\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
 \toprule
 \textbf{Quantity}& \textbf{Truth} \\
 \cmidrule(lr){1-1}\cmidrule(lr){2-2} Pr(Recurrence-free survival)& 0.082 \\
 Pr(Recurrence) & 0.551 \\
 Pr(Death w/ recur.) & 0.349 \\
 Pr(Death w/o recur.) & 0.367 \\
 RM RFST & 0.423 \\
 Time to recur. $\mid$ recur. & 0.371 \\
 RM time to recur. or EoF & 0.654 \\
 RM time to death $\mid$ recur. & 0.378 \\\bottomrule
 \end{tabular}
\caption{Ground truth for key quantities for the illness-death simulation in Section . Time is given in years. RM RFST is the restricted mean (RM) recurrence-free survival time, which is the minimum of the time to recurrence, death, or end of follow-up (EoF). Time to recurrence conditional on recurrence is the time to recurrence among individuals whose disease recurred. RM time to recurrence or EoF is the minimum of the time to recurrence or EoF, treating participants who die prior to recurrence as having recurrence time equal to the duration of follow-up. RM time to death conditional on recurrence is the minimum of the time to death or EoF starting at disease recurrence.}
\end{table}
\end{document}

stat

Raphael Morsomme
C. Jason Liang
Allyson Mateja
Dean A. Follmann
Meagan P. O'Brien
Chenguang Wang
Jonathan Fintzi

"Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams" by Raphael Morsomme, C. Jason Liang, Allyson Mateja, Dean A. Follmann, Meagan P. O'Brien, Chenguang Wang, and Jonathan Fintzi, arXiv:2501.14097v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc} \toprule
 Previous stage & \multicolumn{5}{c}{State at next stage}\\ \cline{2-6}
 State & $S_1$ & $S_2$ & $S_3$ & $S_4$ & $S_5$ \\ \hline
 $S_1$ & \checkmark & \checkmark & & & \\
 $S_2$ & \checkmark & \checkmark & \checkmark & & \\
 $S_3$ & & \checkmark & \checkmark & \checkmark & \\
 $S_4$ & & & \checkmark & \checkmark & \checkmark \\
 $S_5$ & & & & \checkmark & \checkmark \\ \bottomrule
\end{tabular}
\caption{Possible transition between different scenarios}
\end{table}
\end{document}

math

Babooshka Shavazipour
Theodor J. Stewart

"A novel multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty" by Babooshka Shavazipour and Theodor J. Stewart, arXiv:2312.11745v1, 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.


q-fin

"Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market" by Beatrice Foroni, Luca Merlo, and Lea Petrella, arXiv:2307.06400v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||l||c|c|c|c||}
 \hline \hline
 \textbf{Expected energy} & \textbf{Consumed} & \textbf{Battery} & \textbf{Bought} & \textbf{Sold} \\
 \hline
 Base scenario & $4199.6$ & $4079.9$ & $119.63$ & $49.64$ \\
 Scenario A & $4040$ & $3295.7$ & $744.36$ & $29.94$ \\
 Scenario B & $4584.3$ & $4440.6$ & $143.68$ & $43.5$ \\
 Scenario C & $4402.8$ & $2347.5$ & $2055.2$ & $5363.6$ \\
 Scenario D & $3883.3$ & $3811.6$ & $71.64$ & $50.99$ \\
 Scenario E & $3870.8$ & $3763.3$ & $107.48$ & $52.35$ \\
 \hline
 \end{tabular}
\caption{Comparison of the expected energy balance (in Wh) balance using the optimal power procurement policy from the optimal $\bar{u}$ obtained from the dual optimization Algorithm~ for each scenario.}
\end{table}
\end{document}

math

Nadhir Ben Rached
Shyam Mohan Subbiah Pillai
Raúl Tempone

"Optimal power procurement for green cellular wireless networks under uncertainty and chance constraints" by Nadhir Ben Rached, Shyam Mohan Subbiah Pillai, and Raúl Tempone, arXiv:2503.03051v1, 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{Waiting Status Within Group}
\begin{tabular}{cccc}

 Groups & Wait & No Wait (Allocate to DAA) & Percent Reduction \\
 \hline \hline
 B-1 & 0 & 78842 & - \\
 B-2 & 0 & 87153 & -\\
 IC-1 & 77732 & 10229 & 88.26\% \\
 ID-1 & 59295 & 21078 & 75.81\% \\
 ID-2 & 80650 & 10278 & 88.20\% \\

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

eess

"Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid" by Asma Tabassum and He Bai, arXiv:2103.07820v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|r|r|r|r|r|}
		\hline
		Stock tag $i$ with $j\in \mathbb{N}_0$ & $1 + 5j$ & $2 + 5j$ & $3 + 5j$ & $4 + 5j$ & $5 + 5j$ \\ \hline
		Dividend rate $q_i$ & 3\% & 2\% & 5\% & 0\% & 4\% \\ \hline
		Black-Scholes Volatility $\sigma_i^{BS}$ & 20\% & 30\% & 25\% & 24\% & 15\% \\ \hline
		Local Volatility $\sigma^{LV}_i(S_i(t),t)$ & \multicolumn{5}{c|}{$\sigma_i^{BS}e^{-0.05\sqrt{t}}\left(1.5-e^{-0.1t-5e^{-0.05t}\ln^2\frac{S_i(t)}{S(0)}}\right)$} \\ \hline
	\end{tabular}
\caption{Underlying market data.}
\end{table}
\end{document}

q-fin

"Finite Difference Solution Ansatz approach in Least-Squares Monte Carlo" by Jiawei Huo, arXiv:2305.09166v7, 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 attained by employing MIQCP, MILP, MILP+DL, and MILP+SD in solving Nested-VRP\@. }
\begin{tabular}{lcrrrcrrrcrrrcrrl}
\toprule
 & \multicolumn{3}{c}{MIQCP} & &\multicolumn{3}{c}{MILP} & & \multicolumn{3}{c}{MILP+DL} & & \multicolumn{3}{c}{MILP+SD} & \\ \cline{2-4} \cline{6-8} \cline{10-12} \cline{14-16}
 & $T_{\text{sol}}$ (seconds) & $N_{\text{node}}$ & $\gamma_0$ (\%) &
 &$T_{\text{sol}}$ (seconds) & $N_{\text{node}}$ & $\gamma_0$ (\%) &
 &$T_{\text{sol}}$ (seconds) & $N_{\text{node}}$ & $\gamma_0$ (\%) &
 & $T_{\text{sol}}$ (seconds) & $N_{\text{node}}$ & $\gamma_0$ (\%) & \\
Mean & 141.79 & 760138.10 & 119.82 & & 106.13 & 308942.80 & 118.89 & & 99.75 & 296040 & 118.70 & & 49.50 & 120498.60 & 118.71 & \\
$\text{Change wrt MIQCP} $ & - & - & - &
 & -35.67 & -451195 & -0.93 &
 & -42.04 & -464098 & -1.12 &
 & -92.29 & -639639 & -1.12 & \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Fanruiqi Zeng
Zaiwei Chen
John-Paul Clarke
David Goldsman

"Nested Vehicle Routing Problem: Optimizing Drone-Truck Surveillance Operations" by Fanruiqi Zeng, Zaiwei Chen, John-Paul Clarke, and David Goldsman, arXiv:2103.01528v2, 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
\begin{tabular}{l|c|c|c}
\toprule
\textbf{Model} & \textbf{Parameters} & \textbf{Arch} &\textbf{Methods} \\\midrule
\multirow{1}{*}{{\textsc{CodeT5~}}} & 60 M & Enc-Dec & Fine-tune \\\midrule
\multirow{1}{*}{{\textsc{CodeBERT~}}} & 125 M & Encoder & Fine-tune \\\midrule
\multirow{1}{*}{{\textsc{UnixCoder~}}} & 125 M & Encoder & Fine-tune \\\midrule
\multirow{1}{*}{{\textsc{StarCoder2~}}} & 7 B & Decoder & Fine-tune \\\midrule
\multirow{1}{*}{{\textsc{CodeGen2.5~}}} & 7 B & Decoder & Fine-tune \\\midrule
\multirow{3}{*}{{\textsc{GPT-3.5~}}} & \multirow{3}{*}{$>100$B} & \multirow{3}{*}{Decoder} & Fine-tune \\%\cmidrule{4-4}
&&&Few-shot Prompt\\%\cmidrule{4-4}
&&&Chain-of-thought\\
\midrule
\multirow{2}{*}{{\textsc{GPT-4~}}} & \multirow{2}{*}{$>100$B} & \multirow{2}{*}{Decoder} & Few-shot Prompt\\%\cmidrule{4-4}
&&&Chain-of-thought\\
\midrule
\end{tabular}
\end{table}
\end{document}

cs

Yangruibo Ding
Yanjun Fu
Omniyyah Ibrahim
Chawin Sitawarin
Xinyun Chen
Basel Alomair
David Wagner
Baishakhi Ray
Yizheng Chen

"Vulnerability Detection with Code Language Models: How Far Are We?" by Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair, David Wagner, Baishakhi Ray, and Yizheng Chen, arXiv:2403.18624v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Thrust activation logic}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\textbf{Motion} & \multicolumn{8}{|c|}{\textbf{Thrusters}} \\ \hline
 & $T_{1}$ & $T_{2}$ & $T_{3}$ & $T_{4}$ & $T_{5}$ & $T_{6}$ & $T_{7}$ & $T_{8}$ \\ \hline
Forward & & & \checkmark & & \checkmark & & & \\ \hline
Backward & \checkmark & & & & & & \checkmark & \\ \hline
Left & & \checkmark & & \checkmark & & & & \\ \hline
Right & & & & & & \checkmark & & \checkmark \\ \hline
Clockwise & \checkmark & & &\checkmark & \checkmark & & & \checkmark \\ \hline
C-Clockwise & & \checkmark & \checkmark & & &\checkmark & \checkmark & \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

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

"Slider: On the Design and Modeling of a 2D Floating Satellite Platform" by Avijit Banerjee, Jakub Haluska, Sumeet G. Satpute, Dariusz Kominiak, and George Nikolakopoulos, arXiv:2101.06335v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{r|lll}
$R(59,60)$ & $\geq 0.2$ & $\geq 0.7$ & $\geq 10^3$ \\ \hline
Undamped: & $0.2630$ & $0.2013$ & $0.0830$ \\
Decorrelation: & $0.1667$ & $0.1063$ & $0.0187$ \\
Desmettre et al.: & $0.2293$ & $0.1630$ & $0.0487$ \\
Desmettre et al. + Decorrelation: & $0.1730$ & $0.0937$ & $0.0163$ \\
VolFreeze: & $0.1460$ & $0.0770$ & $0.0083$ \\
VolFreeze + Decorrelation: & $0.1520$ & $0.0770$ & $0.0073$
\end{tabular}
\caption{Relative number of simulated values of $R(59,60)$ with respect to $\mathbb{Q}^*$ that exceed selected thresholds.}
\end{table}
\end{document}

q-fin

The Mean Field Market Model Revisited

Manuel Hasenbichler
Wolfgang Müller
Stefan Thonhauser

"The Mean Field Market Model Revisited" by Manuel Hasenbichler, Wolfgang Müller, and Stefan Thonhauser, arXiv:2402.10215v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of CVXPY Problem}
\begin{tabular}{ll}
\toprule
 Metric & Value \\
\midrule
 Problem Status & optimal \\
 Optimal Value & 12.43618 \\
Compilation Time (s) & 76.8 \\
 Solver Time (s) & 5694.0 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"ConvMesh: Reimagining Mesh Quality Through Convex Optimization" by Alexander Valverde, arXiv:2412.08484v1, 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}{llll}
\hline
\textbf{L1} & \textbf{L2} & \textbf{Model} & \textbf{\% overlap}\\
\hline
 ar & hi & MBERT & 2.12 \\
 ar & hi & XLM-R & 1.98 \\
 ar & fa & MBERT & 14.65 \\
 ar & fa & XLM-R & 15.01 \\
 cs & sk & MBERT & 24.26 \\
 cs & sk & XLM-R & 24.18 \\
 nl & af & MBERT & 22.63 \\
 nl & af & XLM-R & 22.57 \\
 en & sco & MBERT & 29.22 \\
 en & sco & XLM-R & 29.19 \\
 en & cy & MBERT & 17.31 \\
 en & cy & XLM-R & 17.08 \\
 fr & br & MBERT & 9.50 \\
 fr & br & XLM-R & 9.44 \\
 fr & oc & MBERT & 23.09 \\
 fr & oc & XLM-R & 23.04 \\
 id & ms & MBERT & 36.34 \\
 id & ms & XLM-R & 36.34 \\
 it & scn & MBERT & 25.99 \\
 it & scn & XLM-R & 25.86 \\
 es & an & MBERT & 24.80 \\
 es & an & XLM-R & 24.77 \\
 es & ast & MBERT & 29.59 \\
 es & ast & XLM-R & 29.65 \\
 es & ca & MBERT & 17.12 \\
 es & ca & XLM-R & 17.20 \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

"Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets" by Shadi Manafi and Nikhil Krishnaswamy, arXiv:2403.20056v1, 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|}
\hline
Round of DFP & 0 to 500 & 501 to 1000 & 1001 to 1500 \\
\hline
Learning rate & 1e-2 & 1e-3 & 1e-4 \\
\hline
\end{tabular}
\caption{Training schedule for CRRA case.}
\end{table}
\end{document}

q-fin

Robert Balkin
Hector D. Ceniceros
Ruimeng Hu

"Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms" by Robert Balkin, Hector D. Ceniceros, and Ruimeng Hu, arXiv:2307.06450v1, 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.


q-fin

"Introducing the $σ$-Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting" by German Rodikov and Nino Antulov-Fantulin, arXiv:2309.01565v1, 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 the models with the scaling-weighted MSE loss with $\alpha=2$, $\beta=0.5$, $w_{90\%}=1.5$, and $w_{80\%}=1.25$ for two, three, and six lead month SSTA and MHW forecasts.}
\begin{tabular}{llllll}
\textbf{Across All Locations} & \textbf{MSE↓} & \textbf{CSI↑} & \textbf{CSI 80↑} & \textbf{Training Time↓} & \textbf{PUR↓} \\ \hline
Average (two lead months) & 0.6988 & 0.1976 & \textbf{0.3213} & 29.6779 & 0\% \\
Average (three lead months) & 0.8176 & 0.1170 & 0.2300 & 30.1820 & 0\% \\
Average (six lead months) & 1.0395 & 0.0682 & \textbf{0.1913} & 30.3764 & 0\%
\end{tabular}
\end{table}
\end{document}

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}
\usepackage{amsfonts}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study for $\lambda$ on $\mathcal{L}_{Dist}$}
\begin{tabular}{lllcccccccccccc}
\hline
 \multicolumn{2}{l}{\multirow{2}{*}{\textbf{Lambda}}} & \multicolumn{13}{c}{\textbf{TextFooler}} \\
\multicolumn{2}{l}{} & \textbf{CA (↑)} & \textbf{AUA (↑)} & \multicolumn{11}{c}{\textbf{ASR (↓)}} \\ \hline
\multicolumn{2}{l}{0} & 86.0 & 13.8 & \multicolumn{11}{c}{83.95} \\
\multicolumn{2}{l}{0.1} & 86.0 & 31.8 & \multicolumn{11}{c}{63.02} \\
\multicolumn{2}{l}{0.5} & 85.8 & 39.6 & \multicolumn{11}{c}{53.85} \\
\multicolumn{2}{l}{1} & 85.8 & 39.4 & \multicolumn{11}{c}{54.08} \\
\multicolumn{2}{l}{5} & 85.4 & 34.4 & \multicolumn{11}{c}{59.72} \\
\multicolumn{2}{l}{10} & 85.6 & 31.8 & \multicolumn{11}{c}{62.85} \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Brian Formento
Wenjie Feng
Chuan Sheng Foo
Luu Anh Tuan
See-Kiong Ng

"SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks" by Brian Formento, Wenjie Feng, Chuan Sheng Foo, Luu Anh Tuan, and See-Kiong Ng, arXiv:2403.18423v1, 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{Performance based on DINO score.}}
\begin{tabular}{l|cc|cc|cc|cc|cc}
\toprule[1.5pt]
Datasets & \multicolumn{6}{c|}{AgeDB-IT2I} & \multicolumn{2}{c|}{DigiFace-IT2I} & \multicolumn{2}{c}{{VGGFace-IT2I}} \\ \midrule
Size & \multicolumn{2}{c|}{Small} & \multicolumn{2}{c|}{Medium} & \multicolumn{2}{c|}{Large} & \multicolumn{2}{c|}{Large} & \multicolumn{2}{c}{Large} \\ \midrule
Metric & \multicolumn{10}{c}{DINO (cosine similarity) scores~$\uparrow$} \\ \midrule
Shot & All & Few & All & Few & All & Few & All & Few & All & Few \\ \midrule
\textsc{Vanilla} & 0.42 & 0.37 & 0.39 & 0.28 & 0.34 & 0.25 & 0.42 & 0.36 & 0.41 & 0.29 \\
\textsc{CBDM} & 0.54 & 0.09 & 0.38 & 0.11 & 0.41 & 0.26 & 0.34 & 0.16 & 0.46 & 0.22 \\
{\textsc{T2H}} & 0.43 & 0.39 & 0.42 & 0.29 & 0.37 & 0.26 & 0.44 & 0.36 & 0.42 & 0.28 \\
\textsc{PoGDiff (Ours)} & \textbf{0.77} & \textbf{0.73} & \textbf{0.69} & \textbf{0.56} & \textbf{0.66} & \textbf{0.52} & \textbf{0.64} & \textbf{0.49} & \textbf{0.69} & \textbf{0.55}\\
\bottomrule[1.5pt]
\end{tabular}
\end{table}
\end{document}

stat

Ziyan Wang
Sizhe Wei
Xiaoming Huo
Hao Wang

"PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation" by Ziyan Wang, Sizhe Wei, Xiaoming Huo, and Hao Wang, arXiv:2502.08106v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\toprule & No control & Proposed policy & \\
\midrule Case 1 & 102.7 $\pm$ 0.12 & 67.7 $\pm$ 0.07 & 67.7 $\pm$ 0.07 \\
Case 2 & 102.7 $\pm$ 0.12 & 84.2 $\pm$ 0.10 & 84.2 $\pm$ 0.10 \\
Case 3 & 102.7 $\pm$ 0.12 & 85.2 $\pm$ 0.07 & 85.1 $\pm$ 0.07 \\
\bottomrule% & & &
\end{tabular}
\caption{Simulation performance for three class of a three-station example.}
\end{table}
\end{document}

eess

"Singular Control of (Reflected) Brownian Motion: A Computational Method Suitable for Queueing Applications" by Baris Ata, J. Michael Harrison, and Nian Si, arXiv:2312.11823v3, 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{Class-wise and overall accuracies achieved by PixEF on Sentinel-1 images alone (S1), Sentinel-2 images alone (S2) and Sentinel-1/-2 image fusion (S1S2) with the linear protocol and the fine-tuning evaluation.}
\begin{tabular}{ccccccc}
		\hline
		\multirow{2}{*}{class} & \multicolumn{3}{c}{Linear Evaluation} & \multicolumn{3}{c}{Fine-tuning Evaluation} \\ \cline{2-7}
		& S1 & S2 & S1S2& S1 & S2 & S1S2 \\ \hline
		Forest & 84.2 & 90.3 & 91.3 & 86.2 & 92.2 & 92.0 \\
		Shrubland & 27.5 & 48.9 & 53.2 & 31.2 & 44.2 & 62.3 \\
		Grassland & 61.1 & 67.2 & 74.5 & 61.9 & 68.0 & 78.0 \\
		Wetlands & 35.0 & 58.8 & 59.9 & 51.5 & 62.3 & 62.3 \\
		Croplands & 66.0 & 81.4 & 80.0 & 71.1 & 79.6 & 85.7 \\
		Urban & 78.8 & 86.5 & 87.1 & 84.7 & 89.7 & 86.1 \\
		Barren & 3.5 & 30.0 & 37.0 & 7.8 & 30.7 & 38.6 \\
		Water & 98.8 & 99.2 & 99.2 & 99.2 & 99.5 & 99.4 \\ \hline
		Average & 56.9 & 70.0 & 72.8 & 61.7 & 70.8 & 75.6 \\
		mIoU & 0.362& 0.470& 0.490& 0.395& 0.474& 0.521\\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

"Self-supervised SAR-optical Data Fusion and Land-cover Mapping using Sentinel-1/-2 Images" by Yuxing Chen and Lorenzo Bruzzone, arXiv:2103.05543v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

A. Ramirez-Hassan
C. Gomez
S. Velasquez
K. Tangarife

"Marijuana on Main Streets? The Story Continues in Colombia: An Endogenous Three-part Model" by A. Ramirez-Hassan, C. Gomez, S. Velasquez, and K. Tangarife, arXiv:2306.10031v1, 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 $\min \{ x^{\top}x - (\theta + u)^{\top} x \mid x \in [0, 1]^{p} \} = \min \left\{\sum_{k=1}^{p} x_k^2 - \sum_{k=1}^{p} (\theta + u)_k x_k \mid x_k \in [0, 1], \ \forall \ k \in [p] \right\}$ in the \textbf{Noisy Objective Function} 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}{*}{Samplesize} & \multicolumn{4}{c|}{ParameterError} & \multicolumn{4}{c|}{DecisionError} & \multicolumn{4}{c}{Regret} \\ \cline{2-13}
 & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA \\ \hline
50 & \textbf{1.37} & 2.52 & - & 8.80 & \textbf{0.05} & 0.15 & - & 0.85 & \textbf{0.05} & 0.16 & - & 0.85 \\
100 & \textbf{1.26} & 2.67 & - & 8.29 & \textbf{0.04} & 0.15 & - & 0.80 & \textbf{0.04} & 0.16 & - & 0.80 \\
300 & \textbf{1.28} & 2.88 & - & 5.85 & \textbf{0.03} & 0.17 & - & 0.51 & \textbf{0.04} & 0.18 & - & 0.51 \\
500 & \textbf{1.28} & 2.89 & - & 4.57 & \textbf{0.03} & 0.17 & - & 0.37 & \textbf{0.03} & 0.18 & - & 0.37 \\
1000 & \textbf{1.28} & 2.89 & - & 3.38 & \textbf{0.03} & 0.17 & - & 0.23 & \textbf{0.03} & 0.18 & - & 0.23 \\ \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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cccc|cccc}
 \toprule
 \textbf{Method} & \multicolumn{4}{c|}{\textbf{ARI}} & \multicolumn{4}{c}{\textbf{NMI}} \\
 \cmidrule(lr){2-5} \cmidrule(lr){6-9}
 & \textbf{Pearson} & \textbf{Cosine} & \textbf{Spearman} & \textbf{Kendall}
 & \textbf{Pearson} & \textbf{Cosine} & \textbf{Spearman} & \textbf{Kendall} \\
 \midrule
 GreedyModularity & 0.205 & \textbf{0.135} & 0.194 & 0.197
 & 0.534 & \textbf{0.429} & 0.496 & 0.502 \\
 Louvain & 0.493 & \textbf{0.322} & 0.445 & 0.473
 & 0.784 & \textbf{0.666} & 0.768 & 0.781 \\
 Infomap & \textbf{0.000} & \textbf{0.000} & \textbf{0.000} & \textbf{0.000}
 & \textbf{0.000} & \textbf{0.000} & \textbf{0.000} & \textbf{0.000} \\
 Leiden & \textbf{-0.001} & \textbf{-0.001} & -0.000 & -0.000
 & 0.062 & \textbf{0.040} & 0.082 & 0.073 \\
 Multilevel & \textbf{0.032} & \textbf{0.034} & 0.034 & 0.035
 & 0.305 & \textbf{0.289} & 0.325 & 0.319 \\
 Eigenvector & 0.002 & \textbf{0.000} & 0.003 & 0.005
 & 0.097 & \textbf{0.060} & 0.082 & 0.091 \\
 \bottomrule
 \end{tabular}
\caption{Impact of Correlation Methods on Network-Based Clustering Performance. The table evaluates the impact of correlation computation methods—Pearson's, Spearman's, Kendall's Tau, and cosine similarity—on the performance of various network-based clustering methods. The correlation computation method resulting in the worst clustering performance is marked in bold. Cosine similarity, which ignores shared zeros in cell expression profiles, often resulted in the worst clustering performance. These results show that, although using correlation computation methods that treat zeros the same as non-zero signals could inflate correlations, ignoring these zeros, as in cosine similarity, can lead to worse performance.}
\end{table}
\end{document}

stat

Wan He
Daniel I. Bolnick
Samuel V. Scarpino
Tina Eliassi-Rad

"Hypergraph Representations of scRNA-seq Data for Improved Clustering with Random Walks" by Wan He, Daniel I. Bolnick, Samuel V. Scarpino, and Tina Eliassi-Rad, arXiv:2501.11760v3, 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|ccccc}
\hline
Stages (Inclusive) & 0-1 & 2-4 & 5-7 & 8-11 & 12-19 \\ \hline
New Stages & 0 & 1 & 2 & 3 & 4 \\ \hline
\end{tabular}
\caption{Transformation of Stages}
\end{table}
\end{document}

stat

"Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement Learning" by Jinhang Chai, Elynn Chen, and Jianqing Fan, arXiv:2501.04870v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mauricio Herrera
Francisca Kleisinger
Andrés Wilsón

