\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of best regressors discovered through Bayesian hyperparameter optimization compared to Weighted Ensembles Regressor from AutoML.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|}
\hline
Metric & Decision Tree & K-Nearest Neighbors & XGBoost & Neural Network & AutoML \\ \hline
Coefficient of Determination (R2) & 0.440 & 0.306 & 0.595 & 0.545 & \textbf{0.605} \\ \hline
Mean Squared Error (MSE) & 1.156 & 1.270 & \textbf{0.978} & 1.031 & 0.995 \\ \hline
Mean Absolute Error (MAE) & 0.297 & 0.412 & 0.208 & 0.263 & \textbf{0.182} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Lyle Regenwetter
Colin Weaver
Faez Ahmed

"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames" by Lyle Regenwetter, Colin Weaver, and Faez Ahmed, arXiv:2201.10459v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Daniel Jenson
Jhonathan Navott
Mengyan Zhang
Makkunda Sharma
Elizaveta Semenova
Seth Flaxman

"Transformer Neural Processes - Kernel Regression" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, 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{arydshln}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{MISE per method on dry bean dataset} for $\beta = 0$ and varying levels of $\alpha$.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
 $\beta = 0$\\
 \hline \\
 \hline \\
 & \multicolumn{5}{c}{$\alpha$}\\
 \cdashline{2-6}\\
 Model & 0.0 & 1.0 & 2.0 & 3.0 & 4.0 \\
 \hline \\
 Linear Regression & 2.83 \scriptsize{± 0.01} & 3.27 \scriptsize{± 0.03} & 3.80 \scriptsize{± 0.03} & 4.19 \scriptsize{± 0.04} & 4.44 \scriptsize{± 0.04} \\
 CART & 0.86 \scriptsize{± 0.03} & 1.04 \scriptsize{± 0.03} & 1.44 \scriptsize{± 0.07} & 1.93 \scriptsize{± 0.10} & 2.18 \scriptsize{± 0.11} \\
 xgboost & 0.55 \scriptsize{± 0.03} & 0.69 \scriptsize{± 0.02} & 1.11 \scriptsize{± 0.08} & 1.58 \scriptsize{± 0.07} & 1.88 \scriptsize{± 0.10} \\
 MLP & 2.83 \scriptsize{± 0.01} & 0.54 \scriptsize{± 0.08} & 1.17 \scriptsize{± 0.09} & 1.98 \scriptsize{± 0.05} & 2.52 \scriptsize{± 0.09} \\
 \hdashline\\
 DRNet & 0.41 \scriptsize{± 0.03} & 0.60 \scriptsize{± 0.06} & 0.99 \scriptsize{± 0.12} & 1.74 \scriptsize{± 0.19} & 1.92 \scriptsize{± 0.23} \\
 VCNet & 0.34 \scriptsize{± 0.04} & 0.69 \scriptsize{± 0.04} & 1.04 \scriptsize{± 0.09} & 1.41 \scriptsize{± 0.09} & 1.69 \scriptsize{± 0.17} \\
 \hdashline\\
 CBRNet(MMD$_{lin}$) & \textbf{0.25} \scriptsize{± 0.04} & \textit{0.35} \scriptsize{± 0.05} & \textbf{0.41} \scriptsize{± 0.10} & 0.64 \scriptsize{± 0.08} & \textit{0.88} \scriptsize{± 0.14} \\
 CBRNet(MMD$_{rbf}$) & \textit{0.26} \scriptsize{± 0.04} & \textbf{0.34} \scriptsize{± 0.05} & \textit{0.42} \scriptsize{± 0.07} & \textbf{0.61} \scriptsize{± 0.08} & 0.92 \scriptsize{± 0.09} \\
 CBRNet(Wass) & 0.28 \scriptsize{± 0.05} & \textbf{0.34} \scriptsize{± 0.05} & \textit{0.42} \scriptsize{± 0.10} & \textit{0.62} \scriptsize{± 0.08} & \textbf{0.86} \scriptsize{± 0.16} \\
 \hline \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Christopher Bockel-Rickermann
Toon Vanderschueren
Jeroen Berrevoets
Tim Verdonck
Wouter Verbeke

"Using representation balancing to learn conditional-average dose responses from clustered data" by Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, and Wouter Verbeke, arXiv:2309.03731v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A PINN-enriched finite element method for linear elliptic problems" by Xiao Chen, Yixin Luo, and Jingrun Chen, arXiv:2503.14913v1, 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{Statistics of the dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \toprule
 \multirow{2}{*}{Sequence} &
 \multirow{2}{*}{\shortstack{Trajectory \\Length (m)}} &
 \multirow{2}{*}{\shortstack{Duration \\(seconds)}} &
 \multirow{2}{*}{\shortstack{\# Avg. \\Points}} \\
 {} \\
 \midrule
Baker-Barry Tunnel (Empty)
& 860.31 %
& 83.7 %
& 55.7k \\ %
Baker-Barry Tunnel (Vehicles)
& 906.86 %
& 65.5 %
& 36.5k \\ %
Robin Williams Tunnel
& 688.76 %
& 30.0 %
& 43.3k \\ %
Brisbane Lagoon Freeway
& 4941.80 %
& 176.3 %
& 40.0k \\ %
San Francisco City
& 1378.14 %
& 450.0 %
& 120.1k \\ %
CARLA Town04 (Straight Walls)
& 599.91 %
& 46.4 %
& 78.8k \\ %
CARLA Town05 (Curved Walls)
& 426.81 %
& 76.0 %
& 80.4k \\ %
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"DICP: Doppler Iterative Closest Point Algorithm" by Bruno Hexsel, Heethesh Vhavle, and Yi Chen, arXiv:2201.11944v2, 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{Evaluation Performance Comparison with Different Baselines. $CD$: average squared distance between two points, $L_{1}$: norm distance between two points}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\hline
&\multicolumn{2}{|c|}{GPRNet} &\multicolumn{2}{|c|}{PCN} &\multicolumn{2}{|c|}{PF-Net}\\
\hline
\hline
CD & \multicolumn{2}{|c|}{6.214}&\multicolumn{2}{|c|}{6.965} & \multicolumn{2}{|c|}{{7.005}} \\
\emph{L}1 & \multicolumn{2}{|c|}{22.97} &\multicolumn{2}{|c|}{24.90} & \multicolumn{2}{|c|}{{33.68}} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Jinglun Feng
Liang Yang
Ejup Hoxha
Diar Sanakov
Stanislav Sotnikov
Jizhong Xiao

"GPR-based Model Reconstruction System for Underground Utilities Using GPRNet" by Jinglun Feng, Liang Yang, Ejup Hoxha, Diar Sanakov, Stanislav Sotnikov, and Jizhong Xiao, arXiv:2011.02635v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Nuo Chen
Jiqun Liu
Hanpei Fang
Yuankai Luo
Tetsuya Sakai
Xiao-Ming Wu

"Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability" by Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, and Xiao-Ming Wu, arXiv:2403.18462v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|c|c|c|c}
Language & Corpus & {\bf Trankit} & Stanza & Flair & spaCy \\ \hline
Arabic & AQMAR & \textbf{74.8} & 74.3 & 74.0 & - \\ \hline
Chinese & OntoNotes & \textbf{80.0} & 79.2 & - & 69.3 \\ \hline
\multirow{2}{*}{Dutch} & CoNLL02 & \textbf{91.8} & 89.2 & 91.3 & 73.8 \\ \cline{2-6}
 & WikiNER & \textbf{94.8} & \textbf{94.8} & \textbf{94.8} & 90.9 \\ \hline
\multirow{2}{*}{English} & CoNLL03 & 92.1 & 92.1 & \textbf{92.7} & 81.0 \\ \cline{2-6}
 & OntoNotes & \textbf{89.6} & 88.8 & 89.0 & 85.4 \\ \hline
French & WikiNER & 92.3 & \textbf{92.9} & 92.5 & 88.8 \\ \hline
\multirow{2}{*}{German} & CoNLL03 & \textbf{84.6} & 81.9 & 82.5 & 63.9 \\ \cline{2-6}
 & GermEval14 & \textbf{86.9} & 85.2 & 85.4 & 68.4 \\ \hline
Russian & WikiNER & 92.8 & \textbf{92.9} & - & - \\ \hline
Spanish & CoNLL02 & \textbf{88.9} & 88.1 & 87.3 & 77.5 \\ %\hline
\end{tabular}
\end{adjustbox}
\caption{Performance (F1) on NER test sets.}
\end{table}
\end{document}

cs

Minh Van Nguyen
Viet Dac Lai
Amir Pouran Ben Veyseh
Thien Huu Nguyen

"Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing" by Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, and Thien Huu Nguyen, arXiv:2101.03289v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{hhline}
\usepackage{rotating}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The performance of different TI approaches in identifying the correct network topology: the main case.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c}
\hline
\multicolumn{2}{c||}{Method$^{\mathrm{a}}$ $\rightarrow$} &
\multicolumn{2}{c||}{DA} &
\multicolumn{2}{c||}{ANN } &
\multicolumn{2}{c||}{RB } &
\multicolumn{2}{c}{ML } \\
\hline
\multicolumn{2}{c||}{MI of$^{\mathrm{b}}$: $\rightarrow$} &
SC$^{\mathrm{c}}$ &
PDS$^{\mathrm{d}}$ &
SC &
PDS &
SC &
PDS&
SC &
PDS \\
\hline
\multirow{6}{*}{\rotatebox[origin=c]{90}{\parbox[c]{1.5 cm}{\centering Actual SC}}} &
 C1 &
0.00\% &
1.33\% &
0.04\% &
7.29\% &
0.65\% &
2.65\% &
0.00\% &
33.91\% \\
\hhline{~~~~~~~~~~}
&
 C2 &
0.38\% &
3.55\% &
0.71\% &
5.71\% &
61.98\% &
1.53\% &
0.40\% &
28.44\% \\
\hhline{~~~~~~~~~~}
&
 C3 &
0.00\% &
0.20\% &
0.00\% &
2.96\% &
0.00\% &
0.00\% &
2.80\% &
47.43\% \\
\hhline{~~~~~~~~~~}
&
 C4 &
0.00\% &
1.47\% &
0.00\% &
7.67\% &
0.00\% &
3.80\% &
4.21\% &
45.87\% \\
\hhline{~~~~~~~~~~}
&
 C5 &
0.00\% &
0.27\% &
0.00\% &
4.88\% &
0.02\% &
2.82\% &
21.65\% &
27.27\% \\
\hhline{~~~~~~~~~~}
&
 C6 &
0.03\% &
1.82\% &
1.04\% &
3.42\% &
0.16\% &
4.57\% &
11.88\% &
40.40\% \\
\hline
\multicolumn{2}{c||}{Average} &
0.07\% &
1.44\% &
0.30\% &
5.32\% &
10.47\% &
2.66\% &
6.82\% &
37.22\% \\
\hline
\multicolumn{10}{l}{$^{\mathrm{a}}$ANN = Artificial Neural Network, RB = Recursive Bayesian,} \\
\hhline{~~~~~~~~~~}
\multicolumn{10}{l}{ML = Maximum Likelihood;} \\
\hhline{~~~~~~~~~~}
\multicolumn{10}{l}{$^{\mathrm{b}}$Misidentification of; $^{\mathrm{c}}$Switching Configuration; $^{\mathrm{d}}$Protective Devices Status.} \\
\hhline{~~~~~~~~~~}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mohammad Jafarian
Alireza Soroudi
Andrew Keane

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


math

Seyed Mahdi Shavarani
Bela Vizvari
Kovacs Gergely

"Open Loop Layout Optimization: Feasible Path Planning and Exact Door-to-Door Distance Calculation" by Seyed Mahdi Shavarani, Bela Vizvari, and Kovacs Gergely, arXiv:2504.12792v1, 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{Benchmarking the proposed AuthNet model against State-of-the-art models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
\hline
\textbf{Models} & \textbf{Test Accuracy} \\ \hline
\textbf{AuthNet \textit{(Proposed)}} & 0.980 \\ %\hline
FaceNet + Garg \textit{et al.} \\ & 0.611 \\ %\hline
FaceNet + Chung \& Zisserman \\ & 0.652 \\ %\hline
FaceNet + Gergen \textit{et al.} \\ & 0.864 \\ %\hline
FaceNet + LipNet \\ & 0.951 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mohit Raghavendra
Pravan Omprakash
B R Mukesh
Sowmya Kamath

"AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements" by Mohit Raghavendra, Pravan Omprakash, B R Mukesh, and Sowmya Kamath, arXiv:2012.02515v2, 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{An example of the Chi-square for one day (Sunday) vs year}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrr}\toprule
 & Rear-end on Sundays & Rear-end at the year \\ \midrule
2015 & 4.20E+01 & 8.47E+02 \\
2016 & 8.70E+01 & 9.52E+02 \\
2017 & 9.00E+01 & 8.84E+02 \\
2018 & 6.40E+01 & 8.94E+02 \\
2019 & 6.70E+01 & 8.95E+02 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Contribution of temporal correlation of the shortest time lag in the financial time series. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|}
\multicolumn{4}{c}{}\\ \hline
Contribution of correlation.&No.1&No.7&No.9&No.12&No.13\\
 \hline \hline
Data&
BTC/USD	&	USD/CHF	&	USD/CAD	&	EUR/CHF 	& 	EUR/GBP\hspace{0.4cm} \\
\hline
4 th moment $ (\%) $&
 655.84 	&	93.47 	&	98.72	&	112.39 	&	126.18 	\\
\hline
6 th moment $ (\%)$ &
 542.99 	&	86.3	&	96.2	&	111.34 	&	122.89 	\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Deformation of semi-circle law for the correlated time series and Phase transition" by Masato Hisakado and Takuya Kaneko, arXiv:2508.07192v1, licensed under CC0 1.0. 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|ccccc}
\hline\hline
& 0.0 & 1.0 & Accuracy & Macro Average & Weighted Average \\ \hline
Precision & 0.97 & 1.00 & 0.97 & 0.98 & 0.97 \\
Recall & 1.00 & 0.86 & 0.97 & 0.93 & 0.97 \\
f1-score & 0.98 & 0.92 & 0.97 & 0.95 & 0.97 \\
\hline\hline
\end{tabular}
\end{adjustbox}
\caption{Test Evaluation Metrics for Random Forest}
\end{table}
\end{document}

q-fin

Aadi Gupta
Priya Gulati
Siddhartha P. Chakrabarty

"Classification based credit risk analysis: The case of Lending Club" by Aadi Gupta, Priya Gulati, and Siddhartha P. Chakrabarty, arXiv:2210.05136v1, 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{Summary of validation accuracies for LeNet-5 and a convolutional neural network with four convolutional and two linear layers for various training methods on the clean MNIST data set.}
\begin{tabular}{ccc}
 Architecture & Training Type & Accuracy \\ \hline
 LeNet-5 & naturally trained & 99.00\% \\
 LeNet-5 & adversarially trained (fgsm) & 98.99\% \\
 LeNet-5 & adversarially trained (pgd) & 98.55\% \\
 LeNet-5 & adversarially trained (Brownian) & 97.17\% \\
 LeNet-5 & trained on noise ($\sigma^2 = 0.4$) & 99.02\% \\
 CNN & naturally trained & 98.99\% \\
 CNN & adversarially trained (fgsm) & 98.65\% \\
 CNN & trained on noise ($\sigma^2 = 0.4$) & 98.93\% \\
\end{tabular}
\end{table}
\end{document}

cs

Bogdan Georgiev
Lukas Franken
Mayukh Mukherjee

"Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds" by Bogdan Georgiev, Lukas Franken, and Mayukh Mukherjee, arXiv:2101.06061v1, 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{Model Specifications}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \# & \multicolumn{2}{|c|}{Model Specification} & Results \\
 \hline
 \scriptsize{1*}& \multicolumn{2}{|c|}{\parbox{9cm}{\centering\begin{math} \Delta UER_{it} = \boldsymbol{\beta} \begin{pmatrix} \boldsymbol{X}_{it}\\ \Delta log(RealGDPPC)_{it} \\ \end{pmatrix} + \alpha_i + \gamma_t + \varepsilon_{it} \end{math}}} & \parbox{2cm}{\centering \scriptsize Figure - \\Appendix B.1.2: \\Tables 13-14}\\
 \hline
 \footnotesize{2*}& \parbox{4.25cm}{\centering\footnotesize\begin{math} \Delta log(Employed Persons)_{it} =\end{math}} & \multirow[c]{3}{*}[0in]{\parbox{6cm}{\centering\begin{math} \boldsymbol{\beta} \begin{pmatrix} \boldsymbol{X}_{it}\\ \Delta log(RealGDP)_{it} \\ \Delta log(Population)_{it} \\ \end{pmatrix} + \alpha_i + \gamma_t + \varepsilon_{it} \end{math}}} & \multirow[c]{4}{*}[0in]{\parbox{2cm}{\centering\scriptsize Figure \\Appendix B.1.2: \\Tables 13-14}}\\
 \cline{1-2}
 \footnotesize{3*}& \parbox{4.25cm}{\centering\footnotesize\begin{math} \Delta log(Unemployed Persons)_{it} =\end{math}} && \\
 \cline{1-2}
 \footnotesize{4*}& \parbox{4.25cm}{\centering\footnotesize\begin{math} \Delta log(Labor Force)_{it} =\end{math}} &&\\
 \cline{1-3}
 \scriptsize{5*}& \multicolumn{2}{|c|}{\parbox{11cm}{\centering\begin{math} \Delta log(Population)_{it} = \boldsymbol{\beta} \begin{pmatrix} \boldsymbol{X}_{it}\\ \Delta log(RealGDP)_{it} \\ \end{pmatrix} + \alpha_i + \gamma_t + \varepsilon_{it} \end{math}}} &\\
 \hline
 \scriptsize{6*}& \multicolumn{2}{|c|}{\parbox{11cm}{\centering\scriptsize\begin{math} \Delta UER_{it} = \boldsymbol{\beta} \boldsymbol{X}_{it} + \boldsymbol{Z}
 \begin{pmatrix} \Delta Active Mines_{it}*RE Investments_{it}\\
 \Delta Active Mines_{it-1}*RE Investments_{it-1}\\
 \Delta Active Mines_{it-2}*RE Investments_{it-2} \\
 \end{pmatrix} + \alpha_i + \gamma_t + \varepsilon_{it} \end{math}}} & \parbox{2cm}{\centering \scriptsize Appendix B.1.2: \\Table 19}\\
 \hline
\scriptsize{1\textsuperscript{SEM}}& \multicolumn{2}{|c|}{\parbox{9cm}{\centering\begin{math} Y_{it} = \boldsymbol{\beta} \boldsymbol{X}_{it} + \alpha_i + \gamma_t + U_{it} \end{math} \\ \begin{math} U_{it} = \delta W_{i}U_{t} + \varepsilon_{it} \end{math}}} & \multirow[c]{3}{*}[0in]{\parbox{2cm}{\centering\scriptsize Figure \\Appendix B.2: \\Tables 21-23, 25}} \\
 \cline{1-3}
 \scriptsize{1\textsuperscript{SLM}}&\multicolumn{2}{|c|}{\parbox{9cm}{\centering\begin{math} Y_{it} = \boldsymbol{\beta} \boldsymbol{X}_{it} + \rho W_{i}Y_{t} + \alpha_i + \gamma_t + \varepsilon_{it} \end{math}}} &\\
 \cline{1-3}
\scriptsize{1-5\textsuperscript{SARAR}}& \multicolumn{2}{|c|}{\parbox{9cm}{\centering\begin{math} Y_{it} = \boldsymbol{\beta} \boldsymbol{X}_{it} + \rho W_{i}Y_{t} + \alpha_i + \gamma_t + U_{it} \end{math} \\ \begin{math} U_{it} = \delta W_{i}U_{t} + \varepsilon_{it} \end{math}}} & \\
 \hline
 \scriptsize{1-6\textsuperscript{HTT(d)}}& \multicolumn{2}{|c|}{\parbox{9cm}{\centering\begin{math} Y_{it} = \boldsymbol{\beta} \boldsymbol{X}_{it} + \sum_{l=1}^{d} \lambda_{il}f(t) + \alpha_i + \gamma_t + \varepsilon_{it} \end{math}}}& \parbox{2cm}{\centering \scriptsize Figure \\ Appendix B.3: \\Tables 26-28}\\
 \hline
 \multicolumn{4}{|c|}{\parbox{15cm}{\small \ \ \\ \textbf{Variable definitions:}\\
\textbf{\textit{i}} : denotes each county for i=1, 2,\ldots3,072 counties\\
 \textbf{\textit{t}} : denotes each year for t=2002, 2003,\ldots2019 \\
 $\boldsymbol\alpha_{i}$ : unit-fixed effects \\
 $\boldsymbol\gamma_{t}$ : time-fixed effects\\
 $\boldsymbol X_{it}$ : Vector of independent variables ($\Delta$ Active Mines\textsubscript{it}, $\Delta$ Active Mines\textsubscript{it-1}, $\Delta$ Active Mines\textsubscript{it-2}). For Model 6, this includes levels of all interacted variables. For Models 1-5\textsuperscript{[SEM, SLM, SARAR]} and 1-6\textsuperscript{HTT(1),HTT(2)]} this represents all independent variables denoted in Models 1-6, respectively.\\
 $\boldsymbol Y_{it}$ : Relevant dependent variable denoted by Model number.\\
 $\boldsymbol\beta$ : Vector of regression coefficients to be estimated.\\
 $\boldsymbol\rho$ : Spatial autoregression parameter.\\
 $\boldsymbol\delta$ : Spatial autocorrelation parameter.\\
$\boldsymbol W_{i}$ : N X N spatial weight matrix for neighbors of unit \textit{i} where N = 3,072.\\
 $\boldsymbol\lambda_{il}$ : Individual loadings parameter per unit \textit{i} and factor \textit{l} in heterogeneous trend models.\\
\textbf{\textit{f(t)}} : Unobserved time-varying common factors used in heterogeneous trend models.\\
\textbf{\textit{REInvestments\textsubscript{it}}}: dummy indicator of whether renewable energy investments exceeded 0.1\% of GDP\textsubscript{it}.\\
$\boldsymbol\Delta UER_{it}$ : Change in unemployment rate in county \textit{i} in year \textit{t}.\\
\textit{Note: * indicates model was also applied to a subset of coal counties only.}}}\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Spatial-temporal dynamics of employment shocks in declining coal mining regions and potentialities of the 'just transition'" by Ebba Mark, Ryan Rafaty, and Moritz Schwarz, arXiv:2211.12619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Implementation details of horseshoe methods.}
\begin{tabular}{cccc}
 \toprule
 & Global Shrinkage Parameter $\xi$ & Local Shrinkage Parameter $\eta_j$'s & Model Variance $\sigma^2$ \\
 \midrule
 \textbf{m-FAHS} & $\frac{\text{R}_{BH}}{m}$ & standard half-Cauchy prior & Jeffreys prior\\
 \addlinespace
 \textbf{e-FAHS} & $\frac{\sigma \text{R}_{BH}}{m-R_{BH}}$ & standard half-Cauchy prior & fixed at 1\\
 \addlinespace
 \textbf{EBHS} & estimated by MMLE & standard half-Cauchy prior & Jeffreys prior\\
 \addlinespace
 \textbf{FBHS}& standard half-Cauchy prior & standard half-Cauchy prior & Jeffreys prior\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Qiaoyu Liang
Zihan Zhu
Ziang Fu
Michael Evans

"False Discovery Rate Control via Frequentist-assisted Horseshoe" by Qiaoyu Liang, Zihan Zhu, Ziang Fu, and Michael Evans, arXiv:2502.05460v2, 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[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{LASS results on AudioCaps testset. FAD does not apply on unprocessed data as it calculates the difference between two groups of audio, while the target and mixed audio share the same audio events.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
\textbf{Model} & \textbf{FAD} & \textbf{CLAPScore} & \textbf{CLAPScore}$_A$ \\
\midrule
Unprocessed & --- & 11.9 & 64.9 \\
LASS-Net & 5.09 & 14.4 & 70.2 \\
AudioSep & 4.38 & 13.6 & 69.6 \\
\rowcolor{gray!20} FlowSep & 2.86 & 21.9 & \textbf{81.7} \\
\midrule
\rowcolor{gray!20} UniFlow$_{\text{DDPM}}$ & \textbf{2.70} & \textbf{22.7} & 81.2 \\
\rowcolor{gray!20} UniFlow$_{\text{FM}}$ & 2.76 & 22.1 & 80.3 \\
\rowcolor{gray!20} UniFlow$_{\text{MF}}$ & 2.83 & 21.4 & 79.4 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Ziqian Wang
Zikai Liu
Yike Zhu
Xingchen Li
Boyi Kang
Jixun Yao
Xianjun Xia
Chuanzeng Huang
Lei Xie

"UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling" by Ziqian Wang, Zikai Liu, Yike Zhu, Xingchen Li, Boyi Kang, Jixun Yao, Xianjun Xia, Chuanzeng Huang, and Lei Xie, arXiv:2508.07558v1, 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{Out-of-domain model performance on M6. (f) means subset (f) in M6 while (o) means the other remaining ones.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\hline
\textbf{Model} & \textbf{Acc(f)} & \textbf{F1 (f)} & \textbf{Acc(o)} & \textbf{F1 (o)} \\
\hline
Q-SVM & .671 & \textbf{.758} & .780 & .626 \\
ResNet18 & \textbf{.685} & .693 & \textbf{.781} & \textbf{.778} \\
VGG & .667 & .533 & \underline{.274} & \underline{.118} \\
SeNet & .513 & .526 & .687 & .776 \\
MobileNet & .429 & .356 & .772 & .732 \\
CNN+LSTM & \underline{.392} & \underline{.316} & .776 & .737 \\
ViT & .623 & .630 & .762 & .754 \\
Mamba &.623 & .625 & .656 & .670 \\
XLSTM & .405 & .422 & .710 & .712 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Yupei Li
Qiyang Sun
Hanqian Li
Lucia Specia
Björn W. Schuller

"Detecting Machine-Generated Music with Explainability -- A Challenge and Early Benchmarks" by Yupei Li, Qiyang Sun, Hanqian Li, Lucia Specia, and Björn W. Schuller, arXiv:2412.13421v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrrrr} \toprule
 Regularity parameter $\alpha$ & 1.5 & 2 & 2.5 & 3 & 3.5 & 4 \\ \midrule
 Avg. single-chain ESS & 1\,749 & 1\,126 & 252 & 183 & 168 & 635 \\
 Multi-chain ESS & 156\,483 & 126\,163 & 67\,195 & 57\,390 & 56\,423 & 96\,326 \\ \bottomrule
 \end{tabular}
\caption{Effective sample sizes for cost comparison experiment in , rounded to the closest integer.}
\end{table}
\end{document}

math

Jonas Latz
Aretha L. Teckentrup
Simon Urbainczyk

"Deep Gaussian Process Priors for Bayesian Image Reconstruction" by Jonas Latz, Aretha L. Teckentrup, and Simon Urbainczyk, arXiv:2412.10248v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results on the MELD dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|ccc|c|}
\hline
\textbf{Methods} & \multicolumn{3}{c|}{\textbf{Learnable Module}} & \textbf{MELD} \\ \hline
\textbf{} & \textbf{Encoder} & \textbf{LLM} & \textbf{Adapter} & \textbf{WA $\uparrow$} \\ \hline
\textbf{emotion2vec} & Y & - & - & 51.9 \\ \hline
\textbf{Qwen-Audio} & Y & N & Y & 55.7 \\ \hline
\textbf{Qwen2-Audio} & Y & Y & Y & 55.3 \\ \hline
\textbf{SLM-Qwen (ours)} & N & N & Y & 53.3 \\ \hline
\textbf{SLM-Llama (ours)} & N & N & Y & 54.9 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Chun Wang
Chenyang Liu
Wenze Xu
Weihong Deng

"Dual Information Speech Language Models for Emotional Conversations" by Chun Wang, Chenyang Liu, Wenze Xu, and Weihong Deng, arXiv:2508.08095v1, 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{The results of the Chi-square test for week day every year vs Type.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrr} \toprule
Scenario & S & M & T & W & Y & F & S \\ \midrule
statistic & 3.05E+00 & 5.45E+00 & 2.57E+00 & 3.36E+00 & 1.97E+00 & 1.07E+01 & 6.26E+00 \\
P-value & 5.49E-01 & 2.44E-01 & 6.32E-01 & 5.00E-01 & 7.42E-01 & 2.99E-02 & 1.81E-01 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Type B measurement uncertainties.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
		\hline
		type B uncertainty & mean (ps) & $u_{j}$ (ps)\\
		\hline
		TIC temperature dependency & 0.0 & 12.5\\
		DSO temperature dependency & 0.0 & 0.8\\
 TIC reproducibility & 0.0 & 4.0\\
 DSO reproducibility & 0.0 & 0.7\\
 Cable Manipulation & 0.0 & 7.5\\
		Direct Attach Cable Asymmetry & 0.0 & 1.3\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

P. P. M. Jansweijer
N. A. D. Boukadida
K. Hanhijärvi
A. Wallin
B. Eglin
E. Laier English

"First electrical White Rabbit absolute calibration inter-comparison" by P. P. M. Jansweijer, N. A. D. Boukadida, K. Hanhijärvi, A. Wallin, B. Eglin, and E. Laier English, arXiv:2201.08640v1, 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{Fine tuning backbone with NMCE objective. Results shown are from ResNet-18. Fine tuning backbone improves result slightly but notably.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|lll}
\bf Model &\bf ACC &\bf NMI &\bf ARI
\\ \hline \\
CIFAR-10 before &0.819 &0.743 &0.690 \\
CIFAR-10 after &{\bf 0.830} &{\bf 0.761} &{\bf 0.710} \\
CIFAR-20 before &0.422 &0.471 &0.300 \\
CIFAR-20 after &{\bf 0.437} &{\bf 0.488} &{\bf 0.322} \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zengyi Li
Yubei Chen
Yann LeCun
Friedrich T. Sommer

"Neural Manifold Clustering and Embedding" by Zengyi Li, Yubei Chen, Yann LeCun, and Friedrich T. Sommer, arXiv:2201.10000v1, 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{Hyper-parameters in EPT \textbf{with} outer loops on real image datasets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccr}
\toprule
Parameter		& $\ell$ 	& $s$ 	& $n$ 	& $\alpha$ 	& $T$ 	& $IL$ \\
\midrule
Value 		& 128 	& 0.5 	& 1k 		& 0 			& 1 		& 20 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yuan Gao
Jian Huang
Yuling Jiao
Jin Liu
Xiliang Lu
Zhijian Yang

"Generative Learning With Euler Particle Transport" by Yuan Gao, Jian Huang, Yuling Jiao, Jin Liu, Xiliang Lu, and Zhijian Yang, arXiv:2012.06094v1, 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}
 \hline
 \textbf{Parameter} & \textbf{Value} \\
 \hline
 Number of subcarriers & $N_{sc}$ = \{1,256\} \\
 \hline
 Modulation order & $M = 16,\,64$ \\
 \hline
 Transmit antenna gain & $G_t$ = 45 dBi \\
 \hline
 Receive antenna gain & $G_r$ = 14 dBi \\
 \hline
 Bandwidth & $B$ = 1 GHz \\
 \hline
 Noise temperature & $T_n = 290$ K \\
 \hline
 Center frequency & $f_c$ = 315 GHz \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{System Parameters}
\end{table}
\end{document}

eess

Lutfi Samara
Simon Haussmann
Erind Tufa
Antonio Alberto D'Amico
Tommaso Zugno
Ingmar Kallfass
Thomas Kürner

"Sub-THz Power Amplifiers: Measurements, Behavioral Modeling and Predistortion Algorithms" by Lutfi Samara, Simon Haussmann, Erind Tufa, Antonio Alberto D'Amico, Tommaso Zugno, Ingmar Kallfass, and Thomas Kürner, arXiv:2508.09545v1, 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}{lrrrr}
\toprule
plant type & real time & time-invariant& real time & time-invariant \\
&zonal&zonal&nodal&nodal\\\midrule
other\_res & 57.6 & 57.7 & 62.2 & 62.2 \\
conventional & 172.7 & 173.7 & 162.3 & 163.0 \\
solar rooftop & 3.6 & 3.7 & 4.2 & 4.2 \\
Ground-mounted PV & 23.7 & 24.0 & 27.2 & 27.3 \\
(solar park)&&&&\\
solar battery & 0.8 & 0.7 & 0.5 & 0.5 \\
wind onshore & 139.6 & 140.0 & 135.2 & 135.3 \\
wind offshore & 79.0 & 79.3 & 76.3 & 76.2 \\
hydro\_ror & 20.8 & 20.9 & 21.7 & 21.7 \\
(run-of-river)&&&&\\
hydro\_psp & 19.9 & 19.8 & 11.8 & 11.7 \\
(pumped storage)&&&&\\\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Yearly generation by plant type and scenario (TWh).}
\end{table}
\end{document}

q-fin

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

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


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study of normalization methods for multi-domain CTR prediction. STAR FCN is trained BN, LN, and PN respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
	\toprule
	 &STAR FCN (BN) & STAR FCN (LN) & STAR FCN (PN) \\
	\midrule
	Overall AUC &0.6455 & 0.6463 & \textbf{0.6506} \\
	\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiang-Rong Sheng
Liqin Zhao
Guorui Zhou
Xinyao Ding
Binding Dai
Qiang Luo
Siran Yang
Jingshan Lv
Chi Zhang
Hongbo Deng
Xiaoqiang Zhu

"One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction" by Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, and Xiaoqiang Zhu, arXiv:2101.11427v5, 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{Relative width of the 95\% prediction interval for each return level from the concentration approach compared with the standard method. Bootstrapped standard errors appear in brackets.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
 Return level&2&5&10&20\\
 Relative width&5.099 (0.123)&3.448 (0.102)&2.928(0.091)&2.713 (0.066) \\
 \hline
 Return level&50&100&200&500\\
 Relative width&1.880 (0.061)&1.624 (0.054)&1.577 (0.063)&1.546 (0.062)
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Fast return-level estimates for flood insurance via an improved Bennett inequality for random variables with differing upper bounds" by Anna Maria Barlow and Chris Sherlock, arXiv:2311.10001v3, 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{Choices of $N$, $p$ and $\sigma_g$ for the scenario where the genetic variants were simulated based on real genetic data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|ccc}
 \hline
 & \multicolumn{3}{c|}{Low-dimensional ($N>p$)}
 & \multicolumn{3}{c}{High-dimensional ($N<p$)} \\
 \hline
 & $N$ & $p$ & $\sigma_g$ & $N$ & $p$ & $\sigma_g$\\
 \hline
 Linear & 1092 & 500 & 0.02 & 1092 & 1500 & 0.01 \\
 Quadratic & 1092 & 500 & 0.03 & 1092 & 1500 & 0.015\\
 Trigonometric & 1092 & 500 & 0.05 & 1092 & 1500 & 0.05\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Olivia Bley
Elizabeth Lei
Andy Zhou
Xiaoxi Shen

"Estimating Broad Sense Heritability via Kernel Ridge Regression" by Olivia Bley, Elizabeth Lei, Andy Zhou, and Xiaoxi Shen, arXiv:2411.00346v1, 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
\caption{Cost for LLMs}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
\toprule
Model & Prompt & Completion & Cost \\
\midrule
GPT-4o-mini& (\$0.15/MTok) & (\$0.6/MTok) & \$18.35 \\
GPT-4.1 & (\$2/MTok) & 8/MTok) & \$265.47\\
GPT-4.1-mini & (\$0.4/MTok) & (\$1.6/MTok) & \$49.87 \\
GPT-4.1-nano & (\$0.1/MTok) & (\$0.4/MTok) & \$11.22 \\
o3-mini& (\$1.1/MTok) & (\$4.4/MTok) & \$186.94 \\
Claude-3.7-Sonnet& (\$3/MTok) & (\$15/MTok) & \$396.07 \\
DeepSeek-V3-2503& (2RMB/MTok)& (8RMB/MTok)& 239.27RMB \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Junzhe Jiang
Chang Yang
Aixin Cui
Sihan Jin
Ruiyu Wang
Bo Li
Xiao Huang
Dongning Sun
Xinrun Wang

"FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs" by Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Ruiyu Wang, Bo Li, Xiao Huang, Dongning Sun, and Xinrun Wang, arXiv:2505.13533v1, 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{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Forecasting performance of the proposed E-STGCN model in comparison to the temporal-only and spatiotemporal forecasting techniques for 60 60-day ahead forecast horizon of $PM_{2.5}$ and $PM_{10}$ pollutants (best results are \underline{\textbf{highlighted}}).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|cccccc|ccccccc|c|}
\hline \multirow{2}{*}{Pollutant} & Forecast & \multirow{2}{*}{Metric} & \multicolumn{6}{c|}{Temporal-only Model} & \multicolumn{7}{c|}{Spatiotemporal Model} & Proposed \\ \cline{4-16}
 & \multirow{2}{*}{Period} & & \multirow{2}{*}{ARIMA} & \multirow{2}{*}{LSTM} & \multirow{2}{*}{TCN} & \multirow{2}{*}{DeepAR} & \multirow{2}{*}{Transformers} & \multirow{2}{*}{NBeats} & \multirow{2}{*}{STARMA} & \multirow{2}{*}{GSTAR} & \multirow{2}{*}{GpGp} & \multirow{2}{*}{{\color{black}STGCN}} & \multirow{2}{*}{STNN} & Modified & \multirow{2}{*}{DeepKrigging} & \multirow{2}{*}{E-STGCN} \\
 & & & & & & & & & & & & & & STGCN & & \\ \hline
\multirow{36}{*}{PM$_{2.5}$} & \multirow{6}{*}{JAN - FEB} & MAE & 73.48 & 132.20 & 116.87 & 131.90 & 66.52 & 59.28 & 80.56 & 100.35 & 58.53 & {\color{black} 62.00} & 76.26 & 56.73 & 84.47 & \underline{\textbf{55.00}} \\
 & & MASE & 1.72 & 3.11 & 2.73 & 3.10 & 1.54 & 1.39 & 1.87 & 2.36 & 1.36 & {\color{black} 1.44} & 1.82 & 1.32 & 1.99 & \underline{\textbf{1.28}} \\
 & & RMSE & 86.23 & 148.60 & 135.33 & 148.33 & 89.53 & 77.12 & 96.46 & 113.61 & 78.81 & {\color{black} 84.09} & 94.98 & 76.44 & 100.93 & \underline{\textbf{73.00}} \\
 & & SMAPE & 46.50 & 152.00 & 126.90 & 151.30 & 47.40 & 45.20 & 81.50 & 57.00 & 39.90 & {\color{black} 43.58} & 48.80 & 39.60 & 77.20 & \underline{\textbf{37.90}} \\
 && {\color{black}	Pinball Loss	} & {\color{black}	\underline{\textbf{21.51}}	} & {\color{black}	105.76	} & {\color{black}	92.78	} & {\color{black}	105.52	} & {\color{black}	50.69	} & {\color{black}	45.07	} & {\color{black}	62.52	} & {\color{black}	23.37	} & {\color{black}	39.18	} & {\color{black} 46.61} & {\color{black}	34.45	} & {\color{black}	38.63	} & {\color{black}	58.54	} & {\color{black}	37.44	} \\
 && {\color{black}	CRPS	} & {\color{black}	83.42	} & {\color{black}	95.40	} & {\color{black}	94.98	} & {\color{black}	95.40	} & {\color{black}	94.09	} & {\color{black}	83.66	} & {\color{black}	87.92	} & {\color{black}	99.12	} & {\color{black}	85.74	} & {\color{black} 92.50} & {\color{black}	93.58	} & {\color{black}	82.68	} & {\color{black}	83.64	} & {\color{black}	\underline{\textbf{81.90}}	} \\ \cline{2-17}
 & \multirow{6}{*}{MAR - APR} & MAE & 35.03 & 59.96 & 49.14 & 55.51 & 29.62 & 35.06 & 50.95 & 50.86 & 37.44 & {\color{black} 34.42} & 143.20 & 27.68 & 68.10 & \underline{\textbf{22.35}} \\
 & & MASE & 1.82 & 3.02 & 2.46 & 2.78 & 1.58 & 1.84 & 2.56 & 2.55 & 2.07 & {\color{black} 1.81} & 7.71 & 1.39 & 3.47 & \underline{\textbf{1.13}} \\
 & & RMSE & 40.90 & 65.06 & 58.64 & 60.99 & 34.89 & 41.08 & 57.72 & 58.23 & 42.69 & {\color{black} 39.94} & 154.31 & 35.06 & 77.92 & \underline{\textbf{27.69}} \\
 & & SMAPE & 43.70 & 131.80 & 93.70 & 114.10 & 39.20 & 42.80 & 111.50 & 112.70 & 46.50 & {\color{black} 43.28} & 98.80 & 41.70 & 144.30 & \underline{\textbf{31.20}} \\
 && {\color{black}	Pinball Loss	} & {\color{black}	8.10	} & {\color{black}	47.97	} & {\color{black}	35.48	} & {\color{black}	44.41	} & {\color{black}	8.60	} & {\color{black}	8.89	} & {\color{black}	40.47	} & {\color{black}	39.91	} & {\color{black}	9.54	} & {\color{black} 9.65} & {\color{black}	28.74	} & {\color{black}	18.35	} & {\color{black}	48.39	} & {\color{black}	\underline{\textbf{8.08}}	} \\
 && {\color{black}	CRPS	} & {\color{black}	40.07	} & {\color{black}	45.49	} & {\color{black}	46.80	} & {\color{black}	45.50	} & {\color{black}	36.68	} & {\color{black}	40.11	} & {\color{black}	45.67	} & {\color{black}	45.84	} & {\color{black}	40.96	} & {\color{black} 39.48} & {\color{black}	98.36	} & {\color{black}	41.11	} & {\color{black}	49.50	} & {\color{black}	\underline{\textbf{33.74}}	} \\
 \cline{2-17}
 & \multirow{6}{*}{MAY - JUN} & MAE & 37.54 & 40.38 & 42.47 & 38.31 & 46.70 & 22.53 & 43.36 & \underline{\textbf{22.15}} & 49.65 & {\color{black} 50.80} & 43.12 & 30.41 & 37.31 & 43.53 \\
 & & MASE & 2.14 & 2.23 & 2.35 & 2.11 & 2.71 & 1.24 & 2.41 & \underline{\textbf{1.23}} & 2.95 & {\color{black} 2.92} & 2.33 & 1.70 & 2.03 & 2.44 \\
 & & RMSE & 42.70 & 49.77 & 51.29 & 48.09 & 51.41 & 32.55 & 50.27 & \underline{\textbf{30.71}} & 54.99 & {\color{black} 55.57} & 52.49 & 42.70 & 44.79 & 56.56 \\
 & & SMAPE & 57.70 & 97.10 & 117.20 & 88.10 & 66.60 & 39.10 & 127.60 & \underline{\textbf{38.70}} & 68.60 & {\color{black} 69.57} & 70.40 & 58.00 & 99.80 & 94.50 \\
 && {\color{black}	Pinball Loss	} & {\color{black}	11.64	} & {\color{black}	32.29	} & {\color{black}	33.46	} & {\color{black}	30.63	} & {\color{black}	\underline{\textbf{11.17}}	} & {\color{black}	15.27	} & {\color{black}	34.65	} & {\color{black}	13.32	} & {\color{black}	11.69	} & {\color{black} 19.84} & {\color{black}	21.46	} & {\color{black}	19.52	} & {\color{black}	29.10	} & {\color{black}	19.19	} \\
 && {\color{black}	CRPS	} & {\color{black}	37.77	} & {\color{black}	36.76	} & {\color{black}	36.47	} & {\color{black}	36.77	} & {\color{black}	41.25	} & {\color{black}	32.45	} & {\color{black}	36.00	} & {\color{black}	\underline{\textbf{29.82}}	} & {\color{black}	43.42	} & {\color{black} 43.61} & {\color{black}	42.10	} & {\color{black}	37.57	} & {\color{black}	35.14	} & {\color{black}	37.52	} \\ \cline{2-17}
 & \multirow{6}{*}{JUL - AUG} & MAE & 30.37 & 21.33 & 26.49 & 18.44 & 60.75 & 24.10 & 29.51 & \underline{\textbf{12.67}} & 62.11 & {\color{black} 62.03} & 20.80 & 29.41 & 36.68 & 28.05 \\
 & & MASE & 4.07 & 2.68 & 3.38 & 2.30 & 8.17 & 3.25 & 3.77 & \underline{\textbf{1.60}} & 8.35 & {\color{black} 8.33} & 2.60 & 3.89 & 4.71 & 3.70 \\
 & & RMSE & 33.44 & 24.35 & 30.50 & 21.69 & 61.90 & 28.32 & 32.59 & \underline{\textbf{16.06}} & 64.19 & {\color{black} 63.43} & 24.31 & 35.63 & 38.78 & 34.11 \\
 & & SMAPE & 60.90 & 74.00 & 109.00 & 59.70 & 93.40 & 51.00 & 132.80 & \underline{\textbf{38.10}} & 93.60 & {\color{black} 93.83} & 61.90 & 56.70 & 197.50 & 55.40 \\
 && {\color{black}	Pinball Loss	} & {\color{black}	6.55	} & {\color{black}	16.98	} & {\color{black}	20.22	} & {\color{black}	14.57	} & {\color{black}	12.21	} & {\color{black}	\underline{\textbf{5.82}}	} & {\color{black}	23.60	} & {\color{black}	9.45	} & {\color{black}	12.50	} & {\color{black} 13.47} & {\color{black}	13.26	} & {\color{black}	7.14	} & {\color{black}	29.24	} & {\color{black}	6.91	} \\
 && {\color{black}	CRPS	} & {\color{black}	26.97	} & {\color{black}	22.71	} & {\color{black}	23.22	} & {\color{black}	22.46	} & {\color{black}	39.78	} & {\color{black}	24.77	} & {\color{black}	22.95	} & {\color{black}	\underline{\textbf{18.45}}	} & {\color{black}	41.13	} & {\color{black} 40.81} & {\color{black}	23.37	} & {\color{black}	28.61	} & {\color{black}	22.72	} & {\color{black}	22.80	} \\
 \cline{2-17}
 & \multirow{6}{*}{SEP - OCT} & MAE & 36.12 & 54.88 & 63.93 & 52.05 & 44.53 & 40.25 & 58.62 & 34.86 & 44.33 & {\color{black} 34.29} & 53.65 & 33.99 & 57.96 & \underline{\textbf{33.60}} \\
 & & MASE & 2.63 & 3.95 & 4.64 & 3.74 & 3.32 & 2.91 & 4.25 & 2.54 & 3.34 & {\color{black} 2.52} & 3.90 & 2.50 & 4.15 & \underline{\textbf{2.46}} \\
 & & RMSE & 51.27 & 69.38 & 78.49 & 66.93 & 50.97 & 55.78 & 76.23 & 49.13 & 51.34 & {\color{black} 41.26} & 68.24 & 41.05 & 71.90 & \underline{\textbf{40.34}} \\
 & & SMAPE & 55.00 & 107.90 & 148.20 & 97.40 & 59.80 & 64.00 & 120.70 & 52.40 & 59.40 & {\color{black} 49.63} & 108.80 & 49.60 & 123.00 & \underline{\textbf{48.90}} \\
 && {\color{black}	Pinball Loss	} & {\color{black}	26.24	} & {\color{black}	43.73	} & {\color{black}	50.83	} & {\color{black}	41.36	} & {\color{black}	14.96	} & {\color{black}	30.90	} & {\color{black}	46.10	} & {\color{black}	24.12	} & {\color{black}	14.66	} & {\color{black} 15.37} & {\color{black}	42.29	} & {\color{black}	13.65	} & {\color{black}	46.12	} & {\color{black}	\underline{\textbf{13.52}}	} \\
 && {\color{black}	CRPS	} & {\color{black}	46.88	} & {\color{black}	47.82	} & {\color{black}	48.53	} & {\color{black}	47.83	} & {\color{black}	45.98	} & {\color{black}	47.67	} & {\color{black}	49.68	} & {\color{black}	45.92	} & {\color{black}	46.10	} & {\color{black} 39.24} & {\color{black}	47.71	} & {\color{black}	39.02	} & {\color{black}	47.83	} & {\color{black}	\underline{\textbf{38.67}}	} \\
 \cline{2-17}
 & \multirow{6}{*}{NOV - DEC} & MAE & \underline{\textbf{90.77}} & 204.62 & 195.80 & 200.48 & 127.97 & 132.34 & 159.92 & 157.59 & 120.80 & {\color{black} 107.25} & 198.17 & 95.34 & 224.42 & 91.09 \\
 & & MASE & \underline{\textbf{1.91}} & 4.30 & 4.11 & 4.21 & 2.67 & 2.78 & 3.35 & 3.31 & 2.50 & {\color{black} 2.24} & 4.18 & 2.00 & 4.73 & 1.91 \\
 & & RMSE & \underline{\textbf{111.64}} & 217.05 & 210.92 & 213.17 & 146.81 & 149.75 & 175.57 & 173.31 & 139.69 & {\color{black} 127.11} & 211.16 & 115.40 & 244.57 & 112.98 \\
 & & SMAPE & 46.50 & 169.40 & 155.70 & 162.30 & 74.50 & 80.80 & 117.80 & 113.50 & 68.20 & {\color{black} 57.87} & 163.30 & 49.60 & 189.00 & \underline{\textbf{46.30}} \\
