\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Discussion on Amount of Parallel Data. (\% WER)}
\begin{tabular}{lcccc}
 	 \toprule
 	 \toprule
 Training Data & \multicolumn{4}{c}{Test Data}\\
 (Hours)& {\it BLA-L2L}& {\it BLA-NoMic}&{\it BLA-KA6}& {\it L3L-L4L}\\
 	 \midrule
 0.1 & 17.1&	24&	21.3&	20.4\\
 1 & 17&	20.4&	20&	20.1\\
 10 &16.7&	18.1&	18.9&	20.2\\
 81 (All) & 16.6&	17.8&	19.2&	20.1\\
 \bottomrule
 \bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

"Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR" by Ruizhi Li, Gregory Sell, and Hynek Hermansky, arXiv:2102.03055v1, 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{\footnotesize \textbf{Accuracies (standard deviations) on \textsc{CelebA} dataset in JCBM.}}
\begin{tabular}{c|c|c|c|c|c|c|c}
\hline
ANOVA-T$^{1}$PNN & NODE-GA$^{1}$M & NA$^{1}$M & NB$^{1}$M & ANOVA-T$^{2}$PNN & NODE-GA$^{2}$M & NA$^{2}$M & NB$^{2}$M \\ \hline \hline
0.985 (0.001) & 0.981 (0.006) & 0.982 (0.002) & 0.980 (0.002) & $\textbf{0.986}$ (0.001) & 0.981 (0.006) & $\textbf{0.986}$ (0.001) & 0.980 (0.002) \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Seokhun Park
Insung Kong
Yongchan Choi
Chanmoo Park
Yongdai Kim

"Tensor Product Neural Networks for Functional ANOVA Model" by Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, and Yongdai Kim, arXiv:2502.15215v3, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sentiment alignment evaluation of decoded explanations by RMSE. PD is the RMSE between explanation rating and predicted rating, and GT is the RMSE between explanation rating and ground-truth rating.}
\begin{tabular}{|c|cc|cc|}
 \hline
 & \multicolumn{2}{c|}{Yelp} & \multicolumn{2}{c|}{Ratebeer} \\
 & PD & GT & PD & GT \\
 \hline
 NARRE & 1.0932 & 1.4950 & 2.0996 & 2.9641 \\
 NRT & 0.6676 & 1.2086 & 2.3302 & 3.1304 \\
 SAER (topk) & 0.6908 & 1.2216 & 2.1727 & 3.0026 \\
 SAER (reg + topk) & 0.6242 & 1.1849 & 1.6985 & 2.6769 \\
 SAER & \textbf{0.5505} & \textbf{1.1503} & \textbf{1.5911} & \textbf{2.6042} \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Aobo Yang
Nan Wang
Hongbo Deng
Hongning Wang

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Global Parameters}
\begin{tabular}{llrl}
 \hline\hline
Name & Symbol & Value & Unit \\
 \hline
Grid frequency & {\tt f0} & 60 & Hz \\
Line-line voltage & {\tt V} & 480 & V \\
 \hline\hline
\end{tabular}
\end{table}
\end{document}

cs

"Implementing Admittance Relaying for Microgrid Protection" by Arthur K. Barnes and Adam Mate, arXiv:2101.07783v2, 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}{ll}
 \toprule
 \textbf{Software} & \textbf{Use In Project} \\
 \midrule
 NumPy & A library for numerical computing. \\
 Pandas & Structured data processing. \\
 Scikit-learn (sklearn) & Library for clustering. \\
 Yahoo Finance & Retrieve financial data from Yahoo Finance. \\
 Matplotlib & A visualization library. \\
 JSON & Build and read the Headlines dataset. \\
 Shutil & Duplicating datasets for recoverable operations. \\
 Threading & Optimize headline embedding, model training. \\
 Queue & Optimize headline embeddings. \\
 Multiprocessing & Optimize model training. \\
 PyTorch & An open-source deep learning framework. \\
 Optuna & Streamline hyperparameter optimization. \\
 Seaborn & Visualization for correlation matrices. \\
 \bottomrule
 \end{tabular}
\caption{Software Descriptions}
\end{table}
\end{document}

q-fin

"News Sentiment Embeddings for Stock Price Forecasting" by Ayaan Qayyum, arXiv:2507.01970v1, 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}{llc}
 \toprule
 Format & Example & Accuracy \\
 \midrule
 NumeroLogic & \texttt{\{1:1\}*\{1:1\}=\{1:1\}} & 31.03\% \\
 White-spaces & \texttt{\_\_\_1\_*\_\_\_1\_=\_\_\_1\_} & 24.37\% \\
 Random white-spaces & \texttt{\_\_\_\_1*\_\_1\_\_=1\_\_\_\_} & 27.76\% \\
 Plain & \texttt{1*1=1} & 24.73\% \\
 \bottomrule
 \end{tabular}
\caption{\textbf{Extra tokens effect:} Just adding filler white space tokens is not helpful and is comparable to the plain format. The random white-space method of adding filler tokens at random locations is helpful but less effective compared to NumeroLogic.}
\end{table}
\end{document}

cs

Eli Schwartz
Leshem Choshen
Joseph Shtok
Sivan Doveh
Leonid Karlinsky
Assaf Arbelle

"NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning" by Eli Schwartz, Leshem Choshen, Joseph Shtok, Sivan Doveh, Leonid Karlinsky, and Assaf Arbelle, arXiv:2404.00459v2, 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{Performance Metrics}
\begin{tabular}{|c|c|c|c|c|}
 \hline & precision & recall & f1-score & support\\
 \hline 0 & 0.79 & 0.52 & 0.63 & 2314\\
 \hline 1 & 0.29 & 0.57 & 0.39 & 566\\
 \hline 2 & 0.20 & 0.37 & 0.26 & 299\\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Predicting Stock Price Movement as an Image Classification Problem" by Matej Steinbacher, arXiv:2303.01111v1, 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{Computational Complexity of Different Schemes.}
\begin{tabular}{|c|c|c|c|c|c|c|}
		\hline % 顶部线
		\bf Scheme&\bf Computational complexity \\
		\hline % 中部线
		FAS-OMP & ${\cal O}\left(LPM{N^2}\right)$ \\ \hline
		FAS-ML &${\cal O}\left({I_o}PML\left( {PM + N} \right)\right)$ \\ \hline
		S-BAR (Stage 1) & ${\cal O}\left({P}{M}\left( {{P^2}{M^2} + NPM + {N^2}} \right)\right)$ \\ \hline
		S-BAR (Stage 2) & ${\cal O}\left(PMN\right)$\\
		\hline % 底部线
	\end{tabular}
\end{table}
\end{document}

math

Zijian Zhang
Jieao Zhu
Linglong Dai
Robert W. Heath

"Successive Bayesian Reconstructor for Channel Estimation in Fluid Antenna Systems" by Zijian Zhang, Jieao Zhu, Linglong Dai, and Robert W. Heath, arXiv:2312.06551v5, licensed under CC0 1.0. 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}{lcccc}
			\toprule
			\textbf{Model} & \textbf{$\mathbb{E}[\text{prob tie across}]$} & \textbf{$\mathbb{V}[\text{prob tie across}]$} & \textbf{$\mathbb{E}[\text{prob tie within}]$} & \textbf{$\mathbb{V}[\text{prob tie within}]$} \\
			\midrule
			Independent DP & 0 & 0 & 0.672 & 0.049\\
			Independent PYP & 0 & 0 & 0.669 & 0.047\\
			+DP & 0.388 & 0.092 & 0.666 & 0.052\\
			+PY & 0.400 & 0.064 & 0.628 & 0.038\\
			HDP & 0.389 & 0.056 & 0.671 & 0.041 \\
			HPY & 0.397 & 0.043 & 0.638 & 0.033 \\
			\bottomrule
	\end{tabular}
\caption{ Expected probabilities of ties within and across as functions of the hyperparameters and corresponding variances. Values are obtained via Monte Carlo approximation by simulating 2000 samples of the hyperparameters from the hyperpriors of each model. }
\end{table}
\end{document}

math

Multivariate Species Sampling Models

Beatrice Franzolini
Antonio Lijoi
Igor Prünster
Giovanni Rebaudo

"Multivariate Species Sampling Models" by Beatrice Franzolini, Antonio Lijoi, Igor Prünster, and Giovanni Rebaudo, arXiv:2503.24004v2, 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{Distribution shift dataset characteristics}
\begin{tabular}{cccc}
\toprule
Dataset & M & Training set size & Test set size \\
\toprule
Wine & 8 & 69 & 31 \\
Parkinson & 10 & 320 & 197 \\
Fire & 17 & 1877 & 3998 \\
Fertility & 11 & 4898 & 1599 \\
Triazines & 60 & 139 & 47 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data" by Koby Bibas, arXiv:2412.07520v1, 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}{lcc}
 \toprule
 \textbf{Script} & \textbf{Training} & \textbf{Inference} \\
 \midrule
 Japanese & 17,963 & 17,963 \\
 Simplified Chinese & 6,621 & 7,806 \\
 Traditional Chinese & 8,415 & 8,628 \\
 Korean & 3,686 & 3,729 \\
 \midrule
 \textbf{Total} & \textbf{36,685} & \textbf{38,126} \\
 \bottomrule
 \end{tabular}
\caption{\textbf{Training and Inference Sizes}. This table shows the training and inference sizes for different language scripts.}
\end{table}
\end{document}

q-fin

Xinmei Yang
Abhishek Arora
Shao-Yu Jheng
Melissa Dell

"Quantifying Character Similarity with Vision Transformers" by Xinmei Yang, Abhishek Arora, Shao-Yu Jheng, and Melissa Dell, arXiv:2305.14672v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

David Rossell
Arnold Kisuk Kseung
Ignacio Saez
Michele Guindani

"Semi-parametric local variable selection under misspecification" by David Rossell, Arnold Kisuk Kseung, Ignacio Saez, and Michele Guindani, arXiv:2401.10235v3, 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 the $\gamma$-scaled (scale-invariant) quadratic functional outputs for uniform thermophysical properties ($\kappa = 1, \sigma = 1$) and several canonical geometries. The domain $\Omega_\text{right triangle}(W)$ is defined by vertices $(0,0)$, $(W,0)$, and $(0,1)$.}
\begin{tabular}{l|c|c|c}
$\Omega$ & $\phi$ & $\gamma\,\chi$ & $\gamma^2\,\Upsilon$\\
\hline\hline
$\Omega_\text{interval}$ & 1/3 & 1/9 & 1/45\\
$\Omega_\text{disk}$ & 1/2 & 1/4 & 1/12\\
$\Omega_\text{sphere}$ & 3/5 & 9/25 & 27/175\\
$\Omega_\text{isosceles right triangle}$ & 4/3 & $\frac{4}{5}(3+2\sqrt{2})$ & $\frac{4}{15}(3+2\sqrt{2})$\\
$\Omega_\text{equilateral triangle}$ & 1 & 9/5 & 3/5\\
$\Omega_\text{right triangle}(W)$ & $\sim \frac{2}{3}W^{-2} $ & $\sim \frac{28}{15} W^{-4}$ & $ \sim \frac{28}{45} W^{-4}$
\end{tabular}
\end{table}
\end{document}

math

Kento Kaneko
Claude Le Bris
Anthony T. Patera

"Certified Lumped Approximations for the Conduction Dunking Problem" by Kento Kaneko, Claude Le Bris, and Anthony T. Patera, arXiv:2412.16357v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \toprule %添加表格头部粗线
 Field& Type& Description\\
 \midrule %添加表格中横线
 $event\_type$& string& data record or control event\\
 $evnet\_time$& long& the logical timestamp\\
 $migration$& bool& trigger state-migration or not\\
 $sender$& string& the sender name of the state\\
 $receiver$& string& the receiver name of the state\\
 $slot\_ids$& list of strings& which state slots to send\\
 \bottomrule %添加表格底部粗线
 \end{tabular}
\caption{Control messages sent by the scheduler operator}
\end{table}
\end{document}

