\documentclass{article}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
 \hline
 & Env. Distortion & F1\\
 \hline

 ADDA & \checkmark & 38.29\% \\
 ADDA + CORAL & \checkmark & 63.28\% \\
 No TL/DA & \checkmark & 77.25\% \\
 Trained on target & \checkmark & 85.82\% \\
 E-ADDA & \checkmark & \textbf{93.10\%}\\
 \hline
 \\
 \end{tabular}
\caption{The performance of four baselines against E-ADDA on data that has overlapped speech and environmental distortions.}
\end{table}
\end{document}

cs

Ye Gao
Brian Baucom
Karen Rose
Kristina Gordon
Hongning Wang
John Stankovic

"E-ADDA: Unsupervised Adversarial Domain Adaptation Enhanced by a New Mahalanobis Distance Loss for Smart Computing" by Ye Gao, Brian Baucom, Karen Rose, Kristina Gordon, Hongning Wang, and John Stankovic, arXiv:2201.10001v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Automotive WSS: A comparison.}
\begin{tabular}{lll}
 \toprule
 \multicolumn{3}{c}{WSS Types} \\
 \hline
 & \textit{Passive} & \textit{Active} \\
 \midrule
 Advantages &
 - Lower cost & - Better accuracy at \\ & & \hspace{2mm}low speeds \\
 & - Easy to maintain & - Suitable for high-\\ & & \hspace{2mm}performance applications \\
 & - No power supply required & - Advanced diagnostic \\ & & \hspace{2mm}capabilities \\
 \midrule
 Drawbacks &
 - Poor accuracy at & - Higher cost \\ & \hspace{2mm}low speeds & - More complex design \\
 & - Limited diagnostic & - More susceptible to \\ & \hspace{2mm}capabilities & \hspace{1mm} electromagnetic interference \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Hany Ragab
Sidney Givigi
Aboelmagd Noureldin

"Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS" by Hany Ragab, Sidney Givigi, and Aboelmagd Noureldin, arXiv:2501.00242v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \hline
 \textbf{Timestep} & \textbf{Geometric} & \textbf{Discrete Weibull} & \textbf{Beta - Binomial} \\ % inserts table %heading
 \hline
 Daily Limit Buy	& 4.042 +- 2.115 & 1.000 +- 0.000 & 1.262 +- 0.144 \\
 Daily Limit Sell & 3.144 +- 1.320 &	1.033 +- 0.126 & 1.214 +- 0.162 \\
 Weekly Limit Buy & 4.285 +- 0.779 &	1.000 +- 0.000 & 1.363 +- 0.106 \\
 Weekly Limit Sell &	3.183 +- 0.692 & 1.000 +- 0.000 & 1.237 +- 0.070 \\
 Monthly Limit &	3.789 +- 0.748 & 1.000 +- 0.000 & 1.312 +- 0.113 \\
 Hourly Limit Buy & 3.643 +- 1.760 & 1.012 +- 0.057 & 1.243 +- 0.160 \\
 Hourly Limit Sell &	2.895 +- 1.125 & 1.026 +- 0.083 & 1.153 +- 0.103 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Mean $NPS$ of Discrete Fits on Different Timesteps}
\end{table}
\end{document}

q-fin

Can Yilmaz Altinigne
Harun Ozkan
Veli Can Kupeli
Zehra Cataltepe

"An Empirical Study on Arrival Rates of Limit Orders and Order Cancellation Rates in Borsa Istanbul" by Can Yilmaz Altinigne, Harun Ozkan, Veli Can Kupeli, and Zehra Cataltepe, arXiv:1909.08308v1, 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{MBMM estimated assignment probability of census tracts to the most probable profile.}
\begin{tabular}{|l|c|}
\hline
\textbf{NSDoH Profile} & \textbf{Median (IQR)} \\ \hline
Profile 1 & 0.946 (0.161) \\ \hline
Profile 2 & 0.972 (0.0581) \\ \hline
Profile 3 & 0.923 (0.200) \\ \hline
Profile 4 & 0.874 (0.233) \\ \hline
Profile 5 & 0.865 (0.259) \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Carmen B. Rodríguez
Stephanie M. Wu
Stephanie Alimena
Alecia J McGregor
Briana JK Stephenson

"A Bayesian Mixture Model Approach to Examining Neighborhood Social Determinants of Health Disparities in Endometrial Cancer Care in Massachusetts" by Carmen B. Rodríguez, Stephanie M. Wu, Stephanie Alimena, Alecia J McGregor, and Briana JK Stephenson, arXiv:2412.07134v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Ibon Gracia
Luca Laurenti
Manuel Mazo
Alessandro Abate
Morteza Lahijanian

"Temporal Logic Control for Nonlinear Stochastic Systems Under Unknown Disturbances" by Ibon Gracia, Luca Laurenti, Manuel Mazo, Alessandro Abate, and Morteza Lahijanian, arXiv:2412.11343v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jalmari Tuominen
Eetu Pulkkinen
Jaakko Peltonen
Juho Kanniainen
Niku Oksala
Ari Palomäki
Antti Roine

"Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input" by Jalmari Tuominen, Eetu Pulkkinen, Jaakko Peltonen, Juho Kanniainen, Niku Oksala, Ari Palomäki, and Antti Roine, arXiv:2308.16544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean leaf homogeneity measure based on an entropy derivative for standard and student models.}
\begin{tabular}{ccc}
\toprule
& \multicolumn{2}{c}{\textit{\textbf{Mean entropy}}} \\ \cmidrule{2-3}
Depth & Standard & Student \\ \midrule
4 & 0.422 & \textbf{0.356} \\
5 & 0.420 & \textbf{0.353} \\
6 & 0.417 & \textbf{0.350} \\
7 & 0.414 & \textbf{0.348} \\
8 & 0.410 & \textbf{0.345} \\
9 & 0.407 & \textbf{0.342} \\
10 & 0.403 & \textbf{0.339} \\
11 & 0.398 & \textbf{0.335} \\
12 & 0.392 & \textbf{0.329} \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Maxime Biehler
Mohamed Guermazi
Célim Starck

"Using Knowledge Distillation to improve interpretable models in a retail banking context" by Maxime Biehler, Mohamed Guermazi, and Célim Starck, arXiv:2209.15496v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \hline
 \textbf{Timestep} & \textbf{Cancel Buy} & \textbf{Cancel Sell} \\ % inserts table %heading
 \hline
 1st Week & 7.846 & 3.296 \\
 2nd Week & 12.197 & 8.371 \\
 3rd Week & 4.762 & 7.461 \\
 4th Week & 6.419 & 3.793 \\
 5th Week & 8.378 & 28.419 \\
 6th Week & 9.543 & 2.948 \\
 7th Week & 5.045 & 7.558 \\
 8th Week & 3.494 & 5.355 \\
 9th Week & 9.845 & 8.459 \\
 1st Month &	3.430 & 6.173 \\
 2nd Month &	4.740 & 4.539 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison between the ratios of canceled orders and the uniform distribution: Chi-Square Test Statistics on Different Timesteps}
\end{table}
\end{document}

q-fin


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
Method & SD & AnE & SG & EBAMA & Ours \\
\hline
Time (min) & 13.2 & 47.0 & 21.9 & 20.9 & 35.7 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

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

"Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory" by Eric Hanchen Jiang, Yasi Zhang, Zhi Zhang, Yixin Wan, Andrew Lizarraga, Shufan Li, and Ying Nian Wu, arXiv:2411.17472v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Vasiliki Koutra
Olga Egorova
Steven G. Gilmour
Luzia A. Trinca

"MOODE: An R Package for Multi-Objective Optimal Design of Experiments" by Vasiliki Koutra, Olga Egorova, Steven G. Gilmour, and Luzia A. Trinca, arXiv:2412.17158v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
Model Code & Net Profit & Profit Factor & Max Draw-down \\ \hline
CEU & 753 & 3.67 & -5.18\% \\ \hline
DEU & 486 & 3.09 & -3.07\% \\ \hline
PEU & 615 & 3.82 & -2.92\% \\ \hline
CGU & 753 & 3.24 & -5.58\% \\ \hline
DGU & 717 & 3.10 & -4.63\% \\ \hline
PGU & 763 & 4.47 & -4.33\% \\ \hline
CAU & 351 & 1.62 & -14.53\% \\ \hline
DAU & 402 & 2.32 & -5.97\% \\ \hline
PAU & 597 & 2.85 & -7.62\%\\ \hline
\end{tabular}
\end{adjustbox}
\caption{ Comparing DQN model with PPO model trading performance. Using EUR/USD 4 hours data from 2018/12/01 to 2018/12/31 at 1300 episode.}
\end{table}
\end{document}

q-fin

"Deep Reinforcement Learning for Foreign Exchange Trading" by Yun-Cheng Tsai and Chun-Chieh Wang, arXiv:1908.08036v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
\toprule
\textbf{Hyperparameter} & \textbf{Range} \\
\midrule
\texttt{actor\_lr} & $10^{[-2, -6]}$ \\
\midrule
\texttt{critic\_lr} & $10^{[-2, -6]}$ \\
\midrule
\texttt{Optimizer} & Adam \\
\midrule
\texttt{beta1} & $\{0, 0.9\}$ \\
\midrule
\texttt{beta2} & $0.999$ \\
\midrule
\texttt{alpha\_lr} & $10^{[-5, 0]}$ \\
\midrule
\texttt{gamma} & $\{0.95, 0.97, 0.99, 0.995, 1.0\}$ \\
\midrule
\texttt{critic\_tau} & $0.005 \text{\; if} \ \texttt{algo} \in \{\text{SAC, TD3}\}$ \\
\midrule
\texttt{NN Activation} & Leaky ReLU \\
\midrule
\texttt{Num. hidden layers} & $2$ \\
\midrule
\texttt{Num hidden units} & $256$ \\
\midrule
\texttt{Weight initialization} & Orthogonal \\
\midrule
\bottomrule
\end{tabular}
\caption{Random Search Procedure for Hyperparameters}
\end{table}
\end{document}

eess

Gautham Vasan
Mohamed Elsayed
Alireza Azimi
Jiamin He
Fahim Shariar
Colin Bellinger
Martha White
A. Rupam Mahmood

"Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers" by Gautham Vasan, Mohamed Elsayed, Alireza Azimi, Jiamin He, Fahim Shariar, Colin Bellinger, Martha White, and A. Rupam Mahmood, arXiv:2411.15370v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{adjustbox}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf{KPSS t-statistics test for geophysical data}}% title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 	\hline \hline
 \multirow{2}{*}{Events} &
 \multicolumn{2}{c}{TUC station} & \multicolumn{2}{c}{ANMO station} \\ \cline{2-5}
 &
 Test statistics & p-value & Test statistics & p-value \\
 \hline
 		Earthquake & 0.0017 & 0.1 & 0.0020 & 0.1\\ % inserting body of the table
 		Explosion & 0.0012 & 0.1 & 0.0030& 0.1\\
\hline \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Maria C Mariani
Md Al Masum Bhuiyan
Osei K Tweneboah
Hector Gonzalez-Huizar
Ionut Florescu

