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2307.04457v2
\begin{tabular}{l|cc|cc|cc} \hline & \multicolumn{2}{c}{\textbf{Heat stability}} & \multicolumn{2}{|c}{\textbf{Casein content}} &\multicolumn{2}{|c}{\textbf{RCT}} \\ & RMSEP&$\widehat{\ttQ}_y$ &RMSEP&$\widehat{\ttQ}_y$& RMSEP&$\widehat{\ttQ}_y$ \\ \hline BBPLS & 0.875 (0.172) & 6.5 (2.5) & 0.376 (0.027) & 6.8 (1...
Predicting milk traits from spectral data using Bayesian probabilistic partial least squares regression
Accuracy of predictions of the standardised heat stability, casein content and RCT traits from the milk MIR spectral data using each of the proposed BPLS and existing methods. Estimates are given as ``mean (standard deviation)'' of thee RMSEP over the four test folds, with bold font indicating optimal performance. $^*$...
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stat.ME, stat.AP
2309.04013v1
\begin{tabular}{|c|c|c|} \hline Species & \bf Mean & \bf Std.~Dev. \\ \hline 1 & 3.4 & 1.2 \\ 2 & 5.4 & 0.6 \\ \hline \end{tabular}
An Element-wise RSAV Algorithm for Unconstrained Optimization Problems
Example table.
['\\newcommand{\\bx}{\\boldsymbol{x}}']
math.OC, cs.LG, stat.ML, 90C26, 68T99, 68W40
2307.00516v1
\begin{tabular}{|c|c|c|c|c|c|} \hline $m\times n$ & 500$\times$ 1000 & 500$\times$ 2000 & 500$\times$ 4000 & 500$\times$ 8000 \\ \hline GP-P1 & 4.015903385 & 16.7774142 & 70.75651123 & 388.644996 \\ \hline GP-P2 & 3.56448103 & 24.74755891 & 68.75088813 & 327.9051987 \\ \hline GP-P3 & 3.63770008 & 28.0462...
A re-examination to the SCoTLASS problems for SPCA and two projection-based methods for them
The solving speed comparison of different constrained algorithms for $m$ is fixed at 500 (in seconds)
null
math.ST, stat.TH
2311.09497v1
\begin{tabular}{|l|c|} \hline $\alpha_f$ (Objective Quality Variance) & 0.581\\ \hline $\alpha_{b,1} / \alpha_f$ (Meta-Reviewer Offset Variance) & 0.458 \\ \hline $\alpha_{b,2} / \alpha_f$ (Reviewer Offset Variance) & 0.432 \\ \hline $\alpha_{b,3} / \alpha_f$ (Author Offset...
Peer Reviews of Peer Reviews: A Randomized Controlled Trial and Other Experiments
Fit parameters of linear calibration model.
['\\newcommand{\\ns}[1]{{\\color{blue} \\textbf{ns: #1}}}', '\\newcommand{\\is}[1]{{\\color{green} \\textbf{is: #1}}}', '\\newcommand{\\ag}[1]{{\\color{red} \\textbf{ag: #1}}}', '\\newcommand{\\confx}{NeurIPS 2022{}}', '\\newcommand{\\externalrev}{external reviewers}', '\\newcommand{\\understanding}{understanding}', '\...
cs.DL, cs.GT
2310.16470v1
\begin{tabular}{crrr|crrr} \hline & Coefficient & Std. err. & $t$ value & & Coefficient & Std. err. & $t$ value\\ \hline $\gamma$ & 216.41 & 0.81 & $^{*}$268.16 & & & & \\ $\alpha_{\rm{c}1}$ & 161.30 & 9.51 & $^{*}$16.97 & $\alpha_{\rm{s}1}$ & 138.52 & 11.52 & $^{*}$12.02 \\ $\alpha_{\rm{c}2}$ & -30.96 & 2.67 & $...
Understanding Impact of Angle in Urban Transportation
Estimated parameters of Case 3.
null
stat.AP
2312.09955v1
\begin{tabular}{ c c c c c } \hline Metrics & Fattal's & DehazeNet & Gao \emph{et al.} & Proposed \\ \hline MPSNR & 17.74 & 22.39 & 23.26 & 26.83 \\ MSSIM & 0.803 & 0.817 & 0.917 & 0.924 \\ MFSIM & 0.901 & 0.952 & 0.921 & 0.978 \\ \hline \\ \end{tabular}
DHFormer: A Vision Transformer-Based Attention Module for Image Dehazing
Mean PSNR, SSIM, and FSIM of the given methods over the HSTS dataset.
null
cs.CV, eess.IV
2311.18446v1
\begin{tabular}{cccc} \hline & \multicolumn{1}{c}{Beran} & \multicolumn{2}{c}{SBeran} \\ \noalign{\hrule height 1pt} \hspace*{0.2cm} $x$ \hspace*{0.2cm} & \hspace*{0.2cm} $ h^*$ \hspace*{0.2cm} & $h_2^*$ & $g_2^*$ \\ 40 & 4.765306 & 5.507370 & 1.266695 \\ 60 & 4.571429 & 5.548651 & 0.784...
Length-of-stay times in hospital for COVID-19 patients using the smoothed Beran's estimator with bootstrap bandwidth selection
Bootstrap bandwidth for Beran's estimation and the smoothed Beran's estimation of the conditional survival function of the time in ward for some different values of age.
null
stat.CO, stat.AP, stat.ME
2305.17922v1
\begin{tabular}{|l|r|r||r|r|} \hline & \multicolumn{2}{|c||}{IM EN-prior} & \multicolumn{2}{c|}{IM PC-prior} \\ \hline RMSE & $4.22$ & $4.24$ & $4.21$ & $4.21$\\ \hline Bias & $0.71$ & $0.68$ & $0.73$ & $0.86$ \\ \hline & \multicolumn{1}{|c|}{Base} & \multicolumn{1}{c||}{Feedback} & \multicol...
Bayesian feedback in the framework of ecological sciences
Median values of the RMSE along the $108$ scenarios for the median posterior prediction values.
['\\newcommand{\\iosu}{\\color{orange}}', '\\newcommand{\\david}{\\color{orange}}']
stat.AP, 62P10
2310.02046v1
\begin{tabular}{|p{54mm}|p{54mm}|p{54mm}|} \hline \textbf{Examples that indicate that Comparison operator is used to find the target} & \textbf{Examples that indicate that Semantic understanding is used to find the target} & \textbf{Examples that indicate that Context awareness is used to find the target} \\ \hline Bot...
Improving web element localization by using a large language model
Example motivations from GPT-4 classified as comparison operator, semantic understanding, or context awareness.
null
cs.SE
2309.00239v2
\begin{tabular}{lll} \hline Parameters & Montreal$^{1}$ & La Plata\\ \hline \Teff & $1460-150\,000$\,K & $2750-80\,000$\,K\\ \logg & $6.7-9.3$ & $6-9.45$\\ Mass ($\rm{M_{WD}}$) & $0.2-1.3$\,\Msun & $0.2-1.3$\,\Msun\\ Core composition & CO core & He Core $(\rm{M_{WD}}<0.5\,\Msun)^{2}$\\ & entire mass range & CO core...
An HST COS ultra-violet spectroscopic survey of 311 DA white dwarfs.I. Fundamental parameters and comparative studies
Model parameters of the two mass-radius relations from the Montreal and La Plata models for a progenitor metallicity of $Z=0.02$.
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astro-ph.SR
2311.13498v1
\begin{tabular}{ |c| } \hline $\Omega_{\rm m0}h^2 = 0.139 \pm 0.017$ \\ \hline \end{tabular}
Unveiling $Ω_{\rm m0}$ independently: a journey and consistency quest with first-order perturbation theory
The estimated value of the $\Omega_{\rm m0}h^2$ parameter and the associated 1$\sigma$ error.
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astro-ph.CO, gr-qc
2310.20138v2
\begin{tabular}{c|c|c|cc|cc|c} \hline \multirow{2}*{Models} & \multirow{2}*{\shortstack{\# Edited Neurons}} & \multirow{2}*{Time} &\multicolumn{2}{c|}{Before Editing} & \multicolumn{2}{c|}{After Editing} & \multirow{2}*{Reduction Rate} \\ \cline{4-7} & & & Valid-PPL & Exposure & Valid-P...
DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models
The privacy leakage risk reduction rate for models of different sizes.
['\\newcommand{\\theHalgorithm}{\\arabic{algorithm}}']
cs.CR, cs.CL
2310.14702v2
\begin{tabular}{c|l} \hline Blocks & \multicolumn{1}{c}{Settings} \\ \hline Voxel Feature Encoder (VFE) & use normalization and absolute 3D coordinates, 64 filters \\ \hline PointPillar Scatter & 64-channel output \\ \hline \multirow{6}{*}{BEV backbone} & ResNet backbone: \\ & layer...
BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities
Details of unified network architecture.
null
cs.CV, cs.AI, cs.RO
2307.05467v1
\begin{tabular}{p{4.0cm}p{2.3cm}} \hline %\multicolumn{4}{|c|}{Country List} \\ \hline $\kappa$ & $T_c$ \\ \hline 1.0 & 1.728(5) \\ %\hline 0.8 & 1.894(5) \\ %\hline 0.6 & 2.041(5) \\ %\hline 0.4 & 2.161(5) \\ %\hline 0.2 & 2.241(5) \\ %\hline 0.001 & 2.269(5) \\ \hline \...
Is Kaniadakis $κ$-generalized statistical mechanics general?
Estimates of critical temperatures of 2D Ising model with the $\kappa$-generalized Metropolis rate in Eq.\ (\ref{ising-metropolis-kaniadakis}), for some values of $\kappa$. Estimates of the critical temperature $T_c$ were obtained from the crossings of Binder Cumulant, as seen in panel (a) of Fig.\ \ref{ising-collapses...
null
hep-th, cond-mat.stat-mech, math-ph, math.MP
2312.15382v1
\begin{tabular}{|c|c|c|c|} \hline Nodes & Reference & Computed value & Error \\ \hline (2, 10, 12) & 0.5389714947317054 & 0.5386865717999949 & $2.84922 \cdot 10^{-4} $ \\ (2, 10, 14) & 0.5953434982171909 & 0.5951583168446205 & $1.85181 \cdot 10^{-4}$ \\ (4, 12, 18) & 0.7121629047455362 & 0.7125335341761537 & $3.7062...
