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10th Asian Conference on Applied Superconductivity and Cryogenics, ACASC 2020, 2nd International Cryogenic Materials Conference in Asia, Asian-ICMC 2020 and CSSJ Joint Conference

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Aso, Y. Bae, S.-W. Bae, Y.-B. Baiotti, L. Bajpai, R. Barton, M.A. Cannon, K. Capocasa, E. Chan, M.-L. Chen, C.-S. Chen, K.-H. Chen, Y.-R. Chu, H.-Y. Chu, Y.-K. Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fukunaga, M. Fukushima, M. Ge, G.-G. Hagiwara, A. Haino, S. Hasegawa, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B.-H. Huang, G.-Z. Huang, P.-W. Huang, Y.-J. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Itoh, Y. Izumi, K. Jung, K. Jung, P. Kajita, T. Kamiizumi, M. Kanda, N. Kang, G.-W. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, W. 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.-M. Kuo, H.-S. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H.-K. Lee, H.-W. Lee, R.-K. Leonardi, M. Lin, C.-Y. Lin, F.-L. Lin, L.C.-C. Liu, G.-C. Luo, L.-W. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Narikawa, T. Negishi, R. Ni, W.-T. Nishizawa, A. Obuchi, Y. Ogaki, W. Oh, J.J. Oh, S.-H. Ohashi, M. Ohishi, N. Ohkawa, M. Okutomi, K. Oohara, K. Ooi, C.-P. Oshino, S. Pan, K.-C. Pang, H.-F. Park, J. Peiia Arellano, F.E. Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E.J. Sotani, H. Sugimoto, R. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H. Takahashi, R. Takamori, A. Takano, S. Takeda, H. Takeda, M. Tanaka, H. Tanaka, K. Tanaka, K. Tanaka, T. Tanaka, T. Tanioka, S. Tapia San Martin, E.N. Telada, S. Tomaru, T. Tomigami, Y. 2-21-1 Osawa Mitaka-City Tokyo181-8588 Japan University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Physics University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa-City Chiba277-8582 Japan 1-1 Oho Tsukuba-City Ibaraki305-0801 Japan Earthquake Research Institute University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo113-0032 Japan 238 Higashi-Mozumi Kamioka-cho Hida-City Gifu Pref. Japan 2-21-1 Osawa Mitaka-City Tokyo181-8588 Japan 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of National Institute for Mathematical Sciences Yuseong-daero 1689 beon-gil 70 Daejeon Yuseong-gu34047 Korea Republic of Graduate School of Science Osaka University 1-1 Machikaneyama-cho Toyonaka-City Osaka560-0043 Japan 1-1 Oho Tsukuba-City Ibaraki305-0801 Japan Institute for Gravitational Research University of Glasgow Kelvin BuildingG12 8QQ United Kingdom Department of Applied Physics Fukuoka University Jonan Nanakuma Fukuoka814-0180 Japan Department of Physics Tamkang University No.151 Yingzhuan Rd. Danshui Dist. New Taipei-City25137 Taiwan Department of Physics Center for High Energy and High Field Physics National Central University No. 300 Zhongda Road Zhongyi District Taiyuan-City320 Taiwan Institute of Astronomy National Tsing Hua University No. 101 Kuang-Fu Road Section 2 Hsinchu30013 Taiwan Department of Physics National Tsing Hua University No. 101 Kuang-Fu Road Section 2 Hsinchu30013 Taiwan Department of Physics National Taiwan Normal University Sceince Campus National Taiwan Normal University Taipei116 Taiwan Univ. Grenoble Alpes Universite Savoie Mont Blanc CNRS IN2P3 AnnecyF-74941 France Department of Astronomy University of Tokyo 2-21-1 Osawa Mitaka-City Tokyo181-8588 Japan 2-21-1 Osawa Mitaka-City Tokyo181-8588 Japan State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of

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

关键词： Gravity waves

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A novel unsupervised method for new word extraction

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Science China(Information Sciences) 2016年第9期59卷 11-21页

作者： Lili MEI Heyan HUANG Xiaochi WEI Xianling MAO Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Department of Computer Science and TechnologyBeijing Institute of Technology

New words could benefit many NLP tasks such as sentence chunking and sentiment analysis. However, automatic new word extraction is a challenging task because new words usually have no fixed language pattern, and even appear with the new meanings of existing words. To tackle these problems, this paper proposes a novel method to extract new words. It not only considers domain specificity, but also combines with multiple statistical language knowledge. First, we perform a filtering algorithm to obtain a candidate list of new words. Then, we employ the statistical language knowledge to extract the top ranked new words. Experimental results show that our proposed method is able to extract a large number of new words both in Chinese and English corpus, and notably outperforms the state-of-the-art methods. Moreover, we also demonstrate our method increases the accuracy of Chinese word segmentation by 10% on corpus containing new words.

