\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cc|c}
\hline
\# of SSM block pairs & STOI (\%) & PESQ & Params. \\ \hline
1 & 94.27 & 3.09 & 4.20 M \\
2 & 94.85 & 3.16 & 5.26 M \\
3 & 95.71 & 3.25 & 6.31 M \\
4 & 95.94 & 3.27 & 7.37 M \\
5 & 96.08 & 3.27 & 8.43 M \\ \hline
\end{tabular}
\caption{Ablation study on the number of SSM block pairs.}
\end{table}
\end{document}

eess

"SE Territory: Monaural Speech Enhancement Meets the Fixed Virtual Perceptual Space Mapping" by Xinmeng Xu, Yuhong Yang, and Weiping Tu, arXiv:2311.01679v2, 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}{lcc}\hline
 & $\mathcal{L}^2$-like norm & $\mathcal{L^{\infty}}$-like norm \\\hline
 asymptotic expansion up to 1st-order & 1.9521 & 0.0154 \\\hline
 asymptotic expansion up to 2nd-order & 0.4636 & 0.0037 \\\hline
 asymptotic expansion up to 3rd-order & 0.2471 & 0.0019 \\\hline
 asymptotic expansion up to 4th-order & 0.2113 & 0.0017 \\\hline
 \end{tabular}
\end{table}
\end{document}

eess

Haruki Tadano
Yoshihiko Susuki
Atsushi Ishigame

"Asymptotic Assessment of Distribution Voltage Profile Using a Nonlinear ODE Model" by Haruki Tadano, Yoshihiko Susuki, and Atsushi Ishigame, arXiv:2101.09945v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
Method & $d$ & Exact solution & Numerical value & (Std.)\tabularnewline
\hline
\hline
\multirow{4}{*}{Full} & $2$ & $1.4729$ & $1.4135$ & ($0.0075$)\tabularnewline
\cline{2-5} \cline{3-5} \cline{4-5} \cline{5-5}
 & $3$ & $0.8486$ & $0.6536$ & ($0.0025$)\tabularnewline
\cline{2-5} \cline{3-5} \cline{4-5} \cline{5-5}
 & $4$ & $2.1673$ & $1.7105$ & ($0.0111$)\tabularnewline
\cline{2-5} \cline{3-5} \cline{4-5} \cline{5-5}
 & $5$ & $1.7716$ & $1.1472$ & ($0.0057$)\tabularnewline
\hline
\multirow{4}{*}{Reduced} & $2$ & $1.4729$ & $1.4958$ & ($0.0115$)\tabularnewline
\cline{2-5} \cline{3-5} \cline{4-5} \cline{5-5}
 & $3$ & $0.8486$ & $0.7784$ & ($0.0028$)\tabularnewline
\cline{2-5} \cline{3-5} \cline{4-5} \cline{5-5}
 & $4$ & $2.1673$ & $1.9891$ & ($0.0109$)\tabularnewline
\cline{2-5} \cline{3-5} \cline{4-5} \cline{5-5}
 & $5$ & $1.7716$ & $1.4709$ & ($0.0054$)\tabularnewline
\hline
\end{tabular}
\caption{Comparison between exact and numerical values.}
\end{table}
\end{document}

q-fin

Joshua Zoen-Git Hiew
Tongseok Lim
Brendan Pass
Marcelo Cruz de Souza

"Geometry of vectorial martingale optimal transport and robust option pricing" by Joshua Zoen-Git Hiew, Tongseok Lim, Brendan Pass, and Marcelo Cruz de Souza, arXiv:2309.04947v2, 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{$R^2$ score for spatial generalization using the Random Forest (RF) model with and without transfer learning (TL). Higher scores are better.}
\begin{tabular}{c|c|c|c|c}
\hline\hline
\multirow{2}{*}{Test Road} & \multicolumn{2}{l|}{PL Features} & \multicolumn{2}{l}{TA Features} \\ \cline{2-5}
 & No TL & TL & No TL & TL \\ \hline
1 & 0.02 & -0.47 & -0.96 & \textbf{0.71} \\ \hline
2 & 0.02 & -0.75 & -0.96 & \textbf{0.72} \\ \hline
3 & 0.02 & -0.86 & -0.96 & \textbf{0.42} \\ \hline\hline
Mean & 0.02 & -0.69 & -0.96 & \textbf{0.62} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Forough Yaghoubi
Armin Catovic
Arthur Gusmao
Jan Pieczkowski
Peter Boros

"Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning" by Forough Yaghoubi, Armin Catovic, Arthur Gusmao, Jan Pieczkowski, and Peter Boros, arXiv:2101.09143v1, 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 Split Configurations for Experimental Study}
\begin{tabular}{llll}
\toprule
Data Split & Training Data & Validation Data & Testing Data \\
\hline
SEP-28k-E & 4-DS & DS-Set 1 & DS-Set 2 \\
SEP-28k-T & DS-Set 1 & DS-Set 2 & 4-DS \\
SEP-28k-D & DS-Set 2 & DS-Set 1 & 4-DS \\
SEP-28k-E-merged & 4-DS + DS-Set 1 & DS-Set 2 & FB \\
SEP-28k-T-merged & DS-Set 1 + DS-Set 2 & 4-DS & FB \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Huma Ameer
Seemab Latif
Rabia Latif
Sana Mukhtar

"Whisper in Focus: Enhancing Stuttered Speech Classification with Encoder Layer Optimization" by Huma Ameer, Seemab Latif, Rabia Latif, and Sana Mukhtar, arXiv:2311.05203v1, 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{Hasbrouck's Information Share}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Market} & & \\ \hline
Centralized Market & 0.965 & 0.923 \\ \hline
Decentralized Market & 0.076 & 0.003 \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

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

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


cs

"Interpretable Models for Granger Causality Using Self-explaining Neural Networks" by Ričards Marcinkevičs and Julia E. Vogt, arXiv:2101.07600v1, 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}{||ccc||}
 \hline
 Algorithm 1 & Algorithm 2 & P-Value \\
 \hline\hline
 Logistic Regression & Decision Tree & 0.012 \\
 \hline
 Decision Tree & Neural Network & 0.0044 \\
 \hline
 Logistic Regression & Neural Network & 0.5 \\
 \hline
\end{tabular}
\caption{P-values for the algorithm pairs}
\end{table}
\end{document}

cs

"Comparing Classification Models on Kepler Data" by Rohan Saha, arXiv:2101.01904v2, 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|}
\hline
 & Column 1 & Column 2 & Column 3 & Column 4 & Column 5 \\ \hline
Row 1 & 0 & 0 & 2 & 0 & 0 \\ \hline
Row 2 & 0 & 2 & 0 & 2 & 0 \\ \hline
Row 3 & 2 & 0 & 0 & 0 & 2 \\ \hline
\end{tabular}
\caption{Contingency Table showing that Goodman-Kruskal association measure $\tau_b$ is not symmetric: a perfect association in one direction but only a moderate association in the other direction.}
\end{table}
\end{document}

math

Renato Assunção
Flávio Figueiredo
Francisco N. Tinoco Júnior
Léo M. de Sá-Freire
Fábio Silva

"An Interpretable Measure for Quantifying Predictive Dependence between Continuous Random Variables -- Extended Version" by Renato Assunção, Flávio Figueiredo, Francisco N. Tinoco Júnior, Léo M. de Sá-Freire, and Fábio Silva, arXiv:2501.10815v1, 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{List of Components}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Components & Manufacturer & Product type/Specification \\ \hline
Solenoid valve & Festo & MFH-2-M5 \\ \hline
12V coil & Festo & MSFG-12-OD \\ \hline
Regulator & Festo & MS2-LR-QS6-D6-AR-BAR-B \\ \hline
Airsoft regulator & Polarstar & MRS \\ \hline
Tank regulator & Ninja & HP UL Reg 4500psi \\ \hline
Air tank & DYE & UL \\ \hline
Relay module & Seeed & Groove -2-Channel SPDT Relay \\ \hline
Upboard Computer & Arduino & Micro-controller\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Avijit Banerjee
Jakub Haluska
Sumeet G. Satpute
Dariusz Kominiak
George Nikolakopoulos

"Slider: On the Design and Modeling of a 2D Floating Satellite Platform" by Avijit Banerjee, Jakub Haluska, Sumeet G. Satpute, Dariusz Kominiak, and George Nikolakopoulos, arXiv:2101.06335v1, 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}{lrllr}
 \toprule
 & & Model & & MedQA Test \\
 Model & Params & Openness & Method & Accuracy \\
 \midrule
 Med-PaLM 2 & -- & closed & few-shot & 85.4 \\
 GPT-4 & -- & closed & few-shot & 81.4 \\
 Flan-PaLM & 540B & closed & few-shot & 67.2 \\
 BioMedLM (MedMCQA data + classifier) & 2.7B & fully open & fine-tune & 54.7 \\
 GPT-3.5 & 175B & closed & few-shot & 53.6 \\
 BioMedLM (classifier) & 2.7B & fully open & fine-tune & 50.3 \\
 DRAGON & 360M & fully open & fine-tune & 47.5 \\
 BioLinkBERT & 340M & fully open & fine-tune & 45.1 \\
 Galactica & 120B & open weights & zero-shot & 44.4 \\
 GPT-Neo 2.7B & 2.7B & fully open & fine-tune & 37.7 \\
 \bottomrule
 \end{tabular}
\caption{MedQA Performance of Various Systems}
\end{table}
\end{document}

cs

Elliot Bolton
Abhinav Venigalla
Michihiro Yasunaga
David Hall
Betty Xiong
Tony Lee
Roxana Daneshjou
Jonathan Frankle
Percy Liang
Michael Carbin
Christopher D. Manning

"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text" by Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, and Christopher D. Manning, arXiv:2403.18421v1, 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{Heterogeneity in Bidimensional Productivity Spillovers}
\begin{tabular}{lcc}
		\toprule[1pt]
		& $SP^0$ & $SP^1$ \\
		\midrule
		$\omega_{i,t-1} $ 			& --0.720 & --0.070 \\
		& (--0.882, --0.571) & (--0.125, --0.019) \\
		$G_{i,t-1}$ 						& --0.185 & 0.036 \\
		& (--0.355, --0.073) & (--0.151, 0.130) \\
		$\sum_j s_{ij,t-1}^0\omega_{j,t-1}$ & 1.206 & --0.147 \\
		& (0.591, 1.459) & (--0.276, --0.023) \\
		$\sum_j s_{ij,t-1}^1\omega_{j,t-1}$ & --0.147 & 0.144 \\
		& (--0.276, --0.023) & (0.101, 0.178) \\
		\midrule
		\multicolumn{3}{p{8.4cm}}{\footnotesize {\sc Notes:} Reported are the parameter estimates for the $SP^0$ and $SP^1$ functions derived from the polynomial approximation of the conditional mean of $\omega_{it}$ in the productivity process formulation with bidimensional spillovers in . Two-sided 95\% bootstrap percentile confidence intervals in parentheses. These correspond to our baseline specification, with (i) each firm's peers restricted to the firms located in the same province and the industrial scope of spillovers defined at the level of the entire 2-digit industry, (ii) the technical change flexibly controlled for using a series of year effects. }\\
		\bottomrule[1pt]
	\end{tabular}
\end{table}
\end{document}

q-fin

"On the Estimation of Cross-Firm Productivity Spillovers with an Application to FDI" by Emir Malikov and Shunan Zhao, arXiv:2302.14602v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Eigenvector fluctuations and limit results for random graphs with infinite rank kernels" by Minh Tang and Joshua R. Cape, arXiv:2501.15725v1, 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}{lr}
 \toprule
 \bf{Activation type} & Interpretability \\
 \midrule
 Dense (random) & 32.6 \\
 Dense (agreement) & 30.2 \\
 Dense (BiB) & 36.0 \\
 \midrule
 Sparse (random) & 52.8 \\
 Sparse (agreement) & 62.3 \\
 Sparse (BiB) & 81.5 \\
 \bottomrule
 \end{tabular}
\caption{Human interpretability ratings for dense (neuron) vs.\ sparse (autoencoder) features. We present mean interpretability scores across features on a 0--100 scale. We show scores for features that were either uniformly sampled (random), the top 30 by $\hat{\text{IE}}$ from the subject-verb agreement across RC task (agreement; \S), or the top 30 by $\hat{\text{IE}}$ for the Bias in Bios task (BiB; \S).}
\end{table}
\end{document}

cs

Samuel Marks
Can Rager
Eric J. Michaud
Yonatan Belinkov
David Bau
Aaron Mueller

"Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models" by Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, and Aaron Mueller, arXiv:2403.19647v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Kangning Cui
Ruoning Li
Sam L. Polk
Yinyi Lin
Hongsheng Zhang
James M. Murphy
Robert J. Plemmons
Raymond H. Chan

"Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering" by Kangning Cui, Ruoning Li, Sam L. Polk, Yinyi Lin, Hongsheng Zhang, James M. Murphy, Robert J. Plemmons, and Raymond H. Chan, arXiv:2312.15447v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The time spent (approximately in \textbf{minutes}) and the number of elements generated per second by each method (in parentheses) for small-size meshes is shown when processing the CBC3D mesh converted from the segmented image of the first aneurysm. Instead of elements per second, the values in parentheses for Build\_Sizing/Metric represent the CBC3D volume mesh points processed per second. K means thousand.}
\begin{tabular}{l|ccc}
\hline
& \multicolumn{3}{c}{CPU Cores} \\
Methods & 1 & 10 & 20 \\\hline
Build\_Sizing/Metric & 191 (5K) & 19 (55K) & 9 (117K) \\
TetGen (iso-sizing) & 0.1 (20K) & - & - \\
TetGen (iso) & 0.06 (32K) & - & - \\
AFLR (bl) & 0.2 (67K) & - & - \\
CDT3D (iso) & 0.6 (17K) & 0.3 (42K) & 0.1 (78K) \\
CDT3D (aniso) & 20 (1K) & 3 (8K) & 2 (14K) \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Kevin Garner
Fotis Drakopoulos
Chander Sadasivan
Nikos Chrisochoides

"Towards Real-time Adaptive Anisotropic Image-to-mesh Conversion for Vascular Flow Simulations" by Kevin Garner, Fotis Drakopoulos, Chander Sadasivan, and Nikos Chrisochoides, arXiv:2412.13222v1, 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 5-shot learning}
\begin{tabular}{c|c|c|c}
Model & English & Italian & Spanish\\\hline
Supervised & $24.33\%$ & $16.19\%$ & $20.11\%$ \\
MetaSER & $65.21\%$ & $64.13\%$ & $64.95\%$ \\
F-MAML & \textbf{69.71\%} & \textbf{69.13\%} & \textbf{68.85\%}\\ \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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ $\rho$ VERSUS QUANTIZATION BITS $ b $}
\begin{tabular}{cccccc}
			\toprule
			$ b $ & 1 & 2 & 3 & 4 & 5 \\
			\midrule
			$\rho $ & 0.3634 & 0.1175 & 0.03454 & 0.009497 & 0.002499\\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

eess

Jianxin Dai
Yuanyuan Wang
Cunhua Pan
Kangda Zhi
Hong Ren
Kezhi Wang

"Reconfigurable Intelligent Surface aided Massive MIMO Systems with Low-Resolution DACs" by Jianxin Dai, Yuanyuan Wang, Cunhua Pan, Kangda Zhi, Hong Ren, and Kezhi Wang, arXiv:2103.08871v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
Par & Zon(2) & Zon(4) & CZ(2,5) & CZ(4,5) \\
\hline
1 & 2.9 & 4.8 & 2042.4 & 3521.8 \\
\hline
\end{tabular}
\caption{Randomly generated systems: average relative times for one iteration of approximation algorithms, with respect to one iteration of $Par$.}
\end{table}
\end{document}

eess

Marco Casini
Andrea Garulli
Antonio Vicino

"Adaptive Threshold Selection for Set Membership State Estimation with Quantized Measurements" by Marco Casini, Andrea Garulli, and Antonio Vicino, arXiv:2311.00611v2, 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{\it QUIS evaluation of VivaRoutes system}			\centering
\begin{tabular}{l|c|c} \hline%\noalign{\smallskip}
\hspace{50pt} \textbf{Questions} & \textbf{Mean} & \textbf{Std. } \\	\hspace{30pt} &\textbf{Value} & \textbf{Dev.} \\
\hline%\noalign{\smallskip}
Reading labels and icons on the screen. &8.2 & 0.8\\
 ~~0(very hard)-9(very easy) &&\\
 \hline

Selecting and highlighting items/areas. &7.8& 1.0 \\
~~ 0(not at all)-9(very much) && \\
 \hline
Organizing information in the interface with positions and layouts. &7.3& 1.3 \\
~~ 0(confusing)-9(very clear) && \\
\hline
Sequential operations on the interface. &8.1& 0.9 \\
~~ 0 (confusing)-9 (very clear) &&\\
\hline
Interaction on visual interface &7.9&1.4\\
~~0 (very hard) - 9 (very easy) &&\\
\hline
Learning to operate the system. &8.1 &0.8 \\
~~ 0 (difficulty) - 9 (easy) &&\\
\hline
System response with good speed. &8.1 &0.8 \\
~~0 (very slow) - 9 (fast enough) &&\\
\hline
\hline%\noalign{\smallskip}
\end{tabular}
\end{table}
\end{document}

cs

Tsung Heng Wu
Md Amiruzzaman
Ye Zhao
Deepshikha Bhati
Jing Yang

"Visualizing Routes with AI-Discovered Street-View Patterns" by Tsung Heng Wu, Md Amiruzzaman, Ye Zhao, Deepshikha Bhati, and Jing Yang, arXiv:2404.00431v1, 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 for one-shot devices with multiple stress levels observed at different inspection times}
\begin{tabular}{|c|c|c|c|c|c|c|}
		\hline
		Condition & Inspection Time & Failures & Stress Factor 1 & ... & Stress Factor $J$ & Devices \\
		\hline
		1 & $\tau_1$ & $n_1$ & $x_{11}$ & ... & $x_{1J}$ & $K_1$ \\
		2 & $\tau_2$ & $n_2$ & $x_{21}$ & ... & $x_{2J}$ & $K_2$ \\
		... & ... & ... & ... & ... & ... & ... \\
		$j$ & $\tau_j$ & $n_j$ & $x_{j1}$ & ... & $x_{jJ}$ & $K_j$ \\
		... & ... & ... & ... & ... & ... & ... \\
		$I$ & $\tau_I$ & $n_I$ & $x_{I1}$ & ... & $x_{IJ}$ & $K_I$ \\
		\hline
	\end{tabular}
\end{table}
\end{document}

math

María González-Calderón
María Jaenada
Leandro Pardo

"Robust statistical inference for accelerated life-tests with one-shot devices under log-logistic distributions" by María González-Calderón, María Jaenada, and Leandro Pardo, arXiv:2502.20467v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Flavia Chorobura
Ion Necoara
Jean-Christophe Pesquet

"An adaptive forward-backward-forward splitting algorithm for solving pseudo-monotone inclusions" by Flavia Chorobura, Ion Necoara, and Jean-Christophe Pesquet, arXiv:2503.01070v1, 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{IOC Payoff Matrix (where $0 < k < n$)}
\begin{tabular}{|c|c|c|}
 \hline
 Others / This & Diligent & Lazy\\
 \hline
 All diligent & $r - c(1)$ & $rq - (f + b(n - 1))(1 - q) - c(q)$\\
 \hline
 $k$ lazy & $r + b(1 - q) - c(1)$ & $rq - (f + \frac{b(n - k - 1)}{k + 1})(1 - q) - c(q)$\\
 \hline
 All lazy & $r + b(1 - q) - c(1)$ & $r - c(q)$\\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Ameer Taweel
Burcu Yıldız
Alptekin Küpçü

"Gamu Blue: A Practical Tool for Game Theory Security Equilibria" by Ameer Taweel, Burcu Yıldız, and Alptekin Küpçü, arXiv:2403.19130v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Automating the Design of Multigrid Methods with Evolutionary Program Synthesis" by Jonas Schmitt, arXiv:2312.14875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracy comparison of the baseline (\textit{B1, B2}) and proposed model (\textit{P: PhonemeBERT}) at different WER range.}
\begin{tabular}{l|lll|lll}
\multirow{2}{*}{WER} & \multicolumn{3}{c|}{TREC-50} & \multicolumn{3}{c}{SST-5} \\ \cline{2-7}
 & B1 & B2 & P & B1 & B2 & P \\ \hline
10-20 & 80.00 & \textbf{81.43} & 80.00 & 46.94 & \textbf{51.47} & 50.94 \\
20-30 & 81.82 & 82.82 & \textbf{84.85} & 43.62 & 43.62 & \textbf{46.29} \\
30+ & 69.53 & 72.53 & \textbf{77.52} & 41.04 & 40.57 & \textbf{43.50} \\ \hline
Overall & 76.20 & 77.80 & \textbf{81.40} & 43.12 & 44.52 & \textbf{46.74}
\end{tabular}
\end{table}
\end{document}

eess

Mukuntha Narayanan Sundararaman
Ayush Kumar
Jithendra Vepa

"Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript" by Mukuntha Narayanan Sundararaman, Ayush Kumar, and Jithendra Vepa, arXiv:2102.00804v2, 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}{lccccccc}
\hline
 & \multicolumn{3}{c}{SST} & & \multicolumn{3}{c}{N\underline{o} of Records per device} \\
\cline{2-4} \cline{6-8}
 & N & Mean & SD & & Mean & Min & Max \\
\hline
Bucket & 36 & 19.3 & 1.5 & & 1.0 & 1 & 1 \\
D.Buoy & 10 & 19.9 & 0.9 & & 2.1 & 1 & 3 \\
ERI & 35 & 19.6 & 1.4 & & 1.1 & 1 & 2 \\
F.Buoy & 5 & 20.4 & 0.9 & & 48.0 & 23 & 94 \\
\hline
Overall & 86 & 19.6 & 1.4 & & 3.9 & 1 & 94 \\
\hline
\end{tabular}
\caption{Summary statistics of SST by device type.}
\end{table}
\end{document}

stat

"Advances in Bayesian Modeling: Applications and Methods" by Yifei Yan, Juan Sosa, and Carlos A. Martínez, arXiv:2502.11321v1, 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{hhline}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|cccc|ccc|ccc|ccc|}
\hhline{~|----|---|---|---|}
\rowcolor{lightgray!10} \cellcolor{lightgray!00} &\multicolumn{4}{c|}{$p=n$, $h=1/10$} & \multicolumn{3}{c|}{$p=2$, $h=2^{-n}/10$} & \multicolumn{3}{c|}{$p=3$, $h=2^{-n}/10$} & \multicolumn{3}{c|}{$p=n+2$, $h = 2^{-n}/10$}\\
\hline
\cellcolor{lightgray!10} $n$ & 3 &10 & 17 & 24 & 1& 3& 5& 0& 2 & 4 & 0& 2 & 4 \\
\hline
\cellcolor{lightgray!10} GMRES Its.& 16.38&23.63 &23.54&23.38& 20.33 & 32.08 & 36.54 & 16.38& 26.79 &35.13&14.21&29.33&35.29\\
\hline
\cellcolor{lightgray!10} GMRES Time & 0.00 &0.19 &1.67&7.99& 0.00 & 0.12 & 2.64 & 0.00& 0.06 &1.58&0.00&0.23&12.07\\
\hline
\cellcolor{lightgray!10} LU Time & 0.00 & 0.22 &2.75 &15.28& 0.01 & 0.15 & 3.10 & 0.01& 0.10 &2.40&0.00&0.20&13.38\\
\hline
\end{tabular}
\caption{A comparison of the average right-preconditioned GMRES iterations and wall-clock timings (in seconds) per Newton iteration for various choices of $h$ and $p$ over a run of the hpG algorithm to solve the obstacle problem with the setup . We utilize the preconditioner outlined in with a GMRES relative tolerance of $10^{-5}$. We use the stabilization $E_\beta$ with $\beta = 10^{-4}$ and include the average wall-clock timings of a sparse LU factorization for comparison. We observe a bounded iteration count with respect to $p$ and a mild polylogarithmic growth with respect to $h$. The GMRES solver is almost always faster than the LU factorization.}
\end{table}
\end{document}

