\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|l|l|l|l|}
\hline
& \multicolumn{2}{|c|}{FO scheme} & \multicolumn{2}{|c|}{SO scheme} \\ \hline
$h$ & $\|{\rho}_h - {\rho}_{h/2}\|_{{L}^1}$& $\gamma$ &$\|{\rho}_h - {\rho}_{h/2}\|_{{L}^1}$ & $\gamma $ \\ \hline
0.05 &0.6700479 & -& 0.6758311 & - \\ \hline
0.025 & 0.6735401& -0.0074994& 0.4313976 & 0.6476447 \\ \hline
0.0125 &0.5576313 & 0.2724521& 0.2618499 & 0.7202780 \\ \hline
0.00625 &0.4034235 & 0.4670164 & 0.1437489 & 0.8651884 \\ \hline
\end{tabular}
\caption{ Example . $L^1$ errors and E.O.C obtained for the FO and SO schemes to solve the problem with initial data at time $T=1.0$ with a time step of $\Delta t = 0.07 \Delta x.$}
\end{table}
\end{document}

math

Nikhil Manoj
G. D. Veerappa Gowda
Sudarshan Kumar K

"A positivity preserving second-order scheme for multi-dimensional system of non-local conservation laws" by Nikhil Manoj, G. D. Veerappa Gowda, and Sudarshan Kumar K, arXiv:2412.18475v2, 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{Comparison of AGILS with different proximal lower-level problem solvers on the sparse group Lasso bilevel hyperparameter selection problem with $n_{\text{tr}} = n_{\text{val}} =n_{\text{test}} = 200, m = 300$.}
\begin{tabular}{cccccc}
						\hline
						\textbf{Method} & \textbf{Time(s)} & \textbf{Val. Err.} & \textbf{Test. Err.} & \textbf{Test. Err. Infeas.} & \textbf{Feasibility} \\
						\hline
 AGILS(PGM) & 12.25(2.11) & 95.93(9.71) & 153.75(12.72) & 153.14(12.24) & 0.00(0.00)\\
						AGILS(FISTA) & 11.85(2.04) & 95.93(9.71) & 153.75(12.72) & 153.14(12.24) & 0.00(0.00) \\
						AGILS(ADMM) & 12.13(2.06) & 95.60(10.11) & 154.27(12.30) & 154.58(12.11) & 0.00(0.00) \\
						\hline
					\end{tabular}
\end{table}
\end{document}

math

Xiaoning Bai
Shangzhi Zeng
Jin Zhang
Lezhi Zhang

"Alternating Gradient-Type Algorithm for Bilevel Optimization with Inexact Lower-Level Solutions via Moreau Envelope-based Reformulation" by Xiaoning Bai, Shangzhi Zeng, Jin Zhang, and Lezhi Zhang, arXiv:2412.18929v2, 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.


eess

"On Energy Optimal Speed Trajectories: The Impact of Electric Powertrain Efficiency Characteristics" by Arian Ahmadi and Peter H. Bauer, arXiv:2102.05807v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c} \hline
 Method& T\_1& T\_2& T\_3& T\_4\\ \hline
 CoW& 0.39& 0.22& 0.08& 0.03\\
 CLIP Map& 0.33& 0.19& 0.06& 0.0\\
 VLMap& 0.56& 0.39& 0.19& 0.08\\
 \textbf{IVLMap(Ours)}& \textbf{0.64}& \textbf{0.44}& \textbf{0.25}& \textbf{0.14}\\ \hline
 \end{tabular}
\caption{Outcome of Zero-Shot Instance Level Object Goal Navigation from Natural Language. T\_k for achieving the kth subgoal out of the total 4 subgoals in each task. }
\end{table}
\end{document}

cs

Jiacui Huang
Hongtao Zhang
Mingbo Zhao
Zhou Wu

"IVLMap: Instance-Aware Visual Language Grounding for Consumer Robot Navigation" by Jiacui Huang, Hongtao Zhang, Mingbo Zhao, and Zhou Wu, arXiv:2403.19336v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Fundamental groups of moduli spaces of real weighted stable curves" by Jake Levinson and Haggai Liu, arXiv:2503.23253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Shumin Ma
Zhiri Yuan
Qi Wu
Yiyan Huang
Xixu Hu
Cheuk Hang Leung
Dongdong Wang
Zhixiang Huang

"Deep into The Domain Shift: Transfer Learning through Dependence Regularization" by Shumin Ma, Zhiri Yuan, Qi Wu, Yiyan Huang, Xixu Hu, Cheuk Hang Leung, Dongdong Wang, and Zhixiang Huang, arXiv:2305.19499v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jitao Wang
Chengchun Shi
John D. Piette
Joshua R. Loftus
Donglin Zeng
Zhenke Wu

"Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing" by Jitao Wang, Chengchun Shi, John D. Piette, Joshua R. Loftus, Donglin Zeng, and Zhenke Wu, arXiv:2501.06366v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Daniel Beaglehole
Adityanarayanan Radhakrishnan
Enric Boix-Adserà
Mikhail Belkin

"Aggregate and conquer: detecting and steering LLM concepts by combining nonlinear predictors over multiple layers" by Daniel Beaglehole, Adityanarayanan Radhakrishnan, Enric Boix-Adserà, and Mikhail Belkin, arXiv:2502.03708v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Alexander Roth
Carlos Gaete-Morales
Dana Kirchem
Wolf-Peter Schill

"Power sector benefits of flexible heat pumps" by Alexander Roth, Carlos Gaete-Morales, Dana Kirchem, and Wolf-Peter Schill, arXiv:2307.12918v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Celia Rubio-Madrigal
Adarsh Jamadandi
Rebekka Burkholz

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


stat

Wei Miao
Jad Beyhum
Jonas Striaukas
Ingrid Van Keilegom

"High-dimensional censored MIDAS logistic regression for corporate survival forecasting" by Wei Miao, Jad Beyhum, Jonas Striaukas, and Ingrid Van Keilegom, arXiv:2502.09740v1, 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{Evaluation metrics for the DR results on the 50 dimensional synthetic hierarchical dataset. The first two quantify the preservation of local structure, and the following two quantify the preservation of global structure (see Section~ for additional details). RT Accuracy stands for Random Triplet Accuracy, and CT Accuracy stands for Centroid Triplet Accuracy.}
\begin{tabular}{lccccc}
\toprule
Metric & t-SNE & LargeVis & UMAP & TriMap & PaCMAP \\
\midrule
1NN accuracy & \textbf{1.000 $\pm$ 0.000} & \textbf{1.000 $\pm$ 0.000} & \textbf{1.000 $\pm$ 0.000} & \textbf{1.000 $\pm$ 0.000} & \textbf{1.000 $\pm$ 0.000} \\
SVM accuracy & \textbf{1.000 $\pm$ 0.000} & \textbf{1.000 $\pm$ 0.000} & \textbf{1.000 $\pm$ 0.000} & \textbf{1.000 $\pm$ 0.000} & \textbf{1.000 $\pm$ 0.000}\\
RT accuracy & 0.508 $\pm$ 0.003 & 0.506 $\pm$ 0.004 & {0.506 $\pm$ 0.001} & 0.665 $\pm$ 0.003 & \textbf{{0.801 $\pm$ 0.002}}\\
CT accuracy& 0.499 $\pm$ 0.002 & 0.496 $\pm$ 0.006 & {0.497 $\pm$ 0.001} & 0.651 $\pm$ 0.003 & \textbf{0.794 $\pm$ 0.001}\\
Running Time & 00:02:18 & 00:15:73 & 00:00:45 & {00:00:54} & \textbf{00:00:24}\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Yingfan Wang
Haiyang Huang
Cynthia Rudin
Yaron Shaposhnik

"Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization" by Yingfan Wang, Haiyang Huang, Cynthia Rudin, and Yaron Shaposhnik, arXiv:2012.04456v2, 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{FVA High Yield Wasserstein Radii}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
Confidence Level & 0.80 & 0.85 & 0.90 & 0.95 & 0.99 & 0.999 \\
 \hline
W Radius delta & 3.8 & 4.2 & 4.6 & 5.3 & 6.6 & 8.1 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.03993v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Perfect sampling of stochastic matching models with reneging" by Thomas Masanet and Pascal Moyal, arXiv:2202.09341v2, 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{Percentage of $p > 0.05$, across 50 Trials.}
\begin{tabular}{ccc}
\hline
\textbf{Model} & \textbf{RCT} & \textbf{Non-RCT} \\
\hline
TARNet & 0 & 0 \\
CausalForest & 12\% & 6\%\\
S-BART & 12\% & 8\% \\
T-BART & 12\% & 6\% \\
S-engression & 18\% & 6\%\\
T-engression & 24\% & 8\%\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Daniel de Vassimon Manela
Linying Yang
Robin J. Evans

"Testing Generalizability in Causal Inference" by Daniel de Vassimon Manela, Linying Yang, and Robin J. Evans, arXiv:2411.03021v2, 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}{lcc}
\hline
Estimand & Estimate & Monte-Carlo SD \\
\hline
&&\\
\multicolumn{3}{c}{{\bf Setting 1: Biomarker response}} \\
Policy & -0.085 & 0.044 \\
Policy in S+-/Hypothetical & -0.250 & 0.130 \\
Responder Analysis & -0.059 & 0.045 \\
Policy in S+* & -0.130 & 0.065 \\
Hypothetical in S+* & -0.150 & 0.078 \\
&&\\
\multicolumn{3}{c}{{\bf Setting 2: Adherence}} \\
Policy & -0.37 &0.047 \\
Policy in S$_{++}$ & -0.32 &0.038 \\
Policy in S$_{+*}$ & -0.39 &0.051 \\
Per-Protocol & -0.26 &0.045 \\
$\alpha_{A}$ & -0.40 &0.071 \\
\hline
\end{tabular}
\caption{ {\it Point estimates and Monte-Carlo standard deviations (SD) for the estimands considered in settings 1 and 2 across 1000 simulated data sets.}}
\end{table}
\end{document}

stat

Jack Bowden
Bjoern Bornkamp
Ekkehard Glimm
Frank Bretz

"Connecting Instrumental Variable methods for causal inference to the Estimand Framework" by Jack Bowden, Bjoern Bornkamp, Ekkehard Glimm, and Frank Bretz, arXiv:2012.03786v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of TrueLearn algorithms are evaluated using Precision (Prec.), Recall (Rec.) and F1 Score (F1). The best and second best performance is indicated in \textbf{bold} and \emph{italic} faces respectively. TrueLearn models outperform the best baseline most times ($p< 0.01$ in a one-tailed paired t-test are marked with $\cdot^{*}$).}
\begin{tabular}{cccccc}
\hline
& Model & Acc. & Prec. & Rec. & F1 \\
\hline
&\texttt{Cosine}& {55.08} & {57.86} & 58.45 & 54.06 \\
&$\texttt{Jaccard}_{\mathcal{C}}$& 55.46 & 57.81 & 60.36 & 55.03 \\
Baselines&$\texttt{Jaccard}_{\mathcal{U}}$ & 64.06 & 57.85 & {72.76} & {61.22} \\
&TF (Binary)	& 55.19 &	56.71 & 66.60 &	57.38 \\
&TF (Cosine) & 55.11 &	56.75	& 65.95	 & 57.11 \\
&KT & 54.99 & 53.25 & 28.56 & 34.51 \\
\hline
&Interest & 58.13 & 52.08 & \textit{78.61}$^{*}$ & 63.00$^{*}$ \\
TrueLearn & Novelty & \textit{64.78}$^{*}$	 & \textit{58.52}$^{*}$ & \textbf{80.91}$^{*}$ & \textbf{65.53}$^{*}$ \\
&INK & \textbf{78.32}$^{*}$	 & \textbf{64.32}$^{*}$	 & {64.03} & \textit{64.00}$^{*}$ \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Yuxiang Qiu
Karim Djemili
Denis Elezi
Aaneel Shalman
María Pérez-Ortiz
Emine Yilmaz
John Shawe-Taylor
Sahan Bulathwela

