\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccccccc}
\toprule \\
\textbf{Model} &
 \textbf{lr} &
 \textbf{B} &
 \textbf{w\_decay} &
 \textbf{temp} &
 \textbf{im\_wt} &
 \textbf{m} &
 \textbf{k} &
 \textbf{epochs} &
 \textbf{nm\_thresh}
 \\
 \midrule
Language-image Pretraining & 5e-5 & 153 & 0.001 & 0.048 & - & - & - & 40 & - \\
\textit{Supervised models} & & & & & & & & & \\
ViT (synthetic) & 5.8e-5 & 256 & 0.0398 & 0.048 & - & 8 & 8 & 5 & - \\
ViT (labelled) & 2e-6 & 252 & 0.1 & 0.09 & - & 3 & 8 & 10 & 0.88 \\
Sup. Lang.-only (synthetic) & 5e-6 & 153 & 0.001 & 0.1 & 0 & 3 & 3 & 30 & 0.85, 0.85 \\
Sup. Image-only (synthetic) & 5e-6 & 153 & 0.001 & 0.1 & 1 & 3 & 3 & 30 & 0.76 \\
Sup. Mean-pool (synthetic) & 5e-6 & 153 & 0.001 & 0.1 & 0.5 & 3 & 3 & 30 & 0.81, 0.80 \\
Sup. Lang-only (labelled) & 5e-6 & 153 & 0.001 & 0.1 & 0 & 3 & 3 & 30 & 0.84,0.82 \\
Sup. Image-only (labelled) & 5e-6 & 153 & 0.001 & 0.1 & 1 & 3 & 3 & 30 & 0.79 \\
Sup. Mean-pooling (labelled) & 5e-6 & 153 & 0.001 & 0.1 & 0.5 & 3 & 3 & 30 & 0.82,0.82 \\
\bottomrule
\end{tabular}
\caption{Training Hyperparameters: $lr$ is the maximum learning rate, $B$ is the batch size, $w\_decay$ is the AdamW weight decay, $im\_wt$ is the weight of the image embedding in the pooled embedding, $m$ is the number of views sampled in each epoch, $k$ is the number of nearest neighbours in a hard-negative set, and epochs is the number of epochs. $nm\_threshold$ is a tuple with two tuned similarity thresholds (for noisy and clean OCR respectively) under which a retrieved neighbor is considered to not match with any of the target images.}
\end{table}
\end{document}

q-fin

Abhishek Arora
Xinmei Yang
Shao-Yu Jheng
Melissa Dell

"Linking Representations with Multimodal Contrastive Learning" by Abhishek Arora, Xinmei Yang, Shao-Yu Jheng, and Melissa Dell, arXiv:2304.03464v3, 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{Variance and reliability analysis for the number of blocked ads and trackers across five runs of browser-based experiment. CV means coefficient of variation. ICC means intraclass correlation coefficient and we report its values with 95\% confidence intervals. Multiplying the 9 browser instances (Adblock Plus MV2, Adblock Plus MV3, AdGuard MV2, AdGuard MV3, Stands MV2, Stands MV3, uBlock MV2, uBlock MV3, and MV3+) with the number of websites (924) yields the number of observations (N = 8,316).}
\begin{tabular}{lcc}
\toprule
\textbf{Metric} & \textbf{Blocked Ads} & \textbf{Blocked Trackers} \\
\midrule
Average SD & 0.59 & 2.70 \\
Average CV & 0.67 & 0.20 \\
No deviation & 4,443 (53.43\%) & 1,581 (19.01\%) \\
Max-min difference $\leq$ 1 & 5,612 (67.48\%) & 2,609 (31.37\%) \\
\midrule
ICC [95\% CI] & 0.77 [0.77, 0.78] & 0.89 [0.88, 0.89] \\
\midrule
N & 8,316 & 8,316 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Privacy vs. Profit: The Impact of Google's Manifest Version 3 (MV3) Update on Ad Blocker Effectiveness" by Karlo Lukic and Lazaros Papadopoulos, arXiv:2503.01000v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Datasets and Important Element}
\begin{tabular}{|l|l|}
\hline
\textbf{Dataset} & \textbf{Important Elements} \\
\hline
OpenStreetMap Data & Intersection details, Road types, Traffic controls \\
\hline
UK Accident Dataset & Accident location, Time, Vehicles involved \\
\hline
UK Traffic Count Dataset & Average daily traffic volume \\
\hline
Building Geographic Dataset & Building coordinates, Physical dimensions\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Hanlin Tian
Yuxiang Feng
Wei Zhou
Anupriya
Mohammed Quddus
Yiannis Demiris
Panagiotis Angeloudis

"The Impact of Building-Induced Visibility Restrictions on Intersection Accidents" by Hanlin Tian, Yuxiang Feng, Wei Zhou, Anupriya, Mohammed Quddus, Yiannis Demiris, and Panagiotis Angeloudis, arXiv:2503.05706v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ll}
\textbf{Error type} & \textbf{Share} & \textbf{Number} \\
\midrule
too broad definition & 0.41 & 15 \\
wrong sense & 0.32 & 12 \\
similar words but not a definition & 0.16 & 6 \\
redundant whitespace & 0.08 & 3 \\
ambiguous definition, ambiguous word & 0.03 & 1 \\
repetitions & 0.05 & 2 \\
factual mistake & 0.03 & 1 \\
non-existing word & 0.03 & 1 \\
\midrule
Total & 1.00 & 37 \\
\bottomrule
\end{tabular}
\caption{Erroneous English definitions by error type. Some definitions were annotated with two error types, for instance, some too broad definitions also suffer from a redundant whitespace.}
\end{table}
\end{document}

cs

Mariia Fedorova
Andrey Kutuzov
Nikolay Arefyev
Dominik Schlechtweg

"Enriching Word Usage Graphs with Cluster Definitions" by Mariia Fedorova, Andrey Kutuzov, Nikolay Arefyev, and Dominik Schlechtweg, arXiv:2403.18024v1, 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{Overall and class-specific prediction rates with 95\% prediction intervals (public transport versus car usage)}
\begin{tabular}{lccc}
		\hline
		Mode & Mean Accuracy & 95\% PI Lower & 95\% PI Upper \\
		\midrule
		\textbf{Overall} & \textbf{0.7230} & \textbf{0.7148} & \textbf{0.7312} \\
		Car & 0.741 & 0.728 & 0.755 \\
		Public Transport & 0.708 & 0.695 & 0.721 \\
		\hline
	\end{tabular}
\end{table}
\end{document}

q-fin

"Predicting Children's Travel Modes for School Journeys in Switzerland: A Machine Learning Approach Using National Census Data" by Hannes Wallimann and Noah Balthasar, arXiv:2504.09947v1, 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|}
			\hline
			\textbf{Entity Type} & \textbf{Test Precision} & \textbf{Test Recall} \\
			\hline
			PER & 88.6 & 89.9 \\
			\hline
			LOC & 77.0 & 76.2 \\
			\hline
			ORG & 74.8 & 77.8 \\
			\hline
			GPE & 87.7 & 86.7 \\
			\hline
			FAC & 82.0 & 77.8 \\
			\hline
			VEH & 72.3 & 71.4 \\
			\hline
			WEA & 75.0 & 73.8 \\
			\hline
			Overall & 85.2 & 85.9 \\
			\hline
		\end{tabular}
\caption{\small \textbf{Precision and Recall by Category. (ACE2005)} }
\end{table}
\end{document}

cs

"Named Entity Recognition in the Style of Object Detection" by Bing Li, arXiv:2101.11122v1, 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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{OLS Estimation Results - Dependent Variable: Swim Out Times}
\begin{tabular}{lcccc}
 \toprule
 & Estimate & Std. Error & t-value & Pr(>|t|) \\
 \midrule
 Drafter & -23.8886 & 4.1184 & -5.8004 & $6.63 \times 10^{-9}$ *** \\
 Pre-period $\times$ Drafter & -8.5779 & 4.6013 & -1.8642 & 0.0623 . \\
 Covid-year $\times$ Drafter & 19.4150 & 8.1469 & 2.3831 & 0.0172 * \\
 \midrule
 Observations & \multicolumn{4}{c}{168,391} \\
 Fixed-effects & \multicolumn{4}{c}{Athlete ID: 29,194, Event ID: 1,339, Cluster (Swim Group): 224} \\
 Standard-errors & \multicolumn{4}{c}{Heteroskedasticity-robust} \\
 RMSE & \multicolumn{4}{c}{175.1} \\
 Adj. $R^2$ & \multicolumn{4}{c}{0.9683} \\
 Within $R^2$ & \multicolumn{4}{c}{0.0035} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"Strategic Effort and Bandwagon Effects in Finite Multi-Stage Games with Non-Linear Externalities: Evidence from Triathlon" by Felix Reichel, arXiv:2505.03247v2, 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{Inverted pendulum: number of equations}
\begin{tabular}{lc}
 \toprule
 \textbf{Component} & \textbf{Value} \\
 \midrule
 Number of bodies & $2$ \\
 States per body & $7$ \\
 Total differentiable variables for dynamics & $2 \times 7 = 14$ \\
 First-order equations of motion & $2 \times 14 = 28$ \\
 Degrees of freedom & $5$ \\
 Lagrange multipliers/Constraints & $14 - 5 = 9$ \\
 Total dynamic equations & $28 + 9 = 37$ \\
 Number of free-variables (parameters) & $3$\\
 Total number of sensitivities & $3\times 37 = 111$\\
 Total differential-algebraic equations & $37+111=148$\\
 Total objective function(s) & $1$\\
 Total objective function gradients & $3$\\
 \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{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|c||}
	 \hline
	 Parameter & Distribution \\
	 \hline\hline
	 $\bar{P}_\mathrm{tx}$ & $\log_{10}(\bar{P}_\mathrm{tx}) \sim \mathfrak{U}[3,4]$ \\
 $\sigma_0$ & $\sigma_0 \sim \mathcal{N} \left( 3.1623 \times 10^{-8}, 10^{-16} \right)$ \\
	 $\bar{P}_R$ & $\log_{10}(\bar{P}_R) \sim \mathfrak{U}[2,4]$ \\
	 $\bar{A}$ & $\log_{10}(\bar{A}) \sim \mathfrak{U}[3,4]$ \\
	 $w$ & $w \sim \mathfrak{U}[0,1]$ \\
	 $\phi_\mathrm{th}$ & $\log_{10}(\phi_\mathrm{th}) \sim \mathfrak{U}[-2,-4]$ \\
	 $A_0$ & $A_0 \sim \mathfrak{U}[0,1]$ \\
	 $K_\text{net}$ & $\log_{10}(K_\text{net}) \sim \mathfrak{U}[-1,1]$ \\
	 \hline
	 \end{tabular}
\caption{Distribution of model and algorithmic parameters used to run randomized multi-scenario simulations of Algorithm~.}
\end{table}
\end{document}

math

Nadhir Ben Rached
Shyam Mohan Subbiah Pillai
Raúl Tempone

"Optimal power procurement for green cellular wireless networks under uncertainty and chance constraints" by Nadhir Ben Rached, Shyam Mohan Subbiah Pillai, and Raúl Tempone, arXiv:2503.03051v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of IoT Datasets}
\begin{tabular}{|l|r|r|r|r|}
\hline
\textbf{Dataset} &
 \textbf{Length} &
 \textbf{Duration} &
 \textbf{Samples} &
 \textbf{Labels} \\ \hline
Urban Observatory & 864 & 1 day & 1065 & 16 \\ \hline
Swiss Experiment & 445 & Variable & 346 & 11 \\ \hline
IOWA ASOS & 168 & 1 week & 1000 & 8 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Muhammad Sakib Khan Inan
Kewen Liao
Haifeng Shen
Prem Prakash Jayaraman
Dimitrios Georgakopoulos
Ming Jian Tang

"DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data" by Muhammad Sakib Khan Inan, Kewen Liao, Haifeng Shen, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, and Ming Jian Tang, arXiv:2403.19996v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Eeshwar Prasad Poudel
Pitambar Acharya
Jeevan Kafle
Shreeram Khadka

"On One Dimensional Advection -- Diffusion Equation with Variable Diffusivity" by Eeshwar Prasad Poudel, Pitambar Acharya, Jeevan Kafle, and Shreeram Khadka, arXiv:2312.06493v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Thomas Möbius
Mira Watermeyer
Oliver Grothe
Felix Müsgens