eess

Pengqi Lu
Liang Yuan
Yunquan Zhang
Hang Cao
Kun Li

"AutoFlow: Hotspot-Aware, Dynamic Load Balancing for Distributed Stream Processing" by Pengqi Lu, Liang Yuan, Yunquan Zhang, Hang Cao, and Kun Li, arXiv:2103.08888v1, 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|c|c} \hline
Province & Year of policy & Year of Effect \\ \hline \hline
Alberta & 1976 & 1977 \\
Quebec & 1988 & 1989 \\
Saskatchewan & 1989 & 1990 \\
British Columbia & 1993 & 1994 \\
Ontario & 1993 & 1994 \\
New Brunswick & 1993 & 1994 \\
Prince Edward Island & 1993 & 1994 \\
Nova Scotia & 1993 & 1994 \\
Manitoba & 1994 & 1994 \\
Newfoundland and Labrador & 1994 & 1994 \\ \hline
\end{tabular}
\caption{Year of policy change by province. Note that Manitoba and Newfoundland and Labrador implemented policies abolishing rotating medical residencies in 1994 but these affected the residency class of 1992-1993.}
\end{table}
\end{document}

q-fin

"Effects of extending residencies on the supply and quality of family medicine practitioners; difference-in-differences evidence from the implementation of mandatory family medicine residencies in Canada" by Stephenson Strobel, arXiv:2303.14232v2, 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}{cccc}\toprule
 problem & s.o.t.a. hyperparams. & same dofs hyperparams. & our hyperparams. \\
 \cmidrule{1-4}
 Poisson eq. & $ (5.61\pm 0.094) \%$ & $ (1.46 \pm 0.071) \% $ & $ \mathbf{(1.04 \pm 0.090)\%} $ \\
 Darcy eq. & $(1.05 \pm 0.008) \%$ & $ \mathbf{(0.81\pm 0.015)} \% $ & $ (1.00 \pm 0.006) \% $ \\
 Navier-Stokes eq. & $ (3.83\pm 0.096) \%$ & $ (3.26 \pm 0.083) \% $ & $ \mathbf{(3.26 \pm 0.083)} \% $ \\
 Wave eq. & $ (1.27\pm 0.048) \%$ & $ \mathbf{(0.95\pm 0.094)} \% $ & $ (1.08 \pm 0.038) \% $ \\
 Euler eq. & $ (0.48\pm 0.017) \%$ & $ (0.42 \pm 0.002) \% $ & $ \mathbf{(0.42 \pm 0.002)} \% $ \\
 Smooth transport eq. & $ (0.40\pm 0.026) \%$ & $ (0.17\pm 0.006) \% $ & $ \mathbf{(0.036 \pm 0.0004)} \% $ \\
 Discontinuous transport eq. & $ (1.30\pm 0.080) \%$ & $ (1.09\pm 0.071) \% $ & $ \mathbf{(1.01 \pm 0.018)} \% $ \\
 Allen-Cahn eq. & $ (0.48\pm 0.052)\%$ & $ \mathbf{(0.22\pm 0.006)} \% $ & $ (0.25 \pm 0.028)\%$ \\
 \bottomrule
 \end{tabular}
\caption{The tests are performed for all the benchmarks previously described with the FNO architecture. In the table we report the resulting mean $L^1$ errors obtained with the s.o.t.a. hyperparameters (second column), the optimized hyperparameter configuration (last column) and the hyperparameters with the constraint to keep the number of trainable parameters fixed and equal to the s.o.t.a. configuration (third column). For each test, we report the mean and the standard deviation after three trials.}
\end{table}
\end{document}

math

"HyperNOs: Automated and Parallel Library for Neural Operators Research" by Massimiliano Ghiotto, arXiv:2503.18087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance results of the w2v-SELD BASE model pre-trained using various datasets and fine-tuned on the TAU-2019 dataset.}
\begin{tabular}{ccccccc}
			 \hline \hline
 & \textbf{Pre-training Set} & $\downarrow$\textbf{ER} & 	$\uparrow$\textbf{F1}\% & $\downarrow$\textbf{DOA-error} & $\uparrow$\textbf{FR}\% & $\downarrow$\textbf{SELD\textsubscript{score}} \\
				\hline
 & - & 0.17 & 89.45 & 4.97 & 92.01 & 0.10\\
 & { \footnotesize LS-960} & 0.14 & 91.50 & \textbf{4.80} & 91.66 & 0.08\\
 & { \footnotesize L3DAS21-SELD} & \textbf{0.10} & \textbf{94.20} & 4.88 & \textbf{93.12} & \textbf{0.06}
 \\
 \hline
 \hline
		\end{tabular}
\end{table}
\end{document}

eess

Orlem Lima dos Santos
Karen Rosero
Roberto de Alencar Lotufo

"w2v-SELD: A Sound Event Localization and Detection Framework for Self-Supervised Spatial Audio Pre-Training" by Orlem Lima dos Santos, Karen Rosero, and Roberto de Alencar Lotufo, arXiv:2312.06907v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Energy stable Runge-Kutta discontinuous Galerkin schemes for fourth order gradient flows" by Hailiang Liu and Peimeng Yin, arXiv:2101.00152v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The implemented Autoencoder consists of a dense layer, two LSTM layers, and a dense output layer. Input data are provided in the three-dimensional format: number of samples, time steps (24), and features (20).}
\begin{tabular}{cc}
\toprule
\textbf{Layer} & \textbf{Output shape} \\ \midrule
Input & 24x20 \\
Dense & 24x20 \\
LSTM & 24x18 \\
LSTM & 24x18 \\
Dense & 24x20 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Giulia Crocioni
Giambattista Gruosso
Danilo Pau
Davide Denaro
Luigi Zambrano
Giuseppe di Giore

"Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers" by Giulia Crocioni, Giambattista Gruosso, Danilo Pau, Davide Denaro, Luigi Zambrano, and Giuseppe di Giore, arXiv:2103.00201v1, 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
\begin{tabular}{llr}
\hline\hline
&3-digit SIC Industry&Fraction\tabularnewline
\hline
1&Educational Services&$32.6\%$\tabularnewline
2&Apparel \& Accessory Stores&$31.8\%$\tabularnewline
3&Non-Classifiable Establishments&$31.6\%$\tabularnewline
4&Business Services&$27.7\%$\tabularnewline
5&Instruments \& Related Products&$24.9\%$\tabularnewline
6&Leather&$23.9\%$\tabularnewline
7&Pipelines, Except Natural Gas&$23.7\%$\tabularnewline
8&Transportation Services&$23.1\%$\tabularnewline
9&Chemical \& Allied products&$22.5\%$\tabularnewline
10&Engineering \& Management Services&$22.5\%$\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

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.


cs

"Censorship of Online Encyclopedias: Implications for NLP Models" by Eddie Yang and Margaret E. Roberts, arXiv:2101.09294v1, 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{For both datasets, the fraction of times the $k$-th local minimum of the sum of variances of same phase samples is within 5\% of the correct value}
\begin{tabular}{lrrrr}
\toprule
& Dataset 1 & \multicolumn{3}{c}{Dataset 2} \\
$k$ & & $J^{(1)}$ & $J^{(2)}$ & $J^{(3)}$ \\
\midrule
$1$ & $0.95$ & $0.65$ & $0.68$ & $0.67$ \\
$2\leq k \leq 5$ & $0.00$ & $0.00$ & $0.00$ & $0.00$ \\
$\geq 6$ & $0.05$ & $0.35$ & $0.32$ & $0.33$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Simão da Graça Marto
Massimiliano Vasile
Andrew Campbell
Paul Murray
Stephen Marshall
Vasili Savitski

"Hyperspectral Lightcurve Inversion for Attitude Determination" by Simão da Graça Marto, Massimiliano Vasile, Andrew Campbell, Paul Murray, Stephen Marshall, and Vasili Savitski, arXiv:2401.05397v1, 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{Average execution time (in seconds) per outer iteration with $m_r=20$ and $m_f=5$. }
\begin{tabular}{rrrrrr}
 \toprule\toprule
 & & \multicolumn{4}{c}{T+1} \\
 \cmidrule{3-6}
 d & & 2 & 4 & 8 & 16 \\
 \cmidrule{1-1}\cmidrule{3-6}
 2 & & 0.71 & 1.44 & 3.26 & 8.05 \\
 4 & & 0.73 & 1.46 & 3.28 & 8.83 \\
 8 & & 0.75 & 1.54 & 3.39 & 10.10 \\
 \bottomrule\bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

Silvana M. Pesenti
Sebastian Jaimungal
Yuri F. Saporito
Rodrigo S. Targino

"Risk Budgeting Allocation for Dynamic Risk Measures" by Silvana M. Pesenti, Sebastian Jaimungal, Yuri F. Saporito, and Rodrigo S. Targino, arXiv:2305.11319v5, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zero-Leverage Puzzle


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison between VRoC and baselines on the rumor tracking task. }
\begin{tabular}{lcccccccc}
			\toprule
			& & & S & G & F & C & O \\
			& Accuracy & Macro-F1 & F1 & F1 & F1 & F1 & F1 \\\hline
			CNN & $0.570$ & $0.574$ & $0.589$ & $\textbf{0.534}$ & $0.777$ & $0.571$ & $0.400$ \\
			LSTM & $0.585$ & $0.585$ & $0.607$ & $0.352$ & $\textbf{0.804}$ & $\textbf{0.711}$ & $0.453$\\
			VAE-LSTM & $0.609$ & $0.612$ & $\textbf{0.666}$ & $0.515$ & $0.641$ & $0.694$ & $0.545$ \\
			VRoC & $\textbf{0.644}$ & $\textbf{0.632}$ & $0.611$ & $0.520$ & $0.640$ & $0.685$ & $\textbf{0.703}$ \\
 \bottomrule
		\end{tabular}
\end{table}
\end{document}

cs

Mingxi Cheng
Shahin Nazarian
Paul Bogdan

"VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text" by Mingxi Cheng, Shahin Nazarian, and Paul Bogdan, arXiv:2102.00816v1, 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{Distribution of health shocks between men and women.}
\begin{tabular}{lccc}
 \hline\hline
 & Man & Woman & p-value \\
 \hline
Acute myocardial infarction & 58.98\% & 50.05\% & 0.00 \\
 Cerebral infarction & 41.02\% & 49.95\% & 0.00 \\
 & 100\% & 100\% & \\
 \hline
Nr. unique individuals & 30,103 & 15,140 & \\
 \hline\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Nadja van 't Hoff
Giovanni Mellace
Seetha Menon

"Gender Differences in Healthcare Utilisation -- Evidence from Unexpected Adverse Health Shocks" by Nadja van 't Hoff, Giovanni Mellace, and Seetha Menon, arXiv:2509.01310v1, 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{AE's attack performance against SV systems.}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
 & Target & SpeechBrain & Google & Siri & Bixby \\
\hline
i-vector & 10/10 & 0/10 & 0/10 & 0/10 & 0/10 \\
\hline
GMM & 10/10 & 0/10 & 0/10 & 0/10 & 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}
\usepackage[T1]{fontenc}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
\toprule
\multirow{2}{*}{Method} & \multicolumn{3}{c}{Dice Similarity [\%]} \\ \cline{2-4}
 & LV & MYO & RV \\
\midrule
Peter M. Full & 91.0 & 84.9 & 88.4 \\
Yao Zhang (Ours) & 90.6 & 84.0 & 87.8 \\
Jun Ma & 90.2	 & 83.5 & 87.4 \\
Mario Parreño & 91.2 & 83.8 & 85.3 \\
Fanwei Kong & 90.2 & 82.8 & 85.7 \\
\bottomrule
\end{tabular}
\caption{Results on test set}
\end{table}
\end{document}

eess

Yao Zhang
Jiawei Yang
Feng Hou
Yang Liu
Yixin Wang
Jiang Tian
Cheng Zhong
Yang Zhang
Zhiqiang He

"Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer" by Yao Zhang, Jiawei Yang, Feng Hou, Yang Liu, Yixin Wang, Jiang Tian, Cheng Zhong, Yang Zhang, and Zhiqiang He, arXiv:2012.14785v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of Equal Filtered Results}
\begin{tabular}{llrrrr}
 \toprule
 Model & Cut & OptimalTimes & GapWithoutInf & \#Inf \\
 \midrule
 DD-JS & IIS & 411.9 & 0.4 & 0 \\
 DD-JS & No-Good & 372.1 & 0.6 & 0 \\
 DD-LJ & IIS & 444.0 & 0.6 & 0 \\
 DD-LJ & No-Good & 415.3 & 0.7 & 0 \\
 IP & IIS & 222.9 & 2.7 & 58 \\
 IP & No-Good & 529.1 & 1.1 & 0 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Nicolás Casassus
Margarita Castro
Gustavo Angulo

"A Decision Diagram Approach for the Parallel Machine Scheduling Problem with Chance Constraints" by Nicolás Casassus, Margarita Castro, and Gustavo Angulo, arXiv:2504.20889v1, 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}{|l|c|}
 \hline
 \emph{Pattern} & \emph{Count} \\
 \hline
 \hline
 TTFFTT & 69 \\ \hline
 TTFFT & 44 \\ \hline
 TTFFTTT & 10 \\ \hline
 TFTFF & 1 \\ \hline
 predictable & 12281 \\ \hline
 \end{tabular}
\caption{Patterns when $G$ is co-chordal with 9 vertices.}
\end{table}
\end{document}

math

Trung Chau
Kanoy Kumar Das
Aryaman Maithani

"Edge ideals with linear quotients and without homological linear quotients" by Trung Chau, Kanoy Kumar Das, and Aryaman Maithani, arXiv:2503.11424v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
 \toprule
 Problem & $\theta_1$ & $\theta_2$ & $\theta_3$ & Interpolated & Target \\
 \midrule
 Airy	 & 0.38 (1.87) & 0.69 (1.91) & 0.62 (2.02) & 2.76 (3.28) & 0.76 (1.84) \\
 Fractional Laplacian	& 0.51 (2.38) & 0.95 (2.81) & 0.65 (1.87) & 0.99 (2.94) & 0.41 (2.21) \\
 Advection Diffusion	 & 0.24 (1.57) & 0.26 (1.49) & 0.29 (1.49) & 0.54 (1.61) & 0.3 (1.54) \\
 \bottomrule
 \end{tabular}
\caption{Summary of the test error for the Airy, Fractional Laplacian, and Advection-Diffusion problems. The error for experiments with noisy dataset are shown in parenthesis.}
\end{table}
\end{document}

math

Harshwardhan Praveen
Jacob Brown
Christopher Earls

"chebgreen: Learning and Interpolating Continuous Empirical Green's Functions from Data" by Harshwardhan Praveen, Jacob Brown, and Christopher Earls, arXiv:2501.18715v5, 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{MRI classes after Data Augmentation}
\begin{tabular}{|c|c|}
		\hline
			\textbf{ID Class} & \textbf{Number of elements} \\
			\hline
			1 & 375 \\
			\hline
			2 & 375 \\
			\hline
			3 & 375 \\
			\hline
			4 & 375 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Maria Frasca
Davide La Torre
Gabriella Pravettoni
Ilaria Cutica

"Predicting Parkinson's disease evolution using deep learning" by Maria Frasca, Davide La Torre, Gabriella Pravettoni, and Ilaria Cutica, arXiv:2312.17290v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc|cccc}
			\huge{$\clubsuit$} & \huge{$ \heartsuit $} & \huge{$\spadesuit$} & Precision & Recall & F1 & Accuracy\\ \hline \hline
			$\surd$ &$\surd$ &$\surd$ & 76.6 & 78.1 & 77.3 & 43.8 \\ \hline
			$\surd$ & $ \times $ & $ \times $ & \cellcolor[HTML]{A9A9A9}-2.4 & \cellcolor[HTML]{A9A9A9}-2.2 & \cellcolor[HTML]{A9A9A9}-2.3 & \cellcolor[HTML]{A9A9A9}-5.6 \\
			$ \times $&$\surd$ & $ \times $ & \cellcolor[HTML]{D9D9D9}-0.6 & \cellcolor[HTML]{D9D9D9}-0.7 & \cellcolor[HTML]{D9D9D9}-0.6 & \cellcolor[HTML]{D9D9D9}-0.8 \\
			$ \times $&$ \times $&$\surd$ & \cellcolor[HTML]{C9C9C9}-1.1 & \cellcolor[HTML]{C9C9C9}-1.8 & \cellcolor[HTML]{C9C9C9}-1.5 & \cellcolor[HTML]{C9C9C9}-3.3 \\
			$\surd$ &$\surd$ & $ \times $ & \cellcolor[HTML]{E9E9E9}-0.5 & \cellcolor[HTML]{E9E9E9}-0.8 & \cellcolor[HTML]{E9E9E9}-0.6 & \cellcolor[HTML]{E9E9E9}-0.9 \\
			$\surd$ & $ \times $ &$\surd$ & \cellcolor[HTML]{B9B9B9}-1.3 & \cellcolor[HTML]{B9B9B9}-1.9 & \cellcolor[HTML]{B9B9B9}-1.6 & \cellcolor[HTML]{B9B9B9}-3.5 \\
			$ \times $&$\surd$ &$\surd$ & \cellcolor[HTML]{F9F9F9}-0.3 & \cellcolor[HTML]{F9F9F9}-0.4 & \cellcolor[HTML]{F9F9F9}-0.3 & \cellcolor[HTML]{F9F9F9}-0.6 \\ \hline
			$ \times $& $ \times $ & $ \times $ & -3.1 & -2.7 & -3.0 & -5.9 \\ \noalign{\hrule height 1.15pt}
		\end{tabular}
\caption{Ablation results of the different coonection types of HGNN$ _{\text{base}} $ by fused operation. $\clubsuit$ denotes the intra-sentence connection, $\heartsuit $ denotes the inter-sentence connection and $\spadesuit$ denotes the global connection. $\surd$ means the connection is kept in Equation . The last raw with no connection reduced to the BERT$ _{\text{base}} $ model.}
\end{table}
\end{document}

cs

Bingning Wang
Ting Yao
Weipeng Chen
Jingfang Xu
Xiaochuan Wang

"ComQA:Compositional Question Answering via Hierarchical Graph Neural Networks" by Bingning Wang, Ting Yao, Weipeng Chen, Jingfang Xu, and Xiaochuan Wang, arXiv:2101.06400v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llll}
 \toprule
 \textbf{\textcolor{blue}{Sensing Scenario}} & \textbf{\textcolor{blue}{$f=0$}} & \textbf{\textcolor{blue}{$\bar{f}$ known}} & \textbf{\textcolor{blue}{$\bar{f}$ unknown}} \\
 \midrule
 \textcolor{blue}{Full Sensing} & \textcolor{blue}{$L^2$-GES} & \textcolor{blue}{$L^2$-GpA} & \textcolor{blue}{$L^2$-GpA} \\
 \textcolor{blue}{Intermittent Sensing} & \textcolor{blue}{$L^2$-GES} & \textcolor{blue}{$L^2$-ISS} & \textcolor{blue}{$L^2$-GUUB} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Mohamed Camil Belhadjoudja
Mohamed Maghenem
Emmanuel Witrant
Christophe Prieur

"Adaptive Boundary Control of the Kuramoto-Sivashinsky Equation Under Intermittent Sensing" by Mohamed Camil Belhadjoudja, Mohamed Maghenem, Emmanuel Witrant, and Christophe Prieur, arXiv:2403.18055v2, 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
 & Change\\
 \midrule
 Social sciences & +0.1\%\\
 Humanities & +0.43\%\\
 STEM & +0.79\%\\
 Others & +1.19\%\\
 \bottomrule
 \end{tabular}
\caption{MMLU accuracy change due to NumeroLogic encoding on tasks from different fields. STEM tasks which are more likely to require numerical understanding enjoy higher improvement.}
\end{table}
\end{document}

cs

Eli Schwartz
Leshem Choshen
Joseph Shtok
Sivan Doveh
Leonid Karlinsky
Assaf Arbelle

"NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning" by Eli Schwartz, Leshem Choshen, Joseph Shtok, Sivan Doveh, Leonid Karlinsky, and Assaf Arbelle, arXiv:2404.00459v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Stijn Cambie
Wouter Cames van Batenburg
Xuding Zhu

"Disjoint list-colorings for planar graphs" by Stijn Cambie, Wouter Cames van Batenburg, and Xuding Zhu, arXiv:2312.17233v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sample selection with noise rate estimation in noise learning of medical image analysis" by Maolin Li and Giacomo Tarroni, arXiv:2312.15233v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Required iteration numbers of the residual of LOBPCG in solving a methane molecule without and with the elliptic preconditioner utilizing different orders, where the number $1000$ means that the accuracy is not touched within maximum iteration numbers $1000$.}
\begin{tabular}{cc|cccc}
\hline
 & & $p=2$ & $p=3$ & $p=4$ & $p=5$ \\ \hline
\multirow{2}{*}{$\varepsilon_1$} & without pre & $210$ & $1000$ & $1000$ & $1000$ \\
 & with pre & $23$ & $20$ & $23$ & $21$ \\ \hline
\multirow{2}{*}{$\varepsilon_2$} & without pre & $142$ & $662$ & $1000$ & $1000$ \\
 & with pre & $34$ & $35$ & $35$ & $36$ \\ \hline
\multirow{2}{*}{$\varepsilon_3$} & without pre & $148$ & $666$ & $1000$ & $1000$ \\
 & with pre & $34$ & $35$ & $35$ & $36$ \\ \hline
\multirow{2}{*}{$\varepsilon_4$} & without pre & $147$ & $653$ & $1000$ & $1000$ \\
 & with pre & $35$ & $36$ & $36$ & $36$ \\ \hline
\multirow{2}{*}{$\varepsilon_5$} & without pre & $503$ & $775$ & $1000$ & $1000$ \\
 & with pre & $165$ & $39$ & $102$ & $43$ \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Tao Wang
Yang Kuang
Ran Zhang
Guanghui Hu

"A hierarchical splines-based $h$-adaptive isogeometric solver for all-electron Kohn--Sham equation" by Tao Wang, Yang Kuang, Ran Zhang, and Guanghui Hu, arXiv:2412.12580v1, 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{Evaluation results of Llama 60M pre-training experiments.}
\begin{tabular}{l|c}
\toprule
Optimizer & Perplexity$\downarrow$ \\
\midrule
Adam & 49.83 \\
C-Adam & \underline{43.21} \\
Lion & 50.25\\
C-Lion & 53.21 \\
Grams (ours) & \textbf{38.60} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Grams: Gradient Descent with Adaptive Momentum Scaling" by Yang Cao, Xiaoyu Li, and Zhao Song, arXiv:2412.17107v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Normalised mean $\sum|u|$ without disturbance.}
\begin{tabular}{c|cc}
\toprule
Setpoint & Model-based & Learning-based\\
\midrule
1 & 0.1365 & \textbf{0.1361}\\
2 & 0.1575 & \textbf{0.1507}\\
3 & 0.1806 & \textbf{0.1464}\\
4 & 0.1907 & \textbf{0.1494}\\
5 & \textbf{0.1036} & 0.1528\\
6 & \textbf{0.0880} & 0.1526\\
7 & \textbf{0.1209} & 0.1428\\
8 & \textbf{0.1141} & 0.1408\\
9 & \textbf{0.1289} & 0.1579\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Thomas Chaffre
Jonathan Wheare
Andrew Lammas
Paulo Santos
Gilles Le Chenadec
Karl Sammut
Benoit Clement