"A Toolbox for Modelling Engagement with Educational Videos" by Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor, and Sahan Bulathwela, arXiv:2401.05424v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Eric Chen
Xi Chen
Arian Maleki
Shirin Jalali

"Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems" by Eric Chen, Xi Chen, Arian Maleki, and Shirin Jalali, arXiv:2501.04608v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Cumulative return Comparison}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Model} & \textbf{Number of Drawdown \(> \)0.001} & \textbf{Cumulative Return (30mins)} \\ \hline
Quantile-Attention LSTM & 320 & 0.102117\% \\ \hline
FutureQuant Transformer & 193 & 0.1193\% \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Wenhao Guo
Yuda Wang
Zeqiao Huang
Changjiang Zhang
Shumin ma

"Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer" by Wenhao Guo, Yuda Wang, Zeqiao Huang, Changjiang Zhang, and Shumin ma, arXiv:2505.05595v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Jean-Philippe Boussemart
Walter Briec
Raluca Parvulescu
Paola Ravelojaona

"$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle" by Jean-Philippe Boussemart, Walter Briec, Raluca Parvulescu, and Paola Ravelojaona, arXiv:2212.04724v2, 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[table]{xcolor}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 \Xhline{6\arrayrulewidth}
 \multirow{2}{*}{Environment}& Task & Difficulty Settings &Environments Type & Input\\
 & \# of Stages & Point of View & Action Space & Observation Types\\
 \Xhline{6\arrayrulewidth}
 \rowcolor[HTML]{EEEEEE}
 &LunarLander-V2&-&2D& Coordinate \& Velocity \& Angle \& Boolean flag value \\
 \rowcolor[HTML]{EEEEEE}
 \multirow{-2}{*}{\cellcolor[HTML]{EEEEEE} OpenAI Gym}&2-stage & StaticView &Continous& State-based RL\\
 \multirow{2}{*}{MuJoCo} &CartPole-Reacher&-&3D& Joint Value \& Goal Position\\
 &2-stage &StaticView&Continuous& State-based RL\\
 \rowcolor[HTML]{EEEEEE}
 &UR5-Reacher&-&3D& Joint Value \& Goal Position\\
 \rowcolor[HTML]{EEEEEE}
 \multirow{-2}{*}{\cellcolor[HTML]{EEEEEE} MuJoCo}&2-stage &StaticView&Continuous& State-based RL\\
 & Seen \& Unseen Navigation & - &3D& RGB-D\\
 \multirow{-2}{*}{ViZDoom} & 2-stage &Egocentric View&Discrete& Visual RL\\
 \rowcolor[HTML]{EEEEEE}&Toddler playroom \& Cross Maze &-&3D& RGB\\
 \rowcolor[HTML]{EEEEEE}
 \multirow{-2}{*}{Minecraft}& 2-stage& Egocentric View& Discrete&Visual RL\\
 \hline
 \hline
 \rowcolor[HTML]{EEEEEE}
 &Shelf-delivery&Level3&2D& Internal state of the surrounding tiles\\
 \rowcolor[HTML]{EEEEEE}
 \multirow{-2}{*}{RWARE}&Non-humanoid&StaticView& Discrete&3-stage \& State-based RL\\
 \rowcolor[HTML]{FFFFFF}
 & Navigation & - &2D& Position-based value\\
 \rowcolor[HTML]{FFFFFF}
 \multirow{-2}{*}{\cellcolor[HTML]{FFFFFF} Gridworld} &2-stage &StaticView&Discrete& State-based RL\\
 \Xhline{6\arrayrulewidth}
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Junseok Park
Hyeonseo Yang
Min Whoo Lee
Won-Seok Choi
Minsu Lee
Byoung-Tak Zhang

"From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning" by Junseok Park, Hyeonseo Yang, Min Whoo Lee, Won-Seok Choi, Minsu Lee, and Byoung-Tak Zhang, arXiv:2501.17842v1, 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}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Total costs values for setting A and B.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
			\hline
			$N$ & $\mathcal{C}^N_A(H)$ & $\mathcal{C}^N_B(H)$\\
			\hline
			0 & 32.9653 & 201.7153 \\
			1 & 27.3081 & 64.8081 \\
			2 & 28.0268 & 42.6101 \\
			3 & 30.3572 & 37.3884 \\
			4 & 33.3387 & 37.0887 \\
			5 & 36.6489 & 38.7322 \\
			\hline
		\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.


q-fin

"The Composition of Wage Differentials between Migrants and Natives" by Panagiotis Nanos and Christian Schluter, arXiv:1306.1781v3, licensed under CC BY 3.0. 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{An Interesting Table.}
\begin{tabular}{rl}
 \toprule % from booktabs package
 \bfseries Dataset & \bfseries Result\\
 \midrule % from booktabs package
 Data1 & 0.12345\\
 Data2 & 0.67890\\
 Data3 & 0.54321\\
 Data4 & 0.09876\\
 \bottomrule % from booktabs package
 \end{tabular}
\end{table}
\end{document}

stat

Brady Neal
Chin-Wei Huang
Sunand Raghupathi

"RealCause: Realistic Causal Inference Benchmarking" by Brady Neal, Chin-Wei Huang, and Sunand Raghupathi, arXiv:2011.15007v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison of proposed model with state-of-the-art result on GRID }
\begin{tabular}{|c|c|c|c|c|}
\hline
Test SNR & \multicolumn{2}{c|}{-5 dB} & \multicolumn{2}{c|}{0 dB} \\ \hline
Evaluation Metrics & \multicolumn{4}{c|}{$\Delta$PESQ} \\ \hline
Deep-learning-based AV-SE & \multicolumn{2}{c|}{1.09} & \multicolumn{2}{c|}{0.77} \\ \hline
OVA Approach & \multicolumn{2}{c|}{0.24} & \multicolumn{2}{c|}{0.13} \\ \hline
L2L Model & \multicolumn{2}{c|}{0.28} & \multicolumn{2}{c|}{0.16} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"AMFFCN: Attentional Multi-layer Feature Fusion Convolution Network for Audio-visual Speech Enhancement" by Xinmeng Xu and Jianjun Hao, arXiv:2101.06268v3, 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{DoE table}
\begin{tabular}{|c|c|}
		\hline
		Time-delay ($L$) & Friction values ($f_S$, $f_D$)\\
		\hline
		Low & Low \\
		Low & High \\
		High & Low \\
		High & High \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

"Reinforcement Learning for Control of Valves" by Rajesh Siraskar, arXiv:2012.14668v2, 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{Percentage of Frequency for Each Classification}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|r|}
\hline
\textbf{Classification} & \textbf{Percentage (\%)} \\
\hline\hline
Perfective & 51.76 \\
Adaptive & 22.58 \\
Corrective & 18.54 \\
Adaptive Perfective & 4.97 \\
Corrective Perfective & 1.23 \\
Corrective Adaptive & 0.46 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Krishna Upadhyay
Vinaik Chhetri
A. B. Siddique
Umar Farooq

"Analyzing the Evolution and Maintenance of Quantum Software Repositories" by Krishna Upadhyay, Vinaik Chhetri, A. B. Siddique, and Umar Farooq, arXiv:2501.06894v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs" by He Yu and Jing Liu, arXiv:2501.11880v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Ablation study over our design choices.} Adding our proposed adaptations continuously improves the glioma subtype classification performance.}
\begin{tabular}{lcccccccccc}
 \toprule
 Semi-supervised & \multicolumn{2}{c}{MCC} & \multicolumn{2}{c}{AUC} & \multicolumn{6}{c}{F1 Score} \\ \cmidrule(l){2-11}
 & & & & & \multicolumn{2}{c}{Astro} & \multicolumn{2}{c}{GBM} & \multicolumn{2}{c}{Oligo} \\ \cmidrule(l){6-11}
 & Int. & Ext. & Int. & Ext. & Int. & Ext. & Int. & Ext. & Int. & Ext. \\ \midrule
 Concat Tokens & 0.40 & 0.29 & 0.84 & 0.73 & 0.41 & 0.31 & 0.93 & 0.82 & 0.29 & \textbf{0.22} \\
 + Per-Image Pos Embedding & 0.63 & 0.36 & 0.93 & 0.79 & 0.68 & 0.45 & 0.95 & 0.83 & 0.35 & 0.12 \\
 + MRI Sequence Embedding & \textbf{0.74} & 0.49 & 0.94 & \textbf{0.86} & 0.71 & 0.65 & \textbf{0.97} & 0.86 & \textbf{0.71} & 0.13 \\
 + Full Sequence Masking & \textbf{0.74} & \textbf{0.57} & \textbf{0.95} & \textbf{0.86} & \textbf{0.76} & \textbf{0.71} & 0.96 & \textbf{0.89} & 0.67 & 0.21 \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Daniel Scholz
Ayhan Can Erdur
Viktoria Ehm
Anke Meyer-Baese
Jan C. Peeken
Daniel Rueckert
Benedikt Wiestler

"MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysis" by Daniel Scholz, Ayhan Can Erdur, Viktoria Ehm, Anke Meyer-Baese, Jan C. Peeken, Daniel Rueckert, and Benedikt Wiestler, arXiv:2509.06617v1, 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{Column $n$ is the number of solutions in the surrogate model, column a) is the time to compute all surrogate elements, $u_{\theta_i}$, in FreeFem, column b) is the time required for the MCMC exploration with $800,000$ iterations, column c) is the total time required, and column $\varepsilon_\rho$ is the error estimate. The sum in the last line is the time required to compute all models. }
\begin{tabular}{ccccc}
 \hline
 \textbf{n} & \textbf{a)} & \textbf{b)} & \textbf{c)} & \textbf{$\varepsilon_\rho$} \\
 \hline
$n = 5$ & 0.769 min & 2.2 min & 2.969 min & $30/2^2$ \\
$n = 9$ & 1.369 min & 2.4 min & 3.769 min & $30/2^3$\\
$n = 17$ & 2.567 min & 3.3 min & 5.867 min & $30/2^4$ \\
$n = 33$ & 4.970 min & 3.5 min & 8.470 min & $30/2^5$ \\
 \hline
 & & Sum = & 21.075 min &
\end{tabular}
\end{table}
\end{document}

stat

"A Physics Based Surrogate Model in Bayesian Uncertainty Quantification involving Elliptic PDEs" by A. Galaviz, J. A. Christen, and A. Capella, arXiv:2312.09303v1, 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}{|c|ccc|ccc|}
\midrule
 Team Size & Only Male & Only Female & P-Value & More Male Authors & More Female Authors & P-Value\\ \midrule
1 & 3.49 & 3.36 & 0.016 & - & - & - \\
2 & 4.62 & 4.2 & 0.6646 & - & - & - \\
3 & 5.32 & 4.12 & 0.0004 & 4.85 & 4.56 & 0.1186 \\
4 & 4.91 & 4.37 & 0.6437 & 5.02 & 4.74 & 0.1708 \\
5 & 5.36 & 4.66 & 0.3596 & 5.44 & 4.91 & 0.3583 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{The table shows the average number of citations received by papers based on the gender composition of the author teams. It reveals that sole-authored papers by male authors receive slightly more citations on average than those by female authors, with a statistically significant difference. For teams of three or more authors, papers authored by male-only teams tend to receive more citations than those by female-only teams, but the differences are generally not statistically significant when considering teams with a mix of male and female authors. This suggests that gender-related citation disparities may be more pronounced in sole or small-group authorship scenarios.}
\end{table}
\end{document}

cs

"Navigating Gender Disparities in Communication Research Leadership: Academic Recognition, Career Development, and Compensation" by Diego F. M. Oliveira and Qian Huang, arXiv:2501.08401v2, 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|c|}
 \hline
 \textbf{Metric} & \textbf{Value} \\
 \hline

