\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Gap in dB from the ModCod convergence threshold at ${\mathrm{CER}=10^{-5}}$ using Strategy 2 with $\beta=0.2$.}
\begin{tabular}{|c|c|}
			\hline
			ModCod & Gap from LDPC threshold \\
			\hline\hline
			1 & -0.86 \\
			\hline
			2 & -1.04 \\
			\hline
			3 & -1.67 \\
			\hline
			6 & -4.35 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Decoding of Variable Length PLH Codes

Marco Morini
Alessandro Ugolini
Giulio Colavolpe

"Decoding of Variable Length PLH Codes" by Marco Morini, Alessandro Ugolini, and Giulio Colavolpe, arXiv:2103.08991v1, 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|c|}
\hline
\multicolumn{2}{|c|}{} & \multicolumn{2}{|c|}{No.~Pentatopes} & \multicolumn{2}{|c|}{20\% AMG} \\
\hline
No.~Points & No. Flips & Initial & Final & Initial & Final \\
\hline
50 & 47 & 492 & 524 & 0.6781 & 0.6826 \\
100 & 125 & 1421 & 1545 & 0.6879 & 0.6899 \\
150 & 237 & 2443 & 2641 & 0.6787 & 0.6854 \\
200 & 350 & 3583 & 3841 & 0.6745 & 0.6852 \\
250 & 477 & 4767 & 5039 & 0.6736 & 0.6829 \\
300 & 608 & 6010 & 6296 & 0.6732 & 0.6796 \\
\hline
\end{tabular}
\caption{Information for $\eta^{(2)}$ quality improvement cases on randomized meshes.}
\end{table}
\end{document}

math

"Anisotropic Delaunay hypervolume meshing for space-time applications: point insertion, quality heuristics, and bistellar flips" by Jude T. Anderson and David M. Williams, arXiv:2312.17414v2, 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.


stat

"Direct sampling from conditional distributions by sequential maximum likelihood estimations" by Shuhei Mano, arXiv:2502.00812v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{Total $LR$ for IEEE 118-bus system in different strategies}}
\begin{tabular}{ccccc}
\hline
Strategy & $c_1$ & $c_2$ & $c_3$ & $c_4$ \\ \hline
LR (MW) & 76.389 & 51.799 & 53.253 & 45.255 \\ \hline
\hline
 \end{tabular}
\end{table}
\end{document}

eess

"Controllable Transmission Networks under Demand Uncertainty with Modular FACTS" by Alireza Soroudi, arXiv:2105.10402v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Capacity allocation under Scenario 2, with regulation and minimum quota restriction.}
\begin{tabular}{|c|c|c|c|} \hline
 Products & Original Grouping & New Grouping & Transported Amount \\ \hline
 Crude Oil & $G_1$ & $G_1$ & 128,185,505 \\
 Corn & $G_1$ & $G_1$ & 440,916,666 \\
 Barley & $G_2$ & $G_3$ & 25,782,263 \\
 Oat & $G_3$ & $G_3$ & 55,115,565 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

Mostafa Pazoki
Hamed Samarghandi
Mehdi Behroozi

"Increasing Supply Chain Resiliency Through Equilibrium Pricing and Stipulating Transportation Quota Regulation" by Mostafa Pazoki, Hamed Samarghandi, and Mehdi Behroozi, arXiv:2308.00681v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the results obtained for $k=7$}
\begin{tabular}{|c|c|c|c|}
 			\hline
 			Girth $g$ & $n(7,g)$ & Orders to be investigated & $N(7,g)$ \\
 			\hline
 			3 & 8 & -- & 8 \\
 			\hline
 			4 & 14 & -- & 14 \\
 			\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.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cccc}
\toprule
 C-Eval& 1/4& 1/2& 3/4& 1\\ \midrule
Ours& 56.61& 56.08& 55.80& 56.23\\
\bottomrule
\end{tabular}
\caption{"This is the result of studying the variation in the size of the held-out dataset after merging on the 2200B and 2420B checkpoints.}
\end{table}
\end{document}

cs

Deyuan Liu
Zecheng Wang
Bingning Wang
Weipeng Chen
Chunshan Li
Zhiying Tu
Dianhui Chu
Bo Li
Dianbo Sui

"Checkpoint Merging via Bayesian Optimization in LLM Pretraining" by Deyuan Liu, Zecheng Wang, Bingning Wang, Weipeng Chen, Chunshan Li, Zhiying Tu, Dianhui Chu, Bo Li, and Dianbo Sui, arXiv:2403.19390v1, 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|}
 \hline
 index & frequency & weight & distance to $X$\\\hline
 1 & 2944 & 73 & 22 \\\hline
 2 & 2727 & 73 & 26 \\\hline
 3 & 1560 & 73 & 20 \\\hline
 4 & 1521 & 73 & 16 \\\hline
 5 & 125 & 73 & 30 \\\hline
 6 & 110 & 73 & 26 \\\hline
 7 & 91 & 73 & 16 \\\hline
 8 & 68 & 73 & 20 \\\hline
 9 & 66 & 73 & 24 \\\hline
 10 & 32 & 75 & 28 \\\hline
 \end{tabular}
\end{table}
\end{document}

math

Hanwen Yao
Waleed Abu Laban
Christian Häger
Alexandre Graell i Amat
Henry D. Pfister

"Belief Propagation Decoding of Quantum LDPC Codes with Guided Decimation" by Hanwen Yao, Waleed Abu Laban, Christian Häger, Alexandre Graell i Amat, and Henry D. Pfister, arXiv:2312.10950v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Ricardo Fraiman
Leonardo Moreno
Thomas Ransford

"Two statistical problems for multivariate mixture distributions" by Ricardo Fraiman, Leonardo Moreno, and Thomas Ransford, arXiv:2503.12147v3, 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{Overview over the four simulation scenarios, their DGPs and the models corresponding to these DGPs.}
\begin{tabular}{ccc}
 \hline
 Scenario & Effect of $x_2$ in the DGP & Model \\
 \hline
 (I) & Heterogeneous time-variation & (i) \\
 (II) & Heterogeneity \& time-variation but no interaction & (ii) \\
 (III) & Heterogeneity only & (iii) \\
 (IV) & Time-variation only & (iv) \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Niklas Hagemann
Thomas Kneib
Kathrin Möllenhoff

"Capturing heterogeneous time-variation in covariate effects in non-proportional hazard regression models" by Niklas Hagemann, Thomas Kneib, and Kathrin Möllenhoff, arXiv:2501.13525v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Robust MCVaR Portfolio Optimization with Ellipsoidal Support and Reproducing Kernel Hilbert Space-based Uncertainty" by Rupendra Yadav and Aparna Mehra, arXiv:2509.00447v1, 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{Generator architecture.}
\begin{tabular}{ccc}
 \hline
 \textbf{Generator} & Activation & Output Shape \\
 \hline
 Latent Vector & - & 1 x 1 x 512\\
 $a_*$ & - & 1 x 1 x 1\\
 Concatenate & - & 1 x 1 x 513\\
 Dense & LReLU & 1 x 1 x 8192\\
 Reshape & - & 4 x 4 x 512\\
 Conv 4 x 4 & LReLU & 4 x 4 x 512\\
 Conv 3 x 3 & LReLU & 4 x 4 x 512\\

 Upsample & - & 8 x 8 x 512\\
 Conv 3 x 3 & LReLU & 8 x 8 x 512\\
 Conv 3 x 3 & LReLU & 8 x 8 x 256\\

 Upsample & - & 16 x 16 x 256\\
 Conv 3 x 3 & LReLU & 16 x 16 x 256\\
 Conv 3 x 3 & LReLU & 16 x 16 x 128\\

 Upsample & - & 32 x 32 x 128\\
 Conv 3 x 3 & LReLU & 32 x 32 x 128\\
 Conv 3 x 3 & LReLU & 32 x 32 x 64\\

 Upsample & - & 64 x 64 x 64\\
 Conv 3 x 3 & LReLU & 64 x 64 x 64\\
 Conv 3 x 3 & LReLU & 64 x 64 x 32\\

 Upsample & - & 128 x 128 x 32\\
 Conv 3 x 3 & LReLU & 128 x 128 x 32\\
 Conv 3 x 3 & LReLU & 128 x 128 x 16\\
 Conv 1 x 1 & tanh & 128 x 128 x 2\\
 \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.


math

Eiichi Bannai
Hirotake Kurihara
Hiroshi Nozaki

"On the existence and non-existence of spherical $m$-stiff configurations" by Eiichi Bannai, Hirotake Kurihara, and Hiroshi Nozaki, arXiv:2504.17184v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 \multirow{2}{*}{Matrix} & \multicolumn{3}{c|}{$s$ for 5\% relative error} & \multicolumn{3}{c|}{$s$ for 1\% relative error}\\
 \cline{2-7}
 & Low-rank & SVD-Zeroth& SVD-First& Low-rank & SVD-Zeroth& SVD-First\\
 \hline
 Symmetric \& Toeplitz & 24 & 1& 1& -& 1& 1\\
 \hline
 Symmetric \& Symmetric& 16& 1& 1& -& 1& 1 \\
 \hline
 Symmetric \& Hankel &25 & 1& 1& - & 1& 1 \\
 \hline
 General \& Symmetric &25 & 1& 1 & - & 1 & 1 \\
 \hline
 DFT Model \& DFT Model &8 &1 &1 & -&1 & 1 \\
 \hline
 Images[1]& 9& 1&1 & - & 5&2 \\
 \hline
 Bus494 \& Bus494[2]& 50 & 4&2 &62 &10 &3 \\
 \hline
 Type-1 \& Type-1[4]& - &5 &3 &- &7 & 4 \\
 \hline
 Type-1 \& Toeplitz& - & 73& 4&- & -&6 \\
 \hline
 Toeplitz \& Toeplitz &34 & 1& 1& - & 31 & 9\\
 \hline
 Block Toeplitz \& Block Toeplitz & 34& 1& 1 & - & 30 & 9\\
 \hline
 Topelitz \& Hankel& 33& 1 & 1 & - & 31& 9 \\
 \hline
 General \& Toeplitz& 34& 1& 1 & - & 30 & 9 \\
 \hline
 Hankel \& Hankel& 34 &1 & 1& -& 31& 9\\
 \hline
 General \& Hankel& 36 & 1 & 1& -& 30& 9 \\
 \hline
 LLM-1($Q_1I_1,K_1I_1,V_1I_1$)[4]&-&171&8&-&183&9\\
 \hline
 LLM-2($Q_2I_2, K_2I_2, V_2I_2$)[4]&-&171&8&-&184&9\\
 \hline
 Bus662 \& Bus662[3]& 74 &18 &7 &74 &39 &15 \\
 \hline
 Type-2 \& Type-2[4]& - &27 &2 & - & 67& 16 \\
 \hline
 Kappa \& Kappa &- &68 & 40& - & 76& 60 \\
 \hline
 Kappa \& Toeplitz &- & 69& 39 & - &- & 61 \\
 \hline
 Kappa \& General& - & 75 &55 &- &- & 68 \\
 \hline
 Type-3 \& Toeplitz&- &74 & 56&- &- & 69 \\
 \hline
 General \& General &- & 75& 61& - & -& 70\\
 \hline
 Type-3 \& Type-3[4]& - &76 & 62& - & -&71 \\
 \hline

