id
string | image
image | latex
string | title
string | caption
string | newcommands
string | categories
string |
|---|---|---|---|---|---|---|
2304.08967v2
|
\begin{tabular}{p{0.35\linewidth} | p{0.5\linewidth} | p{0.15\linewidth}}
\textbf{Email subject line} & \textbf{Hypothesis} & \textbf{Wave(s)} \\ \hline
Diversify Learning & Concise subject line that highlights project topic & Test, 1\\
Diversify Learning and get \$20 for a school & A small incentive that board members can allocate to a teacher of their choice may increase engagement & 1\\
Data science to diversify attendance boundaries & ``Data science'' might pique interest, especially in the context of boundary planning & 2\\
\$20 for your views on diversifying attendance boundaries & A small incentive that board members can allocate to a teacher of their choice may increase engagement & 2\\
Data science to improve learning experiences & ``Data science'', focusing on learning instead of diversity may increase engagement & 2 \\
Data science to diversify learning experiences & Same as above, but mentioning diversity and learning together & 2, 5 \\
More diversity with shorter commutes? & Stating the main finding of~ up front may increase engagement & 3, 5 \\
More diversity with shorter commutes? \$20 for your thoughts! & Stating the main finding of~ up front and adding the incentive may increase engagement & 3 \\
More diversity, less driving? & Pithy description of findings from~ & 4 \\
2,000 pennies for your thoughts & Pithy version of incentive email & 4 \\
Improve discussions about school diversity with families & Heard from districts that they find it difficult to talk with families about diversifying schools & 5 \\
Data science to diversify learning and reduce commuting times & Combing learning, diversity, and shorter commute messages from earlier & 6 \\
Data science to decide which schools to open or close & Framing in terms of a problem school districts often face that triggers boundary planning may increase engagement & 6 \\
Data science to diversify learning and reduce commute times & Has elements of promising subject lines throughout runs & Final \\
\end{tabular}
|
All a-board: sharing educational data science research with school districts
|
Email subject lines and corresponding hypotheses motivating their inclusion across different email waves.
| null |
cs.CY, stat.AP
|
|
2310.09433v2
|
\begin{tabular}[c]{|c|c|c|c|}
\hline
NMSE & $\tau_ \textrm{th}$ [ns] & $\Delta\omega/2\pi$ [GHz] & $\overline{P}_{\textrm {in}}$ [dBm]\\
\hline
0.0178 $\pm$ 0.0018 & 50 & -30 & -5.0 \\
\hline
0.0283 $\pm$ 0.0026 & 100 & -65 & -2.5 \\
\hline
0.0412 $\pm$ 0.0030 & 150 & -95 & 7.5 \\
\hline
0.0611 $\pm$ 0.0033 & 200 & -145 & 15 \\
\hline
0.0736 $\pm$ 0.0044 & 300 & 75 & -7.5 \\
\hline
0.0748 $\pm$ 0.0044 & 400 & 80 & -7.5 \\
\hline
\end{tabular}
|
Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing
|
Minimum NMSE of testing set prediction for selected values of $\tau_ \textrm{th}$ ($\tau_ \textrm{FC}$ = 10 ns, $\alpha$ = 0.8 dB/cm).
|
['\\newcommand\\crefrangeconjunction{--}']
|
physics.optics, cs.ET, cs.LG, cs.NE
|
|
2310.06082v2
|
\begin{tabular}{ |c|c|c|c|c| }
\hline
\multicolumn{5}{|c|}{Cross Section $p_{n}$, $135^{o}\,<\,\alpha_{3D}\,<\,180^{o}$} \\
\hline
\hline
Bin \# & Low edge [GeV/\textit{c}] & High edge [GeV/\textit{c}] & Cross Section [$10^{-38}\frac{cm^{2}}{(GeV/\textit{c})\,^{40}Ar}$] & Uncertainty [$10^{-38}\frac{cm^{2}}{(GeV/\textit{c})\,^{40}Ar}$] \\
\hline
\hline
1 & 0 & 0.08 & 0.0167 & 0.011364\\
2 & 0.08 & 0.15 & 0.08459 & 0.016098\\
3 & 0.15 & 0.23 & 0.12713 & 0.019017\\
4 & 0.23 & 0.3 & 0.14042 & 0.02328\\
5 & 0.3 & 0.4 & 0.098465 & 0.017631\\
6 & 0.4 & 0.85 & 0.068701 & 0.014215\\
\hline
\end{tabular}
|
Measurement of nuclear effects in neutrino-argon interactions using generalized kinematic imbalance variables with the MicroBooNE detector
|
Double differential cross section measurement as a function of $p_{n}$ for $135^{o}\,<\,\alpha_{3D}\,<\,180^{o}$.
|
['\\newcommand{\\ANL}{Argonne National Laboratory (ANL), Lemont, IL, 60439, USA}', '\\newcommand{\\Bern}{Universit{\\"a}t Bern, Bern CH-3012, Switzerland}', '\\newcommand{\\BNL}{Brookhaven National Laboratory (BNL), Upton, NY, 11973, USA}', '\\newcommand{\\UCSB}{University of California, Santa Barbara, CA, 93106, USA}', '\\newcommand{\\Cambridge}{University of Cambridge, Cambridge CB3 0HE, United Kingdom}', "\\newcommand{\\CIEMAT}{Centro de Investigaciones Energ\\'{e}ticas, Medioambientales y Tecnol\\'{o}gicas (CIEMAT), Madrid E-28040, Spain}", '\\newcommand{\\Chicago}{University of Chicago, Chicago, IL, 60637, USA}', '\\newcommand{\\Cincinnati}{University of Cincinnati, Cincinnati, OH, 45221, USA}', '\\newcommand{\\CSU}{Colorado State University, Fort Collins, CO, 80523, USA}', '\\newcommand{\\Columbia}{Columbia University, New York, NY, 10027, USA}', '\\newcommand{\\Edinburgh}{University of Edinburgh, Edinburgh EH9 3FD, United Kingdom}', '\\newcommand{\\FNAL}{Fermi National Accelerator Laboratory (FNAL), Batavia, IL 60510, USA}', '\\newcommand{\\Granada}{Universidad de Granada, Granada E-18071, Spain}', '\\newcommand{\\Harvard}{Harvard University, Cambridge, MA 02138, USA}', '\\newcommand{\\IIT}{Illinois Institute of Technology (IIT), Chicago, IL 60616, USA}', '\\newcommand{\\Indiana}{Indiana University, Bloomington, IN 47405, USA}', '\\newcommand{\\KSU}{Kansas State University (KSU), Manhattan, KS, 66506, USA}', '\\newcommand{\\Lancaster}{Lancaster University, Lancaster LA1 4YW, United Kingdom}', '\\newcommand{\\LANL}{Los Alamos National Laboratory (LANL), Los Alamos, NM, 87545, USA}', '\\newcommand{\\Louisiana}{Louisiana State University, Baton Rouge, LA, 70803, USA}', '\\newcommand{\\Manchester}{The University of Manchester, Manchester M13 9PL, United Kingdom}', '\\newcommand{\\MIT}{Massachusetts Institute of Technology (MIT), Cambridge, MA, 02139, USA}', '\\newcommand{\\Michigan}{University of Michigan, Ann Arbor, MI, 48109, USA}', '\\newcommand{\\MSU}{Michigan State University, East Lansing, MI 48824, USA}', '\\newcommand{\\Minnesota}{University of Minnesota, Minneapolis, MN, 55455, USA}', '\\newcommand{\\Nankai}{Nankai University, Nankai District, Tianjin 300071, China}', '\\newcommand{\\NMSU}{New Mexico State University (NMSU), Las Cruces, NM, 88003, USA}', '\\newcommand{\\Oxford}{University of Oxford, Oxford OX1 3RH, United Kingdom}', '\\newcommand{\\Pitt}{University of Pittsburgh, Pittsburgh, PA, 15260, USA}', '\\newcommand{\\Rutgers}{Rutgers University, Piscataway, NJ, 08854, USA}', '\\newcommand{\\SLAC}{SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA}', '\\newcommand{\\SDSMT}{South Dakota School of Mines and Technology (SDSMT), Rapid City, SD, 57701, USA}', '\\newcommand{\\Maine}{University of Southern Maine, Portland, ME, 04104, USA}', '\\newcommand{\\Syracuse}{Syracuse University, Syracuse, NY, 13244, USA}', '\\newcommand{\\TelAviv}{Tel Aviv University, Tel Aviv, Israel, 69978}', '\\newcommand{\\Tennessee}{University of Tennessee, Knoxville, TN, 37996, USA}', '\\newcommand{\\UTA}{University of Texas, Arlington, TX, 76019, USA}', '\\newcommand{\\Tufts}{Tufts University, Medford, MA, 02155, USA}', '\\newcommand{\\UCL}{University College London, London WC1E 6BT, United Kingdom}', '\\newcommand{\\VTech}{Center for Neutrino Physics, Virginia Tech, Blacksburg, VA, 24061, USA}', '\\newcommand{\\Warwick}{University of Warwick, Coventry CV4 7AL, United Kingdom}', '\\newcommand{\\Yale}{Wright Laboratory, Department of Physics, Yale University, New Haven, CT, 06520, USA}']
|
nucl-ex, hep-ex
|
|
2304.06858v3
|
\begin{tabular}{l|cccc}
\hline
Model & Accuracy & Precision & Recall & F1 \\
\hline
BERTweet-cov19 & 88.1 & 47.9 & 28.9 & 33.9 \\
BERT-base & 88.2 & 54.5 & 25.3 & 31.7\\
BERT-large & 88.7 & 57 & 32 & 38\\
RoBERTa-base & 88.2 & 48.8 & 31.9 & 37\\
RoBERTa-large & 89.2 & 51.6 & 41.2 & 44.7\\
\end{tabular}
|
Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter
|
Evaluating baselines on the subjects of criticism prediction task.
|
['\\newcommand{\\blue}[1]{\\textcolor{blue}{#1}}', '\\newcommand{\\red}[1]{\\textcolor{cardinal}{#1}}', '\\newcommand{\\green}[1]{\\textcolor{officegreen}{#1}}', '\\newcommand{\\brown}[1]{\\textcolor{lightbrown}{#1}}']
|
cs.SI, cs.CL, cs.LG
|
|
2305.19775v1
|
\begin{tabular}{c|c|c}
\hline
\textbf{Parameter} & \textbf{Description} & \textbf{Unit} \tabularnewline
\hline
$T_0$ & Initial temperature. & Kelvin \tabularnewline
$T_w$ & Ambient temperature. & Kelvin \tabularnewline
$\rho$ & Density. & $km/m^3$ \tabularnewline
$\eta$ & Temperature averaging factor for shear plane. & - \tabularnewline
$\psi$ & Temperature averaging factor for tool-chip interface. & - \tabularnewline
$jc\_A$ & $A$ coefficient in the Johnson-Cook law. & Pa \tabularnewline
$jc\_B$ & $B$ coefficient in the Johnson-Cook law. & Pa \tabularnewline
$jc\_n$ & $n$ coefficient in the Johnson-Cook law. & - \tabularnewline
$jc\_C$ & $C$ coefficient in the Johnson-Cook law. & - \tabularnewline
$jc\_m$ & $m$ coefficient in the Johnson-Cook law. & - \tabularnewline
$T_m$ & Melting temperature. & Kelvin \tabularnewline
$jc\_\dot{\epsilon}_0$ & $\dot{\epsilon}_0$ coefficient for the Johnson-Cook law. & $1/s$ \tabularnewline
\hline
\end{tabular}
|
Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization
|
Description of material parameters that define a material and a task. Taken from and their provided repository.
|
['\\newcommand{\\leo}[1]{{\\color{blue}[#1]}}']
|
cs.NE
|
|
2307.02760v1
|
\begin{tabular}{crrrr}
\hline
\multirow{2}{*}{} & \multicolumn{4}{c}{I tried cannabis $\dots$} \\ \cline{2-5}
Alcohol consumption & Never & Once or twice & More often & Total \\ \hline
At most once/month & 204 & 6 & 1 & 211 \\
Twice/month & 211 & 13 & 5 & 229 \\
Twice/week & 357 & 44 & 38 & 439 \\
More often & 92 & 34 & 49 & 175 \\ \hline
Total & 864 & 97 & 93 & 1054 \\ \hline
\end{tabular}
|
Geometric Mean Type of Proportional Reduction in Variation Measure for Two-Way Contingency Tables
|
Students’ survey about cannabis use at the University of Ioannina
|
['\\newcommand{\\ep}{\\varepsilon}', '\\newcommand{\\tr}{{\\rm tr}}', '\\newcommand{\\Var}{{\\rm Var}}', '\\newcommand{\\Cov}{{\\rm Cov}}', '\\newcommand{\\diag}{{\\rm diag}}', '\\newcommand{\\ve}{{\\rm vec}}', '\\newcommand{\\pd}{\\partial}', '\\newcommand{\\Bias}{{\\rm Bias}}', '\\newcommand{\\MSE}{{\\rm MSE}}', '\\newcommand{\\E}{\\mathbb{E}}', '\\newcommand{\\iid}{ \\stackrel{i.i.d.}{\\sim} }', '\\newcommand{\\mbf}[1]{\\mathbf{#1}}', '\\newcommand{\\mbb}[1]{\\mathbb{#1}}', '\\newcommand{\\trm}[1]{\\textrm{#1}}', '\\newcommand{\\tit}[1]{\\textit{#1}}', '\\newcommand{\\mcal}[1]{\\mathcal{#1}}']
|
stat.ME
|
|
2311.09167v1
|
\begin{tabular}{|l|c|c|c|}
\hline
Sample & A & B & C \\
\hline
$\tilde{m}$ & $31.5 \pm 5.2$ & $28.6$ & $25.4$ \\
$\tilde{r}$ & $0.156 \pm 0.025$ & $0.139$ & $0.170$ \\
$x_B$ & $0.997 \pm 0.160$ & $0.856$* & $0.807$ \\
$k_S$ (N$/$m) & $0.17 \pm 0.03$ & $0.137 \pm 0.002$ & $0.160 \pm 0.002$ \\
\hline
\end{tabular}
|
Thermal noise calibration of functionalized cantilevers for force microscopy: effects of the colloidal probe position
|
Best fit parameters, single point contact model, with $\tilde{m}$, $\tilde{r}$ and $x_B$ extracted from the resonance frequency ratio of the loaded samples, while $k_S$ is fitted from the thermal profiles of Figs.~\ref{FitModes} and \ref{FitModesAC}. Those best fit parameters are close to the ones of Tab.~\ref{tab:mrxB}, showing the equivalence of both approaches. Estimated uncertainty is not reported when below the last displayed digit. *Note that for sample B, the fit is not converging unless we fix the value of $x_B$.
|
['\\newcommand{\\CP}{\\mathrm{B}}']
|
physics.ins-det, cond-mat.mes-hall, cond-mat.soft
|
|
2310.10356v1
|
\begin{tabular}{ccccccc}
\hline
\hline
sources & $^{13}$CS & C$^{34}$S & SO & SO$^+$ & NS & NO \\
\hline
\multicolumn{7}{c}{HMSCs} \\
\hline
00117b & --36.02(0.08) & --36.07(0.02) & --35.77(0.02) & -- & -- & --36.1(0.12) \\
AFGLa & --2.71(0.05) & --2.87(0.04) & --2.04(0.05) & -- & --2.79(0.14) & --2.79(0.04) \\
05358a & --16.46(0.08) & --16.48(0.08) & --16.17(0.08) & --16.0(0.2) & --15.70(0.02) &--16.18(0.08) \\
20293a & 6.39(0.05) & 7.00(0.02) & 7.08(0.04) & -- & -- & 6.37(0.16) \\
22134b & --18.46(0.08) & --18.55(0.02) & 18.53(0.02) & -- & -- &--18.46(0.13) \\
\hline
\multicolumn{7}{c}{HMPOs} \\
\hline
00117a & --36.02(0.08) & --36.20(0.03) & --36.21(0.02) & -- & --35.83(0.12 & --36.14(0.09) \\
05358b & --16.33(0.07) & --16.43(0.06) & --16.12(0.03) & --16.1(0.2) & --15.91(0.06 & --16.10(0.08) \\
18517 & 43.70(0.03) & 44.14(0.02) & 43.94(0.02) & 43.2(0.3) & 43.82(0.14) & 44.44(0.08) \\
21307 & --46.29(0.06) & --46.35(0.02) & --46.33(0.03) & -- & -- & -- \\
23385 & --49.8(0.1) & --49.9(0.1) & --49.54(0.03) & --49.9(0.3) & --50.3(0.1) & --49.24(0.15) \\
\hline
\multicolumn{7}{c}{UCHIIs} \\
\hline
G75 & --0.30(0.07) & 0.22(0.07) & --0.01(0.5) & 0.4(0.2) & 0.30(0.09) & --0.04(0.11) \\
19410 & 22.58(0.03) & 23.07(0.04) & 22.75(0.02) & 22.7(0.1) & 22.67(0.02) & 22.80(0.06) \\
22134 & --18.29(0.03) & --18.41(0.02) & --17.97(0.02) & --17.8(0.2) & --18.1(0.2) & --18.40(0.10) \\
23033 & --52.96(0.03) & --53.06(0.02) & --53.02(0.02) & --53.3(0.2) & --53.4(0.1) & --53.09(0.05) \\
NGC7538 & --56.93(0.08) & --57.06(0.07) & --56.96(0.02) & --56.6(0.2) & --57.11(0.09) & --57.04(0.08) \\
\hline
\end{tabular}
|
The evolution of sulphur-bearing molecules in high-mass star-forming cores
|
Best fit centroid velocities in \kms\ for the molecules with two atoms analysed. Numbers in brackets give the uncertainties.
|
['\\newcommand{\\asec}{$^{\\prime\\prime}$}', '\\newcommand{\\pas}{.\\hskip-2pt$^{\\prime\\prime}$}']
|
astro-ph.GA
|
|
2303.10117v1
|
\begin{tabular}{llcccccc}
\hline\hline
& & \multicolumn{3}{c}{Random groups} & \multicolumn{3}{c}{Fixed groups} \\
& $N\setminus T$ & 200 & 300 & 400 & 200 & 300 & 400 \\
${\sf RMSE}_{{\rm pre}}$ & 100 & 0.061 & 0.050 & \multicolumn{1}{r|}{0.048} & 0.066 & 0.052 & 0.030 \\
& & (0.058) & (0.031) & \multicolumn{1}{r|}{(0.052)} & (0.006) & (0.004) & (0.002) \\
& 200 & 0.116 & 0.074 & \multicolumn{1}{r|}{0.055} & 0.123 & 0.076 & 0.055 \\
& & (0.009) & (0.005) & \multicolumn{1}{r|}{(0.003)} & (0.010) & (0.005) & (0.003) \\
${\sf RMSE}_{{\rm post}}$ & 100 & 0.048 & 0.041 & \multicolumn{1}{r|}{0.043} & 0.049 & 0.040 & 0.036 \\
& & (0.007) & (0.011) & \multicolumn{1}{r|}{ (0.044)} & (0.007) & (0.006) & (0.005) \\
& 200 & 0.068 & 0.043 & \multicolumn{1}{r|}{0.039} & 0.064 & 0.043 & 0.038 \\
& & (0.058) & (0.007) & \multicolumn{1}{r|}{(0.006)} & (0.005) & (0.006) & (0.005) \\
${\sf RMSE}_{{\rm ora}}$ & 100 & 0.048 & 0.040 & \multicolumn{1}{r|}{0.038} & 0.049 & 0.040 & 0.036 \\
& & (0.007) & (0.006) & \multicolumn{1}{r|}{(0.007)} & (0.007) & (0.006) & (0.005) \\
& 200 & 0.052 & 0.043 & \multicolumn{1}{r|}{0.039} & 0.051 & 0.043 & 0.038 \\
& & (0.008) & (0.006) & \multicolumn{1}{r|}{(0.006)} & (0.007) & (0.006) & (0.005) \\
\hline\hline
\end{tabular}
|
Inference of Grouped Time-Varying Network Vector Autoregression Models
|
Estimation performance of the time-varying coefficients
| null |
stat.ME, econ.EM
|
|
2301.11491v1
|
\begin{tabular}{lcccr}
\hline
& \multicolumn{2}{c}{$T = 150$} & \multicolumn{2}{c}{$T = 300$} \\
Method & $p = 3$ & $p = 5$ & $p = 3$ & $p = 5$ \\
\hline
\multicolumn{5}{c}{propotion of times $\widehat{K} \neq K$} \\
MNSBS & \textbf{\textit{0.040}} & \textbf{\textit{0.025}} & \textbf{\textit{0.140 }} & \textbf{\textit{0.105}} \\
NMP & 0.170 & 0.325 & 0.255 & 0.420 \\
ECP & \textbf{0.010} & \textbf{0} & 0.570 & 0.630 \\
SBS & 0.705 & 0.540 & \textbf{0.115} & \textbf{0.010} \\
DCBS & 0.210 & 0.120 & 0.145 & 0.115 \\
\multicolumn{5}{c}{average (standard deviation) of $d_{\mathrm{H}}$} \\
MNSBS & \textbf{\textit{0.026}} (0.042) & \textbf{\textit{0.012}} (0.024) & \textbf{\textit{0.028}} (0.045) & \textbf{\textit{0.016}} (0.038) \\
NMP & 0.064 (0.060) & 0.077 (0.051) & 0.045 (0.050) & 0.064 (0.059) \\
ECP & \textbf{0.017} (0.021) & \textbf{0.007} (0.009) & 0.080 (0.065) & 0.087 (0.066) \\
SBS & 0.245 (0.138) & 0.190 (0.157) & 0.051 (0.096) & \textbf{0.010} (0.028) \\
DCBS & 0.061 (0.087) & 0.024 (0.035) & \textbf{0.025} (0.036) & 0.017 (0.033) \\
\hline
\end{tabular}
|
Change point detection and inference in multivariable nonparametric models under mixing conditions
|
Localisation results of Scenario 1.
|
['\\newcommand{\\ou}{\\"{o}}', '\\newcommand{\\p}{\\mathcal P}', '\\newcommand{\\f}{\\mathcal F}', '\\newcommand{\\diag}{\\text{diag}}', '\\newcommand{\\op}{\\text{op}}', '\\newcommand{\\sign}{\\text{sign}}', '\\newcommand{\\cov}{\\text{Cov}}', '\\newcommand{\\var}{\\text{Var}}', '\\newcommand{\\I}{{\\mathcal I }} ', '\\newcommand{\\lc}{ \\lceil} ', '\\newcommand{\\rc}{ \\rceil } ', '\\newcommand{\\kse}{\\kappa_{\\max}^{s,e}}', '\\newcommand{\\dint}{\\,\\mathrm{d}}', '\\newcommand{\\lb}{ \\langle}', '\\newcommand{\\rb}{ \\rangle}', '\\newcommand{\\N}{\\mathcal N }', '\\newcommand{\\h}{\\mathcal H}', '\\newcommand{\\lt}{ {\\mathcal L^2}}', '\\newcommand{\\lf}{\\lfloor}', '\\newcommand{\\rf}{\\rfloor}', '\\newcommand{\\hr}{{ \\mathcal H^{r} } }']
|
math.ST, stat.ME, stat.TH
|
|
2311.08476v3
|
\begin{tabular}{*{2}{ll@{\hspace{4em}}}ll}
\oe & \verb"\oe" & \aa & \verb"\aa" & \l & \verb"\l" \\
\OE & \verb"\OE" & \AA & \verb"\AA" & \L & \verb"\L" \\
\ae & \verb"\ae" & \o & \verb"\o" & \ss & \verb"\ss" \\
\AE & \verb"\AE" & \O & \verb"\O" & & \\
\end{tabular}
|
Deconstructing Alien Hunting
|
National symbols
|
['\\newcommand{\\vdag}{(v)^\\dagger}', '\\newcommand\\aastex{AAS\\TeX}', '\\newcommand\\latex{La\\TeX}']
|
astro-ph.IM
|
|
2303.06106v1
|
\begin{tabular}{l | r r r r r r}
& \multicolumn{6}{c}{students} \\
professors & Physics & Chemistry & Medicine & Economics & Any & None \\ \hline
Physics & 98 & 16 & 2 & 0 & 116 & 107\\
Chemistry & 12 & 66 & 18 & 0 & 96 & 92 \\
Medicine & 0 & 15 & 91 & 0 & 106 & 119 \\
Economics & 0 & 0 & 0 & 42 & 42 & 49 \\ \hline
\end{tabular}
|
The Nobel Family
|
Nobel laureates as PhD advisors.
| null |
econ.GN, q-fin.EC
|
|
2309.00851v2
|
\begin{tabular}{|l|l|l|}\hline
{Type } & a bound on the hitting time $m(X_k)$ &source \\\hline
$r_{k,\ell}$ lower & $m(X_k) \ge \displaystyle\min_{X_k \in S_k}\left\{ \frac{1}{p(X_k, S_{[0,k-1]})} + \sum^{k-1}_{\ell=1}\frac{p(X_k,S_{\ell})}{p(X_k,S_{[0,k-1]})} d_{\ell}\right\} $ &Theorems~\ref{theoremLowerBound1},\ref{theoremTightestLowerBound1} \\
\hline
$r_{k,\ell}$ upper & $\displaystyle m(X_k) \le \max_{X_k\in S_k}\left\{ \frac{1}{p(X_k, S_{[0,k-1]})} + \sum^{k-1}_{\ell=1}\frac{p(X_k,S_{\ell})}{p(X_k,S_{[0,k-1]})} d_{\ell}\right\}$ &Theorems~\ref{theoremUpperBound1}, \ref{theoremTightestUpperBound1} \\
\hline $c_{k,\ell} $ lower & $\displaystyle m(X_k) \ge\frac{1}{p_{{\scriptscriptstyle\max}}(X_{k},S_{[0,k-1]})} +\sum^{k-1}_{\ell=1} \frac{c_{k,\ell}}{ p_{{\scriptscriptstyle\max}}(X_{\ell},S_{[0,\ell-1]})}$ &Theorem~\ref{theoremLowerBound2} \\
\hline $c_{k,\ell} $ upper & $\displaystyle m(X_k) \le \frac{1}{p_{{\scriptscriptstyle\min}}(X_{k},S_{[0,k-1]})} +\sum^{k-1}_{\ell=1} \frac{c_{k,\ell}}{ p_{{\scriptscriptstyle\min}}(X_{\ell},S_{[0,\ell-1]})}$ &Theorem~\ref{theoremUpperBound2} \\
\hline
$c_{\ell}$ & $c_{k,\ell}=c_\ell$, a special case of Type-$c_{k,\ell}$ bounds &Corollaries~\ref{corollaryLowerBound-cl}, \ref{corollaryUpperBound-cl}
\\ \hline $c$ & $c_{k,\ell}=c$, a special case of Type-$c_{k,\ell}$ bounds &Corollaries \ref{corollaryLowerBound-c}, \ref{corollaryUpperBound-c}
\\
\hline
\end{tabular}
|
Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms
|
Type-$c_{k,\ell}$, $c_\ell$ and $c$ bounds. Notation refers to Table~\ref{tab:notation}.
