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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.06203">arXiv:2405.06203</a> <span> [<a href="https://arxiv.org/pdf/2405.06203">pdf</a>, <a href="https://arxiv.org/format/2405.06203">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fonteles%2C+J">Joyce Fonteles</a>, <a href="/search/cs?searchtype=author&query=Davalos%2C+E">Eduardo Davalos</a>, <a href="/search/cs?searchtype=author&query=S.%2C+A+T">Ashwin T. S.</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yike Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+M">Mengxi Zhou</a>, <a href="/search/cs?searchtype=author&query=Ayalon%2C+E">Efrat Ayalon</a>, <a href="/search/cs?searchtype=author&query=Lane%2C+A">Alicia Lane</a>, <a href="/search/cs?searchtype=author&query=Steinberg%2C+S">Selena Steinberg</a>, <a href="/search/cs?searchtype=author&query=Anton%2C+G">Gabriella Anton</a>, <a href="/search/cs?searchtype=author&query=Danish%2C+J">Joshua Danish</a>, <a href="/search/cs?searchtype=author&query=Enyedy%2C+N">Noel Enyedy</a>, <a href="/search/cs?searchtype=author&query=Biswas%2C+G">Gautam Biswas</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.06203v1-abstract-short" style="display: inline;"> Investigating children's embodied learning in mixed-reality environments, where they collaboratively simulate scientific processes, requires analyzing complex multimodal data to interpret their learning and coordination behaviors. Learning scientists have developed Interaction Analysis (IA) methodologies for analyzing such data, but this requires researchers to watch hours of videos to extract and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.06203v1-abstract-full').style.display = 'inline'; document.getElementById('2405.06203v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.06203v1-abstract-full" style="display: none;"> Investigating children's embodied learning in mixed-reality environments, where they collaboratively simulate scientific processes, requires analyzing complex multimodal data to interpret their learning and coordination behaviors. Learning scientists have developed Interaction Analysis (IA) methodologies for analyzing such data, but this requires researchers to watch hours of videos to extract and interpret students' learning patterns. Our study aims to simplify researchers' tasks, using Machine Learning and Multimodal Learning Analytics to support the IA processes. Our study combines machine learning algorithms and multimodal analyses to support and streamline researcher efforts in developing a comprehensive understanding of students' scientific engagement through their movements, gaze, and affective responses in a simulated scenario. To facilitate an effective researcher-AI partnership, we present an initial case study to determine the feasibility of visually representing students' states, actions, gaze, affect, and movement on a timeline. Our case study focuses on a specific science scenario where students learn about photosynthesis. The timeline allows us to investigate the alignment of critical learning moments identified by multimodal and interaction analysis, and uncover insights into students' temporal learning progressions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.06203v1-abstract-full').style.display = 'none'; document.getElementById('2405.06203v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.04427">arXiv:2307.04427</a> <span> [<a href="https://arxiv.org/pdf/2307.04427">pdf</a>, <a href="https://arxiv.org/format/2307.04427">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Astrophysical Phenomena">astro-ph.HE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1126/science.adc9818">10.1126/science.adc9818 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Observation of high-energy neutrinos from the Galactic plane </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/cs?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/cs?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/cs?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/cs?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/cs?searchtype=author&query=Ahrens%2C+M">M. Ahrens</a>, <a href="/search/cs?searchtype=author&query=Alameddine%2C+J+M">J. M. Alameddine</a>, <a href="/search/cs?searchtype=author&query=Alves%2C+A+A">A. A. Alves Jr.</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/cs?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/cs?searchtype=author&query=Anderson%2C+T">T. Anderson</a>, <a href="/search/cs?searchtype=author&query=Anton%2C+G">G. Anton</a>, <a href="/search/cs?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/cs?searchtype=author&query=Ashida%2C+Y">Y. Ashida</a>, <a href="/search/cs?searchtype=author&query=Athanasiadou%2C+S">S. Athanasiadou</a>, <a href="/search/cs?searchtype=author&query=Axani%2C+S">S. Axani</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/cs?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/cs?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/cs?