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Physical Review D 2024年第2期110卷 023029-023029页

作者： Yunyang Luo Zihao Bo Shibo Zhang Abdusalam Abdukerim Wei Chen Xun Chen Chen Cheng Zhaokan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Lisheng Geng Karl Giboni Xuyuan Guo Chencheng Han Ke Han Changda He Jinrong He Di Huang Junting Huang Zhou Huang Ruquan Hou Yu Hou Xiangdong Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shuaijie Li Tao Li Qing Lin Jianglai Liu Congcong Lu Xiaoying Lu Lingyin Luo Wenbo Ma Yugang Ma Yajun Mao Yue Meng Xuyang Ning Binyu Pang Ningchun Qi Zhicheng Qian Xiangxiang Ren Nasir Shaheed Xiaofeng Shang Xiyuan Shao Guofang Shen Lin Si Wenliang Sun Andi Tan Yi Tao Anqing Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Xu Wang Zhou Wang Yuehuan Wei Mengmeng Wu Weihao Wu Yuan Wu Mengjiao Xiao Xiang Xiao Binbin Yan Xiyu Yan Yong Yang Chunxu Yu Ying Yuan Zhe Yuan Youhui Yun Xinning Zeng Minzhen Zhang Peng Zhang Shu Zhang Tao Zhang Wei Zhang Yang Zhang Yingxin Zhang Yuanyuan Zhang Li Zhao Jifang Zhou Ning Zhou Xiaopeng Zhou Yong Zhou Yubo Zhou Zhizhen Zhou Department of Modern Physics University of Science and Technology of China Hefei 230026 China School of Physics and Astronomy Shanghai Jiao Tong University Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China School of Physics Sun Yat-Sen University Guangzhou 510275 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Department of Physics Yantai University Yantai 264005 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Beihang University Beijing 102206 China International Research Center for Nuclei and Particles in the Cosmos and Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China School of Physics and Microelectronics Zhengzhou University Zhengzhou Henan 450001 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park Maryland 20742 USA State Key Laboratory of Particle Detection and Electronics New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Qingdao 266237 Shandong China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao 266237 Shandong China School of Physics Peking University Beijing 100871 China School of Physics Nankai University Tianjin 300071 China SJTU Paris Elite Insti

PandaX-4T experiment is a deep-underground dark matter direct search experiment that employs a dual-phase time projection chamber with a sensitive volume containing 3.7 tonne of liquid xenon. The detector of PandaX-4T is capable of simultaneously collecting the primary scintillation and ionization signals, utilizing their ratio to discriminate dark matter signals from background sources such as gamma rays and beta particles. The signal response model plays a crucial role in interpreting the data obtained by PandaX-4T. It describes the conversion from the deposited energy by dark matter interactions to the detectable signals within the detector. The signal response model is utilized in various PandaX-4T results. This work provides a comprehensive description of the procedures involved in constructing and parameter-fitting the signal response model for the energy range of approximately 1 keV to 25 keV for electronic recoils and 6 keV to 90 keV for nuclear recoils. It also covers the signal reconstruction, selection, and correction methods, which are crucial components integrated into the signal response model.

关键词： Particle dark matter Particle data analysis

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Geographic information science in the era of geospatial big data: A cyberspace perspective

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The Innovation 2022年第5期3卷 21-22页