math

"Hierarchical proximal Galerkin: a fast $hp$-FEM solver for variational problems with pointwise inequality constraints" by Ioannis P. A. Papadopoulos, arXiv:2412.13733v3, 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}{|cc|}
\hline
I am a [BLANK]\\
I work as a [BLANK]\\
My occupation is [BLANK]\\
My dream job is [BLANK]\\
works as a [BLANK]\\
wants to become a [BLANK]\\
Overall i am quite happy with being a [BLANK] \\
I am depressed working as a [BLANK] \\
In conclusion i am neutral towards my job as a [BLANK] \\
I am working as a [BLANK] \\
This is my job: [BLANK]\\
I am conflicted about being a [BLANK]\\
\hline
\end{tabular}
\caption{ List of neutral sentences used in the experiments.} %TODO cite bolukbasi implementation as source
\end{table}
\end{document}

cs

Philip Kenneweg
Sarah Schröder
Barbara Hammer

"Neural Architecture Search for Sentence Classification with BERT" by Philip Kenneweg, Sarah Schröder, and Barbara Hammer, arXiv:2403.18547v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
 \toprule
 Data manifold & Mean squared error (MSE) \\
 \hline
 Sphere ($K = 1$) & $0.4915 (\pm 0.0086)$ \\
 Hyperbolic ($K = -1$) & $0.4228 (\pm 0.0021)$ \\
 \bottomrule
 \end{tabular}
\caption{Evaluation of Fréchet regression on different spaces.}
\end{table}
\end{document}

stat

"Theoretical and Practical Analysis of Fréchet Regression via Comparison Geometry" by Masanari Kimura and Howard Bondell, arXiv:2502.01995v1, 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}{ll}
Size & log(Total Assets) \\
Age	& number of years the firm is in Compustat \\
Profitability & $\frac{EBIT}{Total Assets}$ \\
Tangibility	 & $\frac{Property,\ Plant\ and Equipment}{Total\ Assets}$ \\
Cash & $\frac{Cash\ and\ Short-Term\ Investments}{Total\ Assets}$ \\
R\&D & $\frac{Research\ and\ Development\ Expense}{Total\ Assets}$ \\
MB (Market-to-Book) & $\frac{Total\ Assets - Total\ Common\ Equity + Market\ Value}{Total\ Assets}$ \\
P/S (Price/Sales) & $\frac{(Common\ Shares\ Outstanding)(Price\ Close)}{Sales}$ \\
ROA & $\frac{Net\ Income}{Total\ Assets}$ \\
ROE & $\frac{Net\ Income}{Total\ Common\ Equity}$ \\
Asset Growth & $\frac{Total\ Assets_{t}}{Total\ Assets_{t-1}}$ - 1 \\
Dividends & $\frac{Dividends\ Common}{Total\ Assets}$ \\
Capex & $\frac{Capital\ Expenditures}{Assets\ Total}$ \\
Cash Flow & $\frac{Income\ Before\ Extraordinary\ Items + Depreciation\ and\ Amortization}{Total\ Assets}$ \\
Net Debt Issuance & $\frac{Tota\l Debt_{t}}{Total\ Debt_{t-1}}$ - 1\\
Equity Issuance & $\frac{Sale\ of\ Common\ and\ Preferred\ Stock - Purchase\ of\ Common\ and\ Preferred\ Stock}{Total\ Assets}$ \\
Tobin`s Q & $\frac{Total\ Assets + Market\ Value + Common\ Equity + Deferred\ Taxes}{Total\ Assets}$ \\
Acquisitions & $\frac{Acquisitions}{Total\ Assets}$ \\
Book Leverage & $\frac{LongTerm Debt + ShortTerm Debt}{Total\ Assets}$ \\
After-IPO Cash Ratio & $\frac{Cash_{it}}{Cash_{i0}}$ \\
SA-index & $ (0.737*Size) + (0.043*Size^2) - (0.040*Age)$ \\
Board\ Independence & $\frac{Number of external directors}{Board Size}$ \\
Delisting\ Shock & $\max{(\frac{Fraction\ of\ delistings\ due\ to\ merger\ in\ 2-digit\ SIC\ industry_t}{Fraction\ of\ delistings\ due\ to\ merger\ in\ 2-digit\ SIC\ industry_{t-1}} - 1, 0)}$ \\
\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.


stat

Huawei Lin
Jun Woo Chung
Yingjie Lao
Weijie Zhao

"Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data" by Huawei Lin, Jun Woo Chung, Yingjie Lao, and Weijie Zhao, arXiv:2502.01634v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


q-fin

"Parameter Estimation Methods of Required Rate of Return" by Battulga Gankhuu, arXiv:2305.19708v3, 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{Autonomous robot computing tasks}
\begin{tabular}{lll}
\toprule
Task & Period (ms) & WCET (ms) \\ \midrule
SLAM & 1000 & 500 \\
Path Planning & 2000 & 1188 \\
Control & 40 & 37 \\
Task Allocation & 10000 & 10000 \\
Depth Estimation & 500 & 400 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Sen Wang
Dong Li
Ashrarul H. Sifat
Shao-Yu Huang
Xuanliang Deng
Changhee Jung
Ryan Williams
Haibo Zeng

"Optimizing Logical Execution Time Model for Both Determinism and Low Latency" by Sen Wang, Dong Li, Ashrarul H. Sifat, Shao-Yu Huang, Xuanliang Deng, Changhee Jung, Ryan Williams, and Haibo Zeng, arXiv:2310.19699v3, 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{Summary of Our Dataset}
\begin{tabular}{lccccc}
 \toprule
 \textbf{Sequence Name} & \textbf{Spatial Resolution} & \textbf{\#Frames} & \textbf{Frame rate (fps)} & \textbf{Scene Feature} \\
 \midrule
 PetraJordan & 3840 $\times$ 1920 & 1200 & 30 & Outdoor \\
 \midrule
 CapeTownCityPenorama & 3840 $\times$ 1920& 1200 & 30 & Outdoor \\
 \midrule
 CapeTownCityBeach & 3840 $\times$ 1920 & 1200 & 30 & Outdoor \\
 \midrule
 CapeTownCityGarden & 3840 $\times$ 1920 & 1200 & 30 & Outdoor \\
 \midrule
 CapeTownCitySquare & 3840 $\times$ 1920 & 1200 & 30 & Outdoor \\
 \midrule
 FreeStyleParaGliding & 5120 $\times$ 1920 & 1200 & 30 & Sports \\
 \midrule
 StPetersBergMuseum & 3840 $\times$ 1920 & 1200 & 30 & Night \\
 \midrule
 NorthPoleTrip & 3840 $\times$ 1920 & 1200 & 30 & Motion \\
 \midrule
 DubaiVertical & 3840 $\times$ 1920 & 2000 & 50 & Vertical Motion \\
 \midrule
 AbuDhabiCity & 3840 $\times$ 1920 & 2000 & 50 & City Panorama \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Mohit K. Sharma
Chen-Feng Liu
Ibrahim Farhat
Nassim Sehad
Wassim Hamidouche
Merouane Debbah

"UAV Immersive Video Streaming: A Comprehensive Survey, Benchmarking, and Open Challenges" by Mohit K. Sharma, Chen-Feng Liu, Ibrahim Farhat, Nassim Sehad, Wassim Hamidouche, and Merouane Debbah, arXiv:2311.00082v1, 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{List of Alberta towns, extracted from SemTab Round 1.}
\begin{tabular}{l|l|l|l|l|l}
\hline
col0 & col1 & col2 &col3&col4 & col5\\
\hline
Grande Prairie &city in Alberta&canada&Sexsmith&650&Alberta\\
\hline
Sundre	&town in Alberta&canada&Mountain View County&1093&Alberta\\
\hline
Peace River & town in clberta&Canada & Northern Sunrise County & 330 & Alberta\\
\hline
Vegreville&town in Alberta&canada&Mundare&635&Alberta\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

"AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model" by Rabia Azzi and Gayo Diallo, arXiv:2101.06637v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Valentina Lenarduzzi
Savanna Lujan
Nyyti Saarimaki
Fabio Palomba

"A Critical Comparison on Six Static Analysis Tools: Detection, Agreement, and Precision" by Valentina Lenarduzzi, Savanna Lujan, Nyyti Saarimaki, and Fabio Palomba, arXiv:2101.08832v1, 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{The Butcher tableau of the 4s3pB method}
\begin{tabular}{c|cccc}
$c_1$ & $a_{11}^{\epsilon}$ & 0 & 0 & 0 \\
$c_2$ & $a_{21}+a_{21}^{\epsilon}$ & $a_{22}^{\epsilon}$ & 0 & 0 \\
$c_3$ &$a_{31}+a_{31}^{\epsilon}$ & $a_{32}+a_{32}^{\epsilon}$ & $a_{33}^{\epsilon}$ & 0 \\
$c_4$ & $a_{41}+a_{41}^{\epsilon}$ & $a_{42}+a_{42}^{\epsilon}$ & $a_{43}+a_{43}^{\epsilon}$ & $a_{44}^{\epsilon}$ \\ \hline
 & $3/2$ & $-3/2$ & $1/2$ & $1/2$
\end{tabular}
\end{table}
\end{document}

math

Ivo Dravins
Marcel Koch
Victoria Griehl
Katharina Kormann

"Performance evaluation of mixed-precision Runge-Kutta methods for the solution of partial differential equations" by Ivo Dravins, Marcel Koch, Victoria Griehl, and Katharina Kormann, arXiv:2412.16638v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Tim Matthies
Thomas Löhden
Stephan Leible
Jun-Patrick Raabe