 Recall & 93\%\\
 $TNR$ & 87\%\\
 Balanced Accuracy & 90\%\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Metrics of the model obtained for the VRPTW.}
\end{table}
\end{document}

math

Mouad Morabit
Guy Desaulniers
Andrea Lodi

"Machine-learning-based arc selection for constrained shortest path problems in column generation" by Mouad Morabit, Guy Desaulniers, and Andrea Lodi, arXiv:2201.02535v1, 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{\footnotesize{\textbf{Diffusion Policy Baselines:} Performance on LIBERO-10 tasks using diffusion policies (DP)~ without language conditioning for BehaviorRetrieval (BR)~, FlowRetrieval (FR)~. These experiments replicate the training setup for BR and FR.}}
\begin{tabular}{lccccc}
 \toprule
 Method & Stove-Moka & Bowl-Cabinet & Soup-Cheese & Mug-Mug & Book-Caddy \\
 \midrule
 BR & $36.67\% \pm 1.44$ & $68.0\% \pm 2.49$ & $34.0\% \pm 2.49$ & $55.33\% \pm 1.44$ & $42.0\% \pm 1.63$\\
 FR & $68.67\% \pm 2.37$ & $56.0\% \pm 4.32$ & $18.0\% \pm 3.4$ & $56.0\% \pm 3.4$ & $35.33\% \pm 6.28$ \\
 \midrule
 \midrule
 Method & Mug-Microwave & Pots-On-Stove & Soup-Sauce & Cream cheese-Butter & Mug-Pudding \\
 \midrule
 BR & $30.67\% \pm 0.54$ & $0.00\% \pm 0.00$ & $10.67\% \pm 1.96$ & $24.0\% \pm 0.94$& $9.33\% \pm 1.44$\\
 FR & $32.67\% \pm 3.31$ & $68.0\% \pm 2.49$ & $6.0\% \pm 0.00$ & $35.33\% \pm 0.54$ & $8.0\% \pm 1.89$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Marius Memmel
Jacob Berg
Bingqing Chen
Abhishek Gupta
Jonathan Francis

"STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning" by Marius Memmel, Jacob Berg, Bingqing Chen, Abhishek Gupta, and Jonathan Francis, arXiv:2412.15182v2, 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}{rrrrr}
 \hline
Tickers & ADV & $\hat{\sigma}_{D, \text{1HR}}^2$ & $\hat{\sigma}_{D, \text{10MIN}}^2$ & $\hat{\sigma}_{D, \text{1MIN}}^2$ \\
 \hline
BTI & 628652.6 & 0.0075 & 0.0135 & 0.0501 \\
 NPN & 558210.8 & 0.0017 & 0.0093 & 0.0494 \\
 AGL & 1150362.2 & 0.0040 & 0.0107 & 0.0341 \\
 (**)MNP & 340381.4 & 0.0059 & 0.0105 & 0.0457 \\
 SOL & 1209754.6 & 0.0057 & 0.0206 & 0.0868 \\
 SBK & 1885551.0 & 0.0037 & 0.0151 & 0.0664 \\
 NED & 903670.8 & 0.0048 & 0.0195 & 0.0821 \\
 ABG & 1321528.8 & 0.0080 & 0.0239 & 0.1080 \\
 SHP & 751731.0 & 0.0104 & 0.0200 & 0.0919 \\
 (*)FSR & 7698648.0 & 0.0055 & 0.0207 & 0.0715 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{The table indicates the Average Daily Volume (ADV) for the various tickers over the period of consideration where (**) and (*) indicate the least and most liquid ticker respectively. The table further shows the daily volatility for the various tickers by re-scaling the volatility computed using Algorithm from Figure (a) to (c) with $\sqrt{24}, \sqrt{6*24}$ and $\sqrt{60*24}$ respectively. The fact that the re-scaled volatilities do not line up demonstrate that the price discovery in a continuous double auction market is not well represented as a Brownian motion.}
\end{table}
\end{document}

q-fin

"Revisiting the Epps effect using volume time averaging: An exercise in R" by Patrick Chang, Roger Bukuru, and Tim Gebbie, arXiv:1912.02416v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}\hline
& $\theta=\theta_{1}$ & $\theta=\theta_{2}$\\\hline
$P_{a}(\hat{\theta}(X)=\theta\,|\,A_{1}=\{1,2\})$ & $1/2$ & $1/2$\\\hline
$P_{b}(\hat{\theta}(X)=\theta\,|\,A_{1}=\{1,2\})$ & $1/4$ & $3/4$\\\hline
$P_{a}(\hat{\theta}(X)=\theta\,|\,A_{2}=\{1,3\})$ & $1/3$ & $2/3$\\\hline
$P_{b}(\hat{\theta}(X)=\theta\,|\,A_{2}=\{1,3\})$ & $1/6$ & $5/6$\\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On Resolving Problems with Conditionality and Its Implications for Characterizing Statistical Evidence" by Michael Evans and Constantine Frangakis, arXiv:2202.09911v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sample table title}
\begin{tabular}{lll}
 \toprule
 \multicolumn{2}{c}{Part} \\
 \cmidrule(r){1-2}
 Name & Description & Size ($\mu$m) \\
 \midrule
 Dendrite & Input terminal & $\sim$100 \\
 Axon & Output terminal & $\sim$10 \\
 Soma & Cell body & up to $10^6$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"In-context learning for medical image segmentation" by Eichi Takaya and Shinnosuke Yamamoto, arXiv:2412.13299v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Global Bias-Corrected Divide-and-Conquer by Quantile-Matched Composite for General Nonparametric Regressions" by Yan Chen and Lu Lin, arXiv:2201.12597v1, 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|c|cccccc}
	\toprule
	\textbf{Vessel} & \textbf{0D model} & $\varepsilon_P^{\text{RMS}} (\%)$ & $\varepsilon_Q^{\text{RMS}} (\%)$ & $\varepsilon_P^{\text{SYS}} (\%)$ & $\varepsilon_Q^{\text{SYS}} (\%)$ & $\varepsilon_P^{\text{DIAS}} (\%)$ & $\varepsilon_Q^{\text{DIAS}} (\%)$ \\
	\midrule
	\multirow{2}{*}{Abdominal aorta} & 0D-NL & 0.231 & 0.620 & -0.027 & 0.424 & -0.013 & 0.127\\
	& 0D-L & 0.437 & 0.680 & 0.551 & 0.970 & -0.482 & 0.092\\
	\midrule
	\multirow{2}{*}{Iliac arteries} & 0D-NL & 0.149 & 0.406 & 0.051 & -0.484 & 0.010 & 0.212\\
	& 0D-L & 0.462 & 0.695 & 0.823 & 1.010 & -0.458 & -0.445\\
	\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Aortic bifurcation. Relative errors (in \%) for pressure and flow between for both nonlinear (0D-NL) and linear (0D-L) 0D results with respect to the 1D results (1D) at the midpoint of the vessel, computed according to the relative error metrics ().}
\end{table}
\end{document}

math

Beatrice Ghitti
Eleuterio F. Toro
Lucas O. Müller

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Details of the training datasets' construction. For the UR5e, the $\vec{q}_j$ quantities are defined as\textsuperscript{*} $(x,\;y,\; z)$ positions of the end effector.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\hline
 & Pendulum & SCARA & UR5e \\
\hline
\# data & $808$ & $43344$ & $33658$ \\
\# trajectories & $8$ & $144$ & $231$ \\
sampling freq. (Hz) & $100$ & $100$ & $50$ \\
ranges $t_f$ (s) & $[1.0, 1.5]$ & $[2.5, 3.5]$ & $[2.5, 3.0]$ \\
\# samples $t_f$ & $2$ & $3$ & $2$ \\
ranges $\vec{q}_i$ (rad or m) & $[-\pi/4 , \pi/4]$ & $[0,0]$ & $[0.3, 0.5]$ \\
 & & $\left[ 0, \frac{3}{4}\pi \right]$ & $ 0.0 \times -0.5$ \\
 & & & $[0.0, 0.5]$ \\
\# samples $\vec{q}_i$ & $4$ & $1 \times 4$ & $3 \times 3 \times 3$ \\
ranges $\vec{q}_f$ (rad or m) & $[-\pi/4, \pi/4]$ & $[0, \pi ]$ & $[0.3, 0.5]$ \\
 & & $[0, \frac{3}{4}\pi]$ & $[0.0, 0.5]$ \\
 & & & $[0.0, 0.5]$ \\
\# samples $\vec{q}_f$ & $4$ & $4 \times 4$ & $3 \times 3 \times 3$ \\
 add. requirement & $\lvert q_f \rvert > \lvert q_i\rvert$ & $q_{f,2} > q_{i,2}$ & $\sum_i \lvert\Delta x_i\rvert > 0.1$\\
 & $ \lvert \Delta q \rvert > \pi/16$ & & $z_f - z_i>0$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Domenico Dona'
Giovanni Franzese
Cosimo Della Santina
Paolo Boscariol
Basilio Lenzo

"Real-Time Generation of Near-Minimum-Energy Trajectories via Constraint-Informed Residual Learning" by Domenico Dona', Giovanni Franzese, Cosimo Della Santina, Paolo Boscariol, and Basilio Lenzo, arXiv:2501.09450v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


\documentclass{article}
\usepackage{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
Model & Algorithm & Currencies\\ \hline
CEU & Constant & EUR/USD\\ \hline
DEU & DQN & EUR/USD\\ \hline
PEU & PPO & EUR/USD \\ \hline
CGU & Constant & GBP/USD\\ \hline
DGU & DQN & GBP/USD\\ \hline
PGU & PPO & GBP/USD \\ \hline
CAU & Constant & AUD/USD\\ \hline
DAU & DQN & EUR/USD\\ \hline
PAU & PPO & EUR/USD \\ \hline
\end{tabular}
\end{adjustbox}
\caption{ Model Code Information}
\end{table}
\end{document}

q-fin

"Deep Reinforcement Learning for Foreign Exchange Trading" by Yun-Cheng Tsai and Chun-Chieh Wang, arXiv:1908.08036v2, 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{Comparison between vanilla GNNs and certified GNNs under ELEGANT over three popular GNNs across three real-world datasets. Here ACC denotes node classification accuracy, and E- prefix marks out the GNNs under ELEGANT with certification. $\uparrow$ denotes the larger, the better; $\downarrow$ represents the opposite. Numerical values are in percentage, and the best ones are in bold.}
\begin{tabular}{cccccccccccc}
\hline
\hline
 & \multicolumn{3}{c}{\textbf{German Credit}} & & \multicolumn{3}{c}{\textbf{Recidivism}} & & \multicolumn{3}{c}{\textbf{Credit Defaulter}} \\
 \cline{2-4} \cline{6-8} \cline{10-12}
 & \textbf{ACC ($\uparrow$)} & \textbf{Bias ($\downarrow$)} & \textbf{FCR ($\uparrow$)} & & \textbf{ACC ($\uparrow$)} & \textbf{Bias ($\downarrow$)} & \textbf{FCR ($\uparrow$)} & & \textbf{ACC ($\uparrow$)} & \textbf{Bias ($\downarrow$)} & \textbf{FCR ($\uparrow$)} \\
 \hline
 \hline
\textbf{SAGE} &67.3 $_{\pm 2.14}$ & 50.6 $_{\pm 15.9}$ & N/A & & 89.8 $_{\pm 0.66}$ & 9.36 $_{\pm 3.15}$ & N/A & & \textbf{75.9 $_{\pm 2.18}$} & 13.0 $_{\pm 4.01}$ & N/A \\
\hline
\textbf{E-SAGE} &\textbf{71.0 $_{\pm 1.27}$} & \textbf{16.3 $_{\pm 10.9}$} & 98.7 $_{\pm 1.89}$ & & \textbf{89.9 $_{\pm 0.90}$} &\textbf{6.39 $_{\pm 2.85}$} & 94.3 $_{\pm 6.65}$ & & 73.4 $_{\pm 0.50}$ & \textbf{8.94 $_{\pm 0.99}$} & 94.3 $_{\pm 3.30}$ \\
\hline
\hline
\textbf{GCN} & \textbf{59.6 $_{\pm 3.64}$} & 37.4 $_{\pm 3.24}$ & N/A & & \textbf{90.5 $_{\pm 0.73}$} & 10.1 $_{\pm 3.01}$ & N/A & & \textbf{65.8 $_{\pm 0.29}$} & 11.1 $_{\pm 3.22}$ & N/A \\
\hline
\textbf{E-GCN} & 58.2 $_{\pm 1.82}$ &\textbf{3.52 $_{\pm 3.77}$} & 96.3 $_{\pm 1.89}$ & & 89.6 $_{\pm 0.74}$ & \textbf{9.56 $_{\pm 3.22}$} & 96.0 $_{\pm 3.56}$ & & 65.2 $_{\pm 0.99}$ &\textbf{7.28 $_{\pm 1.46}$} & 92.7 $_{\pm 5.19}$ \\
\hline
\hline
\textbf{JK} & \textbf{63.3 $_{\pm 4.11}$} & 41.2 $_{\pm 18.1}$ & N/A & & \textbf{91.9 $_{\pm 0.54}$} & 10.1 $_{\pm 3.15}$ & N/A & & 76.6 $_{\pm 0.69}$ & 9.24 $_{\pm 0.60}$ & N/A \\
\hline
\textbf{E-JK} & 62.3 $_{\pm 4.07}$ & \textbf{22.4 $_{\pm 1.95}$} & 97.0 $_{\pm 3.00}$ & &89.3 $_{\pm 0.33}$ & \textbf{6.26 $_{\pm 2.78}$} & 89.5 $_{\pm 10.5}$ & &\textbf{77.7 $_{\pm 0.27}$} & \textbf{3.37 $_{\pm 2.64}$} & 99.3 $_{\pm 0.47}$ \\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

