\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
			\toprule
			Noise parameter & (2.5e-3, 1e-2) & (6.4e-3, 2e-2) & (1e-2, 5e-2)\\
			\midrule
			Average receptive field &6.8326& 7.294 & 7.7152 \\
			\bottomrule
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance Metrics by Event Label (2-Day Horizon)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrr}
\toprule
Label & Samples & MeanRet & StdRet & IC & Sharpe & p-value & Sig \\
\midrule
Social Media Backlash & 46 & 0.0036 & 0.0208 & 0.3979 & 0.1708 & 0.2527 & \\
Negative Press & 42 & 0.0007 & 0.0214 & 0.0205 & 0.0308 & 0.8430 & \\
Brand Boycott & 23 & -0.0018 & 0.0151 & 0.6781 & -0.1165 & 0.5821 & \\
Viral Marketing Campaign & 17 & -0.0047 & 0.0192 & -0.3636 & -0.2475 & 0.3227 & \\
Speculation/Rumor & 130 & -0.0058 & 0.0217 & -0.0586 & -0.2668 & 0.0028 & ** \\
Retail Investor Buzz & 92 & -0.0049 & 0.0176 & 0.0097 & -0.2770 & 0.0093 & ** \\
Geopolitical Tension & 25 & -0.0176 & 0.0274 & -0.1254 & -0.6426 & 0.0037 & ** \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading" by Yueyi Wang and Qiyao Wei, arXiv:2508.07408v1, 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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
\textbf{Variable} & \textbf{Update rule} \\
\midrule
\textbf{Labor}
& \(\dot{L}(t)= g_L\,L(t)\) \quad (Perfect reallocation) \\
& \(\dot{L}_i(t)= g_L\,L_i(t)\) \quad (No reallocation) \\
\textbf{Capital stock}
& \(\dot{K}(t) = I_k(t)-\delta_K\,K(t)\) \\
\textbf{Compute stock}
& \(\dot{Q}(t) = I_q(t)\,H(t)-\delta_Q\,Q(t)\) \\
\textbf{Size largest training run}
& \(\dot{C}_{T}(t)=D(t)\) \\
\textbf{Hardware efficiency}
& \(\frac{1}{H(t)} \frac{dH(t)}{dt} = \theta_H [H(t)]^{-\phi_H} [I_H^{RD}(t)]^{\lambda_H}\) \\
\textbf{Software efficiency}
& \(\frac{1}{S(t)} \frac{dS(t)}{dt} = \theta_S [S(t)]^{-\phi_S} [I_S^{RD}(t)]^{\lambda_S}\) \\
\textbf{Total effective compute}
& \(\dot{C}(t)=C(t)\,\frac{\dot{S}(t)}{S(t)}-\delta_Q\,C(t) + I_q(t)\,H(t)\,S(t)\) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{\small Summary of update rules for state variables in the dynamic model. The labor update rule depends on whether perfect reallocation (where labor moves freely across tasks) or no reallocation (where workers remain in their initial task) is assumed. Laws of motion for Total effective compute apply to the regime when heating bottlenecks are sufficiently distant; otherwise, those need to be incorporated (see ). Constraints on hardware and software efficiency are omitted for clarity (see ).}
\end{table}
\end{document}

q-fin

Ege Erdil
Andrei Potlogea
Tamay Besiroglu
Edu Roldan
Anson Ho
Jaime Sevilla
Matthew Barnett
Matej Vrzla
Robert Sandler

"GATE: An Integrated Assessment Model for AI Automation" by Ege Erdil, Andrei Potlogea, Tamay Besiroglu, Edu Roldan, Anson Ho, Jaime Sevilla, Matthew Barnett, Matej Vrzla, and Robert Sandler, arXiv:2503.04941v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Manuel Schlenkrich
Jean-François Cordeau
Sophie N. Parragh

"Progressive hedging for multi-stage stochastic lot sizing problems with setup carry-over under uncertain demand" by Manuel Schlenkrich, Jean-François Cordeau, and Sophie N. Parragh, arXiv:2503.08477v2, 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{Trajectory Generation And Open-Loop Control Results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Trajectory} & \textbf{Average Tip Error } & \textbf{Real-Time Speedup Factor} \\
\hline
A & $7.6332 \times 10^{-5}$ m & 22.2x \\
\hline
B & $6.0634 \times 10^{-5}$ m & 24.3x \\
\hline
C & $4.0483 \times 10^{-5}$ m & 23.8x \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Akua Dickson
Juan C. Pacheco Garcia
Ran Jing
Meredith L. Anderson
Andrew P. Sabelhaus

"Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness" by Akua Dickson, Juan C. Pacheco Garcia, Ran Jing, Meredith L. Anderson, and Andrew P. Sabelhaus, arXiv:2412.08568v1, 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{SIR millseconds elapsed for different image sizes. 128x128 and 256x256 are averaged over 1000 samples each. 1024x1024 is averaged over 500 samples.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
\toprule
Model & 128x128 & 256x256 & 1024x1024 \\
\midrule
NP & $0.1\pm0.0$ & $0.1\pm0.0$ & $0.1\pm0.0$ \\
CNP & $0.1\pm0.0$ & $0.1\pm0.0$ & $0.1\pm0.0$ \\
ANP & $0.1\pm0.0$ & OOM & OOM \\
CANP & $0.1\pm0.0$ & OOM & OOM \\
ConvCNP & $1.1\pm0.0$ & OOM & OOM \\
TNP-D & $0.8\pm0.0$ & OOM & OOM \\
TNP-KR: PERF & $0.3\pm0.0$ & $0.3\pm0.0$ & $1.2\pm0.0$ \\
TNP-KR: DKA & $0.3\pm0.0$ & $0.3\pm0.0$ & $1.2\pm0.0$ \\
TNP-KR: SA & $1.3\pm0.0$ & $163.6\pm0.1$ & $49505.6\pm1.2$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison of classification accuracy. Numbers are in percentage.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
 & \textbf{German} & \textbf{Recidivism} & \textbf{Credit} \\
\midrule
 \textbf{SAGE} & 67.3 $\pm$ 2.14 & 89.8 $\pm$ 0.66 & 75.9 $\pm$ 2.18 \\
 \textbf{E-SAGE} & \textbf{71.0 $\pm$ 1.27} & \textbf{89.9 $\pm$ 0.90} & \textbf{73.4 $\pm$ 0.50} \\
 \textbf{GCN} & 59.6 $\pm$ 3.64 & 90.5 $\pm$ 0.73 & 65.8 $\pm$ 0.29 \\
 \textbf{E-GCN} & \textbf{58.2 $\pm$ 1.82} & \textbf{89.6 $\pm$ 0.74} & \textbf{65.2 $\pm$ 0.99} \\
 \textbf{JK} & 63.3 $\pm$ 4.11 & 91.9 $\pm$ 0.54 & 76.6 $\pm$ 0.69 \\
 \textbf{E-JK} & \textbf{62.3 $\pm$ 4.07} & \textbf{89.3 $\pm$ 0.33} & \textbf{77.7 $\pm$ 0.27} \\
 \textbf{APPNP} & 69.9 $\pm$ 2.17 & 95.3 $\pm$ 0.78 & 74.4 $\pm$ 3.05 \\
 \textbf{E-APPNP} & \textbf{69.4 $\pm$ 0.83} & \textbf{95.9 $\pm$ 0.02} & \textbf{74.6 $\pm$ 0.32} \\
 \textbf{GCNII} & 60.9 $\pm$ 1.00 & 90.4 $\pm$ 0.95 & 77.7 $\pm$ 0.22 \\
 \textbf{E-GCNII} & \textbf{60.4 $\pm$ 4.45} & \textbf{88.8 $\pm$ 0.24} & \textbf{77.6 $\pm$ 0.02} \\
\bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yushun Dong
Binchi Zhang
Hanghang Tong
Jundong Li

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mutual information between registered brain volumes and the brain atlas on ten test subjects using direct patient registration and inpainted volume registration.}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c}
\hline
Methods& Sub1 & Sub2 & Sub3 & Sub4 & Sub5 & Sub6 & Sub7 & Sub8 & Sub9 & Sub10 \\
\hline\hline
Direct registration	&0.303&0.311&0.308&0.324&0.315&0.299&0.309&0.303&0.317&0.308\\
Inpainted registration &0.309&0.311&0.309&0.324&0.316&0.304&0.312&0.313&0.320&0.312\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Xiaofeng Liu
Fangxu Xing
Chao Yang
C. -C. Jay Kuo
Georges ElFakhri
Jonghye Woo

"Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology" by Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Georges ElFakhri, and Jonghye Woo, arXiv:2101.06775v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Initial and convergent parameters and iteration - CPU time of activator-inhibitor example}\footnotesize
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
Initial guess&$\begin{bmatrix}
1\\2\\1\\2
\end{bmatrix}$&$\begin{bmatrix}
10\\0\\3\\0.1
\end{bmatrix}$&$\begin{bmatrix}
0\\0\\-10\\0
\end{bmatrix}$&$\begin{bmatrix}
-1\\1\\-10\\9
\end{bmatrix}$&$\begin{bmatrix}
-10\\11\\12\\13
\end{bmatrix}$\\
\hline
\multirow{3}{*}{NR}&6-iter &9-iter&9-iter&7-iter&11-iter\\
&0.0089 s&0.0155 s&0.0098 s&0.0091 s&0.0115 s\\
&\multicolumn{5}{|c|}{$\begin{bmatrix}
2.0117& 3.02825& 0.0948& 0.3737
\end{bmatrix}^\top$}\\
\hline
\multirow{3}{*}{GD}&1210-iter &3897-iter&799-iter&462-iter&3865-iter\\
&0.6407 s&1.9940 s&0.4111 s &2.0562 s&0.2417 s\\
& \multicolumn{5}{|c|}{$\begin{bmatrix}
2.0117& 3.02825& 0.0948& 0.3737
\end{bmatrix}^\top$}\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

S. Syafiie
Aries Subiantoro
Vivi Andasari
Fernando Tadeo

"Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods" by S. Syafiie, Aries Subiantoro, Vivi Andasari, and Fernando Tadeo, arXiv:2501.12856v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Markus Kreer
Ayse Kizilersu
Anthony W. Thomas

"Censored EM algorithm for Weibull mixtures: application to arrival times of market orders" by Markus Kreer, Ayse Kizilersu, and Anthony W. Thomas, arXiv:2012.10601v1, 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{Descriptive network measures by layer and for the aggregate network.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllllll}
\toprule
 & & & & & & \multicolumn{2}{l}{Degree Distribution} &&&\\
 \cmidrule(lr){7-11}
Layer & Nodes & Ties & Clustering & Components & \% giant comp. & Pctl. 5 & Mean & Median & Pctl. 95 & SD \\ \hline
Family & 166,393 & 199,781 & 0.76 & 41235 & 0.01 & 1 & 2.40 & 2 & 4 & 1.07 \\
Household & 164,013 & 273,344 & 1 & 41627 & 0.05 & 1 & 3.33 & 3 & 5 & 2.07 \\
School & 58,079 & 1,386,609 & 1 & 1566 & 0.27 & 16 & 47.75 & 45 & 91 & 24.08 \\
Work & 1,446,817 & 58,860,777 & 0.70 & 13022 & 0.93 & 3 & 81.37 & 100 & 164 & 54.90 \\ \hline
Aggregate & 1,570,812 & 60,538,563 & 0.70 & 1 & 100 & 3 & 77.08 & 81 & 163 & 55.43 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Christine Hedde-von Westernhagen
Javier Garcia-Bernardo
Ayoub Bagheri

