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Machine Learning in the Search for New Fundamental Physics [article]

Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz, Benjamin Nachman, David Shih
<span title="2021-12-07">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics.  ...  We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event  ...  A Living Review of ML for particle physics that is continually updated with the latest methods and results can be found at Ref. [10] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.03769v1">arXiv:2112.03769v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nwfq77xx2vehbfxg6oazrnulpe">fatcat:nwfq77xx2vehbfxg6oazrnulpe</a> </span>
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Optimising Particle Accelerators with Adaptive Machine Learning

<span title="2020-07-29">2020</span> <i title="Research Outreach"> Research Outreach </i> &nbsp;
Ideally, Optimising Particle Accelerators with Adaptive Machine Learning Machine learning has become a staple of research into many of today's most cutting-edge technologies.  ...  Alexander Scheinker Particle dynamics live in 6D space.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32907/ro-115-182185">doi:10.32907/ro-115-182185</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ybheeozxpbhbnhymr74ha6qn2a">fatcat:ybheeozxpbhbnhymr74ha6qn2a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210427155654/https://researchoutreach.org/wp-content/uploads/2020/07/Alexander-Scheinker.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/72/fc/72fcd235ee0b529a5fb853f9196f8800dc2950e5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32907/ro-115-182185"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Phase separation of tunable biomolecular condensates predicted by an interacting particle model [article]

Gorka Munoz-Gil, Catalina Romero, Nicolas Mateos, Lara Isabel de Llobet Cucalon, Miguel Beato, Maciej Lewenstein, Maria Filomena Garcia-Parajo, Juan Andres Torreno-Pina
<span title="2020-09-10">2020</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Here we propose a theoretical model, where phase separation is explained by means of interaction probabilities between particles.  ...  Phase separation, condensate sizes, diffusion behavior, and mobility parameters, quantified by data analysis and machine learning, are fully recapitulated by our model.  ...  ACKNOWLEDGEMENTS We thank Gordon Hager for providing the pGFP-PRB plasmid and Luke Lavis for kindly providing the JF549 SNAP dye. We would like to thank the Advanced Light Microscopy  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.09.09.289876">doi:10.1101/2020.09.09.289876</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ai7qnqxyengstde65rhog4own4">fatcat:ai7qnqxyengstde65rhog4own4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201213143019/https://www.biorxiv.org/content/biorxiv/early/2020/09/10/2020.09.09.289876.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6d/69/6d69b32716b801e024a185350c648cba739ddf0a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.09.09.289876"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Page 428 of Community College Journal Vol. 25, Issue 7 [page]

<span title="">1955</span> <i title="American Association of Community Colleges"> <a target="_blank" rel="noopener" href="https://archive.org/details/pub_community-college-journal" style="color: black;">Community College Journal </a> </i> &nbsp;
Tew: INTRODUCTION TO PHYSICS by FRANK M. DURBIN, Oklahoma A. & M. College This new text's contents are divided equally between classical physics and “small particle" physics.  ...  There are more than 700 graded problems, 100 of which pertain to "small particle" physics. estimated 770 pages : 6”x 9” : March 1955 BASIC MATHEMATICS FOR GENERAL EDUCATION 2nd. Edition (1955) by H.  ... 
<span class="external-identifiers"> </span>
<a target="_blank" rel="noopener" href="https://archive.org/details/sim_community-college-journal_1955-03_25_7/page/428" title="read fulltext microfilm" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Archive [Microfilm] <div class="menu fulltext-thumbnail"> <img src="https://archive.org/serve/sim_community-college-journal_1955-03_25_7/__ia_thumb.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a>

Particle-identification techniques and performance at LHCb in Run 2

M. Hushchyn, V. Chekalina
<span title="">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wlymb735mjbbhbp4jd3xjlbbhy" style="color: black;">Nuclear Instruments and Methods in Physics Research Section A : Accelerators, Spectrometers, Detectors and Associated Equipment</a> </i> &nbsp;
A B S T R A C T One of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles.  ...  In this proceedings we review the new approaches used for particle identification at the LHCb experiment.  ...  Conclusion Advanced machine learning techniques allow to increase particle identification performance both for charged and neutral particles.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.nima.2018.10.144">doi:10.1016/j.nima.2018.10.144</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jv7s5k5wirabxc6fxskrnux7pm">fatcat:jv7s5k5wirabxc6fxskrnux7pm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201103123800/http://cds.cern.ch/record/2629876/files/1-s2.0-S0168900218314608-main.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/26/fe/26fee4b1552b9725cb666f8a9992eadaf2ecaad6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.nima.2018.10.144"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Using Machine Learning for the Calibration of Airborne Particulate Sensors

