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IEEE International Conference on Bioinformatics and Biomedicine

作者： Hong Zhang Fei Ren Zhonglie Wang Xiaosong Rao Li Li Junbo Hao Rui Yan Jiancheng Luo Ming Du Fa Zhang College of Computer Science and Technology Donghua University High Performance Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Department of Pathology Peking University International Hospital Peking University Department of Oncology Peking University International Hospital Peking University School of Electronic Engineering Xidian University Aiyi Technology Co. Ltd

ISBN: (纸本)9781728118680

Tumor mutational burden (TMB) is the most important and most promising biomarker in the era of tumor immunotherapy, and it can predict the immunotherapy efficiency of patients in various cancers including liver cancer. TMB is mainly obtained by next generation sequencing technology such as whole exome sequencing (WES). However, conditions such as excessive testing costs, lengthy detection cycles, and tissue sample dependence severely limit the clinical application of TMB. Inspired by the inner link between the intrinsic characteristics of the tumor cell genome and the pathological features of tumor cells and their microenvironment-related cells, we propose a deep learning method for predicting the level of TMB (high or low) directly from pathological images. This study found that the feature scale (receptive field) is the biggest factor affecting the classification effect of TMB prediction, and further determined the best receptive field through a series of experiments. Experimental results show that our method is far more out performance of the commonly used panel sequencing (99.7% VS 79.2%). To the best of our knowledge, this is the first research to predict TMB and the highest level of accuracy of genomic characteristic predicted by pathological images. The proposed method has the potential to provide immunotherapy to a much broader subset of patients with liver cancer.

关键词： tumor mutational burden liver cancer receptive field pathological image convolutional neural network Cancer of Liver Receptive field Neoplastic Cell pathology immunotherapy cancer immunotherapy

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Referral for disease-related visual impairment using retinal photograph-based deep learning: a proof-of-concept, model development study

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The Lancet Digital Health 2021年第1期3卷 e29-e40页

作者： Tham, Yih-Chung Anees, Ayesha Zhang, Liang Goh, Jocelyn Hui Lin Rim, Tyler Hyungtaek Nusinovici, Simon Hamzah, Haslina Chee, Miao-Li Tjio, Gabriel Li, Shaohua Xu, Xinxing Goh, Rick Tang, Fangyao Cheung, Carol Yim-Lui Wang, Ya Xing Nangia, Vinay Jonas, Jost B Gopinath, Bamini Mitchell, Paul Husain, Rahat Lamoureux, Ecosse Sabanayagam, Charumathi Wang, Jie Jin Aung, Tin Liu, Yong Wong, Tien Yin Cheng, Ching-Yu Singapore Eye Research Institute Singapore National Eye Centre Singapore Duke-NUS Medical School Singapore Institute of High Performance Computing A*STAR Singapore School of Chemical and Biomedical Engineering Division of Bioengineering Nanyang Technological University Singapore Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region Hong Kong Beijing Institute of Ophthalmology Beijing Ophthalmology and Visual Science Key Lab Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing China Suraj Eye Institute Nagpur India Department of Ophthalmology Medical Faculty Mannheim of the Ruprecht-Karis-University Heidelberg Mannheim Germany Centre for Vision Research Department of Ophthalmology The Westmead Institute for Medical Research The University of Sydney Westmead Hospital Westmead NSW Australia Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore

Background: In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of deep learning technology offers new opportunities to revolutionise this clinical referral pathway. We aimed to assess the performance of a newly developed deep learning algorithm for detection of disease-related visual impairment. Methods: In this proof-of-concept study, using retinal fundus images from 15 175 eyes with complete data related to best-corrected visual acuity or pinhole visual acuity from the Singapore Epidemiology of Eye Diseases Study, we first developed a single-modality deep learning algorithm based on retinal photographs alone for detection of any disease-related visual impairment (defined as eyes from patients with major eye diseases and best-corrected visual acuity of <20/40), and moderate or worse disease-related visual impairment (eyes with disease and best-corrected visual acuity of <20/60). After development of the algorithm, we tested it internally, using a new set of 3803 eyes from the Singapore Epidemiology of Eye Diseases Study. We then tested it externally using three population-based studies (the Beijing Eye study [6239 eyes], Central India Eye and Medical study [6526 eyes], and Blue Mountains Eye Study [2002 eyes]), and two clinical studies (the Chinese University of Hong Kong's Sight Threatening Diabetic Retinopathy study [971 eyes] and the Outram Polyclinic Study [1225 eyes]). The algorithm's performance in each dataset was assessed on the basis of the area under the receiver operating characteristic curve (AUC). Findings: In the internal test dataset, the AUC for detection of any disease-related visual impairment was 94·2% (95% CI 93·0–95·3;sensitivity 90·7% [87·0–93·6];specificity 86·8% [85·6–87·9]). The AUC for moderate or worse disease-related visual impairment was 93·9% (95% CI 92·2–95

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Energy Load Forecasting using Deep Learning Approach-LSTM and GRU in Spark Cluster

Energy Load Forecasting using Deep Learning Approach-LSTM an...

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International Conference on Emerging Applications of Information Technology (EAIT)

作者： Sumit Kumar Lasani Hussain Sekhar Banarjee Motahar Reza B. Tech CSE 3rd Year Students High Performance Computing Lab School of Computer Science and Engineering National Institute of Science & Technology Berhampur India

Electric load forecasting has a significant role in power grids in order to facilitate the decision making process of energy generation & consumption. Long term forecasting is not feasible as there might be an uncertainty in the prediction because of irregular increase in the demand for power with the growing population and dependency on electric power. Since the behaviour of electric load time series is very much non-linear and seasonal, Neural Networks are best suited model for learning the Non-Linear behaviour within the data and for forecasting purpose. This paper deals with the Recurrent Neural Networks based Models: Long-Short-Term-Memory (LSTM) and Gated-Recurrent-Unit (GRU) to deal with this challenge. Observations have been made based on the distributed implementation of various configurations of LSTM-RNN & GRU-RNN on spark clusters for hyper parameter tuning purpose and deploying best suited configuration with least RMSE value using apache memos resource management.

关键词： Load modeling Predictive models Logic gates Computational modeling Load forecasting Forecasting Sparks

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Zero-field Sub-50-nm skyrmions in monolayer Janus Van der Waals magnets

arXiv

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arXiv 2019年

作者： Yuan, Jiaren Yang, Yumeng Cai, Yongqing Wu, Yihong Feng, Yuan Ping Chen, Yuanping Yan, Xiaohong Shen, Lei College of Science School of Material Science and Engineering Jiangsu University Zhenjiang212013 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Institute of High Performance Computing A*STAR 1 Fusionopolis Way Singapore138632 Singapore Department of Physics and Centre for Advanced Two-Dimensional Materials National University of Singapore 2 Science Drive 3 117551 Singapore Department of Mechanical Engineering and Engineering Science National University of Singapore 9 Engineering Drive 1 117542 Singapore

Skyrmions are localized solitonic spin textures with protected topology, which are promising as information carriers in ultra-dense and energy-efficient logics and memories. Magnetic skyrmions have been recently observed in thick magnetic multilayers and are stabilized by the extrinsic interfacial Dzyaloshinskii–Moriya interaction (DMI) and external magnetic fields. Here, we investigate the intrinsic magnetic skyrmions in four monolayer Janus van der Waals magnets, MnSSe, MnSTe, MnSeTe, and VSeTe, by the first-principles calculations combined with micromagnetic simulations. It is found that the monolayer Janus MnSTe and MnSeTe with out-of-plane asymmetry and strong spin-orbit coupling (SOC) have a large intrinsic DMI. Using micromagnetic simulations, we demonstrate that skyrmions can be stabilized in monolayer MnSTe and MnSeTe within sub-50 nm scale in the absence of magnetic fields. On the other hand, VSeTe (in-plane easy axis) forms chiral magnetic domains. Finally, the size and shape of skyrmions can be regulated using an external magnetic field. Our work opens a new vista for seeking intrinsic skyrmions in atomic-scale van der Waals magnets. Copyright © 2019, The Authors. All rights reserved.

