\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the time-complexities of the parallel algorithms studied in this work.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
 \textbf{Parallel Algorithm} & \textbf{time complexity} \\
 \midrule
 Imminent collisions & $O(d)$ \\
 End collisions & $O(\frac{d^2}{p} + d \log d)$ \\
 Search collisions & $O(\frac{d \log^2 d}{p} + \log^3 d)$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Weight-Sharing Regularization

Mehran Shakerinava
Motahareh Sohrabi
Siamak Ravanbakhsh
Simon Lacoste-Julien

"Weight-Sharing Regularization" by Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, and Simon Lacoste-Julien, arXiv:2311.03096v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Choices of IEEE standard precisions for three-precision GMRES-IR presented in , and their convergence conditions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccc|ccc|}
		\hline
		\multirow{2}{*}{$u_f$} & \multirow{2}{*}{$u$} & \multirow{2}{*}{$u_r$} & \multirow{2}{*}{$\kappa_\infty(A)$} & \multicolumn{2}{c}{Backward error}& \\
		& & & & Normwise & Componentwise & Forward error \\ \hline
		half & half & single & $10^{4}$ & half & half & half \\
		half & single & double & $10^{8}$ & single & single & single \\
		half & double & quad & $10^{12}$ & double & double & double \\
		single & single & double & $10^{8}$ & single & single & single \\
		single & double & quad & $10^{16}$ & double & double & double \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Mixed Precision GMRES-based Iterative Refinement with Recycling" by Eda Oktay and Erin Carson, arXiv:2201.09827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 & \multicolumn{2}{|c|}{Phone error rate}\\\hline
Model & WSJ & CUSENT \\\hline
CD-GMM-HMM & 6.98\% & 9.39\% \\
SGMMs-HMM & 6.95\% &8.78\% \\%\hline
DNN/DBN-HMM & 6.26\% &7.02\% \\\hline
Multilingual DNN-BN & 5.37\% & 8.32\% \\%\hline
Multilingual DNN-SBN & 5.33\% & 7.96\% \\\hline

 \end{tabular}
\end{adjustbox}
\caption{Performances on WSJ and CUSENT corpora using different models}
\end{table}
\end{document}

eess

"Unsupervised Spoken Term Discovery on Untranscribed Speech" by Man-Ling Sung, arXiv:2011.14060v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{KL-Divergences for Different Trajectories.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
\textit{p} & \textit{q} & \textit{$D_{KL(p||q)}$} & \textit{$D_{KL(p||q)}$} \\
\hline
Trajectory 1 & Trajectory 2 & 7.21 & 2.82 \\
Trajectory 1 & Trajectory 3 & 1.28 & 1.83 \\
Trajectory 1 & Trajectory 4 & 19.07 & 1269.47 \\
Trajectory 1 & Trajectory 5 & 3.08 & 996.17 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Weizhu Qian
Fabrice Lauri
Franck Gechter

"A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data" by Weizhu Qian, Fabrice Lauri, and Franck Gechter, arXiv:1911.09355v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
Regime & $\mathbb{M}$ & Log-Likelihood & $\hat{p}$ & $1/\hat{p}$\\
\hline
\hline
$x^+$ & $8$ & $23.0935$ & $0.236527$ & $4.22785$\\
\hline
$x^-$ & $66$ & $130.306$ & $0.34617$ & $2.88876$\\
\end{tabular}
\end{adjustbox}
\caption{Maximum likelihood estimation output of the proportion $p$.}
\end{table}
\end{document}

q-fin

"Keynesian Beauty Contest in Morocco's Public Procurement Reform" by Nizar Riane, arXiv:2503.00883v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc} % centered columns (4 columns)
			\hline %inserts double horizontal lines
		Copula & ~~~~~~~~~~~~~~~~~Parameter &~~~~~~~~~~~~~~~ p-value \\
			\hline
		Frank& ~~~~~~~~~~~~~~~~~1.3776&~~~~~~~~~~~~~~~~~~0.488\\
		Gumbel-Hougaarad& ~~~~~~~~~~~~~~~~~1.1542&~~~~~~~~~~~~~~~~~~0.036\\
	Joe& ~~~~~~~~~~~~~~~~~1.1977&~~~~~~~~~~~~~~~~~~0\\
	Product& ~~~~~~~~~~~~~~~~~0&~~~~~~~~~~~~~~~~~~0\\
			\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Multivariate Rényi inaccuracy measures based on copulas: properties and application" by Shital Saha and Suchandan Kayal, arXiv:2502.17215v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jeanne Clelland
Haley Colgate
Daryl DeFord
Beth Malmskog
Flavia Sancier-Barbosa

"Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado" by Jeanne Clelland, Haley Colgate, Daryl DeFord, Beth Malmskog, and Flavia Sancier-Barbosa, arXiv:2011.06049v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of computational complexity (run time in second) of the algorithms}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
				\hline
				Algorithm & This work & Linear & Sign & Finite & ADMM & DTAC-ADMM\\
				\hline
				Run time & 0.0175 & 0.0171 & 0.0178 & 0.0188 & 0.0162 & 0.0165 \\
				\hline
				\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mohammadreza Doostmohammadian
Hamid R. Rabiee

"Distributed Automatic Generation Control subject to Ramp-Rate-Limits: Anytime Feasibility and Uniform Network-Connectivity" by Mohammadreza Doostmohammadian and Hamid R. Rabiee, arXiv:2509.06588v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Clustering algorithms used for the LFR benchmark}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l}
\textbf{Method} & \textbf{Source} & \textbf{Parameters} \\ \hline
Geometric modularity & this paper & $-0.5<log_{10}\tau<0$, $\kappa_0$=0 \\
Modularity & & Resolution parameter = 1 \\
Belief propagation & & Max. iterations=100, Tolerance=$10^{-4}$, Re-runs=5, Threshold=0.005, $q_{max}$=7 \\
Spectral & & Number of clusters = 40 \\
Infomap & & \\
Ricci-flow & & Number of iterations = 100 \\
Spinglass & &
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Unfolding the multiscale structure of networks with dynamical Ollivier-Ricci curvature" by Adam Gosztolai and Alexis Arnaudon, arXiv:2106.05847v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\toprule {Parameters} & {Values} & {Units} \\ \midrule% centered columns (4 columns)
$b$ & 50 & $N s/m$\\ % inserting body of the table
$m$ & 1000 & $kg$\\
initial speed & 0 & $m/s$\\
desired speed & 15 & $m/s$\\
rise time & $<5$ & $s$\\
steady-state error & $<5$\% & -\\ \midrule
\end{tabular}
\end{adjustbox}
\caption{Vehicle parameters}
\end{table}
\end{document}

eess

"Verifying a Cruise Control System using Simulink and SpaceEx" by Nikolaos Kekatos, arXiv:2101.00102v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Labels included in the VLEngagement dataset with their variable type, value interval and category.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllc} \hline
 Type&Label&Interval&Category\\ \hline
 cont.&Mean Star Rating & $[1,5)$ & Explicit Rating\\
 cont.&View Count & $(5,\infty)$ & Popularity\\

 cont.&SMNET (Eq. ) & $(0,1)$ & Watch Time \\
 cont.&SANET (Eq. ) & $[0,1)$ & Watch Time \\

 cont.&Std. of NET & $(0,1)$ & Watch Time \\
 cont.&Number of User Sessions & $(5,\infty)$ & Watch Time \\
 cont.&Engagement Times (NET) & $[0,1)$ & Watch Time \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Sahan Bulathwela
Maria Perez-Ortiz
Emine Yilmaz
John Shawe-Taylor

"VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement" by Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, and John Shawe-Taylor, arXiv:2011.02273v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters of clustered federated learning (CFL) experiments.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
\textbf{Parameter (s)} & \textbf{Value (s)} \\ \hline
Communication Rounds & 30, 50, \& 100 \\ \hline
Epochs & 5 \& 10 \\ \hline
Batch Size & 16 \& 32 \\ \hline
Learning Rate & $1e^{-3}$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Adnan Qayyum
Kashif Ahmad
Muhammad Ahtazaz Ahsan
Ala Al-Fuqaha
Junaid Qadir

"Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge" by Adnan Qayyum, Kashif Ahmad, Muhammad Ahtazaz Ahsan, Ala Al-Fuqaha, and Junaid Qadir, arXiv:2101.07511v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \textbf{GNN + MGM (Memory \%)} & \textbf{Fact} & \textbf{Bias} \\
 \midrule
 FAGCN + MGM (60\%) & 39.07 & 43.09 \\
 FAGCN + MGM (80\%) & 41.31 & \textbf{46.02} \\
 FAGCN + MGM (90\%) & 46.88 & 44.36 \\
 FAGCN + MGM (100\%) & \textbf{48.77} & 45.02 \\
 \hdashline
 Gatv2 + MGM (60\%) & 45.06 & 45.27 \\
 Gatv2 + MGM (80\%) & 49.77 & \textbf{52.44} \\
 Gatv2 + MGM (90\%) & \textbf{54.51} & 50.44 \\
 Gatv2 + MGM (100\%) & 54.13 & 52.41 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Performance comparison of GNN + MGM at different memory allocations.}
\end{table}
\end{document}

stat

Muhammad Arslan Manzoor
Ruihong Zeng
Dilshod Azizov
Preslav Nakov
Shangsong Liang

"MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media" by Muhammad Arslan Manzoor, Ruihong Zeng, Dilshod Azizov, Preslav Nakov, and Shangsong Liang, arXiv:2412.10467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Subject Performance and Estimated Group Performance After the Test}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline
 \hline
 & & \multicolumn{2}{c}{Pooled sample} \\\cline{3-4}
 Variable & & Mean & Std. dev. \\
 \hline
 Actual own performance & & 12.84 & 3.551 \\
 Estimated group performance & & 12.52 & 2.594 \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)" by Marius Protte, arXiv:2507.15568v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Heterogeneous Response of Investment to $\epsilon^{X}_t$ \\ \footnotesize Long term Leverage }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 & \multicolumn{4}{c}{Firm investment - $\Delta \log k_{i,t}$ } \\
 & (1) & (2) & (3) & (4) \\ \hline \hline
 & & & & \\
$\left(\tilde{l}_{i,t-1} - \mathbb{E}_{i} \left[ \tilde{l}_{t}\right] \right) \epsilon^{X}_t$ & -0.278*** & -0.275*** & -0.205* & -0.202* \\
 & (0.0961) & (0.0936) & (0.114) & (0.112) \\
 & & & & \\
Firm FE & yes & yes & yes & yes \\
Sector-Time FE & yes & yes & yes & yes \\
Control for Lagged Inv. & no & yes & yes & yes \\
Control for Lagged Currency Mismatch & no & no & yes & yes \\
Control for $l_{i,t-1}$ & no & no & no & yes \\
 & & & & \\
Observations & 11,670 & 11,516 & 10,064 & 9,995 \\ \hline
$R^{2}$ & 0.149 & 0.155 & 0.161 & 0.170 \\ \hline \hline
\multicolumn{5}{c}{ Clustered standard errors in parentheses} \\
\multicolumn{5}{c}{ *** p$<$0.01, ** p$<$0.05, * p$<$0.1} \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Santiago Camara
Sebastian Ramirez Venegas