 \end{tabular}
\caption{Results for different types of matrices of size $700 \times 700$. The average numbers $s$ reported indicate the corresponding front constants in the $\mathcal{O}(n^2 \log n)$ scaling of arithmetic operations, using $\lceil s \log n \rceil$ components of the Singular Value Decomposition (SVD). `-' indicates that it did not achieve the desired tolerance in relative error even after using $2n^3$ arithmetic operations required in the conventional multiplication. DFT - Density Functional Theory; LLM - Large Language Model. All structured matrices with some periodicity in entries are of low effective ranks.}
\end{table}
\end{document}

math

Suvendu Kar
Hariprasad M.
Sai Gowri J. N.
Murugesan Venkatapathi

"Efficient approximations of matrix multiplication using truncated decompositions" by Suvendu Kar, Hariprasad M., Sai Gowri J. N., and Murugesan Venkatapathi, arXiv:2504.19308v2, 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{Demand per district per period for the increasing demand scenario and decreasing demand scenario}
\begin{tabular}{llll}
\hline
District & Demand per period & UAV costs & Truck costs \\ \hline
1 & 200 & 150 & 900 \\ \hline
District & Demand per period & UAV costs & Truck costs \\ \hline
1 & 300 & 100 & 600 \\
2 & 100 & 200 & 1200 \\ \hline
District & Demand per period & UAV costs & Truck costs \\ \hline
1 & 200 & 50 & 300 \\
2 & 300 & 150 & 900 \\
3 & 100 & 250 & 1500 \\ \hline
District & Demand per period & UAV costs & Truck costs \\ \hline
1 & 200 & 50 & 300 \\
2 & 300 & 150 & 900 \\
3 & 100 & 200 & 1200 \\
4 & 150 & 300 & 1800 \\ \hline
District & Demand per period & UAV costs & Truck costs \\ \hline
1 & 200 & 50 & 300 \\
2 & 300 & 100 & 600 \\
3 & 100 & 150 & 900 \\
4 & 150 & 200 & 1200 \\
5 & 250 & 250 & 1500 \\ \hline
District & Demand per period & UAV costs & Truck costs \\ \hline
1 & 200 & 50 & 300 \\
2 & 300 & 100 & 600 \\
3 & 100 & 150 & 900 \\
4 & 150 & 200 & 1200 \\
5 & 250 & 250 & 1500 \\
6 & 200 & 300 & 1800 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Robert van Steenbergen
Wouter van Heeswijk
Martijn Mes

"The Stochastic Dynamic Post-Disaster Inventory Allocation Problem with Trucks and UAVs" by Robert van Steenbergen, Wouter van Heeswijk, and Martijn Mes, arXiv:2312.00140v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|lcc}
 \toprule
 \textbf{Test Class} & \textbf{Method} & \textbf{cIoU $\uparrow$} & \textbf{AUC $\uparrow$} \\
 \hline
 \multirow{9}{*}{Heard 110}
 & LVS~$_{\text{CVPR}21}$ & 28.90 & 36.20 \\
 & EZ-VSL (w/o OGL)~$_{\text{ECCV}22}$ & 31.86 & 36.19 \\
 & EZ-VSL (w/ OGL)~$_{\text{ECCV}22}$ & 37.25 & 38.97 \\
 & SLAVC (w/o OGL)~$_{\text{NeurIPS}22}$ & 35.84 & - \\
 & SLAVC (w/ OGL)~$_{\text{NeurIPS}22}$ & 38.22 & - \\
 & FNAC (w/ OGL)~$_{\text{CVPR}23}$ & 39.54 & 39.83 \\
 & Alignment (w/o OGL)~$_{\text{ICCV}23}$ & 38.31 & 39.05 \\
 & Alignment (w OGL)~$_{\text{ICCV}23}$ & 41.85 & 40.93 \\
 \cline{2-4}
 & CLIPSeg (w/ GT Text) & 49.65 & 45.74 \\
 & CLIPSeg (w/ WAV2CLIP Text) & 23.24 & 24.78 \\
 & CLIPSeg (Sup. AudioTokenizer) & 49.73 & 45.35 \\
 & \cellcolor{lightgray!25}\textbf{Ours (w/o OGL)} & \cellcolor{lightgray!25} 48.44 & \cellcolor{lightgray!25} 45.06 \\
 \hline \hline

 \multirow{9}{*}{Unheard 110}
 & LVS~$_{\text{CVPR}21}$ & 26.30 & 34.70 \\
 & EZ-VSL (w/o OGL)~$_{\text{ECCV}22}$ & 32.66 & 36.72 \\
 & EZ-VSL (w/ OGL)~$_{\text{ECCV}22}$ & 39.57 & 39.60 \\
 & SLAVC (w/o OGL)~$_{\text{NeurIPS}22}$ & 36.50 & - \\
 & SLAVC (w/ OGL)~$_{\text{NeurIPS}22}$ & 38.87 & - \\
 & FNAC (w/ OGL)~$_{\text{CVPR}23}$ & 42.91 & 41.17 \\
 & Alignment (w/o OGL)~$_{\text{ICCV}23}$ & 39.11 & 39.80 \\
 & Alignment (w OGL)~$_{\text{ICCV}23}$ & 42.94 & 41.54 \\
 \cline{2-4}
 & CLIPSeg (w/ GT Text) & 49.13 & 44.77 \\
 & CLIPSeg (w/ WAV2CLIP Text) & 26.25 & 27.03 \\
 & CLIPSeg (Sup. AudioTokenizer) & 43.65 & 41.05 \\
 & \cellcolor{lightgray!25}\textbf{Ours (w/o OGL)} & \cellcolor{lightgray!25} 41.98 & \cellcolor{lightgray!25} 41.55 \\
 \bottomrule
 \end{tabular}
\caption{\textbf{Comparison results on open-set audio-visual localization experiments trained and tested on the splits of~.}}
\end{table}
\end{document}

eess

Sooyoung Park
Arda Senocak
Joon Son Chung

"Can CLIP Help Sound Source Localization?" by Sooyoung Park, Arda Senocak, and Joon Son Chung, arXiv:2311.04066v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Interpretable Models for Granger Causality Using Self-explaining Neural Networks" by Ričards Marcinkevičs and Julia E. Vogt, arXiv:2101.07600v1, 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
 \textbf{Language} & \textbf{Seq2seq} & \textbf{Support} \\
 \midrule
PL & 89.92 & 2~986 \\
CS & 88.13 & 1~567 \\
RU & 85.13 & 3~458 \\
BG & 89.58 & 2~872 \\
SL & 84.86 & 1~976 \\
UK & 91.09 & 1~100 \\
\midrule
All & 87.84 & 13~959\\
 \bottomrule
 \end{tabular}
\caption{Evaluation of the entity linking on the \textbf{cross topics} split---accuracy for each language.}
\end{table}
\end{document}

cs

Jakub Piskorski
Michał Marcińczuk
Roman Yangarber

"Cross-lingual Named Entity Corpus for Slavic Languages" by Jakub Piskorski, Michał Marcińczuk, and Roman Yangarber, arXiv:2404.00482v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameters used for training in \texttt{Nocturne} scenarios.}
\begin{tabular}{lrrr}
\toprule
\textbf{Parameter} & \textbf{PPO} & \textbf{HR-PPO} \\
\midrule
$\gamma$ & 0.99 & 0.99 \\
$\lambda_{\text{GAE}}$ & 0.95 & 0.95 \\
PPO rollout length & 4096 & 4096 \\
PPO epochs & 10 & 10\\
PPO mini-batch size & 512 & 512 \\
PPO clip range & 0.2 & 0.2 \\
Adam learning rate & 3e-4 & 3e-4 \\
Adam $\epsilon$ & 1e-5 & 1e-5\\
normalize advantage & yes & yes \\
entropy bonus coefficient & 0.001 & 0.001 \\
value loss coefficient & 0.5 & 0.5 \\
human regularization coefficient $\lambda$ & 0.0 & 0.06 \\
total timesteps & 140 M & 140 M \\
seed & 42 & 42 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"Human-compatible driving partners through data-regularized self-play reinforcement learning" by Daphne Cornelisse and Eugene Vinitsky, arXiv:2403.19648v2, 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{rotating}
\usepackage{multirow}
\usepackage{graphicx}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Confusion matrix for Baichuan2 model response evaluation.}
\begin{tabular}{|c|c|c|c|}
\hline
\multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{\textbf{Predicted}} \\ \cline{3-4}
\multicolumn{2}{|c|}{} & Positive & Negative \\ \hline
\multirow{2}{*}{\rotatebox[origin=c]{90}{\textbf{Actual}}} & Positive & 184 & 123 \\ \cline{2-4}
& Negative & 63 & 130 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

"Exploring the Privacy Protection Capabilities of Chinese Large Language Models" by Yuqi Yang, Xiaowen Huang, and Jitao Sang, arXiv:2403.18205v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Atsumoto Ohashi
Ukyo Honda
Tetsuro Morimura
Yuu Jinnai

"On the True Distribution Approximation of Minimum Bayes-Risk Decoding" by Atsumoto Ohashi, Ukyo Honda, Tetsuro Morimura, and Yuu Jinnai, arXiv:2404.00752v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Debdas Ghosh
Anshika
Jen-Chih Yao
Xiaopeng Zhao

"Quasi-Newton Method for Set Optimization Problems with Set-Valued Mapping Given by Finitely Many Vector-Valued Functions" by Debdas Ghosh, Anshika, Jen-Chih Yao, and Xiaopeng Zhao, arXiv:2501.04711v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Minghao Fu
Biwei Huang
Zijian Li
Yujia Zheng
Ignavier Ng
Yingyao Hu
Kun Zhang

"Identification of Nonparametric Dynamic Causal Structure and Latent Process in Climate System" by Minghao Fu, Biwei Huang, Zijian Li, Yujia Zheng, Ignavier Ng, Yingyao Hu, and Kun Zhang, arXiv:2501.12500v1, 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{Alignment accuracy (in \%) using binary matrix}
\begin{tabular}{cccc} \toprule
 \textbf{Model} & \textbf{$<$50ms} & \textbf{$<$100ms} & \textbf{$<$200ms} \\ \midrule
 MATCH & 71.4 & 77.6 & 83.7 \\ \midrule
 $DTW_{Chroma}$ & 70.3 & 76.2 & 82.9 \\ \midrule
 \begin{math}\mathit{SCNN_{base}}\end{math} & 74.9 & 80.2 & 86.8 \\ \midrule
$SCNN_{sal}$ & 76.7 & 82.1 & 88.4 \\ \midrule
 $SCNN_{DA}$ & 75.9 & 81.7 & 87.6 \\ \midrule
 $SCNN_{sal+DA}$ & \textbf{78.2} & \textbf{83.3} & \textbf{90.1} \\ \midrule
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Learning Frame Similarity using Siamese networks for Audio-to-Score Alignment" by Ruchit Agrawal and Simon Dixon, arXiv:2011.07546v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Babooshka Shavazipour
Theodor J. Stewart

"A novel multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty" by Babooshka Shavazipour and Theodor J. Stewart, arXiv:2312.11745v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Tuning parameters}
\begin{tabular}{l|l|l}

 		\hline
 		\textbf{Algorithm } & \textbf{Parameter} & \textbf{Value} \\
 		\hline

 		&$P_c$: crossover probability & 0.8 \\
 		GA based heuristic 	&$P_s$: mutation probability & 0.01 \\
 		(Deterministic model) &$P_{size} $ : population size & $s^2$ \\
 		& Stopping criterion & $5s$ \\
 		&Tournament size & $c+1$ \\

 		\hline

 		& Shaking phase & $h=c+1$ \\
 		GVNS	based heuristic & Stopping criterion & $2s$ \\
 		& shaking phases order &switch, inter-swap, intra-swap and shift \\
 		& local search methods order & shift, switch, inter-swap and intra-swap \\

 		\hline

 		& $\epsilon$ & $0.05$ \\
 		Monte Carlo simulation	& $MaxIterMCS$ & 100 \\
 		& $MaxIterGap$ & 10 \\

 		\hline

 		&$P_c$: crossover probability & 0.6 \\
 		GA based heuristic 	 &$P_s$: mutation probability & 0.08 \\
 		(SPR model) &$P_{size} $ : population size & 100 \\
 		& Stopping criterion & $50$ \\
 		&Tournament size & $c+1$ \\

