\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{rrrrrrrrrrr}
 \hline
 & \multicolumn{2}{c}{Model 1} &\multicolumn{2}{c}{Model 2}&
 \multicolumn{2}{c}{Model 3} & \multicolumn{2}{c}{Model 4} &\multicolumn{2}{c}{Model 5}\\
 \hline
 & FPR & sd & FPR & sd & FPR & sd & FPR & sd & FPR & sd \\
 \hline
$\theta=0.1$ & 2.81 & 1.54 & 2.41 & 1.41 & 2.67 & 1.50 & 2.64 & 1.51 & 2.67 & 1.48 \\
 $\theta=0.15$ & 1.95 & 1.25 & 1.41 & 1.29 & 1.69 & 1.25 & 1.70 & 1.26 & 1.78 & 1.24 \\
 $\theta=0.2$ & 1.13 & 1.01 & 0.58 & 0.96 & 0.84 & 1.04 & 0.85 & 0.98 & 0.96 & 1.03 \\
 $\theta=0.25$ & 0.36 & 0.71 & 0.17 & 0.54 & 0.27 & 0.69 & 0.27 & 0.71 & 0.33 & 0.72 \\
 $\theta=0.3$ & 0.04 & 0.26 & 0.15 & 0.58 & 0.04 & 0.26 & 0.04 & 0.27 & 0.05 & 0.30 \\
 \hline
\end{tabular}
\caption{FPR variation with different contamination rates. FPR refers to the average across 500 experiments, sd denotes the corresponding standard deviation}
\end{table}
\end{document}

stat

Cristian F. Jimenez-Varon
Fouzi Harrou
Ying Sun

"Pointwise Data Depth for Univariate and Multivariate Functional Outlier Detection" by Cristian F. Jimenez-Varon, Fouzi Harrou, and Ying Sun, arXiv:2311.02634v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
 \toprule
 \textbf{Source} & \textbf{Size}\\
 \midrule
 \multicolumn{2}{l}{\textit{Romanian (300B)}} \\
 \quad Web Sources & 621 GB \\
 \quad Discussions, Curated \& Parallel & 10 GB \\
 \midrule
 \multicolumn{2}{l}{\textit{English (700B)}} \\
 \quad FineWebEdu & -- \\
 \quad Dolma Subset & 109 GB \\
 \midrule
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Dataset Composition by Language and Source}
\end{table}
\end{document}

cs

Vlad-Andrei Bădoiu
Mihai-Valentin Dumitru
Alexandru M. Gherghescu
Alexandru Agache
Costin Raiciu

"LLMic: Romanian Foundation Language Model" by Vlad-Andrei Bădoiu, Mihai-Valentin Dumitru, Alexandru M. Gherghescu, Alexandru Agache, and Costin Raiciu, arXiv:2501.07721v1, 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{graphicx}
\usepackage{rotating}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Literature on age-based maintenance models with age-dependent costs}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccc}
			& \rotatebox{90}{Tilquin and Cleroux (1975)} & \rotatebox{90}{Boland (1982)} & \rotatebox{90}{Boland and Proschan (1982)} & \rotatebox{90}{Segawa et al. (1992)} & \rotatebox{90}{Dagpunar and Jack (1994)} & \rotatebox{90}{Chien (2010)} & \rotatebox{90}{Sonoubar et al. (2021)} & \rotatebox{90}{Schouten et al. (2022)} & \rotatebox{90}{\textbf{Our paper}} \\
			\midrule
			\textbf{Planning horizon } & & & & & & & & & \\
			{\it Finite (deterministic)} & & & x & & x & & & & x \\
			{\it Infinite} & x & x & x & x & & x & x & x & \\
			\midrule
			\textbf{Time} & & & & & & & & & \\
			{\it Continuous} & x & x & x & x & x & x & x & & x \\
			{\it Discrete} & & & & & & & & x & \\
			\midrule
			\textbf{Preventive action} & & & & & & & & & \\
			{\it Perfect} & x & x & x & x & & x & x & x & x \\
			{\it Imperfect} & & & & & x & & & & \\
			\midrule
			\textbf{Effect of corrective action} & & & & & & & & & \\
			{\it New condition} & & & & x & & x & x & & \\
			\multicolumn{1}{p{14em}}{{\it Condition just before failure (i.e., minimal repair)}} & x & x & x & x & x & & & & x \\
			\midrule
			\textbf{Age-dependent costs} & & & & & & & & & \\
			{\it Minimal repair} & x & x & x & x & & & & & x \\
			{\it Corrective replacement} & & & & x & & & x & x & \\
			{\it Preventive replacement} & & & & & x & x & x & x & x \\
			{\it Operating cost} & & & & & & & & & x \\
			\midrule
			\multicolumn{1}{p{16em}}{\textbf{Schedule of low-cost preventive action moments}} & & & & & & & & & \\
			{\it No} & x & x & x & x & x & x & x & x & \\
			{\it Yes} & & & & & & & & & x \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Fiona Sloothaak
Alp Akçay
Matthieu van der Heijden
Geert-Jan van Houtum

