检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Lee, Chia-Hao Khan, Abid Luo, Di Santos, Tatiane P. Shi, Chuqiao Janicek, Blanka E. Kang, Sangmin Zhu, Wenjuan Sobh, Nahil A. Schleife, André Clark, Bryan K. Huang, Pinshane Y. Department of Materials Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL61801 United States Materials Research Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States

2D materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so-far limited electron microscopy methods to probe these atomic-scale strain fields. Here, we demonstrate an approach to probe single-atom defects with sub-picometer precision in a monolayer 2D transition metal dichalcogenide, WSe2-2xTe2x. We utilize deep learning to mine large datasets of aberration-corrected scanning transmission electron microscopy images to locate and classify point defects. By combining hundreds of images of nominally identical defects, we generate high signal-to-noise class-averages which allow us to measure 2D atomic coordinates with up to 0.3 pm precision. Our methods reveal that Se vacancies introduce complex, oscillating strain fields in the WSe2-2xTe2xlattice which cannot be explained by continuum elastic theory. These results indicate the potential impact of computer vision for the development of high-precision electron microscopy methods for beam-sensitive materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Transition metals

来源：评论

学校读者我要写书评

暂无评论

Filamentary Network and Magnetic Field Structures Revealed with BISTRO in the High-Mass Star-Forming Region NGC2264 – Global Properties and Local Magneto-Gravitational Configurations

arXiv

引用

arXiv 2024年

作者： Wang, Jia-Wei Koch, Patrick M. Clarke, Seamus D. Fuller, Gary Peretto, Nicolas Tang, Ya-Wen Yen, Hsi-Wei Lai, Shih-Ping Ohashi, Nagayoshi Arzoumanian, Doris Johnstone, Doug Furuya, Ray Inutsuka, Shu-Ichiro Lee, Chang Won Ward-Thompson, Derek Le Gouellec, Valentin J.M. Liu, Hong-Li Fanciullo, Lapo Hwang, Jihye Pattle, Kate Poidevin, Frédérick Tahani, Mehrnoosh Onaka, Takashi Rawlings, Mark G. Chung, Eun Jung Liu, Junhao Lyo, A-Ran Priestley, Felix Hoang, Thiem Tamura, Motohide Berry, David Bastien, Pierre Ching, Tao-Chung Coudé, Simon Kwon, Woojin Chen, Mike Eswaraiah, Chakali Soam, Archana Hasegawa, Tetsuo Qiu, Keping Bourke, Tyler L. Byun, Do-Young Chen, Zhiwei Chen, Huei-Ru Vivien Chen, Wen Ping Cho, Jungyeon Choi, Minho Choi, Yunhee Choi, Youngwoo Chrysostomou, Antonio Dai, Sophia Francesco, James Di Pham, Ngoc Diep Doi, Yasuo Duan, Yan Duan, Hao-Yuan Eden, David Fiege, Jason Fissel, Laura M. Franzmann, Erica Friberg, Per Friesen, Rachel Gledhill, Tim Graves, Sarah Greaves, Jane Griffin, Matt Gu, Qilao Han, Ilseung Hayashi, Saeko Houde, Martin Inoue, Tsuyoshi Iwasaki, Kazunari Jeong, Il-Gyo Könyves, Vera Kang, Ji-Hyun Kang, Miju Karoly, Janik Kataoka, Akimasa Kawabata, Koji Khan, Zacariyya Kim, Mi-Ryang Kim, Kee-Tae Kim, Kyoung Hee Kim, Shinyoung Kim, Jongsoo Kim, Hyosung Kim, Gwanjeong Kirchschlager, Florian Kirk, Jason Kobayashi, Masato I.N. Kusune, Takayoshi Kwon, Jungmi Lacaille, Kevin Law, Chi-Yan Lee, Sang-Sung Lee, Hyeseung Lee, Jeong-Eun Lee, Chin-Fei Li, Dalei Li, Hua-Bai Li, Guangxing Li, Di Lin, Sheng-Jun Liu, Tie Liu, Sheng-Yuan Lu, Xing Mairs, Steve Matsumura, Masafumi Matthews, Brenda Moriarty-Schieven, Gerald Nagata, Tetsuya Nakamura, Fumitaka Nakanishi, Hiroyuki Ngoc, Nguyen Bich Park, Geumsook Parsons, Harriet Pyo, Tae-Soo Qian, Lei Rao, Ramprasad Rawlings, Jonathan Retter, Brendan Richer, John Rigby, Andrew Sadavoy, Sarah Saito, Hiro Savini, Giorgio Seta, Masumichi Sharma, Ekta Shimajiri, Yoshito Shinnaga, Hiroko Tang, Xindi Thuong, Hoang Duc Tomisaka, Kohji Tram, Le Ngoc Tsukamoto, Yusuke Vi Academia Sinica Institute of Astronomy and Astrophysics No.1 Sec. 4. Roosevelt Road Taipei10617 Taiwan Jodrell Bank Centre for Astrophysics School of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Physikalisches Institut University of Cologne Zülpicher Str. 77 KölnD-50937 Germany School of Physics and Astronomy Cardiff University The Parade CardiffCF24 3AA United Kingdom Institute of Astronomy Department of Physics National Tsing Hua University Hsinchu30013 Taiwan Division of Science National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan NRC Herzberg Astronomy and Astrophysics 5071 West Saanich Rd VictoriaBCV9E 2E7 Canada Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Institute of Liberal Arts and Sciences Tokushima University Minami Jousanajima-machi 1-1 Tokushima770-8502 Japan Department of Physics Graduate School of Science Nagoya University Furo-cho Chikusa-ku Nagoya464-8602 Japan Korea Astronomy and Space Science Institute 776 Daedeokdae-ro Yuseong-gu Daejeon34055 Korea Republic of University of Science and Technology Korea 217 Gajeong-ro Yuseong-gu Daejeon34113 Korea Republic of Jeremiah Horrocks Institute University of Central Lancashire PrestonPR1 2HE United Kingdom NASA Ames Research Center Space Science and Astrobiology Division M.S. 245-6 Moffett FieldCA94035 United States School of physics and astronomy Yunnan University Kunming650091 China National Chung Hsing University 145 Xingda Rd. South Dist. Taichung City402 Taiwan Department of Physics and Astronomy University College London LondonWC1E 6BT United Kingdom Instituto de Astrofisíca de Canarias La Laguna Tenerife Canary Islands38200 Spain La Laguna Tenerife38206 Spain Stanford University StanfordCA United States Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Gem