"The Transmission of US Monetary Policy Shocks: The Role of Investment & Financial Heterogeneity" by Santiago Camara and Sebastian Ramirez Venegas, arXiv:2209.11150v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small Comparison of average acoustic characteristic values. SILA outperforms the baseline, with scores within the expected range for each feature, indicating improved disentanglement between audio and its characteristics.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Categories & Sub Categories & Baseline (w/o SILA) & SILA \\
\midrule \midrule
\multirow{4}{*}{Reverb (RT60)} & \textit{dry} & 0.55 & 0.11 \\
 & \textit{slightly wet} & 0.52 & 0.52 \\
 & \textit{wet} & 0.50 & 0.73 \\
 & \textit{very wet} & 0.51 & 0.84 \\ \midrule
\multirow{2}{*}{Noise (SNR)} & \textit{silent background} & 4.31 & 6.78 \\
 & \textit{noisy background} & 2.43 & 1.72 \\ \midrule
\multirow{2}{*}{Brightness (spec. centroid)} & \textit{dull} & 57.53 & 41.71 \\
 & \textit{bright} & 59.24 & 71.37 \\ \midrule
\multirow{2}{*}{Pitch (octave)} & \textit{low} & 2.31 & 1.17 \\
 & \textit{high} & 2.95 & 3.78 \\ \midrule
\multirow{2}{*}{Duration (sec.)} & \textit{3 seconds} & 4.87 & 3.12 \\
 & \textit{4 seconds} & 4.94 & 3.97 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sonal Kumar
Prem Seetharaman
Justin Salamon
Dinesh Manocha
Oriol Nieto

"SILA: Signal-to-Language Augmentation for Enhanced Control in Text-to-Audio Generation" by Sonal Kumar, Prem Seetharaman, Justin Salamon, Dinesh Manocha, and Oriol Nieto, arXiv:2412.09789v1, 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}{cllcl}
 \toprule
 \textbf{Label} & \textbf{Purpose} &&\textbf{Label} & \textbf{Purpose}\\
 \midrule
 p\textsubscript1 & Improve service && p\textsubscript{15} & Research \\
 p\textsubscript2 & Provide service && p\textsubscript{16} & Extract vocal and disorder relationships \\
 p\textsubscript3 & Identify && p\textsubscript{17} & Fighting spam/malware \\
 p\textsubscript4 & Collect, measure, \& process autism risk && p\textsubscript{18} & Facilitate data collection \\
 p\textsubscript5 & Extracting acoustic features && p\textsubscript{19} & Identify web browser \\
 p\textsubscript6 & Send alert && p\textsubscript{20} & Find site visit statistics \\
 p\textsubscript7 & Fulfill request && p\textsubscript{21} & Marketing \\
 p\textsubscript8 & Anti-fraud && p\textsubscript{22} & Send research participation request \\
 p\textsubscript9 & Keep customer posted && p\textsubscript{23} & Promote service \\
 p\textsubscript{10} & Internal improvement && p\textsubscript{24} & Any \\
 p\textsubscript{11} & Send service information && p\textsubscript{25} & Understand customer behavior \\
 p\textsubscript{12} & Send notice && p\textsubscript{26} & Facilitate interaction \\
 p\textsubscript{13} & Auditing && p\textsubscript{27} & Provide product \\
 p\textsubscript{14} & Data analysis \\
 \bottomrule
 \end{tabular}
\caption{ChatterBaby\texttrademark's purposes}
\end{table}
\end{document}

cs

"The Privacy Policy Permission Model: A Unified View of Privacy Policies" by Maryam Majedi and Ken Barker, arXiv:2403.17414v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fire Segmentation Accuracy of Q-Rough Sets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 Sequence & $FP(\%)$ & $FN(\%)$ & $Precision$ & $Recall$\\
 \hline
 PosVideo1 & 0.5 & 15 & 0.99 & 0.87\\
 \hline
PosVideo2 & 3 & 5 & 0.97 & 0.95\\
\hline
PosVideo4 & 4 & 1 & 0.96 & 0.99\\
\hline
PosVideo5 & 3 & 2 & 0.97 & 0.98\\
\hline
PosVideo11 & 2 & 10 & 0.98 & 0.9\\
\hline
WeddingDress & 2 & 8 & 0.98 & 0.91\\
\hline
ChristmasTree & 23 & 5 & 0.77 & 0.93\\
\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Debarati B. Chakrabortya
Vinay Detania
Shah Parshv Jigneshkumar

"Fire Threat Detection From Videos with Q-Rough Sets" by Debarati B. Chakrabortya, Vinay Detania, and Shah Parshv Jigneshkumar, arXiv:2101.08459v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effect of Euler sample steps N on flow matching module. Objective metrics improve rapidly in the first 5 steps and continued qualitative improvements up to 20 steps.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\hline
Model & N & UTMOS $\uparrow$ & WER $\downarrow$ & SECS $\uparrow$ & RTF $\downarrow$ \\ \hline
\multirow{4}{*}{StableVC} & 1 & 3.42 & 3.87 & 0.54 & 0.031 \\
 & 2 & 3.49 & 3.94 & 0.55 & 0.043 \\
 & 5 & 3.82 & 2.98 & 0.61 & 0.068 \\
 & 10 & 4.12 & 2.03 & 0.67 & 0.146 \\
 & 20 & 4.15 & 2.07 & 0.66 & 0.215 \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jixun Yao
Yuguang Yang
Yu Pan
Ziqian Ning
Jiaohao Ye
Hongbin Zhou
Lei Xie

"StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching" by Jixun Yao, Yuguang Yang, Yu Pan, Ziqian Ning, Jiaohao Ye, Hongbin Zhou, and Lei Xie, arXiv:2412.04724v2, 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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
 \toprule
 Symbol & Value & Symbol & Value & \\
 \midrule
 $\alpha$ & 0.5 & $j_{0,\text{ref}}$ & $1.08 \cdot 10^{-17}$ A/m$^2$ \\
 $p_{H_2}$ & $1$ bar & $d_{\text{membrane}}$ & $2.54\cdot 10^{-4}$ m \\
 $p_{O_2}$ & $1$ bar & $d_{\text{electrode}}$ & $8\cdot 10^{-4}$ m \\
 $a_w$ & $1$ bar & $\sigma_{\text{electrode}}$ & $5.53\cdot 10^{6}$ ($\Omega\cdot$ m)$^{-1}$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Parameters used in the PEM electrolyser electrochemical model }
\end{table}
\end{document}

q-fin

Matthias Derez
Alexander Hoogsteyn
Erik Delarue

"Optimisation of Electrolyser Operation: Integrating External Heat" by Matthias Derez, Alexander Hoogsteyn, and Erik Delarue, arXiv:2507.06796v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 \toprule
 Graph ($G$)& $\gamma_2$ & $\gamma_n$ & Lower bound & $K(G)$ & Upper bound \\
 \midrule
 Complete bipartite $K_{10,15}$ & 10 & 25 & 23.47 & 23.50 & 23.50 \\
 Windmill $W(3,10)$ & 1 & 31 & 44.32 & 45.45&45.45 \\
 $W'(3,10,5)$ & 5 & 35 & 34.49 & 35.49&35.49 \\
 $W''(3,10,5)$ & 5 & 35 & 35.21 &35.54 &35.54 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Values of the lower and upper bounds from Eq. and $K(G)$ for several slightly regular graphs.}
\end{table}
\end{document}

math

Aida Abiad
Ángeles Carmona
Andrés M. Encinas
Maria José Jiménez
Álvaro Samperio

"A Spectral Approach to Kemeny's Constant" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, 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{Mean squared errors of the estimates from for each of the methods}
\begin{tabular}{|c|c|c|}
					\hline
					Method & Mean Squared Error \\ \hline
					Fearnhead & $1.5\cdot 10^{-5}$ \\
					LNA (with restart) & $6.5\cdot 10^{-6}$ \\
					Diffusion guiding term & $2.1\cdot 10^{-5}$ \\
					Poisson guiding term & $4.0\cdot 10^{-7}$ \\ \hline
				\end{tabular}
\end{table}
\end{document}

math

"Guided simulation of conditioned chemical reaction networks" by Marc Corstanje and Frank van der Meulen, arXiv:2312.04457v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule
\textbf{Correlation} & \textbf{Feature} \\ \midrule
-0.219 & sadness score from $s_{18}$ (E) \\
-0.196 & sadness score from $s_{17}$ (E) \\
-0.175 & positive score from $s_{18}$ (E) \\
0.173 & std-dev($\Delta$ no. of words per turn) (T) \\
0.172 & std-dev(no. of words per turn) (T) \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Top 5 features most correlated with suicidal risk (feature set in parentheses). All correlations statistically significant $(p<0.05)$}
\end{table}
\end{document}

eess

Sandeep Nallan Chakravarthula
Md Nasir
Shao-Yen Tseng
Haoqi Li
Tae Jin Park
Brian Baucom
Craig J. Bryan
Shrikanth Narayanan
Panayiotis Georgiou

"Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations" by Sandeep Nallan Chakravarthula, Md Nasir, Shao-Yen Tseng, Haoqi Li, Tae Jin Park, Brian Baucom, Craig J. Bryan, Shrikanth Narayanan, and Panayiotis Georgiou, arXiv:1911.11927v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{Average running times (seconds) of one realisation of estimators for the $q$-GLE model.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule \\
 Experiment & $ \hat{\theta}_{2,n}$ & $\hat{\theta}_{3,n} $
 \\ \midrule
 \\
 \textbf{Design A} - \textbf{Case I}
 & 38.25 & 44.33
 \\
 \textbf{Design A} - \textbf{Case II}
 & 24.46 & 28.34
 \\
 \textbf{Design B} - \textbf{Case I}
 & 27.48 & 28.71
 \\
 \textbf{Design B} - \textbf{Case II}
 & 90.26 & 104.12
 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Parameter Inference for Hypo-Elliptic Diffusions under a Weak Design Condition" by Yuga Iguchi and Alexandros Beskos, arXiv:2312.04444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l}
 authblk & babel & cjk & dvips \\
 epsf & epsfig & euler & float \\
 fullpage & geometry & graphics & hyperref \\
 layout & linespread & lmodern & maltepaper \\
 navigator & pdfcomment & pgfplots & psfig \\
 pstricks & t1enc & titlesec & tocbind \\
 ulem
\end{tabular}
\end{adjustbox}
\caption{LaTeX style packages that must not be used.}
\end{table}
\end{document}

stat

Rohan Sukumaran
Parth Patwa
T V Sethuraman
Sheshank Shankar
Rishank Kanaparti
Joseph Bae
Yash Mathur
Abhishek Singh
Ayush Chopra
Myungsun Kang
Priya Ramaswamy
Ramesh Raskar

"COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms" by Rohan Sukumaran, Parth Patwa, T V Sethuraman, Sheshank Shankar, Rishank Kanaparti, Joseph Bae, Yash Mathur, Abhishek Singh, Ayush Chopra, Myungsun Kang, Priya Ramaswamy, and Ramesh Raskar, arXiv:2101.10266v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
\toprule
Dataset & \multicolumn{2}{c|}{CIFAR-10} & \multicolumn{2}{c}{CIFAR-100} \\
\hline
Noise & Sym 80 & Asym 40 & Sym 80 & Asym 40\\
\hline
ResNet-32 & 74.8 $\pm$.3 & 87.5$\pm$1.3 & 30.3$\pm$.4 & 55.5$\pm$.8 \\
ResNet-34 & 90.8$\pm$.6 & 90.4$\pm$.4 & 69.4$\pm$.3& 65.2$\pm$.2 \\\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yihao Xue
Kyle Whitecross
Baharan Mirzasoleiman

"Investigating Why Contrastive Learning Benefits Robustness Against Label Noise" by Yihao Xue, Kyle Whitecross, and Baharan Mirzasoleiman, arXiv:2201.12498v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance (accuracy in \%) comparison of the proposed multi-modality (speech-text) model with speech-only model for dysarthric detection and assessment tasks across different settings: speaker-dependent and unseen words (SD), speaker-independent seen words (SID-1), and unseen words (SID-2).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccc||ccc|}
\hline
 & \multicolumn{3}{c||}{Dysarthric detection} & \multicolumn{3}{c|}{Severity assessment} \\ \hline
\hline
Modality & SD & SID-1 & SID-2 & SD & SID-1 & SID-2 \\ \hline
Speech-only & 95.38 & 89.61 & \textbf{87.76} & 97.50 & 49.14 & 54.71 \\ \hline
Speech-text &
 \textbf{99.53} &
 \textbf{93.20} &
 85.30 &
 \textbf{98.12} &
 \textbf{51.97} &
 \textbf{57.42} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Anuprabha M
Krishna Gurugubelli
V Kesavaraj
Anil Kumar Vuppala

"A Multi-modal Approach to Dysarthria Detection and Severity Assessment Using Speech and Text Information" by Anuprabha M, Krishna Gurugubelli, V Kesavaraj, and Anil Kumar Vuppala, arXiv:2412.16874v4, 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{Tokens used in our framework.}
\begin{tabular}{c|c}
			\toprule[1.5pt]
			Category & Examples \\
			\midrule[1pt]
			Operators & \emph{CS-Log}, \emph{CS-Add}, \emph{TS-Mean}, \emph, $\dotsc$ \\
 Features & \emph{\$open}, \emph{\$volume}, $\dotsc$ \\
			Constants & $-30, -10, -5, -2, -1, -0.5, -0.01, 0.01, 0.5, 1, 2, 5, 10, 30$ \\
			Time Deltas & $10d, 20d, 30d, 40d, 50d$ \\
			Sequence Indicator & \emph{BEG}(begin), \emph{SEP}(end of expression) \\
			\bottomrule[1.5pt]
	\end{tabular}
\end{table}
\end{document}

q-fin

Shuo Yu
Hongyan Xue
Xiang Ao
Feiyang Pan
Jia He
Dandan Tu
Qing He

"Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning" by Shuo Yu, Hongyan Xue, Xiang Ao, Feiyang Pan, Jia He, Dandan Tu, and Qing He, arXiv:2306.12964v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{SOLs for different load configurations of the CLOD model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccc}
		\toprule
		& \multicolumn{6}{c}{\textbf{Complex load (CLOD) model parameters}} & \textbf{Scenario A} \\
		\cmidrule(lr){2-7}\cmidrule(lr){8-8}
 \textbf{Scenario} & LIM & SIM & DL & TS & MVA & Kp & \textbf{SOL} \\
	 \textbf{number} & [\%] & [\%] & [\%] & [\%] & [\%] & [MW]\\
	 \hline
	 \addlinespace
	 16 & 35 &	35 & 5 & 5 & 10 & 2 & -\\
	 17 & 30	& 30 & 5 & 5 & 10 & 2 & 64\\
	 18 & 20	& 20 & 5 & 5 & 10 & 2 & 377\\
	 \addlinespace
	 19 & 35	& 30 & 5 & 5 & 10 & 2 & 12\\
	 20 & 35	& 20 & 5 & 5 & 10 & 2 & 92\\
	 21 & 35 & 10 & 5 & 5 & 10 & 2 & 334\\
	 \addlinespace
	 22 & 30	& 35 & 5 & 5 & 10 & 2 & 28\\
	 23 & 20	& 35 & 5 & 5 & 10 & 2 & 198\\
	 24 & 10	& 35 & 5 & 5 & 10 & 2 & 375\\
	 \addlinespace
		\bottomrule

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

eess

Hannes Hagmar
Robert Eriksson
Le Anh Tuan

"Comparison of security margin estimation methods under various load configurations" by Hannes Hagmar, Robert Eriksson, and Le Anh Tuan, arXiv:2012.00336v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The average increase in inference time per answer of ITI and RADIANT across base models. The unit is \%.}
\begin{tabular}{lccc}
\toprule
Base models & ITI & RADIANT & RADIANT-P \\ \midrule
Gemma-2B & 2.53 & 6.82 & 1.75 \\
GPT-2 Large & 2.43 & 3.01 & 1.65 \\
Llama-7B & 2.46 & 3.09 & 1.45 \\
Llama3-8B & 2.51 & 3.32 & 1.55 \\
Llama2-chat-13B & 2.51 & 4.72 & 1.57 \\ \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{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Top-1 ImageNet validation accuracy (in $\%$) for W4A8 post-training per-tensor quantization with base and extended variants of AdaRound.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 Method & ResNet-18 & ResNet-50 & InceptionV3 & MobileNetV2 \\
 \midrule
 Full Precision & 69.68 & 76.07 & 77.40 & 71.72 \\
 \midrule
 Nearest (W4A32) & 23.99 & 35.60 & 1.67 & 8.09 \\
 AdaRound &
 68.48$\pm{}$0.08 &
 74.96$\pm{}$0.05 &
 75.66$\pm{}$0.12 &
 69.21$\pm{}$0.07 \\
 + extensions &
 \textbf{68.74}$\pm{}$\textbf{0.07} &
 \textbf{75.21}$\pm{}$\textbf{0.11} &
 \textbf{75.87}$\pm{}$\textbf{0.05} &
 \textbf{69.54}$\pm{}$\textbf{0.11} \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sangeetha Siddegowda
Marios Fournarakis
Markus Nagel
Tijmen Blankevoort
Chirag Patel
Abhijit Khobare

"Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)" by Sangeetha Siddegowda, Marios Fournarakis, Markus Nagel, Tijmen Blankevoort, Chirag Patel, and Abhijit Khobare, arXiv:2201.08442v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Assessing Estimation Uncertainty under Model Misspecification" by Rong Li, Yichen Qin, and Yang Li, arXiv:2312.11393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\multirow{2}{*}{\textbf{Modality}} & \multicolumn{2}{c}{\textbf{Sampling Scheme}} \\ \cmidrule(l){2-3}
 & \textbf{Contiguous} & \textbf{Uniformly Dist.} \\ \cmidrule(r){1-1} \cmidrule(l){2-2} \cmidrule(l){3-3}
Video & 65.55 & \underline{73.95} \\
Audio & 53.36 & \underline{64.50} \\
\textsc{MMDL} & 61.55 & \textbf{\underline{74.60}} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{\textbf{Performance} (accuracy in \%) of single modalities vs a multimodal (\textsc{MMDL}) assessment for contiguous and uniformly distributed sampling schemes.}
\end{table}
\end{document}

