\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Detection results of $AVTENet_{sf}$ (Score fusion).}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\textbf {Test-Set type} & \textbf {Class} & \textbf {Precision} & \textbf {Recall} & \textbf {F1-Score} & \textbf {Accuracy} \\
\hline \hline
\multirow{2}{*}{Testset-I} & Real & 0.96 & 0.91 & 0.93 & \multirow{2}{*}{0.94} \\
\cline{2-5}
& Fake & 0.92 & 0.96 & 0.94 & \\
\hline
\multirow{2}{*}{Testset-II} & Real & 1.00 & 0.91 & 0.96 & \multirow{2}{*}{0.96} \\
\cline{2-5}
& Fake & 0.92 & 1.00 & 0.96 & \\
\hline
\multirow{2}{*}{faceswap} & Real & 0.93 & 0.91 & 0.92 & \multirow{2}{*}{0.92} \\
\cline{2-5}
& Fake & 0.92 & 0.93 & 0.92 & \\
\hline
\multirow{2}{*}{faceswap-wav2lip} & Real & 1.00 & 0.91 & 0.96 & \multirow{2}{*}{0.96} \\
\cline{2-5}
& Fake & 0.92 & 1.00 & 0.96 & \\
\hline
\multirow{2}{*}{fsgan} & Real & 0.93 & 0.91 & 0.92 & \multirow{2}{*}{0.92} \\
\cline{2-5}
& Fake & 0.92 & 0.93 & 0.92 & \\
\hline
\multirow{2}{*}{fsgan-wav2lip} & Real & 1.00 & 0.91 & 0.96 & \multirow{2}{*}{ 0.96} \\
\cline{2-5}
& Fake & 0.92 & 1.00 & 0.96 & \\
\hline
\multirow{2}{*}{RTVC} & Real & 0.96 & 0.91 & 0.93 & \multirow{2}{*}{0.94} \\
\cline{2-5}
& Fake & 0.92 & 0.96 & 0.94 & \\
\hline
\multirow{2}{*}{wav2lip} & Real & 1.00 & 0.91 & 0.96 & \multirow{2}{*}{0.96} \\
\cline{2-5}
& Fake & 0.92 & 1.00 & 0.96 & \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccr}
\toprule
Data set & Naive & Flexible & Better? \\
\midrule
Breast & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
Glass2 & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
Credit & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6& \\
Horse & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
Meta & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
Pima & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5& \\
Vehicle & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Yihan He
Hong-Yu Chen
Yuan Cao
Jianqing Fan
Han Liu

"Transformers versus the EM Algorithm in Multi-class Clustering" by Yihan He, Hong-Yu Chen, Yuan Cao, Jianqing Fan, and Han Liu, arXiv:2502.06007v1, 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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The effect of GNN compared to vanilla \textsc{Dragonnet}.}
\begin{tabular}{clcccc}
 \toprule
 \multirow{2}{*}{\textsl{Dataset}} & ~~\multirow{2}{*}{\textsl{Model}}& \multicolumn{2}{c}{20\% training size} & \multicolumn{2}{c}{5\% training size} \\
 \cmidrule{3-4} \cmidrule{5-6}
 & & up@40 & up@20 & up@40 & up@20 \\% & \textbf{MSE} \\
 \midrule
\multirow{2}{*}{\textsl{RHC}} & \textsc{Dragonnet}&~ $2.24 \pm 0.14$ & $3.08 \pm 0.34$ &
$2.26 \pm 0.04$ & $3.01 \pm 0.10$\\% & nan \\%0.0492 &
 & \textsc{UMGNet-Dr}&~ $2.41 \pm 1.27$ & $5.20 \pm 1.64$ & $3.00 \pm 0.48$& $5.70 \pm 0.50$ \\ % AC
 \midrule
 \multicolumn{2}{c}{Improvement (\%)} & +7.6\% & +68.8\% & +32.7\% & +89.4\%
 \\
 \midrule
 \multirow{2}{*}{\textsl{RHP}} & \textsc{Dragonnet}&~ $0.21 \pm 0.16$ & $-0.01 \pm 0.26$ &~ $0.21 \pm 0.01$ &$-0.10 \pm 0.01$ \\%&
& \textsc{UMGNet-Dr}&~ $4.19 \pm 1.33$ & $6.15 \pm 2.42$ &~ $3.47 \pm 0.82$& $4.33 \pm 0.90$ \\ % AP
\midrule
 \multicolumn{2}{c}{Improvement (\%)} & +1895.2\% & +2700\% & +1552.4\% & +4430\%
 \\
\bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

George Panagopoulos
Daniele Malitesta
Fragkiskos D. Malliaros
Jun Pang

"Uplift Modeling Under Limited Supervision" by George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, and Jun Pang, arXiv:2403.19289v4, 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{This table shows the computations of our algorithm when applied on the linear system associated with the polynomial $p(x)$ in Eq. . The polynomials in the table are the polynomials associated with linear systems obtained from algorithm of Theorem .}
\begin{tabular}{c|cccc}
\hline\hline
 &&&&\\
\textbf{ Step} &&$p(x)$ && $\|p\|_1$ \\
 \hline\hline
 &&&&\\
 \textbf{1} & &$x^5+\frac{1}{2}x^4-\frac{1}{2}x-\frac{1}{2}$ && $1+\frac{3}{2}$ \\
 &&&&\\
 \textbf{2}& &$x^6-\frac{1}{4}x^4-\frac{1}{2}x^2-\frac{1}{4}x+\frac{1}{4}$ & & $1+\frac{5}{4}$ \\
 &&&&\\
 \textbf{3}& &$x^7+\frac{1}{8}x^4-\frac{1}{2}x^3-\frac{1}{4}x^2+\frac{1}{8}x-\frac{1}{8}$ && $1+\frac{9}{8}$ \\
 &&&&\\
 \textbf{4}& &$x^8-\frac{9}{16}x^4-\frac{1}{4}x^3+\frac{1}{8}x^2-\frac{1}{16}x+\frac{1}{16}$ && $1+\frac{17}{16}$ \\
 &&&&\\
 \textbf{5}& &$x^9+\frac{1}{32}x^4+\frac{1}{8}x^3-\frac{1}{16}x^2-\frac{7}{32}x-\frac{9}{32}$ && ${\bf 1+\frac{23}{32}<2}$ \\
 &&&&\\
 \hline\hline
\end{tabular}
\end{table}
\end{document}

math

Ziyad AlSharawi
Jose S. Cánovas
Sadok Kallel

"Constrained polynomial roots and a modulated approach to Schur stability" by Ziyad AlSharawi, Jose S. Cánovas, and Sadok Kallel, arXiv:2503.11252v1, 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{Models MAE}
\begin{tabular}{cccc}
\hline
Model & MAE & MAPE & Training (s)\\
\hline
XGBoost 10 & 0.8093 & 42.23 & 1917 \\
AutoML & 1.0248 & 42.73 & 174420 \\
XGBoost 5 & 1.6362 & 187.02 & 971 \\
5 Layer FFNN & 4.6374 & 243.90 & 3288 \\
3 Layer FFNN & 8.8075 & 323.77 & 3066 \\
Black Scholes & 8.0082 & 63.88 & NA \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Pricing European Options with Google AutoML, TensorFlow, and XGBoost" by Juan Esteban Berger, arXiv:2307.00476v1, 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{HAPT dataset results. SS: self-supervised, FT: fine-tuned.}
\begin{tabular}{llll}
\hline
\textbf{Method} & \textbf{Accuracy} & \textbf{F1(m)} & \textbf{F1(w)} \\ \hline\hline
Saeed et al. (SS) & -- & -- & 0.863$\pm$0.045 \\
\textbf{Ours} (SS) &\textbf{0.899}$\pm$\textbf{0.034} & \textbf{0.796}$\pm$\textbf{0.025} & \textbf{0.898}$\pm$\textbf{0.034} \\ \hline
Saeed et al. (SS, FT) & -- & -- & 0.896$\pm$0.051 \\
\textbf{Ours} (SS, FT) &\textbf{0.901}$\pm$\textbf{0.038} & \textbf{0.792}$\pm$\textbf{0.029} & \textbf{0.900}$\pm$\textbf{0.038} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Setareh Rahimi Taghanaki
Michael Rainbow
Ali Etemad

"Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion" by Setareh Rahimi Taghanaki, Michael Rainbow, and Ali Etemad, arXiv:2010.13713v2, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Retrieval-augmented Generation with first retrieved document Result}
\begin{tabular}{ccccccc}
 \toprule
 \multirow{2}{*}{Method} & \multirow{2}{*}{Parameter}&\multicolumn{5}{c}{Test} \\
 \cline{3-7}
 & & EM & Recall & Precision & F1 & Contains \\
 \midrule
 Mistral & 7B & 0.0 &24.46 & 5.35& 8.01 &8.47 \\
 Mixtral & 8x7B &0.03& 8.33 &2.03 & 2.94 & 2.46 \\
 LLama & 70B & 0.0 & 9.63 & 1.45 & 2.35 & 4.10 \\
 Qwen &14B & 0.25 & 29.94 & 12.72 & 16.08 & 15.17 \\
 \textbf{GPT3.5} & \textbf{175B} & \textbf{0.037} & \textbf{36.80} & \textbf{13.77} & \textbf{18.32} & \textbf{17.53} \\
 \bottomrule

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

cs

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

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


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{array}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Optimal plant design variable values and corresponding merit function values for optimization with varied targets. The tower-only (T) CCD problem solves for tower design variables in conjunction with control trajectories. The tower and blades (T\&B) CCD problem solves for the tower design parameters, blade design parameters, and control trajectories. Values with gray shading represent parameters are not optimized and held constant.}
\begin{tabular}{cccrrr}%{p{0.15in}<{\centering} @{\hskip 0.3in} p{0.50in}<{\centering} @{\hskip 0.02in} p{0.38in}<{\centering} p{0.42in}<{\raggedleft} p{0.42in}<{\raggedleft} p{0.42in}<{\raggedleft}}
 \toprule
 & & & \multicolumn{3}{c}{Optimization target} \\
 \cmidrule(){4-6}
 Group & Variable & Unit
 & \hspace*{-0.5em}\shortstack[r]{None\\(baseline)}
 & \shortstack[r]{\;\;Tower\\only}
 & \shortstack[r]{Tower \&\\blades} \\
 \midrule
 \multirow{4}{*}{\shortstack[c]{Tower\\parameters}}
 & $t_{\text{base}}$ & m
 & \cellcolor[HTML]{cfcfcf} 0.027 & 0.042 & 0.042 \\
 & $t_{\text{tip}}$ & m
 & \cellcolor[HTML]{cfcfcf} 0.019 & 0.012 & 0.012 \\
 & $d_{\text{tip}}$ & m
 & \cellcolor[HTML]{cfcfcf} 3.870 & 4.95 & 5.00\\ % Significant figures should be maintained for mm digit, considering other quantities.
 & $l$ & m
 & \cellcolor[HTML]{cfcfcf} 77.600 & 83.04 & 81.38 \\
 \cmidrule(r){1-3}\cmidrule(){4-6}
 \multirow{10}{*}{\shortstack[c]{Blade\\parameters}}
 & $\phi_{4}$ & deg
 & \cellcolor[HTML]{cfcfcf} 13.31 & \cellcolor[HTML]{cfcfcf} 13.31 & 12.09 \\
 & $\phi_{6}$ & deg
 & \cellcolor[HTML]{cfcfcf} 11.48 & \cellcolor[HTML]{cfcfcf} 11.48 & 9.23 \\
 & $\phi_{9}$ & deg
 & \cellcolor[HTML]{cfcfcf} 6.54 & \cellcolor[HTML]{cfcfcf} 6.54 & 3.96 \\
 & $\phi_{12}$ & deg
 & \cellcolor[HTML]{cfcfcf} 1.53 & \cellcolor[HTML]{cfcfcf} 1.53 & 1.52 \\
 & $\phi_{17}$ & deg
 & \cellcolor[HTML]{cfcfcf} 0.11 & \cellcolor[HTML]{cfcfcf} 0.11 & 0.06 \\
 & $\zeta_{4}$ & m
 & \cellcolor[HTML]{cfcfcf} 4.557 & \cellcolor[HTML]{cfcfcf} 4.557 & 5.580 \\
 & $\zeta_{6}$ & m
 & \cellcolor[HTML]{cfcfcf} 4.007 & \cellcolor[HTML]{cfcfcf} 4.007 & 5.056 \\
 & $\zeta_{9}$ & m
 & \cellcolor[HTML]{cfcfcf} 3.502 & \cellcolor[HTML]{cfcfcf} 3.502 & 3.136 \\
 & $\zeta_{12}$ & m
 & \cellcolor[HTML]{cfcfcf} 2.764 & \cellcolor[HTML]{cfcfcf} 2.764 & 2.458 \\
 & $\zeta_{17}$ & m
 & \cellcolor[HTML]{cfcfcf} 1.419 & \cellcolor[HTML]{cfcfcf} 1.419 & 1.458 \\
 \cmidrule(r){1-3}\cmidrule(){4-6}
 \multirow{2}{*}{\shortstack[c]{Merit\\functions}}
 & $-J^{*}_{\text{out}}$ & GWh
 & 26.24 & 29.22& 29.34 \\
 & $m_{\text{tower}}$ & tonne
 & 249.6 & 344.4 & 342.0 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Saeid Bayat
Yong Hoon Lee
James T. Allison

