\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\toprule
Method & Positional Encoding & Kinetics & SSv2$^\dagger$ \\ \midrule
$\Omega{=}\{1\}$ & $\times$ & 85.2 & 53.0 \\
$\Omega{=}\{1\}$ & \checkmark & 85.2 & 53.3 \\
$\Omega{=}\{2,3\}$ & $\times$ & 85.5 & 58.5 \\
$\Omega{=}\{2,3\}$ & \checkmark & {\bf 85.9} & {\bf 59.1} \\
\bottomrule
\end{tabular}
\caption{The importance of incorporating positional encoding for single frames and the proposed model ${\Omega{=}\{2,3\}}$.}
\end{table}
\end{document}

cs

Toby Perrett
Alessandro Masullo
Tilo Burghardt
Majid Mirmehdi
Dima Damen

"Temporal-Relational CrossTransformers for Few-Shot Action Recognition" by Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, and Dima Damen, arXiv:2101.06184v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Ulrich Langer
Richard Löscher
Olaf Steinbach
Huidong Yang

"Parallel iterative solvers for discretized reduced optimality systems" by Ulrich Langer, Richard Löscher, Olaf Steinbach, and Huidong Yang, arXiv:2312.12282v1, 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.


cs

Shiguang Wu
Wenda Wei
Mengqi Zhang
Zhumin Chen
Jun Ma
Zhaochun Ren
Maarten de Rijke
Pengjie Ren

"Generative Retrieval as Multi-Vector Dense Retrieval" by Shiguang Wu, Wenda Wei, Mengqi Zhang, Zhumin Chen, Jun Ma, Zhaochun Ren, Maarten de Rijke, and Pengjie Ren, arXiv:2404.00684v1, 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}{|ccccc|}
\hline
Dataset & \# Graphs & Avg. \# Nodes & Avg. \# Edges & \# Classes \\
\hline\hline
HIV & 41127 & 25.5 & 27.5 & 2 \\
\hline
BACE & 1513 & 34.1 & 36.9 & 2 \\
\hline
BBBP & 2039 & 24.1 & 26.0 & 2 \\
\hline
\end{tabular}
\caption{The properties of datasets used for testing fingerprints in prediction tasks.}
\end{table}
\end{document}

cs

Michał Szafarczyk
Piotr Ludynia
Przemysław Kukla

"A Python library for efficient computation of molecular fingerprints" by Michał Szafarczyk, Piotr Ludynia, and Przemysław Kukla, arXiv:2403.19718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"One Parameter Families of Conformal Mappings of Bounded Doubly Connected Polygonal Domains" by A. Dyutin and S. Nasyrov, arXiv:2312.01112v1, 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}{lll}
\hline
Noise & Newton's iterations & Fixed point iterations \\ \hline
$\sigma = 0.01$ &
\begin{minipage}{4.6cm}{\begin{equation*}
\begin{split}
&\dot{x}_{1}(t) = -0.102 x_{1}(t) + 2.000 x_{2}(t)\\
&\dot{x}_{2}(t) = -2.001 x_{1}(t) - 0.099 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} &
\begin{minipage}{4.6cm}{\begin{equation*}
\begin{split}
&\dot{x}_{1}(t) = -0.102 x_{1}(t) + 2.000 x_{2}(t)\\
&\dot{x}_{2}(t) = -2.001 x_{1}(t) - 0.099 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} \\ \hline
$\sigma = 0.04$ &
\begin{minipage}{4.6cm}{\begin{equation*}
\begin{split}
&\dot{x}_{1}(t) = -0.105 x_{1}(t) + 1.995 x_{2}(t)\\
&\dot{x}_{2}(t) = -2.009 x_{1}(t) - 0.097 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} &
\begin{minipage}{4.6cm}{\begin{equation*}
\begin{split}
&\dot{x}_{1}(t) = -0.105 x_{1}(t) + 1.995 x_{2}(t)\\
&\dot{x}_{2}(t) = -2.008 x_{1}(t) - 0.097 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} \\ \hline
$\sigma = 0.08$ &
\begin{minipage}{4.6cm}{\begin{equation*}
\begin{split}
&\dot{x}_{1}(t) = -0.117 x_{1}(t) + 2.013 x_{2}(t)\\
&\dot{x}_{2}(t) = -1.972 x_{1}(t) - 0.105 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} &
\begin{minipage}{4.6cm}{\begin{equation*}
\begin{split}
&\dot{x}_{1}(t) = -0.117 x_{1}(t) + 2.013 x_{2}(t)\\
&\dot{x}_{2}(t) = -1.972 x_{1}(t) - 0.105 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} \\ \hline
$\sigma = 0.16$ &
\begin{minipage}{4.6cm}{\begin{equation*}
\begin{split}
&\dot{x}_{1}(t) = -0.147 x_{1}(t) + 1.991 x_{2}(t)\\
&\dot{x}_{2}(t) = -2.006 x_{1}(t) - 0.086 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} &
\begin{minipage}{4.6cm}{\begin{equation*}
\begin{split}
&\dot{x}_{1}(t) = -0.148 x_{1}(t) + 1.991 x_{2}(t)\\
&\dot{x}_{2}(t) = -2.006 x_{1}(t) - 0.088 x_{2}(t)
\end{split}
\end{equation*}}\end{minipage} \\ \hline
\end{tabular}
\caption{Linear damped oscillator: the discovered governing equations using IRK-SINDy in the approach of Newton's iterations and fixed point iterations for various noise levels $\sigma$.}
\end{table}
\end{document}

math

Mehrdad Anvari
Hamidreza Marasi
Hossein Kheiri

"Impilict Runge-Kutta based sparse identification of governing equations in biologically motivated systems" by Mehrdad Anvari, Hamidreza Marasi, and Hossein Kheiri, arXiv:2502.20319v1, 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{Information of four datasets. Num means the select data volume. }
\begin{tabular}{cccccc}
		\toprule
		\textbf{Dataset}&\textbf{Subject}&\textbf{Activity}&\textbf{Sample}&\textbf{Location}&\textbf{Num}\\
		\hline
		DSADS&8&19&1.14M&Tarso, Right Arm, Left Arm, Right Leg, Left Leg&2400\\
		UCI-HAR&30&6&1.31M&Waist&6616\\
		USC-HAD&14&12&2.81M&Front Right Hip&4187\\
		PAMAP&9&18&2.84M&Wrist, Chest, Ankle&1688\\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Wang Lu
Yiqiang Chen
Jindong Wang
Xin Qin

"Cross-domain Activity Recognition via Substructural Optimal Transport" by Wang Lu, Yiqiang Chen, Jindong Wang, and Xin Qin, arXiv:2102.03353v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc}
\hline
 & \multicolumn{3}{c}{{\small{}Panasonic}} & \multicolumn{3}{c}{{\small{}Toshiba}}\tabularnewline
\cline{2-7} \cline{3-7} \cline{4-7} \cline{5-7} \cline{6-7} \cline{7-7}
 & {\small{}(A-1)} & {\small{}(A-2)} & {\small{}(A-3)} & {\small{}(B-1)} & {\small{}(B-2)} & {\small{}(B-3)}\tabularnewline
 & {\small{}6000h \& 10000h} & {\small{}6000h only} & {\small{}10000h only} & {\small{}6000h \& 12000h} & {\small{}6000h only} & {\small{}12000h only}\tabularnewline
\hline
\hline
{\small{}Joint profit} & {\small{}24.67} & {\small{}24.53} & {\small{}24.58} & {\small{}24.67} & {\small{}24.19} & {\small{}23.32}\tabularnewline
{\small{}Profit (Panasonic)} & {\small{}10.77} & {\small{}9.65} & {\small{}8.53} & {\small{}10.77} & {\small{}11.24} & {\small{}12.27}\tabularnewline
{\small{}Profit (Toshiba)} & {\small{}13.9} & {\small{}14.88} & {\small{}16.05} & {\small{}13.9} & {\small{}12.95} & {\small{}11.05}\tabularnewline
\hline
{\small{}No inventory consumers (\%)} & {\small{}18.61} & {\small{}19.33} & {\small{}18.16} & {\small{}18.61} & {\small{}19.55} & {\small{}18.25}\tabularnewline
{\small{}Average price (1000h Inc.; yen)} & {\small{}94.73} & {\small{}94.05} & {\small{}93.07} & {\small{}94.73} & {\small{}94.8} & {\small{}94.94}\tabularnewline
{\small{}Average price (2000h Inc.; yen)} & {\small{}172.57} & {\small{}171.81} & {\small{}170.84} & {\small{}172.57} & {\small{}172.71} & {\small{}172.74}\tabularnewline
{\small{}Average price (CFL; yen)} & {\small{}796.53} & {\small{}777.03} & {\small{}836.82} & {\small{}796.53} & {\small{}772.14} & {\small{}830.21}\tabularnewline
{\small{}Disposal (million)} & {\small{}3.04} & {\small{}3.15} & {\small{}2.95} & {\small{}3.04} & {\small{}3.19} & {\small{}2.96}\tabularnewline
\hline
{\small{}$\Delta$CS} & {\small{}-} & {\small{}-1.68} & {\small{}-3.57} & {\small{}-} & {\small{}-0.96} & {\small{}-2.96}\tabularnewline
{\small{}$\Delta$PS (excluding fixed cost)} & {\small{}-} & {\small{}-0.1} & {\small{}0.00} & {\small{}-} & {\small{}-0.46} & {\small{}-1.27}\tabularnewline
{\small{}$\Delta$TS (excluding Ext. / fixed costs)} & {\small{}-} & {\small{}-1.78} & {\small{}-3.57} & {\small{}-} & {\small{}-1.42} & {\small{}-4.22}\tabularnewline
{\small{}$\Delta$Ext. (electricity usage)} & {\small{}-} & {\small{}0.15} & {\small{}0.37} & {\small{}-} & {\small{}0.01} & {\small{}0.41}\tabularnewline
{\small{}$\Delta$Ext. (waste disposal)} & {\small{}-} & {\small{}0.00} & {\small{}-0.05} & {\small{}-} & {\small{}0.01} & {\small{}-0.03}\tabularnewline
{\small{}$\Delta$TS (excluding fixed costs)} & {\small{}-} & {\small{}-1.93} & {\small{}-3.9} & {\small{}-} & {\small{}-1.44} & {\small{}-4.6}\tabularnewline
{\small{}Upper bound of Fixed cost savings} & {\small{}-} & {\small{}1.05} & {\small{}1.98} & {\small{}-} & {\small{}0.9} & {\small{}2.57}\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"When do firms sell high durability products? The case of light bulb industry" by Takeshi Fukasawa, arXiv:2503.23792v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Marina T. Dietrich
Dennis Dobler
Mathisca C. M. de Gunst

"Wild Bootstrap for Counting Process-Based Statistics" by Marina T. Dietrich, Dennis Dobler, and Mathisca C. M. de Gunst, arXiv:2310.17308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Yihong Zhou
Yuxin Xia
Hanbin Yang
Thomas Morstyn