We report 850 µm continuum polarization observations toward the filamentary high-mass star-forming region NGC 2264, taken as part of the B-fields In STar forming Regions Observations (BISTRO) large program on the James Clerk Maxwell Telescope (JCMT). These data reveal a well-structured non-uniform magnetic field in the NGC 2264C and 2264D regions with a prevailing orientation around 30◦ from north to east. Field strengths estimates and a virial analysis for the major clumps indicate that NGC 2264C is globally dominated by gravity while in 2264D magnetic, gravitational, and kinetic energies are roughly balanced. We present an analysis scheme that utilizes the locally resolved magnetic field structures, together with the locally measured gravitational vector field and the extracted filamentary network. From this, we infer statistical trends showing that this network consists of two main groups of filaments oriented approximately perpendicular to one another. Additionally, gravity shows one dominating converging direction that is roughly perpendicular to one of the filament orientations, which is suggestive of mass accretion along this direction. Beyond these statistical trends, we identify two types of filaments. The type-I filament is perpendicular to the magnetic field with local gravity transitioning from parallel to perpendicular to the magnetic field from the outside to the filament ridge. The type-II filament is parallel to the magnetic field and local gravity. We interpret these two types of filaments as originating from the competition between radial collapsing, driven by filament self-gravity, and the longitudinal collapsing, driven by the region’s global gravity. © 2024, CC BY.

