\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|cccc}
 \textbf{NOK} & $\text{MOON}_{1,t}$& $\text{MOON}_{2,t}$& $\text{YOLO}_{1,t}$& $\text{YOLO}_{2,t}$\\
 \hline
$V_t$& 0.608 &-0.061 &0.509 &0.391 \\
$\text{MOON}_{1,t}$& &-0.118 &0.464 &0.779 \\
$\text{MOON}_{2,t}$& & &-0.102 &0.137 \\
$\text{YOLO}_{1,t}$& & & &0.401
 \end{tabular}
\caption{NOK Correlation}
\end{table}
\end{document}

q-fin

Tim Matthies
Thomas Löhden
Stephan Leible
Jun-Patrick Raabe

"To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis" by Tim Matthies, Thomas Löhden, Stephan Leible, and Jun-Patrick Raabe, arXiv:2308.09968v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Linduni M. Rodrigo
Robert Kohn
Hadi M. Afshar
Sally Cripps

"A Beta Cauchy-Cauchy (BECCA) shrinkage prior for Bayesian variable selection" by Linduni M. Rodrigo, Robert Kohn, Hadi M. Afshar, and Sally Cripps, arXiv:2501.07061v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


math

"Finite Horizon Optimization: Framework and Applications" by Yushun Zhang, Dmitry Rybin, and Zhi-Quan Luo, arXiv:2412.21068v1, 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}{llll|lll}
 \toprule
 &&&& \multicolumn{3}{c}{Flipping augmentation option} \\
 \cmidrule(r){5-7}
 Train crop & Test crop & Epochs & TTA & None & Random & Alternating \\
 \midrule
 Heavy RRC & CC(256, 0.875) & 16 & No & \textbf{66.78\%}$_{n=8}$ & 66.54\%$_{n=28}$ & 66.58\%$_{n=28}$ \\
 Heavy RRC & CC(192, 1.0) & 16 & No & 64.43\%$_{n=8}$ & 64.62\%$_{n=28}$ & 64.63\%$_{n=28}$ \\
 Light RRC & CC(256, 0.875) & 16 & No & 59.02\%$_{n=4}$ & 61.84\%$_{n=26}$ & \textbf{62.19\%}$_{n=26}$ \\
 Light RRC & CC(192, 1.0) & 16 & No & 61.79\%$_{n=4}$ & 64.50\%$_{n=26}$ & \textbf{64.93\%}$_{n=26}$ \\
 \midrule
 Heavy RRC & CC(256, 0.875) & 16 & Yes & 67.52\%$_{n=8}$ & 67.65\%$_{n=28}$ & 67.60\%$_{n=28}$ \\
 Heavy RRC & CC(192, 1.0) & 16 & Yes & 65.36\%$_{n=8}$ & 65.48\%$_{n=28}$ & 65.51\%$_{n=28}$ \\
 Light RRC & CC(256, 0.875) & 16 & Yes & 61.08\%$_{n=4}$ & 62.89\%$_{n=26}$ & \textbf{63.08\%}$_{n=26}$ \\
 Light RRC & CC(192, 1.0) & 16 & Yes & 63.91\%$_{n=4}$ & 65.63\%$_{n=26}$ & \textbf{65.87\%}$_{n=26}$ \\
 \midrule
 Light RRC & CC(192, 1.0) & 20 & Yes & not measured & 65.80\%$_{n=16}$ & \textbf{66.02\%}$_{n=16}$ \\
 \midrule
 Heavy RRC & CC(256, 0.875) & 88 & Yes & 72.34\%$_{n=2}$ & 72.45\%$_{n=4}$ & 72.46\%$_{n=4}$ \\
 \bottomrule
\end{tabular}
\caption{\small ImageNet validation accuracy for ResNet-18 trainings. Alternating flip improves over random flip for those trainings where random flip improves significantly over not flipping at all. The single best flipping option in each row is bolded when the difference is statistically significant.}
\end{table}
\end{document}

cs

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||c|cccc||}
 \hline
 $M$ & $G$ & $K_-$ & $K_+$ & $H$\\
 \hline\hline
 $\mathbb{S}^4$ & $\mathrm{SO}(3) \times \mathrm{SO}(2)$ & $\mathrm{SO}(2)\times \mathrm{SO}(2)$ & $\mathrm{SO}(2)\times \mathrm{SO}(2)$ & $\mathrm{SO}(2)\times 1$\\
 \hline
 $\mathbb{S}^2\times \mathbb{S}^2$ & $\mathrm{SO}(3) \times \mathrm{SO}(2)$ & $\mathrm{SO}(3)\times 1$ & $\mathrm{SO}(2)\times \mathrm{SO}(2)$ & $\mathrm{SO}(2)\times 1$\\
 \hline
 $\mathbb{S}^4$ & $\mathrm{SO}(3)$ & $\mathrm{S}(\mathrm{O}(2)\times \mathrm{O}(1))$ & $\mathrm{S}(\mathrm{O}(1)\times \mathrm{O}(2))$ & $\mathrm{S}(\mathrm{O}(1)\times\mathrm{O}(1)\times \mathrm{O}(1))$\\
 \hline
 $\mathbb{CP}^2$ & $\mathrm{SO}(3)$ & $\mathrm{S}(\mathrm{O}(1)\times \mathrm{O}(2))$ & $\mathrm{SO}(2)\times \mathrm{SO}(1)$ & $\mathbb{Z}_2$\\
 \hline
 $\mathbb{S}^4$ & $\mathrm{SU}(2)$ & $\mathrm{SU}(2)$ & $\mathrm{SU}(2)$ & $1$\\
 \hline
 $\mathbb{CP}^2$ & $\mathrm{SU}(2)$ & $\mathrm{SU}(2)$ & $\mathrm{U}(1)$ & $1$\\
 \hline
 $\mathbb{CP}^2\# \overline{\mathbb{CP}^2}$ & $\mathrm{SU}(2)$ & $\mathrm{U}(1)$ & $\mathrm{U}(1)$ & $\mathbb{Z}_n$, $n$ \text{odd}\\
 \hline
 $\mathbb{S}^2\times \mathbb{S}^2$ & $\mathrm{SO}(3)$ & $\mathrm{SO}(2)$ & $\mathrm{SO}(2)$ & $\mathbb{Z}_n$\\
 \hline
 \end{tabular}
\caption{Simply-connected compact $4$-manifolds that admit a cohomogeneity one group action and their effective irreducible group diagrams.}
\end{table}
\end{document}

math

"Cohomogeneity one 4-dimensional gradient Ricci solitons" by Patrick Donovan, arXiv:2503.15033v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
		\hline
		\textbf{Case} & \textbf{Mean Hitting Time} & \textbf{95\% Confidence Interval} \\
		\hline
		Controlled & 1.79 & (1.78, 1.80) \\
		Uncontrolled & 2.23 & (2.22, 2.24) \\
		\hline
	\end{tabular}
\caption{Comparison of mean hitting times and 95\% confidence intervals for controlled and uncontrolled scenarios.}
\end{table}
\end{document}

math

"Deterministic and Stochastic Studies on Additional Food Provided Prey-Predator Systems with Group Defence among Prey and Mutual Interference among Predators" by D Bhanu Prakash and D K K Vamsi, arXiv:2504.21114v1, 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{H\'{e}non-Heiles system diffusion estimation}
\begin{tabular}{|c|c|} \hline
 True $\sigma_1$& Estimated $\hat{\sigma_1}$\\ \hline
 0.0700& 0.0700\\ \hline \hline
 True $\sigma_2$&Estimated $\hat{\sigma_2}$\\ \hline
 0.0500&0.0500\\ \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{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Production values}
\begin{tabular}{lll}
 \hline
		energy source & EJ/yr & ratio \\
		\toprule
		Nuclear & 10 & 27.4\% \\
 Hydropower & 15.2 & 41.6\% \\
 Biopower & 2.55 & 7.0\% \\
 Wind & 5.75 & 15.75\% \\
 Solar & 3.0 & 8.2\% \\
 Batteries & - & -\\
 Multi-day storage & - & - \\
 Electrolyzers & - & - \\
		\bottomrule
	\end{tabular}
\end{table}
\end{document}

q-fin

Michael Barnett
William Brock
Lars Peter Hansen
Ruimeng Hu
Joseph Huang

"A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty" by Michael Barnett, William Brock, Lars Peter Hansen, Ruimeng Hu, and Joseph Huang, arXiv:2310.13200v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{2D metrics on CAV3D datasets. The bold numbers indicate the best tracker. The category of trackers (class) is classified using the principles in Table .}
\begin{tabular}{|c|c|ccc|}
\hline
\multirow{2}{*}{\textbf{Sequences}} & \multirow{2}{*}{\textbf{Metrics}} & \multicolumn{3}{c|}{\textbf{Trackers (Class)}} \\ \cline{3-5}
 & & Tracker (4) & AV3T (4) & GAVT (4) \\ \hline
\multirow{2}{*}{SOT} & TLR & \textbf{2.50} & 7.00 & 13.93 \\ \cline{2-2}
 & MAE & \textbf{12.00} & 16.50 & 26.76 \\ \hline
\multirow{2}{*}{MOT} & TLR & - & \textbf{11.20} & 21.01 \\ \cline{2-2}
 & MAE & - & 24.80 & \textbf{13.47} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Jinzheng Zhao
Yong Xu
Xinyuan Qian
Davide Berghi
Peipei Wu
Meng Cui
Jianyuan Sun
Philip J. B. Jackson
Wenwu Wang

"Audio-Visual Speaker Tracking: Progress, Challenges, and Future Directions" by Jinzheng Zhao, Yong Xu, Xinyuan Qian, Davide Berghi, Peipei Wu, Meng Cui, Jianyuan Sun, Philip J. B. Jackson, and Wenwu Wang, arXiv:2310.14778v5, 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{$R^2$ score of surrogate models for predicting the price, deltas, and gammas of a Bachelier modelled basket option.}
\begin{tabular}{rrrrr}
 \toprule
 \multirow{2}{*}{Predict} & \multirow{2}{*}{Oversized} & \multirow{2}{*}{Pruned} & \multicolumn{2}{c}{Sobolev fine-tuning} \\
 \cmidrule{4-5} & NN & NN & NN Data & Bachelier \\
 \hline
 Values & 0.999545 & 0.999296 & 0.999805 & \textbf{0.999962} \\
 Deltas & 0.998700 & 0.996718 & 0.999479 & \textbf{0.999863} \\
 Gammas & 0.997033 & 0.902470 & 0.987393 & \textbf{0.997374} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

Towards Sobolev Pruning

"Towards Sobolev Pruning" by Neil Kichler, Sher Afghan, and Uwe Naumann, arXiv:2312.03510v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llcc}
\toprule
Dataset & Model & $m_{X_t}$ & $\sigma_{X_t}$ \\
\hline
\midrule
\multirow{2}{*}{Crypto} & CTFP & $0.083 \pm 0.007$ & $0.232 \pm 0.009$\\
 & TCNF & $\mathbf{0.025} \pm \mathbf{0.001}$ & $\mathbf{0.131} \pm \mathbf{0.005}$\\
\midrule
\multirow{2}{*}{ECL} & CTFP & $0.771 \pm 0.108$ & $26.972 \pm 8.634$ \\
 & TCNF & $\mathbf{0.299} \pm \mathbf{0.007}$ & $\mathbf{2.182} \pm \mathbf{2.081}$ \\
\bottomrule
\end{tabular}
\caption{Quantitative analysis of real-world dataset: We display mean and standard deviation estimation errors.}
\end{table}
\end{document}

stat

Naoufal El Bekri
Lucas Drumetz
Franck Vermet

"Time-changed normalizing flows for accurate SDE modeling" by Naoufal El Bekri, Lucas Drumetz, and Franck Vermet, arXiv:2312.14698v2, 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{Projection head $n(.)$}
\begin{tabular}{|l|cc|}
\hline
Layer & Size & Bias \\
\hline
Global Average Pooling Layer& - & - \\
Dense Layer & 2048 & True \\
Batch Normalization Layer \& ReLU & - & - \\
Dense Layer & 128 & False\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Matej Gazda
Jakub Gazda
Jan Plavka
Peter Drotar