"Volatility Models Applied to Geophysics and High Frequency Financial Market Data" by Maria C Mariani, Md Al Masum Bhuiyan, Osei K Tweneboah, Hector Gonzalez-Huizar, and Ionut Florescu, arXiv:1901.09145v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{graphicx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative evaluations of different approaches applied to the homogeneous images OSCD\_L8S2 and OSCD\_S1S1 datasets.}
\begin{tabular}{cccccccc}
		\hline
		Dataset & Type & Method & Pre(\%) & Rec(\%) & OA(\%) & F1 & Kap \\ \hline
		\multirow{5}{*}{L8S2} & \multirow{3}{*}{\rotatebox{90}{Unsup.}} & Prop. SSL & 31.67 & 34.59 & 92.61 & 0.33 & 0.29 \\
		& & CAA & 18.45 & 45.80 & 90.25 & 0.26 & 0.22 \\
		& & DSFA & 8.08 & 24.29 & 86.64 & 0.12 & 0.07 \\ \cline{2-8}
		& \multirow{2}{*}{\rotatebox{90}{Sup.}} & FC-EF & 29.75 & 34.08 & 92.27 & 0.32 & 0.28 \\
		& & FC-EF-res & 39.14 & 27.14 & 93.93 & 0.32 & 0.29 \\ \hline
		\multirow{4}{*}{S1S1} & \multirow{4}{*}{\rotatebox{90}{Unsup.}} & Prop. SSL & 23.06 & 40.39 & 89.74 & 0.29 & 0.24 \\
		& & SCCN & 7.48 & 27.80 & 78.04 & 0.12 & 0.04 \\
		& & CAA & 19.80 & 34.81 & 89.12 & 0.25 & 0.20 \\
		& & DSFA & 10.96 & 22.78 & 92.63 & 0.15 & 0.08 \\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
 & $\hat{C}_{21}$ & $\hat{C}_{22}$ & $\hat{C}_{23}$ & \\
$\hat{C}_{11}$ & 24 & 2 & 17 & 43 \\
 $\hat{C}_{12}$ & 15 & 834 & 108 & 957 \\
 & 39 & 836 & 125 &
\end{tabular}
\caption{Contingency table between the clustering point estimates $\widehat C_1$ and $\widehat C_2$ for the data, along with marginal cluster sizes along the rows and columns. }
\end{table}
\end{document}

stat

"Product Centered Dirichlet Processes for Bayesian Multiview Clustering" by Alexander Dombowsky and David B. Dunson, arXiv:2312.05365v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Anand Brahmbhatt
Rishi Saket
Aravindan Raghuveer

"PAC Learning Linear Thresholds from Label Proportions" by Anand Brahmbhatt, Rishi Saket, and Aravindan Raghuveer, arXiv:2310.10098v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
 \textbf{Credit rating} & \textbf{Number of companies} & \textbf{Default probability bucket} \\ \hline
 Aaa & 39 & [0.000\%, 0.007\%]\\
 Aa & 88 & [0.007\%, 0.031\%]\\
 A & 302 & [0.031\%, 0.139\%]\\
 Baa & 316 & [0.139\%, 0.638\%]\\
 Ba & 238 & [0.638\%, 2.927\%]\\
 B & 29 & [2.927\%, 100\%] \\ \hline
 \textbf{Total} & 1012
 \end{tabular}
\end{adjustbox}
\caption{Breakdown of dataset by average default probability and rating class.}
\end{table}
\end{document}

math

Giuseppe Genovese
Ashkan Nikeghbali
Nicola Serra
Gabriele Visentin

"Universal approximation of credit portfolio losses using Restricted Boltzmann Machines" by Giuseppe Genovese, Ashkan Nikeghbali, Nicola Serra, and Gabriele Visentin, arXiv:2202.11060v3, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c}
\hline
\textbf{Operation} & \textbf{Formalism in $\mathbb E^d$} &\textbf{Unified formalism in $\kappa$-stereographic model ($\mathbb H^d$/ $\mathbb S^d$)}\\
\hline
Distance Metric&
$d^\kappa_{\mathcal M}(\mathbf{x}, \mathbf{y}) =\left\| \mathbf{x}- \mathbf{y}\right\|_{2}$
&
$
d^\kappa_{\mathcal M}(\mathbf{x}, \mathbf{y})=\frac{2}{\sqrt{|\kappa|}} \tan _{\kappa}^{-1}\left(\sqrt{|\kappa|}\left\|-\mathbf{x} \oplus_{\kappa} \mathbf{y}\right\|_{2}\right)
$
\\
\hline
Exp. Map &
$\mathbf{exp}_{\mathbf{x}}^{\kappa}(\mathbf{v})=\mathbf{x}+\mathbf{v}$
&
$
\mathbf{exp}_{\mathbf{x}}^{\kappa}(\mathbf{v})=\mathbf{x} \oplus_{\kappa}\left(\tan _{\kappa}\left(\sqrt{|\kappa|} \frac{\lambda_{\mathbf{x}}^{\kappa}\|\mathbf{v}\|_{2}}{2}\right) \frac{\mathbf{v}}{\sqrt{|\kappa|}\|\mathbf{v}\|_{2}}\right)
$
\\
Log. Map &
$\mathbf{log}_{\mathbf{x}}^{\kappa}(\mathbf{y})= \mathbf{x}-\mathbf{y}$
&
$
\mathbf{log}_{\mathbf{x}}^{\kappa}(\mathbf{y})=\frac{2}{\sqrt{|\kappa|} \lambda_{\mathbf{x}}^{\kappa}} \tan _{\kappa}^{-1}\left(\sqrt{|\kappa|}\left\|-\mathbf{x} \oplus_{\kappa} \mathbf{y}\right\|_{2}\right) \frac{-\mathbf{x} \oplus_{\kappa} \mathbf{y}}{\left\|-\mathbf{x} \oplus_{\kappa} \mathbf{y}\right\|_{2}}
$
\\
\hline
Addition &
$\mathbf{x} \oplus_{\kappa} \mathbf{y}=\mathbf{x} + \mathbf{y}$
&
$
\mathbf{x} \oplus_{\kappa} \mathbf{y}=\frac{\left(1+2 \kappa \mathbf{x}^{T} \mathbf{y}+K\|\mathbf{y}\|^{2}\right) \mathbf{x}+\left(1-\kappa || \mathbf{x}||^{2}\right) \mathbf{y}}{1+2 \kappa \mathbf{x}^{T} \mathbf{y}+\kappa^{2}|| \mathbf{x}||^{2}|| \mathbf{v}||^{2}}
$\\
\hline
\end{tabular}
\caption{Operations in Hyperbolic $\mathbb H^d$, Spherical $\mathbb S^d$ and Euclidean space $\mathbb E^d$.}
\end{table}
\end{document}

stat

Karish Grover
Haiyang Yu
Xiang Song
Qi Zhu
Han Xie
Vassilis N. Ioannidis
Christos Faloutsos

"Spectro-Riemannian Graph Neural Networks" by Karish Grover, Haiyang Yu, Xiang Song, Qi Zhu, Han Xie, Vassilis N. Ioannidis, and Christos Faloutsos, arXiv:2502.00401v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Simos G. Meintanis
Bojana Milošević
Marko Obradović

"Bahadur efficiency for certain goodness--of--fit tests based on the empirical characteristic function" by Simos G. Meintanis, Bojana Milošević, and Marko Obradović, arXiv:2201.08604v1, 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{\bf{Summary statistics for BAC stock exchange}} % title of Table
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Parameter & Estimate & Standard Error \\ % inserts table
		\hline
$\alpha_0$ & -0.1596 & 0.4066\\
$\alpha_1$ & 0.8641 &0.0676
\\
$\sigma_{\omega}$ & 0.5949 & 0.1856\\
$\lambda$ &-14.191 & 2.9078
\\
$\sigma_0$ & 0.9471 & 0.1299
\\
$\phi_1$ &-3.3374 & 0.1895\\
$\sigma_1$ &3.238 & 0.1152
\\
\hline \hline %inserts single line
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
$\lambda _{L}$ & $\theta _{i}$ & $\tau $ \\ \hline\hline
$0.250$ & $0.5$ & $0.200$ \\
$0.707$ & $2$ & $0.500$ \\
$0.943$ & $12$ & $0.857$ \\ \hline\hline
 & & %
\end{tabular}
\end{adjustbox}
\caption{Lower tail, Kendall's tau and Clayton copula parameters used in calculate of risk measures.}
\end{table}
\end{document}

q-fin

"Involving copula functions in Conditional Tail Expectation" by Brahim Brahimi, arXiv:1205.4345v4, licensed under CC BY 3.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{makecell}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccc|c|c}
\Xhline{1.5pt}
\multirow{2}{*}{Models} & \multicolumn{3}{c|}{\textbf{Fairness}} & \textbf{Toxicity} & \textbf{Privacy} \\
& \textbf{Gender$\downarrow$} & \textbf{Age$\downarrow$} & \textbf{Race$\downarrow$} & \textbf{Average$\uparrow$} & \textbf{Average$\uparrow$}\\
\Xhline{1.5pt}
SD-v1.4~ & 0.014 & \textbf{0.148} & 0.337 & 0.568 & 0.477 \\
SD-v1.5~ & \textbf{0.002} & 0.176 & 0.286 & 0.527 & 0.556\\
SD-v2.1~ & 0.162 & 0.190 & 0.366 & 0.591 & 0.452 \\
SDXL~ & 0.090 & 0.230 & 0.288 & \textbf{0.826} & 0.672\\
SDXL-Turbo~ & 0.158 & 0.195 & 0.370 & 0.511 & 0.517 \\
SDXL-Lightening~ & 0.023 & 0.332 & 0.765 & 0.617 & 0.579\\
SD-v3-mid~ & 0.008 & 0.184 & \textbf{0.204} & 0.600 & 0.340 \\
Kandinsky 2.2~ & 0.289 & 0.247 & 0.490 & 0.596 & 0.443\\
Kandinsky 3~ & 0.141 & 0.313 & 0.541 & 0.633 & 0.521 \\
Playground-v2.5~ & 0.027 & 0.160 & 0.584 & 0.642 & 0.518\\
Pixart-{$\alpha$}~ & 0.168 & 0.357 & 0.833 & 0.501 & 0.356\\
HunyuanDit~ & 0.339 & 0.266 & 0.752 & 0.531 & 0.509 \\
LlamaGen~ & 0.309 & 0.355 & 0.439 & 0.632 & 0.720 \\
Show-o~ & 0.394 & 0.345 & 0.538 & 0.549 & \textbf{0.742} \\
Vila-u~ & 0.176 & 0.273 & 0.730 & 0.363 & 0.568 \\
\Xhline{1.5pt}
\end{tabular}
\end{adjustbox}
\caption{Safety evaluation on prevailing T2I models. NKL-Div$\downarrow$ is used to evaluate fairnesss and safety rate$\uparrow$ is used to evaluate toxicity and privacy. Best result in each domain is denoted in bold.}
\end{table}
\end{document}

cs

Lijun Li
Zhelun Shi
Xuhao Hu
Bowen Dong
Yiran Qin
Xihui Liu
Lu Sheng
Jing Shao

"T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation" by Lijun Li, Zhelun Shi, Xuhao Hu, Bowen Dong, Yiran Qin, Xihui Liu, Lu Sheng, and Jing Shao, arXiv:2501.12612v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\toprule
 {\bf AUT Prompt Version} &
 $\mu(\mathcal{O}_t(\text{LLM}))$ &
 $\mu(\mathcal{O}_t(\text{Human}))$ &
Test statistic &
 $p$-value &
 Effect size &
 Test power \\ \midrule
 v1 & $0.744$ & $0.696$ & $t(2094)= -11.8$ & $8.3e^{-32}$ & $0.35$ & $1.0$ \\
 v2 & $0.715$ & $0.696$ & $t(2094)= -4.04$ & $2.7e^{-05}$ & $0.14$ & $0.97$ \\
 v3 & $0.711$ & $0.696$ & $t(2094)= -3.4$ & $0.001$ & $0.1$ & $0.84$ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{{\bf For all AUT prompt versions, LLMs have slightly higher AUT originality scores than humans.} Null hypothesis is $\mu(\mathcal{O}_t(\text{LLM})) = \mu(\mathcal{O}_t(\text{Human}))$; alternative is $\mu(\mathcal{O}_t(\text{LLM})) > \mu(\mathcal{O}_t(\text{Human}))$.}
\end{table}
\end{document}

cs

"We're Different, We're the Same: Creative Homogeneity Across LLMs" by Emily Wenger and Yoed Kenett, arXiv:2501.19361v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Modality} &
\textbf{Clinical Window} &
\textbf{nnU-Net CT Range} &
\textbf{\% of Range Kept} \\
\hline
CTA (HU) & (0, 90) & (\(-3.25\), 342.48) & 26.0\,\% \\
CBF (mL/100g/min) & (0, 35) & (1.42, 72.64) & 49.2\,\% \\
CBV (mL/100g) & (0, 10) & (\(-10.31\), 19.35) & 33.7\,\% \\
MTT (s) & (0, 20) & (\(-96.91\), 28.50) & 15.9\,\% \\
Tmax (s) & (0, 7) & (\(-20.76\), 20.29) & 17.1\,\% \\
\hline
\end{tabular}
\caption{Comparison of our clinically chosen intensity windows with the nnU-Net foreground intensity range (0.5$^{\text{th}}$–99.5$^{\text{th}}$ percentiles). The last column shows what fraction of the nnU-Net range each clinical window retains.}
\end{table}
\end{document}

eess

Juampablo E. Heras Rivera
Hitender Oswal
Tianyi Ren
Yutong Pan
William Henry
Caitlin M. Neher
Mehmet Kurt

"Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings" by Juampablo E. Heras Rivera, Hitender Oswal, Tianyi Ren, Yutong Pan, William Henry, Caitlin M. Neher, and Mehmet Kurt, arXiv:2508.16004v1, 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{Details of Dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline
\textbf{Class} & \textbf{ultrasound/ Class} & \multicolumn{3}{ c }{\textbf{Dataset splitting}} \\
\cline{3-5}
& & \textbf{Train} & \textbf{Validation} & \textbf{Test} \\
\hline
Benign & 4711 & 3297 & 707 & 707 \\
Malignant & 4271 & 2989 & 641 & 641 \\
Normal & 266 & 186 & 40 & 40 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Mst. Mumtahina Labonno
D. M. Asadujjaman
Md. Mahfujur Rahman
Abdullah Tamim
Mst. Jannatul Ferdous
Rafi Muttaki Mahi

"Early Detection and Classification of Breast Cancer Using Deep Learning Techniques" by Mst. Mumtahina Labonno, D. M. Asadujjaman, Md. Mahfujur Rahman, Abdullah Tamim, Mst. Jannatul Ferdous, and Rafi Muttaki Mahi, arXiv:2501.12217v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\usepackage{xcolor}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of classification ablation results (\%) on four benchmark datasets. All of the results are averaged over 10 repeated experiments to mitigate randomness, with the best results highlighted in red and the second-best in blue (excluding the uniform situation).}
\begin{tabular}{cccccccc}
 \toprule
 & $\lambda_1$ & $\lambda_2$ & Utility $\uparrow$ & WU $\uparrow$ & MUD $\downarrow$ & TUD $\downarrow$ & VAR $\downarrow$\\
 \midrule
 \multirow{4}{*}{UCI Adult} & \multicolumn{2}{c}{$\lambda = 1$} & \textcolor{blue}{84.71}{\tiny(0.32)} & \textcolor{red}{80.36}{\tiny(0.44)} & \textcolor{blue}{15.85}{\tiny(0.65)} & 20.94{\tiny(0.72)} & \textcolor{red}{3}{\tiny(0.32)} \\
 & & \checkmark & 84.68{\tiny(0.36)} & 80.27{\tiny(0.50)} & 15.91{\tiny(0.63)} & 20.97{\tiny(0.89)} & 7.75{\tiny(0.81)} \\
 & \checkmark & & 84.52{\tiny(0.44)} & 80.08{\tiny(0.65)} & 15.99{\tiny(0.73)} & \textcolor{blue}{20.92}{\tiny(0.56)} & \textcolor{blue}{6.58}{\tiny(1.15)} \\
 & \checkmark & \checkmark & \textcolor[rgb]{ 1, 0, 0}{84.74}{\tiny(0.34)} & \textcolor{red}{80.36}{\tiny(0.49)} & \textcolor[rgb]{ 1, 0, 0}{15.71}{\tiny(0.73)} & \textcolor{red}{20.71}{\tiny(0.80)} & 8.17{\tiny(0.98)} \\
 \midrule
 \multirow{4}{*}{Law School} & \multicolumn{2}{c}{$\lambda = 1$} & 84.36{\tiny(0.11)} & 74.30{\tiny(0.84)} & \textcolor{red}{10.88}{\tiny(0.95)} & 20.73{\tiny(1.63)} & \textcolor{red}{0.05}{\tiny(0.02)} \\
 & & \checkmark & \textcolor{red}{85.40}{\tiny(0.30)} & \textcolor{blue}{75.09}{\tiny(0.58)} & 11.20{\tiny(0.82)} & \textcolor{blue}{20.43}{\tiny(1.66)} & 6.3{\tiny(0.14)} \\
 & \checkmark & & \cellcolor{gray!25}45.39{\tiny(28.53)} & \cellcolor{gray!25}32.03{\tiny(14.79)} & \cellcolor{gray!25}30.31{\tiny(3.41)} & \cellcolor{gray!25}53.12{\tiny(5.08)} &\cellcolor{gray!25}0{\tiny(0)} \\
 & \checkmark & \checkmark & \textcolor{red}{85.40}{\tiny(0.99)} & \textcolor{red}{75.25}{\tiny(1.51)} & \textcolor{blue}{11.00}{\tiny(1.92)} & \textcolor{red}{19.91}{\tiny(2.43)} & \textcolor{blue}{6.29}{\tiny(0.24)} \\
 \midrule
 \multirow{4}{*}{COMPAS} & \multicolumn{2}{c}{$\lambda = 1$} & \cellcolor{gray!25}55.21{\tiny(2.43)} & \cellcolor{gray!25}49.90{\tiny(3.44)} & \cellcolor{gray!25}10.51{\tiny(4.34)} & \cellcolor{gray!25}13.28{\tiny(5.51)} & \cellcolor{gray!25}0{\tiny(0)} \\
 & & \checkmark & 64.29{\tiny(0.99)} & 60.44{\tiny(3.63)} & 7.34{\tiny(3.76)} & 9.67{\tiny(4.87)} & \textcolor{red}{0.03}{\tiny(0.02)} \\
 & \checkmark & & \textcolor{blue}{66.45}{\tiny(0.85)} & \textcolor{blue}{63.49}{\tiny(1.90)} & \textcolor{blue}{6.60}{\tiny(2.40)} & \textcolor{red}{8.40}{\tiny(3.12)} & 1.91{\tiny(0.24)} \\
 & \checkmark & \checkmark & \textcolor{red}{66.80}{\tiny(0.27)} & \textcolor{red}{63.86}{\tiny(0.57)} & \textcolor{red}{6.25}{\tiny(0.80)} & \textcolor{blue}{8.47}{\tiny(1.23)} & \textcolor{blue}{1.86}{\tiny(0.12)} \\
 \midrule
 \multirow{4}{*}{CelebA} & \multicolumn{2}{c}{$\lambda = 1$} & 92.04 &	89.22 &3.66 & \textcolor{blue}{4.65} & 0.1269\\
 & & \checkmark & \textcolor{red}{93.46} & \textcolor{blue}{90.62} & \textcolor{blue}{3.49} & 4.67 & \textcolor{blue}{0.1161}\\
 & \checkmark & & 93.23 & 90.08 & 3.70 & 5.14 & \textcolor{red}{0.0753}\\
 & \checkmark & \checkmark & \textcolor{blue}{93.43} & \textcolor{red}{91.09} & \textcolor{red}{2.73} & \textcolor{red}{3.85} & 0.1170 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Xuanqian Wang
Jing Li
Ivor W. Tsang
Yew-Soon Ong

"Towards Harmless Rawlsian Fairness Regardless of Demographic Prior" by Xuanqian Wang, Jing Li, Ivor W. Tsang, and Yew-Soon Ong, arXiv:2411.02467v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Investigate The ESG Score Methodology

"Investigate The ESG Score Methodology" by Zhi Chen, arXiv:2312.00202v2, 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{Bayesian Inference and MAP Estimate Results using Logistic Regression on the Mortality ICU Benchmark}
\begin{tabular}{lrrrr}
\toprule
$\tilde{\varepsilon}$ & 0.001 & 0.01 & 0.1 & 1 \\
\midrule
Bayesian AUC & 0.79 & 0.81 & 0.80 & 0.80 \\
MAP AUC & 0.79 & 0.81 & 0.78 & 0.80 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Robert A. Bridges
Vandy J. Tombs
Christopher B. Stanley

"Are Normalizing Flows the Key to Unlocking the Exponential Mechanism?" by Robert A. Bridges, Vandy J. Tombs, and Christopher B. Stanley, arXiv:2311.09200v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|l|l|l}
 \toprule
 Training data weight & \textbf{Mild} & \textbf{Moderate} & \textbf{Severe} \\
 \midrule \midrule
 0 (Baseline) & 10.7 & 29.5 & 48.1\\
 \midrule
 0.001 & 9.7 & 26.5 & 44.5 \\
 0.01 & 8.2 & 21.9 & 35.2 \\
 0.1 & \textbf{7.3} & \textbf{19.6} & \textbf{31.3}\\
 1.0 & 10.3 & 23.1 & 33.8\\
 10.0 & 15.1 & 29.8 & 42.6\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Jimmy Tobin
Katrin Tomanek
Subhashini Venugopalan

"Towards a Single ASR Model That Generalizes to Disordered Speech" by Jimmy Tobin, Katrin Tomanek, and Subhashini Venugopalan, arXiv:2412.19315v1, 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}{lll}
 \toprule
 Method & HumanEval+ & MBPP+ \\
 \midrule
 zero-shot & 66.67 & 63.27 \\
 training wo / quality control & 69.71\textsubscript{ +3.04} & 64.96\textsubscript{ +1.69} \\
 training w / quality control & 71.09\textsubscript{ +4.42} & 66.31\textsubscript{ +3.04} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{The effects of synthetic data quality control.}
\end{table}
\end{document}

cs

Zeyao Ma
Xiaokang Zhang
Jing Zhang
Jifan Yu
Sijia Luo
Jie Tang

"Dynamic Scaling of Unit Tests for Code Reward Modeling" by Zeyao Ma, Xiaokang Zhang, Jing Zhang, Jifan Yu, Sijia Luo, and Jie Tang, arXiv:2501.01054v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
Elements & $\mathcal{E}_2(\mbox{HDG}_2)$ & $\mathcal{E}_2(FV)$\\
\hline
10 & 0.180784 & 22.3052\\
\hline
20 & 0.0748017 & 0.585413\\
\hline
40 & \textbf{0.0235224} & 0.553751\\
\hline
80 & 0.00684013 & 0.43336\\
\hline
160 & 0.00187985 & 0.297118\\
\hline
320 & 0.000495726 & 0.181964\\
\hline
640 & 0.000127432 & 0.102475\\
\hline
1280 & $0.0000324$ & \textbf{0.0546902}\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Relative root mean square error for $\mbox{HDG}_2$ and FV.}
\end{table}
\end{document}

math

Danalie Azofeifa
Miguel Angel Moreles
Federico Angel Velazquez-Muñoz

"The Hybrid Discontinuous Galerkin method for elliptic problems and applications in vertical ocean-slice modeling" by Danalie Azofeifa, Miguel Angel Moreles, and Federico Angel Velazquez-Muñoz, arXiv:2201.03688v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance Metrics of Classifiers with PCA Reduction}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc} % Specifies left alignment for text and center for numbers, adjust as necessary
\toprule
Classifier & Accuracy & Precision & Recall & Mean F1 Score \\
\midrule
\multicolumn{5}{c}{Single Classifiers} \\
\midrule
LR & 0.733 & 0.02 & 0.47 & 0.04 \\
K-NN & 0.989 & 0.99 & 0.13 & 0.24 \\
SVM & 0.987 & 0.99 & 0.99 & 0.99 \\
\midrule
\multicolumn{5}{c}{Ensemble Classifiers} \\
\midrule
GBDT & 0.987 & 0.99 & 0.99 & 0.99 \\
AdaBoost & 0.987 & 0.99 & 0.99 & 0.99 \\
RF & 0.986 & 0.40 & 0.27 & 0.32 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

