\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{csquotes}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|ccc|cccccc|}
 \hline
 & \multicolumn{3}{c|}{Accuracy} & \multicolumn{6}{c|}{Fidelity} \\
 & I\;$\uparrow$ & C\;$\uparrow$ & TS\;$\uparrow$ & ND\;$\downarrow$ & P\;$\uparrow$ & V\;$\uparrow$ & D\;$\uparrow$ & $s_c\uparrow$ & $s_g\uparrow$ \\
 \hline
 FIGARO & .960 & .593 & .997 & .238 & .827 & .735 & .748 & .790 & .853 \\
 \hline
 Expert Similarity & \textbf{.819} & \textbf{.351} & \textbf{.993} & \textbf{.453} & .625 & .354 & .473 & \textbf{.595} & .621 \\
 Learned Similarity & .288 & .057 & .753 & .696 & \textbf{.699} & \textbf{.670} & \textbf{.623} & .398 & \textbf{.741} \\
 \hline
 Mean & .554 & .204 & .873 & .575 & .662 & .512 & .548 & .497 & .681 \\
 Best case & .819 & .351 & .993 & .453 & .699 & .670 & .623 & .595 & .741 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Description mixing performance on similarity metrics. \enquote{FIGARO} refers to the performance on unaltered descriptions. Expert/learned similarity refers to similarity of generated samples to the sequence from which the expert/learned description was extracted from. Highlights indicate which of the two descriptions was more closely adhered to in terms of the respective metric.}
\end{table}
\end{document}

cs

Dimitri von Rütte
Luca Biggio
Yannic Kilcher
Thomas Hofmann

"FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control" by Dimitri von Rütte, Luca Biggio, Yannic Kilcher, and Thomas Hofmann, arXiv:2201.10936v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c}
Dataset & Majority Vote (\%) & Fleiss' Kappa \\ \hline
AudioSet & 65.66 & 0.4385 \\
VGG-Sound & 84.00 & 0.4634 \\
Ours (2M) & 69.00 & 0.5110 \\
Random & 44.00 & 0.6112 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{\textbf{Human evaluation results} assessing the perceived audio-visual correspondence in videos from different datasets.}
\end{table}
\end{document}

cs

Sangho Lee
Jiwan Chung
Youngjae Yu
Gunhee Kim
Thomas Breuel
Gal Chechik
Yale Song

"ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning" by Sangho Lee, Jiwan Chung, Youngjae Yu, Gunhee Kim, Thomas Breuel, Gal Chechik, and Yale Song, arXiv:2101.10803v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Estimated Effects of Mergers (Synthetic DiD)}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\toprule
& \multicolumn{3}{c}{Merger Cases} \\
\cmidrule(lr){2-4}
Outcomes & DL-NW & UA-CO & AA-US \\
\midrule
\multicolumn{4}{l}{\textit{Panel A: Levels}} \\
Log (Price) & -0.015 & -0.004 & -0.035 \\
& (0.022) & (0.012) & (0.032) \\
Log (Passengers) & 0.048 & 0.011 & 0.178 \\
& (0.034) & (0.026) & (0.052) \\
Log (Offered Seats) & 0.130 & 0.213$^{***}$ & 0.130$^{**}$ \\
& (0.099) & (0.015) & (0.030) \\
\multicolumn{4}{l}{\textit{Panel B: First Differences}} \\
Log (Price) & -0.003 & -0.008$^{*}$ & -0.000 \\
& (0.007) & (0.004) & (0.0006) \\
Log (Passengers) & 0.005 & 0.005 & 0.018 \\
& (0.034) & (0.013) & (0.011) \\
Log (Offered Seats) & 0.020 & 0.053$^{***}$ & 0.020$^{**}$ \\
& (0.017) & (0.013) & (0.009) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Gaurab Aryal
Anirban Chattopadhyaya
Federico Ciliberto

"Bridging Retrospective and Prospective Merger Analyses: The Case of US Airline Mergers" by Gaurab Aryal, Anirban Chattopadhyaya, and Federico Ciliberto, arXiv:2503.15785v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Spring and damper conditions in the dataset. Only Condition \#1 is considered as healthy, while all others are faulty. Two sub-conditions from Condition \#2 (nSdD100 and nSdD120) are combined into nSdD and other two from condition \#5 (lSdD100 and lSdD120) are lSdD.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 Condition \# & Spring & Damper & Notation & \# samples\\
 \hline
 1 & normal & normal & nSnD & 60 \\
 \hline
 \multirow{2}{*}{2} & \multirow{2}{*}{normal} & degraded 100 & nSdD100 & 30 \\
 & & degraded 120 & nSdD120 & 30 \\
 \hline
 3 & high & normal & hSnD & 96 \\
 \hline
 4 & low & normal & lSnD & 65 \\
 \hline
 \multirow{2}{*}{5} & \multirow{2}{*}{low} & degraded 100 & lSdD100 & 29 \\
 & & degraded 120 & lSdD120 & 30 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Chi-Ching Hsu
Gaëtan Frusque
Florent Forest
Felipe Macedo
Christian M. Franck
Olga Fink

"Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers" by Chi-Ching Hsu, Gaëtan Frusque, Florent Forest, Felipe Macedo, Christian M. Franck, and Olga Fink, arXiv:2507.19168v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Calibration Errors for Sinusoidal Hull-White Model}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \toprule
 Maturity & Observed (\%) & Fitted (\%) & Error (\%) \\
 \midrule
 1 & 1.22 & 1.43 & -0.21 \\
 2 & 1.75 & 1.56 & 0.19 \\
 3 & 1.91 & 1.79 & 0.12 \\
 5 & 1.96 & 1.87 & 0.09 \\
 7 & 2.01 & 2.00 & 0.01 \\
 10 & 2.00 & 2.14 & -0.14 \\
 20 & 2.45 & 2.25 & 0.20 \\
 30 & 2.36 & 2.35 & 0.01 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"A Sinusoidal Hull-White Model for Interest Rate Dynamics: Capturing Long-Term Periodicity in U.S. Treasury Yields" by Amit Kumar Jha, arXiv:2506.06317v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||cl||}
 \hline
 Species Symbol & Definition \\
 \hline\hline
 mRNA-Bax & Messenger mRNA for Bax \\
 Bad-Bcl-xL & Bad bound to Bcl-xL \\
 Casp-pro & Inactive caspase \\
 Bax-Bcl-xL & Bax bound to Bcl-xL \\
 Casp-act & Activated caspase \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Table of measurable species in the \textit{PARP1} inhibited ODE model. As in , all initial values for these species is 0. The ODE model computes concentrations in dimensionless units \textit{[Molec.]} representing molecules-per-cell.}
\end{table}
\end{document}

stat

Natalie M. Isenberg
Susan D. Mertins
Byung-Jun Yoon
Kristofer Reyes
Nathan M. Urban

"Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models" by Natalie M. Isenberg, Susan D. Mertins, Byung-Jun Yoon, Kristofer Reyes, and Nathan M. Urban, arXiv:2309.06540v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
\hline \bf Model & \bf Aggreability Rate \\
\hline
 Word2Vec 2019 & 0.64 \\
 \hline
 Word2Vec 2018 & 0.52 \\
 \hline
 Word2Vec 2017 & 0.49 \\
\hline
\end{tabular}
\caption{ Expert validation for the knowledge base. }
\end{table}
\end{document}

cs

Clark Emmanuel Paulo
Arvin Ken Ramirez
David Clarence Reducindo
Rannie Mark Mateo
Joseph Marvin Imperial

"A Simple Disaster-Related Knowledge Base for Intelligent Agents" by Clark Emmanuel Paulo, Arvin Ken Ramirez, David Clarence Reducindo, Rannie Mark Mateo, and Joseph Marvin Imperial, arXiv:2101.10014v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c}
\hline\hline
& roc\_auc\_score \\
\hline
Random Forest & 0.928\\
Logistic Regression & 0.910 \\
\hline\hline
\end{tabular}
\end{adjustbox}
\caption{AUC Score for Logistic Regression and Random Forest}
\end{table}
\end{document}

q-fin

Aadi Gupta
Priya Gulati
Siddhartha P. Chakrabarty

"Classification based credit risk analysis: The case of Lending Club" by Aadi Gupta, Priya Gulati, and Siddhartha P. Chakrabarty, arXiv:2210.05136v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{change detection results of different methods on the Florence dataset}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccccc}
\hline\hline
Methods & FP & FN & OE & PCC (\%) & KC(\%) & F1(\%)\\ \hline
PCAKM & 1426 & 535 & 1961 & 97.01 & 79.13 & 82.20\\
NR-ELM & 1273 & 520 & 1793 & 97.26 & 80.67 & 82.88\\
GaborPCANet & 1055 & 564 & 1619 & 97.53 & 82.11 & 84.86\\
LR-CNN & 776 & 714 & 1490 & 97.73 & 82.84 & 85.76\\
MLFN & 702 & 397 & 1099 & 98.32 & 87.64 & 88.54\\
DBN & 796 & \textbf{371} & 1167 & 98.22 & 87.03 &88.53\\
DCNet & 454 & 602 & 1056 & 98.39 & 87.58 & 88.67\\
GKSNet & \textbf{209} & 576 & \textbf{785} & \textbf{98.80} & \textbf{90.56} &\textbf{91.95} \\
\hline \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Junjie Wang
Feng Gao
Junyu Dong
Shan Zhang
Qian Du

"Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network" by Junjie Wang, Feng Gao, Junyu Dong, Shan Zhang, and Qian Du, arXiv:2201.08954v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Inclusion relationships for categories of networks.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
Line & First network class & Relationship & Second network class & Source \\ \hline
1 & Time-consistent galled trees & identical to & Normal galled trees & , p.~3 \\
2 & Time-consistent galled trees & proper subset of & Normal networks & , p.~3 \\
3 & Time-consistent galled trees & proper subset of & Galled trees & , p.~3 \\
4 & Galled trees & proper subset of & Galled tree-child networks & , Table 1 \#13 \\
5 & Galled tree-child networks & proper subset of & Galled networks & , Remark 4\\
6 & Galled tree-child networks & proper subset of & Tree-child networks & , Remark 4 \\
7 & Normal networks & proper subset of & Tree-child networks & , Fig.~12\\
8 & Tree-child networks & proper subset of & Reticulation-visible networks & , Fig.~12 \\
9 & Galled networks & proper subset of & Reticulation-visible networks & , Fig.~12 \\
10 & Reticulation-visible networks & proper subset of & Phylogenetic networks & , Fig.~5\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Lily Agranat-Tamir
Michael Fuchs
Bernhard Gittenberger
Noah A. Rosenberg

"Enumerative combinatorics of unlabeled and labeled time-consistent galled trees" by Lily Agranat-Tamir, Michael Fuchs, Bernhard Gittenberger, and Noah A. Rosenberg, arXiv:2504.16302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Random Forest Classification Performance Metrics}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lc}
\toprule
\textbf{Metric} & \textbf{Value} \\
\midrule
\textbf{Predictive Performance} & \\
AUC-ROC & 0.8825 \\
Accuracy & 0.7968 \\
Precision (Up Moves) & 0.80 \\
Precision (Down Moves) & 0.80 \\
Recall (Up Moves) & 0.80 \\
Recall (Down Moves) & 0.80 \\
F1-Score & 0.80 \\
& \\
\textbf{Cross-Validation Results} & \\
Mean CV AUC & 0.8747 \\
CV Standard Deviation & 0.0100 \\
CV Range & [0.8678, 0.8792] \\
& \\
\textbf{Feature Analysis} & \\
Number of Features & 17 \\
Top Feature Importance & 0.432 (OFI) \\
Feature Stability (CV) & 0.956 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Akash Deep
Chris Monico
W. Brent Lindquist
Svetlozar T. Rachev
Frank J. Fabozzi

"Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach" by Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.16701v1, 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{Summary of the main dataset's statistics}
\begin{tabular}{lrr}
	& \textbf{smartphones} & \textbf{cameras} \\ \hline
	\# collected images & 24.908 & 8.347\\
	\# devices & 372 & 114 \\
	\# different models & 45 & 25 \\
	\# reference images & 12.752 & 3.586\\
	\# matching tests & 12.141 & 1.563 \\
	\# mismatching tests & 61.655 & 19.382 \\
\end{tabular}
\end{table}
\end{document}

eess

Massimo Iuliani
Marco Fontani
Alessandro Piva

"A leak in PRNU based source identification. Questioning fingerprint uniqueness" by Massimo Iuliani, Marco Fontani, and Alessandro Piva, arXiv:2009.04878v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|l}
 \hline
 & Top 10 most representative words per topic \\
 \hline
Computer Science & ``learning" ``network" ``networks" ``model" "can" ``neural" \\ &
 ``deep" ``using" "models" ``data" \\

 Physics & ``model" ``can" ``system" ``field" ``energy" ``systems" \\ &
 ``magnetic" ``models" ``using" ``phase"
 \\
 Mathematics & ``problem" ``can" ``algorithm" ``show" ``method"
 ``paper" \\& ``results" ``also" ``time" ``using" \\
 Statistics & ``data" ``model" ``can" ``learning" ``using" ``models" \\&
 ``method" ``approach" ``based" ``paper" \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Most common words found by our method}
\end{table}
\end{document}

stat

"Sparse topic modeling via spectral decomposition and thresholding" by Huy Tran, Yating Liu, and Claire Donnat, arXiv:2310.06730v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
\toprule
Description & Form.Name & Grand.Total\\
\midrule
Changes in ownership & 4 & 6,757,053\\
Current report filing & 8-K & 1,549,474\\
5\% passive ownership triggers amendments & SC 13G/A & 655,696\\
Initial Ownership Report & 3 & 613,615\\
Quarterly report & 10-Q & 574,774\\
\addlinespace
Definite materials & 497 & 417,177\\
Curret report of Foreign issuer & 6-K & 379,844\\
5\% passive ownership triggers & SC 13G & 369,518\\
Change on prospectus & 424B3 & 276,705\\
Quarterly hodings, institutional managers & 13F-HR & 238,794\\
\addlinespace
Prospectus filed pursuant to Rule 424(b)(2) & 424B2 & 236,485\\
5\% active ownership triggers amendments & SC 13D/A & 222,739\\
Changes in ownership amendments & 4/A & 217,095\\
Offering made without registration & D & 211,275\\
Annual report on ownership changes & 5 & 199,887\\
\addlinespace
A correspondence & CORRESP & 186,948\\
Upload Notification & UPLOAD & 183,620\\
Annual report & 10-K & 183,148\\
Post-effective amendment & 485BPOS & 171,995\\
Summary Prospectus & 497K & 169,519\\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Text analysis in financial disclosures

"Text analysis in financial disclosures" by Sridhar Ravula, arXiv:2101.04480v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Running time for Algorithm 1 and the algorithm in with $e_t =1$}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
		\hline
		& \textbf{Algorithm 1} & Algorithm in \\
		\hline
		$r=0.1$ & {2.03s} & 5.37s\\
		$r=0.3$ & {1.83s} & 4.44s\\
		$r=0.5$ & {1.83s} & 4.35s\\
		$r=0.8$ & {1.84s} & 4.39s\\
		\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"MIMO Radar Waveform-Filter Design for Extended Target Detection from a View of Games" by Zhou Xu, Chongyi Fan, and Xiaotao Huang, arXiv:2011.11346v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"On the improved estimation of ordered parameters based on doubly type-II censored sample" by Shrajal Bajpai and Lakshmi Kanta Patra, arXiv:2411.06888v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Crypto Factor and Risk-Free Rate Descriptive Statistics. Values are returns in percentage points and are at the weekly frequency. There are 105 weekly observations.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
\hline
 & Crypto Market & SMB & Momentum & Risk-Free Rate\\
\hline
mean & 1.44 & -10.04 & 0.68 & 0.09\\
std & 6.39 & 33.46 & 7.68 & 0.01\\
min & -15.42 & -206.53 & -30.14 & 0.06\\
25\% & -1.89 & -6.41 & -4.54 & 0.09\\
50\% & 0.25 & -0.93 & 0.55 & 0.10\\
75\% & 5.72 & 2.18 & 4.33 & 0.10\\
max & 18.70 & 18.54 & 24.93 & 0.11\\
\hline
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Surprising Irrelevance of Total-Value-Locked on Cryptocurrency Returns" by Matt Brigida, arXiv:2506.03287v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Dynamics of Liquidity Surfaces in Uniswap v3" by Jimmy Risk, Shen-Ning Tung, and Tai-Ho Wang, arXiv:2509.05013v1, 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}
\toprule
 A sound of \texttt{label} \\
 a sound of \texttt{label}. \\
 A constant sound of \texttt{label}\\
 a constant sound of \texttt{label}\\
 A constant sound of \texttt{label}. \\
 a constant sound of \texttt{label}. \\
 A big sound of \texttt{label} \\
 a big sound of \texttt{label} \\
 A big sound of \texttt{label}. \\
 A small sound of \texttt{label} \\
 a small sound of \texttt{label} \\
 A small sound of \texttt{label}. \\
 A \texttt{label} is making a sound. \\
 a \texttt{label} is making a sound \\
 An \texttt{label} is making a sound. \\
 an \texttt{label} is making a sound \\
 A sound of \texttt{label} followed by a sound of \texttt{label} \\
 A sound of \texttt{label} followed by \texttt{label} \\
 A \texttt{label}@ \\
 A \texttt{label} \\
 An \texttt{label} \\
 an \texttt{label} \\
 \texttt{label}@ \\
 \texttt{label} \\
 \texttt{label} and \texttt{label} \\
 A \texttt{label} is running. \\
 A \texttt{label} is running \\
 A \texttt{label} is happening. \\
 A \texttt{label} is happening \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Mengjie Zhao
Junya Ono
Zhi Zhong
Chieh-Hsin Lai
Yuhta Takida
Naoki Murata
Wei-Hsiang Liao
Takashi Shibuya
Hiromi Wakaki
Yuki Mitsufuji

"On the Language Encoder of Contrastive Cross-modal Models" by Mengjie Zhao, Junya Ono, Zhi Zhong, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Wei-Hsiang Liao, Takashi Shibuya, Hiromi Wakaki, and Yuki Mitsufuji, arXiv:2310.13267v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Optimal confidence interval for the difference of proportions" by Almog Peer and David Azriel, arXiv:2308.16650v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cl}
 \toprule
 \textbf{N} & \textbf{Industry} \\
 \midrule
 \textbf{1} & Farms \\
 \textbf{2} & Forestry, fishing, and related activities \\
 \textbf{3} & Oil and gas extraction \\
 \textbf{4} & Mining, except oil and gas \\
 \textbf{5} & Support activities for mining \\
 \textbf{6} & Utilities \\
 \textbf{7} & Construction \\
 \textbf{8} & Wood products \\
 \textbf{9} & Nonmetallic mineral products \\
 \textbf{10} & Primary metals \\
 \textbf{11} & Fabricated metal products \\
 \textbf{12} & Machinery \\
 \textbf{13} & Computer and electronic products \\
 \textbf{14} & Electrical equipment, appliances, and components \\
 \textbf{15} & Motor vehicles, bodies and trailers, and parts \\
 \textbf{16} & Other transportation equipment \\
 \textbf{17} & Furniture and related products \\
 \textbf{18} & Miscellaneous manufacturing \\
 \textbf{19} & Food and beverage and tobacco products \\
 \textbf{20} & Textile mills and textile product mills \\
 \textbf{21} & Apparel and leather and allied products \\
 \textbf{22} & Paper products \\
 \textbf{23} & Printing and related support activities \\
 \textbf{24} & Petroleum and coal products \\
 \textbf{25} & Chemical products \\
 \textbf{26} & Plastics and rubber products \\
 \textbf{27} & Wholesale trade \\
 \textbf{28} & Retail trade \\
 \textbf{29} & Air transportation \\
 \textbf{30} & Rail transportation \\
 \textbf{31} & Water transportation \\
 \textbf{32} & Truck transportation \\
 \textbf{33} & Transit and ground passenger transportation \\
 \textbf{34} & Pipeline transportation \\
 \textbf{35} & Other transportation and support activities \\
 \textbf{36} & Warehousing and storage \\
 \textbf{37} & Publishing industries (includes software) \\
 \textbf{38} & Motion picture and sound recording industries \\
 \textbf{39} & Broadcasting and telecommunications \\
 \textbf{40} & Information and data processing services \\
 \textbf{41} & Federal Reserve banks, credit intermediation, and related activities \\
 \textbf{42} & Securities, commodity contracts, and investments \\
 \textbf{43} & Insurance carriers and related activities \\
 \textbf{44} & Funds, trusts, and other financial vehicles \\
 \textbf{45} & Real estate \\
 \textbf{46} & Rental and leasing services and lessors of intangible assets \\
 \textbf{47} & Legal services \\
 \textbf{48} & Computer systems design and related services \\
 \textbf{49} & Miscellaneous professional, scientific, and technical services \\
 \textbf{50} & Management of companies and enterprises \\
 \textbf{51} & Administrative and support services \\
 \textbf{52} & Waste management and remediation services \\
 \textbf{53} & Educational services \\
 \textbf{54} & Ambulatory health care services \\
 \textbf{55} & Hospitals and nursing and residential care facilities \\
 \textbf{56} & Social assistance \\
 \textbf{57} & Performing arts, spectator sports, museums, and related activities \\
 \textbf{58} & Amusements, gambling, and recreation industries \\
 \textbf{59} & Accommodation \\
 \textbf{60} & Food services and drinking places \\
 \textbf{61} & Other services, except government \\
 \textbf{62} & Federal government \\
 \textbf{63} & State and local government \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Jean-Philippe Boussemart
Walter Briec
Raluca Parvulescu
Paola Ravelojaona

"$Λ$-Returns to Scale and Individual Minimum Extrapolation Principle" by Jean-Philippe Boussemart, Walter Briec, Raluca Parvulescu, and Paola Ravelojaona, arXiv:2212.04724v2, 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{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small System-focused results under nodal, zonal, and copper-plate modeling. The results are reported for the base system and two market bidding REPs, both with 1000 MW electrolyzer installed.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llrrrr} \toprule
\textbf{} &
 \textbf{} &
 \textbf{System} &
 \textbf{Cost of} &
 \textbf{System} &
 \textbf{Renewable}
 \\
\textbf{} &
 \textbf{} &
 \textbf{load} &
 \textbf{generation} &
 \textbf{emissions } &
 \textbf{curtailment}
 \\
\multicolumn{2}{c}{\textbf{Case}} &
 [TWh] &
 [$\$$/MWh] &
 [t/MWh] &
 [GWh]
 \\ \toprule
\multirow{3}{*}{Base} & Copper & 37.66 & 18.54 & 0.36 & 348.75 \\
 & Zonal & 37.66 & 18.55 & 0.36 & 369.97 \\
 & Nodal & 37.66 & 18.67 & 0.37 & 608.09 \\
 \midrule
\multirow{3}{*}{$\$$1.5/kg} & Copper & 45.07 & 18.81 & 0.43 & 40.47 \\
 & Zonal & 44.94 & 18.80 & 0.42 & 40.77 \\
 & Nodal & 43.19 & 18.72 & 0.41 & 67.29 \\
 \midrule
\multirow{3}{*}{$\$$6/kg}& Copper & 46.44 & 19.00 & 0.43 & 40.47 \\
 & Zonal & 46.44 & 19.04 & 0.43 & 40.77 \\
 & Nodal & 44.06 & 19.25 & 0.42 & 302.07 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Andrea Gloppen Johnsen
Lesia Mitridati
Jalal Kazempour
Line Roald