"Enhancing Energy System Models Using Better Load Forecasts" by Thomas Möbius, Mira Watermeyer, Oliver Grothe, and Felix Müsgens, arXiv:2302.11017v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Selection of \( r \) for the third and sixth settings across 100 independent trials.}
\begin{tabular}{cccccc}
\hline
\multirow{2}{*}{ID} & \multirow{2}{*}{True \( r \)} & \multicolumn{4}{c}{Estimated \( r \)} \\ \cline{3-6}
 & & 1 & 2 & 3 & \( \geq 4 \) \\ \hline
3 & 1 & 100 & 0 & 0 & 0 \\
6 & 2 & 0 & 92 & 0 & 8 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Xinyuan Fan
Feiyan Ma
Chenlei Leng
Weichi Wu

"Low-Rank Approaches to Graphon Learning in Networks" by Xinyuan Fan, Feiyan Ma, Chenlei Leng, and Weichi Wu, arXiv:2501.18785v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline
 & M & \textbf{M-OF} & D$_1$ & \textbf{D$_1$-OF} & D$_2$ & \textbf{D$_2$-OF} & D$_3$ & \textbf{D$_3$-OF} \\
 \hline
 \text{RRE} & 0.815 & 0.314 & 0.418 & 0.323 & 0.489 & 0.399 & 0.777 & 0.368 \\
 \hline
 \text{SSIM} & 0.301 & 0.926 & 0.863 & 0.922 & 0.806 & 0.880 & 0.384 & 0.896 \\
 \hline
 \end{tabular}
\caption{\textit{\textbf{Test 5}: Mean RRE and SSIM.}}
\end{table}
\end{document}

math

Toluwani Okunola
Mirjeta Pasha
Misha Kilmer
Melina Freitag

"Efficient Dynamic Image Reconstruction with motion estimation" by Toluwani Okunola, Mirjeta Pasha, Misha Kilmer, and Melina Freitag, arXiv:2501.12497v1, 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{Parameters of the Q-ALNS, their corresponding levels, and tuned values}
\begin{tabular}{cccccc}
\hline
\multirow{2}{*}{Parameter} & \multirow{2}{*}{Notation} & \multicolumn{3}{c}{Levels} & \multirow{2}{*}{Tuned value} \\
 & & -1 & 0 & 1 & \\ \hline
Temperature scale & $\tau$ & 0.01 & 0.105 & 0.2 & 0.2\\
Epsilon-greedy & $\epsilon$ & 0.7 & 0.85 & 1 & 1\\
Epsilon-decay & $\beta$ & 0.99 & 0.995 & 1 & 0.99\\
Learning rate & $\alpha$ & 0.5 & 0.75 & 1 & 0.5\\
Discount factor & $\gamma$ & 0.7 & 0.85 & 1 & 0.7\\
Non\_improve times & $t$ & 10 & 25 & 40 & 20\\
Local/global improvement weight & $\eta$ & 0.6 & 0.7 & 0.8 & 0.8\\
Learning loop & $l$ & 100 & 200 & 300 & 200\\\hline
\end{tabular}
\end{table}
\end{document}

math

Yueyi Li
Mehrdad Mohammadi
Xiaodong Zhang
Yunxing Lan
Willem van Jaarsveld

"Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm" by Yueyi Li, Mehrdad Mohammadi, Xiaodong Zhang, Yunxing Lan, and Willem van Jaarsveld, arXiv:2412.09090v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling" by Diana Koldasbayeva and Alexey Zaytsev, arXiv:2502.03480v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

Lorenzo Mercuri
Andrea Perchiazzo
Edit Rroji

"Investigating Short-Term Dynamics in Green Bond Markets" by Lorenzo Mercuri, Andrea Perchiazzo, and Edit Rroji, arXiv:2308.12179v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets" by Shuo Sun, Molei Qin, Xinrun Wang, and Bo An, arXiv:2302.00586v2, 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 of the genetic programming configuration parameters.}
\begin{tabular}{lc}
		\toprule
		Parameter & Value \\
		\midrule
		Evolutionary algorithm type & $(\mu + \lambda)$ \\
		\midrule
		Objectives & $t, \rho$ \\
		\midrule
		Number of generations & 250 \\
		\midrule
		Initial population size & 2048 \\
		\midrule
		$\lambda$ & 256 \\
		\midrule
		$\mu$ & 256 \\
		\midrule
		Number of MPI processes & 64 \\
		\midrule
		Non-dominated sorting procedure & \\
		\midrule
		Selection operator & \\
		\midrule
		Crossover operator & Subtree crossover \\
		\midrule
		Crossover probability & $2/3$ \\
		\midrule
		Mutation operator & Random subtree insertion \\
		\midrule
		Probability to mutate a terminal symbol & $1/3$ \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

math

"Automating the Design of Multigrid Methods with Evolutionary Program Synthesis" by Jonas Schmitt, arXiv:2312.14875v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Performance on AgeDB-IT2I based on GPT-4o evaluation.} The scores are from 0 to 10, with higher scores indicating the individual resembles the well-known person.}
\begin{tabular}{l|cc|cc|cc|cc}
\toprule[1.5pt]
Datasets & \multicolumn{6}{c|}{AgeDB-IT2I} & \multicolumn{2}{c}{{VGGFace-IT2I}} \\ \midrule
Size & \multicolumn{2}{c|}{Small} & \multicolumn{2}{c|}{Medium} & \multicolumn{2}{c|}{Large} & \multicolumn{2}{c}{Large} \\ \midrule
Metric & \multicolumn{8}{c}{GPT-4o Evaluation~$\uparrow$} \\ \midrule
Shot & All & Few & All & Few & All & Few & All & Few \\ \midrule
\textsc{Vanilla} & 5.20 & 3.20 & 4.30 & 2.90 & 4.90 & 3.60 & 4.50 & 2.90 \\
\textsc{CBDM} & 4.50 & 1.10 & 1.30 & 1.00 & 3.10 & 1.70 & 2.80 & 1.30 \\
{\textsc{T2H}} & 5.50 & 3.10 & 4.60 & 3.00 & 4.70 & 3.90 & 4.60 & 3.10 \\
\textsc{PoGDiff (Ours)} & \textbf{9.10} & \textbf{8.40} & \textbf{8.80} & \textbf{8.20} & \textbf{8.50} & \textbf{8.00} & \textbf{8.20} & \textbf{7.60} \\
\bottomrule[1.5pt]
\end{tabular}
\end{table}
\end{document}

stat

Ziyan Wang
Sizhe Wei
Xiaoming Huo
Hao Wang

"PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation" by Ziyan Wang, Sizhe Wei, Xiaoming Huo, and Hao Wang, arXiv:2502.08106v2, 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{Scenarios of technological unemployment. }
\begin{tabular}{lccccc}
\toprule
& $\frac{\partial U^{A}_{t}}{\partial L_{t}} \; ( \text{if } \dot{M}_{t} > 0)$ & $\frac{\partial U^{A}_{t}}{\partial L_{t}} \; ( \text{if } \dot{M}_{t} < 0)$ & $ \partial U^{A}_{L_{t}}/\partial \dot{M}_{t} $ & $\frac{\partial U^{A}_{L_{t}} }{\partial \dot{m}^{*}_{t}} \; ( \text{if } m^{*}_{t}=m_{t}) $ & $\frac{\partial U^{A}_{L_{t}} }{\partial m^{*}_{t}}$
\\\cmidrule{2-6}
$\sigma >1 $ & $ < 0 $ & $>0$ & $<0$ & $<0$ & $=0$\\
$\sigma \in (0,1) $ & $ > 0 $ & $<0$ & $>0$& $<0$ & $=0$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

There is power in general equilibrium

"There is power in general equilibrium" by Juan Jacobo, arXiv:2309.00909v1, 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{RMSE Results for predicting FMC, Driest Month.}
\begin{tabular}{lrrr}
\toprule
Loss & Mean & Min & Max \\
\midrule
MSE & 2.507 & 1.758 & 3.334 \\
$\text{exp}\_0.0367$ & 2.433 & 1.711 & 3.232 \\
ROS & 2.408 & 1.693 & 3.211 \\
\bottomrule
\end{tabular}
\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{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Correlation coefficients between the estimated and calculated parameters.}
\begin{tabular}{lllllll}

			\toprule
			& $T_{60}$ & EDT & $C_{80}$ & $D_{50}$ & $T_s$ & STI \\ \midrule
			Simulated rooms & 0.996 & 0.996 & 0.992 & 0.994 & 0.996 & 0.997 \\ \midrule
			Real rooms & 0.915 & 0.870 & 0.918 & 0.818 & 0.822 & 0.902\\ \midrule

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

eess

Suradej Duangpummet
Jessada Karnjana
Waree Kongprawechnon
Masashi Unoki

"Blind Estimation of Room Acoustic Parameters and Speech Transmission Index using MTF-based CNNs" by Suradej Duangpummet, Jessada Karnjana, Waree Kongprawechnon, and Masashi Unoki, arXiv:2103.07904v1, 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{Information of the real-world datasets used in this paper.}
\begin{tabular}{cccc}
\toprule
\textbf{Dataset} & $\sharp$\textbf{Instances} & $\sharp$\textbf{Features} & \textbf{Task} \\
\midrule
Retail credit data & 1100000 & 69 & Classification \\
Equity price data & 71242 & 22 & Regression \\
UCI wine quality & 6197 & 12 & Regression \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Shumin Ma
Zhiri Yuan
Qi Wu
Yiyan Huang
Xixu Hu
Cheuk Hang Leung
Dongdong Wang
Zhixiang Huang

"Deep into The Domain Shift: Transfer Learning through Dependence Regularization" by Shumin Ma, Zhiri Yuan, Qi Wu, Yiyan Huang, Xixu Hu, Cheuk Hang Leung, Dongdong Wang, and Zhixiang Huang, arXiv:2305.19499v1, licensed under CC0 1.0. 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{Classification performance on multiple CASAS datasets using different preprocessing techniques - applying forward imputation, using a deep learning based interpolation network IP-Net, and applying no preprocessing. We report the mean and standard deviation of the 3-fold test F1-score across three runs.}
\begin{tabular}{l|l|l|l|l|l}
\toprule
\multirow{2}{*}{Methods \textbackslash Datasets} & Kyoto8 & Milan & Kyoto7 & Aruba & Cairo \\ \cmidrule{2-6}
 & F1 score & F1 score & F1 score & F1 score & F1 score \\ \midrule
No preprocessing & 50.9 $\pm$ 0.45 & 64.6 $\pm$ 1.05 & 76.1 $\pm$ 2.10 & 75.3 $\pm$ 1.22 & 69.4 $\pm$ 0.89 \\
IP-Net & 55.3 $\pm$ 0.42 & 68.8 $\pm$ 1.43 & 82.7 $\pm$ 1.84 & 90.4 $\pm$ 0.64 & 83.8 $\pm$ 0.42 \\
Forward imputation (Our approach) & \textbf{78.3 $\pm$ 0.95} & \textbf{80.4 $\pm$ 0.52} & \textbf{88.7 $\pm$ 0.60} & \textbf{92.4 $\pm$ 0.34} & \textbf{88.7 $\pm$ 0.22} \\\midrule
\end{tabular}
\end{table}
\end{document}

eess

"Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homes" by Srivatsa P and Thomas Plötz, arXiv:2311.09514v1, 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{NYISO: mean and std over 50 tests.}
\begin{tabular}{lcccccccc}
\toprule
\# Scenarios & mean (k\$) & std (k\$)\\
\midrule
\textit{10} & 310.00 & 6.73 \\
\textit{20} & 303.89 & 6.70 \\
\textit{30} & 302.71 & 5.95 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Dongwei Zhao
Vladimir Dvorkin
Stefanos Delikaraoglou
Alberto J. Lamadrid L.
Audun Botterud