关键词： Tellurium compounds

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performance evaluation of container-based high performance computing ecosystem using OpenPOWER 32nd

Performance evaluation of container-based high performance c...

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32nd International Conference on high performance computing, ISC high performance 2017

作者： Kuity, Animesh Peddoju, Sateesh Kumar High Performance Computing Lab Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India

ISBN: (纸本)9783319676296

Container-based high performance computing (HPC) has started gaining popularity due to its almost negligible performance penalty compared to the BareMetal hardware. Although HPC hardware architectures and programming models are continuously evolving, the platform models are suffering from the HPC community’s awareness. Power-awareness, hardware and application-aware co-design along with security related concerns have attracted the most in recent time to empower the platform models. Otherside, the OpenPOWER ecosystems have stepped into our life to fulfill the thirst of exploiting the last drop of a performance benefit from our invested system. It has Power8 compliant processor with a larger cache, big fat instructions and data path accompanied by the popular coherent accelerator. In this paper, we have proposed a container-based HPC ecosystem established using OpenPOWER machine. The performance of the designed and developed ecosystem is evaluated stressing on different subcomponents of the system such as processor, memory, IO, and interconnect. Finally, the results are compared with the performance of the equivalent environments made of virtual machines and BareMetal hardware. © Springer International Publishing AG 2017.

关键词： Ecosystems

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Observing Geometry of Quantum States in a Three-Level System

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Physical Review Letters 2020年第15期125卷 150401-150401页

作者： Jie Xie Aonan Zhang Ningping Cao Huichao Xu Kaimin Zheng Yiu-Tung Poon Nung-Sing Sze Ping Xu Bei Zeng Lijian Zhang National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and Manipulation (Ministry of Education) College of Engineering and Applied Sciences and School of Physics Nanjing University Nanjing 210093 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China Department of Mathematics & Statistics University of Guelph Guelph N1G 2W1 Ontario Canada Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Ontario Canada Department of Mathematics Iowa State University Ames Iowa 50011 USA Department of Applied Mathematics The Hong Kong Polytechnic University 999077 Hong Kong China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong China

In quantum mechanics, geometry has been demonstrated as a useful tool for inferring nonclassical behaviors and exotic properties of quantum systems. One standard approach to illustrate the geometry of quantum systems is to project the quantum state space onto the Euclidean space via measurements of observables on the system. Despite the great success of this method in studying two-level quantum systems (qubits) with the celebrated Bloch sphere representation, it is still difficult to reveal the geometry of multidimensional quantum systems. Here we report the first experiment measuring the geometry of such projections beyond the qubit. Specifically, we observe the joint numerical ranges of a triple of observables in a three-level photonic system, providing a complete classification of these ranges. We further show that the geometry of different classes reveals ground-state degeneracies of a Hamiltonian as a linear combination of the observables, which is related to quantum phases in the thermodynamic limit. Our results offer a versatile geometric approach for exploring the properties of higher-dimensional quantum systems.

关键词： Geometric & topological phases Quantum foundations Quantum measurements Quantum phase transitions

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Calibration of the gravitational wave telescope KAGRA 38

Calibration of the gravitational wave telescope KAGRA

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38th International Cosmic Ray Conference, ICRC 2023

