\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l}
\cline{1-3}
\textbf{Cost category} & \textbf{NAICS description} & \textbf{NAICS} & \\ \cline{1-3}
Array System & Communication and energy wire and cable manufacturing & 33592 & \\ \cline{1-3}
Export System & Communication and energy wire and cable manufacturing & 33592 & \\ \cline{1-3}
Offshore Substation & Substation and switching station, power transmission line, construction & 237130 & \\ \cline{1-3}
Scour Protection & Other heavy and civil engineering construction & 237990 & \\ \cline{1-3}
Substructure & Wind Turbine Manufaturing & 333611 & \\ \cline{1-3}
Array System Installation & Cable Laying & 237130 & \\ \cline{1-3}
Export System Installation & Cable Laying & 237130 & \\ \cline{1-3}
Offshore Substation Installation & Substation and switching station, power transmission line, construction & 237130 & \\ \cline{1-3}
Scour Protection Installation & Other heavy and civil engineering construction & 237990 & \\ \cline{1-3}
Substructure Installation & Structural Steel and Precast Concrete Contractors & 238120 & \\ \cline{1-3}
Turbine Installation & Heavy and civil engineering construction & 237 & \\ \cline{1-3}
Turbines & Wind Turbine Manufaturing & 333 & \\ \cline{1-3}
Insurance & Insurance agencies and brokerages & 524210 & \\ \cline{1-3}
Financing & Credit Intermediation and related activities & 522 & \\ \cline{1-3}
Contingency & Emergency and other relief services & 624230 & \\ \cline{1-3}
Commissioning & Building commissioning services & 541350 & \\ \cline{1-3}
Decommissioning & Site preparation contractors & 238910 & \\ \cline{1-3}
Site Auction & Real Estate & 531 & \\ \cline{1-3}
Site Assessment & Site preparation contractors & 238910 & \\ \cline{1-3}
Construction Plan & Construction of buildings & 236 & \\ \cline{1-3}
Installation Plan & Professional, scientific and technical services & 541 & \\ \cline{1-3}
\end{tabular}
\end{adjustbox}
\caption{Mapping costs from ORBIT cost categories to NAICS Codes}
\end{table}
\end{document}

q-fin

Apoorva Bademi
Miriam Stevens
Isha Sura
Shweta Singh

"Estimating the spatial economic and environmental impact of planned offshore wind energy in the USA using Environmentally Extended Multiregional Input-Output analysis" by Apoorva Bademi, Miriam Stevens, Isha Sura, and Shweta Singh, arXiv:2508.15012v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{An Example of a Table}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|}
\hline
One & Two\\
\hline
Three & Four\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Bradford Derby
Lucas Dunker
Samarth Galchar
Shashank Jarmale
Akash Setti

"Source Separation & Automatic Transcription for Music" by Bradford Derby, Lucas Dunker, Samarth Galchar, Shashank Jarmale, and Akash Setti, arXiv:2412.06703v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Buddha Nath Sharma
Anish Rai
SR Luwang
Md. Nurujjaman
Sushovan Majhi

"Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective" by Buddha Nath Sharma, Anish Rai, SR Luwang, Md. Nurujjaman, and Sushovan Majhi, arXiv:2502.14431v1, 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{Statistical Description of Used Datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
\toprule
Dataset &\#Nodes & \#Edges& \#Classes & \#Features & Avg.~Degree\\
\midrule
Cora &$2,708$ & $5,278$ &$7$& $1,433$ & $3.90$\\
Citeseer &$2,110$ & $3,668$ &$6$& $3,703$ & $2.74$\\
LASTFM &$7,083$ & $25,814$ &$10$& $7,842$ & $7.29$\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Towards Private Learning on Decentralized Graphs with Local Differential Privacy" by Wanyu Lin, Baochun Li, and Cong Wang, arXiv:2201.09398v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracy estimate of different RRAM-based schemes}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Network & Structure & Top-1 Error Rate \\
 \hline
 \multirow{5}{*}{LeNet on MNIST}& Software Based & 0.90 \%\\
 \cline{2-3}
 &MBRAI&0.97 \%\\
 \cline{2-3}
 &MBHS-mCNN&2.44 \%\\
 \cline{2-3}
 &RPN\&BLM&0.90 \%\\
 \cline{2-3}
 &Proposed&\textbf{ 0.91 \%}\\
 \hline
 \multirow{4}{*}{AlexNet on ILSVRC12}& Software Based & 42.70 \%\\
 \cline{2-3}
 &MBRAI&44.16 \%\\
 \cline{2-3}
 &RPN\&BLM&43.60 \%\\
 \cline{2-3}
 &Proposed&\textbf{43.10 \%}\\
 \hline
 \multirow{2}{*}{ResNet34 on ILSVRC12}& Software Based & 26.70 \%\\
 \cline{2-3}
 &Proposed&\textbf{27.80 \%}\\
 \hline
 \multirow{2}{*}{VGG16 on ILSVRC12}& Software Based & 28.40 \%\\
 \cline{2-3}
 &Proposed&\textbf{29.30 \%}\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Rui Xiao
Kejie Huang
Yewei Zhang
Haibin Shen

"A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights" by Rui Xiao, Kejie Huang, Yewei Zhang, and Haibin Shen, arXiv:2101.02419v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Branding through responsibility: the advertising impact of CSR activities in the Korean instant noodles market" by Youngjin Hong, In Kyung Kim, and Kyoo il Kim, arXiv:2507.05782v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
 \hline
 & $\|\Pi_{\mathcal{I}}(\pmb{y})\|_F$ & $\|\Pi_{\mathcal{N}}(\pmb{y})\|_F$ & $\|\pmb{J}\pmb{J}^T\pmb{y}\|_F/\|\pmb{J}\pmb{J}^T\|_F$ \\
 \hline
 Pretrained for 1000 epochs& 10.063 & 29.979 & 3.184 \\
 \hline
 Pretrained for 100 epochs & 10.036 & 29.988 & 3.175 \\
 \hline
 Randomly initialized & 10.014 & 29.995 & 3.055 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Alignment between the Jacobian matrix and the clean labels.}
\end{table}
\end{document}

cs

Yihao Xue
Kyle Whitecross
Baharan Mirzasoleiman

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 Model & Active Parameters & One-Step SPE \\
\hline
GNAR(1, [1]) & 2 & 7.86 \\
 VAR(1) & $140^2$ & 12.804 \\
 Res. VAR(1) & 3712 & 11.504 \\
 Sparse VAR(1) & 2992 & 11.533 \\
 AR(1) & $140$ & 78.748 \\
 GNAR(1, [1])* & 141 & 7.833
\end{tabular}
\end{adjustbox}
\caption{One-step prediction error, $\hat{\boldsymbol{X}}_{444}$ is predicted using the previous 443 observations.}
\end{table}
\end{document}

stat

Guy Nason
Daniel Salnikov
Mario Cortina-Borja

"New tools for network time series with an application to COVID-19 hospitalisations" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Günay Dogan
Javier Bernal
Charles Hagwood

"FFT-based Alignment of 2d Closed Curves with Application to Elastic Shape Analysis" by Günay Dogan, Javier Bernal, and Charles Hagwood, arXiv:2501.17779v1, 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{First special class of circles with apparent symmetric lattice points on each boundary.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}
\hline
$k$ & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\
\hline
$f(k)$ & 4 & 16 & 32 & 60 & 88 & 124 & 172 & 216 & 276 & 332 \\
\hline
$\pi R_{f(k)}^2\approx $ & 8 & 20 & 39 & 64 & 96 & 134 & 176 & 228 & 284 & 347 \\
\hline
$k$ & 11 & 12 & 13 & 14 & 15 & 16 & 17 & 18 &19 &20\\
\hline
$f(k)$ & 408 & 484 & 560 & 648 & 740 & 848 & 952 & 1060 &1184 & 1304\\
\hline
$\pi R_{f(k)}^2\approx $ & 416& 492& 573& 661& 756& 856& 963& 1076 & 1195 &1321 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"The Largest Circle Enclosing $n$ Lattice Points" by Jianqiang Zhao, arXiv:2505.06234v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Rutwig Campoamor-Stursberg
Danilo Latini
Ian Marquette
Junze Zhang
Yao-Zhong Zhang

"On the construction of polynomial Poisson algebras: a novel grading approach" by Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang, and Yao-Zhong Zhang, arXiv:2503.03490v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrrrlr}
\toprule
 Name & $N_\text{output}$ & $N_\text{halo}$ & $\sigma$ & $\alpha$ & $\rho_{SR}$ & $\beta$ & $N$ & $\sigma_b$ & readout & $\rho_A$ \\
\midrule
 Local RC & 2 & 2 & 0.011 & 0.49 & 0.85 & 1.155972e-08 & 720 & 1.11 & linear & 0.98 \\
 Single RC & 40 & 0 & 0.001 & 1.00 & 0.07 & 1.758120e-07 & 6000 & 0.63 & linear & 0.98 \\
Model 3 from & 2 & 4 & 0.052 & 0.41 & 0.34 & 3.611497e-06 & 6000 & 0.00 & linear & 0.99 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Fig. }
\end{table}
\end{document}

cs

Jason A. Platt
Stephen G. Penny
Timothy A. Smith
Tse-Chun Chen
Henry D. I. Abarbanel

"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, 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{Baseline WER (\%) achieved on single-speaker or two-speaker mixtures before enhancement/separation. All results reported in this paper are with reverberated speech.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{$1$ speaker} & \textbf{$1$ speaker + noise} & \textbf{$2$ speakers + noise} \\ \hline
12.2 & 23.6 & 58.2 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sunit Sivasankaran
Emmaneul Vincent
Dominique Fohr

"Analyzing the impact of speaker localization errors on speech separation for automatic speech recognition" by Sunit Sivasankaran, Emmaneul Vincent, and Dominique Fohr, arXiv:1910.11114v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Tabacof attack}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ll|ll}
 & \multicolumn{2}{c}{Base} & \multicolumn{2}{c}{DVIB} \\\hline
Model & Acc@1 & \#\,Hits & Acc@1 & \#\,Hits\\\hline
\cellcolor[HTML]{EFEFEF}Resnet-18 & \cellcolor[HTML]{EFEFEF}61.6 & \cellcolor[HTML]{EFEFEF}927 & \cellcolor[HTML]{EFEFEF}52.26 & \cellcolor[HTML]{EFEFEF}1802 \\
Resnet-50 & 76.57 & 879 & 72.66 & 1126 \\
\cellcolor[HTML]{EFEFEF}Resnet-101 & \cellcolor[HTML]{EFEFEF}33.17 & \cellcolor[HTML]{EFEFEF}448 &\cellcolor[HTML]{EFEFEF}34.73 & \cellcolor[HTML]{EFEFEF}1641 \\\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Alireza Furutanpey
Pantelis A. Frangoudis
Patrik Szabo
Schahram Dustdar

"Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication" by Alireza Furutanpey, Pantelis A. Frangoudis, Patrik Szabo, and Schahram Dustdar, arXiv:2412.10265v1, 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}

eess

"Localization using Angle-of-Arrival Triangulation" by Amod K. Agrawal, arXiv:2508.16908v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Japan: Parameter Estimates and NLL for DCC, Scalar BEKK, and LSTM-BEKK Models.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccc}
 \toprule
 \textbf{Portfolio Size} & & NLL & $a$ & $b$ \\
 \midrule
 \multirow{3}{*}{100} & DCC & 164.067 & 0.009 & 0.697 \\
 & Scalar BEKK & 163.322 & 0.007 & 0.931 \\
 & LSTM-BEKK & \textbf{162.731} & 0.002 & 0.998 \\
 \midrule
 \multirow{3}{*}{175} & DCC & 289.456 & 0.005 & 0.696 \\
 & Scalar BEKK & 289.320 & 0.006 & 0.945 \\
 & LSTM-BEKK & \textbf{285.631} & 0.002 & 0.993 \\
 \midrule
 \multirow{3}{*}{250} & DCC & 423.885 & 0.004 & 0.698 \\
 & Scalar BEKK & 421.414 & 0.003 & 0.971 \\
 & LSTM-BEKK & \textbf{417.788} & 0.002 & 0.997 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Haoyuan Wang
Chen Liu
Minh-Ngoc Tran
Chao Wang

"Deep Learning Enhanced Multivariate GARCH" by Haoyuan Wang, Chen Liu, Minh-Ngoc Tran, and Chao Wang, arXiv:2506.02796v1, 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{Comparison of processing time of different algorithms}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cc|}
 \hline
NA & Processing Time (ms) \\
 \hline\hline
 Deep Q-Network & 210 \\
 Exhaustive Search & 4117 \\
 K-Means & 387 \\
 Fixed & 0 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Xukai Zhong
Yiming Huo
Xiaodai Dong
Zhonghua Liang

"Deep Q-Network Based Dynamic Movement Strategy in a UAV-Assisted Network" by Xukai Zhong, Yiming Huo, Xiaodai Dong, and Zhonghua Liang, arXiv:2008.03162v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{pifont}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The iteration number stats of phase \ding{172} optimal cutting post-processing and phase \ding{173} optimal nodal shape design for different models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llllllll}
 \hline
 \multirow{2}{*}{Model} & \multirow{2}{*}{Degree} & \multicolumn{3}{c}{Iteration number of \ding{172}} & \multicolumn{3}{c}{Iteration number of \ding{173}} \\
 \cline{3-8}
 & & Min & Max & Average & Min & Max & Average \\
 \hline
 Screw & 5-10 & 5 & 27 & 14 & 4 & 65 & 16\\
 Boot & 5-10 & 6 & 26 & 13 & 4 & 69 & 18\\
 Bracket & 5-10 & 5 & 26 & 13 & 6 & 62 & 16\\
 Kitten & 9-15 & 5 & 30 & 17 & 6 & 78 & 23\\
 Hinge & 9-15 & 7 & 35 & 19 & 6 & 85 & 26\\
 Teddy & 9-15 & 6 & 35 & 17 & 5 & 71 & 24\\
 Gear & 12-20 & 7 & 43 & 21 & 7 & 87 & 31\\
 Impeller & 12-20 & 8 & 49 & 23 & 5 & 90 & 35\\
 Connector & 12-20 & 8 & 48 & 22 & 7 & 85 & 33\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"A Quasi-Optimal Shape Design Method for Lattice Structure Construction" by Sifan Chen, Yuan Kong, and Qiang Zou, arXiv:2502.02602v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|rrr|ll}
Contest & D votes & R votes & Other votes & \% D & \% R\\ \hline
Governor & 1,348,888 &1,080,801 & 95,373 &55.52 & 44.48\\
Secretary of State & 1,313,716 & 1,113,927 & 64,992 & 54.11& 45.89\\
State Treasurer & 1,292,281 & 1,111,641 & 70,475 & 53.76 & 46.24\\
Attorney General & 1,285,464 & 1,124,757 & 81,733 & 55.64& 44.36\\
U of CO Regent (at large) & 1,246,318 & 1,031,993 & 120,714 & 54.70 & 45.30
\end{tabular}
\end{adjustbox}
\caption{\footnotesize Voting Data for Statewide Offices in Colorado, 2018}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Distribution of global envelope $p$-values for 30 cross inhomogeneous $J$-functions}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccc}
 $p$-value & 0.01 & 0.02 & 0.03 & 0.04 & $>$0.05 \\ \hline
 frequency & 8 & 3 & 2 & 2 & 15
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Kristian Bjørn Hessellund
Ganggang Xu
Yongtao Guan
Rasmus Waagepetersen

"Second order semi-parametric inference for multivariate log Gaussian Cox processes" by Kristian Bjørn Hessellund, Ganggang Xu, Yongtao Guan, and Rasmus Waagepetersen, arXiv:2012.02155v2, 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}{ccccc}
\hline
\textbf{Duration (secs)} & \textbf{NPTEL-dt} & \textbf{NPTEL-et} & \textbf{NPTEL-Unseen\_1} & \textbf{NPTEL-Unseen\_2} \\ \hline \hline
\textbf{less\_5} & 1317-25.0\% & 1226-24.0\% & 207-21.0\% & 224-23.0\% \\ %\hline
\textbf{5\_15} & 3713-72.0\% & 3790-73.0\% & 737-75.0\% & 718-73.0\% \\ %\hline
\textbf{15\_above} & 153-3.0\% & 1683-3.0\% & 38-4.0\% & 40-4.0\% \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Data split in terms of ``Number of Utterances'' based on utterance duration in NPTEL test sets}
\end{table}
\end{document}

eess

Vishwas M. Shetty
Metilda Sagaya Mary N J
S. Umesh

"Investigation of Speaker-adaptation methods in Transformer based ASR" by Vishwas M. Shetty, Metilda Sagaya Mary N J, and S. Umesh, arXiv:2008.03247v2, 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{Weather-Performance Correlation Matrix}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Weather Variable} & \textbf{Revenue} & \textbf{Wait Time} & \textbf{Utilization} & \textbf{Earnings} \\
\midrule
Rain Intensity & 0.575*** & 0.551*** & 0.428*** & 0.522*** \\
Temperature Extreme & 0.442*** & 0.287*** & 0.356*** & 0.398*** \\
Low Visibility & -0.384*** & -0.298*** & -0.267*** & -0.341*** \\
High Wind Speed & 0.234*** & 0.167*** & 0.201*** & 0.198*** \\
\bottomrule
\multicolumn{5}{l}{\footnotesize *** $p < 0.001$}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Weather-Aware AI Systems versus Route-Optimization AI: A Comprehensive Analysis of AI Applications in Transportation Productivity" by Tatsuru Kikuchi, arXiv:2507.17099v1, 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}{ccc}
 \hline
 $n=7500$ & Predicted: Default & Predicted: Not default \\ \hline
Actual: Default & 397 & 1198 \\ \hline
Actual: Not default & 168 & 5737 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Confusion matrix of the logistic regression for the Taiwan credit score dataset.}
\end{table}
\end{document}

q-fin

"Interpretable Selective Learning in Credit Risk" by Dangxing Chen, Weicheng Ye, and Jiahui Ye, arXiv:2209.10127v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Separability matrix of the GGNAM for the bankruptcy dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
 \hline
 Feature & $x_{17}$ & $x_{19}$ & $x_{22}$ \\ \hline
 $x_{17}$ & 0.913 & 0.908 & 0.881 \\ \hline
 $x_{19}$ & & 0.913 & 0.910 \\ \hline
 $x_{22}$ & & & 0.913 \\ \hline
 \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance" by Dangxing Chen and Weicheng Ye, arXiv:2209.10082v2, 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{Intensity of price reversal}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
			& Competitive Agents & Market Makers \\
			\hline
			Insider trader & $\frac{2}{1 +\sqrt{1 +\frac{4}{\rho_M^2}}-2s}$& $\frac{2}{1 +\sqrt{1 +\frac{4}{\rho_M^2}}-2s}$\\
			\hline
			Strategic trader & $\frac{\rho_M^2}{4+\rho_M^2(1-s)}$	& $\frac{\rho_M^2}{4}$ \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Order routing and market quality: Who benefits from internalisation?" by Umut Çetin and Alaina Danilova, arXiv:2212.07827v1, 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|ccc}
Node $i$ & $L_i$ & $W_i$ & $X_i$ \\
\hline
1 & 0 & 1 & 2368 \\
2 & 2 & 2 & 1368 \\
3 & 1 & 1 & 2000 \\
4 & 3 & 2 & 2000 \\
5 & 3 & $2 + e^{-1}$ & 2368 \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

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


stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

"Abnormal component analysis" by Romain Valla, Pavlo Mozharovskyi, and Florence d'Alché-Buc, arXiv:2312.16139v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Short-time behavior of the At-The-Money implied volatility for the jump-diffusion stochastic volatility Bachelier model" by Elisa Alòs, Òscar Burés, and Josep Vives, arXiv:2503.22282v1, 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{Simulation Error of Complete Simulation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccc}
\toprule
Estimates & \multicolumn{6}{c}{Simulation RMSE}\\ \cmidrule(lr){2-9}
 &\multicolumn{2}{c}{CC} & \multicolumn{2}{c}{DC} & \multicolumn{2}{c}{Stoich Mis} & \multicolumn{2}{c}{$\varepsilon_s^-$ Mis}\\\cmidrule(lr){2-9}
 & V+E & V & V+E & V & V+E & V & V+E & V \\ \midrule
 $\hat{V}_{t}$ & {7} & {7.7} & {5.2} & {5.4} & {3.2}& {3.6} & {7.2} & {7.6} \\
 $\hat{c}_{ss}^-$ & {89} & {70} & {76} & {62} & {90} & {431} & {350} & {277} \\
 $\hat{c}_{s,avg}^-$ & {89} & {97} & {76} & {104} & {90} & {419} & {314} & {234} \\
 $\hat{c}_{ss}^+$ & {94} & {93} & {51} & {70} & {83} & {156} & {116} & {107} \\
 $\hat{c}_{s,avg}^+$ & {139} & {143} & {135} & {152} & {75} & {140} & {167} & {154} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sravan Pannala
Puneet Valecha
Peyman Mohtat
Jason B. Siegel
Anna G. Stefanopoulou