"After-sales services during an asset's lifetime: collaborative planning of system upgrades" by Fiona Sloothaak, Alp Akçay, Matthieu van der Heijden, and Geert-Jan van Houtum, arXiv:2202.12128v2, 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{\textsc{rmse} for each time series on test set \\ with cnn, 4-rf and three hybrid models}
\begin{tabular}{|c|cccc|c|}
			\hline
			method & ego speed & ego accel & target speed & distance & mean
			\\
			\hline
			\textsc{cnn} & 0.23 & 3.34 & {1.00} & {2.52} & {1.77} %& $\star \star \star$
			\\
			4-\textsc{rf} & 0.13 & {1.36} & 2.36 & 9.84 & 3.42 %& $\star \star \star$
			\\
			\hline
			Hybrid 1 & 0.12 & 1.35 & 1.12 & 2.52 & 1.28 %& $\star \star \star$
			\\
			Hybrid 2 & 0.12 & 1.35 & \textbf{1.00} & 2.52 & \textbf{1.25} %& $\star \star \star$
			\\
			Aggregated & 0.50 & 3.66 & 1.08 & 3.38 & 2.16 %& $\star \star \star$
			\\
			\hline
		\end{tabular}
\end{table}
\end{document}

stat

Clara Carlier
Arnaud Franju
Matthieu Lerasle
Mathias Obrebski

"Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model" by Clara Carlier, Arnaud Franju, Matthieu Lerasle, and Mathias Obrebski, arXiv:2212.07918v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Mohammad Jafarian
Alireza Soroudi
Andrew Keane

"Resilient Identification of Distribution Network Topology" by Mohammad Jafarian, Alireza Soroudi, and Andrew Keane, arXiv:2011.07981v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc|ccc}
\toprule
\multirow{2}{*}{Methods} & \multicolumn{5}{c|}{Symptom Summary} & \multicolumn{3}{c}{Severity Prediction} \\
& Rouge-1 & Rouge-2 & Rouge-L & BLEU & BERTScore & Precision & Recall & Macro \\ \midrule
\textbf{PhqMML} &\cellcolor{gray!30}\textbf{76.59} &\cellcolor{gray!30}\textbf{55.83} &\cellcolor{gray!30}\textbf{72.93} &\cellcolor{gray!30}\textbf{21.44}
&\cellcolor{gray!30}\textbf{78.32}
&\cellcolor{gray!30}\textbf{91.18} &\cellcolor{gray!30}\textbf{95.45} &\cellcolor{gray!30}\textbf{92.78} \\
- rm IC Label &74.65 &53.39 &70.73 &20.06 &77.22 &85.00 &90.91 &86.18 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Wenjie Zheng
Qiming Xie
Zengzhi Wang
Jianfei Yu
Rui Xia

"Towards Explainable Multimodal Depression Recognition for Clinical Interviews" by Wenjie Zheng, Qiming Xie, Zengzhi Wang, Jianfei Yu, and Rui Xia, arXiv:2501.16106v1, 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{ SPICEsat Data Requirements (MB)}
\begin{tabular}{clllll}
 \hline\hline %inserts double horizontal lines
 \textbf{ } & \textbf{VSS} & \textbf{LSS} & \textbf{MSS} & \textbf{SoH} & \textbf{Total} \\
 \hline % inserts a single horizontal line
 With Camera & 104 & 12.85 & 2.9 & 7 & 126.75 \\
 Without Camera & N/A & 12.85 & 2.9 & 7 & 22.75 \\
 \hline
 \end{tabular}
\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.


