\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Convertible bond price based on the AFV model at $t = 0$ ans $S = 100$, computed using non-uniform, weighted cubic NURBS. The numerical parameters are given in Table~.}
\begin{tabular}{c|c|ccc} \hline
& & \multicolumn{3}{c}{ \underline{Weights}} \\
$nE$ & $n_\tau$ & Optimal & Adjusted & Non-optimal \\ \hline \hline
$2^{6}$ & 50 & 124.8745 & 124.8777 & 125.1409 \\
$2^{7}$ & 100 & 124.8745 & 124.6833 & 124.9433 \\
$2^{8}$ & 200 & 124.8745 & 124.5956 & 124.8542 \\
$2^{9}$ & 400 & & 124.5541 & 124.8120 \\
$2^{10}$ & 800 & & 124.5339 & 124.7915 \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Rakhymzhan Kazbek
Yogi Erlangga
Yerlan Amanbek
Dongming Wei

"Isogeometric Analysis for the Pricing of Financial Derivatives with Nonlinear Models: Convertible Bonds and Options" by Rakhymzhan Kazbek, Yogi Erlangga, Yerlan Amanbek, and Dongming Wei, arXiv:2412.08987v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Scalar Field Static Spherically Symmetric Solutions in Teleparallel $F(T)$ Gravity" by Alexandre Landry, arXiv:2503.14465v1, 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{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ccc|ccc}
\hline
\multirow{2}{*}{Run (Retriever)} & \multicolumn{3}{c|}{Cutoff = 10} & \multicolumn{3}{c}{Cutoff = 20} \\ \cline{2-7}
 & DEJA-VU & nDCG & Recall & DEJA-VU & nDCG & Recall \\ \hline
ANCE & 0.846 & 0.646 & 0.206 & 0.909 & 0.607 & 0.287 \\
BM25 & 0.756 & 0.480 & 0.164 & 0.843 & 0.472 & 0.248 \\
SBERT & 0.846 & 0.634 & 0.202 & 0.888 & 0.603 & 0.291 \\
SPLADE++ & \underline{0.910} & \textbf{0.720} & \textbf{0.245} & \underline{0.966} & \textbf{0.702} & \textbf{0.360} \\
TCT-ColBERT &\textbf{0.914} & \underline{0.688} & \underline{0.228} & \textbf{0.969} & \underline{0.659} & \underline{0.329} \\
uniCOIL & 0.834 & 0.652 & 0.224 & 0.897 & 0.611 & 0.311 \\ \hline
\end{tabular}
\caption{Scores of DEJA-VU, nDCG, and Recall for the six runs on TRDL 20 at cutoffs of 10 and 20 respectively. Bold indicates the highest score for a run, while underscore denotes the second highest score.}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{An input-output table of a primitive society with three basic industries: farming, housing, and garment.}
\begin{tabular}{|ll|lll|}
\cline{1-5}
 & & \multicolumn{3}{c|}{Ouput} \\ \cline{3-5}
 & & Farming & Housing & Garment \\ \hline
\multirow{3}{*}{Consumption} & Farming & 0.4 & 0.2 & 0.3 \\ \cline{2-5}
 & Housing & 0.2 & 0.6 & 0.5 \\ \cline{2-5}
 & Garment & 0.4 & 0.2 & 0.2 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Minh Hoang Trinh
Nhat-Minh Le-Phan
Hyo-Sung Ahn

"The networked input-output economic problem" by Minh Hoang Trinh, Nhat-Minh Le-Phan, and Hyo-Sung Ahn, arXiv:2412.13564v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Quotas for scholarship recipients: an efficient race-neutral alternative to affirmative action?" by Louis Gleyo, arXiv:2507.17191v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Xenia Haslinger
Elisabeth Gaar
Sophie N. Parragh

"An exact approach for the multi-depot electric vehicle scheduling problem" by Xenia Haslinger, Elisabeth Gaar, and Sophie N. Parragh, arXiv:2504.13063v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean Absolute Difference between prescribed and actual treatments for unstructured datasets.}
\begin{tabular}{ccccccc}
& \multicolumn{2}{c}{\textbf{TAVR models}} & \multicolumn{2}{c}{\textbf{Liver trauma models}} \\
\textbf{Method} & \textbf{Tabular} & \textbf{Multimodal} & \textbf{Tabular} & \textbf{Multimodal} \\ \midrule
PNN & $0.4561$ & $\mathbf{0.6144}$ & $0.3590$ & $0.3837$ \\
Mirrored OCT & $\mathbf{0.4280}$ & $0.6539$ & $\mathbf{0.3351}$ & $\mathbf{0.3089}$\\
\end{tabular}
\end{table}
\end{document}

stat

Multimodal Prescriptive Deep Learning

Dimitris Bertsimas
Lisa Everest
Vasiliki Stoumpou

"Multimodal Prescriptive Deep Learning" by Dimitris Bertsimas, Lisa Everest, and Vasiliki Stoumpou, arXiv:2501.14152v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The MSE values of the proposed model corresponding to different CNN filter numbers.}
\begin{tabular}{|c|c|c|c|}
\hline
 CNN Filter Number & 64 & 128 & 256 \\ \hline
MSE (Training) & 0.039 & 0.0335 & 0.019 \\ \hline
MSE (Validation) & 0.037 & 0.0316 & 0.017 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Praveen Damacharla
Hamid Rajabalipanah
Mohammad Hosein Fakheri

"LSTM-CNN Network for Audio Signature Analysis in Noisy Environments" by Praveen Damacharla, Hamid Rajabalipanah, and Mohammad Hosein Fakheri, arXiv:2312.07059v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|r|r|}
\hline
\textbf{Statistic} & \textbf{Value BTC Futures CME} & \textbf{Value Binance} \\ \hline
Price Change During Event (\%) & -0.85 & -0.77 \\ \hline
Event Maximum Price(USD) & 56440
 & 56149 \\ \hline
Event Minimum Price (USD) & 	56000 & 55667.3 \\ \hline
\end{tabular}
\caption{Descriptive statistics for the event on August 5th 2024: Micro BTC Q24 futures contract on CME and spot BTC on Binance}
\end{table}
\end{document}

q-fin

Juan Plazuelo Pascual
Carlos Tardon Rubio
Juan Toro Cebada
Angel Hernando Veciana

"Price Discovery in Cryptocurrency Markets" by Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, and Angel Hernando Veciana, arXiv:2506.08718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}

			\hline
			\textbf{Previous Methods} & \textbf{Accuracy\%} \\\hline

			Yao et al.~ & 94.5 \\\hline

			\textbf{Proposed Method} & \textbf{98.1} \\
			\hline
		\end{tabular}
\caption{ Comparison of Accuracies of proposed method with previous methods on HHAR dataset}
\end{table}
\end{document}

eess

"Multidomain Multimodal Fusion For Human Action Recognition Using Inertial Sensors" by Zeeshan Ahmad and Naimul Khan, arXiv:2008.09748v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
\hline
 & \textbf{w/o style} & \textbf{w/o content} & \textbf{w/o marg.} & \textbf{GDA (Ours)} \\
\textbf{Rendition} & 37.7 & 37.9 & 39.4 & 44.5 \\
\textbf{Sketch} & 23.3 & 23.5 & 23.9 & 25.5 \\ \hline
\end{tabular}
\caption{The Impact of Different Loss Term}
\end{table}
\end{document}

cs

Yun-Yun Tsai
Fu-Chen Chen
Albert Y. C. Chen
Junfeng Yang
Che-Chun Su
Min Sun
Cheng-Hao Kuo

"GDA: Generalized Diffusion for Robust Test-time Adaptation" by Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen, Junfeng Yang, Che-Chun Su, Min Sun, and Cheng-Hao Kuo, arXiv:2404.00095v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative comparison of different algorithms on the HS-MS image fusion experiments. The best one is shown in bold.}
\begin{tabular}{c||cccc}
\toprule[1.5pt] Methods & RMSE & ERGAS & SA & SSIM \\
\hline \hline
CNMF & 6.404 & 0.715 & 4.89 & 0.8857\\
ICCV'15 & \bf 5.203 & \bf 0.589 & \bf 4.64 & \bf 0.9139\\
HySure & 8.537 & 0.812 & 9.45 & 0.8527\\
FUSE & 8.652 & 0.869 & 9.51 & 0.8401\\
CSTF & 8.32 & 0.841 & 8.34 & 0.8419\\
STEREO & 9.4425 & 0.891 & 9.78 & 0.8231\\
NLSTF & 8.254 & 0.819 & 8.36 & 0.8424\\
\bottomrule[1.5pt]
\end{tabular}
\end{table}
\end{document}

eess

Danfeng Hong
Wei He
Naoto Yokoya
Jing Yao
Lianru Gao
Liangpei Zhang
Jocelyn Chanussot
Xiao Xiang Zhu

"Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing" by Danfeng Hong, Wei He, Naoto Yokoya, Jing Yao, Lianru Gao, Liangpei Zhang, Jocelyn Chanussot, and Xiao Xiang Zhu, arXiv:2103.01449v1, 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{Regional Distributions of VOTE400 Read Dataset}
\begin{tabular}{lrrr}
\hline
Region(\texttt{G}) & No. Persons & No. Sent. & Len.($\mu/\sigma$) \\ \hline \hline
Gyeongsangnam-do(GB) & 20 & 22,575 & 3.18/1.38 \\ \hline
Seoul-si(SE) & 18 & 19,220 & 3.31/1.49 \\ \hline
Jeollanam-do(JN) & 21 & 21,393 & 3.36/1.52 \\ \hline
Daegu-si(DG) & 25 & 26,950 & 3.60/1.87 \\ \hline
Gangwon-do(GW) & 20 & 21,676 & 2.73/1.12 \\ \hline \hline
Total & 104 & 111,814 & 3.25/1.54 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Minsu Jang
Sangwon Seo
Dohyung Kim
Jaeyeon Lee
Jaehong Kim
Jun-Hwan Ahn

"VOTE400(Voide Of The Elderly 400 Hours): A Speech Dataset to Study Voice Interface for Elderly-Care" by Minsu Jang, Sangwon Seo, Dohyung Kim, Jaeyeon Lee, Jaehong Kim, and Jun-Hwan Ahn, arXiv:2101.11469v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zero-Leverage Puzzle

"Zero-Leverage Puzzle" by Mykola Pinchuk, arXiv:2302.00761v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
\hline
 & (1) & (2) & (3)\tabularnewline
Panasonic & 1000h \& 2000h & 1000h only & 1000h only\tabularnewline
Toshiba & 1000h \& 2000h & 1000h only & 1000h only\tabularnewline
Price & - & Fixed & Not fixed\tabularnewline
\hline
\hline
Joint profit & \textsf{24.67} & \textsf{25.41} & 26.06\tabularnewline
Profit (Panasonic) & 10.77 & 10.49 & 10.99\tabularnewline
Profit (Toshiba) & \textsf{13.9} & \textsf{14.92} & 15.07\tabularnewline
\hline
{\small{}No inventory consumers (\%)} & 18.61 & 19.28 & 19.21\tabularnewline
{\small{}Disposal (million)} & 3.04 & 3.15 & 3.13\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"When do firms sell high durability products? The case of light bulb industry" by Takeshi Fukasawa, arXiv:2503.23792v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Computation Time Comparison}
\begin{tabular}{lccc}
 Method & Mean & SD & Max\\ \hline
 Proposed safety filter & 0.1446 s & 0.0225 s & 0.2558 s\\
 Nonlinear RMPC from & 5.1962 s & 7.9486 s & 42.1694 s\\
 Feasibility checking for RMPC & 4.3684 s & 0.5716 s & 6.7651 s\\\hline
 \end{tabular}
\end{table}
\end{document}

eess

Zeyang Li
Chuxiong Hu
Weiye Zhao
Changliu Liu

"Learning Predictive Safety Filter via Decomposition of Robust Invariant Set" by Zeyang Li, Chuxiong Hu, Weiye Zhao, and Changliu Liu, arXiv:2311.06769v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Monotone Curve Estimation via Convex Duality" by Tongseok Lim, Kyeongsik Nam, and Jinwon Sohn, arXiv:2501.06975v2, 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{Values of $a$ and $b$ in for different binary modulations.}
\begin{tabular}{|c|c|c|}
		\hline
		Modulation & $a$ & $b$ \\
		\hline 	\hline
		BPSK & $1$ & $\frac{1}{2}$ \\
		\hline
		DBPSK & $1$ & $1$ \\
		\hline
		BFSK & $\frac{1}{2}$ & $\frac{1}{2}$ \\
		\hline
		NBFSK & $\frac{1}{2}$ & $1$ \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Sotiris A. Tegos
Dimitrios Tyrovolas
Panagiotis D. Diamantoulakis
Christos K. Liaskos
George K. Karagiannidis

