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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

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LANCET GLOBAL HEALTH 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob Health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of Health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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Improving higher education institutions' innovative teaching and learning in Tanzania through information communications technology tools

Improving higher education institutions' innovative teaching...

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2018 International Conference on Intelligent and Innovative Computing Applications, ICONIC 2018

作者： Mwandosya, Godfrey Isaac Suero Montero, Calkin Mbise, Esther Rosinner Tandi Lwoga, Edda Casmir, Respickius Mwasaga, Moses Nkundwe Dar es Salaam Tanzania United Republic of School of Computing University of Eastern Finland Joensuu Finland Marketing Department College of Business Education Dar es Salaam Tanzania United Republic of Academic Research and Consultancy College of Business Education Dar es Salaam Tanzania United Republic of Undergraduate Studies College of Business Education Dar es Salaam Tanzania United Republic of Computer Studies Department Dar es Salaam Institute of Technology Dar es Salaam Tanzania United Republic of

ISBN: (纸本)9781538664773

The application of information communications technologies (ICTs) in higher education institutions has created opportunities for improving teaching and learning. One such avenue is innovation. However, in the Tanzanian HEIs environment little is known on the extent ICTs tools are utilized for innovative teaching and learning. The aim of this study was to find the extent ICT infrastructure, teachers' skills, and readiness influence innovative teaching and learning. Guided by the Activity Theory, the sample for the study was 80 teachers and Information technology (IT) staff from three HEIs. Data was collected through an administered survey questionnaire and interviews. The results indicate that the infrastructure is adequate and teachers have skills and are ready to use ICT tools in classrooms. However, wireless technology is inadequate in all the three HEIs. Furthermore, the results show that innovative curriculums and collaborative innovation projects among the different HEIs are factors for improving innovative teaching and learning. © 2018 IEEE.

关键词： Surveys

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Post-mortem human Iris recognition

arXiv

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arXiv 2018年

作者： Trokielewicz, Mateusz Czajka, Adam Maciejewicz, Piotr Research and Academic Computer Network Wawozowa 18 Warsaw02-796 Poland Institute of Control and Computation Engineering Warsaw University of Technology Nowowiejska 15/19 Warsaw00-665 Poland Department of Ophthalmology Medical University of Warsaw Lindleya 4 Warsaw02-005 Poland

This paper presents a unique analysis of post-mortem human iris recognition. Post-mortem human iris images were collected at the university mortuary in three sessions separated by approximately 11 hours, with the first session organized from 5 to 7 hours after demise. Analysis performed for four independent iris recognition methods shows that the common claim of the iris being useless for biometric identification soon after death is not entirely true. Since the pupil has a constant and neutral dilation after death (the so called "cadaveric position"), this makes the iris pattern perfectly visible from the standpoint of dilation. We found that more than 90% of irises are still correctly recognized when captured a few hours after death, and that serious iris deterioration begins approximately 22 hours later, since the recognition rate drops to a range of 13.3-73.3% (depending on the method used) when the cornea starts to be cloudy. There were only two failures to enroll (out of 104 images) observed for only a single method (out of four employed in this study). These findings show that the dynamics of postmortem changes to the iris that are important for biometric identification are much more moderate than previously believed. To the best of our knowledge, this paper presents the first experimental study of how iris recognition works after death, and we hope that these preliminary findings will stimulate further research in this area. Copyright © 2018, The Authors. All rights reserved.

关键词： Biometrics

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Knowledge and Awareness of Generative Artificial Intelligence Use in Medicine Among International Stakeholders: A Cross-Sectional Study

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Journal of evidence-based medicine 2025年第2期18卷 e70034页