"To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis" by Tim Matthies, Thomas Löhden, Stephan Leible, and Jun-Patrick Raabe, arXiv:2308.09968v1, 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{Cross-sectional Comparison of Customers' Monthly Spend}
\begin{tabular}{lcccc}
\midrule\midrule
DV: & \multicolumn{2}{c}{Spend} \\
\midrule
Model: & (1) & (2) \\
\midrule
\emph{Variables} \\
Constant & 236.5 (0.412) & \\
 & [0.000] & \\
Multichannel & 161.7 (0.595) & 149.0 (0.610) \\
 & [0.000] & [0.000] \\
Offline-Only & -15.0 (0.509) & -31.2 (0.561) \\
 & [0.000] & [0.000] \\
\midrule
\emph{Control Variables}\\
Age & & Yes \\
Gender & & Yes \\
Zipcode & & Yes \\
Household Income & & Yes \\
\midrule
\emph{Fixed-effects}\\
YearMonth & & Yes \\
\midrule
\emph{Fit statistics}\\
Observations & 123,748,697 & 123,748,697 \\
R$^2$ & 0.01307 & 0.04867 \\
Within R$^2$ & & 0.01165 \\
\midrule\midrule
\multicolumn{3}{l}{\emph{Clustered (Customer) standard‐errors are presented in standard brackets,}}\\
\multicolumn{3}{l}{\emph{while p‑values are shown in square brackets.}}
\end{tabular}
\end{table}
\end{document}

q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"Channel Choice and Customer Value" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Object detection: mean intersection over the union (mIoU$\uparrow$) of objects of interest. We use pre-trained model form~ for object detection on the generated images and error of depth estimation in RMSE. }
\begin{tabular}{|c||c|c|c|c|}
\hline
Method & Car & People & Bicycle & Depth (RMSE$\downarrow$) \\
\hline
GESCO & 68.27 & 62.89 & 62.26 & 0.1489\\ %-0.01
\hline
OD & 73.52 & \textbf{68.96} & 67.30 & 0.1183\\ %-0.01
\hline
Diff-GO 100 & \textbf{73.55} & 67.57 & \textbf{67.74}& \textbf{0.1077} \\ %-0.01
\hline
\end{tabular}
\end{table}
\end{document}

eess

Achintha Wijesinghe
Songyang Zhang
Suchinthaka Wanninayaka
Weiwei Wang
Zhi Ding

"Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency" by Achintha Wijesinghe, Songyang Zhang, Suchinthaka Wanninayaka, Weiwei Wang, and Zhi Ding, arXiv:2312.02984v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
Entity Name & Total Count \\
\hline
(Header) Description of Securities & 367 \\
(Header) Dividend Policy & 304 \\
(Header) Prospectus Summary & 320 \\
(Header) Risks To The Business & 332 \\
Agent Address & 320 \\
Agent Name & 323 \\
Agent Telephone & 311 \\
Amount Registered & 875 \\
Attorney Names & 1230 \\
Company Address & 322 \\
Company Name & 328 \\
Company Officer & 2485 \\
Company Officer Title & 2506 \\
Date of Prospectus & 316 \\
Description of Securities (1st Para) & 374 \\
Dividend Policy (1st Para) & 307 \\
EIN & 317 \\
Joint Book Runners & 611 \\
Law Firm Address & 877 \\
Law Firm Name & 638 \\
Max Price & 493 \\
Prospectus Summary (1st Para) & 3051 \\
Risk Clauses & 23916 \\
Title of Security Registered & 910 \\
\hline
\end{tabular}
\caption{S1 Label List and Counts }
\end{table}
\end{document}

cs

Benjamin Townsend
Madison May
Christopher Wells

"RealKIE: Five Novel Datasets for Enterprise Key Information Extraction" by Benjamin Townsend, Madison May, and Christopher Wells, arXiv:2403.20101v1, 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}{l|ccc|cc}
 \toprule
 & $\ $ $\ $ ViT $\ $ $\ $& $\ $ RoPE $\ $& $\ $ RoPE\textrm{-}M $\ $ & $\ $ Circulant\textrm{-}S $\ $ & $\ $ Cayley\textrm{-}S \\
 \midrule
 $\mathrm{ImageNet}$ & $80.04$ & $80.18$ & $80.86$ & $\textbf{81.22}$ & $\underline{81.09}$ \\
 $\mathrm{Places365}$ & $56.79$ & \underline{$56.97$} & $56.69$ & $56.77$ & $\textbf{57.16}$ \\
 \midrule
 Mean & $68.42$ & $68.58$ & $68.78$ & $\underline{69.00}$ & $\textbf{69.12}$ \\
 \bottomrule
 \end{tabular}
\caption{Image classification $\%$ test accuracy. Best numbers are highlighted in \textbf{bold} and the second-best numbers are \underline{underlined}.}
\end{table}
\end{document}

stat

Connor Schenck
Isaac Reid
Mithun George Jacob
Alex Bewley
Joshua Ainslie
David Rendleman
Deepali Jain
Mohit Sharma
Avinava Dubey
Ayzaan Wahid
Sumeet Singh
René Wagner
Tianli Ding
Chuyuan Fu
Arunkumar Byravan
Jake Varley
Alexey Gritsenko
Matthias Minderer
Dmitry Kalashnikov
Jonathan Tompson
Vikas Sindhwani
Krzysztof Choromanski

"Learning the RoPEs: Better 2D and 3D Position Encodings with STRING" by Connor Schenck, Isaac Reid, Mithun George Jacob, Alex Bewley, Joshua Ainslie, David Rendleman, Deepali Jain, Mohit Sharma, Avinava Dubey, Ayzaan Wahid, Sumeet Singh, René Wagner, Tianli Ding, Chuyuan Fu, Arunkumar Byravan, Jake Varley, Alexey Gritsenko, Matthias Minderer, Dmitry Kalashnikov, Jonathan Tompson, Vikas Sindhwani, and Krzysztof Choromanski, arXiv:2502.02562v1, 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}{lccc}
 \toprule
 MLE parameters & $\gamma$ & $c$ & $\tilde{d}$\\
 \midrule
 MLE density & $1.97$ & $5.46$ & $0.0154$\\
 \bottomrule
 \end{tabular}
\caption{MLE parameters of the lower-truncated and right-censored gamma distribution rounded to three significant digits (Section ).}
\end{table}
\end{document}

stat

"Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class" by Selim Gatti and Mario V. Wüthrich, arXiv:2310.11471v2, 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{Model Predictions vs Empirical Evidence from Literature}
\begin{tabular}{lccc}
\toprule
Metric & Model & Empirical Studies & Source \\
\midrule
ESG Price Premium & 2.8-4.6\% & 3-7\% & MSCI (2020), \\
Green vs Brown TS Response & 50-60\% lower & 50-60\% lower & \\
Path Surprise Differential & -1.7 bp & -2.0 bp & Our empirical analysis \\
Investor WTP for ESG & 100 bp & 63-240 bp & , NN Investment \\
Green/Brown Return Spread & 11\% & 10\% vs 21\% & \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Green Shields: The Role of ESG in Uncertain Time" by Fatih Kansoy and Dominykas Stasiulaitis, arXiv:2506.02143v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
		\toprule
		Framework & Aggregation(s) & ITR (bit/min)\\
		\midrule
		MFF & Choquet Integral & 901.40\\
		EMF & Choquet, OWA$_3$ & 1710.74 \\
		\bottomrule
	\end{tabular}
\caption{ITR comparison table.}
\end{table}
\end{document}

cs

Javier Fumanal-Idocin
Yu-Kai Wang
Chin-Teng Lin
Javier Fernández
Jose Antonio Sanz
Humberto Bustince

"Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions" by Javier Fumanal-Idocin, Yu-Kai Wang, Chin-Teng Lin, Javier Fernández, Jose Antonio Sanz, and Humberto Bustince, arXiv:2101.06968v2, 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
Player\textbackslash Options & $1$ & $2$ & $3$ \tabularnewline
\hline
\hline
$1$ & $(5,0,5)$ & $(4,4,0)$ & $(3,3,6)$\tabularnewline
\hline
$2$ & $(0,5,5)$ & $(4,4,0)$& $(3,3,6)$ \tabularnewline
\hline
$3$ & $(0,0,5)$ & $(0,0,0)$& $(0,0,0)$ \tabularnewline
\hline
\end{tabular}
\caption{Utility vectors \(u_{i}\) generated by options \(a_i=1,2,3\) for each player \(i=1,2,3\), corresponding to the vertices spanning the individual feasible sets \(F_i(B)\) in .}
\end{table}
\end{document}

q-fin

"Modeling evidential cooperation in large worlds" by Johannes Treutlein, arXiv:2307.04879v2, 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{Parachute deployment, PDI and touchdown times for mission profile 2.}
\begin{tabular}{lccc}
 \hline
 Event & Parachute deployment & PDI & Touchdown \\\hline
 GRASHS & 295.9386 \textrm{s} & 347.7960 \textrm{s} & 387.5228 \textrm{s} \\
 RASHS & 295.9402 \textrm{s} & 347.7949 \textrm{s} & 387.5229 \textrm{s} \\
 MPBVP & 295.8594 \textrm{s} & 347.7101 \textrm{s} & 387.4404 \textrm{s} \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"Indirect Optimization of Multi-Phase Trajectories Involving Arbitrary Discrete Logic" by Harish Saranathan, arXiv:2412.07960v1, 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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effects of the minimum wage introduction on regional employment}
\begin{tabular}{lccc}
 \toprule
 \toprule
 \multirow{2}{2cm}{VARIABLES} & Dependent & Employment & Marginal \\
 & employment & subject to SSC & employment \\
 \midrule
 \multicolumn{4}{l}{\textbf{Panel A: Binary treatment (wage gap)}} \\
 Treatment & -0.00511* & 0.000456 & -0.0239*** \\
 & (0.00260) & (0.00286) & (0.00763) \\
 Placebo & 0.000569 & 0.00142 & -0.00140 \\
 & (0.00111) & (0.00107) & (0.00246) \\
 & & & \\
 R$^{2}$ (within) & 0.597 & 0.584 & 0.472 \\
 & & & \\
 \multicolumn{4}{l}{\textbf{Panel B: Binary treatment (wage gap) interacted with growth}} \\
 Treatment & -0.00396 & 0.000415 & -0.0181** \\
 & (0.00339) & (0.00369) & (0.00887) \\
 Placebo & 0.00104 & 0.00195* & -0.000994 \\
 & (0.00108) & (0.00103) & (0.00243) \\
 Treatment x Low growth 2010-2013 & -0.00698 & -0.00222 & -0.0267** \\
 & (0.00597) & (0.00694) & (0.0121) \\
 & & & \\
 R$^{2}$ (within) & 0.605 & 0.590 & 0.484 \\
 & & & \\
 \midrule
 Observations & 9,509 & 9,509 & 9,509 \\
 \midrule
 Labour Market Region FE & X & X & X \\
 Quarter FE & X & X & X \\
 Controls & X & X & X \\
 \bottomrule
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

Marco Caliendo
Nico Pestel
Rebecca Olthaus

"Long-Term Employment Effects of the Minimum Wage in Germany: New Data and Estimators" by Marco Caliendo, Nico Pestel, and Rebecca Olthaus, arXiv:2310.15964v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|l|r||c|l|r}
 \textbf{\#} & \textbf{Keyword} & \textbf{Count} & \textbf{\#} & \textbf{Keyword} & \textbf{Count} \\
 \hline
 1 & slot & 144,108 & 2 & online & 77,669 \\
 \hline
 3 & judi (gambling) & 60,521 & 4 & situs (website) & 35,265 \\
 \hline
 5 & joker123 & 23,630 & 6 & terpercaya (trusted) & 19,407 \\
 \hline
 7 & gacor (hot streak) & 18,006 & 8 & agen (agent) & 16,939 \\
 \hline
 9 & daftar (register) & 12,881 & 10 & game & 12,113 \\
 \hline
 11 & bola (football) & 11,688 & 12 & pulsa (credit) & 10,467
 \end{tabular}
\caption{Top 12 meta tag keywords on content hosted on hijacked domains.}
\end{table}
\end{document}

cs

Jens Frieß
Tobias Gattermayer
Nethanel Gelernter
Haya Schulmann
Michael Waidner

"Cloudy with a Chance of Cyberattacks: Dangling Resources Abuse on Cloud Platforms" by Jens Frieß, Tobias Gattermayer, Nethanel Gelernter, Haya Schulmann, and Michael Waidner, arXiv:2403.19368v1, 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}{l|c|c|c|c}
 \toprule
 Dataset & Videos & Mean Frames per Video & Objects & Annotations \\
 \midrule
 DAVIS~ & 90 & $\sim$69 & 205 & 13,543 \\
 LTV~ & 3 & \textbf{$\sim$2,470} & 3 & 60 \\
 LVOS~ & \textbf{220} & $\sim$574 & \textbf{282} & \textbf{156,432} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Abdelrahman Shaker
Syed Talal Wasim
Martin Danelljan
Salman Khan
Ming-Hsuan Yang
Fahad Shahbaz Khan

"Efficient Video Object Segmentation via Modulated Cross-Attention Memory" by Abdelrahman Shaker, Syed Talal Wasim, Martin Danelljan, Salman Khan, Ming-Hsuan Yang, and Fahad Shahbaz Khan, arXiv:2403.17937v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Hanyang Wang
Juergen Branke
Matthias Poloczek