"Predicting COVID-19 Infections Using Multi-layer Centrality Measures in Population-scale Networks" by Christine Hedde-von Westernhagen, Javier Garcia-Bernardo, and Ayoub Bagheri, arXiv:2310.14767v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 $\delta$ & $\gamma$ & $\bar{\eta}$(k\$) & $U(h)$ & $\rho$ & $T$ \\
 \hline
 $1$ & $0.05$ & $10$ & $\sqrt h$ & $0.2$ & $1$ \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Parameters of the optimization problem.}
\end{table}
\end{document}

q-fin

Giorgia Callegaro
Claudio Fontana
Caroline Hillairet
Beatrice Ongarato

"A stochastic Gordon-Loeb model for optimal cybersecurity investment under clustered attacks" by Giorgia Callegaro, Claudio Fontana, Caroline Hillairet, and Beatrice Ongarato, arXiv:2505.01221v1, 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|c|c|c}
\toprule
Collection & Users & Items & Interactions \\
\hline
\midrule
BAYC & 1,230 & 6,726 & 13,737 \\
Cool Cats & 1,357 & 6,824 & 14,890 \\
Doodles & 804 & 4,771 & 7,250 \\
Meebits & 1,184 & 6,693 & 21,104 \\
\hline
Total & 4,575 & 25,014 & 56,981
\end{tabular}
\caption{The number of users, items and interactions for each collection.}
\end{table}
\end{document}

q-fin

Seonmi Kim
Youngbin Lee
Yejin Kim
Joohwan Hong
Yongjae Lee

"NFTs to MARS: Multi-Attention Recommender System for NFTs" by Seonmi Kim, Youngbin Lee, Yejin Kim, Joohwan Hong, and Yongjae Lee, arXiv:2306.10053v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{multi-terminal hybrid AC/DC test cases.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccc}
\hline
Test case & \# AC & \# DC & \#AC/DC & \# AC & \# DC \\
 & \ buses & buses & conv & branches & branches \\
\hline
5-buses~ & 5 & 3 & 3 & 7 & 3 \\
39-buses~ & 39 & 10 & 10 & 46 & 12 \\
67-buses~ & 67 & 9 & 9 & 102 & 11 \\
588-buses~ & 588 & 7 & 7 & 686 & 8 \\
3120-buses~ & 3120 & 5 & 5 & 3693 & 5 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Giacomo Bastianel
Marta Vanin
Dirk Van Hertem
Hakan Ergun

"Optimal Transmission Switching and Busbar Splitting in Hybrid AC/DC Grids" by Giacomo Bastianel, Marta Vanin, Dirk Van Hertem, and Hakan Ergun, arXiv:2412.00270v1, 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}{ll}
\toprule
d & SDCD-GPU \\
\midrule
1000 & 1438.7 ${\scriptstyle \pm 59.2}$ \\
2000 & 3356.7 ${\scriptstyle \pm 70.0}$ \\
3000 & 5172.5 ${\scriptstyle \pm 89.8}$ \\
4000 & 7567.0 ${\scriptstyle \pm 343.7}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Detailed results of SHD means and standard deviations from .}
\end{table}
\end{document}

stat

Stable Differentiable Causal Discovery

Achille Nazaret
Justin Hong
Elham Azizi
David Blei

"Stable Differentiable Causal Discovery" by Achille Nazaret, Justin Hong, Elham Azizi, and David Blei, arXiv:2311.10263v2, 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{The asymptotic variance of the ATT estimator when weights are unknown ($\Sigma$) and known ($\Sigma^*$), and the ratio (Unknown / Known) of the asymptotic standard deviations (SD Ratio).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rrrr}
 \hline
 Scenario & $\Sigma$ & $\Sigma^*$ & SD Ratio \\
 \hline
(i) & 3.90 & 2.26 & 1.31 \\
 (ii) & 1.36 & 4.33 & 0.56 \\
 (iii) & 4.37 & 3.59 & 1.10 \\
 (iv) & 11.28 & 24.50 & 0.68 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"On variance of the treatment effect in the treated using inverse probability weighting" by Sarah A. Reifeis and Michael G. Hudgens, arXiv:2011.11874v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Marco Caliendo
Nico Pestel
Rebecca Olthaus

"Long-Term Employment Effects of the Minimum Wage in Germany: New Data and Estimators" by Marco Caliendo, Nico Pestel, and Rebecca Olthaus, arXiv:2310.15964v1, 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}
& $T_1$ & $T^C_1$ & $T_2$ & $T^C_2$\\
 \hline
 Fidelity & 0.51 & 0.45 & 0.57 & 0.90\\
 Diversity & 1.0 & 1.0 & 1.0 & 0.99\\
 TopF1 & 0.68 & 0.62 & 0.72 & 0.94\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{TopP\&R scores for VQalAttent}
\end{table}
\end{document}

eess

Armani Rodriguez
Silvija Kokalj-Filipovic

"VQalAttent: a Transparent Speech Generation Pipeline based on Transformer-learned VQ-VAE Latent Space" by Armani Rodriguez and Silvija Kokalj-Filipovic, arXiv:2411.14642v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Moritz Kulessa
Eneldo Loza Mencía
Johannes Fürnkranz

"Revisiting Non-Specific Syndromic Surveillance" by Moritz Kulessa, Eneldo Loza Mencía, and Johannes Fürnkranz, arXiv:2101.12246v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\hline
$n$ & 4-fold CV d.f. & MSE of $\hat{\alpha}(X)$ & 4-fold CV d.f. & MSE of $\hat{\alpha}(X, \hat{\eta})$ \\ \hline
$5,000$ & 3 & 0.16 & 3 & 0.22 \\ \hline
$10,000$ & 3 & 0.04 & 3 & 0.08 \\ \hline
$20,000$ & 3 & 0.04 & 3 & 0.05 \\ \hline
$50,000$ & 3 & 0.01 & 3 & 0.03 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{The MSEs of $\hat{\alpha}(X)$ and $\hat{\alpha}(X, \hat{\eta})$ tend to decrease as the sample size $n$ increases.}
\end{table}
\end{document}

math

Kevin Christian Wibisono
Debarghya Mukherjee
Moulinath Banerjee
Ya'acov Ritov

"Estimation and Inference for the Average Treatment Effect in a Score-Explained Heterogeneous Treatment Effect Model" by Kevin Christian Wibisono, Debarghya Mukherjee, Moulinath Banerjee, and Ya'acov Ritov, arXiv:2504.17126v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Fractional order induced bifurcations in Caputo-type denatured Morris-Lecar neurons" by Indranil Ghosh and Hammed Olawale Fatoyinbo, arXiv:2502.17798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{pifont}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Potential choices of \( \mathcal{I}(\cdot) \) and their properties. En, CoEn and RlEn represent entropy, conditional entropy and relative entropy, respectively.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \hline
 & Mean & Variance & En & CoEn & RlEn \\ \hline
 Transformation invariant & \ding{56} & \ding{56} & \ding{56} & \ding{56} & \ding{52} \\
 Background noise-free & \ding{52} & \ding{56} & \ding{56} & \ding{56} & \ding{52} \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jie Li
Jian Zhang
Samantha L. Winter
Mark Burnley

"Modelling Loss of Complexity in Intermittent Time Series and its Application" by Jie Li, Jian Zhang, Samantha L. Winter, and Mark Burnley, arXiv:2411.14635v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Conditional-mean Multiplicative Operator Models for Count Time Series" by Christian H. Weiß and Fukang Zhu, arXiv:2212.05831v2, 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{Component-wise Stress Impact During the 2008 Global Financial Crisis}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
\textbf{Latent Factor} & $\Delta$VaR & $\Delta$ES & $\Delta$Drawdown \\
\midrule
Z1 & $-0.0066$ & $-0.0061$ & $+0.2367$ \\
Z2 & $+0.0012$ & $+0.0011$ & $-0.3211$ \\
Z3 & $+0.0014$ & $+0.0012$ & $-0.3899$ \\
Z4 & $-0.0063$ & $-0.0062$ & $+0.2251$ \\
Z5 & $+0.0017$ & $+0.0016$ & $-0.4353$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Vidya Sagar G
Shifat Ali
Siddhartha P. Chakrabarty

"Machine Learning Based Stress Testing Framework for Indian Financial Market Portfolios" by Vidya Sagar G, Shifat Ali, and Siddhartha P. Chakrabarty, arXiv:2507.02011v1, 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{Symbols and definitions.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
		\hline
		\textbf{Symbol} & \textbf{Definition} \\
		\hline
		$G_t = (V_t, E_t)$ & the snapshot $G_t$ with node set $V_t$ and edge set $E_t$ \\
		\hline
		$N$ & the final timestamp \\
		\hline
		$\textbf{W}_t$, $\textbf{H}_t$ & the similarity matrices for $G_t$ \\
		\hline
		$\textbf{U}$ & the global representation matrix \\
		\hline
		$\textbf{V}_t$ & the temporary representation matrix for $G_t$ \\
		\hline
		$m$ & the dimension of node representation \\
		\hline
		$\theta$ & decay weight\\
		\hline
		$\lambda$ & smoothness weight\\
		\hline
		$\gamma$ & control the importance of constraint terms \\
		\hline
		$k$ & the steps of random walk \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Link Prediction and Unlink Prediction on Dynamic Networks" by Christina Muro, Boyu Li, and Kun He, arXiv:2101.06919v2, 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}{r|c|c|c}
 & PCA & PB & FPC \\
 \hline
$\hat\Lambda_{\text{inf}}$ & 107.26 & 12306.19 & 51776.87 \\
 \end{tabular}
\end{adjustbox}
\caption{Teststatistic for the independent noise test on the residuals of the spatial Canadian Weather Data.}
\end{table}
\end{document}

stat

"Preprocessing noisy functional data: a multivariate perspective" by Siegfried Hörmann and Fatima Jammoul, arXiv:2012.05824v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Antonio Cabrales
Manu García
David Ramos Muñoz
Angel Sánchez

"The Interactions of Social Norms about Climate Change: Science, Institutions and Economics" by Antonio Cabrales, Manu García, David Ramos Muñoz, and Angel Sánchez, arXiv:2208.09239v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Willie Aboumrad
Phani R V Marthi
Suman Debnath
Martin Roetteler
Evgeny Epifanovsky

"A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem" by Willie Aboumrad, Phani R V Marthi, Suman Debnath, Martin Roetteler, and Evgeny Epifanovsky, arXiv:2505.00145v1, 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}{lllll}
 \ & \multicolumn{2}{c}{ \textbf{Original data}} & \multicolumn{2}{c}{ \textbf{After removing outliers}} \\
 \textbf{Independent variables} & \textbf{Kurtosis} & \textbf{Skewness} & \textbf{Kurtosis} & \textbf{Skewness} \\
cf\_total\_seconds & 130.29 & 8.97 & 44.65 & 5.21 \\
cf\_total\_days & 108.01 & 9.98 & 101.01 & 9.76 \\
givaways\_totals & 168.89 & 12.31 & 212.32 & 14.16 \\
pt\_totals & 238.28 & 14.61 & 212.39 & 14.15 \\
fm\_total & 190.58 & 13.07 & 212.32 & 14.16 \\
sch\_total & 169.63 & 11.56 & 1.14 & 1.22 \\
ad\_total & 535.18 & 22.62 & 314.46 & 17.23
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Moisés Ramírez
Raziel Ruíz
Nathan Klarer