"Uncertainty-Informed Renewable Energy Scheduling: A Scalable Bilevel Framework" by Dongwei Zhao, Vladimir Dvorkin, Stefanos Delikaraoglou, Alberto J. Lamadrid L., and Audun Botterud, arXiv:2312.03868v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Differences between averages of falling depth with the price limit and with the circuit breaker.}
\begin{tabular}{cr|rrrrrrr}
&& \multicolumn{5}{c}{$tr$} \\
&& 1000 & 2000 & 5000 & 10000 & 20000 \\ \hline
 & 10 & 146 & 54 & 16 & 12 & 11 \\
 & 20 & 280 & 95 & 27 & 21 & 9 \\
 & 50 & \cellcolor[gray]{0.90}525 & 234 & 47 & 4 & 5 \\
 $Pr$ & 100 & \cellcolor[gray]{0.90}778 & \cellcolor[gray]{0.90}427 & 92 & 0 & 1 \\
 & 200 & \cellcolor[gray]{0.90}240 & \cellcolor[gray]{0.90}589 & 319 & 1 & 1 \\
 & 500 & \cellcolor[gray]{0.90}0 & \cellcolor[gray]{0.90}0 & \cellcolor[gray]{0.90}15 & \cellcolor[gray]{0.90}359 & 403 \\
 & 1000 & \cellcolor[gray]{0.90}0 & \cellcolor[gray]{0.90}0 & \cellcolor[gray]{0.90}0 & \cellcolor[gray]{0.90}0 & \cellcolor[gray]{0.90}427
\end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative performance for domain generalization segmentation. A to B indicates A for training and B for testing. Best and second-best performances are bold and underlined, respectively. $^\dagger$ indicates fewer trainable parameters than GMS.}
\begin{tabular}{l|cc|cc}
\hline
\multirow{2}*{Model} &\multicolumn{2}{c|}{BUSI to BUS} &\multicolumn{2}{c}{BUS to BUSI} \\
\cline{2-5}
 &DSC$\uparrow$ &HD95$\downarrow$ &DSC$\uparrow$ &HD95$\downarrow$ \\
\hline
UNet &62.99 &47.26 &53.83 &96.81 \\
MultiResUNet &61.53 &53.97 &56.25 &94.31 \\
ACC-UNet &64.60 &42.87 &47.80 &135.24 \\
nnUNet &\underline{78.39} &\underline{20.53} &\underline{59.13} &\underline{89.32} \\
EGE-UNet$^\dagger$ &69.04 &34.63 &54.46 &105.23 \\
\hline
SwinUNet &78.38 &21.94 &57.47 &91.63 \\
SME-SwinUNet &74.78 &25.81 &58.28 &91.26 \\
UCTransNet &72.76 &28.47 &56.94 &94.32 \\
\hline
MixStyle &73.07 &26.52 &57.97 &93.54 \\
DSU &66.15 &40.03 &56.70 &95.31 \\
\hline
MedSegDiff-V2 &69.56 &32.51 &55.21 &98.57 \\
SDSeg &74.03 &26.32 &57.03 &94.61 \\
GSS &68.74 &35.74 &58.72 &92.57 \\
\hline
\textbf{GMS (Ours)} &\textbf{80.31} &\textbf{18.55} &\textbf{61.60} &\textbf{85.25} \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Generative Medical Segmentation

Jiayu Huo
Xi Ouyang
Sébastien Ourselin
Rachel Sparks

"Generative Medical Segmentation" by Jiayu Huo, Xi Ouyang, Sébastien Ourselin, and Rachel Sparks, arXiv:2403.18198v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{diagbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The 3D Example. The number of MINRES iterations required to reach convergence for the preconditioned system $\mathcal{P}_d^{-1} \mathcal{A}$ with block diagonal Schur complement preconditioning.}
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
\diagbox{$\mu$}{$N$} & 4046 & 7915 & 32724 & 112078 & 266555\\
 \hline
 $1$ & 59&59 &63 & 67 & 67 \\
 \hline
 $10^{-4}$ & 62 &62 & 70 & 76 &78 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"Consistency enforcement for the iterative solution of weak Galerkin finite element approximation of Stokes flow" by Weizhang Huang and Zhuoran Wang, arXiv:2412.09865v1, 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}{lcccccc}
 \toprule
 & \ \ HP\ \ \ & NORM & \multicolumn{2}{c}{TA} & \multicolumn{2}{c}{TVA}\\
 & & & HG & LG & HG & LG \\
 \midrule
 Sensitivity	&0.86	&0.79	&0.60 &0.50	&0.78	&0.52 \\
 Specificity	&0.93 &0.87	&0.92 &0.94	&0.96	&0.92 \\
 BA	&0.89	&0.83	&0.76 &0.72	&0.87	&0.72 \\
 \bottomrule
 \end{tabular}
\caption{ Sensitivity, Specificity and BA per class.}
\end{table}
\end{document}

eess

Carlo Alberto Barbano
Daniele Perlo
Enzo Tartaglione
Attilio Fiandrotti
Luca Bertero
Paola Cassoni
Marco Grangetto

"UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading" by Carlo Alberto Barbano, Daniele Perlo, Enzo Tartaglione, Attilio Fiandrotti, Luca Bertero, Paola Cassoni, and Marco Grangetto, arXiv:2101.09991v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|c|c|}
\hline
\textbf{Policy} & \textbf{Mean Correct Rate} & \textbf{SD Correct Rate} & \textbf{Mean outcome} & \textbf{SD outcome} & \textbf{WAPTS Superior Count} \\
\hline
TS\_multi & 0.654 & 0.241 & 0.564 & 0.075 & 131 \\
\hline
UR\_multi & 0.496 & 0.068 & 0.546 & 0.075 & 133 \\
\hline
WAPTS\_multi & 0.879 & 0.149 & 0.590 & 0.072 & -- \\
\hline
\end{tabular}
\caption{Comparison of policies: Mean and standard deviation (SD) of correct rates and outcomes for experiments with \(N = 50\) participants and \(K = 2\) treatments under \textbf{Scenario 1}. The final column indicates the number of simulations (out of 200) in which WAPTS outperformed the respective policy.}
\end{table}
\end{document}

stat

Haochen Song
Ilya Musabirov
Ananya Bhattacharjee
Audrey Durand
Meredith Franklin
Anna Rafferty
Joseph Jay Williams

"Adaptive Experiments Under High-Dimensional and Data Sparse Settings: Applications for Educational Platforms" by Haochen Song, Ilya Musabirov, Ananya Bhattacharjee, Audrey Durand, Meredith Franklin, Anna Rafferty, and Joseph Jay Williams, arXiv:2501.03999v2, 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}{cc}
 \toprule
 \textbf{Parameters} & \textbf{Value}\\ \hline
 $\varphi_0$ & $10^{-4}$ \\
$\varphi_1$ & $0.02$ \\
$\varphi_2$ & $0.99$ \\
$\varphi_3$ & $0.02$ \\
$\eta$ & $10^{-3}$ \\
$h_1$ & $10^{-2}$ \\
$h_2$ & $10^{-2}$ \\
$h_3$ & $10^{-2}$ \\
$\lambda_1$ & $10^{-4}$ \\
$\lambda_2$ & $10^{-4}$
\\ \bottomrule
 \end{tabular}
\caption{Choice of hyperparameters.}
\end{table}
\end{document}

math

"Mean-field underdamped Langevin dynamics and its spacetime discretization" by Qiang Fu and Ashia Wilson, arXiv:2312.16360v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

"Censorship of Online Encyclopedias: Implications for NLP Models" by Eddie Yang and Margaret E. Roberts, arXiv:2101.09294v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Parameters for CARA case of competition between portfolio managers with delayed tax effects.}
\begin{tabular}{|c||c|c|c|c|c|c|c|c|c|c|}
\hline
Parameter & $N$ & $T$ & $\mu_1$ & $\sigma$ & $r$ & $\lambda$ & $\mu_2$ & $\delta_i$ & $\theta_i$ & $X^i_{(-\infty,0]} = x^i_0$ \\
\hline
Value & 10 & 10.0 & 0.08 & 0.2 & 0.04 & 2.0 & 0.01 & $0.3 + \frac{4}{9}(i-1)$ & $0.3 + \frac{4}{9}(i-1)$ & $2 + \frac{1}{10}(i-1)$ \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Robert Balkin
Hector D. Ceniceros
Ruimeng Hu

"Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms" by Robert Balkin, Hector D. Ceniceros, and Ruimeng Hu, arXiv:2307.06450v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|}
\hline
Description & References & Geometry \\
\hline\hline
Equal-mass banana graphs & , this paper & CY 2-, 3- and 4-folds \\
\hline
Single scale triangle graphs & & Elliptic curve \\
\hline
3-loop corrections to the electron & & Sunrise elliptic curve, \\
and photon self-energies in QED & & banana K3 surface \\
\hline
3- and 4-loop ice cone integrals & , this paper & Two copies of sunrise elliptic \\
 & & curve and banana K3 surface \\
\hline
Deformed CY operators & this paper & CY 2-, 3- and 4-folds \\
\hline
Equal-mass banana graphs with & unpublished & CY 1-, 2-, 3-folds \\
one massless propagator & & \\
\hline
Gravitational scattering & & Sym. square of Legendre curve, \\
at 5PM-1SF & & CY 3-fold AESZ 3 \\
\hline
Gravitational scattering & this paper & Ap\'ery family of K3 surfaces, \\
at 5PM-2SF & & CY 3-fold \\
\hline
Generic three-mass sunset & & Elliptic curve \\
\hline
2-loop 3-point integrals for $gg\rightarrow H$ & & Two-mass sunrise elliptic curve \\
\hline
2-parameter triangle graph & & Elliptic curve \\
\hline
2-loop 4-point integrals for Bhabha & unpublished & Elliptic curve \\
and M\o ller scattering & & \\
\hline
2-loop 4-point integrals for diphoton & & Elliptic curve \\
\hline
2-loop 4-point acnode integral & unpublished & Elliptic curve \\
(diagonal box) & & \\
\hline
3-parameter double box & & Elliptic curve \\
\hline
2-loop 5-point integrals for $t\bar{t}+$jet & & Elliptic curve \\
\hline
3-loop two-mass banana graph & unpublished & K3 surface \\
\hline
4-loop two-mass banana graph & unpublished & CY 3-fold \\
\hline
Maximal cut of a non-planar & & Hyperelliptic curve of genus 2 \\
double box & & \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Claude Duhr
Sara Maggio
Christoph Nega
Benjamin Sauer
Lorenzo Tancredi
Fabian J. Wagner

"Aspects of canonical differential equations for Calabi-Yau geometries and beyond" by Claude Duhr, Sara Maggio, Christoph Nega, Benjamin Sauer, Lorenzo Tancredi, and Fabian J. Wagner, arXiv:2503.20655v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Latency Ablation.} Evaluation of a different multi-segment values ($K$) for super-resolution on the CelebA-Test dataset. Each model was trained with a fixed size of 27M parameters. We note that the FPS decreases as $K$ increases.}
\begin{tabular}{cccccccc}
\toprule
 & \multicolumn{3}{c}{Perceptual Quality} & \multicolumn{3}{c}{Distortion} & \\ \cmidrule(l){2-4} \cmidrule(l){5-7}
\multirow{-2}{*}{K} & FID($\downarrow$) & NIQE($\downarrow$) & MUSIQ($\uparrow$) & PSNR($\uparrow$) & SSIM($\uparrow$) & LPIPS($\downarrow$) & \multirow{-2}{*}{FPS($\uparrow$)} \\ \midrule\midrule
1 & 45.76 & 5.08 & 65.07 & 23.91 & 0.6612 & 0.3207 & 70.30 \\
3 & \cellcolor[HTML]{FFFFFF}{\color[HTML]{000000} 44.81} & \cellcolor[HTML]{FFFFFF}{\color[HTML]{000000} 5.01} & \cellcolor[HTML]{FFFFFF}{\color[HTML]{000000} 64.06} & \cellcolor[HTML]{FFFFFF}{\color[HTML]{000000} 23.87} & \cellcolor[HTML]{FFFFFF}{\color[HTML]{000000} 0.6579} & \cellcolor[HTML]{FFFFFF}{\color[HTML]{000000} 0.3256} & \cellcolor[HTML]{FFFFFF}{\color[HTML]{333333} 49.26} \\
5 & 44.64 & 4.96 & 64.46 & 23.85 & 0.6573 & 0.3262 & 36.35 \\
7 & 44.20 & 4.92 & 64.61 & 23.79 & 0.6548 & 0.3278 & 30.25 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Elad Cohen
Idan Achituve
Idit Diamant
Arnon Netzer
Hai Victor Habi

