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 8,684 results for author: <span class="mathjax">Li, S</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="Li, S"> </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=Li%2C+S&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="Li, S"> <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=Li%2C+S&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Li%2C+S&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+S&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+S&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+S&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+S&start=200" class="pagination-link " aria-label="Page 5" aria-current="page">5 </a> </li> <li><span class="pagination-ellipsis">…</span></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/2502.10291">arXiv:2502.10291</a> <span> [<a href="https://arxiv.org/pdf/2502.10291">pdf</a>, <a href="https://arxiv.org/format/2502.10291">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 Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Angular analysis of $B^0\rightarrow K^{*0}e^{+}e^{-}$ decays </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=LHCb+collaboration"> LHCb collaboration</a>, <a href="/search/?searchtype=author&query=Aaij%2C+R">R. Aaij</a>, <a href="/search/?searchtype=author&query=Abdelmotteleb%2C+A+S+W">A. S. W. Abdelmotteleb</a>, <a href="/search/?searchtype=author&query=Beteta%2C+C+A">C. Abellan Beteta</a>, <a href="/search/?searchtype=author&query=Abudin%C3%A9n%2C+F">F. Abudin茅n</a>, <a href="/search/?searchtype=author&query=Ackernley%2C+T">T. Ackernley</a>, <a href="/search/?searchtype=author&query=Adefisoye%2C+A+A">A. A. Adefisoye</a>, <a href="/search/?searchtype=author&query=Adeva%2C+B">B. Adeva</a>, <a href="/search/?searchtype=author&query=Adinolfi%2C+M">M. Adinolfi</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Agapopoulou%2C+C">C. Agapopoulou</a>, <a href="/search/?searchtype=author&query=Aidala%2C+C+A">C. A. Aidala</a>, <a href="/search/?searchtype=author&query=Ajaltouni%2C+Z">Z. Ajaltouni</a>, <a href="/search/?searchtype=author&query=Akar%2C+S">S. Akar</a>, <a href="/search/?searchtype=author&query=Akiba%2C+K">K. Akiba</a>, <a href="/search/?searchtype=author&query=Albicocco%2C+P">P. Albicocco</a>, <a href="/search/?searchtype=author&query=Albrecht%2C+J">J. Albrecht</a>, <a href="/search/?searchtype=author&query=Alessio%2C+F">F. Alessio</a>, <a href="/search/?searchtype=author&query=Alexander%2C+M">M. Alexander</a>, <a href="/search/?searchtype=author&query=Aliouche%2C+Z">Z. Aliouche</a>, <a href="/search/?searchtype=author&query=Cartelle%2C+P+A">P. Alvarez Cartelle</a>, <a href="/search/?searchtype=author&query=Amalric%2C+R">R. Amalric</a>, <a href="/search/?searchtype=author&query=Amato%2C+S">S. Amato</a>, <a href="/search/?searchtype=author&query=Amey%2C+J+L">J. L. Amey</a>, <a href="/search/?searchtype=author&query=Amhis%2C+Y">Y. Amhis</a> , et al. (1115 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="2502.10291v1-abstract-short" style="display: inline;"> An angular analysis of $B^0\rightarrow K^{*0}e^{+}e^{-}$ decays is presented using proton-proton collision data collected by the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of 9 fb$^{-1}$. The analysis is performed in the region of the dilepton invariant mass squared of 1.1-6.0 GeV$^{2}/c^{4}$. In addition, a test of lepton flavour unive… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10291v1-abstract-full').style.display = 'inline'; document.getElementById('2502.10291v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.10291v1-abstract-full" style="display: none;"> An angular analysis of $B^0\rightarrow K^{*0}e^{+}e^{-}$ decays is presented using proton-proton collision data collected by the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of 9 fb$^{-1}$. The analysis is performed in the region of the dilepton invariant mass squared of 1.1-6.0 GeV$^{2}/c^{4}$. In addition, a test of lepton flavour universality is performed by comparing the obtained angular observables with those measured in $B^0\rightarrow K^{*0}渭^{+}渭^{-}$ decays. In general, the angular observables are found to be consistent with the Standard Model expectations as well as with global analyses of other $b \rightarrow s \ell^{+} \ell^{-}$ processes, where $\ell$ is either a muon or an electron. No sign of lepton-flavour-violating effects is observed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10291v1-abstract-full').style.display = 'none'; document.getElementById('2502.10291v1-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> 14 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">Comments:</span> <span class="has-text-grey-dark mathjax">All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1628/ (LHCb public pages)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LHCb-PAPER-2024-022, CERN-EP-2025-001 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09847">arXiv:2502.09847</a> <span> [<a href="https://arxiv.org/pdf/2502.09847">pdf</a>, <a href="https://arxiv.org/format/2502.09847">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Compressive Sensing Empirical Wavelet Transform for Frequency-Banded Power Measurement Considering Interharmonics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+J">Jian Liu</a>, <a href="/search/?searchtype=author&query=Zhao%2C+W">Wei Zhao</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shisong Li</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="2502.09847v1-abstract-short" style="display: inline;"> Power measurement algorithms based on Fourier transform are susceptible to errors caused by interharmonics, while wavelet transform algorithms are particularly sensitive to even harmonics due to band decomposition effects. The empirical wavelet transform (EWT) has been demonstrated to improve measurement accuracy by effectively partitioning transition bands. However, for detecting interharmonic co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09847v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09847v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09847v1-abstract-full" style="display: none;"> Power measurement algorithms based on Fourier transform are susceptible to errors caused by interharmonics, while wavelet transform algorithms are particularly sensitive to even harmonics due to band decomposition effects. The empirical wavelet transform (EWT) has been demonstrated to improve measurement accuracy by effectively partitioning transition bands. However, for detecting interharmonic components, the limitation of the observation time window restricts spectral resolution, thereby limiting measurement accuracy. To address this challenge, this paper proposes a Compressive Sensing Empirical Wavelet Transform (CSEWT). The approach aims to enhance frequency resolution by integrating compressive sensing with the EWT, allowing precise identification of components across different frequency bands. This enables accurate determination of the power associated with the fundamental frequency, harmonics, and interharmonics. Test results indicate that the proposed CSEWT method can significantly improve the precision of individual frequency component measurements, even under dynamic and noisy conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09847v1-abstract-full').style.display = 'none'; document.getElementById('2502.09847v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">10 figures, 12 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09838">arXiv:2502.09838</a> <span> [<a href="https://arxiv.org/pdf/2502.09838">pdf</a>, <a href="https://arxiv.org/format/2502.09838">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> </div> </div> <p class="title is-5 mathjax"> HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lin%2C+T">Tianwei Lin</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wenqiao Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Sijing Li</a>, <a href="/search/?searchtype=author&query=Yuan%2C+Y">Yuqian Yuan</a>, <a href="/search/?searchtype=author&query=Yu%2C+B">Binhe Yu</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Haoyuan Li</a>, <a href="/search/?searchtype=author&query=He%2C+W">Wanggui He</a>, <a href="/search/?searchtype=author&query=Jiang%2C+H">Hao Jiang</a>, <a href="/search/?searchtype=author&query=Li%2C+M">Mengze Li</a>, <a href="/search/?searchtype=author&query=Song%2C+X">Xiaohui Song</a>, <a href="/search/?searchtype=author&query=Tang%2C+S">Siliang Tang</a>, <a href="/search/?searchtype=author&query=Xiao%2C+J">Jun Xiao</a>, <a href="/search/?searchtype=author&query=Lin%2C+H">Hui Lin</a>, <a href="/search/?searchtype=author&query=Zhuang%2C+Y">Yueting Zhuang</a>, <a href="/search/?searchtype=author&query=Ooi%2C+B+C">Beng Chin Ooi</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="2502.09838v1-abstract-short" style="display: inline;"> We present HealthGPT, a powerful Medical Large Vision-Language Model (Med-LVLM) that integrates medical visual comprehension and generation capabilities within a unified autoregressive paradigm. Our bootstrapping philosophy is to progressively adapt heterogeneous comprehension and generation knowledge to pre-trained large language models (LLMs). This is achieved through a novel heterogeneous low-r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09838v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09838v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09838v1-abstract-full" style="display: none;"> We present HealthGPT, a powerful Medical Large Vision-Language Model (Med-LVLM) that integrates medical visual comprehension and generation capabilities within a unified autoregressive paradigm. Our bootstrapping philosophy is to progressively adapt heterogeneous comprehension and generation knowledge to pre-trained large language models (LLMs). This is achieved through a novel heterogeneous low-rank adaptation (H-LoRA) technique, which is complemented by a tailored hierarchical visual perception approach and a three-stage learning strategy. To effectively learn the HealthGPT, we devise a comprehensive medical domain-specific comprehension and generation dataset called VL-Health. Experimental results demonstrate exceptional performance and scalability of HealthGPT in medical visual unified tasks. Our project can be accessed at https://github.com/DCDmllm/HealthGPT. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09838v1-abstract-full').style.display = 'none'; document.getElementById('2502.09838v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09625">arXiv:2502.09625</a> <span> [<a href="https://arxiv.org/pdf/2502.09625">pdf</a>, <a href="https://arxiv.org/format/2502.09625">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Finance">q-fin.CP</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"> Transformer Based Time-Series Forecasting for Stock </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S">Shuozhe Li</a>, <a href="/search/?searchtype=author&query=Schulwol%2C+Z+B">Zachery B Schulwol</a>, <a href="/search/?searchtype=author&query=Miikkulainen%2C+R">Risto Miikkulainen</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="2502.09625v1-abstract-short" style="display: inline;"> To the naked eye, stock prices are considered chaotic, dynamic, and unpredictable. Indeed, it is one of the most difficult forecasting tasks that hundreds of millions of retail traders and professional traders around the world try to do every second even before the market opens. With recent advances in the development of machine learning and the amount of data the market generated over years, appl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09625v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09625v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09625v1-abstract-full" style="display: none;"> To the naked eye, stock prices are considered chaotic, dynamic, and unpredictable. Indeed, it is one of the most difficult forecasting tasks that hundreds of millions of retail traders and professional traders around the world try to do every second even before the market opens. With recent advances in the development of machine learning and the amount of data the market generated over years, applying machine learning techniques such as deep learning neural networks is unavoidable. In this work, we modeled the task as a multivariate forecasting problem, instead of a naive autoregression problem. The multivariate analysis is done using the attention mechanism via applying a mutated version of the Transformer, "Stockformer", which we created. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09625v1-abstract-full').style.display = 'none'; document.getElementById('2502.09625v1-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 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09604">arXiv:2502.09604</a> <span> [<a href="https://arxiv.org/pdf/2502.09604">pdf</a>, <a href="https://arxiv.org/format/2502.09604">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"> SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chuang%2C+Y">Yung-Sung Chuang</a>, <a href="/search/?searchtype=author&query=Cohen-Wang%2C+B">Benjamin Cohen-Wang</a>, <a href="/search/?searchtype=author&query=Shen%2C+S+Z">Shannon Zejiang Shen</a>, <a href="/search/?searchtype=author&query=Wu%2C+Z">Zhaofeng Wu</a>, <a href="/search/?searchtype=author&query=Xu%2C+H">Hu Xu</a>, <a href="/search/?searchtype=author&query=Lin%2C+X+V">Xi Victoria Lin</a>, <a href="/search/?searchtype=author&query=Glass%2C+J">James Glass</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shang-Wen Li</a>, <a href="/search/?searchtype=author&query=Yih%2C+W">Wen-tau Yih</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="2502.09604v1-abstract-short" style="display: inline;"> We introduce SelfCite, a novel self-supervised approach that aligns LLMs to generate high-quality, fine-grained, sentence-level citations for the statements in their generated responses. Instead of only relying on costly and labor-intensive annotations, SelfCite leverages a reward signal provided by the LLM itself through context ablation: If a citation is necessary, removing the cited text from t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09604v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09604v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09604v1-abstract-full" style="display: none;"> We introduce SelfCite, a novel self-supervised approach that aligns LLMs to generate high-quality, fine-grained, sentence-level citations for the statements in their generated responses. Instead of only relying on costly and labor-intensive annotations, SelfCite leverages a reward signal provided by the LLM itself through context ablation: If a citation is necessary, removing the cited text from the context should prevent the same response; if sufficient, retaining the cited text alone should preserve the same response. This reward can guide the inference-time best-of-N sampling strategy to improve citation quality significantly, as well as be used in preference optimization to directly fine-tune the models for generating better citations. The effectiveness of SelfCite is demonstrated by increasing citation F1 up to 5.3 points on the LongBench-Cite benchmark across five long-form question answering tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09604v1-abstract-full').style.display = 'none'; document.getElementById('2502.09604v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">Implementation available at https://github.com/voidism/SelfCite</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09439">arXiv:2502.09439</a> <span> [<a href="https://arxiv.org/pdf/2502.09439">pdf</a>, <a href="https://arxiv.org/format/2502.09439">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> The Third Generation of Nanogenerators: The Irreplaceable Potential Source Enabled by the Flexoelectric Nanogenerator </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S+R">Shang Ru Li</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Q+K">Qi Kang Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+X+X">Xiao Xiong 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="2502.09439v1-abstract-short" style="display: inline;"> The electroneutrality assumption has long been adopted by scholars; however, this assumption may lead to an oversight of certain physical effects. Using derivations from a discontinuous medium, we have obtained an expression for the potential and energy of a many-body unipolar charge system, which corresponds well to its counterpart in a continuous medium. The compressed form of this expression su… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09439v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09439v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09439v1-abstract-full" style="display: none;"> The electroneutrality assumption has long been adopted by scholars; however, this assumption may lead to an oversight of certain physical effects. Using derivations from a discontinuous medium, we have obtained an expression for the potential and energy of a many-body unipolar charge system, which corresponds well to its counterpart in a continuous medium. The compressed form of this expression suggests that compressing a macroscale charged body to the nanoscale can yield an enormous electric potential and energy, thereby establishing a concrete research framework for third-generation nanogenerators. This effect may serve as a crucial reference for understanding anomalous spatial electromagnetic distributions and divergent energy fields. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09439v1-abstract-full').style.display = 'none'; document.getElementById('2502.09439v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">No additional comments</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09346">arXiv:2502.09346</a> <span> [<a href="https://arxiv.org/pdf/2502.09346">pdf</a>, <a href="https://arxiv.org/format/2502.09346">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="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Fluid Dynamics">physics.flu-dyn</span> </div> </div> <p class="title is-5 mathjax"> Machine learning for modelling unstructured grid data in computational physics: a review </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cheng%2C+S">Sibo Cheng</a>, <a href="/search/?searchtype=author&query=Bocquet%2C+M">Marc Bocquet</a>, <a href="/search/?searchtype=author&query=Ding%2C+W">Weiping Ding</a>, <a href="/search/?searchtype=author&query=Finn%2C+T+S">Tobias Sebastian Finn</a>, <a href="/search/?searchtype=author&query=Fu%2C+R">Rui Fu</a>, <a href="/search/?searchtype=author&query=Fu%2C+J">Jinlong Fu</a>, <a href="/search/?searchtype=author&query=Guo%2C+Y">Yike Guo</a>, <a href="/search/?searchtype=author&query=Johnson%2C+E">Eleda Johnson</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Siyi Li</a>, <a href="/search/?searchtype=author&query=Liu%2C+C">Che Liu</a>, <a href="/search/?searchtype=author&query=Moro%2C+E+N">Eric Newton Moro</a>, <a href="/search/?searchtype=author&query=Pan%2C+J">Jie Pan</a>, <a href="/search/?searchtype=author&query=Piggott%2C+M">Matthew Piggott</a>, <a href="/search/?searchtype=author&query=Quilodran%2C+C">Cesar Quilodran</a>, <a href="/search/?searchtype=author&query=Sharma%2C+P">Prakhar Sharma</a>, <a href="/search/?searchtype=author&query=Wang%2C+K">Kun Wang</a>, <a href="/search/?searchtype=author&query=Xiao%2C+D">Dunhui Xiao</a>, <a href="/search/?searchtype=author&query=Xue%2C+X">Xiao Xue</a>, <a href="/search/?searchtype=author&query=Zeng%2C+Y">Yong Zeng</a>, <a href="/search/?searchtype=author&query=Zhang%2C+M">Mingrui Zhang</a>, <a href="/search/?