 		\hline

 		Others	& $MaxIterSyn$ & $2c$\\
 		& $\beta$ & 100 \\

 		\hline

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

math

"Mathematical models and heuristics for the home health care routing and scheduling problem" by Mohammed Bazirha, arXiv:2312.08032v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llrrrr}
\toprule
State & Model & Accuracy & Precision & Recall & AUC \\
\midrule
 NC & LR & 0.72 & 0.75 & 0.81 & 0.75 \\
 & RF & 0.72 & 0.72 & 0.89 & 0.76 \\
 SC & LR & 0.67 & 0.69 & 0.77 & 0.71 \\
 & RF & 0.67 & 0.67 & 0.86 & 0.71 \\
 LA & LR & 0.70 & 0.73 & 0.84 & 0.72 \\
 & RF & 0.70 & 0.71 & 0.91 & 0.73 \\
 GA & LR & 0.69 & 0.70 & 0.71 & 0.75 \\
 & RF & 0.69 & 0.68 & 0.78 & 0.75 \\
 AL & LR & 0.67 & 0.69 & 0.74 & 0.72 \\
 & RF & 0.67 & 0.67 & 0.80 & 0.72 \\
 FL & LR & 0.67 & 0.69 & 0.76 & 0.71 \\
 & RF & 0.67 & 0.67 & 0.85 & 0.72 \\
\bottomrule
\end{tabular}
\caption{Accuracy, precision, recall, and AUC for voter turnout prediction for all six states considered in L2. We evaluate two different model performances for turnout prediction: logistic regression (LR) and random forests (RF).}
\end{table}
\end{document}

stat

Hadi Elzayn
Emily Black
Patrick Vossler
Nathanael Jo
Jacob Goldin
Daniel E. Ho

"Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features" by Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, and Daniel E. Ho, arXiv:2310.01679v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the technical indicators used.}
\begin{tabular}{c|c}
 \toprule[1.5pt]
 \textbf{Type} & \textbf{Indicators}\\
 \midrule[1pt]
 \multirow{3}{*}{Trend} & Arithmetic Ratio, Open, Close, \\
 & Close SMA, Volume SMA, \\
 &Close EMA, Volume EMA, ADX \\
 \midrule
 Oscillator & RSI, MACD, MACD Signal, K, MFI\\
 \midrule
 Volatility & ATR, BB Middle, OBV\\
 \bottomrule[1.5pt]
 \end{tabular}
\end{table}
\end{document}

q-fin

Junzhe Jiang
Chang Yang
Xinrun Wang
Bo Li

"Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction" by Junzhe Jiang, Chang Yang, Xinrun Wang, and Bo Li, arXiv:2506.03153v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Incident-Specific Cyber Insurance

Wing Fung Chong
Daniel Linders
Zhiyu Quan
Linfeng Zhang

"Incident-Specific Cyber Insurance" by Wing Fung Chong, Daniel Linders, Zhiyu Quan, and Linfeng Zhang, arXiv:2308.00921v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

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


\documentclass{article}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Attack success rates (ASRs) achieved by different attack strategies in training data mismatch scenario. }
\begin{tabular}{|lcccc|}
				\hline
			\multicolumn{5}{|c|}{\textbf{Proposed Anti-Forensic GAN}}\\
			 \hline
				\textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification} &\textbf{ Parameterization} & \textbf{Avg.}\\
				MISLnet &1.00& 0.87 & 1.00 &0.96\\
				TransferNet &1.00& 0.99& 0.98&0.99\\
				PHNet&0.98& 1.00 & 0.96&0.98\\
				SRNet &0.93 &0.97 &0.78&0.89\\
		 	 DenseNet\textunderscore BC & 0.99 &0.31 & 0.64&0.65\\
		 	 VGG-19 & 0.98 &0.95 & 0.86&0.93\\
			\textcolor{blue}{\textbf{Avg. }} & \textcolor{blue}{\textbf{0.98}} & \textcolor{blue}{\textbf{0.85}}& \textcolor{blue}{\textbf{0.87}}&\textcolor{blue}{\textbf{0.90}}\\
	 \hline
 \multicolumn{5}{|c|}{\textbf{MISLGAN~}}\\\hline
	 \textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification} &\textbf{ Parameterization} & \textbf{Avg.}\\
	 	MISLnet & 0.78 &0.25 & 0.91&0.64\\
			TransferNet & 1.00 &1.00 &1.00 &1.00\\
			PHNet & 0.62 &0.00 &0.19 &0.27\\
			SRNet & 0.46 & 0.06& 0.34&0.29\\
		 	DenseNet\textunderscore BC & 0.21 &0.08 &0.01 &0.10\\
		 	VGG-19 & 0.10 & 0.53& 0.52&0.38\\
			\textbf{Avg. } & \textbf{0.53} &\textbf{0.32}&\textbf{0.50} &\textbf{0.45}\\
			 \hline
	 \multicolumn{5}{|c|}{\textbf{Removing Architecture Diversity}}\\
			 \hline
	\textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification} &\textbf{ Parameterization} & \textbf{Avg.}\\
				MISLnet & 0.79& 0.17 & 0.99& 0.65\\
				TransferNet & 0.99& 1.00 & 0.88 & 0.95\\
				PHNet& 0.97& 0.68 &0.83 & 0.82\\
				SRNet & 0.53&0.21 & 0.01& 0.25\\
		 	 DenseNet\textunderscore BC & 0.70 & 0.12 & 0.71 &0.51\\
		 	 VGG-19 & 0.98& 0.97& 0.65 & 0.87\\
		 	 \textbf{Avg.}& \textbf{0.83}&\textbf{0.53} &\textbf{0.68} & \textbf{0.68}\\
		 	 \hline
		 	 \multicolumn{5}{|c|}{\textbf{Training without Pixel to Pixel Correspondence}}\\
			 \hline
				\textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification} &\textbf{ Parameterization} & \textbf{Avg.}\\
				MISLnet & 0.95& 0.71 &0.88 &0.85\\
				TransferNet & 1.00 & 0.98&0.98 &0.96\\
				PHNet & 0.97 & 1.00& 0.88&0.95\\
				SRNet & 0.93 &0.92 &0.73 &0.86\\
		 	 DenseNet\textunderscore BC & 0.70 &0.01 & 0.13&0.28 \\
		 	 VGG-19 & 0.90 & 0.79 &0.51 & 0.73\\
		 	 \textbf{Avg.} & \textbf{0.91} & \textbf{0.74}&\textbf{0.69} &\textbf{0.78} \\\hline
			\end{tabular}
\end{table}
\end{document}

eess

"A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network" by Xinwei Zhao, Chen Chen, and Matthew C. Stamm, arXiv:2101.09568v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

stat

""Medium-n studies" in computing education conferences" by Michael Guerzhoy, arXiv:2311.14679v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lr}
 \toprule
 \textbf{parameter} & \textbf{value} \\
 \midrule
 epochs & 5 \\
 learning rate & 1e-5\\
 batch size & 8 \\
 gradient accumulation & 4 \\
 \bottomrule
 \end{tabular}
\caption{Hyperparameters of the target model.}
\end{table}
\end{document}

cs

Janis Goldzycher
Paul Röttger
Gerold Schneider

"Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset" by Janis Goldzycher, Paul Röttger, and Gerold Schneider, arXiv:2403.19559v1, 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 normalized error calculated using for financial data. Sample size $N=760$. }
\begin{tabular}{cccccc}
\hline
ICSS & ICSS {[}BMID{]} & ICSS {[}QCV{]} & OLS & OLS {[}BMID{]} & OLS {[}QCV{]} \\ \hline
0.00526 & 0.00658 & 0.00658 & 0.04605 & 0.00789 & 0.00789 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc} \hline
		 	& 		& X (3) 	& Y (2) & Z (1)	\\ \hline
					& Budget	& 		& 		&		\\ \hline
		 Agent 1 	& 1 		& 	& + 	& +		\\ \hline
		 Agent 2 	& 1	 	& 	& + 	& +		\\ \hline
		 Agent 3 	& 3		& + 		& 		& 		\\ \hline
		\end{tabular}
\caption{Example instance where rules are not strategyproof.}
\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{TrashCan dataset classifications}
\begin{tabular}{|c|c|c|c|}
 \hline
 \multicolumn{2}{|c|}{TrashCan-Material} & \multicolumn{2}{|c|}{TrashCan-Instance} \\
\hline
 animal\_crab & trash\_metal & animal\_crab & trash\_can
 \\
 \hline
 animal\_eel & trash\_paper & animal\_eel & trash\_clothing \\
 \hline
 animal\_etc & trash\_plastic & animal\_etc & trash\_container \\
 \hline
 animal\_fish & trash\_rubber & animal\_fish & trash\_cup \\
 \hline
 animal\_shells & trash\_wood & animal\_shells & trash\_net \\
 \hline
 animal\_starfish & & animal\_starfish & trash\_pipe \\
 \hline
 plant & & plant & trash\_rope \\
 \hline
 rov & & rov & trash\_snack\_wrapper \\
 \hline
 trash\_etc & & trash\_bag & trash\_tarp \\
 \hline
 trash\_fabric & & trash\_bottle & trash\_unknown\_instance \\
 \hline
 trash\_fishing\_gear & & trash\_branch & trash\_wreckage \\
 \hline
\end{tabular}
\end{table}
\end{document}

cs

"State of the art applications of deep learning within tracking and detecting marine debris: A survey" by Zoe Moorton, Zeyneb Kurt, and Wai Lok Woo, arXiv:2403.18067v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c||c|c}\toprule
			$u^1_1$ & 6.94016 & $u^1_2$ & 6.94036 \\
			\midrule
			$u^2_1$ & 6.94018 & $u^2_2$ & 6.94034\\
			\midrule
			$u^3_1$& 6.94020 & $u^3_2$ & 6.94032\\
			\midrule
			$u^4_1$& 6.94024 & $u^4_2$ & 6.94028\\
			\midrule
			$u^5_1$& 6.94025 & $u^5_2$ & 6.94027\\
			\bottomrule
		\end{tabular}
\caption{Driving frequencies for the $\widetilde{q}_1$ mode in MHz.}
\end{table}
\end{document}

math

Jan Bartsch
Ahmed A. Barakat
Simon Buchwald
Gabriele Ciaramella
Stefan Volkwein
Eva M. Weig

"Reconstructing the system coefficients for coupled harmonic oscillators" by Jan Bartsch, Ahmed A. Barakat, Simon Buchwald, Gabriele Ciaramella, Stefan Volkwein, and Eva M. Weig, arXiv:2412.07301v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{marvosym}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{pifont}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{An example of $\mathbb T^*_1$, $\mathbb T^*_2$, $\mathbb T^1$ and $\mathbb T^2$ when $T=16$ and $p=2$}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}
 \hline
 \textbf{Design} & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16
 \\ \hline
 $\mathbb{T}^*_1$ & \ding{51} & \ding{55} & \ding{55} & \ding{55} & \ding{51} & \ding{55} & \ding{51} & \ding{55} & \ding{51} & \ding{55} & \ding{51} & \ding{55} & \ding{51} & \ding{55} & \ding{55} & \ding{55} \\ \hline
 $\mathbb{T}^*_2$ & \ding{51} & \ding{55} & \ding{55} & \ding{55} & \ding{55} & \ding{51} & \ding{55} & \ding{55} & \ding{51} & \ding{55} & \ding{55} & \ding{51} & \ding{55} & \ding{55} & \ding{55} & \ding{55} \\ \hline
 $\mathbb{T}^1$ & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} & \ding{51} \\ \hline
 $\mathbb{T}^2$ & \ding{51} & \ding{55} & \ding{55} & \ding{51} & \ding{55} & \ding{55} & \ding{51} & \ding{55} & \ding{55} & \ding{51} & \ding{55} & \ding{55} & \ding{51} & \ding{55} & \ding{55} & \ding{55} \\ \hline
 \end{tabular}
\end{table}
\end{document}

stat

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


q-fin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Transfer experiment results (Algorithm before/after $\rightarrow$ indicates distillation/evaluation)}
\begin{tabular}{lccc}
\toprule
Dataset & Img/Cls & SVM$\to$LR & LR$\to$SVM \\
\midrule
MNIST & 1 & 99.97±0.02 & 99.76±0.00 \\
& 10 & 99.98±0.02 & 99.76±0.00 \\
& 50 & 99.93±0.02 & 99.78±0.04 \\
\midrule
F-MNIST & 1 & 97.30±0.39 & 96.72±0.69 \\
& 10 & 98.63±0.18 & 98.39±0.07 \\
& 50 & 98.36±0.11 & 98.58±0.08 \\
\midrule
CIFAR-10 & 1 & 85.59±1.04 & 83.23±2.60 \\
& 10 & 85.06±1.52 & 89.73±0.18 \\
& 50 & 88.21±1.09 & 90.41±0.11 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Tatsuya Aoyama
Hanting Yang
Hiroyuki Hanada
Satoshi Akahane
Tomonari Tanaka
Yoshito Okura
Yu Inatsu
Noriaki Hashimoto
Taro Murayama
Hanju Lee
Shinya Kojima
Ichiro Takeuchi