stat

Yushun Dong
Binchi Zhang
Hanghang Tong
Jundong Li

"Certified Defense on the Fairness of Graph Neural Networks" by Yushun Dong, Binchi Zhang, Hanghang Tong, and Jundong Li, arXiv:2311.02757v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll} \toprule
			Variable & Description \\ \midrule
			$CR_{init}$ & Churn Rate at time period 0 \\
			$CR_{nat}$ & Natural Rate of Churn to which churn rate decays \\
			$CR_{t}$ & Churn Rate at time period t\\
			$k$ & decay constant describing rate of decay of churn rate\\ \bottomrule
		\end{tabular}
\end{adjustbox}
\caption{Additional variable definitions for the exponential model}
\end{table}
\end{document}

q-fin

"A generalized Bayesian framework for the analysis of subscription based businesses" by Rahul Madhavan and Ankit Baraskar, arXiv:1704.05729v1, 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}{cccc}
 \hline
Layer & Type & Maps and size & Kernel size \\
\hline
 \centering
 0 & Input & 1 map of $61 \times 61$ neurons & \\
 1 & Convolution2D & 32 maps of $56 \times 56$ neurons & $6 \times 6$ \\
 2 & Maxpooling2D & 32 maps of $28 \times 28$ neurons & $2 \times 2$ \\
 3 & Convolution2D & 32 maps of $24 \times 24$ neurons & $5 \times 5$ \\
 4 & Maxpooling2D & 32 maps of $12 \times 12$ neurons & $2 \times 2$ \\
 5 & Convolution2D & 32 maps of $9 \times 9$ neurons & $4 \times 4$ \\
 6 & Maxpooling2D & 32 maps of $5 \times 5$ neurons & $2 \times 2$ \\
 7 & Dense & $150$ neurons & \\
 8 & Dropout & & \\
 9 & Dense & 1 neuron & \\
\hline
 \end{tabular}
\end{table}
\end{document}

eess

Ylenia Giarratano
Eleonora Bianchi
Calum Gray
Andrew Morris
Tom MacGillivray
Baljean Dhillon
Miguel O. Bernabeu

"Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics" by Ylenia Giarratano, Eleonora Bianchi, Calum Gray, Andrew Morris, Tom MacGillivray, Baljean Dhillon, and Miguel O. Bernabeu, arXiv:1912.09978v2, 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{Comparison of the CONV, Lewis-FRFT, COS and SFP methods in error convergence and CPU time for pricing European deep at/in/out-of-the-money put options under the BSM model. The range of $K$ is from 1 to 200. Spectral convergence is observed in the SFP method. The parameters are taken from \textbf{BSM--Para1}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|c||c|cc|c|} % centered columns (4 columns)
\hline
\multicolumn{4}{|c||}{\textbf{CONV}}&\multicolumn{4}{c|}{\textbf{Lewis-FRFT}}\\
\hline
N&$R_\infty$&$R_2$&Time&N&$R_\infty$&$R_2$&Time\\
\hline
8&20.49&180.1&0.00806&8&32.53&356&0.02111\\
16&6.292&53.54&0.00941&16&16.1&156.4&0.02525\\
32&1.998&13.83&0.01012&32&7.933&69.38&0.04571\\
64&0.5709&3.286&0.01641&64&4.765&38.81&0.09151\\
\hline
\multicolumn{8}{c}{\textbf{}}\\
\hline
\multicolumn{4}{|c||}{\textbf{COS}}&\multicolumn{4}{c|}{\textbf{SFP}}\\
\hline
N&$R_\infty$&$R_2$&Time&U&$R_\infty$&$R_2$&Time\\
\hline
8&5.046&10.06&0.00706&8&1.028e-01&5.893e-01&0.00901\\
16&5.109&8.447&0.00841&16&2.818e-04&1.559e-03&0.00951\\
32&5.111&8.449&0.01113&32&1.598e-09&3.732e-09&0.01312\\
64&5.111&8.449&0.01423&64&1.991e-13&5.801e-13&0.01731\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Singular Fourier-Padé Series Expansion of European Option Prices" by Tat Lung Chan, arXiv:1706.06709v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging" by Wei He, Naoto Yokoya, and Xin Yuan, arXiv:2012.15104v1, 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{Performance of BasisChange w/ and w/o BAT}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}\hline
limb\_in &limb\_out & Degree &No-BAT &BAT &Speedup \\\hline
12 &28 &65536 &364.31 &85.42 & \textbf{4.26$\times$} \\
12 &36 &65536 &698.05 &96.73 & \textbf{7.22$\times$} \\
16 &40 &65536 &839.83 &96.16 & \textbf{8.73$\times$} \\
20 &48 &65536 &838.52 &106.38 & \textbf{7.88$\times$} \\
24 &56 &65536 &768.10 &121.21 & \textbf{6.34$\times$} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jianming Tong
Tianhao Huang
Leo de Castro
Anirudh Itagi
Jingtian Dang
Anupam Golder
Asra Ali
Jevin Jiang
Arvind
G. Edward Suh
Tushar Krishna

"Leveraging ASIC AI Chips for Homomorphic Encryption" by Jianming Tong, Tianhao Huang, Leo de Castro, Anirudh Itagi, Jingtian Dang, Anupam Golder, Asra Ali, Jevin Jiang, Arvind, G. Edward Suh, and Tushar Krishna, arXiv:2501.07047v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Calibration Accuracy, by Sub-groups}
\begin{tabular}{llcccc}
 \hline
 \hline
 & & & Fraction & & Pearson $\chi^2$-Test \\ \cline{3-6}
 \multicolumn{2}{l}{\textbf{Sub-group}} & & & & \\
 \hline
 \multicolumn{2}{l}{\textbf{Vote}} & & & & \\
 & Test & & 0.641 & & \multirow{2}{*}{$\chi^2(1) = 5.63, p = 0.018$} \\
 & Lottery & & 0.788 & & \\
 \hline
 \multicolumn{2}{l}{\textbf{Gender}} & & & & \\
 & Men & & 0.704 & & \multirow{2}{*}{$\chi^2(1) = 0.65, p = 0.422$} \\
 & Women & & 0.658 & & \\
 \hline
 \multicolumn{2}{l}{\textbf{Student status}} & & & & \\
 & Students & & 0.693 & & \multirow{2}{*}{$\chi^2(1) = 0.05, p = 0.818$} \\
 & Non-students & & 0.679 & & \\
 \hline
 \multicolumn{2}{l}{\textbf{Degree in higher education}} & & & & \\
 & Yes & & 0.731 & & \multirow{2}{*}{$\chi^2(1) = 4.37, p = 0.037$} \\
 & No & & 0.610 & & \\
 \hline
 \multicolumn{2}{l}{\textbf{Sample question performance}} & & & & \\
 & 0 correct & & 0.900 & & \multirow{4}{*}{$\chi^2(3) = 5.78, p = 0.123$} \\
 & 1 correct & & 0.590 & & \\
 & 2 correct & & 0.636 & & \\
 & 3 correct & & 0.726 & & \\
 \hline
 \hline
 \end{tabular}
\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{amsfonts}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 & $\mathbb{E}(\Delta t)$ & $\sigma(\Delta t)$ & $Q_1(\Delta t)$ & $Q_2(\Delta t)$ & $Q_3(\Delta t)$& Number of trades \\ %\hline
{\textsc{itxeb}} & 7~min & 10~min & 1~min & 3~min & 8 min & 22~227 \\ %\hline
{\textsc{itxex}}& 8~min & 12~min & 2~min & 4~min & 10 min & 18~152 \\ %\hline
{\textsc{itxes}}& 17~min & 24~min & 3~min & 9~min & 21 min & 7~194 \\ %\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Achraf Bahamou
Maud Doumergue
Philippe Donnat

"Hawkes processes for credit indices time series analysis: How random are trades arrival times?" by Achraf Bahamou, Maud Doumergue, and Philippe Donnat, arXiv:1902.03714v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Temporal Analysis of Adversarial Attacks in Federated Learning" by Rohit Mapakshi, Sayma Akther, and Mark Stamp, arXiv:2501.11054v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"An Active Parameter Learning Approach to The Identification of Safe Regions" by Aneesh Raghavan and Karl H Johansson, arXiv:2412.10627v1, 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}{lccc}
			\hline
			& Setting A & Setting B & Setting C \\
			\hline
			$n=1,500$ & & & \\
			$n_{12}$ & 256(22)& 189(13) & 164(34) \\
			$n_{prev}$ & 109(12)& 81(9) & 64(8) \\
			$n_{13}$ & 484(19)& 293(15) & 352(111) \\
			$n_{23}$ & 186(18)& 99(11) & 102(38) \\
			\hline
			$n=10,000$ & & & \\
			$n_{12}$ & 1806(88) & 1252 (35) & 1092(209) \\
			$n_{prev}$ & 759(44)& 542(23) & 423(21) \\
			$n_{13}$ & 3148(54)& 1968 (39) & 2373(720) \\
			$n_{23}$ & 1310(64)& 657 (24) & 678(239) \\
			\hline
		\end{tabular}
\caption{Number of observed events per transition in the simulation study: means (standard deviations), where $n_{12}$, $n_{prev}$, $n_{13}$ and $n_{23}$ stand for the numbers of $1\rightarrow2$ (including prevalent), prevalent, $1\rightarrow3$ and $2\rightarrow3$ cases, respectively. }
\end{table}
\end{document}