"On the Distribution of the Sum of Double-Nakagami-m Random Vectors and Application in Randomly Reconfigurable Surfaces" by Sotiris A. Tegos, Dimitrios Tyrovolas, Panagiotis D. Diamantoulakis, Christos K. Liaskos, and George K. Karagiannidis, arXiv:2102.05591v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{United Nations Sustainable Development Goals}
\begin{tabular}{cll}
\hline
\textbf{UNSDG}&\textbf{Description}&\textbf{Class}\\
\hline
SDG 1 & No Poverty & Social \\
SDG 2 & Zero Hunger & Social \\
SDG 3 & Good Health and Well-being & Social \\
SDG 4 & Quality Education & Social \\
SDG 5 & Gender Equality & Social \\
SDG 6 & Clean Water and Sanitation & Environmental \\
SDG 7 & Affordable and Clean Energy & Environmental \\
SDG 8 & Decent Work and Economic Growth & Economic \\
SDG 9 & Industry, Innovation and Infrastructure & Economic \\
SDG 10 & Reduced Inequalities & Social \\
SDG 11 & Sustainable Cities and Communities & Social \\
SDG 12 & Responsible Consumption and Production & Economic \\
SDG 13 & Climate Action & Environmental \\
SDG 14 & Life Below Water & Environmental \\
SDG 15 & Life on Land & Environmental \\
SDG 16 & Peace, Justice and Strong Institutions & Social \\
SDG 17 & Partnerships & Social \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Ali Kaan Kurbanzade
Ansaar M. Baig
Sanjay Mehrotra

"Elevating Industries with Unmanned Aerial Vehicles: Integrating Sustainability and Operational Innovation" by Ali Kaan Kurbanzade, Ansaar M. Baig, and Sanjay Mehrotra, arXiv:2312.13551v1, 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{Execution times and acceptance rates of the Markov chain simulations with starting points \((-9,-9)\) for HMC and RWMH, and \((-9,-9)\), \((-8,-8)\) for t-walk. }
\begin{tabular}{lrrrrc}
\hline
& HMC & RWMH & t-walk \\
\hline
Acceptance rate & 93.9\% s & 88.9\% & 17.1\%\\
Execution time (s)& 115.8 & 171.4 & 119.8 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Understanding the Hamiltonian Monte Carlo through its Physics Fundamentals and Examples" by Abraham Granados and Isaías Bañales, arXiv:2501.13932v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
\toprule
\midrule
\multicolumn{2}{l}{\textbf{SGLT2-i Prevalence Scenarios}} \\
\midrule
\textbf{Baseline} & $S_k \sim \text{Bernoulli}(\text{expit}(-9.5 + 0.5 B + 0.1 L_k - 1.5 A))$. \\
& Annual take-up: 20\% in placebo, 5\% in semaglutide group. \\
\textbf{High drop-in} & $S_k \sim \text{Bernoulli}(\text{expit}(-9 + 0.5 B + 0.1 L_k - 2 A))$. \\
& Annual take-up: 30\% in placebo, 5\% in semaglutide group. \\
\textbf{Low drop-in} & $S_k \sim \text{Bernoulli}(\text{expit}(-9 + 0.5 B + 0.1 L_k - A))$. \\
& Annual take-up: 13\% in placebo, 5\% in semaglutide group. \\
\textbf{No drop-in} & $S_k = 0$. No SGLT2-i use in both groups. \\
\midrule
\multicolumn{2}{l}{\textbf{Effect Scenarios}} \\
\midrule
\textbf{Baseline} & $\beta_S = \log(0.9)$, $\beta_{AS} = 0.05$. \\
\textbf{Strong effect} & $\beta_S = \log(0.85)$, $\beta_{AS} = 0.1$. \\
\textbf{Weak effect} & $\beta_S = \log(0.95)$, $\beta_{AS} = 0.1$. \\
\bottomrule
\end{tabular}
\caption{Descriptions of SGLT2-i prevalence and efficacy scenarios. The prevalence scenarios vary the annual uptake of SGLT2-i in the placebo and semaglutide groups, while efficacy scenarios are defined by combinations of $\beta_S$ and $\beta_{AS}$, representing the isolated and interaction effects, respectively.}
\end{table}
\end{document}

stat

Xi Lin
Daniel de Vassimon Manela
Chase Mathis
Jens Magelund Tarp
Robin J. Evans

"Exact Simulation of Longitudinal Data from Marginal Structural Models" by Xi Lin, Daniel de Vassimon Manela, Chase Mathis, Jens Magelund Tarp, and Robin J. Evans, arXiv:2502.07991v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|l}
 \hline
 Sampling & Space & Method \\
 \hline
 \hline
 \multirow{3}{*}{Uniform} & \multirow{2}{*}{Objective} & \texttt{equi-spaced}~ \\
 & & \texttt{equi-dist} (this paper) \\
 \cline{2-3}
 & Non-objective & \texttt{equi-jaccard} (this paper) \\
 \hline
 \multirow{6}{*}{\shortstack{Non- \\ uniform}} & \multirow{4}{*}{Objective} & \texttt{hv-ss}~ \\
 & & \texttt{igd-ss}~ \\
 & & \texttt{igd+-ss}~ \\
 & & \texttt{hvc-ss} (this paper) \\
 & & \texttt{k-medoids-pr}~ \\
 \cline{2-3}
 & Non-objective & \texttt{k-medoids-jaccard} (this paper) \\
 \hline
\end{tabular}
\caption{Categorization of SSF methods.}
\end{table}
\end{document}

q-fin

"On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech Applications" by Chengyao Wen and Yin Lou, arXiv:2311.00964v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|l}
 \hline
 \textbf{Detector} & \textbf{RMSE}\\
 \hline
 Proposed Detector (Algorithm~) & 1.55\\
 Random Forest Model & 38.63\\
 \hline
 \end{tabular}
\caption{Comparison of root mean squared error (RMSE) in estimating the onset of the eclipse attack on a blockchain network. Our test dataset consisted of 83 data points, each corresponding to a sequence of 1000 adjacency matrices for the BCNs (Sec. ). The RMSE values were averaged over 5 runs.}
\end{table}
\end{document}

cs

Anurag Gupta
Vikram Krishnamurthy
Brian M. Sadler

"Eclipse Attack Detection on a Blockchain Network as a Non-Parametric Change Detection Problem" by Anurag Gupta, Vikram Krishnamurthy, and Brian M. Sadler, arXiv:2404.00538v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Monetary policy and the joint distribution of income and wealth: The heterogeneous case of the euro area" by Anna Stelzer, arXiv:2304.14264v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of the mean accuracies over all participants obtained by our selected state-of-the-art classifiers using various feature selection algorithm and min-max normalization for all participants. The ``*" marked classifiers can have varying results due to their implementation.}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
Classifier & None & SFS & RFECV & PPG & PPG+EDA \\
\hline\hline
SVC & \textbf{0.7917} & \textbf{0.7708} & 0.7083 & 0.6875 & \textbf{0.7917} \\
\hline
DTC* & 0.5417 & 0.5625 & 0.5625 & 0.5833 & 0.5625 \\
\hline
KNN & 0.75 & 0.6458 & N.A. & 0.7292 & 0.6458 \\
\hline
GNB & 0.6042 & 0.5833 & N.A. & 0.6667 & 0.6042 \\
\hline
LR & 0.5625 & 0.6875 & 0.6667 & 0.6875 & 0.7083 \\
\hline
LDA & 0.7708 & 0.6458 & \textbf{0.75} & \textbf{0.7708} & \textbf{0.7917} \\
\hline
QDA & 0.625 & 0.5625 & N.A. & 0.6042 & 0.625 \\
\hline
RF* & 0.6875 & 0.5208 & 0.7083 & 0.6875 & 0.625\\
\hline
GB* & 0.6458 & 0.5417 & 0.625 & 0.6042 & 0.5833 \\
\hline
AB & 0.6458 & 0.5 & 0.5833 & 0.6458 & 0.6458 \\
\hline
XGB & 0.5625 & 0.4167 & 0.6458 & 0.6667 & 0.5833\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Till Aust
Eirini Balta
Argiro Vatakis
Heiko Hamann

"Automatic Classification of Subjective Time Perception Using Multi-modal Physiological Data of Air Traffic Controllers" by Till Aust, Eirini Balta, Argiro Vatakis, and Heiko Hamann, arXiv:2404.15213v3, 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{Comparison of substitution errors across different bases and average bit error}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
Base to base & G to A & G to T & C to A & C to T & T to C & A to G & T to A & A to T & T to G & G to C & A to C & C to G \\ \hline
Sub. error prob.$(\%)$& 14.133 & 13.773 & 8.894 & 7.842 & 7.142 & 7.067 & 7.050 & 7.046 & 6.948 & 6.889 & 6.826 & 6.387 \\ \hline
Average bit error & 2 & 2.357 & 2.214 & 2.357 & 2.357 & 2 & 2.5 & 2.5 & 2.357 & 2.857 & 2.214 & 2.857 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Seong-Joon Park
Yongwoo Lee
Jong-Seon No

"Iterative DNA Coding Scheme With GC Balance and Run-Length Constraints Using a Greedy Algorithm" by Seong-Joon Park, Yongwoo Lee, and Jong-Seon No, arXiv:2103.03540v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sample table title}
\begin{tabular}{lll}
 \toprule
 \multicolumn{2}{c}{Part} \\
 \cmidrule(r){1-2}
 Name & Description & Size ($\mu$m) \\
 \midrule
 Dendrite & Input terminal & $\sim$100 \\
 Axon & Output terminal & $\sim$10 \\
 Soma & Cell body & up to $10^6$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Complex-valued Neural Networks -- Theory and Analysis" by Rayyan Abdalla, arXiv:2312.06087v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|}\hline
\verb|C2__C2_C2__C2|\\\hline
\verb|C2__C2_Ceta__C2|\\\hline
\verb|C2__C2h4__S0|\\\hline
\verb|C2__C2h5__S0|\\\hline
\verb|C2__C2h6__S0|\\\hline
\verb|C2__CW_2_V_eta__S2|\\\hline
\verb|C2__CW_2_eta__S0|\\\hline
\verb|C2__CW_2_eta_nu__Cnu|\\\hline
\verb|C2__CW_2_eta_nu_sigma__CW_nu_sigma|\\\hline
\verb|CW_2_eta__CW_2_eta_nu__S0|\\\hline
\verb|CW_2_eta__CW_2_eta_nu_sigma__Csigma|\\\hline
\verb|CW_2_eta__Joker__Ceta|\\\hline
\verb|CW_2_eta_nu__CW_2_eta_nu_sigma__S0|\\\hline
\verb|CW_eta_2__RP1_6__CW_2_eta|\\\hline
\verb|CW_eta_nu__CW_eta_nu_sigma__S0|\\\hline
\verb|CW_nu_eta__CW_nu_eta_2__S0|\\\hline
\verb|Ceta__C2_Ceta__Ceta|\\\hline
\verb|Ceta__CW_2_V_eta__S1|\\\hline
\verb|Ceta__CW_eta_2__S0|\\\hline
\verb|Ceta__CW_eta_nu__S0|\\\hline
\verb|Ceta__CW_eta_nu_sigma__Csigma|\\\hline
\verb|Ceta__Joker__CW_eta_2|\\\hline
\verb|Cnu__CW_nu_eta_2__C2|\\\hline
\verb|Cnu__CW_nu_eta__S0|\\\hline
\verb|Cnu__CW_nu_sigma__S0|\\\hline
\verb|Csigma__CW_sigma_2sigma__S0|\\\hline
\verb|Csigma__CW_sigma_nu__S0|\\\hline
\verb|Csigmasq__DC2h4__S0|\\\hline
\verb|Ctheta4__DC2h5__S0|\\\hline
\verb|Ctheta5__DC2h6__S0|\\\hline
\verb|Fphi__RP1_256__S0|\\\hline
\verb|RP1_2__RP1_256__RP3_256|\\\hline
\verb|RP1_2__RP1_4__RP3_4|\\\hline
\verb|RP1_2__RP1_6__RP3_6|\\\hline
\verb|RP3_4__RP3_6__RP5_6|\\\hline
\verb|S0__C2__S0|\\\hline
\verb|S0__C2h4__Csigmasq|\\\hline
\verb|S0__C2h5__Ctheta4|\\\hline
\verb|S0__C2h6__Ctheta5|\\\hline
\verb|S0__C2sigma__S0|\\\hline
\verb|S0__CW_2_A_eta__C2|\\\hline
\verb|S0__CW_2_eta__Ceta|\\\hline
\verb|S0__CW_2_eta_nu__CW_eta_nu|\\\hline
\verb|S0__CW_2_eta_nu_sigma__CW_eta_nu_sigma|\\\hline
\end{tabular}
\caption{Cofiber sequences in the database}
\end{table}
\end{document}