"Spatiotemporal Forecasting in Climate Data Using EOFs and Machine Learning Models: A Case Study in Chile" by Mauricio Herrera, Francisca Kleisinger, and Andrés Wilsón, arXiv:2502.17495v1, 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{Final ATE Estimates (DoubleMLIRM) for All Outcomes in Experiment 2}
\begin{tabular}{lrrrr}
\toprule
\textbf{Outcome} & \textbf{ATE} & \textbf{StdErr} & \textbf{p-value} \\
\midrule
avg\_rev\_last\_1000 & -0.1185 & 0.0078 & 0.0000 \\
time\_to\_converge & 0.0392 & 0.0274 & 0.1529 \\
avg\_regret\_of\_seller & 0.0627 & 0.0026 & 0.0000 \\
no\_sale\_rate & 0.0022 & 0.0020 & 0.2863 \\
price\_volatility & 0.0360 & 0.0023 & 0.0000 \\
winner\_entropy & 0.0022 & 0.0011 & 0.0415 \\
\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.


q-fin

Matteo Prata
Giuseppe Masi
Leonardo Berti
Viviana Arrigoni
Andrea Coletta
Irene Cannistraci
Svitlana Vyetrenko
Paola Velardi
Novella Bartolini

"LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study" by Matteo Prata, Giuseppe Masi, Leonardo Berti, Viviana Arrigoni, Andrea Coletta, Irene Cannistraci, Svitlana Vyetrenko, Paola Velardi, and Novella Bartolini, arXiv:2308.01915v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Eeshwar Prasad Poudel
Pitambar Acharya
Jeevan Kafle
Shreeram Khadka

"On One Dimensional Advection -- Diffusion Equation with Variable Diffusivity" by Eeshwar Prasad Poudel, Pitambar Acharya, Jeevan Kafle, and Shreeram Khadka, arXiv:2312.06493v1, 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{\small Experimental setup on a Stampede2 SKX node.}
\begin{tabular}{l|l}
\hline
Processor & Intel Xeon Platinum 8160 (Skylake / SKX)\\
Cores & 24 cores per socket, 2 sockets (total: 48 cores)\\
Cache sizes & L1 32 KB / core, L2 1 MB / core, L3 33 MB / socket\\
Memory & 144GB /tmp partition on a 200GB SSD\\ \hline
Compiler & Intel C++ Compiler (ICC) v18.0.2\\
Compiler flags & \texttt{-O3 -xhost -ansi-alias -ipo -AVX512}\\
Parallelization & OpenMP 5.0\\
Thread affinity & \texttt{GOMP\_CPU\_AFFINITY} \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Zafar Ahmad
Reilly Browne
Rezaul Chowdhury
Rathish Das
Yushen Huang
Yimin Zhu

"Fast American Option Pricing using Nonlinear Stencils" by Zafar Ahmad, Reilly Browne, Rezaul Chowdhury, Rathish Das, Yushen Huang, and Yimin Zhu, arXiv:2303.02317v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effect of Recommendation on Fund Purchases}
\begin{tabular}{lcccc}
 \midrule
 Dependent Variable: & (1) & (2) & (3) \\
 log(NumberOfPurchases) & & & \\
 \midrule
 Recommended & 0.055*** & 0.058*** & 0.044*** \\
 & (0.006) & (0.006) & (0.007) \\
 Polynomial Degree & 2 & 2 & 2 \\
 Fund Type Dummies & Yes & Yes & No \\
 Month Dummies & Yes & Yes & No \\
 Day of Week Dummies & Yes & Yes & No \\
 Control Variables & Yes & No & No \\
 Number of Observations & 21,834 & 21,834 & 21,834 \\
 \textcolor[rgb]{ .133, .133, .133}{$R^2$} & 0.153 & 0.081 & 0.010 \\
 \midrule
 \multicolumn{4}{p{28em}}{\scriptsize Notes: {***} $p<0.01$, {**} $p<0.05$, {*} $p<0.1$. The dependent variable in Columns (1) - (3) is log(NumberOfPurchases). Control variables include management fees, purchase fees, custodial fees, asset size, dummy for valuable fund brand, fund past returns, fund risk level, and fund tenure. Robust standard errors are in parentheses.} \\
 \end{tabular}
\end{table}
\end{document}

q-fin

"The Effect of Product Recommendations on Online Investor Behaviors" by Ruiqi Rich Zhu, Cheng He, and Yu Jeffrey Hu, arXiv:2303.14263v2, 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{ Tested instances details}
\begin{tabular}{|l|l|l|l|l|l|l|}

 		\hline

 		Set & Subset & Size& $\# N$ &$\#S$ &$\#K$ \\

 		\hline
 		& A &	7	& 10	&	10 & 3 \\

 		SS & B &	7	& 25	&	25 & 5 \\

 		& C &	7	& 50	&	50 & 10 \\
 		\hline
 		& D &	7	& 10	&	13 & 3 \\

 		MSS 	& E &	7	& 25	&	33	 & 5 \\

 		& F &	7	& 50	&	65 	& 10 \\

 		\hline

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

math

"Mathematical models and heuristics for the home health care routing and scheduling problem" by Mohammed Bazirha, arXiv:2312.08032v1, 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{Damage detection summary results at multiple $\alpha$ values for path 3-12 (damage-intersecting case) in the CFRP panel .}
\begin{tabular}{lccc} % defines the alignment
\hline % adds horizontal line
Method & False & \multicolumn{2}{|c}{Missed damage ($\%$)} \\
\cline{3-4}
 & alarms ($\%$) & D5/6/7/8 & D9/10/11 \\
\hline
DI$^a$$^\dagger$ & 7.5 & 51.25 & 100 \\
$F$ Statistic$^b$$^\dagger$ & 0 & 75 & 100 \\
$F_m$ Statistic$^b$$^{\dagger\dagger}$ & 0 & 75 & 33 \\
$Z$ Statistic$^a$$^{\dagger\dagger}$ & 0 & 0 & 0 \\
\hline
\multicolumn{3}{l}{{\bf False alarms} presented as percentage of 20 test cases.} \\
\multicolumn{3}{l}{{\bf Missed damages} presented as percentage of all test cases per damage group.} \\
\multicolumn{3}{l}{$^a$ $\alpha = 95\%$.; $^b$ $\alpha = 80\%$} \\
\multicolumn{3}{l}{$^\dagger$ All 20 baseline data sets were used as reference signals consecutively.} \\
\multicolumn{3}{l}{$^{\dagger\dagger}$ 15 out of 20 baseline data sets were used to calculate the baseline mean.}
\end{tabular}
\end{table}
\end{document}

eess

"Statistical guided-waves-based SHM via stochastic non-parametric time series models" by Ahmad Amer and Fotis Kopsaftopoulos, arXiv:2101.11208v2, 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}{ccccccccccccc}
\hline
$\mu_1$ & $\sigma_1$ & $\lambda_1$ & $\nu_1$ & $\iota_1$ & $\varsigma_1$ &
 $\mu_2$ & $\sigma_2$ & $\lambda_2$ & $\nu_2$ & $\iota_2$ & $\varsigma_2$ & $\rho$ \\ \hline
0.051 & 0.146 & 0.178 & 0.2 & 7.13 & 7.33 & -0.014 & 0.017 & 0.321 & 0 & N/A & 44.48 & 0.14 \\ \hline
\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{siunitx}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters for LoRA fine-tuning on T5-base model.}
\begin{tabular}{ccccccc}
 \toprule
 Epoch & Optimizer & $(\beta_1, \beta_2)$ & $\epsilon$ & Batch Size \\
 \midrule
 1 & AdamW & (0.9, 0.999) & $\SI{1e-8}{}$ & 32 \\
 \midrule
 Warm-up Ratio & LoRA Alpha & $s$ (if needed) & $\lambda$ (if needed) & \#Runs \\
 \midrule
 0.03 & 16 & 16 & $\SI{1e-6}{}$ & 3 \\
 \midrule
 Weight Decay & LR Scheduler & Sequence Length & Precision & \\
 \midrule
 0 & cosine & 128 & FP32 & \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"One-step full gradient suffices for low-rank fine-tuning, provably and efficiently" by Yuanhe Zhang, Fanghui Liu, and Yudong Chen, arXiv:2502.01235v1, 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|cccc|c}
\toprule
& \textbf{\small B-4} & \textbf{\small R-L} & \textbf{\small Meteor} & \textbf{\small CIDEr} &\textbf{\small Human} \\
 \midrule
w/o AttnM & 13.51 & \textbf{18.29} & 13.18 & 47.69 &14\% \\
w/ AttnM (ours) & \textbf{13.52}& 18.01 & 13.18 & \textbf{48.20} &\textbf{33\%} \\ \bottomrule
\end{tabular}
\caption{Evaluations of the fine-tune GPT2-L on $\alpha$NLG using greedy decoding.}
\end{table}
\end{document}

cs

Yue Dong
Chandra Bhagavatula
Ximing Lu
Jena D. Hwang
Antoine Bosselut
Jackie Chi Kit Cheung
Yejin Choi

"On-the-Fly Attention Modulation for Neural Generation" by Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, and Yejin Choi, arXiv:2101.00371v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|l|l|l|}
		\hline
		Knot id & $\sigma$ & $s$ & $d(Kh, \mathbb{Q})$ & $Z_2$ diagonals & $Z_4$ diagonals & $d(Kh, \mathbb{Z})$ &w \\ \hline
		$17nh_{0000460}$ & 8 & 14 & \{7, 9, 11, 13, 15\} & \{9, 11, 13, 7\} & 9 & \{7, 9, 11, 13, 15\} & 5 \\ \hline
	\end{tabular}
\caption{The only knot up to 17 crossings with difference between s and signature is equal to 6.}
\end{table}
\end{document}

math

Data Driven Perspectives on Knot Theory

Pawel Dlotko
Davide Gurnari
Radmila Sazdanovic

"Data Driven Perspectives on Knot Theory" by Pawel Dlotko, Davide Gurnari, and Radmila Sazdanovic, arXiv:2503.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|r|}
 \hline
 feature\_dims & 128 \\
 normalize\_images & True \\
 shared\_lstm & True \\
 enable\_critic\_lstm & False \\
 n\_lstm\_layers & 1 \\
 lstm\_hidden\_size & 128 \\
 net\_arch & [ [64,64], [64,64] ] \\
 \hline
 \end{tabular}
\caption{Policy arguments for the the neural agents.}
\end{table}
\end{document}

cs

Philipp Sadler
Sherzod Hakimov
David Schlangen

"Sharing the Cost of Success: A Game for Evaluating and Learning Collaborative Multi-Agent Instruction Giving and Following Policies" by Philipp Sadler, Sherzod Hakimov, and David Schlangen, arXiv:2403.17497v1, 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|}
 \hline
 \bf Layer&\bf Layer Name& \bf Size & Activation \\
 \hline
 1&Input & $f_s$ & None\\ \hline
 2&Dense & 40 & Relu\\ \hline
 3&BatchNorm & 40 & None\\ \hline
 4&Dense & 10 & Relu\\ \hline
 5&BatchNorm & 10 & None\\ \hline
 6&Output & 2/3 (Classification), 1 (regression) & Softmax/Relu\\ \hline