| null |
cs.NE
|
|
2309.06373v1
|
\begin{tabular}{|c|c|c|c|c|c|c|c|}
\hline
Number of particles(N) & 10 & 20 & 50 & 100 & 200 & 500 & 1000 \\
\hline\hline
log-bias & -3.67 & -4.05 & -4.46 & -4.87 & -5.24 & -5.64 & -5.97 \\
log-MSE & -6.94 & -7.64 & -8.43 & -9.27 & -9.98 & -10.81 & -11.44 \\
\hline
\end{tabular}
|
Chebyshev Particles
|
The log-bias and the log-MSE of the filtered states under 100 Riesz particles for varying N.
| null |
cs.AI, cs.IT, math.IT, 53-04, F.2
|
|
2310.18948v2
|
\begin{tabular}{r|l|c|c|c}
\hline
\multicolumn{5}{c}{\textbf{Probabilistc Model Results}} \\ \hline
\textbf{Test Type} & \textbf{Coordinate Type} & \textbf{Precision (\%)} & \textbf{Recall (\%)} & \textbf{F1 Score (\%)} \\ \hline
\multirow{2}{*}{Cargo Test} & Route & 83.13 & 83.16 & 81.19 \\ \cline{2-5}
& Destination & 75.28 & 79.09 & 75.85 \\ \hline
\multirow{2}{*}{Tanker Test} & Route & 86.89 & 89.92 & 87.84 \\ \cline{2-5}
& Destination & 74.85 & 77.12 & 74.90 \\ \hline
\multirow{2}{*}{Cargo Train} & Route & 81.69 & 83.25 & 80.43 \\ \cline{2-5}
& Destination & 89.51 & 89.72 & 89.02 \\ \hline
\multirow{2}{*}{Tanker Train}& Route & 83.43 & 84.29 & 82.31 \\ \cline{2-5}
& Destination & 88.64 & 87.95 & 87.48 \\ \hline
\end{tabular}
|
Building a Safer Maritime Environment Through Multi-Path Long-Term Vessel Trajectory Forecasting
|
Performance of the probabilistic model in forecasting a vessel's route and destination.
| null |
cs.LG, cs.AI, cs.DM, math.PR
|
|
2309.12766v3
|
\begin{tabular}{c||c||c||c}
\hline
\hline
\textbf{Features} &\textbf{LCC} & \textbf{SRCC} & \textbf{MSE} \\ [0.5ex] \cline{2-4}
\hline\hline
\multicolumn{4}{c} {Speech Quality Prediction}
\\ \hline
HuBERT&0.777&0.724&0.411\\\hline
W2V FT&0.804&0.758&0.360\\\hline
MMS&0.811&0.766&0.362\\\hline
Whisper&0.815&0.776&0.344\\\hline
%Whisper+HuBERT&0.665&0.598&0.559\\\hline
Whisper+W2V FT&\textbf{0.816}&\textbf{0.778}&\textbf{0.343}\\\hline
\hline
\multicolumn{4}{c} {Speech Intelligibility Prediction} \\
\hline
HuBERT&0.740&0.698&0.023\\\hline
W2V FT&0.796&0.712&0.018\\\hline
MMS&\textbf{0.809}&0.732&0.018\\\hline
Whisper&0.807&\textbf{0.738}&\textbf{0.017}\\\hline
%Whisper+HuBERT&0.721&0.657&0.023\\\hline
Whisper+W2V FT&0.807&0.733&\textbf{0.017}\\\hline
\hline
\end{tabular}
|
A Study on Incorporating Whisper for Robust Speech Assessment
|
LCC, SRCC, and MSE from MOSA-Net+ with different cross-domain features from HuBERT, W2V FT, MMS, and Whisper for Human Listening test prediction.
| null |
eess.AS, cs.SD
|
|
2308.04441v1
|
\begin{tabular}{|c||c|c|c|}
\hline
& Initial value & min value & max value\\
\hline
$\theta_1$ &1.0 &0.0 &10000.0 \\
\hline
$\theta_2$ & 1.0& 0.0 &10000.0 \\
\hline
$\theta_3$ &1.0 & -10000.0 &10000.0\\
\hline
$\theta_4$ &1.0 & 0.0 &10000.0\\
\hline
\end{tabular}
|
Hug model: parameter estimation via the ABC Shadow algorithm
|
Initialization of the parameter for the ABC Shadow algorithm.
|
['\\newcommand{\\noopsort}[2]{#2}', '\\newcommand{\\m}[1]{{\\color{red} [#1]}}', '\\newcommand{\\1}{\\mathbb{1}}', '\\newcommand{\\cB}{\\mathcal{B}}', '\\newcommand{\\cF}{\\mathcal{F}}', '\\newcommand{\\PP}{\\mathbb{P}}', '\\newcommand{\\RR}{\\mathbb{R}}', '\\newcommand{\\NN}{\\mathbb{N}}', '\\newcommand{\\XX}{\\mathbb{X}}', '\\newcommand{\\cs}{\\mathbf{s}}', '\\newcommand{\\xx}{\\mathbf{x}}', '\\newcommand{\\yy}{\\mathbf{y}}', '\\newcommand{\\dd}{\\mathbf{d}}', '\\newcommand{\\RS}[1]{{\\color{cyan} [RS - #1]}}']
|
physics.geo-ph
|
|
2304.03520v1
|
\begin{tabular}{l|l|l|l|l|l}
\textbf{\#} & CA & CPPN & Direct & Parametric & Dictionary \\
1 & 19,2\% & 0\% & 0\% & 31.2\% & 49.6\% \\
2 & 13.2\% & 4.4\% & 2.8\% &22.8\% &56.8\% \\
3 & 12.8\% & 0\% & 0\% & 32.8\% & 54.4\% \\
4 & 16.4\% & 0\% & 0\% & 40.4\% & 43.2\% \\
5 & 15.6\% & 0\% & 0\% & 30.8\% & 53.6\% \\
6 & 14.8\% & 0\% & 0\% & 38.0\% & 47.2\% \\
7 & 0\% & 52.4\% & 0\% & 19.2\% & 28.4\% \\
8 & 13.6\% & 0\% & 0.4\% & 40.8\% & 45.2\% \\
9 & 13.6\% & 0\% & 1.6\% & 30.8\% & 54.0\%\\
10 & 15.2\% & 0\% & 0\% & 34.4\% & 50.4\% \\
\end{tabular}
|
On the Suitability of Representations for Quality Diversity Optimization of Shapes
|
Proportion of encodings in multi-encoding archive. Each line is a separate run (\#).
| null |
cs.NE
|
|
2310.07599v1
|
\begin{tabular}{|ccccccc|}
\hline
$L$ & $N$ & $1-n_s$ & $C_a$ & $p_a$ & $C_b$ & $p_b$\\
\hline
10& 1& -0& 4.000& -0.000& 0.800& -0.000\\
10& 2& -1& 4.160& -1.000& 1.600& -1.000\\
10& 3& -2& 4.511& -2.002& 2.449& -2.002\\
10& 4& -3& 5.085& -3.005& 3.335& -3.000\\
10& 5& -4& 11.51& -4.005& - & - \\
12& 1& -0& 4.000& -0.000& 0.800& -0.000\\
12& 2& -1& 4.160& -1.000& 1.600& -1.000\\
12& 3& -2& 4.511& -2.002& 2.449& -2.002\\
12& 4& -3& 5.037& -3.003& 3.338& -3.003\\
12& 5& -4& 5.810& -4.007& 4.363& -4.002\\
12& 6& -5& 13.13& -5.000& - & - \\
14& 1& -0& 4.000& -0.000& 0.807& -0.000\\
14& 2& -1& 4.160& -1.000& 1.615& -1.000\\
14& 3& -2& 4.511& -2.002& 2.449& -2.002\\
14& 4& -3& 5.037& -3.003& 3.338& -3.003\\
14& 5& -4& 5.753& -4.005& 4.410& -4.005\\
14& 6& -5& 6.582& -5.000& 5.657& -5.011\\
14& 7& -6& 20.23& -6.102& - & - \\
\hline
\end{tabular}
|
Many-body entanglement and spectral clusters in the extended hard-core bosonic Hatano-Nelson model
|
The fitting parameters of the major and minor axes $2a$ and $2b$ with respect to $V$ for the cluster $n_s=N$ with $L=10,12,14$, $t=1$ and $\gamma=0.2$.
\label{tab:ab_U_L10_14}
|
['\\newcommand{\\ket}[1]{|#1\\rangle}', '\\newcommand{\\bra}[1]{\\langle#1|}', '\\newcommand{\\braket}[2]{\\langle#1| #2\\rangle}', '\\newcommand{\\psfg}[1]{\\psfrag{#1}{\\S mall{$#1$}}}', '\\newcommand{\\bs}{\\boldsymbol}', '\\newcommand{\\mjh}[1]{\\textcolor{red}{#1}}']
|
cond-mat.str-el, cond-mat.stat-mech, quant-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 & $0.64 \pm 0.13$ & $0.83 \pm 0.08$ & $0.69$ & $0.12$\\
Stable non-crop & $0.91 \pm 0.07$ & $0.59 \pm 0.05$ & $0.41$ & $0.11$ \\
Cropland gain & $0.08 \pm 0.08$ & $0.29 \pm 0.36$ & $0.60$ & $0.18$ \\
Cropland loss & $0.00 \pm 0.00$ & $0.00 \pm 0.00$ & $0.00$ & $0.23$ \\
\hline
\end{tabular}
|
Satellite Data Shows Resilience of Tigrayan Farmers in Crop Cultivation During Civil War
|
Accuracy metrics for four-class cropland change reference sample in Western zone of Tigray. Overall accuracy is $0.66 \pm 0.06$.
| null |
cs.CY
|
|
2303.03008v4
|
\begin{tabular}{ccc}
\hline
Scintillator & $\alpha_{LN_2}$ & $\alpha_{LHe}$ \\
\hline \hline
EJ-200 & 0.90 $\pm$ 0.01 $\pm$ 0.05 & 0.90 $\pm$ 0.01 $\pm$ 0.05 \\
EJ-244 & 0.89 $\pm$ 0.01 $\pm$ 0.01 & 0.93 $\pm$ 0.01 $\pm$ 0.03 \\
EJ-248 & 0.89 $\pm$ 0.01 $\pm$ 0.01 & 0.91 $\pm$ 0.01 $\pm$ 0.01 \\
EJ-208 & 0.83 $\pm$ 0.01 $\pm$ 0.02 & - \\
EJ-230 & 0.81 $\pm$ 0.01 $\pm$ 0.04 & - \\
EJ-240 & 0.90 $\pm$ 0.01 $\pm$ 0.02 & - \\
\hline
\end{tabular}
|
Characterization of the performances of plastic commercial scintillators in cryogenic environments
|
Evalutation of the $\alpha_{LN_2}$ and $\alpha_{LHe}$ factors for the tested commercial scintillators.
| null |
physics.ins-det, nucl-ex
|
|
2310.16170v1
|
\begin{tabular}{|c|c c|c c|cc| c c|}
\cline{1-9}
& \multicolumn{4}{|c|} {Fourth order SSP multistep} & \multicolumn{4}{|c|} {Fourth SSP Runge-Kutta} \\
\cline{1-9}
\hline N & $L^1$ error & order & $L^\infty$ error & order & $L^1$ error & order & $L^\infty$ error & order\\
\cline{1-9}
$20$ & 1.59E-2 & - & 5.26E-2 & - & 1.62E-2 & - & 5.39E-2 & - \\
\cline{1-9}
$40$ & 2.10E-3 & 2.92& 1.38E-2 & 1.93 & 2.11E-3 & 2.94 & 1.39E-2 & 1.95\\
\cline{1-9}
$80$ & 6.35E-4 & 1.73 & 6.56E-3 & 1.07 & 6.48E-4 & 1.70 & 7.01E-3 & 0.99\\
\cline{1-9}
$160$ & 1.48E-4 & 2.10 & 1.65E-3 & 1.99 & 1.51E-4 & 2.10 & 1.66E-3 & 2.08\\
\cline{1-9}
$320$ & 3.12E-5 & 2.25 & 6.10E-4 & 1.43 & 3.14E-5 & 2.26 & 6.13E-4 & 1.44\\
% \hline
% $640$ & 8.34E-6 & 1.90 & 1.87E-4 & 1.70 & 8.31E-6 & 1.92 & 1.87E-4 & 1.71\\
% \hline
% $1280$ & 2.16E-6 & 1.95 & 7.18E-5 & 1.38 & 2.16E-6 & 1.94 & 7.17E-5 & 1.38\\
\hline
\end{tabular}
|
A high order accurate bound-preserving compact finite difference scheme for scalar convection diffusion equations
|
Burgers' equation. The errors are measured in the smooth region away from the shock.
|
['\\newcommand{\\creflastconjunction}{, and~}']
|
math.NA, cs.NA, 65M06, 65M12
|
|
2401.03338v1
|
\begin{tabular}{|c|c|c|c|c|}
\hline
$\hat{\varepsilon}$ & $\hat{a}$ & $\hat{b}$ & $\hat{c}$ & $R^2$\\
\hline
%$\hat{\varepsilon}_{wg, EKF0}$ & $0.69361734$ & $-1.2872899$ & $0.35014224$ & $0.8918754439239495$\\
$\hat{\varepsilon}_{wg, \textrm{EKF0}}$ & $0.694$ & $-1.287$ & $0.350$ & $0.892$\\
\hline
\end{tabular}
|
Modelling pathwise uncertainty of Stochastic Differential Equations samplers via Probabilistic Numerics
|
Linear regression summary of the model~\eqref{eq:linear_model} for Algorithm~\ref{alg:ekf0-marginal-scheme} using EKF0.
|
['\\newcommand{\\dd}[0]{\\mathrm{d}}', '\\newcommand{\\bbE}[0]{\\mathbb{E}}', '\\newcommand{\\bbR}[0]{\\mathbb{R}}', '\\newcommand{\\bbV}[0]{\\mathbb{V}}', '\\newcommand{\\bbC}[0]{\\mathbb{C}}', '\\newcommand{\\calL}[0]{\\mathcal{L}}', '\\newcommand{\\calN}[0]{\\mathcal{N}}', '\\newcommand{\\wtX}[0]{\\widetilde{X}}', '\\newcommand{\\eqname}[1]{\\tag*{#1}}', '\\newcommand{\\adrien}[1]{\\textcolor{red}{AC: #1}}', '\\newcommand{\\yvann}[1]{\\textcolor{blue}{YLF: #1}}', '\\newcommand{\\simo}[1]{\\textcolor{purple}{[Simo: #1]}}']
|
math.NA, cs.NA, stat.CO, stat.ME, 65C30, 60G15
|
|
2311.10420v1
|
\begin{tabular}{l | l | c | r}
\hline
\bf Change & \bf Impact &\bf Severity &\bf Occurences\\ \hline
CSS property: \texttt{margin-\{top,bottom\}} & Failure of margin collapsing & SEVERE & 252\\
$\{{\Delta}CCN {\ne} {\Delta}FFN {\ne} {\Delta}WWN$\} \& $\{{\Delta}CF {\ne} {\Delta}CW {\ne} {\Delta}FW\}$ & No impact & IRRITANT & 225\\
Missing image \texttt{SRC} reference & Failure of lazy loading & SEVERE & 101\\
CSS property: \texttt{white-space: wrap} & Failure of soft-wrap & SEVERE & 99\\
Change of CSS attribute(s) & Change of inline CSS & MODERATE & 83\\
CSS property: \texttt{page-break-\{before,after\}} & Unnecessary blank lines & SEVERE & 74\\
iFrame \texttt{width} | \texttt{height} & Displaced iframe & MODERATE & 48\\
$\{{\Delta}CCN = {\Delta}FFN = {\Delta}WWN\} \ne \{{\Delta}CF = {\Delta}CW = {\Delta}FW\}$ & Content restriction & UNUSABLE & 38\\
CAPTCHA or 403 Error or Browser Error & Browser not identified & UNUSABLE & 22\\
CSS \texttt{:disabled} | \texttt{:inactive} & Disabled component & UNUSABLE & 7\\
No pattern & - & - & 6\\
\hline
\end{tabular}
|
UA-Radar: Exploring the Impact of User Agents on the Web
|
Change impact analysis of the 955 UA-dependent websites: this table details the specific changes detected, their associated impact, the problem severity level, and the number of occurrences, providing a comprehensive overview of how changes in the UA affect different aspects of the web page.
|
['\\newcommand{\\radar}[0]{\\textsc{UA-Radar}\\xspace}']
|
cs.CR
|
|
2312.07149v1
|
\begin{tabular}{llll}
\hline
\multicolumn{2}{c}{Elastic modulus} & \multicolumn{2}{c}{Yield Strength}\\
\hline
Feature removed & $R^2$ Score & Feature removed & $R^2$ Score\\
\hline
-- & -0.23 & -- & -0.16 \\
Molecular weight & -0.23 & Molecular weight & -0.16 \\
Valence electrons & -0.14 & Valence electrons & -0.06 \\
Rotatable bonds & 0.21 & Rotatable bonds & 0.30 \\
Secondary amines & 0.36 & Carbon atoms & 0.74 \\
SP3 carbon atoms & 0.39 & Oxygen atoms & 0.79 \\
Oxygen atoms & 0.39 & Stoichiometric ratio & 0.79 \\
Stoichiometric ratio & 0.39 & Radical electrons & 0.79 \\
Radical electrons & 0.39 & SP3 carbon atoms & 0.79 \\
Aliphatic rings & 0.39 & Aliphatic rings & 0.81 \\
Aromatic rings & 0.41 & Secondary amines & 0.79 \\
\hline
\end{tabular}
|
Feature-based prediction of properties of cross-linked epoxy polymers by molecular dynamics and machine learning techniques
|
Prediction accuracy of GPR model during backward sequential feature selection.
| null |
cond-mat.mtrl-sci
|
|
2305.12872v2
|
\begin{tabular}{cc|cc|cc}
\hline
Hyperparameter & Value & Hyperparameter & Value & Hyperparameter & Value\\ \hline
rollouts & 10 & mini-batch num & 1 & gamma & 0.99 \\
actor network & MLP & actor lr & 5e-5 & eval episode & 32 \\
hidden dim & 256 & critic lr & 5e-5 & optimizer & Adam \\
buffer size & 1000000 & batch size & 1000 & epsilon & 0.1 \\\hline
\end{tabular}
|
Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game
|
Hyperparameters for MADDPG and M3DDPG in toy environment.
|
['\\newcommand{\\ie}{\\textit{i}.\\textit{e}.}', '\\newcommand{\\eg}{\\textit{e}.\\textit{g}.}', '\\newcommand{\\etal}{\\textit{et} \\textit{al}.}', '\\newcommand{\\etc}{\\textit{etc}.}', '\\newcommand{\\cf}{\\textit{c}.\\textit{f}. }', '\\newcommand\\lsm[1]{\\textit{\\textcolor{blue}{[Simin:] #1}}}']
|
cs.GT
|
|
2311.01525v1
|
\begin{tabular}{|c|cccc|cccc|}
\hline
$T$ [MeV] & $M_b/T$ & $\langle v^2\rangle$ & $\langle p^2\rangle/(3MT)$ & $2\pi TD_s$ & $M_c$ [GeV] & $\langle v^2\rangle$ & $\langle p^2\rangle/(3MT)$ & $2\pi TD_s$\\
\hline
195 & 23.08 & 0.117 & 1.112 & 1.243(267) & 1.74(12) & 0.264(15) & 1.303(23) & 1.340(279)(12)\\
& 24.62 & 0.111 & 1.105 & 1.240(267) & & & & \\
\hline
220 & 20.45 & 0.131 & 1.127 & 1.668(448) & 1.65(7) & 0.302(9) & 1.364(16) & 1.812(465)(10)\\
& 21.82 & 0.124 & 1.118 & 1.664(448) & & & & \\
\hline
251 & 17.93 & 0.147 & 1.145 & 2.093(627) & 1.58(5) & 0.342(8) & 1.438(16) & 2.327(664)(12)\\
& 19.12 & 0.139 & 1.136 & 2.088(626) & & & & \\
\hline
293 & 15.35 & 0.168 & 1.170 & 2.630(850) & 1.53(5) & 0.390(9) & 1.536(19) & 3.107(973)(23)\\
& 16.38 & 0.159 & 1.159 & 2.616(846) & & & & \\
\hline
\end{tabular}
|
Quark Mass Dependence of Heavy Quark Diffusion Coefficient from Lattice QCD
|
The dependence of $\langle v^2\rangle$, $\langle p^2\rangle/(3MT)$ and $2\pi TD_s$ on the quark mass. The four columns in the middle are for the bottom quark. The uncertainties of $2\pi TD_s$ are from $\kappa$ solely. The first row at each temperature is for $m_b$=4.5 GeV while the second row for 4.8 GeV. The four columns on the right are for the charm quark. Note that the uncertainties in the first brackets of the last column inherit from $\kappa$ and the second brackets from the uncertainties in the charm quark mass.
|
['\\newcommand{\\Gnorm}{G^\\mathrm{norm}}', '\\newcommand{\\tauf}{\\tau_\\mathrm{F}}']
|
hep-lat, hep-ex, hep-ph, nucl-th
|
|
2312.08788v1
|
\begin{tabular}{p{60pt}c}
\hline
$J$ & $256$\\
$K$ & $85$\\
$\alpha/\K{v_0/\Lambda_0}$ & $-0.27$\\
$\beta/\K{1/\K{v_0\Lambda_0}}$ & $0.27$\\
$\Gamma_0/\K{v_0\Lambda_0}$ & $-0.078$\\
$\Gamma_2/\K{v_0\Lambda_0^3}$ & $0.00099$\\
$d/\Lambda_0$ & $0.31$\\
\hline
\end{tabular}
|
Oscillating edge current in polar active fluid
|
Fixed parameters through all the simulations. $v_0=\sqrt{\ABS{\alpha}/\beta}$ and $\Lambda_0=2\pi\sqrt{2\Gamma_2/\ABS{\Gamma_0}}$.
|
['\\newcommand{\\Tensor}[1]{\\overset{\\text{\\tiny$\\leftrightarrow$}}{#1}}', '\\newcommand{\\R}[1]{{\\mathrm{#1}}}', '\\newcommand{\\F}[2]{\\frac{#1}{#2}}', '\\newcommand{\\Int}[3]{{\\int_{#1}^{#2} #3}}', '\\newcommand{\\K}[1]{\\left(#1 \\right)}', '\\newcommand{\\BK}[1]{\\left[#1 \\right]}', '\\newcommand{\\OD}[2]{\\frac{d #1}{d #2}}', '\\newcommand{\\PD}[2]{\\frac{\\partial #1}{\\partial #2}}', '\\newcommand{\\PDn}[3]{\\frac{\\partial^{#3} #1}{\\partial #2^{#3}}}', '\\newcommand{\\FD}[2]{\\frac{\\delta #1}{\\delta #2}}', '\\newcommand{\\LD}[2]{\\frac{\\R{D}#1}{\\R{D}#2}}', '\\newcommand{\\CK}[1]{\\left\\{#1 \\right\\}}', '\\newcommand{\\ABS}[1]{\\left| #1 \\right|}', '\\newcommand{\\QUADtwo}{\\quad\\quad}', '\\newcommand{\\QUADthree}{\\quad\\quad\\quad}', '\\newcommand{\\QUADfour}{\\quad\\quad\\quad\\quad}', '\\newcommand{\\QUADfive}{\\quad\\quad\\quad\\quad\\quad}', '\\newcommand{\\QUADsix}{\\quad\\quad\\quad\\quad\\quad\\quad}', '\\newcommand{\\QUADseven}{\\quad\\quad\\quad\\quad\\quad\\quad\\quad}', '\\newcommand{\\RED}[1]{\\color{red}#1 \\color{black}}', '\\newcommand{\\BLUE}[1]{\\color{blue}#1 \\color{black}}', '\\newcommand{\\MYBLACK}[1]{\\color{black}#1 \\color{black}}']
|
cond-mat.soft
|
|
2308.03834v2
|
\begin{tabular}{*{2}{ll@{\hspace{4em}}}ll}
\oe & \verb"\oe" & \aa & \verb"\aa" & \l & \verb"\l" \\
\OE & \verb"\OE" & \AA & \verb"\AA" & \L & \verb"\L" \\
\ae & \verb"\ae" & \o & \verb"\o" & \ss & \verb"\ss" \\
\AE & \verb"\AE" & \O & \verb"\O" & & \\
\end{tabular}
|
Systematic Investigation of Very Early-Phase Spectra of Type Ia Supernovae
|
National symbols
|
['\\newcommand{\\head}[1]{\\subsubsection*{#1}}', '\\newcommand{\\btx}{\\textsc{Bib}\\TeX}', '\\newcommand{\\thestyle}{\\texttt{\\filename}}']
|
astro-ph.HE, astro-ph.SR
|
|
2307.16105v1
|
\begin{tabular}{cccc}
\hline
&&Airfoil&Friedman-1\\
\hline
&k=2, p=5 & $0.87\pm0.01$& $0.99\pm0.00$\\
$W_i=0$&k=3, p=5 & $0.92\pm0.01$& $0.99\pm0.00$\\
$i\neq1$&k=3,p=10 & $0.89\pm0.05$&$0.99\pm0.00$ \\
$W_1=I$&k=3,p=20 & $0.87\pm0.11$& $0.99\pm0.00$\\
&k=4, p=5 & $0.80\pm0.12$&$0.99\pm0.00$ \\
\hline
&k=2, p=5 & $0.66\pm0.02$& $0.99\pm0.00$\\
$W_i=0$&k=3, p=5 & $-1.07\pm2.44$&$0.99\pm0.00$ \\
&k=4, p=5 & $-3.86\pm4.29$& --\\
\hline
$W_i=\epsilon_i,$&k=2, p=5 & $0.86\pm0.02$&$0.99\pm0.00$\\
$i\neq1$&k=3, p=5 & $0.91\pm0.1$&$0.99\pm0.00$ \\
$W_1=I+\epsilon_1$&k=4, p=5 & $0.74\pm0.24$& -- \\
\hline
\end{tabular}
|
TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization
|
R2 score for variations of TMPNN. $\epsilon_j\subset \mathcal N (0, 0.0001)$. Sign "--" stands for the failure to converge.