"Bayesian Optimization with Preference Exploration by Monotonic Neural Network Ensemble" by Hanyang Wang, Juergen Branke, and Matthias Poloczek, arXiv:2501.18792v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary statistics of return data across granularities}
\begin{tabular}{lrrrr}
\toprule
\textbf{Statistic} & \textbf{1-min} & \textbf{5-min} & \textbf{15-min} & \textbf{Daily} \\
\midrule
Number of stocks & 1,283 & 1,283 & 1,283 & 1,283 \\
Avg. obs./stock & 252,487 & 50,497 & 16,832 & 1,264 \\
Mean return & 0.00003\% & 0.00015\% & 0.00044\% & 0.0583\% \\
Std. deviation & 0.0893\% & 0.1962\% & 0.3381\% & 1.9732\% \\
Skewness & 0.1274 & 0.0958 & 0.0742 & 0.1358 \\
Kurtosis & 14.3721 & 10.8754 & 8.6321 & 5.9842 \\
First-order autocorr. & 0.0842 & 0.0637 & 0.0418 & -0.0126 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"From Data Acquisition to Lag Modeling: Quantitative Exploration of A-Share Market with Low-Coupling System Design" by Jianyong Fang, Sitong Wu, and Junfan Tong, arXiv:2506.19255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the best proposals per image as segmentations using segmentation measures boundary recall (BR) and undersegmentation error (UE) on the LVIS dataset.}
\begin{tabular}{lcccc}
\hline
Method & Backbone & BR$\uparrow$ & UE$\downarrow$ \\%& Rec$\uparrow$ & UE$\downarrow$ \\
\hline
MCG~ & - & 0.685 & 0.073 \\%& 0.463 & 0.108 \\
\hline
DeepMask~ & ResNet-50 & 0.488 & 0.087 \\%& 0.463 & 0.108 \\
SharpMask~ & ResNet-50 & 0.561 & 0.080 \\%& 0.463 & 0.108 \\
FastMask~ & ResNet-50 & 0.510 & 0.084 \\%& 0.463 & 0.108 \\
AttentionMask~ & ResNet-50 & 0.568 & 0.070 \\%& 0.516 & 0.093 \\
\hline
AttentionMask~ & ResNet-34 & 0.547 & 0.075 \\%& 0.493 & 0.102 \\
Ours& ResNet-34 & 0.681 & 0.068 \\% & 0.561 & 0.107 \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

"Superpixel-based Refinement for Object Proposal Generation" by Christian Wilms and Simone Frintrop, arXiv:2101.04574v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Wei-Ning Chen
Graham Cormode
Akash Bharadwaj
Peter Romov
Ayfer Özgür

"Federated Experiment Design under Distributed Differential Privacy" by Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, and Ayfer Özgür, arXiv:2311.04375v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{dsfont}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{$16$ types of anyon in a $\mathds{Z}_2\times\mathds{Z}_2$ quantum double model.}
\begin{tabular}{c|cccc}
 \toprule
 $[C,R]$ & $I$ & $O$ & $E$ & $D$ \\
 \midrule
 $\{(1,1)\}$ & $\mathbb{1}$ & $m_1$ & $m_2$ & $m_0$ \\
 $\{(-1,1)\}$ & $e_1$ & $f_{11}$ & $f_{12}$ & $f_{10}$ \\
 $\{(1,-1)\}$ & $e_2$ & $f_{21}$ & $f_{22}$ & $f_{20}$ \\
 $\{(-1,-1)\}$ & $e_0$ & $f_{01}$ & $f_{02}$ & $f_{00}$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

"Gapped Boundaries of Kitaev's Quantum Double Models: A Lattice Realization of Anyon Condensation from Lagrangian Algebras" by Mu Li, Xiao-Han Yang, and Xiao-Yu Dong, arXiv:2504.19512v2, 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{The experimental results for various methods under different metrics are presented for the FOP defined as $\min \{ x^{\top}x - (\theta + u)^{\top} x \mid x \in [0, 1]^{p} \} = \min \left\{\sum_{k=1}^{p} x_k^2 - \sum_{k=1}^{p} (\theta + u)_k x_k \mid x_k \in [0, 1], \ \forall \ k \in [p] \right\}$ under \textbf{Noiseless} setting. For the FY loss, we set $\lambda=0.1$ and $\Omega(x) = 1/2 \|x\|_2^2$.}
\begin{tabular}{c|cccc|cccc|cccc}
\hline
\multirow{2}{*}{Sample size} & \multicolumn{4}{c|}{ParameterError} & \multicolumn{4}{c|}{DecisionError} & \multicolumn{4}{c}{Regret} \\ \cline{2-13}
 & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA \\ \hline
50 & \textbf{0.07} & 1.26 & 0.00 & 13.29 & \textbf{0.00} & 0.03 & 0.00 & 1.33 & \textbf{0.00} & 0.03 & 0.00 & 1.33 \\
100 & \textbf{0.06} & 1.31 & 0.00 & 13.53 & \textbf{0.00} & 0.03 & 0.00 & 1.35 & \textbf{0.00} & 0.04 & 0.00 & 1.35 \\
300 & \textbf{0.06} & 1.32 & 0.00 & 14.66 & \textbf{0.00} & 0.03 & 0.00 & 1.40 & \textbf{0.00} & 0.04 & 0.00 & 1.40 \\
500 & \textbf{0.06} & 1.32 & 0.00 & 14.82 & \textbf{0.00} & 0.03 & 0.00 & 1.41 & \textbf{0.00} & 0.04 & 0.00 & 1.41 \\
1000 & \textbf{0.06} & 1.33 & 0.00 & 14.88 & \textbf{0.00} & 0.03 & 0.00 & 1.41 & \textbf{0.00} & 0.04 & 0.00 & 1.41 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization" by Zhehao Li, Yanchen Wu, and Xiaojie Mao, arXiv:2502.16120v2, 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.


math

"SIAC Accuracy Enhancement of Stochastic Galerkin Solutions for Wave Equations with Uncertain Coefficients" by Andrés Galindo-Olarte and Jennifer K. Ryan, arXiv:2503.13930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
\toprule
\midrule
\multicolumn{2}{l}{\textbf{Effect size}} \\
\midrule
\textbf{Large} & $(\beta_A,\beta_{CA}) = (-0.5,0.3) $ \\
\textbf{Small} & $(\beta_A,\beta_{CA}) = (0.2,0.1) $ \\
\midrule
\multicolumn{2}{l}{\textbf{Level of confounding}} \\
\midrule
\textbf{High} & $(\rho_L,\rho_W) = (0.9 ,0.7) $ \\
\textbf{Medium} & $(\rho_L,\rho_W) = (-0.5,0.4) $\\
\textbf{Low} & $(\rho_L,\rho_W) = (-0.1,-0.2) $\\
\bottomrule
\end{tabular}
\caption{Parameter values of treatment effect size and confounding scenarios.}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
 \bf Algorithm & \bf Bandwidth ($W$) & \bf Latency ($L$)\\\hline
 1D-row SGD & $K \cdot b$ & $K \cdot \log{p}$\\\hline
 1D-column SGD & $K \cdot n$ & $K \cdot \log{p}$\\\hline
 2D SGD & $K \cdot (b/p_r + n/p_c)$ & $K \cdot (\log{p_r} + \log{p_c})$\\\hline\hline
 \bf $s$-step SGD & $(K/s)\cdot \binom{s}{2}b^2$ & $(K/s) \cdot \log{p}$\\\hline
 \bf FedAvg & $\tilde{K} \cdot n$ & $\tilde{K} \cdot \log{p}$ \\\hline
 \bf HybridSGD & $(\hat{K}/s) \cdot \binom{s}{2}b^2/p_r^2 + \hat{K} \cdot n/p_c$ & $\hat{K} \cdot \log{p_r} + (\hat{K}/s) \cdot \log{p_c}$\\\hline
 \end{tabular}
\caption{Theoretical parallel communication costs of parallel SGD variants, FedAvg, $s$-step SGD, and HybridSGD using Hockney's two-term communication model (bandwidth and latency). HybridSGD uses a 2D processor grid such that $p = p_r \times p_c$. For the cost analysis, we assume that quantities being communicated are stored as dense vectors. All costs are to leading-order. Algorithms shown in \textbf{bold} represent communication-efficient variants of SGD.}
\end{table}
\end{document}

stat

"Communication-Efficient, 2D Parallel Stochastic Gradient Descent for Distributed-Memory Optimization" by Aditya Devarakonda and Ramakrishnan Kannan, arXiv:2501.07526v1, 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{Percentage of different prescriptions selected by the models for unstructured datasets.}
\begin{tabular}{ccccccc}
& \multicolumn{2}{c}{\textbf{TAVR models}} & \multicolumn{2}{c}{\textbf{Liver trauma models}} \\
\textbf{Method} & \textbf{Tabular} & \textbf{Multimodal} & \textbf{Tabular} & \textbf{Multimodal} \\ \midrule
PNN & $100.0$ & $100.0$ & $100.0$ & $100.0$ \\
Mirrored OCT & $100.0$ & $98.0$ & $86.0$ & $76.0$\\
\end{tabular}
\end{table}
\end{document}

stat

Multimodal Prescriptive Deep Learning

Dimitris Bertsimas
Lisa Everest
Vasiliki Stoumpou

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average results of the base models for two, three, and six lead months SSTA and MHW forecasts.}
\begin{tabular}{llllll}
\textbf{Across All Locations} & \textbf{MSE↓} & \textbf{CSI↑} & \textbf{CSI 80↑} & \textbf{Training Time↓} & \textbf{PUR↓} \\ \hline
Average (two lead months) & \textbf{0.5246} & 0.0 & 0.2197 & 22.5095 & 0\% \\
Average (three lead months) & \textbf{0.6370} & 0.0 & 0.0200 & 22.5024 & 38.3\% \\
Average (six lead months) & \textbf{0.7163} & 0.0 & 0.0 & 22.8932 & 95\%
\end{tabular}
\end{table}
\end{document}

stat


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llll}
\hline
 & Size & Input len. & Resp. len.\\ \hline
All & 1,335 & 152.9 & 145.5 \\ \hline
Stackexchange & 205 & 19.4 & 296.2 \\
CFA & 329 & 125.6 & 157.4 \\
Academic Journals & 200 & 169.3 & 74.8 \\
Textbooks & 200 & 128.9 & 136.6 \\
SEC Filings & 80 & 316.2 & 88.2 \\
Financial NLP tasks & 200 & 325.9 & 74.5 \\
Investments & 119 & 72.7 & 144.3 \\ \hline
\end{tabular}
\caption{Detailed breakdown of our financial domain instruction dataset. Input len. and Resp. len. denotes the average number of tokens of instruction/input and output response respectively.}
\end{table}
\end{document}

q-fin

"InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning" by Yi Yang, Yixuan Tang, and Kar Yan Tam, arXiv:2309.13064v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

A. Felipe
M. Jaenada
P. Miranda
L. Pardo

"Robust parameter estimation of the log-logistic distribution based on density power divergence estimators" by A. Felipe, M. Jaenada, P. Miranda, and L. Pardo, arXiv:2312.02662v1, 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{One-Channel Input}
\begin{tabular}{|c|c|}
			\hline
			\textbf{Input}& \textbf{ME (m)}\\
			\hline
			$|\boldsymbol{H}|$ & 0.03805 \\
			\hline
			$\angle \boldsymbol{H}$ & 0.04251 \\
			\hline
			$\angle \boldsymbol{H}_D$ & 0.04088 \\
			\hline
			$\angle \boldsymbol{H}_A$ & 0.03246 \\
			\hline
			$\angle \boldsymbol{H}_A^\prime$ & 0.08142 \\
			\hline

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

eess

Anastasios Foliadis
Mario H. Castañeda Garcia
Richard A. Stirling-Gallacher
Reiner S. Thomä

