math

Aida Abiad
Ángeles Carmona
Andrés M. Encinas
Maria José Jiménez
Álvaro Samperio

"A Spectral Approach to Kemeny's Constant" by Aida Abiad, Ángeles Carmona, Andrés M. Encinas, Maria José Jiménez, and Álvaro Samperio, arXiv:2503.13382v1, 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{Samples, average NET2 lesion counts, and percentage of patients with NET2 lesion activity in the \textit{training set} at 1 and 2 years post-treatment. w048 values are measured relative to pre-treatment; w096 values are relative to w048.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
 \toprule
 \textbf{Treatment} & \textbf{Samples} & \multicolumn{2}{c}{\textbf{Avg. NET2 Count}} & \multicolumn{2}{c}{\textbf{NET2 Activity (\%)}} \\
 \cmidrule(lr){3-4} \cmidrule(lr){5-6}
 & & \textbf{w048} & \textbf{w096} & \textbf{w048} & \textbf{w096} \\
 \midrule
 Placebo & 267 & 4.60 & 3.33 & 63.7 & 58.1 \\
 Laquinimod (NE) & 184 & 5.51 & 4.49 & 64.7 & 57.1 \\
 Avonex (LE) & 214 & 4.37 & 4.37 & 54.7 & 51.9 \\
 Interferon Beta-1a (MiE) & 418 & 0.63 & 1.59 & 24.9 & 33.7 \\
 Dimethyl Fumarate (MoE) & 153 & 0.60 & 1.03 & 19.6 & 26.1 \\
 Ocrelizumab (HE) & 466 & 0.07 & 0.07 & 3.20 & 1.50 \\
 \midrule
 \textbf{Total} & 1702 & & & & \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

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

"Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments" by Gian Mario Favero, Ge Ya Luo, Nima Fathi, Justin Szeto, Douglas L. Arnold, Brennan Nichyporuk, Chris Pal, and Tal Arbel, arXiv:2508.07006v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{F1 Score (IN \%) for N-d-p-K combinations on PE-ZTWCC+SDC features with TDNN Classifier}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
\textbf{SDC Combinations} & \textbf{Repetitions} & \textbf{Filled Pause} & \textbf{Prolongation} \\
\hline
13-\textbf{1}-3-7 & 80.89 & 81.28 & 79.87 \\
13-\textbf{2}-3-7 & 81.34 & 81.89 & \textbf{82.78} \\
13-\textbf{3}-3-7 & 79.12 & 80.12 & 79.23 \\
\hline
13-2-3-\textbf{5} & 82.56 & \textbf{85.45} & 80.17\\
13-2-3-\textbf{6} & \textbf{86.98} & 84.67 & 80.34 \\
13-2-3-\textbf{7} & 81.34 & 81.89 & \textbf{82.78} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Priyanka Kommagouni
Vamshiraghusimha Narasinga
Purva Barche
Sai Akarsh C
Anil Vuppala

"Typical vs. Atypical Disfluency Classification: Introducing the IIITH-TISA Corpus and Temporal Context-Based Feature Representations" by Priyanka Kommagouni, Vamshiraghusimha Narasinga, Purva Barche, Sai Akarsh C, and Anil Vuppala, arXiv:2411.17149v1, 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{Results for Constant Axial Strain Patch Test}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Strain Component & Computed Average & Expected Value & Absolute Error \\
\midrule
\( \varepsilon_r \) (Radial strain) & 0.000000 & 0.000000 & 0.000000 \\
\( \varepsilon_z \) (Axial strain) & 0.010000 & 0.010000 & 0.000000 \\
\( \varepsilon_\theta \) (Hoop strain) & 0.000000 & 0.000000 & \(7.20\times10^{-8}\) \\
\( \gamma_{rz} \) (Shear strain) & 0.000000 & 0.000000 & \(2.17\times10^{-19}\) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Virtual Element Method Applied to Two Dimensional Axisymmetric Elastic Problems" by Paulo Akira F. Enabe and Rodrigo Provasi, arXiv:2504.21305v2, 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}{cccccccc} \toprule[1pt]
 CRSP & $\mu^s$ & $\sigma^s$ & $\lambda^s$ & $u^s$ &
 $\eta_1^s$ & $\eta_2^s$ & $\rho_{sb}$ \\ \midrule
 & 0.087323 & 0.147716& 0.316326 & 0.225806 & 4.3591 & 5.53370 & 0.095933\\
 \midrule[1pt]
30-day T-bill & $\mu^b$ & $\sigma^b$ & $\lambda^b$ & $u^b$ &
 $\eta_1^b$ & $\eta_2^b$ & $\rho_{sb}$ \\ \midrule
 & 0.0032 & 0.0140& 0.3878 & 0.3947 & 61.5350 & 53.4043 &0.095933
\\
\bottomrule[1pt]
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Risk Measures for DC Pension Plan Decumulation" by Peter A. Forsyth and Yuying Li, arXiv:2502.16364v1, 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{makecell}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cc|cc}
\Xhline{2\arrayrulewidth} \hline
 \multirow{2}{*}{} & \multicolumn{2}{c}{Fed-EF-SGD} & \multicolumn{2}{c}{Fed-EF-AMS} \\ %\cline{2-5}
 & $\eta$ & $\eta_l$ & $\eta$ & $\eta_l$ \\ \hline
MNIST & $10$ & $10^{-3}$ & $10^{-3}$ & $10^{-2}$ \\
FMNIST & $1$ & $10^{-1}$ & $10^{-2}$ & $10^{-1}$ \\
CIFAR-10 & 1 & $10^{-1}$ & $10^{-3}$ & $10^{-2}$ \\ \hline
\Xhline{2\arrayrulewidth}
\end{tabular}
\end{adjustbox}
\caption{Optimal global ($\eta$) and local ($\eta_l$) learning rate combinations to attain highest test accuracy.}
\end{table}
\end{document}

stat

"Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression" by Xiaoyun Li and Ping Li, arXiv:2211.14292v1, 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{CNN Model Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 & \textbf{Basic CNN} & \multicolumn{3}{c}{\textbf{Deep CNN}} \\ \cline{2-5}
\textbf{Parameter} & \textbf{Block 1} & \textbf{Block 1} & \textbf{Block 2} & \textbf{Block 3} \\ \hline
Convolutional 1D layer & & & & \\
Filter Size & 64 & 32 & 64 & 128 \\
Kernel Size & 2 & 3 & 2 & 1 \\
Padding & same & same & same & same \\
Dense Layer: Relu Activation & 265 & - & - & - \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis" by Tanja Aue, Adam Jatowt, and Michael Färber, arXiv:2212.11765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation Parameters}
\begin{tabular}{ll|ll}
 \hline
 Parameter & Value & Parameter & Value \\
 \hline
 Carrier frequency ($f_c$) & 2.4 GHz & Maximum latency ($\tau^\mathrm{max}$) & 5 ms \\
 Bandwidth (W) & 5 MHz & Channel noise power & -11.5 dBm \\
 DT required error variances $(\xi^2_{pos}, \xi^2_{vel})$ & (0.01, 0.001) & Max. distance ($d^{\max}$) & 20 m \\
 Max. connection ($C$) & 10 & Leaning rate (both actor and critic) & $1e^{-3}$ \\
 Rician factor ($G$) & 15 dB & Outage probability factor $(\varepsilon)$& $1e^{-5}$\\
 System parameter ($\Gamma$) & 1 & Data packet size $(D)$& $1024$ bits\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Van-Phuc Bui
Shashi Raj Pandey
Pedro M. de Sant Ana
Petar Popovski

"Value-Based Reinforcement Learning for Digital Twins in Cloud Computing" by Van-Phuc Bui, Shashi Raj Pandey, Pedro M. de Sant Ana, and Petar Popovski, arXiv:2311.15985v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small Mean and standard error (in brackets) from $50$ replicates of parameter estimates in the Jansen-Rit neural mass model (), under scenario \textbf{JR-3}.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \hline
 Parameter & True value & {Local Gaussian contrast} & {New contrast}
 \\
 \hline
 \\
 $C$ & 135.0 & 134.80 (0.0062) & 134.80 (0.0062) \\
 $\mu$ & 220.0 & 220.84 (0.6167) & 220.84 (0.6176) \\
 $\sigma_2 $ & 2000.0 &
 1843.49 (4.3520) & 1989.00 (4.750)
 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yuga Iguchi
Alexandros Beskos
Matthew M. Graham

"Parameter Estimation with Increased Precision for Elliptic and Hypo-elliptic Diffusions" by Yuga Iguchi, Alexandros Beskos, and Matthew M. Graham, arXiv:2211.16384v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Anisotropic local constant smoothing for change-point regression function estimation" by John R. J. Thompson and W. John Braun, arXiv:2012.00180v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Ammarah Hashmi
Sahibzada Adil Shahzad
Chia-Wen Lin
Yu Tsao
Hsin-Min Wang

"AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection" by Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao, and Hsin-Min Wang, arXiv:2310.13103v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A Randomization-Based Method for Evaluating Time-Varying Treatment Effects" by Sangjin Lee and Kwonsang Lee, arXiv:2412.09697v3, 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}{cc}
 \toprule
 Abbrevation & Definition \\
 \midrule
 AdaRound & Adaptive Rounding \\
 AIMET & AI Model Efficiency Toolkit \\
 API & Application Programming Interface \\
 BNF & Batch Normalization Folding \\
 CLE & Cross-layer Equalization\\
 DFQ & Data-free Quantization\\
 DLC & Deep Learning Container\\
 PTQ & Post-training Quantization \\
 QAT & Quantization-aware Training\\
 QuantSim & Quantization Simulation \\
 SDK & Software Development Kit\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Abbreviations}
\end{table}
\end{document}

cs

Sangeetha Siddegowda
Marios Fournarakis
Markus Nagel
Tijmen Blankevoort
Chirag Patel
Abhijit Khobare

"Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)" by Sangeetha Siddegowda, Marios Fournarakis, Markus Nagel, Tijmen Blankevoort, Chirag Patel, and Abhijit Khobare, arXiv:2201.08442v1, 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{List of representative efficient transformer-based models. The data of the Table is from .}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c}
				\hline
				Models & Compress Type & $\#$Layer &Params & Speed Up \\
				\hline
				BERT$_{BASE}$ & Baseline & 12 & 110M & $\times$1\\
				\hline
				ALBERT & Decomposition & 12 & 12M & $\times$5.6 \\
				\hline
				BERT-& Architecture & \multirow{2}*{6} & \multirow{2}*{66M} & \multirow{2}*{$\times$1.94}\\
				of-Theseus & design & & & \\
				\hline
				Q-BERT & \multirow{2}*{Quantization} & 12 & \multirow{2}*{-} & \multirow{2}*{-} \\
				Q8BERT & &12 & & \\
				\hline
				TinyBERT & \multirow{5}*{Distillation} & 4 & 14.5M & $\times$9.4 \\
				DistilBERT~ & & 6 & 6.6m & $\times$1.63 \\
				BERT-PKD~ & & 3$\sim$6 & 45.7$\sim$67M & $\times$3.73$\sim$1.64 \\
				MobileBERT~ & &24 &25.3M & $\times$4.0 \\
				PD~ & & 6 &67.5M &$\times$2.0\\
				\hline
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Fahad Shamshad
Salman Khan
Syed Waqas Zamir
Muhammad Haris Khan
Munawar Hayat
Fahad Shahbaz Khan
Huazhu Fu

