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A secure and robust certificateless public key steganography based on SVD-DDWT

International Journal of Network Security 2016年第5期18卷 888-899页

作者： Wahballa, Osman Wahaballa, Abubaker Li, Fagen Xu, Chunxiang School of Computer Science and Engineering University of Electronic Science and Technology of China No.4 Section 2 North Jianshe Road Chengdu China

Security and undetectability are main goals of steganographic systems. This paper proposes a novel certificateless public key steganography that allows two parties that have no prior knowledge of each other to communicate covertly over public channel. Firstly, secure and high efficient rate of key distribution are provided. Secondly, proper stego and destego are introduced based on Distributed Discrete Wavelet Transform (DDWT) and Singular Value Decomposition (SVD).Thirdly, we present the Matlab analysis of the original and stego images, which proves the robustness of our scheme. Finally, the analyses demonstrate that our scheme meets all security requirements of steganographic system and resists various kinds of sophisticated attacks.

关键词： Discrete wavelet transforms

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Implementing, Connecting, and Evaluating a Standard-Based Integrated Operating Room within a German University Hospital

ACI Open

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ACI Open 2018年第1期02卷 e10-e20页

作者： Raluca Dees Angela Merzweiler Gerd Schneider Martin Kasparick Lars Mündermann Janko Ahlbrandt Martin Wagner Hannes Kenngott Beat P. Müller-Stich Björn Bergh Department of Medical Information Systems Heidelberg University Hospital Heidelberg Germany Institute of Applied Microelectronics and Computer Engineering University of Rostock Rostock Germany Department of Research and Technology Karl Storz GmbH and Co. KG Tuttlingen Germany Center for Information and Medical Technology Heidelberg University Hospital Heidelberg Germany Minimally Invasive Surgery Section Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany

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Quantum computing at the frontiers of biological sciences

arXiv

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arXiv 2019年

作者： Emani, Prashant S. Warrell, Jonathan Anticevic, Alan Bekiranov, Stefan Gandal, Michael McConnell, Michael J. Sapiro, Guillermo Aspuru-Guzik, Alán Baker, Justin Bastiani, Matteo McClure, Patrick Murray, John Sotiropoulos, Stamatios N. Taylor, Jacob Senthil, Geetha Lehner, Thomas Gerstein, Mark B. Harrow, Aram W. Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Yale School of Medicine Department of Psychiatry New HavenCT06511 United States University of Virginia School of Medicine Department of Biochemistry and Molecular Genetics CharlottesvilleVA22903 United States Department of Psychiatry Semel Institute David Geffen School of Medicine University of California–Los Angeles 695 Charles E. Young Drive South Los AngelesCA90095 United States Univ. of Virginia School of Medicine Department of Neuroscience CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry University of Toronto 80 St. George Street TorontoONM5S 3H6 Canada TorontoONM5S 1M1 Canada CIFAR Artificial Intelligence Research Chair Vector Institute TorontoONM5S 1M1 Canada Department of Computer Science University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada Schizophrenia and Bipolar Disorder Program McLean Hospital BelmontMA02478 United States Department of Psychiatry Harvard Medical School BostonMA02114 United States Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham NottinghamNG7 2RD United Kingdom Machine Learning Team National Institute of Mental Health BethesdaMD20892 United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD20892 United States Department of Psychiatry Yale University School of Medicine New HavenCT06511 United States Department of Physics Yale University New HavenCT06511 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States National Institute of Standards and Technology GaithersburgMD20899 United States Office of Genomics Research Coordination N

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Observability-driven Sensor Deployment in Smart Academic Environments: Demo Abstract 16

Observability-driven Sensor Deployment in Smart Academic Env...

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Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM

作者： A. Agarwal K. Jaiswal U. Gudhaka V. Munigala Krithi Ramamritham Gopinath Karmakar Department of Computer Science & Engineering Indian Institute of Technology Bombay Reactor Control System Design Section Bhabha Atomic Research Centre

ISBN: (纸本)9781450342636

Saving energy without causing discomfort and without demanding human intervention is the need of the day. It is important to develop sensor systems which not only satisfy user requirements, but also take energy consumption into consideration. In this demo, we present the sensing subsystems of a Smart Academic Environment, based upon the core concept of 'Observability' [1] which -- while satisfying user needs -- helps to reduce the number of sensors required.