作者： Xintao Liu Min Chen Christophe Claramunt Michael Batty Mei-Po Kwan Ahmad MSenousi Tao Cheng Josef Strobl Arzu Cöltekin John Wilson Temenoujka Bandrova Milan Konecny Paul MTorrens Fengyuan Zhang Li He Jinfeng Wang Carlo Ratti Olaf Kolditz Alexander Klippel Songnian Li Hui Lin Guonian Lu Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong Kong SARKowloonChina Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China The French Naval Academy Research Institute 29240 Lanveoc-PoulmicFrance The Bartlett Centre for Advanced Spatial Analysis(CASA) University College London(UCL)WC1E 6BT LondonUK Institute of Space and Earth Information Science The Chinese University of Hong KongHong Kong SARShatin 999077China Department of Civil Engineering Cairo University12613 CairoEgypt SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College London(UCL)WC1E 6BT LondonUK Department of Geoinformatics University of Salzburg5020 SalzburgAustria Institute of Interactive Technologies(IIT) University of Applied Sciences and Arts Northwestern Switzerland FHNW5210 WindischSwitzerland Spatial Sciences Institute University of Southern CaliforniaLos AngelesCA 90007USA Laboratory on Cartography Department of Photogrammetry and CartographyUniversity of ArchitectureCivil Engineering and Geodesy1164 SofiaBulgaria Laboratory on Geoinformatics and Cartography Department of GeographyMasaryk University61137 BrnoCeskáRepublika Computer Science and Engineering Tandon School&Center for Urban Science+ProgressNew York UniversityNew YorkNY 10012USA School of Humanities and Social Science Xi’an Jiaotong UniversityXi’an 710049China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Urban Studies and Planning Massachusetts Institute of TechnologyCambridgeMA 02139USA Centre for Environmental Research Helmholtz Centre for Environmental Research-UFZ04318 LeipzigGermany Laboratory of Geo-information Science and Remote Sensing Wageningen University&ResearchWageningen 6708the Netherlands Department of Civil Engineering Toronto Metropolitan UniversityTorontoON M5B 2K3Canada School of Geography and Env

The advent of information and communication technology and the Internet of Things have led our society toward a digital *** proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to *** transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different *** instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

关键词： computer cyber instance

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Search for lepton portal dark matter in the PandaX-4T experiment

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Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics 2025年 864卷

作者： Ning, Xuyang Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Gao, Zhixing Geng, Lisheng Giboni, Karl Guo, Xunan Guo, Xuyuan Guo, Zichao Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Houqi Huang, Junting Hou, Ruquan Hou, Yu Ji, Xiangdong Ji, Xiangpan Ju, Yonglin Li, Chenxiang Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Tao Li, Zhiyuan Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Lingyin Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Pang, Binyu Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shan, Dong Shang, Xiaofeng Shao, Xiyuan Shen, Guofang Shen, Manbin Sun, Wenliang Tao, Yi Wang, Anqing Wang, Guanbo Wang, Hao Wang, Jiamin Wang, Lei Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Xu Wang, Zhou Wei, Yuehuan Wu, Weihao Wu, Yuan Xiao, Mengjiao Xiao, Xiang Xiong, Kaizhi Xu, Yifan Yao, Shunyu Yan, Binbin Yan, Xiyu Yang, Yong Ye, Peihua Yu, Chunxu Yuan, Ying Yuan, Zhe Yun, Youhui Zeng, Xinning Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Shu Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zhou, Jifang Zhou, Jiaxu Zhou, Jiayi Zhou, Ning Zhou, Xiaopeng Zhou, Yubo Zhou, Zhizhen Bai, Yang Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 201210 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Dark Matter Physics Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology New Cornerstone Science Laboratory Shanghai Jiao Tong University Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province Liangshan 615000 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Department of Physics Yantai University Yantai 264005 China Key Laboratory of Nuclear Physics Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Beihang University Beijing 102206 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai 200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park 20742 MD United States School of Physics Nankai University Tianjin 300071 China School of Physics Sun Yat-Sen University Guangzhou 510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Department of Mod

We report a search for a lepton-portal dark matter model, where dark matter couples to a charged lepton in the standard model. This simplified model naturally leads to photon-mediated dark matter interactions with nuclei, making it suitable for direct dark matter detection experiments. Matching to the framework of non-relativistic effective field theory for dark matter, we report the first sensitive search for this model using data from the PandaX-4T commissioning run. Our results yield strong constraints on Dirac fermion dark matter but relatively weaker constraints on Majorana dark matter due to the suppression of effective photon interactions. These constraints complement those obtained from the collider and indirect detection experiments. © 2025 The Author(s)

