\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The precision, recall, f1-score produced by the proposed approach for different classes}
\begin{tabular}{|l|l|l|l|l|}
\hline
\textit{} &Precision &Recall &F1-score &Support \\ \hline
 Angry & 0.73 &0.74 &0.73 & 1173 \\ \hline
 Disgust&0.51 & 0.58&0.55 &1155 \\ \hline
 Fear&0.63 &0.49 &0.55 &1157 \\ \hline
 Happy& 0.60 &0.57 &0.59 &1175 \\ \hline
 Neutral& 0.60& 0.65 & 0.62&1153 \\ \hline
 Sad& 0.64& 0.67& 0.66 & 1207 \\ \hline
 Surprise& 0.86 &0.86 &0.86 &377 \\ \hline
 \textbf{accuracy}& & &0.63 & 7397\\ \hline
 \textbf{macro avg}&0.65 & 0.65 & 0.65 & 7397 \\ \hline
 \textbf{weighted avg}& 0.63 & 0.63 & 0.63 & 7397 \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Hanan Hamza
Fiza Gafoor
Fathima Sithara
Gayathri Anil
V. S. Anoop

"EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks" by Hanan Hamza, Fiza Gafoor, Fathima Sithara, Gayathri Anil, and V. S. Anoop, arXiv:2310.12851v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c||c|c|c||c||c||c|c||c|c|c|c|}
 \hline &\multicolumn{3}{|c||}{$area \times 2$ } &$total$& $average$& rank\\ \hline \hline
 A	& 12&15&15&42&14	&1 \\
 B	& 40&42&42&124&41.33&4 \\
 C& 16&15&15&46&15.33&2 \\
 D& 80&80&80&240&80&5 \\
 E	& 24&25&21&70&23.33&3 \\ 	\hline
 	 \end{tabular}
\end{table}
\end{document}

cs

Marcel Ausloos
Giulia Rotundo
Roy Cerqueti

"A theory of best choice selection through objective arguments grounded in Linear Response Theory concepts" by Marcel Ausloos, Giulia Rotundo, and Roy Cerqueti, arXiv:2405.00041v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textbf{Test set evaluation.} Performance of the agent over the test set with a buffer size equal to 2,000. The agent was evaluated with 30 trials for each of the 10 test starting points. The agent performance is based on summary statistics of episode length and the collision-free rate (CFR).}
\begin{tabular}{l|c|c|c|c}
 \toprule
 Strategy & Average & Standard Deviation & Min & CFR \\
 \midrule
 \emph{Decreasing $\epsilon$-greedy}& 1,232.26 & 818.09 & 69 & 44.48\%\\
 \emph{Constant $\epsilon$-greedy}& 1,240.52 & 744.12 & 41 & 35.73\%\\
 \emph{VDBE}& 1,593.84 & 700.01 & 40 & 68.64\%\\
 \emph{BMC}& 1,604.47 & 718.93 & 40 & 72.47\%\\
 \emph{Softmax}& \textbf{1,666.6} & \textbf{655.72}& \textbf{92} & \textbf{75.92}\%\\
 \emph{VDBE-Softmax}& 1,609.05 & 749.3 & 84 & 75.85\%\\
 \emph{MBE}& 1,561.56 & 731.04 & \textbf{92} & 69.31\%\\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

stat

"Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning" by Valentina Zangirolami and Matteo Borrotti, arXiv:2310.08331v2, 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{Approximate number of computations in each iteration of KMAP-IIC}
\begin{tabular}{ll}
\hline\noalign{\smallskip}
 Steps involved in KMAP-IIC & Computational complexity \\
\noalign{\smallskip}\hline\noalign{\smallskip}
Finding low-complexity matrix inverse (required only once) & $(2N_r-1)MU$ \\
Finding favorable MAPs & $2MN_rU$ \\
Performing low-complexity search & $(2N_r+4)KU$ \\
Finding the solution using greedy search & $(6N_r-1)U^2 $ \\
\noalign{\smallskip}\hline
\end{tabular}
\end{table}
\end{document}

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.


eess

Risk-Adaptive Local Decision Rules

"Risk-Adaptive Local Decision Rules" by Johannes O. Royset and Miguel A. Lejeune, arXiv:2310.09844v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Sky Qiu
Jens Tarp
Andrew Mertens
Mark van der Laan

"An Estimator-Robust Design for Augmenting Randomized Controlled Trial with External Real-World Data" by Sky Qiu, Jens Tarp, Andrew Mertens, and Mark van der Laan, arXiv:2501.17835v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Multi-kernel property in high-frequency price dynamics under Hawkes model" by Kyungsub Lee, arXiv:2302.11822v1, 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{List of cryptocurrencies from Binance.}
\begin{tabular}{|l|l||l|l||l|l|}
\hline
Ticker & Name & Ticker & Name & Ticker & Name \\ \hline\hline
ADA & cardano & FET & fetch & QTUM & qtum \\ \hline
ALGO & algorand & FTM & fantom & REN & ren \\ \hline
ANKR & ankr & FUN & funtoken & RLC & iexec \\ \hline
ARPA & arpa chain & HBAR & hedera & RVN & ravencoin \\ \hline
ATOM & cosmos & HOT & holo & STX & stacks \\ \hline
BAND & band protocol & ICX & icon & TFUEL & theta fuel \\ \hline
BAT & basic atention token & IOST & iost & THETA & theta \\ \hline
BCH & bitcoin cash & IOTA & miota & TOMO & tomochain \\ \hline
BEAM & beam & IOTX & iotex & TROY & troy \\ \hline
BNB & binance coin & KAVA & kava & TRX & tron \\ \hline
BTC & bitcoin & KEY & key & VET & vechain \\ \hline
CELR & celer network & LINK & chainlink & VITE & vite \\ \hline
CHZ & chiliz & LTC & litecoin & WAN & wanchain \\ \hline
COS & contentos & MATIC & polygon & WAVES & waves \\ \hline
CTXC & cortex & MFT & hifi finance & WIN & winklink \\ \hline
DASH & dash & MTL & metal & XLM & stellar \\ \hline
DENT & dent & NEO & neo & XMR & monero \\ \hline
DOCK & dock & NKN & nkn & XRP & ripple \\ \hline
DOGE & dogecoin & NULS & nuls & XTZ & tezos \\ \hline
DUSK & dusk network & OMG & omg network & ZEC & zcash \\ \hline
ENJ & enj coin & ONE & harmony & ZIL & zilliqa \\ \hline
EOS & eos & ONG & ontology gas & ZRX & 0x \\ \hline
ETC & ethereum classic & ONT & ontology & & \\ \hline
ETH & ethereum & PERL & perl & & \\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Stanisław Drożdż
Jarosław Kwapień
Marcin Wątorek

"What is mature and what is still emerging in the cryptocurrency market?" by Stanisław Drożdż, Jarosław Kwapień, and Marcin Wątorek, arXiv:2305.05751v1, 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{Reasons for part-time work}
\begin{tabular}{lccc}
\hline\hline
\\
& Women (\%)&Men (\%)&Full sample (\%)\\
\hline
\\
\multicolumn{4}{c}{\emph{Reasons for part-time work}}\\
\\
Student or at school& 9.69& 30.57 & 13.33 \\
Ill or disabled& 2.15& 4.65 &2.59\\
Could not find full-time job&9.95 & 25.88 &12.74 \\
Did not want full-time job& 78.21 & 38.91&71.34\\
Total &100&100&100\\
\\
\hline
\\
\multicolumn{4}{c}{\emph{Reasons for not wanting full-time job}}\\
\\
Financially secure -- work because want & 7.22&24.26&8.74\\
Earn enough part-time& 7.64& 17.41& 8.52\\
Want to spend more time with family & 40.35 &12.30 &37.85 \\
Domestic commitments prevent full-time& 28.20&10.70 & 26.64 \\
Insufficient child-care facilities & 3.49& 0.96 & 3.26\\
Another reason& 13.09 &34.37&14.99 \\
Total &100&100&100\\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Riccardo Leoncini
Mariele Macaluso
Annalivia Polselli

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{{Notation used}}
\begin{tabular}{cc}
 \toprule
\text{name} & \text{source}
 \\
 \midrule
$k$ & Lemma \\
$t_0$ & Lemma \\
$t_1$ & Theorem \\
$t_2$ & Theorem \\
$t_3$ & Proposition \\
$h(\cdot)$ & Remark \\
$\mu(\cdot)$ & Remark \\
$\eta$ & Theorem \\
$\rho$ & Theorem \\
 \hline
 \end{tabular}
\end{table}
\end{document}

math

Diego Cifuentes
Santanu S. Dey
Jingye Xu

"Sensitivity analysis for mixed binary quadratic programming" by Diego Cifuentes, Santanu S. Dey, and Jingye Xu, arXiv:2312.06714v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}
& Never & Occasionally & Often & Very often & Constantly \\
\hline
Organization of household tasks & [ ] & [ ] & [ ] & [ ] & [ ] \\
\hline
Organization of childcare activities & [ ] & [ ] & [ ] & [ ] & [ ]\\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Francesca Barigozzi
Pietro Biroli
Chiara Monfardini
Natalia Montinari
Elena Pisanelli
Sveva Vitellozzi

"Beyond Time: Unveiling the Invisible Burden of Mental Load" by Francesca Barigozzi, Pietro Biroli, Chiara Monfardini, Natalia Montinari, Elena Pisanelli, and Sveva Vitellozzi, arXiv:2505.11426v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Additive and Multiplicative Coinvariant Spaces of Weyl Groups in the Light of Harmonics and Graded Transfer" by Sebastian Debus and Tobias Metzlaff, arXiv:2412.17099v1, 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{Performance of speaker recognition systems.}
\begin{tabular}{c|c|ccc|cc}
 \toprule
 \multirow{2}{*}{Task} & CSI & \multicolumn{3}{c|}{OSI} & \multicolumn{2}{c}{SV} \\
 & Accuracy & FAR & FRR & OSIER & FAR & FRR \\ \midrule
 DeepSpeaker & 98.89\% & 11.42\% & 1.11\% & 0.83\% & 6.96\% & 0.41\% \\ \midrule
 ECAPA-TDNN & 99.58\% & 9.74\% &0.42\% & 0.03\% & 4.87\% & 0.42\% \\ \midrule
 GMM-UBM & 99.44\% & 10.72\% & 5.15\% & 2.65\% & 10.02\% & 5.01\% \\ \midrule
 i-vector-PLDA & 99.72\% & 7.93\% & 2.36\% & 0.27\% & 12.25\% & 0.97\% \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Rui Duan
Zhe Qu
Leah Ding
Yao Liu
Zhuo Lu