math

"Machine Proofs for Adams Differentials and Extension Problems among CW Spectra" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10876v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Operating characteristics of the MAMSAP design and competing approaches for non-binding stopping boundaries.}
\begin{tabular}{c|c|c|c|c|c|c}
\multirow{4}{*}{Design} & \multirow{4}{*}{$\begin{pmatrix}
u_1\\
u_2\\
u_3
\end{pmatrix}$} & \multirow{4}{*}{$\begin{pmatrix}
u_1^\star\\
u_2^\star\\
u_3^\star
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
\text{FWER}\\
\text{Power}\\
\end{matrix}$} & \multirow{4}{*}{$\begin{pmatrix}
n_1\\
n_2\\
n_3
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
\max(N)\\
\end{matrix}$} & \multirow{4}{*}{$\begin{matrix}
E(N|\Theta_0) \\
E(N|\Theta_1)\\
E(N|\Theta_2) \\
E(N|\Theta_3)
\end{matrix}$} \\
& & & & & & \\
& & & & & & \\
& & & & & & \\
\hline
 & \multirow{4}{*}{$\begin{pmatrix}
3.181\\
2.811\\
2.755
\end{pmatrix}$} & \multirow{4}{*}{$\begin{pmatrix}
0.000\\
1.687\\
2.755
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
0.048\\
0.903
\end{matrix}$} & \multirow{4}{*}{$\begin{pmatrix}
82\\
164\\
246
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
984\\
\end{matrix}$} & \multirow{4}{*}{$\begin{matrix}
758.0\\
654.5\\
636.6\\
677.2
\end{matrix}$} \\
 \multirow{1}{*}{MAMSAP} & & & & & & \\
\multirow{1}{*}{ design}& & & & & & \\
 & & & & & & \\
\hline
 & \multirow{4}{*}{$\begin{pmatrix}
2.517\\
2.225\\
2.180
\end{pmatrix}$} & \multirow{4}{*}{$\begin{pmatrix}
0.000\\
1.335\\
2.180
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
0.201\\
0.813
\end{matrix}$} &\multirow{4}{*}{$\begin{pmatrix}
51\\
102\\
153
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
612\\
\end{matrix}$} & \multirow{4}{*}{$\begin{matrix}
497.8\\
406.8\\
402.1\\
437.1
\end{matrix}$} \\
 \multirow{1}{*}{Whitehead} & & & & & & \\
 \multirow{1}{*}{ design}& & & & & & \\
 & & & & & & \\
\hline
 \multirow{1}{*}{Bonferroni} & \multirow{4}{*}{$\begin{pmatrix}
3.235\\
2.859\\
2.801
\end{pmatrix}$} & \multirow{4}{*}{$\begin{pmatrix}
0\\
1.715\\
2.801
\end{pmatrix}$} &\multirow{4}{*}{$\begin{matrix}
0.042\\
0.930
\end{matrix}$} & \multirow{4}{*}{$\begin{pmatrix}
90\\
180\\
270
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
1080\\
832.0
\end{matrix}$} & \multirow{4}{*}{$\begin{matrix}
698.2\\
684.1\\
734.0
\end{matrix}$} \\
 \multirow{1}{*}{adjusted} & & & & & & \\
 \multirow{1}{*}{Whitehead} & & & & & & \\
 \multirow{1}{*}{design} & & & & & & \\
\hline
 & \multirow{4}{*}{$\begin{pmatrix}
2.517\\
2.225\\
2.180
\end{pmatrix}$} & \multirow{4}{*}{$\begin{pmatrix}
0.000\\
1.335\\
2.180
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
0.248\\
0.739
\end{matrix}$} & \multirow{4}{*}{$\begin{pmatrix}
51\\
102\\
153
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
1836\\
\end{matrix}$} &\multirow{4}{*}{$\begin{matrix}
1308.9\\
1224.6\\
1196.5\\
1224.6
\end{matrix}$} \\
\multirow{1}{*}{Separate} & & & & & & \\
 \multirow{1}{*}{trials}& & & & & & \\
 & & & & & & \\
\hline
\multirow{1}{*}{FWER} & \multirow{4}{*}{$\begin{pmatrix}
3.227\\
2.852\\
2.794
\end{pmatrix}$} & \multirow{4}{*}{$\begin{pmatrix}
0\\
1.711\\
2.794
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
0.047\\
0.901
\end{matrix}$} & \multirow{4}{*}{$\begin{pmatrix}
89\\
178\\
267
\end{pmatrix}$} & \multirow{4}{*}{$\begin{matrix}
3204\\
\end{matrix}$} & \multirow{4}{*}{$\begin{matrix}
2222.0 \\
2095.7\\
2053.7\\
2095.7
\end{matrix}$} \\
\multirow{1}{*}{controlled} & & & & & &\\
\multirow{1}{*}{separate} & & & & & &\\
\multirow{1}{*}{trials} & & & & & & \\
\end{tabular}
\end{table}
\end{document}

stat

Peter Greenstreet
Thomas Jaki
Alun Bedding
Pavel Mozgunov

"A multi-arm multi-stage design for trials with all pairwise testing" by Peter Greenstreet, Thomas Jaki, Alun Bedding, and Pavel Mozgunov, arXiv:2502.07013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|lrr}
 Sensor & FOV & $n_{points}/$ scan & LiDAR Height \\
 \hline
 HLD-64 (Kitti) & $26.9^\circ$ & $\approx 118000$ & $\approx1.6$\,m \\
 VLD-64 (CS64)& $26.9^\circ$ & $\approx 100000$ & $\approx2$\,m \\
 HLD-64 (Waymo) & $26.9^\circ$ & $\approx 144512$ & $\approx1.6$\,m \\
 VLD-32 (CS32) & $40^\circ$ & $\approx 63900$ &$2$\,m\\
 VLD-32 (nuScenes) & $40^\circ$ & $\approx 34688$ &$1.6$\,m\\
 VLD-16 (CS16) & $30^\circ$ & $\approx 22000$ & $2$\,m\\
 VLP-16 (robot) & $30^\circ$ & $\approx 22000$ & $0.6$\,m\\
 \end{tabular}
\end{table}
\end{document}

cs

Maciej K Wozniak
Mattias Hansson
Marko Thiel
Patric Jensfelt

"UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps" by Maciej K Wozniak, Mattias Hansson, Marko Thiel, and Patric Jensfelt, arXiv:2403.17633v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{~ Breusch and Pagan LM Test}
\begin{tabular}{lrrrr}
 \toprule
 \multicolumn{5}{c}{\textbf{Breusch and Pagan Lagrangian multiplier test for random effects}} \\
 \midrule
 \midrule
 \multicolumn{5}{c}{nim[bank,t] = Xb + u[bank] + e[bank,t]} \\
 \midrule
 \midrule
 Estimated results: & & & & \\
 \midrule
 \midrule
 & Var & $sd = \sqrt{Var}$ & & \\
\cmidrule{1-3} & & & & \\
 nim & 12.22123 & 3.495887 & & \\
 e & 5.688468 & 2.385051 & & \\
 u & 0 & 0 & & \\
 Test: Var(u) $=$ 0 & & & & \\
 chibar2(01)$=$ 0.00 & & & & \\
 Prob $> \chi^2 = 1.0000$ & & & & \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"The Determinants of Net Interest Margin in the Turkish Banking Sector: Does Bank Ownership Matter?" by Fatih Kansoy, arXiv:2506.04384v2, 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{Attack success rates (ASRs) achieved by different attack strategies in the perfect knowledge scenario.}
\begin{tabular}{|lcccc|}
		\hline
		 \multicolumn{5}{|c|}{\textbf{Proposed Anti-Forensic GAN}} \\
		\hline
		\textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification} &\textbf{ Parameterization}& \textbf{Avg.}\\
		MISLnet& 0.98& 0.98& 0.98&0.98\\
		TransferNet &1.00 &0.99&1.00&1.00 \\
		PHNet &0.84 & 0.99 &0.99&0.94\\
		SRNet &0.96 & 0.99&0.97&0.97\\
		DenseNet\textunderscore BC & 0.99 &0.96 &0.98&0.98\\
		VGG-19 & 0.99 &0.96 &0.98&0.98\\
		\textcolor{blue}{\textbf{Avg.} }& \textcolor{blue}{\textbf{0.96}} & \textcolor{blue}{\textbf{0.98}} & \textcolor{blue}{ \textbf{ 0.99}} & \textcolor{blue}{\textbf{0.98}}\\
 \hline
	 \multicolumn{5}{|c|}{\textbf{Removing Discriminator}} \\
	 	\hline
 \textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification} &\textbf{ Parameterization}& \textbf{Avg.}\\
 MISLnet&0.90&0.97&1.00&0.95\\
 TransferNet &1.00 &1.00&1.00&1.00\\
 PHNet&0.78&0.93&0.98&0.89\\
		SRNet&0.53&0.93&0.98&0.81\\
		DenseNet\textunderscore BC & 0.89&1.00 &1.00 &0.96\\
		VGG-19 &0.87 & 0.99 &0.99 &0.95\\
		\textbf{Avg.} & \textbf{0.83} &\textbf{0.97}&\textbf{0.99} &\textbf{0.92} \\
	 	\hline
 \multicolumn{5}{|c|}{\textbf{MISLGAN~}} \\
	 	\hline
 \textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification} &\textbf{ Parameterization}& \textbf{Avg.}\\
 MISLnet& 0.55 & 0.95& 0.84 &0.78\\
	TransferNet & 0.99 & 1.00 & 1.00&1.00 \\
	PHNet&0.90 & 0.97&0.94&0.94\\
	SRNet & 0.88& 0.90 & 0.82&0.87\\
	DenseNet &0.90&0.94&0.94&0.93\\
	VGG-19 & 0.71 &0.97&0.96&0.88\\
		\textbf{Avg.} & \textbf{0.82} & \textbf{0.96} & \textbf{0.92}& \textbf{0.90}\\
		\hline
 \multicolumn{5}{|c|}{\textbf{Standard GAN}} \\
 \hline
 \textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification} &\textbf{Parameterization}& \textbf{Avg.}\\
 MISLnet&0.08&0.01 &0.00&0.03\\
 TransferNet & 0.02&0.00 &0.00 &0.01\\
 PHNet&0.06 &0.00&0.00&0.02\\
		SRNet & 0.04&0.20 &0.18&0.14\\
		DenseNet\textunderscore BC & 0.08 & 0.24& 0.26 &0.19\\
		VGG-19 &0.05 & 0.75 & 0.57 &0.46\\
		\textbf{Avg.} &\textbf{0.06}& \textbf{0.20} & \textbf{0.17} &\textbf{0.14} \\
 \hline
 	\end{tabular}
\end{table}
\end{document}

cs

"A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network" by Xinwei Zhao, Chen Chen, and Matthew C. Stamm, arXiv:2101.09568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{International students enrolled in Italian three-year, master's or single-cycle programmes, basic index numbers (N.I. base academic year 2013/14) and \% of total enrolment \\ Source: Elaboration on MUR (Ministry of University and research) data}
\begin{tabular}{lccccccccccc}
\toprule
 & 13/14 & 14/15 & 15/16 & 16/17 & 17/18 & 18/19 & 19/20 & 20/21 & 21/22 & 22/23 \\
 \midrule
N.I. & & 99.5 & 102.7 & 110.3 & 118.9 & 120.0 & 130.0 & 139.9 & 149.8 & 165.2 \\
\% & 4.8 & 4.8 & 4.8 & 4.9 & 5.3 & 5.2 & 5.3 & 5.4 & 5.8 & 6.4 \\
\bottomrule \\
\end{tabular}
\end{table}
\end{document}

stat

Lorenzo Giammei
Laura Terzera
Fulvia Mecatti

"Statistical Challenges in Analyzing Migrant Backgrounds Among University Students: a Case Study from Italy" by Lorenzo Giammei, Laura Terzera, and Fulvia Mecatti, arXiv:2501.06166v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
\hline
Dimension & Mechanism \\ \hline
Central collateral management & Collateral is pooled together and the issuer functions as a central entity or is a smart contract responsible for managing the collateral. \\
Decentral collateral management & “Everyone” has the ability to issue central-debt positions, which are subject to liquidation once a specific critical liquidation ratio is reached. \\
Exogenous collateral source & External to the blockchain system; the collateral has no interactions with the stablecoin other than backing the stablecoin. \\
Endogenous collateral source & Internal to the blockchain system; stablecoin value is derived from future trading fees or other financial remunerations. \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Matthias Hafner
Marco Henriques Pereira
Helmut Dietl
Juan Beccuti