关键词： Magnetic fields

来源：评论

学校读者我要写书评

暂无评论

JASMINE: Near-Infrared Astrometry and Time Series Photometry science

arXiv

引用

arXiv 2023年

作者： Kawata, Daisuke Kawahara, Hajime Gouda, Naoteru Secrest, Nathan J. Kano, Ryouhei Kataza, Hirokazu Isobe, Naoki Ohsawa, Ryou Usui, Fumihiko Yamada, Yoshiyuki Graham, Alister W. Pettitt, Alex R. Asada, Hideki Baba, Junichi Bekki, Kenji Dorland, Bryan N. Fujii, Michiko Fukui, Akihiko Hattori, Kohei Hirano, Teruyuki Kamizuka, Takafumi Kashima, Shingo Kawanaka, Norita Kawashima, Yui Klioner, Sergei A. Kodama, Takanori Koshimoto, Naoki Kotani, Takayuki Kuzuhara, Masayuki Levine, Stephen E. Majewski, Steven R. Masuda, Kento Matsunaga, Noriyuki Miyakawa, Kohei Miyoshi, Makoko Morihana, Kumiko Nishi, Ryoichi Notsu, Yuta Omiya, Masashi Sanders, Jason Tanikawa, Ataru Tsujimoto, Masahiro Yano, Taihei Aizawa, Masataka Arimatsu, Ko Biermann, Michael Boehm, Celine Chiba, Masashi Debattista, Victor P. Gerhard, Ortwin Hirabayashi, Masayuki Hobbs, David Ikenoue, Bungo Izumiura, Hideyuki Jordi, Carme Kohara, Naoki Löffler, Wolfgang Luri, Xavier Mase, Ichiro Miglio, Andrea Mitsuda, Kazuhisa Newswander, Trent Nishiyama, Shogo Obuchi, Yoshiyuki Ootsubo, Takafumi Ouchi, Masami Ozaki, Masanobu Perryman, Michael Prusti, Timo Ramos, Pau Read, Justin I. Rich, R. Michael Schönrich, Ralph Shikauchi, Minori Shimizu, Risa Suematsu, Yoshinori Tada, Shotaro Takahashi, Aoi Tatekawa, Takayuki Tatsumi, Daisuke Tsujimoto, Takuji Tsuzuki, Toshihiro Urakawa, Seitaro Uraguchi, Fumihiro Utsunomiya, Shin Van Eylen, Vincent van Leeuwen, Floor Wada, Takehiko Walton, Nicholas A. National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Mullard Space Science Laboratory University College London Holmbury St. Mary Dorking SurreyRH5 6NT United Kingdom Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo-ku Kanagawa Sagamihara252-5210 Japan Astronomical Science Program Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-1855 Japan U.S. Naval Observatory 3450 Massachusetts Ave NW WashingtonDC20392-5420 United States Department of Physics Kyoto University Kitashirakawa-oiwake-cho Sakyo-ku Kyoto606-8502 Japan Centre for Astrophysics and Supercomputing Swinburne University of Technology HawthornVIC3122 Australia Department of Physics and Astronomy California State University Sacramento 6000 J Street SacramentoCA95819-6041 United States Graduate School of Science and Technology Hirosaki University Aomori036-8561 Japan Graduate School of Science and Engineering Kagoshima University 1-21-35 Korimoto Kagoshima890-0065 Japan International Centre for Radio Astronomy Research The University of Western Australia 7 Fairway CrawleyWA6009 Australia Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Earth and Planetary Science Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute of Statistical Mathematics 10-3 Midoricho Tachikawa Tokyo190-8562 Japan Astrobiology Center 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Institute of Astronomy Graduate School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Center for Gravitational Physics and Quantum Information Yukawa Institute for Theoretical Physics Kyoto University Kitashirakawa-oiwake-cho Sakyo-ku Kyoto606-8502 Japan Cluster for Pioneering Research RIKEN 2-1 Hirosawa Saitama Wako351-0198 Japan Lohrmann Observ

Japan Astrometry Satellite Mission for INfrared Exploration (JASMINE) is a planned M-class science space mission by the Institute of Space and Astronautical science, the Japan Aerospace Exploration Agency. JASMINE has two main science goals. One is the Galactic archaeology with Galactic center Survey, which aims to reveal the Milky Way's central core structure and formation history from Gaia-level (∼25 µas) astrometry in the Near-Infrared (NIR) Hw-band (1.0-1.6 µm). The other is the Exoplanet Survey, which aims to discover transiting Earth-like exoplanets in the habitable zone from NIR time-series photometry of M dwarfs when the Galactic center is not accessible. We introduce the mission, review many science objectives, and present the instrument concept. JASMINE will be the first dedicated NIR astrometry space mission and provide precise astrometric information of the stars in the Galactic center, taking advantage of the significantly lower extinction in the NIR. The precise astrometry is obtained by taking many short-exposure images. Hence, the JASMINE Galactic center survey data will be valuable for studies of exoplanet transits, asteroseismology, variable stars and microlensing studies, including discovery of (intermediate mass) black holes. We highlight a swath of such potential science, and also describe synergies with other missions. © 2023, CC BY.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Thou Shalt Not Reject the P-value

