\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
\textbf{Identifier} & \textbf{Compound} \\
\hline
adp\_c & ADP C10H12N5O10P2 \\
atp\_c & ATP C10H12N5O13P3 \\
f6p\_c & D-Fructose 6-phosphate \\
fdp\_c & D-Fructose 1 \\
h\_c & H+ \\
accoa\_c & Acetyl-CoA \\
coa\_c & Coenzyme A \\
for\_c & Formate \\
pyr\_c & Pyruvate \\
g6p\_c & D-Glucose 6-phosphate \\
13dpg\_c & 3-Phospho-D-glyceroyl phosphate \\
3pg\_c & 3-Phospho-D-glycerate \\
6pgc\_c & 6-Phospho-D-gluconate \\
6pgl\_c & 6-phospho-D-glucono-1 \\
h2o\_c & H2O H2O \\
acald\_c & Acetaldehyde \\
nad\_c & Nicotinamide adenine dinucleotide \\
nadh\_c & Nicotinamide adenine dinucleotide - reduced \\
2pg\_c & D-Glycerate 2-phosphate \\
etoh\_c & Ethanol \\
ac\_c & Acetate \\
actp\_c & Acetyl phosphate \\
co2\_c & CO2 CO2 \\
oaa\_c & Oxaloacetate \\
pep\_c & Phosphoenolpyruvate \\
pi\_c & Phosphate \\
acon\_C\_c & Cis-Aconitate \\
cit\_c & Citrate \\
icit\_c & Isocitrate \\
amp\_c & AMP C10H12N5O7P \\
akg\_c & 2-Oxoglutarate \\
succoa\_c & Succinyl-CoA \\
h\_e & H+ \\
e4p\_c & D-Erythrose 4-phosphate \\
g3p\_c & Glyceraldehyde 3-phosphate \\
gln\_\_L\_c & L-Glutamine \\
glu\_\_L\_c & L-Glutamate \\
nadp\_c & Nicotinamide adenine dinucleotide phosphate \\
nadph\_c & Nicotinamide adenine dinucleotide phosphate - reduced \\
r5p\_c & Alpha-D-Ribose 5-phosphate \\
ru5p\_\_D\_c & D-Ribulose 5-phosphate \\
xu5p\_\_D\_c & D-Xylulose 5-phosphate \\
o2\_c & O2 O2 \\
q8\_c & Ubiquinone-8 \\
q8h2\_c & Ubiquinol-8 \\
fum\_c & Fumarate \\
succ\_c & Succinate \\
s7p\_c & Sedoheptulose 7-phosphate \\
dhap\_c & Dihydroxyacetone phosphate \\
fru\_e & D-Fructose \\
mal\_\_L\_c & L-Malate \\
glc\_\_D\_e & D-Glucose \\
nh4\_c & Ammonium \\
gln\_\_L\_e & L-Glutamine \\
glx\_c & Glyoxylate \\
lac\_\_D\_c & D-Lactate
\end{tabular}
\caption{Table of identifier and compound names for the E.\ coli dataset.}
\end{table}
\end{document}

math

Víctor Blanco
Gabriel González
Praful Gagrani

"Identifying Self-Amplifying Hypergraph Structures through Mathematical Optimization" by Víctor Blanco, Gabriel González, and Praful Gagrani, arXiv:2412.15776v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Austin Adams
Benjamin Y Chan
Sarit Markovich
Xin Wan

"Don't Let MEV Slip: The Costs of Swapping on the Uniswap Protocol" by Austin Adams, Benjamin Y Chan, Sarit Markovich, and Xin Wan, arXiv:2309.13648v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{AD Test Statistics and Critical Values}
\begin{tabular}{l|cccc}
 \toprule
 \textbf{Parameters}& \textbf{SU 1} \\
 \midrule
 AD Test Statistic & 46.89 \\
 Critical Value at 15\% &0.57 \\
 Critical Value at 10\% &0.65 \\
 Critical Value at 5\% &0.78 \\
 Critical Value at 2.5\% &0.90 \\
 Critical Value at 1\% &1.08 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"Hardware-in-the-Loop Evaluation of Goodness of Fit (GoF) Testing for Dynamic Spectrum Sharing" by Mir Lodro, Simon Armour, and Mark A. Beach, arXiv:2501.12693v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger

"Supervised Large Neighbourhood Search for MIPs" by Charly Robinson La Rocca, Jean-François Cordeau, and Emma Frejinger, arXiv:2501.10778v1, 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{Hyper-parameter tuning ranges and optimal values for SWAM model}
\begin{tabular}{ccc}
 \hline
 & Range & Optimal Value \\ \hline
 $\eta$ & 0.0001,0.0003,& 0.001 \\
 (learning rate) & 0.001,0.003 & \\
 $k$ & 1-10 & 4 \\
 (filter size) \\
 $d_{c}$ & 50-500 & 500 \\
 (number of filters) \\
 $q$ & 0.2-0.8 & 0.2 \\
 (dropout probability) \\
 \hline

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

cs

"An Explainable CNN Approach for Medical Codes Prediction from Clinical Text" by Shu Yuan Hu and Fei Teng, arXiv:2101.11430v1, 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|ccccccccc}
 Electrode & 1 & 3 & 5 & 7 & 9 & 11 & 13 & 29 & 31 \\
 \hline
 R [$\Omega$] & 2200 & 2700 & 2600 & 1000 & 800 & 2000 & - & 1900 & 2400 \\
 Tape [\%] & - & 50 & 50 & 33 & 33 & 66 & 66 & - & -
 \end{tabular}
\end{table}
\end{document}

math

Altti Jääskeläinen
Jussi Toivanen
Asko Hänninen
Ville Kolehmainen
Nuutti Hyvönen

"Projection-based preprocessing for electrical impedance tomography to reduce the effect of electrode contacts" by Altti Jääskeläinen, Jussi Toivanen, Asko Hänninen, Ville Kolehmainen, and Nuutti Hyvönen, arXiv:2412.15009v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Reconstruction of space-dependence and nonlinearity of a reaction term in a subdiffusion equation" by Barbara Kaltenbacher and William Rundell, arXiv:2502.21243v1, 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|c|c|c}
\toprule
Collection & Users & Items & Interactions \\
\midrule
BAYC & 2,469 & 929 & 3,504 \\
Azuki & 9,384 & 3,931 & 16,401 \\
Meebits & 4,581 & 1,694 & 7,317 \\
Doodles & 9,321 & 3,497 & 15,176 \\
Cool Cats & 4,806 & 1,647 & 6,667 \\
\end{tabular}
\caption{NFT data description}
\end{table}
\end{document}

cs

Minjoo Choi
Seonmi Kim
Yejin Kim
Youngbin Lee
Joohwan Hong
Yongjae Lee

"A Recommender System for NFT Collectibles with Item Feature" by Minjoo Choi, Seonmi Kim, Yejin Kim, Youngbin Lee, Joohwan Hong, and Yongjae Lee, arXiv:2403.18305v2, 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 Uniswap v2} & \textbf{Value Binance} \\ \hline
Price Change During Event (\%) & -0.95 & -0.83 \\ \hline
Event Maximum Price(USD) & 2395.46
 & 2396.94 \\ \hline
Event Minimum Price (USD) & 	2364.68 & 2356.48 \\ \hline
\end{tabular}
\caption{Descriptive statistics for the event on September 6th 2024: Uniswap v2 pool WETH/USDT and ETH on Binance}
\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.


math

Mirco Ciallella
Stephane Clain
Elena Gaburro
Mario Ricchiuto

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{List of Parameters.}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Notation} & \textbf{Value} & \textbf{Notation} & \textbf{Value}\\
\hline
$N_g$ & $500$ & $\eta^{(LoS)}$, $\eta^{(NLoS)}$ & $2.5$, $3$ \\ \hline
$N_f$ & $180$ & $h_k$ & $100$ m\\ \hline
$N_u$ & $10$ & $\varpi$, $\psi$ & $2$, $20$ \\ \hline
$N_s$ & $7$ & $L_c$ & $37.5$ MB\\ \hline
$N_c$ & $40724$ & $\tau_p$ & $0-5$ s \\ \hline
$R_u$ & $500$ m & $P_k$ & $15$ dBm\\ \hline
$R_f$ & $30$ m & $\chi_{\sigma}^{(LoS)}$, $\chi_{\sigma}^{(NLoS)}$ & $3.5$, $3 $\\ \hline
$P_{T}(t)$, $P_{R}(t)$ & $0.5$ , $0.25$ W & $N_0$ & $-94$ dBm \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Zohreh HajiAkhondi-Meybodi
Arash Mohammadi
Jamshid Abouei
Ming Hou
Konstantinos N. Plataniotis

"Joint Transmission Scheme and Coded Content Placement in Cluster-centric UAV-aided Cellular Networks" by Zohreh HajiAkhondi-Meybodi, Arash Mohammadi, Jamshid Abouei, Ming Hou, and Konstantinos N. Plataniotis, arXiv:2101.11787v2, 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{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{VIFs, $R^2$, MSE and correlations without clustering and with clustering in males and females}
\begin{tabular}{ccccccc}
		\toprule
		\textbf{Sex}&\textbf{Cluster}&\textbf{Type}&\textbf{VIFs}&\textbf{$R^2$}&\textbf{MSE}&\textbf{Correlation}\\\midrule
		\multirow{4}{*}{Female}&\multirow{2}{*}{without clustering}&training&\multirow{2}{*}{$>10^5$}&.73&.09&.91\\
		&&testing&&.37&.22&.63\\\cline{2-7}
		&\multirow{2}{*}{9 clusters}&training&\multirow{2}{*}{$<5$}&.66&.12&.87\\
		&&testing&&.34&.23&.61\\\hline
		\multirow{4}{*}{Male}&\multirow{2}{*}{without clustering}&training&\multirow{2}{*}{$>10^5$}&.78&.13&.90\\
		&&testing&&.57&.25&.77\\\cline{2-7}
		&\multirow{2}{*}{8 clusters}&training&\multirow{2}{*}{$<5$}&.70&.17&.86\\
		&&testing&&.53&.28&.73\\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Fuling Chen
Roberto Togneri
Murray Maybery
Diana Tan

"Voice Gender Scoring and Independent Acoustic Characterization of Perceived Masculinity and Femininity" by Fuling Chen, Roberto Togneri, Murray Maybery, and Diana Tan, arXiv:2102.07982v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ {\it SV results with nontarget sample TASE-SVnet.}}
\begin{tabular}{c|cc}\toprule
 model & overlap-spk & vehicle-spk \\
\cline{1-3}
 TDNN (ours T/S) & 15.91\% & 3.93\% \\
 TASE-SVNet (vanilla) & 6.83\% & 3.72\% \\
 TASE-SVNet (nontarget) & 6.02\% & 3.15\% \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Towards Robust Speaker Verification with Target Speaker Enhancement" by Chunlei Zhang, Meng Yu, Chao Weng, and Dong Yu, arXiv:2103.08781v1, 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}{lccrrrrrrr}
		\toprule
		Model & $\varepsilon$ & $\delta$ & Mean & Std & Min & 5\% & 10\% & 50\% & Max \\
		\midrule
		DQN & - & - & \textbf{0.077} & 0.029 & \textbf{0.015} & \textbf{0.039} & \textbf{0.042} & \textbf{0.075} & \textbf{0.155} \\
		RDQN & 0.05 & 0.0001 & 0.051 & \textbf{0.027} & -0.008 & 0.013 & 0.021 & 0.048 & 0.122 \\
		RDQN & 0.1 & 0.0001 & 0.058 & 0.037 & 0.000 & 0.007 & 0.010 & 0.057 & 0.147 \\
		RDQN & 0.1 & 0.01 & 0.058 & 0.031 & 0.000 & 0.012 & 0.020 & 0.055 & 0.135 \\
		RDQN & 0.2 & 0.0001 & 0.031 & 0.030 & 0.000 & 0.001 & 0.002 & 0.023 & 0.132 \\
		\bottomrule
	\end{tabular}
\caption{Performance of the strategies in terms of average reward per step as in Table but using the \emph{reference distribution} instead of the \emph{true distribution} for evaluation.}
\end{table}
\end{document}

q-fin

Distributionally Robust Deep Q-Learning

"Distributionally Robust Deep Q-Learning" by Chung I Lu, Julian Sester, and Aijia Zhang, arXiv:2505.19058v1, 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{Analysis of the energy ratio used in our M-VoxelHop. *Our choice can be a good balance of performance and ccomplexity} % title of Table
\begin{tabular}{l|c|c} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Energy Ratio&Accuracy& AUC \\ % inserts table
\hline\hline % inserts single horizontal line
95\% & {86.95$\pm$0.7\%} & {0.8746$\pm$0.010} \\ \hline
96\% & {89.13$\pm$0.8\%} & {0.9023$\pm$0.014} \\ \hline
97\% & {91.30$\pm$0.6\%} & {0.9155$\pm$0.012} \\ \hline
98\%* & \textbf{93.48$\pm$0.7\%} & {0.9394$\pm$0.012} \\ \hline
99\% & \textbf{93.48$\pm$0.6\%} & \textbf{0.9405$\pm$0.013} \\
\hline\hline

