\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lllrc}
\toprule
\textbf{Model} & \textbf{Initial Weights} & \textbf{Corpus} & \textbf{Release} & \textbf{MedQA} \\
\midrule
PMC-LLaMA-7B~ & LLaMA 7B & PMC, Textbooks & Apr 2023 & \\
LLaMA-2-7B~ & - & & Jul 2023 & 59.1$_{\pm0.9}$ \\
Mistral-7B~ & - & & Oct 2023 & \textbf{63.3}$_{\pm0.5}$ \\
MediTron-7B~ & LLaMA 7B & PMC & Nov 2023 & 62.2$_{\pm0.7}$ \\
OLMo-7B~ & - & & Feb 2024 & \\
BioMistral-7B~ & Mistral 7B & PMC & Feb 2024 & 62.7$_{\pm0.4}$ \\
Gemma-7B~ & - & Unknown & Feb 2024 & 63.1$_{\pm0.3}$ \\
\midrule
Mixtral-8x7B~ & & Unknown & Jan 2024 & \\
\midrule
\end{tabular}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters for BitFit for each task}
\begin{tabular}{l|rrrrrr}
\toprule
\textbf{Hyperparameter} & \textbf{CoLA} & \textbf{STS-B} & \textbf{MRPC} & \textbf{QNLI} & \textbf{SST-2} & \textbf{RTE} \\
\midrule
\textbf{Learning Rate} & $7.94e^{-4}$ & $5.53e^{-4}$ & $8.61e^{-4}$ & $7.91e^{-4}$ & $3.36e^{-4}$ & $1.00e^{-3}$ \\
\textbf{Weight Decay} & $2.00e^{-2}$ & $8.89e^{-2}$ & $9.89e^{-2}$ & $4.70e^{-3}$ & $3.16e^{-2}$ & $1.11e^{-2}$ \\
\textbf{Warmup Ratio} & $1.00e^{-1}$ & $2.75e^{-2}$ & $8.10e^{-2}$ & $7.07e^{-2}$ & $8.33e^{-2}$ & $6.19e^{-2}$ \\
\textbf{Scheduler Type} & Cosine & Linear & Cosine & Linear & Cosine & Linear \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

Abdessalam Ed-dib
Zhanibek Datbayev
Amine Mohamed Aboussalah

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

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Network Structure of the proposed fully CNN backbone for SCI reconstruction.}
\begin{tabular}{c|c|c|c|c}
 \toprule
 & Module & Stride & Kernel Size & Output Size \\ \midrule
 \multirow{4}{*}{$\times 1$} & Conv. + Leaky Relu & 1 & $5 \times 5 \times (B+1) \times 32$ & $256 \times 256 \times 32$ \\
 & Conv. + Leaky Relu & 1 & $3 \times 3 \times 32 \times 64$ & $256 \times 256 \times 64$ \\
 & Conv. + Leaky Relu & 1 & $1 \times 1 \times 64 \times 64$ & $256 \times 256 \times 64$ \\
 & Conv. + Leaky Relu & 2 & $3 \times 3 \times 64 \times 128$ & $128 \times 128 \times 128$ \\ \midrule
 \multirow{6}{*}{$\times 3$} & Conv. + Leaky Relu & 1 & $3 \times 3 \times 128 \times 128$ & $128 \times 128 \times 128$ \\
 & Conv. + Leaky Relu & 1 & $1 \times 1 \times 128 \times 128$ & $128 \times 128 \times 128$ \\
 & Conv. + Leaky Relu & 1 & $3 \times 3 \times 128 \times 128$ & $128 \times 128 \times 128$ \\
 & Conv. + Leaky Relu & 1 & $3 \times 3 \times 128 \times 128$ & $128 \times 128 \times 128$ \\
 & Summation & -- & -- & $128 \times 128 \times 128$ \\
 & Conv. + Leaky Relu & 1 & $3 \times 3 \times 128 \times 128$ & $128 \times 128 \times 128$ \\
 & Conv. + Leaky Relu & 1 & $1 \times 1 \times 128 \times 128$ & $128 \times 128 \times 128$ \\ \midrule
 \multirow{4}{*}{$\times 1$} & dConv. + Leaky Relu & 1 & $3 \times 3 \times 64 \times 128$ & $256 \times 256 \times 64$ \\
 & Conv. + Leaky Relu & 1 & $3 \times 3 \times 64 \times 32$ & $256 \times 256 \times 32$ \\
 & Conv. + Leaky Relu & 1 & $1 \times 1 \times 32 \times 16$ & $256 \times 256 \times 16$ \\
 & Conv. + Leaky Relu & 1 & $3 \times 3 \times 16 \times B$ & $256 \times 256 \times B$ \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Zhengjue Wang
Hao Zhang
Ziheng Cheng
Bo Chen
Xin Yuan

"MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing" by Zhengjue Wang, Hao Zhang, Ziheng Cheng, Bo Chen, and Xin Yuan, arXiv:2103.01786v1, 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}{lccccc}
 \toprule
 & Point {m}ass & Mean & $\ell_{Z|_{\{Z<1\}}}$ & $\ell_Z$ & AIC \\
 \midrule
 Empirical density (Blue) & 0.034 & 0.339 & - & - & - \\
 MLE of the standard problem (Green) & 0.020 & 0.337 &32 680 &13 475 & -26 945 \\
 MLE of the extended problem (Red) & 0.034 & 0.336 &33 257 &14 587 & -29 166 \\
 \bottomrule
 \end{tabular}
\caption{Power logarithmic linked exposure example: results.}
\end{table}
\end{document}

stat

"Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class" by Selim Gatti and Mario V. Wüthrich, arXiv:2310.11471v2, 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{Different kernel sizes for Centerpoint~. For the unsupervised task of \textit{VLD-64} $\rightarrow$ \textit{VLD-16}.}
\begin{tabular}{c|c|ccc|c}
 & \multicolumn{5}{c}{Centerpoint} \\
 \cline{2-6}
 & & \multicolumn{3}{|c|}{Kernel Size} & \\
 & Source-Only & $1$ & $3$ & $5$ & Oracle \\
 \hline
 $AP_{3D, V}$ & 32.30 & 43.90 & \textbf{46.99 }& 46.55 & 53.54 \\
 $AP_{3D, P}$ & 11.63 & 32.92 & \textbf{38.22} & 37.52 & 47.78 \\
 $AP_{3D, C}$ & 23.06 & 46.20 & 45.56 & \textbf{46.62} & 53.11 \\
 \end{tabular}
\end{table}
\end{document}

cs

Maciej K Wozniak
Mattias Hansson
Marko Thiel
Patric Jensfelt

"UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps" by Maciej K Wozniak, Mattias Hansson, Marko Thiel, and Patric Jensfelt, arXiv:2403.17633v4, 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}{|cccc|}
\hline
Fingerprint & logistic regression & Random Forest & LightGBM \\
\hline
ECFP & 69.04 & 82.00 ± 0.82 & 80.32\\
\hline
Atom Pair & 80.31 & 85.19 ± 1.07 & 84.59\\
\hline
Topological Torsion & 69.23 & \textbf{85.58 ± 0.79} & 81.13\\
\hline
MACCS Keys & 74.44 & 79.03 ± 1.15 & 76.00\\
\hline
ErG & 66.24 & 79.45 ± 0.95 & 78.60\\
\hline
MAP4 & 44.01 & 58.91 ± 3.50 & 63.92\\
\hline
MHFP & 50.10 & 58.58 ± 4.09 & 55.12\\
\hline
\end{tabular}
\caption{Test AUROC scores for BACE dataset.}
\end{table}
\end{document}

cs

Michał Szafarczyk
Piotr Ludynia
Przemysław Kukla

"A Python library for efficient computation of molecular fingerprints" by Michał Szafarczyk, Piotr Ludynia, and Przemysław Kukla, arXiv:2403.19718v1, 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{Flyball governor parameter values}
\begin{tabular}{lllll}
 \toprule
 \textbf{Parameter} & \textbf{Description} & \textbf{Initial Est.} & \textbf{Bounds} & \textbf{Optima}\\
 \midrule
 $\rho_1$ & Top arm length & 0.135 m & [0.1 m, 0.15 m] & 0.1247 m\\
 $\rho_2$ & Bottom arm length & 0.08 m & [0.06 m, 0.1 m] & 0.0853 m\\
 $\rho_3$ & Overhang ratio & 0.3 & [0.2, 0.4] & 0.20\\
 $\rho_4$ & Top arm to Pillar offset & 0.025 m & [0.02 m, 0.03 m] & 0.02 m\\
 $\rho_5$ & Collar outer radius & 0.025 m & [0.02 m, 0.03 m] & 0.02 m\\
 $\rho_6$ & Pillar height & 0.225 m & [0.2 m, 0.25 m] & 0.2 m\\
 $\rho_7$ & Flyweight mass & 0.03 kg & [0.02, 0.04] & 0.0242 kg\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Adwait Verulkar
Corina Sandu
Adrian Sandu
Daniel Dopico

"Simultaneous Optimal System and Controller Design for Multibody Systems with Joint Friction using Direct Sensitivities" by Adwait Verulkar, Corina Sandu, Adrian Sandu, and Daniel Dopico, arXiv:2312.15771v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|} \hline
 method & sample ACVF & sample NCV & trimm. ($c=0.015$) & trimm. ($c=0.025$) & Kendall & Spearman \\ \hline
 $\tau_\gamma$ & 0.0495 & 0.0744 & 0.0708 & 0.0641 & 0.0841 & 0.0590\\ \hline
 \end{tabular}
\caption{Performance indicator $\tau_\gamma$ values obtained for Dataset 2.}
\end{table}
\end{document}

stat

Wojciech Żuławiński
Jérôme Antoni
Radosław Zimroz
Agnieszka Wyłomańska

"Applications of robust statistics for cyclostationarity detection in non-Gaussian signals for local damage detection in bearings" by Wojciech Żuławiński, Jérôme Antoni, Radosław Zimroz, and Agnieszka Wyłomańska, arXiv:2502.07478v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The effect of modifying the number of gradient steps using the optimizer configuration reported in the main section for Adam on the toy nonlinear system. Ordered by RMSE.}
\begin{tabular}{lc}
\toprule
\textbf{Method} & \textbf{RMSE} \\
\midrule
Adam ($K = 1, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $10.510 \pm 0.263$ \\
Adam ($K = 25, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $10.478 \pm 0.409$ \\
Adam ($K = 3, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $9.749 \pm 0.288$ \\
Adam ($K = 5, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $9.244 \pm 0.266$ \\
Adam ($K = 10, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $9.218 \pm 0.291$ \\
Adam ($K = 100, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $6.575 \pm 0.347$ \\
Adam ($K = 50, \eta = 0.1, \beta_1, \beta_2 = 0.1$) & $5.842 \pm 0.231$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Gianluca M. Bencomo
Jake C. Snell
Thomas L. Griffiths

"Implicit Maximum a Posteriori Filtering via Adaptive Optimization" by Gianluca M. Bencomo, Jake C. Snell, and Thomas L. Griffiths, arXiv:2311.10580v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Lucca Portes Cavalheiro
Simon Bernard
Jean Paul Barddal
Laurent Heutte