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Bagging of Xgboost Classifiers with Random Under-sampling and Tomek Link for Noisy Label-imbalanced Data

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IOP Conference Series: Materials Science and engineering 2018年第1期428卷

作者： Luo Ruisen Dian Songyi Wang Chen Cheng Peng Tang Zuodong Yu YanMei Wang Shixiong College of Electrical Engineering and Information Technology Sichuan University 24 South Section 1 One Ring Road Chengdu China 610065 Department of Computer Science University College London Gower Street London United Kingdom WC1E 6BT School of Electronics and Information Northwestern Polytechnical University 1 Dongxiang Road Chang'an District Xi'an China 710129

Fitting label-imbalanced data with high level of noise is one of the major challenges in learning-based intelligent system design. In this paper, for the two-class problem, we propose a bagging-based algorithm with Xgboost classifier (Gradient Boosting Machine) and under-sampling approaches to overcome the challenge. To avoid model misspecification caused by imbalanced data, random sampling with replacement is employed to obtain several balanced training sets; and to mitigate the problem of misleading information produced by noise, Tomek Link method is introduced to eliminate the cross-class overlapped instances, which are the primal sources of noise. And to obtain robust individual learners, we utilize Xgboost, a novel Gradient Boosting Machine-based classifier with convenient parameter tuning interface, to fit each component of the bagging ensemble. The performance of the proposed method is tested with Mandarin radio records (MFCC features) with the task of keywords recognition, and experimental results show that the new method could outperform single Xgboost classifier, verified the rationality and effectiveness of the bagging scheme. The method proposed in the paper could offer a novel solution to the challenge of noisy imbalanced data classification, and the implementation of Xgboost in this area could also serve as an innovative work.

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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Erratum to: Search for single top-quark production via flavour-changing neutral currents at 8 TeV with the ATLAS detector

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The European Physical Journal C 2022年第1期82卷 1-15页

作者： Aad, G. Abbott, B. Abdallah, J. Abdinov, O. Aben, R. Abolins, M. AbouZeid, O. S. Abramowicz, H. Abreu, H. Abreu, R. Abulaiti, Y. Acharya, B. S. Adamczyk, L. Adams, D. L. Adelman, J. Adomeit, S. Adye, T. Affolder, A. A. Agatonovic-Jovin, T. Agricola, J. Aguilar-Saavedra, J. A. Ahlen, S. P. Ahmadov, F. Aielli, G. Akerstedt, H. Åkesson, T. P. A. Akimov, A. V. Alberghi, G. L. Albert, J. Albrand, S. Alconada Verzini, M. J. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexander, G. Alexopoulos, T. Alhroob, M. Alimonti, G. Alio, L. Alison, J. Alkire, S. P. Allbrooke, B. M. M. Allport, P. P. Aloisio, A. Alonso, A. Alonso, F. Alpigiani, C. Altheimer, A. Alvarez Gonzalez, B. Álvarez Piqueras, D. Alviggi, M. G. Amadio, B. T. Amako, K. Amaral Coutinho, Y. Amelung, C. Amidei, D. Amor Dos Santos, S. P. Amorim, A. Amoroso, S. Amram, N. Amundsen, G. Anastopoulos, C. Ancu, L. S. Andari, N. Andeen, T. Anders, C. F. Anders, G. Anders, J. K. Anderson, K. J. Andreazza, A. Andrei, V. Angelidakis, S. Angelozzi, I. Anger, P. Angerami, A. Anghinolfi, F. Anisenkov, A. V. Anjos, N. Annovi, A. Antonelli, M. Antonov, A. Antos, J. Anulli, F. Aoki, M. Aperio Bella, L. Arabidze, G. Arai, Y. Araque, J. P. Arce, A. T. H. Arduh, F. A. Arguin, J-F. Argyropoulos, S. Arik, M. Armbruster, A. J. Arnaez, O. Arnal, V. Arnold, H. Arratia, M. Arslan, O. Artamonov, A. Artoni, G. Asai, S. Asbah, N. Ashkenazi, A. Åsman, B. Asquith, L. Assamagan, K. Astalos, R. Atkinson, M. Atlay, N. B. Augsten, K. Aurousseau, M. Avolio, G. Axen, B. Ayoub, M. K. Azuelos, G. Baak, M. A. Baas, A. E. Baca, M. J. Bacci, C. Bachacou, H. Bachas, K. Backes, M. Backhaus, M. Bagiacchi, P. Bagnaia, P. Bai, Y. Bain, T. Baines, J. T. Baker, O. K. Baldin, E. M. Balek, P. Balestri, T. Balli, F. Banas, E. Banerjee, Sw. Bannoura, A. A. E. Bansil, H. S. Barak, L. Barberio, E. L. Barberis, D. Barbero, M. Barillari, T. Barisonzi, M. Barklow, T. Barlow, N. Barnes, S. L. Barnett, B. M. Barnett, R. M. Barnovska, Z. Baroncelli, A. Barone, G. Barr, A. J. Barreiro, F. Barreiro Guimarães da Costa, J. Bartol CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman USA Institute of Physics Academia Sinica Taipei Taiwan Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam The Netherlands Department of Physics and Astronomy Michigan State University East Lansing USA Department of Physics University of Toronto Toronto Canada Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel Center for High Energy Physics University of Oregon Eugene USA Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London UK AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Physics Department Brookhaven National Laboratory Upton USA Department of Physics Northern Illinois University De Kalb USA Fakultät für Physik Ludwig-Maximilians-Universität München Munich Germany Particle Physics Department Rutherford Appleton Laboratory Didcot UK Oliver Lodge Laboratory University of Liverpool Liverpool UK Institute of Physics University of Belgrade Belgrade Serbia II Physikalisches Institut Georg-August-Universität Göttingen Germany Laboratório de Instrumentação e Física Experimental de Partículas-LIP Lisbon Portugal Departamento de Fisica Teorica y del Cosmos and CAFPE Universidad de Granada Granada Spain Department of Physics Boston University Boston USA Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN Sezione di Roma Tor Vergata Rome Italy Dipartimento di Fisica Università di Roma Tor Vergata Rome Italy Fysiska institutionen Lunds uni

