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arXiv 2025年

作者： Audard, Marc Awaki, Hisamitsu Ballhausen, Ralf Bamba, Aya Behar, Ehud Boissay-Malaquin, Rozenn Brenneman, Laura Brown, Gregory V. Corrales, Lia Costantini, Elisa Cumbee, Renata Diaz-Trigo, Maria Done, Chris Dotani, Tadayasu Ebisawa, Ken Eckart, Megan E. Eckert, Dominique Enoto, Teruaki Eguchi, Satoshi Ezoe, Yuichiro Foster, Adam Fujimoto, Ryuichi Fujita, Yutaka Fukazawa, Yasushi Fukushima, Kotaro Furuzawa, Akihiro Gallo, Luigi García, Javier A. Gu, Liyi Guainazzi, Matteo Hagino, Kouichi Hamaguchi, Kenji Hatsukade, Isamu Hayashi, Katsuhiro Hayashi, Takayuki Hell, Natalie Hodges-Kluck, Edmund Hornschemeier, Ann Ichinohe, Yuto Ishida, Manabu Ishikawa, Kumi Ishisaki, Yoshitaka Kaastra, Jelle Kallman, Timothy Kara, Erin Katsuda, Satoru Kanemaru, Yoshiaki Kelley, Richard Kilbourne, Caroline Kitamoto, Shunji Kobayashi, Shogo Kohmura, Takayoshi Kubota, Aya Leutenegger, Maurice Loewenstein, Michael Maeda, Yoshitomo Markevitch, Maxim Matsumoto, Hironori Matsushita, Kyoko McCammon, Dan McNamara, Brian Mernier, François Miller, Eric D. Miller, Jon M. Mitsuishi, Ikuyuki Mizumoto, Misaki Mizuno, Tsunefumi Mori, Koji Mukai, Koji Murakami, Hiroshi Mushotzky, Richard Nakajima, Hiroshi Nakazawa, Kazuhiro Ness, Jan-Uwe Nobukawa, Kumiko Nobukawa, Masayoshi Noda, Hirofumi Odaka, Hirokazu Ogawa, Shoji Ogorzalek, Anna Okajima, Takashi Ota, Naomi Paltani, Stephane Petre, Robert Plucinsky, Paul Porter, Frederick Scott Pottschmidt, Katja Sato, Kosuke Sato, Toshiki Sawada, Makoto Seta, Hiromi Shidatsu, Megumi Simionescu, Aurora Smith, Randall Suzuki, Hiromasa Szymkowiak, Andrew Takahashi, Hiromitsu Takeo, Mai Tamagawa, Toru Tamura, Keisuke Tanaka, Takaaki Tanimoto, Atsushi Tashiro, Makoto Terada, Yukikatsu Terashima, Yuichi Tsuboi, Yohko Tsujimoto, Masahiro Tsunemi, Hiroshi Tsuru, Takeshi G. Uchida, Hiroyuki Uchida, Nagomi Uchida, Yuusuke Uchiyama, Hideki Ueda, Yoshihiro Uno, Shinichiro Vink, Jacco Watanabe, Shin Williams, Brian J. Yamada, Satoshi Yamada, Shinya Yamaguchi, Hiroya Yamaoka, Kazutaka Yamasaki, Noriko Yamauchi, Makoto Yamauchi Department of Astronomy University of Geneva VersoixCH-1290 Switzerland Department of Physics Ehime University Ehime790-8577 Japan Department of Astronomy University of Maryland College ParkMD20742 United States NASA / Goddard Space Flight Center GreenbeltMD20771 United States GreenbeltMD20771 United States Department of Physics University of Tokyo Tokyo113-0033 Japan Department of Physics Technion Technion City Haifa3200003 Israel BaltimoreMD21250 United States Center for Astrophysics Smithsonian HarvardMA02138 United States Lawrence Livermore National Laboratory CA94550 United States Department of Astronomy University of Michigan MI48109 United States SRON Netherlands Institute for Space Research Leiden Netherlands ESO Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany Centre for Extragalactic Astronomy Department of Physics University of Durham South Road DurhamDH1 3LE United Kingdom Kanagawa252-5210 Japan Department of Physics Kyoto University Kyoto606-8502 Japan Department of Economics Kumamoto Gakuen University Kumamoto862-8680 Japan Department of Physics Tokyo Metropolitan University Tokyo192-0397 Japan Department of Physics Hiroshima University Hiroshima739-8526 Japan Department of Physics Fujita Health University Aichi470-1192 Japan Department of Astronomy and Physics Saint Mary’s University Nova ScotiaB3H 3C3 Canada Cahill Center for Astronomy and Astrophysics California Institute of Technology PasadenaCA91125 United States Noordwijk2200 AG Netherlands Faculty of Engineering University of Miyazaki Miyazaki889-2192 Japan RIKEN Nishina Center Saitama351-0198 Japan Leiden Observatory University of Leiden P.O. Box 9513 LeidenNL-2300 RA Netherlands Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology MA02139 United States Department of Physics Saitama University Saitama338-8570 Japan Department of Physics Rikkyo University Tokyo171-8501 Japa

