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arXiv 2022年

作者： Jelescu, Ileana O. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

arXiv

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

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Joint searches by FACT, H.E.S.S., MAGIC and VERITAS for VHE gamma-ray emission associated with neutrinos detected by IceCube

arXiv

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arXiv 2023年

作者： Schüssler, Fabian Ashkar, H. Bernardini, E. Berti, A. Bradascio, F. Buson, S. Dorner, D. Jin, W. Mezek, G. Kukec Santander, M. Satalecka, K. Schleicher, B. Senniappan, M. Viale, I. Abhir, J. Baack, D. Balbo, M. Biland, A. Brand, K. Bretz, T. Buss, J. Dorner, D. Eisenberger, L. Elsaesser, D. Günther, P. Hildebrand, D. Mannheim, K. Noethe, M. Paravac, A. Rhode, W. Schleicher, B. Sliusar, V. Hasan, S. Walter, R. Aharonian, F. Benkhali, F. Ait Alkan, A. Aschersleben, J. Ashkar, H. Backes, M. Baktash, A. Martins, V. Barbosa Barnacka, A. Barnard, J. Batzofin, R. Becherini, Y. Beck, G. Berge, D. Bernlöhr, K. Bi, B. Böttcher, M. Boisson, C. Bolmont, J. de Bony de Lavergne, M. Borowska, J. Bouyahiaoui, M. Bradascio, F. Breuhaus, M. Brose, R. Brown, A. Brun, F. Bruno, B. Bulik, T. Burger-Scheidlin, C. Bylund, T. Cangemi, F. Caroff, S. Casanova, S. Cecil, R. Celic, J. Cerruti, M. Chambery, P. Chand, T. Chandra, S. Chen, A. Chibueze, J. Chibueze, O. Collins, T. Cotter, G. Cristofari, P. Mbarubucyeye, J. Damascene Davids, I.D. Davies, J. de Jonge, L. Devin, J. Djannati-Ataï, A. Dmytriiev, A. Doroshenko, V. Dreyer, L. Du Plessis, L. Egberts, K. Einecke, S. Ernenwein, J.-P. Fegan, S. Feijen, K. de Clairfontaine, G. Fichet Fontaine, G. Lott, F. Füßling, M. Funk, S. Gabici, S. Gallant, Y.A. Ghafourizadeh, S. Giavitto, G. Giunti, L. Glawion, D. Glicenstein, J.F. Glombitza, J. Goswami, P. Grolleron, G. Grondin, M.-H. Haerer, L. Hattingh, S. Haupt, M. Hermann, G. Hinton, J.A. Hofmann, W. Holch, T.L. Holler, M. Horns, D. Huang, Zhiqiu Jaitly, A. Jamrozy, M. Jankowsky, F. Jardin-Blicq, A. Joshi, V. Jung-Richardt, I. Kasai, E. Katarzyński, K. Katjaita, H. Khangulyan, D. Khatoon, R. Khélifi, B. Klepser, S. Kluźniak, W. Komin, Nu. Konno, R. Kosack, K. Kostunin, D. Kundu, A. Lamanna, G. Lang, R.G. Le Stum, S. Lefranc, V. Leitl, F. Lemière, A. Lemoine-Goumard, M. Lenain, J.-P. Leuschner, F. Luashvili, A. Lypova, I. Mackey, J. Malyshev, D. IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Laboratoire Leprince-Ringuet Ecole Polytechnique CNRS Institut Polytechnique de Paris Palaiseau France Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Padova Italy Max-Planck-Institut für Physik München München Germany Julius-Maximilians-Universität Würzburg Fakultät für Physik und Astronomie Institut für Theoretische Physik und Astrophysik Lehrstuhl für Astronomie Emil-Fischer-Str. 31 WürzburgD-97074 Germany ETH Zurich Institute for Particle Physics and Astrophysics Otto-Stern-Weg 5 Zürich8093 Switzerland Department of Physics and Astronomy University of Alabama TuscaloosaAL United States Department of Physics and Electrical Engineering Linnaeus University Växjö Sweden Zeuthen Germany Institute for Particle Physics and Astrophysics Otto-Stern-Weg 5 Zürich8093 Switzerland TU Dortmund Experimental Physics 5 Otto-Hahn-Str. 4a Dortmund44227 Germany University of Geneva Department of Astronomy Chemin d’Ecogia 16 Versoix1290 Switzerland Fakultät für Physik und Astronomie Institut für Theoretische Physik und Astrophysik Lehrstuhl für Astronomie Emil-Fischer-Str. 31 WürzburgD-97074 Germany RWTH Aachen University Germany Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut für Kernphysik P.O. Box 103980 HeidelbergD 69029 Germany Yerevan State University 1 Alek Manukyan St Yerevan0025 Armenia Landessternwarte Universität Heidelberg Königstuhl HeidelbergD 69117 Germany IRFU CEA Université Paris-Saclay Gif-sur-YvetteF-91191 France Kapteyn Astronomical Institute University of Groningen Landleven 12 Groningen9747 AD Netherlands Laboratoire Leprince-Ringuet École Polytechnique CNRS Institut Polytechnique de Paris PalaiseauF-91128 France University of Namibia Department of Physics Private Bag 13301 Windhoek10005 Namibia Centre for Space Research North-West University Potchefstroom2520 South Africa Universität Hambu