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Offline handwritten pre-segmented character recognition of gurmukhi script

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Machine Graphics and Vision 2016年第1-4期25卷 45-55页

作者： Kumar, Munish Jindal, Manish K. Sharma, Rajendra K. Jindal, Simpel R. Maharaja Ranjit Singh Punjab Technical University Department of Computer Applications GZS Campus College of Engineering and Technology Bathinda Punjab India Panjab University Regional Centre Department of Computer Science and Applications Muktsar Punjab India Thapar University Department of Computer Science and Engineering Patiala Punjab India Yadavindra College of Engineering Talwandi Sabo Computer Science and Engineering Section Bathinda Punjab India

In this paper, we have proposed a feature extraction technique for recognition of segmented handwritten characters of Gurmukhi script. The experiments have been performed with 7000 specimens of segmented offine handwritten Gurmukhi characters collected from 200 different writers. We have considered the set of 35 basic characters of the Gurmukhi script and have proposed the feature extraction technique based on boundary extents of the character image. PCA based feature selection technique has also been implemented in this work to reduce the dimension of data. We have used k-NN, SVM and MLP classiffers. SVM has been used with four different kernels. In this work, we have achieved maximum recognition accuracy of 93.8% for the 35-class problem when SVM with RBF kernel and 5-fold cross validation technique were employed.

关键词： Feature extraction

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Radiative Cooling of the Thermally Isolated System in KAGRA Gravitational Wave Telescope

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Journal of Physics: Conference Series 2021年第1期1857卷

