\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Quantitative results for the differential diagnosis of TTS using echocardiogram video dataset. }
\begin{tabular}{lcccc}
 \toprule
		\textbf{Method} & \textbf{Sensitivity} & \textbf{Specificity} & \textbf{F1-score} & \textbf{Accuracy} \\
 		\midrule
 \midrule
 DCNN (2D [SCI]) & $0.67$ & $0.78$ & $0.69$ & $0.73$ \\
 DCNN (2D [MCI]) & $0.73$ & $0.77$ & $0.73$ & $0.75$ \\
 RNN & $0.71$ & $0.79$ & $0.72$ & $0.75$ \\
 DCNN (2D+t) & $0.79$ & $0.80$ & $0.78$ & $0.80$ \\
 \midrule
 LV-SegNet + SVMC & $0.76$ & $0.84$ & $0.80$ & $0.80$ \\
 LV-SegNet + MLP & $\textbf{0.81}$ & $0.79$ & $0.81$ & $0.80$ \\
 LV-SegNet + RFC & $0.67$ & $0.75$ & $0.71$ & $0.71$ \\
 \midrule
 LV-SegNet + FSR + SVMC & $0.76$ & $0.72$ & $0.75$ & $0.74$ \\
 LV-SegNet + FSR + MLP & $0.75$ & $0.66$ & $0.73$ & $0.71$ \\
 LV-SegNet + FSR + RFC & $0.58$ & $0.75$ & $0.57$ & $0.67$ \\
 \midrule
 LV-SegNet + FSL + SVMC & $0.73$ & $0.82$ & $0.76$ & $0.78$ \\
 LV-SegNet + FSL + MLP & $0.73$ & $\textbf{0.87}$ & $0.80$ & $0.81$ \\
 \textbf{LV-SegNet + FSL + RFC} & $0.78$ & $0.85$ & $\textbf{0.82}$ & $\textbf{0.82}$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Fahim Ahmed Zaman
Wahidul Alam
Tarun Kanti Roy
Amanda Chang
Kan Liu
Xiaodong Wu

"Diagnosis Of Takotsubo Syndrome By Robust Feature Selection From The Complex Latent Space Of DL-based Segmentation Network" by Fahim Ahmed Zaman, Wahidul Alam, Tarun Kanti Roy, Amanda Chang, Kan Liu, and Xiaodong Wu, arXiv:2312.12653v2, 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
		Trajectories & USL & LSL \\
		\hline
		\textbf{Parameter 1} & 120 & 370 \\ \hline
		\textbf{Parameter 2} & -300 & 1000 \\ \hline
		\textbf{Parameter 3} & -1.9 & 1.9\\ \hline
		\textbf{Parameter 4} & -1.9 & 1.9 \\ \hline

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

stat

"Quality Control of Lifetime Drift in Discrete Electrical Parameters in Semiconductor Devices via Transition Modeling" by Lukas Sommeregger and Jürgen Pilz, arXiv:2501.07115v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
\multirow{2}{*}{Normality Test}&\multicolumn{4}{c|}{$t$}\\
\cline{2-5}
 & $10$ & $1000$ & $5000$ & $10000$\\
\hline
\multirow{2}{*}{Shapiro}&0.952 &0.968 &\textbf{0.997} &\textbf{0.998}\\
 &$(1.55\times10^{-17})$ &$(5.7\times10^{-14})$ &\textbf{(0.11)} &\textbf{(0.86)}\\
\hline
\multirow{2}{*}{Normal Test}&714.16 &67.55 &\textbf{4.19} &\textbf{0.282}\\
 &$(8.34\times10^{-156})$ &$(2.15\times10^{-15})$ &\textbf{(0.12)} &\textbf{(0.86)} \\
\hline
\multirow{2}{*}{Jarque-Bera}&61.83 &79.73 &\textbf{4.26} &\textbf{0.33}\\
 &$(3.7\times10^{-14})$ &$(4.85\times10^{-18}$ &\textbf{(0.11)} &\textbf{(0.84)} \\
\hline
\end{tabular}
\caption{Normality tests for the variable $\left(\frac{S_t}{\sqrt{t}}\right)$ for different values of $t$. Each row reports the value af the tests and, between parentheses, the $p$-value.}
\end{table}
\end{document}

q-fin

F. Lillo
G. Livieri
S. Marmi
A. Solomko
S. Vaienti

"Unimodal maps perturbed by heteroscedastic noise: an application to a financial systems" by F. Lillo, G. Livieri, S. Marmi, A. Solomko, and S. Vaienti, arXiv:2305.13475v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{siunitx}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Evaluation parameters.}
\begin{tabular}{ll}
			\toprule
			Parameter & Value \\
				\midrule
			Network size ($N$) & \num{10} nodes\\
			Packet length & [\SI{10}-\SI{100}{\byte}] \\
			AoI Threshold & [\SI{20}-\SI{200}{\milli\second}] \\
			Packet drop probability & \num{10}\% \\
			$\alpha$ & \num{0.01}\\
			$\grave{\alpha}$ & \num{0.01}\\
			$\gamma$ & \num{100}\\
			$\rho$ & \num{5}\\
			\bottomrule
		\end{tabular}
\end{table}
\end{document}

eess

Hossam Farag
Mikael Gidlund
Cedomir Stefanovic

"A Deep Reinforcement Learning Approach for Improving Age of Information in Mission-Critical IoT" by Hossam Farag, Mikael Gidlund, and Cedomir Stefanovic, arXiv:2311.13861v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
 Symbol & Meaning & Indexing \\
 \hline
 $S$ & samples & $S_i, i \in \{1, \dots, s\}$ \\
 $V$ & observed predictor variables & $V_j, j \in \{1, \dots, v\}$ \\
 $U$ & unobserved predictor variables & $U_l, l \in \{1, \dots, u\}$ \\
 $O$ & outcomes (sinks) & $O_k, k \in \{1, \dots, o\}$ \\
 $D$ & $\{V, O\}$ - observable data matrix & \\
 $\bar{D}$ & estimate of variables in D & \\
 & from their parents & \\
 $D_u$ & $\{V, O, U\}$ - implied data matrix& \\
 $\theta$ & parameters & $\theta_i, i \in \{1, \dots, t\}$ \\
 $Pa^N$ & parents of variable $N$ & $Pa^N_i, i \in \{1, \dots, p\}$\\
 $C^N$ & children of variable $N$ & $C^N_i, i \in \{1, \dots, c\}$\\
 $G$ & graph over $D$ &\\
 $G_u$ & graph over $D_u$& \\
 $R$ & residuals - matrix matching $D$ &\\
\end{tabular}
\caption{Notation}
\end{table}
\end{document}

cs

Boris Hayete
Fred Gruber
Anna Decker
Raymond Yan

"Identification of Latent Variables From Graphical Model Residuals" by Boris Hayete, Fred Gruber, Anna Decker, and Raymond Yan, arXiv:2101.02332v1, 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{Training times (in seconds) for JEVD-PCA and Baseline Approaches.}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Method} & \textbf{Diabetes} & \textbf{LSAC} & \textbf{NPHA} & \textbf{Obesity} \\ \hline
covFairPCA & 55.9304 & 0.8066 & 0.3013 & 1.1090 \\ \hline
FairPCA\_SDP\_LP & 136.7052 & 13.6891 & 0.7191 & 514.3610 \\ \hline
JevdPCA & 5.1725 & 0.4644 & 0.7174 & 12.5314 \\ \hline
StandardPCA & 17.0916 & 0.3382 & 0.0267 & 0.3314 \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"Achieving Fair PCA Using Joint Eigenvalue Decomposition" by Vidhi Rathore and Naresh Manwani, arXiv:2502.16933v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|} \hline
 M \textbackslash Guess &1 & 2 & 3 & 4 & 5 & 6 \\ \hline
 1& 99.97 & 99.91 & 99.98 & 99.66 & 99.99 & \textbf{99.95} \\ \hline
 2& 99.97 & 99.92 & 99.98 & 99.81 & 99.98 & 99.96 \\ \hline
 3& 99.97& \textbf{99.51} & \textbf{99.69} & 99.50 & 99.98 & \textbf{99.93} \\ \hline
 4& 99.97 & \textbf{99.75} & \textbf{99.97} & 99.72 & 99.96 & \textbf{99.87} \\ \hline
 5& \textbf{99.96} & 99.92 & 99.98 & 99.92 & \textbf{96.15} & \textbf{99.49} \\ \hline
 \end{tabular}
\caption{Cost functional reduction for the initial calibration of the coarse model.}
\end{table}
\end{document}

math

Anna Clevenhaus
Claudia Totzeck
Matthias Ehrhardt

"A Space Mapping approach for the calibration of financial models with the application to the Heston model" by Anna Clevenhaus, Claudia Totzeck, and Matthias Ehrhardt, arXiv:2501.14521v1, 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{Per-domain and overall inter-annotator agreement (Cohen's $\kappa$ and MUC) for coreference resolution annotation in our STM corpus.}
\begin{tabular}{l|rrrrrrrrrr|r}
	& \textit{Mat}	&\textit{Med}	&\textit{Ast}	&\textit{CS}	&\textit{Bio}	&\textit{Agr}	&\textit{ES}	&\textit{Eng}	&\textit{Che}	&\textit{MS} &\textit{Overall} \\ \hline
$\kappa$	& 0.84 & 0.80 & 0.78 & 0.72 & 0.70 & 0.66 & 0.61 & 0.58 & 0.56 & 0.52 & 0.68 \\
MUC & 0.83 & 0.69 & 0.78 & 0.73 & 0.70 & 0.72 & 0.61 & 0.66 & 0.56 & 0.63 & 0.69 \\
\end{tabular}
\end{table}
\end{document}

cs

Arthur Brack
Daniel Uwe Müller
Anett Hoppe
Ralph Ewerth

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Learning rates used for Real-ESRGAN.}
\begin{tabular}{lc}
 \toprule
 Label & Learning rate\\
 \midrule
 Linear-L1 & $1 \times 10^{-4}$
\\
 PQ-L1 & $1 \times 10^{-4}$\\
 PU21-L1 & $1 \times 10^{-4}$\\
 $\mu$-L1 & $1 \times 10^{-4}$\\
 Linear-PQ & $1 \times 10^{-5}$\\
 Linear-PU21 & $1 \times 10^{-5}$\\
 Linear-$\mu$ & $1 \times 10^{-5}$\\
 Linear-SMAPE & $1 \times 10^{-5}$\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Andrew Yanzhe Ke
Lei Luo
Xiaoyu Xiang
Yuchen Fan
Rakesh Ranjan
Alexandre Chapiro
Rafał K. Mantiuk

