\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
Graph-size &
solutions without normalization & solutions
with normalization \\
\hline
8 & 4 & 9\\
9 & 4 & 8\\
10 & 3 & 8\\
\hline
\end{tabular}
\caption{The table shows the number of runs vs the number of feasible solutions with and without normalization.}
\end{table}
\end{document}

math

Venkat Padmasola
Zhaotong Li
Rupak Chatterjee
Wesley Dyk

"Solving the Traveling Salesman Problem via Different Quantum Computing Architectures" by Venkat Padmasola, Zhaotong Li, Rupak Chatterjee, and Wesley Dyk, arXiv:2502.17725v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
 & parameters & properties of Arnold tongues \\ \hline
Domain I & $A<B-1$ & very thin tongues \\ \hline
Domain II & $B-1<A<B+1$ & very thin spacings between tongues, no constrictions \\ \hline
Domain III & $A>B+1$ & parquet-like tongues with constrictions \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Artem Alexandrov
Alexey Glutsyuk
Alexander Gorsky

"Phase-locking in dynamical systems and quantum mechanics" by Artem Alexandrov, Alexey Glutsyuk, and Alexander Gorsky, arXiv:2504.20181v1, 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.


math

"Two sample test for covariance matrices in ultra-high dimension" by Xiucai Ding, Yichen Hu, and Zhenggang Wang, arXiv:2312.10796v1, 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}{lccccc}
 \toprule
		Model	&	lr	&
		weight decay	&	T\_0	&	T\_mult	&	Epochs	\\
\cmidrule{1-6}
Japanese - (No HN) 	&	2e-5	&	5e-3	&	1	&	2	&	100	\\
\ \ distance \\
Japanese - (HN) 	&	2e-5	&	5e-3	&	1	&	2	&	30	\\
\ \ distance \\
Simplified Chinese - (No HN) 	&	2e-5	&	5e-3	&	1	&	2	&	30	\\
\ \ distance \\
Simplified Chinese - (HN) 	&	2e-5	&	5e-3	&	1	&	2	&	30	\\
\ \ distance \\
Traditional Chinese - (No HN) 	&	2e-5	&	5e-3	&	1	&	2	&	30	\\
\ \ distance \\
Traditional Chinese - (HN) 	&	2e-5	&	5e-3	&	1	&	2	&	30	\\
\ \ distance \\
Korean - (No HN) 	&	2e-5	&	5e-3	&	1	&	2	&	60	\\
\ \ distance \\
Korean - (HN) 	&	2e-5	&	5e-3	&	1	&	2	&	30	\\
\ \ distance \\
Ancient Chinese - (No HN) 	&	2e-5	&	5e-3	&	300	&	1	&	200	\\
\ \ distance \\
Ancient Chinese - (HN) 	&	2e-5	&	5e-3	&	300	&	3	&	24	\\
\ \ distance \\
 \bottomrule
 \end{tabular}
\caption{\textbf{Training Hyperparameters}. This table reports the training hyperparameters used for the models. The lr stands for learning rate, weight decay represents the weight decay factor, T\_0 is the number of steps until the first restart of the learning rate scheduler, T\_mult denotes the factor by which T\_0 is multiplied at each restart, and Epochs indicates the total number of training epochs. Parameters not mentioned here use PyTorch defaults. HN denotes offline hard-negative mining.}
\end{table}
\end{document}

q-fin

Xinmei Yang
Abhishek Arora
Shao-Yu Jheng
Melissa Dell

"Quantifying Character Similarity with Vision Transformers" by Xinmei Yang, Abhishek Arora, Shao-Yu Jheng, and Melissa Dell, arXiv:2305.14672v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
Example & RBF Kernel & Mat{\' e}rn Kernel & RF(Cauchy) & RF(Gaussian) \\\hline
Burgers' & 3.76 & {\bf 2.03} & 3.82 & 2.70 \\\hline
Darcy & 4.93 & 4.47 & {\bf 3.08} & 3.74 \\\hline
Helmholtz & 5.05 & 3.76 & 6.66 & {\bf 3.63} \\\hline
Structural Mechanics & 10.31 & 7.73 & {\bf 7.67} & 8.71 \\\hline
Navier-Stokes & 1.09 & {\bf 0.91} & 2.54 & 1.06 \\\hline
\end{tabular}
\caption{Summary of relative test errors of random features methods and kernel methods.}
\end{table}
\end{document}

math

Chunyang Liao
Deanna Needell
Hayden Schaeffer

"Cauchy Random Features for Operator Learning in Sobolev Space" by Chunyang Liao, Deanna Needell, and Hayden Schaeffer, arXiv:2503.00300v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Table Type Styles}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Table}&\multicolumn{3}{|c|}{\textbf{Table Column Head}} \\
\cline{2-4}
\textbf{Head} & \textbf{\textit{Table column subhead}}& \textbf{\textit{Subhead}}& \textbf{\textit{Subhead}} \\
\hline
copy& More table copy$^{\mathrm{a}}$& & \\
\hline
\multicolumn{4}{l}{$^{\mathrm{a}}$Sample of a Table footnote.}
\end{tabular}
\end{table}
\end{document}

eess

G. Encinas-Lago
M. Rossanese
V. Sciancalepore
Marco Di Renzo
Xavier Costa-Perez

"RIShield: Enabling Electromagnetic Blackout in Radiation-Sensitive Environments" by G. Encinas-Lago, M. Rossanese, V. Sciancalepore, Marco Di Renzo, and Xavier Costa-Perez, arXiv:2312.13203v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Neetu Gupta
S. K. Neogy
Qazi J. Azhad
Bhagwati Devi

"Inference of Half Logistic Geometric Distribution Based on Generalized Order Statistics" by Neetu Gupta, S. K. Neogy, Qazi J. Azhad, and Bhagwati Devi, arXiv:2502.01255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
\textbf{Dataset} & \textbf{Weight} \\ \hline
MNIST & 1/21 \\ \hline
CelebA & 4/21 \\ \hline
CIFAR10 & 3/21 \\ \hline
CIFAR10 Gray & 2/21 \\ \hline
Bigset & 6/21 \\ \hline
Bigset Gray & 5/21 \\ \hline
\end{tabular}
\caption{Weights of Datasets}
\end{table}
\end{document}

eess

Jonathan Zhao
Matthew Westerham
Mark Lakatos-Toth
Zhikang Zhang
Avi Moskoff
Fengbo Ren

"OpenICS: Open Image Compressive Sensing Toolbox and Benchmark" by Jonathan Zhao, Matthew Westerham, Mark Lakatos-Toth, Zhikang Zhang, Avi Moskoff, and Fengbo Ren, arXiv:2103.00652v4, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Rewards for a real agent at each state-action pair in the MTD problem.}
\begin{tabular}{c|lll}\toprule
 State & \texttt{wait} & \texttt{defend} & \texttt{reset}\\ \midrule
 \texttt{N} & $R$ & $R-C_D$ & $R - C_R$\\
 \texttt{T} & $R-C_T$ & $R-C_D -C_T$ & $R - C_R$\\
 \texttt{E} & $R-C_E$ & $R-C_D -C_E$ & $R - C_R$\\
 \texttt{B} & $R-C_B$ & $R-C_D -C_B$& $R - C_R$ \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Yerin Kim
Alexander Benvenuti
Bo Chen
Mustafa Karabag
Abhishek Kulkarni
Nathaniel D. Bastian
Ufuk Topcu
Matthew Hale

"Deceptive Sequential Decision-Making via Regularized Policy Optimization" by Yerin Kim, Alexander Benvenuti, Bo Chen, Mustafa Karabag, Abhishek Kulkarni, Nathaniel D. Bastian, Ufuk Topcu, and Matthew Hale, arXiv:2501.18803v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Estimation problems for some perturbations of the independence copula" by Martial Longla and Mous-Abou Hamadou, arXiv:2308.14282v1, 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{Lychee picking points dataset split used for training.}%title
\begin{tabular}{ccc}% four columns
\hline %begin the first line
{ } & {Point cloud} & {Picking point ground truth} \\
\hline %begin the second line
Train & 640 & 1964 \\
Validation & 160 & 516 \\
\hline %begin the third line
\end{tabular}
\end{table}
\end{document}

cs

Gengming Zhang
Hao Cao
Kewei Hu
Yaoqiang Pan
Yuqin Deng
Hongjun Wang
Hanwen Kang

"Accurate Cutting-point Estimation for Robotic Lychee Harvesting through Geometry-aware Learning" by Gengming Zhang, Hao Cao, Kewei Hu, Yaoqiang Pan, Yuqin Deng, Hongjun Wang, and Hanwen Kang, arXiv:2404.00364v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average Gross Salary Per Occupation}
\begin{tabular}{ccc}
 \toprule
 \textbf{Occupation} & \textbf{Average Gross Monthly Wage} & \textbf{Level} \\
 \midrule
 Climate Change Officer & € 3,338.00 & Educated \\
 Teacher & € 3,359.00 & Educated \\
 Programmer & € 3,650.00 & Educated \\
 Human Resource & € 3,777.00 & Educated \\
 Software Developer & € 3,815.00 & Educated \\
 Researcher (WO) & € 3,858.00 & Educated \\
 Accountancy \& Finance & € 3,863.00 & Educated \\
 Basic Doctors & € 3,887.00 & Educated \\
 Marketing & € 3,915.00 & Educated \\
 Architect & € 4,251.00 & Educated \\
 Management & € 4,706.00 & Educated \\
 Occupational Physician & € 5,000.00 & Educated \\
 Psychologist & € 5,563.00 & Educated \\
 Lawyer & € 6,500.00 & Educated \\
 General Practitioner & € 8,150.00 & Educated \\
 Facilitair & € 2,326.00 & Mixed \\
 Administration & € 2,771.00 & Mixed \\
 Designer & € 2,844.00 & Mixed \\
 Sales & € 3,081.00 & Mixed \\
 Media & € 3,220.00 & Mixed \\
 Nurse & € 3,406.00 & Mixed \\
 Social Worker & € 3,909.00 & Mixed \\
 Pedagogical & € 2,238.00 & Practically \\
 Beauty Care & € 2,332.00 & Practically \\
 Customer Service & € 2,489.00 & Practically \\
 Production & € 2,515.00 & Practically \\
 Caretaker & € 2,555.00 & Practically \\
 Doctor's Assistant & € 2,615.00 & Practically \\
 Transportation & € 2,855.00 & Practically \\
 Carpenter & € 2,900.00 & Practically \\
 Painter & € 3,000.00 & Practically \\
 Electrician & € 3,050.00 & Practically \\
 Plumber & € 3,100.00 & Practically \\
 Security & € 3,116.00 & Practically \\
 Contractor & € 3,350.00 & Practically \\
 Executor (Construction) & € 3,900.00 & Practically \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"Simulating Tertiary Educational Decision Dynamics: An Agent-Based Model for the Netherlands" by Jean-Paul Daemen and Silvia Leoni, arXiv:2505.01142v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Fusion rule for the Haagerup fusion category \(\mathcal{H}_3\). In this table, the elements of the first column are fused with the elements of the first row (``column labels'' $\otimes$ ``row labels'').}
\begin{tabular}{l|l|l|l|l|l|l}
\hline
\hline
$\otimes$ & $\mathbf{1}$ & $\alpha$ & $\alpha^2$ & $\rho$ & ${}_{\alpha}\rho$ & ${}_{\alpha^2}\rho$\\
\hline
$\mathbf{1}$ & $\mathbf{1}$ & $\alpha$ & $\alpha^2$ & $\rho$ & ${}_{\alpha}\rho$ & ${}_{\alpha^2}\rho$\\
\hline
$\alpha$ & $\alpha$ & $\alpha^2$ & $\mathbf{1}$ & ${}_{\alpha}\rho$ & ${}_{\alpha^2}\rho$ & $\rho$\\
\hline
$\alpha^2$ & $\alpha^2$ & $\mathbf{1}$ & $\alpha$ & ${}_{\alpha^2}\rho$ & $\rho$ & ${}_{\alpha}\rho$\\
\hline
$\rho$ & $\rho$ & ${}_{\alpha^2}\rho$ & ${}_{\alpha}\rho$ & $\mathbf{1} \oplus \rho \oplus {}_{\alpha}\rho \oplus {}_{\alpha^2}\rho$ & $\alpha^2 \oplus \rho \oplus {}_{\alpha}\rho \oplus {}_{\alpha^2}\rho$ & $\alpha \oplus \rho \oplus {}_{\alpha}\rho \oplus {}_{\alpha^2}\rho$\\
\hline
${}_{\alpha}\rho$ & ${}_{\alpha}\rho$ & $\rho$ & ${}_{\alpha^2}\rho$ & $\alpha \oplus \rho \oplus {}_{\alpha}\rho \oplus {}_{\alpha^2}\rho$ & $\mathbf{1} \oplus \rho \oplus {}_{\alpha}\rho \oplus {}_{\alpha^2}\rho$ & $\alpha^2 \oplus \rho \oplus {}_{\alpha}\rho \oplus {}_{\alpha^2}\rho$ \\
\hline
${}_{\alpha^2}\rho$& ${}_{\alpha^2}\rho$ & ${}_{\alpha}\rho$ & $\rho$ & $\alpha^2 \oplus \rho \oplus {}_{\alpha}\rho \oplus {}_{\alpha^2}\rho$ & $\alpha \oplus \rho \oplus {}_{\alpha}\rho \oplus {}_{\alpha^2}\rho$ & $\mathbf{1} \oplus \rho \oplus {}_{\alpha}\rho \oplus {}_{\alpha^2}\rho$ \\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

math

"Quantum Cluster State Model with Haagerup Fusion Category Symmetry" by Zhian Jia, arXiv:2412.19657v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

cs

Haoyu Dong
Tram Thi Minh Tran
Rutger Verstegen
Silvia Cazacu
Ruolin Gao
Marius Hoggenmüller
Debargha Dey
Mervyn Franssen
Markus Sasalovici
Pavlo Bazilinskyy
Marieke Martens

