\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Attention changes within regime}
\begin{tabular}{lccc}
\hline \hline\\
& $\widehat{\delta}_1$ & $\widehat{\delta}_2$ & $\widehat{\delta}_3$ \\\hline\\
2-year windows & $0.193^{***}$ & -0.002 & 0.022 \\\\
s.e.\ & (0.067) & (0.005) & (0.032)\\\\
5-year windows & $0.119^{***}$ & -0.004 & 0.001 \\\\
s.e.\ & (0.030) & (0.003) & (0.005) %\\\\
\\\\
\hline \hline
\end{tabular}
\end{table}
\end{document}

q-fin

"The Inflation Attention Threshold and Inflation Surges" by Oliver Pfäuti, arXiv:2308.09480v3, 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{The number of weights in generator and discriminator, respectively.}
\begin{tabular}{c|c|c|c} %表格7列 全部居中显示
		\hline
		\ &\ \ \ \ AWGN\ \ \ \ &Rayleigh fading &DeepMIMO \\ \hline
		\multirow{1}*{GAN~}&22030\ /\ 513&23822\ /\ 2017&23822\ /\ 2017\\ %纵向合并2行单元格
		\cline{2-4} %为第二列到第三列添加横线
		\hline
		\multirow{1}*{WGAN~}&26208\ /\ 13176&28096\ /\ 15512&28096\ /\ 15512\\ %纵向合并2行单元格
		\cline{2-4} \hline
		\multirow{1}*{RA-GAN}&22030\ /\ 513&23822\ /\ 2017&23822\ /\ 2017\\ %纵向合并2行单元格
		\cline{2-4} \hline
	\end{tabular}
\end{table}
\end{document}

eess

Hao Jiang
Shuangkaisheng Bi
Linglong Dai
Hao Wang
Jiankun Zhang

"Residual-Aided End-to-End Learning of Communication System without Known Channel" by Hao Jiang, Shuangkaisheng Bi, Linglong Dai, Hao Wang, and Jiankun Zhang, arXiv:2102.10786v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters of algorithms with local search}
\begin{tabular}{cccccccc}
 \toprule
 \multirow{2}{*}{Sampling Type} & \multicolumn{3}{c}{IRDSA} & & \multicolumn{2}{c}{CPFF} \\ \cmidrule{2-4} \cmidrule{6-7}
 & $R_0$ & $R_{max}$ & $M$ & & ($\mu_0,\,\rho_0,\,\mu$) & Q \\
 \midrule
 Polynomial & $0.5$ & $2.5$ & $\lfloor25\times (N/2)\rfloor$ & & ($0.05,\,0.05,\,0.5$) / ($0.1,\,0.1,\,1$) & $2N$\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Randomized directional search for nonconvex optimization" by Yuxuan Zhang and Wenxun Xing, arXiv:2501.00469v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Covariance Regression based on Basis Expansion" by Kwan-Young Bak and Seongoh Park, arXiv:2502.15416v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccccccc}
\toprule
\multicolumn{10}{c}{\textbf{NoCrash Benchmark (\% Success Episodes)}} \\ \hline
\textbf{Task} & \multicolumn{9}{c}{\textbf{Training Conditions (\textit{Town 01})}} \\ \hline
 & \textit{CIL} & \textit{CAL} & \textit{CILRS} & \textit{LBC} & \textit{IA} & \textit{AT} & \textit{Ours (A)} & \textit{Ours (A+I)} & \textit{Ours (I)} \\
\textit{Empty} & $79 \pm 1$ & $81 \pm 1$ & $87 \pm 1$ & $97 \pm 1$ & \textbf{100} & $\textbf{100} \pm 0$ & $\textbf{100} \pm 0$ & $\textbf{100} \pm 0$ & $94 \pm 10$ \\
\textit{Regular} & $60 \pm 1$ & $73 \pm 2$ & $83 \pm 0$ & $93 \pm 1$ & 96 & $\textbf{99} \pm 1$ & $98 \pm 2$ & $90 \pm 2$ & $90 \pm 4$ \\
\textit{Dense} & $21 \pm 2$ & $42 \pm 1$ & $42 \pm 2$ & $71 \pm 5$ & 70 & $86 \pm 3$ & $\textbf{95} \pm 2$ & $94 \pm 4$ & $89 \pm 4$ \\
\hline
\textbf{Task} & \multicolumn{9}{c}{\textbf{Testing Conditions (\textit{Town 02})}} \\ \hline
 & \textit{CIL} & \textit{CAL} & \textit{CILRS} & \textit{LBC} & \textit{IA} & \textit{AT} & \textit{Ours (A)} & \textit{Ours (A+I)} & \textit{Ours (I)} \\
\textit{Empty} & $48 \pm 3$ & $36 \pm 6$ & $51 \pm 1$ & $\textbf{100} \pm 0$ & 99 & $\textbf{100} \pm 0$ & $\textbf{100} \pm 0$ & $\textbf{100} \pm 0$ & $98 \pm 3$ \\
\textit{Regular} & $27 \pm 1$ & $26 \pm 2$ & $44 \pm 5$ & $94 \pm 3$ & 87 & $\textbf{99} \pm 1$ & $98 \pm 1$ & $96 \pm 2$ & $96 \pm 2$ \\
\textit{Dense} & $10 \pm 2$ & $9 \pm 1$ & $38 \pm 2$ & $51 \pm 3$ & 42 & $60 \pm 3$ & $\textbf{91} \pm 1$ & $89 \pm 2$ & $79 \pm 3$ \\
\bottomrule
\end{tabular}
\caption{Quantitative comparison with the baselines on the NoCrash benchmark . The table reports the percentage (\%) of successfully completed episodes for each task in the training and testing town. \textbf{Higher} is better. The baselines include \textit{CIL} , \textit{CAL} , \textit{CILRS} , \textit{LBC} , \textit{IA} and CARLA built-in autopilot control (\textit{AT}) compared with our PPO method. Results denote average of 3 seeds across 5 trials. \textbf{Bold} values correspond to the best mean success rate.}
\end{table}
\end{document}

cs

Tanmay Agarwal
Hitesh Arora
Jeff Schneider

"Affordance-based Reinforcement Learning for Urban Driving" by Tanmay Agarwal, Hitesh Arora, and Jeff Schneider, arXiv:2101.05970v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average time required for each step performed within one generation of the evolutionary algorithm.}
\begin{tabular}{lc}
		\toprule
		Step & Average Time \\
		\midrule
		Parent Selection & 0.68 ms \\
		\midrule
		Child Creation & 0.32 s \\
		\midrule
		Child Evaluation & 3.31 h \\
		\midrule
		Population Selection & 0.20 s \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

math

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Demographic characteristics - February 1986 to April 1987}
\begin{tabular}{lcccc}
 \hline
 & SIPP 1985 & SIPP 1986 & SIPP 1987 & p-value \\
 & & & & (no difference) \\ \hline \hline
 \bf{Education} & & & & \\
less than high school & 14.72& 15.27& 14.55& 0.386\\
high school grad & 38.10& 37.36& 36.80& 0.361 \\
some college & 24.51& 24.94& 24.71& 0.546 \\
college degree & 22.67& 22.43& 23.95& 0.746 \\
\bf{Age category} & & & & \\
19-24 & 12.29	& 12.62	& 12.90 & 0.458 \\
25-29 & 16.72	& 16.15 & 	 16.36 & 0.357 \\
 30-34 & 15.84	& 15.40	& 16.00 &	0.512 \\
35-39 & 15.02 & 15.30 &	13.99 &	0.806 \\
40-44 & 11.65	& 11.50	& 11.80 &	0.804 \\
45-49 & 9.10 &	8.87 & 9.22 &	0.667 \\
50-54 & 7.72	& 7.90 &	8.25	& 0.600 \\
55-59 & 7.07 &	7.31	& 7.01 & 0.600 \\
60-64 & 4.20 &	 4.64 & 4.05 &	0.221 \\
\bf{Ethicity}& & & & \\
white & 86.29 & 86.57 & 86.25 &	0.729 \\
black & 10.62 & 10.81 & 10.73 &	0.782 \\
american indian, eskimo & 0.49	& 0.62 &	0.42 & 0.401 \\
asian or pacific islander & 2.60 &	2.01 & 2.60 &	0.090 \\
\bf{Other} & & & & \\
men & 54.20& 55.27& 53.93& 0.112 \\
married& 65.51 &	66.15 &	64.54 & 0.550 \\
living in metro area & 76.33 & 76.24 &	75.97 & 0.885\\ \hline
\multicolumn{5}{p{0.8\textwidth}}{\tiny{Workers aged 19-66, not enrolled in school, in two adjacent waves, measured in the first month of the current wave, with employment in one firm only in the previous wave, and employment in one (but possibly different) employer only in the wave that follows, without any self-employment, with un-imputed occupations reported in both waves. Person weights are used to scale observations per month within panel group (1985 versus 1986+1987).}}
 \end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrr}
 \toprule
 onset & duration & description \\
 \midrule
 15985.234375 & 0.0 & Chewing motion \\
 15990.93359375 & 30.0 & Sleep stage W \\
 16002.09375 & 0.0 & Movement \\
 16002.34375 & 1.21875 & Limb Movement \\
 \bottomrule
 \end{tabular}
\caption{Example annotations from a .tsv file. ``Chewing motion'' and ``Movement'' are free text entries by the NCH technicion, while ``Limb Movement'' is a standard sleep event labeled by Natus Sleepworks.}
\end{table}
\end{document}

eess

Harlin Lee
Boyue Li
Shelly DeForte
Mark Splaingard
Yungui Huang
Yuejie Chi
Simon Lin Linwood

"A Large Collection of Real-world Pediatric Sleep Studies" by Harlin Lee, Boyue Li, Shelly DeForte, Mark Splaingard, Yungui Huang, Yuejie Chi, and Simon Lin Linwood, arXiv:2102.13284v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The detailed structure of our five consecutive 3-channel VoxelHop} % title of Table
\begin{tabular}{l|l|l} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Input Size&Type& Filter Shape \\ % inserts table
\hline % inserts single horizontal line
$[110\times110\times(30\times1)]\times3$&M-VoxelHop& [$F_1$ kernels of $3\times3\times3$]$\times$3\\
$[108\times108\times(28\times F_1)]\times3$&MaxPool& (2$\times$2$\times$1)-(1$\times$1$\times$1)\\
\hline

$[54\times54\times(28\times F_1)]\times3$&M-VoxelHop& [$F_2$ kernels of $3\times3\times3$]$\times$3\\
$[52\times52\times(26\times F_2)]\times3$&MaxPool& (2$\times$2$\times$1)-(1$\times$1$\times$1)\\
\hline
$[26\times26\times(26\times F_2)]\times3$&M-VoxelHop& [$F_3$ kernels of $3\times3\times3$]$\times$3\\
$[24\times24\times(24\times F_3)]\times3$&MaxPool& (2$\times$2$\times2F_3$)-(1$\times$1$\times F_3$)\\
\hline
$[12\times12\times(12\times F_3)]\times3$&M-VoxelHop& [$F_4$ kernels of $3\times3\times3$]$\times$3\\
$[10\times10\times(10\times F_4)]\times3$&MaxPool& (2$\times$2$\times2F_4$)-(1$\times$1$\times F_4$)\\
\hline
$[5\times5\times(5\times F_4)]\times3$&M-VoxelHop& [$F_5$ kernels of $3\times3\times3$]$\times$3\\
$[3\times3\times(3\times F_5)]\times3$&MaxPool& (2$\times$2$\times2F_5$)-(1$\times$1$\times F_5$)\\
\hline\hline

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

eess

Xiaofeng Liu
Fangxu Xing
Chao Yang
C. -C. Jay Kuo
Suma Babu
Georges El Fakhri
Thomas Jenkins
Jonghye Woo

"VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI" by Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Suma Babu, Georges El Fakhri, Thomas Jenkins, and Jonghye Woo, arXiv:2101.05131v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Elvis Han Cui
Xuyang Lu
Jin Zhou
Hua Zhou
Gang Li