"Strengthened and Faster Linear Approximation to Joint Chance Constraints with Wasserstein Ambiguity" by Yihong Zhou, Yuxin Xia, Hanbin Yang, and Thomas Morstyn, arXiv:2412.12992v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
 \hline
 \multicolumn{3}{|c|}{Method} & Val $mIoU$ (\%) \\
 \hline
 \multicolumn{3}{|c|}{Teacher: DeepLabV3-R101} & 78.07 \\
 \hline
 \multicolumn{3}{|c|}{Student: DeepLabV3-R18} & 74.21 \\
 \hline
 $\mathcal{L}_{seg}$ & $\mathcal{L}_{SM}$ & $\mathcal{L}_{I2CKD}$ & \\
 \hline
 $\checkmark$ & & & 74.21 \\
 $\checkmark$ & $\checkmark$ & & 75.33 \\
 $\checkmark$ & $\checkmark$ & $\checkmark$ & \textbf{76.03} \\
 \hline
 \end{tabular}
\caption{Ablation study on the validation set of Cityscapes.}
\end{table}
\end{document}

cs

Ayoub Karine
Thibault Napoléon
Maher Jridi

"I2CKD : Intra- and Inter-Class Knowledge Distillation for Semantic Segmentation" by Ayoub Karine, Thibault Napoléon, and Maher Jridi, arXiv:2403.18490v2, 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
\caption{Comparison of Real and Synthetic Networks (\(n = 1000\)).}
\begin{tabular}{lcc}\hline
 \textbf{Network Type} & \textbf{Failure Probability ($>5$ assets)} & \textbf{Avg. Failed Assets} \\\hline
 Real (Exposure-Based) & 0.000 & 2.000 \\
 Synthetic (Erdős-Rényi) & 0.000 & 0.000 \\\hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Systemic Risk and Default Cascades in Global Equity Markets: Extending the Gai-Kapadia Framework with Stochastic Simulations and Network Analysis" by Ana I. C. Pereda, arXiv:2504.01969v2, 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 & 28.45 & 13.43 \\
1 & 2 & 0.9432 & 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|ccc|}
\hline
\multirow{2}{*}{Models} & \multicolumn{3}{c|}{Metrics} \\ \cline{2-4}
 & Micro-Precision & Micro-Recall & \ Micro-F1\ \\ \hline
BR-SVM & 0.5356 & 0.3386 & 0.4608 \\ \hline
SGM & 0.5489 & 0.4179 & 0.4745 \\ \hline
BERT & 0.5408 & 0.4339 & 0.5069 \\ \hline
HABERT & \bf{0.6092} & 0.5083 &\bf{0.5542} \\ \hline
HABERT-R & \bf{0.6093} & 0.5082 & \bf{0.5541} \\ \hline
HABERT-L & 0.5743 & \bf{0.5188} &0.5452 \\ \hline
HABERT-RL & 0.5843 & 0.5084 & 0.5437 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

"Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports" by Xinyu Zhao, Hao Yan, and Yongming Liu, arXiv:2403.17914v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"SASA: Saliency-Aware Self-Adaptive Snapshot Compressive Imaging" by Yaping Zhao and Edmund Y. Lam, arXiv:2401.00875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}
 \toprule
 & Positives & Negative & Reproduced & Singleton & Total \\
 & Pairs & Pairs & Articles & Articles & Articles \\
 \midrule
 \textbf{Training Data} \\
 Training	&	36,291	&	37,637	&	891	&	--	&	7,728 \\
Validation	&	3,042	&	3,246	&	20	&	--	&	283 \\
\midrule
\textbf{Full Day Evaluation}	&		&		&		&		&	\\
Validation	&	28,547	&	12,409,031	&	447	&	2,162	&	4,988 \\
Test	&	54,996	&	100,914,159	&	1,236	&	8,046	&	14,211 \\
\midrule
\textbf{Full Dataset}	&	122,876	&	113,364,073	&	2,594	&	10,208 	&	27,210 \\
 \bottomrule
 \end{tabular}
\caption{This table provides summary statistics from the \texttt{NEWS-COPY} dataset, decomposed into the training sample and the full day evaluation data.}
\end{table}
\end{document}

q-fin

"A Massive Scale Semantic Similarity Dataset of Historical English" by Emily Silcock and Melissa Dell, arXiv:2306.17810v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


q-fin

Zafar Ahmad
Reilly Browne
Rezaul Chowdhury
Rathish Das
Yushen Huang
Yimin Zhu

"Fast American Option Pricing using Nonlinear Stencils" by Zafar Ahmad, Reilly Browne, Rezaul Chowdhury, Rathish Das, Yushen Huang, and Yimin Zhu, arXiv:2303.02317v2, 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{Parameters for Deep Learning Model}
\begin{tabular}{ccc}
\cline{1-3}
\hline\hline
 & \multicolumn{2}{c}{ \textbf{Value}} \\
\multirow{-2}{*}{\textbf{Name}} & \textbf{Problem 1} & \textbf{Problem 2} \\
\hline
Input sequence length & 10 & 10 \\
Predicted future time steps & 1-40 & 1-40 \\
Hidden state of RNN & 20 & 20 \\
Output dimension & 2 & 1 \\
Number of RNN layer & 1 & 1 \\
Dropout rate & 0.2 & 0.2 \\
Epoch & 1000 & 1000 \\
\hline\hline
\end{tabular}
\end{table}
\end{document}

eess

Shunyao Wu
Muhammad Alrabeiah
Andrew Hredzak
Chaitali Chakrabarti
Ahmed Alkhateeb

"Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements" by Shunyao Wu, Muhammad Alrabeiah, Andrew Hredzak, Chaitali Chakrabarti, and Ahmed Alkhateeb, arXiv:2101.06886v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Xueqing Liu
Tianchen Qian
Lauren Bell
Bibhas Chakraborty

"Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes" by Xueqing Liu, Tianchen Qian, Lauren Bell, and Bibhas Chakraborty, arXiv:2310.18905v3, 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
 Method & LSD (dB) \\
 \midrule
 Random RIEC Subject & 8.23 \\
 Generic HRTF & 7.32 \\
 Zandi et. al & 4.5 \\
 \textbf{Ours} & \textbf{4.38} \\
 Hu et. al & 3.5 \\
 \bottomrule
 \end{tabular}
\caption{Log-spectral distortion between ground-truth HRTF and the output HRTF for several methods. We note that the method in requires additional physical measurements and the method in requires significantly more active input from the user.}
\end{table}
\end{document}

eess

HRTF Estimation in the Wild

Vivek Jayaram
Ira Kemelmacher-Shlizerman
Steven M. Seitz

"HRTF Estimation in the Wild" by Vivek Jayaram, Ira Kemelmacher-Shlizerman, and Steven M. Seitz, arXiv:2311.03560v1, 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{Description for the nodes in the illustrative sample.}
\begin{tabular}{cccccc}
\hline
 & No.Lines($m$) & No.Gens($q$) & No.RES($g$) & No. Load ($d$) & No. Integers\\ \hline
14-bus & 20 & 5 & 5 & 10 & 14 \\
39-bus & 46 & 10 & 5 & 30 & 39 \\
118-bus & 186 & 54 & 25 & 80 & 118\\
1888-bus & 2308 & 285 & 500 & 1500 & 1888\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach" by Xuan He, Danny H. K. Tsang, and Yize Chen, arXiv:2312.06144v2, 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
 & AIC & BIC & Theorem \\ \hline
Subject 1 (Shedder) & 1.05 & 1.15 & 1.73 \\ \hline
Subject 2 (Shedder) & 0.99 & 0.79 & 1.70 \\ \hline
Subject 3 (Non-Shedder) & 0.97 & 0.76 & 0.93 \\ \hline
Subject 4 (Non-Shedder) & 1.02 & 1.07 & 1.17 \\ \hline
\end{tabular}
\caption{ Comparison between the ratio of after to before inoculation of the averaged standard deviation of the optimal value of $k$ achieved from AIC, BIC and Theorem for the subjects in Figure . Note that only for the optimal $k$ of Theorem , this ratio is considerably greater than one for shedders and approximately one for non-shedders, which can be used for detection and classification purposes.}
\end{table}
\end{document}

eess

Elyas Sabeti
Peter X. K. Song
Alfred O. Hero

"Data Discovery Using Lossless Compression-Based Sparse Representation" by Elyas Sabeti, Peter X. K. Song, and Alfred O. Hero, arXiv:2103.08765v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yiwei Shi
Jingyu Hu
Yu Zhang
Mengyue Yang
Weinan Zhang
Cunjia Liu
Weiru Liu

"Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering" by Yiwei Shi, Jingyu Hu, Yu Zhang, Mengyue Yang, Weinan Zhang, Cunjia Liu, and Weiru Liu, arXiv:2501.18501v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The $p$-values of the tests for the S\&P100 stock return.}
\begin{tabular}{cccccccccccc}
\toprule
 MATES& CM& GET& BD& GED& RF& MT& GPK& RISE& MMD& xMMD& mMMD \\
\midrule
 $<$\textbf{0.001} & 0.786 & 0.071 & \textbf{0.025} & 0.148 & 0.208 & 0.065 & \textbf{0.039} & 0.08 & 0.213 & 0.304 & 0.637\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"MATES: Multi-view Aggregated Two-Sample Test" by Zexi Cai, Wenbo Fei, and Doudou Zhou, arXiv:2412.16684v1, 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{Function values for $x_1,x_2,x_3$ by the MNAM in the GMSC dataset. $f$ is weakly monotonic with respect to $x_3$ over $x_2$ and $x_2$ over $x_1$. Individual and weak pairwise monotonicity are preserved.}
\begin{tabular}{l|cccr}
\toprule
 $f \backslash x$ & 0 & 1 & 2 \\ \hline
 $f_1$ & $0$ & $0.8$ & $1.0$ \\
 $f_2$ & 0 & 1.4 & 1.7 \\
 $f_3$ & 0 & 1.7 & 2.2 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"How to address monotonicity for model risk management?" by Dangxing Chen and Weicheng Ye, arXiv:2305.00799v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \hline
 \textbf{Year} & \textbf{Affordable LNG} & \textbf{Costly LNG} \\
 2020 & 20.86 & 50.98\\
 2025 & 22.57 & 55.15\\
 2030 & 24.55 & 59.98\\
 2035 & 26.22 & 64.06\\
 2040 & 27.10 & 66.22\\
 2045 & 27.66 & 67.57\\
 2050 & 28.08 & 68.62\\
 2055 & 28.08 & 68.62\\
 \hline
 \end{tabular}
\caption{Price for LNG in affordable and costly case}
\end{table}
\end{document}

q-fin

Goran Durakovic
Hongyu Zhang
Brage Rugstad Knudsen
Asgeir Tomasgard
Pedro Crespo del Granado

"Decarbonizing the European energy system in the absence of Russian gas: Hydrogen uptake and carbon capture developments in the power, heat and industry sectors" by Goran Durakovic, Hongyu Zhang, Brage Rugstad Knudsen, Asgeir Tomasgard, and Pedro Crespo del Granado, arXiv:2308.08953v1, 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{12 selected locations with the corresponding results of monthly SSTA and MHW forecasts (Part 3).}
\begin{tabular}{ll}
\textbf{Location} & \textbf{Three lead months} \\ \hline
\multirow{3}{*}{BOP} & MSE, WMSE, FR \\
 & SWMSE1, SWMSE3 \\
 & \\ \hline
\multirow{3}{*}{BP} & Huber, SWMSE1, SWMSE2, SWMSE3 \\
 & \\
 & \\ \hline
\multirow{3}{*}{CI} & MSE, Huber, WMSE, FR, SWMSE1, SWMSE2, SWMSE3 \\
 & BMSE \\
 & BMSE \\ \hline
\multirow{3}{*}{CR} & \\
 & \\
 & \\ \hline
\multirow{3}{*}{CS} & MSE, WMSE, SWMSE1 \\
 & SWMSE3 \\
 & SWMSE3 \\ \hline
\multirow{3}{*}{F} & MSE, Huber, WMSE, FR \\
 & BMSE, SWMSE3 \\
 & SWMSE3 \\ \hline
\multirow{3}{*}{HG} & Huber, WMSE \\
 & \\
 & \\ \hline
\multirow{3}{*}{OP} & Huber, SWMSE1, SWMSE2 \\
 & Huber, BMSE, SWMSE1, SWMSE2, SWMSE3 \\
 & BMSE, SWMSE1, SWMSE2, SWMSE3 \\ \hline
\multirow{3}{*}{R} & MSE, WMSE, SWMSE1 \\
 & \\
 & \\ \hline
\multirow{3}{*}{SI} & MSE, Huber, SWMSE1 \\
 & BMSE, SWMSE1, SWMSE2, SWMSE3 \\
 & BMSE, SWMSE1, SWMSE2 \\ \hline
\multirow{3}{*}{T} & MSE, WMSE, FR, SWMSE1 \\
 & \\
 & \\ \hline
W & MSE, Huber, WMSE, FR, SWMSE1, SWMSE2 \\
 &
\end{tabular}
\end{table}
\end{document}

stat

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

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cccc}
\hline
\textbf{Crossings} & \textbf{Total knots} & \textbf{Hyperbolic knots} & \textbf{$J_2$ Computed} & \textbf{$J_3$ Computed} \\
\hline
$\leq 13$ & 12{,}965 & 12{,}955 & 12{,}955 & 11{,}941 \\
14 & 46{,}972 & 46{,}969 & 46{,}969 & 18{,}353 \\
15 & 253{,}293 & 253{,}285 & 253{,}285 & 147{,}022 \\
16 & 1{,}388{,}705 & 1{,}388{,}694 & 1{,}388{,}694 & 0 \\
\hline
\end{tabular}
\caption{Summary of total knots, hyperbolic knots, and the numbers of knots for which $J_2$ and $J_3$ are known, organized by crossing number.}
\end{table}
\end{document}

math

Mark Hughes
Vishnu Jejjala
P. Ramadevi
Pratik Roy
Vivek Kumar Singh

"Colored Jones Polynomials and the Volume Conjecture" by Mark Hughes, Vishnu Jejjala, P. Ramadevi, Pratik Roy, and Vivek Kumar Singh, arXiv:2502.18575v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{RL-based simulation parameters and the decision methodologies for actualizing a STPS}}
\begin{tabular}{|c|c|}

