stat

"Finite-Sample Valid Randomization Tests for Monotone Spillover Effects" by Shunzhuang Huang, Xinran Li, and Panos Toulis, arXiv:2501.02454v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{mpc.gmd\_bus}
\begin{tabular}{|c|c|c|c|}
\hline
\textit{parent} & \textit{status} & \textit{g\_gnd} & \textit{name} \\
\hline
\hline
1 & 1 & 5 & `dc\_sub1' \\
\hline
2 & 1 & 5 & `dc\_sub2' \\
\hline
1 & 1 & 0 & `dc\_bus1' \\
\hline
2 & 1 & 0 & `dc\_bus2' \\
\hline
3 & 1 & 0 & `dc\_bus3' \\
\hline
4 & 1 & 0 & `dc\_bus4' \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Adam Mate
Arthur K. Barnes
Russell W. Bent
Eduardo Cotilla-Sanchez

"Analyzing and Mitigating the Impacts of GMD and EMP Events on the Electrical Grid with PowerModelsGMD.jl" by Adam Mate, Arthur K. Barnes, Russell W. Bent, and Eduardo Cotilla-Sanchez, arXiv:2101.05042v2, 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{Empirical mean squared errors, over 250 runs, of the trimmed mean, \texttt{Bun}, with $t=1$, and the clipped mean, $\tilde\mu$, with $\eta=0$, \textbf{before the noise is added}, compared across various population distributions. Here, $C$ and $m$ (as defined in ) were randomly selected between 1 and 100.}
\begin{tabular}{l|rr|rr|rr}
 & \multicolumn{2}{c}{Gaussian} & \multicolumn{2}{c}{Gaussian Mixture} & \multicolumn{2}{c}{Skewed}\\
 $n$ & \texttt{Bun} & $\tilde\mu',\ \eta=0$ & \texttt{Bun} & $\tilde\mu',\ \eta=0$ & \texttt{Bun} & $\tilde\mu',\ \eta=0$ \\
 \hline
50 & \textbf{0.0201} & 0.0222 & 0.5333 & \textbf{0.5517} & \textbf{0.0250} & 0.0297 \\
100 &\textbf{0.0105} & 0.0107 & 0.3132 & \textbf{0.2889} & 0.0147 & \textbf{0.0130} \\
500 &\textbf{0.0025} & \textbf{0.0025} & 0.0507 & \textbf{0.0464} & 0.0072 & \textbf{0.0024} \\
1000 & \textbf{0.0013} & \textbf{0.0013} & 0.0284 & \textbf{0.0258} & 0.0068 & \textbf{0.0014} \\
\end{tabular}
\end{table}
\end{document}

stat

"Improved subsample-and-aggregate via the private modified winsorized mean" by Kelly Ramsay and Dylan Spicker, arXiv:2501.14095v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

AI exposure predicts unemployment risk

"AI exposure predicts unemployment risk" by Morgan Frank, Yong-Yeol Ahn, and Esteban Moro, arXiv:2308.02624v1, 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|cccccc}
 \hline
 Method & Acc $\downarrow$ & Comp $\downarrow$ & C-$\mathcal{L}_1$ $\downarrow$ & Prec $\uparrow$ & Recall $\uparrow$ & F-score $\uparrow$ \\
 \hline
 COLMAP & 0.047 & 0.235 & 0.141 & 71.1 & 44.1 & 53.7\\
 UNISURF & 0.554 & 0.164 & 0.359 & 21.2 & 36.2 & 26.7\\
 VolSDF & 0.414 & 0.120 & 0.267 & 32.1 & 39.4 & 34.6\\
 NeuS & 0.179 & 0.208 & 0.194 & 31.3 & 27.5 & 29.1 \\
 Manhattan-SDF & 0.072 & 0.068 & 0.070 & 62.1 & 56.8 & 60.2\\
 NeuRIS & 0.050 & 0.049 & 0.050 & 71.7 & 66.9 & 69.2\\
 MonoSDF & \textbf{0.035} & 0.048 & \textbf{0.042} & \textbf{79.9} & 68.1 & 73.3\\
 {ObjSDF++} & 0.039 & 0.045 & \textbf{0.042} & {78.1} & {70.6} & {74.0}\\
 \textbf{Ours} & 0.044 & \textbf{0.040} & \textbf{0.042} & {74.7} & \textbf{74.8} & \textbf{74.7}\\
 \hline
 \end{tabular}
\caption{Quantitative assessments of the proposed model against previous works on the ScanNet dataset. }
\end{table}
\end{document}

cs

Xiaoyang Lyu
Chirui Chang
Peng Dai
Yang-Tian Sun
Xiaojuan Qi

"Total-Decom: Decomposed 3D Scene Reconstruction with Minimal Interaction" by Xiaoyang Lyu, Chirui Chang, Peng Dai, Yang-Tian Sun, and Xiaojuan Qi, arXiv:2403.19314v2, 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 an $\alpha$ value of $95\%$ for path 3-12 (damage-non-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 ($\%$) & D21/22/23/24 & D25/26/27/28 \\
\hline
DI$^a$$^\dagger$ & 7.5 & 100 & 93.75 \\
$F$ Statistic$^b$$^\dagger$ & 0 & 100 & 75 \\
$F_m$ Statistic$^b$$^{\dagger\dagger}$ & 0 & 100 & 75 \\
$Z$ Statistic$^a$$^{\dagger\dagger}$ & 0 & 50 & 75 \\
\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}{$^a$ $\alpha = 95\%$.; $^b$ $\alpha = 70\%$} \\
\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}{|c||c|c|}
 \hline
 type of quadratic form & $|V_0|$ & $|V_a|$\\ \hline\hline
 $\dim(q) = 2m$, $q$ hyperbolic & $f^{2m - 1} + f^m - f ^{m - 1}$ & $f^{2m - 1} - f ^{m - 1}$ \\ \hline
 $\dim(q) = 2m$, $q$ not hyperbolic & $f^{2m - 1} - f^m + f^{m - 1}$ & $f^{2m - 1} + f^{m - 1}$ \\ \hline
 $\dim(q) = 2m + 1$ & $f^{2m}$ & $f^{2m} \pm f^m$ \\ \hline
 \end{tabular}
\caption{Size of the pre-image under a quadratic form}
\end{table}
\end{document}

math

"Diameter and Girth of Representation Graphs of Quadratic Forms" by Nico Lorenz and Marc Christian Zimmermann, arXiv:2503.01721v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Ziyad AlSharawi
Jose S. Cánovas
Sadok Kallel

"Constrained polynomial roots and a modulated approach to Schur stability" by Ziyad AlSharawi, Jose S. Cánovas, and Sadok Kallel, arXiv:2503.11252v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Justyna Witulska
Anna Zaleska
Natalia Kremzer-Osiadacz
Agnieszka Wyłomańska
Ireneusz Jabłoński

"Robust variance estimators in application to segmentation of measurement data distorted by impulsive and non-Gaussian noise" by Justyna Witulska, Anna Zaleska, Natalia Kremzer-Osiadacz, Agnieszka Wyłomańska, and Ireneusz Jabłoński, arXiv:2502.10275v1, 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
 & {\bf Ablated} & {\bf Counterfactual State} & {\bf Actual Game} \\
 & {\bf Version} & {\bf Explanations} & {} \\
\hline
{\bf Score} & 1.93 & 4.00 & 4.97\\
\hline
\end{tabular}
\caption{Average results on a 6 point Likert scale from the fidelity user study.}
\end{table}
\end{document}

cs

Matthew L. Olson
Roli Khanna
Lawrence Neal
Fuxin Li
Weng-Keen Wong

"Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning" by Matthew L. Olson, Roli Khanna, Lawrence Neal, Fuxin Li, and Weng-Keen Wong, arXiv:2101.12446v1, 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}{r|ccc}
 \toprule
 & SA=0\% & SA=50\% & SA=100\% \\\midrule
 number of auctions & 43 & 127 & 39 \\
 number of items & 762 & 12215 & 215 \\
 number of bids & 1659 & 94218 & 597 \\
 Small bidder pool & 4 & 14 & 8 \\
 Large bidder pool & 1 & 5 & 0 \\
 Average number of small bidders & 1.835 & 6.656 & 3.147 \\
 & (0.466) & (1.602) & (1.042) \\
 Average number of large bidders & 0.507 & 1.788 & 0 \\
 & (0.533) & (1.082) & (0) \\
 Mean Offer price & 2.966 & 2.587 & 2.708 \\
 & (0.593) & (0.354) & (0.374) \\
 Mean Winning price & 2.742 & 2.469 & 2.577 \\
 & (0.468) & (0.332) & (0.322) \\
 Mean Item Quantity & 32841 & 40707 & 38022 \\
 & (9088) & (4412) & (5086) \\
 Exist in all years & Yes & Yes & Yes \\
 \bottomrule
 \end{tabular}
\caption{Summary Statistics}
\end{table}
\end{document}

q-fin

"Set-Asides in USDA Food Procurement Auctions" by Ni Yan and WenTing Tao, arXiv:2302.05772v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Decision rule at month 3, mean of the blip estimates over 25 imputed datasets, SPRINT trial, 2010-2013. }
\begin{tabular}{|l|c|c|c|}
 \hline
 Variable & QLOMA & WOMA & No. of times stat. significant$^*$ \\ \hline
 Intercept visit & -8.3 & -2.4 & 2 \\ \hline
 Intercept add-on & -3.1 & -5.5 & 0 \\ \hline
 \multicolumn{4}{|c|}{Visit interaction with:} \\ \hline
 Intensive group & -0.5 & -1.3 & 0 \\ \hline
 Age & 0.0 & -0.0 & 0 \\ \hline
 Female sex & 0.2 & 0.8 & 2 \\ \hline
 Race Black & \multicolumn{3}{|c|}{Reference} \\ \hline
 Race Hispanic & 0.6 & -0.2 & 0 \\ \hline
 Race White & 0.7 & 1.9 & 0 \\ \hline
 \hspace{0.2cm} Other & -0.1 & 0.4 & 0 \\ \hline
 Smoking (ever) & -1.4 & -1.8 & 1 \\ \hline
 BMI & 0.0 & -0.1 & 0 \\ \hline
 HDL & 0.0 & -0.0 & 1 \\ \hline
 SBP Baseline & 0.0 & 0.0 & 2 \\ \hline
 SBP Current month & 0.0 & 0.0 & 1 \\ \hline
 CVD & 0.9 & 1.6 & 2 \\ \hline
 Aspirin use & 0.0 & -1.1 & 0 \\ \hline
 Statin use & 0.3 & 0.6 & 0 \\ \hline
 \end{tabular}
\end{table}
\end{document}

stat

Janie Coulombe
Dany El-Riachi
Fanxing Du
Tianze Jiao

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


math

Diego Cifuentes
Santanu S. Dey
Jingye Xu

"Sensitivity analysis for mixed binary quadratic programming" by Diego Cifuentes, Santanu S. Dey, and Jingye Xu, arXiv:2312.06714v2, 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|cccc}
Experiment/Type & $x = 1$ &$x = 2$ & $\cdots$ &$x = |\mathcal{X}_n|$ \\
\hline \hline
$e = 1$ & $\phi(x = 1,e=1)$ & $\phi(x = 2,e=1)$ &$\cdots$ & $\phi(x = |\mathcal{X}_n|,e=1)$\\
$e = 2$ & $\phi(x = 1,e=2)$ & $\phi(x = 2,e=2)$ &$\cdots$ & $\phi(x = |\mathcal{X}_n|,e=2)$\\
$\vdots$ & $\vdots$ & $\cdots$ & $\cdots$ & $\vdots$ \\
$e = E$ & $\phi(x = 1,e=E)$ & $\phi(x = 2,e=E)$ &$\cdots$ & $\phi(x = |\mathcal{X}_n|,e=E)$\\
\hline \hline
Generalizable? & Yes & No & $\cdots$ & Yes \\
Predicted effect: & $\bar{\phi}^\star(x = 1)$ & $\boldsymbol{?}$ & $\cdots$ & $\bar{\phi}^\star(x = |\mathcal{X}_n|)$
\end{tabular}
\caption{Joint prediction problem of forming predictions for generalizable sets with $T_e = 1$ as defined in Definition .}
\end{table}
\end{document}