|
['\\newcommand{\\XX}{\\mathbf{X}}', '\\newcommand{\\UU}{\\mathbf{U}}', '\\newcommand{\\YY}{\\mathbf{Y}}', '\\newcommand{\\ZZ}{\\mathbf{Z}}', '\\newcommand{\\FF}{\\mathbf{F}}', '\\newcommand{\\MM}{\\mathcal{M}}', '\\newcommand{\\x}{\\mathbf{x}}', '\\newcommand{\\y}{\\mathbf{y}}', '\\newcommand\\colb{\\cellcolor{blue!10}}', '\\newcommand\\colr{\\cellcolor{red!20}}', '\\newcommand\\coly{\\cellcolor{yellow!50}}', '\\newcommand\\gold{\\cellcolor{gold}}', '\\newcommand\\silver{\\cellcolor{silver}}', '\\newcommand\\bronze{\\cellcolor{bronze}}']
|
cs.LG, cs.NE
|
|
2307.14694v2
|
\begin{tabular}{l|rrrr|rrrr}
\hline & \multicolumn{4}{c}{cc-pVQZ} & \multicolumn{4}{c}{cc-pVTZ} \\
B$_2$ & $\Delta R_e$ & $\Delta\omega_e$ & $\Delta\omega_ex_e$ & $\Delta\alpha_e$ & $\Delta R_e$ & $\Delta\omega_e$ & $\Delta\omega_ex_e$ & $\Delta\alpha_e$ \\
\hline
CCSD(T) & -0.00408 & 8.20 & -0.004 & 0.00002 & -0.00424 & 8.49 & -0.016 & 0.00001 \\
CCSD(T)$_\Lambda$ & -0.00196 & 5.76 & -0.010 & -0.00001 & -0.00221 & 5.95 & -0.022 & -0.00002 \\
CCSDT & -0.00059 & -0.52 & 0.050 & 0.00010 & -0.00061 & -0.42 & 0.041 & 0.00009 \\
CCSDT(Q) & -0.00036 & -0.01 & 0.021 & 0.00004 & -0.00032 & -0.05 & 0.018 & 0.00004 \\
CCSDT(Q)/B & -0.00067 & 0.95 & 0.034 & 0.00003 & -0.00062 & 0.87 & 0.032 & 0.00003 \\
CCSDT(Q)$_\Lambda$ & -0.00043 & 0.48 & 0.024 & 0.00003 & -0.00043 & 0.47 & 0.021 & 0.00003 \\
CCSDTQ & -0.00002 & -0.31 & 0.012 & 0.00002 & -0.00001 & -0.33 & 0.010 & 0.00002 \\
CCSDTQ(5) & -0.00013 & 0.47 & -0.003 & -0.00001 & -0.00012 & 0.45 & -0.003 & -0.00001 \\
CCSDTQ(5)$_B$ & -0.00013 & 0.44 & -0.001 & -0.00001 & -0.00012 & 0.42 & -0.002 & -0.00001 \\
\hline
C$_2$ & $\Delta R_e$ & $\Delta\omega_e$ & $\Delta\omega_ex_e$ & $\Delta\alpha_e$ & $\Delta R_e$ & $\Delta\omega_e$ & $\Delta\omega_ex_e$ & $\Delta\alpha_e$ \\
\hline
CCSD(T) & -0.00195 & 15.44 & -1.030 & -0.00061 & -0.00204 & 15.63 & -0.996 & -0.00059 \\
CCSD(T)$_\Lambda$ & -0.00077 & 17.77 & -1.299 & -0.00085 & -0.00092 & 18.12 & -1.271 & -0.00084 \\
CCSDT & -0.00218 & 17.53 & -0.886 & -0.00058 & -0.00217 & 17.50 & -0.870 & -0.00057 \\
CCSDT(Q) & 0.00031 & -1.67 & -0.098 & -0.00004 & 0.00032 & -1.55 & -0.100 & -0.00004 \\
CCSDT(Q)/B & -0.00016 & 1.93 & -0.086 & -0.00007 & -0.00015 & 2.02 & -0.083 & -0.00008 \\
CCSDT(Q)$_\Lambda$ & 0.00006 & 2.65 & -0.482 & -0.00028 & 0.00006 & 2.75 & -0.479 & -0.00028 \\
CCSDTQ & -0.00049 & 2.99 & -0.052 & -0.00004 & -0.00048 & 2.93 & -0.046 & -0.00004 \\
CCSDTQ(5) & 0.00010 & -0.60 & 0.051 & 0.00002 & 0.00010 & -0.63 & 0.052 & 0.00002 \\
CCSDTQ(5)$_B$ & 0.00005 & -0.27 & 0.051 & 0.00002 & 0.00005 & -0.30 & 0.051 & 0.00002 \\
\hline
N$_2$ & $\Delta R_e$ & $\Delta\omega_e$ & $\Delta\omega_ex_e$ & $\Delta\alpha_e$ & $\Delta R_e$ & $\Delta\omega_e$ & $\Delta\omega_ex_e$ & $\Delta\alpha_e$ \\
\hline
CCSD(T) & -0.00095 & 12.58 & -0.319 & -0.00024 & -0.00098 & 12.89 & -0.335 & -0.00025 \\
CCSD(T)$_\Lambda$ & -0.00124 & 15.86 & -0.374 & -0.00029 & -0.00125 & 15.94 & -0.391 & -0.00030 \\
CCSDT & -0.00163 & 22.50 & -0.577 & -0.00045 & -0.00162 & 22.55 & -0.593 & -0.00046 \\
CCSDT(Q) & 0.00015 & -2.73 & 0.157 & 0.00009 & 0.00014 & -2.65 & 0.160 & 0.00009 \\
CCSDT(Q)/B & 0.00010 & -2.00 & 0.140 & 0.00008 & 0.00009 & -1.96 & 0.146 & 0.00008 \\
CCSDT(Q)$_\Lambda$ & 0.00005 & -0.98 & 0.070 & 0.00004 & 0.00004 & -0.88 & 0.070 & 0.00004 \\
CCSDTQ & -0.00024 & 3.82 & -0.141 & -0.00010 & -0.00024 & 3.75 & -0.142 & -0.00010 \\
CCSDTQ(5) & 0.00002 & -0.66 & 0.060 & 0.00003 & 0.00002 & -0.68 & 0.061 & 0.00003 \\
CCSDTQ(5)$_B$ & 0.00001 & -0.47 & 0.049 & 0.00003 & 0.00002 & -0.49 & 0.049 & 0.00003 \\
\hline
P$_2$& $\Delta R_e$ & $\Delta\omega_e$ & $\Delta\omega_ex_e$ & $\Delta\alpha_e$ & $\Delta R_e$ & $\Delta\omega_e$ & $\Delta\omega_ex_e$ & $\Delta\alpha_e$ \\
\hline
CCSD(T) & -0.00324 & 9.40 & -0.160 & -0.00005 & -0.00360 & 10.45 & -0.186 & -0.00006 \\
CCSD(T)$_\Lambda$ & -0.00527 & 14.60 & -0.213 & -0.00007 & -0.00562 & 15.58 & -0.241 & -0.00008 \\
CCSDT & -0.00512 & 14.85 & -0.188 & -0.00007 & -0.00528 & 15.25 & -0.194 & -0.00007 \\
CCSDT(Q) & 0.00023 & -1.22 & 0.060 & 0.00001 & 0.00021 & -1.18 & 0.069 & 0.00001 \\
CCSDT(Q)/B & 0.00005 & -0.73 & 0.057 & 0.00001 & 0.00003 & -0.73 & 0.066 & 0.00001 \\
CCSDT(Q)$_\Lambda$ & -0.00040 & 1.01 & 0.006 & 0.00000 & -0.00045 & 1.10 & 0.010 & 0.00000 \\
CCSDTQ & -0.00084 & 2.76 & -0.045 & -0.00002 & -0.00086 & 2.77 & -0.044 & -0.00002 \\
CCSDTQ(5) & -0.00005 & -0.23 & 0.024 & 0.00001 & -0.00002 & -0.32 & 0.025 & 0.00001 \\
CCSDTQ(5)$_B$ & -0.00008 & -0.11 & 0.020 & 0.00000 & -0.00005 & -0.19 & 0.021 & 0.00000 \\
\hline
BN & $\Delta R_e$ & $\Delta\omega_e$ & $\Delta\omega_ex_e$ & $\Delta\alpha_e$ & $\Delta R_e$ & $\Delta\omega_e$ & $\Delta\omega_ex_e$ & $\Delta\alpha_e$ \\
\hline
CCSD(T) & -0.01004 & 44.54 & 5.456 & 0.00081 & -0.01110 & 42.10 & 6.313 & 0.00130\\
CCSD(T)$_\Lambda$ &-0.00376 & 77.56 & 4.088 & -0.00127& -0.00486 & 82.44 & 6.511 & -0.00073\\
CCSDT & -0.00263 & 7.77 & 0.991 & 0.00059& -0.00256 & 6.91 & 0.937 & 0.00060\\
CCSDT(Q) & -0.00443 & -20.58 & 2.561 & 0.00175& -0.00409 & -23.25 & 2.444 & 0.00178\\
CCSDT(Q)/B & 0.00323 & -35.31 & -2.098 &-0.00056& 0.00352 & -33.99 & -2.167 & -0.00071\\
CCSDT(Q)$_\Lambda$ & 0.00069 & -8.98 & 0.071 & 0.00034& 0.00074 & -9.39 & -0.007 & 0.00033\\
CCSDTQ & -0.00078 & 2.51 & 0.294 & 0.00017& -0.00076 & 2.27 & 0.275 & 0.00017\\
CCSDTQ(5) & 0.00132 & -1.02 & -0.181 & -0.00008& 0.00126 & -0.83 & -0.173 & -0.00009\\
CCSDTQ(5)$_B$ & 0.00134 & -2.51 & -0.368 & -0.00010& 0.00129 & -2.22 & -0.370 & -0.00012\\
\hline
\end{tabular}
|
Post-CCSD(T) corrections to bond distances and vibrational frequencies: the power of $Λ$
|
Comparison for selected diatomics between cc-pVQZ and cc-pVTZ basis sets for differences (\AA,cm$^{-1}$) with CCSDTQ(5)$_\Lambda$
| null |
physics.chem-ph
|
|
2309.10582v1
|
\begin{tabular}{*{2}{ll@{\hspace{4em}}}ll}
\`{o} & \verb"\`{o}" & \={o} & \verb"\={o}" & \t{oo} & \verb"\t{oo}" \\
\'{o} & \verb"\'{o}" & \.{o} & \verb"\.{o}" & \c{o} & \verb"\c{o}" \\
\^{o} & \verb"\^{o}" & \u{o} & \verb"\u{o}" & \d{o} & \verb"\d{o}" \\
\"{o} & \verb#\"{o}# & \v{o} & \verb"\v{o}" & \b{o} & \verb"\b{o}" \\
\~{o} & \verb"\~{o}" & \H{o} & \verb"\H{o}" & & \\
\end{tabular}
|
Investigating the Magnetic Structure of Interplanetary Coronal Mass Ejections using Simultaneous Multi-Spacecraft In situ Measurements
|
Text-mode accents
|
['\\newcommand{\\head}[1]{\\subsubsection*{#1}}', '\\newcommand{\\btx}{\\textsc{Bib}\\TeX}', '\\newcommand{\\thestyle}{\\texttt{\\filename}}']
|
astro-ph.SR, physics.space-ph
|
|
2306.06060v1
|
\begin{tabular}{*{2}{ll@{\hspace{4em}}}ll}
\`{o} & \verb"\`{o}" & \={o} & \verb"\={o}" & \t{oo} & \verb"\t{oo}" \\
\'{o} & \verb"\'{o}" & \.{o} & \verb"\.{o}" & \c{o} & \verb"\c{o}" \\
\^{o} & \verb"\^{o}" & \u{o} & \verb"\u{o}" & \d{o} & \verb"\d{o}" \\
\"{o} & \verb#\"{o}# & \v{o} & \verb"\v{o}" & \b{o} & \verb"\b{o}" \\
\~{o} & \verb"\~{o}" & \H{o} & \verb"\H{o}" & & \\
\end{tabular}
|
Ion-Driven Instabilities in the Inner Heliosphere II: Classification and Multi-Dimensional Mapping
|
Text-mode accents
|
['\\newcommand{\\head}[1]{\\subsubsection*{#1}}', '\\newcommand{\\btx}{\\textsc{Bib}\\TeX}', '\\newcommand{\\thestyle}{\\texttt{\\filename}}']
|
astro-ph.SR, physics.plasm-ph, physics.space-ph
|
|
2308.14292v1
|
\begin{tabular}{|c|c|c|}
\hline
& Old injection & New injection \\ \hline \hline
NF-number (from SCExAO) & \multicolumn{2}{c|}{f/27.3} \\ \hline
Collimation lens focal length & 120 mm & 125 mm \\ \hline
Beam size on collimation lens & 4.24 mm & 4.58 mm \\ \hline
MEMS & Iris AO & Boston Micromachines \\ \hline
Number of Hex-segments & \multicolumn{2}{c|}{37} \\ \hline
MEMS - full aperture size & 4.24 mm & 4.55 mm \\ \hline
MEMS - inscribed circle in each segment & 606.2 $\mu$m & 649.5 $\mu$m\\ \hline
First telescope lens & 85 mm & 88.9 mm \\ \hline
Second telescope lens & 35 mm & 35 mm \\ \hline
Pupil size on MLA & 1.75 mm & 1.79 mm \\ \hline
Subpupil size on each $\mu$-lens & 250 $\mu$m & 255.7 $\mu$m \\ \hline
$\mu$-lens diameter & \multicolumn{2}{c|}{250 $\mu$m } \\ \hline
$\mu$-lens focal length & \multicolumn{2}{c|}{1 mm }\\ \hline
Fiber bundle pitch & \multicolumn{2}{c|}{250 $\mu$m } \\ \hline
\end{tabular}
|
Photonic spectro-interferometry with SCExAO/FIRST at the Subaru Telescope: towards H-alpha imaging of protoplanets
|
FIRST injection module optical elements before and after the MEMS upgrade.
|
['\\newcommand{\\manon}[1]{\\textcolor{cyan}{#1}} ', '\\newcommand{\\V}[1]{\\boldsymbol{#1}} %\\vec{#1}} %vect discret 2D', '\\newcommand{\\Vx}{\\V{x}} %pixel vector', '\\newcommand{\\Vr}{\\V{r}} %pixel vector', '\\newcommand{\\GM}[1]{\\left|#1\\right|}', '\\newcommand{\\GP}[1]{\\left(#1\\right)}', '\\newcommand{\\GC}[1]{\\left[#1\\right]}', '\\newcommand{\\R}{\\mathcal{R}}', '\\newcommand{\\EST}[1]{\\widehat{#1}}', '\\newcommand{\\upgrades}[1]{\\textcolor{red}{#1}} ', '\\newcommand{\\correction}[1]{\\textcolor{red}{#1}} ']
|
astro-ph.IM
|
|
2310.05761v1
|
\begin{tabular}{lcc}
\hline \hline \textit{Notation} & \textit{Definition} & \textit{Relative Weights} \\ \hline \hline
\multicolumn{3}{c}{\textit{Calibration (fixed parameters)} } \\ \hline
$\rho$ & \textit{Subjective discount factor} & 0.006 \\
$\eta$ & \textit{Nominal rigidity} & 0.03 \\
$\omega$ & \textit{Elasticity of marginal cost to output} & 0 \\
$\gamma$ & \textit{Coeff on lagged inflation} & 0.006 \\
$\phi$ & \textit{Endogenous feedback in Taylor rule} & 0.148\\
$\pi$ & \textit{Inflation target shock} & 0.674\\
$\delta$ & \textit{Elasticity of substitution across varieties} & 0 \\
$\sigma$ & \textit{Intertemporal el of substitution} & 0\\
$\rho_{1}$ & \textit{Autoregressive first root of
monetary shock} & 0.13 \\
$\rho_{2}$ & \textit{Autoregressive second root of
monetary shock} & 0.003 \\
$b$ & \textit{Consumption Habit} & 0.008
\\\hline \hline
\end{tabular}
|
Robust Minimum Distance Inference in Structural Models
|
\textbf{ Contribution to Direction of Maximum Local Power.}
|
['\\newcommand{\\blue}[1]{\\textcolor{blue}{#1}}']
|
econ.EM
|
|
2310.16609v1
|
\begin{tabular}{lrrrrr}
\hline
\textbf{TTS} &
\textbf{total} &
\textbf{utt} &
\textbf{aug} &
\textbf{both} &
\textbf{resemblance} \\
\hline
Tacotron & 121 & 73 & 19 & 29 & 84.30\% \\
FastSpeech & 115 & 66 & 16 & 33 & 86.09\% \\
\hline
\end{tabular}
|
Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors
|
\label{tab:tts_evaluation_results}
TTS evaluation results.
| null |
cs.CL, cs.AI, cs.SD, eess.AS
|
|
2310.20564v1
|
\begin{tabular}{cc}
\hline \hline
Parameters& Priors \\
\hline
$P_{R\mathrm{p}}$ & Log-Uniform$\left(-3,1\right)$ \\
$f_{\mathrm{p}}$ $\left[\mathrm{Hz}\right]$ & Log-Uniform$\left(-10,-5\,\right)$ \\
$\alpha$ & 4\\
$\beta$ & Uniform$\left(0,5\right) $\\
$\gamma$ & 2.6\\
$A_a$ & Log-Uniform$\left(-20,-11\right)$\\
$\gamma_a$ & Uniform$\left(0,7\right) $\\
\hline \hline
\end{tabular}
|
Constraints on ultra-slow-roll inflation with the NANOGrav 15-Year Dataset
|
\label{table:prior} Priors on the Model Parameters. From a physical perspective, \(P_{R\mathrm{p}}\) is constrained to be less than \({\cal{O}}(1)\). However, we relax this constraint to better observe the trend of \( \log_{10}P_{\mathrm{Rp}} - \log_{10}f_{\mathrm{p}} \).
%We fix $\gamma$ to the value at which Eq.~\ref{eq:param} best fits the numerical curve during the MCMC runs.
|
['\\newcommand{\\red}[1]{\\textcolor{red}{{#1}}}', '\\newcommand{\\blue}[1]{\\textcolor{blue}{{#1}}}', '\\newcommand{\\be}{\\begin{equation}}', '\\newcommand{\\ba}{\\begin{eqnarray}}']
|
astro-ph.CO, gr-qc, hep-ph
|
|
2311.17556v1
|
\begin{tabular}{|l|c|c |c|}
\hline
Title & Definitions & Choices for $\mc{X}$ and $\mc{Y}$ & Restriction\\
\hline
DMP inverse & $\mc{D}^{\D,\dagger}=\mc{D}^\D\,\s\,\mc{D}\,\s\,\mc{D}^{\dagger}$ & $\mc{X}=\mc{D}^\D$,\ $\mc{Y}=\mc{D}^{\dagger}$ & $\mathrm{ind}(\mc{D}) =k$\\
\hline
MPD inverse &$\mc{D}^{\dagger,\D}=\mc{D}^{\dagger}\,\s\,\mc{D}\,\s\,\mc{D}^{\D}$ & $\mc{X}=\mc{D}^{\dagger}$,\ $\mc{Y}=\mc{D}^{\D}$ & $\mathrm{ind}(\mc{D}) =k$\\
\hline
CMP inverse & $\mc{D}^{c,\dagger}=\mc{D}^{\dagger}\,\s\,\mc{D}\,\s\,\mc{D}^{\D,\dagger}=\mc{D}^{\dagger,\D}\,\s\,\mc{D}\,\s\,\mc{D}^{\dagger}$ & $\mc{X}=\mc{D}^{\dagger}$, \ $\mc{Y}=\mc{D}^{\D,\dagger}$& $\mathrm{ind}(\mc{D}) =k$ \\\hline
MPCEP inverse & $\mc{D}^{\dagger,\ep}=\mc{D}^{\dagger}\,\s\,\mc{D}\,\s\,\mc{D}^{\ep}$ & $\mc{X}=\mc{D}^{\dagger}$,\ $\mc{Y}=\mc{D}^{\ep}$ & - \\
\hline
CEPMP inverse & $\mc{D}^{\ep,\dagger}=\mc{D}^{\ep}\,\s\,\mc{D}\,\s\,\mc{D}^{\dagger}$ & $\mc{X}=\mc{D}^{\ep}$,\ $\mc{Y}=\mc{D}^{\dagger}$ & - \\
\hline
\end{tabular}
|
Computing Tensor Generalized bilateral inverses
|
Composite generalized inverses for the tensor $\mc{D}\in \mathbb C^{\textbf{N}(s)\times \textbf{N}(s)}$
|
['\\newcommand\\NoDo{\\renewcommand\\algorithmicdo{}}', '\\newcommand{\\Break}{\\State \\textbf{break} }', '\\newcommand{\\kronecker}{\\raisebox{1pt}{\\ensuremath{\\:\\otimes\\:}}}', '\\newcommand{\\T}{{\\mathrm T}}', '\\newcommand{\\D}{{\\mathrm D}}', '\\newcommand{\\mc}[1]{\\mathcal {#1}}', '\\newcommand{\\dg}{{\\dagger}}', '\\newcommand{\\n}{{*\\!\\!_n}}', '\\newcommand{\\kl}{{*_l}}', '\\newcommand{\\p}{{*\\!\\!_p}}', '\\newcommand{\\1}{{*_1}}', '\\newcommand{\\s}{{*\\!\\!_s}}', '\\newcommand{\\f}{{*_r}}', '\\newcommand{\\m}{{*\\!_m}}', '\\newcommand{\\kj}{{*_j}}', '\\newcommand{\\kk}{{*\\!_k}}', '\\newcommand{\\tp}[1]{\\textup{#1}}', '\\newcommand{\\rra}[1]{\\mathrm{rshrank}({#1})} % tensor rank', '\\newcommand{\\inv}{\\mathrm{inv}}', '\\newcommand{\\ra}[1]{\\mathrm{rank}({#1})} % rank', '\\newcommand{\\ind}[1]{\\mathrm{ind}({#1})} % index', '\\newcommand{\\tl}{{\\textnormal}}', '\\newcommand{\\rg}{{\\mathscr{R}}}', '\\newcommand{\\nl}{{\\mathscr{N}}}', '\\newcommand{\\ep}{\\scriptsize\\mbox{\\textcircled{$\\dagger$}}}', '\\newcommand{\\core}{\\scriptsize\\mbox{\\textcircled{\\#}}}']
|
math.NA, cs.NA
|
|
2312.17525v1
|
\begin{tabular}{|l|c|c|c|c|}
\hline
\textbf{}&\multicolumn{2}{|c|}{\textbf{Matrix Mult.}}
\textbf{}&\multicolumn{2}{|c|}{\textbf{FIR}} \\
\cline{2-5}
\textbf{Benchmarks} & \textit{10x10}& \textit{50x50}& \textit{100}&\textit{200} \\
\cline{4-5}
\hline \hline
\multicolumn{5}{|c|}{$\Delta$ Power Consumption (mW)}\\
\cline{1-5}
min & 15 & 0.55 & 529.515 & 1059.345 \\ \hline
\textbf{solution} & 415.3 & 753.72 & 10850.855 & 1237.247 \\ \hline
max & 418.4 & 1552.017 & 17344.390 & 34699.1 \\ \hline \hline
\multicolumn{5}{|c|}{$\Delta$ Computation time (ns)}\\
\cline{1-5}
min& 50 & -90 & 563.135 & 1126.605 \\ \hline
\textbf{solution} & 1780 & 1460.8 & 2664.385 & 3951.525 \\ \hline
max & 1840 & 5707.6 & 6547.495 & 13098.89 \\ \hline \hline
\multicolumn{5}{|c|}{Accuracy degradation}\\
\cline{1-5}
min & 0.02 & 0 & 1096.03 & 395.74 \\ \hline
\textbf{solution} & 19.95 & 0.736 & 1096.03 & 27580.345 \\ \hline
max & 204.71 & 26.7964 & 31671.43 & 27580.35 \\ \hline \hline
\multicolumn{5}{|c|}{Configuration}\\
\cline{1-5}
\textbf{Adder Type} & 00M & 6R6 & 0GN & 067 \\ \hline
\textbf{Multiplier Type} & 17MJ & L93 & 043 & 018 \\ \hline
\end{tabular}
|
Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach
|
Explorations results for power, computation time, and accuracy
|
['\\newcommand\\Myperm[2][^n]{\\prescript{#1\\mkern-2.5mu}{}P_{#2}}', '\\newcommand\\Mycomb[2][^n]{\\prescript{#1\\mkern-0.5mu}{}C_{#2}}']
|
cs.AR, cs.LG, cs.PF, C.1
|
|
2312.09947v1
|
\begin{tabular}{|l|l|l|l|l|l|l|}
\hline
\textbf{Date} & \textbf{Location} & \textbf{CGAI} & \textbf{Questionnaires} & \textbf{Conversations} & \textbf{Audio} & \textbf{Teams} \\ \hline
18/05 & London & ChatGPT3.5 & 9 & 9 & N/A & N/A \\ \hline
7/07 & Berlin & ChatGPT4 & 5 & 10 & Yes & \\ \hline
12/07 & London & Bard & 9 & 8 & Yes & Yes \\ \hline
\textbf{Total} & 4 & 3 & 23 & 27 & 2 & 1 \\ \hline
\end{tabular}
|
Prompting Datasets: Data Discovery with Conversational Agents
|
Research data collection.
| null |
cs.HC
|
|
2310.08818v1
|
\begin{tabular}{ c c c c c c c c c c c c c}
\hline
\hline
$N$ && PCHIP && MQSI &&\multicolumn{3}{c}{DBI} && \multicolumn{3}{c}{PPI} \\
&&$\mathcal{P}_{3}$ && $\mathcal{P}_{5}$ && $\mathcal{P}_{3}$ & $\mathcal{P}_{4}$ & $\mathcal{P}_{8}$
&&$\mathcal{P}_{3}$ & $\mathcal{P}_{4}$ & $\mathcal{P}_{8}$ \\
\hline
17 && 3.99E-2 && 3.63E-2 && 5.10E-2 & 2.91E-2 & 4.61E-2 && 5.10E-2 & 2.91E-2 & 4.61E-2 \\
33 && 4.52E-3 && 4.32E-3 && 6.31E-3 & 9.57E-3 & 3.05E-3 && 6.31E-3 & 9.57E-3 & 3.05E-3 \\
65 && 2.79E-3 && 2.67E-3 && 2.44E-3 & 2.49E-3 & 1.33E-3 && 2.44E-3 & 2.49E-3 & 9.92E-4 \\
129 && 6.23E-4 && 6.71E-4 && 2.22E-4 & 1.21E-4 & 1.05E-4 && 2.22E-4 & 1.21E-4 & 2.43E-5 \\
257 && 1.17E-4 && 9.89E-5 && 1.51E-5 & 1.15E-5 & 1.07E-5 && 1.51E-5 & 4.68E-6 & 9.89E-8 \\
\hline
\hline
\end{tabular}
|
Algorithm xxxx: HiPPIS A High-Order Positivity-Preserving Mapping Software for Structured Meshes
|
$L^2$-errors when using the PCHIP, MQSI, DBI, and PPI methods to approximate the function $f_{1}(x)$.