"The Effects of Just-in-time Delivery on Social Engagement: A Cluster Analysis" by Moisés Ramírez, Raziel Ruíz, and Nathan Klarer, arXiv:2212.12285v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc|}
\hline
Instances & Potential Sites & Customers & $p$ & $\#$ Instances \\ \hline
\multirow{4}{*}{AP} & 10 & 10 & 3, 5 & 10 \\
 & 15 & 15 & 3, 5, 7 & 15 \\
 & 20 & 20 & 3, 5, 7 & 15 \\
 & 25 & 25 & 3, 5, 7 & 15 \\ \hline
\multirow{4}{*}{CAB} & 10 & 10 & 3, 5 & 10 \\
 & 15 & 15 & 3, 5, 7 & 15 \\
 & 20 & 20 & 3, 5, 7 & 15 \\
 & 25 & 25 & 3, 5, 7 & 15 \\ \hline
\multirow{4}{*}{TR} & 10 & 10 & 3, 5 & 10 \\
 & 15 & 15 & 3, 5, 7 & 15 \\
 & 20 & 20 & 3, 5, 7 & 15 \\
 & 25 & 25 & 3, 5, 7 & 15 \\ \hline
 & & & Total & 165 \\ \cline{4-5}
\end{tabular}
\end{adjustbox}
\caption{Summary of the tested instances.}
\end{table}
\end{document}

math

Víctor Blanco
José-Fernando Camacho-Vallejo
Carlos Corpus

"A Novel Co-Evolutionary Algorithm for Solving a Bilevel Pricing and Hubs Location Problem under a Tree Topology" by Víctor Blanco, José-Fernando Camacho-Vallejo, and Carlos Corpus, arXiv:2503.00561v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of Multi-turn Memory}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
\textbf{Model} & \textbf{Accuracy (\%) $\uparrow$} \\
\hline
GLM-4-Voice & 80.00 \\
MiniCPM-o 2.6 & 86.67 \\
Baichuan-Omni-1.5 & 78.67 \\
SpeechGPT-2.0-preview & 20.00 \\
Freeze-Omni & 62.67 \\
Qwen2.5-Omni & 88.67 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

BoSS: Beyond-Semantic Speech

Qing Wang
Zehan Li
Hang Lv
Hongjie Chen
Yaodong Song
Jian Kang
Jie Lian
Jie Li
Yongxiang Li
Zhongjiang He
Xuelong Li

"BoSS: Beyond-Semantic Speech" by Qing Wang, Zehan Li, Hang Lv, Hongjie Chen, Yaodong Song, Jian Kang, Jie Lian, Jie Li, Yongxiang Li, Zhongjiang He, and Xuelong Li, arXiv:2507.17563v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Generalized Steepest Descent Methods on Riemannian Manifolds and Hilbert Spaces: Convergence Analysis and Stochastic Extensions" by Rashid A. and Amal A Samad, arXiv:2502.19929v1, 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{Average cross-domain meta-test accuracy scores over 5 runs using a Conv-4 backbone. Techniques trained on tasks from one data set and were evaluated on tasks from another data set. The 95\% confidence intervals, computed over the mean performances of the 3 different random seeds, are displayed as $\pm$ x. The ``-" indicates that the method did not finish within 2 days of running time. The best performances are dislpayed in bold font.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\toprule
& \multicolumn{2}{c}{MIN $\rightarrow$ CUB} & \multicolumn{2}{c}{CUB $\rightarrow$ MIN} \\
\cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & 1-shot & 5-shot & 1-shot & 5-shot \\
\midrule
MAML & 37.9 $\pm$ 2.22 & 53.6 $\pm$ 0.67 & 31.1 $\pm$ 1.19 & 45.8 $\pm$ 2.06 \\
Warp-MAML & 42.0 $\pm$ 0.85 & 56.9 $\pm$ 4.16 & 31.1 $\pm$ 1.59 & 41.3 $\pm$ 1.37 \\
SAP & 41.5 $\pm$ 3.72 & 58.0 $\pm$ 1.79 & 33.3 $\pm$ 2.33 & 47.1 $\pm$ 1.28 \\
ProtoNet & 39.7 $\pm$ 4.11 & 56.0 $\pm$ 4.89 & 31.7 $\pm$ 0.20 & 45.3 $\pm$ 1.84 \\
\midrule
LSTM & 20.1 $\pm$ 0.77 & 20.0 $\pm$ 0.40 & - & -\\
OP-LSTM (ours) & \textbf{42.3} $\pm$ 1.90 & \textbf{58.5} $\pm$ 1.49 & \textbf{35.8} $\pm$ 2.98 & \textbf{49.0} $\pm$ 0.80 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Are LSTMs Good Few-Shot Learners?

Mike Huisman
Thomas M. Moerland
Aske Plaat
Jan N. van Rijn

"Are LSTMs Good Few-Shot Learners?" by Mike Huisman, Thomas M. Moerland, Aske Plaat, and Jan N. van Rijn, arXiv:2310.14139v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Rajmadan Lakshmanan
Alois Pichler
Miloš Kopa

"Higher-Order Stochastic Dominance Constraints in Optimization" by Rajmadan Lakshmanan, Alois Pichler, and Miloš Kopa, arXiv:2501.14565v3, 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}{lr}
\hline
Configurations	 &	Values				\\
\hline
\multicolumn{2}{c}{\bf Pretraining} \\
\hline
Model Architecture 	 & Transformer (base) \\
Devices & 8 Nvidia V100 GPU \\
Max tokens per GPU		& 5120					\\
Update Frequency & 8 \\
Optimizer 				& Adam 					\\
						& ($\beta_1$=0.9, $\beta_2$=0.98, $\epsilon$=$1\times10^{-8}$)	\\
Learning rate 			& [$5\times10^{-4}$, $7\times10^{-4}$]			\\
Learning rate scheduler & polynomial decay \\
Warmup & 8000 \\
weight decay & 0.0 \\
Loss Function 			& label smoothed cross entropy \\
						& (label-smoothing=0.1) \\
Dropout					& [0.1, 0.2]	\\
Source Dropout & 0.2 \\
Target Dropout & 0.1 \\
Edit-weighted MLE & 3 \\
\hline
\multicolumn{2}{c}{\bf Fine-tuning} \\
\hline
Devices & 1 Nvidia V100 GPU \\
Update Frequency & [2, 4] \\
Learning rate 			& [$3\times10^{-4}$, $5\times10^{-4}$, $7\times10^{-4}$]			\\
Dropout					& [0.1, 0.2, 0.3]	\\
Warmup & 2000 \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Hyper-parameters values of pretraining and fine-tuning on Falko-Merlin (German) and RULEC-GEC (Russian).}
\end{table}
\end{document}

cs

Xin Sun
Tao Ge
Shuming Ma
Jingjing Li
Furu Wei
Houfeng Wang

"A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model" by Xin Sun, Tao Ge, Shuming Ma, Jingjing Li, Furu Wei, and Houfeng Wang, arXiv:2201.10707v1, 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{Fr\'{e}chet inception distance (FID) as a quantitative measurement of the generated image's similarity compared to the real images for each of the three GAN architectures. WGAN-GP-SN showed the highest similarity to the real data in terms of FID.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
		\toprule
		GAN architecture & FID \\
		\midrule
		DCGAN & 105.96 \\
		WGAN-GP & 52.53 \\
		WGAN-GP-SN & \textbf{38.05} \\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Tabea Kossen
Pooja Subramaniam
Vince I. Madai
Anja Hennemuth
Kristian Hildebrand
Adam Hilbert
Jan Sobesky
Michelle Livne
Ivana Galinovic
Ahmed A. Khalil
Jochen B. Fiebach
Dietmar Frey

"Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks" by Tabea Kossen, Pooja Subramaniam, Vince I. Madai, Anja Hennemuth, Kristian Hildebrand, Adam Hilbert, Jan Sobesky, Michelle Livne, Ivana Galinovic, Ahmed A. Khalil, Jochen B. Fiebach, and Dietmar Frey, arXiv:2009.04227v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of real-world graph datasets used in the experiments.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
 \hline
 Dataset & Type & Task & \#Nodes & \#Edges \\ \hline
 ogbn-mag & Heterogeneous & Classify 349 venues for each paper & 1,939,743 & 21,111,007 \\
 ogbn-arxiv & Homogeneous & Classify 40 primary categories for arXiv papers & 169,343 & 1,166,243 \\
 ogbn-papers100m & Homogeneous & Classify 172 subject areas for arXiv papers & 111,059,956 & 1,615,685,872 \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs" by Sejun Park, Joo Young Park, and Hyunwoo Park, arXiv:2411.10957v1, 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|cccccccc||}
 \hline
 env & $\pi_{size}$ & $Q_{size}$ & $\alpha_{\pi}$ & $\alpha_{Q}$ & $O_{\pi}$ & $O_{Q}$ & $I_G$ & $I_V$ \\
 \hline \hline
 CartPole & 32 & [64,64] & .00125 & .005 & RMS & RMS & 3 & 3 \\
 LunarLander & 64 & [64,64] & .0025 & .005 & RMS & RMS & 5 & 3 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Low-Variance Policy Gradient Estimation with World Models" by Michal Nauman and Floris Den Hengst, arXiv:2010.15622v1, 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}{ccccccc}
\toprule
\textbf{Model} & \textbf{Dim.} & $\mathbf{D}_w$ & $\mathbf{D}_a$ & ${}^I_w\mathbf{R}$ & ${}^I_a\mathbf{R}$ & $\mathbf{T}_g$ \\ \midrule
\textit{imu0} & 0 & - & - & - & - & - \\ \midrule
\textit{imu1} & 15 & $\mathbf{D}_{w6}$ & $\mathbf{D}_{a6}$ & ${}^I_w\mathbf{R}$ & - & - \\
\textit{imu2} & 15 & $\mathbf{D}_{w6}$ & $\mathbf{D}_{a6}$ & - & ${}^I_a\mathbf{R}$ & - \\
\textit{imu3} & 15 & $\mathbf{D}_{w9}$ & $\mathbf{D}_{a6}$ & - & - & - \\
\textit{imu4} & 15 & $\mathbf{D}_{w6}$ & $\mathbf{D}_{a9}$ & - & - & - \\ \midrule
\textit{imu5} & 18 & $\mathbf{D}_{w6}$ & $\mathbf{D}_{a6}$ & ${}^I_w\mathbf{R}$ & ${}^I_a\mathbf{R}$ & - \\
\textit{imu6} & 24 & $\mathbf{D}'_{w6}$ & $\mathbf{D}'_{a6}$ & ${}^I_w\mathbf{R}$ & - & $\mathbf{T}_{g9}$ \\ \midrule
\textit{imu11} & 21 & $\mathbf{D}_{w6}$ & $\mathbf{D}_{a6}$ & ${}^I_w\mathbf{R}$ & - & $\mathbf{T}_{g6}$ \\
\textit{imu12} & 21 & $\mathbf{D}_{w6}$ & $\mathbf{D}_{a6}$ & - & ${}^I_a\mathbf{R}$ & $\mathbf{T}_{g6}$ \\
\textit{imu13} & 21 & $\mathbf{D}_{w9}$ & $\mathbf{D}_{a6}$ & - & - & $\mathbf{T}_{g6}$ \\
\textit{imu14} & 21 & $\mathbf{D}_{w6}$ & $\mathbf{D}_{a9}$ & - & - & $\mathbf{T}_{g6}$ \\ \midrule
\textit{imu21} & 24 & $\mathbf{D}_{w6}$ & $\mathbf{D}_{a6}$ & ${}^I_w\mathbf{R}$ & - & $\mathbf{T}_{g9}$ \\
\textit{imu22} & 24 & $\mathbf{D}_{w6}$ & $\mathbf{D}_{a6}$ & - & ${}^I_a\mathbf{R}$ & $\mathbf{T}_{g9}$ \\
\textit{imu23} & 24 & $\mathbf{D}_{w9}$ & $\mathbf{D}_{a6}$ & - & - & $\mathbf{T}_{g9}$ \\
\textit{imu24} & 24 & $\mathbf{D}_{w6}$ & $\mathbf{D}_{a9}$ & - & - & $\mathbf{T}_{g9}$ \\ \midrule
\textit{imu31} & 9 & - & $\mathbf{D}_{a9}$ & - & - & - \\
\textit{imu32} & 9 & $\mathbf{D}_{w9}$ & - & - & - & - \\
\textit{imu33} & 6 & - & - & - & - & $\mathbf{T}_{g6}$ \\
\textit{imu34} & 9 & - & - & - & - & $\mathbf{T}_{g9}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yulin Yang
Patrick Geneva
Xingxing Zuo
Guoquan Huang

"Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy" by Yulin Yang, Patrick Geneva, Xingxing Zuo, and Guoquan Huang, arXiv:2201.09170v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrrrrrr}
\toprule
{} & maize & pork & poultry & eggs & maize g. oil & beer & alc. beverages & sweeteners \\
\midrule
Africa N & 17.1 & 2.5 & 4.9 & 4.9 & 1.2 & 0.2 & 0.7 & 5.5 \\
Africa S & 1.9 & 1.0 & 0.9 & 0.4 & 1.6 & 1.3 & 0.3 & 0.3 \\
America S & 1.7 & 0.8 & 0.9 & 1.4 & 2.2 & 0.9 & 0.6 & 0.4 \\
America N & 0.1 & 0.1 & 0.0 & 0.0 & 0.1 & 0.4 & 0.4 & 0.0 \\
Asia C & 2.2 & 0.7 & 2.5 & 0.3 & 1.8 & 0.3 & 0.7 & 11.4 \\
Asia E & 1.0 & 0.3 & 0.5 & 0.4 & 1.6 & 0.8 & 0.3 & 1.2 \\
Asia S & 5.9 & 0.1 & 5.6 & 2.3 & 6.5 & 0.0 & 0.0 & 0.1 \\
Asia SE & 2.6 & 0.6 & 0.8 & 0.8 & 1.9 & 0.4 & 0.2 & 0.2 \\
Asia W & 22.2 & 5.1 & 3.9 & 5.5 & 10.8 & 2.1 & 2.4 & 1.1 \\
Australia & 9.3 & 0.6 & 0.3 & 0.3 & 0.7 & 0.7 & 1.7 & 0.2 \\
Europe E & 5.4 & 1.7 & 1.4 & 1.1 & 5.8 & 1.9 & 1.4 & 1.3 \\
Europe N & 39.1 & 2.1 & 2.6 & 2.6 & 6.8 & 0.7 & 3.6 & 2.3 \\
Eurpe S & 30.1 & 11.4 & 15.8 & 15.8 & 23.0 & 4.5 & 0.7 & 10.0 \\
Europe W & 15.7 & 3.4 & 3.9 & 4.4 & 4.8 & 4.6 & 1.8 & 3.2 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Relative loss in \% of different food products in different world regions after a shock to the maize production of Ukraine. These numbers correspond to left half of each cell in the upper part of fig. 5 in the main text.}
\end{table}
\end{document}

q-fin

Moritz Laber
Peter Klimek
Martin Bruckner
Liuhuaying Yang
Stefan Thurner

"Shock propagation from the Russia-Ukraine conflict on international multilayer food production network determines global food availability" by Moritz Laber, Peter Klimek, Martin Bruckner, Liuhuaying Yang, and Stefan Thurner, arXiv:2210.01846v3, 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}
(n,m)&$ (2000,10)$& $(5000,20)$ & $(10000,50)$\\ \hline
Benchmark & 35.85s (6.48s) & 1292.2s (84.9s) & 10000s (1362s) \\
 Random & 4.97s (1.32s) & 26.7s (0.8s) & 61.4s (12.8s)\\
Spatial & 5.25s (2.49s) & 25.8s (1.7s) & 58.6s (12.1s)\\
\end{tabular}
\end{adjustbox}
\caption{\scriptsize Hierarchical Bayes rescaling of the Mat\'ern Gaussian process prior. Average run time for computing the posterior. Benchmark: Non-distributed method. Method 1: Random partitioning, Method 2: Spatial partitioning.}
\end{table}
\end{document}

stat

Botond Szabo
Amine Hadji
Aad van der Vaart

"Adaptation using spatially distributed Gaussian Processes" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \toprule
 \textbf{Measure} & \textbf{FT} & \textbf{ST} & \textbf{\% improvement} \\
 \hline
 True Positives & 155 & 165 & 6.5 \% \\
 False Positive & 159 & 64 & \textbf{59.7 \%}\\
 False Negative & 162 & 168 & -3.7 \% \\
 True Negative & 5,940 & 6,035 & 1.5 \% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Comparison of error categories in percentage between the fine-tuned model (FT) and the model combining fine-tuned+self-trained (ST) model, based on the dialectal part of the dev set of the NER task. }
\end{table}
\end{document}

cs

Muhammad Khalifa
Muhammad Abdul-Mageed
Khaled Shaalan

"Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling" by Muhammad Khalifa, Muhammad Abdul-Mageed, and Khaled Shaalan, arXiv:2101.04758v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A note on Erdős matrices and Marcus\unicode{x2013}Ree inequality" by Aman Kushwaha and Raghavendra Tripathi, arXiv:2503.09542v3, 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}
\hline
 & Multilayer Perceptron (MLP) \\
\hline
Layers & $2, 3$ \\
Nodes per layer & $50, 100, 150, 200, 250, 500$ \\
Activation function & ReLU \\
Loss function & MSE \\
Learning rate & $10^{-5}$ \\
Batch size & 64 \\
\hline
\end{tabular}
\caption{Overview of MLP configurations.}
\end{table}
\end{document}

q-fin

Laurens Van Mieghem
Antonis Papapantoleon
Jonas Papazoglou-Hennig

"Machine learning for option pricing: an empirical investigation of network architectures" by Laurens Van Mieghem, Antonis Papapantoleon, and Jonas Papazoglou-Hennig, arXiv:2307.07657v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
 \hline
 \textbf{Hyper-parameters} & \textbf{Values} \\
 \hline
 Dropout rate & $0.35$ \\
 \hline
 Batch Normalization & Quaternion Batch Normalization \\
 \hline
 Kernel Initializer & Glorot \\
 \hline
 Optimizer & Adam \\
 \hline
 Loss Function & Binary Cross-entropy \\
 \hline
 Epochs & $20$ \\
 \hline
 Batch Size & $128$ \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Hyper-parameters}
\end{table}
\end{document}

eess

Deepak Kumar
Nalin Kumar
Subhankar Mishra

"QUARC: Quaternion Multi-Modal Fusion Architecture For Hate Speech Classification" by Deepak Kumar, Nalin Kumar, and Subhankar Mishra, arXiv:2012.08312v1, 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 regression slopes of each proximity type corresponding to the points in Figure~. RF-GAP does not exhibit bias towards the training data as they have a slope close to one, while the larger slope of the other proximity definitions indicates they are overfitting the training data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|}
 \hline
 Type & Slope\\
 \hline
 RF-GAP & 1.040\\
 Original & 2.434\\
 OOB & 1.543\\
 PBK & 2.287\\
 RFProxIH & 2.147\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Jake S. Rhodes
Adele Cutler
Kevin R. Moon

"Geometry- and Accuracy-Preserving Random Forest Proximities" by Jake S. Rhodes, Adele Cutler, and Kevin R. Moon, arXiv:2201.12682v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \toprule
 scenario & increase & decrease \\ \midrule
 real time zonal & 156.7 & 31.8 \\
 time invariant zonal & 157.2 & 30.7 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Total annual generation increase and decrease after the prosumer decision stage (TWh).}
\end{table}
\end{document}

q-fin

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

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


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Statistics for Cartpole environment.}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c|c|c||}
 \hline
 Algorithm & Return average & Variance average & Return median & Total running time(s)\\
 \hline\hline
 PFedDQN-Rep & 143 &43 &154 &466\\
 \hline
 DQN & 135 &54 &127 &3840\\
 \hline
 FedDQN & 101 &67 &88 &387\\
 \hline
 FedQ-K & 112 &34 &107 &490\\
 \hline
 LFRL & 117 &47 &99 &434\\
 \hline
 PerDQNAvg & 127 &48 &131 &520\\
 \hline
 FedAsynQ-ImAvg & 119 &51 &117 &501\\
 \hline

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

stat

Guojun Xiong
Shufan Wang
Daniel Jiang
Jian Li

"On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations" by Guojun Xiong, Shufan Wang, Daniel Jiang, and Jian Li, arXiv:2411.15014v2, 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}{rrrr}
 \hline
 & random & kmeans & kmed \\
 \hline
Decathlon & 8.2 (1.8) & 23.3 (3) & 22.4 (3.7) \\
 Ames & 16.0 (4.3) & 33.6 (8.4) & 25.4 (7.6) \\
 Normal & 1.0 (1.0) & 1.8 (0.2) & 1.8 (1.1)\\
 Uniform & 1.1 (1.0) & 0.9 (0.1) & 1.3 (1.0)\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Catherine B. Hurley
Mark O'Connell
Katarina Domijan

"Interactive slice visualization for exploring machine learning models" by Catherine B. Hurley, Mark O'Connell, and Katarina Domijan, arXiv:2101.06986v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Shihong Song
Guanlin Mo
Qingyuan Yang
Hu Ding

"Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems" by Shihong Song, Guanlin Mo, Qingyuan Yang, and Hu Ding, arXiv:2411.01115v3, 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}{lc}
 \hline
 \textbf{Stock Ticker} & \textbf{Weight(\%)} \\
 \hline
 JPM & 252.71 \\
 MMM & 230.60 \\
 KO & 193.30 \\
 CAT & 185.56 \\
 MSFT & 156.86 \\
 GS	& -121.70 \\
 AAPL & -130.76 \\
 PG	& -142.19 \\
 AXP	& -252.97 \\
 HON	& -481.37 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Top five long and short positions in the optimal eigen-portfolio based on absolute weight magnitude, which sums up to 100\%}
\end{table}
\end{document}

q-fin

"Eigen Portfolios: From Single Component Models to Ensemble Approaches" by ZhengXiang Zhou and Yuqi Luan, arXiv:2508.15586v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c}
		\toprule[1pt]
		 Methods & mIOU (\%) \\
		\midrule[0.5pt]
		\midrule[0.5pt]
		HRNetV2 + OCR ~ & 40.5\\
		DRAN & 41.2 \\
		HRNetV2 + OCR + RMI & 45.2 \\
		\midrule[0.5pt]
		\textbf{Our CAA (EfficientNet-B7)} & \textbf{45.4}\\
		\bottomrule[1pt]
	\end{tabular}
\end{adjustbox}
\caption{Result comparison with the state-of-the-art approaches on the COCO-Stuff-10K test set for multi-scale prediction. Note that, the listed methods were not trained under the same settings, or using same backbone. }
\end{table}
\end{document}

cs

Ye Huang
Di Kang
Wenjing Jia
Xiangjian He
Liu Liu

"Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation" by Ye Huang, Di Kang, Wenjing Jia, Xiangjian He, and Liu Liu, arXiv:2101.07434v5, 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{MDP Elements for Bank Agents in EconGym}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
& \textbf{Non-Profit Platform} & \textbf{Commercial Bank} \\
\midrule
\textbf{Observation}
& --- & \( o_t^{\text{bank}} = \{ \iota_t, \phi_t, A_{t-1}, K_{t-1}, B_{t-1} \} \) \\
& --- & Interest benchmark, reserve ratio, deposits, firm loans, government bonds \\
\midrule
\textbf{Action}
& Static allocation rule & \(a_t^{\text{bank}} = \{ r^d_t, r^l_t \} \) \\
& No control over rates & Deposit and lending rate decisions \\
\midrule
\textbf{Reward}
& --- & \( a_t^{\text{bank}} = r^l_t (K_{t+1} + B_{t+1}) - r^d_t A_{t+1} \) \\
& Passive agent & Interest margin under reserve constraint \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Qirui Mi
Qipeng Yang
Zijun Fan
Wentian Fan
Heyang Ma
Chengdong Ma
Siyu Xia
Bo An
Jun Wang
Haifeng Zhang

"EconGym: A Scalable AI Testbed with Diverse Economic Tasks" by Qirui Mi, Qipeng Yang, Zijun Fan, Wentian Fan, Heyang Ma, Chengdong Ma, Siyu Xia, Bo An, Jun Wang, and Haifeng Zhang, arXiv:2506.12110v1, 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{Most Active Block Proposers Since the Merge}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrl}
\hline
 Fee receiver address & Blocks vldtd. & No. Trans. & Validator name \\
\hline \hline
0xdafea492d9c6733ae3d56b7ed1adb60692c98bc5 & 45,688 & 8,809,237 & Flashbots \\
0x388c818ca8b9251b393131c08a736a67ccb19297 & 36,753 & 5,536,598 & Lido Execution Layer \\
0x4675c7e5baafbffbca748158becba61ef3b0a263 & 32,827 & 4,265,292 & Coinbase \\
0xf2f5c73fa04406b1995e397b55c24ab1f3ea726c & 10,891 & 2,176,154 & bloXroute: Max Profit \\
0xebec795c9c8bbd61ffc14a6662944748f299cacf & 10,540 & 1,383,861 & Contract1 \\
0xe688b84b23f322a994a53dbf8e15fa82cdb71127 & 9,197 & 1,221,402 & Address1 \\
0x690b9a9e9aa1c9db991c7721a92d351db4fac990 & 7,903 & 1,694,011 & builder0x69 \\
0x199d5ed7f45f4ee35960cf22eade2076e95b253f & 5,045 & 1,057,766 & bloXroute: Regulated \\
0x6d2e03b7effeae98bd302a9f836d0d6ab0002766 & 4,793 & 587,538 & Address2 \\
0xb646d87963da1fb9d192ddba775f24f33e857128 & 3,577 & 766,706 & MEV Builder \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"An Event Study of the Ethereum Transition to Proof-of-Stake" by Elie Kapengut and Bruce Mizrach, arXiv:2210.13655v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l|}
 \hline
 \textbf{class} & \textbf{class size} & \textbf{graph category} & \textbf{category size} \\ \hline\hline
 \multirow{4}{*}{biological} & \multirow{4}{*}{1219} & bio & 24 \\ \cline{3-4}
 & & eco & 6 \\ \cline{3-4}
 & & protein & 1178 \\ \cline{3-4}
 & & bn & 11 \\ \hline\hline
 \multirow{6}{*}{infrastructure} & \multirow{6}{*}{29} & inf & 4 \\ \cline{3-4}
 & & massive & 0 \\ \cline{3-4}
 & & power & 8 \\ \cline{3-4}
 & & road & 3 \\ \cline{3-4}
 & & tech & 5 \\ \cline{3-4}
 & & web & 9 \\ \hline\hline
 \multirow{13}{*}{social} & \multirow{13}{*}{23} & ca & 7 \\ \cline{3-4}
 & & cit & 1 \\ \cline{3-4}
 & & dynamic & 43 \\ \cline{3-4}
 & & econ & 12 \\ \cline{3-4}
 & & email & 6 \\ \cline{3-4}
 & & ia & 17 \\ \cline{3-4}
 & & proximity & 6 \\ \cline{3-4}
 & & rec & 2 \\ \cline{3-4}
 & & retweet\_graphs & 28 \\ \cline{3-4}
 & & rt & 31 \\ \cline{3-4}
 & & soc & 21 \\ \cline{3-4}
 & & socfb & 27 \\ \cline{3-4}
 & & tscc & 33 \\ \hline\hline
 \multirow{8}{*}{miscellaneous} & \multirow{8}{*}{1820} & dimacs & 62 \\ \cline{3-4}
 & & dimacs10 & 17 \\ \cline{3-4}
 & & graph500 & 0 \\ \cline{3-4}
 & & heter & 0 \\ \cline{3-4}
 & & labeled & 47 \\ \cline{3-4}
 & & misc & 1555 \\ \cline{3-4}
 & & rand & 139 \\ \cline{3-4}
 & & sc & 0 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Classification of graphs into four classes, composed from all 31 categories in the Network Repository. The counts reflect the 500\,kB size limit of the compressed graph archives, which accounts for some categories being empty.}
\end{table}
\end{document}

math

Laslo Hunhold
James Quinlan
Stefan Wesner

"Numerical Performance of the Implicitly Restarted Arnoldi Method in OFP8, Bfloat16, Posit, and Takum Arithmetics" by Laslo Hunhold, James Quinlan, and Stefan Wesner, arXiv:2504.21130v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"On the physics of nested Markov models: a generalized probabilistic theory perspective" by Xingjian Zhang and Yuhao Wang, arXiv:2411.11614v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study : different way of feature fusion in GCM on PASCAL VOC 2007. $\checkmark$ indicates that the component is used.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llll}
		\hline\noalign{\smallskip}
		multiplication & addition & multiplication + addition & mAP \\
		\noalign{\smallskip}\hline\noalign{\smallskip}
		& & & 46 \\
		$\checkmark$ & & & 47.8 \\
		& $\checkmark$ & & 47.0 \\
		& & $\checkmark$ & \textbf{48.1} \\
		\noalign{\smallskip}\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

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.


stat

"Dynamic Causal Structure Discovery and Causal Effect Estimation" by Jianian Wang and Rui Song, arXiv:2501.06534v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llr}
\toprule
\bf Corpus						& \bf Type of Instances	& \bf Instances	\\
\midrule
ClaimRev\textsubscript{{\tiny{BASE}}}	& \bf Total claim pairs	& \bf 210\,222	\\
 & Claim Clarification	& 63 729	\\
 & Typo/Grammar Correction	& 59 690		\\
 & Corrected/Added Links & 17 882		\\
 & Changed Meaning of Claim & 1 178		\\
 & Misc	& 10 464		\\
 & None	& 57 279		\\
\midrule
ClaimRev\textsubscript{{\tiny{EXT}}}		& \bf Total claim pairs	& \bf 377\,659	\\
 & Revision distance 1				& 77 217\\
 & Revision distance 2				& 27 819\\
 & Revision distance 3			& 10 753\\
 & Revision distance 4				& 4 460	 \\
 & Revision distance 5				& 2 055 \\
 & Revision distance 6+				& 2 008 \\
 \midrule
Both Corpora					& \bf Claim revision chains			& \bf 124\,312	\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Statistics of the two provided corpus versions. ClaimRev\textsubscript{{\tiny{BASE}}}: Number of claim pairs in total and of each revision type. ClaimRev\textsubscript{{\tiny{EXT}}}: Number of claim pairs in total and of each revision distance. The bottom line shows the number of unique revision chains in the corpora.}
\end{table}
\end{document}

cs

Gabriella Skitalinskaya
Jonas Klaff
Henning Wachsmuth

"Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale" by Gabriella Skitalinskaya, Jonas Klaff, and Henning Wachsmuth, arXiv:2101.10250v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|ccccccccc}
			\hline\hline
			& $\omega_1$ & $\alpha_1$ & $\beta_1$ & $\gamma_1$ & $\omega_2$ & $\alpha_2$ & $\beta_2$ & $\gamma_2$ & $\theta$\\
			\hline
			$I$ & 0.028 & 0.063 & 0.025 & 0.037 & 0.033 & 0.034 & 0.030 & 0.029 & 0.054\\
			$\hat{A}$ & 0.024 & 0.059 & 0.024 & 0.036 & 0.028 & 0.030 & 0.026 & 0.024 & 0.049\\
			\hline\hline
	\end{tabular}
\end{adjustbox}
\caption{ Relative mean square error using the identity matrix $I$ and matrix $\hat{A}$ reported in Table .}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{BLEU scores of generated explanations.}
\begin{tabular}{|cc|ccc|}
 \hline

 Dataset & Model & BLEU-1 & BLEU-2 & BLEU-4 \\
 \hline
 & NARRE & 20.46 & 5.72 & 2.12 \\
 & NRT & 26.25 & 8.84 & 2.97 \\
 Yelp & SAER (topk) & 27.43 & 9.53 & 3.18 \\
 & SAER (reg + topk) & 28.69 & 10.29 & 3.37 \\
 & SAER & \textbf{28.88} & \textbf{10.44} & \textbf{3.44} \\
 \hline
 & NARRE & 29.78 & 9.47 & 3.27 \\
 & NRT & 42.16 & 17.54 & 5.63 \\
 Ratebeer & SAER (topk) & 43.92 & 19.60 & 6.56 \\
 & SAER (reg + topk) & 45.69 & 21.09 & 7.02 \\
 & SAER & \textbf{46.01} & \textbf{21.60} & \textbf{7.32} \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Aobo Yang
Nan Wang
Hongbo Deng
Hongning Wang

"Explanation as a Defense of Recommendation" by Aobo Yang, Nan Wang, Hongbo Deng, and Hongning Wang, arXiv:2101.09656v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average EH Efficiency for SU Scenario}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
 Algorithm & TS & HERA \\ \hline
Exhaustive search & 64.6\% & 84.6\% \\ \hline
Proposed & 57.5\% & 81.5\% \\ \hline
TD3 & 43.9\% & 74.3\% \\ \hline
DDPG & 54.9\% & 40.4\% \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Mahmoud M. Salim
Khaled M. Rabie
Ali H. Muqaibel

