\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The result of lock box approach. The training result on the hyperparameter optimization set and the testing result on the lock box set.}
\begin{tabular}{|c|c|c|}
\hline
Modality & Train (\%) & Test (\%) \\ \hline
\multicolumn{3}{|c|}{EEG Signals (DEAP)} \\ \hline
Alpha & 89.70 & 89.18 \\ \hline
Beta & 89.92 & 90.41 \\ \hline
Gamma & 73.94 & 73.83 \\ \hline
Theta & 81.15 & 81.03 \\ \hline
EEG\# & 93.82 & 93.96 \\ \hline
\multicolumn{3}{|c|}{Peripheral Physiological Signals (DEAP)} \\ \hline
hEOG & 73.26 & 73.03 \\ \hline
vEOG & 85.92 & 85.73 \\ \hline
zEMG & 73.50 & 72.54 \\ \hline
tEMG & 84.87 & 84.27 \\ \hline
GSR & 73.15 & 71.39 \\ \hline
Respiration belt & 69.23 & 68.45 \\ \hline
Plethysmograph & 74.03 & 74.08 \\ \hline
Temperature & 51.98 & 52.04 \\ \hline
\multicolumn{3}{|c|}{EEG Signals (AMIGOS)} \\ \hline
Alpha & 81.21 & 81.46 \\ \hline
Beta & 87.32 & 87.62 \\ \hline
Gamma & 85.64 & 84.79 \\ \hline
Theta & 77.35 & 76.87 \\ \hline
EEG\# & 96.50 & 96.14 \\ \hline
\multicolumn{3}{|c|}{Peripheral Physiological Signals (AMIGOS)} \\ \hline
ECG Right & 99.67 & 99.75 \\ \hline
ECG Left & 99.48 & 99.31 \\ \hline
GSR & 85.64 & 86.82 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Yuxuan Zhao
Xinyan Cao
Jinlong Lin
Dunshan Yu
Xixin Cao

"Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model" by Yuxuan Zhao, Xinyan Cao, Jinlong Lin, Dunshan Yu, and Xixin Cao, arXiv:1911.12918v2, 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{Hyperparameter for experiments in the real world.}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Hyperparameter} & Greedy & CP-SAT \\ \hline
Learning Rate ($\eta$) & $0.01$ & $0.01$ \\ \hline
Exploration parameter ($\alpha$) & $0.11$ & $0.1$ \\ \hline
Number of vantage points ($k$) & $25$ & $25$ \\ \hline
Alpha planner ($\alpha_p$) & $0.49$ & - \\ \hline
Map Resolution & $75$ & $75$ \\ \hline
Positional Embedding Size & $20$ & $30$ \\ \hline
Feature Vector Normalization & $l^2\text{-norm}$ & $l^2\text{-norm}$ \\ \hline
Sigmoid Scale & $18.38$ & $15.78$ \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Sohan Rudra
Saksham Goel
Anirban Santara
Claudio Gentile
Laurent Perron
Fei Xia
Vikas Sindhwani
Carolina Parada
Gaurav Aggarwal

"A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations" by Sohan Rudra, Saksham Goel, Anirban Santara, Claudio Gentile, Laurent Perron, Fei Xia, Vikas Sindhwani, Carolina Parada, and Gaurav Aggarwal, arXiv:2211.16309v1, 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}{|r|r|r|r|r|r|}%
\hline
$n^3$ & $1024^3$ & $2048^3$ & $4096^3$ & $8192^3$ & $16384^3$\\
 \hline
FFT$_3$ & $\sim 6000$ & --&--& -- & $\sim 2$ years\\
 \hline
$C\ast C$ & $8.8$ & $20.0$ & $61.0 $ & $157.5 $ & $ 299.2 $\\
\hline
{\footnotesize C2T } & $6.9$ & $10.9$ & $20.0$ & $37.9$ & $86.0$\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Times (sec) for the C2T transform and the 3D tensor product convolution vs. 3D FFT convolution.}
\end{table}
\end{document}

math

"Reduced Higher Order SVD: ubiquitous rank-reduction method in tensor-based scientific computing" by Venera Khoromskaia and Boris N. Khoromskij, arXiv:2201.12663v1, 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{Created Multiview Mic-Array and RGBD Dataset Summary w.r.t. each Physical Object Category.}
\begin{tabular}{l|cccc}
 \toprule
 Object & Texture-homo. & Texture-disc. & Source Num. & View Num.\\
 \midrule
 wall & 975 & 717 & 1-5 & 4\\
 ceiling & 727 & 614 & 1-5 & 4 \\
 table & 464 & 461 & 1-5 & 4\\
 door & 712 & 702 & 1-5 & 4\\
 cabinet & 286 & 292 & 1-5 & 4\\
 chair & 100 & 222 & 1-5 & 4\\
 \midrule
 sum & 3264 & 3008 & / & / \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Yuhang He
Sangyun Shin
Anoop Cherian
Niki Trigoni
Andrew Markham

"SoundLoc3D: Invisible 3D Sound Source Localization and Classification Using a Multimodal RGB-D Acoustic Camera" by Yuhang He, Sangyun Shin, Anoop Cherian, Niki Trigoni, and Andrew Markham, arXiv:2412.16861v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 \rowcolor[HTML]{EEEEEE}
 {\color[HTML]{000000} \textbf{Reward}} & {\color[HTML]{000000} \textbf{LunarLander-V2}} & {\color[HTML]{000000} \textbf{CartPole-Reacher}} & {\color[HTML]{000000} \textbf{UR5-Reacher}} & {\color[HTML]{000000} \textbf{ViZDoom-Seen}} & {\color[HTML]{000000} \textbf{ViZDoom-Unseen}} & {\color[HTML]{000000} \textbf{Cross Maze}} & {\color[HTML]{000000} \textbf{Toddler Playroom Maze}} \\ \hline
 \textbf{Sparse} & $||s-g||_2 < 1$ & $||s-g||_2 < 0.02$ & $||s-g||_2 < 0.02$ & $||s-g||_2 < 0.0075$ & $||s-g||_2 < 0.0075$ & $||s-g||_1 < 2$ & $||s-g||_1 < 2$ \\ \hline
 \textbf{Dense} & $\gamma \Phi(s_{t+1}) - \Phi(s_t)< 0.3$ & \multicolumn{2}{c|}{ $\gamma \Phi(s_{t+1}) - \Phi(s_t)<1$ } & \multicolumn{2}{c|}{ $\gamma \Phi(s_{t+1}) - \Phi(s_t)<0.14$ } & \multicolumn{2}{c|}{ $\gamma \Phi(s_{t+1}) - \Phi(s_t)< 5$ } \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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

"From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning" by Junseok Park, Hyeonseo Yang, Min Whoo Lee, Won-Seok Choi, Minsu Lee, and Byoung-Tak Zhang, arXiv:2501.17842v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Relative Accuracy Improvement for Different $(\alpha, \omega)$ Values in TPO Objective}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\toprule
$(\alpha, \omega)$ & Relative Accuracy Improvement \\
\midrule
(1.0, 0.0) & 13.23\% \\
(0.9, 0.1) & 14.29\% \\
(0.8, 0.2) & 14.77\% \\
(0.7, 0.3) & 13.76\% \\
(0.6, 0.4) & 11.40\% \\
(0.5, 0.5) & 10.84\% \\
(0.4, 0.6) & 9.89\% \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yongzhi Qi
Hao Hu
Dazhou Lei
Jianshen Zhang
Zhengxin Shi
Yulin Huang
Zhengyu Chen
Xiaoming Lin
Zuo-Jun Max Shen

"TimeHF: Billion-Scale Time Series Models Guided by Human Feedback" by Yongzhi Qi, Hao Hu, Dazhou Lei, Jianshen Zhang, Zhengxin Shi, Yulin Huang, Zhengyu Chen, Xiaoming Lin, and Zuo-Jun Max Shen, arXiv:2501.15942v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{arydshln}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Design and properties of the tether-net system}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
			\toprule
			Variable & Description & Value \\
			\midrule
			$L$ & Side Length of Net & 22.0 m \\
			$L_{\texttt{mesh}}$ & Side Length of Mesh & 1.375 m \\
 $r_{\texttt{n}}$ & Thread Radius & 6 mm \\
 $m_{\texttt{n}}$ & Total Mass of Net & 100 kg \\
 $m_c$ & Mass of Corner Mass & 10.0 kg \\
			\hdashline
			$\mathbf{V}_{\texttt{eb}}$ & Launching Velocity & $[3.30, 3.54, 7.16]$ m/s \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Chen Zeng
Grant Hecht
Prajit KrisshnaKumar
Raj K. Shah
Souma Chowdhury
Eleonora M. Botta

"Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System" by Chen Zeng, Grant Hecht, Prajit KrisshnaKumar, Raj K. Shah, Souma Chowdhury, and Eleonora M. Botta, arXiv:2201.04180v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Pre-filtering parameters for the texture images; again the feature maps are obtained by summing all filtered images. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|r|}
 \toprule
 Region & $\phi_1$ & $\phi_2$ & $\phi_3$ & $\phi_4$ \\
 \midrule
Brodatz 3 &
$n=2,3 \colon (0,\frac{2^{n+1/2}}{256})$ &
$n=4,5 \colon (\frac{\pi}{4}, \frac{2^{n+1/2}}{256}) $ &
 $n=1,2 \colon \frac{\pi}{4}, \frac{2^{n+1/2}}{256}) $ & (none)
 \\
& $ n=4 \colon (\frac{\pi}{4}, \frac{2^{n+1/2}}{256})$
& $ n=3,5 \colon (\frac{3\pi}{4}, \frac{2^{n+1/2}}{256})$ &
 $ n=2 \colon (\frac{\pi}{2}, \frac{2^{n+1/2}}{256}) $ &
\\
 & $n=4 \colon (\frac{3\pi}{4}, \frac{2^{n+1/2}}{256})$
 & $ n=1 \colon (\frac{3\pi}{4}, \frac{2^{n+1/2}}{256})$ & &\\
 \midrule
 Brodatz 5 &
 $n=4 \colon (0,\frac{2^{n+1/2}}{256})$ &
 $n=4 \colon (0,\frac{2^{n+1/2}}{256})$ &
 $n=1,2 \colon (0, \frac{2^{n+1/2}}{256})$ &
 $1- \phi_{1}$
 \\ &
 $n = 4 \colon (\frac{\pi}{4}, \frac{2^{n+1/2}}{256})$ &
 $n = 3,4 \colon (\frac{\pi}{4}, \frac{2^{n+1/2}}{256})$ &
 $n = 2 \colon (\frac{\pi}{4}, \frac{2^{n+1/2}}{256})$ &
 $- \phi_{2}$
 \\
 &
 $n=4 \colon (\frac{\pi}{2}, \frac{2^{n+1/2}}{256})$ &
 $n=3 \colon (\frac{3\pi}{4}, \frac{2^{n+1/2}}{256})$ &
 &
 $- \phi_{3}$ \\
 & $n=4 \colon (\frac{3\pi}{4}, \frac{2^{n+1/2}}{256})$
 & & & \\
 \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{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Node classification accuracy (\%) on Pubmed under Gaussian noise settings (Best results highlighted by bold).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc||c|cccc|}
\hline
 Noise (\%) & Clean & 5 & 10 & 20 & & Clean & 5 & 10 & 20\\
\hline
Spectral
& 73.9 & 37.4$\pm$3.5 & 36.7$\pm$3.2 & 36.5$\pm$3.1 & GWNN & 79.1 & 20.9$\pm$0.4 & 19.6$\pm$0.3 & 19.6$\pm$0.2 \\
ChebyNet
& 74.4 & 37.6$\pm$3.3 & 35.7$\pm$2.8 & 36.8$\pm$3.0 &UFG
& 79.9 & 43.3$\pm$2.1 & 42.9$\pm$1.5 & 41.3$\pm$1.9 \\
H2GCN & 78.8 & 32.4\(\pm\)2.6 & 32.2\(\pm\)3.6 &31.3\(\pm\)4.4 &
GPRGNN & 79.7 & 37.2\(\pm\)3.1 & 36.8\(\pm\)2.9 & 38.1\(\pm\)4.5\\
GCN
& 79.0 & \textbf{44.1$\pm$1.4} & 42.6$\pm$1.4 & 41.1$\pm$2.7 & QUFG & \textbf{80.6}
& 43.6$\pm$2.9 & \textbf{43.8$\pm$1.7} & \textbf{41.5$\pm$2.2} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mengxi Yang
Dai Shi
Xuebin Zheng
Jie Yin
Junbin Gao

"Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution" by Mengxi Yang, Dai Shi, Xuebin Zheng, Jie Yin, and Junbin Gao, arXiv:2201.04728v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c} % <-- Alignments: 1st column left, 2nd middle and 3rd right, with vertical lines in between
 \textbf{Method} & \textbf{MCD$\downarrow$} & \textbf{GPE$\downarrow$} & \textbf{VDE$\downarrow$} & \textbf{FFE$\downarrow$} \\
 \hline
 BM+P+E & 17.11& 37.37 & 40.85 & 56.43 \\
 BM+SE & 16.49& 33.86 & 36.43 & 46.32 \\
 BM+SE+P & 13.01 & 28.65 & 18.76 & 35.76\\
 FSM-SS & \textbf{10.12}& \textbf{25.34} & \textbf{15.71} & \textbf{30.56} \\

 \end{tabular}
\caption{Ablation Study of FSM-SS on 4 shot approach }
\end{table}
\end{document}

eess

Neeraj Kumar
Srishti Goel
Ankur Narang
Brejesh Lall

"Few Shot Adaptive Normalization Driven Multi-Speaker Speech Synthesis" by Neeraj Kumar, Srishti Goel, Ankur Narang, and Brejesh Lall, arXiv:2012.07252v1, licensed under CC BY-SA 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c||r|r|c|}
\hline
 & $\theta_0$ & $\theta_1$ & $\kappa_0$ & $\kappa_1$ & $L$ & $\alpha_0 \quad$ & $\alpha_1 \quad$ & Bending energy \\
\hline
I & \multirow{4}*{$\frac{\pi}{4}$} & \multirow{4}*{$ \frac{\pi}{4}$} &
\multirow{4}*{$-0.5$} & \multirow{4}*{$0.5$} & \multirow{4}*{$1.5$} &
$1.60884$ & $1.60884$ & $26.2939$\\
\cline{7-9}
II & & & & & & $-1.60884$ & $-1.60884$ & $2928.06$\\
\cline{7-9}
III & & & & & & $-1.31667$ & $1.31667$ & $239.358$\\
\cline{7-9}
IV & & & & & & $1.31667$ & $-1.31667$ & $239.358$\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Data values, solutions $\alpha_0$, $\alpha_1$, and the bending energy of the PH biarcs from Example~. }
\end{table}
\end{document}

math

Marjeta Knez
Francesca Pelosi
Maria Lucia Sampoli

"Construction of $G^2$ planar Hermite interpolants with prescribed arc lengths" by Marjeta Knez, Francesca Pelosi, and Maria Lucia Sampoli, arXiv:2202.11371v1, 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{\textbf{Quantitative results in terms of SSIM average values on synthetic videos ($\sigma = 0 $ ) and their corrupted counterparts ($\sigma = 0.001 $).} The average values of SSIM and the corresponding standard deviations are highlighted. }
\begin{tabular}{c|c|c}\hline
 \multicolumn{3}{c}{\textbf{Average SSIM}} \\
\hline
\textbf{Methods} & $\sigma = 0 $ & $\sigma = 0.001 $ \\
\hline
ESRGAN & 0.9592 $\pm$ 2.2 $\times$ $10^{-3}$ & 0.5781 $\pm$ 1.7 $\times$ $10^{-2}$ \\ \hline
proSRGAN & 0.9833 $\pm$ 7.7 $\times$ $10^{-4}$ & 0.6742 $\pm$ 5.0 $\times$ $10^{-3}$ \\ \hline
proSR & 0.9916 $\pm$ 5.3 $\times$ $10^{-4}$ & 0.9553 $\pm$ 1.4 $\times$ $10^{-4}$ \\ \hline
proSRs & 0.9924 $\pm$ 3.7 $\times$ $10^{-4}$ & 0.9532 $\pm$ 1.7 $\times$ $10^{-4}$ \\ \hline
RCAN & 0.9924 $\pm$ 4.0 $\times$ $10^{-4}$ & 0.9546 $\pm$ 1.7 $\times$ $10^{-4}$ \\ \hline
EDSR & 0.9913 $\pm$ 1.3 $\times$ $10^{-4}$ & 0.9549 $\pm$ 1.9 $\times$ $10^{-4}$ \\ \hline
DPV & 0.9806 $\pm$ 8.9 $\times$ $10^{-4}$ & 0.9765 $\pm$ 1.1 $\times$ $10^{-3}$ \\ \hline
RDPV & 0.9906 $\pm$ 7.3 $\times$ $10^{-4}$ & 0.9886 $\pm$ 1.0 $\times$ $10^{-3}$ \\ \hline
RDPV-TVi & 0.9920 $\pm$ 6.5 $\times$ $10^{-4}$ & 0.9916 $\pm$ 4.2 $\times$ $10^{-4}$ \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Pasquale Cascarano
Maria Colomba Comes
Arianna Mencattini
Maria Carla Parrini
Elena Loli Piccolomini
Eugenio Martinelli

"Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments" by Pasquale Cascarano, Maria Colomba Comes, Arianna Mencattini, Maria Carla Parrini, Elena Loli Piccolomini, and Eugenio Martinelli, arXiv:2011.09855v1, 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
\ & Random & Human & Simulated & Mixed\\
\midrule
 Sparse & $7.38 \pm 1.13$& $8.7 \pm 0.11$ & $\boldsymbol{9.26 \pm 0.07}$ & $8.81 \pm 0.06$ \\
Dense & $38.57 \pm 5.81 $ & $43.49 \pm 0.54$ & $\boldsymbol{46.35 \pm 0.38}$ & $44.03 \pm 0.32$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Ofir Nabati
Guy Tennenholtz
ChihWei Hsu
Moonkyung Ryu
Deepak Ramachandran
Yinlam Chow
Xiang Li
Craig Boutilier

"Preference Adaptive and Sequential Text-to-Image Generation" by Ofir Nabati, Guy Tennenholtz, ChihWei Hsu, Moonkyung Ryu, Deepak Ramachandran, Yinlam Chow, Xiang Li, and Craig Boutilier, arXiv:2412.10419v2, 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}{l|ccc}
		\toprule
		& \multicolumn{3}{|c}{\textbf{CPU time per card.cycle}}\\
		\midrule
		\multirow{2}{*}{\textbf{Test case}} & \textbf{1D sim.} & \textbf{0D-NL sim.} & \textbf{0D-L sim.}\\
		& & \textbf{(speed-up)} & \textbf{(speed-up)}\\
		\toprule
		Aortic bifurcation & 51.251 & 0.400 (128.096) & 0.332 (154.179) \\
		37-artery network & 6245.066 & 27.010 (231.215) & 19.566 (339.424) 19.923 (313.455) \\
		ADAN56 model & 6492.655 & 84.887 (76.486) & 78.486 (82.724) \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\caption{Comparison between mean CPU times per cardiac cycle (in seconds) of the 1D, nonlinear 0D (0D-NL) and linear 0D (0D-L) simulations for the aortic bifurcation model, the 37-artery network and ADAN56 model. The speed-up gained in the 0D simulation (either linear or nonlinear) with respect to the 1D simulation is also reported between brackets, measured as the ratio CPUtime1D/CPUtime0D.}
\end{table}
\end{document}

math

Beatrice Ghitti
Eleuterio F. Toro
Lucas O. Müller

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}%
			\textbf{Study} & \textbf{W} & \textbf{p-value} \\ \hline
H vs \textbf{NeHPFI} & 18461386 & $\approx$0 \\ \hline
NeHPFI vs \textbf{NeHPF} & 98581813 & $\approx$0 \\ \hline
\textbf{Ne8HI} vs NeHI & 37076704 & $\approx$0 \\ \hline
\textbf{Ne8HPFI} vs NeHPFI & 41639500 & $3.03e^{-206}$ \\ \hline
\textbf{Ne8HPF} vs NeHPF & 53285564 & $1.09e^{-20}$ \\ \hline
\textbf{Ne8HPFI} vs Ne8HPF & 52985562 & $5.48e^{-23}$ \\ \hline
		\end{tabular}
\end{adjustbox}
\caption{Wilcoxon contrast using paired data, with best algorithm in bold. Algorithms that contain an 8 in their name are the same as those in the line above, but they use reflections.}
\end{table}
\end{document}

cs

Solving nonograms using Neural Networks

José María Buades Rubio
Antoni Jaume-i-Capó
David López González
Gabriel Moyà Alcover

"Solving nonograms using Neural Networks" by José María Buades Rubio, Antoni Jaume-i-Capó, David López González, and Gabriel Moyà Alcover, arXiv:2501.05882v1, 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}{lccc}
\hline
\textbf{Model} & \textbf{Size (Billion Parameters)} & \textbf{Dataset} & \textbf{Accuracy} \\
\hline
Phi+ACD & 3.8 & ACD-temporal-QA & 50.34 \\
Qwen & 8.4 & ACD-temporal-QA & 44.87 \\
GAMA & 7 & ACD-temporal-QA & \textbf{57.53} \\
\midrule
Phi+ACD & 3.8 & ACD-timestamp-QA & \textbf{37.57} \\
Qwen & 8.4 & ACD-timestamp-QA & 30.66 \\
GAMA & 7 & ACD-timestamp-QA & 28.56 \\
\hline
\end{tabular}
\caption{Model Performance on Different Datasets}
\end{table}
\end{document}

eess

Vakada Naveen
Arvind Krishna Sridhar
Yinyi Guo
Erik Visser

"Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding" by Vakada Naveen, Arvind Krishna Sridhar, Yinyi Guo, and Erik Visser, arXiv:2412.03980v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Quasi-Bayes empirical Bayes: a sequential approach to the Poisson compound decision problem" by Stefano Favaro and Sandra Fortini, arXiv:2411.07651v2, 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}{rrrrr}
 \hline
 & mean errors out of 25& sd & mean(supp) & sd(supp) \\
 \hline
Lasso multinomial& 0.96 & 0.03 & 18.68 & 0.08 \\
 UniLasso OVR & 0.18 & 0.01 & 27.02 & 0.05 \\
Lasso OVR &0.22 & 0.01 & 58.08 & 0.08\\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Univariate-Guided Sparse Regression

Sourav Chatterjee
Trevor Hastie
Robert Tibshirani

"Univariate-Guided Sparse Regression" by Sourav Chatterjee, Trevor Hastie, and Robert Tibshirani, arXiv:2501.18360v7, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Markov decision processes with observation costs: framework and computation with a penalty scheme" by Christoph Reisinger and Jonathan Tam, arXiv:2201.07908v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Cascade Attentive Dropout for Weakly Supervised Object Detection" by Wenlong Gao, Ying Chen, and Yong Peng, arXiv:2011.10258v1, 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}{lcccr}
\toprule
Data set & Naive & Flexible & Better? \\
\midrule
Breast & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
Glass2 & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
Credit & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6& \\
Horse & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
Meta & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
Pima & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5& \\
Vehicle & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Bugra Can
Mert Gurbuzbalaban
Necdet Serhat Aybat

"A Variance-Reduced Stochastic Accelerated Primal Dual Algorithm" by Bugra Can, Mert Gurbuzbalaban, and Necdet Serhat Aybat, arXiv:2202.09688v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


cs

Jakob Nolte
Maureen M. J. Guichelaar
Donald E. Bouman
Stephanie M. van den Berg
Maryam Amir Haeri

