule
 \multirow{3}{*}{\textbf{Dataset}} & \multirow{3}{*}{\textbf{\# Intents}} & \multirow{3}{*}{\textbf{\# Slots}} & \multicolumn{4}{c}{\textbf{Avg. prompt length}} \\
 & & & \multicolumn{2}{c}{\textbf{zero-shot}} & \multicolumn{2}{c}{\textbf{few-shot}} \\
 \cmidrule(lr){4-5} \cmidrule(lr{0em}){6-7}
 & & & IC & SF & IC & SF \\
 \toprule
 SNIPS & 7 & 45 & 75.4 & 115.2 & 294.4 & 493.8 \\
 MASSIVE & 60 & 55 & 336.7 & 160.5 & 603.6 & 551.9 \\
 MultiWoz & 11 & 24 & 83.9 & 90.8 & 450.9 & 303.5 \\
 \bottomrule
 \end{tabular}
\caption{Datasets for IC and SF experiments.}
\end{table}
\end{document}

cs

Paramita Mirza
Viju Sudhi
Soumya Ranjan Sahoo
Sinchana Ramakanth Bhat

"ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler" by Paramita Mirza, Viju Sudhi, Soumya Ranjan Sahoo, and Sinchana Ramakanth Bhat, arXiv:2403.17536v1, 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}{lcc}
		\toprule & Treatment values & Control values \\
		\midrule
		Global & 9.8144 $\pm$ 0.0007 & 8.8040 $\pm$ 0.0007 \\
		Weighted & 9.8132 $\pm$ 0.0008 & 8.8032 $\pm$ 0.0008 \\
		Data splitting & 9.7953 $\pm$ 0.0009 & 8.7946 $\pm$ 0.0009 \\
		Data pooling & 9.8312 $\pm$ 0.0007 & 8.8151 $\pm$ 0.0007 \\
		Snapshot & 9.8383 $\pm$ 0.0009 & 8.8215 $\pm$ 0.0008 \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

stat

"Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach" by Nian Si, arXiv:2310.17496v5, 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{Cost-adjusted performance, Jan~1994–Dec~2024}
\begin{tabular}{lcccc}
\toprule
Portfolio & Gross CAGR & Net CAGR & Avg.\ turn.\ (\%/mo) & Max DD \\ \midrule
Market (VW) & 7.1\% & 7.1\% & 0.4 & --57\% \\
Entropy FGP & 11.0\% & 10.0\%& 3.7 & --63\% \\
Diversity ($p=0.7$)& 12.2\% & 10.7\%& 4.1 & --65\% \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Functionally Generated Portfolios Under Stochastic Transaction Costs: Theory and Empirical Evidence" by Nader Karimi and Erfan Salavati, arXiv:2507.09196v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{A very nice table}
\begin{tabular}{rrr}
 \toprule
 & \textit{Treatment 1} & \textit{Treatment 2} \\\midrule
 \textit{Setting A} & 125 & 95 \\
 \textit{Setting B} & 85 & 102 \\
 \textit{Setting C} & 98 & 85 \\\bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1" by Daniele Rege Cambrin and Paolo Garza, arXiv:2403.18116v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Hoon Hong
Daniel Profili
J. Rafael Sendra

"Conditions for eigenvalue configurations of two real symmetric matrices" by Hoon Hong, Daniel Profili, and J. Rafael Sendra, arXiv:2401.00866v3, 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{array}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{hhline}
\usepackage[table]{xcolor}
\usepackage{colortbl}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Spearman rank-order correlation coefficient with the associated p-value between expert testability scores and ARTA}
\begin{tabular}{lllll}
 \hline
 \rowcolor[rgb]{0.949,0.949,0.949} {\cellcolor[rgb]{0.949,0.949,0.949}} & \multicolumn{2}{l}{Softened
 value for testability} & \multicolumn{2}{l}{Hardened
 value for testability} \\
 \hhline{>{\arrayrulecolor[rgb]{0.949,0.949,0.949}}->{\arrayrulecolor{black}}----}
 \rowcolor[rgb]{0.949,0.949,0.949} \multirow{-2}{*}{{\cellcolor[rgb]{0.949,0.949,0.949}}Project} & Spearman correlation & P-value & Spearman correlation & P-value \\
 \hline
 EIRENE & 0.9547 & 6.3744$\times 10^{-11}$ & 0.9349 & 1.5555$\times 10^{-9}$ \\
 ERTMS/ETCS & 0.8590 & 1.2358$\times 10^{-6}$ & 0.8590 & 1.2358$\times 10^{-6}$ \\
 CCTNS & 0.9324 & 2.1803$\times 10^{-9}$ & 0.9423 & 5.4530$\times 10^{-10}$ \\
 Gamma-J & 0.8749 & 4.4837$\times 10^{-7}$ & 0.8749 & 4.4837$\times 10^{-7}$ \\
 KeePass & 0.8900 & 1.4816$\times 10^{-7}$ & 0.8973 & 8.2228$\times 10^{-8}$ \\
 Peering & 0.5399 & $1.3910 \times 10^{-2}$ & 0.5399 & $1.3990 \times 10^{-2}$ \\
 \rowcolor[rgb]{0.949,0.949,0.949} Total & 0.9310 & 1.7530$\times 10^{-53}$ & 0.9321 & 6.5681$\times 10^{-8}$ \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

"Natural Language Requirements Testability Measurement Based on Requirement Smells" by Morteza Zakeri-Nasrabadi and Saeed Parsa, arXiv:2403.17479v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{User study participant demographic information.}
\begin{tabular}{l|ccc|cccc}
 \toprule[1.5pt]
 \multirow{2}{*}{Category} & \multicolumn{3}{c}{Gender Groups} & \multicolumn{4}{c}{Age Groups} \\
 \cmidrule{2-8}
 & Male&Female&Others & 18-25, &25-35, &35-45, & 45-55\\
 \midrule
 Number & 65 &53 &2 & 42 & 68 & 41 & 11\\

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

eess

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

"Practical Speech Re-use Prevention in Voice-driven Services" by Yangyong Zhang, Maliheh Shirvanian, Sunpreet S. Arora, Jianwei Huang, and Guofei Gu, arXiv:2101.04773v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{proposed models}
\begin{tabular}{|l|c|}
 \hline
 Functions & Models \\
 \hline
 $C(s_k)$ & $7 \times 10^{-10}s_k^{1.5}+0.083$ TFLOPS\\
 \hline
 $A(s_k)$ & $1-1.578e^{-6.5 \times 10^{-3} \sqrt{s_k}}$\\
 \hline
 $P^{tr}(R_k)$ & $0.018R_k + 0.7$ \\
 \hline
 $P^{bs}(f_k)$ & $0.079f_k + 0.59$ \\
 \hline
 $P^{pre}(f_k)$ & $-0.01071f_k^3 + 0.06055f_k^2 - 0.1028f_k + 0.107$ \\
 \hline
 $P_n(x)$ & $0.083x^2 + 0.32$\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Optimization in Mobile Augmented Reality Systems for the Metaverse over Wireless Communications" by Tianming Lan and Jun Zhao, arXiv:2311.17630v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Two-Level Private Information Retrieval

Ruida Zhou
Chao Tian
Hua Sun
James Plank

"Two-Level Private Information Retrieval" by Ruida Zhou, Chao Tian, Hua Sun, and James Plank, arXiv:2101.04821v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrrrr}
\toprule
\textbf{Layer} & \textbf{\% Variance Explained} & \textbf{L1} & \textbf{L0} & \textbf{\% Alive} & \textbf{CE Diff} & \textbf{\% CE Recovered} \\
\midrule
Resid 0 & 92\% & 11 & 59 & 41\% & 0.24 & 97\% \\
Resid 1 & 85\% & 13 & 54 & 38\% & 0.45 & 95\% \\
Resid 2 & 96\% & 24 & 108 & 27\% & 0.55 & 94\% \\
Resid 3 & 96\% & 23 & 68 & 22\% & 0.58 & 95\% \\
Resid 4 & 88\% & 23 & 61 & 27\% & 0.48 & 95\% \\
Resid 5 & 90\% & 35 & 72 & 45\% & 0.55 & 92\% \\
\bottomrule
\end{tabular}
\caption{Residual (Resid) SAE evaluation by layer.}
\end{table}
\end{document}

cs

Samuel Marks
Can Rager
Eric J. Michaud
Yonatan Belinkov
David Bau
Aaron Mueller

"Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models" by Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, and Aaron Mueller, arXiv:2403.19647v3, 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{Relative discrepancy between Monte Carlo and DLRA in $L^2$ norm.}
\begin{tabular}{ccccc}
 \toprule
 &P$_{37}$, $\vartheta =0.1$ &P$_{37}$, $\vartheta =0.01$ &P$_{95}$, $
 \vartheta =0.1$ & P$_{95}$, $\vartheta =0.01$\\
 \midrule
 homogeneous & 0.058 & 0.047 & 0.041	&0.039 \\
 heterogeneous & 0.066 & 0.067 &	0.059	& 0.057 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Pia Stammer
Tiberiu Burlacu
Niklas Wahl
Danny Lathouwers
Jonas Kusch