"Training Neural Networks on RAW and HDR Images for Restoration Tasks" by Andrew Yanzhe Ke, Lei Luo, Xiaoyu Xiang, Yuchen Fan, Rakesh Ranjan, Alexandre Chapiro, and Rafał K. Mantiuk, arXiv:2312.03640v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Causal Inference under Interference: Regression Adjustment and Optimality" by Xinyuan Fan, Chenlei Leng, and Weichi Wu, arXiv:2502.06008v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}
 \toprule
 Tokenization & $\mathbf{TSE_{type}}$ $\downarrow$ & $\mathbf{TSE_{time}}$ $\downarrow$ & $\mathbf{TSE_{dupn}}$ $\downarrow$ & $\mathbf{TSE_{nnon}}$ $\downarrow$ & $\mathbf{TSE_{nnof}}$ $\downarrow$ \\
 \midrule
 \textit{TS} + \textit{Dur} & $<10^{-3}$ & - & 0.014 & - & - \\
 \textit{TS} + \textit{NOff} & $<10^{-3}$ & - & 0.001 & 0.109 & 0.040 \\
 \textit{Pos} + \textit{Dur} & 0.002 & 0.113 & 0.032 & - & - \\
 \textit{Pos} + \textit{NOff} & 0.002 & 0.127 & 0.005 & 0.095 & 0.066 \\
 \bottomrule
 \end{tabular}
\caption{Prediction error ratios when performing autoregressive generation. - symbol stands for not concerned, and can be interpreted as 0.}
\end{table}
\end{document}

eess

Nathan Fradet
Nicolas Gutowski
Fabien Chhel
Jean-Pierre Briot

"Impact of time and note duration tokenizations on deep learning symbolic music modeling" by Nathan Fradet, Nicolas Gutowski, Fabien Chhel, and Jean-Pierre Briot, arXiv:2310.08497v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Sample size and power calculation for propensity score analysis of observational studies" by Bo Liu, Xiaoxiao Zhou, and Fan Li, arXiv:2501.11181v2, 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}{lll}
 \toprule
 Bundle & Columns Name & Description \\
 \midrule
 Funding Rounds & uuid & Funding round unique identifier\\
 & name & Funding round name (e.g., Angel Round - Facebook)\\
 & permalink & \\
 & cb\_url & Crunchbase url\\
 & rank & Crunchbase company rank\\
 & created\_at & Record creation date\\
 & updated\_at & Record last update\\
 & country\_code & \\
 & state\_code & \\
 & region & \\
 & city & \\
 & investment\_type & Investment type (e.g., angel, seed, series a)\\
 & announced\_on & \\
 & raised\_amount\_usd & \\
 & raised\_amount & \\
 & raised\_amount\_currency\_code & \\
 & post\_money\_valuation\_usd & \\
 & post\_money\_valuation & \\
 & post\_money\_valuation\_currency\_code & \\
 & investor\_count & Number of investors\\
 & org\_uuid & Investee unique identifier\\
 & org\_name & Investee name\\
 & lead\_investor\_uuids & Lead investor's unique identifier.\\
 \bottomrule
 \end{tabular}
\caption{Data entries in the Crunchbase funding rounds bundle.}
\end{table}
\end{document}

q-fin

Luca Mungo
Silvia Bartolucci
Laura Alessandretti

"Cryptocurrency co-investment network: token returns reflect investment patterns" by Luca Mungo, Silvia Bartolucci, and Laura Alessandretti, arXiv:2301.02027v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Monnie McGee
Jacob Turner
Bianca Luedeker

"Layered Dirichlet Modeling to Assess the Changing Contributions of MLB Players as they Age" by Monnie McGee, Jacob Turner, and Bianca Luedeker, arXiv:2501.09153v1, 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{DER~(\%) comparison among different audio-visual decoders in AVSD. Both the audio encoder and the speaker are ResNet.}
\begin{tabular}{lcccc}
\toprule
\hline
Method & MISS & FA & SPKERR & DER \\ \hline
BLSTM & 2.19 & 5.49 & 2.22 & 9.97 \\
Conformer & 2.01 & 5.50 & 2.10 & 9.61 \\
Cross-Attention & 1.35 & 6.26 & 1.95 & 9.57 \\
Transformer & \textbf{1.36} & \textbf{6.23} & \textbf{1.92} & \textbf{9.54} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Huan Zhao
Li Zhang
Yue Li
Yannan Wang
Hongji Wang
Wei Rao
Qing Wang
Lei Xie

"Joint Training or Not: An Exploration of Pre-trained Speech Models in Audio-Visual Speaker Diarization" by Huan Zhao, Li Zhang, Yue Li, Yannan Wang, Hongji Wang, Wei Rao, Qing Wang, and Lei Xie, arXiv:2312.04131v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"European Option Pricing Under Generalized Tempered Stable Process: Empirical Analysis" by A. H. Nzokem, arXiv:2304.06060v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Dibyendu Adak
Felipe Lepe
Gonzalo Rivera

"A noncoforming virtual element approximation for the Oseen eigenvalue problem" by Dibyendu Adak, Felipe Lepe, and Gonzalo Rivera, arXiv:2412.16813v1, 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{Tabulated Model Parameters.}
\begin{tabular}{||c||c|c|c|c|c|c|c||}
 \hline
 \bf{Site Location} & \bf{p} & \bf{d} & \bf{q} & \bf{P} & \bf{D} & \bf{Q} & \bf{Model MAPE} \\
 \hline
 \hline
 Touristic Area 1 & 0 & 0.6 & 1 & 0 & 1 & 1 & $12.12\%$ \\
 City 1 Downtown 1 & 1 & 0.8 & 1 & 0 & 1 & 1 & $8.15\%$ \\
 City 2 Mall 1 & 1 & 0.8 & 0 & 1 & 1 & 0 & $14.28\%$ \\
 Rural Area 1 & 0 & 0.4 & 1 & 0 & 1 & 1 & $10.83\%$ \\

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

math

Nassr Al-Dahabreh
Mohammad Ali Sayed
Khaled Sarieddine
Mohamed Elhattab
Maurice Khabbaz
Ribal Atallah
Chadi Assi

"A Data-Driven Framework for Improving Public EV Charging Infrastructure: Modeling and Forecasting" by Nassr Al-Dahabreh, Mohammad Ali Sayed, Khaled Sarieddine, Mohamed Elhattab, Maurice Khabbaz, Ribal Atallah, and Chadi Assi, arXiv:2312.05333v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Exchange Rate Pass-Through and Data Frequency: Firm-Level Evidence from Bangladesh" by Md Deluair Hossen, arXiv:2303.04101v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c||c|c|c|c|c|c|} \hline
 \textbf{Hyperparameter}& \textbf{40x}& \textbf{20x}& \textbf{10x}& \textbf{5x}& \textbf{2.5x} & \textbf{1.25x} \\ \hline
 Learning Rate& 1e-3& 5e-4 & 5e-4 & 5e-4 & 1e-3 & 5e-4 \\ \hline
 Weight Decay& 1e-4& 1e-4 & 1e-4 & 1e-6 & 1e-5 & 1e-5\\ \hline
 First Moment Decay& 0.95& 0.99 & 0.8 & 0.95 & 0.9 & 0.9\\ \hline
 Second Moment Decay& 0.99& 0.99 & 0.99 & 0.999 & 0.9999 & 0.999\\ \hline
 Stability Parameter& 1e-10& 1e-8 & 1e-4 & 1e-14 & 1e-4 & 1e-14\\ \hline
 Attention Layer Size& 512& 256 & 256 & 128 & 256 & 256\\ \hline
 Dropout& 0.6& 0.7 & 0.6& 0.6 & 0.7 & 0.5\\ \hline
 Max Patches& 50000& 10000& 1000& 400 & 40 & 7\\ \hline
 \end{tabular}
\caption{The hyperparameters used to train the final model at each resolution, determined through an iterative hyperparameter tuning procedure to minimise average balanced cross-entropy loss on the 5-fold cross-validation validation sets. The first five hyperparamters are all inputs to the Adam optimiser. The attention layer size was also used to set the subsequent fully connected layer size, which was set to half of this size. [This table should be transposed to match the other ones] }
\end{table}
\end{document}

eess

Jack Breen
Katie Allen
Kieran Zucker
Nicolas M. Orsi
Nishant Ravikumar

"Reducing Histopathology Slide Magnification Improves the Accuracy and Speed of Ovarian Cancer Subtyping" by Jack Breen, Katie Allen, Kieran Zucker, Nicolas M. Orsi, and Nishant Ravikumar, arXiv:2311.13956v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Fanruiqi Zeng
Zaiwei Chen
John-Paul Clarke
David Goldsman

"Nested Vehicle Routing Problem: Optimizing Drone-Truck Surveillance Operations" by Fanruiqi Zeng, Zaiwei Chen, John-Paul Clarke, and David Goldsman, arXiv:2103.01528v2, 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{Some signals and their FST along with the information on whether MT exists. We observe that the ROC is the entire $s$-plane, if there are no poles; otherwise, it is always right-side of the rightmost pole. Also, the FT exists, if the ROC includes the imaginary axis $s= j\omega$.}
\begin{tabular}{|l|l|l|}
		\hline
		Signals & Proposed FST and ROC & MT \\ \hline
	$\delta(t-t_0), \, t_0\in(0,1]$	& $t_0^{s-1}$, all $s$ & Yes \\

	$\delta(t-t_0), \, t_0\in[1,\infty)$	& $t_0^{-s^*-1}$, all $s$ & Yes \\

	$e^{-at}\,\mathrm{u}(t)$, $a>0$	& $\frac{1}{a^s}\Gamma_L(s,a)+{a^{s^*}}\Gamma_U(-s^*,a)$ & Yes\\

	${e^{-t}}\,\mathrm{u}(t)$	& $\Gamma_L(s,1)+ \Gamma_U(-s^*,1)$, $\text{Re}\{s\}>0$ & Yes\\

	$\frac{1}{e^t-1}\,\mathrm{u}(t)$	& $\sum_{\ell=1}^{\infty} \left[ \ell^{-s}\,\Gamma_L(s,\ell)+\ell^{s^*}\, \Gamma_U(-s^*,\ell)\right]$, $\text{Re}\{s\}>0$ & Yes\\

	$\mathrm{u}(t)$	& $\frac{1}{s}+\frac{1}{s^*}$, $\text{Re}\{s\}>0$ & No \\

	$t^{a}\,\mathrm{u}(t)$	& $\frac{1}{s+a}+\frac{1}{s^*-a}$, $\text{Re}\{s\}>\text{Re}\{a\}$ & No\\

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

eess

Pushpendra Singh
Anubha Gupta
Shiv Dutt Joshi

"The Generalized Fourier Transform: A Unified Framework for the Fourier, Laplace, Mellin and $Z$ Transforms" by Pushpendra Singh, Anubha Gupta, and Shiv Dutt Joshi, arXiv:2103.11905v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{FUNSD: Class distribution of the semantic entities. The dataset has two splits, i.e., training and test, and four classes of semantic entities, i.e., header, question, answer, and other.}
\begin{tabular}{cccccc}
 \toprule
 \textbf{Split} & \textbf{Header} & \textbf{Question} & \textbf{Answer} & \textbf{Other} & \textbf{Total} \\
 \midrule
 \multirow{1}{*}{Training}
 & 441 & 3,266 & 2,802 & 902 & 7,411 \\
 \multirow{1}{*}{Test}
 & 122 & 1,077 & 821 & 312 & 2,332 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Hsiu-Wei Yang
Abhinav Agrawal
Pavlos Fragkogiannis
Shubham Nitin Mulay

"Can AI Models Appreciate Document Aesthetics? An Exploration of Legibility and Layout Quality in Relation to Prediction Confidence" by Hsiu-Wei Yang, Abhinav Agrawal, Pavlos Fragkogiannis, and Shubham Nitin Mulay, arXiv:2403.18183v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Monotone Curve Estimation via Convex Duality" by Tongseok Lim, Kyeongsik Nam, and Jinwon Sohn, arXiv:2501.06975v2, 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}
 & Cluster 1 & Cluster 2 & Cluster 3 \\
Node & weight: $\frac{145}{365}$ & weight: $\frac{117}{365}$ & weight: $\frac{103}{365}$ \\ \hline \hline
 & demand: day 43 & demand: day 263 & demand: day 177 \\
Finland & wind: day 12 & wind: day 257 & wind: day 204 \\
 & solar: day 12 & solar: day 257 & solar: day 200 \\ \hline
 & demand: day 12 & demand: day 257 & demand: day 204 \\
Sweden & wind: day 10 & wind: day 320 & wind: day 199 \\
 & solar: day 6 & solar: day 91 & solar: day 159 \\ \hline
 & demand: day 12 & demand: day 257 & demand: day 204 \\
Norway & wind: day 12 & wind: day 257 & wind: day 204 \\
 & solar: day 6 & solar: day 77 & solar: day 210 \\ \hline
 & demand: day 12 & demand: day 257 & demand: day 204 \\
Denmark & wind: day 354 & wind: day 257 & wind: day 204 \\
 & solar: day 313 & solar: day 104 & solar: day 200 \\ \hline
 & demand: day 26 & demand: day 267 & demand: day 190 \\
Baltics & wind: day 88 & wind: day 258 & wind: day 141 \\
 & solar: day 323 & solar: day 89 & solar: day 150 \\ \hline \hline
\end{tabular}
\caption{Representative days for each of the clusters and nodes}
\end{table}
\end{document}

q-fin

Nikita Belyak
Steven A. Gabriel
Nikolay Khabarov
Fabricio Oliveira

"Renewable Energy Expansion under Taxes and Subsidies: A Transmission Operator's Perspective" by Nikita Belyak, Steven A. Gabriel, Nikolay Khabarov, and Fabricio Oliveira, arXiv:2302.10562v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lr}
\toprule
\textbf{(Hyper)parameter} & \textbf{Value} \\ \midrule
Architecture & OPT \\
Embed size & 768 \\
FFN dimension & 3,072 \\
Num. layers & 12 \\
Attention heads & 12 \\
Vocab size & 16,384 \\
Max. seq. length & 256 \\
Batch size & 32 \\
Warmup steps & 32,000 \\
Epochs & 20 \\
Total parameters & 97M \\
Training size & 100M tokens \\
Compute & 1x NVIDIA A40 \\
Training time & 21 hours \\ \bottomrule
\end{tabular}
\caption{LM Training details}
\end{table}
\end{document}

cs

"Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs" by Kanishka Misra and Kyle Mahowald, arXiv:2403.19827v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
 \hline
 Dataset & Nodes & Spectral Recovery Rate & QSD Recovery Rate\\
 \hline
 SBM-connected & 2000 & \textbf{1} & 0.992 \\
 SBM-bounded-degree & 2000 & .502 & \textbf{0.81}\\
 IONOSPHERE & 351 & \textbf{.69} & 0.67\\
 DIABETES & 768 & .522 & \textbf{0.67} \\
 WDBC & 683 & \textbf{.70} & 0.55\\
 POLBLOGS & 1224 & .915 & \textbf{0.943}\\
 SPAM & 4601 & .673 & \textbf{0.70 }\\
 GISETTE & 7000 & .906 & \textbf{0.959}\\
 \hline
 \end{tabular}
\caption{Comparison of Recovery Rates for the Standard Spectral and QSD methods with a $\delta = 0.1$ fraction of revealed nodes.}
\end{table}
\end{document}

math

"Semi-Supervised Community Detection via Quasi-Stationary Distributions" by Nicolas Fraiman and Michael Nisenzon, arXiv:2412.09793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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.