 \end{tabular}
\caption{{The shallow FFNN Model Architecture. $f_s$ = No. of features = 26.}}
\end{table}
\end{document}

eess

Muhammad Saran Khalid
Ikramah Shahid Quraishi
Hadia Sajjad
Hira Yaseen
Ahsan Mehmood
Muhammad Mahboob Ur Rahman
Qammer H. Abbasi

"A low-cost PPG sensor-based empirical study on healthy aging based on changes in PPG morphology" by Muhammad Saran Khalid, Ikramah Shahid Quraishi, Hadia Sajjad, Hira Yaseen, Ahsan Mehmood, Muhammad Mahboob Ur Rahman, and Qammer H. Abbasi, arXiv:2312.13404v1, 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{Training and Inference times for our implementation (SYCL), the fully-fused implementation from~ (CUDA) and PyTorch using both IPEX and CUDA. The numbers in the brackets indicate the time relative to our implementation.}
\begin{tabular}{lll|l}
 & & Training [s]	$\downarrow$ & Inference [s]	$\downarrow$ \\
 \hline
\multirow{4}{*}{Benchmark} & SYCL & \textbf{0.58} & \textbf{0.083} \\
 & CUDA & 0.79 (1.36) & 0.236 (2.84) \\
 & PyTorch (IPEX) & 4.57 (7.88) & 2.178 (26.24) \\
 & PyTorch (CUDA) & 4.32 (7.45) & 1.543 (18.55) \\
 \hline
\multirow{4}{*}{Image compr.} & SYCL & \textbf{9.20} & \textbf{2.041} \\
 & CUDA & 16.10 (1.75) & 3.514 (1.72) \\
 & PyTorch (IPEX) & 41.75 (4.54) & 12.84 (6.29) \\
 & PyTorch (CUDA) & 36.66 (3.98) & 15.46 (7.57) \\
 \hline
\multirow{4}{*}{NeRF} & SYCL & \textbf{1.93} & \textbf{0.302} \\
 & CUDA & 2.05 (1.06) & 0.477 (1.58) \\
 & PyTorch (IPEX) & 15.09 (7.82) & 9.050 (29.97) \\
 & PyTorch (CUDA) & 10.07 (5.21) & 3.841 (12.72) \\
 \hline
\multirow{4}{*}{PINNs} & SYCL & \textbf{0.55} & \textbf{0.088} \\
 & CUDA & 0.60 (1.09) & 0.108 (1.22) \\
 & PyTorch (IPEX) & 1.92 (3.49) & 0.788 (8.95) \\
 & PyTorch (CUDA) & 1.85 (3.36) & 0.646 (7.34)
\end{tabular}
\end{table}
\end{document}

cs

Kai Yuan
Christoph Bauinger
Xiangyi Zhang
Pascal Baehr
Matthias Kirchhart
Darius Dabert
Adrien Tousnakhoff
Pierre Boudier
Michael Paulitsch

"Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs" by Kai Yuan, Christoph Bauinger, Xiangyi Zhang, Pascal Baehr, Matthias Kirchhart, Darius Dabert, Adrien Tousnakhoff, Pierre Boudier, and Michael Paulitsch, arXiv:2403.17607v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of ORS Filtered Results}
\begin{tabular}{llrrrr}
 \toprule
 Model & Cut & OptimalTimes & GapWithoutInf & \#Inf \\
 \midrule
 DD-JS & IIS & 427.1 & 0.8 & 0 \\
 DD-JS & No-Good & 490.0 & 0.8 & 0 \\
 DD-LJ & IIS & 518.0 & 1.1 & 0 \\
 DD-LJ & No-Good & 547.3 & 1.0 & 0 \\
 IP & IIS & 136.7 & 1.6 & 101 \\
 IP & No-Good & 537.0 & 1.5 & 2 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Nicolás Casassus
Margarita Castro
Gustavo Angulo

"A Decision Diagram Approach for the Parallel Machine Scheduling Problem with Chance Constraints" by Nicolás Casassus, Margarita Castro, and Gustavo Angulo, arXiv:2504.20889v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c||rr}
\hline
 & LT & LP \\
\hline
Number of instructions & 2,654,347 & 68,434 \\
Average daily number & & \\
of instructions & 4,720 & 471 \\
\hline
Total USD volume & $\approx$ \$ 262$\times 10^9$	 & $\approx$ \$ 232 $\times 10^9$ \\
Average daily USD volume & \$ 554,624,500	 & \$ 863,285 \\
\hline
Average LT transaction & & \\
or LP operation size & \$ 98,624 & \$ 3,611,197 \\
Average interaction frequency & 13 seconds & 590 seconds \\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Álvaro Cartea
Fayçal Drissi
Marcello Monga

"Decentralised Finance and Automated Market Making: Predictable Loss and Optimal Liquidity Provision" by Álvaro Cartea, Fayçal Drissi, and Marcello Monga, arXiv:2309.08431v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter Settings for Model Evaluation}
\begin{tabular}{ll}
 \toprule
 \textbf{Hyperparameter} & \textbf{Value} \\ \midrule
 Number of Samples & 25 \\
 Max Length & 20 \\
 Temperature & 1 \\
 Top-p (sampling) & 0.9 \\
 Top-k (sampling) & 0 \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Bao Nguyen
Binh Nguyen
Duy Nguyen
Viet Anh Nguyen

"Risk-Aware Distributional Intervention Policies for Language Models" by Bao Nguyen, Binh Nguyen, Duy Nguyen, and Viet Anh Nguyen, arXiv:2501.15758v1, 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{Classification results for the TR problem on real datasets. For each method (Gr: Grassmann optimization~, Fl: flag optimization~, Fl-U: flag optimization + uniform soft voting, Fl-W: flag optimization + optimal soft voting~), we give the cross-entropy of the projected-predictions with respect to the true labels.}
\begin{tabular}{ccccccccc}
 \toprule
 dataset & $n$ & $p$ & $q_{1:d}$ & Gr & Fl & Fl-U & Fl-W & weights\\
 \midrule
 breast & $569$ & $30$ & $(1, 2, 5)$ & $0.0986$ & $0.0978$ & $0.0942$ & $0.0915$ & $(0.754, 0, 0.246)$\\
 iris & $150$ & $4$ & $(1, 2, 3)$ & $0.0372$ & $0.0441$ & $0.0410$ & $0.0368$ & $(0.985, 0, 0.015)$\\
 wine & $178$ & $13$ & $(1, 2, 5)$ & $0.0897$ & $0.0800$ & $0.1503$ & $0.0677$ & $(0, 1, 0)$\\
 digits & $1797$ & $64$ & $(1, 2, 5, 10)$ & $0.4507$ & $0.4419$ & $0.5645$ & $0.4374$ & $(0, 0, 0.239, 0.761)$\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Nested subspace learning with flags

"Nested subspace learning with flags" by Tom Szwagier and Xavier Pennec, arXiv:2502.06022v1, 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
 Knot & $TNB$ word for shifted piece & $\tau$\\
 \hline
 $9_{35}$ & $fufdlflurffflduflufurfluffffflffff$ & $\tau = 1$\\
 \hline
 $9_{40}$ & $fffflfffflffflffdfflfful$ & $\tau(N) =B, \tau(B) = -N$.\\
 \hline
 $9_{41}$ & $udfffdfffffffflfffldffufffdffrfffdflfflf$ & $\tau = 1$\\
 \hline
 \end{tabular}
\caption{The code for the pieces of $9_{35}, 9_{40}$ and $9_{41}$ in $TNB$ frame.}
\end{table}
\end{document}

math

Knots and Coxeter Groups

Dylan Burke
Geoffrey Cuff-Chartrand
Malors Espinosa
Mateusz Kazimierczak
Mohammadamin Mobedi

"Knots and Coxeter Groups" by Dylan Burke, Geoffrey Cuff-Chartrand, Malors Espinosa, Mateusz Kazimierczak, and Mohammadamin Mobedi, arXiv:2503.10785v1, 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{Example 1: Distribution of the Available Persons by Age Group and Blindness}
\begin{tabular}{lcccccccc}
\toprule
 \textbf{Blindness} & \textbf{50–54 yrs} & \textbf{55–59 yrs} & \textbf{60–64 yrs} & \textbf{65–69 yrs} & \textbf{70–74 yrs} & \textbf{75–79 yrs} & \textbf{$\geq$ 80 yrs} & \textbf{Total}\\
\midrule
 None & 873& 541& 469& 257 &242 &127& 104& 2613\\
 Unilateral &23 &17 &18 &16 &32 &30 &29 &165 \\
 Bilateral & 2 &8 &4 &5 &3 &9 &10 &41 \\
\hline
 \textbf{Total} & 898 & 566 &491 &278 &277 &166 &143 &2819 \\
\bottomrule
\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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|}
\hline
\textbf{Measure} & \textbf{Type} & \textbf{Description} \\ \hline
pre\_difficulty & 1(low)\textasciitilde 5(high) & User perceived task difficulty \\ \hline
pre\_knowledge & 1(low)\textasciitilde 5(high) & User’s prior knowledge about a task \\ \hline
pre\_interest & 1(low)\textasciitilde 5(high) & User’s interest about a task \\ \hline
usefulness & 1(low)\textasciitilde 4(high) & User’s usefulness feedback on a document \\ \hline
satisfaction & 1(low)\textasciitilde 5(high) & User’s satisfaction feedback on a search session \\ \hline
\end{tabular}
\caption{User self-rating variables and their definitions. in the study by~..}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
			\toprule
			\multirow{4}{*}{Method} & \multicolumn{3}{c}{ImageNet(FID $\downarrow$ / IS $\uparrow$)} &LSUN-Bed(FID $\downarrow$ / SFID $\downarrow$) \\

 &\multicolumn{3}{c}{(FP:11.42/245.39)} &(FP:3.16/7.84) \\
 \cmidrule(r){2-4} \cmidrule(r){5-5}
 & W8A8 &W4A8 & W4A6 &W8A8 \\
 \cmidrule(r){1-1} \cmidrule(r){2-4} \cmidrule(r){5-5}
 $PTQD^*$ &11.94/153.92 &10.40/214.73 &- &3.75/9.89 \\
 $TDQ^*$ &- &- &41.23/- &- \\
 Q-Diffusion &10.92/229.31 &9.56/219.64 &41.25/89.82 & 4.03/10.15 \\

 \rowcolor{gray!25}
 QNCD &\textbf{10.57/231.85} &\textbf{9.48/221.62} &\textbf{20.14/136.49} &3.82/\textbf{9.65} \\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

cs

Huanpeng Chu
Wei Wu
Chengjie Zang
Kun Yuan

"QNCD: Quantization Noise Correction for Diffusion Models" by Huanpeng Chu, Wei Wu, Chengjie Zang, and Kun Yuan, arXiv:2403.19140v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Pingping Zeng
Gongqiu Zhang
Weinan Zhang

