\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccccc}
\multicolumn{7}{c} {\textbf{TABLE 2 -- Summary Statistics for the Effe-Cost Example}} \\
Variable & Min. & 1st.Qt & Median & Mean & 3rd.Qt & Max. \\
 & & & & & & \\
99 Std. Treatment Patients & & & & & & \\
effe & 0.076 & 2.436 & 3.615 & 3.653 & 4.624 & 8.373 \\
cost & 0.288 & 53.857 & 78.073 & 76.497 & 98.436 & 172.0 \\
 & & & & & & \\
101 New Treatment Patients & & & & & & \\
effe & 0.816 & 3.210 & 3.890 & 4.000 & 5.079 & 8.373 \\
cost & 11.99 & 48.33 & 63.39 & 68.82 & 90.68 & 150.81 \\
\end{tabular}
\end{table}
\end{document}

stat

"Incremental Cost-Effectiveness Statistical Inference: Calculations and Communications" by Robert L. Obenchain, arXiv:2311.08604v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Simple GNNs with Low Rank Non-parametric Aggregators" by Luciano Vinas and Arash A. Amini, arXiv:2310.05250v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.

Extracted the table LaTeX from the article source.

Wrapped it as a standalone LaTeX document.

Rendered and cropped it into a PNG image.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|}
\hline
Method & LCR\% & AR\% \\
\hline
ResNet18 & 81.70 & 95.92 \\
CharNet & 84.10 & 96.64 \\
CRNN~ & 86.10 & 97.04 \\
ConvS2S & 85.80 & 97.28 \\
FCSRN & 85.60 & 96.98 \\
SRN & 89.60 & 97.82 \\
\hline
SuperOCR(CNN-DSA) & {\bf 90.50} & {\bf 97.98}\% \\
\hline
\end{tabular}
\caption{Accuracy comparison on watermeter dataset. The results of the first six methods are given by~, where AR is defined as the Accuracy Rate, and LCR is defined as the Line Correct Rate to denote the accuracy for the whole line of characters. The SuperOCR implementation on the CNN-DSA implementation uses the CNN accelerator chip with 300mW power consumption.}
\end{table}
\end{document}

eess

"SuperOCR: A Conversion from Optical Character Recognition to Image Captioning" by Baohua Sun, Michael Lin, Hao Sha, and Lin Yang, arXiv:2012.02033v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

"FiloBass: A Dataset and Corpus Based Study of Jazz Basslines" by Xavier Riley and Simon Dixon, arXiv:2311.02023v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
\hline
minute & $1$ & $2$ & $\ldots$ & $T_2$ \\
\hline
fight & $p_s \tilde{p}$ & $p_s \tilde{p}\left(1-\frac{m_2}{N_2-e_2}\right)$ & $\ldots$ & $p_s \tilde{p}\prod_{k=1}^{T_2-1}\left(1-\frac{m_2}{N'_k}\right)$ \\
hide & $p_s \left(1-\frac{m_2}{N_2-e_2}\right)$ & $p_s\prod_{k=1}^2 \left(1-\frac{m_2}{N_k'}\right)$ & $\ldots$ & $p_s \prod_{k=1}^{T_2}\left(1-\frac{m_2}{N_k'} \right)$ \\
run & $p_s \tilde{p}_r $ & $p_s \tilde{p}_r \left(1-\frac{m_2}{N_2-e_2}\right)$ & $\ldots$ & $ p_s \tilde{p}_r\prod_{k=1}^{T_2-1} \left(1-\frac{m_2}{N_k'}\right)$ \\
\hline
\end{tabular}
\caption{Survival probability of the civilian of focus in a complex arena during Phase 2 when $T_1>T_2$, where $p_s$ is given by (), $N'_k=(N_2-e_2)-(k-1)(e_2+m_2)$, and the $i$-th cell of the ``fight'' (respectively ``run'') row is the survival probability of the civilian when he hides for the first $(i-1)$ minutes and starts to fight back (respectively ``run'') at the beginning of the $i$-th minute.}
\end{table}
\end{document}

stat

"Analytic models for active shooter incidents with civilian resistance" by Liang Hong, arXiv:2310.17111v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"An efficient Chorin-Temam projection proper orthogonal decomposition based reduced-order model for nonstationary Stokes equations" by Xi Li, Yan Luo, and Minfu Feng, arXiv:2201.07398v4, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c}
$m$&2&3&4&5&6&7&8&9&10&11&12&13&14&15&16&17&18&19&20\\
\hline
$\pi(m)$&3&8&6&20&24&16&12&24&60&10&24&28&48&40&24&36&24&18&60\\
\end{tabular}
\end{adjustbox}
\caption{Pisano period of the first $20$ integers.}
\end{table}
\end{document}

math

"The Fibonacci Sequence is Normal Base 10" by Brennan Benfield and Michelle Manes, arXiv:2202.08986v1, 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|ccc}
 \hline
 \multirow{2}{*}{Pre-training Sensor} & \multicolumn{3}{c}{Downstream Sensor}\\
 & NAIP & Sentinel-2 & Both\\
 \hline
 No Pre-training & 67.73 & 65.32 & 66.67\\
 NAIP & \textbf{71.21} & - & -\\
 Sentinel-2 & - & 66.50 & -\\
 Both & 70.81 & \textbf{67.62} & \textbf{72.53}\\
 \hline
 \end{tabular}
\caption{Comparison between multi-sensor and single sensor pre-training on USatlas. We report mAP across the USatlas validation set tasks.}
\end{table}
\end{document}

stat

Jeremy Irvin
Lucas Tao
Joanne Zhou
Yuntao Ma
Langston Nashold
Benjamin Liu
Andrew Y. Ng

"USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery" by Jeremy Irvin, Lucas Tao, Joanne Zhou, Yuntao Ma, Langston Nashold, Benjamin Liu, and Andrew Y. Ng, arXiv:2312.02199v1, 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{\bf Confidence Interval for Queue Variables of System Two}
\begin{tabular}{|c|c|c|c|}
\hline
\multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{\textbf{Confidence Interval}} \\
\hline
 \textbf{Variables} & \textbf{Values} & \textbf{ Lower Bound }& \textbf{Upper Bound} \\
\hline
 \(\bar{N}\) & 1.75 & 1.409 & 2.091 \\
\(V_N\)& 2.027& & \\
\(\bar{Q}\)& 0.075& -0.025& 0.175 \\
\(V_{N_q}\)& 0.177 & & \\
\(\bar{T}\)& 4.275& 3.275& 5.275 \\
\(V_T\)& 17.356 & & \\
\(\bar{W}\)& 0.183& -0.004& 0.37\\
\(V_{T_q}\)& 0.611& &\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Optimization of COVID-19 Vaccination Process Using Queueing Theory" by Sarkis Der Wartanian and Baydaa Al Ayoubi, arXiv:2312.13522v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{xcolor}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Simulation results of $\hat{s}$, $\hat{\kappa}$ and $\hat{\eta}$ }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|ccc} \hline
		&$\hat{s}$&$\hat{\kappa}$&$\hat{\eta}$
\\ \hline
{\color{black} true value} &5 & 1 & 1
\\ \hline
mean &4.784& 0.987& 0.992
 \\
s.d. & (0.153)& (0.037)& (0.036)
 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Yozo Tonaki
Yusuke Kaino
Masayuki Uchida

"Parameter estimation for linear parabolic SPDEs in two space dimensions based on high frequency data" by Yozo Tonaki, Yusuke Kaino, and Masayuki Uchida, arXiv:2201.09036v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|l|c|c|c|c|c|c|}
 \hline
 \textbf{Model} & \textbf{Avg. Keypoint Prec. / Rec.} \\
 \hline
 Du \textit{et al.} 2018[1]~ & 83.0 / 83.7 \\
 \hline
 DeepLabv3 (single-frame)~ & 100.0 / 91.0 \\
 \hline
 DeepLabv3+MFCNet-B (K=3) & 99.9 / 86.6 \\
 \hline
 DeepLabv3+MFCNet-W (K=3) & 100.0 / 89.8 \\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{\textbf{EndoVis'15 results}. Showing precision and recall scores averaged over 5 keypoints. Our proposed models perform better than Du \textit{et al.} 2018~.}
\end{table}
\end{document}

cs

Bhargav Ghanekar
Lianne R. Johnson
Jacob L. Laughlin
Marcia K. O'Malley
Ashok Veeraraghavan

"Video-based Surgical Tool-tip and Keypoint Tracking using Multi-frame Context-driven Deep Learning Models" by Bhargav Ghanekar, Lianne R. Johnson, Jacob L. Laughlin, Marcia K. O'Malley, and Ashok Veeraraghavan, arXiv:2501.18361v1, 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{(Scenario I) Averages of the 5 smallest of EO/KS-GEOs whose AUCs are greater than the thresholds. Those scores are selected in the Pareto solutions appearing in Figure~. Standard deviations are in the parentheses next to the averages.}
\begin{tabular}{c|cc|cc|cc}
\toprule
& \multicolumn{2}{c|}{\textbf{Adult} (AUC $\geq$ 0.85)} & \multicolumn{2}{|c|}{\textbf{Cred. Card.} (AUC $\geq$ 0.75)} & \multicolumn{2}{|c}{\textbf{Law School.} (AUC $\geq$ 0.85)} \\
\hline
Model & EO ($\downarrow$) & KS-GEO ($\downarrow$) & EO ($\downarrow$) & KS-GEO ($\downarrow$) & EO ($\downarrow$) & KS-GEO ($\downarrow$) \\
\hline
\hline
SBP & 0.009 (0.005) & 0.070 (0.002) & 0.003 (0.001) & 0.040 (0.001) & 0.166 (0.027) & 0.140 (0.005) \\
CON & 0.019 (0.008) & 0.066 (0.002) & 0.001 (0.001) & 0.038 (0.002) & 0.217 (0.033) & 0.190 (0.009) \\
HGR & 0.164 (0.031) & 0.170 (0.006) & 0.002 (0.001) & 0.047 (0.002) & 0.891 (0.080) & 0.409 (0.014) \\
KDE & 0.383 (0.013) & 0.172 (0.003) & 0.002 (0.001) & 0.050 (0.001) & 0.311 (0.128) & 0.262 (0.006) \\
NEU & 0.450 (0.028) & 0.205 (0.005) & 0.030 (0.019) & 0.046 (0.001) & 0.421 (0.022) & 0.299 (0.003) \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

"Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes" by Jinwon Sohn, Qifan Song, and Guang Lin, arXiv:2311.05866v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of the Speed of the Two Instance Segmentation Models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ccc}
 \toprule
 \textbf{Model} & \textbf{Time (msec)} & \textbf{Speed (fps)} \\
 \midrule
 Mask R-CNN & 55 & 18 \\
 YOLO-v7 Instance Segmentation & 24.9 & 40 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Saeid Ataei
Saeed Adibnazari
Seyyed Taghi Ataei

"Data-driven Detection and Evaluation of Damages in Concrete Structures: Using Deep Learning and Computer Vision" by Saeid Ataei, Saeed Adibnazari, and Seyyed Taghi Ataei, arXiv:2501.11836v1, 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{\small Comparison of $5$-way, $1$-shot and $5$-shot classification accuracies between the Experiment 2(a) and 2(b) based on \textit{new subjects with few-shot observation.}}
\begin{tabular}{c|cccc}
\hline
\hline
\multirow{3}[2]{*}{\textbf{The Embedding Module}}
& \multicolumn{2}{|c|}{\textbf{Experiment 2(a)}}
& \multicolumn{2}{|c}{\textbf{Experiment 2(b)}}
\\
\cline{2-5}
&
\multicolumn{4}{c}{\multirow{1}[2]{*}{\textbf{5-way Accuracy}}}
\\
& \textbf{1-shot}
& \textbf{5-shot}
&\textbf{1-shot}
& \textbf{5-shot}
\\
\hline
\textbf{FC Embedding}
& 62.87$\%$
&78.90$\%$
& 72.69$\%$
& 86.08$\%$
\\
\textbf{LSTM Embedding}
& 64.46$\%$
& 79.82$\%$
& 75.56$\%$
& 89.14$\%$
\\
\textbf{T-Block Embedding I}
& \textbf{67.81}$\%$
& 81.08$\%$
& 75.11$\%$
& 89.66$\%$
\\
\textbf{T-Block Embedding II}
& 66.98$\%$
& \textbf{81.29}$\%$
& \textbf{77.08}$\%$
& \textbf{90.47}$\%$
\\
\hline
\hline
\end{tabular}
\end{table}
\end{document}

eess

Elahe Rahimian
Soheil Zabihi
Amir Asif
Dario Farina
Seyed Farokh Atashzar
Arash Mohammadi

"FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography" by Elahe Rahimian, Soheil Zabihi, Amir Asif, Dario Farina, Seyed Farokh Atashzar, and Arash Mohammadi, arXiv:2011.06104v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||l||ll||ll|}
\hline
 Segment & Ref. & Trilinear && Fake-Trilinear &\\ \cline{3-6}
 & & mean value & abs. error & mean value & abs. error \\ \hline
 0 & 0. & $1.979\cdot 10^{-3}$ & $1.979\cdot 10^{-3}$ & $0.986\cdot 10^{-16}$ & $0.986\cdot 10^{-16}$ \\
 1 &1. & 0.823 & 0.177 & 1.000 & $0.9333\cdot10^{-4}$ \\
2 & 0.05 & $0.569 \cdot 10^{-1}$ &$0.686\cdot10^{-2}$ & $0.500\cdot 10^{-1}$ & $1.379\cdot 10^{-6}$ \\
3 &0.2 & 0.216 & $0.157 \cdot 10^{-1}$ &$0.200$ &$0.204\cdot10^{-5}$ \\
4 &0.3 & 0.296 &$0.390\cdot10^{-2}$ & $0.300$ & $0.797\cdot10^{-5}$ \\
5 &0.15 & 0.160 & $0.995\cdot10^{-2}$ & $1.499\cdot10^{-1}$ & $0.608\cdot10^{-4}$ \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Segment index, Reference value, mean value and absolute error per segment relative to the oversamplings of the Shepp-Logan phantom with Trilinear interpolation and Trilinear interpolation with Fake Nodes.}
\end{table}
\end{document}

math

Davide Poggiali
Diego Cecchin
Stefano De Marchi

"Reducing the Gibbs effect in multimodal medical imaging by the Fake Nodes Approach" by Davide Poggiali, Diego Cecchin, and Stefano De Marchi, arXiv:2202.10325v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

María Jaenada
Elena Castilla
Nirian Martín
Leandro Pardo

"Robust approach for comparing two dependent normal populations through Wald-type tests based on Rényi's pseudodistance estimators" by María Jaenada, Elena Castilla, Nirian Martín, and Leandro Pardo, arXiv:2202.00982v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|cc|c|cc|c|cc|}
			\hline
			\multicolumn{3}{|c|}{algorithm without adaptivity}&\multicolumn{6}{c|}{adaptive algorithm}\\
			\cline{4-9}
			\multicolumn{3}{|c|}{ }
			&\multicolumn{3}{c|}{one level $\mathcal{M}=1$}&\multicolumn{3}{c|}{two levels $\mathcal{M}=2$}\\
			\hline
			cells &$L^1$-error &rate&cells	&$L^1$-error &rate&cells	&$L^1$-error &rate\\
			\hline
			$2\times100\times100$&0.0256&&13,516 &0.0257&&12,800&0.0265&	\\
			$2\times200\times200$&0.0127	&1.01&40,292 &0.0128&1.00&38,284 & 0.0133&0.99\\
			$2\times400\times400$&0.0047&1.43&153,152 &0.0049&1.39&60,326&0.0050&1.41\\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Adaptive Central-Upwind Scheme on Triangular Grids for the Shallow Water Model with variable density" by Thuong Nguyen, arXiv:2201.10073v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Yifan Hu
Guibin Zhang
Peiyuan Liu
Disen Lan
Naiqi Li
Dawei Cheng
Tao Dai
Shu-Tao Xia
Shirui Pan

"TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting" by Yifan Hu, Guibin Zhang, Peiyuan Liu, Disen Lan, Naiqi Li, Dawei Cheng, Tao Dai, Shu-Tao Xia, and Shirui Pan, arXiv:2501.13041v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The evaluation dataset composition of the data challenge where the number in the brackets indicates the number of melodies. ``MTrans'', ``MVAE'' and ``MNet'' represent for music transformer, MusicVAE and MidiNet respectively. The number in the brackets indicates the number of melodies. The title of each column is explained in the context.}
\begin{tabular}{cccccccccccc}
		\toprule[1pt]
		\multicolumn{12}{c}{Computer-generated melodies (2000)} \\
		\midrule[1pt]
		\multicolumn{4}{c}{MTrans (1200)} & \multicolumn{4}{c}{MVAE (400)} & \multicolumn{4}{c}{MNet (400)} \\
		\midrule[0.5pt]
		\multicolumn{2}{c}{Bach} & \multicolumn{2}{c}{Pop} & \multicolumn{2}{c}{Bach} & \multicolumn{2}{c}{Pop} & \multicolumn{2}{c}{Bach} & \multicolumn{2}{c}{Pop} \\
 \midrule[0.5pt]
 \multicolumn{2}{c}{600} & \multicolumn{2}{c}{600} & \multicolumn{2}{c}{200} & \multicolumn{2}{c}{200} & \multicolumn{2}{c}{200} & \multicolumn{2}{c}{200} \\
 \midrule[1pt]
 \multicolumn{12}{c}{Human-composed melodies (2000)} \\
 \midrule[1pt]
 \multicolumn{6}{c}{Training (1900)} & \multicolumn{6}{c}{Unpublished (100)} \\
 \midrule[0.5pt]
 \multicolumn{3}{c}{Bach} & \multicolumn{3}{c}{Pop} & \multicolumn{3}{c}{Bach} & \multicolumn{3}{c}{Pop} \\
 \midrule[0.5pt]
 \multicolumn{3}{c}{950} & \multicolumn{3}{c}{950} & \multicolumn{3}{c}{50} & \multicolumn{3}{c}{50} \\
		\bottomrule[1pt]
	\end{tabular}
\end{table}
\end{document}

eess

"A novel dataset for the identification of computer generated melodies in the CSMT challenge" by Shengchen Li, Yinji Jing, and György Fazekas, arXiv:2012.03646v3, 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}{|cc|c|}
\hline
 & Physics-Informed Priors & Non-Informed Priors \\
Bias$^{2}$ $\left(10^{-2}\right)$ & $0.15~(0.12,0.19)$ & $15.69~(15.54,15.94)$ \\
Variance $\left(10^{-2}\right)$ & $1.08~(0.91,1.26)$ & $16.73~(14.11,18.47)$ \\
MSE $\left(10^{-2}\right)$ & $1.25~(1.06,1.42)$ & $32.36~(29.81,34.34)$ \\ \hline
Runtime (mm:ss) & $02:54$ & $02:58$ \\
 \hline
\end{tabular}
\caption{Physics-informed vs. non-informed priors simulation using $n=50$ observations of the Burgers' equation with viscosity $\nu=0.05$. Displayed are the median and inter-quartile ranges across $N_{\text{sim}}=100$ different realizations for the bias, variance, and MSE as defined in equation . The computational demand does not materially change across the simulations, so we just report the median value.}
\end{table}
\end{document}

stat

Luca Menicali
David H. Richter
Stefano Castruccio

"Physics-Informed Priors with Application to Boundary Layer Velocity" by Luca Menicali, David H. Richter, and Stefano Castruccio, arXiv:2311.12978v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lll}
Method & Acquisition time (s) & Total time (s) \\
\toprule
GP-RS & $0.00002 \textcolor{gray}{(0.00001)}$ & $28 \textcolor{gray}{(7)}$ \\
GP-US & $0.07 \textcolor{gray}{(0.01)}$ & $29 \textcolor{gray}{(7)}$ \\
GP-DUS & $0.38 \textcolor{gray}{(0.02)}$ & $ 44 \textcolor{gray}{(5)}$ \\
T-EIG & $1558 \textcolor{gray}{(376)}$ & $15613 \textcolor{gray}{(3627)}$ \\
D-EIG & $572 \textcolor{gray}{(105)}$ & $5746 \textcolor{gray}{(1002)}$ \\
TDNP (ours) & $0.015 \textcolor{gray}{(0.004)}$ & $0.31 \textcolor{gray}{(0.06)}$ \\
 \bottomrule
\end{tabular}
\caption{Comparison of computational costs across different methods. We report the mean value and \textcolor{gray}{(standard deviation)} derived from 10 runs with different seeds.}
\end{table}
\end{document}

stat

Daolang Huang
Yujia Guo
Luigi Acerbi
Samuel Kaski

"Amortized Bayesian Experimental Design for Decision-Making" by Daolang Huang, Yujia Guo, Luigi Acerbi, and Samuel Kaski, arXiv:2411.02064v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


eess

Fatih Ayten
Mehmet C. Ilter
Akshay Jain
Ossi Kaltiokallio
Jukka Talvitie
Elena Simona Lohan
Henk Wymeersch
Mikko Valkama

"Failure Tolerant Phase-Only Indoor Positioning via Deep Learning" by Fatih Ayten, Mehmet C. Ilter, Akshay Jain, Ossi Kaltiokallio, Jukka Talvitie, Elena Simona Lohan, Henk Wymeersch, and Mikko Valkama, arXiv:2508.14739v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|ccccc}
 \hline
 Methods & Artifacts & Blurring & Anatomical & Diseased & Overall\\
 \hline
 Pix2pix & 3.78 & 3.93 & 4.40 & 4.38 & 3.73 \\

 CycleGAN & 3.20 & 3.93 & 4.38 & 4.35 & 3.45\\

 UDDN & 3.53 & 4.05 & 4.33 & 4.28 & 3.63 \\

 DR2 & \underline{4.43} & \underline{4.13} & \underline{4.55} & \underline{4.48} & \underline{4.15} \\

 GDP & 4.38 & 4.03 & 4.33 & 4.30 & 3.98 \\

 \cellcolor[HTML]{E8E5E5} PFAD & \cellcolor[HTML]{E8E5E5}\textbf{4.78} & \cellcolor[HTML]{E8E5E5}\textbf{4.38} & \cellcolor[HTML]{E8E5E5}\textbf{4.73} & \cellcolor[HTML]{E8E5E5}\textbf{4.78} & \cellcolor[HTML]{E8E5E5}\textbf{4.63}\\
 \hline
 \end{tabular}
\caption{Evaluated by radiologists using Likert 5-scale scoring system. Higher scores indicate higher performance. Only the mean results are shown here, complete results can be found in Table~ in supplementary material B.2.}
\end{table}
\end{document}

eess

Jiahua Xu
Dawei Zhou
Lei Hu
Jianfeng Guo
Feng Yang
Zaiyi Liu
Nannan Wang
Xinbo Gao

"Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model" by Jiahua Xu, Dawei Zhou, Lei Hu, Jianfeng Guo, Feng Yang, Zaiyi Liu, Nannan Wang, and Xinbo Gao, arXiv:2412.07590v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{ccccc}
 \toprule
 & \multicolumn{3}{c}{\textbf{Center E (Unseen)}} \\
 \cmidrule{2-5}
 \textbf{Model} & \textbf{MAE [HU]} & \textbf{SSIM} & \textbf{PSNR [dB]} & \textbf{Time} \\
 \midrule
 \textbf{U-Net} & \textbf{110.4 (91.2-126.1)} & \textbf{0.88 (0.85-0.89)} & \textbf{26.44 (25.42-27.27)} & 6h 00min \\
 Pix2Pix & 122.0 (104.6-139.1) & 0.87 (0.85-0.87) & 26.31 (25.27-26.98) & \textbf{4h 00min} \\
 CycleGAN & 121.4 (115.7-130.9) & 0.87 (0.85-0.88) & 26.30 (25.32-27.07) & 10h 28min \\
 MSEP & 240.5 (220.7-250.7) & 0.79 (0.77-0.80) & 23.70 (23.09-24.29) & 85h 37min\textbf{*}\\
 \bottomrule
 \end{tabular}
\caption{The median and interquartile range (IR) for the mean absolute error (MAE), the structural similarity index (SSIM), and the peak signal-to-noise ratio (PSNR) obtained using different architectures, as well as the total time required for the experiments.\\{(\textbf{*}}) Time required to perform a single round.}
\end{table}
\end{document}

eess

Ciro Benito Raggio
Mathias Krohmer Zabaleta
Nils Skupien
Oliver Blanck
Francesco Cicone
Giuseppe Lucio Cascini
Paolo Zaffino
Lucia Migliorelli
Maria Francesca Spadea

"FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis" by Ciro Benito Raggio, Mathias Krohmer Zabaleta, Nils Skupien, Oliver Blanck, Francesco Cicone, Giuseppe Lucio Cascini, Paolo Zaffino, Lucia Migliorelli, and Maria Francesca Spadea, arXiv:2412.06690v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage[table]{xcolor}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcc}
 \toprule
 \rowcolor[HTML]{EEEEEE}
 \textbf{Environment} & \textbf{Description} & \textbf{Sparse Reward} & \textbf{Dense Reward} \\ \midrule
 \textbf{LunarLander} & 2D landing simulation & $||s-g||_2 < 1$ & $\gamma \Phi(s_{t+1}) - \Phi(s_t) < 0.3$ \\
 \textbf{CartPole} & Pole balancing & $||s-g||_2 < 0.02$ & $\gamma \Phi(s_{t+1}) - \Phi(s_t) < 1$ \\
 \textbf{UR5} & Robotic arm reaching & $||s-g||_2 < 0.02$ & $\gamma \Phi(s_{t+1}) - \Phi(s_t) < 1$ \\
 \textbf{ViZDoom-Seen} & First-person maze (trained) & $||s-g||_2 < 0.0075$ & $\gamma \Phi(s_{t+1}) - \Phi(s_t) < 0.14$ \\
 \textbf{ViZDoom-Unseen} & First-person maze (unseen) & $||s-g||_2 < 0.0075$ & $\gamma \Phi(s_{t+1}) - \Phi(s_t) < 0.14$ \\
 \textbf{Cross Maze} & 2D navigation task & $||s-g||_1 < 2$ & $\gamma \Phi(s_{t+1}) - \Phi(s_t) < 5$ \\
 \textbf{Playroom Maze} & 3D toddler exploration & $||s-g||_1 < 2$ & $\gamma \Phi(s_{t+1}) - \Phi(s_t) < 5$ \\ \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Junseok Park
Hyeonseo Yang
Min Whoo Lee
Won-Seok Choi
Minsu Lee
Byoung-Tak Zhang