"Transformers in Medical Imaging: A Survey" by Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, and Huazhu Fu, arXiv:2201.09873v1, 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
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 Census Tract & Population & SVI \\
 \hline
 1.01 & 5015 & 0.7236 \\
 1.02 & 5696 & 0.7282 \\
 2.03 & 1901 & 0.2533 \\
 2.04 & 4430 & 0.7931 \\
 2.05 & 6001 & 0.2650 \\
 2.06 & 4725 & 0.0761 \\
 2.07 & 6231 & 0.7299 \\
 3.01 & 3575 & 0.1122 \\
 3.02 & 5679 & 0.8001 \\
 4.01 & 3577 & 0.8111 \\
 4.02 & 6561 & 0.7432
 \end{tabular}
\end{adjustbox}
\caption{A table of SVI for Harrisonburg, VA by census tract.}
\end{table}
\end{document}

stat

Miranda Bihler
Hala Nelson
Erin Okey
Noe Reyes Rivas
John Webb
Anna White

"Modeling Supply and Demand in Public Transportation Systems" by Miranda Bihler, Hala Nelson, Erin Okey, Noe Reyes Rivas, John Webb, and Anna White, arXiv:2309.06299v2, 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}
 $\gamma$& white & lightgray & darkgray & black \\ \hline
 0.01 &[0,\, 0.011)&[0.011,\, 0.05)&[0.05,\, 150)&$[150,\, \infty)$ \\ \hline
 0.1 &[0,\, 0.061)&[0.061,\, 0.22)&[0.22,\, 150)&$[150,\, \infty)$ \\ \hline
 1 	 &[0,\, 0.125)&[0.125,\, 0.7)&[0.7,\, 150)&$[150,\, \infty)$ \\ \hline
 5 &[0,\, 1.25)&[1.25,\, 4.0)&[4.0,\, 150)&$[150,\, \infty)$
 \end{tabular}
\end{adjustbox}
\caption{Time intervals during which tipping was observed for each of the different gray shadings in Figure , as in Table~.}
\end{table}
\end{document}

math

"The effect of timescale separation on the tipping window for chaotically forced systems" by Raphael Römer and Peter Ashwin, arXiv:2504.12530v1, 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}{lcccc}
 \hline
 & NSI & Poverty Rate & Med HH Income & Emp-Pop Ratio \\
 \hline
 ATT Estimate & 0.198(0.067) & -0.020(0.009) & 0.081(0.031)& 0.016(0.007)\\
 p-value & 0.00 & 0.03 & 0.01 & 0.02\\
 Baseline Mean & 0 & 0.37 & \$27k & 0.39\\
 Pre-trends robust p & 0.23 & 0.29 & 0.26 & 0.36 \\
 Pre-trends robust 95\% CI & (-0.09,0.36) & (-0.06, 0.02) & (-0.02, 0.11) & (-0.013, 0.037)\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{ATT Estimates in Primary Event Studies}
\end{table}
\end{document}

q-fin

"Place-Based Policies for Neighborhood Improvement: Evidence from Promise Zones" by Adamson Bryant, arXiv:2503.05946v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Ramin Mousa
Saeed Chamani
Mohammad Morsali
Mohammad Kazzazi
Parsa Hatami
Soroush Sarabi

"Enhancing Skin Cancer Diagnosis (SCD) Using Late Discrete Wavelet Transform (DWT) and New Swarm-Based Optimizers" by Ramin Mousa, Saeed Chamani, Mohammad Morsali, Mohammad Kazzazi, Parsa Hatami, and Soroush Sarabi, arXiv:2412.00472v1, 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{Performance of NER systems for Mutation NER using single-labeled data for training.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|llll|}
 \hline
 \textbf{System} & \textbf{Precision} & \textbf{Recall}&\textbf{F-score (Std Dev)}\\ \hline
BioBERT& 73.4 & 63.3 & 67.9 (2.8)\\
Bilstm-crf&71.4&64.1&67.6\\
\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

David Martinez Iraola
Antonio Jimeno Yepes

"Single versus Multiple Annotation for Named Entity Recognition of Mutations" by David Martinez Iraola and Antonio Jimeno Yepes, arXiv:2101.07450v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Total $LR$ for 5 bus system in different strategies}
\begin{tabular}{ccccc}
\hline
Strategy & $c_1$ & $c_2$ & $c_3$ & $c_4$ \\ \hline
LR (MW) & 17.427 & 8.926 & 10.396 & 3.255 \\ \hline
$\bar{\alpha}^{LR}_{i=1,max}$ & 0&0&0& 0\\ \hline
$\bar{\alpha}^{LR}_{i=2,max}$ & 0.70&0.57&0.62& 0.33\\ \hline
$\bar{\alpha}^{LR}_{i=3,max}$ & 0.70&0.58&0.61& 0.32\\ \hline
$\bar{\alpha}^{LR}_{i=4,max}$ & 0.80&0.55&0.60& 0.35\\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Controllable Transmission Networks under Demand Uncertainty with Modular FACTS" by Alireza Soroudi, arXiv:2105.10402v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
\toprule
\textbf{Cluster} & 1 & 2 & 3 & 4 & 5 \\ \midrule
2 & 2.187589$\cdot 10^{-4}$ & \\
3 & 3.039844$\cdot 10^{-11}$ & 1.41$\cdot 10^{-3}$ & \\
4 & 1.070993$\cdot 10^{-10}$ & \textcolor{blue}{0.300540} & 2.98464$\cdot 10^{-4}$ &\\
5 & 3.038979$\cdot 10^{-16}$ & \textcolor{blue}{0.093018} & 6.015797$\cdot 10^{-5}$ & \textcolor{blue}{0.105446} & \\
6 & 1.729616$\cdot 10^{-6}$ & 0.010612 & 9.290826$\cdot 10^{-9}$ & 2.105$\cdot 10^{-3}$ & 5.624624$\cdot 10^{-5}$ \\\bottomrule
\end{tabular}
\end{adjustbox}
\caption{$p$-values computed under model retrieved after testing on the protein data presented in Section~. The hierarchical clustering algorithm was set to find six clusters using average linkage. In blue, adjusted $p$-values for which the null is not rejected at level $\alpha=0.05$.}
\end{table}
\end{document}

stat

Javier González-Delgado
Mathis Deronzier
Juan Cortés
Pierre Neuvial

"Post-clustering Inference under Dependence" by Javier González-Delgado, Mathis Deronzier, Juan Cortés, and Pierre Neuvial, arXiv:2310.11822v2, 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
 \hline
 Methods & MOS\\
 \hline
 \hline
 Ground truth &4.45$\pm$ 0.07\\
 WaveNet &4.3$\pm$ 0.130 \\
 WaveGlow &3.95$\pm$ 0.161\\
 DiffWave &4.325$\pm$ 0.123\\
 WaveGrad &4.1$\pm$ 0.158\\
 \hline
 \hline
 It\^oWave & 4.35$\pm$ 0.115 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"ItôWave: Itô Stochastic Differential Equation Is All You Need For Wave Generation" by Shoule Wu and Ziqiang Shi, arXiv:2201.12519v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An Exact SIR Series Solution and an Exploration of the Related Parameter Space" by Daniel P Hobbs, arXiv:2504.17026v1, 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{Median $l_2$ Distortion of Targeted Soft-Label Attacks under the BD$(\theta, \sigma)$ defense. $\sigma = 0.1$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|ccc}

		\toprule
		 Dataset & Attacks & No defense & $\theta_1$ = 0.5 & $\theta_2$ = 0.7 \\
		 	\midrule
	 & AZ & 0.0473 & 0.0478 & 0.0478 \\
 MNIST & GenAttack & 4.8086 & - & -\\
 & SimBA & 0.0957 & 0.0694 & 0.0823 \\
		 \midrule
	 & AZ & 0.0672 & 0.0672 & 0.0672 \\
	 CIFAR10 & GenAttack & 1.3874 & - & - \\
	 & SimBA & 0.0323 & 0.0236 & 0.0218\\
	 \midrule \midrule
		 Dataset & Attacks & No Defense & $\theta_1$ = 0.1 & $\theta_2$ = 0.3 \\
		 \midrule
	 & AZ & 0.1566 & - & - \\
	IMAGENET & GenAttack & 0.0377 & - & - \\
	 & SimBA & 0.0163 & 0.0092 & 0.0075\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Boundary Defense Against Black-box Adversarial Attacks" by Manjushree B. Aithal and Xiaohua Li, arXiv:2201.13444v1, 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{Channels - Kharif season}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
		\midrule
		\midrule
		 & (1) & (2) & (3) & (4) \\
				& Sev./Extr.	& Sev./Extr. 	& Sev./Extr. 	& Sev./Extr.	 \\
	& Depression 	& Depression 	& Depression 	& Depression \\

		 & Urban & Urban & Rural & Rural \\ \hline
 & & & & \\
\# Days \textgreater 27°C & -1.134 & & -1.272** & \\
& (1.056) & & (0.511) & \\
\# Days \textgreater 27°C * Kharif & 1.538 & & 1.992*** & \\
& (0.992) & & (0.482) & \\
Heatwave ($\geq$2 days $\geq$27°) & & -2.331 & & -4.621** \\
& & (3.232) & & (2.017) \\
Heatwave ($\geq$2 days $\geq$27°) * Kharif & & 2.969 & & 8.083*** \\
& & (3.359) & & (2.045) \\
& & & & \\
Observations & 6,751 & 6,751 & 20,535 & 20,535 \\
R-squared & 0.107 & 0.104 & 0.067 & 0.064 \\
\# of PSUs & 89 & 89 & 282 & 282 \\
Mean of dependent variable & 6.76\% & 6.76\% & 9.03\% & 9.03\%\\
		\midrule
		\midrule
		\multicolumn{5}{p{14cm}}{\textit{Notes:} All regressions include weather controls, individual controls, year-month fixed-effects, state fixed-effects and climate zone fixed effects. The temperature bin regressions (Columns (1) and (3)) include all bins and its interactions with the Kharif dummy, but on the top bin is shown in the table. The Kharif dummy is absorbed by the year-month fixed effects. Robust standard errors are clustered at the PSU level and presented in parentheses. *** p\textless{}0.01, ** p\textless{}0.05, * p\textless{}0.1.}
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Beyond the heat: The mental health toll of temperature and humidity in India" by Manuela Fritz, arXiv:2503.08761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Dynamic Portfolio Optimization with Inverse Covariance Clustering" by Yuanrong Wang and Tomaso Aste, arXiv:2112.15499v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Information about the benchmark datasets from DS1 to DS8.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllllllll}
\toprule
Dataset & \# Species & \# Sites & Reference \\
\midrule
DS1 & 27 & 1949 & \\
DS2 & 29 & 2520 & \\
DS3 & 36 & 1812 & \\
DS4 & 41 & 1137 & \\
DS5 & 50 & 378 & \\
DS6 & 50 & 1133 & \\
DS7 & 59 & 1824 & \\
DS8 & 64 & 1008 & \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Mingyang Zhou
Zichao Yan
Elliot Layne
Nikolay Malkin
Dinghuai Zhang
Moksh Jain
Mathieu Blanchette
Yoshua Bengio

"PhyloGFN: Phylogenetic inference with generative flow networks" by Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, and Yoshua Bengio, arXiv:2310.08774v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ARMSE comparison of positions of different segments for scenario \textbf{R1}~(units: m)}
\begin{tabular}{c|cccc}
		\hline \hline % 表格的横线
		\textbf{Segment} & \textbf{VBACIF}& \textbf{VBAKF-CM}& \textbf{SSVBAKF}& \textbf{CVIAKF}\\ \hline

 A : [0, 13) &419.99 & \textbf{\textcolor{blue}{341.10}} &361.40 & 346.06
 \\
 B : [13, 34) &186.07 & \textbf{\textcolor{blue}{156.18}} &177.24 & 159.45
 \\
 C : [34, 65) &237.36 & 238.56 &234.03 & \textbf{\textcolor{blue}{226.87}}
 \\
 D : [65, 81) &943.45 & 1077.90 & \textbf{\textcolor{blue}{835.06}} & 840.71
 \\
 E : [81, 100) &\textbf{\textcolor{blue}{877.71}} & 908.06 &904.38 & 893.37
 \\
 F : [100, 121] &1269.20 & 1046.80 & 930.95&\textbf{\textcolor{blue}{878.05}}
 \\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