"Self-supervised deep convolutional neural network for chest X-ray classification" by Matej Gazda, Jakub Gazda, Jan Plavka, and Peter Drotar, arXiv:2103.03055v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
\textbf{Parameter} & \textbf{Value}\\
\hline
Flying speed, $v$ & 12 $m/s$ \\
Maximum flying speed, $v^{max}$ & 20 $m/s$\\
Flying height, $z$ & 100 $m$ \\
Safe angle, $\alpha_{\text{safe}}$ & 40 \\
Lookahead distance, $L$ & 20 $m$ \\
Power at constant speed, $P(v)$ & 30 $W$ \\
Solar spectral density, $P_{\mathrm{sd}}$ & 380 $W/m^2$ \\
Solar cell efficiency, $\eta$ & 20$\%$ \\
Solar panel area, $S$ & 0.3 $m^2$ \\
Battery Capacity, $E_{\text{Batt}}$ & 750 $J$ \\
\hline
\end{tabular}
\caption{Parameters Used in the Simulation of Section }
\end{table}
\end{document}

cs

"Advanced Algorithms for Autonomous Guidance of Solar-powered UAVs" by Siyuan Li, arXiv:2404.02920v1, 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|c|c|c} % <-- Alignments: 1st column left, 2nd middle and 3rd right, with vertical lines in between
 \textbf{Speaker ID } & \textbf{Gender} & \textbf{Nationality} \\
 \hline
 229 & Female & english \\
 238 & Female & NorthernIrish \\
 266 & Female & Irish \\
 228 & Female & English \\
 231 & Femle & English \\
 288 & Female & Irish \\
 243 & Male & English \\
 245 & Male & irish \\
 251 & Male & Indian \\
 275 & Male & Scottish \\
 273 & Male & English \\

 \end{tabular}
\caption{Test Speakers from VCTK dataset }
\end{table}
\end{document}

eess

Neeraj Kumar
Srishti Goel
Ankur Narang
Brejesh Lall

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter ranges and types for each model.}
\begin{tabular}{lcc}
\toprule
\textbf{Algorithm (package)} & \textbf{Hyperparameter} & \textbf{Values} \\
\midrule
Gradient Boosting (gbm) & n.tree & \{50, 100, 250, 500, 750, 1000\} \\
 & shrinkage & \{0.005, 0.01\} \\
 & interaction.depth & \{3, 4, 5, 6\} \\
 & n.minobsinnode & \{5, 10, 15, 20\} \\
Random Forest (randomForest) & n.tree & \{50, 100, 250, 500, 750, 1000\} \\
 & max depth & \{3, 4, 5, 6, 7\} \\
 & min samples split & \{5, 10, 15, 20\} \\
XGBoost (xgboost) & n.rounds & \{50, 100, 500, 1000\} \\
 & max.depth & \{3, 5\} \\
 & eta & \{0.01, 0.05, 0.1\} \\
 & subsample & \{0.6, 0.7, 0.8\} \\
 & min.child.weight & \{1, 5, 10\} \\
 & gamma & \{0, 0.1, 1\} \\
 & colsample.bylevel & \{0.6, 0.7, 0.8\} \\
LightGBM (lightgbm) & num.iterations & \{50, 100, 500, 1000\} \\
 & num.leaves & \{10, 20, 30, 40\} \\
 & learning.rate & \{0.01, 0.05, 0.1\} \\
 & subsample & \{0.6, 0.7, 0.8\} \\
 & colsample.bytree & \{0.6, 0.7, 0.8\} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cc}
 \hline
 \textbf{Parameter} & \textbf{Value} \\
 \hline
 Transmitter Antenna Pattern& Dipole\\
 Transmitter Antenna Polarization & Vertical \\
 Receiver Antenna Pattern & Isotropic\\
 Receiver Antenna Polarization& Cross\\
 Radio Frequency & 2.4 GHz\\
 Maximum Interaction & 4 \\
 Num. of Ray Launched & 100,000\\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Lihao Zhang
Haijian Sun
Jin Sun
Rose Qingyang Hu

"WiSegRT: Dataset for Site-Specific Indoor Radio Propagation Modeling with 3D Segmentation and Differentiable Ray-Tracing" by Lihao Zhang, Haijian Sun, Jin Sun, and Rose Qingyang Hu, arXiv:2312.11245v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Yassir Bendou
Giulia Lioi
Bastien Pasdeloup
Lukas Mauch
Ghouthi Boukli Hacene
Fabien Cardinaux
Vincent Gripon

"LLM meets Vision-Language Models for Zero-Shot One-Class Classification" by Yassir Bendou, Giulia Lioi, Bastien Pasdeloup, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux, and Vincent Gripon, arXiv:2404.00675v3, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance of Algorithm on Example }
\begin{tabular}{cccc}
\hline
Number of& Algorithm &Iterations &CPU time\\
initial points& &(Min, Max, Mean, Median, Mode, SD) &(Min, Max, Mean, Median, $\lceil\text{Mode}\rceil$, SD) \\
\hline
$100$ & QNM&($1,~33,~22.7200,~23,~29,~6.4418$) & ($3.2551,~67.4360,~45.8573,~46.2144,~3,~12.9461$) \\
 & SD &($1,~33,~22.4500,~22.5000,~25,~6.3776$) & ($1.2551,~63.9492,~43.9482,~44.1527,~3,~12.1495$) \\
\hline
\end{tabular}
\end{table}
\end{document}

math

Debdas Ghosh
Anshika
Jen-Chih Yao
Xiaopeng Zhao

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


stat

Tommaso Rigon
Ching-Lung Hsu
David B. Dunson

"A Bayesian theory for estimation of biodiversity" by Tommaso Rigon, Ching-Lung Hsu, and David B. Dunson, arXiv:2502.01333v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
\toprule
\textbf{Method} & \textbf{CHAIR\_I $\downarrow$} & \textbf{CHAIR\_S $\downarrow$} \\
\midrule
InstructBLIP & 12.2 & 21.4 \\
VCD & 9.0 & 16.6 \\
\textbf{ICD} & \textbf{7.7} & \textbf{14.4} \\
\bottomrule
\end{tabular}
\caption{\textbf{Evaluation on MSCOCO training and validation sets (500 samples)}, using metrics CHAIR\_I and CHAIR\_S, instance-level and sentence-level hallucinations, followed by .}
\end{table}
\end{document}

cs

Xintong Wang
Jingheng Pan
Liang Ding
Chris Biemann

"Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding" by Xintong Wang, Jingheng Pan, Liang Ding, and Chris Biemann, arXiv:2403.18715v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Maxim Olshanskii
Jan-Phillip Bäcker
Dmitri Kuzmin

"Projected gradient stabilization of sharp and diffuse interface formulations in unfitted Nitsche finite element methods" by Maxim Olshanskii, Jan-Phillip Bäcker, and Dmitri Kuzmin, arXiv:2501.16594v2, 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|ccccc} \hline
 \textbf{Methods} & \textbf{RRE} & \textbf{RTE} & \textbf{FMR} & \textbf{RR} \\ \hline \hline
 GeoTr~+A2A & 1.9398 & 4.96 & 98.37 & 98.37 \\
 GeoTr+OPO & \underline{5.9528} & \underline{15.46} & \underline{99.63} & \underline{94.69} \\
 GeoTr+O2O & \textbf{1.4443} & \textbf{3.67} & \textbf{99.32} & \textbf{99.05} \\ \hline
 \end{tabular}
\caption{\textbf{Ablation study on different registration strategies.}}
\end{table}
\end{document}

cs

"SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks" by Yaxu Xie, Alain Pagani, and Didier Stricker, arXiv:2403.19474v1, 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}{ccccc}
 \toprule
 & \textbf{\# Pairs} & \textbf{\# Utter.} & \textbf{Segment length (s)} \\ \midrule
 val & 263,486 & 140,185 & 2.05/8.18/144.92\\
 test & 1,695,248 & 118,439 & 2.04/5.02/81.04\\
 \bottomrule
 \end{tabular}
\caption{\small{ Statistics of the Speaker Verification val and test sets (Tracks 1--3). \textbf{\# Pairs} refers to the number of evaluation trial pairs, whereas \textbf{\# Utter.} refers to the total number of unique speech segments in the test set. Segment lengths are reported as min/mean/max.}}
\end{table}
\end{document}

eess

Arsha Nagrani
Joon Son Chung
Jaesung Huh
Andrew Brown
Ernesto Coto
Weidi Xie
Mitchell McLaren
Douglas A Reynolds
Andrew Zisserman

"VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge" by Arsha Nagrani, Joon Son Chung, Jaesung Huh, Andrew Brown, Ernesto Coto, Weidi Xie, Mitchell McLaren, Douglas A Reynolds, and Andrew Zisserman, arXiv:2012.06867v1, 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{Numerical results for the MFG in with $\Gamma(m)=m^\alpha$. Recovered power $\alpha$ and $\overline H$ with $I_V=20$ data points of $V$, $I=40$ observations on $m$, and $M=400$ sample points. $\alpha$ and $\overline{H}$ stand for the references and $\alpha^\dagger$ and $\overline{H}^\dagger$ represent the recovered variables. }
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
 \hline
 $\alpha$ & 0.5 & 1 & 2 & 3 & 5\\
 \hline
 $\alpha^{\dagger}$& 0.504769 & 1.182533 & 1.990808 & 2.89700 & 4.776276 \\
 \hline
 $\overline{H}^{}$ & -0.970467 & -0.971760& -0.975970 & -0.985608 &-0.998791 \\
 \hline
 $\overline H^{\dagger}$ &-0.970979 & -0.991179& -0.966402 &-0.985758 &-0.981125\\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Jinyan Guo
Chenchen Mou
Xianjin Yang
Chao Zhou

"Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes" by Jinyan Guo, Chenchen Mou, Xianjin Yang, and Chao Zhou, arXiv:2312.06625v2, 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 VRP Filtered Results}
\begin{tabular}{llrrrr}
 \toprule
 Model & Cut & OptimalTimes & GapWithoutInf & \#Inf \\
 \midrule
 DD-JS & IIS & 263.8 & 0.4 & 1 \\
 DD-JS & No-Good & 283.5 & 0.4 & 0 \\
 DD-LJ & IIS & 397.8 & 0.4 & 1 \\
 DD-LJ & No-Good & 393.0 & 0.5 & 0 \\
 IP & IIS & 448.9 & 0.7 & 70 \\
 IP & No-Good & 408.5 & 0.9 & 0 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Nicolás Casassus
Margarita Castro
Gustavo Angulo

"A Decision Diagram Approach for the Parallel Machine Scheduling Problem with Chance Constraints" by Nicolás Casassus, Margarita Castro, and Gustavo Angulo, arXiv:2504.20889v1, 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{Repeat Mobility - PSID 1968-1997}
\begin{tabular}{l|cc|cc}
 \hline
 & \multicolumn{2}{c|}{All workers } & \multicolumn{2}{c}{Male workers} \\
 \hline
 & Occ. Mobility & Occ.+Ind. Mobility & Occ. Mobility & Occ.+Ind. Mobility \\
 \hline \hline
 \emph{1-digit} & & & & \\
 Stayer - Stayer & 67.3 & 77.2 & 67.4 & 77.0 \\
 Stayer - Mover & 32.7 & 22.8 & 32.6 & 23.0 \\
 \hline
 Mover - Stayer & 46.9 & 58.8 & 45.8 & 57.8 \\
 Mover - Mover & 53.1 & 41.2 & 54.2 & 42.2 \\
 \hline \hline
 \emph{2-digits} & & & & \\
 Stayer - Stayer & 61.9 & 69.9 & 62.8 & 71.0 \\
 Stayer - Mover & 38.1 & 30.1 & 37.2 & 29.0 \\
 \hline
 Mover - Stayer & 40.6 & 49.7 & 40.5 & 48.8 \\
 Mover - Mover & 59.4 & 50.2 & 59.5 & 51.2 \\
 \hline \hline
 \emph{3-digits} & & & & \\
 Stayer - Stayer & 54.3 & 61.2 & 57.8 & 64.1 \\
 Stayer - Mover & 45.7 & 38.8 & 42.2 & 35.9 \\
 \hline
 Mover - Stayer & 25.9 & 33.2 & 27.5 & 34.8 \\
 Mover - Mover & 74.1 & 66.8 & 72.5 & 65.2 \\
 \hline
 \multicolumn{5}{l}{\footnotesize{Note: Total number of observations among all workers (male) = 3,261 (2,467).}}
 \end{tabular}
\end{table}
\end{document}