Efficient simulation of mixed boundary value problems and conformal mappings
Moduli of quadrilaterals \( (Q_{B}; e^{im\pi/24}\), \(e^{in\pi/24}\), \(e^{ir\pi/24}, 1) \) for several integer triples \( (m, n, r) \)
null
math.NA, cs.NA, math.CV, math.PR, 30-08, 30C20, 31-08, 31A15, 60J65, 65C05, G.1.8; G.3; I.3.5
2308.06279v2
\begin{tabular}{lccccc} \hline & L13 & L14 & L15 & L16 & L17 \\ VARIABLES & W & W & W & W & W \\ \hline & & & & & \\ Closed & -0.465** & -0.448** & -0.670 & -0.391 & -0.476** \\ & (0.211) & (0.216) & (0.498) & (0.240) & (0.221) \\ Home Team & & & & & YES \\ Away Team & & & & & YES \\ Calendar & & & &...
Visitors Out! The Absence of Away Team Supporters as a Source of Home Advantage in Football
Logits regressions of models in Table \ref{tab:REG_ADD1}
null
econ.GN, q-fin.EC
2305.09065v2
\begin{tabular}{c || c | c | c | c | c | c | c | c | c | c } \hline $a/b$ & $10^{-4}$ & 0.01 & 0.05 & 0.10 & 0.20 & 0.25 & 0.30 & 0.50 & 0.75 & 0.99 \\ [0.5ex] \hline%\hline $n=1$ & 0.0979 & 0.1784 & 0.2503 & 0.3028 & 0.3832 & 0.4191 & 0.4537 & 0.5906 & 0.7766 & 0.9900 \\ $n=2$ & 0.1086 & 0.2158 & 0.3228 & 0.403...
Robust Auction Design with Support Information
Maximin ratio as a function of relative support information $a/b$ for various numbers of buyers $n$.
null
econ.TH, cs.GT, cs.MA
2302.06098v1
\begin{tabular}{l|cccccc} \hline Arrangement & B-1 & B-4 & M & R & C & S \\ \hline LSA + SA & 81.1 & 39.6 & 29.1 & 59.0 & 133.4 & \textbf{22.8} \\ SA + LSA & \textbf{81.2} & \textbf{39.7} ...
Towards Local Visual Modeling for Image Captioning
Ablation studies on various arrangements of SA and LSA. + is sequential connection, \& represents parallel connection.
null
cs.CV, cs.MM
2311.11231v2
\begin{tabular}{c|c|c|c|c|cc} \hline \multirow{2}[4]{*}{Industry} & \multirow{2}[4]{*}{Total} & \multicolumn{5}{c}{Percentage} \\ \cline{3-7} & & $G_1$ & \multicolumn{1}{c|}{$R_1$} & \multicolumn{1}{c|}{$R_2$} & $R_3$ & $R_4$ \\ \hline $S_1$& 1780 & 29.2 & 85.9 & 5.9 ...
Workforce pDEI: Productivity Coupled with DEI
Employed Persons by Detailed Occupation .
null
econ.GN, q-fin.EC
2312.07312v1
\begin{tabular}{c|c|c|c} & \(\Lambda < 0\) & \(\Lambda = 0\) & \(\Lambda > 0\)\\[0pt] \hline \(\kappa = 1\) & no solution & no solution & de Sitter\\[0pt] & & & \(a = \cosh(q t) / q\)\\[0pt] \hline \(\kappa = 0\) & no solution & Minkowski & de Sitter\\[0pt] & & \(a = 1\) & \(a = \exp(q t)\)\\[0pt] \hline \(\kappa...
Inflationary scenarios in an effective polynomial affine model of gravity
\label{tab:summary-vacuum-friedmann-cosmologies}Classification of vacuum Friedmann cosmologies in General Relativity . The quantity \(q\) is defined as \(q = \sqrt{|\Lambda|/3}\).
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gr-qc, hep-th, math-ph, math.MP
2309.11390v1
\begin{tabular}{lcccccccccc} \hline\hline \noalign{\smallskip} Observatory & Aperture & Filter & Date & Start & End & Length & Exp. Time & Airmass & Comp. & Precision\\ & (m) & & (UTC)& (UTC) & (UTC) & (min.) & (sec.) & Range & Stars...
TOI-858 B b: A hot Jupiter on a polar orbit in a loose binary
TFOP photometric follow-up observation log.
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astro-ph.EP
2307.12087v1
\begin{tabular}{|c|c|} \hline encoding & 734401 \\ \hline round id & 1 \\ \hline number of Pairs & 3 \\ \hline number of Pongs or Kongs & 2 \\ \hline number of Character tiles & 3 \\ \hline number of wind tiles & 11 \\ \hline ...
CFR-p: Counterfactual Regret Minimization with Hierarchical Policy Abstraction, and its Application to Two-player Mahjong
An Example of Nodes
null
cs.AI, econ.GN, q-fin.EC
2306.04992v1
\begin{tabular}{c|c|ccccccccc} \hline \hline Crust &Core & $M_{\rm max}[M_{\odot}]$ & $R_{\rm max}$[km] & $\rho_c\rm[fm^{-3}]$ & $R_{1.4}$[km] & $\Lambda_{1.4}$ & $R_{0.77}$[km] & $\rho_{0.77}\rm[fm^{-3}]$ \\ \hline \multirow{3}*{$L=47$} &$L=26$ & 1.93 & 10.10 & 1.23 & 11.61 & 316 & 11.50 & 0.34 ...
The hadronic equation of state of HESS J1731-347 from the relativistic mean-field model with tensor coupling
Neutron star properties generated by the sets from Table. \ref{table.differL}.
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nucl-th, astro-ph.HE
2308.11991v1
\begin{tabular}{lllll} \hline & \multicolumn{2}{c}{\textbf{Rock-Paper-Scissors}} & \multicolumn{2}{c}{\textbf{Hanoi}} \\ & \multicolumn{1}{c}{\textbf{Before Interv.}} & \multicolumn{1}{c}{\textbf{After Interv.}} & \multicolumn{1}{c}{\textbf{Before Interv.}} & \multicolumn{1}{c}{\textbf{After Interv.}} \\ \hline R-CBM...
Relational Concept Based Models
\textbf{CBMs response to interventions}.
['\\newcommand{\\note}[1]{\\textcolor{red}{#1}}', '\\newcommand{\\mc}{\\mathcal}', '\\newcommand{\\B}{\\mathbf}']
cs.LG, cs.AI, cs.NE
2305.08559v3
\begin{tabular}{l|l|l} \hline & Later Sunset Counties (Robust) & Manipulation \\ \hline County Index & $0.083^{***}(0.024)$ & No \\ \hline Family Unity & $0.098^{***}(0.022)$ & No \\ \hline Community Health & $-0.0443^{**}(0.019)$ & No \\ \hline Institutional Health & $0.073^{**}(0.024)$ & No \\ \hline Efficacy & $0.0...
Designing Discontinuities
Designed Discontinuity Counterfactual Prediction on Social Capital: Local non-parametric regression discontinuity estimates
['\\newcommand{\\lav}[1]{\\textcolor{blue}{#1}}', '\\newcommand{\\ibtihal}[1]{\\textcolor{red}{#1}}']
cs.IT, cs.LG, econ.EM, math.IT
2311.03120v2
\begin{tabular}{ll@{\hspace{4em}}ll} \dag & \verb"\dag" & \S & \verb"\S" \\ \copyright & \verb"\copyright"& \ddag & \verb"\ddag"\\ \P & \verb"\P" & \pounds & \verb"\pounds" \\ \# & \verb"\#" & \$ & \verb"\$"\\ \% & \verb"\%" & \& & \verb"\&" \\ \_ & \verb"\_" & \{ & \verb"\{" \\ \} & \verb"\}" ...
Near-Infrared Ca II Triplet As An Stellar Activity Indicator: Library and Comparative Study
Miscellaneous symbols
['\\newcommand{\\head}[1]{\\subsubsection*{#1}}', '\\newcommand{\\btx}{\\textsc{Bib}\\TeX}', '\\newcommand{\\thestyle}{\\texttt{\\filename}}']
astro-ph.SR, astro-ph.EP
2312.14692v1
\begin{tabular}{c|l} 0 & No measures\\ 1 & Screening\\ 2 & Quarantine arrivals from high-risk regions\\ 3 & Ban on high-risk regions\\ 4 & Total border closure\\ \end{tabular}
Socioeconomic reorganization of communication and mobility networks in response to external shocks
International travel controls
null
physics.soc-ph
2312.10819v1
\begin{tabular}{|c|c|c|c|c|} \hline Metric & Precision (UA) & Recall (PA) & True Positive Rate & False Positive Rate \\ \hline \hline Stable cropland & $0.42 \pm 0.14$ & $0.44 \pm 0.10$ & $0.32$ & $0.11$\\ Stable non-crop & $0.83 \pm 0.05$ & $0.75 \pm 0.04$ & $0.72$ & $0.38$ \\ ...
Satellite Data Shows Resilience of Tigrayan Farmers in Crop Cultivation During Civil War
Accuracy metrics for four-class cropland change reference sample in Southern zone of Tigray. Overall accuracy is $0.65 \pm 0.04$.
null
cs.CY
2308.15553v1
\begin{tabular}{lrrrrl} {} & length (cm) & width (cm) \\ sepal & 5.4 & 3.0 & \\ petal & 4.5 & 1.5 & \\ \end{tabular}
Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster Analysis
Transformed instance\label{Tab:Tcr_restructured}
null
cs.IR, cs.LG, 62H30 (Primary), E.4; G.2.1
2308.02849v1
\begin{tabular}{lrccccccccc} \hline\hline Line & \multicolumn{1}{c}{$\lambda$} & $L_\mathrm{flx}$ & $L_\mathrm{lum}$\\ & \multicolumn{1}{c}{[nm]} & [erg s$^{-1}$ cm$^{-2}$] & [L$_\odot$] \\ \hline H15 & 371.20 & \sci{3.12}{-14} $\pm$ \sci{7.10}{-16} & \sci{2.35}{-5} $\pm$ \sci{5.33}{-7}\\ H14 & 37...
Brightness and mass accretion rate evolution during the 2022 burst of EX~Lupi
Line flux and line luminosities for the 2022 July 29 observations. Their calculation in explained in Sect.~\ref{ss:acclum}.\label{table:lflx_llum_202207}
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astro-ph.SR
2308.06540v1
\begin{tabular}{lrrr} Parameter & Measured arrival flux & Trapezoid arrival flux & Rectangular arrival flux \\ \hline $t_1$ (hour) & - & 9:00 & 9:00 \\ $t_2$ (hour) & - & 23:00 & 23:00 \\ b & - & 4.98 & 4.98 \\ $a_1^{Sun}$ & - & 26.94 & 23.38 \\ $a_1^{Mid}$ & - & 22.39 & 20.42 \\ $a_1^{Fri}$ & - & 21.58 & 17.54 \...
