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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Chia-Yuan Lin Yi-Chun Lin Che-Jui Chang Chi-Feng Chen Ming-Fong Tsai Department of Information Engineering and Computer Science Feng Chia University Taiwan Information and Communications Research Laboratories Industrial Technology Research Institute Taiwan Industrial Ph.D. Program of Internet of Things Feng Chia University Taiwan Departrnent of Electronic Engineering National United University Taiwan

This paper implements an automatic bird data capture system which uploads data captured from social network websites to the server and stores them in the database. When using the Facebook API to capture data of interest, the system needs to filter the captured data to avoid false data storage. This paper takes building an automatic system that includes reinforcing the training database of the bird community as a top priority.

关键词： Birds Databases Facebook Machine learning Servers System implementation

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Eavesdropper's ability to attack a free-space quantum-key-distribution receiver in atmospheric turbulence

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Physical Review A 2019年第6期99卷 062315-062315页

作者： Poompong Chaiwongkhot Katanya B. Kuntz Yanbao Zhang Anqi Huang Jean-Philippe Bourgoin Shihan Sajeed Norbert Lütkenhaus Thomas Jennewein Vadim Makarov Institute for Quantum Computing University of Waterloo Waterloo ON Canada N2L 3G1 Department of Physics and Astronomy University of Waterloo Waterloo ON Canada N2L 3G1 NTT Basic Research Laboratories NTT Corporation Atsugi Kanagawa 243-0198 Japan NTT Research Center for Theoretical Quantum Physics NTT Corporation Atsugi Kanagawa 243-0198 Japan Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 People's Republic of China Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada N2L 3G1 Aegis Quantum Waterloo ON Canada Department of Electrical and Computer Engineering University of Toronto Toronto Canada M5S 3G4 Quantum Information Science Program Canadian Institute for Advanced Research Toronto ON Canada M5G 1Z8 Russian Quantum Center Skolkovo Moscow 143025 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People's Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia

The ability of an eavesdropper (Eve) to perform an intercept-resend attack on a free-space quantum-key-distribution (QKD) receiver by precisely controlling the incidence angle of an attack laser has been previously demonstrated. However, such an attack could be ineffective in the presence of atmospheric turbulence due to beam wander and spatial mode aberrations induced by the air's varying index of refraction. We experimentally investigate the impact turbulence has on Eve's attack on a free-space polarization-encoding QKD receiver by emulating atmospheric turbulence with a spatial light modulator. Our results identify how well Eve would need to compensate for turbulence to perform a successful attack by either reducing her distance to the receiver or using beam wavefront correction via adaptive optics. Furthermore, we use an entanglement-breaking scheme to find a theoretical limit on the turbulence strength that hinders Eve's attack.

关键词： Quantum cryptography

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Eavesdropper’s ability to attack a free-space quantum-key-distribution receiver in atmospheric turbulence

arXiv

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arXiv 2019年

作者： Chaiwongkhot, Poompong Kuntz, Katanya B. Zhang, Yanbao Huang, Anqi Bourgoin, Jean-Philippe Sajeed, Shihan Lütkenhaus, Norbert Jennewein, Thomas Makarov, Vadim Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada NTT Basic Research Laboratories NTT Corporation AtsugiKanagawa Japan NTT Research Center for Theoretical Quantum Physics NTT Corporation AtsugiKanagawa Japan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Aegis Quantum WaterlooON Canada Department of Electrical and Computer Engineering University of Toronto M5S 3G4 Canada Quantum Information Science Program Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada Russian Quantum Center Skolkovo Moscow143025 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

The ability of an eavesdropper (Eve) to perform an intercept-resend attack on a free-space quantum key distribution (QKD) receiver by precisely controlling the incidence angle of an attack laser has been previously demonstrated. However, such an attack could be ineffective in the presence of atmospheric turbulence due to beam wander and spatial mode aberrations induced by the air’s varying index of refraction. We experimentally investigate the impact turbulence has on Eve’s attack on a free-space polarization-encoding QKD receiver by emulating atmospheric turbulence with a spatial light modulator. Our results identify how well Eve would need to compensate for turbulence to perform a successful attack by either reducing her distance to the receiver, or using beam wavefront correction via adaptive optics. Furthermore, we use an entanglement-breaking scheme to find a theoretical limit on the turbulence strength that hinders Eve’s attack. Copyright © 2019, The Authors. All rights reserved.

