id string | image image | latex string | title string | caption string | newcommands string | categories string |
|---|---|---|---|---|---|---|
2310.06808v1 | \begin{tabular}{ccc}
\hline
&Lack of evidence&Evidence for infection \\
Birth defect presence&501&91\\
Birth defect absence&16533&1784\\
\hline
\end{tabular} | Odds are the sign is right | A contingency table showing an association between Zika virus infection and birth defects. | ['\\newcommand{\\Dconv}{\\overset{\\mathcal{D}}{\\longrightarrow}}', '\\newcommand{\\ds}{\\displaystyle}', '\\newcommand{\\vare}{\\varepsilon}', '\\newcommand{\\bu}{{\\bf u}}', '\\newcommand{\\bi}{{\\bf i}}', '\\newcommand{\\A}{{\\bf A}}', '\\newcommand{\\argmin}{\\operatornamewithlimits{argmin}}'] | stat.ME | |
2311.13118v1 | \begin{tabular}{|p{0.25\textwidth}|p{0.65\textwidth}|}
\hline
\textbf{Research Question} & \textbf{Coding Scheme} \\
\hline
RQ1 & Problem, Methods, Algorithms, ML Features, Results \\
RQ2 & Binary coding of indicators, Definitions \\
RQ3 & Binary coding of connectors, ... | Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements | Data extraction fields corresponding to each research question. | null | cs.LG, cs.AI, cs.CL, cs.CY, cs.SI, 68T50, 62H30, 91C99, 68T068T50, 62H30, 91C99, 68T01, I.2.7; I.5.4; K.4.1; K.4.2 | |
2309.16933v1 | \begin{tabular}{ccc}
\hline
$\mathrm{\{\theta\}}$ & Network HN & Network LN \\ \hline
N\textsubscript{conv} & 0 & 4 \\
N\textsubscript{param} & 11k & 10k \\
N\textsubscript{kernel} & 63 & 21 \\
N\textsubscript{batch size} & 32 & ... | Retrieving genuine nonlinear Raman responses in ultrafast spectroscopy via deep learning | Hyperparameters for the HN and LN networks obtained by training with the HN and LN datasets and optimization with a grid search over the network architecture shown in Fig. \ref{fig:architecture}. | ['\\newcommand{\\mean}[1]{\\left\\langle {#1} \\right\\rangle}', '\\newcommand{\\E}{\\mathcal{E}}', '\\newcommand{\\om}{\\tilde{\\omega}}', '\\newcommand{\\?}{\\stackrel{?}{=}}', '\\newcommand{\\icm}{$\\mathrm{cm}^{-1}\\,$}'] | physics.optics, physics.chem-ph | |
2312.03211v1 | \begin{tabular}{ll@{\hspace{4em}}ll}
$\hat{a}$ & \verb"\hat{a}" & $\dot{a}$ & \verb"\dot{a}" \\
$\check{a}$ & \verb"\check{a}" & $\ddot{a}$ & \verb"\ddot{a}" \\
$\tilde{a}$ & \verb"\tilde{a}" & $\breve{a}$ & \verb"\breve{a}" \\
$\acute{a}$ & \verb"\acute{a}" & $\bar{a}$ & \verb"\bar{a}" \\
$\grave{a}$ & \... | A Morpho-Kinematic Study of the Enigmatic Emission Nebula NGC 6164/5 Surrounding the Magnetic O-type Star HD 148937 | Math-mode accents | ['\\newcommand{\\vdag}{(v)^\\dagger}', '\\newcommand\\aastex{AAS\\TeX}', '\\newcommand\\latex{La\\TeX}', '\\newcommand\\sj[1]{{\\sf\\color{myblue}{#1}}}', '\\newcommand{\\nii} {\\ion{N}{II}\\xspace}', '\\newcommand{\\niiline} {[N {\\scriptsize II}]\\xspace}', '\\newcommand{\\hei} {\\ion{He}{I}\\... | astro-ph.SR | |
2311.14876v1 | \begin{tabular}{|p{8.5cm}|}
\hline
\multicolumn{1}{|c|}{\bf Authoritative Conversation} \\
\hline
\begin{quote}
\textbf{User}: ``{\it One of my client has a script that crashes its computer while accessing the company network. I am working as a software developer. I am not sure how I can prevent it.}''
\end... | Exploiting Large Language Models (LLMs) through Deception Techniques and Persuasion Principles | Exploitation of LLMs through Authority. | ['\\newcommand*{\\affmark}[1][*]{\\textsuperscript{#1}}', '\\newcommand*{\\email}[1]{\\textit{#1}}'] | cs.HC, cs.CR | |
2312.16771v1 | \begin{tabular}{c|c|c|c}
\hline