"Random Forest Kernel for High-Dimension Low Sample Size Classification" by Lucca Portes Cavalheiro, Simon Bernard, Jean Paul Barddal, and Laurent Heutte, arXiv:2310.14710v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Combinatorial Results on Barcode Lattices" by Alex Bouquet and Andrés R. Vindas-Meléndez, arXiv:2312.08705v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter selection for NanoGPT experiment}
\begin{tabular}{lcccccc}
 \toprule
 \textbf{Optimizer} & \textbf{learning rate} & \textbf{$\beta_1$} & \textbf{$\beta_2$} & \textbf{warmup factor} & \textbf{update frequency} & \textbf{inverse order}\\
 \midrule
 Muon & 0.00349 & 0.9881 & N/A & 0.3 & N/A & N/A \\
 AdamW & 0.00450 & 0.9332 & 0.9528 & 0.2 & N/A & N/A \\
 DASGO & 0.060 & 0.9584 & 0.9435 & 0.2 & N/A & N/A \\
 Shampoo & 0.00593 & 0.9402 & 0.9760 & 0.3 & 15 & $-\frac{1}{4}$ \\
 ASGO & 0.01470 & 0.9541 & 0.8487 & 0.3 & 15 & N/A \\\hline
 \end{tabular}
\end{table}
\end{document}

math

Kang An
Yuxing Liu
Rui Pan
Yi Ren
Shiqian Ma
Donald Goldfarb
Tong Zhang

"ASGO: Adaptive Structured Gradient Optimization" by Kang An, Yuxing Liu, Rui Pan, Yi Ren, Shiqian Ma, Donald Goldfarb, and Tong Zhang, arXiv:2503.20762v2, 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}{lrr}
\hline\hline
Constraints&Number of filings&Fraction, \%\tabularnewline
\hline
0&$51535$&$85.952$\tabularnewline
1&$ 3725$&$ 6.213$\tabularnewline
2&$ 2074$&$ 3.459$\tabularnewline
3&$ 873$&$ 1.456$\tabularnewline
4&$ 466$&$ 0.777$\tabularnewline
5&$ 306$&$ 0.510$\tabularnewline
6&$ 231$&$ 0.385$\tabularnewline
7&$ 148$&$ 0.247$\tabularnewline
8&$ 119$&$ 0.198$\tabularnewline
9&$ 103$&$ 0.172$\tabularnewline
10&$ 78$&$ 0.130$\tabularnewline
11&$ 48$&$ 0.080$\tabularnewline
12&$ 54$&$ 0.090$\tabularnewline
13&$ 36$&$ 0.060$\tabularnewline
14&$ 24$&$ 0.040$\tabularnewline
15&$ 28$&$ 0.047$\tabularnewline
16&$ 20$&$ 0.033$\tabularnewline
17&$ 19$&$ 0.032$\tabularnewline
18&$ 19$&$ 0.032$\tabularnewline
19&$ 8$&$ 0.013$\tabularnewline
20&$ 12$&$ 0.020$\tabularnewline
21&$ 10$&$ 0.017$\tabularnewline
22&$ 3$&$ 0.005$\tabularnewline
23&$ 4$&$ 0.007$\tabularnewline
24&$ 4$&$ 0.007$\tabularnewline
25&$ 2$&$ 0.003$\tabularnewline
26&$ 1$&$ 0.002$\tabularnewline
27&$ 2$&$ 0.003$\tabularnewline
29&$ 1$&$ 0.002$\tabularnewline
32&$ 1$&$ 0.002$\tabularnewline
34&$ 2$&$ 0.003$\tabularnewline
35&$ 1$&$ 0.002$\tabularnewline
47&$ 1$&$ 0.002$\tabularnewline
\hline
Total&$ 59958 $&$ 100.000$\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Zero-Leverage Puzzle

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


stat

"Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age" by Marcos Matabuena, arXiv:2501.06868v1, 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{Summary of the results obtained for $(4,g)$-spectra}
\begin{tabular}{|c|c|c|c|}
 			\hline
 			Girth $g$ & $n(4,g)$ & Orders to be investigated & $N(4,g)$ \\
 			\hline
 			4 & 8 & -- & 10 \\
 			\hline
 			5 & 19 & -- & 19 \\
 			\hline
 			6 & 26 & 37 & $\leq$ 38\\
 			\hline
 			7 & 67 & 69, 71, 81--83 & $\leq$ 84\\
 			\hline
 			8 & 80 & 83--95, 97--99, 101--107, 109, 111--119, 121--123, 125, 127, 129 & $\leq$ 130 \\
 			\hline
 		\end{tabular}
\end{table}
\end{document}

math

L. C. Eze
R. Jajcay
T. Jajcayová
D. Závacká

"Theoretical and Computational Approaches to Determining Sets of Orders for $(k,g)$-Graphs" by L. C. Eze, R. Jajcay, T. Jajcayová, and D. Závacká, arXiv:2503.06466v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Fleurianne Bertrand
Maximilian Brodbeck
Tim Ricken
Henrik Schneider

"Least-Squares Finite Element Methods for nonlinear problems: A unified framework" by Fleurianne Bertrand, Maximilian Brodbeck, Tim Ricken, and Henrik Schneider, arXiv:2503.18739v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c} % <-- Alignments: 1st column left, 2nd middle and 3rd right, with vertical lines in between
			\textbf{Parameters} & \textbf{Value} \\
			\hline
			$\tau^{s}$& 1/10/2023 \\
			$\tau^{e}$ & 31/10/2023 \\
			$u_{max}$ & $1,\dots, 31$ \\
			$d_{max}$ & $1,\dots, 31$ \\
			$K$ & $39.83$ (EUR/MWh)
		\end{tabular}
\caption{Swing contract parameters for a delivery period $\left[\tau^{s} ,\tau^{e} \right]$ from 1/10/2023 to 31/10/2023.}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrr}
 & Definition & Sources\tabularnewline
 & & \tabularnewline
\hline
\multicolumn{2}{l}{Bank Variables} & \tabularnewline
\hline
 & & \tabularnewline
Non-Core Ratio & Foreign liabilities over deposits & IFS, national CBs\\
Loan-to-Deposit Ratio & Loans to private sector over deposits & IFS, national CBs\\
Capital-to-Asset Ratio & Bank capital over total assets & IFS, Fred, FSI, national CBs\\
BIS Cross-border Liabilities & Cross-border bank liabilities over deposits & IFS, BIS LBS \\
BIS Foreign-currency, cross-border Liabilities & Cross-border bank liabilities denominated in foreign currency over deposits & IFS, BIS LBS \\
BIS Domestic-currency, cross-border Liabilities & Cross-border bank liabilities denominated in domestic currency over deposits & IFS, BIS LBS \\
BIS Cross-border Liabilities to Banks & Cross-border bank liabilities vis-a-vis banks over deposits & IFS, BIS LBS \\
BIS Cross-border Liabilities to Nonbanks & Cross-border bank liabilities vis-a-vis the nonbank financial sector over deposits & IFS, BIS LBS \\
BIS Cross-border Liabilities to Other Sectors & Cross-border bank liabilities vis-a-vis all other sectors (other than banks or nonbanks) over deposits & IFS, BIS LBS \\
 & & \\
\hline
\multicolumn{2}{l}{Domestic Controls} & \\
\hline
 & & \\
GDP Growth & Quarter-to-quarter real GDP growth rate & IFS \\
Real Interest Rate & The difference between the short-term nominal interest rate and the one-quarter lead inflation rate & IFS, OECD \\
REER & The real effective exchange rate based on CPI & IFS, Fred \\
Money-to-GDP Ratio & The broad money to GDP ratio & IFS, Fred \\
Liquidity Regulation & country-specific cumulative macroprudential liquidity
regulation & \\
Capital Regulation & country-specific cumulative macroprudential capital
regulation & \\
Fixed & Dummy=1 for strictly fixed exchange rate regimes (pegs) & \\
Macroprudential Tightness & Dummy=1 for countries with a cumulative macroprudential tightness stance above sample median & \\
 & & \\
\hline
\multicolumn{2}{l}{Global Variables} & \\
\hline
 & & \\
VIX & Logarithm of the implied volatility of S\&P 500 index options & Fred \\
REER US & The US real effective exchange rate & Fred \\
Shadow Rate & The US shadow interest rate & \\
Oil Price & WTI oil prices in USD & FRED \\
Nonbank Share & Worldwide share of financial intermediation by nonbanks & FSB \\
World GDP Growth & Current USD GDP weighted average quarter-to-quarter real GDP growth of high-income countries & IFS \\
World Inflation & Current USD GDP weighted average quarter-to-quarter inflation rate of high-income countries & IFS \\
World Macroprudential & Current USD GDP weighted average cumulative macroprudential tightness variable & \\ \hline
\end{tabular}
\caption{\scshape Variable Definitions and Sources}
\end{table}
\end{document}

q-fin

Luís A. V. Catão
Jan Ditzen
Daniel Marcel te Kaat

"Global Factors in Non-core Bank Funding and Exchange Rate Flexibility" by Luís A. V. Catão, Jan Ditzen, and Daniel Marcel te Kaat, arXiv:2310.11552v6, 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 optimal strategies and value functions}
\begin{tabular}{cccccccccc}
		\toprule %添加表格头部粗线
		Predetermined:&$x$	&$B$ &$\pi^*(x,y)/x$&$c^*(x,y)/x$ &$\frac{y}{\kappa}$ \\
		 \midrule
	Values:	&1 & 5 &33.482 &1.477& 55 \\
	\midrule
	Predetermined:&$x$	&$B$&$\pi^*(x,y)/x$&$c^*(x,y)/x$ && \\
	\midrule
	Values:	&1 &0.351&32.216 &1.479& \\
	\midrule
	Controlled:&$x$&$B^*_0(x,y)/x$	&$\pi^*(x,y)/x$&$c^*(x,y)/x$ &\\
	\midrule
	Values:	&1& 0.351 &32.216 &1.479& \\				\bottomrule
	\end{tabular}
\end{table}
\end{document}

q-fin

"Striking the Balance: Life Insurance Timing and Asset Allocation in Financial Planning" by An Chen, Giorgio Ferrari, and Shihao Zhu, arXiv:2312.02943v1, 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{Classification $R^{2}$ scores on different train and test datasets, where values closer to one correspond to a better match.}
\begin{tabular}{cccc}
 \hline
 $R_\mathrm{high}$ & TRTR & TSTR & T(S+R)TR \\
 \hline
 1 & 0.975 & 0.978 & 0.985 \\
 5 & 0.926 & 0.909 & 0.931\\
 10 & 0.917 & 0.935 & 0.943\\
 20 & 0.988 & 0.879 & 0.915\\
 40 & 0.976 & 0.903 & 0.981\\
 80 & 0.905 & 0.971 & 0.963\\
 160 & 0.938 & 0.910 & 0.950\\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Arya Mohan
Pavlos Protopapas
Keerthi Kunnumkai
Cecilia Garraffo
Lindy Blackburn
Koushik Chatterjee
Sheperd S. Doeleman
Razieh Emami
Christian M. Fromm
Yosuke Mizuno
Angelo Ricarte

"Generating Images of the M87* Black Hole Using GANs" by Arya Mohan, Pavlos Protopapas, Keerthi Kunnumkai, Cecilia Garraffo, Lindy Blackburn, Koushik Chatterjee, Sheperd S. Doeleman, Razieh Emami, Christian M. Fromm, Yosuke Mizuno, and Angelo Ricarte, arXiv:2312.01005v1, 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}{llllll}
\hline
 & MSE & MAE & RMSE & MS-SSIM & PSNR \\\midrule
Conventional tomography & 0.0018 & 0.0262 & 0.0420 & 0.8888 & 27.5299 \\
Proposed tomography & 0.0006 & 0.0110 & 0.0236 & 0.9519 & 32.5294 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Jiawei Sun
Bin Yang
Nektarios Koukourakis
Jochen Guck
Juergen W. Czarske