 && {\color{black}	Pinball Loss	} & {\color{black}	71.38	} & {\color{black}	163.70	} & {\color{black}	156.29	} & {\color{black}	160.39	} & {\color{black}	102.18	} & {\color{black}	105.13	} & {\color{black}	127.54	} & {\color{black}	125.72	} & {\color{black}	96.23	} & {\color{black} 85.93} & {\color{black}	158.54	} & {\color{black}	74.93	} & {\color{black}	177.00	} & {\color{black}	\underline{\textbf{69.81}}	} \\
 && {\color{black}	CRPS	} & {\color{black}	131.98	} & {\color{black}	136.38	} & {\color{black}	137.02	} & {\color{black}	136.39	} & {\color{black}	135.65	} & {\color{black}	136.02	} & {\color{black}	136.20	} & {\color{black}	136.15	} & {\color{black}	134.78	} & {\color{black} 134.44} & {\color{black}	136.42	} & {\color{black}	133.45	} & {\color{black}	139.13	} & {\color{black}	\underline{\textbf{131.94}}	} \\
 \hline
\multirow{36}{*}{PM$_{10}$} &
\multirow{6}{*}{JAN - FEB} & MAE & 94.47 & 252.19 & 245.06 & 245.01 & 135.51 & 95.19 & 120.61 & 116.70 & 101.61 & {\color{black} 82.81} & 102.18 & 80.35 & 125.87 & \underline{\textbf{72.53}} \\
 & & MASE & 1.53 & 3.99 & 3.87 & 3.88 & 2.11 & 1.53 & 1.88 & 1.83 & 1.58 & {\color{black} 1.30} & 1.63 & 1.27 & 1.97 & \underline{\textbf{1.15}} \\
 & & RMSE & 113.55 & 269.06 & 280.32 & 262.36 & 163.79 & 116.76 & 147.89 & 143.09 & 130.47 & {\color{black} 110.99} & 127.15 & 106.36 & 152.58 & \underline{\textbf{94.10}} \\
 & & SMAPE & 35.10 & 183.00 & 148.20 & 171.80 & 61.20 & 35.40 & 61.00 & 56.50 & 40.70 & {\color{black} 32.34} & 40.00 & 31.40 & 58.10 & \underline{\textbf{28.20}} \\
 & & {\color{black}Pinball Loss}	&	{\color{black}\underline{\textbf{30.47}}}	&	{\color{black}201.75}	&	{\color{black}189.48}	&	{\color{black}196.01}	&	{\color{black}107.88}	&	{\color{black}34.42}	&	{\color{black}91.92}	&	{\color{black}90.83}	&	{\color{black}74.52} & {\color{black} 60.41}	&	{\color{black}56.99}	&	{\color{black}55.78}	&	{\color{black}89.08}	&	{\color{black}45.19} \\
 & & {\color{black}CRPS} &	{\color{black}124.50}	&	{\color{black}163.35}	&	{\color{black}164.21}	&	{\color{black}163.35}	&	{\color{black}162.62}	&	{\color{black}124.74}	&	{\color{black}152.08}	&	{\color{black}153.03}	&	{\color{black}149.54}	&	{\color{black}131.16}	&	{\color{black}125.64}	&	{\color{black}149.42}	& {\color{black} 131.16} &	{\color{black}\underline{\textbf{109.46}}} \\
 \cline{2-17}
 & \multirow{6}{*}{MAR - APR} & MAE & 61.48 & 168.41 & 174.61 & 164.51 & 62.63 & 61.36 & 103.94 & 95.72 & 57.14 & {\color{black} 54.37} & 165.95 & 51.94 & 181.87 & \underline{\textbf{49.59}} \\
 & & MASE & 1.43 & 3.87 & 4.02 & 3.78 & 1.39 & 1.42 & 2.35 & 2.18 & 1.32 & {\color{black} 1.27} & 4.06 & 1.20 & 4.19 & \underline{\textbf{1.14}} \\
 & & RMSE & 73.69 & 178.80 & 203.57 & 175.13 & 78.19 & 75.55 & 125.97 & 117.40 & 70.86 & {\color{black} 65.85} & 212.95 & 63.17 & 191.54 & \underline{\textbf{60.07}} \\
 & & SMAPE & 33.40 & 167.10 & 135.50 & 159.10 & 35.80 & 33.80 & 76.30 & 67.70 & 32.20 & {\color{black} 30.45} & 60.90 & 29.20 & 198.90 & \underline{\textbf{28.10}} \\
 & & {\color{black}Pinball Loss}	&	{\color{black}\underline{\textbf{19.10}}}	&	{\color{black}134.73}	&	{\color{black}127.74}	&	{\color{black}131.61}	&	{\color{black}45.14}	&	{\color{black}28.45}	&	{\color{black}79.35}	&	{\color{black}72.03}	&	{\color{black}32.15} & {\color{black} 24.67}	&	{\color{black}36.13}	&	{\color{black}21.88}	&	{\color{black}145.49}	&	{\color{black}23.67} \\
 & & {\color{black}CRPS}	&	{\color{black}83.08}	&	{\color{black}113.16}	&	{\color{black}121.38}	&	{\color{black}113.16}	&	{\color{black}95.51}	&	{\color{black}87.98}	&	{\color{black}115.24}	&	{\color{black}114.65}	&	{\color{black}85.66} & {\color{black} 77.98}	&	{\color{black}155.32}	&	{\color{black}75.52}	&	{\color{black}113.16}	&	{\color{black}\underline{\textbf{73.97}}} \\
 \cline{2-17}
 & \multirow{6}{*}{MAY - JUN} & MAE & 71.99 & 145.59 & 145.40 & 143.09 & 83.16 & 67.01 & 129.13 & 111.88 & 67.53 & {\color{black} 75.81} & 87.48 & 76.14 & 77.28 & \underline{\textbf{66.47}} \\
 & & MASE & 1.48 & 2.95 & 2.92 & 2.90 & 1.65 & \underline{\textbf{1.35}} & 2.61 & 2.27 & 1.40 & {\color{black} 1.57} & 1.83 & 1.55 & 1.53 & 1.36 \\
 & & RMSE & 89.34 & 167.01 & 173.28 & 164.85 & 113.32 & 95.91 & 150.33 & 134.00 & \underline{\textbf{87.32}} & {\color{black} 90.26} & 107.85 & 100.07 & 107.10 & 89.67 \\
 & & SMAPE & 43.60 & 155.20 & 133.70 & 149.30 & 56.00 & 42.90 & 128.80 & 100.80 & 41.70 & {\color{black} 45.48} & 48.30 & 47.80 & 50.50 & \underline{\textbf{41.10}} \\
 & & {\color{black}Pinball Loss}	&	{\color{black}30.71}	&	{\color{black}116.47}	&	{\color{black}111.33}	&	{\color{black}114.47}	&	{\color{black}64.84}	&	{\color{black}50.75}	&	{\color{black}103.25}	&	{\color{black}89.01}	&	{\color{black}33.56} & {\color{black} 32.42}	&	{\color{black}\underline{\textbf{28.02}}}	&	{\color{black}44.45}	&	{\color{black}58.81}	&	{\color{black}37.86} \\
 & & {\color{black}CRPS}	&	{\color{black}86.48}	&	{\color{black}104.87}	&	{\color{black}109.70}	&	{\color{black}104.87}	&	{\color{black}104.04}	&	{\color{black}98.39}	&	{\color{black}103.80}	&	{\color{black}103.36}	&	{\color{black}87.12} & {\color{black} 86.72}	&	{\color{black}97.49}	&	{\color{black}101.79}	&	{\color{black}101.82}	&	{\color{black}\underline{\textbf{86.46}}} \\
 \cline{2-17}
 & \multirow{6}{*}{JUL - AUG} & MAE & 58.10 & 87.62 & 95.24 & 82.17 & 59.70 & \underline{\textbf{42.82}} & 76.47 & 77.41 & 70.26 & {\color{black} 104.24} & 58.97 & 49.79 & 72.79 & 49.79 \\
 & & MASE & 2.90 & 4.18 & 4.53 & 3.90 & 3.05 & \underline{\textbf{2.04}} & 3.62 & 3.69 & 3.65 & {\color{black} 5.22} & 2.79 & 2.46 & 3.43 & 2.46 \\
 & & RMSE & 65.40 & 97.74 & 109.72 & 92.87 & 67.62 & \underline{\textbf{54.78}} & 91.86 & 93.15 & 78.60 & {\color{black} 111.69} & 72.70 & 59.03 & 84.92 & 59.03 \\
 & & SMAPE & 50.40 & 142.70 & 142.60 & 125.20 & 52.50 & \underline{\textbf{40.70}} & 110.10 & 112.70 & 58.20 & {\color{black} 73.42} & 64.00 & 43.70 & 105.10 & 43.70 \\
 & & {\color{black}Pinball Loss}	&	{\color{black}13.75}	&	{\color{black}70.09}	&	{\color{black}72.58}	&	{\color{black}65.73}	&	{\color{black}15.87}	&	{\color{black}21.15}	&	{\color{black}60.88}	&	{\color{black}61.64}	&	{\color{black}17.58}	& {\color{black} 22.79} &	{\color{black}39.40}	&	{\color{black}\underline{\textbf{12.37}}}	&	{\color{black}57.79}	&	{\color{black} \underline{\textbf{12.37}}} \\
 & & {\color{black}CRPS}	&	{\color{black}59.61}	&	{\color{black}64.63}	&	{\color{black}68.49}	&	{\color{black}64.63}	&	{\color{black}61.12}	&	{\color{black}57.72}	&	{\color{black}66.10}	&	{\color{black}66.26}	&	{\color{black}66.90}	& {\color{black} 82.76} &	{\color{black}66.03}	&	{\color{black}\underline{\textbf{56.00}}}	&	{\color{black}64.96} &	{\color{black}\underline{\textbf{56.00}}} \\ \cline{2-17}
 & \multirow{6}{*}{SEP - OCT} & MAE & 64.76 & 153.86 & 160.36 & 148.98 & 67.84 & 77.56 & 119.55 & 118.63 & 70.16 & {\color{black} 74.00} & 132.91 & 69.95 & 164.33 & \underline{\textbf{63.70}} \\
 & & MASE & 2.38 & 5.74 & 5.96 & 5.55 & 2.50 & 2.88 & 4.44 & 4.42 & 2.62 & {\color{black} 2.74} & 4.95 & 2.59 & 6.14 & \underline{\textbf{2.36}} \\
 & & RMSE & 81.32 & 174.76 & 184.58 & 170.42 & 87.21 & 97.58 & 151.29 & 150.95 & 88.90 & {\color{black} 89.91} & 155.66 & 84.98 & 185.21 & \underline{\textbf{81.31}} \\
 & & SMAPE & 40.50 & 156.20 & 149.10 & 145.10 & 42.70 & 47.90 & 93.70 & 92.00 & 43.80 & {\color{black} 44.80} & 119.00 & 43.20 & 175.80 & \underline{\textbf{40.00}} \\
 & & {\color{black}Pinball Loss}	&	{\color{black}27.79}	&	{\color{black}123.08}	&	{\color{black}124.20}	&	{\color{black}119.15}	&	{\color{black}38.93}	&	{\color{black}42.61}	&	{\color{black}89.79}	&	{\color{black}88.02}	&	{\color{black}35.99} & {\color{black} 29.03}	&	{\color{black}105.59}	&	{\color{black}28.50}	&	{\color{black}130.88}	&	{\color{black}\underline{\textbf{26.66}}} \\
 & & {\color{black}CRPS}	&	{\color{black} \underline{\textbf{85.45}} }	&	{\color{black}109.81}	&	{\color{black}113.53}	&	{\color{black}109.81}	&	{\color{black}92.85}	&	{\color{black}101.60}	&	{\color{black}115.41}	&	{\color{black}116.21}	&	{\color{black}92.69} & {\color{black} 91.66}	&	{\color{black}109.09}	&	{\color{black}88.29}	&	{\color{black}110.79}	&	{\color{black}87.29} \\
 \cline{2-17}
 & \multirow{6}{*}{NOV - DEC} & MAE & \underline{\textbf{114.94}} & 342.22 & 328.89 & 338.54 & 202.89 & 156.01 & 204.62 & 188.63 & 186.24 & {\color{black} 164.65} & 175.93 & 162.09 & 350.87 & 162.09 \\
 & & MASE & \underline{\textbf{1.71}} & 5.07 & 4.85 & 5.01 & 2.97 & 2.30 & 3.00 & 2.79 & 2.71 & {\color{black} 2.43} & 2.58 & 2.40 & 5.20 & 2.40 \\
 & & RMSE & \underline{\textbf{139.63}} & 356.98 & 351.94 & 353.46 & 226.80 & 177.83 & 226.54 & 210.06 & 208.70 & {\color{black} 191.68} & 208.93 & 187.66 & 366.66 & 187.66 \\
 & & SMAPE & \underline{\textbf{35.00}} & 179.80 & 157.70 & 175.80 & 72.60 & 52.30 & 81.90 & 72.00 & 64.90 & {\color{black} 54.60} & 61.20 & 53.80 & 187.60 & 53.80 \\
 & & {\color{black}Pinball Loss}	&	{\color{black}\underline{\textbf{87.31}}}	&	{\color{black}273.77}	&	{\color{black}260.48}	&	{\color{black}270.84}	&	{\color{black}162.18}	&	{\color{black}121.19}	&	{\color{black}160.86}	&	{\color{black}148.22}	& {\color{black} 134.06} & 	{\color{black}146.36}	&	{\color{black}127.14}	&	{\color{black}128.73}	&	{\color{black}280.63}	&	{\color{black}128.73} \\
 & & {\color{black}CRPS}	&	{\color{black}\underline{\textbf{180.03}}}	&	{\color{black}220.48}	&	{\color{black}222.80}	&	{\color{black}220.48}	&	{\color{black}220.48}	&	{\color{black}209.88}	&	{\color{black}216.08} & {\color{black} 219.38}	&	{\color{black}215.31}	&	{\color{black}219.38}	&	{\color{black}214.57}	&	{\color{black}219.31}	&	{\color{black}220.86}	&	{\color{black}219.31} \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Madhurima Panja
Tanujit Chakraborty
Anubhab Biswas
Soudeep Deb