关键词： new word extraction word segmentation domain specificity statistical language knowledge domain word extraction

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Correlation-compressed direct-coupling analysis

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Physical Review E 2018年第3期98卷 032407-032407页

作者： Chen-Yi Gao Hai-Jun Zhou Erik Aurell Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha Hunan 410081 China Department of Computational Biology KTH Royal Institute of Technology 10044 Stockholm Sweden Department of Applied Physics and Department of Computer Science Aalto University 00076 Aalto Finland

Learning Ising or Potts models from data has become an important topic in statistical physics and computational biology, with applications to predictions of structural contacts in proteins and other areas of biological data analysis. The corresponding inference problems are challenging since the normalization constant (partition function) of the Ising or Potts distribution cannot be computed efficiently on large instances. Different ways to address this issue have resulted in a substantial amount of methodological literature. In this paper we investigate how these methods could be used on much larger data sets than studied previously. We focus on a central aspect, that in practice these inference problems are almost always severely undersampled, and the operational result is almost always a small set of leading predictions. We therefore explore an approach where the data are prefiltered based on empirical correlations, which can be computed directly even for very large problems. Inference is only used on the much smaller instance in a subsequent step of the analysis. We show that in several relevant model classes such a combined approach gives results of almost the same quality as inference on the whole data set. It can therefore provide a potentially very large computational speedup at the price of only marginal decrease in prediction quality. We also show that the results on whole-genome epistatic couplings that were obtained in a recent computation-intensive study can be retrieved by our approach. The method of this paper hence opens up the possibility to learn parameters describing pairwise dependences among whole genomes in a computationally feasible and expedient manner.

关键词： Sequencing analysis

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Estimating the urban heat-related mortality burden due to greenness: a global modelling study

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The Lancet Planetary Health 2025年

作者： Wu, Yao Wen, Bo Ye, Tingting Huang, Wenzhong Liu, Yanming Gasparrini, Antonio Sera, Francesco Tong, Shilu Lavigne, Eric Roye, Dominic Achilleos, Souzana Ryti, Niilo Pascal, Mathilde Zeka, Ariana das Neves Pereira da Silva, Susana Madureira, Joana Mistry, Malcolm Armstrong, Ben Bell, Michelle L Schwartz, Joel Guo, Yuming Li, Shanshan Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Environment & Health Modelling Lab Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom Department of Statistics Computer Science and Applications “G Parenti” University of Florence Florence Italy National Institute of Environmental Health Chinese Center for Disease Control and Prevention Beijing China School of Public Health and Social Work Queensland University of Technology Brisbane QLD Australia School of Epidemiology & Public Health Faculty of Medicine University of Ottawa Ottawa ON Canada Environmental Health Science and Research Bureau Health Canada Ottawa ON Canada Biological Mision of Galicia (MBG) Spanish Council for Scientific Research (CSIC) Madrid Spain Climate Research Foundation (FIC) CIBER of Epidemiology and Public Health Madrid Spain Department of Primary Care and Population Health University of Nicosia Medical School Nicosia Cyprus Center for Environmental and Respiratory Health Research (CERH) and Medical Research Center Oulo (MRC Oulu) University of Oulu Oulu Finland Santé Publique France Department of Environmental and Occupational Health French National Public Health Agency Saint Maurice France UK Health Security Agency London United Kingdom Department of Epidemiology Lazio Regional Health Service ASL Roma 1 Rome Italy Department of Epidemiology Instituto Nacional de Saúde Dr Ricardo Jorge Lisbon Portugal Department of Environmental Health Instituto Nacional de Saúde Dr Ricardo Jorge Lisbon Portugal EPIUnit Instituto de Saúde Pública Universidade do Porto Porto Portugal Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional Porto Portugal Department of Economics Ca' Foscari University of Venice Venice Italy School of

Background: Heat exposure poses a substantial public health threat. Increasing greenness has been suggested as a mitigation strategy due to its cooling effect and potential to modify the heat–mortality association. This study aimed to comprehensively estimate the effects of increased greenness on heat-related deaths. Methods: We applied a multistage meta-analytical approach to estimate the potential reduction in global heat-related deaths by increasing greenness in the warm season in 2000–19 in 11 534 urban areas. We used the enhanced vegetation index (EVI) to indicate greenness and a random forest model to predict daily temperatures in counterfactual EVI scenarios. In the factual EVI scenarios, daily mortality and weather variables from 830 locations in 53 countries were extracted from the Multi-Country Multi-City Collaborative Research Network and used to assess heat–mortality associations. These associations were then extrapolated to each urban area under both factual and counterfactual EVI scenarios based on meta-regression models. Findings: We estimated that EVI increased by 10% would decrease the global population-weighted warm-season mean temperature by 0·08°C, EVI increased by 20% would decrease temperature by 0·14°C, and EVI increased by 30% would decrease temperature by 0·19°C. In the factual scenario, 3 153 225 (2·48%) of 127 179 341 total deaths could be attributed to heat exposure. The attributable fraction of heat-related deaths (as a fraction of total deaths) in 2000–19 would decrease by 0·67 (95% empirical CI 0·53–0·82) percentage points in the 10% scenario, 0·80 (0·63–0·97) percentage points in the 20% scenario, and 0·91 (0·72–1·10) percentage points in the 30% scenario, compared with the factual scenario. South Europe was modelled to have the largest decrease in attributable fraction of heat-related mortality. Interpretation: This modelling study suggests that increased greenness could substantially reduce the heat-related mortality burden. Preserv