"Generalized Kernel Inducing Points by Duality Gap for Dataset Distillation" by Tatsuya Aoyama, Hanting Yang, Hiroyuki Hanada, Satoshi Akahane, Tomonari Tanaka, Yoshito Okura, Yu Inatsu, Noriaki Hashimoto, Taro Murayama, Hanju Lee, Shinya Kojima, and Ichiro Takeuchi, arXiv:2502.12607v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
 \toprule [0.2em]
 \multirow{1}{*}{Model} %
 & \textbf{Param.} & \textbf{Speed $\uparrow$} & BLEU\\
 \midrule[0.1em]
 DeLighT & 37M & 0.30x & 27.6 \\
 \midrule
 \midrule
 Transformer & 61M & 1.00x & 27.7 \\
 SAFE, $d_e=256$ & 54M & 1.17x & 27.7 \\
 \textsc{Sandwich-base} & 38M & 1.26x & 27.3 \\
 \textsc{Subformer-base} & 52M & 0.75x & 28.1 \\
 \bottomrule[0.15em]
 \end{tabular}
\caption{Inference speed for our models measured on a single V100 GPU on WMT'14 En-De (batch size: 384, 1.00x = 5135 tokens)}
\end{table}
\end{document}

cs

Machel Reid
Edison Marrese-Taylor
Yutaka Matsuo

"Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers" by Machel Reid, Edison Marrese-Taylor, and Yutaka Matsuo, arXiv:2101.00234v3, 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{Bounds and optimal values at the four Pareto optimal solutions shown in Figure for all decision variables in the bioethanol process design problem.}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline
 & $F$ (kmol/hr)& $T_{\text{rxn}}$ ($^{\circ}\text{C}$)& $P_{\text{rxn}}$ (atm)& $P_1$ (atm)& $RR_1$& $P_2$ (atm)& $RR_2$& $p$\\ \hline
 Range & $[90,110]$ & $[30,40]$ & $[1,5]$ & $[1,5]$ & $[0.1,10]$ &$[1,5]$ & $[2,10]$ & $[0.5,0.7]$ \\ \hline
 Point A& 90.5& 30.0& 5.0& 4.0& 6.9& 3.7& 2.0& 0.7\\ \hline
 Point B& 95.4& 30.6& 5.0& 4.6& 5.4& 2.5& 2.0& 0.7\\ \hline
 Point C& 98.2& 35.1& 4.9& 5.0& 1.8& 2.5& 3.0& 0.7\\ \hline
 Point D& 101.0& 36.7& 5.0& 2.3& 5.1& 3.3& 3.5&0.7\\\hline
 \end{tabular}
\end{table}
\end{document}

stat

Akshay Kudva
Wei-Ting Tang
Joel A. Paulson

"Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designs" by Akshay Kudva, Wei-Ting Tang, and Joel A. Paulson, arXiv:2502.14121v1, 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{Mahjong Tile Count}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Category} & \textbf{Name} & \textbf{Count} \\ \hline
\multirow{3}{*}{Simples} & Dots & 36 \\ \cline{2-3}
 & Bamboo & 36 \\ \cline{2-3}
 & Characters & 36 \\ \hline
\multirow{2}{*}{Honors} & Winds & 16 \\ \cline{2-3}
 & Dragons & 12 \\ \hline
\multirow{2}{*}{Bonus} & Flowers & 4 \\ \cline{2-3}
 & Seasons & 4 \\ \hline
\multicolumn{2}{|c|}{Total} & 144 \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Externalizing (R-scores) mean (SD) 4.03 (5.44), sex, baseline age, parent marital status, and race/ethnicity as Native American, Asian, Black, Hispanic/Latinx, Pacific Islander, White, or Other Race Count (\%), which can add to more than $100\%$ since endorsement of more than 1 is allowed. Ordinal categorical features family income in the past 12 months 7.44 (2.31) and parental highest education 17.37 (2.47) are treated as continuous. }
\begin{tabular}{lll}
\hline
\textbf{Variable} & \textbf{Level} & \textbf{Value} \\
\hline
n & & 7370 \\
Externalizing Problems (Raw) (mean (SD)) & & 4.03 (5.44) \\
Sex (At Birth) (\%) & Male & 3861 (52.4) \\
 & Female & 3509 (47.6) \\
Age (Months) (mean (SD)) & & 119.07 (7.49) \\
Race/Ethnicity Count (\%) & Native American % & No & 1510 (20.5) \\
& 5860 (79.5) \\
& Asian % & No & 6123 (83.1) \\
& 1247 (16.9) \\
& Black % & No & 7145 (96.9) \\
& 225 (3.1) \\
& Hispanic/Latinx % & No & 7323 (99.4) \\
& 47 (0.6) \\
& Pacific Islander % & No & 6895 (93.6) \\
& 475 (6.4) \\
& Other Race % & No & 6931 (94.0) \\
& 439 (6.0) \\
& White % & No & 6006 (81.5) \\
& 1364 (18.5) \\
Total Family Income (Past 12 Months) (mean (SD)) & & 7.44 (2.31) \\
Highest Parent Education Completed (mean (SD)) & & 17.37 (2.47) \\
Parent Marital Status (\%) & Married & 5446 (73.9) \\
 & Widowed & 43 (0.6) \\
 & Divorced & 576 (7.8) \\
 & Separated & 217 (2.9) \\
 & Never married & 708 (9.6) \\
 & Living with partner & 380 (5.2) \\
\end{tabular}
\end{table}
\end{document}

stat

Aidan Neher
Apostolos Stamenos
Mark Fiecas
Sandra Safo
Thierry Chekouo

"A Bayesian Integrative Mixed Modeling Framework for Analysis of the Adolescent Brain and Cognitive Development Study" by Aidan Neher, Apostolos Stamenos, Mark Fiecas, Sandra Safo, and Thierry Chekouo, arXiv:2501.17705v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Cyrus Mehta
Ajoy Mukhopadhyay
Martin Posch

"Graph Based, Adaptive, Multi Arm, Multiple Endpoint, Two Stage Design" by Cyrus Mehta, Ajoy Mukhopadhyay, and Martin Posch, arXiv:2501.03197v1, 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 baseline AEs during the unsupervised pre-training.}
\begin{tabular}{ll}
\toprule
Hyperparameters & \\ \midrule
Training Steps & 100K \\
Learning Rate & 1e-04 \\
Batch Size & 32 \\
Optimizer & Adam \\
Decay Steps & 100K \\
Gradient Norm Clipping & 1.0 \\ \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

Elisa Alòs
Eulalia Nualart
Makar Pravosud

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


\documentclass{article}
\usepackage{siunitx}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
		\hline
		\textbf{element shape} & tetrahedra \\
		\textbf{\#elements} & 5'268'810 \\
		\textbf{\#nodes(DoFs)} & 927'097 \\
		\textbf{aspect ratio} & 1.57 $\pm$ 0.53 \\
		\textbf{scaled jacobian} & 0.63 $\pm$ 0.14 \\
		\textbf{average cell diameter} & \SI{0.927}{\milli\meter}\\
		\textbf{min cell diameter} & \SI{0.377}{\milli\meter} \\
		\textbf{max cell diameter} & \SI{1.365}{\milli\meter} \\
		\hline
	\end{tabular}
\caption{Characteristics of the 3D mesh used in numerical simulations. Aspect ratio and scaled jacobian are two mesh quality metrics, defined as in~, indicating that the mesh has acceptable regularity.}
\end{table}
\end{document}

math

Samuele Brunati
Michele Bucelli
Roberto Piersanti
Luca Dede'
Christian Vergara

"Coupled Eikonal problems to model cardiac reentries in Purkinje network and myocardium" by Samuele Brunati, Michele Bucelli, Roberto Piersanti, Luca Dede', and Christian Vergara, arXiv:2412.13837v1, 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
Method & MCD (dB) $\downarrow$ & ASR-CER (\%) $\downarrow$ \\
\midrule
Ground truth & - & [16.21] \\
FastSpeech 2 & \textbf{40.13} & 27.04 \\
Ours & 40.24 & \textbf{24.85} \\
\bottomrule
\end{tabular}
\caption{Multimodality's effect on audio. The audio produced from NEUTART is more intelligible than a plain TTS model of the same architecture, indicating the effectiveness of visual supervision for speech.}
\end{table}
\end{document}

eess

Georgios Milis
Panagiotis P. Filntisis
Anastasios Roussos
Petros Maragos

"Neural Text to Articulate Talk: Deep Text to Audiovisual Speech Synthesis achieving both Auditory and Photo-realism" by Georgios Milis, Panagiotis P. Filntisis, Anastasios Roussos, and Petros Maragos, arXiv:2312.06613v1, 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}{lcrl}
\hline
Variable & Symbol & Value & Units \\
\hline \hline
Total load real power & $P$ & 10 & kW \\
Total load reactive power & $Q$ & 5 & kVAR \\
Line-line RMS source voltage & $V_{ll}$ & 240 & V \\
Source resistance & $R_s$ & 19.2& $\Omega$ \\
Source inductance & $L_s$ & 25.465 & mH \\
Fault resistance & $R_f$ & 1 & m$\Omega$ \\
Ground resistance & $R_g$ & 10 & m$\Omega$ \\
Cable positive-sequence resistance & $R_c$ & 183.7 & m$\Omega$ \\
Cable positive-sequence reactance & $L_c$ & 26.6 & m$\Omega$ \\
Simulation time & T & 200 & ms \\
Fault start time & $T_f$ & 50 & ms \\
\hline
\end{tabular}
\caption{Common Parameters for Three-Phase Dynamic Models}
\end{table}
\end{document}

cs

"Dynamic State Estimation for Radial Microgrid Protection" by Arthur K. Barnes and Adam Mate, arXiv:2101.07774v2, 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|r}
 \toprule
 Augmentation Operation & Accuracy\cr
 \midrule
 \em None & $75.0\pm3.0$\cr
 \hline
 Random Rotation & {$92.4\pm0.3$}\cr
 Random Masking & {$92.4\pm0.8$}\cr
 Random Mixing & $86.7\pm0.6$\cr
 Random Rotation + Masking & $92.7\pm0.4$ \cr
 Random Rotation + Mixing & $92.6\pm0.7$ \cr
 Random Masking + Mixing & $93.4\pm0.7$ \cr
 Random Rotation + Masking + Mixing (HDA) &
 \boldmath{$93.5\pm0.4$}\cr
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Few-Shot Website Fingerprinting Attack

Mantun Chen
Yongjun Wang
Zhiquan Qin
Xiatian Zhu

"Few-Shot Website Fingerprinting Attack" by Mantun Chen, Yongjun Wang, Zhiquan Qin, and Xiatian Zhu, arXiv:2101.10063v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Eiichi Bannai
Hirotake Kurihara
Hiroshi Nozaki

"On the existence and non-existence of spherical $m$-stiff configurations" by Eiichi Bannai, Hirotake Kurihara, and Hiroshi Nozaki, arXiv:2504.17184v2, 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|cccc}
& NMF & MDNMF & DNMF & D+MDNMF \\
\hline
$\tau_W$ & $=1$ & $=1$ & $=0$ & $>0$ \\
$\tau_A$ & $=0$ & $>0$ & $=0$ & $>0$ \\
$\tau_S$ & $=0$ & $=0$ & $=1$ & $>0$
\end{tabular}
\end{table}
\end{document}

cs

"Maximum Discrepancy Generative Regularization and Non-Negative Matrix Factorization for Single Channel Source Separation" by Martin Ludvigsen and Markus Grasmair, arXiv:2404.15296v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Rhuaidi Antonio Burke
Benjamin A. Burton
Jonathan Spreer

"Small Triangulations of $4$-Manifolds: Introducing the $4$-Manifold Census" by Rhuaidi Antonio Burke, Benjamin A. Burton, and Jonathan Spreer, arXiv:2412.04768v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\usepackage{xcolor}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{(Section ) The $\mathcal{H}_{\infty}$ values and computational times for the Lyapunov-based approach and NLA corresponding to the Dense $\mathcal{H}_{\infty}$ estimator with $S = \mathbf{1}_{n_x \times n_y}$ and $B_{\Delta f} = 0.1I_{n_x}$ for the IEEE test systems.}}
\begin{tabular}{|c|c|c|c|}
\hline
\textrm{Approach} & $\|T_{z\tilde{w}}(s)\|_{\mathcal{H}_{\infty}}$ & Computational Time & $(n_x,n_y)$ \\
\hline
non-Lyap $9$-bus & \cellcolor{lightgray}$1.5100$ & $4.76$ s & (36,14) \\
\hline
Lyap $9$-bus & \cellcolor{lightgray}$1.5100$ & \cellcolor{lightgray} $2.26$ s & $(36,14)$ \\
\hline
non-Lyap $14$-bus & \cellcolor{lightgray}$1.4140$ & $44.30$ s & $(58,28)$ \\
\hline
Lyap $14$-bus & $2.6656$ & \cellcolor{lightgray} $7.58$ s & $(58,28)$ \\
\hline
non-Lyap $39$-bus & \cellcolor{lightgray}$3.1129$ & $6841.34$ s & $(138,58)$ \\
\hline
Lyap $39$-bus & $4.7038$ & \cellcolor{lightgray} $943.74$ s & $(138,58)$ \\
\hline
non-Lyap $57$-bus & \cellcolor{lightgray}$2.1155$ & $29639.22$ s & $(156,80)$ \\
\hline
Lyap $57$-bus & $3.6180$ & \cellcolor{lightgray}$4113.78$ s & $(156,80)$ \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