"AI-driven projection tomography with multicore fibre-optic cell rotation" by Jiawei Sun, Bin Yang, Nektarios Koukourakis, Jochen Guck, and Juergen W. Czarske, arXiv:2312.07631v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The SCs index mapper for $N=4$ and $K=2$.}
\begin{tabular}{|c|c|c|}\hline
Input bits & Indices & {AFDM-IM subblocks $({\bf{x}}_g)^T$} \\\hline
00 & $\{1,2\}$ &{$[a_{g,1} \ a_{g,2} \ 0 \ 0]$} \\\hline
01 & $\{2,3\}$ & {$[0 \ a_{g,1} \ a_{g,2} \ 0]$} \\\hline
10 & $\{3,4\}$ & {$[0 \ 0 \ a_{g,1} \ a_{g,2}]$}\\\hline
11 & $\{1,4\}$ & {$[a_{g,1} \ 0 \ 0 \ a_{g,2}]$}\\\hline
\end{tabular}
\end{table}
\end{document}

math

Jing Zhu
Qu Luo
Gaojie Chen
Pei Xiao
Lixia Xiao

"Design and Performance Analysis of Index Modulation Empowered AFDM System" by Jing Zhu, Qu Luo, Gaojie Chen, Pei Xiao, and Lixia Xiao, arXiv:2312.01125v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
(n,m)&$ (2000,10)$& $(5000,20)$ & $(10000,50)$\\ \hline
 BM& 1.00 & 1.00 & 1.00\\
 M1& 0.18 & 0.45 & 0.00 \\
 M2& 0.98 & 1.00 & 1.00\\
 M3& 0.00 & 1.00 & 1.00\\
 M4& 0.96 & 1.00 & 1.00\\
\end{tabular}
\caption{\scriptsize Hierarchical Bayes rescaling of the Mat\'ern Gaussian process prior. Proportion of experiments when the true function $f_0$ was inside in the $L_2$-credible ball.}
\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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{An example of the output table for joint estimating ASC and SED results.}
\begin{tabular}{cccc}
\toprule
Start(s) & End(s) & Scene & Event \\ \hline
0 & 1 & city\_center & car \\
... & ... & ... & ... \\
16 & 17 & metro\_station & metro leaving \\
... & ... & ... & ... \\
40 & 41 & residential\_area & birds\_singing \\
... & ... & ... & ... \\
59 & 60 & residential\_area & birds\_singing \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Jisheng Bai
Han Yin
Mou Wang
Dongyuan Shi
Woon-Seng Gan
Jianfeng Chen
Susanto Rahardja

"AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive Learning" by Jisheng Bai, Han Yin, Mou Wang, Dongyuan Shi, Woon-Seng Gan, Jianfeng Chen, and Susanto Rahardja, arXiv:2311.12371v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
\hline
Cryptocurrency & Correlation coefficient
\\
\hline
BTC & $-0.1111$
\\
ETH & $0.6733$
\\
XRP & $0.3253$
\\
BCH & $0.4611$
\\
LTC & $-0.2087$
\\ \hline
\end{tabular}
\caption{Pearson correlation coefficient between $I_{\mathrm{T}}$ and $I^3_{*}$ for all the analyzed cryptocurrencies.}
\end{table}
\end{document}

q-fin

Jessica Morales Herrera
Raúl Salgado-García

"Trend patterns statistics for assessing irreversibility in cryptocurrencies: time-asymmetry versus inefficiency" by Jessica Morales Herrera and Raúl Salgado-García, arXiv:2307.08612v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Margarita A. Guerrero
Braghadeesh Lakshminarayanan
Cristian R. Rojas

"Data-Driven Estimation of Structured Singular Values" by Margarita A. Guerrero, Braghadeesh Lakshminarayanan, and Cristian R. Rojas, arXiv:2503.13410v1, 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.


math

Tinggang Zhao
Zhenyu Zhao
Changpin Li
Dongxia Li

"Spectral approximation of $ψ$-fractional differential equation based on mapped Jacobi functions" by Tinggang Zhao, Zhenyu Zhao, Changpin Li, and Dongxia Li, arXiv:2312.16426v1, 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}{cccccc}
 \toprule
 Epochs & 3 & 5 & 7 \\
 \midrule
 Last & 77.32 & 78.21 & 78.18 \\
 \bottomrule
 \end{tabular}
\caption{Accuracy vs. Training epochs}
\end{table}
\end{document}

cs

"CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models" by Saurav Jha, Dong Gong, and Lina Yao, arXiv:2403.19137v3, 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{Top Export Destinations}
\begin{tabular}{lcc}
 \toprule
 Destination & Export share (\%) & Export share (\%) \\
 & by volume & by shipping frequency \\
 \midrule
 United States & 23.37 & 17.08 \\
 Germany & 15.45 & 10.81 \\
 United Kingdom & 9.95 & 11.72\\
 France & 6.28 & 6.44\\
 Spain & 4.46 & 3.43 \\
 \bottomrule
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"Financing Costs, Per-Shipment Costs and Shipping Frequency: Firm-Level Evidence from Bangladesh" by Md Deluair Hossen, arXiv:2303.04223v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Multi-stage Euler-Maruyama methods for backward stochastic differential equations driven by continuous-time Markov chains" by Akihiro Kaneko, arXiv:2311.08826v2, 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}
Artin groups that are... & spherical & right-angled & affine \\ \hline \hline
...are torsion free. & , & & \\ \hline
...have cyclic or trivial center. & , & & \\ \hline
...have solvable word problem. & , & & \\ \hline
...satisfy the $K(\pi,1)$ conjecture. & & &
\end{tabular}
\end{table}
\end{document}

math

"Orbifold braid groups and complex braid groups" by Jonas Flechsig, arXiv:2312.10498v1, 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}{cccccccc}

		graph & alg.time & end-to-end \\
		& improvement & improvement \\\hline

		twitter-2010 & 1.13x & 1.10x\\\hline
		com-friendster& 1.40x & 1.35x\\\hline
		uk-2002 & 1.17x & 1.14x \\\hline
		arabic-2005 & 1.09x & 1.07x \\\hline
		graph500-26 & 1.10x & 1.07x \\\hline
	\end{tabular}
\caption{Runtime improvement table for Connected Components.}
\end{table}
\end{document}

math

Oleg Kruglov
Anna Mastikhina
Oleg Senkevich
Dmitry Sirotkin
Stanislav Moiseev

"Graph partition method based on finite projective planes" by Oleg Kruglov, Anna Mastikhina, Oleg Senkevich, Dmitry Sirotkin, and Stanislav Moiseev, arXiv:2312.13861v1, 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}{llcc}
 \hline
 \textbf{Model} & \textbf{Training Corpus} & \textbf{Presence of Hindi} & \textbf{Parameters}\\ \hline

 XLM-R & Wikipedia + CommonCrawl & YES & $125M$\\
 Bernice & Twitter Data & YES & $270M$\\
 IndicBERT & News article + Indian Websites & YES & $12M$\\
 Muril & CommonCrawl + Wikipedia & YES & $236M$\\
 mT5 & CommonCrawl& YES & $580M$\\
 mGPT & CommonCrawl + Wikipedia & YES & $13B$\\
 Llama v2 & -- & NO & $7B$\\
 Mistral & -- & NO & $7B$\\
 Bloom & Wikipedia & YES & $7B$\\
 \hline
 \end{tabular}
\caption{Details of models used for bias measurement. Llama v2 and Mistral models have not specified the pretraining datasets.}
\end{table}
\end{document}

cs

Nihar Ranjan Sahoo
Pranamya Prashant Kulkarni
Narjis Asad
Arif Ahmad
Tanu Goyal
Aparna Garimella
Pushpak Bhattacharyya

"IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context" by Nihar Ranjan Sahoo, Pranamya Prashant Kulkarni, Narjis Asad, Arif Ahmad, Tanu Goyal, Aparna Garimella, and Pushpak Bhattacharyya, arXiv:2403.20147v2, 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{Quantitative comparison of maximum position error for different subsets of landmarks }
\begin{tabular}{lrr}
			\toprule
			\textbf{Landmarks Selected} & \textbf{Max Error} \\
			\midrule
			Proposed ILA & $6.6~$m \\
			Only GPS & $20.2~$m \\
			All GPS and vision & $13.9~$m \\
			Random (300 runs) & $25.2~$m \\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

cs

"Robust GPS-Vision Localization via Integrity-Driven Landmark Attention" by Sriramya Bhamidipati and Grace Xingxin Gao, arXiv:2101.04836v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
\hline \hline
\textbf{Personality}: & \textbf{PC1} & \textbf{PC2} \\
\hline
1. Extraversion 		 & $-$ & \\
2. Agreeableness 	 & &\\
3. Openness 	 	 & & \\
4. Conscientiousness & &$+$ \\
5. Neuroticism 		 & &$+$ \\
6. Self-esteem 		 & $-$ & \\
7. Cognitive engagement 		 &$-$ &\\
\hline
Eigenvalue & 1.41 & 1.23 \\
Variance share & 28.58\% &21.75\% \\
\hline \hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Does personality affect the allocation of resources within households?" by Gastón P. Fernández, arXiv:2307.02918v1, 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||c|}
 \hline
 Domain & \small Orig. & \small L1& \small SPL& \small Pix2Pix& \small CRN*& \small P2PHD* & Ours\\\hline\hline
 AuROC & \small 0.8142& \small 0.8102& \small 0.8061& \small 0.8051& \small 0.8038& \small 0.8019 & \small \textbf{0.8420} \\\hline

 \end{tabular}
\caption{\textit{Pulmonary Embolism}-Classification results of a ResNet-50 trained on images from different image domains of various Image-Translation methods. Best result in \textbf{bold}.}
\end{table}
\end{document}

eess

Constantin Seibold
Matthias A. Fink
Charlotte Goos
Hans-Ulrich Kauczor
Heinz-Peter Schlemmer
Rainer Stiefelhagen
Jens Kleesiek

"Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism" by Constantin Seibold, Matthias A. Fink, Charlotte Goos, Hans-Ulrich Kauczor, Heinz-Peter Schlemmer, Rainer Stiefelhagen, and Jens Kleesiek, arXiv:2102.01445v2, 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}{lcc}
\toprule
{\bf Model} & Params & Pass@1 \\
\midrule
{\it Closed-source LLMs} \\
{\bf ChatGPT} & - & {39.6} \\
{\bf GPT-4} & >=175B & {47.0} \\
\midrule
{\it Open-source LLMs} \\
InstructCodeT5+* & 16B & 2.7 \\
BLOOMZ* & 176B & 16.6 \\
StarCoder* & 15.5B & 8.7 \\
CodeLlama* & 13B & 15.2 \\
CodeGeeX2* & 6B & 15.9 \\
OctoCoder* & 15.5B & 30.4 \\
WizardCoder & 15.5B & 31.8 \\
WizardCoder-Python-13B & 13B & 47.7 \\
\midrule
{\it StarCoder backbone} \\
{\bf Instruct tuning} & 15.5B & 33.7 \\
{\bf CCT-StarCoder (Ours)} & 15.5B & 38.3 \\
\midrule
{\it CodeLlama-Python-13B backbone} \\
{\bf Instruct tuning} & 13B & 43.5 \\
{\bf CCT-CodeLlama (Ours)} & 13B & {\bf 47.7} \\
\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.


cs

Zahra Abbasiantaeb
Simon Lupart
Mohammad Aliannejadi

"Generating Multi-Aspect Queries for Conversational Search" by Zahra Abbasiantaeb, Simon Lupart, and Mohammad Aliannejadi, arXiv:2403.19302v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
\toprule
Country & Rachev ratio \\ \midrule
UK & 1.0587 \\
Japan & 0.9994 \\
Canada & 0.9889 \\
France & 0.7723 \\
Brazil & 0.5862 \\
US & 0.4637 \\
Germany & 0.4048 \\
Australia & 0.3404 \\
India & -0.2435 \\
China & -0.5550 \\
\bottomrule
\end{tabular}
\caption{Rachev ratio for each country index for $\alpha =\beta = 0.5$}
\end{table}
\end{document}

q-fin

Thisari K. Mahanama
Abootaleb Shirvani
Svetlozar Rachev
Frank J. Fabozzi

"The Financial Market of Environmental Indices" by Thisari K. Mahanama, Abootaleb Shirvani, Svetlozar Rachev, and Frank J. Fabozzi, arXiv:2308.15661v1, 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{ {Comparison of different attention layers and ELGCA module on LEVIR-CD. The best results are shown in bold text.}}
\begin{tabular}{|l|c|c|c|c|c|} %\toprule[0.08em]
\hline
Layers & Params (M) & FLOPs (M) & IoU & F1 & OA \\
\hline
\hline
Self Attention (Baseline) & 11.63 & 138557.9 & 81.93 & 90.07 & 99.00\\
Separable Attention & 11.32 & 124104.8 & 83.13 & 90.78 & 99.07\\
EfficientFormer Attention & 15.07 & 267518.1 & 81.64 & 89.89 & 98.99\\
ELGCA & \textbf{10.57} & \textbf{123590.5} & \textbf{83.83} & \textbf{91.20} & \textbf{99.12}\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Mubashir Noman
Mustansar Fiaz
Hisham Cholakkal
Salman Khan
Fahad Shahbaz Khan

"ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection" by Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal, Salman Khan, and Fahad Shahbaz Khan, arXiv:2403.17909v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

A. Artemov
A. Veselovskiy
I. Khasenevich
I. Bolokhov

"Analysis of Basic Emotions in Texts Based on BERT Vector Representation" by A. Artemov, A. Veselovskiy, I. Khasenevich, and I. Bolokhov, arXiv:2101.11433v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


q-fin

"Bitcoin versus S&P 500 Index: Return and Risk Analysis" by A. H. Nzokem, arXiv:2310.02436v1, 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|cccccccc|}
\cline{2-9}
 & \multicolumn{8}{c|}{Thyroid data set} \\ \cline{2-9}
 & \multicolumn{2}{c|}{PCA} & \multicolumn{2}{c|}{robPCA} & \multicolumn{2}{c|}{ICA} & \multicolumn{2}{c|}{ACA} \\ \cline{2-9}
 & PC1 & PC2 & robPC1 & robPC2 & IC1 & IC2 & AC1 & AC2 \\ \hline
Var1 & 4 (28\%) & 5 (28\%) & 1 (90\%) & 5 (91\%) & 1 (73\%) & 4 (33\%) & 2 (75\%) & 6 (64\%) \\
Var2 & 6 (22\%) & 1 (21\%) & 3 (4\%) & 6 (8\%) & 4 (11\%) & 5 (26\%) & 6 (12\%) & 4 (18\%) \\
Var3 & 3 (22\%) & 6 (20\%) & 4 (4\%) & 4 (1\%) & 6 (6\%) & 3 (22\%) & 4 (8\%) & 2 (12\%) \\ \hline
\end{tabular}
\caption{Contributions of three most important (for each component) variables for PCA, robPCA, ICA, and ACA for Satellite image (2) data set.}
\end{table}
\end{document}

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}{c|c}
\textbf{Market} & \textbf{Test period} \\ \hline
OMIE-SP & 01.01.2022 - 31.05.2023 \\
EPEX-DE & 01.01.2022 - 31.05.2023 \\
EPEX-BE & 04.01.2015 - 31.12.2016 \\
EPEX-FR & 04.01.2015 - 31.12.2016 \\
NP & 27.12.2016 - 24.12.2018
\end{tabular}
\caption{Test period of every dataset considered}
\end{table}
\end{document}

stat

Carlos Sebastián
Carlos E. González-Guillén
Jesús Juan

"An adaptive standardisation methodology for Day-Ahead electricity price forecasting" by Carlos Sebastián, Carlos E. González-Guillén, and Jesús Juan, arXiv:2311.02610v3, licensed under CC BY-NC-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cccccc}
	\hline
	Region & $x$ & $\alpha$ & $\beta$ & $\mu$ & $\delta$\\
	\hline
	small & (-5, 5) & (0.001, 5) & (-5, 5) & (-5, 5) & (0.001, 5)\\
	large & (-10, 10) & (0.001, 50) & (-50, 50) & (-10, 10) & (0.001, 50)\\
	\hline
	\end{tabular}
\caption{General case: Parameter range for small and large region.}
\end{table}
\end{document}

stat

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
 \hline
 Model & Price \\
 \hline
 Black Scholes & $1.93 \pm 2.33 \times 0.04$\\
 \hline
 Local Volatility & $2.91 \pm 2.33 \times 0.05$\\
 \hline
 LSV as in & $2.77 \pm 2.33 \times 0.05$\\
 \hline
 LSV PD & $3.12 \pm 2.33 \times 0.05$\\
 \hline
 \end{tabular}
\caption{Price Call Up \& Out K=100,B=120.}
\end{table}
\end{document}

q-fin

"SPX, VIX and scale-invariant LSV\footnote{Local Stochastic Volatility}" by Alexander Lipton and Adil Reghai, arXiv:2302.08819v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A Bayesian decision-theoretic approach to sparse estimation" by Aihua Li, Surya T. Tokdar, and Jason Xu, arXiv:2502.00126v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc|c|c|c|c|c|}
\cline{3-7}
& & \multicolumn{5}{c|}{\textit{\textbf{Predicted class}}} \\
\cline{3-7}
& & \textbf{Snow} & \textbf{Sunset} & \textbf{Trees} & \textbf{Rocks} & \textbf{Night} \\
\hline
\multirow{5}{*}{\textit{\textbf{Actual class}}} & \textbf{Snow} & 99.5 & 0 & 0 & 0 & 0.5 \\ \cline{2-7}
 & \textbf{Sunset} & 0 & 99.5 & 0.5 & 0 & 0 \\
\cline{2-7}
 & \textbf{Trees} & 1 & 0.5 & 56.5 & 42 & 0 \\
\cline{2-7}
 & \textbf{Rocks} & 2 & 0.5 & 7 & 91 & 0 \\ \cline{2-7}
 & \textbf{Night} & 1 & 8 & 9 & 2.5 & 79.5
\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Manel Mateos
Alejandro González
Xavier Sevillano

"Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation" by Manel Mateos, Alejandro González, and Xavier Sevillano, arXiv:2101.00990v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{clrlr}
\toprule
\multirow{2}{*}{\textbf{Slot}} & \multicolumn{2}{c}{\textbf{High Variability}} & \multicolumn{2}{c}{\textbf{Low Variability}} \\ \cmidrule(l){2-5}
 & Instance & Freq. & Instance & Freq. \\ \midrule
\multirow{3}{*}{All} & impressive 30 appearances & 1 & great many things & 42 \\
 & massive 108 years & 1 & good many years & 21 \\
 & reported 14 million dolars & 1 & additional two years & 4 \\ \midrule
\multirow{3}{*}{Adj} & career-high & 38 & great & 355 \\
 & staggering & 12 & additional & 111 \\
 & measly & 7 & mere & 60 \\ \midrule
\multirow{3}{*}{Num} & 20 & 32 & two & 174 \\
 & couple & 17 & five & 67 \\
 & seven to eight & 1 & few & 64 \\ \midrule
\multirow{3}{*}{Noun} & dollars & 15 & years & 254 \\
 & students & 8 & miles & 77 \\
 & kangaroos & 1 & hours & 42 \\ \bottomrule
\end{tabular}
\caption{Examples of slot fillers that were ablated as part of our variability experiments, along with their frequency in the training data, across all slots considered (All open slots, Adjective-only, Numeral-only, and Noun-only).}
\end{table}
\end{document}

cs

"Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs" by Kanishka Misra and Kyle Mahowald, arXiv:2403.19827v2, 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{Annualized Sharpe Ratio}
\begin{tabular}{ccc}
 \toprule
 p\% & Strategy 1 & Strategy 2 \\
 \midrule
 45\% & 0.688 & 0.45 \\
 30\% & 0.581 & 0.581 \\
 15\% & 0.703 & 1.36 \\
 10\% & 0.877 & 0.726 \\
 \midrule
 Buy-\&-Hold & 0.828 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"Principal Component Analysis and Hidden Markov Model for Forecasting Stock Returns" by Eugene W. Park, arXiv:2307.00459v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc} %{c|c|c|c|c|c}
		\toprule
			Metric & SN
			 & ONI & OCNN & Vanilla & {ConvNorm} \\
 \midrule
 \multirow{1}{*} IS & \textbf{8.12} & {7.07} & {7.54} & {7.13} & {7.62}\\
 \midrule
 \multirow{1}{*} FID & \textbf{14.53} & {29.49} & {22.15} & {29.47} & {21.37}\\
	\bottomrule
	\end{tabular}
\caption{\textbf{Comparison of ConvNorm to baseline methods on GAN training.} Inception score (IS) (the higher, the better) and FID score (the lower, the better) of ResNet with different normalizations. For each pair of model and method, we generate $50k$ images $10$ times and compute the mean of IS.}
\end{table}
\end{document}

eess

Sheng Liu
Xiao Li
Yuexiang Zhai
Chong You
Zhihui Zhu
Carlos Fernandez-Granda
Qing Qu

"Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training" by Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, and Qing Qu, arXiv:2103.00673v2, 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{Fourfold distinction of forward~vs.~backward and empirical vs. prescriptive human--AI alignment and our positions. \\}
\begin{tabular}{l|l|l}
\toprule
\toprule
 & \textbf{Empirical} & \textbf{Prescriptive} \\
 & \textbf{Alignment} & \textbf{Alignment} \\
\midrule
\textbf{Forward} & Contra (Section~) & Pro (Section~) \\
\midrule
\textbf{Backward} & Pro (Section~) & Pro (Section ) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Julian Rodemann
Esteban Garces Arias
Christoph Luther
Christoph Jansen
Thomas Augustin

"A Statistical Case Against Empirical Human-AI Alignment" by Julian Rodemann, Esteban Garces Arias, Christoph Luther, Christoph Jansen, and Thomas Augustin, arXiv:2502.14581v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
		Parameters & values\\
		\toprule
		$\beta_f$ & 1.86 / 1000\\
		$\varsigma$ & 1.2 * 1.86 / 1000\\
		$\gamma_1$ & 0.00017675 \\
		$\gamma_2$ & 2 * 0.0022 \\
		$\gamma_3$ & $\frac{1}{3} \frac{m-1}{19}, m = 1, 2, \dots, 20$\\
		$\bar{y}$ & 2\\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

q-fin

Michael Barnett
William Brock
Lars Peter Hansen
Ruimeng Hu
Joseph Huang

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
 \hline
 Model & Parameters & ~~~P~~~ & ~~~G~~~ & ~~~ D$_1$ ~~~ & ~~~ D$_\infty$ ~~~ \\
 \hline
 M$_0$ & 18 & 23.21 & 20.38 & 33.4 & 43.59 \\
 M$_2$ & 7 & 21.46 & 20.88 & 31.9 & 42.34 \\
 \hline
 \end{tabular}
\caption{Posterior predictive loss of fully parameterized second-order Markov chain model (\(M_0\)) and second-order MTD model (\(M_2\)).}
\end{table}
\end{document}