"A Deterministic Dynamical Low-rank Approach for Charged Particle Transport" by Pia Stammer, Tiberiu Burlacu, Niklas Wahl, Danny Lathouwers, and Jonas Kusch, arXiv:2412.09484v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?" by Steve Phelps and Yvan I. Russell, arXiv:2305.07970v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
Entity Name & Total Count \\
\hline
(Header) Contract Area Description & 252 \\
(Header)Environmental protections & 217 \\
(Header)Governing law & 233 \\
(Header)Hardship clause or force majeure & 200 \\
(Header)Income tax: rate & 177 \\
(Header)Reporting requirements & 517 \\
(Header)Term & 282 \\
(Header)Water use & 38 \\
Contract Area Description & 780 \\
Country & 432 \\
Date Signed & 290 \\
Environmental protections & 495 \\
Governing law & 246 \\
Hardship clause or force majeure & 260 \\
Income tax: rate & 186 \\
Participants & 957 \\
Project & 218 \\
Renewal or extension of term & 459 \\
Reporting requirements & 1331 \\
Signatories, company & 568 \\
Term & 381 \\
Type & 356 \\
Water use & 130 \\
\hline
\end{tabular}
\caption{Resource Contracts Label List and Counts }
\end{table}
\end{document}

cs

Benjamin Townsend
Madison May
Christopher Wells

"RealKIE: Five Novel Datasets for Enterprise Key Information Extraction" by Benjamin Townsend, Madison May, and Christopher Wells, arXiv:2403.20101v1, 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{Johansen Cointegration Test using Trace Test Statistic (10\% Significance Level)}
\begin{tabular}{cccc}
\hline
\textbf{Rank (r)} & \textbf{Cointegrating Relations} & \textbf{Test Statistic} & \textbf{Critical Value} \\
\hline
0 & 2 & 37.36 & 13.43 \\
1 & 2 & 1.8435 & 2.705 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Juan Plazuelo Pascual
Carlos Tardon Rubio
Juan Toro Cebada
Angel Hernando Veciana

"Price Discovery in Cryptocurrency Markets" by Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, and Angel Hernando Veciana, arXiv:2506.08718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{KS-test results and computational time from $10^4$ simulations.}
\begin{tabular}{llllll}
\hline\noalign{\smallskip}
$\Delta$&\multicolumn{2}{l}{Euler-Maruyama} & \multicolumn{2}{l}{Imp. Euler-Maruyama} & Exact \\
\noalign{\smallskip}\hline\noalign{\smallskip}
 & p-value &Time(s) & p-value &Time(s)& Time(s) \\
\noalign{\smallskip}\hline\noalign{\smallskip}
$2^{-4}$& $0$ & $0.53$ & $0$ & $0.67$ & \multirow{7}{*}{$11.97$}\\
 $2^{-5}$& $0$ & $0.66$ & $0$ & $0.9$ & \\
 $2^{-6}$& $0$ & $0.93$ & $0$ & $1.19$ & \\
 $2^{-7}$& $0$ & $1.34$ & $1.264\times10^{-12}$ & $2$ & \\
 $2^{-8}$& $0$ & $2.34$ & $1.665\times10^{-5}$ & $3.51$ & \\
 $2^{-9}$& $2.859\times10^{-7}$ & $4.56$ & $0.0002324$ & $6.28$ & \\
 $2^{-10}$& $0.0008803$ & $8.35$ & $0.02755$ & $12.14$ & \\
\noalign{\smallskip}\hline
\end{tabular}
\end{table}
\end{document}

math

Devika Khurana
Sascha Desmettre
Evelyn Buckwar

"Exact simulation of the first-passage time of SDEs to time-dependent thresholds" by Devika Khurana, Sascha Desmettre, and Evelyn Buckwar, arXiv:2412.13060v2, 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{Machine learning performances from the direct approach without behavior analysis}
\begin{tabular}{lccccc}
\hline
Method & Accuracy(\%) & Precision(\%) & Recall(\%) & F1 Score(\%) & AUC \\ \hline
logistic regression & 95.60& 98.38 & 95.60& 96.62& 0.9872\\
decision tree & 94.86& 98.29 & 94.86& 96.14& 0.9408\\
random forest & 92.78 & 98.11 & 92.78 & 94.85 & 0.9246 \\
K-Nearest neighbor & 96.59& 98.40& 96.59& 97.25& 0.9559\\
multilayer perceptron &95.88&98.49& 95.88&96.82&0.9899\\
support vector classifier & 95.89&98.50&95.89& 96.83&0.9853\\
extreme gradient boosting & 97.58& 98.62& 97.58& 97.94& 0.9908\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Jin Yuan
Xuelan Qiu
Jinran Wu
Jiesi Guo
Weide Li
You-Gan Wang

"Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study" by Jin Yuan, Xuelan Qiu, Jinran Wu, Jiesi Guo, Weide Li, and You-Gan Wang, arXiv:2406.11847v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccccc}
 \toprule
 &\multicolumn{3}{c}{Direct Eval.} &\multicolumn{3}{c}{Ind.\ Downstream Eval.}\\
 \cmidrule(lr){2-4}\cmidrule(l){5-7}
 Emo. & GPT3.5 & GPT3 & Llama2 & GPT3.5 & GPT3 & Llama2 \\
 \cmidrule(r){1-1} \cmidrule(lr){2-4}\cmidrule(l){5-7}
 J & 81.0 & 73.9 & 52.2 & 76.9 & 71.7 & 68.0\\
 A & 74.5 & 61.5 & 52.1 & 71.5 & 65.9 & 49.4\\
 F & 76.2 & 51.9 & 39.5 & 70.9 & 54.7 & 49.4\\
 S & 80.3 & 71.9 & 64.1 & 72.1 & 64.7 & 62.7\\
 \cmidrule(r){1-1} \cmidrule(lr){2-4}\cmidrule(l){5-7}
 Avg. & 78.1 & 64.6& 51.5 & 73.5 & 65.3& 58.6 \\
 \bottomrule
 \end{tabular}
\caption{Evaluation across models.}
\end{table}
\end{document}

cs

Christopher Bagdon
Prathamesh Karmalker
Harsha Gurulingappa
Roman Klinger

""You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling" by Christopher Bagdon, Prathamesh Karmalker, Harsha Gurulingappa, and Roman Klinger, arXiv:2403.17612v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{AFIO Dataset Specifications}
\begin{tabular}{ll}
 \toprule
 Acquisition Machine & Topcon 3D OCT 1000 \\\hline
 Scan Reference & Optic Nerve Head (ONH) Centered \\
 Examination	& Dilated Pupil with Ø4.0mm (45º) Diameter\\
 Images & 196 ONH SD-OCT Images\\
 Scan Type & B-scan\\
 Resolution & 951x456\\
 Subjects & 101\\
 Categories & Healthy: 50\\
 & Glaucoma: 146\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Hina Raja
Taimur Hassan
Muhammad Usman Akram
Naoufel Werghi

"Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression" by Hina Raja, Taimur Hassan, Muhammad Usman Akram, and Naoufel Werghi, arXiv:2010.03872v1, 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{CIQ-MI: CIQ agent with an extended multi-interference (MI) architecture testing in Env$_1$ (noise level $P=20\%$). As a proof of concept, we consider two interference types together, Gaussian noise and adversarial perturbation. In this setting every observation (state) can possibly undergo an interference with either Gaussian noise or Adversarial perturbation. CIQ-MI is capable of making correct action to solve (over 195.0) the testing environment when training with mixed interference types.}
\begin{tabular}{|l|c|c|c|}
\hline
Train \textbf{/ Test} & \textbf{Gaussian} & \textbf{Adversarial} & \textbf{Gaussian + Adversarial} \\ \hline
Gaussian & \textbf{195.1} & 154.2 & 96.3 \\ \hline
Adversarial & 153.9 & \textbf{195.0} & 105.1 \\ \hline
Gaussian + Adversarial & \textbf{195.0} & \textbf{195.0} & \textbf{195.0} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Chao-Han Huck Yang
I-Te Danny Hung
Yi Ouyang
Pin-Yu Chen

