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 840 results for author: <span class="mathjax">Ding, J</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="Ding, J"> </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=Ding%2C+J&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="Ding, J"> <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=Ding%2C+J&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Ding%2C+J&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Ding%2C+J&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Ding%2C+J&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Ding%2C+J&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a href="/search/?searchtype=author&query=Ding%2C+J&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/2411.17482">arXiv:2411.17482</a> <span> [<a href="https://arxiv.org/pdf/2411.17482">pdf</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="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> An Improved Quantum Algorithm of the Multislice Method </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+Y+C">Y. C. Wang</a>, <a href="/search/?searchtype=author&query=Sun%2C+Y">Y. Sun</a>, <a href="/search/?searchtype=author&query=Ding%2C+Z+J">Z. J. Ding</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.17482v1-abstract-short" style="display: inline;"> The multisilce method is an important algorithm for electron diffraction and image simulations in transmission electron microscopy. We have proposed a quantum algorithm of the multislice method based on quantum circuit model previously. In this work we have developed an improved quantum algorithm. We reconstruct the phase-shifting quantum circuit without using the multi-controlled quantum gates, t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.17482v1-abstract-full').style.display = 'inline'; document.getElementById('2411.17482v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.17482v1-abstract-full" style="display: none;"> The multisilce method is an important algorithm for electron diffraction and image simulations in transmission electron microscopy. We have proposed a quantum algorithm of the multislice method based on quantum circuit model previously. In this work we have developed an improved quantum algorithm. We reconstruct the phase-shifting quantum circuit without using the multi-controlled quantum gates, thereby significantly improve the computation efficiency. The new quantum circuit also allows further gate count reduction at the cost of a controllable error. We have simulated the quantum circuit on a classical supercomputer and analyzed the result to prove the feasibility and correctness of the improved quantum algorithm. We also provide proper parameter settings through testing, allowing the minimization of the necessary number of quantum gates while limiting the relative error within 1%. This work demonstrates the potential of applying quantum computing to electron diffraction simulations and achieving quantum advantages. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.17482v1-abstract-full').style.display = 'none'; document.getElementById('2411.17482v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">26 pages, 15 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14499">arXiv:2411.14499</a> <span> [<a href="https://arxiv.org/pdf/2411.14499">pdf</a>, <a href="https://arxiv.org/format/2411.14499">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"> Understanding World or Predicting Future? A Comprehensive Survey of World Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jingtao Ding</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yunke Zhang</a>, <a href="/search/?searchtype=author&query=Shang%2C+Y">Yu Shang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yuheng Zhang</a>, <a href="/search/?searchtype=author&query=Zong%2C+Z">Zefang Zong</a>, <a href="/search/?searchtype=author&query=Feng%2C+J">Jie Feng</a>, <a href="/search/?searchtype=author&query=Yuan%2C+Y">Yuan Yuan</a>, <a href="/search/?searchtype=author&query=Su%2C+H">Hongyuan Su</a>, <a href="/search/?searchtype=author&query=Li%2C+N">Nian Li</a>, <a href="/search/?searchtype=author&query=Sukiennik%2C+N">Nicholas Sukiennik</a>, <a href="/search/?searchtype=author&query=Xu%2C+F">Fengli Xu</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yong 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="2411.14499v1-abstract-short" style="display: inline;"> The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence. This survey offers a comprehensive review of the literature on world models. Generally, world models are regarded as tools for either understanding the pres… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14499v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14499v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14499v1-abstract-full" style="display: none;"> The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial general intelligence. This survey offers a comprehensive review of the literature on world models. Generally, world models are regarded as tools for either understanding the present state of the world or predicting its future dynamics. This review presents a systematic categorization of world models, emphasizing two primary functions: (1) constructing internal representations to understand the mechanisms of the world, and (2) predicting future states to simulate and guide decision-making. Initially, we examine the current progress in these two categories. We then explore the application of world models in key domains, including autonomous driving, robotics, and social simulacra, with a focus on how each domain utilizes these aspects. Finally, we outline key challenges and provide insights into potential future research directions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14499v1-abstract-full').style.display = 'none'; document.getElementById('2411.14499v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14061">arXiv:2411.14061</a> <span> [<a href="https://arxiv.org/pdf/2411.14061">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="Chemical Physics">physics.chem-ph</span> </div> </div> <p class="title is-5 mathjax"> One-step Synthesis of Cubic Gauche Polymeric Nitrogen with High Yield Just by Heating </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wu%2C+L">Liangfei Wu</a>, <a href="/search/?searchtype=author&query=Xu%2C+Y">Yuxuan Xu</a>, <a href="/search/?searchtype=author&query=Chen%2C+G">Guo Chen</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Junfeng Ding</a>, <a href="/search/?searchtype=author&query=Li%2C+M">Ming Li</a>, <a href="/search/?searchtype=author&query=Zeng%2C+Z">Zhi Zeng</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xianlong 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="2411.14061v1-abstract-short" style="display: inline;"> A high-efficient one-step synthesis of cubic gauche polymeric nitrogen was developed just by thermal treatment of KN3 powders. The Raman and infrared spectra confirm the formation of polymeric nitrogen networks. Thermogravimetric differential scanning calorimeter measurements show that the content of cubic gauche polymeric nitrogen is as high as 1.5 wt% with high thermal stability, which is the hi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14061v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14061v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14061v1-abstract-full" style="display: none;"> A high-efficient one-step synthesis of cubic gauche polymeric nitrogen was developed just by thermal treatment of KN3 powders. The Raman and infrared spectra confirm the formation of polymeric nitrogen networks. Thermogravimetric differential scanning calorimeter measurements show that the content of cubic gauche polymeric nitrogen is as high as 1.5 wt% with high thermal stability, which is the highest content value so far. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14061v1-abstract-full').style.display = 'none'; document.getElementById('2411.14061v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">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/2411.12972">arXiv:2411.12972</a> <span> [<a href="https://arxiv.org/pdf/2411.12972">pdf</a>, <a href="https://arxiv.org/format/2411.12972">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"> A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yuan%2C+Y">Yuan Yuan</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jingtao Ding</a>, <a href="/search/?searchtype=author&query=Han%2C+C">Chonghua Han</a>, <a href="/search/?searchtype=author&query=Jin%2C+D">Depeng Jin</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yong 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="2411.12972v1-abstract-short" style="display: inline;"> Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses. Traditional approaches have relied on separate models tailored to either grid-based data, representing cities as uniform cells, or graph-based data, modeling cities as networks of nodes and edges. In this paper, we build UniF… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12972v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12972v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12972v1-abstract-full" style="display: none;"> Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses. Traditional approaches have relied on separate models tailored to either grid-based data, representing cities as uniform cells, or graph-based data, modeling cities as networks of nodes and edges. In this paper, we build UniFlow, a foundational model for general urban flow prediction that unifies both grid-based and graphbased data. We first design a multi-view spatio-temporal patching mechanism to standardize different data into a consistent sequential format and then introduce a spatio-temporal transformer architecture to capture complex correlations and dynamics. To leverage shared spatio-temporal patterns across different data types and facilitate effective cross-learning, we propose SpatioTemporal Memory Retrieval Augmentation (ST-MRA). By creating structured memory modules to store shared spatio-temporal patterns, ST-MRA enhances predictions through adaptive memory retrieval. Extensive experiments demonstrate that UniFlow outperforms existing models in both grid-based and graph-based flow prediction, excelling particularly in scenarios with limited data availability, showcasing its superior performance and broad applicability. The datasets and code implementation have been released on https://github.com/YuanYuan98/UniFlow. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12972v1-abstract-full').style.display = 'none'; document.getElementById('2411.12972v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12164">arXiv:2411.12164</a> <span> [<a href="https://arxiv.org/pdf/2411.12164">pdf</a>, <a href="https://arxiv.org/format/2411.12164">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"> UrbanDiT: A Foundation Model for Open-World Urban Spatio-Temporal Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yuan%2C+Y">Yuan Yuan</a>, <a href="/search/?searchtype=author&query=Han%2C+C">Chonghua Han</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jingtao Ding</a>, <a href="/search/?searchtype=author&query=Jin%2C+D">Depeng Jin</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yong 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="2411.12164v1-abstract-short" style="display: inline;"> The urban environment is characterized by complex spatio-temporal dynamics arising from diverse human activities and interactions. Effectively modeling these dynamics is essential for understanding and optimizing urban systems In this work, we introduce UrbanDiT, a foundation model for open-world urban spatio-temporal learning that successfully scale up diffusion transformers in this field. UrbanD… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12164v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12164v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12164v1-abstract-full" style="display: none;"> The urban environment is characterized by complex spatio-temporal dynamics arising from diverse human activities and interactions. Effectively modeling these dynamics is essential for understanding and optimizing urban systems In this work, we introduce UrbanDiT, a foundation model for open-world urban spatio-temporal learning that successfully scale up diffusion transformers in this field. UrbanDiT pioneers a unified model that integrates diverse spatio-temporal data sources and types while learning universal spatio-temporal patterns across different cities and scenarios. This allows the model to unify both multi-data and multi-task learning, and effectively support a wide range of spatio-temporal applications. Its key innovation lies in the elaborated prompt learning framework, which adaptively generates both data-driven and task-specific prompts, guiding the model to deliver superior performance across various urban applications. UrbanDiT offers three primary advantages: 1) It unifies diverse data types, such as grid-based and graph-based data, into a sequential format, allowing to capture spatio-temporal dynamics across diverse scenarios of different cities; 2) With masking strategies and task-specific prompts, it supports a wide range of tasks, including bi-directional spatio-temporal prediction, temporal interpolation, spatial extrapolation, and spatio-temporal imputation; and 3) It generalizes effectively to open-world scenarios, with its powerful zero-shot capabilities outperforming nearly all baselines with training data. These features allow UrbanDiT to achieves state-of-the-art performance in different domains such as transportation traffic, crowd flows, taxi demand, bike usage, and cellular traffic, across multiple cities and tasks. UrbanDiT sets up a new benchmark for foundation models in the urban spatio-temporal domain. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12164v1-abstract-full').style.display = 'none'; document.getElementById('2411.12164v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12120">arXiv:2411.12120</a> <span> [<a href="https://arxiv.org/pdf/2411.12120">pdf</a>, <a href="https://arxiv.org/format/2411.12120">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cosmology and Nongalactic Astrophysics">astro-ph.CO</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1093/mnras/stae2601">10.1093/mnras/stae2601 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Miscentering of Optical Galaxy Clusters Based on Sunyaev-Zeldovich Counterparts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jupiter Ding</a>, <a href="/search/?searchtype=author&query=Dalal%2C+R">Roohi Dalal</a>, <a href="/search/?searchtype=author&query=Sunayama%2C+T">Tomomi Sunayama</a>, <a href="/search/?searchtype=author&query=Strauss%2C+M+A">Michael A. Strauss</a>, <a href="/search/?searchtype=author&query=Oguri%2C+M">Masamune Oguri</a>, <a href="/search/?searchtype=author&query=Okabe%2C+N">Nobuhiro Okabe</a>, <a href="/search/?searchtype=author&query=Hilton%2C+M">Matt Hilton</a>, <a href="/search/?searchtype=author&query=Monteiro-Oliveira%2C+R">Rog茅rio Monteiro-Oliveira</a>, <a href="/search/?searchtype=author&query=Sif%C3%B3n%2C+C">Crist贸bal Sif贸n</a>, <a href="/search/?searchtype=author&query=Staggs%2C+S+T">Suzanne T. Staggs</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12120v1-abstract-short" style="display: inline;"> The "miscentering effect," i.e., the offset between a galaxy cluster's optically-defined center and the center of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalog from the Hyper Suprime-Cam (HSC) Survey S19A Data Release and the Sunyaev-Zeldovich clust… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12120v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12120v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12120v1-abstract-full" style="display: none;"> The "miscentering effect," i.e., the offset between a galaxy cluster's optically-defined center and the center of its gravitational potential, is a significant systematic effect on brightest cluster galaxy (BCG) studies and cluster lensing analyses. We perform a cross-match between the optical cluster catalog from the Hyper Suprime-Cam (HSC) Survey S19A Data Release and the Sunyaev-Zeldovich cluster catalog from Data Release 5 of the Atacama Cosmology Telescope (ACT). We obtain a sample of 186 clusters in common in the redshift range $0.1 \leq z \leq 1.4$ over an area of 469 deg$^2$. By modeling the distribution of centering offsets in this fiducial sample, we find a miscentered fraction (corresponding to clusters offset by more than 330 kpc) of ~25%, a value consistent with previous miscentering studies. We examine the image of each miscentered cluster in our sample and identify one of several reasons to explain the miscentering. Some clusters show significant miscentering for astrophysical reasons, i.e., ongoing cluster mergers. Others are miscentered due to non-astrophysical, systematic effects in the HSC data or the cluster-finding algorithm. After removing all clusters with clear, non-astrophysical causes of miscentering from the sample, we find a considerably smaller miscentered fraction, ~10%. We show that the gravitational lensing signal within 1 Mpc of miscentered clusters is considerably smaller than that of well-centered clusters, and we suggest that the ACT SZ centers are a better estimate of the true cluster potential centroid. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12120v1-abstract-full').style.display = 'none'; document.getElementById('2411.12120v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 15 figures. Accepted for publication in MNRAS</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11574">arXiv:2411.11574</a> <span> [<a href="https://arxiv.org/pdf/2411.11574">pdf</a>, <a href="https://arxiv.org/ps/2411.11574">ps</a>, <a href="https://arxiv.org/format/2411.11574">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater Condition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+K">Kunpeng Zhang</a>, <a href="/search/?searchtype=author&query=Yi%2C+X">Xinlei Yi</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jinliang Ding</a>, <a href="/search/?searchtype=author&query=Cao%2C+M">Ming Cao</a>, <a href="/search/?searchtype=author&query=Johansson%2C+K+H">Karl H. Johansson</a>, <a href="/search/?searchtype=author&query=Yang%2C+T">Tao Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.11574v1-abstract-short" style="display: inline;"> This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint funct… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11574v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11574v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11574v1-abstract-full" style="display: none;"> This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal-dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes sublinear network regret and cumulative constraint violation bounds. When Slater condition holds, the network cumulative constraint violation bound is reduced. In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced. For the case where strongly convex parameters are known, the network regret and cumulative constraint violation bounds are further reduced. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater condition. Finally, a numerical example is provided to verify the theoretical results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11574v1-abstract-full').style.display = 'none'; document.getElementById('2411.11574v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: text overlap with arXiv:2406.14060, arXiv:2306.00149</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11280">arXiv:2411.11280</a> <span> [<a href="https://arxiv.org/pdf/2411.11280">pdf</a>, <a href="https://arxiv.org/format/2411.11280">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"> AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xiao%2C+X">Xu Xiao</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jiacheng Ding</a>, <a href="/search/?searchtype=author&query=Luo%2C+X+L">Xiao Lin Luo</a>, <a href="/search/?searchtype=author&query=Lan%2C+S+K">Sun Ke Lan</a>, <a href="/search/?searchtype=author&query=Xiao%2C+L">Liang Xiao</a>, <a href="/search/?searchtype=author&query=Liu%2C+S">Shuai Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xin Wang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+L">Le Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xiao-Dong 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="2411.11280v1-abstract-short" style="display: inline;"> In the study of cosmology and galaxy evolution, the peculiar velocity and density field of dark matter (DM) play a crucial role in studying many issues. Here, we propose a UNet-based deep learning to reconstruct the real-space DM velocity field from the spatial distribution of a sparse sample of DM halos in redshift space. By comparing and testing various properties, we demonstrate that the recons… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11280v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11280v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11280v1-abstract-full" style="display: none;"> In the study of cosmology and galaxy evolution, the peculiar velocity and density field of dark matter (DM) play a crucial role in studying many issues. Here, we propose a UNet-based deep learning to reconstruct the real-space DM velocity field from the spatial distribution of a sparse sample of DM halos in redshift space. By comparing and testing various properties, we demonstrate that the reconstructed velocity field is in good agreement with the actual situation. At $k<0.3~h/{\rm Mpc}$, the reconstruction of various velocity field components, including velocity magnitude and divergence, outperforms traditional linear perturbation theory. Additionally, the effects of redshift space distortions (RSD) are well corrected using the UNet model. Compared to the true real-space power spectra, the UNet reconstruction provides an unbiased estimate of the density, velocity, and momentum fields, remaining consistent within $2蟽$ level. We also demonstrate that the UNet model remains effective even with limited information about halo masses. Thus, our proposed UNet model has a wide range of applications in various aspects of cosmology, such as RSD, cosmic web analysis, the kinetic Sunyaev-Zel'dovich effect, BAO reconstruction, and so on. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11280v1-abstract-full').style.display = 'none'; document.getElementById('2411.11280v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages,14 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07538">arXiv:2411.07538</a> <span> [<a href="https://arxiv.org/pdf/2411.07538">pdf</a>, <a href="https://arxiv.org/format/2411.07538">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="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Unraveling the Gradient Descent Dynamics of Transformers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Song%2C+B">Bingqing Song</a>, <a href="/search/?searchtype=author&query=Han%2C+B">Boran Han</a>, <a href="/search/?searchtype=author&query=Zhang%2C+S">Shuai Zhang</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/?searchtype=author&query=Hong%2C+M">Mingyi Hong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07538v1-abstract-short" style="display: inline;"> While the Transformer architecture has achieved remarkable success across various domains, a thorough theoretical foundation explaining its optimization dynamics is yet to be fully developed. In this study, we aim to bridge this understanding gap by answering the following two core questions: (1) Which types of Transformer architectures allow Gradient Descent (GD) to achieve guaranteed convergence… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07538v1-abstract-full').style.display = 'inline'; document.getElementById('2411.07538v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07538v1-abstract-full" style="display: none;"> While the Transformer architecture has achieved remarkable success across various domains, a thorough theoretical foundation explaining its optimization dynamics is yet to be fully developed. In this study, we aim to bridge this understanding gap by answering the following two core questions: (1) Which types of Transformer architectures allow Gradient Descent (GD) to achieve guaranteed convergence? and (2) Under what initial conditions and architectural specifics does the Transformer achieve rapid convergence during training? By analyzing the loss landscape of a single Transformer layer using Softmax and Gaussian attention kernels, our work provides concrete answers to these questions. Our findings demonstrate that, with appropriate weight initialization, GD can train a Transformer model (with either kernel type) to achieve a global optimal solution, especially when the input embedding dimension is large. Nonetheless, certain scenarios highlight potential pitfalls: training a Transformer using the Softmax attention kernel may sometimes lead to suboptimal local solutions. In contrast, the Gaussian attention kernel exhibits a much favorable behavior. Our empirical study further validate the theoretical findings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07538v1-abstract-full').style.display = 'none'; document.getElementById('2411.07538v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.06491">arXiv:2411.06491</a> <span> [<a href="https://arxiv.org/pdf/2411.06491">pdf</a>, <a href="https://arxiv.org/format/2411.06491">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> MBL-CPDP: A Multi-objective Bilevel Method for Cross-Project Defect Prediction via Automated Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+J">Jiaxin Chen</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jinliang Ding</a>, <a href="/search/?searchtype=author&query=Tan%2C+K+C">Kay Chen Tan</a>, <a href="/search/?searchtype=author&query=Qian%2C+J">Jiancheng Qian</a>, <a href="/search/?searchtype=author&query=Li%2C+K">Ke 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="2411.06491v1-abstract-short" style="display: inline;"> Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. However, developing a robust ML pipeline with optimal hyperparameters that effectively use cross-project information and yield satisfactory performance remains challenging. In this paper, we resolve this bottleneck by formulat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.06491v1-abstract-full').style.display = 'inline'; document.getElementById('2411.06491v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.06491v1-abstract-full" style="display: none;"> Cross-project defect prediction (CPDP) leverages machine learning (ML) techniques to proactively identify software defects, especially where project-specific data is scarce. However, developing a robust ML pipeline with optimal hyperparameters that effectively use cross-project information and yield satisfactory performance remains challenging. In this paper, we resolve this bottleneck by formulating CPDP as a multi-objective bilevel optimization (MBLO) method, dubbed MBL-CPDP. It comprises two nested problems: the upper-level, a multi-objective combinatorial optimization problem, enhances robustness and efficiency in optimizing ML pipelines, while the lower-level problem is an expensive optimization problem that focuses on tuning their optimal hyperparameters. Due to the high-dimensional search space characterized by feature redundancy and inconsistent data distributions, the upper-level problem combines feature selection, transfer learning, and classification to leverage limited and heterogeneous historical data. Meanwhile, an ensemble learning method is proposed to capture differences in cross-project distribution and generalize across diverse datasets. Finally, a MBLO algorithm is presented to solve this problem while achieving high adaptability effectively. To evaluate the performance of MBL-CPDP, we compare it with five automated ML tools and $50$ CPDP techniques across $20$ projects. Extensive empirical results show that MBL-CPDPoutperforms the comparison methods, demonstrating its superior adaptability and comprehensive performance evaluation capability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.06491v1-abstract-full').style.display = 'none'; document.getElementById('2411.06491v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">37 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/2411.06191">arXiv:2411.06191</a> <span> [<a href="https://arxiv.org/pdf/2411.06191">pdf</a>, <a href="https://arxiv.org/format/2411.06191">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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"> Generalizing Hyperedge Expansion for Hyper-relational Knowledge Graph Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+Y">Yu Liu</a>, <a href="/search/?searchtype=author&query=Yang%2C+S">Shu Yang</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jingtao Ding</a>, <a href="/search/?searchtype=author&query=Yao%2C+Q">Quanming Yao</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yong 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="2411.06191v1-abstract-short" style="display: inline;"> By representing knowledge in a primary triple associated with additional attribute-value qualifiers, hyper-relational knowledge graph (HKG) that generalizes triple-based knowledge graph (KG) has been attracting research attention recently. Compared with KG, HKG is enriched with the semantic qualifiers as well as the hyper-relational graph structure. However, to model HKG, existing studies mainly f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.06191v1-abstract-full').style.display = 'inline'; document.getElementById('2411.06191v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.06191v1-abstract-full" style="display: none;"> By representing knowledge in a primary triple associated with additional attribute-value qualifiers, hyper-relational knowledge graph (HKG) that generalizes triple-based knowledge graph (KG) has been attracting research attention recently. Compared with KG, HKG is enriched with the semantic qualifiers as well as the hyper-relational graph structure. However, to model HKG, existing studies mainly focus on either semantic information or structural information therein, which however fail to capture both simultaneously. To tackle this issue, in this paper, we generalize the hyperedge expansion in hypergraph learning and propose an equivalent transformation for HKG modeling, referred to as TransEQ. Specifically, the equivalent transformation transforms a HKG to a KG, which considers both semantic and structural characteristics. Then an encoder-decoder framework is developed to bridge the modeling research between KG and HKG. In the encoder part, KG-based graph neural networks are leveraged for structural modeling; while in the decoder part, various HKG-based scoring functions are exploited for semantic modeling. Especially, we design the sharing embedding mechanism in the encoder-decoder framework with semantic relatedness captured. We further theoretically prove that TransEQ preserves complete information in the equivalent transformation, and also achieves full expressivity. Finally, extensive experiments on three benchmarks demonstrate the superior performance of TransEQ in terms of both effectiveness and efficiency. On the largest benchmark WikiPeople, TransEQ significantly improves the state-of-the-art models by 15\% on MRR. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.06191v1-abstract-full').style.display = 'none'; document.getElementById('2411.06191v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.05554">arXiv:2411.05554</a> <span> [<a href="https://arxiv.org/pdf/2411.05554">pdf</a>, <a href="https://arxiv.org/format/2411.05554">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="Dynamical Systems">math.DS</span> </div> </div> <p class="title is-5 mathjax"> Time-to-reach Bounds for Verification of Dynamical Systems Using the Koopman Spectrum </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jianqiang Ding</a>, <a href="/search/?searchtype=author&query=Deka%2C+S+A">Shankar A. Deka</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.05554v1-abstract-short" style="display: inline;"> In this work, we present a novel Koopman spectrum-based reachability verification method for nonlinear systems. Contrary to conventional methods that focus on characterizing all potential states of a dynamical system over a presupposed time span, our approach seeks to verify the reachability by assessing the non-emptiness of estimated time-to-reach intervals without engaging in the explicit comput… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05554v1-abstract-full').style.display = 'inline'; document.getElementById('2411.05554v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.05554v1-abstract-full" style="display: none;"> In this work, we present a novel Koopman spectrum-based reachability verification method for nonlinear systems. Contrary to conventional methods that focus on characterizing all potential states of a dynamical system over a presupposed time span, our approach seeks to verify the reachability by assessing the non-emptiness of estimated time-to-reach intervals without engaging in the explicit computation of reachable set. Based on the spectral analysis of the Koopman operator, we reformulate the problem of verifying existence of reachable trajectories into the problem of determining feasible time-to-reach bounds required for system reachability. By solving linear programming (LP) problems, our algorithm can effectively estimate all potential time intervals during which a dynamical system can enter (and exit) target sets from given initial sets over an unbounded time horizon. Finally, we demonstrate our method in challenging settings, such as verifying the reachability between non-convex or even disconnected sets, as well as backward reachability and multiple entries into target sets. Additionally, we validate its applicability in addressing real-world challenges and scalability to high-dimensional systems through case studies in verifying the reachability of the cart-pole and multi-agent consensus systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05554v1-abstract-full').style.display = 'none'; document.getElementById('2411.05554v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This work has been submitted to the IEEE 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/2411.03753">arXiv:2411.03753</a> <span> [<a href="https://arxiv.org/pdf/2411.03753">pdf</a>, <a href="https://arxiv.org/format/2411.03753">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"> Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yu%2C+Z">Zihan Yu</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jingtao Ding</a>, <a href="/search/?searchtype=author&query=Li%2C+Y">Yong 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="2411.03753v1-abstract-short" style="display: inline;"> Symbolic regression, a task discovering the formula best fitting the given data, is typically based on the heuristical search. These methods usually update candidate formulas to obtain new ones with lower prediction errors iteratively. However, since formulas with similar function shapes may have completely different symbolic forms, the prediction error does not decrease monotonously as the search… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03753v1-abstract-full').style.display = 'inline'; document.getElementById('2411.03753v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03753v1-abstract-full" style="display: none;"> Symbolic regression, a task discovering the formula best fitting the given data, is typically based on the heuristical search. These methods usually update candidate formulas to obtain new ones with lower prediction errors iteratively. However, since formulas with similar function shapes may have completely different symbolic forms, the prediction error does not decrease monotonously as the search approaches the target formula, causing the low recovery rate of existing methods. To solve this problem, we propose a novel search objective based on the minimum description length, which reflects the distance from the target and decreases monotonically as the search approaches the correct form of the target formula. To estimate the minimum description length of any input data, we design a neural network, MDLformer, which enables robust and scalable estimation through large-scale training. With the MDLformer's output as the search objective, we implement a symbolic regression method, SR4MDL, that can effectively recover the correct mathematical