"A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma Prediction" by Jakob Nolte, Maureen M. J. Guichelaar, Donald E. Bouman, Stephanie M. van den Berg, and Maryam Amir Haeri, arXiv:2501.10733v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Garrett Honke
Irina Higgins
Nina Thigpen
Vladimir Miskovic
Katie Link
Sunny Duan
Pramod Gupta
Julia Klawohn
Greg Hajcak

"Representation learning for improved interpretability and classification accuracy of clinical factors from EEG" by Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, and Greg Hajcak, arXiv:2010.15274v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results achieved on AMP dataset}
\begin{tabular}{|c|c|c|c|}
\hline
\thead{Method} & \thead{Time-steps}
 & \multicolumn{2}{c|}{\thead{Avg.MAPE \%}}
 \\ \cline{3-4} &&
	\thead{DeepDeFF}
	&
	\thead{Basic}\\
\hline
BGRU & 2 & 25.08 & 28.17 \\
\hline
GRU & 2 & 25.48 & 27.65 \\
\hline
BLSTM & 2 & 25.44 & 28.14 \\
\hline
LSTM & 2 & 25.57 & 28.57 \\
\hline
BRNN & 2 & 24.77 & 26.88 \\
\hline
\bfseries{RNN} & \bfseries{2} & 25.62 & \bfseries{26.26} \\
\hline
\bfseries{BGRU} & \bfseries{6} & \bfseries{24.64} & 31.58 \\
\hline
GRU & 6 & 25.33 & 30.49 \\
\hline
BLSTM & 6 & 25.98 & 32.32 \\
\hline
LSTM & 6 & 26.00 & 33.47 \\
\hline
BRNN & 6 & 25.47 & 30.10 \\
\hline
RNN & 6 & 25.32 & 27.97 \\
\hline
BGRU & 12 & 25.19 & 32.85 \\
\hline
GRU & 12 & 25.17 & 30.00 \\
\hline
BLSTM & 12 & 26.62 & 36.08 \\
\hline
LSTM & 12 & 25.81 & 30.65 \\
\hline
BRNN & 12 & 25.35 & 31.62 \\
\hline
RNN & 12 & 25.87 & 29.54 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

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

"Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets" by Abdul Wahab, Muhammad Anas Tahir, Naveed Iqbal, Faisal Shafait, and Syed Muhammad Raza Kazmi, arXiv:2011.14137v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluation results for the SciCap test dataset. The ROUGE (F1-score) and BLEU (4-gram) scores of MLBCAP are opposite to the human preference (Figure~).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} \hline
 & \textbf{ROUGE-1} & \textbf{ROUGE-2} & \textbf{ROUGE-L} & \textbf{BLEU-4} \\ \hline
Pegasus & \textbf{0.460} & \textbf{0.282} & \textbf{0.418} & 0.124 \\
LLaMA-3-8B & 0.405 & 0.237 & 0.346 & 0.126 \\
Yi-1.5-9B & 0.412 & 0.244 & 0.354 & \textbf{0.134} \\ \hline
MLBCAP (short) & 0.369 & 0.174 & 0.310 & 0.043 \\
MLBCAP (long) & 0.333 & 0.150 & 0.257 & 0.049 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jaeyoung Kim
Jongho Lee
Hong-Jun Choi
Ting-Yao Hsu
Chieh-Yang Huang
Sungchul Kim
Ryan Rossi
Tong Yu
Clyde Lee Giles
Ting-Hao 'Kenneth' Huang
Sungchul Choi

"Multi-LLM Collaborative Caption Generation in Scientific Documents" by Jaeyoung Kim, Jongho Lee, Hong-Jun Choi, Ting-Yao Hsu, Chieh-Yang Huang, Sungchul Kim, Ryan Rossi, Tong Yu, Clyde Lee Giles, Ting-Hao 'Kenneth' Huang, and Sungchul Choi, arXiv:2501.02552v1, 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{The Top 15 companies on the S\&P 500 index.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
			\hline
			\# & Company & Symbol \\
			\hline
			1 & Apple Inc. & AAPL \\
			\hline
			2 & Microsoft Corporation& MSFT \\
			\hline
			3& Amazon.com Inc. & AMZN \\
			\hline
			4& Tesla Inc.& TSLA \\
			\hline
			5& Alphabet Inc. Class A&GOOGL \\
			\hline
			6& Alphabet Inc. Class C& GOOG \\
			\hline
			7& Meta Platforms Inc. Class A& FB \\
			\hline
			8& NVIDIA Corporation& NVDA \\
			\hline
			9& Berkshire Hathaway Inc. Class B& BRK.B \\
			\hline
			10& JPMorgan Chase \& Co.& JPM\\
			\hline
			11& Johnson \& Johnson & JNJ \\
			\hline
			12& UnitedHealth Group Incorporated & UNH\\
			\hline
			13& 	Procter \& Gamble Company& PG \\
			\hline
			14& Home Depot Inc.& HD \\
			\hline
			15& Visa Inc. Class A & V \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On Solving Robust Log-Optimal Portfolio: A Supporting Hyperplane Approximation Approach" by Chung-Han Hsieh, arXiv:2202.03858v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Fabio Centofanti
Antonio Lepore
Alessandra Menafoglio
Biagio Palumbo
Simone Vantini

"Adaptive Smoothing Spline Estimator for the Function-on-Function Linear Regression Model" by Fabio Centofanti, Antonio Lepore, Alessandra Menafoglio, Biagio Palumbo, and Simone Vantini, arXiv:2011.12036v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c||c|c|c|c|c|}
 \hline
 & & $c_{\mathrm{obs}}= 0.1$ & $c_{\mathrm{obs}} = 0.25$ & $c_{\mathrm{obs}} = 0.5$ & $c_{\mathrm{obs}}=0.75$\\
 \hline
 & (a) & 22.48 & 22.2 & 21.2 & 17.55 \\
 $\rho_0 \sim \mathrm{Beta}(2,5)$ & (b) & 20.622 & 17.15 & 11.26 & 6.0375 \\
 & (c) & 0.2341 & 0.2360 & 0.2455 & 0.2844 \\
 \hline
 & (a) & 21.4 & 20.97 & 18.36 & 11.27 \\
 $\rho_0 \sim \mathrm{Beta}(3,3)$ & (b) & 17.99 & 14.6475 & 8.92 & 2.5775 \\
 & (c) & 0.2437 & 0.2459 & 0.2696 & 0.3624 \\
 \hline
 & (a) & 19.22 & 17.3 & 11.21 & 3.34 \\
 $\rho_0 \sim \mathrm{Beta}(5,2)$ & (b) & 10.628 & 7.55 & 1.825 & -0.835 \\
 & (c) & 0.2488 & 0.2583 & 0.3302 & 0.5034 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Numerical results for the parameter uncertainty problem. Line (a): average number of observations. Line (b): average profit ($N=50$). Line (c): average credible interval width (HDI 95\%).}
\end{table}
\end{document}

math


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of datasets used for ASR finetuning.}
\begin{tabular}{lcl}
 \toprule
 Dataset & Duration (hrs) & Speech Type\\
 \midrule
 Librispeech & 100 \& 960 & American read speech \\
 TEDLIUMv3 & 452 & Spontaneous speech \\
 \multirow{2}{*}{NSC subset} & \multirow{2}{*}{420} & Singapore English/Singlish with read, \\
 & & spontaneous and code-switch speech \\
\bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Muhammad Huzaifah
Geyu Lin
Tianchi Liu
Hardik B. Sailor
Kye Min Tan
Tarun K. Vangani
Qiongqiong Wang
Jeremy H. M. Wong
Jinyang Wu
Nancy F. Chen
Ai Ti Aw

"MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond" by Muhammad Huzaifah, Geyu Lin, Tianchi Liu, Hardik B. Sailor, Kye Min Tan, Tarun K. Vangani, Qiongqiong Wang, Jeremy H. M. Wong, Jinyang Wu, Nancy F. Chen, and Ai Ti Aw, arXiv:2412.11538v3, 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{Frailty Correlations for Simulated Data}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|}
					\hline
					Corr & true & estimate & CI & & true & estimate & CI & & true & estimate & CI\\
					-elations& value& & & & value & & & & value & & \\
					\hline
					$\rho_{12}$ & 0.401 & 0.487 & (0.433,0.512) &$\rho_{13}$ & 0.377 & 0.490 & (0.462,0.522) &$\rho_{23}$ & 0.353 & 0.495 & (0.458,0.536)\\
					& & & EmpSE=0.307& & & & EmpMSE=0.334 & & & & EmpSE=0.330\\
					& & & RMSE=0.310& & & & RMSE=0.343 & & & &RMSE=0.351\\
					\hline
				\end{tabular}
\end{table}
\end{document}

stat

Atanu Bhattacharjee
Gajendra K. Vishwakarma
Souvik Banerjee

"A modified risk detection approach of biomarkers by frailty effect on multiple time to event data" by Atanu Bhattacharjee, Gajendra K. Vishwakarma, and Souvik Banerjee, arXiv:2012.02102v2, 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
 Module & Linear Layer Neurons\\
 \midrule
 CLIP Projector & 768$\times$2, 1536$\times$2, 3072$\times$2\\
 CLAP Projector & 768$\times$2, 1536$\times$2, 3072$\times$2\\
 Reconstructor & 768$\times$2, 896$\times$2, 1024$\times$2, 2048$\times$2\\
 \bottomrule
 \end{tabular}
\caption{\textbf{Neuron numbers for each CMSS module.} Note that we add a residual connection every two layers.}
\end{table}
\end{document}

eess

"Gotta Hear Them All: Towards Sound Source Aware Audio Generation" by Wei Guo, Heng Wang, Jianbo Ma, and Weidong Cai, arXiv:2411.15447v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"On the Battery Consumption of Mobile Browsers" by Matteo Varvello and Benjamin Livshits, arXiv:2009.03740v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Precision of $\boldsymbol{K}$ retrieved representations, $\boldsymbol{v}$, in the validation set of Chapman and PTB-XL, that are closest to the query when CROCS is trained with 10\% of labelled data.} Results are shown for partial and exact matches of the attributes (\# attribute matches) represented by the query and retrieved cardiac signals, and are averaged across five random seeds. Brackets indicate standard deviation and bold reflects the top-performing method. The strong performance of $\mathrm{TP \ CROCS}$ provides evidence in support of our CROCS framework.}
\begin{tabular}{cc|ccc|ccc}
 \toprule
 \# attribute & \multirow{2}{*}{Query} & \multicolumn{3}{c}{Chapman} & \multicolumn{3}{c}{PTB-XL} \\
 matches & & $K=1$ & $5$ & $10$ & $1$ & $5$ & $10$ \\
 \midrule

 \multirow{3}{*}{$\geq1$} & DTC & $71.9$ {\scriptsize(0.0)} & $100.0$ {\scriptsize(0.0)} & $100.0$ {\scriptsize(0.0)} & $70.0$ {\scriptsize(0.0)} & $100.0$ {\scriptsize(0.0)} & $100.0$ {\scriptsize(0.0)} \\

 & TP CROCS & $\boldsymbol{91.3}$ {\scriptsize(1.3)} & $98.1$ {\scriptsize(3.8)} & $100.0$ {\scriptsize(0.0)} & $\boldsymbol{91.8}$ {\scriptsize(3.4)} & $99.5$ {\scriptsize(1.0)} & $100.0$ {\scriptsize(0.0)} \\

 & CP CROCS & $89.4$ {\scriptsize(2.5)} & $98.1$ {\scriptsize(3.8)} & $100.0$ {\scriptsize(0.0)} & $88.0$ {\scriptsize(6.2)} & $100.0$ {\scriptsize(0.0)} & $100.0$ {\scriptsize(0.0)} \\