"Training a Resilient Q-Network against Observational Interference" by Chao-Han Huck Yang, I-Te Danny Hung, Yi Ouyang, and Pin-Yu Chen, arXiv:2102.09677v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|llllll|l}
 \# $\geq$ 99500 & j\_k1 & j\_k2 & mat & phy & clust & path & sum \\
 \hline &&&&&&& \\
 Euclidean, dim 0 & 6 & \textbf{6} & 2 & 6 & 4 & 0 & 24 \\
 Euclidean, dim 1 & \textbf{8} & 4 & 4 & 7 & 12 & 0 & 35 \\
 Euclidean, dim 2 & 7 & 3 & \textbf{8} & 6 & 9 & 0 & 33 \\
 cosine, dim 0 & 2 & 1 & 2 & 5 & 7 & 0 & 17 \\
 cosine, dim 1 & 5 & 3 & 6 & \textbf{13} & \textbf{14} & \textbf{1} & \textbf{42} \\
 cosine, dim 2 & 2 & 0 & 4 & 10 & 10 & 0 & 26 \\
 \hline &&&&&&& \\
 bottleneck & 4 & 2 & 3 & 9 & 12 & 0 & 30 \\
 sliced Wasserstein & 10 & \textbf{7} & 9 & 14 & \textbf{19} & 0 & 59 \\
 persistence image & 5 & 3 & 4 & 7 & 8 & 0 & 27 \\
 bars statistics & \textbf{11} & 5 & \textbf{10} & \textbf{17} & 17 & \textbf{1} & \textbf{61} \\
 \hline &&&&&&& \\
 nj & 12 & 7 & 8 & 15 & 21 & 0 & 63 \\
 upgma & \textbf{18} & \textbf{10} & \textbf{18} & \textbf{32} & \textbf{35} & \textbf{1} & \textbf{114}
 \end{tabular}
\caption{The number of dots in Figure~ corresponding to parameters that performed better than 99,500 random permutations of the tree leaves (labeled by star in the figure). The total number of dots for a fixed distance that meet the parameters in each block is 24, 36 and 72, respectively.}
\end{table}
\end{document}

cs

"The Shape of Word Embeddings: Quantifying Non-Isometry With Topological Data Analysis" by Ondřej Draganov and Steven Skiena, arXiv:2404.00500v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|}
\hline
Ticker & Name \\ \hline
AC.MX & Arca Continental, S.A.B. de C.V. \\
AEROMEX.MX & Grupo Aeroméxico, S.A.B. de C.V. \\
AGUA.MX & Grupo Rotoplas S.A.B. de C.V. \\
ALFA.MX & Alfa S.A.B. de C.V. \\
ALSEA.MX & Alsea, S.A.B. de C.V. \\
ARA.MX & Consorcio ARA, S. A. B. de C. V. \\
BBVA.MX & Banco Bilbao Vizcaya Argentaria, S.A. \\
CETETRC.MX & iShares S\&P/VALMER Mexico CETETRAC \\
CORPTRC.MX & iShares Mexico Corporate Bond TRAC \\
CUERVO.MX & Becle, S.A.B. de C.V. \\
ELEKTRA.MX & Grupo Elektra, S.A.B. de C.V. \\
GCC.MX & GCC, S.A.B. de C.V. \\
GENTERA.MX & Gentera, S.A.B. de C.V. \\
GMXT.MX & GMéxico Transportes, S.A.B. de C.V. \\
HCITY.MX & Hoteles City Express, S.A.B. de C.V. \\
HERDEZ.MX & Grupo Herdez, S.A.B. de C.V. \\
HOMEX.MX & Desarrolladora Homex, S.A.B. de C.V. \\
HOTEL.MX & Grupo Hotelero Santa Fe, S.A.B. de C.V. \\
IVVPESO.MX & iShares S\&P 500 Peso Hedged TRAC \\
M10TRAC.MX & iShares S\&P/VALMER Mexico M10TRAC \\
NAFTRAC.MX & iShares NAFTRAC \\
ORBIA.MX & Orbia Advance Corporation, S.A.B. de C.V. \\
PE\&OLES.MX & Industrias Peñoles, S.A.B. de C.V. \\
PINFRA.MX & Promotora y Operadora de Infraestructura, S. A. B. de C. V. \\
Q.MX & Quálitas Controladora, S.A.B. de C.V. \\
UDITRAC.MX & iShares S\&P/VALMER Mexico UDITRAC \\
VESTA.MX & Corporación Inmobiliaria Vesta, S.A.B. de C.V. \\
WALMEX.MX & Wal-Mart de México, S.A.B. de C.V. \\
\hline
\end{tabular}
\caption{Ticker and name of the considered $p=28$ financial instruments trading on the BMV.}
\end{table}
\end{document}

q-fin

Andrés García-Medina
Benito Rodriguéz-Camejo

"Random matrix theory and nested clustered portfolios on Mexican markets" by Andrés García-Medina and Benito Rodriguéz-Camejo, arXiv:2306.05667v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Alaeddine Zahir
Khalide Jbilou
Ahmed Ratnani

"A low-rank non-convex norm method for multiview graph clustering" by Alaeddine Zahir, Khalide Jbilou, and Ahmed Ratnani, arXiv:2312.11157v1, 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{\textbf{Performance of minimum-variance parametric portfolios with marginal screening}}
\begin{tabular}{rr}
	\toprule
	& Std. dev. of OOS \\
	& portfolio returns \\
	\cmidrule{2-2} & \\
	Top-3 predictors & 0.107*** \\
	& \\
	Top-5 predictors & 0.105*** \\
	& \\
	Top-10 predictors & 0.095** \\
	& \\
	Top-50 predictors & 0.103*** \\
	& \\
	\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Guilherme V. Moura
André P. Santos
Hudson S. Torrent

"Variable selection for minimum-variance portfolios" by Guilherme V. Moura, André P. Santos, and Hudson S. Torrent, arXiv:2508.14986v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the SAT, ACT, and ENEM college admission exams}
\begin{tabular}{llll}
					\toprule
					& SAT & ACT & ENEM \\ \midrule
					Cost & $\sim$\$60 & $\sim$\$88 & $\sim$\$17 \\
					Grading & Score conversion chart using raw scores & Score conversion chart using raw scores & Item Response Theory (IRT) \\
					Starting time & Between 8:30 and 9am & Between 8:30 and 9am & 1pm Brasilia time \\
					Number of items & 154 questions & 215 questions & 180 questions \\
					Total length & 3 hours and 50 mins over 1 testing day & 3 hours and 35 mins over 1 testing day & 10 hours over 2 testing days \\
					Time per question & 1 minute and 10 seconds & 50 seconds & 3 minutes \\
					Breaks & 10 mins break after reading section & 10 min break after math section & N/A \\
					& 5 min break between math sections & 5 min break before essay & \\
					& 2 min break before the essay & & \\\midrule
					Sections & Reading (65 mins, 52 items) & English (45 mins, 75 items) & Social science (day 1, 45 items) \\
					& Writing and Language (35 mins, 44 items) & Math (60 mins, 60 items) & Natural science (day 1, 45 items) \\
					& Math w/o calculator (25 mins, 20 items) & Reading (35 mins, 40 items) & Language arts (day 2, 45 items) \\
					& Math w/ calculator (55 mins, 38 items) & Science (35 mins, 40 items) & Math (day 2, 45 items) \\
					& Optional essay (50 mins) & Optional essay (40 mins) & Mandatory essay (day 2) \\
					\midrule

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

q-fin

"Cognitive Endurance, Talent Selection, and the Labor Market Returns to Human Capital" by Germán Reyes, arXiv:2301.02575v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Pingping Zeng
Gongqiu Zhang
Weinan Zhang

"Drawdowns, Drawups, and Occupation Times under General Markov Models" by Pingping Zeng, Gongqiu Zhang, and Weinan Zhang, arXiv:2506.00552v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Gianluca Biggi
Andrea Mina
Federico Tamagni