arXiv

引用

arXiv 2020年

作者： Chén, Oliver Y. Saraiva, Raúl G. Phan, Huy Di, Junrui Nagels, Guy Schwantje, Tom Cao, Hengyi Gou, Jiangtao Reinen, Jenna M. Xiong, Bin Zhi, Bangdong Wang, Xiaojun de Vos, Maarten Faculty of Social Sciences and Law University of Bristol United Kingdom Apple Tree Partners United States Department of Computer Science Queen Mary University of London United Kingdom Pfizer United States Department of Engineering University of Oxford United Kingdom Department of Economics University of Oxford United Kingdom Department of Psychology Yale University United States Department of Mathematics and Statistics Villanova University United States IBM Thomas J. Watson Research Center United States Department of Statistics Northwestern University United States School of Management University of Bristol United Kingdom Faculties of Engineering Science and Medicine KU Leuven Belgium

Since its debut in the 18th century, the P-value has been an important part of hypothesis testing-based scientific discoveries. As the statistical engine accelerates, questions are beginning to be raised, asking to what extent scientific discoveries based on P-values are reliable and reproducible, and the voice calling for adjusting the significance level or banning the P-value has been increasingly heard. Inspired by these questions and discussions, here we enquire into the useful roles and misuses of the P-value. For common misuses and misinterpretations, we provide modest recommendations. In parallel, we present the Bayesian alternatives for seeking evidence. Finally, we discuss the promises and risks of using meta-analysis to pool P-values from multiple studies to aggregate evidence. Taken together, the P-value underpins a useful probabilistic decision-making system and provides evidence at a continuous scale. But its interpretation must be contextual, considering the scientific question, experimental design (including the model specification, sample size, and significance level), statistical power, effect size, and reproducibility. © 2020, CC BY-NC-SA.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

PINT: Maximum-likelihood estimation of pulsar timing noise parameters

arXiv

引用

arXiv 2024年

作者： Susobhanan, Abhimanyu Kaplan, David L. Archibald, Anne M. Luo, Jing Ray, Paul S. Pennucci, Timothy T. Ransom, Scott M. Agazie, Gabriella Fiore, William Larsen, Bjorn O’Neill, Patrick van Haasteren, Rutger Anumarlapudi, Akash Bachetti, Matteo Bhakta, Deven Champagne, Chloe A. Cromartie, H. Thankful Demorest, Paul B. Jennings, Ross J. Kerr, Matthew Levina, Sasha McEwen, Alexander Shapiro-Albert, Brent J. Swiggum, Joseph K. Callinstraße 38 HannoverD-30167 Germany Leibniz Universität Hannover HannoverD-30167 Germany Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 MilwaukeeWI53201 United States Newcastle University NE1 7RU United Kingdom Department of Astronomy & Astrophysics University of Toronto 50 Saint George Street TorontoONM5S 3H4 Canada Space Science Division Naval Research Laboratory WashingtonDC20375-5352 United States Institute of Physics and Astronomy Eötvös Loránd University Pázmány P. s. 1/A Budapest1117 Hungary National Radio Astronomy Observatory 520 Edgemont Road CharlottesvilleVA22903 United States Department of Physics and Astronomy West Virginia University P.O. Box 6315 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States Department of Physics Yale University New HavenCT06520 United States I-09047 Italy University of Virginia Department of Astronomy P.O. Box 400325 CharlottesvilleVA22904 United States Electrical and Computer Engineering Department Vanderbilt University NashvilleTN United States National Research Council Research Associate National Academy of Sciences WashingtonDC20001 United States Naval Research Laboratory WashingtonDC20375 United States National Radio Astronomy Observatory 1003 Lopezville Rd. SocorroNM87801 United States William H. Miller III Department of Physics and Astronomy Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States Giant Army 915A 17th Ave SeattleWA98122 United States Department of Physics Lafayette College EastonPA18042 United States