"Parrot-Trained Adversarial Examples: Pushing the Practicality of Black-Box Audio Attacks against Speaker Recognition Models" by Rui Duan, Zhe Qu, Leah Ding, Yao Liu, and Zhuo Lu, arXiv:2311.07780v2, 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{\\Sensitivity Analysis of Parameters}
\begin{tabular}{c|c}
\hline\hline
\textbf{Parameter} & \textbf{Sensitivity} \\ \hline
 $K_\mathrm{A}$ & 1.75 \\ \hline
 $T_\mathrm{B}$ & 1.35 \\ \hline
 $a_{23}$ & 1.13 \\ \hline
 $T_\mathrm{pdo}$ & 1.11 \\ \hline\hline
\end{tabular}
\end{table}
\end{document}

eess

Seyyed Rashid Khazeiynasab
Junjian Qi
Issa Batarseh

"Generator Parameter Estimation by Q-Learning Based on PMU Measurements" by Seyyed Rashid Khazeiynasab, Junjian Qi, and Issa Batarseh, arXiv:2012.00803v1, 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}{rrrrrrrrrr}
\boldmath{}\textbf{$\tau$}\unboldmath{} & & 0.10 & & & 0.50 & & & 0.90 & \\
\midrule
 & AIC & BIC & ICL & AIC & BIC & ICL & AIC & BIC & ICL \\
Panel A: Gaussian errors & & & & & & & & & \\
K = 2 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
K = 3 & 0 & 29 & 100 & 69 & 100 & 100 & 0 & 36 & 100 \\
K = 4 & 100 & 71 & 0 & 31 & 0 & 0 & 100 & 64 & 0 \\
 & & & & & & & & & \\
Panel B: skew-$t$ errors & & & & & & & & & \\
K = 2 & 0 & 0 & 27 & 0 & 0 & 83 & 0 & 0 & 35 \\
K = 3 & 0 & 20 & 72 & 0 & 0 & 15 & 0 & 0 & 58 \\
K = 4 & 100 & 80 & 1 & 100 & 100 & 2 & 100 & 100 & 8 \\
\bottomrule
\end{tabular}
\caption{Percentage frequency distribution of the selected number of hidden states $K$ under Gaussian and skew-$t$ errors over 300 replications.}
\end{table}
\end{document}

q-fin

"Expectile hidden Markov regression models for analyzing cryptocurrency returns" by Beatrice Foroni, Luca Merlo, and Lea Petrella, arXiv:2301.09722v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the GLUE benchmark datasets.}
\begin{tabular}{l|l|r|r|r|c|l}
 \toprule
 \bf Corpus & \bf Task & \bf \#Train & \bf \#Dev & \bf \#Test & \bf \#Label & \bf Metrics \\
 \midrule
 CoLA & Acceptability & 8.5k & 1k & 1k & 2 & Matthews Corr. \\ \midrule
 SST-2 & Sentiment & 67k & 872 & 1.8k & 2 & Accuracy \\ \midrule
 RTE & NLI & 2.5k & 276 & 3k & 2 & Accuracy \\ \midrule
 MRPC & Paraphrase & 3.7k & 408 & 1.7k & 2 & Accuracy \\ \midrule
 QNLI & QA/NLI & 108k & 5.7k & 5.7k & 2 & Accuracy \\ \midrule
 STS-B & Similarity & 7k & 1.5k & 1.4k & -- & Pearson/Spearman Corr. \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

Abdessalam Ed-dib
Zhanibek Datbayev
Amine Mohamed Aboussalah

"GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning" by Abdessalam Ed-dib, Zhanibek Datbayev, and Amine Mohamed Aboussalah, arXiv:2412.09250v3, 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{ Decision rule at month 8, mean of the blip estimates over 25 imputed datasets, SPRINT trial, 2010-2013. }
\begin{tabular}{|l|c|c|c|}
 \hline
 Variable & QLOMA & WOMA & No. of times stat. significant$^*$ \\ \hline
 Intercept visit & 10.4 & 9.6 & 12 \\ \hline
 Intercept add-on & -3.3 & -4.1 & 0 \\ \hline
 \multicolumn{4}{|c|}{Visit interaction with:} \\ \hline
 Intensive group & -2.3 & -1.8 & 22 \\ \hline
 Age & -0.0 & 0.0 & 0 \\ \hline
 Female sex & 0.3 & -0.0 & 0 \\ \hline
 Race Black & \multicolumn{3}{|c|}{Reference} \\ \hline
 Race Hispanic & 0.8 & 1.2 & 1 \\ \hline
 Race White & -0.2 & -0.5 & 0 \\ \hline
 \hspace{0.2cm} Other & 1.9 & 0.7 & 1 \\ \hline
 Smoking (ever) & -0.0 & -0.2 & 0 \\ \hline
 BMI & 0.0 & 0.0 & 0 \\ \hline
 HDL & 0.0 & 0.1 & 3 \\ \hline
 SBP Baseline & -0.0 & -0.0 & 0 \\ \hline
 SBP Current month & -0.1 & -0.1 & 25 \\ \hline
 CVD & 1.3 & 1.1 & 6 \\ \hline
 Aspirin use & -0.4 & -0.7 & 0 \\ \hline
 Statin use & 1.2 & 1.6 & 5 \\ \hline
 \end{tabular}
\end{table}
\end{document}

stat

Janie Coulombe
Dany El-Riachi
Fanxing Du
Tianze Jiao

"Constructing optimal dynamic monitoring and treatment regimes: An application to hypertension care" by Janie Coulombe, Dany El-Riachi, Fanxing Du, and Tianze Jiao, arXiv:2501.08274v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Mirco Ciallella
Stephane Clain
Elena Gaburro
Mario Ricchiuto

"Very high order treatment of embedded curved boundaries in compressible flows: ADER discontinuous Galerkin with a space-time Reconstruction for Off-site data" by Mirco Ciallella, Stephane Clain, Elena Gaburro, and Mario Ricchiuto, arXiv:2312.07170v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Irreducible representations of $\mathbb{Z}_2\times \mathbb{Z}_2$ group elements.}
\begin{tabular}{ccccc}
 \toprule
 $\mathbb{Z}_2\times \mathbb{Z}_2$ & $I$ & $O$ & $E$ & $D$ \\
 \midrule
 $(1,1)$ & $1$ & $1$ & $1$ & $1$ \\
 $(-1,1)$ & $1$ & $-1$ & $1$ & $-1$ \\
 $(1,-1)$ & $1$ & $1$ & $-1$ & $-1$ \\
 $(-1,-1)$ & $1$ & $-1$ & $-1$ & $1$ \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

math

"Gapped Boundaries of Kitaev's Quantum Double Models: A Lattice Realization of Anyon Condensation from Lagrangian Algebras" by Mu Li, Xiao-Han Yang, and Xiao-Yu Dong, arXiv:2504.19512v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
 & \multicolumn{2}{c}{Mean-squared Prediction Error} &&\\
 Model & One-Step & Two-Step & No. Parameters & Sig. Parameters \\
 \hline
 GNAR(1, [1]) & 5.43 & 12.74 & 2 & 2 \\
 GNAR(1, [6]) & 5.46 & 12.73 & 7 & 7 \\
 GNAR(6, [6, ..., 6]) & 5.87 & 12.99 & 42 & 20
 \end{tabular}
\caption{Comparison of different GNAR model orders for the series corresponding to the number of mechanical ventilation beds needed during the second COVID-19 wave for hospital trusts in the network shown by Figure .}
\end{table}
\end{document}

stat

Guy Nason
Daniel Salnikov
Mario Cortina-Borja

"New tools for network time series with an application to COVID-19 hospitalisations" by Guy Nason, Daniel Salnikov, and Mario Cortina-Borja, arXiv:2312.00530v1, 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 considered simulation scenarios.}
\begin{tabular}{crr|rr}
 & \multicolumn{2}{c|}{Sampling} & \multicolumn{2}{c} {Sample} \\
 & \multicolumn{2}{c|}{fraction} & \multicolumn{2}{c} {size} \\ \hline
Scenario & {$f_c$} & {$f_r$} & {$n_c$} & {$n_r$}\\ \hline
& & & \\
 & \multicolumn{4}{c}{}\\
S1 & 0.01 & 0.01 & 600 & 600 \\
S2 & 0.01 & 0.01 & 100 & 100 \\ %\hline
 & \multicolumn{4}{c}{}\\
S3 & 0.10 & 0.10 & 600 & 600 \\
S4 & 0.10 & 0.10 & 100 & 100 \\ %\hline
 & \multicolumn{4}{c}{}\\
S5 & 0.01 & 0.10 & 100 & 1,000 \\
S6 & 0.10 & 0.01 & 1,000 & 100 \\ %\hline
 & \multicolumn{4}{c}{}\\
S7 & 0.50 & 1.00 & 500 & 1,000
\end{tabular}
\end{table}
\end{document}

stat

Vladislav Beresovsky
Julie Gershunskaya
Terrance D. Savitsky

"Review of Quasi-Randomization Approaches for Estimation from Non-probability Samples" by Vladislav Beresovsky, Julie Gershunskaya, and Terrance D. Savitsky, arXiv:2312.05383v3, 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{Function values for $x_1,x_2$ by the MNAM and MGNAM of the COMPAS dataset. There are multiple violations of monotonicity for the NAM, for example, between $(2,2)$ and $(2,3)$, and between $(0,3)$ and $(1,1)$. Violations are also observed for the MNAM, for example, between $(0,2)$ and $(1,1)$. The MGNAM preserves monotonicity.}
\begin{tabular}{l|cccr}
\toprule
 MGNAM & & & & \\ \hline
 $x_1 \backslash x_2$ & $0$ & $1$ & $2$ & 3 \\ \hline
 $0$ & 0 & 0.35 & 0.54 & 0.56 \\
 $1$ & 0.21 & 0.53 & 0.56 & 0.56 \\
 $2$ & 0.49 & 0.55 & 0.56 & 0.56 \\
 3 & 0.55 & 0.56 & 0.56 & 0.56 \\ \hline
 NAM & & & & \\ \hline
 $x_1 \backslash x_2$ & $0$ & $1$ & $2$ & 3 \\ \hline
 $0$ & 0 & 0.41 & 0.40 & {\color{red} \textbf{0.37}} \\
 $1$ & 0.24 & {\color{red} \textbf{0.65}} & 0.65 & 0.62 \\
 $2$ & 0.32 & 0.72 & {\color{blue} \textbf{0.72}} & {\color{blue} \textbf{0.69}} \\
 3 & 0.33 & 0.74 & 0.73 & 0.70 \\ \hline
 MNAM & & & & \\ \hline
 $x_1 \backslash x_2$ & $0$ & $1$ & $2$ & 3 \\ \hline
 $0$ & 0 & 0.33 & {\color{purple} \textbf{0.37}} & 0.37 \\
 $1$ & 0.17 & {\color{purple} \textbf{0.50}} & 0.54 & 0.54 \\
 $2$ & 0.19 & 0.53 & 0.57 & 0.57 \\
 3 & 0.20 & 0.53 & 0.57 & 0.57 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"How to address monotonicity for model risk management?" by Dangxing Chen and Weicheng Ye, arXiv:2305.00799v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Proposed approach vs. zero rule baseline, timespan onward =3.}
\begin{tabular}{lcccc}
 \toprule
 \bf Method & \bf Thresh & \bf Precision & \bf Recall & \bf Macro F1\\
 \midrule
 Zero-rule baseline & & 0.3000 & 0.5000 & 0.3700\\
\midrule
Contextual Leap2Trend & 0 & \bf 0.5600 & \bf 0.5476 & \bf 0.5388 \\
Original Leap2Trend & 0 & 0.5384 & 0.5330 & 0.5276 \\

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

q-fin

Nhu Khoa Nguyen
Thierry Delahaut
Emanuela Boros
Antoine Doucet
Gaël Lejeune

"Contextualizing Emerging Trends in Financial News Articles" by Nhu Khoa Nguyen, Thierry Delahaut, Emanuela Boros, Antoine Doucet, and Gaël Lejeune, arXiv:2301.11318v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zero-Leverage Puzzle

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


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DER (\%) breakdown for each test set with EEND-TA's relative improvements over EEND-EDA.}
\begin{tabular}{llcc}
\toprule
\textbf{Dataset} & \textbf{Attractor} & \textbf{NS1 to NS4} & \textbf{Rel. Improvement} \\ \midrule
\multirow{2}{*}{DIHARD III} & EDA & 14.07 & - \\
 & TA & 12.93 & 8.10\% \\ \midrule
\multirow{2}{*}{VoxConverse} & EDA & 15.75 & - \\
 & TA & 9.89 & 37.2\% \\ \midrule
\multirow{2}{*}{MagicData-RAMC} & EDA & 14.45 & - \\
 & TA & 13.58 & 6.02\% \\ \midrule
\multirow{2}{*}{AMI Mix} & EDA & 19.85 & - \\
 & TA & 17.88 & 9.92\% \\ \midrule
\multirow{2}{*}{AMI SDM1} & EDA & 32.24 & - \\
 & TA & 24.64 & 23.55\% \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Lahiru Samarakoon
Samuel J. Broughton
Marc Härkönen
Ivan Fung