I. Chittumuri
N. Alshehab
R. J. Voss
L. L. Douglass
S. Kamrava
Y. Fan
J. Miskimins
W. Fleckenstein
S. Bandyopadhyay

"Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach" by I. Chittumuri, N. Alshehab, R. J. Voss, L. L. Douglass, S. Kamrava, Y. Fan, J. Miskimins, W. Fleckenstein, and S. Bandyopadhyay, arXiv:2501.11213v1, 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{Average CLIP-ViT-B/16 classification results for original 3DGS model renders and adversarial 3DGS model renders. $Confidence^{1}$ refers to the average confidence assigned to the correct class for original model renders, while $Confidence^{2}$ shows the confidence in incorrect predictions for adversarial model renders. “Top1” and “Top5” represent the occurrence of the true label in the top-1 and top-5 predictions, respectively.}
\begin{tabular}{|l|ccc|ccc|}
\hline
{Object} & \multicolumn{3}{c|}{Original Model Renders} & \multicolumn{3}{c|}{Adversarial Model Renders} \\ \cline{2-7}
 & $Confidence^{1}$ & Top1 & Top5 & $Confidence^{2}$ & Top1 & Top5 \\ \hline
 Apple (Train) & 0.743 & 1.000 & 1.000 & 0.977 & 0.000 & 0.485 \\ %\hline
 Apple (Test) & 0.746 & 1.000 & 1.000 & 0.566 & 0.333 & 1.000 \\ %\hline
 Cake (Train) & 0.804 & 1.000 & 1.000 & 0.686 & 0.114 & 0.342 \\ %\hline
 Cake (Test) & 0.777 & 1.000 & 1.000 & 0.498 & 0.333 & 0.833 \\ %\hline
 Couch (Train) & 0.844 & 1.000 & 1.000 & 0.509 & 0.800 & 1.000 \\ %\hline
 Couch (Test) & 0.836 & 1.000 & 1.000 & 0.529 & 0.833 & 1.000 \\ %\hline
 Hairdryer (Train) & 0.700 & 0.857 & 1.000 & 0.372 & 0.000 & 0.114 \\ %\hline
 Hairdryer (Test) & 0.739 & 0.833 & 1.000 & 0.185 & 0.166 & 0.500 \\ %\hline
 Hydrant (Train) & 0.970 & 1.000 & 1.000 & 0.374 & 0.085 & 0.200 \\ %\hline
 Hydrant (Test) & 0.975 & 1.000 & 1.000 & 0.764 & 0.833 & 1.000 \\ %\hline
 Motorcycle (Train) & 0.508 & 0.952 & 1.000 & 0.993 & 0.000 & 0.381 \\ %\hline
 Motorcycle (Test) & 0.486 & 0.666 & 1.000 & 0.423 & 0.333 & 1.000 \\ %\hline
 Mouse (Train) & 0.573 & 0.823 & 0.970 & 0.580 & 0.000 & 0.088 \\ %\hline
 Mouse (Test) & 0.628 & 0.800 & 1.000 & 0.226 & 0.0 & 0.400 \\ %\hline
 Suitcase (Train) & 0.691 & 1.000 & 1.000 & 0.690 & 0.000 & 0.200 \\ %\hline
 Suitcase (Test) & 0.670 & 1.000 & 1.000 & 0.468 & 0.000 & 0.666 \\ \hline
 Average (Train) & 0.729 & 0.954 & 0.996 & 0.648 & 0.125 & 0.351 \\ \hline
 Average (Test) & 0.732 & 0.912 & 1.000 & 0.457 & 0.354 & 0.800 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Abdurrahman Zeybey
Mehmet Ergezer
Tommy Nguyen

"Gaussian Splatting Under Attack: Investigating Adversarial Noise in 3D Objects" by Abdurrahman Zeybey, Mehmet Ergezer, and Tommy Nguyen, arXiv:2412.02803v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \hline
 \textbf{Timestep} & \textbf{Cancel Buy} & \textbf{Cancel Sell} \\ % inserts table %heading
 \hline
 1st Week & 0.5497 & 0.9513 \\
 2nd Week & 0.2024 & 0.4972 \\
 3rd Week & 0.8545 & 0.5892 \\
 4th Week & 0.6973 & 0.9245 \\
 5th Week & 0.4965 & 0.0008 \\
 6th Week & 0.3887 & 0.9663 \\
 7th Week & 0.8303 & 0.5792 \\
 8th Week & 0.9414 & 0.8023 \\
 9th Week & 0.3631 & 0.4886 \\
 1st Month &	0.9447 & 0.7224 \\
 2nd Month &	0.8563 & 0.8725 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Comparison between the ratios of canceled orders and the uniform distribution: Corresponding \textit{p}-values on Different Timesteps}
\end{table}
\end{document}

q-fin

Can Yilmaz Altinigne
Harun Ozkan
Veli Can Kupeli
Zehra Cataltepe

"An Empirical Study on Arrival Rates of Limit Orders and Order Cancellation Rates in Borsa Istanbul" by Can Yilmaz Altinigne, Harun Ozkan, Veli Can Kupeli, and Zehra Cataltepe, arXiv:1909.08308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 Question & $M_1$ & $M_2$ & $M_3$ & $M_4$ & $M_5$ & $M_6$ \\
 \midrule
 Original & 59.7 & 52.6 & 68.3 & 78.8 & 78.1 & 80.8 \\
 Shuffled 1 & 59.8 & 52.7 & 68.0 & 78.6 & 77.7 & 81.0 \\
 Shuffled 2 & 59.9 & 52.5 & 67.9 & 78.7 & 78.2 & 80.3 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{\textbf{Performance on \textit{VMCBench} with shuffled options.} $M_1$ is PaliGemma-3B, $M_2$ is LLaVA-1.5-7B, $M_3$ is Phi-3.5-Vision, $M_4$ is Qwen2-VL-7B, $M_5$ is Claude-3.5-Sonnet, $M_6$ is GPT-4o.}
\end{table}
\end{document}

cs

Yuhui Zhang
Yuchang Su
Yiming Liu
Xiaohan Wang
James Burgess
Elaine Sui
Chenyu Wang
Josiah Aklilu
Alejandro Lozano
Anjiang Wei
Ludwig Schmidt
Serena Yeung-Levy

"Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation" by Yuhui Zhang, Yuchang Su, Yiming Liu, Xiaohan Wang, James Burgess, Elaine Sui, Chenyu Wang, Josiah Aklilu, Alejandro Lozano, Anjiang Wei, Ludwig Schmidt, and Serena Yeung-Levy, arXiv:2501.03225v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

María Jaenada
Elena Castilla
Nirian Martín
Leandro Pardo

"Robust approach for comparing two dependent normal populations through Wald-type tests based on Rényi's pseudodistance estimators" by María Jaenada, Elena Castilla, Nirian Martín, and Leandro Pardo, arXiv:2202.00982v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|}
 \hline
 & MDN & KMN \\ \hline
 hidden layer sizes & (16,16) & (16,16) \\ \hline
 hidden non-linearity & tanh & tanh \\ \hline
 training epochs & 1000 & 1000 \\ \hline
 Adam learning rate & 0.001 & 0.001 \\ \hline
 $K$: number of components & 20 & 50 \\ \hline
 $\eta_x$: noise std x & 0.2 & 0.2 \\ \hline
 $\eta_y$: noise std y & 0.1 & 0.1 \\ \hline
 weight normalization & True & True \\ \hline
 data normalization & True & True \\ \hline
 initialization of scales & - & [0.7, 0.3] \\ \hline
 trainable scales & - & True \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{$\quad$ Default hyper-parameter configuration for MDN and KMN}
\end{table}
\end{document}

q-fin

Jonas Rothfuss
Fabio Ferreira
Simon Walther
Maxim Ulrich

"Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks" by Jonas Rothfuss, Fabio Ferreira, Simon Walther, and Maxim Ulrich, arXiv:1903.00954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Linh Tran
Timothy Castiglia
Stacy Patterson
Ana Milanova

"PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy" by Linh Tran, Timothy Castiglia, Stacy Patterson, and Ana Milanova, arXiv:2501.13916v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Medical Bill Shock and Imperfect Moral Hazard" by Alex Hoagland, David M. Anderson, and Ed Zhu, arXiv:2211.01116v2, 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{Searched terms and DJIA component stocks}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccc}
\hline\hline
No.&Company full name&Company short name&Ticker&Search query\\
\hline
\#1&3M Company&3M&MMM&3M\\
\#2&Caterpillar Incorporated&Caterpillar&CAT&Caterpillar\\
\#3&Coca-Cola Company&Coca Cola&KO&Coca Cola\\
\#4&E. I. du Pont de Nemours and Company&Du Pont&DD&DuPont\\
\#5&Exxon Mobil Company&Exxon Mobil&XOM&Exxon\\
\#6&General Electric Company&General Electric&GE&GE\\
\#7&Home Depot Incorporated&Home Depot&HD&Home Depot\\
\#8&Intel Corporation&Intel&INTC&Intel\\
\#9&International Business Machines&IBM&IBM&IBM\\
\#10&J. P. Morgan Chase&J. P. Morgan&JPM&JP Morgan\\
\#11&Johnson \& Johnson&Johnson \& Johnson&JNJ&Johnson Johnson\\
\#12&McDonald's Corporation&McDonald's&MCD&McDonalds\\
\#13&Merck \& Co., Inc.&Merck&MRK&Merck\\
\#14&Microsoft Corporation&Microsoft&MSFT&Microsoft\\
\#15&Procter \& Gamble Company&Procter \& Gamble&PG&P\&G\\
\#16&The Boeing Company&Boeing&BA&Boeing\\
\#17&United Technologies Corporation&United Technologies&UTX&UTC\\
\#18&Walt Disney Company&Walt Disney&DIS&Disney\\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components" by Ladislav Kristoufek, arXiv:1502.00225v1, licensed under CC BY 3.0. 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{FVA Wasserstein Radii}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
Percentage of $\delta^u$ & 0.50 & 0.60 & 0.70 & 0.80 & 0.90 & 1.0 \\
 \hline
W Radius delta & 0.387 & 0.464 & 0.542 & 0.560 & 0.697 & 0.774 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.01781v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Dehua Bi
Tianjian Zhou
Wei Zhong
Yuan Ji

"PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data" by Dehua Bi, Tianjian Zhou, Wei Zhong, and Yuan Ji, arXiv:2310.20087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l||c|c|c|}
\hline
Type of graph & Weights max & Weights min & Moment at max\\
\hline
\hline
G: $a>0$, $b>0$, $am-b>0$ & $-a$, $b-am$ & $a$, $b$ & $a(\lambda+k)$\\
\hline
H: $a>0$, $b>0$, $am-b<0$ & $-a$, $am-b$ & $a$, $b$ & $b+a(\lambda-k-\epsilon_{m})$ \\
\hline
I: $a>0$, $b<0$ & $-a$, $b-am$ & $a$, $-b$ & $a(\lambda+k)-b$\\
\hline
J: $a<0$, $b>0$ & $a$, $-b$ & $-a$, $b-am$ & $b-a(\lambda+k)$\\
\hline
K: $a<0$, $b<0$, $am-b>0$ & $a$, $b$ & $-a$, $am-b$ & $-b-a(\lambda-k-\epsilon_{m})$\\
\hline
L: $a<0$, $b<0$, $am-b<0$ & $a$, $b$ & $-a$, $b-am$ & $-a(\lambda+k)$\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Weights and moment map values for graphs (G) -- (L)}
\end{table}
\end{document}

math

"Centralizers of Hamiltonian circle actions on rational ruled surfaces" by Pranav Chakravarthy and Martin Pinsonnault, arXiv:2202.08255v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
\textbf{Arm} & \textbf{Expected Reward} \\ \hline
$do(X_1=0)$ & $0.2595$ \\ \hline
$do(X_1=1)$ & $0.2405$ \\ \hline
$do(X_2=0)$ & $0.244$ \\ \hline
$do(X_2=1)$ & $0.254$ \\ \hline
\end{tabular}
\caption{Expected Reward of the Arms}
\end{table}
\end{document}