cs

Nuo Chen
Jiqun Liu
Hanpei Fang
Yuankai Luo
Tetsuya Sakai
Xiao-Ming Wu

"Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability" by Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, and Xiao-Ming Wu, arXiv:2403.18462v2, 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.


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.


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.


math

On the Last Kervaire Invariant Problem

"On the Last Kervaire Invariant Problem" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10879v2, 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
 $k'$ & $v_p(\Theta)$ & $v_p(x)$ & $\Delta(-1)$ \\ \hline\hline
 $k'=1$ & $-\frac{1}{2}$ & $[-\frac{1}{2},\infty]$ & $\delta_1(-1)=-1$ \\\hline
 $k'=\frac{3}{2}$ & $[-\frac{1}{2},0]$ & $-\frac{1}{2}$ & $\delta_2(-1,x)=-1+\frac{1}{x+1}$ \\\hline
 $k'=2$ & $\frac{1}{2}+v_p(x)$ & $(-\infty,-\frac{1}{2}]$ & $\delta_2(-1,x)=-1+\frac{1}{x+1}$ \\ \hline
 \end{tabular}
\caption{For $r=2$}
\end{table}
\end{document}

math

"On families of strongly divisible modules of rank 2" by Seongjae Han and Chol Park, arXiv:2503.03994v2, 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{The effect of different active learning policies in \textsc{UMGNet}.}
\begin{tabular}{clcccc}
 \toprule
 \multirow{2}{*}{\textsl{Dataset}}~ & \multirow{2}{*}{\textsc{UMGNet}} & \multicolumn{2}{c}{20\% training size} & \multicolumn{2}{c}{5\% training size} \\
 \cmidrule{3-4} \cmidrule{5-6}
 & & ~~up@40~~ & ~~ up@20~~ & ~~up@40~~ & up@20 \\% & \textbf{MSE} \\
 \midrule
\multirow{2}{*}{\textsl{RHC}} & \textsc{EG}~ & $6.01\pm 4.33$ & $3.94 \pm 3.18$~~ & $4.12 \pm 5.00$ & $4.18 \pm 3.00$\\ %
& \textsc{AL} & $6.27\pm 3.00$ & $4.64 \pm 3.60$ ~~& $5.83\pm 2.75$& $6.83 \pm 3.77$\\ % AC
\midrule
 \multicolumn{2}{c}{Improvement (\%)} & +4.3\% & +17.8\% & +41.5\% & +63.4\%
 \\
 \midrule
 \multirow{2}{*}{\textsl{RHP}} & \textsc{EG}&~ $1.91 \pm2.40$ & $3.59 \pm4.51$& $3.91 \pm 3.20$ &~	$5.66 \pm	3.45$ \\
&\textsc{AL}&~ $4.11\pm2.59$ & $4.69 \pm2.88$ &
$5.89 \pm 2.48$ &~	$6.04 \pm 2.46$ \\\midrule
\multicolumn{2}{c}{Improvement (\%)} & +115.2\% & +30.6\% & +50.6\% & +6.7\%\\
\bottomrule
 \end{tabular}
\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{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Single-objective binding affinity optimization. Report top-3 lowest $\mathrm{K_D}$ (in nanomoles/liter) found by each model. Baseline results obtained from~.}
\begin{tabular}{lcccccc}
\toprule
\multirow{2}{*}{\bf Method} & \multicolumn{3}{c}{\bf ESR1 $\mathrm{K_D}$ $(\downarrow)$} & \multicolumn{3}{c}{\bf ACAA1 $\mathrm{K_D}$ $(\downarrow)$}\\
 & 1st & 2rd & 3rd & 1st & 2rd & 3rd \\ \midrule
GCPN & 6.4 & 6.6 & 8.5 & 75 & 83 & 84 \\
MolDQN & 373 & 588 & 1062 & 240 & 337 & 608 \\
MARS & 25 & 47 & 51 & 370 & 520 & 590 \\
GraphDF & 17 & 64 & 69 & 163 & 203 & 236 \\
LIMO & 0.72 & 0.89 & 1.4 & 37 & 37 & 41 \\
LEBM-SGDS & 0.03 & 0.03 & 0.04 & 0.11 & 0.11 & 0.12 \\
\midrule
\textbf{LPT-SGDS} & \bf 0.004 &\bf 0.005 &\bf 0.014 & \bf 0.037 & \bf 0.046 & \bf 0.084
\end{tabular}
\end{table}
\end{document}

stat

Deqian Kong
Yuhao Huang
Jianwen Xie
Ying Nian Wu

"Molecule Design by Latent Prompt Transformer" by Deqian Kong, Yuhao Huang, Jianwen Xie, and Ying Nian Wu, arXiv:2310.03253v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

stat

"Dynamic Causal Structure Discovery and Causal Effect Estimation" by Jianian Wang and Rui Song, arXiv:2501.06534v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Antonio Guerriero
Roberto Pietrantuono
Stefano Russo

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sample table title}
\begin{tabular}{ll}
\bf PART &\bf DESCRIPTION
\\ \hline \\
Dendrite &Input terminal \\
Axon &Output terminal \\
Soma &Cell body (contains cell nucleus) \\
\end{tabular}
\end{table}
\end{document}

math

Rui Pan
Yuxing Liu
Xiaoyu Wang
Tong Zhang

"Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise" by Rui Pan, Yuxing Liu, Xiaoyu Wang, and Tong Zhang, arXiv:2312.14567v2, 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{MaxSpread parameter}
\begin{tabular}{|c|c|cl}
\hline
Markets & Max Spread vs Mid Market \\
\hline
BTC, ETH & 20 bps \\
\hline
Remaining & 40 bps \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

"dYdX: Liquidity Providers' Incentive Programme Review" by Colin Chan, arXiv:2307.03935v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}\hline
index & mon & stem & s & d2 \\\hline
0 & 0 & 0 & 0 & \\\hline
0 & 1,1,0 & 1 & 1 & \\\hline
0 & 1 & 2 & 1 & \\\hline
0 & 1,2,0 & 2 & 2 & \\\hline
0 & 2,1,0 & 3 & 1 & \\\hline
\end{tabular}
\caption{\texttt{C2\_AdamsE2\_basis.csv}}
\end{table}
\end{document}

math

"Machine Proofs for Adams Differentials and Extension Problems among CW Spectra" by Weinan Lin, Guozhen Wang, and Zhouli Xu, arXiv:2412.10876v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Transit-Length Distribution for Particle Transport in Binary Markovian Mixed Media" by Brian C. Kiedrowski and Emily H. Vu, arXiv:2412.19359v1, 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{System performance (DER) on development dataset (task 1)}
\begin{tabular}{lcc}
 \toprule
 \textbf{Dataset} & \textbf{Method} & \textbf{DER (\%)} \\
 \midrule
 NCTS & att-v2s + SC & 16.05 \\
 CTS & Cosine + AHC & 15.07 \\
 CTS & TSVAD & 10.60 \\
 CTS (adapt) & TSVAD round 1 & 7.80\\
 CTS (adapt) & TSVAD round 2 & 7.63 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Weiqing Wang
Qingjian Lin
Danwei Cai
Lin Yang
Ming Li

"The DKU-Duke-Lenovo System Description for the Third DIHARD Speech Diarization Challenge" by Weiqing Wang, Qingjian Lin, Danwei Cai, Lin Yang, and Ming Li, arXiv:2102.03649v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
 \toprule
 Method & Frobnability \\
 \midrule
 Theirs & Frumpy \\
 Yours & Frobbly \\
 Ours & Makes one's heart Frob\\
 \bottomrule
 \end{tabular}
\caption{Results. Ours is better.}
\end{table}
\end{document}

stat

A Power Transform

"A Power Transform" by Jonathan T. Barron, arXiv:2502.10647v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The results of the MOS test for ablation studies.}
\begin{tabular}{lcc} \toprule
Models & Quality & Similarity \\ \midrule
w/o Timbre tokens \& CMSD & 4.0 & 4.0 \\
w/o CMSD & 4.0 & 4.0 \\
w/o Two-stage training ($M_1$) & 4.0 & 4.0 \\ \midrule
EA-SVC & 4.0 & 4.0 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Haohan Guo
Heng Lu
Na Hu
Chunlei Zhang
Shan Yang
Lei Xie
Dan Su
Dong Yu

"Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training" by Haohan Guo, Heng Lu, Na Hu, Chunlei Zhang, Shan Yang, Lei Xie, Dan Su, and Dong Yu, arXiv:2012.01837v1, 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{AUSE Loss Term ablation.}
\begin{tabular}{lcccc}
 & PSNR (dB) $\uparrow$ & SSIM $\uparrow$ & LPIPS $\downarrow$& AUSE RMSE $\downarrow$\\ \hline
No AUSE Loss & \textbf{26.65} & \textbf{0.869} & \textbf{0.082} & 0.0291 \\
\textbf{SGS (Ours)} & 24.20 & 0.842 & 0.121 & \textbf{0.0147} \\ \hline \\
\end{tabular}
\end{table}
\end{document}

cs

"Modeling uncertainty for Gaussian Splatting" by Luca Savant, Diego Valsesia, and Enrico Magli, arXiv:2403.18476v1, 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}{lcccc}
 & \multicolumn{2}{c}{\textbf{No Feedback}} & \multicolumn{2}{c}{\textbf{Feedback}} \\
Data set & Precision & Recall & Precision & Recall \\
Adult & $0.575\pm0.003$ & $0.441\pm0.007$ & $0.598\pm0.003$ & $0.485\pm0.006$ \\
\end{tabular}
\caption{Results for classification feedback model, precision and recall for a model evaluated on synthetic generated by the base GAN model, and precision, recall for a model trained on synthetic data generated by the DSF-GAN with the feedback mechanism. All models are evaluated using a validation set comprised of real samples}
\end{table}
\end{document}

cs

"DSF-GAN: DownStream Feedback Generative Adversarial Network" by Oriel Perets and Nadav Rappoport, arXiv:2403.18267v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lc}
 \toprule
 Projection ($\gamma$) & Label \\
 \midrule
 No projection (Identity) & NP \\
 PCA 10 components & PCA10 \\
 PCA 100 components & PCA100 \\
 \bottomrule
 \end{tabular}
\caption{Projection options considered in this study}
\end{table}
\end{document}