"E-STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting" by Madhurima Panja, Tanujit Chakraborty, Anubhab Biswas, and Soudeep Deb, arXiv:2411.12258v2, 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|ccc}
$\epsilon$ & Attack & LPIPS & NAD & Attack & LPIPS & NAD \\
\hline \hline
0.000 & FGSM & 0.0000 & 0.0000 & PGD & 0.0000 & 0.0000 \\
0.005 & & 0.0026 & 0.0580 & & 0.0016 & 0.0379 \\
0.020 & & 0.0524 & 0.2266 & & 0.0145 & 0.1005 \\
0.050 & & 0.2454 & 0.5644 & & 0.0470 & 0.1768 \\
0.100 & & 0.5596 & 1.1287 & & 0.1187 & 0.2973 \\
0.200 & & 0.9913 & 2.2576 & & 0.2871 & 0.5523 \\
0.300 & & 1.2105 & 3.3857 & & 0.4485 & 0.8163
\end{tabular}
\end{adjustbox}
\caption{Statistics for different $\epsilon$ values on FGSM and PGD.}
\end{table}
\end{document}

math

"Property-Preserving Hashing for $\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks" by Hassan Asghar, Chenhan Zhang, and Dali Kaafar, arXiv:2504.16355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Non-parametric multiple change-point detection" by Andreas Anastasiou and Piotr Fryzlewicz, arXiv:2504.21379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc|cc}
\toprule
Method & e(n, o) & e(n, n) & e(o, o) & e(o, n)&er(n, o)&er(o, n)\\
\midrule
\midrule
A-GEM & 0 & 177 & 0 & 9500&0\%&100\% \\
ER & 37 &148 & 2269 & 5852&20\%&72\% \\
MIR & 54 & 113 & 2770 & 5330&32\%&66\%\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Zheda Mai
Ruiwen Li
Jihwan Jeong
David Quispe
Hyunwoo Kim
Scott Sanner

"Online Continual Learning in Image Classification: An Empirical Survey" by Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, and Scott Sanner, arXiv:2101.10423v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Luca Benedetti
Eric Boniardi
Leonardo Chiani
Jacopo Ghirri
Marta Mastropietro
Andrea Cappozzo
Francesco Denti

"Variational Inference for Semiparametric Bayesian Novelty Detection in Large Datasets" by Luca Benedetti, Eric Boniardi, Leonardo Chiani, Jacopo Ghirri, Marta Mastropietro, Andrea Cappozzo, and Francesco Denti, arXiv:2212.01865v1, 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{(Scenario I) Averages of the 5 smallest of SP/KS-GSPs whose AUCs are greater than the thresholds. Those scores are selected in the Pareto solutions appearing in Figure~. Standard deviations are in the parentheses next to the averages. Note CON is not available for SP.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc|cc}
\toprule
& \multicolumn{2}{c|}{\textbf{Adult} (AUC $\geq$ 0.80)} & \multicolumn{2}{|c|}{\textbf{Cred. Card.} (AUC $\geq$ 0.70)} & \multicolumn{2}{|c}{\textbf{Law School.} (AUC $\geq$ 0.85)} \\
\hline
Model & SP ($\downarrow$) & KS-GSP ($\downarrow$) & SP ($\downarrow$) & KS-GSP ($\downarrow$) & SP ($\downarrow$) & KS-GSP ($\downarrow$) \\
\hline
\hline
SBP & 0.001 ($\approx 0$) & 0.014 ($\approx 0$) & $\approx 0$ ($\approx 0$) & 0.011 (0.001) & 0.199 (0.032) & 0.112 (0.011) \\
HGR & 0.070 (0.005) & 0.040 ($\approx 0$) & 0.001 (0.001) & 0.020 (0.001) & 0.123 (0.005) & 0.089 (0.001) \\
KDE & 0.280 (0.018) & 0.110 (0.005) & 0.005 (0.003) & 0.023 (0.001) & 0.252 (0.006) & 0.151 (0.001) \\
NEU & 0.372 (0.018) & 0.137 (0.002) & 0.002 (0.001) & 0.013 (0.001) & 0.385 (0.008) & 0.159 (0.006) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes" by Jinwon Sohn, Qifan Song, and Guang Lin, arXiv:2311.05866v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small{Statistics of the results obtained for crosshole GPR data in a multi-Gaussian domain. The naming convention is the same and the letters (a-x) refer to the models in Figure . RMSE$_{\rm data}$ denotes the RMSE in data space, ${\rm l}_1$ refers to the ${\rm l}_1$-norm and SSIM to the structural similarity index. The ${\rm l}_1$-norm is calculated in terms of velocity (m/ns) while the SSIM is computed in the rescaled $\left[0,1\right]$ domain. Closest TR means the closest training model in data space and Predicted \#1 and Predicted \#2 signify predicted models from two different noise realizations.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}%
			\hline
			True model & RMSE$_{\rm data}$ (ns) & ${\rm l}_1$ (m/ns) & SSIM (-)\\
			\hline
			(a) True & 0.5 & 0 & 1 \\
			(b) Closest TR & 1.73 & 18.92 & 0.72 \\
			(c) Predicted \#1 & 0.72 & 8.07 & 0.92 \\
			(d) Predicted \#2 & 0.73 & 8.10 & 0.92 \\

			(e) True & 0.5 & 0 & 1 \\
			(f) Closest TR & 1.44 & 15.35 & 0.77 \\
			(g) Predicted \#1 & 0.63 & 6.26 & 0.93 \\
			(h) Predicted \#2 & 0.68 & 6.60 & 0.92 \\

			(i) True & 0.5 & 0 & 1 \\
			(j) Closest TR & 0.98 & 13.31 & 0.79 \\
			(k) Predicted \#1 & 0.56 & 6.26 & 0.93 \\
			(l) Predicted \#2 & 0.59 & 5.89 & 0.94 \\

			(m) True & 0.5 & 0 & 1 \\
			(n) Closest TR & 1.01 & 12.37 & 0.82 \\
			(o) Predicted \#1 & 0.60 & 7.20 & 0.93 \\
			(p) Predicted \#2 & 0.63 & 7.23 & 0.92 \\

			(q) True & 0.5 & 0 & 1 \\
			(r) Closest TR & 1.16 & 13.17 & 0.78 \\
			(s) Predicted \#1 & 0.58 & 5.89 & 0.93 \\
			(t) Predicted \#2 & 0.61 & 6.65 & 0.92 \\

			(u) True & 0.5 & 0 & 1 \\
			(v) Closest TR & 1.05 & 12.65 & 0.81 \\
			(w) Predicted \#1 & 0.63 & 7.83 & 0.90 \\
			(x) Predicted \#2 & 0.67 & 7.55 & 0.91 \\

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

eess

Eric Laloy
Niklas Linde
Diederik Jacques

"Approaching geoscientific inverse problems with vector-to-image domain transfer networks" by Eric Laloy, Niklas Linde, and Diederik Jacques, arXiv:1912.09954v3, 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{Mutually unbiased weighing matrices of order $n$ and weight $9$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
\noalign{\hrule height1pt}
$n$ & $W_{\max}(n,9)$ & References\\
\hline
10 & $0$ & Proposition~\\
12 & $0$ & Proposition~\\
13 & $3$ & Proposition~\\
14 & $0$ & Proposition~\\
15 & $7$ & Proposition~ \\
16 & $15$ & Proposition~ \\
17 & $5$--$18$ & Proposition~\\
18 & $4$--$21$ & Proposition~\\
19 & $6$--$27$ & Proposition~\\
20 & $4$--$34$ & Proposition~\\
21 & $3$--$45$ & Proposition~\\
22 & $2$--$63$ & Proposition~\\
23 & $9$--$99$ & Proposition~\\
24 & $6$--$96$ & Proposition~\\
\noalign{\hrule height1pt}
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Makoto Araya
Masaaki Harada
Hadi Kharaghani
Sho Suda
Wei-Hsuan Yu

"Unbiased weighing matrices of weight $9$" by Makoto Araya, Masaaki Harada, Hadi Kharaghani, Sho Suda, and Wei-Hsuan Yu, arXiv:2501.15444v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
		\hline
		\textbf{Recognition mode} & \textbf{TTS errors} & \textbf{Corrector errors} & \textbf{Relative WER} \\
		\hline
		Basic & 897 & 774 & 13.6 \% \\
		\hline
		With context & 828 & 727 & 12.2\% \\
		\hline
	\end{tabular}
\caption{Number of edition errors and relative WER.}
\end{table}
\end{document}

eess

Diego Campos-Sobrino
Mario Campos-Soberanis
Iván Martínez-Chin
Víctor Uc-Cetina

"Fixing Errors of the Google Voice Recognizer through Phonetic Distance Metrics" by Diego Campos-Sobrino, Mario Campos-Soberanis, Iván Martínez-Chin, and Víctor Uc-Cetina, arXiv:2102.09680v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sound Classification of Four Insect Classes" by Yinxuan Wang and Sudip Vhaduri, arXiv:2412.12395v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
 \texttt{settings} Field & \textbf{Application} \\\hline
 \texttt{eppos} & Nonnegative (small) scalar to enforce strict positivity of \texttt{Pop.P}\\
 \texttt{eppos2} & Nonnegative (small) scalar to enforce strict positivity of \texttt{Pop.R0}\\
 \texttt{epneg} & Nonnegative (small) scalar to enforce strict negativity of \texttt{Dop}\\
 \texttt{sosineq} & Binary value, set 1 to use \texttt{sosineq}\\
 \texttt{override1} & Binary value, set 1 to let $\texttt{P2op}=0$\\
 \texttt{override2} & Binary value, set 1 to let $\texttt{De2op}=0$\\
 \texttt{dd1} & 1x3 cell structure defining monomial degrees for \texttt{Pop}\\
 \texttt{dd12} & 1x3 cell structure defining monomial degrees for \texttt{P2op}\\
 \texttt{dd2} & 1x3 cell structure defining monomial degrees for \texttt{Deop}\\
 \texttt{dd3} & 1x3 cell structure defining monomial degrees for \texttt{De2op}\\
 \texttt{ddZ} & 1x3 array defining monomial degrees for \texttt{Zop}\\
 \texttt{options1} & Structure of \texttt{poslpivar} options for \texttt{Pop}\\
 \texttt{options12} & Structure of \texttt{poslpivar} options for \texttt{P2op}\\
 \texttt{options2} & Structure of \texttt{poslpivar} options for \texttt{Deop}\\
 \texttt{options3} & Structure of \texttt{poslpivar} options for \texttt{De2op}\\
 \texttt{opts} & Structure of \texttt{lpi\_ineq} options for enforcing $\texttt{Dop}\leq0$\\
 \texttt{sos\_opts} & Structure of \texttt{sossolve} options for solving the LPI
\end{tabular}
\end{adjustbox}
\caption{Fields of settings structure passed on to PIETOOLS executive files}
\end{table}
\end{document}

math

PIETOOLS 2024: User Manual

Sachin Shivakumar
Declan Jagt
Danilo Braghini
Amritam Das
Yulia Peet
Matthew Peet