"There are different shades of green: heterogeneous environmental innovations and their effects on firm performance" by Gianluca Biggi, Andrea Mina, and Federico Tamagni, arXiv:2310.08353v1, 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}{ccccccccc}

		graph & partition& \# & part. & alg. & end-to \\
		& alg. & parts & time & time & -end t. \\\hline

		twitter & Edge2D & 600 & 35.9 & 99.2 & 135.1 \\
		-2010 & FPP & 651 & 34.9 & 87.9 & 122.8 \\\hline
		com- & Edge2D & 500 & 50.6 & 217.3 & 267.8 \\
		friendster & FPP & 651 & 43.5 & 155.5 & 199.0 \\\hline
		uk & Edge2D & 273 & 11.3 & 60.0 & 71.4 \\
		-2002 & FPP & 273 & 11.3 & 51.2 & 62.5 \\\hline
		arabic & Edge2D & 381 & 17.3 & 68.3 & 85.6 \\
		-2005 & FPP & 381 & 17.75 & 62.5 & 80.2 \\\hline
		graph500 & Edge2D & 381 & 27.9 & 39.4 & 67.3 \\
		-26 & FPP & 381 & 27.5 & 35.7 & 63.2 \\\hline
	\end{tabular}
\caption{A runtime (in seconds) table for Connected Components.}
\end{table}
\end{document}

math

Oleg Kruglov
Anna Mastikhina
Oleg Senkevich
Dmitry Sirotkin
Stanislav Moiseev

"Graph partition method based on finite projective planes" by Oleg Kruglov, Anna Mastikhina, Oleg Senkevich, Dmitry Sirotkin, and Stanislav Moiseev, arXiv:2312.13861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
 \hline
 Source Name & Proportion (\%) \\
 \hline
 Hithink RoyalFlush & 59.57 \\
 \hline
 Sina Finance & 33.65\\
 \hline
 Tencent & 4.88\\
 \hline
 Hexun & 1.55\\
 \hline
 Caifuhao & 0.23 \\
 \hline
 Netease Finance & 0.1\\
 \hline
 Dazhong Securities & 0.02\\
 \hline
 \end{tabular}
\caption{The Distribution of News Sources within Our Data-set}
\end{table}
\end{document}

q-fin

Haohan Zhang
Fengrui Hua
Chengjin Xu
Hao Kong
Ruiting Zuo
Jian Guo

"Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?" by Haohan Zhang, Fengrui Hua, Chengjin Xu, Hao Kong, Ruiting Zuo, and Jian Guo, arXiv:2306.14222v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"MGCNN: a learnable multigrid solver for sparse linear systems from PDEs on structured grids" by Yan Xie, Minrui Lv, and Chensong Zhang, arXiv:2312.11093v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter settings for all methods}
\begin{tabular}{ll}
\hline
General parameters & Setting \\
\hline
Solving time limit per epoch - Re-optimization & 900s \\
Training/solving time limit - other methods & 14,400s \\
Shortage margin & $2 \sigma d_{t,t+1,n}$ \\
Algorithmic discount factor $\gamma$ & 0.9 \\
\hline
Parameters PPO & \\
\hline
Buffer size & 100 \\
Network learning rate & 0.0001 \\
Network training batch size & 64 \\
Network architecture actor and critic & 64-64-64 \\
\hline
Parameters DL-VFA & \\
\hline
Initial $\epsilon$ & 0.2 \\
$\epsilon$ decay & 0.98 \\
Initial $\alpha$ & 0.2 \\
$\alpha$ decay & 0.99 \\
Buffer size & 1000 \\
Update frequency & 10 \\
\hline
Parameters NN-VFA & \\
\hline
Initial $\epsilon$ & 0.2 \\
$\epsilon$ decay & 0.98 \\
Buffer size & 1000 \\
Update frequency & 10 \\
Network learning rate & 0.001 \\
Network training batch size & 256 \\
Network architecture & 16-16 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Robert van Steenbergen
Wouter van Heeswijk
Martijn Mes

"The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs" by Robert van Steenbergen, Wouter van Heeswijk, and Martijn Mes, arXiv:2312.00140v1, 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{Lead-lag relationships by industry categories}
\begin{tabular}{lrr}
\toprule
\textbf{Relationship} & \textbf{Freq.} & \textbf{Avg. CCF} \\
\midrule
Within Banking & 23 & 0.2412 \\
Within Real Estate & 18 & 0.2187 \\
Within Technology & 14 & 0.1983 \\
Within Consumer Goods & 11 & 0.1872 \\
Within Energy & 9 & 0.1745 \\
Finance → Real Estate & 12 & 0.1684 \\
Tech → Electronics & 8 & 0.1537 \\
Energy → Materials & 7 & 0.1498 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"From Data Acquisition to Lag Modeling: Quantitative Exploration of A-Share Market with Low-Coupling System Design" by Jianyong Fang, Sitong Wu, and Junfan Tong, arXiv:2506.19255v1, 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}{clllll}
\hline
\\
& \textbf{OIL DRILLING} & : & ``oil-drilling'' ($82\%$) & ``gas-pipeline'' ($9\%$) & ``fracking'' ($7\%$)
\\
& \textbf{METAL}& : & ``metal'' ($46\%$) & ``ferrous'' ($37\%$) & ``nonferrous'' ($14\%$)
\\
& \textbf{WOOD}& : & ``wood'' ($64\%$) & ``timber'' ($27\%$) & ``lumber'' ($10\%$)
\\
& \textbf{MEAT}& : & ``meat'' ($49\%$) & ``red-meat'' ($18\%$) & ``poultry'' ($16\%$)
\\
& \textbf{MARIJUANA}& : & ``marijuana'' ($54\%$) & ``cannabis'' ($44\%$) & ``t.h.c.'' ($2\%$)
\\
\hline
\end{tabular}
\caption{Subset of MIS Commodities Industries}
\end{table}
\end{document}

q-fin

Maksim Papenkov
Chris Meredith
Claire Noel
Jai Padalkar
Temple Hendrickson
Daniel Nitiutomo
Thomas Farrell

"Multi-Industry Simplex : A Probabilistic Extension of GICS" by Maksim Papenkov, Chris Meredith, Claire Noel, Jai Padalkar, Temple Hendrickson, Daniel Nitiutomo, and Thomas Farrell, arXiv:2310.04280v2, 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{Suggested Allocation }
\begin{tabular}{|l|l|l|l|}
 \hline
 ~ & Fee Revenue & Allocation (if 80\%) & Allocation (if 100\%) \\ \hline
 Tier 1 & 555494.79 & 40\% & 50\% \\ \hline
 Tier 2 & 276592.38 & 20\% & 30\% \\ \hline
 Tier 3 & 199288.70 & 15\% & 15\% \\ \hline
 Tier 4 & 97303.20 & 5\% & 5\% \\ \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"dYdX: Liquidity Providers' Incentive Programme Review" by Colin Chan, arXiv:2307.03935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c|c|c}
 \toprule
 & RSR & RFR & min-path &
 $33\%$-path & $50\%$-path &
 $66\%$-path & max-path \\
 \midrule
 $\eta = 4$ & 73.03 & 75.49 & 77.75 & 71.30 & \textbf{92.12} & 72.14 & 69.53 \\
 $\eta = 3$ & 69.18 & 71.67 & 62.20 & 63.01 & \textbf{93.33} & 82.39 & 82.39 \\
 $\eta = 2$ & 72.69 & 52.70 & 60.55 & \textbf{78.62} & 74.34 & 72.83 & 65.85 \\
 $\eta = 1$ & 63.13 & 53.05 & 63.62 & \textbf{77.20} & 76.83 & 63.39 & 62.75 \\
 \bottomrule
 \end{tabular}
\caption{Average percentage of feasible-to-change individuals reclassified as healthy based on our simulations for the different methodologies.}
\end{table}
\end{document}