"Exploring Holistic HMI Design for Automated Vehicles: Insights from a Participatory Workshop to Bridge In-Vehicle and External Communication" by Haoyu Dong, Tram Thi Minh Tran, Rutger Verstegen, Silvia Cazacu, Ruolin Gao, Marius Hoggenmüller, Debargha Dey, Mervyn Franssen, Markus Sasalovici, Pavlo Bazilinskyy, and Marieke Martens, arXiv:2403.19153v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
Type & Feature \\
\hline \hline
\multirow{3}{4em}{caller feature} & caller\_basic\_block\_count \\
& caller\_conditionally\_executed\_blocks \\
& caller\_users \\
\hline
\multirow{3}{4em}{callee feature} & callee\_basic\_block\_count \\
& callee\_conditionally\_executed\_blocks \\
& callee\_users \\
\hline
\multirow{3}{4em}{call site feature} & callsite\_height \\
& cost\_estimate \\
& number\_constant\_params \\
\hline
\multirow{2}{4em}{call graph feature} & edge\_count \\
& node\_count \\
\hline
\end{tabular}
\caption{Features for Inlining for Size}
\end{table}
\end{document}

cs

Mircea Trofin
Yundi Qian
Eugene Brevdo
Zinan Lin
Krzysztof Choromanski
David Li

"MLGO: a Machine Learning Guided Compiler Optimizations Framework" by Mircea Trofin, Yundi Qian, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, and David Li, arXiv:2101.04808v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"No-Regret Caching via Online Mirror Descent" by T. Si Salem, G. Neglia, and S. Ioannidis, arXiv:2101.12588v5, 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{Characteristics of the annotated STM corpus with 110 abstracts per concept type in terms of number of scientific concept mentions, number of coreferent mentions, number of coreference clusters and singleton clusters, and the number of overall clusters. MIXED denotes clusters consisting of mentions with different concept types, NONE denotes coreference mentions and clusters without a scientific concept mention.}
\begin{tabular}{l|rrrr|rr|r}
 & Data & Material & Method & Process & MIXED & NONE & Total \\ \hline
\# mentions & 1,658 & 2,099 & 258 & 2,112 & 0 & 0 & 6,127 \\
\# coreferent mentions & 351 & 910 & 101 & 510 & 0 & 705 & 2,577 \\ \hline
\# coreference clusters & 153 & 339 & 30 & 198 & 50 & 138 & 908 \\
\# singleton clusters & 1,307 & 1,189 & 157 & 1,602 & 0 & 0 & 4,255 \\ \hline
\# overall clusters & 1,460 & 1,528 & 187 & 1,800 & 50 & 138 & 5,163 \\
\end{tabular}
\end{table}
\end{document}

cs

Arthur Brack
Daniel Uwe Müller
Anett Hoppe
Ralph Ewerth

"Coreference Resolution in Research Papers from Multiple Domains" by Arthur Brack, Daniel Uwe Müller, Anett Hoppe, and Ralph Ewerth, arXiv:2101.00884v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Description of the non-articulated vehicle parameters}
\begin{tabular}{lll}
\hline
Description & Parameter & Value\\
\hline
Distance from the front axle to the CG & $a_{1}$ & $3.19\hspace{1mm}m$\\
Distance from the rear axle to the CG & $b_{1}$ & $1.62\hspace{1mm}m$\\
Vehicle wheelbase & $l_{1}$ & $4.81\hspace{1mm}m$\\
Vehicle mass & $m_{1}$ & $16030\hspace{1mm}kg$\\
Payload mass & $m_{2}$ & $35000\hspace{1mm}kg$\\
Vehicle moment of inertia & $J_{1}$ & $215717\hspace{1mm}kg \hspace{1mm} m^{2}$\\
Front axle cornering stiffness & $c_{1}$ & $540419\hspace{1mm}N/rad$ \\
Rear axle cornering stiffness & $c_{2}$ & $1064462\hspace{1mm}N/rad$\\
\multirow{12}{*}{Gear ratios} & $\xi_1$ & 11.32 \\
 & $\xi_2$ & 9.164 \\
 & $\xi_3$ & 7.194 \\
 & $\xi_4$ & 5.823 \\
 & $\xi_5$ & 4.632 \\
 & $\xi_6$ & 3.750 \\
 & $\xi_7$ & 3.019 \\
 & $\xi_8$ & 2.444 \\
 & $\xi_9$ & 1.918 \\
 & $\xi_{10}$ & 1.553 \\
 & $\xi_{11}$ & 1.235 \\
 & $\xi_{12}$ & 1.000 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Kenny A. Q. Caldas
Filipe M. Barbosa
Junior A. R. Silva
Tiago C. Santos
Iago P. Gomes
Luis A. Rosero
Denis F. Wolf
Valdir Grassi

"Autonomous driving of trucks in off-road environment" by Kenny A. Q. Caldas, Filipe M. Barbosa, Junior A. R. Silva, Tiago C. Santos, Iago P. Gomes, Luis A. Rosero, Denis F. Wolf, and Valdir Grassi, arXiv:2312.07382v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Tensor Topic Modeling Via HOSVD

"Tensor Topic Modeling Via HOSVD" by Yating Liu and Claire Donnat, arXiv:2501.00535v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cc|cc|cc}
 &
\multicolumn{ 6}{c}{$GAP_{P_{sub}}\%$} \\
No &
\multicolumn{ 2}{c}{$\gamma=10$} &
\multicolumn{ 2}{c}{$\gamma=30$} &
\multicolumn{ 2}{c}{$\gamma=50$} \\
\hline
1 & 3.52 & 0.35 & \textbf{2.07} & 0.35 & \textbf{1.70} & 0.35 \\
2 & 2.34 & 0.39 & \textbf{1.28} & \textbf{0.36} & \textbf{1.07} & \textbf{0.34}\\
3 & 0.55 & 1.23 & \textbf{0.41} & \textbf{0.68} & \textbf{0.39} & \underline{0.73} \\
4 & 0.82 & 0.46 & \textbf{0.43} & \textbf{0.37} & \underline{0.47} & \underline{0.40} \\
5 & 0.78 & 0.57 & \textbf{0.62} & 0.57 & \textbf{0.58} & \textbf{0.49} \\
\end{tabular}
\caption{Chute2, $GAP_{P_{sub}}\%$ for test problem Bi6.1 and $\gamma \in{\{10,30,50\}}$}
\end{table}
\end{document}

math

"A general framework for providing interval representations of Pareto optimal outcomes for large-scale bi- and tri-criteria MIP problems" by Grzegorz Filcek and Janusz Miroforidis, arXiv:2401.00292v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
 \toprule
 Hyperparameters & Value at Initialization \\
 \midrule
 $T$ & 1 \\
 $\Delta t$ & 0.1 \\
 $Q$ & $\left(\begin{matrix}
 -1 & 1 \\
 1 & -1
 \end{matrix}\right)$ \\
 $\xi$ & 0.5 \\
 $x_0$ & 1 \\
 $z$ & 1.4 \\
 $\eta$ for OC loss & $(1\times 10^4, 1\times 10^4, 1\times 10^4, 1\times 10^4)$ \\
 $(r_1, r_2)$ & $(0.01, 0.05)$ \\
 $\theta_{true}$ & $(\sigma_{true, 1} = 0.2, \sigma_{true,2} = 0.3, \rho_{true,1} = 0.95, \rho_{true,2} = -0.833)$ \\
 $\theta^{(0)}$ & (0.1, 0.1, 0.5, -0.5) \\
 \bottomrule
 \end{tabular}
\caption{Configuration of the More Comprehensive Simulation}
\end{table}
\end{document}

stat

"Exploratory Mean-Variance Portfolio Optimization with Regime-Switching Market Dynamics" by Yuling Max Chen, Bin Li, and David Saunders, arXiv:2501.16659v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The attack performance of DB-DFMS against ResNet-34-8x trained on CIFAR-10 with different batch sizes. The accuracy of the victim model is 0.930, and the clone model is ResNet-18-8x.}
\begin{tabular}{lcccccccccc}
\toprule
\rowcolor{white}
Batch Size & 16 & 32 & 64 & 128 & 200 & 256 & 300 & 400 & 512 & 1024 \\
\midrule
Accuracy & 0.790 & 0.849 & 0.874 & 0.875 & 0.890 & 0.885 & 0.887 & 0.867 & 0.860 & 0.829\\
Agreement & 0.815 & 0.876 & 0.904 & 0.909 & 0.928 & 0.921 & 0.923 & 0.895 & 0.888 & 0.849\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Yiyong Liu
Rui Wen
Michael Backes
Yang Zhang

"Efficient Data-Free Model Stealing with Label Diversity" by Yiyong Liu, Rui Wen, Michael Backes, and Yang Zhang, arXiv:2404.00108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zero-Leverage Puzzle

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluating SCIP performance for different values of the strong branching parameters $L$ and $K$.}
\begin{tabular}{lrrrrrrr}
 \toprule
 &\multicolumn{2}{c}{affected} & \multicolumn{2}{c}{affected-solved} \\
 \cmidrule(lr){2-3}\cmidrule(lr){4-5}
 Setting & \# &time(\%)& time(\%) &nodes(\%) \\
 \midrule
 $L=9, K=10^6$ (default) & - & 100.0 &100.0 &100.0 \\
 $L=7, K=10^6$& 278 & 104.3 &104.4 &104.8\\
 $L=11, K=10^6$& 269 & 102.1 &104.0 & 105.0\\
 $L=11, K=1.2\cdot10^6$& 271 & 102.4 &105.2&106.5\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

math

Gioni Mexi
Somayeh Shamsi
Mathieu Besançon
Pierre Le Bodic

"Probabilistic Lookahead Strong Branching via a Stochastic Abstract Branching Model" by Gioni Mexi, Somayeh Shamsi, Mathieu Besançon, and Pierre Le Bodic, arXiv:2312.07041v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccc}
 \hline
 & GP-COMB & GP-LIN & GP-RBF & COK-SPHE & COK-EXP \\
 \hline
 Nord-Nord Est & \textbf{+1.1} & \textbf{+1.27} & \textbf{+1.9} & -0.2 & -0.2\\
 Est & \textbf{+1.56} & \textbf{+1.32} & -0.4 & +0.1 & +0.1 \\
 Ouest & +0.2 & +0.2 & \textbf{-0.8} & -0.14 & -0.14 \\
 Sud-Sud Est & \textbf{-1.2} & -0.24 & \textbf{+0.8} & -0.1 & -0.1 \\
 \hline
 \end{tabular}
\caption{Difference de recouvrement moyens entre les points du jeu de données sans augmentation et les jeux de données aumgentés par les différentes techniques d'interpolation. Les différences signicatives sont en gras. }
\end{table}
\end{document}

stat

Frederick Fabre Ferber
Dominique Gay
Jean-Christophe Soulie
Jean Diatta
Odalric-Ambrym Maillard

"Interpolation pour l'augmentation de donnees : Application à la gestion des adventices de la canne a sucre a la Reunion" by Frederick Fabre Ferber, Dominique Gay, Jean-Christophe Soulie, Jean Diatta, and Odalric-Ambrym Maillard, arXiv:2501.12400v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Decomposition set-up for the structural reliability 4R model (UHSS stands for ultra-high steel strength).}
\begin{tabular}{lllllllllll}
\hline
\multicolumn{3}{l}{Residual stress} & & \multicolumn{3}{l}{Stress ratio} & & \multicolumn{3}{l}{Steel grade} \\ \cline{1-3} \cline{5-7} \cline{9-11}
State & min & max & & State & min & max & & State & min & max \\ \hline
Low & $-400$ & 100 & & Reversed & $-1.2$ & $-0.25$ & & Mild & 255 & 657 \\
Medium & 100 & 650 & & Pulsating & $-0.25$ & 0.7 & & UHSS & 657 & 1060 \\
High & 650 & 950 & & - & - & - & & - & - & - \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Mariia Kozlova
Antti Ahola
Pamphile T. Roy
Julian Scott Yeomans