作者： Xufei Luo Bingyi Wang Yule Li Shuang Liu Haodong Li Yaxuan Ren Wah Yang Kyle Lam Stephen R Ali Gemma Sharp Fabio Ynoe Moraes Ye Wang Di Zhu Zhenhua Yang Daher Mohammad Robert Fruscio Maged N Kamel Boulos Zhicheng Lin Kazuki Ide Xuping Song Lu Zhang Yih Chung Tham Hui Liu Long Ge Yaolong Chen Zhaoxiang Bian Evidence-Based Medicine Center School of Basic Medical Sciences Lanzhou University Lanzhou China. Research Unit of Evidence-Based Evaluation and Guidelines Chinese Academy of Medical Sciences (2021RU017) School of Basic Medical Sciences Lanzhou University Lanzhou China. World Health Organization Collaboration Center for Guideline Implementation and Knowledge Translation Lanzhou China. Institute of Health Data Science Lanzhou University Lanzhou China. Key Laboratory of Evidence Based Medicine of Gansu Province Lanzhou University Lanzhou China. Editorial Office of Medical Journal of Peking Union Medical College Hospital Peking Union Medical College Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China. Chinese Journal of Hematology National Clinical Research Center for Blood Diseases Haihe Laboratory of Cell Ecosystem Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China. Tianjin Institutes of Health Science Tianjin China. School of Public Health Lanzhou University Lanzhou China. School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China. Department of Metabolic and Bariatric Surgery The First Affiliated Hospital of Jinan University Guangzhou China. Department of Surgery and Cancer Imperial College London London UK. Reconstructive Surgery and Regenerative Medicine Research Centre Institute of Life Sciences Swansea University Medical School Swansea UK. School of Psychology University of Queensland Brisbane Australia. Department of Oncology Queen's University Kingston Canada. School of Chinese Medicine Vincent V.C. Woo Chinese Medicine Clinical Research Institute Hong Kong Baptist University Hong Kong China. Orthopedic Department Hôtel Dieu de France Beirut Lebanon. UO Gynecology Fondazione IRCCS San Gerardo dei Tintori Monza Italy. Department of Medicine and Surgery University of Milan-Bicocca Milan Italy.

OBJECTIVE:To assess the knowledge, attitudes, and practices (KAP) of medical stakeholders regarding the use of generative artificial intelligence (GAI) tools. METHODS:A cross-sectional survey was conducted among stakeholders in medicine. Participants included researchers, clinicians, and medical journal editors with varying degrees of familiarity with GAI tools. The survey questionnaire comprised 40 questions covering four main dimensions: basic information, knowledge, attitudes, and practices related to GAI tools. Descriptive analysis, Pearson's correlation, and multivariable regression were used to analyze the data. RESULTS:The overall awareness rate of GAI tools was 93.3%. Participants demonstrated moderate knowledge (mean score 17.71 ± 5.56), positive attitudes (mean score 73.32 ± 15.83), and reasonable practices (mean score 40.70 ± 12.86). Factors influencing knowledge included education level, geographic region, and attitudes (p < 0.05). Attitudes were influenced by work experience and knowledge (p < 0.05), while practices were driven by both knowledge and attitudes (p < 0.001). Participants from outside China scored higher in all dimensions compared to those from China (p < 0.001). Additionally, 74.0% of participants emphasized the importance of reporting GAI usage in research, and 73.9% advocated for naming the specific tool used. CONCLUSION:The findings highlight a growing awareness and generally positive attitude toward GAI tools among medical stakeholders, alongside the recognition of their ethical implications and the necessity for standardized reporting practices. Targeted training and the development of clear reporting guidelines are recommended to enhance the effective use of GAI tools in medical research and practice.

关键词： ChatGPT cross‐sectional survey generative artificial intelligence knowledge and awareness

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Human iris recognition in post-mortem subjects: Study and database

arXiv

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arXiv 2018年

作者： Trokielewicz, Mateusz Czajka, Adam Maciejewicz, Piotr Biometrics Laboratory Research and Academic Computer Network Wawozowa 18 Warsaw02-796 Poland Institute of Control and Computation Engineering Warsaw University of Technology Nowowiejska 15/19 Warsaw00-665 Poland Department of Ophthalmology Medical University of Warsaw Lindleya 4 Warsaw02-005 Poland

This paper presents a unique study of post-mortem human iris recognition and the first known to us database of near-infrared and visible-light iris images of deceased humans collected up to almost 17 days after death. We used four different iris recognition methods to analyze the dynamics of iris quality decay in short-term comparisons (samples collected up to 60 hours after death) and longterm comparisons (for samples acquired up to 407 hours after demise). This study shows that post-mortem iris recognition is possible and occasionally works even 17 days after death. These conclusions contradict a promulgated rumor that iris is unusable shortly after decease. We make this dataset publicly available to let others verify our findings and to research new aspects of this important and unfamiliar topic. We are not aware of any earlier papers offering post-mortem human iris images and such comprehensive analysis employing four different matchers. Copyright © 2018, The Authors. All rights reserved.