"Sim-to-Real Transfer of Adaptive Control Parameters for AUV Stabilization under Current Disturbance" by Thomas Chaffre, Jonathan Wheare, Andrew Lammas, Paulo Santos, Gilles Le Chenadec, Karl Sammut, and Benoit Clement, arXiv:2310.11075v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


stat

Ding Ning
Varvara Vetrova
Sébastien Delaux
Rachael Tappenden
Karin R. Bryan
Yun Sing Koh

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, 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|}\hline
iC & stem & s & base & diff & level\\\hline
1 & 87 & 42 & 0 & 1 & 1\\\hline
1 & 127 & 3 & 0 & \verb|[NULL]| & 9996\\\hline
1 & 126 & 7 & 1 & \verb|[NULL]| & 9998\\\hline
1 & 118 & 19 & 0 & 1,3 & 10000\\\hline
1 & 134 & 4 & 0 & \verb|[NULL]| & 9996\\\hline
1 & 118 & 21 & 0 & 0 & 10000\\\hline
\end{tabular}
\caption{\texttt{cofseq\_S0\_\_C2\_\_S0.csv}}
\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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{1-NN classification accuracy of 6 methods. }
\begin{tabular}{|c|cccccc|}\hline
		& \multicolumn{6}{c|}{Method} \\ \hline
		Data & $k$-mer topology & NVM & FPS-JS & FFP-KL & Markov & FPS \\\hline
		NCBI 2020 & \textbf{0.933} & 0.879 & 0.862 & 0.862 & 0.734 & 0.732 \\
		NCBI 2022 & \textbf{0.920} & 0.875 & 0.870 & 0.870 & 0.735 & 0.732 \\
		NCBI 2024 & \textbf{0.898} & 0.829 & 0.825 & 0.826 & 0.637 & 0.656 \\
		NCBI 2024 All & \textbf{0.901} & 0.825 & 0.832 & 0.832 & 0.647 & 0.647 \\ \hline
	\end{tabular}
\end{table}
\end{document}

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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Key sounder parameters.}
\begin{tabular}{|l|l|}
\hline
\textbf{Parameter} & \textbf{Value} \\ \hline
Carrier Frequency & 3.5 GHz \\ \hline
Bandwidth & 46 MHz \\ \hline
Transmit power & 27 dBm \\ \hline
Sampling rate TX & 50 MS/s \\ \hline
Sampling rate RX & 250 MS/s \\ \hline
SISO signal duration & 50 $\mu s$ \\ \hline
SIMO duration & 6.4 ms \\ \hline
Number of frequency points (RX) & 1841 \\ \hline
Number of TX antennas & 1 \\ \hline
Number of RX antennas & 128 \\ \hline
Number of SIMO captures per burst & 3 \\ \hline
Rate of burst & 20 Hz \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Jorge Gomez Ponce
Thomas Choi
Naveed A. Abbasi
Aldo Adame
Alexander Alvarado
Colton Bullard
Ruiyi Shen
Fred Daneshgaran
Harpreet S. Dhillon
Andreas F. Molisch

"Air-to-Ground Directional Channel Sounder With 64-antenna Dual-polarized Cylindrical Array" by Jorge Gomez Ponce, Thomas Choi, Naveed A. Abbasi, Aldo Adame, Alexander Alvarado, Colton Bullard, Ruiyi Shen, Fred Daneshgaran, Harpreet S. Dhillon, and Andreas F. Molisch, arXiv:2103.09135v1, 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{\centering Examples of reliability estimates in economics and psychology}
\begin{tabular}{ccc}
				\midrule
				Construct & Reliability estimate & Reference \\
				(1) & (2) & (3) \\ \midrule
				IQ & 0.80 & \\
				Risk aversion & 0.20--0.40 & \\
				Big 5 personality traits & 0.60--0.73 & \\
				Present bias			 & 0.36 & \\
				Loss aversion & 0.88 & \\
				Teacher value added & 0.23--0.47 & \\
				Life satisfaction & 0.67 & \\
				Self-esteem & 0.71 & \\
				Academic ability & 0.61--0.77 & This paper \\
				Cognitive endurance & 0.14--0.30 & This paper \\
				\midrule
			\end{tabular}
\end{table}
\end{document}

q-fin

"Cognitive Endurance, Talent Selection, and the Labor Market Returns to Human Capital" by Germán Reyes, arXiv:2301.02575v1, 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}{lccccc}
\toprule
\textbf{Method} & \textbf{CIDEr} & \textbf{BLEU1} & \textbf{BLEU2} & \textbf{BLEU3} & \textbf{BLEU4} \\
\midrule
InstructBLIP & 0.56 & 0.29 & 0.22 & 0.19 & 0.19 \\
\textbf{VCD} & \textbf{0.71} & \textbf{0.36} & \textbf{0.32} & \textbf{0.30} & \textbf{0.31} \\
\textbf{ICD} & \textbf{0.69} & \textbf{0.35} & \textbf{0.30} & \textbf{0.29} & \textbf{0.30} \\
\bottomrule
\end{tabular}
\caption{\textbf{Evaluation on TextVQA test set} using metrics CIDEer and BLEU 1, 2, 3, and 4.}
\end{table}
\end{document}

cs

Xintong Wang
Jingheng Pan
Liang Ding
Chris Biemann

"Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding" by Xintong Wang, Jingheng Pan, Liang Ding, and Chris Biemann, arXiv:2403.18715v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Lorenzo Mercuri
Andrea Perchiazzo
Edit Rroji

"Investigating Short-Term Dynamics in Green Bond Markets" by Lorenzo Mercuri, Andrea Perchiazzo, and Edit Rroji, arXiv:2308.12179v1, 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}{ll}
Operation & Latency (ms) \\
\hline
(g,p) calculation & 51.3939 \\
\hline
Carry propagation (Level 1) & 93.4178 \\
Carry propagation (Level 2) & 93.5273 \\
Carry propagation (Level 3) & 80.342 \\
Carry propagation (Level 4) & 70.8481 \\
\hline
Sum calculation & 34.2846 \\
\hline
Total latency, including overhead & 528.482
\end{tabular}
\caption{16-bit adder latency}
\end{table}
\end{document}

cs

"CryptoEmu: An Instruction Set Emulator for Computation Over Ciphers" by Xiaoyang Gong and Dan Negrut, arXiv:2101.03403v1, 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{Balance Check of Swim-Out Times across different Event Categories and Periods}
\begin{tabular}{llrrrr}
 \toprule
 Event Category & Period & Mean Swim-Out Time & SD Swim-Out Time & Count \\
 \midrule
 Short & Covid & 1855 & 380 & 4694 \\
 Short & Post-Covid & 1899 & 403 & 3726 \\
 Short & Pre-Covid & 1696 & 415 & 37722 \\
 \midrule
 Long & Covid & 4594 & 804 & 1130 \\
 Long & Post-Covid & 4572 & 1035 & 1014 \\
 Long & Pre-Covid & 4553 & 773 & 10201 \\
 \midrule
 Middle & Covid & 2311 & 380 & 4658 \\
 Middle & Post-Covid & 2315 & 394 & 3527 \\
 Middle & Pre-Covid & 2216 & 436 & 26711 \\
 \midrule
 Sprint & Covid & 854 & 295 & 7985 \\
 Sprint & Post-Covid & 906 & 296 & 6152 \\
 Sprint & Pre-Covid & 774 & 258 & 60871 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cccc}
 \hline
 Proj. Head & 1 & 0.8 & 0.5 & 0.2 \\ \hline
 FFN & 13.21\% & 8.3\% & 18.43\% & \textbf{35.43\% } \\\hline
 TF & 4.25\% & 6.97\% & 11.08\% & \textbf{40.19\% } \\ \hline \hline
 Proj. Head & 0.1 & 0.05 & 0.02 \\ \hline
 FFN & 33.95\% & 29.51\% & 26.65\%\\\hline
 TF & 34.09\% & 31.88\% & 1.18\% \\ \hline
 \end{tabular}
\caption{Unsupervised Accuracy reported for ablation on different \textbf{loss temperatures} on CIFAR100 with ResNet18 and small projection head.}
\end{table}
\end{document}

cs

"Deep Fusion: Capturing Dependencies in Contrastive Learning via Transformer Projection Heads" by Huanran Li and Daniel Pimentel-Alarcón, arXiv:2403.18681v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Liquidity Dynamics in RFQ Markets and Impact on Pricing" by Philippe Bergault and Olivier Guéant, arXiv:2309.04216v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{makecell}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|cc|ccc|}
 \Xhline{1.2pt}
 & & \multicolumn{3}{c|}{\bf Neural approximation} \\
 & & \multicolumn{3}{c|}{$\rho_{NN(k,m)}(\Sigma_2)$} \\
 \hline
 $k$ & $m$ & Best & Mean & Std.\\
 \Xhline{1.2pt}
 1 layer & 5 neurons & $8.6977$ & $9.0251$ & $0.8800$ \\
 & 10 neurons & $8.6910$ & $8.6969$ & $0.0056$ \\
 \hline
 2 layers & 5 neurons & $8.6983$ & $8.9312$ & $0.4645$\\
 & 10 neurons & $8.6944$ & $8.7049$ & $0.0077$ \\
 \hline
 3 layers & 5 neurons & $8.6967$ & $9.1984$ & $0.7293$ \\
 & 10 neurons & $8.6946$ & $8.7130$ & $0.0175$ \\
 \Xhline{1.2pt}
 \end{tabular}
\caption{Best and mean/std. (over $20$ seeds) approximation of the JSR of system~ provided by a ReLU neural networks with different architectures.}
\end{table}
\end{document}

math

Virginie Debauche
Alec Edwards
Raphael M. Jungers
Alessandro Abate

"Stability Analysis of Switched Linear Systems with Neural Lyapunov Functions" by Virginie Debauche, Alec Edwards, Raphael M. Jungers, and Alessandro Abate, arXiv:2312.08190v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ccccc}
\toprule
Model & Base & Cross-H\"{o}lder & \cellcolor{lightgray} \textsc{JacHess} \\
\midrule
BERT & $.213$ & $.202$ & $\mathbf{.184}$ \\
OPT-125m & $.256$ & $.233$ & $\mathbf{.204}$ \\
OPT-1.3b & $.184$ & $.193$ & $\mathbf{.157}$ \\
OPT-6.7b* & $.189$ & $.157$ & $\mathbf{.094}$ \\
LLaMA-2-7b* & $.167$ & $.163$ & $\mathbf{.089}$ \\
\bottomrule
\end{tabular}
\caption{Average Brier scores across binary classification tasks. We report the average Brier scores for the CoLA, SST-2, MRPC, RTE, QQP, and QNLI datasets, all of which involve binary classification tasks. Notably, lower Brier scores indicate better performance. Best scores are indicated in \textbf{bold}.}
\end{table}
\end{document}

cs

"From Robustness to Improved Generalization and Calibration in Pre-trained Language Models" by Josip Jukić and Jan Šnajder, arXiv:2404.00758v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
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\begin{document}
\begin{table}
\centering
\caption{Confusion matrix of the video-based binary classification task with DSC+Augmentation (without key-frame label for training) when setting the threshold to \(\widetilde{y}\) = 0.5.}
\begin{tabular}{l|c|c}
\hline\hline
Frame-independent aggregation & \(y =\) Negative & \(y =\) Positive\\\hline
\(\widetilde{y} =\)Positive & 0.073 & 0.918\\
\(\widetilde{y} =\)Negative & 0.902 & 0.082\\\hline\hline
Recurrent neural network & \(y =\) Negative & \(y =\)Positive\\\hline
\(\widetilde{y} =\)Positive & 0.085 & 0.939\\
\(\widetilde{y} =\)Negative & 0.914 & 0.061\\\hline\hline
Non-local aggregation & \(y =\) Negative & \(y =\)Positive\\\hline
\(\widetilde{y} =\)Positive & 0.080 & 0.939\\
\(\widetilde{y} =\)Negative & 0.920 & 0.061\\\hline\hline
Temporal convolution & \(y =\) Negative & \(y =\)Positive\\\hline
\(\widetilde{y} =\)Positive & 0.073 & 0.959\\
\(\widetilde{y} =\)Negative & 0.927 & 0.041\\\hline\hline
\end{tabular}
\end{table}
\end{document}

eess

Jing Wang
Xiaofeng Liu
Fangyun Wang
Lin Zheng
Fengqiao Gao
Hanwen Zhang
Xin Zhang
Wanqing Xie
Binbin Wang