"Drawdowns, Drawups, and Occupation Times under General Markov Models" by Pingping Zeng, Gongqiu Zhang, and Weinan Zhang, arXiv:2506.00552v1, 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{Attribute 1} & \textbf{Attribute 2} & \textbf{New Attribute}\\
 \midrule
 DOB	& Order list &	Interest \\
 \bottomrule
 \end{tabular}
\caption{Attribute connections}
\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}{lll}
			Symbol & Value & Dimension \\
			\hline
			$p_\text{ref}$ & 1972 & Pa \\
			$Q_\text{ref}$ & $2.59\cdot 10^{-4}$ & m s$^{-1}$ \\
			$t_\text{ref}$ & $7.59\cdot 10^{6}$ & s \\
			$R_E$ & 23 984 & - \\
			$\beta$ & 0.1823 & - \\
			$\gamma$ & 0.349 & - \\
			\hline
		\end{tabular}
\caption{Overview of reference and non-dimensional values for base case.}
\end{table}
\end{document}

math

C. Bringedal
S. Kiemle
C. J. van Duijn
R. Helmig

"Impact of saturation on evaporation-driven density instabilities in porous media: mathematical and numerical analysis" by C. Bringedal, S. Kiemle, C. J. van Duijn, and R. Helmig, arXiv:2501.12784v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

I. Cherkaoui
S. Belabssir
J. Horgan
I. Dey

"Quasi-cyclic Linear Error-Block Code-based Post-quantum Signature" by I. Cherkaoui, S. Belabssir, J. Horgan, and I. Dey, arXiv:2503.23405v1, 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}{ccccccc}
 \toprule
 &\multicolumn{3}{c}{AnimeFace}&\multicolumn{3}{c}{CelebA} \\
 \cmidrule(r){2-7}
 & CMMD & FD\textsubscript{Dinov2} & WD\textsubscript{latent}& CMMD & FD\textsubscript{Dinov2}& WD\textsubscript{latent} \\
 \midrule
 Cramer~ &0.73 & 953.99 & 0.6294 &0.72 &722.86 &0.6795 \\
 Cramer + PPM-Reg &0.56 & 780.68 & 0.6080 &0.58 & 700.73 & 0.6666 \\
 \bottomrule
 \end{tabular}
\caption{Quantitative evaluation on $32\times 32$ image generation, values are reported at the epoch with the smallest CMMD.}
\end{table}
\end{document}

math

"Towards Scalable Topological Regularizers" by Hiu-Tung Wong, Darrick Lee, and Hong Yan, arXiv:2501.14641v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

"Robust parameter estimation of the log-logistic distribution based on density power divergence estimators" by A. Felipe, M. Jaenada, P. Miranda, and L. Pardo, arXiv:2312.02662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The fundamental Saaty scale.}
\begin{tabular}{ll} \toprule
Intensity of importance & Meaning \\ \midrule
1 & Equal importance \\
2 & Weak \\
3 & Moderate importance \\
4 & Moderate plus \\
5 & Strong importance \\
6 & Strong plus \\
7 & Very strong or demonstrated importance \\
8 & Very, very strong \\
9 & Extreme importance \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Jarosław Wątróbski
Aleksandra Bączkiewicz
Iga Rudawska

"A Strong Sustainability Paradigm Based Analytical Hierarchy Process (SSP-AHP) Method to Evaluate Sustainable Healthcare Systems" by Jarosław Wątróbski, Aleksandra Bączkiewicz, and Iga Rudawska, arXiv:2306.00718v1, 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}{lcccc}
\hline
 & period 0 & period 1 & \multicolumn{2}{c}{mobility}\tabularnewline
 & $\mathbf{x}_{0}$ & $\mathbf{x}_{1}$ & $1-\hat{\rho}$ & $1-\hat{\beta}$ \tabularnewline
\hline
\hline
case 1 & $(1,2,3)$ & $(3,2,3)$ & 1.0 & 1.0\tabularnewline
case 2 & $(1,2,3)$ & $(3,1,5)$ & 0.5 & 0.0\tabularnewline
\hline
\end{tabular}
\caption{ statistical mobility indices}
\end{table}
\end{document}

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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
\hline
\textbf{Content word} & \textbf{MBERT option} & \textbf{XLM-R option}\\
 \hline
 channels & shots & broadcasts \\
 bred & lived & assistant \\
 population & parted & people \\
 serve & carried & arrangement \\
 place & event & there \\
 journalist & lawyer & activist \\
 female & woman & woman \\
 hijackers & triumphs & males \\
 defeated & won & defeating \\
 \hline
\end{tabular}
\end{table}
\end{document}

cs

"Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets" by Shadi Manafi and Nikhil Krishnaswamy, arXiv:2403.20056v1, 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 Immunization Nudges}
\begin{tabular}{cccc}
\hline\hline
 & EWM & PoLeCe & Control\\
 \hline
 \multicolumn{4}{l}{Panel A. Outcome: Measles shots. Control: } \\
Estimated Value: $\widehat{V} (\widehat{\pi})$ & 10.23 & 9.21 & 4.85 \\
LCB: $\widehat{V}(\widehat{\pi})-\widehat q_{0.95,\Pi}\widehat{s}(\widehat{\pi})$ & 6.56 & 7.8 & 2.05\\
 \hline
 \multicolumn{4}{l}{Panel B. Outcome: Shots per dollar} \\
Estimated Value: $\widehat{V} (\widehat{\pi})$ & 0.045 & 0.045 & 0.043 \\
LCB: $\widehat{V}(\widehat{\pi})-\widehat q_{0.95,\Pi}\widehat{s}(\widehat{\pi})$ & 0.042 & 0.042 & 0.038\\

\hline
 \multicolumn{4}{l}{Panel C. Optimal Treatment Policy: } \\
 \texttt{Measles shots} & \emph{trusted info hub,} & \emph{info hub,}& \emph{no info,}\\
 & \emph{high slope} & \emph{low slope} &\emph{no incentive} \\
 & \emph{high SMS} & \emph{low SMS} &\emph{no reminder} \\
 \texttt{Shots per dollar} & \emph{trusted info hub,} & \emph{trusted info hub,} & \emph{no info,}\\
 & \emph{no incentive} & \emph{no incentive} & \emph{no incentive} \\
 & \emph{no reminder} & \emph{no reminder} & \emph{no reminder} \\
\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.


q-fin

"A Markovian empirical model for the VIX index and the pricing of the corresponding derivatives" by Ying-Li Wang, Cheng-Long Xu, and Ping He, arXiv:2309.08175v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llcccccc} \toprule
 i & j & $r_{ij}$ & $x_{ij}$ & $b_{ij}$ & $\overline S_{ij}$ & $\tau_{ij}$ & $\theta_{ij}$ \\
 \midrule
0 & 1 & 0.0036 & 0.1834 & 0 & 32 & 1 & 0 \\
1 & 2 & 0.03 & 0.022 & 0 & 25 & 1 & 0 \\
1 & 3 & 0.0307 & 0.0621 & 0 & 18 & 1 & 0 \\
2 & 4 & 0.0303 & 0.0611 & 0 & 18 & 1 & 0 \\
2 & 5 & 0.0159 & 0.0502 & 0 & 18 & 1 & 0 \\
 \bottomrule
\end{tabular}
\caption{Description of each branch $e_{ij} \in \mathcal E$ of ANM6-Easy.}
\end{table}
\end{document}

eess

"Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems" by Robin Henry and Damien Ernst, arXiv:2103.07932v2, 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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters for LoRA fine-tuning on Llama 2-7B model.}
\begin{tabular}{ccccc}
 \toprule
 Epoch & Optimizer & $(\beta_1, \beta_2)$ & $\epsilon$ & Batch Size \\
 \midrule
 1 & AdamW & (0.9, 0.999) & $\SI{1e-8}{}$ & 32\\
 \midrule
 Warm-up Ratio & LoRA Alpha & $s$ (if needed) & $\lambda$ (if needed) & \#Runs\\
 \midrule
 0.03 & 16 & 64 & $\SI{1e-6}{}$ & 3 \\
 \midrule
 Weight Decay & LR Scheduler & Sequence Length & Precision & \\
 \midrule
 0 & cosine & 1024 & FP32 & \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"One-step full gradient suffices for low-rank fine-tuning, provably and efficiently" by Yuanhe Zhang, Fanghui Liu, and Yudong Chen, arXiv:2502.01235v1, 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{Number of iterations for sparse settings across 100 independent trials.}
\begin{tabular}{ccccc}
\hline
ID & \(\hat{\lambda}_1\) Iterations & Std. Dev. & \(\hat{\lambda}_2\) Iterations & Std. Dev. \\ \hline
2 & 13 & 0 & & \\
3 & 17.15 & 0.3571 & & \\
4 & 31.91 & 1.8713 & 500 & 0 \\
5 & 17.98 & 0.3995 & 500 & 0 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Xinyuan Fan
Feiyan Ma
Chenlei Leng
Weichi Wu

"Low-Rank Approaches to Graphon Learning in Networks" by Xinyuan Fan, Feiyan Ma, Chenlei Leng, and Weichi Wu, arXiv:2501.18785v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
 \toprule
 \toprule
 \textbf{Team} & \textbf{Possession Value (\%)} \\
 \midrule
 Olympique Marseille & 63.85\% \\
PSG & 60.63\% \\
Rennes & 60.15\% \\
\midrule
Olympique Lyonnais & 58.36\% \\
Nice & 55.15\% \\
Lille & 54.77\% \\
\midrule
Lens & 51.05\% \\
Clermont & 49.30\% \\
Saint-Étienne & 48.71\% \\
\midrule
Montpellier & 48.32\% \\
Bordeaux & 48.29\% \\
Strasbourg & 47.72\% \\
\midrule
Brest & 46.87\% \\
Monaco & 46.62\% \\
Angers SCO & 45.71\% \\
\midrule
Metz & 45.19\% \\
Nantes & 45.02\% \\
Troyes & 44.22\% \\
\midrule
Lorient & 43.43\% \\
Reims & 41.63\% \\
 \bottomrule
 \bottomrule
 \end{tabular}
\caption{Possession values for each team in the analysis, displayed as percentages. Teams are sorted in descending order.}
\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.


stat

Qazi J. Azhad
Abdul Nasir Khan
Bhagwati Devi
Jahangir Sabbir Khan
Ayush Tripathi

"Bayesian estimation of Unit-Weibull distribution based on dual generalized order statistics with application to the Cotton Production Data" by Qazi J. Azhad, Abdul Nasir Khan, Bhagwati Devi, Jahangir Sabbir Khan, and Ayush Tripathi, arXiv:2502.02927v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Deep incremental learning models for financial temporal tabular datasets with distribution shifts" by Thomas Wong and Mauricio Barahona, arXiv:2303.07925v10, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PICP evaluation for the diffusion equation}
\begin{tabular}{|c|c|}
		\hline
		Kernel function & PICP \\ \hline
		Gaussian+Laplacian & 80.0 \% \\ \hline
		Gaussian+Exponential & 80.0 \% \\ \hline
		Gaussian & 60.0 \% \\ \hline
		Rational Quadratic+Laplacian & 80.0 \% \\ \hline
		Matérn+Laplacian & 80.0 \% \\ \hline
		Rational Quadratic+Gaussian & 80.0 \% \\ \hline
		Matérn+Gaussian+Laplacian & 80.0 \% \\ \hline
	\end{tabular}
\end{table}
\end{document}

math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

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


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c} % <-- Alignments: 1st column left, 2nd middle and 3rd right, with vertical lines in between
 \textbf{Speaker ID } & \textbf{Gender} \\
 \hline
 6829 & Female \\
 9026 & Female \\
 8975 & Female \\
 6696 & Female \\
 192 & Femle \\
 557 & Male \\
 1355 & Male \\
 176 & Male \\
 3144 & Male \\
 4345 & Male \\
 1065 & Male \\