q-fin

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results by data sets}
\begin{tabular}{lllrrr}
 \toprule
 Model & Cut & Dataset & \#Problems & \#Optimal & TotalTime \\
 \midrule
 DD-JS & IIS & Equal & 135 & 84 & 709.9 \\
 DD-JS & IIS & ORS & 135 & 21 & 1080.3 \\
 DD-JS & IIS & VRP & 135 & 79 & 652.4 \\
 DD-JS & NoGood & Equal & 135 & 72 & 758.8 \\
 DD-JS & NoGood & ORS & 135 & 21 & 1090.1 \\
 DD-JS & NoGood & VRP & 135 & 80 & 657.1 \\
 DD-LJ & IIS & Equal & 135 & 67 & 825.2 \\
 DD-LJ & IIS & ORS & 135 & 18 & 1109.6 \\
 DD-LJ & IIS & VRP & 135 & 75 & 754.6 \\
 DD-LJ & NoGood & Equal & 135 & 63 & 844.6 \\
 DD-LJ & NoGood & ORS & 135 & 19 & 1122.7 \\
 DD-LJ & NoGood & VRP & 135 & 78 & 740.3 \\
 IP & IIS & Equal & 135 & 17 & 1121.1 \\
 IP & IIS & ORS & 135 & 1 & 1237.5 \\
 IP & IIS & VRP & 135 & 46 & 981.1 \\
 IP & NoGood & Equal & 135 & 49 & 957.6 \\
 IP & NoGood & ORS & 135 & 14 & 1132.7 \\
 IP & NoGood & VRP & 135 & 66 & 813.9 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math


math

Maria Vasilyeva
Golo A. Wimmer
Ben S. Southworth

"Multiscale approximation and two-grid preconditioner for extremely anisotropic heat flow" by Maria Vasilyeva, Golo A. Wimmer, and Ben S. Southworth, arXiv:2412.08355v1, 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}{lrr}
\hline\hline
Constraints&Number of filings&Fraction, \%\tabularnewline
\hline
0&$51535$&$85.952$\tabularnewline
1&$ 3725$&$ 6.213$\tabularnewline
2&$ 2074$&$ 3.459$\tabularnewline
3&$ 873$&$ 1.456$\tabularnewline
4&$ 466$&$ 0.777$\tabularnewline
5&$ 306$&$ 0.510$\tabularnewline
6&$ 231$&$ 0.385$\tabularnewline
7&$ 148$&$ 0.247$\tabularnewline
8&$ 119$&$ 0.198$\tabularnewline
9&$ 103$&$ 0.172$\tabularnewline
10&$ 78$&$ 0.130$\tabularnewline
11&$ 48$&$ 0.080$\tabularnewline
12&$ 54$&$ 0.090$\tabularnewline
13&$ 36$&$ 0.060$\tabularnewline
14&$ 24$&$ 0.040$\tabularnewline
15&$ 28$&$ 0.047$\tabularnewline
16&$ 20$&$ 0.033$\tabularnewline
17&$ 19$&$ 0.032$\tabularnewline
18&$ 19$&$ 0.032$\tabularnewline
19&$ 8$&$ 0.013$\tabularnewline
20&$ 12$&$ 0.020$\tabularnewline
21&$ 10$&$ 0.017$\tabularnewline
22&$ 3$&$ 0.005$\tabularnewline
23&$ 4$&$ 0.007$\tabularnewline
24&$ 4$&$ 0.007$\tabularnewline
25&$ 2$&$ 0.003$\tabularnewline
26&$ 1$&$ 0.002$\tabularnewline
27&$ 2$&$ 0.003$\tabularnewline
29&$ 1$&$ 0.002$\tabularnewline
32&$ 1$&$ 0.002$\tabularnewline
34&$ 2$&$ 0.003$\tabularnewline
35&$ 1$&$ 0.002$\tabularnewline
47&$ 1$&$ 0.002$\tabularnewline
\hline
Total&$ 59958 $&$ 100.000$\tabularnewline
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Zero-Leverage Puzzle

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}
 \hline
 Method & Efficiency \\
 \hline
 WENO5-LW5 & $1.44$ \\
 \hline
 WENO5-LWA5 & $1.54$ \\
 \hline
 WENO5-LWF5 & $1.33$ \\
 \hline
 WENO5-LWAF5 & $1.44$ \\
 \hline
 \end{tabular}
\caption{Performance table.}
\end{table}
\end{document}

math

"High Order in Space and Time Schemes Through an Approximate Lax-Wendroff Procedure" by Antonio Baeza, Pep Mulet, and David Zorío, arXiv:2502.07798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

math

Lehel Csillag
Julio Marny Hoff da Silva
Tudor Patuleanu

"Enlargement of symmetry groups in physics: a practitioner's guide" by Lehel Csillag, Julio Marny Hoff da Silva, and Tudor Patuleanu, arXiv:2412.04695v1, 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|cccccccc}
 {\bf Judge} & {\bf Problem} & {\bf Problem} & {\bf Analytical} & {\bf Computational} & {\bf Paper} & {\bf Field} & {\bf Paper} \\
 {\bf } & {\bf Importance} & {\bf Modeling} & {\bf Results} & {\bf Results} & {\bf Writing} & {\bf Contribution} & {\bf Ranking} \\ \hline
 21 & 8 & 10 & 8 & 8 & 5 & 8 & 3 \\
 24 & 8 & 9 & 8 & 10 & 7 & 8 & 1 \\
 14 & 7 & 2 & 3 & 2 & 2 & 2 & 5 \\
 26 & 8 & 8 & 7 & 8 & 8 & 7 & 3 \\
 49 & 10 & 7 & 6 & 9 & 9 & 8 & 1
\end{tabular}
\end{table}
\end{document}

cs

"Joint aggregation of cardinal and ordinal evaluations with an application to a student paper competition" by Dorit S. Hochbaum and Erick Moreno-Centeno, arXiv:2101.04765v1, 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 summaries of layers for detection}
\begin{tabular}{ccccc}
 \hline
 \textbf{Layers for Detection} & \textbf{Stride} & \textbf{Receptive Field}& \textbf{Anchor Size} & \textbf{Anchor Aspect Ratio}\\
 \hline
 $conv4\_3$ & 8 & 108& 32 & 1, $\frac{3}{2}$, 3, $\frac{2}{3}$, $\frac{1}{3}$\\
 $conv5\_3$ & 16 & 228& 64 & 1, $\frac{3}{2}$, 3, $\frac{2}{3}$, $\frac{1}{3}$\\
 $conv\_fc7$ & 32 & 340& 128 & 1, $\frac{3}{2}$, 3, $\frac{2}{3}$, $\frac{1}{3}$\\
 $ conv6\_2$ & 64 & 468& 256& 1, $\frac{3}{2}$, $\frac{2}{3}$\\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

"Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectors" by Richard Schmit, arXiv:2505.00044v1, 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{linear and nonlinear bias of the pretrained embedding models without any finetuning to a specific task. Higher bias scores indicate lower bias present in the embeddings. Baseline indicates the average prediction.}
\begin{tabular}{ccc}
 \toprule
method & linear bias $\uparrow$ & nonlinear bias $\uparrow$ \\
 \cmidrule(r){1-1} \cmidrule(l){2-3}
original Bert & 0.0047 & 0.0018 \\
Sent-Debias & 0.0046 & 0.0020 \\
Null-It-Out & 0.0157 & 0.0021 \\
$pre^p$ & \textbf{0.0159} & \textbf{0.0159} \\
baseline & 0.0197 & 0.0197 \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Philip Kenneweg
Sarah Schröder
Alexander Schulz
Barbara Hammer

"Debiasing Sentence Embedders through Contrastive Word Pairs" by Philip Kenneweg, Sarah Schröder, Alexander Schulz, and Barbara Hammer, arXiv:2403.18555v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Generalized Linear Model Regression Results on Manchester City}
\begin{tabular}{ll}
\hline
\textbf{Metric} & \textbf{Feature Values} \\
\hline
No. Observations & 243 \\
Df Residuals & 238 \\
Link Function & Log \\
Method & IRLS \\
Log-Likelihood & -1938.5 \\
Deviance & 2836.7 \\
Pearson chi2 & 4.24e+03 \\
No. Iterations & 6 \\
Pseudo R-squ. (CS) & 0.8664 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Efficiency Analysis on Raspberry Pi 4.}
\begin{tabular}{ccccc}
 \toprule
 Metrics & Params & Size & Inference Time & CPU Usage \\
 \midrule
 Efficiency & 270K & 284 KB & 4 ms & 25\% \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Xiyuan Zhang
Xiaohan Fu
Diyan Teng
Chengyu Dong
Keerthivasan Vijayakumar
Jiayun Zhang
Ranak Roy Chowdhury
Junsheng Han
Dezhi Hong
Rashmi Kulkarni
Jingbo Shang
Rajesh Gupta

"Physics-Informed Data Denoising for Real-Life Sensing Systems" by Xiyuan Zhang, Xiaohan Fu, Diyan Teng, Chengyu Dong, Keerthivasan Vijayakumar, Jiayun Zhang, Ranak Roy Chowdhury, Junsheng Han, Dezhi Hong, Rashmi Kulkarni, Jingbo Shang, and Rajesh Gupta, arXiv:2311.06968v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|l|}
\hline
\textit{Methods} & \textit{FID Score} \\ \hline
\textbf{Our work} & \textbf{39.2574} \\ \hline
Cartoon-to-real & 45.2566 \\ \hline
UNIT & 55.9214 \\ \hline
\end{tabular}
\caption{FID score of our proposed model, in comparison with our previous work (Cartoon-to-real) and UNIT model.}
\end{table}
\end{document}

eess

K. M. Arefeen Sultan
Mohammad Imrul Jubair
MD. Nahidul Islam
Sayed Hossain Khan

"toon2real: Translating Cartoon Images to Realistic Images" by K. M. Arefeen Sultan, Mohammad Imrul Jubair, MD. Nahidul Islam, and Sayed Hossain Khan, arXiv:2102.01143v1, 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{A sample vector obtained from semantic segmentation from a street-view image in Fig. }
\begin{tabular}{|l|l|l|l|l|}
\hline
Road & Sidewalk & Bicycle & $\cdots$ & Building \\\hline
0.31 & 0.03 & 0 & $\cdots$ & 0.18 \\\hline
\end{tabular}
\end{table}
\end{document}

cs

Tsung Heng Wu
Md Amiruzzaman
Ye Zhao
Deepshikha Bhati
Jing Yang

"Visualizing Routes with AI-Discovered Street-View Patterns" by Tsung Heng Wu, Md Amiruzzaman, Ye Zhao, Deepshikha Bhati, and Jing Yang, arXiv:2404.00431v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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