"Automated interpretation of congenital heart disease from multi-view echocardiograms" by Jing Wang, Xiaofeng Liu, Fangyun Wang, Lin Zheng, Fengqiao Gao, Hanwen Zhang, Xin Zhang, Wanqing Xie, and Binbin Wang, arXiv:2311.18788v1, 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}{lrrrrr}
\toprule
Species & h & z & s & m & total \\
\midrule
\# DAGs & 11783 & 15664 & 40870 & 13122 & 81439\\
$\%$ non-triv. & 0.45 & 0.29 & 0.36 & 0.36 & 0.36 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Efficient Sparse Flow Decomposition Methods for RNA Multi-Assembly" by Mathieu Besançon, arXiv:2501.14662v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Continuation evaluation results of VALL-E zero-shot TTS system in Amphion v0.1.}
\begin{tabular}{ccccc}
 \toprule
 \textbf{Systems} & \textbf{Training Dataset} & \textbf{Test Dataset} & \textbf{SIM-O $\uparrow$} & \textbf{WER $\downarrow$} \\ \midrule
 \multirow{2}{*}{Proprietary (VALL-E)} & \multirow{2}{*}{Librilight} & Librispeech & \multirow{2}{*}{0.51} & \multirow{2}{*}{0.038} \\
 &&test-clean (4-10s) && \\ \midrule
 \multirow{2}{*}{Amphion v0.1(VALL-E)} & \multirow{2}{*}{MLS (10-20s)} & Librispeech & \multirow{2}{*}{0.51} & \multirow{2}{*}{0.034} \\
 &&test-clean (10-20s)&& \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Xueyao Zhang
Liumeng Xue
Yicheng Gu
Yuancheng Wang
Jiaqi Li
Haorui He
Chaoren Wang
Songting Liu
Xi Chen
Junan Zhang
Zihao Fang
Haopeng Chen
Tze Ying Tang
Lexiao Zou
Mingxuan Wang
Jun Han
Kai Chen
Haizhou Li
Zhizheng Wu

"Amphion: An Open-Source Audio, Music and Speech Generation Toolkit" by Xueyao Zhang, Liumeng Xue, Yicheng Gu, Yuancheng Wang, Jiaqi Li, Haorui He, Chaoren Wang, Songting Liu, Xi Chen, Junan Zhang, Zihao Fang, Haopeng Chen, Tze Ying Tang, Lexiao Zou, Mingxuan Wang, Jun Han, Kai Chen, Haizhou Li, and Zhizheng Wu, arXiv:2312.09911v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison with different fusion strategies}
\begin{tabular}{c|cc|cc}
\hline
\multirow{2}{*}{Fusion Strategy} & \multicolumn{2}{c|}{-5 dB} & \multicolumn{2}{c}{0 dB} \\ \cline{2-5}
 & STOI (\%) & PESQ & STOI (\%) & PESQ \\ \hline
Early Fusion & 75.23 & 2.24 & 81.41 & 2.66 \\ \hline
Late Fusion & 75.01 & 2.21 & 81.54 & 2.59 \\ \hline
Intermediate Fusion (VSE) & 76.68 & 2.28 & 83.54 & 2.71 \\ \hline
Intermediate Fusion (AV(SE)$^2$) & 79.31 & 2.37 & 84.88 & 2.79 \\ \hline
Multi-layer Feature Fusion & 80.47 & 2.39 & 85.02 & 2.83 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Multi-layer Feature Fusion Convolution Network for Audio-visual Speech Enhancement" by Xinmeng Xu and Jianjun Hao, arXiv:2101.05975v3, 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{ CPU time (in hours) by AccNEPv on the four text datasets.}
\begin{tabular}{lcccc}
		\hline
		dataset & RCV1\_4Class & Reuters21578 & TDT2 & 20NewsHome \\\hline
		AccNEPv & $9.98$ & $10.97$ & $11.19$ & $12.46$ \\
		\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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data Statistics.}
\begin{tabular}{lll}
 \toprule[1pt]
 & \textit{Kaggle} & \textit{RecSys15} \\
 \midrule
 \#interactions & 195,523 & 200,000 \\
 \#items & 70,852 & 26,702 \\
 \#clicks & 1,176,680 & 1,110,965 \\
 \#purchases & 57,269 & 43,946 \\
 \bottomrule[1pt]
 \end{tabular}
\end{table}
\end{document}

stat

Jialin Liu
Xinyan Su
Peng Zhou
Xiangyu Zhao
Jun Li

"A General Neural Causal Model for Interactive Recommendation" by Jialin Liu, Xinyan Su, Peng Zhou, Xiangyu Zhao, and Jun Li, arXiv:2310.19519v1, licensed under CC BY-NC-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{Iteration numbers and elapsed times (seconds) in a format $\mathtt{iter}(\text{time})$ w.r.t. different $\mathtt{cr}$, where ``gamg'' stands for PETSc's default algebraic multigrid preconditioner and ``$-$'' for breakdown error occurring, and the coefficient profile used is from ``B-frac. cfg.'' in .}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
$\mathtt{cr}$ & $0$ & $2$ & $4$ & $6$ & $8$ \\
\hline
gamg & $9(27.8)$ & $18(33.1)$ & $99(77.8)$ & $-$ & $-$ \\
\hline
$(L_\star, m, \mathtt{sd})=(4, 2, 4)$ & $23(40.8)$ & $44(61.1)$ & $55(77.2)$ & $60(81.5)$ & $55(86.8)$ \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Changqing Ye
Shubin Fu
Eric T. Chung
Jizu Huang

"A robust two-level overlapping preconditioner for Darcy flow in high-contrast media" by Changqing Ye, Shubin Fu, Eric T. Chung, and Jizu Huang, arXiv:2403.19356v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sample selection with noise rate estimation in noise learning of medical image analysis" by Maolin Li and Giacomo Tarroni, arXiv:2312.15233v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Active flux for triangular meshes for compressible flows problems" by Rémi Abgrall, Jianfang Lin, and Yongle Liu, arXiv:2312.11271v2, 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{Summary of the blue core from Fig. .}
\begin{tabular}{ll}
\hline
\textbf{Category} & \textbf{Details} \\ \hline
\textbf{Source Set} & C1a formaldehyde \\ \hline
\textbf{Autocatalytic Set} & C3b, C3c dihydroxy acetone, C4b, C4c, C5d, C5e, C6e \\ \hline
\textbf{Amplification Factor} & 1.1127643944856531 \\ \hline
\textbf{Reactions} & \\ \hline
R4 & C3c dihydroxy acetone $\to$ C3b \\
R5 & C1a formaldehyde + C3b $\to$ C4b \\
R7 & C4b $\to$ C4c \\
R10 & C1a formaldehyde + C4c $\to$ C5d \\
R12 & C5d $\to$ C5e \\
R16 & C1a formaldehyde + C5e $\to$ C6e \\
R37 & C6e $\to$ C3b + C3c dihydroxy acetone \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Víctor Blanco
Gabriel González
Praful Gagrani

"Identifying Self-Amplifying Hypergraph Structures through Mathematical Optimization" by Víctor Blanco, Gabriel González, and Praful Gagrani, arXiv:2412.15776v2, 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 empirical comparison of the estimated causal graph for the synthetic data in the dynamic LSEM setup, when $m=1, T=100$. The number in parenthesis denotes the standard deviation. }
\begin{tabular}{llllll}
\toprule
Metric & CD-NOD & Proposed & & \\
\midrule
 FDR & 0.77(0.02) & \textbf{0.01 (0.00)} & & \\
 TPR & 0.34(0.03) & \textbf{0.94(0.00)} & & \\
 SHD & 3.09(0.22) & \textbf{0.07(0.00)} & & \\
 MSE & 0.41(0.01), & \textbf{0.03(0.00)} & & \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

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


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c|c}
			\hline
			\multirow{2}{*}{rule}
			& training & test	&number
			&\multicolumn{3}{c}{suboptimality}\\
			\cline{5-7}
			& data & data & feasible & min &avg & max\\
			\hline
			$B^\nu,b^\nu$ & unif & unif & 60 &0.000 &0.005 &0.043\\
			MDR & unif & unif & 100 &0.000 &0.003 &0.027\\
			AMDR & unif & unif & 100 &0.000 &0.002 &0.020\\
			\hline
			$B^\nu,b^\nu$ & unif & beta & 39 &0.000 &0.027 &0.077\\
			MDR & unif & beta & 100 &0.000 &0.018 &0.075\\
			AMDR & unif & beta & 100 &0.000 &0.012 &0.037\\
			\hline
			$B^\nu,b^\nu$ & beta & beta & 34 &0.003 &0.054 &0.135\\
			MDR & beta & beta & 100 &0.025 &0.069 &0.200\\
			AMDR & beta & beta & 100 &0.000 &0.006 &0.025\\
 \hline
		\end{tabular}
\caption{Performance of decision rules obtain from ($\beta$-SP1)$^\nu$ against dispersed target. Suboptimality statistics are computed over feasible decisions.}
\end{table}
\end{document}

eess

Risk-Adaptive Local Decision Rules

"Risk-Adaptive Local Decision Rules" by Johannes O. Royset and Miguel A. Lejeune, arXiv:2310.09844v2, 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}{lccccc}
\hline \hline
\textbf{Base model} & \textbf{AUC} & \textbf{ACC} & \textbf{SEN} & \textbf{SPC} & \textbf{PPV} \\
\hline
NASNet & 96.24 & 91.00 & 86.67 & 92.86 & 83.87 \\
ResNetV2 & 95.71 & 88.00 & 83.33 & 90.00 & 78.13 \\
Xception & 92.29 & 87.00 & 76.67 & 91.43 & 79.31 \\
DenseNet169 & 95.24 & 89.00 & 83.33 & 91.43 & 80.65 \\
ResNet152 & 89.29 & 85.00 & 73.33 & 90.00 & 75.86 \\
\hline\hline
\end{tabular}
\caption{PI scenario: Classification evaluation metrics reported for SST data set resulted from different base models in the proposed recognition pipeline, including SST positional information in the input.}
\end{table}
\end{document}

eess

Mostafa Jahanifar
Neda Zamani Tajeddin
Meisam Hasani
Babak Shekarchi
Kamran Azema

"Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks" by Mostafa Jahanifar, Neda Zamani Tajeddin, Meisam Hasani, Babak Shekarchi, and Kamran Azema, arXiv:2011.11777v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"CMHSU: An R Statistical Software Package to Detect Mental Health Status, Substance Use Status, and their Concurrent Status in the North American Healthcare Administrative Databases" by Mohsen Soltanifar and Chel Hee Lee, arXiv:2501.06435v3, 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|} \hline
$\underline{\eta}$ & HJB3d & NN (2,5)\\ \hline
0&	134&	124\\ \hline
3.65&	111&	105	\\ \hline
7.3&	80&	80	\\ \hline
10.95&	53&	53	\\ \hline
14.6&	37&	37	\\ \hline
18.25&	25&	25	\\ \hline
21.9&	19&	20	\\ \hline
25.55&	14&	10	\\ \hline
36.5&	0&	0	\\ \hline
\end{tabular}
\caption{ $-P \times 10^4$ for different values of $\underline{\eta}$ computed with a 3d HJB PDE and neural networks with 2 hidden layers of dimension 5. $\sigma=0.2$.}
\end{table}
\end{document}

q-fin

Mohamed Hamdouche
Pierre Henry-Labordere
Huyen Pham

"Policy gradient learning methods for stochastic control with exit time and applications to share repurchase pricing" by Mohamed Hamdouche, Pierre Henry-Labordere, and Huyen Pham, arXiv:2302.07320v1, licensed under CC0 1.0. 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{Peak classification accuracies, averaged over 5 runs, for all datasets and optimization methods. Best performing optimization method is marked in \textbf{bold}. }
\begin{tabular}{cccc}
 \toprule
 & ADAM & ADAM + SLS & ALSALS \\
 \cmidrule(r){1-1} \cmidrule(r){2-4}
\it{MNLI} & 0.8340 & \textbf{0.8347} & 0.8188 \\
\it{QNLI} & 0.9090 & 0.9044 & \textbf{0.9102} \\
\it{MRPC} & 0.8279 & \textbf{0.8667} & 0.8603 \\
\it{SST2} & \textbf{0.9271} & 0.9261 & 0.9128 \\
 \cmidrule(r){1-1} \cmidrule(r){2-4}
 ResNet34 \\
\it{CIFAR10} & 0.9273 & 0.9393 & \textbf{0.9446} \\
\it{CIFAR100} & 0.675 & 0.7131 & \textbf{0.7607} \\
 ResNet50 \\
\it{ImageNet} & 0.5860 & 0.3069 & \textbf{0.6314} \\
 \cmidrule(r){1-1} \cmidrule(r){2-4}
average & 0.8123 & 0.7844 & \textbf{0.8341} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Philip Kenneweg
Tristan Kenneweg
Barbara Hammer