关键词： Biometrics

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Cataract influence on iris recognition performance

arXiv

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arXiv 2018年

This paper presents the experimental study revealing weaker performance of the automatic iris recognition methods for cataract-affected eyes when compared to healthy eyes. There is little research on the topic, mostly incorporating scarce databases that are often deficient in images representing more than one illness. We built our own database, acquiring 1288 eye images of 37 patients of the Medical University of Warsaw. Those images represent several common ocular diseases, such as cataract, along with less ordinary conditions, such as iris pattern alterations derived from illness or eye trauma. Images were captured in near-infrared light (used in biometrics) and for selected cases also in visible light (used in ophthalmological diagnosis). Since cataract is a disorder that is most populated by samples in the database, in this paper we focus solely on this illness. To assess the extent of the performance deterioration we use three iris recognition methodologies (commercial and academic solutions) to calculate genuine match scores for healthy eyes and those influenced by cataract. Results show a significant degradation in iris recognition reliability manifesting by worsening the genuine scores in all three matchers used in this study (12% of genuine score increase for an academic matcher, up to 175% of genuine score increase obtained for an example commercial matcher). This increase in genuine scores affected the final false non-match rate in two matchers. To our best knowledge this is the only study of such kind that employs more than one iris matcher, and analyzes the iris image segmentation as a potential source of decreased reliability. Copyright © 2018, The Authors. All rights reserved.

关键词： Biometrics

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Assessment of iris recognition reliability for eyes affected by ocular pathologies

arXiv

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arXiv 2018年

This paper presents an analysis of how the iris recognition is impacted by eye diseases and an appropriate dataset comprising 2996 iris images of 230 distinct eyes (including 184 illness-affected eyes representing more than 20 different eye conditions). The images were collected in near infrared and visible light during a routine ophthalmological practice. The experimental study shows four valuable results. First, the enrollment process is highly sensitive to those eye conditions that make the iris obstructed or introduce geometrical distortions. Second, even those conditions that do not produce visible changes to the iris structure may increase the dissimilarity among samples of the same eyes. Third, eye conditions affecting iris geometry, its tissue structure or producing obstructions significantly decrease the iris recognition reliability. Fourth, for eyes afflicted by a disease, the most prominent effect of the disease on iris recognition is to cause segmentation errors. To our knowledge this is the first database of iris images for diseaseaffected eyes made publicly available to researchers, and the most comprehensive study of what we can expect when the iris recognition is deployed for non-healthy eyes. Copyright © 2018, The Authors. All rights reserved.

关键词： Biometrics

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Noise subtraction from KAGRA O3GK data using Independent Component Analysis

arXiv

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arXiv 2022年

作者： Abe, H. Akutsu, T. Ando, M. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Bajpai, R. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, D. Chen, K. Chen, Y. Chiang, C.-Y. Chu, Y.-K. Eguchi, S. Eisenmann, M. Enomoto, Y. Flaminio, R. Fong, H.K. Fujii, Y. Fujikawa, Y. Fujimoto, Y. Fukunaga, I. Gao, D. Ge, G.-G. Ha, S. Hadiputrawan, I.P.W. Haino, S. Han, W.-B. Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hirata, N. Hirose, C. Ho, T.-C. Hsieh, B.-H. Hsieh, H.-F. Hsiung, C. Huang, H.-Y. Huang, P. Huang, Y.-C. Huang, Y.-J. Hui, D.C.Y. Ide, S. Inayoshi, K. Inoue, Y. Ito, K. Itoh, Y. Jeon, C. Jin, H.-B. Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanda, N. Kato, T. Kawaguchi, K. Kim, C. Kim, J. Kim, J.C. Kim, Y.-M. Kimura, N. Kiyota, T. Kobayashi, Y. Kohri, K. Kokeyama, K. Kong, A.K.H. Koyama, N. Kozakai, C. Kume, J. Kuromiya, Y. Kuroyanagi, S. Kwak, K. Lee, E. Lee, H.W. Lee, R. Leonardi, M. Li, K.L. Li, P. Lin, L.C.-C. Lin, C.-Y. Lin, E.T. Lin, F.-K. Lin, F.-L. Lin, H.L. Liu, G.C. Luo, L.-W. Ma'arif, M. Majorana, E. Michimura, Y. Mio, N. Miyakawa, O. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Morisue, N. Moriwaki, Y. Nagano, K. Nakamura, K. Nakano, H. Nakano, M. Nakayama, Y. Narikawa, T. Naticchioni, L. Nguyen Quynh, L. Ni, W.-T. Nishimoto, T. Nishizawa, A. Nozaki, S. Obayashi, Y. Ogaki, W. Oh, J.J. Oh, K. Ohashi, M. Ohashi, T. Ohkawa, M. Ohta, H. Okutani, Y. Oohara, K. Oshino, S. Otabe, S. Pan, K.-C. Parisi, A. Park, J. Peña Arellano, F.E. Saha, S. Saito, Y. Sakai, K. Sawada, T. Sekiguchi, Y. Shao, L. Shikano, Y. Shimizu, H. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Song, I. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takano, S. Takeda, H. Takeda, M. Tanaka, K. Tanaka, T. Tanaka, T. Tanioka, S. Taruya, A. Graduate School of Science Tokyo Institute of Technology Meguro-ku Tokyo152-8551 Japan Mitaka City Tokyo181-8588 Japan Mitaka City Tokyo181-8588 Japan Department of Physics The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan The University of Tokyo Bunkyo-ku Tokyo113-0033 Japan Earthquake Research Institute The University of Tokyo Bunkyo-ku Tokyo113-0032 Japan Department of Mathematics and Physics Gravitational Wave Science Project Hirosaki University Aomori Hirosaki City036-8561 Japan Kamioka-cho Hida City Gifu506-1205 Japan Mitaka City Tokyo181-8588 Japan Yuseong-gu Daejeon34141 Korea Republic of National Institute for Mathematical Sciences Yuseong-gu Daejeon34047 Korea Republic of Ibaraki Tsukuba City305-0801 Japan Department of Astronomy Beijing Normal University Beijing100875 China Department of Applied Physics Fukuoka University Jonan Fukuoka Fukuoka City814-0180 Japan Department of Physics Tamkang University Danshui Dist. New Taipei City25137 Taiwan Department of Physics Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Department of Physics Center for High Energy and High Field Physics National Central University Zhongli District Taoyuan City32001 Taiwan Department of Physics National Tsing Hua University Hsinchu30013 Taiwan Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Université Savoie Mont Blanc CNRS IN2P3 AnnecyF-74941 France Department of Astronomy The University of Tokyo Mitaka City Tokyo181-8588 Japan Faculty of Engineering Niigata University Nishi-ku Niigata Niigata City950-2181 Japan Department of Physics Graduate School of Science Osaka City University Sumiyoshi-ku Osaka City Osaka558-8585 Japan Chinese Academy of Sciences Xiao Hong Shan Wuhan430071 China Ulju-gun Ulsan44919 Korea Republic of Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China KAGRA Observatory The University of Tok