cs

Piano Skills Assessment

Paritosh Parmar
Jaiden Reddy
Brendan Morris

"Piano Skills Assessment" by Paritosh Parmar, Jaiden Reddy, and Brendan Morris, arXiv:2101.04884v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example 2oo3 vote structure function}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|c||c|}
			\hline Component & $c_1$ & $c_2$& $c_3$ & 	\\
			\cline{1-4} State $\mathbf x$ & $x_1 $ & $x_2$ & $x_3$ & $\varphi(\mathbf x)$\\
			\hline $\mathbf x_0$ & 0 & 0 & 0 & 0 \\
			\hline $\mathbf x_1$ & 1 & 0 & 0 & 0 \\
			\hline $\mathbf x_2$ & 0 & 1 & 0 & 0 \\
			\hline $\mathbf x_3$ & 0 & 0 & 1 & 0 \\
			\hline $\mathbf x_4$ & 1 & 1 & 0 & 1 \\
			\hline $\mathbf x_5$ & 1 & 0 & 1 & 1 \\
			\hline $\mathbf x_6$ & 0 & 1 & 1 & 1 \\
			\hline $\mathbf x_7$ & 1 & 1 & 1 & 1 \\
\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Optimising the reliability that can be claimed for a software-based system based on failure-free tests of its components" by Peter Bishop and Andrey Povyakalo, arXiv:2201.12829v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data for the investigation of the variable dimension characteristic of the UAV problem.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll} \hline
 & \multicolumn{2}{c}{$DV = 5$} & \multicolumn{2}{c}{$DV = 10$} & \multicolumn{2}{c}{$DV = 15$} & \multicolumn{2}{c}{$DV = 20$} \\
 & mean & number & mean & number & mean & number & mean & number
 \\ $B$ & rel. error & of wins & rel. error & of wins & rel. error & of wins & rel. error & of wins \\ \hline
$1e3$ & 0.008 & 45 & 0.136 & 7 & 0.292 & 3 & 0.524 & 1 \\
$1e4$ & 0.020 & 50 & 0.106 & 5 & 0.237 & 1 & 0.579 & 0 \\
$1e5$/3h & 0.005 & 51 & 0.155 & 1 & 0.127 & 2 & 0.222 & 2 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Benchmarking global optimization techniques for unmanned aerial vehicle path planning" by Mhd Ali Shehadeh and Jakub Kudela, arXiv:2501.14503v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach" by Henrika Langen, arXiv:2209.00409v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Comparison of execution times between Secant and Newton's methods for various test functions. Where $f$ is explicitly provided, the gradient function is computed via automatic differentiation. For the line search problems, the gradient is provided directly and not computed via automatic differentiation, as those are instances extracted from the line search routine. Timings are averaged over $1000$ runs.\newline}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 Function & Secant Time (s) & Newton's Time (s) & Newton/Secant \\
 \midrule
 $f(x) = x^2 - 2$ & 3.42e-06 & 4.32e-05 & 12.62 \\
 $f(x) = x^2 - 5$ & 3.14e-06 & 5.47e-05 & 17.42 \\
 $f(x) = x^2 - 10$ & 2.82e-06 & 4.13e-05 & 14.65 \\
 $f(x) = x^2 - x - 2$ & 3.70e-06 & 4.63e-05 & 12.52 \\
 $f(x) = x^2 + 2x - 7$ & 3.47e-06 & 4.11e-05 & 11.85 \\
 $f(x) = x^3 - 2$ & 4.81e-06 & 4.64e-05 & 9.65 \\
 $f(x) = x e^x - 7$ & 1.01e-05 & 5.89e-05 & 5.83 \\
 $f(x) = x - \cos(x)$ & 6.39e-06 & 4.39e-05 & 6.88 \\
 \midrule
 Line Search (quadratic) & 1.92e-05 & 7.97e-05 & 4.15 \\
 Line Search (arbitrary) & 3.75e-05 & 1.24e-04 & 3.31 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Deborah Hendrych
Mathieu Besançon
David Martínez-Rubio
Sebastian Pokutta

"Secant Line Search for Frank-Wolfe Algorithms" by Deborah Hendrych, Mathieu Besançon, David Martínez-Rubio, and Sebastian Pokutta, arXiv:2501.18775v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Md Umar Hashmi
Jonathan Cavaleiro
Lucas Pereira
Ana Bušić

"Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal" by Md Umar Hashmi, Jonathan Cavaleiro, Lucas Pereira, and Ana Bušić, arXiv:1911.10458v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Textured models used for fa\c{c}ade modeling.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllll}
			\toprule
			Name & Model Source & Texture Source & GSD ($cm$) & Coverage & City \\ \midrule
			Bank & UAV Photogrammetry & UAV & 2 & Individual & Huzhou, CN \\
			Central Business District (CBD) & 3D Warehouse & Aerial & 10 & Individual & London, UK \\
			State Capitol & 3D Warehouse & Ground & 2 & Individual & Denver, US \\
			Borough High Street & AccuCities & Ground & 2 & Block & London, UK \\
			Broad Street & 3D Warehouse & UAV & 5 & Block & Rome, US \\ \bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Han Hu
Xinrong Liang
Yulin Ding
Qisen Shang
Bo Xu
Xuming Ge
Min Chen
Ruofei Zhong
Qing Zhu

"Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Façade Modeling" by Han Hu, Xinrong Liang, Yulin Ding, Qisen Shang, Bo Xu, Xuming Ge, Min Chen, Ruofei Zhong, and Qing Zhu, arXiv:2201.08977v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrr}
\toprule
\textbf{track id} & \textbf{tāḷa}
 & \multicolumn{3}{c}{\textbf{Beat}} \\
\cmidrule(lr){3-5}
 & & \textbf{F-measure} & \textbf{CML$_t$} & \textbf{AML$_t$} \\
\midrule
10047 & adi & 0.192975 & 0.100241 & 0.458864 \\
11024 & rupakam & 0.605962 & 0.000145 & 0.903054 \\
\midrule
\textbf{track id} & \textbf{tāḷa}
 & \multicolumn{3}{c}{\textbf{Downbeat}} \\
\cmidrule(lr){3-5}
 & & \textbf{F-measure} & \textbf{CML$_t$} & \textbf{AML$_t$} \\
\midrule
10048 & adi & 0.000000 & 0.0 & 1.000000 \\
11040 & rupakam & 0.032501 & 0.0 & 0.000000 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{TCN-FS : Worst Performing Tracks by Beat and Downbeat F-Measure}
\end{table}
\end{document}

eess

"Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches" by Satyajeet Prabhu, arXiv:2509.11241v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Distribution of Welfare Effects, 2020}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
& \multicolumn{2}{c}{Welfare Ratio: Baseline/Counterfactual} \\
\cmidrule(lr){2-3}
& Tariff Changes & Geopolitical Changes \\
\midrule
Mean & 1.0123 & 0.9943 \\
Median & 1.0069 & 0.9970 \\
Std.\ Deviation & 0.0134 & 0.0150 \\
Skewness & 1.82 & $-3.41$ \\
\midrule
Countries gaining & 70 (94.6\%) & 27 (36.5\%) \\
Countries losing & 4 (5.4\%) & 47 (63.5\%) \\
\midrule
Range of effects & [$-1.08\%$, $+5.51\%$] & [$-8.56\%$, $+1.55\%$] \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Geopolitical Barriers to Globalization

"Geopolitical Barriers to Globalization" by Tianyu Fan, Mai Wo, and Wei Xiang, arXiv:2509.12084v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fit measures for the proposed functional random coefficient and its competitors including a simple linear effect. The fit is measured in terms of the log-likelihood (logLik), the integrated Brier score (IBS) and the AIC.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline & logLik & IBS & AIC \\
 \hline
 Heterogeneous time-variation & -4099.36 & 0.1162 & 8238.87 \\
 Heterogeneity and time-variation & -4108.27 & 0.1174 & 8250.03 \\
 Heterogeneity only & -4111.04 & 0.1185 & 8250.50 \\
 Time-variation only & -4111.00 & 0.1184 & 8251.44 \\
 Linear effect & -4111.01 & 0.1184 & 8251.20 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Niklas Hagemann
Thomas Kneib
Kathrin Möllenhoff

"Capturing heterogeneous time-variation in covariate effects in non-proportional hazard regression models" by Niklas Hagemann, Thomas Kneib, and Kathrin Möllenhoff, arXiv:2501.13525v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{OLS Estimation Results - Dependent Variable: Swim Out Times}
\begin{tabular}{lcccc}
 \toprule
 & Estimate & Std. Error & t-value & Pr(>|t|) \\
 \midrule
 Drafter & -23.8886 & 4.1184 & -5.8004 & $6.63 \times 10^{-9}$ *** \\
 Pre-period $\times$ Drafter & -8.5779 & 4.6013 & -1.8642 & 0.0623 . \\
 Covid-year $\times$ Drafter & 19.4150 & 8.1469 & 2.3831 & 0.0172 * \\
 \midrule
 Observations & \multicolumn{4}{c}{168,391} \\
 Fixed-effects & \multicolumn{4}{c}{Athlete ID: 29,194, Event ID: 1,339, Cluster (Swim Group): 224} \\
 Standard-errors & \multicolumn{4}{c}{Heteroskedasticity-robust} \\
 RMSE & \multicolumn{4}{c}{175.1} \\
 Adj. $R^2$ & \multicolumn{4}{c}{0.9683} \\
 Within $R^2$ & \multicolumn{4}{c}{0.0035} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"Strategic Effort and Bandwagon Effects in Finite Multi-Stage Games with Non-Linear Externalities: Evidence from Triathlon" by Felix Reichel, arXiv:2505.03247v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Multiply Robust Federated Estimation of Targeted Average Treatment Effects" by Larry Han, Zhu Shen, and Jose Zubizarreta, arXiv:2309.12600v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccccc}
 \hline
 Cutoff & PSNR & SSIM & Lpips & FSIM & VIF & VSI & GMSD \\
 \hline
 $\pi/5$ & 25.89 & 0.711 & 0.283 & 0.826 & 0.306 & 0.950 & 0.112\\

 $\pi/10$ & 27.60 & 0.829 & 0.147 & 0.896 & 0.426 & 0.970 & 0.061\\

 $\pi/20$ & 26.34 & 0.779 & 0.180 & 0.867 & 0.343 & 0.962 & 0.085\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Experiment on the cutoff frequency of the filter.}
\end{table}
\end{document}

eess

Jiahua Xu
Dawei Zhou
Lei Hu
Jianfeng Guo
Feng Yang
Zaiyi Liu
Nannan Wang
Xinbo Gao

"Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model" by Jiahua Xu, Dawei Zhou, Lei Hu, Jianfeng Guo, Feng Yang, Zaiyi Liu, Nannan Wang, and Xinbo Gao, arXiv:2412.07590v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Algorithms vs. Peers: Shaping Engagement with Novel Content" by Shan Huang, Yi Ji, and Leyu Lin, arXiv:2503.11561v2, 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{Available elevation angle in different environment for frequencies between 1.5 and 3~GHz}
\begin{tabular}{|c|l|}
\hline
\textbf{Environment} & \textbf{Available Elevation Angles (degrees)} \\
\hline
Urban & 20, 30, 45, 60, 70 \\
\hline
Suburban & 20, 30, 45, 60, 70 \\
\hline
Village & 20, 30, 45, 60, 70 \\
\hline
Rural wooded & 20, 30, 45, 60, 70 \\
\hline
Residential & 20, 30, 60, 70 \\
\hline
\end{tabular}
\end{table}
\end{document}

math

"Evaluating the Influence of Satellite Systems on Terrestrial Networks: Analyzing S-Band Interference" by Lingrui Zhang, Zheng Li, and Sheng Yang, arXiv:2501.05462v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Word error rate on development and evaluation sets of FS-2 ASR challenge track~2 (reference segmentation). Comparing models trained on the FS-2 data only, with Apollo 11 original noisy transcriptions (A11) and with semi-supervised training (SST) of the acoustic model (AM) and the language model (LM), using n-gram and recurrent neural network (RNN) LMs.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ll|ll|rr}
\toprule
\multirow{2}{*}{N\textsuperscript{\underline{o}}} & \multicolumn{2}{|c|}{AM} & \multicolumn{2}{|c|}{LM} & \multicolumn{2}{|c}{WER, \%} \\
 & Model & Data & Model & Data & Dev & Eval \\
 \midrule
1 & GMM & FS-2 & 3-gram & FS-2 & 53.8 & 55.6 \\
2 & TDNN & FS-2 & 3-gram & FS-2 & 28.6 & 31.4 \\
3 & TDNN & FS-2 & 3-gram & FS-2+A11 & 26.3 & 29.1 \\
\midrule
4 & TDNN & +SST1 & 3-gram & FS-2+A11 & 23.7 & 26.0 \\
5 & TDNN & +SST1 & 3-gram & FS-2+A11+SST1 & 23.5 & 25.8 \\
6 & TDNN & +SST1 & 4-gram & FS-2+A11+SST1 & 23.0 & 25.6 \\
7 & TDNN & +SST1 & +RNN & FS-2+A11+SST1 & 21.8 & 24.3 \\
 \midrule
8 & TDNN & +SST2 & +RNN & FS-2+A11+SST2 & 22.2 & 24.6 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Arseniy Gorin
Daniil Kulko
Steven Grima
Alex Glasman