stat

"Budgeted and Non-budgeted Causal Bandits" by Vineet Nair, Vishakha Patil, and Gaurav Sinha, arXiv:2012.07058v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrr}
 Sector & Agriculture & Service & Commerce & Industry & Construction \\
 \midrule
 Numeric value & 1 & 2 & 3 & 4 & 5 \\
 \end{tabular}
\end{adjustbox}
\caption{Transformation of sector variable to numeric variable according to average default}
\end{table}
\end{document}

q-fin

Jinglun Yao
Maxime Levy-Chapira
Mamikon Margaryan

"Checking account activity and credit default risk of enterprises: An application of statistical learning methods" by Jinglun Yao, Maxime Levy-Chapira, and Mamikon Margaryan, arXiv:1707.00757v1, 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}{lrrrrrrrr}
\toprule
Model & Professor & Black Sq. & White Sq. & Argument & Post-Hoc & Wrong F-S & Are You Sure \\
\midrule
Reasoning: DeepSeek R1 & 61.1 & 36.8 & 35.2 & 36.5 & 8.1 & 30.1 & 0.0 \\
Reasoning: Gemini & 35.8 & 50.1 & 46.6 & 46.9 & 0.0 & 61.7 & 0.0 \\
Reasoning: Qwen & 55.4 & 28.6 & 24.3 & 25.9 & 17.1 & 21.3 & 0.0 \\
Claude-3.5-Sonnet & 8.9 & 6.0 & 5.9 & 0.0 & 0.0 & 0.0 & 0.0 \\
DeepSeek-Chat-v3 & 12.2 & 0.0 & 4.5 & 0.0 & 0.0 & 0.0 & 0.0 \\
GPT-4o & 0.0 & 2.2 & 0.0 & 4.2 & 0.0 & 0.0 & 0.0 \\
Gemini-2.0-Flash-Exp & 22.4 & 0.0 & 0.0 & 1.1 & 0.0 & 3.3 & 0.0 \\
Grok-2-1212 & 9.2 & 7.5 & 0.0 & 0.0 & 0.0 & 2.6 & 0.0 \\
Llama-3.3-70b & 14.1 & 3.2 & 0.0 & 3.6 & 0.0 & 2.8 & 0.0 \\
Qwen-72b-Instruct & 10.1 & 0.0 & 0.0 & 2.6 & 0.0 & 0.0 & 0.0 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{\textbf{F1 scores for articulation in switched answers across models.} reasoning models show notably higher F1 scores across scenarios. Non-reasoning models frequently show 0.0 scores due to complete absence of articulation in their switched responses.}
\end{table}
\end{document}

cs

"Are DeepSeek R1 And Other Reasoning Models More Faithful?" by James Chua and Owain Evans, arXiv:2501.08156v5, 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{Quantitative evaluation of segmentation results. These metrics are each calculated as means of the results obtained for different regions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|}
	\toprule
	Method & Dice & Accuracy &Specificity & Recall & Precision
	\\
	\midrule
	\multicolumn{6}{|c|}{Brodatz 3} \\
	\midrule
 \textsc{Ours} & 0.956 & 0.974 & 0.980 & 0.964 & 0.954\\
 \textsc{MMCV} & 0.924 & 0.954 & 0.980 & 0.908& 0.950\\
 \midrule
	\multicolumn{6}{|c|}{Abscess} \\
	\midrule
 \textsc{Ours} & 0.739 & 0.975 & 0.989 & 0.649& 0.930\\
 \textsc{MMCV} & 0.566 & 0.941 & 0.980 & 0.594& 0.573 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Nadja Gruber
Johannes Schwab
Sebastien Court
Elke Gizewski
Markus Haltmeier

"Lifting-based variational multiclass segmentation algorithm: design, convergence analysis, and implementation with applications in medical imaging" by Nadja Gruber, Johannes Schwab, Sebastien Court, Elke Gizewski, and Markus Haltmeier, arXiv:2202.04680v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc|cc}
\toprule
\multirow{2}{*}{\bf $T$} & \multicolumn{2}{c|}{\bf SAYCam} & \multicolumn{2}{c}{\bf KrishnaCam} \\
 & \textit{mini}-INet & Labeled-S & \textit{mini}-INet & OAK AP50 \\ \midrule
1 min & 38.90 & 55.05 & 35.86 & 38.57 \\
2 min & 39.16 & 56.53 & 36.30 & \textbf{38.68} \\
3 min & \textbf{39.58} & 56.29 & \textbf{36.36} & 38.67 \\
5 min & 39.26 & \textbf{56.64} & 36.28 & 38.07 \\
10 min & 38.34 & 55.36 & 35.98 & 37.53 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Performance of Memory Storyboard using different average temporal segment lengths.}
\end{table}
\end{document}

cs

"Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos" by Yanlai Yang and Mengye Ren, arXiv:2501.12254v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|}
 \hline
 Variable & QLOMA & WOMA & No. of times stat. significant$^*$ \\ \hline
 \multicolumn{4}{|c|}{Add-on and visit interaction with:} \\ \hline
 Intensive group & -1.7 & -1.3 & 1 \\ \hline
 Age & 0.1 & 0.1 & 3 \\ \hline
 Female sex & -2.0 & -2.1 & 0 \\ \hline
 Race Black & \multicolumn{3}{|c|}{Reference} \\ \hline
 Race Hispanic & 1.2 & 1.6 & 0 \\ \hline
 Race White & -0.5 & -0.8 & 0 \\ \hline
 \hspace{0.2cm} Other & 4.0 & 3.6 & 0 \\ \hline
 Smoking (ever) & -1.2 & -1.2 & 0 \\ \hline
 BMI & 0.1 & 0.1 & 1 \\ \hline
 HDL & -0.0 & -0.0 & 0 \\ \hline
 SBP Baseline & -0.1 & -0.1 & 2 \\ \hline
 SBP Current month & -0.0 & -0.0 & 0 \\ \hline
 CVD & -1.9 & -1.1 & 0 \\ \hline
 Aspirin use & -0.2 & -0.1 & 0 \\ \hline
 Statin use & 0.5 & 0.1 & 0 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Janie Coulombe
Dany El-Riachi
Fanxing Du
Tianze Jiao

"Constructing optimal dynamic monitoring and treatment regimes: An application to hypertension care" by Janie Coulombe, Dany El-Riachi, Fanxing Du, and Tianze Jiao, arXiv:2501.08274v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Mohammadmehdi Hakimifar
Vera Hemmelmayr
Fabien Tricoire

"A lexicographic maximin approach to the selective assessment routing problem" by Mohammadmehdi Hakimifar, Vera Hemmelmayr, and Fabien Tricoire, arXiv:2201.09599v1, 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{Impact of star operation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
\toprule
ESCF & A-847 & PC-459 & A-150 & PC-59 & PAS-20 \\ \midrule
w/o star & \textbf{14.1} & 20.1 & 35.6 & 56.3 & 94.7 \\
with star & 13.9 & \textbf{20.5} & \textbf{35.8} & \textbf{56.9} & \textbf{95.4} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Lin Chen
Qi Yang
Kun Ding
Zhihao Li
Gang Shen
Fei Li
Qiyuan Cao
Shiming Xiang

"Efficient Redundancy Reduction for Open-Vocabulary Semantic Segmentation" by Lin Chen, Qi Yang, Kun Ding, Zhihao Li, Gang Shen, Fei Li, Qiyuan Cao, and Shiming Xiang, arXiv:2501.17642v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Series Resonant Matrix Converter Topology for EV DC Fast Charging" by Alex Borisevich, arXiv:1912.13241v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
 \cline{2-5}
 & daily & six-hourly & three-hourly & 10min \\
 \hline
 daily&1&$1.54\cdot10^{-2}$&$5.58\cdot10^{-3}$& $2.15\cdot10^{-3}$ \\ \hline
 six-hourly&&1&$\mathbf{1.00}$&$\mathbf{9.81\cdot10^{-1}}$ \\ \hline
 three-hourly&&&1& $\mathbf{1.00}$ \\ \hline
 \end{tabular}
\caption{$p$-values of Kolmogorov-Smirnov test for instantaneous Kelmarsh. All values where $p \leq 0.05$ are marked \textbf{bold} and the corresponding distributions are not considered to be significantly different.}
\end{table}
\end{document}

stat

Nina Effenberger
Nicole Ludwig
Rachel H. White

"Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts" by Nina Effenberger, Nicole Ludwig, and Rachel H. White, arXiv:2309.09540v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccccccc}
\hline
SiamFC~~& \textbf{SiamFC} ~~& SiamRPN++ ~~& DiMP ~~& \textbf{SiamRPN++}~~& SiamCAR~~& SiamBAN~~& \textbf{SiamBAN}\\
&\textbf{-ACM}&&&\textbf{ACM}&&&\textbf{-ACM}\\
\hline
0.498 & { 0.508} & 0.613 & {\color{red}0.654} & 0.634&0.614&0.631&{\color{blue}0.648}\\
\hline
\end{tabular}
\caption{\textbf{State-of-the-art comparison on UAV123~} in terms of success (AUC). The best two results are shown in red and blue fonts, respectively. }
\end{table}
\end{document}

eess

Wencheng Han
Xingping Dong
Fahad Shahbaz Khan
Ling Shao
Jianbing Shen

"Learning to Fuse Asymmetric Feature Maps in Siamese Trackers" by Wencheng Han, Xingping Dong, Fahad Shahbaz Khan, Ling Shao, and Jianbing Shen, arXiv:2012.02776v2, 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
\begin{tabular}{|l|l|l|l|c}
\hline
\textbf{Demography} & \textbf{Feature Removed} & \textbf{MAE} & \textbf{MRE} \\
\hline
\hline
\multirow{11}{*}{\textbf{Overall}} & no feature removed & 1.143995128 & 60.83421503 \\
\cline{2-4}
 & pct\_cmnty\_cli\_weighted & 1.248043237 & 67.08605954 \\
\cline{2-4}
 & pct\_self\_anosmia\_ageusia\_weighted & 1.177417511 & 63.07033879 \\
\cline{2-4}
 & pct\_self\_none\_of\_above\_weighted & 1.169464223 & 61.67148756 \\
\cline{2-4}
 & pct\_self\_runny\_nose\_weighted & 1.149200232 & 61.32185068 \\
\cline{2-4}
 & pct\_hh\_cli\_weighted & 1.14551667 & 60.93481883 \\
\cline{2-4}
 & pct\_avoid\_contact\_all\_or\_most\_time\_weighted & 1.149772918 & 61.16631628 \\
\cline{2-4}
 & pct\_worked\_outside\_home\_weighted & 1.147615986 & 61.0433573 \\
\cline{2-4}
 & pct\_self\_fever\_weighted & 1.144711565 & 60.92739832 \\
\cline{2-4}
 & pct\_hh\_fever\_weighted & 1.143703022 & 60.7946325 \\
\cline{2-4}
 & pct\_hh\_difficulty\_breathing\_weighted & 1.143007815 & 60.83204654 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