"Nested Control Co-design of a Spar Buoy Horizontal-axis Floating Offshore Wind Turbine" by Saeid Bayat, Yong Hoon Lee, and James T. Allison, arXiv:2310.15463v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{RMSE results on the test dataset for different imputation schemes compared to baseline }
\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|c|}
\hline
\rowcolor[HTML]{BFBFBF}
 Data Imputation &
 Grass &

 Clover &

 White Clover &

 Red Clover &

 Weeds
 \\ \hline
\rowcolor[HTML]{FFFFC7}
Baseline &
 9.05 &

 9.91 &

 9.51 &

 \textbf{6.68} &

 \textbf{6.49}
 \\ \hline
Regression &
 8.98 &

 10.03 &

 8.78 &

 10.46 &

 6.86
 \\ \hline
Mean &
 \textbf{8.64} &

 \textbf{8.73} &

 8.16 &

 10.11 &

 6.95
 \\ \hline
Median &
 8.67 &

 9.93 &

 \textbf{8.09} &

 9.87 &

 7.73
 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Badri Narayanan
Mohamed Saadeldin
Paul Albert
Kevin McGuinness
Brian Mac Namee

"Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset" by Badri Narayanan, Mohamed Saadeldin, Paul Albert, Kevin McGuinness, and Brian Mac Namee, arXiv:2101.03198v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Zhen Tian
Wayne Xin Zhao
Changwang Zhang
Xin Zhao
Zhongrui Ma
Ji-Rong Wen

"EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention" by Zhen Tian, Wayne Xin Zhao, Changwang Zhang, Xin Zhao, Zhongrui Ma, and Ji-Rong Wen, arXiv:2403.17729v2, 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}{ccccccc}
\hline
Method & Count & min & 25\% & Median & 75\% & max\\
\hline
Integration & 949 (18.48\%) & 0 & 0 & $2.22\cdot 10^{-16}$ & $4.44\cdot 10^{-16}$ & $6.00\cdot 10^{-15}$\\
Series & 4051 (81.02\%) & 0 & 0 & $2.22\cdot 10^{-16}$ & $5.55\cdot 10^{-16}$ & $2.48\cdot 10^{-13}$\\
	\hline
	\end{tabular}
\caption{Precision metrics of the numerical methods used for computing in the small region. The errors are the absolute relative errors compared to the reference solutions obtained using mpmath. Percentiles: 25, 50 (median), 75.}
\end{table}
\end{document}

stat

"On the computation of the cumulative distribution function of the Normal Inverse Gaussian distribution" by Guillermo Navas-Palencia, arXiv:2502.16015v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
\toprule
\toprule
\textbf{Cluster} & \textbf{Percentage of Frames} & \textbf{Average Possession} \\
\midrule
 1 & 9.73\% & 22.34\% \\
 2 & 8.26\% & 27.96\% \\
 3 & 11.10\% & 33.08\% \\
\midrule
 4 & 13.30\% & 40.19\% \\
 5 & 11.86\% & 41.88\% \\
 6 & 12.31\% & 48.80\% \\
\midrule
 7 & 9.51\% & 61.02\% \\
 8 & 9.72\% & 63.14\% \\
 9 & 7.93\% & 75.47\% \\
 10 & 6.28\% & 76.52\% \\
\bottomrule
\bottomrule
\end{tabular}
\caption{Distribution of percentage of frames and average possession across clusters.}
\end{table}
\end{document}

stat

Ali Baouan
Mathieu Rosenbaum
Sergio Pulido

"An optimal transport based embedding to quantify the distance between playing styles in collective sports" by Ali Baouan, Mathieu Rosenbaum, and Sergio Pulido, arXiv:2501.10299v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{EVALUATION METRICS FOR THE PRODUCTION PREDICTION MODEL}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textbf{Model} & \textbf{MSE} & \textbf{RMSE} & \textbf{R-Squared} \\
 \hline
 DTR & 1.06 & 1.03 & 0.997 \\
 RFR & 1.19 & 1.09 & 0.996 \\
 LR & 42.89 & 6.54 & 0.891 \\
 GBR & 4.54 & 2.13 & 0.988 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Forkan Uddin Ahmed
Annesha Das
Md Zubair

"A Machine Learning Approach for Crop Yield and Disease Prediction Integrating Soil Nutrition and Weather Factors" by Forkan Uddin Ahmed, Annesha Das, and Md Zubair, arXiv:2403.19273v1, 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{Parameter settings for Experiment~1 (Constant Valuations).}
\begin{tabular}{ll}
\toprule
\textbf{Parameter} & \textbf{Possible Values}\\
\midrule
Number of bidders ($n$) & $\{2,4,6\}$\\
Reserve price ($r$) & $\{0.0,0.1,0.2,0.3,0.5\}$\\
Learning rate ($\alpha$) & e.g.\ $\{0.001,0.005,0.01,0.05,0.1\}$\\
Discount factor ($\gamma$) & e.g.\ $\{0.0,0.5,0.9,0.99\}$\\
Exploration & $\varepsilon$-greedy or Boltzmann\\
Q-update mode & Asynchronous or synchronous\\
State features & None / median-of-others / previous-winner\\
Number of episodes & e.g.\ $10^4$ or $10^5$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{r|cc}
 \toprule
 & \multicolumn{2}{c}{WLS} \\
 & Coefficient & Robust Error \\\midrule
 Constant & 0.3995 & (0.0322) \\
 Small & 1.5193 & (0.0288) \\
 SA50\%, Large & -0.7710 & (0.0919) \\
 SA50\%, Small & 1.8050 & (0.0904) \\
 SA100\%, Small & 0.1864 & (0.0381) \\
 Demand & 0.2259 & (0.0163) \\
 Demand$^2$ & -0.0083 & (0.0011) \\
 SDVOSB & 0.4870 & (0.0584) \\ \midrule
 $n=26169$ &\multicolumn{2}{c}{$R^2=0.8197$} \\ \bottomrule
 \end{tabular}
\caption{Replicating Number of Bidders Model (Table 2)}
\end{table}
\end{document}

q-fin

"Set-Asides in USDA Food Procurement Auctions" by Ni Yan and WenTing Tao, arXiv:2302.05772v1, 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{Manufactured solution: characteristics of the conformal linear meshes for the test case presented in~.}
\begin{tabular}{cccc} \hline %\hline
 Grid level &Nodes &Triangles &$h$ \\
 \hline
 0 & 61 & 98 & 1.54E-1 \\
 1 & 219 & 392 & 7.72E-2 \\
 2 & 829 & 1,568 & 3.86E-2 \\
 3 & 3,225 & 6,272 & 1.93E-2 \\
 \hline %\hline
 \end{tabular}
\end{table}
\end{document}

math

Mirco Ciallella
Stephane Clain
Elena Gaburro
Mario Ricchiuto

"Very high order treatment of embedded curved boundaries in compressible flows: ADER discontinuous Galerkin with a space-time Reconstruction for Off-site data" by Mirco Ciallella, Stephane Clain, Elena Gaburro, and Mario Ricchiuto, arXiv:2312.07170v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Model assessment for the proposed algorithm BGWR using different kernels for the simulated data.}
\begin{tabular}{llll}
\hline
 & Exponential kernel & Bi-square kernel & Gaussian kernel \\ \hline
WAIC & 181878.4 & 181854 & 181855 \\
DIC & 35846.3 & 35831.9 & 35853.4 \\
$P_D$ & 391.7 & 381.8 & 404.7 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Wala Draidi Areed
Aiden Price
Helen Thompson
Conor Hassan
Reid Malseed
Kerrie Mengersen

"Bayesian Cluster Geographically Weighted Regression for Spatial Heterogeneous Data" by Wala Draidi Areed, Aiden Price, Helen Thompson, Conor Hassan, Reid Malseed, and Kerrie Mengersen, arXiv:2311.12347v1, 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{Frequencies of the Number of Diseased Eyes}
\begin{tabular}{lccccc}
\hline
Number of Diseased Eyes & Group 1 & Group 2 & \dots & Group $g$ & Total \\
\hline
0 & $m_{10}$ & $m_{20}$ & \dots & $m_{g0}$ & $S_0$ \\
1 & $m_{11}$ & $m_{21}$ & \dots & $m_{g1}$ & $S_1$ \\
2 & $m_{12}$ & $m_{22}$ & \dots & $m_{g2}$ & $S_2$ \\
\hline
\textbf{Total} & $m_1$ & $m_2$ & \dots & $m_g$ & $N$ \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Shuyi Liang
Takeshi Emura
Chang-Xing Ma
Yijing Xin
Xin-Wei Huang

"Testing the Homogeneity of Two Proportions for Correlated Bilateral Data via the Clayton Copula" by Shuyi Liang, Takeshi Emura, Chang-Xing Ma, Yijing Xin, and Xin-Wei Huang, arXiv:2502.00523v1, 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{Accuracy (\%) of various methods on ESC-50 in the few-shot settings.}
\begin{tabular}{llc}
\toprule
 & \multicolumn{2}{c}{Accuracy$\uparrow$} \\ \hline
 & 5-way & 12-way \\ \cline{2-3}
\multicolumn{3}{l}{\textit{Specialised systems trained with task-specific examples}} \\
ProtoNet~ & 88.2 & 77.7 \\
MatchNet~ & 86.8 & 71.8 \\
HPN~ & 88.7 & 78.7 \\ \midrule
\multicolumn{3}{l}{\textit{Audio language models trained with contrastive learning}} \\
TIP-adapter~ & 97.5 & 95.6 \\
Treff adapter~ & 98.5 & 96.3 \\ \midrule
\multicolumn{3}{l}{\textit{One-for-all models for various audio tasks}} \\
APT-LLM & 91.0 & 54.2 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Jinhua Liang
Xubo Liu
Wenwu Wang
Mark D. Plumbley
Huy Phan
Emmanouil Benetos

"Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities" by Jinhua Liang, Xubo Liu, Wenwu Wang, Mark D. Plumbley, Huy Phan, and Emmanouil Benetos, arXiv:2312.00249v2, 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{Statistics calculated in the data plane.}
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{Statistics} & \textbf{Notation} & \textbf{Calculation} \\ \hline
 Weight & $w$ & $w$ \\ \hline
 Mean & $\mu$ & $\frac{LS}{w}$ \\ \hline
 Std. Deviation & $\sigma_{S_{i}}$ & $\sqrt{\mid\frac{SS}{w} - (\frac{LS}{w})^{2}\mid}$ \\ \hline
 Magnitude\textbf{*} & $\mid\mid S_{i},S_{j} \mid\mid$ & $\sqrt{\mu^{2}_{S_{i}} + \mu^{2}_{S_{j}}}$ \\ \hline
 Radius\textbf{*} & $R_{S_{i},S_{j}}$ & $\sqrt{(\sigma^{2}_{S_{i}})^{2} + (\sigma^{2}_{S_{j}})^{2}}$ \\ \hline
 Approx. Covariance\textbf{*} & $Cov_{S_{i},S{j}}$ & $\frac{SR_{ij}}{w_{i} + w_{j}}$ \\ \hline
 Pearson Corr. Coeff.\textbf{*} & $PCC_{S_{i},S_{j}}$ & $\frac{Cov_{S_{i},S_{j}}}{\sigma_{S_{i}}\sigma_{S_{j}}}$ \\ \hline
 \end{tabular}
\end{table}
\end{document}

cs

João Romeiras Amado
Francisco Pereira
David Pissarra
Salvatore Signorello
Miguel Correia
Fernando M. V. Ramos