"Efficient Image Restoration via Latent Consistency Flow Matching" by Elad Cohen, Idan Achituve, Idit Diamant, Arnon Netzer, and Hai Victor Habi, arXiv:2502.03500v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|}
\hline
\textbf{Embedded Regime ($\tilde{d}$) } & \textbf{Stage 1} & \textbf{Stage 2 if Lapse} & \textbf{Stage 2 if No Lapse} \\ \hline
1 & SOC & SOC outreach & Continue \\ \hline
2 & SMS & SOC outreach & Continue \\ \hline
3 & CCT & SOC outreach & Continue \\ \hline
4 & SOC & SMS + CCT & Continue \\ \hline
5 & SMS & SMS + CCT & Continue \\ \hline
6 & CCT & SMS + CCT & Continue \\ \hline
7 & SOC & Navigator & Continue \\ \hline
8 & SMS & Navigator & Continue \\ \hline
9 & CCT & Navigator & Continue \\ \hline
10 & SMS & SOC outreach & Discontinue \\ \hline
11 & CCT & SOC outreach & Discontinue \\ \hline
12 & SMS & SMS + CCT & Discontinue \\ \hline
13 & CCT & SMS + CCT & Discontinue \\ \hline
14 & SMS & Navigator & Discontinue \\ \hline
15 & CCT & Navigator & Discontinue \\ \hline
\end{tabular}
\caption{List of 15 dynamic treatment regimes embedded within the Adaptive Strategies for Preventing and Treating Lapses of Retention in HIV Care (ADAPT-R) study (i.e., ADAPT-R's 15 embedded regimes). Acronyms: SOC is standard-of-care; SMS is Short Message Service; CCT is conditional cash transfer.}
\end{table}
\end{document}

stat

Lina M. Montoya
Elvin H. Geng
Harriet F. Adhiambo
Eliud Akama
Starley B. Shade
Assurah Elly
Thomas Odeny
Maya L. Petersen

"Cost Effectiveness Analyses for Sequential Multiple Assignment Randomized Trials" by Lina M. Montoya, Elvin H. Geng, Harriet F. Adhiambo, Eliud Akama, Starley B. Shade, Assurah Elly, Thomas Odeny, and Maya L. Petersen, arXiv:2502.07973v1, 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{Toy model drift estimation summary}
\begin{tabular}{|c|c|} \hline
 Relative $L^2(\rho)$ Error& 0.017\\ \hline
 Relative Trajectory Error& $4.2e-3$ $\pm$ $8.6e-3$\\ \hline
 Wasserstein Distance at $t = 25$& 0.0144\\ \hline
 Wasserstein Distance at $t = 50$&0.0149\\\hline
 Wasserstein Distance at $t = 100$& 0.0151\\\hline
 \end{tabular}
\end{table}
\end{document}

math

"Learning Stochastic Dynamical Systems with Structured Noise" by Ziheng Guo, James Greene, and Ming Zhong, arXiv:2503.01077v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Function shapes of risk measures. }
\begin{tabular}{ll}
\toprule
Risk measure & Function shape \\
\midrule
Risk-neutral & $\Psi(\cdot)=\mathbb{E}[\cdot]$ \\
Mean-variance & $\Psi(Z)=\mathbb{E}[Z]-\beta \sqrt{\mathbb{V}[Z]}$ \\
VaR & ${\rm VaR}_{\beta}(Z)=\min_{z}\left\{ z|F_{Z}(z)>\beta \right\}$ \\
Distorted expectation & $\Psi(Z)=\int_{0}^{1}F_{Z}^{-1}(\tau)dg(\tau)$ \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Yirui Zhou
Xiaowei Liu
Xiaofeng Zhang
Yangchun Zhang

"On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration" by Yirui Zhou, Xiaowei Liu, Xiaofeng Zhang, and Yangchun Zhang, arXiv:2501.12785v1, 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{Overall comparison of the Q-ALNS with Gurobi based on Pareto front}
\begin{tabular}{c|c|c|c|c|c|c|c} % 直接使用普通的 tabular
\toprule
\multicolumn{2}{c|}{Instance} & \multicolumn{2}{c|}{NR} & \multicolumn{2}{c|}{HV} & \multicolumn{2}{c}{HCC} \\
\cmidrule{1-2} \cmidrule{3-4} \cmidrule{5-6} \cmidrule{7-8}
Dock & Truck & Gurobi & Q-ALNS & Gurobi & Q-ALNS & Gurobi & Q-ALNS \\
\midrule
6 & 20 & 30.0\% & \cellcolor{gray!30}\textbf{70.0\%} & \cellcolor{gray!30}\textbf{0.313} & 0.310 & \cellcolor{gray!30}\textbf{3.588} & 2.096 \\
7 & 20 & \cellcolor{gray!30}\textbf{50.0\%} &\cellcolor{gray!30}\textbf{50.0\%} & 0.721 &\cellcolor{gray!30}\textbf{0.768} &\cellcolor{gray!30}\textbf{3.254} & 2.535 \\
8 & 20 & 64.3\% & \cellcolor{gray!30}\textbf{35.7\%} & 0.698 & \cellcolor{gray!30}\textbf{0.835} & \cellcolor{gray!30}\textbf{3.679} & 2.509 \\ % 添加空行
\multicolumn{2}{c|}{Average} & 48.1\% & \cellcolor{gray!30}\textbf{51.9\%} & 0.577 & \cellcolor{gray!30}\textbf{0.638} & \cellcolor{gray!30}\textbf{3.507} & 2.380 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

"Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm" by Yueyi Li, Mehrdad Mohammadi, Xiaodong Zhang, Yunxing Lan, and Willem van Jaarsveld, arXiv:2412.09090v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|l|l|l|l|l|}
\hline \textbf{Data} & \textbf{AMD} & \textbf{CSR} & \textbf{DR} & \textbf{MH} & \textbf{Normal} & \textbf{Total} \\ \hline
\textbf{Training} & 44 & 82 & 86 & 82 & 165 & 459\\ \hline
\textbf{Test} & 11 & 20 & 21 & 20 & 41 & 113\\ \hline
\textbf{Total} & 55 & 102 & 107 & 102 & 206 & 572\\ \hline
\textit{\% of total} & \textit{9.62\%} & \textit{17.83\%} & \textit{18.71\%} & \textit{17.83\%} & \textit{36.01\%} & \textit{100\%} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Amitojdeep Singh
Sourya Sengupta
Mohammed Abdul Rasheed
Varadharajan Jayakumar
Vasudevan Lakshminarayanan

"Uncertainty aware and explainable diagnosis of retinal disease" by Amitojdeep Singh, Sourya Sengupta, Mohammed Abdul Rasheed, Varadharajan Jayakumar, and Vasudevan Lakshminarayanan, arXiv:2101.12041v1, 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
		 UTIL 	& 18			& 37	 		& 35 			\\ \hline
		 NASH 	& 160		& 1539	 	& 1521			\\ \hline
		\end{tabular}
\caption{Perceived welfares of certain project sets to be funded if Agent 3 misrepresents her preferences.}
\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.


q-fin

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


stat

Adil Rengim Cetingoz
Charles-Albert Lehalle

"Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance" by Adil Rengim Cetingoz and Charles-Albert Lehalle, arXiv:2501.03993v4, 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{4 Node State of the System - balanced Approach}
\begin{tabular}{cccccccc}
$V_0$ & $\theta_0$ & $V_1$ & $\theta_1$ & $V_2$ & $\theta_2$ & $V_3$ & $\theta_3$ \\
\hline 1.000 & 0.00 & 0.987 & -1.59 & 0.981 & -2.40 & 0.981 & -2.40
\\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"A Simplified Formulation for the Backward/Forward Sweep Power Flow Method" by Paulo M. De Oliveira-De Jesus, arXiv:2010.06389v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|rrccc}
\textbf{Name} & $n$ & $d$ & \textbf{\# Anomalies} & \textbf{\% Anomalies} & \textbf{Category} \\ \hline
ALOI & - & 27 & 1508 & 3.04 & Image \\
Annthyroid & 7200 & 6 & 534 & 7.42 & Healthcare \\
Breastw & 683 & 9 & 239 & 34.99 & Healthcare \\
Cardio & 1831 & 21 & 176 & 9.61 & Healthcare \\
Cardiotocography & 2114 & 21 & 466 & 22.04 & Healthcare \\
Celeba & - & 39 & 4547 & 2.24 & Image \\
Cover & - & 10 & 2747 & 0.96 & Botany \\
Fault & 1941 & 27 & 673 & 34.67 & Physical \\
Glass & 214 & 7 & 9 & 4.21 & Forensic \\
Hepatitis & 80 & 19 & 13 & 16.25 & Healthcare \\
Ionosphere & 351 & 32 & 126 & 35.90 & Oryctognosy \\
Landsat & 6435 & 36 & 1333 & 20.71 & Astronautics \\
Letter & 1600 & 32 & 100 & 6.25 & Image \\
Lymphography & 148 & 18 & 6 & 4.05 & Healthcare \\
Optdigits & 5216 & 64 & 150 & 2.88 & Image \\
PageBlocks & 5393 & 10 & 510 & 9.46 & Document \\
Pendigits & 6870 & 16 & 156 & 2.27 & Image \\
Pima & 768 & 8 & 268 & 34.90 & Healthcare \\
Satellite & 6435 & 36 & 2036 & 31.64 & Astronautics \\
SpamBase & 4207 & 57 & 1679 & 39.91 & Document \\
Stamps & 340 & 9 & 31 & 9.12 & Document \\
Vertebral & 240 & 6 & 30 & 12.50 & Biology \\
Vowels & 1456 & 12 & 50 & 3.43 & Linguistics \\
Waveform & 3443 & 31 & 100 & 2.90 & Physics \\
WBC & 223 & 9 & 10 & 4.48 & Healthcare \\
WDBC & 367 & 30 & 10 & 2.72 & Healthcare \\
Wilt & 4819 & 5 & 257 & 5.33 & Botany \\
Wine & 129 & 13 & 10 & 7.75 & Chemistry \\
WPBC & 198 & 33 & 47 & 23.74	& Healthcare \\
Yeast & 1484 & 8 & 507 & 34.16 & Biology \\
\end{tabular}
\caption{Datasets description (- signifies datasets with $n>10\,000$ and randomly sampled to $n=10\,000$)}
\end{table}
\end{document}

stat

Abnormal component analysis


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Statistics of the subset of Global Voices used as validation data for tuning the NLLB hyperparameters.}
\begin{tabular}{lrr}
 \toprule
 \textbf{Language Pair} & \textbf{Sentence pairs} & \textbf{Words (M)} \\
 \hline

 \texttt{ENG} -> \texttt{CMN} & 137,737 & 2.83 \\
 \texttt{ENG} -> \texttt{SWH} & 30,338 & 1.13 \\
 \texttt{ENG} -> \texttt{IND} & 15,266 & 0.54 \\
 \texttt{ENG} -> \texttt{JPN} & 8,595 & 0.18 \\
 \texttt{ENG} -> \texttt{TUR} & 7,479 & 0.24 \\
 \texttt{ENG} -> \texttt{RON} & 4,265 & 0.17\\

