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Erratum: “Two-dimensional porous graphitic carbon nitride C6N7 monolayer: First-principles calculations” [Appl. Phys. Lett. 119, 142102 (2021)]

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Applied Physics Letters 2022年第18期120卷

作者： A. Bafekry M. Faraji M. M. Fadlallah I. Abdolhosseini Sarsari H. R. Jappor S. Fazeli M. Ghergherehchi 1Department of Radiation Application Shahid Beheshti University Tehran Iran 2Micro and Nanotechnology Graduate Program TOBB University of Economics and Technology Sogutozu Caddesi No 43 Sogutozu 06560 Ankara Turkey 3Department of Physics Faculty of Science Benha University 13518 Benha Egypt 4Department of Physics Isfahan University of Technology Isfahan 84156-83111 Iran 5Department of Physics College of Education for Pure Sciences University of Babylon Hilla Iraq 6Department of Material Science and Engineering Sharif University of Technology PO Box 11155-9466 Tehran Iran 7Department of Electrical and Computer Engineering Sungkyunkwan University 16419 Suwon South Korea

The original article1 contains misprints in the values of the elastic parameters. The values of C11 = 258.6 GPa, C22 = 290.8 GPa, and C12 = 70.73 GPa and C13 =

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ARCTIC TRAFFICABILITY program - A REVIEW

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NAVAL ENGINEERS JOURNAL 1984年第3期96卷 169-175页

作者： VOELKER, R GLEN, IF SEIBOLD, F BAYLY, I Richard Voelker:is Vice President of ARCTEC Incorporated a firm specializing in cold regions technology. He has been responsible for the management of thePolarClass Traffic-ability Program since its inception and annually participates in the field data collection in the Arctic. His prior experience includes positions with the U.S. Coast Guard in the icebreaker design project the Military Sealift Command and at Newport News Shipbuilding. He is a graduate of N. Y.S. Maritime College and has a MS degree from the University of Michigan. I.F. Glen:received his professional degrees in naval architecture from the Royal Naval Engineering College Manadon Plymouth and RN College Greenwich London entering the Royal Corps of Naval Constructors in 1967. After serving as a Constructor Lieutenant in the Royal Navy's Far East Fleet for a short period he joined the Polaris submarine project team in Bath England in 1968. In 1971 he was seconded to the Canadian Department of National Defense in Ottawa as a Constructor Lieutenant Commander under NATO exchange arrangements where he had responsibilities initially for conventional submarines and latterly for computer aided conceptual design. He ventured to Bath England in 1974 and joined Forward Design Group. In 1975 he took a position as a civilian engineer in the Canadian Defense Department and was Head of Hull Systems Engineering from 1977 to 1979. He joined ARCTEC CANADA LIMITED in 1980 and in addition to managing ice model testing projects and full scale trials has specialized in structural response of ships to ice impact. He headed ARCTEC's Kanata Laboratory from 1981 to 1983 when he was promoted to president. Frederick Seibold:is a research program manager with the Maritime Administration's Office of Advanced Ship Development and Technology. He is responsible for the marine science program which includes research in the areas of ship powering structures and propeller performance and Arctic technology. Mr. Seibold has been employed by Mar Ad since 1961 having hel

This paper describes a multiyear program to make an operational assessment on the feasibility of a year-round Arctic marine transportation system to serve Alaska. Specifically, the three objectives were to: collect meteorological and ice data along potential marine routes; instrument the hull and propulsion machinery to improve design critera for ice-worthy ships; and demonstrate that ships can operate in midwinter Alaskan Arctic ice conditions. The U.S. Coast Guard's Polar class icebreakers were used to make the operational assessment by annually extending the route northward and by operating throughout the winter season. This paper reviews some of the operational and technical achievements to date, as well as plans for future Arctic deployments.

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MetaMOPE: a web service for mobile phase determination and fast chromatography peaks evaluation for metabolomics

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Bioinformatics Advances 2023年第1期3卷

作者： Tsai, Dong-Ming Chang, Ching-Yao Lin, Shih-Ming Kuo, Tien-Chueh Wang, San-Yuan Chen, Guan-Yuan Kuo, Ching-Hua Tseng, Yufeng Jane Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 106216 Taiwan The Metabolomics Core Laboratory Centers of Genomic and Precision Medicine National Taiwan University Taipei 100225 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 106216 Taiwan Master Program for Clinical Pharmacogenomics and Pharmacoproteomics School of Pharmacy Taipei Medical University Taipei 110301 Taiwan Forensic Medicine College of Medicine National Taiwan University Taipei 100225 Taiwan School of Pharmacy College of Medicine National Taiwan University Taipei 100225 Taiwan

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Euclid: Early Release Observations – A preview of the Euclid era through a galaxy cluster magnifying lens

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Astronomy and Astrophysics 2025年 697卷

