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Earthquake risk assessment in NE India using deep learning and geospatial analysis

Geoscience Frontiers 2021年第3期12卷 541-556页

作者： Ratiranjan Jena Biswajeet Pradhan Sambit Prasanajit Naik Abdullah M.Alamri The Center for Advanced Modeling and Geospatial Information Systems(CAMGIS) Faculty of Engineering and Information TechnologyUniversity of Technology SydneyNSW 2007Australia Department of Energy and Mineral Resources Engineering Sejong UniversityChoongmu-gwan209Neungdong-roGwangin-guSeoul 05006Republic of Korea Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKMBangiSelangorMalaysia Active Fault and Earthquake Hazard Mitigation Research Institute Pukyong National UniversityBusan 48513South Korea Department of Geology&Geophysics College of ScienceKing Saud UniversityRiyadh 11451Saudi Arabia

Earthquake prediction is currently the most crucial task required for the probability,hazard,risk mapping,and mitigation *** prediction attracts the researchers'attention from both academia and ***,the risk assessment approaches have used various traditional and machine learning ***,deep learning techniques have been rarely tested for earthquake probability ***,this study develops a convolutional neural network(CNN)model for earthquake probability assessment in NE *** conducts vulnerability using analytical hierarchy process(AHP),Venn's intersection theory for hazard,and integrated model for risk mapping.A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine *** classification results and intensity variation were then used for probability and hazard mapping,***,earthquake risk map was produced by multiplying hazard,vulnerability,and coping *** vulnerability was prepared by using six vulnerable factors,and the coping capacity was estimated by using the number of hospitals and associated variables,including budget available for disaster *** CNN model for a probability distribution is a robust technique that provides good *** show that CNN is superior to the other algorithms,which completed the classification prediction task with an accuracy of 0.94,precision of 0.98,recall of 0.85,and F1 score of *** indicators were used for probability mapping,and the total area of hazard(21,412.94 km^(2)),vulnerability(480.98 km^(2)),and risk(34,586.10 km^(2))was estimated.

关键词： Earthquake Convolutional neural network Geospatial information systems Hazard Vulnerability Risk North-East India

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Co-Contraction Analyses of the Body Wall Muscles in Caenorhabditis Elegans by Calcium Imaging Assay and Simulation

Research Square

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Research Square 2021年

作者： Soh, Zu Yamashita, Hiroki Suzuki, Michiyo Sakamoto, Kazuma Tsuji, Toshio Department of Electrical Systems and Control Engineering Faculty of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739–8527 Japan Department of Radiation-Applied Biology Research Takasaki Advanced Radiation Research Institute National Institutes for Quantum and Radiological Science and Technology Gunma Takasaki370-1292 Japan

Caenorhabditis elegans can generate locomotion under various environments with completely different drag levels. Therefore, animals should have strategies for adapting to the changes in the dynamics of locomotion imposed by various environments. We hypothesized that co-contraction between the ventral and dorsal body wall muscles plays such a role and validated the presence of a co-contraction strategy through both experimental and mathematical modeling approaches. To this end, the fluorescence of calcium ion (Ca2+) corresponding to a part of activities of the body wall muscles was measured. The results indicated a significant difference in the co-fluorescence rate between the animals moving in low- and high-drag environments. The contribution of co-contraction to the dynamics of locomotion was then analysed using a body dynamics model. The simulation results suggested that co-contraction allows the dominance of body stiffness over viscous drag so that the phase difference between the local curvature of the body and muscle activities can be maintained under different environmental drag levels. Therefore, co-contraction can be an effective strategy for adapting to environmental drag that changes the dynamics of locomotion. © 2021, CC BY.