"Peregrine: ML-based Malicious Traffic Detection for Terabit Networks" by João Romeiras Amado, Francisco Pereira, David Pissarra, Salvatore Signorello, Miguel Correia, and Fernando M. V. Ramos, arXiv:2403.18788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccccc}
\toprule
\textbf{Model} & \textbf{Cliniq K} & \textbf{Med Gen} & \textbf{Anat} & \textbf{Pro Med} & \textbf{C Bio} & \textbf{C Med} \\
\midrule
\multicolumn{3}{l}{\textbf{Closed-source Model}} \\
\midrule
GPT-4 (5-shot) & \underline{86.4} & \underline{92.0} & \underline{80.0} & \underline{93.8} & \underline{93.8} & \underline{76.3}
 \\
GPT-3.5 (5-shot) & 68.7 & 68.0 & 60.7 & 69.9 & 72.9 & 63.6 \\
\midrule
\multicolumn{3}{l}{\textbf{Open-source Model (70B)}} \\
\midrule
Clinical Camel-70B~ & 65.1 & 62.6 & 56.0 & 71.6 & 69.2 & 56.3 \\
LLaMA-2-70B (Ens)~ & {76.5} & 81.1 & {70.9} & {82.3} & \underline{90.9} & {72.1} \\
MediTron-70B (Ens)~ & 75.5 & {85.9} & 69.4 & {82.3} & 86.7 & 68.0 \\
Meerkat-70B (\textbf{Ours}) & \underline{87.2} & \underline{88.2} & \underline{84.4} & \underline{87.2} & {87.9} & \underline{86.6} \\
\midrule
\multicolumn{3}{l}{\textbf{Open-source Model ($<$ 10B)}} \\
\midrule
MediTron-7B~ & 57.7 & 63.8 & 56.9 & 56.0 & 57.1 & 48.9 \\
BioMistral-7B~ & 59.9 & 64.0 & 56.5 & 60.4 & {59.0} & 54.7 \\
Meerkat-7B (\textbf{Ours}) & {71.6} & {74.8} & {63.2} & \underline{77.3} & {70.8} & {65.2} \\
Meerkat-8B (\textbf{Ours}) & \underline{74.3} & \underline{76.7} & \underline{74.8} & {75.3} & \underline{76.1} & \underline{74.3} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Hyunjae Kim
Hyeon Hwang
Jiwoo Lee
Sihyeon Park
Dain Kim
Taewhoo Lee
Chanwoong Yoon
Jiwoong Sohn
Donghee Choi
Jaewoo Kang

"Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks" by Hyunjae Kim, Hyeon Hwang, Jiwoo Lee, Sihyeon Park, Dain Kim, Taewhoo Lee, Chanwoong Yoon, Jiwoong Sohn, Donghee Choi, and Jaewoo Kang, arXiv:2404.00376v2, 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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Similarity measures for the different XAI methods using the DT model and the \textit{minimum} set of features.}
\begin{tabular}{ccccccc}
\toprule
 & MDI-DT & MDA-DT & SHAP-DT & LIME-DT & Guidelines & Experts \\
\cmidrule(lr){1-1}\cmidrule(lr){2-5}\cmidrule(lr){6-7}
MDI-DT & \cellcolor[gray]
 {0.8}1.00 & 0.71 & 0.75 & 0.71 & 0.55 & 0.64 \\
MDA-DT & 0.71 & \cellcolor[gray]
 {0.8}1.00 & 0.71 & 0.67 & 0.36 & 0.45 \\
SHAP-DT & 0.75 & 0.71 & \cellcolor[gray]
 {0.8}1.00 & 0.71 & 0.42 & 0.50 \\
LIME-DT & 0.71 & 0.67 & 0.71 & \cellcolor[gray]
 {0.8}1.00 & 0.50 & 0.45 \\
\midrule
Guidelines & 0.55 & 0.36 & 0.42 & 0.50 & \cellcolor[gray]
 {0.8}1.00 & 0.75 \\
Experts & 0.64 & 0.45 & 0.50 & 0.45 & 0.75 & \cellcolor[gray]
 {0.8}1.00 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

José Bobes-Bascarán
Eduardo Mosqueira-Rey
Ángel Fernández-Leal
Elena Hernández-Pereira
David Alonso-Ríos
Vicente Moret-Bonillo
Israel Figueirido-Arnoso
Yolanda Vidal-Ínsua

"Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach" by José Bobes-Bascarán, Eduardo Mosqueira-Rey, Ángel Fernández-Leal, Elena Hernández-Pereira, David Alonso-Ríos, Vicente Moret-Bonillo, Israel Figueirido-Arnoso, and Yolanda Vidal-Ínsua, arXiv:2403.19820v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

"A Study on Monthly Marine Heatwave Forecasts in New Zealand: An Investigation of Imbalanced Regression Loss Functions with Neural Network Models" by Ding Ning, Varvara Vetrova, Sébastien Delaux, Rachael Tappenden, Karin R. Bryan, and Yun Sing Koh, arXiv:2502.13495v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Johansen Cointegration Test using Trace Test Statistic (10\% Significance Level)}
\begin{tabular}{cccc}
\hline
\textbf{Rank (r)} & \textbf{Cointegrating Relations} & \textbf{Test Statistic} & \textbf{Critical Value} \\
\hline
0 & 2 & 50.29 & 13.43 \\
1 & 2 & 2.113 & 2.705 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc} \hline\hline
$v_1^*$ & $v_2^*$ & $v_3^*$ & $v_4^*$& $u_1^*$ & $u_2^*$ &{ $\psi^*$ }\\ \hline
0.407493 & 5.69403 & 10.8921 & 1.28423& 0.959148 & 7.22785&{ 15201.7} \\ \hline\hline
\end{tabular}
\end{table}
\end{document}

math

"Closest targets in Russell graph measure of strongly monotonic efficiency for an extended facet production possibility set" by Kazuyuki Sekitani and Yu Zhao, arXiv:2312.15422v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

J. Bernier
S. Blanes
F. Casas
A. Escorihuela-Tomàs

"On alternating-conjugate splitting methods" by J. Bernier, S. Blanes, F. Casas, and A. Escorihuela-Tomàs, arXiv:2503.08453v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Anubha Goel
Puneet Pasricha
Martin Magris
Juho Kanniainen

"Foundation Time-Series AI Model for Realized Volatility Forecasting" by Anubha Goel, Puneet Pasricha, Martin Magris, and Juho Kanniainen, arXiv:2505.11163v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Preference test: \textbf{w/} or \textbf{w/o} timbre tokens}
\begin{tabular}{c|ccc|c}
& w/o & neutral & w/ & p-value\\ \hline
Quality & 20\% & 30\% & 50\% & 0.01 \\ \hline
Similarity & 20\% & 30\% & 50\% & 0.01 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Haohan Guo
Heng Lu
Na Hu
Chunlei Zhang
Shan Yang
Lei Xie
Dan Su
Dong Yu

"Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training" by Haohan Guo, Heng Lu, Na Hu, Chunlei Zhang, Shan Yang, Lei Xie, Dan Su, and Dong Yu, arXiv:2012.01837v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
 \hline
 Proj. Head & Number of Layers & Backbone & Projection Head \\ \hline
 \multirow{2}{*}{FFN}
 & 1 & \textbf{42.51\%} & 39.46\%\\
 & 3 & 39.10\% & 36.25\% \\ \hline
 \multirow{2}{*}{TF}
 & 1 & \textbf{46.12\%} & 44.16\%\\
 & 3 & 41.85\% & 30.34\%\\ \hline
 \end{tabular}
\caption{Ablation on different setups to measure supervised accuracy on CIFAR100 with ResNet18 and small projection head.}
\end{table}
\end{document}

cs

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


cs

"Low-Complexity Interference Cancellation Algorithms for Detection in Media-based Modulated Uplink Massive-MIMO Systems" by Manish Mandloi and Devendra Singh Gurjar, arXiv:2101.01905v1, 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{Comparison of TTM references}
\begin{tabular}{llrrrrr}
\hline
\hline
Method & N & ICC & R2 & MAE & MAPE & r\\
\hline
Proposed & 4,483 & \textbf{0.996} & \textbf{0.992} & \textbf{0.173} & \textbf{1.7} & \textbf{0.997} \\
Field 23275 & 4,483 & 0.958 & 0.919 & 0.561 & 5.6 & 0.959 \\
\hline
Proposed & 8,144 & \textbf{0.997} & \textbf{0.993} & \textbf{0.161} & \textbf{1.6} & \textbf{0.997} \\
Return 981 & 8,144 & 0.989 & 0.978 & 0.284 & 2.8 & 0.989 \\
\hline
\hline
\multicolumn{7}{l}{*Comparison to the target values, listing both the proposed predictions}\\ \multicolumn{7}{l}{ and alternative UK Biobank reference values on the same subjects}
\end{tabular}
\end{table}
\end{document}

cs

Taro Langner
Fredrik K. Gustafsson
Benny Avelin
Robin Strand
Håkan Ahlström
Joel Kullberg

"Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI" by Taro Langner, Fredrik K. Gustafsson, Benny Avelin, Robin Strand, Håkan Ahlström, and Joel Kullberg, arXiv:2101.06963v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Sooyoung Park
Arda Senocak
Joon Son Chung

"Can CLIP Help Sound Source Localization?" by Sooyoung Park, Arda Senocak, and Joon Son Chung, arXiv:2311.04066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Chrysostomos Marasinou
Bo Li
Jeremy Paige
Akinyinka Omigbodun
Noor Nakhaei
Anne Hoyt
William Hsu

"Segmentation of Breast Microcalcifications: A Multi-Scale Approach" by Chrysostomos Marasinou, Bo Li, Jeremy Paige, Akinyinka Omigbodun, Noor Nakhaei, Anne Hoyt, and William Hsu, arXiv:2102.00754v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Michal Klein
Louis Bethune
Eugene Ndiaye
Marco Cuturi

"Multivariate Conformal Prediction using Optimal Transport" by Michal Klein, Louis Bethune, Eugene Ndiaye, and Marco Cuturi, arXiv:2502.03609v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{arydshln}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ELPD results for different models with data simulated under balanced data-generating processes.}
\begin{tabular}{llrr}
 \toprule
DGP & Model & $\widehat{\operatorname{elpd}}$ & $\operatorname{se}(\widehat{\operatorname{elpd}})$ \\
 \midrule
\multirow{4}{*}{ZANIM} & ZANIM & $-2464.647$ & 22.269 \\
 & ZANIDM & $-2548.417$ & 11.145 \\
 & DM & $-3012.544$ & 18.185 \\
 & Multinomial & $-3802.310$ & 113.858 \\
 \hdashline
 \multirow{4}{*}{ZANIDM} & ZANIDM & $-2978.564$ & 18.977 \\
 & DM & $-3007.946$ & 17.219 \\
 & ZANIM & $-4781.668$ & 112.203 \\
 & Multinomial & $-7459.453$ & 178.316 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

math

André F. B. Menezes
Andrew C. Parnell
Keefe Murphy

"Finite mixture representations of zero-&-$N$-inflated distributions for count-compositional data" by André F. B. Menezes, Andrew C. Parnell, and Keefe Murphy, arXiv:2501.13879v1, 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{Statistics of the SQuAD dataset.}
\begin{tabular}{l|cc}
 \toprule
 & \# Train & \# Validation \\
 \midrule
 SQuAD v1.1 & 87,599 & 10,570 \\
 SQuAD v2.0 & 130,319 & 11,873 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Abdessalam Ed-dib
Zhanibek Datbayev
Amine Mohamed Aboussalah

"GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning" by Abdessalam Ed-dib, Zhanibek Datbayev, and Amine Mohamed Aboussalah, arXiv:2412.09250v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
 \hline
 $i$ & 1 & 2 \\ \hline
 $\lambda_i$ & 1 & 1 \\
 $\mu_i$ & 1/4 & 1/4 \\
 $\mu_i'$ & 1/4 & - \\
 $\theta_i$ & 1 & 2 \\
 $k_i$ & 5 & 5 \\
 $\gamma_{i}$ & 1 & 2 \\ \hline
 \end{tabular}
\caption{Parameter values for the discrete-time example.}
\end{table}
\end{document}

math

Mark Fackrell
Hritika Gupta
Peter G. Taylor

"Minimising Numbers of Losses and Abandonments in Small Call Centres Under a Transient Regime" by Mark Fackrell, Hritika Gupta, and Peter G. Taylor, arXiv:2312.03941v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math


\documentclass{article}
\usepackage{amsfonts}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Complexity of computing a single sample for permutation null distribution, $X_0$ with m-out-of-n bootstrap, $X_0$ with derivative bootstrap, and ${UB}_0$ given by Theorem (b). Recall that $k$ is the number of measures $\mu_1,\hdots, \mu_k$, and $N$ is the cardinality of the underlying metric space $\mathcal{X}=\{x_1,\hdots,x_N \} \subset \mathbb{R}^d$. Algorithm (theory) row reports an algorithm used to prove theoretical complexity, while algorithm (practice) rows report algorithms implemented in this paper and available for use. The algorithm of Altschuler \& Boix-Adser{\`a} is abbreviated as AB-A and assumes fixed $d$.}
\begin{tabular}{c||c|c|c}
distribution to sample & permut. or $X_0$ (m-out-of-n) &
$X_0$ (deriv.)
& $UB_0$ \\
hypothesis & null, alternative& null& null\\
optimization program & equation & equation & equation \\
\hline \hline
$\#$ variables &$N^k$ & $kN$ & $(k-1)N$\\
$\#$ equality constraints& $kN$ & $N$ & none \\
$\#$ inequality constraints& none & $N^k$& $(k-1)N(N-1)$ \\
\hline \hline
theoretical complexity & $\text{poly}(N,k,\log U)$ & $\text{poly}(N,k,\log U)$ & $\text{poly}(N,k,\log U)$ \\
reference &Theorem 2 of & Lemma here & Theorem 6 of \\
algorithm (theory) & AB-A & AB-A & Ellipsoid \\
\hline
\multirow{3}{4em}{algorithm (practice)} & AB-A & AB-A & \\
 & Simplex & Simplex & Simplex \\
& Interior point & Interior point & Interior point\\
\hline
\multirow{3}{4em}{software} & Github for (d=2) & & \\
& GUROBI & GUROBI & GUROBI \\
& RSymphony &RSymphony &RSymphony \\
\end{tabular}
\end{table}
\end{document}

stat

"k-Sample inference via Multimarginal Optimal Transport" by Natalia Kravtsova, arXiv:2501.05645v1, 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{Flash [Kb], RAM [Kb], and average inference time [ms] required by the CNN LSTM, for each MCU.}
\begin{tabular}{cccc}
\toprule
\textbf{Device} & \textbf{Flash {[}Kb{]}} & \textbf{RAM {[}Kb{]}} & \textbf{Average inference time {[}ms{]}} \\ \midrule
SPC584B & 35.13 & 2.25 & 6.34 \\
SPC58EC & 35.13 & 2.25 & 4.38 \\
SPC58NH & 35.13 & 2.25 & 3.86 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