PINT is a pure-Python framework for high-precision pulsar timing developed on top of widely used and well-tested Python libraries, supporting both interactive and programmatic data analysis workflows. We present a new frequentist framework within PINT to characterize the single-pulsar noise processes present in pulsar timing datasets. This framework enables the parameter estimation for both uncorrelated and correlated noise processes as well as the model comparison between different timing and noise models in a computationally inexpensive way. We demonstrate the efficacy of the new framework by applying it to simulated datasets as well as a real dataset of PSR B1855+09. We also describe the new features implemented in PINT since it was first described in the literature. © 2024, CC BY.

关键词： Pulsars

来源：评论

学校读者我要写书评

暂无评论

Protein Shape Retrieval Contest

Protein Shape Retrieval Contest

引用

12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer Graphics, EG 2019

作者： Langenfeld, Florent Axenopoulos, Apostolos Benhabiles, Halim Daras, Petros Giachetti, Andrea Han, Xusi Hammoudi, Karim Kihara, Daisuke Lai, Tuan M. Liu, Haiguang Melkemi, Mahmoud Mylonas, Stelios K. Terashi, Genki Wang, Yufan Windal, Feryal Montes, Matthieu Laboratoire GBCM EA7528 Conservatoire National des Arts-et-Métiers 2 rue Conté Paris75003 France Team Information Technologies Institute Centre for Research and Technology Hellas Greece ISEN-Lille Yncréa Hauts de France Department of Computer Science Università di Verona Strada le Grazie 15 Verona37134 Italy Department of Biological Sciences Purdue University West LafayetteIN United States Université Haute-Alsace IRIMAS EA 7499 MulhouseF-68100 France Department of Computer Science Purdue University West LafayetteIN United States Complex Systems Division Beijing Computational Science Research Center Beijing100193 China School of Software Engineering University of Science and Technology China Suzhou Jiangsu215123 China

ISBN: (纸本)9783038680772

This track aimed at retrieving protein evolutionary classification based on their surfaces meshes only. Given that proteins are dynamic, non-rigid objects and that evolution tends to conserve patterns related to their activity and function, this track offers a challenging issue using biologically relevant molecules. We evaluated the performance of 5 different algorithms and analyzed their ability, over a dataset of 5,298 objects, to retrieve various conformations of identical proteins and various conformations of ortholog proteins (proteins from different organisms and showing the same activity). All methods were able to retrieve a member of the same class as the query in at least 94% of the cases when considering the first match, but show more divergent when more matches were considered. Last, similarity metrics trained on databases dedicated to proteins improved the results. © 2019 The Author(s)

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Full-space Cloud of Random Points with a Scrambling Metasurface

引用

Light(science & Applications) 2018年第1期7卷 467-474页

作者： Zile Li Qi Dai Muhammad Q.Mehmood Guangwei Hu Boris Luk’yanchuk Jin Tao Chenglong Hao Inki Kim Heonyeong Jeong Guoxing Zheng Shaohua Yu Andrea Alù Junsuk Rho Cheng-Wei Qiu School of Electronic Information Wuhan UniversityWuhan 430072China NOEIC State Key Laboratory of Optical Communication Technologies and NetworksWuhan Research Institute of Posts and TelecommunicationsWuhan 430074China Department of Electrical Engineering Information Technology University of the PunjabFerozpur Road54000 LahorePakistan Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Singapore Advanced Science Research Center City University of New YorkNew York 10031USA Data Storage Institute A*STAR(Agency for ScienceTechnology and Research)2 Fusionopolis Way#08-01Innovis 138634Singapore Department of Mechanical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea Department of Chemical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea National Institute of Nanomaterials Technology(NINT) Pohang 37673Republic of Korea

With the rapid progress in computer science,including artificial intelligence,big data and cloud computing,full-space spot generation can be pivotal to many practical applications,such as facial recognition,motion detection,augmented reality,*** opportunities may be achieved by using diffractive optical elements(DOEs)or light detection and ranging(LIDAR).However,DOEs suffer from intrinsic limitations,such as demanding depth-controlled fabrication techniques,large thicknesses(more than the wavelength),Lambertian operation only in half space,*** nevertheless relies on complex and bulky scanning systems,which hinders the miniaturization of the spot ***,inspired by a Lambertian scatterer,we report a Hermitian-conjugate metasurface scrambling the incident light to a cloud of random points in full space with compressed information density,functioning in both transmission and reflection *** 4044 random spots are experimentally observed in the entire space,covering angles at nearly 90°.Our scrambling metasurface is made of amorphous silicon with a uniform subwavelength height,a nearly continuous phase coverage,a lightweight,flexible design,and low-heat ***,it may be mass produced by and integrated into existing semiconductor foundry *** work opens important directions for emerging 3D recognition sensors,such as motion sensing,facial recognition,and other applications.

