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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/1911.02479">arXiv:1911.02479</a> <span> [<a href="https://arxiv.org/pdf/1911.02479">pdf</a>, <a href="https://arxiv.org/ps/1911.02479">ps</a>, <a href="https://arxiv.org/format/1911.02479">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</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"> Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nord%2C+B">Brian Nord</a>, <a href="/search/cs?searchtype=author&query=Connolly%2C+A+J">Andrew J. Connolly</a>, <a href="/search/cs?searchtype=author&query=Kinney%2C+J">Jamie Kinney</a>, <a href="/search/cs?searchtype=author&query=Kubica%2C+J">Jeremy Kubica</a>, <a href="/search/cs?searchtype=author&query=Narayan%2C+G">Gautaum Narayan</a>, <a href="/search/cs?searchtype=author&query=Peek%2C+J+E+G">Joshua E. G. Peek</a>, <a href="/search/cs?searchtype=author&query=Schafer%2C+C">Chad Schafer</a>, <a href="/search/cs?searchtype=author&query=Tollerud%2C+E+J">Erik J. Tollerud</a>, <a href="/search/cs?searchtype=author&query=Avestruz%2C+C">Camille Avestruz</a>, <a href="/search/cs?searchtype=author&query=Babu%2C+G+J">G. Jogesh Babu</a>, <a href="/search/cs?searchtype=author&query=Birrer%2C+S">Simon Birrer</a>, <a href="/search/cs?searchtype=author&query=Burke%2C+D">Douglas Burke</a>, <a href="/search/cs?searchtype=author&query=Caldeira%2C+J">Jo茫o Caldeira</a>, <a href="/search/cs?searchtype=author&query=Caldwell%2C+D+A">Douglas A. Caldwell</a>, <a href="/search/cs?searchtype=author&query=Carlberg%2C+J+K">Joleen K. Carlberg</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yen-Chi Chen</a>, <a href="/search/cs?searchtype=author&query=Dong%2C+C">Chuanfei Dong</a>, <a href="/search/cs?searchtype=author&query=Feigelson%2C+E+D">Eric D. Feigelson</a>, <a href="/search/cs?searchtype=author&query=Golkhou%2C+V+Z">V. Zach Golkhou</a>, <a href="/search/cs?searchtype=author&query=Kashyap%2C+V">Vinay Kashyap</a>, <a href="/search/cs?searchtype=author&query=Li%2C+T+S">T. S. Li</a>, <a href="/search/cs?searchtype=author&query=Loredo%2C+T">Thomas Loredo</a>, <a href="/search/cs?searchtype=author&query=Lucie-Smith%2C+L">Luisa Lucie-Smith</a>, <a href="/search/cs?searchtype=author&query=Mandel%2C+K+S">Kaisey S. Mandel</a>, <a href="/search/cs?searchtype=author&query=Mart%C3%ADnez-Galarza%2C+J+R">J. R. Mart铆nez-Galarza</a> , et al. (13 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.02479v1-abstract-short" style="display: inline;"> The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.02479v1-abstract-full').style.display = 'inline'; document.getElementById('1911.02479v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.02479v1-abstract-full" style="display: none;"> The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era. We discuss challenges for discovery in large and complex data sets; challenges and requirements for the next stage of development of statistical methodologies and algorithmic tool sets; how we might change our paradigms of collaboration and education; and the ethical implications of scientists' contributions to widely applicable algorithms and computational modeling. We start with six distinct recommendations that are supported by the commentary following them. This white paper is related to a larger corpus of effort that has taken place within and around the Petabytes to Science Workshops (https://petabytestoscience.github.io/). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.02479v1-abstract-full').style.display = 'none'; document.getElementById('1911.02479v1-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 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">arXiv admin note: substantial text overlap with arXiv:1905.05116</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> FERMILAB-FN-1093-A-AE-SCD </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1801.06590">arXiv:1801.06590</a> <span> [<a href="https://arxiv.org/pdf/1801.06590">pdf</a>, <a href="https://arxiv.org/format/1801.06590">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Algebraic Topology">math.AT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Geometry">cs.CG</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"> Persistent Homology of Morse Decompositions in Combinatorial Dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dey%2C+T+K">Tamal K. Dey</a>, <a href="/search/cs?searchtype=author&query=Juda%2C+M">Mateusz Juda</a>, <a href="/search/cs?searchtype=author&query=Kapela%2C+T">Tomasz Kapela</a>, <a href="/search/cs?searchtype=author&query=Kubica%2C+J">Jacek Kubica</a>, <a href="/search/cs?searchtype=author&query=Lipinski%2C+M">Michal Lipinski</a>, <a href="/search/cs?searchtype=author&query=Mrozek%2C+M">Marian Mrozek</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="1801.06590v2-abstract-short" style="display: inline;"> We investigate combinatorial dynamical systems on simplicial complexes considered as {\em finite topological spaces}. Such systems arise in a natural way from sampling dynamics and may be used to reconstruct some features of the dynamics directly from the sample. We study the homological persistence of {\em Morse decompositions} of such systems, an important descriptor of the dynamics, as a tool f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.06590v2-abstract-full').style.display = 'inline'; document.getElementById('1801.06590v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1801.06590v2-abstract-full" style="display: none;"> We investigate combinatorial dynamical systems on simplicial complexes considered as {\em finite topological spaces}. Such systems arise in a natural way from sampling dynamics and may be used to reconstruct some features of the dynamics directly from the sample. We study the homological persistence of {\em Morse decompositions} of such systems, an important descriptor of the dynamics, as a tool for validating the reconstruction. Our framework can be viewed as a step toward extending the classical persistence theory to "vector cloud" data. We present experimental results on two numerical examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1801.06590v2-abstract-full').style.display = 'none'; document.getElementById('1801.06590v2-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 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 January, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2018. </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 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