In April 2020, KAGRA conducted its first science observation in combination with the GEO 600 detector (O3GK) for two weeks. According to the noise budget estimation, suspension control noise in the low frequency band and acoustic noise in the middle frequency band are identified as the dominant contribution. In this study, we show that such noise can be reduced in offline data analysis by utilizing a method called Independent Component Analysis (ICA). Here the ICA model is extended from the one studied in iKAGRA data analysis by incorporating frequency dependence while linearity and stationarity of the couplings are still assumed. By using optimal witness sensors, those two dominant contributions are mitigated in the real observational data. We also analyze the stability of the transfer functions for whole two weeks data in order to investigate how the current subtraction method can be practically used in gravitational wave search. Copyright © 2022, The Authors. All rights reserved.

关键词： Data handling

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Inuence maximization for fixed heterogeneous thresholds

arXiv

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arXiv 2018年

作者： Karampourniotis, P.D. Szymanski, B.K. Korniss, G. Ibm Research CambridgeMA United States Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute 110 8th Street TroyNY12180-3590 United States Social Cognitive Networks Academic Research Center Rensselaer Polytechnic Institute 110 8th Street TroyNY12180-3590 United States Department of Computer Science Rensselaer Polytechnic Institute 110 8th Street TroyNY12180-3590 United States Faculty of Computer Science and Management Wroclaw University of Science and Technology

Inuence Maximization is a NP-hard problem of selecting the optimal set of inuencers in a network. Here, we propose two new approaches to inuence maximization based on two very different metrics. The first metric, termed Balanced Index (BI), is fast to compute and assigns top values to two kinds of nodes: Those with high resistance to adoption, and those with large out-degree. This is done by linearly combining three properties of a node: its degree, susceptibility to new opinions, and the impact its activation will have on its neighborhood. Controlling the weights between those three terms has a huge impact on performance. The second metric, termed Group Performance Index (GPI), measures performance of each node as an initiator when it is a part of randomly selected initiator set. In each such selection, the score assigned to each teammate is inversely proportional to the number of initiators causing the desired spread. These two metrics are applicable to various cascade models;here we test them on the Linear Threshold Model with fixed and known thresholds. Furthermore, we study the impact of network degree assortativity and threshold distribution on the cascade size for metrics including ours. The results demonstrate our two metrics deliver strong performance for inuence maximization. Copyright © 2018, The Authors. All rights reserved.

