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Intell.) </option> <option value="jlpea" > Journal of Low Power Electronics and Applications (JLPEA) </option> <option value="jmmp" > Journal of Manufacturing and Materials Processing (JMMP) </option> <option value="jmse" > Journal of Marine Science and Engineering (JMSE) </option> <option value="jmahp" > Journal of Market Access & Health Policy (JMAHP) </option> <option value="jmp" > Journal of Molecular Pathology (JMP) </option> <option value="jnt" > Journal of Nanotheranostics (JNT) </option> <option value="jne" > Journal of Nuclear Engineering (JNE) </option> <option value="ohbm" > Journal of Otorhinolaryngology, Hearing and Balance Medicine (JOHBM) </option> <option value="jop" > Journal of Parks </option> <option value="jpm" > Journal of Personalized Medicine (JPM) </option> <option value="jpbi" > Journal of Pharmaceutical and BioTech Industry (JPBI) </option> <option value="jor" > Journal of Respiration (JoR) </option> <option value="jrfm" > Journal of Risk and Financial Management (JRFM) </option> <option value="jsan" > Journal of Sensor and Actuator Networks (JSAN) </option> <option value="joma" > Journal of the Oman Medical Association (JOMA) </option> <option value="jtaer" > Journal of Theoretical and Applied Electronic Commerce Research (JTAER) </option> <option value="jvd" > Journal of Vascular Diseases (JVD) </option> <option value="jox" > Journal of Xenobiotics (JoX) </option> <option value="jzbg" > Journal of Zoological and Botanical Gardens (JZBG) </option> <option value="journalmedia" > Journalism and Media </option> <option value="kidneydial" > Kidney and Dialysis </option> <option value="kinasesphosphatases" > Kinases and Phosphatases </option> <option value="knowledge" > Knowledge </option> <option value="labmed" > LabMed </option> <option value="laboratories" > Laboratories </option> <option value="land" > Land </option> <option value="languages" > Languages </option> <option value="laws" > Laws </option> <option value="life" > Life 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Mathematics </option> <option value="medsci" > Medical Sciences </option> <option value="msf" > Medical Sciences Forum </option> <option value="medicina" > Medicina </option> <option value="medicines" > Medicines </option> <option value="membranes" > Membranes </option> <option value="merits" > Merits </option> <option value="metabolites" > Metabolites </option> <option value="metals" > Metals </option> <option value="meteorology" > Meteorology </option> <option value="methane" > Methane </option> <option value="mps" > Methods and Protocols (MPs) </option> <option value="metrics" > Metrics </option> <option value="metrology" > Metrology </option> <option value="micro" > Micro </option> <option value="microbiolres" > Microbiology Research </option> <option value="micromachines" > Micromachines </option> <option value="microorganisms" > Microorganisms </option> <option value="microplastics" > Microplastics </option> <option value="minerals" > Minerals </option> <option value="mining" > Mining </option> <option value="modelling" > Modelling </option> <option value="mmphys" > Modern Mathematical Physics </option> <option value="molbank" > Molbank </option> <option value="molecules" > Molecules </option> <option value="mti" > Multimodal Technologies and Interaction (MTI) </option> <option value="muscles" > Muscles </option> <option value="nanoenergyadv" > Nanoenergy Advances </option> <option value="nanomanufacturing" > Nanomanufacturing </option> <option value="nanomaterials" > Nanomaterials </option> <option value="ndt" > NDT </option> <option value="network" > Network </option> <option value="neuroglia" > Neuroglia </option> <option value="neurolint" > Neurology International </option> <option value="neurosci" > NeuroSci </option> <option value="nitrogen" > Nitrogen </option> <option value="ncrna" > Non-Coding RNA (ncRNA) </option> <option value="nursrep" > Nursing Reports </option> <option value="nutraceuticals" > Nutraceuticals </option> <option value="nutrients" > Nutrients </option> <option value="obesities" > Obesities </option> <option value="oceans" > Oceans </option> <option value="onco" > Onco </option> <option value="optics" > Optics </option> <option value="oral" > Oral </option> <option value="organics" > Organics </option> <option value="organoids" > Organoids </option> <option value="osteology" > Osteology </option> <option value="oxygen" > Oxygen </option> <option value="parasitologia" > Parasitologia </option> <option value="particles" > Particles </option> <option value="pathogens" > Pathogens </option> <option value="pathophysiology" > Pathophysiology </option> <option value="pediatrrep" > Pediatric Reports </option> <option value="pets" > Pets </option> <option value="pharmaceuticals" > Pharmaceuticals </option> <option value="pharmaceutics" > Pharmaceutics </option> <option value="pharmacoepidemiology" > Pharmacoepidemiology </option> <option value="pharmacy" > Pharmacy </option> <option value="philosophies" > Philosophies </option> <option value="photochem" > Photochem </option> <option value="photonics" > Photonics </option> <option value="phycology" > Phycology </option> <option value="physchem" > Physchem </option> <option value="psf" selected='selected'> Physical Sciences Forum </option> <option value="physics" > Physics </option> <option value="physiologia" > Physiologia </option> <option value="plants" > Plants </option> <option value="plasma" > Plasma </option> <option value="platforms" > Platforms </option> <option value="pollutants" > Pollutants </option> <option value="polymers" > Polymers </option> <option value="polysaccharides" > Polysaccharides </option> <option value="populations" > Populations </option> <option value="poultry" > Poultry </option> <option value="powders" > Powders </option> <option value="proceedings" > Proceedings </option> <option value="processes" > Processes </option> <option value="prosthesis" > Prosthesis </option> <option value="proteomes" > Proteomes </option> 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Pharm.) </option> <option value="sclerosis" > Sclerosis </option> <option value="seeds" > Seeds </option> <option value="sensors" > Sensors </option> <option value="separations" > Separations </option> <option value="sexes" > Sexes </option> <option value="signals" > Signals </option> <option value="sinusitis" > Sinusitis </option> <option value="smartcities" > Smart Cities </option> <option value="socsci" > Social Sciences </option> <option value="siuj" > Société Internationale d’Urologie Journal (SIUJ) </option> <option value="societies" > Societies </option> <option value="software" > Software </option> <option value="soilsystems" > Soil Systems </option> <option value="solar" > Solar </option> <option value="solids" > Solids </option> <option value="spectroscj" > Spectroscopy Journal </option> <option value="sports" > Sports </option> <option value="standards" > Standards </option> <option value="stats" > Stats </option> <option value="stresses" > Stresses </option> <option value="surfaces" > Surfaces </option> <option value="surgeries" > Surgeries </option> <option value="std" > Surgical Techniques Development </option> <option value="sustainability" > Sustainability </option> <option value="suschem" > Sustainable Chemistry </option> <option value="symmetry" > Symmetry </option> <option value="synbio" > SynBio </option> <option value="systems" > Systems </option> <option value="targets" > Targets </option> <option value="taxonomy" > Taxonomy </option> <option value="technologies" > Technologies </option> <option value="telecom" > Telecom </option> <option value="textiles" > Textiles </option> <option value="thalassrep" > Thalassemia Reports </option> <option value="therapeutics" > Therapeutics </option> <option value="thermo" > Thermo </option> <option value="timespace" > Time and Space </option> <option value="tomography" > Tomography </option> <option value="tourismhosp" > Tourism and Hospitality </option> <option value="toxics" > Toxics </option> <option value="toxins" > Toxins </option> <option value="transplantology" > Transplantology </option> <option value="traumacare" > Trauma Care </option> <option value="higheredu" > Trends in Higher Education </option> <option value="tropicalmed" > Tropical Medicine and Infectious Disease (TropicalMed) </option> <option value="universe" > Universe </option> <option value="urbansci" > Urban Science </option> <option value="uro" > Uro </option> <option value="vaccines" > Vaccines </option> <option value="vehicles" > Vehicles </option> <option value="venereology" > Venereology </option> <option value="vetsci" > Veterinary Sciences </option> <option value="vibration" > Vibration </option> <option value="virtualworlds" > Virtual Worlds </option> <option value="viruses" > Viruses </option> <option value="vision" > Vision </option> <option value="waste" > Waste </option> <option value="water" > Water </option> <option value="wild" > Wild </option> <option value="wind" > Wind </option> <option 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class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_HUNGARY"> <i class="material-icons">remove_circle_outline</i> </a> <label> Hungary </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_IRAQ"> <i class="material-icons">remove_circle_outline</i> </a> <label> Iraq </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_ISRAEL"> <i class="material-icons">remove_circle_outline</i> </a> <label> Israel </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_KYRGYZSTAN"> <i class="material-icons">remove_circle_outline</i> </a> <label> Kyrgyzstan </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_MALAYSIA"> <i class="material-icons">remove_circle_outline</i> </a> <label> Malaysia </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_NETHERLANDS"> <i class="material-icons">remove_circle_outline</i> </a> <label> The Netherlands </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_NIGERIA"> <i class="material-icons">remove_circle_outline</i> </a> <label> Nigeria </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_PORTUGAL"> <i class="material-icons">remove_circle_outline</i> </a> <label> Portugal </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_SINGAPORE"> <i class="material-icons">remove_circle_outline</i> </a> <label> Singapore </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_SLOVAKIA"> <i class="material-icons">remove_circle_outline</i> </a> <label> Slovakia </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_SLOVENIA"> <i class="material-icons">remove_circle_outline</i> </a> <label> Slovenia </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_SYRIAN_ARAB_REPUBLIC"> <i class="material-icons">remove_circle_outline</i> </a> <label> Syrian Arab Republic </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_UKRAINE"> <i class="material-icons">remove_circle_outline</i> </a> <label> Ukraine </label> </div/> <div class="remove-filter-container remove-filter-container--hidden"> <a href="#" class="remove-filter-link link--red " data-filterid="refine_countries_UZBEKISTAN"> <i class="material-icons">remove_circle_outline</i> </a> <label> Uzbekistan </label> </div/> </div> <div class="filter-actions-container filter-actions-container--filled filter-actions-container--hidden"> <a class="js-refine-filter" data-reveal-id="refine-modal-countries" data-filter="countries"><strong>Add Countries / Regions</strong></a> <a href="#" class="remove-refines link--red right" data-refineid="countries"><strong>Reset</strong></a> </div> <div class="filter-actions-container filter-actions-container--empty "> <a class="button button--color button--full-width js-refine-filter" href="#" data-reveal-id="refine-modal-countries" data-filter="countries" class="all">Select Countries / Regions</a> </div> <div style="clear:both;"></div> <div id="refine-modal-countries" class="reveal-modal reveal-modal-new" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"> <div