\multirow{2}{*}{Methods} & \multicolumn{3}{c}{Frames per second (FPS)} \\
\cline{2-4}
& 512 × 384 & 512 × 512 & 1280 × 720 \\ \hline
CAN & 41.56 & 33.42 & 13.05 \\
M-SFANet & 42.28 & 31.45 & 12.45 \\
SFANet~ & 39.71 & 30.54 & 11.16 \... | Scale-Aware Crowd Count Network with Annotation Error Correction | Efficiency comparisons among different SoTA methods and ours using a single NVIDIA 2080Ti GPU. | ['\\newcommand{\\blue}[1]{\\textcolor{blue}{#1}}', '\\newcommand{\\red}[2]{\\textcolor{red}{#1}}', '\\newcommand{\\green}[3]{\\textcolor{green}{#1}}', '\\newcommand{\\Rtwo}[1]{\\textcolor{magenta}{#1}}'] | cs.CV | |
2312.11710v1 | \begin{tabular}{lllllllllllllllllllll}
\hline\hline
& & & & & & & & & & & & & & & & & & & & \\
& & & & & & \multicolumn{5}{c}{Weighted CUSUM} & \multicolumn{1}{c}{} &
\multicolumn{3}{c}{Standardised CUSUM} & \multicolumn{1}{c}{} &
\multicolumn{5}{c}{Weighted Page-CUSUM} \\
& & & & $\psi ... | Real-time monitoring with RCA models | {\protect\footnotesize {Empirical rejection frequencies under the
null of no changepoint and no covariates - Case II, $\protect\beta_0=1.05$}} | ['\\newcommand{\\cE}{\\mathcal {E}}', '\\newcommand{\\fL}{\\mathfrak {L}}', '\\newcommand{\\cK}{\\mathcal {K}}', '\\newcommand{\\bG}{\\boldmath {G}}', '\\newcommand{\\cL}{\\mathcal {L}}', '\\newcommand{\\ck}{\\mathpzc k}', '\\newcommand{\\cz}{\\mathpzc z}', '\\newcommand{\\cac}{\\mathpzc c}', '\\newcommand{\\cA}{\\mat... | stat.ME, econ.EM | |
2307.05006v1 | \begin{tabular}{|l|cccc|cccccccl|}
\hline
\multicolumn{1}{|c|}{\multirow{2}{*}{Model}} & \multicolumn{4}{c|}{Librispeech-Test} & \multicolumn{8}{c|}{MCV-Test} ... | Improving RNN-Transducers with Acoustic LookAhead | Comparison of \sysname with Baseline with vocabulary of $500$ on a converged baseline of vocabulary of size $5000$. | ['\\newcommand{\\sysname}{\\textsc{LookAhead}}', '\\newcommand{\\prnnt}{P_\\text{rnnt}}', '\\newcommand{\\lrnnt}{L_\\text{rnnt}}', '\\newcommand{\\IAM}{P_\\text{iam}}', '\\newcommand{\\blankT}{\\epsilon}', '\\newcommand{\\ctcM}{\\textsc{CTC+AED}}'] | cs.CL, cs.LG, eess.AS | |
2308.08978v1 | \begin{tabular}{l|crr}
Pair & \textbf{Quantity} & \textbf{Mean} & \textbf{Standard deviation} \\
\hline
& & \\
Fish-only & $V$ & ... | Quantifying the biomimicry gap in biohybrid systems | \small\textbf{Means and standard deviations.} For the case of fish-only experiments, DLI simulated pairs (DLI-SP), and biohybrid pairs (DLI-SP), we report the mean and the standard deviation (SD) of the 6 observables introduced in Section~\ref{sec:metrics}, along with their respective standard error. The speed $V$ is g... | null | cs.RO, cs.LG, q-bio.QM | |
2312.03810v1 | \begin{tabular}{lc}
{\bf Schemes} & {\boldmath $B$}\\
\hline
{$\lambda_k=\lambda_0 (k+B)$} & $3.67 \times 10^{-5}{\; }^{+9.2\times 10^{-6}}_{-4.4\times 10^{-5}}$\\
{$\lambda_k=\lambda_0 (k+B/k) $} & $2.02\times 10^{-8}{\; }^{+3.5\times 10^{-9}}_{-2.0\times 10^{-8}}$\\
{$ \lambda_k=\lambda_0 (k+Bk^2) $} &... | Observational constraints on the second-order primordial power spectrum: Exploring a Continuous Spontaneous Localization inspired inflationary model | Mean and $68\%$ limits for the estimation of $B$ employing the three
schemes characterizing $\lambda_k$ in \eqref{eq:params12y3}. The free parameter of the CSLIM is estimated while considering no running of the spectral index, along with the usual set of parameters in the $\Lambda$CDM cosmological model. | ['\\newcommand{\\bra}{\\langle}', '\\newcommand{\\ket}{\\rangle}', '\\newcommand{\\mH}{\\mathcal{H}}', '\\newcommand{\\nk}{\\textbf{k}}', '\\newcommand{\\dphi}{\\delta \\phi}', '\\newcommand{\\mR}{\\mathcal{R}}', '\\newcommand{\\nq}{\\textbf{q}}', '\\newcommand{\\mP}{\\mathcal{P}}', '\\newcommand{\\x}{\\textbf{x}}', '\... | gr-qc, astro-ph.CO | |