"Simple binning algorithm and SimDec visualization for comprehensive sensitivity analysis of complex computational models" by Mariia Kozlova, Antti Ahola, Pamphile T. Roy, and Julian Scott Yeomans, arXiv:2310.13446v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Architecture details of the discriminator network.}
\begin{tabular}{ccc}
 \toprule
 \textbf{Input} & \textbf{Output} & \textbf{Layer} \\
 \midrule
 10 & 500 & Linear \\
 500 & 500 & ReLU \\
 500 & 500 & Linear \\
 500 & 500 & ReLU \\
 500 & 1 & Linear \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Guillaume Quétant
Pavlo Molchanov
Slava Voloshynovskiy

"TwinTURBO: Semi-Supervised Fine-Tuning of Foundation Models via Mutual Information Decompositions for Downstream Task and Latent Spaces" by Guillaume Quétant, Pavlo Molchanov, and Slava Voloshynovskiy, arXiv:2503.07851v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary statistics and top-1 performance for all datasets. Notation: $K$ -- number of classes, $N$ -- number of samples, Top-1 acc. -- top-1 accuracy of hierarchical classifier.}
\begin{tabular}{ccccc|c}
 \toprule
 \textsc{Dataset} & $K$ & $N_{train}$ & $N_{cal}$ & $N_{test}$ & \textsc{Top-1 acc.} \\
 \midrule
 \textsc{CIFAR-10} & 10 & 50000 & 5000 & 5000 & 0.8817 \\
 \textsc{Caltech-101} & 97 & 4338 & 2169 & 2169 & 0.9039 \\
 \textsc{Caltech-256} & 256 & 14890 & 7445 & 7445 & 0.7578 \\
 \textsc{PlantCLEF 2015} & 1000 & 91758 & 10723 & 10723 & 0.4156 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

Thomas Mortier
Alireza Javanmardi
Yusuf Sale
Eyke Hüllermeier
Willem Waegeman

"Conformal Prediction in Hierarchical Classification" by Thomas Mortier, Alireza Javanmardi, Yusuf Sale, Eyke Hüllermeier, and Willem Waegeman, arXiv:2501.19038v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

"Kernel-learning parameter prediction and evaluation in algebraic multigrid method for several PDEs" by Juan Zhang, Junyue Luo, Fangfang Zhang, and Xiaoqiang Yue, arXiv:2504.14930v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|}
\hline
\textbf{Parameter} & \textbf{Value} \\
\hline
n\_estimators & 50 \\
max\_depth & 8 \\
min\_samples\_split & 2 \\
min\_samples\_leaf & 1 \\
max\_features & 0.8 \\
bootstrap & true \\
\hline
\end{tabular}
\caption{Random Forest Hyperparameters}
\end{table}
\end{document}

stat

"Custom Loss Functions in Fuel Moisture Modeling" by Jonathon Hirschi, arXiv:2501.10401v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Summary of the results for the best models for each technique.}
\begin{tabular}{lcc}
\toprule
\textbf{Attractor} & \textbf{NS1 to NS4} & \textbf{NS1 to NS9} \\ \midrule
EDA & 17.45 & 21.68 \\
EDA + CSV & 17.13 & 21.34 \\
TA & 14.77 & 18.78 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Lahiru Samarakoon
Samuel J. Broughton
Marc Härkönen
Ivan Fung

"Transformer Attractors for Robust and Efficient End-to-End Neural Diarization" by Lahiru Samarakoon, Samuel J. Broughton, Marc Härkönen, and Ivan Fung, arXiv:2312.06253v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistical significance of networks trained with different learning strategies across all motion categories at a 5\% significance level. \\\hspace{\textwidth} s = shuffled, c = curriculum, S = Skull, C = Clean, M = Motion, n = no}
\begin{tabular}{c|c|c|c|c|c} % @ property can be modified
\toprule
 & UNet Attention & UNet 2.5D & LSTM UNet & LSTM ResNet34 & LSTM CorNet \\
\midrule
$s_{nSC}$ \quad vs. \quad $s_{SC}$ & 0.665 & 0.068 & 0.211 & 0.058 & 0.724 \\
$s_{SC}$ \quad vs. \quad $s_{SM}$ & 0.0001 & 0.0001 & 0.0007 & 0.001 & 0.001 \\
$s_{SM}$ \quad vs. \quad $c_{SM}$ & 0.0001 & 0.0001 & 0.0001 & 0.001 & 0.004 \\
$s_{SC}$ \quad vs. \quad $c_{SM}$ & 0.525 & 0.125 & 0.059 & 0.688 & 0.508 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Tejas Sudharshan Mathai
Yi Wang
Nathan Cross

"Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI" by Tejas Sudharshan Mathai, Yi Wang, and Nathan Cross, arXiv:2010.06027v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Yiwei Shi
Jingyu Hu
Yu Zhang
Mengyue Yang
Weinan Zhang
Cunjia Liu
Weiru Liu

"Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering" by Yiwei Shi, Jingyu Hu, Yu Zhang, Mengyue Yang, Weinan Zhang, Cunjia Liu, and Weiru Liu, arXiv:2501.18501v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zero-Leverage Puzzle


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll|ll}
 \toprule
 Dataset & Flipping? & Cutout? & ResNet-18 & \texttt{airbench96} \\
 \midrule
 CIFAR-10 & Yes & No & 95.55\% & 95.61\% \\
 CIFAR-10 & Yes & Yes & 96.01\% & 96.05\% \\
 CIFAR-100 & Yes & No & 77.54\% & 79.27\% \\
 CIFAR-100 & Yes & Yes & 78.04\% & 79.76\% \\
 CINIC-10 & Yes & No & 87.58\% & 87.78\% \\
 CINIC-10 & Yes & Yes & not measured & 88.22\% \\
 SVHN & No & No & 97.35\% & 97.38\% \\
 SVHN & No & Yes & not measured & 97.64\% \\
 \bottomrule
\end{tabular}
\caption{Comparison of \texttt{airbench96} to standard ResNet-18 training across a variety of tasks. We directly apply \texttt{airbench96} to each task without re-tuning any hyperparameters (besides turning off flipping for SVHN).}
\end{table}
\end{document}

cs

"94% on CIFAR-10 in 3.29 Seconds on a Single GPU" by Keller Jordan, arXiv:2404.00498v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{r|cc}
 \toprule
 & \multicolumn{2}{c}{WLS} \\
 & Coefficient & Robust Error \\\midrule
 Constant & -1.2423 & (0.2137) \\
 Small & -0.0099 & (0.0185) \\
 SA50\%, Large & -0.0527 & (0.0279) \\
 SA50\%, Small & -0.1058 & (0.0253) \\
 SA100\%, Small & 0.0197 & (0.0113) \\
 Demand & 0.0246 & (0.0039) \\
 Demand$^2$ & -0.0011 & (0.0002) \\
 Number of bidders & -0.0245 & (0.0069) \\
 Number of bidders$^2$ & 0.0009 & (0.0005) \\
 log(USDA price) & 1.7384 & (0.2090) \\
 log(wholesale price) & 0.6616 & (0.0390) \\
 SDVOSB & 0.0157 & (0.0039) \\ \midrule
 $n=12677$ &\multicolumn{2}{c}{$R^2=0.6129$} \\ \bottomrule
 \end{tabular}
\caption{Replicating Log of Winning Price Model (Table 4)}
\end{table}
\end{document}

q-fin

"Set-Asides in USDA Food Procurement Auctions" by Ni Yan and WenTing Tao, arXiv:2302.05772v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Probabilistic Overview of Probabilities of Default for Low Default Portfolios by K. Pluto and D. Tasche" by Andrius Grigutis, arXiv:2303.06148v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline
 $m$ &1 & 2 & 3 & 4 & 5 & 6 & 7 & 8\\
\hline
 $H_2(m)$ & 0 & 1 & 4 & 12 & 30 & 73 & 172 & 400\\
 \hline
 $h_2(m)$ & 0.000 & 0.250 & 0.333 & 0.375 & 0.375 & 0.380 & 0.384 & 0.391\\
 \hline
\end{tabular}
\end{table}
\end{document}

math

"An intermediate case of exponential multivalued forbidden matrix configuration" by Wallace Peaslee, Attila Sali, and Jun Yan, arXiv:2312.11446v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
 & mean & std \cr
 \hline
 $(a_c-a_2)/a_2$ &$-$5.0803$\times$10$^{-4}$ &0.0778 \cr
 $e_c-e_2$ &$-$0.003 &0.0341 \cr
 $i_c-i_2$ &$-$3.8397$\times$10$^{-4}$ &0.0045 \cr
 $\Omega_c-\Omega_2$ &0.0019 &0.0232 \cr
 $\omega_c-\omega_2$ &$-$0.0021 &0.0244 \cr
 \hline
 $\delta({\cal T}_c,{\cal T}_2)$ &0.0264 &0.0877 \cr
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Erica Scantamburlo
Giovanni F. Gronchi
Giulio Baù