		\hline
		\textbf{Decision methodology} & \textbf{Time slot of the agents} \\ \hline
Individual & $t-1$ \\ \hline
Joint & Simultaneously \\
\hline
\multicolumn{2}{c}{}\\
		\multicolumn{2}{c}{\textbf{Simulation Parameters}} \\ \hline
Number of agents, $I$ & 2 \\ \cline{1-2}
 \multirow{2}{*}{Discount rate} & $0.75$ (for individual decisions) \\ & $0.85$ (for joint decisions) \\ \cline{1-2}
 \multirow{2}{*}{Learning rate} & $0.02$ (for individual decisions) \\ & $0.035$ (for joint decisions) \\ \cline{1-2}
 \multirow{2}{*}{ $\epsilon$} & $0.95$ (for individual decisions) \\ & $0.7$ (for joint decisions) \\ \cline{1-2}
 \multirow{2}{*}{Number of Training Episodes} & $200$ (for individual decisions) \\ & $300$ (for joint decisions) \\ \cline{1-2}
 Number of time slots, $T$ & 50 \\ \cline{1-2}
 \multirow{2}{*}{$\delta_i^{k_i\rightarrow k'_i}$} & $0.95$ for $k_i \neq k'_i$\\
 & $1$ for $k_i = k'_i$ \\
 \cline{1-2}
\end{tabular}
\end{table}
\end{document}

eess

Mehmet Özgün Demir
Ozan Alp Topal
Ali Emre Pusane
Guido Dartmann
Gerd Ascheid
Güneş Karabulut Kurt

"An Adaptive Multi-Agent Physical Layer Security Framework for Cognitive Cyber-Physical Systems" by Mehmet Özgün Demir, Ozan Alp Topal, Ali Emre Pusane, Guido Dartmann, Gerd Ascheid, and Güneş Karabulut Kurt, arXiv:2101.02446v1, 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{FID results obtained on the test set.}
\begin{tabular}{|c|c|c|c|}
\hline
Model & FID $\downarrow$ \\ \hline
Low Resolution (LR) & 66.05 \\
SwinIR & 50.79 \\
SR3 & 16.59 \\
StableSR & 12.41 \\
StableShip-SR (Ours) & \textbf{11.72} \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Luigi Sigillo
Riccardo Fosco Gramaccioni
Alessandro Nicolosi
Danilo Comminiello

"Ship in Sight: Diffusion Models for Ship-Image Super Resolution" by Luigi Sigillo, Riccardo Fosco Gramaccioni, Alessandro Nicolosi, and Danilo Comminiello, arXiv:2403.18370v2, 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
\begin{tabular}{l|l|l}
&PDMP & MDP \\ \hline
$S$ & & State space \\
$E$ & State space & \\
$A$ & Action space & Action space \\
$\Omega$ & & Observation space \\
$\mathbb{O}$ & Observation space & \\
$C$ & Cost function & \\
$C'$ & Cost function in belief space & \\
$P$, $P_n$ & Transition kernel of $(X_n)$ & Transition kernel of $s$ \\
$Pr$, & & Transition kernel of $b$ \\
$R$ & Transition kernel of $(X,O)$ & Reward function \\
$R'$ & Transition kernel of $(\Theta,O)$ & \\
$\rho$ & & Reward function in belief space \\
$H$ & Horizon (physical time) & Horizon (number of iterations) \\
$\mathcal{S}$ & Strategy & \\
$\pi$ & & Policy \\
$\mathcal{J}(\mathcal{S},x)$ & Expected cost & \\
$V_H(\pi,s)$ & & Expected cost \\
$\mathcal{V}$ & Value function & \\
$V^\star(s)$ & & Value function \\
$\theta_n$ & Filter & \\
$b$ & &Filter \\
$\Psi$ &Filter updating operator & \\
$\tau$ & &Filter updating operator \\
$\phi$, $\Phi$ & Flow & BAMDP hyperparameters \\
\end{tabular}
\caption{Classical notations in PDMP and MDP frameworks}
\end{table}
\end{document}

stat

Alice Cleynen
Benoîte de Saporta
Orlane Rossini
Régis Sabbadin
Amélie Vernay

"Bridging Impulse Control of Piecewise Deterministic Markov Processes and Markov Decision Processes: Frameworks, Extensions, and Open Challenges" by Alice Cleynen, Benoîte de Saporta, Orlane Rossini, Régis Sabbadin, and Amélie Vernay, arXiv:2501.04120v1, 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{Summary Statistics: Evictions dataset, measures per weekly-census tract.}
\begin{tabular}{lccc}
\hline
Statistic & \texttt{evict} & \texttt{filing} & \texttt{atrisk} \\
\hline
25th Perc. & 1 & 1 & 1 \\
Median & 1 & 1 & 1 \\
Mean & 1.219 & 1.770 & 1.676 \\
75th Perc. & 1 & 2 & 2 \\
Std. Dev. & 1.26 & 1.59 & 1.49 \\
\hline
\multicolumn{2}{c}{\footnotesize Observations: 498793} & \multicolumn{2}{c}{\footnotesize Unique Census Tracts: 3980} \\
\hline\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.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Please write your table caption here}
\begin{tabular}{lll}
\hline\noalign{\smallskip}
first & second & third \\
\noalign{\smallskip}\hline\noalign{\smallskip}
number & number & number \\
number & number & number \\
\noalign{\smallskip}\hline
\end{tabular}
\end{table}
\end{document}

eess

Qazi Ammar Arshad
Mohsen Ali
Saeed-ul Hassan
Chen Chen
Ayisha Imran
Ghulam Rasul
Waqas Sultani

"A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images" by Qazi Ammar Arshad, Mohsen Ali, Saeed-ul Hassan, Chen Chen, Ayisha Imran, Ghulam Rasul, and Waqas Sultani, arXiv:2102.08708v1, 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{Statistics of the tested datasets.}
\begin{tabular}{lrrrc}
 \toprule
 \textbf{Datasets} & \textbf{\# Users} & \textbf{\# Items} & \textbf{\# Interactions} & \textbf{Sparsity (\%)}\\ \cmidrule{1-5}
 Amazon Baby & 19,445 & 7,050 & 139,110 & 99.899 \\
 Amazon Toys & 19,412 & 11,924 & 167,597 & 99.928 \\
 Amazon Sports & 35,598 & 18,357 & 256,308 & 99.961 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Daniele Malitesta
Emanuele Rossi
Claudio Pomo
Fragkiskos D. Malliaros
Tommaso Di Noia

"Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach" by Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Fragkiskos D. Malliaros, and Tommaso Di Noia, arXiv:2403.19841v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Philippe Bergault
Sébastien Bieber
Leandro Sánchez-Betancourt

"Optimal Exit Time for Liquidity Providers in Automated Market Makers" by Philippe Bergault, Sébastien Bieber, and Leandro Sánchez-Betancourt, arXiv:2509.06510v1, 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}{llll}
\toprule
Case & Net s.t.m & Vocal s.t.m & Pakhawaj s.t.m \\ \midrule
Accuracy & 75.23 & 69.07 & 76.84 \\ \bottomrule
\end{tabular}
\caption{Average 3-fold cross-validation accuracies (\%) for surface tempo multiple estimation.}
\end{table}
\end{document}

eess

"Structure and Automatic Segmentation of Dhrupad Vocal Bandish Audio" by Rohit M. A. and Preeti Rao, arXiv:2008.00756v1, 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}{c|c}
\toprule
 & \textbf{\# Patients} \\
\midrule
Positive PH & 97 \\
Negative / Unknown PH & 23 \\
\bottomrule
\end{tabular}
\caption{Statistics for our ground truth hand-labeled pulmonary hypertension labels.}
\end{table}
\end{document}

eess

Shih-Cheng Huang
Zepeng Huo
Ethan Steinberg
Chia-Chun Chiang
Matthew P. Lungren
Curtis P. Langlotz
Serena Yeung
Nigam H. Shah
Jason A. Fries

"INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis" by Shih-Cheng Huang, Zepeng Huo, Ethan Steinberg, Chia-Chun Chiang, Matthew P. Lungren, Curtis P. Langlotz, Serena Yeung, Nigam H. Shah, and Jason A. Fries, arXiv:2311.10798v1, 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}
 \textbf{Model Parameter} & \textbf{Value} \\\hline
 Embedding Size & 256 \\
 CNN Kernel Size & 3 \\
 No. CNN Filters & 256 \\
 No. LSTM Layers & 1(or2) \\
 LSTM Hidden Size & 256 \\
 Batch Normalization & Flase
\end{tabular}
\caption{Baseline Parameter Details}
\end{table}
\end{document}

eess

Moayad Elamin
Muhammad Omer
Yonas Chanie
Henslaac Ndlovu

"Creating Spoken Dialog Systems in Ultra-Low Resourced Settings" by Moayad Elamin, Muhammad Omer, Yonas Chanie, and Henslaac Ndlovu, arXiv:2312.06266v1, 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{Types of Faults for TE Process .}
\begin{tabular}{ccc}
			\toprule
			Fault ID & Fault Name & Type \\ \midrule
			Fault 1 & A/C feed ratio, B composition constant (stream 4) & Step \\
			Fault 2 & B composition, A/C ratio constant (stream 4) & Step \\
			Fault 3 & D feed temperature (stream 2) & Step \\
			Fault 4 & Reactor cooling water inlet temperature & Step \\
			Fault 5 & Condenser cooling water inlet temperature & Step \\
			Fault 6 & A feed loss (stream 1) & Step \\
			Fault 7 & C header pressure loss - reduced availability (stream 4) & Step \\
			Fault 8 & A, B, C feed composition (stream 4) & Random variation \\
			Fault 9 & D feed temperature (stream 2) & Random variation \\
			Fault 10 & C feed temperature (stream 4) & Random variation \\
			Fault 11 & Reactor cooling water inlet temperature & Random variation \\
			Fault 12 & Condenser cooling water inlet temperature & Random variation \\
			Fault 13 & Reaction kinetics & Slow drift \\
			Fault 14 & Reactor cooling water valve & Sticking \\
			Fault 15 & Condenser cooling water valve & Sticking \\
			Fault 16-20 & Unknown & Unknown \\ \bottomrule
		\end{tabular}
\end{table}
\end{document}

cs

"Convolutional Neural Nets in Chemical Engineering: Foundations, Computations, and Applications" by Shengli Jiang and Victor M. Zavala, arXiv:2101.04869v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|l}
			\textbf{Main subscale} & \textbf{Subscale} & \textbf{Question} \\\hline

			\multirow{16}{*}{Food Approach (FAp)}
			& \multirow{5}{*}{Food Responsiveness (FR)}
			& My child is always asking for food \\
			& & If allowed to, my child would eat too much \\
			& & Even if my child is full up s/he finds room to eat his/her favourite food \\
			& & If given the chance, my child would always have food in his/her mouth \\
			& & Given the choice, my child would eat most of the time \\
 			& \multirow{4}{*}{Emotional Over-Eating (EOE)}
			& My child eats more when annoyed \\
			& & My child eats more when worried \\
			& & My child eats more when anxious \\
			& & My child eats more when s/he has nothing else to do \\

 			& \multirow{4}{*}{Enjoyment of Food (EF)}
 			& My child loves food \\
			& & My child is interested in food \\
			& & My child looks forward to mealtimes \\
			& & My child enjoys eating \\
 			& \multirow{3}{*}{Desire to Drink (DD)}
			& If given the chance, my child would drink continuously throughout the day \\
			& & If given the chance, my child would always be having a drink \\
			& & My child is always asking for a drink \\\hline