MirSaleh Bahavarnia
Muhammad Nadeem
Ahmad F. Taha

"On Scaling Robust Feedback Control and State Estimation Problems in Power Networks" by MirSaleh Bahavarnia, Muhammad Nadeem, and Ahmad F. Taha, arXiv:2311.17836v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccccccc}
 \hline
 \textbf{$R$} & \textbf{1} & \textbf{2} & \textbf{3} & \textbf{4} & \textbf{5} & \textbf{6} & \textbf{7}\\
 \hline
 $F_1$& 61.74 & 62.05 & 62.31 & 63.01 & \cellcolor{magenta!20}\textbf{63.16} & 62.99 & 61.23 \\
 \hline
 \end{tabular}
\caption{F1 for various settings of $R$. All values are averaged across all datasets for all low-resource settings.}
\end{table}
\end{document}

cs

Chandra Kiran Reddy Evuru
Sreyan Ghosh
Sonal Kumar
Ramaneswaran S
Utkarsh Tyagi
Dinesh Manocha

"CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP" by Chandra Kiran Reddy Evuru, Sreyan Ghosh, Sonal Kumar, Ramaneswaran S, Utkarsh Tyagi, and Dinesh Manocha, arXiv:2404.00415v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 \multirow{3}{*}{Example} & \multicolumn{6}{c|}{Times (seconds)} \\
 \cline{2-7}
 & Z3 8.9 & Yices 2.6 & PolyAR+Z3 & PolyAR+Z3 & PolyAR+Yices & PolyAR+Yices\\
 & & & (1 thread) & (max threads) & (1 thread) & (max threads)\\
 \hline
 \hline
 1 & \textit{timeout} & \textit{timeout} & \textit{timeout} & $7.552$ & $\mathbf{2.405}$ & $2.442$\\
 \hline
 2 & \textit{timeout} & \textit{timeout} & $83.776$ & $114.453$ & $timeout$ & $\mathbf{3.766}$ \\
 \hline
 3 & \textit{timeout} & \textit{timeout} & $23.551$ & $23.970$ & \textit{timeout} & $\mathbf{8.725}$ \\
 \hline
 4 & \textit{timeout} & \textit{timeout} & $0.718$ & $0.729$ & $\mathbf{0.416}$ & $0.432$\\
 \hline
 5 & \textit{timeout} & \textit{timeout} & $3.636$ & $3.768$ & $0.621$ & $\mathbf{0.498}$ \\
 \hline
 \hline
 \# Problems & $0$ & $0$ & $4$ & $5$ & $3$ & $\mathbf{5}$ \\
 Solved & & & & & & \\
\hline
 Total Time & \textit{timeout} & \textit{timeout} & $111.681$ & $150.472$ & $3.442$ & $\mathbf{15.863}$ \\
 (seconds) & & & & & &\\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

"PolyAR: A Highly Parallelizable Solver For Polynomial Inequality Constraints Using Convex Abstraction Refinement" by Wael Fatnassi and Yasser Shoukry, arXiv:2101.04655v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Andreas A. Buchheit
Jonathan Busse
Ruben Gutendorf

"Computation and properties of the Epstein zeta function with high-performance implementation in EpsteinLib" by Andreas A. Buchheit, Jonathan Busse, and Ruben Gutendorf, arXiv:2412.16317v1, 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}{lcccc}
 & \multicolumn{2}{c}{\textbf{Multi-horizon DMPC}} & \multicolumn{2}{c}{\textbf{Islanded operation}}\\ \hline
 & \multirow{2}{*}{Total cost (CHF)} & Total elec. & \multirow{2}{*}{Total cost (CHF)} & Total elec. \\
 & & from grid (kWh) & & from grid (kWh)\\ \hline
 Exp. 1 & -871 & -5,517 & -374 & -5,396\\
 Exp. 2 & 434 & 1,206 & 645 & 1,273
 \end{tabular}
\caption{Comparison of the total cost and energy imports from the electricity grid for the complete network using the MH-DMPC and in islanded operation}
\end{table}
\end{document}

eess

Varsha Behrunani
Philipp Heer
John Lygeros

"Experimental Validation for Distributed Control of Energy Hubs" by Varsha Behrunani, Philipp Heer, and John Lygeros, arXiv:2310.18037v1, 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}{lcrl}
\hline
Variable & Symbol & Value & Units \\
\hline \hline
Total load real power & $P$ & 10 & kW \\
Total load reactive power & $Q$ & 5 & kVAR \\
Line-line RMS source voltage & $V_{ll}$ & 480 & V \\
Simulation time & T & 10 & ms \\
Sample rate & $T_s$ & 100 & $\mu$s \\
\hline
\end{tabular}
\caption{Parameters for Single-Phase Dynamic Load}
\end{table}
\end{document}

eess


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Discriminator Architecture. $m$ is the number of output channels which varies across different experiments.}
\begin{tabular}{cccc}
 \toprule
 Block & Operation & Activation & Output Shape \\
 \midrule
 & Image 1 & - & m x 256 x 256 \\
 & From RGB & - & 64 x 256 x 256 \\
 1. & MiniBatchStd & - & 65 x 256 x 256 \\
 & Conv 3x3 & LReLU & 64 x 256 x 256 \\
 & Conv 3x3 & LReLU & 128 x 256 x 256 \\
 & Max Pool & - & 128 x 128 x 128 \\
 & Image 2 & - & m x 128 x 128 \\
 & Concat & - & (128+m) x 128 x 128 \\
 2. & MiniBatchStd & - & (128+m+1) x 128 x 128 \\
 & Conv 3x3 & LReLU & 128 x 128 x 128 \\
 & Conv 3x3 & LReLU & 256 x 128 x 128 \\
 & Max Pool & - & 256 x 64 x 64 \\
 & Image 3 & - & m x 64 x 64 \\
 & Concat & - & (256+m) x 64 x 64 \\
 3. & MiniBatchStd & - & (256+m+1) x 64 x 64 \\
 & Conv 3x3 & LReLU & 256 x 64 x 64 \\
 & Conv 3x3 & LReLU & 512 x 64 x 64 \\
 & Max Pool & - & 512 x 32 x 32 \\
 & Image 4 & - & m x 32 x 32 \\
 & Concat & - & (512+m) x 32 x 32 \\
 4. & MiniBatchStd & - & (512+m+1) x 32 x 32 \\
 & Conv 3x3 & LReLU & 512 x 32 x 32 \\
 & Conv 3x3 & LReLU & 512 x 32 x 32 \\
 & Max Pool & - & 512 x 16 x 16 \\
 & Image 4 & - & m x 16 x 16 \\
 & Concat & - & (512+m) x 16 x 16 \\
 5. & MiniBatchStd & - & (512+m+1) x 16 x 16 \\
 & Conv 3x3 & LReLU & 512 x 16 x 16 \\
 & Conv 3x3 & LReLU & 512 x 16 x 16 \\
 & Max Pool & - & 512 x 8 x 8 \\
 & Image 5 & - & m x 8 x 8 \\
 & Concat & - & (512+m) x 8 x 8 \\
 6. & MiniBatchStd & - & (512+m+1) x 8 x 8 \\
 & Conv 3x3 & LReLU & 512 x 8 x 8 \\
 & Conv 3x3 & LReLU & 512 x 8 x 8 \\
 & Max Pool & - & 512 x 4 x 4 \\
 & Image 6 & - & m x 4 x 4 \\
 & Concat & - & (512+m) x 4 x 4 \\
 7. & MiniBatchStd & - & (512+m+1) x 4 x 4 \\
 & Conv 3x3 & LReLU & 512 x 4 x 4 \\
 & Conv 3x3 & LReLU & 512 x 4 x 4 \\
 & Fully Connected & Linear & 1 x 1 x 1 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Tharun Mohandoss
Aditya Kulkarni
Daniel Northrup
Ernest Mwebaze
Hamed Alemohammad

"Generating Synthetic Multispectral Satellite Imagery from Sentinel-2" by Tharun Mohandoss, Aditya Kulkarni, Daniel Northrup, Ernest Mwebaze, and Hamed Alemohammad, arXiv:2012.03108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling" by Diana Koldasbayeva and Alexey Zaytsev, arXiv:2502.03480v1, licensed under CC0 1.0. 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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of win rates between RLHF (optimized) and WRO-KL-BT-logit without additional optimization (non-optimized). RLHF's dropping of the constant in the objective decreases variance in the gradient estimates and generally improves results with small enough $\beta$. However, this change does not yield systematic benefits for every setting, suggesting that there is still room for improvement.}
\begin{tabular}{llllll}
 \toprule
 & & $\beta=0.001$ & $\beta=0.01$ & $\beta=0.1$ & $\beta=1$ \\
 Dataset & & & & & \\
 \midrule
 HH & non-optimized & 51.79 (1.12) & 54.59 (0.90) & 68.68 (0.80) & \textbf{56.88 (0.46) }\\
 & optimized & \textbf{70.46 (0.87)} & \textbf{63.93 (1.24)} & 69.44 (0.90) & 54.60 (0.67) \\
 OASST & non-optimized & 60.74 (3.65) & 61.79 (3.64) & \textbf{73.11 (1.16)} & 60.41 (2.05) \\
 & optimized & 62.05 (3.65) & 63.95 (3.38) & 66.72 (1.16) & 58.45 (1.82) \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Preference learning made easy: Everything should be understood through win rate" by Lily H. Zhang and Rajesh Ranganath, arXiv:2502.10505v1, 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 Models Based on Classification Accuracy (Class. Acc.) and Runtime}
\begin{tabular}{lcc}
 \toprule
 \textbf{Model} & \textbf{Class. Acc.} & \textbf{Runtime [min]} \\
 \midrule
 VB-PMF & 86.850 & 9.88 \\
 Proposed & \textbf{87.483} & \textbf{5.83} \\
 CTF-AO-KL & 86.315 & 46.55 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Mustafa Musab
Joseph K. Chege
Arie Yeredor
Martin Haardt

"A Unified MDL-based Binning and Tensor Factorization Framework for PDF Estimation" by Mustafa Musab, Joseph K. Chege, Arie Yeredor, and Martin Haardt, arXiv:2504.18686v1, 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{Parameter settings for simulation.}
\begin{tabular}{ccc}
		\hline \textbf{Symbol} & \textbf{Parameter} & \textbf{Value} \\
		\hline $f$ & Carrier frequency & $100 \mathrm{~GHz}$ \\
		$B$ & Bandwidth & $2 \mathrm{~GHz}$ \\
		$P_{T}$ & Transmit power & {$[20 \mathrm{dBm}, 30 \mathrm{dBW}]$} \\
		${K}$ & Number of training subcarriers & $6$ \\
		$T_b$ & Solar brightness temperature & $6000 \mathrm{~K}$ \\
		$T_0$ & Ambient noise temperature in LEO & $1000 \mathrm{~K}$ \\
		$\mu$ & Regularization value for ALS & $[0.06,0.08,0.1]$ \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Olivier Daoust
Hasan Nayir
Irfan Azam
Antoine Lesage-Landry
Gunes Karabulut Kurt

"Tensor-based Space Debris Detection for Satellite Mega-constellations" by Olivier Daoust, Hasan Nayir, Irfan Azam, Antoine Lesage-Landry, and Gunes Karabulut Kurt, arXiv:2311.11838v1, 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{PRIMARILY SELECTED CROPS}
\begin{tabular}{|c|c|}
 \hline
 \textbf{Initial Order} & \textbf{Crop Name} \\
 \hline
 1 & Garlic \\
 2 & Lentil \\
 3 & Papaya \\
 4 & Rice \\
 5 & Soyabean \\
 6 & Sugarcane \\
 7 & Tomato \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Forkan Uddin Ahmed
Annesha Das
Md Zubair

"A Machine Learning Approach for Crop Yield and Disease Prediction Integrating Soil Nutrition and Weather Factors" by Forkan Uddin Ahmed, Annesha Das, and Md Zubair, arXiv:2403.19273v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Christian Møller Dahl
Nadja van 't Hoff
Giovanni Mellace
Sinne Smed