作者： T. Akutsu M. Ando K. Arai Y. Arai S. Araki A. Araya N. Aritomi Y. Aso S.-W. Bae Y.-B. Bae L. Baiotti R. Bajpai M. A. Barton K. Cannon E. Capocasa M.-L. Chan C.-S. Chen K.-H. Chen Y.-R. Chen H.-Y. Chu Y.-K. Chu S. Eguchi Y. Enomoto R. Flaminio Y. Fujii M. Fukunaga M. Fukushima G.-G. Ge A. Hagiwara S. Haino K. Hasegawa H. Hayakawa K. Hayama Y. Himemoto Y. Hiranuma N. Hirata E. Hirose Z. Hong B.-H. Hsieh G.-Z. Huang P.-W. Huang Y.-J. Huang B. Ikenoue S. Imam K. Inayoshi Y. Inoue K. Ioka Y. Itoh K. Izumi K. Jung P. Jung T. Kajita M. Kamiizumi N. Kanda G.-W. Kang K. Kawaguchi N. Kawai T. Kawasaki C. Kim J. Kim W. Kim Y.-M. Kim N. Kimura N. Kita H. Kitazawa Y. Kojima K. Kokeyama K. Komori A. K. H. Kong K. Kotake C. Kozakai R. Kozu R. Kumar J. Kume C.-M. Kuo H.-S. Kuo S. Kuroyanagi K. Kusayanagi K. Kwak H.-K. Lee H.-W. Lee R.-K. Lee M. Leonardi C.-Y. Lin F.-L. Lin L. C.-C. Lin G.-C. Liu L.-W. Luo M. Marchio Y. Michimura N. Mio O. Miyakawa A. Miyamoto Y. Miyazaki K. Miyo S. Miyoki S. Morisaki Y. Moriwaki K. Nagano S. Nagano K. Nakamura H. Nakano M. Nakano R. Nakashima T. Narikawa R. Negishi W.-T. Ni A. Nishizawa Y. Obuchi W. Ogaki J. J. Oh S.-H. Oh M. Ohashi N. Ohishi M. Ohkawa K. Okutomi K. Oohara C.-P. Ooi S. Oshino K.-C. Pan H.-F. Pang J. Park F. E. Peiia Arellano I. Pinto N. Sago S. Saito Y. Saito K. Sakai Y. Sakai Y. Sakuno S. Sato T. Sato T. Sawada T. Sekiguchi Y. Sekiguchi S. Shibagaki R. Shimizu T. Shimoda K. Shimode H. Shinkai T. Shishido A. Shoda K. Somiya E. J. Son H. Sotani R. Sugimoto T. Suzuki H. Tagoshi H. Takahashi R. Takahashi A. Takamori S. Takano H. Takeda M. Takeda H. Tanaka K. Tanaka T. Tanaka S. Tanioka E. N. Tapia San Martin S. Telada T. Tomaru Y. Tomigami T. Tomura F. Travasso L. Trozzo T. T. L. Tsang K. Tsubono S. Tsuchida T. Tsuzuki D. Tuyenbayev N. Uchikata T. Uchiyama A. Ueda T. Uehara K. Ueno G. Ueshima F. Uraguchi T. Ushiba M. H. P. M. van Putten H. Vocca J. Wang C.-M. Wu H.-C. Wu S.-R. Wu W.-R. Xu T. Yamada K. Yamamoto T. Yamamoto K. Yokogawa J. Yokoyama T. Yokozawa T. Yoshioka H. Yuzurihar National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka-City Tokyo 181-8588 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research (ICRR) The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa-City Chiba 277-8582Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 OhoTsukuba-City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1Yayoi Bunkyo-ku Tokyo 113-0032Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) 238 Higashi-Mozumi Kamioka-cho Hida-City Gifu Pref. 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 (KISTI) 245 Daehak-ro Yuseong-gu Daejeon 34141 South Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 beon-gil Yuseong-gu Daejeon 34047 South Korea Graduate School of Science Osaka University 1-1 Machikaneyama-cho Toyonaka-City Osaka560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba-City Ibaraki 305-0801 Japan Institute for Gravitational Research University of Glasgow Kelvin Building G12 8QQ UK Department of Applied Physics Fukuoka University Fukuoka Jonan Nanakuma 814-0180 Japan Department of Physics Tamkang university No.151 Yingzhuan Rd. Danshui Dist. New Taipei-City 25137 Taiwan Department of Physics the Center for High Energy and High Field Physics National Central University No. 300 Zhongda Road Zhongyi District Taiyuan-City 320 Taiwan Institute of Astronomy National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan Department of Physics National Tsing Hua University No. 101 Section 2

Radiative cooling of the thermally isolated system is investigated in KAGRA gravitational wave telescope. KAGRA is a laser interferometer-based detector and main mirrors constituting optical cavities are cool down to 20K to reduce noises caused by the thermal fluctuation. The mirror is suspended with the multi-stage pendulum to isolate any vibration. Therefore, this mirror suspension system has few heat links to reduce vibration injection. Thus, this system is mainly cooled down with thermal radiation. In order to understand the process of radiative cooling of the mirror, we analyzed cooling curve based on mass and specific heat. As a result, it was newly found that a cryogenic part called 'cryogenic duct-shield' seems to have large contribution in the beginning of the mirror cooling. This finding will help to design new cooling system for the next generation cryogenic gravitational wave detector.

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