"Orbit determination from one position vector and a very short arc of optical observations" by Erica Scantamburlo, Giovanni F. Gronchi, and Giulio Baù, arXiv:2312.14764v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}
 \toprule
 & \multicolumn{2}{c}{Scraped} & & \multicolumn{2}{c}{Thomson-Reuters} \\
 \midrule
 & In-Sample & Out-of-Sample & & In-Sample & Out-of-Sample \\
 \midrule
 \rowcolor[rgb]{ .875, .89, .898} orig\_score & 45.9*** & 23.5*** & & 15.7*** & 13.4*** \\
 std. error & 10.06 & 7.54 & & 2.15 & 5.03 \\
 \rowcolor[rgb]{ .875, .89, .898} t-stat & (4.556) & (3.116) & & (7.328) & (2.661) \\
 rep\_score & 49.3*** & 23.5 & & 20.9*** & 22.36*** \\
 \rowcolor[rgb]{ .875, .89, .898} std. error & 10.83 & 7.60 & & 2.64 & 6.15 \\
 t-stat & (4.55) & (3.086) & & (7.881) & (2.867) \\
 \midrule
 \rowcolor[rgb]{ .875, .89, .898} Wald test & 1.587 & 0.000391 & & 24.239 & 3.315 \\
 p-value & 0.2077 & 0.9842 & & 0.0000 & 0.0686 \\
 \midrule
 \rowcolor[rgb]{ .875, .89, .898} No. of obs. & 199650 & 59212 & & 314376 & 49440 \\
 \bottomrule
 \end{tabular}
\caption{\textbf{Stacked regressions to compare the coefficients of the original GPT score and the replaced GPT score for statistically significant differences.} The formula of the regression is given by equation (). Standard errors are clustered by time and firm. Each Wald test tests equality of the corresponding orig\_score and rep\_score coefficients.}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Values} of $ G_{-\frac{1}{2},b}\left(r(T)\right)$ special function as defined by Equation where $a=-\frac{1}{2}$.}
\begin{tabular}{cc}
			\hline
			\boldmath{$b$}	& \boldmath{$G_{-\frac{1}{2},b}\left(r(T)\right)$} \\
			\hline
			$1$	 			& $-\frac{\delta\,b_0}{2}\sqrt{r(T)}\left(r^{-1}(T) -2\delta\,b_0\right)\exp\left[-\delta\,b_0\,r(T)\right]$ \\
			\hline
			$-1$ 			& $\frac{-\exp\left[\frac{\delta\,b_0}{r(T)}\right]}{\left(r(T)+2\delta\,b_0\right)^2}\sqrt{r(T)}\Bigg[\frac{3}{2}r^2(T)+{\delta\,b_0}\,r(T) -\frac{2\left[\frac{3}{4}\,r^{2}(T)+b_0^2\right]}{\left[r(T)+2\delta\,b_0\right]} \left(r(T)+\delta\,b_0\right)\Bigg] $ \\
			\hline
			$2$	 			& $\frac{\exp\left[-\frac{\delta\,b_0}{2} r^2(T)\right]}{2\left(1+2\delta\,b_0\,r^2(T)\right)^{\frac{1}{2}}} \Bigg[\frac{\left(3-2\delta\,b_0r^2(T)\right)}{r^{5/2}(T)}-\frac{\left[3r^{-4}(T)-4b_0^2\right]}{\left[r^{-2}(T)+2\delta\,b_0\right]}{\left(r^{-2}(T)+\delta\,b_0\right)}\left[r(T)\right]^{3/2}\Bigg]$ \\
			\hline
			$-2$ 			& $\frac{-\left[r(T)\right]^{1/2}\exp\left[\frac{\delta\,b_0}{2r^2(T)}\right]}{2\left(r^2(T)+2\delta\,b_0\right)^{3/2}}\Bigg[5r^{4}(T)+2\delta\,b_0\,r^2(T)
			-\frac{\left[5r^{4}(T)+4b_0^2\right]}{\left[r^{2}(T)+2\delta\,b_0\right]}\left(r^{2}(T)+\delta\,b_0\right)\Bigg]$ \\
			\hline
			$-3$	 		& $\frac{-r^{\frac{7}{2}}(T)\,\exp\left[\frac{\delta\,b_0}{3r^3(T)}\right]}{2\left(r^3(T)+2\delta\,b_0\right)^{\frac{4}{3}}}\Bigg[\left(7r^{3}(T)+2\delta\,b_0\right) -\frac{\left[7r^{6}(T)+4b_0^2\right]}{\left[r^{3}(T)+2\delta\,b_0\right]}\frac{\left(r^{3}(T)+\delta\,b_0\right)}{r^{3}(T)}\Bigg]$ \\
			\hline
			$4$	 			& $\frac{\exp\left[-\frac{\delta\,b_0}{4}\,r^4(T)\right]}{2\left[r(T)\right]^{\frac{9}{2}}\left(1+2\delta\,b_0 r^4(T)\right)^{\frac{3}{4}}}\Bigg[\left[7-\delta\,b_0 r^4(T)\right] -\frac{\left[7-4b_0^2r^8(T)\right]}{\left[1+2\delta\,b_0 r^4(T)\right]}\left[1+\delta\,b_0 r^4(T)\right]\Bigg]$ \\
			\hline
			$-4$ 			& $\frac{-\left[r(T)\right]^{\frac{9}{2}}\exp\left[\frac{\delta\,b_0}{4r^4(T)}\right]}{\left(r^4(T)+2\delta\,b_0\right)^{\frac{5}{4}}}\Bigg[\frac{9}{2}r^{4}(T)+\delta\,b_0 -\frac{\left[\frac{9}{4}\,r^{8}(T)+b_0^2\right]}{\left[\frac{1}{2}\,r^{4}(T)+\delta\,b_0\right]}\frac{\left(r^{4}(T)+\delta\,b_0\right)}{r^4(T)}\Bigg]$ \\
			\hline
			$6$	 			& $\frac{\exp\left[-\frac{\delta\,b_0}{6}r^6(T)\right]}{2\left[r(T)\right]^{\frac{13}{2}}\left(1+2\delta\,b_0 r^6(T)\right)^{\frac{5}{6}}}
			\Bigg[11-2\delta\,b_0 r^{6}(T) -\frac{\left[11-4b_0^2 r^{12}(T)\right]}{\left[1+2\delta\,b_0 r^6(T)\right]}\left(1+\delta\,b_0 r^6(T)\right)\Bigg]$ \\
			\hline
			$-6$ 			& $\frac{-\left[r(T)\right]^{\frac{13}{2}}\exp\left[\frac{\delta\,b_0}{6r^6(T)}\right]}{2\left(r^6(T)+2\delta\,b_0\right)^{\frac{7}{6}}}
			\Bigg[13 r^{6}(T)+2\delta\,b_0 -\frac{\left[13 r^{12}(T)+4b_0^2\right]}{\left[r^{6}(T)+2\delta\,b_0\right]}\frac{\left(r^{6}(T)+\delta\,b_0\right)}{r^{6}(T)}\Bigg]$ \\
			\hline
			$-8$	 		& $\frac{-\left[r(T)\right]^{\frac{17}{2}}\exp\left[\frac{\delta\,b_0}{8 r^8(T)}\right]}{2\left(r^8(T)+2\delta\,b_0\right)^{\frac{9}{8}}}\Bigg[17 r^{8}(T)+2\delta\,b_0 -\frac{\left[17 r^{16}(T)+4b_0^2\right]}{\left[r^{8}(T)+2\delta\,b_0\right]}\frac{\left(r^{8}(T)+\delta\,b_0\right)}{r^{8}(T)}\Bigg]$ \\
			\hline
		\end{tabular}
\end{table}
\end{document}

math

"Scalar Field Static Spherically Symmetric Solutions in Teleparallel $F(T)$ Gravity" by Alexandre Landry, arXiv:2503.14465v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
 \toprule
 Metric & Logistic Regression & GBM \\
 \midrule
 Accuracy & 0.79 & 0.89 \\
 \midrule
 Confusion Matrix &
 $\begin{pmatrix}
 & \text{0} & \text{1} \\
 \text{0} & 12502 & 1697 \\
 \text{1} & 3579 & 7222
 \end{pmatrix}$
 &
 $\begin{pmatrix}
 & \text{0} & \text{1} \\
 \text{0} & 25872 & 2427 \\
 \text{1} & 3243 & 18458
 \end{pmatrix}$ \\
 \midrule
 Precision (RZ: Class 0) & 0.8805 & 0.9142 \\
 Recall (RZ: Class 0) & 0.7775 & 0.8886 \\
 Precision (NRZ: Class 1) & 0.8097 & 0.8838 \\
 Recall (NRZ: Class 1) & 0.6686 & 0.8506 \\
 \midrule
 Logistic Model's Parameters: & \multicolumn{2}{l}{$b_0=-7.2757, b_1=1.1029, b_2=5.6285$} \\
 \bottomrule
 \end{tabular}
\caption{Comparison of Logistic Regression and Gradient Boosting Machines for Predicting Trajectory Reaching Zero}
\end{table}
\end{document}

q-fin

Matej Steinbacher
Mitja Steinbacher
Matjaz Steinbacher

"Bimodal Dynamics of the Artificial Limit Order Book Stock Exchange with Autonomous Traders" by Matej Steinbacher, Mitja Steinbacher, and Matjaz Steinbacher, arXiv:2508.17837v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Technical details in the GrEASE experiments for all datasets.}
\begin{tabular}{cccccc}
 & Moon & Cifar10 & Appliances & KMNIST & Parkinsons\\
 \hline
 Batch size & 2048 & 2048 & 2048 & 2048 & 512 \\
 n\_neighbors & 20 & 20 & 20 & 20 & 5 \\
 Initial LR & \(10^{-2}\) & \(10^{-2}\) & \(10^{-3}\) & \(10^{-3}\) & \(10^{-2}\)\\
 Optimizer & ADAM & ADAM & ADAM & ADAM & ADAM\\
 \end{tabular}
\end{table}
\end{document}

stat

"Generalizable Spectral Embedding with an Application to UMAP" by Nir Ben-Ari, Amitai Yacobi, and Uri Shaham, arXiv:2501.11305v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llccccc}
\toprule
 & & $\beta$ & $\pi_{gm}$ & $\pi_{alt}$ & $\pi_{coop}$ & $\pi_{free}$ \\ \midrule
\multirow{3}{*}{$T_1$} & True & 0.900 & 0.188 & 0.281 & 0.375 & 0.156 \\
 & Mean Estimate & 0.899 & 0.189 & 0.281 & 0.372 & 0.158 \\
 & s.d. & 0.011 & 0.015 & 0.010 & 0.019 & 0.004 \\
 & & & & & & \\
\multirow{3}{*}{$T_2$} & True & 0.900 & 0.188 & 0.281 & 0.375 & 0.156 \\
 & Mean Estimate & 0.900 & 0.180 & 0.286 & 0.378 & 0.156 \\
 & s.d. & 0.014 & 0.070 & 0.043 & 0.046 & 0.001 \\
 & & & & & & \\
\multirow{3}{*}{$T_3$} & True & 0.900 & 0.219 & 0.344 & 0.438 & - \\
 & Mean Estimate & 0.899 & 0.219 & 0.343 & 0.438 & - \\
 & s.d. & 0.011 & 0.000 & 0.010 & 0.010 & - \\
 & & & & & & \\ \bottomrule
\end{tabular}
\caption{The Table reports the results from the SFEM simulation for the low noise ($\beta=0.90$). $\pi^{k}$ is the mixing probability (proportion of subjects) of strategy $k$. \(gm\) stands for the G\&M type, \(alt\) for the altruist, \(coop\) for the conditional co-operator, and \(free\) for the free rider. The frequency of types is set to 6 (0.188), 9 (0.281), 12 (0.375) and 5 (0.156) subjects for each type respectively, in $T_1$ and $T_2$, and 7 (0.219), 11 (0.344) and 14 (0.438) subjects in $T_3$ for the G\&M the altruist, and the conditional co-operator.}
\end{table}
\end{document}

q-fin

Chowdhury Mohammad Sakib Anwar
Konstantinos Georgalos

"Position Uncertainty in a Sequential Public Goods Game: An Experiment" by Chowdhury Mohammad Sakib Anwar and Konstantinos Georgalos, arXiv:2308.00179v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{RERR (\%) on LibriSpeech dev-clean when greedy decoding was applied. We evaluated the performance at each stage of TutorNet in the case of RNN$_{DS}$ $\rightarrow$ CNN$_{Mini}$.}
\begin{tabular}{clcc}
\toprule
Teacher model &\hspace{8mm}TutorNet & WER (\%) & RERR (\%) \\
\midrule
&SKD & 7.32 & 15.47\\
RNN$_{DS1}$&SKD+RKD w/o $M_{FW}$ & 6.40 & 26.10 \\
&SKD+RKD w/ $M_{FW}$ & 6.26 & 27.71\\
\midrule
&SKD & 6.65 & 23.21\\
RNN$_{DS2}$&SKD+RKD w/o $M_{FW}$ & 6.32 & 27.02\\
&SKD+RKD w/ $M_{FW}$ & 6.25 & 27.83\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Ji Won Yoon
Hyeonseung Lee
Hyung Yong Kim
Won Ik Cho
Nam Soo Kim

"TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition" by Ji Won Yoon, Hyeonseung Lee, Hyung Yong Kim, Won Ik Cho, and Nam Soo Kim, arXiv:2008.00671v2, 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{Equivariant Anti-Aliasing - ablation}
\begin{tabular}{llll}
\hline
Group & Sub Groups & Equivariant & Non-equivariant \\ \hline
D16 & D8, D4, D2 & & \\
C16 & C8, C4, C2 & & \\ \hline
\end{tabular}
\end{table}
\end{document}

math

"Group Downsampling with Equivariant Anti-aliasing" by Md Ashiqur Rahman and Raymond A. Yeh, arXiv:2504.17258v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Arbaz Khan
Felipe Lepe
David Mora
Jesus Vellojin

"Finite element analysis of the nearly incompressible linear elasticity eigenvalue problem with variable coefficients" by Arbaz Khan, Felipe Lepe, David Mora, and Jesus Vellojin, arXiv:2312.10715v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c} % <-- Alignments: 1st column left, 2nd middle and 3rd right, with vertical lines in between
 \textbf{Method} & \textbf{VCTK$\uparrow$} & \textbf{LibriTTS$\uparrow$}\\
 \hline
 GT & \textbf{4.05 $\pm$ 0.05}& \textbf{ 4.10$\pm$ 0.24 } \\
 GTmel+waveglow & 3.84 $\pm$ 0.14 & 3.92 $\pm$ 0.46 \\
 Conv+waveglow & 3.75 $\pm$ 0.56 & 3.45 $\pm$ 0.68 \\
 Attention+waveglow & 3.72 $\pm$ 0.24 & 3.38 $\pm$ 0.08 \\
 NVS& 3.13 $\pm$ 0.42 & - \\
 \end{tabular}
\caption{MOS score on FSM-SS with 95$\%$ confidence interval for VCTK and LibriTTS dataset. GT - ground Truth , GTmel+waveglow - groung truth mel spectrogram with waveglow as vocoder, Conv+waveglow - Convolution based normalsation in FSM-SS with waveglow vocoder , Attention+waveglow - Multi head attention based normalisation in FSM-SS with waveglow architecture, NVS : Neural Voice cloning with few samples method }
\end{table}
\end{document}

eess

Neeraj Kumar
Srishti Goel
Ankur Narang
Brejesh Lall

"Few Shot Adaptive Normalization Driven Multi-Speaker Speech Synthesis" by Neeraj Kumar, Srishti Goel, Ankur Narang, and Brejesh Lall, arXiv:2012.07252v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Number of Subjects per Dataset}
\begin{tabular}{clcccc}
\hline
\hline
Field ID & Target && Cross-validation & Testing & Artifacts \\%\textit{$D_{cv}$} & \textit{$D_{test}$} & \textit{$D_{art}$} \\
\hline
22407 & Visceral Adipose Tissue & (VAT) & 8,534 & 1,096 & 327\\% & 29,234 & 1,179\\
22408 & Abdominal Subcutaneous Adipose Tissue & (SAT) & 8,534 & 1,097 & 326\\% & 29,234 & 1,179\\
22415 & Total Adipose Tissue & (TAT) & 8,270 & 0 & 242\\% & 29,234 & 1,179\\
22416 & Total Lean Tissue & (TLT) & 8,270 & 0 & 242\\% & 29,234 & 1,179\\
22409 & Total Thigh Muscle & (TTM) & 8,478 & 1,038 & 284\\% & 29,234 & 1,179\\
22436 & Liver Fat Fraction & (LFF) & 8,474 & 1,061 & 323\\% & 29,234 & 1,179\\
\hline
\hline
\multicolumn{6}{l}{*UK Biobank Field IDs and number of available subjects with known reference values per target }\\
\multicolumn{6}{l}{ in cross-validation on dataset \textit{$D_{cv}$}, testing on dataset \textit{$D_{test}$}, and artifact dataset \textit{$D_{art}$}.}
\end{tabular}
\end{table}
\end{document}

cs

Taro Langner
Fredrik K. Gustafsson
Benny Avelin
Robin Strand
Håkan Ahlström
Joel Kullberg