关键词： Atmospheric turbulence

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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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The hybrid algorithm for product design in multimedia

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International Journal of Applied Systemic Studies 2017年第1-3期7卷 78-91页

作者： Huang, Ching-Lien Chen, Yung-Hui Tseng, Chun-Hsiung Wan, Tian-Long Shen, Mars Department of Industrial Management Lunghwa University of Science and Technology No. 300 Sec. 1 Wanshou Rd. Guishan Taoyuan City33306 Taiwan Department of Computer Information and Network Engineering LungHwa University of Science and Technology No. 300 Sec. 1 Wanshou Rd. Guishan District Taoyuan City33306 Taiwan Department of Communications Engineering Yuan Ze University No. 135 Yuandong Rd. Zhongli Dist. Taoyuan City320 Taiwan Department of Information Management Lunghwa University of Science and Technology No. 300 Sec. 1 Wanshou Rd. Guishan Taoyuan City33306 Taiwan Department of Master’s Program of Business Administration Lunghwa University of Science and Technology No. 300 Sec. 1 Wanshou Rd. Guishan Taoyuan City33306 Taiwan

It is the key successful factors for the manufacture industry that create and select the key parameters in product design to match customer requirements in multimedia. Previous literature studies are focused on the development of customer requirements or parameter selection in product design but not the development of customer demand modelling and product parameter selections in multimedia. In this way we can achieve the purpose of meeting customer demand for product design in multimedia. Kano is a successful method to solve the customer requirements selections issues. And, the theory of inventive problem solving (TRIZ) method is a method to solve the contradiction of product design. Therefore;the integration of the Kano and theory of inventive problem solving (TRIZ) methods is proposed to solve the cable product design in multimedia. This algorithm is also verified in an electronic manufacture. In summary, the proposed algorithm can be helpful by creating a product system in multimedia in this study. Copyright © 2017 Inderscience Enterprises Ltd.

关键词： Product design

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Bagging-based active learning model for named entity recognition with distant supervision

Bagging-based active learning model for named entity recogni...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sunghee Lee Yeongkil Song Maengsik Choi Harksoo Kim Program of Computer and Communications Engineering Kangwon National University Chuncheon-si Gangwon-do Korea

Named entity recognition (NER) is a preliminary step to performing information extraction and question answering. Most previous studies on NER have been based on supervised machine learning methods that need a large amount of human-annotated training corpus. In this paper, we propose a semi-supervised NER model to minimize the time-consuming and labor-intensive task for constructing the training corpus. The proposed model generates weakly labeled training corpus using a distant supervision method. Then, it improves NER accuracy by refining the weakly labeled training corpus using a bagging-based active learning method. In the experiments, the proposed model outperformed the previous semi-supervised model. It showed F1-measure of 0.764 after 15 times of bagging-based active learning.

关键词： Training Dictionaries Data models Bagging Biological system modeling Refining Data mining

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A real-time moving objects detection and classification approach for static cameras

A real-time moving objects detection and classification appr...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Hong-Son Vu Jia-Xian Guo Kuan-Hung Chen Shu-Jui Hsieh De-Sheng Chen Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung Taiwan R.O.C Department of Electronic Engineering Feng Chia University Taichung Taiwan R.O.C Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan R.O.C

ISBN: (纸本)9781509020744

Moving objects recognition plays an important role in camera-only active safety systems and intelligent autonomous vehicles. For these applications, reliable detection performance is required; however, pedestrian detection is challenging due to their divergent dressing and action variety. Besides, real-time detection and recognition performance is also critical. This paper aims to optimize the pedestrian detection and recognition by combining both temporal-domain and spatial-domain methods. Accordingly, we first use Background Subtraction (BS) technique to detect moving objects. Then, we use AdaBoost algorithm to classify the detected moving objects into their categories. Experimental results on our datasets show that the proposed approach can speed up 3.3 times in terms of processing rate, with significantly improved detection performance, i.e., at least 17% detection rate increment and 38% false alarm decrement for daytime out-door applications.

关键词： Classification algorithms Object detection Real-time systems Object recognition Lighting Training Histograms

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Overview of KAGRA : Data transfer and management

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Progress of Theoretical and Experimental Physics 2023年第10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-Y Chu, H Chu, Y-K Eguchi, S Enomoto, Y Flaminio, R Fujii, Y Fujikawa, Y Fukunaga, M Fukushima, M Gao, D Ge, 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 Huang, G-Z Huang, H-Y Huang, P Huang, Y-C Huang, Y 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 Kanda, N Kang, G Kawaguchi, K Kawai, N Kawasaki, T Kim, C Kim, J Kim, J C Kim, W S Kim, Y-M Kimura, N Kita, N Kitazawa, H Kobayashi, Y 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, K Oh, S H Ohashi, M Ohashi, T Ohishi, N Ohkawa, M Ohta, H Okutani, Y Okutomi, K Oohara, K Ooi, C Oshino, S Otabe, S Pan, K Pang, H Parisi, A Park, J Arellano, F E Peña Pinto, I Sago, N Saito, S Saito, Y Sakai, K Sakai, Y Sakuno, Y Sasaki, 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 Tagoshi, H Takahashi, H Takahashi, R Takamori, A Takano, S Takeda, H Takeda, M Tanaka, H Tanaka, K Tanaka, T Tanioka, S Martin, E N Tapia San Telada, S Tomaru, T Tomigami, Y Tomura, T Travasso, F Trozzo, L Tsang, T Ts Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) 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 (RESCEU) The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research (ICRR) 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 Hirosaki University 3 Bunkyo-cho Hirosaki City Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) 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 (KISTI) 245 Daehak-ro Yuseong-gu Daejeon 34141 Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 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 No. 19 Xinjiekou Street 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 Rd. Danshui Dist. New Taipei City 25137 Taiwan Department of Physics and Institute of Astronomy Nation