stat

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Robustness DiD State Capacity Analysis: Extensive Margin, Events in Over 60\% of Years}
\begin{tabular}{lccccccc}
\toprule
& \multicolumn{2}{c}{Tax Revenue per GDP} & \multicolumn{2}{c}{Tax Revenue per Nighttime Light} & \multicolumn{3}{c}{No Windsorizing} \tabularnewline & Tax Rev. to & Anderson & Tax Rev. to & Anderson & Operational & Ratio Gov. Trans. & Anderson \tabularnewline & GDP (DANE) & Index & Nighttime Light & Index & Costs (pc) & to Revenue & Anderson \tabularnewline
{}&{(1)}&{(2)}&{(3)}&{(4)}&{(5)}&{(6)}&{(7)} \tabularnewline
\midrule
\midrule Ceasefire \( \times \) FARC&--0.000&0.040&5.201&0.041&--0.002&--0.050&0.075 \tabularnewline
&(0.001)&(0.045)&(4.541)&(0.044)&(0.009)&(0.539)&(0.102) \tabularnewline
&&&&&&& \tabularnewline
Treated Municip.&216&216&216&216&216&216&216 \tabularnewline
Control Municip.&41&41&41&41&41&41&41 \tabularnewline
Mean Dependent Var.&0.012&0.001&--0.091&0.003&0.118&7.149&--0.016 \tabularnewline
Observations&2,308&2,827&2,822&2,827&2,822&2,815&2,827 \tabularnewline
\(R^2\)&0.659&0.662&0.096&0.668&0.219&0.676&0.507 \tabularnewline
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Wikipedia vs. \emph{People's Daily}}
\begin{tabular}{lrrrr}
\toprule
 & \multicolumn{2}{c}{Propaganda Attributes} & \multicolumn{2}{c}{Evaluative Attributes} \\
\cmidrule(l{3pt}r{3pt}){2-3} \cmidrule(l{3pt}r{3pt}){4-5}
 & effect size & p-value & effect size & p-value\\
\midrule
Freedom & -0.29 & 0.11 & -0.51 & 0.01\\
Democracy & -0.40 & 0.09 & -0.97 & 0.00\\
Election & -0.43 & 0.04 & -0.91 & 0.00\\
Collective Action & -0.81 & 0.00 & -0.10 & 0.34\\
Negative Figures & 0.44 & 0.91 & -0.06 & 0.41\\
\addlinespace
Social Control & 0.82 & 0.01 & 0.58 & 0.03\\
Surveillance & 0.31 & 0.06 & 0.84 & 0.00\\
CCP & 1.39 & 0.00 & 1.22 & 0.00\\
Historical Events & 0.29 & 0.08 & 0.22 & 0.04\\
Positive Figures & 1.51 & 0.00 & 1.29 & 0.00\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"Censorship of Online Encyclopedias: Implications for NLP Models" by Eddie Yang and Margaret E. Roberts, arXiv:2101.09294v1, 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}{lcccccccc} \hline
			 & & A (7) & B (6) & C (1) & D (1) & E (8) & F (7) \\ \hline
						 & Budget	& & & & & & \\ \hline
			 Agent 1 & 3 	 	& + & & + & & + & \\ \hline
			 Agent 2 & 3	 	& + & & & + & + & \\ \hline
			 Agent 3 & 3 	& & + & + & & & + \\ \hline
			 Agent 4 & 2 	& & + & & + & & + \\ \hline
			 Agent 5 & 1 	& + & & & & + & \\ \hline
			\end{tabular}
\caption{Example of an PFC instance. }
\end{table}
\end{document}

cs

"Participatory Funding Coordination: Model, Axioms and Rules" by Haris Aziz and Aditya Ganguly, arXiv:2101.11722v1, 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{Grid cases in terms of SCR at WT MV terminal.}
\begin{tabular}{ccc}
 \hline
 Grid Case & Base-Case SCR & X/R Ratio\\
 \hline\hline
 Weak Grid & 1.6 & 5 \\
 Normal Grid & 3.2 & 14.8\\
 Strong Grid & 4.12 & 14.8\\\hline
 \end{tabular}
\end{table}
\end{document}

eess

Sulav Ghimire
Kanakesh V. Kkuni
Emerson D. Guest
Kim H. Jensen
Guangya Yang

"Small-Signal Stability and SCR Enhancement of Offshore WPPs with Synchronous Condensers" by Sulav Ghimire, Kanakesh V. Kkuni, Emerson D. Guest, Kim H. Jensen, and Guangya Yang, arXiv:2310.06457v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
		\toprule
		Framework 	& Mean agg. & Best Frequency Fusion\\
		\midrule
		Diff-traditional SVM & $79.98$ & $80.67$ \\
		Diff-traditional LDA & $67.30$& $74.38$\\
		Diff-traditional QDA & $72.13$ & $83.25$\\
		Diff-traditional KNN & $86.06$ & $86.06$\\
		Diff-traditional GP & $85.95$ & $86.51$\\
		\bottomrule
	\end{tabular}
\caption{Performance for the traditional BCI frameworks using the differentiation. We compare the usage of the base aggregation (the arithmetic mean) against the best possible one.}
\end{table}
\end{document}

cs

Javier Fumanal-Idocin
Yu-Kai Wang
Chin-Teng Lin
Javier Fernández
Jose Antonio Sanz
Humberto Bustince

"Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions" by Javier Fumanal-Idocin, Yu-Kai Wang, Chin-Teng Lin, Javier Fernández, Jose Antonio Sanz, and Humberto Bustince, arXiv:2101.06968v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||ccc||}
 \hline
 Volatility Methods & Total Return (\%) & Sharpe Ratio \\
 \hline\hline
 EWMA & 144.13 & 0.45 \\
 \hline
 250VOL & 202.17 & 0.52 \\
 \hline
 500VOL & 206.71 & 0.53 \\
 \hline
 PWMA & 185.49 & 0.50 \\
 \hline
 NoVOL & 151.46 & 0.41 \\
\hline
 MAXFLAT & 208.51 & 0.53 \\
 \hline
\end{tabular}
\caption{Different Volatility Estimator with CSI 500 universe.}
\end{table}
\end{document}

q-fin

"Adjust factor with volatility model using MAXFLAT low-pass filter and construct portfolio in China A share market" by Ke Zhang, arXiv:2304.04676v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
 \hline
 \textbf{UQ algorithm} & \textbf{Average Uncertainty} \\
 \hline
 MCD & 0.86 \\
 \hline
 EDL & 0.51 \\
 \hline
 CP & 0.08 \\
 \hline
 \end{tabular}
\caption{Average Uncertainty Values for Different uncertainty quantification algorithms. The core network is trained on HAM10000 dataset and evaluated on DMF dataset as an OOD example. Results illustrate that CP achieves the best performance in terms of OOD detection.}
\end{table}
\end{document}

eess

Jamil Fayyad
Shadi Alijani
Homayoun Najjaran

"Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification" by Jamil Fayyad, Shadi Alijani, and Homayoun Najjaran, arXiv:2312.07460v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model Calibration with Literature Support}
\begin{tabular}{llcc}
\toprule
Parameter & Description & Value & Primary Sources \\
\midrule
$A$ & Baseline firm value & 100 & Normalization \\
$\kappa$ & ESG investment cost & 0.02 & \\
$\gamma$ & ESG benefit & 0.05 & McKinsey (2022), \\
$\mu$ & ESG investor share & 0.30 & GSIA (2020, 2022) \\
$\alpha$ & ESG preference intensity & 0.01 & \\
$\lambda$ & Risk aversion & 2.0 & \\
$\sigma_D$ & Dividend volatility & 0.15 & \\
$r_f$ & Risk-free rate & 0.03 & Current environment \\
$\psi$ & Path surprise sensitivity & 0.5 & Calibrated to match empirics \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Green Shields: The Role of ESG in Uncertain Time" by Fatih Kansoy and Dominykas Stasiulaitis, arXiv:2506.02143v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"SIAC Accuracy Enhancement of Stochastic Galerkin Solutions for Wave Equations with Uncertain Coefficients" by Andrés Galindo-Olarte and Jennifer K. Ryan, arXiv:2503.13930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"High Order in Space and Time Schemes Through an Approximate Lax-Wendroff Procedure" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2502.07798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mohammad Kaviul Anam Khan
Olli Saarela
Rafal Kustra

"Marginal and Conditional Importance Measures from Machine Learning Models and Their Relationship with Conditional Average Treatment Effect" by Mohammad Kaviul Anam Khan, Olli Saarela, and Rafal Kustra, arXiv:2501.16988v2, 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{ESG Quintile Analysis}
\begin{tabular}{lcccc}
 \toprule
 & \multicolumn{2}{c}{Target Surprise} & \multicolumn{2}{c}{Path Surprise} \\
 & \multicolumn{2}{c}{Interaction} & \multicolumn{2}{c}{Interaction} \\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5}
 & Coef. & SE & Coef. & SE \\
 \midrule
 \multicolumn{5}{l}{\textit{Base Category: Quintile 1 (Lowest ESG)}} \\
 \midrule
 Quintile 2 $\times$ MP & 1.008* & (0.600) & -1.217** & (0.508) \\
 Quintile 3 $\times$ MP & 1.699** & (0.773) & -2.166*** & (0.754) \\
 Quintile 4 $\times$ MP & 2.099 & (1.357) & -2.567** & (1.129) \\
 Quintile 5 $\times$ MP (Highest ESG) & 2.773** & (1.374) & -2.855*** & (1.065) \\
 \midrule
 \multicolumn{5}{l}{\textit{Main Effects:}} \\
 Target Surprise & -7.705*** & (1.438) & & \\
 Path Surprise & & & -1.939 & (1.241) \\
 \midrule
 Observations & \multicolumn{4}{c}{51,529} \\
 R-squared & \multicolumn{4}{c}{0.245} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|ccccccc}
 \hline \hline
 & & Degree & Imbalance & Fraction & Betweenness & Eigenvector & Closeness\\\hline
 \multirow{ 2}{*}{Relative error} & Pearson & 0.208 & 0.640 & 0.19 & -0.016 & 0.235 & 0.192 \\
 & Spearman &0.140 & 0.688 & 0.147 & -0.080 & 0.178 & 0.124\\\hline
 \multirow{ 2}{*}{Precision} & Pearson & 0.530 & -0.795 & -0.521 & -0.348 & -0.544 & -0.516 \\
 & Spearman &-0.463 & -0.792 & -0.467 & -0.286 & -0.483 & -0.45\\\hline
 \multirow{ 2}{*}{Recall} & Pearson & -0.492 & -0.796 & -0.486 & -0.289 & -0.508 & -0.475\\
 & Spearman &0.14 & 0.688 & 0.147 & -0.08 & 0.178 & 0.124\\\hline
 \end{tabular}
\caption{The correlations between clustering accuracy and centere'dity metrics Imbalanced observations.}
\end{table}
\end{document}

stat

Lijia Wang
Xiao Han
Yanhui Wu
Y. X. Rachel Wang

"Local Information for Global Network Estimation in Latent Space Models" by Lijia Wang, Xiao Han, Yanhui Wu, and Y. X. Rachel Wang, arXiv:2502.16504v1, 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{System Parameters of Outdoor Test}
\begin{tabular}{|c|c|}
		\hline\hline
		\textbf{Parameter} & \textbf{Value} \\ \hline
		 Center frequency & 5.8 GHz \\ \hline
		Modulation method & OFDM \\ \hline
		 Bandwidth & 20 MHz \\ \hline
		 TX-RX distance & 50 m or 500 m \\ \hline
		 Transmit power & 13 dBm or 23 dBm \\ \hline\hline
	\end{tabular}
\end{table}
\end{document}

eess

Xilong Pei
Haifan Yin
Li Tan
Lin Cao
Zhanpeng Li
Kai Wang
Kun Zhang
Emil Björnson

"RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials" by Xilong Pei, Haifan Yin, Li Tan, Lin Cao, Zhanpeng Li, Kai Wang, Kun Zhang, and Emil Björnson, arXiv:2103.00534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Renato Assunção
Flávio Figueiredo
Francisco N. Tinoco Júnior
Léo M. de Sá-Freire
Fábio Silva