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/?searchtype=author&query=Zhu%2C+K">Kewei Zhu</a>, <a href="/search/?searchtype=author&query=Arcucci%2C+R">Rossella Arcucci</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="2502.09346v1-abstract-short" style="display: inline;"> Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discuss… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09346v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09346v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09346v1-abstract-full" style="display: none;"> Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09346v1-abstract-full').style.display = 'none'; document.getElementById('2502.09346v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09280">arXiv:2502.09280</a> <span> [<a href="https://arxiv.org/pdf/2502.09280">pdf</a>, <a href="https://arxiv.org/format/2502.09280">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</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/10.1109/TIA.2025.3541007">10.1109/TIA.2025.3541007 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Adaptive Multi-Objective Bayesian Optimization for Capacity Planning of Hybrid Heat Sources in Electric-Heat Coupling Systems of Cold Regions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yang%2C+R">Ruizhe Yang</a>, <a href="/search/?searchtype=author&query=Yi%2C+Z">Zhongkai Yi</a>, <a href="/search/?searchtype=author&query=Xu%2C+Y">Ying Xu</a>, <a href="/search/?searchtype=author&query=Chen%2C+G">Guiyu Chen</a>, <a href="/search/?searchtype=author&query=Yang%2C+H">Haojie Yang</a>, <a href="/search/?searchtype=author&query=Yi%2C+R">Rong Yi</a>, <a href="/search/?searchtype=author&query=Li%2C+T">Tongqing Li</a>, <a href="/search/?searchtype=author&query=Li%2C+M+S">Miaozhe ShenJin Li</a>, <a href="/search/?searchtype=author&query=Gao%2C+H">Haoxiang Gao</a>, <a href="/search/?searchtype=author&query=Duan%2C+H">Hongyu Duan</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="2502.09280v1-abstract-short" style="display: inline;"> The traditional heat-load generation pattern of combined heat and power generators has become a problem leading to renewable energy source (RES) power curtailment in cold regions, motivating the proposal of a planning model for alternative heat sources. The model aims to identify non-dominant capacity allocation schemes for heat pumps, thermal energy storage, electric boilers, and combined storage… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09280v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09280v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09280v1-abstract-full" style="display: none;"> The traditional heat-load generation pattern of combined heat and power generators has become a problem leading to renewable energy source (RES) power curtailment in cold regions, motivating the proposal of a planning model for alternative heat sources. The model aims to identify non-dominant capacity allocation schemes for heat pumps, thermal energy storage, electric boilers, and combined storage heaters to construct a Pareto front, considering both economic and sustainable objectives. The integration of various heat sources from both generation and consumption sides enhances flexibility in utilization. The study introduces a novel optimization algorithm, the adaptive multi-objective Bayesian optimization (AMBO). Compared to other widely used multi-objective optimization algorithms, AMBO eliminates predefined parameters that may introduce subjectivity from planners. Beyond the algorithm, the proposed model incorporates a noise term to account for inevitable simulation deviations, enabling the identification of better-performing planning results that meet the unique requirements of cold regions. What's more, the characteristics of electric-thermal coupling scenarios are captured and reflected in the operation simulation model to make sure the simulation is close to reality. Numerical simulation verifies the superiority of the proposed approach in generating a more diverse and evenly distributed Pareto front in a sample-efficient manner, providing comprehensive and objective planning choices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09280v1-abstract-full').style.display = 'none'; document.getElementById('2502.09280v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 11 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Transactions on Industry Applications 2025 ( Early Access ) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.09071">arXiv:2502.09071</a> <span> [<a href="https://arxiv.org/pdf/2502.09071">pdf</a>, <a href="https://arxiv.org/format/2502.09071">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"> Foreground Removal in Ground-Based CMB Observations Using a Transformer Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yan%2C+Y">Ye-Peng Yan</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Si-Yu Li</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/?searchtype=author&query=Xia%2C+J">Jun-Qing Xia</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Hong Li</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="2502.09071v1-abstract-short" style="display: inline;"> We present a novel method for Cosmic Microwave Background (CMB) foreground removal based on deep learning techniques. This method employs a Transformer model, referred to as \texttt{TCMB}, which is specifically designed to effectively process HEALPix-format spherical sky maps. \texttt{TCMB} represents an innovative application in CMB data analysis, as it is an image-based technique that has rarely… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09071v1-abstract-full').style.display = 'inline'; document.getElementById('2502.09071v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.09071v1-abstract-full" style="display: none;"> We present a novel method for Cosmic Microwave Background (CMB) foreground removal based on deep learning techniques. This method employs a Transformer model, referred to as \texttt{TCMB}, which is specifically designed to effectively process HEALPix-format spherical sky maps. \texttt{TCMB} represents an innovative application in CMB data analysis, as it is an image-based technique that has rarely been utilized in this field. Using simulated data with noise levels representative of current ground-based CMB polarization observations, the \texttt{TCMB} method demonstrates robust performance in removing foreground contamination. The mean absolute variance for the reconstruction of the noisy CMB Q/U map is significantly less than the CMB polarization signal. To mitigate biases caused by instrumental noise, a cross-correlation approach using two half-mission maps was employed, successfully recovering CMB EE and BB power spectra that align closely with the true values, and these results validate the effectiveness of the \texttt{TCMB} method. Compared to the previously employed convolutional neural network (CNN)-based approach, the \texttt{TCMB} method offers two significant advantages: (1) It demonstrates superior effectiveness in reconstructing CMB polarization maps, outperforming CNN-based methods. (2) It can directly process HEALPix spherical sky maps without requiring rectangular region division, a step necessary for CNN-based approaches that often introduces uncertainties such as boundary effects. This study highlights the potential of Transformer-based models as a powerful tool for CMB data analysis, offering a substantial improvement over traditional CNN-based techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.09071v1-abstract-full').style.display = 'none'; document.getElementById('2502.09071v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 13 figures, 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08996">arXiv:2502.08996</a> <span> [<a href="https://arxiv.org/pdf/2502.08996">pdf</a>, <a href="https://arxiv.org/format/2502.08996">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Masked Modulation: High-Throughput Half-Duplex ISAC Transmission Waveform Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xiong%2C+Y">Yifeng Xiong</a>, <a href="/search/?searchtype=author&query=Mu%2C+J">Junsheng Mu</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shuangyang Li</a>, <a href="/search/?searchtype=author&query=Lops%2C+M">Marco Lops</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jianhua Zhang</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="2502.08996v1-abstract-short" style="display: inline;"> Integrated sensing and communication (ISAC) enables numerous innovative wireless applications. Communication-centric design is a practical choice for the construction of the sixth generation (6G) ISAC networks. Continuous-wave-based ISAC systems, with orthogonal frequency-division multiplexing (OFDM) being a representative example, suffer from the self-interference (SI) problem, and hence are less… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08996v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08996v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08996v1-abstract-full" style="display: none;"> Integrated sensing and communication (ISAC) enables numerous innovative wireless applications. Communication-centric design is a practical choice for the construction of the sixth generation (6G) ISAC networks. Continuous-wave-based ISAC systems, with orthogonal frequency-division multiplexing (OFDM) being a representative example, suffer from the self-interference (SI) problem, and hence are less suitable for long-range sensing. On the other hand, pulse-based half-duplex ISAC systems are free of SI, but are also less favourable for high-throughput communication scenarios. In this treatise, we propose MASked Modulation (MASM), a half-duplex ISAC waveform design scheme, which minimises a range blindness metric, referred to as "range glint", given a duty cycle (proportional to communication throughput) constraint. In particular, MASM is capable of supporting high-throughput communication (~50% duty cycle) under mild range glint. Moreover, MASM can be flexibly adapted to frame-level waveform designs by operating on the slow-time scale. In terms of optimal transmit mask design, a set of masks is shown to be ideal in the sense of sidelobe level and range glint intensity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08996v1-abstract-full').style.display = 'none'; document.getElementById('2502.08996v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to IEEE JSAC for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08987">arXiv:2502.08987</a> <span> [<a href="https://arxiv.org/pdf/2502.08987">pdf</a>, <a href="https://arxiv.org/format/2502.08987">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"> Neural Force Field: Learning Generalized Physical Representation from a Few Examples </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S">Shiqian Li</a>, <a href="/search/?searchtype=author&query=Shen%2C+R">Ruihong Shen</a>, <a href="/search/?searchtype=author&query=Zhang%2C+C">Chi Zhang</a>, <a href="/search/?searchtype=author&query=Zhu%2C+Y">Yixin Zhu</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="2502.08987v2-abstract-short" style="display: inline;"> Physical reasoning is a remarkable human ability that enables rapid learning and generalization from limited experience. Current AI models, despite extensive training, still struggle to achieve similar generalization, especially in Out-of-distribution (OOD) settings. This limitation stems from their inability to abstract core physical principles from observations. A key challenge is developing rep… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08987v2-abstract-full').style.display = 'inline'; document.getElementById('2502.08987v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08987v2-abstract-full" style="display: none;"> Physical reasoning is a remarkable human ability that enables rapid learning and generalization from limited experience. Current AI models, despite extensive training, still struggle to achieve similar generalization, especially in Out-of-distribution (OOD) settings. This limitation stems from their inability to abstract core physical principles from observations. A key challenge is developing representations that can efficiently learn and generalize physical dynamics from minimal data. Here we present Neural Force Field (NFF) a modeling framework built on Neural Ordinary Differential Equation (NODE) that learns interpretable force field representations which can be efficiently integrated through an Ordinary Differential Equation ( ODE) solver to predict object trajectories. Unlike existing approaches that rely on high-dimensional latent spaces, NFF captures fundamental physical concepts such as gravity, support, and collision in an interpretable manner. Experiments on two challenging physical reasoning tasks demonstrate that NFF, trained with only a few examples, achieves strong generalization to unseen scenarios. This physics-grounded representation enables efficient forward-backward planning and rapid adaptation through interactive refinement. Our work suggests that incorporating physics-inspired representations into learning systems can help bridge the gap between artificial and human physical reasoning capabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08987v2-abstract-full').style.display = 'none'; document.getElementById('2502.08987v2-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> 14 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 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">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08973">arXiv:2502.08973</a> <span> [<a href="https://arxiv.org/pdf/2502.08973">pdf</a>, <a href="https://arxiv.org/ps/2502.08973">ps</a>, <a href="https://arxiv.org/format/2502.08973">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> </div> </div> <p class="title is-5 mathjax"> Utilizing 3D Fast Spin Echo Anatomical Imaging to Reduce the Number of Contrast Preparations in $T_{1蟻}$ Quantification of Knee Cartilage Using Learning-Based Methods </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhong%2C+J">Junru Zhong</a>, <a href="/search/?searchtype=author&query=Huang%2C+C">Chaoxing Huang</a>, <a href="/search/?searchtype=author&query=Yu%2C+Z">Ziqiang Yu</a>, <a href="/search/?searchtype=author&query=Xiao%2C+F">Fan Xiao</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Siyue Li</a>, <a href="/search/?searchtype=author&query=Ong%2C+T+M">Tim-Yun Michael Ong</a>, <a href="/search/?searchtype=author&query=Ho%2C+K+K">Ki-Wai Kevin Ho</a>, <a href="/search/?searchtype=author&query=Chan%2C+Q">Queenie Chan</a>, <a href="/search/?searchtype=author&query=Griffith%2C+J+F">James F. Griffith</a>, <a href="/search/?searchtype=author&query=Chen%2C+W">Weitian Chen</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="2502.08973v1-abstract-short" style="display: inline;"> Purpose: To propose and evaluate an accelerated $T_{1蟻}$ quantification method that combines $T_{1蟻}$-weighted fast spin echo (FSE) images and proton density (PD)-weighted anatomical FSE images, leveraging deep learning models for $T_{1蟻}$ mapping. The goal is to reduce scan time and facilitate integration into routine clinical workflows for osteoarthritis (OA) assessment. Methods: This retrospect… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08973v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08973v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08973v1-abstract-full" style="display: none;"> Purpose: To propose and evaluate an accelerated $T_{1蟻}$ quantification method that combines $T_{1蟻}$-weighted fast spin echo (FSE) images and proton density (PD)-weighted anatomical FSE images, leveraging deep learning models for $T_{1蟻}$ mapping. The goal is to reduce scan time and facilitate integration into routine clinical workflows for osteoarthritis (OA) assessment. Methods: This retrospective study utilized MRI data from 40 participants (30 OA patients and 10 healthy volunteers). A volume of PD-weighted anatomical FSE images and a volume of $T_{1蟻}$-weighted images acquired at a non-zero spin-lock time were used as input to train deep learning models, including a 2D U-Net and a multi-layer perceptron (MLP). $T_{1蟻}$ maps generated by these models were compared with ground truth maps derived from a traditional non-linear least squares (NLLS) fitting method using four $T_{1蟻}$-weighted images. Evaluation metrics included mean absolute error (MAE), mean absolute percentage error (MAPE), regional error (RE), and regional percentage error (RPE). Results: Deep learning models achieved RPEs below 5% across all evaluated scenarios, outperforming NLLS methods, especially in low signal-to-noise conditions. The best results were obtained using the 2D U-Net, which effectively leveraged spatial information for accurate $T_{1蟻}$ fitting. The proposed method demonstrated compatibility with shorter TSLs, alleviating RF hardware and specific absorption rate (SAR) limitations. Conclusion: The proposed approach enables efficient $T_{1蟻}$ mapping using PD-weighted anatomical images, reducing scan time while maintaining clinical standards. This method has the potential to facilitate the integration of quantitative MRI techniques into routine clinical practice, benefiting OA diagnosis and monitoring. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08973v1-abstract-full').style.display = 'none'; document.getElementById('2502.08973v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to Magnetic Resonance in Medicine</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08944">arXiv:2502.08944</a> <span> [<a href="https://arxiv.org/pdf/2502.08944">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> </div> <p class="title is-5 mathjax"> Frequency-comb-steered ultrawideband quasi-true-time-delay beamformer for integrated sensing and communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+M">Mian Wang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wenxin Zhang</a>, <a href="/search/?searchtype=author&query=Ren%2C+Z">Zeyu Ren</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shangyuan Li</a>, <a href="/search/?searchtype=author&query=Zheng%2C+X">Xiaoping Zheng</a>, <a href="/search/?searchtype=author&query=Xue%2C+X">Xiaoxiao Xue</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="2502.08944v1-abstract-short" style="display: inline;"> Phased array antennas (PAAs) possessing broadband beamforming capabilities are crucial for advanced radar and wireless communication systems. Nevertheless, traditional phase-shifter-based PAA beamformers frequently encounter the beam-squint issue, which substantially restricts their instantaneous bandwidth. Photonic true-time-delay (TTD) beamformers have the potential to overcome this challenge, o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08944v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08944v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08944v1-abstract-full" style="display: none;"> Phased array antennas (PAAs) possessing broadband beamforming capabilities are crucial for advanced radar and wireless communication systems. Nevertheless, traditional phase-shifter-based PAA beamformers frequently encounter the beam-squint issue, which substantially restricts their instantaneous bandwidth. Photonic true-time-delay (TTD) beamformers have the potential to overcome this challenge, offering ultrawide bandwidth and immunity to electromagnetic interference. However, their practical application is impeded by the high complexity, which typically involves a vast array of optical switches and delay lines. Here, we introduce a novel frequency-comb-steered photonic quasi-TTD beamformer that eliminates the need for delay lines by leveraging the concepts of frequency-diverse arrays and photonic microwave mixing arrays. This beamformer enables squint-free beamforming of ultrawideband linear frequency modulation waveforms, which is essential for high-resolution radar applications. It ensures seamless and continuous beam steering, effectively delivering infinite spatial resolution. We present a prototype with an 8-element PAA, demonstrating an instantaneous bandwidth of 6 GHz across the entire Ku-band. Additionally, we explore the system's capabilities in integrated inverse synthetic aperture radar imaging and high-speed communication, achieving a high imaging resolution of 2.6 cm * 3.0 cm and a transmission rate of 3 Gbps. Compared to conventional delay-line-based beamformers, our new concept markedly reduces hardware complexity and enhances scalability, positioning it as a potent enabler for future integrated sensing and communication applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08944v1-abstract-full').style.display = 'none'; document.getElementById('2502.08944v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 4 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/2502.08929">arXiv:2502.08929</a> <span> [<a href="https://arxiv.org/pdf/2502.08929">pdf</a>, <a href="https://arxiv.org/ps/2502.08929">ps</a>, <a href="https://arxiv.org/format/2502.08929">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 Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Precise Measurement of the $蠂_{c0}$ Resonance Parameters and Branching Fractions of $蠂_{c0,c2}\to蟺^+蟺^-/K^+K^-$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=BESIII+Collaboration"> BESIII Collaboration</a>, <a href="/search/?searchtype=author&query=Ablikim%2C+M">M. Ablikim</a>, <a href="/search/?searchtype=author&query=Achasov%2C+M+N">M. N. Achasov</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Afedulidis%2C+O">O. Afedulidis</a>, <a href="/search/?searchtype=author&query=Ai%2C+X+C">X. C. Ai</a>, <a href="/search/?searchtype=author&query=Aliberti%2C+R">R. Aliberti</a>, <a href="/search/?searchtype=author&query=Amoroso%2C+A">A. Amoroso</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y">Y. Bai</a>, <a href="/search/?searchtype=author&query=Bakina%2C+O">O. Bakina</a>, <a href="/search/?searchtype=author&query=Balossino%2C+I">I. Balossino</a>, <a href="/search/?searchtype=author&query=Ban%2C+Y">Y. Ban</a>, <a href="/search/?searchtype=author&query=Bao%2C+H+-">H. -R. Bao</a>, <a href="/search/?searchtype=author&query=Batozskaya%2C+V">V. Batozskaya</a>, <a href="/search/?searchtype=author&query=Begzsuren%2C+K">K. Begzsuren</a>, <a href="/search/?searchtype=author&query=Berger%2C+N">N. Berger</a>, <a href="/search/?searchtype=author&query=Berlowski%2C+M">M. Berlowski</a>, <a href="/search/?searchtype=author&query=Bertani%2C+M">M. Bertani</a>, <a href="/search/?searchtype=author&query=Bettoni%2C+D">D. Bettoni</a>, <a href="/search/?searchtype=author&query=Bianchi%2C+F">F. Bianchi</a>, <a href="/search/?searchtype=author&query=Bianco%2C+E">E. Bianco</a>, <a href="/search/?searchtype=author&query=Bortone%2C+A">A. Bortone</a>, <a href="/search/?searchtype=author&query=Boyko%2C+I">I. Boyko</a>, <a href="/search/?searchtype=author&query=Briere%2C+R+A">R. A. Briere</a>, <a href="/search/?searchtype=author&query=Brueggemann%2C+A">A. Brueggemann</a> , et al. (648 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="2502.08929v1-abstract-short" style="display: inline;"> By analyzing a $蠄(3686)$ data sample containing $(107.7\pm0.6)\times10^{6}$ events taken with the BESIII detector at the BEPCII storage ring in 2009, the $蠂_{c0}$ resonance parameters are precisely measured using $蠂_{c0,c2} \to 蟺^+蟺^-/K^+K^-$ events. The mass of $蠂_{c0}$ is determined to be $M(蠂_{c0})=(3415.67\pm0.07\pm0.06\pm0.07$)~MeV/$c^2$, and its full width is… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08929v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08929v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08929v1-abstract-full" style="display: none;"> By analyzing a $蠄(3686)$ data sample containing $(107.7\pm0.6)\times10^{6}$ events taken with the BESIII detector at the BEPCII storage ring in 2009, the $蠂_{c0}$ resonance parameters are precisely measured using $蠂_{c0,c2} \to 蟺^+蟺^-/K^+K^-$ events. The mass of $蠂_{c0}$ is determined to be $M(蠂_{c0})=(3415.67\pm0.07\pm0.06\pm0.07$)~MeV/$c^2$, and its full width is $螕(蠂_{c0})=(12.44\pm0.12\pm0.12)~{\rm MeV}$, where the first uncertainty is statistical, the second systematic, and the third for mass comes from $蠂_{c2}$ mass uncertainty. These measurements improve the precision of $蠂_{c0}$ mass by a factor of four and width by one order of magnitude over the previous individual measurements, and significantly boost our knowledge about the charmonium spectrum. Together with additional $(345.4\pm2.6)\times10^{6}$ $蠄(3686)$ data events taken in 2012, the decay branching fractions of $蠂_{c0,c2}\to蟺^+蟺^-/K^+K^-$ are measured as well, with precision improved by a factor of three compared to previous measurements. These $蠂_{c0}$ decay branching fractions provide important inputs for the study of glueballs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08929v1-abstract-full').style.display = 'none'; document.getElementById('2502.08929v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 1 figure</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08745">arXiv:2502.08745</a> <span> [<a href="https://arxiv.org/pdf/2502.08745">pdf</a>, <a href="https://arxiv.org/format/2502.08745">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"> IHEval: Evaluating Language Models on Following the Instruction Hierarchy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zhihan Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shiyang Li</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zixuan Zhang</a>, <a href="/search/?searchtype=author&query=Liu%2C+X">Xin Liu</a>, <a href="/search/?searchtype=author&query=Jiang%2C+H">Haoming Jiang</a>, <a href="/search/?searchtype=author&query=Tang%2C+X">Xianfeng Tang</a>, <a href="/search/?searchtype=author&query=Gao%2C+Y">Yifan Gao</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zheng Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+H">Haodong Wang</a>, <a href="/search/?searchtype=author&query=Tan%2C+Z">Zhaoxuan Tan</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yichuan Li</a>, <a href="/search/?searchtype=author&query=Yin%2C+Q">Qingyu Yin</a>, <a href="/search/?searchtype=author&query=Yin%2C+B">Bing Yin</a>, <a href="/search/?searchtype=author&query=Jiang%2C+M">Meng Jiang</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="2502.08745v1-abstract-short" style="display: inline;"> The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent and safe behavior in language models (LMs). Despite its importance, this topic receives limited attention, and there is a lack of comprehensive benchmarks for evaluating models' ability to follow the instruction hierarchy.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08745v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08745v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08745v1-abstract-full" style="display: none;"> The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent and safe behavior in language models (LMs). Despite its importance, this topic receives limited attention, and there is a lack of comprehensive benchmarks for evaluating models' ability to follow the instruction hierarchy. We bridge this gap by introducing IHEval, a novel benchmark comprising 3,538 examples across nine tasks, covering cases where instructions in different priorities either align or conflict. Our evaluation of popular LMs highlights their struggle to recognize instruction priorities. All evaluated models experience a sharp performance decline when facing conflicting instructions, compared to their original instruction-following performance. Moreover, the most competitive open-source model only achieves 48% accuracy in resolving such conflicts. Our results underscore the need for targeted optimization in the future development of LMs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08745v1-abstract-full').style.display = 'none'; document.getElementById('2502.08745v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to NAACL 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/2502.08482">arXiv:2502.08482</a> <span> [<a href="https://arxiv.org/pdf/2502.08482">pdf</a>, <a href="https://arxiv.org/format/2502.08482">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"> Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yu%2C+Q">Qifan Yu</a>, <a href="/search/?searchtype=author&query=He%2C+Z">Zhenyu He</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Sijie Li</a>, <a href="/search/?searchtype=author&query=Zhou%2C+X">Xun Zhou</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/?searchtype=author&query=Xu%2C+J">Jingjing Xu</a>, <a href="/search/?searchtype=author&query=He%2C+D">Di He</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="2502.08482v1-abstract-short" style="display: inline;"> Chain-of-Thought (CoT) prompting has emerged as a powerful technique for enhancing language model's reasoning capabilities. However, generating long and correct CoT trajectories is challenging. Recent studies have demonstrated that Looped Transformers possess remarkable length generalization capabilities, but their limited generality and adaptability prevent them from serving as an alternative to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08482v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08482v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08482v1-abstract-full" style="display: none;"> Chain-of-Thought (CoT) prompting has emerged as a powerful technique for enhancing language model's reasoning capabilities. However, generating long and correct CoT trajectories is challenging. Recent studies have demonstrated that Looped Transformers possess remarkable length generalization capabilities, but their limited generality and adaptability prevent them from serving as an alternative to auto-regressive solutions. To better leverage the strengths of Looped Transformers, we propose RELAY (REasoning through Loop Alignment iterativelY). Specifically, we align the steps of Chain-of-Thought (CoT) reasoning with loop iterations and apply intermediate supervision during the training of Looped Transformers. This additional iteration-wise supervision not only preserves the Looped Transformer's ability for length generalization but also enables it to predict CoT reasoning steps for unseen data. Therefore, we leverage this Looped Transformer to generate accurate reasoning chains for complex problems that exceed the training length, which will then be used to fine-tune an auto-regressive model. We conduct extensive experiments, and the results demonstrate the effectiveness of our approach, with significant improvements in the performance of the auto-regressive model. Code will be released at https://github.com/qifanyu/RELAY. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08482v1-abstract-full').style.display = 'none'; document.getElementById('2502.08482v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">work in progress</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08185">arXiv:2502.08185</a> <span> [<a href="https://arxiv.org/pdf/2502.08185">pdf</a>, <a href="https://arxiv.org/format/2502.08185">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey 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.111.075119">10.1103/PhysRevB.111.075119 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Chiral Topological Phononic Quasiparticles in Enantiomeric Crystals SrSi$_2$ and BaSi$_2$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+Y">Yong-Kun Wang</a>, <a href="/search/?searchtype=author&query=Fan%2C+A">An-Dong Fan</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jin-Yang Li</a>, <a href="/search/?searchtype=author&query=Huang%2C+H">Huaqing Huang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Si Li</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="2502.08185v1-abstract-short" style="display: inline;"> Chiral crystals have recently garnered significant interest in condensed matter physics due to their unique electronic and optical properties. In this paper, we explore the connection between the chirality of crystal structures and the chirality of topological quasiparticles. We specifically predict and analyze several chiral enantiomeric materials, such as SrSi$_2$ and BaSi$_2$, which crystallize… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08185v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08185v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08185v1-abstract-full" style="display: none;"> Chiral crystals have recently garnered significant interest in condensed matter physics due to their unique electronic and optical properties. In this paper, we explore the connection between the chirality of crystal structures and the chirality of topological quasiparticles. We specifically predict and analyze several chiral enantiomeric materials, such as SrSi$_2$ and BaSi$_2$, which crystallize in the chiral space groups $P4{_3}32$ and $P4{_1}32$. Based on first-principles calculations and theoretical analysis, we reveal that the phonon spectra of these materials host various topological phononic quasiparticles, including charge-2 triple points, charge-2 Dirac points, charge-2 Weyl points, and charge-1 Weyl points. Our paper shows that in these enantiomeric materials, the opposite chirality of the crystal structure results in topological quasiparticles with opposite chiral topological charges and distinct topological surface states. Our paper elucidates the intrinsic relationship between the chirality of crystal structures and the chirality of topological quasiparticles, providing promising theoretical guidance and material platform for investigating the physical properties of chiral crystals. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08185v1-abstract-full').style.display = 'none'; document.getElementById('2502.08185v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. B 111, 075119 (2025) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08155">arXiv:2502.08155</a> <span> [<a href="https://arxiv.org/pdf/2502.08155">pdf</a>, <a href="https://arxiv.org/ps/2502.08155">ps</a>, <a href="https://arxiv.org/format/2502.08155">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"> DGSense: A Domain Generalization Framework for Wireless Sensing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhou%2C+R">Rui Zhou</a>, <a href="/search/?searchtype=author&query=Cheng%2C+Y">Yu Cheng</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Songlin Li</a>, <a href="/search/?searchtype=author&query=Zhang%2C+H">Hongwang Zhang</a>, <a href="/search/?searchtype=author&query=Liu%2C+C">Chenxu Liu</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="2502.08155v1-abstract-short" style="display: inline;"> Wireless sensing is of great benefits to our daily lives. However, wireless signals are sensitive to the surroundings. Various factors, e.g. environments, locations, and individuals, may induce extra impact on wireless propagation. Such a change can be regarded as a domain, in which the data distribution shifts. A vast majority of the sensing schemes are learning-based. They are dependent on the t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08155v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08155v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08155v1-abstract-full" style="display: none;"> Wireless sensing is of great benefits to our daily lives. However, wireless signals are sensitive to the surroundings. Various factors, e.g. environments, locations, and individuals, may induce extra impact on wireless propagation. Such a change can be regarded as a domain, in which the data distribution shifts. A vast majority of the sensing schemes are learning-based. They are dependent on the training domains, resulting in performance degradation in unseen domains. Researchers have proposed various solutions to address this issue. But these solutions leverage either semi-supervised or unsupervised domain adaptation techniques. They still require some data in the target domains and do not perform well in unseen domains. In this paper, we propose a domain generalization framework DGSense, to eliminate the domain dependence problem in wireless sensing. The framework is a general solution working across diverse sensing tasks and wireless technologies. Once the sensing model is built, it can generalize to unseen domains without any data from the target domain. To achieve the goal, we first increase the diversity of the training set by a virtual data generator, and then extract the domain independent features via episodic training between the main feature extractor and the domain feature extractors. The feature extractors employ a pre-trained Residual Network (ResNet) with an attention mechanism for spatial features, and a 1D Convolutional Neural Network (1DCNN) for temporal features. To demonstrate the effectiveness and generality of DGSense, we evaluated on WiFi gesture recognition, Millimeter Wave (mmWave) activity recognition, and acoustic fall detection. All the systems exhibited high generalization capability to unseen domains, including new users, locations, and environments, free of new data and retraining. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08155v1-abstract-full').style.display = 'none'; document.getElementById('2502.08155v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08151">arXiv:2502.08151</a> <span> [<a href="https://arxiv.org/pdf/2502.08151">pdf</a>, <a href="https://arxiv.org/format/2502.08151">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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/10.1109/TIFS.2024.3515793">10.1109/TIFS.2024.3515793 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=You%2C+Z">Zhichao You</a>, <a href="/search/?searchtype=author&query=Dong%2C+X">Xuewen Dong</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shujun Li</a>, <a href="/search/?searchtype=author&query=Liu%2C+X">Ximeng Liu</a>, <a href="/search/?searchtype=author&query=Ma%2C+S">Siqi Ma</a>, <a href="/search/?searchtype=author&query=Shen%2C+Y">Yulong Shen</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="2502.08151v1-abstract-short" style="display: inline;"> Reconstruction attacks against federated learning (FL) aim to reconstruct users' samples through users' uploaded gradients. Local differential privacy (LDP) is regarded as an effective defense against various attacks, including sample reconstruction in FL, where gradients are clipped and perturbed. Existing attacks are ineffective in FL with LDP since clipped and perturbed gradients obliterate mos… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08151v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08151v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08151v1-abstract-full" style="display: none;"> Reconstruction attacks against federated learning (FL) aim to reconstruct users' samples through users' uploaded gradients. Local differential privacy (LDP) is regarded as an effective defense against various attacks, including sample reconstruction in FL, where gradients are clipped and perturbed. Existing attacks are ineffective in FL with LDP since clipped and perturbed gradients obliterate most sample information for reconstruction. Besides, existing attacks embed additional sample information into gradients to improve the attack effect and cause gradient expansion, leading to a more severe gradient clipping in FL with LDP. In this paper, we propose a sample reconstruction attack against LDP-based FL with any target models to reconstruct victims' sensitive samples to illustrate that FL with LDP is not flawless. Considering gradient expansion in reconstruction attacks and noise in LDP, the core of the proposed attack is gradient compression and reconstructed sample denoising. For gradient compression, an inference structure based on sample characteristics is presented to reduce redundant gradients against LDP. For reconstructed sample denoising, we artificially introduce zero gradients to observe noise distribution and scale confidence interval to filter the noise. Theoretical proof guarantees the effectiveness of the proposed attack. Evaluations show that the proposed attack is the only attack that reconstructs victims' training samples in LDP-based FL and has little impact on the target model's accuracy. We conclude that LDP-based FL needs further improvements to defend against sample reconstruction attacks effectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08151v1-abstract-full').style.display = 'none'; document.getElementById('2502.08151v1-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 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> IEEE Transactions on Information Forensics and Security, 2025 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08077">arXiv:2502.08077</a> <span> [<a href="https://arxiv.org/pdf/2502.08077">pdf</a>, <a href="https://arxiv.org/format/2502.08077">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"> Cascading Bandits Robust to Adversarial Corruptions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xie%2C+J">Jize Xie</a>, <a href="/search/?searchtype=author&query=Chen%2C+C">Cheng Chen</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Zhiyong Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shuai Li</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="2502.08077v1-abstract-short" style="display: inline;"> Online learning to rank sequentially recommends a small list of items to users from a large candidate set and receives the users' click feedback. In many real-world scenarios, users browse the recommended list in order and click the first attractive item without checking the rest. Such behaviors are usually formulated as the cascade model. Many recent works study algorithms for cascading bandits,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08077v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08077v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08077v1-abstract-full" style="display: none;"> Online learning to rank sequentially recommends a small list of items to users from a large candidate set and receives the users' click feedback. In many real-world scenarios, users browse the recommended list in order and click the first attractive item without checking the rest. Such behaviors are usually formulated as the cascade model. Many recent works study algorithms for cascading bandits, an online learning to rank framework in the cascade model. However, the performance of existing methods may drop significantly if part of the user feedback is adversarially corrupted (e.g., click fraud). In this work, we study how to resist adversarial corruptions in cascading bandits. We first formulate the ``\textit{Cascading Bandits with Adversarial Corruptions}" (CBAC) problem, which assumes that there is an adaptive adversary that may manipulate the user feedback. Then we propose two robust algorithms for this problem, which assume the corruption level is known and agnostic, respectively. We show that both algorithms can achieve logarithmic regret when the algorithm is not under attack, and the regret increases linearly with the corruption level. The experimental results also verify the robustness of our methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08077v1-abstract-full').style.display = 'none'; document.getElementById('2502.08077v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.08067">arXiv:2502.08067</a> <span> [<a href="https://arxiv.org/pdf/2502.08067">pdf</a>, <a href="https://arxiv.org/format/2502.08067">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="Atomic Physics">physics.atom-ph</span> </div> </div> <p class="title is-5 mathjax"> Reducing thermal noises by a quantum refrigerator </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Bi%2C+H">Han-Jia Bi</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Sheng-Wen Li</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="2502.08067v1-abstract-short" style="display: inline;"> Reducing the thermal noises in microwave (MW) resonators can bring about significant progress in many research fields. Recently, a bench-top cooling method using "quantum refrigerators" has been adopted to reduce the thermal noises, reaching around liquid nitrogen temperature. In this study, we investigate the possible cooling limit of the MW resonator by using three-level or four-level systems as… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08067v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08067v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08067v1-abstract-full" style="display: none;"> Reducing the thermal noises in microwave (MW) resonators can bring about significant progress in many research fields. Recently, a bench-top cooling method using "quantum refrigerators" has been adopted to reduce the thermal noises, reaching around liquid nitrogen temperature. In this study, we investigate the possible cooling limit of the MW resonator by using three-level or four-level systems as the quantum refrigerator. In this refrigerator system, proper light pump makes the multilevel systems concentrated into their ground states, which continuously absorb the thermal photons in the MW resonator. By adiabatic elimination, we give a more precise description for this cooling process. It turns out, though the multilevel systems can be efficiently cooled down, the laser driving also significantly perturbs their energy levels. For three-level refrigerators, such perturbation causes the atom-resonator interaction to become off-resonant, impeding the heat transfer from the MW resonator to the refrigerator, which greatly weakens the cooling effect. We also find that, by using four-level systems as the refrigerator, this issue can be well overcome. Based on practical parameters, our estimation shows the cooling limit could reach the liquid helium temperature. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08067v1-abstract-full').style.display = 'none'; document.getElementById('2502.08067v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 1 figure, comments 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/2502.08057">arXiv:2502.08057</a> <span> [<a href="https://arxiv.org/pdf/2502.08057">pdf</a>, <a href="https://arxiv.org/format/2502.08057">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Nuclear Theory">nucl-th</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Nuclear Experiment">nucl-ex</span> </div> </div> <p class="title is-5 mathjax"> Directly probing existence of $伪$-cluster structure in $^{20}$Ne by relativistic heavy-ion collisions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+L">Lu-Meng Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+H">Hai-Cheng Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Song-Jie Li</a>, <a href="/search/?searchtype=author&query=Zhang%2C+C">Chunjian Zhang</a>, <a href="/search/?searchtype=author&query=Xu%2C+J">Jun Xu</a>, <a href="/search/?searchtype=author&query=Ren%2C+Z">Zhong-Zhou Ren</a>, <a href="/search/?searchtype=author&query=Jia%2C+J">Jiangyong Jia</a>, <a href="/search/?searchtype=author&query=Huang%2C+X">Xu-Guang Huang</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="2502.08057v2-abstract-short" style="display: inline;"> Can relativistic heavy-ion collisions only probe the global shape of colliding nuclei, or their detailed internal structure as well? Taking $^{20}$Ne as an example, we attempt to directly probe its internal $伪$-cluster structure, by comparing experimentally measured observables in collisions at relativistic energies from density distributions of $^{20}$Ne with and without $伪$-cluster structure. Si… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08057v2-abstract-full').style.display = 'inline'; document.getElementById('2502.08057v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08057v2-abstract-full" style="display: none;"> Can relativistic heavy-ion collisions only probe the global shape of colliding nuclei, or their detailed internal structure as well? Taking $^{20}$Ne as an example, we attempt to directly probe its internal $伪$-cluster structure, by comparing experimentally measured observables in collisions at relativistic energies from density distributions of $^{20}$Ne with and without $伪$-cluster structure. Since the two density distributions give the same nucleus size and deformation, they lead to similar mid-rapidity observables. However, the $伪$-cluster structure may considerably reduce the free spectator nucleon yield and enhance the spectator light nuclei yield, as a result of more compact initial phase-space distribution of nucleons inside $伪$ clusters. We propose to measure the scaled yield ratio of free spectator neutrons to charged particles with mass-to-charge ratio $A/Z = 3$, 3/2, and 2 in ultra-central $^{20}$Ne+$^{20}$Ne collisions, which is found to be reduced by about $25\%$ at $\sqrt{s_\mathrm{NN}} = 7$ TeV and about $20\%$ at $\sqrt{s_\mathrm{NN}} = 200$ GeV with $伪$-cluster structure in $^{20}$Ne. This scaled yield ratio thus serves as a robust and direct probe of the existence of $伪$-cluster structure in $^{20}$Ne free from the uncertainty of mid-rapidity dynamics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08057v2-abstract-full').style.display = 'none'; document.getElementById('2502.08057v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 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">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 4 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/2502.07901">arXiv:2502.07901</a> <span> [<a href="https://arxiv.org/pdf/2502.07901">pdf</a>, <a href="https://arxiv.org/format/2502.07901">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> StarCast: A Secure and Spectrum-Efficient Group Communication Scheme for LEO Satellite Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+C">Chaoyu Zhang</a>, <a href="/search/?searchtype=author&query=Yu%2C+H">Hexuan Yu</a>, <a href="/search/?searchtype=author&query=Shi%2C+S">Shanghao Shi</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shaoyu Li</a>, <a href="/search/?searchtype=author&query=Shi%2C+Y">Yi Shi</a>, <a href="/search/?searchtype=author&query=Burger%2C+E">Eric Burger</a>, <a href="/search/?searchtype=author&query=Hou%2C+Y+T">Y. Thomas Hou</a>, <a href="/search/?searchtype=author&query=Lou%2C+W">Wenjing Lou</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="2502.07901v1-abstract-short" style="display: inline;"> Low Earth Orbit (LEO) satellite networks serve as a cornerstone infrastructure for providing ubiquitous connectivity in areas where terrestrial infrastructure is unavailable. With the emergence of Direct-to-Cell (DTC) satellites, these networks can provide direct access to mobile phones and IoT devices without relying on terrestrial base stations, leading to a surge in massive connectivity demands… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07901v1-abstract-full').style.display = 'inline'; document.getElementById('2502.07901v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07901v1-abstract-full" style="display: none;"> Low Earth Orbit (LEO) satellite networks serve as a cornerstone infrastructure for providing ubiquitous connectivity in areas where terrestrial infrastructure is unavailable. With the emergence of Direct-to-Cell (DTC) satellites, these networks can provide direct access to mobile phones and IoT devices without relying on terrestrial base stations, leading to a surge in massive connectivity demands for the serving satellite. To address this issue, group communication is an effective paradigm that enables simultaneous content delivery to multiple users and thus optimizes bandwidth reuse. Although extensive research has been conducted to improve group communication performance, securing this communication without compromising its inherent spectrum efficiency remains a critical challenge. To address this, we introduce StarCast, a secure group encryption scheme for LEO satellite networks. Our solution leverages ciphertext-policy attribute-based encryption (CP-ABE) to implement fine-grained access control by embedding access policies directly within the ciphertext. Unlike standard secure communication approaches that require dedicated per-user channels and significantly deplete limited satellite spectrum resources, StarCast maintains efficient spectrum reuse within user groups while ensuring that only authorized users can access transmitted data. Additionally, it significantly reduces the costly key management overhead associated with conventional encryption schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07901v1-abstract-full').style.display = 'none'; document.getElementById('2502.07901v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.07685">arXiv:2502.07685</a> <span> [<a href="https://arxiv.org/pdf/2502.07685">pdf</a>, <a href="https://arxiv.org/format/2502.07685">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"> Matrix3D: Large Photogrammetry Model All-in-One </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lu%2C+Y">Yuanxun Lu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jingyang Zhang</a>, <a href="/search/?searchtype=author&query=Fang%2C+T">Tian Fang</a>, <a href="/search/?searchtype=author&query=Nahmias%2C+J">Jean-Daniel Nahmias</a>, <a href="/search/?searchtype=author&query=Tsin%2C+Y">Yanghai Tsin</a>, <a href="/search/?searchtype=author&query=Quan%2C+L">Long Quan</a>, <a href="/search/?searchtype=author&query=Cao%2C+X">Xun Cao</a>, <a href="/search/?searchtype=author&query=Yao%2C+Y">Yao Yao</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shiwei Li</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="2502.07685v1-abstract-short" style="display: inline;"> We present Matrix3D, a unified model that performs several photogrammetry subtasks, including pose estimation, depth prediction, and novel view synthesis using just the same model. Matrix3D utilizes a multi-modal diffusion transformer (DiT) to integrate transformations across several modalities, such as images, camera parameters, and depth maps. The key to Matrix3D's large-scale multi-modal traini… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07685v1-abstract-full').style.display = 'inline'; document.getElementById('2502.07685v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07685v1-abstract-full" style="display: none;"> We present Matrix3D, a unified model that performs several photogrammetry subtasks, including pose estimation, depth prediction, and novel view synthesis using just the same model. Matrix3D utilizes a multi-modal diffusion transformer (DiT) to integrate transformations across several modalities, such as images, camera parameters, and depth maps. The key to Matrix3D's large-scale multi-modal training lies in the incorporation of a mask learning strategy. This enables full-modality model training even with partially complete data, such as bi-modality data of image-pose and image-depth pairs, thus significantly increases the pool of available training data. Matrix3D demonstrates state-of-the-art performance in pose estimation and novel view synthesis tasks. Additionally, it offers fine-grained control through multi-round interactions, making it an innovative tool for 3D content creation. Project page: https://nju-3dv.github.io/projects/matrix3d. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07685v1-abstract-full').style.display = 'none'; document.getElementById('2502.07685v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">Project Page: https://nju-3dv.github.io/projects/matrix3d</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.07406">arXiv:2502.07406</a> <span> [<a href="https://arxiv.org/pdf/2502.07406">pdf</a>, <a href="https://arxiv.org/format/2502.07406">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 Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Search for $e^+e^-\to K_S^0 K_S^0 h_c$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=BESIII+Collaboration"> BESIII Collaboration</a>, <a href="/search/?searchtype=author&query=Ablikim%2C+M">M. Ablikim</a>, <a href="/search/?searchtype=author&query=Achasov%2C+M+N">M. N. Achasov</a>, <a href="/search/?searchtype=author&query=Adlarson%2C+P">P. Adlarson</a>, <a href="/search/?searchtype=author&query=Afedulidis%2C+O">O. Afedulidis</a>, <a href="/search/?searchtype=author&query=Ai%2C+X+C">X. C. Ai</a>, <a href="/search/?searchtype=author&query=Aliberti%2C+R">R. Aliberti</a>, <a href="/search/?searchtype=author&query=Amoroso%2C+A">A. Amoroso</a>, <a href="/search/?searchtype=author&query=An%2C+Q">Q. An</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y">Y. Bai</a>, <a href="/search/?searchtype=author&query=Bakina%2C+O">O. Bakina</a>, <a href="/search/?searchtype=author&query=Balossino%2C+I">I. Balossino</a>, <a href="/search/?searchtype=author&query=Ban%2C+Y">Y. Ban</a>, <a href="/search/?searchtype=author&query=Bao%2C+H+-">H. -R. Bao</a>, <a href="/search/?searchtype=author&query=Batozskaya%2C+V">V. Batozskaya</a>, <a href="/search/?searchtype=author&query=Begzsuren%2C+K">K. Begzsuren</a>, <a href="/search/?searchtype=author&query=Berger%2C+N">N. Berger</a>, <a href="/search/?searchtype=author&query=Berlowski%2C+M">M. Berlowski</a>, <a href="/search/?searchtype=author&query=Bertani%2C+M">M. Bertani</a>, <a href="/search/?searchtype=author&query=Bettoni%2C+D">D. Bettoni</a>, <a href="/search/?searchtype=author&query=Bianchi%2C+F">F. Bianchi</a>, <a href="/search/?searchtype=author&query=Bianco%2C+E">E. Bianco</a>, <a href="/search/?searchtype=author&query=Bortone%2C+A">A. Bortone</a>, <a href="/search/?searchtype=author&query=Boyko%2C+I">I. Boyko</a>, <a href="/search/?searchtype=author&query=Briere%2C+R+A">R. A. Briere</a> , et al. (642 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="2502.07406v1-abstract-short" style="display: inline;"> Using $e^+e^-$ collision data at 13 center-of-mass energies ranging from 4.600 to 4.950 GeV collected with the BESIII detector, we search for the unmeasured $e^+e^-\to K_S^0 K_S^0 h_c$ process . No significant signal is observed, and the upper limits of the Born cross sections at each center-of-mass energy are presented. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07406v1-abstract-full" style="display: none;"> Using $e^+e^-$ collision data at 13 center-of-mass energies ranging from 4.600 to 4.950 GeV collected with the BESIII detector, we search for the unmeasured $e^+e^-\to K_S^0 K_S^0 h_c$ process . No significant signal is observed, and the upper limits of the Born cross sections at each center-of-mass energy are presented. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07406v1-abstract-full').style.display = 'none'; document.getElementById('2502.07406v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.07331">arXiv:2502.07331</a> <span> [<a href="https://arxiv.org/pdf/2502.07331">pdf</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"> ERANet: Edge Replacement Augmentation for Semi-Supervised Meniscus Segmentation with Prototype Consistency Alignment and Conditional Self-Training </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S">Siyue Li</a>, <a href="/search/?searchtype=author&query=Yao%2C+Y">Yongcheng Yao</a>, <a href="/search/?searchtype=author&query=Zhong%2C+J">Junru Zhong</a>, <a href="/search/?searchtype=author&query=Zhao%2C+S">Shutian Zhao</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yudong Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Shuihua Wang</a>, <a href="/search/?searchtype=author&query=Hong%2C+J">Jin Hong</a>, <a href="/search/?searchtype=author&query=Chen%2C+W">Weitian Chen</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="2502.07331v1-abstract-short" style="display: inline;"> Manual segmentation is labor-intensive, and automatic segmentation remains challenging due to the inherent variability in meniscal morphology, partial volume effects, and low contrast between the meniscus and surrounding tissues. To address these challenges, we propose ERANet, an innovative semi-supervised framework for meniscus segmentation that effectively leverages both labeled and unlabeled im… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07331v1-abstract-full').style.display = 'inline'; document.getElementById('2502.07331v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07331v1-abstract-full" style="display: none;"> Manual segmentation is labor-intensive, and automatic segmentation remains challenging due to the inherent variability in meniscal morphology, partial volume effects, and low contrast between the meniscus and surrounding tissues. To address these challenges, we propose ERANet, an innovative semi-supervised framework for meniscus segmentation that effectively leverages both labeled and unlabeled images through advanced augmentation and learning strategies. ERANet integrates three key components: edge replacement augmentation (ERA), prototype consistency alignment (PCA), and a conditional self-training (CST) strategy within a mean teacher architecture. ERA introduces anatomically relevant perturbations by simulating meniscal variations, ensuring that augmentations align with the structural context. PCA enhances segmentation performance by aligning intra-class features and promoting compact, discriminative feature representations, particularly in scenarios with limited labeled data. CST improves segmentation robustness by iteratively refining pseudo-labels and mitigating the impact of label noise during training. Together, these innovations establish ERANet as a robust and scalable solution for meniscus segmentation, effectively addressing key barriers to practical implementation. We validated ERANet comprehensively on 3D Double Echo Steady State (DESS) and 3D Fast/Turbo Spin Echo (FSE/TSE) MRI sequences. The results demonstrate the superior performance of ERANet compared to state-of-the-art methods. The proposed framework achieves reliable and accurate segmentation of meniscus structures, even when trained on minimal labeled data. Extensive ablation studies further highlight the synergistic contributions of ERA, PCA, and CST, solidifying ERANet as a transformative solution for semi-supervised meniscus segmentation in medical imaging. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07331v1-abstract-full').style.display = 'none'; document.getElementById('2502.07331v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.07317">arXiv:2502.07317</a> <span> [<a href="https://arxiv.org/pdf/2502.07317">pdf</a>, <a href="https://arxiv.org/format/2502.07317">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Position reconstruction and surface background model for the PandaX-4T detector </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Qian%2C+Z">Zhicheng Qian</a>, <a href="/search/?searchtype=author&query=Gu%2C+L">Linhui Gu</a>, <a href="/search/?searchtype=author&query=Cheng%2C+C">Chen Cheng</a>, <a href="/search/?searchtype=author&query=Bo%2C+Z">Zihao Bo</a>, <a href="/search/?searchtype=author&query=Chen%2C+W">Wei Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+X">Xun Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yunhua Chen</a>, <a href="/search/?searchtype=author&query=Cheng%2C+Z">Zhaokan Cheng</a>, <a href="/search/?searchtype=author&query=Cui%2C+X">Xiangyi Cui</a>, <a href="/search/?searchtype=author&query=Fan%2C+Y">Yingjie Fan</a>, <a href="/search/?searchtype=author&query=Fang%2C+D">Deqing Fang</a>, <a href="/search/?searchtype=author&query=Gao%2C+Z">Zhixing Gao</a>, <a href="/search/?searchtype=author&query=Geng%2C+L">Lisheng Geng</a>, <a href="/search/?searchtype=author&query=Giboni%2C+K">Karl Giboni</a>, <a href="/search/?searchtype=author&query=Guo%2C+X">Xunan Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+X">Xuyuan Guo</a>, <a href="/search/?searchtype=author&query=Guo%2C+Z">Zichao Guo</a>, <a href="/search/?searchtype=author&query=Han%2C+C">Chencheng Han</a>, <a href="/search/?searchtype=author&query=Han%2C+K">Ke Han</a>, <a href="/search/?searchtype=author&query=He%2C+C">Changda He</a>, <a href="/search/?searchtype=author&query=He%2C+J">Jinrong He</a>, <a href="/search/?searchtype=author&query=Huang%2C+D">Di Huang</a>, <a href="/search/?searchtype=author&query=Huang%2C+H">Houqi Huang</a>, <a href="/search/?searchtype=author&query=Huang%2C+J">Junting Huang</a>, <a href="/search/?searchtype=author&query=Hou%2C+R">Ruquan Hou</a> , et al. (78 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="2502.07317v1-abstract-short" style="display: inline;"> We report the position reconstruction methods and surface background model for the PandaX-4T dark matter direct search experiment. This work develops two position reconstruction algorithms: template matching (TM) method and photon acceptance function (PAF) method. Both methods determine the horizontal position of events based on the light pattern of secondary scintillation collected by the light s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07317v1-abstract-full').style.display = 'inline'; document.getElementById('2502.07317v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07317v1-abstract-full" style="display: none;"> We report the position reconstruction methods and surface background model for the PandaX-4T dark matter direct search experiment. This work develops two position reconstruction algorithms: template matching (TM) method and photon acceptance function (PAF) method. Both methods determine the horizontal position of events based on the light pattern of secondary scintillation collected by the light sensors. After a comprehensive evaluation of resolution, uniformity, and robustness, the PAF method was selected for position reconstruction, while the TM method was employed for verification. The PAF method achieves a bulk event resolution of 1.0 mm and a surface event resolution of 4.4 mm for a typical $S2$ signal with a bottom charge of 1500 PE (about 14 keV). The uniformity is around 20\%. Robustness studies reveal average deviations of 5.1 mm and 8.8 mm for the commissioning run (Run0) and the first science run (Run1), respectively, due to the deactivation of certain PMTs. A data-driven surface background model is developed based on the PAF method. The surface background is estimated to be $0.09 \pm 0.06$ events for Run0 (0.54 tonne$\cdot$year) and $0.17 \pm 0.11$ events for Run1 (1.00 tonne$\cdot$year). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07317v1-abstract-full').style.display = 'none'; document.getElementById('2502.07317v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, 15 figures, 2 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.07299">arXiv:2502.07299</a> <span> [<a href="https://arxiv.org/pdf/2502.07299">pdf</a>, <a href="https://arxiv.org/format/2502.07299">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="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Genomics">q-bio.GN</span> </div> </div> <p class="title is-5 mathjax"> Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+Z">Zicheng Liu</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Siyuan Li</a>, <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhiyuan Chen</a>, <a href="/search/?searchtype=author&query=Xin%2C+L">Lei Xin</a>, <a href="/search/?searchtype=author&query=Wu%2C+F">Fang Wu</a>, <a href="/search/?searchtype=author&query=Yu%2C+C">Chang Yu</a>, <a href="/search/?searchtype=author&query=Yang%2C+Q">Qirong Yang</a>, <a href="/search/?searchtype=author&query=Guo%2C+Y">Yucheng Guo</a>, <a href="/search/?searchtype=author&query=Yang%2C+Y">Yujie Yang</a>, <a href="/search/?searchtype=author&query=Li%2C+S+Z">Stan Z. Li</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="2502.07299v1-abstract-short" style="display: inline;"> The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. While modern biological pre-trained models have achieved great success in analyzing these macromolecules individually, their interconnected nature remains under-explored. In this paper, we follow the guidance of the central dogma to redesign both the da… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07299v1-abstract-full').style.display = 'inline'; document.getElementById('2502.07299v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07299v1-abstract-full" style="display: none;"> The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. While modern biological pre-trained models have achieved great success in analyzing these macromolecules individually, their interconnected nature remains under-explored. In this paper, we follow the guidance of the central dogma to redesign both the data and model pipeline and offer a comprehensive framework, Life-Code, that spans different biological functions. As for data flow, we propose a unified pipeline to integrate multi-omics data by reverse-transcribing RNA and reverse-translating amino acids into nucleotide-based sequences. As for the model, we design a codon tokenizer and a hybrid long-sequence architecture to encode the interactions of both coding and non-coding regions with masked modeling pre-training. To model the translation and folding process with coding sequences, Life-Code learns protein structures of the corresponding amino acids by knowledge distillation from off-the-shelf protein language models. Such designs enable Life-Code to capture complex interactions within genetic sequences, providing a more comprehensive understanding of multi-omics with the central dogma. Extensive Experiments show that Life-Code achieves state-of-the-art performance on various tasks across three omics, highlighting its potential for advancing multi-omics analysis and interpretation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07299v1-abstract-full').style.display = 'none'; document.getElementById('2502.07299v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages main text with 6 pages Appendix</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.07264">arXiv:2502.07264</a> <span> [<a href="https://arxiv.org/pdf/2502.07264">pdf</a>, <a href="https://arxiv.org/format/2502.07264">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> </div> <p class="title is-5 mathjax"> Precision Control of Resistive Power in Kibble Balance Coils: An Advanced Method for Minimizing Temperature-Related Magnetic Errors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+W">Weibo Liu</a>, <a href="/search/?searchtype=author&query=Schlamminger%2C+S">Stephan Schlamminger</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shisong Li</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="2502.07264v1-abstract-short" style="display: inline;"> Temperature changes affect the coercivity of permanent magnets, thereby impacting the $Bl$ factor and potentially introducing systematic errors in Kibble balance measurements. While the thermal-magnetic effect is negligible in large magnet systems, it increases substantially as the magnet size decreases, posing an engineering difficulty for tabletop Kibble balance systems. We discuss the mechanism… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07264v1-abstract-full').style.display = 'inline'; document.getElementById('2502.07264v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.07264v1-abstract-full" style="display: none;"> Temperature changes affect the coercivity of permanent magnets, thereby impacting the $Bl$ factor and potentially introducing systematic errors in Kibble balance measurements. While the thermal-magnetic effect is negligible in large magnet systems, it increases substantially as the magnet size decreases, posing an engineering difficulty for tabletop Kibble balance systems. We discuss the mechanism of thermal-magnetic effects through finite element analysis, which has not been sufficiently emphasized in previous studies. A bifilar-coil power regulator is proposed to eliminate thermal-magnetic errors in Kibble balances. The approach aims to keep the power of the internal heating source -- coil ohmic power -- constant over time, allowing the $Bl$ drift to be mitigated through ABA or ABBA measurement sequences. Experimental results validate the proposal, demonstrating that the thermal effect can be reduced by more than two orders of magnitude compared to the conventional two-mode, two-phase measurement scheme, and by about one order of magnitude compared to the one-mode, two-phase scheme. The proposed approach can eliminate the influence of thermal-magnetic effects on the measurement results, thus further breaking down the limitations on the minimum size of tabletop Kibble balances. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.07264v1-abstract-full').style.display = 'none'; document.getElementById('2502.07264v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">23 pages, 9 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/2502.06994">arXiv:2502.06994</a> <span> [<a href="https://arxiv.org/pdf/2502.06994">pdf</a>, <a href="https://arxiv.org/format/2502.06994">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</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="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Guo%2C+X">Xuehang Guo</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xingyao Wang</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yangyi Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Sha Li</a>, <a href="/search/?searchtype=author&query=Han%2C+C">Chi Han</a>, <a href="/search/?searchtype=author&query=Li%2C+M">Manling Li</a>, <a href="/search/?searchtype=author&query=Ji%2C+H">Heng Ji</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="2502.06994v1-abstract-short" style="display: inline;"> Software engineering (SE) is increasingly collaborative, with developers working together on shared complex codebases. Effective collaboration in shared environments requires participants -- whether humans or AI agents -- to stay on the same page as their environment evolves. When a collaborator's understanding diverges from the current state -- what we term the out-of-sync challenge -- the collab… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06994v1-abstract-full').style.display = 'inline'; document.getElementById('2502.06994v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06994v1-abstract-full" style="display: none;"> Software engineering (SE) is increasingly collaborative, with developers working together on shared complex codebases. Effective collaboration in shared environments requires participants -- whether humans or AI agents -- to stay on the same page as their environment evolves. When a collaborator's understanding diverges from the current state -- what we term the out-of-sync challenge -- the collaborator's actions may fail, leading to integration issues. In this work, we introduce SyncMind, a framework that systematically defines the out-of-sync problem faced by large language model (LLM) agents in collaborative software engineering (CSE). Based on SyncMind, we create SyncBench, a benchmark featuring 24,332 instances of agent out-of-sync scenarios in real-world CSE derived from 21 popular GitHub repositories with executable verification tests. Experiments on SyncBench uncover critical insights into existing LLM agents' capabilities and limitations. Besides substantial performance gaps among agents (from Llama-3.1 agent <= 3.33% to Claude-3.5-Sonnet >= 28.18%), their consistently low collaboration willingness (<= 4.86%) suggests fundamental limitations of existing LLM in CSE. However, when collaboration occurs, it positively correlates with out-of-sync recovery success. Minimal performance differences in agents' resource-aware out-of-sync recoveries further reveal their significant lack of resource awareness and adaptability, shedding light on future resource-efficient collaborative systems. Code and data are openly available on our project website: https://xhguo7.github.io/SyncMind/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06994v1-abstract-full').style.display = 'none'; document.getElementById('2502.06994v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.06913">arXiv:2502.06913</a> <span> [<a href="https://arxiv.org/pdf/2502.06913">pdf</a>, <a href="https://arxiv.org/format/2502.06913">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</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"> A Simple yet Effective DDG Predictor is An Unsupervised Antibody Optimizer and Explainer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wu%2C+L">Lirong Wu</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yunfan Liu</a>, <a href="/search/?searchtype=author&query=Lin%2C+H">Haitao Lin</a>, <a href="/search/?searchtype=author&query=Huang%2C+Y">Yufei Huang</a>, <a href="/search/?searchtype=author&query=Zhao%2C+G">Guojiang Zhao</a>, <a href="/search/?searchtype=author&query=Gao%2C+Z">Zhifeng Gao</a>, <a href="/search/?searchtype=author&query=Li%2C+S+Z">Stan Z. Li</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="2502.06913v2-abstract-short" style="display: inline;"> The proteins that exist today have been optimized over billions of years of natural evolution, during which nature creates random mutations and selects them. The discovery of functionally promising mutations is challenged by the limited evolutionary accessible regions, i.e., only a small region on the fitness landscape is beneficial. There have been numerous priors used to constrain protein evolut… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06913v2-abstract-full').style.display = 'inline'; document.getElementById('2502.06913v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06913v2-abstract-full" style="display: none;"> The proteins that exist today have been optimized over billions of years of natural evolution, during which nature creates random mutations and selects them. The discovery of functionally promising mutations is challenged by the limited evolutionary accessible regions, i.e., only a small region on the fitness landscape is beneficial. There have been numerous priors used to constrain protein evolution to regions of landscapes with high-fitness variants, among which the change in binding free energy (DDG) of protein complexes upon mutations is one of the most commonly used priors. However, the huge mutation space poses two challenges: (1) how to improve the efficiency of DDG prediction for fast mutation screening; and (2) how to explain mutation preferences and efficiently explore accessible evolutionary regions. To address these challenges, we propose a lightweight DDG predictor (Light-DDG), which adopts a structure-aware Transformer as the backbone and enhances it by knowledge distilled from existing powerful but computationally heavy DDG predictors. Additionally, we augmented, annotated, and released a large-scale dataset containing millions of mutation data for pre-training Light-DDG. We find that such a simple yet effective Light-DDG can serve as a good unsupervised antibody optimizer and explainer. For the target antibody, we propose a novel Mutation Explainer to learn mutation preferences, which accounts for the marginal benefit of each mutation per residue. To further explore accessible evolutionary regions, we conduct preference-guided antibody optimization and evaluate