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 1–45 of 45 results for author: <span class="mathjax">Pan, L</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/eess" aria-role="search"> Searching in archive <strong>eess</strong>. <a href="/search/?searchtype=author&query=Pan%2C+L">Search in all archives.</a> <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="Pan, L"> </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=Pan%2C+L&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="Pan, L"> <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> <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/2411.15274">arXiv:2411.15274</a> <span> [<a href="https://arxiv.org/pdf/2411.15274">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Liangrui Pan</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+Q">Qingchun Liang</a>, <a href="/search/eess?searchtype=author&query=Zeng%2C+W">Wenwu Zeng</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+Y">Yijun Peng</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+Z">Zhenyu Zhao</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+Y">Yiyi Liang</a>, <a href="/search/eess?searchtype=author&query=Luo%2C+J">Jiadi Luo</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xiang Wang</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+S">Shaoliang Peng</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="2411.15274v1-abstract-short" style="display: inline;"> Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard for STAS detection, yet traditional methods are subjective, time-consuming, and prone to misdiagnosis, limiting large-scale applications. We present VERN, an image analysis model utilizing a feature-interactive Siamese… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15274v1-abstract-full').style.display = 'inline'; document.getElementById('2411.15274v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.15274v1-abstract-full" style="display: none;"> Spread through air spaces (STAS) is a distinct invasion pattern in lung cancer, crucial for prognosis assessment and guiding surgical decisions. Histopathology is the gold standard for STAS detection, yet traditional methods are subjective, time-consuming, and prone to misdiagnosis, limiting large-scale applications. We present VERN, an image analysis model utilizing a feature-interactive Siamese graph encoder to predict STAS from lung cancer histopathological images. VERN captures spatial topological features with feature sharing and skip connections to enhance model training. Using 1,546 histopathology slides, we built a large single-cohort STAS lung cancer dataset. VERN achieved an AUC of 0.9215 in internal validation and AUCs of 0.8275 and 0.8829 in frozen and paraffin-embedded test sections, respectively, demonstrating clinical-grade performance. Validated on a single-cohort and three external datasets, VERN showed robust predictive performance and generalizability, providing an open platform (http://plr.20210706.xyz:5000/) to enhance STAS diagnosis efficiency and accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15274v1-abstract-full').style.display = 'none'; document.getElementById('2411.15274v1-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 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">accept for publication in npj Precision Oncology</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.05607">arXiv:2410.05607</a> <span> [<a href="https://arxiv.org/pdf/2410.05607">pdf</a>, <a href="https://arxiv.org/format/2410.05607">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Single picture single photon single pixel 3D imaging through unknown thick scattering medium </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Long Pan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yunan Wang</a>, <a href="/search/eess?searchtype=author&query=Lou%2C+Y">Yijie Lou</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+X">Xiaohua Feng</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="2410.05607v1-abstract-short" style="display: inline;"> Imaging through thick scattering media presents significant challenges, particularly for three-dimensional (3D) applications. This manuscript demonstrates a novel scheme for single-image-enabled 3D imaging through such media, treating the scattering medium as a lens. This approach captures a comprehensive image containing information about objects hidden at various depths. By leveraging depth from… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05607v1-abstract-full').style.display = 'inline'; document.getElementById('2410.05607v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.05607v1-abstract-full" style="display: none;"> Imaging through thick scattering media presents significant challenges, particularly for three-dimensional (3D) applications. This manuscript demonstrates a novel scheme for single-image-enabled 3D imaging through such media, treating the scattering medium as a lens. This approach captures a comprehensive image containing information about objects hidden at various depths. By leveraging depth from focus and the reduced thickness of the scattering medium for single-pixel imaging, the proposed method ensures robust 3D imaging capabilities. We develop both traditional metric-based and deep learning-based methods to extract depth information for each pixel, allowing us to explore the locations of both positive and negative objects, whether shallow or deep. Remarkably, this scheme enables the simultaneous 3D reconstruction of targets concealed within the scattering medium. Specifically, we successfully reconstructed targets buried at depths of 5 mm and 30 mm within a total medium thickness of 60 mm. Additionally, we can effectively distinguish targets at three different depths. Notably, this scheme requires no prior knowledge of the scattering medium, no invasive procedures, reference measurements, or calibration. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05607v1-abstract-full').style.display = 'none'; document.getElementById('2410.05607v1-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/2410.04743">arXiv:2410.04743</a> <span> [<a href="https://arxiv.org/pdf/2410.04743">pdf</a>, <a href="https://arxiv.org/format/2410.04743">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="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"> Smart energy management: process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+L">Long Wu</a>, <a href="/search/eess?searchtype=author&query=Yin%2C+X">Xunyuan Yin</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lei Pan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+J">Jinfeng 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="2410.04743v1-abstract-short" style="display: inline;"> Integrated energy systems (IESs) are complex systems consisting of diverse operating units spanning multiple domains. To address its operational challenges, we propose a physics-informed hybrid time-series neural network (NN) surrogate to predict the dynamic performance of IESs across multiple time scales. This neural network-based modeling approach develops time-series multi-layer perceptrons (ML… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04743v1-abstract-full').style.display = 'inline'; document.getElementById('2410.04743v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.04743v1-abstract-full" style="display: none;"> Integrated energy systems (IESs) are complex systems consisting of diverse operating units spanning multiple domains. To address its operational challenges, we propose a physics-informed hybrid time-series neural network (NN) surrogate to predict the dynamic performance of IESs across multiple time scales. This neural network-based modeling approach develops time-series multi-layer perceptrons (MLPs) for the operating units and integrates them with prior process knowledge about system structure and fundamental dynamics. This integration forms three hybrid NNs (long-term, slow, and fast MLPs) that predict the entire system dynamics across multiple time scales. Leveraging these MLPs, we design an NN-based scheduler and an NN-based economic model predictive control (NEMPC) framework to meet global operational requirements: rapid electrical power responsiveness to operators requests, adequate cooling supply to customers, and increased system profitability, while addressing the dynamic time-scale multiplicity present in IESs. The proposed day-ahead scheduler is formulated using the ReLU network-based MLP, which effectively represents IES performance under a broad range of conditions from a long-term perspective. The scheduler is then exactly recast into a mixed-integer linear programming problem for efficient evaluation. The real-time NEMPC, based on slow and fast MLPs, comprises two sequential distributed control agents: a slow NEMPC for the cooling-dominant subsystem with slower transient responses and a fast NEMPC for the power-dominant subsystem with faster responses. Extensive simulations demonstrate that the developed scheduler and NEMPC schemes outperform their respective benchmark scheduler and controller by about 25% and 40%. Together, they enhance overall system performance by over 70% compared to benchmark approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04743v1-abstract-full').style.display = 'none'; document.getElementById('2410.04743v1-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/2403.07923">arXiv:2403.07923</a> <span> [<a href="https://arxiv.org/pdf/2403.07923">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <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> <span class="tag is-small is-grey 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="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jingyu Xu</a>, <a href="/search/eess?searchtype=author&query=Wan%2C+W">Weixiang Wan</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Linying Pan</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+W">Wenjian Sun</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yuxiang 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="2403.07923v1-abstract-short" style="display: inline;"> In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07923v1-abstract-full').style.display = 'inline'; document.getElementById('2403.07923v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.07923v1-abstract-full" style="display: none;"> In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabling monitoring and optimization of industrial objectives. Additionally, a dynamic resource allocation mechanism is designed to ensure rational scheduling of edge computing resources, achieving global optimization. Results demonstrate that this approach reduces cloud-edge communication latency, accelerates response to abnormal situations, reduces system failure rates, extends average equipment operating time, and saves costs for manual maintenance and replacement. This ensures real-time and stable control. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07923v1-abstract-full').style.display = 'none'; document.getElementById('2403.07923v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.06993">arXiv:2403.06993</a> <span> [<a href="https://arxiv.org/pdf/2403.06993">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Automatic driving lane change safety prediction model based on LSTM </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Sun%2C+W">Wenjian Sun</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Linying Pan</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jingyu Xu</a>, <a href="/search/eess?searchtype=author&query=Wan%2C+W">Weixiang Wan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yong 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="2403.06993v1-abstract-short" style="display: inline;"> Autonomous driving technology can improve traffic safety and reduce traffic accidents. In addition, it improves traffic flow, reduces congestion, saves energy and increases travel efficiency. In the relatively mature automatic driving technology, the automatic driving function is divided into several modules: perception, decision-making, planning and control, and a reasonable division of labor can… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06993v1-abstract-full').style.display = 'inline'; document.getElementById('2403.06993v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.06993v1-abstract-full" style="display: none;"> Autonomous driving technology can improve traffic safety and reduce traffic accidents. In addition, it improves traffic flow, reduces congestion, saves energy and increases travel efficiency. In the relatively mature automatic driving technology, the automatic driving function is divided into several modules: perception, decision-making, planning and control, and a reasonable division of labor can improve the stability of the system. Therefore, autonomous vehicles need to have the ability to predict the trajectory of surrounding vehicles in order to make reasonable decision planning and safety measures to improve driving safety. By using deep learning method, a safety-sensitive deep learning model based on short term memory (LSTM) network is proposed. This model can alleviate the shortcomings of current automatic driving trajectory planning, and the output trajectory not only ensures high accuracy but also improves safety. The cell state simulation algorithm simulates the trackability of the trajectory generated by this model. The research results show that compared with the traditional model-based method, the trajectory prediction method based on LSTM network has obvious advantages in predicting the trajectory in the long time domain. The intention recognition module considering interactive information has higher prediction and accuracy, and the algorithm results show that the trajectory is very smooth based on the premise of safe prediction and efficient lane change. And autonomous vehicles can efficiently and safely complete lane changes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06993v1-abstract-full').style.display = 'none'; document.getElementById('2403.06993v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.09442">arXiv:2402.09442</a> <span> [<a href="https://arxiv.org/pdf/2402.09442">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <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"> Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wan%2C+W">Weixiang Wan</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+W">Wenjian Sun</a>, <a href="/search/eess?searchtype=author&query=Zeng%2C+Q">Qiang Zeng</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Linying Pan</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jingyu Xu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+B">Bo 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="2402.09442v3-abstract-short" style="display: inline;"> In the era of Internet of Things, how to develop a smart sensor system with sustainable power supply, easy deployment and flexible use has become a difficult problem to be solved. The traditional power supply has problems such as frequent replacement or charging when in use, which limits the development of wearable devices. The contact-to-separate friction nanogenerator (TENG) was prepared by usin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.09442v3-abstract-full').style.display = 'inline'; document.getElementById('2402.09442v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.09442v3-abstract-full" style="display: none;"> In the era of Internet of Things, how to develop a smart sensor system with sustainable power supply, easy deployment and flexible use has become a difficult problem to be solved. The traditional power supply has problems such as frequent replacement or charging when in use, which limits the development of wearable devices. The contact-to-separate friction nanogenerator (TENG) was prepared by using polychotomy thy lene (PTFE) and aluminum (AI) foils. Human motion energy was collected by human body arrangement, and human motion posture was monitored according to the changes of output electrical