cs

"Measuring Audio Prompt Adherence with Distribution-based Embedding Distances" by Maarten Grachten, arXiv:2404.00775v4, 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}{rlll}
		\toprule\toprule
		& Ticker & Name & Country \\
		\midrule
		1 & \verb|^|NDX & NASDAQ 100 & USA \\
		2 & \verb|^|GSPC & S\&P 500 & USA \\
		3 & \verb|^|HSI & Hang Seng Index & HKG \\
		4 & \verb|^|FTSE & FTSE 100 & GBR \\
		5 & \verb|^|DJI & Dow Jones Industrial Average & USA \\
		6 & \verb|^|GDAXI & DAX Performance-Index & GER \\
		7 & \verb|^|RUT & Russell 2000 & GBR \\
		8 & \verb|^|FCHI & CAC 40 & FRA \\
		9 & \verb|^|BVSP & Ibovespa & BRA \\
		10 & 000001.SS & SSE Composite Index & CHN \\
		11 & \verb|^|N225 & Nikkei 225 & JPN \\
		\bottomrule\bottomrule
	\end{tabular}
\caption{Universe of assets.}
\end{table}
\end{document}

q-fin

Bernardo Freitas Paulo da Costa
Silvana M. Pesenti
Rodrigo S. Targino

"Risk Budgeting Portfolios from Simulations" by Bernardo Freitas Paulo da Costa, Silvana M. Pesenti, and Rodrigo S. Targino, arXiv:2302.01196v1, 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{Training settings}
\begin{tabular}{|c|c|c|}
 \hline
 \textbf{Parameter} & \textbf{Value} & \textbf{Description}\\
 \hline
 epochs & 50 & -- \\
 num & 1000 & number of batches in one epoch \\ \hline
 lr & 0.003 & initial learning rate \\
 optimizer & Adam & step\_size=2, gamma=0.8 \\ \hline
 size\_step & 10 & change data\_size, level, batch\_size every size\_step epochs\\
 size & 31 & initial data size, double sizes every size\_step \\
 level & 4 & initial level, increase 1 every size\_step \\
 batch\_size & 16 & initial batch size, reduce to its half every size\_step \\
 max\_size & 511 & maximum data size\\
 min\_batch\_size & 2 & maximum batch size\\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"MGCNN: a learnable multigrid solver for sparse linear systems from PDEs on structured grids" by Yan Xie, Minrui Lv, and Chensong Zhang, arXiv:2312.11093v2, 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}{lccccccccc}
\toprule
 & \multicolumn{3}{c}{THUIR2016} & \multicolumn{3}{c}{THU-KDD} & \multicolumn{3}{c}{THUIR2018} \\
 & Target & Control & Sig. Lv. & Target & Control & Sig. Lv. & Target & Control & Sig. Lv. \\
\midrule
Clickthrough prob. & 0.139 & 0.139 & - & 0.227 & 0.207 & - & 0.306 & 0.175 & *** \\
Browsing duration & 5.323 & 4.677 & - & 8.181 & 6.744 & - & 79.403 & 28.200 & *** \\
Usefulness score & 0.361 & 0.322 & - & 0.867 & 0.780 & - & 0.598 & 0.237 & *** \\
\midrule
\# Observations & 982 & 741 & & 922 & 1790 & & 413 & 219 & \\
\bottomrule
\end{tabular}
\caption{The clickthrough probability as well as the mean value of browsing duration and usefulness score across THUIR2016, THU-KDD and THUIR2018. \textit{Sig. Lv.} stands for ``Significance Level''. *** indicates $ p < 0.001$ under t-test.}
\end{table}
\end{document}

cs

"Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability" by Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, and Xiao-Ming Wu, arXiv:2403.18462v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{User study participant demographic information.}
\begin{tabular}{l|ccc|cccc}
 \toprule[1.5pt]
 \multirow{2}{*}{Category} & \multicolumn{3}{c}{Gender Groups} & \multicolumn{4}{c}{Age Groups} \\
 \cmidrule{2-8}
 & Male&Female&Others & 18-25, &25-35, &35-45, & 45-55\\
 \midrule
 Number & 65 &53 &2 & 42 & 68 & 41 & 11\\

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

cs

Yangyong Zhang
Maliheh Shirvanian
Sunpreet S. Arora
Jianwei Huang
Guofei Gu

"Practical Speech Re-use Prevention in Voice-driven Services" by Yangyong Zhang, Maliheh Shirvanian, Sunpreet S. Arora, Jianwei Huang, and Guofei Gu, arXiv:2101.04773v1, 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{Sample table title. This is where the description of the table should go.}
\begin{tabular}{cccc}
 \hline
 & B1 &B2 & B3\\ \hline
 A1 & 0.1 & 0.2 & 0.3\\
 A2 & ... & .. & .\\
 A3 & .. & . & .\\ \hline
 \end{tabular}
\end{table}
\end{document}

cs

"Classifying Conspiratorial Narratives At Scale: False Alarms and Erroneous Connections" by Ahmad Diab, Rr. Nefriana, and Yu-Ru Lin, arXiv:2404.00141v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Kentaro Ohno
Tatsuhiko Shirai
Nozomu Togawa

"Toward Practical Benchmarks of Ising Machines: A Case Study on the Quadratic Knapsack Problem" by Kentaro Ohno, Tatsuhiko Shirai, and Nozomu Togawa, arXiv:2403.19175v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Manoj Kumar Yadav
Chirala Satyanarayana
A. Sreedhar

"Comparative study on higher order compact RBF-FD formulas with Gaussian and Multiquadric radial functions" by Manoj Kumar Yadav, Chirala Satyanarayana, and A. Sreedhar, arXiv:2412.10036v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage{rotating}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of Dataset}
\begin{tabular}{cc|c|c|c|c}
\toprule
\multicolumn{6}{c}{\textbf{Demographics Statistics}} \\
\midrule
\textbf{Attributes} & & \textbf{All} & \textbf{Train} & \textbf{Val} & \textbf{Test} \\
\midrule
\midrule
\multirow{2}{*}{\rotatebox[origin=c]{90}{\textbf{}}}
& Cases & -- & -- & -- & -- \\
& Patients & -- & -- & -- & -- \\
\multirow{3}{*}{\textbf{Gender}}
& Female & -- & -- & -- & -- \\
& Male & -- & -- & -- & -- \\
& Unknown & -- & -- & -- & -- \\
\midrule
\multirow{3}{*}{\textbf{Age}}
& 0-20 & -- & -- & -- & -- \\
& 21-40 & -- & -- & -- & -- \\
& 41-60 & -- & -- & -- & -- \\
& 61-80 & -- & -- & -- & -- \\
& 81+ & -- & -- & -- & -- \\
& Unknown & -- & -- & -- & -- \\
\midrule
\multirow{3}{*}{\textbf{Race}}
& White & -- & -- & -- & -- \\
& Black & -- & -- & -- & -- \\
& Hispanic & -- & -- & -- & -- \\
& Asian & -- & -- & -- & -- \\
& Native & -- & -- & -- & -- \\
& Other & -- & -- & -- & -- \\
& Unknown & -- & -- & -- & -- \\
\midrule
\midrule
\multicolumn{6}{c}{\textbf{Labels Statistics}} \\
\midrule
\multirow{3}{*}{\textbf{Pulmonary Embolism}}
& Positive & -- & -- & -- & -- \\
& Negative & -- & -- & -- & -- \\
\midrule
\multirow{3}{*}{\textbf{Mortality}}
& 30 day & -- & -- & -- & -- \\
& 6 months & -- & -- & -- & -- \\
& 1 year & -- & -- & -- & -- \\
\midrule
\multirow{3}{*}{\textbf{Re-admission}}
& 30 day & -- & -- & -- & -- \\
& 6 months & -- & -- & -- & -- \\
& 1 year & -- & -- & -- & -- \\
\midrule
\multirow{1}{*}{\textbf{Pulmonary Hypertension}}
& 1 year & -- & -- & -- & -- \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Shih-Cheng Huang
Zepeng Huo
Ethan Steinberg
Chia-Chun Chiang
Matthew P. Lungren
Curtis P. Langlotz
Serena Yeung
Nigam H. Shah
Jason A. Fries

"INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis" by Shih-Cheng Huang, Zepeng Huo, Ethan Steinberg, Chia-Chun Chiang, Matthew P. Lungren, Curtis P. Langlotz, Serena Yeung, Nigam H. Shah, and Jason A. Fries, arXiv:2311.10798v1, 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{Performance of the best prediction models on the test set for each externalizing score, imputation model and regularization procedure.}
\begin{tabular}{llcccccc}
\hline
 & & \multicolumn{3}{c}{Lasso mixed model} & \multicolumn{3}{c}{Adaptive lasso mixed model} \\
 Externalizing & & Number of selected & & & Number of selected \\
 score & Analysis & genetic predictors & MSPE & $R^2_{MSPE}$ & genetic predictors & MSPE & $R^2_{MSPE}$ \\
 \hline
Aggression & Complete case analysis & 601 & 0.083 & 0.261 & 419 & 0.086 & 0.235 \\
 & Single imputation & 965 & 0.066 & 0.403 & 744 & 0.069 & 0.376 \\
 Hyperactivity & Complete case analysis & 771 & 0.147 & 0.304 & 533 & 0.147 & 0.301 \\
 & Single imputation & 1080 & 0.119 & 0.420 & 905 & 0.121 & 0.411 \\
 Opposition & Complete case analysis & 851 & 0.135 & 0.310 & 628 & 0.138 & 0.294 \\
 & Single imputation & 1163 & 0.109 & 0.441 & 966 & 0.109 & 0.439 \\
 \hline
\end{tabular}
\end{table}
\end{document}

stat

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

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


q-fin

"Robust MCVaR Portfolio Optimization with Ellipsoidal Support and Reproducing Kernel Hilbert Space-based Uncertainty" by Rupendra Yadav and Aparna Mehra, arXiv:2509.00447v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{PPML Estimation Results of Labor Supply Elasticities}
\begin{tabular}{lcccc}
\hline\hline
 & \multicolumn{2}{c}{1980--2000} & \multicolumn{2}{c}{1980--2010} \\ \cline{2-5}
 & CES & CNCES & CES & CNCES \\ \hline
\(\theta\) & 3.12 (0.20) & 1.10 (0.32) & 2.85 (0.20) & 1.02 (0.30) \\
\(\rho_{\text{Cog}}\) & 0 & 0.78 (0.08) & 0 & 0.76 (0.14) \\
\(\rho_{\text{Man}}\) & 0 & 0.48 (0.11) & 0 & 0.44 (0.11) \\
\(\rho_{\text{Int}}\) & 0 & 0.75 (0.14) & 0 & 0.72 (0.18) \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

"The Labor Market Incidence of New Technologies" by Tianyu Fan, arXiv:2504.04047v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Nikolas Michael
Mihai Cucuringu
Sam Howison