Lakitha O.H. Wijeratne, Daniel R. Kiv, Adam R. Aker, Shawhin Talebi, David J. Lary
<span title="2019-12-23">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
In this study we show that machine learning can be used to effectively calibrate lower cost optical particle counters.  ...  For this calibration it is critical that measurements of the atmospheric pressure, humidity, and temperature are also made.  ...  Acknowledgments: We warmly thank the anonymous reviewers for their suggestions to improve this paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s20010099">doi:10.3390/s20010099</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31877977">pmid:31877977</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6982762/">pmcid:PMC6982762</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dbuztyxtfbamrjnl354edhr3s4">fatcat:dbuztyxtfbamrjnl354edhr3s4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191225014321/https://res.mdpi.com/d_attachment/sensors/sensors-20-00099/article_deploy/sensors-20-00099.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/af/c2/afc2cd838ed0fbb5495270e65ccd5986597d5be2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s20010099"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982762" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Detecting Protein Communities in Native Cell Extracts by Machine Learning: A Structural Biologist's Perspective

Fotis L. Kyrilis, Jaydeep Belapure, Panagiotis L. Kastritis
<span title="2021-04-15">2021</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zqzewb742bdglfxhkef3ke6cla" style="color: black;">Frontiers in Molecular Biosciences</a> </i> &nbsp;
We argue that the progress in, and the integration of, machine learning, cryo-EM, and complementary structural proteomics approaches would provide the basis for a multi-scale molecular description of protein  ...  The application of image-processing workflows inspired by machine-learning techniques would provide improvements in distinguishing structural signatures, correlating proteomic and network data to structural  ...  machine learning, e.g., using a random forest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fmolb.2021.660542">doi:10.3389/fmolb.2021.660542</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33937337">pmid:33937337</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8082361/">pmcid:PMC8082361</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4t4sxenhnbdbdfstofs44yaf3u">fatcat:4t4sxenhnbdbdfstofs44yaf3u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210425204438/https://fjfsdata01prod.blob.core.windows.net/articles/files/660542/pubmed-zip/.versions/1/.package-entries/fmolb-08-660542/fmolb-08-660542.pdf?sv=2018-03-28&amp;sr=b&amp;sig=vCi1bR16UknvnS8VGPk8li1ZYhr1ffO%2Fs49y%2FJYjrSc%3D&amp;se=2021-04-25T20%3A45%3A07Z&amp;sp=r&amp;rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fmolb-08-660542.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/00/b1/00b1230e91d0b184a82d8e272ce6700334dd0683.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fmolb.2021.660542"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082361" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Unsupervised machine learning for detection of phase transitions in off-lattice systems. I. Foundations

R. B. Jadrich, B. A. Lindquist, T. M. Truskett
<span title="2018-11-21">2018</span> <i title="AIP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hnc5ddfunnd3taca63uf5ggldm" style="color: black;">Journal of Chemical Physics</a> </i> &nbsp;
We demonstrate the utility of an unsupervised machine learning tool for the detection of phase transitions in off-lattice systems.  ...  As we demonstrate, PCA autonomously discovers order-parameter-like quantities that report on phase transitions, mitigating the need for a priori construction or identification of a suitable order parameter  ...  We acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1063/1.5049849">doi:10.1063/1.5049849</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30466285">pmid:30466285</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wiqttipisffythqzzo3mmmb7ge">fatcat:wiqttipisffythqzzo3mmmb7ge</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191016073914/https://arxiv.org/pdf/1808.00084v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/20/0b/200b09510bb65f6b82923abe894f7b6f278a7754.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1063/1.5049849"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Introduction to Graph Neural Networks for HEP

Joosep Pata
<span title="2021-05-25">2021</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Lecture slides for "Intro to Graph Neural Networks from a HEP perspective" at the Quantum Universe Data Science Basics school at Hamburg (virtual).  ...  Acknowledgements • Thanks to Jan Kieseler and Jean-Roch Vlimant for comments & discussion  ...  MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks. Eur. Phys. J.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4786841">doi:10.5281/zenodo.4786841</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/evrolezr6rf4bff3sk6nrpcmfu">fatcat:evrolezr6rf4bff3sk6nrpcmfu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210529003811/https://zenodo.org/record/4786841/files/gnn_lecture_hamburg.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/88/a0/88a03f9209149e0c3016e4135dd10b514e4b2806.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.4786841"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine Learning Applications in Type 1 Diabetes (Preprint)