作者： Chen, Dan Abe, H. Akutsu, T. Ando, M. Aoumi, M. Araya, A. Aritomi, N. Aso, Y. Bae, S. Bajpai, R. Cannon, K. Cao, Z. Chang, R.-J. Chen, A.H.-Y. Chen, D. Chen, H. Chen, Y. Chiba, A. Chiba, R. Chou, C. Eisenmann, M. Fujii, S. Fukunaga, I. Haba, D. Haino, S. Han, W.-B. Hayakawa, H. Hayama, K. Himemoto, Y. Hirata, N. Hirose, C. Hoshino, S. Hsieh, H.-F. Hsiung, C. Hsu, S.-C. Hui, D.C.Y. Inayoshi, K. Itoh, Y. Iwaya, M. Jin, H.B. Jung, K. Kajita, T. Kamiizumi, M. Kanda, N. Kato, J. Kato, T. Kim, S. Kimura, N. Kiyota, T. Kohri, K. Kokeyama, K. Komori, K. Kong, A.K.H. Koyama, N. Kume, J. Kuroyanagi, S. Kuwahara, S. Kwak, K. Lai, S. Lee, H.W. Lee, R. Lee, S. Leonardi, M. Li, K.L. Lin, L.C.-C. Lin, C.-Y. Lin, E.T. Liu, G.C. Ma, L.-T. Maeda, K. Matsuyama, M. Meyer-Conde, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, S. Miyoki, S. Morisaki, S. Moriwaki, Y. Murakoshi, M. Nakamura, K. Nakano, H. Narikawa, T. Nguyen Quynh, L. Nishino, Y. Nishizawa, A. Obayashi, K. Oh, J.J. Oh, K. Ohashi, M. Ohkawa, M. Oohara, K. Oshima, Y. Oshino, S. Page, M.A. Pan, K.-C. Park, J. Peña Arellano, F.E. Saha, S. Sakai, K. Sako, T. Sato, R. Sato, S. Sato, Y. Sawada, T. Sekiguchi, Y. Shao, L. Shikano, Y. Shimode, K. Shinkai, H. Shiota, J. Somiya, K. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takatani, K. Takeda, H. Takeda, M. Tamaki, M. Tanaka, K. Tanaka, S.J. Tanaka, T. Taruya, A. Tomaru, T. Tomita, K. Tomura, T. Toriyama, A. Trani, A.A. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueno, K. Ushiba, T. van Putten, M.H.P.M. Wang, H. Washimi, T. Wu, C. Wu, H. Yamamoto, K. Yamamoto, M. Yamamoto, T. Yamamoto, T.S. Yamamura, S. Yamazaki, R. Yang, L.-C. Yang, Y. Yeh, S.-W. Yokoyama, J. Yokozawa, T. Yuzurihara, H. Zhao, Y. Zhu, Z.-H. Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8551 Japan Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo113-0032 Japan LIGO Hanford Observatory RichlandWA99352 United States 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing100875 China Department of Physics National Cheng Kung University No.1 University Road Tainan City701 Taiwan Institute of Physics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Department of Physics National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Faculty of Science University of Toyama 3190 Gofuku Toyama City Toyama930-8555 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Chiba Kashiwa City277-8582 Japan Department of Electrophysics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Department of Physics Graduate School of Science Osaka Metropolitan University 3-3-138 Sugimoto-cho Sumiyoshi-ku Osaka City Osaka558-8585 Japan Institute of Physics Academia Sinica 128 Sec. 2 Academia Rd. Na

KAGRA is a laser interferometric gravitational wave (GW) telescope in Japan. It joined O4, the fourth international GW observation run, with other telescopes, namely, Laser Interferometer Gravitational Wave Observatory (LIGO) and Virgo. In the previous GW observation runs: O1, O2, and O3, 90 events were reported, and many more events are expected to be observed in O4. With the beginning of GW astronomy, the calibration of observed GW signals is becoming more important than ever before. The main calibration system used in KAGRA, LIGO, and Virgo is a photon calibration system (Pcal), which injects reference signals into a telescope by the radiation pressure of a laser. On the basis of the preparation and operation of KAGRA Pcals in O3, we prepared an upgrading plan for O4. This plan includes three main improvements: improving the Pcal laser beam alignment system, reducing noise, and dereasing uncertainty. Improvement works were conducted between the end of O3 and the start of O4 in accordance with this plan. The following results were obtained: first, precise remote alignment was enabled in the critical Pcal laser path region. Second, a Pcal noise source was identified and noise was significantly improved, resulting in a remarkable 50 dB reduction and sufficient reduction of the Pcal noise below the design sensitivity of KAGRA. Third, studies on the temperature dependence and laser incidence state dependence of the integrating sphere-type power meter were conducted to reduce the uncertainty of the Pcal, which is directly related to the uncertainty of the telescope signal. The details of the KAGRA Pcal and the improvements made between O3 and O4 are reported in this study. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Laser interferometry