作者： Atek, H. Gavazzi, R. Weaver, J.R. Diego, J.M. Schrabback, T. Hatch, N.A. Aghanim, N. Dole, H. Hartley, W.G. Taamoli, S. Congedo, G. Jimenez-Teja, Y. Cuillandre, J.-C. Bañados, E. Belladitta, S. Bowler, R.A.A. Franco, M. Jauzac, M. Mahler, G. Richard, J. Rocci, P.-F. Serjeant, S. Toft, S. Abriola, D. Bergamini, P. Biviano, A. Dimauro, P. Ezziati, M. Golden-Marx, J.B. Grillo, C. Hughes, A.C.N. Kang, Y. Kneib, J.-P. Lombardi, M. Mamon, G.A. McPartland, C.J.R. Meneghetti, M. Miyatake, H. Montes, M. Mortlock, D.J. Oesch, P.A. Okabe, N. Rosati, P. Taylor, A.N. Tarsitano, F. Weller, J. Kluge, M. Laureijs, R. Paltani, S. Saifollahi, T. Schirmer, M. Stone, C. Mora, A. Altieri, B. Amara, A. Andreon, S. Auricchio, N. Baldi, M. Balestra, A. Bardelli, S. Basset, A. Bender, R. Bodendorf, C. Bonino, D. Branchini, E. Brescia, M. Brinchmann, J. Camera, S. Candini, G.P. Capobianco, V. Carbone, C. Cardone, V.F. Carretero, J. Casas, S. Castander, F.J. Castellano, M. Cavuoti, S. Cimatti, A. Conselice, C.J. Conversi, L. Copin, Y. Corcione, L. Courbin, F. Courtois, H.M. Da Silva, A. Degaudenzi, H. Di Giorgio, A.M. Dinis, J. Douspis, M. Dubath, F. Dupac, X. Dusini, S. Ealet, A. Farina, M. Farrens, S. Ferriol, S. Fotopoulou, S. Frailis, M. Franceschi, E. Galeotta, S. Gillard, W. Gillis, B. Giocoli, C. Gómez-Alvarez, P. Grazian, A. Grupp, F. Guzzo, L. Hailey, M. Haugan, S.V.H. Hoar, J. Hoekstra, H. Holliman, M.S. Institut d’Astrophysique de Paris UMR 7095 CNRS Sorbonne Université 98 bis boulevard Arago Paris75014 France Aix-Marseille Université CNRS CNES LAM Marseille France Department of Astronomy University of Massachusetts AmherstMA01003 United States Instituto de Física de Cantabria Edificio Juan Jordá Avenida de los Castros Santander39005 Spain Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria School of Physics and Astronomy University of Nottingham University Park NottinghamNG7 2RD United Kingdom Université Paris-Saclay CNRS Institut d’astrophysique spatiale Orsay91405 France Department of Astronomy University of Geneva ch. d’Ecogia 16 Versoix1290 Switzerland Physics and Astronomy Department University of California 900 University Ave. RiversideCA92521 United States Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Instituto de Astrofísica de Andalucía CSIC Glorieta de la Astronomía Granada18080 Spain Observatorio Nacional Rua General Jose Cristino 77-Bairro Imperial de Sao Cristovao Rio de Janeiro20921-400 Brazil Université Paris-Saclay Université Paris Cité CEA CNRS AIM Gif-sur-Yvette91191 France Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester Oxford Road ManchesterM13 9PL United Kingdom The University of Texas at Austin AustinTX78712 United States Department of Physics Centre for Extragalactic Astronomy Durham University South Road DH1 3LE United Kingdom Department of Physics Institute for Computational Cosmology Durham University South Road DH1 3LE United Kingdom Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of

We present the first analysis of the Euclid Early Release Observations (ERO) program that targets fields around two lensing clusters, Abell 2390 and Abell 2764. We use imaging data from the Visible instrument (VIS) and the Near-Infrared Spectrometer and Photometer (NISP) to produce photometric catalogs for a total of ∼500 000 objects. The imaging data reach a typical depth of 5 σ in the range 25.1–25.4 AB in the NISP bands and 27.1–27.3 AB in the VIS band. Using the Lyman-break method in combination with photometric redshifts, we searched for high-redshift galaxies. We identified 30 Lyman-break galaxy (LBG) candidates at z > 6 and 139 extremely red sources (ERSs), most of which likely lie at lower redshift. The VIS imaging is deeper than the NISP imaging, which means that we can routinely identify high-redshift Lyman-break galaxies at about a magnitude of 3, which reduces contamination by brown dwarf stars and low-redshift galaxies. The difficulty of spatially resolving most of these sources in 0′′ . 3 pix−1 imaging means that it is difficult to distinguish between galaxies and quasars. Spectroscopic follow-up campaigns of these bright sources will help us to constrain the bright end of the ultraviolet galaxy luminosity function and the quasar luminosity function at z > 6, and it will constrain the physical nature of these objects. Additionally, we performed a combined strong- and weak-lensing analysis of A2390, and we show that Euclid will contribute to constraining the virial mass of galaxy clusters better. We also identify optical and near-infrared counterparts of known z > 0.6 clusters in these data. These counterparts exhibit strong-lensing features. This establishes that Euclid can characterize high-redshift clusters. Finally, we provide a glimpse of the ability of Euclid to map the intracluster light out to larger radii than current facilities, which enables us to understand the cluster assembly history better and to map the dark matter distribution. This ini