class="row"> <div class="small-12 columns"> <h2>Filter Countries / Regions</h2> <input class="js-filter" type="text" form="temp" placeholder="Search for Countries / Regions" style="width: 100%; max-width: 300px;" /> <p class="reveal-modal-new__description"> Select countries / regions you want to add to your filter. </p> </div> <div class="js-refinement-values-container"> <div class="large-6 medium-6 small-12 columns end js-data-filter" data-filter="usa" style="padding-top: 0px; padding-bottom: 0px; margin-top: 0;" > <div> <input type="checkbox" class="refine_checkbox refine_countries" id="refine_countries_UNITED_STATES" value="UNITED_STATES"> <label class="search_refine_label" style="margin-bottom: 10px;" for="refine_countries_UNITED_STATES">USA (62)</label> </div> </div> <div class="large-6 medium-6 small-12 columns end js-data-filter" data-filter="france" style="padding-top: 0px; padding-bottom: 0px; margin-top: 0;" > <div> <input type="checkbox" class="refine_checkbox refine_countries" id="refine_countries_FRANCE" value="FRANCE"> <label class="search_refine_label" style="margin-bottom: 10px;" for="refine_countries_FRANCE">France (49)</label> </div> </div> <div class="large-6 medium-6 small-12 columns end js-data-filter" data-filter="germany" style="padding-top: 0px; padding-bottom: 0px; margin-top: 0;" > <div> <input type="checkbox" class="refine_checkbox refine_countries" id="refine_countries_GERMANY" value="GERMANY"> <label class="search_refine_label" style="margin-bottom: 10px;" for="refine_countries_GERMANY">Germany 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Different Far-UVC Sources with Regards to Intensity Stability, Estimated Antimicrobial Efficiency and Potential Human Hazard in Comparison to a Conventional UVC Lamp" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/10/1/1">Comparison of Different Far-UVC Sources with Regards to Intensity Stability, Estimated Antimicrobial Efficiency and Potential Human Hazard in Comparison to a Conventional UVC Lamp</a> <div class="authors"> by <span class="inlineblock "><strong>Ben Sicks</strong>, </span><span class="inlineblock "><strong>Florian Maiss</strong>, </span><span class="inlineblock "><strong>Christian Lingenfelder</strong>, </span><span class="inlineblock "><strong>Cornelia Wiegand</strong> and </span><span class="inlineblock "><strong>Martin Hessling</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2024</b>, <em>10</em>(1), 1; <a href="https://doi.org/10.3390/psf2024010001">https://doi.org/10.3390/psf2024010001</a> - 19 Nov 2024 </div> Viewed by 134 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The recently much noticed Far-UVC spectral range offers the possibility of inactivating pathogens without necessarily posing a major danger to humans. Unfortunately, there are various Far-UVC sources that differ significantly in their longer wavelength UVC emission and, subsequently, in their risk potential. Therefore, <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/10/1/1/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The recently much noticed Far-UVC spectral range offers the possibility of inactivating pathogens without necessarily posing a major danger to humans. Unfortunately, there are various Far-UVC sources that differ significantly in their longer wavelength UVC emission and, subsequently, in their risk potential. Therefore, a simple assessment method for Far-UVC sources is presented here. In addition, the temporal intensity stability of Far-UVC sources was examined in order to reduce possible errors in irradiation measurements. For this purpose, four Far-UVC sources and a conventional Hg UVC lamp were each spectrally measured for about 100 h and mathematically evaluated for their antimicrobial effect and hazard potential using available standard data. The two filtered KrCl lamps were found to be most stable after a warm-up time of 30 min. With regard to the antimicrobial effect, the radiation efficiencies of all examined (Far-) UVC sources were more or less similar. However, the calculated differences in the potential human hazard to eyes and skin were more than one order of magnitude. The two filtered KrCl lamps were the safest, followed by an unfiltered KrCl lamp, a Far-UVC LED and, finally, the Hg lamp. When experimenting with these Far-UVC radiation sources, the irradiance should be checked more than once. If UVC radiation is to be or could be applied in the presence of humans, filtered KrCl lamps are a much better choice than any other available Far-UVC sources. <a href="/2673-9984/10/1/1">Full article</a> </div> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/10/1/1/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1528360"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1528360"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1528360" data-cycle-prev="#prev1528360" data-cycle-progressive="#images1528360" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1528360-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-10-00001/article_deploy/html/images/psf-10-00001-g001-550.jpg?1732507878" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1528360" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1528360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-10-00001/article_deploy/html/images/psf-10-00001-g002-550.jpg?1732507879'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1528360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-10-00001/article_deploy/html/images/psf-10-00001-g003-550.jpg?1732507881'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1528360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-10-00001/article_deploy/html/images/psf-10-00001-g004-550.jpg?1732507884'><p>Figure 4</p></div></script></div></div><div id="article-1528360-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-10-00001/article_deploy/html/images/psf-10-00001-g001-550.jpg?1732507878" title=" <strong>Figure 1</strong><br/> <p>The schematic setup for the determination of the spectrally resolved (Far-) UVC lamp irradiances.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/10/1/1'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-10-00001/article_deploy/html/images/psf-10-00001-g002-550.jpg?1732507879" title=" <strong>Figure 2</strong><br/> <p>Spectrally resolved relative antimicrobial impact and potential hazards to human eyes and skin for UV radiation in range 200–400 nm according to DIN 5031-10 and ACGIH-TLVs [<a href="#B11-psf-10-00001" class="html-bibr">11</a>,<a href="#B13-psf-10-00001" class="html-bibr">13</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/10/1/1'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-10-00001/article_deploy/html/images/psf-10-00001-g003-550.jpg?1732507881" title=" <strong>Figure 3</strong><br/> <p>The time-dependent intensity variation in the various (Far-) UVC sources over a period of about 100 h.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/10/1/1'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-10-00001/article_deploy/html/images/psf-10-00001-g004-550.jpg?1732507884" title=" <strong>Figure 4</strong><br/> <p>The spectral irradiances of the different (Far-) UVC sources, normalized to a UV irradiation of 1 mW/cm<sup>2</sup>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/10/1/1'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1397877" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 7 pages, 967 KiB   </span> <a href="/2673-9984/9/1/26/pdf?version=1716192696" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Nested Sampling for Detection and Localization of Sound Sources Using a Spherical Microphone Array" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/26">Nested Sampling for Detection and Localization of Sound Sources Using a Spherical Microphone Array</a> <div class="authors"> by <span class="inlineblock "><strong>Ning Xiang</strong> and </span><span class="inlineblock "><strong>Tomislav Jasa</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 26; <a href="https://doi.org/10.3390/psf2023009026">https://doi.org/10.3390/psf2023009026</a> - 20 May 2024 </div> Viewed by 833 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Since its inception in 2004, nested sampling has been used in acoustics applications. This work applies nested sampling within a Bayesian framework to the detection and localization of sound sources using a spherical microphone array. Beyond an existing work, this source localization task <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/26/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Since its inception in 2004, nested sampling has been used in acoustics applications. This work applies nested sampling within a Bayesian framework to the detection and localization of sound sources using a spherical microphone array. Beyond an existing work, this source localization task relies on spherical harmonics to establish parametric models that distinguish the background sound environment from the presence of sound sources. Upon a positive detection, the parametric models are also involved to estimate an unknown number of potentially multiple sound sources. For the purpose of source detection, a no-source scenario needs to be considered in addition to the presence of at least one sound source. Specifically, the spherical microphone array senses the sound environment. The acoustic data are analyzed via spherical <i>Fourier</i> transforms using a Bayesian model comparison of two different models accounting for the absence and presence of sound sources for the source detection. Upon a positive detection, potentially multiple source models are involved to analyze direction of arrivals (DoAs) using Bayesian model selection and parameter estimation for the sound source enumeration and localization. These are two levels (enumeration and localization) of inferential estimations necessary to correctly localize potentially multiple sound sources. This paper discusses an efficient implementation of the nested sampling algorithm applied to the sound source detection and localization within the Bayesian framework. <a href="/2673-9984/9/1/26">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/26/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1397877"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1397877"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1397877" data-cycle-prev="#prev1397877" data-cycle-progressive="#images1397877" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1397877-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-09-00026/article_deploy/html/images/psf-09-00026-g001-550.jpg?1716192809" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1397877" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1397877-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00026/article_deploy/html/images/psf-09-00026-g002-550.jpg?1716192810'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1397877-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00026/article_deploy/html/images/psf-09-00026-g003-550.jpg?1716192811'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1397877-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00026/article_deploy/html/images/psf-09-00026-g004-550.jpg?1716192812'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1397877-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00026/article_deploy/html/images/psf-09-00026-g005-550.jpg?1716192813'><p>Figure 5</p></div></script></div></div><div id="article-1397877-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-09-00026/article_deploy/html/images/psf-09-00026-g001-550.jpg?1716192809" title=" <strong>Figure 1</strong><br/> <p>Spherical microphone array of radius <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>3.5</mn> </mrow> </semantics></math> cm. Altogether, 32 microphones are nearly uniformly flush-mounted over the rigid spherical surface.