\documentclass{article}
\usepackage{pifont}
\usepackage[table]{xcolor}
\usepackage{amsmath}
\usepackage{marvosym}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccccccc}
 \toprule
 &\multicolumn{3}{c}{} & \multicolumn{2}{c}{\textbf{VGG-SS}} & \multicolumn{2}{c}{\textbf{AVS (S4)}} & \multicolumn{2}{c}{\textbf{Extended VGG-SS}} \\
 \textbf{} & $ACL_I$ & $ACL_F$ & $Reg$ & \textbf{cIoU $\uparrow$} & \textbf{AUC $\uparrow$} & \textbf{mIoU $\uparrow$} & \textbf{F-score $\uparrow$} & \textbf{AP $\uparrow$} & \textbf{max-F1 $\uparrow$} \\ \midrule
 (A) & \ding{51} & \ding{55} & \ding{55} & 40.42 & 40.84 & 38.55 & 45.94 & 28.59 & 35.90 \\
 (B) & \ding{55} & \ding{51} & \ding{55} & 2.30 & 7.46 & 4.08 & 22.59 & 0.86 & 1.80 \\
 (C) & \ding{51} & \ding{51} & \ding{55} & 46.61 & 44.71 & 53.06 & 63.01 & 40.72 & 47.90 \\
 (D) & \ding{51} & \ding{55} & \ding{51} & 41.08 & 41.01 & 41.93 & 48.99 & 33.37 & 41.30 \\
 (E) & \ding{55} & \ding{51} & \ding{51} & 35.15 & 38.36 & 32.06 & 41.05 & 39.91 & 47.20 \\
 \rowcolor{lightgray!25}
 (F) & \ding{51} & \ding{51} & \ding{51} & \textbf{49.46} & \textbf{46.32} & \textbf{59.76} & \textbf{69.03} & \textbf{40.79} & \textbf{49.10} \\
 \bottomrule
 \end{tabular}
\caption{\textbf{Ablative experiments on our method by using different combinations of loss functions.}}
\end{table}
\end{document}

eess

Sooyoung Park
Arda Senocak
Joon Son Chung

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
$s$ &ESS &Metropolis & Direct & 1 table\\
\hline
1 &444 &3.877 & 25.4&0.057\\
2 &844 &4.012 & 83.7&0.099\\
5 &717 &4.241 &130.3&0.182\\
10 &544 &4.574 &165.0&0.303\\
\hline
\end{tabular}
\end{table}
\end{document}

stat


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|cccc|}
\hline
Fingerprint & logistic regression & Random Forest & LightGBM [\%] \\
\hline
ECFP & 66.80 & 76.69 ± 1.26 & 74.62\\
\hline
Atom Pair & 56.42 & \textbf{79.47 ± 1.00} & 73.15\\
\hline
Topological Torsion & 66.20 & 76.75 ± 1.02 & 72.38\\
\hline
MACCS Keys & 71.71 & 75.08 ± 1.55 & 74.95\\
\hline
ErG & 72.82 & 74.38 ± 2.06 & 74.03\\
\hline
MAP4 & 70.54 & 66.13 ± 1.30 & 66.58\\
\hline
MHFP & 52.61 & 65.29 ± 1.41 & 54.23\\
\hline
\end{tabular}
\caption{Test AUROC scores for HIV dataset.}
\end{table}
\end{document}

cs

Michał Szafarczyk
Piotr Ludynia
Przemysław Kukla

"A Python library for efficient computation of molecular fingerprints" by Michał Szafarczyk, Piotr Ludynia, and Przemysław Kukla, arXiv:2403.19718v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Random Matrices and the Free Energy of Ising-Like Models with Disorder" by Nils Gluth, Thomas Guhr, and Alfred Hucht, arXiv:2312.13231v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|}
		\hline
		Description & Parameter& Value \\
		\hline
		Number of antennas at Base Station & $M$& 8 \\
		\hline
		Number of RIS elements & $N$& 32 \\
		\hline
		Number of Users & $K$ & 4 \\
		\hline
		Max transmitted power & $P_{\max}$ & 10 W \\
		\hline
		Temporal evolution coefficient & $\rho$ & 0.95 \\
		\hline
		Noise power density & $\sigma_k^2$& $-174$ dBm/Hz \\
		\hline
		Carrier frequency & $f_c$ & 5 GHz \\
		\hline
		Rician factor & $K_f$& 3 dB \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

Meng-Qian Alexander Wu
Tzu-Hsien Sang
Luisa Schuhmacher
Ming-Jie Guo
Khodr Hammoud
Sofie Pollin

"An MRL-Based Design Solution for RIS-Assisted MU-MIMO Wireless System under Time-Varying Channels" by Meng-Qian Alexander Wu, Tzu-Hsien Sang, Luisa Schuhmacher, Ming-Jie Guo, Khodr Hammoud, and Sofie Pollin, arXiv:2311.08840v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Overview of different training trajectories and conditional vector fields.} Note that the conditional vector field is the time-derivative of the training trajectory.}
\begin{tabular}{lrrr}
 \toprule
 Name & Training Trajectory $\mathbf{x}_t$ & \quad & Conditional Vector Field $v_t$ \\
 \midrule
 Linear & $(1-t) \mathbf{x}_0 + t \mathbf{x}_1$ & & $\mathbf{x}_1 - \mathbf{x}_0$ \\
 Sine/Cosine & $\cos{(\frac{\pi}{2}t)} \mathbf{x}_0 + \sin{(\frac{\pi}{2}t)} \mathbf{x}_1$ & & $\cos{(\frac{\pi}{2}t)} \mathbf{x}_1-\sin{(\frac{\pi}{2}t)} \mathbf{x}_0$ \\
 Sine & $ (1-\sin{(\frac{\pi}{2}t})) \mathbf{x}_0 + \sin{(\frac{\pi}{2}t)} \mathbf{x}_1$ & & $\cos{(\frac{\pi}{2}t)} (\mathbf{x}_1 - \mathbf{x}_0)$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Julian Jorge Andrade Guerreiro
Naoto Inoue
Kento Masui
Mayu Otani
Hideki Nakayama

"LayoutFlow: Flow Matching for Layout Generation" by Julian Jorge Andrade Guerreiro, Naoto Inoue, Kento Masui, Mayu Otani, and Hideki Nakayama, arXiv:2403.18187v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"A Standardization Procedure to Incorporate Variance Partitioning Based Priors in Latent Gaussian Models" by Luisa Ferrari and Massimo Ventrucci, arXiv:2501.16057v1, 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{Learning hyperparameters}
\begin{tabular}{c|c}
\toprule
Actor network & [512, 256, 128] \\
Critic network & [512, 256, 128] \\
Activation & Elu \\
Discount factor & 0.99 \\
GAE coefficient & 0.95 \\
PPO clipping & 0.2 \\
Entropy coefficient & 1e-3 \\
Learning rate & 3e-4 \\
Learning rate schedule & Adaptive \\
KL threshold for adaptive schedule & 8e-3 \\
Maximum gradient norm & 1.0 \\
Horizon length & 24 \\
Minibatch size & 16384 \\
Mini epochs & 5 \\
Critic coefficient & 2 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Elliot Chane-Sane
Pierre-Alexandre Leziart
Thomas Flayols
Olivier Stasse
Philippe Souères
Nicolas Mansard

"CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning" by Elliot Chane-Sane, Pierre-Alexandre Leziart, Thomas Flayols, Olivier Stasse, Philippe Souères, and Nicolas Mansard, arXiv:2403.18765v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of Core 2 (Blue) from Fig. .}
\begin{tabular}{ll}
\hline
\textbf{Category} & \textbf{Details} \\ \hline
\textbf{Source Set} & atp\_c, akg\_c, nadph\_c, nh4\_c \\ \hline
\textbf{Sink Set} & adp\_c, pi\_c, nadp\_c \\ \hline
\textbf{Non-autocatalytic Set} & gln\_\_L\_c \\ \hline
\textbf{Autocatalytic Set} & h\_c, glu\_\_L\_c \\ \hline
\textbf{Growth Factor} & 1.4142011834319526 \\ \hline
\textbf{Reactions} & \\ \hline
R11 & atp\_c + glu\_\_L\_c + nh4\_c $\to$ adp\_c + h\_c + pi\_c + gln\_\_L\_c \\
R12 & h\_c + akg\_c + gln\_\_L\_c + nadph\_c $\to$ 2glu\_\_L\_c + nadp\_c \\ \hline
\end{tabular}
\end{table}
\end{document}

math

Víctor Blanco
Gabriel González
Praful Gagrani

"Identifying Self-Amplifying Hypergraph Structures through Mathematical Optimization" by Víctor Blanco, Gabriel González, and Praful Gagrani, arXiv:2412.15776v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{diagbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccccccccc}
 	\hline
 	\diagbox{$\alpha$}{$g_{\alpha\beta}$}{$\beta$} & $t$ & $D_\theta$ & $s$ & $y$ & $z$ & $\overline{y}$ & $\overline{z}$ \\ % <-- added & and content for each column
 	\hline
 	$t$ & & $\surd_{L}$ & & $\surd_{BL}$ &
 $\surd_{BL}$ & $\surd_{BL}$ & $\surd_{BL}$ \\
 $D_\theta$ & $\surd_{L}$ & & & $\surd_{BL}$ & $\surd_{BL}$ & & \\% <--
 	$s$ & & & & & \\
 	$y$ & $\surd_{BL}$ & $\surd_{BL}$ & & $\surd_{BL}$ & $\surd_{BL}$ & $\surd_{BL}$ & $\surd_{BL}$ \\
 	$z$ & $\surd_{BL}$ & $\surd_{BL}$ & & $\surd_{BL}$ & $\surd_{BL}$ & $\surd_{BL}$ & $\surd_{BL}$ \\
 	$\overline{y}$ & $\surd_{BL}$ & & & $\surd_{BL}$ & $\surd_{BL}$ & & \\
 $\overline{z}$ & $\surd_{BL}$ & & & $\surd_{BL}$ & $\surd_{BL}$ & & \\
 	\hline
 \end{tabular}
\caption{Second-order derivatives of $g$ required to be bounded and Lipschitz continuous}
\end{table}
\end{document}

math

"A Malliavin Calculus Approach to Backward Stochastic Volterra Integral Equations" by Qian Lei and Chi Seng Pun, arXiv:2412.19236v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Validation Results of Information Retrieval}
\begin{tabular}{lcc}
\hline
\textbf{Variable} & \textbf{Accuracy} & \textbf{F1 Score} \\
\hline
Method: DiD & 0.7762 & 0.8447 \\
Method: RCT & 0.7063 & 0.8269 \\
Method: RDD & 0.9371 & 0.9644 \\
Method: IV & 0.7413 & 0.8183 \\
Field: Urban Economics & 0.9138 & 0.9545 \\
Field: Finance & 0.8788 & 0.9295 \\
Field: Macroeconomics & 0.9744 & 0.9870 \\
Field: Development & 0.6643 & 0.7937 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

Causal Claims in Economics

"Causal Claims in Economics" by Prashant Garg and Thiemo Fetzer, arXiv:2501.06873v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"How to detect the spacetime curvature without rulers and clocks. II. Three-dimensional spacetime" by A. V. Nenashev and S. D. Baranovskii, arXiv:2312.03487v1, 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{Statistics of datasets used in our work.}
\begin{tabular}{ccccc}
 \toprule
 Dataset & \#User & \#Item & \#Interactions & Density\\
 \midrule
 Gowalla & 29,858 & 40,981 & 1,027,370 & 0.084\%\\
 Yelp2020 & 71,135 & 45,063 & 1,782,999 & 0.056\%\\
 Amazon-book & 52,643 & 91,599 & 2,984,108 & 0.062\%\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Xurong Liang
Tong Chen
Lizhen Cui
Yang Wang
Meng Wang
Hongzhi Yin

"Lightweight Embeddings for Graph Collaborative Filtering" by Xurong Liang, Tong Chen, Lizhen Cui, Yang Wang, Meng Wang, and Hongzhi Yin, arXiv:2403.18479v2, 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}
Parameter & Value \\
\hline
$m$ & $6$ \\
$n$ & $1$-$9$ \\
$T$ & $1$ \\
$\mu$ & $8\%$ \\
$\sigma$ & $20\%$ \\
$u$ & $\sim$$1.09$ \\
$q$ & $\sim$$0.56$ \\
$\theta_\mathrm{u}\frac{180^\circ}{\pi}$ & $\sim$$97.1^\circ$
\end{tabular}
\end{table}
\end{document}