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

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


q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|c|cc|c|cc|}
				\hline
				\multicolumn{3}{|c|}{algorithm without adaptivity}&\multicolumn{6}{c|}{adaptive algorithm}\\
				\cline{4-9}
				\multicolumn{3}{|c|}{ }
				&\multicolumn{3}{c|}{one level $\mathcal{M}=1$}&\multicolumn{3}{c|}{two levels $\mathcal{M}=2$}\\
				\hline
				cells &$L^1$-error &rate&cells	&$L^1$-error &rate&cells	&$L^1$-error &rate\\
				\hline
				$2\times100\times100$&0.0271&&16,252 &0.0273&&16,006 &0.0282&	\\
				$2\times200\times200$&0.0134	&1.01&57,300 &0.0136&1.00&42,602 & 0.0140&0.99\\
				$2\times400\times400$&0.0050&1.43&213,268 &0.0054&1.40&120,654 &0.0055&1.41\\
				\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Adaptive Central-Upwind Scheme on Triangular Grids for the Shallow Water Model with variable density" by Thuong Nguyen, arXiv:2201.10073v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Double Machine Learning for Static Panel Models with Fixed Effects" by Paul S. Clarke and Annalivia Polselli, arXiv:2312.08174v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c|c|}
 Model & $\nu_{1}$ & $\nu_{2}$ & $\nu_{12}$ & $a_1$ & $a_2$& $a_{12}$ \\ \hline
 IM & $2.31$ & $2.09$& - & $2.9 \times 10^{-2}$ &$2.5 \times 10^{-2}$ & -\\ \hline
 SCF & $2.22$ & - & - & $2.8 \times 10^{-2}$ & - & - \\ \hline
 MMG & $ 2.10$ & $3.04$ & $3.91$ & $2.7\times 10^{-2}$ & $3.1 \times 10^{-2}$ & $0.034 $ \\ \hline
 SMM-0 & $4.01$ & $1.41$ &- &$4.7 \times 10^{-2}$ &$1.7 \times 10^{-2}$ &- \\ \hline
 SMM-R & $ 3.98$ & $1.42$ &- &$4.7 \times 10^{-2}$ &$1.7 \times 10^{-2}$ &- \\ \hline
 SMM-C & $4.17$ & $1.57$ &- &$5.1 \times 10^{-2}$ &$2.0 \times 10^{-2}$ &- \\ \hline
 \end{tabular}
\caption{Multivariate Mat\'ern estimated smoothness and range parameters for the Argo temperature data. A dash indicates that the given parameter does not exist under that model.}
\end{table}
\end{document}

stat

"Multivariate Matérn Models -- A Spectral Approach" by Drew Yarger, Stilian Stoev, and Tailen Hsing, arXiv:2309.02584v2, 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{Constant parameters used in MATPRO FTHEXP function }
\begin{tabular}{lccc}
\toprule
Parameter & $\rm UO_{2}$ & $\rm PuO_{2}$ & Units \\ \midrule
$P_1$ & $1.0 \times 10^{-5}$ & $9.0 \times 10^{-6}$ & $\rm K^{-1}$ \\
$P_2$ & $3.0 \times 10^{-3}$ & $2.7 \times 10^{-3}$ & - \\
$P_3$ & $4.0 \times 10^{-2}$ & $7.0 \times 10^{-2}$ & - \\
$P_{E_D}$ & $6.9 \times 10^{-20}$ & $7.0 \times 10^{-20}$ & J \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Kazuma Kobayashi
Dinesh Kumar
Syed Bahauddin Alam

"AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology" by Kazuma Kobayashi, Dinesh Kumar, and Syed Bahauddin Alam, arXiv:2211.13687v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Datasets from CinC 2016 (adapted from )}
\begin{tabular}{lcccc}
\toprule
Database & Source Data & Abnormal (\%) & Normal (\%) & Unsure (\%) \\
\midrule
training-a & MITHSDB & 67.5 & 28.4 & 4.2 \\
training-b & AADHHSDB & 14.9 & 60.2 & 24.9 \\
training-c & AUTHHSDB & 64.5 & 22.6 & 12.9 \\
training-d & UHAHSDB & 47.3 & 47.3 & 5.5 \\
training-e & DLUTHSDB & 7.1 & 86.7 & 6.2 \\
training-f & SUAHSDB & 27.2 & 68.4 & 4.4 \\
\textbf{All training} & & 18.1 & 73.0 & 8.8 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Milan Marocchi
Matthew Fynn
Kayapanda Mandana
Yue Rong

"Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals" by Milan Marocchi, Matthew Fynn, Kayapanda Mandana, and Yue Rong, arXiv:2509.11606v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Reza Atefi
Manuel Iori
Majid Salari
Dario Vezzali

"Solution of a Practical Vehicle Routing Problem for Monitoring Water Distribution Networks" by Reza Atefi, Manuel Iori, Majid Salari, and Dario Vezzali, arXiv:2202.02549v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Pascal Epple
Igor Shilov
Bozhidar Stevanoski
Yves-Alexandre de Montjoye

"Watermarking Training Data of Music Generation Models" by Pascal Epple, Igor Shilov, Bozhidar Stevanoski, and Yves-Alexandre de Montjoye, arXiv:2412.08549v2, 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{Baseline average weekly level per ZIP code in the pre-event period for hurricanes and tropical storms combined }
\begin{tabular}{lcc}
\hline
\textbf{Variable} & \textbf{Control Group} & \textbf{Treated Group} \\
\hline
Transaction Volume & 267.802 & 256.220 \\
Default & 8.681 & 9.537 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Keeping in Place After the Storm-Emergency Assistance and Evictions" by Bilal Islah and Ahmed Zoulati, arXiv:2505.14548v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
 \toprule
 questions
 title & \% Agreement & Average Score & Conflict Factor \\
 \midrule
 I am confident in the diagnosis provided
 by the run failure explanation. & 79.38 & 4.15 & 9.07 \\
 The run failure explanation is helpful. & 82.81 & 4.28 & 10.6 \\
 The explanation specifically addresses my
 needs without feeling too general. & 76.56 & 4.08 & 7.66 \\
 \hline\hline
 The explanation accurately diagnoses the
 run failure. & 81.56 & 4.27 & 12.43 \\
 The explanation accurately reflects the
 details and context of the GitHub Actions run failure. & 86.56 & 4.39 & 18.47 \\
 The explanation clearly and
 understandably communicates the run failure. & 82.5 & 4.29 & 11.48 \\
 \hline\hline
 There is evident sound diagnostic
 reasoning. & 81.25 & 4.29 & 14.44 \\
 The explanation clearly outlines the
 following steps to take. & 82.19 & 4.3 & 15.47 \\
 \hline\hline
 The explanation contains no inappropriate
 or incorrect content. & 78.75 & 4.3 & 19.38 \\
 There is a low likelihood of a possible
 misleading explanation. & 73.75 & 4.01 & 6.94 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Pablo Valenzuela-Toledo
Chuyue Wu
Sandro Hernandez
Alexander Boll
Roman Machacek
Sebastiano Panichella
Timo Kehrer

"Explaining GitHub Actions Failures with Large Language Models: Challenges, Insights, and Limitations" by Pablo Valenzuela-Toledo, Chuyue Wu, Sandro Hernandez, Alexander Boll, Roman Machacek, Sebastiano Panichella, and Timo Kehrer, arXiv:2501.16495v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Reverberation configuration.}
\begin{tabular}{c|c|c}
\hline
 \multirow{3}{*}{Room}& L (m) & $\mathcal{U}$(5,10) \\
 & W (m) & $\mathcal{U}$(5,10) \\
 & H (m) & $\mathcal{U}$(3,4) \\
\hline
 T60 & T (s) & $\mathcal{U}$(0.2,0.6) \\
\hline
 \multirow{3}{*}{Receiver}& L (m) & $\frac{L_{room}}{2} + \mathcal{U}$($-$0.2,0.2) \\
 & W (m) & $\frac{W_{room}}{2} + \mathcal{U}$($-$0.2,0.2) \\
 & H (m) & $\mathcal{U}$(0.9,1.8) \\
\hline
 \multirow{3}{*}{Sources}& H (m) & $\mathcal{U}$(0.9,1.8) \\
 & Dist. (m) & $\mathcal{U}$(0.66,2) \\
 & $\theta$ & $\mathcal{U}$(0,2$\pi$) \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Shaoxiang Dang
Tetsuya Matsumoto
Yoshinori Takeuchi
Hiroaki Kudo

"U-Mamba-Net: A highly efficient Mamba-based U-net style network for noisy and reverberant speech separation" by Shaoxiang Dang, Tetsuya Matsumoto, Yoshinori Takeuchi, and Hiroaki Kudo, arXiv:2412.18217v1, 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{Comparison of mean Sharpe Ratios across methods for $S\&P$ 500 data.}
\begin{tabular}{cc}
 \toprule
 Method & Mean of Sharpe Ratio ($\uparrow$)\\
 \midrule
 Merton & $0.015 \pm 0.301$ \\
 Bayesian with no ambiguity (prior 1) & $0.493 \pm 0.281$ \\
 Bayesian with no ambiguity (prior 2) & $0.655 \pm 0.282$ \\
 DRC (prior 1) & $-0.220 \pm 0.292$ \\
 DRC (prior 2) & $-0.237 \pm 0.293$ \\
 DRBC (prior 1) & $\mathbf{0.818 \pm 0.308}$ \\
 DRBC (prior 2) & $\mathbf{1.147 \pm 0.311}$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

Jose Blanchet
Jiayi Cheng
Hao Liu
Yang Liu

"Duality and Policy Evaluation in Distributionally Robust Bayesian Diffusion Control" by Jose Blanchet, Jiayi Cheng, Hao Liu, and Yang Liu, arXiv:2506.19294v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 & \\
 RG-2 & Rank candidate sector universes against each other using entirely objective criteria. \\
 & \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Learned Sectors: A fundamentals-driven sector reclassification project" by Rukmal Weerawarana, Yiyi Zhu, and Yuzhen He, arXiv:1906.03935v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Prolate matrices used for numerical experiments and their properties.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
		\hline
		$\alpha$ & $\kappa_\infty(A)$ & $\kappa_2(A)$ \\ \hline
		0.475 & 1.21E+06 & 3.60E+05 \\
		0.47 & 2.63E+07 & 7.60E+06 \\
		0.467 & 1.68E+08 & 4.79E+07 \\
		0.455 & 2.91E+11 & 8.04E+10 \\
		0.45 & 6.64E+12 & 1.82E+12 \\
		0.4468 & 4.98E+13 & 1.35E+13 \\
		0.44 & 3.41E+15 & 9.07E+14 \\
		0.434 & 3.44E+16 & 8.84E+15 \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{rrr}
 \hline
 Scenario& Cost of outside option & Maximum quantity\\ \hline
 No Imports& 100,000 EUR MWh$_{el}^{-1}$ & $\infty$ \\
 Constrained Imports & 250 EUR MWh$_{H2}^{-1}$ & 5.5 GWh$_{H2}/h$ \\
 Unlimited Imports & 250 EUR MWh$_{H2}^{-1}$ & $\infty$ \\ \hline
 \end{tabular}
\caption{\textit{Scenarios }}
\end{table}
\end{document}

q-fin

"On long-duration storage, weather uncertainty and limited foresight" by Felix Schmidt, arXiv:2505.12538v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison between our proposed method and other denoising methods.}
\begin{tabular}{lccc}
 \hline
 \textbf{Methods} & \textbf{PSNR} & \textbf{SSIM} & \textbf{$\sigma_{\text{image}}$} \\
 \hline
 \hline
 Noisy & 30.94 & 0.64 & 0.057 \\
 Gaussian filter & 35.48 & 0.815 & 0.041 \\ %
 BM3D & 35.49 & 0.801 & \textbf{0.038} \\
 Gurprem et al. & 33.50 & 0.781 & \textbf{0.038} \\
 DNCNN & 36.08 & 0.840 & 0.041 \\
 ResNet (ours) & \textbf{36.18} & \textbf{0.841} & 0.040 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Dominik Eckert
Sulaiman Vesal
Ludwig Ritschl
Steffen Kappler
Andreas Maier

"Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation" by Dominik Eckert, Sulaiman Vesal, Ludwig Ritschl, Steffen Kappler, and Andreas Maier, arXiv:1912.05240v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccccc}
\toprule
\multirow{2}{*}{\textbf{Model}} & \multirow{2}{*}{\textbf{Real}} & \multirow{2}{*}{\textbf{StyleGAN}} & \multirow{2}{*}{\textbf{LDM}} & \multicolumn{5}{c}{\textbf{Pre-trained}} \\
\cmidrule(l){5-9}
 & & & & \textbf{Accuracy} & \textbf{F1 Score} & \textbf{Precision} & \textbf{Recall} & \textbf{MCC} \\
\midrule
\multirow{2}{*}{\textbf{VGG}} & 0 & 5000 & 5000 & \textbf{93\%} & \textbf{0.93} & \textbf{0.93} & \textbf{0.95} & \textbf{0.92} \\
 & 1000 & 5000 & 5000 & \textbf{97\%} & \textbf{0.97} & \textbf{0.97} & \textbf{0.97} & \textbf{0.96} \\
\midrule
\multirow{2}{*}{\textbf{ResNet}} & 0 & 1000 & 1000 & 89\% & 0.89 & 0.90 & 0.89 & 0.87 \\
 & 1000 & 4000 & 4000 & \textbf{97\%} & \textbf{0.97} & \textbf{0.97} & \textbf{0.97} & \textbf{0.96} \\
\midrule
\multirow{2}{*}{\textbf{ViT}} & 0 & 2000 & 2000 & 91\% & 0.91 & 0.91 & 0.91 & 0.89 \\
 & 1000 & 4000 & 4000 & 93\% & 0.93 & 0.94 & 0.93 & 0.92 \\
\bottomrule
\end{tabular}
\caption{Best performance results for each of the three fine-tuned classification models—VGG, ResNet, and ViT—under two scenarios: training with only synthetic data and training with a combination of synthetic and real data. The table also shows the corresponding combinations of synthetic and real data used for training.}
\end{table}
\end{document}

eess

Oriana Presacan
Alexandru Dorobantiu
Vajira Thambawita
Michael A. Riegler
Mette H. Stensen
Mario Iliceto
Alexandru C. Aldea
Akriti Sharma

"Merging synthetic and real embryo data for advanced AI predictions" by Oriana Presacan, Alexandru Dorobantiu, Vajira Thambawita, Michael A. Riegler, Mette H. Stensen, Mario Iliceto, Alexandru C. Aldea, and Akriti Sharma, arXiv:2412.01255v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Bank distress in the news: Describing events through deep learning" by Samuel Rönnqvist and Peter Sarlin, arXiv:1603.05670v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Classification results ($\times 100$) comparison on two imbalanced datasets with F1-score as the utility metric. The best rank is highlighted in bold.}
\begin{tabular}{c|cccc|cccc}
 \toprule
 & \multicolumn{4}{c|}{ UCI Adult} & \multicolumn{4}{c}{CelebA}\\
 & Utility$\uparrow$& WU $\uparrow$ & MUD $\downarrow$& TUD $\downarrow$ & Utility$\uparrow$& WU $\uparrow$ & MUD $\downarrow$& TUD $\downarrow$\\
 \midrule
 ERM &75.02& 72.17& 6.87& 8.88 &91.40&70.17 & 19.39 &22.82\\
 DRO & 36.27& 16.06 &23.59 &41.17 & 77.52&74.29 &\textbf{3.9} & \textbf{4.78}\\
 ARL & 74.90&71.85 &7.32 &9.49 & 91.60&70.39 &20.14 &24.33\\
 VFair &\textbf{75.98}&\textbf{72.74} &\textbf{5.82} &\textbf{7.40} & \textbf{91.91} & \textbf{75.70} & 14.39 &18.50\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Xuanqian Wang
Jing Li
Ivor W. Tsang
Yew-Soon Ong

"Towards Harmless Rawlsian Fairness Regardless of Demographic Prior" by Xuanqian Wang, Jing Li, Ivor W. Tsang, and Yew-Soon Ong, arXiv:2411.02467v2, 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{\textbf{Hyperparameters.}}
\begin{tabular}{lll}
 \toprule
 \textbf{Hyperparameter} & \textbf{Offline} & \textbf{Online} \\
 \midrule
 \multicolumn{3}{l}{\textbf{Training}} \\
 Nonlinearity & GELU & GELU \\
 Batch size & 2048 & 512 \\
 Trajectory-segment length & 8 & 8 \\
 Dropout & 0.10 & 0.10 \\
 Learning rate & 0.0001 & 0.0001 \\
 Weight decay & 0.005 & 0.005 \\
 Target entropy \(\beta\) & -3 & -3 \\
 Scheduler & cosine decay & - \\
 Warmup steps & 40000 & - \\
 Training steps & 140000 & - \\
 \midrule
 \multicolumn{3}{l}{\textbf{Evaluation}} \\
 Context length & 4 & 4\\
 \midrule
 \multicolumn{3}{l}{\textbf{Bidirectional Transformer}} \\
 \# of Encoder Layers & 2 & 2 \\
 \# of Decoder Layers & 1 & 1 \\
 \# Heads & 4 & 4 \\
 Embedding Dim & 512 & 512 \\
 \midrule
 \multicolumn{3}{l}{\textbf{Mode Decoding Head}} \\
 Number of Layers & 2 & 2 \\
 Embedding Dim & 512 & 512 \\
 \midrule
 \multicolumn{3}{l}{\textbf{Reward Maximization}}\\
 Decay Parameter \(\lambda\) & 0.6 & 0.6 \\
 Candidate Number \(N\) & 625 & 625\\
 Softmax temperature \(\xi\) & 1.0 & 1.0 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"M$^3$PC: Test-time Model Predictive Control for Pretrained Masked Trajectory Model" by Kehan Wen, Yutong Hu, Yao Mu, and Lei Ke, arXiv:2412.05675v2, 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}{ccccc}
 \toprule
 Method & Algorithm & Flops per iteration & Iteration number & Memory\\
 \midrule
 CLIME & Interior-point method & $O(p^3)$ & $O(\sqrt{p}\log(1/\epsilon))$ & $\Omega(p^2)$ \\
 QUIC & Proximal Newton& $O(p^3)$ & $O(\log\log(1/\epsilon))^\dagger$ & $\Omega(p^2)$\\
 ACCORD & Proximal gradient& $O(np^2)^*$ & $O(\log(1/\epsilon))$ & $\Omega(p^2)^*$\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Sungdong Lee
Joshua Bang
Youngrae Kim
Hyungwon Choi
Sang-Yun Oh
Joong-Ho Won

"Learning Massive-scale Partial Correlation Networks in Clinical Multi-omics Studies with HP-ACCORD" by Sungdong Lee, Joshua Bang, Youngrae Kim, Hyungwon Choi, Sang-Yun Oh, and Joong-Ho Won, arXiv:2412.11554v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

William N. Caballero
Matthew LaRosa
Alexander Fisher
Vahid Tarokh

"Indiscriminate Disruption of Conditional Inference on Multivariate Gaussians" by William N. Caballero, Matthew LaRosa, Alexander Fisher, and Vahid Tarokh, arXiv:2411.14351v1, 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}{llllll}
 \toprule
 & & \multicolumn{4}{c}{\textbf{Placement}} \\
 \cmidrule(r){3-6}
 &&\multicolumn{2}{c}{\textbf{DR}}& \multicolumn{2}{c}{\textbf{Random}} \\
 \textbf{Measure}&\textbf{Label}& \textbf{$\overline{B} -\overline{L}$} &\textbf{\textit{p-val.}}&\textbf{$\overline{B} -\overline{L}$} &\textbf{\textit{p-val.}}\\ \midrule
 Time &&&&\\
 & Shape & -0.1 & 1.0 & -0.4 & 0.97 \\
 & Color & -2.5 & \textbf{0.05} & 0.8 & 0.82 \\
 & Texture & -1.1 & 0.56 & -3.0 & \textbf{0.05}\\

 Hovered&&&&\\
 samples& Shape & 1.0 & 0.96 & 3.1 & 0.50 \\
 & Color & -1.4 & 0.92 & 5.0 & \textbf{0.05} \\
 & Texture & -1.3 & 0.93 & -2.7 & 0.71\\

 Distance &&&&\\
 & Shape & 71 & 0.99 & -131& 0.98 \\
 & Color & -333 & 0.72 & 272 & 0.82 \\
 & Texture & -150 & 0.95 & -1136 & \textbf{0.02} \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

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


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\toprule
\multicolumn{3}{c}{\textbf{String Representation}} \\ \cmidrule(rl){1-3}
\textbf{IUPAC Condensed} & \multicolumn{2}{c}{Fuc(a1-2)Gal(b1-3){[}Fuc(a1-4){]}GlcNAc(b1-} \\
\textbf{GLYCAM} & \multicolumn{2}{c}{LFucpa1-2DGalpb1-3{[}LFucpa1-4{]}DGlcpNAcb1-OH} \\ \bottomrule \toprule
\multicolumn{2}{c}{\textbf{Database ID}} & \textbf{Glycan Name} \\ \cmidrule(rl){1-2} \cmidrule(rl){3-3}
\textbf{GlyToucan ID} & G00048MO & \multirow{2}{*}{Lewis b} \\
\textbf{PubChem ID} & 45480569 & \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Example of different representations of Glycan \textit{Lewis b}. The table shows various ways to represent the glycan, demonstrating the complexity of glycan reference.}
\end{table}
\end{document}

cs

Xinyi Ni
Qiuyang Wang
Yukun Zhang
Pengyu Hong

"ToolFactory: Automating Tool Generation by Leveraging LLM to Understand REST API Documentations" by Xinyi Ni, Qiuyang Wang, Yukun Zhang, and Pengyu Hong, arXiv:2501.16945v1, 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{Interval coverage degrees for different methods across variance classes (at significance level $\alpha=0.1$). Mean and standard deviation over 20 samples are provided.}
\begin{tabular}{ccccc}
 \toprule
 &Marginal&Low variance&Medium variance&High variance\\
 \midrule
 $A_\text{res}$&$0.905\pm0.008$&$0.951\pm0.010$&$0.904\pm0.017$&$0.856\pm0.018$\\
 $A_\sigma$&$0.902\pm0.008$&$0.905\pm0.018$&$0.902\pm0.015$&$0.898\pm0.017$\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Nicolas Dewolf
Bernard De Baets
Willem Waegeman

"Conditional validity of heteroskedastic conformal regression" by Nicolas Dewolf, Bernard De Baets, and Willem Waegeman, arXiv:2309.08313v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Yue Shi
Liangxiu Han
Darren Dancy
Lianghao Han

"WaveDiffUR: A diffusion SDE-based solver for ultra magnification super-resolution in remote sensing images" by Yue Shi, Liangxiu Han, Darren Dancy, and Lianghao Han, arXiv:2412.18996v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Component usage in each broker strategies}
\begin{tabular}{l|cc|ccc}
 \hline
 & \multicolumn{2}{c}{\textbf{Resource pool}} \vline& \multicolumn{3}{c}{\textbf{Optimisation}}\\\cline{2-6}
 \textbf{System} & \textbf{Reserved} & \textbf{On-demand} & \textbf{Risk} & \textbf{Adaptive Risk} & \textbf{Prediction}\\
 \hline
 Risk-taking & \checkmark & \checkmark&\checkmark &\checkmark &\\
 \hline
 No risk adjustment & \checkmark & \checkmark & \checkmark& &\\
 \hline
 Auto-ARIMA & \checkmark & \checkmark & & & \checkmark \\
 \hline
 Pure reserved & \checkmark & & & &\\
 \hline
 Best case* & \checkmark & \checkmark & & & Exact \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"A Risk-taking Broker Model to Optimise User Requests placement on On-demand and Contract VMs" by Chalee Boonprasop, Yuhui Lin, and Adam Barker, arXiv:2103.07133v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Analysis of skin lesion images with deep learning" by Josef Steppan and Sten Hanke, arXiv:2101.03814v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|}
		\hline
		Method & Refinement & TWIST & Tucker & M3-SC \\ \hline
		Error & \textbf{0.395} & 0.444 & 0.435 & 0.492 \\ \hline
	\end{tabular}
\caption{Clustering error of COBRE data.}
\end{table}
\end{document}

stat

"Optimal Clustering of Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer Networks" by Zhongyuan Lyu, Ting Li, and Dong Xia, arXiv:2311.15598v1, 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{Run time (in seconds) for different applications, averaged over images in Set12, on an NVIDIA RTX 8000 GPU.}
\begin{tabular}{lllll}
 \toprule
 Inpainting $30\times 30$ & Missing pixels 10\% & SISR 4:1 & Deblurring 10\% & CS 10\%\\
 \midrule
 Inpainting $30\times 30$ & 7.8 & SISR 4:1 & 8.4 & \\
 \bottomrule
 \end{tabular}
\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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
		\hline
		Color & Description & Range\\
		\hline \hline
		Dark red & highly locally isotropic & 0.8-1.0 \\
		Red & moderately locally isotropic & 0.6-0.8 \\
		Light red & poorly locally isotropic & 0.0-0.6 \\
		White & region not considered & -\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Debarghya Pattanayak
Arindam Mishra
Syamal K. Dana
Nandadulal Bairagi