Mitigating Emergency Department Crowding With Stochastic Population Models
Fitted parameter values, for the measured arrival flux (A), trapezoid arrival-flux model (B), and rectangular arrival-flux model (C), used in the main text. The latter resembles model (B) [Eq.~(\ref{trap_flux})], but with a constant value during rush hours: $a^i_1 = a^i_2$ for every part of the week.
null
physics.soc-ph, cond-mat.stat-mech, q-bio.PE
2308.08918v1
\begin{tabular}{l|cccc} \hline % \hline & EPnL[$10^3$] & MAP[unit] & PnLMAP % & RPT[$\%$] & \#T \\ \hline IMM$_{PnL}$ %RB % & 16143.91 & { 68071.77} & 1735.32 & 182.77 & 8.10 & 38.58 & -0.33 & 1.33 & 3.80 & 0....
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making
Performance of IMM variations trained with different reward preferemces on FU dataset.
null
cs.LG, cs.AI, q-fin.TR
2307.10453v1
\begin{tabular}{l|c c c c c c c c c c} Facility & $E_l$[J] & $\Delta\tau$[fs] & $P_{\rm peak}$ [PW] & $w_0$[$\mu$m] & $\lambda_l$[$\mu$m] & $I_{\rm peak}$[W/cm$^2$] & $a_{\rm peak}$ & $\epsilon_w$ & $\epsilon_t$ & Rep.[Hz] \\\hline %Baseline & 20 & 300 & 2.6 & 1 & $4.25\times 10^{20}$ & 17.7 & 0.0612 & 0.0111 & ?? \\\h...
Luminosity for laser-electron colliders
The pulse duration is measured as intensity full-width half-max. For peak intensity estimates, the temporal profile is assumed to be gaussian. \label{tab:facilities}
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hep-ph, physics.acc-ph, physics.plasm-ph
2304.02723v1
\begin{tabular}{|c|ccccccccc|c|} \hline Data Set & \multicolumn{9}{|c|}{Number of Accidents} & Total Number \\[-0.5ex] & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & $\geq 8$ & of Policies \\ \hline \hline O (original) & 7,840 & 1,317 & 239 & 42 & 14 & 4 & 4 & 1 & 0 & 9,461 \\ M1 (modified \#1) & {\em 7,700} & 1,317 & {\em 379} & ...
Measuring Discrete Risks on Infinite Domains: Theoretical Foundations, Conditional Five Number Summaries, and Data Analyses
Original and modified data sets consisting of the numbers of accidents per policy.
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stat.AP, econ.GN, q-fin.EC, stat.ME
2310.04022v1
\begin{tabular}{|c|cccc|} \hline & $\tau = 1$ & $25$ & $75$ & $100$ \\ \hline NI Grid&\multicolumn{4}{c|}{Iterations}\\ \hline $M_1$ & $26$ & $34$ & $39$ & $41$ \\ $M_2$ & $15$ & $39$ & $70$ & $59$ \\ $M_3$ ...
Nonlinear Methods for Shape Optimization Problems in Liquid Crystal Tactoids
Subproblem A: Iteration count for QN with NI on each grid level. Iteration count for QN without NI is given on level $M_6$ in parenthesis. The final energy, $F^k$, and runtime in seconds for the full simulation of QN with NI (QN without NI in parenthesis) are also given. Both QN and QN with NI converge to the same ener...
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math.NA, cs.NA
2306.10946v4
\begin{tabular}{lccc} \hline \textbf{d} & \textbf{AUC}& \textbf{F1-score} \\\hline 8 & 0.973 & \bfseries 0.944 \\\hline 16 & \bfseries 0.976 & 0.923 \\\hline 32 & 0.941 & 0.897 \\\hline 64 & 0.936 & 0.892 \\\hline 128 & 0.884 & 0.8...
Att-KGCN: Tourist Attractions Recommendation System by using Attention mechanism and Knowledge Graph Convolution Network
$AUC$ and $F1$-$score$ Results on different d for Att-KGCN model. The best performance is highlighted in bold.
['\\newcommand{\\AF}[1]{\\textcolor{red}{[Afifa Khaled: #1]}}', '\\newcommand{\\AM}[1]{\\textcolor{blue}{[Ahmed Mubarak: #1]}}']
cs.IR, cs.AI, cs.GR, cs.LG
2307.14231v1
\begin{tabular}{ |c|c|c| } \hline Substrates & Normalised Area & Raman Shift (cm$^{-1}$) of quinoid mode \\ \hline Pristine & 0.8 & 1408 \\ Glass & 2.5 & 1404 \\ PDMS & 4.3 & 1402 \\ \hline \end{tabular}
Giant conductance of PSS:PEDOT micro-surfaces induced by microbubble lithography
Characteristics of the Raman peak due to the quinoid mode in PEDOT:PSS in the pristine form and after patterning on different substrates.
['\\newcommand*\\mycommand[1]{\\texttt{\\emph{#1}}}']
physics.chem-ph, cond-mat.mtrl-sci
2311.04148v1
\begin{tabular}{c c} \hline \textbf{Spoof} & \textbf{Number of Images} \\ \hline DRAGONSKIN & 1700 \\ ECOFLEX & 300 \\ GELAFIX & 100 \\ GELATIN & 100 \\ GLUE & 200 \\ KNETOSIL & 200 \\ LATEX & 100 \\ MOULDABLE-CLAY & 100 \\ MOULDABLE-GLUE & 900 \\ PAPER PRINTOUT & 1200 \\ PLAYDOH & 1700 \\ SILLY-PUTTY & 600...
Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach
Statisitcs of the COLFISPOOF dataset
null
cs.CV, cs.AI, cs.CR, cs.LG
2305.08394v1
\begin{tabular}{l l l l} stand\_sce 0 & KAI0-blue & KAI1-blue \\ \hline KAI0-red & 0.450/114.7 & 0.500/278.3\\ KAI1-red & 0.510/109.1 & 0.470/110.2 \\ \hline \end{tabular}
More Like Real World Game Challenge for Partially Observable Multi-Agent Cooperation
Results of built-in bots compete against each other in all scenarios of all sub-environments, and the number xxx/xxx means win rate/average time steps used.
null
cs.MA
2310.04620v2
\begin{tabular}{c|c|lll} \multicolumn{1}{l|}{Dive Type} & Parameter Estimate & Descent & Bottom & Ascent \\ \hline \multirow{3}{*}{1} & $\hat \mu$ & 0.70 & 0.02 & -0.67 \\ & $\hat \sigma$ & 0.45 & 0.23 & 0.40 \\ & $\hat p$ & ...
Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models
Maximum likelihood parameter estimates from the Killer Whale case study described in the main text. Recall that $D_t$ represents the change in depth in meters at time index $t$ and $E_t \in \{0,1\}$ encodes whether a dive ends at time index $t$. The parameter $\mu$ corresponds to the state-dependent mean of $D_t$, $\si...
['\\newcommand{\\conditional}[3][]{\\bbE_{#1}\\bigCond*{#2}{#3}}', '\\newcommand{\\argdot}{{\\,\\vcenter{\\hbox{\\tiny$\\bullet$}}\\,}} %generic argument dot', '\\newcommand{\\C}{{\\mathbb{C}}}', '\\newcommand{\\F}{{\\mathcal{F}}}', '\\newcommand{\\R}{{\\mathbb{R}}}', '\\newcommand{\\N}{{\\mathbb{N}}}', '\\newcommand{\...
stat.CO
2309.06039v1
\begin{tabular}{c|c|c|c|c} \hline & Water outflow [m$^3$/h] & Volume [m$^3$] & Surface [m$^2$] & Ventilation [m$^3$/h]\\ \hline Main hall & Top: 0.4, bottom: 70 & 120,000 & 5500 & 52,000 \\ \hline LS room & 4 & 2700 & 650 & 3000 \\ \hline Tunnel & 450 & 180,000 & - & 100,000 \\ \hline \e...
Environmental radon control in the 700-m underground laboratory at JUNO
\label{tab:ventilation} The optimized ventilation inside the experimental hall at the JUNO site from October 2022. The area of the surface includes both the rock and floor, and the ventilation is calculated based on the wind speed shown in Fig.~\ref{fig:ventilation}.
null
physics.ins-det, hep-ex
2307.01107v1
\begin{tabular}{|c c c c c|} \hline $A^{(0)}_{HT}$ & $B^{(0)}_{HT}$ & $B1^{(0)}_{HT}$ & $B2^{(0)}_{HT}$ & $\chi^2/d.o.f.$ \\ [0.5ex] \hline\hline $-0.335823(2)$ & $-0.5234(2)$ & $-0.179(7)$ & $-0.51(7)$ & $1.89$ \\ \hline \end{tabular}
Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
Results for the coefficients of the fit of eq.~(\ref{eq:logZHT2}) (upper table), for the coefficients of eq.~(\ref{eq:perHT}) (middle table) and for the coefficients of eq.~(\ref{eq:logZHT3}) (lower table).
['\\newcommand{\\dd}{{\\rm{d}}}', '\\newcommand{\\DD}{{\\rm{D}}}', '\\newcommand{\\tmb}[1]{{\\mbox{\\tiny{#1}}}}', '\\newcommand{\\Scl}{S_{\\mbox{\\tiny{cl}}}}', '\\newcommand{\\eq}{\\begin{equation}} ', '\\newcommand{\\en}{\\end{equation}} ', '\\newcommand{\\eqa}{\\begin{eqnarray}}', '\\newcommand{\\ena}{\\end{eqnarra...
hep-lat, cs.LG, hep-th
2312.01273v1
\begin{tabular}{|c|cc|cc|} \hline & \multicolumn{2}{c|}{SDPCP} & \multicolumn{2}{c|}{SDPNAL+} \\ \cline{1-5} success & 69 & 100.0\% & 69 & 100.0\% \\ fastest & 53 & 76.8\% & 16 & 23.2\% \\ fastest under success & 53 & 76.8\% & 16 & 23.2\% \\ not slower 1.2 times & 60 & 87.0\% & 25 & 36.2\% \\ not slower 1.2 times ...