"Energy-Efficient Irregular RIS-aided UAV-Assisted Optimization: A Deep Reinforcement Learning Approach" by Mahmoud M. Salim, Khaled M. Rabie, and Ali H. Muqaibel, arXiv:2504.15031v1, 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} \hline
 Layer (type) & Output Shape & Param \# \\ \hline
 spectrogram (Conv2D) & (None, 511, 619, 32) & 320 \\
 max\_pooling2d (MaxPooling2D) & (None, 255, 309, 32) & 0 \\
 conv2d (Conv2D) & (None, 253, 307, 64) & 18496 \\
 max\_pooling2d (MaxPooling2D)& (None, 126, 153, 64) & 0 \\
 flatten (Flatten) & (None, 1233792) & 0 \\
 dense (Dense) & (None, 128) & 157925504 \\
 {\textit{[odour]}} {subject} (Dense) & (None, {\textit{[13]}} 11) & {\textit{[1677]}} 1419 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Signal Transformation for Effective Multi-Channel Signal Processing" by Sunil Kumar Kopparapu, arXiv:2412.17478v1, 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{Continuous versus continuous summary statistics of $p$-values computed by each approximation.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|}
\hline
&& \multicolumn{7}{c|}{Numerical summary of observed $p$-values}\\
&& \multicolumn{7}{c|}{}\\
Approximation & $p$ & minimum & $Q(0.25)$ & median & average & $Q(0.75)$ & maximum &IQR\\
\hline
$\chi^2$ estimated d.f. & 0.05 & 0.034 & 0.053 & 0.060 & 0.065 & 0.077 & 0.122 & 0.024 \\
$\chi^2$ simple d.f. & & 0.022 & 0.035 &0.041 & 0.044 & 0.053 & 0.086 & 0.018\\
$\Gamma(\alpha, \theta)$& &0.014 & 0.027 & 0.034 & 0.037 & 0.042 & 0.083 & 0.015 \\
\hline
$\chi^2$ estimated d.f. & 0.01 & 0.006 & 0.011 & 0.013 &0.016 & 0.019 & 0.045 & 0.008\\
$\chi^2$ simple d.f. & & 0.003 & 0.007 & 0.008 & 0.010 & 0.012 & 0.029 & 0.005 \\
$\Gamma(\alpha, \theta)$& &0.002 & 0.004 & 0.006 & 0.007 & 0.008 & 0.024& 0.004\\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Recursive random binning to detect and display pairwise dependence" by Chris Salahub and Wayne Oldford, arXiv:2311.08561v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of open-sourced audiobook corpora. Duration in ``hours.''}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccccc}
 Lang. & Corpus & Duration & \# of speakers & Professional & Parallel \\ \hline
 En & Blizzard2018~ & 6.5 & 1 & Yes & No\\
 En & Blizzard2013~ & 300 & 1 & Yes & No \\
 En & LibriTTS~ & 585 & 2,456 & No & Yes \\
 Fr & SynPaFlex~ & 87 & 1 & No & No \\
 Multi & TUNDRA~ & 60 & 14 & No & No \\
 Ja & J-KAC~ & 9 & 1 & Yes & No \\
 Ja & \textbf{J-MAC (ours)} & 32 & 39 & \textbf{Yes} & \textbf{Yes} \\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Shinnosuke Takamichi
Wataru Nakata
Naoko Tanji
Hiroshi Saruwatari

"J-MAC: Japanese multi-speaker audiobook corpus for speech synthesis" by Shinnosuke Takamichi, Wataru Nakata, Naoko Tanji, and Hiroshi Saruwatari, arXiv:2201.10896v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
 \hline
 & $\mbox{\textit{D-IFWGO}}_q$ \\ \hline
 $P_1$ & $\langle (0.59,0.41);0.73 \rangle$\\
 $P_2$ & $\langle (0.61,0.39);0.61 \rangle$\\
 $P_3$ & $\langle (0.56,0.44);0.42 \rangle$\\
 $P_4$ & $\langle (0.59,0.41);0.66 \rangle$\\
 $P_5$ & $\langle (0.55,0.45);0.70 \rangle$\\ \hline
 $P_*$ & $\langle (0.61,0.39);0.73 \rangle$\\
 $P_\neg$ & $\langle (0.55,0.45);0.73 \rangle$\\ \hline

 \end{tabular}
\end{adjustbox}
\caption{Aggregation results with the displaced ideal and anti-ideal ratings}
\end{table}
\end{document}

math

"Solar Panel Selection using Extended WASPAS with Disc Intuitionistic Fuzzy Choquet Integral Operators: CASPAS Methodology" by Mahmut Can Bozyiğit and Mehmet Ünver, arXiv:2501.12251v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average Performance in Cross-Database Comparison}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Methods 		& PLCC 		&SRCC			&KRCC 	\\ \midrule
DIIVIVE 	 	& 0.4639 		& 0.4566		&0.3324 \\
BRISQUE 		& 0.5301 		& 0.5305		&0.3965 \\
CNN-IQA 		& 0.3994 	& 0.4040		&0.2900 \\
HOSA 	 	& 0.5964			& 0.5819		&0.4443 	\\
LPC-SI			& 0.6538 			& 0.5458 		&0.4277		\\
MLV 			& 0.5631 	& 0.4248		&0.3326 \\
GPC 			& 0.6312 	& 0.5965		&0.4794	 \\
SPARISH 		& 0.6575 	& {\color[HTML]{3166FF} \textbf{0.6609}}		&{\color[HTML]{3166FF} \textbf{0.5110}} \\
Synthetic-MaxPol	& 0.4873 	& 0.3656		&0.2824 \\
HVS-MaxPol 		& 0.5788 	& 0.5464		&0.4157 \\
FQPath 				& 0.5310 	& 0.5209		&0.3979 \\
FocusLiteNN 		& {\color[HTML]{3166FF} \textbf{0.6651}} 	& 0.5640		&0.4424 \\
HYQM 			& 0.3625 	& 0.2599		&0.2008 \\ \midrule
NSS-SR 			& 0.4293 & 0.3478		&0.2558 \\
LNQM 			& 0.6633 	& 0.6413		&0.4606		 \\
BSRIQA 			& 0.6587 		& 0.5639 &0.3987 	\\
DISQ 	 		&{\color[HTML]{CB0000} \textbf{0.7615}}	& {\color[HTML]{CB0000} \textbf{0.7643}} 	&{\color[HTML]{CB0000} \textbf{0.5784}}\\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Tiesong Zhao
Yuting Lin
Yiwen Xu
Weiling Chen
Zhou Wang

"Learning-Based Quality Assessment for Image Super-Resolution" by Tiesong Zhao, Yuting Lin, Yiwen Xu, Weiling Chen, and Zhou Wang, arXiv:2012.08732v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"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.


eess

"Sample selection with noise rate estimation in noise learning of medical image analysis" by Maolin Li and Giacomo Tarroni, arXiv:2312.15233v2, 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{Granger causality test results for lag $1$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}

\hline
\hline
number of lags (no zero) 1\\
\hline
ssr based F test: & F=23.1736 &p=0.0000 & df\_denom=825 & df\_num=1\\
ssr based chi2 test: & chi2=23.2579 & p=0.0000 & df=1 &\\
likelihood ratio test: &chi2=22.9372 &p=0.0000 & df=1&\\
parameter F test: &F=23.1736 &p=0.0000 & df\_denom=825 & df\_num=1\\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Xinwen Ni
Wolfgang Karl Härdle
Taojun Xie

"A Machine Learning Based Regulatory Risk Index for Cryptocurrencies" by Xinwen Ni, Wolfgang Karl Härdle, and Taojun Xie, arXiv:2009.12121v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{\color{black}A toy data example with numerical values having ten observations (eight class 0 examples and two class 1 examples).}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}
\hline Feature 1 & 2.0 & 2.2 & 2.4 & 2.6 & 2.8 & 1.8 & 1.9 & 2.3 & 4.0 & 4.1 \\ \hline
 Feature 2 & 2.0 & 2.2 & 2.4 & 2.6 & 2.8 & 1.8 & 2.1 & 2.1 & 4.0 & 4.1 \\ \hline
 Class & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Shraddha M. Naik
Tanujit Chakraborty
Madhurima Panja
Abdenour Hadid
Bibhas Chakraborty

"Skew-Probabilistic Neural Networks for Learning from Imbalanced Data" by Shraddha M. Naik, Tanujit Chakraborty, Madhurima Panja, Abdenour Hadid, and Bibhas Chakraborty, arXiv:2312.05878v2, 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{Precision, Recall, and F-measures for different systems. MV\_6 is the majority vote of all, and MV\_3 is majority vote of GS, MS, and CH.}
\begin{tabular}{|c||c|c|c|c|c|c||c|c|}
\hline
Systems & NSP & CH & STS & LSS & GS & MS & MV\_6 & MV\_3 \\ \hline
Precision & 0.12 & 0.42 & 0.32 & 0.21 & \textbf{0.50} & 0.37 & 0.39 & \textbf{0.59} \\ \hline
Recall & 0.13 & 0.29 & 0.44 & 0.32 & 0.49 & \textbf{0.76} & \textbf{0.71} & 0.69 \\ \hline
F-measure & 0.12 & 0.34 & 0.37 & 0.15 & \textbf{0.50} & \textbf{0.50} & 0.49 & \textbf{0.65} \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Mihir Parmar
Ashwin Karthik Ambalavanan
Hong Guan
Rishab Banerjee
Jitesh Pabla
Murthy Devarakonda

"COVID-19: Comparative Analysis of Methods for Identifying Articles Related to Therapeutics and Vaccines without Using Labeled Data" by Mihir Parmar, Ashwin Karthik Ambalavanan, Hong Guan, Rishab Banerjee, Jitesh Pabla, and Murthy Devarakonda, arXiv:2101.02017v1, 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{Bit error rate $q$ versus BSC SNR (dB)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
{BSC SNR (dB)} & {Bit Error Rate $q$} \\
\hline
0 & 0.07865 \\
1 & 0.05628 \\
2 & 0.03751 \\
3 & 0.02288 \\
4 & 0.01250 \\
5 & 0.00595 \\
6 & 0.00239 \\
8 & $1.91 \times 10^{-4}$\\
12 & $9.01 \times 10^{-9}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Liangshun Wu
Wen Chen
Qingqing Wu
Xudong Bai
Kunlun Wang

"RIS Codebook Index Assignment under Imperfect Control Links Using TSP-Inspired Optimization" by Liangshun Wu, Wen Chen, Qingqing Wu, Xudong Bai, and Kunlun Wang, arXiv:2507.18727v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc}
\hline
\textbf{Features extracted from} & \textbf{LR} & \textbf{RF} & \textbf{SVM} & \textbf{KNN} & \textbf{GB} \\ \hline
Price Time Series (P)& 65.81\% & 94.37\% & 66.86\% & 84.59\% & 91.63\%\\
Return Rate Time Series (R) & 70.02\% & 86.04\% & 61.44\% & 72.67\% & 84.58\%\\
Normalized Price Time Series (NP)& 65.68\% & 78.40\% & 65.33\% & 63.92\% & 74.20\%\\
Normalized Return Rate Time Series (NR) & 69.46\% & 80.96\% & 68.09\% & 70.86\% & 79.16\%\\
P+R & 64.81\% & 96.05\% & 66.36\% & 87.92\% & 93.47\%\\
NP+NR & 70.12\% & 84.88\% & 71.21\% & 71.42\% & 82.88\% \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks" by Yu Zhang, Zelin Wu, and Claudio Tessone, arXiv:2504.12771v1, 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{\textbf{Aggregate Card Spending in England by Dec 2020 Tiers \\ (Year-on-Year \% Changes)}}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} \hline
		\textbf{Tier 2} & \multicolumn{4}{c}{Month}		\\
		\hline
		& April & July & October & November \\
		Overall & -28.1\% & -9.1\% & +15.6\% & +15.1\% \\
		Offline & -45.3\% & -22.6\% & -3.2\% & -12.4\% \\
		Online & +3.6\% & +17.2\% & + 47.9\% & + 60.3\% \\
		\hline \\
		 \\
		\hline
		\textbf{Tier 3} & \multicolumn{4}{c}{Month}		\\
		\hline
		& April & July & October & November \\
		Overall & -27.9\% & -10.1\% & +8.5\% & +9.3\% \\
		Offline & -44.5\% & -22.4\% & -4.8\% & -14.9\% \\
		Online & +2.3\% & +9.8\% & + 31.9\% & + 49.4\% \\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