			\multirow{19}{*}{Food Avoidance (FAv)}
			& \multirow{5}{*}{Satiety Responsiveness (SR)}
			& My child has a big appetite \\
			& & My child leaves food on his/her plate at the end of a meal \\
			& & My child gets full before his/her meal is finished \\
			& & My child gets full up easily \\
			& & My child cannot eat a meal if s/he has had a snack just before \\

			& \multirow{4}{*}{Slowness in Eating (SE)}
			& My child finishes his/her meal quickly \\
			& & My child eats slowly \\
			& & My child takes more than 30 minutes to finish a meal \\
			& & My child eats more and more slowly during the course of a meal \\

			& \multirow{4}{*}{Emotional Under-Eating (EUE)}
			& My child eats less when angry \\
			& & My child eats less when s/he is tired \\
			& & My child eats more when she is happy \\
			& & My child eats less when upset \\

			& \multirow{6}{*}{Food Fussiness (FF)}
			& My child refuses new foods at first \\
			& & My child enjoys tasting new foods \\
 			& & My child enjoys a wide variety of foods \\
			& & My child is difficult to please with meals \\
			& & My child is interested in tasting food s/he hasn't tasted before \\
			& & My child decides that s/he doesn't like a food, even without tasting it
		\end{tabular}
\end{table}
\end{document}

stat

Andrea Cremaschi
Beatrice Franzolini
Maria De Iorio
Mary Chong
Toh Jia Ying
Navin Michael
Varsha Gupta
Fabian Yap
Yung Seng Lee
Johan Erikkson
Anna Fogel

"A Bayesian semi-parametric model for longitudinal growth and appetite phenotypes in children" by Andrea Cremaschi, Beatrice Franzolini, Maria De Iorio, Mary Chong, Toh Jia Ying, Navin Michael, Varsha Gupta, Fabian Yap, Yung Seng Lee, Johan Erikkson, and Anna Fogel, arXiv:2501.17040v1, licensed under CC BY-SA 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{Speech enhancement benchmark}
\begin{tabular}{cccccc}
 \toprule
 & PESQ & CSIG & CBAK & COVL & SSNR\\
 \midrule
 Noisy & 1.97 & 3.35 & 2.44 & 2.63 & 1.68\\
 Wiener & 2.22 & 3.23 & 2.68 & 2.67 & 5.07\\
 SEGAN & 2.16 & 3.48 & 2.94 & 2.80 & 7.73\\
 WaveNet & N/A & 3.62 & 3.23 & 2.98 & N/A\\
 MMSE-GAN & 2.53 & 3.80 & 3.12 & 3.14 & N/A\\
 D+M & 2.73 & 3.94 & 3.35 & 3.33 & \textbf{9.40}\\
 UNet. & 2.90 & 4.22 & 3.32 & 3.58 & N/A\\
 Ours & \textbf{3.043} & \textbf{4.30} & \textbf{3.42} & \textbf{3.69} & 7.35\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Oscar Chang
Dung N. Tran
Kazuhito Koishida

"Single-channel speech enhancement using learnable loss mixup" by Oscar Chang, Dung N. Tran, and Kazuhito Koishida, arXiv:2312.17255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison with other Surveys} % title of Table
\begin{tabular}{ccc} % centered columns (7 columns)
\hline %inserts double horizontal lines
\bf{Survey} & \bf{Reference} & \bf{Mutual test case} \\ % inserts table
\hline % inserts single horizontal line
Adadi et al., 2018 & & $\times$ \\
Mueller et al., 2019 & & $\times$ \\
Samek et al., 2017 & & $\times$ \\
Molnar et al., 2019 & & $\times$ \\
Staniak et al., 2018 & & $\times$ \\
Gilpin et al., 2018 & & $\times$ \\
Collaris et al., 2018 & & $\times$ \\
Ras et al., 2018 & & $\times$ \\
Dosilovic et al., 2018 & & $\times$ \\
Tjoa et al., 2019 & & $\times$ \\
Dosi-Valez et al., 2017 & & $\times$ \\
Rudin et al., 2019 & & $\times$ \\
Arrieta et al., 2020 & & $\times$ \\
Miller et al., 2018 & & $\times$ \\
Zhang et al., 2018 & & $\times$ \\
{\bf{This Survey}} & & $\checkmark$ \\
\hline %inserts single l
\end{tabular}
\end{table}
\end{document}

cs

Sheikh Rabiul Islam
William Eberle
Sheikh Khaled Ghafoor
Mohiuddin Ahmed

"Explainable Artificial Intelligence Approaches: A Survey" by Sheikh Rabiul Islam, William Eberle, Sheikh Khaled Ghafoor, and Mohiuddin Ahmed, arXiv:2101.09429v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l||l|l|}
\hline
\textbf{Parameter} & \textbf{Value} & \textbf{Parameter} & \textbf{Value} \\ \hline
$I$ & $25$ &$U$ & $3$ \\ \hline
$x_{max}$ & $1000m$ & $y_{max}$ & $1000m$ \\ \hline
$H_u$ & $[80,100]m$ & $R_{min}$ & $150$ Kbit/s \\ \hline
$\tau$ & $3ms$ &$k_i$ & $[1,5]$ \\ \hline
$B_{i,u}$ & $[1.5,2]$ GHz &$\sigma^{2}$ & $-120dBm$ \\ \hline
$P_{i}$ & $[0,1]$ mW &$T$ & $500\tau$ \\ \hline
\end{tabular}
\caption{Simulation setup}
\end{table}
\end{document}

math

Mouhamed Naby Ndiaye
El Houcine Bergou
Hajar El Hammouti

"Age-of-Information in UAV-assisted Networks: a Decentralized Multi-Agent Optimization" by Mouhamed Naby Ndiaye, El Houcine Bergou, and Hajar El Hammouti, arXiv:2312.15778v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Overlapped Arithmetic Codes

"Overlapped Arithmetic Codes" by Yong Fang, arXiv:2502.20781v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis" by Paul Glasserman and Caden Lin, arXiv:2309.17322v1, 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}{rrrrrr}
\toprule % \textbf{Comp. time (s)}\textbf{Abs. err.}
$d$ & time (s) & abs. err. & $d$ & time (s) & abs. err.\\
\cmidrule(r){1-3} \cmidrule(r){4-6}
2 & 0.04 & 4.32e-14 & 3 & 0.15 & 2.05e-12\\
8 & 0.61 & 1.48e-13 & 7 & 0.91 & 1.95e-12\\
16 & 10.22 & 1.46e-12 & 21 & 42.32 & 1.31e-11\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

Chengkai Zhu
Renfeng Peng
Bin Gao
Xin Wang

"Riemannian Optimization for Holevo Capacity" by Chengkai Zhu, Renfeng Peng, Bin Gao, and Xin Wang, arXiv:2501.11576v1, 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}{ccccc} \hline
 {\textbf{}} & \multicolumn{4}{c}{\textbf{Brisbane Zones}} \\
 & \textbf{Business} & \textbf{Residential} & \textbf{Education} & \textbf{Recreation} \\ \hline

 Sydney Cluster 1 & \textbf{0.030} & 0.238 & 0.267 & 0.536 \\ %\hline

 Sydney Cluster 2 & 0.266 & \textbf{0.114} & 0.691 & 0.355 \\%\hline

 Sydney Cluster 3 & 0.398 & 1.204 & \textbf{0.198} & 1.613 \\%\hline

 Sydney Cluster 4 & 0.397 & 0.321 & 1.190 & \textbf{0.149} \\\hline
 \end{tabular}
\caption{MSE between Sydney \& Brisbane reference clusters}
\end{table}
\end{document}

cs

"Land Use Detection & Identification using Geo-tagged Tweets" by Saeed Khan and Md Shahzamal, arXiv:2101.03337v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Marco Caliendo
Nico Pestel
Rebecca Olthaus

"Long-Term Employment Effects of the Minimum Wage in Germany: New Data and Estimators" by Marco Caliendo, Nico Pestel, and Rebecca Olthaus, arXiv:2310.15964v1, 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{List of simulation related properties}
\begin{tabular}{clccc}
\hline
\textbf{} & \textbf{Property} & \textbf{Symbol} & \textbf{Value} \\ \hline
1 & Frontal area & $A$ & 0.78 m$^2$ \\
2 & Wheel radius & $r$ & 0.226 m \\
3 & Inertia front wheel & $J_f$ & 0.327 Nms$^2$ \\
4 & Inertia rear wheel & $J_r$ & 1.3228 Nms$^2$ \\
5 & Mass scooter & $m_{sc}$ & 99 kg \\
6 & Mass rider & $m_r$ & 80 kg \\
7 & Air resistance coeff. & $c_w$ & 0.64 \\
8 & Rolling resis. coeff. & $c_r$ & 0.015 \\
9 & Air density & $\rho$ & 1.225 kg/m$^3$ \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Jannis Kreß
Jens Rau
Hektor Hebert
Fernando Perez-Peña
Karsten Schmidt
Arturo Morgado-Estévez

"Fuel Saving Effect and Performance of Velocity Control for Modern Combustion-Powered Scooters" by Jannis Kreß, Jens Rau, Hektor Hebert, Fernando Perez-Peña, Karsten Schmidt, and Arturo Morgado-Estévez, arXiv:2311.09865v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Shuyi Liang
Takeshi Emura
Chang-Xing Ma
Yijing Xin
Xin-Wei Huang

"Testing the Homogeneity of Two Proportions for Correlated Bilateral Data via the Clayton Copula" by Shuyi Liang, Takeshi Emura, Chang-Xing Ma, Yijing Xin, and Xin-Wei Huang, arXiv:2502.00523v1, 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{Elements of the action $a_t$}
\begin{tabular}{llll}
 \noalign{\global\arrayrulewidth=1pt} \hline \noalign{\global\arrayrulewidth=0.4pt}
 content & symbol & size & range\\ \hline
 gain of the ZEM-ZEV control & $K_R$ & 1 & [5, 7] \\
 gain of the ZEM-ZEV control & $K_V$ & 1 & [1, 3] \\
 degradation of time-to-go from previous timestep & $\delta t_{go}$ & 1 & [4.25,5.75] \\
 target landing point position within the DEM FOV& $\alpha_x, \alpha_y$ & 2 & [-0.5, 0.5]\\ \hline
 total & & 5 \\
 \noalign{\global\arrayrulewidth=1pt} \hline \noalign{\global\arrayrulewidth=0.4pt}
 \end{tabular}
\end{table}
\end{document}