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

cs

Andreea Iana
Goran Glavaš
Heiko Paulheim

"MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation" by Andreea Iana, Goran Glavaš, and Heiko Paulheim, arXiv:2403.17876v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"The Ramanujan Library -- Automated Discovery on the Hypergraph of Integer Relations" by Itay Beit-Halachmi and Ido Kaminer, arXiv:2412.12361v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Assessing the Performance of Mixed-Precision ILU(0)-Preconditioned Multiple-Precision Real and Complex Krylov Subspace Methods" by Tomonori Kouya, arXiv:2504.14498v1, 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{Probability of having a high school diploma grade (even in the event of failure) listed in the Parcoursup data (\%)}
\begin{tabular}{lcccccc}
 \toprule
 \textbf{Year} & \multicolumn{2}{c}{\textbf{General}} & \multicolumn{2}{c}{\textbf{Technological}} & \multicolumn{2}{c}{\textbf{Vocational}} \\
 \cmidrule(lr){2-3} \cmidrule(lr){4-5} \cmidrule(lr){6-7}
 & \textbf{Scholarship} & \textbf{Non-Schol.} & \textbf{Scholarship} & \textbf{Non-Schol.} & \textbf{Scholarship} & \textbf{Non-Schol.} \\
 \midrule
 2014 & 99 & 99 & 96 & 95 & 87 & 84 \\
 2015 & 99 & 99 & 97 & 95 & 90 & 86 \\
 2016 & 99 & 99 & 96 & 95 & 90 & 87 \\
 2017 & 99 & 99 & 97 & 95 & 91 & 87 \\
 2018 & 99 & 99 & 97 & 95 & 91 & 87 \\
 2019 & 99 & 99 & 97 & 95 & 90 & 87 \\
 2020 & 98 & 99 & 95 & 94 & 81 & 77 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"Quotas for scholarship recipients: an efficient race-neutral alternative to affirmative action?" by Louis Gleyo, arXiv:2507.17191v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{diagbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison between different matching schemes.}
\begin{tabular}{cccc}
		\toprule
		\textbf{Type}&\textbf{Assumption}&\textbf{Formulation}&\textbf{Limitations}\\
		\hline
		Domain-level&domain-invariant features&$p(\mathbf{x}_s)\longleftrightarrow p(\mathbf{x}_t)$&too coarse matching\\

		Class-level&class-invariant features&$p(\mathbf{x}_s|y_s)\longleftrightarrow p(\mathbf{x}_t|y_t)$&coarse matching\\

		Substructure-level&substructure-invariant features &$p(\mathbf{x}_s,y_s|o_s)\longleftrightarrow p(\mathbf{x}_t|o_t)$&\diagbox{}{}\\

		Sample-level&no strict restrictions&$(\mathbf{x}_s,y_s)\longleftrightarrow \mathbf{x}_t$& affected by noise, low-efficiency \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

cs

Wang Lu
Yiqiang Chen
Jindong Wang
Xin Qin

"Cross-domain Activity Recognition via Substructural Optimal Transport" by Wang Lu, Yiqiang Chen, Jindong Wang, and Xin Qin, arXiv:2102.03353v3, 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{Illustrations of the datasets}
\begin{tabular}{ccccc}
 \toprule
 \cmidrule(r){1-2}
 dataset & \#samples & size$_{original}$ & size$_{final}$ & \#classes\\
 \midrule
 ETH80 & 3280 & 32*32 & 8*8 & 8 \\
 MNIST & 3000 & 28*28 & 10*10 & 10 \\
 USPS & 2000 & 16*16 & 7*8 & 10 \\
 COIL20 & 1440 & 32*32 & 8*8 & 20 \\
	ORL & 400 & 32*32 & 6*6 & 40 \\
	Olivetti & 400 & 64*64 & 8*8 & 40 \\
	CMU PIE & 2500 & 32*32 & 8*8 & 50 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Riemannian Manifold Optimization for Discriminant Subspace Learning" by Wanguang Yin, Zhengming Ma, and Quanying Liu, arXiv:2101.08032v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccccccc}
 \toprule
method & sst2 & cola & mrpc & mnli & rte & qqp & average \\
 \cmidrule(r){1-1} \cmidrule(l){2-7} \cmidrule(l){8-8}
$BERT base$ & 0.925 & 0.831 & 0.821 & 0.829 & 0.700 & 0.899 & 0.833 \\
$BERT tuned$ & \textbf{0.930} & 0.831 & \textbf{0.860} & \textbf{0.835} & 0.700 & \textbf{0.900} & \textbf{0.842} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Philip Kenneweg
Sarah Schröder
Barbara Hammer

"Neural Architecture Search for Sentence Classification with BERT" by Philip Kenneweg, Sarah Schröder, and Barbara Hammer, arXiv:2403.18547v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{GRU layer Parameters}
\begin{tabular}{|c|c|c|}
		\hline
			\textbf{Layer (type)} & \textbf{Output Shape} & \textbf{Param} \\
			\hline
			GRU & [(None, 128)] & 148224 \\
			\hline
 Dense & [(None, 1024)] & 132096 \\
			\hline
 Dropout & [(None, 1024)] & 0 \\
			\hline
 Dense & [(None, 512)] & 524800 \\
			\hline
 Dropout & [(None, 512)] & 0 \\
			\hline
 Dense & [(None, 128)] & 65664 \\
			\hline
 Dropout & [(None, 128)] & 0 \\
			\hline
 Dense & [(None, 64)] & 8256 \\
			\hline
 Dense & [(None, 4)] & 260 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

eess

Maria Frasca
Davide La Torre
Gabriella Pravettoni
Ilaria Cutica

"Predicting Parkinson's disease evolution using deep learning" by Maria Frasca, Davide La Torre, Gabriella Pravettoni, and Ilaria Cutica, arXiv:2312.17290v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

A. Felipe
M. Jaenada
P. Miranda
L. Pardo

"Robust parameter estimation of the log-logistic distribution based on density power divergence estimators" by A. Felipe, M. Jaenada, P. Miranda, and L. Pardo, arXiv:2312.02662v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Material properties required for the simulations.}
\begin{tabular}{|l|l|l|}
\hline
\textbf{ Material Property} & \textbf{Values of BNT} & \textbf{Values of PDMS Matrix }\tabularnewline
\hline
\textbf{Elastic coefficients (GPa):} & & \tabularnewline
$c_{11}$ & 153.9 & $\lambda_{m}+2\mu_{m}$\tabularnewline
$c_{13}$ & 52.1 & $\lambda_{m}$\tabularnewline
$c_{33}$ & 168.1 & $\lambda_{m}+2\mu_{m}$\tabularnewline
$c_{44}$ & 82.3 & $\mu_{m}$\tabularnewline
Young\textquoteright s Modulus ($E_{m}$) & 93.0 & 0.002\tabularnewline
Poisson\textquoteright s Ratio ($\nu_{m}$) & 0.23 & 0.499\tabularnewline
\hline
\textbf{Relative permittivity: } & & \tabularnewline
$\frac{\epsilon_{11}}{\epsilon_{0}}$ & 367 & 2.72\tabularnewline
$\frac{\epsilon_{33}}{\epsilon_{0}}$ & 343 & 2.72\tabularnewline
\hline
\textbf{Piezoelectric coefficients $(pC/N)$:} & & \tabularnewline
$d_{15}$ & 87.3 & Non-piezoelectric\tabularnewline
$d_{31}$ & -15.0 & \tabularnewline
$d_{33}$ & 72.9 & \tabularnewline
\hline
\textbf{Flexoelectric coefficients $(cm^{-1})$:} & & \tabularnewline
$\mu_{11}$, longitudinal & $10^{-6}$ & $10^{-9}$\tabularnewline
$\mu_{12}$, transverse & $10^{-6}$ & $10^{-9}$\tabularnewline
$\mu_{44}$, shear & 0 & 0\tabularnewline
\hline
\textbf{Thermo-elastic coefficients:} & & \tabularnewline
$\lambda_{ij}$ & $2\times10^{-5}$.$K^{-1}$ & 3.$2\times10^{-4}$$K^{-1}$\tabularnewline
\hline
\textbf{Thermoelectric coefficient: } & & \tabularnewline
$\eta_{i}$ & $27.3\times10^{-4}$$C/m^{2}K$ . & Non-thermoelectric\tabularnewline
\hline
\textbf{Heat capacity coefficient:} & & \tabularnewline
$a_{T}$ & 500 $J/kgK$ . & 1460 $J/kgK.$\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

math

Akshayveer
Federico C Buroni
Roderick Melnik
Luis Rodriguez-Tembleque
Andres Saez
Sundeep Singh

"The influence of thermo-electromechanical coupling on the performance of lead-free BNT-type piezoelectric materials" by Akshayveer, Federico C Buroni, Roderick Melnik, Luis Rodriguez-Tembleque, Andres Saez, and Sundeep Singh, arXiv:2312.15807v1, 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{Classification results with Recurrent Neural Networks}
\begin{tabular}{|c|c|c|c|c|}
\hline
Category & Precision (\%) & Recall (\%) & F1 Score (\%) & Accuracy (\%) \\ \hline
General Level & 88.22 & 87.71 & 87.68 & 87.71 \\ \hline
Specific Level & 72.31 & 69.93 & 70.13 & 69.93 \\ \hline
Course Level & 59.49 & 52.91 & 53.99 & 52.91 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

"Classification of Pedagogical content using conventional machine learning and deep learning model" by Vedat Apuk and Krenare Pireva Nuçi, arXiv:2101.07321v1, 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{Example 2: Distribution of Male Subjects by Lens Design}
\begin{tabular}{lccc}
\toprule
 \textbf{No. of eyes with vision improvement} & \textbf{VST} & \textbf{CRT} & \textbf{Total}\\
\midrule
 0 & 11& 6& 17 \\
 1 &4 &2 &6 \\
 2 & 3 &2 &5 \\
\hline
\textbf{Total} & 18 & 10 &28 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Shuyi Liang
Takeshi Emura
Chang-Xing Ma
Yijing Xin
Xin-Wei Huang

"Testing the Homogeneity of Two Proportions for Correlated Bilateral Data via the Clayton Copula" by Shuyi Liang, Takeshi Emura, Chang-Xing Ma, Yijing Xin, and Xin-Wei Huang, arXiv:2502.00523v1, 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{Baseline average monthly level per 1000 renters per ZIP code in the pre-event period. }
\begin{tabular}{lcc}
\hline
\textbf{Variable} & \textbf{Control Group} & \textbf{Treated Group} \\
\hline
Eviction Rate & 2.664 & 2.423 \\
Filing Rate & 3.950 & 3.829 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"Keeping in Place After the Storm-Emergency Assistance and Evictions" by Bilal Islah and Ahmed Zoulati, arXiv:2505.14548v2, licensed under CC BY-SA 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{Communication system parameters}
\begin{tabular}{|l|c|c|c|c|}\hline
			Sub-carrier interval&\multicolumn{4}{|c|}{15KHz}\\ 		\hline
			Sampling rate& \multicolumn{4}{|c|}{15.36MHz} \\ 		\hline
			IFFT Size& \multicolumn{4}{|c|}{1024}\\ \hline
			Modulate& \multicolumn{4}{|c|}{QPSK}\\ \hline
			Traditional Algorithm&\multicolumn{2}{|c|}{LS}&\multicolumn{2}{|c|}{MMSE} \\ 		\hline
			NPS&2&4&8&16\\ 		\hline
		\end{tabular}
\end{table}
\end{document}