关键词： conjugate directions amorphous

来源：评论

学校读者我要写书评

暂无评论

Erratum: “Preparation, thermoelectric properties, and crystal structure of boron-doped Mg2Si single crystals” [AIP Advances 10, 035115 (2020)]

引用

AIP Advances 2021年第2期11卷

作者： Kei Hayashi Wataru Saito Kazuya Sugimoto Kenji Ohoyama Kouichi Hayashi Naohisa Happo Masahide Harada Kenichi Oikawa Yasuhiro Inamura Yuzuru Miyazaki 1Department of Applied Physics Graduate School of Engineering Tohoku University Sendai 980-8579 Japan 2Institute of Quantum Beam Science Graduate School of Science and Engineering Ibaraki University Tokai 319-1106 Japan 3Department of Physical Science and Engineering Nagoya Institute of Technology Nagoya 466-8555 Japan 4Frontier Research Institute for Materials Science Nagoya Institute of Technology Nagoya 466-8555 Japan 5Department of Computer and Network Engineering Graduate School of Information Sciences Hiroshima City University Hiroshima 731-3194 Japan 6Materials and Life Science Division J-PARC Center Tokai 319-1195 Japan

Mg/B occupancy (occ.) in Table II of the original article1 was not correct. This erratum presents the corrected version of Table II. The B occ. should be half o

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification of Electron and Muon Neutrino Events for the ESSVSB Near Water Cherenkov Detector using Graph Neural Networks

arXiv

引用

arXiv 2025年

作者： Aguilar, J. Anastasopoulos, M. Barčot, D. Baussan, E. Bhattacharyya, A.K. Bignami, A. Blennow, M. Bogomilov, M. Bolling, B. Bouquerel, E. Bramati, F. Branca, A. Brunetti, G. Burgman, A. Bustinduy, I. Carlile, C.J. Cederkall, J. Choi, T.W. Choubey, S. Christiansen, P. Collins, M. Morales, E. Cristaldo Cupial, P. D'Ago, D. Danared, H. de André, J.P.A.M. Dracos, M. Efthymiopoulos, I. Ekelöf, T. Eshraqi, M. Fanourakis, G. Farricker, A. Fasoula, E. Fukuda, T. García-Marcos, J. Gazis, N. Geralis, Th. Ghosh, M. Giarnetti, A. Gokbulut, G. Hagner, C. Halić, L. Hooft, M. Iversen, K.E. Jachowicz, N. Jenssen, M. Johansson, R. Karakoulias, I. Kasimi, E. Topaksu, A. Kayis Kildetoft, B. Kliček, B. Kordas, K. Leisos, A. Lindroos, M. Longhin, A. Maiano, C. Marangoni, S. Marrelli, C. Meloni, D. Mezzetto, M. Milas, N. Muñoz, J.L. Niewczas, K. Oglakci, M. Ohlsson, T. Olvegård, M. Pari, M. Park, J. Patrzalek, D. Petkov, G. Petridou, Ch. Poussot, P. Psallidas, A. Pupilli, F. Saiang, D. Sampsonidis, D. Schwab, C. Sordo, F. Sosa, A. Stavropoulos, G. Stipčević, M. Tarkeshian, R. Terranova, F. Tolba, T. Trachanas, E. Tsenov, R. Tsirigotis, A. Tzamarias, S.E. Vanderpoorten, M. Vankova-Kirilova, G. Vassilopoulos, N. Vihonen, S. Wurtz, J. Zeter, V. Zormpa, O. Consorcio ESS-bilbao Parque Científico y Tecnológico de Bizkaia Laida Bidea Edificio 207-B Bizkaia Derio48160 Spain European Spallation Source Box 176 LundSE-221 00 Sweden IPHC Université de Strasbourg CNRS IN2P3 Strasbourg France Department of Physics School of Engineering Sciences KTH Royal Institute of Technology Roslagstullsbacken 21 Stockholm106 91 Sweden The Oskar Klein Centre AlbaNova University Center Roslagstullsbacken 21 Stockholm106 91 Sweden Sofia University St. Kliment Ohridski Faculty of Physics Sofia1164 Bulgaria University of Milano-Bicocca INFN Sez. di Milano-Bicocca Milano20126 Italy Department of Physics Lund University P.O Box 118 Lund221 00 Sweden Department of Physics and Astronomy FREIA Division Uppsala University P.O. Box 516 Uppsala751 20 Sweden Faculty of Engineering Lund University P.O Box 118 Lund221 00 Sweden AGH University of Krakow al. A. Mickiewicza 30 Krakow30-059 Poland CERN Geneva 231211 Switzerland Institute of Nuclear and Particle Physics NCSR Demokritos Neapoleos 27 Agia Paraskevi15341 Greece Liverpool University WarringtonWA4 4AD United Kingdom Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece Thessaloniki Greece Institute for Advanced Research Nagoya University Nagoya464–8601 Japan Center of Excellence for Advanced Materials and Sensing Devices Ruđer Bošković Institute Zagreb10000 Croatia Dipartimento di Matematica e Fisica Universitá di Roma Tre Via della Vasca Navale 84 Rome00146 Italy University of Cukurova Faculty of Science and Letters Department of Physics Adana01330 Turkey Department of Physics and Astronomy Ghent University Proeftuinstraat 86 GhentB-9000 Belgium Institute for Experimental Physics Hamburg University Hamburg22761 Germany Physics Laboratory School of Science and Technology Hellenic Open University Patras26335 Greece Department of Physics and Astronomy "G. Galilei" University of Padova INFN Sezione di Padova Italy INF