$N$ represents the number of input points used to build the approximation.
The parameters $\epsilon_{0}$, $\epsilon_{1}$, and $st$ are set to $0.01, 1.0,$ and $3$, respectively.
| null |
math.NA, cs.MS, cs.NA, 65D05, 65D15
|
|
2308.14065v2
|
\begin{tabular}{lcccccccr} % four columns, alignment for each
\hline
\hline
{} & Channels & $<\delta e_1>$ & 1$\sigma$ width & $<\delta e_2>$ & 1$\sigma$ width & $<\delta R^2/R^2>$ & 1$\sigma$ width & number of contrast \\
\hline
& F277W & 0.002 & 0.008 & -0.001 & 0.005 & -0.002 & 0.017 & 27 \\
HybPSF & F356W & -0.001 & 0.006 & 0.001 & 0.004 & -0.003 & 0.011 & 23 \\
& F444W & -0.0009 & 0.005 & 0.0005 & 0.005 & -0.001 & 0.010 & 23 \\
\hline
\hline
\end{tabular}
|
HybPSF: Hybrid PSF reconstruction for the observed JWST NIRCam image
|
The mean value and 1$\sigma$ width of the residual distributions for coadded data.
|
['\\newcommand{\\vdag}{(v)^\\dagger}', '\\newcommand\\aastex{AAS\\TeX}', '\\newcommand\\latex{La\\TeX}']
|
astro-ph.IM
|
|
2401.00146v1
|
\begin{tabular}{|p{1.5cm}|p{3cm}|p{4cm}|p{4cm}|}
\hline
S No. & Properties & COW & DPS\tabularnewline
\hline
1 & Encoding & Combining vacuum and coherent pulse & Phase difference between consecutive coherent pulse\tabularnewline
\hline
2 & Source & WCP & WCP\tabularnewline
\hline
3 & $\mu$ & $0.5$ & $0.2$\tabularnewline
\hline
4 & PNS attack effect & No & No\tabularnewline
\hline
5 & No of detectors & $3$ & $2$\tabularnewline
\hline
6 & Phase & Constant & Modulated\tabularnewline
\hline
7 & Intensity & Modulated & Constant\tabularnewline
\hline
8 & Polarization & Insensitive & Insensitive\tabularnewline
\hline
\end{tabular}
|
Experimental implementation of distributed phase reference quantum key distribution protocols
|
\label{tab:COW-DPS} Comparison of COW and DPS QKD protocol.
| null |
quant-ph
|
|
2309.00404v2
|
\begin{tabular}{lclc}
\hline
\multicolumn{2}{c}{WR21} & \multicolumn{2}{c}{WR31} \\
HJD-2450000. & RV (\kms) & HJD-2450000. & RV (\kms) \\
\hline
8090.571 & 279.5 & 8840.533 & --149.2 \\
8135.466 &--171.0 & 8843.533 & 203.1 \\
8152.396 &--135.7 & 8862.469 & 209.0 \\
8173.385 & 307.4 & 8863.517 & 101.9 \\
8174.333 & 69.6 & 9202.579 & --71.9 \\
8177.545 &--143.5 & 9213.508 & --96.3 \\
8238.390 & 272.5 & 9214.516 & 126.6 \\
8815.584 & 148.3 & 9219.544 & 190.9 \\
8822.573 & --40.2 & 9221.497 & --31.1 \\
8839.529 & 46.3 & 9267.366 & 63.4 \\
8842.509 & 271.1 & 9295.290 & --175.1 \\
8843.503 & 100.1 & 9303.283 & 77.9 \\
8846.523 &--141.6 & 9304.475 & --128.0 \\
8849.525 & 281.3 & 9629.402 & 2.3 \\
8860.448 & 8.6 & 9630.419 & 231.7 \\
8861.451 &--113.0 & 9655.520 & 147.1 \\
9186.564 & 76.2 & 9714.381 & --30.4 \\
9206.509 & 123.4 \\
9207.494 & --46.8 \\
9309.250 & --66.1 \\
9758.253 & 304.4 \\
\hline
\end{tabular}
|
Colliding winds in WR21 and WR31 -- I. The X-ray view
|
Radial velocities of WR21 and WR31 ($\sigma=20$\,km\,s$^{-1}$).
|
['\\newcommand{\\sw}{\\emph{Swift}}', '\\newcommand{\\ch}{\\emph{Chandra}}', '\\newcommand{\\xmm}{\\emph{XMM-Newton}}', '\\newcommand{\\te}{\\emph{TESS}}', '\\newcommand{\\gc}{$\\gamma$\\,Cas}', '\\newcommand{\\kms}{km\\,s$^{-1}$}']
|
astro-ph.SR, astro-ph.HE
|
|
2312.15233v1
|
\begin{tabular}{|c|c|c|c|c|c|}
\hline
&Baseline & &Ours & \\ \hline
&Validation set & Test set & Validation set & Test set \\ \hline
Precision &88.84 &72.47 &91.82 &80.75 \\ \hline
Recall &89.04 &69.10 &92.17 &74.60 \\ \hline
F1 &88.87 &65.76 &91.90 &72.16 \\ \hline
\end{tabular}
|
Sample selection with noise rate estimation in noise learning of medical image analysis
|
Precision, Recall and F1 score (\%, Median of three runs) under noise rate 0.2. \label{Tab:distance}
| null |
eess.IV, cs.CV, 68T07, I.4.8.b
|
|
2305.00936v1
|
\begin{tabular}{l|ccc}
\noalign{\smallskip}
\hline
Method
& LPIPS↓ & PSNR↑ & SSIM↑ \\
\hline
\hline
w/o curriculum learning
& 0.2238 & 18.09 & 0.5920 \\
w/ curriculum learning
& \textbf{0.2220} & \textbf{18.18} & \textbf{0.5935} \\
\hline
\noalign{\smallskip}
\end{tabular}
|
Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks
|
Ablation study for the method trained with and without curriculum learning.
| null |
cs.CV, cs.GR
|
|
2311.13844v1
|
\begin{tabular}{ccccccc}
\hline\hline
Date & MJD & Telescope & \textit{B} & \textit{V} & \textit{R} & \textit{I} \\
(DD-MM-YYYY) & & & (mag) & (mag) & (mag) & (mag) \\
\hline
20-11-2017 & 58077.42 & 60cm & 15.200 $\pm$ 0.009 & 14.324 $\pm$ 0.008 & 13.674 $\pm$ 0.009 & 13.020 $\pm$ 0.013 \\
21-11-2017 & 58078.46 & 60cm & 15.196 $\pm$ 0.011 & 14.326 $\pm$ 0.010 & 13.682 $\pm$ 0.010 & 13.026 $\pm$ 0.014 \\
22-11-2017 & 58079.45 & 60cm & 15.203 $\pm$ 0.011 & 14.306 $\pm$ 0.012 & 13.677 $\pm$ 0.009 & 13.071 $\pm$ 0.050 \\
23-11-2017 & 58080.48 & 60cm & 15.181 $\pm$ 0.011 & 14.317 $\pm$ 0.009 & 13.673 $\pm$ 0.008 & 13.033 $\pm$ 0.011 \\
08-09-2018 & 58369.59 & 80cm & 15.249 $\pm$ 0.010 & 14.348 $\pm$ 0.008 & 13.722 $\pm$ 0.007 & 13.077 $\pm$ 0.010 \\
09-09-2018 & 58370.53 & 80cm & 15.251 $\pm$ 0.010 & 14.359 $\pm$ 0.008 & 13.736 $\pm$ 0.007 & 13.109 $\pm$ 0.010 \\
16-09-2018 & 58377.52 & 80cm & 15.240 $\pm$ 0.008 & 14.337 $\pm$ 0.007 & 13.712 $\pm$ 0.007 & 13.057 $\pm$ 0.010 \\
08-11-2018 & 58430.47 & 80cm & 15.266 $\pm$ 0.009 & 14.378 $\pm$ 0.008 & 13.767 $\pm$ 0.008 & 13.135 $\pm$ 0.011 \\
24-09-2019 & 58750.52 & 80cm & 15.318 $\pm$ 0.008 & 14.439 $\pm$ 0.007 & 13.806 $\pm$ 0.007 & 13.173 $\pm$ 0.009 \\
25-09-2019 & 58751.52 & 80cm & 15.297 $\pm$ 0.008 & 14.405 $\pm$ 0.007 & 13.780 $\pm$ 0.007 & 13.141 $\pm$ 0.009 \\
04-10-2019 & 58760.49 & 80cm & 15.366 $\pm$ 0.009 & 14.476 $\pm$ 0.009 & 13.858 $\pm$ 0.008 & 13.212 $\pm$ 0.011 \\
03-11-2019 & 58790.47 & 80cm & 15.274 $\pm$ 0.010 & 14.390 $\pm$ 0.008 & 13.788 $\pm$ 0.007 & 13.161 $\pm$ 0.010 \\
04-11-2019 & 58791.45 & 80cm & 15.274 $\pm$ 0.009 & 14.401 $\pm$ 0.008 & 13.792 $\pm$ 0.007 & 13.160 $\pm$ 0.010 \\
15-09-2020 & 59107.59 & 80cm & 15.443 $\pm$ 0.009 & 14.540 $\pm$ 0.008 & 13.885 $\pm$ 0.007 & 13.211 $\pm$ 0.011 \\
16-09-2020 & 59108.53 & 80cm & 15.450 $\pm$ 0.009 & 14.557 $\pm$ 0.008 & 13.912 $\pm$ 0.007 & 13.255 $\pm$ 0.010 \\
09-10-2020 & 59131.49 & 80cm & 15.422 $\pm$ 0.014 & 14.642 $\pm$ 0.011 & 13.921 $\pm$ 0.009 & 13.289 $\pm$ 0.011 \\
12-10-2020 & 59134.49 & 80cm & 15.422 $\pm$ 0.009 & 14.553 $\pm$ 0.008 & 13.938 $\pm$ 0.007 & 13.318 $\pm$ 0.009 \\
20-11-2020 & 59173.50 & 80cm & 15.341 $\pm$ 0.022 & 14.473 $\pm$ 0.026 & 13.889 $\pm$ 0.028 & 13.207 $\pm$ 0.032 \\
22-11-2020 & 59175.42 & 80cm & 15.313 $\pm$ 0.012 & 14.449 $\pm$ 0.009 & 13.854 $\pm$ 0.008 & 13.242 $\pm$ 0.011 \\
21-12-2020 & 59204.44 & 80cm & 15.263 $\pm$ 0.012 & 14.392 $\pm$ 0.010 & 13.812 $\pm$ 0.008 & 13.193 $\pm$ 0.011 \\
10-10-2021 & 59497.50 & 80cm & 15.485 $\pm$ 0.011 & 14.612 $\pm$ 0.008 & 13.987 $\pm$ 0.008 & 13.361 $\pm$ 0.011 \\
12-10-2022 & 59864.62 & YAHPT & 15.275 $\pm$ 0.009 & 14.375 $\pm$ 0.007 & 13.773 $\pm$ 0.018 & 13.157 $\pm$ 0.024 \\
13-10-2022 & 59865.51 & YAHPT & 15.262 $\pm$ 0.006 & 14.373 $\pm$ 0.006 & 13.754 $\pm$ 0.006 & 13.144 $\pm$ 0.009 \\
18-10-2022 & 59870.65 & YAHPT & 15.372 $\pm$ 0.097 & 14.107 $\pm$ 0.107 & 13.618 $\pm$ 0.120 & ... \\
20-10-2022 & 59872.50 & YAHPT & 15.264 $\pm$ 0.006 & 14.373 $\pm$ 0.006 & 13.757 $\pm$ 0.006 & 13.151 $\pm$ 0.009 \\
22-10-2022 & 59874.50 & YAHPT & 15.271 $\pm$ 0.006 & 14.376 $\pm$ 0.006 & 13.758 $\pm$ 0.006 & 13.153 $\pm$ 0.009 \\
28-10-2022 & 59880.65 & YAHPT & 15.279 $\pm$ 0.007 & 14.377 $\pm$ 0.006 & 13.738 $\pm$ 0.006 & 13.121 $\pm$ 0.010 \\
29-10-2022 & 59881.64 & YAHPT & 15.268 $\pm$ 0.007 & 14.362 $\pm$ 0.006 & 13.741 $\pm$ 0.006 & 13.133 $\pm$ 0.009 \\
30-10-2022 & 59882.60 & YAHPT & 15.285 $\pm$ 0.007 & 14.366 $\pm$ 0.006 & 13.744 $\pm$ 0.006 & 13.140 $\pm$ 0.009 \\
01-11-2022 & 59884.59 & YAHPT & 15.292 $\pm$ 0.007 & 14.386 $\pm$ 0.006 & 13.752 $\pm$ 0.006 & 13.149 $\pm$ 0.009 \\
02-11-2022 & 59885.63 & YAHPT & 15.275 $\pm$ 0.008 & 14.380 $\pm$ 0.007 & 13.741 $\pm$ 0.007 & 13.121 $\pm$ 0.010 \\
03-11-2022 & 59886.62 & YAHPT & 15.267 $\pm$ 0.008 & 14.379 $\pm$ 0.007 & 13.757 $\pm$ 0.007 & 13.134 $\pm$ 0.010 \\
06-11-2022 & 59889.63 & YAHPT & 15.268 $\pm$ 0.011 & 14.358 $\pm$ 0.008 & 13.747 $\pm$ 0.007 & 13.101 $\pm$ 0.011 \\
07-11-2022 & 59890.62 & YAHPT & 15.287 $\pm$ 0.016 & 14.352 $\pm$ 0.009 & 13.715 $\pm$ 0.009 & 13.087 $\pm$ 0.013 \\
08-11-2022 & 59891.62 & YAHPT & 15.271 $\pm$ 0.017 & 14.394 $\pm$ 0.009 & 13.745 $\pm$ 0.009 & 13.125 $\pm$ 0.013 \\
09-11-2022 & 59892.61 & YAHPT & 15.273 $\pm$ 0.016 & 14.358 $\pm$ 0.008 & 13.727 $\pm$ 0.007 & 13.120 $\pm$ 0.011 \\
10-11-2022 & 59893.62 & YAHPT & 15.277 $\pm$ 0.012 & 14.368 $\pm$ 0.008 & 13.733 $\pm$ 0.008 & 13.069 $\pm$ 0.012 \\
12-11-2022 & 59895.61 & YAHPT & 15.285 $\pm$ 0.010 & 14.374 $\pm$ 0.008 & 13.748 $\pm$ 0.007 & 13.095 $\pm$ 0.011 \\
15-11-2022 & 59898.60 & YAHPT & 15.291 $\pm$ 0.008 & 14.385 $\pm$ 0.007 & 13.758 $\pm$ 0.007 & 13.116 $\pm$ 0.010 \\
17-11-2022 & 59900.60 & YAHPT & 15.292 $\pm$ 0.008 & 14.384 $\pm$ 0.007 & 13.742 $\pm$ 0.007 & 13.121 $\pm$ 0.010 \\
20-11-2022 & 59903.50 & YAHPT & 15.303 $\pm$ 0.006 & 14.411 $\pm$ 0.006 & 13.783 $\pm$ 0.006 & 13.164 $\pm$ 0.009 \\
21-11-2022 & 59904.49 & YAHPT & 15.306 $\pm$ 0.006 & 14.416 $\pm$ 0.006 & 13.786 $\pm$ 0.006 & 13.167 $\pm$ 0.009 \\
24-11-2022 & 59907.50 & YAHPT & 15.316 $\pm$ 0.007 & 14.420 $\pm$ 0.006 & 13.792 $\pm$ 0.006 & 13.168 $\pm$ 0.009 \\
25-11-2022 & 59908.49 & YAHPT & 15.310 $\pm$ 0.006 & 14.422 $\pm$ 0.006 & 13.793 $\pm$ 0.006 & 13.165 $\pm$ 0.009 \\
27-11-2022 & 59910.50 & YAHPT & 15.308 $\pm$ 0.007 & 14.420 $\pm$ 0.006 & 13.784 $\pm$ 0.006 & 13.177 $\pm$ 0.009 \\
28-11-2022 & 59911.49 & YAHPT & 15.316 $\pm$ 0.007 & 14.424 $\pm$ 0.006 & 13.793 $\pm$ 0.006 & 13.161 $\pm$ 0.009 \\
01-12-2022 & 59914.49 & YAHPT & 15.321 $\pm$ 0.007 & 14.438 $\pm$ 0.006 & 13.807 $\pm$ 0.006 & 13.199 $\pm$ 0.009 \\
02-12-2022 & 59915.49 & YAHPT & 15.328 $\pm$ 0.007 & 14.440 $\pm$ 0.006 & 13.815 $\pm$ 0.006 & 13.207 $\pm$ 0.010 \\
03-12-2022 & 59916.56 & YAHPT & 15.301 $\pm$ 0.012 & 14.402 $\pm$ 0.008 & 13.782 $\pm$ 0.007 & 13.178 $\pm$ 0.011 \\
05-12-2022 & 59918.49 & YAHPT & 15.313 $\pm$ 0.008 & 14.440 $\pm$ 0.007 & 13.819 $\pm$ 0.007 & 13.191 $\pm$ 0.010 \\
06-12-2022 & 59919.48 & YAHPT & 15.304 $\pm$ 0.008 & 14.427 $\pm$ 0.007 & 13.616 $\pm$ 0.083 & 13.209 $\pm$ 0.010 \\
07-12-2022 & 59920.52 & YAHPT & 15.314 $\pm$ 0.011 & 14.420 $\pm$ 0.007 & 13.815 $\pm$ 0.007 & 13.210 $\pm$ 0.011 \\
08-12-2022 & 59921.52 & YAHPT & 15.297 $\pm$ 0.012 & 14.421 $\pm$ 0.009 & 13.825 $\pm$ 0.008 & 13.230 $\pm$ 0.014 \\
09-12-2022 & 59922.49 & YAHPT & 15.319 $\pm$ 0.008 & 14.434 $\pm$ 0.007 & 13.812 $\pm$ 0.007 & 13.201 $\pm$ 0.010 \\
\hline
\end{tabular}
|
Multiwavelength observations of KS 1947+300
|
Photometric observations of KS 1947+300 from 60 cm , 80 cm, 2.4 m, and YAHPT telescopes--continued.
|
['\\newcommand{\\PR}[1]{{\\color{magenta} #1}}', '\\newcommand{\\comment}[1]{{\\color{blue} #1}}', '\\newcommand{\\WL}[1]{{\\color{red} #1}}']
|
astro-ph.HE
|
|
2310.06655v1
|
\begin{tabular}{lc}
\hline
Parameter & Fiducial Value\\ \hline
$m_{\rm ej}$ $(10^{-3} M_{\odot})$ & 1.00 \\
$n$ & 1.00\\
$m$ & 9.00\\
$R_{*,0}$ ($10^{11}$ cm) & 1.00\\
$v_{*,0}$ ($10^{9}$ cm s$^{-1}$) & 1.00\\
$H_0$ ($10^{46}$ erg s$^{-1}$) & 1.00 \\
$t_c/t_*$ & 1.00 \\
$\delta$ & 1.30 \\
$\kappa$ (cm$^2$ g$^{-1}$) & 0.20 \\
\hline
\end{tabular}
|
On the optical transients from double white-dwarf mergers
|
Parameter values used to model thermal and synchrotron radiation from the expansion of ejected material.
|
['\\newcommand{\\vdag}{(v)^\\dagger}', '\\newcommand\\aastex{AAS\\TeX}', '\\newcommand\\latex{La\\TeX}']
|
astro-ph.SR, astro-ph.HE
|
|
2307.02820v1
|
\begin{tabular}{ccccccc}
\hline
\textbf{DATASET} & \textbf{SVM} & \textbf{RF} & \textbf{DT} & \textbf{NB} & \textbf{MV} & \textbf{STCK} \\ \hline
EMO-DB & \textbf{81.30} & 71.14 & 52.03 & 51.26 & 64.92 & 69.91 \\
RAVDESS & \textbf{71.06} & 69.53 & 54.65 & 29.53 & 49.81 & 40.64 \\
TESS & 97.23 & 98.23 & 89.2 & 86.04 & \textbf{98.52} & 95.95 \\
CREMA & \textbf{50.26} & 45.66 & 44.50 & 30.75 & 42.65 & 41.25 \\
SAVEE & \textbf{71.20} & 69.72 & 48.61 & 48.33 & 70.55 & 63.05 \\
TESS+RAVDESS & 85.62 & \textbf{86.47} & 70.12 & 64.08 & 82.01 & 67.51 \\ \hline
\end{tabular}
|
Evaluating raw waveforms with deep learning frameworks for speech emotion recognition
|
Evaluation results of machine learning methods with MFCC features
| null |
cs.SD, cs.AI, eess.AS
|
|
2308.09191v1
|
\begin{tabular}{ l | r | r | r | r}
\hline
\textbf{ImpGreedy} & \multicolumn{1}{c|}{AT:0.9} & \multicolumn{1}{c|}{AT:0.8} & \multicolumn{1}{c|}{AT:0.7} & \multicolumn{1}{c}{AT:0.6} \\ \hline
Total number of riders served & 29657 & 28579 & 27218 & 24655 \\
Avg number of riders served per interval & 411.9 & 396.9 & 378.0 & 342.4 \\
Total time saved of all served riders (minute) & 295310.4 & 333864.3 & 361674.1 & 370964.7 \\
Avg time saved of served riders per interval (minute) & 4101.5 & 4637.0 & 5023.3 & 5152.3 \\
Avg time saved per served rider (minute) & 9.96 & 11.68 & 13.29 & 15.05 \\
Avg time saved per rider (minute) & 6.52 & 7.37 & 7.98 & 8.19 \\ \hline
\textbf{Exact} & \multicolumn{1}{c|}{AT:0.9} & \multicolumn{1}{c|}{AT:0.8} & \multicolumn{1}{c|}{AT:0.7} & \multicolumn{1}{c}{AT:0.6} \\ \hline
Total number of riders served & 31168 & 30214 & 29122 & 26973 \\
Avg number of riders served per interval & 432.9 & 419.6 & 404.5 & 374.6 \\
Total time saved of all served riders (minute) & 305837.6 & 354127.2 & 386819.1 & 405339.6 \\
Avg time saved of served riders per interval (minute) & 4247.7 & 4918.4 & 5372.5 & 5629.7 \\
Avg time saved per served rider (minute) & 9.81 & 11.72 & 13.28 & 15.03 \\
Avg time saved per rider (minute) & 6.75 & 7.81 & 8.54 & 8.95 \\ \hline
\multicolumn{2}{| l |}{Total number of riders and public transit duration} & \multicolumn{3}{l |}{45314 and 1384100.97 minutes}\\ \hline
\end{tabular}
|
Algorithms and Computational Study on a Transportation System Integrating Public Transit and Ridesharing of Personal Vehicles
|
Overall solution comparison between different acceptance thresholds for Huge3 Config.
|
['\\newcommand{\\OPT}{{\\mathop{\\rm OPT}}}']
|
cs.DS, F.2.2; J.0
|
|
2302.09064v1
|
\begin{tabular}{c|cccccc}
\hline
run & $\mathrm{Re=Re_m}$ & $v_{rms}^*$ & ${v_{rms}^0}/{b_{rms}^0}$ & $\mathrm{Ma}$ & $t^*$ & $\xi$ \\ \hline
I & $\sim$ 1500 & $8.5\cdot 10^{-4}$ & $\sim 0.5$ & 0.005 & 79.5 & 2.4 \\
II & $\sim$ 900 & $2.7\cdot 10^{-3}$ & $\sim 1.0$ & 0.005 & 51.9 & 1.7 \\
III & $\sim$ 800 & $2.5\cdot 10^{-3}$ & $\sim 2.0$ & 0.001 & 45.3 & 1.8 \\ \hline
\hline
\end{tabular}
|
Helios 2 observations of solar wind turbulence decay in the inner heliosphere
|
Adimensional parameters at the current peak $t^*$ (in turnover time units). $\mathrm{Re}$ and $\mathrm{Re_m}$ are respectively the Reynolds and magnetic Reynolds numbers; $\mathrm{Ma}$ is the Mach number; $v_{rms}^0/b_{rms}^0$ is the ratio between the $rms$ velocity and magnetic fluctuations at the initial time of the simulation ($t=0$);
%, corresponding to the Orszag-Tang vortex);
$\xi$ is the fitted exponent (see Figure~\ref{fig:LB-epsilon}).