stat

"Unlocking Retrospective Prevalent Information in EHRs -- a Pairwise Pseudolikelihood Approach" by Nir Keret and Malka Gorfine, arXiv:2309.01128v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of RMSE under the scenarios of consistency and inconsistency shown in Panel (d-1), (d-2) and (e-1), (e-2) in Figure . ``Remove 5\% outliers'' means that calculates RMSE after removing the top 5\% of data (under bias measure).}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{Scenarios} & \multirow{2}{*}{ Data} & \multicolumn{6}{c|}{Root mean square error (RMSE)} \\ \cline{3-8}
 & & BCMAP & MAP & rBCMAP & rMAP & NPP & MEM \\ \hline
\multirow{2}{*}{Consistency} & All & 0.141 & 0.129 & 0.131 & 0.150 & 0.285 & 0.209 \\ \cline{2-8}
 & Remove 5\% outliers & 0.109 & 0.113 & 0.103 & 0.119 & 0.207 & 0.149 \\ \hline \hline
\multirow{2}{*}{Inconsistency} & All & 0.170 & 0.107 & 0.149 & 0.133 & 0.242 & 0.238 \\ \cline{2-8}
 & Remove 5\% outliers & 0.162 & 0.085 & 0.139 & 0.099 & 0.098 & 0.157 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"Bayesian Clustering Prior with Overlapping Indices for Effective Use of Multisource External Data" by Xuetao Lu and J. Jack Lee, arXiv:2412.06098v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Model $(l_1, l_2, .. l_p)$} & \multicolumn{3}{|c|}{\textbf{Mean F1}} \\
\hline
 & \textbf{h=100} & \textbf{h=200} & \textbf{h=500} \\
\hline
VLSTM \{1\} & 57.33\% & 55.27\% & 51.63\% \\
\hline
VLSTM \{5\} & 58.97\% & 57.86\% & 55.29\% \\
\hline
VLSTM \{20\} & \emph{63.36\%} & \emph{61.74\%} & \emph{58.78\%} \\
\hline
VLSTM \{100\} & 47.38\% & 46.69\% & 46.21\% \\
\hline
\hline
VLSTM \{1, 5, 20, 100\} & \textbf{69.70\%} & \textbf{68.41\%} & \textbf{65.90\%} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Performance of LSTM at various levels}
\end{table}
\end{document}

q-fin

"VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading" by Prakhar Ganesh and Puneet Rakheja, arXiv:1809.01506v3, 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|cc|}
\hline
\textbf{Target (hyper-)rough Heston} & \multicolumn{2}{c|}{\textbf{Calibrated reversionary Heston}} \\ \hline
$H$ & $\hat \epsilon$ & $\hat H$ \\ \hline
0.1 & 0.10183756 & -0.29183935 \\ \hline
0 & 0.06258637 & -0.33057822 \\ \hline
-0.05 & 0.05932449 & -0.38692275 \\ \hline
\end{tabular}
\caption{Calibrated values of $\left( \hat \epsilon, \hat H \right)$ of the reversionary Heston model to the hyper-rough Heston volatility surfaces. The other parameters are fixed as in .}
\end{table}
\end{document}

q-fin

Reconciling rough volatility with jumps

"Reconciling rough volatility with jumps" by Eduardo Abi Jaber and Nathan De Carvalho, arXiv:2303.07222v2, 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 1B. A comparison of $L^2$-error by SAV/BDF2, R-SAV/BDF2, GSAV/BDF2 and R-GSAV/BDF2 schemes for Allen-Cahn equation at $T=200$ with different time step.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllll}
		\hline
		 & SAV & R-SAV & GSAV & R-GSAV \\
		\hline
	1e-1 & 4.30E-04 & 2.72E-04 & 1.27E-03 & 2.65E-04 \\
	1e-2 & 4.53E-05 & 2.93E-06 & 1.47E-04 & 2.90E-06 \\
	1e-3 & 6.26E-07 & 2.96E-08 & 2.38E-06 & 2.94E-08 \\
	\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A generalized SAV approach with relaxation for dissipative systems" by Yanrong Zhang and Jie Shen, arXiv:2201.12587v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Antonio Ramires
Frederic Font
Dmitry Bogdanov
Jordan B. L. Smith
Yi-Hsuan Yang
Joann Ching
Bo-Yu Chen
Yueh-Kao Wu
Hsu Wei-Han
Xavier Serra

"The Freesound Loop Dataset and Annotation Tool" by Antonio Ramires, Frederic Font, Dmitry Bogdanov, Jordan B. L. Smith, Yi-Hsuan Yang, Joann Ching, Bo-Yu Chen, Yueh-Kao Wu, Hsu Wei-Han, and Xavier Serra, arXiv:2008.11507v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c|c|c||}
 \hline
 method & $\#$ steps& $\|(u-u^h)(10)\|$ & $\|(u-u^h)(10)\|_{L^\infty}$ & $\|(p-p^h)(10)\|_{L^\infty}$ \\
 \hline
 first &3450&0.0609278&0.0512269 &0.348461 \\
 \hline
 second & 447 &0.0567343 &0.0476828 &0.344317 \\
 \hline
 vsvo &566&0.00364638 &0.00314834 &0.190205 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Variable time-step error comparison}
\end{table}
\end{document}

math

"A Doubly Adaptive Penalty Method for the Navier Stokes Equations" by Kiera Kean, Xihui Xie, and Shuxian Xu, arXiv:2201.03978v2, 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
				PA Key Registers & Used at & Configurable at \\ \hline
				\texttt{pac\_key\_u\_ns} & U-NS & P-NS and P-S \\ \hline
				\texttt{pac\_key\_p\_ns} & P-NS & P-NS and P-S \\ \hline
				\texttt{pac\_key\_u\_s} & U-S & P-S \\ \hline
				\texttt{pac\_key\_p\_s} & P-S & P-S \\ \hline
			\end{tabular}
\end{adjustbox}
\caption{The 128-bit PA key registers (not memory mapped) and their usage and configuration settings.}
\end{table}
\end{document}

cs

"ENOLA: Efficient Control-Flow Attestation for Embedded Systems" by Md Armanuzzaman, Engin Kirda, and Ziming Zhao, arXiv:2501.11207v1, 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}{cccc}
 \hline
 Parameter & Description & Value & Units \\
 \hline
 $\tau$ & time interval of collision & 0.05-0.005 & hour\\
 $\mu$ & motility coefficient & 0.25 & cm$^2$/hour\\
 $c$ & band speed & 1.5 & cm/hour\\
 $\beta$ & chemotactic coefficient & 0.16-0.6 & cm$^2$/hour\\%0.1625, 0.25, 0.375, 0.625
 $d$ & $\frac{2 \beta}{\mu}$ & 0.3 - 5 & unit less\\
 $\gamma_0$ & quorum rate & 12-100 & 1/hour\\
 $C_5$ & Integrating constant & 1 & unit less\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Parameter values}
\end{table}
\end{document}

math

"Einstein's Brownian motion model for chemotactic system and traveling band" by Rahnuma Islam and Akif Ibragimov, arXiv:2201.12980v1, 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{Accuracy Improvements with 2-Layer TAGCN for Synthetic Datasets with 30 Percent Training Data}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 Dataset & Clustering & Intra & $\widehat{H}$ & Improvement\\ \hline
 dense\_low & \textbf{0.45} & \textbf{0.8} & \textbf{0.521} & \textbf{51.3} \\
 \hline
 sparse\_low & 0.121 & \textbf{0.8} & \textbf{0.521} & 36.2 \\
 \hline
 dense\_high & 0.31 & 0.42 & 0.974 & 32 \\
 \hline
 sparse\_high & 0.077 & 0.42 & 0.974 & 19.2 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Lavender Yao Jiang
John Shi
Mark Cheung
Oren Wright
José M. F. Moura

"Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification" by Lavender Yao Jiang, John Shi, Mark Cheung, Oren Wright, and José M. F. Moura, arXiv:2012.08698v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
text & schoolchild & precis \\ \hline
sound & audio engineer & faithful perception \\ \hline
web page & search provider & interest for reader \\ \hline
web click history & advertiser & effective ad placement \\ \hline
Brownian path & numerical analysis & effective simulation \\ \hline
rough paths & analyst & RDEs \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Examples of contexts where streams are summarised while retaining their essence.}
\end{table}
\end{document}

q-fin

"Rough paths, Signatures and the modelling of functions on streams" by Terry Lyons, arXiv:1405.4537v1, licensed under CC BY 3.0. 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{$R$ given $E$}
\begin{tabular}{lccc}
 &$R=1$ & $R=0$ & Sum\\
 $E=1$ & 0.68 & 0.32 & 1\\
 $E=0$ & 0.68 & 0.32 & 1\\
 \end{tabular}
\end{table}
\end{document}

stat

Individual Causation with Biased Data

"Individual Causation with Biased Data" by Monica Musio and Philip Dawid, arXiv:2311.08242v1, 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{{\textbf{Optimistic and pessimistic DEA models}}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
			\hline

			Optimistic DEA model {(Model 1)} & Pessimistic DEA model {(Model 2)}\\\hline
			For $k=1,2,3,...,n,$ & For $k=1,2,3,...,n,$\\
			$\max E_{k}^O = \dfrac{\sum\limits_{r=1}^{s}y_{rk}{v_{rk}}}{\sum\limits_{i=1}^{m}x_{ik}{u_{ik}}}$ & $\min E_{k}^P = \dfrac{\sum\limits_{r=1}^{s}y_{rk}{v_{rk}}}{\sum\limits_{i=1}^{m}x_{ik}{u_{ik}}}$ \\
			{\hbox {subject to }}$ \dfrac{\sum\limits_{r=1}^{s}y_{rj}{v_{rk}}}{\sum\limits_{i=1}^{m}x_{ij}{u_{ik}}}\leq 1\,\, \forall j$, & {\hbox {subject to }} $ \dfrac{\sum\limits_{r=1}^{s}{y_{rj}v_{rk}}}{\sum\limits_{i=1}^{m}{x_{ij}u_{ik}}}\geq 1\,\, \forall j,$\\
			$u_{ik}\geq \varepsilon\,\,\forall i,\,\, v_{rk}\geq \varepsilon\,\,\forall r,\,\,\,\varepsilon > 0.$ & $u_{ik}\geq \varepsilon\,\,\forall i, \,\, v_{rk}\geq \varepsilon\,\,\forall r,\,\,\forall k,\,\,\varepsilon > 0.$ \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Performance evaluation of DMUs using hybrid fuzzy multi-objective data envelopment analysis" by Awadh Pratap Singh and Shiv Prasad Yadav, arXiv:2202.01585v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results achieved on SGSC dataset}
\begin{tabular}{|c|c|c|c|}
\hline
\thead{Method} & \thead{Time-steps}
 & \multicolumn{2}{c|}{\thead{Avg.MAPE \%}}
 \\ \cline{3-4} &&
	\thead{DeepDeFF}
	&
	\thead{Basic}\\\cline{3-4}
\hline
\bfseries{BGRU} & \bfseries{2} & \bfseries{34.87}& 42.83\\
\hline
GRU & 2 & 35.01 & 42.85 \\
\hline
BLSTM & 2 & 35.40 & 43.04 \\
\hline
LSTM & 2 & 36.88 & 43.21 \\
\hline
RNN & 2 & 35.94 & 42.04 \\
\hline
BRNN & 2 & 35.79 & 42.74 \\
\hline
BGRU & 6 & 36.02 & 42.08 \\
\hline
GRU & 6 & 35.72 & 41.78 \\
\hline
BLSTM & 6 & 36.41 & 42.96 \\
\hline
LSTM & 6 & 37.28 & 42.41 \\
\hline
RNN & 6 & 38.97 & 42.28 \\
\hline
BRNN & 6 & 38.60 & 42.61 \\
\hline
BGRU & 12 & 36.48 & 42.40 \\
\hline
\bfseries{GRU} & \bfseries{12} & 36.34 & \bfseries{41.54} \\
\hline
BLSTM & 12 & 37.74 & 43.88 \\
\hline
LSTM & 12 & 38.09 & 42.46 \\
\hline
RNN & 12 & 40.57 & 43.29 \\
\hline
BRNN & 12 & 39.64 & 43.20 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Abdul Wahab
Muhammad Anas Tahir
Naveed Iqbal
Faisal Shafait
Syed Muhammad Raza Kazmi

"Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets" by Abdul Wahab, Muhammad Anas Tahir, Naveed Iqbal, Faisal Shafait, and Syed Muhammad Raza Kazmi, arXiv:2011.14137v1, 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{$\varepsilon_{\text{ATE}}$ across various CATE estimation models, with COCOA augmentation (w/ aug.) and without augmentation (w/o aug.) in Twins, Linear, and Non-Linear datasets. Lower $\varepsilon_{\text{ATE}}$ corresponds to the better performance.}
\begin{tabular}{l|cc|cc|cc}
\hline
& \multicolumn{2}{c|}{\textbf{\texttt{Twins}}} & \multicolumn{2}{c|}{\textbf{\texttt{Linear}}} & \multicolumn{2}{c}{\textbf{\texttt{Non-linear}}} \\
\textbf{Model} & \textbf{w/o aug.} & \textbf{w/ aug.} & \textbf{w/o aug.} & \textbf{w/ aug.} & \textbf{w/o aug.} & \textbf{w/ aug.} \\
\hline
TARNet & $0.33 \scriptstyle \pm .19$ & $0.41 \scriptstyle \pm .29$ & $0.10 \scriptstyle \pm .02$ & $0.04 \scriptstyle \pm .02$ & $0.23 \scriptstyle \pm .13$ & $0.04 \scriptstyle \pm .02$ \\
CFR-Wass & $0.47 \scriptstyle \pm .16$ & $0.14 \scriptstyle \pm .09$ & $0.13 \scriptstyle \pm .04$ & $0.06 \scriptstyle \pm .01$ & $0.19 \scriptstyle \pm .09$ & $0.03 \scriptstyle \pm .01$ \\
CFR-MMD & $0.19 \scriptstyle \pm .09$ & $0.18 \scriptstyle \pm .12$ & $0.12 \scriptstyle \pm .05$ & $0.05 \scriptstyle \pm .03$ & $0.25 \scriptstyle \pm .15$ & $0.04 \scriptstyle \pm .01$ \\
T-Learner & $0.02 \scriptstyle \pm .02$ & $0.05 \scriptstyle \pm .03$ & $0.01 \scriptstyle \pm .01$ & $0.01 \scriptstyle \pm .01$ & $0.05 \scriptstyle \pm 0.02$ & $0.05 \scriptstyle \pm .01$ \\
S-Learner & $0.89 \scriptstyle \pm .03$ & $0.79 \scriptstyle \pm .07$ & $0.03 \scriptstyle \pm .01$ & $0.05 \scriptstyle \pm .01$ & $0.45 \scriptstyle \pm .05$ & $0.27 \scriptstyle \pm .02$ \\
BART & $0.28 \scriptstyle \pm .08$ & $0.21 \scriptstyle \pm .10$ & $0.37 \scriptstyle \pm .00$ & $0.07 \scriptstyle \pm .01$ & $0.80 \scriptstyle \pm .00$ & $0.26 \scriptstyle \pm .00$ \\
CF & $0.28 \scriptstyle \pm .06$ & $0.14 \scriptstyle \pm .15$ & $0.39 \scriptstyle \pm .00$ & $0.06 \scriptstyle \pm .01$ & $0.77 \scriptstyle \pm .00$ & $0.32 \scriptstyle \pm .00$ \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Ahmed Aloui
Juncheng Dong
Cat P. Le
Vahid Tarokh

"CATE Estimation With Potential Outcome Imputation From Local Regression" by Ahmed Aloui, Juncheng Dong, Cat P. Le, and Vahid Tarokh, arXiv:2311.03630v2, 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{Notation for $k$-Flow Decomposition ILP}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
\textit{Headers}& \\ \midrule
$x_{uvi}$ & binary variable corresponding to the usage of edge $(u,v) \in E$ in flow path $i \in \{1,\dots,k\}$ \\
$w_{k}$ & integer variable corresponding to the weight of flow path $i \in \{1,\dots,k\}$ \\
$\pi_{uvi}$ & integer variable corresponding to the product of the weight of flow path $i \in \{1,\dots,k\}$ and the usage of edge $(u,v) \in E$ in the same flow path \\
$\overline{w}$ & sufficiently large upper bound for any $w_i$, for all $i \in \{1,\dots,k\}$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Fernando H. C. Dias
Lucia Williams
Brendan Mumey
Alexandru I. Tomescu

"Fast, Flexible, and Exact Minimum Flow Decompositions via ILP" by Fernando H. C. Dias, Lucia Williams, Brendan Mumey, and Alexandru I. Tomescu, arXiv:2201.10923v3, 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{Relative von Neumann entropy Method, 1000 sample points}
\begin{tabular}{|c|c|c|c|c|}
\hline
\bf{$SD | \beta_{0}$} & \bf{1} & \bf{2} & \bf{3} & \bf{$>$4} \\ \hline
\bf{0.01} & 0 & 0 & 100 & 0 \\ \hline
\bf{0.02} & 0 & 0 & 100 & 0 \\ \hline
\bf{0.03} & 0 & 1.334 & 98.667 & 0 \\ \hline
\bf{0.04} & 0 & 51.334 & 48.667 & 0 \\ \hline
\bf{0.05} & 0 & 96.667 & 3.334 & 0 \\ \hline

\end{tabular}
\caption{Each row in the table contains the results of the trial for points samples at the corresponding noise level (SD in the table). The number in each cell is the percent of the experiments for that noise level whose output ($\beta_0$) was the value at the top of the column.}
\end{table}
\end{document}

stat

"Noncommutative Model Selection for Data Clustering and Dimension Reduction Using Relative von Neumann Entropy" by Araceli Guzmán-Tristán and Antonio Rieser, arXiv:2411.19902v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c}
\textbf{System} & \textbf{LSE-D $\uparrow$} & \textbf{LSE-C $\downarrow$} \\ \hline
\textbf{Ours + Updated TM} & 10.18 & 2.91 \\ \hline
\textbf{Length Match} & 10.35 & 2.66 \\ \hline
\textbf{Original Translation} & 10.67 & 2.60 \\
\end{tabular}
\caption{Comparison of \textit{Ours+Updated TM} with length matching and original translation approaches for the English-Spanish setting.}
\end{table}
\end{document}

eess

Lucas Goncalves
Prashant Mathur
Xing Niu
Brady Houston
Chandrashekhar Lavania
Srikanth Vishnubhotla
Lijia Sun
Anthony Ferritto

"Improving Lip-synchrony in Direct Audio-Visual Speech-to-Speech Translation" by Lucas Goncalves, Prashant Mathur, Xing Niu, Brady Houston, Chandrashekhar Lavania, Srikanth Vishnubhotla, Lijia Sun, and Anthony Ferritto, arXiv:2412.16530v1, 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{Resulting RMSE and MAPE for global models in the considered scenarios for the 180 participating clients}
\begin{tabular}{|c|c|c|c|c|c|c|}
 		\hline
 			 &\multicolumn{3}{c|}{RMSE} & \multicolumn{3}{c|}{MAPE}\\
 			\hline
 			Scenario & Min & Max & Mean & Min & Max & Mean \\
 			\hline
 			1 & 0.070 & 2.652 & 0.605 & 10.65\% & 83.35\% & 41.40\% \\ \hline
 			2 & 0.045 & 2.55 & 0.578 & 9.18\% & 87.63\% & 38.39\% \\ \hline
 			3 & 0.026 & 2.652 & 0.576 & 9.45\% & 96.84\% & 37.43\% \\ \hline
 			4 & 0.047 & 2.68 & 0.583 & 9.71\% & 93.74\% & 38.91\% \\ \hline

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

cs

"Electrical Load Forecasting Using Edge Computing and Federated Learning" by Afaf Taik and Soumaya Cherkaoui, arXiv:2201.11248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Perfect sampling of stochastic matching models with reneging" by Thomas Masanet and Pascal Moyal, arXiv:2202.09341v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{hhline}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|c|c|c}
			& $T_{comp}$, s & \# of successful (failed) steps & \# of Newton iterations \\ \hhline{=|=|=|=}
			TPFA, 0 & 47.2 & 17 (36) & 383 \\
			 1 & 20.1 & 11 (23) & 186 \\ \hline
			MFD, 0 & 335.5 & 18 (39) & 510 \\
			1 & 185.5 & 19 (41) & 312
		\end{tabular}
\end{adjustbox}
\caption{Comparison of zero- and first-order predictor for the realistic test}
\end{table}
\end{document}

math

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


cs

Xiang Wu
Xunkai Li
Rong-Hua Li
Kangfei Zhao
Guoren Wang

"ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning" by Xiang Wu, Xunkai Li, Rong-Hua Li, Kangfei Zhao, and Guoren Wang, arXiv:2501.16002v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rll|cc|cc|cc} \hline \hline
& & & \multicolumn{2}{|c|}{Unskilled}&\multicolumn{2}{c|}{Skilled}&\multicolumn{2}{c}{Clerks} \\
& Counterfactually &
Remaining & Wage &
Migrant & Wage &
Migrant & Wage & Migrant\\
& equalised para. & differing para.
& differential & effect &
differential & effect & differential & effect\\
 \hline
(1)& & $\underline{p},\alpha,\mu,\sigma,\lambda,\delta$ & 61.717 & 2.779 & 62.463 & 9.560 & 43.426 & 16.929 \\ \hline
(2)& $\mu,\sigma$ & $\underline{p},\alpha,\lambda,\delta$ & 61.740 & 2.793 & 60.964 & 4.609 & 38.357 & 6.995 \\
(3)& $\delta$ &$\underline{p},\alpha,\mu,\sigma,\lambda$ & 60.806 & 0.349 & 61.260 & 7.691 & 40.637 & 14.205 \\
(4)& $\lambda$ & $\underline{p},\alpha,\mu,\sigma,\delta$ & 64.804 & 11.272 & 63.437 & 11.146 & 40.639 & 14.206 \\
(5)& $\mu,\sigma,\delta$ & $\underline{p},\alpha,\lambda$ & 60.828 & 0.373 & 59.900 & 3.150 & 36.253 & 5.321 \\
(6)& $\mu,\sigma,\lambda$ & $\underline{p},\alpha,\delta$ & 64.803 & 11.134 & 61.804 & 5.798 & 36.254 & 5.322 \\
(7)& $\lambda,\delta$ & $\underline{p},\alpha,\mu,\sigma$ & 63.917 & 8.793 & 62.237 & 9.201 & 37.861 & 11.681 \\
(8)& $\mu,\sigma,\lambda,\delta$ & $\underline{p},\alpha$ & 63.930 & 8.729 & 60.766 & 4.334 & 34.028 & 3.660 \\
(9)& $\underline{p},\alpha$ & $\mu,\sigma,\lambda,\delta$ & & -4.336 & & 4.142 & & 11.569 \\
(10)& $\underline{p},\alpha,\mu,\sigma$ & $\lambda,\delta$ & & -4.962 & & 0.264 & & 3.501 \\
(11)& $\underline{p},\alpha,\delta$ & $\mu,\sigma,\lambda$ & & -6.137 & & 2.610 & & 9.065 \\
(12)& $\underline{p},\alpha,\lambda$ & $\mu,\sigma,\delta$ & & 2.989 & & 5.529 & & 9.066 \\
(13)& $\underline{p},\alpha,\mu,\sigma,\delta$ & $\lambda$ & & -6.756 & & -1.088 & & 1.710 \\
(14)& $\underline{p},\alpha,\mu,\sigma,\lambda$ & $\delta$ & & 2.330 & & 1.458 & & 1.711 \\
(15)& $\underline{p},\alpha, \lambda,\delta$ & $\mu, \sigma$ & & 0.647 & & 3.840 & & 6.812 \\
\hline\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Composition of Wage Differentials between Migrants and Natives" by Panagiotis Nanos and Christian Schluter, arXiv:1306.1781v3, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Self-supervised Change Detection in Multi-view Remote Sensing Images" by Yuxing Chen and Lorenzo Bruzzone, arXiv:2103.05969v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|cc|}
 \hline
 \textbf{Model} & \textbf{Score} & \textbf{Change (\%)} \\
 \hline
 Ours & 0.344 & -- \\
 \hline
 - $R_{PAC}$ & 0.338 & \textcolor{red!70!black}{-1.74\%} \\
 \hline
 - $L_{out}$ & 0.336 & \textcolor{red!70!black}{-0.59\%} \\
 \hline
 - $L_{sim}$ & 0.331 & \textcolor{red!70!black}{-1.48\%} \\
 \hline
 \end{tabular}
\caption{ABC-6K Dataset}
\end{table}
\end{document}

stat

Eric Hanchen Jiang
Yasi Zhang
Zhi Zhang
Yixin Wan
Andrew Lizarraga
Shufan Li
Ying Nian Wu

"Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory" by Eric Hanchen Jiang, Yasi Zhang, Zhi Zhang, Yixin Wan, Andrew Lizarraga, Shufan Li, and Ying Nian Wu, arXiv:2411.17472v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Functions used in the paper.}
\begin{tabular}{cll}
 \hline
 Notation & Function name & Parameters \\
 \hline
 $\|\cdot\|_d$ & Euclidean norm in $\mathbb{R}^d$ &\\
 $(\cdot)_+$ & Positive part function &\\
 $(\cdot)_n$ & Pochhammer symbol (rising factorial)& $n \in \mathbb{N}$\\
 $\Gamma$ & Gamma function &\\
 $J_{\nu}$ & Bessel function of the first kind & $\nu \in \mathbb{R}$\\
 ${\cal K}_{\nu}$ & Modified Bessel function of the second kind & $\nu \in \mathbb{R}$\\
 ${}_2F_1({\alpha,\beta;\gamma};\cdot)$ & Gauss hypergeometric function & $\alpha,\beta,\gamma \in \mathbb{R}$\\ ${}_pF_q({\boldsymbol{\beta};\boldsymbol{\gamma}};\cdot)$ & Generalized hypergeometric function & $p,q \in \mathbb{N}$, $\boldsymbol{\beta} \in \mathbb{R}^p, \boldsymbol{\gamma}\in \mathbb{R}^q$\\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Xavier Emery
Moreno Bevilacqua
Emilio Porcu

"Matern and Generalized Wendland correlation models that parameterize hole effect, smoothness, and support" by Xavier Emery, Moreno Bevilacqua, and Emilio Porcu, arXiv:2501.00558v2, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters for Algorithm }
\begin{tabular}{cc}
 \hline
 $Parameter$ & Value \\
 \hline
 $\alpha_J$ & 0.5 \\
 $\eta_1$ & $10^{-3}$ \\
 $\eta_2$ & $10^{-2}$ \\
 $t_2$ & 0.5 \\
 $t_1$ & 2 \\
 $t_3$ & 0.5\\
 $\mathcal{R}_i$ & 0.3\\
 \hline
 \end{tabular}
\end{table}
\end{document}

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{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation on 10\,k windows}
\begin{tabular}{lccc}
\toprule
\textbf{Configuration} & \textbf{Sil.($\uparrow$)} & \textbf{DB($\downarrow$)} & \textbf{CH($\uparrow$)} \\
\midrule
SSL+PH+HDBSCAN (SSL-TDA) & \textbf{0.72} & \textbf{0.34} & 6\,173 \\
SSL+HDBSCAN (no PH) & 0.05 & 4.60 & 29 \\
SSL+PH+$k$-means (no density) & 0.39 & 0.89 & \textbf{7\,177} \\
SSL+$k$-means (SSL only) & 0.01 & 7.33 & 141 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Wei Shao
Ruoyu Zhang
Zequan Liang
Ehsan Kourkchi
Setareh Rafatirad
Houman Homayoun

"Self-Supervised and Topological Signal-Quality Assessment for Any PPG Device" by Wei Shao, Ruoyu Zhang, Zequan Liang, Ehsan Kourkchi, Setareh Rafatirad, and Houman Homayoun, arXiv:2509.12510v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Qinyang Wang
Hoileong Lee
Xiaodi Pu
Yuanming Lai
Yiming Ma

"SLENet: A Novel Multiscale CNN-Based Network for Detecting the Rats Estrous Cycle" by Qinyang Wang, Hoileong Lee, Xiaodi Pu, Yuanming Lai, and Yiming Ma, arXiv:2507.19566v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 Airfoil & Chord [m]& $U_\mathrm{app}$ [m/s] & $Re/10^6$ [-]& $\alpha$ [\textdegree] \\ \hline
 FFA-W3-301 & 3.4 & 26.7&6.1 & 0.28 \\
 FFA-W3-241 & 2.7& 42.47&7.5 & -1.10\\
 NACA 63-221 & 2.0& 55.0& 7.5& 0.22\\
 NACA 63-218 & 1.6& 64.6& 6.7& 1.12\\
 NACA 63-218 & 1.2& 71.9& 5.7& 1.70\\
 NACA 63-218 & 0.9& 77.6& 4.7& 2.10\\
 NACA 63-218 & 0.7& 81.7& 3.6& 2.20\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Distribution of the conditions for the WPS prediction of the SWT-2.3-93 wind turbine.}
\end{table}
\end{document}

cs

Laura Botero Bolívar
David Huergo
Fernanda L. dos Santos
Cornelis H. Venner
Leandro D. de Santana
Esteban Ferrer

"An Empirical Wall-Pressure Spectrum Model for Aeroacoustic Predictions Based on Symbolic Regression" by Laura Botero Bolívar, David Huergo, Fernanda L. dos Santos, Cornelis H. Venner, Leandro D. de Santana, and Esteban Ferrer, arXiv:2501.08134v1, 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|c|c|c|c|c|c|} \hline
Parameter & $\alpha$ & $\theta$ & $u$ & $k$ & $\l$ & \\\hline
& 0.573 & 0.893 & 3.803 & 1.550 & 2.908 & \\\hline
Parameter & $\mu_0$& $\mu_1$& $\mu_2$& $\sigma_0$& $\sigma_1$& $\sigma_2$ \\\hline
& 1.134 & 0.892 & 0.225 & 0.005 & 0.120 & 0.455 \\\hline
\end{tabular}
\end{adjustbox}
\caption{Parameters of the joint probability density function of significant wave height $H_S$ (m) and wave period $T_p$ (s) for the Northern North Sea .}
\end{table}
\end{document}

math

Stan Tendijck
Jonathan Tawn
Philip Jonathan

"Extremal Characteristics of Conditional Models" by Stan Tendijck, Jonathan Tawn, and Philip Jonathan, arXiv:2202.11673v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The hierarchical generalized linear model and the bootstrap estimator of the error of prediction of loss reserves in a non-life insurance company" by Alicja Wolny-Dominiak, arXiv:1612.04126v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Lihu Xu
Fang Yao
Qiuran Yao
Huiming Zhang

"Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption" by Lihu Xu, Fang Yao, Qiuran Yao, and Huiming Zhang, arXiv:2201.03182v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Model} & \multicolumn{3}{|c|}{\textbf{Mean F1}} \\
\hline
 & \textbf{h=100} & \textbf{h=200} & \textbf{h=500} \\
\hline
MLP & 51.33\% & 50.97\% & 50.68\% \\
\hline
LSTM & 57.33\% & 55.27\% & 51.63\% \\
\hline
LSTM + Attention & 60.20\% & 59.58\% & 56.36\% \\
\hline
Multi-Scale LSTM & 66.56\% & 63.98\% & 60.80\% \\
\hline
VLSTM & \textbf{69.70\%} & \textbf{68.41\%} & \textbf{65.90\%} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Comparison with other baseline models}
\end{table}
\end{document}

q-fin

"VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading" by Prakhar Ganesh and Puneet Rakheja, arXiv:1809.01506v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
\toprule
Statistic & Log-correlation \\
\midrule
Mimimum & -0.43 \\
Mean & -0.44 \\
Variance & -0.57 \\
Skew & 0.35 \\
Kurtosis & 0.22 \\
\bottomrule
\end{tabular}
\caption{Log-correlation of various statistics of the charge difference curve, $\Delta Q^c_{190-10}$, with Ah-throughput life.}
\end{table}
\end{document}

eess

Valentin Sulzer
Peyman Mohtat
Suhak Lee
Jason B. Siegel
Anna G. Stefanopoulou

"Promise and Challenges of a Data-Driven Approach for Battery Lifetime Prognostics" by Valentin Sulzer, Peyman Mohtat, Suhak Lee, Jason B. Siegel, and Anna G. Stefanopoulou, arXiv:2010.07460v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Caihua Chen
Xindan Li
Caleb Tolman
Suyang Wang
Yinyu Ye

"Sparse Portfolio Selection via Quasi-Norm Regularization" by Caihua Chen, Xindan Li, Caleb Tolman, Suyang Wang, and Yinyu Ye, arXiv:1312.6350v1, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Author reputation and institution impact on acceptance.} This logistic regression model has 93\% accuracy on a hold-out set containing 30\% of 2020 papers.}
\begin{tabular}{lcccc}
\bf Variable &\bf Coefficient &\bf Std. Error &\bf Z-score &\bf p-value
\\ \hline \\
\texttt{mean reviewer score} & 2.4354 & 0.098 & 24.807 & 0.000\\% & 2.243 & 2.628 \\
\texttt{log(citations/papers+1)} & 0.1302 & 0.057 & 2.270 & 0.023\\% & 0.018 & 0.243 \\
\texttt{top ten school?} & 0.2969 & 0.165 & 1.802 & 0.072\\%& -0.026 & 0.620 \\
\texttt{constant} & -13.6620 & 0.562 & -24.313 & 0.000\\% &-14.763 & -12.561 \\
\end{tabular}
\end{table}
\end{document}

stat

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

"Analyzing the Machine Learning Conference Review Process" by David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, and Tom Goldstein, arXiv:2011.12919v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccccccccc|} \hline
 $r$ & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline
{$s_r$} & 1 & 0 & 1 & 1 & 2 & 2 & 4 & 5 & 8 & 11 \\
$n_r$ & 2 & 1 & 2 & 3 & 6 & 9 & 18 & 30 & 56 & 99 \\
$\ell_r$ & 2 & 1 & 2 & 2 & 4 & 5 & 10 & 14 & 25 & 39 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Runge-Kutta-Nyström symplectic splitting methods of order 8" by F. Casas, S. Blanes, and A. Escorihuela-Tomàs, arXiv:2202.01541v2, 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
\caption{Comparison of the SWIFT and CONLeg methods for pricing daily-monitored ($L = 12$) UO call and UO put under the CGMY model with parameters taken from \textbf{CGMY2}. 725 option prices are computed in a range of $S$ from 90 to 110 and $K$ equal to 100. The barrier level $H$ is equal to 120.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|ccc|} % centered columns (4 columns)
\hline
&\multicolumn{4}{c|}{\textbf{SWIFT}}&\multicolumn{3}{c|}{\textbf{CONLeg}}\\
&$scale$&$R_\infty$&$R_2$&Time (sec.)&$R_\infty$&$R_2$&Time (sec.)\\
\hline
UO Call&6&3.181e-10&7.641e-09&7.100&5.186e-09&6.886e-08&7.201\\
\hline
UO Put&6&1.901e-11&8.621e-10&6.901&4.330e-10&6.131e-09&6.891\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Hedging and Pricing European-type, Early-Exercise and Discrete Barrier Options using Algorithm for the Convolution of Legendre Series" by Tat Lung Chan and Nicholas Hale, arXiv:1811.09257v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Episodic Memories Generation and Evaluation Benchmark for Large Language Models" by Alexis Huet, Zied Ben Houidi, and Dario Rossi, arXiv:2501.13121v1, 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{The main results.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline
 \multirow{5}{*}{} &
 \multicolumn {4}{c|}{$\lambda'_{1,2}(G)$} \\
 \hline
 \textbf{Grid} & $T_3$ & $T_4$ & $T_6$ & $T_8$ \\
 \hline
 \textbf{Known} & 7-8 ~ & 10-11 ~ & 16-20 ~ & 25-28 ~ \\
 \hline
 \textbf{Ours} & 7 & 11 & 18-20 & 26-28\\
 \hline