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Scene Parsing with Deep Features and Per-Exemplar Detectors

Scene Parsing with Deep Features and Per-Exemplar Detectors

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2017中国智能自动化会议(CIAC 2017)

作者： Xiaofei Cui Hanbing Qu Xi Chen Ziliang Qi Liang Dong School of Computer Science and Engineering Hebei University of Technology Beijing Institute of New Technology Applications Beijing Academy of Science and Technology

1 Introduction This paper addresses the problem of scene parsing,i.e.,labeling each pixel in an image with its semantic category,which is a challenging task in computer *** involves many types of problems,e.g.,object detection problems,image segmentation problems,and multi-label recognition *** the past few years,scene parsing had been extensively studied.

关键词： image Scene Parsing with Deep Features and Per-Exemplar Detectors

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Boron-doped microporous nano carbon as cathode material for high-performance Li-S batteries

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Nano Research 2017年第2期10卷 426-436页

作者： Feng Wu Ji Qian Weiping Wu Yusheng Ye Zhiguo Sun Bin Xu Xiaoguang Yang Yuhong Xu Jiatao Zhang Renjie Chen Beijing Key Laboratory of Environmental Science and Engineering School of Material Science and Engineering Beijing Institute ofTechnology Beijing 10008 I China Collaborative Innovation Center of Electric Vehicles in 8eijing Beijing 100081 China School of Computer Science Mathematics and Engineering City University of London Northampton Square London EC 1 V OHB UK State Key Laboratory of Chemical Resource Engineering Beijing Key Laboratory of Electrochemical Process and Technology for Materials 8eijing University of Chemical Technology 8eijing 100029 China Research and Advanced Engineering Ford Motor Company M148121 USA Electrified Powertrain Engineering Ford Motor Research and Engineering (Nanjing) Co. Ltd. Nanjing 211100 China Beijing Key Laboratory of Construction-Tailorable Advanced Functional Materials and Green Applications School of MaterialScience and Engineering 8eijing Institute of Technology Beijing 100081 China

In this study, a boron-doped microporous carbon （BMC）/sulfur nanocomposite is synthesized and applied as a novel cathode material for advanced Li-S batteries. The cell with this cathode exhibits an ultrahigh cycling stability and rate capability. After activation, a capacity of 749.5 mAh/g was obtained on the 54t＂ cycle at a discharge current of 3.2 A/g. After 500 cycles, capacity of 561.8 mAh/g remained （74.96% retention）, with only a very small average capacity decay of 0.056%. The excellent reversibility and stability of the novel sulfur cathode can be attributed to the ability of the boron-doped microporous carbon host to both physically confine polysulfides and chemically bind these species on the host surface. Theoretical calculations confirm that boron-doped carbon is capable of significantly stronger interactions with the polysulfide species than undoped carbon, most likely as a result of the lower electronegativity of boron. We believe that this doping strategy can be extended to other metal-air batteries and fuel cells, and that it has promising potential for many different applications.

关键词： boron-doping microporous carbon binding energy Li-S batteries

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Near duplicate product image detection based on binary hashing 17

Near duplicate product image detection based on binary hashi...

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2017 International Conference on Deep Learning Technologies, ICDLT 2017

作者： An, Shan Huang, Zhibiao Weng, Dennis Chen, Yu Beijing Jingdong Shangke Information Technology Co. Ltd. North Star Century Center Beijng China Chengdu Institute of Computer Applications Chinese Academy of Sciences Wuhou District Renminnan Road No.9 Chendu China

ISBN: (纸本)9781450352321

This paper proposes a near duplicate product image detection system for large scale datasets based on binary hashing. The product images are represented using global features generated by deep neural networks. An automatic evaluation method is used to choose the best feature description. A compact feature description is learnt using discriminative metric learning. A method called subspace learning for hashing is used to index images. A distributed system is designed to process large scale product images, which utilize five strategies including removing the logo area of the product images, accelerating Hamming distance computation by using SSE2, filtering results using color information, dividing the dataset into buckets, distributing the computing using Spark clusters. The experimental results show that the system can detect near duplicate product images in large scale datasets rapidly and accurately. 2017 Copyright is held by the owner/author(s).