An Augmented Lagrangian Primal-Dual Semismooth Newton Method for Multi-Block Composite Optimization
A statistic of computational results of SSNCP and SDPNAL+ for theta problems
['\\newcommand{\\comm}[1]{{\\color{red}#1}}', '\\newcommand{\\revise}[1]{{\\color{blue}#1}}', '\\newcommand{\\T}{\\top}', '\\newcommand{\\E}{\\mathbb{E}}', '\\newcommand\\numberthis{\\addtocounter{equation}{1}\\tag{\\theequation}}', '\\newcommand{\\etal}{ et al. }', '\\newcommand{\\br}{\\mathbb{R}}', '\\newcommand{\\ba...
math.OC
2309.05974v1
\begin{tabular}{|c|c|} \hline Decision Variable & References \\ \hline \hline Blocklength & ,, \\ \hline Transmit power and blocklength & \\ \hline Time duration of energy collected & \\ \hline Blocklength with ARQ & \\ \hline Bl...
Optimizing Reported Age of Information with Short Error Correction and Detection Codes
A summary of decision variables in previous works on finite-blocklength communication for AoI optimization.
['\\newcommand{\\argmax}{\\arg\\!\\max}', '\\newcommand{\\argmin}{\\arg\\!\\min}', '\\newcommand{\\sgn}{\\operatorname{sgn}}', '\\newcommand\\numberthis{\\addtocounter{equation}{1}\\tag{\\theequation}}']
cs.IT, math.IT
2304.05189v1
\begin{tabular}{l|c|c|c|c} \multicolumn{5}{c}{Short data}\\ Cosine & General & Conformal & Split & Jackknife \\ \hline diffpred & 0.57 & 0.34 & 0.39 & 0.98 \\ diffpredr & 0.31 & 0.28 & 0.4 & 0.26 \\ diffpredrs & 0.29 & 0.27 & 0.35 & 0.25 \\ diffpredl & 0.55 & 0.34 & 0.34 & 0.98 \\ diffpredlr & 0.3 & 0.28 & 0.4 & 0.22 \...
Individualized Conformal
Short data cosine
null
stat.ME, stat.OT, 62, G.3
2306.04704v1
\begin{tabular}{|c|c|c|c|c|c|c|c|} \hline Experiment & $N_{\rm dat}$ & Observable & Target & $\sqrt{s}$ [GeV]& $Q^2$ [GeV$^2$] &$y$ & Ref. \\ \hline \hline HERA (p) & 136 & $A_{\rm PV}$ for $e^+$ & proton & 319 & 120 - 30000 & 0.033 - 0.9 & \\ \hline HERA (p) & 138 & $A_{\rm PV}$ for $e^-$ & proton & ...
Signals of strong parity violation in deep inelastic scattering
Breakdown of the data sets considered in this analysis. For each data set, the table includes information on: the number of data points ($N_{\rm dat}$), the measured observable, the hadronic target, the center-of-mass energy $\sqrt{s}$, the covered range(s) in $Q^2$, the inelasticity $y$, and the published refer...
['\\newcommand{\\dd}{\\mathop{}\\!\\mathrm{d}}', '\\newcommand{\\Pperp}{\\boldsymbol{P}_{hT}}', '\\newcommand{\\PhT}{\\boldsymbol{P}_{hT}}', '\\newcommand{\\kperp}{\\boldsymbol{k}_T}', '\\newcommand{\\Phperp}{\\bm{P}_{hT}}', '\\newcommand{\\bT}{\\xi_T}', '\\newcommand{\\xbj}{x}', '\\newcommand{\\nslash}{n\\kern -0.50em...
hep-ph, hep-ex, nucl-ex, nucl-th
2310.12581v1
\begin{tabular}{ccc|ccc} \hline $p'$ & $p''$ & event scale & $A$ & $h^{\rm{A}}(p')$ & $p=f^{A}(p',p'')$ \\ \hline \multirow{2}{*}{$1-k_{i} e_2$} & \multirow{2}{*}{$1-k_{j} e_2$} & \multirow{2}{*}{$O(1)$ or $O(e_2)$} & $\rm{C}$ & $1-k_{i}e_2$ & $1-e_2$ \\ & & & $\rm{D}$ & $k_{i}e_2$ & $(k_{i}+1)e_2$ ...
Evolutionary stability of cooperation by the leading eight norms in indirect reciprocity under noisy and private assessment
How to calculate $\phi_{t+1}(p)$ from $\phi_{t}(p)$ for Type-1 norms.
['\\newcommand{\\bs}{\\boldsymbol} % by Fujimoto', '\\newcommand{\\mc}{\\mathcal} % by Fujimoto', '\\newcommand{\\CG}{\\mathrm{CG}} % by Fujimoto', '\\newcommand{\\CB}{\\mathrm{CB}} % by Fujimoto', '\\newcommand{\\DG}{\\mathrm{DG}} % by Fujimoto', '\\newcommand{\\DB}{\\mathrm{DB}} % by Fujimoto', '\\newcommand{\\GG}{\\...
q-bio.PE, cs.GT, cs.MA, physics.soc-ph
2312.06579v1
\begin{tabular}{|c|c|} \hline Locker Name & $\%$ Accuracy \\ \hline \hline Boson & 98.9 \\ Seth & 92.6 \\ Berlin & 96.1 \\ Grape & 99.3 \\ \hline \end{tabular}
Amazon Locker Capacity Management
Simulation system accuracy metrics
['\\newcommand{\\elite}{\\mathcal{E}}']
math.OC, cs.AI, 68T05, 90B05, 90B06, 90C90, G.1.6; I.2.6; I.2.8; I.6.3
2301.08274v3
\begin{tabular}{l|c|c} \hline \hline Source & \quad $\delta \amuLW$ (\%) \quad & \quad $\delta \amuLWTwo$ (\%) \quad \\ \hline Monte Carlo statistics & 0.19 & 2.44 \\ Continuum extrapolation ($a \to 0$, $\Delta_{\textrm{TB}}$) & 0.34 & 1.05\\ Finite-volume correction ($\Delta_{\textrm{FV}}$) & 0.16 & 0.23 \\ P...
Light-quark connected intermediate-window contributions to the muon $g-2$ hadronic vacuum polarization from lattice QCD
Approximate error budgets for $\amuLW$ and $\amuLWTwo$. \vspace{1mm}
['\\newcommand{\\creflastconjunction}{, and\\nobreakspace}', '\\newcommand{\\bi}{\\begin{itemize}}', '\\newcommand{\\ei}{\\end{itemize}}', '\\newcommand{\\ii}{\\item}', '\\newcommand{\\ben}{\\begin{enumerate}}', '\\newcommand{\\een}{\\end{enumerate}} ', '\\newcommand{\\be}{\\begin{equation}}', '\\newcommand{\\ee}{\\end...
hep-lat, hep-ph
2307.10618v1
\begin{tabular}{cccccc} \hline \multirow{2}{*}{Monitors} & \multicolumn{5}{c}{Memory size (MB) on frequency intervals} \\ & {[}0,20) & {[}20,40) & {[}40,60) & {[}60,80) & {[}80,100{]} \\ \hline {\bf Base scan (baseline)} & {\bf 19171} & {\bf 184} & {\bf...
FHPM: Fine-grained Huge Page Management For Virtualization
Full monitoring Results on Redis.
null
cs.OS
2307.12707v1
\begin{tabular}{c l l l} \hline Parameters & Value ($n=1$) & Unit & Source\\ \hline $\Lambda$ & $3032$ & day$^{-1}$ & assumed based on \\ $\beta$ & $0.15\times 10^{-8}$ & day$^{-1}$ & assumed based on \\ $\mu$ & $3.653 \times 10^{-5} $ & day$^{-1}$ & assumed based ...
Dynamics of a mathematical model of virus spreading incorporating the effect of a vaccine
Biologically meaningful parameters used in Fig. \ref{Fig:SIRV}.
['\\newcommand{\\e}{{\\rm e}}']
math.DS, q-bio.QM, 00A71 34D20 37M05 37N25 92D30
2311.00820v2
\begin{tabular}{l c cccc c cccc c cccc} & & \multicolumn{4}{c}{$\rho = 0.90$} & & \multicolumn{4}{c}{$\rho = 0.95$} & & \multicolumn{4}{c}{$\rho = 0.99$}\\ & & $\beta_1$ & $\beta_2$ & $\beta_3$ & $\beta_4$ & & $\beta_1$ & $\beta_2$ & $\beta_3$ & $\beta_4$ & & $\beta_1$ & $\beta_2$ & $\beta_3$ & $\beta_4$ \\ \textsc{l...
Bayesian inference for generalized linear models via quasi-posteriors
\small Heteroscedastic continuous data example. Frequentist coverage of credible intervals with nominal level $\rho \in \{0.90, 0.95, 0.99\}$ for Gaussian linear model (\textsc{lm}) and quasi-posterior (\textsc{qp}).
['\\newcommand{\\Y}{\\bm{Y}}', '\\newcommand{\\y}{\\bm{y}}', '\\newcommand{\\X}{\\bm{X}}', '\\newcommand{\\x}{\\bm{x}}', '\\newcommand{\\bmbeta}{\\bm{\\beta}}', '\\newcommand{\\hbmbeta}{\\Hat{\\bm{\\beta}}}', '\\newcommand{\\dd}{\\mathrm{d}}', '\\newcommand{\\var}{\\mathrm{var}}', '\\newcommand{\\E}{\\mathds{E}}', '\\n...
stat.ME
2309.08249v1
\begin{tabular}{|c|c|c|c|} \hline & $\check{d}(v,u)$ & $\hat{d}(v,u)$ & $\bar{d}(v)$ \\ \hline $\beta=0$ & $vu^{-1}$ & $\log(u)$ & $u(\log(v)-1)$ \\ $\beta \in [1,2] $ & $d_{\beta}(v,u)$ & 0 & 0 \\ \hline \end{tabular}
Deep Nonnegative Matrix Factorization with Beta Divergences
Differentiable convex-concave-constant decomposition of the $\beta$-divergence under the form \eqref{eq:3}~.
['\\newcommand{\\ngc}[1]{{\\color{brightpink} (\\textbf{NG:} #1)}}', '\\newcommand{\\ngi}[1]{{{\\color{brightpink} #1}}}', '\\newcommand{\\vlc}[1]{{\\color{blue} (\\textbf{VL:} #1)}}', '\\newcommand{\\vli}[1]{{{\\color{blue} #1}}}', '\\newcommand{\\Akwum}[1]{{{\\color{red} Akwum: #1}}}', '\\newcommand{\\hien}[1]{{{\\co...
cs.LG, cs.NA, eess.SP, math.NA, stat.ML
2308.15270v2
\begin{tabular}{cllll} & $A$ & 118 & 120 & 122 \\\hline & $Q_\beta$ (keV) & 527(21) & 1770(40) & 2960(50) \\ AME20& $T_{1/2}$ & 50.3(2) m & 50.80(21) s & 5.24(3) s \\ & $\log(ft)$ & 3.93(6) & 4.09(4) & 4.02(4) \\\hline & $Q_\beta$ (keV) & 587.1(28) & 1752.1(46) & 2861.1(25) \\ JYFLTRAP & $T_{1/2}$ & 50.3(2) m & ...