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/cs?searchtype=author&query=Baur%2C+S">S. Baur</a>, <a href="/search/cs?searchtype=author&query=Bay%2C+R">R. Bay</a>, <a href="/search/cs?searchtype=author&query=Beatty%2C+J+J">J. J. Beatty</a>, <a href="/search/cs?searchtype=author&query=Becker%2C+K+-">K. -H. Becker</a>, <a href="/search/cs?searchtype=author&query=Tjus%2C+J+B">J. Becker Tjus</a> , et al. (364 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.04427v1-abstract-short" style="display: inline;"> The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth's atmosphere, has been a mystery for over a century. Due to deflection in interstellar magnetic fields, cosmic rays from the Milky Way arrive at Earth from random directions. However, near their sources and during propagation, cosmic rays interact with matter and produce high-energy neutrinos. We search for neutrin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.04427v1-abstract-full').style.display = 'inline'; document.getElementById('2307.04427v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.04427v1-abstract-full" style="display: none;"> The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth's atmosphere, has been a mystery for over a century. Due to deflection in interstellar magnetic fields, cosmic rays from the Milky Way arrive at Earth from random directions. However, near their sources and during propagation, cosmic rays interact with matter and produce high-energy neutrinos. We search for neutrino emission using machine learning techniques applied to ten years of data from the IceCube Neutrino Observatory. We identify neutrino emission from the Galactic plane at the 4.5$蟽$ level of significance, by comparing diffuse emission models to a background-only hypothesis. The signal is consistent with modeled diffuse emission from the Galactic plane, but could also arise from a population of unresolved point sources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.04427v1-abstract-full').style.display = 'none'; document.getElementById('2307.04427v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted on May 12th, 2022; Accepted on May 4th, 2023</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Science 380, 6652, 1338-1343 (2023) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.06311">arXiv:2303.06311</a> <span> [<a href="https://arxiv.org/pdf/2303.06311">pdf</a>, <a href="https://arxiv.org/format/2303.06311">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/1748-0221/18/06/P06005">10.1088/1748-0221/18/06/P06005 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Generative Adversarial Networks for Scintillation Signal Simulation in EXO-200 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+S">S. Li</a>, <a href="/search/cs?searchtype=author&query=Ostrovskiy%2C+I">I. Ostrovskiy</a>, <a href="/search/cs?searchtype=author&query=Li%2C+Z">Z. Li</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+L">L. Yang</a>, <a href="/search/cs?searchtype=author&query=Kharusi%2C+S+A">S. Al Kharusi</a>, <a href="/search/cs?searchtype=author&query=Anton%2C+G">G. Anton</a>, <a href="/search/cs?searchtype=author&query=Badhrees%2C+I">I. Badhrees</a>, <a href="/search/cs?searchtype=author&query=Barbeau%2C+P+S">P. S. Barbeau</a>, <a href="/search/cs?searchtype=author&query=Beck%2C+D">D. Beck</a>, <a href="/search/cs?searchtype=author&query=Belov%2C+V">V. Belov</a>, <a href="/search/cs?searchtype=author&query=Bhatta%2C+T">T. Bhatta</a>, <a href="/search/cs?searchtype=author&query=Breidenbach%2C+M">M. Breidenbach</a>, <a href="/search/cs?searchtype=author&query=Brunner%2C+T">T. Brunner</a>, <a href="/search/cs?searchtype=author&query=Cao%2C+G+F">G. F. Cao</a>, <a href="/search/cs?searchtype=author&query=Cen%2C+W+R">W. R. Cen</a>, <a href="/search/cs?searchtype=author&query=Chambers%2C+C">C. Chambers</a>, <a href="/search/cs?searchtype=author&query=Cleveland%2C+B">B. Cleveland</a>, <a href="/search/cs?searchtype=author&query=Coon%2C+M">M. Coon</a>, <a href="/search/cs?searchtype=author&query=Craycraft%2C+A">A. Craycraft</a>, <a href="/search/cs?searchtype=author&query=Daniels%2C+T">T. Daniels</a>, <a href="/search/cs?searchtype=author&query=Darroch%2C+L">L. Darroch</a>, <a href="/search/cs?searchtype=author&query=Daugherty%2C+S+J">S. J. Daugherty</a>, <a href="/search/cs?searchtype=author&query=Davis%2C+J">J. Davis</a>, <a href="/search/cs?searchtype=author&query=Delaquis%2C+S">S. Delaquis</a>, <a href="/search/cs?searchtype=author&query=Der+Mesrobian-Kabakian%2C+A">A. Der Mesrobian-Kabakian</a> , et al. (65 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.06311v2-abstract-short" style="display: inline;"> Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial N… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.06311v2-abstract-full').style.display = 'inline'; document.getElementById('2303.06311v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.06311v2-abstract-full" style="display: none;"> Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network - a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.06311v2-abstract-full').style.display = 'none'; document.getElementById('2303.06311v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">As accepted by JINST</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> JINST 18 P06005 2023 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.03042">arXiv:2209.03042</a> <span> [<a href="https://arxiv.org/pdf/2209.03042">pdf</a>, <a href="https://arxiv.org/format/2209.03042">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/1748-0221/17/11/P11003">10.1088/1748-0221/17/11/P11003 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/cs?