"An Interpretable Measure for Quantifying Predictive Dependence between Continuous Random Variables -- Extended Version" by Renato Assunção, Flávio Figueiredo, Francisco N. Tinoco Júnior, Léo M. de Sá-Freire, and Fábio Silva, arXiv:2501.10815v1, 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{List of Components (serial No. 26- 55) indicated in Figs }
\begin{tabular}{|l|l|l|l|l|}
\hline
	ITEM & No. of QTY & Product type/Specification & Material & Description \\ \hline
	26 & 1 & Valve holder-V3 & PLA & \\ \hline
	27 & 1 & Zippy compact 1400 & & Battery \\ \hline
	28 & 1 & Battery-case-zippy-1400i-V2 & PLA & 3D print \\ \hline
	29 & 1 & Regulator-mount-v2 & PLA & 3D print \\ \hline
	30 & 1 & 8086628 MS2-LR-M5-D6-AR-BAR-B & & Regulator w filter Air bearings \\ \hline
	31 & 2 & 5003640 MS2-LR/LFR-B & & MS2-WR (p)-Mounting bracket \\ \hline
	32 & 1 & 8086644 MS2-LFR-QS6-D6-AR-BAR-C-M-B & & Regulator Thrusters \\ \hline
	33 & 1 & Regulator-mount-long-v3 & PLA & PLA 3D print \\ \hline
	34 & 1 & T-piece-holder & PLA & 3D print \\ \hline
	35 & 2 & 130618 QSW-6HL & & QSW-HL-Push-in connector \\ \hline
	36 & 1 & Splice-sla & Resin "Grey pro" & 3D print (SLA) \\ \hline
	37 & 1 & 186096 QS-G1/8-6 & & QS\_G-Push-in fitting \\ \hline
	38 & 1 & Bottle mount V3-extended & PLA & 3D print \\ \hline
	39 & 1 & Bottle mount V3 & PLA & 3D print \\ \hline
	40 & 1 & Arduino shield & & Arduino shield \\ \hline
	41 & 1 & Arduino Nano 33 IoT & & Arduino \\ \hline
	42 & 1 & Arduino-w-shiled-holder & Resin "Black" & 3D print (SLA) \\ \hline
	43 & 1 & Polarstar-regulator & & Airsoft regulator \\ \hline
	44 & 1 & paintball air tank-DYE & & Paintball tank 1,1l \\ \hline
	45 & 1 & top ring-6MM-V2 & PLA & 3D print \\ \hline
	46 & 1 & UP\_BOARD\_\&\_HEAT\_SINK\_by\_JMJV & & Up-boad computer \\ \hline
	47 & 1 & Futaba-reciever & PLA & RC reciever \\ \hline
	48 & 1 & Futaba-controller-hodler & PLA & 3D print \\ \hline
	49 & 1 & Valve holder-V4 & PLA & 3D print \\ \hline
	50 & 1 & holder\_ps & PLA & 3D print \\ \hline
	51 & 1 & ps-camera & & Playstation camera \\ \hline
	52 & 1 & Gopro-mount & PLA & 3D print \\ \hline
	53 & 2 & Valve-holder-v3-mirror & PLA & 3D print \\ \hline
	54 & 1 & Spacer & PLA & 3D print \\ \hline
	55 & 1 & Low-top-platform-mount-front-V4-2nd part & PLA & 3D print \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|}
 \hline
 \multirow{2}{*}{Datset} &
 \multicolumn{2}{c}{Train} &
 \multicolumn{2}{c}{Eval} \\
 & Duration & Files & Duration & Files\\
 \hline
 English & 44.21 & 15963 & 11.28 & 4074 \\
 \hline
 Kannada & 35.95 & 12988 & 8.90 & 3216 \\
 \hline
 Gujarati & 30.24 & 10933 & 7.312 & 2642 \\
 \hline
 Hindi & 38.79 & 14004 & 9.72 & 3510 \\
 \hline
 Malayalam & 34.98 & 12636 & 8.94 & 3228 \\
 \hline
 Tamil & 63.15 & 22774 & 15.95 & 5753 \\
 \hline
 Telugu & 35.07 & 12666 & 8.54 & 3087 \\
 \hline
 Marathi & 16.23 & 5873 & 4.013 & 1449 \\
 \hline
 \end{tabular}
\caption{Train and evaluation splits for different languages (Duration is in Hrs)}
\end{table}
\end{document}

eess

"End-to-End Language Identification using Multi-Head Self-Attention and 1D Convolutional Neural Networks" by Krishna D N and Ankita Patil, arXiv:2102.00306v1, 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{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation Study on The LIVE, CSIQ, TID2008, and TID2013 Databases.}
\begin{tabular}{cc|cccc}
		\hline
 \hline
		Databases & Methods & Distorted Image & Residual Map & Direct Concat & Proposed \\ \hline
 \multirow{2}{*}{LIVE}
 & SRCC & 0.911 & 0.976 & 0.981 & \textbf{0.984} \\
 & PLCC & 0.913 & 0.978 & 0.981 & \textbf{0.984} \\ \hline
 \multirow{2}{*}{CSIQ}
 & SRCC & 0.745 & 0.917 & 0.949 & \textbf{0.960} \\
 & PLCC & 0.810 & 0.893 & 0.952 & \textbf{0.952} \\ \hline
 \multirow{2}{*}{TID2008}
 & SRCC & 0.529 & 0.894 & 0.923 & \textbf{0.928} \\
 & PLCC & 0.660 & 0.898 & 0.933 & \textbf{0.937} \\ \hline
 \multirow{2}{*}{TID2013}
 & SRCC & 0.577 & 0.854 & 0.900 & \textbf{0.906} \\
 & PLCC & 0.713 & 0.872 & 0.918 & \textbf{0.922} \\ \hline
 \hline
	\end{tabular}
\end{table}
\end{document}

eess

"Deep Multi-Scale Features Learning for Distorted Image Quality Assessment" by Wei Zhou and Zhibo Chen, arXiv:2012.01980v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Model selection focusing on longtime behavior of differential equations" by Cordula Reisch and Hannah Burmester, arXiv:2312.05128v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Standard deviation of out-of-sample minimum-variance portfolio returns}}
\begin{tabular}{lc}
				\toprule
				& Std. dev. of OOS \\
				& portfolio returns \\
				\cmidrule{2-2}market, size, value & 0.150*** \\
				market, size, value, profit., invest. & 0.146*** \\
				equally weighted & 0.183*** \\
				value-weighted & 0.158*** \\
				& \\
				OLS & 0.176*** \\
				& \\
				Lasso & \\
				(95 characteristics) & 0.091* \\
				(4,610 characteristics) & 0.083 \\
				& \\
				Adalasso & \\
				(95 characteristics) & 0.097** \\
				(4,610 characteristics) & 0.091** \\
				& \\
				Elastic net & \\
				(95 characteristics) & 0.092 \\
				(4,610 characteristics) & 0.084 \\
				& \\
				Ridge & \\
				(95 characteristics) & 0.117*** \\
				(4,610 characteristics) & 0.158*** \\
				& \\
				Horseshoe & \\
				(95 characteristics) & 0.089* \\
				(4,610 characteristics) & 0.094** \\
				& \\
				Scad & \\
				(95 characteristics) & 0.100*** \\
				(4,610 characteristics) & 0.091** \\
				& \\
				$L2$-boosting & \\
				(95 characteristics) & 0.090** \\
				(4,610 characteristics) & 0.087 \\
				\bottomrule
			\end{tabular}
\end{table}
\end{document}

q-fin

Guilherme V. Moura
André P. Santos
Hudson S. Torrent

"Variable selection for minimum-variance portfolios" by Guilherme V. Moura, André P. Santos, and Hudson S. Torrent, arXiv:2508.14986v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llllll}
 \toprule
 & &\multicolumn{2}{c}{$\mathcal{NC}1$} & \multicolumn{2}{c}{$\mathcal{NC}4$}\\
 \cmidrule(lr){3-4}\cmidrule(lr){5-6}
 Model&Dataset&Orig.&Proj.&Orig.&Proj.\\
 \cmidrule(lr){1-1}\cmidrule(lr){2-2}\cmidrule(lr){3-3}\cmidrule(lr){4-4}\cmidrule(lr){5-5}\cmidrule(lr){6-6}
 Resnet-18&MNIST&0.28&0.28& 0.99&0.99\\
 WRN&CIFAR10&0.73& 0.75& 0.95&0.95\\
 Resnet50&CIFAR10&0.28& 0.50 & 0.95& 0.94\\
 VGG-16&MNIST&0.26&0.36& 0.97&0.97\\
 \bottomrule
 \end{tabular}
\caption{\textbf{Projection of intermediate representations to a lower-dimensional space preserves sensitivity ($\mathcal{NC}1$) and smoothness ($\mathcal{NC}4$).} Neural collapse metrics before (Orig.) and after projection (Proj.) for various networks and datasets.}
\end{table}
\end{document}

stat

"Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks" by Felix Jimenez and Matthias Katzfuss, arXiv:2501.04816v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Ablation Study on CIFAR-10 and CIFAR-100} It represents the mean accuracy and standard deviation with the VGG model. The mean and standard deviation were calculated based on five different seeds. L represents the number of layers utilized by the student. K signifies the number of teachers utilized. T stands for the the duration of cycle \( T_{\text{cycle}} \). M indicates the re-initialization method used for reset. When M is marked as O, it means that re-initialization was done using the mean of the teachers' weights. X signifies that random weights were utilized for re-initialization. The notation - signifies the use of the default setting. The default setting for (L, K, T, M) is (3, 4, 100, O).}
\begin{tabular}{lcccccr}
\toprule
L & K & T & M & CIFAR-10 & CIFAR-100 \\
\midrule
X & X & X & X & 93.64 ± 0.14 & 71.85 ± 0.32 \\
3 & 4 & 100 & O & \textbf{93.78 ± 0.10} & \textbf{72.94 ± 0.44} \\
\hline
1 & - & - & - & 93.49 ± 0.31 & 72.38 ± 0.18 \\
3 & - & - & - & \textbf{93.78 ± 0.78} & \textbf{72.94 ± 0.44} \\
\hline
- & 1 & - & - & 93.71 ± 0.12 & 72.89 ± 0.33 \\
- & 2 & - & - & 93.77 ± 0.18 & \textbf{73.03 ± 0.24} \\
- & 4 & - & - & \textbf{93.78 ± 0.10} & 72.94 ± 0.24 \\
- & 8 & - & - & 93.22 ± 0.13 & 68.00 ± 2.09 \\
\hline
- & - & 1 & - & 93.53 ± 0.12 & 72.10 ± 0.30 \\
- & - & 20 & - & 93.48 ± 0.15 & 71.63 ± 0.34 \\
- & - & 50 & - & 93.61 ± 0.06 & 72.56 ± 0.22 \\
- & - & 100 & - & \textbf{93.78 ± 0.10} & \textbf{72.94 ± 0.44} \\
\hline
- & - & - & X & 93.64 ± 0.14 & \textbf{73.04 ± 0.09} \\
- & - & - & O & \textbf{93.78 ± 0.10} & 72.94 ± 0.44 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"Diverse Feature Learning by Self-distillation and Reset" by Sejik Park, arXiv:2403.19941v1, 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|c|c|c}
\toprule
 & Total Time (s) & Time/Iteration (s) & PSNR (dB) \\ \hline
Plug $\&$ Play & 1.24 & 0.025 & 31.56 \\ \hline
{\sc DU-Prox} & 0.25 & 0.025 & 32.49 \\ \hline
{\sc DE-Prox} & 1.22 & 0.025 & 31.86 \\ \hline
{\sc DE-Prox} (Anderson) & 1.17 & 0.046 & 33.66 \\ \hline
{\sc DE-Prox} (Broyden) & 1.85 & 0.039 & 33.61
\end{tabular}
\caption{Mean computation time required to reach convergence and resulting mean reconstruction PSNR in $4\times$ accelerated MRI reconstruction with complex image size $320\times320$, as computed over the test set.}
\end{table}
\end{document}

eess

Davis Gilton
Gregory Ongie
Rebecca Willett

"Deep Equilibrium Architectures for Inverse Problems in Imaging" by Davis Gilton, Gregory Ongie, and Rebecca Willett, arXiv:2102.07944v2, 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|ccc}
\hline
 & \textbf{ResNet50} & \textbf{ConvNext-T} & \textbf{Swin-T} \\ \hline
\textbf{Standard} & 37.30 & 59.60 & 54.33 \\
\textbf{Diffpure~} & 15.83 & 47.23 & 35.69 \\
\textbf{$DDA_{10}$}~ & 38.90 & 63.26 & 49.65 \\
\textbf{w/o marg.} & 40.9 & 59.70 & 55.86 \\
\textbf{GDA (ours)} & \textbf{41.70} & \textbf{65.24} & \textbf{59.35} \\ \hline
\end{tabular}
\caption{Classification accuracy on the ImageNet-C under severity level 3 for three model architectures. We compare the result between GDA and the four baselines, including Standard, Diffpure~, DDA~, and w/o marginal. GDA consistently achieves the highest accuracy (numbers in bold) .}
\end{table}
\end{document}

cs

Yun-Yun Tsai
Fu-Chen Chen
Albert Y. C. Chen
Junfeng Yang
Che-Chun Su
Min Sun
Cheng-Hao Kuo