""This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II)" by Arseniy Gorin, Daniil Kulko, Steven Grima, and Alex Glasman, arXiv:2008.01504v1, 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{Objective results of different audio codecs. $^\dag$ denotes that Encodec operates audio sampels in 24 kHz sampling rate.}
\begin{tabular}{l|ccccc}
			\toprule
			Method &Bitrate &Bandwidth &POLQA &ViSQOL &eSTOI(\%) \\
 \hline
 Opus &6 kbps &WB &1.82 &2.03 &58.45\\
 Opus &8 kbps &WB &2.96 &3.46 &76.03\\
 Opus &10 kbps &SWB &3.26 &3.71 &77.93\\
 Opus &16 kbps &WB &4.26 &4.30 &85.30\\
 \hline
 Lyra-v2 &9.2 kbps &WB &3.49 &3.92 &90.81\\
 Encodec &12 kbps &SWB$^\dag$ &3.72 &4.16 &94.21\\
			\hline
			WB-NC &6 kbps &WB &3.66 &3.88 &91.59\\
			\quad+PM loss &6 kbps &WB &3.86 &4.19 &93.00\\
			\qquad+GAN-KD &6 kbps &WB &4.06 &4.22 &94.63\\
 \quad\qquad+SWB-BWE &8 kbps &SWB &4.16 &4.20 &94.37\\
			\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Wenzhe Liu
Wei Xiao
Meng Wang
Shan Yang
Yupeng Shi
Yuyong Kang
Dan Su
Shidong Shang
Dong Yu

"A High Fidelity and Low Complexity Neural Audio Coding" by Wenzhe Liu, Wei Xiao, Meng Wang, Shan Yang, Yupeng Shi, Yuyong Kang, Dan Su, Shidong Shang, and Dong Yu, arXiv:2310.10992v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
		parameter & $ \overline{\text{AXI}} $ & $ \sigma(\text{AXI}) $ & $ \bar{\Delta} $ & $ \sigma(\Delta) $ \\
		\hline
		value & 0.5141\% & 0.2987\% & -0.0020\% & 0.3156\% \\
	\end{tabular}
\end{adjustbox}
\caption{Assumed parameters for potential spread discount}
\end{table}
\end{document}

q-fin

A Case for AXI

"A Case for AXI" by Viktor Tsyrennikov, arXiv:2509.03035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|c}
\hline
Dataset & Detectable Joints \\ \hline
Kinetics 400~ & 41.0\% \\ \hline
UCF101~ & 37.8\% \\ \hline
HMDB51~ & 41.8\% \\ \hline
FineGym~ & 44.7\% \\ \hline
\textbf{HAA500} & \textbf{69.7\%} \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Detectable joints of video action datasets. We use AlphaPose~ to detect the largest person in the frame, and count the number of joints with a score higher than $0.5$. }
\end{table}
\end{document}

eess

Jihoon Chung
Cheng-hsin Wuu
Hsuan-ru Yang
Yu-Wing Tai
Chi-Keung Tang

"HAA500: Human-Centric Atomic Action Dataset with Curated Videos" by Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, and Chi-Keung Tang, arXiv:2009.05224v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
		\toprule
		Attention Base & Eval OS & Channelized & mIOU (\%) \\
		\midrule[0.5pt]
		\midrule[0.5pt]
		\multirow{2}{*}{Axial Attention }& 16 & & 50.27 \\
		& 16 	 & \checkmark & 51.06 \\
		\midrule
		\multirow{2}{*}{ Self Attention} & 16 	 & & 50.42 \\
		& 16 	 & \checkmark & 51.09 \\
		\bottomrule[0.5pt]
	\end{tabular}
\end{adjustbox}
\caption{Ablation study of applying our Channelized Attention on self-attention with ResNet-101. \textbf{Eval OS}: Output strides~ during evaluation.}
\end{table}
\end{document}

cs

Ye Huang
Di Kang
Wenjing Jia
Xiangjian He
Liu Liu

"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fine-tuning hyperparameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
Learning rate & Cosine annealing scheduler (lr=3e-7, T=10e3) \\ \hline
Training steps & 50e3 \\ \hline
Batch size & 8 \\ \hline
Gradient norm clipping & 0.5 \\ \hline
Update target network phase & 256 \\ \hline
Optimizer & AdamW (weight decay = 1e-2) \\ \hline
$\kappa_2$ & 0.01 \\ \hline
$\kappa_3$ & 1 \\ \hline
$\kappa_4$ & 0.1 \\ \hline
$\alpha_{prior}$ & 0.999 \\ \hline
$\tau_{\max}$ & 3 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ofir Nabati
Guy Tennenholtz
ChihWei Hsu
Moonkyung Ryu
Deepak Ramachandran
Yinlam Chow
Xiang Li
Craig Boutilier

"Preference Adaptive and Sequential Text-to-Image Generation" by Ofir Nabati, Guy Tennenholtz, ChihWei Hsu, Moonkyung Ryu, Deepak Ramachandran, Yinlam Chow, Xiang Li, and Craig Boutilier, arXiv:2412.10419v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
 \hline
 & \textbf{Option A} & \textbf{Option B} \\
 \hline
 1) & €30 with a probability of 50\%, €0 with a probability of 50\% & €1 safe\\
 2) & €30 with a probability of 50\%, €0 with a probability of 50\% & €2 safe \\
 3) & €30 with a probability of 50\%, €0 with a probability of 50\% & €3 safe \\
 4) & €30 with a probability of 50\%, €0 with a probability of 50\% & €4 safe \\
 5) & €30 with a probability of 50\%, €0 with a probability of 50\% & €5 safe \\
 6) & €30 with a probability of 50\%, €0 with a probability of 50\% & €6 safe \\
 7) & €30 with a probability of 50\%, €0 with a probability of 50\% & €7 safe \\
 8) & €30 with a probability of 50\%, €0 with a probability of 50\% & €8 safe \\
 9) & €30 with a probability of 50\%, €0 with a probability of 50\% & €9 safe \\
 10) & €30 with a probability of 50\%, €0 with a probability of 50\% & €10 safe \\
 11) & €30 with a probability of 50\%, €0 with a probability of 50\% & €11 safe \\
 12) & €30 with a probability of 50\%, €0 with a probability of 50\% & €12 safe \\
 13) & €30 with a probability of 50\%, €0 with a probability of 50\% & €13 safe \\
 14) & €30 with a probability of 50\%, €0 with a probability of 50\% & €14 safe \\
 15) & €30 with a probability of 50\%, €0 with a probability of 50\% & €15 safe \\
 16) & €30 with a probability of 50\%, €0 with a probability of 50\% & €16 safe \\
 17) & €30 with a probability of 50\%, €0 with a probability of 50\% & €17 safe \\
 18) & €30 with a probability of 50\%, €0 with a probability of 50\% & €18 safe \\
 19) & €30 with a probability of 50\%, €0 with a probability of 50\% & €19 safe \\
 20) & €30 with a probability of 50\%, €0 with a probability of 50\% & €20 safe \\
 21) & €30 with a probability of 50\%, €0 with a probability of 50\% & €21 safe \\
 22) & €30 with a probability of 50\%, €0 with a probability of 50\% & €22 safe \\
 23) & €30 with a probability of 50\%, €0 with a probability of 50\% & €23 safe \\
 24) & €30 with a probability of 50\%, €0 with a probability of 50\% & €24 safe \\
 25) & €30 with a probability of 50\%, €0 with a probability of 50\% & €25 safe \\
 26) & €30 with a probability of 50\%, €0 with a probability of 50\% & €26 safe \\
 27) & €30 with a probability of 50\%, €0 with a probability of 50\% & €27 safe \\
 28) & €30 with a probability of 50\%, €0 with a probability of 50\% & €28 safe \\
 29) & €30 with a probability of 50\%, €0 with a probability of 50\% & €29 safe \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Explaining Apparently Inaccurate Self-assessments of Relative Performance: A Replication and Adaptation of 'Overconfident: Do you put your money on it?' by Hoelzl and Rustichini (2005)" by Marius Protte, arXiv:2507.15568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Old Scheme&New Scheme&Ratio\\
\hline
127.18&124.44&1.022\\
\hline
\end{tabular}
\end{adjustbox}
\caption{\sf Example 5: The CPU times (in seconds) consumed by the studied schemes.}
\end{table}
\end{document}

math

New More Efficient A-WENO Schemes

Shaoshuai Chu
Alexander Kurganov
Ruixiao Xin

"New More Efficient A-WENO Schemes" by Shaoshuai Chu, Alexander Kurganov, and Ruixiao Xin, arXiv:2503.10221v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Settings of the GPOPS-\uppercase\expandafter{\romannumeral2} toolbox }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 Stage & Parameter & Value \\
 \midrule
 Problem 3 and 4 & setup.nlp.solver & snopt\\
 Problem 3 and 4 & setup.derivatives.derivativelevel & second\\
 Problem 3 and 4 & setup.derivatives.supplier & sparseCD\\
 Problem 3 and 4 & setup.method & RPMintegration\\
 Problem 3 and 4 & setup.mesh.method & hp1\\
 Problem 3 and 4 & setup.mesh.phase.colpoints & 4*ones(1,1)\\
 Problem 3 and 4 & setup.mesh.phase.fraction & ones(1,1)/1\\
 Problem 3 & setup.mesh.tolerance & $10^{-2}$\\
 Problem 4 & setup.mesh.tolerance & $10^{-6}$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yingqi Liu
Tianlu Pan
Jingjun Tan
Renxin Zhong
Can Chen