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|}

			\hline
			\textbf{Training Parameters} & \textbf{Values} \\\hline
			Momentum & 0.9 \\\hline
			Initial Learn Rate & 0.001 \\\hline
			Learn Rate Drop Factor & 0.5 \\\hline
			Learn Rate Drop Period & 10 \\\hline
			$L_2$ Regularization & 0.004 \\\hline
			Max Epochs & 70\\\hline
			MiniBatchSize & 64 \\\hline

		\end{tabular}
\caption{Training Parameters for CNN}
\end{table}
\end{document}

eess

"Multidomain Multimodal Fusion For Human Action Recognition Using Inertial Sensors" by Zeeshan Ahmad and Naimul Khan, arXiv:2008.09748v1, 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{Inference time and Image Quality Assessment(IQA) for MS-COCO via Stable Diffusion (512$*$512, 50 steps).}
\begin{tabular}{lcccc}
\toprule
 & FP16 & Original PTQ(W8A8) & Q-Diffusion(W8A8) & QNCD(W8A8) \\
 \cmidrule(r){1-1} \cmidrule(r){2-2} \cmidrule(r){3-5}
Inference Time & 959.5ms & \textbf{601.8ms} & 628.3ms & 631.2ms \\
IQA Score$\uparrow$(0 $\sim$ 1) & 0.847 & 0.728 & 0.775 & \textbf{0.793} \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Huanpeng Chu
Wei Wu
Chengjie Zang
Kun Yuan

"QNCD: Quantization Noise Correction for Diffusion Models" by Huanpeng Chu, Wei Wu, Chengjie Zang, and Kun Yuan, arXiv:2403.19140v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ Sensitivity indices for the modified engineering model with correlated inputs.}
\begin{tabular}{rrrrrrrrrr}
\hline
 & $H$ & $W$ & $E$ & $A_c$ & $I_c$ & $A_b$ & $I_b$ & $P$ & $q_0$ \\
 & (m) & (m) & (N/m$^{2}$) & (m$^{2}$) & (m$^{4}$) & (m$^{2}$) & (m$^{4}$) & (N) & (N/m) \\ \hline
First-order indices & 21 \% & 27 \% & 23 \% & {\color[HTML]{9B9B9B} 0 \%} & 21 \% & {\color[HTML]{C0C0C0} 0 \%} & {\color[HTML]{C0C0C0} 1 \%} & {\color[HTML]{C0C0C0} 0 \%} & 22 \% \\ \hline
 & & & & & & & & & \\
Second-order indices & & & & & & & & & \\ \hline
$H$ (m) & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} 1 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{000000} -20 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 2 \%} \\
$W$ (m) & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 1 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} \\
$E$ (N/m$^{2}$) & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} \\
$A_c$ (m$^{2}$) & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} \\
$I_c$ (m$^{4}$) & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 2 \%} \\
$A_b$ (m$^{2}$) & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} \\
$I_b$ (m$^{4}$) & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} 0 \%} & {\color[HTML]{9B9B9B} 0 \%} \\
$P$ (N) & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} } & {\color[HTML]{9B9B9B} 0 \%} \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Mariia Kozlova
Antti Ahola
Pamphile T. Roy
Julian Scott Yeomans

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


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparisons of deep-learning-based trackers. The bold numbers indicate the best tracker.}
\begin{tabular}{|l|l|l|l|}
\hline
 & \textbf{MOTA $\uparrow$} & \textbf{HOTA$\uparrow$} & \textbf{ID Swit.$\downarrow$} \\ \hline
Trackformer & 74.1 & 57.3 & 2,829 \\ \hline
TransCenter & 73.2 & 54.5 & 4,614 \\ \hline
TransTrack & 75.2 & 54.1 & 3,603 \\ \hline
ByteTrack & 80.3 & 63.1 & 2,196 \\ \hline
MotionTrack & 81.1 & 65.1 & \textbf{1,140} \\ \hline
C-BIoU & \textbf{82.8} & \textbf{66.0} & 1,194 \\ \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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|cc}
\multirow{2}{*}{Node} & \multirow{2}{*}{VRE availability} & \multirow{2}{*}{Demand profile} & \multicolumn{2}{c}{Inverse demand function} \\ \cline{4-5}
 & & & Slope & Intercept \\ \hline \hline
1 & Moderate (50 \%) & Low & 0.04 & 260 \\ \hline
2 & High (70 \%) & Low & 0.04 & 260 \\ \hline
3 & Low (30 \%) & High & 0.0075 & 195 \\ \hline \hline
\end{tabular}
\caption{Illustrative energy system demand and VRE availability profiles. }
\end{table}
\end{document}

q-fin

Nikita Belyak
Steven A. Gabriel
Nikolay Khabarov
Fabricio Oliveira

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Johansen Cointegration Test using Trace Test Statistic (10\% Significance Level)}
\begin{tabular}{cccc}
\hline
\textbf{Rank (r)} & \textbf{Cointegrating Relations} & \textbf{Test Statistic} & \textbf{Critical Value} \\
\hline
0 & 2 & 18.56 & 13.43 \\
1 & 2 & 0.9834 & 2.705 \\
\hline
\end{tabular}
\end{table}
\end{document}

q-fin

Juan Plazuelo Pascual
Carlos Tardon Rubio
Juan Toro Cebada
Angel Hernando Veciana

"Price Discovery in Cryptocurrency Markets" by Juan Plazuelo Pascual, Carlos Tardon Rubio, Juan Toro Cebada, and Angel Hernando Veciana, arXiv:2506.08718v1, 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{Test top-1, top-10 ($\%$) accuracy on the LeanStep unseen lemma prediction task.}
\begin{tabular}{llllll}
 \toprule
 Model & Top-1 Acc. & Top-10 Acc. \\
 \midrule
 No pretrain & 15.8 & 27.4 \\
 LIME \texttt{Deduct} &25.8 &38.0 \\
 LIME \texttt{Abduct} &26.0 &38.6 \\
 LIME \texttt{Induct} &25.0 &38.2 \\
 LIME \texttt{Mix} &\textbf{29.8} &\textbf{41.8} \\
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

cs

Yuhuai Wu
Markus Rabe
Wenda Li
Jimmy Ba
Roger Grosse
Christian Szegedy

"LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning" by Yuhuai Wu, Markus Rabe, Wenda Li, Jimmy Ba, Roger Grosse, and Christian Szegedy, arXiv:2101.06223v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textit{FlowReg-A} model, details of architecture in Figure .}
\begin{tabular}{lllll}
\toprule
Layer & Filters & Kernel & Stride & Activation \\
\midrule
fixedInput & - & - & - & - \\
movingInput & - & - & - & - \\
concatenate & - & - & - & - \\
conv3D & 16 & 7x7x7 & 2, 2, 1 & ReLu \\
conv3D & 32 & 5x5x5 & 2, 2, 1 & ReLu \\
conv3D & 64 & 3x3x3 & 2, 2, 2 & ReLu \\
conv3D & 128 & 3x3x3 & 2, 2, 2 & ReLu \\
conv3D & 256 & 3x3x3 & 2, 2, 2 & ReLu \\
conv3D & 512 & 3x3x3 & 2, 2, 2 & ReLu \\
flatten & - & - & - & - \\
dense & 12 & - & - & Linear
\\ \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Sergiu Mocanu
Alan R. Moody
April Khademi

"FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow" by Sergiu Mocanu, Alan R. Moody, and April Khademi, arXiv:2101.09639v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|}
\hline
d & Par & Zon(2) & Zon(4) & CZ(2,5) & CZ(4,5) \\
\hline
3 & 5.52 & 1.98 & 1.36 & 1.09 & 0.87 \\
5 & 3.08 & 1.62 & 1.22 & 0.97 & 0.81 \\
10 & 2.01 & 1.26 & 1.03 & 0.82 & 0.71 \\
15 & 1.56 & 1.07 & 0.93 & 0.72 & 0.65 \\
20 & 1.35 & 0.97 & 0.87 & 0.67 & 0.61 \\
\hline
\end{tabular}
\caption{Randomly generated systems: average uncertainty associated to the state estimates for different $d$: fixed thresholds.}
\end{table}
\end{document}

eess

Marco Casini
Andrea Garulli
Antonio Vicino

"Adaptive Threshold Selection for Set Membership State Estimation with Quantized Measurements" by Marco Casini, Andrea Garulli, and Antonio Vicino, arXiv:2311.00611v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{\textcolor{black}{List of the datasets. `TV Series' and `12 Angry Men' datasets are taken from the web, and the rest others are self-collected.}}
\begin{tabular}{ccccc}
 \toprule
 Dataset Name&Location/Description&Length&Purpose&Labeled\\
 \midrule
 Lab-1& a research lab &10 hours&No. of Nodes&No\\
 Reception Area& an institute&10 hours&No. of Nodes&No\\
 Outdoor Canteen-1& an open canteen&10 hours&No. of Nodes&No\\
 TV Series& Breaking Bad~ &10 hours&No. of Nodes&No\\
 12 Angry Men~& single room movie&1.5 hours &Performance&Yes\\
 Outdoor Canteen-2& an open canteen&\textcolor{black}{3 hours}&Performance&Yes\\
 An Outdoor Area& an institute&\textcolor{black}{3 hours}&Performance&Yes\\
 Lab-2& a research lab &1.33 hours&Adaptiveness&No\\
 \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}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{ {Comparison of loss functions on LEVIR-CD applied during the training of our model. The best results are highlighted in bold text.}}
\begin{tabular}{|l|c|c|c|}
\hline
Loss Function & IoU & F1 & OA \\
\hline
\hline
Focal Loss & 79.27 & 88.44 & 98.84\\
mIoU Loss & 77.51 & 87.32 & 98.75\\
Cross-Entropy Loss & \textbf{83.83} & \textbf{91.20} & \textbf{99.12} \\
\hline
\end{tabular}
\end{table}
\end{document}

cs

Mubashir Noman
Mustansar Fiaz
Hisham Cholakkal
Salman Khan
Fahad Shahbaz Khan

"ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection" by Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal, Salman Khan, and Fahad Shahbaz Khan, arXiv:2403.17909v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Ashutosh Singh
Priyanka Sharma
Om Prakash

"Construction of $(σ,δ)$-cyclic codes over a non-chain ring and their applications in DNA codes" by Ashutosh Singh, Priyanka Sharma, and Om Prakash, arXiv:2312.08109v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the classification performance} % title of Table
\begin{tabular}{l|c|c} % centered columns (4 columns)
\hline\hline %inserts double horizontal lines
Methods&Accuracy& AUC \\ % inserts table
\hline\hline % inserts single horizontal line
M-VoxelHop & \textbf{93.48$\pm$0.7\%} & \textbf{0.9394$\pm$0.012} \\ \hline
M-3D ResNet + & 91.30$\pm$0.6\% & 0.9048$\pm$0.010 \\
M-3D VGG~~+ & 89.13$\pm$0.5\% & 0.8808$\pm$0.014 \\
M-3D DenseNet + & 86.96$\pm$0.8\% & 0.8762$\pm$0.012 \\
M-3D AlexNet + & 84.78$\pm$1.0\% & 0.8575$\pm$0.013 \\

\hline\hline

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

cs

Xiaofeng Liu
Fangxu Xing
Chao Yang
C. -C. Jay Kuo
Suma Babu
Georges El Fakhri
Thomas Jenkins
Jonghye Woo

"VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI" by Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Suma Babu, Georges El Fakhri, Thomas Jenkins, and Jonghye Woo, arXiv:2101.05131v1, 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.}
\begin{tabular}{c||c}
 \hline \hline
 Initial Data (bits), $D_{k,1}$ &$192$ Mbit \\
 \hline
 Final channel outputs, $C_{out}$ & 20 \\
 \hline
 Wavelength, $\lambda$ & $0.05$ m \\
 \hline
 Distance from BS, $d$ & $50$ m \\
 \hline
 Path loss exponent, $n$ & $2.4$ \\
 \hline
 Transmit power, $P_{k}$ & $1$ W \\
 \hline
 Antenna gains, $G_t , G_r$ & $1 , 10$ dBi \\
 \hline
 Total bandwidth, $B_{\mathrm{tot}}$ & $200$ MHz \\
 \hline
 Number of devices, $K$ & $10$ \\
 \hline
 Number of BMs in ENet, $L$ & $30$ \\
 \hline
 Device computational resources, $f_k$ & $30$ GFLOPS \\
 \hline
 Central server computational resources, $f_{\mathrm{max}}$ & $300$ GFLOPS \\
 \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.


math

"Imperfect diffusion-controlled reactions on a torus and on a pair of balls" by Denis S. Grebenkov, arXiv:2504.21449v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amssymb}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}\hline
\multirow{2}{*}{Variable}	&	\multicolumn{3}{c}{Posterior percentiles}					\\
	&	2.5\%	&	50\%	&	97.5\%	\\
\hline
\multicolumn{4}{c}{Extensive margin equation}							\\
\hline
Constant	&	\textbf{-1.96}	&	\textbf{-1.55}	&	\textbf{-1.20}	\\
Drug dealer in neighborhood	&	-0.01	&	0.06	&	0.14	\\
Alcohol and cigarette use	&	\textbf{0.75}	&	\textbf{0.84}	&	\textbf{0.93}	\\
Age: 30s	&	\textbf{-0.44}	&	\textbf{-0.34}	&	\textbf{-0.24}	\\
Age: 40s	&	\textbf{-0.63}	&	\textbf{-0.50}	&	\textbf{-0.37}	\\
Age: 50s and older	&	\textbf{-1.12}	&	\textbf{-0.97}	&	\textbf{-0.83}	\\
Stratum medium	&	\textbf{0.02}	&	\textbf{0.10}	&	\textbf{0.18}	\\
Stratum high	&	-0.07	&	0.07	&	0.20	\\
High risk perception drug use	&	\textbf{-0.97}	&	\textbf{-0.82}	&	\textbf{-0.66}	\\
Medium risk perception drug use	&	-0.36	&	-0.17	&	0.01	\\
Years of education	&	\textbf{-0.03}	&	\textbf{-0.02}	&	\textbf{-0.01}	\\
Female	&	\textbf{-0.50}	&	\textbf{-0.42}	&	\textbf{-0.34}	\\
Good mental health	&	\textbf{-0.28}	&	\textbf{-0.20}	&	\textbf{-0.11}	\\
Marijuana consumers in network	&	\textbf{0.84}	&	\textbf{0.95}	&	\textbf{1.08}	\\
Working	&	\textbf{-0.19}	&	\textbf{-0.10}	&	\textbf{-0.02}	\\
Region fixed effects	&	$\checkmark$	&	$\checkmark$	&	$\checkmark$	\\
\hline
\multicolumn{4}{c}{Intensive margin equation}							\\
\hline
Constant	&	\textbf{4.58}	&	\textbf{6.45}	&	\textbf{8.31}	\\
Drug dealer in neighborhood	&	-0.17	&	0.09	&	0.35	\\
Alcohol and cigarette use	&	-0.01	&	0.18	&	0.37	\\
$\log\left\{\text{price of marijuana}\right\}$	&	\textbf{-0.70}	&	\textbf{-0.51}	&	\textbf{-0.33}	\\
Age 30s	&	-2.60	&	0.14	&	2.82	\\
Age 40s	&	-3.84	&	0.47	&	4.66	\\
Age 50s and older	&	-8.46	&	-3.89	&	1.35	\\
Stratum medium	&	-0.23	&	-0.02	&	0.17	\\
Stratum high	&	-0.36	&	-0.08	&	0.22	\\
High risk perception drug use	&	\textbf{-1.04}	&	\textbf{-0.71}	&	\textbf{-0.38}	\\
Medium risk perception drug use	&	\textbf{-1.00}	&	\textbf{-0.65}	&	\textbf{-0.30}	\\
Years of education	&	\textbf{-0.12}	&	\textbf{-0.09}	&	\textbf{-0.06}	\\
Female	&	\textbf{-0.71}	&	\textbf{-0.50}	&	\textbf{-0.28}	\\
Good mental health	&	-0.06	&	0.14	&	0.35	\\
Marijuana consumers in network	&	-0.15	&	0.26	&	0.68	\\
Working	&	-0.01	&	0.17	&	0.37	\\
Age 30s $\times \log\left\{\text{price of marijuana}\right\}$	&	-0.34	&	0.01	&	0.37	\\
Age 40s $\times \log\left\{\text{price of marijuana}\right\}$	&	-0.57	&	-0.02	&	0.53	\\
Age 50s and older $\times \log\left\{\text{price of marijuana}\right\}$	&	-0.14	&	0.48	&	1.07	\\
Region fixed effects	&	$\checkmark$	&	$\checkmark$	&	$\checkmark$	\\
					\hline
				\end{tabular}
\end{table}
\end{document}

q-fin

A. Ramirez-Hassan
C. Gomez
S. Velasquez
K. Tangarife

"Marijuana on Main Streets? The Story Continues in Colombia: An Endogenous Three-part Model" by A. Ramirez-Hassan, C. Gomez, S. Velasquez, and K. Tangarife, arXiv:2306.10031v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{WER results for our proposed noise vectors (offline and MLE) compared with other embedding-based techniques from literature. Methods marked with $^\dagger$ require environment labels for training embedding extractor.}
\begin{tabular}{lcccccc}
\toprule
\multirow{2}{*}{\textbf{Method}} & \phantom{a} & \multicolumn{2}{c}{\textbf{AMI}} & \phantom{a} & \multicolumn{2}{c}{\textbf{Aurora-4}} \\
 \cmidrule(l{2pt}r{2pt}){3-4} \cmidrule(l{2pt}r{2pt}){6-7}
 && \textbf{Dev} & \textbf{Eval} && \textbf{Eval-92} & \textbf{0166} \\
\midrule
Base model && 38.8 & 42.6 && 7.94 & 8.12 \\
CMN && 38.5 & 42.8 && 7.79 & 7.94 \\
utt-mean && 36.8 & 40.8 && 7.77 & 7.78 \\
\midrule
i-vector (offline)~ && 35.2 & 39.4 && 8.24 & 8.53 \\
i-vector (online) && 35.6 & 40.0 && 8.51 & 8.78 \\
NAT-vector~ && 38.1 & 42.0 && 8.17 & 8.38 \\
e-vector (LDA)$^{\dagger}$~ && 37.6 & 41.5 && 7.63 & 7.73 \\
Bottleneck NN$^{\dagger}$~ && 38.7 & 42.4 && 7.77 & 7.98 \\
\midrule
Noise vector (offline) && \textbf{34.9} & \textbf{38.8} && \textbf{7.37} & \textbf{7.61} \\
Noise vector (MLE) && 35.4 & 39.2 && 7.72 & 7.93 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Desh Raj
Jesus Villalba
Daniel Povey
Sanjeev Khudanpur

"Frustratingly Easy Noise-aware Training of Acoustic Models" by Desh Raj, Jesus Villalba, Daniel Povey, and Sanjeev Khudanpur, arXiv:2011.02090v2, 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
 $\hat{\varepsilon}$ & $\hat{a}$ & $\hat{b}$ & $\hat{c}$ & $R^2$ \\
 \hline
 $\hat{\varepsilon}_{wg, \textrm{EKF0}}$ & $0.694$ & $-1.287$ & $0.350$ & $0.892$ \\
 \hline
 \end{tabular}
\caption{Linear regression summary of the model~ for Algorithm~ using EKF0.}
\end{table}
\end{document}

stat

Yvann Le Fay
Simo Särkkä
Adrien Corenflos

"Modelling pathwise uncertainty of Stochastic Differential Equations samplers via Probabilistic Numerics" by Yvann Le Fay, Simo Särkkä, and Adrien Corenflos, arXiv:2401.03338v2, 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{95\% Approval Rate}
\begin{tabular}{|c|c|c|c|c|}
 \hline True / Prediction & 0 & 1 & 2 & SUM\\
 \hline 0 & 46 & 1 & 5 & 52\\
 \hline 1 & 2 & 14 & 0 & 16\\
 \hline 2 & 0 & 0 & 3 & 3\\
 \hline SUM & 48 & 15 & 8 & 71\\
 \hline
 \end{tabular}
\end{table}
\end{document}

q-fin

"Predicting Stock Price Movement as an Image Classification Problem" by Matej Steinbacher, arXiv:2303.01111v1, 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}{ll}
Size & log(Total Assets) \\
Age	& number of years the firm is in Compustat \\
Profitability & $\frac{EBIT}{Total Assets}$ \\
Tangibility	 & $\frac{Property,\ Plant\ and Equipment}{Total\ Assets}$ \\
Cash & $\frac{Cash\ and\ Short-Term\ Investments}{Total\ Assets}$ \\
R\&D & $\frac{Research\ and\ Development\ Expense}{Total\ Assets}$ \\
MB (Market-to-Book) & $\frac{Total\ Assets - Total\ Common\ Equity + Market\ Value}{Total\ Assets}$ \\
P/S (Price/Sales) & $\frac{(Common\ Shares\ Outstanding)(Price\ Close)}{Sales}$ \\
ROA & $\frac{Net\ Income}{Total\ Assets}$ \\
ROE & $\frac{Net\ Income}{Total\ Common\ Equity}$ \\
Asset Growth & $\frac{Total\ Assets_{t}}{Total\ Assets_{t-1}}$ - 1 \\
Dividends & $\frac{Dividends\ Common}{Total\ Assets}$ \\
Capex & $\frac{Capital\ Expenditures}{Assets\ Total}$ \\
Cash Flow & $\frac{Income\ Before\ Extraordinary\ Items + Depreciation\ and\ Amortization}{Total\ Assets}$ \\
Net Debt Issuance & $\frac{Tota\l Debt_{t}}{Total\ Debt_{t-1}}$ - 1\\
Equity Issuance & $\frac{Sale\ of\ Common\ and\ Preferred\ Stock - Purchase\ of\ Common\ and\ Preferred\ Stock}{Total\ Assets}$ \\
Tobin`s Q & $\frac{Total\ Assets + Market\ Value + Common\ Equity + Deferred\ Taxes}{Total\ Assets}$ \\
Acquisitions & $\frac{Acquisitions}{Total\ Assets}$ \\
Book Leverage & $\frac{LongTerm Debt + ShortTerm Debt}{Total\ Assets}$ \\
After-IPO Cash Ratio & $\frac{Cash_{it}}{Cash_{i0}}$ \\
SA-index & $ (0.737*Size) + (0.043*Size^2) - (0.040*Age)$ \\
Board\ Independence & $\frac{Number of external directors}{Board Size}$ \\
Delisting\ Shock & $\max{(\frac{Fraction\ of\ delistings\ due\ to\ merger\ in\ 2-digit\ SIC\ industry_t}{Fraction\ of\ delistings\ due\ to\ merger\ in\ 2-digit\ SIC\ industry_{t-1}} - 1, 0)}$ \\
\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}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lrrrrrr}
 \toprule\toprule
Solver & Run time & Asset 1 & Asset 2 & Asset 3 & Asset 4 & Asset 5\\
 \midrule
 CP & 1.55 & 0.109 & 0.0854 & 0.115 & 0.0667 & 0.121 \\
 SCS & 120.97 & 0.256 & $2.17 \times 10^{-5}$ & 0.125 & $1.36 \times 10^{-5}$ & 0.198 \\
 MOSEK & 1.43 & 0.256 & 0.000643 & 0.120 & 0.00176 & 0.199 \\
 IpOpt & 3382.49 & 0.117 & $2.03 \times 10^{-19}$ & 0.176 & $1.51 \times 10^{-19}$ & 0.298
 \\ \bottomrule\bottomrule
\end{tabular}
\caption{Run time (in seconds) and EVaR risk contributions of each one of the $d=5$ assets.}
\end{table}
\end{document}