"A Semiparametric Bayesian Method for Instrumental Variable Analysis with Partly Interval-Censored Time-to-Event Outcome" by Elvis Han Cui, Xuyang Lu, Jin Zhou, Hua Zhou, and Gang Li, arXiv:2501.14837v1, 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{Local Average Shift Effect Simulation Results}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{Method} & \textbf{Avg. Estimate} & \textbf{Avg. Est. SD} & \textbf{RMSE} & \textbf{Empirical SD} & \textbf{Coverage (95\%)} \\ \hline
RieszBoost & 94.921 & 1.768 & 1.859 & 1.855 & 0.946 \\
Indirect & 94.758 & 1.753 & 1.789 & 1.789 & 0.940 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"RieszBoost: Gradient Boosting for Riesz Regression" by Kaitlyn J. Lee and Alejandro Schuler, arXiv:2501.04871v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ilaria Bombelli
Domenica Fioredistella Iezzi
Emiliano Seri
Maurizio Vichi

"Spherical Double K-Means: a co-clustering approach for text data analysis" by Ilaria Bombelli, Domenica Fioredistella Iezzi, Emiliano Seri, and Maurizio Vichi, arXiv:2501.04562v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
 \hline
 $N$ & Near-zone grid size \\
 \hline
 3096 & $17^3$\\
 12175 & $25^3$ \\
 53601 & $41^3$ \\
 92233 & $49^3$ \\
 177973 & $59^3$ \\
 418308 & $77^3$ \\
 1391742 & $111^3$
\end{tabular}
\caption{\textcolor{black}{Grid size for near-zone PSP component evaluation. The near-zone grid is much larger than the far-zone grid when the problem size $N$ is large. Second-order projection/interpolation for near-zone evaluation is used.}}
\end{table}
\end{document}

math

"Fast Fourier Transform periodic interpolation method for superposition sums in a periodic unit cell" by Fangzhou Ai and Vitaliy Lomakin, arXiv:2312.02376v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Table of the main notations/parameters and variables}
\begin{tabular}{c||l}
			\hline\textbf{Notation(s)} & \textbf{Definition} \\
			\hline \multicolumn{2}{l}{~~~~~~~~~~~~\textbf{Notations/parameters}}
			\\
			\hline $N/\mathcal{N}/n$ & Number/set/index of physical nodes \\
			\hline $L/\mathcal{L}/l_{mn}$ & Number/set of physical links/index of physical \\& link connecting physical node $m$ and $n$ \\
			\hline$P / \mathcal{P}/p$ & Number/set/type of resources \\
			\hline$C_n^p / C_{mn}^{BW} $ & maximum customizable amount of, resource\\& type $p$ in node $n$/physical link $l_{mn}$ bandwidth \\

			\hline$W_n^p(t) / W_{mn}^{BW}(t) $ & Allocated ratio of, resource type $ p $ in node $n$/\\& physical link $l_{mn}$ bandwidth, at time slot $t$ \\
			\hline$ t/\delta$ & Index/duration of each time slot
			\\	\hline	$ K/\mathcal{K}/k $& Number/set/index of embedded SFCs
			\\
			\hline $\mathcal{H}_k/H_k$ &The set/number of sequenced VNFs in SFC $k$ \\
			\hline $V_h^k$ & The $h$-th VNF in SFC $k$ \\
			\hline $\mathcal{\varDelta}_k/\mathcal{\sigma}_k$ &Maximum down time/traffic rate (packet/s) of\\& SFC $k$ \\
			\hline $ \phi_{(k,h)}^p /U_h^k$ & The amount of resources type $p$ needed/the\\& resource use cost of a backup instance for $V_h^k$ \\
			\hline $ \alpha^k_h(t)$ & Ratio coefficient managing the backup\\& placement cost influence for $V_h^k$ on each state\\
			\hline $Z_h^k(t)/d_h^k/ b^k_h(t)$ & Accumulated statelet size (in bits)/delay/\\&bandwidth of logical statelet synchronization\\& link of $V_h^k$ \\
			\hline $P_{\textit{nn}}/P_{\textit{nw}}$ & State transition probability from normal to \\& normal/warning\\
			\hline $P_{\textit{ww}}/P_{\textit{wc}}/P_{\textit{wc}}$& State transition probability from warning to\\& warning/critical/normal\\
			\hline $q_v$& Number of least time slots VNF $v$ would \\& stay in warning state\\
			\hline $\theta_v(t)$& State information AoI of VNF $v$ at time $ t $\\
			\hline $\kappa_v^s(t)$& AoI constraint depending on VNF $v$ and its \\& state $s$ at time slot $t$\\
			\hline ${\rho}_h^k(t)$& Binary variable indicating weather if $v_h^k$ is in\\& critical state at time slot $t$\\
			\hline \multicolumn{2}{l}{~~~~~~~~~~~~\textbf{ Optimization Variables}} %($ t $ $\rightarrow$ denotes time slot )
			\\
			\hline$y^{\prime (k,h)}_n (t)$ & Binary variable for embedding VNF $ (k,h) $ in\\& physical node $ n $\\
			\hline$y^{\prime (k,h)}_{mn} (t)$ & Binary variable for embedding VNF $ (k,h) $ in\\& physical link $ l_{nm} $
			\\ \hline $m_h^k(t)$ &Binary variable indicating if $ V_h^k $ is supported \\& by a backup at time slot $ t $
			\\
			\hline
			${\beta}_h^k(t)$& Binary variable for failure recovery decision \\& on $ V_h^k $ at time slot $ t $
			\\\hline
		\end{tabular}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters for the setup and algorithms.}
\begin{tabular}{ll}
\toprule
Dimension & $d=3$\\
Volume & 10 m $\times$ 10 m $\times $ 3 m \\
M sensors & 7 to 12 \\
K sources & 7 to 12\\
Time offset & [-1,1] s \\
Speed of sound & 343 m/s\\
Solver & SeDuMi \\
LM iterations & At most 1000\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Dalia El Badawy
Viktor Larsson
Marc Pollefeys
Ivan Dokmanić

"Localizing Unsynchronized Sensors with Unknown Sources" by Dalia El Badawy, Viktor Larsson, Marc Pollefeys, and Ivan Dokmanić, arXiv:2102.03565v2, 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}{lc}
 \hline
Aggregation & The highest probability weight result \\
\hline
 $[3,1]+ [2,1]$ & $[4,2]$ \\
 $[4,2]+ [4,1]$ & $[6,4]$\\
 $[4,2]+ [3,1]$ & $[5,4]$ \\
$[3,1,1]+[3,1,1]$ & $[4,3,2]$ \\
 $[\lambda]+[1^l]$ & $(\lambda_1\ge \dots \ge \lambda_{l-1}\ge \lambda_l+1>0)$\\
\hline
 \end{tabular}
\caption{Examples of the highest probability weight results of partitions aggregations }
\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.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of Student Models with Knowledge Distillation}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Model & Acc. & Prec. & Recall & F-Measure & FPR & FNR \\
\hline
KD-FS2 & 87.0 & 83.7 & 89.4 & 86.4 & 15.2 & 10.6 \\ \hline
KD-FS4 & \textbf{87.3} & \textbf{84.5} & 88.3 & \textbf{86.6} & \textbf{13.6} & 11.7 \\ \hline
KD-FS8 & 86.6 & 83.1 & 89.5 & 86.2 & 15.9 & 10.5 \\ \hline
KD-FS16 & 85.0 & 80.0 & \textbf{90.4} & 84.9 & 19.7 & \textbf{9.6} \\ \hline
KD-FS32 & 81.5 & 79.4 & 81.3 & 80.3 & 18.4 & 18.7 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Soumava Paul
Gurunath Reddy M
K Sreenivasa Rao
Partha Pratim Das

"Knowledge Distillation for Singing Voice Detection" by Soumava Paul, Gurunath Reddy M, K Sreenivasa Rao, and Partha Pratim Das, arXiv:2011.04297v2, 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{Average correlation values of $48$ subjects from NMED-H Dataset}
\begin{tabular}{|c|c|c|}
 \hline
 & LMCCA & DMCCA \\
 \hline
 Envelope & 0.0694 & 0.3268 \\
 PC1 & 0.0237 & 0.3552 \\
 RMS & 0.0305 & 0.3169 \\
 Spectral Flux & 0.0432 & 0.3261 \\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

"Deep Multiway Canonical Correlation Analysis for Multi-Subject EEG Normalization" by Jaswanth Reddy Katthi and Sriram Ganapathy, arXiv:2103.06478v1, 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{Model performance of the heart dataset. All ML models perform similarly. }
\begin{tabular}{l|cccr}
\toprule
 Model/Metrics & Classification error & AUC \\
 \midrule
 FCNN & $20.3\%$ & $87.0\%$ \\
 NAM & $18.9\%$ & $89.8\%$ \\
 MGNAM & $17.6\%$ & $90.6\%$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"How to address monotonicity for model risk management?" by Dangxing Chen and Weicheng Ye, arXiv:2305.00799v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llc}
 \toprule
 Task & Tested concepts and objects & Supercategory \\
 \midrule
 20th century events & Civil rights movements, Keynesianism, Soweto uprising, ... & Humanities \\
 Accounting & Fiscal year, depreciation, Sage 50, FreshBooks, Xero, ... & Other \\
 Architecture & Biomimicry, visual transparency, the Sagrada Familia, ... & Humanities \\
 Astronomy & Doppler effect, stellar parallax, pulsar PSR B1937+21, ... & STEM \\
 Biology & Osmosis, genetic recombination, Elizabeth Blackburn, ... & STEM \\
 Business ethics & Triple bottom line, tax evasion, Boeing 737 Max crash, ... & Other \\
 Celebrities & Philanthropy, paprazzi culture, Benedict Cumberbatch, ... & Humanities \\
 Chemistry & Photolysis, stereochemistry, Gilbert N. Lewis, PCR, ... & STEM \\
 Clinical knowledge & Blood-brain barrier, hemostasis, Karolinska Institute, ... & STEM \\
 Computer science & Evolutionary computing, Apache Hadoop, Project Loon, ... & STEM \\
 Computer security & Multi-factor authentication, VPN, Fortigate, Signal, ... & STEM \\
 Economics & Income inequality, externalities, Swiss National Bank, ... & Social Sciences \\
 Electrical engineering & Hysteresis, Schmitt Trigger, Eaton Powerware 9355 UPS, ... & STEM \\
 Gaming & Procedural generation, Xbox Series X, Cyberpunk 2077, ... & Other \\
 Geography & Fjords, thermohaline circulation, Mount Kilimanjaro, ... & Social Sciences \\
 Global facts & Global water crisis, Pyramids of Giza, Niagara Falls, ... & Other \\
 History & Crusades, Greek city-states, Battle of Agincourt, ... & Humanities \\
 Immigration law & Chain migration, visa waiver program, Ira J. Kurzban, ... & Social Sciences \\
 International law & Diplomatic immunity, Treaty of Lisbon, Paris Agreement, ... & Humanities \\
 Jurisprudence & Legal formalism, Robert Shapiro, \textit{Loving v. Virginia}, ... & Humanities \\
 Machine learning & Linear regression, random forests, Keras, TensorFlow, ... & STEM \\
 Management & Balanced scorecard approach, Susan Wojcicki, Tim Cook, ... & Social Sciences \\
 Marketing & Personalization, viral marketing, ``Open Happiness,'' ... & Social Sciences \\
 Mathematics & Vector spaces, P vs NP, the Fields Medal, Terence Tao, ... & STEM \\
 Medicine & Optogenetics, bioinformatics, Mount Sinai Hospital, ... & STEM \\
 Moral disputes & Consequentialism, libertarianism, Panama Papers leak, ... & Humanities \\
 Movies & Neo-noir, color grading, ``A Ghost Story,'' ``The Room,'' ... & Humanities \\
 Music & Just intonation, leitmotif, ``Beethoven's Symphony No. 5,'' ... & Humanities \\
 Philosophy & Rationalism, virtue ethics, Ludwig Wittgenstein, Lao Tzu, ... & Humanities \\
 Physics & Dark energy, quantum entanglement, Dark Energy Survey, ... & STEM \\
 Prehistory & Use of fire, stone tool technologies, Tower of Jericho, ... & Social Sciences \\
 Psychology & Bystander effect, self-efficacy, Bobo Doll experiment, ... & Social Sciences \\
 Public relations & Crisis management, Galaxy Note 7 crisis, Project Blue, ... & Social Sciences \\
 Sociology & Intersectionality, interactionism, Charles Horton Cooley, ... & Social Sciences \\
 Sports & Overtraining, muscle hypertrophy, Vince Lombardi trophy, ... & Social Sciences \\
 US foreign policy & Nixon Doctrine, manifest destiny, Iran Nuclear Deal, ... & Social Sciences \\
 Virology & Viral latency, antiviral drug design, Merck \& Co. Inc., ... & STEM \\
 World religions & Tikkun olam, dukkha, the Lumbini, the Dome of the Rock, ... & Humanities \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Jerry Wei
Chengrun Yang
Xinying Song
Yifeng Lu
Nathan Hu
Jie Huang
Dustin Tran
Daiyi Peng
Ruibo Liu
Da Huang
Cosmo Du
Quoc V. Le