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

cs

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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Official Test Data Results. 3-class submissions include an additional \textit{cannot-determine} (CD) class when the model confidence is lower than 0.4.}
\begin{tabular}{|l|c|c|}
			\hline
			\textbf{Submission} & \textbf{Classes} & \textbf{MCC} \\ \hline
			Run 1 (4-SUM) & 3+CD & 0.5717 \\ \hline
			Run 2 (4-CAT) & 3+CD & 0.5773 \\ \hline
			Run 3 (4-SUM) & 2 & 0.5986 \\ \hline
			Run 4 (4-CAT) & 2 & 0.6083 \\ \hline
		\end{tabular}
\end{table}
\end{document}

cs

Gullal S. Cheema
Sherzod Hakimov
Ralph Ewerth

"TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy" by Gullal S. Cheema, Sherzod Hakimov, and Ralph Ewerth, arXiv:2101.03529v1, 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|lcr|lcr}%{l|lr|lr|lr}
\toprule
{} & \textbf{Parsing} & \textbf{Pos} & \textbf{NER} & \textbf{XNLI} & \textbf{ANLI} & \textbf{TyDiQA} \\
{} & &\textbf{Tagging} & & & & \\
\midrule
mBERT & 30.6 & 31.0 & 31.8 & 0 & 0 & 30.1 \\
xlmr & 20.5 & 33.2 & 41.1 & 44.4 & 41.6 & 17.0\\
\bottomrule
\end{tabular}
\caption{Average percentage of languages receiving positive transfer (avg. \textit{+(\%))} across models. Unlike mBERT, xlmr provides positive transferring in NLI.}
\end{table}
\end{document}

cs

"An Efficient Approach for Studying Cross-Lingual Transfer in Multilingual Language Models" by Fahim Faisal and Antonios Anastasopoulos, arXiv:2403.20088v1, 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{Correct Frequency and running Time$\times10^{2}~$(second) of different optimization methods in finding the global maximum of different functions in Subsection , implemented using \texttt{R} and \texttt{Matlab}. SMCOtree has highest Correct Frequency with low running time in all cases. }
\begin{tabular}{ccccccccccccccccccc}
\hline
\multicolumn{3}{c}{Case} && \multicolumn{3}{c}{Method} && \multicolumn{3}{c}{Frequency (\texttt{R} | \texttt{Matlab})} && \multicolumn{3}{c}{Time$\times10^{2}~$(\texttt{R} | \texttt{Matlab})} && \multicolumn{3}{c}{RMSE (\texttt{R} | \texttt{Matlab})}\\
\hline
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMBS} && \multicolumn{3}{c}{1.00 | } && \multicolumn{3}{c}{9.95 | } && \multicolumn{3}{c}{1.65$\times10^{-6}$ | }\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{1.00 | } && \multicolumn{3}{c}{12.48 | } && \multicolumn{3}{c}{1.39$\times10^{-6}$ | }\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{GD} && \multicolumn{3}{c}{0.09 | 0.26} && \multicolumn{3}{c}{4.44 | 2.51} && \multicolumn{3}{c}{1.11 | }\\
\multicolumn{3}{c}{Case 1, $d=1$} && \multicolumn{3}{c}{SA$_1$} && \multicolumn{3}{c}{1.00 | 0.87} && \multicolumn{3}{c}{23.80 | 6.29} && \multicolumn{3}{c}{4.11$\times10^{-5}$ | }\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_2$} && \multicolumn{3}{c}{1.00 | 0.83} && \multicolumn{3}{c}{31.73 | 6.79} && \multicolumn{3}{c}{3.31$\times10^{-5}$ | }\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{1.00 | 0.88} && \multicolumn{3}{c}{23.93 | 2.07} && \multicolumn{3}{c}{1.30$\times10^{-4}$ | }\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{1.00 | 0.92} && \multicolumn{3}{c}{50.49 | 2.76} && \multicolumn{3}{c}{6.09$\times10^{-5}$ | }\\
\hline
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{\textbf{0.99} | \textbf{1.00}} && \multicolumn{3}{c}{\textbf{1.29} | \textbf{0.21}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{0.99} | \textbf{1.00}} && \multicolumn{3}{c}{2.51 | 3.38}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.90 | 0.88} && \multicolumn{3}{c}{10.39 | 10.55}\\
\multicolumn{3}{c}{Case 2, $d=2$} && \multicolumn{3}{c}{SA$_3$} && \multicolumn{3}{c}{0.31 | \textbf{0.97}} && \multicolumn{3}{c}{10.05 | 20.35}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_4$} && \multicolumn{3}{c}{0.29 | \textbf{0.98}} && \multicolumn{3}{c}{12.54 | 20.48}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{0.38 | \textbf{1.00}} && \multicolumn{3}{c}{8.76 | 3.97}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{0.47 | \textbf{1.00}} && \multicolumn{3}{c}{12.70 | 4.62}\\
\hline
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{\textbf{1.00} | \textbf{1.00}} && \multicolumn{3}{c}{\textbf{0.92} | \textbf{0.11}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00} | \textbf{1.00}} && \multicolumn{3}{c}{2.33 | 1.32}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.81 | 0.78} && \multicolumn{3}{c}{9.86 | 11.46}\\
\multicolumn{3}{c}{Case 3, Griewank, $d=2$} && \multicolumn{3}{c}{SA$_3$} && \multicolumn{3}{c}{0.01 | 0.43} && \multicolumn{3}{c}{8.91 | 13.13}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_4$} && \multicolumn{3}{c}{0.04 | 0.40} && \multicolumn{3}{c}{11.08 | 13.21}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{\textbf{0.99} | \textbf{1.00}} && \multicolumn{3}{c}{8.04 | 1.99}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{\textbf{1.00} | \textbf{1.00}} && \multicolumn{3}{c}{11.60 | 2.37}\\
\hline
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{0.88 | 0.88} && \multicolumn{3}{c}{6.44 | 0.07}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00} | \textbf{0.93}} && \multicolumn{3}{c}{\textbf{8.47} | \textbf{2.58}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.73 | 0.75} && \multicolumn{3}{c}{10.04 | 11.30}\\
\multicolumn{3}{c}{Case 4, Rastrigin, $d=2$} && \multicolumn{3}{c}{SA$_3$} && \multicolumn{3}{c}{0.16 | 0.65} && \multicolumn{3}{c}{11.57 | 14.07}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_4$} && \multicolumn{3}{c}{0.18 | 0.58} && \multicolumn{3}{c}{14.42 | 14.48}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{\textbf{0.99} | 0.62} && \multicolumn{3}{c}{9.07 | 4.33}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{\textbf{1.00} | 0.68} && \multicolumn{3}{c}{13.15 | 5.94}\\
\hline
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00} | \textbf{0.99}} && \multicolumn{3}{c}{\textbf{74.07} | \textbf{80.57}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.00 | 0.01} && \multicolumn{3}{c}{51.17 | 62.14}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_5$} && \multicolumn{3}{c}{0.00 | 0.00} && \multicolumn{3}{c}{98.67 | 71.20}\\
\multicolumn{3}{c}{Case 5, Rastrigin, $d=10$} && \multicolumn{3}{c}{SA$_6$} && \multicolumn{3}{c}{0.00 | 0.00} && \multicolumn{3}{c}{127.31 | 76.06}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_3$} && \multicolumn{3}{c}{0.64 | 0.47} && \multicolumn{3}{c}{101.20 | 98.84}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_4$} && \multicolumn{3}{c}{0.91 | 0.60} && \multicolumn{3}{c}{158.94 | 207.17}\\
\hline
\end{tabular}
\end{table}
\end{document}

math

Xiaohong Chen
Zengjing Chen
Wayne Yuan Gao
Xiaodong Yan
Guodong Zhang

"Optimization via Strategic Law of Large Numbers" by Xiaohong Chen, Zengjing Chen, Wayne Yuan Gao, Xiaodong Yan, and Guodong Zhang, arXiv:2412.05604v2, 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}{llll}
\toprule
 & a=1.1 & a=1.3 & a=1.6 \\ \midrule
action skipped [\%] & 24.5 & 43.8 & 7.8 \\
sell levels (bid, mid, ask) {[}\%{]} & (95.4, 3.1, 1.5) & (94.6, 2.8, 2.65) & (97.2, 1.9, 0.9) \\
buy levels (bid, mid, ask) {[}\%{]} & (1.1, 1.3, 97.5) & (1.6, 52.9, 45.5) & (1.7, 13.0, 85.3) \\ \bottomrule
\end{tabular}
\caption{Actions taken by RL policy for the three different noise levels: the first row shows how often the policy chooses the ``skip'' action. Not choosing this action does however not necessarily result in an order being placed, e.g. if inventory constraints are binding. The last two rows show the relative proportion of limit order placement levels for sell orders, and buy orders, respectively.}
\end{table}
\end{document}

q-fin

Peer Nagy
Jan-Peter Calliess
Stefan Zohren

"Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets" by Peer Nagy, Jan-Peter Calliess, and Stefan Zohren, arXiv:2301.08688v2, 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{Components and data sources for estimates of growth rates}
\begin{tabular}{lll}
		\hline\hline
		Component	& Unit of measurement & Data source \\ \hline
		Forest area	 & Hectare & WorldBank (2023) \\
		Living Planet Index (LPI) & Dimensionless & Zoological Society of London,\\
		&& and WWF 2022 \\
		Red List Index (RLI) & Various & IUCN RedList (2023),\\
	&&	based on 	\\
		Air quality & Micrograms per m$^3$ & WorldBank (2023)	\\
		(mean annual PM2.5) &&\\
		Climate regulation & Degrees Celsius & NOAA (2023)	\\ \hline
		GDP per capita & US dollars & WorldBank (2023) \\
		\hline\hline
	\end{tabular}
\end{table}
\end{document}

q-fin

Moritz A. Drupp
Zachary M. Turk
Ben Groom
Jonas Heckenhahn

"Global evidence on the income elasticity of willingness to pay, relative price changes and public natural capital values" by Moritz A. Drupp, Zachary M. Turk, Ben Groom, and Jonas Heckenhahn, arXiv:2308.04400v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The statistics of the datasets.}
\begin{tabular}{cccc}
		\noalign{\smallskip}\hline\noalign{\smallskip}
		Datasets & Smaples 	& Dimension 		& Classes\\
		\noalign{\smallskip}\hline\noalign{\smallskip}
		EYaleB	& 2414		& 256 				& 38\\
		AR		& 2600		& 192 				& 100\\
		USPS	& 9298 		& 256				& 10\\
		CIFAR-10 & 20000	& 256 				& 10\\
		Network & 25192		& 41				& 2\\
		MNIST	& 70000		& 784 				& 10\\
		\noalign{\smallskip}\hline\noalign{\smallskip}
	\end{tabular}
\end{table}
\end{document}

math

Wentao Qu
Lingchen Kong
Linglong Kong
Bei Jiang

"Fast Online $L_0$ Elastic Net Subspace Clustering via A Novel Dictionary Update Strategy" by Wentao Qu, Lingchen Kong, Linglong Kong, and Bei Jiang, arXiv:2412.07335v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DID Results of Profit -- \textit{Black Friday Adopters}, \textit{Organic Adopters}, and \textit{Offline-only} Customers}
\begin{tabular}{lcc}
 \toprule
 DV: & \multicolumn{2}{c}{Profit} \\
 Model: & (1) & (2) \\
 \midrule
 \emph{Variables} \\
 Constant & 96.13 (0.187) & \\
 & [0.000] & \\
 Organic\_Adopter & 58.55 (3.523) & \\
 & [0.000] & \\
 Post & -22.83 (0.152) & \\
 & [0.000] & \\
 Organic\_Adopter * Post & 26.14 (3.166) & 23.57 (3.160) \\
 & [0.000] & [0.000] \\
 BlackFriday\_Adopter & 65.89 (5.809) & \\
 & [0.000] & \\
 BlackFriday\_Adopter * Post & 5.621 (4.164) & 7.813 (4.014) \\
 & [0.177] & [0.051] \\
 \midrule
 \emph{Fixed-effects} \\
 Customer & & Yes \\
 YearMonth & & Yes \\
 \midrule
 \emph{Fit statistics} \\
 Observations & 9,505,999 & 9,505,999 \\
 R$^2$ & 0.00451 & 0.34750 \\
 Within R$^2$ & & $2.88\times10^{-5}$ \\
 \midrule\midrule
 \multicolumn{3}{l}{\emph{Clustered (Customer) standard-errors are presented in standard brackets,}}\\
 \multicolumn{3}{l}{\emph{while p‑values are shown in square brackets.}}\\
 \end{tabular}
\end{table}
\end{document}