eess

Panagiotis Petsagkourakis
Federico Galvanin

"Safe model-based design of experiments using Gaussian processes" by Panagiotis Petsagkourakis and Federico Galvanin, arXiv:2011.10009v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of Over-Encoding on MoE architecture with 500B tokens' training. The column `Emb. P.' represents `Embedding Parameters'. `Downstream' stands for the average of MMLU-Var, Hellaswag, ARC-Challenge, ARC-Easy, and PIQA. For `+OE' rows, we provide metric difference with blue labels.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc|ll}
\toprule
Model & \# Emb. P. & Loss$\downarrow$ & Downstream$\uparrow$ \\
\midrule
OLMoE-1.3B & 51M & 2.554 & 0.510 \\
+OE-12.8M & 13.1B & 2.472 \scriptsize \textcolor{blue}{-0.082} & 0.524 \scriptsize \textcolor{blue}{+0.014} \\
\hline
OLMoE-7B & 102M & 2.305 & 0.601 \\
+OE-12.8M & 26.3B & 2.229 \scriptsize \textcolor{blue}{-0.076} & 0.608 \scriptsize \textcolor{blue}{+0.007} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Hongzhi Huang
Defa Zhu
Banggu Wu
Yutao Zeng
Ya Wang
Qiyang Min
Xun Zhou

"Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling" by Hongzhi Huang, Defa Zhu, Banggu Wu, Yutao Zeng, Ya Wang, Qiyang Min, and Xun Zhou, arXiv:2501.16975v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|l|l|l}
\hline\toprule
\multirow{2}{*}{\textbf{Hyperparameters}} & \multicolumn{4}{c}{\textbf{Values}}\\\cmidrule{2-5}
& {\bf \emph{Transfer}} & {\bf \emph{GraphProp}} & {\bf \emph{LRGB}} & {\bf \emph{G-Bench}}\\\midrule
Optimizer & Adam & Adam & AdamW & AdamW\\
Learning rate & 0.001 & 0.003 & 0.001, \ 0.0005, 0.0001 & 0.001, 0.0005 ,0.0001\\
Weight decay & 0 & $10^{-6}$ & 0, \ 0.0001 & 0, 0.0001 \\
Dropout & 0 & 0 & 0, \ 0.3, \ 0.5 & 0, \ 0.3, \ 0.5 \\
Activation function ($\sigma$) & ReLU & ReLU & ELU, GELU, ReLU & ELU, GELU, ReLU\\
Embedding dim (d) & 64 & 10, 20, 30 & 64, \ 128 & 64, 128, 256\\
Sequence Length (L) & 1, 3, 5, 10, 50 & 1, 5, 10, 20 & 2, 4, 8, 16 & 2, 4, 8, 16 \\
Blocks (S)& 1, 2 & 1, 2 & 1, 2, 4 & 1, 2, 4 \\
Graph Backbone & \multicolumn{4}{c}{{GCN, GPS, GatedGCN}}\\
\bottomrule\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Moshe Eliasof
Alessio Gravina
Andrea Ceni
Claudio Gallicchio
Davide Bacciu
Carola-Bibiane Schönlieb

"GRAMA: Adaptive Graph Autoregressive Moving Average Models" by Moshe Eliasof, Alessio Gravina, Andrea Ceni, Claudio Gallicchio, Davide Bacciu, and Carola-Bibiane Schönlieb, arXiv:2501.12732v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Harsh Gupta
Mainak Bhattacharyya
Ritik Jain
Ankur Raina

"Fault-tolerant syndrome extraction in [[n,1,3]] non-CSS code family generated using measurements on graph states" by Harsh Gupta, Mainak Bhattacharyya, Ritik Jain, and Ankur Raina, arXiv:2501.12072v2, 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{Recovery rate of phrasal representations. {\em phrase pair} (k=1) and {\em target only} (k=3) have a recovery rate close to 100\%.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 {\em representation} & {\em recovery rate} \\
 \hline
 {\em phrase pair ($k=1$)} & 99.98\% \\
 \hline
 {\em target only ($k=1$)} & 96.80\% \\
 {\em target only ($k=2$)} & 99.50\% \\
 \hline
 {\em target only ($k=3$)} & 99.80\% \\
 \hline
 {\em target only ($k=4$)} & 99.80\% \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing" by Hao Zhang, Felix Stahlberg, and Shankar Kumar, arXiv:2501.13831v1, 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}{lc}
Variable & VIF \\ \hline
Conditioning stage video like count & 58.7064 \\
Conditioning stage video play count & 47.1637 \\
Recommendation stage video play count & 27.6304 \\
Conditioning stage video comment count & 25.0417 \\
Recommendation stage channel verification status & 22.8050 \\
Conditioning stage video share count & 16.5838 \\
Recommendation stage video like count & 14.3400 \\
Conditioning stage channel verification status & 13.4786 \\
Conditioning stage channel video count & 12.1204 \\
Conditioning stage channel cumulative like count & 8.8994 \\
Recommendation stage video comment count & 7.7629 \\
Conditioning stage channel follower count & 5.7607 \\
Recommendation stage video share count & 5.1790 \\
Recommendation stage channel video count & 2.4859 \\
Recommendation stage channel follower count & 2.2943 \\
Recommendation stage channel cumulative like count & 2.1489 \\
Week of experiment & 1.8398 \\
Top account X Republican bot & 1.7770 \\
Top account X Democrat bot & 1.6151 \\
Number of videos watched during conditioning stage & 1.6803 \\
Recommendation stage video likes per recommendation & 1.3990 \\
Recommendation stage video comments per recommendation & 1.3096 \\
Opposition comment proportion & 1.2853 \\
Video Duration & 1.0427 \\\hline
\end{tabular}
\end{adjustbox}
\caption{VIF values for control variables in logistic regression when considering top account status.}
\end{table}
\end{document}