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Development of Gravity Field Calibrator for KAGRA 38

Development of Gravity Field Calibrator for KAGRA

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38th International Cosmic Ray Conference, ICRC 2023

作者： Bajpai, Rishabh Abe, H. Akutsu, T. Ando, M. Aoumi, M. Araya, A. Aritomi, N. Aso, Y. Bae, S. Bajpai, R. Cannon, K. Cao, Z. Chang, R.-J. Chen, A.H.-Y. Chen, D. Chen, H. Chen, Y. Chiba, A. Chiba, R. Chou, C. Eisenmann, M. Fujii, S. Fukunaga, I. Haba, D. Haino, S. Han, W.-B. Hayakawa, H. Hayama, K. Himemoto, Y. Hirata, N. Hirose, C. Hoshino, S. Hsieh, H.-F. Hsiung, C. Hsu, S.-C. Hui, D.C.Y. Inayoshi, K. Itoh, Y. Iwaya, M. Jin, H.-B. Jung, K. Kajita, T. Kamiizumi, M. Kanda, N. Kato, J. Kato, T. Kim, S. Kimura, N. Kiyota, T. Kohri, K. Kokeyama, K. Komori, K. Kong, A.K.H. Koyama, N. Kume, J. Kuroyanagi, S. Kuwahara, S. Kwak, K. Lai, S. Lee, H.W. Lee, R. Lee, S. Leonardi, M. Li, K.L. Lin, L.C.-C. Lin, C.-Y. Lin, E.T. Liu, G.C. Ma, L.-T. Maeda, K. Matsuyama, M. Meyer-Conde, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, S. Miyoki, S. Morisaki, S. Moriwaki, Y. Murakoshi, M. Nakamura, K. Nakano, H. Narikawa, T. Nguyen Quynh, L. Nishino, Y. Nishizawa, A. Obayashi, K. Oh, J.J. Oh, K. Ohashi, M. Ohkawa, M. Oohara, K. Oshima, Y. Oshino, S. Page, M.A. Pan, K.-C. Park, J. Peña Arellano, F.E. Saha, S. Sakai, K. Sako, T. Sato, R. Sato, S. Sato, Y. Sawada, T. Sekiguchi, Y. Shao, L. Shikano, Y. Shimode, K. Shinkai, H. Shiota, J. Somiya, K. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takatani, K. Takeda, H. Takeda, M. Tamaki, M. Tanaka, K. Tanaka, S.J. Tanaka, T. Taruya, A. Tomaru, T. Tomita, K. Tomura, T. Toriyama, A. Trani, A.A. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueno, K. Ushiba, T. van Putten, M.H.P.M. Wang, H. Washimi, T. Wu, C. Wu, H. Yamamoto, K. Yamamoto, M. Yamamoto, T. Yamamoto, T.S. Yamamura, S. Yamazaki, R. Yang, L.-C. Yang, Y. Yeh, S.-W. Yokoyama, J. Yokozawa, T. Yuzurihara, H. Zhao, Y. Zhu, Z.-H. National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo Japan Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8551 Japan Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo113-0032 Japan LIGO Hanford Observatory RichlandWA99352 United States 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing100875 China Department of Physics National Cheng Kung University No.1 University Road Tainan City701 Taiwan Institute of Physics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Department of Physics National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Faculty of Science University of Toyama 3190 Gofuku Toyama City Toyama930-8555 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Chiba Kashiwa City277-8582 Japan Department of Electrophysics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Department of Physics Graduate School of Science Osaka Metropolitan University 3-3-138 Sugimoto-cho Sumiyoshi-ku Osaka City Osaka558-