q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

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


stat

Lukas Pin
Sofía S. Villar
William F. Rosenberger

"Revisiting Optimal Proportions for Binary Responses: Insights from Incorporating the Absent Perspective of Type-I Error Rate Control" by Lukas Pin, Sofía S. Villar, and William F. Rosenberger, arXiv:2502.06381v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ccccccccccc|c}
 \toprule
 & GUS & S.Per. & FPR & FDR & FWE & K.B. & UMAP & LDA & PCA & FOCI & \textit{vec}- & \textit{vec}- \\
 \midrule
Student & 13.30 & 14.14 & 11.36 & 11.64 & 11.36 & 10.53 & 8.87 &\textbf{8.31} & 8.59 & 10.25& \textbf{8.41 $\pm$ 0.82} & \textbf{8.52 $\pm$ 0.89} \\
Bank & 3.45 & 3.19 & 3.19 & 3.19 & 3.19 & 3.10 & 3.32 & 3.01 & 3.32 & 3.01 & \textbf{2.68 $\pm$ 0.04} & 2.90 $\pm$ 0.07 \\
Income & 10.39 & 10.39 & 7.62 & 7.62 & 7.62 & 7.71 & \textbf{7.49} & 7.86 & 8.02 & 7.96 & 7.65 $\pm$ 0.01 & 7.92 $\pm$ 0.01 \\
Employment. & 12.09 & 11.23 & 8.31 & 8.31 & 8.31 & 8.41 & 14.60 & 8.67 & 9.01 & 8.43 & \textbf{7.81 $\pm$ 0.03} & 7.97 $\pm$ 0.02 \\ \bottomrule
 \end{tabular}
\caption{Fairness experiments and test log-loss. \textit{vec}- achieves best performance by not conditioning on sensitive attributes, though \textit{vec}- remains competitive even though it conditioning out the information regarding the sensitive data $\mathbf{X_S}$. }
\end{table}
\end{document}

stat

Krunoslav Lehman Pavasovic
David Lopez-Paz
Giulio Biroli
Levent Sagun

"A Differentiable Rank-Based Objective For Better Feature Learning" by Krunoslav Lehman Pavasovic, David Lopez-Paz, Giulio Biroli, and Levent Sagun, arXiv:2502.09445v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fixed material parameters for the viscoplastic element.}
\begin{tabular}{ccc}
 \textbf{Parameter} & \textbf{Value} & \textbf{Unit} \\ \hline
 $n_1$ & $3$ & $-$ \\
 $\beta_1$ & $4.8\times 10^{-3}$ & MPa$^{-1}$ \\
 $\beta$ & $0.995$ & $-$ \\
 $m$ & $-0.5$ & $-$ \\
 $\gamma$ & $0.095$ & $-$ \\
 $F_0$ & $1.0$ & MPa$^2$ \\
 $k$ & $0.0$ & $-$ \\
 \end{tabular}
\end{table}
\end{document}

cs

Hermínio T. Honório
Maartje Houben
Kevin Bisdom
Arjan van der Linden
Karin de Borst
Lambertus J. Sluys
Hadi Hajibeygi

"A multi-step calibration strategy for reliable parameter determination of salt rock mechanics constitutive models" by Hermínio T. Honório, Maartje Houben, Kevin Bisdom, Arjan van der Linden, Karin de Borst, Lambertus J. Sluys, and Hadi Hajibeygi, arXiv:2403.19426v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Class-wise and overall accuracies achieved on the test set by a linear classifier used with the different methods considering 2000 samples.}
\begin{tabular}{ccccccccc}
		\hline
		Method & Forest & Sparse Veg. & Grass. & Built. & Barren & Water & AA & mIoU\\ \hline
		MCL & 87.3 & 36.9 & 53.3 & 68.8 & 42.5 & 99.0 & 64.7 & 0.408 \\ \hline
		PixIF & 89.6 & 39.3 & 55.7 & 73.0 & 47.4 & 99.2 & 67.4 & 0.438 \\ \hline
		Fine. & 91.6 & 38.2 & 57.2 & 74.4 & 47.1 & 99.3 & 68.0 & 0.449 \\ \hline
	\end{tabular}
\end{table}
\end{document}

eess

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllllll}
\hline \hline
\multicolumn{7}{c}{Small GEB} \\ \hline
 & \multicolumn{2}{c|}{VRES} & \multicolumn{2}{c|}{Welfare} & \multicolumn{2}{c}{Generation amount} \\ \hline
 & Low tax & & Low tax & & Low tax & \\
Largest effect & High incentive & + 42.28\% & High incentive & + 66.75 \% & High incentive & + 105.41 \% \\
 & High TEB & & High TEB & & High TEB & \\ \hline
 & Low tax & & Low tax & & Low tax & \\
Smallest effect & Low incentive & + 28.00\% & Low incentive & + 58.69 \% & Low incentive & + 99.94 \% \\
 & Low TEB & & Low TEB & & Low TEB & \\ \hline \hline
\multicolumn{7}{c}{High GEB} \\ \hline \hline
 & \multicolumn{2}{c|}{VRES} & \multicolumn{2}{c|}{Welfare} & \multicolumn{2}{c}{Generation amount} \\ \hline
 & Low tax & & Low tax & & Low tax & \\
Largest effect & High incentive & + 56.04 \% & High incentive & + 78.32 \% & High incentive & + 165.24 \% \\
 & High TEB & & High TEB & & High TEB & \\ \hline
 & Low tax & & Low tax & & Low tax & \\
Smallest effect & Low incentive & + 53.32 \% & Low incentive & + 71.05 \% & Low incentive & +147.43 \% \\
 & Low TEB & & Low TEB & & Low TEB & \\ \hline
\end{tabular}
\caption{Nordics case study: summary. Percentage values (\%) are calculated relative to the baseline case.}
\end{table}
\end{document}

q-fin

Nikita Belyak
Steven A. Gabriel
Nikolay Khabarov
Fabricio Oliveira

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Theoretical and actual thresholds on the number of time-steps where the one-touch option solution starts diverging for a uniform grid composed of $M=100$ space-steps, varying the grid boundary $S_{\max}$. .}
\begin{tabular}{rrrrr}\toprule
$S_{\max}$ & $L^+ - S_{u-1}$ & $(L^+ - S_{u-1}) / \Delta S$ & Theoretical Threshold & Actual Threshold\\\midrule
13662.0 & 67.26 & 0.492 & 122 & 115 \\
13702.0 & 45.26 & 0.330 & 153 & 134 \\
13760.0 & 13.36 & 0.097 & 373 & 338 \\
13772.0 & 6.76 & 0.049 & 677 & 638 \\
13778.0 & 3.46 & 0.021& 1266 & 1223 \\
13782.0 & 1.26 & 0.009& 3370 & 3322\\
13784.0 & 0.16 & 0.001 & 26121 &26071 \\\bottomrule
	\end{tabular}
\end{table}
\end{document}

q-fin

"Instabilities of explicit finite difference schemes with ghost points on the diffusion equation" by Fabien Le Floc'h, arXiv:2308.04629v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Jarosław Wątróbski
Aleksandra Bączkiewicz
Iga Rudawska

"A Strong Sustainability Paradigm Based Analytical Hierarchy Process (SSP-AHP) Method to Evaluate Sustainable Healthcare Systems" by Jarosław Wątróbski, Aleksandra Bączkiewicz, and Iga Rudawska, arXiv:2306.00718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study. Reconstruction performances were progressively improved as more blocks were added to deep ADMM (* denotes statistical significance; $p < 0.05$).}
\begin{tabular}{c|c|c} \hline
 & PSNR ($\uparrow$) & SSIM ($\uparrow$) \\
\hline
Deep ADMM & 40.95 $\pm$ \ 3.72* & 0.9820 $\pm$ \ 0.0257*\\
Deep ADMM + single SPO & 41.77 $\pm$ \ 3.24* & 0.9839 $\pm$ \ 0.0091*\\
Deep ADMM + TFF & 42.24 $\pm$ \ 3.28* & 0.9864 $\pm$ \ 0.0074*\\
Deep ADMM + TFF + single SPO & 42.77 $\pm$ \ 3.38* & 0.9867 $\pm$ \ 0.0076*\\
Deep ADMM + TFF + multi SPO & $\textbf{43.75} \ \pm$ \ 3.02 & $\textbf{0.9894} \ \pm$ \ 0.0058\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Jinwei Zhang
Hang Zhang
Chao Li
Pascal Spincemaille
Mert Sabuncu
Thanh D. Nguyen
Yi Wang

"Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction" by Jinwei Zhang, Hang Zhang, Chao Li, Pascal Spincemaille, Mert Sabuncu, Thanh D. Nguyen, and Yi Wang, arXiv:2103.05878v1, 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
 Method & Frobnability \\
 \midrule
 Theirs & Frumpy \\
 Yours & Frobbly \\
 Ours & Makes one's heart Frob\\
 \bottomrule
 \end{tabular}
\caption{Results. Ours is better.}
\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{The normalized error calculated using for medical data. Sample size $N=990$. }
\begin{tabular}{cccccc}
\hline
ICSS & ICSS {[}BMID{]} & ICSS {[}QCV{]} & OLS & OLS {[}BMID{]} & OLS {[}QCV{]} \\ \hline
0.062 & 0.000 & 0.004 & 0.062 & 0.007 & 0.019 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

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

"Robust variance estimators in application to segmentation of measurement data distorted by impulsive and non-Gaussian noise" by Justyna Witulska, Anna Zaleska, Natalia Kremzer-Osiadacz, Agnieszka Wyłomańska, and Ireneusz Jabłoński, arXiv:2502.10275v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Workforce pDEI: Productivity Coupled with DEI" by Lanqing Du and Jinwook Lee, arXiv:2311.11231v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mingyu Chen
Yiding Chen
Wen Sun
Xuezhou Zhang

"Avoiding $\mathbf{exp(R_{max})}$ scaling in RLHF through Preference-based Exploration" by Mingyu Chen, Yiding Chen, Wen Sun, and Xuezhou Zhang, arXiv:2502.00666v2, 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}{r|l|l|l|l|l}
 & CNt & CN & PYIN & BP & MT3 \\
\hline
Recall & 88.26 & \textbf{88.61} & 50.32 & 80.62 & 40.67 \\
Precision & \textbf{77.18} & 76.91 & 69.50 & 71.18 & 45.78 \\
F-measure & \textbf{82.31} & \textbf{82.31} & 58.28 & 75.54 & 42.97 \\
Overlap & 88.54 & \textbf{89.91} & 87.36 & 83.45 & 72.96 \\
\hline
Parameters & 0.5M & 22M & N/A & 17K & 77M
\end{tabular}
\end{table}
\end{document}

eess

"CREPE Notes: A new method for segmenting pitch contours into discrete notes" by Xavier Riley and Simon Dixon, arXiv:2311.08884v1, 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|ccc|ccc|}
\hline
 & \multicolumn{3}{c|}{\textbf{SplitS Maneuver}} & \multicolumn{3}{c|}{\textbf{Chandelle Maneuver}} \\ \hline
 & \textbf{BC} & \textbf{C-DAgger Iter-1} & \textbf{C-DAgger Iter-2} & \textbf{BC} & \textbf{C-DAgger Iter-1} & \textbf{C-DAgger Iter-2} \\ \hline
\textbf{P-mse ($rad^2/s^2$)} & 0.2402 & 0.0334 & 0.0252 & 0.04008 & 0.05479 & 0.01364 \\ \hline
\textbf{Q-mse ($rad^2/s^2$)} & 0.1555 & 0.0119 & 0.0072 & 0.00193 & 0.00336 & 0.00083 \\ \hline
\textbf{R-mse ($rad^2/s^2$)} & 0.0663 & 0.00041 & 0.000076 & 0.00367 & 0.00088 & 0.00027 \\ \hline
\textbf{PQR-mse ($rad^2/s^2$)} & 0.1540 & 0.0152 & 0.0111 & 0.015 & 0.019679 & 0.00491 \\ \hline
\end{tabular}
\caption{The Mean Squared Error (MSE) values for $P-Q-R$ tracking performance of the Split-S maneuver and the Chandelle maneuver for each policy are presented in this table. The learned policies compared are the Behavior Cloning (BC) algorithm, C-DAgger Iteration-1, and C-DAgger Iteration-2.}
\end{table}
\end{document}

eess

Gulay Goktas Sever
Umut Demir
Abdullah Sadik Satir
Mustafa Cagatay Sahin
Nazim Kemal Ure