form of the formula. Extensive experiments illustrate its excellent performance in recovering formulas from data. Our method successfully recovers around 50 formulas across two benchmark datasets comprising 133 problems, outperforming state-of-the-art methods by 43.92%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03753v1-abstract-full').style.display = 'none'; document.getElementById('2411.03753v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.00035">arXiv:2411.00035</a> <span> [<a href="https://arxiv.org/pdf/2411.00035">pdf</a>, <a href="https://arxiv.org/ps/2411.00035">ps</a>, <a href="https://arxiv.org/format/2411.00035">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Physics and Society">physics.soc-ph</span> </div> </div> <p class="title is-5 mathjax"> Home Swapping -- An Innovative Approach to Reduce Traffic Congestion and Carbon Emissions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhao%2C+C">Chen Zhao</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yuqing Liu</a>, <a href="/search/?searchtype=author&query=Hou%2C+X">Xiaoyue Hou</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jianghui Ding</a>, <a href="/search/?searchtype=author&query=Yeung%2C+C+H">Chi Ho Yeung</a>, <a href="/search/?searchtype=author&query=Zeng%2C+A">An Zeng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.00035v1-abstract-short" style="display: inline;"> Urban traffic congestion, worsened by the rapid urbanization and the increasing prevalence of private vehicles, has significantly increased commuting time for everyone. In this paper, we used a dataset with over 400,000 real mobility trajectories of individuals spanning 9 days in a major Chinese city to investigate an innovative approach to swap homes between households in addressing the challenge… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00035v1-abstract-full').style.display = 'inline'; document.getElementById('2411.00035v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.00035v1-abstract-full" style="display: none;"> Urban traffic congestion, worsened by the rapid urbanization and the increasing prevalence of private vehicles, has significantly increased commuting time for everyone. In this paper, we used a dataset with over 400,000 real mobility trajectories of individuals spanning 9 days in a major Chinese city to investigate an innovative approach to swap homes between households in addressing the challenge of peak-hour traffic congestion. We observed that, empirically, households choose their home location strategically such that the average commuting distance is roughly 3 times less than that when their home is randomly located, showing features of self-organization. Remarkably, we found that the average commuting distance can be further reduced by 50% through home swapping at the city-level, leading to a large reduction in traffic congestion. To make home swapping more realistic, we swap homes only if the following socio-demographic factors including the distance from the city center, housing price and amenity accessibility are preserved for both households, such that the average commuting distance can still be reduced by 13%. As both home-workplace distance and traffic congestion are reduced, as a side benefit, carbon emissions from vehicles are also greatly reduced by almost 80%, and 40% when socio-demographic factors are considered. The distance from the city center is shown to be the most influential factor affecting the benefit brought by home swapping, and further analysis indicates that developing a polycentric city layout could significantly enhance such benefit. This study suggests that mitigating traffic congestion requires a long-term, holistic and strategic approach to urban planning, suggesting a need for coordinating individual residence locations and a polycentric city layout. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00035v1-abstract-full').style.display = 'none'; document.getElementById('2411.00035v1-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> 29 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">56 pages, 6+23 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/2410.22925">arXiv:2410.22925</a> <span> [<a href="https://arxiv.org/pdf/2410.22925">pdf</a>, <a href="https://arxiv.org/format/2410.22925">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Caglayan%2C+B">Bora Caglayan</a>, <a href="/search/?searchtype=author&query=Wang%2C+M">Mingxue Wang</a>, <a href="/search/?searchtype=author&query=Kelleher%2C+J+D">John D. Kelleher</a>, <a href="/search/?searchtype=author&query=Fei%2C+S">Shen Fei</a>, <a href="/search/?searchtype=author&query=Tong%2C+G">Gui Tong</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jiandong Ding</a>, <a href="/search/?searchtype=author&query=Zhang%2C+P">Puchao 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="2410.22925v1-abstract-short" style="display: inline;"> NL2SQL (Natural Language to Structured Query Language) transformation has seen wide adoption in Business Intelligence (BI) applications in recent years. However, existing NL2SQL benchmarks are not suitable for production BI scenarios, as they are not designed for common business intelligence questions. To address this gap, we have developed a new benchmark focused on typical NL questions in indust… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.22925v1-abstract-full').style.display = 'inline'; document.getElementById('2410.22925v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.22925v1-abstract-full" style="display: none;"> NL2SQL (Natural Language to Structured Query Language) transformation has seen wide adoption in Business Intelligence (BI) applications in recent years. However, existing NL2SQL benchmarks are not suitable for production BI scenarios, as they are not designed for common business intelligence questions. To address this gap, we have developed a new benchmark focused on typical NL questions in industrial BI scenarios. We discuss the challenges of constructing a BI-focused benchmark and the shortcomings of existing benchmarks. Additionally, we introduce question categories in our benchmark that reflect common BI inquiries. Lastly, we propose two novel semantic similarity evaluation metrics for assessing NL2SQL capabilities in BI applications and services. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.22925v1-abstract-full').style.display = 'none'; document.getElementById('2410.22925v1-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> 30 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been accepted by ICSOC (International Conference on Service-Oriented Computing) 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.22075">arXiv:2410.22075</a> <span> [<a href="https://arxiv.org/pdf/2410.22075">pdf</a>, <a href="https://arxiv.org/format/2410.22075">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mathematical Physics">math-ph</span> </div> </div> <p class="title is-5 mathjax"> Percolation of thick points of the log-correlated Gaussian field in high dimensions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jian Ding</a>, <a href="/search/?searchtype=author&query=Gwynne%2C+E">Ewain Gwynne</a>, <a href="/search/?searchtype=author&query=Zhuang%2C+Z">Zijie Zhuang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.22075v1-abstract-short" style="display: inline;"> We prove that the set of thick points of the log-correlated Gaussian field contains an unbounded path in sufficiently high dimensions. This contrasts with the two-dimensional case, where Aru, Papon, and Powell (2023) showed that the set of thick points is totally disconnected. This result has an interesting implication for the exponential metric of the log-correlated Gaussian field: in sufficientl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.22075v1-abstract-full').style.display = 'inline'; document.getElementById('2410.22075v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.22075v1-abstract-full" style="display: none;"> We prove that the set of thick points of the log-correlated Gaussian field contains an unbounded path in sufficiently high dimensions. This contrasts with the two-dimensional case, where Aru, Papon, and Powell (2023) showed that the set of thick points is totally disconnected. This result has an interesting implication for the exponential metric of the log-correlated Gaussian field: in sufficiently high dimensions, when the parameter $尉$ is large, the set-to-set distance exponent (if it exists) is negative. This suggests that a new phase may emerge for the exponential metric, which does not appear in two dimensions. In addition, we establish similar results for the set of thick points of the branching random walk. As an intermediate result, we also prove that the critical probability for fractal percolation converges to 0 as $d \to \infty$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.22075v1-abstract-full').style.display = 'none'; document.getElementById('2410.22075v1-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> 29 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">38 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/2410.20457">arXiv:2410.20457</a> <span> [<a href="https://arxiv.org/pdf/2410.20457">pdf</a>, <a href="https://arxiv.org/format/2410.20457">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mathematical Physics">math-ph</span> </div> </div> <p class="title is-5 mathjax"> Dynamical random field Ising model at zero temperature </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jian Ding</a>, <a href="/search/?searchtype=author&query=Yang%2C+P">Peng Yang</a>, <a href="/search/?searchtype=author&query=Zhuang%2C+Z">Zijie Zhuang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.20457v2-abstract-short" style="display: inline;"> In this paper, we study the evolution of the zero-temperature random field Ising model as the mean of the external field $M$ increases from $-\infty$ to $\infty$. We focus on two types of evolutions: the ground state evolution and the Glauber evolution. For the ground state evolution, we investigate the occurrence of global avalanche, a moment where a large fraction of spins flip simultaneously fr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20457v2-abstract-full').style.display = 'inline'; document.getElementById('2410.20457v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20457v2-abstract-full" style="display: none;"> In this paper, we study the evolution of the zero-temperature random field Ising model as the mean of the external field $M$ increases from $-\infty$ to $\infty$. We focus on two types of evolutions: the ground state evolution and the Glauber evolution. For the ground state evolution, we investigate the occurrence of global avalanche, a moment where a large fraction of spins flip simultaneously from minus to plus. In two dimensions, no global avalanche occurs, while in three or higher dimensions, there is a phase transition: a global avalanche happens when the noise intensity is small, but not when it is large. Additionally, we study the zero-temperature Glauber evolution, where spins are updated locally to minimize the Hamiltonian. Our results show that for small noise intensity, in dimensions $d =2$ or $3$, most spins flip around a critical time $c_d = \frac{2 \sqrt{d}}{1 + \sqrt{d}}$ (but we cannot decide whether such flipping occurs simultaneously or not). We also connect this process to polluted bootstrap percolation and solve an open problem on it. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20457v2-abstract-full').style.display = 'none'; document.getElementById('2410.20457v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <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">41 pages, 9 figures; updated references on polluted bootstrap percolation</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.19198">arXiv:2410.19198</a> <span> [<a href="https://arxiv.org/pdf/2410.19198">pdf</a>, <a href="https://arxiv.org/format/2410.19198">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> MAP: Multi-Human-Value Alignment Palette </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+X">Xinran Wang</a>, <a href="/search/?searchtype=author&query=Le%2C+Q">Qi Le</a>, <a href="/search/?searchtype=author&query=Ahmed%2C+A">Ammar Ahmed</a>, <a href="/search/?searchtype=author&query=Diao%2C+E">Enmao Diao</a>, <a href="/search/?searchtype=author&query=Zhou%2C+Y">Yi Zhou</a>, <a href="/search/?searchtype=author&query=Baracaldo%2C+N">Nathalie Baracaldo</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/?searchtype=author&query=Anwar%2C+A">Ali Anwar</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.19198v1-abstract-short" style="display: inline;"> Ensuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs. Since human values can be personalized and dynamically change over time, the desirable levels of value alignment vary across different ethnic groups, industry sectors, and user cohorts. Within existing frameworks, it is hard to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.19198v1-abstract-full').style.display = 'inline'; document.getElementById('2410.19198v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.19198v1-abstract-full" style="display: none;"> Ensuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs. Since human values can be personalized and dynamically change over time, the desirable levels of value alignment vary across different ethnic groups, industry sectors, and user cohorts. Within existing frameworks, it is hard to define human values and align AI systems accordingly across different directions simultaneously, such as harmlessness, helpfulness, and positiveness. To address this, we develop a novel, first-principle approach called Multi-Human-Value Alignment Palette (MAP), which navigates the alignment across multiple human values in a structured and reliable way. MAP formulates the alignment problem as an optimization task with user-defined constraints, which define human value targets. It can be efficiently solved via a primal-dual approach, which determines whether a user-defined alignment target is achievable and how to achieve it. We conduct a detailed theoretical analysis of MAP by quantifying the trade-offs between values, the sensitivity to constraints, the fundamental connection between multi-value alignment and sequential alignment, and proving that linear weighted rewards are sufficient for multi-value alignment. Extensive experiments demonstrate MAP's ability to align multiple values in a principled manner while delivering strong empirical performance across various tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.19198v1-abstract-full').style.display = 'none'; document.getElementById('2410.19198v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.17536">arXiv:2410.17536</a> <span> [<a href="https://arxiv.org/pdf/2410.17536">pdf</a>, <a href="https://arxiv.org/format/2410.17536">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"> Adaptive Wireless Image Semantic Transmission: Design, Simulation, and Prototype Validation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jiarun Ding</a>, <a href="/search/?searchtype=author&query=Jiang%2C+P">Peiwen Jiang</a>, <a href="/search/?searchtype=author&query=Wen%2C+C">Chao-Kai Wen</a>, <a href="/search/?searchtype=author&query=Jin%2C+S">Shi Jin</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.17536v1-abstract-short" style="display: inline;"> The rapid development of artificial intelligence has significantly advanced semantic communications, particularly in wireless image transmission. However, most existing approaches struggle to precisely distinguish and prioritize image content, and they do not sufficiently incorporate semantic priorities into system design. In this study, we propose an adaptive wireless image semantic transmission… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.17536v1-abstract-full').style.display = 'inline'; document.getElementById('2410.17536v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.17536v1-abstract-full" style="display: none;"> The rapid development of artificial intelligence has significantly advanced semantic communications, particularly in wireless image transmission. However, most existing approaches struggle to precisely distinguish and prioritize image content, and they do not sufficiently incorporate semantic priorities into system design. In this study, we propose an adaptive wireless image semantic transmission scheme called ASCViT-JSCC, which utilizes vision transformer-based joint source-channel coding (JSCC). This scheme prioritizes different image regions based on their importance, identified through object and feature point detection. Unimportant background sections are masked, enabling them to be recovered at the receiver, while the freed resources are allocated to enhance object protection via the JSCC network. We also integrate quantization modules to enable compatibility with quadrature amplitude modulation, commonly used in modern wireless communications. To address frequency-selective fading channels, we introduce CSIPA-Net, which allocates power based on channel information, further improving performance. Notably, we conduct over-the-air testing on a prototype platform composed of a software-defined radio and embedded graphics processing unit systems, validating our methods. Both simulations and real-world measurements demonstrate that ASCViT-JSCC effectively prioritizes object protection according to channel conditions, significantly enhancing image reconstruction quality, especially in challenging channel environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.17536v1-abstract-full').style.display = 'none'; document.getElementById('2410.17536v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.17494">arXiv:2410.17494</a> <span> [<a href="https://arxiv.org/pdf/2410.17494">pdf</a>, <a href="https://arxiv.org/format/2410.17494">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jun-En Ding</a>, <a href="/search/?searchtype=author&query=Hsu%2C+C">Chien-Chin Hsu</a>, <a href="/search/?searchtype=author&query=Liu%2C+F">Feng 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="2410.17494v2-abstract-short" style="display: inline;"> The classification of medical images is a pivotal aspect of disease diagnosis, often enhanced by deep learning techniques. However, traditional approaches typically focus on unimodal medical