"PIETOOLS 2024: User Manual" by Sachin Shivakumar, Declan Jagt, Danilo Braghini, Amritam Das, Yulia Peet, and Matthew Peet, arXiv:2501.17854v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{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
Optimal policy & ACPI & MVPI & Expert \\
\hline
iv low, vaso low & $0.72 $ & $0.36$ & 1\\
\hline
iv low, vaso med & $0.18 $ & 0.018 & 0\\
\hline
iv low, vaso high & $< 0.0001$ & <0.0001 & 0\\
\hline
iv med, vaso low & $<0.0001$ & $0.43$ & 0 \\
\hline
iv med, vaso med & $<0.0001$ & <0.0001& 0 \\
\hline
iv med, vaso high & $<0.0001$ & 0.11 & 0\\
\hline
iv high, vaso low & $0.09$ & $<0.0001$ & 0\\
\hline
iv high, vaso med & $<0.001$ & 0.075 & 0\\
\hline
iv high, vaso high & <0.001 & <0.0001 & 0\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Recommended action by policy estimated by ACPI, MVPI and the expert for a sample patient from group 2.}
\end{table}
\end{document}

cs

Elynn Y. Chen
Rui Song
Michael I. Jordan

"Reinforcement Learning with Heterogeneous Data: Estimation and Inference" by Elynn Y. Chen, Rui Song, and Michael I. Jordan, arXiv:2202.00088v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Yassir Bendou
Giulia Lioi
Bastien Pasdeloup
Lukas Mauch
Ghouthi Boukli Hacene
Fabien Cardinaux
Vincent Gripon

"LLM meets Vision-Language Models for Zero-Shot One-Class Classification" by Yassir Bendou, Giulia Lioi, Bastien Pasdeloup, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux, and Vincent Gripon, arXiv:2404.00675v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|}
 \hline
 scale $\theta$ & shape $\nu$ & powershape $\gamma$ & abbreviation \\
 \hline
 fixed to prior mean & Gamma & Gamma & fix-gam-gam \\
 Gamma & Gamma & Gamma & gam-gam-gam\\
 fixed to prior mean & Lognormal & Lognormal & fix-log-log \\
 Lognormal & Lognormal & Lognormal & log-log-log \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Prior distributional assumptions for the scale $\theta$, and shapes $\nu$ and $\gamma$ of the PgW distribution.}
\end{table}
\end{document}

stat

"The BPgWSP test: a Bayesian Weibull Shape Parameter signal detection test for adverse drug reactions" by Julia Dyck and Odile Sauzet, arXiv:2412.05463v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables" by Ping Guo, Qingfu Zhang, and Xi Lin, arXiv:2404.00251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

S. Syafiie
Aries Subiantoro
Vivi Andasari
Fernando Tadeo

"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, 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}{ccc|ccc|ccc}
 		\hline
 		&&&\multicolumn{3}{c|}{ISFLP1}&\multicolumn{3}{c}{ISFLP2}\\
 		\hline

 		Instance&$n$&$\mathbf{\bar{x}}=(\bar{x},\bar{y})$&$C^*$&$iter$&CPU&$C^*$&$iter$&CPU\\
 		&&&&&(in sec)&&&(in sec)\\
 		\hline
 		Example &4&$(-2,5)$&$39.3674$&$39$
 	&273.8595&$39.1801$&$2111$&$6181.4753$\\
 	 		\hline
 		\end{tabular}
\end{table}
\end{document}

math

"Inverse single facility location problem in the plane with variable coordinates" by Nazanin Tour-Savadkoohi and Jafar Fathali, arXiv:2503.07016v1, 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{Selected parameter values for the optimizer}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}\toprule[1.25pt]
 \textbf{Option} & \textbf{Selected Value} \\
 \hline
 Algorithm & sqp \\
 Specify Objective Gradient & True \\
 Constraint Tolerance & 0.002\\
 Optimality Tolerance & 0.005\\
 Step Tolerance & 0.001\\
 Scale Problem & True\\
 Typical X & 0.05\\
 \bottomrule[1.25pt]
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Demetrius Gulewicz
Uduak Inyang-Udoh
Trevor Bird
Neera Jain

"Nonlinear Model Predictive Control of a Hybrid Thermal Management System" by Demetrius Gulewicz, Uduak Inyang-Udoh, Trevor Bird, and Neera Jain, arXiv:2411.15929v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Ankur Mali
Alexander Ororbia
Daniel Kifer
Lee Giles

"An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems" by Ankur Mali, Alexander Ororbia, Daniel Kifer, and Lee Giles, arXiv:2201.11782v1, 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}{llllll}
 \toprule
 \multicolumn{6}{c}{Algorithm} \\
 \cmidrule(r){2-6}
 Factor & $MM^Tx$ & DIP & DeepRED & Ours & Ours - avg\\
 \midrule
 4:1 & 26.35 (0.826) & 30.04 (0.902) & 30.22 (0.904) & 29.47 (0.894) & {\bf 31.20} ({\bf 0.913}) \\
 8:1 & 23.02 (0.673) & 24.98 (0.760) & 24.95 (0.760) & 25.07 (0.767) & {\bf 25.64} ({\bf 0.792}) \\
 \bottomrule \\
 \end{tabular}
\end{adjustbox}
\caption{Spatial super-resolution performance averaged over Set5 images. Values indicate YCbCr-PSNR (SSIM).}
\end{table}
\end{document}

eess

"Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser" by Zahra Kadkhodaie and Eero P. Simoncelli, arXiv:2007.13640v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c}
\hline
Experiments & Average Improvement \\ \hline
four-battery system & 15.2$\%$ \\ \hline
eight-battery system & 31.9$\%$\\ \hline
four-second-life-battery system & 151.0$\%$
\end{tabular}
\end{adjustbox}
\caption{Main results of the average improvement between the baseline among 5000 different random initialization with random load profiles}
\end{table}
\end{document}

cs

Yuan Tian
Minghao Han
Chetan Kulkarni
Olga Fink

"A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning" by Yuan Tian, Minghao Han, Chetan Kulkarni, and Olga Fink, arXiv:2201.08445v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Daniel Jenson
Jhonathan Navott
Mengyan Zhang
Makkunda Sharma
Elizaveta Semenova
Seth Flaxman

"Transformer Neural Processes - Kernel Regression" by Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma, Elizaveta Semenova, and Seth Flaxman, arXiv:2411.12502v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Neural networks architectures for the MNIST experiment.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
 \toprule
 Parameter & Value \\
 \midrule
 Nr. of CNN layers in inference network & 3 \\
 Nr. of CNN layers in generative network & 3 \\
 Nr. of filters per CNN layer & 8 \\
 Filter size & $3 \times 3$ \\
 Nr. of feedforward layers in inference network & 1 \\
 Nr. of feedforward layers in generative network & 1 \\
 Activation function in CNN layers & ELU \\
 Dimensionality of latent space (L) & 16 \\
 Number of latent channels for angle info (J) & 8 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Metod Jazbec
Michael Pearce
Vincent Fortuin

"Factorized Gaussian Process Variational Autoencoders" by Metod Jazbec, Michael Pearce, and Vincent Fortuin, arXiv:2011.07255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Veronica Bitonti
Bishal Deb
Alan D. Sokal

"Thron-type continued fractions (T-fractions) for some classes of increasing trees" by Veronica Bitonti, Bishal Deb, and Alan D. Sokal, arXiv:2412.10214v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 $n$ & Error $\|\cdot\|_1$ & Order $\|\cdot\|_1$ & Error
 $\|\cdot\|_{\infty}$ & Order $\|\cdot\|_{\infty}$ \\
 \hline
 40 & 9.81E$-6$ & $-$ & 2.39E$-5$ & $-$ \\
 \hline
 80 & 3.06E$-7$ & 5.00 & 7.56E$-7$ & 4.98 \\
 \hline
 160 & 9.52E$-9$ & 5.00 & 2.28E$-8$ & 5.05 \\
 \hline
 320 & 2.97E$-10$ & 5.00 & 7.03E$-10$ & 5.02 \\
 \hline
 640 & 9.23E$-12$ & 5.01 & 2.12E$-11$ & 5.06 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Lagrange extrapolation (node removal).}
\end{table}
\end{document}

math

"High Order Boundary Extrapolation Technique for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|l|l|l|}
\hline
\multirow{3}{*}{Models} & \multicolumn{4}{c|}{MARS} \\
\cline{2-5}
 & \multicolumn{2}{c|}{I2V} & \multicolumn{2}{c|}{V2V} \\
\cline{2-5}
 & cmc1 & mAP & cmc1 & mAP \\
\hline
with CE loss & 84.29 & 76.94 & 88.69 & 82.23 \\
\hline
without CE loss & \textbf{85.65} & \textbf{78.02} & \textbf{89.48} & \textbf{82.90} \\
\hline
 & \multicolumn{4}{c|}{Duke-video} \\
\hline
 & \multicolumn{2}{c|}{I2V} & \multicolumn{2}{c|}{V2V} \\
\hline
 & cmc1 & mAP & cmc1 & mAP \\
\hline
with CE loss & 85.83 & 84.66 & 94.07 & 93.44 \\
\hline
without CE loss & \textbf{86.78} & \textbf{84.82} & \textbf{95.26} & \textbf{93.83} \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Ablation study on the impact of cross-entropy loss.}
\end{table}
\end{document}

cs

"Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer" by Pichao Wang, Fan Wang, and Hao Li, arXiv:2201.08887v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parachute deployment, PDI and touchdown times for mission profile 1 derived from GRASHS, RASHS and MPBVP solutions.}
\begin{tabular}{lccc}
 \hline
 Method & Parachute deployment & PDI & Touchdown \\\hline
 GRASHS & 300.3052 \textrm{s} & 336.4748 \textrm{s} & 376.7966 \textrm{s} \\
 RASHS & 300.3600 \textrm{s} & 336.4492 \textrm{s} & 376.7730 \textrm{s} \\
 MPBVP & 300.3537 \textrm{s} & 336.4169 \textrm{s} & 376.7456 \textrm{s} \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"Indirect Optimization of Multi-Phase Trajectories Involving Arbitrary Discrete Logic" by Harish Saranathan, arXiv:2412.07960v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Causal X-ray Results. We report in nats, with mean and standard error averaged over the test Causal X-ray data for 5 runs. The rows given are decomposed from the terms in the ELBO. Finetuning the encoder $q$ outperforms $z$ sampled from MLE estimates of $q$. In this case, higher likelihood samples of $z$ result in a tighter ELBO.\\}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
 &Separate Training & Finetuned \\
 \midrule
 $\mathbb{E}_{x,y\sim D}\mathbb{E}_{q(z\mid x,y)}\log p(z)$ & 979.67 (1.67) & 2012.51 (0.10) \\
 \midrule
 $\mathbb{E}_{x,y\sim D}\mathbb{E}_{q(z\mid x,y)}\log p(y\mid x, z)$ & 1705.91 (0.23) & 1837.63 (0.08)\\
 \midrule
 $\mathbb{E}_{x,y\sim D}\mathbb{E}_{-q(z\mid x,y)}\log q(z\mid x,y)$ & -1923.18 (0.08) & -2589.35 (0.08) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Daniel Israel
Aditya Grover
Guy Van den Broeck