John Gathergood
Fabian Gunzinger
Benedict Guttman-Kenney
Edika Quispe-Torreblanca
Neil Stewart

"Levelling Down and the COVID-19 Lockdowns: Uneven Regional Recovery in UK Consumer Spending" by John Gathergood, Fabian Gunzinger, Benedict Guttman-Kenney, Edika Quispe-Torreblanca, and Neil Stewart, arXiv:2012.09336v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Confusion matrix for the decision tree algorithm on the no-chin dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|ccc|}
 \cline{3-5}
 \multicolumn{2}{c|}{\multirow{2}{*}{Diagnosis}} & \multicolumn{3}{|c|}{Actual} \\
 \cline{3-5}
 \multicolumn{2}{c|}{} & P & C & H \\
 \hline
 & P & 81.4\,\% & 35.0\,\% & 13.3\,\% \\
 & C & 11.8\,\% & 47.5\,\% & 21.7\,\% \\
 \multirow{-3}{*}{Predicted} & H & 6.7\,\% & 17.5\,\% & 65.0\,\% \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

C. V. Vletter
H. L. Burger
H. Alers
N. Sourlos
Z. Al-Ars

"Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features" by C. V. Vletter, H. L. Burger, H. Alers, N. Sourlos, and Z. Al-Ars, arXiv:2201.11852v1, 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}{lcccc}
\toprule
\textbf{Method} & \textbf{Expert 1} & \textbf{Expert 2} & \textbf{Expert 3} & \textbf{Average}$\uparrow$ \\
\midrule
RL & 4.5 & 5.0 & 4.5 & \textbf{4.67} \\
GAN & 4.0 & 4.0 & 4.5 & 4.17 \\
Actual & 3.5 & 3.5 & 4.0 & 3.67 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Expert Evaluation Scores for Different Methods. Best score denoted by \textbf{bold}.}
\end{table}
\end{document}

q-fin

Jaskaran Singh Walia
Aarush Sinha
Srinitish Srinivasan
Srihari Unnikrishnan

"Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation" by Jaskaran Singh Walia, Aarush Sinha, Srinitish Srinivasan, and Srihari Unnikrishnan, arXiv:2502.17011v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c}
 \hline
 Region & (1) & (2) &ARIMA \\
 \hline
 Matara& 11.38& \bf{11.36} & 11.92\\
 Anuradhapura&14.70&\bf{12.81}&14.70\\
 Polonnaruwa & 8.65&\bf{8.04}&8.65\\
 Kurunegala & 14.70 &\bf{10.08} & 10.35\\
 Hambantota & 32.76&\bf{32.12} &34.63 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{RMSE prediction errors of different models}
\end{table}
\end{document}

cs

"An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics" by Zhijian Li, Jack Xin, and Guofa Zhou, arXiv:2201.09394v1, 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}{lccc}
\hline
Pulsar & Dataset & Min Entropy (bits per bit) \\
\hline
PSR J0030+0451 & EPTA & 0.974\\
PSR J1918-0642 & EPTA & 0.826\\
PSR J2124-3358 & EPTA & 0.801\\
PSR J1843-1113 & EPTA & 0.911\\
PSR J2322+2057 & EPTA & 0.699\\
PSR J1832-0836 & NANOGrav & 0.679\\
PSR J2302+4442 & NANOGrav & 0.909\\
PSR J0030+0451 & NANOGrav & 0.882\\
PSR J1918-0642 & NANOGrav & 0.739\\
PSR J1012+5307 & NANOGrav & 0.798\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Empirical results validating non-trivial (significantly larger than $0$) min-entropy for $10$ pulsars, $5$ from NANOGrav and $5$ from EPTA. Note that the maximum possible min-entropy is $1$.}
\end{table}
\end{document}

math

Random Number Generation from Pulsars

"Random Number Generation from Pulsars" by Hayder Tirmazi, arXiv:2502.18430v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

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

"A Systematic Exploration of Reservoir Computing for Forecasting Complex Spatiotemporal Dynamics" by Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, and Henry D. I. Abarbanel, arXiv:2201.08910v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Classification of vacuum Friedmann cosmologies in General Relativity . The quantity \(q\) is defined as \(q = \sqrt{|\Lambda|/3}\).}
\begin{tabular}{c|c|c|c}
 & \(\Lambda < 0\) & \(\Lambda = 0\) & \(\Lambda > 0\)\\
\hline
\(\kappa = 1\) & no solution & no solution & de Sitter\\
 & & & \(a = \cosh(q t) / q\)\\
\hline
\(\kappa = 0\) & no solution & Minkowski & de Sitter\\
 & & \(a = 1\) & \(a = \exp(q t)\)\\
\hline
\(\kappa = -1\) & anti de Sitter & Minkowski & de Sitter\\
 & \(a = \sin(q t) / q\) & \(a = t\) & \(a = \sinh(q t) / q\)\\
\end{tabular}
\end{table}
\end{document}

math

Oscar Castillo-Felisola
Bastian Grez
Jose Perdiguero
Aureliano Skirzewski

"Inflationary scenarios in an effective polynomial affine model of gravity" by Oscar Castillo-Felisola, Bastian Grez, Jose Perdiguero, and Aureliano Skirzewski, arXiv:2312.07312v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Ben Zhe Wang
Jeffrey Sheen
Stefan Trück
Shih-Kang Chao
Wolfgang Karl Härdle

"A note on the impact of news on US household inflation expectations" by Ben Zhe Wang, Jeffrey Sheen, Stefan Trück, Shih-Kang Chao, and Wolfgang Karl Härdle, arXiv:2009.11557v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
\hline
Parameter & Value & Description \\ \hline
$Lm$ & 344 & number of points in longitude direction \\ \hline
$Mm$ & 228 & number of points in latitude direction \\ \hline
$N$ & 10 & number of vertical (sigma) levels \\ \hline
$h_\textrm{max}$ & 146 & maximum depth of the domain (metres) \\ \hline
$h_\textrm{min}$ & 0.29 & minimum depth of the domain (metres) \\ \hline
$\theta_s$ & 5.0 & sigma coordinate surface stretching factor \\ \hline
$\theta_b$ & 0.4 & sigma coordinate bottom stretching factor \\ \hline
$\Delta_t$ & 10 & baroclinic time-step (seconds) \\ \hline
$\Delta_{tt}$ & 7 & barotropic time-step (seconds) \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Numerical values for the parameters associated with the setup of the ocean model.}
\end{table}
\end{document}