Hua Lan
Shijie Zhao
Jinjie Hu
Zengfu Wang
Jing Fu

"Joint State Estimation and Noise Identification Based on Variational Optimization" by Hua Lan, Shijie Zhao, Jinjie Hu, Zengfu Wang, and Jing Fu, arXiv:2312.09585v1, 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{Accuracy (in \%) of logistic regression using SGD with a decreasing learning rate. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllccc}
\hline
 Method & Dataset & Model & \!\!\!\!Epochs\!\!\!\! & $\epsilon$ & Acc. \\
 \hline
 DP-SGLD\!\! & CIFAR10 & Resnet18 & 30 & 1.0 & 70.1 \\
 DP-SGD & CIFAR10 & Resnet18 & 30 & 1.0 & 68.1 \\
 No DP & CIFAR10 & Resnet18 & 30 & - & 70.2 \\
\hline
 DP-SGLD\!\! & CIFAR10 & Alexnet & 30 & 1.0 & 57.3 \\
 DP-SGD & CIFAR10 & Alexnet & 30 & 1.0 & 56.4 \\
 No DP & CIFAR10 & Alexnet & 30 & - & 57.6 \\
\hline
DP-SGLD\!\! & Pneumonia\!\!\!\! & Resnet18 & 50 & 1.0 & 58.8 \\
DP-SGD & Pneumonia\!\!\!\! & Resnet18 & 50 & 1.0 & 58.8 \\
No DP & Pneumonia\!\!\!\! & Resnet18 & 50 & - & 59.3 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Théo Ryffel
Francis Bach
David Pointcheval

"Differential Privacy Guarantees for Stochastic Gradient Langevin Dynamics" by Théo Ryffel, Francis Bach, and David Pointcheval, arXiv:2201.11980v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of different methods on the SMD dataset. \(\uparrow\) and \(\downarrow\) indicate whether higher or lower values are better.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll|ccc}
 \toprule
 \multicolumn{2}{c|}{\multirow{2}{*}{\textbf{Model \& Inputs}}} & \multicolumn{3}{c}{\textbf{SMD dataset}} \\
 \cmidrule{3-5}
 & & MAE \(\downarrow\) & STD \(\downarrow\) & Recall \(\uparrow\) \\
 \midrule
 \textbf{Score-informed Methods} & & & & \\
 DiffVel & multi. & 19.7 & 13.1 & 53.0\% \\
 FiLM Conv & multi. & 15.1 & 12.3 & 85.8\% \\
 FiLM U-Net & multi. & \textbf{9.9} & 7.8 & \textbf{89.7\%} \\
 Score-HPT (ours) & & & & \\
 audio + onset & multi. & 13.0 & 9.0 & 70.3\% \\
 audio + onset \& frame$_{\text{ex}}$ & multi. & 13.5 & 8.8 & 72.6\% \\
 \midrule
 \textbf{AMT Systems} & & & & \\
 HPT & audio & 13.9 & 9.4 & 68.7\% \\
 hFT-Transformer & audio & \textbf{9.9} & \textbf{7.3} & 78.0\% \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Zhanhong He
Roberto Togneri
Defeng
Huang

"Score-Informed BiLSTM Correction for Refining MIDI Velocity in Automatic Piano Transcription" by Zhanhong He, Roberto Togneri, Defeng, and Huang, arXiv:2508.07757v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \toprule
 Category & Example \\
 \toprule
 \textbf{\em As valid as gold} \\
 \midrule
 \multirow{4}{*}{Semantically the same}
 & Q: When did israel split into israel and judah \\
 & Gold: about 930 BCE / Prediction: around 930 BCE \\
 & Q: What movie is bring it on the musical based off of \\
 & Gold: Bring It On / Prediction: 2000 film of the same name \\
 \midrule
 \multirow{2}{*}{Open-ended question}
 & Q: City belonging to mid west of united states \\
 & Gold: Des Moines / Prediction: kansas city\\
 \midrule
 \multirow{2}{*}{Ambiguous entity/event references}
 & Q: Gold woman in guardians of the galaxy 2 \\
 & Gold: Ayesha / Prediction: Elizabeth Debicki \\
 \midrule
 \multirow{4}{*}{Answer has different granularity}
 & Q: When did the musical cats open on broadway \\
 & Gold: 1982, 7 October 1982 / Prediction: october 1982 \\
 & Q: Where did the industrial revolution in the united states begin \\
 & Gold: in Pawtucket / Prediction: rhode island \\
 \midrule
 \multirow{2}{*}{Gold is incorrect}
 & Q: When did universal studios become a theme park \\
 & Gold: 1965 / Prediction: july 15 1964, 1964 \\
 \toprule
 \textbf{\em Valid but not the best} \\
 \midrule
 \multirow{2}{*}{Ambiguous answer type}
 & Q: When did naruto find out about jiraiya's death \\
 & Gold: ``Somber News'' / Prediction: february 23 2013 \\
 \midrule
 \multirow{2}{*}{Answer is time-dependent}
 & Q: Who goes to the big 12 championship game \\
 & Gold: Oklahoma Sooners / Prediction: tcu horned frogs \\
 \toprule
 \textbf{\em Less plausible} \\
 \midrule
 \multirow{2}{*}{Conflicting info in Wikipedia}
 & Q: Based on population what is the largest military base in the \\
 & united states / Gold: Fort Bragg / Prediction: fort hood \\
 \midrule
 \multirow{2}{*}{Plausible only in certain condition}
 & Q: Who appointed the chief justice of high court \\
 & Gold: President / Prediction: president of india \\
 \midrule
 \multirow{2}{*}{Mismatch with question intent}
 & Q: While the united states dealt with the depression it was also facing \\
 & Gold: Racial tensions / Prediction: great depression, economic downturns \\
 \midrule
 \multirow{2}{*}{Incorrect}
 & Q: When were banded iron formations formed on the sea floor \\
 & Gold: Precambrian / Prediction: some 185 billion years ago \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sewon Min
Jordan Boyd-Graber
Chris Alberti
Danqi Chen
Eunsol Choi
Michael Collins
Kelvin Guu
Hannaneh Hajishirzi
Kenton Lee
Jennimaria Palomaki
Colin Raffel
Adam Roberts
Tom Kwiatkowski
Patrick Lewis
Yuxiang Wu
Heinrich Küttler
Linqing Liu
Pasquale Minervini
Pontus Stenetorp
Sebastian Riedel
Sohee Yang
Minjoon Seo
Gautier Izacard
Fabio Petroni
Lucas Hosseini
Nicola De Cao
Edouard Grave
Ikuya Yamada
Sonse Shimaoka
Masatoshi Suzuki
Shumpei Miyawaki
Shun Sato
Ryo Takahashi
Jun Suzuki
Martin Fajcik
Martin Docekal
Karel Ondrej
Pavel Smrz
Hao Cheng
Yelong Shen
Xiaodong Liu
Pengcheng He
Weizhu Chen
Jianfeng Gao
Barlas Oguz
Xilun Chen
Vladimir Karpukhin
Stan Peshterliev
Dmytro Okhonko
Michael Schlichtkrull
Sonal Gupta
Yashar Mehdad
Wen-tau Yih

"NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned" by Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong Shen, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, and Wen-tau Yih, arXiv:2101.00133v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc|ccc|cc}
Unit & X & Z & Y & $U_X$ & $U_Z$ & $U_Y$ & $Y(0)$ & $Y(1)$ \\ \hline
1 & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 2 \\
2 & 0 & 0 & 2 & 0 & 0 & 2 & 2 & 3 \\
3 & 1 & 0 & 1 & 1 & 0 & 0 & 1 & 2 \\
4 & 1 & 0 & 2 & 1 & 0 & 1 & 2 & 3
\end{tabular}
\end{adjustbox}
\caption{Observed data ($X, Z, Y$), inferred exogenous variables ($U_X, U_Z, U_Y$), and predicted potential outcomes ($Y(0), Y(1)$) corresponding to the SCM in Equation~.}
\end{table}
\end{document}

stat

Counterfactuals for the Future

Lucius E. J. Bynum
Joshua R. Loftus
Julia Stoyanovich

"Counterfactuals for the Future" by Lucius E. J. Bynum, Joshua R. Loftus, and Julia Stoyanovich, arXiv:2212.03974v1, 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||c|c||c|c||c|c}
 \multicolumn{8}{c}{\textbf{ODE Terms:}}\\
Eqn.~ & \texttt{NDS.} & Eqn.~ & \texttt{NDS.} & Eqn.~ & \texttt{NDS.}\\
\hline
$A_0$ & \texttt{A0} & $B_{1}$ & \texttt{B1} &$B_{2}$&\texttt{B2}&&\\
$C_{1}$ & \texttt{C1} & $D_{11}$ &\texttt{D11}&$D_{12}$&\texttt{D12}&&\\
$C_{2}$ & \texttt{C2} & $D_{21}$ &\texttt{D21}&$D_{22}$&\texttt{D22}&& \\ \hline \\
 \multicolumn{8}{c}{ \textbf{Discrete Delay Terms:}}\\
Eqn.~ & \texttt{NDS.} & Eqn.~ & \texttt{NDS.} & Eqn.~ & \texttt{NDS.}& Eqn.~ & \texttt{NDS.}\\
\hline
$A_i$ & \texttt{Ai\{i\}} & $B_{1i}$ & \texttt{B1i\{i\}} &$B_{2i}$&\texttt{B2i\{i\}}&$E_i$& \texttt{Ei\{i\}}\\
$C_{1i}$ & \texttt{C1i\{i\}} & $D_{11i}$ &\texttt{D11i\{i\}}&$D_{12i}$&\texttt{D12i\{i\}}&$E_{1i}$& \texttt{E1i\{i\}}\\
$C_{2i}$ & \texttt{C2i\{i\}} & $D_{21i}$ &\texttt{D21i\{i\}}&$D_{22i}$&\texttt{D22i\{i\}}&$E_{2i}$& \texttt{E2i\{i\}}\\
\hline \\ \multicolumn{8}{c}{\textbf{Distributed Delay Terms: May be functions of \texttt{pvar s}}}\\
Eqn.~ & \texttt{NDS.} & Eqn.~ & \texttt{NDS.} & Eqn.~ & \texttt{NDS.}& Eqn.~ & \texttt{NDS.}\\
\hline
$A_{di} $ & \texttt{Adi\{i\}} & $B_{1di}$ & \texttt{B1di\{i\}} &$B_{2di}$&\texttt{B2di\{i\}}&$E_{di}$& \texttt{Edi\{i\}}\\
$C_{1di} $& \texttt{C1di\{i\}} &$ D_{11di} $&\texttt{D11di\{i\}}&$D_{12di}$&\texttt{D12di\{i\}}&$E_{1di}$& \texttt{E1di\{i\}}\\
$C_{2di}$ & \texttt{C2di\{i\}} & $D_{21di} $&\texttt{D21di\{i\}}&$D_{22di}$&\texttt{D22di\{i\}}&$E_{2di}$& \texttt{E2di\{i\}}\\
\end{tabular}
\end{adjustbox}
\caption{ Equivalent names of Matlab elements of the \texttt{NDS} structure terms for terms in Eqn.~. For example, to set term \texttt{XX} to \texttt{YY}, we use \texttt{NDS.XX=YY}. In addition, the delay $\tau_i$ is specified using the vector element \texttt{NDS.tau(i)} so that if $\tau_1=1, \tau_2=2, \tau_3=3$, then \texttt{NDS.tau=[1 2 3]}. }
\end{table}
\end{document}

math

PIETOOLS 2024: User Manual

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cccc|}
\hline
Label & B & OBA & LUCE & Updating \\
\hline
$x$ & X & & & \\
$\alpha$ & & X & X & \\
$L$ & X & & & X \\
$U$ & X & & & X \\
$M$ & & X & X & \\
$D$ & & X & X & \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