q-fin

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

"Risk Budgeting Portfolios from Simulations" by Bernardo Freitas Paulo da Costa, Silvana M. Pesenti, and Rodrigo S. Targino, arXiv:2302.01196v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Balancing Flexibility and Interpretability: A Conditional Linear Model Estimation via Random Forest" by Ricardo Masini and Marcelo Medeiros, arXiv:2502.13438v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{arydshln}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Expected log-predictive density/mass $(\widehat{\operatorname{elpd}})$ and its standard error ($\operatorname{se}(\widehat{\operatorname{elpd}})$) for different models with data simulated under different data-generating processes.}
\begin{tabular}{llrr}
 \toprule
DGP & Model & $\widehat{\operatorname{elpd}}$ & $\operatorname{se}(\widehat{\operatorname{elpd}})$ \\
 \midrule
\multirow{4}{*}{ZANIM} & ZANIM & $-2051.199$ & 20.499 \\
 & ZANIDM & $-2085.094$ & 14.126 \\
 & DM & $-2749.009$ & 25.796 \\
 & Multinomial & $-4736.613$ & 262.969 \\
 \hdashline
\multirow{4}{*}{ZANIDM} & ZANIDM & $-2201.241$ & 19.684 \\
 & ZANIM & $-2298.938$ & 34.754 \\
 & DM & $-2656.433$ & 27.609 \\
 & Multinomial & $-4749.662$ & 264.850 \\
 \bottomrule
\end{tabular}
\end{table}
\end{document}

math

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

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


cs

"Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports" by Xinyu Zhao, Hao Yan, and Yongming Liu, arXiv:2403.17914v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Zhong-Heng Tan
Tiexiang Li
Wen-Wei Lin
Shing-Tung Yau

"A Novel Algorithm for Periodic Conformal Flattening of Genus-one and Multiply Connected Genus-zero Surfaces" by Zhong-Heng Tan, Tiexiang Li, Wen-Wei Lin, and Shing-Tung Yau, arXiv:2412.19052v2, 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{Financial market returns correlation---period 2018--2021.}
\begin{tabular}{lrrrrrr}
\toprule
 & \textbf{BTC-USD} & \textbf{FCHI} & \textbf{FTSE} & \textbf{GDAXI} & \textbf{N100} & \textbf{SSMI} \\ \midrule
BTC-USD & 1 & & & & & \\
FCHI & 0.276 & 1 & & & & \\
FTSE & 0.263 & 0.900 & 1 & & & \\
GDAXI & 0.274 & 0.944 & 0.871 & 1 & & \\
N100 & 0.284 & 0.989 & 0.913 & 0.948 & 1 & \\
SSMI & 0.274 & 0.830 & 0.816 & 0.814 & 0.851 & 1 \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

"Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins" by Apostolos Ampountolas, arXiv:2307.08853v1, 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
Sample & Top5 & Top2 & Top10\\
Mean Restricted & 0.74 & 1 & 0.59\\
Mean Closed & 0.22 & 0.57 & 0\\
$Pr(T<t)$ & 0.9235 & 0.7477 & 0.9906\\
$Pr(T>t)$ & 0.075 & 0.2523 & 0.0094\\ \hline \hline
Sample & NotTop5 & NotTop2 & NotTop10\\
Mean Restricted & 0.19 & 0.24 & 0.15\\
Mean Closed & -0.25 & -0.22 & -0.27\\
$Pr(T<t)$ & 0.9962 & 0.9985 & 0.9793\\
$Pr(T>t)$ & 0.0038 & 0.0015 & 0.0207\\ \hline
 \end{tabular}
\caption{Difference of means test for the period $2018-2022$}
\end{table}
\end{document}

q-fin

Federico Fioravanti
Fernando Delbianco
Fernando Tohmé

"Visitors Out! The Absence of Away Team Supporters as a Source of Home Advantage in Football" by Federico Fioravanti, Fernando Delbianco, and Fernando Tohmé, arXiv:2308.06279v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Arbaz Khan
Felipe Lepe
David Mora
Jesus Vellojin

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lcc}
		\toprule
		Framework 	& Mean agg. & Best Frequency Fusion\\
		\midrule
		Diff-traditional SVM & $79.98$ & $80.67$ \\
		Diff-traditional LDA & $67.30$& $74.38$\\
		Diff-traditional QDA & $72.13$ & $83.25$\\
		Diff-traditional KNN & $86.06$ & $86.06$\\
		Diff-traditional GP & $85.95$ & $86.51$\\
		\bottomrule
	\end{tabular}
\caption{Performance for the traditional BCI frameworks using the differentiation. We compare the usage of the base aggregation (the arithmetic mean) against the best possible one.}
\end{table}
\end{document}

eess

Javier Fumanal-Idocin
Yu-Kai Wang
Chin-Teng Lin
Javier Fernández
Jose Antonio Sanz
Humberto Bustince

"Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions" by Javier Fumanal-Idocin, Yu-Kai Wang, Chin-Teng Lin, Javier Fernández, Jose Antonio Sanz, and Humberto Bustince, arXiv:2101.06968v2, 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{State and control input}
\begin{tabular}{llll}
			\hline\hline \\
			Mode & Name & Symbol&
			Unit\\ \hline
		state&Lateral velocity &$v_y$ & [m/s] \\
		& Yaw rate at center of gravity (CG) &$\omega_r$ & [rad/s] \\
		& Longitudinal velocity &$v_x$ & [m/s] \\
		& Yaw angle &$\phi$ & [rad]\\
		& trajectory&$y$ & [m] \\
		input & Front wheel angle &$\delta$ & [rad] \\
			\hline\hline
		\end{tabular}
\end{table}
\end{document}

eess

Recurrent Model Predictive Control

Zhengyu Liu
Jingliang Duan
Wenxuan Wang
Shengbo Eben Li
Yuming Yin
Ziyu Lin
Qi Sun
Bo Cheng

"Recurrent Model Predictive Control" by Zhengyu Liu, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Yuming Yin, Ziyu Lin, Qi Sun, and Bo Cheng, arXiv:2102.11736v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary statistics for the distribution of the regional effect on GVA per capita}
\begin{tabular}{lccc}
 \hline
 & 7 years & 14 years & Average \\
 \hline
 $\%$ of regions with $\widehat{\tau}_i>0$ & 64.5 & 48.9 & 60.4 \\
Skewness & 0.81 & -0.06 & 0.15 \\
 Kurtosis & 3.81 & 2.33 & 2.42 \\
 \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Angelos Alexopoulos
Ilias Kostarakos
Christos Mylonakis
Petros Varthalitis

"The heterogeneous causal effects of the EU's Cohesion Fund" by Angelos Alexopoulos, Ilias Kostarakos, Christos Mylonakis, and Petros Varthalitis, arXiv:2504.13223v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|c|c|}
 \hline
 Date & Open & High & Low & Close & Adj Close & Volume \\ \hline
 2024-01-02 & 720.00 & 725.00 & 710.00 & 715.00 & 715.00 & 15,200,000 \\ \hline
 2024-01-03 & 715.50 & 730.00 & 705.50 & 725.00 & 725.00 & 17,500,000 \\ \hline
 \end{tabular}
\caption{Sample rows from the Tesla Dataset}
\end{table}
\end{document}

q-fin

Daksh Dave
Gauransh Sawhney
Vikhyat Chauhan

"Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies" by Daksh Dave, Gauransh Sawhney, and Vikhyat Chauhan, arXiv:2502.15853v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Badr-Eddine Cherief-Abdellatif
Randal Douc
Arnaud Doucet
Hugo Marival

"On the Asymptotics of Importance Weighted Variational Inference" by Badr-Eddine Cherief-Abdellatif, Randal Douc, Arnaud Doucet, and Hugo Marival, arXiv:2501.08477v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


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

math

Dinesh Parthasarathy
Tommaso Bevilacqua
Martin Lanser
Axel Klawonn
Harald Köstler

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Performance comparison when training with momentum line or rise-or-fail task.}
\begin{tabular}{llll}
\hline
Backbone & Task & IC $\uparrow$ & RankIC $\uparrow$ \\ \hline
\multirow{2}{*}{LSTM} & Rise-or-Fall & 0.0457(4.5) & 0.0436(4.5) \\
 & Momentum & \textbf{0.0632(0.9)}& \textbf{0.0604(1.4)} \\ \hline
\multirow{2}{*}{GATs} & Rise-or-Fall & 0.0501(3.4)& 0.0484(3.9) \\
 & Momentum & \textbf{0.0622(1.5)} & \textbf{0.0590(0.8)}\\ \hline
\multirow{2}{*}{HIST} & Rise-or-Fall & 0.0519(2.6) & 0.0507(2.2)\\
 & Momentum & \textbf{0.0667(1.1)} & \textbf{0.0633(1.0)}\\ \hline
\end{tabular}
\end{table}
\end{document}

q-fin

Hao Wang
Jingshu Peng
Yanyan Shen
Xujia Li
Lei Chen

"Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization" by Hao Wang, Jingshu Peng, Yanyan Shen, Xujia Li, and Lei Chen, arXiv:2509.10461v1, 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{Test statistics results for six covariates}
\begin{tabular}{lcccccc}
\hline
 & PE & PS & PB & PFCF & DER & ROE \\ \hline
$\lambda_{\text{stat}}$ & 190.74 & 1242.49 & 1046.17 & 1492.37 & 1277.67 & 36.71 \\
$\lambda_{\text{stat\_shuffle}}$-avg & 22.43 & 21.98 & 24.37 & 16.83 & 21.47 & 23.89\\
$\lambda_{\text{stat\_shuffle}}$-95\%quantile & 56.79 & 50.25 & 59.18 & 38.51 & 50.75 & 55.89\\
$\lambda_{\text{stat\_shuffle}}$-99\%quantile & 75.78 & 74.58 & 84.57 & 54.30 & 79.78 & 91.90\\ \hline
\end{tabular}
\end{table}
\end{document}

stat

"Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees" by Jianqing Fan, Jiawei Ge, and Jikai Hou, arXiv:2502.06671v1, 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}{lcc}
 \toprule
 Data Partition & MultiWOZ & SGD
 \\
 \midrule
 Satisfaction & 64.6 & 86.2
 \\
 Dissatisfaction & 47.5 & 14.5
 \\
 Overall & 63.8 & 80.3
 \\ \bottomrule
 \end{tabular}
\caption{Counter Satisfaction Status (CSS). CSS demonstrates the success rate of LLMs in generating counterfactual system utterances.}
\end{table}
\end{document}

cs

Amin Abolghasemi
Zhaochun Ren
Arian Askari
Mohammad Aliannejadi
Maarten de Rijke
Suzan Verberne

"CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems" by Amin Abolghasemi, Zhaochun Ren, Arian Askari, Mohammad Aliannejadi, Maarten de Rijke, and Suzan Verberne, arXiv:2403.19056v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Fanwei Kong
Nathan Wilson
Shawn C. Shadden