"OFTER: An Online Pipeline for Time Series Forecasting" by Nikolas Michael, Mihai Cucuringu, and Sam Howison, arXiv:2304.03877v1, 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{Overlap ratios and the search results on 128 TSP-1000 instances instances in with $m=1500$ using training instances with different sizes. We select top 5 elements from each row in the heat maps. The first column denotes different training sizes.}
\begin{tabular}{lcccr}
\toprule
$n$& Overlap Ratio(\%)& Performance Gap(\%) \\
\midrule
400 & 68.83 & 3.0762 $\pm$ 1.3141 \\
1000 & 93.48 & 1.5563 $\pm$ 0.2345\\
2000 & 94.93 & 1.4145 $\pm$ 0.2005\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"On Size and Hardness Generalization in Unsupervised Learning for the Travelling Salesman Problem" by Yimeng Min and Carla P. Gomes, arXiv:2403.20212v2, 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}{lrlllrr}
\toprule
 & \multicolumn{2}{c}{\textbf{Point Coverage}} & \multicolumn{2}{c}{\textbf{Bound Coverage}} & \multicolumn{2}{c}{\textbf{Bound Narrowness}} \\
 \cmidrule(lr){2-3}\cmidrule(lr){4-5}\cmidrule(lr){6-7}
 \texttt{Adjustment} & Lagrangian & DP-DAG & Lagrangian & DP-DAG & Lagrangian & DP-DAG \\
\midrule
\texttt{parent} & $0.98 \pm 0.0$ & $\mathbf{1 \pm 0.0}$ & $0.90 \pm 0.0$ & $\mathbf{0.99 \pm 0.0}$ & $\mathbf{2.33 \pm 0.51}$ & $2.69 \pm 0.20$ \\
\texttt{optimal} & $0.98 \pm 0.0$ & $\mathbf{1 \pm 0.0}$ & $0.92 \pm 0.0$ & $\mathbf{0.99 \pm 0.0}$ & $\mathbf{1.99 \pm 0.05}$ & $2.85 \pm 0.22$ \\
\bottomrule
\end{tabular}
\caption{Metric values across different adjustment types for the random uncertainty generation.}
\end{table}
\end{document}

stat

Kirtan Padh
Zhufeng Li
Cecilia Casolo
Niki Kilbertus

"Your Assumed DAG is Wrong and Here's How To Deal With It" by Kirtan Padh, Zhufeng Li, Cecilia Casolo, and Niki Kilbertus, arXiv:2502.17030v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c||ccc|ccc}
\midrule[1pt]
 \multirow{2}{*}{Side outputs}
 & \multicolumn{3}{c|}{\textit{w/ BPM} (\textbf{Ours})} & \multicolumn{3}{c}{\textit{w/o BPM}}\\
 \cmidrule(l){2-4} \cmidrule(l){5-7}
 & $\mathcal{J} \uparrow$ & $\mathcal{S}_{\lambda} \uparrow$
 & $w\mathcal{F}_{\beta} \uparrow$
 & $\mathcal{J} \uparrow$ & $\mathcal{S}_{\lambda} \uparrow$
 & $w\mathcal{F}_{\beta} \uparrow$\\
\midrule[1pt]
$\mathbf{S}^{(5)}_{bpm}$ 	 & 62.0 & 71.6 & 67.8 & 60.2 \tiny{-1.8} & 70.4 \tiny{-1.2} & 66.0 \tiny{-1.8} \\
$\mathbf{S}^{(4)}_{bpm}$	 & 69.2 & 77.5 & 75.5 & 68.2 \tiny{-1.0} & 76.9 \tiny{-0.6} & 74.8 \tiny{-0.7} \\
$\mathbf{S}^{(3)}_{bpm}$ 	 & 72.6 & 80.2 & 78.9 & 71.9 \tiny{-0.7} & 79.8 \tiny{-0.4} & 78.3 \tiny{-0.6} \\
$\mathbf{S}^{(2)}_{bpm}$ 	 & 73.7 & 81.1 & 79.9 & 72.9 \tiny{-0.8} & 80.6 \tiny{-0.5} & 79.4 \tiny{-0.5} \\
\hline
\hline
$\mathbf{S}^{(1)}$ & \textbf{73.7} & \textbf{81.1} & \textbf{80.0} & \textbf{73.0} \tiny{-0.7} & \textbf{80.7} \tiny{-0.4} & \textbf{79.5} \tiny{-0.5} \\
\toprule[1pt]
\end{tabular}
\end{table}
\end{document}

cs

Gongyang Li
Zhi Liu
Ran Shi
Zheng Hu
Weijie Wei
Yong Wu
Mengke Huang
Haibin Ling

"Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation" by Gongyang Li, Zhi Liu, Ran Shi, Zheng Hu, Weijie Wei, Yong Wu, Mengke Huang, and Haibin Ling, arXiv:2101.09014v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
		\hline
		Kernel function & PICP \\
		\hline
		Gaussian+Laplacian & 100.0 \% \\ \hline
		Gaussian+Exponential & 100.0 \% \\ \hline
		Gaussian & 100.0\% \\ \hline
		Rational Quadratic+Laplacian & 100.0\% \\ \hline
		Matérn+Laplacian & 100.0 \% \\ \hline
		Rational Quadratic+Gaussian & 90.0 \% \\ \hline
		Matérn+Gaussian+Laplacian & 100.0 \% \\
		\hline
	\end{tabular}
\end{table}
\end{document}

math

Juan Zhang
Junyue Luo
Fangfang Zhang
Xiaoqiang Yue

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


stat

"On Quantile Regression Forests for Modelling Mixed-Frequency and Longitudinal Data" by Mila Andreani, arXiv:2502.17137v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{arydshln}
\usepackage{amsfonts}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrrr} \toprule
 & Distribution & $\mathbb{E}\lbrack Y_j\rbrack$ & $\mathrm{Var}\lbrack Y_j \rbrack$ & $\mathrm{DI}\lbrack Y_j \rbrack$ & $\mathrm{ZI}\lbrack Y_j \rbrack$ \\ \midrule
\multirow{2}{*}{$j = 1$} & ZANIM & 2.320 & 14.326 & 6.174 & 0.341 \\
 & ZANIDM & 2.320 & 16.392 & 7.064 & 0.492 \\
 \hdashline
\multirow{2}{*}{$j = 2$} & ZANIM & 18.496 & 69.178 & 3.740 & 0.897 \\
 & ZANIDM & 18.496 & 72.723 & 3.932 & 0.897 \\
 \hdashline
\multirow{2}{*}{$j = 3$} & ZANIM & 9.161 & 50.409 & 5.502 & 0.749 \\
 & ZANIDM & 9.161 & 54.658 & 5.966 & 0.750 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

André F. B. Menezes
Andrew C. Parnell
Keefe Murphy

"Finite mixture representations of zero-&-$N$-inflated distributions for count-compositional data" by André F. B. Menezes, Andrew C. Parnell, and Keefe Murphy, arXiv:2501.13879v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cccc}
\toprule
\bf Model & En$\to$Fr & En$\to$De & Fr$\to$En & De$\to$En\\
\midrule
Text-only MNMT & 63.8 & 40.2 & 52.0 & 42.5 \\
\midrule
ResNet50 & 64.2 & 40.6 & 52.3 & 43.1 \\
ResNet101 & 64.4 & 40.8 & 52.4 & 43.4 \\
ViT-B/32 & 64.8 & 41.6 & \bf 53.8 & 45.0 \\
ViT-B/16 & 65.1 & 41.8 & 53.6 & 44.8 \\
ViT-B/14 & \bf 65.2 & \bf 41.9 & 53.4 & \bf 45.2 \\
\bottomrule
\end{tabular}
\caption{Comparison of different vision backbones (e.g., CNN and Transformer backbones) on the Flickr2016 test set.}
\end{table}
\end{document}

cs

Jian Yang
Hongcheng Guo
Yuwei Yin
Jiaqi Bai
Bing Wang
Jiaheng Liu
Xinnian Liang
Linzheng Cahi
Liqun Yang
Zhoujun Li

"m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt" by Jian Yang, Hongcheng Guo, Yuwei Yin, Jiaqi Bai, Bing Wang, Jiaheng Liu, Xinnian Liang, Linzheng Cahi, Liqun Yang, and Zhoujun Li, arXiv:2403.17556v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"The Geopolitical Determinants of Economic Growth, 1960-2019" by Tianyu Fan, arXiv:2507.04833v2, 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}{lrrrrrrrrrrl}
\toprule
& Med & SD & Min & Max & Skew & Kurt & ADF\textsubscript{12} & ADF\textsubscript{1} & KPSS & $N$ & Time\\
\midrule
DE & 138.53 & 80.91 & 28.43 & 498.06 & 2.60 & 9.51 & 0.22 & 0.01 & 0.10 & 2 & 1993M1-2021M1\\
IN & 80.10 & 50.27 & 24.94 & 283.69 & -1.49 & 5.82 & 0.42 & 0.01 & 0.01 & 7 & 2003M1-2021M1\\
JP & 105.02 & 34.52 & 48.37 & 240.24 & -1.50 & 7.51 & 0.28 & 0.01 & 0.08 & 6 & 1990M1-2021M1\\
KR & 129.70 & 70.87 & 37.31 & 538.18 & 2.74 & 11.68 & 0.05 & 0.01 & 0.10 & 3 & 1990M1-2021M1\\
US & 116.25 & 69.98 & 44.78 & 503.96 & 2.56 & 11.23 & 0.89 & 0.01 & 0.09 & 10 & 1985M1-2021M1\\
\bottomrule
\end{tabular}
\caption{Summary statistics of the EPU variables, with $p$-values for the Augmented Dickey-Fuller (ADF) and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. $N$ is the number of newspapers used for construction.}
\end{table}
\end{document}

q-fin

"Adaptive local VAR for dynamic economic policy uncertainty spillover" by Niels Gillmann and Ostap Okhrin, arXiv:2302.02808v1, 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{Descriptives}
\begin{tabular}{lcccccccccccccccccccc}
\toprule
			 & N 	& 	mean & sd \\
\midrule
Years of education 			& 83,647 & 13.94 & (5.140) \\
Birth weight (in g)			& 47,476 & 3331.8 & (672.2) \\
BMI	 	 				& 83,647 & 27.57 & (4.790) \\
Height (in cm) 				& 83,647 & 168.52 & (9.216) \\
Cardiovascular disease		& 83,647 & 0.33 & (0.471) \\
Type-2 diabetes 			& 83,647 & 0.05 & (0.222) \\
Sugar intake 			& 36,131 & 0.23 & (0.069) \\
Carbohydrate intake 		& 36,131 & 0.48 & (0.082) \\
Fat intake 			& 36,131 & 0.33 & (0.067) \\
Male 						& 	 84,669 & 0.45 & (0.498) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Gerard J. van den Berg
Stephanie von Hinke
R. Adele H. Wang

"Prenatal Sugar Consumption and Late-Life Human Capital and Health: Analyses Based on Postwar Rationing and Polygenic Scores" by Gerard J. van den Berg, Stephanie von Hinke, and R. Adele H. Wang, arXiv:2301.09982v1, 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{Population Characteristics in Altanta Birthweight Study.}
\begin{tabular}{lc}
 \hline
 \textbf{Characteristics} & \textbf{Overall (N = 273,711)} \\
 \hline
 \textbf{Maternal race/ethnicity} & \\
 \hspace{5mm} White & 121,056 (44.2\%) \\
 \hspace{5mm} Black & 84,193 (30.8\%) \\
 \hspace{5mm} Asian & 13,114 (4.8\%) \\
 \hspace{5mm} Hispanic & 53,831 (19.7\%) \\
 \hspace{5mm} Other & 1,517 (0.6\%) \\
 \textbf{Maternal age} & \\
 \hspace{5mm} 16 - 25 years & 89,401 (32.7\%) \\
 \hspace{5mm} 25 - 31 years & 104,352 (38.1\%) \\
 \hspace{5mm} 31 - 43 years & 79,958 (29.2\%) \\
 \textbf{Maternal education} & \\
 \hspace{5mm} Less than 9th grade & 22,656 (8.3\%) \\
 \hspace{5mm} 9th-12th grade & 39,236 (14.3\%) \\
 \hspace{5mm} High School & 76,481 (27.9\%)\\
 \hspace{5mm} College and above & 135,338 (49.4\%) \\
 \textbf{Maternal tobacco use} & 12,644 (4.6\%)\\
 \textbf{Maternal alcohol use} & 1,723 (0.6\%) \\
 \textbf{Maternal married} & 174,009 (63.6\%) \\
 \textbf{Previous birth} & 164,202 (60.0\%)\\
 \textbf{Gestational weeks} & \\
 \hspace{5mm} Mean (SD) & 38.7 (1.8) \\
 \hspace{5mm} [Min, Max] & [28.0, 44.0] \\
 \textbf{Census block group percent poverty levels} &\\
 \hspace{5mm} Mean (SD) & 0.10 (0.10) \\
 \hspace{5mm} [Min, Max] & [0, 0.77] \\
 \hline
 \end{tabular}
\end{table}
\end{document}

stat

Danlu Zhang
Stephanie M. Eick
Howard H. Chang

"Approximate Bayesian Kernel Machine Regression via Random Fourier Features for Estimating Joint Health Effects of Multiple Exposures" by Danlu Zhang, Stephanie M. Eick, and Howard H. Chang, arXiv:2502.13157v1, 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{WD, FAD and KL Divergence metrics}
\begin{tabular}{|c|c|c|c|c|c|c|}\hline
 & \multicolumn{3}{|c|}{Training}& \multicolumn{3}{|c|}{Test}\\\hline
 & WD&FAD& KL & WD&FAD&KL\\ \hline
 LZMidi& \textbf{8.57}&$\textbf{0.69}$& \textbf{1.42} & \textbf{8.39}& \textbf{0.64}&\textbf{1.37}\\ \hline
 ASD3PM& 27.91&4.22&$2.29$ & 27.96&4.05&2.26\\\hline
 \end{tabular}
\end{table}
\end{document}

math

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The table lists the average standard deviation of mean capacity for each of the zero, one and two period foresight models. That is a for a single path of 1000 periods, we compute the standard deviation of the mean of the population capacity. We then do this over 1000 paths to compute the expected variability. We observe that the standard deviation in mean capacity increases with the degree of foresight.}
\begin{tabular}{c||c|c|c}
 & \textbf{Zero} & \textbf{One} & \textbf{Two} \\
 \hline
 $\sigma_{\bar{x}}$ & 0.087 & 0.093 & 0.101 \\
 \end{tabular}
\end{table}
\end{document}

q-fin

"Bounded Foresight Equilibrium in Large Dynamic Economies with Heterogeneous Agents and Aggregate Shocks" by Bilal Islah and Bar Light, arXiv:2502.16536v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Leonardo Neumann
Antonio Guimarães
Diego F. Aranha
Edson Borin