antibody candidates quickly using Light-DDG to identify desirable mutations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06913v2-abstract-full').style.display = 'none'; document.getElementById('2502.06913v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.06782">arXiv:2502.06782</a> <span> [<a href="https://arxiv.org/pdf/2502.06782">pdf</a>, <a href="https://arxiv.org/format/2502.06782">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"> Lumina-Video: Efficient and Flexible Video Generation with Multi-scale Next-DiT </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+D">Dongyang Liu</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shicheng Li</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yutong Liu</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zhen Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+K">Kai Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xinyue Li</a>, <a href="/search/?searchtype=author&query=Qin%2C+Q">Qi Qin</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yufei Liu</a>, <a href="/search/?searchtype=author&query=Xin%2C+Y">Yi Xin</a>, <a href="/search/?searchtype=author&query=Li%2C+Z">Zhongyu Li</a>, <a href="/search/?searchtype=author&query=Fu%2C+B">Bin Fu</a>, <a href="/search/?searchtype=author&query=Si%2C+C">Chenyang Si</a>, <a href="/search/?searchtype=author&query=Cao%2C+Y">Yuewen Cao</a>, <a href="/search/?searchtype=author&query=He%2C+C">Conghui He</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Ziwei Liu</a>, <a href="/search/?searchtype=author&query=Qiao%2C+Y">Yu Qiao</a>, <a href="/search/?searchtype=author&query=Hou%2C+Q">Qibin Hou</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Hongsheng Li</a>, <a href="/search/?searchtype=author&query=Gao%2C+P">Peng Gao</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="2502.06782v2-abstract-short" style="display: inline;"> Recent advancements have established Diffusion Transformers (DiTs) as a dominant framework in generative modeling. Building on this success, Lumina-Next achieves exceptional performance in the generation of photorealistic images with Next-DiT. However, its potential for video generation remains largely untapped, with significant challenges in modeling the spatiotemporal complexity inherent to vide… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06782v2-abstract-full').style.display = 'inline'; document.getElementById('2502.06782v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06782v2-abstract-full" style="display: none;"> Recent advancements have established Diffusion Transformers (DiTs) as a dominant framework in generative modeling. Building on this success, Lumina-Next achieves exceptional performance in the generation of photorealistic images with Next-DiT. However, its potential for video generation remains largely untapped, with significant challenges in modeling the spatiotemporal complexity inherent to video data. To address this, we introduce Lumina-Video, a framework that leverages the strengths of Next-DiT while introducing tailored solutions for video synthesis. Lumina-Video incorporates a Multi-scale Next-DiT architecture, which jointly learns multiple patchifications to enhance both efficiency and flexibility. By incorporating the motion score as an explicit condition, Lumina-Video also enables direct control of generated videos' dynamic degree. Combined with a progressive training scheme with increasingly higher resolution and FPS, and a multi-source training scheme with mixed natural and synthetic data, Lumina-Video achieves remarkable aesthetic quality and motion smoothness at high training and inference efficiency. We additionally propose Lumina-V2A, a video-to-audio model based on Next-DiT, to create synchronized sounds for generated videos. Codes are released at https://www.github.com/Alpha-VLLM/Lumina-Video. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06782v2-abstract-full').style.display = 'none'; document.getElementById('2502.06782v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.06781">arXiv:2502.06781</a> <span> [<a href="https://arxiv.org/pdf/2502.06781">pdf</a>, <a href="https://arxiv.org/format/2502.06781">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Lyu%2C+C">Chengqi Lyu</a>, <a href="/search/?searchtype=author&query=Gao%2C+S">Songyang Gao</a>, <a href="/search/?searchtype=author&query=Gu%2C+Y">Yuzhe Gu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wenwei Zhang</a>, <a href="/search/?searchtype=author&query=Gao%2C+J">Jianfei Gao</a>, <a href="/search/?searchtype=author&query=Liu%2C+K">Kuikun Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Ziyi Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shuaibin Li</a>, <a href="/search/?searchtype=author&query=Zhao%2C+Q">Qian Zhao</a>, <a href="/search/?searchtype=author&query=Huang%2C+H">Haian Huang</a>, <a href="/search/?searchtype=author&query=Cao%2C+W">Weihan Cao</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jiangning Liu</a>, <a href="/search/?searchtype=author&query=Liu%2C+H">Hongwei Liu</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Junnan Liu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+S">Songyang Zhang</a>, <a href="/search/?searchtype=author&query=Lin%2C+D">Dahua Lin</a>, <a href="/search/?searchtype=author&query=Chen%2C+K">Kai Chen</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="2502.06781v1-abstract-short" style="display: inline;"> Reasoning abilities, especially those for solving complex math problems, are crucial components of general intelligence. Recent advances by proprietary companies, such as o-series models of OpenAI, have made remarkable progress on reasoning tasks. However, the complete technical details remain unrevealed, and the techniques that are believed certainly to be adopted are only reinforcement learning… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06781v1-abstract-full').style.display = 'inline'; document.getElementById('2502.06781v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06781v1-abstract-full" style="display: none;"> Reasoning abilities, especially those for solving complex math problems, are crucial components of general intelligence. Recent advances by proprietary companies, such as o-series models of OpenAI, have made remarkable progress on reasoning tasks. However, the complete technical details remain unrevealed, and the techniques that are believed certainly to be adopted are only reinforcement learning (RL) and the long chain of thoughts. This paper proposes a new RL framework, termed OREAL, to pursue the performance limit that can be achieved through \textbf{O}utcome \textbf{RE}w\textbf{A}rd-based reinforcement \textbf{L}earning for mathematical reasoning tasks, where only binary outcome rewards are easily accessible. We theoretically prove that behavior cloning on positive trajectories from best-of-N (BoN) sampling is sufficient to learn the KL-regularized optimal policy in binary feedback environments. This formulation further implies that the rewards of negative samples should be reshaped to ensure the gradient consistency between positive and negative samples. To alleviate the long-existing difficulties brought by sparse rewards in RL, which are even exacerbated by the partial correctness of the long chain of thought for reasoning tasks, we further apply a token-level reward model to sample important tokens in reasoning trajectories for learning. With OREAL, for the first time, a 7B model can obtain 94.0 pass@1 accuracy on MATH-500 through RL, being on par with 32B models. OREAL-32B also surpasses previous 32B models trained by distillation with 95.0 pass@1 accuracy on MATH-500. Our investigation also indicates the importance of initial policy models and training queries for RL. Code, models, and data will be released to benefit future research\footnote{https://github.com/InternLM/OREAL}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06781v1-abstract-full').style.display = 'none'; document.getElementById('2502.06781v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">We released our code, data, and model on https://github.com/InternLM/OREAL</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.06637">arXiv:2502.06637</a> <span> [<a href="https://arxiv.org/pdf/2502.06637">pdf</a>, <a href="https://arxiv.org/format/2502.06637">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 Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=DUNE+Collaboration"> DUNE Collaboration</a>, <a href="/search/?searchtype=author&query=Abud%2C+A+A">A. Abed Abud</a>, <a href="/search/?searchtype=author&query=Acciarri%2C+R">R. Acciarri</a>, <a href="/search/?searchtype=author&query=Acero%2C+M+A">M. A. Acero</a>, <a href="/search/?searchtype=author&query=Adames%2C+M+R">M. R. Adames</a>, <a href="/search/?searchtype=author&query=Adamov%2C+G">G. Adamov</a>, <a href="/search/?searchtype=author&query=Adamowski%2C+M">M. Adamowski</a>, <a href="/search/?searchtype=author&query=Adams%2C+D">D. Adams</a>, <a href="/search/?searchtype=author&query=Adinolfi%2C+M">M. Adinolfi</a>, <a href="/search/?searchtype=author&query=Adriano%2C+C">C. Adriano</a>, <a href="/search/?searchtype=author&query=Aduszkiewicz%2C+A">A. Aduszkiewicz</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+J">J. Aguilar</a>, <a href="/search/?searchtype=author&query=Akbar%2C+F">F. Akbar</a>, <a href="/search/?searchtype=author&query=Alemanno%2C+F">F. Alemanno</a>, <a href="/search/?searchtype=author&query=Alex%2C+N+S">N. S. Alex</a>, <a href="/search/?searchtype=author&query=Allison%2C+K">K. Allison</a>, <a href="/search/?searchtype=author&query=Alrashed%2C+M">M. Alrashed</a>, <a href="/search/?searchtype=author&query=Alton%2C+A">A. Alton</a>, <a href="/search/?searchtype=author&query=Alvarez%2C+R">R. Alvarez</a>, <a href="/search/?searchtype=author&query=Alves%2C+T">T. Alves</a>, <a href="/search/?searchtype=author&query=Aman%2C+A">A. Aman</a>, <a href="/search/?searchtype=author&query=Amar%2C+H">H. Amar</a>, <a href="/search/?searchtype=author&query=Amedo%2C+P">P. Amedo</a>, <a href="/search/?searchtype=author&query=Anderson%2C+J">J. Anderson</a>, <a href="/search/?searchtype=author&query=Andreopoulos%2C+C">C. Andreopoulos</a> , et al. (1313 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="2502.06637v1-abstract-short" style="display: inline;"> The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06637v1-abstract-full').style.display = 'inline'; document.getElementById('2502.06637v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06637v1-abstract-full" style="display: none;"> The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06637v1-abstract-full').style.display = 'none'; document.getElementById('2502.06637v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">32 pages, 18 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-PUB-25-0037-LBNF </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.06635">arXiv:2502.06635</a> <span> [<a href="https://arxiv.org/pdf/2502.06635">pdf</a>, <a href="https://arxiv.org/format/2502.06635">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"> Steel-LLM:From Scratch to Open Source -- A Personal Journey in Building a Chinese-Centric LLM </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Gu%2C+Q">Qingshui Gu</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shu Li</a>, <a href="/search/?searchtype=author&query=Zheng%2C+T">Tianyu Zheng</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zhaoxiang Zhang</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="2502.06635v2-abstract-short" style="display: inline;"> Steel-LLM is a Chinese-centric language model developed from scratch with the goal of creating a high-quality, open-source model despite limited computational resources. Launched in March 2024, the project aimed to train a 1-billion-parameter model on a large-scale dataset, prioritizing transparency and the sharing of practical insights to assist others in the community. The training process prima… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06635v2-abstract-full').style.display = 'inline'; document.getElementById('2502.06635v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06635v2-abstract-full" style="display: none;"> Steel-LLM is a Chinese-centric language model developed from scratch with the goal of creating a high-quality, open-source model despite limited computational resources. Launched in March 2024, the project aimed to train a 1-billion-parameter model on a large-scale dataset, prioritizing transparency and the sharing of practical insights to assist others in the community. The training process primarily focused on Chinese data, with a small proportion of English data included, addressing gaps in existing open-source LLMs by providing a more detailed and practical account of the model-building journey. Steel-LLM has demonstrated competitive performance on benchmarks such as CEVAL and CMMLU, outperforming early models from larger institutions. This paper provides a comprehensive summary of the project's key contributions, including data collection, model design, training methodologies, and the challenges encountered along the way, offering a valuable resource for researchers and practitioners looking to develop their own LLMs. The model checkpoints and training script are available at https://github.com/zhanshijinwat/Steel-LLM. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06635v2-abstract-full').style.display = 'none'; document.getElementById('2502.06635v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.06390">arXiv:2502.06390</a> <span> [<a href="https://arxiv.org/pdf/2502.06390">pdf</a>, <a href="https://arxiv.org/format/2502.06390">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"> When Data Manipulation Meets Attack Goals: An In-depth Survey of Attacks for VLMs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Dai%2C+A">Aobotao Dai</a>, <a href="/search/?searchtype=author&query=Ma%2C+X">Xinyu Ma</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Lei Chen</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Songze Li</a>, <a href="/search/?searchtype=author&query=Wang%2C+L">Lin 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="2502.06390v2-abstract-short" style="display: inline;"> Vision-Language Models (VLMs) have gained considerable prominence in recent years due to their remarkable capability to effectively integrate and process both textual and visual information. This integration has significantly enhanced performance across a diverse spectrum of applications, such as scene perception and robotics. However, the deployment of VLMs has also given rise to critical safety… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06390v2-abstract-full').style.display = 'inline'; document.getElementById('2502.06390v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06390v2-abstract-full" style="display: none;"> Vision-Language Models (VLMs) have gained considerable prominence in recent years due to their remarkable capability to effectively integrate and process both textual and visual information. This integration has significantly enhanced performance across a diverse spectrum of applications, such as scene perception and robotics. However, the deployment of VLMs has also given rise to critical safety and security concerns, necessitating extensive research to assess the potential vulnerabilities these VLM systems may harbor. In this work, we present an in-depth survey of the attack strategies tailored for VLMs. We categorize these attacks based on their underlying objectives - namely jailbreak, camouflage, and exploitation - while also detailing the various methodologies employed for data manipulation of VLMs. Meanwhile, we outline corresponding defense mechanisms that have been proposed to mitigate these vulnerabilities. By discerning key connections and distinctions among the diverse types of attacks, we propose a compelling taxonomy for VLM attacks. Moreover, we summarize the evaluation metrics that comprehensively describe the characteristics and impact of different attacks on VLMs. Finally, we conclude with a discussion of promising future research directions that could further enhance the robustness and safety of VLMs, emphasizing the importance of ongoing exploration in this critical area of study. To facilitate community engagement, we maintain an up-to-date project page, accessible at: https://github.com/AobtDai/VLM_Attack_Paper_List. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06390v2-abstract-full').style.display = 'none'; document.getElementById('2502.06390v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.06287">arXiv:2502.06287</a> <span> [<a href="https://arxiv.org/pdf/2502.06287">pdf</a>, <a href="https://arxiv.org/format/2502.06287">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> CT-UIO: Continuous-Time UWB-Inertial-Odometer Localization Using Non-Uniform B-spline with Fewer Anchors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Sun%2C+J">Jian Sun</a>, <a href="/search/?searchtype=author&query=Sun%2C+W">Wei Sun</a>, <a href="/search/?searchtype=author&query=Zhang%2C+G">Genwei Zhang</a>, <a href="/search/?searchtype=author&query=Yang%2C+K">Kailun Yang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Song Li</a>, <a href="/search/?searchtype=author&query=Meng%2C+X">Xiangqi Meng</a>, <a href="/search/?searchtype=author&query=Deng%2C+N">Na Deng</a>, <a href="/search/?searchtype=author&query=Tan%2C+C">Chongbin Tan</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="2502.06287v1-abstract-short" style="display: inline;"> Ultra-wideband (UWB) based positioning with fewer anchors has attracted significant research interest in recent years, especially under energy-constrained conditions. However, most existing methods rely on discrete-time representations and smoothness priors to infer a robot's motion states, which often struggle with ensuring multi-sensor data synchronization. In this paper, we present an efficient… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06287v1-abstract-full').style.display = 'inline'; document.getElementById('2502.06287v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06287v1-abstract-full" style="display: none;"> Ultra-wideband (UWB) based positioning with fewer anchors has attracted significant research interest in recent years, especially under energy-constrained conditions. However, most existing methods rely on discrete-time representations and smoothness priors to infer a robot's motion states, which often struggle with ensuring multi-sensor data synchronization. In this paper, we present an efficient UWB-Inertial-odometer localization system, utilizing a non-uniform B-spline framework with fewer anchors. Unlike traditional uniform B-spline-based continuous-time methods, we introduce an adaptive knot-span adjustment strategy for non-uniform continuous-time trajectory representation. This is accomplished by adjusting control points dynamically based on movement speed. To enable efficient fusion of IMU and odometer data, we propose an improved Extended Kalman Filter (EKF) with innovation-based adaptive estimation to provide short-term accurate motion prior. Furthermore, to address the challenge of achieving a fully observable UWB localization system under few-anchor conditions, the Virtual Anchor (VA) generation method based on multiple hypotheses is proposed. At the backend, we propose a CT-UIO factor graph with an adaptive sliding window for global trajectory estimation. Comprehensive experiments conducted on corridor and exhibition hall datasets validate the proposed system's high precision and robust performance. The codebase and datasets of this work will be open-sourced at https://github.com/JasonSun623/CT-UIO. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06287v1-abstract-full').style.display = 'none'; document.getElementById('2502.06287v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">The codebase and datasets will be open-sourced at https://github.com/JasonSun623/CT-UIO</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.06179">arXiv:2502.06179</a> <span> [<a href="https://arxiv.org/pdf/2502.06179">pdf</a>, <a href="https://arxiv.org/format/2502.06179">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Actual Achieved Gain and Optimal Perceived Gain: Modeling Human Take-over Decisions Towards Automated Vehicles' Suggestions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+S">Shuning Zhang</a>, <a href="/search/?searchtype=author&query=Yi%2C+X">Xin Yi</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shixuan Li</a>, <a href="/search/?searchtype=author&query=Hong%2C+C">Chuye Hong</a>, <a href="/search/?searchtype=author&query=Chen%2C+G">Gujun Chen</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jiarui Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xueyang Wang</a>, <a href="/search/?searchtype=author&query=Hu%2C+Y">Yongquan Hu</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yuntao Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Hewu Li</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="2502.06179v1-abstract-short" style="display: inline;"> Driver decision quality in take-overs is critical for effective human-Autonomous Driving System (ADS) collaboration. However, current research lacks detailed analysis of its variations. This paper introduces two metrics--Actual Achieved Gain (AAG) and Optimal Perceived Gain (OPG)--to assess decision quality, with OPG representing optimal decisions and AAG reflecting actual outcomes. Both are calcu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06179v1-abstract-full').style.display = 'inline'; document.getElementById('2502.06179v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.06179v1-abstract-full" style="display: none;"> Driver decision quality in take-overs is critical for effective human-Autonomous Driving System (ADS) collaboration. However, current research lacks detailed analysis of its variations. This paper introduces two metrics--Actual Achieved Gain (AAG) and Optimal Perceived Gain (OPG)--to assess decision quality, with OPG representing optimal decisions and AAG reflecting actual outcomes. Both are calculated as weighted averages of perceived gains and losses, influenced by ADS accuracy. Study 1 (N=315) used a 21-point Thurstone scale to measure perceived gains and losses-key components of AAG and OPG-across typical tasks: route selection, overtaking, and collision avoidance. Studies 2 (N=54) and 3 (N=54) modeled decision quality under varying ADS accuracy and decision time. Results show with sufficient time (>3.5s), AAG converges towards OPG, indicating rational decision-making, while limited time leads to intuitive and deterministic choices. Study 3 also linked AAG-OPG deviations to irrational behaviors. An intervention study (N=8) and a pilot (N=4) employing voice alarms and multi-modal alarms based on these deviations demonstrated AAG's potential to improve decision quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.06179v1-abstract-full').style.display = 'none'; document.getElementById('2502.06179v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05988">arXiv:2502.05988</a> <span> [<a href="https://arxiv.org/pdf/2502.05988">pdf</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"> SNAT-YOLO: Efficient Cross-Layer Aggregation Network for Edge-Oriented Gangue Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S">Shang Li</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="2502.05988v1-abstract-short" style="display: inline;"> To address the issues of slow detection speed,low accuracy,difficulty in deployment on industrial edge devices,and large parameter and computational requirements in deep learning-based coal gangue target detection methods,we propose a lightweight coal gangue target detection algorithm based on an improved YOLOv11.First,we use the lightweight network ShuffleNetV2 as the backbone to enhance detectio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05988v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05988v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05988v1-abstract-full" style="display: none;"> To address the issues of slow detection speed,low accuracy,difficulty in deployment on industrial edge devices,and large parameter and computational requirements in deep learning-based coal gangue target detection methods,we propose a lightweight coal gangue target detection algorithm based on an improved YOLOv11.First,we use the lightweight network ShuffleNetV2 as the backbone to enhance detection speed.Second,we introduce a lightweight downsampling operation,ADown,which reduces model complexity while improving average detection accuracy.Third,we improve the C2PSA module in YOLOv11 by incorporating the Triplet Attention mechanism,resulting in the proposed C2PSA-TriAtt module,which enhances the model's ability to focus on different dimensions of images.Fourth,we propose the Inner-FocalerIoU loss function to replace the existing CIoU loss function.Experimental results show that our model achieves a detection accuracy of 99.10% in coal gangue detection tasks,reduces the model size by 38%,the number of parameters by 41%,and the computational cost by 40%,while decreasing the average detection time per image by 1 ms.The improved model demonstrates enhanced detection speed and accuracy,making it suitable for deployment on industrial edge mobile devices,thus contributing positively to coal processing and efficient utilization of coal resources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05988v1-abstract-full').style.display = 'none'; document.getElementById('2502.05988v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05881">arXiv:2502.05881</a> <span> [<a href="https://arxiv.org/pdf/2502.05881">pdf</a>, <a href="https://arxiv.org/ps/2502.05881">ps</a>, <a href="https://arxiv.org/format/2502.05881">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="General Relativity and Quantum Cosmology">gr-qc</span> </div> </div> <p class="title is-5 mathjax"> Does acceleration always degrade quantum entanglement for tetrapartite Unruh-DeWitt detectors? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S">Si-Han Li</a>, <a href="/search/?searchtype=author&query=Shang%2C+S">Si-Han Shang</a>, <a href="/search/?searchtype=author&query=Wu%2C+S">Shu-Min Wu</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="2502.05881v1-abstract-short" style="display: inline;"> Previous studies have shown that the Unruh effect completely destroys quantum entanglement and coherence of bipartite states, as modeled by entangled Unruh-DeWitt detectors. But does the Unruh effect have a different impact on quantum entanglement of multipartite states within this framework? In this paper, we investigate the influence of the Unruh effect on 1-3 entanglement in the context of enta… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05881v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05881v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05881v1-abstract-full" style="display: none;"> Previous studies have shown that the Unruh effect completely destroys quantum entanglement and coherence of bipartite states, as modeled by entangled Unruh-DeWitt detectors. But does the Unruh effect have a different impact on quantum entanglement of multipartite states within this framework? In this paper, we investigate the influence of the Unruh effect on 1-3 entanglement in the context of entangled tetra-partite Unruh-DeWitt detectors. We find that quantum entanglement of tetrapartite W state first decreases to a minimum value and then increases to a fixed value with the growth of the acceleration. This indicates that the Unruh effect can, under certain conditions, enhance quantum entanglement. In other words, the Unruh effect plays a dual role in the behavior of quantum entanglement-both diminishing and enhancing it. This discovery challenges and overturns the traditional view that the Unruh effect is solely detrimental to quantum entanglement and coherence in entangled Unruh-DeWitt detectors, offering a fresh and profound perspective on its impact. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05881v1-abstract-full').style.display = 'none'; document.getElementById('2502.05881v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">19 pages, 3 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/2502.05755">arXiv:2502.05755</a> <span> [<a href="https://arxiv.org/pdf/2502.05755">pdf</a>, <a href="https://arxiv.org/format/2502.05755">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"> Filter, Obstruct and Dilute: Defending Against Backdoor Attacks on Semi-Supervised Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+X">Xinrui Wang</a>, <a href="/search/?searchtype=author&query=Geng%2C+C">Chuanxing Geng</a>, <a href="/search/?searchtype=author&query=Wan%2C+W">Wenhai Wan</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shao-yuan Li</a>, <a href="/search/?searchtype=author&query=Chen%2C+S">Songcan Chen</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="2502.05755v1-abstract-short" style="display: inline;"> Recent studies have verified that semi-supervised learning (SSL) is vulnerable to data poisoning backdoor attacks. Even a tiny fraction of contaminated training data is sufficient for adversaries to manipulate up to 90\% of the test outputs in existing SSL methods. Given the emerging threat of backdoor attacks designed for SSL, this work aims to protect SSL against such risks, marking it as one of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05755v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05755v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05755v1-abstract-full" style="display: none;"> Recent studies have verified that semi-supervised learning (SSL) is vulnerable to data poisoning backdoor attacks. Even a tiny fraction of contaminated training data is sufficient for adversaries to manipulate up to 90\% of the test outputs in existing SSL methods. Given the emerging threat of backdoor attacks designed for SSL, this work aims to protect SSL against such risks, marking it as one of the few known efforts in this area. Specifically, we begin by identifying that the spurious correlations between the backdoor triggers and the target class implanted by adversaries are the primary cause of manipulated model predictions during the test phase. To disrupt these correlations, we utilize three key techniques: Gaussian Filter, complementary learning and trigger mix-up, which collectively filter, obstruct and dilute the influence of backdoor attacks in both data pre-processing and feature learning. Experimental results demonstrate that our proposed method, Backdoor Invalidator (BI), significantly reduces the average attack success rate from 84.7\% to 1.8\% across different state-of-the-art backdoor attacks. It is also worth mentioning that BI does not sacrifice accuracy on clean data and is supported by a theoretical guarantee of its generalization capability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05755v1-abstract-full').style.display = 'none'; document.getElementById('2502.05755v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05584">arXiv:2502.05584</a> <span> [<a href="https://arxiv.org/pdf/2502.05584">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> </div> </div> <p class="title is-5 mathjax"> Softening of Vibrational Modes and Anharmonicity Induced Thermal Conductivity Reduction in a-Si:H at High Temperatures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhuo Chen</a>, <a href="/search/?searchtype=author&query=Yuan%2C+Y">Yuejin Yuan</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yanzhou Wang</a>, <a href="/search/?searchtype=author&query=Ying%2C+P">Penghua Ying</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shouhang Li</a>, <a href="/search/?searchtype=author&query=Shao%2C+C">Cheng Shao</a>, <a href="/search/?searchtype=author&query=Ding%2C+W">Wenyang Ding</a>, <a href="/search/?searchtype=author&query=Zhang%2C+G">Gang Zhang</a>, <a href="/search/?searchtype=author&query=An%2C+M">Meng An</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="2502.05584v2-abstract-short" style="display: inline;"> Hydrogenated amorphous silicon (a-Si:H) has garnered considerable attention in the semiconductor industry, particularly for its use in solar cells and passivation layers for high performance silicon solar cells, owing to its exceptional photoelectric properties and scalable manufacturing processes. A comprehensive understanding of thermal transport mechanism in a-Si:H is essential for optimizing t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05584v2-abstract-full').style.display = 'inline'; document.getElementById('2502.05584v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05584v2-abstract-full" style="display: none;"> Hydrogenated amorphous silicon (a-Si:H) has garnered considerable attention in the semiconductor industry, particularly for its use in solar cells and passivation layers for high performance silicon solar cells, owing to its exceptional photoelectric properties and scalable manufacturing processes. A comprehensive understanding of thermal transport mechanism in a-Si:H is essential for optimizing thermal management and ensuring the reliable operation of these devices. In this study, we developed a neuroevolution machine learning potential based on first-principles calculations of energy, forces, and virial, which enables accurate modeling of interatomic interactions in both a-Si:H and a-Si systems. Using the homogeneous nonequilibrium molecular dynamics (HNEMD) method, we systematically investigated the thermal conductivity of a-Si:H and a-Si across a temperature range of 300-1000 K and hydrogen concentrations ranging from 6 to 12 at%. Our simulation results found that thermal conductivity of a-Si:H with 12 at% hydrogen was significantly reduced by 12% compared to that of a-Si at 300 K. We analyzed the spectral thermal conductivity, vibrational density of states and lifetimes of vibrational modes, and revealed the softening of vibrational modes and anharmonicity effects contribute to the reduction of thermal conductivity as temperature and hydrogen concentration increase. Furthermore, the influence of hydrogen concentration and temperature on diffuson and propagon contribution to thermal conductivity of a-Si:H was revealed. This study provides valuable insights for developing thermal management strategies in silicon-based semiconducting devices and advances the understanding of thermal transport in amorphous systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05584v2-abstract-full').style.display = 'none'; document.getElementById('2502.05584v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 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">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 6 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/2502.05580">arXiv:2502.05580</a> <span> [<a href="https://arxiv.org/pdf/2502.05580">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> </div> </div> <p class="title is-5 mathjax"> Hyperparameter Optimization and Force Error Correction of Neuroevolution Potential for Predicting Thermal Conductivity of Wurtzite GaN </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+Z">Zhuo Chen</a>, <a href="/search/?searchtype=author&query=Yuan%2C+Y">Yuejin Yuan</a>, <a href="/search/?searchtype=author&query=Ding%2C+W">Wenyang Ding</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shouhang Li</a>, <a href="/search/?searchtype=author&query=An%2C+M">Meng An</a>, <a href="/search/?searchtype=author&query=Zhang%2C+G">Gang Zhang</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="2502.05580v2-abstract-short" style="display: inline;"> As a representative of wide-bandgap semiconductors, wurtzite gallium nitride (GaN) has been widely utilized in high-power devices due to high breakdown voltage and low specific on resistance. Accurate prediction of wurtzite GaN thermal conductivity is a prerequisite for designing effective thermal management systems of electronic applications. Machine learning driven molecular dynamics simulation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05580v2-abstract-full').style.display = 'inline'; document.getElementById('2502.05580v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05580v2-abstract-full" style="display: none;"> As a representative of wide-bandgap semiconductors, wurtzite gallium nitride (GaN) has been widely utilized in high-power devices due to high breakdown voltage and low specific on resistance. Accurate prediction of wurtzite GaN thermal conductivity is a prerequisite for designing effective thermal management systems of electronic applications. Machine learning driven molecular dynamics simulation offers a promising approach to predicting the thermal conductivity of large-scale systems without requiring predefined parameters. However, these methods often underestimate the thermal conductivity of materials with inherently high thermal conductivity due to the large predicted force error compared with first-principle calculation, posing a critical challenge for their broader application. In this study, we successfully developed a neuroevolution potential for wurtzite GaN and accurately predicted its thermal conductivity, 259 W/m-K at room temperatue, achieving excellent agreement with reported experimental measurements. The hyperparameters of neuroevolution potential (NEP) were optimized based on systematic analysis of reproduced energy and force, structural feature, computational efficiency. Furthermore, a force prediction error correction method was implemented, effectively reducing the error caused by the additional force noise in the Langevin thermostat by extrapolating to the zero-force error limit. This study provides valuable insights and hold significant implication for advancing efficient thermal management technologies in wide bandgap semiconductor devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05580v2-abstract-full').style.display = 'none'; document.getElementById('2502.05580v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 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">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 5 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/2502.05491">arXiv:2502.05491</a> <span> [<a href="https://arxiv.org/pdf/2502.05491">pdf</a>, <a href="https://arxiv.org/format/2502.05491">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Lie-algebra Adaptive Tracking Control for Rigid Body Dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Tang%2C+J">Jiawei Tang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shilei Li</a>, <a href="/search/?searchtype=author&query=Shi%2C+L">Ling Shi</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="2502.05491v1-abstract-short" style="display: inline;"> Adaptive tracking control for rigid body dynamics is of critical importance in control and robotics, particularly for addressing uncertainties or variations in system model parameters. However, most existing adaptive control methods are designed for systems with states in vector spaces, often neglecting the manifold constraints inherent to robotic systems. In this work, we propose a novel Lie-alge… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05491v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05491v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05491v1-abstract-full" style="display: none;"> Adaptive tracking control for rigid body dynamics is of critical importance in control and robotics, particularly for addressing uncertainties or variations in system model parameters. However, most existing adaptive control methods are designed for systems with states in vector spaces, often neglecting the manifold constraints inherent to robotic systems. In this work, we propose a novel Lie-algebra-based adaptive control method that leverages the intrinsic relationship between the special Euclidean group and its associated Lie algebra. By transforming the state space from the group manifold to a vector space, we derive a linear error dynamics model that decouples model parameters from the system state. This formulation enables the development of an adaptive optimal control method that is both geometrically consistent and computationally efficient. Extensive simulations demonstrate the effectiveness and efficiency of the proposed method. We have made our source code publicly available to the community to support further research and collaboration. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05491v1-abstract-full').style.display = 'none'; document.getElementById('2502.05491v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05490">arXiv:2502.05490</a> <span> [<a href="https://arxiv.org/pdf/2502.05490">pdf</a>, <a href="https://arxiv.org/format/2502.05490">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantum Physics">quant-ph</span> </div> </div> <p class="title is-5 mathjax"> Stark Shift from Quantum Defects in Hexagonal Boron Nitride </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+P">Pei Li</a>, <a href="/search/?searchtype=author&query=Xu%2C+R">Ran Xu</a>, <a href="/search/?searchtype=author&query=Huang%2C+B">Bing Huang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Song Li</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="2502.05490v1-abstract-short" style="display: inline;"> Color centers in hexagonal boron nitride have emerged as promising candidates for quantum information applications, owing to their efficient and bright single photon emission. Despite the challenges in directly characterizing these emitters, the interaction between external fields and defects, such as the Stark shift, offers valuable insights into their local geometric configurations. In this stud… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05490v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05490v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05490v1-abstract-full" style="display: none;"> Color centers in hexagonal boron nitride have emerged as promising candidates for quantum information applications, owing to their efficient and bright single photon emission. Despite the challenges in directly characterizing these emitters, the interaction between external fields and defects, such as the Stark shift, offers valuable insights into their local geometric configurations. In this study, we focus on clarifying the possible origin of the distinct Stark shift characteristics observed experimentally, particularly in the emission range around 2 eV. We find that the local symmetry of the defects plays a crucial role in determining the nature of the Stark shift, which can be either linear or quadratic. Additionally, the local dielectric environment significantly influences the Stark shift response. Our calculations not only enhance the understanding of the micro-structure of these hitherto unknown emitters but also pave the way for their more effective utilization as single-photon sources and qubits in quantum technologies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05490v1-abstract-full').style.display = 'none'; document.getElementById('2502.05490v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 4 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/2502.05378">arXiv:2502.05378</a> <span> [<a href="https://arxiv.org/pdf/2502.05378">pdf</a>, <a href="https://arxiv.org/format/2502.05378">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="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> NextBestPath: Efficient 3D Mapping of Unseen Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S">Shiyao Li</a>, <a href="/search/?searchtype=author&query=Gu%C3%A9don%2C+A">Antoine Gu茅don</a>, <a href="/search/?searchtype=author&query=Boittiaux%2C+C">Cl茅mentin Boittiaux</a>, <a href="/search/?searchtype=author&query=Chen%2C+S">Shizhe Chen</a>, <a href="/search/?searchtype=author&query=Lepetit%2C+V">Vincent Lepetit</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="2502.05378v1-abstract-short" style="display: inline;"> This work addresses the problem of active 3D mapping, where an agent must find an efficient trajectory to exhaustively reconstruct a new scene. Previous approaches mainly predict the next best view near the agent's location, which is prone to getting stuck in local areas. Additionally, existing indoor datasets are insufficient due to limited geometric complexity and inaccurate ground truth meshes.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05378v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05378v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05378v1-abstract-full" style="display: none;"> This work addresses the problem of active 3D mapping, where an agent must find an efficient trajectory to exhaustively reconstruct a new scene. Previous approaches mainly predict the next best view near the agent's location, which is prone to getting stuck in local areas. Additionally, existing indoor datasets are insufficient due to limited geometric complexity and inaccurate ground truth meshes. To overcome these limitations, we introduce a novel dataset AiMDoom with a map generator for the Doom video game, enabling to better benchmark active 3D mapping in diverse indoor environments. Moreover, we propose a new method we call next-best-path (NBP), which predicts long-term goals rather than focusing solely on short-sighted views. The model jointly predicts accumulated surface coverage gains for long-term goals and obstacle maps, allowing it to efficiently plan optimal paths with a unified model. By leveraging online data collection, data augmentation and curriculum learning, NBP significantly outperforms state-of-the-art methods on both the existing MP3D dataset and our AiMDoom dataset, achieving more efficient mapping in indoor environments of varying complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05378v1-abstract-full').style.display = 'none'; document.getElementById('2502.05378v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">To appear at ICLR 2025. Project webpage: https://shiyao-li.github.io/nbp/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.05282">arXiv:2502.05282</a> <span> [<a href="https://arxiv.org/pdf/2502.05282">pdf</a>, <a href="https://arxiv.org/format/2502.05282">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> </div> </div> <p class="title is-5 mathjax"> Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=He%2C+Y">Yuting He</a>, <a href="/search/?searchtype=author&query=Wang%2C+B">Boyu Wang</a>, <a href="/search/?searchtype=author&query=Ge%2C+R">Rongjun Ge</a>, <a href="/search/?searchtype=author&query=Chen%2C+Y">Yang Chen</a>, <a href="/search/?searchtype=author&query=Yang%2C+G">Guanyu Yang</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shuo Li</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="2502.05282v1-abstract-short" style="display: inline;"> Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image-dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05282v1-abstract-full').style.display = 'inline'; document.getElementById('2502.05282v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05282v1-abstract-full" style="display: none;"> Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image-dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We propose a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels' correspondence under topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via a gradient. We also propose a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code on a companion link: https://github.com/YutingHe-list/GEMINI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05282v1-abstract-full').style.display = 'none'; document.getElementById('2502.05282v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by T-PAMI 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/2502.05259">arXiv:2502.05259</a> <span> [<a href="https://arxiv.org/pdf/2502.05259">pdf</a>, <a href="https://arxiv.org/format/2502.05259">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"> JAGB 2.0: Improved Constraints on the J-region Asymptotic Giant Branch-based Hubble Constant from an Expanded Sample of JWST Observations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Li%2C+S">Siyang Li</a>, <a href="/search/?searchtype=author&query=Riess%2C+A+G">Adam G. Riess</a>, <a href="/search/?searchtype=author&query=Scolnic%2C+D">Daniel Scolnic</a>, <a href="/search/?searchtype=author&query=Casertano%2C+S">Stefano Casertano</a>, <a href="/search/?searchtype=author&query=Anand%2C+G+S">Gagandeep S. Anand</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="2502.05259v2-abstract-short" style="display: inline;"> The J-region Asymptotic Giant Branch (JAGB) is an overdensity of stars in the near-infrared, attributed to carbon-rich asymptotic giant branch stars, and recently used as a standard candle for measuring extragalactic distances and the Hubble constant. Using JWST in Cycle 2, we extend JAGB measurements to 6 hosts of 9 Type Ia supernovae (SNe Ia) (NGC 2525, NGC 3147, NGC 3370, NGC 3447, NGC 5468, an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05259v2-abstract-full').style.display = 'inline'; document.getElementById('2502.05259v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.05259v2-abstract-full" style="display: none;"> The J-region Asymptotic Giant Branch (JAGB) is an overdensity of stars in the near-infrared, attributed to carbon-rich asymptotic giant branch stars, and recently used as a standard candle for measuring extragalactic distances and the Hubble constant. Using JWST in Cycle 2, we extend JAGB measurements to 6 hosts of 9 Type Ia supernovae (SNe Ia) (NGC 2525, NGC 3147, NGC 3370, NGC 3447, NGC 5468, and NGC 5861), with two at $D \sim 40$ Mpc, all calibrated by the maser host NGC 4258. We investigate the effects of incompleteness and find that we are unable to recover a robust JAGB measurement in one of the two most distant hosts at $R \sim 40$ Mpc, NGC 3147. We compile all JWST JAGB observations in SNe Ia hosts, 15 galaxies hosting 18 SNe Ia, from the SH0ES and CCHP programs and employ all literature measures (mode, mean, median, model). We find no significant mean difference between these distances and those from HST Cepheids, $-0.03\pm0.02$ (stat) $\pm$ 0.05 (sys) mag. We find a difference of 0.11 $\pm$ 0.02 mag between JAGB mode measurements in the CCHP analyses of two fields in NGC 4258, a feature also seen in two SH0ES fields (see field-to-field variations in Li et al. 2024a), indicating significant field-to-field variation of JAGB measurements in NGC 4258 which produce a large absolute calibration uncertainty. Variations are also seen in the shape of the JAGB LF across galaxies so that different measures produce different values of the Hubble constant. We look for but do not (yet) find a standardizing relation between JAGB LF skew or color dependence and the apparent variation. Using the middle result of all JAGB measures to calibrate SNe Ia yields a Hubble constant of $H_0$ = 73.3 $\pm$ 1.4 (stat) $\pm$ 2.0 (sys) km/s/Mpc with the systematic dominated by apparent differences across NGC 4258 calibrating fields or their measures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.05259v2-abstract-full').style.display = 'none'; document.getElementById('2502.05259v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 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">Comments:</span> <span class="has-text-grey-dark mathjax">29 pages, 18 figures, 7 tables, submitted to ApJ</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.04848">arXiv:2502.04848</a> <span> [<a href="https://arxiv.org/pdf/2502.04848">pdf</a>, <a href="https://arxiv.org/format/2502.04848">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"> Broadband $纬$-ray spectrum of supernova remnant Cassiopeia A </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhen Cao</a>, <a href="/search/?searchtype=author&query=Aharonian%2C+F">F. Aharonian</a>, <a href="/search/?searchtype=author&query=Bai%2C+Y+X">Y. X. Bai</a>, <a href="/search/?searchtype=author&query=Bao%2C+Y+W">Y. W. Bao</a>, <a href="/search/?searchtype=author&query=Bastieri%2C+D">D. Bastieri</a>, <a href="/search/?searchtype=author&query=Bi%2C+X+J">X. J. Bi</a>, <a href="/search/?searchtype=author&query=Bi%2C+Y+J">Y. J. Bi</a>, <a href="/search/?searchtype=author&query=Bian%2C+W">W. Bian</a>, <a href="/search/?searchtype=author&query=Bukevich%2C+A+V">A. V. Bukevich</a>, <a href="/search/?searchtype=author&query=Cai%2C+C+M">C. M. Cai</a>, <a href="/search/?searchtype=author&query=Cao%2C+W+Y">W. Y. Cao</a>, <a href="/search/?searchtype=author&query=Cao%2C+Z">Zhe Cao</a>, <a href="/search/?searchtype=author&query=Chang%2C+J">J. Chang</a>, <a href="/search/?searchtype=author&query=Chang%2C+J+F">J. F. Chang</a>, <a href="/search/?searchtype=author&query=Chen%2C+A+M">A. M. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+E+S">E. S. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+H+X">H. X. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Liang Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+L">Long Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+J">M. J. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+M+L">M. L. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q+H">Q. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S">S. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+H">S. H. Chen</a>, <a href="/search/?searchtype=author&query=Chen%2C+S+Z">S. Z. Chen</a> , et al. (293 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="2502.04848v1-abstract-short" style="display: inline;"> The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of $\sim$ 2.5 $\arcmin$. Although no extension of this source has been detected in the $纬$-ray band, using more than 1000 days of LHAASO data above $\sim 0.8$ TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telesc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04848v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04848v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04848v1-abstract-full" style="display: none;"> The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of $\sim$ 2.5 $\arcmin$. Although no extension of this source has been detected in the $纬$-ray band, using more than 1000 days of LHAASO data above $\sim 0.8$ TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telescopes (IACTs) and its flux near $\sim 1$ TeV is about two times higher. In combination with analyses of more than 16 years of \textit{Fermi}-LAT data covering $0.1 \, \mathrm{GeV} - 1 \, \mathrm{TeV}$, we find that the spectrum above 30 GeV deviates significantly from a single power-law, and is best described by a smoothly broken power-law with a spectral index of $1.90 \pm 0.15_\mathrm{stat}$ ($3.41 \pm 0.19_\mathrm{stat}$) below (above) a break energy of $0.63 \pm 0.21_\mathrm{stat} \, \mathrm{TeV}$. Given differences in the angular resolution of LHAASO-WCDA and IACTs, TeV $纬$-ray emission detected with LHAASO may have a significant contribution from regions surrounding the SNR illuminated by particles accelerated earlier, which, however, are treated as background by IACTs. Detailed modelling can be used to constrain acceleration processes of TeV particles in the early stage of SNR evolution. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04848v1-abstract-full').style.display = 'none'; document.getElementById('2502.04848v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.04735">arXiv:2502.04735</a> <span> [<a href="https://arxiv.org/pdf/2502.04735">pdf</a>, <a href="https://arxiv.org/format/2502.04735">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Affine Frequency Division Multiplexing: Extending OFDM for Scenario-Flexibility and Resilience </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yin%2C+H">Haoran Yin</a>, <a href="/search/?searchtype=author&query=Tang%2C+Y">Yanqun Tang</a>, <a href="/search/?searchtype=author&query=Bemani%2C+A">Ali Bemani</a>, <a href="/search/?searchtype=author&query=Kountouris%2C+M">Marios Kountouris</a>, <a href="/search/?searchtype=author&query=Zhou%2C+Y">Yu Zhou</a>, <a href="/search/?searchtype=author&query=Zhang%2C+X">Xingyao Zhang</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yuqing Liu</a>, <a href="/search/?searchtype=author&query=Chen%2C+G">Gaojie Chen</a>, <a href="/search/?searchtype=author&query=Yang%2C+K">Kai Yang</a>, <a href="/search/?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/?searchtype=author&query=Masouros%2C+C">Christos Masouros</a>, <a href="/search/?searchtype=author&query=Li%2C+S">Shuangyang Li</a>, <a href="/search/?searchtype=author&query=Caire%2C+G">Giuseppe Caire</a>, <a href="/search/?searchtype=author&query=Xiao%2C+P">Pei Xiao</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="2502.04735v1-abstract-short" style="display: inline;"> Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04735v1-abstract-full').style.display = 'inline'; document.getElementById('2502.04735v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.04735v1-abstract-full" style="display: none;"> Next-generation wireless networks are conceived to provide reliable and high-data-rate communication services for diverse scenarios, such as vehicle-to-vehicle, unmanned aerial vehicles, and satellite networks. The severe Doppler spreads in the underlying time-varying channels induce destructive inter-carrier interference (ICI) in the extensively adopted orthogonal frequency division multiplexing (OFDM) waveform, leading to severe performance degradation. This calls for a new air interface design that can accommodate the severe delay-Doppler spreads in highly dynamic channels while possessing sufficient flexibility to cater to various applications. This article provides a comprehensive overview of a promising chirp-based waveform named affine frequency division multiplexing (AFDM). It is featured with two tunable parameters and achieves optimal diversity order in doubly dispersive channels (DDC). We study the fundamental principle of AFDM, illustrating its intrinsic suitability for DDC. Based on that, several potential applications of AFDM are explored. Furthermore, the major challenges and the corresponding solutions of AFDM are presented, followed by several future research directions. Finally, we draw some instructive conclusions about AFDM, hoping to provide useful inspiration for its development. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.04735v1-abstract-full').style.display = 'none'; document.getElementById('2502.04735v1-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 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">Comments:</span> <span class="has-text-grey-dark mathjax">Magazine paper submitted to IEEE</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=Li%2C+S&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Li%2C+S&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+S&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+S&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+S&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a href="/search/?searchtype=author&query=Li%2C+S&start=200" class="pagination-link " aria-label="Page 5" aria-current="page">5 </a> </li> <li><span class="pagination-ellipsis">…</span></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