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</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/2205.15832">arXiv:2205.15832</a> <span> [<a href="https://arxiv.org/pdf/2205.15832">pdf</a>, <a href="https://arxiv.org/format/2205.15832">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Plasma Physics">physics.plasm-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TPS.2023.3268170">10.1109/TPS.2023.3268170 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> 2022 Review of Data-Driven Plasma Science </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Anirudh%2C+R">Rushil Anirudh</a>, <a href="/search/physics?searchtype=author&query=Archibald%2C+R">Rick Archibald</a>, <a href="/search/physics?searchtype=author&query=Asif%2C+M+S">M. Salman Asif</a>, <a href="/search/physics?searchtype=author&query=Becker%2C+M+M">Markus M. Becker</a>, <a href="/search/physics?searchtype=author&query=Benkadda%2C+S">Sadruddin Benkadda</a>, <a href="/search/physics?searchtype=author&query=Bremer%2C+P">Peer-Timo Bremer</a>, <a href="/search/physics?searchtype=author&query=Bud%C3%A9%2C+R+H+S">Rick H. S. Bud茅</a>, <a href="/search/physics?searchtype=author&query=Chang%2C+C+S">C. S. Chang</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+L">Lei Chen</a>, <a href="/search/physics?searchtype=author&query=Churchill%2C+R+M">R. M. Churchill</a>, <a href="/search/physics?searchtype=author&query=Citrin%2C+J">Jonathan Citrin</a>, <a href="/search/physics?searchtype=author&query=Gaffney%2C+J+A">Jim A Gaffney</a>, <a href="/search/physics?searchtype=author&query=Gainaru%2C+A">Ana Gainaru</a>, <a href="/search/physics?searchtype=author&query=Gekelman%2C+W">Walter Gekelman</a>, <a href="/search/physics?searchtype=author&query=Gibbs%2C+T">Tom Gibbs</a>, <a href="/search/physics?searchtype=author&query=Hamaguchi%2C+S">Satoshi Hamaguchi</a>, <a href="/search/physics?searchtype=author&query=Hill%2C+C">Christian Hill</a>, <a href="/search/physics?searchtype=author&query=Humbird%2C+K">Kelli Humbird</a>, <a href="/search/physics?searchtype=author&query=Jalas%2C+S">S枚ren Jalas</a>, <a href="/search/physics?searchtype=author&query=Kawaguchi%2C+S">Satoru Kawaguchi</a>, <a href="/search/physics?searchtype=author&query=Kim%2C+G">Gon-Ho Kim</a>, <a href="/search/physics?searchtype=author&query=Kirchen%2C+M">Manuel Kirchen</a>, <a href="/search/physics?searchtype=author&query=Klasky%2C+S">Scott Klasky</a>, <a href="/search/physics?searchtype=author&query=Kline%2C+J+L">John L. Kline</a>, <a href="/search/physics?searchtype=author&query=Krushelnick%2C+K">Karl Krushelnick</a> , et al. (38 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="2205.15832v1-abstract-short" style="display: inline;"> Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.15832v1-abstract-full').style.display = 'inline'; document.getElementById('2205.15832v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.15832v1-abstract-full" style="display: none;"> Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.15832v1-abstract-full').style.display = 'none'; document.getElementById('2205.15832v1-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> May 2022. </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">112 pages (including 700+ references), 44 figures, submitted to IEEE Transactions on Plasma Science as a part of the IEEE Golden Anniversary Special Issue</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> Los Alamos Report number LA-UR-22-24834 </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Transactions on Plasma Science 51, 1750 - 1838 (2023) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.11310">arXiv:2111.11310</a> <span> [<a href="https://arxiv.org/pdf/2111.11310">pdf</a>, <a href="https://arxiv.org/format/2111.11310">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Plasma Physics">physics.plasm-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Phenomenology">hep-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41586-021-03382-w">10.1038/s41586-021-03382-w <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The data-driven future of high energy density physics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Hatfield%2C+P+W">Peter W. Hatfield</a>, <a href="/search/physics?searchtype=author&query=Gaffney%2C+J+A">Jim A. Gaffney</a>, <a href="/search/physics?searchtype=author&query=Anderson%2C+G+J">Gemma J. Anderson</a>, <a href="/search/physics?searchtype=author&query=Ali%2C+S">Suzanne Ali</a>, <a href="/search/physics?searchtype=author&query=Antonelli%2C+L">Luca Antonelli</a>, <a href="/search/physics?searchtype=author&query=Pree%2C+S+B+d">Suzan Ba艧e臒mez du Pree</a>, <a href="/search/physics?searchtype=author&query=Citrin%2C+J">Jonathan Citrin</a>, <a href="/search/physics?searchtype=author&query=Fajardo%2C+M">Marta Fajardo</a>, <a href="/search/physics?searchtype=author&query=Knapp%2C+P">Patrick Knapp</a>, <a href="/search/physics?searchtype=author&query=Kettle%2C+B">Brendan Kettle</a>, <a href="/search/physics?searchtype=author&query=Kustowski%2C+B">Bogdan Kustowski</a>, <a href="/search/physics?searchtype=author&query=MacDonald%2C+M+J">Michael J. MacDonald</a>, <a href="/search/physics?searchtype=author&query=Mariscal%2C+D">Derek Mariscal</a>, <a href="/search/physics?searchtype=author&query=Martin%2C+M+E">Madison E. Martin</a>, <a href="/search/physics?searchtype=author&query=Nagayama%2C+T">Taisuke Nagayama</a>, <a href="/search/physics?searchtype=author&query=Palmer%2C+C+A+J">Charlotte A. J. Palmer</a>, <a