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/cs?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/cs?searchtype=author&query=Aggarwal%2C+N">N. Aggarwal</a>, <a href="/search/cs?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/cs?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/cs?searchtype=author&query=Ahrens%2C+M">M. Ahrens</a>, <a href="/search/cs?searchtype=author&query=Alameddine%2C+J+M">J. M. Alameddine</a>, <a href="/search/cs?searchtype=author&query=Alves%2C+A+A">A. A. Alves Jr.</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/cs?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/cs?searchtype=author&query=Anderson%2C+T">T. Anderson</a>, <a href="/search/cs?searchtype=author&query=Anton%2C+G">G. Anton</a>, <a href="/search/cs?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/cs?searchtype=author&query=Ashida%2C+Y">Y. Ashida</a>, <a href="/search/cs?searchtype=author&query=Athanasiadou%2C+S">S. Athanasiadou</a>, <a href="/search/cs?searchtype=author&query=Axani%2C+S">S. Axani</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/cs?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/cs?searchtype=author&query=Baricevic%2C+M">M. Baricevic</a>, <a href="/search/cs?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/cs?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/cs?searchtype=author&query=Bay%2C+R">R. Bay</a>, <a href="/search/cs?searchtype=author&query=Beatty%2C+J+J">J. J. Beatty</a>, <a href="/search/cs?searchtype=author&query=Becker%2C+K+-">K. -H. Becker</a> , et al. (359 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2209.03042v3-abstract-short" style="display: inline;"> IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.03042v3-abstract-full').style.display = 'inline'; document.getElementById('2209.03042v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.03042v3-abstract-full" style="display: none;"> IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1-100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed false positive rate (FPR), compared to current IceCube methods. Alternatively, the GNN offers a reduction of the FPR by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%-20% compared to current maximum likelihood techniques in the energy range of 1-30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.03042v3-abstract-full').style.display = 'none'; document.getElementById('2209.03042v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Prepared for submission to JINST</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.00444">arXiv:2207.00444</a> <span> [<a href="https://arxiv.org/pdf/2207.00444">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Implicit adaptation of mesh model of transient heat conduction problem </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Petr%2C+Z">Zhukov Petr</a>, <a href="/search/cs?searchtype=author&query=Anton%2C+G">Glushchenko Anton</a>, <a href="/search/cs?searchtype=author&query=Andrey%2C+F">Fomin Andrey</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.00444v1-abstract-short" style="display: inline;"> Considering high-temperature heating, the equations of transient heat conduction model require an adaptation, i.e. the dependence of thermophysical parameters of the model on the temperature is to be identified for each specific material to be heated. This problem is most often solved by approximation of the tabular data on the measurements of the required parameters, which can be found in the lit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.00444v1-abstract-full').style.display = 'inline'; document.getElementById('2207.00444v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.00444v1-abstract-full" style="display: none;"> Considering high-temperature heating, the equations of transient heat conduction model require an adaptation, i.e. the dependence of thermophysical parameters of the model on the temperature is to be identified for each specific material to be heated. This problem is most often solved by approximation of the tabular data on the measurements of the required parameters, which can be found in the literature, by means of regression equations. But, for example, considering the steel heating process, this approach is difficult to be implemented due to the lack of tabular discrete measurements for many grades of steel, such as alloyed ones. In this paper, the new approach is proposed, which is based on a solution of a related variational problem. Its main idea is to substitute the adaptation process in the classical sense (i.e., to find the dependencies of thermophysical parameters on temperature) with 'supervised learning' of a mesh model on the basis of the technological data received from the plant. The equations to adjust the parameters of the transient heat conduction model, which are related to the thermophysical coefficients, have been derived. A numerical experiment is conducted for steel of a particular group of grades, for which enough both technological as well as tabular data are available. As a result, the 'trained' mesh model, which has not received explicitly any information about the physical and chemical properties of the heated substance, demonstrated an average error of 18.820 C, which is quite close to the average error of the model adapted classically on the basis of the tabular data (18.10 C). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.00444v1-abstract-full').style.display = 'none'; document.getElementById('2207.00444v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">in Russian language</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.11589">arXiv:2101.11589</a> <span> [<a href="https://arxiv.org/pdf/2101.11589">pdf</a>, <a href="https://arxiv.org/format/2101.11589">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1088/1748-0221/16/07/P07041">10.1088/1748-0221/16/07/P07041 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abbasi%2C+R">R. Abbasi</a>, <a href="/search/cs?searchtype=author&query=Ackermann%2C+M">M. Ackermann</a>, <a href="/search/cs?searchtype=author&query=Adams%2C+J">J. Adams</a>, <a href="/search/cs?searchtype=author&query=Aguilar%2C+J+A">J. A. Aguilar</a>, <a href="/search/cs?searchtype=author&query=Ahlers%2C+M">M. Ahlers</a>, <a href="/search/cs?searchtype=author&query=Ahrens%2C+M">M. Ahrens</a>, <a href="/search/cs?searchtype=author&query=Alispach%2C+C">C. Alispach</a>, <a href="/search/cs?searchtype=author&query=Alves%2C+A+A">A. A. Alves Jr.</a>, <a href="/search/cs?searchtype=author&query=Amin%2C+N+M">N. M. Amin</a>, <a href="/search/cs?searchtype=author&query=An%2C+R">R. An</a>, <a href="/search/cs?searchtype=author&query=Andeen%2C+K">K. Andeen</a>, <a href="/search/cs?searchtype=author&query=Anderson%2C+T">T. Anderson</a>, <a href="/search/cs?searchtype=author&query=Ansseau%2C+I">I. Ansseau</a>, <a href="/search/cs?searchtype=author&query=Anton%2C+G">G. Anton</a>, <a href="/search/cs?searchtype=author&query=Arg%C3%BCelles%2C+C">C. Arg眉elles</a>, <a href="/search/cs?searchtype=author&query=Axani%2C+S">S. Axani</a>, <a href="/search/cs?searchtype=author&query=Bai%2C+X">X. Bai</a>, <a href="/search/cs?searchtype=author&query=V.%2C+A+B">A. Balagopal V.</a>, <a href="/search/cs?searchtype=author&query=Barbano%2C+A">A. Barbano</a>, <a href="/search/cs?searchtype=author&query=Barwick%2C+S+W">S. W. Barwick</a>, <a href="/search/cs?searchtype=author&query=Bastian%2C+B">B. Bastian</a>, <a href="/search/cs?searchtype=author&query=Basu%2C+V">V. Basu</a>, <a href="/search/cs?searchtype=author&query=Baum%2C+V">V. Baum</a>, <a href="/search/cs?searchtype=author&query=Baur%2C+S">S. Baur</a>, <a href="/search/cs?searchtype=author&query=Bay%2C+R">R. Bay</a> , et al. (343 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.11589v2-abstract-short" style="display: inline;"> Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.11589v2-abstract-full').style.display = 'inline'; document.getElementById('2101.11589v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.11589v2-abstract-full" style="display: none;"> Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction method based on convolutional architectures and hexagonally shaped kernels is presented. The presented method is robust towards systematic uncertainties in the simulation and has been tested on experimental data. In comparison to standard reconstruction methods in IceCube, it can improve upon the reconstruction accuracy, while reducing the time necessary to run the reconstruction by two to three orders of magnitude. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.11589v2-abstract-full').style.display = 'none'; document.getElementById('2101.11589v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">39 pages, 15 figures, submitted to Journal of Instrumentation; added references</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> JINST 16 (2021) P07041 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.04457">arXiv:1811.04457</a> <span> [<a href="https://arxiv.org/pdf/1811.04457">pdf</a>, <a href="https://arxiv.org/format/1811.04457">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> </div> <p class="title is-5 mathjax"> A 3-D Projection Model for X-ray Dark-field Imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hu%2C+S">Shiyang Hu</a>, <a href="/search/cs?searchtype=author&query=Felsner%2C+L">Lina Felsner</a>, <a href="/search/cs?searchtype=author&query=Maier%2C+A">Andreas Maier</a>, <a href="/search/cs?searchtype=author&query=Ludwig%2C+V">Veronika Ludwig</a>, <a href="/search/cs?searchtype=author&query=Anton%2C+G">Gisela Anton</a>, <a href="/search/cs?searchtype=author&query=Riess%2C+C">Christian Riess</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1811.04457v2-abstract-short" style="display: inline;"> Talbot-Lau X-ray phase-contrast imaging is a novel imaging modality, which provides not only an X-ray absorption image, but also additionally a differential phase image and a dark-field image. The dark-field image is related to small angle scattering and has an interesting property when canning oriented structures: the recorded signal depends on the relative orientation of the structure in the ima… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.04457v2-abstract-full').style.display = 'inline'; document.getElementById('1811.04457v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.04457v2-abstract-full" style="display: none;"> Talbot-Lau X-ray