Search | arXiv e-print repository

<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"/> <meta name="viewport" content="width=device-width, initial-scale=1"/>  <link rel="apple-touch-icon" sizes="180x180" href="https://static.arxiv.org/static/base/1.0.0a5/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="https://static.arxiv.org/static/base/1.0.0a5/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="https://static.arxiv.org/static/base/1.0.0a5/images/icons/favicon-16x16.png"> <link rel="manifest" href="https://static.arxiv.org/static/base/1.0.0a5/images/icons/site.webmanifest"> <link rel="mask-icon" href="https://static.arxiv.org/static/base/1.0.0a5/images/icons/safari-pinned-tab.svg" color="#b31b1b"> <link rel="shortcut icon" href="https://static.arxiv.org/static/base/1.0.0a5/images/icons/favicon.ico"> <meta name="msapplication-TileColor" content="#b31b1b"> <meta name="msapplication-config" content="images/icons/browserconfig.xml"> <meta name="theme-color" content="#b31b1b">  <title>Search | arXiv e-print repository</title> <script defer src="https://static.arxiv.org/static/base/1.0.0a5/fontawesome-free-5.11.2-web/js/all.js"></script> <link rel="stylesheet" href="https://static.arxiv.org/static/base/1.0.0a5/css/arxivstyle.css" /> <script type="text/x-mathjax-config"> MathJax.Hub.Config({ messageStyle: "none", extensions: ["tex2jax.js"], jax: ["input/TeX", "output/HTML-CSS"], tex2jax: { inlineMath: [ ['$','$'], ["\$","\$"] ], displayMath: [ ['$$','$$'], ["\\[","\\]"] ], processEscapes: true, ignoreClass: '.*', processClass: 'mathjax.*' }, TeX: { extensions: ["AMSmath.js", "AMSsymbols.js", "noErrors.js"], noErrors: { inlineDelimiters: ["$","$"], multiLine: false, style: { "font-size": "normal", "border": "" } } }, "HTML-CSS": { availableFonts: ["TeX"] } }); </script> <script src='//static.arxiv.org/MathJax-2.7.3/MathJax.js'></script> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/notification.js"></script> <link rel="stylesheet" href="https://static.arxiv.org/static/search/0.5.6/css/bulma-tooltip.min.css" /> <link rel="stylesheet" href="https://static.arxiv.org/static/search/0.5.6/css/search.css" /> <script src="https://code.jquery.com/jquery-3.2.1.slim.min.js" integrity="sha256-k2WSCIexGzOj3Euiig+TlR8gA0EmPjuc79OEeY5L45g=" crossorigin="anonymous"></script> <script src="https://static.arxiv.org/static/search/0.5.6/js/fieldset.js"></script> <style> radio#cf-customfield_11400 { display: none; } </style> </head> <body> <header><a href="#main-container" class="is-sr-only">Skip to main content</a>  <div class="attribution level is-marginless" role="banner"> <div class="level-left"> <a class="level-item" href="https://cornell.edu/"><img src="https://static.arxiv.org/static/base/1.0.0a5/images/cornell-reduced-white-SMALL.svg" alt="Cornell University" width="200" aria-label="logo" /></a> </div> <div class="level-right is-marginless"><p class="sponsors level-item is-marginless"><span id="support-ack-url">We gratefully acknowledge support from<br /> the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors. <a href="https://info.arxiv.org/about/donate.html">Donate</a></span></p></div> </div>  <div class="identity level is-marginless"> <div class="level-left"> <div class="level-item"> <a class="arxiv" href="https://arxiv.org/" aria-label="arxiv-logo"> <img src="https://static.arxiv.org/static/base/1.0.0a5/images/arxiv-logo-one-color-white.svg" aria-label="logo" alt="arxiv logo" width="85" style="width:85px;"/> </a> </div> </div> <div class="search-block level-right"> <form class="level-item mini-search" method="GET" action="https://arxiv.org/search"> <div class="field has-addons"> <div class="control"> <input class="input is-small" type="text" name="query" placeholder="Search..." aria-label="Search term or terms" /> <p class="help"><a href="https://info.arxiv.org/help">Help</a> | <a href="https://arxiv.org/search/advanced">Advanced Search</a></p> </div> <div class="control"> <div class="select is-small"> <select name="searchtype" aria-label="Field to search"> <option value="all" selected="selected">All fields</option> <option value="title">Title</option> <option value="author">Author</option> <option value="abstract">Abstract</option> <option value="comments">Comments</option> <option value="journal_ref">Journal reference</option> <option value="acm_class">ACM classification</option> <option value="msc_class">MSC classification</option> <option value="report_num">Report number</option> <option value="paper_id">arXiv identifier</option> <option value="doi">DOI</option> <option value="orcid">ORCID</option> <option value="author_id">arXiv author ID</option> <option value="help">Help pages</option> <option value="full_text">Full text</option> </select> </div> </div> <input type="hidden" name="source" value="header"> <button class="button is-small is-cul-darker">Search</button> </div> </form> </div> </div>  <div class="container"> <div class="user-tools is-size-7 has-text-right has-text-weight-bold" role="navigation" aria-label="User menu"> <a href="https://arxiv.org/login">Login</a> </div> </div> </header> <main class="container" id="main-container"> <div class="level is-marginless"> <div class="level-left"> <h1 class="title is-clearfix"> Showing 1–50 of 175 results for author: <span class="mathjax">Tiwari, P</span> </h1> </div> <div class="level-right is-hidden-mobile">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> <div class="content"> <form method="GET" action="/search/" aria-role="search"> <div class="field has-addons-tablet"> <div class="control is-expanded"> <label for="query" class="hidden-label">Search term or terms</label> <input class="input is-medium" id="query" name="query" placeholder="Search term..." type="text" value="Tiwari, P"> </div> <div class="select control is-medium"> <label class="is-hidden" for="searchtype">Field</label> <select class="is-medium" id="searchtype" name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> </div> <div class="control"> <button class="button is-link is-medium">Search</button> </div> </div> <div class="field"> <div class="control is-size-7"> <label class="radio"> <input checked id="abstracts-0" name="abstracts" type="radio" value="show"> Show abstracts </label> <label class="radio"> <input id="abstracts-1" name="abstracts" type="radio" value="hide"> Hide abstracts </label> </div> </div> <div class="is-clearfix" style="height: 2.5em"> <div class="is-pulled-right"> <a href="/search/advanced?terms-0-term=Tiwari%2C+P&terms-0-field=author&size=50&order=-announced_date_first">Advanced Search</a> </div> </div> <input type="hidden" name="order" value="-announced_date_first"> <input type="hidden" name="size" value="50"> </form> <div class="level breathe-horizontal"> <div class="level-left"> <form method="GET" action="/search/"> <div style="display: none;"> <select id="searchtype" name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Tiwari, P"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Tiwari%2C+P&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Tiwari%2C+P&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Tiwari%2C+P&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Tiwari%2C+P&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Tiwari%2C+P&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.03925">arXiv:2502.03925</a> <span> [<a href="https://arxiv.org/pdf/2502.03925">pdf</a>, <a href="https://arxiv.org/format/2502.03925">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1103/PhysRevB.110.235414">10.1103/PhysRevB.110.235414 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Electric field tunable bands in doubly aligned bilayer graphene hBN moire superlattice </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Tiwari%2C+P">Priya Tiwari</a>, <a href="/search/?searchtype=author&query=Watanabe%2C+K">Kenji Watanabe</a>, <a href="/search/?searchtype=author&query=Taniguchi%2C+T">Takashi Taniguchi</a>, <a href="/search/?searchtype=author&query=Bid%2C+A">Aveek Bid</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.03925v1-abstract-short" style="display: inline;"> In this letter, we demonstrate electric field-induced band modification of an asymmetrically twisted hBN/BLG/hBN supermoire lattice. Distinct from unaligned BLG/hBN systems, we observe regions in the density-displacement field (n-D) plane where the device conductance is independent of n and decreases as |D| increases. This distinction arises due to the angle asymmetry between the layers, which ind… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03925v1-abstract-full').style.display = 'inline'; document.getElementById('2502.03925v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03925v1-abstract-full" style="display: none;"> In this letter, we demonstrate electric field-induced band modification of an asymmetrically twisted hBN/BLG/hBN supermoire lattice. Distinct from unaligned BLG/hBN systems, we observe regions in the density-displacement field (n-D) plane where the device conductance is independent of n and decreases as |D| increases. This distinction arises due to the angle asymmetry between the layers, which induces field-controlled layer polarization. We identify D-dependent additional band gaps near the charge neutrality point that appear in the conduction (valence) band for negative (positive) D values. In the quantum Hall regime, new 6-fold degenerate Landau levels are observed. Our findings establish that in an asymmetric supermoire heterostructure, an external vertical displacement field affects the valence and conduction bands very differently and sheds light on the asymmetric conductance patterns noted in previous studies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03925v1-abstract-full').style.display = 'none'; document.getElementById('2502.03925v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. B 110, 235414 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.10507">arXiv:2501.10507</a> <span> [<a href="https://arxiv.org/pdf/2501.10507">pdf</a>, <a href="https://arxiv.org/format/2501.10507">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> Gamma-ray burst prompt emission spectra at high energies </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Macera%2C+S">Samanta Macera</a>, <a href="/search/?searchtype=author&query=Banerjee%2C+B">Biswajit Banerjee</a>, <a href="/search/?searchtype=author&query=Mei%2C+A">Alessio Mei</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pawan Tiwari</a>, <a href="/search/?searchtype=author&query=Oganesyan%2C+G">Gor Oganesyan</a>, <a href="/search/?searchtype=author&query=Branchesi%2C+M">Marica Branchesi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.10507v1-abstract-short" style="display: inline;"> Despite more than fifty years of gamma-ray burst (GRB) observations, several questions regarding the origin of the prompt emission, particularly at high energies, remain unresolved. We present a comprehensive analysis of 35 GRBs observed by \textit{Fermi}/GBM and \textit{Fermi}/LAT over the past 15 years, focusing on the nature of high-energy (HE, E$>$100 MeV) emission during the prompt emission p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.10507v1-abstract-full').style.display = 'inline'; document.getElementById('2501.10507v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.10507v1-abstract-full" style="display: none;"> Despite more than fifty years of gamma-ray burst (GRB) observations, several questions regarding the origin of the prompt emission, particularly at high energies, remain unresolved. We present a comprehensive analysis of 35 GRBs observed by \textit{Fermi}/GBM and \textit{Fermi}/LAT over the past 15 years, focusing on the nature of high-energy (HE, E$>$100 MeV) emission during the prompt emission phase. Our study combines temporal and spectral analyses to investigate the synchrotron origin of the observed emission spanning the energy range from 10 keV to 100 GeV and explore the possible contribution of additional spectral components. Temporal modeling of \textit{Fermi}/LAT light curves for 12 GRBs in our sample reveals deviations from standard afterglow scenarios during the early phases, suggesting a significant contamination from prompt emission. We find that most GRB spectra align with synchrotron emission extending to GeV energies, with the slope $p$ of the non-thermal electron distribution clustering around $p\sim2.7$, consistently with theoretical predictions. For three GRBs, an additional power law component is required to explain the high-energy emission, but the nature and temporal evolution of this component remain unclear due to the limited quality of \textit{Fermi}/LAT data. When the power law component is needed, the synchrotron spectrum shows a sharp MeV suppression. It could be explained by the pair loading effects in the early afterglow. These findings emphasize the importance of multi-wavelength observations in unveiling the mechanisms driving early HE prompt emission in GRBs. We briefly discuss the implications of our findings for future very-high-energy (VHE, E$>$100 GeV) gamma-ray observatories, such as the Cherenkov Telescope Array, and address the detection prospects of additional non-thermal components in GRB spectra. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.10507v1-abstract-full').style.display = 'none'; document.getElementById('2501.10507v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Comments and suggestions are welcome</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.01495">arXiv:2501.01495</a> <span> [<a href="https://arxiv.org/pdf/2501.01495">pdf</a>, <a href="https://arxiv.org/format/2501.01495">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> Search for continuous gravitational waves from known pulsars in the first part of the fourth LIGO-Virgo-KAGRA observing run </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=The+LIGO+Scientific+Collaboration"> The LIGO Scientific Collaboration</a>, <a href="/search/?searchtype=author&query=the+Virgo+Collaboration"> the Virgo Collaboration</a>, <a href="/search/?searchtype=author&query=the+KAGRA+Collaboration"> the KAGRA Collaboration</a>, <a href="/search/?searchtype=author&query=Abac%2C+A+G">A. G. Abac</a>, <a href="/search/?searchtype=author&query=Abbott%2C+R">R. Abbott</a>, <a href="/search/?searchtype=author&query=Abouelfettouh%2C+I">I. Abouelfettouh</a>, <a href="/search/?searchtype=author&query=Acernese%2C+F">F. Acernese</a>, <a href="/search/?searchtype=author&query=Ackley%2C+K">K. Ackley</a>, <a href="/search/?searchtype=author&query=Adhicary%2C+S">S. Adhicary</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+N">N. Adhikari</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+R+X">R. X. Adhikari</a>, <a href="/search/?searchtype=author&query=Adkins%2C+V+K">V. K. Adkins</a>, <a href="/search/?searchtype=author&query=Agarwal%2C+D">D. Agarwal</a>, <a href="/search/?searchtype=author&query=Agathos%2C+M">M. Agathos</a>, <a href="/search/?searchtype=author&query=Abchouyeh%2C+M+A">M. Aghaei Abchouyeh</a>, <a href="/search/?searchtype=author&query=Aguiar%2C+O+D">O. D. Aguiar</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+I">I. Aguilar</a>, <a href="/search/?searchtype=author&query=Aiello%2C+L">L. Aiello</a>, <a href="/search/?searchtype=author&query=Ain%2C+A">A. Ain</a>, <a href="/search/?searchtype=author&query=Ajith%2C+P">P. Ajith</a>, <a href="/search/?searchtype=author&query=Akutsu%2C+T">T. Akutsu</a>, <a href="/search/?searchtype=author&query=Albanesi%2C+S">S. Albanesi</a>, <a href="/search/?searchtype=author&query=Alfaidi%2C+R+A">R. A. Alfaidi</a>, <a href="/search/?searchtype=author&query=Al-Jodah%2C+A">A. Al-Jodah</a>, <a href="/search/?searchtype=author&query=All%C3%A9n%C3%A9%2C+C">C. All茅n茅</a> , et al. (1794 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.01495v1-abstract-short" style="display: inline;"> Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent ana… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.01495v1-abstract-full').style.display = 'inline'; document.getElementById('2501.01495v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.01495v1-abstract-full" style="display: none;"> Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.01495v1-abstract-full').style.display = 'none'; document.getElementById('2501.01495v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">main paper: 12 pages, 6 figures, 4 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LIGO-P2400315 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.09163">arXiv:2411.09163</a> <span> [<a href="https://arxiv.org/pdf/2411.09163">pdf</a>, <a href="https://arxiv.org/format/2411.09163">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</span> </div> </div> <p class="title is-5 mathjax"> Impact of Large-Scale Anisotropies on Galaxy Clustering and Cosmological Constraints </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prabhakar Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.09163v1-abstract-short" style="display: inline;"> We critically assess the impact of significant dipole and large-scale anisotropies on galaxy clustering signals, with a focus on radio continuum surveys. Our study reveals that these anisotropies -- resulting from intrinsic cosmological effects and/or observational systematics -- profoundly influence the two-point correlation function (2PCF) and angular power spectrum ($C_\ell$). Notably, large-sc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09163v1-abstract-full').style.display = 'inline'; document.getElementById('2411.09163v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.09163v1-abstract-full" style="display: none;"> We critically assess the impact of significant dipole and large-scale anisotropies on galaxy clustering signals, with a focus on radio continuum surveys. Our study reveals that these anisotropies -- resulting from intrinsic cosmological effects and/or observational systematics -- profoundly influence the two-point correlation function (2PCF) and angular power spectrum ($C_\ell$). Notably, large-scale anisotropies can obscure or simulate non-Gaussianity signals, complicating the extraction of precise cosmological information. The results emphasize that it is crucial to address systematics and rigorously mask the dipole and its surrounding multipoles to obtain accurate cosmological constraints. This approach is essential for extracting cosmological results from clustering signals, particularly for future surveys such as SKA, DESI, and LSST, to ensure the precision and reliability of cosmological analyses. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.09163v1-abstract-full').style.display = 'none'; document.getElementById('2411.09163v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 2 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.05697">arXiv:2411.05697</a> <span> [<a href="https://arxiv.org/pdf/2411.05697">pdf</a>, <a href="https://arxiv.org/format/2411.05697">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> IPMN Risk Assessment under Federated Learning Paradigm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Pan%2C+H">Hongyi Pan</a>, <a href="/search/?searchtype=author&query=Hong%2C+Z">Ziliang Hong</a>, <a href="/search/?searchtype=author&query=Durak%2C+G">Gorkem Durak</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Aktas%2C+H+E">Halil Ertugrul Aktas</a>, <a href="/search/?searchtype=author&query=Taktak%2C+Y">Yavuz Taktak</a>, <a href="/search/?searchtype=author&query=Medetalibeyoglu%2C+A">Alpay Medetalibeyoglu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Velichko%2C+Y">Yury Velichko</a>, <a href="/search/?searchtype=author&query=Spampinato%2C+C">Concetto Spampinato</a>, <a href="/search/?searchtype=author&query=Schoots%2C+I">Ivo Schoots</a>, <a href="/search/?searchtype=author&query=Bruno%2C+M+J">Marco J. Bruno</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pallavi Tiwari</a>, <a href="/search/?searchtype=author&query=Bolan%2C+C">Candice Bolan</a>, <a href="/search/?searchtype=author&query=Gonda%2C+T">Tamas Gonda</a>, <a href="/search/?searchtype=author&query=Miller%2C+F">Frank Miller</a>, <a href="/search/?searchtype=author&query=Keswani%2C+R+N">Rajesh N. Keswani</a>, <a href="/search/?searchtype=author&query=Wallace%2C+M+B">Michael B. Wallace</a>, <a href="/search/?searchtype=author&query=Xu%2C+Z">Ziyue Xu</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.05697v2-abstract-short" style="display: inline;"> Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 652 T1-weighted and 655 T2-weighted MRI images, accompanied by corresponding IPMN… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05697v2-abstract-full').style.display = 'inline'; document.getElementById('2411.05697v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.05697v2-abstract-full" style="display: none;"> Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 652 T1-weighted and 655 T2-weighted MRI images, accompanied by corresponding IPMN risk scores from 7 leading medical institutions, making it the largest and most diverse dataset for IPMN classification to date. We assess the performance of DenseNet-121 in both centralized and federated settings for training on distributed data. Our results demonstrate that the federated learning approach achieves high classification accuracy comparable to centralized learning while ensuring data privacy across institutions. This work marks a significant advancement in collaborative IPMN classification, facilitating secure and high-accuracy model training across multiple centers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05697v2-abstract-full').style.display = 'none'; document.getElementById('2411.05697v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been accepted to ISBI 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.22530">arXiv:2410.22530</a> <span> [<a href="https://arxiv.org/pdf/2410.22530">pdf</a>, <a href="https://arxiv.org/format/2410.22530">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Pan%2C+H">Hongyi Pan</a>, <a href="/search/?searchtype=author&query=Durak%2C+G">Gorkem Durak</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Taktak%2C+Y">Yavuz Taktak</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Aktas%2C+H+E">Halil Ertugrul Aktas</a>, <a href="/search/?searchtype=author&query=Medetalibeyoglu%2C+A">Alpay Medetalibeyoglu</a>, <a href="/search/?searchtype=author&query=Velichko%2C+Y">Yury Velichko</a>, <a href="/search/?searchtype=author&query=Spampinato%2C+C">Concetto Spampinato</a>, <a href="/search/?searchtype=author&query=Schoots%2C+I">Ivo Schoots</a>, <a href="/search/?searchtype=author&query=Bruno%2C+M+J">Marco J. Bruno</a>, <a href="/search/?searchtype=author&query=Keswani%2C+R+N">Rajesh N. Keswani</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pallavi Tiwari</a>, <a href="/search/?searchtype=author&query=Bolan%2C+C">Candice Bolan</a>, <a href="/search/?searchtype=author&query=Gonda%2C+T">Tamas Gonda</a>, <a href="/search/?searchtype=author&query=Goggins%2C+M+G">Michael G. Goggins</a>, <a href="/search/?searchtype=author&query=Wallace%2C+M+B">Michael B. Wallace</a>, <a href="/search/?searchtype=author&query=Xu%2C+Z">Ziyue Xu</a>, <a href="/search/?searchtype=author&query=Bagci%2C+U">Ulas Bagci</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.22530v2-abstract-short" style="display: inline;"> Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains due to variations in imaging protocols and patient demographics across institutions. This challenge is part… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.22530v2-abstract-full').style.display = 'inline'; document.getElementById('2410.22530v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.22530v2-abstract-full" style="display: none;"> Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains due to variations in imaging protocols and patient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that incorporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.22530v2-abstract-full').style.display = 'none'; document.getElementById('2410.22530v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.21338">arXiv:2410.21338</a> <span> [<a href="https://arxiv.org/pdf/2410.21338">pdf</a>, <a href="https://arxiv.org/format/2410.21338">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> FinTeamExperts: Role Specialized MOEs For Financial Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yu%2C+Y">Yue Yu</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.21338v2-abstract-short" style="display: inline;"> Large Language Models (LLMs), such as ChatGPT, Phi3 and Llama-3, are leading a significant leap in AI, as they can generalize knowledge from their training to new tasks without fine-tuning. However, their application in the financial domain remains relatively limited. The financial field is inherently complex, requiring a deep understanding across various perspectives, from macro, micro economic t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21338v2-abstract-full').style.display = 'inline'; document.getElementById('2410.21338v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.21338v2-abstract-full" style="display: none;"> Large Language Models (LLMs), such as ChatGPT, Phi3 and Llama-3, are leading a significant leap in AI, as they can generalize knowledge from their training to new tasks without fine-tuning. However, their application in the financial domain remains relatively limited. The financial field is inherently complex, requiring a deep understanding across various perspectives, from macro, micro economic trend to quantitative analysis. Motivated by this complexity, a mixture of expert LLMs tailored to specific financial domains could offer a more comprehensive understanding for intricate financial tasks. In this paper, we present the FinTeamExperts, a role-specialized LLM framework structured as a Mixture of Experts (MOEs) for financial analysis. The framework simulates a collaborative team setting by training each model to specialize in distinct roles: Macro Analysts, Micro analysts, and Quantitative Analysts. This role-specific specialization enhances the model's ability to integrate their domain-specific expertise. We achieve this by training three 8-billion parameter models on different corpus, each dedicated to excelling in specific finance-related roles. We then instruct-tune FinTeamExperts on downstream tasks to align with practical financial tasks. The experimental results show that FinTeamExperts outperform all models of the same size and larger on three out of four datasets. On the fourth dataset, which presents a more complex task, FinTeamExperts still surpass all models of the same size. This highlights the success of our role-based specialization approach and the continued training approach for FinTeamExperts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21338v2-abstract-full').style.display = 'none'; document.getElementById('2410.21338v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16565">arXiv:2410.16565</a> <span> [<a href="https://arxiv.org/pdf/2410.16565">pdf</a>, <a href="https://arxiv.org/format/2410.16565">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> Search for gravitational waves emitted from SN 2023ixf </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=The+LIGO+Scientific+Collaboration"> The LIGO Scientific Collaboration</a>, <a href="/search/?searchtype=author&query=the+Virgo+Collaboration"> the Virgo Collaboration</a>, <a href="/search/?searchtype=author&query=the+KAGRA+Collaboration"> the KAGRA Collaboration</a>, <a href="/search/?searchtype=author&query=Abac%2C+A+G">A. G. Abac</a>, <a href="/search/?searchtype=author&query=Abbott%2C+R">R. Abbott</a>, <a href="/search/?searchtype=author&query=Abouelfettouh%2C+I">I. Abouelfettouh</a>, <a href="/search/?searchtype=author&query=Acernese%2C+F">F. Acernese</a>, <a href="/search/?searchtype=author&query=Ackley%2C+K">K. Ackley</a>, <a href="/search/?searchtype=author&query=Adhicary%2C+S">S. Adhicary</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+N">N. Adhikari</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+R+X">R. X. Adhikari</a>, <a href="/search/?searchtype=author&query=Adkins%2C+V+K">V. K. Adkins</a>, <a href="/search/?searchtype=author&query=Agarwal%2C+D">D. Agarwal</a>, <a href="/search/?searchtype=author&query=Agathos%2C+M">M. Agathos</a>, <a href="/search/?searchtype=author&query=Abchouyeh%2C+M+A">M. Aghaei Abchouyeh</a>, <a href="/search/?searchtype=author&query=Aguiar%2C+O+D">O. D. Aguiar</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+I">I. Aguilar</a>, <a href="/search/?searchtype=author&query=Aiello%2C+L">L. Aiello</a>, <a href="/search/?searchtype=author&query=Ain%2C+A">A. Ain</a>, <a href="/search/?searchtype=author&query=Akutsu%2C+T">T. Akutsu</a>, <a href="/search/?searchtype=author&query=Albanesi%2C+S">S. Albanesi</a>, <a href="/search/?searchtype=author&query=Alfaidi%2C+R+A">R. A. Alfaidi</a>, <a href="/search/?searchtype=author&query=Al-Jodah%2C+A">A. Al-Jodah</a>, <a href="/search/?searchtype=author&query=All%C3%A9n%C3%A9%2C+C">C. All茅n茅</a>, <a href="/search/?searchtype=author&query=Allocca%2C+A">A. Allocca</a> , et al. (1758 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.16565v1-abstract-short" style="display: inline;"> We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16565v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16565v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16565v1-abstract-full" style="display: none;"> We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16565v1-abstract-full').style.display = 'none'; document.getElementById('2410.16565v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LIGO-P2400125 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.09151">arXiv:2410.09151</a> <span> [<a href="https://arxiv.org/pdf/2410.09151">pdf</a>, <a href="https://arxiv.org/format/2410.09151">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=The+LIGO+Scientific+Collaboration"> The LIGO Scientific Collaboration</a>, <a href="/search/?searchtype=author&query=the+Virgo+Collaboration"> the Virgo Collaboration</a>, <a href="/search/?searchtype=author&query=the+KAGRA+Collaboration"> the KAGRA Collaboration</a>, <a href="/search/?searchtype=author&query=Abac%2C+A+G">A. G. Abac</a>, <a href="/search/?searchtype=author&query=Abbott%2C+R">R. Abbott</a>, <a href="/search/?searchtype=author&query=Abouelfettouh%2C+I">I. Abouelfettouh</a>, <a href="/search/?searchtype=author&query=Acernese%2C+F">F. Acernese</a>, <a href="/search/?searchtype=author&query=Ackley%2C+K">K. Ackley</a>, <a href="/search/?searchtype=author&query=Adhicary%2C+S">S. Adhicary</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+N">N. Adhikari</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+R+X">R. X. Adhikari</a>, <a href="/search/?searchtype=author&query=Adkins%2C+V+K">V. K. Adkins</a>, <a href="/search/?searchtype=author&query=Agarwal%2C+D">D. Agarwal</a>, <a href="/search/?searchtype=author&query=Agathos%2C+M">M. Agathos</a>, <a href="/search/?searchtype=author&query=Abchouyeh%2C+M+A">M. Aghaei Abchouyeh</a>, <a href="/search/?searchtype=author&query=Aguiar%2C+O+D">O. D. Aguiar</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+I">I. Aguilar</a>, <a href="/search/?searchtype=author&query=Aiello%2C+L">L. Aiello</a>, <a href="/search/?searchtype=author&query=Ain%2C+A">A. Ain</a>, <a href="/search/?searchtype=author&query=Ajith%2C+P">P. Ajith</a>, <a href="/search/?searchtype=author&query=Akutsu%2C+T">T. Akutsu</a>, <a href="/search/?searchtype=author&query=Albanesi%2C+S">S. Albanesi</a>, <a href="/search/?searchtype=author&query=Alfaidi%2C+R+A">R. A. Alfaidi</a>, <a href="/search/?searchtype=author&query=Al-Jodah%2C+A">A. Al-Jodah</a>, <a href="/search/?searchtype=author&query=All%C3%A9n%C3%A9%2C+C">C. All茅n茅</a> , et al. (1758 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.09151v1-abstract-short" style="display: inline;"> The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09151v1-abstract-full').style.display = 'inline'; document.getElementById('2410.09151v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.09151v1-abstract-full" style="display: none;"> The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09151v1-abstract-full').style.display = 'none'; document.getElementById('2410.09151v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages of text including references, 4 figures, 5 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LIGO-P2400192 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.08218">arXiv:2410.08218</a> <span> [<a href="https://arxiv.org/pdf/2410.08218">pdf</a>, <a href="https://arxiv.org/format/2410.08218">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Atmospheric and Oceanic Physics">physics.ao-ph</span> </div> </div> <p class="title is-5 mathjax"> A Visual-Analytical Approach for Automatic Detection of Cyclonic Events in Satellite Observations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Agrawal%2C+A">Akash Agrawal</a>, <a href="/search/?searchtype=author&query=Mohapatra%2C+M">Mayesh Mohapatra</a>, <a href="/search/?searchtype=author&query=Raja%2C+A">Abhinav Raja</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Paritosh Tiwari</a>, <a href="/search/?searchtype=author&query=Pattanaik%2C+V">Vishwajeet Pattanaik</a>, <a href="/search/?searchtype=author&query=Jaiswal%2C+N">Neeru Jaiswal</a>, <a href="/search/?searchtype=author&query=Agarwal%2C+A">Arpit Agarwal</a>, <a href="/search/?searchtype=author&query=Rathore%2C+P">Punit Rathore</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.08218v1-abstract-short" style="display: inline;"> Estimating the location and intensity of tropical cyclones holds crucial significance for predicting catastrophic weather events. In this study, we approach this task as a detection and regression challenge, specifically over the North Indian Ocean (NIO) region where best tracks location and wind speed information serve as the labels. The current process for cyclone detection and intensity estimat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08218v1-abstract-full').style.display = 'inline'; document.getElementById('2410.08218v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.08218v1-abstract-full" style="display: none;"> Estimating the location and intensity of tropical cyclones holds crucial significance for predicting catastrophic weather events. In this study, we approach this task as a detection and regression challenge, specifically over the North Indian Ocean (NIO) region where best tracks location and wind speed information serve as the labels. The current process for cyclone detection and intensity estimation involves physics-based simulation studies which are time-consuming, only using image features will automate the process for significantly faster and more accurate predictions. While conventional methods typically necessitate substantial prior knowledge for training, we are exploring alternative approaches to enhance efficiency. This research aims to focus specifically on cyclone detection, intensity estimation and related aspects using only image input and data-driven approaches and will lead to faster inference time and automate the process as opposed to current NWP models being utilized at SAC. In context to algorithm development, a novel two stage detection and intensity estimation module is proposed. In the first level detection we try to localize the cyclone over an entire image as captured by INSAT3D over the NIO (North Indian Ocean). For the intensity estimation task, we propose a CNN-LSTM network, which works on the cyclone centered images, utilizing a ResNet-18 backbone, by which we are able to capture both temporal and spatial characteristics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08218v1-abstract-full').style.display = 'none'; document.getElementById('2410.08218v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 22 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.18983">arXiv:2409.18983</a> <span> [<a href="https://arxiv.org/pdf/2409.18983">pdf</a>, <a href="https://arxiv.org/format/2409.18983">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Relativity and Quantum Cosmology">gr-qc</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Theory">hep-th</span> </div> </div> <p class="title is-5 mathjax"> Negative non-Gaussianity as a salvager for PBHs with PTAs in bounce </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Choudhury%2C+S">Sayantan Choudhury</a>, <a href="/search/?searchtype=author&query=Dey%2C+K">Kritartha Dey</a>, <a href="/search/?searchtype=author&query=Ganguly%2C+S">Siddhant Ganguly</a>, <a href="/search/?searchtype=author&query=Karde%2C+A">Ahaskar Karde</a>, <a href="/search/?searchtype=author&query=Singh%2C+S+K">Swapnil Kumar Singh</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pranjal Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.18983v2-abstract-short" style="display: inline;"> Non-Gaussianity in the primordial curvature perturbation is a crucial element of the early universe due to its significant impact on the primordial black hole (PBH) production. In this work, we focus on the effects of negative non-Gaussianity on PBH abundance through the lens of the compaction function criterion for PBH formation. Our setup utilizes an effective field theory of non-singular bounce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18983v2-abstract-full').style.display = 'inline'; document.getElementById('2409.18983v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.18983v2-abstract-full" style="display: none;"> Non-Gaussianity in the primordial curvature perturbation is a crucial element of the early universe due to its significant impact on the primordial black hole (PBH) production. In this work, we focus on the effects of negative non-Gaussianity on PBH abundance through the lens of the compaction function criterion for PBH formation. Our setup utilizes an effective field theory of non-singular bounce, including the standard slow-roll inflation with an ultra-slow roll phase for amplifying the curvature perturbations to form PBHs. We investigate with two separate values of the non-Gaussianity parameter, $f_{\rm NL}=(-39.95,-35/8)$, found within the ekpyrotic contraction and the matter bounce scenarios, respectively, and show that a negatively large amount of $f_{\rm NL}$ can provide sizeable abundance, $10^{-3}\leq f_{\rm PBH}\leq 1$, and completely mitigates the PBH overproduction issue. We also highlight that the case with the effective sound speed $c_{s}=1$, coupled with $f_{\rm NL}=-39.95$, provides an agreement under $1蟽$ for the scalar-induced gravitational wave explanation of the latest PTA (NANOGrav15 and EPTA) signal. Lastly, we extract an upper bound on the most negative value of, $f_{\rm NL}\sim -60$, below which we show breaching of the underlying perturbativity constraints on the power spectrum amplitude. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18983v2-abstract-full').style.display = 'none'; document.getElementById('2409.18983v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">51 pages, 6 figures, 2 tables, Comments are welcome, Reference list updated</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.09946">arXiv:2409.09946</a> <span> [<a href="https://arxiv.org/pdf/2409.09946">pdf</a>, <a href="https://arxiv.org/format/2409.09946">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3847/1538-4357/ad815b">10.3847/1538-4357/ad815b <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> An Independent Measure of the Kinematic Dipole from SDSS </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prabhakar Tiwari</a>, <a href="/search/?searchtype=author&query=Schwarz%2C+D+J">Dominik J. Schwarz</a>, <a href="/search/?searchtype=author&query=Zhao%2C+G">Gong-Bo Zhao</a>, <a href="/search/?searchtype=author&query=Durrer%2C+R">Ruth Durrer</a>, <a href="/search/?searchtype=author&query=Kunz%2C+M">Martin Kunz</a>, <a href="/search/?searchtype=author&query=Padmanabhan%2C+H">Hamsa Padmanabhan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.09946v2-abstract-short" style="display: inline;"> We utilize the Sloan Digital Sky Survey (SDSS) extended Baryon Oscillation Spectroscopic Survey (eBOSS) and Baryon Oscillation Spectroscopic Survey (BOSS) catalogs with precise spectroscopic redshifts to estimate the kinematic redshift dipole caused by the proper motion of the Solar system. We find that the velocity extracted from the kinematic dipole is consistent with Cosmic Microwave Background… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.09946v2-abstract-full').style.display = 'inline'; document.getElementById('2409.09946v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.09946v2-abstract-full" style="display: none;"> We utilize the Sloan Digital Sky Survey (SDSS) extended Baryon Oscillation Spectroscopic Survey (eBOSS) and Baryon Oscillation Spectroscopic Survey (BOSS) catalogs with precise spectroscopic redshifts to estimate the kinematic redshift dipole caused by the proper motion of the Solar system. We find that the velocity extracted from the kinematic dipole is consistent with Cosmic Microwave Background inferred values. Although the small sky coverage and limited number density of the SDSS sources constrain us from obtaining precise and robust measurements, we leverage the redshift dipole method to estimate the kinematic dipole. The velocity measurements in this study are insensitive to intrinsic clustering, associated with the source count dipole. The kinematic dipole measured in this work and its consistency with CMB values do not guarantee isotropy at large scales. The anisotropy (excess dipole) measured with the NRAO VLA Sky Survey (NVSS) and the WISE Catalog (CatWISE) could be due to the intrinsic distribution of galaxies. The results in this work focus solely on the kinematic dipole term. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.09946v2-abstract-full').style.display = 'none'; document.getElementById('2409.09946v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Footnotes 5, 6, and 7 were added post-acceptance to the journal version for clarification, in response to comments and questions from peers and colleagues</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> The Astrophysical Journal, Volume 975, Number 2, 279 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.08917">arXiv:2409.08917</a> <span> [<a href="https://arxiv.org/pdf/2409.08917">pdf</a>, <a href="https://arxiv.org/format/2409.08917">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liang%2C+G">Guojun Liang</a>, <a href="/search/?searchtype=author&query=Abiri%2C+N">Najmeh Abiri</a>, <a href="/search/?searchtype=author&query=Hashemi%2C+A+S">Atiye Sadat Hashemi</a>, <a href="/search/?searchtype=author&query=Lundstr%C3%B6m%2C+J">Jens Lundstr枚m</a>, <a href="/search/?searchtype=author&query=Byttner%2C+S">Stefan Byttner</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.08917v1-abstract-short" style="display: inline;"> Accurate imputation is essential for the reliability and success of downstream tasks. Recently, diffusion models have attracted great attention in this field. However, these models neglect the latent distribution in a lower-dimensional space derived from the observed data, which limits the generative capacity of the diffusion model. Additionally, dealing with the original missing data without labe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.08917v1-abstract-full').style.display = 'inline'; document.getElementById('2409.08917v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.08917v1-abstract-full" style="display: none;"> Accurate imputation is essential for the reliability and success of downstream tasks. Recently, diffusion models have attracted great attention in this field. However, these models neglect the latent distribution in a lower-dimensional space derived from the observed data, which limits the generative capacity of the diffusion model. Additionally, dealing with the original missing data without labels becomes particularly problematic. To address these issues, we propose the Latent Space Score-Based Diffusion Model (LSSDM) for probabilistic multivariate time series imputation. Observed values are projected onto low-dimensional latent space and coarse values of the missing data are reconstructed without knowing their ground truth values by this unsupervised learning approach. Finally, the reconstructed values are fed into a conditional diffusion model to obtain the precise imputed values of the time series. In this way, LSSDM not only possesses the power to identify the latent distribution but also seamlessly integrates the diffusion model to obtain the high-fidelity imputed values and assess the uncertainty of the dataset. Experimental results demonstrate that LSSDM achieves superior imputation performance while also providing a better explanation and uncertainty analysis of the imputation mechanism. The website of the code is \textit{https://github.com/gorgen2020/LSSDM\_imputation}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.08917v1-abstract-full').style.display = 'none'; document.getElementById('2409.08917v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.00369">arXiv:2409.00369</a> <span>  </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> An Empirical Study on Information Extraction using Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Han%2C+R">Ridong Han</a>, <a href="/search/?searchtype=author&query=Yang%2C+C">Chaohao Yang</a>, <a href="/search/?searchtype=author&query=Peng%2C+T">Tao Peng</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Wan%2C+X">Xiang Wan</a>, <a href="/search/?searchtype=author&query=Liu%2C+L">Lu Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Benyou Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.00369v3-abstract-short" style="display: inline;"> Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.00369v3-abstract-full').style.display = 'inline'; document.getElementById('2409.00369v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.00369v3-abstract-full" style="display: none;"> Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstrate the latest representative progress in LLMs' information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types. Our results suggest a visible performance gap between GPT-4 and state-of-the-art (SOTA) IE methods. To alleviate this problem, considering the LLMs' human-like characteristics, we propose and analyze the effects of a series of simple prompt-based methods, which can be generalized to other LLMs and NLP tasks. Rich experiments show our methods' effectiveness and some of their remaining issues in improving GPT-4's information extraction ability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.00369v3-abstract-full').style.display = 'none'; document.getElementById('2409.00369v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This submission was intended instead as the replacement of arXiv:2305.14450 , where it now appears as arXiv:2305.14450v2</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.11319">arXiv:2408.11319</a> <span> [<a href="https://arxiv.org/pdf/2408.11319">pdf</a>, <a href="https://arxiv.org/format/2408.11319">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yazhou Zhang</a>, <a href="/search/?searchtype=author&query=Zou%2C+C">Chunwang Zou</a>, <a href="/search/?searchtype=author&query=Lian%2C+Z">Zheng Lian</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Qin%2C+J">Jing Qin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.11319v2-abstract-short" style="display: inline;"> In the era of large language models (LLMs), the task of ``System I''~-~the fast, unconscious, and intuitive tasks, e.g., sentiment analysis, text classification, etc., have been argued to be successfully solved. However, sarcasm, as a subtle linguistic phenomenon, often employs rhetorical devices like hyperbole and figuration to convey true sentiments and intentions, involving a higher level of ab… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11319v2-abstract-full').style.display = 'inline'; document.getElementById('2408.11319v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.11319v2-abstract-full" style="display: none;"> In the era of large language models (LLMs), the task of ``System I''~-~the fast, unconscious, and intuitive tasks, e.g., sentiment analysis, text classification, etc., have been argued to be successfully solved. However, sarcasm, as a subtle linguistic phenomenon, often employs rhetorical devices like hyperbole and figuration to convey true sentiments and intentions, involving a higher level of abstraction than sentiment analysis. There is growing concern that the argument about LLMs' success may not be fully tenable when considering sarcasm understanding. To address this question, we select eleven SOTA LLMs and eight SOTA pre-trained language models (PLMs) and present comprehensive evaluations on six widely used benchmark datasets through different prompting approaches, i.e., zero-shot input/output (IO) prompting, few-shot IO prompting, chain of thought (CoT) prompting. Our results highlight three key findings: (1) current LLMs underperform supervised PLMs based sarcasm detection baselines across six sarcasm benchmarks. This suggests that significant efforts are still required to improve LLMs' understanding of human sarcasm. (2) GPT-4 consistently and significantly outperforms other LLMs across various prompting methods, with an average improvement of 14.0\%$\uparrow$. Claude 3 and ChatGPT demonstrate the next best performance after GPT-4. (3) Few-shot IO prompting method outperforms the other two methods: zero-shot IO and few-shot CoT. The reason is that sarcasm detection, being a holistic, intuitive, and non-rational cognitive process, is argued not to adhere to step-by-step logical reasoning, making CoT less effective in understanding sarcasm compared to its effectiveness in mathematical reasoning tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11319v2-abstract-full').style.display = 'none'; document.getElementById('2408.11319v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.19472">arXiv:2407.19472</a> <span> [<a href="https://arxiv.org/pdf/2407.19472">pdf</a>, <a href="https://arxiv.org/format/2407.19472">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Alonso-Fernandez%2C+F">Fernando Alonso-Fernandez</a>, <a href="/search/?searchtype=author&query=Hernandez-Diaz%2C+K">Kevin Hernandez-Diaz</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Bigun%2C+J">Josef Bigun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.19472v2-abstract-short" style="display: inline;"> We apply pre-trained architectures, originally developed for the ImageNet Large Scale Visual Recognition Challenge, for periocular recognition. These architectures have demonstrated significant success in various computer vision tasks beyond the ones for which they were designed. This work builds on our previous study using off-the-shelf Convolutional Neural Network (CNN) and extends it to include… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.19472v2-abstract-full').style.display = 'inline'; document.getElementById('2407.19472v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.19472v2-abstract-full" style="display: none;"> We apply pre-trained architectures, originally developed for the ImageNet Large Scale Visual Recognition Challenge, for periocular recognition. These architectures have demonstrated significant success in various computer vision tasks beyond the ones for which they were designed. This work builds on our previous study using off-the-shelf Convolutional Neural Network (CNN) and extends it to include the more recently proposed Vision Transformers (ViT). Despite being trained for generic object classification, middle-layer features from CNNs and ViTs are a suitable way to recognize individuals based on periocular images. We also demonstrate that CNNs and ViTs are highly complementary since their combination results in boosted accuracy. In addition, we show that a small portion of these pre-trained models can achieve good accuracy, resulting in thinner models with fewer parameters, suitable for resource-limited environments such as mobiles. This efficiency improves if traditional handcrafted features are added as well. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.19472v2-abstract-full').style.display = 'none'; document.getElementById('2407.19472v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at IEEE WIFS 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.18976">arXiv:2407.18976</a> <span> [<a href="https://arxiv.org/pdf/2407.18976">pdf</a>, <a href="https://arxiv.org/format/2407.18976">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Relativity and Quantum Cosmology">gr-qc</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Theory">hep-th</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/1475-7516/2024/09/013">10.1088/1475-7516/2024/09/013 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Obviating PBH overproduction for SIGWs generated by Pulsar Timing Arrays in loop corrected EFT of bounce </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Choudhury%2C+S">Sayantan Choudhury</a>, <a href="/search/?searchtype=author&query=Ganguly%2C+S">Siddhant Ganguly</a>, <a href="/search/?searchtype=author&query=Panda%2C+S">Sudhakar Panda</a>, <a href="/search/?searchtype=author&query=SenGupta%2C+S">Soumitra SenGupta</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pranjal Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.18976v3-abstract-short" style="display: inline;"> In order to unravel the present situation of the PBH overproduction problem, our study emphasizes the critical role played by the equation of state (EoS) parameter $w$ within the framework of effective field theory (EFT) of non-singular bounce. Our analysis focuses on a wide range of EoS parameter values that are still optimal for explaining the latest data from the pulsar timing array (PTA). As a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18976v3-abstract-full').style.display = 'inline'; document.getElementById('2407.18976v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.18976v3-abstract-full" style="display: none;"> In order to unravel the present situation of the PBH overproduction problem, our study emphasizes the critical role played by the equation of state (EoS) parameter $w$ within the framework of effective field theory (EFT) of non-singular bounce. Our analysis focuses on a wide range of EoS parameter values that are still optimal for explaining the latest data from the pulsar timing array (PTA). As a result of our study, the most advantageous window, $0.31 \leq w \leq 1/3$, is identified as the location of a substantial PBH abundance, $f_{\rm PBH} \in (10^{-3},1)$ with large mass PBHs, $M_{\rm PBH}\sim {\cal O}(10^{-7}-10^{-3})M_{\odot}$, in the SIGW interpretation of the PTA signal. When confronted with PTA, we find that the overproduction avoiding circumstances are between $1蟽-2蟽$, while the EoS parameter lies inside the narrow window, $0.31<w\leq 1/3$. We propose a regularized-renormalized-resummed (RRR) scalar power spectrum that is large enough to produce EoS dependent scalar generated gravitational waves compatible with PTA evidence, while satisfying the perturbativity, causality, and unitarity criteria, within the range of $0.88 \leq c_{s} \leq 1$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.18976v3-abstract-full').style.display = 'none'; document.getElementById('2407.18976v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">32 pages (17 pages material+ 1-page Appendix+ 14 pages refs), 6 figures, Reference updated, Comments are welcome, Accepted for publication in Journal of Cosmology and Astroparticle Physics (Direct Editorial Acceptance)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> JCAP 09 (2024) 013 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.13809">arXiv:2407.13809</a> <span> [<a href="https://arxiv.org/pdf/2407.13809">pdf</a>, <a href="https://arxiv.org/format/2407.13809">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Quantum Machine Learning: The Power of Non-Linear Optical Reproducing Kernels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Dehdashti%2C+S">Shahram Dehdashti</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Safty%2C+K+H+E">Kareem H. El Safty</a>, <a href="/search/?searchtype=author&query=Bruza%2C+P">Peter Bruza</a>, <a href="/search/?searchtype=author&query=Notzel%2C+J">Janis Notzel</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.13809v3-abstract-short" style="display: inline;"> Amidst the array of quantum machine learning algorithms, the quantum kernel method has emerged as a focal point, primarily owing to its compatibility with noisy intermediate-scale quantum devices and its promise to achieve quantum advantage. This method operates by nonlinearly transforming data into feature space constructed with quantum states, enabling classification and regression tasks. In thi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13809v3-abstract-full').style.display = 'inline'; document.getElementById('2407.13809v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.13809v3-abstract-full" style="display: none;"> Amidst the array of quantum machine learning algorithms, the quantum kernel method has emerged as a focal point, primarily owing to its compatibility with noisy intermediate-scale quantum devices and its promise to achieve quantum advantage. This method operates by nonlinearly transforming data into feature space constructed with quantum states, enabling classification and regression tasks. In this study, we present a novel feature space constructed using Kerr coherent states, which generalize su(2), su(1, 1) coherent states, and squeezed states. Notably, the feature space exhibits constant curvature, comprising both spherical and hyperbolic geometries, depending on the sign of the Kerr parameter. Remarkably, the physical parameters associated with the coherent states, enable control over the curvature of the feature space. Our study employs Kerr kernels derived from encoding data into the phase and amplitude of Kerr coherent states. We analyze various datasets ranging from Moon to breast cancer diagnostics. Our findings demonstrate the robustness of Kerr coherent states, attributed to their flexibility in accommodating different hyperparameters, thereby offering superior performance across noisy datasets and hardware setups. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13809v3-abstract-full').style.display = 'none'; document.getElementById('2407.13809v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.01982">arXiv:2407.01982</a> <span> [<a href="https://arxiv.org/pdf/2407.01982">pdf</a>, <a href="https://arxiv.org/format/2407.01982">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Strongly Correlated Electrons">cond-mat.str-el</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> </div> <p class="title is-5 mathjax"> Atypical antiferromagnetic ordering in single crystalline quasi-2D honeycomb magnet YbI$_3$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Pistawala%2C+N">Nashra Pistawala</a>, <a href="/search/?searchtype=author&query=Harnagea%2C+L">Luminita Harnagea</a>, <a href="/search/?searchtype=author&query=Ramakrishnan%2C+S">Sitaram Ramakrishnan</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Priyanshi Tiwari</a>, <a href="/search/?searchtype=author&query=Saravanan%2C+M+P">M. P. Saravanan</a>, <a href="/search/?searchtype=author&query=Rawat%2C+R">Rajeev Rawat</a>, <a href="/search/?searchtype=author&query=Singh%2C+S">Surjeet Singh</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.01982v1-abstract-short" style="display: inline;"> Here, we study YbI$_3$, a quasi-2D layered material with Yb atoms arranged on an ideal honeycomb network of edge-sharing YbI$_6$ octahedra, analogous to the low-temperature phase of $伪-$RuCl$_3$. High quality single crystals of YbI$_3$ are grown from Yb and I as starting precursors, using the vapor transport technique. The grown crystals are characterized by single crystal x-ray diffraction, Raman… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.01982v1-abstract-full').style.display = 'inline'; document.getElementById('2407.01982v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.01982v1-abstract-full" style="display: none;"> Here, we study YbI$_3$, a quasi-2D layered material with Yb atoms arranged on an ideal honeycomb network of edge-sharing YbI$_6$ octahedra, analogous to the low-temperature phase of $伪-$RuCl$_3$. High quality single crystals of YbI$_3$ are grown from Yb and I as starting precursors, using the vapor transport technique. The grown crystals are characterized by single crystal x-ray diffraction, Raman spectroscopy, magnetization, and heat capacity probes. The crystal-field split ground state of Yb$^{3+}$ in \Yb~ is a well-isolated Kramers doublet with an effective moment $\rm J_{eff} = 1/2$. Upon cooling, the low-temperature heat capacity of \Yb~ reveals a broad peak at $\rm T_1 = 0.95$~K due to short-range ordering of the Yb moments, followed by a sharp peak at $\rm T_2 = T_N = 0.6$~K due to long-range ordering. The magnetic behavior is found to be weakly anisotropic with $蠂^\parallel > 蠂^\perp$, where $蠂^\parallel$ and $蠂^\perp$ refers to the in-plane ($H \parallel ab$) and out-of-plane ($H \perp ab$) susceptibilities. The 2~K isothermal magnetization saturates at $\rm \approx~1.5~渭_B/Yb^{3+}$ (in-plane) and $\rm \approx~1~渭_B/Yb^{3+}$ (out-of-plane), suggesting the anisotropy to be easy-plane type. Low-temperature heat capacity, well below T$_N$, is found to vary as T$^伪$ with $伪~\approx~2.5$, indicating a possible unconventional magnetic ground state for YbI$_3$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.01982v1-abstract-full').style.display = 'none'; document.getElementById('2407.01982v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 5 figures, Supplemental Material available on request</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.00105">arXiv:2407.00105</a> <span> [<a href="https://arxiv.org/pdf/2407.00105">pdf</a>, <a href="https://arxiv.org/format/2407.00105">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Multiple Kronecker RLS fusion-based link propagation for drug-side effect prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Qian%2C+Y">Yuqing Qian</a>, <a href="/search/?searchtype=author&query=Zheng%2C+Z">Ziyu Zheng</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Ding%2C+Y">Yijie Ding</a>, <a href="/search/?searchtype=author&query=Zou%2C+Q">Quan Zou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.00105v1-abstract-short" style="display: inline;"> Drug-side effect prediction has become an essential area of research in the field of pharmacology. As the use of medications continues to rise, so does the importance of understanding and mitigating the potential risks associated with them. At present, researchers have turned to data-driven methods to predict drug-side effects. Drug-side effect prediction is a link prediction problem, and the rela… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00105v1-abstract-full').style.display = 'inline'; document.getElementById('2407.00105v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.00105v1-abstract-full" style="display: none;"> Drug-side effect prediction has become an essential area of research in the field of pharmacology. As the use of medications continues to rise, so does the importance of understanding and mitigating the potential risks associated with them. At present, researchers have turned to data-driven methods to predict drug-side effects. Drug-side effect prediction is a link prediction problem, and the related data can be described from various perspectives. To process these kinds of data, a multi-view method, called Multiple Kronecker RLS fusion-based link propagation (MKronRLSF-LP), is proposed. MKronRLSF-LP extends the Kron-RLS by finding the consensus partitions and multiple graph Laplacian constraints in the multi-view setting. Both of these multi-view settings contribute to a higher quality result. Extensive experiments have been conducted on drug-side effect datasets, and our empirical results provide evidence that our approach is effective and robust. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00105v1-abstract-full').style.display = 'none'; document.getElementById('2407.00105v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Transactions on Machine Learning Research (TMLR 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.00024">arXiv:2407.00024</a> <span> [<a href="https://arxiv.org/pdf/2407.00024">pdf</a>, <a href="https://arxiv.org/format/2407.00024">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> LMVD: A Large-Scale Multimodal Vlog Dataset for Depression Detection in the Wild </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=He%2C+L">Lang He</a>, <a href="/search/?searchtype=author&query=Chen%2C+K">Kai Chen</a>, <a href="/search/?searchtype=author&query=Zhao%2C+J">Junnan Zhao</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yimeng Wang</a>, <a href="/search/?searchtype=author&query=Pei%2C+E">Ercheng Pei</a>, <a href="/search/?searchtype=author&query=Chen%2C+H">Haifeng Chen</a>, <a href="/search/?searchtype=author&query=Jiang%2C+J">Jiewei Jiang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+S">Shiqing Zhang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jie Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zhongmin Wang</a>, <a href="/search/?searchtype=author&query=He%2C+T">Tao He</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.00024v1-abstract-short" style="display: inline;"> Depression can significantly impact many aspects of an individual's life, including their personal and social functioning, academic and work performance, and overall quality of life. Many researchers within the field of affective computing are adopting deep learning technology to explore potential patterns related to the detection of depression. However, because of subjects' privacy protection con… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00024v1-abstract-full').style.display = 'inline'; document.getElementById('2407.00024v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.00024v1-abstract-full" style="display: none;"> Depression can significantly impact many aspects of an individual's life, including their personal and social functioning, academic and work performance, and overall quality of life. Many researchers within the field of affective computing are adopting deep learning technology to explore potential patterns related to the detection of depression. However, because of subjects' privacy protection concerns, that data in this area is still scarce, presenting a challenge for the deep discriminative models used in detecting depression. To navigate these obstacles, a large-scale multimodal vlog dataset (LMVD), for depression recognition in the wild is built. In LMVD, which has 1823 samples with 214 hours of the 1475 participants captured from four multimedia platforms (Sina Weibo, Bilibili, Tiktok, and YouTube). A novel architecture termed MDDformer to learn the non-verbal behaviors of individuals is proposed. Extensive validations are performed on the LMVD dataset, demonstrating superior performance for depression detection. We anticipate that the LMVD will contribute a valuable function to the depression detection community. The data and code will released at the link: https://github.com/helang818/LMVD/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.00024v1-abstract-full').style.display = 'none'; document.getElementById('2407.00024v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.17924">arXiv:2406.17924</a> <span> [<a href="https://arxiv.org/pdf/2406.17924">pdf</a>, <a href="https://arxiv.org/format/2406.17924">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> </div> </div> <p class="title is-5 mathjax"> Flux dependence of redshift distribution and clustering of LOFAR radio sources </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bhardwaj%2C+N">Nitesh Bhardwaj</a>, <a href="/search/?searchtype=author&query=Schwarz%2C+D+J">Dominik J. Schwarz</a>, <a href="/search/?searchtype=author&query=Hale%2C+C+L">Catherine L. Hale</a>, <a href="/search/?searchtype=author&query=Duncan%2C+K+J">Kenneth J. Duncan</a>, <a href="/search/?searchtype=author&query=Camera%2C+S">Stefano Camera</a>, <a href="/search/?searchtype=author&query=Heneka%2C+C+S">Caroline S. Heneka</a>, <a href="/search/?searchtype=author&query=Nakoneczny%2C+S+J">Szymon J. Nakoneczny</a>, <a href="/search/?searchtype=author&query=R%C3%B6ttgering%2C+H+J+A">Huub J. A. R枚ttgering</a>, <a href="/search/?searchtype=author&query=Siewert%2C+T+M">Thilo M. Siewert</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prabhakar Tiwari</a>, <a href="/search/?searchtype=author&query=Zheng%2C+J">Jinglan Zheng</a>, <a href="/search/?searchtype=author&query=Miley%2C+G">George Miley</a>, <a href="/search/?searchtype=author&query=Tasse%2C+C">Cyril Tasse</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.17924v1-abstract-short" style="display: inline;"> In this work we study the flux density dependence of the redshift distribution of low-frequency radio sources observed in the LOFAR Two-metre Sky Survey (LoTSS) deep fields and apply it to estimate the clustering length of the large-scale structure of the Universe, examining flux density limited samples (1 mJy, 2 mJy, 4 mJy and 8 mJy) of LoTSS wide field radio sources. We utilise and combine the p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.17924v1-abstract-full').style.display = 'inline'; document.getElementById('2406.17924v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.17924v1-abstract-full" style="display: none;"> In this work we study the flux density dependence of the redshift distribution of low-frequency radio sources observed in the LOFAR Two-metre Sky Survey (LoTSS) deep fields and apply it to estimate the clustering length of the large-scale structure of the Universe, examining flux density limited samples (1 mJy, 2 mJy, 4 mJy and 8 mJy) of LoTSS wide field radio sources. We utilise and combine the posterior probability distributions of photometric redshift determinations for LoTSS deep field observations from three different fields (Bo枚tes, Lockman hole and ELAIS-N1, together about $26$ square degrees of sky), which are available for between $91\%$ to $96\%$ of all sources above the studied flux density thresholds and observed in the area covered by multi-frequency data. We estimate uncertainties by a bootstrap method. We apply the inferred redshift distribution on the LoTSS wide area radio sources from the HETDEX field (LoTSS-DR1; about $424$ square degrees) and make use of the Limber approximation and a power-law model of three dimensional clustering to measure the clustering length, $r_0$, for various models of the evolution of clustering. We find that the redshift distributions from all three LoTSS deep fields agree within expected uncertainties. We show that the radio source population probed by LoTSS at flux densities above $1$ mJy has a median redshift of at least $0.9$. At $2$ mJy, we measure the clustering length of LoTSS radio sources to be $r_0 = (10.1\pm 2.6) \ h^{-1}$Mpc in the context of the comoving clustering model. Our findings are in agreement with measurements at higher flux density thresholds at the same frequency and with measurements at higher frequencies in the context of the comoving clustering model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.17924v1-abstract-full').style.display = 'none'; document.getElementById('2406.17924v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.13822">arXiv:2406.13822</a> <span> [<a href="https://arxiv.org/pdf/2406.13822">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> Association of neighborhood disadvantage with cognitive function and cortical disorganization in an unimpaired cohort </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Safai%2C+A">Apoorva Safai</a>, <a href="/search/?searchtype=author&query=Jonaitis%2C+E">Erin Jonaitis</a>, <a href="/search/?searchtype=author&query=Langhough%2C+R+E">Rebecca E Langhough</a>, <a href="/search/?searchtype=author&query=Buckingham%2C+W+R">William R Buckingham</a>, <a href="/search/?searchtype=author&query=Johnson%2C+S+C">Sterling C. Johnson</a>, <a href="/search/?searchtype=author&query=Powell%2C+W+R">W. Ryan Powell</a>, <a href="/search/?searchtype=author&query=Kind%2C+A+J+H">Amy J. H. Kind</a>, <a href="/search/?searchtype=author&query=Bendlin%2C+B+B">Barbara B. Bendlin</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pallavi Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.13822v1-abstract-short" style="display: inline;"> Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs could capture changes in cortical patterns related to neighborhood disadvantage and cognitive function. This cross-sectional study included cognitivel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.13822v1-abstract-full').style.display = 'inline'; document.getElementById('2406.13822v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.13822v1-abstract-full" style="display: none;"> Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs could capture changes in cortical patterns related to neighborhood disadvantage and cognitive function. This cross-sectional study included cognitively unimpaired participants from two large Alzheimers studies at University of Wisconsin-Madison. Neighborhood disadvantage status was obtained using the Area Deprivation Index (ADI). Cognitive performance was assessed on memory, processing speed and executive function. Morphological Similarity Networks (MSN) were constructed for each participant based on the similarity in distribution of cortical thickness of brain regions, followed by computation of local and global network features. Association of ADI with cognitive scores and MSN features were examined using linear regression and mediation analysis. ADI showed negative association with category fluency,implicit learning speed, story recall and modified pre-clinical Alzheimers cognitive composite scores, indicating worse cognitive function among those living in more disadvantaged neighborhoods. Local network features of frontal and temporal regions differed based on ADI status. Centrality of left lateral orbitofrontal region showed a partial mediating effect between association of neighborhood disadvantage and story recall performance. Our preliminary findings suggest differences in local cortical organization by neighborhood disadvantage, which partially mediated the relationship between ADI and cognitive performance, providing a possible network-based mechanism to, in-part, explain the risk for poor cognitive functioning associated with disadvantaged neighborhoods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.13822v1-abstract-full').style.display = 'none'; document.getElementById('2406.13822v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.01274">arXiv:2406.01274</a> <span> [<a href="https://arxiv.org/pdf/2406.01274">pdf</a>, <a href="https://arxiv.org/format/2406.01274">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Expected Grad-CAM: Towards gradient faithfulness </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Buono%2C+V">Vincenzo Buono</a>, <a href="/search/?searchtype=author&query=Mashhadi%2C+P+S">Peyman Sheikholharam Mashhadi</a>, <a href="/search/?searchtype=author&query=Rahat%2C+M">Mahmoud Rahat</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Byttner%2C+S">Stefan Byttner</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.01274v2-abstract-short" style="display: inline;"> Although input-gradients techniques have evolved to mitigate and tackle the challenges associated with gradients, modern gradient-weighted CAM approaches still rely on vanilla gradients, which are inherently susceptible to the saturation phenomena. Despite recent enhancements have incorporated counterfactual gradient strategies as a mitigating measure, these local explanation techniques still exhi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.01274v2-abstract-full').style.display = 'inline'; document.getElementById('2406.01274v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.01274v2-abstract-full" style="display: none;"> Although input-gradients techniques have evolved to mitigate and tackle the challenges associated with gradients, modern gradient-weighted CAM approaches still rely on vanilla gradients, which are inherently susceptible to the saturation phenomena. Despite recent enhancements have incorporated counterfactual gradient strategies as a mitigating measure, these local explanation techniques still exhibit a lack of sensitivity to their baseline parameter. Our work proposes a gradient-weighted CAM augmentation that tackles both the saturation and sensitivity problem by reshaping the gradient computation, incorporating two well-established and provably approaches: Expected