The output of a gravitational wave telescope is a voltage signal which needs to be calibrated to get the strain signal h(t). This signal h(t) can then be used to derive various parameters of the gravitational wave sources. Any uncertainty in the calibration is directly transferred to these parameters. Therefore, absolute calibration of gravitation wave detectors using a precise reference (known) signal is essential. Currently, KAGRA uses Photon Calibrator (PCAL) to generate reference signals for calibration. PCAL injects a power-modulated laser onto the test mass to calibrate the absolute displacement of the mirror using radiation pressure. Hence, the absolute calibration of KAGRA is limited to 3% by the absolute laser power measurement uncertainty due to the deviation in the laser power standards. KAGRA proposed a new method combining PCAL with Gravity Field Calibrator (GCAL) to reduce the calibration uncertainty. GCAL modulates the test mass using a dynamic gravitational field generated by rotating multipole masses. Since the injected force by GCAL depends on the gravitational constant, mass, distance, rotation frequency, and radius, calibration uncertainty in the sub-percent region can be achieved. In this paper, we will report on the progress of the development of the gravity field calibrator and our future plans. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Laser mirrors

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Segmentation Guided HEp-2 Cell Classification with Adversarial Networks

Segmentation Guided HEp-2 Cell Classification with Adversari...

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computing, Communications and Applications Conference (ComComAp)

作者： Hai Xie Yejun He Haijun Lei Jong Yih Kuo Baiying Lei Guangdong Engineering Research Center of Base Station Antennas and Propagation Shenzhen Key Lab of Antennas and Propagation College of Electronics and Information Engineering Shenzhen University China Guangdong Province Key Laboratory of Popular High-performance Computers School of Computer and Software Engineering Shenzhen University Computer Science and Information Engineering National Taipei University of Technology Taiwan China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University China

ISBN: (数字)9781728119731

ISBN: (纸本)9781728119748

The analysis of indirect immuno fluorescence (IIF) on human epithelial type 2 (HEp-2) cells is of paramount importance for the autoimmune diseases diagnosis. Essentially, accurate segmentation masks can generate rich boundary information, which is beneficial for improving the performance of the classification task For this reason, this paper proposes a novel segmentation guided HEp-2 cell classification method via generative adversarial networks (GANs), which employs the GANs as the segmentor to generate accurate masks for the subsequent classification task. Specifically, the proposed network architecture consists of three modules (i.e., the generator, discriminator and classifier). The first two modules constitute GANs model, which is trained to obtain better segmentation results via playing a min-max game. The segmentation masks and the corresponding original images are fed to the third module together to identify the category of the trained cell. Furthermore, the Xception and ResNet-50 model are used as the backbone of the segmentation and classification network, respectively. Besides, an improved classification loss function via Gaussian Mixture (GM) is proposed to optimize the classification network The proposed architecture can learn rich boundary information and well represent the class label of HEp2 cell images. Experimental results on the HEp-2 International Conference on Pattern Recognition (ICPR) 2016 task1 dataset demonstrate our proposed model achieves quite promising performance.

关键词： Image segmentation computer architecture Task analysis Microprocessors Gallium nitride Feature extraction Generators