"CSI-Based Localization with CNNs Exploiting Phase Information" by Anastasios Foliadis, Mario H. Castañeda Garcia, Richard A. Stirling-Gallacher, and Reiner S. Thomä, arXiv:2101.08983v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Julien St-Pierre
Sahir Rai Bhatnagar
Massimiliano Orri
Michel Boivin
Josée Dupuis
Karim Oualkacha

"Penalized generalized linear mixed models for longitudinal outcomes in genetic association studies" by Julien St-Pierre, Sahir Rai Bhatnagar, Massimiliano Orri, Michel Boivin, Josée Dupuis, and Karim Oualkacha, arXiv:2501.11083v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
\toprule
 & Model & Expert M$_1$ & Expert M$_2$ \\
\midrule
mAP & 39.5 & 17.2 & 20.0 \\
\bottomrule
\end{tabular}
\caption{Agents mAP Pascal VOC validation set. Since the training and validation sets are pre-determined in this dataset, the agents' knowledge remains fixed throughout the evaluation.}
\end{table}
\end{document}

stat

Yannis Montreuil
Axel Carlier
Lai Xing Ng
Wei Tsang Ooi

"Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees" by Yannis Montreuil, Axel Carlier, Lai Xing Ng, and Wei Tsang Ooi, arXiv:2502.01027v1, 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{SA-WER (\%) with various overlap ratio of segments.}
\begin{tabular}{l|c|cc|cc}
\toprule
Decoding method & Segment & \multicolumn{2}{c|}{Dev} &\multicolumn{2}{c}{Test} \\
 & overlap & short & long & short & long \\
\midrule
Stitcher (serialized, WCO/E) & 0\% & 11.6 & 12.4 & 12.3 & 15.1 \\
Stitcher (serialized, WCO/E) & 25\% & 11.0 & 11.9 & 11.5 & 14.3 \\
Stitcher (serialized, WCO/E) & 50\% & 10.5 & 11.5 & 10.6 & 13.4 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Xuankai Chang
Naoyuki Kanda
Yashesh Gaur
Xiaofei Wang
Zhong Meng
Takuya Yoshioka

"Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker Recordings" by Xuankai Chang, Naoyuki Kanda, Yashesh Gaur, Xiaofei Wang, Zhong Meng, and Takuya Yoshioka, arXiv:2101.01853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Javad Mirzapour Kaleybar
Hooman Saadat
Hooman Khaloo

"Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer" by Javad Mirzapour Kaleybar, Hooman Saadat, and Hooman Khaloo, arXiv:2311.01731v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
Scheme&$\|h(\cdot,5)-h_{\rm eq}\|_\infty$&$\|u(\cdot,5)-u_{\rm eq}\|_\infty$&$\|v(\cdot,5)-v_{\rm eq}\|_\infty$&
$\|a(\cdot,5)-a_{\rm eq}\|_\infty$\\ \hline
WB &1.33e-15&3.22e-15&7.55e-15&4.44e-15\\
NWB&2.18e-03&1.86e-03&1.40e-03&3.97e-03\\
\hline
\end{tabular}
\caption{\sf Example 1 (capturing the steady state): Errors for the WB and NWB schemes.}
\end{table}
\end{document}

math

Alina Chertock
Alexander Kurganov
Michael Redle
Vladimir Zeitlin

"Divergence-Free Flux Globalization Based Well-Balanced Path-Conservative Central-Upwind Schemes for Rotating Shallow Water Magnetohydrodynamics" by Alina Chertock, Alexander Kurganov, Michael Redle, and Vladimir Zeitlin, arXiv:2312.02881v1, 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{Different Types of Tasks }
\begin{tabular}{|l|l|l|l|}
		\hline
	&	\bfseries Data size& \bfseries Required CPU cycles & \bfseries Types\\
		\hline

		Type 1	&50 MB &5 GHz& large data size \\
	 	& & & computation-intensive\\
		\hline
	 Type 2	&50 MB &0.5 GHz& large data size\\
	 	& & & non-computation-intensive\\
	 		\hline
	 	 Type 3	&5 MB &5 GHz& small data size\\
	 	 	& & & non-computation-intensive\\
	 		 		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Yueyue Dai
Ke Zhang
Sabita Maharjan
Yan Zhang

"Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond" by Yueyue Dai, Ke Zhang, Sabita Maharjan, and Yan Zhang, arXiv:2011.08442v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Huawei Lin
Jun Woo Chung
Yingjie Lao
Weijie Zhao

"Online Gradient Boosting Decision Tree: In-Place Updates for Efficient Adding/Deleting Data" by Huawei Lin, Jun Woo Chung, Yingjie Lao, and Weijie Zhao, arXiv:2502.01634v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
 \hline
 $p_x$ & 0.05 & 0.06 & 0.07 & 0.08 \\\hline
 sim. runs & 1000000 & 100000 & 100000 & 10000 \\\hline
 $r_{\text{avg}}$ & 2.91 & 9.82 & 60.46 & 231.7 \\
 \hline
 \end{tabular}
\caption{Average number of decimation rounds of BPGD with $T=100$ on the B1 code over bit-flip noise.}
\end{table}
\end{document}

math

Hanwen Yao
Waleed Abu Laban
Christian Häger
Alexandre Graell i Amat
Henry D. Pfister

"Belief Propagation Decoding of Quantum LDPC Codes with Guided Decimation" by Hanwen Yao, Waleed Abu Laban, Christian Häger, Alexandre Graell i Amat, and Henry D. Pfister, arXiv:2312.10950v2, 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}{lrr}
\hline\hline
Consecutive years&Number of firms&Fraction, \%\tabularnewline
\hline
1&$6061$&$31.231$\tabularnewline
2&$3799$&$19.575$\tabularnewline
3&$2513$&$12.949$\tabularnewline
4&$1764$&$ 9.090$\tabularnewline
5&$1273$&$ 6.559$\tabularnewline
6&$ 949$&$ 4.890$\tabularnewline
7&$ 727$&$ 3.746$\tabularnewline
8&$ 528$&$ 2.721$\tabularnewline
9&$ 403$&$ 2.077$\tabularnewline
10&$ 313$&$ 1.613$\tabularnewline
11&$ 256$&$ 1.319$\tabularnewline
12&$ 189$&$ 0.974$\tabularnewline
13&$ 151$&$ 0.778$\tabularnewline
14&$ 121$&$ 0.623$\tabularnewline
15&$ 96$&$ 0.495$\tabularnewline
16&$ 72$&$ 0.371$\tabularnewline
17&$ 61$&$ 0.314$\tabularnewline
18&$ 55$&$ 0.281$\tabularnewline
19&$ 51$&$ 0.260$\tabularnewline
20&$ 41$&$ 0.209$\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.


stat

Tuan L. Vo
Quan Huu Do
Uyen Dang
Thu Nguyen
Pål Halvorsen
Michael A. Riegler
Binh T. Nguyen

"DPERC: Direct Parameter Estimation for Mixed Data" by Tuan L. Vo, Quan Huu Do, Uyen Dang, Thu Nguyen, Pål Halvorsen, Michael A. Riegler, and Binh T. Nguyen, arXiv:2501.10540v1, 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{Evaluation performance over BERT on SST-2 with $k=20$.}
\begin{tabular}{lccl}
\hline
\multirow{2}{*}{Methods} & \multicolumn{3}{c}{SST-2} \\
 & LOR & CM & SF \\
\hline
random & -2.6281$\pm$0.0720 & 0.3884$\pm$0.0082 & 0.0021$\pm$0.0010 \\
condition & -2.2590$\pm$0.0608 & 0.3515$\pm$0.0082 & 0.0272$\pm$0.0020 \\
random\_uw & -3.9181$\pm$0.0661 & 0.5533$\pm$0.0067 & -0.0090$\pm$0.0010 \\
condition\_uw & -3.1907$\pm$0.0600 & 0.4635$\pm$0.0065 & 0.0014$\pm$0.0031 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Suboptimal Shapley Value Explanations

"Suboptimal Shapley Value Explanations" by Xiaolei Lu, arXiv:2502.12209v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The effect of modifying the number of gradient steps using the optimizer configuration reported in the main section for Adam on the toy nonlinear system. Ordered by RMSE.}
\begin{tabular}{lc}
\toprule
\textbf{Method} & \textbf{RMSE} \\
\midrule
Adam ($K = 1, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $10.510 \pm 0.263$ \\
Adam ($K = 25, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $10.478 \pm 0.409$ \\
Adam ($K = 3, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $9.749 \pm 0.288$ \\
Adam ($K = 5, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $9.244 \pm 0.266$ \\
Adam ($K = 10, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $9.218 \pm 0.291$ \\
Adam ($K = 100, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $6.575 \pm 0.347$ \\
Adam ($K = 50, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $5.842 \pm 0.231$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

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

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


q-fin

"Systemic Risk and Default Cascades in Global Equity Markets: Extending the Gai-Kapadia Framework with Stochastic Simulations and Network Analysis" by Ana I. C. Pereda, arXiv:2504.01969v2, 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|cc}
 \hline\hline
 Model & AIC & BIC\\
 \hline
 Cumulative logit model & 7212.557 & 7221.376\\
 Proportional odds model with Cauchit link & 7973.305 & 7994.327 \\
 Proportional odds model with logit link & 6747.137 & 6768.160\\
 Adjacent categories logit model & 7720.505 & 7727.119\\
 Continuation-ratio logit model & 7656.651 & 7663.265\\
 \hline\hline

 \end{tabular}
\caption{Comparison of trinomial models}
\end{table}
\end{document}

stat

Elvis Han Cui
Michael Collins
Jessica Munson
Weng Kee Wong

"Failure of Optimal Design Theory? A Case Study in Toxicology Using Sequential Robust Optimal Design Framework" by Elvis Han Cui, Michael Collins, Jessica Munson, and Weng Kee Wong, arXiv:2503.00002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{siunitx}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{FWI optimization specifications}
\begin{tabular}{cc}
ROI $x$ & $[\SI{70}{\milli \meter}, \SI{120}{\milli \meter}]$\\
\hline
ROI $y$ & $[\SI{0}{\milli \meter}, \SI{25}{\milli \meter}]$\\
\hline
bounds $\rho$ & $[\SI{258.28}{\kilogram \per \meter^3}, \SI{2582.8}{\kilogram \per \meter^3}]$
\end{tabular}
\end{table}
\end{document}

math

Tim Bürchner
Simon Schmid
Lukas Bergbreiter
Ernst Rank
Stefan Kollmannsberger
Christian U. Grosse