KAGRA is a newly built gravitational wave observatory, a laser interferometer with a 3 km arm length, located in Kamioka, Gifu prefecture, Japan. In this article, we describe the KAGRA data management system, i.e., recording of data, transfer from the KAGRA experiment site to computing resources, as well as data distribution to tier sites, including international sites in Taiwan and Korea. The amount of KAGRA data exceeded 1.0 PiB and increased by about 1.5 TB per day during operation in 2020. Our system has succeeded in data management, and has achieved performance that can withstand observations after 2023, that is, a transfer rate of 20 MB s-1or more and file storage of sufficient capacity for petabyte class. We also discuss the sharing of data between the global gravitational-wave detector network with other experiments, namely LIGO and Virgo. The latency, which consists of calculation of calibrated strain data and transfer time within the global network, is very important from the view of multi-messenger astronomy using gravitational waves. Real-time calbrated data delivered from the KAGRA detector site and other detectors to our computing system arrive with about 4–15 seconds of latency. These latencies are sufficiently short compared to the time taken for gravitational wave event search computations. We also established a high-latency exchange of offline calibrated data that was aggregated with a better accuracy compared with real-time data.

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Middle East and North African Health Informatics Association (MENAHIA): Building Sustainable Collaboration

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Yearbook of Medical Informatics 2018年第1期27卷 286-291页

作者： Najeeb Al-Shorbaji Mowafa Househ Adel Taweel Abdullah Alanizi Bennani Othmani Mohammed Haitham Abaza Hala Bawadi Hamayon Rasuly Khalid Alyafei Luis Fernandez-Luque Mohamed Shouman Osama El-Hassan Rada Hussein Riyad Alshammari Salah Mandil Sarah Shouman Shahrad Taheri Tamer Emara Wasmiya Dalhem Zaid Al-Hamdan Zineb Serhier eHealth and Knowledge Management Consultant Retired Director of Knowledge Ethics and Research Department WHO/HQ Amman Jordan Department of Health Informatics College of Public Health and Health Informatics King Saud Bin Abdulaziz University for Health Sciences Ministry of National Guard-Health Affairs Riyadh Saudi Arabia Department of Computer Science BirZeit University Ramallah Palestine Head of Medical Informatics Unit 20 August Hospital Ibn Rochd University Hospital Casablanca Head of Casablanca Medical Informatics Laboratory Medical Faculty of Casablanca Morocco Department of Biomedical Engineering Misr University for Science & Technology Cairo Egypt Associate Professor College of Nursing University of Jordan Amman Jordan eHealth Program Coordinator Bamyan Provincial Hospital Bamyan City Afghanistan Chief Medical Informatics Officer (CMIO) - SIDRA Assistant Professor Clinical Pediatrics at Weill Cornell Medicine Qatar Qatar Computing Research Institute Hamad Bin Khalifa University Doha Qatar Faculty of Medicine Ain Shams University Cairo Egypt Vice-Chair of Emirates Health Informatics Society Head of eHealth Section at Dubai Health Authority Dubai United Arab Emirates Founder and Former Director of the Biomedical Informatics Center of Excellence (BMICoE) Information Technology Institute (ITI) Ministry of Communications and Information Technology (MCIT) Egypt President of Saudi Association for Health Informatics Riyadh Saudi Arabia Senior Consultant on eStrategies and eHealth to WHO and ITU Geneva Switzerland Department of Medicine Weill Cornell Medical College in Qatar Qatar Foundation Doha Qatar Executive Director of Nursing Informatics Hamad Medical Corporation Qatar Associate Professor of Nursing Management Faculty of Nursing Jordan University of Science and Technology Irbid Jordan Professor at Medical Informatics Laboratory Faculty of Medicine and Pharmacy Hassan II University Casablanca Morocco

There has been a growing interest in Health Informatics applications, research, and education within the Middle East and North African Region over the past twenty years. People of this region share similar cultural and religious values, primarily speak the Arabic language, and have similar health care related issues, which are in dire need of being addressed. Health Informatics efforts, organizations, and initiatives within the region have been largely under-represented within, but not ignored by, the International Medical Informatics Association (IMIA). Attempts to create bonds and collaboration between the different organizations of the region have remained scattered, and often, resulted in failure despite the fact that the need for a united health informatics collaborative within the region has never been more crucial than today. During the 2017 MEDINFO, held in Hangzhou, China, a new organization, the Middle East and North African Health Informatics Association (MENAHIA) was conceived as a regional non-governmental organization to promote and facilitate health informatics uptake within the region endorsing health informatics research and educational initiatives of the 22 countries represented within the region. This paper provides an overview of the collaboration and efforts to date in forming MENAHIA and displays the variety of initiatives that are already occurring within the MENAHIA region, which MENAHIA will help, endorse, support, share, and improve within the international forum of health informatics.

关键词： eHealth Middle East and North Africa Health Informatics collaboration MENAHIA Universal Health Coverage Bioinformatics International Medical Informatics Association

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