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/26'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00026/article_deploy/html/images/psf-09-00026-g002-550.jpg?1716192810" title=" <strong>Figure 2</strong><br/> <p>Beamforming superposition of two sound sources using a spherical order <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/26'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00026/article_deploy/html/images/psf-09-00026-g003-550.jpg?1716192811" title=" <strong>Figure 3</strong><br/> <p>Comparison between the experimental data (<b>a</b>) processed according to Equation (<a href="#FD1-psf-09-00026" class="html-disp-formula">1</a>) with the prediction model (<b>b</b>) in Equation (<a href="#FD4-psf-09-00026" class="html-disp-formula">4</a>) of two sound sources using a spherical order <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/26'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00026/article_deploy/html/images/psf-09-00026-g004-550.jpg?1716192812" title=" <strong>Figure 4</strong><br/> <p>Sound source detection based on Bayesian model comparison. Bayesian evidence is estimated using both ’no-source’ model <math display="inline"><semantics> <msub> <mi>M</mi> <mn>0</mn> </msub> </semantics></math> and one-source model <math display="inline"><semantics> <msub> <mi>M</mi> <mn>1</mn> </msub> </semantics></math>. The evidence is expressed in unit [decibans] in honor of Thomas Bayes [<a href="#B8-psf-09-00026" class="html-bibr">8</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/26'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00026/article_deploy/html/images/psf-09-00026-g005-550.jpg?1716192813" title=" <strong>Figure 5</strong><br/> <p>The sound source enumeration based on Bayes factor estimation. The Bayes factors are expressed in unit [decibans] in honor of Thomas Bayes [<a href="#B8-psf-09-00026" class="html-bibr">8</a>]. A two-source model is preferred by the Bayesian model selection. The evidence estimated using nested sampling also provides the posterior as a byproduct.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/26'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1395669" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 6 pages, 669 KiB   </span> <a href="/2673-9984/9/1/25/pdf?version=1715908756" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Manifold-Based Geometric Exploration of Optimization Solutions" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/25">Manifold-Based Geometric Exploration of Optimization Solutions</a> <div class="authors"> by <span class="inlineblock "><strong>Guillaume Lebonvallet</strong>, </span><span class="inlineblock "><strong>Faicel Hnaien</strong> and </span><span class="inlineblock "><strong>Hichem Snoussi</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 25; <a href="https://doi.org/10.3390/psf2023009025">https://doi.org/10.3390/psf2023009025</a> - 16 May 2024 </div> Viewed by 724 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> This work introduces a new method for the exploration of solutions space in complex problems. This method consists of the build of a latent space which gives a new encoding of the solution space. We map the objective function on the latent space <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/25/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> This work introduces a new method for the exploration of solutions space in complex problems. This method consists of the build of a latent space which gives a new encoding of the solution space. We map the objective function on the latent space using a manifold, i.e., a mathematical object defined by an equations system. The latent space is built with some knowledge of the objective function to make the mapping of the manifold easier. In this work, we introduce a new encoding for the Travelling Salesman Problem (TSP) and we give a new method for finding the optimal round. <a href="/2673-9984/9/1/25">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/25/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1395669"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1395669"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1395669" data-cycle-prev="#prev1395669" data-cycle-progressive="#images1395669" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1395669-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-09-00025/article_deploy/html/images/psf-09-00025-g001-550.jpg?1715908844" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1395669" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1395669-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00025/article_deploy/html/images/psf-09-00025-g002-550.jpg?1715908845'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1395669-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00025/article_deploy/html/images/psf-09-00025-g003a-550.jpg?1715908847'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1395669-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00025/article_deploy/html/images/psf-09-00025-g003b-550.jpg?1715908849'><p>Figure 3 Cont.</p></div></script></div></div><div id="article-1395669-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-09-00025/article_deploy/html/images/psf-09-00025-g001-550.jpg?1715908844" title=" <strong>Figure 1</strong><br/> <p>Encoding tree: each intermediate node (round shape) represents a splitting of the solution space based on the use of a link (the left branch keeps the solutions using the link, the right branch the solutions without the link), the number in square brackets indicates the number of solutions; the leaf nodes (rectangle shape) represents the solutions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/25'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00025/article_deploy/html/images/psf-09-00025-g002-550.jpg?1715908845" title=" <strong>Figure 2</strong><br/> <p>Graph of the new encoding: the horizontal axis represents the rank of a solution (0 is the optimum) and the vertical axis the encoding of that solution.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/25'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00025/article_deploy/html/images/psf-09-00025-g003a-550.jpg?1715908847" title=" <strong>Figure 3</strong><br/> <p>Simulations of the new encoding on several TSP problems with 7 nodes.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/25'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00025/article_deploy/html/images/psf-09-00025-g003b-550.jpg?1715908849" title=" <strong>Figure 3 Cont.</strong><br/> <p>Simulations of the new encoding on several TSP problems with 7 nodes.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/25'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1380189" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 8 pages, 2751 KiB   </span> <a href="/2673-9984/8/1/73/pdf?version=1713775424" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="NuMI Beam Monitoring Simulation and Data Analysis" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/8/1/73">NuMI Beam Monitoring Simulation and Data Analysis</a> <div class="authors"> by <span class="inlineblock "><strong>Yiding Yu</strong>, </span><span class="inlineblock "><strong>Thomas Joseph Carroll</strong>, </span><span class="inlineblock "><strong>Sudeshna Ganguly</strong>, </span><span class="inlineblock "><strong>Karol Lang</strong>, </span><span class="inlineblock "><strong>Eduardo Ossorio</strong>, </span><span class="inlineblock "><strong>Pavel Snopok</strong>, </span><span class="inlineblock "><strong>Jennifer Thomas</strong>, </span><span class="inlineblock "><strong>Don Athula Wickremasinghe</strong> and </span><span class="inlineblock "><strong>Katsuya Yonehara</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>8</em>(1), 73; <a href="https://doi.org/10.3390/psf2023008073">https://doi.org/10.3390/psf2023008073</a> - 22 Apr 2024 </div> Viewed by 642 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Following the decommissioning of the Main Injector Neutrino Oscillation Search (MINOS) experiment, muon and hadron monitors have emerged as vital diagnostic tools for the NuMI Off-axis <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>ν</mi><mi>μ</mi></msub></semantics></math></inline-formula> Appearance (NOvA) experiment at Fermilab. These tools are crucial for overseeing the Neutrinos at the <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/8/1/73/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Following the decommissioning of the Main Injector Neutrino Oscillation Search (MINOS) experiment, muon and hadron monitors have emerged as vital diagnostic tools for the NuMI Off-axis <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>ν</mi><mi>μ</mi></msub></semantics></math></inline-formula> Appearance (NOvA) experiment at Fermilab. These tools are crucial for overseeing the Neutrinos at the Main Injector (NuMI) beam. This study endeavors to ensure the monitor signal quality and to correlate them with the Neutrino beam profile. Leveraging muon monitor simulations, we systematically explore the monitor responses to variations in proton-beam and lattice parameters. Through the amalgamation of individual pixel data from muon monitors, pattern-recognition algorithms, simulations, and measured data, we devise machine-learning-based models to predict muon monitor responses and Neutrino flux. <a href="/2673-9984/8/1/73">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/8/1">The 23rd International Workshop on Neutrinos from Accelerators</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/8/1/73/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1380189"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1380189"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1380189" data-cycle-prev="#prev1380189" data-cycle-progressive="#images1380189" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1380189-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g001-550.jpg?1713775530" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1380189" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1380189-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g002-550.jpg?1713775532'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1380189-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g003-550.jpg?1713775535'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1380189-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g004-550.jpg?1713775536'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1380189-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g005-550.jpg?1713775537'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1380189-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g006-550.jpg?1713775538'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1380189-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g007-550.jpg?1713775539'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1380189-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g008-550.jpg?1713775540'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1380189-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g009-550.jpg?1713775542'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1380189-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g010-550.jpg?1713775542'><p>Figure 10</p></div></script></div></div><div id="article-1380189-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g001-550.jpg?1713775530" title=" <strong>Figure 1</strong><br/> <p>Schematic of the NuMI beamline [<a href="#B3-psf-08-00073" class="html-bibr">3</a>] featuring three muon monitors and two magnetic horns.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/73'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g002-550.jpg?1713775532" title=" <strong>Figure 2</strong><br/> <p>Two−dimensional histogram at MM1. On the (<b>left</b>) (simulation), pixels indicate event counts divided by <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>5</mn> </msup> </mrow> </semantics></math>. On the (<b>right</b>) (measurement), pixels display voltage signals normalized to beam intensity.