math

Víctor Blanco
Alberto Japón
Justo Puerto
Peter Zhang

"Optimal probabilistic feature shifts for reclassification in tree ensembles" by Víctor Blanco, Alberto Japón, Justo Puerto, and Peter Zhang, arXiv:2412.03722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{xcolor}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{(Section ) The $\mathcal{H}_{\infty}$ values and computational times for the Lyapunov-based approach and NLA corresponding to DSSF $\mathcal{H}_{\infty}$ controller with $S = \mathbf{1}_{n_u \times n_y}$, $C_y = I_{n_x}$, $D_y = 0$, and $B_h = 0.1I_{n_x}$ for the IEEE test systems.}}
\begin{tabular}{|c|c|c|c|}
\hline
\textrm{Approach} & $\|T_{z\tilde{w}}(s)\|_{\mathcal{H}_{\infty}}$ & Computational Time & $(n_u,n_y)$ \\
\hline
non-Lyap $9$-bus & \cellcolor{lightgray}$3.1856$ & \cellcolor{lightgray} $2.09$ s & $(6,36)$\\
\hline
Lyap $9$-bus & $3.1857$ & $2.72$ s & $(6,36)$\\
\hline
non-Lyap $14$-bus & \cellcolor{lightgray}$7.4260$ & \cellcolor{lightgray} $6.05$ s & $(10,58)$\\
\hline
Lyap $14$-bus & $7.4274$ & $19.41$ s & $(10,58)$\\
\hline
non-Lyap $39$-bus & $9.2018$ & \cellcolor{lightgray} $259.56$ s & $(20,138)$\\
\hline
Lyap $39$-bus & \cellcolor{lightgray}$9.1701$ & $1611.45$ s & $(20,138)$\\
\hline
non-Lyap $57$-bus & \cellcolor{lightgray}$23.5716$ & \cellcolor{lightgray} $111.31$ s & $(14,156)$\\
\hline
Lyap $57$-bus & $23.5731$ & $5361.67$ s & $(14,156)$\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

MirSaleh Bahavarnia
Muhammad Nadeem
Ahmad F. Taha

"On Scaling Robust Feedback Control and State Estimation Problems in Power Networks" by MirSaleh Bahavarnia, Muhammad Nadeem, and Ahmad F. Taha, arXiv:2311.17836v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
			\hline
			\small
			Strategy & Accumulated \\
			& Revenue (\%) \\
			\hline \hline
			UTLPL & 1.010922 \\ \hline
			UTLPP & 1.009505 \\ \hline
			UTPPL & 1.011206 \\ \hline
			UTPPP & 1.008875 \\ \hline
			ATLPL & 1.008409 \\ \hline
			ATLPP & 1.011454 \\ \hline
			ATPPL & 1.008456 \\ \hline
			ATPPP & 1.011473 \\ \hline
			OTLPL & 1.006268 \\ \hline
			OTLPP & 1.010698 \\ \hline
			OTPPL & 1.007157 \\ \hline
			OTPPP & 1.011098 \\ \hline
			NS & 1.000000 \\ \hline
		\end{tabular}
\caption{Cumulative revenue as a percentage of NS}
\end{table}
\end{document}

q-fin

"Optimal liquidation with temporary and permanent price impact, an application to cryptocurrencies" by Hugo E. Ramirez and Julián Fernando Sanchez, arXiv:2303.10043v1, 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{Baseline average weekly level per ZIP code in the pre-event period for tropical storms}
\begin{tabular}{lcc}
\hline
\textbf{Variable} & \textbf{Control Group} & \textbf{Treated Group} \\
\hline
Transaction Volume & 286.228 & 291.139 \\
Default & 8.666 & 8.655 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Keeping in Place After the Storm-Emergency Assistance and Evictions" by Bilal Islah and Ahmed Zoulati, arXiv:2505.14548v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Estimation problems for some perturbations of the independence copula" by Martial Longla and Mous-Abou Hamadou, arXiv:2308.14282v1, 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|ccc}
\hline
$x$ & $\alpha$ & $\beta$ & $\mu$ & $\delta$ & & & \\
\hline
	1/2 & 2 & 1 & 1/4 & 3 & 27 & 32 & 15\\
	1/3 & 5 & -1 & 1/4 & 1 & 12 & 17 & 14\\
	4 & 15 & -6 & 7/2 & 10 & 63 & 67 & 17\\
	2/10 & 1 & 1/2 & 1/10 & 1/2 & 22 & 25 & 19\\
	2 & 3 & 2 & 1 & 2 & 42 & 152 & 52\\
	3 & 5 & 1/2 & 1 & 4 & 29 & 63 & 66\\
	\hline
	\end{tabular}
\caption{Comparison on the number of terms to achieve machine precision with the three series expansions for $|x-\mu| \to 0$.}
\end{table}
\end{document}

stat

"On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution" by Guillermo Navas-Palencia, arXiv:2502.16015v1, 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{Capacity allocation under scenario 2 and with regulation.}
\begin{tabular}{|c|c|c|c|} \hline
 Products & Original Grouping & New Grouping & Transported Amount \\ \hline
 Crude Oil & $G_1$ & $G_1$ & 128,185,505 \\
 Corn & $G_1$ & $G_2$ & 246,236,668 \\
 Barley & $G_2$ & $G_3$ & 0 \\
 Oat & $G_3$ & $G_3$ & 275,577,827 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Mostafa Pazoki
Hamed Samarghandi
Mehdi Behroozi

"Increasing Supply Chain Resiliency Through Equilibrium Pricing and Stipulating Transportation Quota Regulation" by Mostafa Pazoki, Hamed Samarghandi, and Mehdi Behroozi, arXiv:2308.00681v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
Entity Name & Total Count \\
\hline
Effective Date & 420 \\
Jurisdiction & 431 \\
Party & 948 \\
\hline
\end{tabular}
\caption{NDA Label List and Counts }
\end{table}
\end{document}

cs

Benjamin Townsend
Madison May
Christopher Wells

"RealKIE: Five Novel Datasets for Enterprise Key Information Extraction" by Benjamin Townsend, Madison May, and Christopher Wells, arXiv:2403.20101v1, 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{Results on AOLP dataset}
\begin{tabular}{llrrr}
\hline
 & & ResNet50 & MobileNet2 & \\
\hline
\multirow{2}{*}{AC} & mAP & 100\% & 100\% & - \\
 & recog. rate & 97.00\% & 95.00\% & - \\
\hline
\multirow{2}{*}{LE} & mAP & 98.11\% & 98.15\% & - \\
 & recog. rate & 91.67\% & 87.96\% & - \\
\hline
\multirow{2}{*}{RP} & mAP & 100.00\% & 99.00\% & - \\
 & recog. rate & 89.00\% & 85.00\% & - \\
\hline
\multirow{2}{*}{Overall} & mAP & \textbf{99.35\%} & 99.02\% & 99.00\% \\
 & recog. rate & \textbf{92.21\%} & 89.29\% & 78.00\% \\
\hline
\multicolumn{2}{l}{GFLOPs} & 35.13 & \textbf{2.93} & - \\
\hline
\multicolumn{2}{l}{Params (M)} & 27.2 & \textbf{3.4} & - \\
\hline
\multicolumn{2}{l}{Time (ms)} & 70.86 & \textbf{52.54} & 800 - 1000 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"QuickBrowser: A Unified Model to Detect and Read Simple Object in Real-time" by Thao Do and Daeyoung Kim, arXiv:2102.07354v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Lead Times in Flux: Analyzing Airbnb Booking Dynamics During Global Upheavals (2018-2022)" by Harrison Katz, Erica Savage, and Peter Coles, arXiv:2501.10535v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

""Medium-n studies" in computing education conferences" by Michael Guerzhoy, arXiv:2311.14679v2, 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}{lll}
\hline
& \multicolumn{2}{c}{Collateral management} \\
Collateral source & Central & Decentral \\ \hline
Exogenous & USDT, USDC & Dai \\
Endogenous & TerraUSD & sUSD \\ \hline
\end{tabular}
\caption{2 × 2 matrix for categorizing stablecoins with examples of each type.}
\end{table}
\end{document}

q-fin

Matthias Hafner
Marco Henriques Pereira
Helmut Dietl
Juan Beccuti

"The four types of stablecoins: A comparative analysis" by Matthias Hafner, Marco Henriques Pereira, Helmut Dietl, and Juan Beccuti, arXiv:2308.07041v1, 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{Dataset structure.}
\begin{tabular}{cc}
 \hline
 Parameter & Values \\
 \hline
 Accretion & SANE\\
 Frequency & 230 GHz, 345 GHz \\
 $a_*$ & $-0.94$, $-0.50$, $0.00$, $0.50$, $0.94$ \\
 $R_\mathrm{high}$ & 1, 5, 10, 20, 40, 80, 160 \\
 Frames & 1, 2, 3, 4, ..., 100, 101 \\
 \textbf{Inclination Angle} & \textbf{163 deg}\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Arya Mohan
Pavlos Protopapas
Keerthi Kunnumkai
Cecilia Garraffo
Lindy Blackburn
Koushik Chatterjee
Sheperd S. Doeleman
Razieh Emami
Christian M. Fromm
Yosuke Mizuno
Angelo Ricarte