In the effort to obtain a precise measurement of leptonic CP-violation with the ESSvSB experiment, accurate and fast reconstruction of detector events plays a pivotal role. In this work, we examine the possibility of replacing the currently proposed likelihood-based reconstruction method with an approach based on Graph Neural Networks (GNNs). As the likelihood-based reconstruction method is reasonably accurate but computationally expensive, one of the benefits of a Machine Learning (ML) based method is enabling fast event reconstruction in the detector development phase, allowing for easier investigation of the effects of changes to the detector design. Focusing on classification of flavour and interaction type in muon and electron events and muon- and electron neutrino interaction events, we demonstrate that the GNN reconstructs events with greater accuracy than the likelihood method for events with greater complexity, and with increased speed for all events. Additionally, we investigate the key factors impacting reconstruction performance, and demonstrate how separation of events by pion production using another GNN classifier can benefit flavour classification. © 2025, CC BY.

关键词： Neutrons

来源：评论

学校读者我要写书评

暂无评论

Deep belief network based vertebra segmentation for CT images 13th

Deep belief network based vertebra segmentation for CT image...

引用

13th Conference on Image and Graphics Technologies and Applications, IGTA 2018

作者： Qadri, Syed Furqan Ahmad, Mubashir Ai, Danni Yang, Jian Wang, Yongtian School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811317019

Automatic vertebra segmentation is a challenging task from CT images due to anatomically complexity, shape variation and vertebrae articulation with each other. Deep Learning is a machine learning paradigm that focuses on deep hierarchical learning modeling of input data. In this paper, we propose a novel approach of automatic vertebrae segmentation from computed tomography (CT) images by using deep belief networks (BDNs) modeling. Using the DBN model, the contexture features of vertebra from CT images are extracted automatically by an unsupervised pattern called pre-training and followed by supervised training called back-propagation algorithm;then segmentation the vertebra from other abdominal structure. To evaluate the performance, we computed the overall accuracy (94.2%), sensitivity (83.2%), specificity (94.8%) and mean Dice coefficients (0.85 ± 0.03) for segmentation evaluation. Experimental results show that our proposed model provides a more accuracy in vertebra segmentation compared to the previous state of art methods. © Springer Nature Singapore Pte Ltd., 2018.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：