Galaxy clusters, the largest gravitationally bound structures in the Universe, contain vast amounts of dark matter, galaxies, and hot ionised gas known as the intracluster medium (ICM). In relaxed cluster cores, the ICM appears to cool radiatively faster than the age of the cluster, but the absence of line emission from the predicted cooling rate suggests heating mechanisms that offset the cooling, with feedback from active galactic nuclei (AGNs) being the most likely source [1, 2]. Turbulence and bulk motions, such as the oscillating ("sloshing") motion of the core gas in the cluster potential well, have also been proposed as mechanisms for the distribution of heat from the outside of the core [3, 4]. Here we present high-resolution X-ray spectroscopic observations of the core of the Centaurus galaxy cluster with the XRISM satellite. We find that the hot gas is streaming along the line of sight relative to the central galaxy (NGC 4696), with relative velocities varying from 130 km s−1 to 310 km s−1 within ∼ 30 kpc of the centre, indicating a structured bulk flow ("wind") blowing in the core. This wind is consistent with the core gas sloshing. While the wind may prevent excessive accumulation of cooled gas at the centre of the cluster, it could also distribute the heat injected by the central AGN and/or bring in thermal energy from the surrounding ICM, thus contributing to the thermal balance at the cluster centre. The velocity dispersion (turbulent velocity) of the gas is found to be only 120 km s−1 (corresponding to a Mach number M 0.2) in the core, even within ∼ 10 kpc of the AGN. This may indicate that the influence of the AGN on the motion of the surrounding ICM is limited in the Centaurus cluster. Copyright © 2025, The Authors. All rights reserved.

关键词： Dark Matter

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SocialText: A Framework for Understanding the Relationship Between Digital Communication Patterns and Mental Health

SocialText: A Framework for Understanding the Relationship B...

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International Conference on Semantic Computing

作者： Sanjana Mendu Mehdi Boukhechba Anna Baglione Sonia Baee Congyu Wu Laura Barnes Department of Systems Engineering University of Virginia Charlottesville Virginia Data Science Institute University of Virginia Charlottesville Virginia

As social media platforms have grown to form the foundation of modern digital communication, digital text message datasets that document interpersonal exchanges on these platforms have proliferated. These exchanges comprise a rich corpus of social context data, which can provide insight into how mental health challenges manifest in social contexts. To date, researchers have employed a variety of methods for extracting mental health-centric features from digital text communication data, including natural language processing, social network analysis, sentiment analysis, time series analysis, and discourse analysis. However, there is a marked divide in current literature between qualitative and quantitative feature extraction methods. To effectively identify and analyze key underlying social contexts and related mental health factors from digital text communication data, researchers must extract a comprehensive corpus of features from raw textual data streams. In this paper, we present a generalized framework for extracting features from digital text communication datasets that leverages methodological approaches from diverse fields. This framework will serve to bridge the gap between quantitative and qualitative research approaches to analyzing digital text communications with respect to mental health.

关键词： Feature extraction Semantics Social networking (online) Metadata Sensors Vocabulary Microsoft Windows

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Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

arXiv

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arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

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On-chip mode-division multiplexing with modal crosstalk mitigation

On-chip mode-division multiplexing with modal crosstalk miti...

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Optical Fiber Communication Conference, OFC 2020

作者： Huang, Yetian Zhang, Ruihuan Chen, Haoshuo Huang, Hanzi Zhu, Qingming He, Yu Yuan, Yuan Song, Yingxiong Fontaine, Nicolas K. Ryf, Roland Zhang, Yong Su, Yikai Wang, Min Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Nokia Bell Labs 791 Holmdel Rd. HolmdelNJ07733 United States

We experimentally demonstrate modal crosstalk mitigation over an on-chip mode-division multiplexing link employing low-coherence matched detection. 20-Gbaud QPSK and 8-PSK mode-multiplexed signals are successfully tra... 详细信息

ISBN: (纸本)9781943580712

关键词： Crosstalk

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Improving the performance of AgCdF 3 -based perovskite solar cells using machine learning-driven adjustment of active layer and charge transport materials with SCAPS-1D