关键词： Deep neural networks

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Deep Transfer Learning for Power Substation Recognition with Google Earth 15

Deep Transfer Learning for Power Substation Recognition with...

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15th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2018

作者： Peng, Jing Liu, Yong-Bo Yima, Y. He, Yong Zheng, Ze-Zhong Zhang, Ya-Meng Li, Jiang Yunnan Electric Power Research Institute Yunnan Power Grid Co. Ltd. Kunming650217 China School of Resources and Environment University of Electronic Science and Technology of China Chengdu611731 China Sichuan Institute of Geo-Environment Monitoring Chengdu Sichuan610081 China State Key Laboratory of Remote Sensing Science Beijing100101 China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Land and Resources Shenzhen518040 China Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin541004 China State Key Laboratory of Water Resources and Hydropower Engineering Sciences Wuhan University Wuhan430072 China Department of Electrical and Computer Engineering Old Dominion University Norfolk23529 United States

ISBN: (纸本)9781728115351

Power substation is a vital component of power transmission systems. However, their location information, longitude and latitude, maybe incorrect due to human errors. For example, an employee of a power grid company may carelessly input wrong location information to the system database. In practice, these errors residue in database and are difficult to be corrected or even unaware of because human naturally are insensitive to these long numbers. Correct location information is critical and it will ensure destroyed power substations to be quickly pinpointed and repaired after earthquake. In this paper, we develop a deep transfer learning approach to automatically verify power substation location information based upon Google Earth (GE)images. We first extract an image patch for a substation from GE using the recorded location information in database. We then apply the deep transfer learning model to verify whether the image patch contains a power substation. We validated the proposed method on more than 1000 substations in Yunnan Electric Power Grid. Our experimental results showed that the Xception deep transfer model achieved the best accuracy of 98.5% among four compared machine learning methods with an image size of 299×299 pixels. The developed model is a promising tool to verify the location information of power substations in database. © 2018 IEEE.

关键词： Database systems

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Utilizing Crowdsourcing for the Construction of Chinese-Mongolian Speech Corpus with Evaluation Mechanism

Utilizing Crowdsourcing for the Construction of Chinese-Mong...

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The Third International Conference of Pioneering computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Rihai Su Shumin Shi Meng Zhao Heyan Huang School of Computer Sciences and Technology Beijing Institute of Technology Beijing Engineering Research Centre of High Volume Language Information Processing and Cloud Computing Applications

Crowdsourcing has been used recently as an alternative to traditional costly annotation by many natural language processing groups. In this paper, we explore the use of Wechat Official Account Platform(WOAP) in order to build a speech corpus and to assess the feasibility of using WOAP followers(also known as contributors) to assemble speech corpus of Mongolian. A Mongolian language qualification test was used to filter out potential non-qualified participants. We gathered natural speech recordings in our daily life, and constructed a ChineseMongolian Speech Corpus(CMSC) of 31472 utterances from 296 native speakers who are fluent in Mongolian, totalling 30.8 h of speech. Then,an evaluation experiment was performed, in where the contributors were asked to choose a correct sentence from a multiple choice list to ensure the high-quality of corpus. The results obtained so far showed that crowdsourcing for constructing CMSC with an evaluation mechanism could be more effective than traditional experiments requiring expertise.

关键词： Crowdsourcing Chinese-Mongolian corpus Speech corpus WOAP Evaluation mechanism

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Multi-Features Group Emotion Analysis Based on CNN for Weibo Events

Multi-Features Group Emotion Analysis Based on CNN for Weibo...

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2017 2nd International Conference on computer Engineering,Information Science and Internet technology(CII 2017)

作者： SHUMIN SHI MENG ZHAO JUN GUAN HEYAN HUANG School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications

With the development of social network,group emotion analysis on social media such as Facebook,Twitter and Weibo becomes a new trend in recent *** different methods have been proposed for group emotion analysis,including traditional methods like SVM and NB and deep learning methods like RNN and *** paper proposes a CNN model with *** first analyze the characteristic of weibos to collect features including basic features,user-based features and content-based *** introduce these features to our CNN model to analyze emotions for Weibo events,which has been proved in experiment that it is effective to get the accurate sentiment of *** crawl 5,319,687 weibos about 724 events from Sina Weibo and apply several feature matrices to classify them to 4 *** use the new model to analyze 4 kinds of events to get the group *** results suggest that the classification we suggested can capture the emotions within different event group.

关键词： Social media Sentiment analysis Group emotion Deep learning Convolutional neural networks

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