"From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning" by Junseok Park, Hyeonseo Yang, Min Whoo Lee, Won-Seok Choi, Minsu Lee, and Byoung-Tak Zhang, arXiv:2501.17842v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{BP Predictions of Our Approach on Different PPG Channels}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{3}{*}{\textbf{Channel}}
& \multicolumn{2}{c|}{\textbf{MAE}}
& \multicolumn{2}{c|}{\textbf{BHS-5}}
& \multicolumn{2}{c|}{\textbf{BHS-10}}
& \multicolumn{2}{c|}{\textbf{BHS-15}} \\
& \multicolumn{2}{c|}{\textbf{(mmHg)}}
& \multicolumn{2}{c|}{\textbf{(\%)}}
& \multicolumn{2}{c|}{\textbf{(\%)}}
& \multicolumn{2}{c|}{\textbf{(\%)}} \\
\cline{2-9}
& \textbf{\textit{SBP}} & \textbf{\textit{DBP}}
& \textbf{\textit{SBP}} & \textbf{\textit{DBP}}
& \textbf{\textit{SBP}} & \textbf{\textit{DBP}}
& \textbf{\textit{SBP}} & \textbf{\textit{DBP}} \\
\hline
660\,nm & 14.9 & 8.6 & \textbf{24.2} & 48.5 & 39.4 & 69.7 & 54.5 & 75.8 \\
\hline
730\,nm & 15.5 & 8.2 & 21.2 & 21.2 & 36.4 & 66.7 & 48.5 & 90.9 \\
\hline
850\,nm & 15.3 & 7.7 & 21.2 & 36.4 & 45.5 & 72.7 & 54.5 & 81.8 \\
\hline
940\,nm & 16.2 & 7.3 & 21.2 & 36.4 & 36.4 & 69.7 & 54.5 & \textbf{93.9} \\
\hline
All & \textbf{14.2} & \textbf{6.4} & \textbf{24.2} & \textbf{51.5} & \textbf{51.5} & \textbf{75.8} & \textbf{57.6} & 90.9 \\
\hline
\end{tabular}
\end{table}
\end{document}

eess

Zequan Liang
Ruoyu Zhang
Wei Shao
Mahdi Pirayesh Shirazi Nejad
Ehsan Kourkchi
Setareh Rafatirad
Houman Homayoun

"Generalizable Blood Pressure Estimation from Multi-Wavelength PPG Using Curriculum-Adversarial Learning" by Zequan Liang, Ruoyu Zhang, Wei Shao, Mahdi Pirayesh Shirazi Nejad, Ehsan Kourkchi, Setareh Rafatirad, and Houman Homayoun, arXiv:2509.12518v1, 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|ccccc}
 & \textbf{Iris} & \textbf{Wine} & \textbf{Ionosphere} & \textbf{LSVT} & \textbf{Parkinson} \\ \hline
\textbf{Dimension ($d$)} & 4 & 13 & 34 & 309 & 754 \\
\textbf{Data points ($\sum n_c$)} & 150 & 178 & 351 & 126 & 756 \\
\textbf{Classes ($C$)} & 3 & 3 & 2 & 2 & 2
\end{tabular}
\caption{UCI datasets description.}
\end{table}
\end{document}

stat

"A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant Analysis" by Dong Min Roh, Zhaojun Bai, and Ren-Cang Li, arXiv:2211.11891v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

"Bayesian semiparametric modelling of covariance matrices for multivariate longitudinal data" by Georgios Papageorgiou, arXiv:2012.09833v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
\toprule
\textbf{Annotator} & \textbf{TPR (\%)} & \textbf{FPR (\%)} \\ \midrule
Nonexpert Annotator 1 & 73.3 & 76.7 \\
 Nonexpert Annotator 2 & 43.3 & 80.0 \\
Nonexpert Annotator 3 & 66.7 & 80.0 \\
Expert Annotator 1 & 96.7 & 93.3 \\
Nonexpert Annotator 4 & 66.7 & 76.7 \\ \midrule
\textbf{Average} & 69.3 & 81.3 \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Story performance of initial 5 annotators, which included 4 nonexpert annotators and expert annotator \# 1, who was used in all article experiments.}
\end{table}
\end{document}

cs

Jenna Russell
Marzena Karpinska
Mohit Iyyer

"People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text" by Jenna Russell, Marzena Karpinska, and Mohit Iyyer, arXiv:2501.15654v2, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{List of abbreviations of the considered cryptocurrencies.}
\begin{tabular}{llllll}
\hline
\multicolumn{4}{l}{Cryptocurrencies}
\\ \hline
ADA & Cardano & EOS & Electro-Optical System & NEM & New Economy Movement\\
BAT & Basic Attention Token & ETC & Ethereum Classic & QTUM & Qtum\\
BCH & Bitcoin Cash & ETH & Ethereum & TRX & TRON\\
BNB & Binance Coin & FIL & Filecoin & USDT & Tether\\
BTC & Bitcoin & GNO & Gnosis & WAVES & WAVES\\
BTG & Bitcoin Gold & IOTA & IOTA & XLM & Stellar\\
DASH & Digital Cash & KCS & KuCoin Shares & XMR & Monero\\
DCR & Decred & LRC & Loopring & XRP & Ripple\\
DOGE & Dogecoin & LTC & Litecoin & XTZ & Tezos\\
ENJ & Enigma & MANA & Decentraland & ZEC & ZCash
\end{tabular}
\end{table}
\end{document}

stat

"A Computational Note on the Graphical Ridge in High-dimension" by A. Bekker, A. Kheyri, and M. Arashi, arXiv:2312.15781v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Performance evaluation of DMUs using hybrid fuzzy multi-objective data envelopment analysis" by Awadh Pratap Singh and Shiv Prasad Yadav, arXiv:2202.01585v1, 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{\small Internal knots placement.}
\begin{tabular}{lccc}
\hline
\hline \\
 \textbf{d.f} & & \textbf{Centiles} & \\
 \hline \\
 2 & 50 & & \\
 3 & 33 & 67 & \\
 4 & 25 & 50 & 75 \\
\hline
\hline \\
\end{tabular}
\end{table}
\end{document}

stat

Marta Cipriani
Lorenzo Di Rocco
Maria Puopolo
Marco Alfò

"A flexible parametric approach to synthetic patients generation using health data" by Marta Cipriani, Lorenzo Di Rocco, Maria Puopolo, and Marco Alfò, arXiv:2412.21056v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter Settings of Different Models}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Models} & \textbf{Learning Rate} & \textbf{Number of Epochs} & \textbf{Optimizer} \\
\hline
DenseNet201 & 0.0001 & 15 & Adam \\
\hline
DenseNet169 & 0.0001 & 10 & Adam \\
\hline
ResNet152 & 0.0001 & 11 & Adam \\
\hline
ResNet101 & 0.0001 & 15 & Adam \\
\hline
MobileNetV3 & 0.0001 & 14 & Adam \\
\hline
InceptionV3 & 0.0001 & 15 & Adam \\
\hline
ResNet50 & 0.0001 & 12 & Adam \\
\hline
Xception & 0.0001 & 13 & Adam \\
\hline
DenseNet121 & 0.0001 & 15 & Adam \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Md. Arafat Alam Khandaker
Ziyan Shirin Raha
Salehin Bin Iqbal
M. F. Mridha
Jungpil Shin

"From Images to Insights: Transforming Brain Cancer Diagnosis with Explainable AI" by Md. Arafat Alam Khandaker, Ziyan Shirin Raha, Salehin Bin Iqbal, M. F. Mridha, and Jungpil Shin, arXiv:2501.05426v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Daan B. Zult
Peter G. M. van der Heijden
Bart F. M. Bakker

"Bias correction in multiple-systems estimation" by Daan B. Zult, Peter G. M. van der Heijden, and Bart F. M. Bakker, arXiv:2311.01297v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c|c|c}
 \hline
 \textbf{Languages} & \textbf{mT5} & \textbf{XLM-R} & \textbf{Falcon} & \textbf{mBERT} & \textbf{BLOOM}\\
 \hline
English & 2733 & 0.30 & 750 & 4.59 & 112 \\
Spanish & 433 & 0.05 & 17 & 1.20 & 40 \\
German & 347 & 0.07 & 18 & 1.56 & - \\
French & 318 & 0.06 & 16 & 1.68 & 46 \\
Turkish & 71 & 0.02 & - & 0.16 & - \\
Hindi & 24 & 0.02 & - & 0.06 & 5.6 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Amirhossein Aliakbarzadeh
Lucie Flek
Akbar Karimi

"Exploring Robustness of Multilingual LLMs on Real-World Noisy Data" by Amirhossein Aliakbarzadeh, Lucie Flek, and Akbar Karimi, arXiv:2501.08322v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


cs

Mathieu Besançon
Sébastien Designolle
Jannis Halbey
Deborah Hendrych
Dominik Kuzinowicz
Sebastian Pokutta
Hannah Troppens
Daniel Viladrich Herrmannsdoerfer
Elias Wirth

"Improved algorithms and novel applications of the FrankWolfe.jl library" by Mathieu Besançon, Sébastien Designolle, Jannis Halbey, Deborah Hendrych, Dominik Kuzinowicz, Sebastian Pokutta, Hannah Troppens, Daniel Viladrich Herrmannsdoerfer, and Elias Wirth, arXiv:2501.14613v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage[T1]{fontenc}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \textbf{Annotator} & \textbf{t} & \textbf{p-value} \\
 \midrule
 Annotator 1 & 13.3 & <0.001 \\
 Annotator 2 & 10.7 & <0.001 \\
 Annotator 3& 5.7 & <0.001 \\
 Annotator 4& 2.4 & <0.02 \\
 Combined & 18.7 & <0.001 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{One-tailed t-test results comparing the average JudgeLM rank of human-preferred answers to the baseline value of 1.5.}
\end{table}
\end{document}

cs

Michael Bennie
Demi Zhang
Bushi Xiao
Jing Cao
Chryseis Xinyi Liu
Jian Meng
Alayo Tripp

"PANDA -- Paired Anti-hate Narratives Dataset from Asia: Using an LLM-as-a-Judge to Create the First Chinese Counterspeech Dataset" by Michael Bennie, Demi Zhang, Bushi Xiao, Jing Cao, Chryseis Xinyi Liu, Jian Meng, and Alayo Tripp, arXiv:2501.00697v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Test accuracy on CIFAR-100 for different values of p (interval steps for estimating eigenvectors) using Eigen-SAM.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{llcccc}
Architechture & p=100 & p=200 & p=500 & p=1000 &SAM\\ \midrule \midrule
ResNet18 & $78.3_{\pm 0.2}$ & $78.2_{\pm 0.2}$ & $78.2_{\pm 0.2}$& $78.3_{\pm 0.1}$ &$77.4_{\pm 0.2}$ \\
 \midrule
WideResNet-28-10 & $82.8_{\pm 0.1}$ & $82.7_{\pm 0.2}$ & $82.7_{\pm 0.1}$ & $82.6_{\pm 0.1}$&$82.0_{\pm 0.2}$ \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Haocheng Luo
Tuan Truong
Tung Pham
Mehrtash Harandi
Dinh Phung
Trung Le

"Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization" by Haocheng Luo, Tuan Truong, Tung Pham, Mehrtash Harandi, Dinh Phung, and Trung Le, arXiv:2501.12666v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Mara Sherlin D. Talento
Sarbojit Roy
Tania Reyes Vallejo
Leena A Ibrahim
Hernando C. Ombao

"KenCoh: A Ranked-Based Canonical Coherence" by Mara Sherlin D. Talento, Sarbojit Roy, Tania Reyes Vallejo, Leena A Ibrahim, and Hernando C. Ombao, arXiv:2412.10521v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|ccccccc}
 \textbf{Method} & SK & SK32 & PFF & SP & NS & PL3D \\
 \hline
 SRU-Net pseudo-dense~ & 56,7 & 57,3 & 61,7 & 46,9 & 49,8 & 38,0 \\
 Same with SLAM pertubations & 45,1 & 41,5 & 58,5 & 39,9 & 36,3 & 30,1 \\
 \end{tabular}
\end{adjustbox}
\caption{Domain generalization results for SRU-Net with pseudo-dense input without and with SLAM perturbations. All models are trained on SemanticKITTI.}
\end{table}
\end{document}

cs

Jules Sanchez
Jean-Emmanuel Deschaud
François Goulette

"3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous Driving" by Jules Sanchez, Jean-Emmanuel Deschaud, and François Goulette, arXiv:2501.14605v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
Quadrant&Variability&Inertia\\
\hline
Q1&-0.06&0.03\\
Q2&-0.06&-0.03\\
Q3&0.02&0.1*\\
Q4&0.03&0.05\\
\hline
\end{tabular}
\caption{Spearman correlations between users' dynamic measures, represented by Variability and Inertia of QPS and K10 scores. (*p\textless0.05)}
\end{table}
\end{document}

eess

"Static and Dynamic Measures of Active Music Listening as Indicators of Depression Risk" by Aayush Surana, Yash Goyal, and Vinoo Alluri, arXiv:2009.13685v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Hyperparameter setting for the Acrobot benchmark.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccr}
\toprule
\textbf{Method} & \textbf{$\eta$} & \textbf{$\alpha$} & \textbf{$p_t$} \\
\midrule
GPOMDP & $10^{-4}$ & -- & --\\
SVRPG & $10^{-5}$ & -- & --\\
SRVRPG & $6\cdot 10^{-6}$ & -- & --\\
STORM-PG & $10^{-4}$ & 0.9 & -- \\
PAGE-PG & $5\cdot 10^{-6}$ & -- & $0.01,\,0.4$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Matilde Gargiani
Andrea Zanelli
Andrea Martinelli
Tyler Summers
John Lygeros

"PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation" by Matilde Gargiani, Andrea Zanelli, Andrea Martinelli, Tyler Summers, and John Lygeros, arXiv:2202.00308v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results of all metrics comparing the best performing system from Table~ with several baselines, over the real portion of the CHiME-3 eval and test data.}
\begin{tabular}{lllllll}
\toprule
 & \multicolumn{2}{c}{PESQ (MOS)} & \multicolumn{2}{c}{SDR (dB)} & \multicolumn{2}{c}{WER (\%)} \\
 (REAL data)& Dev & Test & Dev & Test & Dev & Test \\
 \midrule
MESSL & 2.65 & 2.37 & 6.42 & 5.38 & 19.7 & 32.3 \\
Souden & 2.14 & 2.05 & 3.21 & 2.37 & 37.4 & 52.3 \\
\textbf{Mask Enhancer} &2.73 & 2.47 & 7.07 & 5.97 & 19.3 & 32.6 \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

eess

Felix Grezes
Zhaoheng Ni
Viet Anh Trinh
Michael Mandel

"Enhancement of Spatial Clustering-Based Time-Frequency Masks using LSTM Neural Networks" by Felix Grezes, Zhaoheng Ni, Viet Anh Trinh, and Michael Mandel, arXiv:2012.01576v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of performance, compression ratio (CR), and speed-up (Sp) for various tasks and task combinations with fixed $\lambda$ values and learned sparsity (meta-sparsity experiments). Metrics demonstrate the effectiveness of the proposed approach in balancing task performance and parameter efficiency. In the table, IoU stands for Intersection over Union, MAE is the Mean Absolute Error, and CS represents the Cosine Similarity. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccccc}
\hline
Tasks & $\lambda$ & CR & Sp & Seg. & Depth Est. & SN est. & Edge Det. \\
Combination& & & & IoU($\uparrow$) & MAE($\downarrow$) & CS($\uparrow$) & MAE($\downarrow$) \\ \hline
T1,T2,T3 & $1 \times 10^{-3}$ & 10.29 & 5.51 & 0.3019 & 0.154 & 0.7439 & - \\
T1,T2,T3 & $1 \times 10^{-4}$ & 1.15 & 1.14 & 0.2922 & 0.1483 & 0.7322 & -\\
T1,T2,T3 & learned & 1.88 & 1.82 & 0.2937 & 0.1409 & 0.746 & - \\
T2,T3,T4 & $1 \times 10^{-3}$ & 5.47 & 3.85 & - & 0.1591 & 0.7172 & 0.1806 \\
T2,T3,T4 & $1 \times 10^{-4}$ & 2.38 & 2.1 & - & 0.1461 & 0.7396 & 0.1481 \\
T2,T3,T4 & learned & 1.81 & 1.68 & - & 0.1412 & 0.7402 & 0.1386 \\
T1,T2,T3,T4 & $1 \times 10^{-3}$ & 5.83 & 4.02 & 0.3032 & 0.1526 & 0.7417 & 0.1541 \\
T1,T2,T3,T4 & $1 \times 10^{-4}$ & 1.41 & 1.36 & 0.2957 & 0.1476 & 0.7353 & 0.1628 \\
T1,T2,T3,T4 & learned & 1.83 & 1.72 & 0.2923 & 0.1397 & 0.7414 & 0.1395 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Richa Upadhyay
Ronald Phlypo
Rajkumar Saini
Marcus Liwicki

"Meta-Sparsity: Learning Optimal Sparse Structures in Multi-task Networks through Meta-learning" by Richa Upadhyay, Ronald Phlypo, Rajkumar Saini, and Marcus Liwicki, arXiv:2501.12115v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|c|c|} \hline
		\multicolumn{5}{|c|}{$v_0 = 10$} \\ \hline
		$\varepsilon$ & $10^{-2}$ & $10^{-3}$ & $10^{-4}$ & $10^{-5}$ \\ \hline
		error & 0.0096 & 5.05 $\cdot 10^{-4} $ & 3.52 $\cdot 10^{-5} $ & 8.07 $\cdot 10^{-6} $ \\ \hline \hline
	\multicolumn{5}{|c|}{$v_0 = 1$} \\ \hline
		$\varepsilon$ & $10^{-2}$ & $10^{-3}$ & $10^{-4}$ & $10^{-5}$ \\ \hline
		error & 0.183 & 0.0096 & 5.05 $\cdot 10^{-4} $ & 3.52 $\cdot 10^{-5} $ \\ \hline \hline
	\multicolumn{5}{|c|}{$v_0 = 0.1$} \\ \hline
		$\varepsilon$ & $10^{-2}$ & $10^{-3}$ & $10^{-4}$ & $10^{-5}$ \\ \hline
		error & - & 0.183 & 0.0096 & 5.08 $\cdot 10^{-4} $ \\ \hline
	\multicolumn{5}{|c|}{$v_0 = 0.01$} \\ \hline \hline
		$\varepsilon$ & $10^{-2}$ & $10^{-3}$ & $10^{-4}$ & $10^{-5}$ \\ \hline
		error & - & - & 0.183 & 0.0095 \\ \hline
	\end{tabular}
\end{adjustbox}
\caption{\textit{ Discrepancy $d_e$ on varying $\varepsilon $ and initial volume $v_0$ for the Eq.~. From the values of the table we see the discrepancy strongly depends on the fraction $\varepsilon/v_0$. In these tests $\Delta t = 100\varepsilon$ and $N = 128$.}}
\end{table}
\end{document}

math

Antonio Raudino
Antonio Grassi
Giuseppe Lombardo
Giovanni Russo
Clarissa Astuto
Mario Corti

"Anomalous sorption kinetics of self-interacting particles by a spherical trap" by Antonio Raudino, Antonio Grassi, Giuseppe Lombardo, Giovanni Russo, Clarissa Astuto, and Mario Corti, arXiv:2202.01420v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|c|cccc}
 \toprule
 & \multirow{2}{*}{Method} & \multicolumn{4}{c}{Overall} \\
 & & Acc. & Prec. & Rec. & F1 \\
 \midrule
 \multirow{2}{*}{YETI (Ours)}
 & Global & 86.97 & 52.23 & 87.04 & 65.28 \\
 & Local & 93.76 & 64.51 & 59.73 & 62.02 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\caption{Evaluation of YETI on our proactive interaction detection setting which encompasses the proactive intervention benchmark}
\end{table}
\end{document}

cs

Saptarashmi Bandyopadhyay
Vikas Bahirwani
Lavisha Aggarwal
Bhanu Guda
Lin Li
Andrea Colaco

"YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks" by Saptarashmi Bandyopadhyay, Vikas Bahirwani, Lavisha Aggarwal, Bhanu Guda, Lin Li, and Andrea Colaco, arXiv:2501.09355v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{OverlapBot Evaluation Results}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|rrrr|rrrr|rr}
 \toprule
 & \multicolumn{4}{c|}{Timing} & \multicolumn{4}{c|}{Dialogue Acts} & \multicolumn{2}{c}{Utterance} \\
 Models & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Bleu & Rouge-L \\
 \midrule
 Llama3 8B & 0.46 & 0.50 & 0.50 & 0.46 & 0.59 & 0.79 & 0.50 & 0.37 & 0.27 & 0.11 \\
 GPT4o & 0.50 & 0.47 & 0.47 & 0.47 & 0.73 & 0.74 & 0.76 & 0.73 & 0.08 & 0.12 \\
 GPT4 turbo & 0.50 & 0.46 & 0.46 & 0.46 & 0.75 & 0.77 & 0.77 & 0.73 & 0.26 & 0.12 \\
 \textbf{OverlapBot} & \textbf{0.67} & \textbf{0.65} & \textbf{0.65} & \textbf{0.65} & \textbf{0.85} & \textbf{0.87} & \textbf{0.78} & \textbf{0.80} & \textbf{0.48} & \textbf{0.30} \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"Beyond Turn-taking: Introducing Text-based Overlap into Human-LLM Interactions" by JiWoo Kim, Minsuk Chang, and JinYeong Bak, arXiv:2501.18103v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Results for single task and multi-task experiments on the CelebA dataset. These include the no-sparsity, fixed-sparsity, and meta-sparsity experiments. The results are shown in the form of mean $\pm$ standard deviation across five trials. here $T_1-T_7$ represents all tasks from $T_1$ to $T_7$, similarly for $T_2-T_7$ it considers tasks ranging from $T_2$ to $T_7$. Also, $\lambda = 0$ represents experiments without sparsity.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccccc}
\hline
\textbf{Experiments} & \textbf{Sparsity} & \textbf{Segmentation} & \textbf{male/female} & \textbf{smile/no smile} & \textbf{biglips/no biglips} & \textbf{highcheekbones/no} & \textbf{wearing lipstick/} & \textbf{bushy eyebrows/ no} & \textbf{Parameter} \\
\textbf{} & \textbf{parameter} & \textbf{} & \textbf{} & \textbf{} & \textbf{} & \textbf{highcheekbones} & \textbf{not wearing lipstick} & \textbf{bushy eyebrows} & \textbf{sparsity} \\
 & $\lambda$ & $T_1$ & $T_2$ & $T_3$ & $T_4$ & $T_5$ & $T_6$ & $T_7$ & \\
 & & IoU($\uparrow$) & Accuracy($\uparrow$) & Accuracy($\uparrow$) & Accuracy($\uparrow$) & Accuracy($\uparrow$) & Accuracy($\uparrow$) & Accuracy($\uparrow$) & (\%) \\ \hline
 & & & & & & & & & \\
 & 0 & 0.9195 $\pm$ 0.0001 & - & - & - & - & - & - & - \\
$T_1$ & 1.00E-04 & 0.8996 $\pm$ 0.0026 & - & - & - & - & - & - & 81.2797 $\pm$ 0.6728 \\
 & 1.00E-03 & 0.8685 $\pm$ 0.0033 & - & - & - & - & - & - & 82.2677 $\pm$ 0.7133 \\
 & & & & & & & & & \\
 & 0 & - & 0.9648 $\pm$ 0.0044 & - & - & - & - & - & -\\
$T_2$ & 1.00E-04 & - & 0.9255 $\pm$ 0.0088 & - & - & - & - & - & 74.3208 $\pm$ 4.8320 \\
 & 1.00E-03 & - & 0.7773 $\pm$ 0.0971 & - & - & - & - & - & 81.5482 $\pm$ 1.1930 \\
 & & & & & & & & & \\
 & 0 & - & - & 0.8987 $\pm$ 0.0016 & - & - & - & - & - \\
$T_3$ & 1.00E-04 & - & - & 0.8891 $\pm$ 0.0034 & - & - & - & - & 48.3989 $\pm$ 0.8911 \\
 & 1.00E-03 & - & - & 0.8569 $\pm$ 0.0127 & - & - & - & - & 78.7108 $\pm$ 2.3941 \\
 & & & & & & & & & \\
 & 0 & - & - & - & 0.6358 $\pm$ 0.0061 & & & & - \\
$T_4$ & 1.00E-04 & - & - & - & 0.6270 $\pm$ 0.0067 & - & - & - & 67.6497 $\pm$ 5.9694 \\
 & 1.00E-03 & - & - & - & 0.6259 $\pm$ 0.0069 & - & - & - & 71.6123 $\pm$ 0.4355 \\
 & & & & & & & & & \\
 & 0 & - & - & - & - & 0.8273 $\pm$ 0.0101 & - & - & - \\
$T_5$ & 1.00E-04 & - & - & - & - & 0.6984 $\pm$ 0.0764 & - & - & 80.6301 $\pm$ 0.0829 \\
 & 1.00E-03 & - & - & - & - & 0.5433 $\pm$ 0.0167 & - & - & 81.8055 $\pm$ 1.8168 \\
 & & & & & & & & & \\
 & 0 & - & - & - & - & - & 0.9106 $\pm$ 0.0049 & - & - \\
$T_6$ & 1.00E-04& - & - & - & - & - & 0.8522 $\pm$ 0.0129 & - & 63.3811 $\pm$ 6.2434 \\
 & 1.00E-03 & - & - & - & - & - & 0.7502 $\pm$ 0.0486 & - & 83.2237 $\pm$ 3.1300 \\
 & & & & & & & & & \\
 & 0 & - & - & - & - & - & - & 0.6270 $\pm$ 0.0100 & - \\
$T_7$ & 1.00E-04 & - & - & - & - & - & - & 0.6351 $\pm$ 0.0104 & 65.1942 $\pm$ 5.8890 \\
 & 1.00E-03 & - & - & - & - & - & - & 0.6212 $\pm$ 0.0023 & 74.2825 $\pm$ 6.2813 \\
 & & & & & & & & & \\
 & 0 & 0.8941 $\pm$ 0.0027 & 0.9554 $\pm$ 0.0055 & 0.8909 $\pm$ 0.0047 & 0.6340 $\pm$ 0.0003 & 0.8454 $\pm$ 0.0010 & 0.9122 $\pm$ 0.0031 & 0.7867 $\pm$ 0.0016 & - \\
$T_1$-$T_7$ & 1.00E-05 & 0.8437 $\pm$ 0.0022 & 0.8882 $\pm$ 0.0041 & 0.5253 $\pm$ 0.0011 & 0.6297 $\pm$ 0.0045 & 0.5916 $\pm$ 0.0247 & 0.8412 $\pm$ 0.0172 & 0.6241 $\pm$ 0.0008 & 0.0000 $\pm$ 0.0000 \\
 & 1.00E-04 & 0.8066 $\pm$ 0.0196 & 0.6268 $\pm$ 0.0011 & 0.5215 $\pm$ 0.0020 & 0.6328 $\pm$ 0.0006 & 0.5235 $\pm$ 0.0034 & 0.7274 $\pm$ 0.0152 & 0.6225 $\pm$ 0.0021 & 85.6721 $\pm$ 1.7966 \\
 & & & & & & & & & \\
 & 0 & - & 0.9496 $\pm$ 0.0048 & 0.8790 $\pm$ 0.0128 & 0.6337 $\pm$ 0.0000 & 0.8327 $\pm$ 0.0217 & 0.9034 $\pm$ 0.0084 & 0.7814 $\pm$ 0.0154 & - \\
$T_2$-$T_7$ & 1.00E-05 & - & 0.9350 $\pm$ 0.0054 & 0.8669 $\pm$ 0.0121 & 0.6331 $\pm$ 0.0011 & 0.8177 $\pm$ 0.0047 & 0.8947 $\pm$ 0.0170 & 0.7247 $\pm$ 0.0229 & 0.4095 $\pm$ 0.0884 \\
 & 1.00E-04 & - & 0.6248 $\pm$ 0.0014 & 0.5240 $\pm$ 0.0037 & 0.6284 $\pm$ 0.0019 & 0.5166 $\pm$ 0.0014 & 0.5529 $\pm$ 0.0595 & 0.6216 $\pm$ 0.0058 & 95.5407 $\pm$ 1.3195 \\
 & & & & & & & & & \\
 & 0 & 0.8995 $\pm$ 0.0014 & 0.9513 $\pm$ 0.0010 & 0.8901 $\pm$ 0.0017 & - & - & - & 0.7890 $\pm$ 0.0087 & - \\
$T_1$,$T_2$,$T_3$,$T_7$ & 1.00E-05 & 0.8663 $\pm$ 0.0104 & 0.6270 $\pm$ 0.0010 & 0.5260 $\pm$ 0.0021 & - & - & - & 0.6221 $\pm$ 0.0027 & 0.0811 $\pm$ 0.0659 \\
 & 1.00E-04 & 0.8223 $\pm$ 0.0050 & 0.6261 $\pm$ 0.0043 & 0.5249 $\pm$ 0.0019 & - & - & - & 0.6246 $\pm$ 0.0018 & 85.9002 $\pm$ 2.0208 \\
 & & & & & & & & & \\
$T_1$-$T_7$ & & 0.9007$\pm$0.0012 & 0.9669$\pm$0.0014 & 0.9014$\pm$0.0043 & 0.6330$\pm$0.1680 & 0.8235$\pm$0.0104 & 0.9068$\pm$0.0165 & 0.6885$\pm$0.0936 & 43.6893$\pm$0.3593 \\
$T_2$-$T_7$ & meta-sparsity & - & 0.9004$\pm$0.0203 & 0.7618$\pm$0.1602 & 0.6330$\pm$0.0720 & 0.7562$\pm$0.1109 & 0.8703$\pm$0.0154 & 0.6223$\pm$0.0000 & 30.2145$\pm$0.0016 \\
$T_1$,$T_2$,$T_3$,$T_7$ & & 0.9034$\pm$0.0013 & 0.7386$\pm$0.0188 & 0.5261$\pm$0.0000 & - & - & - & 0.6223$\pm$0.0000 & 30.0187$\pm$0.0011 \\ \hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs


math

Xianyi Zeng
Giovanni Stabile
Efthymios N. Karatzas
Guglielmo Scovazzi
Gianluigi Rozza

"Embedded domain Reduced Basis Models for the shallow water hyperbolic equations with the Shifted Boundary Method" by Xianyi Zeng, Giovanni Stabile, Efthymios N. Karatzas, Guglielmo Scovazzi, and Gianluigi Rozza, arXiv:2201.09546v1, 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 Clinical and Demographic Features}
\begin{tabular}{lc}
\toprule
 Characteristic & Summary \\
\midrule
 Number of Patients & 326 \\
 Age & 73.3±10.6 \\
 Survival Days & 1179.7±1581.1 \\
 Sex & \\
 \quad Male & 42.3\% \\
 \quad Female & 57.7\% \\
 Smoking Status & \\
 \quad Smoker & 19.0\% \\
 \quad Non-Smoker & 81.0\% \\
 Stage & \\
 \quad I & 30.4\% \\
 \quad II & 8.0\% \\
 \quad III & 14.1\% \\
 \quad IV & 47.5\% \\
 Treatment Plan & \\
\quad Unknown/Not Recorded & 69.3\% \\
 \quad Chemotherapy & 20.9\% \\
 \quad Targeted Therapy & 7.1\% \\
 \quad Immunotherapy & 2.8\% \\
 Genomics & \\
 \quad KRAS & 27.9\% \\
 \quad EGFR & 39.0\% \\
 \quad FGFR1 & 5.8\% \\
 \quad ALK & 20.6\% \\
 \quad MET & 11.0\% \\
 \quad PIK3CA & 26.4\% \\
 \quad BRAF & 5.2\% \\
 \quad RET & 42.9\% \\
\bottomrule
\end{tabular}
\end{table}
\end{document}

stat

Narmada Naik
Ayush Khandelwal
Mohit Joshi
Madhusudan Atre
Hollis Wright
Kavya Kannan
Scott Hill
Giridhar Mamidipudi
Ganapati Srinivasa
Carlo Bifulco
Brian Piening
Kevin Matlock

"Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer" by Narmada Naik, Ayush Khandelwal, Mohit Joshi, Madhusudan Atre, Hollis Wright, Kavya Kannan, Scott Hill, Giridhar Mamidipudi, Ganapati Srinivasa, Carlo Bifulco, Brian Piening, and Kevin Matlock, arXiv:2311.07191v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}
			\hline
			\textbf{Dataset} & \textbf{Task Type} & \textbf{Multilingual} &\textbf{Complex Context}\\ \hline
			\textbf{UDC} & RG & No & Yes \\
			\textbf{XCOPA} & MCQA & Yes &No \\
			\textbf{XWinograd} & Reasoning & Yes &- \\
			\textbf{XDailyDialog} & RR & Yes &No \\ \hline
			\textbf{XMP(Ours)} & RG & Yes &Yes \\ \hline
	\end{tabular}
\end{adjustbox}
\caption{Common multilingual or multi-party datasets. Task types include RG for Response Generation, RR for Response Retrieval, and MCQA for Multiple-Choice Question Answering.}
\end{table}
\end{document}

cs

Zhongtian Hu
Yiwen Cui
Ronghan Li
Meng Zhao
Lifang Wang

"Can MLLMs Generalize to Multi-Party dialog? Exploring Multilingual Response Generation in Complex Scenarios" by Zhongtian Hu, Yiwen Cui, Ronghan Li, Meng Zhao, and Lifang Wang, arXiv:2501.11269v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
\hline
\textbf{Model} & \textbf{Window} & \textbf{Accuracy} \\ \hline
DNN\_W1\_V1 & W1 & 0.84 \\ \hline
DNN\_W2\_V1 & W2 & 0.89 \\ \hline
DNN\_W3\_V1 & W3 & 0.85 \\ \hline
\textbf{DNN\_W4\_V1} & \textbf{W4} & \textbf{0.91} \\ \hline
\end{tabular}
\end{adjustbox}
\caption{Model accuracy comparison for different HU window selections. The best performing model corresponds to window W4.}
\end{table}
\end{document}

cs

Juan Manuel Liscano Fierro
Hector J. Hortua

"Enhancing Diagnostic in 3D COVID-19 Pneumonia CT-scans through Explainable Uncertainty Bayesian Quantification" by Juan Manuel Liscano Fierro and Hector J. Hortua, arXiv:2501.10770v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


stat

Jeanne Clelland
Haley Colgate
Daryl DeFord
Beth Malmskog
Flavia Sancier-Barbosa

"Colorado in Context: Congressional Redistricting and Competing Fairness Criteria in Colorado" by Jeanne Clelland, Haley Colgate, Daryl DeFord, Beth Malmskog, and Flavia Sancier-Barbosa, arXiv:2011.06049v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccc}
 \hline
 & \textbf{GSM8k} & \textbf{PrOntoQA} & \textbf{Game24} \\ \hline
 \textbf{avg. $b$} & 4.26 & 1.67 & 4.56 \\
 \textbf{avg. $p$} & 4.54 & 9.45 & 3.99 \\
 \textbf{avg. $L$} & 3.11 & 4.00 & 3.00 \\ \hline
 \textbf{$\widetilde{N}_\text{call}$} & 19.40 & 15.77 & 18.24 \\
 \textbf{$\widetilde{N}_\text{res}$} & 6.23 & 3.94 & 6.08 \\ \hline
 \end{tabular}
\end{adjustbox}
\caption{Settings for BoN and MCTS in GSM8k, PrOntoQA and Game24.}
\end{table}
\end{document}

cs

"Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning" by Zeyu Gan, Yun Liao, and Yong Liu, arXiv:2501.15602v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of the RMSE (pixel) obtained from the synthetic images proposed by Cai et al. 2019. }
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccc}

 Case & Cai et al. & Proposed & $\beta$ \\\hline
 Uniform & 0.059 & 0.044 & 200 \\\cline{2-4}
 Back-step $Re=800$ & \multirow{4}{*}{0.072} & 0.072 & \multirow{4}{*}{100}\\
 Back-step $Re=1000$ & &0.099\\
 Back-step $Re=1200$ & &0.057\\
 Back-step $Re=1500$ & &0.074\\\cline{2-4}
 Cylinder $Re=40$ & \multirow{5}{*}{0.115} &0.052&\multirow{5}{*}{50}\\
 Cylinder $Re=150$ & &0.094\\
 Cylinder $Re=200$ & &0.104\\
 Cylinder $Re=300$ & &0.121\\
 Cylinder $Re=400$ & &0.111\\\cline{2-4}
 DNS turbulence & 0.282 &0.112&10\\
 SQG & 0.294 &0.253&20\\
 JHTDB-channel & 0.155 &0.087&30\\
 JHTDB-mhd1024 & - &0.150&50\\
 JHTDB-isotropic1024 & - &0.564&20\\
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Efraín Magaña
Francisco Sahli Costabal
Wernher Brevis

"Image Velocimetry using Direct Displacement Field estimation with Neural Networks for Fluids" by Efraín Magaña, Francisco Sahli Costabal, and Wernher Brevis, arXiv:2501.18641v1, 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}{ccc} \hline
CMM & LM & CENTR \\ \hline
2.23 $\pm$ 0.98 & 2.05 $\pm$ 1.18 & 0.27 $\pm$ 0.14 \\
\hline
\end{tabular}
\caption{Dataset evaluation}
\end{table}
\end{document}

eess

Sebastian Garcia-Valencia
Alejandro Betancourt
Juan G. Lalinde-Pulido

"A framework to compare music generative models using automatic evaluation metrics extended to rhythm" by Sebastian Garcia-Valencia, Alejandro Betancourt, and Juan G. Lalinde-Pulido, arXiv:2101.07669v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Summary of AUCC results for DQN and T-learner}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrr}
\toprule
Methods & Training AUCC & Test AUCC \\
\midrule
DQN, $\gamma=0.8$ & \textbf{1.2431} & \textbf{1.1426} \\
DQN, $\gamma=0.0$ & 1.0529 & 0.9570 \\
T-learner & 0.6214 & 0.6049 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Tao Liu
Qi Xu
Wei Shi
Zhigang Hua
Shuang Yang

"Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning" by Tao Liu, Qi Xu, Wei Shi, Zhigang Hua, and Shuang Yang, arXiv:2501.05591v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Reinforcement Learning Parameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cc}
 \toprule
 Algorithm & PPO2 \\

 \midrule
 Neural Network type & Multi-Layer Perceptron \\
 Total Timesteps & 1,500,000 \\
 Learning Rate & 2.5e-4 \\
 Discount Factor & 0.999 \\
 Number of Steps & 128 \\
 Entropy Coefficient & 0.01 \\
 Clip Range & 0.2 \\
 Value Function Coefficient & 0.5 \\
 Max. Gradient Norm & 0.5 \\
 \bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Chen Zeng
Grant Hecht
Prajit KrisshnaKumar
Raj K. Shah
Souma Chowdhury
Eleonora M. Botta

"Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System" by Chen Zeng, Grant Hecht, Prajit KrisshnaKumar, Raj K. Shah, Souma Chowdhury, and Eleonora M. Botta, arXiv:2201.04180v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Average Overhead}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lrrrr}
 \toprule
 Test Name & Power \% & Cells \% & Area \% & I/O \% \\
 \midrule
 K=2, g=3, key bit=n/3 & 0.45 & 20.40 & 18.63 & 64.84 \\
 K=3, g=10, key bit=5 bits & 2.93 & 23.07 & 19.52 & 33.14 \\
 K=4, g=10, key bit=5 bits & 10.45 & 41.33 & 33.95 & 33.14 \\
 K=6, g=All, key bit=1 bit & 25.62 & 246.33 & 197.89 & 35.23 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"K-Gate Lock: Multi-Key Logic Locking Using Input Encoding Against Oracle-Guided Attacks" by Kevin Lopez and Amin Rezaei, arXiv:2501.02118v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{cccccccc}\hline
 \multicolumn{4}{c}{training parameter} &
 \multicolumn{2}{c}{accuracy} &
 \multicolumn{2}{c}{\# test with proper score order}\\
 \# tasks & \# normal tasks & \# noise tasks & iteration & test & train & mean & median\\
 \hline
128&112&16&10000&0.727&1.000&70(+1.06$\sigma$)&68(+0.71$\sigma$)\\
256&224&32&10000&0.717&1.000&68(+0.71$\sigma$)&68(+0.71$\sigma$)\\
512&448&64&10000&0.740&1.000&68(+0.71$\sigma$)&67(+0.53$\sigma$)\\
1024&896&128&10000&0.754&0.971&77(+2.30$\sigma$)&72(+1.41$\sigma$)\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Yoshihiro Mitsuka
Shadan Golestan
Zahin Sufiyan
Sheila Schoepp
Shotaro Miwa
Osmar R. Zaiane