Gradients and kernel smoothing. By revisiting the original formulation as the smoothed expectation of the perturbed integrated gradients, one can concurrently construct more faithful, localized and robust explanations which minimize infidelity. Through fine modulation of the perturbation distribution it is possible to regulate the complexity characteristic of the explanation, selectively discriminating stable features. Our technique, Expected Grad-CAM, differently from recent works, exclusively optimizes the gradient computation, purposefully designed as an enhanced substitute of the foundational Grad-CAM algorithm and any method built therefrom. Quantitative and qualitative evaluations have been conducted to assess the effectiveness of our method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.01274v2-abstract-full').style.display = 'none'; document.getElementById('2406.01274v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Updated appendix figures to vector format for improved clarity</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.18302">arXiv:2405.18302</a> <span> [<a href="https://arxiv.org/pdf/2405.18302">pdf</a>, <a href="https://arxiv.org/format/2405.18302">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Deep Network Pruning: A Comparative Study on CNNs in Face Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Alonso-Fernandez%2C+F">Fernando Alonso-Fernandez</a>, <a href="/search/?searchtype=author&query=Hernandez-Diaz%2C+K">Kevin Hernandez-Diaz</a>, <a href="/search/?searchtype=author&query=Rubio%2C+J+M+B">Jose Maria Buades Rubio</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Bigun%2C+J">Josef Bigun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.18302v1-abstract-short" style="display: inline;"> The widespread use of mobile devices for all kind of transactions makes necessary reliable and real-time identity authentication, leading to the adoption of face recognition (FR) via the cameras embedded in such devices. Progress of deep Convolutional Neural Networks (CNNs) has provided substantial advances in FR. Nonetheless, the size of state-of-the-art architectures is unsuitable for mobile dep… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18302v1-abstract-full').style.display = 'inline'; document.getElementById('2405.18302v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.18302v1-abstract-full" style="display: none;"> The widespread use of mobile devices for all kind of transactions makes necessary reliable and real-time identity authentication, leading to the adoption of face recognition (FR) via the cameras embedded in such devices. Progress of deep Convolutional Neural Networks (CNNs) has provided substantial advances in FR. Nonetheless, the size of state-of-the-art architectures is unsuitable for mobile deployment, since they often encompass hundreds of megabytes and millions of parameters. We address this by studying methods for deep network compression applied to FR. In particular, we apply network pruning based on Taylor scores, where less important filters are removed iteratively. The method is tested on three networks based on the small SqueezeNet (1.24M parameters) and the popular MobileNetv2 (3.5M) and ResNet50 (23.5M) architectures. These have been selected to showcase the method on CNNs with different complexities and sizes. We observe that a substantial percentage of filters can be removed with minimal performance loss. Also, filters with the highest amount of output channels tend to be removed first, suggesting that high-dimensional spaces within popular CNNs are over-dimensionated. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18302v1-abstract-full').style.display = 'none'; document.getElementById('2405.18302v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to Pattern Recognition Letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.16671">arXiv:2405.16671</a> <span> [<a href="https://arxiv.org/pdf/2405.16671">pdf</a>, <a href="https://arxiv.org/format/2405.16671">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Mixture of Latent Experts Using Tensor Products </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Su%2C+Z">Zhan Su</a>, <a href="/search/?searchtype=author&query=Mo%2C+F">Fengran Mo</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Benyou Wang</a>, <a href="/search/?searchtype=author&query=Nie%2C+J">Jian-Yun Nie</a>, <a href="/search/?searchtype=author&query=Simonsen%2C+J+G">Jakob Grue Simonsen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.16671v2-abstract-short" style="display: inline;"> In multi-task learning, the conventional approach involves training a model on multiple tasks simultaneously. However, the training signals from different tasks can interfere with one another, potentially leading to \textit{negative transfer}. To mitigate this, we investigate if modular language models can facilitate positive transfer and systematic generalization. Specifically, we propose a novel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.16671v2-abstract-full').style.display = 'inline'; document.getElementById('2405.16671v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.16671v2-abstract-full" style="display: none;"> In multi-task learning, the conventional approach involves training a model on multiple tasks simultaneously. However, the training signals from different tasks can interfere with one another, potentially leading to \textit{negative transfer}. To mitigate this, we investigate if modular language models can facilitate positive transfer and systematic generalization. Specifically, we propose a novel modular language model (\texttt{TensorPoly}), that balances parameter efficiency with nuanced routing methods. For \textit{modules}, we reparameterize Low-Rank Adaptation (\texttt{LoRA}) by employing an entangled tensor through the use of tensor product operations and name the resulting approach \texttt{TLoRA}. For \textit{routing function}, we tailor two innovative routing functions according to the granularity: \texttt{TensorPoly-I} which directs to each rank within the entangled tensor while \texttt{TensorPoly-II} offers a finer-grained routing approach targeting each order of the entangled tensor. The experimental results from the multi-task T0-benchmark demonstrate that: 1) all modular LMs surpass the corresponding dense approaches, highlighting the potential of modular language models to mitigate negative inference in multi-task learning and deliver superior outcomes. 2) \texttt{TensorPoly-I} achieves higher parameter efficiency in adaptation and outperforms other modular LMs, which shows the potential of our approach in multi-task transfer learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.16671v2-abstract-full').style.display = 'none'; document.getElementById('2405.16671v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">https://github.com/microsoft/mttl/tree/zs_code</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.12367">arXiv:2405.12367</a> <span> [<a href="https://arxiv.org/pdf/2405.12367">pdf</a>, <a href="https://arxiv.org/format/2405.12367">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zheyuan Zhang</a>, <a href="/search/?searchtype=author&query=Keles%2C+E">Elif Keles</a>, <a href="/search/?searchtype=author&query=Durak%2C+G">Gorkem Durak</a>, <a href="/search/?searchtype=author&query=Taktak%2C+Y">Yavuz Taktak</a>, <a href="/search/?searchtype=author&query=Susladkar%2C+O">Onkar Susladkar</a>, <a href="/search/?searchtype=author&query=Gorade%2C+V">Vandan Gorade</a>, <a href="/search/?searchtype=author&query=Jha%2C+D">Debesh Jha</a>, <a href="/search/?searchtype=author&query=Ormeci%2C+A+C">Asli C. Ormeci</a>, <a href="/search/?searchtype=author&query=Medetalibeyoglu%2C+A">Alpay Medetalibeyoglu</a>, <a href="/search/?searchtype=author&query=Yao%2C+L">Lanhong Yao</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Bin Wang</a>, <a href="/search/?searchtype=author&query=Isler%2C+I+S">Ilkin Sevgi Isler</a>, <a href="/search/?searchtype=author&query=Peng%2C+L">Linkai Peng</a>, <a href="/search/?searchtype=author&query=Pan%2C+H">Hongyi Pan</a>, <a href="/search/?searchtype=author&query=Vendrami%2C+C+L">Camila Lopes Vendrami</a>, <a href="/search/?searchtype=author&query=Bourhani%2C+A">Amir Bourhani</a>, <a href="/search/?searchtype=author&query=Velichko%2C+Y">Yury Velichko</a>, <a href="/search/?searchtype=author&query=Gong%2C+B">Boqing Gong</a>, <a href="/search/?searchtype=author&query=Spampinato%2C+C">Concetto Spampinato</a>, <a href="/search/?searchtype=author&query=Pyrros%2C+A">Ayis Pyrros</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pallavi Tiwari</a>, <a href="/search/?searchtype=author&query=Klatte%2C+D+C+F">Derk C. F. Klatte</a>, <a href="/search/?searchtype=author&query=Engels%2C+M">Megan Engels</a>, <a href="/search/?searchtype=author&query=Hoogenboom%2C+S">Sanne Hoogenboom</a>, <a href="/search/?searchtype=author&query=Bolan%2C+C+W">Candice W. Bolan</a> , et al. (13 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.12367v3-abstract-short" style="display: inline;"> Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective st… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.12367v3-abstract-full').style.display = 'inline'; document.getElementById('2405.12367v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.12367v3-abstract-full" style="display: none;"> Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1W) and T2-weighted (T2W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We developed a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (std: 7.2%, at case level) with CT, 85.0% (std: 7.9%) with T1W MRI, and 86.3% (std: 6.4%) with T2W MRI. There was a high correlation for pancreas volume prediction with R^2 of 0.91, 0.84, and 0.85 for CT, T1W, and T2W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1W and T2W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.12367v3-abstract-full').style.display = 'none'; document.getElementById('2405.12367v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Peer-reviewer version</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.10995">arXiv:2405.10995</a> <span> [<a href="https://arxiv.org/pdf/2405.10995">pdf</a>, <a href="https://arxiv.org/format/2405.10995">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liang%2C+G">Guojun Liang</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Nowaczyk%2C+S">Slawomir Nowaczyk</a>, <a href="/search/?searchtype=author&query=Byttner%2C+S">Stefan Byttner</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.10995v2-abstract-short" style="display: inline;"> Exploring the missing values is an essential but challenging issue due to the complex latent spatio-temporal correlation and dynamic nature of time series. Owing to the outstanding performance in dealing with structure learning potentials, Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) are often used to capture such complex spatio-temporal features in multivariate time series. H… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10995v2-abstract-full').style.display = 'inline'; document.getElementById('2405.10995v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.10995v2-abstract-full" style="display: none;"> Exploring the missing values is an essential but challenging issue due to the complex latent spatio-temporal correlation and dynamic nature of time series. Owing to the outstanding performance in dealing with structure learning potentials, Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) are often used to capture such complex spatio-temporal features in multivariate time series. However, these data-driven models often fail to capture the essential spatio-temporal relationships when significant signal corruption occurs. Additionally, calculating the high-order neighbor nodes in these models is of high computational complexity. To address these problems, we propose a novel higher-order spatio-temporal physics-incorporated GNN (HSPGNN). Firstly, the dynamic Laplacian matrix can be obtained by the spatial attention mechanism. Then, the generic inhomogeneous partial differential equation (PDE) of physical dynamic systems is used to construct the dynamic higher-order spatio-temporal GNN to obtain the missing time series values. Moreover, we estimate the missing impact by Normalizing Flows (NF) to evaluate the importance of each node in the graph for better explainability. Experimental results on four benchmark datasets demonstrate the effectiveness of HSPGNN and the superior performance when combining various order neighbor nodes. Also, graph-like optical flow, dynamic graphs, and missing impact can be obtained naturally by HSPGNN, which provides better dynamic analysis and explanation than traditional data-driven models. Our code is available at https://github.com/gorgen2020/HSPGNN. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10995v2-abstract-full').style.display = 'none'; document.getElementById('2405.10995v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 7 figures, CIKM 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.08460">arXiv:2405.08460</a> <span> [<a href="https://arxiv.org/pdf/2405.08460">pdf</a>, <a href="https://arxiv.org/format/2405.08460">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Is Your LLM Outdated? Evaluating LLMs at Temporal Generalization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhu%2C+C">Chenghao Zhu</a>, <a href="/search/?searchtype=author&query=Chen%2C+N">Nuo Chen</a>, <a href="/search/?searchtype=author&query=Gao%2C+Y">Yufei Gao</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yunyi Zhang</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Benyou Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.08460v2-abstract-short" style="display: inline;"> The rapid advancement of Large Language Models (LLMs) highlights the urgent need for evolving evaluation methodologies that keep pace with improvements in language comprehension and information processing. However, traditional benchmarks, which are often static, fail to capture the continually changing information landscape, leading to a disparity between the perceived and actual effectiveness of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.08460v2-abstract-full').style.display = 'inline'; document.getElementById('2405.08460v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.08460v2-abstract-full" style="display: none;"> The rapid advancement of Large Language Models (LLMs) highlights the urgent need for evolving evaluation methodologies that keep pace with improvements in language comprehension and information processing. However, traditional benchmarks, which are often static, fail to capture the continually changing information landscape, leading to a disparity between the perceived and actual effectiveness of LLMs in ever-changing real-world scenarios. Our study examines temporal generalization, which includes the ability to understand, predict, and generate text relevant to past, present, and future contexts, revealing significant temporal biases in LLMs. We propose an evaluation framework, for dynamically generating benchmarks from recent real-world predictions. Experiments demonstrate that LLMs struggle with temporal generalization, showing performance decline over time. These findings highlight the necessity for improved training and updating processes to enhance adaptability and reduce biases. Our code, dataset and benchmark are available at https://github.com/FreedomIntelligence/FreshBench. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.08460v2-abstract-full').style.display = 'none'; document.getElementById('2405.08460v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Preprint</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.08163">arXiv:2405.08163</a> <span> [<a href="https://arxiv.org/pdf/2405.08163">pdf</a>, <a href="https://arxiv.org/format/2405.08163">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Relativity and Quantum Cosmology">gr-qc</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Nuclear Theory">nucl-th</span> </div> </div> <p class="title is-5 mathjax"> Deep TOV to characterize Neutron Stars </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Tiwari%2C+P">Praveer Tiwari</a>, <a href="/search/?searchtype=author&query=Pai%2C+A">Archana Pai</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.08163v1-abstract-short" style="display: inline;"> Astrophysical observations, theoretical models, and terrestrial experiments probe different regions of neutron star (NS) interior. Therefore, it is essential to consistently combine the information from these sources. This analysis requires multiple evaluations of Tolman Oppenheimer Volkoff equations which can become computationally expensive with a large number of observations. Further, multi-mes… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.08163v1-abstract-full').style.display = 'inline'; document.getElementById('2405.08163v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.08163v1-abstract-full" style="display: none;"> Astrophysical observations, theoretical models, and terrestrial experiments probe different regions of neutron star (NS) interior. Therefore, it is essential to consistently combine the information from these sources. This analysis requires multiple evaluations of Tolman Oppenheimer Volkoff equations which can become computationally expensive with a large number of observations. Further, multi-messenger astronomy requires rapid NS characterization via gravitational waves for efficient electromagnetic follow-up. In this work, we develop a novel neural network-based map from the EoS curve to the mass and radius of cold non-rotating NS. We estimate a speed-up of an order of magnitude when compared with the state-of-the-art RePrimAnd solver and an average error of 1e-3 when calculating the mass and radius of the neutron star. Additionally, we also develop neural network solvers for obtaining EoS curves from a physics conforming EoS model, FRZ$蠂_{1.5}$. We utilize this efficient continuous map to measure the sensitivity of model parameters of FRZ$蠂_{1.5}$ towards mass and radius. We show that 8 out of 18 parameters of this model are sensitive by at least three orders of magnitude higher than the remaining 10 parameters. This information will be useful in further speeding up, as well as probing the crucial parameter space, in the parameter estimation from astrophysical observations using this physics-conforming EoS model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.08163v1-abstract-full').style.display = 'none'; document.getElementById('2405.08163v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LIGO DCC number LIGO-P2400158 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.04629">arXiv:2405.04629</a> <span> [<a href="https://arxiv.org/pdf/2405.04629">pdf</a>, <a href="https://arxiv.org/format/2405.04629">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> </div> </div> <p class="title is-5 mathjax"> ResNCT: A Deep Learning Model for the Synthesis of Nephrographic Phase Images in CT Urography </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Gardezi%2C+S+J+S">Syed Jamal Safdar Gardezi</a>, <a href="/search/?searchtype=author&query=Aronson%2C+L">Lucas Aronson</a>, <a