stat

Emily Breza
Arun G. Chandrasekhar
Davide Viviano

"Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery" by Emily Breza, Arun G. Chandrasekhar, and Davide Viviano, arXiv:2501.13355v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccccc}
 \toprule
 Parameters & B & m & $\lambda_{\text{STRidge}}$ &$\text{Tol}_{\text{STRidge}}$& $Iter_{\text{Phase1}}$ & $Iter_{\text{Phase2}}$ & $Iter_{\text{update}}$\\
 \midrule
 Linear2D & 200 & 100 & 0 & 0.2 & 2000,3000 & 100,500 & 200\\
 Cubic2D & 200 & 100 & 0 & 0.2 & 2500,4500 & 100,650 &200\\
 Linear3D & 200 & 100 & 0.1 & 0.2 & 4500,5200 & 100,850 &200\\
 Lorenz & 200 & 100 & 0.35 & 0.6 & 10000,13200 & 100,1200 &200\\
 LV4D & 200 & 100 & 0.8 & 0.6 & 7500,15000 & 100,1000 &200\\
 Duffing & 200 & 100 & 0 & 0.05 & 2500,4000 & 100,1200 &200\\
 Pendulum & 200 & 100 & 0.6 & 0.6 & 6000,12000 & 100,450 &200\\
 Pendulum$^{\star}$ & 200 & 100 & 0.6 & 0.6 & 6000,12000& 100,450 &200\\
 \bottomrule
 \end{tabular}
\caption{Implement parameters for the numerical examples.$B$ denotes the batchsize for both two phases, $m$ denotes the direction numbers for the SWD calculation, $\lambda_{\text{STRidge}},\text{Tol}_{\text{STRidge}}$ represent the penalty parameter and the tolerance parameter in STRidge, $(a,b) $ in $Iter_{\text{Phase1}}$ represent the warm up step and the total step in distribution matching phase, $(a,b) $ in $Iter_{\text{Phase2}}$ represents the warm up step and the total step in parameter identification phase. The STRidge updates the parameters every $Iter_{\text{update}}$ step.}
\end{table}
\end{document}

math

Zhijun Zeng
Pipi Hu
Chenglong Bao
Yi Zhu
Zuoqiang Shi

"Reconstruction of dynamical systems from data without time labels" by Zhijun Zeng, Pipi Hu, Chenglong Bao, Yi Zhu, and Zuoqiang Shi, arXiv:2312.04038v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training data settings for different networks.}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Classifier} & \textbf{Class} & \textbf{Category} \\
\hline \hline
\multirow{2}{*}{VN} & Fake & $F_{v}F_{a}$, $F_{v}R_{a}$\\
\cline{2-3}
& Real & $R_{v}F_{a}$, $R_{v}R_{a}$ \\
\cline{2-3}
\hline
\multirow{2}{*}{AN} & Fake & $R_{v}F_{a}$, $F_{v}F_{a}$\\
\cline{2-3}
& Real & $ F_{v}R_{a}$, $ R_{v}R_{a}$ \\
\cline{2-3}
\hline
\multirow{2}{*}{AVN} & Fake & $ F_{v}F_{a}$, $F_{v}R_{a}$, $R_{v}F_{a}$\\
\cline{2-3}
& Real &$ R_{v}R_{a}$ \\
\cline{2-3}
\hline
\end{tabular}
\end{table}
\end{document}

eess

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

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


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.


stat

Abnormal component analysis


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.


math

Isaac Rudich
Quentin Cappart
Manuel López-Ibáñez
Michael Römer
Louis-Martin Rousseau

"An Exact Framework for Solving the Space-Time Dependent TSP" by Isaac Rudich, Quentin Cappart, Manuel López-Ibáñez, Michael Römer, and Louis-Martin Rousseau, arXiv:2312.01404v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Adults in the room? The auditor and dividends in small firms: Evidence from a natural experiment" by Hakim Lyngstadås and Johannes Mauritzen, arXiv:2301.11079v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Shumin Ma
Zhiri Yuan
Qi Wu
Yiyan Huang
Xixu Hu
Cheuk Hang Leung
Dongdong Wang
Zhixiang Huang

"Deep into The Domain Shift: Transfer Learning through Dependence Regularization" by Shumin Ma, Zhiri Yuan, Qi Wu, Yiyan Huang, Xixu Hu, Cheuk Hang Leung, Dongdong Wang, and Zhixiang Huang, arXiv:2305.19499v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

On the Last Kervaire Invariant Problem

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


cs

Changqing Ye
Shubin Fu
Eric T. Chung
Jizu Huang

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{diagbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
\hline
\diagbox{State}{Action} & Go ($a_{1}$) & Not go ($a_{2}$) \\ \hline
0 $(s_{1})$ & $Q_{s_{1},a_{1}}$ & $Q_{s_{1},a_{2}}$ \\
1 $(s_{2})$ & $Q_{s_{2},a_{1}}$ & $Q_{s_{2},a_{2}}$ \\
2 $(s_{3})$ & $Q_{s_{3},a_{1}}$ & $Q_{s_{3},a_{2}}$ \\
3 $(s_{4})$ & $Q_{s_{4},a_{1}}$ & $Q_{s_{4},a_{2}}$ \\
... & ... & ... \\
N $(s_{N+1})$ & $Q_{s_{N+1},a_{1}}$ & $Q_{s_{N+1},a_{2}}$ \\ \hline
\end{tabular}
\caption{Q-table for each individual, where the state corresponds to how many people went to the bar in the last round, which is identical for all players, and the actions are the two choices of going or not going to the bar.}
\end{table}
\end{document}

stat

Guozhong Zheng
Weiran Cai
Guanxiao Qi
Jiqiang Zhang
Li Chen

"Optimal coordination of resources: A solution from reinforcement learning" by Guozhong Zheng, Weiran Cai, Guanxiao Qi, Jiqiang Zhang, and Li Chen, arXiv:2312.14970v2, 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|l}
Symbol & Description \\ \hline \hline
$n \in N$ & Nodes \\
$s \in S$ & Availability scenarios \\
$e \in E$ & Conventional energy sources \\
$r \in R$ & Variable renewable energy sources \\
$i \in I$ & Power producer companies \\
$t \in T$ & Time periods \\ \hline \hline
\end{tabular}
\caption{Indices and sets}
\end{table}
\end{document}

q-fin

Nikita Belyak
Steven A. Gabriel
Nikolay Khabarov
Fabricio Oliveira

"Renewable Energy Expansion under Taxes and Subsidies: A Transmission Operator's Perspective" by Nikita Belyak, Steven A. Gabriel, Nikolay Khabarov, and Fabricio Oliveira, arXiv:2302.10562v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluation metrics tested on video with synthetic turbulence. \(\uparrow\): higher is better, \(\downarrow\): lower is better.}
\begin{tabular}{cccccc}
\toprule
Method & \textbf{PSNR$_{Img}$ $\uparrow$} & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$ & \textbf{E$_{warp}$} $\downarrow$ & \textbf{PSNR$_{x-t}$} $\uparrow$ \\ \hline
TSRWGAN & 23.58 & 0.739 & 0.230 & 0.0026 & 23.77 \\
TurbNet & 23.44 & 0.732 & 0.228 & 0.0057 & 23.54 \\
TurbNet+Real-ESRGAN & 22.48 & 0.713 & 0.213 & 0.0074 & 22.67 \\
ConVRT (Ours) & \textbf{24.90} & \textbf{0.787} & \textbf{0.189} & \textbf{0.0014} & \textbf{25.73} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Haoming Cai
Jingxi Chen
Brandon Y. Feng
Weiyun Jiang
Mingyang Xie
Kevin Zhang
Ashok Veeraraghavan
Christopher Metzler

"ConVRT: Consistent Video Restoration Through Turbulence with Test-time Optimization of Neural Video Representations" by Haoming Cai, Jingxi Chen, Brandon Y. Feng, Weiyun Jiang, Mingyang Xie, Kevin Zhang, Ashok Veeraraghavan, and Christopher Metzler, arXiv:2312.04679v1, 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{The effect of the use of geometric, photometric, shift augmentation and global loss in the proposed approach.}
\begin{tabular}{cccc|cc}
		\hline
		Only Shift & Geometric (affine) & Photometric & Global loss & AA & mIoU \\ \hline
		$\checkmark$ & & & $\checkmark$ & 72.7 & 0.498 \\
		& $\checkmark$ & & $\checkmark$ & 68.4 & 0.460 \\
		& & $\checkmark$ & $\checkmark$ & 68.8 & 0.464 \\
		$\checkmark$ & & & & 70.6 & 0.479 \\ \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.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Percentage of the cases of corresponding classification between $\kappa_{\iota,\tau}$ and $J^{Surprise}$ for each ticker.}
\begin{tabular}{lcccc}
				& Upward Spike & Downward Spike & Boost & Drop \\
				\midrule
				MSFT & 88.24\% & 300.00\% & 1350.00\% & 32.76\% \\
				GS & 93.75\% & 675.00\% & 750.00\% & 26.56\% \\
				JPM & 71.79\% & 437.50\% & 700.00\% & 24.53\% \\
				JNJ & 91.67\% & 428.57\% & 428.57\% & 20.37\% \\
				CAT & 72.50\% & 650.00\% & 1300.00\% & 17.86\% \\
				MMM & 100.00\% & 500.00\% & 525.00\% & 22.41\% \\
				HD & 78.13\% & 487.50\% & 520.00\% & 23.73\%\\
				\bottomrule
			\end{tabular}
\end{table}
\end{document}

q-fin

Giampiero M. Gallo
Demetrio Lacava
Edoardo Otranto

"Volatility jumps and the classification of monetary policy announcements" by Giampiero M. Gallo, Demetrio Lacava, and Edoardo Otranto, arXiv:2305.12192v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
			\toprule
			\textbf{Language} & \textbf{Examples} \\
			\midrule
			\textbf{000000 POS} & \textcolor{red}{NounS} \textcolor{orange}{Subj} \textcolor{blue}{VerbCompPresS}. \\
			\textbf{000000 (Lexicon 0)} & \textcolor{red}{burse} \textcolor{orange}{sub} \textcolor{blue}{lurchifies}. \\
			\textbf{100000 (Lexicon 0)} & \underline{\textcolor{blue}{lurchifies}} \underline{\textcolor{red}{burse} \textcolor{orange}{sub}}. \\
			\textbf{100010 (Lexicon 0)} & \textcolor{blue}{lurchifies} \textcolor{red}{burse} \textcolor{orange}{sub}. \\
			\textbf{000000 (Lexicon 1)} & \textcolor{red}{swopceer} \textcolor{orange}{bus} \textcolor{blue}{rheleates}.\\
			\midrule
			\textbf{000000 POS} & \textcolor{blue}{IVerbPastP} \textcolor{green}{Adj} \textcolor{red}{NounP} \textcolor{orange}{Subj}. \\
			\textbf{000000 (Lexicon 0)} & \textcolor{blue}{rolveda} \textcolor{green}{prask} \textcolor{red}{autoners} \textcolor{orange}{sub}. \\
			\textbf{100000 (Lexicon 0)} & \underline{\textcolor{green}{prask} \textcolor{red}{autoners} \textcolor{orange}{sub}} \underline{\textcolor{blue}{rolveda}}. \\
			\textbf{100010 (Lexicon 0)} & \underline{\textcolor{red}{autoners} \textcolor{orange}{sub}} \underline{\textcolor{green}{prask}} \textcolor{blue}{rolveda}. \\
			\textbf{000000 (Lexicon 1)} & \textcolor{blue}{knyfeateda} \textcolor{green}{wourk} \textcolor{red}{krarfteers} \textcolor{orange}{bus}.\\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