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Inorganic Chemistry Communications 2025年 179卷

作者： Avijit Ghosh Al Shahriar Zishan Mahbuba Moumita Yedluri Anil Kumar Anup Kumar Roy Siful Islam Shahan Ahmed Hasanur Zaman Anonto Asma Tabassum Happy Md Shameem Ahsan H.A. Alrafai Abeer A. Hassan Department of Electrical and Electronic Engineering Begum Rokeya University Rangpur 5400 Bangladesh Department of Electrical and Electronics Engineering American International University – Bangladesh 408/1 Kuratoli Road Kuril Dhaka 1229 Bangladesh Department of Mathematics Lamar University Beaumont TX 77705 USA Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 602105 Tamil Nadu India Phillip M. Drayer Department of Electrical and Computer Engineering Lamar University TX USA Master of Science in Management Information Systems College of Business Lamar University TX USA Master of Arts in Social Research & Data Analysis Montclair State University NJ USA Department of Industrial and Systems Engineering Lamar University Beaumont TX 77710 USA Department of Chemistry Faculty of Science King Khalid University PO Box 9004 Abha 61413 Saudi Arabia

This study investigates the possibility of combining inorganic substances—which act as absorbers, HTLs) in order to produce stable, effective PSCs. A ZnSe ETL, HTLs (MoS 2 , MoTe 2 , and CuI), and an AgCdF 3 perovskite absorber, comprise the suggested architecture for this study. This study examines the impact of metal contacts, doping density, interface defect densities, absorber thickness, HTL and ETL layers, and defect density on the output of a solar device using the SCAPS-1D model. The investigation of how temperature impacts the performance of the proposed PSC is another important aspect of the study. By integrating the MoS 2 /MoTe 2 /CuI layers as an HTL with the studied structure of Al/FTO/ZnSe/AgCdF 3 /HTL/Ni, the V OC climbed to 0.834/0.910/0.969 V, the J SC to 42.122/42.862/42.885 mA cm −2 , the PCE to 30.31/34.05/36.06 %, and the FF to 86.21/87.23/86.69 %. A machine learning model was created to forecast the solar cells’ performance metrics. The model precisely evaluates the importance of each parameter by utilizing SHAP values, providing important insights into their contributions. With an accuracy rate of around 83.75 %, machine learning predicted the performance matrix of the optimal solar cell under investigation. An affordable AgCdF 3 solar cell may be developed as a result of this study’s significant findings and practical design.

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SciServer: A science platform for astronomy and beyond

arXiv

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arXiv 2020年

作者： Taghizadeh-Popp, Manuchehr Kim, Jai Won Lemson, Gerard Medvedev, Dmitry Jordan Raddick, M. Szalay, Alexander S. Thakar, Aniruddha R. Booker, Joseph Chhetri, Camy Dobos, Laszlo Rippin, Michael Institute For Data Intensive Engineering and Science Johns Hopkins University 3701 San Martin Drive BaltimoreMD21218 United States Department of Physics of Complex Systems Eotvos Lorand University Pf. 32 BudapestH-1518 Hungary

We present SciServer, a science platform built and supported by the institute for data Intensive engineering and science at the Johns Hopkins University. SciServer builds upon and extends the SkyServer system of server-side tools that introduced the astronomical community to SQL (Structured Query Language) and has been serving the Sloan Digital Sky Survey catalog data to the public. SciServer uses a Docker/VM based architecture to provide interactive and batch mode server-side analysis with scripting languages like Python and R in various environments including Jupyter (notebooks), RStudio and command-line in addition to traditional SQL-based data analysis. Users have access to private file storage as well as personal SQL database space. A flexible resource access control system allows users toÂă share their resources with collaborators, a feature that has also been very useful in classroom environments. All these services, wrapped in a layer of REST APIs, constituteÂăa scalable collaborative data-driven science platform that is attractive to science disciplines beyond astronomy. Copyright © 2020, The Authors. All rights reserved.