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


\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}{lllllllllll}
\toprule
 & SDCD & SDCD-GPU & DCDI & DCDFG & GIES & DAGMA & NOTEARS & NOBEARS & SCORE & sortnregress \\
d & & & & & & & & & & \\
\midrule
10 & 14.7 ${\scriptstyle \pm 5.5}$ & NA & 24.3 ${\scriptstyle \pm 3.9}$ & 24.6 ${\scriptstyle \pm 6.0}$ & 27.8 ${\scriptstyle \pm 3.9}$ & 25.3 ${\scriptstyle \pm 6.2}$ & 35.3 ${\scriptstyle \pm 1.9}$ & 33.5 ${\scriptstyle \pm 3.0}$ & 14.4 ${\scriptstyle \pm 4.0}$ & 28.6 ${\scriptstyle \pm 4.7}$ \\
20 & 35.7 ${\scriptstyle \pm 6.2}$ & NA & 31.7 ${\scriptstyle \pm 6.5}$ & 108.0 ${\scriptstyle \pm 14.3}$ & 123.4 ${\scriptstyle \pm 12.3}$ & 62.0 ${\scriptstyle \pm 12.0}$ & 75.4 ${\scriptstyle \pm 4.0}$ & 74.2 ${\scriptstyle \pm 3.0}$ & 118.6 ${\scriptstyle \pm 8.5}$ & 81.6 ${\scriptstyle \pm 6.3}$ \\
30 & 53.8 ${\scriptstyle \pm 11.9}$ & NA & 55.5 ${\scriptstyle \pm 10.7}$ & 258.8 ${\scriptstyle \pm 32.6}$ & NA & 89.4 ${\scriptstyle \pm 10.9}$ & 113.1 ${\scriptstyle \pm 4.8}$ & 113.7 ${\scriptstyle \pm 3.3}$ & 275.9 ${\scriptstyle \pm 21.0}$ & 134.6 ${\scriptstyle \pm 8.4}$ \\
40 & 64.0 ${\scriptstyle \pm 13.7}$ & NA & 102.4 ${\scriptstyle \pm 21.6}$ & 426.6 ${\scriptstyle \pm 73.7}$ & NA & 115.3 ${\scriptstyle \pm 13.4}$ & 147.9 ${\scriptstyle \pm 6.0}$ & 151.1 ${\scriptstyle \pm 3.4}$ & 454.3 ${\scriptstyle \pm 52.8}$ & 172.9 ${\scriptstyle \pm 12.2}$ \\
50 & 69.9 ${\scriptstyle \pm 12.3}$ & 68.3 ${\scriptstyle \pm 13.3}$ & NA & 660.8 ${\scriptstyle \pm 126.1}$ & NA & NA & 183.4 ${\scriptstyle \pm 7.4}$ & 192.0 ${\scriptstyle \pm 3.5}$ & 619.4 ${\scriptstyle \pm 59.7}$ & 216.6 ${\scriptstyle \pm 12.4}$ \\
100 & 92.7 ${\scriptstyle \pm 9.1}$ & 89.7 ${\scriptstyle \pm 11.0}$ & NA & 1807.3 ${\scriptstyle \pm 788.2}$ & NA & NA & 327.3 ${\scriptstyle \pm 7.5}$ & 389.0 ${\scriptstyle \pm 3.6}$ & NA & 421.3 ${\scriptstyle \pm 12.0}$ \\
200 & 225.3 ${\scriptstyle \pm 13.7}$ & 228.0 ${\scriptstyle \pm 18.3}$ & NA & 5657.3 ${\scriptstyle \pm 2982.6}$ & NA & NA & 619.0 ${\scriptstyle \pm 4.2}$ & 770.0 ${\scriptstyle \pm 7.8}$ & NA & 824.0 ${\scriptstyle \pm 19.0}$ \\
300 & 350.0 ${\scriptstyle \pm 12.5}$ & 360.0 ${\scriptstyle \pm nan}$ & NA & 7284.7 ${\scriptstyle \pm 5072.3}$ & NA & NA & NA & 1149.0 ${\scriptstyle \pm 14.0}$ & NA & 1190.7 ${\scriptstyle \pm 26.3}$ \\
400 & 466.3 ${\scriptstyle \pm 62.4}$ & 471.7 ${\scriptstyle \pm 68.0}$ & NA & 3779.7 ${\scriptstyle \pm 507.3}$ & NA & NA & NA & 1534.7 ${\scriptstyle \pm 3.1}$ & NA & 1585.0 ${\scriptstyle \pm 59.3}$ \\
500 & 621.7 ${\scriptstyle \pm 10.7}$ & 621.0 ${\scriptstyle \pm 10.5}$ & NA & 7252.7 ${\scriptstyle \pm 3284.6}$ & NA & NA & NA & 1915.7 ${\scriptstyle \pm 18.8}$ & NA & 1974.3 ${\scriptstyle \pm 34.6}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Detailed results of SHD means and standard deviations from . SDCD-GPU was only run for $d\ge 50$. All other \texttt{NA} values correspond to failed runs.}
\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[T1]{fontenc}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|l||l|l|l|}\hline
& \multicolumn{6}{c|}{\textbf{Temperature ($\tau$) > 1.0}} \\ \hline
 & 10 & 100 & 1000 & 10 & 100 & 1000 \\ \hline
\textbf{Src+ Ext} & \multicolumn{3}{|c||}{Prec$@$1} & \multicolumn{3}{|c|}{Recall$@$3} \\ \hline
 Yahoo! + TREC & 0.46 &0.38 & 0.38& 0.73&0.644 & 0.64 \\
 Yahoo! + WHO& 0.37& 0.38& 0.36& 0.64& 0.65&0.64 \\
 Infobot + TREC & 0.44 & 0.59& 0.39& 0.72& 0.81& 0.75 \\
 Infobot + WHO & 0.65 & 0.41& 0.44 & 0.85 & 0.76 & 0.79\\ \hline
\end{tabular}
\end{adjustbox}
\caption{Variation in P$@$1 and R$@$3 across different temperature values.}
\end{table}
\end{document}

cs

Manisha Verma
Kapil Thadani
Shaunak Mishra

"Powering COVID-19 community Q&A with Curated Side Information" by Manisha Verma, Kapil Thadani, and Shaunak Mishra, arXiv:2101.11556v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{makecell}
\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}
 \Xhline{2\arrayrulewidth}
 Encoder & Decoder & \multicolumn{4}{c}{\textbf{GF2} Dataset (Reduced-Resolution)} \\
 \hline
 FFA & HQFE & SAM$\downarrow$ & ERGAS$\downarrow$ & SCC$\uparrow$ & Q4$\uparrow$\\
 \hline
 & & 0.654 $\pm$ 0.112 & 0.661 $\pm$ 0.076 & 0.992 $\pm$ 0.001 & 0.986 $\pm$ 0.007 \\
 \checkmark & & $0.654 \pm 0.112$ & $0.636 \pm 0.077$ & $0.993 \pm 0.002$ & $0.986 \pm 0.007$ \\
 & \checkmark & $0.617 \pm 0.117$ & $0.556 \pm 0.104$ & $0.993 \pm 0.002$ & $0.987 \pm 0.007$ \\
 \checkmark & \checkmark & {\color{red}{\textbf{0.603 $\pm$ 0.102}}} & {\color{red}{\textbf{0.537 $\pm$ 0.077}}} & {\color{red}{\textbf{0.994 $\pm$ 0.001}}} & {\color{red}{\textbf{0.988 $\pm$ 0.006}}} \\
 \Xhline{2\arrayrulewidth}
 \end{tabular}
\end{adjustbox}
\caption{Comparison of Results with and without FFA and HQFE Blocks in FSA-T. Best values are highlighted in {\color{red}{\textbf{red}}}.}
\end{table}
\end{document}

eess

Sungpyo Kim
Jeonghyeok Do
Jaehyup Lee
Munchurl Kim

"U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening" by Sungpyo Kim, Jeonghyeok Do, Jaehyup Lee, and Munchurl Kim, arXiv:2412.06243v1, 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{Rated values and simulation parameters for 4-zone SPS model }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
\hline \hline
\textbf{Parameter/Simulation Description} & \textbf{Parameter Value} \\
 \hline \hline
Rated Power PGM-1 & 29 MW \\
Rated Power PGM-2 & 29 MW \\
Desired Operating Power PGMs ($\mathbf{p}_{g_i}^r$) & 15 MW \\
Rated Power PCM-1 & $\pm$ 10.64 MW \\
Rated Power PCM-2 & $\pm$ 10.64 MW \\
Rated Bus Voltage ($v_{bus}$) & 12 kV \\
Capacity of PCMs ($Q_T$) & 20 AHr \\
\hline
PGMs weight ($\beta_i$) & 1 \\
PCM-1 weight ($\gamma_1$) & 1 \\
Gradient Step ($\alpha$) & 0.1 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Degradation-based Energy Management for Microgrids in the Presence of Energy Storage Elements" by Satish Vedula, arXiv:2503.09028v2, 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{\textit{GridTracer} performance with the "conventional" data handling scheme in Fig. (a) and the "in-domain" scheme in the in Fig. (b), cross-domain training}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l||c|c|c|c}
 \hline
 & Azrizona & Kansas & New Zealand & Average \\ \hline\hline
Data handling & \multicolumn{4}{c}{Tower detection (DmaP)} \\ \hline
(a) & \textbf{0.73} & \textbf{0.54} & \textbf{0.55} & \textbf{0.61} \\
(b) & 0.68 & 0.53 & 0.51 & 0.57 \\ \hline\hline
 & \multicolumn{4}{c}{Line segmentation (IoU)} \\ \hline
(a) & \textbf{54.43} & \textbf{40.04} & 45.62 & \textbf{46.70} \\
(b) & 52.97 & 39.42 & \textbf{46.50} & 46.30 \\ \hline\hline
 & \multicolumn{4}{c}{Graph inference (F1)} \\ \hline
(a) & 0.68 & \textbf{0.59} & \textbf{0.61} & \textbf{0.63} \\
(b) & \textbf{0.70} & 0.56 & 0.48 & 0.58 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Bohao Huang
Jichen Yang
Artem Streltsov
Kyle Bradbury
Leslie M. Collins
Jordan Malof

"GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery" by Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, and Jordan Malof, arXiv:2101.06390v1, 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{Example of RIS codebook index assignment for $K=16$ codewords, where each codeword consists of $N=2$ phases, each quantized to $\{0^\circ, 90^\circ, 180^\circ, 270^\circ\}$ ($b=2$ bits).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
Gray Code & Codebook Index & Codeword\\
\hline
0000 & 6 & $[90^\circ,\,180^\circ]$ \\
0001 & 2 & $[0^\circ,\,180^\circ]$ \\
0011 & 7 & $[90^\circ,\,270^\circ]$ \\
0010 & 3 & $[0^\circ,\,270^\circ]$ \\
0110 & 14 & $[270^\circ,\,180^\circ]$ \\
0111 & 10 & $[180^\circ,\,180^\circ]$ \\
0101 & 15 & $[270^\circ,\,270^\circ]$ \\
0100 & 11 & $[180^\circ,\,270^\circ]$ \\
1100 & 4 & $[90^\circ,\,0^\circ]$ \\
1101 & 0 & $[0^\circ,\,0^\circ]$ \\
1111 & 5 & $[90^\circ,\,90^\circ]$ \\
1110 & 1 & $[0^\circ,\,90^\circ]$ \\
1010 & 12 & $[270^\circ,\,0^\circ]$ \\
1011 & 8 & $[180^\circ,\,0^\circ]$ \\
1001 & 13 & $[270^\circ,\,90^\circ]$ \\
1000 & 9 & $[180^\circ,\,90^\circ]$ \\
\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{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|r|rrrrr|}
 \hline
 & & & Storey & & estimated\\
 SNR & Bonf & Simes & Simes & Fisher & LRT \\
 \hline
 1.00 & 410.52 & 384.66 & 327.55 & {\bf 274.36} & 277.58 \\
 1.50 & 217.69 & 187.36 & 142.98 & {\bf 107.85} & 113.88 \\
 2.00 & 81.63 & 65.52 & 49.12 & {\bf 37.40} & 37.76 \\
 2.50 & 23.51 & 17.98 & 15.08 & 14.60 & {\bf 11.91} \\
 3.00 & 6.42 & 5.35 & 5.18 & 7.78 & {\bf 4.35} \\
 3.50 & 2.46 & 2.24 & 2.27 & 5.20 & {\bf 2.02} \\
 4.00 & 1.39 & 1.34 & 1.37 & 4.38 & {\bf 1.28} \\
 4.50 & 1.07 & 1.06 & 1.08 & 4.03 & {\bf 1.03} \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Average batch prediction set size at each SNR for different combining methods (columns). In bold, the combining method that produces the narrowest prediction region. $\alpha = 0.1$ and $2000$ replications. For a single data generation, the average running time on a standard PC was less than 0.05 seconds for all methods but the estimated LRT, which has an average running time of 5.7 seconds. }
\end{table}
\end{document}

stat

Ulysse Gazin
Ruth Heller
Etienne Roquain
Aldo Solari

"Powerful batch conformal prediction for classification" by Ulysse Gazin, Ruth Heller, Etienne Roquain, and Aldo Solari, arXiv:2411.02239v2, 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{Variables in the wind direction dataset.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
\hline
Variable & Unit \\
\hline
Wind direction & rad \\
Cosine of wind direction in the previous hour & dimensionless \\
Sine of wind direction in the previous hour & dimensionless \\
Total precipitation in the previous hour & mm \\
Atmospheric pressure in the previous hour & mB \\
Air temperature (dry bulb) in the previous hour	& $^\circ\mathrm{C}$ \\
Dew point temperature in the previous hour & $^\circ\mathrm{C}$ \\
Relative humidity in the previous hour & \% \\
Wind gust in the previous hour & m/s \\
Wind speed in the previous hour	& m/s \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Paulo C. Marques F.
Rinaldo Artes
Helton Graziadei

"Projected random forests and conformal prediction of circular data" by Paulo C. Marques F., Rinaldo Artes, and Helton Graziadei, arXiv:2410.24145v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Kent-Andre Mardal
Jarle Sogn
Marius Zeinhofer

"Variational Formulations of the Strong Formulation -- Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks" by Kent-Andre Mardal, Jarle Sogn, and Marius Zeinhofer, arXiv:2312.03496v1, 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}{ccccrr}
\toprule
 & & & & &\\
Site & AgeGroup & Gender & Diagnosis & Axis 1 & Depression \\
\midrule
Site 1 & adult & F & 0.0 & 35 & 35\\ % FSU\_WAVE1
 & & & 1.0 & 59 & 59\\
 & & M & 0.0 & 11 & 11\\
 & & & 1.0 & 12 & 12\\
Site 2 & child & F & 0.0 & 72 & 95 \\ % SDSU\_D2018
 & & & 1.0 & 31 & 8 \\
 & & M & 0.0 & 101 & 119 \\
 & & & 1.0 & 19 & 1 \\
Site 3 & child & F & 0.0 & 82 & 128\\ % fsu\_d2018b50
 & & & 1.0 & 57 & 11\\
 & & M & 0.0 & 98 & 150\\
 & & & 1.0 & 54 & 2\\
Site 4 & child & F & 0.0 & 41 & 58\\ % fsu\_d2018w8
 & & & 1.0 & 21 & 4\\
 & & M & 0.0 & 45 & 63\\
 & & & 1.0 & 20 & 2\\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Participant label distribution}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{rotating}
\usepackage{amsfonts}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative comparisons between our proposed MSIRN and other 15 methods: including traditional methods and deep learning based methods. Extensive experiments are conducted on 7 datasets using S-measure ($S_\alpha$), max F-measure (max $F_\beta$), mean F-measure (mean $F_\beta$), adaptive F-measure (adaptive $F_\beta$), max E-measure (max $E_\xi$), adaptive E-measure (adaptive $E_\xi$) and mean absolute error (MAE). ↑ and ↓ denote that the higher or lower is better.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccc|cccc}
\hline
\hline
 & &ACSD&LBE&DCMC&MDSF&SE&DF&AFNet&CTMF&MMCI\\
 &Metric&ICIP14&CVPR16&SPL16&TIP17&ICME16&TIP17&arXiv19&TOC18&PR19\\
 & &[52]&[19]&[53]&[54]&[55]&[64]&[56]&[57]&[9]\\
\hline
\hline
\multirow{8}*{\rotatebox{90}{\emph{NJU2K}
}} &$S_\alpha$↑ & 0.699 & 0.695 & 0.686 & 0.748 & 0.664 & 0.763 & 0.772 & 0.849 & 0.858 \\
 &max $F_\beta$↑ & 0.711 & 0.748 & 0.715 & 0.775 & 0.748 & 0.804 & 0.775 & 0.845 & 0.852 \\
 &mean $F_\beta$↑ & 0.512 & 0.606 & 0.556 & 0.628 & 0.583 & 0.65 & 0.764 & 0.779 & 0.793 \\
 &adp $F_\beta$↑ & 0.696 & 0.74 & 0.717 & 0.757 & 0.734 & 0.784 & 0.768 & 0.788 & 0.812 \\
 &max $E_\xi$↑ & 0.803 & 0.803 & 0.799 & 0.838 & 0.813 & 0.864 & 0.853 & 0.913 & 0.915 \\
 &mean $E_\xi$↑ & 0.593 & 0.655 & 0.619 & 0.677 & 0.624 & 0.696 & 0.826 & 0.846 & 0.851 \\
 &adp $E_\xi$↑ & 0.786 & 0.791 & 0.791 & 0.812 & 0.772 & 0.835 & 0.846 & 0.864 & 0.878 \\
 &$\mathcal{M}$↓ & 0.202 & 0.153 & 0.172 & 0.157 & 0.169 & 0.141 & 0.100 & 0.085 & 0.079 \\
\hline
\multirow{8}*{\rotatebox{90}{\emph{LFSD}
}} &$S_\alpha$↑&0.862 & 0.736 & 0.753 & 0.700 & 0.698 & 0.791 & 0.738 & 0.826 & 0.787 \\
 &max $F_\beta$↑ &0.863 & 0.726 & 0.817 & 0.783 & 0.791 & 0.817 & 0.744 & 0.791 & 0.771 \\
 &mean $F_\beta$↑ &0.838 & 0.611 & 0.655 & 0.521 & 0.640 & 0.679 & 0.735 & 0.756 & 0.722 \\
 &adp $F_\beta$↑ &0.851 & 0.708 & 0.816 & 0.799 & 0.778 & 0.806 & 0.742 & 0.782 & 0.779 \\
 &max $E_\xi$↑ &0.898 & 0.804 & 0.856 & 0.826 & 0.840 & 0.865 & 0.815 & 0.865 & 0.839 \\
 &mean $E_\xi$↑ &0.873 & 0.670 & 0.683 & 0.588 & 0.653 & 0.726 & 0.796 & 0.810 & 0.775 \\
 &adp $E_\xi$↑ &0.885 & 0.770 & 0.842 & 0.817 & 0.784 & 0.844 & 0.810 & 0.851 & 0.840 \\
 &$\mathcal{M}$↓ &0.077 & 0.208 & 0.155 & 0.190 & 0.167 & 0.138 & 0.133 & 0.119 & 0.132\\
\hline
\multirow{8}*{\rotatebox{90}{\emph{STERE}
}} &$S_\alpha$↑&0.692 & 0.660 & 0.731 & 0.728 & 0.708 & 0.757 & 0.825 & 0.848 & 0.873 \\
 &max $F_\beta$↑ &0.669 & 0.633 & 0.740 & 0.719 & 0.755 & 0.757 & 0.823 & 0.831 & 0.863 \\
 &mean $F_\beta$↑ &0.478 & 0.501 & 0.590 & 0.527 & 0.610 & 0.617 & 0.806 & 0.758 & 0.813 \\
 &adp $F_\beta$↑ &0.661 & 0.595 & 0.742 & 0.744 & 0.748 & 0.742 & 0.807 & 0.771 & 0.829 \\
 &max $E_\xi$↑ &0.806 & 0.787 & 0.819 & 0.809 & 0.846 & 0.847 & 0.887 & 0.912 & 0.927 \\
 &mean $E_\xi$↑ &0.592 & 0.601 & 0.655 & 0.614 & 0.665 & 0.691 & 0.872 & 0.841 & 0.873 \\
 &adp $E_\xi$↑ &0.793 & 0.749 & 0.831 & 0.830 & 0.825 & 0.838 & 0.886 & 0.864 & 0.901 \\
 &$\mathcal{M}$↓ &0.200 & 0.250 & 0.148 & 0.176 & 0.143 & 0.141 & 0.075 & 0.086 & 0.068\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ze-yu Liu
Jian-wei Liu
Xin Zuo
Ming-fei Hu

"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, 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}{c|c|c|c|c}
 \toprule
 $n$ & QR & RQ & $Q(A^TA)^{1/2}$ & $(AA^T)^{1/2}Q$ \\
 \midrule
 10 & 1 & 12 & 1 & 3\\
 100 & 1 & - & 1 & 3\\
 1000& 1 & - & 2* & 5\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lorenzo Lazzarino
Yuji Nakatsukasa
Umberto Zerbinati

"Preconditioned normal equations for solving discretised partial differential equations" by Lorenzo Lazzarino, Yuji Nakatsukasa, and Umberto Zerbinati, arXiv:2502.17626v2, 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{Simulations results by varying basket dimension.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
\toprule
Dim &	MC Price & BSDE Price	& Err.\% & Time$^*$ \\
\midrule\midrule
5	&	0.903	&	0.908	&	0.59\%	&	1984	\\
10	&	1.614	&	1.612	&	0.15\%	&	1861	\\
25	&	3.618	&	3.612	&	0.17\%	&	2105	\\
50	&	6.858	&	6.902	&	0.64\%	&	2450	\\
75	&	10.138	&	10.093	&	0.44\%	&	2840	\\
100	&	13.607	&	13.479	&	0.95\%	&	4438	\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

A deep solver for BSDEs with jumps

Kristoffer Andersson
Alessandro Gnoatto
Marco Patacca
Athena Picarelli

"A deep solver for BSDEs with jumps" by Kristoffer Andersson, Alessandro Gnoatto, Marco Patacca, and Athena Picarelli, arXiv:2211.04349v3, 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{The ablation study of AMamba with different modules and feature fusion.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccccc}
\hline
TMamba & SMamba & Concate & FEM & $\overline{\text{APE}}(m)$& $\overline{\text{Acc}}(\%)$\\
\hline
$\checkmark$ & & & & {0.89} & 97.4 & \\
&$\checkmark$ & & & 1.16 & 95.2 & \\
$\checkmark$& $\checkmark$ & $\checkmark$ & & 1.12 & 96.8 & \\
$\checkmark$& $\checkmark$ & & $\checkmark$ & \textbf{0.86} & \textbf{97.9} & \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Zhenyuan Xiao
Yizhuo Yang
Guili Xu
Xianglong Zeng
Shenghai Yuan

"AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification" by Zhenyuan Xiao, Yizhuo Yang, Guili Xu, Xianglong Zeng, and Shenghai Yuan, arXiv:2412.16928v1, 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{Computational Performance for Data Processing Steps}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
Processing Step & Average Time (min) & Memory Usage (GB) & Processor Utilization (\%) \\
\midrule
Data importation & 1.2 & 2.8 & 15 \\
Noise reduction & 2.7 & 4.5 & 45 \\
Surface trimming & 4.1 & 5.2 & 30 \\
Pre-positioning & 3.5 & 6.8 & 65 \\
Sequential merging & 18.6 & 18.4 & 85 \\
Final alignment & 7.2 & 12.6 & 70 \\
Deviation mapping & 14.9 & 22.3 & 90 \\
Feature extraction & 9.3 & 15.7 & 75 \\
\textbf{Total} & \textbf{61.5} & \textbf{22.3 (peak)} & \textbf{90 (peak)} \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Hamidreza Samadi
Md Manjurul Ahsan
Shivakumar Raman

"Hybrid Machine Learning Framework for Predicting Geometric Deviations from 3D Surface Metrology" by Hamidreza Samadi, Md Manjurul Ahsan, and Shivakumar Raman, arXiv:2508.06845v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Lattice aggregations of boxes and symmetric functions" by Natasha Rozhkovskaya, arXiv:2312.03168v1, 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{Running time for Algorithm 2 and the algorithm in with $e_t =1$ and $\delta = 1$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
		\hline
		& \textbf{Algorithm 2} & Algorithm in \\
		\hline
		$r=0.1$ & {19.81s} & 15.80s\\
		$r=0.3$ & {28.74s} & 15.83s\\
		$r=0.5$ & {41.42s} & 15.98s\\
		$r=0.8$ & {110.63s} & 16.11s\\
		\hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"MIMO Radar Waveform-Filter Design for Extended Target Detection from a View of Games" by Zhou Xu, Chongyi Fan, and Xiaotao Huang, arXiv:2011.11346v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\toprule
\textbf{Gender \textbackslash Label} & \textit{none} & \textit{ideation} & \textit{attempt} \\ \midrule
Husband & 31 & 22 & 9 \\
Wife & 34 & 15 & 13 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Suicidal risk demographics of 62 couples in our data}
\end{table}
\end{document}

eess

Sandeep Nallan Chakravarthula
Md Nasir
Shao-Yen Tseng
Haoqi Li
Tae Jin Park
Brian Baucom
Craig J. Bryan
Shrikanth Narayanan
Panayiotis Georgiou

"Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations" by Sandeep Nallan Chakravarthula, Md Nasir, Shao-Yen Tseng, Haoqi Li, Tae Jin Park, Brian Baucom, Craig J. Bryan, Shrikanth Narayanan, and Panayiotis Georgiou, arXiv:1911.11927v1, 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{Running Time Summary (Numerical Features: Standardized).}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\toprule
{Models} & Total Fits & Tuning Time & Average Tuning & Fitting Time (Best Model)\\
 \midrule
LightGBM & 2000 & 159.92 & 0.08 & 0.19 \\
Linear Regression & 150 & 3.32 & 0.02 & 0.07 \\
MLP & 40 & 1948.63 & 48.72 & 116.05\\
Random Forest & 1200 & 1934.51 & 1.61 & 64.58 \\
XGBoost & 5760 & 2429.88 & 0.42 & 4.30 \\
\bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yinpu Li
Siqi Mao
Yaping Yuan
Ziren Wang
Yixin Kang
Yuanxin Yao

"Beyond Tides and Time: Machine Learning Triumph in Water Quality" by Yinpu Li, Siqi Mao, Yaping Yuan, Ziren Wang, Yixin Kang, and Yuanxin Yao, arXiv:2309.16951v2, 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{Nodes on the designed ISIG BNs with assumed states}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \hline
node & description & states \\
 \hline
 SC & signal controller & N,L,M,H,C \\
 RSE & roadside equipment & L,M,H \\
 TC & traffic composition & UP,EP,UV,CR,EM,FR,TR \\
 UP & unequipped pedestrians & N,L,M,H,C \\
 EP & equipped pedestrians & N,L,M,H,C \\
 UV & unequipped vehicles & N,L,M,H,C \\
 CR & regular equipped vehicles & N,L,M,H,C \\
 EM & emergency vehicles & N,L,M,H,C \\
 FR & freight trucks & N,L,M,H,C \\
 TR & transit vehicles & N,L,M,H,C \\
 ITS & other ITS devices & L,M,H \\
 TMC & traffic management center & L,M,H \\
 S & sensor & True,False \\
 DD & detection by driver & L,M,H \\
 DP & detection by pedestrian & L,M,H \\
 DS & detection by system (vehs) & L,M,H \\
 PDS & detection by system (pedes) & L,M,H \\
 RDS & detection by system (RSE) & L,M,H \\
 SDS & detection by system (SA) & L,M,H \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Gurcan Comert
Mashrur Chowdhury
David M. Nicol

"Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks" by Gurcan Comert, Mashrur Chowdhury, and David M. Nicol, arXiv:2011.09436v1, 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{Forward star representation of the network in Figure~. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{rc|c|crc|c|c|c|c|}
		Node &
		 &
		Point &
		 &
		Link &
		 &
		tail &
		head &
		cost &
		capacity \\
		\cline{1-1}
		\cline{3-3}
		\cline{5-5}
		\cline{7-10}
		1 & & 1 & & 1 & & 1 & 3 & $\cdots$ & $\cdots$ \\
		\cline{3-3}
		\cline{7-10}
		2 & & 3 & & 2 & & 1 & 2 & $\cdots$ & $\cdots$ \\
		\cline{3-3}
		\cline{7-10}
		3 & & 5 & & 3 & & 2 & 3 & $\cdots$ & $\cdots$ \\
		\cline{3-3}
		\cline{7-10}
		4 & & 6 & & 4 & & 2 & 4 & $\cdots$ & $\cdots$ \\
		\cline{3-3}
		\cline{7-10}
		5 & & 6 & & 5 & & 3 & 4 & $\cdots$ & $\cdots$ \\
		\cline{3-3}
		\cline{7-10}
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Stephen D. Boyles
Nicholas E. Lownes
Avinash Unnikrishnan

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Continuous versus continuous summary statistics of false positives for 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 proportion of false positives}\\
&& \multicolumn{7}{c|}{}\\
Approximation & $level$ & minimum & $Q(0.25)$ & median & average & $Q(0.75)$ & maximum &IQR\\
\hline
$\chi^2$ estimated d.f. & 0.05 & 0.011 & 0.030 & 0.041 & 0.040 & 0.047 & 0.073& 0.018 \\
$\chi^2$ simple d.f. & & 0.023 & 0.047 & 0.062 & 0.060 & 0.070 & 0.103 & 0.023 \\
$\Gamma(\alpha, \theta)$& &0.028 & 0.058 & 0.072 & 0.072 & 0.087 & 0.128 & 0.029\\
\hline
$\chi^2$ estimated d.f. & 0.01 & 0.000 & 0.005 & 0.008 & 0.007 & 0.009 & 0.017& 0.004\\
$\chi^2$ simple d.f. & & 0.002 & 0.008 & 0.012 & 0.012 & 0.015 & 0.025 & 0.007 \\
$\Gamma(\alpha, \theta)$& &0.003 & 0.012 & 0.017 & 0.017 & 0.021 & 0.037 & 0.009 \\
 \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.


q-fin

Jackson P. Lautier
Vladimir Pozdnyakov
Jun Yan

"On the Convergence of Credit Risk in Current Consumer Automobile Loans" by Jackson P. Lautier, Vladimir Pozdnyakov, and Jun Yan, arXiv:2211.09176v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters of the CAT architecture used in this study.}
\begin{tabular}{lllc}
 \toprule
 Module & Layers & Parameters & Notation \\
 \midrule
 \multirow{8}*{1D-CNN Backbone} & Conv1D, BatchNorm, ReLU & in=1, out=16, kernel=15 & \multirow{10}*{$\mathbf{W}_{\text{E}}$} \\
 & Conv1D, BatchNorm, ReLU & in=16, out=32, kernel=3 \\
 & MaxPool1D & kernel=2, stride=2 \\
 & Conv1D, BatchNorm, ReLU & in=32, out=64, kernel=3 \\
 & Conv1D, BatchNorm, ReLU & in=64, out=128, kernel=3 \\
 & AdaptiveMaxPool1D & out=128$\times$4 \\
 & FC, ReLU & in=128$\times$4, out=256 \\
 & Dropout & p=0.5 \\
 \cmidrule{1-3}
 \multirow{2}*{Bottleneck} & FC, ReLU & in=256, out=256 \\
 & Dropout & p=0.5 \\
 \midrule
 Classification head & FC, Softmax & in=256, out=13 & $\mathbf{W}_{\text{C}}$ \\
 \midrule
 \multirow{4}*{Domain Classifier} & FC, ReLU & in=256, out=1024 & \multirow{4}*{$\mathbf{W}_{\text{DC}}$}\\
 & Dropout & p=0.5 \\
 & FC, ReLU & in=1024, out=1024 & \\
 & Dropout & p=0.5 \\
 & FC, Sigmoid & in=1024, out=1 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis" by Florent Forest and Olga Fink, arXiv:2312.02826v2, 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
Position & Before & After \\ \hline
p. 25 & & Add abbreviations \\ \hline
 & & \\ \hline
\end{tabular}
\end{adjustbox}
\caption{List of edits in the reviewed manuscript}
\end{table}
\end{document}

cs

Evgeny Bobrov
Boris Chinyaev
Viktor Kuznetsov
Dmitrii Minenkov
Daniil Yudakov

"Power Allocation Algorithms for Massive MIMO Systems with Multi-Antenna Users" by Evgeny Bobrov, Boris Chinyaev, Viktor Kuznetsov, Dmitrii Minenkov, and Daniil Yudakov, arXiv:2201.08068v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
 \hline
 Spectral Bands & mAP \\
 \hline
 Red & 61.99 \\
 Red, Green & 65.53 \\
 Red, Green, Blue & 66.73\\
 Red, Red-Edge 1, SWIR1 & 62.43 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{USatlas validation set performance comparison of USatMAE using different subsets of Sentinel-2 spectral bands.}
\end{table}
\end{document}

stat

Jeremy Irvin
Lucas Tao
Joanne Zhou
Yuntao Ma
Langston Nashold
Benjamin Liu
Andrew Y. Ng

"USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery" by Jeremy Irvin, Lucas Tao, Joanne Zhou, Yuntao Ma, Langston Nashold, Benjamin Liu, and Andrew Y. Ng, arXiv:2312.02199v1, 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}{lccccccc} \toprule[1pt]
Rebalancing & E[$R_T$] & Median[$R_T$] & $R_T: 5^{th}$ & $R_T: 95^{th}$ & ES( 5\% )& $\Omega(1)$ &$Prob[R_T > 1]$\\
 interval & & &percentile & percentile & & & \\
 \hline
 & \multicolumn{7}{c}{$\alpha^\ell = 0.30$} \\
 \hline
Yearly & 0.9831 (.0004)& 0.9769 & 0.9026 & 1.0838 & 0.8671 & 0.4690 & 0.3139 \\
\hline
 & \multicolumn{7}{c}{$\alpha^\ell = 0.45$} \\
\hline
Yearly & 1.1561 (.002) & 1.1228 & 0.7712 & 1.6502 & 0.6952 & 5.0141 & 0.7012 \\
Quarterly & 1.1504 (.001) & 1.1313 & 0.7983 & 1.5646 & 0.7239 & 5.6502 & 0.7299 \\
Monthly & 1.1494 (.001) & 1.1330 & 0.8033 & 1.5487 & 0.7308 & 5.8412 & 0.7367 \\
\bottomrule[1pt]
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Peter Forsyth
Pieter van Staden
Yuying Li