cs

Nicolas Guerin
Shane Steinert-Threlkeld
Emmanuel Chemla

"The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation" by Nicolas Guerin, Shane Steinert-Threlkeld, and Emmanuel Chemla, arXiv:2403.18031v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llll|lll}
 \hline
 & \multicolumn{3}{c}{2 observed follow-ups} & \multicolumn{3}{c}{3 observed follow-ups} \\
 & Combined & No MDT & MDT & Combined& No MDT & MDT \\
 \hline
 $\hat{\alpha}$ & 0.35 & 0.36 & 0.34 & 0.33 & 0.37 & 0.32 \\
 $ \hat{\beta}$& 640.00 & 603.41 & 658.44 & 715.33 & 648.81 & 752.05 \\
 $ \hat{\pi}$& 0.23 & 0.22 & 0.24 & 0.20 & 0.19 & 0.20 \\
 \hline
 \end{tabular}
\caption{Model parameter estimates for two and three study follow-ups, across and within the metastasis-directed radiation therapy (MDT) and lack of MDT (No MDT) treatment strata.}
\end{table}
\end{document}

stat

David Swanson
Alexander Sherry
Chad Tang

"Variance component mixture modelling for longitudinal T-cell receptor clonal dynamics" by David Swanson, Alexander Sherry, and Chad Tang, arXiv:2502.04553v1, 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}{lcr}
Left&Centered&Right\\
\hline
1 & 2 & 3\\
10 & 20 & 30\\
100 & 200 & 300\\
\end{tabular}
\end{table}
\end{document}

q-fin

"Scaling and shape of financial returns distributions modeled as conditionally independent random variables" by Hernán Larralde and Roberto Mota Navarro, arXiv:2504.20488v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Full-conformal novelty detection: A powerful and non-random approach" by Junu Lee, Ilia Popov, and Zhimei Ren, arXiv:2501.02703v1, 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|l|l|l|l|}
 \hline
 & $L=10^{9}$ & $L=10^{10}$ & $L=10^{11}$ \\ \hline
 $D_{\text{E}}^{\text{(G)}}[s,d]$ & $7.2 \times 10^{-5}$ & $2.6 \times 10^{-5}$ & $9.4 \times 10^{-6}$\\ \hline
 $D_{\text{CLE}}^{\text{(G)}}[s,d]$ & $3.7 \times 10^{-5}$ & $1.1 \times 10^{-5}$ & $2.8 \times 10^{-6}$\\ \hline
 $D_{\text{CFE}}^{\text{(G)}}[s,d]$ & $3.5 \times 10^{-5}$ & $1.4 \times 10^{-5}$ & $6.6 \times 10^{-6}$\\
 \hline
 \end{tabular}
\caption{The evaulation of $D_{\text{E}}^{\text{(G)}}[s,d]$, $D_{\text{CLE}}^{\text{(G)}}[s,d]$ and $D_{\text{CFE}}^{\text{(G)}}[s,d]$ for various computational limit $L$ values considereing $(128,64)$-random tree codes under BSC with $p=0.02$ where $s$ is optimized using the SBP algorithm and $d$ is in the form where $\gamma=1$. }
\end{table}
\end{document}

math

"The Optimization of Random Tree Codes for Limited Computational Resources" by B. Tan Bacinoglu, arXiv:2501.12834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc|cccc}
 \toprule
 {Potential $\phi$} & {Parameter $q$} & {Sep. mgn. $m$} & {Smoothness $\beta$} & {Order $\alpha$} & {Conv. rate for $T$} \\
 \midrule
 {Shannon} & {---} & {$\infty$} & {$1/4$} & {$1$} & {$\tilde\Omega(\epsilon^{-1})$} \\ \midrule
 {Semi-circle} & {---} & {$\infty$} & {$1/4$} & {$2$} & {$\Omega(\epsilon^{-4})$} \\ \midrule
 \multirow{3}{*}{Tsallis} & {$(0,1)$} & {$\infty$} & \multirow{2}{*}{\footnotesize$\dfrac{2^{q-3}}{q}$} & \multirow{2}{*}{\footnotesize$\dfrac1q$} & {$\Omega(\epsilon^{-2/q})$} \\ \cmidrule(l){3-3}
 {} & {$(1,2]$} & \multirow{2}{*}{\footnotesize$\dfrac{q}{q-1}$} & {} & {} & {$\Omega(\epsilon^{-1/q})$} \\ \cmidrule(l){4-5}
 {} & {$(2,\infty)$} & {} & {$\infty$} & {$1/2$} & {$\Omega(\epsilon^{-1/2})$} \\ \midrule
 \multirow{3}{*}{R{\'e}nyi} & {$(0,1)$} & {$\infty$} & {$1/4q$} & \multirow{2}{*}{\footnotesize$\dfrac{1}{q}$} & {$\Omega(\epsilon^{-2/q})$} \\ \cmidrule(l){3-3}
 {} & {$(1,2)$} & \multirow{2}{*}{\footnotesize$\dfrac{q}{q-1}$} & {\color{gray} $(\ge1/4q)$} & {} & {$\Omega(\epsilon^{-1/q})$} \\ \cmidrule(l){5-5}
 {} & {$2$} & {} & {$\infty$} & {$1/3$} & {$\Omega(\epsilon^{-1/3})$} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"Any-stepsize Gradient Descent for Separable Data under Fenchel--Young Losses" by Han Bao, Shinsaku Sakaue, and Yuki Takezawa, arXiv:2502.04889v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \toprule
 \textbf{Anomaly Type} & \textbf{\# tokens} & \textbf{Percentage in anomalies} \\
 \midrule
 Low Volumes & 478 (67.61\%) & 82.84\% \\
 Low Makers & 470 (66.47\%) & 81.45\% \\
 Low Trading Activity & 456 (64.49\%) & 79.03\% \\
 Top Holders & 359 (50.78\%) & 62.22\% \\
 MarketCap Distortion & 48 (6.79\%) & 8.32\% \\
 Honeypots & 28 (3.96\%) & 4.85\% \\
 Airdrop & 24 (3.39\%) & 4.16\% \\
 Bundle Buys & 20 (2.83\%) & 3.47\% \\
 Fresh Addresses & 8 (1.13) & 1.39\% \\
 \midrule
 \textbf{Total tokens} & 577 (81.61\%) & \\
 \bottomrule
 \end{tabular}
\caption{Anomaly statistics for meme coins with price returns higher than 100\%.}
\end{table}
\end{document}

q-fin

"A Midsummer Meme's Dream: Investigating Market Manipulations in the Meme Coin Ecosystem" by Alberto Maria Mongardini and Alessandro Mei, arXiv:2507.01963v1, 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}{llll}
\toprule
 & \textbf{\(\rho = -0.2\)} & \textbf{\(\rho = -0.4\)} & \textbf{\(\rho = -2\)} \\
\midrule
\textbf{\(f = 5\%\)} & \(6.4 \times 10^7\) & \(3.6 \times 10^4\) & 89 \\
\textbf{\(f = 10\%\)} & \(10^6\) & \(3.2 \times 10^3\) & 32 \\
\textbf{\(f = 25\% \)} & \(4.1 \times 10^3\) & 128 & 8 \\
\bottomrule
\end{tabular}
\caption{\small \centering A table showing the scale-up factors we can get in GDP for various different values of the fraction of tasks that cannot be automated by AI, \( f \); and the substitution parameter \( \rho \) of the CES aggregator function.}
\end{table}
\end{document}

q-fin

"Explosive growth from AI automation: A review of the arguments" by Ege Erdil and Tamay Besiroglu, arXiv:2309.11690v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"f-Betas and Portfolio Optimization with f-Divergence induced Risk Measures" by Rui Ding, arXiv:2302.00452v3, 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|l|l|l|}
\hline
Feature Sets & Sharpe & Calmar & Mean & Volatility & Max Drawdown \\ \hline
All & 0.5158 & 0.0736 & 0.0147 & 	0.0286 &	0.1996 \\ \hline
Signature+Catch22+Stats & 0.2595 & 0.0158 & 0.0085 & 	0.0327 &	0.5379 \\ \hline
Signature & 0.1997 & 	0.0110 & 0.0061 & 	0.0307 &	0.5546 \\ \hline
Catch22 & 0.1632 & 	0.0077 & 0.0048 & 	0.0294 &	0.6262 \\ \hline
Statistics & 0.1600 & 	0.0132 & 0.0050 &	0.0310 &	0.3789 \\ \hline
Financials & 0.3101 & 	0.0293 & 0.0082 &	0.0264 &	0.2794 \\ \hline
Sentiment & 0.5324 & 	0.0816 & 0.0168 &	0.0315 &	0.2060 \\ \hline
\end{tabular}
\caption{Strategy Performance of LightGBM models trained on different feature sets for the Numerai-Signals tournament in the test period for CV 1. In addition to models trained with individual feature sets (stats, signature, Catch22, financials, sentiment), we also report the model trained with all 5 feature sets combined (all). }
\end{table}
\end{document}

q-fin

"Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions" by Thomas Wong and Mauricio Barahona, arXiv:2303.16117v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Rakhymzhan Kazbek
Yogi Erlangga
Yerlan Amanbek
Dongming Wei

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary Statistics: Extensive Margin, Events in Over 60\% of Years}
\begin{tabular}{l|ccc|ccc}
\toprule
& \multicolumn{3}{c|}{\textbf{Mean}} & \multicolumn{3}{c}{\textbf{p-value of Difference}} \tabularnewline & Rest Country & FARC & ELN & Rest-FARC & Rest-ELN & FARC-ELN \tabularnewline
{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)} \tabularnewline
\midrule
\midrule \textbf{Panel A. General Characteristics}&&&&&& \tabularnewline
Population&42.65&25.15&27.36&0.36&0.72&0.66 \tabularnewline
Area (km\(^2\))&783.28&1842.32&711.46&0&0.88&0.04 \tabularnewline
Distance Dept. Capital (km)&81.12&79.73&101.34&0.76&0.04&0.02 \tabularnewline
Distance Bogot\'a (km)&317.89&303.70&345.42&0.34&0.39&0.11 \tabularnewline
Conflict in 1901/30&0.03&0.07&0.11&0.02&0&0.30 \tabularnewline
Spanish Occupation&0.46&0.18&0.07&0&0&0.08 \tabularnewline
&&&&&& \tabularnewline
\midrule \textbf{Panel B. State Capacity}&&&&&& \tabularnewline
Gov. Transfers (pc)&0.07&0.05&0.03&0&0&0.13 \tabularnewline
Tax Revenue (pc)&0.09&0.05&0.05&0&0.06&0.67 \tabularnewline
Savings Capacity&33.81&29.26&32.15&0&0.54&0.30 \tabularnewline
Fiscal Performance&62.45&60.61&61.29&0&0.39&0.62 \tabularnewline
Overall Performance&60.09&55.29&59.41&0&0.78&0.11 \tabularnewline
Aqueduct Coverage&59.5&56.58&57.18&0.27&0.68&0.92 \tabularnewline
Garbage Collection&44.59&47.33&43.04&0.30&0.78&0.45 \tabularnewline
&&&&&& \tabularnewline
\midrule \textbf{Panel C. Economic Conditions}&&&&&& \tabularnewline
Multidimensional Poverty&68.01&73.41&72.80&0&0.07&0.79 \tabularnewline
Municipal Development&67.75&63.84&65.31&0&0.24&0.47 \tabularnewline
Nighttime Light Intensity&0.12&--0.33&--0.20&0&0.06&0.06 \tabularnewline
Cultivated Land (per HA)&0.25&0.28&0.31&0.47&0.27&0.26 \tabularnewline
Agricultural Productivity&7.61&6.88&5.21&0.40&0.15&0.41 \tabularnewline
\# Manufacturing Firms (EAM, pc)&2.42&1.11&0.85&0.01&0.16&0.64 \tabularnewline
Gross Salary Last Month (GEIH)&788.89&716.86&624.29&0&0&0.28 \tabularnewline
Asset Ownership Index (GEIH)&--0.15&--0.68&--0.60&0&0&0 \tabularnewline
Low Socioecon. Stratum (GEIH)&0.64&0.86&0.77&0&0&0 \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example table.}
\begin{tabular}{|c|c|c|} \hline
 Species & \bf Mean & \bf Std.~Dev. \\ \hline
 1 & 3.4 & 1.2 \\
 2 & 5.4 & 0.6 \\ \hline
 \end{tabular}
\end{table}
\end{document}

stat

Jonas Latz
Doris Schneider
Philipp Wacker

"Nested Sampling for Uncertainty Quantification and Rare Event Estimation" by Jonas Latz, Doris Schneider, and Philipp Wacker, arXiv:2310.03040v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average CPU time to output the subset of variables for 200 observations (seconds)}
\begin{tabular}{||c|c||c|c||c|c||c|c||}
 \hline
 \textbf{Model} & \textbf{CPU Time} & \textbf{Model} & \textbf{CPU Time} & \textbf{Model} & \textbf{CPU Time} & \textbf{Model} & \textbf{CPU Time} \\
 \hline
 Enh\_ELRT & 1.396 & Enh\_ELRS & 1.363 & Enh\_ESVMT & 1.456 & Enh\_ESVMS & 1.437 \\
 \hline
 OAL & 0.126 & OAENet & 0.394 & BACR & 1.881 & BCEE & 8.475 \\
 \hline
 Boruta\_T & 1.476 & Boruta\_Y & 2.705 & DWR & 4.444 & CTMLE & 2.652 \\ \hline
 \end{tabular}
\end{table}
\end{document}

stat

"Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation" by Tianyu Yang and Md. Noor-E-Alam, arXiv:2502.00501v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Shi Bo
AmirEmad Ghassami
Debarghya Mukherjee