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/73'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g003-550.jpg?1713775535" title=" <strong>Figure 3</strong><br/> <p>The plot illustrates the muon momentum spectra for MM1 pixels, organized in a central row (X1–X9) and column (Y1–Y9). Noticeable is the shift of peaks to lower momentum as one moves from the center to the edge. Different color lines in the right plots represent spectra for distinct pixels.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/73'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g004-550.jpg?1713775536" title=" <strong>Figure 4</strong><br/> <p>Slopes of muon beam position at MM1 to MM3 plotted against proton−beam position on the target, comparing data and simulation results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/73'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g005-550.jpg?1713775537" title=" <strong>Figure 5</strong><br/> <p>Comparison of MM1 pixels for normalized voltage signals and muon event counts across varying horizontal target beam positions. Blue represents data, while red indicates simulation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/73'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g006-550.jpg?1713775538" title=" <strong>Figure 6</strong><br/> <p>Model of Horn1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/73'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g007-550.jpg?1713775539" title=" <strong>Figure 7</strong><br/> <p>Two-dimensional histograms of ratios (muon events) at MM1–3 for tilt angle −3 mrads (<b>top</b>) and −3 mrads (<b>bottom</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/73'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g008-550.jpg?1713775540" title=" <strong>Figure 8</strong><br/> <p>Ratios of pixels vs. target vertical offset.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/73'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g009-550.jpg?1713775542" title=" <strong>Figure 9</strong><br/> <p>Pixel map of muon monitors.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/73'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00073/article_deploy/html/images/psf-08-00073-g010-550.jpg?1713775542" title=" <strong>Figure 10</strong><br/> <p>Circles indicate differences exceeding 3 sigma at certain pixels, while crosses represent differences within 3 sigma. Here, sigma is the difference in the simulation divided by the statistical error of the simulation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/73'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1342037" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 7 pages, 269 KiB   </span> <a href="/2673-9984/9/1/24/pdf?version=1708420199" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Analysis of Ecological Networks: Linear Inverse Modeling and Information Theory Tools" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/24">Analysis of Ecological Networks: Linear Inverse Modeling and Information Theory Tools</a> <div class="authors"> by <span class="inlineblock "><strong>Valérie Girardin</strong>, </span><span class="inlineblock "><strong>Théo Grente</strong>, </span><span class="inlineblock "><strong>Nathalie Niquil</strong> and </span><span class="inlineblock "><strong>Philippe Regnault</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 24; <a href="https://doi.org/10.3390/psf2023009024">https://doi.org/10.3390/psf2023009024</a> - 20 Feb 2024 </div> Viewed by 896 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In marine ecology, the most studied interactions are trophic and are in networks called food webs. Trophic modeling is mainly based on weighted networks, where each weighted edge corresponds to a flow of organic matter between two trophic compartments, containing individuals of similar <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/24/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In marine ecology, the most studied interactions are trophic and are in networks called food webs. Trophic modeling is mainly based on weighted networks, where each weighted edge corresponds to a flow of organic matter between two trophic compartments, containing individuals of similar feeding behaviors and metabolisms and with the same predators. To take into account the unknown flow values within food webs, a class of methods called Linear Inverse Modeling was developed. The total linear constraints, equations and inequations defines a multidimensional convex-bounded polyhedron, called a polytope, within which lie all realistic solutions to the problem. To describe this polytope, a possible method is to calculate a representative sample of solutions by using the Monte Carlo Markov Chain approach. In order to extract a unique solution from the simulated sample, several goal (cost) functions—also called Ecological Network Analysis indices—have been introduced in the literature as criteria of fitness to the ecosystems. These tools are all related to information theory. Here we introduce new functions that potentially provide a better fit of the estimated model to the ecosystem. <a href="/2673-9984/9/1/24">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1321314" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 6 pages, 515 KiB   </span> <a href="/2673-9984/8/1/72/pdf?version=1705552991" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Development of a Clock Generation and Time Distribution System for Hyper-Kamiokande" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/8/1/72">Development of a Clock Generation and Time Distribution System for Hyper-Kamiokande</a> <div class="authors"> by <span class="inlineblock "><strong>Lucile Mellet</strong>, </span><span class="inlineblock "><strong>Mathieu Guigue</strong>, </span><span class="inlineblock "><strong>Boris Popov</strong>, </span><span class="inlineblock "><strong>Stefano Russo</strong> and </span><span class=" "><strong>Vincent Voisin</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>8</em>(1), 72; <a href="https://doi.org/10.3390/psf2023008072">https://doi.org/10.3390/psf2023008072</a> - 18 Jan 2024 </div> Viewed by 847 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The construction of the next-generation water Cherenkov detector Hyper-Kamiokande (HK) has started. It will have about a ten times larger fiducial volume compared to the existing Super-Kamiokande detector, as well as increased detection performances. The data collection process is planned from 2027 onwards. <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/8/1/72/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The construction of the next-generation water Cherenkov detector Hyper-Kamiokande (HK) has started. It will have about a ten times larger fiducial volume compared to the existing Super-Kamiokande detector, as well as increased detection performances. The data collection process is planned from 2027 onwards. Time stability is crucial, as detecting physics events relies on reconstructing Cherenkov rings based on the coincidence between the photomultipliers. The above requires a distributed clock jitter at each endpoint that is smaller than 100 ps. In addition, since this detector will be mainly used to detect neutrinos produced by the J-PARC accelerator in Tokai, each event needs to be timed-tagged with a precision better than 100 ns, with respect to UTC, in order to be associated with a proton spill from J-PARC or the events observed in other detectors for multi-messenger astronomy. The HK collaboration is in an R&D phase and several groups are working in parallel for the electronics system. This proceeding will present the studies performed at LPNHE (Paris) related to a novel design for the time synchronization system in Kamioka with respect to the previous KamiokaNDE series of experiments. We will discuss the clock generation, including the connection scheme between the GNSS receiver (Septentrio) and the atomic clock (free-running Rubidium), the precise calibration of the atomic clock and algorithms to account for errors on satellites orbits, the redundancy of the system, and a two-stage distribution system that sends the clock and various timing-sensitive information to each front-end electronics module, using a custom protocol. <a href="/2673-9984/8/1/72">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/8/1">The 23rd International Workshop on Neutrinos from Accelerators</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/8/1/72/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1321314"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1321314"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1321314" data-cycle-prev="#prev1321314" data-cycle-progressive="#images1321314" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1321314-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-08-00072/article_deploy/html/images/psf-08-00072-g001-550.jpg?1705553068" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1321314" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1321314-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00072/article_deploy/html/images/psf-08-00072-g002-550.jpg?1705553070'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1321314-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-08-00072/article_deploy/html/images/psf-08-00072-g003-550.jpg?1705553071'><p>Figure 3</p></div></script></div></div><div id="article-1321314-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-08-00072/article_deploy/html/images/psf-08-00072-g001-550.jpg?1705553068" title=" <strong>Figure 1</strong><br/> <p>Detailed scheme of the proposed timing system for HK (redundancy not included).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/72'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00072/article_deploy/html/images/psf-08-00072-g002-550.jpg?1705553070" title=" <strong>Figure 2</strong><br/> <p>Overlapping Allan standard deviation (ADEV) for the free Rb clock and the GNSS receiver.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/72'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-08-00072/article_deploy/html/images/psf-08-00072-g003-550.jpg?1705553071" title=" <strong>Figure 3</strong><br/> <p>Evolution of time differences between the Rubidium clock (deterministic drift removed) and French UTC via GPS signals over 6 days.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/72'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1316127" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 10 pages, 743 KiB   </span> <a href="/2673-9984/9/1/23/pdf?version=1704857639" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Preconditioned Monte Carlo for Gradient-Free Bayesian Inference in the Physical Sciences" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/23">Preconditioned Monte Carlo for Gradient-Free Bayesian Inference in the Physical Sciences</a> <div class="authors"> by <span class="inlineblock "><strong>Minas Karamanis</strong> and </span><span class="inlineblock "><strong>Uroš Seljak</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 23; <a href="https://doi.org/10.3390/psf2023009023">https://doi.org/10.3390/psf2023009023</a> - 9 Jan 2024 </div> <a href="/2673-9984/9/1/23#metrics">Cited by 1</a> | Viewed by 856 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> We present preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference in complex probability distributions. PMC incorporates a normalizing flow (NF) and an adaptive Sequential Monte Carlo (SMC) scheme, along with a novel past resampling scheme to boost the number <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/23/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> We present preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference in complex probability distributions. PMC incorporates a normalizing flow (NF) and an adaptive Sequential Monte Carlo (SMC) scheme, along with a novel past resampling scheme to boost the number of propagated particles without extra computational costs. Additionally, we utilize preconditioned Crank–Nicolson updates, enabling PMC to scale to higher dimensions without the gradient of target distribution. The efficacy of PMC in producing samples, estimating model evidence, and executing robust inference is showcased through two challenging case studies, highlighting its superior performance compared to conventional methods. <a href="/2673-9984/9/1/23">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/23/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="absgraph cycle-slideshow"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1316127-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-09-00023/article_deploy/html/images/psf-09-00023-g001-550.jpg?1704857737" alt="" style="border: 0;"><p>Figure 1</p></div></div></div><div id="article-1316127-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-09-00023/article_deploy/html/images/psf-09-00023-g001-550.jpg?1704857737" title=" <strong>Figure 1</strong><br/> <p>Two-dimensional marginal posteriors of 10-D Rosenbrock (<b>left</b>) and 61-D logistic regression with sonar data (<b>right</b>) as obtained using PMC-PR-p<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>CN (blue) and SMC-RWM (orange).