关键词： Drag

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Prox-DBRO-VR: A Unified Analysis on Byzantine-Resilient Decentralized Stochastic Composite Optimization with Variance Reduction

arXiv

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arXiv 2023年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Guo, Xiaoyu Ran, Liang Huang, Tingwen School of Automation Central South University Hunan Changsha410083 China School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Department of Mechanical Engineering City University of Hong Kong Kowloon Tong Hong Kong Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Decentralized stochastic gradient algorithms efficiently solve large-scale finite-sum optimization problems when all agents in the network are reliable. However, most of these algorithms are not resilient to adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This paper aims to handle a class of general composite optimization problems over multi-agent systems (MASs) in the presence of an unknown number of Byzantine agents. Building on a resilient model-aggregation mechanism and the proximal-gradient mapping method, a Byzantine-resilient decentralized stochastic proximal-gradient algorithmic framework is proposed, dubbed ProxDBRO-VR, which achieves an optimization and control goal using only local computations and communications. To asymptotically reduce the noise variance arising from local gradient estimation and accelerate the convergence, we incorporate two localized variance-reduced (VR) techniques (SAGA and LSVRG) into ProxDBRO-VR to design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. By analyzing the contraction relationships among the gradient-learning error, resilient consensus condition, and convergence error in a unified theoretical framework, it is proved that both Prox-DBRO-SAGA and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sub-linearly) inside an error ball around the optimal solution to the original problem under standard assumptions. A trade-off between convergence accuracy and Byzantine resilience in both linear and sub-linear cases is also characterized. In numerical experiments, the effectiveness and practicability of the proposed algorithms are manifested via resolving a decentralized sparse machine-learning problem under various Byzantine attacks. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous agents

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Volatolomics in healthcare and its advanced detection technology

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Nano Research 2022年第9期15卷 8185-8213页

作者： Wenwen Hu Weiwei Wu Yingying Jian Hossam Haick Guangjian Zhang Yun Qian Miaomiao Yuan Mingshui Yao School of Aerospace Science and Technology Xidian UniversityXi’an 730107China Interdisciplinary Research Center of Smart Sensors School of Advanced Materials and NanotechnologyXidian UniversityXi’an 730107China Faculty of Chemical Engineering and Russell Berrie Nanotechnology Institute Technion-Israel Institute of TechnologyHaifa 3200002Israel Department of Thoracic Surgery The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Stomatology Hospital School of StomatologyZhejiang University School of MedicineClinical Research Center for Oral Diseases of Zhejiang Province Key Laboratory of Oral Biomedical Research of Zhejiang ProvinceCancer Center of Zhejiang UniversityHangzhou 310006China The Eighth Affiliated Hospital Sun Yat-sen UniversityShenzhen 518033China State Key Laboratory of Multi-phase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 310006China Institute for Integrated Cell-Material Sciences Kyoto University Institute for Advanced StudyKyoto UniversityKyoto 606-8501Japan

Various diseases increasingly challenge the health status and life quality of human *** emitted from patients has been considered as a potential family of markers,volatolomics,for diagnosis/*** are two fundamental issues of volatolomics in *** one hand,the solid relationship between the volatolome and specific diseases needs to be clarified and *** the other hand,effective methods should be explored for the precise detection of *** comprehensive review articles had been published in this ***,a timely and systematical summary and elaboration is still *** this review article,the research methodology of volatolomics in healthcare is critically considered and given out,at ***,the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically ***,the advanced electronic nose and photonic nose technologies for volatile organic compounds(VOCs)detection are well *** existed obstacles and future perspectives are deeply thought and *** article could give a good guidance to researchers in this interdisciplinary field,not only understanding the cutting-edge detection technologies for doctors(medicinal background),but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists,materials scientists,electronics engineers,etc.

关键词： volatolomics electronic nose disease diagnosis sensor artificial olfaction volatile organic compounds

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GRAS transcription factors regulate cell division planes in moss overriding the default rule (vol 120, e2210632120, 2023)

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PROCEEDINGS OF THE NATIONAL ACADEMY OF scienceS OF THE UNITED STATES OF AMERICA 2023年第10期120卷 e2210632120-e2210632120页