"The four types of stablecoins: A comparative analysis" by Matthias Hafner, Marco Henriques Pereira, Helmut Dietl, and Juan Beccuti, arXiv:2308.07041v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \toprule
 \textbf{System} & \textbf{F1-Score}\\
 \midrule
 $\textit{ResNet-LSTM-MHA-2Sec}$ & 92.64\%\\
 $\textit{ResNet-LSTM-MHA-3Sec}$ & 94.40\%\\
 $\textit{ResNet-LSTM-MHA-4Sec}$ & \textbf{95.90}\%\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"End-to-End Language Identification using Multi-Head Self-Attention and 1D Convolutional Neural Networks" by Krishna D N and Ankita Patil, arXiv:2102.00306v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The classification accurary and its standard deviation with $(n_s,n_0,d)=(2000,300,60)$ and different $r^*$, dimensions of the representation estimator modules in Example 1.}
\begin{tabular}{lllll}
\toprule
 $r^*$ & TESR & DNN & DDR & TransIRM \\
\midrule
8 & 0.782 (0.015)& 0.661 (0.036) & 0.676 (0.021) & 0.602(0.092) \\
16 & 0.783 (0.015) & 0.664 (0.032) & 0.673 (0.023) & 0.639(0.086) \\
32 & 0.784 (0.014) & 0.652 (0.036)& 0.677 (0.019) & 0.653(0.075) \\
64 & 0.783 (0.013) & 0.657 (0.033)& 0.673 (0.027) & 0.652(0.072) \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"Transfer Learning through Enhanced Sufficient Representation: Enriching Source Domain Knowledge with Target Data" by Yeheng Ge, Xueyu Zhou, and Jian Huang, arXiv:2502.20414v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Description of the Vehicle Battery Data}
\begin{tabular}{lllll}
 \toprule
 \textit{field names} & \textit{data type} & \textit{value range} & \textit{unit} &\textit{source}\\
 \midrule
 cell voltage & double & $[0,5]$ & V & BMS\\
 temperature & double & $[-50,100]$ & \textcelsius & BMS\\
 pack current & double & $[-500,500]$ & A & BMS\\
 battery faults & bool & $[0,1]$ & - & BMS\\
 battery status & enum & - & - & BMS\\
 \toprule
 \end{tabular}
\end{table}
\end{document}

eess

Xiaojun Li
Jianwei Li
Ali Abdollahi
Trevor Jones

"Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering" by Xiaojun Li, Jianwei Li, Ali Abdollahi, and Trevor Jones, arXiv:2103.08796v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline $\otimes$ &$A$ &$B$ &$C$ &$D$ &$E$ &$F$ &$G$ &$H$\\ \hline
$A$ &$A$ &$B$ &$C$ &$D$ &$E$& $F$ &$G$ &$H$\\ \hline
$B$ &$B$ &$A$ &$C$& $E$ &$D$ &$F$ &$G$ &$H$\\ \hline
$C$ &$C$ &$C$ &$A\oplus B\oplus C$& $D\oplus E$ &$D\oplus E$ & $G\oplus H$& $F\oplus H$ &$F\oplus G$\\ \hline
\multirow{2}{*}{$D$} &\multirow{2}{*}{$D$} &\multirow{2}{*}{$E$} &\multirow{2}{*}{$D\oplus E$}& $A\oplus C\oplus F$ & $B\oplus C\oplus F$ & \multirow{2}{*}{$D\oplus E$} & \multirow{2}{*}{$D\oplus E$} & \multirow{2}{*}{$D\oplus E$} \\
& & & & $\oplus G\oplus H$ & $\oplus G\oplus H$ & & & \\ \hline
\multirow{2}{*}{$E$} &\multirow{2}{*}{$E$}& \multirow{2}{*}{$D$}& \multirow{2}{*}{$D\oplus E$} & $B\oplus C\oplus F$ & $A\oplus C\oplus F$ & \multirow{2}{*}{$D\oplus E$} &\multirow{2}{*}{$D\oplus E$} & \multirow{2}{*}{$D\oplus E$} \\
& & & & $\oplus G\oplus H$ & $\oplus G\oplus H$ & & & \\ \hline
$F$ &$F$ & $F$& $G\oplus H$& $D\oplus E$ & $D\oplus E$ & $A\oplus B\oplus F$ & $H\oplus C$ & $G\oplus C$ \\ \hline
$G$ &$G$ & $G$& $F\oplus H$ & $D\oplus E$ & $D\oplus E$ & $H\oplus C$ & $A\oplus B\oplus G$ & $F\oplus C$ \\ \hline
$H$ &$H$ & $H$& $F\oplus G$ & $D\oplus E$ & $D\oplus E$ & $G\oplus C$ & $F\oplus C$ & $A\oplus B\oplus H$\\\hline
\end{tabular}
\caption{Fusion rules of $D(S_3)$ quantum double model.}
\end{table}
\end{document}

math

"Weak Hopf non-invertible symmetry-protected topological spin liquid and lattice realization of (1+1)D symmetry topological field theory" by Zhian Jia, arXiv:2412.15336v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average dice score performance (\%) of the GCN refinement at $\tau=0.5$. The table compares $w_1$ with different combinations of $\lambda$, $\beta$ parameters ($w_{1,(\lambda,\beta)}$). Statistical significance is indicated by (*) for a p-value $<0.05$, and (**) for a p-value $< 0.01$ with respect to the CNN prediction.}
\begin{tabular}{l|c|c|c|c}
			\hline
			Task & CNN & GCN & GCN & GCN \\
			 & 2D U-Net & $w_{1,(0.5,1)}$ & $w_{1,(0.5,0)}$ & $w_{1,(0,1)}$\\
			\hline
			Pancreas & $76.89 \pm 6.6$ & $77.77 \pm 6.3$* & $77.85 \pm 6.3$* & $77.90 \pm 6.2$* \\
			\hline
			Pancreas-10 & $52.14 \pm 22.6$ & $54.15 \pm 22.2$ & $54.28 \pm 22.2$ & $53.03 \pm 23.0$ \\
			\hline
			\hline
			Spleen & $93.17 \pm 2.5$ & $94.98 \pm 1.4$** & $95.20 \pm 1.4$** & $94.74 \pm 1.8$** \\
			\hline
			Spleen-9 & $78.89 \pm 28.4$ & $80.94 \pm 28.8$** & $80.96 \pm 28.9$** & $81.17 \pm 28.9$*\\
			\hline
			\end{tabular}
\end{table}
\end{document}

eess

Roger D. Soberanis-Mukul
Nassir Navab
Shadi Albarqouni

"An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation" by Roger D. Soberanis-Mukul, Nassir Navab, and Shadi Albarqouni, arXiv:2012.03352v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\toprule
Method & Accuracy (\%) & Latency\\
\midrule
Baseline (Convolution layers without skip connection) & 90.7 & 3.71 \\
Addition-based skip connection & 91.1 & 4.89\\
Concatenation-based skip connection & 90.6 & 2.28 \\
 Concatenation-based skip connection + Delay Block & 91.4 & 2.29\\
\bottomrule
\end{tabular}
\caption{Classification Accuracy (\%) and latency of skip connection architectures on the Fashion-MNIST dataset.}
\end{table}
\end{document}

eess

Youngeun Kim
Adar Kahana
Ruokai Yin
Yuhang Li
Panos Stinis
George Em Karniadakis
Priyadarshini Panda

"Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding" by Youngeun Kim, Adar Kahana, Ruokai Yin, Yuhang Li, Panos Stinis, George Em Karniadakis, and Priyadarshini Panda, arXiv:2312.00919v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results on MS-COCO comparing relative frequency of trials for which the resulting decision rule $\lambda$ exceeded the target risk threshold $\alpha$ and average prediction set size.}
\begin{tabular}{lcc}
 \toprule
 Method & Relative Freq. & Pred. Set Size\\
 \midrule
 CRC & 43.76\% & 2.93 \\
 RCPS & 0.0\% & 3.57 \\
 Ours ($\beta = 0.95$) & 5.11\% & 3.04 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"Conformal Prediction as Bayesian Quadrature" by Jake C. Snell and Thomas L. Griffiths, arXiv:2502.13228v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
 \toprule
 Method &Vanilla & Pipeline & SelF-Reasoner \\% & sum\\
 \midrule
 Accuracy & 58.07 & 54.95 & \textbf{58.48 (+3.5)}\\
 \bottomrule
 \end{tabular}
\caption{ Accuracy (\%) on test split of ECQA. The backbone model is UnifiedQA-base. SelF-Reasoner outperforms the pipeline by 3.5\%.}
\end{table}
\end{document}

cs

"Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering" by Yexin Wu, Zhuosheng Zhang, and Hai Zhao, arXiv:2403.19167v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Coefficient estimates for log-Gaussian Cox process Model 2}
\begin{tabular}{lccc}
 \hline
 \textbf{Covariate} & \textbf{Mean} & \textbf{Lower 95\% CRI} & \textbf{Upper 95\% CRI} \\
 \hline
 Traffic intensity & 8.278 & 3.594 & 12.963 \\
 FRC1 & -0.688 & -1.067 & -0.310 \\
 FRC2 & -8.275 & -28.637 & 12.086 \\
 FRC3 & -1.124 & -1.542 & -0.706 \\
 FRC4 & -2.893 & -3.883 & -1.904 \\
 FRC5 & -0.739 & -1.017 & -0.461 \\
 FRC6 & -0.252 & -0.421 & -0.083 \\
 Distance to mosques & -0.008 & -0.245 & 0.229 \\
 Distance to education & -0.667 & -0.917 & -0.417 \\
 Distance to finance & 1.084 & 0.854 & 1.314 \\
 Distance to hospitals & -0.717 & -1.010 & -0.424 \\
 Distance to intersections & 0.115 & -0.119 & 0.348 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

David Bolin
Damilya Saduakhas
Alexandre B. Simas

"Log-Gaussian Cox Processes on General Metric Graphs" by David Bolin, Damilya Saduakhas, and Alexandre B. Simas, arXiv:2501.18558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Demographic information, gaming frequency and participation details of participants (each row represents a pair); Playtime values represent time to complete the tutorial (T) and time playing mission mode (M) and are always rounded down to minutes.}
\begin{tabular}{cccl|cccl|cc|cc}
 {\small \textbf{ID}}
 & {\small \textbf{GN}}
 & {\small \textbf{Age}}
 & {\small \textbf{Plays}}
 & {\small \textbf{ID}}
 & {\small \textbf{GN}}
 & {\small \textbf{Age}}
 & {\small \textbf{Plays}}
 & {\small \textbf{Relation}}
 & {\small \textbf{Country}}
 & {\small \textbf{\textsc{sub} Playtime}}
 & {\small \textbf{\textsc{air} Playtime}}\\
 \toprule
 \textbf{B1}&F&34&Daily&\textbf{S1}&F&53&Monthly&Friends&Australia&27 (T) 18 (M)&22 (T) 14 (M) \\
 \textbf{B2}&M&28&Monthly&\textbf{S2}&F&26&Occasionally&Family&USA&18 (T) 11 (M)&20 (T) 31 (M) \\
 \textbf{B3}&M&41&Daily&\textbf{S3}&F&16&Occasionally&Family&UK&31 (T) 21 (M) &17 (T) 31 (M) \\
 \textbf{B4}&M&38&Monthly&\textbf{S4}&M&38&Occasionally&Friends&Portugal&24 (T) 17 (M)&15 (T) 18 (M) \\
 \textbf{B5}&F&16&Daily&\textbf{S5}&M&18&Weekly&Family&USA&15 (T) 38 (M)&16 (T) 15 (M) \\
 \textbf{B6}&M&32&Occasionally&\textbf{S6}&F&27&Occasionally&Partners&Portugal&21 (T) 18 (M)&21 (T) 29 (M) \\
 \textbf{B7}&F&40&Never&\textbf{S7}&F&32&Weekly&Friends&Portugal&24 (T) 68 (M)&44 (T) 21 (M) \\
 \textbf{B8}&M&38&Occasionally&\textbf{S8}&F&36&Occasionally&Family&Portugal&21 (T) 20 (M)&41 (T) 19 (M)\\
 \textbf{B9}&M&33&Weekly&\textbf{S9}&M&31&Monthly&Friends&Portugal&27 (T) 53 (M)&23 (T) 35 (M) \\
 \textbf{B10}&F&41&Never&\textbf{S10}&F&38&Occasionally&Friends&Portugal&38 (T) 23 (M)&48 (T) 27 (M) \\
 \textbf{B11}&M&25&Monthly&\textbf{S11}&M&26&Weekly&Friends&Portugal&23 (T) 11 (M)&26 (T) 19 (M) \\
 \textbf{B12}&M&37&Never&\textbf{S12}&F&37&Daily&Family&Portugal&33 (T) 23 (M)&51 (T) 12 (M) \\
 \textbf{B13}&F&35&Occasionally&\textbf{S13}&F&24&Occasionally&Family&Portugal&48 (T) 32 (M)&58 (T) 30 (M) \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

David Gonçalves
André Rodrigues
Mike L. Richardson
Alexandra A. de Sousa
Michael J. Proulx
Tiago Guerreiro