eess

Keidai Iiyama
Kento Tomita
Bhavi A. Jagatia
Tatsuwaki Nakagawa
Koki Ho

"Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers" by Keidai Iiyama, Kento Tomita, Bhavi A. Jagatia, Tatsuwaki Nakagawa, and Koki Ho, arXiv:2102.12432v1, 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{Phase I goods}
\begin{tabular}{llcc}
\hline\hline
Good & Description & Amazon Price & Avg. Elic. Val.\\ \hline
{\bf Backpack} & {\bf Fjallraven gray, 16L backpack} & {\bf \$66.95}& {\bf \$28.24}\\
Alarm clock & Aisuo bluetooth alarm/speaker/nightlight & \$37.99 & \$19.46\\
{\bf Water bottle} & {\bf Hydroflask blue, 32 oz. water bottle} & {\bf \$32.96} & {\bf \$22.56}\\
Shower speaker & Donerton bluetooth waterproof speaker & \$29.99 & \$18.75 \\
Laptop stand & Ergonomic universal laptop stand & \$26.99 & \$15.99\\
Outdoor blanket & Bearz blue waterproof blanket & \$24.99 & \$14.04\\
Cold brewer & Takeya 1 qt. carafe & \$21.00 & \$15.04\\
Picture frames & Set of 4 white, wooden frames & \$20.99 & \$9.99\\
Popcorn set & 3 bags of popcorn plus flavors & \$22.00 & \$8.72\\
{\bf Notebook} & {\bf Moleskine 192 age, black notebook} & {\bf \$21.90} & {\bf \$9.11}\\
Tile & Bluetooth chip to track item on phone & \$19.99 & \$14.98\\
Phone mount & Gooseneck phone holder with bracket & \$19.79 & \$8.87\\
Popcorn popper & Silicone microwaveable popcorn maker & \$14.99 & \$7.49\\
Charging pad & Anker phone-charging pad & \$13.99 & \$14.57\\
Frisbee & Discraft yellow 175 g. Frisbee & \$13.76 & \$6.60\\
{\bf Mug} & {\bf Blue ceramic mug} & {\bf \$11.99} & {\bf \$6.53}\\
Playing cards & Deck of black playing cards & \$7.99 & \$4.93\\
{\bf Pens} & {\bf Set of 4 Uni-ball rollerball pens} & {\bf \$6.88} & {\bf \$5.33}\\
Keychain tool & Multi-tool that attaches to keys & \$6.64 & \$5.11\\
Cable spirals & Set of 24 plastic, multicolor cable-protectors & \$6.29 & \$5.03\\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Rankings-Dependent Preferences: A Real Goods Matching Experiment" by Andrew Kloosterman and Peter Troyan, arXiv:2305.03644v3, 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{Validation Accuracy on VCR dataset.}
\begin{tabular}{lccc}
\toprule
Model & Q $\rightarrow$ A (\%) & QA $\rightarrow$ R (\%) & Q $\rightarrow$ AR (\%) \\
\hline
ViLBERT ~ & 72.4 & 74.5 & 54.0 \\
Unicoder-VL ~ & 72.6 & 74.5 & 54.5 \\
VLBERT-L ~ & 75.5 & 77.9 & 58.9 \\
ERNIE-ViL-L ~ & 78.52 & 83.37 & 65.81 \\
VILLA-L ~ & 78.45 & 82.57 & 65.18 \\
GPT4RoI-7B~ & 87.4 & 89.6 & 78.6 \\
ViP-LLaVA-Base-7B~ & 87.66 & 89.80 & 78.93 \\
\hline
\textbf{VP-SPHINX-13B} & \textbf{88.92} & \textbf{90.23} & \textbf{80.65} \\
\textbf{VP-LLaVA-8B} & 88.43 & 89.96 & 79.71 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Weifeng Lin
Xinyu Wei
Ruichuan An
Peng Gao
Bocheng Zou
Yulin Luo
Siyuan Huang
Shanghang Zhang
Hongsheng Li

"Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want" by Weifeng Lin, Xinyu Wei, Ruichuan An, Peng Gao, Bocheng Zou, Yulin Luo, Siyuan Huang, Shanghang Zhang, and Hongsheng Li, arXiv:2403.20271v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
			\hline
			$\nu$ & $\sqrt{\widehat{\kappa}_1}$ \\
			\hline
			0.35 & $2.66229608762752$ \\
			0.49 & $2.56684460283184$ \\
			0.5 & $2.56098604836068$ \\
			\hline
			\hline
		\end{tabular}
\caption{Test . Reference lowest computed eigenvalues for different values of $\nu$.}
\end{table}
\end{document}

math

Arbaz Khan
Felipe Lepe
David Mora
Jesus Vellojin

"Finite element analysis of the nearly incompressible linear elasticity eigenvalue problem with variable coefficients" by Arbaz Khan, Felipe Lepe, David Mora, and Jesus Vellojin, arXiv:2312.10715v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Babooshka Shavazipour
Theodor J. Stewart

"A novel multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty" by Babooshka Shavazipour and Theodor J. Stewart, arXiv:2312.11745v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|c|c||}
 \hline
 Locations of given coefficients & ${\rm{RMSE}}_\alpha$ & ${\rm{RMSE}}_{c^2}$ \\
 \hline
 Diagonal & 1.466 & 0.328
 \\
 Grid & 4.228 & 2.444
 \\
 Random & 1.810 & 0.194
 \\
 \hline
 \end{tabular}
\caption{The RMSEs between the true and recovered PDE coefficients for different settings of locations for given coefficients.}
\end{table}
\end{document}

eess

"Deep Learning based Spatially Dependent Acoustical Properties Recovery" by Ruixian Liu and Peter Gerstoft, arXiv:2310.10970v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|ccc|}
 \hline
 Dataset & Memory Usage, No SOAR & Memory Usage with SOAR \\
 \hline
 Glove-1M & 453.5 MB & 488.4 MB (+7.7\%) \\
 Microsoft Turing-ANNS & 120.03 GB & 140.23 GB (+16.8\%) \\
 Microsoft SPACEV & 120.85 GB & 141.80 GB (+17.3\%) \\
 \hline
\end{tabular}
\caption{{ANN index memory consumption before/after SOAR.}}
\end{table}
\end{document}

cs

Philip Sun
David Simcha
Dave Dopson
Ruiqi Guo
Sanjiv Kumar

"SOAR: Improved Indexing for Approximate Nearest Neighbor Search" by Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo, and Sanjiv Kumar, arXiv:2404.00774v1, 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{ List of contrastive debiasing terms used in the experiments.} %TODO cite bolukbasi implementation as source
\begin{tabular}{|cc|}
\hline
female & male \\
women & men \\
girl & boy \\
she & he \\
actress & actor \\
heroine & hero \\
mother & father \\
lady & gentleman \\
queen & king \\
sister & brother \\
her & him \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Philip Kenneweg
Sarah Schröder
Alexander Schulz
Barbara Hammer

"Debiasing Sentence Embedders through Contrastive Word Pairs" by Philip Kenneweg, Sarah Schröder, Alexander Schulz, and Barbara Hammer, arXiv:2403.18555v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Impact of the details of the feature matching block. } All the results are evaluated on \emph{mini}ImageNet for $5$-way $1$-shot task.}
\begin{tabular}{lclc}
 \toprule
 Model & accuracy & Model & accuracy \\
 \midrule
 $C_m=4$ & $51.63\% $ & $C_m=64$ & $52.98\% $ \\
 $C_m=8$ & $52.14\% $ & $C_m=128$ & $52.49\% $ \\
 $C_m=16$ & $52.52\% $ & w/o softmax & $49.93\% $ \\
 $C_m=32$ & $52.93\% $ & w/o transformation & $52.46\% $ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Mengting Chen
Xinggang Wang
Heng Luo
Yifeng Geng
Wenyu Liu

"Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition" by Mengting Chen, Xinggang Wang, Heng Luo, Yifeng Geng, and Wenyu Liu, arXiv:2101.05018v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

S. Ananya
Channabasavayya
D. Ranganatha
R. G. Veeresha

"Vanishing coefficient results in four families of infinite q-products" by S. Ananya, Channabasavayya, D. Ranganatha, and R. G. Veeresha, arXiv:2503.11670v1, 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{DNA codewords from $(\sigma,\delta)$-cyclic codes}
\begin{tabular}{|c|c|c|c|}

			\hline
		CCTAATCCTAAT & TTACCATTACCA & GGATTAGGATTA & TGAGCGTGAGCG \\
CTCACGCTCACG & ACGGCAACGGCA & CTTCAACTTCAA & CCCCCCCCCCCC \\
CGTGAGCGTGAG & GCCGAAGCCGAA & CGCTCACGCTCA & ATTAGGATTAGG \\
CCAGGACCAGGA & ACTCGCACTCGC & AGTTGAAGTTGA & CTGGTCCTGGTC \\
CAGATACAGATA & AACTTCAACTTC &CGGCTTCGGCTT &ATAGACATAGAC \\
TAATCCTAATCC &ATGTCTATGTCT &AAAAAAAAAAAA &AGGACCAGGACC \\
CATTACCATTAC &AGACATAGACAT &GTCTATGTCTAT &TTCGGCTTCGGC \\
GGCAACGGCAAC &AAGCCGAAGCCG &CTATGTCTATGT &TACAGATACAGA \\
GACCAGGACCAG &CGAAGCCGAAGC &GCCGTTGCCGT &GCGAGTGCGAGT \\
GTTGCCGTTGCC &CAACGGCAACGG &GTGCGAGTGCGA &GATACAGATACA \\
GGGGGGGGGGGG &GGTCCTGGTCCT &TAGGATTAGGAT &CCGTTGCCGTTG \\
TCTGTATCTGTA &GAAGTTGAAGTT &ACATAGACATAG &CACGCTCACGCT \\
TCCTGGTCCTGG &GAGTGCGAGTGC &AATGGTAATGGT &GCTTCGGCTTCG \\
TTGAAGTTGAAG &TTTTTTTTTTTT &TCGCACTCGCAC &TGTATCTGTATC \\
ACCATTACCATT &TGGTAATGGTAA &TATCTGTATCTG &ATCCTAATCCTA \\
GTAATGGTAATG &TCAACTTCAACT &AGCGTGAGCGTG &GCACTCGCACTC\\
			\hline

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

math

Ashutosh Singh
Priyanka Sharma
Om Prakash

"Construction of $(σ,δ)$-cyclic codes over a non-chain ring and their applications in DNA codes" by Ashutosh Singh, Priyanka Sharma, and Om Prakash, arXiv:2312.08109v1, 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{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}

math

Dinesh Parthasarathy
Tommaso Bevilacqua
Martin Lanser
Axel Klawonn
Harald Köstler

"Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations" by Dinesh Parthasarathy, Tommaso Bevilacqua, Martin Lanser, Axel Klawonn, and Harald Köstler, arXiv:2412.08186v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Paulo Dourado
Antonio C. Pedroso-de-Lima
Francisco M. M. Rocha

"Segmented zero-inflated Poisson mixed effects model with random changepoint" by Paulo Dourado, Antonio C. Pedroso-de-Lima, and Francisco M. M. Rocha, arXiv:2310.01694v2, licensed under CC BY-NC-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{Classification accuracies, for the distribution shift experiment. Two numbers per dataset are given, the first denoting the accuracy on the partial dataset before the data shift and the second denoting the same accuracy after the data shift.}
\begin{tabular}{cccccc}
 \toprule
 & \multicolumn{2}{c}{sentence length shift} & \multicolumn{3}{c}{artifical shift} \\
 \cmidrule(r){1-1} \cmidrule(l){2-3} \cmidrule(l){4-6}
method & SST2 & QNLI & SST2 & MRPC & MNLI \\
 \cmidrule(r){1-1} \cmidrule(l){2-3} \cmidrule(l){4-6}
$BERT base$ & 0.931 - 0.933 & 0.903 - 0.905 & 0.909 - 0.900 & 0.845 - 0.888 & 0.814 - 0.826 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Philip Kenneweg
Alexander Schulz
Sarah Schröder
Barbara Hammer

"Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers" by Philip Kenneweg, Alexander Schulz, Sarah Schröder, and Barbara Hammer, arXiv:2404.01317v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Monte Carlo Parameters}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline Class & $\mu$ & $\sigma$ & a & b & Exp1 & Exp2 & Exp3\\
 \hline 1 & 0.03 & 0.015 & 0.02 & 0.15 & 33 & 100 & 50\\
 \hline 2 & 0.0 & 0.01 & -0.02 & 0.02 & 10 & 10 & 10\\
 \hline 3 & -0.03 & 0.015 & -0.15 & -0.02 & 100 & 100 & 300\\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Predicting Stock Price Movement as an Image Classification Problem" by Matej Steinbacher, arXiv:2303.01111v1, 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{MLP Architecture}
\begin{tabular}{lc}
	\hline
	Layer & Output Shape \\
	\hline
 Input & (B, 8, L) \\
 AdaptiveAvgPool & (B, 256, 8) \\
 Flatten & (B, 2048) \\
 Dropout & - \\
 Linear & (B, 1024) \\
 GELU & - \\
 Dropout & -\\
 Linear & (B, 1024) \\
 GELU & - \\
 Linear & (B, 512) \\
 GELU & -\\
 Linear & (B, 256) \\
 GELU & -\\
 Linear & (B, 10) \\
	\hline
	\end{tabular}
\end{table}
\end{document}

eess

"FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification" by Anthony Zhou and Amir Barati Farimani, arXiv:2312.02380v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The performance of RADIANT when varying $\alpha$ and fixing $\Gamma$ of 15.}
\begin{tabular}{cccccccc}
\toprule
$\alpha$ &
 True * Info (\%) $\uparrow$ &
 True (\%) $\uparrow$ &
 Info (\%) $\uparrow$ &
 $\overline{\text{FPR}}$ $\downarrow$ &
 $\overline{\text{FNR}}$ $\downarrow$ &
 CE $\downarrow$ &
 KL $\downarrow$ \\ \midrule
Unintervened & 21.15 & 22.16 & 95.47 & - & - & 2.13 & 0.00 \\
1.0 & 24.39 & 25.95 & 94.00 & 0.32 & 0.32 & 2.14 & 0.01 \\
1.5 & 29.07 & 31.95 & 91.00 & 0.67 & 0.11 & 2.18 & 0.05 \\
2.0 & 34.75 & 39.54 & 91.88 & 0.76 & 0.05 & 2.19 & 0.06 \\
2.5 & 40.36 & 44.48 & 90.75 & 0.78 & 0.00 & 2.19 & 0.07 \\
3.0 & 34.21 & 38.92 & 87.88 & 0.97 & 0.00 & 2.20 & 0.13 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Bao Nguyen
Binh Nguyen
Duy Nguyen
Viet Anh Nguyen