phase-contrast imaging is a novel imaging modality, which provides not only an X-ray absorption image, but also additionally a differential phase image and a dark-field image. The dark-field image is related to small angle scattering and has an interesting property when canning oriented structures: the recorded signal depends on the relative orientation of the structure in the imaging system. Exactly this property allows to draw conclusions about the orientation and to reconstruct the structure. However, the reconstruction is a complex, non-trivial challenge. A lot of research was conducted towards this goal in the last years and several reconstruction algorithms were proposed. A key step of the reconstruction algorithm is the inversion of a forward projection model. Up until now, only 2-D projection models are available, with effectively limit the scanning trajectory to a 2-D plane. To obtain true 3-D information, this limitation requires to combine several 2-D scans, which leads to quite complex, impractical acquisitions schemes. Furthermore, it is not possible with these models to use 3-D trajectories that might allow simpler protocols, like for example a helical trajectory. To address these limitations, we propose in this work a very general 3-D projection model. Our projection model defines the dark-field signal dependent on an arbitrarily chosen ray and sensitivity direction. We derive the projection model under the assumption that the observed scatter distribution has a Gaussian shape. We theoretically show the consistency of our model with more constrained existing 2-D models. Furthermore, we experimentally show the compatibility of our model with dark-field measurements of two matchsticks. We believe that this 3-D projection model is an important step towards more flexible trajectories and imaging protocols that are much better applicable in practice. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.04457v2-abstract-full').style.display = 'none'; document.getElementById('1811.04457v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Shiyang Hu and Lina Felsner contributed equally to this work</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1104.3248">arXiv:1104.3248</a> <span> [<a href="https://arxiv.org/pdf/1104.3248">pdf</a>, <a href="https://arxiv.org/ps/1104.3248">ps</a>, <a href="https://arxiv.org/format/1104.3248">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Methods for Astrophysics">astro-ph.IM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.nima.2010.11.016">10.1016/j.nima.2010.11.016 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Signal Classification for Acoustic Neutrino Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Neff%2C+M">M. Neff</a>, <a href="/search/cs?searchtype=author&query=Anton%2C+G">G. Anton</a>, <a href="/search/cs?searchtype=author&query=Enzenh%C3%B6fer%2C+A">A. Enzenh枚fer</a>, <a href="/search/cs?searchtype=author&query=Graf%2C+K">K. Graf</a>, <a href="/search/cs?searchtype=author&query=H%C3%B6%C3%9Fl%2C+J">J. H枚脽l</a>, <a href="/search/cs?searchtype=author&query=Katz%2C+U">U. Katz</a>, <a href="/search/cs?searchtype=author&query=Lahmann%2C+R">R. Lahmann</a>, <a href="/search/cs?searchtype=author&query=Richardt%2C+C">C. Richardt</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1104.3248v1-abstract-short" style="display: inline;"> This article focuses on signal classification for deep-sea acoustic neutrino detection. In the deep sea, the background of transient signals is very diverse. Approaches like matched filtering are not sufficient to distinguish between neutrino-like signals and other transient signals with similar signature, which are forming the acoustic background for neutrino detection in the deep-sea environment… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1104.3248v1-abstract-full').style.display = 'inline'; document.getElementById('1104.3248v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1104.3248v1-abstract-full" style="display: none;"> This article focuses on signal classification for deep-sea acoustic neutrino detection. In the deep sea, the background of transient signals is very diverse. Approaches like matched filtering are not sufficient to distinguish between neutrino-like signals and other transient signals with similar signature, which are forming the acoustic background for neutrino detection in the deep-sea environment. A classification system based on machine learning algorithms is analysed with the goal to find a robust and effective way to perform this task. For a well-trained model, a testing error on the level of one percent is achieved for strong classifiers like Random Forest and Boosting Trees using the extracted features of the signal as input and utilising dense clusters of sensors instead of single sensors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1104.3248v1-abstract-full').style.display = 'none'; document.getElementById('1104.3248v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 April, 2011; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2011. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 Pages, 6 Figures, ARENA 2010 Conference Proceedings</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" 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