"Electricity Market Bidding for Renewable Electrolyzer Plants: An Opportunity Cost Approach" by Andrea Gloppen Johnsen, Lesia Mitridati, Jalal Kazempour, and Line Roald, arXiv:2501.16844v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"MATES: Multi-view Aggregated Two-Sample Test" by Zexi Cai, Wenbo Fei, and Doudou Zhou, arXiv:2412.16684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Projected Langevin Monte Carlo algorithms in non-convex and super-linear setting" by Chenxu Pang, Xiaojie Wang, and Yue Wu, arXiv:2312.17077v4, 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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fitting Parameters \( a \) and \( b \) for the Function in Equation ()}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
 \toprule
 City & \( a \) & \( b \) \\
 \midrule
 New York & $0.52$ & $38.7$ \\
 Tokyo & $0.51$ & $19.1$ \\
 Boston & $0.37$ & $4.5$ \\
 Chicago & $0.52$ & $14.6$ \\
 Washington D.C. & $0.37$ & $10.1$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Shu-ichi Kinoshita
Yuya Bando
Hiroki Sayama

"Spatio-Temporal Differences in Bike Sharing Usage: A Tale of Six Cities" by Shu-ichi Kinoshita, Yuya Bando, and Hiroki Sayama, arXiv:2412.19294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Computational complexity of feature importance estimation. For Relief algorithms, we used $s = |I|$. The $t$ corresponds to the number of trees.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \hline
 Algorithm & time complexity & space complexity \\ \hline
 ReliefE &$\mathcal{O}(|\nu|^2 \cdot |F| + |I| \cdot d \cdot s)$ & $\mathcal{O}(|\nu|^2)$ \\
 ReliefF & $\mathcal{O}(|F| \cdot |I|^{2})$ & $\mathcal{O}(|F|)$ \\
 Random Forest & $\mathcal{O}(t \cdot |F| \cdot |I| \cdot \log^2 |I|)$ & $\mathcal{O}(|I| + |F|)$\\
 Mutual Information & $\mathcal{O}(|F| \cdot |I|)$ & $\mathcal{O}(|I|)$ \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Blaž Škrlj
Sašo Džeroski
Nada Lavrač
Matej Petković

"ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings" by Blaž Škrlj, Sašo Džeroski, Nada Lavrač, and Matej Petković, arXiv:2101.09577v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameter settings for simulation}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Symbols} & \textbf{Value} \\
\hline
Carrier frequency $f_c$ & $28$ GHz\\
\hline
Subcarrier spacing $\Delta f$ & $120$ kHz\\
\hline
Number of subcarriers $P$ & 1024 \\
\hline
Bandwidth & $122.88$ MHz \\
\hline
Number of OFDM symbols $Q$ & $1024$\\
\hline
CP length $T_{\mathrm{CP}}$ & $2.0833\ \mathrm{\mu s}$ \\
\hline
Noise power density $N_0$ & $-169$ dBm/Hz\\
\hline
Number of users $U$ & $4$\\
\hline
Number of subcarriers per user & 512\\
\hline
Number of OFDM symbols per user & 512\\
\hline
Number of sensing targets $K$ & 3\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Full-Dimensional Beamforming for Multi-User MIMO-OFDM ISAC for Low-Altitude UAV with Zero Sensing Resource Allocation" by Zhiwen Zhou, arXiv:2508.06428v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The QLBS Model within the presence of feedback loops through the impacts of a large trader" by Ahmet Umur Özsoy and Ömür Uğur, arXiv:2311.06790v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c}
Direction & CAR & In-shop \\
\hline
$\mathbf{e^{euc}}$ & \cellcolor[HTML]{F9DEDC}69.5 $\pm$ 0.7 & \cellcolor[HTML]{FFFFFF}83.7 $\pm$ 0.1 \\
$\mathbf{e^{cos}}$ & \cellcolor[HTML]{EA9088}75.5 $\pm$ 0.2 & \cellcolor[HTML]{F3BCB7}85.2 $\pm$ 0.3 \\
$\mathbf{e^{euc-orth}}$ & \cellcolor[HTML]{FFFFFF}66.9 $\pm$ 0.5 & \cellcolor[HTML]{FCEFEE}84.1 $\pm$ 0.1 \\
$\mathbf{e^{cos-orth}}$ & \cellcolor[HTML]{E67C73}77.0 $\pm$ 0.7 & \cellcolor[HTML]{E67C73}86.6 $\pm$ 0.2
\end{tabular}
\end{adjustbox}
\caption{Comparing recall@1 performance of different gradient directions on CAR and In-shop dataset}
\end{table}
\end{document}

cs

"Dissecting the impact of different loss functions with gradient surgery" by Hong Xuan and Robert Pless, arXiv:2201.11307v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc}
 topic 1 & topic 2 &topic 3 &topic 4 &topic 5\\
 ``laundry and other household" &``baby and body care" &``frozen products" &``vegetable, fruits and milk" &"drinking and body care"\\
 \hline
laundry & baby food formula & frozen meals & fresh vegetables & body lotions soap \\
deodorants & diapers wipes & frozen breakfast & fresh fruits & beers coolers \\
mint gum & baby bath body care & frozen pizza & packaged vegetables fruits & muscles joints pain relief \\
cleaning products & energy granola bars & instant foods & yogurt & dog food care \\
dish detergents & fruit vegetable snacks & cereal & packaged cheese & white wines \\
trash bags liners & laundry & frozen appetizers sides & chips pretzels & air fresheners candles \\
oral hygiene & canned fruit applesauce & plates bowls cups flatware & water seltzer sparkling water & ice cream toppings \\
baby accessories & food storage & frozen dessert & frozen produce & red wines \\
more household & cereal & frozen vegan vegetarian & ice cream ice & first aid \\
paper goods & hair care & hair care & milk & spirits \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{5 selected topics and the corresponding top 10 categories from derived from a 20-topic MCTM.}
\end{table}
\end{document}

cs

A Multilayer Correlated Topic Model

"A Multilayer Correlated Topic Model" by Ye Tian, arXiv:2101.02028v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcr}
Left&Centered&Right\\
\hline
1 & 2 & 3\\
10 & 20 & 30\\
100 & 200 & 300\\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"On the Reasoning Capacity of AI Models and How to Quantify It" by Santosh Kumar Radha and Oktay Goktas, arXiv:2501.13833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \toprule
 \multirow{3}{2.5cm}{\centering } & \multicolumn{2}{c}{ShapeNet} & \multicolumn{2}{c}{V2V} \\
 \cmidrule{2-3} \cmidrule{4-5}
 & Clean & Perturbed & Clean & Perturbed \\
 \midrule
 Original & 66.33 & 0.62 & 82.19 & 7.55 \\
 Adv Trained & \textbf{67.29} & \textbf{66.00} & \textbf{82.60} & \textbf{83.44} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

James Tu
Tsunhsuan Wang
Jingkang Wang
Sivabalan Manivasagam
Mengye Ren
Raquel Urtasun

"Adversarial Attacks On Multi-Agent Communication" by James Tu, Tsunhsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun, arXiv:2101.06560v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \hline
 \textbf{Dataset} & \textbf{Sharpe Ratio} & \textbf{T-stat (p-value) }\\
 \hline
 FinMarBa & 0.30 & 10 (0) \\
 Financial-Phrasebank & -0.13 & -4.36 (1.3 e-5) \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Sharpe Ratios of sentiment-based signals from FinMarBa and Financial-Phrasebank. Higher values indicate stronger risk-adjusted returns, with FinMarBa outperforming Financial-Phrasebank.}
\end{table}
\end{document}

q-fin

Baptiste Lefort
Eric Benhamou
Beatrice Guez
Jean-Jacques Ohana
Ethan Setrouk
Alban Etienne

"FinMarBa: A Market-Informed Dataset for Financial Sentiment Classification" by Baptiste Lefort, Eric Benhamou, Beatrice Guez, Jean-Jacques Ohana, Ethan Setrouk, and Alban Etienne, arXiv:2507.22932v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Archimedean copulas and their corresponding generating functions $\phi(t)$ }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
\hline
Family & $\phi(t)$ & $\theta \in$ &$ \mathcal{C}(u_1,u_2;\theta)$ \\
\hline
Frank &-$\ln \dfrac{e^{-\theta t}-1}{e^{-\theta}-1}$ & $\theta \neq0$ &$ -\frac{1}{\theta}\ln\bigl[1+\dfrac{(e^{-\theta u_1}-1)(e^{-\theta u_2}-1)}{e^{-\theta}-1}\bigr] $ \\
Clayton & $t^{-\theta}-1 $ & $\theta>0$ &$ (u_1^{-\theta}+u_2^{-\theta}-1)^{-\frac{1}{\theta}}$ \\
Gumbel-Hugard & $(-\ln t)^\theta $ & $\theta\geq 1$ &$\exp \Bigl\lbrace -\bigl[(-\ln u_1)^\theta+(-\ln u_2)^\theta\bigr]^{\frac{1}{\theta}}\Bigr\rbrace$\\
Ali-Mikhail-Haq & $ \ln \dfrac{1-\theta(1-t)}{t} $ &$-1\leq \theta <1$&$\dfrac{u_1 u_2}{1-\theta(1-u_1)(1-u_2)}$ \\
Joe & $ -\ln(1-(1-t)^{\theta}) $ &$\theta\geq 1$ &$1-\bigr[ (1-u_1)^{\theta} + (1-u_2)^{\theta} -(1-u_1)^{\theta}(1-u_2)^{\theta} \bigr]^{1/ \theta}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Modeling Count Data via Copulas

Hadi Safari-Katesari
S. Yaser Samadi
Samira Zaroudi

"Modeling Count Data via Copulas" by Hadi Safari-Katesari, S. Yaser Samadi, and Samira Zaroudi, arXiv:2012.02021v1, 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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Type of traces used to define membership by organization}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
		\toprule
		short\_name & affectation & nomination & intégration & titularisation\\
		\midrule
		cab & 0 & 1 & 0 & 0\\
		ccomptes & 1 & 1 & 1 & 0\\
		cdiplo & 0 & 1 & 0 & 0\\
		ce & 1 & 1 & 0 & 1\\
		cprefet & 0 & 1 & 0 & 1\\
		csprefet & 0 & 1 & 0 & 1\\
		dgbudget & 1 & 1 & 1 & 1\\
		dgfip & 1 & 1 & 1 & 1\\
		dgtresor & 1 & 1 & 1 & 1\\
		ena & 0 & 1 & 0 & 0\\
		iga & 1 & 1 & 1 & 1\\
		igas & 1 & 1 & 1 & 1\\
		igf & 1 & 1 & 1 & 1\\
		insee & 0 & 1 & 0 & 0\\
		\bottomrule
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