"Long-form factuality in large language models" by Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, and Quoc V. Le, arXiv:2403.18802v4, 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}{ccc} %\hline
 & $n=100$ & $n=1,000$ \\ %\hline
Gaussian shrinkage prior. Cut degree 0 (1 core) & 8.5 sec & 5.5 sec \\
Gaussian shrinkage prior. Cut degree 0 (3 cores) & 5.0 sec & 4.0 sec \\
ICAR+ prior. Cut degree 0 (1 core) & 53.3 sec & 20.3 sec \\
ICAR+ prior. Cut degree 0 (3 cores) & 31.4 sec & 12.3 sec \\
VCBART & 69.2 sec & 7.05 min \\
GAM ($k=12$) & 1.4 sec & 4.2 sec\\
GAM ($k=24$) & 1.3 sec & 4.2 sec\\
\end{tabular}
\caption{Independent errors simulation. Run times for one simulation on an Ubuntu Linux desktop, 13th Gen Intel core i7 processor, 32Gb RAM}
\end{table}
\end{document}

stat

David Rossell
Arnold Kisuk Kseung
Ignacio Saez
Michele Guindani

"Semi-parametric local variable selection under misspecification" by David Rossell, Arnold Kisuk Kseung, Ignacio Saez, and Michele Guindani, arXiv:2401.10235v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Deep Learning Enhanced Realized GARCH

Chen Liu
Chao Wang
Minh-Ngoc Tran
Robert Kohn

"Deep Learning Enhanced Realized GARCH" by Chen Liu, Chao Wang, Minh-Ngoc Tran, and Robert Kohn, arXiv:2302.08002v2, 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|} \hline
Parameter & Values \\
\hline
$k_{1_H}$ & 1.3 \\
\hline
$k_{2_H}$ & 1 \\
\hline
$k_{1_I}$ & 1.2367 \\
\hline
$k_{2_I}$ & 0.4101 \\
\hline
\end{tabular}
\caption{Fixed parameter values corresponding to the two considered functional responses.}
\end{table}
\end{document}

math

"Spatio-temporal pattern formation under varying functional response parametrizations" by Indrajyoti Gaine and Malay Banerjee, arXiv:2504.12933v1, 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{Capacity allocation under no regulation scenario.}
\begin{tabular}{|c|c|c|c|} \hline
 Products & $S_i+g_i-c_i$ & Group & Transported Amount \\ \hline
 Crude Oil & 14.793 & $G_1$ & 128,185,505 \\
 Corn & 0.0342 & $G_1$ & 440,916,666 \\
 Barley & 0.0029 & $G_2$ & 80,897,829 \\
 Oat & -0.0153 & $G_3$ & 0 \\ \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Mostafa Pazoki
Hamed Samarghandi
Mehdi Behroozi

"Increasing Supply Chain Resiliency Through Equilibrium Pricing and Stipulating Transportation Quota Regulation" by Mostafa Pazoki, Hamed Samarghandi, and Mehdi Behroozi, arXiv:2308.00681v2, 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|ccc|cc|c}
 \toprule
 \textbf{True label} & \textbf{Precision} & \textbf{Recall} & \textbf{F1-score} & \multicolumn{2}{c|}{\shortstack{\textbf{Assignment} \\ \textbf{certainty}}} & \textbf{Count} \\
 & & & & \textbf{TP} & \textbf{FP} & \\
 \midrule
 Toys & 0.92 & 0.82 & 0.87 & 0.68 & 0.51 & 8\,092 \\
 Health & 0.75 & 0.46 & 0.57 & 0.64 & 0.60 & 6\,938 \\
 Beauty & 0.68 & 0.79 & 0.73 & 0.71 & 0.54 & 4\,072 \\
 Baby & 0.71 & 0.78 & 0.74 & 0.70 & 0.54 & 4\,635 \\
 Pets & 0.61 & 0.76 & 0.74 & 0.65 & 0.52 & 3\,792 \\
 Grocery & 0.51 & 0.94 & 0.66 & 0.78 & 0.54 & 2\,471 \\
 \midrule
 Macro avg & 0.71 & 0.76 & 0.72 & & & 30\,000 \\
 Weighted avg & 0.75 & 0.73 & 0.73 & & & 30\,000 \\
 \bottomrule
 \end{tabular}
\caption{Predictive performance of the SPF topic model on Amazon customer feedback, including assignment certainties of TP (true positive) and FP (false positive) predictions. The overall accuracy is 0.73.}
\end{table}
\end{document}

q-fin

Bernd Prostmaier
Jan Vávra
Bettina Grün
Paul Hofmarcher

"Seeded Poisson Factorization: Leveraging domain knowledge to fit topic models" by Bernd Prostmaier, Jan Vávra, Bettina Grün, and Paul Hofmarcher, arXiv:2503.02741v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Paola Ferrari
Sara Sommariva
Michele Piana
Federico Benvenuto
Matteo Varbaro

"When algebra twinks system biology: a conjecture on the structure of Gröbner bases in complex chemical reaction networks" by Paola Ferrari, Sara Sommariva, Michele Piana, Federico Benvenuto, and Matteo Varbaro, arXiv:2501.12233v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Lourens Touwen
Doina Bucur
Remco van der Hofstad
Alessandro Garavaglia
Nelly Litvak

"Learning the mechanisms of network growth" by Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia, and Nelly Litvak, arXiv:2404.00793v3, 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{Benign applications' process tree features.}
\begin{tabular}{lrrrrr}
 \toprule
 \textbf{Application} & \textbf{\# processes} & \textbf{Depth} & \textbf{\# leaf nodes} &\textbf{\# unique processes} & \textbf{\# threads}\\ \midrule
 Pycharm & 140 & 4 & 70 & 11 & 993 \\
 Visual Studio & 46 & 4 & 29 &21&568 \\
 Chrome & 42 & 1& 41& 2& 1,480 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Muhammad Ejaz Ahmed
Hyoungshick Kim
Seyit Camtepe
Surya Nepal

"Peeler: Profiling Kernel-Level Events to Detect Ransomware" by Muhammad Ejaz Ahmed, Hyoungshick Kim, Seyit Camtepe, and Surya Nepal, arXiv:2101.12434v1, 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}{lllcll}
\textbf{} & \textbf{Micro F1} & \textbf{Macro F1} & \textbf{mAP} \\ \hline
Multi-label & 0.449 (0.01) & 0.349 (0.02) & 0.400 (0.02) \\
Source Estimates PIT & 0.407 (0.01) & 0.332 (0.01) & 0.347 (0.01) \\
Oracle Sources PIT & \textbf{0.511} (0.02) & \textbf{0.461} (0.04) & \textbf{0.501} (0.04) \\
Oracle sources multi-class & \textbf{0.581} (0.01) & \textbf{0.541} (0.01) & \textbf{0.590} (0.01) \\ \hline
\end{tabular}
\caption{Closed-set classification results on 53 or 54 classes, depending on the dataset variant. All metrics are averaged over the five dataset variants, with standard deviations in parentheses.}
\end{table}
\end{document}

eess

"Multi-label Open-set Audio Classification" by Sripathi Sridhar and Mark Cartwright, arXiv:2310.13759v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf Santa Fe laser data single step benchmark results. }
\begin{tabular}{cccc}
\hline
\textbf{Nodes\#} & \textbf{Mean} & \textbf{Std} & \textbf{Min} \\
\hline
$400$ &$2.71 \times 10^{-2}$ &$6.20\times 10^{-4}$ &$2.54\times 10^{-2}$ \\
$950$ &$6.73\times 10^{-3}$ &$3.34\times 10^{-4}$ &$5.66\times 10^{-3}$ \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Prajnesh Kumar
Mingwei Jin
Ting Bu
Santosh Kumar
Yu-Ping Huang

"Efficient Reservoir Computing using Field Programmable Gate Array and Electro-optic Modulation" by Prajnesh Kumar, Mingwei Jin, Ting Bu, Santosh Kumar, and Yu-Ping Huang, arXiv:2102.09049v1, 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{Example-Based Explanations}
\begin{tabular}{llll}
\toprule
Customer & Delinquency & Credit score & Defaulted \\
\midrule
1 & 162 & 680 & yes \\
2 & 149 & 691 & yes \\
3 & 6 & 728 & yes \\
4 & 6 & 744 & yes \\
5 & 0 & 748 & yes \\
6 & 0 & 749 & no \\
7 & 0 & 763 & no \\
8 & 0 & 790 & no \\
9 & 0 & 794 & no \\
10 & 0 & 806 & no \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Sheikh Rabiul Islam
William Eberle
Sheikh Khaled Ghafoor
Mohiuddin Ahmed

"Explainable Artificial Intelligence Approaches: A Survey" by Sheikh Rabiul Islam, William Eberle, Sheikh Khaled Ghafoor, and Mohiuddin Ahmed, arXiv:2101.09429v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}
\toprule
Dataset & N/D & & RMSE & Log-likelihood & Time (s) \\
\midrule
\multirow{2}{*}{wine} &
\multirow{2}{*}{1599/11}
 & w. last term & 0.47 $\pm$ 0.01 & -0.66 $\pm$ 0.01 & 0.15 $\pm$ 0.00 \\
 & & wo. last term & 0.47 $\pm$ 0.01 & -0.66 $\pm$ 0.01 & 0.03 $\pm$ 0.00 \\
\midrule
\multirow{2}{*}{solar} &
\multirow{2}{*}{1066/10}
 & w. last term & 0.93 $\pm$ 0.07 & -1.57 $\pm$ 0.20 & 0.07 $\pm$ 0.00 \\
 & & wo. last term & 0.93 $\pm$ 0.07 & -1.56 $\pm$ 0.20 & 0.03 $\pm$ 0.00 \\
\midrule
\multirow{2}{*}{pumadyn32nm} &
\multirow{2}{*}{8192/32}
 & w. last term & 1.00 $\pm$ 0.01 & -1.42 $\pm$ 0.01 & 21.12 $\pm$ 0.06 \\
 & & wo. last term & 1.00 $\pm$ 0.01 & -1.42 $\pm$ 0.01 & 0.05 $\pm$ 0.00 \\
\midrule
\bottomrule
\end{tabular}
\caption{RMSE, log-likelihood, and run time for two variants of predictive variance computation. }
\end{table}
\end{document}

stat

Thang D. Bui
Matthew Ashman
Richard E. Turner

"Tighter sparse variational Gaussian processes" by Thang D. Bui, Matthew Ashman, and Richard E. Turner, arXiv:2502.04750v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the classification performance} % title of Table
\begin{tabular}{l|c|c} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Methods&Accuracy& AUC \\ % inserts table
\hline\hline % inserts single horizontal line
M-VoxelHop & \textbf{93.48$\pm$0.7\%} & \textbf{0.9394$\pm$0.012} \\ \hline
M-3D ResNet + & 91.30$\pm$0.6\% & 0.9048$\pm$0.010 \\
M-3D VGG~~+ & 89.13$\pm$0.5\% & 0.8808$\pm$0.014 \\
M-3D DenseNet + & 86.96$\pm$0.8\% & 0.8762$\pm$0.012 \\
M-3D AlexNet + & 84.78$\pm$1.0\% & 0.8575$\pm$0.013 \\