"Improved Battery State Estimation Under Parameter Uncertainty Caused by Aging Using Expansion Measurements" by Sravan Pannala, Puneet Valecha, Peyman Mohtat, Jason B. Siegel, and Anna G. Stefanopoulou, arXiv:2009.14270v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 Number of claims occurred for each policy& 0 & 1 & 2 & 3 & 4 & 5\\
 \midrule
 Number of policies & $653,069$ & $23,571$ & $1298$ & $62$ & $5$ & $2$ \\
 Total exposure & $341,090$ & $16,315$ & $909$ & $42$ & $2$ & $1$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Number of policies and total exposure within the portfolio that is split with respect to the number of claims occurred for each policy.}
\end{table}
\end{document}

math

Łukasz Delong
Selim Gatti
Mario V. Wüthrich

"Calibration Bands for Mean Estimates within the Exponential Dispersion Family" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, 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{Speeding-up all shortest paths computation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
Dataset & Space gain & Speed-up \\ \hline
CA-ASTROPH & $45.82\%$ & $1.496$ \\
BLOG-CATALOG & $46.52\%$ & $1.323$ \\
CA-HEPTH & $31.08\%$ & $1.065$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Abd Errahmane Kiouche
Julien Baste
Mohammed Haddad
Hamida Seba

"A Neighborhood-preserving Graph Summarization" by Abd Errahmane Kiouche, Julien Baste, Mohammed Haddad, and Hamida Seba, arXiv:2101.11559v1, 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{The test performance of three optimized models with different input sizes.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
 & RMSE & $R^2$ & MAE & Huber loss\\
\midrule
$32 \times 32$ & 0.660 & 0.901 & 0.434 & 0.18\\
$64 \times 64$ & 0.674 & 0.897 & 0.438 & 0.19\\
$128 \times 128$ & 0.692 & 0.892 & 0.460 & 0.20\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Weihao Li
Dianne Cook
Emi Tanaka
Susan VanderPlas
Klaus Ackermann

"Automated Assessment of Residual Plots with Computer Vision Models" by Weihao Li, Dianne Cook, Emi Tanaka, Susan VanderPlas, and Klaus Ackermann, arXiv:2411.01001v1, 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}{ccccc}
 \hline
 $n$ & 250 & 500 & 1000 & 2000 \\
 \hline
 Coverage & 0.95 & 0.95 & 0.96 & 0.96 \\
 \hline
 Bias & 0.42 & 0.27 & 0.18 & 0.13 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{ \textbf{NHANES Data.} Bias $ = ||\beta -\hat{\beta}||$ and coverage of the moment condition model based estimates of the regression parameters for varying sample sizes.}
\end{table}
\end{document}

stat

Abhisek Chakraborty
Anirban Bhattacharya
Debdeep Pati

"Constrained Reweighting of Distributions: an Optimal Transport Approach" by Abhisek Chakraborty, Anirban Bhattacharya, and Debdeep Pati, arXiv:2310.12447v2, 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{Dimensions for the Patch Antenna Design }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Patches} & \textbf{1st, 8th} & \textbf{2nd, 7th} & \textbf{3rd, 6th} & \textbf{4th, 5th} \\
\hline
Width & 1.55 & 2.06 & 2.59 & 3.12 \\
\hline
Length & 2.81 & 2.77 & 2.74 & 2.72 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Sravan Pulipati
Viduneth Ariyarathna
Udara De Silva
Najath Akram
Elias Alwan
Arjuna Madanayake
Soumyajit Mandal
Theodore S. Rappaport

"A Direct- Conversion Digital Beamforming Array Receiver with 800 MHz Channel Bandwidth at 28 GHz using Xilinx RF SoC" by Sravan Pulipati, Viduneth Ariyarathna, Udara De Silva, Najath Akram, Elias Alwan, Arjuna Madanayake, Soumyajit Mandal, and Theodore S. Rappaport, arXiv:1911.09180v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
& \multicolumn{2}{c}{MIN $\rightarrow$ CUB} & \multicolumn{2}{c}{Tiered $\rightarrow$ CUB} \\
\cline{2-3} \cline{4-5}
 & 1-shot & 5-shot & 1-shot & 5-shot \\
\midrule
MAML & 37.3 $\pm$ 0.3 & 54.7 $\pm$ 0.3 & 38.1 $\pm$ 0.3 & 55.1 $\pm$ 0.3 \\
T-Net & 38.0 $\pm$ 0.3 & 55.6 $\pm$ 0.3 & 37.5 $\pm$ 0.3 & 54.8 $\pm$ 0.3 \\
MT-Net & 37.1 $\pm$ 0.3 & 53.1 $\pm$ 0.3 & 38.0 $\pm$ 0.3 & 55.5 $\pm$ 0.3 \\
Warp-MAML & \textbf{41.0} $\pm$ 0.3 & 55.3 $\pm$ 0.3 & 40.9 $\pm$ 0.3 & 56.8 $\pm$ 0.3 \\
\midrule
SAP (ours) & 40.9 $\pm$ 0.3 & \textbf{55.8} $\pm$ 0.3 & \textbf{41.1} $\pm$ 0.3 & \textbf{60.7} $\pm$ 0.3 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Average cross-domain meta-test accuracy scores over 5 runs a 32-channel Conv-4 backbone. Techniques trained on tasks from one data set were evaluated on tasks from another data set. The 95\% confidence intervals are displayed as $\pm$ x.}
\end{table}
\end{document}

stat

"Subspace Adaptation Prior for Few-Shot Learning" by Mike Huisman, Aske Plaat, and Jan N. van Rijn, arXiv:2310.09028v1, 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{Parameter settings.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 \textbf{Parameters} & \textbf{Value} \\
 \midrule
 Number of antennas ($M$ ($M_x\times M_y$)) & $16\ (4\times 4)$\\
 Number of subcarriers ($K$) & $8$\\
 BS carrier frequency and bandwidth & $60$ GHz, $50$ MHz\\
 Number of paths ($L$) & $25$\\
 Speed of light ($c$) & $3\times 10^8 $m/s\\
 Image size ($W_I, H_I, C_I$) & $960, 540, 3$\\
 Feature sizes ($d_\text{CSI}$, $d_\text{Vision}$) & $64$\\
 Embedding size ($d_\text{Embed}$) & $16$\\
 Batch size ($B$) & $32$\\
 Temperature parameter ($\alpha$) & $0.07$\\
 Training epochs & $50$\\
 Initial learning rate & $0.001$\\
 Decay mode& $0.5$ per $10$ epochs\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Can Zheng
Jiguang He
Chung G. Kang
Guofa Cai
Henk Wymeersch

"Multimodal Radio and Vision Fusion for Robust Localization in Urban V2I Communications" by Can Zheng, Jiguang He, Chung G. Kang, Guofa Cai, and Henk Wymeersch, arXiv:2508.17640v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison with different fusion strategies}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc}
\hline
\multirow{2}{*}{Fusion Strategy} & \multicolumn{2}{c|}{-5 dB} & \multicolumn{2}{c}{0 dB} \\ \cline{2-5}
 & STOI (\%) & PESQ & STOI (\%) & PESQ \\ \hline
Early Fusion & 75.23 & 2.24 & 81.41 & 2.66 \\ \hline
Late Fusion & 75.01 & 2.21 & 81.54 & 2.59 \\ \hline
Intermediate Fusion (VSE) & 76.68 & 2.28 & 83.54 & 2.71 \\ \hline
Intermediate Fusion (AV(SE)$^2$) & 79.31 & 2.37 & 84.88 & 2.79 \\ \hline
Multi-layer Feature Fusion & 80.47 & 2.39 & 85.02 & 2.83 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Multi-layer Feature Fusion Convolution Network for Audio-visual Speech Enhancement" by Xinmeng Xu and Jianjun Hao, arXiv:2101.05975v3, 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{Evaluation of voice activity detection models, in terms of detection error rate (DetER \%), false alarm (FA \%), and missed detection (Miss \%) rates. Results on the development set are reported using small font size. We report two variants: the first one is based on handcrafted features (MFCCs) and the other one is an end-to-end model processing the waveform directly.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|rr|rrrr|}
 \hline
 & \multicolumn{2}{c|}{DetER \%} & \multicolumn{2}{c|}{FA \%} & \multicolumn{2}{c|}{Miss \%} \\
 \hline
 Baseline & 11.2 & & 6.5 & & 4.7 & \\
 \hline
 MFCC~ & 10.5 & {\scriptsize{10.0}} & 6.8 & {\scriptsize{5.4}} & 3.7 & {\scriptsize{4.6}} \\
 Waveform & \textbf{9.9} & {\scriptsize{9.3}} & 5.7 & {\scriptsize{3.7}} & 4.2 & {\scriptsize{5.6}} \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Marvin Lavechin
Marie-Philippe Gill
Ruben Bousbib
Hervé Bredin
Leibny Paola Garcia-Perera

"End-to-end Domain-Adversarial Voice Activity Detection" by Marvin Lavechin, Marie-Philippe Gill, Ruben Bousbib, Hervé Bredin, and Leibny Paola Garcia-Perera, arXiv:1910.10655v2, 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}{r|r|r|r|}
 $N_f$ & $\varepsilon_2^{(N_f)}$ & $\varepsilon_4^{(N_f)}$ & $\varepsilon_6^{(N_f)}$ \\
 \hline
10 (\phantom{1}30) & 0.47427 & 9.22067 & 162.92010\\
20 (\phantom{1}60) & 0.03533 & 0.35907 & 5.32643\\
40 (120) & 0.00405 & 0.00273 & 0.02023\\
80 (240)& 0.00048 & 0.00030 & 0.00078\\
160 (480)& 0.00006 & 0.00004 & 0.00009\\
\hline
 \end{tabular}
\caption{Absolute error of the far-field with 64 equidistant incident and observation directions for the disk with radius $R=2$ and the physical parameters $a=3$ and $\eta=1$ for varying number of faces (collocation nodes). The wave numbers are $k=2$, $k=4$, and $k=6$, respectively.}
\end{table}
\end{document}

math

Isaac Harris
Victor Hughes
Andreas Kleefeld

"Two direct sampling methods for an anisotropic scatterer with a conductive boundary" by Isaac Harris, Victor Hughes, and Andreas Kleefeld, arXiv:2412.16605v2, 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{Dataset} & \textbf{DFT} & \textbf{DWT} & \textbf{DFT + DWT} \\ \hline
StarGAN & 78.81\% & 79.34\% & 97.67\% \\ \hline
StyleGAN & 61.45\% & 69.11\% & 87.1\% \\ \hline
SITD & 83.61\% & 49.72\% & 90.3\% \\ \hline
BigGAN & 64.92\% & 66.92\% & 72.08\% \\ \hline
StyleGAN2 & 68.02\% & 59.54\% & 91.83\% \\ \hline
CycleGAN & 65.31\% & 55.94\% & 77.53\% \\ \hline
Whichfaceisreal & 39.85\% & 77.40\% & 80.15\% \\ \hline
GauGAN & 50.91\% & 67.22\% & 70.91\% \\ \hline
Deepfake & 56.39\% & 51.88\% & 68.90\% \\ \hline
CRN & 61.75\% & 83.85\% & 55.06\% \\ \hline
IMLE & 47.82\% & 79.90\% & 55.06\% \\ \hline
SAN & 69.18\% & 46.80\% & 49.32\% \\ \hline
\end{tabular}
\end{adjustbox}
\caption{ Evaluation of DFT and DWT combination for fake image detection. Percentage accuracy is reported for the full image using only DFT, only DWT, and the combination DFT+DWT.}
\end{table}
\end{document}