"Generating Images of the M87* Black Hole Using GANs" by Arya Mohan, Pavlos Protopapas, Keerthi Kunnumkai, Cecilia Garraffo, Lindy Blackburn, Koushik Chatterjee, Sheperd S. Doeleman, Razieh Emami, Christian M. Fromm, Yosuke Mizuno, and Angelo Ricarte, arXiv:2312.01005v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yixuan He
Aaron Sandel
David Wipf
Mihai Cucuringu
John Mitani
Gesine Reinert

"Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions" by Yixuan He, Aaron Sandel, David Wipf, Mihai Cucuringu, John Mitani, and Gesine Reinert, arXiv:2502.00302v1, 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{Operating characteristics of the proposed designs under different values of $\theta_1$ and $\theta_2$, for both control of pairwise power and of conjunctive power.}
\begin{tabular}{c|c|c|c|c|c|c}
\multicolumn{7}{c}{\textbf{Design for pairwise power}}
\\
\hline
\multicolumn{2}{c|}{Treatment effect} & \multicolumn{2}{c|}{Pairwise power} & Conjunctive power & Disjunctive power & Expected sample size
\\
$\theta_1$ & $\theta_2$ & $P_{PW,1}$ & $P_{PW,2}$ & $P_{C}$ & $P_{D}$ & $E(N|\theta_1,\theta_2)$ \\
\hline
 $\theta'$ & $\theta'$ & 0.800 & 0.800 & 0.660 & 0.941 & 420.6 \\
 $\theta'$ & $0$ & 0.800 & 0.013 & 0.800 & 0.802 & 372.7 \\
 $\theta'$ & $-\infty$ & 0.800 & 0 & 0.800 & 0.800 & 342.9 \\
 0 & $\theta'$ & 0.013 & 0.800 & 0.800 & 0.802 & 396.6 \\
 0 & $0$ & 0.013 & 0.013 & 1 & 0.025 & 348.7 \\
 $-\infty$ & $\theta'$ & 0 & 0.800 & 0.800 & 0.800 & 381.7 \\
\hline
\multicolumn{7}{c}{\textbf{Design for conjunctive power}}
\\
\hline
\multicolumn{2}{c|}{Treatment effect} & \multicolumn{2}{c|}{Pairwise power} & Conjunctive power & Disjunctive power & Expected sample size
\\
$\theta_1$ & $\theta_2$ & $P_{PW,1}$ & $P_{PW,2}$ & $P_{C}$ & $P_{D}$ & $E(N|\theta_1,\theta_2)$ \\
\hline
 $\theta'$ & $\theta'$ & 0.890 & 0.890 & 0.801 & 0.979 & 508.1 \\
 $\theta'$ & $0$ & 0.890 & 0.013 & 0.890 & 0.890 & 463.0 \\
 $\theta'$ & $-\infty$ & 0.890 & 0 & 0.890 & 0.890 & 425.4 \\
 0 & $\theta'$ & 0.013 & 0.890 & 0.890 & 0.891 & 485.6 \\
 0 & $0$ & 0.013 & 0.013 & 1 & 0.025 & 440.5 \\
 $-\infty$ & $\theta'$ & 0 & 0.890 & 0.890 & 0.890 & 466.7 \\
\end{tabular}
\end{table}
\end{document}

stat

Peter Greenstreet
Thomas Jaki
Alun Bedding
Pavel Mozgunov

"A preplanned multi-stage platform trial for discovering multiple superior treatments with control of FWER and power" by Peter Greenstreet, Thomas Jaki, Alun Bedding, and Pavel Mozgunov, arXiv:2308.12798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean and standard deviations of Absolute Error for MAGI and EFiGP for each parameter on the FN system}
\begin{tabular}{lccccccc}
\toprule
 & & \textbf{41} & \textbf{81} & \textbf{161} & \textbf{321} & \textbf{641} & \textbf{1281} \\
\midrule
\multirow{3}{*}{\textbf{EFiGP}}
 & \textnormal{a} & \hfill .020$\pm$.013 & \hfill .025$\pm$.022 & \hfill .027$\pm$.026 & \hfill .030$\pm$.026 & \hfill .031$\pm$.025 & \hfill .031$\pm$.024 \\
 & \textnormal{b} & \hfill .176$\pm$.116 & \hfill .121$\pm$.099 & \hfill .190$\pm$.111 & \hfill .257$\pm$.114 & \hfill .264$\pm$.112 & \hfill .233$\pm$.103 \\
 & \textnormal{c} & \hfill .100$\pm$.075 & \hfill .174$\pm$.094 & \hfill .068$\pm$.047 & \hfill .075$\pm$.051 & \hfill .067$\pm$.046 & \hfill .050$\pm$.034 \\
\midrule
\multirow{3}{*}{\textbf{MAGI}}
 & \textnormal{a} & \hfill .026$\pm$.019 & \hfill .017$\pm$.014 & \hfill .028$\pm$.017 & \hfill .033$\pm$.017 & \hfill .032$\pm$.017 & \hfill .031$\pm$.018 \\
 & \textnormal{b} & \hfill .120$\pm$.082 & \hfill .229$\pm$.141 & \hfill .388$\pm$.144 & \hfill .475$\pm$.123 & \hfill .500$\pm$.099 & \hfill .500$\pm$.085 \\
 & \textnormal{c} & \hfill .126$\pm$.083 & \hfill .241$\pm$.105 & \hfill .288$\pm$.104 & \hfill .277$\pm$.097 & \hfill .251$\pm$.087 & \hfill .231$\pm$.080 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems" by Jianhong Chen and Shihao Yang, arXiv:2501.14107v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Equity Protection Swaps: A New Type of Investment Insurance for Holders of Superannuation Accounts" by Huansang Xu, Ruyi Liu, and Marek Rutkowski, arXiv:2305.09472v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of existing CMI tests. \textsf{High-Dim}: whether the test has good empirical performance when some or all the dimensions of $X$, $Y$ and $Z$ are large; \textsf{Size Guarantee}: whether the test has theoretical results under $H_0$ that guarantee accurate asymptotic size control in general nonparametric setting; \textsf{Local Alt}: whether the test can detect local alternatives with signal strength $\Delta_n$ converging to zero at the parametric rate $n^{-1/2}$. }
\begin{tabular}{c|cccc}
				\toprule
				\midrule
				Tests & \textsf{High-Dim} & \textsf{Size Guarantee} & \textsf{Local Alt} \\\hline
				 & No& Yes & No \\\hline
				 & No& Yes & Yes \\\hline
				 &No & Yes & No\\\hline
				 &No &Yes & No\\\hline
				 &No & Yes& No\\\hline
				 &Yes & No & No\\\hline
				 &Yes &Yes & No\\\hline
				 &Yes & No& Yes\\\hline
				 &Yes & Yes & No\\\hline
				 &Yes & Yes & No\\\hline
				\textcolor{blue}{\bf{Our method}} &\textcolor{blue}{\bf{Yes}} & \textcolor{blue}{\bf{Yes}} & \textcolor{blue}{\bf{Yes}}\\\hline
			\end{tabular}
\end{table}
\end{document}

stat

Yi Zhang
Linjun Huang
Yun Yang
Xiaofeng Shao

"Testing Conditional Mean Independence Using Generative Neural Networks" by Yi Zhang, Linjun Huang, Yun Yang, and Xiaofeng Shao, arXiv:2501.17345v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Revisiting Stochastic Collocation with Exponential Splines for an Arbitrage-Free Interpolation of Option Prices" by Fabien Le Floc'h, arXiv:2508.12419v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|r|}
 \hline
 \textbf{Category}&\textbf{Value}\\
 \hline\hline
 \# Speakers & 27\\
 \# Moderator & 1\\
 \# Students & 16+2(pilot)\\
 \# Interlocutors & 8\\
 \hline
 \# Meetings &15\\
 Total Duration (h) & 11.9\\
 Speech Duration (h) & 10.5\\
 Average Meeting Duration (h) & 0.8\\
 \hline
 \# Utterances & 6,033\\
 \# Tokens & 94,101\\\hline
\end{tabular}
\caption{Corpus Size Overview}
\end{table}
\end{document}

cs

Injy Hamed
Fadhl Eryani
David Palfreyman
Nizar Habash

"ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus" by Injy Hamed, Fadhl Eryani, David Palfreyman, and Nizar Habash, arXiv:2403.18182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Aristotelis Ballas
Vasileios Papapanagiotou
Christos Diou

"Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning" by Aristotelis Ballas, Vasileios Papapanagiotou, and Christos Diou, arXiv:2312.00502v6, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Tools and applications using ETW framework.}
\begin{tabular}{lccc}
 \toprule
 \textbf{Metric} & \textbf{Command line tools and applications} & \textbf{ETW APIs} & \textbf{Peeler} \\
 \midrule
 Visibility & limited & limited & greater\\
 Real-time & $\times$ & \checkmark & \checkmark \\
 Lightweight &\checkmark & $\times$ & \checkmark \\
 Native & $\times$ & $\times$ & \checkmark \\
 Static support & \checkmark & \checkmark & \checkmark \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Muhammad Ejaz Ahmed
Hyoungshick Kim
Seyit Camtepe
Surya Nepal

"Peeler: Profiling Kernel-Level Events to Detect Ransomware" by Muhammad Ejaz Ahmed, Hyoungshick Kim, Seyit Camtepe, and Surya Nepal, arXiv:2101.12434v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics for the largest angle between $\mathcal{R}(P_\zeta(\sigma, \zeta))$ and $\mathcal{R}(P_\zeta(\sigma_0, \zeta_0))$ as well as for the relative discrepancy ${\rm err}_{\rm F}$ between the associated Jacobians over a sample of 1000 random draws of $\sigma$ and $\zeta$ for the head model of Section~.}
\begin{tabular}{cccccc}
 $\max \theta_{\rm max} $ & $\mathbb{E} [\theta_{\max}]$ & ${\rm std}[ \theta_{\max}]$ & $\max ({\rm err}_{\rm F}) $ & $\mathbb{E} [{\rm err}_{\rm F}]$ & ${\rm std}[ {\rm err}_{\rm F}]$\\
 \hline
 $0.84^{\circ}$ & $0.46^{\circ}$ & $0.12^{\circ}$ & 200.17 & 3.67 & 12.42
 \end{tabular}
\end{table}
\end{document}

math

Altti Jääskeläinen
Jussi Toivanen
Asko Hänninen
Ville Kolehmainen
Nuutti Hyvönen

"Projection-based preprocessing for electrical impedance tomography to reduce the effect of electrode contacts" by Altti Jääskeläinen, Jussi Toivanen, Asko Hänninen, Ville Kolehmainen, and Nuutti Hyvönen, arXiv:2412.15009v1, 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 results for T-test}
\begin{tabular}{lrr}
		\hline & t-statistic & p-value \\
		\hline
		Before Crisis vs During Crisis & -10.3308 & $6,65 \times 10^{-10}$ \\
		Before Crisis vs After Crisis & -4.5497 & $1.57 \times 10^{-4}$ \\
		During Crisis vs After Crisis & 5.4632 & $1.72 \times 10^{-5}$ \\
		\hline
	\end{tabular}
\end{table}
\end{document}

q-fin

"Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropy" by Kleyton da Costa, arXiv:2308.02914v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cccccc}
 \toprule
 \multirow{2.5}{*}{Src Doc.} &
 \multicolumn{3}{c}{\textbf{Sample-Level}} &
 \multicolumn{3}{c}{\textbf{Dataset-Level}} \\
 \cmidrule(r){2-4} \cmidrule(r){5-7} & $\rho$ & $r$ & $\tau$ & $\rho$ & $r$ & $\tau$ \\
 \midrule
 \multicolumn{7}{c}{\textit{UniEval}} \\ \midrule
 Empty ("") & 56.7 & 57.8 & 43.6 & 58.7 & 55.6 & 42.3 \\
 Original Src & 57.5 & 55.4 & 44.2 & 59.2 & 53.3 & 42.5 \\ \midrule
 \multicolumn{7}{c}{\textit{CoUDA}} \\ \midrule
 None & 60.0 & 62.1 & 46.0 & 65.6 & 64.2 & 47.8 \\
 \bottomrule
 \end{tabular}
\caption{Performance Comparison of UniEval w/ or w/o Source Document.}
\end{table}
\end{document}

cs

Dawei Zhu
Wenhao Wu
Yifan Song
Fangwei Zhu
Ziqiang Cao
Sujian Li

"CoUDA: Coherence Evaluation via Unified Data Augmentation" by Dawei Zhu, Wenhao Wu, Yifan Song, Fangwei Zhu, Ziqiang Cao, and Sujian Li, arXiv:2404.00681v1, 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}{ccc}
 Model & Active Parameters & One-Step SPE \\
\hline
GNAR(1, [1]) & 2 & 4.44 \\
 VAR(1) & $140^2$ & 7.978 \\
 Res. VAR(1) & 3738 & 7.716 \\
 Sparse VAR(1) & 3030 & 7.918 \\
 AR(1) & $140$ & 91.004 \\
 GNAR(1, [1])* & 141 & 4.466
\end{tabular}
\caption{One-step prediction error, $\hat{\boldsymbol{X}}_{449}$ is predicted using the previous 448 observations.}
\end{table}
\end{document}

stat

Guy Nason
Daniel Salnikov
Mario Cortina-Borja

"New tools for network time series with an application to COVID-19 hospitalisations" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, 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{amsfonts}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Notations and Descriptions (Part I)}
\begin{tabular}{llll}
 \toprule
 Category & Symbol & Description & Reference\\
 \midrule
 \multirow{5}{*}{Domain} & $x$& Option & Eq.~()\\
 &$x^*$& Consensus (global optimum) & Eq.~()\\
 &$x_t$& Queried option at $t$-th step & Eq.~()\\
 &$\mathcal{X} \in \mathbb{R}^d$& (continuous) domain & Eq.~()\\
 &$d$& Number of dimensions & Eq.~()\\
 \midrule
 \multirow{16}{*}{Utility} &$n$& Number of agents & Eq.~()\\
 & $V$ & Set of $n$ agents & Eq.~()\\
 & $i$ & Index of agents & Eq.~()\\
 &$u(x,i)$& Truthful utility for the $i$-th agent. & Eq.~()\\
 &$v(x,i)$& Non-truthful utility for the $i$-th agent. & Eq.~()\\
 &$u(x,:)$ & Utilities of all agents & Eq.~()\\
 &$\succ_{u(\cdot,i)}$ & Preference of agent $i$ induced by utility $u(\cdot,i)$ & Assumption~\\
 &$\succ_{\mathcal{S}}$ & Preference of group induced by social utility $\mathcal{S}$ & Definition~\\
 &$p(u), p(v)$ & Prior over utilities (uniform prior) & Section~\\
 &$\hat{u}, \hat{v}$& MAP estimate of utilities. & Lemma~\\
 &$v(x,i)$& Non-truthful utility for the $i$-th agent. & Eq.~()\\
 &$u(x,:)$ & Utilities of all agents & Eq.~()\\
 &$\tilde{u}, \tilde{v}$& Utility function sample from confidence set. & Assumption~\\
 &$\hat{u}_t, \hat{v}_t$& MAP estimated utility function at $t$-th step. & Assumption~\\
 &$\underline{u}_t, \underline{v}_t$& The lower confidence bound of utility. & Remark~\\
 &$\overline{u}_t, \overline{v}_t$& The upper confidence bound of utility. & Remark~\\
 \midrule
 \multirow{4}{*}{Likelihood}
 &$\sigma$& Sigmoid function & Assumption~\\
 &$\ell_t(u), \ell_t(v)$& Log Likelihood (LL) function & Corollary~\\
 &$\hat{\ell}^u_t, \hat{\ell}^v_t$& MLE estimate of LL values& Lemma~\\
 &$\mathcal{L}_t(u,A,v)$& Unnormalized negative log posterior & Appendix~\\
 \midrule
 \multirow{5}{*}{\shortstack[l]{Aggregation\\function}}
 &$\mathcal{A}$& Aggregation function & Definition~\\
 &$\mathcal{S}$& Social utility& Definition~\\
 &$\textbf{w}$ & Weight function of $\mathcal{A}$ & Proposition~\\
 &$\rho$ & GSF interpolation parameter & Proposition~\\
 &$\phi$ & Sorting function & Proposition~\\
 \midrule
 \multirow{11}{*}{RKHS}
 &$k_i(x, x^\prime)$&Kernel of $i$-th agent's utility & Assumption~\\
 &$\mathcal{H}_{k_i}$&RKHS corresponding to the kernel $k_i$ & Assumption~\\
 &$||\cdot||_{k_i}$ & Norm induced by inner product in RKHS $\mathcal{H}_{k_i}$ & Assumption~\\
 &$L^v$&Isotropic norm bound of $\mathcal{H}_{k_i}$ & Assumption~\\
 &$\mathcal{B}^{v_i}, \mathcal{B}^{u_i}$ & Set of utility functions of $i$-th agent & Assumption~\\
 &$\mathcal{B}^{v}, \mathcal{B}^{u}$ & Superset of utility functions of all agent & Assumption~\\
 &$\mathcal{B}^{v}_t, \mathcal{B}^{u}_t$ & Superset of MLE-estimated utility functions at $t$-th step & Lemma~\\
 &$\mathcal{B}^{u,A,v}_t$ & Superset of MAP-estimated utility functions at $t$-th step & Lemma~\\
 &$\gamma^u_t, \gamma^v_t, \gamma^{uu}_t, \gamma^{vv}_t$ & Maximum information gain for corresponding kernels & Lemma~\\
 &$L_{k, t}$ & Kernel specific term & Theorem~\\
 &$\nu$ & Matérn kernel smoothness paramter & Table~\\
 \midrule
 \multirow{6}{*}{\shortstack[l]{Confidence\\set}}
 &$\mathcal{F}_t$&Filtration at the step $t$ & Lemma~\\
 &$\delta$ & Probability that $\mathcal{B}^{u,A,v}_t$ does not contain $u,A,v$ & Lemma~\\
 &$\epsilon$ &Radius of the function space ball $L_{k_i}$ & Lemma~\\
 &$\mathcal{N}(\mathcal{B}^u, \epsilon, \lVert \cdot \rVert_\infty)$ & Covering number of the set $\mathcal{B}^u$ & Lemma~\\
 &$\beta^u_t, \beta^v_t$ & MLE-based confidence set bound parameter & Lemma~\\
 &$\beta_t$ & MAP-based confidence set bound parameter & Lemma~\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Masaki Adachi
Siu Lun Chau
Wenjie Xu
Anurag Singh
Michael A. Osborne
Krikamol Muandet