High-precision Penning-trap mass measurements of Cd and In isotopes at JYFLTRAP remove the fluctuations in the two-neutron separation energies
\label{tab:logft}A comparison of the $\log(ft)$ values for the $^{A}\mathrm{Cd}(0^+_{gs})\longrightarrow$ $^{A}\mathrm{In}(1^+_1)$ decay calculated with the $\log(ft)$ calculator using the $Q_\beta$ and the half-lives $T_{1/2}$ values from AME20/NUBASE20 with the results from JYFLTRAP (this work and Ref. ).
['\\newcommand*{\\ak}[1]{\\textcolor{blue}{AK: #1}}', '\\newcommand*{\\ms}[1]{\\textcolor{red}{MS: #1}}']
nucl-ex
2310.09302v2
\begin{tabular}{cl} \hline Model ID & Formula \\ \hline 1 & PM25 $\sim$ X \\ 2 & \texttt{PM25} $\sim$ \texttt{X + Season} \\ 3 & \texttt{PM25} $\sim$ \texttt{X : Season} \\ 4 & \texttt{PM25} $\sim$ \texttt{X * Season} \\ 5 & \texttt{PM25} $\sim$ \texttt{X + Milano} \\ 6 & \texttt{PM25} $\sim$ \texttt{X ...
To what extent airborne particulate matters are influenced by ammonia and nitrogen oxides?
Regression models considered. \\ \texttt{X = lag(PM2.5)+NH3:nl+NOX+T+RH+WS+BLH+RainyDay}
null
physics.ao-ph
2305.06056v2
\begin{tabular}{|c|c|c|c|} \hline $n_{1}$ & $n_{2}$ & Effective Hamiltonian \\ \hline 0 & 0 & $E=0$ \\ \hline 0 & 2 & $E=V$ \\ \hline 2 &0 & $E=V$ \\ \hline 2&2&$E=3V$\\ \hline 0&1&$H=J(a_{2}^{\dagger}b_{2}+ {\rm H.c.})$\\ \hline 1&0&$H=J(a_{1}^{\dagger}b_{1}+ {\rm H.c.})$\\ \hline 1&2&$H=J(a_{1}^{\dagger}b_{1}+ {\...
Dynamical localization and slow thermalization in a class of disorder-free periodically driven one-dimensional interacting systems
\label{effHa_twosite} Allowed configurations and the corresponding effective Hamiltonians for two unit cells at a DL point with $\mu \gg V$ for a period-4 model.
['\\newcommand\\bea{\\begin{eqnarray}}', '\\newcommand\\eea{\\end{eqnarray}}', '\\newcommand\\beq{\\begin{equation}}', '\\newcommand\\eeq{\\end{equation}}', '\\newcommand\\bib{\\bibitem}', '\\newcommand{\\new}{\\newpage}', '\\newcommand{\\noi}{\\noindent}', '\\newcommand{\\non}{\\nonumber}', '\\newcommand{\\al}{\\alpha...
cond-mat.stat-mech, cond-mat.dis-nn, cond-mat.str-el
2309.02354v2
\begin{tabular}{c c c c c } \hline Brick & Height & Support & Controller & Success Rate\\ \hline \multirow{8}{*}{1x2} & \multirow{4}{*}{1} & \multirow{4}{*}{Solid} & \multirow{2}{*}{Joint JPC} & (\textbf{100\%} / 96\%)\\ & & & & [\textbf{100\%}/\textbf{100\%}]\\ & & & \multirow{2}{*}{Cartesian JPC}...
A Lightweight and Transferable Design for Robust LEGO Manipulation
\footnotesize Success rate of LEGO brick manipulation. $(\cdot/\cdot):$ assembling and disassembling success rate without safe learning. $[\cdot/\cdot]:$ optimized assembling and disassembling success rate with safe learning.\label{table:EOAT_performance}
['\\newcommand{\\ie}{\\textit{i}.\\textit{e}., }', '\\newcommand{\\eg}{\\textit{e}.\\textit{g}., }', '\\newcommand{\\st}{\\text{s.t. }}', '\\newcommand\\ruixuan[1]{{\\color{magenta}{#1}}}']
cs.RO, cs.LG, cs.NE
2309.16532v1
\begin{tabular}{c c | c c} \hline\hline \noalign{\smallskip} BJD & $v_{\rm rad}$ & BJD & $v_{\rm rad}$\\ $-$ 2\,450\,000 &(km\,s$^{-1}$) & $-$ 2\,450\,000 & (km\,s$^{-1}$)\\ \noalign{\smallskip} \hline \noalign{\medskip} 9459.6291 & $+$363.2(11) & 9525.4729 & $+$359.0(11)\\ 9461.6065 ...
Exploring extreme brightness variations in blue supergiant MACHO 80.7443.1718: Evidence for companion-driven enhanced mass loss
Barycentric radial velocities $v_{\rm rad}$ of the primary component of ExtEV extracted from the SALT/HRS spectra.
['\\newcommand{\\comm}[1]{\\textcolor{comm}{#1}}', '\\newcommand{\\old}[1]{\\textcolor{old}{#1}}', '\\newcommand{\\new}[1]{\\textcolor{new}{#1}}', '\\newcommand{\\done}[1]{\\textcolor{done}{#1}}', '\\newcommand{\\todo}[1]{\\textcolor{todo}{#1}}', '\\newcommand{\\idea}[1]{\\textcolor{idea}{#1}}', '\\newcommand{\\kms}{km...
astro-ph.SR, astro-ph.HE
2309.03486v1
\begin{tabular}{lcccccc} config. id & cub. & s. cub. & sph. & s. sph. & cyl. & s. cyl. \\ \hline 1 DEISM & 1.918 & 1.214 & 2.268 & 1.143 & 2.062 & 0.950 \\ 1 DEISM-LC & 1.669 & 1.070 & 2.206 & 1.125 & 2.007 & 0.916 \\ \hline 2 DEISM & 1.974 & 1.051 & 2.521 & 1.003 & 1.900 & 0.964 \\ 2 DEISM-LC & 1.855 & 1.003 & 2.060 ...
Simulating room transfer functions between transducers mounted on audio devices using a modified image source method
Root-mean-square log spectral distance in decibel between DEISM and FEM, and between DEISM-LC and FEM. (s. denotes small.)
['\\newcommand{\\round}[1]{\\ensuremath{\\lfloor#1\\rceil}}', '\\newcommand{\\pluseq}{\\mathrel{+}=}', '\\newcommand{\\tc}[1]{\\textcolor{blue}{#1}}']
eess.AS, cs.SD
2306.07228v1
\begin{tabular}{||c||c|c|c|c|c|c|c| c||} \hline ~~~ ensemble ~~~ & ~~~ $\beta$ ~~~ & ~~~ $V/a^{4}$ ~~~ & ~~~ $a$\,(fm) ~~~ & ~ $M_{\pi}$\,(MeV) ~ & ~ $M_{D_{s}}$(GeV) ~ & ~ $L$ (fm) ~ & ~ $N_{g}$ ~ & ~ $N_{\rm s}$ ~\\ \hline cB211.072.64 & $1.778$ & $64^{3}\cdot 128$ & $0.07957~(13)$ & $140.2~(0.2)$ & $...
Spectral-function determination of complex electroweak amplitudes with lattice QCD
\it \small Parameters of the single ETMC ensemble used in this work. We give the lattice spacing $a$, the pion mass $M_\pi$, the $D_{s}$ meson mass $M_{D_{s}}$, the lattice extent $L$, the number of gauge configurations analyzed $N_{g}$, and the number $N_{\rm s}$ of random stochastic sources that have been used for e...
['\\newcommand{\\be}{\\begin{equation}}', '\\newcommand{\\ee}{\\end{equation}}', '\\newcommand{\\bea}{\\begin{eqnarray}}', '\\newcommand{\\eea}{\\end{eqnarray}}', '\\newcommand{\\bs}{\\boldsymbol}', '\\newcommand{\\beq}{\\begin{equation}}', '\\newcommand{\\eeq}{\\end{equation}}', '\\newcommand{\\msbar}{\\overline{\\foo...
hep-lat
2312.08625v1
\begin{tabular}{|c|c|c|} \hline {No.} & {Feature} & {Quantity} \\ \hline {1.} & {${p}^n$} & {current pressure}\\ {2.} & {${S}^n_w$} & {current water saturation}\\ {3.} & {${k}$} & {permeability}\\ {4.} & {${\phi}$} & {porosity}\\ {5.} & {${V}$} & {cell bulk volume}\\ {6.} & {${D}$} & {...
Graph Network Surrogate Model for Subsurface Flow Optimization
Node features used in the GNSM
['\\newcommand{\\Ud}{\\mathrm{d}} ', '\\newcommand{\\Bx}{\\mathbf{x}}', '\\newcommand{\\By}{\\mathbf{y}} ', '\\newcommand{\\Bh}{\\mathbf{h}} ', '\\newcommand{\\Ba}{\\mathbf{a}}', '\\newcommand{\\Bb}{\\mathbf{b}}', '\\newcommand{\\Bxstar}{\\mathbf{x}^{*}}', '\\newcommand{\\Bz}{\\mathbf{z}}', '\\newcommand{\\Bxi}{\\bolds...
physics.geo-ph, cs.LG
2307.02896v1
\begin{tabular}{ll|ll} \hline Parameter & Value & Parameter & Value\\ \hline $K$ & 64 & $T_s$ & 5 $\rm \mu$s \\ $G_t^s,\,G_r^s$ & 30 dB & $N_s$ & 16 \\ $G_r^c$ & 30 dB & $\sigma^2$ & -174 dBm/Hz \\ $G_s^c$ & 0 dB & $|a_k|^2$ & 1 W \\ $\eta$ & 1 m$^2$ & $\mu$ & 0.5 \\ \hline \end{tabular}
Robust Deployment and Resource Allocation for Robotic Aerial Base Station Enabled OFDM Integrated Sensing and Communication
Parameter Settings
null
cs.NI
2306.14691v1
\begin{tabular}{|c|c|c|} \hline \textbf{Time width [ms]} & \textbf{Average $\mu$ [V]} & \textbf{Variance $\sigma$ [V]} \\ \hline 0.05 & 2.31 & 0.38 \\ \hline 0.10 & 2.11 & 0.33 \\ \hline 0.15 & 1.86 & 0.30 \\ \hline 0.50 & 1.73 & 0.22 \\ ...