"An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data" by Gulay Goktas Sever, Umut Demir, Abdullah Sadik Satir, Mustafa Cagatay Sahin, and Nazim Kemal Ure, arXiv:2401.08663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of two-stage model, single-stage models, and baseline model ("$\pm$" represents 95\% confidence intervals)}
\begin{tabular}{lccc}

 \toprule
 & PESQ & SI-SDR & \#Params\\ \midrule
 noisy & 1.81 $\pm$ 0.05 & 12.50 $\pm$ 0.82 & -\\
 DeepFilter2 & \textbf{2.60 $\pm$ 0.05} & \textbf{16.99 $\pm$ 0.50} & 2.1M\\
 \midrule
 Mel($L_1$) & 2.43 $\pm$ 0.06 & 16.99 $\pm$ 0.53 & 300k\\
 Complex & 2.45 $\pm$ 0.07 & 17.01 $\pm$ 0.54 & 600k\\
 Two-stage & \textbf{2.58 $\pm$ 0.06} & \textbf{17.58 $\pm$ 0.55} & 560k\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Lingjun Meng
Jozef Coldenhoff
Paul Kendrick
Tijana Stojkovic
Andrew Harper
Kiril Ratmanski
Milos Cernak

"On real-time multi-stage speech enhancement systems" by Lingjun Meng, Jozef Coldenhoff, Paul Kendrick, Tijana Stojkovic, Andrew Harper, Kiril Ratmanski, and Milos Cernak, arXiv:2312.12415v1, 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{Imperceptibility for Different User Groups.}
\begin{tabular}{lcl}
 \toprule[1.5pt]
 User Group & \parbox[t]{4.5cm}{Imperceptible / Non-disruptive / Disruptive \%} & \parbox[t]{1.1cm}{Number \\ Participant }\\
 \midrule
 Female & 51.6\% / 32.3\%/ 16\% & \\
 Male & 65\% / 33\% / 2\% & \\
 Age: 18-34 & 35.6\% / 42.5\%/ 21.8\% & \\
 Age: 35-49 & 37\% / 55.5\%/ 7.4\% & \\
 Age: 50+ & 62.4\% / 22\%/ 15.6\% & \\

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

cs

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

"Practical Speech Re-use Prevention in Voice-driven Services" by Yangyong Zhang, Maliheh Shirvanian, Sunpreet S. Arora, Jianwei Huang, and Guofei Gu, arXiv:2101.04773v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Riccardo Leoncini
Mariele Macaluso
Annalivia Polselli

"Gender Segregation: Analysis across Sectoral-Dominance in the UK Labour Market" by Riccardo Leoncini, Mariele Macaluso, and Annalivia Polselli, arXiv:2303.04539v3, 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}{lc}
 \hline
 \textbf{Grid Levels} & \textbf{Prices} \\
 \hline
 \verb|Top| & $P * (1+k)^{m}$ \\
 \verb|...| & ... \\
 \verb|Above| & $P * (1+k)$ \\
 \verb|Reference| & $P$ \\
 \verb|Below| & $P *(1+k)^{-1}$ \\
 \verb|...| & ... \\
 \verb|Bottom| & $P * (1+k)^{-(n-m)}$ \\\hline
 \end{tabular}
\caption{Structure of geometric grid}
\end{table}
\end{document}

q-fin

Kai-Yuan Chen
Kai-Hsin Chen
Jyh-Shing Roger Jang

"Dynamic Grid Trading Strategy: From Zero Expectation to Market Outperformance" by Kai-Yuan Chen, Kai-Hsin Chen, and Jyh-Shing Roger Jang, arXiv:2506.11921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Ibrahim Abdelrazeq
Hardy Smith
Dinmukhammed Zhanbyrshy

"How to verify that a given process is a Lévy-Driven Ornstein-Uhlenbeck Process" by Ibrahim Abdelrazeq, Hardy Smith, and Dinmukhammed Zhanbyrshy, arXiv:2501.03434v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|c|c|c|}
\hline
\textbf{Train \textdownarrow} & \textbf{Test \textrightarrow} & \textbf{Paheli} & \textbf{M-CL} & \textbf{M-IT} \\ \hline
 & \textbf{Random} & 19.10 & 7.87
& 9.12 \\ \hline
\textbf{Paheli} & Baseline & \color[HTML]{9B9B9B}62.43 & 56.98 & 57.31 \\
\textbf{} & Disc. Contr. & \color[HTML]{9B9B9B}71.99 & 56.54 & 57.40 \\
\textbf{} & Single Proto. & \color[HTML]{9B9B9B}69.94 & 58.30 & 59.92 \\
\textbf{} & Multi Proto. & \color[HTML]{9B9B9B}71.38 & 57.47 & 59.48 \\
\textbf{} & DC + Single Pr & \color[HTML]{9B9B9B}74.28 & 62.27 & 60.33 \\
\textbf{} & DC + Multi Pr & \color[HTML]{9B9B9B}75.52 & 60.89 & 60.61 \\ \hline
\textbf{M-CL} & Baseline & 54.71 & \color[HTML]{9B9B9B}59.51 & 63.08 \\
\textbf{} & Disc. Contr. & 54.04 & \color[HTML]{9B9B9B}66.94 & 62.98 \\
\textbf{} & Single Proto. & 57.48 & \color[HTML]{9B9B9B}64.42 & 60.23 \\
\textbf{} & Multi Proto. & 56.10 & \color[HTML]{9B9B9B}65.91 & 61.62 \\
\textbf{} & DC + Single Pr & 59.95 & \color[HTML]{9B9B9B}65.87 & 63.92 \\
\textbf{} & DC + Multi Pr & 57.89 & \color[HTML]{9B9B9B}68.66 & 62.37 \\ \hline
\textbf{M-IT} & Baseline & 52.97 & 58.83 & \color[HTML]{9B9B9B}70.76 \\
\textbf{} & Disc. Contr. & 51.89 & 57.16 & \color[HTML]{9B9B9B}72.23 \\
\textbf{} & Single Proto. & 51.57 & 58.58 & \color[HTML]{9B9B9B}72.59 \\
\textbf{} & Multi Proto. & 52.85 & 58.70 & \color[HTML]{9B9B9B}73.02 \\
\textbf{} & DC + Single Pr & 51.03 & 57.23 & \color[HTML]{9B9B9B}72.50 \\
\textbf{} & DC + Multi Pr & 51.77 & 56.99 & \color[HTML]{9B9B9B}73.14 \\ \hline
\end{tabular}
\caption{Macro-F1 scores of our methods across three datasets. The column `train' indicates the source dataset on which the model is trained and each of the dataset columns indicates the target test dataset. Scores in grey indicates the in-domain performance (trained and tested on same dataset). \{DC, Disc. Contr.\} : Discourse-aware contrastive, \{Pr., Proto.\} : Prototypical }
\end{table}
\end{document}

cs

T. Y. S. S Santosh
Hassan Sarwat
Ahmed Abdou
Matthias Grabmair

"Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents" by T. Y. S. S Santosh, Hassan Sarwat, Ahmed Abdou, and Matthias Grabmair, arXiv:2404.01344v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c|c|c|c} %\hline
 & IoU & Dice & Precision & Recall & HD \\
 \hline
 U-net & 60.74\% & 70.00\% & 72.53\% & 69.78\% & 8.56 \\
 CE-net & 67.67\% & 77.63\% & 70.82\% & 87.46\% & 7.98 \\
 Attention U-net & 75.15\% & 85.14\% & 80.35\% & \textbf{92.01}\% & 5.51 \\
 AURA-net & \textbf{78.89}\% & \textbf{87.78}\% & \textbf{86.96}\% & 89.87\% & \textbf{ 5.12}
 \end{tabular}
\caption{Comparison of AURA-net with relevant state-of-the-art alternatives on dataset $2$.}
\end{table}
\end{document}

eess

"aura-net : robust segmentation of phase-contrast microscopy images with few annotations" by Ethan Cohen and Virginie Uhlmann, arXiv:2102.01389v1, 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{Puzzles confirmed to be sufficient conditions}
\begin{tabular}{ll}
		\toprule
		Name\\
		\midrule
		Choco Banana \\
		Kurotto \\
		Fillomino \\
		Inshi no heya \\
		Hitori \\
		Sudoku \\
		Sukoro \\
		Norinori \\
		Shikaku \\
		Slitherlink \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

math

"Mathematical Definition and Systematization of Puzzle Rules" by Itsuki Maeda and Yasuhiro Inoue, arXiv:2501.01433v2, 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{Cone Details}
\begin{tabular}{|l|l|}
\hline
\textbf{Cone Type} & \textbf{Details} \\ \hline
Primal zero / Dual free variables & 7686 \\ \hline
Linear variables & 15360 \\ \hline
SOC variables & 61440 \\ \hline
PSD variables & 3290895 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

"ConvMesh: Reimagining Mesh Quality Through Convex Optimization" by Alexander Valverde, arXiv:2412.08484v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{B-line localization accuracy (\%) at different IoU $\alpha$'s.}
\begin{tabular}{l|llll}
\hline
\hline
IoU & $\alpha$=0.1& $\alpha$=0.2& $\alpha$=0.3& $\alpha$=0.4 \\ \hline
C2D+A & 36.2 & 31.4 & 28.6 & 22.4 \\ \hline
C3D+A & 63.3 & 61.1 & 54.0 & 45.7 \\ \hline
C2D+LSTM+A & \textbf{67.1} & 65.3 & 54.5 & 49.5 \\ \hline
\hline
\end{tabular}
\end{table}
\end{document}

eess

Hamideh Kerdegari
Phung Tran Huy Nhat
Angela McBride
VITAL Consortium
Reza Razavi
Nguyen Van Hao
Louise Thwaites
Sophie Yacoub
Alberto Gomez

"Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients" by Hamideh Kerdegari, Phung Tran Huy Nhat, Angela McBride, VITAL Consortium, Reza Razavi, Nguyen Van Hao, Louise Thwaites, Sophie Yacoub, and Alberto Gomez, arXiv:2102.01059v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"Representation Learning For Speech Recognition Using Feedback Based Relevance Weighting" by Purvi Agrawal and Sriram Ganapathy, arXiv:2102.07390v1, 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{Major hardware components.}
\begin{tabular}{|l|l|l|}
\hline
\textbf{Item} & \textbf{Manufacturer} & \textbf{Model No.} \\ \hline
Drone & DJI & DJI Matrice 600 Pro \\ \hline
USRP & National Instruments & NI2901 \\ \hline
GPS TX & Jackson Labs & Firefly 1A \\ \hline
Controller TX & Intel & NUC10i7FNK \\ \hline
BPF & K\&L Microwave & 3C50-3500/T50-O/O \\ \hline
Switch & Pulsar Microwave & SW16AD-21/SW8AD-A85 \\ \hline
Mixer & Mini Circuits & ZEM-4300MH+ \\ \hline
FS & Phase Matrix & FSW-0020 \\ \hline
PA & Wenteq & ABP1500-03-3730 \\ \hline
LNA & Wenteq & ABL0600-33-4009 \\ \hline
LPF & Pasternack & PE8719 \\ \hline
Controller RX & National Instruments & PXIe-1082 \\ \hline
GPS RX & PTSYST & GPS10eR \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Jorge Gomez Ponce
Thomas Choi
Naveed A. Abbasi
Aldo Adame
Alexander Alvarado
Colton Bullard
Ruiyi Shen
Fred Daneshgaran
Harpreet S. Dhillon
Andreas F. Molisch