"GDA: Generalized Diffusion for Robust Test-time Adaptation" by Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen, Junfeng Yang, Che-Chun Su, Min Sun, and Cheng-Hao Kuo, arXiv:2404.00095v2, 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{Relative errors of the Riemann problem with rarefaction for Burgers' equation}
\begin{tabular}{|c|c|c|}
\hline
Time & $\frac{\|u(\cdot, t_k)- u^{(k)}_{N}\|_{L^2(\Omega)}}{\|u(\cdot, t_k)\|_{L^2(\Omega)}}$ \\ \hline
0.0 & $1.9775 \times 10^{-2}$ \\ \hline
0.1 & $1.6860\times 10^{-3}$\\ \hline
0.2 & $1.2393\times 10^{-3}$ \\ \hline
0.3 & $1.0538\times 10^{-3}$ \\ \hline
0.4 & $9.5347\times 10^{-4}$ \\ \hline
0.5 & $8.9459\times 10^{-4}$ \\ \hline
\end{tabular}
\end{table}
\end{document}

math

"Evolving Neural Network (ENN) Method for One-Dimensional Scalar Hyperbolic Conservation Laws: I Linear and Quadratic Fluxes" by Zhiqiang Cai and Brooke Hejnal, arXiv:2312.06919v1, 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{array}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{RQ3.1: RMSE sensitivity analysis (conf./LSA/DSA)}
\begin{tabular}{c|c|c|c|c|c|c|c} \toprule
 & & \multicolumn{5}{c|}{{Minimum RMSE}} & {Inversions} \\
 & {Technique} & \textit{50} & \textit{100} & \textit{200} & \textit{400} & \textit{800} & \small{\textit{{50}>{800}}} \\ \hline
 & GBS & 0/0/0 & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{34FF34}3/3/3 & 3/3/3 \\ \cline{2-8}
 & DeepEST & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{FFFC9E}0/0/1 & \cellcolor[HTML]{FD6864}3/3/2 & 3/3/3 \\ \cline{2-8}
 & 2-UPS & \cellcolor[HTML]{FFFC9E}1/0/0 & \cellcolor[HTML]{FFFC9E}1/0/0 & 0/0/0 & \cellcolor[HTML]{FFFC9E}0/1/0 & \cellcolor[HTML]{FD6864}1/2/3 & \cellcolor[HTML]{FD6864}1/3/3 \\ \cline{2-8}
 & RHC-S & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{FFFC9E}0/1/1 & \cellcolor[HTML]{FD6864}3/2/2 & \cellcolor[HTML]{FFFFFF}3/3/3 \\ \cline{2-8}
 & SSRS & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{FFFC9E}0/0/1 & \cellcolor[HTML]{FD6864}3/3/2 & 3/3/3 \\ \cline{2-8}
 & SUPS & 0/0/0 & 0/0/0 & \cellcolor[HTML]{FFFC9E}1/0/0 & 0/0/0 & \cellcolor[HTML]{FD6864}2/3/3 & 3/3/3 \\ \cline{2-8}
 & CES & 0 & 0 & 0 & 0 & \cellcolor[HTML]{34FF34}3 & 3 \\ \cline{2-8}
 \multirow{-8}{*}{\rotatebox[origin=c]{90}{MNIST}} & SRS & 0 & 0 & 0 & 0 & \cellcolor[HTML]{34FF34}3 & 3 \\ \cmidrule{1-8}\morecmidrules\cmidrule{1-8}
 & GBS & 0/0/0 & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{34FF34}3/3/3 & 3/3/3 \\ \cline{2-8}
 & DeepEST & 0/0/0 & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{34FF34}3/3/3 & 3/3/3 \\ \cline{2-8}
 & 2-UPS & 0/0/0 & \cellcolor[HTML]{FFFC9E}0/0/1 & 0/0/0 & \cellcolor[HTML]{FFFC9E}1/0/0 & \cellcolor[HTML]{FD6864}2/3/2 & 3/3/3 \\ \cline{2-8}
 & RHC-S & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{FFFC9E}1/0/0 & \cellcolor[HTML]{FD6864}2/3/3 & 3/3/3 \\ \cline{2-8}
 & SSRS & 0/0/0 & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{34FF34}3/3/3 & 3/3/3 \\ \cline{2-8}
 & SUPS & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{FFFC9E}1/0/0 & \cellcolor[HTML]{FD6864}2/3/3 & 3/3/3 \\ \cline{2-8}
 & CES & 0 & 0 & 0 & 0 & \cellcolor[HTML]{34FF34}3 & 3 \\ \cline{2-8}
 \multirow{-8}{*}{\rotatebox[origin=c]{90}{CIFAR10}} & SRS & 0 & 0 & 0 & 0 & \cellcolor[HTML]{34FF34}3 & 3 \\ \cmidrule{1-8}\morecmidrules\cmidrule{1-8}
 & GBS & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{FFFC9E}0/0/1 & \cellcolor[HTML]{FD6864}3/3/2 & 3/3/3 \\ \cline{2-8}
 & DeepEST & 0/0/0 & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{34FF34}3/3/3 & 3/3/3 \\ \cline{2-8}
 & 2-UPS & \cellcolor[HTML]{FFFC9E}0/0/1 & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{FD6864}3/3/2 & \cellcolor[HTML]{FD6864}3/3/2 \\ \cline{2-8}
 & RHC-S & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{FFFC9E}0/1/0 & \cellcolor[HTML]{FD6864}3/2/3 & 3/3/3 \\ \cline{2-8}
 & SSRS & 0/0/0 & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{34FF34}3/3/3 & 3/3/3 \\ \cline{2-8}
 & SUPS & 0/0/0 & 0/0/0 & 0/0/0 & \cellcolor[HTML]{FFFC9E}2/1/0 & \cellcolor[HTML]{FD6864}1/2/3 & \cellcolor[HTML]{FD6864}2/3/3 \\ \cline{2-8}
 & CES & 0 & \cellcolor[HTML]{FFFC9E}1 & \cellcolor[HTML]{FFFC9E}1 & 0 & \cellcolor[HTML]{FD6864}1 & \cellcolor[HTML]{FD6864}2 \\ \cline{2-8}
\multirow{-8}{*}{\rotatebox[origin=c]{90}{CIFAR100}} & SRS & 0 & 0 & 0 & 0 & \cellcolor[HTML]{34FF34}3 & 3 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Antonio Guerriero
Roberto Pietrantuono
Stefano Russo

"DeepSample: DNN sampling-based testing for operational accuracy assessment" by Antonio Guerriero, Roberto Pietrantuono, and Stefano Russo, arXiv:2403.19271v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
 \hline
 \textbf{Cleaning Method} &\textbf{Description} \\
 \hline
 \texttt{LCDSC} & Our Method\\ \hline

 k-Highest & Removal all but the k-highest IMFs \\ \hline
 l-Lowest & Removal all but the k-lowest IMFs \\ \hline
 k-Highest \& l-Lowest & Combination of k-Highest and l-Lowest \\ \hline
 Power Set Cleaning & Perform a best subset selection over all possible subsets. \\ \hline
 WHT & Wavelet Hard Thresholding each IMF \\ \hline
 WIT & Wavelet Interval Thresholding each IMF \\ \hline
 No Cleaning & No Signal cleaning \\ \hline
 \end{tabular}
\caption{List of EEMD Signal Cleaning Techniques}
\end{table}
\end{document}

eess

Kentaro Hoffman
Jonathan M. Lees
Kai Zhang

"Local Change Point Detection and Cleaning of EEMD Signals with Application to Acoustic Shockwaves" by Kentaro Hoffman, Jonathan M. Lees, and Kai Zhang, arXiv:2103.01352v3, 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}{ccccccc}
 \toprule
 \multicolumn{7}{c}{FFNN (n\_quantiles = 50)} \\
 \midrule
 Model & Layer 0 & Layer 1 & Layer 2 & Output Layer & Act. Functions & \# Weights \\
 \midrule
 1 & 50 & & & 50 & relu & 2500 \\
 2 & 50 & 50 & & 50 & relu, tanh & 5000 \\
 3 & 50 & 50 & 50 & 50 & relu, tanh, relu & 7500 \\
 \bottomrule
 \end{tabular}
\caption{FFNN model selection process.}
\end{table}
\end{document}

stat

Bastian Schmidt Jørgensen
Jan Kloppenborg Møller
Peter Nystrup
Henrik Madsen

"Sequential Methods for Error Correction of Probabilistic Wind Power Forecasts" by Bastian Schmidt Jørgensen, Jan Kloppenborg Møller, Peter Nystrup, and Henrik Madsen, arXiv:2501.14805v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of throughput in Mbps and Number of re-transmission for different channel distributions}
\begin{tabular}{|l|l|l|l|l|l|l|l|}
\hline
 & \textbf{$\mu_3$} & \textbf{$\mu_4$} & \textbf{$\mu_5$} & \textbf{$\mu_6$} & \textbf{$\mu_7$} & \textbf{$\mu_8$} & \textbf{Average} \\ \hline
\textbf{QPSK} & 50 (1) & 50 (1) & 50 (1) & 50 (1) & 50 (1) & 50 (1) & 50 (1) \\ \hline
\textbf{QAM} & 38.7 (5) & 76.5 (2) & 38.7 (5) & 38.8 (5) & 45.5 (4) & 38.7 (5) & 43.4 (4.3) \\ \hline
\textbf{Proposed} & 50 (1) & \textbf{90 (1)} & 50 (1) & 50 (1) & 50 (1) & 50 (1) & \textbf{53 (1)} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Neelam Singh
S. V. Sai Santosh
Sumit J. Darak

"Towards Intelligent Reconfigurable Wireless Physical Layer (PHY)" by Neelam Singh, S. V. Sai Santosh, and Sumit J. Darak, arXiv:2012.01153v1, 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}{cccc}
\toprule
Statistics & Know.Enrich. Hall. & Ans.Extract. Hall. \\ \midrule
$s$-$o$ assoc. & 0.47 & 1.17 \\
$s$-$o'$ assoc. & 0.81 & 1.69 \\
Robustness & 0.86 & 0.45 \\
Uncertainty & 4.54 & 4.76 \\ \bottomrule
\end{tabular}
\caption{External data and model prediction features for two types of non-factual hallucination, averaged over three LMs.}
\end{table}
\end{document}

cs

Lei Yu
Meng Cao
Jackie Chi Kit Cheung
Yue Dong

"Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations" by Lei Yu, Meng Cao, Jackie Chi Kit Cheung, and Yue Dong, arXiv:2403.18167v2, 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|cccc}
 & \multicolumn{4}{c@{}}{Cluster}\\
 Linkage & A (red, $n=86$) & B (blue, $n=7$) & C (green, $n=22$)& D (purple, $n=24$)\\ \hline
 average & 1.0 & 1.0 & 1.0 & 1.0 \\
 complete & 0.0 & 1.0 & 0.732 & 1.0 \\
 single & 0.021 & 0.0 & 0.056 & 1.0 \\
 ward & 0.0 & 1.0 & 0.690 & 1.0 \\
 \end{tabular}
\caption{Purity score of the 4 clusters obtained with the average linkage for $X1$ without \texttt{X1-015}, see . The colour and size of each cluster is in parentheses.}
\end{table}
\end{document}

stat

Yossi Bokor Bleile
Pooja Yadav
Patrice Koehl
Florian Rehfeldt

"Persistence diagrams as morphological signatures of cells: A method to measure and compare cells within a population" by Yossi Bokor Bleile, Pooja Yadav, Patrice Koehl, and Florian Rehfeldt, arXiv:2310.20644v2, 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{FD Payoff Matrix}
\begin{tabular}{|c|c|c|}
 \hline
 Others / This & Forward & Drop\\
 \hline
 All drop & $g - c$ & $0$\\
 \hline
 At least one forward & $g - c$ & $g$\\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Ameer Taweel
Burcu Yıldız
Alptekin Küpçü