"A Debiased Estimator for the Mediation Functional in Ultra-High-Dimensional Setting in the Presence of Interaction Effects" by Shi Bo, AmirEmad Ghassami, and Debarghya Mukherjee, arXiv:2412.08827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
		\hline
		Beta & 0 & 0.01 & 0.1 & 1 & 10 & 100 \\
		\hline
		$\rho_p$ & 0.44 & 0.41 & 0.32 & 0.79 & 0.79 & 0.79 \\
		\hline
	\end{tabular}
\caption{The Pearson's correlation coefficients between the reconstruction loss and the KL divergence between the latent vector of sample and normal distribution. The results are calculated by the single subset system using subset `e'.}
\end{table}
\end{document}

eess

"Unsupervised heart abnormality detection based on phonocardiogram analysis with Beta Variational Auto-Encoders" by Shengchen Li, Ke Tian, and Rui Wang, arXiv:2101.05443v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter value variations.}
\begin{tabular}{ll}
\toprule
Parameter & Value variations\\
\midrule
Initial cash $C_0$\qquad~~~~ &10,000\quad 15,000\quad 20.000\quad 25,000\quad 40,000\\
Receipt delay length $L$ &0\quad 1\quad 2\quad 3\quad 4 \\
Overhead Cost $H_t$ &0\quad 1,000\quad 2,000\quad 3,000\quad 4,000\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"A multi-period multi-product stochastic inventory problem with order-based loan" by Zhen Chen and Ren-qian Zhang, arXiv:2012.04850v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of different SE architectures evaluated on Libri2Mix.}
\begin{tabular}{l|cccc}
 & STOI & PESQ$_{\text{WB}}$ & PESQ$_{\text{NB}}$ & SI-SDR \\
 \hline
 DCCRN & 0.8857 & 1.8383 & 2.4285 & \textbf{11.9048} \\
 \hline
 DTLN & 0.8577 & 1.6155 & 2.1245 & 10.9175 \\
 \hline
 DEMUCS & \textbf{0.8978} & \textbf{1.9553} & \textbf{2.4486} & 11.2817
 \end{tabular}
\end{table}
\end{document}

eess

Chi-Chang Lee
Hong-Wei Chen
Chu-Song Chen
Hsin-Min Wang
Tsung-Te Liu
Yu Tsao

"LC4SV: A Denoising Framework Learning to Compensate for Unseen Speaker Verification Models" by Chi-Chang Lee, Hong-Wei Chen, Chu-Song Chen, Hsin-Min Wang, Tsung-Te Liu, and Yu Tsao, arXiv:2311.16604v1, 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}{lrrr}
 \toprule
 \textbf{microphone} & 0 & 1 & TOTAL \\
 \textbf{class} & & & \\
 \midrule
 \textbf{0 } & 3.9 & 24.5 & 28.4 \\
 \textbf{1 } & 11.9 & 59.8 & 71.6 \\
 \midrule
 \textbf{TOTAL } & 15.8 & 84.2 & 100.0 \\
 \bottomrule
\end{tabular}
\caption{Distribution of samples by microphone (\%).}
\end{table}
\end{document}

eess

"The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft" by Blake Downward and Jon Nordby, arXiv:2311.06368v1, 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{Optimal system parameters}
\begin{tabular}{l|l|l|l}
		\cline{1-4}
		\multirow{2}{*}{Target rate} & \multicolumn{3}{l}{Optimal system parameters} \\
		\cline{2-4}
		& $\alpha$ & $\beta$ & $\mu$ \\
		\hline
		1/2 & 0.01 & 0.6918 & 0.95 \\
		\hline
		1/3 & 0.01 & 0.7125 & 0.95 \\
		\hline
		1/4 & 0.01 & 0.6847 & 0.8165 \\
		\hline
		1/5 & 0.01 & 0.7101 & 0.7544 \\
		\hline
		1/6 & 0.01 & 0.7140 & 0.7732 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Addanki Prathima
Devendra Singh Gurjar
Ha H. Nguyen
Ajay Bhardwaj

"Performance Analysis and Optimization of Bidirectional Overlay Cognitive Radio Networks with Hybrid-SWIPT" by Addanki Prathima, Devendra Singh Gurjar, Ha H. Nguyen, and Ajay Bhardwaj, arXiv:2101.01897v1, 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{Initial testing on adaptive adversarial attacks. We explore character and punctuation insertions. The results are on MR and BERT.}
\begin{tabular}{lcccccc}
\hline
\multirow{2}{*}{\textbf{Approach}} & \multicolumn{3}{c}{\textbf{Character Insertions}} & \multicolumn{3}{c}{\textbf{Punctuation Insertions}} \\
& \textbf{CA (↑)} & \textbf{AUA (↑)} & \textbf{ASR (↓)} & \textbf{CA (↑)} & \textbf{AUA (↑)} & \textbf{ASR (↓)} \\
\hline
Baseline & 86.6 & 48.0 & 44.57 & 86.6 & 36.0 & 58.43 \\
TextFooler + AT & 85.8 & 45.8 & 46.62 & 85.8 & 34.6 & 59.67 \\
TextFooler + MMD & 86.0 & 58.8 & 31.63 & 86.0 & 51.0 & 40.7 \\
\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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example table}
\begin{tabular}{|c|c|c|} \hline
 Species & \bf Mean & \bf Std.~Dev. \\ \hline
 1 & 3.4 & 1.2 \\
 2 & 5.4 & 0.6 \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Quang Minh Nguyen
Iain Weissburg
Haewon Jeong

"Coded Computing for Fault-Tolerant Parallel QR Decomposition" by Quang Minh Nguyen, Iain Weissburg, and Haewon Jeong, arXiv:2311.11943v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Computation time of different algorithms under the first tested continuous distribution.}
\begin{tabular}{cccc|cccc|cccc}
	\toprule
	$T$ & Algorithm & Avg. Regret & Avg. Time(s) & $T$ & Algorithm & Avg. Regret & Avg. Time(s) & $T$ & Algorithm & Avg. Regret & Avg. Time(s) \\
	\midrule
	\multirow{3}{*}{$10^3$} & & $12.37$ & $<0.001$ & \multirow{3}{*}{$10^4$} & & $38.24$ & $<0.01$ & \multirow{3}{*}{$10^5$} & & $123.03$ & $0.063$ \\
 & & $4.18$ & $<0.001$ & & & $13.83$ & $<0.01$ & & & $24.00$ & $0.064$ \\
 & & $3.82$ & $0.95$ & & & $4.12$ & $37.5$ & & & $ 5.91 $ & $4742.9$ \\
 \bottomrule
	\end{tabular}
\end{table}
\end{document}

stat

Wenzhi Gao
Dongdong Ge
Chenyu Xue
Chunlin Sun
Yinyu Ye

"Beyond $\mathcal{O}(\sqrt{T})$ Regret: Decoupling Learning and Decision-making in Online Linear Programming" by Wenzhi Gao, Dongdong Ge, Chenyu Xue, Chunlin Sun, and Yinyu Ye, arXiv:2501.02761v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Feature extraction hyperparameters.} Frame lengths (16, 32, 64 samples i.e. 160, 320 and 640 msec long) overlap in such a way that the number of segments (5 and 10) are the same for all events in the dataset.} % title name of the table
\begin{tabular}{c|c|c}
			\hline
			\textbf{Hyperparameter} & \textbf{Description} & \textbf{Range} \\
			\hline
			\hline
			{\multirow{2}{*}{Frame length ($\Psi$)}} & Size of frames in samples & $2^k$ where \\
			 & in which cough is segmented & $k=4, 5, 6$\\
			\hline
			{\multirow{2}{*}{No. of Segments ($C$)}} & Number of segments in & {\multirow{2}{*}{5, 10}} \\
			 & which frames were grouped & \\
			\hline
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Madhurananda Pahar
Igor Miranda
Andreas Diacon
Thomas Niesler

"Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements" by Madhurananda Pahar, Igor Miranda, Andreas Diacon, and Thomas Niesler, arXiv:2102.04997v1, 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}{lcc}
 \hline
$\nu$&$P_{\lambda,\lambda}^\nu$ \\
 \hline
 $(2\lambda_1,2\lambda_2)$&$\frac{1}{2(\lambda_1+\lambda_2)}$\\
 $(2\lambda_1-s,2 \lambda_2), \quad s=1,\dots,\lambda_1 -1 $ & $\frac{1}{2(\lambda_1+\lambda_2)}$\\
 $(2\lambda_1,2 \lambda_2-s), \quad s=1,\dots,\lambda_2 -1 $ & $\frac{1}{2(\lambda_1+\lambda_2)}$\\
$(2\lambda_1,\lambda_2)$&$\frac{1}{4(\lambda_1+\lambda_2)}$\\
$(\lambda_1,2\lambda_2)$&$\frac{1}{4(\lambda_1+\lambda_2)}$\\
 $(\lambda_1+\lambda_2,\lambda_1+\lambda_2)$&$\frac{1}{2(\lambda_1+\lambda_2)}$\\
 $(\lambda_1+\lambda_2,\lambda_1+ \lambda_2-s), \quad s=1,\dots,\lambda_2 -1 $ &$\frac{1}{\lambda_1+\lambda_2}$\\
 $(\lambda_1+\lambda_2,\lambda_1)$ &$\frac{\lambda_1-\lambda_2+1}{2(\lambda_1+\lambda_2)}$\\
 otherwise & $0$\\
 \hline
 \end{tabular}
\caption{Probability distribution for self-aggregation of $[\lambda_1, \lambda_2]$. }
\end{table}
\end{document}

math

"Lattice aggregations of boxes and symmetric functions" by Natasha Rozhkovskaya, arXiv:2312.03168v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