href="/search/?searchtype=author&query=Wawrzyn%2C+P">Peter Wawrzyn</a>, <a href="/search/?searchtype=author&query=Yu%2C+H">Hongkun Yu</a>, <a href="/search/?searchtype=author&query=Abel%2C+E+J">E. Jason Abel</a>, <a href="/search/?searchtype=author&query=Shapiro%2C+D+D">Daniel D. Shapiro</a>, <a href="/search/?searchtype=author&query=Lubner%2C+M+G">Meghan G. Lubner</a>, <a href="/search/?searchtype=author&query=Warner%2C+J">Joshua Warner</a>, <a href="/search/?searchtype=author&query=Toia%2C+G">Giuseppe Toia</a>, <a href="/search/?searchtype=author&query=Mao%2C+L">Lu Mao</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Pallavi Tiwari</a>, <a href="/search/?searchtype=author&query=Wentland%2C+A+L">Andrew L. Wentland</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.04629v2-abstract-short" style="display: inline;"> Purpose: To develop and evaluate a transformer-based deep learning model for the synthesis of nephrographic phase images in CT urography (CTU) examinations from the unenhanced and urographic phases. Materials and Methods: This retrospective study was approved by the local Institutional Review Board. A dataset of 119 patients (mean $\pm$ SD age, 65 $\pm$ 12 years; 75/44 males/females) with three-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.04629v2-abstract-full').style.display = 'inline'; document.getElementById('2405.04629v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.04629v2-abstract-full" style="display: none;"> Purpose: To develop and evaluate a transformer-based deep learning model for the synthesis of nephrographic phase images in CT urography (CTU) examinations from the unenhanced and urographic phases. Materials and Methods: This retrospective study was approved by the local Institutional Review Board. A dataset of 119 patients (mean $\pm$ SD age, 65 $\pm$ 12 years; 75/44 males/females) with three-phase CT urography studies was curated for deep learning model development. The three phases for each patient were aligned with an affine registration algorithm. A custom model, coined Residual transformer model for Nephrographic phase CT image synthesis (ResNCT), was developed and implemented with paired inputs of non-contrast and urographic sets of images trained to produce the nephrographic phase images, that were compared with the corresponding ground truth nephrographic phase images. The synthesized images were evaluated with multiple performance metrics, including peak signal to noise ratio (PSNR), structural similarity index (SSIM), normalized cross correlation coefficient (NCC), mean absolute error (MAE), and root mean squared error (RMSE). Results: The ResNCT model successfully generated synthetic nephrographic images from non-contrast and urographic image inputs. With respect to ground truth nephrographic phase images, the images synthesized by the model achieved high PSNR (27.8 $\pm$ 2.7 dB), SSIM (0.88 $\pm$ 0.05), and NCC (0.98 $\pm$ 0.02), and low MAE (0.02 $\pm$ 0.005) and RMSE (0.042 $\pm$ 0.016). Conclusion: The ResNCT model synthesized nephrographic phase CT images with high similarity to ground truth images. The ResNCT model provides a means of eliminating the acquisition of the nephrographic phase with a resultant 33% reduction in radiation dose for CTU examinations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.04629v2-abstract-full').style.display = 'none'; document.getElementById('2405.04629v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 5 Figures,2 Tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> eess.IV <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> J.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.15908">arXiv:2404.15908</a> <span> [<a href="https://arxiv.org/pdf/2404.15908">pdf</a>, <a href="https://arxiv.org/format/2404.15908">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1103/PhysRevLett.133.203605">10.1103/PhysRevLett.133.203605 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A hybrid source of quantum light for generation of frequency tunable Fock states </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Krsti%C4%87%2C+A">Aleksa Krsti膰</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Priyanshu Tiwari</a>, <a href="/search/?searchtype=author&query=H%C3%B6he%2C+F">Florian H枚he</a>, <a href="/search/?searchtype=author&query=Setzpfandt%2C+F">Frank Setzpfandt</a>, <a href="/search/?searchtype=author&query=Peschel%2C+U">Ulf Peschel</a>, <a href="/search/?searchtype=author&query=Ankerhold%2C+J">Joachim Ankerhold</a>, <a href="/search/?searchtype=author&query=Saravi%2C+S">Sina Saravi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.15908v2-abstract-short" style="display: inline;"> We propose a scheme for quantum-light generation in a nonlinear cavity hybridized with a 2-level system and theoretically show that, when excited by a series of controlled pump pulses, the hybrid source generates Fock states with high probabilities. E.g., 1- and 2-photon states can be generated near-on-demand, and Fock states with up to $7$ photons with a probability above $50\%$. The tailorable n… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.15908v2-abstract-full').style.display = 'inline'; document.getElementById('2404.15908v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.15908v2-abstract-full" style="display: none;"> We propose a scheme for quantum-light generation in a nonlinear cavity hybridized with a 2-level system and theoretically show that, when excited by a series of controlled pump pulses, the hybrid source generates Fock states with high probabilities. E.g., 1- and 2-photon states can be generated near-on-demand, and Fock states with up to $7$ photons with a probability above $50\%$. The tailorable nature of the nonlinear cavity allows for generating Fock states with arbitrary frequencies, even with a fixed 2-level system, creating fundamentally new opportunities in all areas of quantum technologies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.15908v2-abstract-full').style.display = 'none'; document.getElementById('2404.15908v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.05723">arXiv:2404.05723</a> <span> [<a href="https://arxiv.org/pdf/2404.05723">pdf</a>, <a href="https://arxiv.org/ps/2404.05723">ps</a>, <a href="https://arxiv.org/format/2404.05723">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Predicting Overtakes in Trucks Using CAN Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Butt%2C+T+H">Talha Hanif Butt</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Alonso-Fernandez%2C+F">Fernando Alonso-Fernandez</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.05723v1-abstract-short" style="display: inline;"> Safe overtakes in trucks are crucial to prevent accidents, reduce congestion, and ensure efficient traffic flow, making early prediction essential for timely and informed driving decisions. Accordingly, we investigate the detection of truck overtakes from CAN data. Three classifiers, Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM), are employed for the task. Our… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.05723v1-abstract-full').style.display = 'inline'; document.getElementById('2404.05723v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.05723v1-abstract-full" style="display: none;"> Safe overtakes in trucks are crucial to prevent accidents, reduce congestion, and ensure efficient traffic flow, making early prediction essential for timely and informed driving decisions. Accordingly, we investigate the detection of truck overtakes from CAN data. Three classifiers, Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM), are employed for the task. Our analysis covers up to 10 seconds before the overtaking event, using an overlapping sliding window of 1 second to extract CAN features. We observe that the prediction scores of the overtake class tend to increase as we approach the overtake trigger, while the no-overtake class remain stable or oscillates depending on the classifier. Thus, the best accuracy is achieved when approaching the trigger, making early overtaking prediction challenging. The classifiers show good accuracy in classifying overtakes (Recall/TPR > 93%), but accuracy is suboptimal in classifying no-overtakes (TNR typically 80-90% and below 60% for one SVM variant). We further combine two classifiers (Random Forest and linear SVM) by averaging their output scores. The fusion is observed to improve no-overtake classification (TNR > 92%) at the expense of reducing overtake accuracy (TPR). However, the latter is kept above 91% near the overtake trigger. Therefore, the fusion balances TPR and TNR, providing more consistent performance than individual classifiers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.05723v1-abstract-full').style.display = 'none'; document.getElementById('2404.05723v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.04248">arXiv:2404.04248</a> <span> [<a href="https://arxiv.org/pdf/2404.04248">pdf</a>, <a href="https://arxiv.org/format/2404.04248">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="General Relativity and Quantum Cosmology">gr-qc</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3847/2041-8213/ad5beb">10.3847/2041-8213/ad5beb <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Observation of Gravitational Waves from the Coalescence of a $2.5\text{-}4.5~M_\odot$ Compact Object and a Neutron Star </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=The+LIGO+Scientific+Collaboration"> The LIGO Scientific Collaboration</a>, <a href="/search/?searchtype=author&query=the+Virgo+Collaboration"> the Virgo Collaboration</a>, <a href="/search/?searchtype=author&query=the+KAGRA+Collaboration"> the KAGRA Collaboration</a>, <a href="/search/?searchtype=author&query=Abac%2C+A+G">A. G. Abac</a>, <a href="/search/?searchtype=author&query=Abbott%2C+R">R. Abbott</a>, <a href="/search/?searchtype=author&query=Abouelfettouh%2C+I">I. Abouelfettouh</a>, <a href="/search/?searchtype=author&query=Acernese%2C+F">F. Acernese</a>, <a href="/search/?searchtype=author&query=Ackley%2C+K">K. Ackley</a>, <a href="/search/?searchtype=author&query=Adhicary%2C+S">S. Adhicary</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+N">N. Adhikari</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+R+X">R. X. Adhikari</a>, <a href="/search/?searchtype=author&query=Adkins%2C+V+K">V. K. Adkins</a>, <a href="/search/?searchtype=author&query=Agarwal%2C+D">D. Agarwal</a>, <a href="/search/?searchtype=author&query=Agathos%2C+M">M. Agathos</a>, <a href="/search/?searchtype=author&query=Abchouyeh%2C+M+A">M. Aghaei Abchouyeh</a>, <a href="/search/?searchtype=author&query=Aguiar%2C+O+D">O. D. Aguiar</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+I">I. Aguilar</a>, <a href="/search/?searchtype=author&query=Aiello%2C+L">L. Aiello</a>, <a href="/search/?searchtype=author&query=Ain%2C+A">A. Ain</a>, <a href="/search/?searchtype=author&query=Ajith%2C+P">P. Ajith</a>, <a href="/search/?searchtype=author&query=Ak%C3%A7ay%2C+S">S. Ak莽ay</a>, <a href="/search/?searchtype=author&query=Akutsu%2C+T">T. Akutsu</a>, <a href="/search/?searchtype=author&query=Albanesi%2C+S">S. Albanesi</a>, <a href="/search/?searchtype=author&query=Alfaidi%2C+R+A">R. A. Alfaidi</a>, <a href="/search/?searchtype=author&query=Al-Jodah%2C+A">A. Al-Jodah</a> , et al. (1771 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.04248v3-abstract-short" style="display: inline;"> We report the observation of a coalescing compact binary with component masses $2.5\text{-}4.5~M_\odot$ and $1.2\text{-}2.0~M_\odot$ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO-Virgo-KAGRA detector network on 2023 May 29 by the LIGO Livingston Observatory. The primary component of the so… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.04248v3-abstract-full').style.display = 'inline'; document.getElementById('2404.04248v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.04248v3-abstract-full" style="display: none;"> We report the observation of a coalescing compact binary with component masses $2.5\text{-}4.5~M_\odot$ and $1.2\text{-}2.0~M_\odot$ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO-Virgo-KAGRA detector network on 2023 May 29 by the LIGO Livingston Observatory. The primary component of the source has a mass less than $5~M_\odot$ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of $55^{+127}_{-47}~\text{Gpc}^{-3}\,\text{yr}^{-1}$ for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star-black hole merger, GW230529_181500-like sources constitute about 60% of the total merger rate inferred for neutron star-black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star-black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.04248v3-abstract-full').style.display = 'none'; document.getElementById('2404.04248v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">45 pages (10 pages author list, 13 pages main text, 1 page acknowledgements, 13 pages appendices, 8 pages bibliography), 17 figures, 16 tables. Update to match version published in The Astrophysical Journal Letters. Data products available from https://zenodo.org/records/10845779</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LIGO-P2300352 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> ApJL 970, L34 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.07470">arXiv:2402.07470</a> <span> [<a href="https://arxiv.org/pdf/2402.07470">pdf</a>, <a href="https://arxiv.org/format/2402.07470">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Pushing The Limit of LLM Capacity for Text Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yazhou Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+M">Mengyao Wang</a>, <a href="/search/?searchtype=author&query=Ren%2C+C">Chenyu Ren</a>, <a href="/search/?searchtype=author&query=Li%2C+Q">Qiuchi Li</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Benyou Wang</a>, <a href="/search/?searchtype=author&query=Qin%2C+J">Jing Qin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.07470v2-abstract-short" style="display: inline;"> The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) across numerous downstream NLP tasks. In this era of open-ended language modeling, where task boundaries are gradually fading, an urgent question emerges: have we made significant advances in text classification under the fu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.07470v2-abstract-full').style.display = 'inline'; document.getElementById('2402.07470v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.07470v2-abstract-full" style="display: none;"> The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) across numerous downstream NLP tasks. In this era of open-ended language modeling, where task boundaries are gradually fading, an urgent question emerges: have we made significant advances in text classification under the full benefit of LLMs? To answer this question, we propose RGPT, an adaptive boosting framework tailored to produce a specialized text classification LLM by recurrently ensembling a pool of strong base learners. The base learners are constructed by adaptively adjusting the distribution of training samples and iteratively fine-tuning LLMs with them. Such base learners are then ensembled to be a specialized text classification LLM, by recurrently incorporating the historical predictions from the previous learners. Through a comprehensive empirical comparison, we show that RGPT significantly outperforms 8 SOTA PLMs and 7 SOTA LLMs on four benchmarks by 1.36% on average. Further evaluation experiments show a clear surpassing of RGPT over human classification. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.07470v2-abstract-full').style.display = 'none'; document.getElementById('2402.07470v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.11985">arXiv:2312.11985</a> <span> [<a href="https://arxiv.org/pdf/2312.11985">pdf</a>, <a href="https://arxiv.org/format/2312.11985">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Climate Change from Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhu%2C+H">Hongyin Zhu</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.11985v3-abstract-short" style="display: inline;"> Climate change poses grave challenges, demanding widespread understanding and low-carbon lifestyle awareness. Large language models (LLMs) offer a powerful tool to address this crisis, yet comprehensive evaluations of their climate-crisis knowledge are lacking. This paper proposes an automated evaluation framework to assess climate-crisis knowledge within LLMs. We adopt a hybrid approach for data… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.11985v3-abstract-full').style.display = 'inline'; document.getElementById('2312.11985v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.11985v3-abstract-full" style="display: none;"> Climate change poses grave challenges, demanding widespread understanding and low-carbon lifestyle awareness. Large language models (LLMs) offer a powerful tool to address this crisis, yet comprehensive evaluations of their climate-crisis knowledge are lacking. This paper proposes an automated evaluation framework to assess climate-crisis knowledge within LLMs. We adopt a hybrid approach for data acquisition, combining data synthesis and manual collection, to compile a diverse set of questions encompassing various aspects of climate change. Utilizing prompt engineering based on the compiled questions, we evaluate the model's knowledge by analyzing its generated answers. Furthermore, we introduce a comprehensive set of metrics to assess climate-crisis knowledge, encompassing indicators from 10 distinct perspectives. These metrics provide a multifaceted evaluation, enabling a nuanced understanding of the LLMs' climate crisis comprehension. The experimental results demonstrate the efficacy of our proposed method. In our evaluation utilizing diverse high-performing LLMs, we discovered that while LLMs possess considerable climate-related knowledge, there are shortcomings in terms of timeliness, indicating a need for continuous updating and refinement of their climate-related content. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.11985v3-abstract-full').style.display = 'none'; document.getElementById('2312.11985v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.15787">arXiv:2311.15787</a> <span> [<a href="https://arxiv.org/pdf/2311.15787">pdf</a>, <a href="https://arxiv.org/format/2311.15787">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> </div> </div> <p class="title is-5 mathjax"> Topological Thermal Hall Conductance of Even Denominator Fractional States </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Paul%2C+A+K">Arup Kumar Paul</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Priya Tiwari</a>, <a href="/search/?searchtype=author&query=Melcer%2C+R">Ron Melcer</a>, <a href="/search/?searchtype=author&query=Umansky%2C+V">Vladimir Umansky</a>, <a href="/search/?searchtype=author&query=Heiblum%2C+M">Moty Heiblum</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.15787v2-abstract-short" style="display: inline;"> The even denominator fractional quantum Hall (FQH) states $谓=5/2$ and $谓=7/2$ have been long predicted to host non-abelian quasiparticles (QPs). Their present energy-carrying neutral modes are hidden from customary conductance measurements and thus motivate thermal transport measurements, which are sensitive to all energy-carrying modes. While past `two-terminal' thermal conductance ($k_{2t}T$) me… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.15787v2-abstract-full').style.display = 'inline'; document.getElementById('2311.15787v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.15787v2-abstract-full" style="display: none;"> The even denominator fractional quantum Hall (FQH) states $谓=5/2$ and $谓=7/2$ have been long predicted to host non-abelian quasiparticles (QPs). Their present energy-carrying neutral modes are hidden from customary conductance measurements and thus motivate thermal transport measurements, which are sensitive to all energy-carrying modes. While past `two-terminal' thermal conductance ($k_{2t}T$) measurements already proved the non-Abelian nature of the $谓=5/2$ FQH state, they might have been prone to a lack of thermal equilibration among the counter-propagating edge modes. Here, we report a novel thermal Hall conductance measurement of the $谓=5/2$ and $谓=7/2$ states, being insensitive to equilibration among edge modes. We verify the state's non-Abelian nature, with both states supporting a single upstream Majorana edge mode (hence, a particle-hole Pfaffian order). While current numerical works predict a different topological order, this contribution should motivate further theoretical work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.15787v2-abstract-full').style.display = 'none'; document.getElementById('2311.15787v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.00603">arXiv:2311.00603</a> <span> [<a href="https://arxiv.org/pdf/2311.00603">pdf</a>, <a href="https://arxiv.org/format/2311.00603">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ning%2C+E">Enhao Ning</a>, <a href="/search/?searchtype=author&query=Wang%2C+C">Changshuo Wang</a>, <a href="/search/?searchtype=author&query=Zhangc%2C+H">Huang Zhangc</a>, <a href="/search/?searchtype=author&query=Ning%2C+X">Xin Ning</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.00603v1-abstract-short" style="display: inline;"> Person re-identification (Re-ID) technology plays an increasingly crucial role in intelligent surveillance systems. Widespread occlusion significantly impacts the performance of person Re-ID. Occluded person Re-ID refers to a pedestrian matching method that deals with challenges such as pedestrian information loss, noise interference, and perspective misalignment. It has garnered extensive attenti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00603v1-abstract-full').style.display = 'inline'; document.getElementById('2311.00603v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.00603v1-abstract-full" style="display: none;"> Person re-identification (Re-ID) technology plays an increasingly crucial role in intelligent surveillance systems. Widespread occlusion significantly impacts the performance of person Re-ID. Occluded person Re-ID refers to a pedestrian matching method that deals with challenges such as pedestrian information loss, noise interference, and perspective misalignment. It has garnered extensive attention from researchers. Over the past few years, several occlusion-solving person Re-ID methods have been proposed, tackling various sub-problems arising from occlusion. However, there is a lack of comprehensive studies that compare, summarize, and evaluate the potential of occluded person Re-ID methods in detail. In this review, we start by providing a detailed overview of the datasets and evaluation scheme used for occluded person Re-ID. Next, we scientifically classify and analyze existing deep learning-based occluded person Re-ID methods from various perspectives, summarizing them concisely. Furthermore, we conduct a systematic comparison among these methods, identify the state-of-the-art approaches, and present an outlook on the future development of occluded person Re-ID. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.00603v1-abstract-full').style.display = 'none'; document.getElementById('2311.00603v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.15189">arXiv:2310.15189</a> <span> [<a href="https://arxiv.org/pdf/2310.15189">pdf</a>, <a href="https://arxiv.org/format/2310.15189">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Towards Subject Agnostic Affective Emotion Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jaiswal%2C+A+K">Amit Kumar Jaiswal</a>, <a href="/search/?searchtype=author&query=Liu%2C+H">Haiming Liu</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.15189v1-abstract-short" style="display: inline;"> This paper focuses on affective emotion recognition, aiming to perform in the subject-agnostic paradigm based on EEG signals. However, EEG signals manifest subject instability in subject-agnostic affective Brain-computer interfaces (aBCIs), which led to the problem of distributional shift. Furthermore, this problem is alleviated by approaches such as domain generalisation and domain adaptation. Ty… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.15189v1-abstract-full').style.display = 'inline'; document.getElementById('2310.15189v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.15189v1-abstract-full" style="display: none;"> This paper focuses on affective emotion recognition, aiming to perform in the subject-agnostic paradigm based on EEG signals. However, EEG signals manifest subject instability in subject-agnostic affective Brain-computer interfaces (aBCIs), which led to the problem of distributional shift. Furthermore, this problem is alleviated by approaches such as domain generalisation and domain adaptation. Typically, methods based on domain adaptation confer comparatively better results than the domain generalisation methods but demand more computational resources given new subjects. We propose a novel framework, meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our domain adaptation approach is augmented through meta-learning, which consists of a recurrent neural network, a classifier, and a distributional shift controller based on a sum-decomposable function. Also, we present that a neural network explicating a sum-decomposable function can effectively estimate the divergence between varied domains. The network setting for augmented domain adaptation follows meta-learning and adversarial learning, where the controller promptly adapts to new domains employing the target data via a few self-adaptation steps in the test phase. Our proposed approach is shown to be effective in experiments on a public aBICs dataset and achieves similar performance to state-of-the-art domain adaptation methods while avoiding the use of additional computational resources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.15189v1-abstract-full').style.display = 'none'; document.getElementById('2310.15189v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To Appear in MUWS workshop at the 32nd ACM International Conference on Information and Knowledge Management (CIKM) 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.11374">arXiv:2310.11374</a> <span> [<a href="https://arxiv.org/pdf/2310.11374">pdf</a>, <a href="https://arxiv.org/format/2310.11374">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yazhou Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+M">Mengyao Wang</a>, <a href="/search/?searchtype=author&query=Wu%2C+Y">Youxi Wu</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Li%2C+Q">Qiuchi Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Benyou Wang</a>, <a href="/search/?searchtype=author&query=Qin%2C+J">Jing Qin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.11374v4-abstract-short" style="display: inline;"> Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks, which has presented a new vision for the development of NLP. Despite their remarkable performance in natural language generating (NLG), LLMs lack a distinct focus on the emotion understanding domain. As a result, using LLMs for emotion recognition ma… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.11374v4-abstract-full').style.display = 'inline'; document.getElementById('2310.11374v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.11374v4-abstract-full" style="display: none;"> Large language models (LLMs) and their variants have shown extraordinary efficacy across numerous downstream natural language processing (NLP) tasks, which has presented a new vision for the development of NLP. Despite their remarkable performance in natural language generating (NLG), LLMs lack a distinct focus on the emotion understanding domain. As a result, using LLMs for emotion recognition may lead to suboptimal and inadequate precision. Another limitation of LLMs is that they are typical trained without leveraging multi-modal information. To overcome these limitations, we propose DialogueLLM, a context and emotion knowledge tuned LLM that is obtained by fine-tuning LLaMA models with 13,638 multi-modal (i.e., texts and videos) emotional dialogues. The visual information is considered as the supplementary knowledge to construct high-quality instructions. We offer a comprehensive evaluation of our proposed model on three benchmarking emotion recognition in conversations (ERC) datasets and compare the results against the SOTA baselines and other SOTA LLMs. Additionally, DialogueLLM-7B can be easily trained using LoRA on a 40GB A100 GPU in 5 hours, facilitating reproducibility for other researchers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.11374v4-abstract-full').style.display = 'none'; document.getElementById('2310.11374v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.07642">arXiv:2310.07642</a> <span> [<a href="https://arxiv.org/pdf/2310.07642">pdf</a>, <a href="https://arxiv.org/format/2310.07642">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1051/0004-6361/202347728">10.1051/0004-6361/202347728 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Cosmology from LOFAR Two-metre Sky Survey Data Release 2: Cross-correlation with the cosmic microwave background </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Nakoneczny%2C+S+J">S. J. Nakoneczny</a>, <a href="/search/?searchtype=author&query=Alonso%2C+D">D. Alonso</a>, <a href="/search/?searchtype=author&query=Bilicki%2C+M">M. Bilicki</a>, <a href="/search/?searchtype=author&query=Schwarz%2C+D+J">D. J. Schwarz</a>, <a href="/search/?searchtype=author&query=Hale%2C+C+L">C. L. Hale</a>, <a href="/search/?searchtype=author&query=Pollo%2C+A">A. Pollo</a>, <a href="/search/?searchtype=author&query=Heneka%2C+C">C. Heneka</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">P. Tiwari</a>, <a href="/search/?searchtype=author&query=Zheng%2C+J">J. Zheng</a>, <a href="/search/?searchtype=author&query=Br%C3%BCggen%2C+M">M. Br眉ggen</a>, <a href="/search/?searchtype=author&query=Jarvis%2C+M+J">M. J. Jarvis</a>, <a href="/search/?searchtype=author&query=Shimwell%2C+T+W">T. W. Shimwell</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.07642v3-abstract-short" style="display: inline;"> We combine the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) second data release (DR2) catalogue with gravitational lensing maps from the Cosmic Microwave Background (CMB) to place constraints on the bias evolution of LoTSS radio galaxies, and on the amplitude of matter perturbations. We construct a flux-limited catalogue, and analyse its harmonic-space cross-correlation with CMB lensin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.07642v3-abstract-full').style.display = 'inline'; document.getElementById('2310.07642v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.07642v3-abstract-full" style="display: none;"> We combine the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) second data release (DR2) catalogue with gravitational lensing maps from the Cosmic Microwave Background (CMB) to place constraints on the bias evolution of LoTSS radio galaxies, and on the amplitude of matter perturbations. We construct a flux-limited catalogue, and analyse its harmonic-space cross-correlation with CMB lensing maps from Planck, $C_\ell^{g魏}$, as well as its auto-correlation, $C_\ell^{gg}$. We explore the models describing the redshift evolution of the large-scale radio galaxy bias, discriminating between them through the combination of both $C_\ell^{g魏}$ and $C_\ell^{gg}$. Fixing the bias evolution, we then use these data to place constraints on the amplitude of large scale density fluctuations. We report the significance of the $C_\ell^{g魏}$ signal at a level of $26.6蟽$. We determine that a linear bias evolution of the form $b_g(z) = b_{g,D} / D(z)$, where $D(z)$ is the growth rate, is able to provide a good description of the data, and measure $b_{g,D} = 1.41 \pm 0.06$ for a sample flux-limited at $1.5\,{\rm mJy}$, for scales $\ell < 250$ for $C_\ell^{gg}$, and $\ell < 500$ for $C_\ell^{g魏}$. At the sample's median redshift, we obtain $b(z = 0.82) = 2.34 \pm 0.10$. Using $蟽_8$ as a free parameter, while keeping other cosmological parameters fixed to the Planck values, we find fluctuations of $蟽_8 = 0.75^{+0.05}_{-0.04}$. The result is in agreement with weak lensing surveys, and at $1蟽$ difference with Planck CMB constraints. We also attempt to detect the late-time integrated Sachs-Wolfe effect with LOFAR, but with the current sky coverage, the cross-correlation with CMB temperature maps is consistent with zero. Our results are an important step towards constraining cosmology with radio continuum surveys from LOFAR and other future large radio surveys. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.07642v3-abstract-full').style.display = 'none'; document.getElementById('2310.07642v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">The code is available at https://github.com/snakoneczny/cosmo-pipe</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> A&A, 681, A105 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.07627">arXiv:2310.07627</a> <span> [<a href="https://arxiv.org/pdf/2310.07627">pdf</a>, <a href="https://arxiv.org/format/2310.07627">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</span> </div> </div> <p class="title is-5 mathjax"> Cosmology from LOFAR Two-metre Sky Survey Data Release 2: Angular Clustering of Radio Sources </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Hale%2C+C+L">C. L. Hale</a>, <a href="/search/?searchtype=author&query=Schwarz%2C+D+J">D. J. Schwarz</a>, <a href="/search/?searchtype=author&query=Best%2C+P+N">P. N. Best</a>, <a href="/search/?searchtype=author&query=Nakoneczny%2C+S+J">S. J. Nakoneczny</a>, <a href="/search/?searchtype=author&query=Alonso%2C+D">D. Alonso</a>, <a href="/search/?searchtype=author&query=Bacon%2C+D">D. Bacon</a>, <a href="/search/?searchtype=author&query=B%C3%B6hme%2C+L">L. B枚hme</a>, <a href="/search/?searchtype=author&query=Bhardwaj%2C+N">N. Bhardwaj</a>, <a href="/search/?searchtype=author&query=Bilicki%2C+M">M. Bilicki</a>, <a href="/search/?searchtype=author&query=Camera%2C+S">S. Camera</a>, <a href="/search/?searchtype=author&query=Heneka%2C+C+S">C. S. Heneka</a>, <a href="/search/?searchtype=author&query=Pashapour-Ahmadabadi%2C+M">M. Pashapour-Ahmadabadi</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">P. Tiwari</a>, <a href="/search/?searchtype=author&query=Zheng%2C+J">J. Zheng</a>, <a href="/search/?searchtype=author&query=Duncan%2C+K+J">K. J. Duncan</a>, <a href="/search/?searchtype=author&query=Jarvis%2C+M+J">M. J. Jarvis</a>, <a href="/search/?searchtype=author&query=Kondapally%2C+R">R. Kondapally</a>, <a href="/search/?searchtype=author&query=Magliocchetti%2C+M">M. Magliocchetti</a>, <a href="/search/?searchtype=author&query=Rottgering%2C+H+J+A">H. J. A. Rottgering</a>, <a href="/search/?searchtype=author&query=Shimwell%2C+T+W">T. W. Shimwell</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.07627v1-abstract-short" style="display: inline;"> Covering $\sim$5600 deg$^2$ to rms sensitivities of $\sim$70$-$100 $渭$Jy beam$^{-1}$, the LOFAR Two-metre Sky Survey Data Release 2 (LoTSS-DR2) provides the largest low-frequency ($\sim$150 MHz) radio catalogue to date, making it an excellent tool for large-area radio cosmology studies. In this work, we use LoTSS-DR2 sources to investigate the angular two-point correlation function of galaxies wit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.07627v1-abstract-full').style.display = 'inline'; document.getElementById('2310.07627v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.07627v1-abstract-full" style="display: none;"> Covering $\sim$5600 deg$^2$ to rms sensitivities of $\sim$70$-$100 $渭$Jy beam$^{-1}$, the LOFAR Two-metre Sky Survey Data Release 2 (LoTSS-DR2) provides the largest low-frequency ($\sim$150 MHz) radio catalogue to date, making it an excellent tool for large-area radio cosmology studies. In this work, we use LoTSS-DR2 sources to investigate the angular two-point correlation function of galaxies within the survey. We discuss systematics in the data and an improved methodology for generating random catalogues, compared to that used for LoTSS-DR1, before presenting the angular clustering for $\sim$900,000 sources $\geq$$1.5$ mJy and a peak signal-to-noise $\geq$$7.5$ across $\sim$$80\%$ of the observed area. Using the clustering we infer the bias assuming two evolutionary models. When fitting {angular scales of $0.5 \leq胃<5\,掳$, using a linear bias model, we find LoTSS-DR2 sources are biased tracers of the underlying matter, with a bias of $b_{C}= 2.14^{+0.22}_{-0.20}$ (assuming constant bias) and $b_{E}(z=0)= 1.79^{+0.15}_{-0.14}$ (for an evolving model, inversely proportional to the growth factor), corresponding to $b_E= 2.81^{+0.24}_{-0.22}$ at the median redshift of our sample, assuming the LoTSS Deep Fields redshift distribution is representative of our data. This reduces to $b_{C}= 2.02^{+0.17}_{-0.16}$ and $b_{E}(z=0)= 1.67^{+0.12}_{-0.12}$ when allowing preferential redshift distributions from the Deep Fields to model our data. Whilst the clustering amplitude is slightly lower than LoTSS-DR1 ($\geq$2 mJy), our study benefits from larger samples and improved redshift estimates. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.07627v1-abstract-full').style.display = 'none'; document.getElementById('2310.07627v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication in MNRAS. 