"Uncertainty-Aware Body Composition Analysis with Deep Regression Ensembles on UK Biobank MRI" by Taro Langner, Fredrik K. Gustafsson, Benny Avelin, Robin Strand, Håkan Ahlström, and Joel Kullberg, arXiv:2101.06963v3, 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}{ccccccc}
\hline
 & \textbf{Standard} & \textbf{SVP (patch)} & \textbf{SVP (padding)} & \textbf{CVP (3*3)} & \textbf{CVP (5*5)} & \textbf{GDA} \\ \hline
\textbf{Noise} & 28.85 & 29.37 & 29.38 & 31.59 & 30.53 & \textbf{37.03} \\
\textbf{Blur} & 30.45 & 29.59 & 29.58 & 30.80 &31.0 & \textbf{32.4} \\
\textbf{Weather} & 42.99 & 41.18 & 41.22 & 42.27 & 42.45 & \textbf{46.5} \\
\textbf{Digital} & 50.45 & 48.96 & 48.96 & \textbf{52.58} & 51.45 & 50.98 \\ \hline
\textbf{Avg.} & 38.19 & 37.27 & 37.28 & 39.31 & 38.85 & \textbf{41.73} \\ \hline
\end{tabular}
\caption{Compare GDA with input-based adaptation baselines.}
\end{table}
\end{document}

cs

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

"GDA: Generalized Diffusion for Robust Test-time Adaptation" by Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen, Junfeng Yang, Che-Chun Su, Min Sun, and Cheng-Hao Kuo, arXiv:2404.00095v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
 \toprule
 \textbf{Prompts} & why the sky is blue?\\
 \midrule
 \textbf{Base} & {\small why is the sun yellow? why does the moon look like a white spot?} \\
 & {\small why are the stars not visible at night? why the earth is round?}\\
 \midrule
 \textbf{DPO} & {\small The color blue is caused by the reflection of light off of the water, air, and other objects in the atmosphere.} \\
 & {\small The wavelength of visible light, which includes blue, is shorter than other colors, so it is able to pass through these layers of matter, allowing light to travel further and be reflected.} \\
 & {\small In the case of our blue sky, the combination of water droplets, ice crystals, dust, clouds, sunlight, etc. all contribute to the color of blue in our skies.} \\
 & {\small This combination creates a beautiful and vibrant experience for those who witness it, regardless of whether it is the result of human activity or natural processes.} \\
 & {\small Overall, while it may not be fully explained by science, there is still much to admire and appreciate about the beauty of a blue day in nature, no matter where you are in this world. }\\
 \midrule
 \textbf{KTO} & {\small The color blue is due to the presence of a certain wavelength of light in the electromagnetic spectrum.} \\
 & \small This particular wavelength is not absorbed by the materials that make up the atmosphere, so it gets scattered in all directions. \\
 & \small The further away the light is from the source, the less it is scattered and the more blue it appears. \\
 & Since the sun is very far away, it scatters light very little, and therefore appears blue to us. \\
 & \small However, there are other colors that can be seen in nature, such as red, orange, green, etc., and these colors are not present in our atmosphere. \\
 & \small So, why does the blue color exist in sky? The answer is that it's a result of the way the earth reflects and scats light. \\
 & \small As the sunlight passes through the clouds and reaches the Earth's surface, some of it bounces back and some is absorbed. \\
 & \small Some of this light that is reflected and absorbed is then re-emitted in a different direction, creating the beautiful blue sky we see today. \\
 \midrule
 \textbf{BCO} & \small The color of the night sky, blue, is due to the scattering of light by molecules of atmospheric gases such as nitrogen and oxygen. \\
 & \small These gases absorb certain colors of visible light, leaving the blue color to reach us. \\
 & \small The color we see is not the color that the gases themselves emit, but rather the colors they absorb, which is what we perceive as blue. \\
 \midrule
 \textbf{KLDO} & \small The color blue is associated with the atmosphere, which is a mixture of gases such as nitrogen, oxygen, and water vapor. \\
 & \small These gases absorb certain wavelengths of light, making the air around us appear blue. \\
 & \small However, the exact reason why the color of the skies is typically blue varies depending on where you are in the world. \\
 & \small In some parts of Europe and North America, for example, where there is more urban development and pollution, blue skies may not be as common. \\
 & \small Instead, they may be more likely to appear green or brown due to the presence of industrial emissions. \\
 & \small Ultimately, it's a result of how the light is interacting with different elements in our atmosphere and how it reflects off the surface of objects, such the clouds and the ground.\\
 \bottomrule
 \end{tabular}
\caption{Responses from differently aligned Llama3.2-1B models to the sample prompt 'Why is the sky blue?' from the Alpaca Eval dataset, compared to the base pre-trained Llama3.2-1B model.}
\end{table}
\end{document}

stat

Rajdeep Haldar
Ziyi Wang
Qifan Song
Guang Lin
Yue Xing

"LLM Safety Alignment is Divergence Estimation in Disguise" by Rajdeep Haldar, Ziyi Wang, Qifan Song, Guang Lin, and Yue Xing, arXiv:2502.00657v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation study on VCTK with standard error calculated over 5 random seeds. LLM1: Learnable Label Mixup. LM: Loss Mixup. LLM2: Learnable Loss Mixup.}
\begin{tabular}{ccccc}
 \toprule
 & PESQ & CSIG & CBAK & COVL\\
 \midrule
ERM & $3.18\pm0.00$ & $4.47\pm0.00$ & $\textbf{3.52}\pm0.02$ & $3.86\pm0.00$ \\
LLM1 & $3.10\pm0.01$ & $4.44\pm0.00$ & $3.41\pm0.01$ & $3.80\pm0.01$ \\
LM & $3.20\pm0.01$ & $4.48\pm0.00$ & $3.36\pm0.01$ & $3.87\pm0.00$ \\
LLM2 & $\textbf{3.26}\pm0.01$ & $\textbf{4.49}\pm0.00$ & $3.27\pm0.01$ & $\textbf{3.91}\pm0.00$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Oscar Chang
Dung N. Tran
Kazuhito Koishida

"Single-channel speech enhancement using learnable loss mixup" by Oscar Chang, Dung N. Tran, and Kazuhito Koishida, arXiv:2312.17255v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c}
 \hline Dim & $A \otimes-$ & \\
 \hline $\chi_{80}^9$ & $A\otimes Y_i=Y_1\oplus Y_3\oplus Y_6\oplus Y_8\oplus Y_9\oplus Y_{11}\oplus Y_{14}\oplus Y_{16}$ & $i=1,3,6,8,9,11,14,16$ \\
 \hline $\chi_{80}^{10}$ & $A\otimes Z_j=2(Z_{10}\oplus Z_{35}\oplus Z_{75}\oplus Z_{90})$& $j=10,35,75,90$\\
 \hline $\chi_{80}^{10}$ &$A\otimes Z_j=2(Z_{17}\oplus Z_{56}\oplus Z_{101}\oplus Z_{116})$& $j=17,56,101,116$\\
 \hline $\chi_{80}^{10}$ &$A\otimes Z_j=2(Z_{23}\oplus Z_{50}\oplus Z_{62}\oplus Z_{83})$& $j=23,50,62,83$\\
 \hline $\chi_{80}^8$ & $A\otimes W_k=\oplus_{k=1}^4(W_k\oplus W_{k+94})$& $1\leq k\leq 4$, $95\leq k\leq 98$
 \\
 \hline $\chi_{80}^8$ & $A\otimes W_k=\oplus_{k=5}^8(W_k\oplus W_{k+94})$& $5\leq k\leq 8$, $99\leq k\leq 102$
 \\
 \hline
 \end{tabular}
\caption{Action of \'{e}tale algebra A}
\end{table}
\end{document}

math

"Realizing modular data from centers of near-group categories" by Zhiqiang Yu and Qing Zhang, arXiv:2412.20763v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Anisotropic refinable functions and the tile B-splines" by Vladimir Yu. Protasov and Tatyana Zaitseva, arXiv:2312.11182v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccc}
\hline
 & (1) & (2)\tabularnewline
Panasonic & 6000h \& 10000h & 6000h only\tabularnewline
Toshiba & 6000h \& 12000h & 6000h only\tabularnewline
\hline
\hline
Joint profit & \textsf{24.67} & \textsf{24.07}\tabularnewline
Profit (Panasonic) & 10.77 & 10.04\tabularnewline
Profit (Toshiba) & \textsf{13.9} & \textsf{14.03}\tabularnewline
\hline
No inventory consumers (\%) & 18.61 & 20.45\tabularnewline
Disposal (million) & 3.04 & 3.33\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"When do firms sell high durability products? The case of light bulb industry" by Takeshi Fukasawa, arXiv:2503.23792v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{GNSS Signal Classification Methods Using SVMs}
\begin{tabular}{|l|l|c|}
\hline
\textbf{Paper} & \textbf{Task} & \textbf{Accuracy} \\
\hline
Hsu et al. & Categorizing pseudorange measurements & 75\% \\
\hline
Ozeki et al. & NLOS signal detection & $>$80\% \\
\hline
Lee et al. & MP prediction model & 58.4\% horizontal \\
\hline
Suzuki et al. & NLOS MP detection & 97.7\% \\
\hline
Xu et al. & GNSS shadow matching in urban environments & 91.5\% \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

"A Survey of Machine Learning Techniques for Improving Global Navigation Satellite Systems" by Adyasha Mohanty and Grace Gao, arXiv:2406.16873v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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


cs

Mubashara Akhtar
Omar Benjelloun
Costanza Conforti
Luca Foschini
Joan Giner-Miguelez
Pieter Gijsbers
Sujata Goswami
Nitisha Jain
Michalis Karamousadakis
Michael Kuchnik
Satyapriya Krishna
Sylvain Lesage
Quentin Lhoest
Pierre Marcenac
Manil Maskey
Peter Mattson
Luis Oala
Hamidah Oderinwale
Pierre Ruyssen
Tim Santos
Rajat Shinde
Elena Simperl
Arjun Suresh
Goeffry Thomas
Slava Tykhonov
Joaquin Vanschoren
Susheel Varma
Jos van der Velde
Steffen Vogler
Carole-Jean Wu
Luyao Zhang

"Croissant: A Metadata Format for ML-Ready Datasets" by Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Luca Foschini, Joan Giner-Miguelez, Pieter Gijsbers, Sujata Goswami, Nitisha Jain, Michalis Karamousadakis, Michael Kuchnik, Satyapriya Krishna, Sylvain Lesage, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Hamidah Oderinwale, Pierre Ruyssen, Tim Santos, Rajat Shinde, Elena Simperl, Arjun Suresh, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Susheel Varma, Jos van der Velde, Steffen Vogler, Carole-Jean Wu, and Luyao Zhang, arXiv:2403.19546v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
$Par$ & $Zon(2)$ &$Zon(4)$ & $Zon(6)$ & $C\!Z(2,10)$ & $C\!Z(4,10)$ & $C\!Z(6,10)$ \\
\hline
1 & 2.9 & 17.8 & 90.5 & 1879.9 & 3313.8 & 4858.5 \\
\hline
\end{tabular}
\caption{Average relative times for one iteration of each approximation algorithm, with respect to one iteration of $Par$.}
\end{table}
\end{document}

eess

Marco Casini
Andrea Garulli
Antonio Vicino

"Adaptive Threshold Selection for Set Membership State Estimation with Quantized Measurements" by Marco Casini, Andrea Garulli, and Antonio Vicino, arXiv:2311.00611v2, 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{FLOPs and CPU Utilization of the Diffusion Baseline}
\begin{tabular}{|l|c|c|c|c|}
\hline
\textbf{Operation} & \textbf{Calls} & \textbf{Total FLOPs} (MFLOPs) & \textbf{Self CPU \%} & \textbf{CPU Total Time (ms)} \\ \hline
\texttt{aten::addmm} & 145 & 620,622.774 & 37.36\% & 736.399 \\ \hline
\texttt{aten::bmm} & 48 & 12,884.902 & 6.91\% & 126.123 \\ \hline
\texttt{aten::mul} & 1 & 377.487 & 1.08\% & 21.089 \\ \hline
\texttt{aten::add} & 97 & 203.424 & 1.25\% & 14.255 \\ \hline
\texttt{DataParallel::forward} & 50 & 104.858 & 2.14\% & 18.332 \\ \hline
\texttt{aten::expand} & 243 & 0.00 & 0.56\% & 55.675 \\ \hline
\texttt{aten::reshape} & 245 & 0.00 & 0.88\% & 56.436 \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Connor Ding
Abhiram Gorle
Sagnik Bhattacharya
Divija Hasteer
Naomi Sagan
Tsachy Weissman