"Homomorphic WiSARDs: Efficient Weightless Neural Network training over encrypted data" by Leonardo Neumann, Antonio Guimarães, Diego F. Aranha, and Edson Borin, arXiv:2403.20190v1, 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|c|c|c}\toprule\midrule
 Method & PESQ & STOI & SDR \\ \midrule
 Noisy & 1.72 & 0.66 & 0.19 \\
 w/o GCN & 2.08 & 0.71 & 7.05 \\
 w/ GCN & 2.13 & 0.73 & 7.73 \\
 \end{tabular}
\caption{Proposed model with and without the graph representation.}
\end{table}
\end{document}

eess

Panagiotis Tzirakis
Anurag Kumar
Jacob Donley

"Multi-Channel Speech Enhancement using Graph Neural Networks" by Panagiotis Tzirakis, Anurag Kumar, and Jacob Donley, arXiv:2102.06934v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Silvia García-Méndez
Francisco de Arriba-Pérez
Francisco J. González-Castaño
José A. Regueiro-Janeiro
Felipe Gil-Castiñeira

"Entertainment chatbot for the digital inclusion of elderly people without abstraction capabilities" by Silvia García-Méndez, Francisco de Arriba-Pérez, Francisco J. González-Castaño, José A. Regueiro-Janeiro, and Felipe Gil-Castiñeira, arXiv:2404.01327v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{A defaults table showing the number $d_{tv}$ of defaults across cohorts $t=t_1,\dots,t_M$ over their lifetimes $v=1,2,\dots$. From .}
\begin{tabular}{llccccccc}
\toprule
\multirow{2}[3]{*}{
\textbf{Cohort} ($t$)} & \multirow{2}[3]{*}{\textbf{Initial account volume} ($n'_{t0}$)} & \multicolumn{7}{c}{\textbf{Lifetime $(v)$} } \\
\cmidrule(l){3-9}
& & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\
\midrule
201501 & 500 & 10 & 5 & 4 & 8 & 6 & 3 & 3 \\
201502 & 550 & 11 & 5 & 6 & 3 & 7 & 5 & \\
201503 & 600 & 13 & 5 & 7 & 4 & 6 & & \\
201504 & 650 & 14 & 6 & 6 & 5 & & & \\
201505 & 700 & 15 & 5 & 7 & & & & \\
201506 & 750 & 14 & 7 & & & & & \\
201507 & 800 & 16 & & & & & & \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Approaches for modelling the term-structure of default risk under IFRS 9: A tutorial using discrete-time survival analysis" by Arno Botha and Tanja Verster, arXiv:2507.15441v1, 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{The statistics of datasets. \textsc{\# Train} and \textsc{\# Test} denote the number of training and test samples respectively. The unit for OpenWebMath is the number of documents.}
\begin{tabular}{l|rr}
 \toprule
 Dataset & \# Train & \# Test \\ \midrule

 Alpaca GPT-4
 & 52,000
 & - \\
 MT-Bench~
 & -
 & 80 \\

 GSM8K~
 & 7,473
 & 1,319 \\

 MMLU~
 & -
 & 14,079 \\
 AGIEval~
 & -
 & 9316 \\
 WinoGrande~
 & -
 & 44,000 \\

 PubMedQA~
 & 211,269
 & 1,000 \\

 OpenWebMath~
 & 6.3M
 & - \\

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

cs

Rui Pan
Xiang Liu
Shizhe Diao
Renjie Pi
Jipeng Zhang
Chi Han
Tong Zhang

"LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning" by Rui Pan, Xiang Liu, Shizhe Diao, Renjie Pi, Jipeng Zhang, Chi Han, and Tong Zhang, arXiv:2403.17919v4, 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{Overhead factors for different index coding and data organization.}
\begin{tabular}{|c|c|c|c|} \hline
\bf Order &\bf Index & \boldmath $\alpha$ & \boldmath $\beta$ \\ \hline \hline
\sf UNI &\sf I32 & 0 & 4.57 \\ \hline
\sf UNI &\sf RTC & 1 / 8 & 0.82 \\ \hline
\sf F+B &\sf RTC & 1 / 16 & 0.53 \\ \hline
\sf F+R &\sf RTC & 1 / 16 & 0.41 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

"Bitstream Organization for Parallel Entropy Coding on Neural Network-based Video Codecs" by Amir Said, Hoang Le, and Farzad Farhadzadeh, arXiv:2312.00921v1, 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}{rllll}
 \hline
 & \multicolumn{2}{c}{Sensitivity}& \multicolumn{2}{c}{1-Specificity} \\
Membership Threshold: &\hspace{0.5cm} 0.75 & \hspace{0.5cm} 0.95 & \hspace{0.5cm} 0.75 & \hspace{0.5cm} 0.95 \\
 \hline
 2 observed follow-ups & 0.70 (0.69,0.70) & 0.64 (0.64,0.65) & 0.99 (0.99,0.99) & 1.00 (1.00,1.00) \\
 3 observed follow-ups & 0.90 (0.90,0.91) & 0.88 (0.87,0.89) & 1.00 (1.00,1.00) & 1.00 (1.00,1.00) \\
 4 observed follow-ups & 0.97 (0.97,0.97) & 0.96 (0.96,0.96) & 1.00 (1.00,1.00) & 1.00 (1.00,1.00) \\
 \hline \\
 \end{tabular}
\caption{Sensitivity and specificity rates as a function of component membership threshold and number of observed follow-up periods.}
\end{table}
\end{document}

stat

David Swanson
Alexander Sherry
Chad Tang

"Variance component mixture modelling for longitudinal T-cell receptor clonal dynamics" by David Swanson, Alexander Sherry, and Chad Tang, arXiv:2502.04553v1, 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|}
\hline
Users Prefer Baseline & Users Prefer Degraded & Users Tie\\
\hline
29 & 23 & 11\\
\hline
\end{tabular}
\caption{Number of QRPs (total 63) by majority preference. Users considered tied when number of users indicating choice 1 and 2 is equal (regardless of number of no preferences)}
\end{table}
\end{document}

cs

Gineke Wiggers
Suzan Verberne
Arjen de Vries
Roel van der Burg

"High Recall, Small Data: The Challenges of Within-System Evaluation in a Live Legal Search System" by Gineke Wiggers, Suzan Verberne, Arjen de Vries, and Roel van der Burg, arXiv:2403.18962v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Yao Zhang
Jiawei Yang
Feng Hou
Yang Liu
Yixin Wang
Jiang Tian
Cheng Zhong
Yang Zhang
Zhiqiang He

"Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer" by Yao Zhang, Jiawei Yang, Feng Hou, Yang Liu, Yixin Wang, Jiang Tian, Cheng Zhong, Yang Zhang, and Zhiqiang He, arXiv:2012.14785v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Edgar C. Merkle
Sonja D. Winter
Ellen Fitzsimmons

"Identification and Scaling of Latent Variables in Ordinal Factor Analysis" by Edgar C. Merkle, Sonja D. Winter, and Ellen Fitzsimmons, arXiv:2501.06094v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Approximate number of computations in each iteration of IIC algorithm}
\begin{tabular}{lll}
\hline\noalign{\smallskip}
 Steps involved in IIC & Computational complexity \\
\noalign{\smallskip}\hline\noalign{\smallskip}
Interference cancellation & $2N_r $ \\
Performing ML search & $(4N_r-1)MU \vert\mathbb{A}\vert $ \\
Finding the solution using greedy search & $(6N_r-1)U^2 $ \\
\noalign{\smallskip}\hline
\end{tabular}
\end{table}
\end{document}

cs

"Low-Complexity Interference Cancellation Algorithms for Detection in Media-based Modulated Uplink Massive-MIMO Systems" by Manish Mandloi and Devendra Singh Gurjar, arXiv:2101.01905v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c}
Methods &wtd\_range &mean &wtd\_mean&gmean&wtd\_entropy\\
\hline\hline
M2& 13.72 (0.51) & 13.46 (0.35) & 13.14 (0.35) & 13.69 (0.64)& 13.49 (0.31) \\
\hline
M3& 19.25 (1.28) & 24.34 (0.88) & 16.63 (1.40) & 27.57 (1.50) & 28.16 (1.14) \\
\hline
M4& 13.27 (0.42) & 13.14 (0.42) & 12.69 (0.32) & 13.10 (0.41s) & 12.91 (0.23)\\
\end{tabular}
\caption{\scriptsize{RMSE for spatially distributed GP regression methods with squared exponential covariance kernel using different feature variables (in all cases we omitted the "\_atomic\_mass" from their names) for splitting the data in the Superconductivity data set .} }
\end{table}
\end{document}

math

Botond Szabo
Amine Hadji
Aad van der Vaart

"Adaptation using spatially distributed Gaussian Processes" by Botond Szabo, Amine Hadji, and Aad van der Vaart, arXiv:2312.14130v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
 \hline
 \textbf{Method} & \textbf{CIFAR100} & \textbf{STL10} & \textbf{Flower102} & \textbf{Caltech101} & \textbf{MNIST} \\
 \hline Supervised & \textbf{94.22} & 83.26 & 93.34 & 86.07 & 95.56
 \\
 \hline SimCLR & 85.9 & - & 97.0 & \textbf{92.1} & -
 \\
 \hline Ours & 91.8 & \textbf{87.74} & \textbf{99.31} & 91.42 & \textbf{96.51}
 \\
 \hline
 \end{tabular}
\caption{Comparison of transfer learning performance of the methods with several image datasets. These are top-1 accuracy scores for linear evaluation. SimCLR model uses ImageNet pre-trained ResNet-50 model and is fine-tuned with the datasets. Self-supervised learning batch size is 128, fine-tuning size is 1024 for CIFAR10, STL10, MNIST, and the batch size is 256 for Caltech101, and Flower102. For SimCLR paper results, the number of batch sizes is not given. }
\end{table}
\end{document}

cs

"The Bad Batches: Enhancing Self-Supervised Learning in Image Classification Through Representative Batch Curation" by Ozgu Goksu and Nicolas Pugeault, arXiv:2403.19579v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\hline
sender&18 & 9 & 22 & 11 & 15 & 8 & 18 & 13 & 18 \\
\hline
receiver&9 & 18 & 11 & 22 & 13 & 18 & 8 & 17 & 22\\
\hline
$\%$ of total &6.95 & 5.97 & 3.97 & 3.01 & 1.96 & 1.89 & 1.87 & 1.78 & 1.75 \\
\hline
\end{tabular}
\caption{Top 9 sender-receiver for the Ikenet data ranked by the total number of outgoing emails.}
\end{table}
\end{document}

math

Naratip Santitissadeekorn
Martin Short
David J. B. Lloyd

"Influence network reconstruction from discrete time-series of count data modelled by multidimensional Hawkes processes" by Naratip Santitissadeekorn, Martin Short, and David J. B. Lloyd, arXiv:2504.20758v1, 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}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Pearson correlation coefficient $\rho$ and Kendall rank correlation $\tau$ between MAE and MU score in brain tumor region and healthy brain region of the image.}
\begin{tabular}{l|ccc}\toprule
 & MC-Dropout & Deep Ens. & MAF \\ \midrule \rowcolor[gray]{.95}
 Pearson $\rho_\text{healthy}$ & -0.11 & 0.38 & 0.89 \\
 Pearson $\rho_\text{tumor}$ & 0.19 & 0.45 & 0.61 \\ \midrule \rowcolor[gray]{.95}