关键词： Moon

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THz generation by exchange-coupled spintronic emitters

npj Spintronics

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npj Spintronics 2024年第1期2卷 1-8页

作者： Roman Adam Derang Cao Daniel E. Bürgler Sarah Heidtfeld Fangzhou Wang Christian Greb Markus Büscher Claus M. Schneider Jing Cheng Debamitra Chakraborty Ivan Komissarov Roman Sobolewski Martin Mikulics Hilde Hardtdegen Peter Grünberg Institute Research Centre Jülich Jülich Germany College of Physics Qingdao University Qingdao China Faculty of Physics University Duisburg-Essen Duisburg Germany Institut für Laser- und Plasmaphysik Heinrich-Heine Universität Düsseldorf Düsseldorf Germany Materials Science Graduate Program University of Rochester Rochester NY USA Laboratory for Laser Energetics University of Rochester Rochester NY USA Department of Electrical and Computer Engineering University of Rochester Rochester NY USA Ernst Ruska Centre Research Centre Jülich Jülich Germany

The mechanism of THz generation in ferromagnet/metal (F/M) bilayers has been typically ascribed to the inverse spin Hall effect (ISHE). Here, we fabricated Pt/Fe/Cr/Fe/Pt multilayers containing two back-to-back spintronic THz emitters separated by a thin (tCr≤ 3nm) wedge-shaped Cr spacer. In such an arrangement, magnetization alignment of the two Fe films can be controlled by the interplay between Cr-mediated interlayer exchange coupling (IEC) and an external magnetic field. This in turn results in a strong variation of the THz amplitudeA, withA↑↓reaching up to 14 timesA↑↑(arrows indicate the relative alignment of the magnetization of the two magnetic layers). This observed functionality is ascribed to the interference of THz transients generated by two closely spaced THz emitters. Moreover, the magnetic field dependenceA(H) shows a strong asymmetry that points to an additional performance modulation of the THz emitter via IEC and multilayer design.

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Erratum to: The real cost of sequencing: scaling computation to keep pace with data generation

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Genome biology 2016年第1期17卷 78页

作者： Paul Muir Shantao Li Shaoke Lou Daifeng Wang Daniel J Spakowicz Leonidas Salichos Jing Zhang George M Weinstock Farren Isaacs Joel Rozowsky Mark Gerstein Department of Molecular Cellular and Developmental Biology Yale University New Haven CT 06520 USA. Systems Biology Institute Yale University West Haven CT 06516 USA. Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark@***. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark@***. Department of Computer Science Yale University New Haven CT 06520 USA. mark@***.

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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Erratum: An intrinsic mechanism controls reactivation of neural stem cells by spindle matrix proteins

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Nature communications 2017年第1期8卷 1298页

作者： Song Li Chwee Tat Koe Su Ting Tay Angie Lay Keng Tan Shenli Zhang Yingjie Zhang Patrick Tan Wing-Kin Sung Hongyan Wang Neuroscience and Behavioural Disorders Program Duke-NUS Medical School 8 College Road Singapore 169857 Singapore. Cancer and Stem Cell Biology Program Duke-NUS Medical School 8 College Road Singapore 169857 Singapore. NUS Graduate School for Integrative Sciences and Engineering National University of Singapore 28 Medical Drive Singapore 117456 Singapore. Cellular and Molecular Research National Cancer Centre Singapore 169610 Singapore. Cancer Science Institute of Singapore National University of Singapore Singapore 119074 Singapore. Genome Institute of Singapore 60 Biopolis Street Genome 02-01 Singapore 138672 Singapore. Department of Computer Science National University of Singapore Singapore 117417 Singapore. Neuroscience and Behavioural Disorders Program Duke-NUS Medical School 8 College Road Singapore 169857 Singapore. hongyan.wang@duke-nus.edu.sg. NUS Graduate School for Integrative Sciences and Engineering National University of Singapore 28 Medical Drive Singapore 117456 Singapore. hongyan.wang@duke-nus.edu.sg. Department of Physiology Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore. hongyan.wang@duke-nus.edu.sg.

A correction to this article has been published and is linked from the HTML version of this article.

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Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

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PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

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