"LZMidi: Compression-Based Symbolic Music Generation" by Connor Ding, Abhiram Gorle, Sagnik Bhattacharya, Divija Hasteer, Naomi Sagan, and Tsachy Weissman, arXiv:2503.17654v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Normalized cross-covariance with zero lag between measured input variable candidates and emissions.}
\begin{tabular}{l|cc|cc}\hline\toprule
 &\multicolumn{2}{c}{Transient} &\multicolumn{2}{c}{Steady State} \\\cmidrule{2-5}
Measured & $NOx$ &$Soot$ & $NOx$ &$Soot$ \\
Variables & $\rm [ppm]$ &$\rm [\%]$ & $\rm [ppm]$ &$\rm [\%]$ \\\cmidrule{1-5}
Injection Pressure $\rm [MPa]$&0.45 &-0.32 &-0.09 &-0.18 \\\cmidrule{1-5}
Main injection &\multirow{2}{*}{0.27} &\multirow{2}{*}{-0.39} &\multirow{2}{*}{-0.03} & \multirow{2}{*}{-0.19 } \\
timing $\rm [BTDC]$ & & & & \\\cmidrule{1-5}
Main injection fuel &\multirow{2}{*}{0.68} &\multirow{2}{*}{-0.12} &\multirow{2}{*}{0.38} & \multirow{2}{*}{-3.0e-3} \\
flow rate $\rm [mm^3/st]$ & & & & \\\cmidrule{1-5}
Pre-injection fuel &\multirow{2}{*}{0.22} &\multirow{2}{*}{0.07} &\multirow{2}{*}{-8.2e-17} & \multirow{2}{*}{-1.7e-16} \\
flow rate $\rm [mm^3/st]$ & & & & \\\cmidrule{1-5}
Engine torque output $\rm [Nm]$&0.67 &-0.10 &0.38 &-0.02 \\\cmidrule{1-5}
Engine speed $\rm [rpm]$&0.28 &-0.40 &-0.25 &-0.07 \\\cmidrule{1-5}
Intake manifold &\multirow{2}{*}{0.47} &\multirow{2}{*}{-0.21} &\multirow{2}{*}{0.15} & \multirow{2}{*}{0.15} \\
pressure $\rm [kPa]$ & & & & \\\cmidrule{1-5}
Exhaust manifold &\multirow{2}{*}{0.49} &\multirow{2}{*}{-0.22} &\multirow{2}{*}{0.11} & \multirow{2}{*}{-0.04} \\
pressure $\rm [kPa]$ & & & & \\\cmidrule{1-5}
Mass air flow $\rm [G/s]$&0.39 &-0.27 &0.04 &-0.12 \\\cmidrule{1-5}
EGR position $\rm [\%]$&-0.23 &-0.33 &0.10 &0.22 \\\cmidrule{1-5}
VGT position $\rm [\%]$&-0.32 &0.42 &0.08 &0.13 \\
\bottomrule\hline
\end{tabular}
\end{table}
\end{document}

eess

Jiadi Zhang
Xiao Li
Mohammad Reza Amini
Ilya Kolmanovsky
Munechika Tsutsumi
Hayato Nakada

"Modeling and Control of Diesel Engine Emissions using Multi-layer Neural Networks and Economic Model Predictive Control" by Jiadi Zhang, Xiao Li, Mohammad Reza Amini, Ilya Kolmanovsky, Munechika Tsutsumi, and Hayato Nakada, arXiv:2311.03552v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrrrr}
& \multicolumn{3}{c}{\textbf{Indoor: building\_loop}} & \multicolumn{3}{c}{\textbf{Outdoor: penno\_short\_loop}}\\
\toprule
Noise Type $\to$ & None & Gaussian & Impulse & None & Gaussian & Impulse \\
\textbf{Baseline} & 0.475 & 0.484 & 0.490 & 0.474 & 0.484 & 0.488 \\
\textbf{Ours} & 0.163 & 0.160 & 0.162 & 0.162 & 0.163 & 0.172 \\ \bottomrule
\end{tabular}
\caption{\textbf{Estimation improvement for M3ED dataset.} We compare the mean estimation errors of the baseline and our methods under different types of depth measurement noise described in~.}
\end{table}
\end{document}

cs

Siming He
Yuezhan Tao
Igor Spasojevic
Vijay Kumar
Pratik Chaudhari

"An Active Perception Game for Robust Information Gathering" by Siming He, Yuezhan Tao, Igor Spasojevic, Vijay Kumar, and Pratik Chaudhari, arXiv:2404.00769v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||cccc||}
 \hline
 & 5min & 15min & 1hour \\
 \hline\hline
 mean & 714.237kW & 1495.218kW & 7286.616kW \\
 \hline
 median & 581.408kW & 966.566kW & 6518.619kW \\
 \hline
 max & 3905.480kW & 4708.951kW & 17035.819kW \\
 \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Tom Nonnenmacher
Jenny Nelson
Benedict Winchester

"Maximum Return on Investment for a Domestic Photovoltaic Installation" by Tom Nonnenmacher, Jenny Nelson, and Benedict Winchester, arXiv:2310.09323v1, 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{Separated performance for different learning patterns for machine learning algorithms based on the results of the direct approach without behavior analysis}
\begin{tabular}{llccccc}
\hline
Pattern &Method & Accuracy (\%) & Precision (\%) & Recall (\%) & F1 Score (\%) & AUC \\ \hline
 &logistic regression & 96.13& 98.67& 96.13& 97.09& 0.9866
\\
 &decision tree & 95.38& 98.57& 95.38& 96.62& 0.9265\\
low &random forest & 93.30& 98.44& 93.30& 95.38& 0.9140\\
autonomy &K-Nearest neighbor & 97.67& 98.95& 97.67& 98.12& 0.9844\\
&multilayer perceptron &96.62&98.78& 96.62&97.42&0.9903\\
&support vector classifier & 96.35&98.80&96.35& 97.25&0.9869\\
&extreme gradient boosting & 98.30& 98.97& 98.30& 98.54& 0.9936
\\
\hline
 & logistic regression
& 55.76& 72.39& 55.76& 42.73&0.7600\\
 & decision tree
& 60.48& 77.32& 60.48& 51.37&0.5775\\
 & random forest
& 53.46& 75.17& 53.46& 37.48&0.5023\\
motivated & K-Nearest neighbor
& 69.59& 77.85& 69.59& 66.26&0.9014\\
& multilayer perceptron
& 59.93& 75.07& 59.93& 50.76&0.7105\\
& support vector classifier & 62.79& 76.63& 62.79& 55.61&0.7361\\
& extreme gradient boosting
& 71.90& 76.11& 71.90& 70.10&0.7869\\
 \hline
\end{tabular}
\end{table}
\end{document}

cs

Jin Yuan
Xuelan Qiu
Jinran Wu
Jiesi Guo
Weide Li
You-Gan Wang

"Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study" by Jin Yuan, Xuelan Qiu, Jinran Wu, Jiesi Guo, Weide Li, and You-Gan Wang, arXiv:2406.11847v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{General OTFS System Implementation Parameters.}
\begin{tabular}{|c|c|}
\hline
Parameters & Specifications \\ \hline
Modulation scheme & \(32\)-QAM \\ \hline
Operation Frequency & \(400\) MHz \\ \hline
Total Bits & \(20480\) bits \\ \hline
FFT Sampling Rate & \(61.44\) MHz \\ \hline
Power & \(1.45\) W \\ \hline
Latency & \(12.17\) \(\mu\)s \\ \hline
Throughput & \(503.31\) Gbits/sec \\ \hline
Throughput Efficiency & \(347.11\) Gbits/sec/W \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Murat Isik
Malvin Nkomo
Anup Das
Kapil R. Dandekar

"FPGA Implementation of OTFS Modulation for 6G Communication Systems" by Murat Isik, Malvin Nkomo, Anup Das, and Kapil R. Dandekar, arXiv:2310.09671v1, 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{Final ATE Estimates (DoubleMLIRM), Rounded to 2 d.p.}
\begin{tabular}{lccc}
\hline
\textbf{Outcome} & \textbf{ATE} & \textbf{StdErr} & \textbf{p-value} \\
\hline
\texttt{avg\_rev\_last\_1000} & -0.09 & 0.01 & 0.00 \\
\texttt{time\_to\_converge} & 0.02 & 0.03 & 0.59 \\
\texttt{avg\_regret\_of\_seller} & 0.07 & 0.00 & 0.00 \\
\texttt{no\_sale\_rate} & 0.00 & 0.00 & 0.08 \\
\texttt{price\_volatility} & 0.00 & 0.00 & 0.25 \\
\texttt{winner\_entropy} & 0.00 & 0.00 & 0.38 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc} \toprule
 & \multirow{2}{*}{Goals} & \multicolumn{5}{c}{Plausible states (scenarios)}\\ \cline{3-7}
 & & $S_1$ & $S_2$ & $S_3$ & $S_4$ & $S_5$ \\ \hline
 \multirow{2}{*}{\texttt{stage 0}} & $g_1^0$ & $ - $ & $ - $ & $ 5.5 $ & $ - $ & $ - $ \\ \cline{2-7}
 & $g_2^0$ & $ - $ & $ - $ & $ 0.75 $ & $ - $ & $ - $ \\ \hline

 \multirow{2}{*}{\texttt{stage 1}} & $g_1^1$ & $ - $ & $ 6.5 $ & $ 7 $ & $ 7.5 $ & $ - $ \\ \cline{2-7}
 & $g_2^1$ & $ - $ & $ 0.5 $ & $ 0.75 $ & $ 1 $ & $ - $ \\ \hline

 \multirow{2}{*}{\texttt{stage 2}} & $g_1^2$ & $ 7 $ & $ 7.5 $ & $ 8 $ & $ 9 $ & $ 11 $ \\ \cline{2-7}
 & $g_2^2$ & $ 0.5 $ & $ 0.5 $ & $ 0.75 $ & $ 1 $ & $ 1.5 $ \\ \bottomrule

 \end{tabular}
\caption{Desirable levels of total remained funds (million Dollars) after consumption in each state}
\end{table}
\end{document}

math

Babooshka Shavazipour
Theodor J. Stewart

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{rrrr}
 \hline
 & dim 1 & dim 2 & dim 3 \\
 \hline
Acid & 0.08 & 0.26 & 0.42 \\
 Basil & 0.04 & 0.07 & 0.00 \\
 Bitter & 0.00 & 0.02 & 0.01 \\
 Lemon & 0.10 & 0.02 & 0.48 \\
 Licorice & 0.00 & 0.00 & 0.00 \\
 Mint & 0.00 & 0.00 & 0.00 \\
 Salty & 0.22 & 0.30 & 0.02 \\
 Sweet & 0.56 & 0.34 & 0.06 \\
 \hline
\end{tabular}
\caption{Importance of the different states on each dimension of the MFPCA with equal weights~$w_j$.}
\end{table}
\end{document}

stat

"Statistical modeling of categorical trajectories with multivariate functional principal components" by Hervé Cardot and Caroline Peltier, arXiv:2502.09986v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Charlotte Leininger
Simon Rittel
Ludwig Bothmann

"Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective" by Charlotte Leininger, Simon Rittel, and Ludwig Bothmann, arXiv:2501.14710v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Accuracy in 5-shot learning}
\begin{tabular}{c|c|c|c}
Model & English & Italian & Spanish\\\hline
Supervised & $24.33\%$ & $16.19\%$ & $20.11\%$ \\
MetaSER & $65.21\%$ & $64.13\%$ & $64.95\%$ \\
F-MAML & \textbf{69.71\%} & \textbf{69.13\%} & \textbf{68.85\%}\\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Anugunj Naman
Chetan Sinha
Liliana Mancini