"Air-to-Ground Directional Channel Sounder With 64-antenna Dual-polarized Cylindrical Array" by Jorge Gomez Ponce, Thomas Choi, Naveed A. Abbasi, Aldo Adame, Alexander Alvarado, Colton Bullard, Ruiyi Shen, Fred Daneshgaran, Harpreet S. Dhillon, and Andreas F. Molisch, arXiv:2103.09135v1, 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{Producers' parameters.}
\begin{tabular}{cccc}
\toprule
Producer & Zone & $\bar{\Delta}$ (MW) & $\underline{\pi}$ \\ \midrule
0 & 0 & 700 & 7 \\
1 & 0 & 700 & 7 \\
2 & 1 & 150 & 3 \\
3 & 1 & 150 & 3 \\
4 & 0 & 650 & 6 \\
5 & 0 & 600 & 5 \\
6 & 0 & 850 & 8 \\
7 & 1 & 350 & 4 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Francesco Morri
Hélène Le Cadre
Pierre Gruet
Luce Brotcorne

"Game Theory and Multi-Agent Reinforcement Learning for Zonal Ancillary Markets" by Francesco Morri, Hélène Le Cadre, Pierre Gruet, and Luce Brotcorne, arXiv:2505.03288v3, 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|ccc}
 \hline
 Method & AUC@$5^{\circ}$ & AUC@$10^{\circ}$ & AUC@$20^{\circ}$ \\
 \hline
 PointCN~ & 12.38 & 28.15 & 48.04 \\
 NM-Net~ & 12.59 & 30.62 & 52.07 \\
 OANet~ & 16.86 & 36.74 & 57.40 \\
 PointACN~ & 18.64 & 38.76 & 59.56 \\
 \hline
 Our CLNet & \bf{26.19} & \bf{46.33} & \bf{65.48} \\
 \hline
 \end{tabular}
\caption{Pose Estimation without robust estimator. Weighted 8-point algorithm~ instead of a robust estimator, e.g., RANSAC, is used while estimating essential matrices. AUCs on YFCC100M are listed.}
\end{table}
\end{document}

cs

Chen Zhao
Yixiao Ge
Feng Zhu
Rui Zhao
Hongsheng Li
Mathieu Salzmann

"Progressive Correspondence Pruning by Consensus Learning" by Chen Zhao, Yixiao Ge, Feng Zhu, Rui Zhao, Hongsheng Li, and Mathieu Salzmann, arXiv:2101.00591v2, 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{Production Function Parameter Estimates}
\begin{tabular}{lccc}
			\toprule[1pt]
			Parameter & Point Estimate & Lower Bound & Upper Bound\\
			\midrule

			Capital Elasticity & 0.214 & 0.204 & 0.226 \\
			Labor Elasticity & 0.456 & 0.435 & 0.476 \\
			Material Elasticity & 0.288 & 0.284 & 0.291 \\
			Scale Elasticity & 0.957 & 0.938 & 0.975\\

			\midrule
			\multicolumn{4}{p{12.3cm}}{\textit{Notes}: Reported are the input elasticity estimates along with their two-sided 95\% lower and upper confidence bounds. Scale elasticity is the sum of capital, labor and material elasticities.}\\
			\bottomrule[1pt]
		\end{tabular}
\end{table}
\end{document}

q-fin

"Detecting Learning by Exporting and from Exporters" by Jingfang Zhang and Emir Malikov, arXiv:2302.13427v1, 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}{ll}
Operation & Latency (ms) \\
\hline
Mux select (Stage 1) & 101.92 \\
Mux select (Stage 2) & 89.7695 \\
Mux select (Stage 3) & 98.8634 \\
Mux select(Stage 4) & 91.3939 \\
Mux select (Stage 5) & 91.8491 \\
\hline
Total latency, including overhead & 474.739
\end{tabular}
\caption{32-bit LLS latency}
\end{table}
\end{document}

cs

"CryptoEmu: An Instruction Set Emulator for Computation Over Ciphers" by Xiaoyang Gong and Dan Negrut, arXiv:2101.03403v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The experimental results for various methods under different metrics are presented for the FOP defined as $\min \{ (\theta + u)^{\top} x \mid x \succeq 0, \parallel x \parallel_1 \leq a \} = \min \left\{\sum_{k=1}^{p} (\theta_k + u_k) x_k \mid x \succeq 0, \parallel x \parallel_1 \leq a \right\}$ in the \textbf{Noisy Decision} setting. For the FY loss, we set $\lambda=0.1$ and $\Omega(x) = 1/2\|x\|_2^2$.}
\begin{tabular}{c|cccc|cccc|cccc}
\hline
\multirow{2}{*}{Sample size} & \multicolumn{4}{c|}{Parameter Error} & \multicolumn{4}{c|}{Decision Error} & \multicolumn{4}{c}{Regret} \\ \cline{2-13}
 & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA & FY & SPA & KKA & VIA \\ \hline
50 & \textbf{1.30} & 2.46 & 4.25 & 3.81 & \textbf{3.03} & 4.53 & 7.66 & 3.90 & \textbf{0.05} & 0.20 & 0.77 & 0.08 \\
100 & \textbf{1.17} & 1.35 & 4.58 & 4.12 & \textbf{2.47} & 2.94 & 7.79 & 3.17 & \textbf{0.03} & 0.06 & 0.84 & 0.05 \\
300 & \textbf{0.53} & 2.10 & 4.87 & 4.42 & \textbf{2.05} & 2.66 & 8.22 & 2.19 & \textbf{0.02} & 0.08 & 0.93 & 0.03 \\
500 & \textbf{0.53} & 2.44 & 4.92 & 4.52 & \textbf{1.64} & 2.69 & 8.32 & 1.88 & \textbf{0.01} & 0.10 & 0.95 & 0.03 \\
1000 & \textbf{0.38} & 2.59 & 4.96 & 4.56 & \textbf{1.33} & 2.64 & 8.40 & 1.60 & \textbf{0.01} & 0.11 & 0.96 & 0.03 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"A Fenchel-Young Loss Approach to Data-Driven Inverse Optimization" by Zhehao Li, Yanchen Wu, and Xiaojie Mao, arXiv:2502.16120v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|ccc}
 \hline
 \multirow{2}{*}{\textbf{Mtds.}} & \multirow{2}{*}{\shortstack{\textbf{Overlap} \\ \textbf{$\left ( \% \right )$}} }& \multirow{2}{*}{\shortstack{\textbf{Mean}\\ \textbf{RR}} } & \multirow{2}{*}{\textbf{F1}} & \multicolumn{3}{c}{\textbf{Hits} @ } \\
 & & & & \textbf{K=1} & \textbf{K=3} & \textbf{K=5} \\ \hline \hline
 \multirow{4}*{\shortstack{SG-PGM\\(\textit{ours})}} & 10-30 & 94.96 & 74.86 & 91.23 & 98.69 & 99.65\\
 & 30-60 & 97.91 & 87.95 & 96.33 & 99.54 & 99.87\\
 & 60- & 99.15 & 95.21 & 98.48 & 99.83 & 99.93\\ \cline{2-7}
 & overall & \textbf{97.81} & \textbf{88.18} & \textbf{96.16} & \textbf{99.49} & \textbf{99.85}\\ \hline
 \multirow{4}*{\shortstack{SGA*\\ \textbf{}}}
 & 10-30 & 79.93 & 60.46 & 64.64 & 86.54 & 93.50\\
 & 30-60 & 83.20 & 71.84 & 71.25 & 89.61 & 95.28\\
 & 60- & 87.24 & 81.05 & 78.01 & 93.75 & 97.48\\ \cline{2-7}
 & overall & 85.92 & 79.46 & 77.69 & 88.07 & 93.71 \\ \hline
 \end{tabular}
\caption{\textbf{Evaluation of our proposed method on node matching per overlap range.} Even in low-overlap cases, our method still provides accurate alignment results with Hit@1 over 90\%.}
\end{table}
\end{document}

cs

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llr}
\toprule
 \textbf{Target Application} & \textbf{Dataset} & \textbf{\# Examples} \\
\midrule
Multiple-choice QA & MedQA-CoT$^\dag$~ & 9,308 \\
& MedBooks-CoT-18$^\dag$ & 77,776 \\
& MedMCQA~ & 182,821 \\
\midrule
Free-form QA & LiveQA~ & 633 \\
& MedicationQA~ & 689 \\
& ChatDoctor-cleaned$^\dag$~ & 112,165 \\
\midrule
Multi-turn Dialogue &
MedQA-dialog$^\dag$~ & 4,839 \\
\midrule
Clinical Note Generation & MTS-dialog~ & 1,201 \\
\midrule
Miscellaneous & AlpaCare~ & 52,002 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

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

"Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks" by Hyunjae Kim, Hyeon Hwang, Jiwoo Lee, Sihyeon Park, Dain Kim, Taewhoo Lee, Chanwoong Yoon, Jiwoong Sohn, Donghee Choi, and Jaewoo Kang, arXiv:2404.00376v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Earlybird Last}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 \textbf{Name} & \textbf{Added} & \textbf{Exit Reason} & \textbf{Expelled}& \textbf{Used in Capital Growth} \\
 \hline
 Flatiron & 2016-02-01 & longtime & 2018-02-01 & TRUE \\
 \rowcolor{green!25}
Klarna & 2016-02-01 & success & 2021-07-01 & TRUE \\
\rowcolor{green!25}
Looker & 2016-02-01 & success & 2019-07-01 & FALSE \\
\rowcolor{green!25}
Magic Leap & 2016-03-01 & success & 2021-11-01 & TRUE \\
Jumia Group & 2016-04-01 & longtime & 2018-04-01 & TRUE \\
Monedo & 2016-04-01 & longtime & 2019-01-01 & TRUE \\
\rowcolor{green!25}
Vectra & 2016-04-01 & success & 2021-05-01 & FALSE \\
Pivotal & 2016-06-01 & longtime & 2018-06-01 & TRUE \\
Barefoot Networks & 2016-07-01 & longtime & 2020-09-01 & FALSE \\
CommonBond & 2016-08-01 & longtime & 2020-04-01 & FALSE \\
\rowcolor{green!25}
GitHub & 2016-08-01 & success & 2018-07-01 & TRUE \\
\rowcolor{green!25}
Coinbase & 2016-08-01 & success & 2021-05-01 & TRUE \\
\rowcolor{green!25}
Auris Health & 2016-09-01 & success & 2019-03-01 & TRUE \\
Distil Networks & 2016-09-01 & longtime & 2018-09-01 & FALSE \\
Behalf & 2016-09-01 & STILL IN & & FALSE \\
\rowcolor{green!25}
PillPack & 2017-01-01 & success & 2018-07-01 & FALSE \\
Pluralsight & 2017-01-01 & longtime & 2019-01-01 & TRUE \\
\rowcolor{green!25}
Exabeam & 2017-03-01 & success & 2021-06-01 & FALSE \\
Learner-Centered Collaborative & 2017-04-01 & longtime & 2020-05-01 & FALSE \\
\rowcolor{green!25}
Earnest Operations & 2017-04-01 & success & 2017-11-01 & FALSE \\
Aledade & 2017-04-01 & longtime & 2020-01-01 & TRUE \\
Qualtrics & 2017-05-01 & longtime & 2019-05-01 & TRUE \\
\rowcolor{green!25}
Cohesity & 2017-05-01 & success & 2020-05-01 & TRUE \\
\rowcolor{green!25}
Robinhood Markets & 2017-05-01 & success & 2021-08-01 & TRUE \\
Wandera & 2017-06-01 & longtime & 2019-06-01 & FALSE \\
OpenGov & 2017-06-01 & longtime & 2021-10-01 & FALSE \\
\rowcolor{green!25}
Symphony Communication Services & 2017-06-01 & success & 2019-07-01 & TRUE \\
Casper & 2017-07-01 & longtime & 2021-04-01 & TRUE \\
\rowcolor{green!25}
Segment & 2017-08-01 & success & 2020-11-01 & FALSE \\
\rowcolor{green!25}
Revolut & 2017-08-01 & success & 2021-08-01 & TRUE \\
\rowcolor{green!25}
Spire Global & 2017-12-01 & success & 2021-09-01 & FALSE \\
ClearStory Data & 2018-04-01 & longtime & 2020-04-01 & TRUE \\
Helix & 2018-04-01 & longtime & 2020-03-01 & FALSE \\
\rowcolor{green!25}
Aircall & 2018-06-01 & success & 2021-07-01 & FALSE \\
\rowcolor{green!25}
Zoox & 2018-08-01 & success & 2020-07-01 & TRUE \\
Yesware & 2018-09-01 & longtime & 2020-09-01 & FALSE \\
import.io & 2019-01-01 & longtime & 2021-01-01 & FALSE \\
Fair & 2019-01-01 & longtime & 2021-01-01 & TRUE \\
Petal & 2019-02-01 & STILL IN & & TRUE \\
Bolt & 2019-08-01 & STILL IN & & FALSE \\
\rowcolor{green!25}
Ginger & 2019-10-01 & success & 2021-09-01 & TRUE \\
Blue Planet-works Japan & 2019-10-01 & longtime & 2021-10-01 & FALSE \\
\rowcolor{green!25}
Porch Group & 2020-02-01 & success & 2021-01-01 & TRUE \\
\rowcolor{green!25}
OneTrust & 2020-03-01 & success & 2021-05-01 & TRUE \\
Pony.ai & 2020-03-01 & STILL IN & & TRUE \\
\rowcolor{green!25}
Bakkt & 2020-04-01 & success & 2021-11-01 & TRUE \\
\rowcolor{green!25}
DigitalOcean & 2020-06-01 & success & 2021-04-01 & TRUE \\
Wandelbots & 2020-07-01 & STILL IN & & FALSE \\
Withings & 2020-08-01 & STILL IN & & FALSE \\
\rowcolor{green!25}
Northvolt & 2020-10-01 & success & 2021-07-01 & TRUE \\
WireWheel & 2021-03-01 & STILL IN & & TRUE \\
Skycatch & 2021-04-01 & STILL IN & & FALSE \\
Bitstocks Trading Limited & 2021-05-01 & STILL IN & & TRUE \\
Placer.ai & 2021-05-01 & STILL IN & & TRUE \\
Grover & 2021-05-01 & STILL IN & & TRUE \\
Genies & 2021-06-01 & STILL IN & & TRUE \\
Heyday & 2021-06-01 & STILL IN & & TRUE \\
\rowcolor{green!25}
Pipe & 2021-06-01 & success & 2022-01-01 & TRUE \\
Lendbuzz & 2021-07-01 & STILL IN & & FALSE \\
ShipHawk & 2021-07-01 & STILL IN & & FALSE \\
DuckDuckGo & 2021-07-01 & STILL IN & & TRUE \\
Railsbank & 2021-08-01 & STILL IN & & TRUE \\
Ripio & 2021-10-01 & STILL IN & & TRUE \\
Jeeves & 2021-10-01 & STILL IN & & TRUE \\
slice & 2021-12-01 & STILL IN & & TRUE \\
Terra & 2022-01-01 & STILL IN & & FALSE \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Mark Potanin
Andrey Chertok
Konstantin Zorin
Cyril Shtabtsovsky