"Making Leveraged Exchange-Traded Funds Work for your Portfolio" by Peter Forsyth, Pieter van Staden, and Yuying Li, arXiv:2506.19200v1, 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{Abnormality classification performance on patches of size $64\times64$. Metrics computed on fully-sampled images, (A) 4$\times$ and (B) $8\times$ undersampled images, which were reconstructed using the various methods studied in this paper.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c}
\hline\hline
\multicolumn{6}{c}{AF=4$\times$}\\
Input & Sn & Sp & F1 score & Kappa & AUROC\\
\hline
Fully-sampled & 78.43 & 80.47 & 79.31 & 58.89 & 87.93\\
\hline
U-Net & 69.87 & 83.12& 74.90 & 52.62 & 86.51 \\
FNAF U-Net & 70.65 & 83.32 & 75.49 & 53.93 & 86.68\\
B-Box U-Net & 73.08 & 82.73 & 76.85 & 55.78 & 87.00 \\
\hline
I-RIM & 81.54 & 76.45 & 79.60 & 58.00 & 86.58 \\
FNAF I-RIM & 81.44 & 76.45 & 79.54 & 57.90 & 86.63 \\
B-Box I-RIM & 78.91 & 78.31 & 78.61 & 57.22 & 86.49 \\
\hline\hline
 \\
\multicolumn{6}{c}{(A)} \\
\\
\hline\hline
\multicolumn{6}{c}{AF=8$\times$}\\
Input & Sn & Sp & F1 score & Kappa & AUROC\\
\hline
Fully-sampled & 78.43 & 80.47 & 79.31 & 58.89 & 87.93\\
\hline
U-Net & 60.06 & 88.62 & 70.11 & 48.61 & 86.10\\
FNAF U-Net & 62.00 & 88.32 & 71.44 & 50.26 & 86.36 \\
B-Box U-Net &67.93 & 84.79 & 74.24 & 52.68 & 86.85 \\
\hline
I-RIM & 75.61 & 78.61 & 76.84 & 54.21 & 85.21 \\
FNAF I-RIM & 74.44 & 78.70 & 76.14 & 53.13 & 85.31 \\
B-Box I-RIM & 72.01 & 81.45 & 75.65 & 53.44 & 85.27 \\
\hline\hline
 \\
\multicolumn{6}{c}{(B)} \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Francesco Calivá
Kaiyang Cheng
Rutwik Shah
Valentina Pedoia

"Adversarial Robust Training of Deep Learning MRI Reconstruction Models" by Francesco Calivá, Kaiyang Cheng, Rutwik Shah, and Valentina Pedoia, arXiv:2011.00070v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline\hline
\textbf{Parameter} & \textbf{Symbol} & \textbf{Value} \\ % inserts table %heading
\hline
Number of subcarriers&$N$&512\\
Maximum number of frames &$\bar{N}$&100\\
Modulations order &$\bar{m}$&[1, 2, 4, 6]\\
Carrier frequency &$f_c$&60 GHz \\
Wavelength&$\omega$&$5\times10^{-3}$ m \\
Bandwidth&$B_{\text{w}}$&2.16 GHz\\
Maximum range&$d_{max}$& 50 m\\
Maximum velocity&$v_{max}$& 50 m/s\\
Noise level&$N_0$& -174 dBm/Hz\\
Antenna gain &$g^a$& 1.5\\
Transmit power &$\rho_i$& 15 dBm\\
Interference &$\mathbf{I}$&[24 , 24.5, 37, 37.5]~dB\\
Path loss exponent &$\mathbf{\delta}$&[2.10, 1.22, 0.453, 0.240]\\
atmospheric attenuation &$\mathbf{\beta}$&[75.1, 94.6, 126, 135]\\
Blocker type &$\mathbf{B}$&['LOS', '1V','2V','3V']\\
Transmission slot time &$s$&0.002\\
Pilote transmission time &$T_p$&0.01*s\\
sector-level beamwidth &$\psi$& $45^{\circ}$\\
half-power beamwidth &$\varphi$& $10^{\circ}$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mamady Delamou
Ahmed Naeem
Huseyin Arslan
El Mehdi Amhoud

"Joint Adaptive OFDM and Reinforcement Learning Design for Autonomous Vehicles: Leveraging Age of Updates" by Mamady Delamou, Ahmed Naeem, Huseyin Arslan, and El Mehdi Amhoud, arXiv:2412.18500v1, 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 Training statistics of the 5 homophilic, heterophilic and real-world benchmark datasets used in this work for the baseline dDGM, MLP and GCN models.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc} \hline
 & Training Epochs & Learning Rate & Weight Decay \\ \hline
 \textbf{Cora} & 1,500 & $1\times10^{-2}$& $1\times10^{-4}$\\
 \textbf{CiteSeer} & 1,500 & $1\times10^{-2}$& $1\times10^{-4}$\\
 \textbf{Squirrel} &1,000 &$1\times10^{-2}$ & $1\times10^{-3}$\\
\textbf{Chameleon} & 1,000& $1\times10^{-2}$& $1\times10^{-3}$\\
 \textbf{TadPole} & 800& $1\times10^{-3}$& $2\times10^{-4}$\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Yuan Lu
Haitz Sáez de Ocáriz Borde
Pietro Liò

"AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference" by Yuan Lu, Haitz Sáez de Ocáriz Borde, and Pietro Liò, arXiv:2311.11891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The attack success rate (\textbf{higher} indicates better attacks) of targeted attacks under different defenses. We pick $\epsilon_{\infty}=0.18$, $m=100$, $\gamma=0.25$ and select DGCNN as victim model. Other settings are the same as Sec 4.1. Our AOF outperforms other SOTA attacks under IF-Defense and reaches the second best position under SRS, SOR and DUP defenses. The ConvNet-Opt, ONet-Remesh and ONet-Opt are three variants of IF-Defense.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccr}
\toprule
Defenses & 3D-Adv & kNN & AdvPC & AOF \\
\midrule
No defense & \bf99.76 & 75.85 & 76.56 & 77.17 \\
SRS & 3.04 & \bf13.98 & 6.88 & 7.09 \\
SOR & 1.54 & \bf22.00 & 8.54 & 14.05 \\
DUP-Net & 1.38 & \bf13.25 &7.04 & 11.38 \\
ConvNet-Opt & 0.57 & 2.84 & 1.09 & \bf4.66 \\
ONet-Remesh & 0.85 & 1.62 & 1.05 & \bf1.98 \\
ONet-Opt & 0.65 & 1.94 & 1.01 & \bf2.91 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Binbin Liu
Jinlai Zhang
Lyujie Chen
Jihong Zhu

"Boosting 3D Adversarial Attacks with Attacking On Frequency" by Binbin Liu, Jinlai Zhang, Lyujie Chen, and Jihong Zhu, arXiv:2201.10937v1, 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{Fairleads and anchors coordinates as reported in }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
 \textbf{Fairlead/Anchor} & \textbf{Global Coordinates (m)} \\
 \midrule
 Fairlead 1 & $(-0.1,0.1,0.0736)$ \\
 Fairlead 2 & $(-0.1,-0.1,-0.0736)$ \\
 Fairlead 3 & $(0.1,0.1,-0.0736)$ \\
 Fairlead 4 & $(0.1,-0.1,-0.0736)$ \\
 Anchor 1 & $(-1.385,0.423,-0.5)$ \\
 Anchor 2 & $(-1.385,-0.423,-0.5)$ \\
 Anchor 3 & $(1.385,0.423,-0.5)$ \\
 Anchor 4 & $(1.385,-0.423,-0.5)$
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Amirhossein Taran
Seevani Bali
Zeljko Tukovic
Vikram Pakrashi
Philip Cardiff

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Linear-regression transportability, synthetic ($\delta = 0.1$)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\toprule
\textbf{Model} & \textbf{Min} & \textbf{Median} & \textbf{Mean} & \textbf{Max}\\
\midrule
S-Linear & $1.47\times10^{-13}$ & $1.47\times10^{-10}$ & $1.28\times10^{-8}$ & $2.17\times10^{-7}$ \\
T-Linear & $1.54\times10^{-11}$ & $1.10\times10^{-8}$ & $4.57\times10^{-7}$ & $9.10\times10^{-6}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Daniel de Vassimon Manela
Linying Yang
Robin J. Evans

"Testing Generalizability in Causal Inference" by Daniel de Vassimon Manela, Linying Yang, and Robin J. Evans, arXiv:2411.03021v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method" by Yang Yang, Tong Zhang, Jian Wu, and Lijie Su, arXiv:2504.08743v1, 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}
 \toprule
 Dataset & \textit{normal} & \textit{abnormal}\\
 \midrule
 UCR & 83.0 & 46.8~(\textbf{-36.2}) \\
 NASA-SMAP& 72.9 & 48.5~(\textbf{-24.4})\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Jiaxin Zhuang
Leon Yan
Zhenwei Zhang
Ruiqi Wang
Jiawei Zhang
Yuantao Gu

"See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers" by Jiaxin Zhuang, Leon Yan, Zhenwei Zhang, Ruiqi Wang, Jiawei Zhang, and Yuantao Gu, arXiv:2411.02465v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
		\hline
		Training Size & $\tau$ & QKNN & QRF\\
		\hline
		1000 & 0.5 & \textbf{0.1553} (0.0032) & 0.1679 (0.0026)\\
		5000 & 0.5 & \textbf{0.1485} (0.0016) & 0.1646 (0.0016) \\
		10000 & 0.5 & \textbf{0.1479} (0.0017) & 0.1623 (0.0019)\\
		\hline
		\\
		\hline
		Training Size & $\tau$& QKNN & QRF \\
		\hline
		1000 & 0.9 & \textbf{0.8405} (0.0141) & 0.8315 (0.0086) \\
		5000 & 0.9& \textbf{0.8569} (0.0076) & 0.8334 (0.0040)\\
		10000 & 0.9 & \textbf{0.8585} (0.0061) & 0.8337 (0.0041)\\
		1000 & 0.95& \textbf{0.9163} (0.0127) & 0.8887 (0.0084)\\
		5000 & 0.95& \textbf{0.9216} (0.0099) & 0.8891 (0.0040)\\
		10000 & 0.95 & \textbf{0.9274} (0.0097) & 0.8902 (0.0042) \\
		\hline
		\\
	\end{tabular}
\end{adjustbox}
\caption{Average test set prediction error (across the 100 test sets) on California housing data. For median, we report the mean squared errors; for other quantiles ($\tau \in \{0.9,0.95\}$), we report the averaged proportion of the true data located in the predicted confidence interval. Standard Monte Carlo errors are recorded in parentheses. The number of nearest neighbors, $K$ is chosen as 5 for quantile $K$-NN fused lasso.}
\end{table}
\end{document}

stat

Steven Siwei Ye
Oscar Hernan Madrid Padilla

"Non-parametric Quantile Regression via the K-NN Fused Lasso" by Steven Siwei Ye and Oscar Hernan Madrid Padilla, arXiv:2012.01758v4, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Forecasting realized volatility in the stock market: a path-dependent perspective" by Xiangdong Liu, Sicheng Fu, and Shaopeng Hong, arXiv:2503.00851v1, 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{List of the 5 employed CMIP6 models, the modelling groups providing them}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
Model & Modelling centre (or group)\\
\hline
GFDL-ESM4 & NOAA Geophysical Fluid Dynamics Laboratory\\
IPSL-CM6A-LR & Institut Pierre-Simon Laplace\\
MPI-ESM1-2-HR & Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)\\
MRI-ESM2-0 & Meteorological Research Institute NESM3\\
UKESM1-0-LL & Met Office Hadley Centre\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Shruti Nath
Julie Carreau
Kai Kornhuber
Peter Pfleiderer
Carl-Friedrich Schleussner
Philippe Naveau

"MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards" by Shruti Nath, Julie Carreau, Kai Kornhuber, Peter Pfleiderer, Carl-Friedrich Schleussner, and Philippe Naveau, arXiv:2501.04018v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{In-Context Learning with LLMs for MRC}
\begin{tabular}{ccccccc}
 \toprule
 \multirow{2}{*}{Method} & \multirow{2}{*}{Parameter}&\multicolumn{5}{c}{Test} \\
 \cline{3-7}
 & & EM & Recall & Precision & F1 & Contains \\
 \midrule
 Mistral & 7B & 0.067 & 34.80 & 6.110 & 9.311 & 22.28 \\
 Qwen & 14B & 0.185 & 15.81 &6.86 & 8.533 & 7.862 \\
 Mixtral & 8x7B & 0.078 & 33.23 & 7.228 & 10.39 & 14.00 \\
 LlaMA2 & 70B & 0.0 & 16.80 & 2.233 & 16.80 & 7.11 \\
 PPLX & 70B & 0.014 & 69.52 & 13.06 & 20.17 & \textbf{40.23} \\
 GPT3.5 & 175B & 0.164 & 32.16 & \textbf{24.30} & \textbf{32.16} & 32.161\\
 Falcon & 180B &\textbf{0.613} & \textbf{46.94} & 15.57 & 20.93 & 24.79 \\

 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Abdelrahman Abdallah
Mahmoud Kasem
Mahmoud Abdalla
Mohamed Mahmoud
Mohamed Elkasaby
Yasser Elbendary
Adam Jatowt

"ArabicaQA: A Comprehensive Dataset for Arabic Question Answering" by Abdelrahman Abdallah, Mahmoud Kasem, Mahmoud Abdalla, Mohamed Mahmoud, Mohamed Elkasaby, Yasser Elbendary, and Adam Jatowt, arXiv:2403.17848v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
$L(\epsilon)$ & \\
&$\left\lceil \frac{1}{2}+\frac{\log(\widetilde{c}_{\infty}^{1/\alpha}\mathbf{h})}{\log(M)}+\sqrt{\left(\frac{1}{2}+\frac{\log(\widetilde{c}_{\infty}^{1/\alpha}\mathbf{h})}{\log(M)}\right)^{2}+ 2\frac{\log(A/\epsilon)}{\alpha\log(M)}}\right\rceil$\\
& \\\hline
$h(\epsilon)$ & $\mathbf{h}$ \\ \hline
$\mu(\epsilon)$ &\\
&$\mu_{1}=q^{*}(1+\lambda h^{\frac{\beta}{2}})$\\
&$\mu_{l}=q^{*}\lambda h^{\frac{\beta}{2}} \underline{C}_{M,\beta}|\widetilde{W}_l(L,M)|M^{-\frac{(1-\beta)}{2}(l-1)}, l=2,\dots ,L$,~$\sum\limits_{1\leq l \leq L}\mu_{l}=1$\\
& \\ \hline
$N(\epsilon)$ & \\
&$\left(1+\frac{1}{2 \alpha L}\right)\frac{\text{Var}\left(P(X^{0}_{T})\right)\left(1+ \lambda h^{\beta/2}+\lambda h^{\beta/2}\overline{C}_{M,\beta}\sum\limits_{l=2}^{L}|\widetilde{W}_l(L,M)|M^{\frac{1-\beta}{2}(j-1)} \right)}{\epsilon^{2}q^{*}}$ \\
&\\\hline
\end{tabular}
\end{adjustbox}
\caption{Optimal parameters for the ML2R estimator }
\end{table}
\end{document}

q-fin

"Multilevel Richardson-Romberg and Importance Sampling in Derivative Pricing" by Devang Sinha and Siddhartha P. Chakrabarty, arXiv:2209.00821v1, 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{estimated bases of the central mean space. * denotes estimates after omitting influential observations.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
\hline
 & FOLS & FOLS* & sa-OLS & sa-OLS* \\ \hline
\% Non-Hispanic Blacks & 0.0612 & 0.1860 & 0.1728 & 0.3102 \\
\% Hispanics & -0.2771 & -0.0339 & -0.3974 & -0.3431 \\
\% Adults 65+ & -0.7254 & -0.5820 & -0.8304 & -0.8659 \\
\% No high school diploma & 0.3848 & -0.0789 & 0.3949 & -0.1724 \\
\% Living below poverty line & 0.2294 & -0.5378 & 0.0284 & -0.1615 \\
\% Unemployed & -0.0225 & 0.3190 & 0.0455 & 0.2875 \\
\% Renter-occupied homes & -0.8263 & 0.6574 & -0.6710 & 0.0731 \\
\% On public assistance & 0.0359 & -0.1459 & 0.0969 & 0.1479 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Detecting influential observations in single-index Fréchet regression" by Abdul-Nasah Soale, arXiv:2311.17246v3, 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}{clll}
\toprule
\multicolumn{4}{c}{Total People Affected Time Series} \\ \midrule
Mean & Std. Dev. & Skewness & Kurtosis \\
223,664.919 & 561,471.353 & 4.102 & 18.495 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Total number of people affected includes deaths, injuries, those requiring assistance, and individuals displaced from their homes due to natural disasters over the period spanning from 1900 to 2023. Source }
\end{table}
\end{document}

stat

Giacomo Ascione
Michele Bufalo
Giuseppe Orlando

"Modeling Volatility of Disaster-Affected Populations: A Non-Homogeneous Geometric-Skew Brownian Motion Approach" by Giacomo Ascione, Michele Bufalo, and Giuseppe Orlando, arXiv:2309.09287v1, 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{Averaged AD test p-values over rolling windows for selected assets from India (IN), the United States (USA), and Hong Kong (HK). Ticker symbols are shown alongside their corresponding p-values.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcl}
\hline
\textbf{IN} & \textbf{USA} & \textbf{HK} \\
\hline
REL - 0.98 & MSFT - 0.99 & 0005.HK - 0.98 \\
TCS - 0.93 & GOOGL - 0.99 & 0700.HK - 0.97 \\
INFY - 0.97 & NVDA - 0.99 & 0001.HK - 0.99 \\
HDFC - 0.999 & AMZN - 0.95 & 2318.HK - 0.71 \\
ICICI - 0.95 & META - 0.73 & 1299.HK - 0.98 \\
HINDUNILVR - 0.88 & TSLA - 0.92 & 0388.HK - 0.91 \\
ITC - 0.97 & AAPL - 0.998 & 1398.HK - 0.90 \\
KOTAK - 0.99 & HD - 0.96 & 2388.HK - 0.93 \\
LT - 0.99 & ADBE - 0.79 & 0002.HK - 0.97 \\
SBIN - 0.91 & NFLX - 0.95 & 2888.HK - 0.99 \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Semiparametric Dynamic Copula Models for Portfolio Optimization" by Savita Pareek and Sujit K. Ghosh, arXiv:2504.12266v1, 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{Contingency Table for McNemar's Test Between Two Methods}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
 & \textbf{ChatGPT4 Correct} & \textbf{ChatGPT4 Incorrect} \\
\midrule
\textbf{ChatGPT3.5 Correct} & 31 & 4 \\
\textbf{ChatGPT3.5 Incorrect} & 22 & 8 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Equity in the Use of ChatGPT for the Classroom: A Comparison of the Accuracy and Precision of ChatGPT 3.5 vs. ChatGPT4 with Respect to Statistics and Data Science Exams" by Monnie McGee and Bivin Sadler, arXiv:2412.13116v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}
 \hline
User &1 & 2 &3 & 4 &5 & 6 &7 & 8 &9 & 10 \\
\hline
CPU&3&2.9&3.1&2.8&3.2&2.7&3.3&2.6&3.4&2.8\\
\hline
GPU& 30&29&31&28&32&27&33&26&34&28\\
\hline
CPUMem& 6&5.9&6.1&5.8&6.2&5.7&6.3&5.6&6.4&5.8\\
\hline
GPUMem & 60&59&61&58&62&57&63&56&64&58\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Baseline and Nominal Resource}
\end{table}
\end{document}

math

"A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications" by Jiaqi Lei, Akhil Singla, and Sanjay Mehrotra, arXiv:2501.18051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 Licensing & \multirow{2}{*}{\#} & Percentage \\
 properties & & of all entries\\
 \midrule
 Missing & 71 & 37\% \\
 Open license & 56 & 29\%\\
 Copyright & 30 & 16\%\\
 Non-commercial use & 18 & 9\%\\
 Public domain & 18 & 9\%\\
 Research use & 10 & 5\%\\
 Multiple licenses & 7 & 4\%\\
 Do not distribute & 2 & 1\%\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Distribution of licensing properties.}
\end{table}
\end{document}

cs

Angelina McMillan-Major
Zaid Alyafeai
Stella Biderman
Kimbo Chen
Francesco De Toni
Gérard Dupont
Hady Elsahar
Chris Emezue
Alham Fikri Aji
Suzana Ilić
Nurulaqilla Khamis
Colin Leong
Maraim Masoud
Aitor Soroa
Pedro Ortiz Suarez
Zeerak Talat
Daniel van Strien
Yacine Jernite

"Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources" by Angelina McMillan-Major, Zaid Alyafeai, Stella Biderman, Kimbo Chen, Francesco De Toni, Gérard Dupont, Hady Elsahar, Chris Emezue, Alham Fikri Aji, Suzana Ilić, Nurulaqilla Khamis, Colin Leong, Maraim Masoud, Aitor Soroa, Pedro Ortiz Suarez, Zeerak Talat, Daniel van Strien, and Yacine Jernite, arXiv:2201.10066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c}
 \hline
 \bf Method & $F_{0.5}$ \\
 \hline
 \bf Rule(Unimorph)$^\star$ & 60.87 \\
 \bf Rule(Aspell)$^\star$ & 63.49 \\
 \bf Rule(Combine)$^\star$ & 62.55 \\
 \hline
 \bf WikiEdits$^\star$ & 58.00 \\
 \bf Rule + WikiEdits$^\star$ & \bf 66.66 \\
 \hline
 \bf Back-translate & 61.37 \\
 \bf Round-trip translation & 62.91 \\
 \hline
 \bf Ours & \bf 69.17 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{$F_{0.5}$ scores of different data construction approaches on the German dataset. For the approaches with $\star$, their results are from .}
\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{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of estimated and actual MFDs}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cclll}
\toprule
Method & Network Types & \% LD & RMSE (veh/h) & $R^2$ \\ \midrule
\multirow{12}{*}{Statistical} & \multirow{4}{*}{Non-hierarchy} & 30 & 39.9 & 0.98 \\
 & & 20 & 133.5 &0.79 \\
 & & 10 & 137.6 & 0.78 \\
 & & 5 & 175.5 & 0.64 \\ \cmidrule(l){2-5}
 & \multirow{4}{*}{Three-hierarchy} & 30 & \textbf{35.9} & 0.98 \\
 & & 20 & \textbf{36.5} & 0.98 \\
 & & 10 & \textbf{45.3} & 0.97 \\
 & & 5 & \textbf{48.9} & 0.97 \\ \cmidrule(l){2-5}
 & \multirow{4}{*}{Two-hierarchy} & 30 & 37.1 & 0.98 \\
 & & 20 & 37.3 & 0.98 \\
 & & 10 & 45.7 & 0.97 \\
 & & 5 & 51.8 & 0.96\\ \midrule
\multirow{3}{*}{Variogram} & \multirow{3}{*}{Non-hierarchy} & 30 & 51.5 & 0.97 \\
 & & 20 & 54.6 & 0.96 \\
 & & 10 & 55.2 & 0.96 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Nandan Maiti
Manon Seppecher
Ludovic Leclercq

"Scaling Methods To Estimate Macroscopic Fundamental Diagrams in Urban Networks with Sparse Sensor Coverage" by Nandan Maiti, Manon Seppecher, and Ludovic Leclercq, arXiv:2411.19721v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Kernel compensation method for Maxwell eigenproblem with mimetic finite difference discretization" by Chenhao Jin, Yinhua Xia, and Yan Xu, arXiv:2503.19379v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.