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SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database

TechRxiv

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TechRxiv 2022年

作者： Li, Yongfu Zhang, Qing Zhang, Jing Yuan, Jiajun Huang, Huajie Zhang, Yuhang Zhang, Baoqin Lv, Gaomei Lin, Shuzhu Wang, Na Liu, Xin Tang, Mingyu Wang, Yahua Ma, Hui Liu, Lu Yuan, Shuhua Zhou, Hongyuan Zhao, Jian Yin, Yong Zhao, Liebin Wang, Guoxing Lian, Yong Department of Micro-Nano Electronics and MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Pediatric AI Clinical Application and Research Center Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine China Department of Respiratory Medicine Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine China China School of Computer Engineering and Science Shanghai University China Sanya Maternity and Child Care Hospital China Department of Paediatrics Taicang Affiliated Hospital of Soochow University The First People’s Hospital of Taicang China Linyi City People Hospital China Department of Paediatrics Fengcheng Hospital China Department of Pediatrics Fifth Affiliated Hospital of Harbin Medical University China Shanghai Tuoxiao Intelligent Technology Co. Ltd China

It has proved that the auscultation of respiratory sound has advantage in early respiratory diagnosis. Various methods have been raised to perform automatic respiratory sound analysis to reduce subjective diagnosis and physicians’ workload. However, these methods highly rely on the quality of respiratory sound database. In this work, we have developed the first open-access paediatric respiratory sound database, SPRSound. The database consists of 2,683 records and 9,089 respiratory sound events from 292 participants. Accurate label is important to achieve a good prediction for adventitious respiratory sound classification problem. A custom-made sound label annotation software (SoundAnn) has been developed to perform sound editing, sound annotation, and quality assurance evaluation. A team of 11 experienced paediatric physicians is involved in the entire process to establish golden standard reference for the dataset. To verify the robustness and accuracy of the classification model, we have investigated the effects of different feature extraction methods and machine learning classifiers on the classification performance of our dataset. As such, we have achieved a score of 75.22%, 61.57%, 56.71%, and © 2022, CC BY.

关键词： Pediatrics

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Measurement of the Eu( ) cross section up to 500 keV at the CSNS Back-n facility, and the stellar Eu( ) cross section at s-process temperatures

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The European Physical Journal A 2022年第12期58卷 1-16页

作者： Li, Xiankai An, Zhendong Jiang, Wei Yang, Gaole Qiu, Weiwei Liao, Zhengfa Zhuang, Ziyue Zhang, Xiaoping Chen, Shengli Guo, Chenchen Xiao, Erxi Fang, Xiao Li, Xinxiang Wang, Hongwei Hu, Xinrong Jiang, Bing Shen, Wenqing Wang, Jincheng Ren, Jie Ruan, Xichao Wang, Dexin Zhang, Su-Yalatu Luo, Wen Zhu, Zhichao Lan, Haoyang Cao, Zongwei Ma, Xu Liu, Yingdu Wang, Pusen Yang, Yi Su, Ping Deng, Xiangai He, Wanbing Ma, Yugang Ma, Chunwang Wang, Yuting Dai, Zhitao Li, Ertao He, Pengqin Tang, RenGuang Zhou, Tao Wang, Jing Yi, Han Zhang, Yue Chen, Yonghao Fan, Ruirui Gao, Keqing Li, Qiang Sun, Kang Tan, Zhixin Gu, Minhao Jing, Hantao Tang, Jingyu School of Nuclear Science and Technology University of South China Hengyang China School of Physics and Astronomy Sun Yat-sen University Zhuhai China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai China State Key Laboratory of Lunar and Planetary Sciences Macau University of Science and Technology Macau China Institute of High Energy Physics Chinese Academy of Sciences Beijing China Spallation Neutron Source Science Center Dongguan China CNSA Macau Center for Space Exploration and Science Macau China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai China University of Chinese Academy of Sciences Beijing China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing China College of Mathematics and Physics Inner Mongolia Minzu University Tongliao China Institute of Nuclear Physics Inner Mongolia Minzu University Tongliao China State Key Laboratory of Nuclear Physics and Technology and CAPT Peking University Beijing China Beijing Laser Acceleration Innovation Center Huairou Beijing China Shool of Materials Science and Engineering Xiangtan University Xiangtan China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern PhysicsDepartment of Nuclear Science and Technology Fudan University Shanghai China Institute of Particle and Nuclear Physics Henan Normal University Xinxiang China School of Physics Henan Normal University Xinxiang China Shenzhen Center Cancer hospital Chinese Academy of Medical Sciences Shenzhen China Institute for Advanced Study in Nuclear Energy and Safety College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China