"TLXML: Task-Level Explanation of Meta-Learning via Influence Functions" by Yoshihiro Mitsuka, Shadan Golestan, Zahin Sufiyan, Sheila Schoepp, Shotaro Miwa, and Osmar R. Zaiane, arXiv:2501.14271v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"A weak Galerkin method and its two-grid algorithm for the quasi-linear elliptic problems of non-monotone type" by Peng Zhu and Shenglan Xie, arXiv:2202.11164v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Comparison of MSE in Scenario 2.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{c|cccc}
\hline
$n$ &RPackage & CVTF &SURE & Traditional CV \\
\hline
300 & 0.029 & 0.029 & 0.032 & 0.028\\
600 & 0.015 & 0.013 & 0.016 & 0.013\\
1200 & 0.007 & 0.006 & 0.008 & 0.007\\
2400 & 0.004 & 0.003 & 0.004 & 0.003\\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Anamitra Chaudhuri
Sabyasachi Chatterjee

"A Cross Validation Framework for Signal Denoising with Applications to Trend Filtering, Dyadic CART and Beyond" by Anamitra Chaudhuri and Sabyasachi Chatterjee, arXiv:2201.02654v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The distribution of reasoning tasks detected in AR-LSAT and VERUS-LM's accuracy}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 Reasoning task & Distribution (\%) & Exe\_Acc (\%) \\
 \midrule
 Optimization & 6.06 & 71.43 \\
 Determine range & 16.88 & 79.49 \\
 Entailment & 45.89 & 66.04 \\
 Satisfiability & 31.17 & 69.01 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Benjamin Callewaert
Simon Vandevelde
Joost Vennekens

"VERUS-LM: a Versatile Framework for Combining LLMs with Symbolic Reasoning" by Benjamin Callewaert, Simon Vandevelde, and Joost Vennekens, arXiv:2501.14540v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \multirow{2}{*}{Implementation} & i7-4700MQ & i7-5600U \\
 & \texttt{g++} 9.3.0 & \texttt{g++} 10.2.1 \\
 \midrule
 Eq.~ (this paper) & $0.0076$ & $0.0058$ \\
 Morris (DILOG 0011) & $0.0115$ & $0.0085$ \\
 Cephes & $0.0136$ & $0.0119$ \\
 Algorithm 327 & $0.0229$ & $0.0172$ \\
 Chebyshev & $0.0718$ & $0.0515$ \\
 Algorithm 490 & $0.1057$ & $0.0576$ \\
 GSL & $0.2091$ & $0.0898$ \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Comparison of methods for the calculation of the real dilogarithm regarding instruction-level parallelism" by Alexander Voigt, arXiv:2201.01678v1, 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}
 Model & AUROC & F1-score \\ \hline
 Baseline & 0.9407 & 0.7581 \\ \hline
 EGA-GCN & \underline{0.9576} & \underline{0.8065} \\
 EGA-GraphSAGE & 0.8305 & 0.4032 \\
 EGA-GAT & \textbf{0.9661} & \textbf{0.8548} \\
 \end{tabular}
\caption{Model Performance in MDD Downstream Task}
\end{table}
\end{document}

eess

Toyotaro Suzumura
Hiroki Kanezashi
Shotaro Akahori

"Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning" by Toyotaro Suzumura, Hiroki Kanezashi, and Shotaro Akahori, arXiv:2411.16155v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage[table]{xcolor}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{The classification accuracies (in \%) of SAFE-OCC Configuration 2 for each CNN sensor. The shaded and non-shaded values denote the normal and novelty accuracies, respectively, (with respect to the training data of each sensor).}.
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c|c|c|c|c|c|c|}
 \hline
 \multirow{2}{*}{Sensor} & \multicolumn{7}{c|}{Test Data} \\
 \cline{2-8}
 & Original & Blockages & Blurred & Fogged & Noised & Shifted & Splattered \\
 \hline \cline{2-8}
 A & \cellcolor{black!15}97.27 & 100.00 & 0.00 & 100.00 & 100.00 & 96.35 & 100.00 \\
 B & \cellcolor{black!15}97.07 & \cellcolor{black!15}97.07 & 2.02 & 100.00 & 17.84 & 38.09 & 100.00 \\
 C & \cellcolor{black!15}98.31 & 100.00 & \cellcolor{black!15}98.31 & 100.00 & 100.00 & 98.05 & 100.00 \\
 D & \cellcolor{black!15}99.02 & 99.93 & 0.13 & \cellcolor{black!15}99.02 & 39.06 & 74.48 & 100.00 \\
 E & \cellcolor{black!15}98.44 & 100.00 & 3.06 & 100.00 & \cellcolor{black!15}98.44 & 91.67 & 100.00 \\
 F & \cellcolor{black!15}96.29 & 98.31 & 0.00 & 100.00 & 100.00 & \cellcolor{black!15}96.29 & 100.00 \\
 G & \cellcolor{black!15}100.00 & 99.87 & 10.29 & 100.00 & 100.00 & 42.45 & \cellcolor{black!15}100.00 \\
 \hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

Joshua L. Pulsipher
Luke D. J. Coutinho
Tyler A. Soderstrom
Victor M. Zavala

"SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control" by Joshua L. Pulsipher, Luke D. J. Coutinho, Tyler A. Soderstrom, and Victor M. Zavala, arXiv:2202.01816v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

"Perfect sampling of stochastic matching models with reneging" by Thomas Masanet and Pascal Moyal, arXiv:2202.09341v2, 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||cc|cc}
 & \multicolumn{2}{c|}{\textbf{ViT-H}} & \multicolumn{2}{c}{\textbf{ViT-B}} \\
\toprule
$R$ & $\tau$ & $\rho$ & $\tau$ & $\rho$ \\
\midrule
$1$ & $-0.56$ & $-0.73$ & $-0.61$ & $-0.79$ \\
$3$ & $-0.55$ & $-0.72$ & $-0.61$ & $-0.79$ \\
$5$ & $-0.59$ & $-0.76$ & $-0.63$ & $-0.81$ \\
$7$ & $-0.53$ & $-0.7$ & $-0.59$ & $-0.77$ \\
$9$ & $-0.53$ & $-0.71$ & $-0.57$ & $-0.75$ \\
$11$ & $-0.51$ & $-0.69$ & $-0.56$ & $-0.74$ \\
\bottomrule
\end{tabular}
\caption{Rank correlation (Kendall $\tau$ and Spearman $\rho$) between SAM prediction IoU and object tree-likeness as measured by \textbf{CPR} with different hyperparameter values $R$ on \textbf{DIS}, to supplement Table .}
\end{table}
\end{document}

eess

Yixin Zhang
Nicholas Konz
Kevin Kramer
Maciej A. Mazurowski

"Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects" by Yixin Zhang, Nicholas Konz, Kevin Kramer, and Maciej A. Mazurowski, arXiv:2412.04243v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{l|c|c|c}
		\textbf{} &
		\multirow{2}{*}{Centralized planning } &
		\multicolumn{2}{c}{Decentralized planning with CA} \\ \cline{3-4}
		&
		&
		Independent planning &
		{CA with LNF} \\ \hline
		Comput. time (mean$\pm$ std)(ms)
		&345.8$\pm$ 87.2
		& 138.6$\pm$ 62.4 &
		9.97 $\pm$ 0.4
	\end{tabular}
\caption{Reach-avoid mission. Computation times (mean and standard deviation) comparison between centralized planning following~ and decentralized planning (independent planning with LNF) over 100 runs of the scenario.}
\end{table}
\end{document}

eess

Alëna Rodionova
Yash Vardhan Pant
Connor Kurtz
Kuk Jang
Houssam Abbas
Rahul Mangharam

"Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme" by Alëna Rodionova, Yash Vardhan Pant, Connor Kurtz, Kuk Jang, Houssam Abbas, and Rahul Mangharam, arXiv:2101.10404v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Model Selection Results (Validation set). Accuracy and AUC values are reported.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c|c|c}
\hline
\textbf{Model} & \textbf{Parameters (M)} & \textbf{Accuracy} & \textbf{AUC} \\
\hline
MaxViT & 119.9 & \(0.9508\) & \(0.9762\) \\
CoAtNet & 73.9 & \(0.9447\) & \(0.9787\) \\
EVA-02 & 87.1 & \(0.9248\) & \(0.9791\) \\
EfficientNetV2 & 21.6 & \(0.8779\) & \(0.9686\) \\
ConvNeXtV2 & 28.6 & \(0.9118\) & \(0.9670\) \\
\hline
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

MD Sadik Hossain Shanto
Mahir Labib Dihan
Souvik Ghosh
Riad Ahmed Anonto
Hafijul Hoque Chowdhury
Abir Muhtasim
Rakib Ahsan
MD Tanvir Hassan
MD Roqunuzzaman Sojib
Sheikh Azizul Hakim
M. Saifur Rahman

"DFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection" by MD Sadik Hossain Shanto, Mahir Labib Dihan, Souvik Ghosh, Riad Ahmed Anonto, Hafijul Hoque Chowdhury, Abir Muhtasim, Rakib Ahsan, MD Tanvir Hassan, MD Roqunuzzaman Sojib, Sheikh Azizul Hakim, and M. Saifur Rahman, arXiv:2501.16704v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lcc}
 \toprule
 \textbf{Weight Importance Metric} & \textbf{PPL ($\downarrow$)} & \textbf{Avg. Score} \\
 \midrule
 w/o Reordering & 10.78 & 61.50 \\
 $L^1$-Norm & \textbf{9.33} & \textbf{62.83} \\
 Wanda & 9.43 & 61.84 \\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

"MultiPruner: Balanced Structure Removal in Foundation Models" by J. Pablo Muñoz, Jinjie Yuan, and Nilesh Jain, arXiv:2501.09949v1, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{c|c|c}
 & Controller 1 & Controller 2 \\
 \hline
 Inversion & Acceleration Level & Jerk Level \\
 Outer Loop Commands & $\dot{\varPsi}$, $u_{C}$, $w_{C}$ & $r$, $u_{B}$, $w_{C}$ \\
 Inner Loop Commands & $\omega$, $u_{B}$, $w_{B}$ & $\omega$, $f_{x}$, $f_{z}$
 \end{tabular}
\caption{Key differences between the two controllers}
\end{table}
\end{document}

eess

Controller Design of an Airship

"Controller Design of an Airship" by Manuel Schimmer, arXiv:2507.19558v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Firas Laakom
Jenni Raitoharju
Nikolaos Passalis
Alexandros Iosifidis
Moncef Gabbouj

"Non-Linear Spectral Dimensionality Reduction Under Uncertainty" by Firas Laakom, Jenni Raitoharju, Nikolaos Passalis, Alexandros Iosifidis, and Moncef Gabbouj, arXiv:2202.04678v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\usepackage{amsfonts}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Skill Prior Hyperparameters}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{ll}
\toprule
Hyperaparameter & Value \\
 \midrule
 Batch size & 16 \\
 Optimizer & RAdam($\beta_1=0.9, \beta_2=0.999, lr=1e-3$) \\
 Regularization weight $\alpha$ & 1.0 \\ \midrule
 Skill Encoder \\
 $\quad$dim-$\mathcal{Z}$ in VAE & 32 \\
 $\quad$hidden dim & 128 \\
 $\quad$\# LSTM Layers & 1 \\ \midrule
 Skill Prior (Kitchen) \\
 $\quad$hidden dim & 128 \\
 $\quad$\# FC Layers & 6 \\ \midrule
 Skill Prior (Maze, Block Stacking) \\
 $\quad$kernel size & (4, 4) \\
 $\quad$channels & 8, 16, 32 \\
 $\quad$\# Convolution Layers & 3 \\ \midrule
 Skill Decoder \\
 $\quad$hidden dim & 128 \\
 $\quad$\# hidden layers & 6 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Myunsoo Kim
Hayeong Lee
Seong-Woong Shim
JunHo Seo
Byung-Jun Lee