 \midrule
 \multirow{3}{*}{$\geq2$} & DTC & $25.0$ {\scriptsize(0.0)} & $71.3$ {\scriptsize(1.3)} & $89.0$ {\scriptsize(4.6)} & $21.0$ {\scriptsize(0.0)} & $50.1$ {\scriptsize(5.0)} & $78.5$ {\scriptsize(8.0)}\\

 & TP CROCS & $\boldsymbol{53.8}$ {\scriptsize(3.6)} & $85.6$ {\scriptsize(7.0)} & $92.5$ {\scriptsize(1.5)} & $\boldsymbol{62.6}$ {\scriptsize(12.7)} & $\boldsymbol{91.3}$ {\scriptsize(4.8)} & $94.9$ {\scriptsize(1.6)} \\

 & CP CROCS & $\boldsymbol{51.3}$ {\scriptsize(2.5)} & $\boldsymbol{86.9}$ {\scriptsize(7.5)} & $\boldsymbol{93.8}$ {\scriptsize(7.1)} & $57.5$ {\scriptsize(5.2)} & $90.0$ {\scriptsize(5.2)} & $\boldsymbol{97.5}$ {\scriptsize(3.2)} \\
 \midrule
 \multirow{3}{*}{$=3$} & DTC & $3.1$ {\scriptsize(0.0)} & $12.5$ {\scriptsize(0.0)} & $21.9$ {\scriptsize(2.0)} & $2.5$ {\scriptsize(0.0)} & $12.0$ {\scriptsize(1.0)} & $20.5$ {\scriptsize(4.0)}\\
 & TP CROCS & $11.3$ {\scriptsize(1.5)} & $\boldsymbol{30.0}$ {\scriptsize(3.2)} & $40.6$ {\scriptsize(4.4)} & $10.3$ {\scriptsize(3.2)} & $\boldsymbol{28.7}$ {\scriptsize(4.4)} & $37.9$ {\scriptsize(6.4)} \\
 & CP CROCS & $11.3$ {\scriptsize(1.5)} & $28.8$ {\scriptsize(7.0)} & $\boldsymbol{43.8}$ {\scriptsize(5.2)} & $9.5$ {\scriptsize(1.9)} & $27.5$ {\scriptsize(0.0)} & $\boldsymbol{39.0}$ {\scriptsize(5.2)} \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Dani Kiyasseh
Tingting Zhu
David A. Clifton

"CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age" by Dani Kiyasseh, Tingting Zhu, and David A. Clifton, arXiv:2011.14230v2, 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{KIP transfer to NN vs NN baselines on MNIST.} For each group of four experiments, the best number is marked boldface, while the second best number is in italics. Corruption refers to $90\%$ noise corruption. KIP images used FC1-3, Conv1-2 kernel during training.}
\begin{tabular}{l|cc|cc|cc} \toprule
\textbf{Method} & 10 uncrpt & 10 crpt & 100 uncrpt & 100 crpt & 200 uncrpt & 200 crpt \\ \midrule
FC1, KIP & \textbf{73.57$\pm$1.51} & \textit{44.95$\pm$1.23} & \textbf{86.84$\pm$1.65} & \textit{79.73$\pm$1.10} & \textbf{89.55$\pm$0.94} & \textit{83.38$\pm$1.37} \\
FC1, Natural & 42.28$\pm$1.59 & 35.00$\pm$2.33 & 72.65$\pm$1.17 & 45.39$\pm$2.25 & 81.70$\pm$1.03 & 54.20$\pm$2.61 \\ \midrule
LeNet, KIP & \textbf{59.69$\pm$8.98} & 38.25$\pm$6.42 & \textbf{87.85$\pm$1.46} & 69.45$\pm$3.99 & \textbf{91.08$\pm$1.65} & 70.52$\pm$4.39 \\
LeNet, Natural & \textit{48.69$\pm$4.10} & 30.56$\pm$4.35 & \textit{80.32$\pm$1.26} & 59.99$\pm$0.95 & \textit{89.03$\pm$1.13} & 62.00$\pm$0.94 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Timothy Nguyen
Zhourong Chen
Jaehoon Lee

"Dataset Meta-Learning from Kernel Ridge-Regression" by Timothy Nguyen, Zhourong Chen, and Jaehoon Lee, arXiv:2011.00050v3, 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{Hyperparameters for GeLoRA for each task}
\begin{tabular}{l|rrrrrr}
\toprule
\textbf{Hyperparameter} & \textbf{CoLA} & \textbf{STS-B} & \textbf{MRPC} & \textbf{QNLI} & \textbf{SST-2} & \textbf{RTE} \\
\midrule
\textbf{Learning Rate} & $8.00e^{-5}$ & $1.69e^{-4}$ & $7.53e^{-4}$ & $1.88e^{-4}$ & $1.61e^{-4}$ & $1.51e^{-4}$ \\
\textbf{Weight Decay} & $1.00e^{-1}$ & $9.43e^{-2}$ & $5.48e^{-2}$ & $3.00e^{-2}$ & $3.22e^{-2}$ & $6.78e^{-2}$ \\
\textbf{Warmup Ratio} & $6.00e^{-2}$ & $1.65e^{-2}$ & $3.04e^{-2}$ & $5.91e^{-2}$ & $7.63e^{-2}$ & $6.35e^{-2}$ \\
\textbf{LoRA Dropout} & $5.00e^{-2}$ & $5.69e^{-2}$ & $1.88e^{-2}$ & $5.36e^{-2}$ & $4.68e^{-2}$ & $7.16e^{-2}$ \\
\textbf{Scheduler Type} & Linear & Cosine & Linear & Linear & Cosine & Cosine \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Abdessalam Ed-dib
Zhanibek Datbayev
Amine Mohamed Aboussalah

"GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning" by Abdessalam Ed-dib, Zhanibek Datbayev, and Amine Mohamed Aboussalah, arXiv:2412.09250v3, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of various spaces in neighborhood prediction}
\begin{tabular}{lcccccc}
\toprule
Method & Cora & Citeseer & PubMed & Reddit \\
\midrule
Feature Based & 35.78\% & 3.90\% & 26.68\% & 34.35\% \\
Spectral Embedding & 83.86\% & 70.21\% & 79.32\% & 70.02\% \\
$\mathcal{F}_\theta$ Embedding & 62.81\% & 58.76\% & 58.58\% & 68.66\% \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning" by Yahel Jacobs, Reut Dayan, and Uri Shaham, arXiv:2411.01532v2, 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}{cccccc}
 	\toprule
 \multirow{ 2}{*}{Layer } & \multirow{ 2}{4em}{Total accuracy } & \multicolumn{2}{c}{Normal} & \multicolumn{2}{c}{VEB} \\ & & Se & +P & Se & +P\\
		\midrule
 {\bf RGW-VP} & {\bf 98.51\%} & {\bf 99.60\%} & {\bf 98.79\%} & {\bf 85.48\%} & {\bf 94.71\%} \\
 {\bf Ricker-VP} & 95.69\% & 99.37\% & 96.1\% & 51.68\% & 87.27\%\\
		 {\bf Hermite-VP} & 97.42\% & 98.65\% & 98.56\% & 82.75\% & 83.63\% \\ \midrule
 {\bf Literature~} & - & 80-99 \% & 85-99 \% & 77-96 \% & 63-99 \% \\
		\bottomrule
 \end{tabular}
\caption{VEB classification results on the MIT-BiH Arrythmia dataset.}
\end{table}
\end{document}

stat

Attila Miklós Ámon
Kristian Fenech
Péter Kovács
Tamás Dózsa

"Rational Gaussian wavelets and corresponding model driven neural networks" by Attila Miklós Ámon, Kristian Fenech, Péter Kovács, and Tamás Dózsa, arXiv:2502.01282v1, 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|c|}
\hline
\textbf{Model} & \textbf{Low water} & \textbf{Moderate water} & \textbf{High water} \\
\hline
Centralized ML & 2.0s & 2.5s & 3.0s \\
\hline
Standard FL & 1.8s & 2.3s & 2.8s \\
\hline
Meta-Federated Learning & 1.2s & 1.5s & 1.8s \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Comparison of response time across different water densities}
\end{table}
\end{document}

cs

"Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization" by Kevin Cooper and Michael Geller, arXiv:2501.18174v1, licensed under CC0 1.0. 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{Validation losses for different moment closure}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|l|l|}
		\hline
		 Closure &\multicolumn{2}{c|}{$M_1$ $1$D} &\multicolumn{2}{c|}{$M_2$ $1$D} &\multicolumn{2}{c|}{$M_1$ $2$D} \\ \hline
		 Architecture & convex & monotone & convex & monotone & convex & monotone \\ \hline
		 Layout & $10\times 7$ & $30\times 2$ & $15\times 7$ & $50\times 2$ & $18\times 8$& $100\times 3$ \\ \hline
		 MSE$(h^n,h^n_\theta)$ & $7.87\mathrm{e}{-7}$ & $2.09\mathrm{e}{-5}$ & $1.33\mathrm{e}{-5}$&$5.04\mathrm{e}{-4}$ & $1.10\mathrm{e}{-6}$ & $4.01\mathrm{e}{-4}$ \\ \hline
		 MSE$(\alpha^r_u,\alpha^r_\theta)$ & $7.52\mathrm{e}{-4}$ & $5.56\mathrm{e}{-6}$ & $2.81\mathrm{e}{-4}$ & $2.56\mathrm{e}{-4}$ &$3.39\mathrm{e}{-5}$ &$4.54\mathrm{e}{-5}$ \\ \hline
		 MSE$(u^r,u^r_\theta)$& $1.47\mathrm{e}{-6}$ & $3.64\mathrm{e}{-6}$ & $2.81\mathrm{e}{-4}$& $1.27\mathrm{e}{-4}$& $3.39\mathrm{e}{-5}$& $8.09\mathrm{e}{-5}$\\ \hline
		 $L_\text{mono}(u^r)$ & n.a. & $1.60\mathrm{e}{-14}$ & n.a. &$1.38\mathrm{e}{-14}$ & n.a. &$5.31\mathrm{e}{-16}$ \\ \hline
		 MAE$(h^n,h^n_\theta)$ & $7.57\mathrm{e}{-4}$ & $3.05\mathrm{e}{-3}$ & $3.11\mathrm{e}{-3}$& $1.66\mathrm{e}{-2}$ & $1.02\mathrm{e}{-3}$ &$1.49\mathrm{e}{-2}$\\ \hline
		 MAE$(\alpha_u^r,\alpha^r_\theta)$ & $1.26\mathrm{e}{-2}$ & $9.10\mathrm{e}{-3}$ & $1.23\mathrm{e}{-2}$ & $9.74\mathrm{e}{-3}$& $3.36\mathrm{e}{-3}$ & $4.35\mathrm{e}{-3}$\\ \hline
		 MAE$(u^r,u^r_\theta)$ & $9.59\mathrm{e}{-4}$ & $1.51\mathrm{e}{-3}$ &$1.23\mathrm{e}{-2}$ & $7.96\mathrm{e}{-3}$ & $9.62\mathrm{e}{-4}$ &$7.02\mathrm{e}{-3}$ \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Steffen Schotthöfer
Tianbai Xiao
Martin Frank
Cory D. Hauck

"Neural network-based, structure-preserving entropy closures for the Boltzmann moment system" by Steffen Schotthöfer, Tianbai Xiao, Martin Frank, and Cory D. Hauck, arXiv:2201.10364v1, 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|cccc}
 \toprule
 \textbf{Hyper-parameter} & \textbf{BERT} & \textbf{RoBERTa} & \textbf{DistilBERT} & \textbf{DeBERTaV3} \\
 \midrule
 Batch size & 80 & 100 & 120 & 80 \\
 Max length & 512 & 512 & 512 & 512 \\
 Epochs & 3 & 4 & 5 & 5 \\
 Learning rate & 2e-5 & 2e-5 & 2e-5 & 2e-5 \\
 \bottomrule
 \end{tabular}
\caption{Experimental setup for PLMs.}
\end{table}
\end{document}

stat

Muhammad Arslan Manzoor
Ruihong Zeng
Dilshod Azizov
Preslav Nakov
Shangsong Liang

"MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media" by Muhammad Arslan Manzoor, Ruihong Zeng, Dilshod Azizov, Preslav Nakov, and Shangsong Liang, arXiv:2412.10467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Extrapolation of Relative Treatment Effects using Change-point Survival Models" by Philip Cooney and Arthur White, arXiv:2401.00568v1, 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|}
			\hline
			Battery & Inverter & Battery & Battery Cost& euros/cycle/$b_{\text{rated}}$\\
			Model & Cost/kWh & Cost/kWh & ($C_{\text{bat}}$/kWh) & ($C_{\text{cyc}}$)\\
			\hline
			0.25C-0.25C 	& 25 & 400 & 425& 0.1062\\
			1C-1C	& 100 & 600 & 700 & 0.1750 \\
			2C-2C	& 200 & 700 & 900 & 0.2250 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Md Umar Hashmi
Jonathan Cavaleiro
Lucas Pereira
Ana Bušić

"Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal" by Md Umar Hashmi, Jonathan Cavaleiro, Lucas Pereira, and Ana Bušić, arXiv:1911.10458v1, 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}{ccc}
\toprule
Method & 2D Burgers' ($\times 10^{-3}$) & WRF ($\times 10^{-1}$)\\
\midrule
CAE & 0.27 (0.87) & 0.17 (0.18) \\
PCA & 0.42 (1.85) & 7.00 (4.02)\\
IDW & 3.41 (6.26) & 9.93 (6.63) \\
Kriging & 0.48 (1.73) & 8.80 (5.75) \\
FRK & 1.14 (3.06) & 9.47 (5.99) \\
\bottomrule
\end{tabular}
\caption{Median (across space) forecasting MSE for each method for both the simulated two dimensional (2D) Burgers' equation and WRF wind speed ($ms^{-1}$) data. The IQR is reported in parenthesis.}
\end{table}
\end{document}

stat

Matthew Bonas
Paolo Giani
Paola Crippa
Stefano Castruccio

"CESAR: A Convolutional Echo State AutoencodeR for High-Resolution Wind Forecasting" by Matthew Bonas, Paolo Giani, Paola Crippa, and Stefano Castruccio, arXiv:2412.10578v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Chiu-Yen Kao
Braxton Osting
Jackson C. Turner

"Flat tori with large Laplacian eigenvalues in dimensions up to eight" by Chiu-Yen Kao, Braxton Osting, and Jackson C. Turner, arXiv:2202.08351v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Cassandra Bird
Daniel Williamson
Sabina Leonelli

"On the meaning of uncertainty for ethical AI: philosophy and practice" by Cassandra Bird, Daniel Williamson, and Sabina Leonelli, arXiv:2309.05529v1, 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{Matrices from used for numerical experiments and their properties.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
		\hline
		Name & Size & nnz & $\kappa_\infty(A)$ & Group & Kind \\ \hline
		orsirr\_1 & 1030 & 1030 & 9.96E+04 & HB & Computational Fluid Dynamics Problem \\
		comsol & 1500 & 1500 & 3.42E+06 & Langemyr & Structural Problem \\
		circuit204 & 1020 & 1020 & 9.03E+09 & Yzhou & Circuit Simulation Problem \\ \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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation on Synthetic data for Corporate Analysis of Productivity Scores and Key Findings}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|l|}
 \hline
 \textbf{Group} & \textbf{Age Range} & \textbf{Gender} & \textbf{Productivity Score (1-5)} & \textbf{Key Findings} \\
 \hline
 Group A & 18-25 & Male & 4.2 & Higher engagement in collaborative tasks. \\
 Group B & 18-25 & Female & 4.5 & Excels in tasks requiring creativity and multitasking. \\
 Group C & 26-35 & Male & 3.8 & Demonstrates consistent performance in technical roles. \\
 Group D & 26-35 & Female & 4.3 & Strong in strategic planning and decision-making tasks. \\
 Group E & 36-45 & Male & 3.9 & Prefers independent work over group collaborations. \\
 Group F & 36-45 & Female & 4.0 & Shows balanced performance across multiple domains. \\
 Group G & 46-55 & Male & 3.7 & Demonstrates steady but slower adaptability to new tools. \\
 Group H & 46-55 & Female & 4.1 & Excels in mentoring and team management. \\
 Group I & 56+ & Male & 3.5 & Focused expertise but reduced multitasking capabilities. \\
 Group J & 56+ & Female & 3.9 & Contributes effectively in advisory and quality-check roles. \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Enhancing Workplace Productivity and Well-being Using AI Agent" by Ravirajan K and Arvind Sundarajan, arXiv:2501.02368v1, 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{Summary of datasets used, including their data types, the number of classes, the number of groups, and the total dataset size for each.}
\begin{tabular}{lcccc}
 \toprule
 \textbf{Dataset} & \textbf{Data type} & \textbf{Num. of classes} & \textbf{Num. of groups} & \textbf{Train size} \\
 \midrule
 Waterbirds & Image & 2 & 2 & 4795 \\
 CelebA & Image & 2 & 2 & 162770 \\
 MultiNLI & Text & 3 & 6 & 206175 \\
 CivilComments & Text & 2 & 8 & 269038 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Reza Bayat
Mohammad Pezeshki
Elvis Dohmatob
David Lopez-Paz
Pascal Vincent

"The Pitfalls of Memorization: When Memorization Hurts Generalization" by Reza Bayat, Mohammad Pezeshki, Elvis Dohmatob, David Lopez-Paz, and Pascal Vincent, arXiv:2412.07684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c} % <-- Alignments: 1st column left, 2nd middle and 3rd right, with vertical lines in between
 \textbf{Method} & \textbf{MCD$\downarrow$} & \textbf{GPE$\downarrow$} & \textbf{VDE$\downarrow$} & \textbf{FFE$\downarrow$}\\
 \hline
 BM+P+E & 20.91& 40.04 & 44.56 & 65.46 \\
 BM+SE & 19.78& 37.98 & 39.90 & 56.05 \\
 BM+SE+P & 16.20 & 33.68 & 29.94 & 45.72 \\
 FSM-SS & \textbf{13.42}& \textbf{27.56} & \textbf{17.52} & \textbf{34.44}\\
 \end{tabular}
\caption{Ablation Study of FSM-SS on 1 shot approach }
\end{table}
\end{document}

eess

Neeraj Kumar
Srishti Goel
Ankur Narang
Brejesh Lall

"Few Shot Adaptive Normalization Driven Multi-Speaker Speech Synthesis" by Neeraj Kumar, Srishti Goel, Ankur Narang, and Brejesh Lall, arXiv:2012.07252v1, 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{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of MLP-DDQN for Different Distributions on MNIST and Fashion MNIST Datasets. \textbf{Bold} indicates the best performance.}
\begin{tabular}{|l|cc|cc|}
\hline
\multirow{2}{*}{\textbf{Distribution}} &
 \multicolumn{2}{c|}{\textbf{MNIST}} &
 \multicolumn{2}{c|}{\textbf{FashionMNIST}} \\ \cline{2-5}
 &
 \textbf{Train Accuracy} &
 \textbf{Test Accuracy} &
 \textbf{Train Accuracy} &
 \textbf{Test Accuracy} \\ \hline
Bernoulli & 0.57 $\pm$ 0.03 & 0.58 $\pm$ 0.00 & 0.55 $\pm$ 0.02 & 0.54 $\pm$ 0.00 \\ \hline
Gumbel Softmax & 0.15 $\pm$ 0.01 & 0.18 $\pm$ 0.00 & 0.10 $\pm$ 0.02 & 0.24 $\pm$ 0.00 \\ \hline
Gaussian & 0.22 $\pm$ 0.01 & 0.21 $\pm$ 0.00 & 0.22 $\pm$ 0.01 & 0.21 $\pm$ 0.01 \\ \hline
\textbf{Beta} &
 \textbf{0.79 $\pm$ 0.02} &
 \textbf{0.78 $\pm$ 0.00} &
 \textbf{0.68 $\pm$ 0.01} &
 \textbf{0.66 $\pm$ 0.00} \\ \hline
Dirichlet & 0.17 $\pm$ 0.02 & 0.22 $\pm$ 0.00 & 0.23 $\pm$ 0.02 & 0.24 $\pm$ 0.00 \\ \hline
Multinomial & 0.10 $\pm$ 0.02 & 0.12 $\pm$ 0.00 & 0.10 $\pm$ 0.01 & 0.12 $\pm$ 0.00 \\ \hline
Cauchy & 0.12 $\pm$ 0.01 & 0.12 $\pm$ 0.00 & 0.11 $\pm$ 0.01 & 0.12 $\pm$ 0.00 \\ \hline
Laplace & 0.16 $\pm$ 0.02 & 0.18 $\pm$ 0.00 & 0.21 $\pm$ 0.01 & 0.19 $\pm$ 0.00 \\ \hline
Uniform & 0.73 $\pm$ 0.02 & 0.75 $\pm$ 0.00 & 0.69 $\pm$ 0.03 & 0.65 $\pm$ 0.00 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Md Abrar Jahin
M. F. Mridha
Nilanjan Dey

"Human-in-the-Loop Feature Selection Using Interpretable Kolmogorov-Arnold Network-based Double Deep Q-Network" by Md Abrar Jahin, M. F. Mridha, and Nilanjan Dey, arXiv:2411.03740v1, 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}{lrl}
\hline \textbf{Type of Text} & \textbf{Font Size} & \textbf{Style} \\ \hline
paper title & 15 pt & bold \\
author names & 12 pt & bold \\
author affiliation & 12 pt & \\
the word ``Abstract'' & 12 pt & bold \\
section titles & 12 pt & bold \\
subsection titles & 11 pt & bold \\
document text & 11 pt &\\
captions & 10 pt & \\
abstract text & 10 pt & \\
bibliography & 10 pt & \\
footnotes & 9 pt & \\
\hline
\end{tabular}
\caption{ Font guide. }
\end{table}
\end{document}

eess

Yerbolat Khassanov
Saida Mussakhojayeva
Almas Mirzakhmetov
Alen Adiyev
Mukhamet Nurpeiissov
Huseyin Atakan Varol

"A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline" by Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, and Huseyin Atakan Varol, arXiv:2009.10334v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test accuracy (\%) of node classification benchmarks using GRACE with different augmentations. We compare no augmentations (NO Aug), domain-agnostic augmentations (Gaussian), and default domain-specific augmentations (FM+EP). Average accuracy and p-value are reported.}
\begin{tabular}{cccccccc}
 \toprule
 \textbf{Augmentation} & Cora & CiteSeer & PubMed & Photo & Computers & \textbf{Avg} & \textbf{Avg p-value} \\
 \midrule
 NO Aug & 79.56 $\pm$ 2.18 & 71.83 $\pm$ 1.83 & 84.68 $\pm$ 0.58 & 90.99 $\pm$ 1.26 & 82.83 $\pm$ 0.86 & 81.98 & \cellcolor{green!10}0.1051\\
 \cmidrule(r){1-8}
 FM+EP & 84.67 $\pm$ 1.39 & 73.47 $\pm$ 2.32 & 85.80 $\pm$ 0.16 & 91.42 $\pm$ 1.27 & 89.01 $\pm$ 0.60 & 84.87 & -\\
 Gaussian & 82.72 $\pm$ 2.38 & 72.60 $\pm$ 1.21 & 85.24 $\pm$ 0.61 & 91.32 $\pm$ 1.37 & 82.87 $\pm$ 1.09 & 82.95 & \cellcolor{green!10}0.1778\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Xiaojun Guo
Yifei Wang
Zeming Wei
Yisen Wang

"Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning" by Xiaojun Guo, Yifei Wang, Zeming Wei, and Yisen Wang, arXiv:2311.02687v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Lihu Xu
Fang Yao
Qiuran Yao
Huiming Zhang

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Characteristic of the main benchmarks (both produced with Claude 3.5 Sonnet 2024-06-20). Additional benchmarks are available in Appendix~.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\bf Parameter & \bf Short book & \bf Long book \\
\midrule
$N_\text{events}$ (Chapters) & 20 & 200 \\
Nb. of tokens & $\approx$10k (10397) & $\approx$100k (102870) \\
Nb. of dates, locations, entities, contents & 14, 12, 13, 12 & 37, 35, 34, 34 \\
Start -- end dates & \multicolumn{2}{c}{March 23, 2024 -- December 26, 2026} \\ \midrule
Nb. of selected QA pairs & 456 & 686 \\
Nb. of QA related to 0, 1, 2, 3-5, 6+ events & 180, 180, 72, 24, 0 & 180, 180, 108, 128, 90 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Episodic Memories Generation and Evaluation Benchmark for Large Language Models" by Alexis Huet, Zied Ben Houidi, and Dario Rossi, arXiv:2501.13121v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cc|cc}
\toprule
\multirow{2}{*}{$\beta$} & \multicolumn{2}{c|}{JSB-multi} & \multicolumn{2}{c}{Jazznet-multi} \\ \cline{2-5}
 & Val-Acc. & Test-Acc. & Val-Acc. & Test-Acc. \\ \midrule
0.5 & \textbf{97.85} ${\scriptstyle \pm 0.13}$ & \textbf{97.42} ${\scriptstyle \pm 0.06}$ & \textbf{97.00} ${\scriptstyle \pm 0.38}$ & \textbf{81.53} ${\scriptstyle \pm 0.77}$ \\
1.0 & 96.66 ${\scriptstyle \pm 0.34}$ & 96.21 ${\scriptstyle \pm 0.35}$ & 94.37 ${\scriptstyle \pm 0.55}$ & 71.55 ${\scriptstyle \pm 4.77}$ \\
2.0 & 92.23 ${\scriptstyle \pm 0.66}$ & 90.74 ${\scriptstyle \pm 0.94}$ & 73.93 ${\scriptstyle \pm 1.97}$ & 47.90 ${\scriptstyle \pm 8.59}$ \\
4.0 & 82.07 ${\scriptstyle \pm 0.83}$ & 78.95 ${\scriptstyle \pm 1.42}$ & 47.51 ${\scriptstyle \pm 3.71}$ & 11.31 ${\scriptstyle \pm 1.92}$ \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Joonsu Gha
Vincent Herrmann
Benjamin Grewe
Jürgen Schmidhuber
Anand Gopalakrishnan

"Unsupervised Musical Object Discovery from Audio" by Joonsu Gha, Vincent Herrmann, Benjamin Grewe, Jürgen Schmidhuber, and Anand Gopalakrishnan, arXiv:2311.07534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Farshud Sorourifar
Georgios Makrygirgos
Ali Mesbah
Joel A. Paulson

"A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimization" by Farshud Sorourifar, Georgios Makrygirgos, Ali Mesbah, and Joel A. Paulson, arXiv:2011.11841v1, 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{15Hz token rate for speech-to-text translation experiment. \textbf{CALE} refers to our Cross-Attentive Local Encoder.}
\begin{tabular}{lccc}
\toprule
\textbf{Method} & \textbf{EN-CN} & \textbf{EN-FR} & \textbf{EN-ES} \\
\midrule
boundary + max pooling (w/o CALE) & 30.1 & 29.4 & 29.8 \\
boundary + average pooling (w/o CALE) & 29.2 & 28.5 & 29.1 \\
boundary + attention pooling (w/o CALE) & 30.8 & 30.2 & 30.7 \\
\textbf{Ours (with CALE)} & \textbf{31.5} & \textbf{30.9} & \textbf{31.6} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Jialong Zuo
Guangyan Zhang
Minghui Fang
Shengpeng Ji
Xiaoqi Jiao
Jingyu Li
Yiwen Guo
Zhou Zhao

"Entropy-based Coarse and Compressed Semantic Speech Representation Learning" by Jialong Zuo, Guangyan Zhang, Minghui Fang, Shengpeng Ji, Xiaoqi Jiao, Jingyu Li, Yiwen Guo, and Zhou Zhao, arXiv:2509.00503v1, 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{Detection Algorithm Testing Parameters}
\begin{tabular}{|c|c|}
 \hline
 \multicolumn{2}{|c|}{Simulation Parameters} \\
 \hline
 $T_{\text{sym}}$&0.04\\
 $T_{\text{slot}}$&0.0175\\
 $T_{\text{guard}}$&0.005\\
 $N_{P}$&30\\
 $N_{\text{slots}}$& 50\\
 $\lambda^{'}$&$\lambda$\\
 $\rho$&2\%\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Daniel Harman
Ashton Palacios
Philip Lundrigan
Willie K. Harrison

"An Information Theoretic Analysis of Ghost Modulation" by Daniel Harman, Ashton Palacios, Philip Lundrigan, and Willie K. Harrison, arXiv:2412.05249v1, 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{Quantitative comparison of the consumer electronics test dataset. \textbf{Bold} represents the best result. $^{\dag}$ indicates that this method uses the Flare7k dataset as the training dataset.}
\begin{tabular}{c|c|lll}
\hline
Methods & Published & NIQE$\downarrow$ & MUSIQ$\uparrow$ & PI$\downarrow$ \\ \hline
Flare7k$^{\dag}$ & NeurIPS'22 & 5.137 & 58.410 & 3.579 \\
BracketFlare & CVPR'23 & 4.748 & 57.643 & 3.274 \\
IR-SDE & ICML'23 & 5.089 & 59.213 & 3.370 \\
Retinexformer & ICCV'23 & 4.931 & 56.694 & 3.550 \\
Flare7k++ & TPAMI'24 & 5.029 & 58.678 & 3.492 \\
Kotp et al. & ICASSP'24 & 4.923 & 56.913 & 3.478 \\
RDDM & CVPR'24 & 5.203 & 56.874 & 3.619 \\
FPro & ECCV'24 & 4.860 & 57.377 & 3.201 \\
\hline
Ours & - & \textbf{4.646} & \textbf{60.137} & \textbf{3.085} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Minglong Xue
Aoxiang Ning
Shivakumara Palaiahnakote
Mingliang Zhou

"DFDNet: Dynamic Frequency-Guided De-Flare Network" by Minglong Xue, Aoxiang Ning, Shivakumara Palaiahnakote, and Mingliang Zhou, arXiv:2507.17489v1, 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}{l|cccc}
\toprule
Method & Training & Likelihood evaluation & Sampling \\
\midrule
RCNF & Solving ODE $\mathcal{O}(dN)$ & Solving augmented ODE $\mathcal{O}(dN)$ & Solving ODE $\mathcal{O}(N)$ \\ %\midrule
Moser flow & Computing $\text{div}$ $\mathcal{O}(dk)$ or $\mathcal{O}(k)$ & Solving augmented ODE $\mathcal{O}(dN)$ & Solving ODE $\mathcal{O}(N)$ \\ %\midrule
RSGM & Score matching $\mathcal{O}(d)$ or $\mathcal{O}(1)$ & Solving augmented ODE $\mathcal{O}(dN)$ & Solving SDE $\mathcal{O}(N^*)$ \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Valentin De Bortoli
Emile Mathieu
Michael Hutchinson
James Thornton
Yee Whye Teh
Arnaud Doucet

"Riemannian Score-Based Generative Modelling" by Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet, arXiv:2202.02763v3, licensed under CC0 1.0. 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{Open-loop prediction of the different models. Quantative results in terms of MAE en MSE.}
\begin{tabular}{ccccc}
 \toprule
 \textbf{Method} & \multicolumn{2}{c}{\textbf{$10s$ interval}} & \multicolumn{2}{c}{\textbf{Complete trajectory}} \\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & \textbf{MSE} & \textbf{MAE} & \textbf{MSE} & \textbf{MAE}\\
 \midrule
 Simple & $1.97$ & $0.96$ & $-$ & $-$ \\
 \midrule
 Stribeck & $0.14$ & $0.25$ & $1.15$ & $0.61$ \\
 \midrule
 Lugre & $0.027$ & $0.12$ & $0.66$ & $0.42$ \\
 \midrule
 GMS & $0.15$ & $0.28$ & $1.43$ & $0.84$ \\
 \midrule
 Fully connected NN & $0.21$ & $0.26$ & $2.29$ & $1.07$ \\
 \midrule
 RNN & $0.039$ & $0.13$ & $-$ & $-$ \\
 \midrule
 LVM (ours) & $0.024$ & $0.11$ & $0.11$ & $0.22$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Victor Vantilborgh
Sander De Witte
Frederik Ostyn
Tom Lefebvre
Guillaume Crevecoeur

"Probabilistic Latent Variable Modeling for Dynamic Friction Identification and Estimation" by Victor Vantilborgh, Sander De Witte, Frederik Ostyn, Tom Lefebvre, and Guillaume Crevecoeur, arXiv:2412.15756v1, 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{Underestimation of treatment effects in crop burning experiment: Revisited.}
\begin{tabular}{l|ccc}
\toprule
& Common practice & Bias & Causal parameter \\
Estimand & $\widetilde{\theta}$ & $\beta$ & $\theta$ \\
\hline
Estimate & 0.074 & 0.601*** & 0.123 \\
 & (0.049) & (0.068) & (0.086) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Ashesh Rambachan
Rahul Singh
Davide Viviano

"Program Evaluation with Remotely Sensed Outcomes" by Ashesh Rambachan, Rahul Singh, and Davide Viviano, arXiv:2411.10959v2, licensed under CC0 1.0. 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
		$2\times100\times100$&0.0155&&12,164 &0.0145&&8,994 &0.0166&	\\
		$2\times200\times200$&0.0074&1.07&40,814 &0.0074&0.97&30,263 & 0.0071&1.23\\
		$2\times400\times400$&0.0027&1.45&148,473 &0.0028&1.40&87,214 &0.0028&1.34\\
		\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.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\footnotesize{Experimental settings for MNIST and CIFAR10 datasets: Number of iterations $N_{iter}$, batch size $bs$, learning rate $\sigma_{\Theta}$, momentum $m$, update rate $\sigma$ (training and test) }}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 {\bf Dataset} 					& {\bf Classifier} 	& $N_{iter}$ 	& $bs$	 	& $\sigma_{\Theta}$ 	& $m$ 	& $\sigma$ (train) 	& $\sigma$ (test) \\
 \hline
 \multirow{2}{3em}{\bf MNIST} 		& \bf Log. Reg. 		& 3000 		& 250 		& 0.1 	& 0.5 	& 1.0 			& 5.0 \\ \cline{2-8}
 							&\bf CNN4 		& 3500 		& 250 		& 05	 	& 0.5 	& 0.4 			& 0.5 \\
 \hline
 \bf CIFAR10 					& \bf Resnet18 		& 1000 		& 64	 		& 0.01 	& 0.5 	& 1.0 			& 2.5 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Cesar F. Caiafa
Ziyao Wang
Jordi Solé-Casals
Qibin Zhao