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

"Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers" by Giulia Crocioni, Giambattista Gruosso, Danilo Pau, Davide Denaro, Luigi Zambrano, and Giuseppe di Giore, arXiv:2103.00201v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
 \hline
$f$ & $r(f)$ (km)\\ \hline \hline
0.053 & 18.7 \\ \hline
0.091 & 34.7 \\ \hline
0.320 & 86.7 \\ \hline
0.443 & 127.3 \\ \hline
0.828 & 195.7 \\ \hline
 \end{tabular}
\caption{Fraction of included data at the breakpoints and the corresponding VP radii. }
\end{table}
\end{document}

stat

"Valeriepieris Circles Reveal City and Regional Boundaries in England and Wales" by Rudy Arthur and Federico Botta, arXiv:2502.07451v1, 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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison between different adapters when Mantis is fully-fine-tuned to a multi-channel time series classification task. The \texttt{UEA-27} data collection is considered in this experiment, and the results are averaged over 3 random seeds. The number of selected channels is fixed to be $\leq 10$. \texttt{No\,Adapter} means that all channels are independently fed to the TSFM, and \texttt{NaN} in its performance results marks the cases when the model has not fitted to a single V100-32GB GPU card's memory. Best Result summarizes the performance when the best strategy per dataset is chosen.}
\begin{tabular}{l|l||llllll||l}
\toprule
& \multirow{2}{*}{d} & \multirow{2}{*}{\texttt{No Adapter}} & \multicolumn{4}{c}{Standalone Adapter} & Diff. Adapter & \multirow{2}{*}{Best Result}
\\
& & & \multicolumn{4}{l}{} & \\
& & & \texttt{PCA} & \texttt{SVD} & \texttt{Rand Proj} & \texttt{Var Selector} & \texttt{LComb} & \\
\midrule
ArticularyWordRecognition & 9 & \textbf{0.9933}$_{\pm 0.0}$ & 0.9922$_{\pm 0.0019}$ & 0.9878$_{\pm 0.0038}$ & 0.9811$_{\pm 0.0038}$ & \textbf{0.9933}$_{\pm 0.0}$ & 0.9744$_{\pm 0.0069}$ & 0.9933$_{\pm 0.0}$ \\
BasicMotions & 6 & \textbf{1.0}$_{\pm 0.0}$ & \textbf{1.0}$_{\pm 0.0}$ & \textbf{1.0}$_{\pm 0.0}$ & \textbf{1.0}$_{\pm 0.0}$ & \textbf{1.0}$_{\pm 0.0}$ & \textbf{1.0}$_{\pm 0.0}$ & 1.0$_{\pm 0.0}$ \\
CharacterTrajectories & 3 & 0.9928$_{\pm 0.0004}$ & \textbf{0.9947}$_{\pm 0.0004}$ & 0.9923$_{\pm 0.0012}$ & 0.9912$_{\pm 0.0016}$ & 0.9928$_{\pm 0.0004}$ & 0.993$_{\pm 0.0018}$ & 0.9947$_{\pm 0.0004}$ \\
Cricket & 6 & \textbf{1.0}$_{\pm 0.0}$ & 0.9861$_{\pm 0.0}$ & 0.9769$_{\pm 0.008}$ & 0.9722$_{\pm 0.0}$ & \textbf{1.0}$_{\pm 0.0}$ & 0.9907$_{\pm 0.008}$ & 1.0$_{\pm 0.0}$ \\
DuckDuckGeese & 1345 & \texttt{NaN} & 0.5733$_{\pm 0.0115}$ & \textbf{0.6}$_{\pm 0.02}$ & 0.54$_{\pm 0.0872}$ & 0.5133$_{\pm 0.0231}$ & 0.54$_{\pm 0.0693}$ & 0.6$_{\pm 0.02}$ \\
ERing & 4 & \textbf{0.9926}$_{\pm 0.0074}$ & 0.9778$_{\pm 0.0064}$ & 0.9753$_{\pm 0.0113}$ & 0.9642$_{\pm 0.0043}$ & \textbf{0.9926}$_{\pm 0.0074}$ & 0.9778$_{\pm 0.0064}$ & 0.9926$_{\pm 0.0074}$ \\
EigenWorms & 6 & 0.8372$_{\pm 0.0044}$ & 0.8448$_{\pm 0.0117}$ & \textbf{0.8601}$_{\pm 0.0192}$ & 0.8117$_{\pm 0.0233}$ & 0.8372$_{\pm 0.0044}$ & 0.8066$_{\pm 0.0384}$ & 0.8601$_{\pm 0.0192}$ \\
Epilepsy & 3 & \textbf{1.0}$_{\pm 0.0}$ & 0.9976$_{\pm 0.0042}$ & 0.9976$_{\pm 0.0042}$ & 0.9976$_{\pm 0.0042}$ & \textbf{1.0}$_{\pm 0.0}$ & \textbf{1.0}$_{\pm 0.0}$ & 1.0$_{\pm 0.0}$ \\
EthanolConcentration & 3 & \textbf{0.4208}$_{\pm 0.0195}$ & 0.2928$_{\pm 0.0101}$ & 0.3029$_{\pm 0.0122}$ & 0.384$_{\pm 0.0503}$ & \textbf{0.4208}$_{\pm 0.0195}$ & 0.4081$_{\pm 0.0275}$ & 0.4208$_{\pm 0.0195}$ \\
FaceDetection & 144 & \texttt{NaN} & 0.6026$_{\pm 0.0054}$ & 0.6064$_{\pm 0.0037}$ & 0.5638$_{\pm 0.0065}$ & 0.5696$_{\pm 0.0027}$ & \textbf{0.6272}$_{\pm 0.013}$ & 0.6272$_{\pm 0.013}$ \\
FingerMovements & 28 & \texttt{NaN} & 0.5833$_{\pm 0.0058}$ & 0.57$_{\pm 0.06}$ & 0.5467$_{\pm 0.0289}$ & \textbf{0.6167}$_{\pm 0.0058}$ & 0.58$_{\pm 0.0265}$ & 0.6167$_{\pm 0.0058}$ \\
HandMovementDirection & 10 & 0.4009$_{\pm 0.0206}$ & \textbf{0.5135}$_{\pm 0.027}$ & 0.482$_{\pm 0.0546}$ & 0.4279$_{\pm 0.0512}$ & 0.4009$_{\pm 0.0206}$ & 0.4414$_{\pm 0.0624}$ & 0.5135$_{\pm 0.027}$ \\
Handwriting & 3 & 0.482$_{\pm 0.0157}$ & 0.4529$_{\pm 0.0129}$ & 0.4588$_{\pm 0.0224}$ & 0.4235$_{\pm 0.0418}$ & 0.482$_{\pm 0.0157}$ & \textbf{0.5839}$_{\pm 0.0283}$ & 0.5839$_{\pm 0.0283}$ \\
Heartbeat & 61 & \texttt{NaN} & 0.7561$_{\pm 0.0098}$ & 0.7626$_{\pm 0.0123}$ & 0.7707$_{\pm 0.0098}$ & \textbf{0.7951}$_{\pm 0.0129}$ & 0.774$_{\pm 0.0123}$ & 0.7951$_{\pm 0.0129}$ \\
InsectWingbeatSubset & 200 & \texttt{NaN} & 0.4703$_{\pm 0.0051}$ & 0.4733$_{\pm 0.0127}$ & 0.4803$_{\pm 0.0302}$ & \textbf{0.591}$_{\pm 0.005}$ & 0.236$_{\pm 0.0125}$ & 0.591$_{\pm 0.005}$ \\
JapaneseVowels & 12 & \textbf{0.9811}$_{\pm 0.0054}$ & 0.9802$_{\pm 0.0031}$ & \textbf{0.9811}$_{\pm 0.0}$ & 0.9577$_{\pm 0.0271}$ & 0.9784$_{\pm 0.0047}$ & 0.9577$_{\pm 0.0128}$ & 0.9811$_{\pm 0.0054}$ \\
LSST & 6 & \textbf{0.7109}$_{\pm 0.0015}$ & 0.6795$_{\pm 0.0027}$ & 0.6894$_{\pm 0.0021}$ & 0.6929$_{\pm 0.0207}$ & \textbf{0.7109}$_{\pm 0.0015}$ & 0.6941$_{\pm 0.0053}$ & 0.7109$_{\pm 0.0015}$ \\
Libras & 2 & 0.9389$_{\pm 0.0}$ & 0.937$_{\pm 0.0032}$ & 0.9481$_{\pm 0.0064}$ & 0.8111$_{\pm 0.1392}$ & 0.9389$_{\pm 0.0}$ & \textbf{0.9704}$_{\pm 0.0032}$ & 0.9704$_{\pm 0.0032}$ \\
MotorImagery & 64 & \texttt{NaN} & 0.5933$_{\pm 0.0351}$ & 0.5833$_{\pm 0.0208}$ & 0.5867$_{\pm 0.0379}$ & \textbf{0.6}$_{\pm 0.01}$ & 0.59$_{\pm 0.0173}$ & 0.6$_{\pm 0.01}$ \\
NATOPS & 24 & 0.937$_{\pm 0.0116}$ & 0.9537$_{\pm 0.0064}$ & \textbf{0.9611}$_{\pm 0.0}$ & 0.8926$_{\pm 0.0032}$ & 0.8981$_{\pm 0.0116}$ & 0.8796$_{\pm 0.017}$ & 0.9611$_{\pm 0.0}$ \\
PEMS-SF & 963 & \texttt{NaN} & 0.8536$_{\pm 0.0067}$ & 0.8304$_{\pm 0.0145}$ & 0.7457$_{\pm 0.0473}$ & \textbf{0.9114}$_{\pm 0.0067}$ & 0.7476$_{\pm 0.0219}$ & 0.9114$_{\pm 0.0067}$ \\
PhonemeSpectra & 11 & 0.3421$_{\pm 0.0023}$ & 0.3351$_{\pm 0.009}$ & 0.3215$_{\pm 0.0052}$ & 0.3492$_{\pm 0.0033}$ & 0.344$_{\pm 0.0052}$ & \textbf{0.3547}$_{\pm 0.0047}$ & 0.3547$_{\pm 0.0047}$ \\
RacketSports & 6 & \textbf{0.9408}$_{\pm 0.0}$ & 0.9123$_{\pm 0.0152}$ & 0.9101$_{\pm 0.0076}$ & 0.9167$_{\pm 0.0038}$ & \textbf{0.9408}$_{\pm 0.0}$ & 0.9254$_{\pm 0.0038}$ & 0.9408$_{\pm 0.0}$ \\
SelfRegulationSCP1 & 6 & 0.9135$_{\pm 0.0071}$ & \textbf{0.917}$_{\pm 0.0052}$ & 0.9113$_{\pm 0.0034}$ & 0.9135$_{\pm 0.0079}$ & 0.9135$_{\pm 0.0071}$ & 0.901$_{\pm 0.009}$ & 0.917$_{\pm 0.0052}$ \\
SelfRegulationSCP2 & 7 & 0.5389$_{\pm 0.0096}$ & 0.5648$_{\pm 0.0449}$ & \textbf{0.5685}$_{\pm 0.0251}$ & 0.5611$_{\pm 0.0455}$ & 0.5389$_{\pm 0.0096}$ & 0.5482$_{\pm 0.021}$ & 0.5685$_{\pm 0.0251}$ \\
SpokenArabicDigits & 13 & 0.987$_{\pm 0.0009}$ & \textbf{0.9933}$_{\pm 0.0014}$ & 0.9906$_{\pm 0.0019}$ & 0.988$_{\pm 0.0027}$ & 0.9864$_{\pm 0.0028}$ & 0.9882$_{\pm 0.0016}$ & 0.9933$_{\pm 0.0014}$ \\
UWaveGestureLibrary & 3 & \textbf{0.9438}$_{\pm 0.0108}$ & 0.8583$_{\pm 0.0079}$ & 0.8552$_{\pm 0.0095}$ & 0.8635$_{\pm 0.0737}$ & \textbf{0.9438}$_{\pm 0.0108}$ & 0.9177$_{\pm 0.0048}$ & 0.9438$_{\pm 0.0108}$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Vasilii Feofanov
Songkang Wen
Marius Alonso
Romain Ilbert
Hongbo Guo
Malik Tiomoko
Lujia Pan
Jianfeng Zhang
Ievgen Redko

"Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification" by Vasilii Feofanov, Songkang Wen, Marius Alonso, Romain Ilbert, Hongbo Guo, Malik Tiomoko, Lujia Pan, Jianfeng Zhang, and Ievgen Redko, arXiv:2502.15637v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for Self-control}
\begin{tabular}{cccc}
\hline\hline
 & EWM & PoLeCe & Control \\
 \hline
 \multicolumn{4}{l}{Panel A. Outcome: Labor Supply} \\
Estimated Value: $\widehat{V} (\widehat{\pi})$ & $0.904$ & $0.903$ & $0.891$ \\
LCB: $\widehat{V}(\widehat{\pi})-\widehat q_{0.95,\Pi}\widehat{s}(\widehat{\pi})$ & $0.857$ & $0.858$ & $0.841$ \\
\hline
 \multicolumn{3}{l}{Panel B. Optimal Treatment Policy: } \\
(\texttt{Baseline sober}, \texttt{Owns rickshaw}) $=(0,0)$ & Control & Control & Control \\
(\texttt{Baseline sober}, \texttt{Owns rickshaw}) $=(0,1)$ & Control & Incentive & Control \\
(\texttt{Baseline sober}, \texttt{Owns rickshaw}) $=(1,0)$ & Choice & Choice & Control \\
(\texttt{Baseline sober}, \texttt{Owns rickshaw}) $=(1,1)$ & Choice & Choice & Control \\
\hline
\\
\end{tabular}
\end{table}
\end{document}

stat

Policy Learning with Confidence

Victor Chernozhukov
Sokbae Lee
Adam M. Rosen
Liyang Sun

"Policy Learning with Confidence" by Victor Chernozhukov, Sokbae Lee, Adam M. Rosen, and Liyang Sun, arXiv:2502.10653v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c}
Methods &BM&M1&M2&M3&M4\\
\hline\hline
RMSE & 4.046 (0.168) & 4.246 (0.161) & 4.211 (0.153) & 4.213 (0.156) & 4.210 (0.153)\\
\hline
runtime & 2106s (32s) & 70.3s (2.0s) & 70.7s (4.8s) & 70.7s (4.8s)& 70.7s (4.8s)
\end{tabular}
\caption{\scriptsize{Average RMSEs and run times (with standard deviations in brackets) over ten runs of non-distributed (BM) and distributed (M1-M4) GP regression with squared exponential covariance kernel for the Combined Cycle Power Plant data set .} }
\end{table}
\end{document}

math

Botond Szabo
Amine Hadji
Aad van der Vaart

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


math

"Spectral Representation and Simulation of Fractional Brownian Motion" by Konstantin A. Rybakov, arXiv:2412.12207v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Real time factor (RTF) for neural audio codecs.}
\begin{tabular}{c|c|cc}
			\toprule
 \multirow{2}{*}{Model} &\multirow{2}{*}{Bitrate} &\multicolumn{2}{c}{RTF}\\
 \cline{3-4}
			 & &Enc. &Dec.\\
			\hline
			Lyra-v2 &9.2 kbps &0.015 &0.034 \\
			 Encodec &12 kbps &0.103 &0.094\\
			Proposed &6 kbps &0.032 &0.028 \\
			\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Wenzhe Liu
Wei Xiao
Meng Wang
Shan Yang
Yupeng Shi
Yuyong Kang
Dan Su
Shidong Shang
Dong Yu

"A High Fidelity and Low Complexity Neural Audio Coding" by Wenzhe Liu, Wei Xiao, Meng Wang, Shan Yang, Yupeng Shi, Yuyong Kang, Dan Su, Shidong Shang, and Dong Yu, arXiv:2310.10992v1, 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{Tropical polynomial, polytope, and IVNN of the disjoint union of points.}
\begin{tabular}{|l||l|}
\hline
Poset & $\{x,y\}$ \\
\hline
Tropical & $0\oplus y\oplus x \oplus x\otimes y$ \\
 polynomial &$=\left(0\oplus y\otimes(0\oplus x)\right) \oplus \left(0\oplus x\otimes(0\oplus y)\right) $ \\
\hline
Polytope & $\{0\leq x\leq 1, 0\leq y\leq 1\}$ \\
\hline
IVNN & ${\text{ReLU}_0\left(\begin{bmatrix}
\text{ReLU}_0(x)&
\text{ReLU}_{-\infty}(y)\\
\text{ReLU}_0(y)&
\text{ReLU}_{-\infty}(x)
\end{bmatrix}\begin{bmatrix}
1\\
1\\
\end{bmatrix}\right)}$ \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Eric Dolores-Cuenca
Aldo Guzman-Saenz
Sangil Kim
Susana Lopez-Moreno
Jose Mendoza-Cortes

"Order Theory in the Context of Machine Learning" by Eric Dolores-Cuenca, Aldo Guzman-Saenz, Sangil Kim, Susana Lopez-Moreno, and Jose Mendoza-Cortes, arXiv:2412.06097v2, 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{Merged Categories of RELP Feature in ACSIncome and ACSPublicCoverage.}
\begin{tabular}{cl}
\toprule
\textbf{Value} & \textbf{Description} \\ \midrule
1 & Reference Person \\
2 & Immediate Family \\
3 & Extended Family \\
4 & Non-Family Residents \\
4 & Group Quarters Population \\
5 & Unknown \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Yuan Cao
Yihan He
Dennis Wu
Hong-Yu Chen
Jianqing Fan
Han Liu

"Transformers Simulate MLE for Sequence Generation in Bayesian Networks" by Yuan Cao, Yihan He, Dennis Wu, Hong-Yu Chen, Jianqing Fan, and Han Liu, arXiv:2501.02547v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Result for the GPL fine-tuning on the pre-trained MSMARCO model.}
\begin{tabular}{l|c|c} % <-- Changed to S here.
 \textbf{Model} & \textbf{Score 1} & \textbf{Score 2}\\
 \hline
 \texttt{msmarco} & 0.14 & 0.79 \\
 \texttt{msmarco} + GPL & 0.15 & 0.84\\
 Improvement & 7$ \%$ & 6$ \%$ \\
 \end{tabular}
\end{table}
\end{document}

q-fin

Ixandra Achitouv
Dragos Gorduza
Antoine Jacquier

"Natural Language Processing for Financial Regulation" by Ixandra Achitouv, Dragos Gorduza, and Antoine Jacquier, arXiv:2311.08533v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Sahibzada Adil Shahzad
Ammarah Hashmi
Yan-Tsung Peng
Yu Tsao
Hsin-Min Wang

"AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection" by Sahibzada Adil Shahzad, Ammarah Hashmi, Yan-Tsung Peng, Yu Tsao, and Hsin-Min Wang, arXiv:2311.02733v1, 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}
 Model & $\rho_c$ & $T_c$ & $\beta$ & $\gamma$ \\
 \hline
 \hline
 Ising 2D (exact) & 0.5 & & 0.125 & 1.75\\
 $\alpha=1/2$, $\sigma=1$ & 0.309(5) & 0.0983(5) & 0.120(8) & 1.71(5)\\
 $\alpha=3/2$, $\sigma=1$ & 0.271(5) & 0.0620(2) & 0.119(5) & 1.74(5)
 \end{tabular}
\caption{Critical density and exponents for nonequilibrium Sakoda-Schelling model for $\alpha=1/2$ and $\alpha=3/2$.}
\end{table}
\end{document}

q-fin

Ruben Zakine
Jerome Garnier-Brun
Antoine-Cyrus Becharat
Michael Benzaquen

"Socioeconomic agents as active matter in nonequilibrium Sakoda-Schelling models" by Ruben Zakine, Jerome Garnier-Brun, Antoine-Cyrus Becharat, and Michael Benzaquen, arXiv:2307.14270v2, 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}{lc} \toprule
Investment horizon $T$ (years) & 10 \\
Equity market indexes & CRSP cap-weighted/equal-weighted index (real) \\
Bond index & CRSP 30-day/10-year U.S. treasury index (real) \\
Index samples for bootstrap& Concatenated 1940:8-1951:7, 1968:9-1985:10\\
Initial portfolio wealth/annual cash injection & 100/10 \\
Rebalancing frequency & Monthly\\
Maximum leverage & 1.3\\
Outperformance target rate $\beta$ & 2\%\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \toprule
 & Specificity & C.I. (95\%)\\
 \midrule
 Radiologist 1	& 87.1\% & [75.6\% -- 94.3\%]\\
 Radiologist 2 &	87.1\% & [75.6\% -- 94.3\%]\\
 Radiologist 3 &	90.3\% & [79.5\% -- 96.5\%]\\
 \midrule
 AI Model without lung segmentation &	87.1\% & [75.6\% -- 94.3\%]\\
 AI Model with lung segmentation	& \textbf{93.5}\% &\textbf{[83.5\% -- 98.5\%]}\\
 \bottomrule
 \hline
 \end{tabular}
\caption{Specificity (together with 95\% confidence interval) comparison between manual readings of expert radiologists and the AI model for COVID-19 detection without lung segmentation and AI model with segmentation.}
\end{table}
\end{document}

eess

Matteo Pennisi
Isaak Kavasidis
Concetto Spampinato
Vincenzo Schininà
Simone Palazzo
Francesco Rundo
Massimo Cristofaro
Paolo Campioni
Elisa Pianura
Federica Di Stefano
Ada Petrone
Fabrizio Albarello
Giuseppe Ippolito
Salvatore Cuzzocrea
Sabrina Conoci

"An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans" by Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato, Vincenzo Schininà, Simone Palazzo, Francesco Rundo, Massimo Cristofaro, Paolo Campioni, Elisa Pianura, Federica Di Stefano, Ada Petrone, Fabrizio Albarello, Giuseppe Ippolito, Salvatore Cuzzocrea, and Sabrina Conoci, arXiv:2101.11943v1, 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{Parameters of convolutional and pooling layers.}
\begin{tabular}{c||c}
 \hline \hline
 Channel inputs / outputs & $C_{in}$ / $C_{out}$ \\
 \hline \hline
 Input width / height & $W_{in}$ / $H_{in}$ \\
 \hline
 Output width / height & $W_{out}$ / $H_{out}$ \\
 \hline
 Kernel width / height & $K_w$ / $K_h$ \\
 \hline
 Padding width / height & $P_w$ / $P_h$ \\
 \hline
 Stride width / height & $S_w$ / $S_h$ \\
 \hline
 Dilation width / height & $D_w$ / $D_h$ \\
 \hline
 Output padding width / height & $P_{wo}$ / $P_{ho}$ \\
 \hline \hline
 \end{tabular}
\end{table}
\end{document}

math

Nikos G. Evgenidis
Nikos A. Mitsiou
Sotiris A. Tegos
Panagiotis D. Diamantoulakis
George K. Karagiannidis

"Split Learning in Computer Vision for Semantic Segmentation Delay Minimization" by Nikos G. Evgenidis, Nikos A. Mitsiou, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, and George K. Karagiannidis, arXiv:2412.14272v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Insertion (ins), deletion (del), and substitution (sub) error comparison for noise vectors compared with the base model, for AMI (dev) and Aurora-4 (eval92).}
\begin{tabular}{llcccc}
\toprule
\textbf{Dataset} & \textbf{System} & \textbf{Ins} & \textbf{Del} & \textbf{Sub} & \textbf{WER} \\
\midrule
\multirow{2}{*}{AMI (dev)} & TDNN & 4.7 & 12.6 & 21.5 & 38.8 \\
& + noise vector & 3.2 & 11.5 & 20.3 & 34.9 \\
\midrule
\multirow{2}{*}{\shortstack[l]{Aurora-4\\(eval92)}} & TDNN-F & 0.50 & 2.57 & 4.87 & 7.94 \\
& + noise vector & 0.58 & 2.12 & 4.67 & 7.37 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Desh Raj
Jesus Villalba
Daniel Povey
Sanjeev Khudanpur