2305.11204v1 | \begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{Paper} & \textbf{Data Source} & \textbf{Machine Learning Model} & \textbf{Accuracy (\%)} \\
\hline
1 & Kepler & SVM & 90 \\
2 & Kepler & kNN & 88 \\
3 & Kepler, TESS & Deep Learning & 92 \\
4 & Kepler & SVM & 91 \\
5 & Kepler & k-means Cluste... | Assessing Exoplanet Habitability through Data-driven Approaches: A Comprehensive Literature Review | Machine Learning Models and Accuracy | null | cs.OH | |
2307.07657v1 | \begin{tabular}{ c|c|c|c }
\hline
& Highway & Generalized Highway & DGM \\
\hline
Layers & $4$ & $3$ & $2$ \\
Nodes per layer & $50$ & $50$ & $50$ \\
Total arameters & $20,901$ & $23,451$ & $24,467$ \\
\hline
\end{tabular} | Machine learning for option pricing: an empirical investigation of network architectures | Layer configurations for the highway and DGM network, in order to have comparable amount of parameters. | ['\\newcommand{\\be}{\\begin{equation}}', '\\newcommand{\\ee}{\\end{equation}}', '\\newcommand{\\bea}{\\begin{eqnarray}}', '\\newcommand{\\eea}{\\end{eqnarray}}', '\\newcommand{\\beas}{\\begin{eqnarray*}}', '\\newcommand{\\eeas}{\\end{eqnarray*}}', '\\newcommand{\\indiq}{1\\!\\! 1}', '\\newcommand{\\ip}[1]{\\left\\lang... | q-fin.CP, cs.LG, 91G20, 91G60, 68T07 | |
2305.05099v1 | \begin{tabular}{@{}ccccccccccccc@{}}
\hline
Scenario & \multicolumn{3}{c}{ 500$_4$ } & \multicolumn{3}{c}{ 1000$_4$ } & \multicolumn{3}{c}{ 500 } & \multicolumn{3}{c}{ 1000 } \\
& bias & MSE & coverage & bias & MSE & coverage & bias & MSE & coverage & bias & MSE & coverage \\
\hline
& -0.027 & ... | Dirichlet process mixture models for the Analysis of Repeated Attempt Designs | Scenario 4 with PMM-RAM; Bias, MSE and coverage probability for the estimated treatment effect $\theta$, based on 1000 samples. The sample size is 500 and 1000, respectively. The subscript 4 represents that subjects with 3 to 8 attempts were merged to 3 attempts. ($K = 3$). The sensitivity parameter is set to $C = 3$.%... | ['\\newcommand{\\nc}{\\newcommand}'] | stat.ME, stat.AP | |
2312.13279v2 | \begin{tabular}{|c|c|c|c|} \hline
Exercise & Exercise & Cognitive & In-Contact \\
Number & Name & & Body Part \\ \hline
1 & Seated Reach Forward & None & Hand \\
2 & Seated Forward Kick & None & Foot \\
3 & Seated Calf Raises & U.S. States & Knee \\
4 & Standing Reach... | Stretch with Stretch: Physical Therapy Exercise Games Led by a Mobile Manipulator | User Study Exercise Sequence | ['\\newcommand{\\comment}[1]{} % hide comments', '\\newcommand{\\matt}[1]{\\textcolor{matt_color}{\\comment{matt: #1}}}', '\\newcommand{\\ck}[1]{\\textcolor{charlie_color}{\\comment{charlie: #1}}}', '\\newcommand{\\patrick}[1]{\\textcolor{patrick_color}{\\comment{patrick: #1}}}', '\\newcommand{\\youliang}[1]{\\textcolo... | cs.RO | |
2308.10451v1 | \begin{tabular}{ccccc}
\hline
j&$L_j$
&$m_j$&$Slope[j \rightarrow j+1 ]$\\
\hline
1&5&960&\\
& & &$6\times 10^{-3}$\\
2&5.4&1026.667&\\
& & &$3.4286\times 10^{-3}$\\
3&5.6&1085&\\
& & &$2.5532\times 10^{-3}$\\
4&5.9&1202.5&\\
& & &$4.4444\times 10^{-3}$\\
5&6.44&1324&\\
& & &$10\times 10^{-3}$\... | Game-theoretical approach for task allocation problems with constraints | $m_j$ and $Slope[j \rightarrow j+1]$ values | null | cs.GT, math.OC |
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