\hline\hline

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

eess

Xiaofeng Liu
Fangxu Xing
Chao Yang
C. -C. Jay Kuo
Suma Babu
Georges El Fakhri
Thomas Jenkins
Jonghye Woo

"VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI" by Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Suma Babu, Georges El Fakhri, Thomas Jenkins, and Jonghye Woo, arXiv:2101.05131v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc} \toprule
Time Period & Average Annualized Inflation \\
\hline
1940:8-1951:7 & .0564 \\
1968:9-1985:10 & .0661 \\
\bottomrule
\end{tabular}
\caption{Inflation regimes determined using a five-year moving window with a cutoff inflation rate of $0.05$.}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average registration performance (structural integrity, spatial alignment and intensity-based metrics). \textbf{Bold} values are best performance across methods while \underline{underline} is best among deep learning methods. $\simeq X$ indicates a value closest to X is best, $\downarrow$ lowest value is best, $\uparrow$ highest value is best. $\Delta PV_{vent}\times10{^-3}$, $\Delta PV_{wml} \times 10^{-3}$, $MAID \times 10^{-3}$, $MAID-zp \times 10^{-3}$.}
\begin{tabular}{llcccccc}
\toprule
& & ANTs & Demons & SE & VM & FlowReg-A & FlowReg-A+O \\ \midrule
\multirow{6}{*}{\rotatebox{-90}{Structural}}&$\Delta V_{brain}\simeq1$ & 1.28 & 1.47 & 1.25 & \underline{\textbf{1.01}} & 1.14 & 1.11 \\
& $\Delta V_{vent}\simeq1$ & 1.27 & 1.31 & 1.60 & 0.64 & \underline{\textbf{0.96}} & 0.86 \\
& $\Delta V_{wml}\simeq1$ & 0.84 & \textbf{1.01} & 0.81 & 0.35 & \underline{0.67} & 0.53 \\
& $\Delta PV_{vent}\simeq0$ &\textbf{ 0.52} & -2.80 & 7.19 &-23.11 & \underline{-7.31} &-11.52 \\
& $\Delta PV_{wml}\simeq0$ & -5.17 &\textbf{-4.74} &-5.67 &-20.17 & \underline{-7.14} &-11.20 \\
& $\Delta SSD\simeq0$ &\textbf{-4.70} & -8.66 & -21.81 & 29.63 & 14.58 & \underline{13.82}\\ \midrule
\multirow{3}{*}{\rotatebox{-90}{Spatial}}& HA-$\varsigma\downarrow$ & 6.21 & 5.16 & \textbf{2.981} & 10.83 & 6.89 & \underline{3.91} \\
& PWA-$\Sigma\downarrow$ & 1.24 & 2.40 & 1.92 & 1.47 & 1.15 & \underline{\textbf{0.65}} \\
& Brain-DSC$\uparrow$ & \textbf{0.88} & 0.77 & 0.87 & 0.84 & \underline{0.86} & 0.85 \\ \midrule
\multirow{4}{*}{\rotatebox{-90}{Intensity}}& MI$\uparrow$ & 0.24 & 0.13 & 0.16 & 0.20 & 0.25 & \underline{\textbf{0.29}} \\
& R$\uparrow$ & 0.64 & 0.41 & 0.39 & 0.60 & 0.65 & \underline{\textbf{0.80}} \\
& MAID$\simeq0$ & 5.24 & 6.26 & 5.99 & 5.54 & \underline{\textbf{5.08}} & 5.33 \\
& MAID-zp$\simeq0$ & \textbf{0.53} & 1.99 & 1.35 & 1.16 & 0.86 & \underline{0.84} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Sergiu Mocanu
Alan R. Moody
April Khademi

"FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow" by Sergiu Mocanu, Alan R. Moody, and April Khademi, arXiv:2101.09639v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Tiago Mendes-Neves
Luís Meireles
João Mendes-Moreira
Nuno de Almeida

"The Geostrategy of Youth Player Recruitment in Portuguese Clubs" by Tiago Mendes-Neves, Luís Meireles, João Mendes-Moreira, and Nuno de Almeida, arXiv:2501.05858v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Welch's T-test: Inference of Visuo-Proprioceptive Sequences,\\ Training Ratio: 80\%}
\begin{tabular}{cccc}
 \hline
 \bf{Group 1} &\bf{Group 2} &\bf{T-statistic} &\bf{P-value} \\
 \hline
 \bf{A1} &\bf{B1} &-7.43 &9.03x$10^{-5}$ \\
 \bf{A1} &\bf{C1} &-15.48 &1.56x$10^{-6}$ \\
 \bf{A1} &\bf{D1} &-14.48 &5.60x$10^{-7}$ \\
 \bf{B1} &\bf{C1} &-8.45 &4.19x$10^{-5}$ \\
 \bf{B1} &\bf{D1} &-8.57 &4.09x$10^{-5}$ \\
 \bf{C1} &\bf{D1} &-2.30 &5.78x$10^{-2}$ \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Prasanna Vijayaraghavan
Jeffrey Frederic Queisser
Sergio Verduzco Flores
Jun Tani

"Development of Compositionality and Generalization through Interactive Learning of Language and Action of Robots" by Prasanna Vijayaraghavan, Jeffrey Frederic Queisser, Sergio Verduzco Flores, and Jun Tani, arXiv:2403.19995v2, 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	 & MACD & MAMA & MAPS & MAPS2 & RFOR\\
\hline Return &-0.000&0.000&4.139&-0.637 & 130.384\\
\hline Sharpe Ratio&-0.004&0.027&0.017&-0.022 & 0.025\\
\hline PSR(0)&0.407&0.999&0.977&0.072 & 0.934\\
\hline mTRL(0)&155.402&0.791&2.124&4.490 & 3.728\\
\hline PSR(0.1)&0.000&0.000&0.000&0.000 & 0.00\\
\hline mTRL(0.1)&0.258&0.121&0.105&0.156 & 0.420\\
 \hline
		\end{tabular}
\caption{Results for the five strategies for asset GBPUSD in six months. A black cell indicates a situation where the strategy surpasses the proposed Sharpe Ratio threshold: 0 or 0.1}
\end{table}
\end{document}

cs

Murilo Sibrao Bernardini
Paulo Andre Lima de Castro

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


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\toprule[1.5pt]
\multirow{2}{*}{Model} & \multicolumn{2}{c}{Utility} & Number of \\
& Without costs & With costs & parameters \\ \midrule[1.5pt]
Feed-forward (Classical) & $-2.868$ & $-5.064$ & $881$ \\
Feed-forward (Pyramid) & $-2.873$ & $-5.048$ & $521$ \\
Feed-forward (Butterfly) & $-2.874$ & $-5.043$ & $257$ \\
\midrule
Recurrent (Classical) & $-2.933$ & $-5.075$ & $881$ \\
Recurrent (Pyramid) & $-2.939$ & $-5.102$ & $521$ \\
Recurrent (Butterfly) & $-2.931$ & $-4.854$ & $257$ \\
\midrule
LSTM (Classical) & $-2.853$ & $-4.743$ & $569$ \\
LSTM (Pyramid) & $-2.856$ & $-4.755$ & $457$ \\
LSTM (Butterfly) & $-2.879$ & $-4.787$ & $217$ \\
\midrule
Transformer (Classical) & $-2.865$ & $-4.713$ & $1905$ \\
Transformer (Pyramid) & $-2.876$ & $-4.806$ & $1305$ \\
Transformer (Butterfly) & $-2.861$ & $-4.822$ & $865$ \\
\bottomrule[1.5pt]
\end{tabular}
\caption{Comparison of expected utilities without and with transaction costs for models with classical and orthogonal layers using exact simulation over 256 paths and 30 trading days, including the number of trainable parameters.}
\end{table}
\end{document}

q-fin

Quantum Deep Hedging

El Amine Cherrat
Snehal Raj
Iordanis Kerenidis
Abhishek Shekhar
Ben Wood
Jon Dee
Shouvanik Chakrabarti
Richard Chen
Dylan Herman
Shaohan Hu
Pierre Minssen
Ruslan Shaydulin
Yue Sun
Romina Yalovetzky
Marco Pistoia

"Quantum Deep Hedging" by El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, and Marco Pistoia, arXiv:2303.16585v2, 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{Lending club data: performance of the logistic regression model for different feature selection approaches.}
\begin{tabular}{|c|c|c|c|}
	\hline
	& AUROC & Accuracy & Number of features \\
	\hline
	All features & 0.6651 & 0.83 & 8 \\
	\hline
	Best choice of features & {\bf 0.6682} & 0.83 & 6 \\
	\hline
	Selection with RFE & 0.6593 & 0.83 & 1 \\
	\hline
	Selection with LASSO & 0.6681 & 0.83 & 3\\
	\hline
\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{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hardware setup parameters}
\begin{tabular}{ll}
			\toprule
			Hardware parameter 	& Value \\
			\midrule
			SDR model &	 USRP 2954R	\\
			integrated FPGA model & Xilinx Kintex Series 7 \\
			transmit signal power &	 $-10\,\mathrm{dBm}$	\\
			external amplifier gain &	$27\,\mathrm{dB}$	\\
			maximum automatic gain control gain &	$31.5\,\mathrm{dB}$	\\
			antenna type &	 omni-directional dipole	\\
			antenna polarization &	 vertical 	\\
			antenna gain &	 $4\,\mathrm{dBi}$	\\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

eess

Stefan Zelenbaba
Benjamin Rainer
Markus Hofer
David Löschenbrand
Laura Bernadó
Anja Dakić
Thomas Zemen

"Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modelling, and Hardware-in-the-Loop Evaluation" by Stefan Zelenbaba, Benjamin Rainer, Markus Hofer, David Löschenbrand, Laura Bernadó, Anja Dakić, and Thomas Zemen, arXiv:2103.06790v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|cc|}
 \hline
 \textbf{Dataset} & \textbf{Commission Fee (\%)} & \textbf{Max Holding Coin Number} \\ \hline
 BTC/TUSD & 0 & 0.01 \\
 BTC/USDT & 0.015 & 0.01 \\
 ETH/USDT & 0.015 & 0.1 \\
 GALA/USDT & 0.015 & 4000 \\
 \hline
 \end{tabular}
\caption{Trading details of the dataset. Here the max holding coin number indicates the maximum position we can hold, which is determined by the unit price of the coin.}
\end{table}
\end{document}

q-fin

Molei Qin
Shuo Sun
Wentao Zhang
Haochong Xia
Xinrun Wang
Bo An

"EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading" by Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang, and Bo An, arXiv:2309.12891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Wenhao Li
Shikun Wang
Zhe Yin
Brad C. Astor
Wei Yang
Tom H. Greene
Liang Li