"An effective estimation of multivariate density functions using extended-beta kernels with Bayesian adaptive bandwidths" by Sobom M. Somé, Célestin C. Kokonendji, and Francial G. B. Libengué Dobélé-Kpoka, arXiv:2502.05366v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Equity Protection Swaps: A New Type of Investment Insurance for Holders of Superannuation Accounts" by Huansang Xu, Ruyi Liu, and Marek Rutkowski, arXiv:2305.09472v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|r}
Strategy & Achieved income \\
\hline
Reference & 46214.33 $\pm$ 16.84 \\
\textbf{Proposed (A2C)} & \textbf{60027.13 $\pm$ 398.11} \\
Proposed (A2C, no weather forecasts) & 56006.59 $\pm$ 321.92 \\
FARL & 29820.81 $\pm$ 4638.89 \\
Timing (CMA-ES) & 37178.98 $\pm$ 1029.76 \\
Opportunistic (CMA-ES) & 43898.02 $\pm$ 2227.74 \\
\end{tabular}
\caption{Final balances achieved on testing data by different strategies averaged over five testing runs. Descriptions in brackets denote additional information about the given strategy. }
\end{table}
\end{document}

q-fin

"On-line reinforcement learning for optimization of real-life energy trading strategy" by Łukasz Lepak and Paweł Wawrzyński, arXiv:2303.16266v3, 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{Symbols and notation}
\begin{tabular}{|l|l|}
\hline
a&	Strategy vector of each BS	\\
\hline
$h_{k}^{i}$&	Power gain of the channel between source-i and user-k	\\
\hline
$J$&	Signal of the smart jammer	\\
\hline
$n$&	Additive noise	\\
\hline
$P_{J}$&	Maximum jamming power	\\
\hline
$P_{k}$&	The power allocated to user-k	\\
\hline
$p_{BS}$&	Total transmission power of each BS	\\
\hline
$\mathrm{s}$&	State vector of each BS	\\
\hline
$U$&	Utility functions	\\
\hline
$\sigma^{2}$&	Power of the noise	\\
\hline
$6$&	The set of neural network weight	\\
\hline
$\epsilon$&	Exploration factor	\\
\hline
$\delta$&	Discount factor for future rewards	\\
\hline
$\gamma$&	The cost of sending jamming signal with base power	\\
\hline
$\Omega$&	The space of the strategies vector	\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Sina Yousefzadeh Marandy
Mohammad Ali Amirabadi
Mohammad Hossein Kahaei
Seyed Mohammad Razavizadeh

"Deep Reinforcement Learning-based Anti-jamming Power Allocation in a Two-cell NOMA Network" by Sina Yousefzadeh Marandy, Mohammad Ali Amirabadi, Mohammad Hossein Kahaei, and Seyed Mohammad Razavizadeh, arXiv:2101.00270v1, 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}{|cccccc|}
\hline
caretaker & dancer & homemaker & librarian & nurse & hairdresser\\
housekeeper & secretary & teacher & nanny & receptionist & stylist\\
interior designer & clerk & educator & bookkeeper & environmentalist & fashion designer\\
paralegal & therapist & dermatologist & instructor & organist & planner\\
radiologist & singer songwriter & socialite & soloist & treasurer & tutor\\
violinist & vocalist & aide & artist & choreographer & lyricist\\
mediator & naturalist & pediatrician & performer & psychiatrist & publicist\\
realtor & singer & sociologist & baker & councilor & counselor\\
photographer & pianist & poet & flight attendant & substitute & cellist\\
correspondent & employee & entertainer & epidemiologist & freelance writer & gardener\\
guidance counselor & warrior & jurist & musician & novelist & psychologist\\
student & swimmer & understudy & valedictorian & writer & author\\
biologist & comic & consultant & parishioner & photojournalist & protagonist\\
researcher & servant & administrator & campaigner & chemist & civil servant\\
columnist & crooner & curator & envoy & graphic designer & headmaster\\
illustrator & lecturer & narrator & painter & pundit & restaurateur\\
trumpeter & attorney & bartender & cleric & comedian & filmmaker\\
jeweler & journalist & missionary & negotiator & pathologist & pharmacist\\
philanthropist & pollster & principal & promoter & prosecutor & solicitor\\
strategist & worker & accountant & analyst & anthropologist & assistant professor\\
associate dean & associate professor & barrister & bishop & broadcaster & commentator\\
composer & critic & editor & geologist & landlord & medic\\
plastic surgeon & professor & proprietor & provost & screenwriter & adjunct professor\\
adventurer & archbishop & astronomer & barber & broker & bureaucrat\\
butler & cardiologist & cartoonist & chef & cinematographer & detective\\
diplomat & economist & entrepreneur & financier & footballer & goalkeeper\\
guitarist & historian & inspector & inventor & investigator & lawyer\\
playwright & politician & professor emeritus & saxophonist & scientist & sculptor\\
shopkeeper & solicitor general & stockbroker & surveyor & archaeologist & architect\\
banker & cabbie & captain & chancellor & chaplain & conductor\\
constable & cop & director & disc jockey & economics professor & lifeguard\\
manager & mechanic & neurologist & parliamentarian & physician & programmer\\
rabbi & scholar & soldier & technician & trader & vice chancellor\\
welder & wrestler & ambassador & athlete & athletic director & dean\\
dentist & deputy & doctor & fighter pilot & firefighter & industrialist\\
investment banker & judge & lawmaker & legislator & lieutenant & magician\\
marshal & neurosurgeon & pastor & physicist & preacher & ranger\\
senator & sergeant & skipper & surgeon & trucker & tycoon\\
astronaut & ballplayer & cab driver & carpenter & coach & colonel\\
commander & commissioner & electrician & farmer & magistrate & mathematician\\
minister & officer & philosopher & plumber & sailor & sheriff deputy\\
bodyguard & boxer & butcher & custodian & drummer & janitor\\
laborer & president & sportswriter & superintendent & taxi driver & warden\\
\hline
\end{tabular}
\caption{ List of occupations used in the occupation task.} %TODO cite bolukbasi implementation as source
\end{table}
\end{document}

cs

Philip Kenneweg
Alexander Schulz
Sarah Schröder
Barbara Hammer

"Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers" by Philip Kenneweg, Alexander Schulz, Sarah Schröder, and Barbara Hammer, arXiv:2404.01317v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"'Good job!' The impact of positive and negative feedback on performance" by Daniel Goller and Maximilian Späth, arXiv:2301.11776v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Velocity buckets}
\begin{tabular}{|l|c|c|}
		\hline
		\textbf{Music dynamic notation} & \textbf{Velocity} & \textbf{Bucket} \\ \hline
		fortississimo fff very very loud & 86 $\leq$ V <128 & 7 \\ \hline
 fortissimo	ff	very loud & 78 $\leq$ V <86 & 6 \\ \hline
 forte	f	loud & 71 $\leq$ V <78 & 5 \\ \hline
 mezzo-forte	mf	average & 65 $\leq$ V <71 & 4 \\ \hline
 mezzo-piano	mp & 58 $\leq$ V <65 & 3 \\ \hline
 piano	p	soft & 50 $\leq$ V <58 & 2 \\ \hline
 pianissimo	pp	very soft & 40 $\leq$ V <50 & 1 \\ \hline
 pianississimo	ppp	very very soft & 0 $\leq$ V <40 & 0 \\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

"Real-time error correction and performance aid for MIDI instruments" by Georgi Marinov, arXiv:2011.13122v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \hline
 {} & \multicolumn{2}{c}{\textbf{Inversion Model PCCs}} \\
 \textbf{Artificial Context} & \textbf{BiGRU} & \textbf{Transformer}\\
 \hline
 None & 0.612 & 0.774\\
 Silence & 0.704 & 0.771\\
 Vowel & 0.705 & 0.762\\
 HPRC Utterance & 0.720 & \textbf{0.792}\\
 Looped Buffer & 0.704 & 0.769\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Tejas S. Prabhune
Peter Wu
Bohan Yu
Gopala K. Anumanchipalli

"Towards Streaming Speech-to-Avatar Synthesis" by Tejas S. Prabhune, Peter Wu, Bohan Yu, and Gopala K. Anumanchipalli, arXiv:2310.16287v1, 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{ Some Blockchains with quantum-resistant digital signature algorithms}
\begin{tabular}{|l|l|}
			\hline
			Blockchain/Coin/Cryptocurrency & Quantum-Resistant Digital Signature Algorithm \\
			\hline
			QRL & XMSS \\
	 	IOTA & WOTS (Winternitz one-time signature scheme) \\
	 	Mochimo & WOTS (Winternitz one-time signature scheme)\\
			Nexus& FALCON \\
			Hcash& BLISS \\
			Quantum Resistant Coin (QRC)& XMSS \\
			QAN & XMSSMT (hybrid) \\
			enQlave& XMSS\\

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

q-fin

Abha Naik
Esra Yeniaras
Gerhard Hellstern
Grishma Prasad
Sanjay Kumar Lalta Prasad Vishwakarma

"From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance" by Abha Naik, Esra Yeniaras, Gerhard Hellstern, Grishma Prasad, and Sanjay Kumar Lalta Prasad Vishwakarma, arXiv:2307.01155v1, 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|rr}
\hline
system & averaging & our method ($\dot{V}_b$) \\
 \hline
CodeLlama-13B-hf & 0.5926 & 0.1345 \\
Google CodeGemma-7B & 0.5783 & 0.1086 \\
CodeLlama-7B-hf & 0.5768 & 0.0868 \\
StabilityAI Stable Code 3B & 0.5705 & 0.0744 \\
Granite-34B-code-base & 0.5749 & 0.0521 \\
Granite-20B-code-base & 0.5714 & 0.0418 \\
\hline
\end{tabular}
\caption{Top 6 systems under simple aggregate-averaging, and our method. Metric ES has been pre-divided by 100 to match the range of the other metrics.}
\end{table}
\end{document}

stat

Samuel Ackerman
Eitan Farchi
Orna Raz
Assaf Toledo

"Statistical multi-metric evaluation and visualization of LLM system predictive performance" by Samuel Ackerman, Eitan Farchi, Orna Raz, and Assaf Toledo, arXiv:2501.18243v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
\toprule & Treatment values & Control values \\
\midrule Global & 9.8827 $\pm$ 0.0006 & 9.3523 $\pm$ 0.0006 \\
Weighted & 9.8816 $\pm$ 0.0009 & 9.3521 $\pm$ 0.0008 \\
Data splitting & 9.8710 $\pm$ 0.0008 & 9.3431 $\pm$ 0.0008 \\
Data pooling & 9.8861 $\pm$ 0.0008 & 9.3551 $\pm$ 0.0009 \\
Snapshot & 9.8876 $\pm$ 0.0009 & 9.3692 $\pm$ 0.0008 \\
\bottomrule & &
\end{tabular}
\end{table}
\end{document}

stat

"Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach" by Nian Si, arXiv:2310.17496v5, 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{Bayesian Estimation of the Selected Companies}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 \hline
 & \multicolumn{4}{c|}{$\Pi_{*|T}$} & \multicolumn{3}{c|}{$\Sigma_{*|T}$} \\
 \hline
 Johnson \& Johnson & 0.0311 & --0.0034 & --0.0436 & 0.0175 & 0.0109 & 0.0061 & 0.0047 \\
 \hline
 PepsiCo & 0.0294 & 0.1246 & --0.2111 & --0.0320 & 0.0061 & 0.0117 & 0.0093 \\
 \hline
 JPMorgan & 0.0299 & 0.1205 & 0.1007 & --0.1968 & 0.0047 & 0.0093 & 0.0449 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Parameter Estimation Methods of Required Rate of Return" by Battulga Gankhuu, arXiv:2305.19708v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The exam score y and the homework score x averaged upto the time of midterm for 18 students.}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
x & 96 & 77 & 0 & 0 & 78 & 64 & 89 & 47 & 90 & 93 & 18 & 86 & 0 & 30 & 59 & 77 & 74 & 67 \\
\hline
y & 95 & 80 & 0 & 0 & 79 & 77 & 72 & 66 & 98 & 90 & 0 & 95 & 35 & 50 & 72 & 55 & 75 & 66 \\
\hline
\end{tabular}
\end{table}
\end{document}

math

"Introduction to probability and statistics: a computational framework of randomness" by Lakshman Mahto, arXiv:2401.08622v2, 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
\textbf{Source} & \textbf{Quantity} & \textbf{Source} & \textbf{Quantity}\\
\midrule
 Zhihu & 2733 & Douban & 300 \\
 Xiaohongshu & 50 & Segment Fault & 454\\
 Encyclopedia Article & 1350 & Encyclopedia of China & 200\\
 WikiHow & 300 & COIG PC & 3000 \\
 Middle school Exam & 200 & Graduate Entrance Examination & 475 \\
 Logi QA & 422 & CValue & 906 \\
 COIG-Human-Value & 101 & Chinese Traditional & 1110 \\
 Finance NLP Task & 500 & Ruozhiba & 240 \\
 Medical Article & 186 & Law & 400 \\
\midrule
 Total & & 12687 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Yuelin Bai
Xinrun Du
Yiming Liang
Yonggang Jin
Junting Zhou
Ziqiang Liu
Feiteng Fang
Mingshan Chang
Tianyu Zheng
Xincheng Zhang
Nuo Ma
Zekun Wang
Ruibin Yuan
Haihong Wu
Hongquan Lin
Wenhao Huang
Jiajun Zhang
Chenghua Lin
Jie Fu
Min Yang
Shiwen Ni
Ge Zhang

"COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning" by Yuelin Bai, Xinrun Du, Yiming Liang, Yonggang Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, and Ge Zhang, arXiv:2403.18058v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Michael Barnett
William Brock
Lars Peter Hansen
Ruimeng Hu
Joseph Huang

"A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty" by Michael Barnett, William Brock, Lars Peter Hansen, Ruimeng Hu, and Joseph Huang, arXiv:2310.13200v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DiD Analysis -- Alternative Mechanisms -- Corruption: Extensive Margin, Events in Over 60\% of Years}
\begin{tabular}{l|cc|cccc}
\toprule
& \multicolumn{2}{c|}{\textbf{Corruption Cases}} & \multicolumn{4}{c}{\textbf{Disciplinary Actions}} \tabularnewline & Public & Total & Economic & Local Gov. & Serious & Total \tabularnewline & Administrators & Offenses & Offenses & Officials & Offenses & Offenses \tabularnewline
{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)} \tabularnewline
\midrule
\midrule \textbf{Panel A. Per 10.000 People}&&&&&& \tabularnewline
Ceasefire \( \times \) FARC&--0.014&--0.004&0.012&0.027&--0.016&--0.011 \tabularnewline
&(0.028)&(0.029)&(0.016)&(0.044)&(0.065)&(0.067) \tabularnewline
&&&&&& \tabularnewline
Treated Munic.&216&216&216&216&216&216 \tabularnewline
Control Munic.&41&41&41&41&41&41 \tabularnewline
Mean Dep. Var.&0.037&0.039&0.039&0.214&0.471&0.500 \tabularnewline
Observations&2,827&2,827&2,827&2,827&2,827&2,827 \tabularnewline
\(R^2\)&0.146&0.148&0.093&0.152&0.216&0.239 \tabularnewline
&&&&&& \tabularnewline
\midrule \textbf{Panel B. Dummy}&&&&&& \tabularnewline
Ceasefire \( \times \) FARC&--0.008&0.004&--0.000&0.021&0.040&0.043 \tabularnewline
&(0.027)&(0.027)&(0.015)&(0.034)&(0.042)&(0.043) \tabularnewline
&&&&&& \tabularnewline
Treated Munic.&216&216&216&216&216&216 \tabularnewline
Control Munic.&41&41&41&41&41&41 \tabularnewline
Mean Dep. Var.&0.079&0.086&0.061&0.242&0.442&0.457 \tabularnewline
Observations&2,827&2,827&2,827&2,827&2,827&2,827 \tabularnewline
\(R^2\)&0.293&0.293&0.135&0.230&0.330&0.332 \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

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


stat

"Advances in Bayesian Modeling: Applications and Methods" by Yifei Yan, Juan Sosa, and Carlos A. Martínez, arXiv:2502.11321v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c||c|c|c|}
\hline
$\alpha$ & section & $f_{\alpha} (n)$ & $\alpha$ & section & $f_{\alpha} (n)$ \\
\hline
\hline
$(0,0,0,0)$ & & $2^{\lfloor n/2 \rfloor}$
& $(1,0,0,0)$ & & $n$ \\
\hline
$(1,0,1,0)$ & & $ \begin{cases}
 n & \text{$n$ even} \\
 n-1 & \text{$n$ odd}
 \end{cases} $
& $(0,0,1,0)$ & & $ \begin{cases}
 \lfloor n/2 \rfloor + 1 & \text{
 $n \equiv 3 \pmod{4}$} \\
 \lfloor n/2 \rfloor &
 \text{otherwise}
 \end{cases}$ \\
\hline
$(0,1,0,0)$ & & $\sim \sqrt{2n}$ &
$(0,0,1,1)$ & & $ \sim \sqrt{2n}$
 \\
\hline
$(0,1,1,0)$ & & $\sim \sqrt{2n}$
& $(0,0,0,1)$ & & $ \sim \sqrt{2n}$ \\
\hline
\end{tabular}
\caption{Our $4$-wise results for $n\geq 7$}
\end{table}
\end{document}

math

A few new oddtown and eventown problems

"A few new oddtown and eventown problems" by Griffin Johnston and Jason O'Neill, arXiv:2312.13588v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

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


math

"Eigenvector fluctuations and limit results for random graphs with infinite rank kernels" by Minh Tang and Joshua R. Cape, arXiv:2501.15725v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of four public datasets after preprocessing.}
\begin{tabular}{lccc}
\toprule
\textbf{Datasets} & \textbf{\#Users} & \textbf{\#Items} & \textbf{\#Interactions} \\
\midrule
Beauty & 22,363 & 12,101 & 198,502 \\
Sports & 35,598 & 18,357 & 296,337 \\
Phone & 27,879 & 10,429 & 194,439 \\
Recipe & 17,813 & 41,240 & 555,618 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Yidan Wang
Zhaochun Ren
Weiwei Sun
Jiyuan Yang
Zhixiang Liang
Xin Chen
Ruobing Xie
Su Yan
Xu Zhang
Pengjie Ren
Zhumin Chen
Xin Xin

"Content-Based Collaborative Generation for Recommender Systems" by Yidan Wang, Zhaochun Ren, Weiwei Sun, Jiyuan Yang, Zhixiang Liang, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Pengjie Ren, Zhumin Chen, and Xin Xin, arXiv:2403.18480v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison between different distributed estimation (and detection) methods for SLS and LDS systems in terms of network-connectivity $\times$ communication-rate. }
\begin{tabular}{|c|c|c|c|}
			\hline
			Ref. & time-scale & system dynamics & links $\times$ rate \\
			\hline
			 & STS & SLS & $N(N-1)\times 1$ \\
			\hline
			 & STS & LDS & $3N\times 1$ \\\hline
			This work	 & STS & LDS & $N\times 1$ \\\hline
			 &	DTS & SLS/LDS & $N\times L$ with $L\geq d_n$\\
			\hline
			\hline
		\end{tabular}
\end{table}
\end{document}

math

Mohammadreza Doostmohammadian
Themistoklis Charalambous

"Distributed Target Tracking based on Localization with Linear Time-Difference-of-Arrival Measurements: A Delay-Tolerant Networked Estimation Approach" by Mohammadreza Doostmohammadian and Themistoklis Charalambous, arXiv:2412.16988v1, 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{Difference in average birthweight with 95\% confidence/posterior intervals (CI/PI) from single, multi-pollutant models and Fast BKMR. Effect sizes are evaluated with two different exposure contrasts with all exposures set at (1) the $75^{th}$ versus the $25^{th}$ percentile and (2) the $95^{th}$ versus the $50^{th}$ percentile.}
\begin{tabular}{llcc}
 \hline
 \textbf{Model} & \textbf{Pollutant} & \textbf{$75^{th}$ vs $25^{th} \,\ (95\% \,\ \text{CI/PI})$} & \textbf{$95^{th}$ vs $50^{th} \,\ (95\% \,\ \text{CI/PI})$} \\
 \hline
 Single & ns(NO$_2$, 3) & -16.54 (-23.93, -9.14) & -9.41 (-16.88, -1.93)\\
 pollutant & ns(CO, 3) & -12.05 (-18.63, -5.48) & -7.52 (-14.05, -0.99) \\
 & ns(PM$_{2.5}$, 3) & -5.97 (-10.26, -1.68) & -3.09 (-8.12, 1.94)\\
 \hline
 Multi- & ns(NO$_2$, 3) + ns(CO, 3) & \multirow{2}{*}{-24.43 (-33.49, -15.36)} & \multirow{2}{*}{-9.99 (-19.13, -0.84)} \\
 pollutant & + ns(PM$_{2.5}$, 3) & & \\
 & ns(NO$_2$, 3) + ns(CO, 3) + & \multirow{3}{*}{-25.06 (-34.21, -15.91)} & \multirow{3}{*}{-13.03 (-23.06, -3.00)}\\
 & ns(PM$_{2.5}$, 3) + & & \\
 & three pairwise interactions & & \\
 \hline
 Fast BKMR & $h(\text{NO}_2,\text{CO},
 \text{PM}_{2.5})$ & -18.18 (-24.87, -11.48) & -16.24 (-26.08, -6.41) \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Danlu Zhang
Stephanie M. Eick
Howard H. Chang

"Approximate Bayesian Kernel Machine Regression via Random Fourier Features for Estimating Joint Health Effects of Multiple Exposures" by Danlu Zhang, Stephanie M. Eick, and Howard H. Chang, arXiv:2502.13157v1, 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{Comparative Performance Metrics of LLMs.}
\begin{tabular}{lll}
\cmidrule[1.5pt]{1-3}
Parameter/Model & GPT 3.5 & Llama2 \\
\midrule
Accuracy & 58 & 56 \\
mean absolute error & 7 & 7.63 \\
Processing Speed (papers/sec) & 0.2210592478 & 0.03103395873 \\
Max Token Limit & 4,096 tokens & 4096 tokens \\
Latency (s) & 4.5236741273 seconds & 31.2227663099 \\
Memory Consumption (MB) & 166.4164453125 & 91.702734375 \\
\cmidrule[1.5pt]{1-3}
\end{tabular}
\end{table}
\end{document}

cs

Neda Taghizadeh Serajeh
Iman Mohammadi
Vittorio Fuccella
Mattia De Rosa

"LLMs in HCI Data Work: Bridging the Gap Between Information Retrieval and Responsible Research Practices" by Neda Taghizadeh Serajeh, Iman Mohammadi, Vittorio Fuccella, and Mattia De Rosa, arXiv:2403.18173v1, 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|l|l|l|}
 \hline
 & \small{Trial 1} & \small{Trial 2} & \small{Trial 3} & \small{Trial 4} & \small{Trial 5} \\ \hline
 \small{PILCO} & \small{2\%} & \small{4\%} & \small{20\%} & \small{36\%} & \small{42\%} \\ \hline
 \small{Black-DROPS} & \small{0\%} & \small{4\%} & \small{30\%} & \small{68\%} & \small{86\%} \\ \hline
 \small{MC-PILCO} & \small{0\%} & \small{14\%} & \small{78\%} & \small{94\%} & \small{100\%} \\ \hline
 \end{tabular}
\caption{\small Success rate per trial obtained with PILCO, Black-DROPS and MC-PILCO.}
\end{table}
\end{document}

eess

Fabio Amadio
Alberto Dalla Libera
Ruggero Carli
Daniel Nikovski
Diego Romeres

"Model-based Policy Search for Partially Measurable Systems" by Fabio Amadio, Alberto Dalla Libera, Ruggero Carli, Daniel Nikovski, and Diego Romeres, arXiv:2101.08740v1, 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 used for training PLR-CENIE and ACCEL-CENIE in Minigrid, BipedalWalker and CarRacing domains. Note that the we inherit the original PLR$^\perp$ and ACCEL hyperparameters and only adjust the CENIE hyperparameters.}
\begin{tabular}{lrrr}
 \toprule
 Parameter & Minigrid & BipedalWalker & CarRacing \\
 \midrule
 {\em \textbf{PPO}}& \text{ } & \text{ } & \text{ } \\
 $\gamma$ & 0.995 & 0.99 & 0.99 \\
 $\lambda_{GAE}$ & 0.95 & 0.9 & 0.9 \\
 PPO rollout length & 256 & 2048 & 125 \\
 PPO epochs & 5 & 5 & 8 \\
 PPO minibatches per epoch & 1 & 32 & 4 \\
 PPO clip range & 0.2 & 0.2 & 0.2 \\
 PPO number of workers & 32 & 16 & 16 \\
 Adam learning rate & 1e-4 & 3e-4 & 3e-4 \\
 Adam $\epsilon$ & 1e-5 & 1e-5 & 1e-5 \\
 PPO max gradient norm & 0.5 & 0.5 & 0.5 \\
 PPO value clipping & yes & no & no \\
 return normalization & no & yes & yes \\
 value loss coefficient & 0.5 & 0.5 & 0.5 \\
 student entropy coefficient & 0.0 & 1e-3 & 0.0 \\
 \\
 {\em \textbf{PLR}$^\perp$} & \text{ } & \text{ } & \text{ } \\
 Scoring function & positive value loss & positive value loss & positive value loss \\
 Replay rate, \em{p} & 0.5 & 0.5 & 0.5 \\
 Buffer size, \em{K} & 4000 & 1000 & 8000 \\
 \\
 {\em \textbf{ACCEL}} & \text{ } & \text{ } & \text{ } \\
 Edit rate, \em{q} & 1.0 & 1.0 & N/A \\
 Replay rate, \em{p} & 0.8 & 0.9 & N/A \\
 Buffer size, \em{K} & 4000 & 1000 & N/A \\
 Scoring function & positive value loss & positive value loss & N/A \\
 Edit method & random & random & N/A \\
 Number of edits & 5 & 3 & N/A \\
 Levels edited & batch & batch & N/A \\
 Prioritization, $\beta$ & 0.3 & 0.1 & N/A \\
 Staleness coefficient, $\rho$ & 0.5 & 0.5 & N/A \\
 \\
 {\em \textbf{CENIE}} & \text{ } & \text{ } & \text{ } \\
 Initialization strategy & k-means++ & k-means++ & k-means++ \\
 Convergence threshold, $\epsilon$ & 0.001 & 0.001 & 0.001 \\
 GMM components & [6,15] & [6,15] & [6,15] \\
 Covariance regularization & 1e-2 & 1e-6 & 1e-1 \\
 Window size (no. of levels) & 32 & 32 & 32 \\
 Novelty coefficient & 0.5 & 0.5 & 0.5 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Jayden Teoh
Wenjun Li
Pradeep Varakantham