"Career Modeling with Missing Data and Traces" by Théo Voldoire, Robin J. Ryder, and Ryan Lahfa, arXiv:2311.15257v2, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Graph class} & \textbf{Problem type} & \textbf{Algorithm} & \textbf{Lower Bound} \\ \hline \hline
General graphs & Vertex-disjoint = edge-disjoint & ???? & NP-hard for $k=2$\\ \hline
\multirow{3}{*}{DAGs} & \multirow{3}{*}{Vertex-disjoint = edge-disjoint} & \multirow{3}{*}{$n^{O(k)}$~} & $f(k)\cdot n^{o(\sqrt{k})}$~\\
 & & & $f(k)\cdot n^{o(k/\log k)}$~\\
 & & & $f(k)\cdot n^{o(k)}$ \textbf{[this paper]}\\ \hline
\multirow{3}{*}{Planar graphs} & \multirow{2}{*}{Vertex-disjoint} & $n^{O(k)}$~ & \multirow{2}{*}{????} \\
 & & $2^{2^{O(k^2)}}\cdot n^{O(1)}$~ & \\\cline{2-4}
 & Edge-disjoint & ???? & $f(k)\cdot n^{o(k)}$ \textbf{[this paper]} \\ \hline
\multirow{2}{*}{Planar DAGs} & Vertex-disjoint & $2^{2^{O(k^2)}}\cdot n^{O(1)}$~ & ????\\
 & Edge-disjoint & $n^{O(k)}$~ & $f(k)\cdot n^{o(k)}$ \textbf{[this paper]} \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"A Tight Lower Bound for Edge-Disjoint Paths on Planar DAGs" by Rajesh Chitnis, arXiv:2101.10742v1, 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{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Performance on the VoxCeleb1 test data when only the WB VoxCeleb1 training data are given. $\rightarrow$: sub-spectrogram selection operation. SR: Sampling rate.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccccccc}
 \toprule
 \textbf{ID}&\textbf{Method}& \textbf{Training SR and \#Filters} & \textbf{Testing SR}&\textbf{Testing \#Filters}&\textbf{EER (\%)}\\
 \midrule
 1&\multirow{4}{*}{ WB baseline} &\multirow{4}{*}{16k and 64}& 16k & 64 &\textbf{4.35}&\\
 2& &&8k&64&20.13&\\
 3& &&8k&48&8.82&\\
 4& &&16k&64$\rightarrow$48&8.87&\\
 \cmidrule(lr){1-6}
 5& \multirow{4}{*}{ NB baseline} &\multirow{4}{*}{8k and 48}& 8k & 48 &\textbf{4.92}&\\
 6&&&16k&48&18.86\\
 7&&&16k&64&8.01&\\
 8&&&16k&64$\rightarrow$48&4.95&\\
 \cmidrule(lr){1-6}
 \multirow{2}{*}{ \textbf{9}}& \multirow{2}{*}{ \textbf{Proposed MB}}& \multirow{2}{*}{16k and 64\&48}& 16k&64&\textbf{4.07}\\
 &&&8k&48&\textbf{4.37}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"A Unified Deep Speaker Embedding Framework for Mixed-Bandwidth Speech Data" by Weicheng Cai and Ming Li, arXiv:2012.00486v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Path Loss Exponent of RIS Assisted Channels}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
		\hline
		Frequency					&$N$	& $\Delta P_L$ (dB) & PLE ($n$) \\ \hline
		\multirow{3}{*}{$2.4$ GHz} 	&64 & 5.6				& 3.373 	\\
		&256	& 13.3				& 3.068 	\\
		&1024 	& 23.8				& 2.847 	\\ \hline
		\multirow{3}{*}{$28$ GHz} 	&64 & 0.3 				& 3.159 	\\
		&256	& 1 				& 3.130 	\\
		&1024 	& 3.5				& 3.038 	\\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

"Modeling and Analysis of Reconfigurable Intelligent Surfaces for Indoor and Outdoor Applications in Future Wireless Networks" by I. Yildirim, A. Uyrus, and E. Basar, arXiv:1912.07350v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccccc} %\hline
\multicolumn{5}{c}{$M=50$ individuals} \\ %\hline
& \multicolumn{2}{c}{Cut degree 0} & \multicolumn{2}{c}{VC-BART} \\
 Region & Covariate 1 & Covariates 2-10 & Covariate 1 & Covariates 2-10 \\
$z \in $(-3,-2] & 0.06 & 0.080 & 0.60 & 0.58 \\
$z \in $(-2,-1] & 0.06 & 0.078 & 0.61 & 0.57 \\
$z \in $(-1,0] & 0.056 & 0.072 & 0.61 & 0.58 \\
$z \in $(0,1] & 0.73 & 0.084 & 0.82 & 0.56 \\
$z \in $(1,2] & 0.873 & 0.070 & 0.97 & 0.56 \\
$z \in $(2,3] & 1 & 0.057 & 0.98 & 0.54 \\
\multicolumn{5}{c}{$M=100$ individuals} \\ %\hline
& \multicolumn{2}{c}{Cut degree 0} & \multicolumn{2}{c}{VC-BART} \\
 Region & Covariate 1 & Covariates 2-10 & Covariate 1 & Covariates 2-10 \\
$z \in $ (-3,-2] & 0.05 & 0.036 & 0.64 & 0.58 \\
$z \in $ (-2,-1] & 0.045 & 0.038 & 0.72 & 0.52 \\
$z \in $ (-1,0] & 0.0376 & 0.039 & 0.59 & 0.48 \\
$z \in $ (0,1] & 0.99 & 0.036 & 0.89 & 0.52 \\
$z \in $ (1,2] & 0.995 & 0.031 & 1 & 0.54 \\
$z \in $ (2,3] & 1 & 0.027 & 1 & 0.51 \\
\end{tabular}
\end{adjustbox}
\caption{Functional data simulation. Proportion of rejected null hypothesis for our 0-degree cut orthogonal basis, and VC-BART. For covariate 1 and $z>0$ this is the statistical power, otherwise it is the type I error}
\end{table}
\end{document}

stat

David Rossell
Arnold Kisuk Kseung
Ignacio Saez
Michele Guindani

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


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lllll}
\toprule
\multicolumn{3}{@{}l}{\textbf{Variable}} & \texttt{dropout3y}=0 & \texttt{dropout3y}=1 \\\cmidrule{1-3}\cmidrule{4-5}
\textbf{Type} & \textbf{Name} & & {\textbf{Mean (sd)}} & {\textbf{Mean (sd)}} \\
\midrule
\multirow{2}{*}{Numerical}
& \texttt{admission\_score} & & 67.00 (11.46) & 63.61 (11.62) \\
& \texttt{ECTS1sem} & & 49.79 (14.74) & 10.38 (15.39) \\
\midrule
& & \textbf{Category} & \textbf{N (Frequency)} & \textbf{N (Frequency)} \\
\cmidrule{2-3}\cmidrule{4-5}
\multirow{15}{*}{Categorical} & \multirow{3}{*}{\texttt{origins}} &
 \texttt{OnSite\textsuperscript{*}} & 1033 (21.55\%) & 232 (26.61\%) \\
 & & \texttt{Commuter} & 3427 (71.49\%) & 583 (66.86\%) \\
 & & \texttt{Offsite} & 334 (6.96\%) & 57 (6.53\%) \\
 \cmidrule{2-5}
 & \multirow{2}{*}{\texttt{gender}} & \texttt{Male\textsuperscript{*}} & 3200 (66.75\%) & 670 (76.83\%)\\
 & & \texttt{Female} & 31594 (33.25\%) & 202 (23.17\%) \\
 \cmidrule{2-5}
 & \multirow{4}{*}{\texttt{highschool\_type}} & \texttt{Scientific\textsuperscript{*}} & 3181 (66.36\%) & 542 (62.15\%) \\
 & & \texttt{Classical} & 327 (6.82\%) & 55 (6.31\%) \\
 & & \texttt{Others} &607 (12.66\%) & 90 (10.32\%) \\
& & \texttt{Technical} & 679 (14.16\%) & 185 (21.22\%) \\
 \cmidrule{2-5}
 & \multirow{4}{*}{\texttt{income}} & \texttt{Medium\textsuperscript{*}} & 988 (20.61\%) & 125 (14.33\%) \\
 & & \texttt{Grant} & 1404 (29.29\%) & 287 (32.91\%) \\
 & & \texttt{High} & 1771 (36.94\%) & 378 (43.35\%) \\
 & & \texttt{Low} & 631 (13.16\%) & 82 (9.41\%) \\
 \cmidrule{2-5}
 & \multirow{2}{*}{\texttt{age19}} & \texttt{0\textsuperscript{*}} & 4276 (89.19\%) &671 (76.95\%) \\
 & & \texttt{1} & 518 (10.81\%) & 201 (23.05\%) \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{Descriptive statistics for considered covariates after data pre-processing for \texttt{career\_start\_ay}=\lq 2017', according to the dropout status by the end of the third year.}
\end{table}
\end{document}

stat

Alessandra Ragni
Chiara Masci
Anna Maria Paganoni

"Analysis of Higher Education Dropouts Dynamics through Multilevel Functional Decomposition of Recurrent Events in Counting Processes" by Alessandra Ragni, Chiara Masci, and Anna Maria Paganoni, arXiv:2411.13370v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|lllll}
 & MG & SS & CV & FL & HL \\ \hline
\#Utts & 8,423 & 19,266 & 516 & 548 & 198 \\
Dur (h) & 10.2 & 28.2 & 0.6 & 2.2 & 0.7
\end{tabular}
\end{adjustbox}
\caption{Number of utterances and total duration of test sets.}
\end{table}
\end{document}

eess

Liam Lonergan
Ibon Saratxaga
John Sloan
Oscar Maharog
Mengjie Qian
Neasa Ní Chiaráin
Christer Gobl
Ailbhe Ní Chasaide

"Fotheidil: an Automatic Transcription System for the Irish Language" by Liam Lonergan, Ibon Saratxaga, John Sloan, Oscar Maharog, Mengjie Qian, Neasa Ní Chiaráin, Christer Gobl, and Ailbhe Ní Chasaide, arXiv:2501.00509v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"MATES: Multi-view Aggregated Two-Sample Test" by Zexi Cai, Wenbo Fei, and Doudou Zhou, arXiv:2412.16684v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Discordance Minimization-based Imputation Algorithms for Missing Values in Rating Data" by Young Woong Park, Jinhak Kim, and Dan Zhu, arXiv:2311.04035v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|cccc|}
 \hline
 $\rho$& number of scenarios & obj & LB & UB & gap \\
 \hline
0.2
&500&-10.19&-10.23&-10.19&0.43\%%\\
\\
 \hline
0.5
&500&-10.57&-10.68&-10.56&1.09\%%\\
\\
 \hline
1.0
&500&-11.13&-11.35&-11.12&2.06\%%\\
\\
 \hline
2.0
&500&-12.12&-12.57&-12.10&3.89\%%\\
\\
\hline
 \end{tabular}
\end{adjustbox}
\caption{Objective values and 95\% bounds with different sizes of uncertainty with triangle distribution under box demand uncertainty}
\end{table}
\end{document}

math

"A Framework for Stochastic Fairness in Dominant Resource Allocation with Cloud Computing Applications" by Jiaqi Lei, Akhil Singla, and Sanjay Mehrotra, arXiv:2501.18051v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|l|}
			\hline
			& symmetric case & asymmetric case \\ \hline
			$g$ low & . (no hegemon) & . (unipolar, $a$ dominates) \\ \hline
			$g$ mid & . (unipolar, $a$ compromises)
			& . and .
		 (bipolar. $a$ or $b$ compromises)\\ \hline
			$g$ high & . (bipolar)
			& . (unipolar, $b$ dominates) \\ \hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