Tunable Synaptic Working Memory with Volatile Memristive Devices
Values of $\mu$ and $\sigma$ calculated for different pulse time widths.
['\\newcommand{\\erika}[1] {{\\color[rgb]{0 0.8 0.4} { \\textbf{Erika:} #1}}}']
cs.ET
2312.16280v1
\begin{tabular}{|c|c|} \hline $\alpha$ & $(a,\sigma)$ \\ \hline $\beta$ & $(b,\sigma^\prime)$ \\ \hline $\gamma$ & $(c,\sigma^{\prime\prime})$ \\ \hline $\alpha^\prime$ & $(a_1,\sigma_1)$ \\ \hline $\beta^\prime$ & $(a_2,\sigma_2)$ \\ \hline $\gamma^\prime$ & $(a_3,\sigma_3)$ \\ \hline $\delta^\prime$ & $(a_4,\si...
Two-particle self-consistent approach for broken symmetry phases
Relation between indices expressed in the compact and extended notations.
['\\newcommand{\\ket}[1]{\\left|#1\\right>}', '\\newcommand{\\bra}[1]{\\left<#1\\right|}', '\\newcommand{\\LK}[1]{\\textcolor{red}{#1}}', '\\newcommand{\\LC}[1]{\\textcolor{magenta}{#1}}', '\\newcommand{\\LD}[1]{\\textcolor{blue}{#1}}', '\\newcommand{\\up}{\\uparrow}', '\\newcommand{\\dow}{\\downarrow}', '\\newcommand{...
cond-mat.str-el, cond-mat.quant-gas, cond-mat.supr-con
2310.17348v1
\begin{tabular}{|c|c|c|c|} \hline \multicolumn{4}{|c|}{Inductive} \\ \hline Class Name & Precision & Recall & F1-Score \\ \hline Benign & 26.73\% & 92.18\% & 0.41\\ \hline DDos & 100.00\% & 91.70\% &0.96\\ \hline Dos & 39.13\% & 97.71\% &0.56 \\ \hline Reconnaissance & 99.68\% & 85.08\% & 0.92 \\...
Network Intrusion Detection with Edge-Directed Graph Multi-Head Attention Networks
Multi-class classification results of the proposed EDGMAT on dataset NF-BoT-IoT under the inductive setting.
null
cs.CR
2311.13166v1
\begin{tabular}{c|c|cl|c|c} \hline \multicolumn{1}{c|}{\textbf{Type}} & \multicolumn{1}{c|}{\textbf{Device}} & \multicolumn{2}{c|}{\textbf{Comp}} & \textbf{Mem} & \textbf{Num} \\ \hline Client-Weak & Raspberry Pi 4B & \multicolumn{2}{c|}{ARM Cortex-A72 CPU} & 2G & 4 \\ Client-Medium & Jetson Na...
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
Real test-bed platform settings
null
cs.LG, cs.DC
2309.01864v2
\begin{tabular}{c c c c c c} \hline Quantity & L1 & L2 & L3 & L4 & L5 \\ \hline $E_b$ [MeV] & -15.677 & -15.677 & -15.677 & -15.677 & -15.677 \\ $K$ [MeV] & 275 & 275 & 275 & 275 & 275 \\ $J$ [MeV] & 30.7 & 30.7 & 30.7 & 30.7 & 30.7 \\ $L$ [MeV] & 35 & 45 & 55 & 65 & 75 \\ \hline \end{tabular}
Far-from-equilibrium bulk-viscous transport coefficients in neutron star mergers
\label{L EoS nuclear} Nuclear properties of our EoSs L1 to L5. $E_b$ is the binding energy. $K$ is the nuclear compressibility. $J$ is the nuclear symmetry energy. $L$ is the slope of the nuclear symmetry energy. See for details on constraints on nuclear-matter properties.
['\\newcommand{\\cA}{\\mathcal A}', '\\newcommand{\\cB}{\\mathcal B}', '\\newcommand{\\cC}{\\mathcal C}', '\\newcommand{\\cD}{\\mathcal D}', '\\newcommand{\\cE}{\\mathcal E}', '\\newcommand{\\cF}{\\mathcal F}', '\\newcommand{\\cG}{\\mathcal G}', '\\newcommand{\\cH}{\\mathcal H}', '\\newcommand{\\cI}{\\mathcal I}', '\\n...
nucl-th, astro-ph.HE, hep-ph
2311.01879v1
\begin{tabular}{l c c c c c c c c c} \hline & ID & Cr & Al & Ti & Y & C & O & N & Ar \\ \hline Fe12Cr9Al & SP12 & 11.93 & 8.65 & 0.53 & 0.38 & 0.029 & 0.22 & 0.003 & 0.006 \\ Fe15Cr9Al & SP13 & 14.25 & 8.4 & 0.51 & 0.38 & 0.03 & 0.22 & 0.003 & 0.006 \\ Fe18Cr9Al & SP14 & 16.63 & 8.09 & 0.49 & 0.37 & 0.032 & 0.22...
Effects of Cr content on ion-irradiation hardening of FeCrAl ODS ferritic steels with 9 wt\% Al
Chemical compositions of FeCrAl ODS ferritic steels (wt\%, Bal. Fe)
null
cond-mat.mtrl-sci
2310.08941v1
\begin{tabular}{lccc} \hline \hline $\alpha$ & Silicate & Carbonate & Iron \\ \hline $-3.5$ & 1.5$\times 10^{-14}$ & 1.0$\times 10^{-14}$ & 3.6$\times10^{-14}$\\ $-3.0$ & 6.0$\times 10^{-12}$ & 4.1$\times 10^{-12}$ & 1.4$\times10^{-11}$\\ $-2.5$ & 3.0$\times 10^{-9}$ & 2.0$\times 10^{-9}$ & 7....
Polarized microwave emission from space particles in the upper atmosphere of the Earth
Nominal spatial density distribution $\rho_\mathrm{d}$ (g\,cm$^{-3}$) for each dust family and size distribution.
null
astro-ph.EP, astro-ph.IM, physics.space-ph
2312.07208v1
\begin{tabular}{c c c} \hline ML algorithm & Training time in s & Prediction time in s\\ \hline Linear CLF & 0.6493 & 0.0035\\ KNN & 0.1562 & 0.0040 \\ SVC & 0.6760 & 0.3854 \\ CLT & 0.6452 & 0.0045 \\ RFC & 0.7884 & 0.0080 \\ VAE-NN & 34.3143 & 0.0872\\ VAE-GAN & 1074.3471 & 0.1285\\ \e...
Experimental Investigation of Machine Learning based Soft-Failure Management using the Optical Spectrum
Execution time of different ML algorithms for soft-failure identification.
null
cs.NI, cs.LG
2312.03121v2
\begin{tabular}{|c|ll|} \multicolumn{3}{c}{\bf Approval(k=5)}\\ \hline Rank & Agent & Score\\ \hline 1 & {\tt text-davinci-003} & 4\\ 2 & {\tt Cohere Command beta (52.4B)} & 4\\ 3 & {\tt text-davinci-002} & 3\\ 4 & {\tt TNLG v2 (530B)} & 3\\ 5 & {\tt Anthropic-LM v4-s3 (52B)} & 3\\ 6 & {\tt YaLM (100B)} & 2\\ 7 & {\tt ...
Evaluating Agents using Social Choice Theory
VasE methods Approval, Borda, and Copeland on HELM Core Scenarios. \label{tab:helm-core-full1}
['\\newcommand{\\argmin}{\\operatornamewithlimits{argmin}}', '\\newcommand{\\argmax}{\\operatornamewithlimits{argmax}}', '\\newcommand{\\BR}{\\textsc{BR}}', '\\newcommand{\\bE}{\\mathbb{E}}', '\\newcommand{\\bI}{\\mathbb{I}}', '\\newcommand{\\ba}{\\mathbf{a}}', '\\newcommand{\\bpi}{\\bar{\\pi}}', '\\newcommand{\\pik}{{...
cs.AI, cs.GT, cs.MA
2312.17414v1
\begin{tabular}{|p{3.5cm}|p{3.5cm}|p{3.5cm}|} \hline \multicolumn{3}{|c|}{\textbf{4D Extended Flips}} \\ \hline $1D \Rightarrow 4D$ & $2D \Rightarrow 4D$ & $3D \Rightarrow 4D$\\ \hline $(1 \rightarrow 2) \Rightarrow (4 \rightarrow 8)$ & $(1 \rightarrow 3) \Rightarrow (3 \rightarrow 9)$ & $(1 \rightarrow 4) \Rightarrow ...
Space-time hypervolume meshing part 1: Point insertion, geometric predicates, and bistellar flips
Lower dimensional flips extended to four dimensions. The * indicates that the $(8 \rightarrow 8)$ flip has three different realizations.
['\\newcommand{\\bbold}{\\bm{b}}', '\\newcommand{\\ebold}{\\bm{e}}', '\\newcommand{\\fbold}{\\bm{f}}', '\\newcommand{\\gbold}{\\bm{g}}', '\\newcommand{\\hbold}{\\bm{h}}', '\\newcommand{\\Lbold}{\\bm{L}}', '\\newcommand{\\Tbold}{\\bm{T}}', '\\newcommand{\\nbold}{\\bm{n}}', '\\newcommand{\\nhatbold}{\\hat{\\bm{n}}}', '\\...
math.NA, cs.NA, 65M50, 52B11, 31B99, 76M10
2312.07665v1
\begin{tabular}{l|c|c|c|}\hline \hline &$\sigma$ [pb]&$\delta$(PDF)&$\delta$(scale) \\ \hline NNLO (QCD) &0.624& ${}^{+0.008}_{-0.010}$&${}^{+0.002}_{-0.002}$\\ \hline NNLO (QED) &0.621& ${}^{+0.008}_{-0.010}$&${}^{+0.001}_{-0.002}$\\ \hline N${}^3$LO (QCD, NNLO PDF) &0.618& ${}^{+0.008}_{-0.010}$&${}^{+0.002}_{-0.0...
Combining QED and Approximate N${}^3$LO QCD Corrections in a Global PDF Fit: MSHT20qed_an3lo PDFs
\sf $W^- H$ cross section predictions at $\sqrt{s}=$ 14 TeV and their corresponding PDF and scale uncertainties (with the central scale $\mu_F=\mu_R=M_{WH}$. Cross sections are calculated with \texttt{n3loxs}~, while the scale uncertainty is calculated using the 7--point variation described in this reference.