cs

Bilal Yousaf
Muhammad Usama
Waqas Sultani
Arif Mahmood
Junaid Qadir

"Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks" by Bilal Yousaf, Muhammad Usama, Waqas Sultani, Arif Mahmood, and Junaid Qadir, arXiv:2101.00676v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Bankruptcy prediction using disclosure text features" by Sridhar Ravula, arXiv:2101.00719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
 & Bias due to imperfect fit & Bias due to overfitting\\
 \midrule
 $\hat{\gamma}^{sep}$ & $O(1)$ & $O\left(\frac{1}{\sqrt{T_0}}\right)$\\
 $\hat{\gamma}^{cat}$ & $O(1)$ & $O\left(\frac{1}{\sqrt{T_0K}}\right)$\\
 $\hat{\gamma}^{avg}$ & $O\left(\frac{1}{\sqrt{K}}\right)$ & $O\left(\frac{1}{\sqrt{T_0K}}\right)$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Leading terms in high probability bounds on the bias due to imperfect fit and overfitting in Theorem~, with $N$ fixed.}
\end{table}
\end{document}

stat

"Using Multiple Outcomes to Improve the Synthetic Control Method" by Liyang Sun, Eli Ben-Michael, and Avi Feller, arXiv:2311.16260v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Worldwide regulations on 5925 – 7125 MHz spectrum}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|}
\hline
Frequency band {[}MHz{]} & 5925 – 6425 & 6425 – 7125 \\ \hline
Total Bandwidth {[}MHz{]} & 500 & 700 \\ \hline
 & Microwave links & Mobile \\ \cline{2-3}
\multirow{-2}{*}{Licensed users} & \multicolumn{2}{c|}{Fixed - Satellite} \\ \hline
US & \multicolumn{2}{c|}{14 dBm EIRP AFC} \\ \hline
Saudi Arabia & \multicolumn{2}{c|}{Indoor/Outdoor AFC} \\ \hline
Europe & Indoor/Outdoor & Not available \\ \hline
Japan & 23 dBm EIRP for 20 MHz & Not available \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Karim Saifullin
Hussein Al-Shatri
Mohamed-Slim Alouini

"Communications over Unlicensed sub-8 GHz Spectrum: Opportunities and Challenges" by Karim Saifullin, Hussein Al-Shatri, and Mohamed-Slim Alouini, arXiv:2412.11002v1, licensed under CC BY-SA 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{IHDP: MSE statistics over 50 trials}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Model} & \textbf{Min} & \textbf{Max} & \textbf{Min Rank} & \textbf{Max Rank}\\
 \midrule
 TARNet & 10.2 & 86.0 & 6 & 6 \\
 CausalForest & 0.03 & 6.19 & 1 & 5 \\
 S-BART & 0.03 & 6.72 & 1 & 4 \\
 T-BART & 0.02 & 6.25 & 1 & 3 \\
 S-engression & 0.10 & 10.16 & 2 & 5 \\
 T-engression & 0.05 & 6.45 & 1 & 5 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\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}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
 \toprule
 Scenario & $\delta$ (s) & Runtime (ms) \\
 \midrule
 Paris & 0.1 & 13.65 \\
 & 0.2 & 0.01 \\
 New York & 0.1 & 92.50 \\
 Singapore & 0.1 & 33.33 \\
 & 0.2 & 23.01 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Booktabs table}
\end{table}
\end{document}

stat

"Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference" by Kaiwen Hou, arXiv:2311.18826v4, 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||c|}
 \hline
 &T & $T_0$ & $T_1$ &$T_2$&$T_J$ \\
 \hline
 \hline
 $p$-value &0.08 & 0.052 & 0.011& 0.027&0.081 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Do Tran Van
Susovan Pal
Benjamin Eltzner
Stephan F. Huckemann

"Two-Sample Tests for Optimal Lifts, Manifold Stability and Reverse Labeling Reflection Shap" by Do Tran Van, Susovan Pal, Benjamin Eltzner, and Stephan F. Huckemann, arXiv:2503.17879v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the greedily selected $p^1,~p^2$ versus the optimal $p^1,~p^2$ for a $T=2$ rounds data mixing problem. On 2 out of 3 datasets, the greedily selected proportions match the optimal proportions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 \multirow{2}{*}{\textbf{Solving}} & \multicolumn{2}{c}{\textbf{Arxiv/SE}} & \multicolumn{2}{c}{\textbf{GH/C4}} & \multicolumn{2}{c}{\textbf{Books/SE}} \\
 & $p_1^1, p_1^2$ & Avg test PPL & $p_1^1, p_1^2$ & Avg test PPL & $p_1^1, p_1^2$ & Avg test PPL \\
 \midrule
 Greedy & $0.4, 0.4$ & $16.039$ & $0.6, 0.4$ & $36.525$ & $0.3, 0.6$ & $45.513$ \\
 Optimal & $0.4, 0.4$ & $16.039$ & $0.3, 0.6$ & $34.709$ & $0.3, 0.6$ & $45.513$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mayee F. Chen
Michael Y. Hu
Nicholas Lourie
Kyunghyun Cho
Christopher Ré

"Aioli: A Unified Optimization Framework for Language Model Data Mixing" by Mayee F. Chen, Michael Y. Hu, Nicholas Lourie, Kyunghyun Cho, and Christopher Ré, arXiv:2411.05735v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 h & 1/8 & 1/16 & 1/32 & 1/64 & 1/128 & 1/256\\
 \hline
 e & 7.99E-04 & 2.33E-04 & 6.80E-05 & 1.85E-05 & 4.73E-06 & 1.19E-06\\
 r & & 1.78 & 1.78 & 1.88 & 1.97 & 1.98\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{the magnitude of the error and order of convergence.}
\end{table}
\end{document}

math

Vladislav Byankin
Aleksandr Tynda
Denis Sidorov
Aliona Dreglea

"Numerical solution of locally loaded Volterra integral equations" by Vladislav Byankin, Aleksandr Tynda, Denis Sidorov, and Aliona Dreglea, arXiv:2503.21452v1, 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{Sample Table Title}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\textbf{PART} &\textbf{DESCRIPTION} \\
\hline \\
Dendrite &Input terminal \\
Axon &Output terminal \\
Soma &Cell body (contains cell nucleus) \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Eduard Tulchinskii
Daria Voronkova
Ilya Trofimov
Evgeny Burnaev
Serguei Barannikov

"RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks" by Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, and Serguei Barannikov, arXiv:2503.11910v1, 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
 s & $R_2(s)$ &Classic Bound & Our Bound \\\hline\hline
 $5$ & $R_2(5) \leq 46$ & $m_2(5)\leq 2,677$ & $m_2(5)\leq 392$ \\\hline
 $6$ & $R_2(6)\leq 160$ & $m_2(6)\leq 1,293,533$ & $m_2(6)\leq 152,000$ \\\hline
 $7$ & $R_2(7)\leq 492$ & $m_2(7)\leq 1,265,045,472$ & $m_2(7)\leq 282,475,249$ \\\hline
 $8$ & $R_2(8)\leq 1,518$ & $m_2(8)\leq 5,114,696,152,715$ & $m_2(8)\leq 4,065,272,127,504$ \\\hline
 $9$ & $R_2(9)\leq 4,956$ & $m_2(9)\leq 1.437\times 10^{17}$ & $m_2(9)\leq 6.947\times 10^{17}$ \\\hline
 $10$& $R_2(10)\leq 16,064$ & $m_2(10)\leq 1.788\times 10^{22}$ & $m_2(10)\leq 1.032 \times 10^{24}$ \\\hline
 \end{tabular}
\end{adjustbox}
\caption{Values of the bound given by , v.s. the classical probabilistic bound .}
\end{table}
\end{document}

math

"Bounds on the Critical Multiplicity of Ramsey Numbers with Many Colors" by Bryce Christopherson and Casia Steinhaus, arXiv:2501.18869v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation results to the Type I error}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|}
\hline
$n$/$\alpha$ & $\alpha=$ 0.01 & $\alpha=$ 0.05 & $\alpha=$ 0.10 \\ \hline
$n=1000$ & 0.010 & 0.0490 & 0.1010 \\ \hline
$n=10000$ & 0.0140 & 0.0539 & 0.1071 \\ \hline
$n=100000$ & 0.0128 & 0.0566 & 0.1106 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Cristian F. Jimenez-Varon
Fouzi Harrou
Ying Sun

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


\documentclass{article}
\usepackage{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|c|}
 \hline
 Task & 0 & 1 & 2 & 3 & 4 \\
 \hline
 Relaxation gap $\delta$ (\%) & 0.9 & 2.1 & 14.3 & 24.4 & 5.8 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Relaxation gap of the TFR for moving the robot arm to different configurations in minimum time.}
\end{table}
\end{document}

math

Lujie Yang
Tobia Marcucci
Pablo A. Parrilo
Russ Tedrake

"A New Semidefinite Relaxation for Linear and Piecewise-Affine Optimal Control with Time Scaling" by Lujie Yang, Tobia Marcucci, Pablo A. Parrilo, and Russ Tedrake, arXiv:2504.13170v1, 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{Failure case for Wav2Vec2.0 XLSR-53. This highlights a limitation in transcribing Nigerian Pidgin speech, showing how numeric elements may be misinterpreted, thus affecting transcription accuracy.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
\textbf{Reference} & \textbf{Prediction} \\
\midrule
dem go wan kill chief femi fani kayode wife 27 & dem go wan kill chief femi fani kayode wife 217 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Amina Mardiyyah Rufai
Afolabi Abeeb
Esther Oduntan
Tayo Arulogun
Oluwabukola Adegboro
Daniel Ajisafe

"Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin" by Amina Mardiyyah Rufai, Afolabi Abeeb, Esther Oduntan, Tayo Arulogun, Oluwabukola Adegboro, and Daniel Ajisafe, arXiv:2010.11123v2, 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{Data cleaning steps and remaining wind parks for validation in Brazil}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
 \hline
 & Applies to & Remaining \\&&wind parks \\
 \hline
 \hline
 - total number of observed wind park time series
 & & 174 \\ \hline
 1. matching of ONS and ANEEL
 & & 72 \\
 - keep only 100 matching score
 & & 70 \\ \hline
 2. data cleaning & &\\
 - remove constant parts of time series except 0 ($>$24h)
 & 50 & 70 \\
 - remove constant parts of 0 generation ($>$180h)
 & 28 & 70\\
 - remove capacity factors $>$ 1
 & 59 & 70\\
 - remove short time series ($<$2y)
 & 17 & 53 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Katharina Gruber
Peter Regner
Sebastian Wehrle
Marianne Zeyringer
Johannes Schmidt