"Integrated Take-off Management and Trajectory Optimization for Merging Control in Urban Air Mobility Corridors" by Yingqi Liu, Tianlu Pan, Jingjun Tan, Renxin Zhong, and Can Chen, arXiv:2508.15395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l}
		\hline \hline
Num	&Regions				&Presence in the Gambia	&Presence in the data\\
\hline \hline
1	&Kanifing 				&21.96\%		&32.95\%\\
2	&Brikama 				&37.17\%		&39.17\%\\
3	&Mansa Konko				&4.38\%			&10.54\%\\
4	&Kerewan 				&11.74\%		&5.29\%\\
5	&Janjanburey-kuntaur			&12.01\%		&7.83\%\\
6	&Basse 					&12.74\%		&4.14\%\\
\hline \hline
	\end{tabular}
\end{adjustbox}
\caption{Population of the regions in \% nationally and in the dat}
\end{table}
\end{document}

stat

Ousmane Saine
Soumaila Dembélé
Gane Samb Lo
Mohamed Cheikh Haidara

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


cs

Hanhan Zhou
Tian Lan
Guru Venkataramani
Wenbo Ding

"On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning" by Hanhan Zhou, Tian Lan, Guru Venkataramani, and Wenbo Ding, arXiv:2201.11803v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Chi-Squared tests of independence for difference in 14-days MA between (i) red zone and (ii) orange zone}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrr}
 \hline
 Data & Statistic & P-value \\
 \hline
 Diff MA 19-20 & 26082.00 & 0.00 \\
 Diff MA (divided by pop) 19-20 & 26082.00 & 0.00 \\
 Diff MA (divided by pop) 15/19-20 & 26082.00 & 0.00 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

C. Biliotti
F. J. Bargagli-Stoffi
N. Fraccaroli
M. Puliga
M. Riccaboni

"Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic" by C. Biliotti, F. J. Bargagli-Stoffi, N. Fraccaroli, M. Puliga, and M. Riccaboni, arXiv:2212.01705v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 \hline
 Parameter & Value \\
 \hline
 Cell radius & $500$ m \\
 Maximum D2D pair distance & $30$ m \\
 Carrier frequency & $2.1$ GHz \\
 RB bandwidth & $180$ kHz \\
 Number of RBs & $25$ \\
 Number of CUEs & $25$ \\
 Number of DUE pairs & $10, 20, 30, 40, 50$ \\
 CUE transmit power & $23$ dBm \\
 DUE min, max transmit power & $0, 20$ dBm \\
 Path loss model & Log-Distance Shadowing \\
 Path loss exponent & $2.0$ \\
 Shadowing SD $\chi_{\sigma}$ & 2.7 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"GymD2D: A Device-to-Device Underlay Cellular Offload Evaluation Platform" by David Cotton and Zenon Chaczko, arXiv:2101.11188v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
\toprule
region & country \\ \hline
Europe E & Belarus \\
Europe E & Bulgaria \\
Europe E & Czech Republic \\
Europe E & Hungary \\
Europe E & Poland \\
Europe E & Republic of Moldova \\
Europe E & Romania \\
Europe E & Russian Federation \\
Europe E & Slovakia \\
Europe E & Ukraine \\
Europe N & Denmark \\
Europe N & Estonia \\
Europe N & Finland \\
Europe N & Iceland \\
Europe N & Ireland \\
Europe N & Latvia \\
Europe N & Lithuania \\
Europe N & Norway \\
Europe N & Sweden \\
Europe N & United Kingdom \\
Europe S & Albania \\
Europe S & Bosnia and Herzegovina \\
Europe S & Croatia \\
Europe S & Greece \\
Europe S & Italy \\
Europe S & Malta \\
Europe S & Montenegro \\
Europe S & North Macedonia \\
Europe S & Portugal \\
Europe S & Serbia \\
Europe S & Slovenia \\
Europe S & Spain \\
Europe W & Austria \\
Europe W & Belgium \\
Europe W & France \\
Europe W & Germany \\
Europe W & Luxembourg \\
Europe W & Netherlands \\
Europe W & Switzerland \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Countries and world regions of Europe}
\end{table}
\end{document}

q-fin

Moritz Laber
Peter Klimek
Martin Bruckner
Liuhuaying Yang
Stefan Thurner

"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of performance of several R-peak detection methods using the MIT-BIH-AR database}
\begin{tabular}{cccc}
\toprule
\textbf{Method} & \textbf{\textit{Sen} (\%)} & \textbf{\textit{PPR} (\%)} & \textbf{\textit{DER} (\%)}\\
\midrule
\texttt Park et al. & 99.93 & 99.91 & 0.163\\
\texttt Farashi & 99.75 & 99.85 & 0.40 \\
\texttt Sharma and Sunkaria & 99.50 & 99.56 & 0.93\\
\texttt Castells-Rufas and Carrabina & 99.43 & 99.67 & 0.88\\
\textbf {Proposed Model} & \textbf {99.64} & \textbf {99.71} & \textbf {0.19}\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Atiyeh Fotoohinasab
Toby Hocking
Fatemeh Afghah

"A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection" by Atiyeh Fotoohinasab, Toby Hocking, and Fatemeh Afghah, arXiv:2102.01319v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{A thesaurus of network terminology. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
Terminology in this book & Alternative terms \\
\hline
Network & Graph \\
Node & Vertex \\
Link & Arc, edge, line \\
Tree	 & Arborescence
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

"Transportation Network Analysis, Volume I: Static and Dynamic Traffic Assignment" by Stephen D. Boyles, Nicholas E. Lownes, and Avinash Unnikrishnan, arXiv:2502.05182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Network Speed Measures for Polygon}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
 Measure & Pre- & Post- & t-stat & p-val \\
\hline \hline
Transactions per day & 2,821,632 & 2,729,498 & -1.49 & 0.14 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"An Event Study of the Ethereum Transition to Proof-of-Stake" by Elie Kapengut and Bruce Mizrach, arXiv:2210.13655v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|cccc|cc|}
 \hline
 \multirow{2}{*}{Data} & \multirow{2}{*}{\# Rows} & \multirow{2}{*}{\# Cols} & \multicolumn{4}{c|}{Column Missing Rates} & \multicolumn{2}{c|}{\% Rows Rated} \\
 & & & Avg & Med & Min & Max & One RP & All RPs \\ \hline
 US Hospital & 4217 & 4 & 30.2\% & 26.9\% & 11.6\% & 55.2\% & 21.0\% & 41.3\% \\
 Journal & 944 & 11 & 36.0\% & 37.7\% & 5.2\% & 65.8\% & 1.2\% & 13.5\% \\
 ESG & 1356 & 4 & 47.5\% & 53.9\% & 3.8\% & 78.4\% & 44.0\% & 14.0\% \\
 Elementary School & 301 & 5 & 38.9\% & 33.9\% & 25.9\% & 66.8\% & 29.9\% & 24.3\% \\
 High School & 132 & 5 & 55.6\% & 53.8\% & 28.8\% & 81.8\% & 55.3\% & 11.4\% \\
 Movielens & 1102 & 12 & 62.7\%	& 64.8\% & 53.0\% & 67.3\% & 23.1\% & 3.0\%\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Summary statistics of data sets}
\end{table}
\end{document}

stat

"Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data" by Young Woong Park, Jinhak Kim, and Dan Zhu, arXiv:2311.04035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Identification code of the households in UK-SMEC dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
			\toprule
			&H\#1 &H\#2 &H\#3 &H\#4\\
			\hline
			ID &MAC005041 &MAC004970 &MAC004902 &MAC004897\\
			\midrule
			&H\#5 &H\#6 &H\#7 &H\#8\\
			\hline
			ID &MAC004866 &MAC001477 &MAC000415 &MAC000032\\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Alessandro Brusaferri
Matteo Matteucci
Stefano Spinelli
Andrea Vitali

"Probabilistic electric load forecasting through Bayesian Mixture Density Networks" by Alessandro Brusaferri, Matteo Matteucci, Stefano Spinelli, and Andrea Vitali, arXiv:2012.14389v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcc}
 \hline
 \textbf{Method} & \textbf{Dataset} & \textbf{ATE Estimate} & \textbf{Estimated Gap (\%)} \\
 \hline
 Unadjusted Analysis & Google Scholar Subset & -- & 11.71\% \\
 & Full Dataset & -- & 12.80\% \\
 \hline
 OLS Regression & Google Scholar Subset & -0.0321 & 7.12\% \\
 & Full Dataset & -0.0400 & 8.80\% \\
 \hline
 PSM Regression & Google Scholar Subset & -0.0297 & 6.61\% \\
 & Full Dataset & -0.0293 & 6.52\% \\
 \hline
 Causal Forest & Google Scholar Subset & -0.0252 & 5.64\% \\
 & Full Dataset & -0.0266 & 5.94\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Estimated Gender Pay Gaps (log salary scale) by Method and Dataset.}
\end{table}
\end{document}

q-fin

"Estimation of Gender Wage Gap in the University of North Carolina System" by Zihan Zhang and Jan Hannig, arXiv:2505.24078v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Description of evaluation datasets, AvgWPerD denotes average number of words per document and AvgSPerD denotes the average number of sentences per document.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
			\toprule
			Dataset & AvgWPerD & AvgSPerD & Train & Dev & Test\\
			\midrule
			CNSE & 982.7 & 20.1 & 17,438 & 5,813 & 5,812 \\
			CNSS & 996.6 & 20.4 & 20,102 & 6,701 & 6,700 \\
			\hline
			AAN-Abs & 122.7 & 4.9 & 106,592 & 13,324 & 13,324 \\
			AAN-Body & 3270.1 & 111.6 & 104,371 & 12,818 & 12,696 \\
			OC & 190.4 & 7.0 & 240,000 & 30,000 & 30,000 \\
			S2ORC & 263.7 & 9.3 & 152,000 & 19,000 & 19,000 \\
			\hline
			PAN & 1569.7 & 47.4 & 17,968 & 2,908 & 2,906 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching" by Liang Pang, Yanyan Lan, and Xueqi Cheng, arXiv:2101.06423v2, 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}
 Bus type & Known Variables & Unknown variables \\
 \hline
 PV & $P$, $|V|$ & $Q$, $\theta$ \\
 PQ & $P$, $Q$ & $|V|$, $\theta$ \\
 Slack & $V$, $\theta$ & $P$, $Q$
 \end{tabular}
\caption{Buses classification for power flow analysis.}
\end{table}
\end{document}

cs

Luis Piloto
Sofia Liguori
Sephora Madjiheurem
Miha Zgubic
Sean Lovett
Hamish Tomlinson
Sophie Elster
Chris Apps
Sims Witherspoon

"CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations" by Luis Piloto, Sofia Liguori, Sephora Madjiheurem, Miha Zgubic, Sean Lovett, Hamish Tomlinson, Sophie Elster, Chris Apps, and Sims Witherspoon, arXiv:2403.17660v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 \textbf{Type} & All & Rear-end & Sideswipe \\ \midrule
 \textbf{P-value} & 2.12E-7 &1.38E-9 & 0.451 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Contribution by road ID or merge condition for the current case. Segment 1 does not have merge lanes, while Segment 2 has merge lanes. }
\end{table}
\end{document}

cs

Xiwen Chen
Hao Wang
Abolfazl Razi
Brendan Russo
Jason Pacheco
John Roberts
Jeffrey Wishart
Larry Head
Alonso Granados Baca

"Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study" by Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, and Alonso Granados Baca, arXiv:2201.13229v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Estimates of the NB model for STDs, after excluding the non-significant covariates}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
\hline
STDs-NB & Estimate({ $\hat{\boldsymbol\beta}_1$}) & StdDev & $p$-value \\
\hline
Intercept & -2.0317 & 0.1567 & 0.0000 \\
Smoke & 0.8100 & 0.3576 & 0.0235 \\
Dispersion & 0.1557 & &\\
\hline
\multicolumn{2}{l}{AIC = 570.87} & \multicolumn{2}{l}{-2log-Lik. = 564.865} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Modeling Count Data via Copulas

Hadi Safari-Katesari
S. Yaser Samadi
Samira Zaroudi

"Modeling Count Data via Copulas" by Hadi Safari-Katesari, S. Yaser Samadi, and Samira Zaroudi, arXiv:2012.02021v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Antonio Cabrales
Manu García
David Ramos Muñoz
Angel Sánchez

"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \toprule
 \textbf{Technology} & \textbf{Installed capacity [GW]} \\
 \midrule
 Hard coal & 8.1 \\
 Lignite & 9 \\
 Gas & 33.4 \\
 Other conventionals & 5 \\
 Biomass & 8.5 \\
 Hydro & 10.4 \\
 Wind onshore & 85.5 \\
 Wind offshore & 18.3 \\
 Ground-mounted PV (solar park) & 31.2 \\
 Rooftop PV & Endogenous \\
 Home battery & Endogenous \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Installed Capacity by Technology.}
\end{table}
\end{document}

q-fin

Dana Kirchem
Mario Kendziorski
Enno Wiebrow
Wolf-Peter Schill
Claudia Kemfert
Christian von Hirschhausen

"Solar prosumage under different pricing regimes: Interactions with the transmission grid" by Dana Kirchem, Mario Kendziorski, Enno Wiebrow, Wolf-Peter Schill, Claudia Kemfert, and Christian von Hirschhausen, arXiv:2502.21306v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccc}
\hline
\textbf{model name} & \textbf{$\#$layers} & \textbf{$\#$dim} & \textbf{$\#$params} & \textbf{train data} \\ \hline
w2v2\_base & 12 & 768 & 94.4M & 960hr \\
hubert\_base & 12 & 768 & 94.4M & 960hr \\
wavlm\_base & 12 & 768 & 94.4M & 960hr \\
w2v2\_xlsr\_300m & 24 & 1024 & 315M & 436,000hr \\
w2v2\_xlsr\_1b & 48 & 1280 & 962M & 436,000hr \\
w2v2\_xlsr\_2b & 48 & 1920 & 2.16B & 436,000hr \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Specifications of selected SSL models}
\end{table}
\end{document}

eess

Xinyu Liang
Fredrik Cumlin
Victor Ungureanu
Chandan K. A. Reddy
Christian Schuldt
Saikat Chatterjee

"Selection of Layers from Self-supervised Learning Models for Predicting Mean-Opinion-Score of Speech" by Xinyu Liang, Fredrik Cumlin, Victor Ungureanu, Chandan K. A. Reddy, Christian Schuldt, and Saikat Chatterjee, arXiv:2508.08962v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Expected initial state of the Deputy relative to the Chief, e.g., \( \mathbb{E}\big[X_d(t_{0}) - X_c(t_{0})\big] \).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\hline
\( \delta x_0 \) [km] & \( \delta y_0 \) [km] & \( \delta z_0 \) [km] & \( \delta{\dot x_0} \) [km/s] & \( \delta{\dot y_0} \) [km/s] & \( \delta{\dot z_0} \) [km/s] \\
\hline
-25.9809 & 27.8498 & 22.7715 & -0.0350 & -0.0066 & -0.0234 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Taehyeun Kim
Robin Inho Kee
Ilya Kolmanovsky
Anouck Girard

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


\documentclass{article}
\usepackage{pifont}
\usepackage{adjustbox}
\usepackage{marvosym}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|c|c|ccc|c|}
 \hline
 No. & Name & Type & Size & \multicolumn{4}{c|}{Characteristics} \\
 & & & & Artifact & Noise & Material & Reconstruction\\\hline
 D1 & AFA & GAN & - & \ding{51} & \ding{51} & Steel & FDK\\
 \hline

 D2 & Incl-718 & GAN & - & \ding{51} & \ding{51} & - & more defects, few inlcusions\\\hline %synthetic in-dist

 D3 & InD & Experimental & 4020 & \ding{51} & \ding{55} & - & \\\hline % test 3
 D4 & OoD, more pore, few Incl. & Experimental & 920 & \ding{51} & \ding{55} & Titanium & MBIR \\\hline %test2

 D5 & OoD, no-Incl & GAN & 600 & \ding{51}& \ding{51} & - & More pores, No inclusions \\\hline % test5

 D6 & OoD, less pore, more Incl. & Experimental & 920 & \ding{51} & \ding{51} & - & Less pore, many inclusion \\\hline %test1

 D7 & OoD, no-Incl. & Experimental & 1080 & \ding{51} & \ding{55} & - & no inclusions, only pores \\\hline %test4

 \end{tabular}
\end{adjustbox}
\caption{Dataset specification.}
\end{table}
\end{document}

eess

"Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing" by Anika Tabassum and Amirkoushyar Ziabari, arXiv:2412.11381v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small Certified robust accuracy of TPC on CurveNet}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcc}
 \toprule
 \multirow{2}{*}[-2pt]{Transformation} & \multirow{2}{*}[-2pt]{Attack radius} & \multicolumn{2}{c}{Certified Accuracy ($\%$)} \\ \cline{3-4}\\
 & & PointNet& CurveNet \\
 \midrule
 Z-rotation & 180$^\circ$ & 81.3& \textbf{85.4}\\
 Z-shear & 0.2 & 77.7 & \textbf{87.8}\\
 Z-twist & 180$^\circ$ & 64.3 & \textbf{86.2}\\
 Z-taper & 0.2 & 76.5 & \textbf{88.6}\\
 Linear & 0.2 & 59.9 & \textbf{77.7} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"TPC: Transformation-Specific Smoothing for Point Cloud Models" by Wenda Chu, Linyi Li, and Bo Li, arXiv:2201.12733v5, 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}{cccc}
\hline
 & Estimate & Bootstrap Mean&Quantile CI (95\%)\\ \hline
Indirect Effect & 0.2600
 & 0.2531
&(0.1279, 0.3752)
\\ \hline
Direct Effect & -0.0223
 & -0.0178
&(-0.2496, 0.1688)
\\ \hline
Total Effect & 0.2378 & 0.2353&(-0.0079, 0.4346) \\ \hline
\end{tabular}
\caption{Summary of the effects in OLS (gene sites from other papers) with mean and confidence interval (CI) under 300-sample bootstrap.}
\end{table}
\end{document}

math

Shi Bo
AmirEmad Ghassami
Debarghya Mukherjee

"A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects" by Shi Bo, AmirEmad Ghassami, and Debarghya Mukherjee, arXiv:2412.08827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{: Neural network approach using two shallow networks of width 32 for both $u$ and $f$. We use $\operatorname{tanh}$ as activation function for $u_\theta$ and $\operatorname{tanh}'' = 2\operatorname{tanh}^3 - 2\operatorname{tanh}$ for $f_\psi$, hence the Laplacian of $u_\theta$ is representable by the network $f_\psi$. We report relative $H^2$ errors.}
\begin{tabular}{|c||c|c|c|}
 \hline
 $\alpha^2 \;\backslash\; \gamma^2$ & $10^0$ & $10^{2}$ & $10^{4}$ \\ \hline \hline
 $1$ & 1.34e-05 & 7.09e-05 & 3.05e-04 \\ \hline
 $10^{-2}$ & 1.44e-05 & 5.34e-05 & 3.56e-04 \\ \hline
 $10^{-4}$ & 3.17e-05 & 1.78e-04 & 7.30e-04 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Kent-Andre Mardal
Jarle Sogn
Marius Zeinhofer

"Variational Formulations of the Strong Formulation -- Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks" by Kent-Andre Mardal, Jarle Sogn, and Marius Zeinhofer, arXiv:2312.03496v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for the $AAUC_{5\%}$ measure on the synthetic data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l||c|c|c|c}
name & rerun & min. $p$-value & permutation test & imported $p$-values \\
\hline
Eigenevent & 4.993 & -- & -- & 4.391\\
WSARE 2.0 & -- & 2.963 & 3.805 & 4.925\\
WSARE 2.5 & -- & 1.321 & 1.614 & 1.931\\
WSARE 3.0 & -- & 0.899 & 1.325 & 1.610\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Moritz Kulessa
Eneldo Loza Mencía
Johannes Fürnkranz

"Revisiting Non-Specific Syndromic Surveillance" by Moritz Kulessa, Eneldo Loza Mencía, and Johannes Fürnkranz, arXiv:2101.12246v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
 \hline