Box size ($(2\pi)^3$) and magnetic Prandtl number $Pr_m=1$ are the same for all runs.
|
['\\newcommand{\\lsv}[1]{{\\bf \\textcolor{purple}{#1$^{L}$}}}', '\\newcommand{\\eya}[1]{{\\bf \\textcolor{green}{#1$^{E}$}}}', '\\newcommand{\\rda}[1]{{\\bf \\textcolor{blue}{#1$^{RD}$}}}', '\\newcommand{\\raf}[1]{{\\bf \\textcolor{red}{#1$^{RM}$}}}', '\\newcommand{\\dan}[1]{{\\bf \\textcolor{orange}{#1$^{D}$}}}', '\\newcommand{\\bob}[1]{{\\bf \\textcolor{gray}{#1$^{RB}$}}}', '\\newcommand\\bNabla{\\boldsymbol{\\nabla}}', '\\newcommand{\\ann}{ {\\it Annales Geophysicae}}', '\\newcommand{\\aas}{ {\\it Astron. Astrophys. Suppl.}}', '\\newcommand{\\aar}{ {\\it Astron. Astrophys. Rev.}}', '\\newcommand{\\ag}{ {\\it Ann. Geophys.}}', '\\newcommand{\\gafd}{ {\\it Geophys. Astrophys. Fluid Dyn.}}', '\\newcommand{\\ijga}{ {\\it Int. J. Geomag. Aeron.}}', '\\newcommand{\\jastp}{ {\\it J. Atmos. Solar Terr. Phys.}}', '\\newcommand{\\jfm}{ {\\it \tJ. Fluid Mech.}}', '\\newcommand{\\phf}{ {\\it \tPhys. Fluid}}', '\\newcommand{\\natph}{ {\\it Nature Phys.}}', '\\newcommand{\\npg}{ {\\it Nonlinear Processes in Geophys.}}', '\\newcommand{\\sph}{ {\\it Solar Phys.}}', '\\newcommand{\\jph}{ {\\it J. Plasma Phys.}}', '\\newcommand{\\jsp}{ {\\it J. of Stat. Phys.}}']
|
physics.space-ph, physics.plasm-ph
|
|
2305.01537v2
|
\begin{tabular}{lllll}
\hline
& $n_r$ & $n_\theta$ & $n_\varphi$ & $dt$ \\
\hline
\hline
Aligned(64) & 2500 & 64 & 4 & $0.00490$ \\
Aligned(96) & 3750 & 96 & 4 & $0.00327$ \\
FFT(64) & 2500 & 64 & 128 & $0.001$ \\
FFT(96) & 3750 & 96 & 192 & $0.00067$ \\
\hline\hline
\end{tabular}
|
Ameliorating the Courant-Friedrichs-Lewy condition in spherical coordinates: A double FFT filter method for general relativistic MHD in dynamical spacetimes
|
The grid parameters for all simulations. Here $n_r$, $n_\theta$, and $n_\varphi$ are the number of grid points in the radial, polar, and azimuthal directions, respectively, and $dt$ is the timestep.
|
['\\newcommand{\\be}{\\begin{equation}}', '\\newcommand{\\ee}{\\end{equation}}', '\\newcommand{\\NOTE}[1]{{\\bf \\color{red} #1 }}', '\\newcommand{\\BLUE}[1]{{\\bf \\color{blue} #1 }}', '\\newcommand{\\LJ}[1]{{\\bf \\color{cyan} #1}}', '\\newcommand{\\hangup}{{\\it hangup kick}\\xspace}', '\\newcommand{\\super}{{\\it superkick}\\xspace}', '\\newcommand{\\cross}{{\\it cross kick}\\xspace}', '\\newcommand{\\tg}{\\tilde\\gamma}', '\\newcommand{\\tG}{\\tilde\\Gamma}', '\\newcommand{\\tA}{\\tilde A}', '\\newcommand{\\tK}{\\tilde K}', '\\newcommand{\\lb}{{\\cal L}_\\beta}', '\\newcommand{\\dt}{\\partial_0}', '\\newcommand{\\tr}{\\mbox{tr}}', '\\newcommand{\\psibl}{\\psi_{BL}}', '\\newcommand{\\bea}{\\begin{eqnarray}}', '\\newcommand{\\eea}{\\end{eqnarray}}', '\\newcommand{\\beq}{\\begin{equation}}', '\\newcommand{\\eeq}{\\end{equation}}', '\\newcommand{\\KMS}{\\rm km\\ s^{-1}}', '\\newcommand{\\LAZEV}{{\\it LazEv}}', '\\newcommand{\\scri}{\\mathscr{I}}', '\\newcommand{\\SphericalNR}{{\\sc SphericalNR}\\xspace}', '\\newcommand{\\Czbe}[1]{\\textcolor{brown}{\\bf [ZBE: #1]}}', '\\newcommand{\\Ccip}[1]{\\textcolor{orange}{\\bf [FC: #1]}}']
|
gr-qc, astro-ph.HE, physics.comp-ph
|
|
2310.09433v2
|
\begin{tabular}[c]{|c|c|c|c|c|}
\hline
NMSE & $\alpha$ [dB/cm] & $Q$ & $\Delta\omega/2\pi$ [GHz] & $\overline{P}_{\textrm {in}}$ [dBm]\\
\hline
0.0164 $\pm$ 0.0020 & 0.2 & 3.5$\times10^5$ & -10 & -7.5 \\
\hline
0.0169 $\pm$ 0.0020 & 0.5 & 1.4$\times10^5$ & -20 & -5.0 \\
\hline
0.0178 $\pm$ 0.0018 & 0.8 & 8.8$\times10^4$ & -30 & -5.0 \\
\hline
0.0182 $\pm$ 0.0022 & 1.0 & 7.0$\times10^4$ & -45 & -5.0 \\
\hline
0.0190 $\pm$ 0.0024 & 1.5 & 4.7$\times10^4$ & -55 & -2.5 \\
\hline
0.0215 $\pm$ 0.0030 & 2.0 & 3.5$\times10^4$ & 60 & -7.5 \\
\hline
\end{tabular}
|
Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing
|
Minimum NMSE of testing set prediction for selected values of $\alpha$ ($\tau_ \textrm{FC}$ = 10 ns, $\tau_ \textrm{th}$ = 50 ns).
|
['\\newcommand\\crefrangeconjunction{--}']
|
physics.optics, cs.ET, cs.LG, cs.NE
|
|
2310.09021v1
|
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Table}&\multicolumn{3}{|c|}{\textbf{Table Column Head}} \\
\cline{2-4}
\textbf{Head} & \textbf{\textit{Table column subhead}}& \textbf{\textit{Subhead}}& \textbf{\textit{Subhead}} \\
\hline
copy& More table copy$^{\mathrm{a}}$& & \\
\hline
\multicolumn{4}{l}{$^{\mathrm{a}}$Sample of a Table footnote.}
\end{tabular}
|
Generative AI-driven Semantic Communication Framework for NextG Wireless Network
|
Table Type Styles
| null |
cs.NI
|
|
2311.10376v1
|
\begin{tabular}{lccc}
Resonance & \multicolumn{3}{c}{Structure} \\[2ex]
$3031+i0.7$ & \multicolumn{3}{c}{$r_{q\bar{q}}:1.02$;\,\,\,\,\,$r_{\bar{q}\bar{q}}:1.07$;\,\,\,\,\,$r_{c\bar{q}}:0.94$;\,\,\,\,\,$r_{qc}:1.00$} \\
& \multicolumn{3}{c}{$S$: 16.4\%;\, $H$: 14.1\%;\, $Di$: 27.0\%;\, $K$: 42.5\%}\\[1.5ex]
%
$3105+i3.7$ & \multicolumn{3}{c}{$r_{q\bar{q}}:1.26$;\,\,\,\,\,$r_{\bar{q}\bar{q}}:1.33$;\,\,\,\,\,$r_{c\bar{q}}:0.99$;\,\,\,\,\,$r_{qc}:1.26$} \\
& \multicolumn{3}{c}{$S$: 4.0\%;\, $H$: 21.6\%;\, $Di$: 28.0\%;\, $K$: 46.4\%}\\[1.5ex]
%
$3373+i4.2$ & \multicolumn{3}{c}{$r_{q\bar{q}}:1.51$;\,\,\,\,\,$r_{\bar{q}\bar{q}}:1.61$;\,\,\,\,\,$r_{c\bar{q}}:1.45$;\,\,\,\,\,$r_{qc}:1.55$} \\
& \multicolumn{3}{c}{$S$: 8.0\%;\, $H$: 10.6\%;\, $Di$: 26.7\%;\, $K$: 54.7\%}\\[1.5ex]
%
$3455+i12.9$ & \multicolumn{3}{c}{$r_{q\bar{q}}:1.68$;\,\,\,\,\,$r_{\bar{q}\bar{q}}:1.54$;\,\,\,\,\,$r_{c\bar{q}}:1.30$;\,\,\,\,\,$r_{qc}:1.45$} \\
& \multicolumn{3}{c}{$S$: 6.6\%;\, $H$: 9.8\%;\, $Di$: 39.4\%;\, $K$: 44.2\%}
\end{tabular}
|
The $\mathbf{\bar{q}q\bar{s}Q}$ $\mathbf{(q=u,\,d;\,Q=c,\,b)}$ tetraquark system in a chiral quark model
|
\label{GresultR6} Compositeness of exotic resonances obtained in a complete coupled-channel calculation in the $1(2^+)$ state of $\bar{q}q\bar{s}c$ tetraquark. Results are similarly organized as those in Table~\ref{GresultR1}.
| null |
hep-ph, hep-ex, hep-lat, nucl-ex, nucl-th
|
|
2311.04517v2
|
\begin{tabular}{|l|l|l|llllll|llllll|llllll|}
\hline
\multicolumn{1}{|c|}{\multirow{3}{*}{$k$}} & \multicolumn{1}{c|}{\multirow{3}{*}{$f^*$}} & \multicolumn{1}{c|}{\multirow{3}{*}{$\overline{f}$}} & \multicolumn{6}{c|}{Big-means-sequential} & \multicolumn{6}{c|}{Big-means-inner} & \multicolumn{6}{c|}{Big-means-competitive} \\ \cline{4-21}
\multicolumn{1}{|c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{2}{c|}{$\varepsilon$} & \multicolumn{2}{c|}{$\overline{t}$} & \multicolumn{2}{c|}{$t$} & \multicolumn{2}{c|}{$\varepsilon$} & \multicolumn{2}{c|}{$\overline{t}$} & \multicolumn{2}{c|}{$t$} & \multicolumn{2}{c|}{$\varepsilon$} & \multicolumn{2}{c|}{$\overline{t}$} & \multicolumn{2}{c|}{$t$} \\ \cline{4-21}
\multicolumn{1}{|c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{med} & \multicolumn{1}{c|}{std} & \multicolumn{1}{c|}{med} & \multicolumn{1}{c|}{std} & \multicolumn{1}{c|}{med} & \multicolumn{1}{c|}{std} & \multicolumn{1}{c|}{med} & \multicolumn{1}{c|}{std} & \multicolumn{1}{c|}{med} & \multicolumn{1}{c|}{std} & \multicolumn{1}{c|}{med} & \multicolumn{1}{c|}{std} & \multicolumn{1}{c|}{med} & \multicolumn{1}{c|}{std} & \multicolumn{1}{c|}{med} & \multicolumn{1}{c|}{std} & \multicolumn{1}{c|}{med} & \multicolumn{1}{c|}{std} \\ \hline
2 & 15.20433$^*$ & 5.05194 & 1.849 & 0.617 & 1.316 & 1.107 & 1.713 & 1.053 & 1.862 & 0.017 & 0.662 & 0.988 & 2.984 & 0.983 & 1.875 & 0.02 & 0.764 & 0.35 & 1.641 & 0.787 \\
3 & 8.07129$^*$ & 2.91767 & 0.644 & 0.393 & 1.704 & 0.721 & 1.653 & 0.771 & 0.518 & 0.268 & 1.984 & 0.91 & 1.984 & 0.876 & 0.769 & 0.465 & 1.185 & 0.582 & 2.137 & 0.676 \\
5 & 5.30537$^*$ & 1.8961 & 0.735 & 0.491 & 1.116 & 0.376 & 1.677 & 0.755 & 0.505 & 0.403 & 1.098 & 0.558 & 2.488 & 0.762 & 0.952 & 0.552 & 2.008 & 0.477 & 2.786 & 0.53 \\
10 & 3.3767$^*$ & 1.27647 & 0.467 & 4.613 & 2.715 & 0.712 & 2.715 & 0.701 & 0.287 & 4.549 & 1.141 & 0.626 & 1.757 & 0.77 & 0.288 & 0.274 & 3.169 & 0.229 & 3.345 & 0.343 \\
15 & 2.86473$^*$ & 1.09602 & 0.962 & 0.642 & 3.011 & 0.628 & 3.381 & 0.644 & 0.713 & 0.614 & 0.768 & 0.658 & 2.313 & 0.946 & 0.74 & 0.533 & 4.427 & 0.746 & 4.727 & 0.945 \\
20 & 2.5732$^*$ & 0.98992 & 1.375 & 0.577 & 3.078 & 0.906 & 3.224 & 0.984 & 0.643 & 0.582 & 0.977 & 0.42 & 2.734 & 0.799 & 1.091 & 0.594 & 6.975 & 0.979 & 7.135 & 0.878 \\
25 & 2.38539$^*$ & 0.91776 & 1.632 & 0.651 & 4.619 & 1.077 & 4.584 & 0.937 & 1.161 & 0.711 & 1.27 & 0.561 & 2.493 & 0.844 & 0.837 & 0.77 & 7.219 & 0.734 & 7.843 & 0.845 \\
\hline
\multicolumn{3}{|c|}{Mean:} & \textbf{1.095} & & \textbf{2.509} & & \textbf{2.707} & & \textbf{0.813} & & \textbf{1.128} & & \textbf{2.393} & & \textbf{0.936} & & \textbf{3.678} & & \textbf{4.231} & \\ \hline
\end{tabular}
|
Strategies for Parallelizing the Big-Means Algorithm: A Comprehensive Tutorial for Effective Big Data Clustering
|
Summary of the results with Protein Homology ($\times10^{11}$)
|
['\\newcommand{\\eps}{\\varepsilon}']
|
cs.DC, cs.LG, math.OC
|
|
2301.10763v1
|
\begin{tabular}{ l | c | r }
\hline
Source & 2022 estimate & baseline \\ \hline \hline
Berkeley Earth & $+1.24\pm0.03^\circ$C & 1850-1900 \\ \hline
ERA5 & $+1.18^\circ$C & 1850-1900 \\ \hline
GISSTEMP & $+0.89^\circ$C & 1951-1980 \\ \hline
HadCRUT5 & $+1.16\pm0.08^\circ$C & 1850-1900 \\ \hline
NOAAGlobalTemp & $+0.86^\circ$C & 1901-2000 \\ \hline
JRA-55 & -* & - \\ \hline \hline
WMO & $+1.15^\circ$C [1.02 to 1.27] & 1850-1900 \\
\hline
\end{tabular}
|
Very early warning of a moderate-to-strong El Niño in 2023
|
Estimates of the global temperature in 2022 compared to the corresponding baseline periods . (*Estimate not available at time of writing)
| null |
physics.ao-ph
|
|
2307.09257v1
|
\begin{tabular}{crrcccc}
\hline
\multirow{2}{*}{$1-\alpha$} & \multirow{2}{*}{$n$} & \multirow{2}{*}{$m$} & \multirow{2}{*}{Average width} & \multicolumn{3}{c}{Pointwise coverage probabilities} \\ \cline{5-7}
& & & & Minimum & Maximum & Average \\ \hline
\multirow{3}{*}{0.90} & 200 & 50 & 3.35 & 0.008 & 0.865 & 0.73 \\
& 700 & 175 & 2.44 & 0.362 & 0.881 & 0.83 \\
& 2000 & 500 & 1.62 & 0.774 & 0.889 & \textbf{0.86} \\ \hline
\multirow{3}{*}{0.95} & 200 & 50 & 3.93 & 0.01 & 0.924 & 0.79 \\
& 700 & 175 & 2.84 & 0.341 & 0.942 & 0.88 \\
& 2000 & 500 & 1.94 & 0.81 & 0.945 & \textbf{0.92} \\ \hline
\multirow{3}{*}{0.99} & 200 & 50 & 4.93 & 0.003 & 0.979 & 0.84 \\
& 700 & 175 & 3.63 & 0.365 & 0.986 & 0.93 \\
& 2000 & 500 & 2.55 & 0.847 & 0.99 & \textbf{0.97} \\ \hline
\end{tabular}
|
Uniform Confidence Band for Optimal Transport Map on One-Dimensional Data
|
Evaluations of our $(1-\alpha)$-level pointwise confidence intervals of the optimal transport map from $N(0,1)$ to $\operatorname{Gamma}(5,0.5)$ based on 1,000 pairs of samples from each distribution. The table displays the median of average widths and summary of the coverage probabilities of the pointwise confidence intervals over $[-2.5,2.5]$.
|
['\\newcommand{\\R}{\\mathbb{R}}', '\\newcommand{\\N}{\\mathbb{N}}', '\\newcommand{\\bH}{\\mathbb{H}}', '\\newcommand{\\mF}{\\mathcal{F}}', '\\newcommand{\\mG}{\\mathcal{G}}', '\\newcommand{\\bG}{\\mathbb{G}}', '\\newcommand{\\mA}{\\mathcal{A}}', '\\newcommand{\\mB}{\\mathcal{B}}', '\\newcommand{\\mC}{\\mathcal{C}}', '\\newcommand{\\mD}{\\mathcal{D}}', '\\newcommand{\\mJ}{\\mathcal{J}}', '\\newcommand{\\mH}{\\mathcal{H}}', '\\newcommand{\\mK}{\\mathcal{K}}', '\\newcommand{\\mL}{\\mathcal{L}}', '\\newcommand{\\mS}{\\mathcal{S}}', '\\newcommand{\\mT}{\\mathcal{T}}', '\\newcommand{\\mP}{\\mathcal{P}}', '\\newcommand{\\mQ}{\\mathcal{Q}}', '\\newcommand{\\mM}{\\mathcal{M}}', '\\newcommand{\\mN}{\\mathcal{N}}', '\\newcommand{\\mV}{\\mathcal{V}}', '\\newcommand{\\mX}{\\mathcal{X}}', '\\newcommand{\\mY}{\\mathcal{Y}}', '\\newcommand{\\mR}{\\mathcal{R}}', '\\newcommand{\\fD}{\\mathfrak{D}}', '\\newcommand{\\fR}{\\mathfrak{R}}', '\\newcommand{\\bX}{\\mathbb{X}}', '\\newcommand{\\bY}{\\mathbb{Y}}', '\\newcommand{\\Ep}{\\mathbb{E}}', '\\newcommand{\\Tr}{\\mathrm{T}}', '\\newcommand{\\Cr}{\\mathrm{C}}', '\\newcommand{\\Exp}{\\mathrm{Exp}}', '\\newcommand{\\Inj}{\\mathrm{Inj}}', '\\newcommand{\\Dr}{\\mathrm{D}}', '\\newcommand{\\Sr}{\\mathrm{S}}', '\\newcommand{\\mE}{\\mathcal{E}}', '\\newcommand{\\mW}{\\mathcal{W}}', '\\newcommand{\\mZ}{\\mathcal{Z}}', '\\newcommand{\\mfM}{\\mathfrak{M}}', '\\newcommand{\\mfE}{\\mathfrak{E}}', '\\newcommand{\\pr}{\\mbox{Pr}}', '\\newcommand{\\argmin}{\\operatornamewithlimits{argmin}}', '\\newcommand{\\argmax}{\\operatornamewithlimits{argmax}}', '\\newcommand{\\mone}{\\textbf{1}}', '\\newcommand{\\PL}{Polyak-\\L ojasiewicz}', '\\newcommand{\\Z}{\\mathbb{Z}}', '\\newcommand{\\C}{\\mathbb{C}}', '\\newcommand{\\Banach}{\\mathbb{B}}', '\\newcommand{\\Hilbert}{\\mathbb{H}}', '\\newcommand{\\trace}{\\mathrm{tr}}', '\\newcommand{\\sinc}{\\operatorname{sinc}}', '\\newcommand{\\rc}{\\color{red}}', '\\newcommand{\\bc}{\\color{blue}}', '\\newcommand{\\oc}{\\color{orange}}', '\\newcommand{\\mc}{\\color{magenta}}']
|
math.ST, stat.TH
|
|
2310.08183v1
|
\begin{tabular}{ %c |
c c c c c c c}
%\hline
\hline
%Design &
$L$~[m] & $T$~[s] & $n$ & $\Delta z_\mathrm{max}$~[m] & $\delta \phi \, [1/\sqrt{\mathrm{Hz}}]$ & $T_\mathrm{int}$~[s] \\
\hline
%Advanced &
$1000$ & 1.7 & $2500$ & $970$ & $10^{-5}$ & $10^8$ \\
\hline
%\hline
\end{tabular}
|
Terrestrial Very-Long-Baseline Atom Interferometry: Workshop Summary
|
List of experimental parameters used for the computation of the sensitivity plots shown in this Section, which could be implemented in future vertical gradiometers, such as AION-km. For reference, $L$ is the length of the baseline, $T$ is the interrogation time, $4n-1$ is the total number of LMT kicks transferred during a single cycle, $\delta \phi$ is the shot noise-limited phase resolution, $T_\mathrm{int}$ is the integration time, and $\Delta z_\mathrm{max}$ is the maximum gradiometer length given the choice of interferometer parameters. We also consider scenarios where $\Delta z$ is shorter than the maximum value. The set of geological parameters is taken from Ref.~.
| null |
hep-ex, astro-ph.IM, gr-qc, hep-ph, physics.atom-ph
|
|
2305.12632v1
|
\begin{tabular}{|l|c|c|c|c|c|c|}
\multicolumn{4}{c}{}\\ \hline
No. &1&2&3&4&5&6\\
\hline \hline
Data&
USD/CAD & EUR/CHF & EUR/GBP & \hspace{0.4cm} SOY \hspace{0.4cm} &\hspace{0.4cm} VIX \hspace{0.4cm} & NKY 225\\
\hline
Corr&
-0.35 & -0.33 & -0.32 & -0.03 & -0.03 & -0.08
\\
\hline
\end{tabular}
|
Deformation of Marchenko-Pastur distribution for the correlated time series
|
Temporal correlation of the shortest time lag of the financial time sires
| null |
cond-mat.stat-mech, q-fin.ST
|
|
2310.08649v1
|
\begin{tabular}{ll}
\hline
Parameter & Values \\ \hline
$f_a$ & 1.0 \\
$T$ & \texttt{linspace($10^{-2}$,$10^{0}$,$n_{batch}$)} \\
$t_{max}$ & 1.0 \\ \hline
\end{tabular}
|
Time-vectorized numerical integration for systems of ODEs
|
Model parameters used in the timing study for the neural ODE case.
| null |
math.NA, cs.LG, cs.NA, stat.ML
|
|
2312.10078v1
|
\begin{tabular}{lrl}
\hline
\textbf{Parameter}& \textbf{Value} \\
\hline
\# conversations& 87,957\\
Total \# turns & 1,449,998\\
Average \# turns per conversation & 16.48 \\
Median \# turns per conversation & 6.00 \\
Maximum \# turns per conversation & 998 \\
\# of conversations that finish within two turns & 24366 (27.7\%) \\
\# of conversations that finish within four turns & 37684 (42.8\%) \\
\# of conversations that have even turns & 84915 (96.5\%) \\
\hline
\end{tabular}
|
Early ChatGPT User Portrait through the Lens of Data
|
Descriptive statistics of conversation lengths for the shareGPT92K Dataset
| null |
cs.HC, cs.AI, cs.CL, cs.LG
|
|
2312.04182v1
|
\begin{tabular}{|p{25pt}|p{75pt}|p{115pt}|}
\hline
Symbol&
Quantity&
Conversion from Gaussian and \par CGS EMU to SI $^{\mathrm{a}}$ \\
\hline
$\Phi $&
magnetic flux&
1 Mx $\to 10^{-8}$ Wb $= 10^{-8}$ V$\cdot $s \\
$B$&
magnetic flux density, \par magnetic induction&
1 G $\to 10^{-4}$ T $= 10^{-4}$ Wb/m$^{2}$ \\
$H$&
magnetic field strength&
1 Oe $\to 10^{3}/(4\pi )$ A/m \\
$m$&
magnetic moment&
1 erg/G $=$ 1 emu \par $\to 10^{-3}$ A$\cdot $m$^{2} = 10^{-3}$ J/T \\
$M$&
magnetization&
1 erg/(G$\cdot $cm$^{3}) =$ 1 emu/cm$^{3}$ \par $\to 10^{3}$ A/m \\
4$\pi M$&
magnetization&
1 G $\to 10^{3}/(4\pi )$ A/m \\
$\sigma $&
specific magnetization&
1 erg/(G$\cdot $g) $=$ 1 emu/g $\to $ 1 A$\cdot $m$^{2}$/kg \\
$j$&
magnetic dipole \par moment&
1 erg/G $=$ 1 emu \par $\to 4\pi \times 10^{-10}$ Wb$\cdot $m \\
$J$&
magnetic polarization&
1 erg/(G$\cdot $cm$^{3}) =$ 1 emu/cm$^{3}$ \par $\to 4\pi \times 10^{-4}$ T \\
$\chi , \kappa $&
susceptibility&
1 $\to 4\pi $ \\
$\chi_{\rho }$&
mass susceptibility&
1 cm$^{3}$/g $\to 4\pi \times 10^{-3}$ m$^{3}$/kg \\
$\mu $&
permeability&
1 $\to 4\pi \times 10^{-7}$ H/m \par $= 4\pi \times 10^{-7}$ Wb/(A$\cdot $m) \\
$\mu_{r}$&
relative permeability&
$\mu \to \mu_{r}$ \\
$w, W$&
energy density&
1 erg/cm$^{3} \to 10^{-1}$ J/m$^{3}$ \\
$N, D$&
demagnetizing factor&
1 $\to 1/(4\pi )$ \\
\hline
\multicolumn{3}{p{251pt}}{Vertical lines are optional in tables. Statements that serve as captions for
the entire table do not need footnote letters. }\\
\multicolumn{3}{p{251pt}}{$^{\mathrm{a}}$Gaussian units are the same as cg emu for magnetostatics; Mx
$=$ maxwell, G $=$ gauss, Oe $=$ oersted; Wb $=$ weber, V $=$ volt, s $=$
second, T $=$ tesla, m $=$ meter, A $=$ ampere, J $=$ joule, kg $=$
kilogram, H $=$ henry.}
\end{tabular}
|
Bayesian Persuasion for Containing SIS Epidemic with Asymptomatic Infection
|
Units for Magnetic Properties
|
['\\newcommand\\um[1]{{\\color{blue} #1}}', '\\newcommand{\\St}{\\mathtt{S}}', '\\newcommand{\\yint}{y^*_{\\mathtt{INT}}}', '\\newcommand{\\zsint}{z^*_{\\bar{\\mathtt{S}},\\mathtt{INT}}}', '\\newcommand{\\It}{\\mathtt{I}}', '\\newcommand{\\Stb}{\\bar{\\mathtt{S}}}', '\\newcommand{\\Itb}{\\bar{\\mathtt{I}}}', '\\newcommand{\\zsdag}{z^\\dagger_{\\bar{\\mathtt{S}}}}', '\\newcommand{\\zidag}{z^\\dagger_{\\bar{\\mathtt{I}}}}', '\\newcommand{\\zsbar}{z^\\star_{\\bar{\\mathtt{S}}}}', '\\newcommand{\\zibar}{z^\\star_{\\bar{\\mathtt{I}}}}', '\\newcommand{\\zp}{\\mathbf{z}}', '\\newcommand{\\ymax}{y_{\\mathtt{max}}}', '\\newcommand{\\yee}{y_{\\mathtt{EE}}}', '\\newcommand{\\Pt}{\\mathtt{P}}', '\\newcommand{\\Ut}{\\mathtt{U}}', '\\newcommand{\\Rb}{\\mathbb{R}}', '\\newcommand{\\Pb}{\\mathbb{P}}', '\\newcommand{\\betap}{\\beta_{\\mathtt{P}}}', '\\newcommand{\\betau}{\\beta_{\\mathtt{U}}}', '\\newcommand{\\ignore}[1]{}']
|
eess.SY, cs.GT, cs.SY
|
|
2308.15777v1
|
\begin{tabular}{ccc}
\hline
& Computational complexity & Memory usage \\
\hline\hline
Dense block & $\frac{G(G+1)}{2}C^2k^2TF$ & $\frac{G(G+1)}{2}C^2k^2$\\
\hline
Grouped dense block & $\frac{G+1}{2}C^2k^2TF$ & $\frac{G+1}{2}C^2k^2$\\
\hline
\textbf{2D SDB} & $\frac{2G-1}{G^2}C^2k^2TF$ & $\frac{2G-1}{G^2}C^2k^2$\\
\hline
\textbf{1D SDB} & $\frac{2G-1}{G^2}C^2k^2L$ & $\frac{2G-1}{G^2}C^2k^2$\\
\hline
\end{tabular}
|
DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup Processing
|
Comparison of Complexity with Various Dense Blocks \label{tab:DenseCost}
|
['\\newcommand{\\dm}[1]{\\color{red}[#1]\\color{black}}', '\\newcommand{\\dmr}[1]{\\color{blue}[#1]\\color{black}}']
|
eess.AS, eess.SP
|
|
2305.00041v1
|
\begin{tabular}{c|ccc|ccc}
\hline
& \multicolumn{3}{c|}{RealEstate-10K} & \multicolumn{3}{c}{NeRF-LLFF} \\
model &
\textbf{Prec. \textuparrow} & \textbf{Rec. \textuparrow} & \textbf{F1 \textuparrow} &
\textbf{Prec. \textuparrow} & \textbf{Rec. \textuparrow} & \textbf{F1 \textuparrow} \\
\hline
ViP-NeRF & 0.97 & \textbf{0.83} & \textbf{0.89} & 0.82 & \textbf{0.85} & \textbf{0.83} \\
DDP-NeRF & \textbf{0.98} & 0.53 & 0.66 & \textbf{0.86} & 0.33 & 0.47 \\
\hline
\end{tabular}
|
ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields
|
Comparison of reliability of priors used in different models.