"Towards a global dynamic wind atlas: A multi-country validation of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the Global Wind Atlas" by Katharina Gruber, Peter Regner, Sebastian Wehrle, Marianne Zeyringer, and Johannes Schmidt, arXiv:2012.05648v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Tobias Weber
Michael Ingrisch
Bernd Bischl
David Rügamer

"Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddings" by Tobias Weber, Michael Ingrisch, Bernd Bischl, and David Rügamer, arXiv:2311.01349v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cl|}
\hline
 $h_r$ & number of honest PoW queries in slot $r$ \\
 $n_r$ & number of total PoW queries in slot $r$ \\
 $\alpha_0$ & lower bound on the fraction of honest mining power \\
 $sl_{\rm re}$ & recency parameter for PoW blocks \\
 $\Delta$ & network delay in slots \\
 $\kappa$ & security parameter; length of the hash function output \\
 $R \in \mathbb{N}$ & duration of an epoch in number of slots\\
 $(\gamma, s)$ & restriction on the fluctuation of the number of \\
 & honest queries across slots (Definition~) \\
 $f^{(s)}$ & expected PoS mining rate in number of blocks per slot \\
 $f^{(w)}$ & expected PoW mining rate in number of blocks per slot \\
 $\epsilon$ & quality of concentration of random variables \\
 $\sigma$ & PoW fairness parameter \\
 & \& advantage of honest parties (Assumption~.1 and .1) \\
 $\delta$ & ``goodness'' parameter of an epoch/slot (Definition~) \\
 $\lambda$ & typicality parameter of the execution \\
 $\ell$ & minimum number of slots for concentration bounds\\
 $L$ & total number of epochs in the execution\\
 $\omega$ & the weighing parameter \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{The parameters used in our analysis.}
\end{table}
\end{document}

cs

Matthias Fitzi
Xuechao Wang
Sreeram Kannan
Aggelos Kiayias
Nikos Leonardos
Pramod Viswanath
Gerui Wang

"Minotaur: Multi-Resource Blockchain Consensus" by Matthias Fitzi, Xuechao Wang, Sreeram Kannan, Aggelos Kiayias, Nikos Leonardos, Pramod Viswanath, and Gerui Wang, arXiv:2201.11780v2, 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{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
	\hline
		&& Rich. & Rich. & Rich. & Rich. & & \\
		&& (default) & (optimal) & (adaptive) & (2o) & CG & BayesCG \\\hline
		\multirow{2}{*}{\textsc{default}}
& $\textsc{mmd}_{\mathcal{F}_k}^2$ & 1.90e-04 & -3.11e-05 & 9.76e-06 & 5.36e-05 & -2.80e-05 & \textbf{1.14e-03} \\
& $q$ & 0.34 & 0.52 & 0.45 & 0.43 & 0.49 & \textbf{0.03} \\\hline
		\multirow{2}{*}{\textsc{natural}}
& $\textsc{mmd}_{\mathcal{F}_k}^2$ & -1.72e-04 & -2.71e-04 & -2.44e-04 & -3.20e-04 & -2.98e-04 & \textbf{4.18e-03} \\
& $q$ & 0.60 & 0.64 & 0.64 & 0.68 & 0.68 & \textbf{0.00} \\\hline
		\multirow{2}{*}{\textsc{opt}}
& $\textsc{mmd}_{\mathcal{F}_k}^2$ & 3.59e-05 & 1.00e-05 & 4.30e-06 & -6.62e-06 & 3.57e-05 & \textbf{6.57e-03} \\
& $q$ & 0.48 & 0.47 & 0.48 & 0.49 & 0.47 & \textbf{0.00} \\\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jon Cockayne
Ilse C. F. Ipsen
Chris J. Oates
Tim W. Reid

"Probabilistic Iterative Methods for Linear Systems" by Jon Cockayne, Ilse C. F. Ipsen, Chris J. Oates, and Tim W. Reid, arXiv:2012.12615v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l||c|c|}
\hline
\textbf{Model} & \textbf{Valid AUC} & \textbf{Test AUC} \\
\hline
MLP & 0.6507 & 0.6465 \\
GCN & 0.6087 & 0.6135 \\
GraphSAGE & 0.6344 & 0.6344 \\
\hline
AllSetTransformer & \underline{0.6779} & 0.6829 \\
HNHN & 0.6775 & \underline{0.6845} \\
HGNN & 0.6429 & 0.6450 \\
HCHA & 0.6289 & 0.6318 \\
\hline
TGN(Sum) & 0.7517 & 0.7640 \\
TGN(Mean) & 0.7569 & \textbf{0.7747} \\
TGN(GCN) & \textbf{0.7581} & 0.7716 \\
TGN(GAT) & 0.7569 & 0.7691 \\
\hline
\textbf{\textit{improv.}} & 11.83\% & 13.18\% \\
\hline
\end{tabular}
\caption{AUC comparison between the models: the best-performing TGN model is highlighted in bold, and the top baseline model is underlined.}
\end{table}
\end{document}

cs

Yejin Kim
Youngbin Lee
Minyoung Choe
Sungju Oh
Yongjae Lee

"Temporal Graph Networks for Graph Anomaly Detection in Financial Networks" by Yejin Kim, Youngbin Lee, Minyoung Choe, Sungju Oh, and Yongjae Lee, arXiv:2404.00060v1, 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{Classification results on the STL dataset with 0.125 bpp using VGG16 as downstream AI task.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllll}
		\toprule[1pt]
		Method & SI & ACC & F1-score & PSNR & MSE & SSIM \\
		\hline
		original & 672.02 & 88.94\% & 0.8899 & - & 0 & 1 \\
		TDIC & 42.29 & 57.04\% & 0.5733 & 23.55 & 0.0050 & 0.8042 \\
		APIC & 89.67 & 71.04\% & 0.7102 & 18.96 & 0.0137 & 0.6959 \\
		\hline
		SAIC & \textbf{91.65} & \textbf{71.99\%} & \textbf{0.7186} & 18.94 & 0.0138 & 0.6939\\
		\toprule[1pt]
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Semantic-assisted image compression

Qizheng Sun
Caili Guo
Yang Yang
Jiujiu Chen
Xijun Xue

"Semantic-assisted image compression" by Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen, and Xijun Xue, arXiv:2201.12599v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty" by Marius Protte and Behnud Mir Djawadi, arXiv:2507.15439v1, 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{MSE of log NET2 count against an average baseline on the test set.} %128 sampling steps, CFG=1.5 (null image), 1x5x5 opening (t = 0.5).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \hline
 & \textbf{Placebo}~$\downarrow$ & \textbf{NE}~$\downarrow$ & \textbf{LE}~$\downarrow$ & \textbf{MiE}~$\downarrow$ & \textbf{MoE}~$\downarrow$ & \textbf{HE}~$\downarrow$ \\
 \hline
 Proposed Method & \textbf{1.538} & \textbf{1.158} & \textbf{1.093} & \textbf{0.842} & \textbf{0.531} & \textbf{0.006} \\ \hline
 Avg. Baseline & 1.775 & 1.620 & 1.780 & 0.991 & 0.697 & 0.010 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Gian Mario Favero
Ge Ya Luo
Nima Fathi
Justin Szeto
Douglas L. Arnold
Brennan Nichyporuk
Chris Pal
Tal Arbel

"Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments" by Gian Mario Favero, Ge Ya Luo, Nima Fathi, Justin Szeto, Douglas L. Arnold, Brennan Nichyporuk, Chris Pal, and Tal Arbel, arXiv:2508.07006v1, 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}{ccccc}
\hline\hline
&Obs Reserve & NoCalib &Calib & chain-ladder \\
\hline
Amount& 2,698,159 &9,361,273 &2,723,594 &6,103,197\\
 Ratio& 1.00& 3.47& 1.01& 2.26\\
 \hline\hline
\end{tabular}
\end{adjustbox}
\caption{Mean of the BE distribution for the RBNS predictions of the multi-state model and the chain-ladder. }
\end{table}
\end{document}

stat

Emmanuel Jordy Menvouta
Jolien Ponnet
Robin Van Oirbeek
Tim Verdonck

"mCube: Multinomial Micro-level reserving Model" by Emmanuel Jordy Menvouta, Jolien Ponnet, Robin Van Oirbeek, and Tim Verdonck, arXiv:2212.00101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Microscopic Traffic Models, Accidents, and Insurance Losses" by Sojung Kim, Marcel Kleiber, and Stefan Weber, arXiv:2208.12530v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|l|l|l|l|}
\hline
\textbf{Number of clusters} & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline
\textbf{Silhouette score} & 0.4195 & 0.4319 & 0.4092 & 0.3757 & 0.3888 & 0.3940 & 0.3747 & 0.3965 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Table showing the Silhouette scores for clustering analysis of centermost cluster.}
\end{table}
\end{document}

math

"A Topological Data Analysis Framework for Quantifying Necrosis in Glioblastomas" by Francisco Tellez and Enrique Torres-Giese, arXiv:2503.17331v1, 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|cc|cr|cc}
\hline
 & \multicolumn{2}{c|}{Hodge Scattering + SVM} & \multicolumn{2}{c|}{UGT} & \multicolumn{2}{c}{GFN} \\ \hline
Graph & Accuracy & \# Param & Accuracy & \# Param & Accuracy & \# Param \\ \hline
Collab & 80.39 & 256 & 77.84 & 866,746 & 81.50 & 68,754 \\
DD & 72.71 & 256 & 80.23 & 76,928 & 79.37 & 68,754 \\
IMDB-B & 73.10 & 256 & 77.04 & 55,508 & 73.40 & 68,754 \\
IMDB-M & 49.67 & 256 & 53.60 & 48,698 & 51.80 & 68,818 \\
MUTAG & 85.78 & 256 & 80.23 & 4,178 & 85.83 & 68,818 \\
PROTEINS & 75.35 & 256 & 78.53 & 1,878 & 76.46 & 68,818 \\
PTC & 68.28 & 256 & 69.63 & 12,038 & 66.60 & 68,818 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Naoki Saito
Stefan C. Schonsheck
Eugene Shvarts