In nuclear astrophysics, the nuclides $$^{151}$$ Eu and $$^{153}$$ Eu are both in the path of the s-process, and their (n, $$\gamma $$ ) cross sections are important input parameters for the calculation of the nuclear astrophysics network. According to the EXFOR database, the neutron capture cross section of natural europium in the resonance region has not been fully measured. The (n, $$\gamma $$ ) cross section of $$^{nat}$$ Eu was measured using the time-of-flight (TOF) technique at the Back-n white neutron source facility of the China Spallation Neutron Source (CSNS) in the 1 eV–to 500 keV range. Four C $$_{6}$$ D $$_{6}$$ liquid scintillator detectors and pulse height weighting techniques were used to measure prompt $$\gamma $$ rays and analyze the data. The results of the analysis were compared with the evaluated data of ENDF/B-VIII.0 and JENDL-5.0. It shows that there are some differences between the measurement results and different evaluated data libraries. The resonance parameters of $$^{151}$$ Eu and $$^{153}$$ Eu were extracted in the 1 eV–150 eV region using the R-Matrix code SAMMY. The accurate Maxwellian-averaged capture cross section (MACS) is derived over the temperature range of the s-process nucleosynthesis model. The result shows that at kT=30 keV, the MACS value of $$^{151}$$ Eu is 3417 ± 297 mb and the value of $$^{153}$$ Eu is 2718 ± 237 mb.

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First Search for the Absorption of Fermionic Dark Matter with the PandaX-4T Experiment

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Physical Review Letters 2022年第16期129卷 161803-161803页

作者： Linhui Gu Abdusalam Abdukerim Zihao Bo Wei Chen Xun Chen Yunhua Chen Chen Cheng Yunshan Cheng Zhaokan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Xuyuan Guo Ke Han Changda He Jinrong He Di Huang Yanlin Huang Zhou Huang Ruquan Hou Xiangdong Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shu Li Shuaijie Li Qing Lin Jianglai Liu Xiaoying Lu Lingyin Luo Yunyang Luo Wenbo Ma Yugang Ma Yajun Mao Nasir Shaheed Yue Meng Xuyang Ning Ningchun Qi Zhicheng Qian Xiangxiang Ren Changsong Shang Xiaofeng Shang Guofang Shen Lin Si Wenliang Sun Andi Tan Yi Tao Anqing Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Yuehuan Wei Mengmeng Wu Weihao Wu Jingkai Xia Mengjiao Xiao Xiang Xiao Pengwei Xie Binbin Yan Xiyu Yan Jijun Yang Yong Yang Yukun Yao Chunxu Yu Jumin Yuan Ying Yuan Xinning Zeng Dan Zhang Minzhen Zhang Peng Zhang Shibo Zhang Shu Zhang Tao Zhang Yuanyuan Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou Yong Zhou Yubo Zhou School of Physics and Astronomy Shanghai Jiao Tong University Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China School of Physics Sun Yat-Sen University Guangzhou 510275 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Qingdao 266237 Shandong China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao 266237 Shandong China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China School of Physics Nankai University Tianjin 300071 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Peking University Beijing 100871 China School of Physics Beihang University Beijing 102206 China International Research Center for Nuclei and Particles in the Cosmos and Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China School of Physics and Microelectronics Zhengzhou University Zhengzhou Henan 450001 China School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai 200093 China Department of Physics University of Maryland College Park Maryland 20742 USA School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Department of Modern Physics University of Science and Technology of China Hefei 230026 China SJTU Pa