q-fin

"Quantum Monte Carlo simulations for financial risk analytics: scenario generation for equity, rate, and credit risk factors" by Titos Matsakos and Stuart Nield, arXiv:2303.09682v2, 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{Results from the regression discontinuity design with matched control group. The first column shows results with only the treatment indicator (\emph{noAudit}) and year fixed effects. The coefficient on the treatment indicator is positive and significant. In the second column we add operating revenue to the regression so that the coefficient on the \emph{noAudit} indicator is interpreted at the threshold. In the third column we add other control variables. In the fourth column we add sector fixed effects and in the fifth column we limit the sample to firms with operating revenue within one million NOK of the voluntary auditing threshold of 5 million NOK. The coefficient on the \emph{noAudit} indicator remains significantly positive in the range of 0.10 to 0.16 standard deviations. }
\begin{tabular}{lccccc}
\\\hline
\hline \\
 & \multicolumn{5}{c}{\textit{Dependent variable:}} \\
\cline{2-6}
 & \multicolumn{5}{c}{\textit{Dividend}} \\
 & I & II & III & IV & V \\
\midrule
Intercept & -0.300*** & -0.302*** & -0.190*** & -0.152*** & -0.123 \\
 & (0.030) & (0.029) & (0.030) & (0.040) & (0.627) \\
noAudit & 0.056*** & 0.104*** & 0.130*** & 0.107*** & 0.160*** \\
 & (0.015) & (0.019) & (0.018) & (0.020) & (0.043) \\
operating revenue & & 0.074*** & 0.132*** & 0.106*** & 0.145* \\
 & & (0.013) & (0.019) & (0.023) & (0.087) \\
employees & & & -0.014*** & -0.012 & -0.000 \\
 & & & (0.003) & (0.008) & (0.016) \\
total assets & & & 0.095*** & 0.080* & 0.182 \\
 & & & (0.029) & (0.046) & (0.235) \\
risk (roa sd) & & & 0.013 & 0.006 & 0.080 \\
 & & & (0.019) & (0.021) & (0.110) \\
leverage & & & 0.029** & 0.031** & 0.026* \\
 & & & (0.013) & (0.014) & (0.015) \\
cash flow (mean) & & & 0.350*** & 0.405*** & 0.682*** \\
 & & & (0.041) & (0.047) & (0.112) \\
cash flow (sd) & & & -0.100*** & -0.095*** & -0.198 \\
 & & & (0.022) & (0.024) & (0.134) \\
\midrule
Year FE & YES & YES & YES & YES & YES\\
Sector FE & NO & NO & YES & YES & YES \\
N & 3902 & 3902 & 3902 & 3902 & 1341 \\
R2 & 0.01 & 0.02 & 0.14 & 0.18 & 0.26 \\
Adjusted R2 & 0.01 & 0.02 & 0.14 & 0.16 & 0.23 \\
\bottomrule
\textit{Note: } & \multicolumn{5}{r}{Standard errors are adjusted for clustering} \\
\textit{} & \multicolumn{5}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\
\end{tabular}
\end{table}
\end{document}

q-fin

"Adults in the room? The auditor and dividends in small firms: Evidence from a natural experiment" by Hakim Lyngstadås and Johannes Mauritzen, arXiv:2301.11079v1, 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{Effect of threshold on precision (PR), recall (RE), F1 score and support samples}
\begin{tabular}{|c|cccc|cccc|}
\cline{1-9}
 & \multicolumn{4}{c|}{\textbf{Base metrics}} & \multicolumn{4}{c|}{\textbf{After uncertainty}}\\
\cline{2-9}
\textbf{Condition}& \textbf{PR} & \textbf{RE} & \textbf{F1} & \textbf{Support} & \textbf{PR} & \textbf{RE} & \textbf{F1} & \textbf{Support} \\
\hline
AMD & 0.90 & 0.82 & 0.86 & 11 & 0.89 & 0.89 & 0.89 & 9 \\ \hline
CSR & 0.85 & 0.85 & 0.85 & 20 & 0.88 & 0.94 & 0.91 & 16\\ \hline
DR & 0.80 & 0.76 & 0.78 & 21 & 0.92 & 0.80 & 0.86 & 15\\ \hline
MH & 0.81 & 0.85 & 0.83 & 20 & 0.88 & 0.93 & 0.90 & 15 \\ \hline
Normal & 0.95 & 0.95 & 0.95 & 41 & 1.00 & 1.00 & 1.00 & 40 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Amitojdeep Singh
Sourya Sengupta
Mohammed Abdul Rasheed
Varadharajan Jayakumar
Vasudevan Lakshminarayanan

"Uncertainty aware and explainable diagnosis of retinal disease" by Amitojdeep Singh, Sourya Sengupta, Mohammed Abdul Rasheed, Varadharajan Jayakumar, and Vasudevan Lakshminarayanan, arXiv:2101.12041v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Jean Fromentin
Pierre-Louis Giscard
Yohan Hosten

"Fast construction of self-avoiding polygons and efficient evaluation of closed walk fractions on the square lattice" by Jean Fromentin, Pierre-Louis Giscard, and Yohan Hosten, arXiv:2412.12655v1, 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{\small Detailed Statistics of datasets used in our experiments. \textbf{Type} refers to Conversational or Read speech.}
\begin{tabular}{lcccc}
 \toprule
\textbf{Dataset}&\textbf{Language}&\textbf{Domain}&\textbf{Type}&\textbf{Duration}\\
 & & & &(train, dev, test)\\
 \toprule
 MSR & Gujarati & General& Conv.& 40hr, 5hr, 5hr\\
 MSR & Tamil & General& Conv.&40hr, 5hr, 5hr\\
 MSR & Telugu & General& Conv.&40hr, 5hr, 5hr\\
 Gramvani (GV) & Hindi & Call Cent.& Conv.&100hr, 5hr, 3hr\\
 SwitchBoard (SWBD) & English & Call Cent.& Conv. &30hr, 5hr, N.A.\\
 Wall Street Journal (WSJ) & English & Finance & Read & 80hr, 1.1hr, 0.4hr\\\hline

 \toprule

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

eess

Ashish Seth
Sreyan Ghosh
S. Umesh
Dinesh Manocha

"Stable Distillation: Regularizing Continued Pre-training for Low-Resource Automatic Speech Recognition" by Ashish Seth, Sreyan Ghosh, S. Umesh, and Dinesh Manocha, arXiv:2312.12783v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"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.


q-fin

"Shortermism and excessive risk taking in optimal execution with a target performance" by Emilio Barucci and Yuheng Lan, arXiv:2505.15611v1, 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{The average PSNR (left) and SSIM (right) results with five masks by the competing methods.}
\begin{tabular}{|c|c|c|c|}
\hline
Method & DNU~ & TSA-Net~ & Ours \\ \hline
mask1 & 30.29, 0.8588 & 30.96, 0.8804 & \textbf{31.38, 0.8979} \\ \hline
mask2 & 30.46, 0.8516 & 31.23, 0.8875 & \textbf{31.73, 0.9034} \\ \hline
mask3 & 30.80, 0.8663 & 31.43, 0.8904 & \textbf{31.81, 0.9055} \\ \hline
mask4 & 30.65, 0.8610 & 31.15, 0.8863 & \textbf{31.58, 0.9038} \\ \hline
mask5 & 30.74, 0.8631 & 31.46, 0.8939 & \textbf{31.70, 0.9018} \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Tao Huang
Weisheng Dong
Xin Yuan
Jinjian Wu
Guangming Shi

"Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging" by Tao Huang, Weisheng Dong, Xin Yuan, Jinjian Wu, and Guangming Shi, arXiv:2103.07152v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Floris den Hengst
Ralf Wolter
Patrick Altmeyer
Arda Kaygan

"Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition" by Floris den Hengst, Ralf Wolter, Patrick Altmeyer, and Arda Kaygan, arXiv:2403.18973v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average AUC on the main task and standard deviations from different starting points of the network parameter initialization. Results for the vanilla and uncertainty based (unc.) weighting strategies. Inception V3 (IV3) and ResNet 50 (ResNet) are used as backbones. The adversarial task, i.e. \textit{center}, is marked by an overline.}
\begin{tabular}{c|c|c|c|c|c|c|cc|cc}
 \hline
Model &ID&main & area & count & contrast & $\overline{center}$ & \multicolumn{2}{c}{int. test}\vline & \multicolumn{2}{c}{CamExt} \\\hline
IV3&1 & \checkmark & & & & & \multicolumn{2}{c}{$0.819 {\pm 0.001}$}\vline & \multicolumn{2}{c}{ $0.868 {\pm 0.005}$ } \\\hline
{ResNet} & {R1} & \checkmark & & & & & \multicolumn{2}{c}{$0.802 {\pm 0.003}$}\vline & \multicolumn{2}{c}{ $0.821 {\pm 0.004}$ } \\\hline
&&& & & & & vanilla & unc. & vanilla & unc. \\\hline
\multirow{7}{*}{IV3}&2&\checkmark & \checkmark & & & & $0.718{\pm 0.11} $ & $0.834 {\pm 0.01}$ & $ 0.560 {\pm 0.06}$ & $0.871 {\pm 0.01}$ \\
&3&\checkmark & & \checkmark & & & $0.853 {\pm 0.03} $ & $0.836 {\pm 0.005}$ & $ 0.874 {\pm 0.02}$ & $\textbf{0.890} {\pm \textbf{0.009}}$ \\
&4&\checkmark & & & \checkmark & & $0.854 {\pm 0.07}$ & $0.835 {\pm 0.008}$ & $ 0.883 {\pm 0.02}$ & $0.876 {\pm 0.007}$ \\
&5&\checkmark & & & & \checkmark & $0.845 {\pm 0.10}$ & $0.822 {\pm 0.005}$ & $ 0.884 {\pm 0.04}$ & $0.871 {\pm 0.005}$ \\
&6&\checkmark & & \checkmark & & \checkmark & $0.863 {\pm 0.06}$ & $0.841 {\pm 0.004}$ & $ 0.623 {\pm 0.10 }$ & $\
\textbf{{0.890}} {\pm \textbf{0.01}}$ \\
&7&\checkmark & \checkmark & \checkmark & & \checkmark & $0.838 {\pm 0.05}$ & $0.848 {\pm 0.003}$ & $ 0.490 {\pm 0.03}$ & $0.864 {\pm 0.01}$ \\
&8&\checkmark & \checkmark & \checkmark & \checkmark & \checkmark & $0.858 {\pm 0.02}$ & ${0.874} {\pm{ 0.009}}$ & $ 0.686 {\pm 0.20}$ & $0.825 {\pm 0,01}$ \\\hline
 {ResNet}& {R8}&\checkmark & \checkmark & \checkmark & \checkmark & \checkmark & n.a. & {$\textbf{0.893}{\pm \textbf{0.001}}$} & n.a. & {${0.861} {\pm{ 0.01}}$} \\
\end{tabular}
\end{table}
\end{document}

eess

Mara Graziani
Sebastian Otalora
Stephane Marchand-Maillet
Henning Muller
Vincent Andrearczyk

"Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization" by Mara Graziani, Sebastian Otalora, Stephane Marchand-Maillet, Henning Muller, and Vincent Andrearczyk, arXiv:2008.01478v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison between the developing explosive time scales of the 4th, 5th and 6th waves. $\tau_{e}^{0}$ and $\tau_{e}^{end}$ represent the value of the fast explosive time scale at the start of each wave and at the time when the explosive modes disappear, respectively.}
\begin{tabular}{cc|cc|c}
\multicolumn{2}{c}{Wave 4} & \multicolumn{2}{c}{Wave 5} & Wave 6 \\
 \hline
 Period A & Period B & Period C & Period D & Period E \\
\hline \hline
$\tau_{e,A}^{0}$ = 8.33 d & $\tau_{e,B}^{0}$ = 7.24 d	&$\tau_{e,C}^{0}$ = 29.5 d & $\tau_{e,D}^{0}$ = 25.89 d & $\tau_{e,E}^{0}$ = 3.8 d\\
$\tau_{e,A}^{end}$ = 30.53 d & $\tau_{e,B}^{end}$ = 29.14 d	&$\tau_{e,C}^{end}$ = 78.7 d & $\tau_{e,D}^{end}$ = 71.1 d & $\tau_{e,E}^{end}$ = 34.9 d\\
\hline
\end{tabular}
\end{table}
\end{document}

math

Dimitris M. Manias
Dimitris G. Patsatzis
Dimitris A. Goussis

"Time scale dynamics of COVID-19 pandemic waves: The case of Greece" by Dimitris M. Manias, Dimitris G. Patsatzis, and Dimitris A. Goussis, arXiv:2312.07260v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation parameters for the application of feedback particle filter to the epidemiological example.}
\begin{tabular}{|c|c|c|}\hline
		parameter & notation & value \\
		\hline
		time step-size & $\Delta t$ & 1.0 \\
		observation noise & $\sigma_W$ & $0.1$ \\
		process noise & $\sigma_B$ & $0.1$ \\
		number of particles & $N$& $100$ \\
		recovery rate & $\alpha$ & $0.1$\\
		transmission rate & $\beta$ & $0.1$ \\
		\hline
	\end{tabular}
\end{table}
\end{document}

eess

"Optimal Transportation Methods in Nonlinear Filtering: The feedback particle filter" by Amirhossein Taghvaei and Prashant G. Mehta, arXiv:2102.10712v1, 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{NDTM hyperparameters for different tasks.}
\begin{tabular}{c|ccccccc|ccccccc}
\toprule
 & \multicolumn{7}{c|}{FFHQ} & \multicolumn{7}{c}{ImageNet} \\ \midrule
Task & N & $\gamma$ & $\eta$ & $\tau$ & $w_T$ & $w_\text{score}$ & $w_\text{control}$ & N & $\gamma$ & $\eta$ & $\tau$ & $w_T$ & $w_\text{score}$ & $w_\text{control}$ \\ \midrule
Super-Resolution (4x) & 5 & 1.0 & 0.7 & 400 & 50 & ddim & ddim & 2 & 2.0 & 0.1 & 600 & 50 & ddim & ddim \\
Random Inpainting (90\%) & 2 & 4.0 & 0.2 & 500 & 1 & 0 & 0 & 2 & 4.0 & 0.0 & 600 & 50 & ddim & ddim \\
Non-Linear Deblur & 5 & 5.0 & 0.1 & 400 & 1 & 0 & 0 & 2 & 4.0 & 0.1 & 600 & 50 & ddim & ddim \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Kushagra Pandey
Farrin Marouf Sofian
Felix Draxler
Theofanis Karaletsos
Stephan Mandt