"Improving Environment Novelty Quantification for Effective Unsupervised Environment Design" by Jayden Teoh, Wenjun Li, and Pradeep Varakantham, arXiv:2502.05726v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model parameter ranges in the frame model.}
\begin{tabular}{llllllllll}
\hline
 & \(H\)& \(W\) & \(E\)& \(A_c\)& \(I_c\)& \(A_b\)& \(I_b\)& \(P\)& \(q_0\) \\
 & (m) & (m) & (GPa) & (e-3 m$^{2}$) & (e-5 m$^{4}$) & (e-3 m$^{2}$) & (e-5 m$^4$) & (kN) & (kN/m) \\
\hline
Minimum & 2.55 & 3.0 & 150 & 1.5 & 1.2 & 4.5 & 4.05 & 7.5 & 37.5 \\
Maximum & 4.25 & 5.0 & 250 & 2.5 & 2.0 & 7.5 & 6.75 & 12.5 & 62.5 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Mariia Kozlova
Antti Ahola
Pamphile T. Roy
Julian Scott Yeomans

"Simple binning algorithm and SimDec visualization for comprehensive sensitivity analysis of complex computational models" by Mariia Kozlova, Antti Ahola, Pamphile T. Roy, and Julian Scott Yeomans, arXiv:2310.13446v2, 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{PPO Hyperparameters.}
\begin{tabular}{cc}
Parameter & Value \\
\hline
Horizon (T) & 4096 \\
Optimizer & Adam \\
Learning rate & $1 \cdot 10^{-4}$ \\
Number of epochs & 10 \\
Minibatch size & 128 \\
Discount ($\gamma$) & 0.99 \\
GAE prarmeter ($\lambda$) & 0.95 \\
Clipping parameter ($\epsilon$) & 0.2 \\
VF coeff. ($c_1$) & 1 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Guillaume Bellegarda
Yiyu Chen
Zhuochen Liu
Quan Nguyen

"Robust High-speed Running for Quadruped Robots via Deep Reinforcement Learning" by Guillaume Bellegarda, Yiyu Chen, Zhuochen Liu, and Quan Nguyen, arXiv:2103.06484v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An Outer Approximation Method for Solving Mixed-Integer Convex Quadratic Programs with Indicators" by Linchuan Wei and Simge Küçükyavuz, arXiv:2312.04812v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l||c|c|c|c|}\hline
Method&\ mean(probs)&\quad AUC&
\ mean(sqrt(probs))\\\hline\hline
Source&0.010&0.802&0.084\\\hline
Capped scaling&0.050&0.950&0.132\\\hline
Label shift&0.060&0.930&0.160\\\hline
FJS&0.050&0.932&0.142\\\hline
Platt scaling&0.050&0.802&0.179\\\hline
ROC QMM&0.049&0.799&0.191\\\hline
2-param QMM&0.050&0.802&0.191\\\hline
Logistic CSPD&0.050&0.803&0.192\\\hline
Normal CSPD&0.050&0.802&0.192\\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Recalibrating binary probabilistic classifiers" by Dirk Tasche, arXiv:2505.19068v2, 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{Main differences in data augmentation and normalization layer settings for the baseline \textit{nnU-Net default} and the final submission model \textit{mnms nnU-Net}. Values in brackets represent ranges for the given data augmentation technique. Probabilities that the data augmentation technique will be applied to an image is given as $p=X$. All other architecture features of the nnU-Net were kept unmodified.}
\begin{tabular}{l|l|l}
Setting & default nnU-Net & mnms nnU-Net \\ \hline
rotation & p=0.2 & p=0.7 \\
elastic deformations & - & p=0.1 \\
independent scale factor per axis & - & p=0.3 \\
elastic deformation alpha & (0, 200) & (0, 300) \\
elastic deformation sigma & (9, 13) & (9, 15) \\
scale & p=0.2 & p=0.3 \\
gamma range & (0.7, 1.5) & (0.5, 1.6) \\
additive brightness mu & - & 0 \\
additive brightness sigma & - & 0.2 \\ \hline \hline
normalization layers & IN & BN \\
\end{tabular}
\end{table}
\end{document}

eess

Peter M. Full
Fabian Isensee
Paul F. Jäger
Klaus Maier-Hein

"Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI" by Peter M. Full, Fabian Isensee, Paul F. Jäger, and Klaus Maier-Hein, arXiv:2011.07592v1, 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}{lrrrrrrrr}
 \hline
 & Acid & Basil & Bitter & Lemon & Licorice & Mint & Salty & Sweet \\
 \hline
$w_e$ & 0.12 & 0.12 & 0.12 & 0.12 & 0.12 & 0.12 & 0.12 & 0.12 \\
 $w_{p(1-p)}$ & 0.02 & 0.06 & 0.05 & 0.02 & 0.21 & 0.60 & 0.03 & 0.01 \\
 $w_p$ & 0.02 & 0.05 & 0.05 & 0.02 & 0.21 & 0.62 & 0.02 & 0.01 \\
 \hline
\end{tabular}
\caption{Normalized (and rounded at two first digits in the Table) weights used for defining the inner product in $\mathbb{H}$. $w_e$ corresponds to equal weights, $w_{p(1-p)}$ to the scheme given in and $w_p$ to weights proportional to the inverse of the average probability of occurrence, $w_j = (\int_0^1 p_j(t) dt)^{-1}$.}
\end{table}
\end{document}

stat

"Statistical modeling of categorical trajectories with multivariate functional principal components" by Hervé Cardot and Caroline Peltier, arXiv:2502.09986v2, 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
T-learner base model & AUC \\
 \midrule
Vancomycin - BART & 0.75 (0.52 - 0.86) \\
Vancomycin - RF & 0.72 (0.59 - 0.84) \\
Vancomycin - LR & 0.71 (0.50 - 0.84) \\
Alternative - BART & 0.74 (0.59 - 0.82) \\
Alternative - RF & 0.73 (0.62 - 0.83) \\
Alternative - LR & 0.72 (0.51 - 0.81) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Izak Yasrebi-de Kom
Joanna Klopotowska
Dave Dongelmans
Nicolette De Keizer
Kitty Jager
Ameen Abu-Hanna
Giovanni Cinà

"Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury" by Izak Yasrebi-de Kom, Joanna Klopotowska, Dave Dongelmans, Nicolette De Keizer, Kitty Jager, Ameen Abu-Hanna, and Giovanni Cinà, arXiv:2311.09137v1, 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{ Diarization Error Rate (DER) \% for various EEND baselines across all test datasets combined.}
\begin{tabular}{lccccc}
\toprule
\textbf{Model} & \textbf{NS1} & \textbf{NS2} & \textbf{NS3} & \textbf{NS4} & \textbf{NS1 to NS4} \\ \midrule
EDA & 7.81 & 12.83 & 19.67 & 27.21 & 17.45 \\
EDA + CSV & 10.37 & 12.78 & 20.35 & 25.62 & 17.13 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Lahiru Samarakoon
Samuel J. Broughton
Marc Härkönen
Ivan Fung

"Transformer Attractors for Robust and Efficient End-to-End Neural Diarization" by Lahiru Samarakoon, Samuel J. Broughton, Marc Härkönen, and Ivan Fung, arXiv:2312.06253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
\hline
\multicolumn{2}{c}{Parameters} & Est. & S.E.\tabularnewline
\hline
\hline
\multirow{3}{*}{Panasonic} & 40W & 57.7 & 21.5\tabularnewline
 & 60W & 57.1 & 21.5\tabularnewline
 & 100W & 70.8 & 21.5\tabularnewline
\hline
\multirow{3}{*}{Toshiba} & 40W & 73.9 & 21.5\tabularnewline
 & 60W & 74.5 & 21.5\tabularnewline
 & 100W & 87.7 & 21.5\tabularnewline
\hline
\end{tabular}
\caption{Results of marginal cost parameter estimates}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
\toprule
\textbf{Lower Bound} & \textbf{Upper Bound} & \textbf{Ground Truth Effect} \\ \midrule
$2.4 \pm 2.01$ & $7.2 \pm 2.1$ & $4.7 \pm 0.13$ \\ \bottomrule
\end{tabular}
\caption{The estimated bounds and ground truth effect of the IHDP dataset. The errors are the standard deviations to 10 bootstrap sampling iterations with replacement.}
\end{table}
\end{document}

stat

Kirtan Padh
Zhufeng Li
Cecilia Casolo
Niki Kilbertus

"Your Assumed DAG is Wrong and Here's How To Deal With It" by Kirtan Padh, Zhufeng Li, Cecilia Casolo, and Niki Kilbertus, arXiv:2502.17030v2, 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{Example table}
\begin{tabular}{|c|c|c|} \hline
 Species & \bf Mean & \bf Std.~Dev. \\ \hline
 1 & 3.4 & 1.2 \\
 2 & 5.4 & 0.6 \\ \hline
 \end{tabular}
\end{table}
\end{document}

math

"Calculating Spectra by Sequential High-Pass Filtering" by Dongxiao Zhao and Hussein Aluie, arXiv:2412.11891v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llrrrrrrrr}
 \toprule
 & & \multicolumn{2}{c}{\textit{point}} & & \multicolumn{5}{c}{\textit{probabilistic}} \\
 \cmidrule{3-4} \cmidrule{6-6} \cmidrule{8-10}
 & & MAE & RMSE & & CRPS & & 50\%-cov & 90\%-cov & 98\%-cov \\
 \cmidrule{3-4} \cmidrule{6-6} \cmidrule{8-10}
 & Naive & \cellcolor[rgb]{ .973, .412, .42} 9.2559 & \cellcolor[rgb]{ .973, .412, .42} 14.2027 & & \cellcolor[rgb]{ .973, .412, .42} 3.3409 & & \cellcolor[rgb]{ .973, .412, .42} 0.3509 & \cellcolor[rgb]{ .973, .412, .42} 0.6965 & \cellcolor[rgb]{ .973, .412, .42} 0.7915 \\
 & LEAR-Avg & \cellcolor[rgb]{ 1, .914, .518} 4.4655 & \cellcolor[rgb]{ 1, .922, .518} 6.7939 & & - & & - & - & - \\
 & LEAR-QRM & \cellcolor[rgb]{ 1, .922, .518} 4.3848 & \cellcolor[rgb]{ 1, .922, .518} 6.7547 & & \cellcolor[rgb]{ 1, .914, .518} 1.7048 & & \cellcolor[rgb]{ .996, .902, .514} 0.4272 & \cellcolor[rgb]{ .996, .898, .51} 0.8318 & \cellcolor[rgb]{ .996, .882, .51} 0.9348 \\
 & LEAR-QRA & \cellcolor[rgb]{ .937, .902, .514} 4.3230 & \cellcolor[rgb]{ .91, .894, .51} 6.6908 & & \cellcolor[rgb]{ .945, .902, .514} 1.6497 & & \cellcolor[rgb]{ .933, .902, .514} 0.4329 & \cellcolor[rgb]{ .922, .898, .514} 0.8432 & \cellcolor[rgb]{ .788, .863, .506} 0.9577 \\
 & DistrNN & \cellcolor[rgb]{ .388, .745, .482} 3.7507 & \cellcolor[rgb]{ .388, .745, .482} 6.3119 & & \cellcolor[rgb]{ .388, .745, .482} 1.3669 & & \cellcolor[rgb]{ .388, .745, .482} 0.4558 & \cellcolor[rgb]{ .388, .745, .482} 0.8800 & \cellcolor[rgb]{ .388, .745, .482} 0.9792 \\
 \bottomrule
 \end{tabular}
\caption{Quantitative results. Point (median) and probabilistic accuracy results. For MAE, RMSE, and CRPS measures lower $\implies$ better, for $\alpha$-coverage measure closer to nominal \% coverage $\implies$ better. Colours highlight differences in values from red to green are from the worst to the best.}
\end{table}
\end{document}

q-fin

"Learning Probability Distributions of Day-Ahead Electricity Prices" by Jozef Barunik and Lubos Hanus, arXiv:2310.02867v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Wenbo Liu
Akang Wang
Wenguo Yang
Qingjiang Shi