"Nudging Nutrition: Lessons from the Danish "Fat Tax"" by Christian Møller Dahl, Nadja van 't Hoff, Giovanni Mellace, and Sinne Smed, arXiv:2312.11481v3, 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{Inter-country: Number of retractions with respect to document type and journal quartile.}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|}
\hline
\textbf{Document Type} & \textbf{Count} & \textbf{In \%} & \textbf{Q1} & \textbf{Q2} & \textbf{Q3} & \textbf{Q4} & \textbf{Proceedings} & \textbf{No Impact} \\ \hline
Article & 570 & 87.02 & 52.11 & 29.82 & 11.05 & 2.63 & - & 4.39 \\ \hline
Review & 38 & 5.8 & 15.79 & 44.74 & 31.58 & 5.26 & - & 2.63 \\ \hline
Conference & 15 & 2.29 & - & - & - & 26.67 & 66.67 & 6.67 \\ \hline
Clinical Study & 13 & 1.98 & 46.15 & 0.53 & 1.3 & 12 & - & 5.88 \\ \hline
Case Report & 6 & 0.92 & 1.27 & - & 0.32 & & - & 0.32 \\ \hline
Commentary & 5 & 0.76 & 20 & - & - & 20 & 60 & - \\ \hline
Others & 4 & 0.61 & 50 & 50 & - & - & - & - \\ \hline
Book Chapter & 4 & 0.61 & - & - & - & - & - & 100 \\ \hline
\textbf{Total Retractions} & \textbf{655} & \textbf{100} & \textbf{316} & \textbf{190} & \textbf{77} & \textbf{25} & \textbf{13} & \textbf{34} \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

"Two Decades of Scientific Misconduct in India: Retraction Reasons and Journal Quality among Inter-country and Intra-country Institutional Collaboration" by Kiran Sharma, arXiv:2404.15306v1, 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}{llr}
\toprule
\textbf{Rank} & \textbf{Collection} & \textbf{\# tokens} \\
\midrule
1 & Art Blocks & 9,548 \\
2 & The Sandbox & 9,549\\
3 & LOSTPOETS & 4,766\\
4 & Meebits & 2,744\\
5 & Hashmasks & 2,671\\
6 & Mutant Ape Yacht Club & 1,621 \\
7 & RTFKT CLONE X + Murakami & 1,610\\
8 & CryptoPunks & 1,599\\
9 & CyberKongz VX & 1,558\\
10 & Punks Comic & 1,528\\
\bottomrule
\end{tabular}
\caption{Top 10 collections and corresponding number of tokens held by whales}
\end{table}
\end{document}

q-fin

Na Hyeon Park
Hanna Kim
Chanhee Lee
Changhoon Yoon
Seunghyeon Lee
Youngjin jin
Seungwon Shin

"A Deep Dive into NFT Whales: A Longitudinal Study of the NFT Trading Ecosystem" by Na Hyeon Park, Hanna Kim, Chanhee Lee, Changhoon Yoon, Seunghyeon Lee, Youngjin jin, and Seungwon Shin, arXiv:2303.09393v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average dice score performance (\%) of the GCN refinement at $\tau=0.5$. The table compares the performance of $w_3$ with the U-Net prediction and $w_1$. Statistical significance is indicated by (*) for a p-value $<0.05$, and (**) for a p-value $< 0.01$ with respect to the CNN prediction.}
\begin{tabular}{l|c|c|c}
			\hline
			Task & CNN & GCN & GCN \\
			 & 2D U-Net & $w_{1,(0.5,1)}$ & $w_{3,(1,1)}$ \\
			\hline
			Pancreas & $76.89 \pm 6.6$ & $77.77 \pm 6.3$* & $78.19 \pm 6.1$* \\
			\hline
			Pancreas-10 & $52.14 \pm 22.6$ & $54.15 \pm 22.20$ & $52.90 \pm 23.1$ \\
			\hline
			\hline
			Spleen & $93.17 \pm 2.5$ & $94.98 \pm 1.4$** & $94.81 \pm 1.9$** \\
			\hline
			Spleen-9 & $78.89 \pm 28.4$ & $80.94 \pm 28.8$** & $81.08 \pm 28.9$** \\
			\hline
			\end{tabular}
\end{table}
\end{document}

eess

Roger D. Soberanis-Mukul
Nassir Navab
Shadi Albarqouni

"An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation" by Roger D. Soberanis-Mukul, Nassir Navab, and Shadi Albarqouni, arXiv:2012.03352v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
\toprule & $c_4$ & $c_5$ & $c_6$ \\
\midrule Case 1 & 2 & 1 & 1 \\
Case 2 & 2 & 1 & 2 \\
Case 3 & 1.65 & 1 & 2.25 \\
\bottomrule% & & &
\end{tabular}
\caption{The cost parameters.}
\end{table}
\end{document}

math

"Singular Control of (Reflected) Brownian Motion: A Computational Method Suitable for Queueing Applications" by Baris Ata, J. Michael Harrison, and Nian Si, arXiv:2312.11823v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"A New Framework to Estimate Return on Investment for Player Salaries in the National Basketball Association" by Jackson P. Lautier, arXiv:2309.05783v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{graphicx}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of input data with corresponding sources}
\begin{tabular}{lllll}
 & & & This paper (2021) & \\
 \hline
 \multicolumn{2}{c}{ \multirow{14}{*}{ \rotatebox{90}{ \textbf{Building stock}} } }
 & Number of SFDs with electric heating & 1.44 million (of 2.1 million in 2019) & 1.26 million (of 2.0 million in 2012) \\
 & & Number of building types & 14 & 574 \\
 & & Electricity demand for space heating & 19.6\,TWh & 17.5\,TWh \\
 & & Electricity demand for hot water & 1900 kWh/dwelling (2.7\,TWh) & 1900 kWh/dwelling (2.4\,TWh) \\
 & & Total heated floor area & 176 million\,m$^\text{2}$ (122\,m$^\text{2}$ per dwelling) & 192 million\,m$^\text{2}$ (152\,m$^\text{2}$ per dwelling) \\
 & & Building properties of & \multirow{2}{*}{Presented in } & Presented in , \\
 & & \;\;\;\;construction materials & & \;\;\;\;based on \\
 & & Effective heat capacity & varying & fixed, 130,000 $\tfrac{\text{J}}{\text{K m}^\text{2}}$ \\
 & & Indoor temperature setpoint (deadband) & $\theta^\text{set}$=19...23\,$^\text{o}$C, ($\theta^\text{set}\pm\,1^\text{o}$C)& 21.2\,$^\text{o}$C, (21.2\,...\,24\,$^\text{o}$C) \\
 & & Type and efficiency of electric heating & Presented in based on & Presented in , based on \\
 & & Power rating of the heating equipment & Presented in , based on with & Done in , based on \\
 & & Temperature data & Taken from & Taken from \\
 & & Solar irradiation & - & Taken from \\
 & & Aggregate electric power & 6.4\,GW (out of 12.9\,GW installed capacity) & 7.3\,GW \\
 & & Aggregate electric energy capacity & mean: 14.1\,GWh (7.0\,$\tfrac{\text{GWh}}{^\text{o}\text{C}}$), max: 21.3\,GWh & 19.3\,GWh (6.9\,$\tfrac{\text{GWh}}{^\text{o}\text{C}}$)\\
 \hline
 \multirow{4}{*}{ \rotatebox{90}{ \textbf{Power}} } &
 \multirow{4}{*}{ \rotatebox{90}{ \textbf{system}} } &
 Day-ahead energy market spot prices & Taken from Ref. & Taken from Ref. \\
 & & Imbalance settlement prices & Taken from Ref. & - \\
 & & Reserve prices (specifically FCR-N) & Taken from Ref. & - \\
 & & Hourly mean system frequency & Taken from Ref. & - \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Lars Herre
Behrouz Nourozi
Mohammad Reza Hesamzadeh
Qian Wang
Lennart Söder

"A bottom-up quantification of flexibility potential from the thermal energy storage in electric space heating" by Lars Herre, Behrouz Nourozi, Mohammad Reza Hesamzadeh, Qian Wang, and Lennart Söder, arXiv:2103.06734v1, 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 performance of RADIANT when varying $\Gamma$ and fixing $\alpha$ of 2.5.}
\begin{tabular}{cccccccc}
\toprule
$\Gamma$ &
 True * Info (\%) $\uparrow$ &
 True (\%) $\uparrow$ &
 Info (\%) $\uparrow$ &
 MC1 $\uparrow$ &
 MC2 $\uparrow$ &
 CE $\downarrow$ &
 KL $\downarrow$ \\ \midrule
Unintervened & 21.15 & 22.16 & 95.47 & 25.58 & 40.54 & 2.13 & 0.00 \\
5 & 26.14 & 28.40 & 92.04 & 26.81 & 41.91 & 2.14 & 0.01 \\
10 & 33.04 & 36.11 & 91.49 & 27.17 & 43.11 & 2.17 & 0.04 \\
15 & 40.36 & 44.48 & 90.75 & 30.91 & 46.13 & 2.19 & 0.07 \\
20 & 36.59 & 43.46 & 84.20 & 28.15 & 44.92 & 2.29 & 0.18 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Bao Nguyen
Binh Nguyen
Duy Nguyen
Viet Anh Nguyen

"Risk-Aware Distributional Intervention Policies for Language Models" by Bao Nguyen, Binh Nguyen, Duy Nguyen, and Viet Anh Nguyen, arXiv:2501.15758v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccccc}
\hline
 & & \multicolumn{3}{c}{Vague} & \multicolumn{3}{c}{Logistic} \\
Parameter & Truth & $\text{MSE}^*$ & MSE & Cov & $\text{MSE}^*$ & MSE & Cov \\
\hline
$\beta_0$ & -0.50 & 1.00E-04 & 0.0496 & 0.94 & 1.00E-04 & 0.0458 & 0.95 \\
$\beta_1$ & 0.30 & 3.00E-04 & 0.0497 & 0.97 & 0.00E+00 & 0.0457 & 0.96 \\
\hline
\end{tabular}
\caption{$\text{MSE}^*$, MSE and coverage of the 95\% posterior credible interval (Cov), for $n=100$, for the case with one covariate.}
\end{table}
\end{document}

stat

"Logistic regression models: practical induced prior specification" by Ken B. Newman, Cristiano Villa, and Ruth King, arXiv:2501.18106v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

"Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury" by Izak Yasrebi-de Kom, Joanna Klopotowska, Dave Dongelmans, Nicolette De Keizer, Kitty Jager, Ameen Abu-Hanna, and Giovanni Cinà, arXiv:2311.09137v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|c|c|c||}
			\hline\hline
			Method 1 (Ref. )&Bi-filters $J=1$&Bi-filters $J=2$&Bi-filters $J=3$\\
			\hline
			12.$10^{-3}$&11,8.$10^{-3}$&2.$10^{-5}$&2,4.$10^{-6}$\\
			\hline\hline
		\end{tabular}
\caption{Error Estimates.}
\end{table}
\end{document}

eess

Malika Jallouli
Makerem Zemni
Anouar Ben Mabrouk
Mohamed Ali Mahjoub

"Towards New Multiwavelets: Associated Filters and Algorithms. Part I: Theoretical Framework and Investigation of Biomedical Signals, ECG and Coronavirus Cases" by Malika Jallouli, Makerem Zemni, Anouar Ben Mabrouk, and Mohamed Ali Mahjoub, arXiv:2103.08657v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters }
\begin{tabular}{|c|c|}
\hline
{\cellcolor{gray!20}{\textbf{Parameter}}} & {\cellcolor{gray!20}{\textbf{Value}}} \\ \hline
System & 3GPP Release 16\\ \hline
Bandwidth & 10 MHz\\ \hline
Duplex mode & FDD\\ \hline
CP mode & Normal\\ \hline
Modulation & QPSK\\ \hline
\# Rx antennas & 2\\ \hline
Delay profile & Extended Vehicular A model (EVA) \\ \hline
Doppler frequency & 500 Hz\\ \hline
Fading & Rayleigh\\ \hline
Equalization & MMSE\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Environment-Adaptive Multiple Access for Distributed V2X Network: A Reinforcement Learning Framework" by Seungmo Kim, Byung-Jun Kim, and B. Brian Park, arXiv:2101.10447v1, 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}{r|r|r|r|}
 $N_f$ & $\varepsilon_2^{(N_f)}$ & $\varepsilon_4^{(N_f)}$ & $\varepsilon_6^{(N_f)}$ \\
 \hline
10 (\phantom{1}30) & 0.04888 & 0.69465 & 8.75189\\
20 (\phantom{1}60) & 0.00289 & 0.02705 & 0.28886\\
40 (120) & 0.00033 & 0.00016 & 0.00114\\
80 (240)& 0.00004 & 0.00002 & 0.00004\\
160 (480)& 0.00000 & 0.00000 & 0.00000\\
\hline
 \end{tabular}
\caption{Absolute error of the scattered field with 64 equidistant incident and observation directions on a circle with measurement radius $R_0=3$ for the disk with radius $R=2$ and the physical parameters $a=3$ and $\eta=1$ for varying number of faces (collocation nodes). The wave numbers are $k=2$, $k=4$, and $k=6$, respectively.}
\end{table}
\end{document}