eess

Daofeng Li
Kaihe Deng
Ming Zhao
Sihai Zhang
Jinkang Zhu

"Knowledge-Driven Machine Learning: Concept, Model and Case Study on Channel Estimation" by Daofeng Li, Kaihe Deng, Ming Zhao, Sihai Zhang, and Jinkang Zhu, arXiv:2012.11178v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Correct Frequency and running time (second) of different optimization methods in finding the global maximum of different functions in Subsection . SMCOtree has highest Correct Frequency in all cases. }
\begin{tabular}{ccccccccccccccc}
\cline{1-15}
\multicolumn{3}{c}{Case} && \multicolumn{3}{c}{Method (\texttt{R})} && \multicolumn{3}{c}{Frequency (\texttt{R} | \texttt{Matlab})} && \multicolumn{3}{c}{Time$\times10^{2}~$(\texttt{R} | \texttt{Matlab})} \\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{\textbf{0.97}} && \multicolumn{3}{c}{\textbf{0.25}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{0.92}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{GD} && \multicolumn{3}{c}{0.24} && \multicolumn{3}{c}{0.40}\\
\multicolumn{3}{c}{Case 1, $d=1$} && \multicolumn{3}{c}{SA$_1$} && \multicolumn{3}{c}{\textbf{0.98}} && \multicolumn{3}{c}{1.03}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_2$} && \multicolumn{3}{c}{\textbf{0.99}} && \multicolumn{3}{c}{1.28}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{0.67} && \multicolumn{3}{c}{8.02}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{0.74} && \multicolumn{3}{c}{11.62}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{\textbf{0.99}} && \multicolumn{3}{c}{\textbf{1.29}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{0.99}} && \multicolumn{3}{c}{2.51}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.90} && \multicolumn{3}{c}{10.39}\\
\multicolumn{3}{c}{Case 2, $d=2$} && \multicolumn{3}{c}{SA$_3$} && \multicolumn{3}{c}{0.31} && \multicolumn{3}{c}{10.05}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_4$} && \multicolumn{3}{c}{0.29} && \multicolumn{3}{c}{12.54}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{0.38} && \multicolumn{3}{c}{8.76}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{0.47} && \multicolumn{3}{c}{12.70}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{\textbf{0.92}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{10.75}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{ 0.99} && \multicolumn{3}{c}{ 5.69}\\
\multicolumn{3}{c}{Case 3, Griewank, $d=2$} && \multicolumn{3}{c}{SA$_3$} && \multicolumn{3}{c}{0.00} && \multicolumn{3}{c}{38.85}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_4$} && \multicolumn{3}{c}{0.00} && \multicolumn{3}{c}{48.17}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{\textbf{0.98}} && \multicolumn{3}{c}{36.06}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{51.96}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCO} && \multicolumn{3}{c}{0.00} && \multicolumn{3}{c}{1.11}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{\textbf{8.47}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.73} && \multicolumn{3}{c}{10.04}\\
\multicolumn{3}{c}{Case 4, Rastrigin, $d=2$} && \multicolumn{3}{c}{SA$_3$} && \multicolumn{3}{c}{0.16} && \multicolumn{3}{c}{11.57}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_4$} && \multicolumn{3}{c}{0.18} && \multicolumn{3}{c}{14.42}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_1$} && \multicolumn{3}{c}{\textbf{0.99}} && \multicolumn{3}{c}{9.07}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_2$} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{13.15}\\
\cline{1-15}
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SMCOtree} && \multicolumn{3}{c}{\textbf{1.00}} && \multicolumn{3}{c}{\textbf{74.07}}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SPSA} && \multicolumn{3}{c}{0.00} && \multicolumn{3}{c}{51.17}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{SA$_5$} && \multicolumn{3}{c}{0.00} && \multicolumn{3}{c}{98.67}\\
\multicolumn{3}{c}{Case 5, Rastrigin, $d=10$} && \multicolumn{3}{c}{SA$_6$} && \multicolumn{3}{c}{0.00} && \multicolumn{3}{c}{127.31}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_3$} && \multicolumn{3}{c}{0.64} && \multicolumn{3}{c}{101.20}\\
\multicolumn{3}{c}{} && \multicolumn{3}{c}{PSO$_4$} && \multicolumn{3}{c}{0.91} && \multicolumn{3}{c}{158.94}\\
\cline{1-15}
\end{tabular}
\end{table}
\end{document}

math

Xiaohong Chen
Zengjing Chen
Wayne Yuan Gao
Xiaodong Yan
Guodong Zhang

"Optimization via Strategic Law of Large Numbers" by Xiaohong Chen, Zengjing Chen, Wayne Yuan Gao, Xiaodong Yan, and Guodong Zhang, arXiv:2412.05604v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Second-Step Estimates of Locationally-Varying Parameters}
\begin{tabular}{lrrr|rrr}
		\toprule[1pt]
		& \multicolumn{3}{c}{Panel A: Kernel-Smoothing}
		& \multicolumn{3}{c}{Panel B: Sample-Splitting} \\
		& $\beta_K(\cdot)$ & $\rho_1(\cdot)$ & $\rho_2(\cdot)$
		& $\beta_K(\cdot)$ & $\rho_1(\cdot)$ & $\rho_2(\cdot)$ \\
		\midrule

		&\multicolumn{6}{c}{$n=100$} \\
		Mean Bias 		&--0.0113& 0.0065 & 0.0046 &--0.0059&--0.0231& --0.0297 \\
		RMSE 			& 0.0569 & 0.0731 & 0.0329 & 0.1725 & 0.2552 & 0.1152 \\
		MAE 			& 0.0449 & 0.0593 & 0.0267 & 0.1446 & 0.1991 & 0.0885 \\

		&\multicolumn{6}{c}{$n=200$} \\
		Mean Bias 		&--0.0069& 0.0037 & 0.0036 &--0.0117& 0.0016 & --0.0085 \\
		RMSE 			& 0.0388 & 0.0508 & 0.0238 & 0.1196 & 0.1577 & 0.0693 \\
		MAE 			& 0.0311 & 0.0413 & 0.0193 & 0.0937 & 0.1239 & 0.0546 \\

		&\multicolumn{6}{c}{$n=400$} \\
		Mean Bias 		&--0.0070& 0.0030 & 0.0053 &--0.0073& 0.0034 & --0.0028 \\
		RMSE 			& 0.0278 & 0.0356 & 0.0178 & 0.0797 & 0.1027 & 0.0456 \\
		MAE 			& 0.0223 & 0.0290 & 0.0144 & 0.0608 & 0.0808 & 0.0362 \\

		\midrule
		\multicolumn{7}{p{10.5cm}}{\scriptsize Ours is a kernel-smoothing estimator which uses information from \textit{all} locations, albeit weighting it based on the proximity to a location of interest. The sample-splitting estimator is essentially a ``frequency estimator'' which splits the data sample by location to estimates location-specific parameters using information from that location only. $T=10$ throughout.} \\
		\bottomrule[1pt]
	\end{tabular}
\end{table}
\end{document}

q-fin

Emir Malikov
Jingfang Zhang
Shunan Zhao
Subal C. Kumbhakar

"Accounting for Cross-Location Technological Heterogeneity in the Measurement of Operations Efficiency and Productivity" by Emir Malikov, Jingfang Zhang, Shunan Zhao, and Subal C. Kumbhakar, arXiv:2302.13430v1, 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}{lcccccc}
 \hline
 \textbf{La-} & \multicolumn{3}{c}{XLM} & \multicolumn{3}{c}{LF}\\
 \cline{2-7}
 \textbf{ng} & \textbf{en} & \textbf{fr} & \textbf{all} & \textbf{en} & \textbf{fr} & \textbf{all}\\
 \hline
 en & 57.3 & \_ & 51.7 & 56.1 & \_ & 47.6\\
 fr & \_ & 71.5 & 69 & \_ & 72.9 & 59.4\\
 \hline
 \end{tabular}
\caption{Weighted F1 score for impact level classification over the data in the $0^{th}$ fold}
\end{table}
\end{document}

cs

Parag Pravin Dakle
Alolika Gon
Sihan Zha
Liang Wang
SaiKrishna Rallabandi
Preethi Raghavan

"Jetsons at FinNLP 2024: Towards Understanding the ESG Impact of a News Article using Transformer-based Models" by Parag Pravin Dakle, Alolika Gon, Sihan Zha, Liang Wang, SaiKrishna Rallabandi, and Preethi Raghavan, arXiv:2404.00386v1, 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{\textit{FlowReg-O} model details of architecture in Figure .}
\begin{tabular}{lllll}
 \toprule
Layer & Filters & Kernel & Strides & Activation \\
 \midrule
fixedInput & - & - & - & - \\
movingInput & - & - & - & - \\
concatenate & - & - & - & - \\
conv2D & 64 & 7x7 & 2, 2 & L-ReLu \\
conv2D & 128 & 5x5 & 2, 2 & L-ReLu \\
conv2D & 256 & 5x5 & 2, 2 & L-ReLu \\
conv2D & 256 & 3x3 & 1, 1 & L-ReLu \\
conv2D & 512 & 3x3 & 2, 2 & L-ReLu \\
conv2D & 512 & 3x3 & 1, 1 & L-ReLu \\
conv2D & 512 & 3x3 & 2, 2 & L-ReLu \\
conv2D & 512 & 3x3 & 1, 1 & L-ReLu \\
conv2D & 1024 & 3x3 & 2, 2 & L-ReLu \\
conv2D & 1024 & 3x3 & 1, 1 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 512 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 256 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 128 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 64 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 32 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 1, 1 & - \\
upconv2D & 2 & 4x4 & 2, 2 & - \\
upconv2D & 16 & 4x4 & 2, 2 & L-ReLu \\
conv2D & 2 & 3x3 & 2, 2 & - \\
resampler & - & - & - & - \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Sergiu Mocanu
Alan R. Moody
April Khademi

"FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow" by Sergiu Mocanu, Alan R. Moody, and April Khademi, arXiv:2101.09639v2, 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{Learning module parameters}
\begin{tabular}{|l|c|}\hline
			Net architecture&{LSTM+Full Connected Layer}\\ 		\hline
			Activation function& Relu \& Tanh \\ 		\hline
			Loss function& {MSE} \\ 		\hline
			Optimizer&{Adam}\\ 		\hline
			Hidden size&{128}\\ \hline
			Learning rate&{0.01} \\ \hline
			Batch size&{500} \\ \hline
			Training number&{27000} \\ \hline
			Test number&{3000} \\ \hline
			Epoches&{100} \\ \hline
		\end{tabular}
\end{table}
\end{document}

eess


q-fin

Giampiero M. Gallo
Demetrio Lacava
Edoardo Otranto

"Volatility jumps and the classification of monetary policy announcements" by Giampiero M. Gallo, Demetrio Lacava, and Edoardo Otranto, arXiv:2305.12192v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ccccc}
 \hline
 \hline
 Beat Groups & 8th & 16th & 12th & 32nd & 24th \\
 \hline
 \% freq & 31.3 & 20.7 & 3.1 & 1.8 & 1.3 \\
 \hline
 $F_1$ unaligned (\%) & 70.0 & 47.6 & 9.2 & 3.27 & 3.94 \\
 $F_1$ aligned (\%) & \textbf{87.9} & \textbf{64.0} & \textbf{59.2} & \textbf{21.5} & \textbf{26.3} \\
 \hline
 \hline
 \end{tabular}
\caption{Micro-$F_1$ scores of aligned and unaligned models on ``osu!mania'' dataset for each timing group. Frequency of occurrence in dataset is denoted on the second row. }
\end{table}
\end{document}

eess

"Beat-Aligned Spectrogram-to-Sequence Generation of Rhythm-Game Charts" by Jayeon Yi, Sungho Lee, and Kyogu Lee, arXiv:2311.13687v1, 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
\multicolumn{7}{|c|}{FDR @ $\alpha=0.1$} \\
\hline
& \multicolumn{3}{|c|}{Additive} & \multicolumn{3}{|c|}{Nonadditive} \\
\hline
Method & $\tau=1$ & $\tau=3$ & $\tau=5$ & $\tau=1$ & $\tau=3$ & $\tau=5$ \\
\hline
Frequentist & \textbf{ 0.04±0.05 } & \textbf{ 0.02±0.01 } & \textbf{ 0.02±0.0 } & \textbf{ 0.02±0.0 } & \textbf{ 0.02±0.0 } & \textbf{ 0.02±0.0 } \\
Add-C2G & \textbf{ 0.13±0.06 } & \textbf{ 0.11±0.02 } & \textbf{ 0.11±0.01 } & \textbf{ 0.08±0.02 } & \textbf{ 0.05±0.01 } & \textbf{ 0.03±0.01 } \\
NP-C2G & \textbf{ 0.06±0.04 } & \textbf{ 0.05±0.01 } & \textbf{ 0.08±0.01 } & \textbf{ 0.07±0.01 } & \textbf{ 0.09±0.01 } & \textbf{ 0.11±0.01 } \\
NP-Oracle & \textbf{ 0.07±0.03 } & \textbf{ 0.1±0.01 } & \textbf{ 0.1±0.0 } & \textbf{ 0.09±0.01 } & \textbf{ 0.09±0.01 } & \textbf{ 0.12±0.03 } \\
\hline
\hline
\multicolumn{7}{|c|}{Power @ $\alpha=0.1$} \\
\hline
& \multicolumn{3}{|c|}{Additive} & \multicolumn{3}{|c|}{Nonadditive} \\
\hline
Method & $\tau=1$ & $\tau=3$ & $\tau=5$ & $\tau=1$ & $\tau=3$ & $\tau=5$ \\
\hline
Frequentist & 0.0±0.0 & 0.22±0.05 & 0.67±0.06 & 0.38±0.06 & 0.57±0.04 & 0.65±0.03 \\
Add-C2G & 0.01±0.01 & 0.48±0.08 & \textbf{ 0.88±0.03 } & 0.45±0.05 & 0.53±0.03 & 0.54±0.03 \\
NP-C2G & 0.0±0.0 & 0.44±0.06 & \textbf{ 0.84±0.05 } & \textbf{ 0.51±0.06 } & \textbf{ 0.68±0.04 } & \textbf{ 0.74±0.03 } \\
NP-Oracle & \textbf{ 0.04±0.01 } & \textbf{ 0.67±0.05 } & \textbf{ 0.92±0.03 } & \textbf{ 0.58±0.05 } & \textbf{ 0.72±0.03 } & \textbf{ 0.79±0.03 } \\
\hline
\end{tabular}
\caption{Multiple testing results on synthetic data for $N=1$K. $\pm$ denotes 95\% confindence intervals. \textbf{Bolded} results in FDR section indicate that the method(s) achieved valid FDR (up to 95\% confidence intervals). \textbf{Bolded} results in power section indicate that the method(s) achieved the highest power for that setting (up to 95\% confidence intervals).}
\end{table}
\end{document}

stat

Treatment response as a latent variable

Christopher Tosh
Boyuan Zhang
Wesley Tansey

"Treatment response as a latent variable" by Christopher Tosh, Boyuan Zhang, and Wesley Tansey, arXiv:2502.08776v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Ankit Dubey
Chinmoy Kundu
Telex M. N. Ngatched
Octavia A. Dobre
Ranjan K. Mallik