"Learning from Incomplete Features by Simultaneous Training of Neural Networks and Sparse Coding" by Cesar F. Caiafa, Ziyao Wang, Jordi Solé-Casals, and Qibin Zhao, arXiv:2011.14047v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc|cccc|cccc}
\toprule
& & \multicolumn{4}{c|}{Validation} & \multicolumn{4}{c}{Test} \\
\cmidrule(lr){3-6} \cmidrule(lr){7-10}
& & P $\uparrow$ & S $\downarrow$ & R $\downarrow$ & E $\downarrow$ & P $\uparrow$ & S $\downarrow$ & R $\downarrow$ & E $\downarrow$ \\
\midrule
& W/O SA \& IA & 21.4 & 11.5 & 9.3 & 15.1 & 22.1 & 8.5 & 11.9 & 15.6 \\
& IA & 25.5 & 4.1 & 6.3 & 9.2 & 25.8 & 4.3 & 5.9 & 9.6 \\
& SA & 25.1 & 4.4 & 6.8 & 9.1 & 25.7 & 3.6 & 6.4 & 9.7 \\
& IA \& SA & \textbf{26.1} & \textbf{2.9} & \textbf{6.2} & \textbf{8.7} & \textbf{26.3} & \textbf{3.1} &\textbf{5.8} & \textbf{9.6} \\
\bottomrule
\end{tabular}
\caption{Ablation Study on BJTU-UVA Full-Spectrum Data: Performance Comparison of Different Attention Configurations (Spectral Attention (SA) and Illumination Attention (IA))}
\end{table}
\end{document}

eess

Zhuoran Du
Shaodi You
Cheng Cheng
Shikui Wei

"Automatic Spectral Calibration of Hyperspectral Images:Method, Dataset and Benchmark" by Zhuoran Du, Shaodi You, Cheng Cheng, and Shikui Wei, arXiv:2412.14925v2, 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}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluation metrics for multi-class classification on the UKM20 dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\hline
\textbf{Model} & \textbf{Accuracy (\%)} & \textbf{Precision (\%)} & \textbf{Recall (\%)} & \textbf{F1-Score (\%)} \\
\midrule
\hline
\rowcolor{green!20} \textbf{LENS-XAI Teacher} & \textbf{99.92} & \textbf{99.92} & \textbf{99.92} & \textbf{99.92} \\
\rowcolor{green!20} \textbf{LENS-XAI Student} & \textbf{99.80} & \textbf{99.80} & \textbf{99.80} & \textbf{99.80} \\
\hline
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

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


\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
 & PINNs without AA/DW & PINNs with AA & PINNs with DW & XPINNs with AA \\ \hline
$\rho$ & 1.3494e-02 & 6.5783e-03& 4.0532e-03& 3.3864e-03\\
$u$ & 7.4464e-02 & 2.7493e-02& 1.5532e-02& 1.1842e-02 \\
$v$ & 6.9054e-02 & 2.8942e-02& 1.6578e-02& 1.3864e-02 \\
$p$ & 2.2646e-02 & 5.5783e-03& 4.3532e-03& 3.0864e-03 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Expansion wave problem: Relative $L_2$ errors in all the primitive variables for all cases. For XPINNs, we present the total relative error from all the subdomains.}
\end{table}
\end{document}

math

Ameya D. Jagtap
Zhiping Mao
Nikolaus Adams
George Em Karniadakis

"Physics-informed neural networks for inverse problems in supersonic flows" by Ameya D. Jagtap, Zhiping Mao, Nikolaus Adams, and George Em Karniadakis, arXiv:2202.11821v1, 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}{lll}
\textbf{Errors in code} & \textbf{Count} & \textbf{\% of Total} \\ \hline
overflow & 118 & 46.8\% \\ \hline
loss of precision & 86 & 34.1\% \\ \hline
overflow, underflow & 19 & 7.5\% \\\hline
underflow & 16 & 6.3\% \\\hline
overflow, loss of precision & 3 & 1.2\% \\\hline
underflow, loss of precision & 1 & 0.4\% \\\hline
overflow, underflow, loss of precision & 1 & 0.4\% \\\hline
invalid input & 2 & 0.8\% \\\hline
N/A &6 &2.4\%\\\hline
Total & 252 & 100\% \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"DeepStability: A Study of Unstable Numerical Methods and Their Solutions in Deep Learning" by E. Kloberdanz, K. G. Kloberdanz, and W. Le, arXiv:2202.03493v1, 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{Main stats for candidate validation set.}
\begin{tabular}{cccc}
\toprule
\textbf{clips} & \textbf{duration} & \textbf{uploaders} \\
\midrule
4170 & 9.9h & 2224 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Eduardo Fonseca
Xavier Favory
Jordi Pons
Frederic Font
Xavier Serra

"FSD50K: An Open Dataset of Human-Labeled Sound Events" by Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, and Xavier Serra, arXiv:2010.00475v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Konstantin Burlachenko
Samuel Horváth
Peter Richtárik

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


stat

"Local Quadratic Spectral and Covariance Matrix Estimation" by Tucker S. McElroy and Dimitris N. Politis, arXiv:2212.02584v1, 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}{cccc}
\toprule
End of Test & $\mathcal{A}$ & Activation & Hidden Layers \\
\midrule
2022-05-14 & [0, 20, 50] & sigmoid & [6, 4] \\
2022-07-16 & [0, 10, 20] & sigmoid & [4, 2] \\
2022-09-16 & [0, 40, 50, 60] & sigmoid & [4, 2] \\
2022-11-18 & [0, 50, 100] & relu & [6, 6, 6] \\
2023-01-19 & [0, 10, 20, 30] & relu & [6, 4] \\
2023-03-23 & [0, 40, 50, 60] & relu & [4, 2] \\
2023-05-24 & [0, 10, 20, 30] & tanh & [8, 2] \\
2023-07-26 & [0, 40, 50, 60] & sigmoid & [8, 2] \\
2023-09-26 & [0, 20, 50] & sigmoid & [10, 2] \\
2023-11-28 & [0, 50, 100] & sigmoid & [6, 2] \\
2024-01-29 & [0, 20, 50] & tanh & [8, 4] \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Optimized hyperparameters over the training window for each testing window. The table includes the action space ($\mathcal{A}$), the activation function used in the neural networks, and a list indicating the number (lenght of the list) and size of hidden layers (numbers in the list).}
\end{table}
\end{document}

cs

"Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning" by Haonan Xu and Alessio Brini, arXiv:2501.07508v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc}
					\toprule
					Semilinear form &Total cost & Total res & CPU \\
					\midrule
$A_0(x)$ & 1.29 & 0.25 & \textbf{0.3s}\\
$\mathcal{A}(x,\alpha^*)$ & \textbf{1.27} & \textbf{7.7e-10} & 2.5s\\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\caption{Test 2: Comparison between the fixed and the optimal case in terms of total cost and total residual. }
\end{table}
\end{document}

math

"Optimizing semilinear representations for State-dependent Riccati Equation-based feedback control" by Sergey Dolgov, Dante Kalise, and Luca Saluzzi, arXiv:2202.11801v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 \multicolumn{4}{c}{Non-hallucination Samples} \\
 {[0.0,0.25)} & {[0.25,0.5)} & {[0.5,0.75)} & {[0.75,1.0]} \\
 1.0 & 0.0 & 0.0 & 0.0 \\
 \midrule
 \multicolumn{4}{c}{Hallucination Samples} \\
 {[0.0,0.25)} & {[0.25,0.5)} & {[0.5,0.75)} & {[0.75,1.0]} \\
 0.986 & 0.012 & 0.001 & 0.001 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Distribution of non-attentive consistency scores $\mathop r\nolimits^{non\_att}$ with Llama3-8b on the Books dataset.}
\end{table}
\end{document}

cs

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

"Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models" by Qiang Liu, Xinlong Chen, Yue Ding, Bowen Song, Weiqiang Wang, Shu Wu, and Liang Wang, arXiv:2501.09997v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

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


math

"A stabilizer-free $C^0$ weak Galerkin method for the biharmonic equations" by Peng Zhu, Shenglan Xie, and Xiaoshen Wang, arXiv:2201.08062v1, 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}{lcc|cc}
 \toprule
 \multirow{2}{*}{Method} & \multicolumn{2}{c}{In-Domain} & \multicolumn{2}{c}{Out-of-Domain} \\
 & mAP & mAR & mAP & mAR \\
 \midrule
 MaskRCNN & & & & \\
 PointRend & & & & \\
 SparseInst & & & & \\
 QueryInst & & & & \\
 Mask2Former & & & & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Caption}
\end{table}
\end{document}

cs

Constantin Seibold
Hamza Kalisch
Lukas Heine
Simon Reiß
Jens Kleesiek

"Foreign object segmentation in chest x-rays through anatomy-guided shape insertion" by Constantin Seibold, Hamza Kalisch, Lukas Heine, Simon Reiß, and Jens Kleesiek, arXiv:2501.12022v1, 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}{lcccccc}
\toprule
& \multicolumn{3}{c}{p=1} & \multicolumn{3}{c}{p=2} \\
\cmidrule(r){2-4}\cmidrule(l){5-7}
\textbf{k} & $LP^*$ & $LP'$ & $FR$ & $LP^*$ & $LP'$ & $FR$ \\
5 \quad\quad & 591.24 \quad & 559.13 \quad & 572.81 \quad \quad & 21.68 \quad & 21.68 \quad & 21.68 \\
10 \quad\quad & 392.24 \quad & 374.63 \quad & 368.50 \quad \quad & 15.54 & 15.03 & 15.06 \\
15 \quad\quad & 284.64 \quad & 283.34 \quad & 283.34 \quad\quad & 11.95 & 11.95 & 11.95 \\
30 \quad\quad & 128.49 \quad & 128.91 \quad & 125.32 \quad\quad & 7.54 & 7.54 & 7.54 \\
\bottomrule
\end{tabular}
\caption{Comparison of the $k$-median ($p=1$) and $k$-means($p=2$) costs $LP^* = LP(x^*,y^*,z^*),$ $LP'=LP(x',y')$ and $FR=FairRound(x',y')$ for the \emph{Diabetes} dataset. Note that in some of the cases the change in costs across the different stages of the LP rounding is very small (requiring more than just two decimal places of precision).}
\end{table}
\end{document}

stat

Binita Maity
Shrutimoy Das
Anirban Dasgupta

"Linear Programming based Approximation to Individually Fair k-Clustering with Outliers" by Binita Maity, Shrutimoy Das, and Anirban Dasgupta, arXiv:2412.10923v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Jiahui Tang
Kaihua Zhou
Zhijian Luo
Yueen Hou

"Natural Language Supervision for Low-light Image Enhancement" by Jiahui Tang, Kaihua Zhou, Zhijian Luo, and Yueen Hou, arXiv:2501.06546v1, 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{Result of parameter identification}
\begin{tabular}{|c|c|c|c|}
\hline
\cline{1-4}
\textbf{Variable [Unit]} & \textbf{True value}& \textbf{Value after training}& \textbf{Error~ [$\%$]} \\
\hline
\cline{1-4}
$R_0$ [$\Omega$] & 0.06 & 0.0594 & 1 [$\%$] \\
\hline
$R_1$ [$\Omega$]& 0.03 & 0.0286 & 4.7 [$\%$] \\
\hline
$C$ [$F$]& 1000 & 944.1464 & 5.9 [$\%$] \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"Physics-Informed Neural Network for Discovering Systems with Unmeasurable States with Application to Lithium-Ion Batteries" by Yuichi Kajiura, Jorge Espin, and Dong Zhang, arXiv:2311.16374v1, 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}{lcccccccc}
 \toprule
 & \textit{i=1} & \textit{i=2} & \textit{i=3} & \textit{i=4} & \textit{i=5} & \textit{i=6} & \textit{i=7} & \textit{i=8} \\
 \midrule
 $r_i$ & 480 & 672 & 576 & 336 & 528 & 624 & 768 & 816 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On Carbon Taxes Effectiveness to Induce a Clean Technology Transition: An Evaluation Framework Based on Optimal Strategic Capacity Planning" by N. Wolf, P. Escalona, A. Angulo, and J. Weston, arXiv:2202.11507v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Sparse Markov Models for High-dimensional Inference" by Guilherme Ost and Daniel Takahashi, arXiv:2202.08007v3, 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|c|c|c}
\multirow{2}{*}{} & \multicolumn{2}{c|}{Score} & Classification \\ \cline{2-4}
& Mean & SD & Positive \% \\ \hline
Ophthalmologist 1 & 1.94 & 0.7669 & 100.00 \\ \cline{1-1}
Ophthalmologist 2 & 1.84 & 0.9553 & 92.00
\end{tabular}
\caption{Results of perceptual study.}
\end{table}
\end{document}

eess

Peyman Tahghighi
Reza A. Zoroofi
Sare Safi
Alireza Ramezani

"Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning" by Peyman Tahghighi, Reza A. Zoroofi, Sare Safi, and Alireza Ramezani, arXiv:2012.14140v1, 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{Metrics for comparing different PianoTeq configurations to the reference.}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Dynamics (dB)} & \textbf{Loudness Range} & \textbf{Correlation} \\
& \textbf{Delta \(\downarrow\)} & \textbf{Coefficient \(\uparrow\)} \\
\hline
50 & 1.8 & 0.9583 \\ \hline
\textbf{60} & \textbf{0.3} & \textbf{0.9591} \\ \hline
70 & 0.9 & 0.9561 \\ \hline
80 & 1.9 & 0.9535 \\ \hline
90 & 3.1 & 0.9479 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Zhanhong He
David Cooper
Defeng Huang
Roberto Togneri