The reference visibility is obtained using NeRF trained with dense input views.
|
['\\newcommand\\Tau{\\scalerel*{\\tau}{T}}']
|
cs.CV, cs.GR
|
|
2307.02196v1
|
\begin{tabular}{|l|c|c|c|c|}
\hline
\rule[-1ex]{0pt}{3.5ex} Electron parameters & \multicolumn{2}{|c|}{AQUA} & \multicolumn{2}{|c|}{ARIA} \\
\rule[-1ex]{0pt}{3.5ex} & PWFA & X-band & PWFA & X-band \\
\hline
\rule[-1ex]{0pt}{3.5ex} Charge (pC) & 30 & 200 & 30 & 200 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Peak current (kA) & 1.8 & 1.8 & 1.8 & 0.8 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Energy (Max range, GeV) & 1.2 & 1.0 & 1.2 & 1.0 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Normalized emittance (slice, mm-mrad) & 0.6 & 0.5 & 0.8 & 1.5 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Energy spread (slice, \%) & 0.022 & 0.05 & 0.022 & 0.02 \\
\hline
\rule[-1ex]{0pt}{3.5ex} Repetition rate (Hz) & 1-10 & 100 & 1-10 & 100 \\
\hline
\end{tabular}
|
EuPRAXIA@SPARC_LAB Status Update
|
Working points for electron beams in the AQUA and ARIA beamlines. For each beamline, there is a working point that uses PWFA electrons and another that uses only the X-band linac at full power
| null |
physics.acc-ph
|
|
2304.00510v1
|
\begin{tabular}{cccccc}
\hline
order & test-statistics & 10\% critical value & 5\% &1\%\\
\hline
$r <= 1$ & 0.0 & 6.50& 8.18& 11.65\\
$r = 0 $ & 3.6& 15.66 & 17.95 &23.52\\
\hline
\end{tabular}
|
The Tech Decoupling
|
Values of Johansen's Co-integration test statistic and critical values of test
| null |
q-fin.ST, econ.GN, q-fin.EC, q-fin.GN
|
|
2312.03121v2
|
\begin{tabular}{|c|ll|}
\multicolumn{3}{c}{\bf Schulze}\\
\hline
Rank & Agent & Score\\
\hline
1 & {\tt gpt-4} & 123\\
2 & {\tt claude} & 116\\
3 & {\tt gpt-3.5-turbo} & 111\\
4 & {\tt text-davinci-003} & 106\\
5 & {\tt claude-instant} & 101\\
6 & {\tt text-davinci-002} & 96\\
7 & {\tt t-bison-001} & 89\\
8 & {\tt chatglm2} & 85\\
9 & {\tt openchat-13b} & 80\\
10 & {\tt wizardlm-30b} & 75\\
11 & {\tt llama2-13b-chat} & 69\\
12 & {\tt vicuna-13b} & 64\\
13 & {\tt codegeex2-6b} & 60\\
14 & {\tt openchat-8192-13b} & 55\\
15 & {\tt wizardlm-13b} & 50\\
16 & {\tt koala-13b} & 44\\
17 & {\tt baichuan-13b-chat} & 39\\
18 & {\tt llama2-7b-chat} & 34\\
19 & {\tt chatglm-6b} & 29\\
20 & {\tt vicuna-7b} & 25\\
21 & {\tt baichuan-7b} & 20\\
22 & {\tt dolly-v2-12b} & 14\\
23 & {\tt internlm-chat-7b} & 9\\
24 & {\tt oasst-sft-4-pythia-12b} & 5\\
25 & {\tt wizardcoder-15b} & 0\\
\hline
\end{tabular}
|
Evaluating Agents using Social Choice Theory
|
Results for Schulze and STV($k=8$) methods on AgentBench. \label{tab:vase-agentbench-full4}
|
['\\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}{{\\pi^k}}', '\\newcommand{\\bg}{\\mathbf{g}}', '\\newcommand{\\bone}{\\mathbf{1}}', '\\newcommand{\\bp}{\\mathbf{p}}', '\\newcommand{\\bu}{\\mathbf{u}}', '\\newcommand{\\bU}{\\mathbf{U}}', '\\newcommand{\\bx}{\\mathbf{x}}', '\\newcommand{\\by}{\\mathbf{y}}', '\\newcommand{\\bw}{\\mathbf{w}}', '\\newcommand{\\bv}{\\mathbf{v}}', '\\newcommand{\\bX}{\\mathbf{X}}', '\\newcommand{\\bY}{\\mathbf{Y}}', '\\newcommand{\\bZ}{\\mathbf{Z}}', '\\newcommand{\\bPi}{\\mathbf{\\Pi}}', '\\newcommand{\\bR}{\\bar{R}}', '\\newcommand{\\btheta}{\\boldsymbol\\theta}', '\\newcommand{\\cA}{\\mathcal{A}}', '\\newcommand{\\cB}{\\mathcal{B}}', '\\newcommand{\\cD}{\\mathcal{D}}', '\\newcommand{\\cPi}{\\mathcal{\\Pi}}', '\\newcommand{\\cI}{\\mathcal{I}}', '\\newcommand{\\cC}{\\mathcal{C}}', '\\newcommand{\\cH}{\\mathcal{H}}', '\\newcommand{\\cG}{\\mathcal{G}}', '\\newcommand{\\cL}{\\mathcal{L}}', '\\newcommand{\\cN}{\\mathcal{N}}', '\\newcommand{\\cO}{\\mathcal{O}}', '\\newcommand{\\cS}{\\mathcal{S}}', '\\newcommand{\\cT}{\\mathcal{T}}', '\\newcommand{\\cZ}{\\mathcal{Z}}', '\\newcommand{\\cU}{\\mathcal{U}}', '\\newcommand{\\tta}{\\mathtt{a}}', '\\newcommand{\\ttm}{\\mathtt{m}}', '\\newcommand{\\A}{\\mathcal{A}}', '\\newcommand{\\playerset}{\\mathscr{N}}', '\\newcommand{\\gameoracle}{\\mathcal{G}}', '\\newcommand{\\NashConv}{\\textsc{NashConv}\\xspace}', '\\newcommand{\\PW}{\\mbox{PW}}', '\\newcommand{\\ERM}{ERM}', '\\newcommand{\\RM}{RM}', '\\newcommand{\\defword}[1]{\\textbf{\\boldmath{#1}}}', '\\newcommand{\\ie}{{\\it i.e.},~} % AK - so Mike tells me.', '\\newcommand{\\eg}{{\\it e.g.},~} % AK', '\\newcommand{\\Var}{\\mathbb{V}\\text{ar}}', '\\newcommand{\\Cov}{\\mathbb{C}\\text{ov}}', '\\newcommand{\\Proof}{{\\noindent\\bf Proof. }}', '\\newcommand{\\citepjustyear}[1]{\\citep{#1}}', '\\newcommand{\\Qed}{$\\blacksquare$}', '\\newcommand{\\abs}[1]{\\left|#1\\right|}', '\\newcommand{\\breturn}{{\\bf return}\\xspace}']
|
cs.AI, cs.GT, cs.MA
|
|
2312.04720v1
|
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Augmentation}&\multicolumn{3}{|c|}{\textbf{Transformer}} \\
\cline{2-4}
% nazwy transformerów do sprawdzenia/zmiany
\textbf{Type} & \textbf{RoBERTa-small} & \textbf{RoBERTa-base} & \textbf{XtremDistil} \\
\hline
& \multicolumn{3}{|c|}{\textbf{F1 macro}}\\
\hline
Baseline & $ 36 \% \pm 2 \%$ & $ 38 \% \pm 8 \%$ & $ 41 \% \pm 3 \%$ \\
\hline
Para & $ 38 \% \pm 3 \%$ & $ 40 \% \pm 1 \%$ & $ 41 \% \pm 2 \%$ \\
\hline
Para-Conv & $ 39 \% \pm 1 \%$ & $ 41 \% \pm 1 \%$ & $ 43 \% \pm 2 \%$ \\
\hline
Both Para & $ 40\% \pm 2 \%$ & $ 37 \% \pm 1 \%$ & $ 43 \% \pm 2 \%$ \\
\hline
Insp & $ 37 \% \pm 4 \%$ & $ 43 \% \pm 1 \%$ & $ 43 \% \pm 3 \%$ \\
\hline
Insp-Lab & $ 38 \% \pm 2 \%$ & $ 41 \% \pm 2 \%$ & $ 40 \% \pm 2 \%$ \\
\hline
Both Insp & $ 37 \% \pm 1 \%$ & $ 41 \% \pm 2 \%$ & $ 42 \% \pm 3 \%$ \\
\hline
All & $ 39 \% \pm 2 \%$ & $ 39 \% \pm 1 \%$ & $ 43 \% \pm 1 \%$ \\
\hline
& \multicolumn{3}{|c|}{\textbf{Accuracy}}\\
\hline
Baseline & $ 38 \% \pm 2 \%$ & $ 39 \% \pm 8 \%$ & $ 43 \% \pm 5 \%$ \\
\hline
Para & $ 39 \% \pm 4 \% $ & $ 46 \% \pm 1 \%$ & $ 43 \% \pm 3 \%$ \\
\hline
Para-Conv & $ 39 \% \pm 1 \%$ & $ 44 \% \pm 1 \%$ & $ 44 \% \pm 2 \%$ \\
\hline
Both Para & $ 41 \% \pm 3 \%$ & $ 45 \% \pm 3 \%$ & $ 45 \% \pm 3 \%$ \\
\hline
Insp & $ 37 \% \pm 5 \%$ & $ 46 \% \pm 1 \%$ & $ 44 \% \pm 3 \%$ \\
\hline
Insp-Lab & $ 41 \% \pm 2 \%$ & $ 46 \% \pm 4 \%$ & $ 42 \% \pm 2 \%$ \\
\hline
Both Insp & $ 40 \% \pm 1 \%$ & $ 46 \% \pm 2 \%$ & $ 44 \% \pm 3 \%$ \\
\hline
All & $ 44 \% \pm 2 \%$ & $ 46 \% \pm 2 \%$ & $ 46 \% \pm 1 \%$ \\
\hline
\end{tabular}
|
From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis
|
results persent
| null |
cs.CL, cs.AI, cs.LG
|
|
2307.11205v2
|
\begin{tabular}{lccccc}
Case & $c_{du1dx3}/\overline{u}_\tau$ & $c_{-u1u3}/\overline{u}_\tau$ & $c_{u1u1}/\overline{u}_\tau$ & $c_{u2u2}/\overline{u}_\tau$ & $c_{u3u3}/\overline{u}_\tau$\\[3pt]
LL & 0.50 & 0.51 & 0.51 & 0.49 & 0.48\\
LM & 0.55 & 0.54 & 0.60 & 0.57 & 0.58 \\
LH & 0.72 & 0.71 & 0.77 & 0.76 & 0.74 \\
\end{tabular}
|
Mean Flow and Turbulence in Unsteady Canopy Layers
|
Propagation speeds of oscillating waves in $\partial \widetilde{u}_1 /\partial x_3$ and oscillatory resolved Reynolds stresses.
| null |
physics.flu-dyn
|
|
2303.16025v1
|
\begin{tabular}{|c|c|c|}
\hline
$\lambda$ &$a+b/R$ & Reduced-$\chi^2$ \\ \hline
$5$ & $6.67(1) + 0.79(4)/R$ & 0.16 \\ \hline
$10$ & $6.44(2) +0.83(5)/R$ & 0.07 \\ \hline
$15$ & $6.17(1) + 0.82(1)/R$ & 1.5 \\ \hline
$20$ & $6.51(3) +0.81(8)/R$ & 0.04 \\ \hline
$25$ & $6.09(1) +0.86(2)/R$ & 2.1 \\ \hline
$30$ & $6.09(1) + 0.937(1)/R$ & 1.3 \\ \hline
$35$ & $6.05(1) +0.934(2)/R$ & 0.92 \\ \hline
$40$ & $6.25(1) +1.07(2)/R$ ($3.0<R<5.0$) & 3.03 \\ \hline
\end{tabular}
|
Holography from lattice $N=4$ super Yang-Mills
|
\label{fit_l12_n5_a0.35} $1/R$ Fitting results for $L=12^4,\mu=0.05,N_{smear}=5,\alpha=0.55$
|
['\\newcommand{\\cO}{{\\cal O}}', '\\newcommand{\\Tr}{{\\rm Tr\\;}}', '\\newcommand{\\TODO}[1]{\\textcolor{red}{{\\bf #1}}}', '\\newcommand{\\cA}{{\\cal A}}', '\\newcommand{\\cAb}{{\\overline{\\cal A}}}', '\\newcommand{\\cF}{{\\cal F}}', '\\newcommand{\\cFb}{{\\overline{\\cal F}}}', '\\newcommand{\\cD}{{\\cal D}}', '\\newcommand{\\cDb}{{\\overline{\\cal D}}}', '\\newcommand{\\cQ}{{\\cal Q}}', '\\newcommand{\\cU}{{\\cal U}}', '\\newcommand{\\cS}{{\\cal S}}', '\\newcommand{\\cN}{{\\cal N}}', '\\newcommand{\\cUb}{{\\overline{\\cal U}}} ']
|
hep-th, hep-lat
|
|
2307.04377v1
|
\begin{tabular}{c|c|c|c}
\hline
&
\textbf{MAE $\downarrow$} &
\textbf{MedAE $\downarrow$} &
\textbf{Perc $\uparrow$} \\ \hline
\GTSD & 0.10 & 0.09 & 98.2\% \\ \hline
\GTSIH & 0.12 & 0.09 & 98.2\% \\ \hline
\end{tabular}
|
HCLAS-X: Hierarchical and Cascaded Lyrics Alignment System Using Multimodal Cross-Correlation
|
An evaluation on the Mandarin pop song dataset, consisting of 20 songs that contain only vocal tracks and Chinese character lyrics. Pinyin text files are not used in this evaluation.
|
['\\newcommand{\\GTSD}{\\texttt{HX-D}}', '\\newcommand{\\GTSIH}{\\texttt{HX-IH}}', '\\newcommand{\\stoller}{\\texttt{ST}}', '\\newcommand{\\vaglio}{\\texttt{VA}}', '\\newcommand{\\gupta}{\\texttt{GU}}', '\\newcommand{\\emir}{\\texttt{EM}}', '\\newcommand{\\na}{\\textcolor{gray}{N/A}}', '\\newcommand{\\PreserveBackslash}[1]{\\let\\temp=\\\\#1\\let\\\\=\\temp}', '\\newcommand\\scalemath[2]{\\scalebox{#1}{\\mbox{\\ensuremath{\\displaystyle #2}}}}']
|
cs.SD, eess.AS
|
|
2311.08707v1
|
\begin{tabular}{c|c|c|c|c}
\hline
& $e_{x_0,y_0}(m)$& $e_{x_1,y_1}(m)$& $e_{\theta_0}(rad)$ & $e_{\theta_1}(rad)$\\
\hline
K-BMPC& 0.0930 & \textbf{0.1683}& 0.0371& \textbf{0.0193}\\
\hline
NMPC& \textbf{0.0785}& 0.1756& \textbf{0.0337}& 0.0206\\
\hline
LMPC& 0.1402& 0.2323& 0.0479& 0.0299\\
\end{tabular}
|
K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control For Tractor-trailer Trajectory Tracking With Unknown Parameters
|
Mean tracking error using different MPC
| null |
eess.SY, cs.SY
|
|
2312.15182v1
|
\begin{tabular}{@{ }l|c|c@{\ \ }c@{\ \ }c@{ }}
\hline
Model & Avg Dice & RV & Myo & LV \\ \hline
R50-U-Net & 87.55 & 87.10 & 80.63 & 94.92 \\
R50-AttnUNet & 86.75 & 87.58 & 79.20 & 93.47 \\
ViT-CUP & 81.45 & 81.46 & 70.71 & 92.18 \\
R50-ViT-CUP & 87.57 & 86.07 & 81.88 & 94.75 \\
TransUNet & 89.71 & 88.86 & 84.53 & 95.73 \\
Swin-UNet & 90.00 & 88.55 & 85.62 & \textbf{95.83} \\
UNETR & 88.61 & 85.29 & \textbf{86.52} & 94.02 \\
UCTransNet & 89.69 & 87.92 & 85.43 & 95.71 \\ \hline
\textbf{UDTransNet} & \textbf{90.11} & \textbf{89.06} & 86.10 & 95.18 \\
\hline
\end{tabular}
|
Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
|
Quantitative results on the ACDC dataset in terms of average dice value (\%).
| null |
eess.IV, cs.CV, cs.LG
|
|
2309.08215v1
|
\begin{tabular}{l c c c c c}
\hline\hline
Filter & $U$ & $B$ & $V$ & $R$ & $I$ \\
$\delta R'_{F4}$ & $-0.00976$ & $-0.00870$ & $-0.01147$ & $-0.00494$ & $-0.00195$ \\
\hline
\end{tabular}
|
Intrinsic and extinction colour components in SNeIa and the determination of $R_V$
|
Error on the bandpass extinction coefficients arising from the linear
approximation for $X_4 = 0.4$
|
['\\newcommand{\\moy}[1]{\\ensuremath{\\langle #1 \\rangle}}', '\\newcommand{\\SiII}{\\ion{Si}{II}}', '\\newcommand{\\CaII}{\\ion{Ca}{II~H\\&K}}', '\\newcommand{\\ew}[1]{\\ensuremath{\\textup{ew}^{\\textup{#1}}}}', '\\newcommand{\\Si}{\\ensuremath{\\textup{Si}}}', '\\newcommand{\\Ca}{\\ensuremath{\\textup{Ca}}}', '\\newcommand{\\DCaSi}{\\ensuremath{\\textup{DCaSi}}}']
|
astro-ph.SR, astro-ph.CO
|
|
2312.05655v1
|
\begin{tabular}{lr|rr}
\hline
Distribution & c* & Conf. c* & Time c* \\
\hline
Laplace & 1.80 & 2.49 & 0.72 \\
student-$t$ (nu=3) & 1.94 & 2.77 & 0.71 \\
student-$t$ (nu=5) & 1.71 & 2.39 & 0.71 \\
student-$t$ (nu=7) & 1.64 & 2.29 & 0.71 \\
student-$t$ (nu=10) & 1.59 & 2.22 & 0.71 \\
student-$t$ (nu=20) & 1.53 & 2.16 & 0.71 \\
student-$t$ (nu=30) & 1.52 & 2.13 & 0.71 \\
student-$t$ (nu=50) & 1.51 & 2.13 & 0.71 \\
student-$t$ (nu=100) & 1.50 & 2.12 & 0.71 \\
Normal & 1.49 & 2.10 & 0.71 \\
GN(3) & 1.40 & 2.02 & 0.69 \\
Cauchy & 4.51 & 9.04 & 0.50 \\
% Cauchy & 6.00 & 12.01 & 0.50 \\
\hline
\end{tabular}
|
A novel scaling approach for unbiased adjustment of risk estimators
|
The table presents the value of scalars for exotic risk factor empirical estimator and various distributions under the setting described in Example~\ref{ex:6}; GN(3) stands for generalised normal distribution with shape parameter $\beta=3$. One can see that while the time scalar is rather stable, the confidence scalar depends strongly on the underlying distribution.
|
['\\newcommand{\\ig}[1]{\\begin{color}[rgb]{0.00, 0.0, 1.0}{Ig: #1} \\end{color}}', '\\newcommand{\\trb}[1]{\\begin{color}[rgb]{0.98, 0.0, 0.98}{TRB: #1} \\end{color}}', '\\newcommand{\\ti}[1]{\\begin{color}[rgb]{0.00,0.50,0.00}{TI: #1} \\end{color}}', '\\newcommand{\\tx}[1]{\\begin{color}[rgb]{0.00,0.0,1.00}{Tao: #1} \\end{color}}', '\\newcommand{\\lx}[1]{\\begin{color}[rgb]{0.00,0.50,0.00}{LX: #1} \\end{color}}', '\\newcommand{\\recheck}[1]{\\textcolor{Red}{ RECHECK: #1}}', '\\newcommand{\\pd}[1]{\\partial_{#1}} % partial derivative', '\\newcommand{\\1}{\\mathbbm{1}} % preferable way of writing indicator function', '\\newcommand{\\set}[1]{\\{#1\\}} % set: {xyz} to be used for inline formulas', '\\newcommand{\\Set}[1]{\\left\\{#1\\right\\}} % set: {xyz} to be used for seapare (not inline) formulas', '\\newcommand{\\Mid}{\\;\\Big | \\;} % big bar with small spaces before and after:', '\\newcommand{\\norm}[1]{ \\| #1 \\| } % mid bar with small spaces before and after: x | y', '\\newcommand{\\abs}[1]{\\left\\vert#1\\right\\vert} % absolute value', '\\newcommand{\\sgn}{\\text{sgn}} % sgn in formulas', '\\newcommand{\\Ind}{{\\mathds 1}}', '\\newcommand{\\ind}[1]{\\Ind_{\\{#1\\}}}']
|
q-fin.RM, q-fin.CP, q-fin.ST
|
|
2308.09255v1
|
\begin{tabular}{cccc}
Parameter & Value & Uncertainty & Source \\
\hline
Parallax (milliarcsec) & 0.8126 & 0.0148 & Gaia DR2\\
G magnitude & 13.4394 & 0.0002 & Gaia DR2\\
BP magnitude & 13.7217 & 0.0017 & Gaia DR2\\
RP magnitude & 12.9942 & 0.0009 & Gaia DR2\\
J magnitude & 12.499 & 0.023 & 2MASS \\
H magnitude & 12.217 & 0.018 & 2MASS \\
K magnitude & 12.215 & 0.024 & 2MASS \\
$M_A$ ($M_\odot$) & 1.1366 & 0.0728 & This work \\
$M_B$ ($M_\odot$) & 1.0820 & 0.0644 & This work \\
$\rho_M$ & 0.94504 & -- & This work \\
$R_A$ ($R_\odot$) & 1.491 & 0.072 & This work \\
$R_B$ ($R_\odot$) & 1.299 & 0.061 & This work
\end{tabular}
|
A $5M_\text{Jup}$ Non-Transiting Coplanar Circumbinary Planet Around Kepler-1660AB
|
The photometric and astrometric measurements used in the \texttt{isochrones} fit.