math

Isaac Harris
Victor Hughes
Andreas Kleefeld

"Two direct sampling methods for an anisotropic scatterer with a conductive boundary" by Isaac Harris, Victor Hughes, and Andreas Kleefeld, arXiv:2412.16605v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Accurate Linear Cutting-Plane Relaxations for ACOPF" by Daniel Bienstock and Matias Villagra, arXiv:2312.04251v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cccc|c}
\toprule
\multirow{2.5}{*}{\textbf{Model}} & \multicolumn{4}{c|}{\textbf{Test Modalities}} & \multirow{2.5}{*}{\textbf{Avg.}} \\
 \cmidrule(lr){2-5}
 & \textbf{V+E+A} & \textbf{V+E} & \textbf{V+A} & \textbf{V} &\\ \midrule
 VidSeq-Concat & 22.35 & 14.08 & 21.92 & 12.12 & 17.62 \\
 MR-VPC (Ours) & \textbf{22.83} & \textbf{16.97} & \textbf{22.59} & \textbf{16.86} & \textbf{19.81} \\
 \bottomrule
\end{tabular}
\caption{Comparison with Vid2Seq-Concat~ on YouCook2. METEOR metrics are reported. When testing Vid2Seq-Concat without $E$, we trim the video into seven consecutive clips of the same length (seven is the average number of events in YouCook2).}
\end{table}
\end{document}

cs

Sishuo Chen
Lei Li
Shuhuai Ren
Rundong Gao
Yuanxin Liu
Xiaohan Bi
Xu Sun
Lu Hou

"Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality" by Sishuo Chen, Lei Li, Shuhuai Ren, Rundong Gao, Yuanxin Liu, Xiaohan Bi, Xu Sun, and Lu Hou, arXiv:2403.19221v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Components of the risk matrix score in tabular form for the Sydney rainfall example, where each of the weights $w_{i,j}$ is $1$.}
\begin{tabular}{|r|cc|cc|cc|}
\hline
forecast & obs not in MOD+ & obs in MOD+ & obs not in SEV+ & obs in SEV+ & obs not in EXT & obs in EXT \\
\hline
very likely	& $1.2$	& $0$	& $1.2$	& $0$	& $1.2$	& $0$ \\
likely		& $0.5$	& $0.3$	& $0.5$	& $0.3$	& $0.5$	& $0.3$ \\
possible	& $0.1$	& $0.9$	& $0.1$	& $0.9$	& $0.1$	& $0.9$ \\
unlikely	& $0$	& $1.8$	& $0$	& $1.8$	& $0$	& $1.8$ \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Warnings based on risk matrices: a coherent framework with consistent evaluation" by Robert J. Taggart and David J. Wilke, arXiv:2502.08891v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average cross generator performance for \textit{ResNet50} and \textit{Swin-T} given in accuracy (in \%) when trained on raw \textit{GenImage} subsets and our constrained subsets.}
\begin{tabular}{ccccccc}
 \toprule
 \textbf{Training Subset} & \multicolumn{3}{c}{\textbf{ResNet50}} & \multicolumn{3}{c}{\textbf{Swin-T}} \\
 \cmidrule(lr){2-4} \cmidrule(lr){5-7}
 & Classic & Ours & Diff & Classic & Ours & Diff \\
 \midrule
 \textit{SD1.5} & 72.16 & 83.90 & +11.74 & 74.14 & 85.90 & +11.76 \\
 \textit{SD1.4} & 71.27 & 83.39 & +12.12 & 74.93 & 86.80 & +11.87 \\
 \textit{Wukong} & 71.61 & 80.93 & +9.32 & 73.20 & 84.80 & +11.60 \\
 \bottomrule
 Total & 71.68 & 82.74 & +11.06 & 74.09 & 85.83 & +11.74 \\
 \end{tabular}
\end{table}
\end{document}

cs

Patrick Grommelt
Louis Weiss
Franz-Josef Pfreundt
Janis Keuper

"Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasets" by Patrick Grommelt, Louis Weiss, Franz-Josef Pfreundt, and Janis Keuper, arXiv:2403.17608v2, 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{Pricing Bounds and Duality Gaps}
\begin{tabular}{llllllll}
\toprule
Ticker & Method & Lower Bound & Upper Bound & Std Error & Duality Gap & Gap (\%) & Premium Status \\
\midrule
AAPL & Linear Signature & \$2.69 & \$4.12 & \$0.02 & \$1.43 & 53.37\% & Outside \\
 & Extended Linear Signature& \$2.68 & \$4.08 & \$0.02 & \$1.40 & 52.29\% & Outside \\
 & Deep Log-Signature & \$1.89 & \$2.40 & \$0.01 & \$0.51 & 26.98\% & Within \\
 & Deep Kernel Method & \$2.04 & \$2.39 & \$0.01 & \$0.35 & 17.16\% & Within \\
\midrule
META & Linear Signature & \$10.16 & \$15.97 & \$0.07 & \$5.81 & 57.25\% & Outside \\
 & Extended Linear Signature& \$10.15 & \$15.95 & \$0.07 & \$5.80 & 57.16\% & Outside \\
 & Deep Log-Signature & \$5.96 & \$10.99 & \$0.03 & \$5.03 & 84.40\% & Outside \\
 & Deep Kernel Method & \$5.29 & \$8.01 & \$0.03 & \$2.72 & 51.42\% & Within \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"American Option Pricing Under Time-Varying Rough Volatility: A Signature-Based Hybrid Framework" by Roshan Shah, arXiv:2508.07151v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracy on test set for the Clutter Slices Dataset}
\begin{tabular}{ccccccc}
\toprule
\multirow{2}{*}{Classifiers} & \multicolumn{5}{|c|}{Cross validation Accuracy} & \multirow{2}{*}{Overall Accuracy} \\ \cmidrule(lr){2-6}
 & 1st Fold & 2nd Fold & 3rd Fold & 4th Fold & 5th Fold & \\ \midrule
RF & $0.907$ & $0.88$ & $0.94$ & $0.96$ & $0.94$ & $0.928 \pm 0.03$ \\ \midrule
AdaBoost & $0.57$ & $0.396$ & $0.53$ & $0.60$ & $0.37$ & $0.495 \pm 0.09$ \\ \midrule
SVM & $0.83$ & $0.88$ & $0.867$ & $0.924$ & $0.886$ & $0.88 \pm 0.03$ \\ \midrule
Logistic Regression & 0.759 & 0.849 & 0.83 & 0.79 & 0.849 & $0.82 \pm 0.035$ \\ \midrule
CNN & 0.907 & 0.905 & 0.94 & 0.96 & 0.96 & $0.936 \pm 0.03$ \\ \midrule
ANN & 0.87 & 0.87 & 0.925 & 0.96 & 0.89 & $0.90 \pm 0.04$ \\ \midrule
 & & & & & & \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Upinder Kaur
Praveen Abbaraju
Harrison McCarty
Richard M. Voyles

"Clutter Slices Approach for Identification-on-the-fly of Indoor Spaces" by Upinder Kaur, Praveen Abbaraju, Harrison McCarty, and Richard M. Voyles, arXiv:2101.04262v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
\textbf{Dataset} & \textbf{Variable Name} & \textbf{Variable Description} \\
\hline
\hline
RECS 2015 & btung & Household natural gas usage, in btu \\
& btulp & Household propane usage, in btu \\
&btufo & Household oil/kerosene usage, in btu \\
& btuel & Household electricity usage, in btu \\
& cooltype & Type of air conditioning equipment used \\
& insec & Faced some form of energy insecurity in the last year \\
& noac & In last year, was the household ever unable to use A/C because it could not \\
& & afford electricity or equipment repair? \\
& noheat & In the last year, was the household ever unable to use heating equipment \\
& & because it could not afford energy or equipment repair? \\
& dollarng & Household natural gas expenditure, in US dollars \\
& dollarlp & Household propane expenditure, in US dollars \\
& dollarfo & Household oil/kerosene expenditure, in US dollars \\
& dollarel & Household electricity expenditure, in US dollars \\
\hline
AHS 2019 & cold & Flag indicating unit was uncomfortably cold for 24 hours or more last winter \\
& hmreneff & Flag indicating home improvements done to make home more energy efficient \\
& & in last two years \\
& hotwater & Type of hot water system \\
& ratinghs & Rating of unit as a place to live \\
& fsstatus & Rating of overall food security of the household \\
& unitsize & Unit size (square feet) \\
\hline
NHTS 2017 & place & Travel is a financial burden \\
& price & Price of gasoline affects travel \\
& ptrans & Public transportation to reduce financial burden of travel \\
& travel & Walk/bike to reduce financial burden of travel \\
& gstotcst & Annual fuel expenditures in US dollars \\
\hline
CEI 2015 & cloftw & Expenditure on clothing and footwear \\
 & jwlbg & Expenditure on jewelry and handbags \\
 & educ & Expenditure on education services \\
 & stdint & Student loan interest payments \\
 & eltrnp & Expenditure on electronic products \\
 & hotel & Expenditure on hotels and motels \\
 & oeprd & Expenditure on other entertainment products \\
 & oesrv & Expenditure on other entertainment services \\
 & recrp & Expenditure on recreational products \\
 & eathome & Expenditure on eating and drinking at home \\
 & eatout & Expenditure on eating and drinking out \\
 & health & Expenditure on health care and insurance premiums \\
 & furhwr & Expenditure on furniture, housewares, and tools \\
& happl & Expenditure on household appliances \\
 & hhpcp & Expenditure on household and personal care products \\
 & hhpcs & Expenditure on household, personal, and child care services \\
 & hinsp & Expenditure on home insurance, primary \\
 & hmtimp & Expenditure on home maintenance and improvement
\end{tabular}
\caption{List of fused variables for the different datasets (continued on next page)}
\end{table}
\end{document}

stat

Kevin Ummel
Miguel Poblete-Cazenave
Karthik Akkiraju
Nick Graetz
Hero Ashman
Cora Kingdon
Steven Herrera Tenorio
Aaryaman "Sunny" Singhal
Daniel Aldana Cohen
Narasimha D. Rao