作者： Ishikawa, Masaki Fujiwara, Ayaka Kosetsu, Ken Horiuchi, Yuta Kamamoto, Naoya Umakawa, Naoyuki Tamada, Yosuke Zhang, Liechi Matsushita, Katsuyoshi Palfalvi, Gergo Nishiyama, Tomoaki Kitasaki, Sota Masuda, Yuri Shiroza, Yoshiki Kitagawa, Munenori Nakamura, Toru Cui, Hongchang Hiwatashi, Yuji Kabeya, Yukiko Shigenobu, Shuji Aoyama, Tsuyoshi Kato, Kagayaki Murata, Takashi Fujimoto, Koichi Benfey, Philip N. Hasebe, Mitsuyasu Kofuji, Rumiko Division of Evolutionary Biology National Institute for Basic Biology Okazaki 444-8585 Japan Department of Basic Biology The Graduate School for Advanced Studies (SOKENDAI) Okazaki 444-8585 Japan Graduate School of Natural Science and Technology Kanazawa University Kanazawa 920-1192 Japan Department of Biological Sciences Graduate School of Science Osaka University Toyonaka 560-0043 Japan School of Engineering Utsunomiya University Utsunomiya 321-8585 Japan Division of Integrated Omics Research Research Center for Experimental Modeling of Human Disease Kanazawa University Kanazawa 920-0934 Japan Department of Biology Kanazawa University Kanazawa 920-1192 Japan Present Address: National Key Laboratory for Germplasm Innovation and Utilization for Fruit and Vegetable Horticultural Crops Huazhong Agricultural University Wuhan 430070 China. Department of Biology Duke University Durham NC 27516 HHMI Duke University Durham NC 27516 Department of Biological Science Florida State University Tallahassee FL 32306-4295 School of Food Industrial Sciences Miyagi University Sendai 982-0215 Japan Present address: Live Imaging Center Institute of Transformative Bio-Molecules Nagoya University Nagoya 466-8601 Japan Bioimage Informatics Group Exploratory Research Center on Life and Living Systems (ExCELLS) National Institutes of Natural Sciences Okazaki 444-8585 Japan Interdisciplinary Research Unit National Institute for Basic Biology Okazaki 444-8585 Japan Department of Applied Bioscience Kanagawa Institute of Technology Atsugi 243-0292 Japan Faculty of Biological Science and Technology Institute of Science and Engineering Kanazawa University Kanazawa 920-1192 Japan

Plant cells are surrounded by a cell wall and do not migrate, which makes the regulation of cell division orientation crucial for development. Regulatory mechanisms controlling cell division orientation may have contributed to the evolution of body organization in land plants. The GRAS family of transcription factors was transferred horizontally from soil bacteria to an algal common ancestor of land plants. SHORTROOT (SHR) and SCARECROW (SCR) genes in this family regulate formative periclinal cell divisions in the roots of flowering plants, but their roles in nonflowering plants and their evolution have not been studied in relation to body organization. Here, we show that SHR cell autonomously inhibits formative periclinal cell divisions indispensable for leaf vein formation in the moss Physcomitrium patens, and SHR expression is positively and negatively regulated by SCR and the GRAS member LATERAL SUPPRESSOR, respectively. While precursor cells of a leaf vein lacking SHR usually follow the geometry rule of dividing along the division plane with the minimum surface area, SHR overrides this rule and forces cells to divide nonpericlinally. Together, these results imply that these bacterially derived GRAS transcription factors were involved in the establishment of the genetic regulatory networks modulating cell division orientation in the common ancestor of land plants and were later adapted to function in flowering plant and moss lineages for their specific body organizations.

关键词： GRAS TFs geometry rule cell division orientation horizontal gene transfer

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Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside"

arXiv

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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： Robotics

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Frequency Measurement Research with Weight Averaging of Pulse Output Signal of Voltage-to-Frequency Converter

SSRN

引用

SSRN 2022年

作者： Dorozhovets, Mykhaylo Pawlowski, Eligiusz Świsulski, Dariusz Department of Metrology and Diagnostic Systems Faculty of Electrical and Computer Engineering Rzeszow University of Technology ul. Wincentego Pola 2A Rzeszów35-959 Poland Department of Information and Measuring Technology National University Lviv Polytechnic Lviv Ukraine Faculty of Electrical Engineering and Computer Science Lublin University of Technology Lublin Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdańsk Poland

The paper presents investigation of the efficiency of weight averaging of a pulse frequency modulated signal. The sampling effects and the interference influence in the input signal of the voltage-to frequency converter on the result of output frequency weight averaging are analyzed. A general form of the averaging result dependence on the used weighting function, signal value, interference level and modulation parameters is derived. In the case of high interferences whose frequencies are unstable or may change over a wide range, the specified level of their suppression, together with the reduction of the sampling effect, can be achieved using weighting functions, such as trigonometric, with an appropriate level of side lobes. The interference suppression dependently on frequency ranges using selected weighing functions are tested using both the simulation and experimental methods. The obtained results show very good convergence with the values calculated from theoretical formulas. © 2022, The Authors. All rights reserved.