The sources of the astrophysical flux of high-energy neutrinos detected by IceCube are still largely unknown, but searches for temporal and spatial correlation between neutrinos and electromagnetic radiation are a promising approach in this endeavor. All major imaging atmospheric Cherenkov telescopes (IACTs) - FACT, H.E.S.S., MAGIC, and VERITAS - operate an active follow-up program of target-of-opportunity observations of neutrino alerts issued by IceCube. These programs use several complementary neutrino alert streams. A publicly distributed alert stream is formed by individual high-energy neutrino candidate events of potentially astrophysical origin, such as IceCube-170922A (which could be linked to the flaring blazar TXS 0506+056). A privately distributed alert stream is formed by clusters of neutrino events in time and space around either pre-selected gamma-ray sources or anywhere in the sky. Here, we present joint searches for multi-wavelength emission associated with a set of IceCube alerts, both private and public, received through mid-January 2021. We will give an overview of the programs of the participating IACTs. We will showcase the various follow-up and data analysis strategies employed in response to the different alert types and various possible counterpart scenarios. Finally, we will present results from a combined analysis of the VHE gamma-ray observations obtained across all involved instruments, as well as relevant multi-wavelength data. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

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Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array 7

Event reconstruction using pattern spectra and convolutional...

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7th Heidelberg International Symposium on High-Energy Gamma-Ray Astronomy, Gamma 2022

作者： Wilkinson, M.H.F. Peletier, R.F. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, V. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, V. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, V. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Aschersleben, J. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, V. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, V. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, V. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, V. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Breen, S. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O’Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU/DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d’Informació Científica Edifici D Carrer de l’Albareda Cerdanyola del Vallès Bellaterrra08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/ Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin02 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue du Général-Dufour Genève1211 4 Switzerland INFN Dipartimento di Scienze Fisiche e Chimiche U

The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. The application of pattern spectra on a CNN allows the selection of relevant combinations of features within an image. In this work, we generate pattern spectra from simulated gamma-ray images to train a CNN for signal-background separation and energy reconstruction for CTA. We compare our results to a CNN trained with CTA images and find that the pattern spectra-based analysis is computationally less expensive but not competitive with the purely CTA images-based analysis. Thus, we conclude that the CNN must rely on additional features in the CTA images not captured by the pattern spectra. © Copyright owned by the author(s)

关键词： Cosmology

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Relaxation methods for co-simulation of finite element and circuit solvers 17

Relaxation methods for co-simulation of finite element and c...

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17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016

作者： De Dieu Nshimiyimana, Jean Dular, Patrick Gyselinck, Johan Geuzaine, Christophe Dept. of Electrical Engineering and Computer Science University of Liege ULG Belgium Ecole Polytechnique de Bruxelles Université Libre de Bruxelles ULB Belgium

ISBN: (纸本)9781509010325

This paper focuses on a comparison of relaxation methods for the co-simulation schemes for coupled problems with application to coupled electromagnetic field and power-electronic simulations. A co-simulation framework between a finite element (FE) and circuit (CIR) solvers is done using the multilevel-Newton waveform relaxation (WR) schemes whose convergence depends of the kind of transmission coupling conditions (TCCs) on the interface between the sub-problems. The convergence of TCCs is studied on a boost and an LLC resonant converters. © 2016 IEEE.