"Backward Joint Model for the Joint Dynamic Prediction of Time-to-Event and Longitudinal Data: Basic Formulation and New Developments" by Wenhao Li, Shikun Wang, Zhe Yin, Brad C. Astor, Wei Yang, Tom H. Greene, and Liang Li, arXiv:2311.00878v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
\toprule
\multicolumn{2}{c}{Technique} \\
\cmidrule(r){1-2}
Models with similar architecture & WER \\
\midrule
AmNet & 8.60 \\
HMM-SAT-GMM & 7.19 \\
Snips & 6.40 \\
\midrule
Models with different architecture & WER \\
\midrule
Deepspeech2 & 5.83 \\
CTC + policy learning & 5.42 \\
Li-GRU & 6.20 \\
\hline
Ours & 6.65 \\
\bottomrule
\end{tabular}
\caption{A comparative study between our framework and the current SoTA for different ASR Models.}
\end{table}
\end{document}

eess

Ankitha Sudarshan
Vinay Samuel
Parth Patwa
Ibtihel Amara
Aman Chadha

"Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring" by Ankitha Sudarshan, Vinay Samuel, Parth Patwa, Ibtihel Amara, and Aman Chadha, arXiv:2310.09680v4, licensed under CC0 1.0. 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{Corruption robustness of TAD models on THUMOS14-C. $^*$ denotes end-to-end methods. Existing TAD models are particularly vulnerable to temporal corruptions, regardless of whether they are based on transformers or CNN.}
\begin{tabular}{l|l|c|c|c}
\hline
\multirow{2}{*}{Model} & \multirow{2}{*}{Feature} & \multirow{2}{*}{\shortstack{Clean \\ mAP}} & \multirow{2}{*}{\shortstack{Corrupted \\ mAP}} & \multirow{2}{*}{\shortstack{Relative \\ Robustness}} \\
 & & & & \\
\hline
BasicTAD$^*$~ & SlowOnly & 59.17 & 37.72 (21.45 $\downarrow$) & 63.75 \\
E2E-TAD$^*$~ & SlowFast & 56.41 & 30.55 (25.86 $\downarrow$) & 54.16 \\
TemporalMaxer~ & I3D & 60.72 & 47.82 (12.90 $\downarrow$) & 78.76 \\
ActionFormer~ & I3D & 61.53 & 50.61 (10.92 $\downarrow$) & 82.25\\
ActionFormer~ & VideoMAEv2 & 73.84 & 58.33 (15.51 $\downarrow$) & 78.99 \\
AFSD$^*$ ~ & I3D & 46.05 & 34.47 (11.58 $\downarrow$) & 74.85 \\
TriDet~ & I3D & 61.33 & 51.71 (9.62 $\downarrow$) & 84.31 \\
TriDet~ & VideoMAEv2 & 75.16 & 61.10 (14.06 $\downarrow$) & 81.29 \\
\hline
TriDet~+Ours & VideoMAEv2 & \textbf{75.60} & \textbf{68.28 (7.32$\downarrow$)} & \textbf{90.31} \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Runhao Zeng
Xiaoyong Chen
Jiaming Liang
Huisi Wu
Guangzhong Cao
Yong Guo

"Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions" by Runhao Zeng, Xiaoyong Chen, Jiaming Liang, Huisi Wu, Guangzhong Cao, and Yong Guo, arXiv:2403.20254v1, 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
\caption{ \small The value of $P\bigg{(}\bigcap^J_{j=1} B_{S'_{i'},j} \bigg{)}$, for given set of $S'_i \in \mathbf{S'}$ as given in Equation .}
\begin{tabular}{c|ccccccc}
& $S'_1$ & $S'_2$ & $S'_3$ & $S'_4$ & $S'_5$ & $S'_6$ & $S'_7$
\\
\hline
$P\bigg{(}\bigcap^J_{j=1} B_{S'_{i'},j} \bigg{)}$ & 0.972 & 0.972 & 0.972 & 0.972 & 0.979 & 0.979 & 0.979
\end{tabular}
\end{table}
\end{document}

stat

Peter Greenstreet
Thomas Jaki
Alun Bedding
Pavel Mozgunov

"A multi-arm multi-stage design for trials with all pairwise testing" by Peter Greenstreet, Thomas Jaki, Alun Bedding, and Pavel Mozgunov, arXiv:2502.07013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
 \toprule
 \textbf{Superior} & \textbf{Inferior}\\
 \midrule
 Manager & Deliverer \\
 Manager & Analyzer \\
 Analyzer & Marketer \\
 \bottomrule
 \end{tabular}
\caption{Roles connections}
\end{table}
\end{document}

cs

"The Privacy Policy Permission Model: A Unified View of Privacy Policies" by Maryam Majedi and Ken Barker, arXiv:2403.17414v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Online SG capacity at different wind penetration}
\begin{tabular}{c||c|c|c}
 \toprule
 & \multicolumn{3}{c}{\textbf{Online SG Capacity} $\,[\mathrm{MW}]\,$} \\
 \cline{1-4}
 & $\rho_w=0$ & $\rho_w=0.4$ & $\rho_w=0.8$ \\
 \cline{1-4}
 Without SI & $\;\;\, 440 \;\;\,$ & $\;\;\,340\;\;\,$ & $310$\\
 \cline{1-4}
 With SI & $440$ & $200$ & $100$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Short Circuit Current Constrained UC in High IBG-Penetrated Power Systems" by Zhongda Chu and Fei Teng, arXiv:2101.04918v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|ccc|}
\hline
 \multirow{ 2}{*}{Model} & \multicolumn{3}{|c|}{Training Sample Size} \\
 \cline{2-4}
 & 100 & 200 & 400 \\
 \hline
 Transformer-PT & \cellcolor[HTML]{B0B0B0} 0.9327 & \cellcolor[HTML]{B0B0B0} 0.9615 & \cellcolor[HTML]{B0B0B0} 0.9615 \\
 Transformer & 0.8077 & 0.8173 & 0.8462 \\
 LSTM & 0.8654 & 0.9519 & \cellcolor[HTML]{B0B0B0} 0.9615\\
 CNN & 0.9135 & \cellcolor[HTML]{B0B0B0} 0.9615 & \cellcolor[HTML]{B0B0B0} 0.9615 \\
 MLP & 0.7596 & 0.8654 & 0.9038 \\
 \hline
\end{tabular}
\caption{\textbf{Data Scarcity: }Test accuracies of different models given various training sample sizes. The highest accuracy for each sample size is highlighted. Pretraining increases performance in low-data regimes.}
\end{table}
\end{document}

eess

"FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification" by Anthony Zhou and Amir Barati Farimani, arXiv:2312.02380v3, 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{Sample table title}
\begin{tabular}{lll}
 \toprule
 \multicolumn{2}{c}{Part} \\
 \cmidrule(r){1-2}
 Name & Description & Size ($\mu$m) \\
 \midrule
 Dendrite & Input terminal & $\sim$100 \\
 Axon & Output terminal & $\sim$10 \\
 Soma & Cell body & up to $10^6$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Field Evaluations of A Deep Learning-based Intelligent Spraying Robot with Flow Control for Pear Orchards" by Jaehwi Seol, Jeongeun Kim, and Hyoung Il Son, arXiv:2102.07313v1, 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}{lccccccc}
 \toprule
 & & & \multicolumn{3}{c}{Time (s)} & \multicolumn{2}{c}{Rel. Error }\\
 \cmidrule{4-6}\cmidrule{6-8}
 Matrix & $n$ & $\kappa(P)$ & Direct & Recursive & Dir-Rec & Recursive & Dir-Rec \\
 \midrule
 \texttt{Gaertner/big} & 13209 & 58134.53 & 17 & 5.81 & \textbf{4.15} & 1.57e-08 & 4.09e-09 \\
 \texttt{vanHeukelum/cage10} & 11397 & 15378.12 & \textbf{11.06} & 44.83 & 56.76 & 8.28e-13 & 1.25e-11 \\
 \texttt{vanHeukelum/cage11} & 39082 & 51177.08 & \textbf{315.27} & 1259.11 & 1591.04 & 2.59e-10 & 1.53e-10 \\
 \texttt{HB/gre\_1107} & 1107 & 1483.57 & \textbf{0.04} & 0.10 & 0.11 & 1.97e-09 & 1.09e-09 \\
 \texttt{Gaertner/nopoly} & 10774 & 171656.87 & 9.56 & 9.14 & \textbf{6.71} & 3.25e-07 & 3.09e-07 \\
 \texttt{Gaertner/pesa} & 11738 & 131250.78 & 11.45 & 6.36 & \textbf{3.30} & 2.15e-07 & 2.28e-07 \\
 \texttt{Gleich/usroads-48} & 126146 & 1818057.53 & 8243.57 & 743.37 & \textbf{542.88} & 8.65e-07 & 1.60e-07 \\
 \texttt{Barabasi/NotreDame\_www}\textsuperscript{$\dagger$} & 34643 & 1173610.94 & 172.36 & 94.28 & \textbf{93.20} & 2.85e-04 & 3.58e-05 \\
 \texttt{Pajek/USpowerGrid} & 4941 & 30166.55 & 1.32 & 1.48 & \textbf{0.58} & 3.01e-08 & 2.80e-08 \\
 \texttt{Gleich/minnesota}\textsuperscript{$\dagger$} & 2640 & 18243.53 & 0.30 & 0.34 & \textbf{0.22} & 8.37e-08 & 3.53e-08 \\
 \bottomrule
 \end{tabular}
\caption{Performance of the recursive implementation of the divide-and-conquer algorithm for computing Kemeny's constant on some test matrices. If the matrix name has a $\dagger$ symbol, then the experiment has been run on the largest connected component of the graph, i.e, on the largest irreducible sub-chain.}
\end{table}
\end{document}

math

Dario Andrea Bini
Fabio Durastante
Sooyeong Kim
Beatrice Meini

"On Kemeny's constant and stochastic complement" by Dario Andrea Bini, Fabio Durastante, Sooyeong Kim, and Beatrice Meini, arXiv:2312.13201v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hasbrouck's Information Share}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Market} & & \\ \hline
Futures (CME) & 0.563 & 0.562 \\ \hline
Spot (Binance) & 0.436 & 0.438 \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{FID scores obtained between the generated samples from MVG and GMM noise and real test samples for all models on MNIST and CelebA datasets.}
\begin{tabular}{ccccc}
 \hline
 & \multicolumn{2}{c}{\textbf{MNIST}} & \multicolumn{2}{c}{\textbf{CelebA}} \\
 \cline{2-5}
 & $\mathcal{N}$ & GMM & $\mathcal{N}$ & GMM \\
 \hline
 \hline
 AE & 103.08 & 68.97 & 68.13 & 59.43 \\
 \hline
 VAE & 21.01 & 18.86 & 61.87 & 53.63 \\
 \hline
 IRMAE & 26.58 & 22.31 & 58.98 & 48.56\\
 \hline
 \textbf{LoRAE} & \textbf{19.50} & \textbf{11.09} & \textbf{56.29} & \textbf{45.43}\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Alokendu Mazumder
Tirthajit Baruah
Bhartendu Kumar
Rishab Sharma
Vishwajeet Pattanaik
Punit Rathore

"Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical Insights" by Alokendu Mazumder, Tirthajit Baruah, Bhartendu Kumar, Rishab Sharma, Vishwajeet Pattanaik, and Punit Rathore, arXiv:2310.16194v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets" by Shuo Sun, Molei Qin, Xinrun Wang, and Bo An, arXiv:2302.00586v2, 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}

cs

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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{H\'{e}non-Heiles system drift estimation summary}
\begin{tabular}{|c|c|} \hline
 Relative $L^2(\rho)$ Error& 0.106\\ \hline
 Relative Trajectory Error& $0.076$ $\pm$ $0.053$\\ \hline
 Wasserstein Distance at $t = 0.25$& 0.0529\\ \hline
 Wasserstein Distance at $t = 0.5$&0.0694\\\hline
 Wasserstein Distance at $t = 1$&0.0904\\\hline
 \end{tabular}
\end{table}
\end{document}

math

"Learning Stochastic Dynamical Systems with Structured Noise" by Ziheng Guo, James Greene, and Ming Zhong, arXiv:2503.01077v1, 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{ETFs}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Country} & \textbf{Ticker} & \textbf{ETF} \\ \hline \hline
India & PIN & Invesco India ETF \\ \hline
China & FXI & iShares MSCI China Large-Cap ETF \\ \hline
South Korea & EWI & iShares MSCI Italy ETF \\ \hline
Mexico & EWW & iShares MSCI Mexico ETF \\ \hline
South Africa & EZA & iShares MSCI South Africa ETF \\ \hline
Taiwan & EWT & iShares MSCI Taiwan ETF \\ \hline
Japan & EWJ & iShares MSCI Japan ETF \\ \hline
USA & IVV & iShares Core S\&P 500 ETF \\ \hline
UK & EWU & iShares MSCI United Kingdom ETF \\ \hline
EU & EZU & iShares MSCI Eurozone ETF \\ \hline
Australia & EWA & iShares MSCI Australia ETF \\ \hline
Singapore & EWS & iShares MSCI Singapore ETF \\ \hline
Canada & EWC & iShares MSCI Canada ETF \\ \hline
Israel & EIS & iShares MSCI Israel ETF \\ \hline
Brazil & EWZ & iShares MSCI Brazil ETF \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Using Internal Bar Strength as a Key Indicator for Trading Country ETFs" by Aditya Pandey and Kunal Joshi, arXiv:2306.12434v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Christian Chan
Xiaotian Dai
Thierry Chekouo
Quan Long
Xuewen Lu