"Fixed-MAML for Few Shot Classification in Multilingual Speech Emotion Recognition" by Anugunj Naman, Chetan Sinha, and Liliana Mancini, arXiv:2101.01356v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

math

Dinesh Parthasarathy
Tommaso Bevilacqua
Martin Lanser
Axel Klawonn
Harald Köstler

"Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations" by Dinesh Parthasarathy, Tommaso Bevilacqua, Martin Lanser, Axel Klawonn, and Harald Köstler, arXiv:2412.08186v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Tangential homoclinic points for Lozi maps" by Kristijan Kilassa Kvaternik, arXiv:2412.12536v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Alaeddine Zahir
Khalide Jbilou
Ahmed Ratnani

"A low-rank non-convex norm method for multiview graph clustering" by Alaeddine Zahir, Khalide Jbilou, and Ahmed Ratnani, arXiv:2312.11157v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
\hline\hline
$Ad\left( e^{\left( \varepsilon \mathbf{X}_{i}\right) }\right) \mathbf{X}%
_{j} $ & $\mathbf{Z}_{1}$ & $\mathbf{Z}_{2}$ & $\mathbf{Z}_{3}$ \\
$\mathbf{X}_{1}$ & $Z_{1}$ & $\cos \left( f_{0}\varepsilon \right)
Z_{2}-\sin \left( f_{0}\varepsilon \right) Z_{3}$ & $\sin \left(
f_{0}\varepsilon \right) Z_{2}+\sin \left( f_{0}\varepsilon \right) Z_{3}$
\\
$\mathbf{X}_{2}$ & $Z_{1}-\varepsilon X_{2}$ & $Z_{2}$ & $Z_{3}$ \\
$\mathbf{X}_{4}$ & $Z_{1}$ & $Z_{2}$ & $Z_{3}$ \\
$\mathbf{X}_{10}$ & $Z_{1}-2\varepsilon X_{10}$ & $Z_{2}$ & $Z_{3}$ \\
$\mathbf{Z}_{1}$ & $Z_{1}$ & $e^{\varepsilon }Z_{2}$ & $e^{\varepsilon
}Z_{3} $ \\
$\mathbf{Z}_{2}$ & $Z_{1}-\varepsilon Z_{2}$ & $Z_{2}$ & $Z_{3}$ \\
$\mathbf{Z}_{3}$ & $Z_{1}-\varepsilon Z_{3}$ & $Z_{2}$ & $Z_{3}$ \\
\hline\hline
\end{tabular}
\end{table}
\end{document}

math

"Lie Symmetries for the Shallow Water Magnetohydrodynamics Equations in a Rotating Reference Frame" by Andronikos Paliathanasis and Amlan Halder, arXiv:2412.14578v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
 \hline
 \# & Function & Dimension(s) & Domain & Global Minimum \\
 \hline
 1 & Ackley & $D$ & $\mathbf{x} \in [-30, 30]^{D}$ & 0 \\
 \hline
 2 & Levy & $D$ & $\mathbf{x} \in [-10, 10]^D$ & 0 \\
 \hline
 3 & Rosenbrock & $D$ & $\mathbf{x} \in [-5, 10]^D$ & 0 \\
 \hline
 4 & Styblinski-Tang & $D$ & $\mathbf{x} \in [-5, 5]^D$ & $-39.16599 \times D$ \\
 \hline
 5 & Rastrigin Function & $D$ & $\mathbf{x} \in [-5.12, 5.12]^D$ & 0 \\
 \hline
 \end{tabular}
\caption{Benchmark high-dimensional full-rank test problems from .}
\end{table}
\end{document}

math

Luo Long
Coralia Cartis
Paz Fink Shustin

"Dimensionality Reduction Techniques for Global Bayesian Optimisation" by Luo Long, Coralia Cartis, and Paz Fink Shustin, arXiv:2412.09183v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Riccardo Leoncini
Mariele Macaluso
Annalivia Polselli

"Gender Segregation: Analysis across Sectoral-Dominance in the UK Labour Market" by Riccardo Leoncini, Mariele Macaluso, and Annalivia Polselli, arXiv:2303.04539v3, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccccc}
	\hline
	Region & $x$ & $\alpha$ & $\mu$ & $\delta$\\
	\hline
	small & (-5, 5) & (0.001, 5) & (-5, 5) & (0.001, 5)\\
	large & (-10, 10) & (0.001, 50) & (-10, 10) & (0.001, 50)\\
	\hline
	\end{tabular}
\caption{Case $\beta = 0$: Parameter range for small and large region.}
\end{table}
\end{document}

stat

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llllll}
 Model & Specification & % Optimiser &
 L2-reg. & Batch size & MSE \\\hline
 PCA & && & 0.1548\\
 AE & 100 / ``GELU'' & % Adam &
 $10^{-4}$ & 64& 0.1298\\
 AE with LSTM & {\small 100 / ``GELU'' / 27 / LSTM} & $1.25 \cdot 10^{-6}$ & 256 &
 0.1411\\\hline
 Clustered PCA & & & & 0.1915\\
 Clustered AE & enc: 10 / ``Swish'' & - & 64 & 0.1741\\
 & dec: 60 / ``Swish''\\
 Clustered AE with LSTM & {\footnotesize enc: \# factors / ``SELU'' / LSTM} & $10^{-6}$ & 128 & 0.1782\\
 & {\footnotesize dec: LSTM / 27 / ``SELU'' / 27}

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

q-fin

"Risk factor aggregation and stress testing" by Natalie Packham, arXiv:2310.04511v1, 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}{lccc}
\toprule
Text & Label & Prediction & Uncertainty \\ \midrule
Help us \#savethefishies & 0 & 0 & Low \\
6 customers have this in their basket & 1 & 1 & Low \\
Only a few more left! & 1 & 1 & Low \\
Hurry! Limited Quantity Available. & 1 & 1 & Low \\ \midrule
Arthritis Aids & 1 & 1 & High \\
The presence of flowers is enough to ... & 1 & 1 & High \\
Irwin & 0 & 0 & High \\
tedpullin & 0 & 0 & High \\ \bottomrule
\end{tabular}
\caption{Predictions from the Nomic SNGP model with high and low uncertainty.}
\end{table}
\end{document}

math

Javier Muñoz
Álvaro Huertas-García
Carlos Martí-González
Enrique De Miguel Ambite

"Uncertainty Quantification for Transformer Models for Dark-Pattern Detection" by Javier Muñoz, Álvaro Huertas-García, Carlos Martí-González, and Enrique De Miguel Ambite, arXiv:2412.05251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc} \hline
			 	& $\{X, Y\}$	& $\{X, Z\}$	& $\{Y, Z\}$		\\ \hline
		Utilitarian Welfare 	& 22			& 37	 		& 39 			\\ \hline
		Nash Welfare 	& 224		& 1539	 	& 2197			\\ \hline
			\end{tabular}
\caption{Utilitarian and Nash welfares of certain project sets to be funded.}
\end{table}
\end{document}

cs

"Participatory Funding Coordination: Model, Axioms and Rules" by Haris Aziz and Aditya Ganguly, arXiv:2101.11722v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Technical parameters used to model storage nodes.}
\begin{tabular}{|c|cccccc|}
		\hline
		& \small{$\eta^S$} & \small{$\eta^+$} & \small{$\eta^-$} & \small{$\sigma$} & \small{$\rho$} & \small{$\phi$} \\ \hline
		Battery Storage & \small{0.00004} & \small{0.959} & \small{0.959} & \small{0.0} & \small{1.0} & \\
		 & \small{-} & \small{-} & \small{-} & \small{-} & \small{-} &\\
		\hline
		Compressed H$_2$ Storage & \small{1.0} & \small{1.0} & \small{1.0} & \small{0.05} & \small{1.0} & \small{1.3}\\
		 & & & & & & \small{GWh$_{el}$/kt$_{H_2}$}\\
		\hline
		Liquefied CO$_2$ Storage & \small{1.0} & \small{1.0} & \small{1.0} & \small{0.0} & \small{1.0} & \small{0.105}\\
		 & & & & & & \small{GWh$_{el}$/kt$_{CO_2}$}\\
		\hline
		Liquefied CH$_4$ Storage & \small{1.0} & \small{1.0} & \small{1.0} & \small{0.0} & \small{1.0} & \\
		 & \small{-} & \small{-} & \small{-} &\small{-} & \small{-} & \\
		\hline
		H$_2$O Storage & \small{1.0} & \small{1.0} & \small{1.0} & \small{0.0} & \small{1.0} & \small{0.00036}\\
		 & & & & & &\small{GWh$_{el}$/kt$_{H_2O}$}\\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Mathias Berger
David Radu
Ghislain Detienne
Thierry Deschuyteneer
Aurore Richel
Damien Ernst

"Remote Renewable Hubs For Carbon-Neutral Synthetic Fuel Production" by Mathias Berger, David Radu, Ghislain Detienne, Thierry Deschuyteneer, Aurore Richel, and Damien Ernst, arXiv:2102.11375v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of the models on Dataset B}
\begin{tabular}{llllll}
 \toprule
 \textbf{Model} & {\textbf{ROC}} & {\textbf{Recall/Sensitivity}} & {\textbf{Precision}} & {\textbf{Specificity}} & {\textbf{F1-score}} \\
 \midrule
 {\textbf{Random Forest Classifier}} &0.85 &0.87 &0.70 &0.63 &0.78 \\
 {\textbf{Extra Trees Classifier}} & 0.84 &0.85 &0.67 &0.58 &0.75 \\
 {\textbf{Gradient Boosting Classifier}} &0.85 &0.86 &0.72 &0.67 &0.78 \\
 {\textbf{Linear SVM}} &0.85 &0.86 &0.67 &0.58 &0.75 \\
 {\textbf{Logistic Regression Classifier}} & 0.85 & 0.86 &0.67 &0.58 &0.75 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Detection of Malaria Vector Breeding Habitats using Topographic Models" by Aishwarya Jadhav, arXiv:2011.13714v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|cc|cc|}
 \hline & $\mu$ Err. Avg. $\pm 2 \sigma$ %& $\mu$ Err. $\sigma$
 & $\Sigma$ Err. Avg. $\pm 2 \sigma $\\%& $\Sigma$ Err. $\sigma$ \\
 \hline GMM & 0.04512 $\pm$ 0.00433 & 0.09518 $\pm$ 0.00296\\
 \hline Sampling & 0.04604 $\pm $ 0.00989 & 0.09710 $\pm$ 0.01419\\
 \hline Diffusion & 0.05866 $\pm$ 0.00942 & 0.1025 $\pm$ 0.01363\\
 \hline
 \end{tabular}
\caption{The statistics for the mean and covariance estimates of each estimation method obtained from 100 estimates at 201 states. The average error is similar for each model, but the GMM-based method has smaller variance which is important when using its outputs in closed-loop control. Estimates for each method are calculated using 10,000 samples. }
\end{table}
\end{document}

eess

Ryan K. Cosner
Igor Sadalski
Jana K. Woo
Preston Culbertson
Aaron D. Ames

"Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions" by Ryan K. Cosner, Igor Sadalski, Jana K. Woo, Preston Culbertson, and Aaron D. Ames, arXiv:2311.05802v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Umur Hasturk
Albert H. Schrotenboer
Kees Jan Roodbergen
Evrim Ursavas

"Constrained Reinforcement Learning for the Dynamic Inventory Routing Problem under Stochastic Supply and Demand" by Umur Hasturk, Albert H. Schrotenboer, Kees Jan Roodbergen, and Evrim Ursavas, arXiv:2503.05276v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Tom Goodman
Karoline van Gemst
Peter Tino