"Variational Control for Guidance in Diffusion Models" by Kushagra Pandey, Farrin Marouf Sofian, Felix Draxler, Theofanis Karaletsos, and Stephan Mandt, arXiv:2502.03686v1, 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{A $2$-private code with $(N,K)=(4,2)$}
\begin{tabular}{|c|c|c|c|c|c|}\hline
\mbox{Server-1}&\mbox{Server-2}&\mbox{Server-3} &\mbox{Server-4}\\\hline
$a_1, b_1$ & $a_2, b_2$ & $a_3, b_3$ & $a_4, b_4$\\ \hline \hline
$a_5+b_5$ & $a_6+b_6$ & $a_7+b_7$ & $a_8+b_8$ \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Two-Level Private Information Retrieval

Ruida Zhou
Chao Tian
Hua Sun
James Plank

"Two-Level Private Information Retrieval" by Ruida Zhou, Chao Tian, Hua Sun, and James Plank, arXiv:2101.04821v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

q-fin

"Explosive growth from AI automation: A review of the arguments" by Ege Erdil and Tamay Besiroglu, arXiv:2309.11690v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Ablation Study : The table presents PSNR$_{\text{img}}$, SSIM, LPIPS, E$_{\text{warp}}$, and PSNR$_{x-t}$ scores, demonstrating the improvements achieved by each successive refinement, culminating in our proposed ConVRT method. (a), (b), (c), and (d) correspond to the respective indices in Fig \textcolor{red}{9}.}
\begin{tabular}{lccccc}
\toprule
Method & \textbf{PSNR$_{Img}$ $\uparrow$} & \textbf{SSIM} $\uparrow$ & \textbf{LPIPS} $\downarrow$ & \textbf{E$_{warp}$} $\downarrow$ & \textbf{PSNR$_{x-t}$} $\uparrow$ \\ \hline
TSRWGAN & 23.58 & 0.739 & 0.230 & 0.0026 & 23.77 \\
TurbNet & 23.44 & 0.732 & 0.228 & 0.0057 & 23.54 \\
TurbNet+Real-ESRGAN & 22.48 & 0.713 & 0.213 & 0.0074 & 22.67 \\ \hline
Base (a) & 24.91 & 0.781 & 0.209 & 0.0017 & 25.44 \\
\quad + Real-ESRGAN (b) & 24.88 & 0.782 & 0.204 & 0.0020 & 25.11 \\
\quad + CLIP (c) & 24.71 & 0.784 & 0.193 & 0.0014 & 25.40 \\
\quad + temp. consistency (d) & 24.90 & 0.787 & 0.189 & 0.0014 & 25.73 \\
= Ours (ConVRT) (d) & \textbf{24.90} & \textbf{0.787} & \textbf{0.189} & \textbf{0.0014} & \textbf{25.73} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Haoming Cai
Jingxi Chen
Brandon Y. Feng
Weiyun Jiang
Mingyang Xie
Kevin Zhang
Ashok Veeraraghavan
Christopher Metzler

"ConVRT: Consistent Video Restoration Through Turbulence with Test-time Optimization of Neural Video Representations" by Haoming Cai, Jingxi Chen, Brandon Y. Feng, Weiyun Jiang, Mingyang Xie, Kevin Zhang, Ashok Veeraraghavan, and Christopher Metzler, arXiv:2312.04679v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{||cccc||}
 \hline
 Index & Total Return(\%) & Vol & Sharpe \\
 \hline
 930950.CSI & 60.49 & 23.52 & 0.139 \\
 \hline
 000300.CSI & -23.36 & 25.97 & -0.068 \\
 \hline
 000905.CSI & 24.95 & 29.46 & 0.06 \\
 \hline
 930903.CSI & 3.21 & 26.42 & 0.012 \\
\hline
\end{tabular}
\caption{Compare different indices from 2007/12/31 to 2023/03/01, data from uqer.datayes.com}
\end{table}
\end{document}

q-fin

"Construct sparse portfolio with mutual fund's favourite stocks in China A share market" by Ke Zhang, arXiv:2305.01642v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example table}
\begin{tabular}{|c|c|c|} \hline
 Species & \bf Mean & \bf Std.~Dev. \\ \hline
 1 & 3.4 & 1.2 \\
 2 & 5.4 & 0.6 \\ \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Epps Effect and the Signature of Short-Term Momentum Traders" by Jérôme Busca and Léon Thomir, arXiv:2309.06711v1, 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{Shifted geometric mean of the primal gap for different oracle configurations}
\begin{tabular}{lccccc}
\toprule
{Oracles} & \multicolumn{4}{c}{Error rate} \\
{} & {0.0} & {0.1} & {0.3} & {0.5} \\
\midrule
OLNS-2 & 3.06 & 3.71 & 3.65 & 3.93 \\
OLNS-100 & 1.43 & 2.73 & 3.74 & 4.05 \\
OLNS-1000 & 1.39 & 2.65 & 3.69 & 3.99 \\
OLNS-10000 & 1.33 & 2.71 & 3.93 & 4.16 \\
DOLNS & 1.07 & 3.89 & 3.82 & 4.03 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger

"Supervised Large Neighbourhood Search for MIPs" by Charly Robinson La Rocca, Jean-François Cordeau, and Emma Frejinger, arXiv:2501.10778v1, 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{Cliques detected by two notions of similarity on the chimpanzee networks for three network combination methods (learned, unlearned, and binary). Individuals are denoted by their codes.}
\begin{tabular}{clll}
\toprule
Method & Count Similarity & Duration Similarity \\
\midrule
\multirow{3}{*}{learned} &
[ri, hu, ro, wn, ga],& [ri, hu, ro, wn, ga, ws], \\
& [ri, ro, garbo, ga], [hi, mu, cs], & [hi, mu, cs],\\
& [pe, ct], [rh, pi], [bt, pp]
& [pe, ct], [rh, pi]
\\
\hline
\multirow{2}{*}{unlearned} &
[ri, hu, ro, wn, ga], [pe, ct],
 & [ri, hu, ro, wn, ga, ws], \\
&[ri, ro, garbo], [hi, mu, dx]
 & [hi, mu, dx], [pe, ct], [rh, ro]
\\
\hline
\multirow{2}{*}{binary} &
[ri, ro], [ri, wn], [hi, mu], [mu, cs], &[ro, ws, bu], \\
&[ws, bu], [bu, ro], [mu, lo]&[mu, lo]\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Yixuan He
Aaron Sandel
David Wipf
Mihai Cucuringu
John Mitani
Gesine Reinert

"Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions" by Yixuan He, Aaron Sandel, David Wipf, Mihai Cucuringu, John Mitani, and Gesine Reinert, arXiv:2502.00302v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c}
		\hline
		\textbf{Hidden layer type} & \textbf{Hidden layers and units as a tuple} \\
		\hline
		Fully connected input layers & (512,512)
		\\
		\hline
	LSTM layers	& (100,100)
		\\
		\hline
	Fully connected output layers	& (256,256)
		\\
		\hline

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

eess

Amirhossein Shaghaghi
Abolfazl Zakeri
Nader Mokari
Mohammad Reza Javan
Mohammad Behdadfar
Eduard A Jorswieck

"Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach" by Amirhossein Shaghaghi, Abolfazl Zakeri, Nader Mokari, Mohammad Reza Javan, Mohammad Behdadfar, and Eduard A Jorswieck, arXiv:2103.03817v4, 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{Shock properties across regimes}
\begin{tabular}{lccc}
\hline \hline
Regime & Mean & Standard deviation \\
\hline
\underline{Inflation as regime-defining variable} \\
High & -0.029 & 0.576 \\
Low & 0.015 & 0.567 \\
\underline{Google Trends as regime-defining variable} \\
High & -0.011 & 0.786 \\
Low & 0.006 & 0.610
\\\\
\hline \hline
\end{tabular}
\end{table}
\end{document}

q-fin

"The Inflation Attention Threshold and Inflation Surges" by Oliver Pfäuti, arXiv:2308.09480v3, 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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|rl|}
\hline \bf Type of Text & \bf Font Size & \bf Style \\ \hline
paper title & 15 pt & bold \\
author names & 12 pt & bold \\
author affiliation & 12 pt & \\
the word ``Abstract'' & 12 pt & bold \\
section titles & 12 pt & bold \\
document text & 11 pt &\\
captions & 11 pt & \\
abstract text & 10 pt & \\
bibliography & 10 pt & \\
footnotes & 9 pt & \\
\hline
\end{tabular}
\caption{ Font guide. }
\end{table}
\end{document}

cs

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

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


q-fin

"Of Models and Tin Men: A Behavioural Economics Study of Principal-Agent Problems in AI Alignment using Large-Language Models" by Steve Phelps and Rebecca Ranson, arXiv:2307.11137v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Boundary values of alpha for projects used in our study}
\begin{tabular}{lllllll}
 \hline
 \multirow{2}{*}{Project} & \multirow{2}{*}{Domain} & \multirow{2}{*}{Criticality} & \multirow{2}{*}{Type} & \multirow{2}{*}{Template} & \multicolumn{2}{c}{Alpha} \\
 \cline{6-7}
 & & & & & Softened & Hardened \\
 \hline
 EIRENE & EE & Safety-critical & Functional & Multiple sentences & 0.4836 & 0.7535 \\
 ERTMS/
 ETCS & EE + ME & Safety-critical & Functional & Single sentence & 0.6093 & 0.8792 \\
 CCTNS & LW & Business-critical & Functional & Multiple sentences & 0.3102 & 0.5801 \\
 Gamma-J & EC + CS & Business-critical & Functional & Single sentence & 0.3445 & 0.6144 \\
 KeePass & CS & Non-critical & Functional & Single sentence & 0.2075 & 0.4150 \\
 Peering & CS & Business-critical & Functional & Single sentence & 0.2700 & 0.5399 \\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

"Natural Language Requirements Testability Measurement Based on Requirement Smells" by Morteza Zakeri-Nasrabadi and Saeed Parsa, arXiv:2403.17479v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Channel Choice and Customer Value