"Startup success prediction and VC portfolio simulation using CrunchBase data" by Mark Potanin, Andrey Chertok, Konstantin Zorin, and Cyril Shtabtsovsky, arXiv:2309.15552v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c} \hline
 Participant &
 Years of Experience &
 Edited videos \# &
 Assigned video \\ \hline

 P1 & 0 & 2 & EV1\\ \hline
 P2 & 1 & 6 & EV1\\ \hline
 P3 & 1 & 3 & EV1\\ \hline
 P4 & 3 & 5 & EV1\\ \hline
 P5 & 2 & 2 & EV1\\ \hline
 P6 & 5 & 5 & EV2\\ \hline
 P7 & 2 & 5 & EV2\\ \hline
 P8 & 3 & 4 & EV2\\ \hline
 P9 & 1 & 15 & EV2\\ \hline
 P10 & 1 & 1 & EV2\\ \hline
 \end{tabular}
\caption{The table shows the information about user evaluation participants including the number of years of experience, the reported number of videos they edited, and the assigned video for the study.}
\end{table}
\end{document}

cs

Bekzat Tilekbay
Saelyne Yang
Michal Lewkowicz
Alex Suryapranata
Juho Kim

"ExpressEdit: Video Editing with Natural Language and Sketching" by Bekzat Tilekbay, Saelyne Yang, Michal Lewkowicz, Alex Suryapranata, and Juho Kim, arXiv:2403.17693v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c}
 \hline
 & Annular Conv. & Conv. \& Max-pooling \\
 \hline
 mAP5 (\%) & \bf{53.15} & 51.25 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison using different number of levels, using an alternate TS-coarsening, $n_x = n_y = 192$, $n_t = 2^{11}$. On the the coarsest level, 16 processors are used. Other than the coarsest level, all matrix/vectors operations are performed on 32 or 64 processors. The symbol ``--'' means coarser levels that can be fit in the assigned number of processors could not be constructed further.}
\begin{tabular}{|c|c|ccc|ccc|} \hline
 & coarsest level & \multicolumn{6}{|c|}{No. of Processors} \\ \cline{3-8}
 & time-space & \multicolumn{3}{|c|}{32} & \multicolumn{3}{|c|}{64} \\ \cline{3-8}
 Level & grid & \#Iter. & $T_{\text{CPU}}$ & $T_{\text{CGS}}$ &\#Iter. & $T_{\text{CPU}}$ & $T_{\text{CGS}}$ \\ \hline
 7 & $128 \times 48 \times 48$ & 9 & 0.436 & 0.927 & 10 & 0.322 & 0.948 \\
 8 & $64 \times 48 \times 48$ & 10 & 0.428 & 0.913 & 10 & 0.365 & 0.947 \\
 9 & $64 \times 24 \times 24$ &10 & 0.416 & 0.918 & 10 & 0.365 & 0.951 \\
 10 & $32 \times 24 \times 24$ & 10 & 0.422 & 0.916 & -- & -- & -- \\
 11 & $32 \times 12 \times 12$ & 9 & 0.384 & 0.916 & -- & -- & -- \\ \hline
\end{tabular}
\end{table}
\end{document}

math

"Parallel-in-time Multilevel Krylov Methods: A Prototype" by Yogi A. Erlangga, arXiv:2401.00228v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

cs

Hyungjun Yoon
Jaehyun Kwak
Biniyam Aschalew Tolera
Gaole Dai
Mo Li
Taesik Gong
Kimin Lee
Sung-Ju Lee

"SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay" by Hyungjun Yoon, Jaehyun Kwak, Biniyam Aschalew Tolera, Gaole Dai, Mo Li, Taesik Gong, Kimin Lee, and Sung-Ju Lee, arXiv:2404.15305v2, 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}{c|c|c|c}
(n,m)&$ (2000,10)$& $(5000,20)$ & $(10000,50)$\\ \hline
Benchmark & 90.32s (17.51s) & 895.1s (86.5s) & 4767s (611s) \\
 Random & 6.31s (1.95s) & 14.4s (3.3s) & 22.5s (4.0s)\\
Spatial & 6.36s (2.49s) & 13.8s (3.0s) & 21.2s (4.9s)\\
\end{tabular}
\caption{\scriptsize Empirical (MMLE) Bayes rescaling of the Mat\'ern Gaussian process prior. Average run time for computing the posterior. Benchmark: Non-distributed method. Method 1: Random partitioning, Method 2: Spatial partitioning.}
\end{table}
\end{document}

math

Botond Szabo
Amine Hadji
Aad van der Vaart

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


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison on Sachs dataset.}
\begin{tabular}{ccccc}
\toprule
 \multirow{2}{*}{Method} & \multicolumn{2}{c}{Skeleton Prediction }& \multicolumn{2}{c}{CPDAG Prediction} \\
 & s-F1$\uparrow$ & s-Acc.$\uparrow$ & SHD$\downarrow$ & \#v-struc.$\downarrow$\\ \midrule
 PC& $68.6$& $80.0$ &$19$&$12$\\
 GES & $70.6$ & $81.8$ &$19$&$8$\\
 DAG-GNN & $21.1$ & $72.7$&$15$&$\mathbf{0}$ \\
 NOTEARS & $11.1$ & $70.9$ &$16 $ & $ \mathbf{0}$ \\
 GRAN-DAG & $45.5 $ & $78.2 $ &$ 12 $ & $\mathbf{0}$ \\
 GOLEM & $ 36.4$ & $ 74.5$ &$ 14 $ & $\mathbf{0}$ \\
AVICI & $66.7 $&$ 83.5$ &$18 $&$ 14$\\
SiCL & $\mathbf{71.4}$ & $\mathbf{86.8}$&$\mathbf{6}$&$\mathbf{0}$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Jiaru Zhang
Rui Ding
Qiang Fu
Bojun Huang
Zizhen Deng
Yang Hua
Haibing Guan
Shi Han
Dongmei Zhang

"Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning" by Jiaru Zhang, Rui Ding, Qiang Fu, Bojun Huang, Zizhen Deng, Yang Hua, Haibing Guan, Shi Han, and Dongmei Zhang, arXiv:2502.10883v1, 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{Constants for the estimates of attraction region, Example~1}
\begin{tabular}{cccccccccc}
\hline
\multicolumn{10}{c}{Lyapunov--Krasovskii approach}\\
\hline
$\Delta$ & $H_1$ & $H_2$ & $\delta$ & $\chi$ & $a_1$ & $\beta$ & $\mathrm{w}_0$ & $\mathrm{w}_1$ & $\mathrm{w}_2$\\
\hline
$0.067$ & $0.1012$ & $0.3$ & $0.1011$ & $0.32$ & $0.48$ & $17.7$ & $0.25$ & $0.05$ & $0.07$\\
\hline
\multicolumn{10}{c}{Lyapunov--Razumikhin approach}\\
\hline
\multicolumn{3}{c}{$\Delta$} & \multicolumn{2}{c}{$H$} & \multicolumn{3}{c}{$\kappa$} & \multicolumn{2}{c}{$K$}\\
\hline
\multicolumn{3}{c}{$0.0427$} & \multicolumn{2}{c}{$0.0443$} & \multicolumn{3}{c}{$1$} & \multicolumn{2}{c}{$1.001$}\\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

Gerson Portilla
Irina Alexandrova
Sabine Mondié
Alexey Zhabko

"Estimates for solutions of homogeneous time-delay systems: Comparison of Lyapunov-Krasovskii and Lyapunov-Razumikhin techniques" by Gerson Portilla, Irina Alexandrova, Sabine Mondié, and Alexey Zhabko, arXiv:2101.10139v3, 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{Parameter values for base transformation set $\mathcal{B}_2$.}
\begin{tabular}{|l|l|}
\hline
\textbf{Base Transformation} & \textbf{Parameter Value} \\ \hline
Rotation & $[-90, 90]$ \\ \hline
Translation ($x$ and $y$) & $[0\%, 25\%]$ \\ \hline
Scaling & $[0.7, 1.3]$ \\ \hline
Shear ($x$ and $y$) & $[-25, 25]$ \\ \hline
Perspective & $0.5$ \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

"Explicit homography estimation improves contrastive self-supervised learning" by David Torpey and Richard Klein, arXiv:2101.04713v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