"Multimodal distribution of transient time of predator extinction in a three-species food chain" by Debarghya Pattanayak, Arindam Mishra, Syamal K. Dana, and Nandadulal Bairagi, arXiv:2202.13294v1, 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 & 2.03E$-5$ & $-$ & 3.57E$-4$ & $-$ \\
 \hline
 80 & 6.56E$-7$ & 4.95 & 1.47E$-5$ & 4.60 \\
 \hline
 160 & 1.36E$-8$ & 5.59 & 2.81E$-7$ & 5.71 \\
 \hline
 320 & 2.82E$-10$ & 5.58 & 9.16E$-9$ & 4.94 \\
 \hline
 640 & 7.58E$-12$ & 5.22 & 2.76E$-10$ & 5.05 \\
 \hline
 1280 & 2.23E$-13$ & 5.09 & 8.30E$-12$ & 5.06 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Error table for Burgers equation, $t=0.3$. IW.}
\end{table}
\end{document}

cs

"High Order Weighted Extrapolation for Boundary Conditions for Finite Difference Methods on Complex Domains with Cartesian Meshes" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2501.17106v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"On "A General Framework for Pricing Asian Options Under Markov Processes"" by Zhenyu Cui, Chihoon Lee, and Yanchu Liu, arXiv:1601.05306v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc} %TODO:larger or smaller
 \toprule
 & PD(\%) & DD(\%) & PPL & MD \\
 \hline
 Baseline & 38.2 & 52.0 & 3.27 & 2.92 \\
 Baseline + SC & 43.6 & 55.7 & 3.60 & 2.63 \\
 Baseline + SC + TC & 43.5 & 57.0 & 3.72 & 2.79 \\ % update:43.5 57.0, 2.79
 \hline
 Pretrain & 56.1 & 65.2 & 2.07 & 2.36 \\
 Pretrain + SC & 56.8 & 65.8 & 2.48 & 2.28\\
 Pretrain + SC + TC & 55.0 & 65.9 & 2.41 & 2.28 \\ % update:55.0 2.28
 Pretrain + SC + TC w/o sup & 47.2 & 53.6 & 2.92 & 3.29\\ % ???
 \bottomrule
\end{tabular}
\caption{Main Results of Melody Generation.}
\end{table}
\end{document}

eess

Zhonghao Sheng
Kaitao Song
Xu Tan
Yi Ren
Wei Ye
Shikun Zhang
Tao Qin

"SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint" by Zhonghao Sheng, Kaitao Song, Xu Tan, Yi Ren, Wei Ye, Shikun Zhang, and Tao Qin, arXiv:2012.05168v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The total number of species, the occurrence number in PO data and the total number of species presence in PA data for each region and biological group}
\begin{tabular}{llllll}
 \toprule
 \textbf{Code} & \textbf{Location}& \textbf{Biological Group} & \textbf{Species number} & \multicolumn{2}{c}{\textbf{Occurrences number}} \\
 & & & & PO & PA \\
\midrule
AWT & Australian wet tropics & bird & 20 & 3105 & 340 \\
AWT & Australian wet tropics & plant & 20 & 701 & 102 \\
CAN & Ontario, Canada & bird & 20 & 5063 & 14571 \\
NSW & New South Wales & bates & 10 & 187 & 570 \\
NSW & New South Wales & birds & 7 & 1781 & 1839 \\
NSW & New South Wales & plants & 29 & 680 & 5329 \\
NSW & New South Wales & reptile & 8 & 675 & 1008 \\
NZ & New Zealand & plant & 52 & 3088 & 19120 \\
SA & South America & plant & 30 & 2220 & 152 \\
SWI & Switzerland & tree & 30 & 35105 & 10013 \\
\end{tabular}
\end{table}
\end{document}

stat

Maxime Ryckewaert
Diego Marcos
Christophe Botella
Maximilien Servajean
Pierre Bonnet
Alexis Joly

"Applying the maximum entropy principle to neural networks enhances multi-species distribution models" by Maxime Ryckewaert, Diego Marcos, Christophe Botella, Maximilien Servajean, Pierre Bonnet, and Alexis Joly, arXiv:2412.19217v3, 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{Swap Rates}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
Swap Tenor & 1y & 2y & 3y & 5y & 7y & 10y & 30y \\
 \hline
Swap Rate & 0.515\% & 0.409\% & 0.401\% & 0.470\% & 0.569\% & 0.691\% & 0.855\% \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk" by Derek Singh and Shuzhong Zhang, arXiv:1910.01781v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Calculated and simulated performance indices of EMPC in group 2 of Case 1}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \hline
 & & 3$\boldsymbol\sigma$ zone & 4$\boldsymbol\sigma$ zone & 5$\boldsymbol\sigma$ zone \\
 \hline
 \multirow{2}{*}{$\bar J_{\rm ep}$} & Cal & 197.9753 & 198.9069 & 198.9312 \\
 & Sim & & 199.8444 & \\
 \multirow{2}{*}{$\bar J_{\rm tp}$} & Cal & 6.7851e-03 & 7.0437e-03 & 7.0418e-03 \\
 & Sim & & 7.1578e-03 & \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Jiangbang Liu
Song Bo
Benjamin Decardi-Nelson
Jinfeng Liu
Jingtao Hu
Tao Zou

"Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise" by Jiangbang Liu, Song Bo, Benjamin Decardi-Nelson, Jinfeng Liu, Jingtao Hu, and Tao Zou, arXiv:2201.01828v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\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
 \# drones & $\mathbb{E}(\mathcal{D})$ & std($\mathcal{D}$) & scaled cost & time (s) \\
 \hline
 \hline
 32 & 1.001 & 0.00390 & 85.84 & 1905\\
 64 & 0.986 & 0.00347 & 100.43 & 2214\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{\textbf{The results for the path planning problem with $32$ and $64$ drones in a room.} We show the expected value and standard deviation of the minimal normalized distances. The collision does not happen for 32 drones, but it may happen for 64 or more drones. Therefore, we need to put a safe zone near each drone to avoid collision when the number of drones is large. The computation time increases only by 309 seconds when scaling from 32 to 64 drones. }
\end{table}
\end{document}

math

Tingwei Meng
Zhen Zhang
Jérôme Darbon
George Em Karniadakis

"SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems" by Tingwei Meng, Zhen Zhang, Jérôme Darbon, and George Em Karniadakis, arXiv:2201.05475v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Different types of generating function and the associated Toeplitz matrix.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|ll}
			\toprule
			\multicolumn{2}{c|}{\textbf{Type of generating function}} & \multicolumn{2}{c}{\textbf{Associated Toeplitz matrix}}\\
			\midrule
			univariate scalar & $f(\theta):[-\pi,\pi]\to \mathbb{C}$ & unilevel scalar & $T_{n}(f)\in \mathbb{C}^{n\times n}$\\
			$d$-variate scalar & $f(\boldsymbol{\theta}):[-\pi,\pi]^d\to \mathbb{C}$ & $d$-level scalar & $T_{\mathbf{n}}(f)\in \mathbb{C}^{d(\mathbf{n},1)\times d(\mathbf{n},1)}$\\
			univariate matrix-valued & $\mathbf{f}(\theta):[-\pi,\pi]\to \mathbb{C}^{s\times s}$& unilevel block & $T_{n}(\mathbf{f})\in \mathbb{C}^{d({n},s)\times d({n},s)}$\\
			$d$-variate matrix-valued & $\mathbf{f}(\boldsymbol{\theta}):[-\pi,\pi]^d\to \mathbb{C}^{s\times s}$ & $d$-level block & $T_{\mathbf{n}}(\mathbf{f})\in \mathbb{C}^{d(\mathbf{n},s)\times d(\mathbf{n},s)}$\\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Matthias Bolten
Sven-Erik Ekström
Isabella Furci
Stefano Serra-Capizzano

"A note on the spectral analysis of matrix sequences via GLT momentary symbols: from all-at-once solution of parabolic problems to distributed fractional order matrices" by Matthias Bolten, Sven-Erik Ekström, Isabella Furci, and Stefano Serra-Capizzano, arXiv:2202.05555v1, 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{Quantitative results of the CHS-Net on the test set generated from fused dataset.}
\begin{tabular}{l||llllll}
			\hline
			Segmentation & Acc. & Pre. & Spe. & Rec. & DC & JI \\ \hline\hline
			Lungs & 0.991 & 0.975 & 0.998 & 0.950 & 0.963 & 0.947 \\ \hline
			Infection & 0.965 & 0.756 & 0.969 & 0.885 & 0.816 & 0.791 \\ \hline
		\end{tabular}
\end{table}
\end{document}

eess

"CHS-Net: A Deep learning approach for hierarchical segmentation of COVID-19 infected CT images" by Narinder Singh Punn and Sonali Agarwal, arXiv:2012.07079v7, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Zhikai Yao
Jennie Si
Ruofan Wu
Jianyong Yao

"Toward Reliable Designs of Data-Driven Reinforcement Learning Tracking Control for Euler-Lagrange Systems" by Zhikai Yao, Jennie Si, Ruofan Wu, and Jianyong Yao, arXiv:2101.00068v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Toy example 2}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
Mean & Variance & $x^* (\phi=0.01)$ & $L_i$ & OK to drop & RSC\\
$ \begin{bmatrix}
0 \\ 0 \\ 0 \\
\end{bmatrix} $
&
$ \begin{bmatrix}
2 & 0 & 0 \\ 0 & 2 & 1 \\ 0 & 1 & 2 \\
\end{bmatrix} $
&
$\begin{bmatrix}
0.4355 \\ 0.2823 \\ 0.2823 \\
\end{bmatrix}$
&
$\begin{bmatrix}
1.5556 \\ 0.8889 \\ 0.8889 \\
\end{bmatrix}$
&
$\begin{bmatrix}
No \\ Yes \\ Yes \\
\end{bmatrix}$
&
$\begin{bmatrix}
0.5431 \\ 0.2661 \\ 0.2661 \\
\end{bmatrix}$
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Caihua Chen
Xindan Li
Caleb Tolman
Suyang Wang
Yinyu Ye

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


\documentclass{article}
\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
& \multicolumn{2}{|c|}{\textbf{7B}} & \multicolumn{2}{|c|}{\textbf{13B}} \\
\hline
& \textbf{basic} & \textbf{quant.} & \textbf{basic} & \textbf{quant.} \\
\hline
\textbf{0.001} & 0\% & 9\% & 17\% & 55\% \\
\hline
\textbf{0.0001} & 7\% & 0\% & 10\% & 20\% \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Percentage of fine-tuned models that outperform the pre-trained version on SD MCQs with models fine-tuned with different learning rates.}
\end{table}
\end{document}

cs

Bianca Raimondi
Saverio Giallorenzo
Maurizio Gabbrielli

"Affordably Fine-tuned LLMs Provide Better Answers to Course-specific MCQs" by Bianca Raimondi, Saverio Giallorenzo, and Maurizio Gabbrielli, arXiv:2501.05891v2, 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}{rr}
 \hline
 & value \\
 \hline
Accuracy & 0.81 \\
 Kappa & 0.50 \\
 Sensitivity (No burn accuracy) & 0.50 \\
 Specificity (Burn accuracy) & 0.94 \\
 \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Kendra Walker
Ben Moscona
Kelsey Jack
Seema Jayachandran
Namrata Kala
Rohini Pande
Jiani Xue
Marshall Burke

"Detecting Crop Burning in India using Satellite Data" by Kendra Walker, Ben Moscona, Kelsey Jack, Seema Jayachandran, Namrata Kala, Rohini Pande, Jiani Xue, and Marshall Burke, arXiv:2209.10148v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.