关键词： Access control

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Expanding renewable electricity use in global corporate supply chains

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Environmental Research: Energy 2024年第3期1卷 033001-033001页

作者： Katrin Sievert Yi Song Yuang Chen Valerie J Karplus Climate Finance and Policy Group ETH Zurich Zurich Switzerland Institute of Science Technology and Policy ETH Zurich Zurich Switzerland Institute of Energy Environment and Economy Tsinghua University Beijing People’s Republic of China Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA United States of America Department of Engineering and Public Policy Carnegie Mellon University Pittsburgh PA United States of America Wilton E. Scott Institute for Energy Innovation Carnegie Mellon University Pittsburgh PA United States of America

Expanding renewable electricity (RE) use in global corporate supply chains can help to achieve global net-zero greenhouse gas emissions targets by mid-century, but efforts face several challenges. First, corporations and their suppliers may be subject to varying climate policy stringency, leading to a misalignment of incentives to act. Second, measuring true progress is difficult, because counterfactuals are unobserved, and measures of effort vary under policy. Third, relevant policy and broader stakeholder audiences differ in the standards of measurement they recognize. Transparent and broadly accepted, or at least interoperable, standards for assessing effort would help corporations and nations strengthen confidence in corporate claims that RE procurement efforts support international climate goals.

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Using artificial intelligence to document the hidden RNA virosphere

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四川生理科学杂志 2024年第10期46卷 2345-2345页

作者： Xin Hou National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases State Key Laboratory for Biocontrol School of Medicine Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen China Apsara Lab Alibaba Cloud Intelligence Alibaba Group Hangzhou China Wuhan Centers for Disease Control and Prevention Wuhan China Polar Research Institute of China Shanghai China Department of Nursing The Fifth Affiliated Hospital Sun Yat-sen University Zhuhai China School of Geospatial Engineering and Science Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Sun Yat-sen University Zhuhai China Key Laboratory of Comprehensive Observation of Polar Environment Ministry of Education Sun Yat-sen University Zhuhai China Ministry of Education Key Laboratory of Biodiversity Science and Ecological Engineering National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary Institute of Biodiversity Science and Institute of Eco-Chongming School of Life Sciences Fudan University Shanghai Shanghai China Centre for Virus Research Westmead Institute for Medical Research Westmead NSW Australia School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia College of Life Sciences Zhejiang University Hangzhou China School of Life Science Guangzhou University Guangzhou China Key Laboratory of Pathogen Infection Prevention and Control (MOE) State Key Laboratory of Respiratory Health and Multimorbidity National Institute of Pathogen Biology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China School of Public Health (Shenzhen) Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen China Guangzhou National Laboratory Guangzhou International Bio-Island Guangzhou China Department of Infectious Diseases and Public Health Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong SAR China Laboratory of Data Disco

Current metagenomic tools can fail to identify highly divergent RNA *** developed a deep learning algorithm,termed LucaProt,to discover highly divergent RNA-dependent RNA polymerase(RdRP)sequences in 10,487 metatranscriptomes generated from diverse global *** integrates both sequence and predicted structural information,enabling the accurate detection of RdRP *** this approach,we identified 161,979 potential RNA virus species and 180 RNA virus supergroups,including many previously poorly studied groups,as well as RNA virus genomes of exceptional length(up to 47,250 nucleotides)and genomic complexity.

关键词： artificial exceptional integrate

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The PLATO mission

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Experimental Astronomy 2025年第3期59卷 1-111页