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

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{\bf Data used along with train-test split dates.}}
\begin{tabular}{|l|l|l|l|}
\hline
\bf Data & \bf Begin Date & \bf Split Point & \bf End Date \\ \hline
GOOGL & 2010/01/01 & 2018/01/01 & 2020/08/24 \\ \hline
AAPL & 2010/01/01 & 2018/01/01 & 2020/08/24 \\ \hline
KSS & 1999/01/01 & 2018/01/01 & 2020/08/24 \\ \hline
BTC-USD & 2014/09/17 & 2018/01/01 & 2020/08/26 \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models" by Gang Hu, arXiv:2311.05743v1, 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}{lrrr}
\toprule
 & True $\varphi$ & $\varphi$ coverage & $T$ coverage \\
 \midrule
 Expectation & 0.20 & 0.97 & 0.90 \\
Geometric mean & 0.20 & 0.97 & 0.89 \\
No scaling & 0.20 & 0.83 & 0.80 \\
Expectation & 0.50 & 0.98 & 0.93 \\
Geometric mean & 0.50 & 0.97 & 0.93 \\
No scaling & 0.50 & 0.93 & 0.94 \\
Expectation & 0.80 & 0.81 & 0.89 \\
Geometric mean & 0.80 & 0.82 & 0.89 \\
No scaling & 0.80 & 0.91 & 0.86 \\
 \bottomrule
 \end{tabular}
\caption{90\% coverage level for $\varphi$ and $T$ for the VP prior specification}
\end{table}
\end{document}

stat

"A Standardization Procedure to Incorporate Variance Partitioning Based Priors in Latent Gaussian Models" by Luisa Ferrari and Massimo Ventrucci, arXiv:2501.16057v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c|c}
\hline
 & $p_k \to 0$ & $p_{k'} \to 1$ & Combination of $p_k$ and $p_{k'}$\\
 \hline
 Fisher & $\log p_k \to -\infty$ & $\log p_{k'} \to 0$ & FCT $\to -\infty$ \\
 Stouffer & $\Phi^{-1}(p_k) \to -\infty$ & $\Phi^{-1}(p_{k'}) \to +\infty$ & Stouffer $\to +\infty -\infty$ \\
 Cauchy & $\tan((0.5-p_k) \pi) \to -\infty$ & $\tan((0.5-p_{k'}) \pi) \to +\infty$ & CCT $\to +\infty -\infty$ \\
\hline
\end{tabular}
\caption{Behavior of transformed $p$-values under extreme values for Fisher (FCT), Stouffer and Cauchy combination rule (CCT).}
\end{table}
\end{document}

stat

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


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results showing the speaker diarization performance using baseline (B) and proposed methods (M1 and M2) on \textbf{development set} of third DIHARD challenge. M1: domain-dependent threshold, M2: domain-dependent threshold and domain-dependent parameter for PCA.}
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \multirow{2}{*}{Method} & \multicolumn{2}{|c|}{Full} & \multicolumn{2}{|c|}{Core}\\
 \cline{2-5}
 & DER & JER & DER & JER \\
 \hline
 B	& 19.59	&43.01	&20.17&	47.28\\
 M1	& 17.97	&40.33	&18.73	&44.77\\
 M2	& 17.40&	38.08&	17.95&	42.12\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

A Kishore Kumar
Shefali Waldekar
Goutam Saha
Md Sahidullah

"Domain-Dependent Speaker Diarization for the Third DIHARD Challenge" by A Kishore Kumar, Shefali Waldekar, Goutam Saha, and Md Sahidullah, arXiv:2101.09884v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\textbf{Discretization} & 41 & 81 & 161 & 321 & 641 & 1281 \\ \hline
\textbf{E} & 41 & 41 & 81 & 81 & 81 & 81 \\ \hline
\textbf{F} & 21 & 21 & 41 & 41 & 41 & 41 \\ \hline
\end{tabular}
\caption{Lotka-Volterra (LV) System}
\end{table}
\end{document}

stat

"EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems" by Jianhong Chen and Shihao Yang, arXiv:2501.14107v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l}
\toprule
\midrule
{\bf Templates} \\
\midrule
Given the instruction: <Instruction> \\
Here is a piece of code with bugs: <Bug output> \\
Fix the bugs in the code. \\
\midrule
<Bug output> is the code implementation of <Instruction>, \\
However, there are some bugs in the code \\
Please fix bugs in the code. \\
\midrule
Find the bugs in the <Bug output> \\
\midrule
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Yufan Jiang
Qiaozhi He
Xiaomin Zhuang
Zhihua Wu

"Code Comparison Tuning for Code Large Language Models" by Yufan Jiang, Qiaozhi He, Xiaomin Zhuang, and Zhihua Wu, arXiv:2403.19121v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{SCC comparison in IEEE 118-bus system}
\begin{tabular}{c|c||c|c|c}
 \toprule
 \multirow{2}{4.2em}{\textbf{\,\,\,\; SCC\\Constraint} } & \multirow{2}{4em}{\textbf{\quad PE\\Capacity} }& \multirow{2}{4em}{\textbf{ \, SCC\\Violation} } & \multirow{2}{2.8em}{\textbf{ Cost}\\$[\mathrm{k\pounds/h}]$} & \multirow{2}{2.8em}{\textbf{Time}$\,[\mathrm{s}]\,$} \\
 & & & &\\
 \cline{1-5}
 \multirow{2}{2.8em}{w.o.} & $P^C$ & 32.21\% & $104.04$ & $2762.17$ \\
 \cline{2-5}
 & $1.5 P^C$ & $10.22\%$ & $104.07$ & $2859.72$\\
 \cline{1-5}
 \multirow{2}{2.8em}{with} & $P^C$ & $0$ & $107.44$ & $4766.57$ \\
 \cline{2-5}
 & $1.5P^C$ & $0$ & $105.20$ & $3562.02$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

"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.


q-fin

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


\documentclass{article}
\usepackage{siunitx}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overview of the perception hardware components.}
\begin{tabular}{c|c|c|c}
 \toprule
 \textbf{Component} & \textbf{Manufacturer} & \textbf{Model} & \textbf{FOV (h x v)} \\
 \midrule
 LiDAR Mid-Range & \textit{Ouster} & \textit{OS1-128} & \SI{360}{\degree} x \SI{45}{\degree} \\
 LiDAR Long-Range & \textit{Innovusion} & \textit{Falcon Kinetic} & \SI{120}{\degree} x \SI{25}{\degree} \\
 GNSS-IMU & \textit{Novatel} & \textit{PwrPak7D-E2} & - \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Florian Sauerbeck
Dominik Kulmer
Markus Pielmeier
Maximilian Leitenstern
Christoph Weiß
Johannes Betz

"Multi-LiDAR Localization and Mapping Pipeline for Urban Autonomous Driving" by Florian Sauerbeck, Dominik Kulmer, Markus Pielmeier, Maximilian Leitenstern, Christoph Weiß, and Johannes Betz, arXiv:2311.01823v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Averages of falling depth in cases with the circuit breaker.}
\begin{tabular}{cr|rrrrrrr}
&& \multicolumn{5}{c}{$tr$} \\
&& 1000 & 2000 & 5000 & 10000 & 20000 \\ \hline
 & 10 & 152 & 103 & 57 & 34 & 22 \\
 & 20 & 297 & 207 & 107 & 58 & 43 \\
 & 50 & \cellcolor[gray]{0.90} 702 & 487 & 266 & 163 & 105 \\
 $Pr$ & 100 & \cellcolor[gray]{0.90} 1222 & \cellcolor[gray]{0.90} 869 & 520 & 315 & 208 \\
 & 200 & \cellcolor[gray]{0.90} 1954 & \cellcolor[gray]{0.90} 1522 & 821 & 614 & 407 \\
 & 500 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 2038 & \cellcolor[gray]{0.90} 1061 & 503 \\
 & 1000 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 2054 & \cellcolor[gray]{0.90} 1006
\end{tabular}
\end{table}
\end{document}

q-fin

"Comparing effects of price limit and circuit breaker in stock exchanges by an agent-based model" by Takanobu Mizuta and Isao Yagi, arXiv:2309.10220v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\toprule
Cardinalities & Num tuples & Kinetics & SSv2$^\dagger$ \\ \midrule
$\Omega{=}\{1\}$ & - & 85.2 & 53.3 \\
$\Omega{=}\{2\}$ & 28 & 85.0 & 57.8 \\
$\Omega{=}\{3\}$ & 56 & 85.6 & 58.8 \\
$\Omega{=}\{4\}$ & 70 & 84.5 & 58.9 \\
$\Omega{=}\{2,3\}$ & 84 & \textbf{85.9} & \textbf{59.1} \\
$\Omega{=}\{2,4\}$ & 98 & 84.4 & 58.4 \\
$\Omega{=}\{3,4\}$ & 126 & 85.3 & \textbf{59.1} \\
$\Omega{=}\{2,3,4\}$ & 154 & 85.3 & 58.9 \\
\bottomrule
\end{tabular}
\caption{Comparing all values of $\Omega$ for TRX, noting the number of tuples for each model, given by $\sum_{\omega \in \Omega}|\Pi^\omega|$.}
\end{table}
\end{document}

cs

Toby Perrett
Alessandro Masullo
Tilo Burghardt
Majid Mirmehdi
Dima Damen

"Temporal-Relational CrossTransformers for Few-Shot Action Recognition" by Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, and Dima Damen, arXiv:2101.06184v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|c|}
 \hline
 {} & {CWM $(K=50)$} & {RNVP} & {NSF} & {GMM $(K=50)$} \\
 \hline
 {\small \# Parameters} & {$39925$} & {$85780$} & {$79560$} & {$343$} \\
 \hline
 {\small Likelihood} & {$\mathbf{0.221\pm1.6\mathrm{e}-3}$} & {$0.133\pm 1.1\mathrm{e}-2$} & {$0.182\pm 3.9\mathrm{e}-3$} & {$0.150\pm 2.2\mathrm{e}-3$}\\
 \hline
\end{tabular}
\caption{Architectures, parameters and log-likelihood}
\end{table}
\end{document}

stat

Classifier Weighted Mixture models

Elouan Argouarc'h
François Desbouvries
Eric Barat
Eiji Kawasaki
Thomas Dautremer

"Classifier Weighted Mixture models" by Elouan Argouarc'h, François Desbouvries, Eric Barat, Eiji Kawasaki, and Thomas Dautremer, arXiv:2501.02989v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Gianluca Biggi
Andrea Mina
Federico Tamagni

"There are different shades of green: heterogeneous environmental innovations and their effects on firm performance" by Gianluca Biggi, Andrea Mina, and Federico Tamagni, arXiv:2310.08353v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Normalised Mutual Information to determine $K$}
\begin{tabular}{l|ccccccc}
\toprule
No. Clusters $K$ & 4 & 6 & 10 & 13 & 16 & 25 & 36 \\
\midrule
NMI $\uparrow$ & 0.141 & 0.133 & 0.206 & \textbf{0.264} & 0.244 & 0.24 & 0.26 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"Disentangled Sequence Clustering for Human Intention Inference" by Mark Zolotas and Yiannis Demiris, arXiv:2101.09500v4, 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{ANOVA results.}
\begin{tabular}{cccccc}
 \hline
 \, & df & SS & MS & F & Significance \\
 \hline
 Regression & 1 & 17655 & 17655 & 47734 & $\le$0.01 \\
 Residual & 669 & 247.4 & 0.3699 & \, & \, \\
 Total & 670 & 17902.4 & \, & \, & \, \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

"Bayesian adversarial multi-node bandit for optimal smart grid protection against cyber attacks" by Jianyu Xu, Bin Liu, Huadong Mo, and Daoyi Dong, arXiv:2104.02774v1, 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{Token taxonomy for Liquid Staking Tokens (LSTs) with staking reward distribution models supported blockchains. The analyzed tokens are naively minted only on Ethereum and bridged to other blockchain.}
\begin{tabular}{|l|l|l|l|}
\hline
\textbf{Protocol} & \textbf{LST} & \textbf{Model} & \textbf{Blockchain} \\
\hline
Lido & stETH & Rebase & Ethereum \\
Lido & wstETH & Reward & Ethereum, Arbitrum, \\
 & & & Optimism, Polygon \\
Rocket Pool & rETH & Reward & Ethereum, Optimism, \\
 & & & Arbitrum \\
Coinbase & cbETH & Reward & Ethereum \\
Binance & bETH & Rebase & Ethereum \\
Binance & wbETH & Reward & Ethereum \\
Frax & sfrxETH & Reward & Ethereum \\
Ankr & ankrETH & Reward & Ethereum \\
StakeWise & sETH2 & Dual & Ethereum \\
 & rETH2 & & \\
Stader & ETHx & Reward & Ethereum \\
Swell & swETH & Reward & Ethereum \\
Liquid Collective & lcETH & Reward & Ethereum \\
DIVA & divETH & Rebase & Ethereum \\
DIVA & wdivETH & Reward & Ethereum \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Krzysztof Gogol
Yaron Velner
Benjamin Kraner
Claudio Tessone