关键词： Computational complexity

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Noise subtraction from KAGRA O3GK data using Independent Component Analysis

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Classical and Quantum Gravity 2023年第8期40卷 085015-085015页

作者： H Abe T Akutsu M Ando A Araya N Aritomi H Asada Y Aso S Bae Y Bae R Bajpai K Cannon Z Cao E Capocasa M Chan C Chen D Chen K Chen Y Chen C-Y Chiang Y-K Chu S Eguchi M Eisenmann Y Enomoto R Flaminio H K Fong Y Fujii Y Fujikawa Y Fujimoto I Fukunaga D Gao G-G Ge S Ha I P W Hadiputrawan S Haino W-B Han K Hasegawa K Hattori H Hayakawa K Hayama Y Himemoto N Hirata C Hirose T-C Ho B-H Hsieh H-F Hsieh C Hsiung H-Y Huang P Huang Y-C Huang Y-J Huang D C Y Hui S Ide K Inayoshi Y Inoue K Ito Y Itoh C Jeon H-B Jin K Jung P Jung K Kaihotsu T Kajita M Kakizaki M Kamiizumi N Kanda T Kato K Kawaguchi C Kim J Kim J C Kim Y-M Kim N Kimura T Kiyota Y Kobayashi K Kohri K Kokeyama A K H Kong N Koyama C Kozakai J Kume Y Kuromiya S Kuroyanagi K Kwak E Lee H W Lee R Lee M Leonardi K L Li P Li L C -C Lin C-Y Lin E T Lin F-K Lin F-L Lin H L Lin G C Liu L-W Luo M Ma’arif E Majorana Y Michimura N Mio O Miyakawa K Miyo S Miyoki Y Mori S Morisaki N Morisue Y Moriwaki K Nagano K Nakamura H Nakano M Nakano Y Nakayama T Narikawa L Naticchioni L Nguyen Quynh W-T Ni T Nishimoto A Nishizawa S Nozaki Y Obayashi W Ogaki J J Oh K Oh M Ohashi T Ohashi M Ohkawa H Ohta Y Okutani K Oohara S Oshino S Otabe K-C Pan A Parisi J Park F E Pe na Arellano S Saha Y Saito K Sakai T Sawada Y Sekiguchi L Shao Y Shikano H Shimizu K Shimode H Shinkai T Shishido A Shoda K Somiya I Song R Sugimoto J Suresh T Suzuki H Tagoshi H Takahashi R Takahashi S Takano H Takeda M Takeda K Tanaka T Tanaka S Tanioka A Taruya T Tomaru T Tomura L Trozzo T Tsang J-S Tsao S Tsuchida T Tsutsui D Tuyenbayev N Uchikata T Uchiyama A Ueda T Uehara K Ueno G Ueshima T Ushiba M H P M van Putten J Wang T Washimi C Wu H Wu T Yamada K Yamamoto T Yamamoto K Yamashita R Yamazaki Y Yang S Yeh J Yokoyama T Yokozawa T Yoshioka H Yuzurihara S Zeidler M Zhan H Zhang Y Zhao Z-H Zhu The KAGRA Collaboration Graduate School of Science Tokyo Institute of Technology Meguro-ku Tokyo 152-8551 Japan Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Earthquake Research Institute The University of Tokyo Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Gravitational Wave Science Project Hirosaki University Hirosaki City Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences Yuseong-gu Daejeon 34047 Republic of Korea School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Beijing 100875 People’s Republic of China Department of Applied Physics Fukuoka University Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University Danshui Dist. New Taipei City 25137 Taiwan Department of Physics and Institute of Astronomy National Tsing Hua University Hsinchu 30013 Taiwan Department of Physics Center for High Energy and High Field Physics National Central University Zhongli District Taoyuan City 32001 Taiwan Department of Physics National Tsing Hua University Hsinchu 30013 Taiwan Institute of Physics Academia Sinica Nankang Taipei 11529 Taiwan Univ. Grenoble Alpes Laboratoire d’Annecy de Physique des Particules (LAPP) Université Savoie M

During April 7–21 2020, KAGRA conducted its first scientific observation in conjunction with the GEO600 detector. The dominant noise sources during this run were found to be suspension control noise in the low-frequency range and acoustic noise in the mid-frequency range. In this study, we show that their contributions in the observational data can be reduced by a signal processing method called independent component analysis (ICA). The model of ICA is extended from that studied in the initial KAGRA data analysis to account for frequency dependence, while the linearity and stationarity of the coupling between the interferometer and the noise sources are still assumed. We identify optimal witness sensors in the application of ICA, leading to successful mitigation of these two dominant contributions. We also analyze the stability of the transfer functions for the entire two weeks of data to investigate the applicability of the proposed subtraction method in gravitational wave searches.

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