"Multiscale Hodge Scattering Networks for Data Analysis" by Naoki Saito, Stefan C. Schonsheck, and Eugene Shvarts, arXiv:2311.10270v5, 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{ Classification accuracy determined from manually labeled experimental detections for clustering with handpicked features (K-means, GMM, hierarchical) and deep learning (DEC). }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline\hline
Method & Accuracy & Recall & Precision\\
\hline
K-means ($K\!=\!2$) & {\bf 0.66} & 0.42 & 0.47 \\
GMM ($K\!=\!2$) & {\bf 0.55} & 0.84 & 0.40 \\
Hierarchical ($K\!=\!2$) & {\bf 0.63} & 0.23 & 0.38 \\
GMM ($K\!=\!2$), latent features & {\bf 0.78} & 0.33 & 0.90\\
DEC ($P\!=\!15$, $K\!=\!2$) & {\bf 0.66} & 0.75 & 0.48\\
\hline\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Emma Ozanich
Aaron Thode
Peter Gerstoft
Lauren A. Freeman
Simon Freeman

"Deep embedded clustering of coral reef bioacoustics" by Emma Ozanich, Aaron Thode, Peter Gerstoft, Lauren A. Freeman, and Simon Freeman, arXiv:2012.09982v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Measures of Model Fit: / Data}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc}
 \hline \hline
\multirow{2}{*}{Criterion} & \multicolumn{2}{c}{\# of components} \\ \cline{2-3}
 & 1 & 2 \\
\hline
AIC & 50,964.90 & \textbf{49,983.40} \\
BIC & 51,112.52 & \textbf{50,296.42} \\
DIC & 50,917.04 & \textbf{49,885.84} \\
WAIC & 50,972.77 & \textbf{49,992.57} \\
LOO & 50,972.67 & \textbf{49,992.82} \\
 \hline \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Bayesian estimation of finite mixtures of Tobit models" by Caio Waisman, arXiv:2411.09771v1, 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{The validity of the upsampling factor S in the Sub-Pixel Weight branch of SubEC. The best performance is highlighted in bold.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|cc|cc}
 \toprule
 &\multicolumn{3}{c|}{Model Efficiency} &\multicolumn{2}{c|}{CVIU} &\multicolumn{2}{c}{Waterloo} \\
 \midrule
 Upscale Factor S & Params/M & Flops/G & Speed/ms & SRCC $\uparrow$ & PLCC $\uparrow$ & SRCC $\uparrow$ & PLCC $\uparrow$ \\
 \midrule
 2 & 2.220 & 0.179 & 11.010 & \textbf{0.978} & \textbf{0.981} & 0.965& 0.979 \\
 3 & 2.223 & 0.183 & 11.174 & 0.977 & 0.980 & \textbf{0.970} & 0.980\\
 4 & 2.232 & 0.188 & 11.322 & 0.976 & 0.978 & 0.965 & 0.982\\
 5 & 2.241 & 0.195 & 11.205 & 0.978 & 0.981 & 0.965 & 0.980\\
 6 & 2.252 & 0.203 & 11.121 & 0.975 & 0.977 & 0.968 & \textbf{0.983} \\
 8 & 2.277 & 0.223 & 11.141 & 0.963 & 0.967 & 0.965 & 0.981\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yixiao Li
Xiaoyuan Yang
Guanghui Yue
Jun Fu
Qiuping Jiang
Xu Jia
Paul L. Rosin
Hantao Liu
Wei Zhou

"Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment" by Yixiao Li, Xiaoyuan Yang, Guanghui Yue, Jun Fu, Qiuping Jiang, Xu Jia, Paul L. Rosin, Hantao Liu, and Wei Zhou, arXiv:2509.06442v1, 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{Breast cancer data: performance of the optimization approach when selecting features for the logistic regression model with different dependency measures.}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
$\rho_{Feature}^X$ & $\rho_{Target}^Y$ & Optimization value & AUC & ACC & Number of features \\
	\hline
	$X=Correl$ & $Y=Correl$ &-0.91611 & 0.9806 & 0.9209 & 3\\
	\hline
	$X=Correl$ & $Y=MI$ & -0.3300 & 0.9644 & 0.8910 & 1 \\
	\hline
	$X=Correl$ & $Y=ROC$ & -1.7031 & 0.9822 & 0.9192 & 4 \\
	\hline
	$X=Correl$ & $Y=ANOVA$ & -2913.6696 & 0.9839 & {\bf 0.9315} &9 \\
	\hline
	$X=MI$ & $Y=Correl$ & -1.438 & 0.9796 & 0.9262 & 5\\
	\hline
	$X=MI$ & $Y=MI$ &-0.4139 & {\bf 0.9859} & 0.9297 &3\\
	\hline
	$X=MI$ & $Y=ROC$ & -2.6366 & 0.9828 & 0.9279 & 6\\
	\hline
	$X=MI$ & $Y=ANOVA$ & -2914.5083 & 0.9839 & {\bf 0.9315} & 9\\
	\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Gerhard Hellstern
Vanessa Dehn
Martin Zaefferer

"Quantum computer based Feature Selection in Machine Learning" by Gerhard Hellstern, Vanessa Dehn, and Martin Zaefferer, arXiv:2306.10591v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|}
 \hline
 & $L=10^{9}$ & $L=10^{10}$ & $L=10^{11}$ \\ \hline
 $D_{\text{E}}^{\text{(G)}}[s,d]$ & $4.6 \times 10^{-5}$ & $1.7 \times 10^{-5}$ & $7.5 \times 10^{-6}$\\ \hline
 $D_{\text{CLE}}^{\text{(G)}}[s,d]$ & $2.2 \times 10^{-5}$ & $0.6 \times 10^{-5}$ & $1.8 \times 10^{-6}$\\ \hline
 $D_{\text{CFE}}^{\text{(G)}}[s,d]$ & $2.4 \times 10^{-5}$ & $1.1 \times 10^{-5}$ & $5.7 \times 10^{-6}$\\
 \hline
 \end{tabular}
\caption{The evaulation of $D_{\text{E}}^{\text{(G)}}[s,d]$, $D_{\text{CLE}}^{\text{(G)}}[s,d]$ and $D_{\text{CFE}}^{\text{(G)}}[s,d]$ for various computational limit $L$ values considereing $(128,64)$-random tree codes under BSC with $p=0.02$ where $s$ is optimized using the SBP algorithm and $d$ is in the form where $\gamma=0.9992$. }
\end{table}
\end{document}

math

"The Optimization of Random Tree Codes for Limited Computational Resources" by B. Tan Bacinoglu, arXiv:2501.12834v1, 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{Median, Maximum and Minimum test loss of the optimizers for the Heat equation.}
\begin{tabular}{lrrr}
\toprule
 & Median & Minimum & Maximum \\
\midrule
ANaGRAM & \textbf{6.82e-11} & \textbf{1.90e-11} & \textbf{2.50e-10} \\
Adam & 2.37e-04 & 1.11e-04 & 1.41e-03 \\
E-NGD & 2.18e-09 & 5.62e-10 & 1.35e-08 \\
GD & 1.01e-02 & 4.70e-03 & 1.93e-02 \\
L-BFGS & 6.84e-04 & 5.85e-05 & 3.34e-03 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning" by Nilo Schwencke and Cyril Furtlehner, arXiv:2412.10782v2, 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{Vehicle data used for case studies}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
\textbf{Parameters} & \textbf{Values} \\ \hline
Vehicle mass $(M)$ & $2000$ kg \\ \hline
Air density $(\rho_a)$ & $1.22$ kg/m$^3$ \\ \hline
Aerodynamic drag coefficient $(C_d)$ & $0.3$ \\ \hline
Frontal Area $(A_f)$ & $1.6$ m$^2$ \\ \hline
Rolling resistance coefficient $(f_r)$ & $0.01$ \\ \hline
Wheel radius $(r_d)$ & $0.28$ m \\ \hline
Wind speed $(V_w)$ & $0$ \\ \hline
Vehicle mass factor $(\delta)$ & $1.04$ \\ \hline
Gear ratio of final drive/differential $(i_0)$ & $4.18$ \\ \hline
Gear ratio of transmission $(i_g)$ & $1.3$ \\ \hline
Speed ratio, maximum speed to base speed $(x)$ & $4$ \\ \hline
Traction motor efficiency $(\eta_m)$ & $85\%$ \\ \hline
Inverter efficiency $(\eta_i)$ & $95\%$ \\ \hline
Gear-box and final drive efficiency $(\eta_t)$ & $90\%$ \\ \hline
Total travel distance $(S)$ & $5$ miles \\ \hline
Maximum time limit $(T_{max})$ & $420$ sec \\ \hline
Maximum acceleration $(a_{max})$ & $8$ mph/s \\ \hline
Maximum cruising speed $(V_{max})$ & $75$ mph \\ \hline
Overall regenerative efficiency & $50\%$ \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mukesh Gautam
Narayan Bhusal
Mohammed Benidris
Poria Fajri

"A GA-based Approach to Eco-driving of Electric Vehicles Considering Regenerative Braking" by Mukesh Gautam, Narayan Bhusal, Mohammed Benidris, and Poria Fajri, arXiv:2012.15195v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 Model & {Sharpe} & {Return \%} & {Std Dev \%} & {Beta} & {Daily} & {Short} \\
 & {Ratio} & & {Return} & & {Turnover} & {Fraction} \\
 \midrule
Long-Conv & $3.64\,\pm\,0.14$ & 12.8 & 3.51 & 0.033 & 0.97 & 0.48 \\
S4 & $\underline{3.94}\,\pm\,0.29$ & 13.5 & 3.43 & 0.028 & 0.90 & 0.48 \\
H3 & $3.46\,\pm\,0.28$ & 10.6 & 3.08 & 0.026 & 1.12 & 0.48 \\
Transformer & $3.65\,\pm\,0.52$ & 12.9 & 3.62 & 0.035 & 0.83 & 0.47 \\
Hyena & $2.91\,\pm\,0.33$ & 8.7 & 3.02 & 0.032 & 1.28 & 0.47 \\
Set-Seq (Ours) & $\textbf{4.82}\,\pm\,0.12$ & 13.0 & 2.69 & 0.028 & 0.91 & 0.48 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

A Set-Sequence Model for Time Series

Elliot L. Epstein
Apaar Sadhwani
Kay Giesecke

"A Set-Sequence Model for Time Series" by Elliot L. Epstein, Apaar Sadhwani, and Kay Giesecke, arXiv:2505.11243v1, 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{Performance of MindVL and comparison models on multimodal benchmarks. "+" indicates that there is a portion of data with unlabeled quantities. The results of the comparative models are referenced from~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|ccccccc}
 \toprule
 \textbf{Model} & \#Tokens & \textbf{MME} & \textbf{MMBench} & \textbf{OCRBench} & \textbf{DocVQA} & \textbf{ChartQA} & \textbf{InfoVQA} & \textbf{Overall} \\
 \midrule
 Qwen2.5-VL 7B~ & 4.1T+ & 83.8 & 82.6 & \underline{86.4} & \textbf{95.7} & \textbf{87.3} & 82.6 & \underline{86.4}\\
 Keye-VL 8B~& 4.1T+ & \textbf{87.3} & \textbf{85.8} & 82.3 & 87.0 & 72.5 & 63.0 & 79.9\\
 GLM-4.1V 9B~ & 2T+ & 79.1 & 76.3 & 85.3 & 93.3 & 70.0 & 80.3 & 80.7\\
 InternVL3.5 8B~ & 380B+ & \underline{85.0} & 79.5 & 83.2 & 92.3 & 86.7 & 76.2 & 83.8\\
 \midrule
 MindVL 8B & 447B & 84.1 & \underline{84.3} & \textbf{87.6} & \underline{94.7} & \underline{87.2} & \underline{81.1} & \textbf{86.5}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Feilong Chen
Yijiang Liu
Yi Huang
Hao Wang
Miren Tian
Ya-Qi Yu
Minghui Liao
Jihao Wu

"MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs" by Feilong Chen, Yijiang Liu, Yi Huang, Hao Wang, Miren Tian, Ya-Qi Yu, Minghui Liao, and Jihao Wu, arXiv:2509.11662v1, 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}{cccc}
 \toprule
 LLM & Successful detections & Correct proofs \\
 \midrule
 GPT-4o & 1/10 & 2/10 \\
 Gemini 2.5 Pro Preview & 8/10 & 5/10 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Tomer Raz
Michael Shalyt
Elyasheev Leibtag
Rotem Kalisch
Shachar Weinbaum
Yaron Hadad
Ido Kaminer

"From Euler to AI: Unifying Formulas for Mathematical Constants" by Tomer Raz, Michael Shalyt, Elyasheev Leibtag, Rotem Kalisch, Shachar Weinbaum, Yaron Hadad, and Ido Kaminer, arXiv:2502.17533v2, 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{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \textbf{Name} &
 \textbf{Type} &
 \textbf{Channels} \\
 \hline\\
 \textbf{Mean} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Variance} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Skewness} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Kurtosis} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Interquartile range} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Min} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Max} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Hjorth complexity} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Hjorth mobility} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Petrosian fractal dimension} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Intermittency} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Voltage auc} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Spikiness} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Standard deviation} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Zero crossing} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Peak to peak} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Absolute area under signal} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Total signal energy} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Spike count} & $\mathcal{T}$ & $\bullet$ \\
 \textbf{Coastline} & $\mathcal{T}$, $\delta,\theta,\alpha,\beta,\gamma$ & $\bullet$ \\
 \textbf{Power spectral density} & $\delta,\theta,\alpha,\beta,\gamma$ & $\bullet$ \\
 \textbf{Power spectral centroid} & $\delta,\theta,\alpha,\beta,\gamma$ & $\bullet$ \\
 \textbf{Signal monotony} & $\delta,\theta,\alpha,\beta,\gamma$ & $\bullet$ \\
 \textbf{Signal to noise} & $\delta,\theta,\alpha,\beta,\gamma$ & $\bullet$ \\
 \textbf{Energy percentage} & $\delta,\theta,\alpha,\beta,\gamma$ & $\bullet$ \\
 \textbf{Discrete wavelet transform} & $\delta,\theta,\alpha,\beta,\gamma$ & $\bullet$ \\
 \textbf{Cross correlation max coef} & $\rho$ & $\bullet\bullet$ \\
 \textbf{Coherence} & $\rho$ & $\bullet\bullet$ \\
 \textbf{Imaginary coherence} & $\rho$ & $\bullet\bullet$ \\
 \textbf{Phase slope index} & $\rho$ & $\bullet\bullet$ \\
 \textbf{Eccentricity} & $\mathcal{G}$ & $\bigodot$ \\
 \textbf{Clustering coefficient} & $\mathcal{G}$ & $\bigodot$ \\
 \textbf{Betweenness centrality} & $\mathcal{G}$ & $\bigodot$ \\
 \textbf{Local efficiency} & $\mathcal{G}$ & $\bigodot$ \\
 \textbf{Global efficiency} & $\mathcal{G}$ & $\bigodot$ \\
 \textbf{Diameter} & $\mathcal{G}$ & $\bigodot$ \\
 \textbf{Radius} & $\mathcal{G}$ & $\bigodot$ \\
 \textbf{Characteristic path} & $\mathcal{G}$ & $\bigodot$ \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Bartlomiej Chybowski
Shima Abdullateef
Hollan Haule
Alfredo Gonzalez-Sulser
Javier Escudero

"PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets" by Bartlomiej Chybowski, Shima Abdullateef, Hollan Haule, Alfredo Gonzalez-Sulser, and Javier Escudero, arXiv:2508.07253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
 Relative $L_2$ error & IR-PINNs2 & Causal PINNs \\
 \midrule
 $x$ & 3.5086e-03 & 1.139e-02 \\
 $y$ & 5.1053e-03 & 1.656e-02 \\
 $z$ & 2.1651e-03 & 7.038e-03 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\textit{Lorentz system:} Relative $L_2$ errors between the proposed method and causal PINNs.}
\end{table}
\end{document}

math

Xiaodong Feng
Haojiong Shangguan
Tao Tang
Xiaoliang Wan

"Integral regularization PINNs for evolution equations" by Xiaodong Feng, Haojiong Shangguan, Tao Tang, and Xiaoliang Wan, arXiv:2503.23729v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance and efficiency trade-offs on the VoxCeleb dataset. EERs\% are reported for the VoxCeleb1-O/E/H test sets. Speedups measured on an AMD EPYC 7A53 CPU and an AMD MI250 GPU are reported relative to the unpruned model.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
\toprule
\multirow{2}{*}{Model} & \multirow{2}{*}{Sparsity} & \multirow{2}{*}{\#Param} & \multicolumn{2}{c}{Speedup} & \multicolumn{3}{c}{VoxCeleb} \\
\cmidrule(lr){4-5} \cmidrule(lr){6-8}
& & & CPU & GPU & O & E & H \\
\midrule
\multicolumn{2}{c}{ResNet50~} & 11.2 M & 1.1$\times$ & 2.6$\times$ & 0.80 & 0.88 & 1.52 \\
\midrule
\rowcolor{gray!15} \multirow{4}{*}{WavLM Base+} & 0\% & 95.6\,M & - & - & 0.70 & 0.69 & 1.40 \\
& 60\% & 38.9 M & 2.2$\times$ & 2.0$\times$ & 0.70 & 0.78 & 1.50 \\
& 70\% & 29.5 M & 2.9$\times$ & 2.6$\times$ & 0.73 & 0.84 & 1.61 \\
& 80\% & 19.9 M & 3.8$\times$ & 3.4$\times$ & 0.92 & 1.02 & 1.91 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Junyi Peng
Lin Zhang
Jiangyu Han
Oldřich Plchot
Johan Rohdin
Themos Stafylakis
Shuai Wang
Jan Černocký

"Hybrid Pruning: In-Situ Compression of Self-Supervised Speech Models for Speaker Verification and Anti-Spoofing" by Junyi Peng, Lin Zhang, Jiangyu Han, Oldřich Plchot, Johan Rohdin, Themos Stafylakis, Shuai Wang, and Jan Černocký, arXiv:2508.16232v1, 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{Denoising performance in PSNR (dB) for the `house' image for different algorithms with different optimized hyper-parameters. The \textbf{top} category only requires the noisy image. The \textbf{middle} requires additional information such as noise level (KSVD, WNNM, BM3D) or additional noisy images with matched noise level (n2v$^{\dagger}$). The \textbf{bottom} requires large clean datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 & $\sigma$=15 & $\sigma$=25 & $\sigma$=50 \\
 \midrule
 n2v$^{\star}$ & 32.05 & 29.20 & 25.42\\
 \small{MTMKL} & \textbf{34.29} & 31.88 & 28.08 \\
 GSC & 33.78 & 32.01 & 28.35 \\
 S5C & 33.50 & 32.08 & 28.35 \\
 var-BSC & 33.50 & 32.32 & 28.91 \\
 TVAE & 34.27 $\pm$ .02 & \textbf{32.65 $\pm$ .06} & \textbf{29.98 \small{$\pm$ .05}} \\
 \midrule
 n2v$^{\dagger}$ & 33.91 & 32.10 & 28.94 \\
 KSVD & 34.32 & 32.15 & 27.95 \\
 WNNM & 35.13 & 33.22 & 30.33 \\
 BM3D & 34.94 & 32.86 & 29.37 \\
 \midrule
 EPLL & 34.17 & 32.17 & 29.12 \\
 BDGAN & 34.57 & 33.28 & 30.61 \\
 DPDNN & \textbf{35.40} & \textbf{33.54} & \textbf{31.04} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents" by Enrico Guiraud, Jakob Drefs, and Jörg Lücke, arXiv:2011.13704v2, 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}{ll}
\toprule
Data Metric & Description \\ \midrule
Gross Premium Income & Total revenue generated from insurance premiums from policyholders. \\
Claims Paid & Total amount disbursed to settle approved policyholder claims. \\
Claims Incurred & Total expected cost of claims during the period. \\
Underwriting Profit & Profit derived from insurance operations less claims and expenses. \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{\footnotesize Key Financial Data Metrics Used in the Study. These metrics provide insights into the financial performance and operational efficiency of insurance companies.}
\end{table}
\end{document}

q-fin

"Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis" by Evans Kiptoo Korir and Zsolt Vizi, arXiv:2502.17072v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrr}
\toprule
\toprule
Portfolio & Distribution & AIC & BIC & KS statistic \\
\cmidrule{3-5} \\
\multirow{2}{*}{Sharpe}
 & Normal & -553.411 & -548.201 & 0.10450 \\
 & NIG & -590.036 & -579.615 & 0.04144 \\
\midrule
\multirow{2}{*}{CVaR}
 & Normal & -498.331 & -493.121 & 0.10883 \\
 & NIG & -514.163 & -503.742 & 0.05381 \\
\midrule
\multirow{2}{*}{DJIA}
 & Normal & -577.127 & -571.917 & 0.19146 \\
 & NIG & -660.546 & -650.125 & 0.05142 \\
\bottomrule
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Goodness‐of‐fit metrics by distribution and portfolio}
\end{table}
\end{document}

q-fin

Ayush Jha
Abootaleb Shirvani
Ali M. Jaffri
Svetlozar T. Rachev
Frank J. Fabozzi

"Behavioral Probability Weighting and Portfolio Optimization under Semi-Heavy Tails" by Ayush Jha, Abootaleb Shirvani, Ali M. Jaffri, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.04208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Information-theoretic subset selection of multivariate Markov chains via submodular optimization" by Zheyuan Lai and Michael C. H. Choi, arXiv:2503.23340v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||cccc||}
 \hline
 $\nu$ & $\Tilde{\Delta}_{\text{opt}}$ & $\mathbb{E}\{\Tilde{\varsigma}^{2}_{d}\} = \Tilde{b}-\Tilde{a}^2$ & $\Tilde{a}$ \\
 \hline\hline
 1 & 1.596 & 0.2313 & 0.6366 \\
 \hline
 2 & 0.9957 & 0.10472 & 0.88115 \\
 \hline
 3 & 0.586 & 0.036037 & 0.96256 \\
 \hline
 4 & 0.3352 & 0.011409 & 0.98845 \\
 \hline
 5 & 0.1881 & 0.003482 & 0.996505 \\
 \hline
 6 & 0.1041 & 0.0010389 & 0.99896 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Optimal Uniform Quantization Parameters}
\end{table}
\end{document}

eess

Soumyadeep Datta
Dheeraj Naidu Amudala
Ekant Sharma
Rohit Budhiraja
Shivendra S. Panwar