image data, neglecting the integration of diverse non-image patient data. This paper proposes a novel Cross-Graph Modal Contrastive Learning (CGMCL) framework for multimodal medical image classification. The m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.17494v2-abstract-full').style.display = 'inline'; document.getElementById('2410.17494v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.17494v2-abstract-full" style="display: none;"> The classification of medical images is a pivotal aspect of disease diagnosis, often enhanced by deep learning techniques. However, traditional approaches typically focus on unimodal medical image data, neglecting the integration of diverse non-image patient data. This paper proposes a novel Cross-Graph Modal Contrastive Learning (CGMCL) framework for multimodal medical image classification. The model effectively integrates both image and non-image data by constructing cross-modality graphs and leveraging contrastive learning to align multimodal features in a shared latent space. An inter-modality feature scaling module further optimizes the representation learning process by reducing the gap between heterogeneous modalities. The proposed approach is evaluated on two datasets: a Parkinson's disease (PD) dataset and a public melanoma dataset. Results demonstrate that CGMCL outperforms conventional unimodal methods in accuracy, interpretability, and early disease prediction. Additionally, the method shows superior performance in multi-class melanoma classification. The CGMCL framework provides valuable insights into medical image classification while offering improved disease interpretability and predictive capabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.17494v2-abstract-full').style.display = 'none'; document.getElementById('2410.17494v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16734">arXiv:2410.16734</a> <span> [<a href="https://arxiv.org/pdf/2410.16734">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> High-Order Associative Learning Based on Memristive Circuits for Efficient Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wang%2C+S">Shengbo Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+X">Xuemeng Li</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jialin Ding</a>, <a href="/search/?searchtype=author&query=Ma%2C+W">Weihao Ma</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Ying Wang</a>, <a href="/search/?searchtype=author&query=Occhipinti%2C+L">Luigi Occhipinti</a>, <a href="/search/?searchtype=author&query=Nathan%2C+A">Arokia Nathan</a>, <a href="/search/?searchtype=author&query=Gao%2C+S">Shuo 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="2410.16734v1-abstract-short" style="display: inline;"> Memristive associative learning has gained significant attention for its ability to mimic fundamental biological learning mechanisms while maintaining system simplicity. In this work, we introduce a high-order memristive associative learning framework with a biologically realistic structure. By utilizing memristors as synaptic modules and their state information to bridge different orders of assoc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16734v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16734v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16734v1-abstract-full" style="display: none;"> Memristive associative learning has gained significant attention for its ability to mimic fundamental biological learning mechanisms while maintaining system simplicity. In this work, we introduce a high-order memristive associative learning framework with a biologically realistic structure. By utilizing memristors as synaptic modules and their state information to bridge different orders of associative learning, our design effectively establishes associations between multiple stimuli and replicates the transient nature of high-order associative learning. In Pavlov's classical conditioning experiments, our design achieves a 230% improvement in learning efficiency compared to previous works, with memristor power consumption in the synaptic modules remaining below 11 渭W. In large-scale image recognition tasks, we utilize a 20*20 memristor array to represent images, enabling the system to recognize and label test images with semantic information at 100% accuracy. This scalability across different tasks highlights the framework's potential for a wide range of applications, offering enhanced learning efficiency for current memristor-based neuromorphic systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16734v1-abstract-full').style.display = 'none'; document.getElementById('2410.16734v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 7 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/2410.16565">arXiv:2410.16565</a> <span> [<a href="https://arxiv.org/pdf/2410.16565">pdf</a>, <a href="https://arxiv.org/format/2410.16565">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> Search for gravitational waves emitted from SN 2023ixf </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=The+LIGO+Scientific+Collaboration"> The LIGO Scientific Collaboration</a>, <a href="/search/?searchtype=author&query=the+Virgo+Collaboration"> the Virgo Collaboration</a>, <a href="/search/?searchtype=author&query=the+KAGRA+Collaboration"> the KAGRA Collaboration</a>, <a href="/search/?searchtype=author&query=Abac%2C+A+G">A. G. Abac</a>, <a href="/search/?searchtype=author&query=Abbott%2C+R">R. Abbott</a>, <a href="/search/?searchtype=author&query=Abouelfettouh%2C+I">I. Abouelfettouh</a>, <a href="/search/?searchtype=author&query=Acernese%2C+F">F. Acernese</a>, <a href="/search/?searchtype=author&query=Ackley%2C+K">K. Ackley</a>, <a href="/search/?searchtype=author&query=Adhicary%2C+S">S. Adhicary</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+N">N. Adhikari</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+R+X">R. X. Adhikari</a>, <a href="/search/?searchtype=author&query=Adkins%2C+V+K">V. K. Adkins</a>, <a href="/search/?searchtype=author&query=Agarwal%2C+D">D. Agarwal</a>, <a href="/search/?searchtype=author&query=Agathos%2C+M">M. Agathos</a>, <a href="/search/?searchtype=author&query=Abchouyeh%2C+M+A">M. Aghaei Abchouyeh</a>, <a href="/search/?searchtype=author&query=Aguiar%2C+O+D">O. D. Aguiar</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+I">I. Aguilar</a>, <a href="/search/?searchtype=author&query=Aiello%2C+L">L. Aiello</a>, <a href="/search/?searchtype=author&query=Ain%2C+A">A. Ain</a>, <a href="/search/?searchtype=author&query=Akutsu%2C+T">T. Akutsu</a>, <a href="/search/?searchtype=author&query=Albanesi%2C+S">S. Albanesi</a>, <a href="/search/?searchtype=author&query=Alfaidi%2C+R+A">R. A. Alfaidi</a>, <a href="/search/?searchtype=author&query=Al-Jodah%2C+A">A. Al-Jodah</a>, <a href="/search/?searchtype=author&query=All%C3%A9n%C3%A9%2C+C">C. All茅n茅</a>, <a href="/search/?searchtype=author&query=Allocca%2C+A">A. Allocca</a> , et al. (1758 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.16565v1-abstract-short" style="display: inline;"> We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16565v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16565v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16565v1-abstract-full" style="display: none;"> We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16565v1-abstract-full').style.display = 'none'; document.getElementById('2410.16565v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LIGO-P2400125 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16326">arXiv:2410.16326</a> <span> [<a href="https://arxiv.org/pdf/2410.16326">pdf</a>, <a href="https://arxiv.org/format/2410.16326">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> </div> </div> <p class="title is-5 mathjax"> Synthetic Data Generation in Cybersecurity: A Comparative Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ammara%2C+D+A">Dure Adan Ammara</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jianguo Ding</a>, <a href="/search/?searchtype=author&query=Tutschku%2C+K">Kurt Tutschku</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.16326v1-abstract-short" style="display: inline;"> Synthetic data generation faces significant challenges in accurately replicating real data, particularly with tabular data, where achieving high fidelity and utility is critical. While numerous methods have been developed, the most effective approach for creating high-quality synthetic data for network traffic security remains to be seen. This study conducts a comprehensive comparative analysis of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16326v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16326v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16326v1-abstract-full" style="display: none;"> Synthetic data generation faces significant challenges in accurately replicating real data, particularly with tabular data, where achieving high fidelity and utility is critical. While numerous methods have been developed, the most effective approach for creating high-quality synthetic data for network traffic security remains to be seen. This study conducts a comprehensive comparative analysis of non-AI, conventional AI, and generative AI techniques for synthetic tabular data generation using two widely recognized cybersecurity datasets: NSL-KDD and CICIDS-2017. Particular emphasis was placed on prominent GAN models for tabular data generation, including CTGAN, CopulaGAN, GANBLR++, and CastGAN. The results indicate that GAN-based methods, particularly CTGAN and CopulaGAN, outperform non-AI and conventional AI approaches in terms of fidelity and utility. To the best of our knowledge, this research contributes to the field by offering the first comparative evaluation of these methods specifically for cybersecurity network traffic data, filling a critical gap in the literature. It also introduces mutual information for feature selection, further enhancing the quality of the generated synthetic data. These findings provide valuable guidance for researchers seeking the most suitable synthetic data generation method in cybersecurity applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16326v1-abstract-full').style.display = 'none'; document.getElementById('2410.16326v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.14863">arXiv:2410.14863</a> <span> [<a href="https://arxiv.org/pdf/2410.14863">pdf</a>, <a href="https://arxiv.org/format/2410.14863">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Strongly Correlated Electrons">cond-mat.str-el</span> </div> </div> <p class="title is-5 mathjax"> Intrinsic Thermal Hall Effect in Mott Insulators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J+K">Jixun K. Ding</a>, <a href="/search/?searchtype=author&query=Zhang%2C+E+Z">Emily Z. Zhang</a>, <a href="/search/?searchtype=author&query=Wang%2C+W+O">Wen O. Wang</a>, <a href="/search/?searchtype=author&query=Cookmeyer%2C+T">Tessa Cookmeyer</a>, <a href="/search/?searchtype=author&query=Moritz%2C+B">Brian Moritz</a>, <a href="/search/?searchtype=author&query=Kim%2C+Y+B">Yong Baek Kim</a>, <a href="/search/?searchtype=author&query=Devereaux%2C+T+P">Thomas P. Devereaux</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.14863v1-abstract-short" style="display: inline;"> In light of recent experimental data indicating a substantial thermal Hall effect in square lattice antiferromagnetic Mott insulators, we investigate whether a simple Mott insulator can sustain a finite thermal Hall effect. We verify that the answer is "no" if one performs calculations within a spin-only low-energy effective spin model with non-interacting magnons. However, by performing determina… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14863v1-abstract-full').style.display = 'inline'; document.getElementById('2410.14863v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.14863v1-abstract-full" style="display: none;"> In light of recent experimental data indicating a substantial thermal Hall effect in square lattice antiferromagnetic Mott insulators, we investigate whether a simple Mott insulator can sustain a finite thermal Hall effect. We verify that the answer is "no" if one performs calculations within a spin-only low-energy effective spin model with non-interacting magnons. However, by performing determinant quantum Monte Carlo simulations, we show the single-band $t$-$t'$-$U$ Hubbard model coupled to an orbital magnetic field does support a finite thermal Hall effect when $t' \neq 0$ and $B \neq 0$ in the Mott insulating phase. We argue that the (carrier agnostic) necessary conditions for observing a finite thermal Hall effect are time-reversal and particle-hole symmetry breaking. By considering magnon-magnon scattering using a semi-classical Boltzmann analysis, we illustrate a physical mechanism by which finite transverse thermal conductivity may arise, consistent with our symmetry argument and numerical results. Our results contradict the conventional wisdom that square and triangular lattices with SU(2) symmetry do not support a finite thermal Hall effect and call for a critical re-examination of thermal Hall effect data in insulating magnets, as the magnon contribution should not be excluded a priori. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14863v1-abstract-full').style.display = 'none'; document.getElementById('2410.14863v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6+23 pages, 3+11 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/2410.13187">arXiv:2410.13187</a> <span> [<a href="https://arxiv.org/pdf/2410.13187">pdf</a>, <a href="https://arxiv.org/format/2410.13187">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="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> aiXcoder-7B: A Lightweight and Effective Large Language Model for Code Completion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Jiang%2C+S">Siyuan Jiang</a>, <a href="/search/?searchtype=author&query=Li%2C+J">Jia Li</a>, <a href="/search/?searchtype=author&query=Zong%2C+H">He Zong</a>, <a href="/search/?searchtype=author&query=Liu%2C+H">Huanyu Liu</a>, <a href="/search/?searchtype=author&query=Zhu%2C+H">Hao Zhu</a>, <a href="/search/?searchtype=author&query=Hu%2C+S">Shukai Hu</a>, <a href="/search/?searchtype=author&query=Li%2C+E">Erlu Li</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jiazheng Ding</a>, <a href="/search/?searchtype=author&query=Han%2C+Y">Yu Han</a>, <a href="/search/?searchtype=author&query=Ning%2C+W">Wei Ning</a>, <a href="/search/?searchtype=author&query=Wang%2C+G">Gen Wang</a>, <a href="/search/?searchtype=author&query=Dong%2C+Y">Yihong Dong</a>, <a href="/search/?searchtype=author&query=Zhang%2C+K">Kechi Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+G">Ge 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="2410.13187v2-abstract-short" style="display: inline;"> Large Language Models (LLMs) have been widely used in code completion, and researchers are focusing on scaling up LLMs to improve their accuracy. However, larger LLMs will increase the response time of code completion and decrease the developers' productivity. In this paper, we propose a lightweight and effective LLM for code completion named aiXcoder-7B. Compared to existing LLMs, aiXcoder-7B ach… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13187v2-abstract-full').style.display = 'inline'; document.getElementById('2410.13187v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.13187v2-abstract-full" style="display: none;"> Large Language Models (LLMs) have been widely used in code completion, and researchers are focusing on scaling up LLMs to improve their accuracy. However, larger LLMs will increase the response time of code completion and decrease the developers' productivity. In this paper, we propose a lightweight and effective LLM for code completion named aiXcoder-7B. Compared to existing LLMs, aiXcoder-7B achieves higher code completion accuracy while having smaller scales (i.e., 7 billion parameters). We attribute the superiority of aiXcoder-7B to three key factors: (1) Multi-objective training. We employ three training objectives, one of which is our proposed Structured Fill-In-the-Middle (SFIM). SFIM considers the syntax structures in code and effectively improves the performance of LLMs for code. (2) Diverse data sampling strategies. They consider inter-file relationships and enhance the capability of LLMs in understanding cross-file contexts. (3) Extensive high-quality data. We establish a rigorous data collection pipeline and consume a total of 1.2 trillion unique tokens for training aiXcoder-7B. This vast volume of data enables aiXcoder-7B to learn a broad distribution of code. We evaluate aiXcoder-7B in five popular code completion benchmarks and a new benchmark collected by this paper. The results show that aiXcoder-7B outperforms the latest six LLMs with similar sizes and even surpasses four larger LLMs (e.g., StarCoder2-15B and CodeLlama-34B), positioning aiXcoder-7B as a lightweight and effective LLM for academia and industry. Finally, we summarize three valuable insights for helping practitioners train the next generations of LLMs for code. aiXcoder-7B has been open-souced and gained significant attention. As of the submission date, aiXcoder-7B has received 2,193 GitHub Stars. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13187v2-abstract-full').style.display = 'none'; document.getElementById('2410.13187v2-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> 30 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">aiXcoder-7B is available at https://github.com/aixcoder-plugin/aiXcoder-7B</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.13133">arXiv:2410.13133</a> <span> [<a href="https://arxiv.org/pdf/2410.13133">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Digital Libraries">cs.DL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applications">stat.AP</span> </div> </div> <p class="title is-5 mathjax"> Exploring Scientific Contributions through Citation Context and Division of Labor </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Chen%2C+L">Liyue Chen</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jielan Ding</a>, <a href="/search/?searchtype=author&query=Song%2C+D">Donghuan Song</a>, <a href="/search/?searchtype=author&query=Qu%2C+Z">Zihao Qu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.13133v1-abstract-short" style="display: inline;"> Scientific contributions are a direct reflection of a research paper's value, illustrating its impact on existing theories or practices. Existing measurement methods assess contributions based on the authors' perceived or self-identified contributions, while the actual contributions made by the papers are rarely investigated. This study measures the actual contributions of papers published in Natu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13133v1-abstract-full').style.display = 'inline'; document.getElementById('2410.13133v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.13133v1-abstract-full" style="display: none;"> Scientific contributions are a direct reflection of a research paper's value, illustrating its impact on existing theories or practices. Existing measurement methods assess contributions based on the authors' perceived or self-identified contributions, while the actual contributions made by the papers are rarely investigated. This study measures the actual contributions of papers published in Nature and Science using 1.53 million citation contexts from citing literature and explores the impact pattern of division of labor (DOL) inputs on the actual contributions of papers from an input-output perspective. Results show that experimental contributions are predominant, contrasting with the theoretical and methodological contributions self-identified by authors. This highlights a notable discrepancy between actual contributions and authors' self-perceptions, indicating an 'ideal bias'. There is no significant correlation between the overall labor input pattern and the actual contribution pattern of papers, but a positive correlation appears between input and output for specific types of scientific contributions, reflecting a 'more effort, more gain' effect. Different types of DOL input in papers exhibit a notable co-occurrence trend. However, once the paper reaches the dissemination stage, the co-occurrence of different types of actual contributions becomes weaker, indicating that a paper's contributions are often focused on a single type. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.13133v1-abstract-full').style.display = 'none'; document.getElementById('2410.13133v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">25 pages, 5 figures, 6 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/2410.09976">arXiv:2410.09976</a> <span> [<a href="https://arxiv.org/pdf/2410.09976">pdf</a>, <a href="https://arxiv.org/format/2410.09976">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="Mathematical Physics">math-ph</span> </div> </div> <p class="title is-5 mathjax"> Quantum Linear Time-Translation-Invariant Systems: Conjugate Symplectic Structure, Uncertainty Bounds, and Tomography </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jacques Ding</a>, <a href="/search/?searchtype=author&query=Loughlin%2C+H+A">Hudson A. Loughlin</a>, <a href="/search/?searchtype=author&query=Sudhir%2C+V">Vivishek Sudhir</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.09976v2-abstract-short" style="display: inline;"> Linear time-translation-invariant (LTI) models offer simple, yet powerful, abstractions of complex classical dynamical systems. Quantum versions of such models have so far relied on assumptions of Markovianity or an internal state-space description. We develop a general quantization scheme for multimode classical LTI systems that reveals their fundamental quantum noise, is applicable to non-Markov… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09976v2-abstract-full').style.display = 'inline'; document.getElementById('2410.09976v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.09976v2-abstract-full" style="display: none;"> Linear time-translation-invariant (LTI) models offer simple, yet powerful, abstractions of complex classical dynamical systems. Quantum versions of such models have so far relied on assumptions of Markovianity or an internal state-space description. We develop a general quantization scheme for multimode classical LTI systems that reveals their fundamental quantum noise, is applicable to non-Markovian scenarios, and does not require knowledge of an internal description. The resulting model is that of an open quantum LTI system whose dilation to a closed system is characterized by elements of the conjugate symplectic group. Using Lie group techniques, we show that such systems can be synthesized using frequency-dependent interferometers and squeezers. We derive tighter Heisenberg uncertainty bounds, which constrain the ultimate performance of any LTI system, and obtain an invariant representation of their output noise covariance matrix that reveals the ubiquity of "complex squeezing" in lossy systems. This frequency-dependent quantum resource can be hidden to homodyne and heterodyne detection and can only be revealed with more general "symplectodyne" detection. These results establish a complete and systematic framework for the analysis, synthesis, and measurement of arbitrary quantum LTI systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09976v2-abstract-full').style.display = 'none'; document.getElementById('2410.09976v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14+6 pages, 6 figures. See ancillary file for Supplementary Information</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.09151">arXiv:2410.09151</a> <span> [<a href="https://arxiv.org/pdf/2410.09151">pdf</a>, <a href="https://arxiv.org/format/2410.09151">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> </div> </div> <p class="title is-5 mathjax"> A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=The+LIGO+Scientific+Collaboration"> The LIGO Scientific Collaboration</a>, <a href="/search/?searchtype=author&query=the+Virgo+Collaboration"> the Virgo Collaboration</a>, <a href="/search/?searchtype=author&query=the+KAGRA+Collaboration"> the KAGRA Collaboration</a>, <a href="/search/?searchtype=author&query=Abac%2C+A+G">A. G. Abac</a>, <a href="/search/?searchtype=author&query=Abbott%2C+R">R. Abbott</a>, <a href="/search/?searchtype=author&query=Abouelfettouh%2C+I">I. Abouelfettouh</a>, <a href="/search/?searchtype=author&query=Acernese%2C+F">F. Acernese</a>, <a href="/search/?searchtype=author&query=Ackley%2C+K">K. Ackley</a>, <a href="/search/?searchtype=author&query=Adhicary%2C+S">S. Adhicary</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+N">N. Adhikari</a>, <a href="/search/?searchtype=author&query=Adhikari%2C+R+X">R. X. Adhikari</a>, <a href="/search/?searchtype=author&query=Adkins%2C+V+K">V. K. Adkins</a>, <a href="/search/?searchtype=author&query=Agarwal%2C+D">D. Agarwal</a>, <a href="/search/?searchtype=author&query=Agathos%2C+M">M. Agathos</a>, <a href="/search/?searchtype=author&query=Abchouyeh%2C+M+A">M. Aghaei Abchouyeh</a>, <a href="/search/?searchtype=author&query=Aguiar%2C+O+D">O. D. Aguiar</a>, <a href="/search/?searchtype=author&query=Aguilar%2C+I">I. Aguilar</a>, <a href="/search/?searchtype=author&query=Aiello%2C+L">L. Aiello</a>, <a href="/search/?searchtype=author&query=Ain%2C+A">A. Ain</a>, <a href="/search/?searchtype=author&query=Ajith%2C+P">P. Ajith</a>, <a href="/search/?searchtype=author&query=Akutsu%2C+T">T. Akutsu</a>, <a href="/search/?searchtype=author&query=Albanesi%2C+S">S. Albanesi</a>, <a href="/search/?searchtype=author&query=Alfaidi%2C+R+A">R. A. Alfaidi</a>, <a href="/search/?searchtype=author&query=Al-Jodah%2C+A">A. Al-Jodah</a>, <a href="/search/?searchtype=author&query=All%C3%A9n%C3%A9%2C+C">C. All茅n茅</a> , et al. (1758 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.09151v1-abstract-short" style="display: inline;"> The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09151v1-abstract-full').style.display = 'inline'; document.getElementById('2410.09151v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.09151v1-abstract-full" style="display: none;"> The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09151v1-abstract-full').style.display = 'none'; document.getElementById('2410.09151v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages of text including references, 4 figures, 5 tables</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LIGO-P2400192 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.08472">arXiv:2410.08472</a> <span> [<a href="https://arxiv.org/pdf/2410.08472">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey 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="Other Condensed Matter">cond-mat.other</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> Modeling and Simulation of 2D Transducers Based on Suspended Graphene-Based Heterostructures in Nanoelectromechanical Pressure Sensors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+Q">Quan Liu</a>, <a href="/search/?searchtype=author&query=He%2C+C">Chang He</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wendong Zhang</a>, <a href="/search/?searchtype=author&query=Fan%2C+X">Xuge Fan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.08472v1-abstract-short" style="display: inline;"> Graphene-based 2D heterostructures exhibit excellent mechanical and electrical properties, which are expected to exhibit better performances than graphene for nanoelectromechanical pressure sensors. Here, we built the pressure sensor models based on suspended heterostructures of graphene/h-BN, graphene/MoS2, and graphene/MoSe2 by using COMSOL Multiphysics finite element software. We found that sus… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08472v1-abstract-full').style.display = 'inline'; document.getElementById('2410.08472v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.08472v1-abstract-full" style="display: none;"> Graphene-based 2D heterostructures exhibit excellent mechanical and electrical properties, which are expected to exhibit better performances than graphene for nanoelectromechanical pressure sensors. Here, we built the pressure sensor models based on suspended heterostructures of graphene/h-BN, graphene/MoS2, and graphene/MoSe2 by using COMSOL Multiphysics finite element software. We found that suspended circular 2D membranes show the best sensitivity to pressures compared to rectangular and square ones. We simulated the deflections, strains, resonant frequencies, and Young's moduli of suspended graphene-based heterostructures under the conditions of different applied pressures and geometrical sizes, built-in tensions, and the number of atomic layers of 2D membranes. The Young's moduli of 2D heterostructures of graphene, graphene/h-BN, graphene/MoS2, and graphene/MoSe2 were estimated to be 1.001TPa, 921.08 GPa, 551.11 GPa, and 475.68 GPa, respectively. We also discuss the effect of highly asymmetric cavities on device performance. These results would contribute to the understanding of the mechanical properties of graphene-based heterostructures and would be helpful for the design and manufacture of high-performance NEMS pressure sensors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08472v1-abstract-full').style.display = 'none'; document.getElementById('2410.08472v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.05970">arXiv:2410.05970</a> <span> [<a href="https://arxiv.org/pdf/2410.05970">pdf</a>, <a href="https://arxiv.org/format/2410.05970">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xie%2C+X">Xudong Xie</a>, <a href="/search/?searchtype=author&query=Yin%2C+L">Liang Yin</a>, <a href="/search/?searchtype=author&query=Yan%2C+H">Hao Yan</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jing Ding</a>, <a href="/search/?searchtype=author&query=Liao%2C+M">Minghui Liao</a>, <a href="/search/?searchtype=author&query=Liu%2C+Y">Yuliang Liu</a>, <a href="/search/?searchtype=author&query=Chen%2C+W">Wei Chen</a>, <a href="/search/?searchtype=author&query=Bai%2C+X">Xiang Bai</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.05970v1-abstract-short" style="display: inline;"> Document understanding is a challenging task to process and comprehend large amounts of textual and visual information. Recent advances in Large Language Models (LLMs) have significantly improved the performance of this task. However, existing methods typically focus on either plain text or a limited number of document images, struggling to handle long PDF documents with interleaved text and image… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05970v1-abstract-full').style.display = 'inline'; document.getElementById('2410.05970v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.05970v1-abstract-full" style="display: none;"> Document understanding is a challenging task to process and comprehend large amounts of textual and visual information. Recent advances in Large Language Models (LLMs) have significantly improved the performance of this task. However, existing methods typically focus on either plain text or a limited number of document images, struggling to handle long PDF documents with interleaved text and images, especially in academic papers. In this paper, we introduce PDF-WuKong, a multimodal large language model (MLLM) which is designed to enhance multimodal question-answering (QA) for long PDF documents. PDF-WuKong incorporates a sparse sampler that operates on both text and image representations, significantly improving the efficiency and capability of the MLLM. The sparse sampler is integrated with the MLLM's image encoder and selects the paragraphs or diagrams most pertinent to user queries for processing by the language model. To effectively train and evaluate our model, we construct PaperPDF, a dataset consisting of a broad collection of academic papers sourced from arXiv, multiple strategies are proposed to generate automatically 1M QA pairs along with their corresponding evidence sources. Experimental results demonstrate the superiority and high efficiency of our approach over other models on the task of long multimodal PDF understanding, surpassing proprietary products by an average of 8.6% on F1. Our code and dataset will be released at https://github.com/yh-hust/PDF-Wukong. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05970v1-abstract-full').style.display = 'none'; document.getElementById('2410.05970v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.05498">arXiv:2410.05498</a> <span> [<a href="https://arxiv.org/pdf/2410.05498">pdf</a>, <a href="https://arxiv.org/format/2410.05498">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Room-temperature decomposition of the ethaline deep eutectic solvent </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yang%2C+J+H">Julia H. Yang</a>, <a href="/search/?searchtype=author&query=Ooi%2C+A+W+S">Amanda Whai Shin Ooi</a>, <a href="/search/?searchtype=author&query=Goodwin%2C+Z+A+H">Zachary A. H. Goodwin</a>, <a href="/search/?searchtype=author&query=Xie%2C+Y">Yu Xie</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jingxuan Ding</a>, <a href="/search/?searchtype=author&query=Falletta%2C+S">Stefano Falletta</a>, <a href="/search/?searchtype=author&query=Park%2C+A+A">Ah-Hyung Alissa Park</a>, <a href="/search/?searchtype=author&query=Kozinsky%2C+B">Boris Kozinsky</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.05498v1-abstract-short" style="display: inline;"> Environmentally-benign, non-toxic electrolytes with combinatorial design spaces are excellent candidates for green solvents, green leaching agents, and carbon capture sources. Here, we examine one particular green solvent, ethaline, a 2:1 molar ratio of ethylene glycol and choline chloride. Despite its touted green credentials, we find partial decomposition of ethaline into toxic chloromethane and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05498v1-abstract-full').style.display = 'inline'; document.getElementById('2410.05498v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.05498v1-abstract-full" style="display: none;"> Environmentally-benign, non-toxic electrolytes with combinatorial design spaces are excellent candidates for green solvents, green leaching agents, and carbon capture sources. Here, we examine one particular green solvent, ethaline, a 2:1 molar ratio of ethylene glycol and choline chloride. Despite its touted green credentials, we find partial decomposition of ethaline into toxic chloromethane and dimethylaminoethanol at room temperature, limiting its sustainable advantage. We experimentally characterize these decomposition products and computationally develop a general, quantum chemically-accurate workflow to understand decomposition. We find that fluctuations of the hydrogen bonds bind chloride near reaction sites, initiating the reaction between choline cations and chloride anions. In summary, in the design of green solvents, we do not recommend the use of choline chloride due to its susceptibility to undergo decomposition in strongly hydrogen-bound mixtures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05498v1-abstract-full').style.display = 'none'; document.getElementById('2410.05498v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.04766">arXiv:2410.04766</a> <span> [<a href="https://arxiv.org/pdf/2410.04766">pdf</a>, <a href="https://arxiv.org/format/2410.04766">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Statistical Mechanics">cond-mat.stat-mech</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/PhysRevResearch.6.043070">10.1103/PhysRevResearch.6.043070 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Efficient sampling using Macrocanonical Monte Carlo and density of states mapping </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jiewei Ding</a>, <a href="/search/?searchtype=author&query=Su%2C+J">Jiahao Su</a>, <a href="/search/?searchtype=author&query=Tang%2C+H">Ho-Kin Tang</a>, <a href="/search/?searchtype=author&query=Yu%2C+W+C">Wing Chi Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.04766v1-abstract-short" style="display: inline;"> In the context of Monte Carlo sampling for lattice models, the complexity of the energy landscape often leads to Markov chains being trapped in local optima, thereby increasing the correlation between samples and reducing sampling efficiency. This study proposes a Monte Carlo algorithm that effectively addresses the irregularities of the energy landscape through the introduction of the estimated d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04766v1-abstract-full').style.display = 'inline'; document.getElementById('2410.04766v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.04766v1-abstract-full" style="display: none;"> In the context of Monte Carlo sampling for lattice models, the complexity of the energy landscape often leads to Markov chains being trapped in local optima, thereby increasing the correlation between samples and reducing sampling efficiency. This study proposes a Monte Carlo algorithm that effectively addresses the irregularities of the energy landscape through the introduction of the estimated density of states. This algorithm enhances the accuracy in the study of phase transitions and is not model-specific. Although our algorithm is primarily demonstrated on the two-dimensional square lattice model, the method is also applicable to a broader range of lattice and higher-dimensional models. Furthermore, the study develops a method for estimating the density of states of large systems based on that of smaller systems, enabling high-precision density of states estimation within specific energy intervals in large systems without sampling. For regions of lower precision, a re-weighting strategy is employed to adjust the density of states to enhance the precision further. This algorithm is not only significant within the field of lattice model sampling but may also inspire applications of the Monte Carlo method in other domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04766v1-abstract-full').style.display = 'none'; document.getElementById('2410.04766v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 9 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. Research 6, 043070 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.02256">arXiv:2410.02256</a> <span> [<a href="https://arxiv.org/pdf/2410.02256">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Other Condensed Matter">cond-mat.other</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</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/JSEN.2024.3419243">10.1109/JSEN.2024.3419243 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Recent Advances in Graphene-Based Pressure Sensors: A Review </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zhe Zhang</a>, <a href="/search/?searchtype=author&query=Liu%2C+Q">Quan Liu</a>, <a href="/search/?searchtype=author&query=Ma%2C+H">Hongliang Ma</a>, <a href="/search/?searchtype=author&query=Ke%2C+N">Ningfeng Ke</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wendong Zhang</a>, <a href="/search/?searchtype=author&query=Fan%2C+X">Xuge Fan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.02256v1-abstract-short" style="display: inline;"> In recent years, pressure sensors have been widely used as crucial technology components in industrial, healthcare, consumer electronics, and automotive safety applications. With the development of intelligent technologies, there is a growing demand for pressure sensors with higher sensitivity, smaller size, and wider detection range. Graphene and its derivatives, as novel emerging materials in re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02256v1-abstract-full').style.display = 'inline'; document.getElementById('2410.02256v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.02256v1-abstract-full" style="display: none;"> In recent years, pressure sensors have been widely used as crucial technology components in industrial, healthcare, consumer electronics, and automotive safety applications. With the development of intelligent technologies, there is a growing demand for pressure sensors with higher sensitivity, smaller size, and wider detection range. Graphene and its derivatives, as novel emerging materials in recent years, have received widespread attention from researchers due to their unique mechanical and electrical properties, and are considered as promising sensing materials for the high-performance pressure sensors. In general, graphene-based pressure sensors can be classified into flexible pressure sensors and gas pressure sensors. In this paper, we firstly introduce the basic properties of graphene and its derivatives and then review the research progress of both graphene-based flexible pressure sensors and graphene-based gas pressure sensors respectively, focusing on different sensing mechanisms. Finally, the application prospects of graphene-based pressure sensors as well as future challenges are discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02256v1-abstract-full').style.display = 'none'; document.getElementById('2410.02256v1-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> 3 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.02255">arXiv:2410.02255</a> <span> [<a href="https://arxiv.org/pdf/2410.02255">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Other Condensed Matter">cond-mat.other</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</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/JSEN.2024.3398003">10.1109/JSEN.2024.3398003 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Recent Advances in Graphene-Based Humidity Sensors with the Focus of Structural Design: A Review </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ma%2C+H">Hongliang Ma</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Z">Zhe Zhang</a>, <a href="/search/?searchtype=author&query=Gao%2C+Q">Qiang Gao</a>, <a href="/search/?searchtype=author&query=Liu%2C+Q">Quan Liu</a>, <a href="/search/?searchtype=author&query=Wang%2C+G">Gaohan Wang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wendong Zhang</a>, <a href="/search/?searchtype=author&query=Fan%2C+X">Xuge Fan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.02255v1-abstract-short" style="display: inline;"> The advent of the 5G era means that the concepts of robot, VR/AR, UAV, smart home, smart healthcare based on IoT (Internet of Things) have gradually entered human life. Since then, intelligent life has become the dominant direction of social development. Humidity sensors, as humidity detection tools, not only convey the comfort of human living environment, but also display great significance in th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02255v1-abstract-full').style.display = 'inline'; document.getElementById('2410.02255v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.02255v1-abstract-full" style="display: none;"> The advent of the 5G era means that the concepts of robot, VR/AR, UAV, smart home, smart healthcare based on IoT (Internet of Things) have gradually entered human life. Since then, intelligent life has become the dominant direction of social development. Humidity sensors, as humidity detection tools, not only convey the comfort of human living environment, but also display great significance in the fields of meteorology, medicine, agriculture and industry. Graphene-based materials exhibit tremendous potential in humidity sensing owing to their ultra-high specific surface area and excellent electron mobility under room temperature for application in humidity sensing. This review begins with the introduction of examples of various synthesis strategies of graphene, followed by the device structure and working mechanism of graphene-based humidity sensor. In addition, several different structural design methods of graphene are summarized, demonstrating the structural design of graphene can not only optimize the performance of graphene, but also bring significant advantages in humidity sensing. Finally, key challenges hindering the further development and practical application of high-performance graphene-based humidity sensors are discussed, followed by presenting the future perspectives. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02255v1-abstract-full').style.display = 'none'; document.getElementById('2410.02255v1-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> 3 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.02245">arXiv:2410.02245</a> <span> [<a href="https://arxiv.org/pdf/2410.02245">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Other Condensed Matter">cond-mat.other</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</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/10.1039/D4NR02207F">10.1039/D4NR02207F <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Fast response and highly sensitive flexible humidity sensor based on nanocomposite film of MoS2 and graphene oxide </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ge%2C+G">Gengwu Ge</a>, <a href="/search/?searchtype=author&query=Ke%2C+N">Ningfeng Ke</a>, <a href="/search/?searchtype=author&query=Ma%2C+H">Hongliang Ma</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wendong Zhang</a>, <a href="/search/?searchtype=author&query=Fan%2C+X">Xuge Fan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.02245v1-abstract-short" style="display: inline;"> Graphene oxide (GO)-based humidity sensors are attracting widespread attention due to their high responsivity and low cost. However, GO-based humidity sensors generally suffer from slow response and recovery as well as poor stability,etc. Here, we reported a flexible resistive humidity sensor based on a MoS2/GO composite film that was fabricated by mixing different volumes of MoS2 and GO dispersio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02245v1-abstract-full').style.display = 'inline'; document.getElementById('2410.02245v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.02245v1-abstract-full" style="display: none;"> Graphene oxide (GO)-based humidity sensors are attracting widespread attention due to their high responsivity and low cost. However, GO-based humidity sensors generally suffer from slow response and recovery as well as poor stability,etc. Here, we reported a flexible resistive humidity sensor based on a MoS2/GO composite film that was fabricated by mixing different volumes of MoS2 and GO dispersions with adjustable volume ratios. The MoS2/GO composite film has been used as a sensing layer on screen-printed interdigital electrodes. The results show that the best device performance was achieved at a dispersion volume of 0.05 mL with the MoS2/GO volume ratio of 5:1, featuring high responsivity (~98%), fast response/recovery time (1.3/12.1 s), excellent stability and low cost. Further, the humidity sensor exhibits good linearity over a wide humidity range (33% RH-98% RH) at room temperature and can be fabricated easily and feasibly. The application of the humidity sensors we prepared in human respiration detection and human fingertip proximity detection has been demonstrated. These findings indicate the great potential of the composite of MoS2/GO in developing the next generation of high-performance humidity sensors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02245v1-abstract-full').style.display = 'none'; document.getElementById('2410.02245v1-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> 3 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.01484">arXiv:2410.01484</a> <span> [<a href="https://arxiv.org/pdf/2410.01484">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey 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="Other Condensed Matter">cond-mat.other</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</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/10.1021/acsami.4c11194">10.1021/acsami.4c11194 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Humidity Sensing Properties of Different Atomic Layers of Graphene on SiO2/Si Substrate </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Gao%2C+Q">Qiang Gao</a>, <a href="/search/?searchtype=author&query=Ma%2C+H">Hongliang Ma</a>, <a href="/search/?searchtype=author&query=He%2C+C">Chang He</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xiaojing Wang</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wendong Zhang</a>, <a href="/search/?searchtype=author&query=Fan%2C+X">Xuge Fan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.01484v1-abstract-short" style="display: inline;"> Graphene has the great potential to be used for humidity sensing due to ultrahigh surface area and conductivity. However, the impact of different atomic layers of graphene on SiO2/Si substrate on the humidity sensing have not been studied yet. In this paper, we fabricated three types of humidity sensors on SiO2/Si substrate based on one to three atomic layers of graphene, in which the sensing area… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01484v1-abstract-full').style.display = 'inline'; document.getElementById('2410.01484v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.01484v1-abstract-full" style="display: none;"> Graphene has the great potential to be used for humidity sensing due to ultrahigh surface area and conductivity. However, the impact of different atomic layers of graphene on SiO2/Si substrate on the humidity sensing have not been studied yet. In this paper, we fabricated three types of humidity sensors on SiO2/Si substrate based on one to three atomic layers of graphene, in which the sensing areas of graphene are 75 渭m * 72 渭m and 45 渭m * 72 渭m, respectively. We studied the impact of both the number of atomic layers of graphene and the sensing areas of graphene on the responsivity and response/recovery time of the prepared graphene-based humidity sensors. We found the relative resistance change of the prepared devices decreased with the increase of number of atomic layers of graphene under the same change of relative humidity. Further, devices based on tri-layer graphene showed the fastest response/recovery time while devices based on double-layer graphene showed the slowest response/recovery time. Finally, we chose the devices based on double-layer graphene that have relatively good responsivity and stability for application in respiration monitoring and contact-free finger monitoring. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01484v1-abstract-full').style.display = 'none'; document.getElementById('2410.01484v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.01462">arXiv:2410.01462</a> <span> [<a href="https://arxiv.org/pdf/2410.01462">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey 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="Other Condensed Matter">cond-mat.other</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</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/10.1038/s41378-024-00799-x">10.1038/s41378-024-00799-x <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Four ribbons of double-layer graphene suspending masses for NEMS applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Fan%2C+X">Xuge Fan</a>, <a href="/search/?searchtype=author&query=He%2C+C">Chang He</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/?searchtype=author&query=Akbari%2C+S+S+A">Sayedeh Shirin Afyouni Akbari</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wendong 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="2410.01462v1-abstract-short" style="display: inline;"> Graphene ribbons with a suspended proof mass for nanomechanical systems have been rarely studied. Here, we report three types of nanomechanical devices consisting of graphene ribbons (two ribbons, four ribbons-cross and four ribbons-parallel) with suspended Si proof masses and studied their mechanical properties. The resonance frequencies and built-in stresses of three types of devices ranged from… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01462v1-abstract-full').style.display = 'inline'; document.getElementById('2410.01462v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.01462v1-abstract-full" style="display: none;"> Graphene ribbons with a suspended proof mass for nanomechanical systems have been rarely studied. Here, we report three types of nanomechanical devices consisting of graphene ribbons (two ribbons, four ribbons-cross and four ribbons-parallel) with suspended Si proof masses and studied their mechanical properties. The resonance frequencies and built-in stresses of three types of devices ranged from tens of kHz to hundreds of kHz, and from 82.61 MPa to 545.73 MPa, respectively, both of which decrease with the increase of the size of proof mass. The devices with four graphene ribbons featured higher resonance frequencies and spring constants, but lower built-in stresses than two ribbon devices under otherwise identical conditions. The Young's modulus and fracture strain of double-layer graphene were measured to be 0.34 TPa and 1.13% respectively, by using the experimental data and finite element analysis (FEA) simulations. Our studies would lay the foundation for understanding of mechanical properties of graphene ribbons with a suspended proof mass and their potential applications in nanoelectromechanical systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01462v1-abstract-full').style.display = 'none'; document.getElementById('2410.01462v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.01439">arXiv:2410.01439</a> <span> [<a href="https://arxiv.org/pdf/2410.01439">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey 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="Other Condensed Matter">cond-mat.other</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> Graphene MEMS and NEMS </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Fan%2C+X">Xuge Fan</a>, <a href="/search/?searchtype=author&query=He%2C+C">Chang He</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a>, <a href="/search/?searchtype=author&query=Gao%2C+Q">Qiang Gao</a>, <a href="/search/?searchtype=author&query=Ma%2C+H">Hongliang Ma</a>, <a href="/search/?searchtype=author&query=Lemme%2C+M+C">Max C. Lemme</a>, <a href="/search/?searchtype=author&query=Zhang%2C+W">Wendong 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="2410.01439v1-abstract-short" style="display: inline;"> Graphene is being increasingly used as an interesting transducer membrane in micro- and nanoelectromechanical systems (MEMS and NEMS, respectively) due to its atomical thickness, extremely high carrier mobility, high mechanical strength and piezoresistive electromechanical transductions. NEMS devices based on graphene feature increased sensitivity, reduced size, and new functionalities. In this re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01439v1-abstract-full').style.display = 'inline'; document.getElementById('2410.01439v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.01439v1-abstract-full" style="display: none;"> Graphene is being increasingly used as an interesting transducer membrane in micro- and nanoelectromechanical systems (MEMS and NEMS, respectively) due to its atomical thickness, extremely high carrier mobility, high mechanical strength and piezoresistive electromechanical transductions. NEMS devices based on graphene feature increased sensitivity, reduced size, and new functionalities. In this review, we discuss the merits of graphene as a functional material for MEMS and NEMS, the related properties of graphene, the transduction mechanisms of graphene MEMS and NEMS, typical transfer methods for integrating graphene with MEMS substrates, methods for fabricating suspended graphene, and graphene patterning and electrical contact. Consequently, we provide an overview of devices based on suspended and nonsuspended graphene structures. Finally, we discuss the potential and challenges of applications of graphene in MEMS and NEMS. Owing to its unique features, graphene is a promising material for emerging MEMS, NEMS and sensor applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01439v1-abstract-full').style.display = 'none'; document.getElementById('2410.01439v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.20265">arXiv:2409.20265</a> <span> [<a href="https://arxiv.org/pdf/2409.20265">pdf</a>, <a href="https://arxiv.org/ps/2409.20265">ps</a>, <a href="https://arxiv.org/format/2409.20265">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Complex Variables">math.