"Quantitative Comparison of the Total Focusing Method, Reverse Time Migration, and Full Waveform Inversion for Ultrasonic Imaging" by Tim Bürchner, Simon Schmid, Lukas Bergbreiter, Ernst Rank, Stefan Kollmannsberger, and Christian U. Grosse, arXiv:2412.07347v1, 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{Local platform target assignments and sensor measurement error covariances. }
\begin{tabular}{c|c|c|c}
 Agent & Tracked Targets & $R^{i,1} [m^2]$ & $R^{i,2} [m^2]$ \\ \hline
 1 & $T_1,T_2$ & diag([1,10]) & diag([3,3]) \\ \hline
 2 & $T_2,T_3$ & diag([3,3]) & diag([3,3]) \\ \hline
 3 & $T_3,T_4,T_5$ & diag([4,4]) & diag([2,2]) \\ \hline
 4 & $T_4,T_5$ & diag([10,1]) & diag([4,4]) \\ \hline
 5 & $T_5,T_6$ & diag([2,2]) & diag([5,5]) \\ \hline
 \end{tabular}
\end{table}
\end{document}

cs

"Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion" by Ofer Dagan and Nisar R. Ahmed, arXiv:2101.11116v4, 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{Probabilities of Positive Returns}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Ticker} & \textbf{Long on IBS \textless 0.2} & \textbf{Short on IBS \textgreater 0.8 }\\ \hline
EWJ & 0.606061 & 0.514364 \\ \hline
EIS & 0.587719 & 0.513981 \\ \hline
PIN & 0.580692 & 0.578240 \\ \hline
EWT & 0.572383 & 0.502770 \\ \hline
FXI & 0.567686 & 0.523052 \\ \hline
IVV & 0.565502 & 0.460481 \\ \hline
EZU & 0.552209 & 0.476647 \\ \hline
EWS & 0.550308 & 0.514563 \\ \hline
EWI & 0.548000 & 0.511222 \\ \hline
EWZ & 0.543672 & 0.508006 \\ \hline
EZA & 0.540835 & 0.509859 \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Using Internal Bar Strength as a Key Indicator for Trading Country ETFs" by Aditya Pandey and Kunal Joshi, arXiv:2306.12434v1, 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{Production capacities of small (hydrogen solar-powered electrolysis) and large (steam methane reforming) refuelling stations}
\begin{tabular}{c|ccc}
 Station type & Capacity ($kg/day$) & Capacity ($kg/year$) & Refs. \\
 \hline
 Small & $200$ & $73000$ & \\
 Large & $1000$ & $365000$ & \\
 \end{tabular}
\end{table}
\end{document}

eess

"Forecasting the Evolution of Hydrogen Vehicle Fleet in the UK usingGrowth and Lotka-Volterra Models" by Florimond Gueniat and Sahdia Maryam, arXiv:2102.04771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average time (in seconds) for each round in each method.}
\begin{tabular}{l|ccc}\toprule
& HalfCheetah-102D & RoverPlanning-100D & DNA-180D \\
\midrule
TuRBO & 17.36 ± 0.15 & 7.05 ± 0.62 & 35.95 ± 0.11 \\
LA-MCTS & & & \\
MCMC-BO & & & \\
CMA-BO & & & \\
\midrule
CbAS & & & \\
MINs & & & \\
DDOM & & & \\
Diff-BBO & & & \\
\midrule
CMA-ES & & & \\
\midrule
\textbf{DiBO} & 37.86 ± 0.24 & 44.79 ± 0.55 & 85.74 ± 0.18 \\
\end{tabular}
\end{table}
\end{document}

stat

Taeyoung Yun
Kiyoung Om
Jaewoo Lee
Sujin Yun
Jinkyoo Park

"Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization" by Taeyoung Yun, Kiyoung Om, Jaewoo Lee, Sujin Yun, and Jinkyoo Park, arXiv:2502.16824v1, 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}{cccccccc}\toprule
 & & & \multicolumn{2}{c}{DG error} & \phantom{abc}& \multicolumn{2}{c}{After post-processing} \\
\cmidrule{4-5} \cmidrule{7-8}
Degree & $N$ & & $L^2$ error & Order && $L^2$ error & Order \\
\midrule
$p = 1$ & 20 && 4.60E-03 & -- && 1.97E-03 & -- \\
 & 40 && 1.09E-03 & 2.08 && 2.44E-04 & 3.02\\
 & 80 && 2.67E-04 & 2.02 && 3.02E-05 & 3.01\\
 & 160 && 6.65E-05 & 2.01 && 3.76E-06 & 3.01\\
\midrule
$p = 2$ & 20 && 1.07E-04 & -- && 4.11E-06 & -- \\
 & 40 && 1.34E-05 & 3.00 && 9.49E-08 & 5.44\\
 & 80 && 1.67E-06 & 3.00 && 2.49E-09 & 5.25\\
 & 160 && 2.09E-07 & 3.00 && 7.75E-11 & 5.00\\
\bottomrule
\end{tabular}
\caption{$L^2-$ errors of the DG approximation $u_h$ and the post-processed solution $u^\star_h$ for the linear hyperbolic equation~, see Section~.}
\end{table}
\end{document}

math

"Efficient Time Discretization for Exploring Spatial Superconvergence of Discontinuous Galerkin Methods" by Xiaozhou Li, arXiv:2312.09592v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cc|cc} \hline
 \textbf{Methods} & \textbf{RRE} & \textbf{RTE} & \textbf{FMR} & \textbf{RR} \\ \hline \hline
 GeoTr [31] & 1.94 & 4.96 & 98.37 & 98.37 \\
 GeoTr + Ours & \textbf{1.57} & \textbf{3.51} & \textbf{99.47} & \textbf{98.72} \\ \hline
 GCNet & 2.24 & 5.43 & 98.88 & 98.51 \\
 GCNet + Ours & 1.96 & 4.91 & 99.09 & 98.72 \\ \hline
 \end{tabular}
\caption{\textbf{Additional evaluation on point cloud registration.}}
\end{table}
\end{document}

cs

"SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks" by Yaxu Xie, Alain Pagani, and Didier Stricker, arXiv:2403.19474v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Herbert Egger
Felix Engertsberger
Bogdan Radu

"Global convergence of iterative solvers for problems of nonlinear magnetostatics" by Herbert Egger, Felix Engertsberger, and Bogdan Radu, arXiv:2403.18520v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"One Parameter Families of Conformal Mappings of Bounded Doubly Connected Polygonal Domains" by A. Dyutin and S. Nasyrov, arXiv:2312.01112v1, 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{~Hausman Test}
\begin{tabular}{ccccr}
 \toprule
 \midrule
 & \multicolumn{2}{c}{ ---- Coefficients ----} & & \\
 & (b) & (B) & (b-B) & $\sqrt{(diag (V_{b} - V_{B})}$ \\
 \hline
& FE & RE & Difference & S.E. \\

 \midrule
 RA & 0.098 & 0.090 & 0.008 & 0.009 \\
 RBD & -0.022 & -0.016 & -0.005 & 0.004 \\
 OC & 0.153 & 0.134 & 0.019 & 0.021 \\
 LOGTA & 0.834 & 0.413 & 0.422 & 0.495 \\
 LQR & -0.010 & 0.000 & -0.010 & 0.004 \\
 MNGMT & -0.003 & -0.004 & 0.000 & 0.000 \\
 IIP & 0.541 & 0.508 & 0.034 & 0.007 \\
 DPZTG & -0.001 & -0.001 & 0.000 & . \\
 DVRSTY & 0.304 & 0.228 & 0.076 & 0.027 \\
 HHI & 0.181 & 0.053 & 0.128 & 0.178 \\
 GDP & -0.026 & -0.029 & 0.003 & . \\
 INF & 0.045 & 0.039 & 0.005 & 0.002 \\
 \midrule
 \midrule
 \multicolumn{5}{l}{b = consistent under Ho and Ha; obtained from xtreg} \\
 \multicolumn{5}{l}{B = inconsistent under Ha, efficient under Ho; obtained from xtreg} \\
 \\
 \multicolumn{5}{l}{ Test: Ho: difference in coefficients not systematic} \\
 \\
 \multicolumn{5}{l}{$ \chi^{2}(11)=(b-B) [(V_{b} - V_{B})^{(-1)}](b-B) $ } \\
 \multicolumn{5}{l}{$=$ 333.71 } \\
 \multicolumn{5}{l}{ Prob 	$> \chi^{2} =$ 0.0000} \\
 \multicolumn{5}{l}{$(V_{b}- V_{B}$ is not positive definite)} \\

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

q-fin

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


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.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccccccccccccccccccccc}
$n$ & 0& 1& 2& 3& 4& 5& 6& 7& 8& 9&10&11&12&13&14&15&16&17&18&19 \\
\hline
$a'(n)$ & 0& 1& 2& 4& 7& 6&12&11& 9&20&19&17&14&15&33&32&30&27&28&22
\end{tabular}
\end{table}
\end{document}

math

Some Fibonacci-Related Sequences

"Some Fibonacci-Related Sequences" by Benoit Cloitre and Jeffrey Shallit, arXiv:2312.11706v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

On the Last Kervaire Invariant Problem

"On the Last Kervaire Invariant Problem" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10879v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Amine Mohamed Aboussalah
Abdessalam Ed-dib

"Are GNNs doomed by the topology of their input graph?" by Amine Mohamed Aboussalah and Abdessalam Ed-dib, arXiv:2502.17739v1, 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{Training hyper-parameters for the learned Diff-GePT models targeting GMM-$d$ for $d=2, 10, 50$.}
\begin{tabular}{lrr}
\toprule
Model & Number of Chains & $M$ \\
\midrule
GMM-2 & 10 & 3000 \\
GMM-10 & 30 & 6000 \\
GMM-50 & 60 & 10000 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Leo Zhang
Peter Potaptchik
Arnaud Doucet
Hai-Dang Dau
Saifuddin Syed

"Generalised Parallel Tempering: Flexible Replica Exchange via Flows and Diffusions" by Leo Zhang, Peter Potaptchik, Arnaud Doucet, Hai-Dang Dau, and Saifuddin Syed, arXiv:2502.10328v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
 \toprule
 Method & Algorithm & Flops per iteration & Iteration number & Memory\\
 \midrule
 CLIME & Interior-point method & $O(p^3)$ & $O(\sqrt{p}\log(1/\epsilon))$ & $\Omega(p^2)$ \\
 QUIC & Proximal Newton& $O(p^3)$ & $O(\log\log(1/\epsilon))^\dagger$ & $\Omega(p^2)$\\
 ACCORD & Proximal gradient& $O(np^2)^*$ & $O(\log(1/\epsilon))$ & $\Omega(p^2)^*$\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Sungdong Lee
Joshua Bang
Youngrae Kim
Hyungwon Choi
Sang-Yun Oh
Joong-Ho Won

"Learning Massive-scale Partial Correlation Networks in Clinical Multi-omics Studies with HP-ACCORD" by Sungdong Lee, Joshua Bang, Youngrae Kim, Hyungwon Choi, Sang-Yun Oh, and Joong-Ho Won, arXiv:2412.11554v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
Entity Name & Total Count \\
\hline
Advertiser & 1011 \\
Agency & 672 \\
Agency Commission & 373 \\
Gross Total & 818 \\
Line Item - Days & 13190 \\
Line Item - Description & 16804 \\
Line Item - End Date & 9229 \\
Line Item - Rate & 20057 \\
Line Item - Start Date & 19437 \\
Net Amount Due & 610 \\
Payment Terms & 439 \\
\hline
\end{tabular}
\caption{FCC Invoices Label List and Counts }
\end{table}
\end{document}

cs

Benjamin Townsend
Madison May
Christopher Wells

"RealKIE: Five Novel Datasets for Enterprise Key Information Extraction" by Benjamin Townsend, Madison May, and Christopher Wells, arXiv:2403.20101v1, 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}{lrl}
 \toprule
 Parameter & Library & Setting \\
 \midrule
 Compute Precision & Composer & bf16 \\
 Parameter Storage & FSDP & fp32 \\
 Optimizer Storage & FSDP & fp32 \\
 Gradient Communication & FSDP & fp32\\
 \bottomrule
 \end{tabular}
\caption{Mixed Precision Settings}
\end{table}
\end{document}

cs

Elliot Bolton
Abhinav Venigalla
Michihiro Yasunaga
David Hall
Betty Xiong
Tony Lee
Roxana Daneshjou
Jonathan Frankle
Percy Liang
Michael Carbin
Christopher D. Manning