"Full-Duplex Cell-Free mMIMO Systems: Analysis and Decentralized Optimization" by Soumyadeep Datta, Dheeraj Naidu Amudala, Ekant Sharma, Rohit Budhiraja, and Shivendra S. Panwar, arXiv:2010.14110v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

FunAudio-ASR Technical Report

Keyu An
Yanni Chen
Chong Deng
Changfeng Gao
Zhifu Gao
Bo Gong
Xiangang Li
Yabin Li
Xiang Lv
Yunjie Ji
Yiheng Jiang
Bin Ma
Haoneng Luo
Chongjia Ni
Zexu Pan
Yiping Peng
Zhendong Peng
Peiyao Wang
Hao Wang
Wen Wang
Wupeng Wang
Biao Tian
Zhentao Tan
Nan Yang
Bin Yuan
Jieping Ye
Jixing Yu
Qinglin Zhang
Kun Zou
Han Zhao
Shengkui Zhao
Jingren Zhou

"FunAudio-ASR Technical Report" by Keyu An, Yanni Chen, Chong Deng, Changfeng Gao, Zhifu Gao, Bo Gong, Xiangang Li, Yabin Li, Xiang Lv, Yunjie Ji, Yiheng Jiang, Bin Ma, Haoneng Luo, Chongjia Ni, Zexu Pan, Yiping Peng, Zhendong Peng, Peiyao Wang, Hao Wang, Wen Wang, Wupeng Wang, Biao Tian, Zhentao Tan, Nan Yang, Bin Yuan, Jieping Ye, Jixing Yu, Qinglin Zhang, Kun Zou, Han Zhao, Shengkui Zhao, and Jingren Zhou, arXiv:2509.12508v1, 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{Number of students for each class}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
 & N° of Students \\ \hline
Class 1 & 15 \\
Class 2 & 16 \\
Class 3 & 12 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition" by Angelo G. Menezes, arXiv:2101.10845v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Wei Xie
Barry L. Nelson
Russell R. Barton

"Statistical Uncertainty Analysis for Stochastic Simulation" by Wei Xie, Barry L. Nelson, and Russell R. Barton, arXiv:2011.04207v1, 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||c|c|}
 \hline
 \multicolumn{3}{|c|}{} & \multicolumn{2}{c|}{$(P_1)$} & \multicolumn{2}{c|}{$(P_2)$} \\
 \hline
 \hline
 $\Delta_a$ &$N_a$ & $\overline{d}_a$ & $V(31)$ & $V(72)$ & $V(31)$ & $V(72)$ \\
 $day$ & & $mg/kg$ & $mm^3$& $mm^3$ & $mm^3$& $mm^3$ \\
 \hline $7$ & $5$ & $6.4$ & $2600$ & $5726$ & $2698$ & $5764$ \\
 \hline $4$ & $8$ & $4$ & $2492 $ & $5802$ & $2570$ & $ 5835$ \\
 \hline $2$ & $15$ & $2.13$ & $2442$ & $5865$ & $2505$ & $5892$ \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Results for antiangiogenic discrete treatments starting at $t_1 = 0$ with the same total dose.}
\end{table}
\end{document}

math

Luis A. Fernández
Isabel Lasheras
Cecilia Pola

"A mathematical perspective on the paradox that chemotherapy sometimes works backwards" by Luis A. Fernández, Isabel Lasheras, and Cecilia Pola, arXiv:2503.10670v1, 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{Ablation study on using different LLMs.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \bfseries LLM & \bfseries BLEU-4 & \bfseries Rouge-L & \bfseries Meteor & \bfseries BertScore F1 \\
 \midrule
 GPT2 XL 1.5B & 12.30 ± 0.19 & 41.33 ± 0.57 & 26.48 ± 0.33 & 92.00 ± 0.06 \\
 Gemma 2B & 13.78 ± 0.18 & 45.48 ± 0.55 & 28.32 ± 0.23 & 92.01 ± 0.02 \\
 OPT 1.3B & 12.26 ± 0.20 & 41.84 ± 0.52 & 26.21 ± 0.29 & 91.78 ± 0.04 \\
 Llama 3.2 1B & \textbf{13.93} ± 0.21 & \textbf{47.08} ± 0.56 & \textbf{29.17} ± 0.31 & \textbf{92.53} ± 0.07 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

William Han
Chaojing Duan
Michael A. Rosenberg
Emerson Liu
Ding Zhao

"ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling" by William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, and Ding Zhao, arXiv:2412.14373v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yassine Abbahaddou
Fragkiskos D. Malliaros
Johannes F. Lutzeyer
Michalis Vazirgiannis

"Centrality Graph Shift Operators for Graph Neural Networks" by Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer, and Michalis Vazirgiannis, arXiv:2411.04655v1, 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}{ccccc}
 & Gap & Hurdle & Stairs & Steps \\
 \hline
 Without Setup Policies & 74 & \textbf{41} & 64 & 142 \\
 With Setup Policies & \textbf{44} & 51 & \textbf{49} & \textbf{90} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Number of failures by terrain type from 1000 episodes of 4 terrain types \textbf{(without the jump terrain)}, randomly shuffled each episode.}
\end{table}
\end{document}

cs

Brendan Tidd
Nicolas Hudson
Akansel Cosgun
Jurgen Leitner

"Learning Setup Policies: Reliable Transition Between Locomotion Behaviours" by Brendan Tidd, Nicolas Hudson, Akansel Cosgun, and Jurgen Leitner, arXiv:2101.09391v2, 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}{llllll}
\cline{1-5}
 & Aurory & Autoglphs & Bored Ape Kennel Club & Bored Ape Yacht Club & \\ \cline{1-5}
12-month Realized Volatility & 291\% & 1,084\% & 420\% & 340\% & \\ \cline{1-5}
 & & & & & \\ \cline{1-5}
 & Cryptopunks & Degenerate Ape Academy & Meebits & Mutant Ape Yacht Club & \\ \cline{1-5}
12-month Realized Volatility & 1,140\% & 464\% & 1,013\% & 896\% & \\ \cline{1-5}
 & & & & &
\end{tabular}
\end{adjustbox}
\caption{12 Month Realized Volatility of eight profile picture collections}
\end{table}
\end{document}

q-fin

Jason B. Cho
Sven Serneels
David S. Matteson

"Non-fungible token transactions: data and challenges" by Jason B. Cho, Sven Serneels, and David S. Matteson, arXiv:2210.07393v1, 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{Wave gauges coordinates within the wave tank as reported in }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \textbf{WG number} & \textbf{Global Coordinates (m)} \\
 \midrule
 Wave gauge 1 & $(-2.74 , 0)$ \\
 Wave gauge 2 & $(-0.05 , 0.26)$ \\
 Wave gauge 3 & $(0.07 , -0.36)$ \\
 Wave gauge 4 & $(0.55 , 0)$ \\
 Wave gauge 5 & $(1.90 , 0)$ \\
 Wave gauge 6 & $(2.90 , 0)$
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Amirhossein Taran
Seevani Bali
Zeljko Tukovic
Vikram Pakrashi
Philip Cardiff

"A finite volume Simo-Reissner beam method for moored floating body dynamics" by Amirhossein Taran, Seevani Bali, Zeljko Tukovic, Vikram Pakrashi, and Philip Cardiff, arXiv:2504.18248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters used for the distortion of the ground truth centerlines}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|cccccc|}
\hline
density $(mm^{-1})$ & $2$ & $4$ & $10$ & $16$ & $20$ \\
\hline
$\sigma_{radius} (mm)$ & $0.01r$ & $0.05r$ & $0.1r$ & $0.3r$ & $0.5r$ \\
\hline
$\sigma_{spatial} (mm)$ & $0.01r$ & $0.05r$ & $0.1r$ & $0.3r$ & $0.5r$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Méghane Decroocq
Carole Frindel
Pierre Rougé
Makoto Ohta
Guillaume Lavoué

"Modeling and hexahedral meshing of cerebral arterial networks from centerlines" by Méghane Decroocq, Carole Frindel, Pierre Rougé, Makoto Ohta, and Guillaume Lavoué, arXiv:2201.08279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PVT simulation on ENOB}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 \multicolumn{2}{|c|}{Process}&\multicolumn{2}{c|}{ff}&\multicolumn{2}{c|}{ss}&tt \\
 \hline
 \multicolumn{2}{|c|}{Temprature ($^{\circ}$C)}&-40&80&-40&80&27\\
 \hline
 \multirow{3}{*}{Voltage (V)}&1& & & & & 7.42\\
 \cline{2-7}
 &0.9&7.41&7.28&7.29&7.21&\\
 \cline{2-7}
 &1.1&7.11&7.16&7.25&7.11&\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Rui Xiao
Kejie Huang
Yewei Zhang
Haibin Shen

"A Low Power In-Memory Multiplication andAccumulation Array with Modified Radix-4 Inputand Canonical Signed Digit Weights" by Rui Xiao, Kejie Huang, Yewei Zhang, and Haibin Shen, arXiv:2101.02419v1, 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}{cc|ccccccccc|c}
&	$g^3_i$&	7&	5&	0&	9&	9&	7&	12&	11&	13&		\\
Iteration&	$i$&	$L_1$&	$L_2$&	$L_3$&	$L_4$&	$L_5$&	$L_6$&	$L_7$&	$L_8$&	$L_9$&	$SEL$\\
\hline
0&	---&	$\infty$&	$\infty$&	$\infty$&	$\infty$&	0&	$\infty$&	$\infty$&	$\infty$&	$\infty$&	$\{ 5 \}$\\
1&	5&	$\infty$&	4&	$\infty$&	3&	0&	3&	$\infty$&	2&	$\infty$&	$\{ 2, 4, 6, 8 \}$\\
2&	2&	6&	4&	9&	3&	0&	3&	$\infty$&	2&	$\infty$&	$\{ 1, 3, 4, 6, 8 \}$\\
3&	3&	6&	4&	9&	3&	0&	3&	$\infty$&	2&	$\infty$&	$\{ 1, 4, 6, 8 \}$\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

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