\label{tab:phot}
|
['\\newcommand{\\kepler}{{\\sl Kepler}\\ }']
|
astro-ph.EP
|
|
2306.09287v2
|
\begin{tabular}{l l}
\hline
\small \textit{Particle Step with Ancestor Sampling} & \\
\hline\hline
\scriptsize \hspace{1.5cm} Draw $s_{1}^k \sim g_\theta(s_{1}) $ & \scriptsize for $ k = 1, \ldots, K-1$ \\
\scriptsize \hspace{1.5cm} Set $s_{1}^K = s_{1}^{(m)}$ \\
\scriptsize \hspace{1.5cm} Compute $ w^k_1 = W_{1}(s_{1}^k)$ and normalize the weights & \scriptsize for $ k = 1, \ldots, K$ \\
\scriptsize \hspace{1.5cm} for $t = 2 : T$ \\
\scriptsize \hspace{2cm} Re-sampling step: sample $\{s_{t-1}^{k}\}_{k=1}^{K}$ with probabilities given by $\{w_{t-1}^{k}\}_{k=1}^{K}$ \\
\scriptsize \hspace{2cm} Draw $s_{t}^k \sim g_\theta(s_{t})$ & \scriptsize for $ k = 1, \ldots, K-1$ \\
\scriptsize \hspace{2cm} Set $s_{t}^K = s_{t}^{(m)}$ \\
\scriptsize \hspace{2cm} Ancestral sampling step \\
\scriptsize \hspace{2cm} Compute $ w^k_t = W_{t}(s_{t}^k)$ and normalize the weights& \scriptsize for $ k = 1, \ldots, K$ \\
\scriptsize \hspace{1.5cm} end \\
\scriptsize \hspace{1.5cm} Draw $j$ with $Pr(j = k) \propto $ $w_T^k$ \\
\hline \hline
\end{tabular}
|
Modelling and Forecasting Macroeconomic Risk with Time Varying Skewness Stochastic Volatility Models
|
Particle Step in the Gibbs Sampler
|
['\\newcommand{\\Lagr}{\\mathcal{L}}']
|
econ.EM
|
|
2311.12831v2
|
\begin{tabular}{c|ll|ll|l}
scheme & PSNR$\uparrow$ & LPIPS$\downarrow$ & ET$\downarrow$ & DT$\downarrow$ & MS$\downarrow$ \\ \hline
w/o CNN & 44.61 & 0.036 & \textbf{4.7h} & \textbf{25.3s} &\textbf{1.65} \\
w/ CNN & \textbf{45.12} & \textbf{0.028} & 4.8h & 78.6s &1.75\\
\end{tabular}
|
ECNR: Efficient Compressive Neural Representation of Time-Varying Volumetric Datasets
|
Average PSNR (dB) and LPIPS values across all timesteps, as well as ET, DT, and MS (MB) of the half-cylinder dataset.
|
['\\newcommand{\\hot}[1]{{\\color{red} #1}}', '\\newcommand{\\pin}[1]{{\\color{blue} #1}}', '\\newcommand{\\ndm}[1]{\\frac{n}{m}}']
|
cs.CV, cs.GR, cs.LG
|
|
2312.07815v1
|
\begin{tabular}{lcccccc} \hline
\rule{0pt}{10pt}
& \MC{2}{c}{UGC01085=J0131+0747} % \\ \hline
& \MC{2}{c}{AGC124137=J0231+0931 } % \\ \hline
& \MC{2}{c}{ESO199-007=J0258--4922} \\ \hline
\rule{0pt}{10pt}
$\lambda_{0}$(\AA) Ion & F($\lambda$)/F(H$\beta$)&I($\lambda$)/I(H$\beta$) & F($\lambda$)/F(H$\beta$)&I($\lambda$)/I(H$\beta$) & F($\lambda$)/F(H$\beta$)&I($\lambda$)/I(H$\beta$) \\ \hline
%
3727\ [O\ {\sc ii}]\ & 2.434$\pm$ 0.097 & 2.272$\pm$ 0.110 & 2.440$\pm$ 0.521 & 1.289$\pm$ 0.688 & 1.169$\pm$ 0.040 & 1.089$\pm$ 0.044 \\
3868\ [Ne\ {\sc iii}]\ & ... & ... & ... & ... & 0.156$\pm$ 0.014 & 0.145$\pm$ 0.014 \\
3889\ He\ {\sc i}\ +\ H8\ & ... & ... & ... & ... & 0.143$\pm$ 0.013 & 0.253$\pm$ 0.033 \\
3967\ [Ne\ {\sc iii}]\ +\ H7\ & ... & ... & ... & ... & 0.130$\pm$ 0.013 & 0.246$\pm$ 0.036 \\
4101\ H$\delta$\ & 0.133$\pm$ 0.022 & 0.277$\pm$ 0.063 & ... & ... & 0.143$\pm$ 0.013 & 0.240$\pm$ 0.030 \\
4340\ H$\gamma$\ & 0.355$\pm$ 0.098 & 0.449$\pm$ 0.149 & 0.028$\pm$ 0.165 & 0.479$\pm$ 9.148 & 0.403$\pm$ 0.040 & 0.483$\pm$ 0.056 \\
4471\ He\ {\sc i}\ & ... & ... & ... & ... & 0.065$\pm$ 0.013 & 0.059$\pm$ 0.013 \\
4861\ H$\beta$\ & 1.000$\pm$ 0.040 & 1.000$\pm$ 0.091 & 1.000$\pm$ 0.259 & 1.000$\pm$ 2.721 & 1.000$\pm$ 0.037 & 1.000$\pm$ 0.042 \\
4959\ [O\ {\sc iii}]\ & 1.248$\pm$ 0.046 & 1.079$\pm$ 0.046 & 0.431$\pm$ 0.251 & 0.181$\pm$ 0.249 & 0.714$\pm$ 0.023 & 0.648$\pm$ 0.023 \\
5007\ [O\ {\sc iii}]\ & 3.613$\pm$ 0.114 & 3.117$\pm$ 0.113 & 1.303$\pm$ 0.338 & 0.543$\pm$ 0.332 & 2.234$\pm$ 0.070 & 2.026$\pm$ 0.070 \\
6548\ [N\ {\sc ii}]\ & 0.034$\pm$ 0.030 & 0.027$\pm$ 0.028 & ... & ... & 0.000$\pm$ 0.013 & 0.000$\pm$ 0.013 \\
6563\ H$\alpha$\ & 3.399$\pm$ 0.115 & 2.808$\pm$ 0.119 & 6.670$\pm$ 1.249 & 2.716$\pm$ 2.457 & 3.052$\pm$ 0.095 & 2.763$\pm$ 0.103 \\
6584\ [N\ {\sc ii}]\ & 0.138$\pm$ 0.041 & 0.111$\pm$ 0.038 & ... & ... & 0.039$\pm$ 0.026 & 0.035$\pm$ 0.025 \\
%
& & & & & & \\
C(H$\beta$)\ dex & \MC {2}{c}{0.01$\pm$0.04} & \MC {2}{c}{0.30$\pm$0.24} & \MC {2}{c}{0.04$\pm$0.04} \\
EW(abs)\ \AA\ & \MC {2}{c}{2.65$\pm$0.23} & \MC {2}{c}{1.45$\pm$2.84} & \MC {2}{c}{4.40$\pm$0.53} \\
EW(H$\beta$)\ \AA\ & \MC {2}{c}{17.7$\pm$0.5} & \MC {2}{c}{ 1.1$\pm$0.2} & \MC {2}{c}{44.1$\pm$0.9} \\
\hline
$T_{\rm e}$(OIII)(K)\ & \MC {2}{c}{13889$\pm$1164} & \MC {2}{c}{22582$\pm$3630} & \MC {2}{c}{17503$\pm$2273} \\
$T_{\rm e}$(OII)(K)\ & \MC {2}{c}{13265$\pm$749 } & \MC {2}{c}{16324$\pm$723 } & \MC {2}{c}{14794$\pm$461 } \\
O$^{+}$/H$^{+}$($\times$10$^5$)\ & \MC {2}{c}{3.071$\pm$0.610} & \MC {2}{c}{0.907$\pm$0.498} & \MC {2}{c}{1.031$\pm$0.108} \\
O$^{++}$/H$^{+}$($\times$10$^5$)\ & \MC {2}{c}{4.223$\pm$0.952} & \MC {2}{c}{0.244$\pm$0.158} & \MC {2}{c}{1.526$\pm$0.448} \\
O/H($\times$10$^5$)\ & \MC {2}{c}{7.294$\pm$1.131} & \MC {2}{c}{1.151$\pm$0.522} & \MC {2}{c}{2.557$\pm$0.461} \\
12+log(O/H)(s)\ & \MC {2}{c}{... } & \MC {2}{c}{7.08$\pm$0.17} & \MC {2}{c}{7.33$\pm$0.05} \\
12+log(O/H)(mse,c)\ & \MC {2}{c}{7.87$\pm$0.11} & \MC {2}{c}{7.10$\pm$0.20} & \MC {2}{c}{7.43$\pm$0.12} \\
12+log(O/H)(PT05)\ & \MC {2}{c}{7.92$\pm$0.10} & \MC {2}{c}{... } & \MC {2}{c}{7.49$\pm$0.10} \\
\MC{3}{l}{~~} \
\end{tabular}
|
Dwarfs in nearby voids: results of SALT spectroscopy
|
Line intensities and derived parameters of UGC01085, AGC124137 and ESO199-007
|
['\\newcommand{\\apj}{ApJ}', '\\newcommand{\\apjl}{ApJL}', '\\newcommand{\\aap}{A\\&A}', '\\newcommand{\\aaps}{A\\&AS}', '\\newcommand{\\aj}{AJ}', '\\newcommand{\\mnras}{MNRAS}', '\\newcommand{\\nat}{Nature}', '\\newcommand{\\pasp}{PASP}', '\\newcommand{\\apjs}{ApJS}', '\\newcommand{\\MC}{\\multicolumn}', '\\newcommand{\\kms}{km~s$^{-1}$}', '\\newcommand{\\Te}{T$_{\\rm e}$}', '\\newcommand{\\Hb}{H$\\beta$}', '\\newcommand{\\HI}{H{\\sc i}}', '\\newcommand{\\HII}{H{\\sc ii}}', '\\newcommand{\\sunn}{$_{\\odot}$}', '\\newcommand{\\p}{$\\pm$}', '\\newcommand{\\acc}{atoms~cm$^{-2}$}', '\\newcommand{\\logOH}{12+\\log(\\textrm{O/H})}', '\\newcommand{\\dg}{$\\dagger$}', '\\newcommand{\\qq}{\\addtocounter{qub}{1}\\arabic{qub}}']
|
astro-ph.GA
|
|
2311.02876v1
|
\begin{tabular}{|l|l|l|l|l|}
\hline
\multicolumn{1}{|c|}{Parameters} & \multicolumn{1}{c|}{I} & \multicolumn{1}{c|}{II} & \multicolumn{1}{c|}{III}\\ \hline \hline
$T$ (in GeV) & 10 & 1 & 0.039\\ \hline
$\alpha$ & 2.135 & 1.2 &1\\ \hline
$\beta/H$ & 16 & 10 & 8\\ \hline
$\kappa$ & 0.5 & 1 & 1\\ \hline
\end{tabular}
|
Gravitational Waves from Superradiance to Probe the Origin of Primordial Black Holes
|
The relevant parameters describing the different FOPTs.
| null |
gr-qc, astro-ph.HE, hep-ph
|
|
2310.01800v1
|
\begin{tabular}{rr|rrrrr|rrrrrr} \\
\hline \hline
\multicolumn{12}{c}{Prior for the errors: Gamma} \\
\hline
& & & & Model 1 & & & & & Model 2 &&\\ \hline \hline
& & Frequentist & Gamma & Student-t & HS & LA & Frequentist & Gamma & Student-t & HS & LA \\ \hline \hline
Both sexes & R-square & 0.8360 & 0.8362 & 0.8348 & 0.8370 & 0.8386 & 0.7894 & 0.7895 & 0.7930 & 0.7947 & 0.7982 \\
& RMSE & 2.6172 & 2.6171 & 2.6276 & 2.6319 & 2.6612 & 2.6153 & 2.6154 & 2.6210 & 2.6225 & 2.6449 \\
& MAE & 4.2804 & 4.2795 & 4.2971 & 4.3016 & 4.3384 & 4.4552 & 4.4558 & 4.4701 & 4.4738 & 4.5268 \\
Females & R-square & 0.8477 & 0.8474 & 0.8454 & 0.8461 & 0.8432 & 0.8108 & 0.8104 & 0.8145 & 0.8151 & 0.8206 \\
& RMSE & 2.6380 & 2.6389 & 2.6505 & 2.6564 & 2.6974 & 2.6459 & 2.6466 & 2.6546 & 2.6589 & 2.7088 \\
& MAE & 4.2391 & 4.2398 & 4.2564 & 4.2636 & 4.3160 & 4.3755 & 4.3778 & 4.4007 & 4.4100 & 4.5087 \\
Males & R-square & 0.8227 & 0.8229 & 0.8222 & 0.8243 & 0.8261 & 0.7795 & 0.7795 & 0.7851 & 0.7883 & 0.7971 \\
& RMSE & 2.7053 & 2.7041 & 2.7132 & 2.7180 & 2.7483 & 2.7841 & 2.7831 & 2.7896 & 2.7938 & 2.8163 \\
& MAE & 4.3582 & 4.3566 & 4.3691 & 4.3759 & 4.4143 & 4.6852 & 4.6842 & 4.6895 & 4.6999 & 4.7435 \\
\hline \hline
\end{tabular}
|
A Bayesian approach to estimate the completeness of death registration
|
\small
R-squared, RMSE and MAE for the considered models using different HS, LA and Student-t priors for the local scales of the random effects and
a common Gamma prior for the scale of the random effects and errors respectively (without Global-Local structure). Frequentist refers to the original model proposed in fitted using restricted maximum likelihood estimation.
| null |
stat.AP, math.ST, stat.TH
|
|
2309.16851v2
|
\begin{tabular}{cccccccc} \hline
%
& & & \multicolumn{4}{c}{$I_G$} & \\ \cline{4-7}
%
$Q^2$ (GeV$^2$)~~ & ~$x_{\rm low}$~ & ~$x_{\rm high}$~ &
$(0,x_{\rm low})$~ &
$(x_{\rm low}, x_{\rm high})$~ &
~$(x_{\rm high}, 1)$~ &
~$(0.004, 1)$ &
$S_G$ \\ \hline \\
2 &0.005 &0.422 &0.011(6) &0.175(13) &0.039(8) &0.218(16) &\textbf{ 0.228(16) }\\ \\
4 &0.009 &0.603 &0.018(7) &0.188(19) &0.011(2) &0.207(19) &\textbf{ 0.218(20) }\\ \\
6 &0.014 &0.690 &0.025(8) &0.177(21) &0.004(0) &0.195(22) &\textbf{ 0.207(23) }\\ \\
8 &0.024 &0.747 &0.035(7) &0.199(22) &0.002(0) &0.224(22) &\textbf{ 0.236(23) }\\ \\
10 &0.028 &0.781 &0.038(6) &0.175(31) &0.001(0) &0.204(33) &\textbf{ 0.214(31) }\\ \\
12 &0.035 &0.819 &0.045(7) &0.210(14) &0.000(0) &0.245(15) &\textbf{ 0.256(16) }\\ \\
15 &0.037 &0.851 &0.048(7) &0.189(14) &0.000(0) &0.225(14) &\textbf{ 0.237(16) }\\ \\
20 &0.053 &0.877 &0.062(9) &0.189(10) &0.000(0) &0.240(11) &\textbf{ 0.252(13) }\\ \\
30 &0.072 &0.896 &0.077(10) &0.166(13) &0.000(0) &0.232(14) &\textbf{ 0.243(16) }\\ \\
\hline
\end{tabular}
|
Extraction of the neutron F2 structure function from inclusive proton and deuteron deep-inelastic scattering data
|
\setstretch{1.0}
Contributions $I_G(x_{\rm min},x_{\rm max};Q^2)$ to the Gottfried integral from different intervals of $x$ at fixed $Q^2$ values, along with the total integral, $S_G(Q^2)$. The experimentally measured region corresponds to $(x_{\rm low}, x_{\rm high})$. The range $(0.004,1)$ is provided for comparison with the NMC experiment~.\\
|
['\\newcommand{\\AAcom}[1]{ \\textbf{\\color{BrickRed}([AA] #1)}}', '\\newcommand{\\IFcom}[1]{{\\color{magenta} (\\textbf{[IF] #1})}}', '\\newcommand{\\SLcom}[1]{{\\color{blue} (\\textbf{[SL] #1})}}', '\\newcommand{\\JOcom}[1]{{\\color{Green} (\\textbf{[JO] #1})}}', '\\newcommand{\\pdf}{{\\sc{PDF}}}', '\\newcommand{\\CJ}{{\\textsc{\\textrm{CJ}}}}', '\\newcommand{\\CJft}{{\\textsc{\\textit{CJ15}}}}', '\\newcommand{\\cor}{\\sc{Cor}}', '\\newcommand{\\cov}{\\sc{Cov}}', '\\newcommand{\\bonus}{BONuS }', '\\newcommand*\\dd{\\mathop{}\\!\\mathrm{d}}']
|
hep-ph, hep-ex, nucl-ex, nucl-th
|
|
2312.01222v1
|
\begin{tabular}{ccccccc}
\hline
Presented & Identical & Finite & Finite & Finite & Possessing & \\
phase & under & antisaddle & antisaddle & weak & invariant curve & Other reasons\\
portrait & perturbations & focus & node--focus & point & (no separatrix) & \\
\hline
\multirow{2}{*}{ $4S_{59}$} & $4S_{60}$, $4S_{65}$, $4S_{66}$, $4S_{76}$ & $4S_{57}$, $4S_{58}$ & & & & $4S_{71}^{(1)}$, $4S_{72}^{(1)}$, $4S_{73}^{(1)}$, $4S_{74}^{(1)}$ \\
& $4S_{78}$, $4S_{79}$, $4S_{81}$ &$4S_{63}$, $4S_{64}$ & & & & $4S_{75}^{(1)}$, $4S_{77}^{(1)}$, $4S_{80}^{(1)}$, $4S_{82}^{(1)}$ \\
& & $3.4L_{17}$, $3.4L_{19}$ &$4.6L_{20}$, $4.6L_{22}$ & $3.4L_{20}$, $3.4L_{22}$ & $4.8L_{22}$, $4.8L_{25}$ & $3.4L_{18}^{(1)}$, $3.4L_{21}^{(1)}$, $4.6L_{25}^{(1)}$ \\
& & & $4.6L_{26}$, $4.6L_{28}$ & & $4.8L_{27}$, $4.8L_{29}$ & $4.6L_{27}^{(1)}$, $4.6L_{30}^{(1)}$, $4.6L_{32}^{(1)}$ \\
& & & $4.6L_{29}$, $4.6L_{31}$ & & & $4.8L_{24}^{(1)}$, $4.8L_{28}^{(1)}$ \\
& & & $P_{36}$, $P_{38}$, $P_{39}$, $P_{41}$ & & & $P_{37}^{(1)}$, $P_{40}^{(1)}$ \\
\hline
\multirow{1}{*}{$5S_{1}$} &$5S_{13}$ & $5S_{2}$, $5S_{14}$ & & & & $5S_{29}^{(1)}$, $5S_{30}^{(1)}$, $5S_{31}^{(1)}$, $5S_{32}^{(1)}$ \\
& & $3.5L_{1}$, $3.5L_{5}$ & $5.6L_{1}$, $5.6L_{6}$ & & & $3.5L_{12}^{(1)}$, $3.5L_{13}^{(1)}$, $5.6L_{11}^{(1)}$ \\
& & & & & & $5.6L_{12}^{(1)}$ \\
\hline
\multirow{1}{*}{$5S_{3}$} & $5S_{15}$ & & & & & $5S_{28}^{(1)}$, $5S_{33}^{(1)}$ \\
& & & & & & \\
\hline
\multirow{2}{*}{$5S_{6}$} & $5S_{18}$ & $5S_{4}$, $5S_{5}$ & & & & $5S_{27}^{(1)}$, $5S_{34}^{(1)}$, $5S_{35}^{(1)}$, $5S_{36}^{(1)}$ \\
& & $5S_{16}$, $5S_{17}$ & & & & \\
& & $3.5L_{2}$, $3.5L_{6}$ & & & & $3.5L_{11}^{(1)}$, $5.6L_{17}^{(1)}$ \\
& & $5.6L_{2}$, $5.6L_{7}$ & & & & \\
& & & & & & \\
\hline
\multirow{2}{*}{$5S_{9}$} &$5S_{10}$, $5S_{11}$, $5S_{12}$, $5S_{19}$ & $5S_{7}$, $5S_{8}$ & & & & $5S_{26}^{(1)}$, $5S_{37}^{(1)}$, $5S_{38}^{(1)}$, $5S_{39}^{(1)}$ \\
&$5S_{20}$, $5S_{22}$, $5S_{24}$, $5S_{25}$ & $5S_{21}$, $5S_{23}$ & & & & $5S_{40}^{(1)}$, $5S_{41}^{(1)}$, $5S_{42}^{(1)}$ \\
& & $3.5L_{3}$, $3.5L_{7}$ & $5.6L_{3}$, $5.6L_{4}$ &$3.5L_{4}$, $3.5L_{8}$ & $5.8L_{4}$, $5.8L_{5}$ & $3.5L_{9}^{(1)}$, $3.5L_{10}^{(1)}$, $5.6L_{13}^{(1)}$ \\
& & & $5.6L_{5}$, $5.6L_{8}$ & & $5.8L_{9}$, $5.8L_{10}$ & $5.6L_{14}^{(1)}$, $5.6L_{15}^{(1)}$, $5.6L_{16}^{(1)}$ \\
& & & $5.6L_{9}$, $5.6L_{10}$ & & & $5.8L_{11}^{(1)}$, $5.8L_{12}^{(1)}$ \\
& & & $P_{51}$, $P_{52}$ & $P_{56}$, $P_{57}$ & & $P_{59}^{(1)}$, $P_{60}^{(1)}$ \\
\hline
\multirow{1}{*}{$7S_{1}$} & $7S_{2}$ & & & & & $7S_{3}^{(1)}$ \\
\hline
\multirow{1}{*}{$7S_{4}$} &$7S_{5}$ & & & & & $7S_{6}^{(1)}$ \\
\hline
\end{tabular}
|
Quadratic systems possessing an infinite elliptic-saddle or an infinite nilpotent saddle
|
\small Topological equivalences for the family $\QESA$ \textit{(cont.)}
|
['\\newcommand{\\QES}{\\bf{Q}{\\widehat{ES}}}', '\\newcommand{\\QESA}{\\bf{Q}{{\\widehat{ES}(A)}}}', '\\newcommand{\\QESB}{\\bf{Q}{{\\widehat{ES}(B)}}}', '\\newcommand{\\QESC}{\\bf{Q}{{\\widehat{ES}(C)}}}', "\\newcommand{\\journal}[6]{#1 [#5] ``#2,'' \\emph{#3} {\\bf #4}, #6.}"]
|
math.DS
|
|
2308.07579v1
|
\begin{tabular}{|c|c|c|}
\hline
$n$ & $q_{1000}(10^n)$ & $q_{10000}(10^n)$\\ \hline
8 & 21.3\% & 20.22\% \\
9 & 48.1\% & 49.04\% \\
10 & 76.1\% & 76.41\% \\
11 & 90.9\% & 90.78\% \\
12 & 96.6\% & 97.10\% \\
13 & 98.8\% & 98.65\% \\
14 & 99.4\% & 99.44 \%\\
15 & 99.7\% & 99.74\% \\
16 & 99.7\% & 99.88\% \\
17 & 99.9\% & 99.93\% \\
18 & 100\% & 99.97\% \\
19 & 100\% & 99.97\& \\
20 & 99.8\% & 99.97\% \\
21 & 100\% & 99.99\%\\
\hline
\end{tabular}
|
Connectivity of Markoff mod-p graphs and maximal divisors
|
For each value of $8 \leq n \leq 35$, we calculate $q_m(10^n)$, the percentage of the first $m$ primes after $10^n$ for which \autoref{thm:Md} guarantees connectivity of $\Gcal_p$.
|
['\\newcommand{\\commentr}[1]{\\tcp*[r]{\\parbox[t]{7.2cm}{\\raggedright\\normalfont{#1}}}}', '\\newcommand{\\commentf}[1]{\\tcp*[f]{\\parbox[t]{7.2cm}{\\raggedright\\normalfont{#1}}}}', '\\newcommand{\\EF}[1]{\\textcolor{violet}{{\\sf (Elena:} {\\sl{#1})}}}', '\\newcommand{\\RS}[1]{}', '\\newcommand{\\KL}[1]{}', '\\newcommand{\\KS}[1]{}', '\\newcommand{\\HT}[1]{}', '\\newcommand{\\ML}[1]{\\textcolor{blue}{{\\sf (Matt:} {\\sl{#1})}}}', '\\newcommand{\\DM}[1]{\\textcolor{darkgreen}{{\\sf (Daniel:} {\\sl{#1}}{\\sf )}}}', '\\newcommand{\\JE}[1]{\\textcolor{cyan}{{\\sf (Jillian:} {\\sl{#1}}{\\sf )}}}', '\\newcommand{\\SA}[1]{}', '\\newcommand{\\N}{\\mathbb{N}}', '\\newcommand{\\F}{\\mathbb{F}}', '\\newcommand{\\Z}{\\mathbb{Z}}', '\\newcommand{\\R}{\\mathbb{R}}', '\\newcommand{\\Q}{\\mathbb{Q}}', '\\newcommand{\\T}{\\mathcal{T}}', '\\newcommand{\\Dcal}{\\mathcal{D}}', '\\newcommand{\\Mcal}{\\mathcal{M}}', '\\newcommand{\\Gcal}{\\mathcal{G}}', '\\newcommand{\\Ccal}{\\mathcal{C}}', '\\newcommand{\\B}{\\mathcal{B}}', '\\newcommand{\\Max}{\\mathrm{Max}}', '\\newcommand{\\Comax}{\\mathrm{Comax}}', '\\newcommand{\\IP}[1]{\\langle #1 \\rangle}', '\\newcommand{\\OP}[1]{\\left( #1 \\right)}', '\\newcommand{\\OV}[1]{\\left| #1 \\right|}', '\\newcommand{\\OVV}[1]{\\left\\Vert #1 \\right\\Vert}']
|
math.NT
|
|
2310.16122v1
|
\begin{tabular}{l|r|r}
\textbf{Implementations} & \textbf{\# SLOC} & \textbf{\% SLOC} \\ \hline
vISA & 226 & 0.27 \\
Broadcast & 1,511 & 1.77 \\
\textit{SYCL ($-$Broadcast)} & 1,470 & 1.73 \\
\textit{SYCL} & 11,292 & 13.26 \\
\hline
HIP & 116 & 0.14 \\
CUDA & 1,096 & 1.29 \\
\textit{HIP and CUDA} & 6,806 & 7.99 \\
\hline
\textit{All} & 43,862 & 51.49 \\
\textit{Unused} & 18,721 & 21.98 \\
\hline \hline
\textbf{Total} & 85,179 & 100 \\
\end{tabular}
|
A Performance-Portable SYCL Implementation of CRK-HACC for Exascale
|
Breakdown of lines of code across CRK-HACC variants. Sets containing fewer than 50 SLOC are not shown.
|
['\\newcommand{\\pp}{\\textrm{PP}}', '\\newcommand{\\ppm}{$\\pp$\\xspace}', '\\newcommand{\\paper}{paper\\xspace}', '\\newcommand{\\fixme}[1]{\\textcolor{red}{\\uppercase{#1}}}', '\\newcommand{\\citeneeded}{\\textcolor{red}{\\cite{}}}', '\\newcommand{\\Intel}{Intel\\textregistered\\xspace}', '\\newcommand{\\eg}{\\textit{e.g.,}\\xspace}', '\\newcommand{\\ie}{\\textit{i.e.,}\\xspace} ']
|
cs.PF, astro-ph.CO, cs.DC, D.2.7; D.2.8; D.1.3; J.2
|
|
2312.10612v1
|
\begin{tabular}{c c c}
\hline
Symbol & Explanation & Value/expression \\
\hline
$d_{\mathrm{prec}}$ & Controller precision & 1$\mu$m \\
$d_{\mathrm{tol}}$ & Controller end tolerance & 9$\mu$m \\
$\ell$ & Target spacing & 30$\mu$m \\
\hline
\end{tabular}
|
Chemical herding as a multiplicative factor for top-down manipulation of colloids
|
Length scales used in the chemical herding controller.
| null |
cond-mat.soft
|
|
2302.13239v3
|
\begin{tabular}{p{25pt}|p{20pt}|p{40pt}|p{45pt}|p{30pt}|p{30pt}|p{30pt}|p{30pt}}
\hline
Date/ Time& IMD $r_{max}$ &Suggested $r_{max}$&Willoughby et al. $r_{max}$& Tan and Fang $r_{max}$& $E_1$ ($\%$)& $E_2$ ($\%$)& $E_3$ ($\%$) \\
\hline
23/18 & 85 & 32.13 & 39.13 & 60.71 & -62.19 & -53.96 & -28.57 \\
24/00 & 58 & 31.44 & 36.309 & 58.309 & -45.79 & -37.39 & 0.53 \\
24/06 & 58 & 31.16 & 33.74 & 26.103 & -46.27 & -41.82 & -3.27 \\
24/12 & 58 & 30.73 & 31.67 & 54.06 & -47.00 & -45.39 & -6.79 \\
24/18 & 38 & 30.36 & 29.63 & 52.17 & -20.09 & -22.02 & 37.28 \\
25/00 & 38 & 29.69 & 27.68 & 48.74 & -21.85 & -27.15 & 28.26 \\
25/06 & 38 & 29.46 & 25.98 & 47.18 & -22.45 & -31.63 & 24.15 \\
\hline
\end{tabular}
|
Evaluation of the Radius of Maximum Wind over the North Indian Basin with the help of Tropical Cyclone characteristics
|
Results of Very Severe Cyclonic Storm, “YAAS” over the Bay of Bengal during $23-28$ May, 2021, where $E_1$ indicates the error percentage between suggested method and IMD, $E_2$ represents the error percentage between Willoughby et al.'s expression and IMD, and $E_3$ represents the error percentage between Tan and Fang's expression and IMD
| null |
physics.ao-ph, math.OC
|
|
2303.08968v1
|
\begin{tabular}{|c||c|c|c|c|c|c|}
\hline
{\footnotesize{}Parameter} & {\footnotesize{}$\mu$$_{i}$} & {\footnotesize{}$\sigma$$_{i}$} & {\footnotesize{}$\lambda_{i}$} & {\footnotesize{}$\upsilon_{i}$} & {\footnotesize{}$\zeta_{i,1}$} & {\footnotesize{}$\zeta_{i,2}$}\tabularnewline
\hline
{\footnotesize{}Asset 1 (T30)} & {\footnotesize{}0.0043} & {\footnotesize{}-} & {\footnotesize{}-} & {\footnotesize{}-} & {\footnotesize{}-} & {\footnotesize{}-}\tabularnewline
\hline
{\footnotesize{}Asset 2(VWD)} & {\footnotesize{}0.0877} & {\footnotesize{}0.1459} & {\footnotesize{}0.3191} & {\footnotesize{}0.2333} & {\footnotesize{}4.3608} & {\footnotesize{}5.504}\tabularnewline
\hline
\end{tabular}
|
A parsimonious neural network approach to solve portfolio optimization problems without using dynamic programming
|
\label{tab: Params for ground truth DSQ with cont rebal} Calibrated,
inflation-adjusted parameters for asset dynamics in Subsection \ref{subsec:Ground-truth: DSQ with cont rebal}:
Ground truth - $DSQ\left(\gamma\right)$ with continuous rebalancing.