"Frustratingly Easy Noise-aware Training of Acoustic Models" by Desh Raj, Jesus Villalba, Daniel Povey, and Sanjeev Khudanpur, arXiv:2011.02090v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
		\hline
		Parameter & $A_1$ & $A_2$\\
\hline
		$q$ & $0.3$ & $0.8$ \\
		$r$ & $2\%$ & $3\%$ \\
		$R$ & $6\%$ & $10\%$ \\
		$b$ & $0.4$ & $0.6$ \\
		$s$ & $0.25$ & $0.05$ \\
		$\mu$ & $\displaystyle \begin{pmatrix} 8\% \\ 10\% \end{pmatrix}$ & $\displaystyle \begin{pmatrix} 16\% \\ 8\% \end{pmatrix}$ \\
		\hline
	\end{tabular}
\caption{Parameter sets $A_1$ and $A_2.$}
\end{table}
\end{document}

q-fin

"Optimal investment with insurable background risk and nonlinear portfolio allocation frictions" by Hugo E. Ramirez and Rafael Serrano, arXiv:2303.04236v1, 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{Results for each approach per instance size. The NN-VFA approach mostly outperforms all other methods, while both NN-VFA and DL-VFA outperform all benchmarks. The gap for re-optimization is the average gap over the 30 solutions in the horizon.}
\begin{tabular}{lrrrrrrrr}
\hline
\multirow{2}{*}{\textbf{1 district}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 11.99 & 0.00\% & 3238 (±233) & 1483 & 855 & 900 & 16 & 62\% \\
Re-optimization & 19.97 & 0.00\% & 3876 (±120) & 1656 & 1320 & 900 & 19 & 63\% \\
Rule-based & 0.01 & & 4974 (±569) & 924 & 0 & 4050 & 12 & 93\% \\
PPO & 3411.16 & & 4034 (±165) & 1109 & 2925 & 0 & 17 & 69\% \\
DL-VFA & 151.05 & & \textbf{3536 (±335)} & 1361 & 1245 & 900 & 23 & 67\% \\
NN-VFA & 2625.25 & & 3544 (±229) & 1324 & 1320 & 900 & 22 & 67\% \\
\hline
\multirow{2}{*}{\textbf{2 districts}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 82.05 & 0.00\% & 5293 (±199) & 2063 & 1670 & 1560 & 18 & 75\% \\
Re-optimization & 944.78 & 0.00\% & 6107 (±384) & 2147 & 2160 & 1800 & 20 & 78\% \\
Rule-based & 0.01 & & 7492 (±819) & 1772 & 380 & 5340 & 12 & 95\% \\
PPO & 3535.05 & & 6408 (±436) & 2028 & 2220 & 2160 & 23 & 88\% \\
DL-VFA & 265.05 & & 5620 (±151) & 2100 & 1840 & 1680 & 19 & 81\% \\
NN-VFA & 4885.34 & & \textbf{5585 (±213)} & 2225 & 2160 & 1200 & 20 & 75\% \\
\hline
\multirow{2}{*}{\textbf{3 districts}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 1580.71 & 0.00\% & 7525 (±243) & 2730 & 2095 & 2700 & 22 & 79\% \\
Re-optimization & 4713.82 & 0.01\% & 8707 (±320) & 3287 & 3020 & 2400 & 22 & 79\% \\
Rule-based & 0.01 & & 10,822 (±956) & 2757 & 685 & 7380 & 15 & 96\% \\
PPO & 3299.36 & & 10,233 (±473) & 3328 & 3295 & 3610 & 26 & 98\% \\
DL-VFA & 404.46 & & 8350 (±323) & 3005 & 2795 & 2550 & 21 & 79\% \\
NN-VFA & 6768.44 & & \textbf{7973 (±332)} & 2753 & 3150 & 2070 & 21 & 81\% \\
\hline
\multirow{2}{*}{\textbf{4 districts}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 13,569.09 & 6.02\% & 10,191 (±357) & 3111 & 2220 & 4860 & 20 & 80\% \\
Re-optimization & 10,780.63 & 1.14\% & 12,112 (±522) & 4122 & 3270 & 4320 & 25 & 80\% \\
Rule-based & 0.01 & & 14,300 (±1810) & 3180 & 1280 & 9840 & 16 & 99\% \\
PPO & 3350.69 & & 13,935 (±807) & 4695 & 4710 & 4530 & 27 & 100\% \\
DL-VFA & 589.45 & & 11,546 (±347) & 4611 & 4025 & 2910 & 24 & 79\% \\
NN-VFA & 8796.20 & & \textbf{11,066 (±319)} & 4156 & 4510 & 2400 & 19 & 80\% \\
\hline
\multirow{2}{*}{\textbf{5 districts}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 14,404.06 & 21.53\% & 13,357 (±488) & 4352 & 2195 & 6810 & 20 & 80\% \\
Re-optimization & 15,971.49 & 1.81\% & 15,326 (±746) & 4966 & 4780 & 5580 & 23 & 81\% \\
Rule-based & 0.01 & & 16,947 (±1468) & 4357 & 2090 & 10,500 & 18 & 98\% \\
PPO & 3671.79 & & 17,542 (±1014) & 5492 & 6500 & 5550 & 23 & 96\% \\
DL-VFA & 770.24 & & 14,758 (±775) & 4468 & 5700 & 4590 & 20 & 86\% \\
NN-VFA & 13,913.97 & & \textbf{14,206 (±640)} & 5111 & 5585 & 3510 & 23 & 78\% \\
\hline
\multirow{2}{*}{\textbf{6 districts}} & \multirow{2}{*}{Runtime (s)} & \multirow{2}{*}{Gap} & Total costs & Deprivation & UAV & Truck & Max. deprivation & Demand \\
 & & & (±std.dev.) & costs & costs & costs & time (h) & coverage \\ \hline
PI-bound & 14,404.85 & 25.10\% & 17,699 (±729) & 5519 & 2970 & 9210 & 22 & 79\% \\
Re-optimization & 20,314.81 & 2.64\% & 20,652 (±652) & 6552 & 6040 & 8060 & 25 & 80\% \\
Rule-based & 0.01 & & 23,496 (±1018) & 5411 & 3055 & 15,030 & 19 & 98\% \\
PPO & 3372.81 & & 24,784 (±848) & 6724 & 9360 & 8700 & 28 & 98\% \\
DL-VFA & 948.49 & & \textbf{19,041 (±832)} & 6096 & 7425 & 5520 & 22 & 85\% \\
NN-VFA & 14,400.14 & & 20,203 (±640) & 6333 & 9190 & 4680 & 28 & 79\% \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Robert van Steenbergen
Wouter van Heeswijk
Martijn Mes

"The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs" by Robert van Steenbergen, Wouter van Heeswijk, and Martijn Mes, arXiv:2312.00140v1, 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{The experimental results for various methods under different metrics are presented for the FOP defined as $\min \{ -(\theta + u)^{\top} x \mid \parallel x \parallel_2^2 \leq a^2 \} = \min \left\{-\sum_{k=1}^{p} (\theta_k + u_k) x_k \mid \parallel x \parallel_2^2 \leq a^2 \right\}$ in hte \textbf{Noisy Decision} 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|}{Parameter Error} & \multicolumn{4}{c|}{Decision Error} & \multicolumn{4}{c}{Regret} \\ \cline{2-13}
 & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA \\ \hline
50 & \textbf{1.02} & - & 4.59 & - & \textbf{0.24} & - & 1.08 & - & \textbf{0.09} & - & 0.38 & - \\
100 & \textbf{0.67} & - & 4.80 & - & \textbf{0.11} & - & 1.13 & - & \textbf{0.04} & - & 0.40 & - \\
300 & \textbf{0.39} & - & 4.93 & - & \textbf{0.04} & - & 1.16 & - & \textbf{0.01} & - & 0.41 & - \\
500 & \textbf{0.30} & - & 4.96 & - & \textbf{0.02} & - & 1.17 & - & \textbf{0.01} & - & 0.42 & - \\
1000 & \textbf{0.23} & - & 4.98 & - & \textbf{0.01} & - & 1.18 & - & \textbf{0.00} & - & 0.42 & - \\ \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.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Computation time in minutes to fit the lasso regularization path for each modelling strategy for the simulation model with no causal predictor (0\% heritability) and for the simulation model with 100 causal predictors explaining 2\% of heritability for both continuous and binary phenotypes. We present the median value with IQR in brackets.}
\begin{tabular}{ll|cccc}
 \hline
& & \multicolumn{2}{c}{Binary phenotypes} & \multicolumn{2}{c}{Continuous phenotypes} \\
Number of PCs & GRM & 0\% & 2\% & 0\% & 2\% \\
 \hline
0 & full & 82.3 (17.6) & 88.0 (19.8) & 60.8 (18.6) & 66.8 (26.4) \\
 & sparse & 34.2 (10.5) & 35.1 (6.9) & 25.0 (15.2) & 24.2 (9.5) \\
 10 & full & 96.0 (14.8) & 97.2 (15.3) & 113 (29.4) & 81.5 (19.7) \\
 & sparse & 45.2 (8.6) & 40.0 (8.5) & 57.5 (16.3) & 35.1 (8.1) \\
 \hline
\end{tabular}
\end{table}
\end{document}

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}{c|c|c|c|c|c|c|c}
 \toprule
 & RSR & RFR & min-path &
 $33\%$-path & $50\%$-path &
 $66\%$-path & max-path \\
 \midrule
 $\eta = 4$ & 25.22 & 26.07 & 26.85 & 24.62 & \textbf{31.81} & 24.91 & 24.01 \\
 $\eta = 3$ & 23.89 & 24.75 & 21.48 & 21.76 & \textbf{32.23} & 28.45 & 28.45 \\
 $\eta = 2$ & 25.10 & 18.20 & 20.91 & \textbf{27.15} & 25.67 & 25.15 & 22.74 \\
 $\eta = 1$ & 21.80 & 18.32 & 21.97 & \textbf{26.66} & 26.53 & 21.89 & 21.67 \\
 \bottomrule
 \end{tabular}
\caption{Average percentage of individuals reclassified as healthy based on our simulations for the different methodologies.}
\end{table}
\end{document}

math

Víctor Blanco
Alberto Japón
Justo Puerto
Peter Zhang

"Optimal probabilistic feature shifts for reclassification in tree ensembles" by Víctor Blanco, Alberto Japón, Justo Puerto, and Peter Zhang, arXiv:2412.03722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
 \toprule
 \textbf{Purpose} & \textbf{Attribute group} & \textbf{Condition} \\
 \midrule
 Improve service & Personal, Location & \\
 Provide service & Personal & \\
 Identify & Individual & \\
 Collect, measure, and process autism risk & Child & \\
 Extracting acoustic features & Acoustic & \\
 Provide service & Friend & \\
 Fulfill request & Friend & \\
 Anti-fraud & Friend & \\
 Keep customer posted & Personal & \\
 Internal improvement & Personal & \\
 Send service information & Contact information & Consent = True \\
 Send notice & Personal & \\
 Auditing & Personal & \\
 Data analysis & Personal & \\
 Research & Personal & \\
 Research & Non-personal & \\
 Understand customer behavior & Location & \\
 Extract vocal and disorder relationships & Medical & \\
 Fighting spam/malware & Browser &\\
 Facilitate data collection & Browser &\\
 Identify web browser & Cookies & \\
 Find site visit statistics & Cookies & \\
 Marketing & Data & Subscription = True\\
 Send research participation request & Contact information & \\
 Facilitate interaction & Profile\\
 Provide product & Personal & \\
 Promote services & Service information & \\
 \bottomrule
 \end{tabular}
\caption{ChatterBaby\texttrademark's purpose-attribute permissions}
\end{table}
\end{document}

cs

"The Privacy Policy Permission Model: A Unified View of Privacy Policies" by Maryam Majedi and Ken Barker, arXiv:2403.17414v1, 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
$\bar\kappa$ & 0.1 & 0.5 & \textbf{1} & 1.3\\
\hline
rate & -0.104 & -0.171 & \textbf{-0.207} & -0.199\\
\hline
\end{tabular}
\caption{Empirical convergence rate for the conventional estimator for difference choices of hyperparameter $\bar\kappa$, when $\alpha=2$ and $s=0.2$.}
\end{table}
\end{document}

math

Xuhui Zhang
Jose Blanchet
Youssef Marzouk
Viet Anh Nguyen
Sven Wang

"Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes" by Xuhui Zhang, Jose Blanchet, Youssef Marzouk, Viet Anh Nguyen, and Sven Wang, arXiv:2312.09862v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model Configurations}
\begin{tabular}{|c|c|}
\hline
\textbf{Configuration Parameter} & \textbf{Value} \\
\hline
unique\_bytes & 256 \\
\hline
hidden\_size & 768 \\
\hline
num\_hidden\_layers & 12 \\
\hline
num\_attention\_heads & 12 \\
\hline
intermediate\_size & 3072 \\
\hline
max\_position\_embeddings & 1460 \\
\hline
num\_labels & 2 or 3\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Kyle Stein
Arash Mahyari
Guillermo Francia
Eman El-Sheikh

"A Transformer-Based Framework for Payload Malware Detection and Classification" by Kyle Stein, Arash Mahyari, Guillermo Francia, and Eman El-Sheikh, arXiv:2403.18223v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Junzhe Jiang
Chang Yang
Xinrun Wang
Bo Li

"Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction" by Junzhe Jiang, Chang Yang, Xinrun Wang, and Bo Li, arXiv:2506.03153v1, 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}{cccccc}
\hline
 & {\small{}(0)} & {\small{}(1)} & {\small{}(2)} & {\small{}(3)} & {\small{}(4)}\tabularnewline
{\small{}Price cartel} & {\small{}No} & {\small{}Yes} & {\small{}Yes} & {\small{}Yes} & {\small{}Yes}\tabularnewline
{\small{}Panasonic} & {\small{}6000h \& 10000h} & {\small{}6000h \& 10000h} & {\small{}6000h only} & {\small{}6000h only} & {\small{}6000h \& 10000h}\tabularnewline
{\small{}Toshiba} & {\small{}6000h \& 12000h} & {\small{}6000h \& 12000h} & {\small{}6000h only} & {\small{}6000h \& 12000h} & {\small{}6000h only}\tabularnewline
\hline
\hline
{\small{}Joint profit} & {\small{}24.67} & {\small{}40.42} & {\small{}38.05} & {\small{}39.45} & {\small{}39.2}\tabularnewline
{\small{}Profit (Panasonic)} & {\small{}10.77} & {\small{}17.47} & {\small{}15.95} & {\small{}14.69} & {\small{}18.91}\tabularnewline
{\small{}Profit (Toshiba)} & {\small{}13.9} & {\small{}22.96} & {\small{}22.1} & {\small{}24.76} & {\small{}20.29}\tabularnewline
\hline
{\small{}No inventory consumers (\%)} & {\small{}18.61} & {\small{}20.67} & {\small{}22.7} & {\small{}21.19} & {\small{}21.85}\tabularnewline
{\small{}Average price (1000h Inc.; yen)} & {\small{}94.73} & {\small{}126.74} & {\small{}127.02} & {\small{}126.84} & {\small{}126.85}\tabularnewline
{\small{}Average price (2000h Inc.; yen)} & {\small{}172.57} & {\small{}233.57} & {\small{}231.37} & {\small{}232.71} & {\small{}232.32}\tabularnewline
{\small{}Average price (CFL; yen)} & {\small{}796.53} & {\small{}989.49} & {\small{}907.7} & {\small{}952.41} & {\small{}956.26}\tabularnewline
{\small{}Disposal (million)} & {\small{}3.04} & {\small{}3.21} & {\small{}3.51} & {\small{}3.28} & {\small{}3.39}\tabularnewline
\hline
{\small{}$\Delta$CS} & {\small{}-} & {\small{}-23.17} & {\small{}-24.7} & {\small{}-23.82} & {\small{}-23.93}\tabularnewline
{\small{}$\Delta$PS (excluding fixed cost)} & {\small{}-} & {\small{}17.79} & {\small{}15.55} & {\small{}16.87} & {\small{}16.64}\tabularnewline
{\small{}$\Delta$TS (excluding Ext. / fixed costs)} & {\small{}-} & {\small{}-5.38} & {\small{}-9.14} & {\small{}-6.95} & {\small{}-7.3}\tabularnewline
{\small{}$\Delta$Externality (electricity usage)} & {\small{}-} & {\small{}-0.77} & {\small{}-0.71} & {\small{}-0.73} & {\small{}-0.74}\tabularnewline
{\small{}$\Delta$Externality (waste disposal)} & {\small{}-} & {\small{}-0.02} & {\small{}0.01} & {\small{}-0.01} & {\small{}0.00}\tabularnewline
{\small{}$\Delta$TS (excluding fixed costs)} & {\small{}-} & {\small{}-4.59} & {\small{}-8.45} & {\small{}-6.2} & {\small{}-6.55}\tabularnewline
{\small{}Upper bound of Fixed cost savings} & {\small{}-} & {\small{}0} & {\small{}1.95} & {\small{}1.05} & {\small{}0.9}\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fixed points of the Harper model }
\begin{tabular}{lll}
\hline
$(\phi^*,p^*)$ & $E$ & type \\
\hline
$(0,0)$ & $-\epsilon$ & stable \\
$(\pi,\pi)$ & $2a+ \epsilon$ & stable \\
$(\pi,0)$ & $\epsilon$ & hyperbolic \\
$(0,\pi)$ & $2a-\epsilon$ & hyperbolic \\
\hline
\end{tabular}
\end{table}
\end{document}

math

"Quantum chaos on the separatrix of the periodically perturbed Harper model" by Alice C. Quillen and Abobakar Sediq Miakhel, arXiv:2412.14926v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l}
\toprule
\textit{Oracle Phrase Summary} \\
\midrule
1. Ohm's Law (4) \\
2. Resistors/Resistance (6) \\
3. Circuits (4) \\
4. Power (2) \\
5. Real world applications (3) \\
\midrule
\textit{PhraseSum} \\
\midrule
1. the copper wire example \\
2. Ohm's Law \\
3. the resistors \\
4. real world incidences \\
5. how easy calculating resistance \\
\midrule
\textit{GPT-Human} \\
\midrule
1. Ohm's Law and circuit analysis (8)\\ 2. Resistance in series and parallel (4) \\3. Battery voltage and electromotive force (3) \\4. Applying physics concepts to real life (2) \\5. Power and energy in circuits (2)\\
\midrule
\textit{GPT-noun phrase} \\
\midrule
1. Copper wire example \\2. Ohm's Law \\3. Circuits\\ 4. Batteries and resistance\\ 5. Practice problems \\
\midrule
\textit{GPT-Human + noun} \\
\midrule
1. Circuit analysis (4) \\
2. Ohm's Law (3) \\ 3. Resistance calculation (2) \\ 4. Battery behavior (2) \\ 5. Power and energy (2) \\
\midrule
\textit{GPT-noun - one-shot} \\
\midrule
1. Copper wire example \\
2. Ohm's Law \\
3. Circuits \\
4. Resistance calculations \\
5. Batteries and their characteristics \\
\bottomrule
\end{tabular}
\caption{An example of the extractive phrase summary and different model outputs.}
\end{table}
\end{document}

cs

Yang Zhong
Mohamed Elaraby
Diane Litman
Ahmed Ashraf Butt
Muhsin Menekse

"ReflectSumm: A Benchmark for Course Reflection Summarization" by Yang Zhong, Mohamed Elaraby, Diane Litman, Ahmed Ashraf Butt, and Muhsin Menekse, arXiv:2403.19012v2, 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{ {Improve In-context Learning with Speech ICT. {\em positive ratio} is defined as the percentage of ground-truth words in augment.}}
\begin{tabular}{rrc}
\toprule
\multicolumn{2}{c}{Speech ICT training setup} & Eval with 64 words \\
positive ratio, \% & \# of keywords & F-score ({\em P/R}) \\
\midrule
n/a & 0 & 0.38 (0.82/0.25) \\
\midrule
33\% & 3 & 0.52 (0.59/0.47) \\
33\% & 64 & 0.52 (0.62/0.44) \\
 \textbf{6\%} & \textbf{64} & 0.56 (0.74/0.45) \\
3\% & 64 & 0.55 (0.79/0.42) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllll}
\hline
Forecast & Mean & SD & Skew & Kurtosis \\
\hline
\hline
\multicolumn{5}{c}{\textbf{High-Low Sorted Straddle Portfolio Returns}} \\
\hline
PCA + Lagged RVs & 0.0206 & 0.0256 & 2.11 & 18.64 \\
Average Forecasts & 0.0195 & 0.0248 & 1.75 & 14.47 \\
HAR & 0.0139 & 0.0221 & 0.603 & 8.04 \\
Rolling SD Squared & 0.0143 & 0.0248 & 0.966 & 7.35 \\
LASSO & 0.0118 & 0.0206 & 0.501 & 6.33 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Predicting Realized Variance Out of Sample: Can Anything Beat The Benchmark?" by Austin Pollok, arXiv:2506.07928v1, 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{Performance comparison of financial language models across benchmark tasks.}
\begin{tabular}{lrrrrrrr}
 \toprule
 Model & Vocabulary (K) & \#Params (M) & CoLA & SST2 & QQP & MNLI & QNLI \\
 \midrule
 FinBERT & $30$ & $110$ & $0.29$ & $0.89$ & $0.87$ & $0.79$ & $0.86$ \\
 StoriesLM & $30$ & $110$ & \textbf{$0.49$} & $0.90$ & $0.87$ & $0.80$ & $0.87$ \\
 NoLBERT & $30$ & $109$ & $0.43$ & \textbf{$0.91$} & \textbf{$0.91$} & \textbf{$0.82$} & \textbf{$0.89$} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"NoLBERT: A No Lookahead(back) Foundational Language Model for Empirical Research" by Ali Kakhbod and Peiyao Li, arXiv:2509.01110v1, 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{Heat pump data}
\begin{tabular}{llrrrr}
 \hline
 & & \textbf{Reference} & \textbf{Slow} & \textbf{Government} & \textbf{Fast} \\
 \hline
 Number of installed heat pumps & [million] & 1.7 & 3.0 & 6.0 & 10.0 \\
 Heat pump power rating & [GW\textsubscript{e}] & 8.7 & 14.5 & 27.5 & 52.6 \\
 Maximum thermal heat pump output & [GW\textsubscript{th}] & 19.6 & 32.7 & 61.9 & 118.5 \\
 Share of air-sourced heat pumps & & 0.8 & 0.8 & 0.8 & 0.8 \\
 Share of ground-sourced heat pumps & & 0.2 & 0.2 & 0.2 & 0.2 \\
 Yearly heat supplied by heat pumps & [TWh\textsubscript{th}] & 24.7 & 53.2 & 92.9 & 226.3 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Alexander Roth
Carlos Gaete-Morales
Dana Kirchem
Wolf-Peter Schill

"Power sector benefits of flexible heat pumps" by Alexander Roth, Carlos Gaete-Morales, Dana Kirchem, and Wolf-Peter Schill, arXiv:2307.12918v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
 \toprule
 type & nominal power & marginal cost & quadratic marginal cost & ramp limit & max p factor \\
 \midrule
 coal & 200 & 0.005 & 0.0005 & $\mathbf{z}_1$ & -\\
 gas & 100 & 0.015 & 0.0005 & 0.5 &-\\
 wind & 60 &0.02 & 0.005& - & $\mathbf{z}_2$\\
 \bottomrule
 \end{tabular}
\caption{Parameters input into PyPSA generator. `max p factor` refer to `p\_max\_pu`, the maximum power at a snapshot given as a fraction of nominal power.}
\end{table}
\end{document}

stat

BILBO: BILevel Bayesian Optimization

Ruth Wan Theng Chew
Quoc Phong Nguyen
Bryan Kian Hsiang Low

"BILBO: BILevel Bayesian Optimization" by Ruth Wan Theng Chew, Quoc Phong Nguyen, and Bryan Kian Hsiang Low, arXiv:2502.02121v1, 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 Accuracy for The Vessel Segmentation in Pathological Images of STARE Dataset}
\begin{tabular}{|l|c|}
 \hline
 \textbf{Method} & \textbf{Accuracy} \\
 \hline
 \multicolumn{2}{|c|}{Unsupervised Methods} \\
 \hline
 Lam et al. & 0.9556\\
 \hline
 Ricci and Perfetti (Implemented in & 0.9352 \\
 \hline
 Mendonca and Campilho & 0.9426 \\
 \hline
 Jiang and Mojon & 0.9337 \\
 \hline
 Method 1 & 0.9438\\
 \hline
 Method 2 & 0.9404\\
 \hline
 \multicolumn{2}{|c|}{Supervised Methods} \\
 \hline
 Soares et al. (DRIVE) & 0.9428\\
 \hline
 Soares et al. (STARE) & 0.9425\\
 \hline
 Reviewer 2 & 0.9410\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Automated Localization of Blood Vessels in Retinal Images" by Vahid Mohammadi Safarzadeh, arXiv:2401.02962v1, 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{Numerical comparison of four methods using BraTS 2018 testing set. Note that smaller mean L1 error and larger SSIM mean error indicate higher similarity.}
\begin{tabular}{c|c|c|c|cccc|ccc}
\hline
Methods& ~mean L1 error~$\downarrow$ ~~&~~~SSIM~$\uparrow$~~&	~~~PSNR~$\uparrow$~~	& Inception Score $\uparrow$\\\hline\hline
Patch-match 	&445.8&	0.9460&	29.55&	9.13\\
GLC 	&432.6&	0.9506&	30.34&	9.68\\
Partial Conv 	&373.2&	0.9512&	33.57&	9.77\\\hline
Proposed	&292.5&	0.9667&	34.26&	10.26\\
Proposed+symmetry&	\textbf{254.8}&	\textbf{0.9682}	&\textbf{34.52}&	\textbf{10.58}\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The analysis parameters of the STFT losses in our work. Hanning window is used in the signal analysis.}
\begin{tabular}{ccc}
\textbf{Frame shift} & \textbf{Frame length} & \textbf{FFT size} \\ \hline
50 & 240 & 512 \\ \hline
120 & 600 & 1024 \\ \hline
240 & 1200 & 2048 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Haohan Guo
Heng Lu
Na Hu
Chunlei Zhang
Shan Yang
Lei Xie
Dan Su
Dong Yu

"Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training" by Haohan Guo, Heng Lu, Na Hu, Chunlei Zhang, Shan Yang, Lei Xie, Dan Su, and Dong Yu, arXiv:2012.01837v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Maxim Olshanskii
Jan-Phillip Bäcker
Dmitri Kuzmin

"Projected gradient stabilization of sharp and diffuse interface formulations in unfitted Nitsche finite element methods" by Maxim Olshanskii, Jan-Phillip Bäcker, and Dmitri Kuzmin, arXiv:2501.16594v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 \textbf{Method} & \text{Eigendecom.} & \text{Sparse Cons.} & \text{Sparse Samp.} & \text{Sparse Samp.} & \text{Sparse Samp.} & \text{Sparse Samp.} \\ \hline
 \textbf{PCC} & .9427 & .9773 & .9741 & .9625 & .8256 & .7152\\ \hline
 \textbf{Feedbacks} & 134912 & 8390656 $\approx$ (8 mil) & 10 mil & 4 mil & 2 mil & 1 mil\\ \hline
 \end{tabular}
\caption{Feedback sizes and Pearson Correlation Coefficients for the SAE dictionary for the ChessGPT under different feedback methods: (Dimension of the dictionary): $4096 \times 512$, rank of the feature matrix $\sf{D}\sf{D}^\top$ is $512$, $s$-sparsity: 3, sparse distribution: uniform.}
\end{table}
\end{document}

stat

"The Complexity of Learning Sparse Superposed Features with Feedback" by Akash Kumar, arXiv:2502.05407v2, 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{\textbf{Impact of weight factor of matched feature. } All the results are evaluated on \emph{mini}ImageNet for $5$-way $1$-shot task. The best results are bold.}
\begin{tabular}{lc}
 \toprule
 weight factor & accuracy \\
 \midrule
 CFMN with $\lambda=0.00$ & $50.89\% $ \\
 CFMN with $\lambda=0.25$ & $52.02\% $ \\
 CFMN with $\lambda=0.50$ & $\mathbf{52.98\% }$ \\
 CFMN with $\lambda=0.75$ & $50.28\% $ \\
 CFMN with $\lambda=1.00$ & $45.59\% $ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Mengting Chen
Xinggang Wang
Heng Luo
Yifeng Geng
Wenyu Liu

"Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition" by Mengting Chen, Xinggang Wang, Heng Luo, Yifeng Geng, and Wenyu Liu, arXiv:2101.05018v1, 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{Vehicle Parameters}
\begin{tabular}{lll}
			\hline\hline \\
			 Name & Symbol&
			Unit\\ \hline
Front wheel cornering stiffness &$k_1$ & -88000 [N/rad] \\
Rear wheel cornering stiffness &$k_2$ & -94000 [N/rad] \\
Mass &$m$ & 1500 [kg] \\
Distance from CG to front axle &$a$ & 1.14 [m] \\
Distance from CG to rear axle &$b$ & 1.40 [m] \\
Polar moment of inertia at CG &$I_z$ & 2420 [kg$\cdot\mathrm{m}^2$] \\
Tire-road friction coefficient &$\mu$ & 1.0 \\
Sampling frequency &$f$ & 20 [Hz] \\
System frequency & & 20 [Hz] \\
			\hline\hline
		\end{tabular}
\end{table}
\end{document}

eess

Recurrent Model Predictive Control

Zhengyu Liu
Jingliang Duan
Wenxuan Wang
Shengbo Eben Li
Yuming Yin
Ziyu Lin
Qi Sun
Bo Cheng

"Recurrent Model Predictive Control" by Zhengyu Liu, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Yuming Yin, Ziyu Lin, Qi Sun, and Bo Cheng, arXiv:2102.11736v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
\textbf{Controller} & \textbf{Action Space Type} & \textbf{Control Command Dimensions} \\ \hline
\multirow{2}{*}{LaneFollowingController} & \multirow{2}{*}{Mixed} & target speed \\ \cline{3-3}
 & & lane change (+1 or 0 or -1) \\ \hline
TrajectoryTrackingController & - & trajectory \\ \hline
\multirow{3}{*}{ActuatorDynamicController} & \multirow{3}{*}{Continuous} & throttle \\ \cline{3-3}
 & & brake \\ \cline{3-3}
 & & steering rate in rad \\ \hline
\multirow{3}{*}{ContinuousController} & \multirow{3}{*}{Continuous} & throttle \\ \cline{3-3}
 & & brake \\ \cline{3-3}
 & & steering \\ \hline
\end{tabular}
\caption{Mapping from controllers to action spaces.}
\end{table}
\end{document}

eess

Ming Zhou
Jun Luo
Julian Villella
Yaodong Yang
David Rusu
Jiayu Miao
Weinan Zhang
Montgomery Alban
Iman Fadakar
Zheng Chen
Aurora Chongxi Huang
Ying Wen
Kimia Hassanzadeh
Daniel Graves
Dong Chen
Zhengbang Zhu
Nhat Nguyen
Mohamed Elsayed
Kun Shao
Sanjeevan Ahilan
Baokuan Zhang
Jiannan Wu
Zhengang Fu
Kasra Rezaee
Peyman Yadmellat
Mohsen Rohani
Nicolas Perez Nieves
Yihan Ni
Seyedershad Banijamali
Alexander Cowen Rivers
Zheng Tian
Daniel Palenicek
Haitham bou Ammar
Hongbo Zhang
Wulong Liu
Jianye Hao
Jun Wang

"SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving" by Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander Cowen Rivers, Zheng Tian, Daniel Palenicek, Haitham bou Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, and Jun Wang, arXiv:2010.09776v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"dYdX: Liquidity Providers' Incentive Programme Review" by Colin Chan, arXiv:2307.03935v1, 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
 Problem & Per node Communication in each round\\ \midrule
 $\ell_p$ subspace embeddings ($p \ne 2$) & $\tilde{O}_{\Delta}(d^{\max(p/2+2, 3)}\varepsilon^{-2})$ (Theorem~)\\
 $\ell_p$ regression ($p \ne 2$) & $\tilde{O}_{\Delta}(d^{\max(p/2+2, 3)}\varepsilon^{-2})$ (Section~)\\
 $\ell_2$ subspace embeddings & $\tilde{O}_{\Delta}(d^2\varepsilon^{-2})$ (Theorem~)\\
 $\ell_2$ regression & $\tilde{O}_{\Delta}(d^{2}\varepsilon^{-2})$ (Section~)\\
 Rank-$k$ Frobenius LRA & $\tilde{O}_{\Delta}(kd\varepsilon^{-3})$ (Section~)\\ \bottomrule
 \end{tabular}
\caption{Per node communication in each of the $\Delta$ rounds to solve the problems over data in a $\Delta$ neighborhood of each node in the CONGEST model}
\end{table}
\end{document}

cs

Hossein Esfandiari
Praneeth Kacham
Vahab Mirrokni
David P. Woodruff
Peilin Zhong

"Optimal Communication for Classic Functions in the Coordinator Model and Beyond" by Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, and Peilin Zhong, arXiv:2403.20307v1, 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{FOMC Announcements by Year and Period}
\begin{tabular}{lccccc}
 \toprule
 Period & Years & Events & Avg per Year & ZLB Events & Post-Paris \\
 \midrule
 Pre-Crisis & 2005-2007 & 24 & 8.0 & 0 & No \\
 Financial Crisis & 2008-2009 & 16 & 8.0 & 8 & No \\
 Early Recovery & 2010-2014 & 40 & 8.0 & 40 & No \\
 Normalization & 2015-2019 & 39 & 7.8 & 7 & Mixed \\
 Pandemic Era & 2020-2021 & 15 & 7.5 & 8 & Yes \\
 Recent Period & 2022-2025 & 26 & 6.5 & 0 & Yes \\
 \midrule
 Total & 2005-2025 & 160 & 7.6 & 63 & 73 \\
 \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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of solving full AC-UC problem with Knitro}
\begin{tabular}{ccccc}
 \hline\hline
 Case ID & N. Var. & N. Con. & Objective (\$) & Solve time (s) \\
 \hline\hline
 S0N00003-003 & 2260 & 3850 & 9.07e+05 & 71\\
 S0N00014-003 & 22080 & 26183 & 2.23e+06 & 33\\
 S0N00037-003 & 43584 & 47494 & 1.07e+07 & 20\\
 N00073-333 & 159072 & 195415 & 2.30e+08 & 84 \\
 N00617-002 & 627768 & 705544 & N/A & $>$10000 \\
 \hline\hline
 \end{tabular}
\end{table}
\end{document}

cs

"Managing power balance and reserve feasibility in the AC unit commitment problem" by Robert Parker and Carleton Coffrin, arXiv:2404.00200v1, 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{$P^*=0$: Time to Zero}
\begin{tabular}{lc|lc}
 \toprule
 Mean & 3053.5 & Skew & 0.76736 \\
 \hline Median & 2868.5 & Ex. kurt & -0.42302 \\
 \hline Minimum & 2117.0 & 5\% & 2163.8 \\
 \hline Maximum & 4607.0 & 95\% & 4595.8 \\
 \hline STD & 702.58 & IQR & 1130.8 \\
 \hline C.V. & 0.2301 & N & 28 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

Matej Steinbacher
Mitja Steinbacher
Matjaz Steinbacher

"Bimodal Dynamics of the Artificial Limit Order Book Stock Exchange with Autonomous Traders" by Matej Steinbacher, Mitja Steinbacher, and Matjaz Steinbacher, arXiv:2508.17837v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Mengqi Hu
Bian Li
Yi-An Ma
Yifei Lou
Xiu Yang

"A Gradient-Based Optimization Method Using the Koopman Operator" by Mengqi Hu, Bian Li, Yi-An Ma, Yifei Lou, and Xiu Yang, arXiv:2312.14361v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c}
 \textbf{Name} & \textbf{Time} & \textbf{Space} & \textbf{Feature} & \textbf{Projection} & \textbf{Output} \\
 \hline
 \textbf{RFSF} & \(NL D^M\) & \(N L D^M\) & \(MDd\) & - & \(N \times D^M\) \\
 \textbf{RFSF-TRP} & \(NL MDQ\) & \(NLQ\) & \(MDd\) & \(MDQ\) & \(N \times MQ\)\\
 \textbf{RFSF-TS} & \(NL M(Q \log Q + Dd)\) & \(NLQ\) & \(MDd\) & \(M(D+Q)\) & \(N \times MQ\) \\
 \textbf{RFSF-DP} & \(NLD(2^M + Md)\) & \(NL D 2^M\) & \(MDd\) & - & \(N \times 2^M D\) \\
 \textbf{RFSF-DP-1D} & \(NL MDd\) & \(NLD\) & \(MDd\) & - & \(N \times MD\)
 \end{tabular}
\end{table}
\end{document}

stat

Csaba Tóth
Danilo Jr Dela Cruz
Harald Oberhauser

"A User's Guide to $\texttt{KSig}$: GPU-Accelerated Computation of the Signature Kernel" by Csaba Tóth, Danilo Jr Dela Cruz, and Harald Oberhauser, arXiv:2501.07145v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Mobility and Mobility Measures

"Mobility and Mobility Measures" by Frank A. Cowell and Emmanuel Flachaire, arXiv:2502.19608v1, 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{Designs of experiment}
\begin{tabular}{lcc}
\toprule \\
 & \textbf{Case I.} & \textbf{Case II.} \\ \midrule
 \textbf{Design A} & $(\Delta_n, T_n) = (0.008, 1000)$
 & $(\Delta_n, T_n) = (0.01, 1000)$ \\
 \hline
 \\
 \textbf{Design B} & $(\Delta_n, T_n) = (0.005, 500)$ & $(\Delta_n, T_n) = (0.005, 1000)$
 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Parameter Inference for Hypo-Elliptic Diffusions under a Weak Design Condition" by Yuga Iguchi and Alexandros Beskos, arXiv:2312.04444v2, 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{Hyperparameter optimization results on the subset of Global Voices . We report only the results obtained with the best number of beams. We report macro-average sacreBLEU scores over six language pairs.}
\begin{tabular}{lcc}
 \toprule
 \textbf{Decoding Strategy} & \textbf{\# Beams} & \textbf{sacreBLEU}\\ \hline
 Greedy & 1 & 18.42 \\
 Multinomial sampling & 1 & 11.97 \\
 Beam Search & 4 & 19.03 \\
 Beam Search Multinomial Sampling & 4 & 18.87 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Andreea Iana
Goran Glavaš
Heiko Paulheim

"MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation" by Andreea Iana, Goran Glavaš, and Heiko Paulheim, arXiv:2403.17876v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.