"Improving Line Search Methods for Large Scale Neural Network Training" by Philip Kenneweg, Tristan Kenneweg, and Barbara Hammer, arXiv:2403.18519v1, 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{Top Ten Export Destinations of Bangladesh 2005-2013}
\begin{tabular}{lc} \toprule
 Destination & Percentage of export (by value) \\
 \midrule
 United States & 23.49\\
 Germany & 15.69 \\
 United Kingdom & 10.12\\
 France & 6.34 \\
 Spain & 4.56 \\
 Canada & 4.22 \\
 Italy & 4.13\\
 Netherlands & 4.02 \\
 Belgium & 3.00\\
 Turkey & 2.44\\

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

q-fin

"Exchange Rate Pass-Through and Data Frequency: Firm-Level Evidence from Bangladesh" by Md Deluair Hossen, arXiv:2303.04101v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
			\# Pentachora & $4$-Manifold & \# Triangulations & \# PL Classes\\
			\hline
			2 & $\mathbb{S}^4$ & 6 & 1\\
			& $S^3\times S^1$ & 2 & 1
		\end{tabular}
\caption{Topological classification of the closed orientable 2-pentachoron census.}
\end{table}
\end{document}

math

Rhuaidi Antonio Burke
Benjamin A. Burton
Jonathan Spreer

"Small Triangulations of $4$-Manifolds: Introducing the $4$-Manifold Census" by Rhuaidi Antonio Burke, Benjamin A. Burton, and Jonathan Spreer, arXiv:2412.04768v1, 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{Data augmentation and $\lambda_{class}$ effect ablation study}
\begin{tabular}{lrr}
\hline
 & mAP & Recog Rate \\
\hline
ResNet50\_w1 & 91.87\% & 82.20\% \\
ResNet50\_w5 & 34.28\% & 0.20\% \\
ResNet50\_w1\_5 & \textbf{94.38\%} & \textbf{86.90\%} \\
\hline
MobileNet2[:15]\_256\_w1 & \textbf{96.60\%} & 69.85\% \\
MobileNet2[:15]\_256\_w5 & 46.33\% & 0.00\% \\
MobileNet2[:15]\_256\_w1\_5 & 91.62\% & \textbf{75.35\%} \\
\hline
ResNet50\_w1\_no\_elastic & 92.95\% & 80.95\% \\
ResNet50\_w1\_no\_rotate & \textbf{86.62\%} & \textbf{57.90\%} \\
ResNet50\_w1\_no\_shear & 93.85\% & 78.85\% \\
ResNet50\_w1\_no\_blur & 91.23\% & 81.15\% \\
ResNet50\_w1\_no\_noise & 94.38\% & 81.55\% \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"QuickBrowser: A Unified Model to Detect and Read Simple Object in Real-time" by Thao Do and Daeyoung Kim, arXiv:2102.07354v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Tim Tsz-Kit Lau
Weijian Li
Chenwei Xu
Han Liu
Mladen Kolar

"Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism" by Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, and Mladen Kolar, arXiv:2412.21124v2, 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{Land-Cover Classes of the University of Pavia dataset, with Standard Training and Test Sets}
\begin{tabular}{l|l|l|l|l}
\hline
\toprule
\textbf{No.} & \textbf{Class Name} & \textbf{Training} & \textbf{Test} & \textbf{Samples} \\ \hline
1 & Asphalt & 548& 6304 & 6852 \\
2 & Meadows & 540 & 18146 & 18686\\
3 & Gravel &392& 1815& 2207 \\
4 & Trees & 524 & 2912& 3436 \\
5 & Metal Sheets & 265 & 1113 & 1378 \\
6 & Bare Soil & 532& 4572 & 5104 \\
7 & Bitumen & 375 & 981& 1366 \\
8 & Bricks & 514 & 3364 & 3878 \\
9 & Shadows & 231 & 795 & 1026 \\
\hline
 &\textbf Total &3961 & 40002 & 43923\\
 \bottomrule
 \hline
\end{tabular}
\end{table}
\end{document}

cs

Judy X Yang
Jun Zhou
Jing Wang
Hui Tian
Alan Wee Chung Liew

"HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification" by Judy X Yang, Jun Zhou, Jing Wang, Hui Tian, and Alan Wee Chung Liew, arXiv:2404.00272v1, 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
 & x2 & x3 & x4 \\ \hline
VDSR+t & 33.52 & 30.65 & 29.00 \\
NODE-VDSR & \textbf{33.80} & \textbf{30.88} & \textbf{29.20} \\ \hline
\end{tabular}
\caption{Average PSNR values evaluated on RealSR test set. }
\end{table}
\end{document}

eess

"Progressive Image Super-Resolution via Neural Differential Equation" by Seobin Park and Tae Hyun Kim, arXiv:2101.08987v4, 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{\small FR Behavior of BFM and five other competing candidates.}
\begin{tabular}{lc}\toprule
			\large
			Model & FRF ($r(x)$) and its behaviors\\
			\midrule
			\textbf{Proposed BFM} &$\frac{\theta\nu x^{\theta-1}}{ \nu x^{\theta} +1 }+\frac{\tau\zeta \left( \zeta x\right) ^{\tau-1}}{e^{-\left( \zeta x\right) ^{\tau}}}$\\
			& IBBFR, RCFR, BFR\\
			Additive Perks distribution (APD)&$\frac{\alpha\lambda e^{\lambda x}}{1+\alpha e^{\lambda x}}+\frac{\beta\theta e^{\theta x}}{1+\beta e^{\theta x}}$\\
			&BFR\\
			Flexible additive Chen-Gompertz (FACG)	&$\alpha\gamma x^{\gamma-1}e^{x^{\gamma}}+\lambda e^{\lambda x-\theta}$\\
			&BFR\\
			Flexible additive exponential power-Gompertz (FAEPG)	&	$\tau\upsilon \left(\upsilon x \right)^{\tau-1}e^{\left(\upsilon x \right) ^{\tau} }+\alpha e^{\alpha x-\theta}$\\
			&BFR\\
			Exponentiated additive Weibull distribution (EAddW)&$\frac{\theta e^{-\alpha x^{\beta}-\gamma x^{\lambda}}\left(\alpha\beta x^{\beta-1}+\gamma\lambda x^{\lambda-1} \right) }{ \left(1-e^{-\alpha x^{\beta}-\gamma x^{\lambda}}\right)^{-\theta+1}\left[ 1-\left(1-e^{-\alpha x^{\beta}-\gamma x^{\lambda}}\right)^{\theta}\right] }$\\
			& IBFR and BFR\\
			Generalized extended exponential-Weibull (GExtEW)&$\frac{c\alpha(\gamma\beta x^{\gamma-1}+\lambda)e^{-(\beta(x^\gamma) +\lambda x)^c} (1-e^{-(\beta(x^\gamma) +\lambda x)^c})^{\alpha-1}}{(\beta(x^\gamma) +\lambda x)^{1-c}\left( 1-(1-e^{-(\beta(x^\gamma) +\lambda x)^c})^\alpha\right) }$\\
			&IBFR, BIBFR, IBBFR, BFR\\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

stat

Badamasi Abba
Mustapha Muhammad
Muhammad Salihu Isa
Jinbiao Wu

"A Bi-failure Mode Model for Competing Risk Modeling with HMC-Driven Bayesian Framework" by Badamasi Abba, Mustapha Muhammad, Muhammad Salihu Isa, and Jinbiao Wu, arXiv:2502.11507v2, 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{Baseline set of parameters of the model.}
\begin{tabular}{|c|c|c|}
			\hline
			Parameter & Value & Interpretation \\
			\hline
			$b$ & 0.001 & permanent impact \\
			$l$ & 0.001 & temporary impact \\
			$\gamma$ & 0.1 & slippage cost \\
			$\sigma$ & 0.1 & volatility\\
			$Q_0$ & 1 & quantity of shares \\
			$Y_0$ & 1 & initial target value \\
			$k$ & 0.95($Y_0-0.05$) & lower boundary \\
			$h$ & 1.05($Y_0+0.05$) & upper boundary \\
			\hline
		\end{tabular}
\end{table}
\end{document}

q-fin

"Shortermism and excessive risk taking in optimal execution with a target performance" by Emilio Barucci and Yuheng Lan, arXiv:2505.15611v1, 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{Descriptive Data of Shipping Frequency}
\begin{tabular}{ccccc}
 \toprule

 Year & Shipping frequency & Exporting firms & Single shipment & High frequency \\

 & (million) & & firms & firms (shipment$\geq$100) \\
 \midrule

 2006 & 0.46 & 5,789 & 752 & 602 \\

 2007 & 0.35 & 5,828 & 756 & 430 \\

 2008 & 0.49 & 6,579 & 926 & 673 \\

 2009 & 0.52 & 6,678 & 962 & 716 \\

 2010 & 0.64 & 6,947 & 1,027 & 944 \\

 2011 & 0.68 & 7,219 & 1,074 & 996 \\

 2012 & 0.85 & 7,622 & 1,142 & 1,278 \\

 2013 & 0.98 & 9,374 & 1,263 & 1,287 \\

 \midrule
 Total & 4.98 & 18,327 & 2,943 & 7,298 \\
 \bottomrule
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"Financing Costs, Per-Shipment Costs and Shipping Frequency: Firm-Level Evidence from Bangladesh" by Md Deluair Hossen, arXiv:2303.04223v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Sample selection with noise rate estimation in noise learning of medical image analysis" by Maolin Li and Giacomo Tarroni, arXiv:2312.15233v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
 \hline
 & $\beta_{Y_{ZX}}$ & $\beta_{Z_X}$ & $\beta_{Y_Z}$ & $\beta_{Y_X}$ \\
 & Primary\\
 \hline
 Low & $(1, 0, \dots, 0)$ & 0.5 & 0.5 & $(1, 1, 1, 0, \dots, 0)$ \\
 Mid & $(1, 0, \dots, 0)$ & 0.5 & 0.5 & $(1, 1, 1, 0, \dots, 0)$ \\
 High & $(1, 0, \dots, 0)$ & 0.5 & 0.5 & $(1, 1, 1, 0, \dots, 0)$ \\
 \hline
 & Auxiliary\\
 Low & $(0, 0.5, 0.5, 0.5, 0, \dots, 0)$ & 0.5 & 0.5 & $(1, 1, 1, 0, \dots, 0)$ \\
 Mid & $(0, 1.5, 1.5, 1.5, 0, \dots, 0)$ & 1.5 & 1.5 & $(1.5, 1.5, 1.5, 0, \dots, 0)$ \\
 High & $(0, 2, 2, 2, 0, \dots, 0)$ & 2 & 2 & $(2, 2, 2, 0, \dots, 0)$ \\
 \end{tabular}
\caption{Coefficient values used in the simulation study when the Primary and Auxiliary dataset use different parameters. This corresponds to a high level of between study heterogeneity}
\end{table}
\end{document}

stat

Ashwini Venkatasubramaniam
Julian Wolfson

"Multi-Study Causal Forest (MCF): A flexible framework for data borrowing in the presence of varying treatment effect heterogeneity" by Ashwini Venkatasubramaniam and Julian Wolfson, arXiv:2502.02110v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Indicative points in the Pareto front comparing samples covered with disk throughput.}
\begin{tabular}{lcc}
\toprule
& \textbf{Samples} & \textbf{Avg. Disk Write} \\
\textbf{Config Set} & \textbf{Covered} &\textbf{(KBytes/sec)} \\\midrule
vbox\_conf1 & \multirow{3}{*}{1432} & \multirow{3}{*}{397}\\
qemu\_legacy\_conf1 & & \\
qemu\_legacy\_conf2 & & \\ \midrule
qemu\_legacy\_conf2 & 1044 & 209 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Mohsen Ahmadi
Kevin Leach
Ryan Dougherty
Stephanie Forrest
Westley Weimer