signals. In 2012, Academician Wang Zhong lin and his team invented the triboelectric nanogenerator (TENG), which uses Maxwell displacement current as a driving force to directly convert mechanical stimuli into electrical signals, so it can be used as a self-driven sensor. Teng-based sensors have the advantages of simple structure and high instantaneous power density, which provides an important means for building intelligent sensor systems. At the same time, machine learning, as a technology with low cost, short development cycle, strong data processing ability and prediction ability, has a significant effect on the processing of a large number of electrical signals generated by TENG, and the combination with TENG sensors will promote the rapid development of intelligent sensor networks in the future. Therefore, this paper is based on the intelligent sound monitoring and recognition system of TENG, which has good sound recognition capability, and aims to evaluate the feasibility of the sound perception module architecture in ubiquitous sensor networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.09442v3-abstract-full').style.display = 'none'; document.getElementById('2402.09442v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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 aticle was accepted by ieee conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.04992">arXiv:2310.04992</a> <span> [<a href="https://arxiv.org/pdf/2310.04992">pdf</a>, <a href="https://arxiv.org/format/2310.04992">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"> VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qiu%2C+J">Jianing Qiu</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+J">Jian Wu</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+H">Hao Wei</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+P">Peilun Shi</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Minqing Zhang</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Y">Yunyun Sun</a>, <a href="/search/eess?searchtype=author&query=Li%2C+L">Lin Li</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+H">Hanruo Liu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+H">Hongyi Liu</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+S">Simeng Hou</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+Y">Yuyang Zhao</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+X">Xuehui Shi</a>, <a href="/search/eess?searchtype=author&query=Xian%2C+J">Junfang Xian</a>, <a href="/search/eess?searchtype=author&query=Qu%2C+X">Xiaoxia Qu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+S">Sirui Zhu</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lijie Pan</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoniao Chen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xiaojia Zhang</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+S">Shuai Jiang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+K">Kebing Wang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+C">Chenlong Yang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+M">Mingqiang Chen</a>, <a href="/search/eess?searchtype=author&query=Fan%2C+S">Sujie Fan</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+J">Jianhua Hu</a>, <a href="/search/eess?searchtype=author&query=Lv%2C+A">Aiguo Lv</a> , et al. (17 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="2310.04992v1-abstract-short" style="display: inline;"> We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering a broad range of ophthalmic diseases, modalities, imaging devices, and demography. After pre-training, VisionFM provides a foundation to foster multiple ophthalmic artificial intelligence (AI) applications, such as disease screening and diagnosis, disease prognosis, subclassifi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04992v1-abstract-full').style.display = 'inline'; document.getElementById('2310.04992v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.04992v1-abstract-full" style="display: none;"> We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering a broad range of ophthalmic diseases, modalities, imaging devices, and demography. After pre-training, VisionFM provides a foundation to foster multiple ophthalmic artificial intelligence (AI) applications, such as disease screening and diagnosis, disease prognosis, subclassification of disease phenotype, and systemic biomarker and disease prediction, with each application enhanced with expert-level intelligence and accuracy. The generalist intelligence of VisionFM outperformed ophthalmologists with basic and intermediate levels in jointly diagnosing 12 common ophthalmic diseases. Evaluated on a new large-scale ophthalmic disease diagnosis benchmark database, as well as a new large-scale segmentation and detection benchmark database, VisionFM outperformed strong baseline deep neural networks. The ophthalmic image representations learned by VisionFM exhibited noteworthy explainability, and demonstrated strong generalizability to new ophthalmic modalities, disease spectrum, and imaging devices. As a foundation model, VisionFM has a large capacity to learn from diverse ophthalmic imaging data and disparate datasets. To be commensurate with this capacity, in addition to the real data used for pre-training, we also generated and leveraged synthetic ophthalmic imaging data. Experimental results revealed that synthetic data that passed visual Turing tests, can also enhance the representation learning capability of VisionFM, leading to substantial performance gains on downstream ophthalmic AI tasks. Beyond the ophthalmic AI applications developed, validated, and demonstrated in this work, substantial further applications can be achieved in an efficient and cost-effective manner using VisionFM as the foundation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04992v1-abstract-full').style.display = 'none'; document.getElementById('2310.04992v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.05358">arXiv:2306.05358</a> <span> [<a href="https://arxiv.org/pdf/2306.05358">pdf</a>, <a href="https://arxiv.org/format/2306.05358">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Guan%2C+J">Jiwei Guan</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lei Pan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+C">Chen Wang</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+S">Shui Yu</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+L">Longxiang Gao</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+X">Xi Zheng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.05358v1-abstract-short" style="display: inline;"> There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.05358v1-abstract-full').style.display = 'inline'; document.getElementById('2306.05358v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.05358v1-abstract-full" style="display: none;"> There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this paper, we propose the Multimodal Fusion Framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio-vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio-vision tasks reveal the model's uncertainty. Using Expected Calibration Errors, we measure calibration errors and Monte-Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for Advanced Driver Assistance Systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.05358v1-abstract-full').style.display = 'none'; document.getElementById('2306.05358v1-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 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.05652">arXiv:2305.05652</a> <span> [<a href="https://arxiv.org/pdf/2305.05652">pdf</a>, <a href="https://arxiv.org/format/2305.05652">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"> Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+L">Long Wu</a>, <a href="/search/eess?searchtype=author&query=Yin%2C+X">Xunyuan Yin</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lei Pan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+J">Jinfeng 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="2305.05652v1-abstract-short" style="display: inline;"> The close integration of increasing operating units into an integrated energy system (IES) results in complex interconnections between these units. The strong dynamic interactions create barriers to designing a successful distributed coordinated controller to achieve synergy between all the units and unlock the potential for grid response. To address these challenges, we introduce a directed graph… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.05652v1-abstract-full').style.display = 'inline'; document.getElementById('2305.05652v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.05652v1-abstract-full" style="display: none;"> The close integration of increasing operating units into an integrated energy system (IES) results in complex interconnections between these units. The strong dynamic interactions create barriers to designing a successful distributed coordinated controller to achieve synergy between all the units and unlock the potential for grid response. To address these challenges, we introduce a directed graph representation of IESs using an augmented Jacobian matrix to depict their underlying dynamics topology. By utilizing this representation, a generic subsystem decomposition method is proposed to partition the entire IES vertically based on the dynamic time scale and horizontally based on the closeness of interconnections between the operating units. Exploiting the decomposed subsystems, we develop a cooperative distributed economic model predictive control (DEMPC) with multiple global objectives that regulate the generated power at the grid's requests and satisfy the customers cooling and system economic requirements. In the DEMPC, multiple local decision-making agents cooperate sequentially and iteratively to leverage the potential across all the units for system-wide dynamic synergy. Furthermore, we discuss how subsystem decomposition impacts the design of distributed cooperation schemes for IESs and provide a control-oriented basic guideline on the optimal decomposition of complex energy systems. Extensive simulations demonstrate that the control strategies with different levels of decomposition and collaboration will lead to marked differences in the overall performance of IES. The standard control scheme based on the proposed subsystem configuration outperforms the empirical decomposition-based control benchmark by about 20%. The DEMPC architecture further improves the overall performance of the IES by about 55% compared to the benchmark. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.05652v1-abstract-full').style.display = 'none'; document.getElementById('2305.05652v1-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 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.09486">arXiv:2304.09486</a> <span> [<a href="https://arxiv.org/pdf/2304.09486">pdf</a>, <a href="https://arxiv.org/format/2304.09486">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Security and Privacy Problems in Voice Assistant Applications: A Survey </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+J">Jingjin Li</a>, <a href="/search/eess?searchtype=author&query=chen%2C+C">Chao chen</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lei Pan</a>, <a href="/search/eess?searchtype=author&query=Azghadi%2C+M+R">Mostafa Rahimi Azghadi</a>, <a href="/search/eess?searchtype=author&query=Ghodosi%2C+H">Hossein Ghodosi</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J">Jun 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="2304.09486v1-abstract-short" style="display: inline;"> Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.09486v1-abstract-full').style.display = 'inline'; document.getElementById('2304.09486v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.09486v1-abstract-full" style="display: none;"> Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.09486v1-abstract-full').style.display = 'none'; document.getElementById('2304.09486v1-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">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/2303.06324">arXiv:2303.06324</a> <span> [<a href="https://arxiv.org/pdf/2303.06324">pdf</a>, <a href="https://arxiv.org/format/2303.06324">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> OCCL: a Deadlock-free Library for GPU Collective Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lichen Pan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+J">Juncheng Liu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+J">Jinhui Yuan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Rongkai Zhang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+P">Pengze Li</a>, <a href="/search/eess?searchtype=author&query=Xiao%2C+Z">Zhen Xiao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.06324v1-abstract-short" style="display: inline;"> Various distributed deep neural network (DNN) training technologies lead to increasingly complicated use of collective communications on GPU. The deadlock-prone collectives on GPU force researchers to guarantee that collectives are enqueued in a consistent order on each GPU to prevent deadlocks. In complex distributed DNN training scenarios, manual hardcoding is the only practical way for deadlock… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.06324v1-abstract-full').style.display = 'inline'; document.getElementById('2303.06324v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.06324v1-abstract-full" style="display: none;"> Various distributed deep neural network (DNN) training technologies lead to increasingly complicated use of collective communications on GPU. The deadlock-prone collectives on GPU force researchers to guarantee that collectives are enqueued in a consistent order on each GPU to prevent deadlocks. In complex distributed DNN training scenarios, manual hardcoding is the only practical way for deadlock prevention, which poses significant challenges to the development of artificial intelligence. This paper presents OCCL, which is, to the best of our knowledge, the first deadlock-free collective communication library for GPU supporting dynamic decentralized preemption and gang-scheduling for collectives. Leveraging the preemption opportunity of collectives on GPU, OCCL dynamically preempts collectives in a decentralized way via the deadlock-free collective execution framework and allows dynamic decentralized gang-scheduling via the stickiness adjustment scheme. With the help of OCCL, researchers no longer have to struggle to get all GPUs to launch collectives in a consistent order to prevent deadlocks. We implement OCCL with several optimizations and integrate OCCL with a distributed deep learning framework OneFlow. Experimental results demonstrate that OCCL achieves comparable or better latency and bandwidth for collectives compared to NCCL, the state-of-the-art. When used in distributed DNN training, OCCL can improve the peak training throughput by up to 78% compared to statically sequenced NCCL, while introducing overheads of less than 6.5% across various distributed DNN training approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.06324v1-abstract-full').style.display = 'none'; document.getElementById('2303.06324v1-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 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.03598">arXiv:2303.03598</a> <span> [<a href="https://arxiv.org/pdf/2303.03598">pdf</a>, <a href="https://arxiv.org/format/2303.03598">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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Guided Image-to-Image Translation by Discriminator-Generator Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cao%2C+Y">Yuanjiang Cao</a>, <a href="/search/eess?searchtype=author&query=Yao%2C+L">Lina Yao</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Le Pan</a>, <a href="/search/eess?searchtype=author&query=Sheng%2C+Q+Z">Quan Z. Sheng</a>, <a href="/search/eess?searchtype=author&query=Chang%2C+X">Xiaojun Chang</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="2303.03598v1-abstract-short" style="display: inline;"> The goal of Image-to-image (I2I) translation is to transfer an image from a source domain to a target domain, which has recently drawn increasing attention. One major branch of this research is to formulate I2I translation based on Generative Adversarial Network (GAN). As a zero-sum game, GAN can be reformulated as a Partially-observed Markov Decision Process (POMDP) for generators, where generato… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.03598v1-abstract-full').style.display = 'inline'; document.getElementById('2303.03598v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.03598v1-abstract-full" style="display: none;"> The goal of Image-to-image (I2I) translation is to transfer an image from a source domain to a target domain, which has recently drawn increasing attention. One major branch of this research is to formulate I2I translation based on Generative Adversarial Network (GAN). As a zero-sum game, GAN can be reformulated as a Partially-observed Markov Decision Process (POMDP) for generators, where generators cannot access full state information of their environments. This formulation illustrates the information insufficiency in the GAN training. To mitigate this problem, we propose to add a communication channel between discriminators and generators. We explore multiple architecture designs to integrate the communication mechanism into the I2I translation framework. To validate the performance of the proposed approach, we have conducted extensive experiments on various benchmark datasets. The experimental results confirm the superiority of our proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.03598v1-abstract-full').style.display = 'none'; document.getElementById('2303.03598v1-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 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.07475">arXiv:2301.07475</a> <span> [<a href="https://arxiv.org/pdf/2301.07475">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </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/10.1007/s10489-023-04773-4">10.1007/s10489-023-04773-4 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Curvilinear object segmentation in medical images based on ODoS filter and deep learning network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Peng%2C+Y">Yuanyuan Peng</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lin Pan</a>, <a href="/search/eess?searchtype=author&query=Luan%2C+P">Pengpeng Luan</a>, <a href="/search/eess?searchtype=author&query=Tu%2C+H">Hongbin Tu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+X">Xiong 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="2301.07475v3-abstract-short" style="display: inline;"> Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper bac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.07475v3-abstract-full').style.display = 'inline'; document.getElementById('2301.07475v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.07475v3-abstract-full" style="display: none;"> Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on an oriented derivative of stick (ODoS) filter and a deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasize developing deep architectures and ignore capturing the structural features of curvilinear objects, which may lead to unsatisfactory results. Consequently, a new approach that incorporates an ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. Specifically, the input image is transfered into four-channel image constructed by the ODoS filter. In which, the original image is considered the principal part to describe various image appearance and complex background illumination conditions, a multi-step strategy is used to enhance the contrast between curvilinear objects and their surrounding backgrounds, and a vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract various structural features for curvilinear object segmentation in medical images. The performance of the computational model is validated in experiments conducted on the publicly available DRIVE, STARE and CHASEDB1 datasets. The experimental results indicate that the presented model yields surprising results compared with those of some state-of-the-art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.07475v3-abstract-full').style.display = 'none'; document.getElementById('2301.07475v3-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 8 figures. Applied Intelligence, 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.13606">arXiv:2209.13606</a> <span> [<a href="https://arxiv.org/pdf/2209.13606">pdf</a>, <a href="https://arxiv.org/format/2209.13606">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"> On the Adversarial Convex Body Chasing Problem </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Guan%2C+Y">Yue Guan</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Longxu Pan</a>, <a href="/search/eess?searchtype=author&query=Shishika%2C+D">Daigo Shishika</a>, <a href="/search/eess?searchtype=author&query=Tsiotras%2C+P">Panagiotis Tsiotras</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="2209.13606v2-abstract-short" style="display: inline;"> In this work, we extend the convex bodies chasing problem (CBC) to an adversarial setting, where an agent (the Player) is tasked with chasing a sequence of convex bodies generated adversarially by another agent (the Opponent). The Player aims to minimize the total cost associated with its own movements, while the Opponent tries to maximize the same cost. The set of feasible convex bodies is finite… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.13606v2-abstract-full').style.display = 'inline'; document.getElementById('2209.13606v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.13606v2-abstract-full" style="display: none;"> In this work, we extend the convex bodies chasing problem (CBC) to an adversarial setting, where an agent (the Player) is tasked with chasing a sequence of convex bodies generated adversarially by another agent (the Opponent). The Player aims to minimize the total cost associated with its own movements, while the Opponent tries to maximize the same cost. The set of feasible convex bodies is finite and known to both agents, which allows us to provide performance guarantees with max-min optimality. Under certain assumptions, we show the continuity of the optimal value function, and propose an algorithm to numerically approximate the optimal policies for both the Player and the Opponent within a guaranteed tolerance. Finally, the theoretical results are verified through numerical examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.13606v2-abstract-full').style.display = 'none'; document.getElementById('2209.13606v2-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 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.10786">arXiv:2209.10786</a> <span> [<a href="https://arxiv.org/pdf/2209.10786">pdf</a>, <a href="https://arxiv.org/ps/2209.10786">ps</a>, <a href="https://arxiv.org/format/2209.10786">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"> Vector-valued Privacy-Preserving Average Consensus </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lulu Pan</a>, <a href="/search/eess?searchtype=author&query=Shao%2C+H">Haibin Shao</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+Y">Yang Lu</a>, <a href="/search/eess?searchtype=author&query=Mesbahi%2C+M">Mehran Mesbahi</a>, <a href="/search/eess?searchtype=author&query=Li%2C+D">Dewei Li</a>, <a href="/search/eess?searchtype=author&query=Xi%2C+Y">Yugeng Xi</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="2209.10786v1-abstract-short" style="display: inline;"> Achieving average consensus without disclosing sensitive information can be a critical concern for multi-agent coordination. This paper examines privacy-preserving average consensus (PPAC) for vector-valued multi-agent networks. In particular, a set of agents with vector-valued states aim to collaboratively reach an exact average consensus of their initial states, while each agent's initial state… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.10786v1-abstract-full').style.display = 'inline'; document.getElementById('2209.10786v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.10786v1-abstract-full" style="display: none;"> Achieving average consensus without disclosing sensitive information can be a critical concern for multi-agent coordination. This paper examines privacy-preserving average consensus (PPAC) for vector-valued multi-agent networks. In particular, a set of agents with vector-valued states aim to collaboratively reach an exact average consensus of their initial states, while each agent's initial state cannot be disclosed to other agents. We show that the vector-valued PPAC problem can be solved via associated matrix-weighted networks with the higher-dimensional agent state. Specifically, a novel distributed vector-valued PPAC algorithm is proposed by lifting the agent-state to higher-dimensional space and designing the associated matrix-weighted network with dynamic, low-rank, positive semi-definite coupling matrices to both conceal the vector-valued agent state and guarantee that the multi-agent network asymptotically converges to the average consensus. Essentially, the convergence analysis can be transformed into the average consensus problem on switching matrix-weighted networks. We show that the exact average consensus can be guaranteed and the initial agents' states can be kept private if each agent has at least one "legitimate" neighbor. The algorithm, involving only basic matrix operations, is computationally more efficient than cryptography-based approaches and can be implemented in a fully distributed manner without relying on a third party. Numerical simulation is provided to illustrate the effectiveness of the proposed algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.10786v1-abstract-full').style.display = 'none'; document.getElementById('2209.10786v1-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 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.05726">arXiv:2209.05726</a> <span> [<a href="https://arxiv.org/pdf/2209.05726">pdf</a>, <a href="https://arxiv.org/format/2209.05726">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Dynamical Systems">math.DS</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"> Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+C">C. Chen</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+Y+P">Y. P. Huang</a>, <a href="/search/eess?searchtype=author&query=Lam%2C+W+H+K">W. H. K. Lam</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+T+L">T. L. Pan</a>, <a href="/search/eess?searchtype=author&query=Hsu%2C+S+C">S. C. Hsu</a>, <a href="/search/eess?searchtype=author&query=Sumalee%2C+A">A. Sumalee</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+R+X">R. X. Zhong</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="2209.05726v1-abstract-short" style="display: inline;"> Existing data-driven and feedback traffic control strategies do not consider the heterogeneity of real-time data measurements. Besides, traditional reinforcement learning (RL) methods for traffic control usually converge slowly for lacking data efficiency. Moreover, conventional optimal perimeter control schemes require exact knowledge of the system dynamics and thus would be fragile to endogenous… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.05726v1-abstract-full').style.display = 'inline'; document.getElementById('2209.05726v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.05726v1-abstract-full" style="display: none;"> Existing data-driven and feedback traffic control strategies do not consider the heterogeneity of real-time data measurements. Besides, traditional reinforcement learning (RL) methods for traffic control usually converge slowly for lacking data efficiency. Moreover, conventional optimal perimeter control schemes require exact knowledge of the system dynamics and thus would be fragile to endogenous uncertainties. To handle these challenges, this work proposes an integral reinforcement learning (IRL) based approach to learning the macroscopic traffic dynamics for adaptive optimal perimeter control. This work makes the following primary contributions to the transportation literature: (a) A continuous-time control is developed with discrete gain updates to adapt to the discrete-time sensor data. (b) To reduce the sampling complexity and use the available data more efficiently, the experience replay (ER) technique is introduced to the IRL algorithm. (c) The proposed method relaxes the requirement on model calibration in a "model-free" manner that enables robustness against modeling uncertainty and enhances the real-time performance via a data-driven RL algorithm. (d) The convergence of the IRL-based algorithms and the stability of the controlled traffic dynamics are proven via the Lyapunov theory. The optimal control law is parameterized and then approximated by neural networks (NN), which moderates the computational complexity. Both state and input constraints are considered while no model linearization is required. Numerical examples and simulation experiments are presented to verify the effectiveness and efficiency of the proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.05726v1-abstract-full').style.display = 'none'; document.getElementById('2209.05726v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.13223">arXiv:2208.13223</a> <span> [<a href="https://arxiv.org/pdf/2208.13223">pdf</a>, <a href="https://arxiv.org/ps/2208.13223">ps</a>, <a href="https://arxiv.org/format/2208.13223">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"> Structural Adaptivity of Directed Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lulu Pan</a>, <a href="/search/eess?searchtype=author&query=Shao%2C+H">Haibin Shao</a>, <a href="/search/eess?searchtype=author&query=Mesbahi%2C+M">Mehran Mesbahi</a>, <a href="/search/eess?searchtype=author&query=Li%2C+D">Dewei Li</a>, <a href="/search/eess?searchtype=author&query=Xi%2C+Y">Yugeng Xi</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="2208.13223v1-abstract-short" style="display: inline;"> Network structure plays a critical role in functionality and performance of network systems. This paper examines structural adaptivity of diffusively coupled, directed multi-agent networks that are subject to diffusion performance. Inspired by the observation that the link redundancy in a network may degrade its diffusion performance, a distributed data-driven neighbor selection framework is propo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.13223v1-abstract-full').style.display = 'inline'; document.getElementById('2208.13223v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.13223v1-abstract-full" style="display: none;"> Network structure plays a critical role in functionality and performance of network systems. This paper examines structural adaptivity of diffusively coupled, directed multi-agent networks that are subject to diffusion performance. Inspired by the observation that the link redundancy in a network may degrade its diffusion performance, a distributed data-driven neighbor selection framework is proposed to adaptively adjust the network structure for improving the diffusion performance of exogenous influence over the network. Specifically, each agent is allowed to interact with only a specific subset of neighbors while global reachability from exogenous influence to all agents of the network is maintained. Both continuous-time and discrete-time directed networks are examined. For each of the two cases, we first examine the reachability properties encoded in the eigenvectors of perturbed variants of graph Laplacian or SIA matrix associated with directed networks, respectively. Then, an eigenvector-based rule for neighbor selection is proposed to derive a reduced network, on which the diffusion performance is enhanced. Finally, motivated by the necessity of distributed and data-driven implementation of the neighbor selection rule, quantitative connections between eigenvectors of the perturbed graph Laplacian and SIA matrix and relative