29 pages, 24 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.10015">arXiv:2308.10015</a> <span> [<a href="https://arxiv.org/pdf/2308.10015">pdf</a>, <a href="https://arxiv.org/format/2308.10015">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> DyFFPAD: Dynamic Fusion of Convolutional and Handcrafted Features for Fingerprint Presentation Attack Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Rai%2C+A">Anuj Rai</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P+K">Parsheel Kumar Tiwari</a>, <a href="/search/?searchtype=author&query=Baishya%2C+J">Jyotishna Baishya</a>, <a href="/search/?searchtype=author&query=Sharma%2C+R+P">Ram Prakash Sharma</a>, <a href="/search/?searchtype=author&query=Dey%2C+S">Somnath Dey</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.10015v4-abstract-short" style="display: inline;"> Automatic fingerprint recognition systems suffer from the threat of presentation attacks due to their wide range of deployment in areas including national borders and commercial applications. A presentation attack can be performed by creating a spoof of a user's fingerprint with or without their consent. This paper presents a dynamic ensemble of deep CNN and handcrafted features to detect presenta… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.10015v4-abstract-full').style.display = 'inline'; document.getElementById('2308.10015v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.10015v4-abstract-full" style="display: none;"> Automatic fingerprint recognition systems suffer from the threat of presentation attacks due to their wide range of deployment in areas including national borders and commercial applications. A presentation attack can be performed by creating a spoof of a user's fingerprint with or without their consent. This paper presents a dynamic ensemble of deep CNN and handcrafted features to detect presentation attacks in known-material and unknown-material protocols of the liveness detection competition. The proposed presentation attack detection model, in this way, utilizes the capabilities of both deep CNN and handcrafted features techniques and exhibits better performance than their individual performances. We have validated our proposed method on benchmark databases from the Liveness Detection Competition in 2015, 2017, and 2019, yielding overall accuracy of 96.10\%, 96.49\%, and 94.99\% on them, respectively. The proposed method outperforms state-of-the-art methods in terms of classification accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.10015v4-abstract-full').style.display = 'none'; document.getElementById('2308.10015v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note:</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.15164">arXiv:2307.15164</a> <span> [<a href="https://arxiv.org/pdf/2307.15164">pdf</a>, <a href="https://arxiv.org/format/2307.15164">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Leveraging BERT and Stacked Embeddings </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Kumar%2C+V">Vivek Kumar</a>, <a href="/search/?searchtype=author&query=Singh%2C+S">Sushmita Singh</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.15164v1-abstract-short" style="display: inline;"> Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion Classification from essays written in reaction to news articles. Emotion detection from complex dialogues is challenging and often requires context/domain understanding. Therefore in this research, we have focused on developing deep learning (DL) models using the combination of word embedding representations with tailored… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.15164v1-abstract-full').style.display = 'inline'; document.getElementById('2307.15164v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.15164v1-abstract-full" style="display: none;"> Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion Classification from essays written in reaction to news articles. Emotion detection from complex dialogues is challenging and often requires context/domain understanding. Therefore in this research, we have focused on developing deep learning (DL) models using the combination of word embedding representations with tailored prepossessing strategies to capture the nuances of emotions expressed. Our experiments used static and contextual embeddings (individual and stacked) with Bidirectional Long short-term memory (BiLSTM) and Transformer based models. We occupied rank tenth in the emotion detection task by scoring a Macro F1-Score of 0.2717, validating the efficacy of our implemented approaches for small and imbalanced datasets with mixed categories of target emotions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.15164v1-abstract-full').style.display = 'none'; document.getElementById('2307.15164v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.13428">arXiv:2307.13428</a> <span> [<a href="https://arxiv.org/pdf/2307.13428">pdf</a>, <a href="https://arxiv.org/format/2307.13428">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Alonso-Fernandez%2C+F">Fernando Alonso-Fernandez</a>, <a href="/search/?searchtype=author&query=Hernandez-Diaz%2C+K">Kevin Hernandez-Diaz</a>, <a href="/search/?searchtype=author&query=Buades%2C+J+M">Jose M. Buades</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Bigun%2C+J">Josef Bigun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.13428v1-abstract-short" style="display: inline;"> This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to classification. However, in a verification setting,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.13428v1-abstract-full').style.display = 'inline'; document.getElementById('2307.13428v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.13428v1-abstract-full" style="display: none;"> This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to classification. However, in a verification setting, the classes to be recognized have not been seen during training. In addition, instead of using the softmax output, face descriptors are usually obtained from a layer before the classification layer. The model is adapted to achieve explainability via cosine similarity between feature vectors of perturbated versions of the input image. The method is showcased for face biometrics with two CNN models based on MobileNetv2 and ResNet50. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.13428v1-abstract-full').style.display = 'none'; document.getElementById('2307.13428v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.04386">arXiv:2306.04386</a> <span> [<a href="https://arxiv.org/pdf/2306.04386">pdf</a>, <a href="https://arxiv.org/format/2306.04386">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Nuclear Theory">nucl-th</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> </div> </div> <p class="title is-5 mathjax"> Framework for Multi-messenger Inference from Neutron Stars: Combining Nuclear Theory Priors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Tiwari%2C+P">Praveer Tiwari</a>, <a href="/search/?searchtype=author&query=Zhou%2C+D">Dake Zhou</a>, <a href="/search/?searchtype=author&query=Biswas%2C+B">Bhaskar Biswas</a>, <a href="/search/?searchtype=author&query=Forbes%2C+M+M">Michael McNeil Forbes</a>, <a href="/search/?searchtype=author&query=Bose%2C+S">Sukanta Bose</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.04386v2-abstract-short" style="display: inline;"> We construct an efficient parameterization of the pure neutron-matter equation of state (EoS) that incorporates the uncertainties from both chiral effective field theory ($蠂$EFT) and phenomenological potential calculations. This parameterization yields a family of EoSs including and extending the forms based purely on these two calculations. In combination with an agnostic inner core EoS, this par… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04386v2-abstract-full').style.display = 'inline'; document.getElementById('2306.04386v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.04386v2-abstract-full" style="display: none;"> We construct an efficient parameterization of the pure neutron-matter equation of state (EoS) that incorporates the uncertainties from both chiral effective field theory ($蠂$EFT) and phenomenological potential calculations. This parameterization yields a family of EoSs including and extending the forms based purely on these two calculations. In combination with an agnostic inner core EoS, this parameterization is used in a Bayesian inference pipeline to obtain constraints on the e os parameters using multi-messenger observations of neutron stars. We specifically considered observations of the massive pulsar J0740+6620, the binary neutron star coalescence GW170817, and the NICER pulsar J0030+0451. Constraints on neutron star mass-radius relations are obtained and compared. The Bayes factors for the different EoS models are also computed. While current constraints do not reveal any significant preference among these models, the framework developed here may enable future observations with more sensitive detectors to discriminate them. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04386v2-abstract-full').style.display = 'none'; document.getElementById('2306.04386v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LIGO-P2300061 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.00559">arXiv:2306.00559</a> <span> [<a href="https://arxiv.org/pdf/2306.00559">pdf</a>, <a href="https://arxiv.org/format/2306.00559">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> We never go out of Style: Motion Disentanglement by Subspace Decomposition of Latent Space </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Parihar%2C+R">Rishubh Parihar</a>, <a href="/search/?searchtype=author&query=Magazine%2C+R">Raghav Magazine</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Piyush Tiwari</a>, <a href="/search/?searchtype=author&query=Babu%2C+R+V">R. Venkatesh Babu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.00559v1-abstract-short" style="display: inline;"> Real-world objects perform complex motions that involve multiple independent motion components. For example, while talking, a person continuously changes their expressions, head, and body pose. In this work, we propose a novel method to decompose motion in videos by using a pretrained image GAN model. We discover disentangled motion subspaces in the latent space of widely used style-based GAN mode… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.00559v1-abstract-full').style.display = 'inline'; document.getElementById('2306.00559v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.00559v1-abstract-full" style="display: none;"> Real-world objects perform complex motions that involve multiple independent motion components. For example, while talking, a person continuously changes their expressions, head, and body pose. In this work, we propose a novel method to decompose motion in videos by using a pretrained image GAN model. We discover disentangled motion subspaces in the latent space of widely used style-based GAN models that are semantically meaningful and control a single explainable motion component. The proposed method uses only a few $(\approx10)$ ground truth video sequences to obtain such subspaces. We extensively evaluate the disentanglement properties of motion subspaces on face and car datasets, quantitatively and qualitatively. Further, we present results for multiple downstream tasks such as motion editing, and selective motion transfer, e.g. transferring only facial expressions without training for it. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.00559v1-abstract-full').style.display = 'none'; document.getElementById('2306.00559v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">AI for content creation, CVPRW-2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.14450">arXiv:2305.14450</a> <span> [<a href="https://arxiv.org/pdf/2305.14450">pdf</a>, <a href="https://arxiv.org/format/2305.14450">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> An Empirical Study on Information Extraction using Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Han%2C+R">Ridong Han</a>, <a href="/search/?searchtype=author&query=Yang%2C+C">Chaohao Yang</a>, <a href="/search/?searchtype=author&query=Peng%2C+T">Tao Peng</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Wan%2C+X">Xiang Wan</a>, <a href="/search/?searchtype=author&query=Liu%2C+L">Lu Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Benyou Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.14450v2-abstract-short" style="display: inline;"> Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.14450v2-abstract-full').style.display = 'inline'; document.getElementById('2305.14450v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.14450v2-abstract-full" style="display: none;"> Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstrate the latest representative progress in LLMs' information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types. Our results suggest a visible performance gap between GPT-4 and state-of-the-art (SOTA) IE methods. To alleviate this problem, considering the LLMs' human-like characteristics, we propose and analyze the effects of a series of simple prompt-based methods, which can be generalized to other LLMs and NLP tasks. Rich experiments show our methods' effectiveness and some of their remaining issues in improving GPT-4's information extraction ability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.14450v2-abstract-full').style.display = 'none'; document.getElementById('2305.14450v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">52 pages, Version 2.0; This article has an original arxiv version entitled "Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors'', whose url link is arXiv:2305.14450v1</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.09703">arXiv:2305.09703</a> <span> [<a href="https://arxiv.org/pdf/2305.09703">pdf</a>, <a href="https://arxiv.org/format/2305.09703">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liang%2C+G">Guojun Liang</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prayag Tiwari</a>, <a href="/search/?searchtype=author&query=Nowaczyk%2C+S">S艂awomir Nowaczyk</a>, <a href="/search/?searchtype=author&query=Byttner%2C+S">Stefan Byttner</a>, <a href="/search/?searchtype=author&query=Alonso-Fernandez%2C+F">Fernando Alonso-Fernandez</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.09703v1-abstract-short" style="display: inline;"> Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot in spatio-temporal forecasting problems. While many dynamic graph construction methods have been developed, relatively few of them explore the causal relationship between neighbour nodes. Thus, the resulting models lack strong explainability for the causal relationship between the neighbour nodes of the dynamical… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.09703v1-abstract-full').style.display = 'inline'; document.getElementById('2305.09703v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.09703v1-abstract-full" style="display: none;"> Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot in spatio-temporal forecasting problems. While many dynamic graph construction methods have been developed, relatively few of them explore the causal relationship between neighbour nodes. Thus, the resulting models lack strong explainability for the causal relationship between the neighbour nodes of the dynamically generated graphs, which can easily lead to a risk in subsequent decisions. Moreover, few of them consider the uncertainty and noise of dynamic graphs based on the time series datasets, which are ubiquitous in real-world graph structure networks. In this paper, we propose a novel Dynamic Diffusion-Variational Graph Neural Network (DVGNN) for spatio-temporal forecasting. For dynamic graph construction, an unsupervised generative model is devised. Two layers of graph convolutional network (GCN) are applied to calculate the posterior distribution of the latent node embeddings in the encoder stage. Then, a diffusion model is used to infer the dynamic link probability and reconstruct causal graphs in the decoder stage adaptively. The new loss function is derived theoretically, and the reparameterization trick is adopted in estimating the probability distribution of the dynamic graphs by Evidence Lower Bound during the backpropagation period. After obtaining the generated graphs, dynamic GCN and temporal attention are applied to predict future states. Experiments are conducted on four real-world datasets of different graph structures in different domains. The results demonstrate that the proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result while exhibiting higher robustness. Also, by F1-score and probability distribution analysis, we demonstrate that DVGNN better reflects the causal relationship and uncertainty of dynamic graphs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.09703v1-abstract-full').style.display = 'none'; document.getElementById('2305.09703v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.03272">arXiv:2305.03272</a> <span> [<a href="https://arxiv.org/pdf/2305.03272">pdf</a>, <a href="https://arxiv.org/format/2305.03272">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Robust Model Predictive Techno-Economic Control of Active Distribution Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Maharjan%2C+S">Salish Maharjan</a>, <a href="/search/?searchtype=author&query=Tiwari%2C+P">Prashant Tiwari</a>, <a href="/search/?searchtype=author&query=Cheng%2C+R">Rui Cheng</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zhaoyu Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.03272v1-abstract-short" style="display: inline;"> Stochastic controllers are perceived as a promising solution for techno-economic operation of distribution networks having higher generation uncertainties at large penetration of renewables. These controllers are supported by forecasters capable of predicting generation uncertainty by means of lower/upper bounds rather than by probability density function (PDF). Hence, the stochastic controller as… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.03272v1-abstract-full').style.display = 'inline'; document.getElementById('2305.03272v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.03272v1-abstract-full" style="display: none;"> Stochastic controllers are perceived as a promising solution for techno-economic operation of distribution networks having higher generation uncertainties at large penetration of renewables. These controllers are supported by forecasters capable of predicting generation uncertainty by means of lower/upper bounds rather than by probability density function (PDF). Hence, the stochastic controller assumes a suitable PDF for scenario creation and optimization, requiring validation of the assumption. To effectively bridge the forecaster's capability and resolve the assumption issues, the paper proposes a robust model prediction-based techno-economic controller, which essentially utilizes only the lower/upper bounds of the forecast, eliminating the necessity of PDF. Both discrete and continuous control resources such as tap-changers and DERs are utilized for regulating the lower/upper bounds of the network states and robustly minimizing the cost of energy import. The proposed controller is implemented for UKGDS network and validated by comparing performance at various confidence levels of lower/upper bound forecast. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.03272v1-abstract-full').style.display = 'none'; document.getElementById('2305.03272v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to PESGM 2023</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Tiwari%2C+P&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Tiwari%2C+P&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Tiwari%2C+P&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Tiwari%2C+P&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Tiwari%2C+P&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> </ul> </nav> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

CINXE.COM

Search | arXiv e-print repository