"High Dimensional Causal Inference with Variational Backdoor Adjustment" by Daniel Israel, Aditya Grover, and Guy Van den Broeck, arXiv:2310.06100v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Rough Heston model as the scaling limit of bivariate cumulative heavy-tailed INAR($\infty$) processes and applications" by Yingli Wang and Zhenyu Cui, arXiv:2503.18259v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Classification of the experimental environments.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Environment & Cooperative? & Mixed? & Communication? \\
 \midrule
 Cooperative Navi. & \checkmark & &\\
 Cooperative Comm. & \checkmark & &\checkmark \\
 Predator-Prey & &\checkmark& \\
 Covert Comm. & &\checkmark&\checkmark\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning" by Heechang Ryu, Hayong Shin, and Jinkyoo Park, arXiv:2101.06890v1, 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{Ray tracing simulation parameters. For the interaction types, R stands for reflection, D for diffraction and DS for diffuse scattering.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rc}
\noalign{\smallskip}
\textbf{Simulation parameter} & \textbf{Value}\\
\hline
\noalign{\smallskip}
Carrier frequency & 28 GHz\\
BS height from the ground & 21.7 m\\
Rx height from the ground & 1.5 m\\
Rx grid resolution & $2\times 2$ m\\
SUMO simulation sampling time & 0.1 s\\
Number of initial sampled rays & $10^6$\\
Sionna RT ray tracing method & Fibonacci\\
Selected interation types & R, D, DS
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Lorenzo Cazzella
Francesco Linsalata
Damiano Badini
Matteo Matteucci
Maurizio Magarini
Umberto Spagnolini

"High-Fidelity RF Mapping: Assessing Environmental Modeling in 6G Network Digital Twins" by Lorenzo Cazzella, Francesco Linsalata, Damiano Badini, Matteo Matteucci, Maurizio Magarini, and Umberto Spagnolini, arXiv:2507.19173v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \toprule
 LLM classifier & LLM extractor & Successful extractions & Faulty code & Symbolic mistake \\
 \midrule
 \textbf{GPT-4o mini}&\textbf{GPT-4o} & $\mathbf{289}$ ($\mathbf{97.6\%}$) & $\mathbf{2}$ ($\mathbf{0.7\%}$) & $\mathbf{5}$ ($\mathbf{1.7\%}$) \\
 GPT-4o mini&Claude 3.7 Sonnet & $266$ ($89.9\%$) & $21$ ($7.1\%$) & $9$ ($3.0\%$) \\
 GPT-4o mini & GPT-4o mini & $206$ ($69.6\%$) & $70$ ($23.6\%$) & $20$ ($6.8\%$) \\
 \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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Portfolio Results with Proxy $= \text{CFVOL}$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 Portfolio & CGO1 & CGO3 & CGO5 & Diff-in-Diff \\
 \midrule
 P1 & 2.525 & 2.544 & 1.891 & \\
 P3 & 1.615 & 1.666 & 2.013 & \\
 P5 & 1.325 & 1.330 & 1.509 & \\
 P5 - P1 & -1.200 & -1.215 & -0.382 & 0.818 \\
 $t$-stat & (-3.19) & (-3.11) & (-0.85) & (1.87) \\
 FF3-$\alpha$ & -0.764 & -0.410 & 0.115 & 0.879 \\
 $t$-stat & (-2.42) & (-1.30) & (0.32) & (1.97) \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Replication of Reference-Dependent Preferences and the Risk-Return Trade-Off in the Chinese Market" by Penggan Xu, arXiv:2505.20608v1, 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{Degredation Parameters Applied to Different Datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|}
\hline
\textbf{Dataset} & \textbf{Gaussian Noise} & \textbf{Defocus Blur} & \textbf{Motion Blur} & \textbf{Low Contrast} \\ \hline
MNIST & SD: 0.5 & Radius: 1.5 & 5x5 Kernel & Factor: 0.1 \\ \hline
CIFAR-10 & SD: 0.025 & Radius: 2.0 & 5x5 Kernel & Factor: 0.25 \\ \hline
SVHN & SD: 0.025 & Radius: 2.0 & 5x5 Kernel & Factor: 0.25 \\ \hline
ImageNet64 & SD: 0.1 & Radius: 1.25 & 5x5 Kernel & Factor: 0.75 \\ \hline
Felidae & SD: 0.1 & Radius: 2.0 & 5x5 Kernel & Factor: 0.5 \\ \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{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test Set Confusion Matrix for Classification Model}
\begin{tabular}{l|c|c|c|}
\multicolumn{2}{c}{}&\multicolumn{2}{c}{\textbf{Predicted}}\\
\cline{3-4}
\multicolumn{2}{c|}{}& EoE & Normal \\
\cline{2-4}
\multirow{2}{*}{\textbf{True Diagnosis}}& EoE & 7,176 & 455 \\
\cline{2-4}
& Normal & 3 & 3,471 \\
\cline{2-4}
\end{tabular}
\end{table}
\end{document}

eess

William Adorno
Alexis Catalano
Lubaina Ehsan
Hans Vitzhum von Eckstaedt
Barrett Barnes
Emily McGowan
Sana Syed
Donald E. Brown

"Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision" by William Adorno, Alexis Catalano, Lubaina Ehsan, Hans Vitzhum von Eckstaedt, Barrett Barnes, Emily McGowan, Sana Syed, and Donald E. Brown, arXiv:2101.05326v1, 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}{l|c|c}
 \toprule
 Hyperparameter & MixMatch & FixMatch\\ \midrule
 Learning rate & $\{\mathbf{0.01}, 0.001\}$ & $\{\mathbf{0.03}\}$ \\
 Optimizer & $\{ \textbf{Adam} \}$ & $\{$Adam, $\textbf{SGD}\}$ \\
 Number of epochs & $\{500, \mathbf{1000}\}$ & $\{1000, \mathbf{2000}\}$ \\
 $T$ & $\{0.25, \mathbf{0.5}, 0.75, 0.9\}$ & --- \\
 $\alpha$ & $\{ 0.25, 0.5, 0.75, \mathbf{0.9} \}$ & --- \\
 $\lambda_u$ & $\{ 12.5, \mathbf{25}, 50, 100, 150 \}$ & $\{ \mathbf{5.0}, 25.0 \}$ \\ \midrule
 Grid-search size & 320 & 8 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Primary hyperparameter search grid for SSL methods. Best value is marked with bold font. SGD -- stocastic gradient descent with momentum ($\beta = 0.9$) . An epoch is defined by maximum number of batches in labelled subset.}
\end{table}
\end{document}

eess

Valentyn Melnychuk
Evgeniy Faerman
Ilja Manakov
Thomas Seidl

"Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels" by Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, and Thomas Seidl, arXiv:2010.12316v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the accuracies of Only LCP, Only CCP, and CaLoNet.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
			\hline
			Dataset & Only LCP & Only CCP & CaLoNet \\ \hline
			AWR & 0.955 & 0.876 & \textbf{0.960 } \\
			AF & \textbf{0.400} & 0.330 & 0.333 \\
			BM & 0.850 & 0.950 & \textbf{0.975} \\
			CT & 0.985 & 0.976 & \textbf{0.987} \\
			HMD & \textbf{0.540} & 0.391 & 0.527 \\
			NA & \textbf{0.902} & 0.727 & 0.900 \\
			PD & 0.979 & 0.962 & \textbf{0.983} \\
			SR2 & 0.477 & \textbf{0.527} & 0.500 \\
			SAD & 0.982 & 0.933 & \textbf{0.989} \\
			SWJ & \textbf{0.410} & 0.266 & 0.400 \\ \hline
			AVG & 0.748 & 0.693 & \textbf{0.755} \\
			Win & 4 & 1 & \textbf{5} \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mingsen Du
Yanxuan Wei
Xiangwei Zheng
Cun Ji

"Causal and Local Correlations Based Network for Multivariate Time Series Classification" by Mingsen Du, Yanxuan Wei, Xiangwei Zheng, and Cun Ji, arXiv:2411.18008v1, 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}{lll}
 \hline
 \textbf{Label} & \textbf{Target} & \textbf{Examples} \\
 \hline
 & North & My hate for northern people keeps growing \\
 \cline{2-3}
 & South & You idiotic Southerners fighting your own \\
 \cline{2-3}
 & East & IPOBs are animals....They lack tact or strategy. \\
 \cline{2-3}
 Hateful & Muslim & Muslim baboons and their terrorist religion. \\
 \cline{2-3}
 & Women & Nobody believes this \textit{ashawo} (prostitute) woman \\
 \hline
 Offensive & None & Stop spewing rubbish, \textit{mumu} (fool).\\ \hline
 Neutral & None & She don already made up her mind sha. \\ \hline
 \end{tabular}
\caption{Examples of tweets for each class. Offensive tweets have no target as they do not target an identity group. Non-English words are displayed in italic with their English translation in parenthesis.}
\end{table}
\end{document}

cs

Manuel Tonneau
Pedro Vitor Quinta de Castro
Karim Lasri
Ibrahim Farouq
Lakshminarayanan Subramanian
Victor Orozco-Olvera
Samuel P. Fraiberger

"NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data" by Manuel Tonneau, Pedro Vitor Quinta de Castro, Karim Lasri, Ibrahim Farouq, Lakshminarayanan Subramanian, Victor Orozco-Olvera, and Samuel P. Fraiberger, arXiv:2403.19260v3, 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{Reported Accuracy vs weights for MNIST dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \textbf{Setup} & \textbf{Best accuracy $\uparrow$} & \textbf{No. of learnables $\downarrow$}\\
 \midrule
 Huffman on UVGG11& 0.8323 & $\sim 35$M\\
 Huffman on UResnet & 0.6313 & $\sim 5$M \\
 \cmidrule(lr){1-3}
 DCT on FullyConnected & 0.9000 & $1$M \\
 \cmidrule(lr){1-3}
 DCT-LDPC on 12 units GRU& \textbf{0.9060} & \textbf{19k}\\
 DCT-LDPC on 32 units GRU& \textbf{0.9237} & \textbf{52.6k}\\
 \bottomrule

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

math

"Goal-Oriented Source Coding using LDPC Codes for Compressed-Domain Image Classification" by Ahcen Aliouat and Elsa Dupraz, arXiv:2503.11954v1, 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{Average Daybreak Sentiment Prediction Accuracy Across Models for 160 market indices}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
\hline
\textbf{Model} & \textbf{Average Accuracy} \\
\hline
CMG & 0.7672 \\
LSTM & 0.7605 \\
BiLSTM & 0.7602 \\
GRU & 0.7601 \\
Logistic Regression & 0.7562 \\
XGBoost & 0.7550 \\
Random Forest Classifier & 0.7539 \\
Gaussian Naive Bayes & 0.7536 \\
Multi Logistic Regression & 0.7499 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data" by Arif Pathan, arXiv:2506.17244v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
		\hline \\
		\small name & \small $\varphi_\theta (x)$ & $C(u,v)$
		& \small $\theta \in$ \\
		\hline \\
		\small Clayton copula & \small $(1+x)^{-1/\theta}$ & \small$(u^{-\theta} + v^{-\theta} -1)^{-1/\theta}$ &\small $(0,\infty)$ \\
		\small Gumbel copula & \small $\exp\{-x^{1/\theta}\}$ & \small$\exp(-((-\log u)^\theta + (-\log v)^\theta)^{1/\theta})$ &\small $[1,\infty)$ \\
		\small Frank copula & \small $-\log\left(\frac{e^{-\theta x} -1}{e^{-\theta} -1}\right)$ &\small$-\frac{1}{\theta}\log\left(1+ \frac{(e^{-\theta u} -1)(e^{-\theta v} -1)}{e^{-\theta} -1}\right)$ & \small$(-\infty, \infty)\backslash\{0\}$\\
		\hline
	\end{tabular}
\end{adjustbox}
\caption{Archimedean copulas with the respective generator function $\varphi$ and $\theta$ parameter range.}
\end{table}
\end{document}

q-fin

"An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model" by Edoardo Berton and Lorenzo Mercuri, arXiv:2112.11968v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
 \hline\hline
 Methods & $\|\nabla f\|$ & Time(Avg.)& Success rate \\
 \hline
 ASK& 5.4423e-03&2.3591e+00& 1.00 \\
 OGDA& 6.5660e-01&6.9367e+01& 0.00 \\
 GDA& 1.3918e+00&9.8197e-03& 0.00 \\
 NAG& 5.2703e+01&6.2550e-03& 0.00 \\
 HB& 1.8026e+00&6.1683e-03& 0.00 \\
 \hline\hline
 \end{tabular}
\caption{$f(x_1,x_2) = x_{1}x_{2}$, init=$2 \times\text{rand}(2,1)-1.$ The proposed ASK is the only method that can converge to the unique saddle point with 100$\%$ success rates.}
\end{table}
\end{document}

math

Mengqi Hu
Bian Li
Yi-An Ma
Yifei Lou
Xiu Yang

"A Gradient-Based Optimization Method Using the Koopman Operator" by Mengqi Hu, Bian Li, Yi-An Ma, Yifei Lou, and Xiu Yang, arXiv:2312.14361v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.