 Solar Panels & $\mathcal{S}(SD^q)$ &$\mathcal{S}(SD^p)$ \\ \hline
 $P_1$ &$0.596$&$0.600$\\
 $P_2$ &$0.565$&$0.569$\\
 $P_3$ &$0.501$&$0.502$\\
 $P_4$ &$0.567$&$0.568$\\
 $P_5$ &$0.540$&$0.541$\\ \hline
 \end{tabular}
\end{adjustbox}
\caption{The score values of significance degrees $SD^q$ and $SD^p$ }
\end{table}
\end{document}

math

"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \hline
 $n=30067$ & Predicted: Default & Predicted: Not default \\ \hline
Actual: Default & 384 & 1754 \\ \hline
Actual: Not default & 287 & 27642 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Confusion matrix for the neural network of the Kaggle dataset.}
\end{table}
\end{document}

q-fin

"Interpretable Selective Learning in Credit Risk" by Dangxing Chen, Weicheng Ye, and Jiahui Ye, arXiv:2209.10127v1, 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{AIC and BIC comparison for Model 1 and Model 2}
\begin{tabular}{lcc}
\hline
\textbf{Model} & \textbf{AIC} & \textbf{BIC} \\
\hline
\hline
Model 1 & 15406.98 & 5035.25 \\
Model 2 & 15084.18 & 4717.32 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Hanlin Tian
Yuxiang Feng
Wei Zhou
Anupriya
Mohammed Quddus
Yiannis Demiris
Panagiotis Angeloudis

"The Impact of Building-Induced Visibility Restrictions on Intersection Accidents" by Hanlin Tian, Yuxiang Feng, Wei Zhou, Anupriya, Mohammed Quddus, Yiannis Demiris, and Panagiotis Angeloudis, arXiv:2503.05706v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Efficient and Accurate Calibration to FX Market Skew with Fully Parameterized Local Volatility Model" by Dongli Wu, Bufan Zhang, and Xiao Lin, arXiv:2211.14431v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Functions used within the joint VaR and ES scoring function of () to give the five different versions of the score used in our study: QS, AL, NZ, FZG, and AS.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 & $G_1(x)$ & $G_2(x)$ & $\zeta_2(x)$ & $a(x)$ \\
 \midrule
 QS& $x$ & 0& 0 & $\alpha x$ \\
 AL & 0 & $-1/x$ & $-\ln(-x)$ & $1 - \ln(1 - \alpha)$ \\
 NZ & 0 & $1/2(-x)^{-1/2}$ & $-(-x)^{1/2}$ & 0 \\
 FZG & $x$ & $\exp(x)/(1 + \exp(x))$ & $\ln(1 + \exp(x))$ & $\ln(2)$ \\
 AS & $-1/2Wx^2$ & $\alpha x$ & $1/2\alpha x^2$ & 0 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall" by James W. Taylor and Chao Wang, arXiv:2508.16919v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracies on each classification benchmark datasets, averaged across 20 training-testing splits for obliqueBART and axis-aligned methods. Best performing method is bolded and errors that are statistically significantly larger than obliqueBART's are marked with an asterisk.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\hline
data & ERT & obliqueBART & BART & RF & XGB \\\hline
\texttt{banknote} & 0.999 & \textbf{0.999} & 0.997* & 0.992* & 0.991* \\
\texttt{blood transfusion} & 0.776 & 0.768 & 0.795 & 0.768 & \textbf{0.798}\\
\texttt{breast cancer diag.} & 0.965 & 0.922 & 0.966 & 0.961 & \textbf{0.967}\\
\texttt{breast cancer} & 0.975 & 0.974 & 0.973 & \textbf{0.975} & 0.964*\\
\texttt{breast cancer prog.} & 0.754 & 0.758 & \textbf{0.760} & 0.756 & 0.733* \\
\texttt{climate crashes} & 0.919 & 0.917 & 0.944 & 0.926 & \textbf{0.951}\\
\texttt{connectionist sonar} & \textbf{0.871} & 0.796 & 0.824 & 0.835 & 0.832\\
\texttt{credit approval} & 0.874 & 0.866 & 0.87 & \textbf{0.876} & 0.872\\
\texttt{echocardiogram} & \textbf{0.733} & 0.714 & 0.712 & 0.707 & 0.692* \\
\texttt{fertility} & 0.846* & \textbf{0.86} & \textbf{0.860} & 0.842* & 0.846\\
\texttt{german credit} & \textbf{0.765} & 0.751 & 0.76 & 0.763 & 0.745\\
\texttt{hepatitis} & 0.858 & 0.84 & 0.865 & \textbf{0.868} & 0.855\\
\texttt{ILPD} & \textbf{0.725} & 0.714 & 0.71 & 0.706 & 0.692*\\
\texttt{ionosphere} & \textbf{0.936} & 0.902 & 0.926 & 0.928 & 0.924\\
\texttt{ozone1} & 0.969* & \textbf{0.970} & 0.97 & 0.969* & 0.970\\
\texttt{ozone8} & \textbf{0.940} & 0.933 & 0.938 & 0.939 & 0.938\\
\texttt{parkinsons} & \textbf{0.910} & 0.866 & 0.861 & 0.899 & 0.880 \\
\texttt{planning relax} & 0.697* & \textbf{0.717} & 0.708* & 0.686* & 0.621* \\
\texttt{qsar bio.} & \textbf{0.868} & 0.826 & 0.845 & 0.862 & 0.855\\
\texttt{seismic bumps} & 0.930* & \textbf{0.935} & 0.935 & 0.933* & 0.934* \\
\texttt{spambase} & \textbf{0.956} & 0.765 & 0.932 & 0.953 & 0.945\\
\texttt{spectf heart} & 0.802 & 0.799 & 0.813 & \textbf{0.814} & 0.796\\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Paul-Hieu V. Nguyen
Ryan Yee
Sameer K. Deshpande

"Oblique Bayesian additive regression trees" by Paul-Hieu V. Nguyen, Ryan Yee, and Sameer K. Deshpande, arXiv:2411.08849v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Diego Cifuentes
Santanu S. Dey
Jingye Xu

"Sensitivity analysis for mixed binary quadratic programming" by Diego Cifuentes, Santanu S. Dey, and Jingye Xu, arXiv:2312.06714v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter settings of XGBoost models.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
Hyperparameter & Setting \\ \midrule
Trees & 500 \\
Min. node size & 20 \\
Max. tree depth & 4 \\
Learning rate & 0.1 \\
Minimum loss reduction & 0 \\
No. randomly selected predictors & No. predictors \\
Proportion of sampled obs. & 0.5 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Christine Hedde-von Westernhagen
Javier Garcia-Bernardo
Ayoub Bagheri

"Predicting COVID-19 Infections Using Multi-layer Centrality Measures in Population-scale Networks" by Christine Hedde-von Westernhagen, Javier Garcia-Bernardo, and Ayoub Bagheri, arXiv:2310.14767v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ VSS Camera Parameters }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \hline\hline %inserts double horizontal lines
 \textbf{Parameter} & \textbf{Measure} \\ % inserts table
 \hline % inserts a single horizontal line
 No. of Cameras & 1 \\
 Resolution & 960H x 540V \\
 Recording Time & 500 sec \\
 Frame/Sec & 24 fps \\
 No. of Experiments & 1 \\
 Compression & H.264 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Michael fogel
Snigdha Sushil Mishra
Laurent Burlion

"Nanosatellite Design Considerations for a Mission to Explore the Propellant Sloshing Problem" by Michael fogel, Snigdha Sushil Mishra, and Laurent Burlion, arXiv:2412.20659v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}
 \toprule
 \textbf{Order} &1&2 &3&4&5&6 &7 &8 \\
 \midrule
 \textbf{Task type} &$Q_0$ &$P$ &$B_R$&$B_{DR}$&$L_{DR}$&$ Q_1$&$L_{R}$&$Q_2$ \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Old Scheme&New Scheme&Ratio\\
\hline
4028.75&3785.95&1.064\\
\hline
\end{tabular}
\end{adjustbox}
\caption{\sf Example 8: The CPU times (in seconds) consumed by the studied schemes.}
\end{table}
\end{document}

math

New More Efficient A-WENO Schemes

Shaoshuai Chu
Alexander Kurganov
Ruixiao Xin

"New More Efficient A-WENO Schemes" by Shaoshuai Chu, Alexander Kurganov, and Ruixiao Xin, arXiv:2503.10221v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
 Predictor & AIC & R\textsuperscript{2}\textsubscript{m} \\
 \midrule
 Algorithm identities & $1661.9$ & $0.362$ \\
 Participant score & $1697.2$ & $0.363$ \\
 Social perception & $\mathbf{1611.2}$ & $\mathbf{0.436}$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Metrics for fractional response models predicting preferences in Study 1. Lower values of AIC and higher values of R\textsuperscript{2}\textsubscript{m} indicate stronger fits.}
\end{table}
\end{document}

cs

Kevin R. McKee
Xuechunzi Bai
Susan T. Fiske

"Warmth and competence in human-agent cooperation" by Kevin R. McKee, Xuechunzi Bai, and Susan T. Fiske, arXiv:2201.13448v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small Heteroscedastic continuous data example. Frequentist coverage of credible intervals with nominal level $\rho \in \{0.90, 0.95, 0.99\}$ for Gaussian linear model (\textsc{lm}) and quasi-posterior (\textsc{qp}).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccccccccc}
 & & \multicolumn{4}{c}{$\rho = 0.90$} & & \multicolumn{4}{c}{$\rho = 0.95$} & & \multicolumn{4}{c}{$\rho = 0.99$}\\
 & & $\beta_1$ & $\beta_2$ & $\beta_3$ & $\beta_4$ & & $\beta_1$ & $\beta_2$ & $\beta_3$ & $\beta_4$ & & $\beta_1$ & $\beta_2$ & $\beta_3$ & $\beta_4$ \\
\textsc{lm} & & 0.92 & 0.65 & 0.71 & 0.76 & & 0.95 & 0.71 & 0.82 & 0.84 & & 0.99 & 0.86 & 0.93 & 0.93 \\
\textsc{qp} & & 0.94 & 0.91 & 0.90 & 0.93 & & 0.96 & 0.95 & 0.96 & 0.95 & & 0.99 & 1.00 & 0.99 & 1.00 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Davide Agnoletto
Tommaso Rigon
David B. Dunson

"Bayesian inference for generalized linear models via quasi-posteriors" by Davide Agnoletto, Tommaso Rigon, and David B. Dunson, arXiv:2311.00820v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \toprule
 model&$H$&$df$& AIC & BIC & $\sigma_{\epsilon}$\\
 \midrule
	time FE&3&31&23757&23959&2.501\\
	local time FE&6&52&23112&23452&2.343\\
	\bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Location-specific ANN panel model with time FE}
\end{table}
\end{document}

stat

"Regressing on distributions: The nonlinear effect of temperature on regional economic growth" by Malte Jahn, arXiv:2309.10481v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.