"Broken Adaptive Ridge Method for Variable Selection in Generalized Partly Linear Models with Application to the Coronary Artery Disease Data" by Christian Chan, Xiaotian Dai, Thierry Chekouo, Quan Long, and Xuewen Lu, arXiv:2311.00210v1, 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{Tracking error (in terms of RMSE values) of three different RL algorithms for exothermic batch process}
\begin{tabular}{llll}
\hline
\multirow{2}{*}{Reward} & \multicolumn{3}{c}{Continuous Action}\\
\cline{2-4}
& TATD3 &
TD3&
DDPG\\
\hline
PI & 0.7289 & 0.7400 & 0.7782 \\
PID & 0.7022 & 0.7324 & 0.7732 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Tanuja Joshi
Shikhar Makker
Hariprasad Kodamana
Harikumar Kandath

"Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control" by Tanuja Joshi, Shikhar Makker, Hariprasad Kodamana, and Harikumar Kandath, arXiv:2102.13012v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Exchange Rate Pass-Through and Data Frequency: Firm-Level Evidence from Bangladesh" by Md Deluair Hossen, arXiv:2303.04101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Data Description and Coverage}
\begin{tabular}{lccc}
\toprule
Variable & Data Source & Country Coverage & Data Period \\
\midrule
\multicolumn{4}{l}{\textit{Enhanced Solow Fundamentals}} \\
GDP per capita (Constant US Dollar) & WDI & 187 countries & 1960--2019 \\
Real GDP per capita & PWT & 172 countries & 1960--2019 \\
Trade as Share of GDP & WDI & 178 countries & 1960--2019 \\
Investment as Share of GDP & PWT & 173 countries & 1960--2019 \\
Capital Stock & PWT & 171 countries & 1960--2019 \\
Population & PWT & 173 countries & 1960--2019 \\
0--14 Population Share & WDI & 195 countries & 1960--2019 \\
15--65 Population Share & WDI & 195 countries & 1960--2019 \\
\\
\multicolumn{4}{l}{\textit{Political Institutions and Governance}} \\
Democracy (Acemoglu et al. 2019) & Acemoglu et al. (2019) & 184 countries & 1960--2010 \\
Democracy (Acemoglu et al. 2025) & Acemoglu et al. (2025) & 193 countries & 1960--2019 \\
Unrest & Acemoglu et al. (2019) & 180 countries & 1960--2010 \\
Soviet Union & Acemoglu et al. (2019) & 184 countries & 1960--2019 \\
Region & Acemoglu et al. (2025) & 196 countries & 1960--2019 \\
\\
\multicolumn{4}{l}{\textit{Market Institutions and Reforms}} \\
Market Reform Index & Acemoglu et al. (2019) & 153 countries & 1960--2005 \\
Tax-to-GDP & Acemoglu et al. (2019) & 136 countries & 1960--2005 \\
\\
\multicolumn{4}{l}{\textit{Human Capital and Labor Markets}} \\
Human Capital Index & PWT & 143 countries & 1960--2019 \\
Employment & PWT & 173 countries & 1960--2019 \\
Labor Share & PWT & 133 countries & 1960--2019 \\
Primary Enrollment & Acemoglu et al. (2019) & 176 countries & 1970--2010 \\
Secondary Enrollment & Acemoglu et al. (2019) & 176 countries & 1970--2010 \\
\\
\multicolumn{4}{l}{\textit{Productivity and Returns}} \\
TFP & PWT & 109 countries & 1960--2010 \\
Internal Rate of Return & PWT & 132 countries & 1960--2019 \\
Real Consumption & PWT & 173 countries & 1960--2019 \\
Real Domestic Absorption & PWT & 173 countries & 1960--2019 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"The Geopolitical Determinants of Economic Growth, 1960-2019" by Tianyu Fan, arXiv:2507.04833v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
& SSP & lkp-SSP & SSP-DTW & lkp-SSP-DTW \\
\hline
es\_ES & 87.6 & - & \textbf{94.0} & - \\
fr\_FR & 82.3 & 85.9 & \textbf{90.1} & 89.1 \\
en\_GB & 88.5 & 94.4 & 92.6 & \textbf{95.5} \\
en\_US & 88.5 & 93.7 & 92.3 & \textbf{94.2} \\
CMU & 89.5 & 93.6 & 93.4 & \textbf{94.7} \\
\hline
\end{tabular}
\caption{Word accuracies for different language/variations and methods}
\end{table}
\end{document}

eess

"MUST&P-SRL: Multi-lingual and Unified Syllabification in Text and Phonetic Domains for Speech Representation Learning" by Noé Tits, arXiv:2310.11541v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
 Name & $m$ & $n$ & Sparsity ($\%$)\\\hline\hline
 w1a & $2477$ & $300$ & $96.18\%$\\\hline
 breast-cancer & $683$ & $10$ & $0\%$\\ \hline
 url & $2396130$ & $3231961$ & $99.99\%$\\ \hline
 epsilon & $400000$ & $2000$ & $0\%$
 \end{tabular}
\caption{A summary of the datasets used in the convergence and performance experiments presented in this paper. All datasets are binary classification tasks obtained from the LIBSVM repository . We chose small-scale datasets for MATLAB (convergence) experiments and large-scale datasets for parallel experiments.}
\end{table}
\end{document}

stat

"Communication-Efficient, 2D Parallel Stochastic Gradient Descent for Distributed-Memory Optimization" by Aditya Devarakonda and Ramakrishnan Kannan, arXiv:2501.07526v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|r|r|}
\hline
\textbf{Statistic} & \textbf{Value BTC Futures CME} & \textbf{Value Binance} \\ \hline
Price Change During Event (\%) & -1.00 & -1.07 \\ \hline
Event Maximum Price(USD) & 56935
 & 56668 \\ \hline
Event Minimum Price (USD) & 	56325 & 56101 \\ \hline
\end{tabular}
\caption{Descriptive statistics for the event on Sept 6th 2024: Micro BTC U24 futures contract on CME and spot BTC on Binance}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{BEA and BLS classification codes. }
\begin{tabular}{ll}
\toprule
 BEA industry category & BLS industry category \\
 \hline
Utilities & Utilities\\
Construction & Construction\\
Manufacturing & \\
& Wood products\\
& Nonmetallic mineral products\\
& Primary metals\\
& Fabricated metal products\\
& Machinery\\
& Computer and electronic products\\
& Electrical equipment, appliances, and components\\
& Motor vehicles, bodies and trailers, and parts\\
& Other transportation equipment\\
& Furniture and related products\\
& Miscellaneous manufacturing\\
& Food and beverage and tobacco products\\
& Textile mills and textile product mills\\
& Apparel and leather and allied products\\
& Paper products\\
& Printing and related support activities\\
& Petroleum and coal products\\
& Chemical products\\
& Plastics and rubber products\\
Whole sale trade & Whole sale trade\\
Retail trade & Retail trade \\
Transporting and warehousing & \\
& Air transportation\\
& Rail transportation\\
& Water transportation\\
& Truck transportation\\
&Transit and ground passenger transportation\\
& Pipeline transportation\\
& Other transportation and support activities\\
& Warehousing and storage\\
 Information & \\
 & Publishing industries, except internet (includes software)\\
 & Motion picture and sound recording industries\\
& Broadcasting and telecommunications\\
& Data processing, internet publishing, and other information services\\
Administrative and waste management services & \\
& Administrative and support services\\
& Waste management and remediation services\\
Arts, entertainment, and recreation & \\
& Performing arts, spectator sports, museums, and related activities\\
& Amusements, gambling, and recreation industries\\
Accommodation and food services & \\
& Accommodation\\
& Food services and drinking places\\
Other services, except government & Other services, except government\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

There is power in general equilibrium

"There is power in general equilibrium" by Juan Jacobo, arXiv:2309.00909v1, 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
\textbf{Dependent Variable} & \textbf{Transaction} & \textbf{Default} \\
\textbf{Period} & \textbf{Weekly} & \textbf{Weekly} \\
\textbf{Window} & \textbf{[-5 ; 12]} & \textbf{[-5 ; 12]} \\
\hline
\textbf{FEMA$\times$Post} & -12.597$^{*}$ & -3.316$^{***}$ \\
 & [7.130] & [1.112] \\
Event-specific ZIP Code FE & Y & Y \\
Event-specific Time FE & Y & Y \\
Observations & 7366 & 7366 \\
\hline
\end{tabular}
\caption{Results for transaction volume and default rate outcomes at the ZIP code level for the payday loan data. The analysis evaluates the impact of hurricane event across treated groups and interacted with receiving FEMA rental assistance over the weekly window: from 5 weeks before to 12 weeks after the event ([-5 ; 12]). The coefficient of -12.597$^{*}$ for transaction volume in the suggests a significant decrease in transaction volume in the treated ZIP codes that received FEMA assistance, corresponding to approximately 5.7\% lower transaction volume relative to pre-event levels. The coefficient for default rate in the same window is -3.316$^{***}$, indicating a significant reduction in the default rate, corresponding to approximately 31.8\% lower default rates in treated ZIP codes that received FEMA assistance.}
\end{table}
\end{document}

q-fin

"Keeping in Place After the Storm-Emergency Assistance and Evictions" by Bilal Islah and Ahmed Zoulati, arXiv:2505.14548v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zero-Leverage Puzzle

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


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison with ensembles of single-model methods. Ensembling can only reduce the performance gap between single-model baselines and RF-DLC.}
\begin{tabular}{l|cccc|c|c|cccc}
\toprule
\multirow{2}{*}{\textbf{Method}} & \multicolumn{4}{c|}{\textbf{ACC (\%) $\uparrow$}} & \multirow{2}{*}{\textbf{AUC (\%) $\uparrow$}} & \multirow{2}{*}{\textbf{ECE (\%) $\downarrow$}} & \multicolumn{4}{c}{\textbf{FHR (\%) $\downarrow$}} \\ \cline{2-5} \cline{8-11}
 & All & Head & Med & Tail & & & avg & \@25\% & \@50\% & \@75\% \\ \midrule
3$\times$CB Loss~ & 47.37 & 67.72 & 49.16 & 25.28 & 80.67 & 17.39 & 45.56 & 21.88 & 45.41 & 69.38 \\
3$\times$LDAM~ & 48.84 & 68.59 & 47.87 & 28.47 & 80.28 & 19.27 & 39.96 & 19.22 & 40.04 & 60.62 \\
RF-DLC (3 particles) & \textbf{50.24} & \textbf{69.92} & \textbf{51.07} & \textbf{30.34} & \textbf{81.24} & \textbf{10.35} & \textbf{32.08} & \textbf{15.39} & \textbf{31.34} & \textbf{49.51} \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