Shirsho Biswas
Hema Yoganarasimhan
Haonan Zhang

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Example table.}
\begin{tabular}{|c|c|c|} \hline
 Species & \bf Mean & \bf Std.~Dev. \\ \hline
 1 & 3.4 & 1.2 \\
 2 & 5.4 & 0.6 \\ \hline
 \end{tabular}
\end{table}
\end{document}

math

Hussein Albazzal
Alexei Lozinski
Roberta Tittarelli

"An a posteriori error estimate for a 0D/2D coupled model" by Hussein Albazzal, Alexei Lozinski, and Roberta Tittarelli, arXiv:2312.07959v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccc|cccc}
 \toprule
 &\textbf{KD}&\textbf{FB}&\textbf{SCE}&\textbf{MNLI}&\textbf{QNLI}&\textbf{SVAMP}&\textbf{RTE}\\ \midrule
 V0 &&&&56.6&88.3&-&68.5 \\ \midrule
 V1 &\checkmark&&&55.4&90.6&20.0&67.1\\
 V2&\checkmark&\checkmark& &59.5&91.4&23.3&68.6\\
 V3&\checkmark&&\checkmark&59.1&91.0&17.3&63.1\\
 \textbf{\emph{GOLD}}&\checkmark&\checkmark&\checkmark &\textbf{62.5}&\textbf{91.7}&\textbf{25.3}&\textbf{69.6} \\ \bottomrule
\end{tabular}
\caption{Ablation on the main components of \emph{GOLD}. \textit{V0}: pre-trained SLMs. \textit{KD}: knowledge distillation by vanilla data generation. \textit{FB}: the feedback function in the course of data generation. \textit{SCE}: symmetric cross entropy used for training the distilled SLM.}
\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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of the persistence models for one lead month SSTA and MHW forecasts. A persistence model predicts the next observation the same as the current observation, so the model does not involve a training process.}
\begin{tabular}{llll}
\textbf{Location} & \textbf{MSE↓} & \textbf{CSI↑} & \textbf{CSI 80↑} \\ \hline
BOP & 0.1690 & 0.3846 & 0.4717 \\
BP & 0.4665 & 0.1875 & 0.2949 \\
CI & 0.3095 & 0.2174 & 0.4211 \\
CR & 0.2468 & 0.3286 & 0.4959 \\
CS & 0.1247 & 0.4043 & 0.5327 \\
F & 0.3402 & 0.4699 & 0.5500 \\
HG & 0.2668 & 0.4386 & 0.3750 \\
OP & 0.3155 & 0.3478 & 0.4198 \\
R & 0.2377 & 0.5158 & 0.6259 \\
SI & 0.2440 & 0.4545 & 0.4646 \\
T & 0.3926 & 0.4595 & 0.5541 \\
W & 0.4578 & 0.4607 & 0.5541 \\ \hline
Average & 0.2976 & 0.3891 & 0.4800
\end{tabular}
\end{table}
\end{document}

stat

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

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The VIX call option price numerical error estimation, $K=0.2,r=0.05,T=1/6,t=1/12$. The parameter $k=2.362$.}
\begin{tabular}{|l|l|l|l|l|}
 \hline
 ${\rm VIX}_t$ & 6 terms & 11 terms & 21 terms & 31 terms \\ \hline
 0.1 & 0.0010 & 0.0024 & 0.0024 & 0.0024 \\ \hline
 0.3 & 0.1004 & 0.0991 & 0.0991 & 0.0991 \\ \hline
 0.5 & 0.1849 & 0.1871 & 0.1871 & 0.1870 \\ \hline
 0.7 & 0.1952 & 0.1984 & 0.1984 & 0.1984 \\ \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"A Markovian empirical model for the VIX index and the pricing of the corresponding derivatives" by Ying-Li Wang, Cheng-Long Xu, and Ping He, arXiv:2309.08175v1, 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}{lcccc}
\toprule
\textbf{Metric} & \textbf{B\&H} & \textbf{Self Adaptive} & \textbf{Risk Seeking} & \textbf{Risk Averse} \\
\midrule
\textbf{Cumulative Return (\%)} & -66.9497 & \textbf{54.6958} & -19.4132 & -12.4679 \\
\midrule
\textbf{Sharpe Ratio} & -2.0845 & \textbf{2.4960} & -0.7866 & -1.5783 \\
\midrule
\textbf{Daily Volatility (\%)} & 3.9527 & 2.7419 & 3.2722 & \textbf{1.7744} \\
\midrule
\textbf{Annualized Volatility (\%)} & 3.8050 & 2.5960 & 2.9236 & \textbf{0.9358} \\
\midrule
\textbf{Max-Drawdown (\%)} & 67.3269 & \textbf{12.5734} & 45.0001 & 15.9882 \\
\bottomrule
\end{tabular}
\caption{Comparison of overall trading performance during the testing period with different risk inclinations setting in \textsc{FinMem}'s profiling module.}
\end{table}
\end{document}

q-fin

Yangyang Yu
Haohang Li
Zhi Chen
Yuechen Jiang
Yang Li
Denghui Zhang
Rong Liu
Jordan W. Suchow
Khaldoun Khashanah

"FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design" by Yangyang Yu, Haohang Li, Zhi Chen, Yuechen Jiang, Yang Li, Denghui Zhang, Rong Liu, Jordan W. Suchow, and Khaldoun Khashanah, arXiv:2311.13743v2, 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
 $i$ & 1 & 2 & 3 & 4 \\ \hline
 $\lambda_i$ & 1 & 1/2 & 1/5 & 1/8 \\
 $\mu_i$ & 2/3 & 1/2 & 1/4 & 1/6 \\
 $\mu_i'$ & 2/3 & 1/2 & 1/4 & - \\
 $\theta_i$ & 2 & 1 & 1 & 1 \\
 $k_i$ & 3 & 2 & 2 & 2\\
 $\gamma_i$ & 1 & 1 & 1 & 1\\
 \hline
 \end{tabular}
\caption{Parameter values for Example 1.}
\end{table}
\end{document}

math

Mark Fackrell
Hritika Gupta
Peter G. Taylor

"Minimising Numbers of Losses and Abandonments in Small Call Centres Under a Transient Regime" by Mark Fackrell, Hritika Gupta, and Peter G. Taylor, arXiv:2312.03941v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{arydshln}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of features used for classification} %title of the table
\begin{tabular}{c|l} % creating eight columns
\hline\hline %inserting double-line \\
Category & Features\\
\hline
SpO\textsubscript{2} & SpO\textsubscript{2} mean, calibrated SpO\textsubscript{2} mean,\\ & red amplitude mean, infrared (IR) amplitude mean, \\
& calibrated red amplitude mean,\\
& calibrated IR amplitude mean, \\
& red AC/DC ratio mean, IR AC/DC ratio mean, \\
& red peak prominence mean, IR peak prominence mean,\\
& red/IR AC ratio mean, red amplitude variance, \\
& IR amplitude variance. \\\hdashline
Pulse & Heart rate mean, calibrated heart rate mean, \\
& pulse full-width-half-maximum (FWHM) mean, \\
& pulse width ratio$^{\dagger}$ mean, pulse width ratio variance. \\ \hdashline
Breathing & Breathing rate mean, calibrated breathing rate mean\\
& breathing amplitude mean.\\\hline
\hline\hline
\end{tabular}
\end{table}
\end{document}

eess

Harry J. Davies
Ian Williams
Ghena Hammour
Metin Yarici
Barry M. Seemungal
Danilo P. Mandic

"In-Ear SpO2 for Classification of Cognitive Workload" by Harry J. Davies, Ian Williams, Ghena Hammour, Metin Yarici, Barry M. Seemungal, and Danilo P. Mandic, arXiv:2101.00647v1, 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{Output size $X_{out}$ ($X=H,W$) and workload $O$ (FLOPS) for each type of layer.}
\begin{tabular}{c}
 \hline \hline
 {\textbf{Convolutional Layer}} \\
 \hline
 $X_{out} = \left\lfloor \frac{X_{in} + 2P_x - D_x(K_x - 1) - 1}{S_x} + 1 \right\rfloor$ \\
 \hline
 $O = 2 C_{in} C_{out} K_w K_h W_{out} H_{out}$ \\
 \hline
 \hline
 {\textbf{Transpose Convolutional Layer}} \\
 \hline
 $X_{out} = (X_{in} - 1)S_x - 2P_x + D_x(K_x - 1) + P_{xo} + 1$ \\
 \hline
 $O = 2 C_{in} C_{out} K_w K_h W_{out} H_{out}$ \\
 \hline
 \hline
 {\textbf{MaxPooling Layer}} \\
 \hline
 $X_{out} = \left\lfloor \frac{X_{in} + 2P_x - D_x(K_x - 1) - 1}{S_x} + 1 \right\rfloor$ \\
 \hline
 $O = (K_w K_h -1) C_{out} W_{out} H_{out}$ \\
 \hline
 \hline
 {\textbf{MaxUnpooling Layer}} \\
 \hline
 $X_{out} = (X_{in} - 1)S_x - 2P_x + K_x$ \\
 \hline
 \\
 \hline \hline
 \end{tabular}
\end{table}
\end{document}

math

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

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


stat

"Resampling Methods that Generate Time Series Data to Enable Sensitivity and Model Analysis in Energy Modeling" by Kelly Wang and Steven O. Kimbrough, arXiv:2502.08102v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Elisa Alòs
Eulalia Nualart
Makar Pravosud

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ll}
\toprule
Term & Likelihood of outcome \\ \midrule
Virtually certain & \textgreater{}99\% probability \\
Very likely & 90\%-99\% probability \\
Likely & 66\%-90\% probability \\
About as likely as not & 33\%-66\% probability \\
Unlikely & 10\%-33\% probability \\
Very unlikely & 1\%-10\% probability \\
Exceptionally unlikely & 0\%-1\% probability \\ \bottomrule
\end{tabular}
\caption{\small \centering Likelihood scale.}
\end{table}
\end{document}

q-fin

"Explosive growth from AI automation: A review of the arguments" by Ege Erdil and Tamay Besiroglu, arXiv:2309.11690v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Jaesun Shin
Eunjoo Jeon
Taewon Cho
Namkyeong Cho
Youngjune Gwon

"Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning" by Jaesun Shin, Eunjoo Jeon, Taewon Cho, Namkyeong Cho, and Youngjune Gwon, arXiv:2412.17468v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Quantum Error Correction near the Coding Theoretical Bound" by Daiki Komoto and Kenta Kasai, arXiv:2412.21171v3, 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{Random indices $RI$ recommended by Saaty.}
\begin{tabular}{lrrrrrrrrrr} \toprule
n & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \midrule
RI & 0 & 0 & 0.58 & 0.90 & 1.12 & 1.24 & 1.32 & 1.41 & 1.45 & 1.49 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

Jarosław Wątróbski
Aleksandra Bączkiewicz
Iga Rudawska

"A Strong Sustainability Paradigm Based Analytical Hierarchy Process (SSP-AHP) Method to Evaluate Sustainable Healthcare Systems" by Jarosław Wątróbski, Aleksandra Bączkiewicz, and Iga Rudawska, arXiv:2306.00718v1, 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{Failures observed in electroexplosive devices under CSALTs for each temperature and inspection time.}
\begin{tabular}{|c|c|c|c|c|}
		\hline
		\textbf{Group} & \textbf{Inspection Time} & \textbf{Temperature (K)} & \textbf{Number of Devices} & \textbf{Failures} \\ \hline
		1 & 10 & 308 & 10 & 3 \\
		2 & 10 & 318 & 10 & 1 \\
		3 & 10 & 328 & 10 & 6 \\
		4 & 20 & 308 & 10 & 3 \\
		5 & 20 & 318 & 10 & 7 \\
		6 & 20 & 328 & 10 & 7 \\
		7 & 30 & 308 & 10 & 7 \\
		8 & 30 & 318 & 10 & 7 \\
		9 & 30 & 328 & 10 & 9 \\ \hline
	\end{tabular}
\end{table}
\end{document}

math

María González-Calderón
María Jaenada
Leandro Pardo

"Robust statistical inference for accelerated life-tests with one-shot devices under log-logistic distributions" by María González-Calderón, María Jaenada, and Leandro Pardo, arXiv:2502.20467v1, 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{UQ method} & \textbf{Accuracy} & $\mathbf{U_{\text{correct}} \pm \text{std}}$ & $\mathbf{U_{\text{wrong}} \pm \text{std}}$ \\
 \hline
 \multicolumn{4}{|c|}{\textbf{HAM10000}} \\
 \hline
 MCD & 85.8\% & $0.01 \pm 0.03$ & $0.09 \pm 0.04$ \\
 \hline
 EDL & 85.7\% & $0.19 \pm 0.13$ & $0.51 \pm 0.25$ \\
 \hline
 CP & 86.7\% & $0.40 \pm 0.30$ & $0.79 \pm 0.15$ \\
 \hline
 \multicolumn{4}{|c|}{\textbf{DMF}} \\
 \hline
 MCD & 75.2\% &$0.05 \pm 0.03$ & $0.08 \pm 0.02$ \\
 \hline
 EDL & 70.4\% &$0.24 \pm 0.17$ & $0.47 \pm 0.11$ \\
 \hline
 CP & 72.7\% &$0.33 \pm 0.25$ & $0.57 \pm 0.18$ \\
 \hline
 \multicolumn{4}{|c|}{\textbf{BCM}} \\
 \hline
 MCD & 98.0\% & $0.01 \pm 0.02$ & $0.07 \pm 0.03$ \\
 \hline
 EDL & 98.2\% & $0.42 \pm 0.06$ & $0.48 \pm 0.06$ \\
 \hline
 CP & 98.6\% & $0.34 \pm 0.29$ & $0.78 \pm 0.16$ \\
 \hline
 \end{tabular}
\caption{Comparison of UQ Algorithms: Accuracy, Average Uncertainty Estimates for HAM10000, DMF, and BCM datasets. The table presents model accuracy along with average uncertainty values for both correctly classified samples and wrongly classified samples, as determined by the core network and per their respective ground truth labels. Algorithms include Monte Carlo Dropout (MCD), Evidential Deep Learning (EDL), and Conformal Prediction (CP).}
\end{table}
\end{document}