 Kendall $\tau_\text{healthy}$ & -0.16 & 0.31 & 0.63 \\
 Kendall $\tau_\text{tumor}$ & 0.21 & 0.43 & 0.43 \\

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

eess

Ivo M. Baltruschat
Parvaneh Janbakhshi
Melanie Dohmen
Matthias Lenga

"Uncertainty Estimation in Contrast-Enhanced MR Image Translation with Multi-Axis Fusion" by Ivo M. Baltruschat, Parvaneh Janbakhshi, Melanie Dohmen, and Matthias Lenga, arXiv:2311.12153v1, 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
 Error Rates & Pseudo-thresholds & Leading Orders \\
 \hline
 Practical Approach & 0.0002192 & 3092 \\
 \hline
 Modified Approach & 0.0004225 & 1774 \\
 \hline
\end{tabular}
\caption{Metrics for anisotropic noise model }
\end{table}
\end{document}

math

Harsh Gupta
Pranav Maheshwari
Ankur Raina

"Fault-tolerance of [[6, 1, 3]] non-CSS code family generated using measurements on graph states" by Harsh Gupta, Pranav Maheshwari, and Ankur Raina, arXiv:2501.12072v1, 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}{ccc}
 \toprule
 \multicolumn{3}{c}{Dataset statistics}\\
 \midrule
 \midrule
 \multicolumn{3}{c}{\textbf{CT Data}}\\
 \midrule
 \midrule
 CT Scans & & 166\\
 \midrule
 & COVID-19+ & 72\\
 & COVID-19- & 94\\
 \midrule
 \midrule
 \multicolumn{3}{c}{\textbf{Annotations}}\\
 \midrule
 Positive slices	& & 2,390\\
 \midrule
 & Ground Glass		& 1,035\\
 & Crazy Paving	& 	757\\
 & Consolidation	& 	598\\
 \midrule
 Negative slices	& &2,988\\
 \midrule
 \midrule
 \end{tabular}
\caption{CT Dataset for training and testing of the AI models.}
\end{table}
\end{document}

eess

Matteo Pennisi
Isaak Kavasidis
Concetto Spampinato
Vincenzo Schininà
Simone Palazzo
Francesco Rundo
Massimo Cristofaro
Paolo Campioni
Elisa Pianura
Federica Di Stefano
Ada Petrone
Fabrizio Albarello
Giuseppe Ippolito
Salvatore Cuzzocrea
Sabrina Conoci

"An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans" by Matteo Pennisi, Isaak Kavasidis, Concetto Spampinato, Vincenzo Schininà, Simone Palazzo, Francesco Rundo, Massimo Cristofaro, Paolo Campioni, Elisa Pianura, Federica Di Stefano, Ada Petrone, Fabrizio Albarello, Giuseppe Ippolito, Salvatore Cuzzocrea, and Sabrina Conoci, arXiv:2101.11943v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The final classification architectures found for the GLUE $small$ datasets.}
\begin{tabular}{ccccccc}
 \toprule
method & sst2 & cola & mrpc & mnli & rte & qqp \\
 \cmidrule(r){1-1} \cmidrule(l){2-7}
pooling & max & [CLS] & mean & max & mean & [CLS] \\
number linear layers & 1 & 1 & 2 & 2 & 1 & 2 \\
hidden dim linear layers & - & - & 60 & 172 & - & 122 \\
number conv layers & 0 & 0 & 2 & 2 & 5 & 1 \\
number heads conv & - & - & 90 & 75 & 14 & 43 \\
kernel size & - & - & 7 & 11 & 5 & 11 \\
skip connection & - & - & True & False & True & False \\
number attention layers & 1 & 0 & 0 & 0 & 0 & 0 \\
number attention heads & 8 & - & - & - & - & - \\
 \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}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Stability methods comparison on Super-task}
\begin{tabular}{|ccc|}
 \hline
 Methods & Test mean & Test deviation \\
 \hline
 Clipping + weight decay & .9343 & $7.76e^{-4}$ \\
 \hline
 Weight decay & .9341 & $10.52e^{-4}$ \\
 \hline
 None & .9337 & $11.60e^{-4}$ \\
 \hline
 \end{tabular}
\end{table}
\end{document}

eess

Tue Minh Cao
Nhat Hong Tran
Le Phi Nguyen
Hieu Huy Pham
Hung Thanh Nguyen

"IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitation mechanism in ECG analysis" by Tue Minh Cao, Nhat Hong Tran, Le Phi Nguyen, Hieu Huy Pham, and Hung Thanh Nguyen, arXiv:2312.09445v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Spectral Representation and Simulation of Fractional Brownian Motion" by Konstantin A. Rybakov, arXiv:2412.12207v2, 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{\small DNN parameters.}
\begin{tabular}{|l|l|}
 \hline
 \textbf{Parameters} & \textbf{Values} \\ \hline
 Architecture & $2 \times k_{on} ; k_{on} ; 2 \times k_{on}$ \\ \hline
 LC-LSDNN Architecture & $k_{on} ; k_{on}/2 ; k_{on}$ \\ \hline
 Hidden layer activation function & ReLU \\ \hline
 Loss Function & MSE \\ \hline
 Optimizer & ADAM \\ \hline
 Epochs & 500 \\ \hline
 Number of training samples & 20000 \\ \hline
 Number of testing samples & 2000 \\ \hline
 \end{tabular}
\end{table}
\end{document}

eess

Syed Asrar ul haq
Varun Singh
Bhanu Teja Tanaji
Sumit Darak

"Low Complexity High Speed Deep Neural Network Augmented Wireless Channel Estimation" by Syed Asrar ul haq, Varun Singh, Bhanu Teja Tanaji, and Sumit Darak, arXiv:2311.08689v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccccc} \toprule
\multirow{2}[2]{*}{Metric} & \multicolumn{4}{c}{$n=75$} & \multicolumn{4}{c}{$n=300$} \\
 \cmidrule(lr){2-5} \cmidrule(lr){6-9}
 & EB	& BS	& Ens	& IPB	& EB	& BS	& Ens	& IPB
 \\ \midrule
 RMSE 	& $3.1$	& $2.7$	& $2.4$	& $\mathbf{1.4}$
 & $2.9$	& $3.0$	& $2.0$	& $\mathbf{1.1}$ \\
 NLPD	& $3.0$	& $2.0$	& $2.6$	& $\mathbf{1.6}$
 & $2.7$	& $3.0$	& $2.2$	& $\mathbf{1.1}$ \\
 CRPS	& $3.0$	& $2.3$	& $2.6$	& $\mathbf{1.4}$
 & $2.7$	& $3.3$	& $2.1$	& $\mathbf{1.1}$ \\
\bottomrule\end{tabular}
\end{table}
\end{document}

cs

"On Uncertainty Quantification for Near-Bayes Optimal Algorithms" by Ziyu Wang and Chris Holmes, arXiv:2403.19381v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

eess

Pengyuan Lu
Matthew Cleaveland
Oleg Sokolsky
Insup Lee
Ivan Ruchkin

"Repairing Learning-Enabled Controllers While Preserving What Works" by Pengyuan Lu, Matthew Cleaveland, Oleg Sokolsky, Insup Lee, and Ivan Ruchkin, arXiv:2311.03477v2, 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{Shrinkage coefficient}
\begin{tabular}{|l||c|c|}
		\hline
		Algorithm & $\rho_{\textit{st}}$ & $\rho_p$\\
		\hline
		RSKE, KOAS(KNSCM) & 0.0344& 0.2732\\
		RSKE, KOAS(KMLE) & 0.0293& 0.2556\\
		RSKE, CV(KNSCM) & 0.0583& 0.3379\\
		RSKE, CV(KMLE) & 0.0363& 0.3541\\
		RSKE, Oracle & 0& 0.4\\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Lei Xie
Zishu He
Jun Tong
Tianle Liu
Jun Li
Jiangtao Xi

"Regularized Covariance Estimation for Polarization Radar Detection in Compound Gaussian Sea Clutter" by Lei Xie, Zishu He, Jun Tong, Tianle Liu, Jun Li, and Jiangtao Xi, arXiv:2103.09628v3, 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}{llllllll}
\hline\hline
\multicolumn{8}{c}{\textbf{Bullet}}\\
\textbf{Coupon} & \textbf{Maturity} & \textbf{Series} & \textbf{Rating} & \textbf{Announce} & \textbf{Currency} & \textbf{Ask Price} & \textbf{Type}\\
\hline
0.375 & 21/10/2025 & EMTN & A- & 14/10/2019 & EUR & 92.611 & Green \\
0.5 & 24/06/2024 & EMTN & A- & 17/06/2019 & EUR & 96.344 & Brown \\
\hline\hline
\multicolumn{8}{c}{\textbf{Callable}}\\
\textbf{Coupon} & \textbf{Maturity} & \textbf{Series} & \textbf{Rating} & \textbf{Announce} & \textbf{Currency} & \textbf{Ask Price} & \textbf{Type}\\
\hline
3.95 &	22/07/2032	& EMTN	& BBB+	&	13/04/2022	& SGD &	97.697	&	Green \\
3.8	 & 30/04/2031	 & EMTN	& BBB+	&	23/04/2019	& SGD	& 98.364	&	Brown \\
\hline\hline
\multicolumn{8}{c}{\textbf{Bullet Corporate}}\\
\textbf{Coupon} & \textbf{Maturity} & \textbf{Series} & \textbf{Rating} & \textbf{Announce} & \textbf{Currency} & \textbf{Ask Price} & \textbf{Type}\\
\hline
0.875 &	27/03/2024	& EMTN	& BBB+	&	15/03/2017	& EUR &	97.481	&	Green \\
0.375	 & 11/06/2027	 & EMTN	& BBB+	&	04/06/2020	& EUR	& 87.163	&	Brown \\
\hline\hline
\end{tabular}
\caption{Features of bonds included in the dataset. The issuers are Cr\'edit Agricole SA (financial) and Engie SA (corporate). Prices refer to September 19, 2022.}
\end{table}
\end{document}

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.


stat

Mattia Banin
Matteo Barigozzi
Luca Trapin

"Predicting Energy Demand with Tensor Factor Models" by Mattia Banin, Matteo Barigozzi, and Luca Trapin, arXiv:2502.06213v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cccc}
 \toprule
 \textbf{$\sigma$}& 0.01 & 0.1 & 1\\ \midrule
 \textbf{Accuracy} & 0.354 & \textbf{0.357} & 0.350\\
 \bottomrule
\end{tabular}
\caption{Analysis of $\sigma$ and $\lambda$ values in Eq. .}
\end{table}
\end{document}

cs

Mohsen Gholami
Mohammad Akbari
Cindy Hu
Vaden Masrani
Z. Jane Wang
Yong Zhang

"GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation" by Mohsen Gholami, Mohammad Akbari, Cindy Hu, Vaden Masrani, Z. Jane Wang, and Yong Zhang, arXiv:2403.19754v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccc}
 &Unethical Grammar&Unethical Rewrite& Respondents\\
 &(mean) &(mean) & (N)\\
\midrule
\textbf{Native} &&&\\
Yes &0.07&0.46&83\\
No &0.05&0.36&188\\
\midrule
\textbf{Total} &\textbf{0.06}&\textbf{0.39}&\textbf{271}\\
\midrule
\textbf{Role} &&&\\
Professor &0.06&0.38&172\\
Student or Postdoc &0.05&0.39&99\\
\midrule
\textbf{Total} &\textbf{0.06}&\textbf{0.39}&\textbf{271}\\
\end{tabular}
\caption{The survey averages responses to the questions: ``Do you think it is unethical to ask ChatGPT to fix grammar (rewrite text) in a manuscript for an academic journal?'' based on one's role and whether they are a native English speaker.}
\end{table}
\end{document}

q-fin

"Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals" by Nir Chemaya and Daniel Martin, arXiv:2311.14720v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"On Finite Time Span Estimators of Parameters for Ornstein-Uhlenbeck Processes" by Jun S. Han and Nino Kordzakhia, arXiv:2503.23677v1, 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}{cc}
 \textbf{CES} & \textbf{Leontief}\\
 \hline
 Constant, decreasing or increasing returns to scale & Constant returns to scale\\
 Non-linear isoquants & Piecewise linear isoquants \\
 Substitution between inputs possible& No substitution possible
 \end{tabular}
\caption{Characteristics of the CES and Leontief production functions}
\end{table}
\end{document}