"Mixed-Integer Linear Optimization via Learning-Based Two-Layer Large Neighborhood Search" by Wenbo Liu, Akang Wang, Wenguo Yang, and Qingjiang Shi, arXiv:2412.08206v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ayush Mohanty
Nazal Mohamed
Paritosh Ramanan
Nagi Gebraeel

"Federated Granger Causality Learning for Interdependent Clients with State Space Representation" by Ayush Mohanty, Nazal Mohamed, Paritosh Ramanan, and Nagi Gebraeel, arXiv:2501.13890v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cc|cc}
\hline
\multirow{2}{*}{\textbf{Methods}} & \multicolumn{2}{c|}{\textbf{Dev}} & \multicolumn{2}{c}{\textbf{Test}} \\
\cline{2-5} & EM & F1 & EM & F1 \\ \hline
BM25 + HYBRIDER~ & 10.3 & 13.0 & 9.7 & 12.8 \\
BM25 + DUREPA~ & 15.8 & - & - & - \\
Iterative-Retrieval + CBR ~ & 14.4 & 18.5 & 16.9 & 20.9 \\
Fusion-Retrieval + CBR ~ & 28.1 & 32.5 & 27.2 & 31.5 \\
OTTeR + CBR* ~ & 35.8 & 41.5 & 34.8 & 40.7 \\ \hline
\textbf{DoTTeR + CBR (ours)} & \textbf{37.8} & \textbf{43.9} & \textbf{35.9} & \textbf{42.0} \\
\textit{\quad w/o denoising OTT-QA + CBR} & 37.1 & 43.0 & 35.5 & 41.5 \\
\textit{\quad w/o RATE + CBR} & 35.8 & 41.8 & 35.1 & 41.0 \\ \hline
\end{tabular}
\caption{QA results on OTT-QA dev and blind test set. * denotes results reproduced by us.}
\end{table}
\end{document}

cs

Deokhyung Kang
Baikjin Jung
Yunsu Kim
Gary Geunbae Lee

"Denoising Table-Text Retrieval for Open-Domain Question Answering" by Deokhyung Kang, Baikjin Jung, Yunsu Kim, and Gary Geunbae Lee, arXiv:2403.17611v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c||c}
\textbf{Models} & \textbf{F1-micro} & \textbf{F1-macro} & \textbf{LRAP} & \textbf{\#Params} \\ \hline
Bosch et al. & 0.503 & 0.432 & -- & --\\
Pons et al. & 0.589 & 0.516 & -- & -- \\
Han et al. & 0.602 & 0.503 & -- & --\\%$1.45M \\
Kratimenos et al. & \textbf{0.616} & 0.506 & \textbf{0.767} & 24.3M \\
Proposed & 0.608 & \textbf{0.543} & 0.747 & \textbf{1.07M} \\
\end{tabular}
\caption{Comparison of our work with previous performances on the IRMAS Dataset}
\end{table}
\end{document}

eess

Kleanthis Avramidis
Agelos Kratimenos
Christos Garoufis
Athanasia Zlatintsi
Petros Maragos

"Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms" by Kleanthis Avramidis, Agelos Kratimenos, Christos Garoufis, Athanasia Zlatintsi, and Petros Maragos, arXiv:2102.06930v1, 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{Mathematical Notations}
\begin{tabular}{|l|l|}
 \hline
 {\bf Notation} & {\bf Definition} \\
 \hline
 $BDP_t$ & Bandwidth delay product at time window $t$\\\hline
 $\texttt{RTprop}_t$ & Minimum RTT captured at time window $t$ \\\hline
 $\texttt{BtlBw}_t$ & Max. bottleneck bandwidth at time window $t$ \\\hline
 {$lastRTT_t$} & RTT of the last \texttt{ACK} at time window $t$\\\hline
 ${minRTT}_t$ & Minimum value of RTT at time window $t$\\\hline
 \multirow{2}{*}{${dRate}_t$} & Delivery rate of the bottleneck link \\
 & at time window $t$\\\hline
 $maxBw_t$ & Maximum delivery rate at time window $t$ \\\hline
 \multirow{2}{*}{$Inflight_t$} & Estimated volume of in-flight data to utilize \\
 & available bottleneck bandwidth at time window $t$\\\hline
 $\alpha_t$ & RTT fairness threshold at time window $t$ \\\hline
 $Wfcount_t$ & Number of flows at time window $t$ \\\hline
 $WminRTT_t$ & RTT estimate at time window $t$\\\hline
 $\beta$ & Balance factor set to 0.8 \\\hline
 $\gamma$ & Discount factor set to 0.99 \\\hline
 \texttt{PROBE\_INT\_EXP} & Expiration flag of the probe bandwidth phase \\\hline
 $\texttt{RATE\_LIM\_APP}$ & Rate limiting flag \\
 \hline
\end{tabular}
\end{table}
\end{document}

cs

Akshita Abrol
Purnima Murali Mohan
Tram Truong-Huu

"FaiRTT: An Empirical Approach for Enhanced RTT Fairness and Bottleneck Throughput in BBR" by Akshita Abrol, Purnima Murali Mohan, and Tram Truong-Huu, arXiv:2403.19973v1, 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}{lrrrr}
	\toprule
	$p$ & 0.01 & 0.05 & 0.10 & 0.50 \\
	$\epsilon$ & 17.08 & 15.43 & 14.68 & 12.48 \\
	\bottomrule
\end{tabular}
\caption{Conversion of (expected) swap rate $p$ to privacy loss $\epsilon$. Under this swapping scheme, the largest stratum size is $b = 264,331$, the number of all two-person households of Massachusetts.}
\end{table}
\end{document}

stat

"A Refreshment Stirred, Not Shaken (II): Invariant-Preserving Deployments of Differential Privacy for the US Decennial Census" by James Bailie, Ruobin Gong, and Xiao-Li Meng, arXiv:2501.08449v1, 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}{llll|l}
 algorithm &parameter & range & tuned & performance\\
 & & of values & value & (sd-all)\\
 \hline
EA & population size $\mu$ & $[4, 200]$ & 52 & -565 \\
& mutation rate $r_m$ & $[0, 1]$ & 0.042&(73.2)\\
& mutation operator $o_m$ & $\{1, 2, 3\}$ & 1 &\\
& recombination operator $o_r$ & $\{1, 2, 3\}$ & 3 & \\
 \hline
SAEA & population size $\mu$ & $[4,200]$ & 14 & -593 \\
& learning rate $\tau$ & $[10^{-4}, 10^0]$& $10^{-1.32}$ &(25.6)\\
& probability $p_r$ & $[0,1]$& 0.30 & \\
 \hline
UEDA & population size $\mu$ & $[4, 200]$& 15 & -586 \\
& learning rate $\tau$ & $[0, 1]$ & 0.95 &(66.5) \\
 \hline
DCMA & population size $\mu$ & $[4, 200]$& 19 & -576 \\
& initial stand. dev. $\sigma_\text{init}$ & $[10^{-4},10^4]$ & $10^{-2.03}$ &(59.0) \\
& margin $\alpha_m$ & $[(\mu n)^{-1.5}, (\mu n)^{-0.5}]$ & $(\mu n)^{-1.23}$ &\\
\end{tabular}
\end{table}
\end{document}

q-fin

Gerhard Hellstern
Vanessa Dehn
Martin Zaefferer

"Quantum computer based Feature Selection in Machine Learning" by Gerhard Hellstern, Vanessa Dehn, and Martin Zaefferer, arXiv:2306.10591v1, 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}
 \toprule
 \textbf{Method}&{ZeroGen}&{ProGen}&{P2Model}&{\emph{GOLD}}\\ \midrule
 \textbf{Time (s)} & 4.9 & 7.3 & 5.3 & 5.4\\
 \bottomrule
\end{tabular}
\caption{Running time analysis.}
\end{table}
\end{document}

cs

Mohsen Gholami
Mohammad Akbari
Cindy Hu
Vaden Masrani
Z. Jane Wang
Yong Zhang

"GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation" by Mohsen Gholami, Mohammad Akbari, Cindy Hu, Vaden Masrani, Z. Jane Wang, and Yong Zhang, arXiv:2403.19754v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Percentage of quality retained relative to the Full Dataset for Validation RMSE, Validation Loss, and Training Loss, evaluated after training with coresets selected using each method.}
\begin{tabular}{lccccc}
\toprule
\textbf{Method} & \textbf{Aggregation} & \textbf{Dimension} & \textbf{Val RMSE, \%} & \textbf{Val Loss, \%} & \textbf{Train Loss, \%} \\
\midrule
Full Dataset & - & - & 100.00 & 100.00 & 100.00 \\
\midrule
Random Coreset & - & - & 50.23 & 46.33 & 55.32 \\
\midrule
\multirow{3}{*}{Coreset} & Concat & 301.824 & 49.08 & 47.41 & 51.81 \\
 & Mean & 768 & 51.54 & 47.90 & 52.63 \\
 & Sum & 768 & 51.11 & 52.12 & 54.47 \\
\midrule
\multirow{4}{*}{Coreset w/ PCA} & Concat & 512 & 47.73 & 45.43 & 45.37 \\
 & Concat & 1024 & 55.93 & 50.00 & 54.55 \\
 & Concat & 2048 & 47.90 & 43.23 & 52.46 \\
 & Concat & 4096 & 44.00 & 36.50 & 51.77 \\
\midrule
\multirow{2}{*}{Coreset w/ UMAP} & Concat & 512 & 49.29 & 49.23 & 47.44 \\
 & Concat & 1024 & 50.53 & 45.27 & 48.31 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Challenges of Multi-Modal Coreset Selection for Depth Prediction" by Viktor Moskvoretskii and Narek Alvandian, arXiv:2502.15834v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\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{Shots} & \textbf{MCD$\downarrow$} & \textbf{GPE$\downarrow$} & \textbf{VDE$\downarrow$} & \textbf{FFE$\downarrow$}\\
 \hline
 1-shot & 13.42& 27.56 & 17.52 & 34.44\\
 2-shot & 11.78& 27.08 & 17.18 & 33.38 \\
 3-shot & 10.91 & 26.04 & 16.13 & 32.41 \\
 4-shot & 10.12 & 25.34 & 15.71 & 30.56 \\
 5-shot & 09.78 & 24.45 & 14.47 & 28.90 \\
 \end{tabular}
\caption{Metric for few shot approach for convolution based normalization in FSM-SS for VCTK 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. 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.