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


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Contingency table for clustering models $M_1$ and $M_2$.}
\begin{tabular}{cccccc}
\hline
\hline
\multirow{1}{*}{}&&\multicolumn{4}{c}{$M_{2}$}\\
\cline{3-6}
 & &$C_{21}$&$C_{22}$&$\cdots$&$C_{2c}$\\
\hline
 \multirow{4}{*}{$M_{1}$} & $C_{11}$ &$n_{11}$ & $n_{12}$ &$\cdots$ & $n_{1c}$\\
\cline{2-6}
 				 & $C_{12}$ & $n_{21}$ & $n_{22}$& $\cdots$& $n_{2c}$\\ \cline{2-6}
 				 & $\vdots $ & $\vdots$ & $\vdots$ & $\ddots$& $\vdots$\\
\cline{2-6}
 				 & $C_{1r}$ & $n_{r1}$ & $n_{r2}$ & $\cdots$& $n_{rc}$\\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Soccer Goalkeeper Performance Evaluation: Clustering Approach" by Mahdi Teimouri, arXiv:2502.05548v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Introducing the $σ$-Cell: Unifying GARCH, Stochastic Fluctuations and Evolving Mechanisms in RNN-based Volatility Forecasting" by German Rodikov and Nino Antulov-Fantulin, arXiv:2309.01565v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The average estimations and type I error for the A/A test with $\alpha_C=\alpha_T=10$ and $p=1/2$}
\begin{tabular}{lcccccc}
		\toprule & \multicolumn{2}{c}{Proportion of short videos} &
		\multicolumn{2}{c}{Stay durations} & \multicolumn{2}{c}{Finishing rates} \\
		\midrule & Estimation & Type I error & Estimation & Type I error & Estimation
		& Type I error \\
		Weighted & -0.0003 & 0.45 & 0.0008 & 0.09 & -0.0001 & 0.11 \\
		Data splitting & -0.0017 & 0.94 & 0.0039 & 0.65 & -0.0003 & 0.60 \\
		Data pooling & -0.0001 & 0.04 & 0.0011 & 0.07 & -0.0001 & 0.07 \\
		Snapshot & 0.0001 & 0.06 & -0.0015 & 0.06 & 0.0000 & 0.06 \\
		\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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
 \toprule
 {} & \multicolumn{4}{c}{Methods}\\
 \cmidrule{2-5}
 Molecule (qubits) & CS (Pauli)~ & LBCS~ & BRG~ & CS (FGU)\\
 \midrule
 $ \text{H}_2 $ (8) & 51.4 & 17.5 & 22.6 & 69.6\\
 $ \text{LiH} $ (12) & 266 & 14.8 & 7.0 & 155\\
 $ \text{BeH}_2 $ (14) & 1670 & 67.6 & 68.3 & 586\\
 $ \text{H}_2\text{O} $ (14) & 2840 & 257 & 6559 & 8440\\
 $ \text{NH}_3 $ (16) & 14400 & 353 & 3288 & 5846\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

"Learning, Optimizing, and Simulating Fermions with Quantum Computers" by Andrew Zhao, arXiv:2312.10399v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Off-Balance Sheet Activities and Scope Economies in U.S. Banking" by Jingfang Zhang and Emir Malikov, arXiv:2302.14603v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Edson Pindza
Jules Clement Mba
Sutene Mwambi
Nneka Umeorah

"Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model" by Edson Pindza, Jules Clement Mba, Sutene Mwambi, and Nneka Umeorah, arXiv:2310.09622v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccc}
\toprule
\textbf{Name} & \textbf{Produced} & \textbf{Matching} & \textbf{Total Wait} & \textbf{Road} \\
 & \textbf{Tons} & \textbf{Factor} & \textbf{Time} & \textbf{Jams} \\
\midrule
FixedGroupDispatcher & 14909.56 & 1.27 & 308.94 & 605 \\
NaiveDispatcher & 2266.54 & 0.51 & 9547.48 & 120 \\
NearestDispatcher & 6582.21 & 0.48 & 4061.66 & 297 \\
RandomDispatcher & 10627.08 & 0.82 & 484.75 & 407 \\
SQDispatcher & 13232.29 & 1.03 & 314.80 & 499 \\
SPTFDispatcher & 13096.42 & 0.95 & 225.25 & 492 \\
\bottomrule
\end{tabular}
\caption{Dispatcher performance result.}
\end{table}
\end{document}

cs

Shi Meng
Bin Tian
Xiaotong Zhang
Shuangying Qi
Caiji Zhang
Qiang Zhang

"OpenMines: A Light and Comprehensive Mining Simulation Environment for Truck Dispatching" by Shi Meng, Bin Tian, Xiaotong Zhang, Shuangying Qi, Caiji Zhang, and Qiang Zhang, arXiv:2404.00622v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c||c|c|c|c|}
\hline
\multirow{3}{*}{Method} & \multirow{3}{*}{\# Params} & \multirow{3}{*}{Corpus} & \multicolumn{3}{c|}{Semantics} & Content \\
\cline{4-7}
 & & & \multicolumn{2}{c|}{IC} & SF & KS \\
\cline{4-7}
 & & & Acc $\uparrow$ & F1 $\uparrow$ & CER $\downarrow$ & Acc $\uparrow$ \\
\hline
WavLM Base & 94.70M & LS 960 hr & 98.63 & 89.38 & 22.86 & 96.79 \\
\quad - w/o denoising task & 94.70M & LS 960 hr & 98.42 & 88.69 & 23.43 & 96.79 \\
\quad - w/o structure modification & 94.68M & LS 960 hr & 98.31 & 88.56 & 24.00 & 96.79 \\
WavLM Base+ & 94.70M & Mix 94k hr & 99.00 & 90.58 & 21.20 & 97.37 \\
WavLM Large & 316.62M & Mix 94k hr & 99.31 & 92.21 & 18.36 & 97.86 \\
Our model & 94.40M & LS 960 hr & 0.0846 & 0.4325 & 0.4370 & 0.2541 \\
\hline
\end{tabular}
\caption{SUPERB Results for WavLM models and the experimental combination model explored in this paper, for comparison.}
\end{table}
\end{document}

eess

"An Experimental Study: Assessing the Combined Framework of WavLM and BEST-RQ for Text-to-Speech Synthesis" by Via Nielson and Steven Hillis, arXiv:2312.05415v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Broken Adaptive Ridge Method for Variable Selection in Generalized Partly Linear Models with Application to the Coronary Artery Disease Data" by Christian Chan, Xiaotian Dai, Thierry Chekouo, Quan Long, and Xuewen Lu, arXiv:2311.00210v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Transit-Length Distribution for Particle Transport in Binary Markovian Mixed Media" by Brian C. Kiedrowski and Emily H. Vu, arXiv:2412.19359v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

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

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


q-fin

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccccc|c}
 & A & B & C & D & E & Wins \\
\hline
 A & 0 & 1 & 1 & 1 & 0 & 3 \\
 B & 0 & 0 & 1 & 1 & 1 & 3 \\
 C & 0 & 0 & 0 & 1 & 1 & 2 \\
 D & 0 & 0 & 0 & 0 & 1 & 1 \\
 E & 1 & 0 & 0 & 0 & 0 & 1 \\
\end{tabular}
\caption{Five-team round-robin tournament}
\end{table}
\end{document}

math

"The many routes to the ubiquitous Bradley-Terry model" by Ian Hamilton, Nick Tawn, and David Firth, arXiv:2312.13619v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Minimax Optimal Design with Spillover and Carryover Effects" by Haoyang Yu, Wei Ma, and Hanzhong Liu, arXiv:2501.14602v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c} % <-- Alignments: 1st column left, 2nd middle and 3rd right, with vertical lines in between
			\textbf{Approach} & \textbf{Swing value (EUR)} & \textbf{Computational Time (s)} \\
			\hline
			Deterministic & 19,826,114 & 25,920.02 \\
			SDP & 7,457,729 & 1,020.77 \\
			SDP (out sample) & 7,462,901 & 23.04
		\end{tabular}
\caption{Gas storage evaluation.}
\end{table}
\end{document}

q-fin

"A Heath-Jarrow-Morton framework for energy markets: a pragmatic approach" by Matteo Gardini and Edoardo Santilli, arXiv:2305.01485v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results from transfer learning (unweighted source data without applying the domain adaptation)}
\begin{tabular}{|l|c|c|c|c|c|}
 \hline
 Model & Data & accuracy & Specificity & Sensitivity \\
 \hline
 \multirow{2}{4em}{SVM} & Source & 0.65 & 0.47 & 0.81 \\
 & Target & 0.43 & 0.22 & 0.86 \\
 \hline
 \multirow{2}{4em}{LR}& Source & 0.62 & 0.52 & 0.70 \\
 & Target & 0.46 & 0.48 & 0.41 \\
 \hline
 \multirow{2}{4em}{RF} & Source & 0.96 & 0.97 & 0.96 \\
 & Target & 0.44 & 0.46 & 0.42 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Sean-Kelly Palicki
Shereen Fouad
Mariam Adedoyin-Olowe
Zahraa S. Abdallah

"Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets" by Sean-Kelly Palicki, Shereen Fouad, Mariam Adedoyin-Olowe, and Zahraa S. Abdallah, arXiv:2102.00912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison with existing approaches.}
\begin{tabular}{lrrrcrc}
\toprule
\textbf{Method} & \textbf{TPR (\%)} & \textbf{FPR (\%)} & \textbf{Files lost} & \textbf{Screen-locker?} &\textbf{Samples/families} &\textbf{Real-time} \\ \midrule
Redemption~ & 100 & 0.8 & 5 & $\times$ & 677/29& $\times$ \\
CryptoLock~ & 100 & 0.03 & 10 & $\times$ & 492/14 & $\times$\\
UNVEIL~ & 96.3 & 0 & - & $\checkmark$ & 2121/ -& $\times$ \\
REDFISH~& 100 & - & 10 & $\times$ & 54/19& $\times$ \\
RWGuard~ & - & 0.1 & partial recovery & $\times$ & - /14& $\checkmark$ \\
Elderan~ & 93.3 & 1.6 & - & $\times$ & 582/11& $\times$ \\
CM\&CB~& 98 & Vary & - & $\times$ & 8/ -& $\times$ \\
RansHunt~& 97 & 3 & - & $\times$ & 360/20& $\times$ \\
ShieldFS~ & 100 & 0.038 & - & $\times$ & 383/11& $\times$ \\
\textbf{Peeler} & \textbf{99.63} & \textbf{0.58} & \textbf{1} & $\checkmark$ & \textbf{206/43}& $\checkmark$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Muhammad Ejaz Ahmed
Hyoungshick Kim
Seyit Camtepe
Surya Nepal

"Peeler: Profiling Kernel-Level Events to Detect Ransomware" by Muhammad Ejaz Ahmed, Hyoungshick Kim, Seyit Camtepe, and Surya Nepal, arXiv:2101.12434v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccccccccccccccc}
 \toprule
 {Method} &
 \multicolumn{2}{c}{ImageNet-A} & \multicolumn{2}{c}{VTAB} \\
 & ${\bar{\mathcal{A}}}$ & {$\mathcal{A}_N $} & ${\bar{\mathcal{A}}}$ & {$\mathcal{A}_N $}
\\
\midrule
Adapter & \textbf{64.53} & \textbf{53.32} & 91.26 & \textbf{89.64} \\
 \midrule
 LoRA & 63.50 & 52.67 & \textbf{91.85} & 88.53 \\
Convpass & 63.48 & 51.74 & 90.68 & 88.62 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Huiyi Wang
Haodong Lu
Lina Yao
Dong Gong

"Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning" by Huiyi Wang, Haodong Lu, Lina Yao, and Dong Gong, arXiv:2403.18886v3, 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{Iteration number of our trained solver over different coefficient distributions. \footnotesize Re$10$ and Re$10^5$ refer to $Re=10,10^5$ with coefficients generated from white noise. The solver is trained on coefficients from a mixture of white noise and CIFAR10.}
\begin{tabular}{|c|l|l|l|l|l|l|l|}
 \hline
 grid & noise & CIFAR10 & FMNIST & MNIST & mldata & Re$10$ & Re$10^5$ \\ \hline
 31x31 & 7.1 & 7.6 & 9.8 & 10.0 & 7.3 & 7.6 & 7 \\
 63x63 & 7.1 & 7.9 & 9.7 & 10 & 7.5 & 7.1 & 7 \\
 127x127 & 7.2 & 8 & 9.7 & 9.6 & 8 & 7.6 & 7.6 \\
 255x255 & 8 & 8 & 9.5 & 9 & 8 & 8 & 8 \\
 511x511 & 10 & 9.6 & 9.6 & 9 & 9.8 & 8 & 10 \\
 1023x1023 & 12 & 12.4 & 11.7 & 10.1 & 12.6 & 10 & 13 \\
 2047x2047 & 16 & 15.9 & 13.6 & 12.7 & 16.6 & 13 & 17 \\
 4095x4095 & 22 & 22.3 & 19.9 & 17.1 & 22.4 & 17 & 23 \\ \hline
 \end{tabular}
\end{table}
\end{document}

math

"MGCNN: a learnable multigrid solver for sparse linear systems from PDEs on structured grids" by Yan Xie, Minrui Lv, and Chensong Zhang, arXiv:2312.11093v2, 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{Interventional density estimation: average rank across all synthetic datasets. Boldface indicates the best result. See App.~ for full results and significance tests.}
\begin{tabular}{cccccc}
\toprule
$n$ & PB & Ens. & NTKGP & BS & IPB \\ \midrule
$100$	& $3.6$	& $1.9$	& $5.0$	& $3.1$	& $\mathbf{1.0}$	\\
$1000$	& $4.0$	& $1.9$	& $5.0$	& $2.4$	& $\mathbf{1.2}$
\\ \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"On Uncertainty Quantification for Near-Bayes Optimal Algorithms" by Ziyu Wang and Chris Holmes, arXiv:2403.19381v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Inverse single facility location problem in the plane with variable coordinates" by Nazanin Tour-Savadkoohi and Jafar Fathali, arXiv:2503.07016v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Shiyu Qin
Bin Chen
Yujun Huang
Baoyi An
Tao Dai
Shu-Tao Xia

"Perceptual Image Compression with Cooperative Cross-Modal Side Information" by Shiyu Qin, Bin Chen, Yujun Huang, Baoyi An, Tao Dai, and Shu-Tao Xia, arXiv:2311.13847v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Frequency & Method & $N$ & $\sigma^2$ & Epochs & Test error & Training Time (Seconds) \\\hline
\multirow{2}{*}{$a=1$} & RF & 200 & $10^2$ & 1000 & $7.80 \times 10^{-6}$ & 14.27 \\\cline{2-7}
 & PINN & - & - & 1000 & $2.45 \times 10^{-1}$ & 8.64 \\\hline
\multirow{2}{*}{$a=10$} & RF & 200 & $100^2$ & 2000 & $1.12 \times 10^{-4}$ & 25.37 \\\cline{2-7}
 & PINN & - & - & 2000 & $1.04 \times 10^{+1}$ & 18.48 \\\hline
 \multirow{2}{*}{$a=20$} & RF & 400 & $1000^2$ & 1500 & $2.87\times 10^{-1}$ & 86.54 \\\cline{2-7}
 & PINN & - & - & 2000 & $6.23 \times 10^{+1}$ & 26.67 \\\hline
\end{tabular}
\caption{Comparison between random feature model and PINN for the Allen-Cahn equations. We report number of epochs, test error, and training time for each model. For the random feature model, we also report the number of random features and variance of Gaussian random features.}
\end{table}
\end{document}

math

"Solving Partial Differential Equations with Random Feature Models" by Chunyang Liao, arXiv:2501.00288v1, 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
$(1,0,0)$ & & $n$ &
$(0,1,1)$ & & $n-1$ \\
\hline
$(0,0,0)$ & & $2^{\lfloor n/2 \rfloor}$
& $(1,1,1)$ & & $2^{\lfloor (n-1)/2 \rfloor}$ \\
\hline
$(1,0,1)$ & &
$ \begin{cases} n & \text{$n$ even} \\
n-1 & \text{$n$ odd}
\end{cases} $
 & $(0,1,0)$ & & $ \begin{cases}
 n-1 & \text{$n$ even} \\
 n & \text{$n$ odd}
 \end{cases} $ \\
\hline
$(1,1,0)$ & &$ \begin{cases}
 \lfloor n/2 \rfloor + 1 & \text{$n \equiv 2,3 $} \\
 \lfloor n/2 \rfloor & \text{$n \equiv 0,1$}
 \end{cases} $
& $(0,0,1)$ & & $ \begin{cases}
 \lfloor n/2 \rfloor + 1 & \text{
 $n \equiv 3 $} \\
 \lfloor n/2 \rfloor &
 \text{otherwise}
 \end{cases}$ \\
\hline
\end{tabular}
\caption{Our $3$-wise results for $n\geq 7$. For ease of display, the cases in the last row are taken modulo $4$.}
\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.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|l}
Parameter & Value \\ \hline \hline
 $a_y$ & $5.4 ~\mathrm{\mu m} $\\
 $a_x$ & $4 ~\mathrm{\mu m}$\\
 $d_1$ & $17 ~\mathrm{\mu m}$\\
 $d_2$ & $23 ~\mathrm{\mu m}$\\
 $\rho$ & $17 ~\mathrm{\mu m}$\\
 $d_\epsilon$ & $19 ~\mathrm{\mu m}$\\ \hline
 $n_0$ & $1.47$ \\
 $n_A$ & $1.2 \times 10^{-3}$ \\
 $n_B$ & $1.1 \times 10^{-3}$ \\
 $\lambda$ & $1030~\mathrm{nm}$\\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

Ekaterina O. Smolina
Lev A. Smirnov
Daniel Leykam
Franco Nori
Daria A. Smirnova

"Identifying topology of leaky photonic lattices with machine learning" by Ekaterina O. Smolina, Lev A. Smirnov, Daniel Leykam, Franco Nori, and Daria A. Smirnova, arXiv:2308.14407v1, 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{NYISO: Computation time (minutes) for a varying number of uncertainty scenarios and operation hours.}
\begin{tabular}{lcccccccc}
\toprule
\# Scenarios \# Hours & 5S2H & 10S2H & 20S2H & 20S4H & 20S6H\\
\midrule
\textit{MyD} & 0.35 & 0.35 & 0.35 & 0.53 &1.0 \\
\textit{BiD} & 2.0 & 3.1 & 7.4 & 19.4 &48.9 \\
\textit{StD} & 0.76 & 1.2 & 3.0 & 7.3 &16.3\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

Dongwei Zhao
Vladimir Dvorkin
Stefanos Delikaraoglou
Alberto J. Lamadrid L.
Audun Botterud

"Uncertainty-Informed Renewable Energy Scheduling: A Scalable Bilevel Framework" by Dongwei Zhao, Vladimir Dvorkin, Stefanos Delikaraoglou, Alberto J. Lamadrid L., and Audun Botterud, arXiv:2312.03868v1, 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|r|}
 \hline
 Land type/subsidy & Margin/subsidy \\
 \hline
 Oilseed rape & £1,340 \\
 Potatoes & £1,393 \\
 Spring barley & £773 \\
 Spring field beans & £835 \\
 Spring oats & £422 \\
 Winter barley & £1,079 \\
 Winter wheat & £1,315 \\
 Grass (without livestock) & £0 \\
 Maize & £$\infty$ \\
 Other crops & £$\infty$ \\
 \hline
 Woodland plantation subsidy & £1,104 \\
 \hline
 \end{tabular}
\caption{Margins and subsidy for different land types, in GBP per hectare per annum. Values are central estimates from the Farm Management Handbook (FMH) . Unimproved grassland without livestock has unknown value and we consider this to have value low enough that it will change use. FMH does not list margins for maize or other crop types and so we consider them to have value high enough they will not change use.}
\end{table}
\end{document}

q-fin

Christopher J. Banks
Katherine Simpson
Nicholas Hanley
Rowland R. Kao

"Assessing the potential impact of environmental land management schemes on emergent infection disease risks" by Christopher J. Banks, Katherine Simpson, Nicholas Hanley, and Rowland R. Kao, arXiv:2311.07735v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Machine Proofs for Adams Differentials and Extension Problems among CW Spectra" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10876v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Property (speed, accuracy and flexibility) of different algorithms for SCI reconstruction. H: High, M: Middle, L: Low.}
\begin{tabular}{|c||c|c|c|}
			\hline
			Algorithm& Speed & Accuracy & Flexibility\\
			\hline \hline
			Conventional Regularization Optimization & L & H/M/L & H\\
			\hline
			Shallow-learning & M & M/L & H/M \\
			\hline 	\hline
		 E2E-CNN & H& H/M& L \\
			\hline
			Deep Unfolding & H & H & M \\
			\hline
			Plug-and-Play & H/M& H & H \\
			\hline
	\end{tabular}
\end{table}
\end{document}

eess

Xin Yuan
David J. Brady
Aggelos K. Katsaggelos

"Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications" by Xin Yuan, David J. Brady, and Aggelos K. Katsaggelos, arXiv:2103.04421v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
 \hline
 encoding & 734401 \\ \hline
 round id & 1 \\ \hline
 number of Pairs & 3 \\ \hline
 number of Pongs or Kongs & 2 \\ \hline
 number of Character tiles & 3 \\ \hline
 number of wind tiles & 11 \\ \hline
 number of legal actions & 3 \\ \hline
 regret sum & [-0.598, 2.128, -1.356] \\ \hline
 strategy sum & [1.359, 1.975, 0.667] \\ \hline
 \end{tabular}
\caption{An Example of Nodes}
\end{table}
\end{document}

q-fin

"CFR-p: Counterfactual Regret Minimization with Hierarchical Policy Abstraction, and its Application to Two-player Mahjong" by Shiheng Wang, arXiv:2307.12087v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative results obtained for the two proposed methods (DE-UNet and DE-Shape-UNet) compared with the two baselines reported by the challenge organizers in . We report the mean Dice and HD values, and the standard deviation in parentheses.}
\begin{tabular}{ccccccc}
\multirow{2}{*}{Method} & \multicolumn{2}{c}{$\mathcal{D}_\mathrm{test}$ (100)} & \multicolumn{2}{c}{$\mathcal{D}_\mathrm{test-extra}$ (10)} & \multicolumn{2}{c}{Overall} \\ \cline{2-7}
 & Dice & HD (mm) & Dice & HD (mm) & Dice & HD (mm) \\ \hline
Baseline N1 & 0.809 & 5.440 & - & - & - & - \\
Baseline N2 & 0.855 & 5.182 & - & - & - & - \\
DE-UNet & \textbf{0.913 (0.038)} & \textbf{4.067 (1.762)} & 0.769 (0.126) & 8.585 (5.128)& \textbf{0.900 (0.067)} & \textbf{4.477 (2.626)} \\
DE-Shape-UNet & 0.845 (0.107) & 6.414 (9.060) & \textbf{0.816 (0.078)} & \textbf{5.952 (1.258)} & 0.842 (0.105) & 6.372 (8.648) \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Franco Matzkin
Virginia Newcombe
Ben Glocker
Enzo Ferrante

"Cranial Implant Design via Virtual Craniectomy with Shape Priors" by Franco Matzkin, Virginia Newcombe, Ben Glocker, and Enzo Ferrante, arXiv:2009.13704v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of overall accuracy score of between constant and estimated forget rate on MedMNIST datasets}
\begin{tabular}{|c|c|c|c|c|c|c|}
		\hline
		Forget rate & 0 & 0.1 & 0.2 & 0.3 & 0.4 & Estimated \\ \hline
		Path & 88.22 & 88.12 & 88.06 & 89.2 & 89.27 & 88.58 \\ \hline
		OCT & 72.74 & 72.02 & 73.44 & 72.96 & 73.56 & 74.3 \\ \hline
		Pneumonia & 79.01 & 80.32 & 81.54 & 80.42 & 77.76 & 82.97 \\ \hline
		Organ & 81.61 & 82.26 & 81.48 & 83.9 & 83.15 & 83.96 \\ \hline
		Vessel & 85.34 & 82.67 & 87.22 & 88.74 & 86.75 & 87.52 \\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

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