"Multidimensional well-being of US households at a fine spatial scale using fused household surveys: fusionACS" by Kevin Ummel, Miguel Poblete-Cazenave, Karthik Akkiraju, Nick Graetz, Hero Ashman, Cora Kingdon, Steven Herrera Tenorio, Aaryaman "Sunny" Singhal, Daniel Aldana Cohen, and Narasimha D. Rao, arXiv:2309.11512v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc}
 \toprule
 Parameters & $n_{cluster}$ & library & $\alpha\lambda_0$ & $lr$ & $\lambda_{\text{init}}$ &$\lambda_{\text{Phy}}$ \\
 \midrule
 Linear2D & 10 & Poly(3)+Exp & 0.04 & $3e-4$ & 0.5 & $1e-3$ \\
 Cubic2D & 10 & Poly(3)+Exp & 0.06 & $6e-4$ & 0.5 & $3e-5$ \\
 Linear3D & 10 & Poly(3)+Exp & 0.04 & $3e-4$ & 0.5 & $1e-3$ \\
 Lorenz & 10 & Poly(3)+Exp & 0.04 & $3e-4$ & 0.5 & $3e-5$ \\
 LV4D & 10 & Poly(2)+Exp & 0.045 & $6e-4$ & 0.5 & $3e-4$ \\
 Duffing & 10 & Poly(3)+Exp & 0.04 & $3e-4$ & 0.5 & $3e-4$ \\
 Pendulum & 10 & Poly(3)+$\sin$+$\cos$ & 0.04 & $3e-4$ & 0.5 & $3e-4$ \\
 Pendulum$^{\star}$ & 10 & Poly(3) & 0.02 & $3e-4$ & 0.5 & $3e-4$ \\
 \bottomrule
 \end{tabular}
\caption{Implement parameters for the numerical examples.$n_{cluster}$ denotes the cluster number in the trajectory segmentation step, Poly(n) represents the n-th order complete polynomials library in d-dimensional space($C_{p+d}^p$ elements), $\alpha\lambda_0$ is the parameter of the proximal operator in the parameter identification phase, $lr$ is the learning rate for the two phase, $\lambda_{\text{init}}\lambda_{\text{Phy}}$ is the weight of Initial Condition loss and Physics loss in distribution matching phase.}
\end{table}
\end{document}

math

Zhijun Zeng
Pipi Hu
Chenglong Bao
Yi Zhu
Zuoqiang Shi

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average results of the models with the MAE loss for two, three, and six lead months SSTA and MHW forecasts.}
\begin{tabular}{llllll}
\textbf{Across All Locations} & \textbf{MSE↓} & \textbf{CSI↑} & \textbf{CSI 80↑} & \textbf{Training Time↓} & \textbf{PUR↓} \\ \hline
Average (two lead months) & 0.7122 & 0.0 & 0.0 & 18.5255 & 95\% \\
Average (three lead months) & \textbf{0.7376} & 0.0 & 0.0 & 18.3525 & 100\% \\
Average (six lead months) & \textbf{0.7383} & 0.0 & 0.0 & 18.2578 & 100\%
\end{tabular}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter setup for the linear Landau damping, nonlinear Landau damping, two-stream instability, and bump-on-tail instability. The subscripts `sw' and `swsr' correspond to the SW and SW square-root formulations, respectively.}
\begin{tabular}{c|c|c|c|c}
\textbf{Parameter} & \textbf{Linear Landau} & \textbf{Nonlinear Landau} & \textbf{Two-stream} & \textbf{Bump-on-tail}\\
\hline
$\ell$ & $2\pi$ & $4\pi$ & $2\pi$ & $20\pi$\\
\hline
$k$ & $1$ & $0.5$ & $1$ & $0.3$\\
\hline
$\epsilon$ & $0.01$ & $0.5$ & $10^{-3}$ & $0.03$\\
\hline
$t_{f}$ & $10$ & $10$ & $45$ & $20$\\
\hline
$n_{0}$ & $1$ & $1$ & $0.5$ \& $0.5$ & $0.9$ \& $0.1$\\
\hline
$u^{e}$ & 0 & 0 & $-1$ \& $1$ & $0$ \& $4.5$\\
\hline
$\alpha^{e}_{\mathrm{sw}}$ & $1$& $1$& $ 0.5/\sqrt{2}$ \& $ 0.5/\sqrt{2}$& $1$ \& $0.5$\\
\hline
$\alpha^{e}_{\mathrm{swsr}}$ &$\sqrt{2}$ & $\sqrt{2}$ & $0.5$ \& $0.5$ & $\sqrt{2}$ \& $1/\sqrt{2}$\\
\end{tabular}
\end{table}
\end{document}

math

Opal Issan
Oleksandr Koshkarov
Federico D. Halpern
Boris Kramer
Gian Luca Delzanno

"Anti-symmetric and Positivity Preserving Formulation of a Spectral Method for Vlasov-Poisson Equations" by Opal Issan, Oleksandr Koshkarov, Federico D. Halpern, Boris Kramer, and Gian Luca Delzanno, arXiv:2312.05439v3, 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}{lccc}
\hline
Predictor & Estimate & Std. Err & p-value \\ \hline
Intercept & -0.543 & 0.037 & $<$ 2e-16 \\
MeanSed & 0.288 & 0.017 & $<$ 2e-16 \\
MeanSed\textsuperscript{2} & -0.083 & 0.013 & 1.95e-10 \\
EndProp & 0.529 & 0.095 & 2.56e-08 \\ \hline
\end{tabular}
\caption{Fitted parameters for time-varying covariates in the GEE regression on average treatment}
\end{table}
\end{document}

stat

Xiang Meng
Walter Dempsey
Peng Liao
Nick Reid
Pedja Klasnja
Susan Murphy

"Evaluation of the HeartSteps Online Sampling Algorithm" by Xiang Meng, Walter Dempsey, Peng Liao, Nick Reid, Pedja Klasnja, and Susan Murphy, arXiv:2501.02137v1, 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 & CMI \\ \hline
 0 & 0.0598 \\
 0.5 & 0.0735 \\
 1 & 0.1109 \\
 1.5 & 0.1712 \\
 2 & 0.2459 \\
 2.5 & 0.3005 \\
 3 & 0.3443 \\
 3.5 & 0.3787 \\
 4 & 0.4063 \\
 \end{tabular}
\caption{MC estimates for CMI for each value of c}
\end{table}
\end{document}

stat

Targeted Learning for Data Fairness

"Targeted Learning for Data Fairness" by Alexander Asemota and Giles Hooker, arXiv:2502.04309v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Nikolai Lipscomb
Xin Liu
Vidyadhar G. Kulkarni

"Asymptotically Optimal Appointment Scheduling in the Presence of Patient Unpunctuality" by Nikolai Lipscomb, Xin Liu, and Vidyadhar G. Kulkarni, arXiv:2412.18215v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
 \hline
 Distribution & Interaction Rule & Independent & Random & Cluster \\
 \hline
 \multirow{2}{*}{Beta(2,2)} & Average & 0.056 & 0.166 & 0.168 \\
 \cline{2-5}
 & Weighted & 0.057 & 0.162 & 0.160 \\
 \hline
 \multirow{2}{*}{Beta(2,5)} & Average & 0.043 & 0.115 & 0.116 \\
 \cline{2-5}
 & Weighted & 0.043 & 0.112 & 0.110 \\
 \hline
 \multirow{2}{*}{Normal(0,1)} & Average & 0.273 & 0.703 & 0.696 \\
 \cline{2-5}
 & Weighted & 0.274 & 0.685 & 0.675 \\
 \hline
 \end{tabular}
\caption{Mean 1-Wasserstein distance for E-R graphs with edge probability 0.211}
\end{table}
\end{document}

stat

"Evaluating Policy Effects through Network Dynamics and Sampling" by Eugene T. Y. Ang and Yong Sheng Soh, arXiv:2501.08150v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"New technique for parameter estimation and improved fits to experimental data for a set of compound Poisson distributions" by S. R. Mane, arXiv:2502.03237v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrrrrrrr}
 \toprule
 & \multicolumn{4}{c}{Scraped} & & \multicolumn{4}{c}{Thomson Reuters} \\
 \midrule
 & \multicolumn{2}{c}{\small In-Sample} & \multicolumn{2}{c}{\small Out-of-Sample} & & \multicolumn{2}{c}{\small In-Sample} & \multicolumn{2}{c}{\small Out-of-Sample} \\
 & \small Original & \small Replaced & \small Original & \small Replaced & & \small Original & \small Replaced & \small Original & \small Replaced \\
 \midrule
 \rowcolor[rgb]{ .875, .89, .898} \small const & \small12.77*** & \small14.74*** & \small18.78*** & \small14.13*** & & \small9.09*** & \small11.44*** & \small3.77 & \small3.54 \\
 \footnotesize std. error & \footnotesize 3.917 & \footnotesize 4.389 &\footnotesize6.623 & \footnotesize6.384 & & \footnotesize1.783 & \footnotesize2.007 & \footnotesize4.117 & \footnotesize4.979 \\
 \rowcolor[rgb]{ .875, .89, .898} \footnotesize t-stat & \footnotesize(3.26) & \footnotesize(3.358) & \footnotesize(2.837) & \footnotesize(2.213) & & \footnotesize(5.102) & \footnotesize(5.701) & \footnotesize(0.917) & \footnotesize(0.712) \\
 \small rm-rf & \small0.429*** & \small0.348* & \small0.273*** & \small0.0850 & & \small0.317*** & \small0.0677 & \small0.220*** & \small0.0221 \\
 \rowcolor[rgb]{ .875, .89, .898} \footnotesize std. error & \footnotesize0.0420 & \footnotesize0.215 & \footnotesize0.0532 & \footnotesize0.0694 & & \footnotesize0.0192 & \footnotesize0.0488 & \footnotesize0.0331 & \footnotesize0.0677 \\
 \footnotesize t-stat & \footnotesize(10.217) & \footnotesize(1.620) & \footnotesize(5.131) & \footnotesize(1.226) & & \footnotesize(16.571) & \footnotesize(1.389) & \footnotesize(6.654) & \footnotesize(0.326) \\
 \midrule
 \rowcolor[rgb]{ .875, .89, .898} \small No. of obs. & \small 1699 & \small 1699 & \small 314 & \small314 & & \small1695 & \small1695 & \small314 & \small314 \\
 \small R$^2$ & \small 0.058 & \small 0.033 & \small 0.078 & \small 0.009 & & \small 0.14 & \small0.005 & \small0.124 & \small0.001 \\
 \bottomrule
 \multicolumn{5}{l}{\cellcolor{white}\footnotesize* p $<$ 0.10, ** p $<$ 0.05, *** p $<$ 0.01} \\
 \end{tabular}
\caption{\textbf{Regression results of overall long-short portfolio returns on the excess return of the market.} }
\end{table}
\end{document}

q-fin

"Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis" by Paul Glasserman and Caden Lin, arXiv:2309.17322v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Chowdhury Mohammad Sakib Anwar
Konstantinos Georgalos

"Position Uncertainty in a Sequential Public Goods Game: An Experiment" by Chowdhury Mohammad Sakib Anwar and Konstantinos Georgalos, arXiv:2308.00179v2, 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|}
 \hline %These are DG errors for the L^2 norm
 $r/\ell$ & analytical & computed & error (\%) \\ \hline
 $1.0$ & $2.549$ & $2.566$ & $0.7\%$ \\ \hline
 $2.0$ & $2.641$ & $2.660$ & $0.7\%$ \\ \hline
 $3.0$ & $2.719$ & $2.740$ & $0.8\%$ \\ \hline
 $4.0$ & $2.779$ & $2.801$ & $0.8\%$ \\ \hline
 $6.0$ & $2.857$ & $2.881$ & $0.8\%$ \\ \hline
 $8.0$ & $2.902$ & $2.927$ & $0.9\%$ \\ \hline
 $10.0$ & $2.929$ & $2.955$ & $0.9\%$ \\ \hline
 \end{tabular}
\caption{Square plate with a hole: third test, parameter $\ell$, analytical maximal stress, computed maximal stress and error.}
\end{table}
\end{document}

cs

"A variational discrete element method for the computation of Cosserat elasticity" by Frédéric Marazzato, arXiv:2101.08712v3, 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}{lc}
 \toprule
 Encoding & Accuracy\\
 \midrule
 Plain (e.g. \texttt{"100"}) & 34.20\%\\
 Multi special tokens (\texttt{"<3digitnumber>100"}) & 33.56\%\\
 Only prefix (\texttt{"<sn>3<mn>100"}) & 34.93\%\\
 NumeroLogic (\texttt{"<sn>3<mn>100<en>"}) & 35.33\%\\
 \bottomrule
 \end{tabular}
\caption{\textbf{Different encoding alternatives} performance on 3-digit integer multiplications.}
\end{table}
\end{document}

cs

Eli Schwartz
Leshem Choshen
Joseph Shtok
Sivan Doveh
Leonid Karlinsky
Assaf Arbelle

"NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning" by Eli Schwartz, Leshem Choshen, Joseph Shtok, Sivan Doveh, Leonid Karlinsky, and Assaf Arbelle, arXiv:2404.00459v2, 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|c|c|c|c|c|c|c|}
 \hline
 $W$ & $h$ &$z_s$ & $L_0$& $L_1$ & $D$ & $\ell_0$& $\ell_1$ & $\ell_{\rm{SLM}}$ & $k \sigma_0$ & $k \sigma_1$ & $\sigma_{{\rm SLM}}$ &$\mathrm{NA}$ & $\tau$ \\
 \hline
 500$\lambda$ & $W/2048$ &500$\lambda$ & 0.8$z_s$& 0.1$z_s$ & $402 h$& $2\lambda$ & $2\lambda$ & $2\lambda$ & 2 & 2 & 2&0.75& 1\\
 \hline
\end{tabular}
\caption{Parameters used in the simulations.}
\end{table}
\end{document}

math

Randy Bartels
Olivier Pinaud
Maxine Varughese

"Speckle imaging with blind source separation and total variation deconvolution" by Randy Bartels, Olivier Pinaud, and Maxine Varughese, arXiv:2412.06755v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.