cs

Hazem Ibrahim
HyunSeok Daniel Jang
Nouar Aldahoul
Aaron R. Kaufman
Talal Rahwan
Yasir Zaki

"TikTok's recommendations skewed towards Republican content during the 2024 U.S. presidential race" by Hazem Ibrahim, HyunSeok Daniel Jang, Nouar Aldahoul, Aaron R. Kaufman, Talal Rahwan, and Yasir Zaki, arXiv:2501.17831v2, 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|c|c|c|c|c|c|}
 \hline &\multicolumn{2}{c|}{ACAT2}&\multicolumn{2}{c|}{WBACAT2}& \multicolumn{2}{c|}{ACAT4} & \multicolumn{2}{c|}{WBACAT4} \\
 Points & Order & Error & Order & Error & Order & Error & Order& Error \\ \hline
 80 & - & 6.44E-2 & - & 1.07E-2 & - & 1.53E-3 & - & 1.17E-4\\
 \hline
 160 & 1.15 & 2.88E-2 & 1.74 & 3.21E-3 & 2.11 & 3.10E-4 & 2.03 & 2.86E-5\\
 \hline
 320 & 1.45 & 1.05E-2 & 1.89 & 8.61E-4 & 4.28 & 1.59E-5 & 4.45 & 1.31E-6 \\
 \hline
 640 & 1.62 & 3.42E-3 & 1.96 & 2.21E-4 & 4.14 & 8.99E-7 & 3.85 & 8.99E-8 \\
 \hline
 1280 & 1.86 & 9.41E-4 & 1.99 & 5.58E-5 & 4.01 & 5.57E-8 & 3.96 & 5.76E-9 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Well-balanced adaptive compact approximate Taylor methods for systems of balance laws" by H. Carrillo, E. Macca, C. Pares, and G. Russo, arXiv:2202.02068v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Xiaohong Chen
Zengjing Chen
Wayne Yuan Gao
Xiaodong Yan
Guodong Zhang

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


\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}{cccc}
 \toprule
 \multicolumn{4}{c}{Non-hallucination Samples} \\
 {[0.0,0.25)} & {[0.25,0.5)} & {[0.5,0.75)} & {[0.75,1.0]} \\
 0.121 & 0.152 & 0.303 & 0.424 \\
 \midrule
 \multicolumn{4}{c}{Hallucination Samples} \\
 {[0.0,0.25)} & {[0.25,0.5)} & {[0.5,0.75)} & {[0.75,1.0]} \\
 0.670 & 0.294 & 0.032 & 0.004 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Distribution of attentive consistency scores $\mathop r\nolimits^{att}$ with Qwen2.5-14b on the Movies dataset.}
\end{table}
\end{document}