"A Geometric Framework for Pitch Estimation on Acoustic Musical Signals" by Tom Goodman, Karoline van Gemst, and Peter Tino, arXiv:2012.04517v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Deep Evidence regression original vs proposed results on simulated data for varying \(\lambda\). We see MSE (or mean squared error) is similar for both, while NLL (or Negative log likelihood) values are much better captured by proposed version.}
\begin{tabular}{ccccc}
\toprule
\multirow{2}{*}{\(\lambda\)} & \multicolumn{2}{c}{MSE(test)} & \multicolumn{2}{c}{NLL(test)} \\ \cmidrule(l){2-5}
 & benchmark & proposed & benchmark & proposed \\ \cmidrule(r){1-1}
\textit{0.2} & \textbf{0.099303} & \textbf{0.571519} & 70.64365 & \textbf{7.416122} \\
\textit{0.25} & \textbf{0.119299} & \textbf{0.504875} & 41.71667 & \textbf{6.667958} \\
\textit{0.3} & \textbf{0.142722} & 3.871369 & 36.16713 & \textbf{6.202275} \\
\textit{0.35} & \textbf{0.143117} & 3.202328 & 57.02918 & \textbf{5.773156} \\
\textit{0.4} & \textbf{0.172697} & 8.981477 & 42.53032 & \textbf{5.471559} \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"UQ for Credit Risk Management: A deep evidence regression approach" by Ashish Dhiman, arXiv:2305.04967v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation Parameters for DESVN.}
\begin{tabular}{|c|c|c|c|}
 \hline
 \textbf{Parameters} & \textbf{Numerical Values} & \textbf{Parameters} & \textbf{Numerical Values} \\
 \hline
 $h_{D}$ & 200\,m & $B_{R}$ & 1.40\,Gbps \\
 \hline
 $\lambda_{c}$ & 0.15\,m & $\psi_{\textrm{min}}$ & -10\,\text{dB} \\
 \hline
 $\alpha$ & 9.61 & $\sigma_{{n}}$ & -125\,\text{dB} \\
 \hline
 $\beta$ & 0.16 & $\varepsilon^{L}$ & 1\,\text{dB} \\
 \hline
 $\varepsilon^{N}$ & 20\,\text{dB} & $\Upsilon^{c} $ & 0.1\,Watt \\
 \hline
 $\delta$ & $5\times10^{-6}/ m^2$ & $\zeta_{RSU}$ & 200\,m\\
 \hline
 $W_{j}$ & 400\,MHz & $\tau_{j}$ & 20\\
 \hline$
 P_{j}^{max}$ & 1.5\,Watt&$\eta$&0.20 \\
 \hline
$\mathbf{r_\textrm{RSU}}$ &
 \multicolumn{3}{c|}{\{{5, 10, 15, 20, 25}\}\,Mbps} \\
 \hline
\end{tabular}
\end{table}
\end{document}

eess

Muhammad Asaad Cheema
Muhammad Karam Shehzad
Hassaan Khaliq Qureshi
Syed Ali Hassan
Haejoon Jung

"A Drone-Aided Blockchain-Based Smart Vehicular Network" by Muhammad Asaad Cheema, Muhammad Karam Shehzad, Hassaan Khaliq Qureshi, Syed Ali Hassan, and Haejoon Jung, arXiv:2007.12912v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Slider-crank : number of equations}
\begin{tabular}{lc}
 \toprule
 \textbf{Component} & \textbf{Value} \\
 \midrule
 Number of bodies & $3$ \\
 States per body & $7$ \\
 Total differentiable variables for dynamics & $3\times 7 = 21$ \\
 First-order equations of motion & $2 \times 21 = 42$ \\
 Degrees of freedom & $1$ \\
 Lagrange multipliers/Constraints & $21-1 = 20$ \\
 Total dynamic equations & $42+20 = 62$ \\
 Number of free-variables (parameters) & $5$\\
 Total number of sensitivities & $5\times 62 = 310$\\
 Total differential-algebraic equations & $62+310=372$\\
 Total objective function(s) & $1$\\
 Total objective function gradients & $5$\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Adwait Verulkar
Corina Sandu
Adrian Sandu
Daniel Dopico

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Class Meeting Schedule for UChicago's HRI: Research \& Practice in the Winter Quarter of 2024}
\begin{tabular}{lll}
 \toprule
 Week&Topic&Paper Sub-Topics \& Citations\\
 \midrule
 1 & Course Introduction & Robot Embodiment~\\
 2 & Verbal Behavior & Emotion~\\
 & & Self-Disclosure~\\
 3 & Nonverbal Behavior & Gaze~\\
 & & Non-humanoid Gestures~\\
 4 & Social Dynamics & Trust~\\
 & & Conflict Resolution~\\
 & & Robot Role~\\
 5 & Norms/Ethics & Harsh Robot Treatment~\\
 & & Social Norms~\\
 & & Ethical Research Practice~\\
 6 & Collaboration \& & Crowd Navigation~\\
 & Learning & Language Corrections~\\
 & & Interactive Policy Shaping~\\
 7 & Group Interactions & Shaping Group Dynamics~ \\
 & & Carryover Effects~\\
 & & Multiple Robots~\\
 8 & Applications & Public Spaces~\\
 & & Education~\\
 & & Robot-Assisted Feeding~\\
 9 & Grand Challenges & Human-Robot Teaming~\\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"Teaching Introductory HRI: UChicago Course "Human-Robot Interaction: Research and Practice"" by Sarah Sebo, arXiv:2403.18692v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
 \hline
 Probability & Strategies & Statistic \\
 \hline
 \multirow{3}{*}{4 $\times 10^{-6}$} & Independent & 0.002\\
 \cline{2-3}
 & Random & 0.008\\
 \cline{2-3}
 & Cluster & 0.012\\
 \hline
 \multirow{3}{*}{8.9 $\times 10^{-5}$} & Independent & 0.010\\
 \cline{2-3}
 & Random & 0.030\\
 \cline{2-3}
 & Cluster & 0.040\\
 \hline
 \multirow{3}{*}{0.002} & Independent & 0.032\\
 \cline{2-3}
 & Random & 0.202$^*$\\
 \cline{2-3}
 & Cluster & 0.392$^*$\\
 \hline
 \multirow{3}{*}{0.048} & Independent & 0.024\\
 \cline{2-3}
 & Random & 0.728$^*$\\
 \cline{2-3}
 & Cluster & 0.626$^*$\\
 \hline
 \multirow{3}{*}{0.211} & Independent & 0.040\\
 \cline{2-3}
 & Random & 0.724$^*$\\
 \cline{2-3}
 & Cluster & 0.760$^*$\\
 \hline
 \multirow{3}{*}{0.460} & Independent & 0.024\\
 \cline{2-3}
 & Random & 0.770$^*$\\
 \cline{2-3}
 & Cluster & 0.906$^*$\\
 \hline
 \end{tabular}
\caption{Kolmogorov-D statistic from 2-sample K-S test for E-R graph, average v.s. weighted interaction rule under Beta(2,5) distribution, $p$-value $< 0.01$ ($^*$)}
\end{table}
\end{document}

stat

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
 \hline
 $f$ & $r(f)$ (km) \\ \hline \hline
0.055 & 10.5\\ \hline
0.131 & 19.0\\ \hline
0.328 & 83.7\\ \hline
0.885 & 199.5\\ \hline
 \end{tabular}
\caption{Fraction of included population at the breakpoints and the corresponding VP radii. }
\end{table}
\end{document}

stat

"Valeriepieris Circles Reveal City and Regional Boundaries in England and Wales" by Rudy Arthur and Federico Botta, arXiv:2502.07451v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|l|}
\hline
Feature Sets & Sharpe & Calmar & Mean & Volatility & Max Drawdown \\ \hline
All & 0.8342 & 0.3758 & 0.0239 & 0.0287 & 0.0636 \\ \hline
Signature+Catch22+Stats & 0.3763 & 0.0543 & 0.0123 & 0.0326 & 0.2265 \\ \hline
Signature & 0.3561 & 0.0486 & 0.0104 & 0.0292 & 0.4116 \\ \hline
Catch22 & 0.3473 & 0.0471 & 0.0101 & 0.0291 & 0.6742 \\ \hline
Statistics & 0.2716 & 0.0279 & 0.0075 & 0.0276 & 0.4456 \\ \hline
Financials & 0.3225 & 0.0468 & 0.0079 & 0.0245 & 0.1687 \\ \hline
Sentiment & 0.9987 & 0.3846 & 0.0335 & 0.0335 & 0.0871 \\ \hline
\end{tabular}
\caption{Strategy Performance of LightGBM models trained on different feature sets for the Numerai-Signals tournament in the test period for CV 4. In addition to models trained with individual feature sets (stats,signature,Catch22,financials,sentiment), we also report the model trained with all the 5 feature sets combined (all). }
\end{table}
\end{document}

q-fin

"Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions" by Thomas Wong and Mauricio Barahona, arXiv:2303.16117v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcccr}
\toprule
Data set & Naive & Flexible & Better? \\
\midrule
Breast & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
Glass2 & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
Credit & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6& \\
Horse & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
Meta & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
Pima & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5& \\
Vehicle & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Josh Gardner
Simon Durand
Daniel Stoller
Rachel M. Bittner

"LLark: A Multimodal Instruction-Following Language Model for Music" by Josh Gardner, Simon Durand, Daniel Stoller, and Rachel M. Bittner, arXiv:2310.07160v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
 & PG & ES & ES (L) \\
\hline \hline
Size Reduction & $4.95\%$ & $3.74\%$ & $5.94\%$ \\
\hline
Parallelism in Data Collection & $100$ & $488$ & $488$ \\
\hline
Training Time & \textasciitilde $12$h & \textasciitilde $60$h & \textasciitilde $150$h \\
\hline
\end{tabular}
\caption{Policy Gradient v.s. Evolution Strategies}
\end{table}
\end{document}

cs

Mircea Trofin
Yundi Qian
Eugene Brevdo
Zinan Lin
Krzysztof Choromanski
David Li

"MLGO: a Machine Learning Guided Compiler Optimizations Framework" by Mircea Trofin, Yundi Qian, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, and David Li, arXiv:2101.04808v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Multi-channel DSC network architecture. dw denotes the depth-wise convolution with size $H\times W$, and keep the same for $N$ channels. pw denotes the point-wise convolution with size $1\times 1\times N$. We note that the first layer uses the conventional convolution with stride size 2 as in .}} % title of Table
\begin{tabular}{l|l|l} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Input Size&Type / Stride & Filter Shape \\ % inserts table
\hline % inserts single horizontal line
$128\times128\times5$&Conv / s2 & 32 kernels of $3\times3\times5$ \\
\hline
$64\times64\times32$&Conv dw / s1& 32 kernels of $3\times3$ dw \\
$64\times64\times32$& Conv pw / s1& 64 kernels of$1\times1\times32$ pw \\
\hline
$64\times64\times64$& Conv dw / s2& 64 kernels of$3\times3$ dw \\
$32\times32\times64$& Conv pw / s1& 128 kernels of$1\times1\times64$ pw \\
\hline
$32\times32\times128$& Conv dw / s2& 128 kernels of $3\times3$ dw \\
$16\times16\times128$&Conv pw / s1&128 kernels of $1\times1\times128$ pw \\
\hline
$16\times16\times128$&Conv dw / s2&128 kernels of $3\times3$ dw \\
$8\times8\times128$& Conv pw / s1&128 kernels of $1\times1\times128$ pw \\
\hline
$8\times8\times128$ & Flatten & N/A\\
8192& FC1 & 1024 \\
1024& FC2 & 128 \\
\hline
128& Classifier & Softmax; 2 or 3-dim \\
\hline

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

eess

Jing Wang
Xiaofeng Liu
Fangyun Wang
Lin Zheng
Fengqiao Gao
Hanwen Zhang
Xin Zhang
Wanqing Xie
Binbin Wang

"Automated interpretation of congenital heart disease from multi-view echocardiograms" by Jing Wang, Xiaofeng Liu, Fangyun Wang, Lin Zheng, Fengqiao Gao, Hanwen Zhang, Xin Zhang, Wanqing Xie, and Binbin Wang, arXiv:2311.18788v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{pifont}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Time Sharing Transmit Power Consumption Reduction, Noise Power -65 dBm}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}
\hline
\textbf{Time Sharing} & \multicolumn{3}{|c|}{\textbf{Transmit}} & \multicolumn{3}{|c|}{\textbf{Data Rates}} & \textbf{Time-shared} & \multicolumn{3}{|c|}{\textbf{SIC Decoding}} \\
 & \multicolumn{3}{|c|}{\textbf{Power (dBm)}} & \multicolumn{3}{|c|}{\textbf{(Mbps)}} & \textbf{Fraction} & \multicolumn{3}{|c|}{\textbf{Order}} \\
\hline
\multirow{3}{*}{\checkmark} & \multirow{3}{*}{15} & \multirow{3}{*}{14.3} & \multirow{3}{*}{15} & 398.01 & 470.48 & 632.23 & 0.52 & 3 & 2 & 1 \\
& & & & 691.78 & 242.32 & 565.91 & 0.17 & 1 & 3 & 2 \\
& & & & 565.91 & 691.78 & 242.32 & 0.31 & 2 & 1 & 3 \\ \hline
\ding{55} & 15.8 & 16 & 15.4 & 500 & 500 & 500 & 1.00 & 3 & 2 & 1 \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Sagnik Bhattacharya
Kamyar Rajabalifardi
Muhammad Ahmed Mohsin
John M. Cioffi

"Optimum Power-Subcarrier Allocation and Time-Sharing in Multicarrier NOMA Uplink" by Sagnik Bhattacharya, Kamyar Rajabalifardi, Muhammad Ahmed Mohsin, and John M. Cioffi, arXiv:2501.11230v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.