rate of change in agent state are established, respectively. These connections immediately enable a data-driven inference of the reduced neighbor set for each agent using only locally accessible data. As an immediate extension, we further discuss the distributed data-driven construction of directed spanning trees of directed networks using the proposed neighbor selection framework. Numerical simulations are provided to demonstrate the theoretical results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.13223v1-abstract-full').style.display = 'none'; document.getElementById('2208.13223v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.09860">arXiv:2207.09860</a> <span> [<a href="https://arxiv.org/pdf/2207.09860">pdf</a>, <a href="https://arxiv.org/format/2207.09860">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Learning to Solve Soft-Constrained Vehicle Routing Problems with Lagrangian Relaxation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tang%2C+Q">Qiaoyue Tang</a>, <a href="/search/eess?searchtype=author&query=Kong%2C+Y">Yangzhe Kong</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lemeng Pan</a>, <a href="/search/eess?searchtype=author&query=Lee%2C+C">Choonmeng Lee</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="2207.09860v3-abstract-short" style="display: inline;"> Vehicle Routing Problems (VRPs) in real-world applications often come with various constraints, therefore bring additional computational challenges to exact solution methods or heuristic search approaches. The recent idea to learn heuristic move patterns from sample data has become increasingly promising to reduce solution developing costs. However, using learning-based approaches to address more… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09860v3-abstract-full').style.display = 'inline'; document.getElementById('2207.09860v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.09860v3-abstract-full" style="display: none;"> Vehicle Routing Problems (VRPs) in real-world applications often come with various constraints, therefore bring additional computational challenges to exact solution methods or heuristic search approaches. The recent idea to learn heuristic move patterns from sample data has become increasingly promising to reduce solution developing costs. However, using learning-based approaches to address more types of constrained VRP remains a challenge. The difficulty lies in controlling for constraint violations while searching for optimal solutions. To overcome this challenge, we propose a Reinforcement Learning based method to solve soft-constrained VRPs by incorporating the Lagrangian relaxation technique and using constrained policy optimization. We apply the method on three common types of VRPs, the Travelling Salesman Problem with Time Windows (TSPTW), the Capacitated VRP (CVRP) and the Capacitated VRP with Time Windows (CVRPTW), to show the generalizability of the proposed method. After comparing to existing RL-based methods and open-source heuristic solvers, we demonstrate its competitive performance in finding solutions with a good balance in travel distance, constraint violations and inference speed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.09860v3-abstract-full').style.display = 'none'; document.getElementById('2207.09860v3-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 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.01728">arXiv:2206.01728</a> <span> [<a href="https://arxiv.org/pdf/2206.01728">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> A review of machine learning approaches, challenges and prospects for computational tumor pathology </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Liangrui Pan</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhichao Feng</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+S">Shaoliang Peng</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="2206.01728v1-abstract-short" style="display: inline;"> Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles, and cure rates, and helps doctors open up innovative approaches to patient prognosis. In the past decade, rapid advances in artificial… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.01728v1-abstract-full').style.display = 'inline'; document.getElementById('2206.01728v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.01728v1-abstract-full" style="display: none;"> Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles, and cure rates, and helps doctors open up innovative approaches to patient prognosis. In the past decade, rapid advances in artificial intelligence, chip design and manufacturing, and mobile computing have facilitated research in computational pathology and have the potential to provide better-integrated solutions for whole-slide images, multi-omics data, and clinical informatics. However, tumor computational pathology now brings some challenges to the application of tumour screening, diagnosis and prognosis in terms of data integration, hardware processing, network sharing bandwidth and machine learning technology. This review investigates image preprocessing methods in computational pathology from a pathological and technical perspective, machine learning-based methods, and applications of computational pathology in breast, colon, prostate, lung, and various tumour disease scenarios. Finally, the challenges and prospects of machine learning in computational pathology applications are discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.01728v1-abstract-full').style.display = 'none'; document.getElementById('2206.01728v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.10433">arXiv:2205.10433</a> <span> [<a href="https://arxiv.org/pdf/2205.10433">pdf</a>, <a href="https://arxiv.org/format/2205.10433">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"> Economic model predictive control of integrated energy systems: A multi-time-scale framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+L">Long Wu</a>, <a href="/search/eess?searchtype=author&query=Yin%2C+X">Xunyuan Yin</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lei Pan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+J">Jinfeng 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="2205.10433v1-abstract-short" style="display: inline;"> In this work, a composite economic model predictive control (CEMPC) is proposed for the optimal operation of a stand-alone integrated energy system (IES). Time-scale multiplicity exists in IESs dynamics is taken into account and addressed using multi-time-scale decomposition. The entire IES is decomposed into three reduced-order subsystems with slow, medium, and fast dynamics. Subsequently, the CE… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.10433v1-abstract-full').style.display = 'inline'; document.getElementById('2205.10433v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.10433v1-abstract-full" style="display: none;"> In this work, a composite economic model predictive control (CEMPC) is proposed for the optimal operation of a stand-alone integrated energy system (IES). Time-scale multiplicity exists in IESs dynamics is taken into account and addressed using multi-time-scale decomposition. The entire IES is decomposed into three reduced-order subsystems with slow, medium, and fast dynamics. Subsequently, the CEMPC, which includes slow economic model predictive control (EMPC), medium EMPC and fast EMPC, is developed. The EMPCs communicate with each other to ensure consistency in decision-making. In the slow EMPC, the global control objectives are optimized, and the manipulated inputs explicitly affecting the slow dynamics are applied. The medium EMPC optimizes the control objectives correlated with the medium dynamics and applies the corresponding optimal medium inputs to the IES, while the fast EMPC optimizes the fast dynamics relevant objectives and makes a decision on the manipulated inputs directly associated with the fast dynamics. Meanwhile, thermal comfort is integrated into the CEMPC in the form of zone tracking of the building temperature for achieving more control degrees of freedom to prioritize satisfying the electric demand and reducing operating costs of the IES. Moreover, a long-term EMPC based on a simplified slow subsystem model is developed and incorporated into the CEMPC to ensure that the operating state accommodates long-term forecasts for external conditions. Finally, the effectiveness and superiority of the proposed method are demonstrated via simulations and a comparison with a hierarchical real-time optimization mechanism. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.10433v1-abstract-full').style.display = 'none'; document.getElementById('2205.10433v1-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 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.13838">arXiv:2204.13838</a> <span> [<a href="https://arxiv.org/pdf/2204.13838">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Liangrui Pan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+H">Hetian Wang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+L">Lian Wang</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+B">Boya Ji</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+M">Mingting Liu</a>, <a href="/search/eess?searchtype=author&query=Chongcheawchamnan%2C+M">Mitchai Chongcheawchamnan</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+J">Jin Yuan</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+S">Shaoliang Peng</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="2204.13838v1-abstract-short" style="display: inline;"> The degree of malignancy of osteosarcoma and its tendency to metastasize/spread mainly depend on the pathological grade (determined by observing the morphology of the tumor under a microscope). The purpose of this study is to use artificial intelligence to classify osteosarcoma histological images and to assess tumor survival and necrosis, which will help doctors reduce their workload, improve the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.13838v1-abstract-full').style.display = 'inline'; document.getElementById('2204.13838v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.13838v1-abstract-full" style="display: none;"> The degree of malignancy of osteosarcoma and its tendency to metastasize/spread mainly depend on the pathological grade (determined by observing the morphology of the tumor under a microscope). The purpose of this study is to use artificial intelligence to classify osteosarcoma histological images and to assess tumor survival and necrosis, which will help doctors reduce their workload, improve the accuracy of osteosarcoma cancer detection, and make a better prognosis for patients. The study proposes a typical transformer image classification framework by integrating noise reduction convolutional autoencoder and feature cross fusion learning (NRCA-FCFL) to classify osteosarcoma histological images. Noise reduction convolutional autoencoder could well denoise histological images of osteosarcoma, resulting in more pure images for osteosarcoma classification. Moreover, we introduce feature cross fusion learning, which integrates two scale image patches, to sufficiently explore their interactions by using additional classification tokens. As a result, a refined fusion feature is generated, which is fed to the residual neural network for label predictions. We conduct extensive experiments to evaluate the performance of the proposed approach. The experimental results demonstrate that our method outperforms the traditional and deep learning approaches on various evaluation metrics, with an accuracy of 99.17% to support osteosarcoma diagnosis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.13838v1-abstract-full').style.display = 'none'; document.getElementById('2204.13838v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.05416">arXiv:2202.05416</a> <span> [<a href="https://arxiv.org/pdf/2202.05416">pdf</a>, <a href="https://arxiv.org/format/2202.05416">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey 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="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> FAAG: Fast Adversarial Audio Generation through Interactive Attack Optimisation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Miao%2C+Y">Yuantian Miao</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+C">Chao Chen</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lei Pan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/eess?searchtype=author&query=Xiang%2C+Y">Yang Xiang</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="2202.05416v1-abstract-short" style="display: inline;"> Automatic Speech Recognition services (ASRs) inherit deep neural networks' vulnerabilities like crafted adversarial examples. Existing methods often suffer from low efficiency because the target phases are added to the entire audio sample, resulting in high demand for computational resources. This paper proposes a novel scheme named FAAG as an iterative optimization-based method to generate target… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.05416v1-abstract-full').style.display = 'inline'; document.getElementById('2202.05416v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.05416v1-abstract-full" style="display: none;"> Automatic Speech Recognition services (ASRs) inherit deep neural networks' vulnerabilities like crafted adversarial examples. Existing methods often suffer from low efficiency because the target phases are added to the entire audio sample, resulting in high demand for computational resources. This paper proposes a novel scheme named FAAG as an iterative optimization-based method to generate targeted adversarial examples quickly. By injecting the noise over the beginning part of the audio, FAAG generates adversarial audio in high quality with a high success rate timely. Specifically, we use audio's logits output to map each character in the transcription to an approximate position of the audio's frame. Thus, an adversarial example can be generated by FAAG in approximately two minutes using CPUs only and around ten seconds with one GPU while maintaining an average success rate over 85%. Specifically, the FAAG method can speed up around 60% compared with the baseline method during the adversarial example generation process. Furthermore, we found that appending benign audio to any suspicious examples can effectively defend against the targeted adversarial attack. We hope that this work paves the way for inventing new adversarial attacks against speech recognition with computational constraints. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.05416v1-abstract-full').style.display = 'none'; document.getElementById('2202.05416v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.13356">arXiv:2110.13356</a> <span> [<a href="https://arxiv.org/pdf/2110.13356">pdf</a>, <a href="https://arxiv.org/ps/2110.13356">ps</a>, <a href="https://arxiv.org/format/2110.13356">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="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Event-triggered Consensus of Matrix-weighted Networks Subject to Actuator Saturation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lulu Pan</a>, <a href="/search/eess?searchtype=author&query=Shao%2C+H">Haibin Shao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yuanlong Li</a>, <a href="/search/eess?searchtype=author&query=Li%2C+D">Dewei Li</a>, <a href="/search/eess?searchtype=author&query=Xi%2C+Y">Yugeng Xi</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="2110.13356v1-abstract-short" style="display: inline;"> The ubiquitous interdependencies among higher-dimensional states of neighboring agents can be characterized by matrix-weighted networks. This paper examines event-triggered global consensus of matrix-weighted networks subject to actuator saturation. Specifically, a distributed dynamic event-triggered coordination strategy, whose design involves sampled state of agents, saturation constraint and au… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.13356v1-abstract-full').style.display = 'inline'; document.getElementById('2110.13356v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.13356v1-abstract-full" style="display: none;"> The ubiquitous interdependencies among higher-dimensional states of neighboring agents can be characterized by matrix-weighted networks. This paper examines event-triggered global consensus of matrix-weighted networks subject to actuator saturation. Specifically, a distributed dynamic event-triggered coordination strategy, whose design involves sampled state of agents, saturation constraint and auxiliary systems, is proposed for this category of generalized network to guarantee its global consensus. Under the proposed event-triggered coordination strategy, sufficient conditions are derived to guarantee the leaderless and leader-follower global consensus of the multi-agent systems on matrix-weighted networks, respectively. The Zeno phenomenon can be excluded for both cases under the proposed coordination strategy. It turns out that the spectral properties of matrix-valued weights are crucial in event-triggered mechanism design for matrix-weighted networks with actuator saturation constraint. Finally, simulations are provided to demonstrate the effectiveness of proposed event-triggered coordination strategy. This work provides a more general design framework compared with existing results that are only applicable to scalar-weighted networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.13356v1-abstract-full').style.display = 'none'; document.getElementById('2110.13356v1-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 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </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: substantial text overlap with arXiv:2106.06198</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.03211">arXiv:2110.03211</a> <span> [<a href="https://arxiv.org/pdf/2110.03211">pdf</a>, <a href="https://arxiv.org/format/2110.03211">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Accurate Indoor Radio Frequency Imaging using a New Extended Rytov Approximation for Lossy Media </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Dubey%2C+A">Amartansh Dubey</a>, <a href="/search/eess?searchtype=author&query=Deshmukh%2C+S">Samruddhi Deshmukh</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Li Pan</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xudong Chen</a>, <a href="/search/eess?searchtype=author&query=Murch%2C+R">Ross Murch</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="2110.03211v1-abstract-short" style="display: inline;"> Imaging objects with high relative permittivity and large electrical size remains a challenging problem in the field of inverse scattering. In this work we present a phaseless inverse scattering method that can accurately image and reconstruct objects even with these attributes. The reconstruction accuracy obtained under these conditions has not been achieved previously and can therefore open up t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.03211v1-abstract-full').style.display = 'inline'; document.getElementById('2110.03211v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.03211v1-abstract-full" style="display: none;"> Imaging objects with high relative permittivity and large electrical size remains a challenging problem in the field of inverse scattering. In this work we present a phaseless inverse scattering method that can accurately image and reconstruct objects even with these attributes. The reconstruction accuracy obtained under these conditions has not been achieved previously and can therefore open up the area to technologically important applications such as indoor Radio Frequency (RF) and microwave imaging. The novelty of the approach is that it utilizes a high frequency approximation for waves passing through lossy media to provide corrections to the conventional Rytov approximation (RA). We refer to this technique as the Extended Phaseless Rytov Approximation for Low Loss Media (xPRA-LM). Simulation as well as experimental results are provided for indoor RF imaging using phaseless measurements from 2.4 GHz based WiFi nodes. We demonstrate that the approach provides accurate reconstruction of an object up to relative permittivities of $15+j1.5$ for object sizes greater than $20 位$ ($位$ is wavelength inside object). Even at higher relative permittivities of up to $蔚_r=77+j 7$, object shape reconstruction remains accurate, however the reconstruction amplitude is less accurate. These results have not been obtained before and can be utilized to achieve the potential of RF and microwave imaging in applications such as indoor RF imaging. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.03211v1-abstract-full').style.display = 'none'; document.getElementById('2110.03211v1-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, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.12555">arXiv:2109.12555</a> <span> [<a href="https://arxiv.org/pdf/2109.12555">pdf</a>, <a href="https://arxiv.org/ps/2109.12555">ps</a>, <a href="https://arxiv.org/format/2109.12555">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="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Distributed Stabilization of Signed Networks via Self-loop Compensation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Shao%2C+H">Haibin Shao</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lulu Pan</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="2109.12555v7-abstract-short" style="display: inline;"> This paper examines the stability and distributed stabilization of signed multi-agent networks. Here, positive semidefiniteness is not inherent for signed Laplacians, which renders the stability and consensus of this category of networks intricate. First, we examine the stability of signed networks by introducing a novel graph-theoretic objective negative cut set, which implies that manipulating n… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.12555v7-abstract-full').style.display = 'inline'; document.getElementById('2109.12555v7-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.12555v7-abstract-full" style="display: none;"> This paper examines the stability and distributed stabilization of signed multi-agent networks. Here, positive semidefiniteness is not inherent for signed Laplacians, which renders the stability and consensus of this category of networks intricate. First, we examine the stability of signed networks by introducing a novel graph-theoretic objective negative cut set, which implies that manipulating negative edge weights cannot change a unstable network into a stable one. Then, inspired by the diagonal dominance and stability of matrices, a local state damping mechanism is introduced using self-loop compensation. The self-loop compensation is only active for those agents who are incident to negative edges and can stabilize signed networks in a fully distributed manner. Quantitative connections between self-loop compensation and the stability of the compensated signed network are established for a tradeoff between compensation efforts and network stability. Necessary and/or sufficient conditions for predictable cluster consensus of compensated signed networks are provided. The optimality of self-loop compensation is discussed. Furthermore, we extend our results to directed signed networks where the symmetry of signed Laplacian is not free. The correlation between the stability of the compensated dynamics obtained by self-loop compensation and eventually positivity is further discussed. Novel insights into the stability of multi-agent systems on signed networks in terms of self-loop compensation are offered. Simulation examples are provided to demonstrate the theoretical results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.12555v7-abstract-full').style.display = 'none'; document.getElementById('2109.12555v7-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 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.09129">arXiv:2109.09129</a> <span> [<a href="https://arxiv.org/pdf/2109.09129">pdf</a>, <a href="https://arxiv.org/format/2109.09129">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"> Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Li Pan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+J">Jundong Liu</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+M">Mingqin Shi</a>, <a href="/search/eess?searchtype=author&query=Wong%2C+C+W">Chi Wah Wong</a>, <a href="/search/eess?searchtype=author&query=Chan%2C+K+H+K">Kei Hang Katie Chan</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="2109.09129v4-abstract-short" style="display: inline;"> Autism Spectrum Disorder(ASD) is a set of neurodevelopmental conditions that affect patients' social abilities. In recent years, many studies have employed deep learning to diagnose this brain dysfunction through functional MRI (fMRI). However, existing approaches solely focused on the abnormal brain functional connections but ignored the impact of regional activities. Due to this biased prior kno… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.09129v4-abstract-full').style.display = 'inline'; document.getElementById('2109.09129v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.09129v4-abstract-full" style="display: none;"> Autism Spectrum Disorder(ASD) is a set of neurodevelopmental conditions that affect patients' social abilities. In recent years, many studies have employed deep learning to diagnose this brain dysfunction through functional MRI (fMRI). However, existing approaches solely focused on the abnormal brain functional connections but ignored the impact of regional activities. Due to this biased prior knowledge, previous diagnosis models suffered from inter-site measurement heterogeneity and inter-individual phenotypic differences. To address this issue, we propose a novel feature extraction method for fMRI that can learn a personalized lower-resolution representation of the entire brain networking regarding both the functional connections and regional activities. Specifically, we abstract the brain imaging as a graph structure and straightforwardly downsample it to substructures by hierarchical graph pooling. To further recalibrate the distribution of the extracted features under phenotypic information, we subsequently embed the sparse feature vectors into a population graph, where the hidden inter-subject heterogeneity and homogeneity are explicitly expressed as inter- and intra-community connectivity differences, and utilize Graph Convolutional Networks to learn the node embeddings. By these means, our framework can extract features directly and efficiently from the entire fMRI and be aware of implicit inter-individual variance. We have evaluated our framework on the ABIDE-I dataset with 10-fold cross-validation. The present model has achieved a mean classification accuracy of 87.62\% and a mean AUC of 0.92, better than the state-of-the-art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.09129v4-abstract-full').style.display = 'none'; document.getElementById('2109.09129v4-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 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </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">for code and support documents, see https://github.com/jhonP-Li/ASD_GP_GCN</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> J.3; I.5.4; I.2.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.09711">arXiv:2108.09711</a> <span>  </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Liangrui Pan</a>, <a href="/search/eess?searchtype=author&query=Ji%2C+B">Boya Ji</a>, <a href="/search/eess?searchtype=author&query=Xi%2C+P">Peng Xi</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xiaoqi Wang</a>, <a href="/search/eess?searchtype=author&query=Chongcheawchamnan%2C+M">Mitchai Chongcheawchamnan</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+S">Shaoliang Peng</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="2108.09711v2-abstract-short" style="display: inline;"> Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients. However, DR can easily delay the occurrence of blindness through the diagnosis of the fundus. In view of the reality, it is difficult to collect a large amount of diabetic retina data in clinical practice. This paper proposes a few-shot learning model of a deep residual network based on Earth Mover's Distance algorithm t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.09711v2-abstract-full').style.display = 'inline'; document.getElementById('2108.09711v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.09711v2-abstract-full" style="display: none;"> Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients. However, DR can easily delay the occurrence of blindness through the diagnosis of the fundus. In view of the reality, it is difficult to collect a large amount of diabetic retina data in clinical practice. This paper proposes a few-shot learning model of a deep residual network based on Earth Mover's Distance algorithm to assist in diagnosing DR. We build training and validation classification tasks for few-shot learning based on 39 categories of 1000 sample data, train deep residual networks, and obtain experience maximization pre-training models. Based on the weights of the pre-trained model, the Earth Mover's Distance algorithm calculates the distance between the images, obtains the similarity between the images, and changes the model's parameters to improve the accuracy of the training model. Finally, the experimental construction of the small sample classification task of the test set to optimize the model further, and finally, an accuracy of 93.5667% on the 3way10shot task of the diabetic retina test set. For the experimental code and results, please refer to: https://github.com/panliangrui/few-shot-learning-funds. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.09711v2-abstract-full').style.display = 'none'; document.getElementById('2108.09711v2-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 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">The article has been significantly revised</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.12022">arXiv:2107.12022</a> <span> [<a href="https://arxiv.org/pdf/2107.12022">pdf</a>, <a href="https://arxiv.org/ps/2107.12022">ps</a>, <a href="https://arxiv.org/format/2107.12022">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="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Distributed Neighbor Selection in Multi-agent Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Shao%2C+H">Haibin Shao</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lulu Pan</a>, <a href="/search/eess?searchtype=author&query=Mesbahi%2C+M">Mehran Mesbahi</a>, <a href="/search/eess?searchtype=author&query=Xi%2C+Y">Yugeng Xi</a>, <a href="/search/eess?searchtype=author&query=Li%2C+D">Dewei 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="2107.12022v2-abstract-short" style="display: inline;"> Achieving consensus via nearest neighbor rules is an important prerequisite for multi-agent networks to accomplish collective tasks. A common assumption in consensus setup is that each agent interacts with all its neighbors. This paper examines whether network functionality and performance can be maintained-and even enhanced-when agents interact only with a subset of their respective (available) n… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.12022v2-abstract-full').style.display = 'inline'; document.getElementById('2107.12022v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.12022v2-abstract-full" style="display: none;"> Achieving consensus via nearest neighbor rules is an important prerequisite for multi-agent networks to accomplish collective tasks. A common assumption in consensus setup is that each agent interacts with all its neighbors. This paper examines whether network functionality and performance can be maintained-and even enhanced-when agents interact only with a subset of their respective (available) neighbors. As shown in the paper, the answer to this inquiry is affirmative. In this direction, we show that by exploring the monotonicity property of the Laplacian eigenvectors, a neighbor selection rule with guaranteed performance enhancements, can be realized for consensus-type networks. For distributed implementation, a quantitative connection between entries of Laplacian eigenvectors and the "relative rate of change" in the state between neighboring agents is further established; this connection facilitates a distributed algorithm for each agent to identify "favorable" neighbors to interact with. Multi-agent networks with and without external influence are examined, as well as extensions to signed networks. This paper underscores the utility of Laplacian eigenvectors in the context of distributed neighbor selection, providing novel insights into distributed data-driven control of multi-agent systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.12022v2-abstract-full').style.display = 'none'; document.getElementById('2107.12022v2-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 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.09292">arXiv:2107.09292</a> <span> [<a href="https://arxiv.org/pdf/2107.09292">pdf</a>, <a href="https://arxiv.org/ps/2107.09292">ps</a>, <a href="https://arxiv.org/format/2107.09292">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="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Cluster Consensus on Matrix-weighted Switching Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lulu Pan</a>, <a href="/search/eess?searchtype=author&query=Shao%2C+H">Haibin Shao</a>, <a href="/search/eess?searchtype=author&query=Mesbahi%2C+M">Mehran Mesbahi</a>, <a href="/search/eess?searchtype=author&query=Li%2C+D">Dewei Li</a>, <a href="/search/eess?searchtype=author&query=Xi%2C+Y">Yugeng Xi</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="2107.09292v2-abstract-short" style="display: inline;"> This paper examines the cluster consensus problem of multi-agent systems on matrix-weighted switching networks. Necessary and/or sufficient conditions under which cluster consensus can be achieved are obtained and quantitative characterization of the steady-state of the cluster consensus are provided as well. Specifically, if the underlying network switches amongst finite number of networks, a nec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.09292v2-abstract-full').style.display = 'inline'; document.getElementById('2107.09292v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.09292v2-abstract-full" style="display: none;"> This paper examines the cluster consensus problem of multi-agent systems on matrix-weighted switching networks. Necessary and/or sufficient conditions under which cluster consensus can be achieved are obtained and quantitative characterization of the steady-state of the cluster consensus are provided as well. Specifically, if the underlying network switches amongst finite number of networks, a necessary condition for cluster consensus of multi-agent system on switching matrix-weighted networks is firstly presented, it is shown that the steady-state of the system lies in the intersection of the null space of matrix-valued Laplacians corresponding to all switching networks. Second, if the underlying network switches amongst infinite number of networks, the matrix-weighted integral network is employed to provide sufficient conditions for cluster consensus and the quantitative characterization of the corresponding steady-state of the multi-agent system, using null space analysis of matrix-valued Laplacian related of integral network associated with the switching networks. In particular, conditions for the bipartite consensus under the matrix-weighted switching networks are examined. Simulation results are finally provided to demonstrate the theoretical analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.09292v2-abstract-full').style.display = 'none'; document.getElementById('2107.09292v2-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 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.06198">arXiv:2106.06198</a> <span> [<a href="https://arxiv.org/pdf/2106.06198">pdf</a>, <a href="https://arxiv.org/ps/2106.06198">ps</a>, <a href="https://arxiv.org/format/2106.06198">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="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Dynamic Event-Triggered Consensus of Multi-agent Systems on Matrix-weighted Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lulu Pan</a>, <a href="/search/eess?searchtype=author&query=Shao%2C+H">Haibin Shao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+D">Dewei Li</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+L">Lin 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="2106.06198v3-abstract-short" style="display: inline;"> This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxilia… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.06198v3-abstract-full').style.display = 'inline'; document.getElementById('2106.06198v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.06198v3-abstract-full" style="display: none;"> This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network. Specifically, a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks, in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold, which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Distributed event-triggered control protocols are proposed to guarantee leaderless and leader-follower consensus for multi-agent systems on matrix-weighted networks, respectively. Remarkably, the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks, generalizing those results only applicable for scalar-weighted networks. The proposed approach allows each agent to broadcast and receive information only at its triggering instants. Finally, simulation examples are provided to demonstrate the theoretical results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.06198v3-abstract-full').style.display = 'none'; document.getElementById('2106.06198v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.04599">arXiv:2104.04599</a> <span> [<a href="https://arxiv.org/pdf/2104.04599">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-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/10.1002/jrs.6225">10.1002/jrs.6225 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Liangrui Pan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+P">Peng Zhang</a>, <a href="/search/eess?searchtype=author&query=Daengngam%2C+C">Chalongrat Daengngam</a>, <a href="/search/eess?searchtype=author&query=Chongcheawchamnan%2C+M">Mitchai Chongcheawchamnan</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="2104.04599v1-abstract-short" style="display: inline;"> In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. However, the development of Raman spectroscopy, machine learning, and deep learning techniques have paved the way for achieving efficient analytical tools capable of identifying mixture components, making an apparent breakt… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.04599v1-abstract-full').style.display = 'inline'; document.getElementById('2104.04599v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.04599v1-abstract-full" style="display: none;"> In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. However, the development of Raman spectroscopy, machine learning, and deep learning techniques have paved the way for achieving efficient analytical tools capable of identifying mixture components, making an apparent breakthrough in the identification of mixtures beyond the traditional chemical analysis methods. This article summarizes the work of Raman spectroscopy in identifying the composition of substances as well as provides detailed reviews on the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence. This review summarizes the work of Raman spectroscopy in identifying the composition of substances and reviews the preprocessing process of Raman spectroscopy, the analysis methods and applications of artificial intelligence. Finally, the advantages and disadvantages and development prospects of Raman spectroscopy are discussed in detail. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.04599v1-abstract-full').style.display = 'none'; document.getElementById('2104.04599v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.11736">arXiv:2103.11736</a> <span> [<a href="https://arxiv.org/pdf/2103.11736">pdf</a>, <a href="https://arxiv.org/format/2103.11736">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Automatic Pulmonary Artery-Vein Separation in CT Images using Twin-Pipe Network and Topology Reconstruction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lin Pan</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+Y">Yaoyong Zheng</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+L">Liqin Huang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+L">Liuqing Chen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhen Zhang</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+R">Rongda Fu</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+B">Bin Zheng</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+S">Shaohua Zheng</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="2103.11736v2-abstract-short" style="display: inline;"> With the development of medical computer-aided diagnostic systems, pulmonary artery-vein(A/V) separation plays a crucial role in assisting doctors in preoperative planning for lung cancer surgery. However, distinguishing arterial from venous irrigation in chest CT images remains a challenge due to the similarity and complex structure of the arteries and veins. We propose a novel method for automat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.11736v2-abstract-full').style.display = 'inline'; document.getElementById('2103.11736v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.11736v2-abstract-full" style="display: none;"> With the development of medical computer-aided diagnostic systems, pulmonary artery-vein(A/V) separation plays a crucial role in assisting doctors in preoperative planning for lung cancer surgery. However, distinguishing arterial from venous irrigation in chest CT images remains a challenge due to the similarity and complex structure of the arteries and veins. We propose a novel method for automatic separation of pulmonary arteries and veins from chest CT images. The method consists of three parts. First, global connection information and local feature information are used to construct a complete topological tree and ensure the continuity of vessel reconstruction. Second, the Twin-Pipe network proposed can automatically learn the differences between arteries and veins at different levels to reduce classification errors caused by changes in terminal vessel characteristics. Finally, the topology optimizer considers interbranch and intrabranch topological relationships to maintain spatial consistency to avoid the misclassification of A/V irrigations. We validate the performance of the method on chest CT images. Compared with manual classification, the proposed method achieves an average accuracy of 96.2% on noncontrast chest CT. In addition, the method has been proven to have good generalization, that is, the accuracies of 93.8% and 94.8% are obtained for CT scans from other devices and other modes, respectively. The result of pulmonary artery-vein obtained by the proposed method can provide better assistance for preoperative planning of lung cancer surgery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.11736v2-abstract-full').style.display = 'none'; document.getElementById('2103.11736v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.12755">arXiv:2102.12755</a> <span> [<a href="https://arxiv.org/pdf/2102.12755">pdf</a>, <a href="https://arxiv.org/format/2102.12755">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Guo%2C+J">Jinquan Guo</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+R">Rongda Fu</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lin Pan</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+S">Shaohua Zheng</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+L">Liqin Huang</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+B">Bin Zheng</a>, <a href="/search/eess?searchtype=author&query=He%2C+B">Bingwei He</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.12755v1-abstract-short" style="display: inline;"> Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-like structures remain as two mainly challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. Motivated by… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.12755v1-abstract-full').style.display = 'inline'; document.getElementById('2102.12755v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.12755v1-abstract-full" style="display: none;"> Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-like structures remain as two mainly challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. Motivated by these works, a coarse-to-fine segmentation framework is proposed to obtain a complete airway tree. Our framework segments the overall airway and small branches via the multi-information fusion convolution neural network (Mif-CNN) and the CNN-based region growing, respectively. In Mif-CNN, atrous spatial pyramid pooling (ASPP) is integrated into a u-shaped network, and it can expend the receptive field and capture multi-scale information. Meanwhile, boundary and location information are incorporated into semantic information. These information are fused to help Mif-CNN utilize additional context knowledge and useful features. To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches. A voxel classification network (VCN), which can entirely capture the rich information around each voxel, is applied to classify the voxels into airway and non-airway. In addition, a shape reconstruction method is used to refine the airway tree. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.12755v1-abstract-full').style.display = 'none'; document.getElementById('2102.12755v1-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 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.10919">arXiv:2102.10919</a> <span> [<a href="https://arxiv.org/pdf/2102.10919">pdf</a>, <a href="https://arxiv.org/ps/2102.10919">ps</a>, <a href="https://arxiv.org/format/2102.10919">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </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/10.1016/j.cmpb.2021.106363">10.1016/j.cmpb.2021.106363 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zheng%2C+S">Shaohua Zheng</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+Z">Zhiqiang Shen</a>, <a href="/search/eess?searchtype=author&query=Peia%2C+C">Chenhao Peia</a>, <a href="/search/eess?searchtype=author&query=Ding%2C+W">Wangbin Ding</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+H">Haojin Lin</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+J">Jiepeng Zheng</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lin Pan</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+B">Bin Zheng</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+L">Liqin 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="2102.10919v1-abstract-short" style="display: inline;"> Background and Objective:Computer-aided diagnosis (CAD) systems promote diagnosis effectiveness and alleviate pressure of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.10919v1-abstract-full').style.display = 'inline'; document.getElementById('2102.10919v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.10919v1-abstract-full" style="display: none;"> Background and Objective:Computer-aided diagnosis (CAD) systems promote diagnosis effectiveness and alleviate pressure of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based nodule malignancy evaluation depends on heuristic inference from low-dose computed tomography volume to malignant probability, which lacks clinical cognition. Methods:In this paper, we propose a joint radiology analysis and malignancy evaluation network (R2MNet) to evaluate the pulmonary nodule malignancy via radiology characteristics analysis. Radiological features are extracted as channel descriptor to highlight specific regions of the input volume that are critical for nodule malignancy evaluation. In addition, for model explanations, we propose channel-dependent activation mapping to visualize the features and shed light on the decision process of deep neural network. Results:Experimental results on the LIDC-IDRI dataset demonstrate that the proposed method achieved area under curve of 96.27% on nodule radiology analysis and AUC of 97.52% on nodule malignancy evaluation. In addition, explanations of CDAM features proved that the shape and density of nodule regions were two critical factors that influence a nodule to be inferred as malignant, which conforms with the diagnosis cognition of experienced radiologists. Conclusion:Incorporating radiology analysis with nodule malignant evaluation, the network inference process conforms to the diagnostic procedure of radiologists and increases the confidence of evaluation results. Besides, model interpretation with CDAM features shed light on the regions which DNNs focus on when they estimate nodule malignancy probabilities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.10919v1-abstract-full').style.display = 'none'; document.getElementById('2102.10919v1-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 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 8 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/2011.14105">arXiv:2011.14105</a> <span> [<a href="https://arxiv.org/pdf/2011.14105">pdf</a>, <a href="https://arxiv.org/ps/2011.14105">ps</a>, <a href="https://arxiv.org/format/2011.14105">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"> Characterizing Bipartite Consensus on Signed Matrix-Weighted Networks via Balancing Set </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+C">Chongzhi Wang</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lulu Pan</a>, <a href="/search/eess?searchtype=author&query=Shao%2C+H">Haibin Shao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+D">Dewei Li</a>, <a href="/search/eess?searchtype=author&query=Xi%2C+Y">Yugeng Xi</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="2011.14105v2-abstract-short" style="display: inline;"> In contrast with the scalar-weighted networks, where bipartite consensus can be achieved if and only if the underlying signed network is structurally balanced, the structural balance property is no longer a graph-theoretic equivalence to the bipartite consensus in the case of signed matrix-weighted networks. To re-establish the relationship between the network structure and the bipartite consensus… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.14105v2-abstract-full').style.display = 'inline'; document.getElementById('2011.14105v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.14105v2-abstract-full" style="display: none;"> In contrast with the scalar-weighted networks, where bipartite consensus can be achieved if and only if the underlying signed network is structurally balanced, the structural balance property is no longer a graph-theoretic equivalence to the bipartite consensus in the case of signed matrix-weighted networks. To re-establish the relationship between the network structure and the bipartite consensus solution, the non-trivial balancing set is introduced which is a set of edges whose sign negation can transform a structurally imbalanced network into a structurally balanced one and the weight matrices associated with edges in this set have a non-trivial intersection of null spaces. We show that necessary and/or sufficient conditions for bipartite consensus on matrix-weighted networks can be characterized by the uniqueness of the non-trivial balancing set, while the contribution of the associated non-trivial intersection of null spaces to the steady-state of the matrix-weighted network is examined. Moreover, for matrix-weighted networks with a positive-negative spanning tree, necessary and sufficient condition for bipartite consensus using the non-trivial balancing set is obtained. Simulation examples are provided to demonstrate the theoretical results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.14105v2-abstract-full').style.display = 'none'; document.getElementById('2011.14105v2-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 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.15654">arXiv:2010.15654</a> <span> [<a href="https://arxiv.org/pdf/2010.15654">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</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"> Identification of complex mixtures for Raman spectroscopy using a novel scheme based on a new multi-label deep neural network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Liangrui Pan</a>, <a href="/search/eess?searchtype=author&query=Pipitsunthonsan%2C+P">Pronthep Pipitsunthonsan</a>, <a href="/search/eess?searchtype=author&query=Daengngam%2C+C">Chalongrat Daengngam</a>, <a href="/search/eess?searchtype=author&query=Chongcheawchamnan%2C+M">Mitchai Chongcheawchamnan</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="2010.15654v1-abstract-short" style="display: inline;"> With noisy environment caused by fluoresence and additive white noise as well as complicated spectrum fingerprints, the identification of complex mixture materials remains a major challenge in Raman spectroscopy application. In this paper, we propose a new scheme based on a constant wavelet transform (CWT) and a deep network for classifying complex mixture. The scheme first transforms the noisy Ra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.15654v1-abstract-full').style.display = 'inline'; document.getElementById('2010.15654v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.15654v1-abstract-full" style="display: none;"> With noisy environment caused by fluoresence and additive white noise as well as complicated spectrum fingerprints, the identification of complex mixture materials remains a major challenge in Raman spectroscopy application. In this paper, we propose a new scheme based on a constant wavelet transform (CWT) and a deep network for classifying complex mixture. The scheme first transforms the noisy Raman spectrum to a two-dimensional scale map using CWT. A multi-label deep neural network model (MDNN) is then applied for classifying material. The proposed model accelerates the feature extraction and expands the feature graph using the global averaging pooling layer. The Sigmoid function is implemented in the last layer of the model. The MDNN model was trained, validated and tested with data collected from the samples prepared from substances in palm oil. During training and validating process, data augmentation is applied to overcome the imbalance of data and enrich the diversity of Raman spectra. From the test results, it is found that the MDNN model outperforms previously proposed deep neural network models in terms of Hamming loss, one error, coverage, ranking loss, average precision, F1 macro averaging and F1 micro averaging, respectively. The average detection time obtained from our model is 5.31 s, which is much faster than the detection time of the previously proposed models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.15654v1-abstract-full').style.display = 'none'; document.getElementById('2010.15654v1-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.09849">arXiv:2009.09849</a> <span> [<a href="https://arxiv.org/pdf/2009.09849">pdf</a>, <a href="https://arxiv.org/format/2009.09849">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="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Spatio-Temporal Hybrid Graph Convolutional Network for Traffic Forecasting in Telecommunication Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kalander%2C+M">Marcus Kalander</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+M">Min Zhou</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+C">Chengzhi Zhang</a>, <a href="/search/eess?searchtype=author&query=Yi%2C+H">Hanling Yi</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lujia Pan</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="2009.09849v1-abstract-short" style="display: inline;"> Telecommunication networks play a critical role in modern society. With the arrival of 5G networks, these systems are becoming even more diversified, integrated, and intelligent. Traffic forecasting is one of the key components in such a system, however, it is particularly challenging due to the complex spatial-temporal dependency. In this work, we consider this problem from the aspect of a cellul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.09849v1-abstract-full').style.display = 'inline'; document.getElementById('2009.09849v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.09849v1-abstract-full" style="display: none;"> Telecommunication networks play a critical role in modern society. With the arrival of 5G networks, these systems are becoming even more diversified, integrated, and intelligent. Traffic forecasting is one of the key components in such a system, however, it is particularly challenging due to the complex spatial-temporal dependency. In this work, we consider this problem from the aspect of a cellular network and the interactions among its base stations. We thoroughly investigate the characteristics of cellular network traffic and shed light on the dependency complexities based on data collected from a densely populated metropolis area. Specifically, we observe that the traffic shows both dynamic and static spatial dependencies as well as diverse cyclic temporal patterns. To address these complexities, we propose an effective deep-learning-based approach, namely, Spatio-Temporal Hybrid Graph Convolutional Network (STHGCN). It employs GRUs to model the temporal dependency, while capturing the complex spatial dependency through a hybrid-GCN from three perspectives: spatial proximity, functional similarity, and recent trend similarity. We conduct extensive experiments on real-world traffic datasets collected from telecommunication networks. Our experimental results demonstrate the superiority of the proposed model in that it consistently outperforms both classical methods and state-of-the-art deep learning models, while being more robust and stable. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.09849v1-abstract-full').style.display = 'none'; document.getElementById('2009.09849v1-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.04078">arXiv:2009.04078</a> <span> [<a href="https://arxiv.org/pdf/2009.04078">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> </div> <p class="title is-5 mathjax"> Method for classifying a noisy Raman spectrum based on a wavelet transform and a deep neural network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Liangrui Pan</a>, <a href="/search/eess?searchtype=author&query=Pipitsunthonsan%2C+P">Pronthep Pipitsunthonsan</a>, <a href="/search/eess?searchtype=author&query=Daengngam%2C+C">Chalongrat Daengngam</a>, <a href="/search/eess?searchtype=author&query=Channumsin%2C+S">Sittiporn Channumsin</a>, <a href="/search/eess?searchtype=author&query=Sreesawet%2C+S">Suwat Sreesawet</a>, <a href="/search/eess?searchtype=author&query=Chongcheawchamnan%2C+M">Mitchai Chongcheawchamnan</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="2009.04078v1-abstract-short" style="display: inline;"> This paper proposes a new framework based on a wavelet transform and deep neural network for identifying noisy Raman spectrum since, in practice, it is relatively difficult to classify the spectrum under baseline noise and additive white Gaussian noise environments. The framework consists of two main engines. Wavelet transform is proposed as the framework front-end for transforming 1-D noise Raman… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.04078v1-abstract-full').style.display = 'inline'; document.getElementById('2009.04078v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.04078v1-abstract-full" style="display: none;"> This paper proposes a new framework based on a wavelet transform and deep neural network for identifying noisy Raman spectrum since, in practice, it is relatively difficult to classify the spectrum under baseline noise and additive white Gaussian noise environments. The framework consists of two main engines. Wavelet transform is proposed as the framework front-end for transforming 1-D noise Raman spectrum to two-dimensional data. This two-dimensional data will be fed to the framework back-end which is a classifier. The optimum classifier is chosen by implementing several traditional machine learning (ML) and deep learning (DL) algorithms, and then we investigated their classification accuracy and robustness performances. The four MLs we choose included a Naive Bayes (NB), a Support Vector Machine (SVM), a Random Forest (RF) and a K-Nearest Neighbor (KNN) where a deep convolution neural network (DCNN) was chosen for a DL classifier. Noise-free, Gaussian noise, baseline noise, and mixed-noise Raman spectrums were applied to train and validate the ML and DCNN models. The optimum back-end classifier was obtained by testing the ML and DCNN models with several noisy Raman spectrums (10-30 dB noise power). Based on the simulation, the accuracy of the DCNN classifier is 9% higher than the NB classifier, 3.5% higher than the RF classifier, 1% higher than the KNN classifier, and 0.5% higher than the SVM classifier. In terms of robustness to the mixed noise scenarios, the framework with DCNN back-end showed superior performance than the other ML back-ends. The DCNN back-end achieved 90% accuracy at 3 dB SNR while NB, SVM, RF, and K-NN back-ends required 27 dB, 22 dB, 27 dB, and 23 dB SNR, respectively. In addition, in the low-noise test data set, the F-measure score of the DCNN back-end exceeded 99.1% while the F-measure scores of the other ML engines were below 98.7%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.04078v1-abstract-full').style.display = 'none'; document.getElementById('2009.04078v1-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 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.04067">arXiv:2009.04067</a> <span> [<a href="https://arxiv.org/pdf/2009.04067">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> </div> <p class="title is-5 mathjax"> Noise Reduction Technique for Raman Spectrum using Deep Learning Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Liangrui Pan</a>, <a href="/search/eess?searchtype=author&query=Pipitsunthonsan%2C+P">Pronthep Pipitsunthonsan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+P">Peng Zhang</a>, <a href="/search/eess?searchtype=author&query=Daengngam%2C+C">Chalongrat Daengngam</a>, <a href="/search/eess?searchtype=author&query=Booranawong%2C+A">Apidach Booranawong</a>, <a href="/search/eess?searchtype=author&query=Chongcheawchamnan%2C+M">Mitcham Chongcheawchamnan</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="2009.04067v1-abstract-short" style="display: inline;"> In a normal indoor environment, Raman spectrum encounters noise often conceal spectrum peak, leading to difficulty in spectrum interpretation. This paper proposes deep learning (DL) based noise reduction technique for Raman spectroscopy. The proposed DL network is developed with several training and test sets of noisy Raman spectrum. The proposed technique is applied to denoise and compare the per… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.04067v1-abstract-full').style.display = 'inline'; document.getElementById('2009.04067v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.04067v1-abstract-full" style="display: none;"> In a normal indoor environment, Raman spectrum encounters noise often conceal spectrum peak, leading to difficulty in spectrum interpretation. This paper proposes deep learning (DL) based noise reduction technique for Raman spectroscopy. The proposed DL network is developed with several training and test sets of noisy Raman spectrum. The proposed technique is applied to denoise and compare the performance with different wavelet noise reduction methods. Output signal-to-noise ratio (SNR), root-mean-square error (RMSE) and mean absolute percentage error (MAPE) are the performance evaluation index. It is shown that output SNR of the proposed noise reduction technology is 10.24 dB greater than that of the wavelet noise reduction method while the RMSE and the MAPE are 292.63 and 10.09, which are much better than the proposed technique. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.04067v1-abstract-full').style.display = 'none'; document.getElementById('2009.04067v1-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 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.01542">arXiv:2002.01542</a> <span> [<a href="https://arxiv.org/pdf/2002.01542">pdf</a>, <a href="https://arxiv.org/ps/2002.01542">ps</a>, <a href="https://arxiv.org/format/2002.01542">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="Robotics">cs.RO</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"> A family of virtual contraction based controllers for tracking of flexible-joints port-Hamiltonian robots: theory and experiments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Reyes-B%C3%A1ez%2C+R">Rodolfo Reyes-B谩ez</a>, <a href="/search/eess?searchtype=author&query=van+der+Schaft%2C+A">Arjan van der Schaft</a>, <a href="/search/eess?searchtype=author&query=Jayawardhana%2C+B">Bayu Jayawardhana</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Le Pan</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="2002.01542v1-abstract-short" style="display: inline;"> In this work we present a constructive method to design a family of virtual contraction based controllers that solve the standard trajectory tracking problem of flexible-joint robots (FJRs) in the port-Hamiltonian (pH) framework. The proposed design method, called virtual contraction based control (v-CBC), combines the concepts of virtual control systems and contraction analysis. It is shown that… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.01542v1-abstract-full').style.display = 'inline'; document.getElementById('2002.01542v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.01542v1-abstract-full" style="display: none;"> In this work we present a constructive method to design a family of virtual contraction based controllers that solve the standard trajectory tracking problem of flexible-joint robots (FJRs) in the port-Hamiltonian (pH) framework. The proposed design method, called virtual contraction based control (v-CBC), combines the concepts of virtual control systems and contraction analysis. It is shown that under potential energy matching conditions, the closed-loop virtual system is contractive and exponential convergence to a predefined trajectory is guaranteed. Moreover, the closed-loop virtual system exhibits properties such as structure preservation, differential passivity and the existence of (incrementally) passive maps. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.01542v1-abstract-full').style.display = 'none'; document.getElementById('2002.01542v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 4 figures, journal paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.11179">arXiv:2001.11179</a> <span> [<a href="https://arxiv.org/pdf/2001.11179">pdf</a>, <a href="https://arxiv.org/ps/2001.11179">ps</a>, <a href="https://arxiv.org/format/2001.11179">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"> Consensus on Matrix-weighted Time-varying Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lulu Pan</a>, <a href="/search/eess?searchtype=author&query=Shao%2C+H">Haibin Shao</a>, <a href="/search/eess?searchtype=author&query=Mesbahi%2C+M">Mehran Mesbahi</a>, <a href="/search/eess?searchtype=author&query=Xi%2C+Y">Yugeng Xi</a>, <a href="/search/eess?searchtype=author&query=Li%2C+D">Dewei 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="2001.11179v1-abstract-short" style="display: inline;"> This paper examines the consensus problem on time-varying matrix-weighed undirected networks. First, we introduce the matrix-weighted integral network for the analysis of such networks. Under mild assumptions on the switching pattern of the time-varying network, necessary and/or sufficient conditions for which average consensus can be achieved are then provided in terms of the null space of matrix… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.11179v1-abstract-full').style.display = 'inline'; document.getElementById('2001.11179v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.11179v1-abstract-full" style="display: none;"> This paper examines the consensus problem on time-varying matrix-weighed undirected networks. First, we introduce the matrix-weighted integral network for the analysis of such networks. Under mild assumptions on the switching pattern of the time-varying network, necessary and/or sufficient conditions for which average consensus can be achieved are then provided in terms of the null space of matrix-valued Laplacian of the corresponding integral network. In particular, for periodic matrix-weighted time-varying networks, necessary and sufficient conditions for reaching average consensus is obtained from an algebraic perspective. Moreover, we show that if the integral network with period $T>0$ has a positive spanning tree over the time span $[0,T)$, average consensus for the node states is achieved. Simulation results are provided to demonstrate the theoretical analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.11179v1-abstract-full').style.display = 'none'; document.getElementById('2001.11179v1-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 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.04035">arXiv:2001.04035</a> <span> [<a href="https://arxiv.org/pdf/2001.04035">pdf</a>, <a href="https://arxiv.org/ps/2001.04035">ps</a>, <a href="https://arxiv.org/format/2001.04035">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="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> On the Controllability of Matrix-weighted Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lulu Pan</a>, <a href="/search/eess?searchtype=author&query=Shao%2C+H">Haibin Shao</a>, <a href="/search/eess?searchtype=author&query=Mesbahi%2C+M">Mehran Mesbahi</a>, <a href="/search/eess?searchtype=author&query=Xi%2C+Y">Yugeng Xi</a>, <a href="/search/eess?searchtype=author&query=Li%2C+D">Dewei 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="2001.04035v1-abstract-short" style="display: inline;"> This letter examines the controllability of consensus dynamics on matrix-weighed networks from a graph-theoretic perspective. Unlike the scalar-weighted networks, the rank of weight matrix introduces additional intricacies into characterizing the dimension of controllable subspace for such networks. Specifically, we investigate how the definiteness of weight matrices influences the dimension of th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.04035v1-abstract-full').style.display = 'inline'; document.getElementById('2001.04035v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.04035v1-abstract-full" style="display: none;"> This letter examines the controllability of consensus dynamics on matrix-weighed networks from a graph-theoretic perspective. Unlike the scalar-weighted networks, the rank of weight matrix introduces additional intricacies into characterizing the dimension of controllable subspace for such networks. Specifically, we investigate how the definiteness of weight matrices influences the dimension of the controllable subspace. In this direction, graph-theoretic characterizations of the lower and upper bounds on the dimension of the controllable subspace are provided by employing, respectively, distance partition and almost equitable partition of matrix-weighted networks. Furthermore, the structure of an uncontrollable input for such networks is examined. Examples are then provided to demonstrate the theoretical results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.04035v1-abstract-full').style.display = 'none'; document.getElementById('2001.04035v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.07082">arXiv:1905.07082</a> <span> [<a href="https://arxiv.org/pdf/1905.07082">pdf</a>, <a href="https://arxiv.org/format/1905.07082">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="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Miao%2C+Y">Yuantian Miao</a>, <a href="/search/eess?searchtype=author&query=Xue%2C+M">Minhui Xue</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+C">Chao Chen</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lei Pan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+B+Z+H">Benjamin Zi Hao Zhao</a>, <a href="/search/eess?searchtype=author&query=Kaafar%2C+D">Dali Kaafar</a>, <a href="/search/eess?searchtype=author&query=Xiang%2C+Y">Yang Xiang</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="1905.07082v6-abstract-short" style="display: inline;"> With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recog… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.07082v6-abstract-full').style.display = 'inline'; document.getElementById('1905.07082v6-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.07082v6-abstract-full" style="display: none;"> With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recognition (ASR) model under strict black-box access. With user representation of the input audio data and their corresponding translated text, our trained auditor is effective in user-level audit. We also observe that the auditor trained on specific data can be generalized well regardless of the ASR model architecture. We validate the auditor on ASR models trained with LSTM, RNNs, and GRU algorithms on two state-of-the-art pipelines, the hybrid ASR system and the end-to-end ASR system. Finally, we conduct a real-world trial of our auditor on iPhone Siri, achieving an overall accuracy exceeding 80\%. We hope the methodology developed in this paper and findings can inform privacy advocates to overhaul IoT privacy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.07082v6-abstract-full').style.display = 'none'; document.getElementById('1905.07082v6-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 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </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 PoPETs 2021.1</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.09559">arXiv:1904.09559</a> <span> [<a href="https://arxiv.org/pdf/1904.09559">pdf</a>, <a href="https://arxiv.org/ps/1904.09559">ps</a>, <a href="https://arxiv.org/format/1904.09559">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="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</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/TSP.2020.3023008">10.1109/TSP.2020.3023008 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yin%2C+F">Feng Yin</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lishuo Pan</a>, <a href="/search/eess?searchtype=author&query=He%2C+X">Xinwei He</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+T">Tianshi Chen</a>, <a href="/search/eess?searchtype=author&query=Theodoridis%2C+S">Sergios Theodoridis</a>, <a href="/search/eess?searchtype=author&query=Zhi-Quan"> Zhi-Quan</a>, <a href="/search/eess?searchtype=author&query=Luo"> Luo</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="1904.09559v1-abstract-short" style="display: inline;"> Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications. However, GP kernel design and the associated hyper-parameter optimization are still hard and to a large extend open problems. In this paper, we consider the task of GP regression for time series modeling and analysis. The under… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.09559v1-abstract-full').style.display = 'inline'; document.getElementById('1904.09559v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.09559v1-abstract-full" style="display: none;"> Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications. However, GP kernel design and the associated hyper-parameter optimization are still hard and to a large extend open problems. In this paper, we consider the task of GP regression for time series modeling and analysis. The underlying stationary kernel can be approximated arbitrarily close by a new proposed grid spectral mixture (GSM) kernel, which turns out to be a linear combination of low-rank sub-kernels. In the case where a large number of the sub-kernels are used, either the Nystr枚m or the random Fourier feature approximations can be adopted to deal efficiently with the computational demands. The unknown GP hyper-parameters consist of the non-negative weights of all sub-kernels as well as the noise variance; their estimation is performed via the maximum-likelihood (ML) estimation framework. Two efficient numerical optimization methods for solving the unknown hyper-parameters are derived, including a sequential majorization-minimization (MM) method and a non-linearly constrained alternating direction of multiplier method (ADMM). The MM matches perfectly with the proven low-rank property of the proposed GSM sub-kernels and turns out to be a part of efficiency, stable, and efficient solver, while the ADMM has the potential to generate better local minimum in terms of the test MSE. Experimental results, based on various classic time series data sets, corroborate that the proposed GSM kernel-based GP regression model outperforms several salient competitors of similar kind in terms of prediction mean-squared-error and numerical stability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.09559v1-abstract-full').style.display = 'none'; document.getElementById('1904.09559v1-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 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 5 figures, submitted</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1507.07844">arXiv:1507.07844</a> <span> [<a href="https://arxiv.org/pdf/1507.07844">pdf</a>, <a href="https://arxiv.org/ps/1507.07844">ps</a>, <a href="https://arxiv.org/format/1507.07844">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 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/CAC.2015.7382502">10.1109/CAC.2015.7382502 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Composite learning control with application to inverted pendulums </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+Y">Yongping Pan</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+L">Lin Pan</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+H">Haoyong 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="1507.07844v3-abstract-short" style="display: inline;"> Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This paper proposes a novel model reference compo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1507.07844v3-abstract-full').style.display = 'inline'; document.getElementById('1507.07844v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1507.07844v3-abstract-full" style="display: none;"> Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This paper proposes a novel model reference composite learning control (MRCLC) strategy for a class of affine nonlinear systems with parametric uncertainties to guarantee parameter convergence without the PE condition. In the composite learning, an integral during a moving-time window is utilized to construct a prediction error, a linear filter is applied to alleviate the derivation of plant states, and both the tracking error and the prediction error are applied to update parametric estimates. It is proven that the closed-loop system achieves global exponential-like stability under interval excitation rather than PE of regression functions. The effectiveness of the proposed MRCLC has been verified by the application to an inverted pendulum control problem. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1507.07844v3-abstract-full').style.display = 'none'; document.getElementById('1507.07844v3-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 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 July, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Chinese Automation Congress, Wuhan, China, 2015, pp. 232-236 </p> </li> </ol> <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