"MIMOSA: Reducing Malware Analysis Overhead with Coverings" by Mohsen Ahmadi, Kevin Leach, Ryan Dougherty, Stephanie Forrest, and Westley Weimer, arXiv:2101.07328v1, 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|}
 \hline
 Configuration & Terms for convergence & Computation time (s) \\
 \hline
 A & 16 & 0.0412 $\pm$ 0.0086 \\
 \hline
 B & 12 & 0.0044 $\pm$ 0.0041 \\
 \hline
 C & 9 & 0.0008 $\pm$ 0.0003 \\
 \hline
 D & 5 & 0.0004 $\pm$ 0.0002 \\
 \hline
\end{tabular}
\caption{The maximum number of terms required for convergence and the corresponding collision probability computation time. The values correspond to the covariance $diag(0.04,0.04)$ for each of the configurations.}
\end{table}
\end{document}

cs

Antony Thomas
Fulvio Mastrogiovanni
Marco Baglietto

"An Integrated Localisation, Motion Planning and Obstacle Avoidance Algorithm in Belief Space" by Antony Thomas, Fulvio Mastrogiovanni, and Marco Baglietto, arXiv:2101.11566v1, 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}{lcc}
\toprule
Method & Relative Energy Cost & Accuracy (\%) \\
\midrule
ANN & 1 & 92.27 \\
Concat-based skip + Delay & 0.13 & 91.41 \\
\bottomrule
\end{tabular}
\caption{Energy-efficiency comparison between ANN and SNN (with concatenation-based skip + delay) at inference. We use the Fashion-MNIST dataset for the experiments.}
\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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||l|l|l||}
\hline	 \# &	Symbol 	&	 Description		\\
\hline 1 & EURUSD & Euro vs US dollar \\
\hline 2 & USDJPY & US dollar vs Japan Yen \\
\hline 3 & EURJPY & Euro vs Japan Yen \\
\hline 4 & GBPUSD	&	 Great Britain Pound vs US dollar \\
\hline 5 & BTCUSD	&	Bitcoin vs US dollar \\
\hline
		\end{tabular}
\caption{Selected assets from the most traded assets in Foreign Exchange (Forex) and Cryptocurrency (Bitcoin) Market. }
\end{table}
\end{document}

cs

Murilo Sibrao Bernardini
Paulo Andre Lima de Castro

"Is it a great Autonomous FX Trading Strategy or you are just fooling yourself" by Murilo Sibrao Bernardini and Paulo Andre Lima de Castro, arXiv:2101.07217v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{COPA America 2024 match: Canada (goalkeeper {\bf{Maxime Crepeau}}) versus Venezuela (goalkeeper {\bf{Rafael Romero}}).}
\begin{tabular}{lllll}
\hline
\hline
 shoot &Canada & true &Venezuela goalkeeper &goal \\
 number &penalty kicker & cluster &clustering &result \\
\hline
 1 &Jonathan David & 6 &6 &allowed \\
 2 &Liam Millar & {\bf{\color{red}{9}}}&1 &saved \\
 3 &Moïse Bombito & 3 &1 &allowed \\
 4 &Stephen Eustáquio &2 &2 &saved \\
 5 &Alphonso Davies &9 &1 &allowed \\
 6 &Ismaël Koné &1 &3 &allowed \\
\hline
 shoot &Venezuelan& true & Canada goalkeeper &goal \\
 number &penalty kicker & cluster&clustering &result \\
\hline
 1 &Salomón Rondón &3 &1 &allowed \\
 2 &Yangel Herrera &{\bf{\color{red}{1}}}&3 &saved \\
 3 &Tomás Rincón & 2 &1 &allowed \\
 4 &Jefferson Savarino & 4 &4 &saved \\
 5 &Jhonder Cádiz & 3 &1 &allowed \\
 6 &Wilker Ángel & 3 &3 &saved \\
 \hline
 goalkeeper stats & & & &\\
 Crepeau:& \multicolumn{4}{l}{RI=0.666, DDI=0.386, MRDI=0.386, SV=0.500, GSI=0.466}\\
 Romero: & \multicolumn{4}{l}{RI=0.866, DDI=0.350, MRDI=0.350, SV=0.333, GSI=0.350}\\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Soccer Goalkeeper Performance Evaluation: Clustering Approach" by Mahdi Teimouri, arXiv:2502.05548v1, 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{Model trained on combined healthy data from four RMs is able to detect anomalies without raising any false alarms across all four RMs.}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{Machine} & \textbf{Precision}& \textbf{Recall}& \textbf{TPR} & \textbf{FPR} & \textbf{F1-score} \\
 \hline
 RM-2 & 1.0 & 1.0 & 1.0 & 0.0 & 1.0 \\
 \hline
 RM-3 & 0.997 & 0.997 & 0.75 & 0.0 & 0.997\\
 \hline
 RM-4 & 1.0 & 1.0 & 1.0 & 0.0 & 1.0 \\
 \hline
 RM-5 & 1.0 & 1.0 & 1.0 & 0.0 & 1.0 \\
 \hline
\end{tabular}
\end{table}
\end{document}

cs

Sabtain Ahmad
Kevin Styp-Rekowski
Sasho Nedelkoski
Odej Kao

"Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines" by Sabtain Ahmad, Kevin Styp-Rekowski, Sasho Nedelkoski, and Odej Kao, arXiv:2101.11539v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
Model & Model used as input & Portfolio with $\theta_{2}=0.05\%$ & Portfolio with $\theta_{2}=0.1\%$ \\
\hline
Model 3 & Model 1 & $(1.86\%, 8.20\%, 0\%)$ & $(0\%, 3.93\%, 6.93\%)$ \\
Model 3 & Model 2 & $(0\%, 10.21\%, 0\%)$ & $(0\%, 3.93\%, 6.93\%)$ \\
\hline
\end{tabular}
\caption{Results for Model }
\end{table}
\end{document}

q-fin

Deb Narayan Barik
Siddhartha P. Chakrabarty

"Loan portfolio management and Liquidity Risk: The impact of limited liability and haircut" by Deb Narayan Barik and Siddhartha P. Chakrabarty, arXiv:2308.06525v1, 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{Void defense performance against different attack methods.}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
Attack & None & Replay & DV & FB & SP & DW & SMACK \\
\hline
Pass Rate & 77\% & 26\% & 18\% & 0\% & 0\% & 0\% & 20\% \\
\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}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Dinesh Parthasarathy
Tommaso Bevilacqua
Martin Lanser
Axel Klawonn
Harald Köstler

"Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations" by Dinesh Parthasarathy, Tommaso Bevilacqua, Martin Lanser, Axel Klawonn, and Harald Köstler, arXiv:2412.08186v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking" by Paria Rashidinejad and Yuandong Tian, arXiv:2412.09544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Soodeh Habibi
Michal Kocvara
Michael Stingl

"On the numerical solution of Lasserre relaxations of unconstrained binary quadratic optimization problem" by Soodeh Habibi, Michal Kocvara, and Michael Stingl, arXiv:2412.19776v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
			\toprule
			\textbf{Grammars} & \textbf{BLEU} \\
			\midrule
			\footnotesize \textbf{000000}$\leftrightarrow$\textbf{000000} & $98.76$\\
			\footnotesize \textbf{011101}$\leftrightarrow$\textbf{011101} & $98.70$\\
			\footnotesize \textbf{011111}$\leftrightarrow$\textbf{011111} & $99.08$\\
			\footnotesize \textbf{000001}$\leftrightarrow$\textbf{000001} & $98.13$\\
			\footnotesize \textbf{100000}$\leftrightarrow$\textbf{100000} & $98.83$\\
			\footnotesize \textbf{000101}$\leftrightarrow$\textbf{000101} & $97.76$\\
			\footnotesize \textbf{111111}$\leftrightarrow$\textbf{111111} & $97.77$\\
			\footnotesize \textbf{111110}$\leftrightarrow$\textbf{111110} & $97.30$\\
			\bottomrule
		\end{tabular}
\caption{BLEU scores obtained on the test set for within grammar and within vocabulary training. Those scores are the average between the BLEU score obtained when evaluating source to target and target to source respectively.}
\end{table}
\end{document}

cs

Nicolas Guerin
Shane Steinert-Threlkeld
Emmanuel Chemla

"The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation" by Nicolas Guerin, Shane Steinert-Threlkeld, and Emmanuel Chemla, arXiv:2403.18031v1, 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}{lll}
 \toprule
 \textbf{Hydrocephalus/LBD} & \textbf{Cardio/Aging} & \textbf{Other Neuro} \\ \midrule

 bp	& recall & 	social \\
 vitamin	& scan & anxiety \\
 tremor & 	decline & 	speech \\
 oral & donepezil & 	language \\
 nightly & memantine & behavioral \\
 route & behavioral & bilaterally \\
 active & work & pain \\
 outpatient & driving & word \\
 pain & social & recall \\
 mood & language & attention \\\bottomrule \\

 \end{tabular}
\caption{TF-IDF for enriched words within each cluster.}
\end{table}
\end{document}