Compared with the signature of dark matter elastic scattering off nuclei, the absorption of fermionic dark matter by nuclei opens up a new searching channel for light dark matter with a characteristic monoenergetic signal. In this Letter, we explore the 95.0-day data from the PandaX-4T commissioning run and report the first dedicated searching results of the fermionic dark matter absorption signal through a neutral current process. No significant signal was found, and the lowest limit on the dark matter-nucleon interaction cross section is set to be 1.5×10−50 cm2 for a fermionic dark matter mass of 40 MeV/c2 with 90% confidence level.

关键词： Particle dark matter Dark matter detectors

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Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T

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Physical Review Letters 2022年第16期129卷 161804-161804页

作者： Dan Zhang Abdusalam Abdukerim Zihao Bo Wei Chen Xun Chen Yunhua Chen Chen Cheng Zhaokan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Ke Han Changda He Jinrong He Di Huang Yanlin Huang Zhou Huang Ruquan Hou Xiangdong Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shu Li Shuaijie Li Qing Lin Jianglai Liu Xiaoying Lu Lingyin Luo Yunyang Luo Wenbo Ma Yugang Ma Yajun Mao Nasir Shaheed Yue Meng Xuyang Ning Ningchun Qi Zhicheng Qian Xiangxiang Ren Changsong Shang Xiaofeng Shang Guofang Shen Lin Si Wenliang Sun Andi Tan Yi Tao Anqing Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Yuehuan Wei Mengmeng Wu Weihao Wu Jingkai Xia Mengjiao Xiao Xiang Xiao Pengwei Xie Binbin Yan Xiyu Yan Jijun Yang Yong Yang Chunxu Yu Jumin Yuan Ying Yuan Xinning Zeng Minzhen Zhang Peng Zhang Shibo Zhang Shu Zhang Tao Zhang Yuanyuan Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou Yong Zhou Yubo Zhou Shao-Feng Ge Xiao-Gang He Xiao-Dong Ma Jie Sheng Department of Physics University of Maryland College Park Maryland 20742 USA School of Physics and Astronomy Shanghai Jiao Tong University Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China School of Physics Sun Yat-Sen University Guangzhou 510275 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China School of Physics Nankai University Tianjin 300071 China Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Peking University Beijing 100871 China School of Physics Beihang University Beijing 102206 China International Research Center for Nuclei and Particles in the Cosmos and Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China School of Physics and Microelectronics Zhengzhou University Zhengzhou Henan 450001 China School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai 200093 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Department of Modern Physics University of Science and Technology of China Hefei 230026 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Scienc Shandong University Qingdao 266237 Shandong China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao 266237 Shandong China SJTU Par

We report a search on sub-MeV fermionic dark matter absorbed by electrons with an outgoing active neutrino using the 0.63 tonne year exposure collected by the PandaX-4T liquid xenon experiment. No significant signals are observed over the expected background. The data are interpreted into limits to the effective couplings between such dark matter and the electron. For axial-vector or vector interactions, our sensitivity is competitive in comparison to existing astrophysical bounds on the decay of such a dark matter candidate into photon final states. In particular, we present the first direct detection limits for a vector (axial-vector) interaction which are the strongest in the mass range from 35 to 55 (25 to 45) keV/c2 in comparison to other astrophysical and cosmological constraints.