"Exploring Asymmetric Roles in Mixed-Ability Gaming" by David Gonçalves, André Rodrigues, Mike L. Richardson, Alexandra A. de Sousa, Michael J. Proulx, and Tiago Guerreiro, arXiv:2101.05703v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Colon cancer study: Cox-regression coefficients (Est), standard errors (SE) and hazard ratio (HR) from intensity-based analyses.}
\begin{tabular}{lrrrrr}
\toprule
Transition & Covariate & Est & SE & HR & p-value\\
\midrule
Entry to Recurrence, $0 \rightarrow 1$ & 5FU+Lev & -0.508 & 0.106 &0.603 & $< 0.001$\\
& Extent 3 or 4 & 0.649 & 0.168 &1.914 & $< 0.001$\\
& Nodes $>4$ & 0.845 & 0.096 & 2.328 & $< 0.001$\\
\midrule
Recurrence to Death, $1 \rightarrow 2'$ & 5FU+Lev & 0.235 & 0.113 &1.265& 0.037\\
& Extent 3 or 4 & 0.304 & 0.179 &1.355 & 0.091 \\
& Nodes $>4$ & 0.379 & 0.103 & 1.461 &$< 0.001$ \\
\midrule
Entry to Death, $0 \rightarrow 2$& 5FU+Lev & 0.031 & 0.333 &1.035 & 0.917\\
& Extent 3 or 4 & 0.108 & 0.449 & 1.115 & 0.809 \\
& Nodes $>4$ & 0.486 & 0.373 & 1.627 & 0.193 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Malka Gorfine
Richard J. Cook
Per Kragh Andersen
Terry M. Therneau
Pierre Joly
Hein Putter
Maja Pohar Perme
Michal Abrahamowicz

"An Overview and Recent Developments in the Analysis of Multistate Processes" by Malka Gorfine, Richard J. Cook, Per Kragh Andersen, Terry M. Therneau, Pierre Joly, Hein Putter, Maja Pohar Perme, and Michal Abrahamowicz, arXiv:2502.06492v1, 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{Model parameters}
\begin{tabular}{|l|c|}
		\hline
		\textbf{Characteristics} & \textbf{Value} \\
		\hline
		Number of policyholders & 10,000 \\ \hline
		Cohort age & 50 \\ \hline
		Policies duration & 10 years \\ \hline
		2nd order demographic base & Lee-Carter applied on 1852-2019 Italy data \\ \hline
		1st order demographic base & 2nd order $q_x$ stressed of 20\% \\ \hline
		Risk-free rate & Costant and equals to 2\% \\ \hline
		Guaranteed rate $i_{gar}$ & 1\% \\
		\hline
		Average sum insured & 100,000.00 \\
		\hline
		CoV of $s_0$ & 2 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

q-fin

Francesco Della Corte
Gian Paolo Clemente
Nino Savelli

"A cohort-based Partial Internal Model for demographic risk" by Francesco Della Corte, Gian Paolo Clemente, and Nino Savelli, arXiv:2307.03090v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c||cccc}
 \hline
 		$M$ & & $(N_t,p)=(6,6)$ & $(N_t,p)=(7,7)$ & $(N_t,p)=(8,8)$ & $(N_t,p)=(9,9)$\\ \hline
 \multirow{3}*{$4194304$}& mean & $26.1462$ & $26.2239$ & $26.3001$ & $26.3404$ \\
 & std & $0.0457$ & $0.0386$ & $0.0611$ & $0.0580$ \\
 & Runtime [s] & $1436.79$ & $1706.81$ & $2021.94$ & $2340.43$\\ \hline
 \end{tabular}
\caption{Results on numerical solutions $\mathcal Y_0^{0,100}$ of using the LSMC methods with Laguerre polynomials up to $p$-th order for $N_t$ time steps. Here, $M=4194304$ is the number of samples for the Monte-Carlo approximation. We run the LSMC algorithm $50$ times independently and collect each $\mathcal Y_0^{0,100}$. The row of ``mean'' and ``std'' reports their sample means and sample (unbiased) standard deviations, respectively. The bottom of the table reports the total CPU times required for the $50$ experments.}
\end{table}
\end{document}

q-fin

"Multi-stage Euler-Maruyama methods for backward stochastic differential equations driven by continuous-time Markov chains" by Akihiro Kaneko, arXiv:2311.08826v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Numerical comparisons to highlight the contribution of each component during the optimization on the LLFF dataset with 3 views.}
\begin{tabular}{lccc}
 \toprule
 Method & PSNR & SSIM & LPIPS\\
 \toprule
 \midrule
 w/o alignment & 19.06 & 0.679 & 0.217\\ \hline
 w/o implicit decoder & 16.68 & 0.477 & 0.331\\
 w/o tv reg. & 20.20 & 0.724 & 0.186\\
 w/o flow reg. & 20.32 & 0.723 & 0.185\\
 w/o multisampling & 19.99 & 0.718 & 0.194\\ \hline
 Ours & \textbf{20.33} & \textbf{0.725} & \textbf{0.180}\\

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

cs

Avinash Paliwal
Wei Ye
Jinhui Xiong
Dmytro Kotovenko
Rakesh Ranjan
Vikas Chandra
Nima Khademi Kalantari

"CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians" by Avinash Paliwal, Wei Ye, Jinhui Xiong, Dmytro Kotovenko, Rakesh Ranjan, Vikas Chandra, and Nima Khademi Kalantari, arXiv:2403.19495v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline %These are DG errors for the L^2 norm
 stress & $\sigma_{xx}$ & $\sigma_{yy}$ & $\sigma_{xy}$ & $\sigma_{yx}$ & $\mu_x$ & $\mu_y$ \\ \hline
 analytical & $4$ & $4$ & $1.5$ & $1.5$ & $0$ & $0$ \\ \hline
 min computed & $4.00$ & $4.00$ & $1.50$ & $1.50$ & ME & ME \\ \hline
 max computed & $4.00$ & $4.00$ & $1.50$ & $1.50$ & ME & ME \\ \hline
 max relative error & $2.63\cdot 10^{-11}\%$ & $6.21\cdot 10^{-11}\%$ & $4.15\cdot 10^{-11}\%$ & $1.04\cdot 10^{-10}\%$ & & \\ \hline
 \end{tabular}
\caption{First patch test: Analytical solution, computed stresses and relative error.}
\end{table}
\end{document}

cs

"A variational discrete element method for the computation of Cosserat elasticity" by Frédéric Marazzato, arXiv:2101.08712v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Bauer's Spectral Factorization Method for Low Order Multiwavelet Filter Design" by Vasil Kolev, Todor Cooklev, and Fritz Keinert, arXiv:2312.05418v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DID Analysis of Total Metrics (Natural Log Scale) – \textit{Covid Adopters} vs. \textit{Organic Adopters}}
\begin{tabular}{lccccccc}
 \toprule
 DVs: & Spend & Quantity & Orders & Unique Items & Unique Brands & Subcategories & Categories \\
 Model: & (1) & (2) & (3) & (4) & (5) & (6) & (7) \\
 \midrule
 \emph{Variables}\\
 Covid\_Adopter * Post & 0.008 (0.023) & 0.010 (0.008) & -0.007 (0.004) & 0.010 (0.006) & 0.010 (0.006) & 0.011 (0.005) & 0.010 (0.005) \\
 & [0.728] & [0.211] & [0.080] & [0.096] & [0.096] & [0.028] & [0.046] \\
 \midrule
 \emph{Fixed‐effects}\\
 Customer & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
 YearMonth & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\
 \midrule
 \emph{Fit statistics}\\
 Observations & 1,783,949 & 1,783,949 & 1,783,949 & 1,783,949 & 1,783,949 & 1,783,949 & 1,783,949 \\
 R$^2$ & 0.28677 & 0.38486 & 0.34049 & 0.33495 & 0.32103 & 0.31157 & 0.30123 \\
 Within R$^2$ & $2.16\times10^{-7}$ & $4.09\times10^{-6}$ & $6.85\times10^{-6}$ & $5.62\times10^{-6}$ & $7.22\times10^{-6}$ & $9.86\times10^{-6}$ & $9.94\times10^{-6}$ \\
 \midrule\midrule
 \multicolumn{8}{l}{\emph{Clustered (Customer) standard‐errors are presented in standard brackets, while p‑values are shown in square brackets.}}
 \end{tabular}
\end{table}
\end{document}

q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"Channel Choice and Customer Value" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, 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{Statistics of six datasets.}
\begin{tabular}{l|cccccc}
		\hline
		 & COIL20 & COIL100 & USPS & Yale & AR & YaleB\\\hline
		number of data samples ($p$) & 1440 & 7200 & 9298 & 165 & 840 & 2414\\
		number of features ($n$) & 1024 & 1024 & 256 & 1024 & 768 & 1024\\
		number of class labels ($k$) & 20 & 100 & 10 & 15 & 120 & 38\\\hline
	\end{tabular}
\end{table}
\end{document}

math

"An NEPv Approach for Feature Selection via Orthogonal OCCA with the (2,1)-norm Regularization" by Li Wang, Lei-Hong Zhang, and Ren-Cang Li, arXiv:2502.18633v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effectiveness of new semi-supervised trained model for Video Frame interpolation. }
\begin{tabular}{|c|c|c|cc|}
\hline
\multirow{2}{*}{VFI dataset} & \multicolumn{2}{|c|}{Flow Train Dataset} & \multirow{2}{*}{PSNR / SSIM $\uparrow$} & \multirow{2}{*}{LPIPS (A) / (V) $\downarrow$} \\
\cline{2-3}
& Label & Unlabel & &\\
\hline
Sintel & \multirow{6}{*}{C+T} & - & 29.47 / 0.904 & 0.078 / 0.117 \\
(12 FPS $\rightarrow$ 24 FPS)& & OCAI-S-Test & \textbf{29.51} / \textbf{0.905} & \textbf{0.756} / \textbf{0.115} \\
\cline{1-1}
\cline{3-5}
Sintel & & - & \textbf{25.88} / 0.838 & 0.129 / 0.178 \\
(6 FPS $\rightarrow$ 12 FPS)& & OCAI-S-Test & 25.87 / \textbf{0.840} & \textbf{0.127} / \textbf{0.176} \\
\cline{1-1}
\cline{3-5}
KITTI & & - & 22.08 / 0.758 & 0.112 / 0.190 \\
(5 FPS $\rightarrow$ 10 FPS)& & OCAI-K-Test & \textbf{22.29} / \textbf{0.759} & \textbf{0.108} / \textbf{0.186} \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Jisoo Jeong
Hong Cai
Risheek Garrepalli
Jamie Menjay Lin
Munawar Hayat
Fatih Porikli

"OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation" by Jisoo Jeong, Hong Cai, Risheek Garrepalli, Jamie Menjay Lin, Munawar Hayat, and Fatih Porikli, arXiv:2403.18092v1, 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{Results over the MS-MARCO development dataset. Statistical significance tests were used to compare ensembles against individual models for re-ranking the DeepCT results, both for RNN (\dag) and RoBERTa-base (\ddag) models, as well as to compare the learning-to-rank ensembles against the second best ensemble (\textasteriskcentered). The methods whose difference is statistically significant, for a $p$-value of 0.05, are marked on the table. Although this is not reported on the table, not including DeepCT scores in the FGE ensembles is consistently worse (i.e., approx. 0.01 points lower in terms of MRR@10 for RoBERTa-base ensembles, and up to 0.1 points lower for RNN ensembles).}
\begin{tabular}{lccc}

 Method & ~~~~MAP~~~~ & ~~~~MRR~~~~ & ~~MRR@10~~\\
 \hline
 BM25 & 0.1835 & 0.1867 & 0.1758\\
 DeepCT & 0.2506 & 0.2546 & 0.2425\\
 \hline
 RNN & 0.2127 & 0.2160 & 0.2010\\
 RoBERTa-base & 0.3356 & 0.3403 & 0.3311\\
 \hline
 RNN + DeepCT & 0.2888 & 0.2936 & 0.2821\\
 RoBERTa-base + DeepCT & 0.3326 & 0.3378 & 0.3285\\
 \hline
 RNN FGE + DeepCT + Average$^\dag$ & 0.3000 & 0.3056 & 0.2952\\
 RNN FGE + DeepCT + RRFuse & 0.2845 & 0.2891 & 0.2769\\
 RNN FGE + DeepCT + MAPFuse & 0.2847 & 0.2893 & 0.2771\\
 RNN FGE + DeepCT + SlideFuse & 0.2738 & 0.2781 & 0.2645\\
 RNN FGE + DeepCT + MAPSlideFuse$^\dag$~~~~~ & 0.2741 & 0.2784 & 0.2649\\
 RNN FGE + DeepCT + Learning-to-Rank$^{\dag*}$ & 0.3131 & 0.3181 & 0.3080\\
 \hline
 RoBERTa-base FGE + DeepCT + Average$^\ddag$ & 0.3354 & 0.3411 & 0.3324\\
 RoBERTa-base FGE + DeepCT + RRFuse$^\ddag$ & 0.3819 & 0.3879 & 0.3813\\
 RoBERTa-base FGE + DeepCT + MAPFuse$^\ddag$ & 0.3818 & 0.3874 & 0.3806\\
 RoBERTa-base FGE + DeepCT + SlideFuse$^\ddag$ & 0.3787 & 0.3844 & 0.3774\\
 RoBERTa-base FGE + DeepCT + MAPSlideFuse$^\ddag$~~~~~~~~~~~~~~ & 0.3789 & 0.3844 & 0.3774\\
 RoBERTa-base FGE + DeepCT + Learning-to-Rank$^{\ddag*}$ & 0.3856 & 0.3913 & 0.3846\\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

"Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval" by Luís Borges, Bruno Martins, and Jamie Callan, arXiv:2101.08705v1, 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{\small\emph{Summary of the datasets used in the experiments. Train units and test units refer to the number of simulations, videos, or images in the training and test sets, respectively. The batch size refers to the number of simulations or images in a single batch. The batch shape refers to the original shape of a single batch before performing any reshaping/resizing operations. The total size refers to the total size of the dataset on disk. Sims, vids, and ims refer to simulations, videos, and images, respectively.}}
\begin{tabular}{l|ccccc}
 Name & Train units& Test units & Batch size& Batch shape & Total size \\
 \hline
 PDEBench & 480 sims& 120 sims& 1 sim & $64\times 64\times 64\times 5 \times 21\times 1$ & 66 GB \\
 Self-oscillating gels & 8,000 sims& 15,000 sims & 1 sim & $3367\times 3\times 10\times 1$ & 18 GB \\
 Basalt MineRL & 5449 vids & 17 vids & 20 frames & $360\times 640 \times 3\times20$ & 183 GB \\
 BigEarthNet & 566,712 ims & 23,612 ims & 100 ims & $120\times120\times12\times100$ & 104 GB \\
 \end{tabular}
\end{table}
\end{document}

math

"Incremental Hierarchical Tucker Decomposition" by Doruk Aksoy and Alex A. Gorodetsky, arXiv:2412.16544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of parameters of each studied model. PCMCI is non-parametric and is omitted from the table. The first row corresponds to the total number of trained parameters, and the second row corresponds to the number of parameters used to compute next step prediction with a history of size $\tau$. }
\begin{tabular}{lccccc}
 \toprule
 & \textbf{GCN} & \textbf{GAT} & \textbf{GATv2} & \textbf{LSTM} & \textbf{Trans.} \\
 \midrule
 Param. & 108.9K & 109.8K & 208.8K & 113.7K & 693.7K \\
 Param. $\delta\tau$ & 26.4K & 26.6K & 43.1K & 113.7K & 693.7K \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Gaël Gendron
Yang Chen
Mitchell Rogers
Yiping Liu
Mihailo Azhar
Shahrokh Heidari
David Arturo Soriano Valdez
Kobe Knowles
Padriac O'Leary
Simon Eyre
Michael Witbrock
Gillian Dobbie
Jiamou Liu
Patrice Delmas

"Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks" by Gaël Gendron, Yang Chen, Mitchell Rogers, Yiping Liu, Mihailo Azhar, Shahrokh Heidari, David Arturo Soriano Valdez, Kobe Knowles, Padriac O'Leary, Simon Eyre, Michael Witbrock, Gillian Dobbie, Jiamou Liu, and Patrice Delmas, arXiv:2312.14333v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Revealing the Shape of Genome Space via K-mer Topology" by Yuta Hozumi and Guo-Wei Wei, arXiv:2412.20202v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
 \toprule
 Metric & mIoU \\
 \midrule
 Uniform & 0.459 \\
 Position & 0.476 \\
 Color & \textbf{0.527} \\
 DINOv2 & 0.512 \\
 \bottomrule
 \end{tabular}
\caption{Comparison of pairwise weighting metrics}
\end{table}
\end{document}

eess

Isaac Wasserman
Jeova Farias Sales Rocha Neto

"Patch-Based Deep Unsupervised Image Segmentation using Graph Cuts" by Isaac Wasserman and Jeova Farias Sales Rocha Neto, arXiv:2311.01475v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Regular expressions for the file I/O patterns of crypto ransomware.}
\begin{tabular}{lr}
 \toprule
 \textbf{File I/O patterns} & \textbf{Regular expression} \\
 \midrule
 Memory-to-File with Post-Overwrite & \texttt{$C[R^+W^+R^*]^+NDC$} \\
 Memory-to-File with Pre-Overwrite & \texttt{$CNDC[R^+W^+R^*]^+$} \\
 File-to-File with Delete & \texttt{$C^+[R^+C^?W^+R^*]^+D$} \\
 File-to-File with Rename and Delete & \texttt{$C^+[R^+C^?W^+R^*]^+NDC$} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Muhammad Ejaz Ahmed
Hyoungshick Kim
Seyit Camtepe
Surya Nepal

"Peeler: Profiling Kernel-Level Events to Detect Ransomware" by Muhammad Ejaz Ahmed, Hyoungshick Kim, Seyit Camtepe, and Surya Nepal, arXiv:2101.12434v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l||cccc|cc|}
\hline
\multirow{2}{*}{Method} & \multirow{2}{*}{$Z$ (Eq.~)} & \multirow{2}{*}{Warping} & \multirow{2}{*}{Hole Filling} & \multirow{2}{*}{Fusion} & \multicolumn{2}{|c|}{KITTI} \\
\cline{6-7}
& & & & & SSIM $\uparrow$ & LPIPS (V) $\downarrow$\\
\hline
\hline
RIPR & Depth & Image & Image & BHF & 0.733 & 0.195 \\
\hline
\multirow{4}{*}{OCAI} & $M$ & Image & Image & BHF & 0.734 & 0.198 \\
 & $M$ & Flow & - & BHF & 0.721 & 0.213 \\
 & $M$ & Flow & Flow & BHF & 0.739 & 0.195 \\
 & $M$ & Flow & Flow & CBF & \textbf{0.758} & \textbf{0.190} \\
\hline
\end{tabular}
\end{table}
\end{document}

cs


stat

Yiwei Shi
Jingyu Hu
Yu Zhang
Mengyue Yang
Weinan Zhang
Cunjia Liu
Weiru Liu

"Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering" by Yiwei Shi, Jingyu Hu, Yu Zhang, Mengyue Yang, Weinan Zhang, Cunjia Liu, and Weiru Liu, arXiv:2501.18501v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
				& Airlines\\
				\hline
				Cluster 1 & CA, MU, CZ, HU, 3U, ZH (China)\\
				\hline
				Cluster 2 & AA, US (USA)\\
				\hline
				Cluster 3 & AF, AZ, KL (Europe), DL (USA)\\
				\hline
				Cluster 4 & BA, AY, IB (Europe), UA (USA)\\
				\hline
				Cluster 5 & SU, AB, AI, AM, NH, AC, AS, FL, DE, ET, etc. (Mixture)
		\end{tabular}
\caption{Airline clustering results for {\sf HSC + HLloyd}.}
\end{table}
\end{document}

stat

Heteroskedastic Tensor Clustering

"Heteroskedastic Tensor Clustering" by Yuchen Zhou and Yuxin Chen, arXiv:2311.02306v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|rrr|}
 \hline
 Output & Input & Input & Input\\
 \hline
 Cars & \cellcolor{green!30}Motor vehicles parts & \cellcolor{green!30}Electrical Lighting/Signalling & \cellcolor{green!30}Padlocks \\
 Delivery Trucks & \cellcolor{green!30}Motor vehicles parts & \cellcolor{green!30} Electrical Lighting/Signalling & \cellcolor{green!30} Padlocks \\
 Processed Tobacco & \cellcolor{green!30} Raw Tobacco & \cellcolor{green!30} Scented Mixtures & \cellcolor{green!30} Conveyor Belt Textiles \\
 Integrated Circuits & \cellcolor{green!30}Chemicals for Electronics & \cellcolor{green!30} Apparatus for semiconductors & \cellcolor{green!30} Oscilloscopes \\
 Telephones & \cellcolor{green!30} Integrated Circuits & \cellcolor{green!30}Electrical Parts & \cellcolor{green!30}LCDs \\
 Computers & \cellcolor{green!30} Photographic Chemicals & \cellcolor{green!30} Other Measuring Instruments & \cellcolor{green!30} Apparatus for semiconductors\\
 Petroleum Coke & \cellcolor{green!30} Refined Petroleum & \cellcolor{green!30} Surveying Equipment & \cellcolor{green!30} Reaction and Catalytic Products \\
 Refined Petroleum & \cellcolor{green!30} Other Iron Products & \cellcolor{red!30}
 Electric Generating Sets & \cellcolor{green!30} Cranes \\
 Electrical Ignitions & \cellcolor{red!30} Knit Gloves & \cellcolor{green!30} Electromagnets & \cellcolor{red!30} Audio-Video Recording \\
 Leather of Animals & \cellcolor{green!30} Leather Machinery & \cellcolor{green!30} Synthetic Tanning Extracts & \cellcolor{red!30} Synth. Filam. Yarn Woven Fabric \\
 Synthetic Fabrics & \cellcolor{green!30} Looms & \cellcolor{green!30} Unprocessed Artificial Staple Fibers & \cellcolor{red!30} Semi chemical Woodpulp \\
 Corn & \cellcolor{green!30} Mill Machinery & \cellcolor{red!30} Iron Radiators & \cellcolor{green!30} Harvesting Machinery \\
 Jewellery & \cellcolor{red!30} Cars & \cellcolor{red!30} Hard Liquor & \cellcolor{green!30} Pearl Products \\
 Pig Iron & \cellcolor{green!30} Electric Furnaces & \cellcolor{red!30} Tensile Testing Machines & \cellcolor{red!30}
 Soldering/welding Machinery \\
 Plane, Helicop., Spacecraft & \cellcolor{green!30} Aircraft Parts & \cellcolor{green!30} Parts of aircraft and spacecraft & \cellcolor{red!30} Other Furniture \\
 Military Weapons & \cellcolor{red!30}
 Other Furniture & \cellcolor{green!30} Explosive Ammunition & \cellcolor{red!30} Light Fixtures \\
 \hline
 \end{tabular}
\caption{HS4 examples produced by the Backward \& Forward method where the green cell represents a correctly predicted input candidate and red incorrectly. For readability, some of the product names have been shortened.}
\end{table}
\end{document}

q-fin

"Mapping Global Value Chains at the Product Level" by Lea Karbevska and César A. Hidalgo, arXiv:2308.02491v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Various fit statistics of the MLR-models across transition types. The $R^2_\mathrm{McF}$-measure is the McFadden pseudo coefficient of determination. In summarising an ROC-analysis on each transition type $k\rightarrow l$, the AUC is calculated both in-sample within $\mathcal{D}_T$ and out-of-sample within $\mathcal{D}_V$. Each AUC-statistic is accompanied by 95\% confidence intervals, calculated using the DeLong-method from .}
\begin{tabular}{lllllll}
\toprule
\multirow{2}{*}{\textbf{MLR-model} $kl$} & \multicolumn{6}{c}{\textbf{Fit statistics}} \\
 & AIC & $R_\mathrm{McF}^2$ & To state $l$ & Sample size & AUC: $\mathcal{D}_T$ & AUC: $\mathcal{D}_V$ \\ \midrule
\multirow{4}{*}{P$l$} & \multirow{4}{*}{853,356} & \multirow{4}{*}{26.95\%} & P & \footnotesize{9,020,554} & \footnotesize{81.66\% ± 0.141\%} & \footnotesize{81.62\% ± 0.204\%} \\
 & & & D & \footnotesize{27,184} & \footnotesize{98.20\% ± 0.096\%} & \footnotesize{98.23\% ± 0.136\%} \\
 & & & S & \footnotesize{66,643} & \footnotesize{75.95\% ± 0.169\%} & \footnotesize{76.10\% ± 0.243\%} \\
 & & & W & \footnotesize{434} & \footnotesize{93.42\% ± 1.032\%} & \footnotesize{93.77\% ± 1.381\%} \\
\multirow{4}{*}{D$l$} & \multirow{4}{*}{183,464} & \multirow{4}{*}{28.43\%} & P & \footnotesize{457,448} & \footnotesize{96.24\% ± 0.082\%} & \footnotesize{96.33\% ± 0.119\%} \\
 & & & D & \footnotesize{12,810} & \footnotesize{78.23\% ± 0.328\%} & \footnotesize{77.94\% ± 0.485\%} \\
 & & & S & \footnotesize{7,108} & \footnotesize{74.87\% ± 0.570\%}& \footnotesize{73.74\% ± 0.840\%} \\
 & & & W & \footnotesize{6,000} & \footnotesize{79.64\% ± 0.549\%}& \footnotesize{78.23\% ± 0.862\%} \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework" by Arno Botha, Tanja Verster, and Roland Breedt, arXiv:2502.14479v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Instruction datasets used in this work. We report also the number of data samples and the average length of prompts (Avg.~L), the average length of completion (Avg.~C).}
\begin{tabular}{l|rrrr}
\toprule
\textbf{Dataset} & \textbf{Type} & \textbf{\# samples} & \textbf{Avg.~L} & \textbf{Avg.~C }\\
\midrule
Alpaca & LLM & 52,002 & 27.8 & 64.6 \\
Dolly & Human & 15,011 & 118.1 & 91.3 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