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

math

Susobhan Bandopadhyay
Sasthi C. Ghosh
Subhasis Koley

"Improved Bounds on the Span of $L(1,2)$-edge Labeling of Some Infinite Regular Grids" by Susobhan Bandopadhyay, Sasthi C. Ghosh, and Subhasis Koley, arXiv:2201.06801v1, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters of various models in k2SSL for pre-training and fine-tuning. Base models are pre-trained on 8 NVIDIA V100 32GB GPUs, and Large models are on 32 NVIDIA V100 32GB GPUs. All models are fine-tuning on 8 NVIDIA V100 32GB GPUs. ``LR'' represents the learning rate, ``BS'' represents the duration of speech samples in a single batch, and ``GA'' represents gradient accumulation.}
\begin{tabular}{cccccccc}
 \toprule[1.5pt]
 \multirow{2}{*}{\textbf{Backbone}} & \multicolumn{2}{c}{{\bf Stage}} & \multirow{2}{*}{\textbf{Loss}} & \multirow{2}{*}{\textbf{\# Params}} & \multirow{2}{*}{\textbf{LR}} & \multirow{2}{*}{\textbf{BS/GPU}} & \multirow{2}{*}{\textbf{GA}} \\
 & {\bf Pre-train} & {\bf Fine-tune} &&&&& \\
 \midrule
 \multirow{2}{*}{Transformer} & \checkmark & & HuBERT & 94.7M & 0.045 & 87.5s & 4 \\
 & & \checkmark & Pruned RNN-T & 96.5M & 0.001 & 200.0s & 1 \\
 \midrule
 \multirow{2}{*}{Transformer} & \checkmark & & CE & 94.8M & 0.045 & 87.5s & 4 \\
 & & \checkmark & Pruned RNN-T & 96.6M & 0.00075 & 200.0s & 1 \\
 \midrule
 \multirow{3}{*}{Zipformer} & \checkmark & & CE & 94.6M & 0.045 & 600.0s & 1 \\
 & & \checkmark & Pruned RNN-T & 96.4M & 0.002 & 600.0s & 1 \\
 & & \checkmark & CTC & 94.6M & 0.001 & 600.0s & 1 \\
 \midrule
 \multirow{2}{*}{Zipformer} & \checkmark & & CE & 305.9M & 0.045 & 350.0s & 1 \\
 & & \checkmark & Pruned RNN-T & 308.0M & 0.002 & 400.0s & 1 \\
 \bottomrule[1.5pt]
 \end{tabular}
\end{table}
\end{document}

eess

Yifan Yang
Jianheng Zhuo
Zengrui Jin
Ziyang Ma
Xiaoyu Yang
Zengwei Yao
Liyong Guo
Wei Kang
Fangjun Kuang
Long Lin
Daniel Povey
Xie Chen

"k2SSL: A Faster and Better Framework for Self-Supervised Speech Representation Learning" by Yifan Yang, Jianheng Zhuo, Zengrui Jin, Ziyang Ma, Xiaoyu Yang, Zengwei Yao, Liyong Guo, Wei Kang, Fangjun Kuang, Long Lin, Daniel Povey, and Xie Chen, arXiv:2411.17100v2, 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{Attack Types and Their Counts in the Edge-IIoTset Dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
\toprule
\textbf{Attack Type} & \textbf{Count} \\
\midrule
Normal & 1,615,643 \\
DDoS\_UDP & 121,568 \\
DDoS\_ICMP & 116,436 \\
SQL\_injection & 51,203 \\
Password & 50,153 \\
Vulnerability\_scanner & 50,110 \\
DDoS\_TCP & 50,062 \\
DDoS\_HTTP & 49,911 \\
Uploading & 37,634 \\
Backdoor & 24,862 \\
Port\_Scanning & 22,564 \\
XSS & 15,915 \\
Ransomware & 10,925 \\
MITM & 1,214 \\
Fingerprinting & 1,001 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity" by Muhammet Anil Yagiz and Polat Goktas, arXiv:2501.00790v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yingfan Wang
Haiyang Huang
Cynthia Rudin
Yaron Shaposhnik

"Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization" by Yingfan Wang, Haiyang Huang, Cynthia Rudin, and Yaron Shaposhnik, arXiv:2012.04456v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{RED-Diff hyperparameters used for different tasks.}
\begin{tabular}{ccccc}
\toprule
 & \multicolumn{2}{c}{FFHQ} & \multicolumn{2}{c}{ImageNet} \\ \midrule
Task & lr & $\lambda$ & lr & $\lambda$ \\ \midrule
Super-Resolution (4x) & 0.5 & 1.0 & 0.5 & 0.4 \\
Random Inpainting (90\%) & 0.5 & 0.25 & 0.5 & 0.25 \\
Non-Linear Deblur & 0.5 & 0.25 & 0.5 & 0.25 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Kushagra Pandey
Farrin Marouf Sofian
Felix Draxler
Theofanis Karaletsos
Stephan Mandt

"Variational Control for Guidance in Diffusion Models" by Kushagra Pandey, Farrin Marouf Sofian, Felix Draxler, Theofanis Karaletsos, and Stephan Mandt, arXiv:2502.03686v1, 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{Spread of tranches (in 100 bps) with maturity time $T$ in the Base Case (Proposed Model)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
				\hline
				& $T=3$ & $T=4$ & $T=5$ & $T=6$ \\
				\hline
				Tranche 1 & 0.5343 & 0.5584 & 0.5654 & 0.5689 \\
				\hline
				Tranche 2 & 0.0421 & 0.0921 & 0.1359 & 0.1660 \\
				\hline
				Tranche 3 & 0.00006 & 0.0001 & 0.0005 & 0.0012 \\
				\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Puneet Pasricha
Dharmaraja Selvamuthu
Selvaraju Natarajan

"A contagion process with self-exciting jumps in credit risk applications" by Puneet Pasricha, Dharmaraja Selvamuthu, and Selvaraju Natarajan, arXiv:2202.12946v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the COS, filter-COS and SFP methods in terms of absolute error for pricing a vanilla call option under the VG model. The reference values are $10.993703186728190$ ($K=90$) and $0.689027011772653$ ($K = 102.336$). The SFP method is more accurate than the others with fewer summation terms required. The parameters are taken from \textbf{VG--Para1}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccc||cccc|} % centered columns (4 columns)
\hline
$K=90$ &\textbf{COS}&\textbf{filter-COS}&\textbf{SFP}&$K=102.336$&\textbf{COS}&\textbf{filter-COS}&\textbf{SFP}\\
\hline
$N/U$& Error &Error &Error &$N/U$& Error &Error &Error \\
\hline
8&5.783e-01&3.062e-02&5.256e-02&8&1.326&2.413&5.506\\
16&5.843e-02&3.152e-01&1.241e-04&16&4.185e-01&7.921e-01&5.435\\
32&1.405e-02&4.756e-02&2.221e-08&32&1.153e-01&2.488e-01&2.895e-04\\
64&1.603e-03&7.951e-04&1.401e-11&64&2.976e-02&6.836e-02&2.899e-05\\
128&4.281e-04&8.931e-05&5.755e-13&128&7.595e-03&1.762e-02&1.147e-06\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Singular Fourier-Padé Series Expansion of European Option Prices" by Tat Lung Chan, arXiv:1706.06709v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Marco Mingione
Francesco Lagona
Priyanka Nagar
Francois von Holtzhausen
Andriette Bekker
Janine Schoombie
Peter C. le Roux

"Does wind affect the orientation of vegetation stripes? A copula-based mixture model for axial and circular data" by Marco Mingione, Francesco Lagona, Priyanka Nagar, Francois von Holtzhausen, Andriette Bekker, Janine Schoombie, and Peter C. le Roux, arXiv:2411.11461v1, 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}{crrrrrrr}\hline
 & $c_{1,j}$ & $c_{2,j}$ & $c_{3,j}$ & $c_{4,j}$& $c_{5,j}$ \\ \hline
 $ j=0$ & $1$ & $5/4 $ & $4/3$ & $319/192$ & $73/40$ \\
 $ j=1$ & & $-1/8 $ & $-1/6$ & $-107/288$ & $-7/15$ \\
 $ j=2$ & & & & $47/1152$ & $13/240$ \\ \hline
 \end{tabular}
\caption{$c_{p,j}$ values for $p=1,\hdots,5$. They are symmetric, $c_{p,j}=c_{p,-j}$, $j=0,\hdots,\left\lfloor\frac{p}{2}\right\rfloor$.}
\end{table}
\end{document}

cs

Sergio Amat
David Levin
Juan Ruiz-Alvarez
Dionisio F. Yáñez

"Global and explicit approximation of piecewise smooth 2D functions from cell-average data" by Sergio Amat, David Levin, Juan Ruiz-Alvarez, and Dionisio F. Yáñez, arXiv:2201.10823v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Bayesian Trading Cost Analysis and Ranking of Broker Algorithms" by Vladimir Markov, arXiv:1904.01566v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Toy example 3}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
Mean & Variance & $x^* (\phi=0.01)$ & $L_i$ & OK to drop & RSC\\
$ \begin{bmatrix}
1 \\ 3 \\ 2 \\
\end{bmatrix} $
&
$ \begin{bmatrix}
3 & 1 & 2 \\ 1 & 7 & 4 \\ 2 & 4 & 3 \\
\end{bmatrix} $
&
$\begin{bmatrix}
0.6875 \\ 0.1925 \\ 0.1200 \\
\end{bmatrix}$
&
$\begin{bmatrix}
2 \\ 2 \\ 0 \\
\end{bmatrix}$
&
$\begin{bmatrix}
No \\ BEST \\ OK \\
\end{bmatrix}$
&
$\begin{bmatrix}
0.9722 \\ 0.2722 \\ 0.00 \\
\end{bmatrix}$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study of marginal logit temperature $\tau_2$ with respect to FID. $\tau_1$ is fixed as 1.}
\begin{tabular}{lccccc}
\toprule
 $\tau_2$ & 1.0 & 0.8 & 0.7 & 0.5 & 0.3 \\
\midrule
FID & 6.20 & 5.62 & 5.45 & 5.27 & 5.30 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Jiajun Ma
Tianyang Hu
Wenjia Wang
Jiacheng Sun

"Elucidating The Design Space of Classifier-Guided Diffusion Generation" by Jiajun Ma, Tianyang Hu, Wenjia Wang, and Jiacheng Sun, arXiv:2310.11311v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Bank distress in the news: Describing events through deep learning" by Samuel Rönnqvist and Peter Sarlin, arXiv:1603.05670v2, 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{Cost Improvement in Stage 2}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 \toprule
 Number of nodes & 10000 & 50000 & 100000 & 500000\\
 \midrule
 Cost improvement & $3.38\%$ & $2.07\%$ & $3.32\%$ & $2.17\%$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach" by Tan Wan and L. Jeff Hong, arXiv:2201.05868v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Dong Yang
Yuki Saito
Takaaki Saeki
Tomoki Koriyama
Wataru Nakata
Detai Xin
Hiroshi Saruwatari

"Speaker-Conditioned Phrase Break Prediction for Text-to-Speech with Phoneme-Level Pre-trained Language Model" by Dong Yang, Yuki Saito, Takaaki Saeki, Tomoki Koriyama, Wataru Nakata, Detai Xin, and Hiroshi Saruwatari, arXiv:2509.00675v1, 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{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Konstantin Burlachenko
Samuel Horváth
Peter Richtárik

"FL_PyTorch: optimization research simulator for federated learning" by Konstantin Burlachenko, Samuel Horváth, and Peter Richtárik, arXiv:2202.03099v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.