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Status of KAGRA 38

Status of KAGRA

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Itoh, Yousuke Abe, H. Akutsu, T. Ando, M. Aoumi, M. Araya, A. Aritomi, N. Aso, Y. Bae, S. Bajpai, R. Cannon, K. Cao, Z. Chang, R.-J. Chen, A.H.-Y. Chen, D. Chen, H. Chen, Y. Chiba, A. Chiba, R. Chou, C. Eisenmann, M. Fujii, S. Fukunaga, I. Haba, D. Haino, S. Han, W.-B. Hayakawa, H. Hayama, K. Himemoto, Y. Hirata, N. Hirose, C. Hoshino, S. Hsieh, H.-F. Hsiung, C. Hsu, S.-C. Hui, D.C.Y. Inayoshi, K. Itoh, Y. Iwaya, M. Jin, H.B. Jung, K. Kajita, T. Kamiizumi, M. Kanda, N. Kato, J. Kato, T. Kim, S. Kimura, N. Kiyota, T. Kohri, K. Kokeyama, K. Komori, K. Kong, A.K.H. Koyama, N. Kume, J. Kuroyanagi, S. Kuwahara, S. Kwak, K. Lai, S. Lee, H.W. Lee, R. Lee, S. Leonardi, M. Li, K.L. Lin, L.C.-C. Lin, C.-Y. Lin, E.T. Liu, G.C. Ma, L.-T. Maeda, K. Matsuyama, M. Meyer-Conde, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, S. Miyoki, S. Morisaki, S. Moriwaki, Y. Murakoshi, M. Nakamura, K. Nakano, H. Narikawa, T. Nguyen Quynh, L. Nishino, Y. Nishizawa, A. Obayashi, K. Oh, J.J. Oh, K. Ohashi, M. Ohkawa, M. Oohara, K. Oshima, Y. Oshino, S. Page, M.A. Pan, K.-C. Park, J. Peña Arellano, F.E. Saha, S. Sakai, K. Sako, T. Sato, R. Sato, S. Sato, Y. Sawada, T. Sekiguchi, Y. Shao, L. Shikano, Y. Shimode, K. Shinkai, H. Shiota, J. Somiya, K. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takatani, K. Takeda, H. Takeda, M. Tamaki, M. Tanaka, K. Tanaka, S.J. Tanaka, T. Taruya, A. Tomaru, T. Tomita, K. Tomura, T. Toriyama, A. Trani, A.A. Tsuchida, S. Uchikata, N. Uchiyama, T. Uehara, T. Ueno, K. Ushiba, T. van Putten, M.H.P.M. Wang, H. Washimi, T. Wu, C. Wu, H. Yamamoto, K. Yamamoto, M. Yamamoto, T. Yamamoto, T.S. Yamamura, S. Yamazaki, R. Yang, L.-C. Yang, Y. Yeh, S.-W. Yokoyama, J. Yokozawa, T. Yuzurihara, H. Zhao, Y. Zhu, Z.-H. Osaka Metropolitan University Sumiyoshi Osaka3-3-138 Japan Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8551 Japan Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo113-0032 Japan LIGO Hanford Observatory RichlandWA99352 United States 2-21-1 Osawa Mitaka City Tokyo181-8588 Japan 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing100875 China Department of Physics National Cheng Kung University No.1 University Road Tainan City701 Taiwan Institute of Physics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu506-1205 Japan Department of Physics National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Faculty of Science University of Toyama 3190 Gofuku Toyama City Toyama930-8555 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Chiba Kashiwa City277-8582 Japan Department of Electrophysics National Yang Ming Chiao Tung University 101 Univ. Street Hsinchu Taiwan Department of Physics Graduate School of Science Osaka Metropolitan University 3-3-138 Sugimoto-cho Sumiyoshi-ku Osaka City Osaka558-8585 Japan Inst

The gravitational wave detectors LIGO and Virgo have so far detected gravitational wave signals from 90 compact binary coalescences including two from neutron star binary mergers. Those detections are revolutionizing our understanding of the universe. This ongoing revolution will be accelerated by more accurate localizations of the gravitational wave sources and by longer coincident observation by multi-detectors, which will be made possible using as many detectors as possible. KAGRA is a gravitational wave detector built at Kamioka in Gifu prefecture, Japan, well away from the LIGO and Virgo detectors, with unique features of having cryogenic mirrors and being built underground. KAGRA can be a prototype for next-generation gravitational wave detectors and our experiences benefit those detectors that would have those features to achieve further sensitivity to gravitational waves. In this presentation, the status of KAGRA is given. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gravitational effects

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