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/23'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1314867" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 8 pages, 2089 KiB   </span> <a href="/2673-9984/9/1/22/pdf?version=1704698623" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Nested Sampling—The Idea" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/22">Nested Sampling—The Idea</a> <div class="authors"> by <span class="inlineblock "><strong>John Skilling</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 22; <a href="https://doi.org/10.3390/psf2023009022">https://doi.org/10.3390/psf2023009022</a> - 8 Jan 2024 </div> Viewed by 922 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> We seek to add up <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>Q</mi><mo>=</mo><mo>∫</mo><mi>f</mi><mspace width="4pt"></mspace><mi>d</mi><mi>X</mi></mrow></semantics></math></inline-formula> over unit volume in arbitrary dimension. Nested sampling locates the bulk of <i>Q</i> by geometrical compression, using a Monte Carlo ensemble constrained within a progressively more restrictive lower limit <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/22/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> We seek to add up <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>Q</mi><mo>=</mo><mo>∫</mo><mi>f</mi><mspace width="4pt"></mspace><mi>d</mi><mi>X</mi></mrow></semantics></math></inline-formula> over unit volume in arbitrary dimension. Nested sampling locates the bulk of <i>Q</i> by geometrical compression, using a Monte Carlo ensemble constrained within a progressively more restrictive lower limit <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>f</mi><mo>≤</mo><msup><mi>f</mi><mo>*</mo></msup></mrow></semantics></math></inline-formula>. This domain is divided into a core <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>f</mi><mo>></mo><msup><mi>f</mi><mo>*</mo></msup></mrow></semantics></math></inline-formula> and a shell <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>f</mi><mo>=</mo><msup><mi>f</mi><mo>*</mo></msup></mrow></semantics></math></inline-formula>, with the core kept adequately populated. <a href="/2673-9984/9/1/22">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/22/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1314867"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1314867"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1314867" data-cycle-prev="#prev1314867" data-cycle-progressive="#images1314867" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1314867-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g001-550.jpg?1704698691" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1314867" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1314867-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g002-550.jpg?1704698692'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1314867-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g003-550.jpg?1704698694'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1314867-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g004-550.jpg?1704698696'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1314867-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g005-550.jpg?1704698697'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1314867-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g006-550.jpg?1704698698'><p>Figure 6</p></div></script></div></div><div id="article-1314867-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g001-550.jpg?1704698691" title=" <strong>Figure 1</strong><br/> <p>Sample ranked <span class="html-italic">r</span> out of <span class="html-italic">n</span> encloses about <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>/</mo> <mi>n</mi> </mrow> </semantics></math> of the volume.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/22'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g002-550.jpg?1704698692" title=" <strong>Figure 2</strong><br/> <p>(<b>left</b>) unit volume <span class="html-italic">V</span> modulated by <span class="html-italic">F</span>; (<b>right</b>) volume <math display="inline"><semantics> <mrow> <mi>X</mi> <mo stretchy="false">(</mo> <mi>f</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> covering <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>⩾</mo> <mi>f</mi> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/22'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g003-550.jpg?1704698694" title=" <strong>Figure 3</strong><br/> <p>Nested sampling iterate with ensemble size 4.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/22'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g004-550.jpg?1704698696" title=" <strong>Figure 4</strong><br/> <p>Nested sampling trajectory.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/22'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g005-550.jpg?1704698697" title=" <strong>Figure 5</strong><br/> <p>Riemann and Lebesgue.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/22'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00022/article_deploy/html/images/psf-09-00022-g006-550.jpg?1704698698" title=" <strong>Figure 6</strong><br/> <p>(<b>left</b>) set prior object by MC; (<b>right</b>) generate new object by MCMC.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/22'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1313677" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 9 pages, 1337 KiB   </span> <a href="/2673-9984/9/1/21/pdf?version=1704439943" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Flow Annealed Kalman Inversion for Gradient-Free Inference in Bayesian Inverse Problems" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/21">Flow Annealed Kalman Inversion for Gradient-Free Inference in Bayesian Inverse Problems</a> <div class="authors"> by <span class="inlineblock "><strong>Richard D. P. Grumitt</strong>, </span><span class="inlineblock "><strong>Minas Karamanis</strong> and </span><span class="inlineblock "><strong>Uroš Seljak</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 21; <a href="https://doi.org/10.3390/psf2023009021">https://doi.org/10.3390/psf2023009021</a> - 4 Jan 2024 </div> Viewed by 744 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> For many scientific inverse problems, we are required to evaluate an expensive forward model. Moreover, the model is often given in such a form that it is unrealistic to access its gradients. In such a scenario, standard Markov Chain Monte Carlo algorithms quickly <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/21/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> For many scientific inverse problems, we are required to evaluate an expensive forward model. Moreover, the model is often given in such a form that it is unrealistic to access its gradients. In such a scenario, standard Markov Chain Monte Carlo algorithms quickly become impractical, requiring a large number of serial model evaluations to converge on the target distribution. In this paper, we introduce Flow Annealed Kalman Inversion (FAKI). This is a generalization of Ensemble Kalman Inversion (EKI) where we embed the Kalman filter updates in a temperature annealing scheme and use normalizing flows (NFs) to map the intermediate measures corresponding to each temperature level to the standard Gaussian. Thus, we relax the Gaussian ansatz for the intermediate measures used in standard EKI, allowing us to achieve higher-fidelity approximations to non-Gaussian targets. We demonstrate the performance of FAKI on two numerical benchmarks, showing dramatic improvements over standard EKI in terms of accuracy whilst accelerating its already rapid convergence properties (typically in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">O</mi><mo>(</mo><mn>10</mn><mo>)</mo></mrow></semantics></math></inline-formula> steps). <a href="/2673-9984/9/1/21">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/21/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1313677"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1313677"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1313677" data-cycle-prev="#prev1313677" data-cycle-progressive="#images1313677" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1313677-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-09-00021/article_deploy/html/images/psf-09-00021-g001-550.jpg?1704440031" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1313677" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1313677-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00021/article_deploy/html/images/psf-09-00021-g002-550.jpg?1704440034'><p>Figure 2</p></div></script></div></div><div id="article-1313677-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-09-00021/article_deploy/html/images/psf-09-00021-g001-550.jpg?1704440031" title=" <strong>Figure 1</strong><br/> <p>Pair plots for the Rosenbrock target. Panel (<b>a</b>): pair-plot comparison of samples from EKI and a long HMC run. Panel (<b>b</b>): pair-plot comparison of samples from FAKI and a long HMC run. Samples from FAKI were able to correctly capture the highly non-linear target geometry. Standard EKI struggled to fill the tails of the target and required ∼100 iterations to converge, compared to ∼34 iterations for FAKI.