href="/search/physics?searchtype=author&query=Peterson%2C+J+L">J. Luc Peterson</a>, <a href="/search/physics?searchtype=author&query=Rose%2C+S">Steven Rose</a>, <a href="/search/physics?searchtype=author&query=Ruby%2C+J+J">J J Ruby</a>, <a href="/search/physics?searchtype=author&query=Shneider%2C+C">Carl Shneider</a>, <a href="/search/physics?searchtype=author&query=Streeter%2C+M+J+V">Matt J. V. Streeter</a>, <a href="/search/physics?searchtype=author&query=Trickey%2C+W">Will Trickey</a>, <a href="/search/physics?searchtype=author&query=Williams%2C+B">Ben Williams</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="2111.11310v1-abstract-short" style="display: inline;"> The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fusion and fundamental physics. These extreme physical systems are strongly non-linear and very difficult to understand theoretically or optimize experimentally. Here, we argue that machine learning models and data-driven… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.11310v1-abstract-full').style.display = 'inline'; document.getElementById('2111.11310v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.11310v1-abstract-full" style="display: none;"> The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fusion and fundamental physics. These extreme physical systems are strongly non-linear and very difficult to understand theoretically or optimize experimentally. Here, we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proven far too non-linear for human researchers. From a fundamental perspective, our understanding can be helped by the way in which machine learning models can rapidly discover complex interactions in large data sets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to ~daily), moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we advance proposals for the community in terms of research design, training, best practices, and support for synthetic diagnostics and data analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.11310v1-abstract-full').style.display = 'none'; document.getElementById('2111.11310v1-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, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">14 pages, 4 figures. This work was the result of a meeting at the Lorentz Center, University of Leiden, 13th-17th January 2020. This is a preprint of Hatfield et al., Nature, 593, 7859, 351-361 (2021) https://www.nature.com/articles/s41586-021-03382-w</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nature, 593, 7859, 351-361, 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1912.02892">arXiv:1912.02892</a> <span> [<a href="https://arxiv.org/pdf/1912.02892">pdf</a>, <a href="https://arxiv.org/format/1912.02892">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Plasma Physics">physics.plasm-ph</span> </div> </div> <p class="title is-5 mathjax"> Enabling Machine Learning-Ready HPC Ensembles with Merlin </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Peterson%2C+J+L">J. Luc Peterson</a>, <a href="/search/physics?searchtype=author&query=Bay%2C+B">Ben Bay</a>, <a href="/search/physics?searchtype=author&query=Koning%2C+J">Joe Koning</a>, <a href="/search/physics?searchtype=author&query=Robinson%2C+P">Peter Robinson</a>, <a href="/search/physics?searchtype=author&query=Semler%2C+J">Jessica Semler</a>, <a href="/search/physics?searchtype=author&query=White%2C+J">Jeremy White</a>, <a href="/search/physics?searchtype=author&query=Anirudh%2C+R">Rushil Anirudh</a>, <a href="/search/physics?searchtype=author&query=Athey%2C+K">Kevin Athey</a>, <a href="/search/physics?searchtype=author&query=Bremer%2C+P">Peer-Timo Bremer</a>, <a href="/search/physics?searchtype=author&query=Di+Natale%2C+F">Francesco Di Natale</a>, <a href="/search/physics?searchtype=author&query=Fox%2C+D">David Fox</a>, <a href="/search/physics?searchtype=author&query=Gaffney%2C+J+A">Jim A. Gaffney</a>, <a href="/search/physics?searchtype=author&query=Jacobs%2C+S+A">Sam A. Jacobs</a>, <a href="/search/physics?searchtype=author&query=Kailkhura%2C+B">Bhavya Kailkhura</a>, <a href="/search/physics?searchtype=author&query=Kustowski%2C+B">Bogdan Kustowski</a>, <a href="/search/physics?searchtype=author&query=Langer%2C+S">Steven Langer</a>, <a href="/search/physics?searchtype=author&query=Spears%2C+B">Brian Spears</a>, <a href="/search/physics?searchtype=author&query=Thiagarajan%2C+J">Jayaraman Thiagarajan</a>, <a href="/search/physics?searchtype=author&query=Van+Essen%2C+B">Brian Van Essen</a>, <a href="/search/physics?searchtype=author&query=Yeom%2C+J">Jae-Seung Yeom</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="1912.02892v2-abstract-short" style="display: inline;"> With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.02892v2-abstract-full').style.display = 'inline'; document.getElementById('1912.02892v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1912.02892v2-abstract-full" style="display: none;"> With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. In this paper, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. In addition to its design, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.02892v2-abstract-full').style.display = 'none'; document.getElementById('1912.02892v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">28 pages, 9 figures; Submitted to FGCS</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> LLNL-JRNL-821884 </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>

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