"Gamu Blue: A Practical Tool for Game Theory Security Equilibria" by Ameer Taweel, Burcu Yıldız, and Alptekin Küpçü, arXiv:2403.19130v1, 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{Required number of hardware components to realize both behavior 1 and 2.}
\begin{tabular}{|c|c|c|c|}
 \hline
 & \# RF chains & \# Phase-shifters & \# TTDs \\ %[0.5ex]
 \hline
 JPTA & $1$ & $M$ & ${\mathrm{O}}(M)$ \\
 \hline
 HBF-FC & ${\mathrm{O}}(M)$ & $M N_{\mathrm{RF}}$ & $0$ \\
 \hline
 HBF-PC & ${\mathrm{O}}(M)$ & $M$ & $0$ \\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

Vishnu V. Ratnam
Jianhua Mo
Ahmad AlAmmouri
Boon L. Ng
Jianzhong
Zhang
Andreas F. Molisch

"Joint Phase-Time Arrays: A Paradigm for Frequency-Dependent Analog Beamforming in 6G" by Vishnu V. Ratnam, Jianhua Mo, Ahmad AlAmmouri, Boon L. Ng, Jianzhong, Zhang, and Andreas F. Molisch, arXiv:2312.11682v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c||c|c|c|c}
 \toprule
 \multirow{2}{*}{Dataset ($\Delta \textsc{DP}$)} & \multicolumn{4}{c}{\texttt{Acc}} \\
 & LAFTR & sIPM-LFR & & FTM $\checkmark$ \\
 \midrule
 \midrule
 \textsc{Adult} ($\approx$ 0.06) & 0.823 & 0.820 & 0.815 & \textbf{0.835} \\
 \midrule
 \textsc{German} ($\approx$ 0.04) & 0.721 & 0.741 & 0.728 & \textbf{0.743} \\
 \midrule
 \textsc{Dutch} ($\approx$ 0.15) & 0.810 & 0.813 & 0.811 & \textbf{0.820} \\
 \midrule
 \textsc{Bank} ($\approx$ 0.02) & 0.877 & 0.878 & 0.876 & \textbf{0.878} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Fairness Through Matching

Kunwoong Kim
Insung Kong
Jongjin Lee
Minwoo Chae
Sangchul Park
Yongdai Kim

"Fairness Through Matching" by Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, and Yongdai Kim, arXiv:2501.02793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|l|l|l|}
\hline
Regime & Strategy & Mean Corr & Sharpe & Max Drawdown \\ \hline
\multirow{5}{*}{Test} & Example Model & 0.0264 & 0.9626 & 0.2608 \\ \cline{2-5}
 & Baseline Model & 0.0266 & 1.1559 & 0.1646 \\ \cline{2-5}
 & Tail Risk Model & 0.0016 & 0.1068 & 0.3728 \\ \cline{2-5}
 & Static Hedged Model & 0.0207 & 1.1337 & 0.0742 \\ \cline{2-5}
 & Dynamic Hedged Model & 0.0225 & 1.2646 & 0.0330 \\ \hline
\multirow{5}{*}{Bull} & Example Model & 0.0307 & 1.2512 & 0.0693 	 \\ \cline{2-5}
 & Baseline Model & 0.0301 & 1.4305 & 0.0351 \\ \cline{2-5}
 & Tail Risk Model & 0.0007 & 0.0481 & 0.3728 \\ \cline{2-5}
 & Static Hedged Model & 0.0227 & 1.2941 & 0.0380 \\ \cline{2-5}
 & Dynamic Hedged Model & 0.0246 & 1.4305 & 0.0330 \\ \hline
\multirow{5}{*}{Bear} & Example Model & -0.0060 & -0.2306 & 0.2608 \\ \cline{2-5}
 & Baseline Model & 0.0003 & 0.0151 & 0.1646 \\ \cline{2-5}
 & Tail Risk Model & 0.0081 & 0.5826 & 0.0219 \\ \cline{2-5}
 & Static Hedged Model & 0.0054 & 0.3409 & 0.0742 \\ \cline{2-5}
 & Dynamic Hedged Model & 0.0069 & 0.4871 & 0.0312 \\ \hline
\end{tabular}
\caption{Performances of Dynamic Hedged deep IL XGBoost ensemble model based on different training set sizes and V4.2 Example Model from Era 901 to Era 1070 under different market regimes. }
\end{table}
\end{document}

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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|r}
Maximum battery capacity (MWh) & Achieved income \\
\hline
1.0 & 41436.84 $\pm$ 1721.37 \\
1.5 & 50643.02 $\pm$ 1214.04 \\
\textbf{2.0} & \textbf{60027.13 $\pm$ 398.11} \\
\end{tabular}
\caption{Final balances achieved on testing data for different maximum battery capacities with the proposed strategy optimized with the A2C algorithm averaged over five testing runs.}
\end{table}
\end{document}

q-fin

"On-line reinforcement learning for optimization of real-life energy trading strategy" by Łukasz Lepak and Paweł Wawrzyński, arXiv:2303.16266v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Angelica Babei
Andrew Fiori
Cameron Franc

"The moduli space of representations of the modular group into $G_2$" by Angelica Babei, Andrew Fiori, and Cameron Franc, arXiv:2501.17276v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
$\epsilon$ & IER & Variance\\
\toprule
\toprule
$0$ (reference) & \phantom{1}80.81 & 1048.86\\
$1$ & \phantom{1}92.12 & 1274.86\\
$2$ & 103.43 & 1551.31\\
$5$ & 137.37 & 2572.78\\
\bottomrule
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Dimension Reduction of Distributionally Robust Optimization Problems" by Brandon Tam and Silvana M. Pesenti, arXiv:2504.06381v1, 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
\toprule
\textbf{Team} & \textbf{Value} \\
\midrule
PSG & 139.34 \\
Troyes & 129.67 \\
Olympique Marseille & 127.45 \\
\midrule
Nice & 127.32 \\
Metz & 126.43 \\
Reims & 125.91 \\
\midrule
Angers SCO & 125.05 \\
Saint-Etienne & 123.58 \\
Monaco & 121.79 \\
\midrule
Rennes & 118.39 \\
Lille & 115.30 \\
Olympique Lyonnais & 110.59 \\
\midrule
Lorient & 105.40 \\
Clermont & 103.86 \\
Nantes & 103.33 \\
\midrule
Lens & 102.68 \\
Strasbourg & 102.53 \\
Bordeaux & 101.76 \\
\midrule
Brest & 101.18 \\
Montpellier & 101.13 \\
\bottomrule
\bottomrule
\end{tabular}
\caption{Sum of distances to other teams.}
\end{table}
\end{document}

stat

Ali Baouan
Mathieu Rosenbaum
Sergio Pulido

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


math

Hao Sun
Ashkan Ertefaie
Luke Duttweiler
Brent A. Johnson

"Constructing optimal treatment length strategies to maximize quality-adjusted lifetimes" by Hao Sun, Ashkan Ertefaie, Luke Duttweiler, and Brent A. Johnson, arXiv:2412.05108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c} % <-- Alignments: 1st column left, 2nd middle and 3rd right, with vertical lines in between
			\textbf{Parameters} & \textbf{Value} \\
			\hline
			$\tau^{s}$& 1/1/2024 \\
			$\tau^{e}$ & 31/12/2024 \\
			$v_{min}$ & 0 (MWh)\\
			$v_{max}$ & 250,000 (MWh)\\
			$v_{0}$ & 100,000 (MWh)\\
			$v_{T+1}$ & 100,000 (MWh)\\
			$i_{min}$ & -8,000 (MWh)\\
			$i_{max}$ & 8,000 (MWh)
		\end{tabular}
\caption{Gas storage contract parameters for a delivery period $\left[\tau^{s} ,\tau^{e} \right]$ from 1/1/2024 to 31/12/2024.}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Training hyperparameters for the note property prediction task}
\begin{tabular}{ll}
\toprule
Hyperparameters & \\ \midrule
Training steps & 10K \\
Batch Size & 32 \\
Optimizer & Adam \\
Learning Rate & 1e-03 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Joonsu Gha
Vincent Herrmann
Benjamin Grewe
Jürgen Schmidhuber
Anand Gopalakrishnan

"Unsupervised Musical Object Discovery from Audio" by Joonsu Gha, Vincent Herrmann, Benjamin Grewe, Jürgen Schmidhuber, and Anand Gopalakrishnan, arXiv:2311.07534v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Quantile Predictions for Equity Premium using Penalized Quantile Regression with Consistent Variable Selection across Multiple Quantiles" by Shaobo Li and Ben Sherwood, arXiv:2505.16019v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance metrics for assessing GMoE model recognizing multi-class human lifting actions.}
\begin{tabular}{|c|c|c|c|c|}
\hline
 & \textbf{Accuracy} & \textbf{Precision} & \textbf{Recall} & \textbf{F1 score} \\ \hline
\textit{standing} & \multirow{3}{*}{/} & 0.890 & 0.969 & 0.928 \\ \cline{1-1} \cline{3-5}
\textit{rising} & & 0.898 & 0.869 & 0.883 \\ \cline{1-1} \cline{3-5}
\textit{squatting} & & 0.925 & 0.816 & 0.867 \\ \hline
\textit{average} & 0.899 & 0.904 & 0.885 & 0.893 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Cheng Guo
Lorenzo Rapetti
Kourosh Darvish
Riccardo Grieco
Francesco Draicchio
Daniele Pucci

"Online Action Recognition for Human Risk Prediction with Anticipated Haptic Alert via Wearables" by Cheng Guo, Lorenzo Rapetti, Kourosh Darvish, Riccardo Grieco, Francesco Draicchio, and Daniele Pucci, arXiv:2401.05365v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrr}
\toprule
Race description & Count & Percentage \\
\midrule
White & 2,433 & 66.24\% \\
Black or African American & 738 & 20.09\% \\
Multiple races & 277 & 7.54\% \\
Asian & 93 & 2.53\% \\
Others and unknown & 132 & 3.59\% \\
\midrule
Total & 3,673 & 100\% \\
\bottomrule
\end{tabular}
\caption{The distribution of 3,673 unique patients' races.}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Joint probability table of two binary variables $X_i$ and $X_j$.}
\begin{tabular}{c||c|c||c}
 $\tilde{p}_{ij}(X_i, X_j)$ & $X_j = +1$ & $X_j = -1$ & \\ \hline \hline
 $X_i = +1$ & $\xi_{ij}$ & $q_i - \xi_{ij}$ & $q_i$ \\ \hline
 $X_i = -1$ & $q_j - \xi_{ij}$ & $1 + \xi_{ij} - q_i - q_j$ & $1-q_i$ \\ \hline \hline
 & $q_j$ & $1 - q_j$ & \\
\end{tabular}
\end{table}
\end{document}

stat

"Adaptive Variational Inference in Probabilistic Graphical Models: Beyond Bethe, Tree-Reweighted, and Convex Free Energies" by Harald Leisenberger and Franz Pernkopf, arXiv:2502.03341v1, 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}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{Coverage ratio comparison in urban environments.}}
\begin{tabular}{||cccc||}
 \hline
 \text{Methods--}{$\left | \mathcal P \right | $} & 80 & 200 & 450 \\
 \hline\hline
 GA Deployment Method & 99.2\% & 88.6\% & 75.3\% \\
 K-Means & 98.6\% & 82.3\% & 69.4\% \\
 Branch and Cut & 95.6\% & 83.1\% & 69.4\% \\
 Greedy Search & 92.6\% & 79.1\% & 71.4\% \\
 Random & 85.6\% & 72.1\% & 59.4\% \\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

Xukai Zhong
Yiming Huo
Xiaodai Dong
Zhonghua Liang

"QoS-Compliant 3D Deployment Optimization Strategy for UAV Base Stations" by Xukai Zhong, Yiming Huo, Xiaodai Dong, and Zhonghua Liang, arXiv:2008.03125v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.