Christophe Tribes
Sacha Benarroch-Lelong
Peng Lu
Ivan Kobyzev

"Hyperparameter Optimization for Large Language Model Instruction-Tuning" by Christophe Tribes, Sacha Benarroch-Lelong, Peng Lu, and Ivan Kobyzev, arXiv:2312.00949v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
\hline
Problem& Quantity &NBSO & FNO & BFNO & UNet \\
\hline
\multirow{2}{*}{Single-source} & $\#$ PARAM & 10.1M & 10.1M & 10.1M & 20.0M \\
 & $\#$ MEM & 16.4G & 12.0G & 16.4G& 11.8G \\
\multirow{2}{*}{Multi-source} & $\#$ PARAM & 144M & 144M & 144M & 36.0M \\
 & $\#$ MEM & 35.5G & 31.9G & 35.5G & 12.0G \\
\hline
\end{tabular}
\caption{Number of trainable parameters (1st and 3rd row) and memory used (2nd and 4th row) of neural network models in single-source and multi-source setting.}
\end{table}
\end{document}

eess

Zhijun Zeng
Yihang Zheng
Youjia Zheng
Yubing Li
Zuoqiang Shi
He Sun

"Neural Born Series Operator for Biomedical Ultrasound Computed Tomography" by Zhijun Zeng, Yihang Zheng, Youjia Zheng, Yubing Li, Zuoqiang Shi, and He Sun, arXiv:2312.15575v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ajmery Jaman
Ashkan Ertefaie
Michèle Bally
Renée Lévesque
Robert W. Platt
Mireille E. Schnitzer

"Valid post-selection inference for penalized G-estimation with longitudinal observational data" by Ajmery Jaman, Ashkan Ertefaie, Michèle Bally, Renée Lévesque, Robert W. Platt, and Mireille E. Schnitzer, arXiv:2501.09196v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Map-Reduce processing time for various dataset sizes and number of nodes}
\begin{tabular}{lll}
\toprule
Dataset Size & Number of Nodes & Processing Time (s) \\ \midrule
10,000 & 1 & 20.14 \\
10,000 & 2 & 10.87 \\
50,000 & 1 & 98.45 \\
50,000 & 2 & 52.37 \\
100,000 & 1 & 204.97 \\
100,000 & 2 & 108.67 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Zefeng Qiu
Prashanth Umapathy
Qingquan Zhang
Guanqun Song
Ting Zhu

"Map-Reduce for Multiprocessing Large Data and Multi-threading for Data Scraping" by Zefeng Qiu, Prashanth Umapathy, Qingquan Zhang, Guanqun Song, and Ting Zhu, arXiv:2312.15158v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
\hline
Parameter & Estimated Value \\
\hline
Empirical $\mu$ & 8.25bps per day \\
Simulated $\mu$ & 8.12bps/day $\pm$ .23bps/day \\
\end{tabular}
\caption{Parameters for the second type of noise trader M2}
\end{table}
\end{document}

q-fin

"The Pricing And Hedging Of Constant Function Market Makers" by Richard Dewey and Craig Newbold, arXiv:2306.11580v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effect of varying latent dimensions on LoRAE compared to a simple AE using CelebA. (FID Score is used for comparison)}
\begin{tabular}{cccccc}
\hline
Latent \\Dimension & 64 & 128 & 256 & 512 & 1024 \\
\hline
\hline
AE & \textbf{69.08} & 68.13 & 66.94 & 91.42 & 107.98 \\
\hline
\textbf{LoRAE} & 71.58 & \textbf{56.29} & \textbf{62.42} & \textbf{62.93} & \textbf{63.89} \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Alokendu Mazumder
Tirthajit Baruah
Bhartendu Kumar
Rishab Sharma
Vishwajeet Pattanaik
Punit Rathore

"Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical Insights" by Alokendu Mazumder, Tirthajit Baruah, Bhartendu Kumar, Rishab Sharma, Vishwajeet Pattanaik, and Punit Rathore, arXiv:2310.16194v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{diagbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{WaveGuard defense performance against different attack methods.}
\begin{tabular}{|c|c|c|c|c|}
\hline
\diagbox{Attack}{Method} & Down. & Quan. & Mel. & LPC \\
\hline
FakeBob (Digital) & 10/10 & 5/10 & 10/10 & 6/10 \\
\hline
FakeBob (Airborne) & 7/10 & 1/10 & 5/10 & 0/10 \\
\hline
Robust & 6/10 & 0/10 & 5/10 & 0/10 \\
\hline
Devil's Whisper & 8/10 & 2/10 & 9/10 & 2/10 \\
\hline
SpecPatch & 10/10 & 2/10 & 9/10 & 0/10 \\
\hline
SMACK & 3/10 & 0/10 & 1/10 & 0/10 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Yuanda Wang
Qiben Yan
Nikolay Ivanov
Xun Chen

"A Practical Survey on Emerging Threats from AI-driven Voice Attacks: How Vulnerable are Commercial Voice Control Systems?" by Yuanda Wang, Qiben Yan, Nikolay Ivanov, and Xun Chen, arXiv:2312.06010v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Dataset details.}
\begin{tabular}{ll|cc}
 \hline
 Building & Scene(Floor) & \#visits & \#images\\ \hline

 The Oculus & floor 2 & 16 & 13933\\\hline
 Silver Center & floor 2 & 6 & 1580\\\hline
 Silver Center & floor 3 & 6 & 586\\\hline
 Silver Center & floor 4 & 6 & 940\\\hline
 Silver Center & floor 5 & 6 & 834\\\hline
 Silver Center & floor 6 & 6 & 814\\\hline
 Silver Center & floor 9 & 6 & 696\\\hline
 Bobst Library & floor -1 & 10 & 4044\\\hline
 Bobst Library & floor 4 & 10 & 3038\\\hline
 Bobst Library & floor 5 & 10 & 3847\\\hline
 Morton Williams Supermarket & floor 1 & 10 & 2237\\\hline
 Metropolitan Art Museum & floor 1 & 4 & 1266\\\hline
 Fulton Subway Station & floor 1 & 7 & 4627\\\hline
 \end{tabular}
\end{table}
\end{document}

cs

Diwei Sheng
Anbang Yang
John-Ross Rizzo
Chen Feng

"NYC-Indoor-VPR: A Long-Term Indoor Visual Place Recognition Dataset with Semi-Automatic Annotation" by Diwei Sheng, Anbang Yang, John-Ross Rizzo, and Chen Feng, arXiv:2404.00504v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c|c}
\hline
Year & 2012 & 2013 & 2014 & 2015 & 2016 & 2017 & 2018 & 2019 & 2020 & 2021 & Average \\ \hline
Excess return (\%) & 0.21 & 0.99 & -0.77 & 0.45 & 0.15 & 0.70 & -0.30 & 0.23 & 0.27 & 0.74 & 0.27 \\ \hline
\end{tabular}
\caption{Norges Bank Investment Management, relative return to benchmark portfolio}
\end{table}
\end{document}

q-fin

"Neural Network Approach to Portfolio Optimization with Leverage Constraints:a Case Study on High Inflation Investment" by Chendi Ni, Yuying Li, and Peter A. Forsyth, arXiv:2304.05297v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The effect of modifying the number of gradient steps using the optimizer configuration reported in the main section for Gradient Descent on the stochastic Lorenz attractor (RK4). Ordered by RMSE.}
\begin{tabular}{lc}
\toprule
\textbf{Method} & \textbf{RMSE} \\
\midrule
Gradient Descent ($K = 100, \eta = 0.05$) & $1.985 \pm 0.011$ \\
Gradient Descent ($K = 1, \eta = 0.05$) & $1.937 \pm 0.282$ \\
Gradient Descent ($K = 50, \eta = 0.05$) & $1.847 \pm 0.010$ \\
Gradient Descent ($K = 25, \eta = 0.05$) & $1.493 \pm 0.009$ \\
Gradient Descent ($K = 10, \eta = 0.05$) & $0.987 \pm 0.008$ \\
Gradient Descent ($K = 5, \eta = 0.05$) & $0.743 \pm 0.010$ \\
Gradient Descent ($K = 3, \eta = 0.05$) & $0.701 \pm 0.018$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Gianluca M. Bencomo
Jake C. Snell
Thomas L. Griffiths

"Implicit Maximum a Posteriori Filtering via Adaptive Optimization" by Gianluca M. Bencomo, Jake C. Snell, and Thomas L. Griffiths, arXiv:2311.10580v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"An inexact infeasible arc-search interior-point method for linear optimization" by Einosuke Iida and Makoto Yamashita, arXiv:2403.18155v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments" by Pranjal Rawat, arXiv:2306.09437v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
(n,m)&$ (1000,10)$& $(3000,20)$ & $(5000,20)$\\ \hline
 BM& 0.91 & 1.00 & 1.00\\
 M1& 0.05 & 0.00 & 0.00 \\
 M2& 1.00 & 1.00 & 1.00\\
 M3& 1.00 & 1.00 & 1.00\\
 M4& 1.00 & 1.00 & 1.00\\
\end{tabular}
\caption{\scriptsize Hierarchical Bayes rescaling of the GP with squared exponential covariance kernel. Proportion of runs when the $L_2$-credible ball contains $f_0$.}
\end{table}
\end{document}

math

Botond Szabo
Amine Hadji
Aad van der Vaart

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\hline
Method & PSNR $\uparrow$ & SSIM $\uparrow$ & ST-RRED $\downarrow$\\
\hline
Raw2Raw+ISP & \textbf{30.46} & \textbf{0.8399} & \textbf{0.2021} \\
Raw2RGB & 27.42 & 0.8131 & 0.2952 \\
RGB2RGB & 24.84 & 0.7988 & 0.5951 \\
\hline
Raw2Raw+ISP+H264 & \textbf{30.19} & \textbf{0.8416} & \textbf{0.1828} \\
RGB2RGB+H264 & 24.75 & 0.8158 & 0.5622 \\
H264+RGB2RGB & 20.03 & 0.6130 & 1.4228 \\
\hline
\end{tabular}
\caption{Comparison of different low-light video enhancement settings on LLRVD dataset. Metrics are computed in RGB domain.}
\end{table}
\end{document}

cs

Gengchen Zhang
Yulun Zhang
Xin Yuan
Ying Fu

"Binarized Low-light Raw Video Enhancement" by Gengchen Zhang, Yulun Zhang, Xin Yuan, and Ying Fu, arXiv:2403.19944v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Determinants of Net Interest Margin in the Turkish Banking Sector: Does Bank Ownership Matter?" by Fatih Kansoy, arXiv:2506.04384v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c||c|c|c|c|c|c|c|c|c|c|c|} \hline
 & $\nu$ & min. & Max.& Total area& mean & Std Dev & skewness & kurtosis \\
\hline \hline
$sofifa$&	6&2170	&	6885&	29248 & 4874.7 &	1912.2 &	-0.4457	&	-1.4123	\\
LRT&	60&28298	&	29600&	1734432 & 28907 &	437.04 &	0.3076	&	-1.5345	\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Marcel Ausloos
Giulia Rotundo
Roy Cerqueti

"A theory of best choice selection through objective arguments grounded in Linear Response Theory concepts" by Marcel Ausloos, Giulia Rotundo, and Roy Cerqueti, arXiv:2405.00041v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracy (\%) of Functional Pose Prediction}
\begin{tabular}{ccc}
\Xhline{2\arrayrulewidth}
Method & Synthetic data & Real data \\ \hline
\textbf{Ours} & \textbf{92.7} & \textbf{100.0} \\ \hline
Ours w/o Functional Pose Analyzer & 75.3 & 79.2
\\ \hline
BLIP & 55.1 & 60.5 \\
\Xhline{2\arrayrulewidth}
\end{tabular}
\end{table}
\end{document}

cs

Ceng Zhang
Xin Meng
Dongchen Qi
Gregory S. Chirikjian

"RAIL: Robot Affordance Imagination with Large Language Models" by Ceng Zhang, Xin Meng, Dongchen Qi, and Gregory S. Chirikjian, arXiv:2403.19369v2, 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{Traversal results of AMG solving the diffusion equation}
\begin{tabular}{|c|c|c|c|c|c|}
		\hline
		$n$ & $\theta$ & iter & $n$ & $\theta$ & iter \\
		\hline
		128 & 0.131 & 224 & 144 & 0.117 & 240 \\ \hline
		224 & 0.115 & 272 & 240 & 0.112 & 272 \\ \hline
		384 & 0.111 & 304 & 400 & 0.111 & 304 \\ \hline
		448 & 0.111 & 320 & 464 & 0.113 & 320 \\ \hline
		480 & 0.113 & 320 & 496 & 0.112 & 320 \\\hline
		512 & 0.112 & 320 & ~ & ~ & ~ \\ \hline
	\end{tabular}
\end{table}
\end{document}

math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Ulrich Langer
Richard Löscher
Olaf Steinbach
Huidong Yang