"A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction" by Fanwei Kong, Nathan Wilson, and Shawn C. Shadden, arXiv:2102.07899v2, 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{Average results of the fixed-lag models for two, three, and six lead months SSTA and MHW forecasts.}
\begin{tabular}{llll}
\textbf{Across All Locations} & \textbf{MSE↓} & \textbf{CSI↑} & \textbf{CSI 80↑} \\ \hline
Average (two lead months) & 0.6240 & 0.2204 & 0.3173 \\
Average (three lead months) & 0.8089 & 0.1459 & 0.2602 \\
Average (six lead months) & 1.0178 & 0.1125 & 0.1882
\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.


stat

Ayush Jha
Abootaleb Shirvani
Ali Jaffri
Svetlozar T. Rachev
Frank J. Fabozzi

"Advancing Portfolio Optimization: Adaptive Minimum-Variance Portfolios and Minimum Risk Rate Frameworks" by Ayush Jha, Abootaleb Shirvani, Ali Jaffri, Svetlozar T. Rachev, and Frank J. Fabozzi, arXiv:2501.15793v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Madhab Barman
Madhurima Panja
Nachiketa Mishra
Tanujit Chakraborty

"Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak" by Madhab Barman, Madhurima Panja, Nachiketa Mishra, and Tanujit Chakraborty, arXiv:2502.10786v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsfonts}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparemeters of MA4DIV on different dataset.}
\begin{tabular}{c|c|c|c|c}
\hline
Dataset & $|\mathcal{A}|$ & $L$ & $H$ & $z$ \\ \hline
TREC & 30 & 1 & 4 & 64 \\ \hline
DU-DIV & 15 & 1 & 4 & 256 \\ \hline
\end{tabular}
\end{table}
\end{document}

cs

Yiqun Chen
Jiaxin Mao
Yi Zhang
Dehong Ma
Long Xia
Jun Fan
Daiting Shi
Zhicong Cheng
Simiu Gu
Dawei Yin

"MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification" by Yiqun Chen, Jiaxin Mao, Yi Zhang, Dehong Ma, Long Xia, Jun Fan, Daiting Shi, Zhicong Cheng, Simiu Gu, and Dawei Yin, arXiv:2403.17421v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|ccc}
\toprule
 & agg. max attn. & SD & \# \\ \midrule
covered & 0.434 & 0.0040 & 4515 \\
uncovered & 0.376 & 0.0068 & 1473 \\ \bottomrule
\end{tabular}
\caption{Aggregated max sentence-level attention of the fine-tuned GPT2-L; the results are aggregated from the generation to covered or uncovered concepts on the CommonGen test set. agg. max attn., SD, \# refer to aggregated max sentence-level attention, standard deviation, and the number of instances. }
\end{table}
\end{document}

cs

Yue Dong
Chandra Bhagavatula
Ximing Lu
Jena D. Hwang
Antoine Bosselut
Jackie Chi Kit Cheung
Yejin Choi

"On-the-Fly Attention Modulation for Neural Generation" by Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung, and Yejin Choi, arXiv:2101.00371v2, 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 statistics of the estimated networks using Kendall's tau statistic for the correlation matrix estimation. SK = SKEPTIC, SNK = elliptical skew-SKEPTIC, STK = elliptical skew-KEPTIC. DIM = dimension of the dataset. The notation "$>$" indicates that one method has a greater number of edges compared to the other.}
\begin{tabular}{|l|c|c|c|c|c|c|c|}
\hline
\bfseries{Network} & & \multicolumn{3}{c|}{\bfseries \ Edges No.} & \multicolumn{3}{c|}{\bfseries Edges diff.} \\
\cline{2-8}
\hline
& DIM & SK & STK & SNK & SK$>$SNK & SK$>$STK & SNK$>$STK \\
\hline
S\&P 500 & 454 & 399 & 303 & 252 & 96 & 147 & 51 \\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Semiparametric Skew-Elliptical Distributions For High-Dimensional Graphical Models" by Gabriele Di Luzio and Giacomo Morelli, arXiv:2501.08033v1, 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{Number of parameters in GeLoRA for each task.}
\begin{tabular}{l|rrrrrrrr}
\toprule
\textbf{Task} & \textbf{CoLA} & \textbf{STS-B} & \textbf{MRPC} & \textbf{QNLI} & \textbf{SST-2} & \textbf{RTE} & \textbf{MNLI} & \textbf{QQP}\\
\midrule
\textbf{\# Params} & $0.10$M & $0.11$M & $0.13$M & $0.09$M & $0.09$M & $0.13$M & $0.10$M & $0.12$M\\
\textbf{Mean Rank} & $1.33$ & $1.50$ & $1.75$ & $1.25$ & $1.17$ & $1.75$ & $1.33$ & $1.58$\\
\textbf{Rounded Mean Rank} & $1$ & $2$ & $2$ & $1$ & $1$ & $2$ & $1$ & $2$\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

math

Abdessalam Ed-dib
Zhanibek Datbayev
Amine Mohamed Aboussalah

"GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning" by Abdessalam Ed-dib, Zhanibek Datbayev, and Amine Mohamed Aboussalah, arXiv:2412.09250v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c|c|c}
 \toprule
 Settings &$||\boldsymbol{\mathbb{X}}||_{rms}$& $\boldsymbol{E}_{\text{sol}}\downarrow$ & $\boldsymbol{E}_{\text{para}}\downarrow$ & $\boldsymbol{E}_{\text{time}}\downarrow$ & $A$ \\
 \hline
 Linear2D & 1.26 &\textbf{1.4}$\%$ (8.4$\%$)& \textbf{1.9}$\%$ (7.7$\%$)& \textbf{0.056}$\%$ (0.39$\%$)& $\begin{bmatrix}
 -0.12& 1.97\\-2.01&-0.08
 \end{bmatrix}$ \\
 &&&&&\\
 Cubic2D &1.13 &\textbf{0.6}$\%$ (7.0$\%$)& \textbf{0.8}$\%$ (9.2$\%$)& \textbf{0.4}$\%$ (0.9$\%$)& $\begin{bmatrix}
 -0.11& 1.99\\-2.02&-0.99
 \end{bmatrix}$ \\
 &&&&&\\
 Linear3D & 1.31 & \textbf{1.5}$\%$ (5.7$\%$)& \textbf{3.0}$\%$ (6.8$\%$)& \textbf{0.13}$\%$ (0.53$\%$)& $\begin{bmatrix}
 -0.15& 1.97& 0\\-2.01&-0.07&0\\
 0&0&-0.28
 \end{bmatrix}$ \\
 \bottomrule
 \end{tabular}
\caption{\textbf{The results of illustrative examples:} The symbol \(\|\cdot\|_{rms}\) denotes the root mean square of the observational data, reflecting the data scale. We recorded the values of all three metrics \(\boldsymbol{E}_{\text{sol}}\), \(\boldsymbol{E}_{\text{para}}\), and \(\boldsymbol{E}_{\text{time}}\) in both phases. The metrics in brackets are computed using the learned neural solution from the distribution matching phase and the final results are marked in bold. All the inactive terms in library are successfully removed during training in four experiment, so the Parameters row describes the identified systems. }
\end{table}
\end{document}

math

Zhijun Zeng
Pipi Hu
Chenglong Bao
Yi Zhu
Zuoqiang Shi

"Reconstruction of dynamical systems from data without time labels" by Zhijun Zeng, Pipi Hu, Chenglong Bao, Yi Zhu, and Zuoqiang Shi, arXiv:2312.04038v3, 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{NYISO: Computation time (minutes) for a varying number of uncertainty scenarios and operation hours.}
\begin{tabular}{lcccccccc}
\toprule
\# Scenarios \# Hours & 5S2H & 10S2H & 20S2H & 20S4H & 20S6H\\
\midrule
\textit{MyD} & 0.35 & 0.35 & 0.35 & 0.53 &1.0 \\
\textit{BiD} & 2.0 & 3.1 & 7.4 & 19.4 &48.9 \\
\textit{StD} & 0.76 & 1.2 & 3.0 & 7.3 &16.3\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

q-fin

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

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


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{cccc}
\toprule
 & $\alpha_\mathbf{HD}$ & $\alpha_\mathbf{HDHD}$ & $\alpha_\mathbf{HDLK}$ \\
\midrule
 \texttt{is\_clicked} & 1.064** & -1.156* & 0.698\\
 \texttt{duration} & 78.890*** & -84.899*** & 31.535\\
 \texttt{usefulness} & 0.494 *** & -0.519 *** & 0.275* \\
 \midrule
\# Observations & \multicolumn{3}{c}{767}\\
\bottomrule
\end{tabular}
\caption{The regression coefficients of the independent variable \texttt{has\_decoy}~($\alpha_\mathbf{HD}$), \texttt{has\_decoy\_high\_difficulty}~($\alpha_\mathbf{HDHD}$), and \texttt{has\_decoy\_low\_knowledge}~($\alpha_\mathbf{HDLK}$) with the dependent variables \texttt{is\_clicked}, \texttt{duration}, and \texttt{usefulness} on THUIR2018. *, ** and *** respectively indicate \( p < 0.05 \), \( p < 0.01 \), and \( p < 0.001 \).}
\end{table}
\end{document}

cs

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

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


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Different activation functions for $M_\theta$}
\begin{tabular}{cccc}
		\toprule
		Activation func & P (\%) & R (\%) & F1 (\%) \\ \hline
		$\mathrm{tanh}$ & 70.6 & 80.0 & 75.0 \\
		$\mathrm{sigmoid}$ & 62.8 & 81.7 & 71.0 \\
		$\mathrm{softplus}$ & 48.6 & 86.7 & 62.3 \\
		$\mathrm{relu}$ & 67.2 & 71.7 & 69.4 \\ \bottomrule
	\end{tabular}
\end{table}
\end{document}

eess

Jindong Wang
Wenjie Feng
Chang Liu
Chaohui Yu
Mingxuan Du
Renjun Xu
Tao Qin
Tie-Yan Liu

"Learning Invariant Representations across Domains and Tasks" by Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, and Tie-Yan Liu, arXiv:2103.05114v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Abnormal component analysis

Romain Valla
Pavlo Mozharovskyi
Florence d'Alché-Buc

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


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|cccc|} \hline
\textbf{Traffic class} &	\textbf{Recall(\%)} &	\textbf{Precision(\%)} &	\textbf{Accuracy(\%)} \\\hline
Browsing-Unencrypted & 99.2 &	99.5 &	99.6 \\\hline
Browsing-Tor & 	99.5 &	96.6 &	99.8 \\\hline
Chat–Unencrypted & 	92.5 &	88.6 &	99.6 \\\hline
Chat–Tor & 	92.1 &	89.7 &	99.8 \\\hline
Chat–VPN & 	100.0 &	99.2 &	100.0 \\\hline
File Transfer–Unencrypted &	100.0 &	100.0 &	100.0 \\\hline
File Transfer–Tor & 	98.0 &	100.0 &	99.9 \\\hline
File Transfer–VPN & 	97.9 &	100.0 &	100.0 \\\hline
Video–Unencrypted & 	99.5 &	100.0 &	100.0 \\\hline
Video–Tor & 	100.0 &	100.0 &	100.0 \\\hline
Video–VPN & 	100.0 &	100.0 &	100.0 \\\hline
VoIP-Unencrypted & 	84.2 &	99.7 &	96.6 \\\hline
VoIP–Tor & 	98.3 &	100.0 &	99.9 \\\hline
VoIP-VPN & 	99.7 &	72.1 &	96.7 \\\hline
\end{tabular}
\caption{Complete performance results on one vs all categorization of different traffic classes and encryption techniques. The task was to classify each class versus all other classes on the test dataset.}
\end{table}
\end{document}

cs

Ali Rasteh
Florian Delpech
Carlos Aguilar-Melchor
Romain Zimmer
Saeed Bagheri Shouraki
Timothée Masquelier