cs

Qiang Liu
Xinlong Chen
Yue Ding
Bowen Song
Weiqiang Wang
Shu Wu
Liang Wang

"Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models" by Qiang Liu, Xinlong Chen, Yue Ding, Bowen Song, Weiqiang Wang, Shu Wu, and Liang Wang, arXiv:2501.09997v3, 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}{cccc}%\toprule[.1em]
			\toprule[.1em]
			\multicolumn{4}{c}{covtype} \\
			\midrule[0.01em]
			Algorithms & BS-PnP & BSGD & SOX \\\midrule[.05em]
			Test Loss ($\downarrow$) & 0.778 &0.625 $\pm$ 0.018 &{\bf 0.516 $\pm$ 0.003}\\\cmidrule(lr){1-1}
			Time (s) ($\downarrow$)&6043.90 & {\bf 4.20 $\pm$ 0.08}&4.62 $\pm$ 0.10 \\	\midrule[0.1em]
			\multicolumn{4}{c}{ijcnn1} \\
			\midrule[0.01em]
			Algorithms & BS-PnP & BSGD & SOX \\\midrule[.05em]
			Test Loss ($\downarrow$) & 0.268 &0.202 $\pm$ 0.001 &{\bf 0.128 $\pm$ 0.002}\\\cmidrule(lr){1-1}
			Time (s) ($\downarrow$)&648.06 & {\bf 4.02 $\pm$ 0.04}&4.15 $\pm$ 0.06 \\
			\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications" by Bokun Wang and Tianbao Yang, arXiv:2202.12396v7, 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{Channels included in the EEG experiments}
\begin{tabular}{llll}
 \hline\noalign{\smallskip}
 FP1-F7 & F7-T7 & T7-P7 & P7-O1 \\
 FP1-F3 & F3-C3 & C3-P3 & P3-O1 \\
 FP2-F4 & F4-C4 & C4-P4 & P4-O2 \\
 FZ-CZ & CZ-PZ & T7-FT9 & \\
 FT9-FT10 & FT10-T8 & & \\
 \noalign{\smallskip}\hline
 \end{tabular}
\end{table}
\end{document}

stat

Kimmo Suotsalo
Yingying Xu
Jukka Corander
Johan Pensar

"High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood" by Kimmo Suotsalo, Yingying Xu, Jukka Corander, and Johan Pensar, arXiv:2011.01484v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
 \cline{2-5}
 & daily & six-hourly & three-hourly & 10min \\
 \hline
 daily&1&$5.68\cdot10^{-22}$&$3.59\cdot10^{-24}$& $3.84\cdot10^{-27}$ \\ \hline
 six-hourly&&1&$\mathbf{7.13\cdot10^{-1}}$&$\mathbf{3.89\cdot10^{-1}}$ \\ \hline
 three-hourly&&&1& $\mathbf{1.00}$ \\ \hline
 \end{tabular}
\caption{$p$-values of Kolmogorov-Smirnov test for instantaneous Zugspitze wind speed distributions. All values where $p \leq 0.05$ are marked \textbf{bold} and the corresponding distributions are not considered to be significantly different.}
\end{table}
\end{document}

stat

Nina Effenberger
Nicole Ludwig
Rachel H. White

"Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts" by Nina Effenberger, Nicole Ludwig, and Rachel H. White, arXiv:2309.09540v1, 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{Environment details for DeepMind Control Suite. $|S|$ represents the size of the state space and $N$ the number of sub-action spaces. $\prod_i n_i$ is the total number of actions under atomic representation and $\sum_i n_i$ under factorised representation when $n_i=3$.}
\begin{tabular}{ccccc} \toprule
 Environment & $|S|$ & $N$ & $\prod_i n_i$ & $\sum_i n_i$ \\ \midrule
 Finger Spin & 9 & 2 & 9 & 6 \\
 Fish Swim & 24 & 5 & 243 & 15\\
 Cheetah Run & 17 & 6 & 729 & 18\\
 Quadruped Walk & 78 & 12 & $\approx 530k$ & 36\\
 Humanoid Stand & 67 & 21 & $\approx 10^{10}$ & 63 \\
 Dog Trot & 223 & 38 & $\approx 10^{18}$ & 114\\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Alex Beeson
David Ireland
Giovanni Montana