q-fin

Martin Chalkley
Sakshi Mohan
Margherita Molaro
Bingling She
Wiktoria Tafesse

"The properties of a Leontief production technology for Health System Modeling: the Thanzi la Onse model for Malawi" by Martin Chalkley, Sakshi Mohan, Margherita Molaro, Bingling She, and Wiktoria Tafesse, arXiv:2508.21699v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Nonlinear Covariance Shrinkage for Hotelling's $T^2$ in High Dimension" by Benjamin D. Robinson and Van Latimer, arXiv:2502.02006v1, 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{Eigenvalue Decomposition and Variance Explained by Principal Components}
\begin{tabular}{lccc}
\toprule
Component & Eigenvalue & Variance Explained & Cumulative \\
\midrule
First & 3.189 & 63.79\% & 63.79\% \\
Second & 0.919 & 18.39\% & 82.18\% \\
Third & 0.626 & 12.52\% & 94.70\% \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
\hline
Figure & time step & simulated time \\
\hline
18 & 1 & 41 minutes \\
19 & 2 & 1 hours 23 minutes \\
20 & 3 & 2 hours 5 minutes \\
21 & 4 & 2 hours 46 minutes \\
22 & 5 & 3 hours 28 minutes \\
23 & 10 & 6 hours 56 minutes \\
24 & 15 & 10 hours 25 minutes \\
25 & 20 & 13 hours 3 minutes \\
26 & 40 & 1 day 3 hours 46 minutes \\
27 & 60 & 1 day 17 hours 40 minutes \\
28 & 100 & 2 days 21 hours 26 minutes \\
29 & 150 & 4 days 8 hours 10 minutes \\
30 & 200 & 5 days 18 hours 53 minutes \\
31 & 250 & 7 days 5 hours 36 minutes \\
32 & 300 & 8 days 16 hours 20 minutes \\
\hline
 \end{tabular}
\caption{Snapshots of the simulation}
\end{table}
\end{document}

cs

Paweł Maczuga
Marcin Łoś
Eirik Valseth
Albert Oliver Serra
Leszek Siwik
Elisabede Alberdi Celaya
Anna Paszyńska
Maciej Paszyński

"Simulating the aftermath of Northern European Enclosure Dam (NEED) break and flooding of European coast" by Paweł Maczuga, Marcin Łoś, Eirik Valseth, Albert Oliver Serra, Leszek Siwik, Elisabede Alberdi Celaya, Anna Paszyńska, and Maciej Paszyński, arXiv:2405.02310v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of Studied LLMs for Vulnerability Localization}
\begin{tabular}{llll}
\toprule
\textbf{Model} & \textbf{Scale} & \textbf{Language} & \textbf{Type} \\
\midrule
GPT-3.5 & 175B & Multiple& Decoder-only \\
GPT-4 & N.R. & Multiple& Decoder-only \\
Llama 2 & 7B & Multiple & Decoder-only \\
CodeLlama & 7B/13B & Multiple & Decoder-only \\
WizardCoder & 15B & Multiple & Decoder-only \\
CodeBERT & 125M & Java/Python etc. & Encoder-only \\
GraphBERT & 125M & Java/Python etc. & Encoder-only \\
PLBart & 140M & Java\&Python & Encoder-decoder \\
CodeT5 & 60M/220M/770M & C/C\#/Java etc.& Encoder-decoder \\
CodeGen & 350M/2B/6B/16B & C/C++/Go etc. & Decoder-only \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Jian Zhang
Chong Wang
Anran Li
Weisong Sun
Cen Zhang
Wei Ma
Yang Liu

"An Empirical Study of Automated Vulnerability Localization with Large Language Models" by Jian Zhang, Chong Wang, Anran Li, Weisong Sun, Cen Zhang, Wei Ma, and Yang Liu, arXiv:2404.00287v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter Selection on Validation Set}
\begin{tabular}{l|ccc|ccc|ccc}
\toprule
$\text{dim}(\mathbf{z}_G)$ & \multicolumn{3}{c}{16} & \multicolumn{3}{c}{32} & \multicolumn{3}{c}{64} \\
$\text{dim}(\mathbf{z}_{t,L})$ & 16 & 32 & 64 & 16 & 32 & 64 & 16 & 32 & 64 \\
\midrule
Acc (\%) $\uparrow$ & \textbf{77.9} & 54.3 & 22.9 & 72.9 & 41.4 & 15 & 74.9 & 28.8 & 14.5 \\
MSE $\downarrow$ & 4.52 & 4.56 & \textbf{4.43} & 4.69 & 4.69 & 4.45 & 4.47 & 4.55 & 4.5 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

"Disentangled Sequence Clustering for Human Intention Inference" by Mark Zolotas and Yiannis Demiris, arXiv:2101.09500v4, 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{Urban Sound Classification results for the different approaches described. The best score for the mean Accuracy over the 10 folds overall is in \textbf{bold}. the CNN10 architecture from is used after each input front-end. Higher is better.}
\begin{tabular}{lcc}
\hline\hline
Accuracy & Mean & [Min - Max]
\\ \hline
Baseline & &\\
SB-CNN [1] & 79 \% & [71\%-85\%] \\
\hline
Input front-end & &\\
Free 2D conv. 3by3 & \textbf{84\%} & [76\%-93\%] \\
Learnable STRFs & 82\% & [74\%-90\%]\\
\hline\hline
\end{tabular}
\end{table}
\end{document}

eess

Rachid Riad
Julien Karadayi
Anne-Catherine Bachoud-Lévi
Emmanuel Dupoux

"Learning spectro-temporal representations of complex sounds with parameterized neural networks" by Rachid Riad, Julien Karadayi, Anne-Catherine Bachoud-Lévi, and Emmanuel Dupoux, arXiv:2103.07125v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Model} & \textbf{Baseline} & \textbf{Uncalibrated UA} & \textbf{Calibrated UA}\\ \hline
PerAct & $0.382\pm0.012$ & $0.385\pm0.012$ & $\boldsymbol{0.414\pm0.012}$\\ \hline
RVT & $0.602\pm0.012$ & $0.607\pm0.012$ & $\boldsymbol{0.623\pm0.011}$ \\ \hline
CLIPort & $0.803\pm0.005$ & $\boldsymbol{0.833\pm0.005}$ & $\boldsymbol{0.833\pm0.005}$\\ \hline
\end{tabular}
\caption{Summary of results for calibrated and uncalibrated uncertainty-aware approach and the uncertainty oblivious (baseline) approach on PerAct, RVT, and CLIPort. For PerAct and RVT, the values in the table represent the portion of successfully completed tasks. For CLIPort, the values represent the mean reward, where the maximum reward for an episode is one if the desired task is completed successfully. Standard errors are shown. }
\end{table}
\end{document}

cs

Bo Wu
Bruce D. Lee
Kostas Daniilidis
Bernadette Bucher
Nikolai Matni

"Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies" by Bo Wu, Bruce D. Lee, Kostas Daniilidis, Bernadette Bucher, and Nikolai Matni, arXiv:2403.18222v2, 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 table of different symbols and their definitions}
\begin{tabular}{|c|l|} % Added vertical lines
		\hline % Top horizontal line
		\textbf{Symbol} & \textbf{Definition} \\
		\hline % Line between header and body
		$K$ & Total number of users in the system \\
		\hline
		$M$ & The number of antennas at the WiFi transmitter \\
		\hline
		$L$ & The number of LEDs at the LiFi transmitter \\
		\hline
		$s$ & Type of service, where $s \in \{eMBB, URLLC, mMTC\}$ \\
		\hline
		$\mathbf{h}_{k,s}^\text{WiFi}$ & The channel link between the WiFi transmitter and the $k^{th}$ user in slice $s$ \\
		\hline
		$\mathbf{h}_{k,s}^\text{LiFi}$ & The channel link between the LiFi transmitter and the $k^{th}$ user in slice $s$ \\
		\hline
		$\mathbf{f}_{k,s}^\text{WiFi}$ & WiFi precoder for the $k^{th}$ user in slice $s$ \\
		\hline
		$\mathbf{f}_{k,s}^\text{LiFi}$ & LiFi precoder for the $k^{th}$ user in slice $s$ \\
		\hline
		$(\sigma_{k,s}^\text{WiFi})^2$ & Variance of AWGN for the $k^{th}$ user in WiFi slice $s$ \\
		\hline
		$(\sigma_{k,s}^\text{LiFi})^2$ & Variance of AWGN for the $k^{th}$ user in LiFi slice $s$ \\
		\hline
		$\gamma_{k,s}^\text{WiFi}$ & SINR of the $k^{th}$ user in WiFi slice $s$ \\
		\hline
		$\gamma_{k,s}^\text{LiFi}$ & SINR of the $k^{th}$ user in LiFi slice $s$ \\
		\hline
		$R_{k,s}^\text{WiFi}$ & Achievable rate of the $k^{th}$ user in WiFi slice $s$ \\
		\hline
		$R_{k,s}^\text{LiFi}$ & Achievable rate of the $k^{th}$ user in LiFi slice $s$ \\
		\hline
		${\text{S}_{k,s}^\text{WiFi}}$ & Shanon term of the $k^{th}$ user in WiFi slice $s$ \\
		\hline
		${\text{V}_{k,s}^\text{WiFi}}$ & Channel dispersion term of the $k^{th}$ user in WiFi slice $s$ \\
		\hline
		${\text{S}_{k,s}^\text{LiFi}}$ & Shanon term of the $k^{th}$ user in LiFi slice $s$ \\
		\hline
		${\text{V}_{k,s}^\text{LiFi}}$ & Channel dispersion term of the $k^{th}$ user in LiFi slice $s$ \\
		\hline
		$P_{\text{WiFi}}$ & Power consumption of the WiFi transmit signal\\
		\hline
		$P_{\text{LiFi}}$ & Power consumption of the LiFi transmit signal\\
		\hline
		$T_{k,s}$ & The total latency for the transmission to the $k^{th}$ user in slice $s$\\
		\hline
	\end{tabular}
\end{table}
\end{document}

math

Asim Ihsan
Muhammad Asif
Hossein Safi
Iman Tavakkolnia
Harald Haas

"Efficient Service Differentiation and Energy Management in Hybrid WiFi/LiFi Networks" by Asim Ihsan, Muhammad Asif, Hossein Safi, Iman Tavakkolnia, and Harald Haas, arXiv:2503.16061v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Transit-Length Distribution for Particle Transport in Binary Markovian Mixed Media" by Brian C. Kiedrowski and Emily H. Vu, arXiv:2412.19359v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"European Option Pricing Under Generalized Tempered Stable Process: Empirical Analysis" by A. H. Nzokem, arXiv:2304.06060v3, 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{Van der Pol oscillator diffusion estimation}
\begin{tabular}{|c|c|} \hline
 True $\sigma$& Estimated $\hat{\sigma}$\\ \hline
 0.1000& 0.1007\\ \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{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Solution of the Leland model at $t = 0$, calculated with non-uniform, unweighted cubic NURBS. $r = 0.1$, $\sigma = 0.2$, $\hat{K} = \$100$, $Le \approx 0.8$ and $\Delta\tau/\Delta x^2 = 0.1$.}
\begin{tabular}{cccc} \hline
$nE$ & $n_{\tau}$ & $\varepsilon$ & Contraction factor \\ \hline \hline
$2^8$ & 80 & 0.451445 & - \\
$2^9$ & 320 & 0.157192 & 2.87\\
$2^{10}$ & 1280 & 0.050986 & 3.08 \\
$2^{11}$ & 5120 & 0.017970 & 2.83\\
$2^{12}$ & 20480 & 0.006319 & 2.84\\
\hline
\end{tabular}
\end{table}
\end{document}

math

Rakhymzhan Kazbek
Yogi Erlangga
Yerlan Amanbek
Dongming Wei

"Isogeometric Analysis for the Pricing of Financial Derivatives with Nonlinear Models: Convertible Bonds and Options" by Rakhymzhan Kazbek, Yogi Erlangga, Yerlan Amanbek, and Dongming Wei, arXiv:2412.08987v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.