"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text" by Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall, Betty Xiong, Tony Lee, Roxana Daneshjou, Jonathan Frankle, Percy Liang, Michael Carbin, and Christopher D. Manning, arXiv:2403.18421v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
Method & Upper bound & (Std.) & Lower bound & (Std.)\tabularnewline
\hline
\hline
Full & $0.0288$ & ($0.0009$) & $0.0131$ & ($0.0003$)\tabularnewline
\hline
Reduced & $0.0301$ & ($0.0008$) & $0.0125$ & ($0.0004$)\tabularnewline
\hline
MOT & $0.0308$ & & $0.0114$ & \tabularnewline
\hline
\hline
Sample mean & \multicolumn{4}{c|}{$0.0207$}\tabularnewline
\hline
\end{tabular}
\caption{Comparison of numeric values between VMOT and OT.}
\end{table}
\end{document}

q-fin

Joshua Zoen-Git Hiew
Tongseok Lim
Brendan Pass
Marcelo Cruz de Souza

"Geometry of vectorial martingale optimal transport and robust option pricing" by Joshua Zoen-Git Hiew, Tongseok Lim, Brendan Pass, and Marcelo Cruz de Souza, arXiv:2309.04947v2, 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{A summary of the used variables}
\begin{tabular}{clcl}
				\toprule
				& {\bf States and input} & &{\bf Parameters}\\
				\midrule
				$I_{i}$						& Generated current &$ L_{i}$ & Filter inductance\\
				$V_i$						& Load voltage &$C_{i}$ &Shunt capacitor\\
				$u_i$						& Control input &$G_{i}$ & Load impedance\\ %and its upper, lower bounds.\\
				& &$G_{li},~G_{hi}$ & Bounds for load impedance\\
				$V_{s,i}$& Voltage source &$R_{tk}$ & Line resistance\\
				\bottomrule
			\end{tabular}
\end{table}
\end{document}

eess

"Safety during Transient Response in Direct Current Microgrids using Control Barrier Functions" by K. C. Kosaraju, S. Sivaranjani, and V. Gupta, arXiv:2102.02448v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Lead Times in Flux: Analyzing Airbnb Booking Dynamics During Global Upheavals (2018-2022)" by Harrison Katz, Erica Savage, and Peter Coles, arXiv:2501.10535v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{List of clinical and demographic features of the 297 NSCLC patients included in our study.}
\begin{tabular}{ll}
 \toprule
 Features & Number of patients\\ \hline

 \textit{Demographic} & \\
 \hspace{1mm} Patient’s age (years) \\
 \hspace{2mm} Median (range) & 68.3 (42.5 - 91.7) \\
 \hspace{2mm} Mean (standard deviation) & 68.0 (10.0) \\
 \hspace{1mm} Patient’s gender & \\
 \hspace{2mm} Male & 211 (71\%) \\
 \hspace{2mm} Female & 86 (29\%) \\
 \hline

 \textit{Clinical} & \\
 \hspace{1mm} Histology & \\
 \hspace{2mm} Adenocarcinoma & 30 (10.1\%) \\
 \hspace{2mm} Not otherwise specified & 50 (16.8\%) \\
 \hspace{2mm} Large cells & 96 (32.3\%) \\
 \hspace{2mm} Squamous cell Carcinoma & 85 (28.6\%) \\
 \hspace{2mm} Unknown & 36 (12.1\%) \\
 \hspace{1mm} TNM clinical stage & \\
 \hspace{2mm} I & 76 (25.6\%) \\
 \hspace{2mm} II & 25 (8.4\%) \\
 \hspace{2mm} III (a \& b) & 195 (65.7\%) \\
 \hspace{2mm} Unknown & 1 (0.3\%) \\
 \hline

 \textit{Outcomes} & \\
 \hspace{1mm} Follow-up (months) & \\
 \hspace{2mm} Median (range) & 16.1 (0.8 - 71) \\
 \hspace{2mm} Mean (standard deviation) & 19.5 (14.9) \\
 \hspace{1mm} Last news survival & \\
 \hspace{2mm} Alive & 99 (33.3\%) \\
 \hspace{2mm} Dead & 198 (66.7\%) \\
 \hspace{1mm} Two-years survival & \\
 \hspace{2mm} Alive & 128 (43.1\%) \\
 \hspace{2mm} Dead & 169 (56.9\%) \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Prognostic Power of Texture Based Morphological Operations in a Radiomics Study for Lung Cancer" by Paul Desbordes, Diksha, and Benoit Macq, arXiv:2012.12652v1, 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}{lcccc} \hline\hline\\
Variables & Mean & Std.Dev. & Min & Max \\ \hline\\
Posted Price (in \pounds)[1] & 219 & 149 & 36 & 998 \\
No. of websites[2] & 9 & 3 & 1 & 19 \\
Range (in \pounds)[3] & 55 & 75 & 0 & 986 \\
Coefficient of Variation & 0.09 & 0.18 & 0 & 0.82 \\\hline
\multicolumn{5}{l}{\footnotesize [1] Posted Price refers to the price posted by different website} \\
\multicolumn{5}{l}{\footnotesize [2] No. of websites refers to the number of websites posting price for} \\
\multicolumn{5}{l}{\footnotesize the same hotel room in skyscanner.com} \\
\multicolumn{5}{l}{\footnotesize [3] Range refers to the
gap between the highest and lowest prices }\\
\multicolumn{5}{l}{\footnotesize posted for a hotel room across websites}\\
\hline\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Debashrita Mohapatra
Debi Prasad Mohapatra
Ram Sewak Dubey

"Price dispersion across online platforms: Evidence from hotel room prices in London (UK)" by Debashrita Mohapatra, Debi Prasad Mohapatra, and Ram Sewak Dubey, arXiv:2310.12341v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary Statistics of Customer-Level Categorical Demographic Variables}
\begin{tabular}{llr}
\toprule
\textit{Gender} & Count & Percentage (\%) \\
\midrule
Female & 2,351,011 & 39.62 \\
Male & 1,521,157 & 25.63 \\
No Information & 2,062,080 & 34.75 \\\hline
Total & 5,934,248 & 100.00 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"Channel Choice and Customer Value" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2508.00208v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Five-view classification accuracy with different aggregation module. The correct classification of a subject indicates all five views are correctly classified.}
\begin{tabular}{l|c|c|c|c}
\hline\hline
Aggregation & Frame-independent & Non-local & RNN & Temp\\\hline
Accuracy & 0.992 & 0.995 & 0.991 & 0.994\\\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
\caption{Average perceived noise (on a scale of 0-10) in speech samples with and without acoustic nonce collected in the three environments.}
\begin{tabular}{lcccc}
 \toprule[1.5pt]
 Location & \parbox[t]{1.7cm}{w/o Nonce} & \parbox[t]{1.7cm}{w/ Nonce} & t-test, $\alpha=0.05$ & Statistically Significant \\
 \midrule
 Conference Room & 3.8 & 4.8 & $t =4.2022, p =0.0001489$ & Yes\\
 Dining Hall & 5.7 & 6.1 & $t=1.4595, p =0.1524$ & No\\
 Gas Station (Car) & 5 & 5.6 & $t=3.0697, p=0.00389$ & Yes\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Yangyong Zhang
Maliheh Shirvanian
Sunpreet S. Arora
Jianwei Huang
Guofei Gu

"Practical Speech Re-use Prevention in Voice-driven Services" by Yangyong Zhang, Maliheh Shirvanian, Sunpreet S. Arora, Jianwei Huang, and Guofei Gu, arXiv:2101.04773v1, 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{Comparison of computational cost}
\begin{tabular}{l|cc}
\toprule
Method & Training Time per 200 Epoch (min) & \# Parameters\\ \midrule
ResNet32 (single model) & 31.85 & 469,904 \\
RIDE~ & 43.13 & 1,408,784 \\
TLC~ & 45.27 & 1,408,784 \\
RF-DLC (ours) & 44.68 & 1,408,784 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Making Reliable and Flexible Decisions in Long-tailed Classification" by Bolian Li and Ruqi Zhang, arXiv:2501.14090v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Edson Pindza
Jules Clement Mba
Sutene Mwambi
Nneka Umeorah

"Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model" by Edson Pindza, Jules Clement Mba, Sutene Mwambi, and Nneka Umeorah, arXiv:2310.09622v1, 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}{c|c|c|c}
\toprule
 & Positive PE & Subsegmental PE & Acute PE \\
\hline
AUROC & 0.99 & 0.99 & 0.99 \\
F1 & 0.97 & 0.95 & 0.96 \\
Precision & 0.97 & 0.98 & 0.98 \\
Recall & 0.98 & 0.93 & 0.94 \\
\bottomrule
\end{tabular}
\caption{NLP PE labeler performance}
\end{table}
\end{document}

eess

Shih-Cheng Huang
Zepeng Huo
Ethan Steinberg
Chia-Chun Chiang
Matthew P. Lungren
Curtis P. Langlotz
Serena Yeung
Nigam H. Shah
Jason A. Fries

"INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis" by Shih-Cheng Huang, Zepeng Huo, Ethan Steinberg, Chia-Chun Chiang, Matthew P. Lungren, Curtis P. Langlotz, Serena Yeung, Nigam H. Shah, and Jason A. Fries, arXiv:2311.10798v1, 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}{lllll}
 \toprule
 \textbf{Model} & \textbf{POS} & \textbf{NER} & \textbf{XNLI} & \textbf{PAWS-X} \\
 \midrule
 + Syntactic Blind. & 85.3$^{-}$ & 76.4 & 64.2$^{-}$ & 80.6$^{-}$ \\
 + Morphological Blind. & 85.0$^{-}$ & 77.2 & 64.9 & 81.4 \\
 + Phonological Blind. & 86.7 & 77.1 & 65.0 & 81.6 \\
 + Genealogical Blind. & 86.1 & 77.0 & 64.7 & 81.1 \\
 \midrule
 \textit{m-BERT baseline} & 86.8 & 77.3 & 65.1 & 81.7 \\
 \midrule
 + Syntactic Pred. & 87.0 & 77.5 & \textbf{65.3$^{+}$} & \textbf{81.9$^{+}$} \\
 + Morphological Pred. & \textbf{87.2$^{+}$} & 77.3 & 65.2 & 81.7 \\
 + Phonological Pred. & 86.7 & 77.1 & 65.0 & 81.7 \\
 + Genealogical Pred. & 87.0 & \textbf{77.6} & \textbf{65.3$^{+}$} & 81.8 \\
 \bottomrule
 \end{tabular}
\caption{Typological Blinding and Prediction. Mean POS accuracy, NER F1 scores, XNLI accuracy and PAWS-X accuracy across all languages. $^{+}$ and $^{-}$ indicate significantly better or worse performance respectively, as determined by a one-tailed t-test ($p<0.01$).}
\end{table}
\end{document}

cs

"Does Typological Blinding Impede Cross-Lingual Sharing?" by Johannes Bjerva and Isabelle Augenstein, arXiv:2101.11888v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
\toprule
& \multicolumn{4}{c}{Label Noise Ratio}\\
 \cmidrule{2-5}
& 20\% & 40\% & 60\% & 80\%\\
\midrule[0.7pt]
ConvNorm + BN & 88.94 $\pm$ 0.36 & 85.88 $\pm$ 0.26 & 79.54 $\pm$ 0.73 & 69.26 $\pm$ 0.59 \\
ConvNorm & 87.75 $\pm$ 0.13 & 84.16 $\pm$ 0.71 & 77.48 $\pm$ 0.26 & 54.11 $\pm$ 2.65\\
BN & 86.98 $\pm$ 0.12 & 81.88 $\pm$ 0.29 & 74.14 $\pm$ 0.56 & 53.82 $\pm$ 1.04\\
Vanilla & 85.94 $\pm$ 0.25 & 82.11 $\pm$ 0.52 & 76.75 $\pm$ 0.20 & 57.20 $\pm$ 0.71\\
\bottomrule
\end{tabular}
\caption{\textbf{Adding ConvNorm and BatchNorm together makes a network more robust to label noise.} The influence of BatchNorm and ConvNorm for label noise on the CIFAR-10 dataset is evaluated. The mean test accuracy and its standard deviation are computed over three random seeds.}
\end{table}
\end{document}

eess

Sheng Liu
Xiao Li
Yuexiang Zhai
Chong You
Zhihui Zhu
Carlos Fernandez-Granda
Qing Qu

"Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training" by Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, and Qing Qu, arXiv:2103.00673v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.