In this example, the first asset is assumed to be a risk-free asset,
so we set $\mu_{1}=r$, while the second asset follows jump dynamics.
The parametric asset returns are (trivially) uncorrelated, and parameters
are based on the inflation-adjusted returns of the T30 and VWD time
series, respectively, over the period 1926:01 to 2019:12
| null |
q-fin.CP
|
|
2312.17414v1
|
\begin{tabular}{| c| c| c| c| c| c| }
\hline
\multicolumn{2}{|c|}{} & \multicolumn{2}{|c|}{No.~Pentatopes} & \multicolumn{2}{|c|}{Hypervolume} \\
\hline
No.~Points & No. Flips & Initial & Final & Initial & Final \\
\hline
50 & 40 & 492 & 450 & 30,340,041.467995968 & 30,340,041.467995968 \\
100 & 99 & 1,421 & 1,317 & 48,546,097.039964405 & 48,546,097.039964405 \\
150 & 194 & 2,443 & 2,209 & 53,983,703.043834004 & 53,983,703.043834004 \\
200 & 292 & 3,581 & 3,271 & 58,893,885.023952322 & 58,893,885.023952322 \\
250 & 396 & 4,767 & 4,323 & 63,293,417.543503716 & 63,293,417.543503716 \\
300 & 528 & 6,010 & 5,408 & 65,849,297.299100857 & 65,849,297.299100857 \\
\hline
\end{tabular}
|
Space-time hypervolume meshing part 1: Point insertion, geometric predicates, and bistellar flips
|
Information for quality improvement cases on randomized meshes.
|
['\\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}}}', '\\newcommand{\\qbold}{\\bm{q}}', '\\newcommand{\\rbold}{\\bm{r}}', '\\newcommand{\\Rbold}{\\bm{R}}', '\\newcommand{\\Mbold}{\\bm{M}}', '\\newcommand{\\Ubold}{\\bm{U}}', '\\newcommand{\\Gbold}{\\bm{G}}', '\\newcommand{\\Fbold}{\\bm{F}}', '\\newcommand{\\ubold}{\\bm{u}}', '\\newcommand{\\zbold}{\\bm{z}}', '\\newcommand{\\vbold}{\\bm{v}}', '\\newcommand{\\wbold}{\\bm{w}}', '\\newcommand{\\whatbold}{\\hat{\\bm{w}}}', '\\newcommand{\\wtildebold}{\\bm{\\phi}}', '\\newcommand{\\xbold}{\\bm{x}}', '\\newcommand{\\ybold}{\\bm{y}}', '\\newcommand{\\sigmabold}{\\bm{\\sigma}}', '\\newcommand{\\edgeE}{\\mathcal{E}}', '\\newcommand{\\sBox}{\\text{\\scalebox{0.7}{$\\square$}}}', '\\newcommand{\\ipt}[2]{\\left(#1,#2\\right)_{\\mathcal{T}_h}}', '\\newcommand{\\iptT}[2]{\\left(#1,#2\\right)_{\\mathcal{T}_k}}', '\\newcommand{\\iptk}[2]{\\left(#1,#2\\right)_{K}}', '\\newcommand{\\ipbt}[2]{\\left\\langle#1,#2\\right\\rangle_{\\partial \\mathcal{T}_h}}', '\\newcommand{\\ipbtbd}[2]{\\left\\langle#1,#2\\right\\rangle_{\\partial \\Omega}}', '\\newcommand{\\ipbtf}[2]{\\left\\langle#1,#2\\right\\rangle_{\\partial \\mathcal{T}_h/\\mathcal{F}^{\\partial}_{h}}}', '\\newcommand{\\ipbtk}[2]{\\left\\langle#1,#2\\right\\rangle_{\\partial K}}', '\\newcommand{\\ipbf}[2]{\\left\\langle#1,#2\\right\\rangle_{\\mathcal{F}_h}}', '\\newcommand{\\iipbf}[2]{\\left\\langle#1,#2\\right\\rangle_{\\mathcal{F}_h^i}}', '\\newcommand{\\iptj}[2]{\\left(#1,#2\\right)_{\\mathcal{T}_{h,j}}}', '\\newcommand{\\ipbtj}[2]{\\left\\langle#1,#2\\right\\rangle_{\\partial \\mathcal{T}_{h,j}}}', '\\newcommand{\\llbracket}{\\left[\\!\\left[}', '\\newcommand{\\rrbracket}{\\right] \\! \\right]}', '\\newcommand{\\llcurve}{\\left\\{\\!\\left\\{}', '\\newcommand{\\rrcurve}{\\right\\} \\! \\right\\}}', '\\newcommand{\\vertiii}[1]{ \\left\\| #1 \\right\\|}']
|
math.NA, cs.NA, 65M50, 52B11, 31B99, 76M10
|
|
2307.02331v1
|
\begin{tabular}{ccccc}
\hline
& \multicolumn{4}{c}{Method}\\
$(\eta_0, \eta_1)$ & ML & Prop & Prog & Block \\
\hline
(.0,.0) & 0.067 ($\pm$ 0.052) & 0.066 ($\pm$ 0.035) & 0.088 ($\pm$ 0.055) & 0.090 ($\pm$ 0.081) \\
(.1,.1) & 0.068 ($\pm$ 0.053) & 0.068 ($\pm$ 0.035) & 0.089 ($\pm$ 0.055) & 0.091 ($\pm$ 0.082) \\
(.2,.2) & 0.070 ($\pm$ 0.055) & 0.069 ($\pm$ 0.035) & 0.091 ($\pm$ 0.056) & 0.093 ($\pm$ 0.084) \\
(.3,.3) & 0.073 ($\pm$ 0.057) & 0.072 ($\pm$ 0.034) & 0.093 ($\pm$ 0.058) & 0.096 ($\pm$ 0.086) \\
(.4,.4) & 0.076 ($\pm$ 0.061) & 0.075 ($\pm$ 0.033) & 0.097 ($\pm$ 0.059) & 0.100 ($\pm$ 0.089) \\
(.5,.5) & 0.081 ($\pm$ 0.066) & 0.082 ($\pm$ 0.033) & 0.103 ($\pm$ 0.062) & 0.106 ($\pm$ 0.093) \\
\hline
\end{tabular}
|
Differential recall bias in estimating treatment effects in observational studies
|
The effects of recall bias for six values of $\eta_0= \eta_1$. The estimates and 95\% bootstrap confidence intervals are displayed for the maximum likelihood and stratification methods
|
['\\newcommand{\\E}{\\mathbb{E}}', '\\newcommand{\\V}{\\mathbb{V}}', '\\newcommand{\\X}{{\\mathbf X}_{i}}', '\\newcommand{\\Z}{Z_{i}}', '\\newcommand{\\Y}{Y_{i}}']
|
stat.ME
|
|
2310.05084v1
|
\begin{tabular}{ccccccccccccc}
\hline
$h$ & $\frac{\|e_u\|_{L^2(\Omega)}}{\|u\|_{L^2(\Omega)}}$ & CR & $\frac{\|e_u\|_{H^1(\Omega)}}{\|u\|_{H^1(\Omega)}}$ & CR & $\frac{\|e_p\|_{L^2(\Omega)}}{\|p\|_{L^2(\Omega)}}$ & CR & $\frac{\|e_p\|_{H^1(\Omega)}}{\|p\|_{H^1(\Omega)}}$ & CR & $\frac{\|e_T\|_{L^2(\Omega)}}{\|T\|_{L^2(\Omega)}}$ & CR & $\frac{\|e_T\|_{H^1(\Omega)}}{\|T\|_{H^1(\Omega)}}$ & CR \\
\hline
$1/4$ &0.0115& &0.0390 & &0.0488 & &0.2983& &0.0488 & &0.2983& \\
$1/8$ &0.0014& 3.0164 &0.0093 & 2.0713 &0.0106 & 2.2047 &0.1475& 1.0158 &0.0106 & 2.2047 &0.1475& 1.0158 \\
$1/16$ &1.7799e-04&3.0018&0.0023 &2.0412&0.0024 & 2.1152 &0.0733&1.0100&0.0024 & 2.1152&0.0733&1.0100\\
$1/32$ &2.2250e-05&2.9999&5.5480e-04& 2.0218&5.8491e-04& 2.0616 &0.0365&1.0047&5.8491e-04&2.0616&0.0365&1.0047\\
$1/64$ &2.7819e-06&2.9997 &1.3763e-04& 2.0111 &1.4302e-04&2.0320&0.0182& 1.0022 &1.4302e-04&2.0320&0.0182&1.0022\\
\hline
\end{tabular}
|
Analysis of multiphysics finite element method for quasi-static thermo-poroelasticity with a nonlinear convective transport term
|
Error and convergence rates of $u_h^n$, $p_h^n$, $T_h^n$
|
['\\newcommand{\\prodt}[2]{\\bigl(#1,#2\\bigr)}', '\\newcommand{\\dual}[2]{\\left\\langle#1, #2\\right\\rangle}', '\\newcommand{\\abs}[1]{\\left\\vert#1\\right\\vert}', '\\newcommand{\\set}[1]{\\left\\{#1\\right\\}}', '\\newcommand{\\bRM}{\\mathbf{RM}}', '\\newcommand{\\br}{\\mathbf{r}}']
|
math.NA, cs.NA, math.AP, 65N30, G.1.8
|
|
2312.15633v1
|
\begin{tabular}{|l|l|l|l|}
\hline
& UIQM$\uparrow$ & UCIQE$\uparrow$ & NIQE$\downarrow$ \\ \hline
Fusion & 2.92 & 0.545 & 5.40 \\ \hline
IBLA & 1.47 & \textbf{0.626} & 6.38 \\ \hline
U-Transformer& 2.99& 0.55& \textbf{5.21}\\ \hline
Funie-GAN & \underline{3.21} & 0.599 & 5.56 \\ \hline
Water-Net & 3.02 & 0.582 & 6.06 \\ \hline
UWCNN & 2.59 & 0.562 & 5.75 \\ \hline
Ours & \textbf{3.26} & \underline{0.603}& \underline{5.39} \\ \hline
\end{tabular}
|
MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility
|
Non-reference image quantitative comparison on private data using UIQM, UCIQE, and NIQE.
|
['\\newcommand{\\RR}[1]{{\\color{red}{#1}}}']
|
cs.CV, eess.IV
|
|
2308.09630v1
|
\begin{tabular}{ccccccccccc}
\hline\hline
\noalign{\smallskip}
Obs. & {UT Date} & {RTT} & {Baud} & Res. & {Start-Stop} & {Runs} & Radar & Note \\
& {[yyyy-mm-dd]} & {[s]} & {[$\mu\rm s$]} & [m] & {[hh:mm:ss-hh:mm:ss]} & & model \\ \hline
\noalign{\smallskip}
Arecibo & 2001-03-03 & 62 & CW & & 09:40:32-09:56:40 & 8 & & \\
& & & CW & & 09:59:25-10:00:23 & 1 & & \\
& & & 4 & 600 & 10:02:38-10:16:19 & 3 & & Ranging\\
& & & 4.5 & 675 & 10:17:46-10:23:28 & 3 & & Ranging\\
& & & 0.5 & 75 & 10:27:14-10:36:39 & 5 & & Ranging\\ \\
Goldstone & 2001-03-03 & 62 & 1.0 & 150 & 13:14:07-15:02:52 & 49 & \textbullet &\\ \\
Arecibo & 2001-03-04 & 67 & CW & & 09:04:03-09:16:47 & 6 & \textbullet &\\
& & & 0.2 & 30 & 09:18:59-09:24:35 & 3 & \textbullet &\\
& & & 0.1 & 15 & 09:27:01-09:38:27 & 3 & &\\
& & 68 & 0.1 & 15 & 10:09:44-10:31:28 & 10 & \textbullet &\\ \\
Arecibo & 2001-03-05 & 76 & CW & & 09:05:55-09:12:43 & 3 & \textbullet &\\
& & 77 & 0.2 & 30 & 09:15:42-10:52:44 & 38 & \textbullet &\\ \\
Goldstone & 2006-03-07 & 36 & 0.125 & 19 & 19:24:26-19:31:09 & 6 & \textbullet &\\
& & & 0.125 & 19 & 19:31:49-20:30:26 & 48 & \textbullet &\\ \\
Goldstone & 2006-03-10 & 86 & CW & & 12:02:53-14:42:14 & 58 & & Low SNR\\
\hline
\end{tabular}
|
Physical modelling of near-Earth asteroid (23187) 2000 PN9 with ground-based optical and radar observations
|
Delay-Doppler observations of (23187) 2000 PN9
| null |
astro-ph.EP
|
|
2302.09336v1
|
\begin{tabular}{|l|c|c|c|}
\hline
& \multicolumn{3}{c|}{Treatment} \\\cline{2-4}
& A & B & C \\\hline
$\delta_{\textbf{QP[1,200]}}$& ~~0.4298& ~~0.5551& ~~0.5745\\\hline
$\delta_{\textbf{QP[801,1000]}}$& ~~0.3766& ~~0.4047& ~~0.4436\\\hline
~~~~~$\triangle \delta_{\textbf{QP}}$& $-$0.0532& $-$0.1504& $-$0.1309\\\hline
$\delta_{\textbf{Dyn[1-200]}}$& ~~0.0781& ~~0.2088& ~~0.1442\\\hline
$\delta_{\textbf{Dyn[801-1000]}}$& ~~0.0059& ~~0.0097& ~~0.0072\\\hline
~~~~~$\triangle \delta_{\textbf{Dyn}}$& $-$0.0722& $-$0.1991& $-$0.1370\\\hline
~~~~~~~$\triangle \delta$& $-$0.3707& $-$0.3950& $-$0.4364\\\hline
\end{tabular}
|
Pulse in collapse: a game dynamics experiment
|
The Euclidean distance and its evolution. For details, see the paragraph 'Explanation of support [EO1] and [EO2]'.
|
['\\newcommand\\xrowht[2][0]{\\addstackgap[.5\\dimexpr#2\\relax]{\\vphantom{#1}}}']
|
econ.TH, nlin.AO
|
|
2312.15896v1
|
\begin{tabular}{p{2.5cm}|p{1cm}|p{1cm}|p{1cm}|p{1cm}}
\hline
\multirow{2}{*}{\textbf{Attributes}} & \multicolumn{4}{c}{\textbf{CiM Primitive}} \\
\cline{2-5}
& Analog-1 & Analog-2 & Digital-1 & Digital-2 \\
\hline
8b-8b MAC Energy (pJ) & 0.14 & 0.17 & 0.64 & 0.82 \\
\hline
Compute Latency (ns) & 9 & 144 & 18 & 233 \\
\hline
Area increase (x) & 1.34 & 2.17 & 1.4 & 1.1 \\
\hline
n (temporal loop) & 16 & 16 & 1 & 10 \\
\hline
CiM Unit Storage (B) & 16 & 16 & 1 & 32 \\
\hline
(Rp, Cp) & (4,64) & (64,4) & (256,32) & (1,128) \\
\hline
CiM Array size (rows x col) & 512x64 & 64x512 & 256x256 & 256x128 \\
\hline
Mapping (Stationarity) & Weight & Weight & Weight & Weight-Input \\
\hline
\end{tabular}
|
WWW: What, When, Where to Compute-in-Memory
|
CiM primitives used for experiments
|
['\\newcommand{\\edits}[1]{\\textcolor{magenta}{#1}}']
|
cs.AR, cs.DC, cs.LG
|
|
2312.17156v2
|
\begin{tabular}{|lc|}
\hline
Model & F1-measure beat (\%) \\ \hline
w/o positional encoding & 79.48 \\
w/ absolute positional encoding & 80.52 \\
w/ relative positional encoding & \textbf{83.65} \\ \hline
\end{tabular}
|
BEAST: Online Joint Beat and Downbeat Tracking Based on Streaming Transformer
|
\label{table1} Effect of positional encoding on F1-measure of online beat tracking.
| null |
cs.SD, eess.AS
|
|
2303.07540v1
|
\begin{tabular}{l|c|c|c|c}
\hline
Modality & Resolution & AUC & Accuracy & MCC\\
\hline
Unimodal (EHR)~ &-&$0.7300\pm 0.04$&$0.7400\pm 0.03$&$0.1182 \pm 0.03$\\
\hline
Unimodal (SA) &$64\times64$&$0.7391\pm 0.05$&$0.7312 \pm 0.07$&$0.3604\pm0.02$ \\
&$128\times128$&$0.7495\pm 0.05$&$0.7321\pm 0.04$&$0.3277\pm0.01$ \\
\hline
Unimodal (FC) &$64\times64$&$0.8034 \pm 0.02$&$0.7509 \pm 0.04$&$0.4240\pm0.02$ \\
&$128\times128$&$0.8100 \pm 0.04$&$0.7925 \pm 0.05$&$0.4666\pm0.02$ \\
\hline
Bi-modal (SA and FC): &$64\times64$&$0.7998\pm0.01$&$0.7698\pm0.03$&$0.4185\pm0.03$ \\
Early fusion &$128\times128$&$0.7470\pm0.02$&$0.7283\pm0.02$&$0.3512\pm0.02$ \\
\hline
Bi-modal (SA and FC): &$64\times64$&$0.8028\pm0.04$&$0.7509\pm0.03$&$0.3644\pm0.01$ \\
Late fusion &$128\times128$&$0.8122\pm0.03$&$0.7547\pm0.03$&$0.3594\pm0.02$ \\
\hline
Bi-modal (SA and EHR): &$64\times64$&$0.7564\pm0.04$&$0.7585\pm0.02$&$0.3825\pm0.02$ \\
Late fusion &$128\times128$&$0.7629\pm0.03$&$0.7434\pm0.03$&$0.3666\pm0.03$ \\
\hline
Bi-modal (FC and EHR): &$64\times64$&$0.8061 \pm 0.03$&$0.7709 \pm 0.02$&$0.4435 \pm 0.02$ \\
Late fusion &$128\times128$&\underline{$0.8135\pm0.02$}&\underline{$0.7925\pm0.02$}&\underline{$0.4999\pm0.03$} \\
\hline
Tri-modal (FC, SA, and EHR) &$64\times64$&$0.8146 \pm 0.04$&$0.7774 \pm 0.03$&$0.4460 \pm 0.02$ \\
Hybrid fusion &$128\times128$&$\mathbf{0.8327\pm0.06}$&$\mathbf{0.8038 \pm 0.05}$&$\mathbf{0.5099\pm0.04}$ \\
\hline
\end{tabular}
|
Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI
|
Performance comparison using three metrics (with \textbf{best} in bold and \underline{second best} underlined). FC: Four-Chamber features; SA: Short-Axis features; EHR: Electronic Health Record features. The standard deviations of methods were obtained by dividing the test set into $5$ parts based on the diagnosis time.
| null |
cs.LG, cs.CV, q-bio.QM
|
|
2310.15900v1
|
\begin{tabular}{lllllr}
\hline
\(k\) & \(S_{\mathrm{on}}\) & \(a_{q}\) and \(b_{q}\) for \(q\) in \(S_{\mathrm{on}}\) & \(B\) & \((r, \log_{r}L, \delta)\) & (CPU time)/s\\
\hline
5 & Empty & & \(10^{3}\) & & \(<\) 1\\
\hline
6 & Empty & & \(10^{7}\) & & \(<\) 1\\
\hline
7 & Empty & & \(10^{14}\) & & \(<\) 1\\
\hline
8 & \((5)\) & \(a_{5} = a \leq 50\) & \(*\) & & 4\\
\hline
9 & \((5, 31)\) & \(a_{5} = 2\), \(a_{31} = a \leq 94\) & \(*\) & & 7105\\
\hline
9 & \((5, 31)\) & \(a_{5} = 2\), \(b_{31} = 96\) & \(10^{16}\) & \((31, 14, 1)\) & 3\\
\hline
9 & \((5, 19531)\) & \(a_{5} = 6\), \(a_{19531} = a \leq 86\) & \(*\) & & 452\\
\hline
9 & \((5, 19531)\) & \(a_{5} = 6\), \(b_{19531} = 88\) & \(10^{17}\) & \((19531, 6, 1)\) & 61\\
\hline
9 & \((5)\) & \(a_{5} = a\): & & & \\
& & \(a = 4\) & \(10^{18}\) & & 23\\
& & \(a = 8\) & \(10^{11}\) & & \(<\) 1\\
& & \(a = 10\) & \(10^{29}\) & & 26721\\
& & \(a = 12\) & \(10^{29}\) & & 27642\\
& & \(a = 46\) & \(10^{29}\) & & 27913\\
& & \(14 \leq a \leq 64\), \(a \neq 46\) & \(*\) & & 3\\
\hline
\end{tabular}
|
Each friend of 10 has at least 10 nonidentical prime factors
|
Input-related values and CPU times in computer searches for friends \(n\) of \(10\).\label{tableResults}
| null |
math.NT, 11A25
|
|
2310.19790v1
|
\begin{tabular}{clcc}
\hline
Reference ${G}_{\rm mag}$ & \Gaia{} EDR3 & ${G}_{\rm mag}$ & Median Error \\
\hline \hline
12 & 4019458647338779648 & 12.49 & 0.05 \\
13 & 3348071631670500736 & 13.00 & 0.09 \\
14 & 866719456827107712 & 14.17 & 0.10 \\
15 & 958262527212954752 & 14.98 & 0.13 \\
16 & 39124751182907520 & 16.01 & 0.17 \\
17 & 1853351441328008192 & 17.02 & 0.28 \\
18 & 1199686173677816576 & 18.01 & 0.54 \\
19 & 3705413559233426432 & 19.07 & 0.97 \\
20 & 1307679071887310592 & 20.58 & 2.61 \\
\hline
\end{tabular}
|
Detection and Preliminary Characterisation of Polluted White Dwarfs from Gaia EDR3 and LAMOST
|
White dwarf candidates from the full GF21/LAMOST catalogue with a flux error representative of multiple \Gaia{} ${G}_{\rm mag}$ reference magnitudes. The third and fourth columns provide, respectively, their true ${G}_{\rm mag}$ and the median of their flux error between 3,800~\angs{} and 4,000~\angs. \label{tab:names_fluxerror}
|
['\\newcommand\\bedit[1]{\\textcolor{new_color}{\\textbf{#1}}}', '\\newcommand\\blue[1]{\\textcolor{darkblue}{\\textbf{#1}}}', '\\newcommand\\crimson[1]{\\textcolor{crimson}{\\textbf{#1}}}', '\\newcommand\\grey[1]{\\textcolor{grey}{\\textbf{#1}}}', '\\newcommand{\\Msun}{\\mbox{$M_{\\odot}$}}', '\\newcommand{\\Rsun}{\\mbox{$R_{\\odot}$}}', '\\newcommand{\\Rearth}{R$_{\\oplus}$}', '\\newcommand{\\Mearth}{M$_{\\oplus}$}', '\\newcommand{\\Teff}{$T_{\\rm eff}$}', '\\newcommand{\\Teffmontreal}{$T_{\\rm eff; MWDD}$}', '\\newcommand{\\Teffmix}{$T_{\\rm eff; mixed}$}', '\\newcommand{\\logg}{$\\log g$}', '\\newcommand{\\Gcolor}{$G_{\\rm BP}-G_{\\rm RP}$}', '\\newcommand{\\upshapeI}[1]{\\textrm{\\upshape I}}', '\\newcommand{\\Gaia}{\\emph{Gaia}}', '\\newcommand{\\angs}{\\text{\\normalfont\\AA}}', '\\newcommand{\\TESS}{\\emph{TESS}}', '\\newcommand{\\GALEX}{\\emph{GALEX}}', '\\newcommand{\\panstarrs}{\\emph{Pan-STARRS}}', '\\newcommand{\\logHHe}{$\\log_{10}(\\rm{H/He})$} ', '\\newcommand{\\logCaH}{$\\log_{10}(\\rm{Ca/H})$} ', '\\newcommand{\\logCaHe}{$\\log_{10}(\\rm{Ca/He})$} ', '\\newcommand{\\sbcom}[1]{{\\color{teal}{[SB: #1]}}}']
|
astro-ph.SR, astro-ph.EP
|
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