作者： Rauer, Heike Aerts, Conny Cabrera, Juan Deleuil, Magali Erikson, Anders Gizon, Laurent Goupil, Mariejo Heras, Ana Walloschek, Thomas Lorenzo-Alvarez, Jose Marliani, Filippo Martin-Garcia, César Mas-Hesse, J. Miguel O’Rourke, Laurence Osborn, Hugh Pagano, Isabella Piotto, Giampaolo Pollacco, Don Ragazzoni, Roberto Ramsay, Gavin Udry, Stéphane Appourchaux, Thierry Benz, Willy Brandeker, Alexis Güdel, Manuel Janot-Pacheco, Eduardo Kabath, Petr Kjeldsen, Hans Min, Michiel Santos, Nuno Smith, Alexis Suarez, Juan-Carlos Werner, Stephanie C. Aboudan, Alessio Abreu, Manuel Acuña, Lorena Adams, Moritz Adibekyan, Vardan Affer, Laura Agneray, François Agnor, Craig Aguirre Børsen-Koch, Victor Ahmed, Saad Aigrain, Suzanne Al-Bahlawan, Ashraf Alcacera Gil, Ma de los Angeles Alei, Eleonora Alencar, Silvia Alexander, Richard Alfonso-Garzón, Julia Alibert, Yann Allende Prieto, Carlos Almeida, Leonardo Alonso Sobrino, Roi Altavilla, Giuseppe Althaus, Christian Alvarez Trujillo, Luis Alonso Amarsi, Anish Ammler-von Eiff, Matthias Amôres, Eduardo Andrade, Laerte Antoniadis-Karnavas, Alexandros António, Carlos Aparicio del Moral, Beatriz Appolloni, Matteo Arena, Claudio Armstrong, David Aroca Aliaga, Jose Asplund, Martin Audenaert, Jeroen Auricchio, Natalia Avelino, Pedro Baeke, Ann Baillié, Kevin Balado, Ana Ballber Balagueró, Pau Balestra, Andrea Ball, Warrick Ballans, Herve Ballot, Jerome Barban, Caroline Barbary, Gaële Barbieri, Mauro Barceló Forteza, Sebastià Barker, Adrian Barklem, Paul Barnes, Sydney Barrado Navascues, David Barragan, Oscar Baruteau, Clément Basu, Sarbani Baudin, Frederic Baumeister, Philipp Bayliss, Daniel Bazot, Michael Beck, Paul G. Belkacem, Kevin Bellinger, Earl Benatti, Serena Benomar, Othman Bérard, Diane Bergemann, Maria Bergomi, Maria Bernardo, Pierre Biazzo, Katia Bignamini, Andrea Bigot, Lionel Billot, Nicolas Binet, Martin Biondi, David Biondi, Federico Birch, Aaron C. Bitsch, Bertram Bluhm Ceballos, Paz Victoria Bódi, Attila Bognár, Zsófia Boisse, Isabelle Bolmont, Emeline Bonanno, Alfio Bonavita, Institut für Planetenforschung Deutsches Zentrum für Luft- und Raumfahrt DLR Rutherfordstrasse 2 Berlin 12489 Germany Fachbereich Geowissenschaften Institut für Geologische Freie Universität Berlin Malteserstr. 74-100 Berlin 12249 Germany Institute of Astronomy KU Leuven Leuven Belgium CNRS CNES LAM Aix Marseille University Marseille France Max Planck Institute for Solar System Research Göttingen Germany LESIA Paris Observatory PSL University Sorbonne University Paris Cité University CNRS Meudon France ESTEC European Space Agency Noordwijk Netherlands ESAC European Space Agency Madrid Spain Centro de Astrobiología CSIC-INTA Campus ESAC Madrid Spain Center for Space and Habitability University of Bern Bern Switzerland INAF - Osservatorio Astrofisico di Catania Catania Italy Department of Physics and Astronomy University of Padua Padua Italy Department of Physics University of Warwick Coventry United Kingdom Astronomical Observatory of Padova INAF - Italian National Institute for Astrophysics Padua Italy Armagh Observatory and Planetarium College Hill Armagh United Kingdom Geneve Observatory University of Geneve Geneve Switzerland IAS University of Paris-Saclay Orsay France AlbaNove University Centre Stockholm University Stockholm Sweden Department of Astrophysics University of Vienna Vienna Austria Department of Astronomy University of Sao Paulo Sao Paulo Brazil Astronomical Institute of the Czech Academy of Sciences Ondřejov Czech Republic Department of Physics and Astronomy Aarhus University Aarhus Denmark Institute for Space Research SRON Leiden Netherlands Instituto de Astrofísica e Ciências do Espaço CAUP Universidade do Porto Porto Portugal Departamento de Física e Astronomia Faculdade da Ci ncias Universidade do Porto Porto Portugal Mullard Space Science Laboratory University College London Holmbury Saint Mary United Kingdom Dept. Theoretical Physics and the Cosmos University of Granada Granada Spain Centre for Plan

PLATO (PLAnetary Transits and Oscillations of stars) is ESA’s M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2REarth) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5%, 10%, 10% for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO‘s target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile towards the end of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases. © The Author(s) 2025.

关键词： Asteroseismology Exoplanets PLATO mission

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Multilinear state space system identification with matrix product operators

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IFAC-PapersOnLine 2018年第15期51卷 640-645页

作者： Batselier, Kim Ko, Ching-Yun Phan, Anh-Huy Cichocki, Andrzej Wong, Ngai Electrical and Electronic Engineering Department The University of Hong Kong Hong Kong Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia Systems Research Institute Polish Academy of Science Warsaw Poland

In this article, we introduce a matrix product operator, also called tensor train matrix, representation of discrete-time multilinear state space models and develop a corresponding system identification method. Using matrix product operators allows us to store the exponential number of model coefficients with a linear storage complexity. The derived system identification algorithm estimates the matrix product operator of the multilinear state space model directly from the measured data. This results in lower computational complexity compared to traditional nonlinear optimization methods. The effectiveness of our proposed model and method is demonstrated by a numerical experiment, where the identification of a degree-16 multilinear state space system in MATLAB on a standard desktop computer takes about 8 minutes with a relative validation error of 0.003%. © 2018

关键词： Matrix algebra

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