"Encrypted Internet traffic classification using a supervised Spiking Neural Network" by Ali Rasteh, Florian Delpech, Carlos Aguilar-Melchor, Romain Zimmer, Saeed Bagheri Shouraki, and Timothée Masquelier, arXiv:2101.09818v2, 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{Exports of Bangladesh (2006-2013)}
\begin{tabular}{ll}
 \toprule
 Total exports volume (billion US\$) & 156.7 \\
 Number of exporters & 18,327\\
 Number of products & 4,234\\
 Number of destinations & 179\\
 Number of shipments & 4,979,958\\
 Average shipment value (US\$) & 31,472 \\
 Median shipment value (US\$) & 14,245 \\
 \bottomrule
 \bottomrule
 \end{tabular}
\end{table}
\end{document}

q-fin

"Financing Costs, Per-Shipment Costs and Shipping Frequency: Firm-Level Evidence from Bangladesh" by Md Deluair Hossen, arXiv:2303.04223v1, 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{Computational Complexity of Different Schemes.}
\begin{tabular}{|c|c|c|c|c|c|c|}
		\hline % 顶部线
		\bf Scheme&\bf Computational complexity \\
		\hline % 中部线
		FAS-OMP & ${\cal O}\left(LPM{N^2}\right)$ \\ \hline
		FAS-ML &${\cal O}\left({I_o}PML\left( {PM + N} \right)\right)$ \\ \hline
		S-BAR (Stage 1) & ${\cal O}\left({P}{M}\left( {{P^2}{M^2} + NPM + {N^2}} \right)\right)$ \\ \hline
		S-BAR (Stage 2) & ${\cal O}\left(PMN\right)$\\
		\hline % 底部线
	\end{tabular}
\end{table}
\end{document}

eess

Zijian Zhang
Jieao Zhu
Linglong Dai
Robert W. Heath

"Successive Bayesian Reconstructor for Channel Estimation in Fluid Antenna Systems" by Zijian Zhang, Jieao Zhu, Linglong Dai, and Robert W. Heath, arXiv:2312.06551v5, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Revealing the Shape of Genome Space via K-mer Topology" by Yuta Hozumi and Guo-Wei Wei, arXiv:2412.20202v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

Zhi Su
Danni Wu
Zhenkun Zhou
Junran Wu
Libo Yin

"The Impact of Customer Online Satisfaction on Stock Returns: Evidence from the E-commerce Reviews in China" by Zhi Su, Danni Wu, Zhenkun Zhou, Junran Wu, and Libo Yin, arXiv:2306.12119v1, 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
\begin{tabular}{l|c|c}
\toprule[2pt]
Ratio & \#Sampling=2000 & \#Sampling=10000 \\
\midrule[1pt]
0.15 & \textcolor{black}{0.2861 $\pm$ 0.2128} & \textcolor{black}{0.0051 $\pm$ 0.0013} \\
0.25 & \textcolor{black}{0.2927 $\pm$ 0.0944} & \textcolor{black}{0.0033 $\pm$ 0.0015} \\
0.35 & \textcolor{black}{0.0048 $\pm$ 0.0027} & \textcolor{black}{0.0030 $\pm$ 0.0004} \\
0.45 & \textbf{\textcolor{black}{0.0039 $\pm$ 0.0009}} & \textbf{\textcolor{black}{0.0029 $\pm$ 0.0002}} \\
\bottomrule[2pt]
\end{tabular}
\end{table}
\end{document}

stat

Jiaqi Luo
Yahong Yang
Yuan Yuan
Shixin Xu
Wenrui Hao

"An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks" by Jiaqi Luo, Yahong Yang, Yuan Yuan, Shixin Xu, and Wenrui Hao, arXiv:2501.11222v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{makecell}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|cccccc|c}
 \toprule
 & 4 & 8 & 16 & 32 & 40 & 50 & \thead{Static \\ prior} \\
 \midrule
 Accuracy & 76.1 & 76.99 & 77.41 & 78.03 & 78.32 & 77.35 & 78.21 \\

 \end{tabular}
\caption{\textbf{Influence of the context set size} used to derive the data-driven prior on CIFAR100.}
\end{table}
\end{document}

cs

"CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models" by Saurav Jha, Dong Gong, and Lina Yao, arXiv:2403.19137v3, 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{Benchmarking APT for accuracy~(\%) on the natural language audio reasoning task.}
\begin{tabular}{lc}
 \toprule
 Model & Accuracy$\uparrow$ \\ \hline
 AAC+ChatGPT & 27.9 \\
 APT-LLM & 62.9 \\
 APT-LLM$_{1.5}$ & \textbf{63.8} \\ \bottomrule
 \end{tabular}
\end{table}
\end{document}

eess

Jinhua Liang
Xubo Liu
Wenwu Wang
Mark D. Plumbley
Huy Phan
Emmanouil Benetos

"Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities" by Jinhua Liang, Xubo Liu, Wenwu Wang, Mark D. Plumbley, Huy Phan, and Emmanouil Benetos, arXiv:2312.00249v2, 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{Number of PBFA Attacks in Different Bit Positions over 100 rounds}
\begin{tabular}{|c|c|c|c|}
 \hline
 {} & MSB (0 $\rightarrow$ 1) & MSB (1 $\rightarrow$ 0) & others\\
 \hline
 ResNet-20 &334 &666 &0\\
 ResNet-18 &16 &897 &87\\
 \hline
 \end{tabular}
\end{table}
\end{document}

cs

Jingtao Li
Adnan Siraj Rakin
Zhezhi He
Deliang Fan
Chaitali Chakrabarti

"RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery" by Jingtao Li, Adnan Siraj Rakin, Zhezhi He, Deliang Fan, and Chaitali Chakrabarti, arXiv:2101.08254v1, 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{Parameters Used in the Algorithm}
\begin{tabular}{|c|c|c|}
			\hline
			\textbf{Function} & \textbf{Step-size} & \textbf{Lower Bound on Basin Size} \\
			\hline
			Rastrigin & 0.0001 & 0.5 \\
			\hline
			Ackley & 0.0001 & 0.1 \\
			\hline
			Sphere & 0.001 & 0.3 \\
			\hline
			Rosenbrock & 0.001 & 0.5 \\
			\hline
			Beale & 0.0005 & 0.3 \\
			\hline
			Booth & 0.005 & 0.3 \\
			\hline
		\end{tabular}
\end{table}
\end{document}

math

"Uncomputability of Global Optima for Nonconvex Functions in the Oracle Model" by K Lakshmanan, arXiv:2401.09436v2, 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
\begin{tabular}{l|l|lll}
\toprule
\textbf{Model} & \textbf{T}&\textbf{HQA} & \textbf{MS} & \textbf{NQ}
\\
\midrule
\multirow{2}{*}{TinyDolphin} & 0.1 & 14.94& 24.44& 16.13\\
& 0.7 & 13.41& 6.98& 9.89\\
\midrule
\multirow{2}{*}{Mistral-7B} & 0.1 & 49.43& 77.08& 60.82\\
& 0.7 & 47.73& 80.00& 56.38\\

\bottomrule
\end{tabular}
\caption{Direct Generate ACC (\%) Results. For comparison, we evaluate the model's ability to answer questions without any external information. Detailed instructions can be found in Table~.}
\end{table}
\end{document}

cs

Shu Yang
Jiayuan Su
Han Jiang
Mengdi Li
Keyuan Cheng
Muhammad Asif Ali
Lijie Hu
Di Wang

"Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs" by Shu Yang, Jiayuan Su, Han Jiang, Mengdi Li, Keyuan Cheng, Muhammad Asif Ali, Lijie Hu, and Di Wang, arXiv:2404.00486v1, 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{Experiment settings}
\begin{tabular}{|c|c|c|}
\hline
 & \textbf{Parameter} & \textbf{Value} \\ \hline
Microphone & Type & Br\"uel Kj\ae r 4939-A-011 \\ \hline
{}{}{Microphone amplifier} & Type & Br\"uel Kj\ae r NEXUS \\ \cline{2-3}
 & Signal delay & $\approx10\,\mu$s \\ \hline
Loudspeaker & Type & Yamaha MS101III \\ \hline
Stage controller & Type & OptoSigma SHOT-302GS \\ \cline{2-3}
 & Movement step & 10$\,$mm \\ \cline{2-3}
 & Rotation step & 5$^\circ$ \\ \hline
{}{}{LDV} & Type & Polytec VibroFlex \\ \cline{2-3}
 & Decoder & Displacement \\ \cline{2-3}
 & Bandwidth & 20$\,$kHz \\ \cline{2-3}
 & Sensitivity & 5 nm/V @M$\Omega$ \\ \cline{2-3}
 & Range & 10$\,$nm \\ \cline{2-3}
 & Signal delay & 900$\,\mu$s \\ \hline
{}{}{Anechoic chamber} & Temperature & 16.9$^\circ$C \\ \cline{2-3}
 & Humidity & 28.4\,\%\\ \cline{2-3}
 & Pressure & 1000.4\,hPa \\ \hline
\end{tabular}
\end{table}
\end{document}

eess

Phuc Duc Nguyen
Kenji Ishikawa
Noboru Harada
Takehiro Moriya

"Acousto-optic reconstruction of exterior sound field based on concentric circle sampling with circular harmonic expansion" by Phuc Duc Nguyen, Kenji Ishikawa, Noboru Harada, and Takehiro Moriya, arXiv:2311.01715v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft" by Blake Downward and Jon Nordby, arXiv:2311.06368v1, 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{\small Comparison of wall-clock time for computing low rank quantized factors via LPLR, LSVD and DSVD. Each image embedding forms a row of the input matrix, with low rank factors computed for \textbf{each} class of the input dataset. Bit-budgets used are ${\rm B} = {\rm B'} = 8, {\rm B_{nq} = 1}$. We report mean and standard deviation.}
\begin{tabular}{cccc}
\toprule
\textbf{Dataset} & \textbf{LPLR} & \textbf{DSVD} & \textbf{LSVD} \\ \midrule
CIFAR-10 & $71 \pm 11$ ms & $306 \pm 26$ ms & $312 \pm 8$ ms \\ \midrule
CIFAR-100 & $14 \pm 3$ ms & $51 \pm 3$ ms & $56 \pm 3$ ms \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Rajarshi Saha
Varun Srivastava
Mert Pilanci