"NBDI: A Simple and Effective Termination Condition for Skill Extraction from Task-Agnostic Demonstrations" by Myunsoo Kim, Hayeong Lee, Seong-Woong Shim, JunHo Seo, and Byung-Jun Lee, arXiv:2501.12668v3, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{DROID evaluation tasks.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|c}
 \toprule
 Task & Trials\\
 \midrule
 Put the spoon in the dish rack & 4\\
 Put carrot in bowl & 4\\
 Put plate in dish rack & 2\\
 Wipe the table & 2\\
 Put the plate on the table & 2\\
 Clean up the table & 2\\
 Close the drawer & 4\\
 Put the stapler on the notebook & 2\\
 Put stapler in the drawer & 4\\
 Clean the whiteboard & 2\\
 Put the marker in the cup & 4\\
 Put the black sponge in the blue bowl & 2\\
 Put the red bottle in the black bowl & 2\\
 Put the watermelon in the purple bowl & 2\\
 Move the watermelon from the purple bowl to the blue bowl & 2\\
 Put the tape in the purple bowl & 2\\
 Put the water bottle on the left side of the table & 2\\
 \midrule
 \textbf{Total} & \textbf{44}\\
 \bottomrule
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Karl Pertsch
Kyle Stachowicz
Brian Ichter
Danny Driess
Suraj Nair
Quan Vuong
Oier Mees
Chelsea Finn
Sergey Levine

"FAST: Efficient Action Tokenization for Vision-Language-Action Models" by Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine, arXiv:2501.09747v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{tabular}{lccccc}
 \toprule
 & Point mass & Mean & $\ell_{Z|_{\{Z<1\}}}$ & $\ell_Z$ & AIC \\
 \midrule
 Empirical density (Blue) & 0.034 & 0.339 & - & - & - \\
 MLE of the standard problem (Green) & 0.016 & 0.345 &32 101 &12 425 &-24 846 \\
 MLE of the extended problem (Red) & 0.034 & 0.341 &33 397 &14 726 &-29 447 \\
 \bottomrule
 \end{tabular}
\caption{Quadratic exponentially linked exposure example: results.}
\end{table}
\end{document}

stat

"Modeling lower-truncated and right-censored insurance claims with an extension of the MBBEFD class" by Selim Gatti and Mario V. Wüthrich, arXiv:2310.11471v2, 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{Replication Results with WG-hybrid estimator}
\begin{tabular}{lcccccc}
\\
\hline\hline
\\
& OLS& OLS & DML-Lasso & DML-Lasso& DML-CART& DML-RF\\
&(1)&(2)&(3)&(4)&(5)&(6)\\
\hline
\\
\multicolumn{7}{l}{\emph{Dependent variable: ``Vote conservative''}}\\
\\
NMW &0.097**&0.088**&0.093***&0.079**&0.089**&0.131**\\
&(0.045)&(0.045)&(0.045)&(0.045)&(0.05)&(0.074)\\
\\
No. Observations&19,961&19,961&19,961&19,961&19,961&19,961\\
No. Groups&4,927&4,927&4,927&4,927&4,927&4,927\\
\\
Wave FE&Yes&Yes&Yes&Yes&Yes&Yes\\
Region FE&Yes&Yes&Yes&Yes&Yes&Yes\\
Wave x Region FE&No&Yes&No&Yes&No&No\\
Extended dictionary&No&No&No&Yes&No&No\\
\\
\hline
\multicolumn{7}{c}{\emph{Resampling Information}} \\
\\
Estimator&WG&WG&WG&WG&WG&WG\\
No. folds&--&--&5&5&5&5\\
Folds per cluster&--&--&5&5&5&5\\
No. repeated sample splits&--&--&1&1&1&1\\
Cross-fitting&--&--&Yes&Yes&Yes&Yes\\
Score&--&--&PO&PO&PO&PO\\
DML algorithm&--&--&2&2&2&2\\
\hline
\end{tabular}
\end{table}
\end{document}

stat

"Double Machine Learning for Static Panel Models with Fixed Effects" by Paul S. Clarke and Annalivia Polselli, arXiv:2312.08174v5, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Boundary Condition of Grid & Total Grid Couplings for ($N_x = N_y$) & $N_x=100$ \\ \hline
 Periodic & $4N_x^2$ & 40000 \\
 Dirichlet & $4(N_x-2)^2+4(N_x-2)+8$ & 38816 \\
 Modified Dirichlet & $4(N_x-2)^2+ N_x-2$ & 38514 \\
 \hline
\end{tabular}
\end{adjustbox}
\caption{Changes in the number of couplings in a grid of $N$ nodes for different boundary conditions. The 2nd column shows the analytic expression for the number of total couplings in the grid. The third column shows the values of these expressions for the specific case of $N=10000$.}
\end{table}
\end{document}

math

"Effect of Choices of Boundary Conditions on the Numerical Efficiency of Direct Solutions of Finite Difference frequency Domain Systems with Perfectly Matched Layers" by Nathan Zhao and Shanhui Fan, arXiv:2202.13552v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|c|c|c|}
			\hline
			Ra & $n = 0.6$ & $n = 0.7$ & $n = 0.8$ & $n = 0.9$ & $n = 1$ \\
			\hline
			$10^{4}$ & 5.72 & 4.28 & 3.35 & 2.72 & 2.26 \\
			\hline
			$10^{5}$ & 14.49 & 10.26 & 7.61 & 5.90 & 4.72 \\
			\hline
			$10^{6}$ & 33.71 & 22.92 & 16.16 & 11.97 & 9.23 \\
			\hline
		\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

"Refined radial basis function-generated finite difference analysis of non-Newtonian natural convection" by Miha Rot and Gregor Kosec, arXiv:2202.08095v2, 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{Latency and Timing parameters}
\begin{tabular}{|l|c|}
 \hline
 \textbf{Parameter} & \textbf{Value} \\
 \hline
 Slot Boundary Time ($S_T$) & $500 \mu$s \\
 \hline
 Total No. of PRBs ($T_{\text{PRB}}$) & $273$ \\
 \hline
 $1$ RB & $12$ RE\\
 \hline
 Total no. users ($T_u$) & $64$ \\
 \hline
 Latency to Load One Matrix ($T_{\text{load}}$) & $16$ clk cycles \\
 \hline
 Latency to Multiply One Matrix ($T_{\text{mult}}$)& $2 $ clk cycle \\
 \hline
 Time Period of Multiplier ($T_{\text{clk}}$) & $4$ ns \\
 \hline
 Max no. of H matrices ($n_H$) & $64$ \\
 \hline
 Max no. of user matrices ($n_X$) & $273 \times 14 \times 12 = 45864$ \\
 \hline
 Total number of multiplications ($N_{\text{mult}}$) & $\max(n(H), n(X)) = 45864$ \\
 \hline

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

eess

Kalyani Bhukya
Shahid Aamir Sheikh
Radha Krishna Ganti

"Precoder Implementation and Optimization in 5G NR Massive MIMO Radio" by Kalyani Bhukya, Shahid Aamir Sheikh, and Radha Krishna Ganti, arXiv:2501.00366v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


math

Xianyi Zeng
Giovanni Stabile
Efthymios N. Karatzas
Guglielmo Scovazzi
Gianluigi Rozza

"Embedded domain Reduced Basis Models for the shallow water hyperbolic equations with the Shifted Boundary Method" by Xianyi Zeng, Giovanni Stabile, Efthymios N. Karatzas, Guglielmo Scovazzi, and Gianluigi Rozza, arXiv:2201.09546v1, 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
 $N$ & 50 & 100 & 200\\
 \hline
Proposed marketing strategy ($i=1$) & 17 & 33 & 67\\
\hline
Proposed marketing strategy ($i=2$) & 34 & 66 & 132 \\
\hline
Strategy of ($i=1$) & 13 &28& 58 \\
\hline
Strategy of ($i=2$) & 30 & 61 & 124 \\
\hline
Stages required for convergence to $\eta$ & 5 & 6 & 6 \\
\hline
\end{tabular}
\caption{Stage utilities with proposed marketing strategy compared to strategy in after practical convergence.}
\end{table}
\end{document}

eess

Vineeth S. Varma
Samson Lasaulce
Julien Mounthanyvong
Irinel-Constantin Morarescu

"Allocating marketing resources over social networks: A long-term analysis" by Vineeth S. Varma, Samson Lasaulce, Julien Mounthanyvong, and Irinel-Constantin Morarescu, arXiv:2011.09268v1, licensed under CC0 1.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{booktabs}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\caption{Architecture and training configurations for models trained on cyclic experimental data.}
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{lccccc}
\toprule
\textbf{Model} & \textbf{Hidden Layers} & \textbf{Neurons/Layer} & \textbf{Training Loss} & \textbf{Activation} \\%& \textbf{Epochs}\\
\midrule
LSTM($H$=50) & 2 & 128 & Huber & ReLU \\
Encoder-Decoder($H$=50) & 2,2 & 128(per direction) & Huber & ReLU \\
GRU($H$=50) & 2 & 128 & Huber & ReLU \\
FFNN ($H$=1) & 3 & 128 & Huber & ReLU \\
rFFNN ($H$=14) & 3 & 128 & Huber & ReLU \\
rBNN ($H$=14) & 2 & 110 & $-$ ELBO () & ReLU & \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\end{document}

cs

Toiba Noor
Soban Nasir Lone
G. V. Ramana
Rajdip Nayek

"A recursive Bayesian neural network for constitutive modeling of sands under monotonic and cyclic loading" by Toiba Noor, Soban Nasir Lone, G. V. Ramana, and Rajdip Nayek, arXiv:2501.10088v2, 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}{ccccc}
\toprule
\bfseries{Model} & \bfseries{LR} & \bfseries{Momentum} & \bfseries{Weight Decay} & \bfseries{Nesterov} \\
\midrule
SeresNet50 & $1e-1$, $1e-2$, $1e-3$, & 0.0, 0.5, 0.9 & 0.0, $1e-3$, $1e-4$, $3e-4$, & Yes, No \\
\midrule
Hourglass Net & $1e-1$, $1e-2$, $1e-3$, & 0.0, 0.5, 0.9 & 0.0, $1e-4$, & Yes, No \\
\bottomrule
\end{tabular}
\caption{Hyperparameters search space. We kept the optimizer fixed to SGD. Batch size was 32 for SeresNet50 and 24 for the hourglass model (KNEEL~)}
\end{table}
\end{document}

eess

Abu Mohammed Raisuddin
Elias Vaattovaara
Mika Nevalainen
Marko Nikki
Elina Järvenpää
Kaisa Makkonen
Pekka Pinola
Tuula Palsio
Arttu Niemensivu
Osmo Tervonen
Aleksei Tiulpin

"Critical Evaluation of Deep Neural Networks for Wrist Fracture Detection" by Abu Mohammed Raisuddin, Elias Vaattovaara, Mika Nevalainen, Marko Nikki, Elina Järvenpää, Kaisa Makkonen, Pekka Pinola, Tuula Palsio, Arttu Niemensivu, Osmo Tervonen, and Aleksei Tiulpin, arXiv:2012.02577v2, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{amsmath}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c|c|c|}
 \hline
 Grid & $L_{1}$-error & order\\\hline
 32$^2$ & 3.18e-04 & -- \\
 64$^2$ & 8.04e-05 & 1.98 \\
 128$^2$ & 2.02e-05 & 1.99 \\
 256$^2$ & 5.07e-06 & 2 \\\hline
 \end{tabular}
\end{adjustbox}
\end{table}
\end{document}

math

A. Limare
S. Popinet
C. Josserand
Z. Xue
A. Ghigo

"A hybrid level-set / embedded boundary method applied to solidification-melt problems" by A. Limare, S. Popinet, C. Josserand, Z. Xue, and A. Ghigo, arXiv:2202.08300v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{multirow}
\usepackage{graphicx}
\usepackage{rotating}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{|c||c||c|c|c|c||c|c|c|c|c||c|c|c|c|c|}
 \hline
 \multirow{3}{*}{\textbf{Size}} & \multirow{3}{*}{\textbf{Instance}} & \multicolumn{4}{|c||}{\textbf{RedCost-S}} & \multicolumn{5}{|c||}{\textbf{ML-RedCost-S}} &
 \multicolumn{5}{|c|}{\textbf{ML-RedCost-S-2}} \\
 \cline{3-16}
 & & \multirow{2}{*}{\#Itr} & \multicolumn{3}{|c||}{Time (s)}
 & \multirow{2}{*}{\#Itr} & \multicolumn{3}{|c|}{Time (s)} & \multirow{2}{*}{\textbf{Gain}}
 & \multirow{2}{*}{\#Itr} & \multicolumn{3}{|c|}{Time (s)} & \multirow{2}{*}{\textbf{Gain}} \\
 \cline{4-6} \cline{8-10} \cline{13-15}
 & & & PP & RMP & Total
 & & PP & RMP & Total &
 & & PP & RMP & Total & \\