关键词： Weighing

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Anisotropies in the Arrival Direction Distribution of Ultra-High Energy Cosmic Rays Measured by the Telescope Array Surface Detector 38

Anisotropies in the Arrival Direction Distribution of Ultra-...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Kim, Jihyun Ivanov, Dmitri Kawata, Kazumasa Sagawa, Hiroyuki Thomson, Gordon Abbasi, R.U. Abe, Y. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cheon, B.G. Chikawa, M. Fedynitch, A. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Furlich, G. Gerber, Z. Globus, N. Hanlon, W. Hayashida, N. He, H. Hibi, R. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawakami, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, S.W. Kimura, Y. Komae, I. Komori, K. Kusumori, Y. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lee, M.J. Lubsandorzhiev, B. Lundquist, J.P. Matsuyama, T. Matthews, J.A. Matthews, J.N. Mayta, R. Miyashita, K. Mizuno, K. Mori, M. Murakami, M. Myers, I. Nagataki, S. Nakai, K. Nakamura, T. Nishio, E. Nonaka, T. Ogio, S. Ohoka, H. Okazaki, N. Oku, Y. Okuda, T. Omura, Y. Onishi, M. Ono, M. Oshima, A. Oshima, H. Ozawa, S. Park, I.H. Park, K.Y. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rott, C. Rubtsov, G.I. Ryu, D. Sagawa, H. Saito, R. Sakaki, N. Sako, T. Sakurai, N. Sato, D. Sato, K. Sato, S. Sekino, K. Shah, P.D. Shibata, N. Shibata, T. Shikita, J. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, K. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, K. Tanaka, M. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Udo, S. Urban, F. Vaiman, I.A. Warren, D. Wong, T. Yamazaki, K. Yashiro, K. Yoshida, F. Zhezher, Y. Zundel, Z. Department of Physics Loyola University Chicago ChicagoIL60660 United States Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano380-8554 Japan High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT84112-0830 United States Graduate School of Science Osaka Metropolitan University Sugimoto Sumiyoshi Osaka558-8585 Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul426-791 Korea Republic of Institute for Cosmic Ray Research University of Tokyo Kashiwa Chiba277-8582 Japan Institute of Physics Academia Sinica Taipei City115201 Taiwan Nambu Yoichiro Institute of Theoretical and Experimental Physics Osaka Metropolitan University Sugimoto Sumiyoshi Osaka558-8585 Japan Astrophysical Big Bang Laboratory RIKEN Wako Saitama351-0198 Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama221-8686 Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Kofu Yamanashi400-8511 Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama338-8570 Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon16419 Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo158-8557 Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow117312 Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo337-8570 Japan Graduate School of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka572-8530 Japan Service de Physique Théorique Université Libre de Bruxelles Brussels1050 Belgium Department of Physics Yonsei University Seodaemun-gu Seoul120-749 Korea Republic of Center for Astrophysics and Cosmology University of Nova Gorica Nova Gorica5297 Slovenia Faculty of Science Kochi University Kochi Kochi

Ultra-high energy cosmic rays (UHECRs) are extremely energetic, charged particles with energies greater than 1018 eV, originating from outer space. We investigate anisotropic patterns in the arrival direction distribution of UHECRs to identify their source locations. The Telescope Array (TA) experiment, the largest UHECR observatory in the northern hemisphere, has observed evidence of two intermediate-scale anisotropies in UHECR arrival direction distributions: the TA Hotspot and the Perseus-Pisces supercluster excess. In this presentation, we will describe an oversampling analysis that we performed to find the excess of events using the data measured by the TA surface detector array. We will report the latest results of the TA Hotspot and Perseus-Pisces supercluster excesses. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Anisotropy

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

引用

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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Computer systems science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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