eess

Jamil Fayyad
Shadi Alijani
Homayoun Najjaran

"Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification" by Jamil Fayyad, Shadi Alijani, and Homayoun Najjaran, arXiv:2312.07460v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
$\sigma^2$ & 100 & 1 & 0.04 & 0.01 & 0.0025 \\\hline
Test error & $2.14\times10^{-1}$ & $3.62\times10^{-2}$ & $7.31\times10^{-3}$ & $4.81\times10^{-4}$ & $4.05\times10^{-4}$ \\\hline
\end{tabular}
\caption{Variances of Gaussian random features and the corresponding test errors for nonlinear Poisson PDE ($d=8$). The training sample size $M=1024$ and each error is calculated on a set of test samples with size 100.}
\end{table}
\end{document}

math

"Solving Partial Differential Equations with Random Feature Models" by Chunyang Liao, arXiv:2501.00288v1, 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{CV types and their descriptions}
\begin{tabular}{lcc}
\toprule
\textbf{Name} & \textbf{Description} & \textbf{Key details} \\
\midrule
\textbf{Random} & Random splitting & Cross-validation with random partitions, ignoring spatial structure. \\
\textbf{SP 200} & Spatial blocking (200 km) & Spatial blocks with a distance threshold of 200 km to separate folds. \\
\textbf{SP 422} & Spatial blocking (422 km) & Spatial blocks with a distance threshold of 422 km, aligned with SAC range. \\
\textbf{SP 600} & Spatial blocking (600 km) & Spatial blocks with a distance threshold of 600 km to separate folds. \\
\textbf{ENV} & Environmental blocking & Clustering data based on environmental similarity using K-means. \\
\textbf{SPT 200} & Spatio-temporal blocking (200 km) & Spatial blocks (200 km) combined with temporal folds (3 years). \\
\textbf{SPT 422} & Spatio-temporal blocking (422 km) & Spatial blocks (422 km) combined with temporal folds (3 years). \\
\textbf{SPT 600} & Spatio-temporal blocking (600 km) & Spatial blocks (600 km) combined with temporal folds (3 years). \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Prediction results for on Leave-One-Out Cross-Validation (LOOCV) for datasets created from three imputation methods (df\_drop, df\_KNN, df\_median) over two interpretable models (CART and random forest) with various hyper-parameters. CART is evaluated with complexity parameters (cp) ranging from 0.01 to 0.07, and random forest with maxnodes ranging from 3 to 7. Panel A reports the training MSE, Panel B reports the validation MSE, and Panel C shows the mean number of nodes resulted in the models.}
\begin{tabular}{lccccccccc}
 \toprule
 & \multicolumn{4}{c}{CART (cp)} & \multicolumn{4}{c}{random forest (maxnodes)} \\
 \midrule
 & 0.01 & 0.03 & 0.05 & 0.07 & 3 & 5 & 7 \\
 \midrule
 \multicolumn{8}{l}{\textit{Panel A: Training MSE}}\\
 df\_drop & 0.197 & 0.212 & 0.220 & 0.222 & 0.263 & 0.225 & 0.200 \\
 df\_KNN & 0.183 & 0.198 & 0.214 & 0.215 & 0.259 & 0.226 & 0.204 \\
 df\_median & 0.201 & 0.218 & 0.224 & 0.226 & 0.260 & 0.227 & 0.206 \\
 \midrule
 \multicolumn{8}{l}{\textit{Panel B: Validation MSE}} \\
 df\_drop & 0.300 & 0.312 & 0.300 & 0.299 & 0.302 & 0.282 & 0.276 \\
 df\_KNN & 0.335 & 0.350 & 0.313 & 0.313 & 0.295 & 0.278 & 0.273 \\
 df\_median & 0.312 & 0.335 & 0.344 & 0.338 & 0.297 & 0.281 & 0.275 \\
 \midrule
 \multicolumn{8}{l}{\textit{Panel C: Mean number of nodes}} \\
 df\_drop & 10.0 & 5.67 & 4.03 & 3.86 & 3.00 & 5.00 & 7.00 \\
 df\_KNN & 11.1 & 7.02 & 3.98 & 3.88 & 3.00 & 5.00 & 7.00 \\
 df\_median & 10.3 & 4.90 & 3.90 & 3.63 & 3.00 & 5.00 & 7.00 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

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

"Evaluation of the HeartSteps Online Sampling Algorithm" by Xiang Meng, Walter Dempsey, Peng Liao, Nick Reid, Pedja Klasnja, and Susan Murphy, arXiv:2501.02137v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Finite Difference Solution Ansatz approach in Least-Squares Monte Carlo" by Jiawei Huo, arXiv:2305.09166v7, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The hyper-parameter selection. (All the methods were implemented using PyTorch, and training and testing were conducted using two NVIDIA A100 GPUs.) }
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
Initializer & Xavier &
Learning rate for $\Theta$ & $\beta=1e-4$ & $L$ & 100 & $K$ & $10$\\
\hline
Optimizer & ADAM & Learning rate for PD & $\alpha_i = 5e-4$ & $T$ & $5$ & $r$ & $5$\\
\hline
Training data & 20 subjects & Learning rate for T2 & $\alpha_i = 2e-4$ & $\rho_i^{(0)}$ & $0.5$ & $\delta_0$ & $0.5$ \\
\hline
Testing data & 5 subjects & Kernel size & $3\times 3 \times 32$ & $\lambda$ & $1e-4$ & $\mu$ & $1$ \\
\hline
 Image size & $300 \times 300$ & Slices & 30 to 38 & $m$ & $4$ & $c$ & $18$\\
\hline
\end{tabular}
\end{table}
\end{document}

cs

"Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learning" by Wanyu Bian, Albert Jang, and Fang Liu, arXiv:2403.19966v2, 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}{llll}
\hline
\textbf{measure} & \textbf{pt} & \textbf{es} & \textbf{fr}
\\
\hline
accuracy & 0.93 & 0.92 & 0.96 \\
$F_1$~A$_1$A$_2$ & 0.93 & 0.92 & 0.96 \\
$F_1$~A$_2$A$_1$ & 0.94 & 0.92 & 0.96 \\
\textit{$k$} & 0.90 & 0.87 & 0.94 \\
\hline
\end{tabular}
\caption{Inter-annotator agreement for each language in the Cleverly zoning corpus, using Cohen's kappa ($k$), accuracy and $F_1$ between annotators A$_1$ and A$_2$.}
\end{table}
\end{document}

cs

Multilingual Email Zoning

Bruno Jardim
Ricardo Rei
Mariana S. C. Almeida

"Multilingual Email Zoning" by Bruno Jardim, Ricardo Rei, and Mariana S. C. Almeida, arXiv:2102.00461v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Youngbin Lee
Yejin Kim
Javier Sanz-Cruzado
Richard McCreadie
Yongjae Lee

"Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Sampling" by Youngbin Lee, Yejin Kim, Javier Sanz-Cruzado, Richard McCreadie, and Yongjae Lee, arXiv:2404.07223v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{Performance comparison between fixed length Huffman tree and proposed approach on the `Outdoor Canteen-2' dataset. We set $\theta_{cos}$=0.93 and $\alpha$=1e-04 for this experiment.}}
\begin{tabular}{ccc|cccc}
\toprule
\multicolumn{3}{c|}{Fixed Length Tree} & \multicolumn{4}{c}{Variable Length Tree} \\ \hline
N & Replaced & AUC(\%) & AUC(\%) & avg. N & Merged & $\theta_{merge}$ \\ \hline
9 & 2046 & 76.57 & 80.94 &119 & 340 & 0.96 \\
11 & 1805 & 75.44 &\textbf{86.00 } & 130 & 235 & 0.97 \\
13 & 1549 & 76.14 & 82.16 &141 & 132 & 0.98 \\
15 & 1329 & \textbf{79.78} & 82.42 & 157 & 50 & 0.99 \\
17 & 1199 & 79.53&&&&\\
19 & 1141&77.53 &&&&\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

"Anomaly Detection in Audio with Concept Drift using Adaptive Huffman Coding" by Pratibha Kumari and Mukesh Saini, arXiv:2102.10515v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|c|c|c|c|}
\hline
\textbf{Policy} & \textbf{Mean Correct Rate} & \textbf{SD Correct Rate} & \textbf{Mean outcome} & \textbf{SD outcome} & \textbf{WAPTS Superior Count} \\
\hline
TS & 0.048 & 0.036 & 0.506 & 0.029 & 116 \\
\hline
UR & 0.021 & 0.008 & 0.505 & 0.027 & 113 \\
\hline
WAPTS & 0.048 & 0.014 & 0.515 & 0.031 & -- \\
\hline
\end{tabular}
\caption{Comparison of policies: Mean and standard deviation (SD) of correct rates and outcomes for experiments with \(N = 300\) participants and \(K = 50\) treatments under \textbf{Scenario 2}. The final column indicates the number of simulations (out of 200) in which WAPTS outperformed the respective policy.}
\end{table}
\end{document}

stat

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

"Adaptive Experiments Under High-Dimensional and Data Sparse Settings: Applications for Educational Platforms" by Haochen Song, Ilya Musabirov, Ananya Bhattacharjee, Audrey Durand, Meredith Franklin, Anna Rafferty, and Joseph Jay Williams, arXiv:2501.03999v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{RAG-Net\textsubscript{v2} hyper-parameters}
\begin{tabular}{lcc}
 \toprule
 \textbf{Layers} & \textbf{Number of Layers} & \textbf{Parameters} \\ \hline
 Convolution & 16 & 4,847,369\\
 Pooling & 4 Average, 10 Max & 0\\
 Batch Normalization & 15 & 17,920\\
 Activation & 13 ReLU, 2 Softmax & 0\\
 Lambda & 5 & 0 \\
 Input & 2 & 0 \\
 Zero-Padding & 10 & 0 \\
 Concatenation & 1 & 0 \\
 Reshape & 1 & 0 \\
 Fully Connected and Flatten & 2 & 716,810 \\
 Classification & 1 & 22 \\ \hline
 Learnable Parameters & 5,573,161 & \\
 Non-learnable Parameters & 8,960 & \\
 Total Parameters & 5,582,121 & \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Hina Raja
Taimur Hassan
Muhammad Usman Akram
Naoufel Werghi

"Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression" by Hina Raja, Taimur Hassan, Muhammad Usman Akram, and Naoufel Werghi, arXiv:2010.03872v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Values of circuit components}
\begin{tabular}{c|ccccccc}
\hline
Component & $E$ & $L$ & $R_L$ & $C$ & $R_C$ & $T_s$
\\
\hline
Value & $20$ V & $1$ mH & $0.1$ $\Omega$ & $10$ $\mu$F & $0.06$ $\Omega$ & $0.1$ ms
\\
\hline
\end{tabular}
\end{table}
\end{document}

eess

"A Data-Driven Modeling Framework of Time-Dependent Switched Dynamical Systems via Extreme Learning Machine" by Weiming Xiang, arXiv:2101.09863v2, 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{Summary statistics for county-level smoke-related PM$_{2.5}$ ($\mu g/m^3$) per month over 2006-2021}
\begin{tabular}{|l|c|c|c|}
\hline
 & Mean & Mean $|$ burn & Obs\\
 \hline \hline
US & 13.9 & 19.8 & 490,998\\
\hline
Midwest & 14.9 & 23.0 & 177,120\\
Northeast & 7.4 & 15.4 & 36,540\\
South & 11.1 & 14.4 & 212,178\\
West & 20.3 & 37.0 & 64,260\\
 \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Fires and Local Labor Markets

"Fires and Local Labor Markets" by Raphaelle G. Coulombe and Akhil Rao, arXiv:2308.02739v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.