"SoK: Liquid Staking Tokens (LSTs) and Emerging Trends in Restaking" by Krzysztof Gogol, Yaron Velner, Benjamin Kraner, and Claudio Tessone, arXiv:2404.00644v3, 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}{lllll}
\hline
Option Variables & Mean & SD & Skew & Kurtosis\\
\hline
\hline
\multicolumn{5}{c}{\textbf{Panel A: Unconditional Excess Returns}} \\
\hline
ATM Straddle & -0.00006 & 0.8336 & 4.23 & 50.9 \\
\hline
\hline
\multicolumn{5}{c}{\textbf{Panel B: Volatility Variables}} \\
\hline
ATM Straddle Implied Volatility & 0.3274 & 0.1164 & 1.59 & 5.64 \\
Realized Volatility & 0.0165 & 0.0071 & 2.44 & 12.9 \\
$\widehat{RV}$ - PCA + Lagged RVs & 0.0015 & 0.0015 & 4.25 & 29.4 \\
$\widehat{RV}$ - Average Forecasts & 0.0010 & 0.0010 & 4.21 & 28.3 \\
$\widehat{RV}$ - HAR & 0.0004 & 0.0005 & 6.80 & 78.4 \\
$\widehat{RV}$ - Rolling SD Squared & 0.0004 & 0.0003 & 2.50 & 7.12 \\
$\widehat{RV}$ - LASSO & 0.0004 & 0.0004 & 6.40 & 73.2 \\
\hline
\hline
\multicolumn{5}{c}{\textbf{Panel C: Option Return Conditioning Variable - $\ln(RV/IV)$}} \\
\hline
VRP - PCA + Lagged RVs & 0.5082 & 0.2008 & -0.28 & 3.62 \\
VRP - Average Forecasts & 0.1410 & 0.1750 & -.0018 & 4.40 \\
VRP - HAR & -0.1664 & 0.1961 & 0.0373 & 4.91 \\
VRP - Rolling SD Squared & -0.0730 & 0.1777 & -0.2087 & 5.76 \\
VRP - LASSO & -0.1589 & 0.2179 & -0.1653 & 5.18 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Predicting Realized Variance Out of Sample: Can Anything Beat The Benchmark?" by Austin Pollok, arXiv:2506.07928v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of performance between methods (1000 episodes. Mean value)}
\begin{tabular}{lcccc}
 \noalign{\global\arrayrulewidth=1pt} \hline \noalign{\global\arrayrulewidth=0.4pt}
 Criteria & Memory-Less Agent & Memory-Based & Fixed Control & Single Divert \\ \hline
 Safe Landing Ratio [\%] & 94.8 \% & 88.3 \% & 93.2 \% & 89.7 \% \\
 Distance to final target [m] & 2.492 & 4.547 & 3.786 & 0.005 \\
 Final Velocity [m/s] & 0.637 & 1.520 & 1.189 & 0.557 \\
 Propellant Consumption [\%] & 69.88 & 63.70 & 67.84 & 68.89 \\
 Minimum Slant Angle [deg] & 72.429 & 72.570 & 72.795 & 70.85 \\
 Maximum Thrust [N] & 5818 & 6863 & 4553 & 4556 \\
 \noalign{\global\arrayrulewidth=1pt} \hline \noalign{\global\arrayrulewidth=0.4pt}
 \end{tabular}
\end{table}
\end{document}

eess

Keidai Iiyama
Kento Tomita
Bhavi A. Jagatia
Tatsuwaki Nakagawa
Koki Ho

"Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers" by Keidai Iiyama, Kento Tomita, Bhavi A. Jagatia, Tatsuwaki Nakagawa, and Koki Ho, arXiv:2102.12432v1, 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{Strategy for adjusting income and wealth components in the macro-based microsimulation.}
\begin{tabular}{ll}
 \toprule
 \multicolumn{2}{c}{Net wealth assets}\\
 \hline
 Main residence $x_{i2,1}$ & $x_{i2,1} \times \Delta(\text{house prices})_{h}$ \\
 Other real estate $x_{i2,2}$ & $x_{i2,2} \times \Delta(\text{house prices})_{h}$\\
 Self-employment business $x_{i2,3}$ & no adjustment\\
 Publicly traded shares $x_{i2,4}$ & $x_{i2,4} \times \Delta(\text{stock prices})_{h}$\\
 Bonds $x_{i2,5}$ & $x_{i2,5} \times \Delta(\text{bond prices})_{h}$\\
 Voluntary pension or whole life insurance $x_{i2,6}$ & no adjustment\\
 Deposits $x_{i2,7}$ & no adjustment\\
 Other financial assets $x_{i2,8}$ & no adjustment\\
 Total liabilities $x_{i2,9}$ & no adjustment\\
 \bottomrule
 \multicolumn{2}{c}{Income component}\\
 \hline
 Employment income $x_{i1,1}$ & if person becomes employed: imputation\\
 & otherwise: $x_{i1,1} \times \Delta(\text{wages})_{h}$\\
 Self-employment income $x_{i1,2}$ & $x_{i1,2} \times \Delta(\text{wages})_{h}$\\
 Income from pensions $x_{i1,3}$ & no adjustment\\
 Rental income from real estate property $x_{i1,4}$ & no adjustment\\
 Income from financial investments $x_{i1,5}$ & no adjustment\\
 Unemployment benefits and transfers $x_{i1,6}$ & set to zero if employment income is imputed,\\
 & otherwise no adjustment\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"Monetary policy and the joint distribution of income and wealth: The heterogeneous case of the euro area" by Anna Stelzer, arXiv:2304.14264v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Xueqing Liu
Tianchen Qian
Lauren Bell
Bibhas Chakraborty

"Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes" by Xueqing Liu, Tianchen Qian, Lauren Bell, and Bibhas Chakraborty, arXiv:2310.18905v3, 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 used in the physics modeling of the thermal systems.}
\begin{tabular}{rlrl}
\toprule
Parameter & Value & Parameter & Value \\ \hline
CPHX wall mass & 1.15 kg &
LLHX wall mass & 1.2 kg \\
Tank fluid mass & 2.01 kg &
Thermal sink temperature $T^t$ & $15^o$ C \\
initial temperature , $T_{l,0}$ & $15^o$ C &
wall temperature, $T_{w,0}$ & $20^o$ C \\
fluid temperatue , $T_{f,0}$ & $20^o$ C &
Thermal sink mass flow rate, $\dot{m}_t$ & 0.2 kg/s \\
Pupmp mass flow rate $\dot{m}_p$ & 0.4 kg/s &
Valve rate limit $\ddot{m}_{f,\mathrm{max}}$ & 0.05 kg/$\mathrm{s}^2$ \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Saeid Bayat
Nastaran Shahmansouri
Satya RT Peddada
Alex Tessier
Adrian Butscher
James T Allison

"Extracting Design Knowledge from Optimization Data: Enhancing Engineering Design in Fluid Based Thermal Management Systems" by Saeid Bayat, Nastaran Shahmansouri, Satya RT Peddada, Alex Tessier, Adrian Butscher, and James T Allison, arXiv:2310.16324v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training hyperparameters chosen in our experiments}
\begin{tabular}{llll}
\toprule
\textbf{Training params.} & Piano & S. Voice (pre) & S. Voice (f-t) \\ \midrule
Learning rate & $2\times 10^{-4}$ & 2$\times 10^{-5}$ & $1\times 10^{-4}$ \\
Batch size & 4 & 4 & 4 \\
Sampling rate &22.05 kHz & 44.1 kHz & 44.1kHz \\
Segment length &8.35 s& 5.94 s & 5.94 s \\
EMA rate &0.9999& 0.9999& 0.9999\\
$\sigma_\text{data}$ & $6.3 \times 10^{-2}$ &1 &1 \\
Training its. & 850k & 325k & 8k\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Eloi Moliner
Maija Turunen
Filip Elvander
Vesa Välimäki

"A Diffusion-Based Generative Equalizer for Music Restoration" by Eloi Moliner, Maija Turunen, Filip Elvander, and Vesa Välimäki, arXiv:2403.18636v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Deniz Inan
Ufuk Beyaztas
Carmen D. Tekwe
Xiwei Chen
Roger S. Zoh

"Functional Linear Cox Regression Model with Frailty" by Deniz Inan, Ufuk Beyaztas, Carmen D. Tekwe, Xiwei Chen, and Roger S. Zoh, arXiv:2501.07450v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{THE DESIGN OF THE FIRST ABLATION STUDY.}
\begin{tabular}{|c|c|c|c|}
\hline
Methods & MinDP & MaxDP & MeanDP\\
\hline
M1 & \checkmark & & \\
\hline
M2 & & \checkmark & \\
\hline
M3 & & & \checkmark \\
\hline
M4 & \checkmark & \checkmark & \\
\hline
M5 & & \checkmark & \checkmark \\
\hline
M6 & \checkmark & & \checkmark \\
\hline
M7 & \checkmark & \checkmark & \checkmark \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Hieu X. Nguyen
Duong V. Nguyen
Hieu H. Pham
Cuong D. Do

"MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classification" by Hieu X. Nguyen, Duong V. Nguyen, Hieu H. Pham, and Cuong D. Do, arXiv:2311.15041v1, 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{MAEs for each value of the hyperparameter with the rest of the hyperparameters as default. For each hyperparameter, each row's MAE score corresponds to the hyperparameter value inside the parentheses in the same order. For example, when lr is 1e-3, the MAE score is 1.313.}
\begin{tabular}{cccccccccc}
\toprule
lr & seg\_len & window\_size & e\_layers & d\_model & d\_ff & n\_heads \\
(1e-3, 1e-4, 1e-5) & (6, 12, 24) & (2,4,8) & (3,4,5) & (32, 64, 128) & (64, 128, 256) & (2,4,8) \\
\midrule
1.313 & 1.313 & 1.313 & 1.313 & 1.313 & 1.313 & 1.313 \\
1.588 & 1.311 & 1.285 & 1.306 & 1.235 & 1.334 & 1.319 \\
1.673 & 1.288 & 1.368 & 1.279 & 1.372 & 1.302 & 1.312 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Dennis Wu
Jerry Yao-Chieh Hu
Weijian Li
Bo-Yu Chen
Han Liu

"STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction" by Dennis Wu, Jerry Yao-Chieh Hu, Weijian Li, Bo-Yu Chen, and Han Liu, arXiv:2312.17346v1, 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{Peeler's false negative rate analysis.}
\begin{tabular}{lrrr}
 \toprule
 \textbf{Ransomware} & \textbf{TPR (\%)} &\textbf{FPR (\%)} &\textbf{FNR (\%)} \\ \midrule
 Crypto & 100& 0.8 &0\\
 Screen-locker &99.50 & 0.31 & 0.50 \\\midrule
 All ransomware &99.63&0.58&0.37 \\
 \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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mean and Standard deviation of test loss of the optimizers for the Heat equation.}
\begin{tabular}{lrr}
\toprule
 & mean & std \\
\midrule
ANaGRAM & \textbf{8.56e-11} & \textbf{7.05e-11} \\
Adam & 3.63e-04 & 3.93e-04 \\
E-NGD & 3.53e-09 & 3.83e-09 \\
GD & 1.20e-02 & 1.05e-03 \\
L-BFGS & 8.54e-04 & 9.16e-04 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning" by Nilo Schwencke and Cyril Furtlehner, arXiv:2412.10782v2, 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{Mean Absolute Difference between prescribed and actual treatments for structured datasets.}
\begin{tabular}{ccccc}
\textbf{Method} & \textbf{Diabetes} & \textbf{Groceries} & \textbf{Spleen} & \textbf{REBOA} \\ \midrule
Regress \& Compare & $\mathbf{0.4556}$ & $1.033$ & $\mathbf{0.3355}$ & $0.2815$ \\
Causal Forest & $0.5923$& $\mathbf{1.0042}$ & $ 0.7761$ & $0.2492$\\
Optimal Policy Tree & $0.7490$&$1.0221$ & $0.3755$ & $\mathbf{0.1199}$\\
PNN & $0.4903$ & $1.4386$ & $0.5185$ & $0.1568$\\
Mirrored OCT & $0.4936$ & $1.4391$ & $0.5069$ & $0.1435$\\
\end{tabular}
\end{table}
\end{document}

stat

Multimodal Prescriptive Deep Learning

Dimitris Bertsimas
Lisa Everest
Vasiliki Stoumpou

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Known van der Waerden square numbers}
\begin{tabular}{|c||c|c|c|c|c|c|}
 c & 1 & 2 & 3 & 4 & 5 & 6 \\
 \hline
 n & 2 & 5 & 29 & \textbf{58} & $>$\textbf{180} & $>$\textbf{333} \\
 \end{tabular}
\end{table}
\end{document}

math

On SAT Solvers and Ramsey-type Numbers

Burcu Canakci
Hannah Christenson
Robert Fleischman
William Gasarch
Nicole McNabb
Daniel Smolyak

"On SAT Solvers and Ramsey-type Numbers" by Burcu Canakci, Hannah Christenson, Robert Fleischman, William Gasarch, Nicole McNabb, and Daniel Smolyak, arXiv:2312.01159v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Elisa Alòs
Eulalia Nualart
Makar Pravosud

"On the implied volatility of European and Asian call options under the stochastic volatility Bachelier model" by Elisa Alòs, Eulalia Nualart, and Makar Pravosud, arXiv:2308.15341v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.