 \end{tabular}
\caption{Test Speakers from LibriTTS dataset }
\end{table}
\end{document}

eess

Neeraj Kumar
Srishti Goel
Ankur Narang
Brejesh Lall

"Few Shot Adaptive Normalization Driven Multi-Speaker Speech Synthesis" by Neeraj Kumar, Srishti Goel, Ankur Narang, and Brejesh Lall, arXiv:2012.07252v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A fast fractional block-centered finite difference method for two-sided space-fractional diffusion equations on general nonuniform grids" by Meijie Kong and Hongfei Fu, arXiv:2312.10577v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Expectile hidden Markov regression models for analyzing cryptocurrency returns" by Beatrice Foroni, Luca Merlo, and Lea Petrella, arXiv:2301.09722v2, 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}{lllr}
\hline\hline
2-digit SIC Industry&Zero&Normal&Observations\tabularnewline
\hline
Agriculture, Forestry and Fishing&9.61\%&90.39\%&$ 461$\tabularnewline
Mining&14.59\%&85.41\%&$ 6762$\tabularnewline
Construction&5.48\%&94.52\%&$ 1419$\tabularnewline
Manufacturing&20.29\%&79.71\%&$ 50722$\tabularnewline
Transportation, Communication and Utilities&7.42\%&92.58\%&$ 9038$\tabularnewline
Wholesale Trade&10.2\%&89.8\%&$ 4155$\tabularnewline
Retail Trade&14.37\%&85.63\%&$ 7571$\tabularnewline
Services&28.37\%&71.63\%&$ 23160$\tabularnewline
All industries&19.49\%&80.51\%&$104688$\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Zero-Leverage Puzzle

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Coincidence matrix for relevant text annotation.}
\begin{tabular}{ccc}
\toprule
 & \textbf{Relevant} & \textbf{Context}\\ \hline
\textbf{Relevant} & 2752.5 & 1561.5\\
\textbf{Context} & 1561.5 & 16584.5\\\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Silvia García-Méndez
Francisco de Arriba-Pérez
Ana Barros-Vila
Francisco J. González-Castaño
Enrique Costa-Montenegro

"Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation" by Silvia García-Méndez, Francisco de Arriba-Pérez, Ana Barros-Vila, Francisco J. González-Castaño, and Enrique Costa-Montenegro, arXiv:2404.01338v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A Pathwise Coordinate Descent Algorithm for LASSO Penalized Quantile Regression" by Sanghee Kim and Sumanta Basu, arXiv:2502.12363v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{tabular}{ccl}
 \toprule
 Non-English or Math&Frequency&Comments\\
 \midrule
 \O & 1 in 1,000& For Swedish names\\
 $\pi$ & 1 in 5& Common in math\\
 \$ & 4 in 5 & Used in business\\
 $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Haoyu Dong
Tram Thi Minh Tran
Rutger Verstegen
Silvia Cazacu
Ruolin Gao
Marius Hoggenmüller
Debargha Dey
Mervyn Franssen
Markus Sasalovici
Pavlo Bazilinskyy
Marieke Martens

"Exploring Holistic HMI Design for Automated Vehicles: Insights from a Participatory Workshop to Bridge In-Vehicle and External Communication" by Haoyu Dong, Tram Thi Minh Tran, Rutger Verstegen, Silvia Cazacu, Ruolin Gao, Marius Hoggenmüller, Debargha Dey, Mervyn Franssen, Markus Sasalovici, Pavlo Bazilinskyy, and Marieke Martens, arXiv:2403.19153v1, 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}{llcl}
	\multicolumn{4}{l}{\texttt{struct}}\\
 \quad \texttt{params} & $\mu$ & = & $1$\\
 \quad & $\sigma$ & = & $4$\\
	\quad \texttt{methods} & \texttt{cdf} & = & $x \mapsto 2\mu$ \\
	\quad & \texttt{pdf} & = & $x\mapsto 42$ \\
\end{tabular}
\end{table}
\end{document}

cs

Franz J. Király
Markus Löning
Anthony Blaom
Ahmed Guecioueur
Raphael Sonabend

"Designing Machine Learning Toolboxes: Concepts, Principles and Patterns" by Franz J. Király, Markus Löning, Anthony Blaom, Ahmed Guecioueur, and Raphael Sonabend, arXiv:2101.04938v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluating choice of the encoder in our proposed approach on several CASAS datasets. The mean and standard deviation of F1 scores across 5 runs are reported.}
\begin{tabular}{l|l|l|l|l|l}
\toprule
\multirow{2}{*}{Methods \textbackslash Datasets} & Kyoto8 & Milan & Kyoto7 & Aruba & Cairo \\ \cmidrule{2-6}
 & F1 score & F1 score & F1 score & F1 score & F1 score \\ \midrule
 GRU & 75.5 $\pm$ 1.43 & 80.3 $\pm$ 0.08 & \textbf{88.7 $\pm$ 0.84} & 91.2 $\pm$ 0.18 & 88.3 $\pm$ 2.94\\
 LSTM & 74.6 $\pm$ 2.86 & 79.2 $\pm$ 0.23 & 84.7 $\pm$ 0.99 & 90.1 $\pm$ 0.23 & 88.3 $\pm$ 2.33\\
 Transformer & 75.5 $\pm$ 1.74 & 79.8 $\pm$ 0.47 & 84.6 $\pm$ 1.01 & 90.4 $\pm$ 0.04 & 88.4 $\pm$ 2.63 \\
 Linear & 75.5 $\pm$ 0.25 & 80.1 $\pm$ 0.36 & 85.6 $\pm$ 0.95 & 91.1 $\pm$ 0.15 & 88.2 $\pm$ 2.56\\
 BiLSTM & \textbf{78.3 $\pm$ 0.95} & \textbf{80.4 $\pm$ 0.52} & \textbf{88.7 $\pm$ 0.60} & \textbf{92.4 $\pm$ 0.34} & \textbf{88.7 $\pm$ 0.22} \\\midrule
\end{tabular}
\end{table}
\end{document}

eess

"Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes" by Srivatsa P and Thomas Plötz, arXiv:2311.09514v1, 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{State population distribution}
\begin{tabular}{lc}
\hline
\textbf{State} & \textbf{Population} \\
\hline
Baden-W\"urttemberg & 13.4 \\
Bayern & 15.9 \\
Berlin & 4.47 \\
Brandenburg & 3.05 \\
Bremen & 0.817 \\
Hamburg & 2.26 \\
Hessen & 7.58 \\
Mecklenburg-Vorpommern & 1.92 \\
Niedersachsen & 9.64 \\
Nordrhein-Westfalen & 21.5 \\
Rheinland-Pfalz & 4.93 \\
Saarland & 1.17 \\
Sachsen & 4.83 \\
Sachsen-Anhalt & 2.58 \\
Schleswig-Holstein & 3.50 \\
Thüringen & 2.51 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Mortality simulations for insured and general populations" by Asmik Nalmpatian and Christian Heumann, arXiv:2502.08814v1, 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{Comparison of segmentation models in terms of $\mu_{DC}$ for extracting the RNFL, GC-IPL, and GCC regions.}
\begin{tabular}{ccccc}
 \toprule
 Framework	&RNFL	&GC-IPL	&GCC &Mean\\ \hline
 RAG-Net\textsubscript{v2} & 0.8692 & \textbf{0.8703} & \textbf{0.8698} & \textbf{0.8697} \\
 RAG-Net & 0.8192 & 0.7508 & 0.7860 & 0.7853\\
 PSPNet & 0.8748 & 0.8151 & 0.8457 & 0.8452 \\
 SegNet & 0.8111 & 0.6945 & 0.7555 & 0.7537 \\
 UNet & 0.8216 & 0.8253 & 0.8234 & 0.8234 \\
 FCN-32 & 0.8638 & 0.7470 & 0.8083 & 0.8064\\
 FCN-8 & \textbf{0.8749} & 0.8156 & 0.8460 & 0.8455\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Hina Raja
Taimur Hassan
Muhammad Usman Akram
Naoufel Werghi

"Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression" by Hina Raja, Taimur Hassan, Muhammad Usman Akram, and Naoufel Werghi, arXiv:2010.03872v1, 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}{||cc||}
 \hline
 $L_\infty$-algebras & QFT \\
 \hline\hline
 cyclic $L_\infty$-algebra & perturbative field theory \\
 \hline
 \shortstack{minimal model of\\cyclic $L_\infty$-algebra} & \shortstack{non-trivial part of \\classical S-matrix} \\
 \hline
 \shortstack{{minimal model of }\\{quantum cyclic $L_\infty$-algebra}} & \shortstack{{non-trivial part of}\\{quantum S-matrix}} \\
 \hline
 \shortstack{{computing minimal model via} \\{homological perturbation theory}} & {Feynman diagrams} \\
 \hline
\end{tabular}
\caption{A vocabulary of corresponding notions of $L_\infty$-algebras and quantum field theories.}
\end{table}
\end{document}

math

Higher geometry in physics

"Higher geometry in physics" by Luigi Alfonsi, arXiv:2312.07308v2, 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}{lcccc}
 \hline
 &$p_{\rm abort}$ & $p_{\rm succ}$ & $p_{\rm fail}$ & $p_{\rm start}$\vphantom{$f_f^f$}\\
 \hline
 super-reliable & 0.01 & 0.98 & 0.01 & 0.98\vphantom{$f_f^f$}\\
 reliable & 0.05 & 0.90 & 0.05 & 0.90\\
 fairly reliable & 0.10 & 0.80 & 0.10 & 0.80\\
 unreliable & 0.15 & 0.70 & 0.15 & 0.70\vphantom{$f_f^f$}\\
 \hline
 \end{tabular}
\caption{Transition probabilities for different generator classes.}
\end{table}
\end{document}

eess

Laurent Pagnier
Criston Hyett
Robert Ferrando
Igal Goldshtein
Jean Alisse
Lilah Saban
Michael Chertkov

"System-Wide Emergency Policy for Transitioning from Main to Secondary Fuel" by Laurent Pagnier, Criston Hyett, Robert Ferrando, Igal Goldshtein, Jean Alisse, Lilah Saban, and Michael Chertkov, arXiv:2311.08686v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Janaína Marques e Melo
João César Reis Alves
Gabriel Rodrigues Palma
Sílvia Maria de Freitas
Idemauro Antonio Rodrigues de Lara

"Unified Multivariate Ordinal Model for analysis of sensory attributes" by Janaína Marques e Melo, João César Reis Alves, Gabriel Rodrigues Palma, Sílvia Maria de Freitas, and Idemauro Antonio Rodrigues de Lara, arXiv:2502.11990v1, 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{Estimates of Bidimensional Productivity Spillovers}
\begin{tabular}{lccc|c}
			\toprule
			& \multicolumn{3}{c}{\textit{Point Estimates}} & {\textit{Statistically $>0$}} \\
			Estimand & 1st Qu. & Median & 3rd Qu. & (\% Obs.) \\
			\midrule
			$SP^0$ 	& 0.153 & 0.300 & 0.428 & 82.58 \\
			& (0.090, 0.180) & (0.198, 0.349) & (0.302, 0.504) & \\
			$SP^1$ 	& 0.158 & 0.172 & 0.185 & 98.89 \\
			& (0.119, 0.188) & (0.129, 0.204) & (0.137, 0.216) & \\
			\midrule
			\multicolumn{5}{p{12cm}}{\footnotesize {\sc Notes:} Reported are the semiparametric estimates of bidimensional productivity spillovers from under our baseline specification. The left panel summarizes point estimates of $SP_{it}^0$ and $SP_{it}^1$ with the corresponding two-sided 95\% bootstrap percentile confidence intervals in parentheses. The last column reports the share of observations for which the point estimates are statistically positive at the 5\% significance level using one-sided bootstrap percentile confidence intervals.} \\
			\bottomrule[1pt]
		\end{tabular}
\end{table}
\end{document}

q-fin

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


stat

Ilaria Bombelli
Domenica Fioredistella Iezzi
Emiliano Seri
Maurizio Vichi

"Spherical Double K-Means: a co-clustering approach for text data analysis" by Ilaria Bombelli, Domenica Fioredistella Iezzi, Emiliano Seri, and Maurizio Vichi, arXiv:2501.04562v3, 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{Two-Channel Input}
\begin{tabular}{|c|c|c|}
			\hline
			\textbf{Input I}&\textbf{Input II} & \textbf{ME (m)}\\
			\hline
			$|\boldsymbol{H}|$ & $\angle \boldsymbol{H}$ & 0.03126 \\
			\hline
			$|\boldsymbol{H}_D|$ & $\angle \boldsymbol{H}_D$ & 0.03810 \\
			\hline
			$|\boldsymbol{H}_A|$ & $\angle \boldsymbol{H}_A$ & 0.02792 \\
			\hline