math

G. Garcia-Sanchez
A. M. Mancho
A. G. Ramos
J. Coca
J. A. Jimenez-Madrid

"Dynamical systems for remote validation of very high-resolution ocean models" by G. Garcia-Sanchez, A. M. Mancho, A. G. Ramos, J. Coca, and J. A. Jimenez-Madrid, arXiv:2501.16026v1, 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}{lccc}
 \hline
 \hline
 \textbf{Source} & \textbf{Train} & \textbf{Test} & \textbf{Total} \\
 \hline
 \hline
 Twitter, 2013 & 1,368 & 588 & 1,956 \\
 Dialogues, 2016 & 3754 & 938 & 4,692 \\
 Twitter, 2017 & 51,189 & 3,742 & 54,931 \\
 Reddit, 2018 & 154,702 & 64,666 & 219,368 \\
 Headlines, 2019 & 22895 & 5724 & 28,619 \\
 \hline
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media" by Ramya Akula and Ivan Garibay, arXiv:2101.05875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results achieved on RTE dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\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 & 0.84& 1.06\\
\hline
\bfseries{GRU} & \bfseries{2} & \bfseries{0.81} & 1.09 \\
\hline
\bfseries{BLSTM} & \bfseries{2} & 1.63 & \bfseries{1.04} \\
\hline
LSTM & 2 & 1.17 & 1.19 \\
\hline
BRNN & 2 & 1.26 & 1.13 \\
\hline
RNN & 2 & 1.39 & 1.14 \\
\hline
\end{tabular}
\end{adjustbox}
\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{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The fuzziness of parameters of fist convolutional layer in LeNet.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
		\toprule
		& MNIST & CIFAR10 \\ \midrule
		Standard model & 0.4417 & 0.2842 \\
		Adv. model & 0.2611 & 0.1594 \\
		Min-Max model & \textbf{0.0053} & \textbf{0.0016} \\ \bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks" by Haojing Shen, Sihong Chen, and Ran Wang, arXiv:2011.13719v1, 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{Emotion distribution in the IEMOCAP dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc|c}
\toprule
Split & Happy & Sad & Neutral & Angry & Total \\
\midrule
Train + Val & 1262 & 828 & 1319 & 872 & 4290 \\
Test & 442 & 245 & 384 & 170 & 1241 \\
\midrule
Total & 1704 & 1073 & 1703 & 1042 & 5531 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"LPGNet: A Lightweight Network with Parallel Attention and Gated Fusion for Multimodal Emotion Recognition" by Zhining He and Yang Xiao, arXiv:2508.08925v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Career Modeling with Missing Data and Traces" by Théo Voldoire, Robin J. Ryder, and Ryan Lahfa, arXiv:2311.15257v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{GTS Parameter Estimation for Bitcoin}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
\toprule
\textbf{Model} & \textbf{Parameter} & \textbf{Estimate} & \textbf{Std Err} & \textbf{$Pr(Z >|z|)$} \\ \toprule
\multirow{10}{*}{\textbf{GTS}} & \multirow{1}{*}{\textbf{$\mu$}} &\multirow{1}{*}{-0.121571} & \multirow{1}{*}{(0.375)} & \multirow{1}{*}{$7.5*10^{-01}$} \\
 & \multirow{1}{*}{\textbf{$\beta_{+}$}} & \multirow{1}{*}{0.315548} & \multirow{1}{*}{(0.136)} & \multirow{1}{*}{$2.0*10^{-02}$} \\
 &\multirow{1}{*}{ \textbf{$\beta_{-}$}} & \multirow{1}{*}{0.406563} & \multirow{1}{*}{(0.117)} & \multirow{1}{*}{$4.9*10^{-04}$} \\
 & \multirow{1}{*}{\textbf{$\alpha_{+}$}} & \multirow{1}{*}{0.747714} & \multirow{1}{*}{(0.047)} & \multirow{1}{*}{$6.2*10^{-56}$} \\
 & \multirow{1}{*}{\textbf{$\alpha_{-}$}} & \multirow{1}{*}{0.544565} & \multirow{1}{*}{(0.037)} & \multirow{1}{*}{$4.8*10^{-48}$} \\
 &\multirow{1}{*}{\textbf{$\lambda_{+}$}} & \multirow{1}{*}{0.246530} & \multirow{1}{*}{(0.036)} & \multirow{1}{*}{$4.9*10^{-12}$} \\
 & \multirow{1}{*}{\textbf{$\lambda_{-}$}} & \multirow{1}{*}{0.174772} & \multirow{1}{*}{(0.026)} & \multirow{1}{*}{$2.2*10^{-11}$} \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Comparing Bitcoin and Ethereum tail behavior via Q-Q analysis of cryptocurrency returns" by A. H. Nzokem, arXiv:2507.01983v1, 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{\large Asymmetric treatment on all US counties}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \tabularnewline\midrule\midrule
 Dependent Variable: & \multicolumn{3}{c}{$\Delta$ Unemployment Rate}\\
 Model: & (1) & (2) & (3)\\
 \midrule \emph{Variables} & & & \\
 $\Delta$ Active Mines\textsubscript{t} & -0.0193 & -0.0752$^{**}$ & \\
 & (0.0201) & (0.0235) & \\
 $\Delta$ Active Mines\textsubscript{t-1} & 0.0221 & -0.0301 & \\
 & (0.0285) & (0.0219) & \\
 $\Delta$ Active Mines\textsubscript{t-2} & 0.0090 & 0.0546$^{***}$ & \\
 & (0.0236) & (0.0116) & \\
 $\Delta$ (log) Real GDPPC\textsubscript{t} & -0.9724$^{***}$ & -0.9724$^{***}$ & -0.9724$^{***}$\\
 & (0.2138) & (0.2138) & (0.2138)\\
 $\Delta$ Active Mines\textsubscript{t} $\times$ $\Delta_{negative_{t}}$ & -0.0559 & & -0.0752$^{**}$\\
 & (0.0370) & & (0.0235)\\
 $\Delta$ Active Mines\textsubscript{t-1} $\times$ $\Delta_{negative_{t-1}}$ & -0.0521 & & -0.0301\\
 & (0.0422) & & (0.0219)\\
 $\Delta$ Active Mines\textsubscript{t-2} $\times$ $\Delta_{negative_{t-2}}$ & 0.0457 & & 0.0546$^{***}$\\
 & (0.0277) & & (0.0116)\\
 $\Delta$ Active Mines\textsubscript{t} $\times$ $\Delta_{positive_{t}}$ & & 0.0559 & -0.0193\\
 & & (0.0370) & (0.0201)\\
 $\Delta$ Active Mines\textsubscript{t-1} $\times$ $\Delta_{positive_{t-1}}$ & & 0.0521 & 0.0221\\
 & & (0.0422) & (0.0285)\\
 $\Delta$ Active Mines\textsubscript{t-2} $\times$ $\Delta_{positive_{t-2}}$ & & -0.0457 & 0.0090\\
 & & (0.0277) & (0.0236)\\
 \midrule \emph{Fixed-effects} & & & \\
 County FIPS Code & Yes & Yes & Yes\\
 Year & Yes & Yes & Yes\\
 \midrule \emph{Fit statistics} & & & \\
 Observations & 55,295 & 55,295 & 55,295\\
 R$^2$ & 0.61355 & 0.61355 & 0.61355\\
 Within R$^2$ & 0.01513 & 0.01513 & 0.01513\\
 \midrule\midrule\multicolumn{4}{l}{\emph{Clustered (County FIPS Code \& Year) standard-errors in parentheses}}\\
 \multicolumn{4}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05, .: 0.1}}\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
Regret at Period & 50 & 60 & 70 & 80 & 90 & 100 & 110 & 120\\ \hline
One-Time MILP & 36.25 & 55.31 & 60.56 & 60.61 & 60.79 & 60.97 & 61.03 & 61.12 \\ \hline
One-Time LP & 36.25 & 55.31 & 61.95 & 64.99 & 67.78 & 71.07 & 74.31 & 77.16 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Comparison of Regrets Without Cost Structure Assumption (Exploration Period is of Length $60$).}
\end{table}
\end{document}

math

Hansheng Jiang
Chunlin Sun
Zuo-Jun Max Shen
Shunan Jiang

"Learning While Repositioning in On-Demand Vehicle Sharing Networks" by Hansheng Jiang, Chunlin Sun, Zuo-Jun Max Shen, and Shunan Jiang, arXiv:2501.19208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small REP-focused results under nodal, zonal, and copper-plate modeling. Results are reported for a 1000 MW electrolyzer, market bidding REP, under the lower and higher hydrogen price. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrr} \toprule
&
& \textbf{Electrolyzer}
& \textbf{REP}
& \textbf{Average} \\
&
& \textbf{average load}
& \textbf{profit}
& \textbf{LMP}\\
\multicolumn{2}{c}{\textbf{Case}}
& { [}MW{]}
& {[}M $\$${]}
& { [}\$/MWh{]} \\ \toprule
\multirow{3}{*}{$\$$1.5/kg} & Copper & 843.53 & 80.92 & 22.05 \\
 & Zonal & 829.25 & 79.80 & 22.26 \\
 & Nodal & 629.77 & 67.76 & 24.45 \\ \midrule
\multirow{3}{*}{$\$$6/kg} & Copper & 1000.00 & 769.76 & 22.39 \\
 & Zonal & 1000.00 & 766.06 & 22.85 \\
 & Nodal & 729.34 & 320.83 & 81.55 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Andrea Gloppen Johnsen
Lesia Mitridati
Jalal Kazempour
Line Roald

"Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach" by Andrea Gloppen Johnsen, Lesia Mitridati, Jalal Kazempour, and Line Roald, arXiv:2501.16844v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Conditional-mean Multiplicative Operator Models for Count Time Series" by Christian H. Weiß and Fukang Zhu, arXiv:2212.05831v2, 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|l|l|} \hline
Case& \multicolumn{2}{l|}{Description} \\
\hline\hline
True Positive (TP)& \multicolumn{2}{p{5.5cm}|}{\raggedright Set to 1 if at least one failure prediction made during the Correct Prediction Period; otherwise, it is set to 0.}\\
\hline
False Negative (FN) & \multicolumn{2}{p{5.5cm}|}{\raggedright Set to1 if no failure prediction made during the Correct Prediction Period; otherwise, it is set to 0.}\\
\hline
False Positive (FP) & \multicolumn{2}{p{5.5cm}|}{\raggedright The amount of failure predictions during the Early Prediction Period.}\\
\hline
True Negative (TN)& \multicolumn{2}{p{5.5cm}|}{\raggedright The amount of non-failure predictions during the Early Prediction Period.}\\
\hline
Ignored& \multicolumn{2}{p{5.5cm}|}{\raggedright All failure predictions during the Repair Period.}\\
\hline
\end{tabular}
\end{adjustbox}
\caption{Description of prediction cases within an episode.}
\end{table}
\end{document}

cs

Petros Petsinis
Athanasios Naskos
Anastasios Gounaris

"Analysis of key flavors of event-driven predictive maintenance using logs of phenomena described by Weibull distributions" by Petros Petsinis, Athanasios Naskos, and Anastasios Gounaris, arXiv:2101.07033v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Stefanie von Felten
Chiara Vanetta
Christoph M. Rüegger
Sven Wellmann
Leonhard Held

"Outcomes truncated by death in RCTs: a simulation study on the survivor average causal effect" by Stefanie von Felten, Chiara Vanetta, Christoph M. Rüegger, Sven Wellmann, and Leonhard Held, arXiv:2312.11991v3, 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{DiD Analysis -- Violence Spillovers: Extensive Margin, Events in Over 60\% of Years}
\begin{tabular}{l|cccccccc}
\toprule
& Gov. & FARC & ELN & Crim. Bands & FARC Diss. & Paramilitaries & Other & Total \tabularnewline
{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)}&{(7)}&{(8)} \tabularnewline
\midrule
\midrule Ceasefire \( \times \) FARC&--0.154&--0.162***&0.003&0.003&0.031**&0.029&--0.147***&--0.397* \tabularnewline
&(0.094)&(0.035)&(0.033)&(0.031)&(0.012)&(0.075)&(0.045)&(0.202) \tabularnewline
&&&&&&&& \tabularnewline
Mean Sample (Pre)&0.551&0.263&0.036&0.114&0.000&0.230&0.124&1.319 \tabularnewline
Mean Sample (Post)&0.373&0.113&0.109&0.114&0.043&0.264&0.088&1.104 \tabularnewline
Observations&2,827&2,827&2,827&2,827&2,827&2,827&2,827&2,827 \tabularnewline
R Squared&0.361&0.373&0.251&0.209&0.178&0.284&0.264&0.411 \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Peace Dividends: The Economic Effects of Colombia's Peace Agreement" by Miguel Fajardo-Steinhäuser, arXiv:2301.01843v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|l|}
\hline
\textbf{Notation} & \textbf{Description} \\ \hline
$y_{ct}^s(d)$ & Potential GDP level (in logs) at time $t+s$ for country $c$ transitioning to either democracy ($d=1$) or nondemocracy ($d=0$) at time $t$ \\ \hline
$\Delta y_{ct}^s(d)$ & Potential change in (log) GDP per capita from time $t-1$ to time $t+s$ for a country with a change in political regime $d \in \{0,1\}$ \\ \hline
$D_{ct}$ & Indicator variable for whether country $c$ is a democracy at time $t$ \\ \hline
$\beta^s$ & Causal effect of a transition to democracy at time $t$ on GDP $s$ periods thereafter for democratizing countries \\ \hline
$\perp$ & Independence symbol \\ \hline
$X_{ct}$ & Vector of covariates including year fixed effects and four lags of GDP \\ \hline
$\pi^*$ & Coefficient vector for the linear regression model of GDP changes \\ \hline
$\hat{\beta}^s$ & Estimated effect of democracy on GDP $s$ years after a transition \\ \hline
$\hat{\mathbb{E}}[\cdot \mid S]$ & Sample average of $\cdot$ for all observations in set $S$ \\ \hline
$\hat{\pi}^*$ & Ordinary least squares (OLS) estimate of $\pi^*$ \\ \hline
$P(D_{ct} = 1 \mid X_{ct}, D_{ct-1} = 0)$ & Probability of transitioning to democracy conditional on $X_{ct}$, $D_{ct-1} = 0$ estimated using a probit regression \\ \hline
$\Phi(\cdot)$ & Cumulative distribution function of the standard normal distribution \\ \hline
$\hat{P}_{ct}$ & Estimated propensity score \\ \hline
$\hat{w}_{ct}$ & Weighting scheme for observations determined by the estimated propensity scores \\ \hline
$\tilde{\pi}$ & Estimate for the counterfactual model \\ \hline
$\hat{\beta}^*$ & Doubly robust estimator of the effect of democracy on GDP \\ \hline
$\tilde{u}_{tt}$ & Reweighting term in the doubly robust estimator \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Notation and Description for Sequential Unconfounding Type (Long Term Matching Type)}
\end{table}
\end{document}

stat

"Matching $\leq$ Hybrid $\leq$ Difference in Differences" by Yechan Park and Yuya Sasaki, arXiv:2411.07952v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.