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/21'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00021/article_deploy/html/images/psf-09-00021-g002-550.jpg?1704440034" title=" <strong>Figure 2</strong><br/> <p>Comparison of first and second moment estimates along each dimension for the stochastic Lorenz system. Panel (<b>a</b>): comparison between the mean estimates from EKI and a long HMC run. Panel (<b>b</b>): comparison between the mean estimates from FAKI and a long HMC run. Panel (<b>c</b>): comparison between the standard deviation estimates from EKI and a long HMC run. Panel (<b>d</b>): comparison between the standard deviation estimates from FAKI and a long HMC run. Blue bars indicate the moment estimates obtained via HMC along each dimension, with the adjacent orange bars showing the estimates obtained through EKI/FAKI. EKI was unable to obtain accurate mean estimates for much of the <math display="inline"><semantics> <msub> <mi>Z</mi> <mi>t</mi> </msub> </semantics></math> trajectory, whilst FAKI was able to obtain accurate mean estimates for each dimension. FAKI outperformed EKI in its estimates of the marginal standard deviations, with EKI drastically overestimating the standard deviations along many dimensions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/21'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1313318" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 8 pages, 2635 KiB   </span> <a href="/2673-9984/9/1/20/pdf?version=1704936450" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Knowledge-Based Image Analysis: Bayesian Evidences Enable the Comparison of Different Image Segmentation Pipelines" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/20">Knowledge-Based Image Analysis: Bayesian Evidences Enable the Comparison of Different Image Segmentation Pipelines</a> <div class="authors"> by <span class="inlineblock "><strong>Mats Leif Moskopp</strong>, </span><span class="inlineblock "><strong>Andreas Deussen</strong> and </span><span class="inlineblock "><strong>Peter Dieterich</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 20; <a href="https://doi.org/10.3390/psf2023009020">https://doi.org/10.3390/psf2023009020</a> - 4 Jan 2024 </div> Viewed by 884 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The analysis and evaluation of microscopic image data is essential in life sciences. Increasing temporal and spatial digital image resolution and the size of data sets promotes the necessity of automated image analysis. Previously, our group proposed a Bayesian formalism that allows for <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/20/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The analysis and evaluation of microscopic image data is essential in life sciences. Increasing temporal and spatial digital image resolution and the size of data sets promotes the necessity of automated image analysis. Previously, our group proposed a Bayesian formalism that allows for converting the experimenter’s knowledge, in the form of a manually segmented image, into machine-readable probability distributions of the parameters of an image segmentation pipeline. This approach preserved the level of detail provided by expert knowledge and interobserver variability and has proven robust to a variety of recording qualities and imaging artifacts. In the present work, Bayesian evidences were used to compare different image processing pipelines. As an illustrative example, a microscopic phase contrast image of a wound healing assay and its manual segmentation by the experimenter (ground truth) are used. Six different variations of image segmentation pipelines are introduced. The aim was to find the image segmentation pipeline that is best to automatically segment the input image given the expert knowledge with respect to the principle of Occam’s razor to avoid unnecessary complexity and computation. While none of the introduced image segmentation pipelines fail completely, it is illustrated that assessing the quality of the image segmentation with the naked eye is not feasible. Bayesian evidence (and the intrinsically estimated uncertainty <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula> of the image segmentation) is used to choose the best image processing pipeline for the given image. This work illustrates a proof of principle and is extendable to a diverse range of image segmentation problems. <a href="/2673-9984/9/1/20">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/20/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1313318"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1313318"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1313318" data-cycle-prev="#prev1313318" data-cycle-progressive="#images1313318" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1313318-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-09-00020/article_deploy/html/images/psf-09-00020-g001-550.jpg?1704936537" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1313318" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1313318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00020/article_deploy/html/images/psf-09-00020-g002-550.jpg?1704936541'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1313318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00020/article_deploy/html/images/psf-09-00020-g003-550.jpg?1704936544'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1313318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00020/article_deploy/html/images/psf-09-00020-g004-550.jpg?1704936546'><p>Figure 4</p></div></script></div></div><div id="article-1313318-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-09-00020/article_deploy/html/images/psf-09-00020-g001-550.jpg?1704936537" title=" <strong>Figure 1</strong><br/> <p><b>Image data and manual segmentation.</b> (<b>A</b>) A typical region of interest on an image of a wound healing assay can be seen. (<b>B</b>) The same region of interest as in panel (<b>A</b>) is shown with enhanced contrast for illustrative purposes. (<b>C</b>) A manual image segmentation for a cell-free (black) and cell-covered (white) area is shown. (<b>D</b>) The boundary between the black and the white pixels of the manual segmentation is indicated by a green line. This boundary is essential for distances between manually segmented images and pipeline-segmented images (see below). (<b>E</b>) An overlay of the contrast-enhanced original image and the boundary is given for visual clarification.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/20'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00020/article_deploy/html/images/psf-09-00020-g002-550.jpg?1704936541" title=" <strong>Figure 2</strong><br/> <p><b>Image segmentation pipelines.</b> The image segmentation pipelines (Models 1–6) consist of a sequence of image filters and algorithms that depend on one (1P) or two (2P) parameters. Further, some algorithms are applied with a fixed set of parameters (0P), so that no free parameters were used during parameter estimation. Differences of the applied filters with respect to Model 1 are highlighted in yellow. The original image is displayed with enhanced contrast for illustrative purposes only—calculations and shown results are based on the native original image (see <a href="#psf-09-00020-f001" class="html-fig">Figure 1</a>A).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/20'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00020/article_deploy/html/images/psf-09-00020-g003-550.jpg?1704936544" title=" <strong>Figure 3</strong><br/> <p><b>Evaluation of the necessary number of live points.</b> This figure shows the results for estimated logarithmic evidence <math display="inline"><semantics> <mrow> <mi>l</mi> <mi>n</mi> <mo>(</mo> <mi>Z</mi> <mo>)</mo> </mrow> </semantics></math>, estimated uncertainty <math display="inline"><semantics> <mi>σ</mi> </semantics></math>, and the total number of likelihood evaluations (N) for three independent applications of the previously introduced formalism using either 20, 50, 100, 200, 400, or 800 live points. With 100 live points or more, the estimated evidences <math display="inline"><semantics> <mrow> <mi>l</mi> <mi>n</mi> <mo>(</mo> <mi>Z</mi> <mo>)</mo> </mrow> </semantics></math> and estimated uncertainties <math display="inline"><semantics> <mi>σ</mi> </semantics></math> remain stable. Of the tested values with stable results, 100 live points require the least likelihood evaluations and are therefore computationally the most effective. (Data are shown as mean and Bayesian uncertainty (error bars) of the posterior distribution. Each estimation was independently run three times to evaluate reproducibility. In <span class="html-italic">RUN 3</span> with 20 live points, results are <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>3095.7</mn> <mspace width="0.277778em"/> <mo>±</mo> <mspace width="0.277778em"/> <mn>1.2</mn> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>l</mi> <mi>n</mi> <mo>(</mo> <mi>Z</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>40.5</mn> <mspace width="0.277778em"/> <mo>±</mo> <mspace width="0.277778em"/> <mn>0.1</mn> <mspace width="0.277778em"/> <mi>p</mi> <mi>i</mi> <mi>x</mi> <mi>e</mi> <mi>l</mi> </mrow> </semantics></math> for <math display="inline"><semantics> <mi>σ</mi> </semantics></math>; both are off the charts. These extreme results indicate a failure due to very few live points. For illustrative purposes, they were not taken into consideration for the limits of the y-axes.)</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/20'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00020/article_deploy/html/images/psf-09-00020-g004-550.jpg?1704936546" title=" <strong>Figure 4</strong><br/> <p><b>Image segmentation for Models 1–6.</b> After applying the above-introduced formalism, estimated posterior parameters were used to obtain one pipeline-segmented image for Models 1–6. These images are overlays of the original input image (see <a href="#psf-09-00020-f001" class="html-fig">Figure 1</a>A). The pipeline-generated image is superimposed with dark red indicating a cell-free area and blue indicating a cell-covered area. The green line represents the boundary between a cell-free and a cell-covered area in the manually segmented image (see <a href="#psf-09-00020-f001" class="html-fig">Figure 1</a>D). The white box shows a region of interest, which is magnified in the lower part of the figure to magnify the details.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/20'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1299366" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 9 pages, 982 KiB   </span> <a href="/2673-9984/9/1/19/pdf?version=1702464380" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Inferring Evidence from Nested Sampling Data via Information Field Theory" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/19">Inferring Evidence from Nested Sampling Data via Information Field Theory</a> <div class="authors"> by <span class="inlineblock "><strong>Margret Westerkamp</strong>, </span><span class="inlineblock "><strong>Jakob Roth</strong>, </span><span class="inlineblock "><strong>Philipp Frank</strong>, </span><span class="inlineblock "><strong>Will Handley</strong> and </span><span class="inlineblock "><strong>Torsten Enßlin</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 19; <a href="https://doi.org/10.3390/psf2023009019">https://doi.org/10.3390/psf2023009019</a> - 13 Dec 2023 </div> <a href="/2673-9984/9/1/19#metrics">Cited by 1</a> | Viewed by 842 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Nested sampling provides an estimate of the evidence of a Bayesian inference problem via probing the likelihood as a function of the enclosed prior volume. However, the lack of precise values of the enclosed prior mass of the samples introduces probing noise, which <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/19/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Nested sampling provides an estimate of the evidence of a Bayesian inference problem via probing the likelihood as a function of the enclosed prior volume. However, the lack of precise values of the enclosed prior mass of the samples introduces probing noise, which can hamper high-accuracy determinations of the evidence values as estimated from the likelihood-prior-volume function. We introduce an approach based on information field theory, a framework for non-parametric function reconstruction from data, that infers the likelihood-prior-volume function by exploiting its smoothness and thereby aims to improve the evidence calculation. Our method provides posterior samples of the likelihood-prior-volume function that translate into a quantification of the remaining sampling noise for the evidence estimate, or for any other quantity derived from the likelihood-prior-volume function. <a href="/2673-9984/9/1/19">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/19/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1299366"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1299366"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1299366" data-cycle-prev="#prev1299366" data-cycle-progressive="#images1299366" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1299366-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-09-00019/article_deploy/html/images/psf-09-00019-g001-550.jpg?1702464442" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1299366" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1299366-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00019/article_deploy/html/images/psf-09-00019-g002-550.jpg?1702464443'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1299366-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00019/article_deploy/html/images/psf-09-00019-g003-550.jpg?1702464444'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1299366-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00019/article_deploy/html/images/psf-09-00019-g004-550.jpg?1702464444'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1299366-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00019/article_deploy/html/images/psf-09-00019-g005-550.jpg?1702464445'><p>Figure 