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Functional Analysis">math.FA</span> </div> </div> <p class="title is-5 mathjax"> BMO on Weighted Bergman Spaces over Tubular Domains </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jiaqing Ding</a>, <a href="/search/?searchtype=author&query=Li%2C+H">Haichou Li</a>, <a href="/search/?searchtype=author&query=Fu%2C+Z">Zhiyuan Fu</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yanhui 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="2409.20265v1-abstract-short" style="display: inline;"> In this paper, we characterize Bounded Mean Oscillation (BMO) and establish their connection with Hankel operators on weighted Bergman spaces over tubular domains. By utilizing the space BMO, we provide a new characterization of Bloch spaces on tubular domains. Next, we define a modified projection operator and prove its boundedness. Furthermore, we introduce differential operators and demonstrate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.20265v1-abstract-full').style.display = 'inline'; document.getElementById('2409.20265v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.20265v1-abstract-full" style="display: none;"> In this paper, we characterize Bounded Mean Oscillation (BMO) and establish their connection with Hankel operators on weighted Bergman spaces over tubular domains. By utilizing the space BMO, we provide a new characterization of Bloch spaces on tubular domains. Next, we define a modified projection operator and prove its boundedness. Furthermore, we introduce differential operators and demonstrate that these operators belong to Lebesgue spaces on tubular domains. Finally, we establish an integral representation for Bergman functions using these differential operators. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.20265v1-abstract-full').style.display = 'none'; document.getElementById('2409.20265v1-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> 30 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.18492">arXiv:2409.18492</a> <span> [<a href="https://arxiv.org/pdf/2409.18492">pdf</a>, <a href="https://arxiv.org/format/2409.18492">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Probability">math.PR</span> </div> </div> <p class="title is-5 mathjax"> Tightness for random walks driven by the two-dimensional Gaussian free field at high temperature </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jian Ding</a>, <a href="/search/?searchtype=author&query=Wang%2C+J">Jiamin Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.18492v1-abstract-short" style="display: inline;"> We study random walks in random environments generated by the two-dimensional Gaussian free field. More specifically, we consider a rescaled lattice with a small mesh size and view it as a random network where each edge is equipped with an electric resistance given by a regularization for the exponentiation of the Gaussian free field. We prove the tightness of random walks on such random networks… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18492v1-abstract-full').style.display = 'inline'; document.getElementById('2409.18492v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.18492v1-abstract-full" style="display: none;"> We study random walks in random environments generated by the two-dimensional Gaussian free field. More specifically, we consider a rescaled lattice with a small mesh size and view it as a random network where each edge is equipped with an electric resistance given by a regularization for the exponentiation of the Gaussian free field. We prove the tightness of random walks on such random networks at high temperature as the mesh size tends to 0. Our proof is based on a careful analysis of the (random) effective resistances as well as their connections to random walks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.18492v1-abstract-full').style.display = 'none'; document.getElementById('2409.18492v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.17510">arXiv:2409.17510</a> <span> [<a href="https://arxiv.org/pdf/2409.17510">pdf</a>, <a href="https://arxiv.org/format/2409.17510">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Wei%2C+Z">Ziquan Wei</a>, <a href="/search/?searchtype=author&query=Dan%2C+T">Tingting Dan</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jiaqi Ding</a>, <a href="/search/?searchtype=author&query=Wu%2C+G">Guorong 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="2409.17510v3-abstract-short" style="display: inline;"> Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how anatomical structure supports brain function and how spontaneous functional fluctuations emerge remarkable cognition is still elusive. Meanwhile, tremendous efforts have been made in the realm of machine learning to establish the nonlinear mapping between… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17510v3-abstract-full').style.display = 'inline'; document.getElementById('2409.17510v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.17510v3-abstract-full" style="display: none;"> Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how anatomical structure supports brain function and how spontaneous functional fluctuations emerge remarkable cognition is still elusive. Meanwhile, tremendous efforts have been made in the realm of machine learning to establish the nonlinear mapping between neuroimaging data and phenotypic traits. However, the absence of neuroscience insight in the current approaches poses significant challenges in understanding cognitive behavior from transient neural activities. To address this challenge, we put the spotlight on the coupling mechanism of structural connectivity (SC) and functional connectivity (FC) by formulating such network neuroscience question into an expressive graph representation learning problem for high-order topology. Specifically, we introduce the concept of topological detour to characterize how a ubiquitous instance of FC (direct link) is supported by neural pathways (detour) physically wired by SC, which forms a cyclic loop interacted by brain structure and function. In the clich茅 of machine learning, the multi-hop detour pathway underlying SC-FC coupling allows us to devise a novel multi-head self-attention mechanism within Transformer to capture multi-modal feature representation from paired graphs of SC and FC. Taken together, we propose a biological-inspired deep model, coined as NeuroPath, to find putative connectomic feature representations from the unprecedented amount of neuroimages, which can be plugged into various downstream applications such as task recognition and disease diagnosis. We have evaluated NeuroPath on large-scale public datasets including HCP and UK Biobank under supervised and zero-shot learning, where the state-of-the-art performance by our NeuroPath indicates great potential in network neuroscience. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17510v3-abstract-full').style.display = 'none'; document.getElementById('2409.17510v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by NeurIPS 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.17275">arXiv:2409.17275</a> <span> [<a href="https://arxiv.org/pdf/2409.17275">pdf</a>, <a href="https://arxiv.org/format/2409.17275">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="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="Databases">cs.DB</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</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"> On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xian%2C+X">Xun Xian</a>, <a href="/search/?searchtype=author&query=Wang%2C+G">Ganghua Wang</a>, <a href="/search/?searchtype=author&query=Bi%2C+X">Xuan Bi</a>, <a href="/search/?searchtype=author&query=Srinivasa%2C+J">Jayanth Srinivasa</a>, <a href="/search/?searchtype=author&query=Kundu%2C+A">Ashish Kundu</a>, <a href="/search/?searchtype=author&query=Fleming%2C+C">Charles Fleming</a>, <a href="/search/?searchtype=author&query=Hong%2C+M">Mingyi Hong</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.17275v1-abstract-short" style="display: inline;"> Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, finance, and legal contexts. Given a query, RAG retrieves relevant documents from a corpus and integrates them into the LLMs' generation process. In this study, we investigate the adversarial robustness of RAG, focusing specif… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17275v1-abstract-full').style.display = 'inline'; document.getElementById('2409.17275v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.17275v1-abstract-full" style="display: none;"> Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, finance, and legal contexts. Given a query, RAG retrieves relevant documents from a corpus and integrates them into the LLMs' generation process. In this study, we investigate the adversarial robustness of RAG, focusing specifically on examining the retrieval system. First, across 225 different setup combinations of corpus, retriever, query, and targeted information, we show that retrieval systems are vulnerable to universal poisoning attacks in medical Q\&A. In such attacks, adversaries generate poisoned documents containing a broad spectrum of targeted information, such as personally identifiable information. When these poisoned documents are inserted into a corpus, they can be accurately retrieved by any users, as long as attacker-specified queries are used. To understand this vulnerability, we discovered that the deviation from the query's embedding to that of the poisoned document tends to follow a pattern in which the high similarity between the poisoned document and the query is retained, thereby enabling precise retrieval. Based on these findings, we develop a new detection-based defense to ensure the safe use of RAG. Through extensive experiments spanning various Q\&A domains, we observed that our proposed method consistently achieves excellent detection rates in nearly all cases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17275v1-abstract-full').style.display = 'none'; document.getElementById('2409.17275v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.15584">arXiv:2409.15584</a> <span> [<a href="https://arxiv.org/pdf/2409.15584">pdf</a>, <a href="https://arxiv.org/format/2409.15584">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"> FACET: Fast and Accurate Event-Based Eye Tracking Using Ellipse Modeling for Extended Reality </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Junyuan Ding</a>, <a href="/search/?searchtype=author&query=Wang%2C+Z">Ziteng Wang</a>, <a href="/search/?searchtype=author&query=Gao%2C+C">Chang Gao</a>, <a href="/search/?searchtype=author&query=Liu%2C+M">Min Liu</a>, <a href="/search/?searchtype=author&query=Chen%2C+Q">Qinyu 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="2409.15584v1-abstract-short" style="display: inline;"> Eye tracking is a key technology for gaze-based interactions in Extended Reality (XR), but traditional frame-based systems struggle to meet XR's demands for high accuracy, low latency, and power efficiency. Event cameras offer a promising alternative due to their high temporal resolution and low power consumption. In this paper, we present FACET (Fast and Accurate Event-based Eye Tracking), an end… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.15584v1-abstract-full').style.display = 'inline'; document.getElementById('2409.15584v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.15584v1-abstract-full" style="display: none;"> Eye tracking is a key technology for gaze-based interactions in Extended Reality (XR), but traditional frame-based systems struggle to meet XR's demands for high accuracy, low latency, and power efficiency. Event cameras offer a promising alternative due to their high temporal resolution and low power consumption. In this paper, we present FACET (Fast and Accurate Event-based Eye Tracking), an end-to-end neural network that directly outputs pupil ellipse parameters from event data, optimized for real-time XR applications. The ellipse output can be directly used in subsequent ellipse-based pupil trackers. We enhance the EV-Eye dataset by expanding annotated data and converting original mask labels to ellipse-based annotations to train the model. Besides, a novel trigonometric loss is adopted to address angle discontinuities and a fast causal event volume event representation method is put forward. On the enhanced EV-Eye test set, FACET achieves an average pupil center error of 0.20 pixels and an inference time of 0.53 ms, reducing pixel error and inference time by 1.6$\times$ and 1.8$\times$ compared to the prior art, EV-Eye, with 4.4$\times$ and 11.7$\times$ less parameters and arithmetic operations. The code is available at https://github.com/DeanJY/FACET. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.15584v1-abstract-full').style.display = 'none'; document.getElementById('2409.15584v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 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/2409.13997">arXiv:2409.13997</a> <span> [<a href="https://arxiv.org/pdf/2409.13997">pdf</a>, <a href="https://arxiv.org/format/2409.13997">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> Drift to Remember </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Du%2C+J">Jin Du</a>, <a href="/search/?searchtype=author&query=Zhang%2C+X">Xinhe Zhang</a>, <a href="/search/?searchtype=author&query=Shen%2C+H">Hao Shen</a>, <a href="/search/?searchtype=author&query=Xian%2C+X">Xun Xian</a>, <a href="/search/?searchtype=author&query=Wang%2C+G">Ganghua Wang</a>, <a href="/search/?searchtype=author&query=Zhang%2C+J">Jiawei Zhang</a>, <a href="/search/?searchtype=author&query=Yang%2C+Y">Yuhong Yang</a>, <a href="/search/?searchtype=author&query=Li%2C+N">Na Li</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jia Liu</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jie Ding</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.13997v1-abstract-short" style="display: inline;"> Lifelong learning in artificial intelligence (AI) aims to mimic the biological brain's ability to continuously learn and retain knowledge, yet it faces challenges such as catastrophic forgetting. Recent neuroscience research suggests that neural activity in biological systems undergoes representational drift, where neural responses evolve over time, even with consistent inputs and tasks. We hypoth… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13997v1-abstract-full').style.display = 'inline'; document.getElementById('2409.13997v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13997v1-abstract-full" style="display: none;"> Lifelong learning in artificial intelligence (AI) aims to mimic the biological brain's ability to continuously learn and retain knowledge, yet it faces challenges such as catastrophic forgetting. Recent neuroscience research suggests that neural activity in biological systems undergoes representational drift, where neural responses evolve over time, even with consistent inputs and tasks. We hypothesize that representational drift can alleviate catastrophic forgetting in AI during new task acquisition. To test this, we introduce DriftNet, a network designed to constantly explore various local minima in the loss landscape while dynamically retrieving relevant tasks. This approach ensures efficient integration of new information and preserves existing knowledge. Experimental studies in image classification and natural language processing demonstrate that DriftNet outperforms existing models in lifelong learning. Importantly, DriftNet is scalable in handling a sequence of tasks such as sentiment analysis and question answering using large language models (LLMs) with billions of parameters on a single Nvidia A100 GPU. DriftNet efficiently updates LLMs using only new data, avoiding the need for full dataset retraining. Tested on GPT-2 and RoBERTa, DriftNet is a robust, cost-effective solution for lifelong learning in LLMs. This study not only advances AI systems to emulate biological learning, but also provides insights into the adaptive mechanisms of biological neural systems, deepening our understanding of lifelong learning in nature. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13997v1-abstract-full').style.display = 'none'; document.getElementById('2409.13997v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.13712">arXiv:2409.13712</a> <span> [<a href="https://arxiv.org/pdf/2409.13712">pdf</a>, <a href="https://arxiv.org/format/2409.13712">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"> Good Idea or Not, Representation of LLM Could Tell </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Xu%2C+Y">Yi Xu</a>, <a href="/search/?searchtype=author&query=Xue%2C+B">Bo Xue</a>, <a href="/search/?searchtype=author&query=Sheng%2C+S">Shuqian Sheng</a>, <a href="/search/?searchtype=author&query=Deng%2C+C">Cheng Deng</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jiaxin Ding</a>, <a href="/search/?searchtype=author&query=Shen%2C+Z">Zanwei Shen</a>, <a href="/search/?searchtype=author&query=Fu%2C+L">Luoyi Fu</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xinbing Wang</a>, <a href="/search/?searchtype=author&query=Zhou%2C+C">Chenghu Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.13712v1-abstract-short" style="display: inline;"> In the ever-expanding landscape of academic research, the proliferation of ideas presents a significant challenge for researchers: discerning valuable ideas from the less impactful ones. The ability to efficiently evaluate the potential of these ideas is crucial for the advancement of science and paper review. In this work, we focus on idea assessment, which aims to leverage the knowledge of large… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13712v1-abstract-full').style.display = 'inline'; document.getElementById('2409.13712v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13712v1-abstract-full" style="display: none;"> In the ever-expanding landscape of academic research, the proliferation of ideas presents a significant challenge for researchers: discerning valuable ideas from the less impactful ones. The ability to efficiently evaluate the potential of these ideas is crucial for the advancement of science and paper review. In this work, we focus on idea assessment, which aims to leverage the knowledge of large language models to assess the merit of scientific ideas. First, we investigate existing text evaluation research and define the problem of quantitative evaluation of ideas. Second, we curate and release a benchmark dataset from nearly four thousand manuscript papers with full texts, meticulously designed to train and evaluate the performance of different approaches to this task. Third, we establish a framework for quantifying the value of ideas by employing representations in a specific layer of large language models. Experimental results show that the scores predicted by our method are relatively consistent with those of humans. Our findings suggest that the representations of large language models hold more potential in quantifying the value of ideas than their generative outputs, demonstrating a promising avenue for automating the idea assessment process. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13712v1-abstract-full').style.display = 'none'; document.getElementById('2409.13712v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.13504">arXiv:2409.13504</a> <span> [<a href="https://arxiv.org/pdf/2409.13504">pdf</a>, <a href="https://arxiv.org/format/2409.13504">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> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1103/PhysRevLett.133.176401">10.1103/PhysRevLett.133.176401 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Absence of altermagnetic spin splitting character in rutile oxide RuO$_2$ </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Liu%2C+J">Jiayu Liu</a>, <a href="/search/?searchtype=author&query=Zhan%2C+J">Jie Zhan</a>, <a href="/search/?searchtype=author&query=Li%2C+T">Tongrui Li</a>, <a href="/search/?searchtype=author&query=Liu%2C+J">Jishan Liu</a>, <a href="/search/?searchtype=author&query=Cheng%2C+S">Shufan Cheng</a>, <a href="/search/?searchtype=author&query=Shi%2C+Y">Yuming Shi</a>, <a href="/search/?searchtype=author&query=Deng%2C+L">Liwei Deng</a>, <a href="/search/?searchtype=author&query=Zhang%2C+M">Meng Zhang</a>, <a href="/search/?searchtype=author&query=Li%2C+C">Chihao Li</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jianyang Ding</a>, <a href="/search/?searchtype=author&query=Jiang%2C+Q">Qi Jiang</a>, <a href="/search/?searchtype=author&query=Ye%2C+M">Mao Ye</a>, <a href="/search/?searchtype=author&query=Liu%2C+Z">Zhengtai Liu</a>, <a href="/search/?searchtype=author&query=Jiang%2C+Z">Zhicheng Jiang</a>, <a href="/search/?searchtype=author&query=Wang%2C+S">Siyu Wang</a>, <a href="/search/?searchtype=author&query=Li%2C+Q">Qian Li</a>, <a href="/search/?searchtype=author&query=Xie%2C+Y">Yanwu Xie</a>, <a href="/search/?searchtype=author&query=Wang%2C+Y">Yilin Wang</a>, <a href="/search/?searchtype=author&query=Qiao%2C+S">Shan Qiao</a>, <a href="/search/?searchtype=author&query=Wen%2C+J">Jinsheng Wen</a>, <a href="/search/?searchtype=author&query=Sun%2C+Y">Yan Sun</a>, <a href="/search/?searchtype=author&query=Shen%2C+D">Dawei 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="2409.13504v2-abstract-short" style="display: inline;"> Rutile RuO$_2$ has been posited as a potential $d$-wave altermagnetism candidate, with a predicted significant spin splitting up to 1.4 eV. Despite accumulating theoretical predictions and transport measurements, direct spectroscopic observation of spin splitting has remained elusive. Here, we employ spin- and angle-resolved photoemission spectroscopy to investigate the band structures and spin po… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13504v2-abstract-full').style.display = 'inline'; document.getElementById('2409.13504v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13504v2-abstract-full" style="display: none;"> Rutile RuO$_2$ has been posited as a potential $d$-wave altermagnetism candidate, with a predicted significant spin splitting up to 1.4 eV. Despite accumulating theoretical predictions and transport measurements, direct spectroscopic observation of spin splitting has remained elusive. Here, we employ spin- and angle-resolved photoemission spectroscopy to investigate the band structures and spin polarization of thin-film and single-crystal RuO$_2$. Contrary to expectations of altermagnetism, our analysis indicates that RuO$_2$'s electronic structure aligns with those predicted under non-magnetic conditions, exhibiting no evidence of the hypothesized spin splitting. Additionally, we observe significant in-plane spin polarization of the low-lying bulk bands, which is antisymmetric about the high-symmetry plane and contrary to the $d$-wave spin texture due to time-reversal symmetry breaking in altermagnetism. These findings definitively challenge the altermagnetic order previously proposed for rutile RuO$_2$, prompting a reevaluation of its magnetic properties. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13504v2-abstract-full').style.display = 'none'; document.getElementById('2409.13504v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 4 figures. Published in Physical Review Letters</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Phys. Rev. Lett. 133, 176401 (2024) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.12507">arXiv:2409.12507</a> <span> [<a href="https://arxiv.org/pdf/2409.12507">pdf</a>, <a href="https://arxiv.org/format/2409.12507">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"> Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Zhong%2C+X">Xian Zhong</a>, <a href="/search/?searchtype=author&query=Hu%2C+S">Shengwang Hu</a>, <a href="/search/?searchtype=author&query=Liu%2C+W">Wenxuan Liu</a>, <a href="/search/?searchtype=author&query=Huang%2C+W">Wenxin Huang</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jianhao Ding</a>, <a href="/search/?searchtype=author&query=Yu%2C+Z">Zhaofei Yu</a>, <a href="/search/?searchtype=author&query=Huang%2C+T">Tiejun 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="2409.12507v1-abstract-short" style="display: inline;"> Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biological interpretability. Their rich spatio-temporal information processing capability and event-driven nature make them ideally well-suited for neuromorphic datasets. However, current SNNs struggle to balance accuracy and latency in classifying these datasets. In this paper, we propose H… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.12507v1-abstract-full').style.display = 'inline'; document.getElementById('2409.12507v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.12507v1-abstract-full" style="display: none;"> Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biological interpretability. Their rich spatio-temporal information processing capability and event-driven nature make them ideally well-suited for neuromorphic datasets. However, current SNNs struggle to balance accuracy and latency in classifying these datasets. In this paper, we propose Hybrid Step-wise Distillation (HSD) method, tailored for neuromorphic datasets, to mitigate the notable decline in performance at lower time steps. Our work disentangles the dependency between the number of event frames and the time steps of SNNs, utilizing more event frames during the training stage to improve performance, while using fewer event frames during the inference stage to reduce latency. Nevertheless, the average output of SNNs across all time steps is susceptible to individual time step with abnormal outputs, particularly at extremely low time steps. To tackle this issue, we implement Step-wise Knowledge Distillation (SKD) module that considers variations in the output distribution of SNNs at each time step. Empirical evidence demonstrates that our method yields competitive performance in classification tasks on neuromorphic datasets, especially at lower time steps. Our code will be available at: {https://github.com/hsw0929/HSD}. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.12507v1-abstract-full').style.display = 'none'; document.getElementById('2409.12507v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.11377">arXiv:2409.11377</a> <span> [<a href="https://arxiv.org/pdf/2409.11377">pdf</a>, <a href="https://arxiv.org/format/2409.11377">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"> Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ding%2C+J">Jiaqi Ding</a>, <a href="/search/?searchtype=author&query=Dan%2C+T">Tingting Dan</a>, <a href="/search/?searchtype=author&query=Wei%2C+Z">Ziquan Wei</a>, <a href="/search/?searchtype=author&query=Cho%2C+H">Hyuna Cho</a>, <a href="/search/?searchtype=author&query=Laurienti%2C+P+J">Paul J. Laurienti</a>, <a href="/search/?searchtype=author&query=Kim%2C+W+H">Won Hwa Kim</a>, <a href="/search/?searchtype=author&query=Wu%2C+G">Guorong 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="2409.11377v1-abstract-short" style="display: inline;"> An unprecedented amount of existing functional Magnetic Resonance Imaging (fMRI) data provides a new opportunity to understand the relationship between functional fluctuation and human cognition/behavior using a data-driven approach. To that end, tremendous efforts have been made in machine learning to predict cognitive states from evolving volumetric images of blood-oxygen-level-dependent (BOLD)… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11377v1-abstract-full').style.display = 'inline'; document.getElementById('2409.11377v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.11377v1-abstract-full" style="display: none;"> An unprecedented amount of existing functional Magnetic Resonance Imaging (fMRI) data provides a new opportunity to understand the relationship between functional fluctuation and human cognition/behavior using a data-driven approach. To that end, tremendous efforts have been made in machine learning to predict cognitive states from evolving volumetric images of blood-oxygen-level-dependent (BOLD) signals. Due to the complex nature of brain function, however, the evaluation on learning performance and discoveries are not often consistent across current state-of-the-arts (SOTA). By capitalizing on large-scale existing neuroimaging data (34,887 data samples from six public databases), we seek to establish a well-founded empirical guideline for designing deep models for functional neuroimages by linking the methodology underpinning with knowledge from the neuroscience domain. Specifically, we put the spotlight on (1) What is the current SOTA performance in cognitive task recognition and disease diagnosis using fMRI? (2) What are the limitations of current deep models? and (3) What is the general guideline for selecting the suitable machine learning backbone for new neuroimaging applications? We have conducted a comprehensive evaluation and statistical analysis, in various settings, to answer the above outstanding questions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11377v1-abstract-full').style.display = 'none'; document.getElementById('2409.11377v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.11143">arXiv:2409.11143</a> <span> [<a href="https://arxiv.org/pdf/2409.11143">pdf</a>, <a href="https://arxiv.org/format/2409.11143">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"> Semformer: Transformer Language Models with Semantic Planning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Yin%2C+Y">Yongjing Yin</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Junran Ding</a>, <a href="/search/?searchtype=author&query=Song%2C+K">Kai Song</a>, <a href="/search/?searchtype=author&query=Zhang%2C+Y">Yue 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="2409.11143v1-abstract-short" style="display: inline;"> Next-token prediction serves as the dominant component in current neural language models. During the training phase, the model employs teacher forcing, which predicts tokens based on all preceding ground truth tokens. However, this approach has been found to create shortcuts, utilizing the revealed prefix to spuriously fit future tokens, potentially compromising the accuracy of the next-token pred… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11143v1-abstract-full').style.display = 'inline'; document.getElementById('2409.11143v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.11143v1-abstract-full" style="display: none;"> Next-token prediction serves as the dominant component in current neural language models. During the training phase, the model employs teacher forcing, which predicts tokens based on all preceding ground truth tokens. However, this approach has been found to create shortcuts, utilizing the revealed prefix to spuriously fit future tokens, potentially compromising the accuracy of the next-token predictor. In this paper, we introduce Semformer, a novel method of training a Transformer language model that explicitly models the semantic planning of response. Specifically, we incorporate a sequence of planning tokens into the prefix, guiding the planning token representations to predict the latent semantic representations of the response, which are induced by an autoencoder. In a minimal planning task (i.e., graph path-finding), our model exhibits near-perfect performance and effectively mitigates shortcut learning, a feat that standard training methods and baseline models have been unable to accomplish. Furthermore, we pretrain Semformer from scratch with 125M parameters, demonstrating its efficacy through measures of perplexity, in-context learning, and fine-tuning on summarization tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11143v1-abstract-full').style.display = 'none'; document.getElementById('2409.11143v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> EMNLP2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.10016">arXiv:2409.10016</a> <span> [<a href="https://arxiv.org/pdf/2409.10016">pdf</a>, <a href="https://arxiv.org/format/2409.10016">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"> AceParse: A Comprehensive Dataset with Diverse Structured Texts for Academic Literature Parsing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/?searchtype=author&query=Ji%2C+H">Huawei Ji</a>, <a href="/search/?searchtype=author&query=Deng%2C+C">Cheng Deng</a>, <a href="/search/?searchtype=author&query=Xue%2C+B">Bo Xue</a>, <a href="/search/?searchtype=author&query=Jin%2C+Z">Zhouyang Jin</a>, <a href="/search/?searchtype=author&query=Ding%2C+J">Jiaxin Ding</a>, <a href="/search/?searchtype=author&query=Gan%2C+X">Xiaoying Gan</a>, <a href="/search/?searchtype=author&query=Fu%2C+L">Luoyi Fu</a>, <a href="/search/?searchtype=author&query=Wang%2C+X">Xinbing Wang</a>, <a href="/search/?searchtype=author&query=Zhou%2C+C">Chenghu Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.10016v1-abstract-short" style="display: inline;"> With the development of data-centric AI, the focus has shifted from model-driven approaches to improving data quality. Academic literature, as one of the crucial types, is predominantly stored in PDF formats and needs to be parsed into texts before further processing. However, parsing diverse structured texts in academic literature remains challenging due to the lack of datasets that cover various… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10016v1-abstract-full').style.display = 'inline'; document.getElementById('2409.10016v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10016v1-abstract-full" style="display: none;"> With the development of data-centric AI, the focus has shifted from model-driven approaches to improving data quality. Academic literature, as one of the crucial types, is predominantly stored in PDF formats and needs to be parsed into texts before further processing. However, parsing diverse structured texts in academic literature remains challenging due to the lack of datasets that cover various text structures. In this paper, we introduce AceParse, the first comprehensive dataset designed to support the parsing of a wide range of structured texts, including formulas, tables, lists, algorithms, and sentences with embedded mathematical expressions. Based on AceParse, we fine-tuned a multimodal model, named AceParser, which accurately parses various structured texts within academic literature. This model outperforms the previous state-of-the-art by 4.1% in terms of F1 score and by 5% in Jaccard Similarity, demonstrating the potential of multimodal models in academic literature parsing. Our dataset is available at https://github.com/JHW5981/AceParse. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10016v1-abstract-full').style.display = 'none'; document.getElementById('2409.10016v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 3 figures, 3 tables</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=Ding%2C+J&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Ding%2C+J&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Ding%2C+J&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Ding%2C+J&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Ding%2C+J&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a href="/search/?searchtype=author&query=Ding%2C+J&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