"Parallel iterative solvers for discretized reduced optimality systems" by Ulrich Langer, Richard Löscher, Olaf Steinbach, and Huidong Yang, arXiv:2312.12282v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Adaptive Large Neighborhood Search Metaheuristic for Vehicle Routing Problem with Multiple Synchronization Constraints and Multiple Trips" by Faisal Alkaabneh, arXiv:2312.09414v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Antonio Guerriero
Roberto Pietrantuono
Stefano Russo

"DeepSample: DNN sampling-based testing for operational accuracy assessment" by Antonio Guerriero, Roberto Pietrantuono, and Stefano Russo, arXiv:2403.19271v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccccc}
 \toprule
 {\small Method} &
 \multicolumn{2}{c}{\small CIFAR-100}
 & \multicolumn{2}{c}{\small 10-Task IN-R} \\
 & ${\bar{\mathcal{A}}}$ & {$\mathcal{A}_N $}
 & ${\bar{\mathcal{A}}}$ & {$\mathcal{A}_N $}
 \\
 \midrule
 Zero-shot & 76.36 & 66.96 & 79.17 & 77.08 \\
 ADAM & 79.53 & 71.26 & 72.06 & 70.90 \\
 \midrule
 SEMA & \textbf{82.74} & \textbf{73.52} & \textbf{80.94} & \textbf{78.18} \\
 \bottomrule
 \end{tabular}
\caption{Performance on pre-trained CLIP model.}
\end{table}
\end{document}

cs

Huiyi Wang
Haodong Lu
Lina Yao
Dong Gong

"Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning" by Huiyi Wang, Haodong Lu, Lina Yao, and Dong Gong, arXiv:2403.18886v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Outcome metrics used in all experiments.}
\begin{tabular}{ll}
\toprule
\textbf{Metric} & \textbf{Description}\\
\midrule
Average revenue (later rounds) & Mean revenue in the final 1000 rounds \\
Time to converge & Round at which revenue stays in a $\pm 5\%$ band \\
Seller regret & $1-\text{(realized revenue per round)}$\\
No-sale rate & Fraction of rounds with all bids below $r$\\
Price volatility & Standard deviation of winning bids in later rounds \\
Winner entropy & Shannon entropy of bidder identity distribution \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ {ASR Keyword Boosting Results on GTC Talk Test Set.} }
\begin{tabular}{lccc}
\toprule
Systems & boost & WER & F-score ({\em P/R}) \\
\midrule
 \midrule
Fast Conf L-Transducer + & N & 16.2 & 0.36 (0.96/0.22) \\
 \ ASR pretrained encoder & Y & \textbf{15.1} & \textbf{0.67 (0.87/0.55)} \\
 \midrule
\multirow{2}{*}{SALM} & N & 17.0 & 0.35 (0.94/0.21) \\
 & Y & 15.8 & 0.56 (0.74/0.45) \\
\ \ \ \ + nucleus sampling & Y & \textbf{14.9} & \textbf{0.61 (0.66/0.57)} \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Zhehuai Chen
He Huang
Andrei Andrusenko
Oleksii Hrinchuk
Krishna C. Puvvada
Jason Li
Subhankar Ghosh
Jagadeesh Balam
Boris Ginsburg

"SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation" by Zhehuai Chen, He Huang, Andrei Andrusenko, Oleksii Hrinchuk, Krishna C. Puvvada, Jason Li, Subhankar Ghosh, Jagadeesh Balam, and Boris Ginsburg, arXiv:2310.09424v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llrrrrl}
 \hline \hline
 & & Test & \multicolumn{3}{l}{Critical values} & \\
Sample & $H_0$ & Statistic & 10\% & 5\% & 1\% & Decision\\
\multicolumn{7}{l}{}\\
\hline
NYU & EV1 & 51.09 & 35.83 & 36.18 & 36.88 & Reject at 1\% \\
 & Symmetry & 13.64 & 16.49& 16.81 & 17.37 & Do not reject \\
 \multicolumn{7}{l}{}\\ \hline
Ivoirian & EV1 & 223.19 & 37.64 & 38.01 & 38.68 & Reject at 1\%\\
& Symmetry & 20.36& 18.11 & 18.44 & 19.06 & Reject at 1\%\\
\multicolumn{7}{l}{}\\ \hline
SCE & EV1 & 55.91 & 35.86 & 36.19 &36.87& Reject at 1\%\\
& Symmetry &21.83 & 16.80 & 17.12 &36.87 & Reject at 1\%\\
\multicolumn{7}{l}{}\\
 \hline
ASU & EV1 & 68.24 & 37.29 & 37.61 &38.33& Reject at 1\%\\
& Symmetry &18.72 & 17.23 & 17.54 &18.14& Reject at 1\%\\
\multicolumn{7}{l}{}\\
 \hline \hline
 \end{tabular}
\caption{Test results}
\end{table}
\end{document}

q-fin

"Is the distribution of resolvable uncertainty Type I extreme value? A Test for Random Coefficient Models using Choice Probabilities" by Romuald Meango, arXiv:2503.13901v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrr}
 \toprule
 \textbf{Language} & \textbf{Orth} & \textbf{Majority} & \textbf{Seq2seq} & \textbf{Support} \\
 \midrule
PL & 59.16 & 77.41 & 90.13 & 2~549 \\
CS & 54.70 & 74.67 & 90.06 & 1~137 \\
RU & 48.79 & 68.05 & 86.60 & 18~018 \\
BG & 76.61 & 82.81 & 88.51 & 6~085 \\
SL & 52.87 & 79.20 & 93.45 & 7~082 \\
UK & 49.23 & 55.77 & 91.07 & 3~085 \\
\midrule
All & 55.24 & 72.32 & 88.89 & 37~956 \\
 \bottomrule
 \end{tabular}
\caption{Evaluation of the lemmatization on the \textbf{single-out topic} split---accuracy for each language.}
\end{table}
\end{document}

cs

Jakub Piskorski
Michał Marcińczuk
Roman Yangarber

"Cross-lingual Named Entity Corpus for Slavic Languages" by Jakub Piskorski, Michał Marcińczuk, and Roman Yangarber, arXiv:2404.00482v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results on the Validation split for every category (\% Macro F1 Scores)}
\begin{tabular}{|l|l|l|l|}
\hline
\textbf{Metric} & \textbf{Without TAPT}\ \ \ \ & \textbf{With TAPT}\ \ \ \ \ \ \ & \textbf{Gains}\ \ \ \ \\
\hline
Hostility (Coarse)\ \ \ \ \ & 96.84 & 98.19 & \ 1.35 \\
\hline
Defamation & 59.43 & 63.38 & \ 3.95\\
Fake & 83.69 & 86.52 & \ 2.83\\
Hate & 70.77 & 74.20 & \ 3.43\\
Offensive & 68.72 & 74.73 & \ 6.01\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Tathagata Raha
Sayar Ghosh Roy
Ujwal Narayan
Zubair Abid
Vasudeva Varma

"Task Adaptive Pretraining of Transformers for Hostility Detection" by Tathagata Raha, Sayar Ghosh Roy, Ujwal Narayan, Zubair Abid, and Vasudeva Varma, arXiv:2101.03382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Johansen Cointegration Test using Trace Test Statistic (10\% Significance Level)}
\begin{tabular}{cccc}
\hline
\textbf{Rank (r)} & \textbf{Cointegrating Relations} & \textbf{Test Statistic} & \textbf{Critical Value} \\
\hline
0 & 2 & 26.54 & 13.43 \\
1 & 2 & 0.7542 & 2.705 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Juan Plazuelo Pascual
Carlos Tardon Rubio
Juan Toro Cebada
Angel Hernando Veciana

"Price Discovery in Cryptocurrency Markets" by Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, and Angel Hernando Veciana, arXiv:2506.08718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Riccardo Leoncini
Mariele Macaluso
Annalivia Polselli

"Gender Segregation: Analysis across Sectoral-Dominance in the UK Labour Market" by Riccardo Leoncini, Mariele Macaluso, and Annalivia Polselli, arXiv:2303.04539v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sobom M. Somé
Célestin C. Kokonendji
Francial G. B. Libengué Dobélé-Kpoka

"An effective estimation of multivariate density functions using extended-beta kernels with Bayesian adaptive bandwidths" by Sobom M. Somé, Célestin C. Kokonendji, and Francial G. B. Libengué Dobélé-Kpoka, arXiv:2502.05366v1, licensed under CC BY-SA 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{Comparison for using the orthogonal transformation in Algorithm~}
\begin{tabular}{ccccc}
\hline
 & \multicolumn{2}{c}{Variable $U$} & \multicolumn{2}{c}{Fixed $U=I$} \\ \hline
$j$ & Haus. ($\downarrow$) & Wass. ($\downarrow$) & Haus. ($\downarrow$) & Wass. ($\downarrow$) \\ \hline
1 & {\bf 83.141 (10.270)} & {\bf 0.775 (0.137)} & 86.543 (14.505) & 0.873 (0.152) \\
2 & {\bf 7.499 (1.686)} & {\bf 0.196 (0.039)} & 8.264 (3.395) & 0.219 (0.074) \\
3 & {\bf 137.598 (27.392)} & {\bf 2.973 (0.397)} & 181.608 (11.925) & 110.020 (4.215) \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Monotone Curve Estimation via Convex Duality" by Tongseok Lim, Kyeongsik Nam, and Jinwon Sohn, arXiv:2501.06975v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Unintended Consequences of Censoring Digital Technology -- Evidence from Italy's ChatGPT Ban" by David H. Kreitmeir and Paul A. Raschky, arXiv:2304.09339v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average and minimum losses 100 repetitions of removing a randomly selected SOC constraint.}
\begin{tabular}{lrrrrrrr}
 \toprule
 {Case} & Avg Loss & Avg (br) & Min (br) & Jabr & weak Jabr & AC-L & SOC-L \\
 \midrule
 case14 & -0.3808 & -0.4906 & -1.7443 & 8075.12 & 6292.78 & 0.0929 & 0.0918 \\ %minloss -1.1190, minloss branch -1.2222
 case118 & 0.1084 & -0.7046 & -5.1803 & 129340.00 & 126982.72 & 0.7740 & 0.7125 \\ %minloss -4.5642 minlossb -5.5542
 case300 & 1.8485 & -1.1652 & -6.1421 & 718654.00 & 714858.26 & 3.0274 & 2.8064 \\ %minloss -3.8647 minlossb -9.6906
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

"Accurate Linear Cutting-Plane Relaxations for ACOPF" by Daniel Bienstock and Matias Villagra, arXiv:2312.04251v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|r|r|r|}
 \hline
 Land cover type & Land parcels & Area (km$^2$) & \% by area\\
 \hline
 Improved grassland & 6512 & 248.5 & 46.3\\
 Coniferous woodland & 1571 & 95.5 & 17.8\\
 Semi-natural grassland & 703 & 69.8 & 13.0\\
 Mountain/heath/bog & 1301 & 49.4 & 9.2\\
 Deciduous woodland & 2692 & 47.3 & 8.8\\
 Freshwater & 81 & 11.5 & 2.1\\
 Built-up & 566 & 5.1 & 0.9\\
 Arable (Grass) & 72 & 4.0 & 0.7\\
 Arable (Spring barley) & 34 & 2.2 & 0.4\\
 Arable (Other crops) & 40 & 1.4 & 0.3\\
 Arable (Winter barley) & 18 & 1.2 & 0.2\\
 Arable (Winter wheat) & 11 & 0.8 & 0.1\\
 Arable (Maize) & 4 & 0.3 & 0.1\\
 Arable (Spring field beans) & 2 & 0.1 & 0.0\\
 Arable (Oilseed rape) & 1 & 0.1 & 0.0\\
 Arable (Potatoes) & 1 & 0.0 & 0.0\\
 \hline
 \end{tabular}
\caption{Land use in the study area, by land cover area.}
\end{table}
\end{document}

q-fin

Christopher J. Banks
Katherine Simpson
Nicholas Hanley
Rowland R. Kao

"Assessing the potential impact of environmental land management schemes on emergent infection disease risks" by Christopher J. Banks, Katherine Simpson, Nicholas Hanley, and Rowland R. Kao, arXiv:2311.07735v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.