['\\newcommand{\\LHL}[1]{\\textcolor{blue}{\\bf[NOTE: LHL -- #1]}}', '\\newcommand{\\TC}[1]{\\textcolor{green}{\\bf[NOTE: TC -- #1]}}', '\\newcommand{\\RST}[1]{\\textcolor{red}{\\bf[NOTE: RST -- #1]}}', '\\newcommand{\\bs}[1]{\\boldsymbol{#1}}', '\\newcommand{\\MSbar}{\\overline{\\text{MS}}}', '\\newcommand{\\anlo}{a${...
hep-ph, hep-ex
2312.13962v1
\begin{tabular}{|l|c|c||l|c|c|} \hline $j_{\mathrm{eff}}$ & 3 band & 5 band & $d$ & 3 band & 5 band \\ \hline \hline $ \left| 3/2,1/2 \right\rangle$ & 0.987 & 0.985 & $\left| d_{xz} \right\rangle$ & 0.814 & 0.803\\ $ \left| 3/2,3/2 \right\rangle$ ...
The rich phase diagram of the prototypical iridate Ba$_2$IrO$_4$: Effective low-energy models and metal-insulator transition
% Band fillings within DMFT. % For both models, the fillings are reported with respect to the $j_{\mathrm{eff}}$ basis (left) and the orbital basis (right) respectively. % The calculations used cRPA values for the Coulomb tensor and $\beta = 80$ eV$^{-1}$.
['\\newcommand{\\R}{\\textbf{R}}', '\\newcommand{\\cdag}{\\hat{c}^{\\dagger}}', '\\newcommand{\\bairo}{Ba$_2$IrO$_4$}', '\\newcommand{\\jeff}{$j_{\\mathrm{eff}}=1/2$}', '\\newcommand{\\JEFF}{$j_{\\mathrm{eff}}=3/2$}', '\\newcommand{\\jeffT}{$\\tilde{j}_{\\mathrm{eff}}=1/2$}', '\\newcommand{\\JEFFT}{$\\tilde{j}_{\\mathr...
cond-mat.str-el, cond-mat.mtrl-sci
2312.07018v1
\begin{tabular}{|l|r|c|c|c|c|c|c|c} \hline $~~\gamma$ & $-0.2~~~$ & $-0.1$ & $0$ & $0.1$ & $0.2$ \\ \hline $~~b_{c_{1}}$ & $5.01561$ & $5.10779$ & $5.19615$ & $5.28114$ & $5.36311$ \\ \hline $~~r_{ph_{1}}$ & $2.86015$ & $2.93178$ & $3$ & $3.06525$ & $3.12788$ \\ \hline $~~b_{c_{2}}$ & $6.08691$ ...
Observational appearance and additional photon rings of the Horndeski asymmetric thin-shell wormhole
The critical impact parameter $b_{c_{i}}$ of the Horndeski ATW for various values of the parameter $\gamma$. We set $M_{1}=1$ and $M_{2}=1.2$.
null
gr-qc
2310.10526v2
\begin{tabular}{|r|r r r r r r|} \hline $h_1$ & $10^{-4}$ & $10^{-5}$ & $10^{-6}$ & $10^{-7}$ & $10^{-8}$ & $10^{-9}$ \\ \hline $s\,\backslash\,N$ & 626 & 857 & 1088 & 1320 & 1551 & 1783\\ \hline 1~~~~ & *** & *** & *** & *** & **...
A spectrally accurate step-by-step method for the numerical solution of fractional differential equations
\label{tab1} Maximum error for Problem (\ref{prob1}), $r=1.01$ and $k=30$.
null
math.NA, cs.NA, 65L05, 65L03, 65L99
2312.07815v1
\begin{tabular}{lcccccc} \hline \rule{0pt}{10pt} & \MC{2}{c}{AGC227973=J1250+0520 } % \\ \hline & \MC{2}{c}{PGC1264260=J1253+0409 } % \\ \hline & \MC{2}{c}{UGC08055=J1256+0348 } \\ \hline \rule{0pt}{10pt} $\lambda_{0}$(\AA) Ion & F($\lambda$)/F(H$\beta$)&I($\lambda$)/I(H$\beta$) & F($\lambda$)/F(H$\beta$...
Dwarfs in nearby voids: results of SALT spectroscopy
Line intensities and derived parameters of AGC227973, PGC1264260 and UGC08055
['\\newcommand{\\apj}{ApJ}', '\\newcommand{\\apjl}{ApJL}', '\\newcommand{\\aap}{A\\&A}', '\\newcommand{\\aaps}{A\\&AS}', '\\newcommand{\\aj}{AJ}', '\\newcommand{\\mnras}{MNRAS}', '\\newcommand{\\nat}{Nature}', '\\newcommand{\\pasp}{PASP}', '\\newcommand{\\apjs}{ApJS}', '\\newcommand{\\MC}{\\multicolumn}', '\\newcommand...
astro-ph.GA
2307.00393v1
\begin{tabular}{c|cc|cc} \hline & \multicolumn{2}{c|}{Seen Speaker} & \multicolumn{2}{c}{Unseen Speaker} \\ \hline & \multicolumn{1}{c|}{Same Language} & Different Language & \multicolumn{1}{c|}{Same Language} & Different Languag...
Using joint training speaker encoder with consistency loss to achieve cross-lingual voice conversion and expressive voice conversion
Naturalness MOS of XVC
null
eess.AS
2312.07510v1
\begin{tabular}{|c|c|} \hline Parameter & Value \\ \hline $\beta_e$ & $0.5$ \\ $\beta_\mu$ & $0.0$ \\ $\beta_\tau$ & $2.5$ \\ $\lambda_{dark}$ & $5$ \\ $\kappa$ & $-1$ \\ $M_N$ & $50$[GeV]\\ $M_{med}$ & $200-800$[GeV]\\ \hline \end{tabular}
Chasing leptophilic dark fermions at CLIC: the role of helicity
Parameter setting for the simulations, note that $\kappa$ is a non-minimal gauge interaction that is present only in the vector mode, see Ref. for details.
null
hep-ph
2310.14571v1
\begin{tabular}{llcclcrrrrl} \hline \hline GLEAM name & S$_{162}$ & MRC name & Type & $z$ & FLASH & NSI\_fit & NSI\_err & LAS$_{\rm 5GHz}$\\ & (Jy) & & & & components & IPS & IPS & (arcsec) \\ (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) \\ \hline \hline J234045-230243 & 1...
The FLASH pilot survey: an HI absorption search against MRC 1-Jy radio sources
continued..
['\\newcommand{\\kms}{km\\,s$^{-1}$} % kilometres per second', '\\newcommand{\\HI}{H{\\sc i}}', '\\newcommand{\\bibtex}{\\textsc{Bib}\\!\\TeX} % bibtex. ', '\\newcommand\\mjyb{mJy beam$^{-1}$}', '\\newcommand\\Msun{M$_{\\odot}$}', '\\newcommand\\Lsun{L$_{\\odot}$}', '\\newcommand\\cmc{cm$^{-3}$}', '\\newcommand\\hi{\\m...
astro-ph.GA
2309.04000v1
\begin{tabular}{llllllll}%{p{10pt}p{50pt}p{10pt}p{50pt}p{10pt}p{50pt}p{20pt}p{100pt}} \hline $E_1$ & 100 GPa & $E_2$ & 100 GPa & $G_{12}$ & 40 Gpa & $\nu_{12}$ & 0.25\\ $\nu_{23}$ & 0.25 & $l_0$ & $1\times10^{-2}$ m & $\alpha_r$ &0.002 & $\rho_{r}$ & $1.0\times10^{3}$ kg/m$^3$ \\ $k$ &$1\times10^{-9}$ & $c_1$ & 0.4...
Phase field modeling of hydraulic fracture propagation in transversely isotropic poroelastic media
Parameters for an isotropic specimen subjected to internal fluid pressure
null
physics.geo-ph
2303.14280v1
\begin{tabular}{l|ccc} Molecule & $N_A$ & $N_b$ & $N_\mathrm{aux}$ \\ \hline\hline Taxol & 113 & 1,032 & 3,599\\ Olestra & 453 & 3,181 & 11,633\\ Crambin & 642 & 5,559 & 19,500\\ Ubiquitin & 1,231 & 10,292 & 36,419 \end{tabular}
Distributed Memory, GPU Accelerated Fock Construction for Hybrid, Gaussian Basis Density Functional Theory
Representative systems considered in this study. $N_A$ is the number of atoms, $N_b$ is given for Cartesian 6-31G(d) and $N_\mathrm{aux}$ for def2-tzvp-j.
['\\newcommand{\\mat}[1]{\\ensuremath\\mathbf{#1}}', '\\newcommand{\\matspace}[3]{\\ensuremath\\mathbb{#1}^{#2 \\times #3}}', '\\newcommand{\\nbas}[0]{\\ensuremath N_b}', '\\newcommand{\\ngrid}[0]{\\ensuremath N_g}', '\\newcommand{\\needcite}{[{\\color{red} cite}]}', '\\newcommand{\\dbwy}[1]{\\textcolor{red}{DBWY:{#1}}...
physics.comp-ph, physics.chem-ph
2307.15204v1
\begin{tabular}[c]{lccccc}\\ %\hline Estimator & Effects & Emp.Estimates & Emp.Var & Emp.S.D. & Var Estimate \\ \hline $ \widehat{\delta}_{{HT,tot}}$ & 7 & 7.1931 & 9.6277 & 3.1028 & 11.2033 \\ $ \widehat{\delta^*}_{{HT,tot}}$ & 7 & 7.1931 & 9.6277 & 3.1028 & 11.2033 \\ $\widehat{\d...
Estimation of Causal Effects Under K-Nearest Neighbors Interference
Estimates Under Bernoulli Randomization Model 8
['\\newcommand{\\blind}{1}', '\\newcommand{\\nn}{\\nonumber}']
stat.ME
2309.13498v1
\begin{tabular}{|p{0.10\linewidth}|p{0.15\linewidth}|p{0.15\linewidth}|p{0.10\linewidth}|p{0.15\linewidth}|p{0.15\linewidth}|} \hline \textbf{Differences} & \textbf{Goal} & \textbf{Model} & \textbf{Roles} & \textbf{Protocol} & \textbf{Recovery} \\ \hline CFT & Ensure system reliability despite crash failures & ...
Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis
Comparison of CFT Family In Term of Operations.
null
cs.DC, cs.CR
2311.05170v1
\begin{tabular}{cccccccc} \hline $h $ & $|\vec{u}_c -\vec{u}_{ch}^{n+1}|_1$ & Rate & $|p_F -p_{Fh}^{n+1}|_1$& Rate & $\|p_f -p_{fh}^{n+1}\|_0$& Rate \\ \hline $\frac{1}{16} $ & 0.146752 & -- & 0.063144 &-- &0.001342 & -- \\ $\frac{1}{25} $ & 0.090678 & 1.08 & 0.043460 &0.84 &0.000612 & 1.76 \\ $...
A Local Parallel Finite Element Method for Super-Hydrophobic Proppants in a Hydraulic Fracturing System Based on a 2D/3D Transient Triple-Porosity Navier-Stokes Model
\label{T4}The convergence performance and computational cost of Algorithm \ref{Algorithm-1}(Traditional Algorithm) in 3D
['\\newcommand*{\\abs}[1]{\\lvert#1\\rvert}', '\\newcommand*{\\norm}[1]{\\lVert #1 \\rVert}', '\\newcommand*{\\tnorm}[1]{\\interleave#1\\interleave}', '\\newcommand*{\\bi}[1]{\\textbf{\\emph{#1}}}', '\\newcommand*{\\tu}[1]{\\textup{#1}}', '\\newcommand\\mathd{d}', '\\newcommand\\bsm{\\boldsymbol}', '\\newcommand{\\curl...
math.NA, cs.NA
2309.15380v1
\begin{tabular}{c c c c c c} \hline\hline ~~~~Coupled-structure~~~~& ~~~~~~~$E_{th}^{Theo}$ (Channel)~~~~~~~ & ~~~~~~$E_{cc}$~~~~~~ & ~~~~~~~$E_{B}$~~~~~~~ & ~~~~~~$E_{th}^{Exp}$~~~~~~ & ~~~~~$E'$~~~~~ \\ \hline $qss-\bar{q}c$ & 3235 ($\Xi D$) & 3237 & ub & 3187 & 3190...
Investigating excited $Ω_c$ states from pentaquark perspective
\label{cc 1/2}The coupled-channel energies of the $ssc\bar{q}q$ pentaquark system with $J^P=\frac{1}{2}^-$ (unit: MeV).
null
hep-ph, nucl-th
2311.15820v1
\begin{tabular}{ |p{4cm}||p{3cm}|p{3cm}| } \hline & Wind energy&Solar energy\\ \hline Average production 12am-7am (MWh) and $\%$ & 600 MWh /37.69$\%$ &7 MWh /1.01$\%$\\ \hline Average production 7am-7pm (MWh) and $\%$& 601 MWh /37.75$\%$ & 676 MWh /97.97$\%$\\ \hline Average $\%$ of the daily demand &...
Meeting Energy Needs by Balancing Cost and Sustainability through Linear Programming
Production based on the time of the day
null
math.OC, 90C05
2310.16095v1
\begin{tabular}{p{0.97\columnwidth}} % \label{nonexplainable} \hline \textbf{Section of Industry} - {{Example}} {{[Document]}} \\ \hline \hline \textbf{Consumer Non-Durables} - Total general and administrative costs decreased by \$79,000 in 1995 due primarily to the absence of a management ...
CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports
Examples of non-causal rationale of sentences by each section.
['\\newcommand{\\revised}[1]{\\textcolor{black}{#1}}', '\\newcommand\\BibTeX{B\\textsc{ib}\\TeX}']
cs.CL, cs.CE
2308.09075v1
\begin{tabular}{|l|l|} \hline Type & Variable \\ \hline \multirow{3}{*}{Vertiport States} & Availability - $P_a$ \\ & Port type - $P_t$ \\ & Location - $(x_p, y...
Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning
MDP formulation
['\\newcommand{\\vport}{vertiport} % What we call a vertiport ', '\\newcommand{\\port}{helipad} % What we call a port inside of the vertiport', '\\newcommand{\\lenactions}{11 actions} %This number keeps changing', '\\newcommand{\\learningrate}{$1E-5$}', '\\newcommand{\\threshold}{3 meters}']
cs.MA, cs.AI, cs.LG, cs.RO
2303.14510v1
\begin{tabular}{c|c|lllllll} \hline \hline \multirow{2}{*}{\textbf{Dataset}} & \multirow{2}{*}{\textbf{u}} & \multicolumn{6}{c}{\textbf{\# minimum utility by varying $k$}} \\ \cline{3-8} & &$k_1$ & $k_2$ & $k_3$ & $k_4$ & $k_5$ & $k_6$ \\ \hline chainstore & $u_1$ & 1643,851 & 697,152 & 488,009 & 407,079 &...
Targeted Mining of Top-k High Utility Itemsets
The minimum utility of TMKU with different $k$ values
null
cs.DB
2311.06714v1
\begin{tabular}{ccccc} \textbf{Review Group} & \textbf{Median} & \textbf{Mean} & \textbf{Std.} \\ \hline Popular & 1.9\% & 2.12\% & 1.73\%\\ Non-Popular & 1.60\% & 1.87\% & 2.18\% \\ \hline \end{tabular}
What factors influence the popularity of user-generated text in the creative domain? A case study of book reviews
The presence of named-entity
null
cs.CL, cs.IR
2304.04372v1
\begin{tabular}{c|c|c|c||c|c|c||c|c|c} \hline\hline Estimator & SV1F & SV2F & RH & SV1F & SV2F & RH & SV1F & SV2F & RH\\ \hline\hline & \multicolumn{3}{c||}{d=5, $\bar{\sigma}_\eta=3$} & \multicolumn{3}{c||}{d=5, $\bar{\sigma}_\eta=3.5$} & \multicolumn{3}{c}{d=5, $\bar{\sigma}_\eta=4$}\\ \hline PDF & 100\...
Symmetric positive semi-definite Fourier estimator of instantaneous variance-covariance matrix
\% of psd matrix produced by each estimator, when the efficient price process is produced by alternative models, in presence of heteroskedastic noise.
null
stat.ME, q-fin.ST
2306.13428v1
\begin{tabular}{lcccc} & $\alpha$ & $I$ & $\eta$ & $m$\\ \hline NGD (Algorithm \ref{algo:NGD}) &0.990 &10000 &0.003 &- \\ rMLE (Algorithm \ref{algo:rMLE.b}) &0.975&- &- &- \\ ONGD (Algorithm \ref{algo:ONGD}) &- &- &0.001 &100 \\ \end{tabular}
On tracking varying bounds when forecasting bounded time series
Hyperparameter values for each algorithm: forgetting factor $\alpha$, number of iterations $I$, learning rate/step size $\eta$ and minibatch size $m$.
['\\newcommand{\\blind}{0}']
stat.ML, cs.LG, stat.AP
2304.13128v3
\begin{tabular}{lccc} \hline \textbf{Model} & \textbf{Training Time} & \textbf{MAE} &\textbf{MAPE}\\ \hline IV-ANN & 446.204831 & $2.3235e^{-5}$ &$0.044106\%$ \\ GAN-2 & 209.194824 & $2.1376e^{-5}$ & $0.039810\%$\\ \hline \end{tabular}
Computing Volatility Surfaces using Generative Adversarial Networks with Minimal Arbitrage Violations
Timing and error comparison between GAN-2 and IV-ANN
null
q-fin.CP
2309.13498v1
\begin{tabular}{|p{0.10\linewidth}|p{0.05\linewidth}|p{0.05\linewidth}|p{0.05\linewidth}|p{0.05\linewidth}|p{0.05\linewidth}|p{0.05\linewidth}|p{0.05\linewidth}|p{0.05\linewidth}|p{0.05\linewidth}|p{0.08\linewidth}|p{0.05\linewidth}|} \hline \textbf{Attributes} & \textbf{Hard.} & \textbf{Trust } & \textbf{Toler.} &...
Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis
Candidates List 3.
null
cs.DC, cs.CR
2308.13642v1
\begin{tabular}{||c |c |c |c|c||} \hline \textbf{Model} &\textbf{Entanglement Scheme} & \textbf{Dimensionality Reduction} & \textbf{Accuracy} & \textbf{F-Score} \\ [0.5ex] \hline\hline XG Boost &None & None & 51.58\% & 58.18\% \\ \hline SVM & None & PCA-3 & 51.58\% & 5...
The Potential of Quantum Techniques for Stock Price Prediction
Best Models for Honeywell dataset
null
q-fin.CP
2312.00705v1
\begin{tabular}{lcrrrrc} %\hline %\textbf{Molecule} & \textbf{Coupling} & \textbf{CC3$^a$} & \textbf{CCSD$^a$} & \textbf{SOPPA} & \textbf{HRPA(D)} & \textbf{exp$^b$} \\ \hline CO & $^1J_{CO}$ & 14.62 & 15.07 & 20.26 & 18.40 & 16.4 \\ \hline %OF$_2$ & $^2J_{FF}$ & 1327.34 & 1211.78 & 1416.08 & 773.52 & \\ % &...
On the performance of HRPA(D) for NMR spin-spin coupling constants: Smaller molecules, aromatic and fluoroaromatic compounds
All the calculated SSCCs (in Hz) for set I including known experimental values.
['\\newcommand\\spas[1]{\\color{red} #1\\normalcolor}']
physics.chem-ph
2304.07620v1
\begin{tabular}{l|c|c|c} \hline \multicolumn{4}{c}{\textbf{Temperature Anomaly under RCP 4.5 at 2300: Methane Oxidation Inclusion}} \\ \hline Scenario & From Permafrost (1) & None (2) & From Permafrost, Fossil Fuels (3) \\ \hline Baseline & 3.90 & 3.90 & 3.93\\ 1\% & 3.90 & 3.89 & 3.92 \\ 10\% & 3.89 & 3.89 & 3.92...
Effect of Methane Mitigation on Global Temperature under a Permafrost Feedback
Temperature anomaly at 2300 between differing inclusions of methane oxidation. We compare different inclusions between the baseline RCP 4.5, and 1\% and 10\% annual methane emission reduction scenarios. Column (1) shows inclusion of methane oxidation from permafrost methane emissions. Column (2) reflects no methane oxi...
['\\newcommand{\\coo}{\\ensuremath{\\mathrm{CO_2}} }', '\\newcommand{\\meth}{\\ensuremath{\\mathrm{CH_4}} }', '\\newcommand{\\nit}{\\ensuremath{\\mathrm{N_2O}} }', '\\newcommand{\\NPP}{\\ensuremath{\\mathrm{NPP}} }', '\\newcommand{\\RH}{\\ensuremath{\\mathrm{RH}} }', '\\newcommand{\\soo}{\\ensuremath{\\mathrm{SO_2}} }'...
physics.ao-ph, math.DS
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