5</p></div></script></div></div><div id="article-1299366-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-09-00019/article_deploy/html/images/psf-09-00019-g001-550.jpg?1702464442" title=" <strong>Figure 1</strong><br/> <p>(<b>Left</b>): Visualisation of the nested sampling dead point logarithmic likelihoods, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>d</mi> <mo stretchy="false">→</mo> </mover> <mi>L</mi> </msub> </semantics></math>, as a function of logarithmic prior mass data, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>d</mi> <mo stretchy="false">→</mo> </mover> <mi>X</mi> </msub> </semantics></math>, for the normalized simple Gaussian in Equation (<a href="#FD14-psf-09-00019" class="html-disp-formula">14</a>) (<math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>X</mi> </msub> <mo>=</mo> <mn>0.01</mn> <mo>,</mo> <mspace width="3.33333pt"/> <mi>D</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>). The corresponding data was generated by the software package anesthetic [<a href="#B9-psf-09-00019" class="html-bibr">9</a>]. (<b>Right</b>): Visualisation of the reparametrised nested sampling dead point logarithmic likelihoods according to Equation (<a href="#FD13-psf-09-00019" class="html-disp-formula">13</a>) as a function of logarithmic prior mass for the same case as shown left.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/19'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00019/article_deploy/html/images/psf-09-00019-g002-550.jpg?1702464443" title=" <strong>Figure 2</strong><br/> <p>Reconstruction prior samples of the likelihood-prior-volume function plotted together with the ground truth. (<b>Left</b>): Log-log-scale. (<b>Right</b>): Parametrisation according to Equation (<a href="#FD13-psf-09-00019" class="html-disp-formula">13</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/19'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00019/article_deploy/html/images/psf-09-00019-g003-550.jpg?1702464444" title=" <strong>Figure 3</strong><br/> <p>Reconstruction results for the likelihood-prior-volume function for the simple Gaussian example in Equation (<a href="#FD14-psf-09-00019" class="html-disp-formula">14</a>). The plots show the data, the ground truth and the reconstruction as well as its uncertainty. (<b>Left</b>): Reconstruction results on log-log-scale. (<b>Right</b>): Reconstruction results in reparametrised coordinates according to Equation (<a href="#FD13-psf-09-00019" class="html-disp-formula">13</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/19'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00019/article_deploy/html/images/psf-09-00019-g004-550.jpg?1702464444" title=" <strong>Figure 4</strong><br/> <p>Reconstruction results for the prior volumes given the likelihood data <math display="inline"><semantics> <msub> <mover accent="true"> <mi>d</mi> <mo stretchy="false">→</mo> </mover> <mi>L</mi> </msub> </semantics></math> for the simple Gaussian example in Equation (<a href="#FD14-psf-09-00019" class="html-disp-formula">14</a>). The plots show the data, the ground truth and the reconstruction as well as its uncertainty. (<b>Left</b>): Reconstruction results on log-log-scale. (<b>Right</b>): Reconstruction results in reparametrised coordinates according to Equation (<a href="#FD13-psf-09-00019" class="html-disp-formula">13</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/19'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00019/article_deploy/html/images/psf-09-00019-g005-550.jpg?1702464445" title=" <strong>Figure 5</strong><br/> <p>Comparison of histograms for logarithmic evidences for <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mi>samp</mi> </msub> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> samples for the classical nested sampling (NSL) approach and the reconstructed prior volumes.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/19'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1299176" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 10 pages, 7936 KiB   </span> <a href="/2673-9984/9/1/18/pdf?version=1702455939" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A BRAIN Study to Tackle Image Analysis with Artificial Intelligence in the ALMA 2030 Era" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/18">A BRAIN Study to Tackle Image Analysis with Artificial Intelligence in the ALMA 2030 Era</a> <div class="authors"> by <span class="inlineblock "><strong>Fabrizia Guglielmetti</strong>, </span><span class="inlineblock "><strong>Michele Delli Veneri</strong>, </span><span class="inlineblock "><strong>Ivano Baronchelli</strong>, </span><span class="inlineblock "><strong>Carmen Blanco</strong>, </span><span class="inlineblock "><strong>Andrea Dosi</strong>, </span><span class="inlineblock "><strong>Torsten Enßlin</strong>, </span><span class="inlineblock "><strong>Vishal Johnson</strong>, </span><span class="inlineblock "><strong>Giuseppe Longo</strong>, </span><span class="inlineblock "><strong>Jakob Roth</strong>, </span><span class="inlineblock "><strong>Felix Stoehr</strong>, </span><span class="inlineblock "><strong>Łukasz Tychoniec</strong> and </span><span class="inlineblock "><strong>Eric Villard</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 18; <a href="https://doi.org/10.3390/psf2023009018">https://doi.org/10.3390/psf2023009018</a> - 13 Dec 2023 </div> Viewed by 1059 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis, employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at the intersection of observational astronomy, statistics, algorithm development, and data science. In this <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/18/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis, employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at the intersection of observational astronomy, statistics, algorithm development, and data science. In this study, we provide evidence of the benefits of employing these approaches to ALMA imaging for operational and scientific purposes. We show the potential of two techniques, RESOLVE and DeepFocus, applied to ALMA-calibrated science data. Significant advantages are provided with the prospect to improve the quality and completeness of the data products stored in the science archive and the overall processing time for operations. Both approaches evidence the logical pathway to address the incoming revolution in data rates dictated by the planned electronic upgrades. Moreover, we bring to the community additional products through a new package, ALMASim, to promote advancements in these fields, providing a refined ALMA simulator usable by a large community for training and testing new algorithms. <a href="/2673-9984/9/1/18">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/18/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1299176"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1299176"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1299176" data-cycle-prev="#prev1299176" data-cycle-progressive="#images1299176" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1299176-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-09-00018/article_deploy/html/images/psf-09-00018-g001-550.jpg?1702456006" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1299176" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1299176-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00018/article_deploy/html/images/psf-09-00018-g002-550.jpg?1702456008'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1299176-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00018/article_deploy/html/images/psf-09-00018-g003-550.jpg?1702456008'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1299176-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00018/article_deploy/html/images/psf-09-00018-g004-550.jpg?1702456010'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1299176-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00018/article_deploy/html/images/psf-09-00018-g005-550.jpg?1702456012'><p>Figure 5</p></div></script></div></div><div id="article-1299176-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-09-00018/article_deploy/html/images/psf-09-00018-g001-550.jpg?1702456006" title=" <strong>Figure 1</strong><br/> <p>ALMA antennas on the Chajnantor plateau. Credit: ESO.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/18'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00018/article_deploy/html/images/psf-09-00018-g002-550.jpg?1702456008" title=" <strong>Figure 2</strong><br/> <p>Application of RESOLVE to Elias 27 from the DSHARP ALMA project at 240 GHz (1.25 mm) continuum. (<b>A</b>) The fiducial image as given by the DSHARP team [<a href="#B14-psf-09-00018" class="html-bibr">14</a>]. (<b>B</b>) RESOLVE mean sky map of Elias 27. (<b>C</b>) RESOLVE uncertainty map representation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/18'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00018/article_deploy/html/images/psf-09-00018-g003-550.jpg?1702456008" title=" <strong>Figure 3</strong><br/> <p>Comparison of processing time and computing throughput with tCLEAN and DeepFocus on <math display="inline"><semantics> <mrow> <mn>29</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics></math> archived cube data from cycles 7, 8, and 9. This represents a rough estimate because at this stage of development, it is challenging to make a robust comparison between the two techniques.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/18'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00018/article_deploy/html/images/psf-09-00018-g004-550.jpg?1702456010" title=" <strong>Figure 4</strong><br/> <p>Example of ALMA-simulated sources (dirty images) created with the ALMASim package: (<b>A</b>) point-like, (<b>B</b>) Gaussian shape, (<b>C</b>) extended, and (<b>D</b>) diffuse emissions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/18'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00018/article_deploy/html/images/psf-09-00018-g005-550.jpg?1702456012" title=" <strong>Figure 5</strong><br/> <p>Simplified visual explanation of the empirical approach to noise modeling. Different background and noise components measured at scales larger and shorter than the typical beam scale (central panels) are isolated from a real ALMA image (e.g., an ALMA calibrator (left panel)) and then added to a simulated image (right panel). In this example, we considered local fluctuations (center, bottom panel), a large-scale background (central panel), and high spatial frequency patterns (center, top panel). Instead of using a theoretical model to simulate noise and instrumental response, the same effects were directly measured from real observations obtained in comparable situations (telescope configuration and atmospheric conditions).