The Power of Non-Superpowers

"The Power of Non-Superpowers" by Tomoo Kikuchi and Shuige Liu, arXiv:2209.10206v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overall Performance Comparison}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c}
\hline
\textbf{Model} & \textbf{\hspace{1em} SR\hspace{1em} } & \textbf{\hspace{1em} EV\hspace{1em} } & \textbf{ \hspace{1em} XS-$R^2$ \hspace{1em} } & \textbf{\hspace{1em} MSPE\hspace{1em} } \\
\hline
\multicolumn{5}{c}{\textit{Traditional Asset Pricing Models}} \\
\hline
CAPM &0.49 & 0.05 & 0.00 & 2.91 \\
FF3 & 0.52 & 0.06 & 0.00 & 2.37 \\
FF5 & 0.50 & 0.09 & 0.02 & 2.13 \\
\hline
\multicolumn{5}{c}{\textit{Modern Asset Pricing Methods}} \\
\hline
Linear-SDF & 0.84 & 0.13 & 0.00 & 4.17 \\
Elasticnet-SDF & 1.03 & 0.12 & 0.00 & 3.16 \\
IPCA-SDF & 1.56 & 0.11 & 0.00 & 1.57 \\
\hline
\multicolumn{5}{c}{\textit{Deep Learning Methods}} \\
\hline
FFN & 1.28 & 0.13 & 0.05 & 1.35 \\
GAN-SDF & 0.65 & 0.14 & 0.11 & 0.87 \\
\hline
\multicolumn{5}{c}{\textit{Text Integration Methods}} \\
\hline
TF-IDF-SDF & 1.83 & 0.13 & 0.10 & 1.45 \\
BERT-SDF & 1.81 & 0.12 & 0.07 & 1.43 \\
\hline
\multicolumn{5}{c}{\textit{Ablation Studies}} \\
\hline
NewsNet-SDF w/o macro & 1.92 & 0.14 & 0.09 & 1.56 \\
NewsNet-SDF w/o news & 1.65 & 0.13 & 0.08 & 2.67 \\
\hline
\textbf{NewsNet-SDF} & \textbf{2.80} & \textbf{0.14} & \textbf{0.11} & \textbf{0.56} \\
\hline
\multicolumn{5}{p{0.85\linewidth}}{\small \textit{Note:} For Sharpe Ratio, Explained Variation, and XS-$R^2$, higher values indicate better performance. For MSPE, lower values indicate better performance.} \\
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shunyao Wang
Ming Cheng
Christina Dan Wang

"NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks" by Shunyao Wang, Ming Cheng, and Christina Dan Wang, arXiv:2505.06864v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Net Electric Charge Sectors in QED}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|l|}
		\hline
		Sector $\mathcal{H}_Q$ & Examples \\ \hline
		$Q=0$ & $\lvert 0 \rangle$, $\lvert e^-e^+\rangle$, multi-photon states \\ \hline
		$Q=-e$ & $\lvert e^-\rangle$, $\lvert e^-e^-e^+\rangle$ \\ \hline
		$Q=+e$ & $\lvert e^+\rangle$, $\lvert e^+e^+e^-\rangle$ \\ \hline
		$Q=-2e$ & $\lvert e^-e^-\rangle$ \\ \hline
		$\cdots$ & \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Symmetry Packaging II: A Group-Theoretic Framework for Packaging Under Finite, Compact, Higher-Form, and Hybrid Symmetries" by Rongchao Ma, arXiv:2503.20295v3, 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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Regret estimates for BFI and FMAB problems in deterministic case.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccc}
 \toprule
 Function & Cost per iter. & Base optimizer & \# iter for $R_B \leq \varepsilon$ & $R_O(T)$ \\
 \midrule
 Convex $M$-Lipschitz &1 $\nabla$, 1 proj. & PGD & $\sum\limits_{i=1}^K\Bigl \lceil\frac{M_i^2R_i^2}{\max(f_i^*-f^*-\frac{\varepsilon}{2},\frac{\varepsilon}{2})^2}\Bigr \rceil$& $O\left(RM\sqrt{KT}\right)$ \\
 Convex $L$-smooth & 1 $\nabla$ & AGD& $\sum\limits_{i=1}^K\left\lceil \sqrt{\frac{L_iR_i^2} {\max(f_i^*-f^*-\frac{\varepsilon}{2}, \frac{\varepsilon}{2})}} \; \right\rceil$ & $O\left(K LR^2\right)$ \\
 $\mu$-convex $M$-Lipschitz & 1 $\nabla$, 1 proj. & PGD & $\sum\limits_{i=1}^K\Bigl \lceil\frac{M_i^2}{\mu_i \max(f_i^*-f^*-\frac{\varepsilon}{2}, \frac{\varepsilon}{2})}\Bigr \rceil$ & $O\left(\frac{M^2}{\mu} K\log{\frac{T}{K}}\right)$\\
 $\mu$-convex $L$-smooth &1 $\nabla$ & AGD & $\sum\limits_{i=1}^K\Bigl \lceil\sqrt{\frac{L_i}{\mu_i}}\log\left(\frac{\mu_i R_i^2}{\max(f_i^*-f^*-\frac{\varepsilon}{2}, \frac{\varepsilon}{2})}\right)\Bigr \rceil$ & $O\left(KR^2 \exp\left\{ -\frac{T}{K\sqrt{\kappa}}\right\}\right)$\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Yuriy Dorn
Aleksandr Katrutsa
Ilgam Latypov
Anastasiia Soboleva

"Functional multi-armed bandit and the best function identification problems" by Yuriy Dorn, Aleksandr Katrutsa, Ilgam Latypov, and Anastasiia Soboleva, arXiv:2503.00509v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l|r}
 Type & Method & BPC ($\downarrow$) \\ \hline
 Autoregressive Backbone& IAF/SCF & 1.88\\
 & AR Argmax Flow & 1.39\\
 & Discrete Flow & \textbf{1.23}\\
 & Autoregressive & \textbf{1.23}\\ \hline
 Non-autoregressive & Mult. Diffusion & $\le$ 1.72\\
 & MAC & $\le$ 1.40\\
 & BFN & $\le$ 1.41\\
 & D3PM Uniform & $\le$ 1.61\\
 & D3PM Absorb & $\le$ 1.45\\ \hline
 Ours (NAR) & SEDD Uniform & $\le$ 1.47\\
 & SEDD Absorb & $\le$ \textbf{1.39}\\
 \end{tabular}
\end{adjustbox}
\caption{\textbf{Bits Per Character on text8.} Our SEDD models achieve second-best overall result (best for non-autoregressive), only being beaten out by the autoregressive model and a discrete flow (which uses an autoregressive model as a backbone) by a small margin. SEDD also substantially improves upon prior the discrete diffusion model D3PM .}
\end{table}
\end{document}

stat

"Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution" by Aaron Lou, Chenlin Meng, and Stefano Ermon, arXiv:2310.16834v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data" by Arif Pathan, arXiv:2506.17244v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ASR performance when training on either purely real or purely synthetic speech}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lr}
 \toprule
 \textbf{Training data} & \textbf{WER} \\
 \midrule
 80h real & 11.4 \\
 80h synth, unclustered, 50 voices & 36.2 \\
 80h synth, clustered, 5 voices & 47.1 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Deblin Bagchi
Shannon Wotherspoon
Zhuolin Jiang
Prasanna Muthukumar

"Speech Synthesis as Augmentation for Low-Resource ASR" by Deblin Bagchi, Shannon Wotherspoon, Zhuolin Jiang, and Prasanna Muthukumar, arXiv:2012.13004v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{amsfonts}
\usepackage{graphicx}
\usepackage{adjustbox}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation studies for the proposed method on seven datasets. The coarse refinement (CR), channel-wise attention (CA), spatial attention (SA), atrous spatial pyramid pooling (ASPP), fine-grained refinement (FR), feature aggregation (FA) and skip connection (SC) are removed from MSIRN.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc|cccc|ccc|c}
 \hline
 \hline
 \multicolumn{2}{c}{} & \multicolumn{4}{|c|}{Coarse branch}&\multicolumn{3}{c|}{Fine branch} & MSIRN \\
 \cline{3-10}
 &Metric&w/o CR&w/o CA&w/o SA&w/o ASPP&w/o FR&w/o FA&w/o SC&Ours \\
 & &(a)&(b)&(c)&(d)&(e)&(f)&(g)& \\
 \hline
 \hline
\multirow{3}*{\rotatebox{90}{\emph{NJU2K}}} &$S_\alpha$↑ &0.902 & 0.909 & 0.912 & 0.910 & 0.900 & \textbf{0.913} & 0.909 & \textbf{0.912} \\
 &$F_\beta$↑ &0.886 & 0.889 & 0.894 & 0.884 & 0.876 & 0.884 & 0.901 & \textbf{0.904} \\
&$\mathcal{M}$↓ &0.042 & 0.043 & 0.041 & 0.043 & 0.047 & 0.044 & 0.039 & \textbf{0.038}\\
\hline
\multirow{3}*{\rotatebox{90}{\emph{LFSD}}} &$S_\alpha$↑ &0.850 & 0.854 & 0.855 & 0.860 & 0.850 & 0.851 & \textbf{0.862} & \textbf{0.862} \\
 &$F_\beta$↑ &0.816 & 0.826 & 0.835 & 0.827 & 0.816 & 0.822 & \textbf{0.842} & 0.838 \\
&$\mathcal{M}$↓ &0.085 & 0.082 & 0.079 & 0.081 & 0.091 & 0.088 & \textbf{0.074} & 0.077 \\
\hline
\multirow{3}*{\rotatebox{90}{\emph{STERE}}} &$S_\alpha$↑ &0.903 & 0.906 & 0.903 & 0.897 & 0.897 & 0.903 & \textbf{0.904} & \textbf{0.904} \\
 &$F_\beta$↑ &0.865 & 0.872 & 0.875 & 0.851 & 0.849 & 0.863 & 0.877 & \textbf{0.892} \\
&$\mathcal{M}$↓ &0.049 & 0.045 & 0.044 & 0.053 & 0.054 & 0.050 & \textbf{0.043} & 0.045 \\
\hline
\multirow{3}*{\rotatebox{90}{\emph{DES}}} &$S_\alpha$↑ &0.929 & 0.930 & 0.920 & 0.929 & 0.917 & 0.925 & 0.926 & \textbf{0.931} \\
 &$F_\beta$↑ &0.896 & 0.896 & 0.896 & 0.882 & 0.873 & 0.887 & 0.899 & \textbf{0.903} \\
&$\mathcal{M}$↓ &0.025 & \textbf{0.023} & 0.025 & 0.026 & 0.029 & 0.027 & 0.024 & \textbf{0.023}\\
\hline
\multirow{3}*{\rotatebox{90}{\emph{NLPR}}} &$S_\alpha$↑ &0.929 & 0.923 & 0.930 & 0.923 & 0.917 & \textbf{0.931} & 0.928 & \textbf{0.931} \\
 &$F_\beta$↑ &0.883 & 0.880 & 0.891 & 0.874 & 0.862 & 0.884 & 0.893 & \textbf{0.894} \\
&$\mathcal{M}$↓ &0.027 & 0.028 & \textbf{0.025} & 0.029 & 0.032 & 0.027 & \textbf{0.025} & \textbf{0.025}\\
\hline
\multirow{3}*{\rotatebox{90}{\emph{SSD}}} &$S_\alpha$↑ &0.873 & 0.879 & 0.871 & 0.871 & 0.876 & 0.879 & 0.878 & \textbf{0.880} \\
 &$F_\beta$↑ &0.823 & 0.839 & 0.829 & 0.818 & 0.822 & 0.840 & 0.836 & \textbf{0.843} \\
&$\mathcal{M}$↓ &0.055 & \textbf{0.049} & 0.050 & 0.054 & 0.056 & 0.051 & 0.051 & 0.050 \\
\hline
\multirow{3}*{\rotatebox{90}{\emph{SIP}}} &$S_\alpha$↑ &0.866 & 0.861 & 0.868 & 0.852 & 0.862 & 0.864 & 0.865 & \textbf{0.879} \\
 &$F_\beta$↑ &0.846 & 0.849 & 0.857 & 0.823 & 0.840 & 0.848 & 0.859 & \textbf{0.867} \\
&$\mathcal{M}$↓ &0.066 & 0.066 & 0.061 & 0.074 & 0.069 & 0.066 & 0.062 & \textbf{0.056}\\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Ze-yu Liu
Jian-wei Liu
Xin Zuo
Ming-fei Hu

"Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection" by Ze-yu Liu, Jian-wei Liu, Xin Zuo, and Ming-fei Hu, arXiv:2201.09574v1, 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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Properties experimentally tested box model with mooring lines }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lll}
 \textbf{Properties} & \textbf{Value} &\textbf{Units} \\
 \midrule
 Box dimension ($L \times W \times H$) & $0.2 \times 0.2 \times 0.132$ & m\\
 Box mass (+ Connections) & $3.16$ & kg \\
 Centre of gravity ($x$,$y$,$z$) & $(0,0,-0.126)$ & m \\
 Box initial draft & $0.0786$ & m \\
 Box moment of inertia ($\mathbf{I}_{xx}, \mathbf{I}_{yy}, \mathbf{I}_{zz}$) & $(0.015, 0.015, 0.021)$ & kg m$^{2}$ \\
 Mooring line diameter & $0.003656$ & m \\
 Mooring line length & $1.455$ & m
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Amirhossein Taran
Seevani Bali
Zeljko Tukovic
Vikram Pakrashi
Philip Cardiff

"A finite volume Simo-Reissner beam method for moored floating body dynamics" by Amirhossein Taran, Seevani Bali, Zeljko Tukovic, Vikram Pakrashi, and Philip Cardiff, arXiv:2504.18248v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Determinants of Net Interest Margin in the Turkish Banking Sector: Does Bank Ownership Matter?" by Fatih Kansoy, arXiv:2506.04384v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Basic CNN Model Accuracy}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
S/N & Dataset & acc & pre & rec & f1 & sup \\
\hline
\hline
1 & MNIST(Image) & 97\% & 96\% & 96\% & 97\% & 97\%\\
\hline
2 & SocoFing(Biometrics) & 82\% & 82\% & 82\% & 83\% & 82\%\\
\hline
3 & CTU-13(Botnet) & 92\% & 93\% & 93\% & 93\% & 93\%\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Olakunle Ibitoye
M. Omair Shafiq
Ashraf Matrawy

"DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning" by Olakunle Ibitoye, M. Omair Shafiq, and Ashraf Matrawy, arXiv:2101.03218v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Monthly pneumonia and influenza deaths time series optimal gamma.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrr}
 \hline
Ahead & $\gamma$\_mape & $\gamma$\_smape\\
 \hline
1.00 & 0.50 & 0.50 \\
2.00 & 0.22 & 0.22 \\
3.00 & 0.08 & 0.08 \\
 \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Short-term prediction of Time Series based on bounding techniques" by Pedro Cadahía and Jose Manuel Bravo Caro, arXiv:2101.10719v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Tianyu Qiu
Eric Ouano
Fernando Palafox
Christian Ellis
David Fridovich-Keil

"PSN Game: Game-theoretic Planning via a Player Selection Network" by Tianyu Qiu, Eric Ouano, Fernando Palafox, Christian Ellis, and David Fridovich-Keil, arXiv:2505.00213v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Wealth values that show when to annuitize.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c}\hline
 \multirow{2}{*}{$\theta$ values} & \multicolumn{2}{c|}{ $\bar c=1$} &
 \multicolumn{2}{c|}{$\bar c=5$} &
 \multicolumn{2}{c}{$\bar c=10$} \\ \cline{2-7}
 &$\eta=0.01$ &$\eta=1.0 $ & $\eta=0.01$ & $\eta=1.0$ & $\eta=0.01$ & $\eta=1.0$ \\ \hline
 0 & $\star$ & $\star$ & $\star$ & $7.77$ & $\star$ & $14.47$ \\ \hline
 0.2 & $\star$ & $\star$ & $\star$ & $8.47$ & $\star$ & $15.53$ \\ \hline
 0.6 & $3.18$ & $2.82$ & $4.94$ & $12.35$ & $5.29$ & $21.17$ \\ \hline
 \multicolumn{7}{c}{$\star$ means that the difference $\Delta V$ is negative for all $w$} \\ \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Retirement spending problem under Habit Formation Model" by S. Kirusheva, H. Huang, and T. S. Salisbury, arXiv:2210.06255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Approximating Solutions to the Knapsack Problem using the Lagrangian Dual Framework" by Mitchell Keegan and Mahdi Abolghasemi, arXiv:2312.03413v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|lc|cc|}
\hline
\multicolumn{2}{|l|}{\multirow{2}{*}{}} & \multicolumn{2}{c|}{\textbf{Dataset}} \\ \cline{3-4}
\multicolumn{2}{|l|}{} & DATASET-1 & DATASET-2 \\ \hline
\multirow{2}{*}{\vtop{\hbox{\strut \textbf{Performance}}\hbox{\strut \textbf{Scenario}}}} & \textbf{Extracted} & 1218 & 6435 \\ \cline{2-4}
 & \textbf{Missing} & 22 & 1301 \\ \hline
\multirow{2}{*}{\vtop{\hbox{\strut \textbf{Cost}}\hbox{\strut \textbf{Evolution}}}} & \textbf{Extracted} & 752 & 3925 \\ \cline{2-4}
 & \textbf{Missing} & 488 & 3811 \\ \hline
\multirow{2}{*}{\vtop{\hbox{\strut \textbf{Cost}}\hbox{\strut \textbf{Composition}}}} & \textbf{Extracted} & 1153 & 6886 \\ \cline{2-4}
 & \textbf{Missing} & 87 & 850 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Results of the Extractor for the different table types.}
\end{table}
\end{document}

cs

Domenico Lembo
Alessandra Limosani
Francesca Medda
Alessandra Monaco
Federico Maria Scafoglieri

"Information Extraction through AI techniques: The KIDs use case at CONSOB" by Domenico Lembo, Alessandra Limosani, Francesca Medda, Alessandra Monaco, and Federico Maria Scafoglieri, arXiv:2202.01178v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|r|r}
\toprule
Signal & Number of Folds & Mean Importance \\
\midrule
StoreCount & 10 & 0.4528 \\
WrTmpCount & 10 & 0.2721 \\
InsCount & 10 & 0.2178 \\
SBExit & 10 & 0.0374 \\
ExitCount & 10 & 0.0111 \\
SBEnter & 9 & 0.0093 \\
InsAddrDiff & 7 & 0.0004 \\
MaxInsAddr & 3 & 0.0002 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Deborah S. Katz
Christopher S. Timperley
Claire Le Goues

"Using Dynamic Binary Instrumentation to Detect Failures in Robotics Software" by Deborah S. Katz, Christopher S. Timperley, and Claire Le Goues, arXiv:2201.12464v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Addressing Positivity Violations in Extending Inference to a Target Population" by Jun Lu and Sanjib Basu, arXiv:2412.09845v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sohrab Andaz
Carson Eisenach
Dhruv Madeka
Kari Torkkola
Randy Jia
Dean Foster
Sham Kakade

"Learning an Inventory Control Policy with General Inventory Arrival Dynamics" by Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, and Sham Kakade, arXiv:2310.17168v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Two-Way Fixed Effects Model Estimates - Percentile\_Price\_Paid}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
 \tabularnewline \midrule \midrule
 Dependent Variable: & \multicolumn{4}{c}{Percentile\_Price\_Paid}\\
 & Dry Dog Food & Dry Cat Food & Dog Hygiene & Cat Hygiene \\
 Model: & (1) & (2) & (3) & (4)\\
 \midrule
 \emph{Variables}\\
 $\mathcal{I}\{\textrm{Adopter}_{i}\} \times \mathcal{I}\{ t \geq T_{i}^{\textrm{adopt}}\}$ & -1.437$^{**}$ & -3.318$^{**}$ & -0.9569 & -4.104$^{***}$\\
 & (0.4141) & (0.9727) & (0.6351) & (1.100)\\
 \midrule
 \emph{Fixed-effects}\\
 Customer & Yes & Yes & Yes & Yes\\
 YearMonth & Yes & Yes & Yes & Yes\\
 \midrule
 \emph{Fit statistics}\\
 Observations & 99,233 & 27,623 & 40,183 & 42,596\\
 R$^2$ & 0.86731 & 0.61190 & 0.50654 & 0.42914\\
 Within R$^2$ & 0.00057 & 0.00135 & 0.00010 & 0.00126\\
 \midrule \midrule
 \multicolumn{5}{l}{\emph{Clustered (Customer \& YearMonth) standard-errors in parentheses}}\\
 \multicolumn{5}{l}{\emph{Signif. Codes: ***: 0.001, **: 0.01, *: 0.05}}\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

"How does online shopping affect offline price sensitivity?" by Shirsho Biswas, Hema Yoganarasimhan, and Haonan Zhang, arXiv:2506.15103v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Mutual information between registered brain volumes and the brain atlas on ten test subjects using direct patient registration and inpainted volume registration.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c}
\hline
Methods& Sub1 & Sub2 & Sub3 & Sub4 & Sub5 & Sub6 & Sub7 & Sub8 & Sub9 & Sub10 \\
\hline\hline
Direct registration	&0.303&0.311&0.308&0.324&0.315&0.299&0.309&0.303&0.317&0.308\\
Inpainted registration &0.309&0.311&0.309&0.324&0.316&0.304&0.312&0.313&0.320&0.312\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Xiaofeng Liu
Fangxu Xing
Chao Yang
C. -C. Jay Kuo
Georges ElFakhri
Jonghye Woo

"Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology" by Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Georges ElFakhri, and Jonghye Woo, arXiv:2101.06775v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|}
\hline
\textbf{Project Site - Capacity (MW)} & \textbf{Payback period (Years)} \\ \hline
RI - 30 & 15.2 \\ \hline
MD - 252 & 11.4 \\ \hline
MA - 804 & 5.1 \\ \hline
NY - 888 & 6.6 \\ \hline
VA - 2640 & 13.6 \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Economic payback period for the projects of interest}
\end{table}
\end{document}

q-fin

Apoorva Bademi
Miriam Stevens
Isha Sura
Shweta Singh

"Estimating the spatial economic and environmental impact of planned offshore wind energy in the USA using Environmentally Extended Multiregional Input-Output analysis" by Apoorva Bademi, Miriam Stevens, Isha Sura, and Shweta Singh, arXiv:2508.15012v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\small The value and the infeasitility at the $k$th iteration.}
\begin{tabular}{|c|c|c|c|}
 \hline
 Method& Complexity&
 val&
 infeas\\
\hline
SG& $\mathcal{O}(\varepsilon^{-2})$ &$f_0(x^{(k)})$ & $\max\{\overline{f}(x^{(k)}),0\}$\\
\hline
SingleDSG& $\mathcal{O}(\varepsilon^{-2})$ &$f_0(\overline{x}^{(k)})$& $\max\{\overline{f}(\overline{x}^{(k)}),0\}$\\
\hline
MultiDSG& $\mathcal{O}(\varepsilon^{-2})$ &$f_0(\overline{x}^{(k)})$ &$\|F(\overline{x}^{(k)})\|_2+\|A\overline{x}^{(k)}-b\|_2$\\
\hline
PDS&$\mathcal{O}(\varepsilon^{-2r})\,,\,\forall\ r>1$& $f_0(x^{(k)})$& $\|F({x}^{(k)})\|_2+\|A{x}^{(k)}-b\|_2$ \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

"Comparing different subgradient methods for solving convex optimization problems with functional constraints" by Thi Lan Dinh and Ngoc Hoang Anh Mai, arXiv:2101.01045v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \hline\hline
 & \multicolumn{2}{c}{Fraction of hits in cone} \\
 Network & 50\% Prob. Working Point & 95\% Prob. Working Point\\
 \hline
 Spatial & 0.07 & 0.75 \\
 Cluster & 0.006 & 0.70 \\
 Combined & 0.001 & 0.39 \\
 Cluster ($n_{ch0} > 1$) & 0.003 & 0.09 \\
 Combined ($n_{ch0} > 1$) & 0.001 & 0.05 \\
 \hline\hline
 \end{tabular}
\end{adjustbox}
\caption{Efficiency and purity numbers at two possible working points, based on Figure~.}
\end{table}
\end{document}

stat

Patrick J. Fox
Shangqing Huang
Joshua Isaacson
Xiangyang Ju
Benjamin Nachman

"Beyond 4D Tracking: Using Cluster Shapes for Track Seeding" by Patrick J. Fox, Shangqing Huang, Joshua Isaacson, Xiangyang Ju, and Benjamin Nachman, arXiv:2012.04533v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
\textbf{Annual discount rate} &
$\dfrac{\sqrt{\operatorname{cov}(R_{B,1}(0), R_{B,2}(0))}}{\mathbb{E}[V_B(0)]}$ &
$\dfrac{\sqrt{\operatorname{cov}(R_{G,1}(0), R_{G,2}(0))}}{\mathbb{E}[V_G(0)]}$ \\
\hline
0.04 & 0.186 & 0.134 \\
0.05 & 0.238 & 0.162 \\
0.06 & 0.328 & 0.207 \\
0.07 & 0.529 & 0.284 \\
0.08 & 1.389 & 0.462 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Quirini Lorenzo
Vannucci Luigi
Quirini Giovanni

"Modelling Prepayment and Default under Changing Credit Market Conditions for a Net Present Value Analysis" by Quirini Lorenzo, Vannucci Luigi, and Quirini Giovanni, arXiv:2508.07774v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market" by Kasymkhan Khubiev and Mikhail Semenov, arXiv:2503.08696v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Mass }& \textbf{Dimensions }& \textbf{Power Consumption } & \textbf{Dipole Moment} \\
\hline
$30$ g & $8$ cm x $8$ cm x $10$ cm& $300$ mW & $0.12$ A$\text{m}^2$ \\
\hline
\end{tabular}
\caption{Magnetorquers Main Properties}
\end{table}
\end{document}

eess

ADCS Preliminary Design For GNB

Alessio Bocci
Giovanni Mingari Scarpello

"ADCS Preliminary Design For GNB" by Alessio Bocci and Giovanni Mingari Scarpello, arXiv:2103.02017v1, 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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
 \toprule
 Contamination $\delta$ & $0$ & $0.5$ & $1$\\
 \midrule
 Global shift &$16/1000$& $995/1000$ & $1000/1000$ \\
 Local shift of level $l = 10$ & - & $360/1000$ & $986/1000$ \\
 Local shift of level $l = 13$ & - &$769/1000$& $1000/1000$ \\
 Local shift of level $l = 15$ & - & $203/1000$ & $917/1000$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Power of the performed statistical tests with confidence level $1-\alpha = 0.95$.}
\end{table}
\end{document}

math

Łukasz Delong
Selim Gatti
Mario V. Wüthrich

"Calibration Bands for Mean Estimates within the Exponential Dispersion Family" by Łukasz Delong, Selim Gatti, and Mario V. Wüthrich, arXiv:2503.18896v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
\hline
\textbf{Package / Software} & \textbf{Version} \\ \hline
Ubuntu & 18.04 \\
Python3 & 3.6.9 \\
Numpy & 1.18.1 \\
Tensorflow & 2.1.0 \\
Keras & 2.3.1 \\
Scikit-learn & 0.22.1 \\
Pandas & 0.25.3 \\
Autograd & 1.3 \\
Matplotlib & 3.1.1 \\
Pickle & (Available with Python3) \\
Argparse & (Available with Python3) \\
Time & (Available with Python3) \\
\hline
\end{tabular}
\end{adjustbox}
\caption{Software dependencies for running provided codes.}
\end{table}
\end{document}

stat

"ZORB: A Derivative-Free Backpropagation Algorithm for Neural Networks" by Varun Ranganathan and Alex Lewandowski, arXiv:2011.08895v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
 \hline
 Training data parameter & Value\\
 \hline
 starting slice location $x_{0}$& 2.0 \\
 slice distance $\Delta x$ & 1.0\\
 number of slices $m$ & 98 \\
 number of $y$ points $n$ & 128 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{CNN model training data parameters.}
\end{table}
\end{document}

math

Kazuko W. Fuchi
Eric M. Wolf
Christopher R. Schrock
Philip S. Beran

"Acceleration of RANS Solver Convergence via Initialization with Wake Extension Models" by Kazuko W. Fuchi, Eric M. Wolf, Christopher R. Schrock, and Philip S. Beran, arXiv:2501.14699v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the data environment.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cccccccc}
\toprule
Sample $S$ & & Covariate $X$ & & Treatment $D$ & & Outcome $Y$ & & Remotely sensed $R$ \\
\hline
Experimental & & \checkmark & & \checkmark & & Missing && \checkmark \\
Observational: Complete & & \checkmark & & \checkmark & & \checkmark && \checkmark \\
Observational: Incomplete & & \checkmark & & Missing or deterministic & & \checkmark && \checkmark \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Ashesh Rambachan
Rahul Singh
Davide Viviano

"Program Evaluation with Remotely Sensed Outcomes" by Ashesh Rambachan, Rahul Singh, and Davide Viviano, arXiv:2411.10959v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Effect sizes (Cohen's d) and post-hoc power tests for pairwise group comparisons}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llccccccc}
 \hline
 \hline
 \multirow{2}{*}{\textbf{Comparison groups}} & & & \multicolumn{2}{c}{Frequency} & & \multicolumn{2}{c}{Magnitude} & \\ \cline{4-5} \cline{7-8}
 & & & $d$ & $1-\beta$ & & $d$ & $1-\beta$ & \\
 \hline
 Human & Machine & & 0.017 & 0.035 & & -0.120 & 0.069 & \\
 Human & Human Black Box & & 0.168 & 0.096 & & 0.673 & 0.577 & \\
 Machine & Machine Black Box & & 0.181 & 0.110 & & 1.503 & 0.999& \\
 Human Black Box & Machine Black Box & & -0.369 & 0.367 & & -0.751 & 0.662 & \\
 \hline
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty" by Marius Protte and Behnud Mir Djawadi, arXiv:2507.15439v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
			CI & $n$ & Coverage & Mean Width\\
			\hline
			Normal& $10^7$ & 82/100 & $0$ \\
			Poisson & $10^7$ & 100/100& $1.476*10^{-6}$ \\
			Heuristic & $10^7$ & 100/100& $1.476*10^{-6}$ \\
			Normal& $10^8$ & 93/100 & 0 \\
			Poisson & $10^8$ & 100/100& $1.476*10^{-7}$\\
			Heuristic & $10^8$ & 100/100& $1.476*10^{-7}$
		\end{tabular}
\end{adjustbox}
\caption{Results for Dynamic Uniform with $\gamma=1.5$ and $r=3$ for large sample sizes $n$. These are based on $N=100$ replications.}
\end{table}
\end{document}

math

"Confidence Intervals Using Turing's Estimator: Simulations and Applications" by Jie Chang, Michael Grabchak, and Jialin Zhang, arXiv:2503.14313v1, 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{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|cc}
 & \multirow{2}{*}{NLL ($\downarrow$)} & Sampling \\
 & & time ($\downarrow$) \\
 \hline
 & \multicolumn{2}{c}{DW4} \\ \hline
 $E(n)$-NF & 1.72 $\pm$ 0.01 &\textbf{0.024} ms \\ %
 OT-FM & \textbf{1.70} $\pm$ 0.02 &0.034 ms\\ %
 E-OT-FM & \textbf{1.68} $\pm$ 0.01 &0.033 ms \\ %
 $E(n)$-FFF (ours) & \textbf{1.68} $\pm$ 0.01&\textbf{0.026} ms \\ %
 \hline

 & \multicolumn{2}{c}{LJ13} \\ \hline
 $E(n)$-NF & -16.28 $\pm$ 0.04 &0.27 ms \\ %
 OT-FM & -16.54 $\pm$ 0.03 &0.77 ms \\ %
 E-OT-FM & -16.70 $\pm$ 0.12 &0.72 ms \\ %
 $E(n)$-FFF (ours) & \textbf{-17.09} $\pm$ 0.16&\textbf{0.11} ms \\ %
 \hline

 & \multicolumn{2}{c}{LJ55} \\ \hline
 OT-FM & -88.45 $\pm$ 0.04 & 40 ms \\ %
 E-OT-FM & \textbf{-89.27} $\pm$ 0.04 &40 ms \\ %
 $E(n)$-FFF (ours) & -88.72 $\pm$ 0.16 &\textbf{2.1} ms \\ %
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

stat

Felix Draxler
Peter Sorrenson
Lea Zimmermann
Armand Rousselot
Ullrich Köthe

"Free-form Flows: Make Any Architecture a Normalizing Flow" by Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, and Ullrich Köthe, arXiv:2310.16624v2, 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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
& AFEW & FPHA & NTU RGB+D\ & HDM05\\
\midrule
Gyrovector & $23.23$\tiny$\pm 0.98$& $61.33 $\tiny$\pm 4.74$ & $40.77$\tiny$\pm 3.10$ & $5.69 $\tiny$\pm 2.15$\\
Aff-Inv & $35.17$ \tiny $\pm 1.78$&$\mathbf{66.53} $\tiny$\mathbf{\pm 1.64}$ & $41.00$ \tiny $\pm 0.50$& $67.91$ \tiny${\pm 1.27}$\\
Log-Euc&$\mathbf{36.38}$\tiny $\mathbf{\pm 1.29}$& $64.58 $\tiny$\pm 0.98$& $\mathbf{42.99 }$\tiny$\mathbf{\pm 0.23}$& $\mathbf{69.80}$\tiny$\mathbf{ \pm 1.51}$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\caption{We show that the Riemannian ResNet model with M\"obius addition struggles to reach the classification accuracies of our exponential map design. The difference is most pronounced on HDM05, where the gyrovector model struggles to learn meaningful representations.}
\end{table}
\end{document}

stat

Riemannian Residual Neural Networks

Isay Katsman
Eric Ming Chen
Sidhanth Holalkere
Anna Asch
Aaron Lou
Ser-Nam Lim
Christopher De Sa