关键词： Finite element method

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Co-simulation of of finite element and circuit solvers using optimized waveform relaxation

Co-simulation of of finite element and circuit solvers using...

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2016 IEEE International Energy Conference, ENERGYCON 2016

作者： De Dieu Nshimiyimana, Jean Plumier, Frederic Dular, Patrick Geuzaine, Christophe Gyselinck, Johan Dept. of Electrical Engineering and Computer Science University of Liege ULG Belgium Ecole Polytechnique de Bruxelles Université Libre de Bruxelles ULB Belgium

ISBN: (纸本)9781467384636

This paper focuses on a comparison of the co-simulation schemes for coupled problems with application to coupled electromagnetic field and power-electronic simulations. A co-simulation framework between a finite element solver and a circuit solver based on the waveform relaxation scheme results in an iterative process whose convergence depends on the kind of transmission coupling conditions (TCC) on the interface between the sub-problems. The convergence of TCC is illustrated on two test cases: A boost converter and a switched reluctance motor drive. © 2016 IEEE.

关键词： Finite element method

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Co-simulation of of finite element and circuit solvers using optimized waveform relaxation

Co-simulation of of finite element and circuit solvers using...

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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Jean de Dieu Nshimiyimana Frédéric Plumier Patrick Dular Christophe Geuzaine Johan Gyselinck Dept. of Electrical Engineering and Computer Science ULG Belgium Ecole Polytechnique de Bruxelles ULB Belgium

ISBN: (纸本)9781467384643

This paper focuses on a comparison of the co-simulation schemes for coupled problems with application to coupled electromagnetic field and power-electronic simulations. A co-simulation framework between a finite element solver and a circuit solver based on the waveform relaxation scheme results in an iterative process whose convergence depends on the kind of transmission coupling conditions (TCC) on the interface between the sub-problems. The convergence of TCC is illustrated on two test cases: a boost converter and a switched reluctance motor drive.

关键词： Mathematical model Convergence Iron Couplings Integrated circuit modeling Indexes Magnetic circuits

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Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data 37

Application of pattern spectra and convolutional neural netw...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Aschersleben, J. Peletier, R.F. Vecchi, M. Wilkinson, M.H.F. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, V. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, V. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, V. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, V. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, V. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, V. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, V. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O'Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d'Informació Científica Edifici D Carrer de l'Albareda Bellaterrra Cerdanyola del Vallès08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS/IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL Department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM/CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue d

The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 − 10 × better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for convolutional neural networks (CNNs) to determine the energy of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA. Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is different from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Gamma rays

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On the LP-convergence of a Girsanov theorem based particle filter

On the LP-convergence of a Girsanov theorem based particle f...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Simo Särkkä Eric Moulines Dept. of Electrical Engineering and Automation Aalto University Finland Center of Applied Mathematics Ecole Polytechnique France

We analyze the L p -convergence of a previously proposed Girsanov theorem based particle filter for discretely observed stochastic differential equation (SDE) models. We prove the convergence of the algorithm with the number of particles tending to infinity by requiring a moment condition and a step-wise initial condition boundedness for the stochastic exponential process giving the likelihood ratio of the SDEs. The practical implications of the condition are illustrated with an Ornstein-Uhlenbeck model and with a non-linear Benes model.

关键词： Atmospheric measurements Particle measurements Stochastic processes Mathematical model Density measurement Kernel Differential equations

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Relaxation methods for co-simulation of finite element and circuit solvers

Relaxation methods for co-simulation of finite element and c...

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IEEE Conference on Electromagnetic Field Computation

作者： Jean de Dieu Nshimiyimana Patrick Dular Johan Gyselinck Christophe Geuzaine Dept. of Electrical Engineering and Computer Science University of Liege ULG Belgium Ecole Polytechnique de Bruxelles Université Libre de Bruxelles ULB Belgium

ISBN: (纸本)9781509010332

关键词： Convergence Mathematical model Relaxation methods Biological system modeling Computational modeling Integrated circuit modeling Couplings

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