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/18'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1295397" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 5 pages, 255 KiB   </span> <a href="/2673-9984/9/1/17/pdf?version=1701916375" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Snowballing Nested Sampling" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/17">Snowballing Nested Sampling</a> <div class="authors"> by <span class="inlineblock "><strong>Johannes Buchner</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 17; <a href="https://doi.org/10.3390/psf2023009017">https://doi.org/10.3390/psf2023009017</a> - 6 Dec 2023 </div> Viewed by 753 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> A new way to run nested sampling, combined with realistic MCMC proposals to generate new live points, is presented. Nested sampling is run with a fixed number of MCMC steps. Subsequently, snowballing nested sampling extends the run to more and more live points. <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/17/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> A new way to run nested sampling, combined with realistic MCMC proposals to generate new live points, is presented. Nested sampling is run with a fixed number of MCMC steps. Subsequently, snowballing nested sampling extends the run to more and more live points. This stabilizes the MCMC proposal of later MCMC proposals, and leads to pleasant properties, including that the number of live points and number of MCMC steps do not have to be calibrated, that the evidence and posterior approximation improve as more compute is added and can be diagnosed with convergence diagnostics from the MCMC community. Snowballing nested sampling converges to a “perfect” nested sampling run with an infinite number of MCMC steps. <a href="/2673-9984/9/1/17">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/17/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="absgraph cycle-slideshow"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1295397-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-09-00017/article_deploy/html/images/psf-09-00017-g001-550.jpg?1701916443" alt="" style="border: 0;"><p>Figure 1</p></div></div></div><div id="article-1295397-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-09-00017/article_deploy/html/images/psf-09-00017-g001-550.jpg?1701916443" title=" <strong>Figure 1</strong><br/> <p>Estimate of <math display="inline"><semantics> <mrow> <mo form="prefix">ln</mo> <mo>(</mo> <mi>Z</mi> <mo>)</mo> </mrow> </semantics></math> for each algorithm iteration.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/17'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1295387" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 9 pages, 285 KiB   </span> <a href="/2673-9984/9/1/16/pdf?version=1701914455" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Quantum Measurement and Objective Classical Reality" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/9/1/16">Quantum Measurement and Objective Classical Reality</a> <div class="authors"> by <span class="inlineblock "><strong>Vishal Johnson</strong>, </span><span class="inlineblock "><strong>Philipp Frank</strong> and </span><span class="inlineblock "><strong>Torsten Enßlin</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>9</em>(1), 16; <a href="https://doi.org/10.3390/psf2023009016">https://doi.org/10.3390/psf2023009016</a> - 6 Dec 2023 </div> Viewed by 857 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> We explore quantum measurement in the context of Everettian unitary quantum mechanics and construct an explicit unitary measurement procedure. We propose the existence of prior correlated states that enable this procedure to work and therefore argue that correlation is a resource that is <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/16/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> We explore quantum measurement in the context of Everettian unitary quantum mechanics and construct an explicit unitary measurement procedure. We propose the existence of prior correlated states that enable this procedure to work and therefore argue that correlation is a resource that is consumed when measurements take place. It is also argued that a network of such measurements establishes a stable objective classical reality. <a href="/2673-9984/9/1/16">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/9/1">The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/9/1/16/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1295387"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1295387"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1295387" data-cycle-prev="#prev1295387" data-cycle-progressive="#images1295387" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1295387-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g001-550.jpg?1701914609" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1295387" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1295387-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g002-550.jpg?1701914610'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1295387-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g003-550.jpg?1701914610'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1295387-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g004-550.jpg?1701914610'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1295387-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g005-550.jpg?1701914611'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1295387-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g006-550.jpg?1701914611'><p>Figure 6</p></div></script></div></div><div id="article-1295387-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g001-550.jpg?1701914609" title=" <strong>Figure 1</strong><br/> <p>A quantum system (the signal) is measured by an observer. Unitarity of quantum mechanics necessitates the involvement of another system (the environment) in order to facilitate quantum measurement.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/16'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g002-550.jpg?1701914610" title=" <strong>Figure 2</strong><br/> <p>Measurement procedure without environmental correction. The environment influences the measurement procedure as indicated by the term <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>∘</mo> <mi>i</mi> </mrow> </semantics></math>. The clouds indicate systems which are correlated with each other. Two clouds touching indicates that the quantum systems they are part of are correlated (and thereby entangled). The clouds are colored to distinguish between the different quantum systems participating in the measurement procedure.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/16'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g003-550.jpg?1701914610" title=" <strong>Figure 3</strong><br/> <p>Measurement procedure with environmental correction. The redundant information provided by the correlated environment is used for this correction. Also indicated are the measures of correlation in the involved subsystems.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/16'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g004-550.jpg?1701914610" title=" <strong>Figure 4</strong><br/> <p>A highly branched network of decohered states lends stability to the measurement of the signal, as there is a large amount of redundancy in the information. Also, for this information to be deleted, all the involved systems must conspire to come together and undo this correlation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/16'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g005-550.jpg?1701914611" title=" <strong>Figure 5</strong><br/> <p>Measurement in a different basis is no longer objective. Different observers may disagree on what consititutes reality. Transparent clouds over the same quantum system indicate its being observed by observers in different bases.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/16'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/psf/psf-09-00016/article_deploy/html/images/psf-09-00016-g006-550.jpg?1701914611" title=" <strong>Figure 6</strong><br/> <p>Due to the redundant information stored in the environment, it is possible to recover information about the environment and thereby allow for the signal to once again be determined objectively.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/9/1/16'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1294632" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 4 pages, 15553 KiB   </span> <a href="/2673-9984/8/1/71/pdf?version=1702363115" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Three-Dimensional Visualization of Astronomy Data Using Virtual Reality" data-journal="psf"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Proceeding Paper</span></div> <a class="title-link" href="/2673-9984/8/1/71">Three-Dimensional Visualization of Astronomy Data Using Virtual Reality</a> <div class="authors"> by <span class="inlineblock "><strong>Gilles Ferrand</strong></span> </div> <div class="color-grey-dark"> <em>Phys. Sci. Forum</em> <b>2023</b>, <em>8</em>(1), 71; <a href="https://doi.org/10.3390/psf2023008071">https://doi.org/10.3390/psf2023008071</a> - 5 Dec 2023 </div> Viewed by 908 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Visualization is an essential part of research, both to explore one’s data and to communicate one’s findings with others. Many data products in astronomy come in the form of multi-dimensional cubes, and since our brains are tuned for recognition in a 3D world, <a href="#" data-counterslink = "https://www.mdpi.com/2673-9984/8/1/71/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Visualization is an essential part of research, both to explore one’s data and to communicate one’s findings with others. Many data products in astronomy come in the form of multi-dimensional cubes, and since our brains are tuned for recognition in a 3D world, we ought to display and manipulate these in 3D space. This is possible with virtual reality (VR) devices. Drawing from our experiments developing immersive and interactive 3D experiences from actual science data at the Astrophysical Big Bang Laboratory (ABBL), this paper gives an overview of the opportunities and challenges that are awaiting astrophysicists in the burgeoning VR space. It covers both software and hardware matters, as well as practical aspects for successful delivery to the public. <a href="/2673-9984/8/1/71">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Proceedings of <a href="/2673-9984/8/1">The 23rd International Workshop on Neutrinos from Accelerators</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2673-9984/8/1/71/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="absgraph cycle-slideshow"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1294632-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/psf/psf-08-00071/article_deploy/html/images/psf-08-00071-g001-550.jpg?1702363207" alt="" style="border: 0;"><p>Figure 1</p></div></div></div><div id="article-1294632-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/psf/psf-08-00071/article_deploy/html/images/psf-08-00071-g001-550.jpg?1702363207" title=" <strong>Figure 1</strong><br/> <p>Collage of photos taken at the ABBL booth during RIKEN Open Day 2019. At the <b>top left</b> and <b>top right</b> one can see a snapshot of the evolution of the supernova remnant (volume-rendered); the <b>top center</b> panel shows the user interface to select iso-contours of elemental abundances in the supernova (meshes). Even though a flat display cannot convey the VR experience, having a monitor in the room is important so that everyone can have an idea of what is going on inside the headset. The <b>bottom right</b> photo illustrates the flow of the demo: on-boarding using info sheet, the VR navigation per se, and the questions/feedback corner.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2673-9984/8/1/71'>Full article</a></strong> "></a></div> </div> </div> <span class="more" style="display: none;"></span> </div> <div class="row footer"> <div class="listing-select-options"> <div class="columns small-12"> <div class="select generic-item"> <a href="#" class="export-options-show export-element export-expanded"> Show export options <i class="material-icons">expand_more</i> </a> <a href="#" class="export-options-show export-element"> Show export options <i class="material-icons">expand_less</i> </a> </div> <div class="listing-export-options export-element"> <div class="export-element" style="margin-top: 10px; margin-bottom: 10px;"> <input type="checkbox" class="selector selectUnselectAll bb-checkbox" id="selectUnselectAll" 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