"Filling MIDI Velocity using U-Net Image Colorizer" by Zhanhong He, David Cooper, Defeng Huang, and Roberto Togneri, arXiv:2508.07751v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of Security Threats and Defense Types.}
\begin{tabular}{|c|c|}
\hline
\multirow{2}{*}{\textbf{Adversarial Attacks}}
& White Box \\
\cline{2-2}
& Black Box \\
\hline
\multirow{3}{*}{\textbf{Adversarial Defences}}
& Pre-processing \\
\cline{2-2}
& Model Robustness Improving \\
\cline{2-2}
& Malware Detection \\
\hline
\hline
\multirow{3}{*}{\textbf{Poisoning Attacks}}
& Accuracy Drop Attack \\
\cline{2-2}
& Targeted Misclassification Attack \\
\cline{2-2}
& Backdoor Attack \\
\hline
\multirow{3}{*}{\textbf{Poisoning Defences}}
& Against Accuracy Drop Attack \\
\cline{2-2}
& Against Targeted Misclassification Attack \\
\cline{2-2}
& Against Backdoor Attack \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Alexander D. Zemskov
Yao Fu
Runchao Li
Xufei Wang
Vispi Karkaria
Ying-Kuan Tsai
Wei Chen
Jianjing Zhang
Robert Gao
Jian Cao
Kenneth A. Loparo
Pan Li

"Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey" by Alexander D. Zemskov, Yao Fu, Runchao Li, Xufei Wang, Vispi Karkaria, Ying-Kuan Tsai, Wei Chen, Jianjing Zhang, Robert Gao, Jian Cao, Kenneth A. Loparo, and Pan Li, arXiv:2412.13939v1, 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{Neural Networks}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 \cmidrule(r){1-2}
 Models & Accuracy \\
 \midrule
 CNN & 0.8095 \\
 LSTM & 0.8218 \\
 Bi-LSTM & 0.8372 \\
 LLM (Gemma3) & 0.9144 \\
 LLM (llama) & 0.9262 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Embedding-Based Approaches to Hyperpartisan News Detection" by Karthik Mohan, arXiv:2501.01370v3, 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{The statistics of graph datasets}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|}
\hline
Graphs & Nodes & Edges & Features & \#Classes \\
\hline
\textbf{Cora} & 2,708 & 5,429 & 1,433 & 7 \\
\textbf{Citeseer} & 3,327 & 4,732 & 3,703 & 6 \\
\textbf{Pubmed} & 19,717 & 44,338 & 500 & 3 \\ \hline
\textbf{Coauthor-CS} & 18,333 & 163,788 & 6,805 & 15 \\
\textbf{Coauthor-Physics} & 34,493 & 495,924 & 8,415 & 3 \\
\textbf{Polblogs} & 1,490 & 19,025 & index & 2\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mengxi Yang
Dai Shi
Xuebin Zheng
Jie Yin
Junbin Gao

"Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution" by Mengxi Yang, Dai Shi, Xuebin Zheng, Jie Yin, and Junbin Gao, arXiv:2201.04728v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{array}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cc} \toprule \toprule
 Case & Mean error (\%) & Mean absolute error (\%) \\
 \midrule \midrule
 \multicolumn{3}{>{\centering\arraybackslash}l}{\footnotesize \qquad Prior cases (as considered in Sec. ) - point estimates} \\
 Base & -0.345 & 0.345 \\
 Solar constrained & -0.423 & 0.423 \\
 1 building & -3.062 & 3.062 \\
 \midrule
 \multicolumn{3}{>{\centering\arraybackslash}l}{\footnotesize \qquad Posterior cases (as defined in Table of Sec. ) - av. over 256 msrmnts.} \\
 \texttt{type} & 0.683 & 0.811 \\
 \texttt{peak} & -0.113 & 0.899 \\
 \texttt{mean} & -0.633 & 1.052 \\
 All (base) & -0.271 & 0.525 \\
 \bottomrule \bottomrule
 \end{tabular}
\caption{Errors in SP estimates of mean system operating costs for optimized designs (under-estimation +ve).}
\end{table}
\end{document}

eess

"Quantifying the benefit of load uncertainty reduction for the design of district energy systems under grid constraints using the Value of Information" by Max Langtry and Ruchi Choudhary, arXiv:2412.16105v2, 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||}
			\hline \hline
			Events	& $\zeta$ & $\phi$ & $\sigma$ & $\psi$
			\\ \hline \hline
			Non-Seizure & 3.624e+14 & 0.9996 & 0.9996 & 4839 \\
			Seizure 	& 2.03e+15 & 0.9999 & 0.9999 & 1.145e+04 \\
			\hline \hline
		\end{tabular}
\caption{Goodness-of-fit statistics. The weighted sum of squared residuals ($\zeta$) and root mean squared error ($\psi$) show much larger values for seizure events with respect to non-seizure events. While R-square ($\phi$) and adjusted R-squared ($\sigma$) have values near one for both, seizure and non-seizure events. These four parameters show good potential to discriminate between seizure and non-seizures in EEG signals.}
\end{table}
\end{document}

eess

Antonio Quintero-Rincon
Carlos D'Giano
Hadj Batatia

"A quadratic linear-parabolic model-based classification to detect epileptic EEG seizures" by Antonio Quintero-Rincon, Carlos D'Giano, and Hadj Batatia, arXiv:1912.08359v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Paul-Hieu V. Nguyen
Ryan Yee
Sameer K. Deshpande

"Oblique Bayesian additive regression trees" by Paul-Hieu V. Nguyen, Ryan Yee, and Sameer K. Deshpande, arXiv:2411.08849v1, 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{Speed up comparison using GPU's for Batch Prediction}
\begin{tabular}{lll}
\hline
CPU Time(minutes) & GPU Time(minutes) & GPU Speedup \\ \hline
208 & 15 & 13.8X \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Praneet Dutta
Bruce Power
Adam Halpert
Carlos Ezequiel
Aravind Subramanian
Chanchal Chatterjee
Sindhu Hari
Kenton Prindle
Vishal Vaddina
Andrew Leach
Raj Domala
Laura Bandura
Massimo Mascaro

"3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement" by Praneet Dutta, Bruce Power, Adam Halpert, Carlos Ezequiel, Aravind Subramanian, Chanchal Chatterjee, Sindhu Hari, Kenton Prindle, Vishal Vaddina, Andrew Leach, Raj Domala, Laura Bandura, and Massimo Mascaro, arXiv:1911.06932v1, 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{Mean absolute error (MAE), root mean squared error (RMSE), coverage (Cov.), and length (Len.) of $95\%$ CIs based on 500 simulated data sets in covariate mismatch settings.}
\begin{tabular}{lrrrrr}
 \toprule
 & Target & SS & IVW & AIPW-$L_1$ & MR-$L_1$ \\
 \cmidrule{2-6}
 \hspace{.1cm} MAE & 0.108 & 4.331 & 0.150 & 0.107 & 0.053 \\
 \hspace{.1cm} RMSE & 0.136 & 4.401 & 0.186 & 0.134 & 0.067 \\
 \hspace{.1cm} Cov. & 0.946 & 1.000 & 0.882 & 0.950 & 0.944 \\
 \hspace{.1cm} Len. & 0.538 & 26.024 & 0.553 & 0.536 & 0.253 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Multiply Robust Federated Estimation of Targeted Average Treatment Effects" by Larry Han, Zhu Shen, and Jose Zubizarreta, arXiv:2309.12600v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"HorNets: Learning from Discrete and Continuous Signals with Routing Neural Networks" by Boshko Koloski, Nada Lavrač, and Blaž Škrlj, arXiv:2501.14346v2, licensed under CC BY-SA 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{\textit{FlowReg-O} model details of architecture in Figure .}
\begin{tabular}{lllll}
 \toprule
Layer & Filters & Kernel & Strides & Activation \\
 \midrule
fixedInput & - & - & - & - \\
movingInput & - & - & - & - \\
concatenate & - & - & - & - \\
conv2D & 64 & 7x7 & 2, 2 & L-ReLu \\
conv2D & 128 & 5x5 & 2, 2 & L-ReLu \\
conv2D & 256 & 5x5 & 2, 2 & L-ReLu \\
conv2D & 256 & 3x3 & 1, 1 & L-ReLu \\
conv2D & 512 & 3x3 & 2, 2 & L-ReLu \\
conv2D & 512 & 3x3 & 1, 1 & L-ReLu \\
conv2D & 512 & 3x3 & 2, 2 & L-ReLu \\
conv2D & 512 & 3x3 & 1, 1 & L-ReLu \\
conv2D & 1024 & 3x3 & 2, 2 & L-ReLu \\
conv2D & 1024 & 3x3 & 1, 1 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 512 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 256 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 128 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 64 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 32 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 16 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 2, 2 & - \\
resampler & - & - & - & - \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Sergiu Mocanu
Alan R. Moody
April Khademi

"FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow" by Sergiu Mocanu, Alan R. Moody, and April Khademi, arXiv:2101.09639v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Fighting together against the pandemic: learning multiple models on tomography images for COVID-19 diagnosis" by Mario Manzo and Simone Pellino, arXiv:2012.01251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of MetSelect and layer aggregation methods in GCN using F1/100.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccccccc|c}
 \toprule
 & Cora & Citseer & Pubmed & Actor & Chameleon & Squirrel & Cornell & Wisconsin & Texas & Ogba & $\Delta$ Max $\downarrow$ \\
 \midrule
 JKNet-Max & 0.86 & 0.76 & 0.88 & 0.36 & 0.62 & 0.40 & 0.62 & 0.75 & 0.67 & 0.68 & 0.023\\
 JKNet-Cat & 0.86 & 0.76 & 0.88 & 0.35 & 0.61 & 0.42 & 0.66 & 0.78 & 0.71 & 0.69 & 0.011\\
 JKNet-LSTM & 0.71 & 0.72 & 0.88 & 0.35 & 0.50 & 0.29 & 0.70 & 0.79 & 0.75 & 0.68 & 0.046\\
 JKNet-MetSelect &
 0.84 & 0.74 & 0.88 & 0.36 & 0.58 & 0.35 & 0.67 & 0.79 & 0.75 & 0.67 & \textbf{0.009}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Kartik Sharma
Vineeth Rakesh
Yingtong Dou
Srijan Kumar
Mahashweta Das

"Personalized Layer Selection for Graph Neural Networks" by Kartik Sharma, Vineeth Rakesh, Yingtong Dou, Srijan Kumar, and Mahashweta Das, arXiv:2501.14964v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Xinzhi Zhang
Leslie A Hulvershorn
Todd Constable
Yize Zhao
Selena Wang

"Cost efficiency of fMRI studies using resting-state vs task-based functional connectivity" by Xinzhi Zhang, Leslie A Hulvershorn, Todd Constable, Yize Zhao, and Selena Wang, arXiv:2411.01092v1, 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.