"Riemannian Residual Neural Networks" by Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, and Christopher De Sa, arXiv:2310.10013v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The summary of performance test with Dirichlet condition with $\theta=\pi/2$. Severn scenarios (1,5,11-12,14,22,24) approach to $y_b$, eighteen scenarios (2-4,6-10,13,15-21,25) go to the other solution $y_s$, one scenario (23) diverges.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcccc}
		status & number & $\max|y''-f|$ & $\max|y_{opt}-y_{b}|$ & $\max|y_{rk4}-y_{b}|$ \\ \hline\hline
		$\to y_b$ & 17 & 1.0E-07 & 4.1E-10 & 8.1E-07 \\
		$\to y_s$ & 7 & 1.1E-07 & 4.4E-01 & 2.2E-01 \\
		$diverge$ & 1 & diverge & diverge & diverge \\ \hline
	\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Trigonometric Interpolation Based Optimization for Second Order Non-Linear ODE with Mixed Boundary Conditions" by Xiaorong Zou, arXiv:2504.19280v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
\hline
$n$ & $2,000$ & $5,000$ & $12,000$ \\ \hline
Mean of the $\hat{\sigma}_\theta^2$'s & $12.0$ & $11.7$ & 11.9 \\ \hline
90\% Monte-Carlo CR of the $\hat{\sigma}_\theta^2$'s & $(10.2, 13.7)$ & $(10.2, 13.5)$ & (10.3, 13.4) \\ \hline
\end{tabular}
\end{adjustbox}
\caption{The bootstrap provides a good estimate of the true asymptotic variance $\sigma_\theta^2 = 11.455$.}
\end{table}
\end{document}

math

Kevin Christian Wibisono
Debarghya Mukherjee
Moulinath Banerjee
Ya'acov Ritov

"Estimation and Inference for the Average Treatment Effect in a Score-Explained Heterogeneous Treatment Effect Model" by Kevin Christian Wibisono, Debarghya Mukherjee, Moulinath Banerjee, and Ya'acov Ritov, arXiv:2504.17126v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\bf Shape Functions for Quadratic Line Elements}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
\hline
local node & $\{N\}_m$ & $\{\Phi_i\}_m$ $(i=x,y,z)$ \\
\hline
$m = 1$ & $L_1(2L_1-1)$ & $\Phi_{i1}$ \\
$m = 2$ & $L_2(2L_2-1)$ & $\Phi_{i2}$ \\
$m = 3$ & $L_3=4L_1L_2$ & $\Phi_{i3}$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Sara Mesquita
Cláudio Haupt Vieira
Lília Perfeito
Joana Gonçalves-Sá

"Learning from pandemics: using extraordinary events can improve disease now-casting models" by Sara Mesquita, Cláudio Haupt Vieira, Lília Perfeito, and Joana Gonçalves-Sá, arXiv:2101.06774v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccccc}
					\toprule
					& \multicolumn{3}{c} {BoW} &
					\multicolumn{3}{c}{Reduced Sample BoW} & \multicolumn{3}{c}{tf-idf} \\
					& (1) & (2) & (3) & (1) & (2) & (3) & (1) & (2) & (3) \\
					\midrule
					sex cr. x placebo & 0.220 & & & 0.166 & & & -0.941 & & \\
					& (0.584) & & & (0.675) & & & (0.831) & & \\
					sex cr. x post & & 0.535 & & & 0.442 & & & 1.697** & \\
					& & (0.424) & & & (0.438) & & & (0.821) & \\
					sex cr. x post & & & 0.806 & & & 0.836* & & & 4.148* \\
					& & & (0.534) & & & (0.505) & & & (2.254) \\
					post & X & X & X & X & X & X & X & X & X \\
					court FE & X & X & X & X & X & X & X & X & X \\
					\# words & X & X & X & X & X & X & X & X & X \\
					\bottomrule
			\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

"The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach" by Henrika Langen, arXiv:2209.00409v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Learning an Inventory Control Policy with General Inventory Arrival Dynamics" by Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola, Randy Jia, Dean Foster, and Sham Kakade, arXiv:2310.17168v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Taehyeun Kim
Anouck Girard
Ilya Kolmanovsky

"CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor" by Taehyeun Kim, Anouck Girard, and Ilya Kolmanovsky, arXiv:2412.03710v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Raphael Flepp
Tim Pawlowski
Travis Richardson

"Suspense and Surprise in European Football" by Raphael Flepp, Tim Pawlowski, and Travis Richardson, arXiv:2506.21253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Overall generalization performance of the teacher model in $\%$ for ATop-$k$ accuracy and ADBA score.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{r|c|c|c|c}
\hline
Metric &W/o MHA & With MHA & Self-KD & Optimal \\
\hline
Test loss & $1.141$ & $1.050$ & $\bf 1.016$ & $0.8158$ \\
\hline
 ATop-$1$ & $40.77$ & $42.91$ & $\bf 44.94$ & $50.20$ \\
 \hline
 ATop-$3$ & $77.44$ & $79.97$ & $\bf 81.45$ & $ 87.15$ \\
 \hline
 ATop-$5$ & $92.31$ & $93.35$ & $\bf 94.63$ & $96.81$ \\
\hline
 ADBA & $93.50$ & $94.47$ & $9\bf 5.00$ & $96.63$ \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

eess

Mengyuan Ma
Nhan Thanh Nguyen
Nir Shlezinger
Yonina C. Eldar
A. Lee Swindlehurst
Markku Juntti

"Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications" by Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger, Yonina C. Eldar, A. Lee Swindlehurst, and Markku Juntti, arXiv:2509.11419v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{||c|c|c|c||}
 \hline
 \textbf{Algorithm} & \textbf{Unbounded} & \textbf{Adaptive} & \textbf{Regret} \\
 \hline\hline
 & No & Yes & $\widetilde O\Big(\sqrt{\sum_{t=1}^T\|\ell_t\|^2_{2}}\Big) $\\
 & Yes & No & $\widetilde O\Big(\ell_{\infty}\sqrt{nT}\Big)$\\
 Non-Adaptive & Yes & No & $\widetilde O\Big(\ell_{\infty}\sqrt{nT}+\sqrt{n \sum_{t=1}^T \|\ell_t\|^2_2}\Big)$\\
 Adaptive & Yes & Yes & $\widetilde O\Big(\ell_{\infty}\sqrt{n\sum_{t=1}^T \|\ell_t\|_1}+\sqrt{n \sum_{t=1}^T \|\ell_t\|^2_2}\Big)$\\
 \textbf{\texttt{UMAB-G} Non-Adaptive} & Yes & No & $\widetilde O\Big( \ell_\infty^- \sqrt{nT} +\sqrt{n \sum_{t=1}^T \|\ell_t\|_\infty^2}\Big)$\\
 \textbf{\texttt{UMAB-G} Adaptive} & Yes & Yes & $\widetilde O\Big(\ell_{\infty}\sqrt{n \sum_{t=1}^T \|\ell_t\|_\infty}+\sqrt{n \sum_{t=1}^T \|\ell_t\|_\infty^2}\Big)$\\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Comparison between our results and previous works\protect}
\end{table}
\end{document}

stat

"Improved Algorithms for Adversarial Bandits with Unbounded Losses" by Mingyu Chen and Xuezhou Zhang, arXiv:2310.01756v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Graph signal denoising using $t$-shrinkage priors" by Sayantan Banerjee and Weining Shen, arXiv:2012.13696v1, 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}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Option Pricing Results: Microstructure-Enhanced vs Black-Scholes}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Parameter/Model} & \textbf{Value} & \textbf{Notes} \\
\midrule
\textbf{Option Specifications} & & \\
Underlying Asset & SPY & S\&P 500 ETF \\
Current Spot Price & \$600.00 & At-the-money \\
Strike Price & \$600.00 & At-the-money \\
Time to Expiration & 30 days & 0.0822 years \\
Risk-free Rate & 5.0\% & Annualized \\
Tree Time Steps & 10 & Computational constraint \\
Time per Step & 3.0 days & $\Delta t = 0.008219$ \\
& & \\
\textbf{Pricing Results} & & \\
Initial Price (Unscaled) & \$0.38 & Time scaling error \\
Microstructure-Enhanced Price & \$15.41 & After proper scaling \\
Black-Scholes Price & \$17.87 & Traditional benchmark \\
Absolute Difference & \$2.46 & 13.79\% lower \\
Relative Difference & -13.79\% & Economically significant \\
& & \\
\textbf{Implementation Details} & & \\
Scaling Factor Applied & $\sqrt{807.8}$ & Minute to tree time \\
Historical Volatility & 24.3\% & Annualized from data \\
Model Implied Volatility & 21.0\% & From scaled factors \\
Volatility Difference & 3.2pp & Model efficiency gain \\
& & \\
\textbf{Computational Performance} & & \\
Tree Nodes Generated & 2,047 & Exponential growth \\
Tree Construction Time & 87.31 sec & 10 steps only \\
Pricing Time & 0.004 sec & Per option \\
State Mapping Method & Simplified & Closest p\_RF match \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

q-fin

Akash Deep
Chris Monico
W. Brent Lindquist
Svetlozar T. Rachev
Frank J. Fabozzi

"Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach" by Akash Deep, Chris Monico, W. Brent Lindquist, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2507.16701v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparisons of CorLoc on OD100 dataset (subset of Internet Images dataset). `-' indicates that the authors have not provided those results in the respective paper.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|lll|l}
\hline\hline
Method & Airplane & Car & Horse & Mean \\ \hline\hline
Joulin et al. & 32.93 & 66.29 & 54.84 & 51.35 \\
Joulin et al. & 57.32 & 64.04 & 52.69 & 58.02 \\
Rubinstein et al. & 74.39 & 87.64 & 63.44 & 75.16 \\
Tang et al. & 71.95 & 93.26& 64.52 & 76.58 \\
SCDA & 87.80 & 86.52 & 75.37 & 83.20 \\
Cho et al. & 82.93 & 94.38 & 75.27 & 84.19 \\
Vora et al. & 43.9 & 65.17 & 45.16 & 51.41 \\
Vo et al. & - & - & - & 90.2 \\
DDT & \textbf{91.46} & 95.51 & 77.42 & 88.13 \\
DDT+ & \textbf{91.46} & 94.38 & 76.34 & 87.39 \\
Our ASOC & 90.24 & \textbf{98.88} & \textbf{86.02} & \textbf{91.71} \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Koteswar Rao Jerripothula
Prerana Mukherjee

"ASOC: Adaptive Self-aware Object Co-localization" by Koteswar Rao Jerripothula and Prerana Mukherjee, arXiv:2201.11547v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Jake S. Rhodes
Adele Cutler
Kevin R. Moon

"Geometry- and Accuracy-Preserving Random Forest Proximities" by Jake S. Rhodes, Adele Cutler, and Kevin R. Moon, arXiv:2201.12682v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Prakrati Kushwah
Anupama Ghorai
Jitraj Saha

"Convergence-rate and error analysis of sectional-volume average method for the collisional breakage equation with multi-dimensional modelling" by Prakrati Kushwah, Anupama Ghorai, and Jitraj Saha, arXiv:2504.15365v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.