cs

Matthew West
Colin Magdamo
Lily Cheng
Yingnan He
Sudeshna Das

"Leveraging Pre-trained and Transformer-derived Embeddings from EHRs to Characterize Heterogeneity Across Alzheimer's Disease and Related Dementias" by Matthew West, Colin Magdamo, Lily Cheng, Yingnan He, and Sudeshna Das, arXiv:2404.00464v1, 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 Violence Analysis: Extensive Margin, Events in Over 60\% of Years}
\begin{tabular}{lccccccc}
\toprule
{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)}&{(7)} \tabularnewline
\midrule
\midrule \textbf{Panel A. UARIV}&Terror. Act&Threats&Disapp.&Sex Crimes&Child Recruit.&Torture&Prop. Loss \tabularnewline
\midrule Ceasefire \( \times \) FARC&--0.330*&--0.865**&--0.046**&--0.029*&--0.029***&--0.005&--0.608*** \tabularnewline
&(0.195)&(0.342)&(0.019)&(0.016)&(0.006)&(0.003)&(0.135) \tabularnewline
&&&&&&& \tabularnewline
Treated Munic.&216&216&216&216&216&216&216 \tabularnewline
Control Munic.&41&41&41&41&41&41&41 \tabularnewline
Mean Dep. Var.&0.649&2.508&0.107&0.068&0.037&0.014&0.689 \tabularnewline
Observations&2,827&2,827&2,827&2,827&2,827&2,827&2,827 \tabularnewline
\(R^2\)&0.252&0.523&0.308&0.608&0.453&0.360&0.363 \tabularnewline
&&&&&&& \tabularnewline
\midrule \textbf{Panel B. Other}&Kidnap.&Homicides&Theft&Mines&Forced Mig.&Clash/Att.&\textbf{\textbf{And. Index}} \tabularnewline
\midrule Ceasefire \( \times \) FARC&0.001&--0.074**&--0.427**&--0.292***&--10.210***&--0.019***&--0.289*** \tabularnewline
&(0.004)&(0.032)&(0.210)&(0.050)&(3.198)&(0.006)&(0.104) \tabularnewline
&&&&&&& \tabularnewline
Treated Munic.&216&216&216&216&216&216&216 \tabularnewline
Control Munic.&41&41&41&41&41&41&41 \tabularnewline
Mean Dep. Var.&0.013&0.443&1.100&0.460&19.870&0.036&0.079 \tabularnewline
Observations&2,827&2,827&2,827&2,827&2,827&2,570&2,827 \tabularnewline
\(R^2\)&0.148&0.574&0.769&0.618&0.546&0.332&0.431 \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Inferred Coding Error Probabilities and Observed vs. Underlying Occupational Mobility}
\begin{tabular}{lccccc}
 Classification & $\mathbb{P}(\tilde{M} |S)$ & \ \ \ \ $\mathbb{P}(\tilde{M}|U)$ & \ \ \ \ \ \ \ $\mathbb{P}(M|U)$ & \ \ \ \ $\frac{\mathbb{P}(M|U)}{\mathbb{P}(\tilde{M}|U)} - 1$ & $\mathbb{P}(\tilde{o}\neq o | U)$ \\ \hline \hline
 2000 SOC (22 cat) & 0.178 & \ \ \ \ \ 0.531 & \ \ \ \ \ \ 0.444 & \ \ \ \ -0.164 & 0.095 \\
1990 SOC (13 cat) & 0.197 & \ \ \ \ \ 0.507 &\ \ \ \ \ \ 0.401 & \ \ \ \ -0.209 & 0.105 \\
1990 SOC (6 cat) & 0.148 & \ \ \ \ \ 0.402 &\ \ \ \ \ \ 0.317 & \ \ \ \ -0.213 & 0.077 \\
 NR/R Cognitive, NR/R Manual (4 cat) & 0.110 & \ \ \ \ \ 0.332 &\ \ \ \ \ \ \ 0.263 & \ \ \ \ -0.208 & 0.058 \\
 Cognitve, R Manual / NR Manual (3 cat) & 0.083 & \ \ \ \ \ 0.273 &\ \ \ \ \ \ \ 0.218 & \ \ \ \ -0.199 & 0.043 \\
Major industry groups (15 cat) & 0.101 & \ \ \ \ \ 0.523 &\ \ \ \ \ \ 0.477 & \ \ \ -0.088 & 0.055 \\
 \hline
 \multicolumn{6}{p{1\textwidth}}{\tiny{Sample: unemployed between 1983-2013, in 1984-2008 SIPP panels, subject to conditions explained in data construction appendix (most importantly: unimputed occupations (resp. industries), with restrictions to avoid right and left censoring issues.) $\mathbb{P}(\tilde{M} | S)$: probability that the wrong code is assigned to a true stayer; $\mathbb{P}(\tilde{o}\neq o | U)$}: probability that the wrong code is assigned to an unemployed worker; $\mathbb{P}(\tilde{M})$: observed occupational mobility among the unemployed; $\mathbb{P}(M)$: inferred underlying true mobility (proportion of unemployed). \emph{NR/R} refers to routine vs. non-routine. Further details on the classifications are explained in the data construction appendix.}
 \end{tabular}
\end{table}
\end{document}

q-fin

"Unemployment and Endogenous Reallocation over the Business Cycle" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, 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{Performance of our rotation invariant auto-encoder (AE) and the spatial-VAE model on the test set}
\begin{tabular}{cccc}
 \toprule
 Dataset & Method & Average MSE & Worst MSE \\
 \midrule
 5HDB & Spatial-VAE & 2.1 & 3.29 \\
 & Rotation invariant AE & 0.3 & 0.81 \\
 \midrule
 MNIST & Spatial-VAE & 66.07 & 121.82 \\
 & Rotation invariant AE & 0.02	 & 0.18 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Koby Bibas
Gili Weiss-Dicker
Dana Cohen
Noa Cahan
Hayit Greenspan

"Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction" by Koby Bibas, Gili Weiss-Dicker, Dana Cohen, Noa Cahan, and Hayit Greenspan, arXiv:2101.03549v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{diagbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The 3D Example. The number of GMRES iterations required to reach convergence for the preconditioned system $\mathcal{P}_t^{-1} \mathcal{A}$ with block triangular Schur complement preconditioning.}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
\diagbox{$\mu$}{$N$} & 4046 & 7915 & 32724 & 112078 & 266555\\
 \hline
 $1$ &30 & 30 & 31 & 32 & 33 \\
 \hline
 $10^{-4}$ & 34& 35 & 37 & 38 & 38 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"Consistency enforcement for the iterative solution of weak Galerkin finite element approximation of Stokes flow" by Weizhang Huang and Zhuoran Wang, arXiv:2412.09865v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

"Abnormal component analysis" by Romain Valla, Pavlo Mozharovskyi, and Florence d'Alché-Buc, arXiv:2312.16139v2, 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{Performances of different networks used in this paper}
\begin{tabular}{ccccccc}
 \toprule
 \centering
 $Networks$ & $Accuracy(\%)$\\
 \hline
 $audio\_noise\_lstm$ & 100 \\
 $japanese\_vowel\_lstm$ & 93.51 \\
 $japanese\_vowel\_cnnlstm$ & 96.49 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approach" by Neelanjana Pal and Taylor T Johnson, arXiv:2311.12130v1, 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{Average performance of BYOL-A with pretraining datasets}
\begin{tabular}{ll|r|r}
\hline
Pretraining dataset & Size & Average & Difference \\
\hline
\hline
AudioSet (1/10 subset) & 210K & \textbf{72.3\%}& \\
\hline
FSD50K & 40K & 70.1\% & AudioSet -2.2 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Daisuke Niizumi
Daiki Takeuchi
Yasunori Ohishi
Noboru Harada
Kunio Kashino

"BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation" by Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada, and Kunio Kashino, arXiv:2103.06695v2, 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{ Decision rule at month 11, mean of the blip estimates over 25 imputed datasets, SPRINT trial, 2010-2013. }
\begin{tabular}{|l|c|c|c|}
 \hline
 Variable & QLOMA & WOMA & No. of times stat. significant$^*$ \\ \hline
 Intercept visit & 12.9 & 5.1 & 10 \\ \hline
 Intercept add-on & 2.3 & 5.4 & 0 \\ \hline
 \multicolumn{4}{|c|}{Visit interaction with:} \\ \hline
 Intensive group & -2.4 & -1.4 & 21 \\ \hline
 Age & -0.0 & -0.0 & 1 \\ \hline
 Female sex & 0.4 & 0.5 & 0 \\ \hline
 Race Black & \multicolumn{3}{|c|}{Reference} \\ \hline
 Race Hispanic & -0.4 & -1.1 & 0 \\ \hline
 Race White & 0.9 & 1.0 & 2 \\ \hline
 \hspace{0.2cm} Other & 0.6 & 1.0 & 0 \\ \hline
 Smoking (ever) & -1.3 & -1.3 & 0 \\ \hline
 BMI & 0.0 & 0.0 & 0 \\ \hline
 HDL & -0.0 & -0.0 & 0 \\ \hline
 SBP Baseline & 0.0 & 0.0 & 0 \\ \hline
 SBP Current month & -0.1 & -0.1 & 23 \\ \hline
 CVD & 0.7 & 0.2 & 0 \\ \hline
 Aspirin use & -0.6 & -0.8 & 1 \\ \hline
 Statin use & -0.1 & 0.3 & 0 \\ \hline
 \end{tabular}
\end{table}
\end{document}

stat

Janie Coulombe
Dany El-Riachi
Fanxing Du
Tianze Jiao

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean execution times (in seconds) of the repair methods.}
\begin{tabular}{lccc}
\toprule
Data set & Llunatic (S) & Llunatic (FT) & Swipe (best) \\
\midrule
Hospital & 20.31 & 167.09 & \textbf{0.20} \\
Allergen & 1.52 & 264.82 & \textbf{0.28} \\
Eudract & 19.38 & 98.09 & \textbf{7.15} \\
Flight & 910.05 & 3987.90 & \textbf{17.80} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Cleaning data with Swipe

"Cleaning data with Swipe" by Toon Boeckling and Antoon Bronselaer, arXiv:2403.19378v2, 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}{cccc}
\hline \textbf{} & \textbf{Sentences} & \textbf{Train} & \textbf{Test} \\ \hline
POS & 2997 & 2247 & 750 \\
NER & 67719 & 64155 & 3564 \\
\hline
\end{tabular}
\caption{ Statistics of POS and NER datasets }
\end{table}
\end{document}

cs

"BNLP: Natural language processing toolkit for Bengali language" by Sagor Sarker, arXiv:2102.00405v2, 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{Accuracy in 10-shot learning}
\begin{tabular}{c|c|c|c}
Model & English & Italian & Spanish\\\hline
Supervised & $32.11\%$ & $32.54\%$ & $16.57\%$ \\
MetaSERL & $69.11\%$ & $70.13\%$ & $71.15\%$ \\
F-MAML & \textbf{73.71\%} & \textbf{74.22\%} & \textbf{74.55\%}\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Anugunj Naman
Chetan Sinha
Liliana Mancini

"Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion Recognition" by Anugunj Naman, Chetan Sinha, and Liliana Mancini, arXiv:2101.01356v2, 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 with Prior Work on Librispeech in the Streaming Setting. Libri-Light: uses unlabeled data from Libri-Light. Lookahead: the model uses a limited amount of future context.}
\begin{tabular}{lllllllll}
\bf \scriptsize Prior Work &\bf \scriptsize Libri-Light &\bf \scriptsize Lookahead &\bf \scriptsize \#Params &\bf \scriptsize Dev-Clean &\bf \scriptsize Dev-Other &\bf \scriptsize Test-Clean &\bf \scriptsize Test-Other
\\ \hline \\
 & No & No & - & - & - & $4.2$ & $11.3$ \\
 & No & Yes & - & - & - & $3.6$ & $10.0$ \\
 & No & No & $30$M & - & - & $3.7$ & $9.2$ \\
 & No & No & - & $3.2$ & $8.5$ & $3.5$ & $8.7$ \\
 & Yes & No & $0.2$B & $4.0$ & $9.4$ & $4.4$ & $8.6$ \\
 & No & No & - & $2.9$ & $8.1$ & $3.2$ & $8.0$ \\
 & No & No & - & - & - & $3.1$ & $7.5$ \\
 & No & Yes & - & $2.7$ & $7.1$ & $2.8$ & $7.2$ \\
 & Yes & No & $0.6$B & $2.5$ & $6.9$ & $2.8$ & $6.6$ \\
 & No & Yes & $80$M & - & - & $2.4$ & $6.1$\\
\hline \hline \\
Our work & Yes & No & $0.6$B & $\bf{1.8}$ & $\bf{5.2}$ & $\bf{2.0}$ & $\bf{5.2}$ \\
\end{tabular}
\end{table}
\end{document}

eess

Oscar Chang
Dongseong Hwang
Olivier Siohan

"Revisiting the Entropy Semiring for Neural Speech Recognition" by Oscar Chang, Dongseong Hwang, and Olivier Siohan, arXiv:2312.10087v2, 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|}
\hline
Regret at Period & 50 & 60 & 70 & 80 & 90 & 100 & 110 & 120\\ \hline
One-Time MILP & 36.25 & 55.31 & 60.56 & 60.61 & 60.79 & 60.97 & 61.03 & 61.12 \\ \hline
One-Time LP & 36.25 & 55.31 & 61.95 & 64.99 & 67.78 & 71.07 & 74.31 & 77.16 \\ \hline
\end{tabular}
\caption{Comparison of Regrets Without Cost Structure Assumption (Exploration Period is of Length $60$).}
\end{table}
\end{document}

stat

Hansheng Jiang
Chunlin Sun
Zuo-Jun Max Shen
Shunan Jiang

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