"Matrix Compression via Randomized Low Rank and Low Precision Factorization" by Rajarshi Saha, Varun Srivastava, and Mert Pilanci, arXiv:2310.11028v1, 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{Damage detection summary results at multiple $\alpha$ values for path 3-4 (single wave packet) in the CFRP plate.}
\begin{tabular}{lccccccc} % defines the alignment
\hline % adds horizontal line
Method & False & \multicolumn{6}{|c}{Missed damage ($\%$)} \\
\cline{3-8}
 & alarms ($\%$) & 1 Weight & 2 Weights & 3 Weights & 4 Weights & 5 Weights & 6 Weights \\
\hline
DI$^a$$^\dagger$ & 5.25 & 13.25 & 0 & 0 & 0 & 0 & 0 \\
$F$ Statistic$^b$$^\dagger$ & 95 & 0 & 5 & 0 & 0 & 0 & 0 \\
$F_m$ Statistic$^c$$^{\dagger\dagger}$ & 30 & 60 & 0 & 0 & 0 & 0 & 0 \\
$Z$ Statistic$^a$$^{\dagger\dagger}$ & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
\hline
\multicolumn{8}{l}{{\bf False alarms} presented as percentage of 20 test cases.} \\
\multicolumn{8}{l}{{\bf Missed damages} presented as percentage of 20 test cases.} \\
\multicolumn{8}{l}{$^a$ $\alpha = 95\%$.; $^b$ $\alpha = 1\%$; $^c$ $\alpha = 10\%$} \\
\multicolumn{8}{l}{$^\dagger$ All 20 baseline data sets were used as reference signals consecutively.} \\
\multicolumn{8}{l}{$^{\dagger\dagger}$ 15 out of 20 baseline data sets were used to calculate the baseline mean.}
\end{tabular}
\end{table}
\end{document}

eess

"Statistical guided-waves-based SHM via stochastic non-parametric time series models" by Ahmad Amer and Fotis Kopsaftopoulos, arXiv:2101.11208v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c|c}
\hline
\hline
\multicolumn{2}{c|}{} &\textbf{FEMINST}& \textbf{CIFAR-10}\\
\hline
\multicolumn{2}{c|}{\textbf{Client Count}} & 3,550 & 100 \\
\hline
\multicolumn{2}{c|}{\textbf{Sample Count}} & 805,263 & 60,000 \\
\hline
\multirow{2}*{\textbf{Sample per Client}} & Mean & 226.83 & 946.8 \\
\cline{2-4}
 & Stdev & 88.94 & 256.04 \\
\hline
\hline
\end{tabular}
\caption{Statistics of dataset}
\end{table}
\end{document}

cs

"GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning" by Shijie Na, Yuzhi Liang, and Siu-Ming Yiu, arXiv:2403.17833v2, 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}{lrrrr}
 \toprule
 \textbf{language} & \textbf{\#documents} & \textbf{type} & \textbf{annotation} & \textbf{authors} \\ \midrule
 es & 400 & forum & entities & \\
 ru & $^\ast$279 & drug reviews & multi-label & \\
 fr & 3,033 & Twitter & binary & \\
 ru & 9,515 & Twitter & binary, entities & \\
 ja & 169 & forum & entities, normalization & \\
 ru & $^{\ast\ast}$500 & drug reviews & multi-label, entities & \\
 ru & 2,800 & drug reviews & entities & \\
 de & 4,169 & forum & binary & \\ \midrule
 de, fr, ja & 837 & forum, Twitter, YJQA & entities, attributes, relations & ours \\
 \bottomrule
 \end{tabular}
\caption{Other non-English social media corpora focused on ADRs. es=Spanish, fr=French, ru=Russian, ja=Japanese, de=German. $^\ast$Number of documents containing ADRs. $^{\ast\ast}$This is only the annotated part of the \textsc{RuDReC} corpus.}
\end{table}
\end{document}

cs

Lisa Raithel
Hui-Syuan Yeh
Shuntaro Yada
Cyril Grouin
Thomas Lavergne
Aurélie Névéol
Patrick Paroubek
Philippe Thomas
Tomohiro Nishiyama
Sebastian Möller
Eiji Aramaki
Yuji Matsumoto
Roland Roller
Pierre Zweigenbaum

"A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages" by Lisa Raithel, Hui-Syuan Yeh, Shuntaro Yada, Cyril Grouin, Thomas Lavergne, Aurélie Névéol, Patrick Paroubek, Philippe Thomas, Tomohiro Nishiyama, Sebastian Möller, Eiji Aramaki, Yuji Matsumoto, Roland Roller, and Pierre Zweigenbaum, arXiv:2403.18336v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{llcccc}
\toprule
\multirow{7}*{\rotatebox[origin=c]{90}{\emph{Epsilon ($\epsilon$ = 0.02)}}} & \emph{Pseudo-Reference} & de-en & en-de & ru-en & en-ru \\
\cmidrule(lr){2-6}
 & Ancestral & 85.82 & 87.51 & 82.02 & 88.41 \\
 & Beam & \underline{85.62} & \underline{87.40} & \underline{81.64} & \underline{87.78} \\
 & Epsilon ($\epsilon$ = 0.02) & 85.89 & 87.74 & 82.01 & 88.46 \\
 & Epsilon ($\epsilon$ = 0.02)$^*$ & 85.87 & 87.74 & 81.98 & 88.46 \\
 & Nucleus ($p$ = 0.6) & 85.69 & 87.57 & 81.76 & 88.26 \\
 & Nucleus ($p$ = 0.9) & \textbf{86.04} & \textbf{87.82} & \textbf{82.18} & \textbf{88.61} \\
\midrule
\midrule
 & Beam Search & 84.38 & 86.13 & 80.76 & 85.69 \\
 & Beam Search (ensemble) & 84.30 & 86.06 & 80.91 & 85.74 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Atsumoto Ohashi
Ukyo Honda
Tetsuro Morimura
Yuu Jinnai

"On the True Distribution Approximation of Minimum Bayes-Risk Decoding" by Atsumoto Ohashi, Ukyo Honda, Tetsuro Morimura, and Yuu Jinnai, arXiv:2404.00752v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Danlu Zhang
Stephanie M. Eick
Howard H. Chang

"Approximate Bayesian Kernel Machine Regression via Random Fourier Features for Estimating Joint Health Effects of Multiple Exposures" by Danlu Zhang, Stephanie M. Eick, and Howard H. Chang, arXiv:2502.13157v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|l|c|l|c|}
\hline
Total number of parameters & 1,080,214 & Learning Rate & 0.00005 \\ \hline
Trainable parameters & 103,812 & Batch Size & 64 \\ \hline
Non-trainable parameters & 976,402 & Epoch & 2000 \\ \hline
\end{tabular}
\caption{Transfer Learning Parameters}
\end{table}
\end{document}

eess

Gulay Goktas Sever
Umut Demir
Abdullah Sadik Satir
Mustafa Cagatay Sahin
Nazim Kemal Ure

"An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data" by Gulay Goktas Sever, Umut Demir, Abdullah Sadik Satir, Mustafa Cagatay Sahin, and Nazim Kemal Ure, arXiv:2401.08663v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{booktabs}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}\toprule
 & $\mathcal{L_{\textnormal{contra.}}}$ & $\mathcal{L_{\textnormal{C-cyclic}}}$& $\mathcal{L_{\textnormal{I-cyclic}}}$ & $\mathcal{L_{\textnormal{s}}}$ & $\mathcal{L_{\textnormal{n}}}$ \\
\cmidrule{1-6}
CLI (A) P & \checkmark & - & - & - & - \\
CLI (A) Ps & \checkmark & - & - & \checkmark & - \\
CLI (A) Pn & \checkmark & - & - & - & \checkmark \\
CyCLI (A) P & \checkmark & \checkmark & \checkmark & - & - \\
CyCLI (A) Ps & \checkmark & \checkmark & \checkmark & \checkmark & - \\
CyCLI (A) Pn & \checkmark & \checkmark & \checkmark & - & (\checkmark) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

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

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


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Sound descriptor categories used in calculating correspondence between captions.\\ Items marked with * exist only in the dataset of 13 labels.}
\begin{tabular}{ll}
\toprule
Label & Description \\ \midrule
WHO & sound-generating agent \\
WHO/WHAT PROPERTY* & describes object or person \\
WHAT & vibrating object or substance \\
HOW & sound-generating actions/mechanisms \\
HOW PROPERTY* & specifies action \\
WHEN & temporal context \\
WHERE & spatial context \\
WHAT/WHERE* & objects that contribute to acoustics \\
SOUND TYPE & sound-signal categories \\
SOUND PROPERTY & acoustic/auditory sound properties \\
NON-AUDITORY SENSATION & non-auditory attributes of sound \\
OTHER & labels that do not describe sound \\
O & omitted labels \\ \bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Gijs Wijngaard
Elia Formisano
Bruno L. Giordano
Michel Dumontier

"ACES: Evaluating Automated Audio Captioning Models on the Semantics of Sounds" by Gijs Wijngaard, Elia Formisano, Bruno L. Giordano, and Michel Dumontier, arXiv:2403.18572v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Spatial autoregressive fractionally integrated moving average model" by Philipp Otto and Philipp Sibbertsen, arXiv:2309.06880v1, 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{\footnotesize \textbf{Results of local interpretation with and without the monotone constraint.}}
\begin{tabular}{c|c|c|c|c|c|c}
\hline
Image index & Monotone & Heavy Makeup & No beard & Wearing Lipstick & classified label & True label \\ \hline \hline
2-1 & X & 0.030 & 0.035 & 0.093 & male & female \\ \hline
2-1 & O & -0.080 & -0.161 & -0.106 & female & female \\ \hline
2-2 & X & 0.036 & 0.104 & 0.095 & male & male \\ \hline
2-2 & O & -0.081 & -0.183 & -0.106 & female & male \\ \hline
\end{tabular}
\end{table}
\end{document}

stat

Seokhun Park
Insung Kong
Yongchan Choi
Chanmoo Park
Yongdai Kim

"Tensor Product Neural Networks for Functional ANOVA Model" by Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, and Yongdai Kim, arXiv:2502.15215v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|rrr|rrrr}
\toprule
\textbf{Weight} & \textbf{Acc$\uparrow$} & \textbf{F1$\uparrow$} & \textbf{VD-S$\downarrow$} & \textbf{P-C$\uparrow$} & \textbf{P-V$\downarrow$} & \textbf{P-B$\downarrow$} & \textbf{P-R$\downarrow$} \\
\midrule
\midrule
\textbf{1} & 96.86 & 21.43 & 89.21 & 1.60 & 12.06 & 85.11 & 1.24\\\midrule
\textbf{5} & 96.24 & 25.29 & 90.65 & 1.77 & 18.97 & 78.55 & 0.71 \\
\textbf{20} & 95.28 &24.26 & 88.92 & 0.89 & 25.71 & 72.16 & 1.24\\
\textbf{30}& 96.14 & 24.49 & 90.07 & 2.13 & 18.09 & 78.01 & 1.77\\
\textbf{40}& 95.99 & 26.28 & 88.49 & 1.42 & 22.52 & 74.82 & 1.24\\
\bottomrule
\end{tabular}
\end{table}
\end{document}

cs

Yangruibo Ding
Yanjun Fu
Omniyyah Ibrahim
Chawin Sitawarin
Xinyun Chen
Basel Alomair
David Wagner
Baishakhi Ray
Yizheng Chen

"Vulnerability Detection with Code Language Models: How Far Are We?" by Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair, David Wagner, Baishakhi Ray, and Yizheng Chen, arXiv:2403.18624v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Javad Mirzapour Kaleybar
Hooman Saadat
Hooman Khaloo

"Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer" by Javad Mirzapour Kaleybar, Hooman Saadat, and Hooman Khaloo, arXiv:2311.01731v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Lars Herre
Behrouz Nourozi
Mohammad Reza Hesamzadeh
Qian Wang
Lennart Söder

"A bottom-up quantification of flexibility potential from the thermal energy storage in electric space heating" by Lars Herre, Behrouz Nourozi, Mohammad Reza Hesamzadeh, Qian Wang, and Lennart Söder, arXiv:2103.06734v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


q-fin

"Revisiting Hötte (2025): A Companion Analysis with Extended Evidence from UK Inter-Industry Payment Data, 2017-2024" by Kerstin Hötte, arXiv:2508.17695v1, 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}{cccc}
\toprule
\textbf{Label Type} & \textbf{\# Labels} & \textbf{\# Positive} & \textbf{\# Negative} \\
\midrule
Pulmonary Embolism & 4,351 & 1,424 & 2,927 \\
Pulmonary Hypertension & & & \\
\bottomrule
\end{tabular}
\caption{Statistics for the manual expert radiologist labels in our dataset. }
\end{table}
\end{document}

eess

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

"INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis" by Shih-Cheng Huang, Zepeng Huo, Ethan Steinberg, Chia-Chun Chiang, Matthew P. Lungren, Curtis P. Langlotz, Serena Yeung, Nigam H. Shah, and Jason A. Fries, arXiv:2311.10798v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Toluwani Okunola
Mirjeta Pasha
Misha Kilmer
Melina Freitag

"Efficient Dynamic Image Reconstruction with motion estimation" by Toluwani Okunola, Mirjeta Pasha, Misha Kilmer, and Melina Freitag, arXiv:2501.12497v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

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

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