"Incremental Relaying for Power Line Communication: Performance Analysis and Power Allocation" by Ankit Dubey, Chinmoy Kundu, Telex M. N. Ngatched, Octavia A. Dobre, and Ranjan K. Mallik, arXiv:2103.05711v1, 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{example of caregivers’ assignment to patients}
\begin{tabular}{|c|c|c|c|c|c|c|c|}
 			\hline
 			Patients & 5 (2) & 1 (3) & 3 (1) &1 (2)& 4 (3) & 2 (1) & 6 (2) \\
 			\hline
 			Caregivers & 1 & 2 & 2 & 1 & 1 & 2 & 1\\
 			\hline
 		\end{tabular}
\end{table}
\end{document}

math

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


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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Trajectory GOSPA metric, its decomposition and runtime}
\begin{tabular}{ccccccc}
 \hline
 & Total & Localization & Missed & False & Switch & Runtime (s) \\ \hline
 TPMBM-M & 477.7 & 341.2 & 116.5 & 4.6 & 15.5 & 613 \\
 TPMBM-DD & 483.5 & 348.5 & 111.5 & 6.7 & 16.9 & 190 \\
 B-TPMBM-G & 469.7 & 345.2 & 103.2 & 6.0 & 15.4 & 1241 \\
 B-TPMBM-MH & \textbf{454.1} & 349.8 & 82.8 & 6.2 & 15.4 & 769 \\
 T$\text{MBM}_{01}$-M & 597.0 & 358.3 & 115.4 & 107.1 & 16.2 & 410 \\
 $\text{B-TMBM}_{01}$-G & 541.5 & 349.5 & 123.1 & 52.3 & 16.2 & 1354 \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Yuxuan Xia
Ángel F. García-Fernández
Lennart Svensson

"Markov Chain Monte Carlo Multi-Scan Data Association for Sets of Trajectories" by Yuxuan Xia, Ángel F. García-Fernández, and Lennart Svensson, arXiv:2312.03423v2, 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}
 Testing protocol & $D_{KL}(P||Q)$ & $\max|P-Q|$ \\
 \midrule
 Copa & 0.017 & 0.004 \\
 Model Copa & 0.147 & 0.01 \\
 Verus & 0.101 & 0.02 \\
 Model Verus & 0.773 & 0.054 \\
 \end{tabular}
\caption{KL Divergence of the steady-state distribution of the states ($Q$) in the testing set after mixing time w.r.t. the stationary distribution ($P$) computed from the Markov model.}
\end{table}
\end{document}

eess

Muhammad Khan
Yasir Zaki
Shiva Iyer
Talal Ahamd
Thomas Pötsch
Jay Chen
Anirudh Sivaraman
Lakshmi Subramanian

"The case for model-driven interpretability of delay-based congestion control protocols" by Muhammad Khan, Yasir Zaki, Shiva Iyer, Talal Ahamd, Thomas Pötsch, Jay Chen, Anirudh Sivaraman, and Lakshmi Subramanian, arXiv:2102.04911v1, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Different LSSD database ratio with respect to the initial RAW image ratio.}
\begin{tabular}{|c|c|c|c|c|}
		\hline
		\textbf{Base name} & RAW & 100k-2M & 50k & 10k\\
		\hline
		\hline
		ALASKA2 & 62.75\% & = & = & +0.01\% \\
		\hline
		BOSS & 7.84\% & = & = & +0.01\% \\
		\hline
		Dresden & 1.23\% & = & = & +0.01\% \\
		\hline
		RAISE & 6.40\% & = & = & = \\
		\hline
		Stego App DB & 18.92\% & = & = & = \\
		\hline
		Wesaturate & 2.86\% & = & -0.01\% & -0.03\% \\
		\hline
	\end{tabular}
\end{table}
\end{document}

cs

Hugo Ruiz
Mehdi Yedroudj
Marc Chaumont
Frédéric Comby
Gérard Subsol

"LSSD: a Controlled Large JPEG Image Database for Deep-Learning-based Steganalysis "into the Wild"" by Hugo Ruiz, Mehdi Yedroudj, Marc Chaumont, Frédéric Comby, and Gérard Subsol, arXiv:2101.01495v1, 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{Validation Results with Plausibly Exogenous Galore Dataset}
\begin{tabular}{lccccc}
\hline
\textbf{Variable} & \textbf{Mean Similarity} & \textbf{Median} & \textbf{Std Dev} & \textbf{Min} & \textbf{Max} \\
\hline
Cause Similarity & 0.6140 & 0.6245 & 0.1127 & 0.2285 & 0.8798 \\
Effect Similarity & 0.6386 & 0.6467 & 0.0893 & 0.3795 & 0.8452 \\
Exogenous Variation Similarity & 0.8014 & 0.8142 & 0.0975 & 0.4920 & 0.9863 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Causal Claims in Economics

"Causal Claims in Economics" by Prashant Garg and Thiemo Fetzer, arXiv:2501.06873v1, 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
 Dataset & Size & Attributes & Pos. Ratio \\
 \hline
 \hline
 Default & 30,000 & 24 & 22.12\% \\
 Credit & 150,000 & 11 & 6.68\% \\
 Fraud & 284,807 & 31 & 0.17\% \\
 Bank & 45,211 & 17 & 11.70\% \\
 \hline
 A1 & 6,609,266 & 731 & $\delta_1$ \\
 A2 & 990,798 & 210 & $\delta_2$ \\
 A3 & 9,837,541 & 176 & $\delta_3$ \\
 \hline
 \end{tabular}
\caption{Datasets.}
\end{table}
\end{document}

q-fin

"On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech Applications" by Chengyao Wen and Yin Lou, arXiv:2311.00964v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Finite Difference Solution Ansatz approach in Least-Squares Monte Carlo" by Jiawei Huo, arXiv:2305.09166v7, 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|}
		\hline
		Test cases & Dimension & Phases & NEI cost & ESMDA cost \\
		\hline
		Test Case 1 & 100 & Single & 6 sec & 26 sec\\
		\hline
		Test Case 2 & 2000 & Two & 6.4 hours & 13.5 hours \\
		\hline
		Test Case 3 & 25200 & Single & 16 hours & doesn't converge\\
		\hline
	\end{tabular}
\caption{CPU time of NEI and ESMDA on test cases.}
\end{table}
\end{document}

math

Zhao Zhang
Xinpeng Li
Menghan Li
Jiayu Zhai
Piyang Liu
Xia Yan
Kai Zhang

"Nonlinear Expectation Inference for Efficient Uncertainty Quantification and History Matching of Transient Darcy Flows in Porous Media with Random Parameters Under Distribution Uncertainty" by Zhao Zhang, Xinpeng Li, Menghan Li, Jiayu Zhai, Piyang Liu, Xia Yan, and Kai Zhang, arXiv:2312.06923v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An Approximate Lax-Wendroff-Type Procedure for High Order Accurate Schemes for Hyperbolic Conservation Laws" by David Zorío, Antonio Baeza, and Pep Mulet, arXiv:2501.17108v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the risk tuning process}
\begin{tabular}{ccc}
\toprule
\textbf{Risk Tuning Method} & \textbf{Sample and Discard } & \textbf{Incremental Optimization} \\ \midrule
Initial Input Sample Size & 779 & 135 \\
Intial Risk Level & 0.021 & 0.117 \\
Final Input Sample Size & 771 & 324 \\
Iteration Times & 8
& 6 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Efficient Scenario Generation for Chance-constrained Economic Dispatch Considering Ambient Wind Conditions" by Qian Zhang, Apurv Shukla, and Le Xie, arXiv:2311.02250v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Rixin Yu
Marco Herbert
Markus Klein
Erdzan Hodzic

"Koopman Theory-Inspired Method for Learning Time Advancement Operators in Unstable Flame Front Evolution" by Rixin Yu, Marco Herbert, Markus Klein, and Erdzan Hodzic, arXiv:2412.08426v1, 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
 		decoder & source& note \\
 		\hline
 		MBP$_4$+ADOSD$_4$ & & based on Algorithm \\
 		MBP$_4$+OSD$_4$-$w$ & this paper & reliability metric based on Definition~\\
 		MBP$_4$+mOSD$_4$-$w$ & & reliability metric based on Definition~\\
 		\hline
 		AMBP$_4$&& No postprocessing. Can act as \\
 		&&a predecoder for ADOSD$_4$ and OSD$_4$\\
 		\hline
 		BP+mOSD($w$,$\lambda$) & & binary BP+ order-w OSD$_4$\\
 		&&with additional flips on $\lambda$ positions\\
 		\hline
 		BP-OSD-CS$\lambda$&&binary BP-OSD-0 with \\
 		&&a greedy search on ${\lambda\choose 2}$ positions\\
 		\hline
 		BP+GDG && binary BP with guided decimation \\
 		&&guessing based on a decision tree\\
 		\hline
 	\end{tabular}
\caption{Various decoders compared in this paper.}
\end{table}
\end{document}

math

Ching-Feng Kung
Kao-Yueh Kuo
Ching-Yi Lai

"Efficient Approximate Degenerate Ordered Statistics Decoding for Quantum Codes via Reliable Subset Reduction" by Ching-Feng Kung, Kao-Yueh Kuo, and Ching-Yi Lai, arXiv:2412.21118v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Complexity Comparison.}
\begin{tabular}{c||c}
 \hline \hline
 Proposed & $\mathcal{O}\left( (2K)^{3.5} + KL \right)$ \\
 \hline
 Proposed & $\mathcal{O}\left( 2K + KL \right)$ \\
 \hline
 Queue & $\mathcal{O}\left(K^{3.5} + KL \right)$ \\
 \hline
 Queue heuristic (Alg. ) & $\mathcal{O}\left(K\log_2(K) + K + KL \right)$ \\
 \hline
 Min-data layer & $\mathcal{O}\left( (2K)^{3.5} \right)$ \\
 \hline
 First layer & $\mathcal{O}\left( (2K)^{3.5} \right)$ \\
 \hline
 Queue first layer & $\mathcal{O}\left( K^{3.5} \right)$ \\
 \hline \hline
 \end{tabular}
\end{table}
\end{document}

math

Nikos G. Evgenidis
Nikos A. Mitsiou
Sotiris A. Tegos
Panagiotis D. Diamantoulakis
George K. Karagiannidis

"Split Learning in Computer Vision for Semantic Segmentation Delay Minimization" by Nikos G. Evgenidis, Nikos A. Mitsiou, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, and George K. Karagiannidis, arXiv:2412.14272v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Local Eviction Moratoria and the Spread of COVID-19" by Julia Hatamyar and Christopher F. Parmeter, arXiv:2307.00251v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Rachel Longjohn
Giri Gopalan
Emily Casleton

"Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks" by Rachel Longjohn, Giri Gopalan, and Emily Casleton, arXiv:2501.04234v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Anthony Sicilia
Xingchen Zhao
Davneet Minhas
Erin O'Connor
Howard Aizenstein
William Klunk
Dana Tudorascu
Seong Jae Hwang