			$|\boldsymbol{H}_A^\prime|$ & $\angle \boldsymbol{H}_A^\prime$ & 0.03719 \\
			\hline
			Re($\boldsymbol{H}$) & Im($\boldsymbol{H}$) & 0.01809 \\
			\hline
			Re($\boldsymbol{H}_A$) & Im($\boldsymbol{H}_A$) & 0.01614 \\
			\hline
			Re($\boldsymbol{H}_D$) & Im($\boldsymbol{H}_D$) & 0.01316 \\
			\hline
			Re($\boldsymbol{H}_A^\prime$) & Im($\boldsymbol{H}_A^\prime$) & 0.03478 \\
			\hline
			sin($\angle \boldsymbol{H}_D$)& cos($\angle \boldsymbol{H}_D$) & 0.01425 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cccccc}
\toprule
ID Data & \multicolumn{6}{c}{ImageNet-100}\\
\midrule
OOD data & Textures & Places & LSUN-C & LSUN-R & iSUN & Avg \\
\midrule
&\multicolumn{6}{c}{\textit{zero-shot}} \\
MCM &71.03 &70.25 &96.44 &87.42 &89.64 &83.59 \\
GL-MCM &71.03 &69.24 &89.59 &74.29 &79.49 &76.73 \\
\midrule
&\multicolumn{6}{c}{\textit{one-shot}} \\
LoCoOp &\textbf{76.26} &66.38 &96.86 &91.68 &94.88 &85.21 \\
\rowcolor{black!20} CLIP-OS (ours) &71.11 &\textbf{69.82} &\textbf{97.51} &\textbf{97.68} &\textbf{97.54} &\textbf{86.73}\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Hao Sun
Rundong He
Zhongyi Han
Zhicong Lin
Yongshun Gong
Yilong Yin

"CLIP-driven Outliers Synthesis for few-shot OOD detection" by Hao Sun, Rundong He, Zhongyi Han, Zhicong Lin, Yongshun Gong, and Yilong Yin, arXiv:2404.00323v1, 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{Comparisons of classification accuracies (\%) with different upsample methods using the ResNet$18$ as backbone architecture. $\xi =i$ means each category has $i \times 3$ feature channels in the middle-level.}
\begin{tabular}{|c|c|c|c|}
 \toprule
 Method & Base Model & Upsample & Acc. \\
 \midrule
 \midrule
 Ours with $\xi =1$ & ResNet$18$ & nearest & $65.66$ \\
 Ours with $\xi =1$ & ResNet$18$ & bicubic & $65.50$ \\
 Ours with $\xi =1$ & ResNet$18$ & bilinear & $65.33$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Dongliang Chang
Yixiao Zheng
Zhanyu Ma
Ruoyi Du
Kongming Liang

"Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features" by Dongliang Chang, Yixiao Zheng, Zhanyu Ma, Ruoyi Du, and Kongming Liang, arXiv:2102.00367v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Valuation Duration of the Stock Market

"Valuation Duration of the Stock Market" by Ye Li and Chen Wang, arXiv:2310.07110v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Gianluca Biggi
Andrea Mina
Federico Tamagni

"There are different shades of green: heterogeneous environmental innovations and their effects on firm performance" by Gianluca Biggi, Andrea Mina, and Federico Tamagni, arXiv:2310.08353v1, 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|ll}
\toprule
\textbf{Language} & \textbf{English prompt} & \textbf{Native prompt} \\
\midrule
English & 39.14 & 39.14 \\
Norwegian & 28.16 & 27.76 \\
Russian & 17.26 & 17.25 \\
\bottomrule
\end{tabular}
\caption{Performance of \texttt{mT0}-based definition generators (ROUGE-L * 100) on the validation sets.}
\end{table}
\end{document}

cs

Mariia Fedorova
Andrey Kutuzov
Nikolay Arefyev
Dominik Schlechtweg

"Enriching Word Usage Graphs with Cluster Definitions" by Mariia Fedorova, Andrey Kutuzov, Nikolay Arefyev, and Dominik Schlechtweg, arXiv:2403.18024v1, 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{Table Type Styles}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Table}&\multicolumn{3}{|c|}{\textbf{Table Column Head}} \\
\cline{2-4}
\textbf{Head} & \textbf{\textit{Table column subhead}}& \textbf{\textit{Subhead}}& \textbf{\textit{Subhead}} \\
\hline
copy& More table copy$^{\mathrm{a}}$& & \\
\hline
\multicolumn{4}{l}{$^{\mathrm{a}}$Sample of a Table footnote.}
\end{tabular}
\end{table}
\end{document}

stat

"SIGN: A Statistically-Informed Gaze Network for Gaze Time Prediction" by Jianping Ye and Michel Wedel, arXiv:2501.17422v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
 \toprule
 Label & Gender & Count & Percentage (\%) \\
 \midrule
 Toxic & F & 2504 & 5.89 \\
 Toxic & M & 2123 & 4.99 \\
 Non-Toxic & F & 22,465 & 52.83 \\
 Non-Toxic & M & 15,431 & 26.29 \\
 \bottomrule
 \end{tabular}
\caption{Gender and label distribution of Jigsaw training set.}
\end{table}
\end{document}

cs

"Addressing Both Statistical and Causal Gender Fairness in NLP Models" by Hannah Chen, Yangfeng Ji, and David Evans, arXiv:2404.00463v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters during sampling candidates}
\begin{tabular}{cccc}\toprule
 &Inverse Temperature $\beta$ &Local Search Steps $J$ &Buffer Size $L$ \\\midrule
Synthetic 200D &$10^5$ &$10$ &$1000$ (Rastrigin) / $500$ (Others) \\
Synthetic 400D &$10^5$ &$15$ &$1000$ (Rastrigin) / $500$ (Others) \\
\midrule
HalfCheetah 102D &$10^4$ &$10$ &$300$ \\
RoverPlanning 100D &$10^5$ &$30$ &$300$ \\
DNA 180D &$10^5$ &$50$ &$300$ \\\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Taeyoung Yun
Kiyoung Om
Jaewoo Lee
Sujin Yun
Jinkyoo Park

"Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization" by Taeyoung Yun, Kiyoung Om, Jaewoo Lee, Sujin Yun, and Jinkyoo Park, arXiv:2502.16824v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results on STARSS23 development set. $F1$ and $LR$ are in percent\,(\%), $LE$ in deg\,(°). CNN-Conf is employed as audio encoder. I3D:\,Inflated 3D ConvNet; RC:\,ResNet-Conformer; Conf:\,AV-Conformer; CMAF:\,Cross-Modal Attentive Fusion; CA:\,Cross-Attention; GRU:\,Gated Recurrent Units; AO:\,Audio-only; AV:\,audio-visual.}
\begin{tabular}{c|c|c|c|c|c|c}
\hline
\textbf{Visual}&\textbf{Fusion}&\textbf{ER$\downarrow$}&\textbf{F1$\uparrow$}&\textbf{LE$\downarrow$}&\textbf{LR$\uparrow$}&\textbf{SELD$\downarrow$}\\ \hline
I3D & CMAF & 0.57 & 40.5 & 33.2 & 55.3 & 0.45 \\
I3D & Conf & 0.52 & 46.4 & 16.9 & 60.2 & 0.39 \\
RC & CMAF & 0.54 & 41.3 & 31.6 & 53.4 & 0.44 \\
RC & Conf & 0.51 & 49.5 & 15.8 & 60.2 & \textbf{0.38} \\
RC & CA & 0.55 & 34.7 & 30.4 & 47.8 & 0.47 \\
RC & GRU & \textbf{0.50} & 49.4 & 16.2 & 56.8 & \textbf{0.38} \\
Both & Conf & 0.52 & 48.0 & 16.2 & \textbf{60.8} & \textbf{0.38} \\
\hline
\hline
\multicolumn{2}{c|}{Visual-only} & 1.03 & 0.9 & 103 & 11.4 & 0.87 \\
\multicolumn{2}{c|}{Audio-only} & 0.51 & \textbf{50.2} & \textbf{15.4} & 56.4 & \textbf{0.38} \\
\hline
\hline
\multicolumn{2}{c|}{Baseline AO} & 0.57 & 29.9 & 22.0 & 47.7 & 0.48 \\
\multicolumn{2}{c|}{Baseline AV} & 1.07 & 14.3 & 48.0 & 35.5 & 0.71 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Davide Berghi
Peipei Wu
Jinzheng Zhao
Wenwu Wang
Philip J. B. Jackson

"Fusion of Audio and Visual Embeddings for Sound Event Localization and Detection" by Davide Berghi, Peipei Wu, Jinzheng Zhao, Wenwu Wang, and Philip J. B. Jackson, arXiv:2312.09034v1, 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}{|cl||cc|c|}
\hline
& &gamma deviance &RMSE &average\\
\hline
 (0)& null model &2.085& 35,311&24,641\\\hline
 (1a)& gamma GLM &1.717& 32,562&25,105\\
 (1b)& gamma GLM recalibrated with $K=24$&1.641& 31,578&24,641\\\hline
(2a)& gamma FFNN &1.496& 29,673&24,526\\
 (2b)& gamma FFNN recalibrated with $K=22$ &1.452& 28,806&24,641\\
 (2c)& gamma FFNN tree adjustment with 4 bins (seed 1) &1.508& 29,371&24,641\\
 (2d)& gamma FFNN tree adjustment with 8 bins (seed 2) &1.466& 27,942&24,641\\\hline
\end{tabular}
\caption{Losses in the Swedish motorcycle example based on all available covariates. }
\end{table}
\end{document}

q-fin

"Isotonic Recalibration under a Low Signal-to-Noise Ratio" by Mario V. Wüthrich and Johanna Ziegel, arXiv:2301.02692v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The NN structure no LSTM layers, for example in tuple $(x,y)$, length of the tuple indicates the number of hidden layers, and each entity, e.g, $x$, denotes the number of hidden units or dropout ratio.}%}
\begin{tabular}{c|c}
		\hline
		\textbf{Hidden layer type} & \textbf{Hidden layers and units as a tuple} \\
		\hline
		Fully connected layers & (512, 512, 512, 512, 512, 512,\\& 512, 512, 512,
		512,
		256, 128)
		\\
		\hline
		Dropout layers	& (
		0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\\& 0.4, 0.4, 0.4,
		0.4,
		0.2, 0.2)
		\\
		\hline

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

eess

Amirhossein Shaghaghi
Abolfazl Zakeri
Nader Mokari
Mohammad Reza Javan
Mohammad Behdadfar
Eduard A Jorswieck

"Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach" by Amirhossein Shaghaghi, Abolfazl Zakeri, Nader Mokari, Mohammad Reza Javan, Mohammad Behdadfar, and Eduard A Jorswieck, arXiv:2103.03817v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
\hline
\textbf{Cluster \#} & \textbf{Cryptocurrencies} \\
\hline
\textbf{1} & Ethereum\\
\hline
\textbf{2} & Wabi\\
\hline
\textbf{3} & Olympus v2\\
\hline
\textbf{4} & \parbox[c]{6cm}{Polygon, Axie Infinity, Aave, The Graph, yearn.finance, IoTeX, SushiSwap, 0x, SwissBorg, Ontology, Cartesi, Dusk Network, Origin Protocol, Illuvium, Orbs, API3, IDEX, Akropolis, NULS, Bytom, Fusion, Dock, Remme, Bela, Livepeer, DODO, Fantom, Sentinel, Neutrino USD, Atomic Wallet Coin, Band Protocol, Curve DAO Token}\\
\hline
\textbf{5} & \parbox[c]{6cm}{BNB, Solana, Cardano, Polkadot, Avalanche, Cosmos, Internet Computer, Elrond, Tezos, Flow, Kusama, Oasis Network, Waves, Mina, Secret, Decred, WAX, Kava, Synthetix, SKALE Network, Hive, Phantasma, Persistence, Akash Network, Divi, Ark, Telos, PEAKDEFI, Switcheo, e-Money, Hydra, Particl, Peercoin, ChainX, Callisto Network, Tachyon Protocol, Minter Network, Blocknet, OKCash, Veil, Datamine, Rapids, Tendies, Savix, FireStarter, Terra, Lisk, Wagerr, Unification, Stake DAO, Starname, NEAR Protocol, IRISnet, CertiK, v.systems, HTMLCOIN, DeFiChain, Mirror Protocol, LTO Network, Stafi, Harmony, PIVX, Stacks, Zilliqa, Beefy Finance, Nexus, Trittium, Bitcoin Green, Algorand, PancakeSwap, Ardor, Edgeware, Phore, TokenPay, Pinkcoin, Crypto.com Coin, TRON, THORChain, IOST, TomoChain, Aion, FLETA, ReddCoin, Nxt, BlackCoin, CloakCoin, EOS, Celo, NEM, 1inch Network, Qtum, ICON, COTI, HEX, Kyber Network Crystal v2, STAKE, Energi, MANTRA DAO, InsurAce, Validity, Navcoin, Neblio, Enecuum, ChangeNOW Token, SmartCash, Wanchain, Thorstarter, Kalamint} \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Abdulrezzak Zekiye
Semih Utku
Fadi Amroush
Oznur Ozkasap

"AI-Assisted Investigation of On-Chain Parameters: Risky Cryptocurrencies and Price Factors" by Abdulrezzak Zekiye, Semih Utku, Fadi Amroush, and Oznur Ozkasap, arXiv:2308.08554v1, 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{Environment hyperparameters}
\begin{tabular}{c|c}
\toprule
Number of envs. & 4096 \\
Random $v_x$ range & $[-0.3, 1.0]$ m/s \\
Random $v_y$ range & $[-0.7, 0.7]$ m/s \\
Random $\omega_z$ range & $[-0.78, 0.78]$ rad/s \\
Proportional gain & 4.0 Nm/rad \\
Derivative gain & 0.2 Nm/(rad/s) \\
Action scaling & 0.5 \\
Default leg angles & [0.05, 0.4, -0.8] rad \\
Simulation time step & 5 ms \\
Episode length & 10 s \\
Height scan grid & 13 x 11 points \\
Height scan step & 8 cm \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Elliot Chane-Sane
Pierre-Alexandre Leziart
Thomas Flayols
Olivier Stasse
Philippe Souères
Nicolas Mansard

"CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning" by Elliot Chane-Sane, Pierre-Alexandre Leziart, Thomas Flayols, Olivier Stasse, Philippe Souères, and Nicolas Mansard, arXiv:2403.18765v1, 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{Validation of asymptotic expansion by differences from nonlinear ODE for simple distribution feeder model in Figure~}
\begin{tabular}{lccc}\hline
 & $\Delta w(0\,{\rm km})$
 & $\Delta v(L=5\,{\rm km})$ & $\Delta \theta(L=5\,{\rm km})$\\\hline%
 asymptotic expansion up to 1st-order
 & 0.02700 & 0.0542 & 0.0564 \\\hline
 asymptotic expansion up to 2nd-order
 & 0.00951 & 0.0191 & 0.0250 \\\hline
 asymptotic expansion up to 3rd-order
 & 0.00580 & 0.0102 & 0.0145 \\\hline
 asymptotic expansion up to 4th-order
 & 0.00478 & 0.0078 & 0.0099 \\\hline
 \end{tabular}
\end{table}
\end{document}

eess

Haruki Tadano
Yoshihiko Susuki
Atsushi Ishigame

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


q-fin

"Unemployment and Endogenous Reallocation over the Business Cycle" by Carlos Carrillo-Tudela and Ludo Visschers, arXiv:2304.00544v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\footnotesize \textbf{Results of prediction performance and stability scores of ANOVA-T$^{1}$PNN and NBM-T$^{1}$PNN.}}
\begin{tabular}{c|c|c|c}
\hline
Dataset & Measure & ANOVA-T$^{1}$PNN & NBM-T$^{1}$PNN \\ \hline \hline
\multirow{2}{*}{\textsc{Calhousing}} & RMSE $\downarrow$ (std) & 0.614 (0.001) & 0.604 (0.001) \\ \cline{2-4}
 & Stability score $\downarrow$ & 0.012 & 0.009 \\ \hline
\multirow{2}{*}{\textsc{Wine}} & RMSE $\downarrow$ (std) & 0.725 (0.02) & 0.720 (0.02) \\ \cline{2-4}
 & Stability score $\downarrow$ & 0.011 & 0.017 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Seokhun Park
Insung Kong
Yongchan Choi
Chanmoo Park
Yongdai Kim

"Tensor Product Neural Networks for Functional ANOVA Model" by Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, and Yongdai Kim, arXiv:2502.15215v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

"Abnormal component analysis" by Romain Valla, Pavlo Mozharovskyi, and Florence d'Alché-Buc, arXiv:2312.16139v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Distributions in MarsGRAM settings.}
\begin{tabular}{lc}
 \toprule\toprule
 & Distribution/Value \\ \midrule
 Dust Level & $\mathcal{U}(0.1, 3.0)$ \\
 Mean Wave Offset & $\mathcal{U}(1.5, 2.5)$\\
 Random Seed & $\mathcal{U}(1, 9\times10^{8})$ \\
 Density Perturbation Scale & 2 \\
 \bottomrule\bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Jens A. Rataczak
Davide Amato
Jay W. McMahon

"Density Estimation for Entry Guidance Problems using Deep Learning" by Jens A. Rataczak, Davide Amato, and Jay W. McMahon, arXiv:2310.19684v1, 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 the proposed BBO algorithm. The best result in each row is shown in bold font.}
\begin{tabular}{lllll}
\hline
 & Case 1 & Case 2 & Case 3 & Case 4 \\ \hline
Base driver dist. & 2,168,819 & 2,168,819 & 2,168,819 & 2,168,819 \\
Base rider dist. & 3,532,517 & 3,532,517 & 3,532,517 & 3,532,517 \\
Matched trip dist. & 3,483,903 & 3,496,608 & 3,454,594 & {\bf 3,451,805} \\
$d_{ov}$ & 1,315,084 & 1,327,789 & 1,285,775 & {\bf 1,282,986} \\
$M_R$ & 0.479 & {\bf 0.482} & 0.481 & 0.476 \\
Cost & 0.8932 & 0.8938 & {\bf 0.8834} & 0.8871 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Hamed Javidi
Dan Simon
Ling Zhu
Yan Wang

"A multi-objective optimization framework for on-line ridesharing systems" by Hamed Javidi, Dan Simon, Ling Zhu, and Yan Wang, arXiv:2012.05046v1, 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
\begin{tabular}{|l|ccc|cc|ccc|}
 \toprule
 \hline
 \multirow{2}{*}{\shortstack[c]{\large\textbf{Model}}} &
 \multirow{2}{*}{\shortstack[c]{\large\textbf{Mean Monthly}\\\large\textbf{Return(\%)}}} &
 \multirow{2}{*}{\shortstack[c]{\large\textbf{Monthly Return}\\\large\textbf{Std(\%)}}} &
 \multirow{2}{*}{\shortstack[c]{\large\textbf{Monthly}\\\large\textbf{ES}}} &
 \multirow{2}{*}{\shortstack[c]{\large\textbf{Mean}\\\large\textbf{Leverage}}} &
 \multirow{2}{*}{\shortstack[c]{\large\textbf{Mean Daily}\\\large\textbf{Drawdown(\%)}}} &
 \multicolumn{3}{|c|}{\large\textbf{Sharpe}} \\
 \cline{7-9}
 & & & & & &
 \shortstack{\large\textbf{Daily}} &
 \shortstack{\large\textbf{Weekly}} &
 \shortstack{\large\textbf{Monthly}} \\
 \hline
 \large A2-SC & \large 7.445 & \large 11.305 & \large -2.853 & \large 1.681 & \large -5.322 & \large 0.587 & \large 1.365 & \large 0.659 \\
 \large A2-USCC & \large 6.004 & \large 12.190 & \large -3.148 & \large 1.753 & \large -6.441 & \large 0.390 & \large 1.021 & \large 0.493 \\
 \large B2-SC & \large \textbf{13.750} & \large \textbf{9.506} & \large \textbf{-2.092} & \large \textbf{1.269} & \large \textbf{-2.230} & \large \textbf{1.242} & \large \textbf{2.999} & \large \textbf{1.446} \\
 \large B2-USCC & \large \textbf{12.860} & \large \textbf{9.154} & \large \textbf{-2.061} & \large \textbf{1.388} & \large \textbf{-2.432} & \large \textbf{1.135} & \large \textbf{2.912} & \large \textbf{1.405} \\
 \large C2-SC & \large 8.235 & \large 16.118 & \large -3.854 & \large 1.508 & \large -11.202 & \large 0.411 & \large 1.059 & \large 0.511 \\
 \large C2-USCC & \large 5.256 & \large 17.329 & \large -4.757 & \large 1.728 & \large -13.549 & \large 0.274 & \large 0.629 & \large 0.303 \\
 \large D2-SC & \large 8.817 & \large 10.569 & \large -2.680 & \large 1.467 & \large -4.517 & \large 0.716 & \large 1.729 & \large 0.834 \\
 \large D2-USCC & \large 7.247 & \large 11.868 & \large -3.210 & \large 1.664 & \large -5.865 & \large 0.471 & \large 1.266 & \large 0.611 \\
 \hline
 \bottomrule
 \end{tabular}
\caption{Performance Metrics Table For Type 2 Models}
\end{table}
\end{document}

q-fin

"Novel Risk Measures for Portfolio Optimization Using Equal-Correlation Portfolio Strategy" by Biswarup Chakraborty, arXiv:2508.03704v1, 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{Accuracy (\%) comparison by ablating NLAR tasks during training.}
\begin{tabular}{ccc}
 \toprule
 & w/o NLAR & w/ NLAR \\
 \midrule
 Accuracy & 16.4 & 63.8 \\
 \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.


q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
 \toprule
 Method & Front-back confusion rate \\
 \midrule
 Generic & 29.0\% $\pm$ 5.4 \\
 \textbf{Ours} & \textbf{14.8\% $\pm$ 4.6} \\
 GT HRTF & 9.6\% $\pm$ 4.2 \\
 \bottomrule
 \end{tabular}
\caption{Front-back confusion with rendered sounds. We report the percent of times the listeners made an error, along with the first standard deviation}
\end{table}
\end{document}

eess

HRTF Estimation in the Wild

Vivek Jayaram
Ira Kemelmacher-Shlizerman
Steven M. Seitz

"HRTF Estimation in the Wild" by Vivek Jayaram, Ira Kemelmacher-Shlizerman, and Steven M. Seitz, arXiv:2311.03560v1, 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}{lllllll}
 \toprule
 & \bfseries SRN ($H_1$ only) & \bfseries PSS-K & \bfseries PWG-K & \bfseries SW-K & \bfseries PF-K & \bfseries PersLay\\
 \midrule
 Accuracy & $79.6 (\pm 0.3)$ & $72.38 (\pm 0.7)$ & $76.63 (\pm 0.9)$ & $83.6 (\pm 0.9) $ & $85.9 (\pm 0.8)$ & $87.7 (\pm 1.0)$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Jens Agerberg
Andrea Guidolin
Andrea Martinelli
Pepijn Roos Hoefgeest
David Eklund
Martina Scolamiero

"Certifying Robustness via Topological Representations" by Jens Agerberg, Andrea Guidolin, Andrea Martinelli, Pepijn Roos Hoefgeest, David Eklund, and Martina Scolamiero, arXiv:2501.10876v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.