Ashenafi Zebene Woldaregay, Eirik Årsand, Taxiarchis Botsis, David Albers, Lena Mamykina, Gunnar Hartvigsen
<span title="2018-05-11">2018</span> <i title="JMIR Publications Inc."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/f42mlbuaivhxrblrv2cbukmx4i" style="color: black;">Journal of Medical Internet Research</a> </i> &nbsp;
However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification  ...  Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular.  ...  https://preprints.jmir.org/preprint/11030 [unpublished, non-peer-reviewed preprint] Various classes of machine learning algorithms have been adopted for the task.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2196/11030">doi:10.2196/11030</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31042157">pmid:31042157</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6658321/">pmcid:PMC6658321</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cgj7c5tfpbeg3cwtoigmzoq4yq">fatcat:cgj7c5tfpbeg3cwtoigmzoq4yq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428195854/https://s3.ca-central-1.amazonaws.com/assets.jmir.org/assets/preprints/preprint-11030-accepted.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f2/6f/f26fdcd8f8ae1b3ab219affde9d4c920c29d9a8d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2196/11030"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6658321" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines

Gordana Dodig-Crnkovic
<span title="2020-09-01">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fbul63ph45fgpdmfbjk7polj6i" style="color: black;">Philosophies</a> </i> &nbsp;
As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through  ...  We propose that one contribution can be understanding of the mechanisms of 'learning to learn', as a step towards deep learning with symbolic layer of computation/information processing in a framework  ...  Acknowledgments: The author would like to thank anonymous reviewers for very helpful, constructive, and instructive review comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/philosophies5030017">doi:10.3390/philosophies5030017</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6svndrki2nazbfbo5ayubctwsa">fatcat:6svndrki2nazbfbo5ayubctwsa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210716053326/https://research.chalmers.se/publication/522483/file/522483_Fulltext.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ee/c7/eec7374d7c004b52a4bf2f4de4ec66de555155f3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/philosophies5030017"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

On-line computing challenges: detector and readout requirements [article]

Richard Brenner, Christos Leonidopoulos
<span title="2021-11-07">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
such as long-lived non-relativistic particles.  ...  We will review the various challenges on online selection for the most demanding Tera-Z running scenario and the constraints they pose on the design of FCC-ee detectors.  ...  Machine Learning In recent years we have witnessed an explosive growth of machine learning techniques in HEP applications.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.04168v1">arXiv:2111.04168v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/louhtg67hbai3omkiqlxlsz4na">fatcat:louhtg67hbai3omkiqlxlsz4na</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211110182222/https://arxiv.org/pdf/2111.04168v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f6/54/f6540da3926c0a0ea1ebbe7a341a98f1d0a6f72b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.04168v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Machine learning topological states

Dong-Ling Deng, Xiaopeng Li, S. Das Sarma
<span title="2017-11-22">2017</span> <i title="American Physical Society (APS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b7jx7xdhxncvtj2nb24xhplinq" style="color: black;">Physical review B</a> </i> &nbsp;
Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology.  ...  Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases  ...  Lukin, Frank Verstraete, Lei Wang, Cheng Fang, Matthias Troyer, Yang-Le Wu, Mohammad Hafezi, Yi Zhang, Eun-Ah Kim, Alexey Gorshkov, and Subir Sachdev for helpful discussions. This work is supported  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1103/physrevb.96.195145">doi:10.1103/physrevb.96.195145</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dkdengcaivgdlfluxc27c4cw5q">fatcat:dkdengcaivgdlfluxc27c4cw5q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190502050416/https://link.aps.org/accepted/10.1103/PhysRevB.96.195145" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e0/4a/e04ad5407cd0be953e95e5e323d9be683b92ca00.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1103/physrevb.96.195145"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> aps.org </button> </a>

Quantum Support Vector Machines for Continuum Suppression in B Meson Decays [article]

Jamie Heredge, Charles Hill, Lloyd Hollenberg, Martin Sevior
<span title="2021-11-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Machine learning algorithms are ubiquitous in particle physics and as advances are made in quantum machine learning technology there may be a similar adoption of these quantum techniques.  ...  As further improvements to the error rates and availability of quantum computers materialise, they could form a new approach for data analysis in high energy physics.  ...  A review of quantum machine learning in particle physics was carried out by Guan et al [9] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.12257v3">arXiv:2103.12257v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dexvajehgvdupnldicz4weloju">fatcat:dexvajehgvdupnldicz4weloju</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211112033218/https://arxiv.org/pdf/2103.12257v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d9/48/d948c77a10f2843b1cc333fa07fbe4b89045201c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.12257v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Supervised deep learning in high energy phenomenology: a mini review [article]

Murat Abdughani, Jie Ren, Lei Wu, Jin Min Yang, Jun Zhao
<span title="2019-05-15">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Deep learning, a branch of machine learning, have been recently applied to high energy experimental and phenomenological studies.  ...  In this note we give a brief review on those applications using supervised deep learning.  ...  for exploring the parameter space of new physics models, called the machine learning scan (MLS).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.06047v1">arXiv:1905.06047v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dawdwfojgjg3djhujurft66sam">fatcat:dawdwfojgjg3djhujurft66sam</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929135603/https://arxiv.org/pdf/1905.06047v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/56/78/5678439ffde4fbcbac88e8f792adc558d05ecede.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.06047v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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