"Bayesian Optimization for Building Social-Influence-Free Consensus" by Masaki Adachi, Siu Lun Chau, Wenjie Xu, Anurag Singh, Michael A. Osborne, and Krikamol Muandet, arXiv:2502.07166v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Assessing the Performance of Mixed-Precision ILU(0)-Preconditioned Multiple-Precision Real and Complex Krylov Subspace Methods" by Tomonori Kouya, arXiv:2504.14498v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||l||c|c|c||}
 	\hline \hline
 \textbf{} & \textbf{Dual Algorithm~} & \textbf{Problem~} & \textbf{Problem~} \\
 \hline
 Scenario A & $-441.8$ & $-564.9$ & $1941.9$ \\
 Scenario B & $-649.28$ & $-660.27$ & $4160.4$ \\
 Scenario C & $-1334.7$ & $-1338.8$ & $1980.5$ \\
 Scenario D & $-80.52$ & $-92.69$ & $1209.5$ \\
 Scenario E & $-664.03$ & $-667.83$ & $-173.57$ \\
 \hline
 \end{tabular}
\caption{Comparing the optimal dual costs (in \texteuro) achieved with the dual optimization Algorithm~ for each scenario with the optimal primal costs of the corresponding unconstrained and a.s. QoS constrained problems.}
\end{table}
\end{document}

math

Nadhir Ben Rached
Shyam Mohan Subbiah Pillai
Raúl Tempone

"Optimal power procurement for green cellular wireless networks under uncertainty and chance constraints" by Nadhir Ben Rached, Shyam Mohan Subbiah Pillai, and Raúl Tempone, arXiv:2503.03051v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Debdas Ghosh
Anshika
Jen-Chih Yao
Xiaopeng Zhao

"Quasi-Newton Method for Set Optimization Problems with Set-Valued Mapping Given by Finitely Many Vector-Valued Functions" by Debdas Ghosh, Anshika, Jen-Chih Yao, and Xiaopeng Zhao, arXiv:2501.04711v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Empirical variance of the additive noise in the algorithms, over 250 runs, of the trimmed mean, \texttt{Bun}, with $t=1$, and the clipped mean, $\tilde\mu$, with $\eta=0$, compared across various population distributions. Here, $C$ and $m$ (as defined in ) were randomly selected between 1 and 100.}
\begin{tabular}{ll|rr|rr|rr}
& & \multicolumn{2}{c}{Gaussian} & \multicolumn{2}{c}{Gaussian Mixture} & \multicolumn{2}{c}{Skewed}\\
$\rho$ & $n$ & \texttt{Bun} & $\tilde\mu',\ \eta=0$ & \texttt{Bun} & $\tilde\mu',\ \eta=0$ & \texttt{Bun} & $\tilde\mu',\ \eta=0$ \\
 \hline
\multirow[c]{4}{*}{1} & 50 & 1.46e+01 & \textbf{3.81e-03} & 1.50e+01 & \textbf{3.68e-02} & 1.51e+01 & \textbf{4.69e-03} \\
 & 100 & 9.47e-01 & \textbf{8.71e-04} & 1.32e+00 & \textbf{1.13e-02} & 9.74e-01 & \textbf{7.32e-04} \\
 & 500 & 1.46e-02 & \textbf{4.83e-05} & 2.50e-02 & \textbf{4.89e-04} & 1.51e-02 & \textbf{3.88e-05} \\
 & 1000 & 3.39e-03 & \textbf{1.38e-05} & 8.05e-03 & \textbf{1.38e-04} & 3.18e-03 & \textbf{9.37e-06} \\
\multirow[c]{4}{*}{100} & 50 & 4.72e-04 & \textbf{4.44e-05} & \textbf{4.85e-0}4 & 5.33e-04 & 4.79e-04 & \textbf{3.62e-05} \\
 & 100 & 2.66e-05 & \textbf{1.10e-05} & \textbf{3.66e-05} & 1.17e-04 & 2.70e-05 & \textbf{9.76e-06} \\
 & 500 & \textbf{2.83e-07} & 5.17e-07 & \textbf{7.39e-07} & 4.78e-06 & \textbf{2.84e-07} & 5.09e-07 \\
 & 1000 & \textbf{5.14e-08} & 1.30e-07 & \textbf{1.89e-07} & 1.20e-06 & \textbf{4.92e-08} & 1.20e-07 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

"Improved subsample-and-aggregate via the private modified winsorized mean" by Kelly Ramsay and Dylan Spicker, arXiv:2501.14095v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Federico Fioravanti
Fernando Delbianco
Fernando Tohmé

"Visitors Out! The Absence of Away Team Supporters as a Source of Home Advantage in Football" by Federico Fioravanti, Fernando Delbianco, and Fernando Tohmé, arXiv:2308.06279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the MSE of the estimated HTE $\widehat{\tau}(X_i)$ using CSF, MISTR, IPCW-IV, and MISTR-IV on a random test set with $n_{test}=5000$ observations for the IV case. Results are based on 100 replications. Results are multiplied by a readability factor of 100.}
\begin{tabular}{|l|cc|cc|cc|cc|}
\toprule
 & \multicolumn{2}{|c|}{CSF} & \multicolumn{2}{|c|}{MISTR} & \multicolumn{2}{|c|}{IPCW-IV} & \multicolumn{2}{|c|}{MISTR-IV} \\
Type & Mean & std & Mean & std & Mean & std & Mean & std \\
\midrule
200 & 7.21 & 2.74 & 7.06 & 2.73 & 11.60 & 4.03 & 4.00 & 1.57 \\
201 & 10.67 & 3.59 & 10.48 & 3.54 & 21.05 & 7.95 & 6.08 & 2.51 \\
202 & 11.73 & 3.67 & 11.59 & 3.69 & 18.57 & 5.92 & 5.95 & 2.58 \\
203 & 5.40 & 1.81 & 4.59 & 1.63 & 12.85 & 5.21 & 3.21 & 1.36 \\
204 & 4.41 & 2.23 & 3.19 & 1.84 & 16.53 & 10.41 & 2.51 & 1.51 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees" by Tomer Meir, Uri Shalit, and Malka Gorfine, arXiv:2502.01575v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.