"Risk-Aware Distributional Intervention Policies for Language Models" by Bao Nguyen, Binh Nguyen, Duy Nguyen, and Viet Anh Nguyen, arXiv:2501.15758v1, 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}{lccc}
 \toprule
 Model & Method & Winrate$\% \uparrow$ & Attack Success Rate$\% \downarrow$ \\
 \midrule
 \multirow{3}{*}{Llama-3.2-1B} & DPO & $31.90(1.38)$ \\
 & KTO & $50.93(1.46)$ \\
 & BCO & $45.90(1.47)$\\
 \midrule
 \multirow{3}{*}{Archangel-sft-llama7b} & DPO & $30.79(1.38)$ \\
 & KTO & $48.74(1.48)$ \\
 & BCO & $34.98(1.47)$ \\
 \midrule
 \multirow{3}{*}{gemma-2-2b} & DPO & $12.77(1.00)$ \\
 & KTO & $20.20(1.17)$ \\
 & BCO & $14.23(1.02)$ \\
 \midrule
 \multirow{3}{*}{Mistral-7B-v0.1} & DPO & $70.26(1.43)$ \\
 & KTO & $56.17(1.43)$\\
 & BCO & $64.42(1.39)$ \\
 \midrule
 \multirow{3}{*}{Qwen2.5-1.5B} & DPO & $38.18(1.46)$ \\
 & KTO & $46.81(1.41)$ \\
 & BCO & $50.88(1.43)$ \\
 \bottomrule
 \end{tabular}
\caption{Caption}
\end{table}
\end{document}

stat

Rajdeep Haldar
Ziyi Wang
Qifan Song
Guang Lin
Yue Xing

"LLM Safety Alignment is Divergence Estimation in Disguise" by Rajdeep Haldar, Ziyi Wang, Qifan Song, Guang Lin, and Yue Xing, arXiv:2502.00657v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Set-Asides in USDA Food Procurement Auctions" by Ni Yan and WenTing Tao, arXiv:2302.05772v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Model-assisted inference for dynamic causal effects in staggered rollout cluster randomized experiments" by Xinyuan Chen and Fan Li, arXiv:2502.10939v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"On the Randić index and its variants of network data" by Mingao Yuan, arXiv:2308.16830v1, licensed under CC BY-NC-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Revealing the Shape of Genome Space via K-mer Topology" by Yuta Hozumi and Guo-Wei Wei, arXiv:2412.20202v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sida Li
Ioana Marinescu
Sebastian Musslick

"GFN-SR: Symbolic Regression with Generative Flow Networks" by Sida Li, Ioana Marinescu, and Sebastian Musslick, arXiv:2312.00396v1, 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
\begin{tabular}{l|cccccc}
 \hline
 Method & Acc $\downarrow$ & Comp $\downarrow$ & C-$\mathcal{L}_1$ $\downarrow$ & Prec $\uparrow$ & Recall $\uparrow$ & F-score $\uparrow$ \\
 \hline
 COLMAP & 0.047 & 0.235 & 0.141 & 71.1 & 44.1 & 53.7\\
 UNISURF & 0.554 & 0.164 & 0.359 & 21.2 & 36.2 & 26.7\\
 VolSDF & 0.414 & 0.120 & 0.267 & 32.1 & 39.4 & 34.6\\
 NeuS & 0.179 & 0.208 & 0.194 & 31.3 & 27.5 & 29.1 \\
 Manhattan-SDF & 0.072 & 0.068 & 0.070 & 62.1 & 56.8 & 60.2\\
 NeuRIS & 0.050 & 0.049 & 0.050 & 71.7 & 66.9 & 69.2\\
 MonoSDF & \textbf{0.035} & 0.048 & \textbf{0.042} & \textbf{79.9} & 68.1 & 73.3\\
 {ObjSDF++} & 0.039 & 0.045 & \textbf{0.042} & {78.1} & {70.6} & {74.0}\\
 \textbf{Ours} & 0.044 & \textbf{0.040} & \textbf{0.042} & {74.7} & \textbf{74.8} & \textbf{74.7}\\
 \hline
 \end{tabular}
\caption{Quantitative assessments of the proposed model against previous works on the ScanNet dataset. }
\end{table}
\end{document}

cs

Xiaoyang Lyu
Chirui Chang
Peng Dai
Yang-Tian Sun
Xiaojuan Qi

"Total-Decom: Decomposed 3D Scene Reconstruction with Minimal Interaction" by Xiaoyang Lyu, Chirui Chang, Peng Dai, Yang-Tian Sun, and Xiaojuan Qi, arXiv:2403.19314v2, 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}{lc}
 \toprule
 \textbf{Process} & \textbf{Time Distribution (\%)} \\
 \midrule
 Modeling (including importing time) & 58.0 \\
 Checking feasibility of samples (OMPL) & 40.0 \\
 Potential field computation & 0.9 \\
 Importance prediction & 0.8 \\
 \bottomrule
 \end{tabular}
\caption{Average time distribution of entire process in MoMa-Pos}
\end{table}
\end{document}

cs

Beichen Shao
Nieqing Cao
Yan Ding
Xingchen Wang
Fuqiang Gu
Chao Chen

"MoMa-Pos: An Efficient Object-Kinematic-Aware Base Placement Optimization Framework for Mobile Manipulation" by Beichen Shao, Nieqing Cao, Yan Ding, Xingchen Wang, Fuqiang Gu, and Chao Chen, arXiv:2403.19940v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Luigi Brugnano
Gianmarco Gurioli
Felice Iavernaro

"A shooting-Newton procedure for solving fractional terminal value problems" by Luigi Brugnano, Gianmarco Gurioli, and Felice Iavernaro, arXiv:2312.08516v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A symbolic computational approach to the generalized gambler's ruin problem in one and two dimensions" by Lucy Martinez, arXiv:2412.07667v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Hossam Farag
Mikael Gidlund
Cedomir Stefanovic

"A Deep Reinforcement Learning Approach for Improving Age of Information in Mission-Critical IoT" by Hossam Farag, Mikael Gidlund, and Cedomir Stefanovic, arXiv:2311.13861v1, 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|c|}
 \hline
 Boxplots & Initial belief distribution & Interaction Rule \\
 \hline
 Figures , , , & Beta(2,2) & average interaction rule\\
 \hline
 Figures , , , & Beta(2,2) & weighted interaction rule\\
 \hline
 Figures , , , & Beta(2,5) & average interaction rule\\
 \hline
 Figures , , , & Beta(2,5) & weighted interaction rule\\
 \hline
 Figures , , , & Normal(0,1) & average interaction rule\\
 \hline
 Figures , , , & Normal(0,1) & weighted interaction rule\\
 \hline
 \end{tabular}
\caption{Different parameters on initial belief distribution and interaction mechanism over various graphs}
\end{table}
\end{document}

stat

"Evaluating Policy Effects through Network Dynamics and Sampling" by Eugene T. Y. Ang and Yong Sheng Soh, arXiv:2501.08150v1, 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|ccccc}
 & A & B & C & D & E \\
\hline
 Wins & 3(1=) & 3(1=) & 2(3) & 1(4=) & 1(4=) \\
 Bradley-Terry & 7.57(1=) & 7.57(1=) & 2.75(3) & 1.00(4=) & 1(4=) \\
 PageRank & 1.00(1=) & 0.67(3) & 0.44(4) & 0.33(5) & 1(1=) \\
 Scroogefactor & 3.00(1) & 2.00(2) & 0.67(4) & 0.33(5) & 1(3)
\end{tabular}
\caption{Five-team round-robin tournament rating(ranking), with rating of E standardised to 1. PageRank here is undamped.}
\end{table}
\end{document}

math

"The many routes to the ubiquitous Bradley-Terry model" by Ian Hamilton, Nick Tawn, and David Firth, arXiv:2312.13619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Making Reliable and Flexible Decisions in Long-tailed Classification" by Bolian Li and Ruqi Zhang, arXiv:2501.14090v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Geometrical details of the obstacles. }
\begin{tabular}{|c|c|c|c|c|c|}
		\hline
		\multirow{2}{*}{Index} &
		\multicolumn{2}{c|}{Configuration} & \multirow{2}{*}{Index} &
		\multicolumn{2}{c|}{Configuration} \\
		\cline{2-3} \cline{5-6}
		& Center (m) & Radius (m) && Center (m) & Radius (m) \\
		\hline
		$\mathcal{O}_{1}^{\ast}$ & $[-2,-0.55]^{\top}$ & $0.10$ & $\mathcal{O}_{5}^{\ast}$ & $[0.4,0.55]^{\top}$ & $0.25$\\

		$\mathcal{O}_{2}^{\ast}$ & $[-0.9,0.85]^{\top}$ & $0.10$ & $\mathcal{O}_{6}^{\ast}$ & $[0.7,-0.6]^{\top}$ & $0.10$\\

		$\mathcal{O}_{3}^{\ast}$ & $[-0.7,-0.5]^{\top}$ & $0.35$ & $\mathcal{O}_{7}^{\ast}$ & $[2,-0.6]^{\top}$ & $0.25$\\

		$\mathcal{O}_{4}^{\ast}$ & $[-2.1,0.6]^{\top}$ & $0.15$ & $\mathcal{O}_{8}^{\ast}$ & $[1.8,0.7 ]^{\top}$ & $0.15$\\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Xiaodong Shao
Bin Zhang
Hui Zhi
Jose Guadalupe Romero
Bowen Fan
Qinglei Hu
David Navarro-Alarcon

"InPTC: Integrated Planning and Tube-Following Control for Prescribed-Time Collision-Free Navigation of Wheeled Mobile Robots" by Xiaodong Shao, Bin Zhang, Hui Zhi, Jose Guadalupe Romero, Bowen Fan, Qinglei Hu, and David Navarro-Alarcon, arXiv:2312.11930v2, 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{Log-difference of GVA per capita, years under treatment (\%) excluding outliers}
\begin{tabular}{l|ccc}
\toprule
Years & Regions & Observed & Counterfactual \\
\midrule
7 &87 &20.9 &14.2 \\
14 &87 &9.87 &9.33 \\
21 &83 &4.31 &1.78 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Angelos Alexopoulos
Ilias Kostarakos
Christos Mylonakis
Petros Varthalitis

"The heterogeneous causal effects of the EU's Cohesion Fund" by Angelos Alexopoulos, Ilias Kostarakos, Christos Mylonakis, and Petros Varthalitis, arXiv:2504.13223v1, 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}{lrlr}
 \toprule
 & & & MedMCQA Test \\
 Model & Params & Method & Accuracy \\
 \midrule
 GPT-4 & -- & few-shot & 72.4 \\
 Flan-PaLM & 540B & few-shot & 57.6 \\
 BioMedLM & 2.7B & fine-tune & 57.3 \\
 Galactica & 120B & zero-shot & 52.9 \\
 GPT-3.5 & 175B & few-shot & 51.0 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Elliot Bolton
Abhinav Venigalla
Michihiro Yasunaga
David Hall
Betty Xiong
Tony Lee
Roxana Daneshjou
Jonathan Frankle
Percy Liang
Michael Carbin
Christopher D. Manning

"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text" by Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, and Christopher D. Manning, arXiv:2403.18421v1, 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}{lrrr}
		\toprule
		& IAMS & MH-IAMS & RIAMS \\ \midrule
		Acceptance rate for $\boldsymbol{\beta}$ & - & 0.23 & 0.62 \\
		Acceptance rate for $\boldsymbol{\gamma}$ & - & 0.58 & 0.76 \\
		Elapsed time (for $B=100,000$) & 13$s$ & 47$s$ & 56$s$ \\ \bottomrule
	\end{tabular}
\caption{Acceptance rates and elapsed time of the compared algorithms.}
\end{table}
\end{document}

stat

Aldo Gardini
Fedele Greco
Carlo Trivisano

"A note on auxiliary mixture sampling for Bayesian Poisson models" by Aldo Gardini, Fedele Greco, and Carlo Trivisano, arXiv:2502.04938v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Celia Rubio-Madrigal
Adarsh Jamadandi
Rebekka Burkholz

"GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring" by Celia Rubio-Madrigal, Adarsh Jamadandi, and Rebekka Burkholz, arXiv:2502.04891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Equilibrium rates in Case A with term debt maturity of 5 years.}
\begin{tabular}{|l|c|c|}
\hline
 $r_{\min}=0.0598$ & $r_{\mathrm{fix}}=$n.a. & $r_{\max} = 0.3211$ \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Gianmarco Bet
Francesco Dainelli
Eugenio Fabrizi

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


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|ccccc|}
			\hline
			\multirow{2}{*}{\textbf{Article}} &
			\multirow{2}{*}{\textbf{BAI}} & \textbf{Batch} &
			\textbf{Bayesian} & \multirow{2}{*}{\textbf{Algorithm}} \\
			& & \textbf{grid} & \textbf{bandit} & \\
			\hline
			 & no & static & no
			& UCB type \\ \hline
			 & no & static/adaptive & no & UCB type \\ \hline
			 & no & static & no & UCB type \\ \hline
			 & no & adaptive & no & UCB type \\ \hline
			 & no & adaptive & yes & Thompson \\ \hline
			 & no & adaptive & yes & Thompson \\ \hline
			 & yes & adaptive & no &
			UCB type \\ \hline
			 & yes & adaptive & no & sample mean based \\ \hline
			 & yes & adaptive & yes & UCB type \\ \hline
			This paper & yes & adaptive & yes & novel \\ \hline
	\end{tabular}
\caption{Summary of literature on batched bandit problem.}
\end{table}
\end{document}

stat

Shengyu Cao
Simai He
Ruoqing Jiang
Jin Xu
Hongsong Yuan

"Best Arm Identification in Batched Multi-armed Bandit Problems" by Shengyu Cao, Simai He, Ruoqing Jiang, Jin Xu, and Hongsong Yuan, arXiv:2312.13875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c|c}
 \toprule
 Settings & $||\boldsymbol{\mathbb{X}}||_{rms}$ & $\boldsymbol{E}_{\text{sol}}\downarrow$ & $\boldsymbol{E}_{\text{para}}\downarrow$ & \multicolumn{2}{c|}{$\boldsymbol{E}_{\text{time}}\downarrow$} & Parameters \\
 \cline{5-6}
 & & & & Our & PC & \\
 \hline
 Lorenz & 25.36 & \textbf{1.5}\% (2.8\%) & \textbf{0.58}\% (0.65\%) & \textbf{0.10}\% (0.40\%) & 25.9\% & $\begin{bmatrix}
 \sigma \\
 \rho\\
 \beta
 \end{bmatrix} =\begin{bmatrix}
 9.94 \\
 27.89\\
 2.59
 \end{bmatrix}$ \\
 &&&&&& \\
 LV4D & 1.12 & \textbf{2.3}\% (4.2\%) & \textbf{4.3}\% (5.6\%) & \textbf{0.31}\% (0.69\%) & 13.0\% & $\begin{bmatrix}
 \alpha_1\\
 \alpha_2\\
 \beta_1\\
 \beta_2
 \end{bmatrix} =\begin{bmatrix}
 0.97 \\
 0.98\\
 2.93\\
 4.65
 \end{bmatrix}$ \\
 &&&&&& \\
 Duffing & 1.17 & \textbf{1.63}\% (3.88\%) & \textbf{2.03}\% (8.53\%) & \textbf{0.20}\% (0.30\%) & 19.4\% & $\begin{bmatrix}
 \alpha\\
 \gamma\\
 \rho\\
 \beta\\
 \end{bmatrix} =\begin{bmatrix}
 0.99 \\
 0\\
 0.21\\
 1.06
 \end{bmatrix}$ \\
 \bottomrule
\end{tabular}
\caption{\textbf{Benchmark Results:} The symbol \(||\cdot||_{rms}\) denotes the root mean square of the observational data. The metrics \(\boldsymbol{E}_{\text{sol}}\) and \(\boldsymbol{E}_{\text{para}}\) are computed in the first phase (using the learned neural solution), while the two columns under \(\boldsymbol{E}_{\text{time}}\) denote the results of our method (Our) and the traditional Principal Curve (PC) method, respectively. \textbf{Bold} indicates the best performance.}
\end{table}
\end{document}

math

Zhijun Zeng
Pipi Hu
Chenglong Bao
Yi Zhu
Zuoqiang Shi

"Reconstruction of dynamical systems from data without time labels" by Zhijun Zeng, Pipi Hu, Chenglong Bao, Yi Zhu, and Zuoqiang Shi, arXiv:2312.04038v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Janaína Marques e Melo
João César Reis Alves
Gabriel Rodrigues Palma
Sílvia Maria de Freitas
Idemauro Antonio Rodrigues de Lara

"Unified Multivariate Ordinal Model for analysis of sensory attributes" by Janaína Marques e Melo, João César Reis Alves, Gabriel Rodrigues Palma, Sílvia Maria de Freitas, and Idemauro Antonio Rodrigues de Lara, arXiv:2502.11990v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Model Compression for Domain Adaptation through Causal Effect Estimation" by Guy Rotman, Amir Feder, and Roi Reichart, arXiv:2101.07086v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{F1-score for Various Strategies on SEP28k-E-Merged Dataset}
\begin{tabular}{lcccccc}
\toprule
& \multicolumn{3}{c}{\textbf{Semi-cleaned SEP28k-E-Merged}} & \multicolumn{3}{c}{\textbf{Cleaned SEP28k-E-Merged}} \\
\cmidrule(lr){2-4} \cmidrule(lr){5-7}
& Base & Strategy 1 & Strategy 2 & Base & \textbf{Strategy 1} & Strategy 2 \\
\midrule
\textbf{No Stuttered Words} & 0.27 & 0.18 & 0.16 & 0.16 & \textbf{0.23} & 0 \\
\textbf{Word Repetition} & 0.72 & 0.7 & 0.67 & 0.73 & \textbf{0.72} & 0.68 \\
\textbf{Sound Repetition} & 0.89 & 0.9 & 0.84 & 0.88 & \textbf{0.89} & 0.84 \\
\textbf{Prolongation} & 0.73 & 0.68 & 0.59 & 0.71 & \textbf{0.73} & 0.63 \\
\textbf{Interjection} & 0.72 & 0.71 & 0.5 & 0.68 & \textbf{0.7} & 0.51 \\
\textbf{Block} & 0.67 & 0.69 & 0.41 & 0.74 & \textbf{0.72} & 0.35 \\
\midrule
\textbf{Average F1-score} & 0.8 & 0.8 & 0.71 & 0.8 & \textbf{0.81} & 0.71 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Huma Ameer
Seemab Latif
Rabia Latif
Sana Mukhtar

"Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimization" by Huma Ameer, Seemab Latif, Rabia Latif, and Sana Mukhtar, arXiv:2311.05203v1, 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{Event Study for Spend - Black Friday Adopters vs. Organic Adopters.}
\begin{tabular}{lc}
 \midrule \midrule
 Dependent Variable: & Spend\\
 Model: & (1)\\
 \midrule
 \emph{Variables}\\
 BlackFriday\_Adopter $\times$ Lag -10 & -39.54\\
 & (50.73)\\
 BlackFriday\_Adopter $\times$ Lag -9 & -44.26\\
 & (38.91)\\
 BlackFriday\_Adopter $\times$ Lag -8 & -26.81\\
 & (35.61)\\
 BlackFriday\_Adopter $\times$ Lag -7 & -25.93\\
 & (30.61)\\
 BlackFriday\_Adopter $\times$ Lag -6 & 5.206\\
 & (34.45)\\
 BlackFriday\_Adopter $\times$ Lag -5 & -4.364\\
 & (27.55)\\
 BlackFriday\_Adopter $\times$ Lag -4 & -18.65\\
 & (27.25)\\
 BlackFriday\_Adopter $\times$ Lag -3 & -9.695\\
 & (25.51)\\
 BlackFriday\_Adopter $\times$ Lag -2 & -38.55\\
 & (23.94)\\
 BlackFriday\_Adopter $\times$ Adoption Month & 128.6$^{***}$\\
 & (32.46)\\
 BlackFriday\_Adopter $\times$ Lead 1 & -134.3$^{***}$\\
 & (25.36)\\
 BlackFriday\_Adopter $\times$ Lead 2 & -114.4$^{***}$\\
 & (25.65)\\
 BlackFriday\_Adopter $\times$ Lead 3 & -105.5$^{***}$\\
 & (25.58)\\
 BlackFriday\_Adopter $\times$ Lead 4 & -88.21$^{**}$\\
 & (28.63)\\
 BlackFriday\_Adopter $\times$ Lead 5 & -55.82$^{*}$\\
 & (28.28)\\
 BlackFriday\_Adopter $\times$ Lead 6 & -126.9$^{***}$\\
 & (27.95)\\
 BlackFriday\_Adopter $\times$ Lead 7 & -88.91$^{**}$\\
 & (28.21)\\
 BlackFriday\_Adopter $\times$ Lead 8 & -69.56$^{*}$\\
 & (27.24)\\
 BlackFriday\_Adopter $\times$ Lead 9 & -25.79\\
 & (28.13)\\
 BlackFriday\_Adopter $\times$ Lead 10 & -90.03$^{**}$\\
 & (28.42)\\
 BlackFriday\_Adopter $\times$ Lead 11 & -67.00$^{*}$\\
 & (28.53)\\
 \midrule
 \emph{Fixed-effects}\\
 Customer & Yes\\
 YearMonth & Yes\\
 \midrule
 \emph{Fit statistics}\\
 Observations & 81,283\\
 R$^2$ & 0.42086\\
 Within R$^2$ & 0.00211\\
 \midrule \midrule
 \multicolumn{2}{l}{\emph{Clustered (Customer) standard-errors in parentheses}}\\
 \multicolumn{2}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\
 \end{tabular}
\end{table}
\end{document}

q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"Channel Choice and Customer Value" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, 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{ Ablative study of two streams in CNN10\textcircled{+}GL-AT}
\begin{tabular}{|c|cc|c|c|c|}
 \hline
 \textbf{Method} & \textbf{Global} & \textbf{Local} & \textbf{mAP} & \textbf{mAUC}\\
 \hline
 CNN10 & $\surd$ & & 0.382 & 0.969\\
 \hline
 \multirow{3}{*}{CNN10\textcircled{+}GL-AT} & $\surd$ & & 0.389 & 0.970\\
 & & $\surd$ & 0.400 & 0.972\\
 & $\surd$ & $\surd$ & 0.408 & 0.974\\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

"A Global-local Attention Framework for Weakly Labelled Audio Tagging" by Helin Wang, Yuexian Zou, and Wenwu Wang, arXiv:2102.01931v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulated vs. Real-world Dataset Statistics}
\begin{tabular}{r|r|cc}
\Xhline{1.5pt}
\textbf{Dataset} & \textbf{Type} & \textbf{Mean} & \textbf{Var.} \\
\Xhline{1.5pt}
CH. Simul. & observed sil. ratio & 0.1409 & 0.0045 \\
CHAES & real-world sil. ratio & 0.1473 & 0.0061 \\
\hline
CH. Simul. & observed ovl. ratio & 0.0759 & 0.0019 \\
CHAES & real-world ovl. ratio & 0.0754 & 0.0020 \\
\Xhline{1.5pt}
AMI Simul. & observed sil. ratio & 0.1804 & 0.0077 \\
AMI & real-world sil. ratio & 0.1814 & 0.0081 \\
\hline
AMI. Simul. & observed ovl. ratio & 0.1711 & 0.0092 \\
AMI & real-world ovl. ratio & 0.1473 & 0.0047 \\
\Xhline{1.5pt}
\end{tabular}
\end{table}
\end{document}

eess

Tae Jin Park
He Huang
Coleman Hooper
Nithin Koluguri
Kunal Dhawan
Ante Jukic
Jagadeesh Balam
Boris Ginsburg

"Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation" by Tae Jin Park, He Huang, Coleman Hooper, Nithin Koluguri, Kunal Dhawan, Ante Jukic, Jagadeesh Balam, and Boris Ginsburg, arXiv:2310.12371v1, 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{Spearman $R$ between \textit{BoTier} and \textit{Chimera}, evaluated on a grid of 10,000 samples from each surface.}
\begin{tabular}{lr}
\toprule
\textbf{Surface} & \textbf{Spearman $R$} \\ \midrule \midrule
BNH & 0.38311 \\
BNH* & 0.57663 \\
DH4 & 0.99729 \\
DH4* & 0.99376 \\
DTLZ5 & 0.97902 \\
DTLZ5* & 0.98092 \\
ZDT1 & 0.99417 \\
ZDT1* & 0.99429 \\
Heterocyclic Suzuki–Miyaura Coupling & 0.99229 \\
Benzylation of $\alpha$-methylbenzylamine & 0.99844 \\
Enzymatic Alkoxylation & 0.99795 \\
Silver Nanoparticle Synthesis & 0.99057 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Mohammad Haddadnia
Leonie Grashoff
Felix Strieth-Kalthoff

"BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives" by Mohammad Haddadnia, Leonie Grashoff, and Felix Strieth-Kalthoff, arXiv:2501.15554v1, 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{Hyperparameters used for training one-step generators with Score-of-Mixture Training and Distillation.}
\begin{tabular}{l|cc|cc}
 \toprule
 Hyperparameter & \multicolumn{2}{c|}{CIFAR-10} & \multicolumn{2}{c}{ImageNet $64\times 64$} \\
 & Scratch & Distillation & Scratch & Distillation \\
 \midrule
 Generator learning rate & 1e-4 & 5e-5 & 5e-6 & 2e-6 \\
 Score learning rate & 5e-4 & 5e-5 & 5e-5 & 2e-6 \\
 Score learning rate decay & cosine & None & cosine & None\\
 Batch size & 280 & 280 & 280 & 280 \\
 Diffusion pretraining steps & 15k & N/A & 40k & N/A\\
 Training iterations & 150k & 150k & 200k & 200k \\
 Score dropout probability & 0.13 & 0.00 & 0.00 & 0.00 \\
 Number of GPUs & 2 $\times$ A100 & 4$\times$ A100 & 7$\times$ A100 & 7$\times$ A100 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Tejas Jayashankar
J. Jon Ryu
Gregory Wornell

"Score-of-Mixture Training: Training One-Step Generative Models Made Simple via Score Estimation of Mixture Distributions" by Tejas Jayashankar, J. Jon Ryu, and Gregory Wornell, arXiv:2502.09609v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

"Bayesian estimation of Unit-Weibull distribution based on dual generalized order statistics with application to the Cotton Production Data" by Qazi J. Azhad, Abdul Nasir Khan, Bhagwati Devi, Jahangir Sabbir Khan, and Ayush Tripathi, arXiv:2502.02927v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
 \hline
 \textbf{Method} & \textbf{Batch Size} &\textbf{Epochs} & \textbf{Top-1 Accuracy} \\
 \hline
 \hline Supervised & 128 & 200 & \textbf{99.87}
 \\
 \hline CaCo & 128 & 200 & 92.6
 \\

 \hline ReSSL & 256 & 200 & 89.37
 \\
 \hline SimSiam & 128 & 200 & 70.0
 \\
 \hline SimCLR & 128 & 200 & 62.5
 \\
 \hline SimSiam & 512 & 800 & 91.8
 \\
 \hline SimCLR & 256 & 200 & 87.5
 \\
 \hline DINO & - & 800 & 99.1
 \\
 \hline Mixed Barlow Twins & 128 & 2000 & 92.58
 \\
 \hline Ours & 128 & 200 & \textbf{99.67}
 \\
 \hline
 \end{tabular}
\caption{These are top-1 accuracy scores for the linear classifier testing on CIFAR10. }
\end{table}
\end{document}

cs

"The Bad Batches: Enhancing Self-Supervised Learning in Image Classification Through Representative Batch Curation" by Ozgu Goksu and Nicolas Pugeault, arXiv:2403.19579v1, 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}{ccc}
 Type & Mean & Percentiles (25:50:75:90) \\
 \toprule
 Per document tokens & 183 & 62 : 120 : 217 : 388 \\
 Concatenated instance tokens & 6420 & 429 : 1323 : 3658 : 11427 \\
 \end{tabular}
\caption{Descriptive statistics about the number of tokens across clinical notes related to individual referral instances.}
\end{table}
\end{document}

cs

Niall Taylor
Andrey Kormilitzin
Isabelle Lorge
Alejo Nevado-Holgado
Dan W Joyce

"Bespoke Large Language Models for Digital Triage Assistance in Mental Health Care" by Niall Taylor, Andrey Kormilitzin, Isabelle Lorge, Alejo Nevado-Holgado, and Dan W Joyce, arXiv:2403.19790v1, 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}{lccc}
\toprule
\textbf{Method} & \textbf{Mandarin} & \textbf{German} & \textbf{Spanish} \\ \midrule
\textbf{RFM} & 3.92$\pm$0.33 & 3.76$\pm$0.55 & 3.87$\pm$0.39 \\
\textbf{Linear} & \textbf{3.95$\pm$0.21} & 3.75$\pm$0.57 & 3.81$\pm$0.48 \\
\textbf{Logistic} & \textbf{3.95$\pm$0.26} & 3.75$\pm$0.55 & 3.86$\pm$0.42 \\
\textbf{Difference-in-means} & 3.94$\pm$0.27 & 3.76$\pm$0.53 & 3.86$\pm$0.44 \\
\textbf{PCA} & 3.91$\pm$0.34 & \textbf{3.79$\pm$0.57} & \textbf{3.89$\pm$0.34} \\
\bottomrule
\end{tabular}
\caption{Comparison of average scores given by GPT-4o judge model for language translation by steering Llama-3.1-8B-it. Maximum score of 4 for a perfect translation. Steering direction is extracted using RFM, Linear or Logistic regression, Difference-in-means, and PCA.}
\end{table}
\end{document}

stat

Daniel Beaglehole
Adityanarayanan Radhakrishnan
Enric Boix-Adserà
Mikhail Belkin

"Aggregate and conquer: detecting and steering LLM concepts by combining nonlinear predictors over multiple layers" by Daniel Beaglehole, Adityanarayanan Radhakrishnan, Enric Boix-Adserà, and Mikhail Belkin, arXiv:2502.03708v1, licensed under CC0 1.0. 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}{lcccr}
\toprule
Data set & Naive & Flexible & Better? \\
\midrule
Breast & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
Glass2 & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
Credit & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6& \\
Horse & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
Meta & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
Pima & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5& \\
Vehicle & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Feature maps for the Laplacian kernel and its generalizations" by Sudhendu Ahir and Parthe Pandit, arXiv:2502.15575v1, 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
\begin{tabular}{lccccccc} \toprule

			Quadrotor Body& Value & Unit & Arms & Value & Unit & Operator & Value \\ \midrule
			$m$ & 0.5 & $kg$ & $L_c$ & 0.21 & $m$ & $K_p$ & 0.59\\
			$J_x$ & $4.85 \times 10^{-3}$ & $kgm^2$ & $\rho_c$ & 1370 & $kgm^3$ & $T_p$ & 0.41 \\
			$J_y$ & $4.85 \times 10^{-3}$ & $kgm^2$ & $E_c$ & 2.91 & $GPa$ & $\tau_h$ & 0.20 \\
			$J_z$ & $8.81 \times 10^{-3}$ & $kgm^2$ & & & & & \\
			$J_r$ & $3.36 \times 10^{-5}$ & $kgm^2$ & & & & & \\
			\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

"Modeling, Control and Human-In-The-Loop Stability Analysis of an Elastic Quadrotor" by Emre Eraslan and Yildiray Yildiz, arXiv:2012.05747v1, 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 the Dense $\mathcal{H}_{\infty}$ estimator with $S = \mathbf{1}_{n_x \times n_y}$, $B_{\Delta f} = 0.1I_{n_x}$, $C_y = I_{n_y,n_x}$, and $n_y = 2N$ for the IEEE test systems.}}
\begin{tabular}{|c|c|c|c|}
\hline
\textrm{Approach} & $\|T_{z\tilde{w}}(s)\|_{\mathcal{H}_{\infty}}$ & Computational Time & $(n_x,n_y)$ \\
\hline
non-Lyap $9$-bus & \cellcolor{lightgray}$3.0921$ & \cellcolor{lightgray}$0.85$ s & (36,6) \\
\hline
Lyap $9$-bus & \cellcolor{lightgray}$3.0921$ & $6.06$ s & $(36,6)$ \\
\hline
non-Lyap $14$-bus & \cellcolor{lightgray}$3.2893$ & \cellcolor{lightgray}$1.95$ s & $(58,10)$ \\
\hline
Lyap $14$-bus & $4.3332$ & $7.25$ s & $(58,10)$ \\
\hline
non-Lyap $39$-bus & \cellcolor{lightgray}$2.6402$ & \cellcolor{lightgray}$45.25$ s & $(138,20)$ \\
\hline
Lyap $39$-bus & \cellcolor{lightgray}$2.6402$ & $556.77$ s & $(138,20)$ \\
\hline
non-Lyap $57$-bus & \cellcolor{lightgray}$17.5631$ & \cellcolor{lightgray}$17.28$ s & $(156,14)$ \\
\hline
Lyap $57$-bus & \cellcolor{lightgray}$17.5631$ & $1337.05$ s & $(156,14)$ \\
\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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Percentage error Simulated single-cell Data (lower is better)}
\begin{tabular}{lrr}
		\toprule
		 & Bernoulli & Uniform \\
		\midrule
		Noisy & 50.0\% & 28.1\% \\
		Ours & \textbf{7.9\%} & \textbf{25.5\%} \\
		Local Avg & 39.9\% & 29.3\% \\
		Low Pass & 49.7\% & 28.8\% \\
		High Pass & 100.4\% & 100.3\% \\
		MAGIC & 40.1\% & 30.3\% \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Sam Leone
Xingzhi Sun
Michael Perlmutter
Smita Krishnaswamy

"Bayesian Formulations for Graph Spectral Denoising" by Sam Leone, Xingzhi Sun, Michael Perlmutter, and Smita Krishnaswamy, arXiv:2311.16378v2, 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{Configuration Parameters for Gradient Boosting Regression}
\begin{tabular}{|c|c|}
\hline
\textbf{Parameter} & \textbf{Value} \\ \hline
Random State & 0 \\ \hline
Number of Estimators & 100 \\ \hline
Maximum Leaf Nodes & 25 \\ \hline
Maximum Depth & 3 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Machine Learning based Soil VWC and Field Capacity Estimation Using Low Cost Sensors" by Idrees Zaman, Nandit Jain, and Anna Förster, arXiv:2102.03354v1, 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{Tropical polynomial, polytope, and IVNN of the two chain.}
\begin{tabular}{|l||l|}
\hline
Poset & $\{x<y\}$ \\
\hline
Tropical & $0\oplus y\oplus x\otimes y$ \\
 polynomial & $=0\oplus y\otimes (0\oplus x)$\\
\hline
Polytope & $\{0\leq x\leq y\leq 1\}$ \\
\hline
IVNN & ${\text{ReLU}_0\left(\begin{bmatrix}
\text{ReLU}_0(x)&
\text{ReLU}_{-\infty}(y)
\end{bmatrix}\begin{bmatrix}
1\\
1\\
\end{bmatrix}\right)}$ \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Eric Dolores-Cuenca
Aldo Guzman-Saenz
Sangil Kim
Susana Lopez-Moreno
Jose Mendoza-Cortes

"Order Theory in the Context of Machine Learning" by Eric Dolores-Cuenca, Aldo Guzman-Saenz, Sangil Kim, Susana Lopez-Moreno, and Jose Mendoza-Cortes, arXiv:2412.06097v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Janie Coulombe
Dany El-Riachi
Fanxing Du
Tianze Jiao

"Constructing optimal dynamic monitoring and treatment regimes: An application to hypertension care" by Janie Coulombe, Dany El-Riachi, Fanxing Du, and Tianze Jiao, arXiv:2501.08274v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.