"Multi-Domain Learning by Meta-Learning: Taking Optimal Steps in Multi-Domain Loss Landscapes by Inner-Loop Learning" by Anthony Sicilia, Xingchen Zhao, Davneet Minhas, Erin O'Connor, Howard Aizenstein, William Klunk, Dana Tudorascu, and Seong Jae Hwang, arXiv:2102.13147v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the Computational Complexity of }
\begin{tabular}{ccc}
			\hline\hline \\
			Method & \# of optimization variables & \# of constraints \\ \hline
			 					& $6Hn-n$ 	&	$11Hn+5n+H$ \\
			 in our approach 					& $6Hk-k$ 	&	$11Hk+5k+H$ \\	\hline\hline
$k \ll n$
		\end{tabular}
\end{table}
\end{document}

eess

Amir Farakhor
Di Wu
Yebin Wang
Huazhen Fang

"Scalable Optimal Power Management for Large-Scale Battery Energy Storage Systems" by Amir Farakhor, Di Wu, Yebin Wang, and Huazhen Fang, arXiv:2310.16333v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The averaged accuracy on the Digit-5 dataset. The standard deviation of the metric between clients is reported in parentheses. w. is the abbreviation of 'weighted', and the $\uparrow$ denotes that the higher the metric is, the better performance a model achieved, and the best performance is highlighted.}
\begin{tabular}{l|c|c}
 ~ & w. AUC ($10^{-2}$)$\uparrow$ & w. F1 ($10^{-2}$)$\uparrow$ \\
 \hline
 Local Only & 84.03(9.75) &49.73(20.38)\\
 FedAvg+FT & 96.11(1.98) &75.64(4.94) \\
 FedAvg+BN & 97.82(3.11) &83.40(12.96) \\
 FedBN & 95.71(3.79) &74.07(10.90)\\
 FedRep & 82.94(11.54) &50.14(20.88)\\
 PerFL & 96.10(2.47) &74.63(6.71)\\
 \hline
 FedCS-FC1(ours) & \bf{98.74}(0.95) &\bf{87.89}(4.76)\\
 FedCS-FC2(ours) & 98.60(1.09) &87.66(5.15)\\
 FedCS-FC3(ours) & 98.57(1.09) &87.66(5.19)\\
\end{tabular}
\end{table}
\end{document}

cs

"Client-supervised Federated Learning: Towards One-model-for-all Personalization" by Peng Yan and Guodong Long, arXiv:2403.19499v1, 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{\textit{A-set}, baseline classification accuracies for fsix CNN architectures and three class definitions.}
\begin{tabular}{|l|ccc|}
\hline
\textbf{CNN Architect.} & \textbf{Detection} & \textbf{Classification}&\textbf{Parameterization}\\\hline
MISLnet &99.12\% & 99.08\% & 87.44\%\\
TransferNet& 99.54\%& 98.66\%& 67.81\% \\
PHNet & 99.58\%& 98.76\%&84.79\%\\
SRNet & 99.47\%& 98.50\%& 83.78\%\\
DenseNet\textunderscore BC &98.89\%& 95.91\%&68.60\%\\
VGG-19& 99.90\% &99.41\% &82.11\%\\
\textbf{Avg.}&\textbf{99.42\%} & \textbf{98.39\%} & \textbf{79.08\%}\\\hline
\end{tabular}
\end{table}
\end{document}

cs

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc} \hline
		 	& 		& X (10) 	& Y (4) & Z (9)	\\ \hline
					& Budget	& 		& 		&		\\ \hline
		 Agent 1 	& 8 		& 	& + 	& +		\\ \hline
		 Agent 2 	& 1	 	& 	& + 	& +		\\ \hline
		 Agent 3 	& 10		& + 		& 		& +
		\end{tabular}
\caption{Instance where Agent 3 is misrepresenting her preferences.}
\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
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 & \textbf{Up@40} & \textbf{Up@20} & \textbf{AUUC}& \textbf{Qini} & \textbf{MSE} \\
 \hline
\textbf{S-XGB} & & 0.048 & 0.016 & 0.010 & $6*10^{-6}$ \\
\textbf{T-XGB} & & 0.056 & 0.0& 0.014 & $5*10^{-6}$\\
\textbf{X-XGB}& &0.043 &0.009 & 0.006& $21*10^{-6}$\\
\textbf{Uplift Trees}& & 0.036 & 0 & 0 & $0.93$ \\
\textbf{Double ML} && 0.04 & 0.007& 0.005 & $6*10^{-6}$\\
\textbf{Dragonnet}& & & - & - & - \\
\hline
\textbf{XGBoost} & & \textbf{0.061} & \textbf{0.029} & \textbf{0.019} & $23*10^{-6}$\\
\textbf{LogisticRegression} & & 0.053 & 0.019& 0.012 & $24*10^{-6}$ \\
\textbf{MLP} & & 0.042 &0.007 &0.005 & 0.842 \\
\textbf{NetDeconf} & & & - & - & - \\
\hline
\textbf{BipartiteGNN} & & & \textbf{0.300} & 0.009 &\textbf{0.047}\\
\textbf{HeteroSAGE} & & & \textbf{0.300} & 0.009 &\textbf{0.047}\\
\textbf{MLP+Graph} & & & 0.221	& -0.048 &	1.67\\
\hline
\textbf{Optimum} & & & 0.221	& -0.048 &	1.67\\
\hline
 \end{tabular}
\caption{Summary results on the \textsc{RetailHero} for the \textbf{purchase sum after treatment} for $20$-fold cross validation with binary outcome.}
\end{table}
\end{document}

cs

George Panagopoulos
Daniele Malitesta
Fragkiskos D. Malliaros
Jun Pang

"Uplift Modeling Under Limited Supervision" by George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, and Jun Pang, arXiv:2403.19289v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Bayes estimates of UW$(\alpha,\beta)$ for $c=0.5$, $t=0.5$ and Prior I: Lower Record Values}
\begin{tabular}{l|rrr|rrr|rrr}
		\toprule
		\multirow{2}[3]{*}{Method} & & SELF & & & LINEX & & & GELF & \\
		\cmidrule{2-10} & $\alpha$ & $\beta$ & $R(t)$ & $\alpha$ & $\beta$ & $R(t)$ & $\alpha$ & $\beta$ & $R(t)$ \\
		\midrule
		Lindley & 0.1231 & 1.5871 & 0.0876 & 0.1023 & 1.6251 & 0.0871 & 0.1104 & 1.6326 & 0.0611 \\
		T-K & 0.1461 & 1.8000 & 0.0797 & 0.1269 & 1.7078 & 0.0629 & 0.1004 & 1.6811 & 0.0482 \\
		MCMC & 0.2340 & 2.9930 & 0.1049 & 0.2038 & 2.6531 & 0.0982 & 0.0302 & 2.5683 & 0.0074 \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

stat

Qazi J. Azhad
Abdul Nasir Khan
Bhagwati Devi
Jahangir Sabbir Khan
Ayush Tripathi

"Bayesian estimation of Unit-Weibull distribution based on dual generalized order statistics with application to the Cotton Production Data" by Qazi J. Azhad, Abdul Nasir Khan, Bhagwati Devi, Jahangir Sabbir Khan, and Ayush Tripathi, arXiv:2502.02927v1, 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|ccc|ccc} \hline
& \multicolumn{3}{c|}{Speaker-balanced} & \multicolumn{3}{c}{Speaker-imbalanced} \\ \hline
Method & MCD & F0 & DUR & MCD & F0 & DUR \\ \hline \hline
DNN & 5.66 & 239 & 25.6 & \bf{5.96} & 271 & 28.0 \\
DGP & 5.66 & \bf{227} & 25.4 & 6.29 & 280 & 27.7 \\
DGPLVM & \bf{5.65} & 228 & \bf{24.9} & 6.15 & \bf{264} & \bf{27.6} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Kentaro Mitsui
Tomoki Koriyama
Hiroshi Saruwatari

"Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes" by Kentaro Mitsui, Tomoki Koriyama, and Hiroshi Saruwatari, arXiv:2008.02950v1, 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{BBO parameters for four different sets of parameters (see Algorithm for parameters)}
\begin{tabular}{llllll}
\hline
 & Symbol & Case 1 & Case 2 & Case 3 & Case 4 \\ \hline
Generation limit & $G_{max}$ & 10 & 10 & 10 & 10 \\
Population size & $N$ & 20 & 20 & 20 & 20 \\
Hybrid init. rate & $H_{ratio}$ & 0.85 & 0.85 & 1 & 1 \\
Number of elites & $E$ & 1 & 1 & 1 & 1 \\
Roll back & $RB$ & On & Off & On & Off \\
Objective weight & $\alpha$ & 0.5 & 0.5 & 0.5 & 0.5 \\
Cache size & - & 2\textasciicircum{}30 & 2\textasciicircum{}30 & 2\textasciicircum{}30 & 2\textasciicircum{}30 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Hamed Javidi
Dan Simon
Ling Zhu
Yan Wang

"A multi-objective optimization framework for on-line ridesharing systems" by Hamed Javidi, Dan Simon, Ling Zhu, and Yan Wang, arXiv:2012.05046v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Elvis Han Cui
Michael Collins
Jessica Munson
Weng Kee Wong

"Failure of Optimal Design Theory? A Case Study in Toxicology Using Sequential Robust Optimal Design Framework" by Elvis Han Cui, Michael Collins, Jessica Munson, and Weng Kee Wong, arXiv:2503.00002v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
\bf H vs BD & \bf H vs BPD & \bf BD vs BPD \\ \hline
\color{blue}\textit{(DEPID, MATTR, BI)} & \color{blue}\textit{(Nonflu., CONJ)} & \color{blue}\textit{(BI, MATTR, MLS)} \\
\color{blue}\textit{(Nonflu., Verbs)} & \color{blue}\textit{(ABS, ADV, Articles)} & \color{blue}\textit{(We, PREP)} \\
\color{blue}\textit{(PPRO, CONJ, CONJ)} & \color{green}\textit{(WPS, SP\_avg, RFC\_t)} & \color{blue}\textit{(PREP, We)} \\
\color{blue}\textit{(NEG, AUXV, NEG)} & \color{blue}\textit{(CONJ, Nonflu.)} & \color{blue}\textit{(ABS, ADV, NEG)} \\
\color{blue}\textit{(PPRO, Swear, Verbs)} & \color{blue}\textit{(You, Verbs, Nonflu.)} & \color{red}\textit{(SOC, DRI, DRI)} \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Bo Wang
Yue Wu
Niall Taylor
Terry Lyons
Maria Liakata
Alejo J Nevado-Holgado
Kate E A Saunders

"Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews" by Bo Wang, Yue Wu, Niall Taylor, Terry Lyons, Maria Liakata, Alejo J Nevado-Holgado, and Kate E A Saunders, arXiv:2008.03408v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sample table title}
\begin{tabular}{lll}
 \toprule
 \multicolumn{2}{c}{Part} \\
 \cmidrule(r){1-2}
 Name & Description & Size ($\mu$m) \\
 \midrule
 Dendrite & Input terminal & $\sim$100 \\
 Axon & Output terminal & $\sim$10 \\
 Soma & Cell body & up to $10^6$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

"Building Ears for Robots: Machine Hearing in the Age of Autonomy" by Xuan Zhong, arXiv:2312.01554v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Cyrille Feudjio
Victoire Djimna Noyum
Younous Perieukeu Mofendjou
Rockefeller
Ernest Fokoué

"A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data" by Cyrille Feudjio, Victoire Djimna Noyum, Younous Perieukeu Mofendjou, Rockefeller, and Ernest Fokoué, arXiv:2102.01647v1, 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}{lll}
\toprule
\textbf{$f_0$} & \textbf{$f_1$} & \textbf{$f_3$} \\ \midrule
$\omega^{-1}$ & - & $\omega^2$ \\
 & & $\omega\delta^{-1}$ \\
$\omega^{-2}$ & $\omega$ & - \\
 & $\delta^{-1}$ & $\omega^2$ \\
 & & $\omega\delta^{-1}$ \\
$(\omega\delta)^{-1}$ & $\omega$ & - \\
 & $\delta^{-1}$ & $\omega^2$ \\
 & & $\omega\delta^{-1}$ \\ \midrule
 & Total & 8 $\times$ 3 = 24 \\ \bottomrule \\
\end{tabular}
\caption{Table showing the enumeration of possible basic edge cases for the $\vdash\Gamma$ configuration.}
\end{table}
\end{document}

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.


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.