"An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces" by Alex Beeson, David Ireland, and Giovanni Montana, arXiv:2411.11088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Grouping options with associated generation parameters.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{$n_{csr}$} & \textbf{$n_{G}$} & \multicolumn{4}{c|}{\textbf{Generation Parameters for CSRs}} \\ \hline
\multirow{2}{*}{\textbf{1 CSR}} & \textbf{} & \multicolumn{4}{c|}{\textbf{CSR1}} \\ \cline{2-6}
 & 1 & \multicolumn{4}{c|}{$\lambda_{H}$, $\lambda_{D}$, $\lambda_{M}$, $T_{C}$} \\ \hline
\multirow{8}{*}{\textbf{2 CSRs}} & \textbf{} & \multicolumn{2}{c|}{\textbf{CSR 1}} & \multicolumn{2}{c|}{\textbf{CSR 2}} \\ \cline{2-6}
 & 2 & \multicolumn{2}{c|}{$T_{C}$} & \multicolumn{2}{c|}{$\lambda_{H}$, $\lambda_{D}$, $\lambda_{M}$} \\ \cline{2-6}
 & 3 & \multicolumn{2}{c|}{$\lambda_{H}$} & \multicolumn{2}{c|}{$\lambda_{D}$, $T_{C}$, $\lambda_{M}$} \\ \cline{2-6}
 & 4 & \multicolumn{2}{c|}{$\lambda_{D}$} & \multicolumn{2}{c|}{$\lambda_{H}$, $T_{C}$, $\lambda_{M}$} \\ \cline{2-6}
 & 5 & \multicolumn{2}{c|}{$\lambda_{M}$} & \multicolumn{2}{c|}{$\lambda_{H}$, $T_{C}$, $\lambda_{D}$} \\ \cline{2-6}
 & 6 & \multicolumn{2}{c|}{$\lambda_{H}$, $T_{C}$} & \multicolumn{2}{c|}{$\lambda_{D}$, $\lambda_{M}$} \\ \cline{2-6}
 & 7 & \multicolumn{2}{c|}{$\lambda_{H}$, $\lambda_{M}$} & \multicolumn{2}{c|}{$\lambda_{D}$, $T_{C}$} \\ \cline{2-6}
 & 8 & \multicolumn{2}{c|}{$\lambda_{H}$, $\lambda_{D}$} & \multicolumn{2}{c|}{$T_{C}$, $\lambda_{M}$} \\ \hline
\multirow{7}{*}{\textbf{3 CSRs}} & & \textbf{CSR 1} & \textbf{CSR 2} & \multicolumn{2}{c|}{\textbf{CSR 3}} \\ \cline{2-6}
 & 9 & $T_{C}$ & $\lambda_{H}$ & \multicolumn{2}{c|}{$\lambda_{D}$, $\lambda_{M}$} \\ \cline{2-6}
 & 10 & $\lambda_{H}$ & $\lambda_{D}$ & \multicolumn{2}{c|}{$T_{C}$, $\lambda_{M}$} \\ \cline{2-6}
 & 11 & $T_{C}$ & $\lambda_{M}$ & \multicolumn{2}{c|}{$\lambda_{H}$, $\lambda_{D}$} \\ \cline{2-6}
 & 12 & $\lambda_{D}$ & $T_{C}$ & \multicolumn{2}{c|}{$\lambda_{H}$, $\lambda_{M}$} \\ \cline{2-6}
 & 13 & $\lambda_{D}$ & $\lambda_{M}$ & \multicolumn{2}{c|}{$\lambda_{H}$, $T_{C}$} \\ \cline{2-6}
 & 14 & $\lambda_{H}$ & $\lambda_{M}$ & \multicolumn{2}{c|}{$\lambda_{D}$, $T_{C}$} \\ \hline
\multirow{2}{*}{\textbf{4 CSRs}} & \textbf{} & \textbf{CSR 1} & \textbf{CSR 2} & \textbf{CSR 3} & \textbf{CSR 4} \\ \cline{2-6}
 & 15 & $\lambda _{H}$ & $\lambda_{D}$ & $T_{C}$ & $\lambda_{M}$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

G. P. Wijesiri N. B. A
T. Samarasinghe
J. Haapola

"Performance Enhancement of C-V2X Mode 4 Utilizing Multiple Candidate Single-subframe Resources" by G. P. Wijesiri N. B. A, T. Samarasinghe, and J. Haapola, arXiv:2202.10869v1, 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|}
\hline
Heading level & Example & Font size and style\\
\hline
Title (centered) & {\Large\bfseries Lecture Notes} & 14 point, bold\\
1st-level heading & {\large\bfseries 1 Introduction} & 12 point, bold\\
2nd-level heading & {\bfseries 2.1 Printing Area} & 10 point, bold\\
3rd-level heading & {\bfseries Run-in Heading in Bold.} Text follows & 10 point, bold\\
4th-level heading & {\itshape Lowest Level Heading.} Text follows & 10 point, italic\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mohit Tekriwal
Joshua Miller
Jean-Baptiste Jeannin

"Formalization of Asymptotic Convergence for Stationary Iterative Methods" by Mohit Tekriwal, Joshua Miller, and Jean-Baptiste Jeannin, arXiv:2202.05587v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.