 \hline \hline
 \multirow{18}{*}{\rotatebox[origin=c]{90}{\textbf{200 clients}}} & \textbf{R2\_2\_1} & 439 & 23 & 12 & 34
 & 578 (403) & 13 & 12 & 24 & \textbf{31\%}
 & 420 (235) & 8 & 10 & 18 & \textbf{48\%}\\
 & \textbf{R2\_2\_2} & 179 & 25 & 5 & 29
 & 309 (127) & 13 & 7 & 21 & \textbf{30\%}
 & 343 (158) & 16 & 8 & 24 & \textbf{17\%}\\
 & \textbf{R2\_2\_3} & 441 & 20 & 8 & 28
 & 406 (255) & 8 & 9 & 17 & \textbf{38\%}
 & 378 (219) & 8 & 8 & 16 & \textbf{44\%}\\
 & \textbf{R2\_2\_4} & 152 & 34 & 2 & 37
 & 217 (98) & 8 & 3 & 11 & \textbf{69\%}
 & 245 (113) & 9 & 4 & 12 & \textbf{66\%}\\
 & \textbf{R2\_2\_5} & 367 & 22 & 11 & 33
 & 430 (244) & 11 & 12 & 23 & \textbf{32\%}
 & 444 (245) & 11 & 12 & 23 & \textbf{32\%}\\
 \cline{2-16}
 & \textbf{Average}
 & \textbf{316} & \textbf{25} & \textbf{7} & \textbf{32}
 & \textbf{388 (225)} & \textbf{11} & \textbf{8} & \textbf{19} & \textbf{40\%}
 & \textbf{366 (194)} & \textbf{10} & \textbf{8} & \textbf{18} & \textbf{41\%}\\
 \cline{2-16}
 \noalign{\vskip-2\tabcolsep \vskip-3\arrayrulewidth \vskip\doublerulesep} \\
 \cline{2-16}
 & \textbf{RC2\_2\_1} & 372 & 32 & 6 & 38
 & 586 (378) & 18 & 10 & 28 & \textbf{28\%}
 & 568 (376) & 17 & 9 & 26 & \textbf{32\%}\\
 & \textbf{RC2\_2\_2} & 218 & 17 & 3 & 20
 & 332 (179) & 12 & 5 & 17 & \textbf{15\%}
 & 282 (142) & 11 & 5 & 15 & \textbf{25\%}\\
 & \textbf{RC2\_2\_3} & 852 & 40 & 11 & 51
 & 912 (695) & 28 & 11 & 39 & \textbf{23\%}
 & 670 (446) & 22 & 10 & 32 & \textbf{38\%}\\
 & \textbf{RC2\_2\_4} & 609 & 34 & 13 & 47
 & 675 (476) & 18 & 14 & 33 & \textbf{30\%}
 & 531 (352) & 17 & 13 & 29 & \textbf{37\%}\\
 & \textbf{RC2\_2\_5} & 313 & 32 & 6 & 39
 & 451 (226) & 15 & 7 & 23 & \textbf{41\%}
 & 497 (270) & 20 & 9 & 29 & \textbf{25\%}\\
 \cline{2-16}
 & \textbf{Average}
 & \textbf{473} & \textbf{31} & \textbf{8} & \textbf{39}
 & \textbf{591 (391)} & \textbf{18} & \textbf{10} & \textbf{28} & \textbf{28\%}
 & \textbf{510 (317)} & \textbf{17} & \textbf{9} & \textbf{26} & \textbf{31\%}\\
 \cline{2-16}
 \noalign{\vskip-2\tabcolsep \vskip-3\arrayrulewidth \vskip\doublerulesep} \\
 \cline{2-16}
 & \textbf{C2\_2\_1} & 760 & 72 & 11 & 83
 & 924 (552) & 54 & 15 & 69 & \textbf{17\%}
 & 1020 (676) & 62 & 15 & 77 & \textbf{6\%}\\
 & \textbf{C2\_2\_2} & 316 & 121 & 6 & 126
 & 416 (242) & 76 & 8 & 84 & \textbf{34\%}
 & 385 (210) & 71 & 8 & 79 & \textbf{38\%}\\
 & \textbf{C2\_2\_3} & 272 & 54 & 7 & 61
 & 505 (208) & 49 & 11 & 60 & \textbf{2\%}
 & 489 (190) & 51 & 11 & 62 & \textbf{-1\%}\\
 & \textbf{C2\_2\_4} & 181 & 65 & 5 & 70
 & 442 (113) & 61 & 11 & 72 & \textbf{-3\%}
 & 464 (122) & 65 & 12 & 77 & \textbf{-10\%}\\
 & \textbf{C2\_2\_5} & 237 & 140 & 6 & 146
 & 501 (159) & 127 & 12 & 139 & \textbf{5\%}
 & 479 (174) & 121 & 11 & 132 & \textbf{10\%}\\
 \cline{2-16}
 & \textbf{Average}
 & \textbf{353} & \textbf{90} & \textbf{7} & \textbf{97}
 & \textbf{558 (255)} & \textbf{73} & \textbf{11} & \textbf{84} & \textbf{11\%}
 & \textbf{567 (274)} & \textbf{74} & \textbf{11} & \textbf{85} & \textbf{10\%}\\
 \cline{1-16}
 \noalign{\vskip-2\tabcolsep \vskip-3\arrayrulewidth \vskip\doublerulesep} \\
 \cline{1-16}
 \multirow{28}{*}{\rotatebox[origin=c]{90}{\textbf{400 clients}}} & \textbf{R2\_4\_1} & 373 & 66 & 119 & 185
 & 647 (180) & 39 & 119 & 158 & \textbf{15\%}
 & 678 (230) & 41 & 122 & 163 & \textbf{12\%}\\
 & \textbf{R2\_4\_2} & 289 & 45 & 84 & 129
 & 436 (127) & 24 & 81 & 105 & \textbf{19\%}
 & 429 (117) & 24 & 81 & 105 & \textbf{19\%}\\
 & \textbf{R2\_4\_3} & 235 & 97 & 37 & 134
 & 392 (111) & 40 & 54 & 94 & \textbf{30\%}
 & 440 (157) & 44 & 55 & 99 & \textbf{0\%}\\
 & \textbf{R2\_4\_4} & 239 & 142 & 20 & 162
 & 297 (106) & 40 & 29 & 69 & \textbf{57\%}
 & 314 (134) & 48 & 29 & 77 & \textbf{52\%}\\
 & \textbf{R2\_4\_5} & 361 & 51 & 127 & 178
 & 619 (165) & 32 & 114 & 146 & \textbf{18\%}
 & 573 (159) & 30 & 114 & 144 & \textbf{19\%}\\
 & \textbf{R2\_4\_6} & 277 & 44 & 78 & 122
 & 523 (196) & 32 & 94 & 126 & \textbf{-3\%}
 & 460 (139) & 25 & 81 & 106 & \textbf{13\%}\\
 & \textbf{R2\_4\_7} & 218 & 81 & 44 & 125
 & 390 (114) & 38 & 60 & 98 & \textbf{22\%}
 & 384 (116) & 31 & 53 & 84 & \textbf{33\%}\\
 & \textbf{R2\_4\_8} & 242 & 144 & 23 & 167
 & 328 (131) & 60 & 33 & 93 & \textbf{44\%}
 & 309 (119) & 45 & 31 & 76 & \textbf{54\%}\\
 & \textbf{R2\_4\_9} & 322 & 101 & 116 & 217
 & 615 (168) & 56 & 131 & 187 & \textbf{14\%}
 & 632 (188) & 50 & 121 & 171 & \textbf{21\%}\\
 & \textbf{R2\_4\_10} & 380 & 154 & 108 & 262
 & 691 (230) & 76 & 119 & 195 & \textbf{26\%}
 & 719 (201) & 64 & 116 & 180 & \textbf{31\%}\\
 \cline{2-16}
 & \textbf{Average}
 & \textbf{294} & \textbf{93} & \textbf{76} & \textbf{168}
 & \textbf{494 (153)} & \textbf{44} & \textbf{83} & \textbf{127} & \textbf{24\%}
 & \textbf{494 (156)} & \textbf{40} & \textbf{80} & \textbf{121} & \textbf{28\%}\\
 \cline{2-16}
 \noalign{\vskip-2\tabcolsep \vskip-3\arrayrulewidth \vskip\doublerulesep} \\
 \cline{2-16}
 & \textbf{RC2\_4\_1} & 390 & 45 & 102 & 147
 & 762 (232) & 42 & 124 & 166 & \textbf{-13\%}
 & 801 (310) & 50 & 132 & 182 & \textbf{-24\%}\\
 & \textbf{RC2\_4\_2} & 295 & 37 & 67 & 104
 & 630 (210) & 37 & 76 & 113 & \textbf{-9\%}
 & 651 (309) & 43 & 89 & 132 & \textbf{-27\%}\\
 & \textbf{RC2\_4\_3} & 301 & 108 & 27 & 135
 & 446 (188) & 52 & 35 & 87 & \textbf{36\%}
 & 486 (206) & 53 & 36 & 89 & \textbf{34\%}\\
 & \textbf{RC2\_4\_4} & 183 & 81 & 16 & 97
 & 263 (76) & 29 & 23 & 52 & \textbf{46\%}
 & 278 (86) & 31 & 23 & 54 & \textbf{44\%}\\
 & \textbf{RC2\_4\_5} & 1164 & 84 & 186 & 270
 & 1600 (882) & 83 & 196 & 279 & \textbf{-3\%}
 & 1810 (1084) & 101 & 251 & 352 & \textbf{-30\%}\\
 & \textbf{RC2\_4\_6} & 434 & 98 & 118 & 216
 & 773 (244) & 62 & 130 & 192 & \textbf{11\%}
 & 867 (314) & 62 & 122 & 184 & \textbf{15\%}\\
 & \textbf{RC2\_4\_7} & 415 & 100 & 98 & 198
 & 792 (348) & 79 & 118 & 197 & \textbf{1\%}
 & 674 (274) & 60 & 119 & 179 & \textbf{10\%}\\
 & \textbf{RC2\_4\_8} & 435 & 90 & 72 & 162
 & 834 (357) & 78 & 84 & 162 & \textbf{0\%}
 & 669 (240) & 56 & 74 & 130 & \textbf{20\%}\\
 & \textbf{RC2\_4\_9} & 371 & 83 & 91 & 174
 & 723 (304) & 67 & 116 & 183 & \textbf{-5\%}
 & 952 (561) & 111 & 108 & 219 & \textbf{-26\%}\\
 & \textbf{RC2\_4\_10} & 438 & 40 & 91 & 131
 & 679 (274) & 39 & 118 & 157 & \textbf{-20\%}
 & 689 (266) & 35 & 91 & 126 & \textbf{4\%}\\
 \cline{2-16}
 & \textbf{Average}
 & \textbf{443} & \textbf{77} & \textbf{87} & \textbf{163}
 & \textbf{750 (312)} & \textbf{57} & \textbf{102} & \textbf{159} & \textbf{4\%}
 & \textbf{788 (365)} & \textbf{60} & \textbf{105} & \textbf{165} & \textbf{2\%}\\
 \cline{2-16}
 \noalign{\vskip-2\tabcolsep \vskip-3\arrayrulewidth \vskip\doublerulesep} \\
 \cline{2-16}
 & \textbf{C2\_4\_1} & 966 & 144 & 183 & 327
 & 1692 (669) & 162 & 190 & 352 & \textbf{-8\%}
 & 1785 (768) & 170 & 186 & 356 & \textbf{-9\%}\\
 & \textbf{C2\_4\_2} & 445 & 71 & 88 & 159
 & 867 (438) & 99 & 117 & 216 & \textbf{-36\%}
 & 726 (239) & 68 & 110 & 178 & \textbf{-12\%}\\
 & \textbf{C2\_4\_3} & 484 & 176 & 55 & 231
 & 741 (333) & 215 & 78 & 293 & \textbf{-27\%}
 & 913 (463) & 210 & 87 & 297 & \textbf{-29\%}\\
 & \textbf{C2\_4\_4} & 332 & 267 & 30 & 297
 & 522 (249) & 327 & 44 & 371 & \textbf{-25\%}
 & 536 (287) & 290 & 47 & 337 & \textbf{-13\%}\\
 & \textbf{C2\_4\_5} & 417 & 194 & 63 & 257
 & 887 (266) & 248 & 101 & 349 & \textbf{-36\%}
 & 895 (278) & 222 & 101 & 323 & \textbf{-26\%}\\
 \cline{2-16}
 & \textbf{Average}
 & \textbf{529} & \textbf{170} & \textbf{84} & \textbf{254}
 & \textbf{942 (391)} & \textbf{210} & \textbf{106} & \textbf{316} & \textbf{-26\%}
 & \textbf{971 (407)} & \textbf{192} & \textbf{106} & \textbf{298} & \textbf{-17\%}\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{Additional VRPTW results.}
\end{table}
\end{document}

math

Mouad Morabit
Guy Desaulniers
Andrea Lodi

"Machine-learning-based arc selection for constrained shortest path problems in column generation" by Mouad Morabit, Guy Desaulniers, and Andrea Lodi, arXiv:2201.02535v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.


\documentclass{article}
\usepackage{adjustbox}
\pagestyle{empty}
\begin{document}
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth, max height=0.8\textheight, keepaspectratio}
\begin{tabular}{l|l}
 Retrieval model & Model class\\
 \hline
 \hline
 bm25 & lexical\\
 aggretriever-distilbert & dense\\
 tct\_colbert-v2-hnp-msmarco & dense\\
 bm25 + aggretriever-distilbert & lexical + dense\\
 bm25 + tct\_colbert-v2-hnp-msmarco & lexical + dense\\
 bm25 + MonoT5 & lexical + re-ranker\\
 aggretriever-distilbert + MonoT5 & dense + re-ranker\\
 tct\_colbert-v2-hnp-msmarco + MonoT5 & dense + reranker\\
 bm25 + aggretriever-distilbert + MonoT5 & lexical + dense + reranker\\
 bm25 + tct\_colbert-v2-hnp-msmarco + MonoT5 & lexical + dense + reranker\\
 \hline
 \end{tabular}
\end{adjustbox}
\caption{10 IR systems to evaluate}
\end{table}
\end{document}

cs

Mehmet Deniz Türkmen
Mucahid Kutlu
Bahadir Altun
Gokalp Cosgun

"GenTREC: The First Test Collection Generated by Large Language Models for Evaluating Information Retrieval Systems" by Mehmet Deniz Türkmen, Mucahid Kutlu, Bahadir Altun, and Gokalp Cosgun, arXiv:2501.02408v1, licensed under CC BY 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.