关键词： Effective field theory Fermions Particle dark matter Dark matter detectors Multi-purpose particle detectors

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Results of dark matter search using the full PandaX-Ⅱ exposure

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Chinese Physics C 2020年第12期44卷 184-200页

作者： Qiuhong Wang Abdusalam Abdukerim Wei Chen Xun Chen Yunhua Chen Chen Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Ke Han Changda He Di Huang Yan Huang Yanlin Huang Zhou Huang Xiangdong Ji Yonglin Ju Shuaijie Li Huaxuan Liu Jianglai Liu Wenbo Ma Yugang Ma Yajun Mao Yue Meng Kaixiang Ni Jinhua Ning Xuyang Ning Xiangxiang Ren Changsong Shang Lin Si Guofang Shen Andi Tan Anqing Wang Hongwei Wang Meng Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Mengmeng Wu Shiyong Wu Weihao Wu Jingkai Xia Mengjiao Xiao Pengwei Xie Binbin Yan Jijun Yang Yong Yang Chunxu Yu Jumin Yuan Ying Yuan Xinning Zeng Dan Zhang Tao Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou INPAC and School of Physics and Astronomy Shanghai Jiao Tong UniversityMOE Key Lab for Particle PhysicsAstrophysics and CosmologyShanghai Key Laboratory for Particle Physics and CosmologyShanghai 200240China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China University of Chinese Academy of Sciences Beijing 100049China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213China Yalong River Hydropower Development Company Ltd.288 Shuanglin RoadChengdu 610051China School of Physics Sun Yat-Sen UniversityGuangzhou 510275China Tsung-Dao Lee Institute Shanghai 200240China School of Physics Nankai UniversityTianjin 300071China Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China School of Physics Peking UniversityBeijing 100871China School of Physics Beihang UniversityBeijing 100191China International Research Center for Nuclei and Particles in the Cosmos&Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing 100191China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Physics University of MarylandCollege ParkMaryland 20742USA School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physics and Key Laboratory of Particle Physics and Particle Irradiation(MOE) Shandong UniversityJinan 250100China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Center for High Energy Physics Peking UniversityBeijing 100871China

We report the dark matter search results obtained using the full 132 ton·day exposure of the PandaX-II experiment,including all data from March 2016 to August *** significant excess of events is identified above the expected *** limits are set on the spin-independent dark matter-nucleon *** lowest 90%confidence level exclusion on the spin-independent cross section is 2.2×10−46 cm2 at a WIMP mass of 30 GeV/c2.

关键词： dark matter direct detection liquid xenon

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

arXiv

引用

arXiv 2022年

作者： Han, Chu Pan, Xipeng Yan, Lixu Lin, Huan Li, Bingbing Yao, Su Lv, Shanshan Shi, Zhenwei Mai, Jinhai Lin, Jiatai Zhao, Bingchao Xu, Zeyan Wang, Zhizhen Wang, Yumeng Zhang, Yuan Wang, Huihui Zhu, Chao Lin, Chunhui Mao, Lijian Wu, Min Duan, Luwen Zhu, Jingsong Hu, Dong Fang, Zijie Chen, Yang Zhang, Yongbing Li, Yi Zou, Yiwen Yu, Yiduo Li, Xiaomeng Li, Haiming Cui, Yanfen Han, Guoqiang Xu, Yan Xu, Jun Yang, Huihua Li, Chunming Liu, Zhenbing Lu, Cheng Chen, Xin Liang, Changhong Zhang, Qingling Liu, Zaiyi The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi541004 China The Department of Pathology Guangdong Provicial People's Hospital Ganzhou Hospital Ganzhou China The College of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer Science and Engineering South China University of Technology Guangzhou510640 China The Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China The School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China The Department of Radiology Guangzhou First People's Hospital Hospital of South China University of Technology Guangzhou510180 China The Zhejiang Dahua Technology Co. Ltd Zhejiang310053 China The School of Biomedical Engineering University of Science and Technology of China Hefei230022 China The Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Electronic and Computer Engineering & Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong The School of Automation Science and Engineering Xi'An Jiaotong University Xian710049 Chi

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques for histopathology images of LUAD. Participants have to design the algorithm to segment tumor epithelial, tumor-associated stroma and normal tissue with only patch-level labels. This challenge includes 10,091 patch-level annotations (the training set) and over 130 million labeled pixels (the validation and test sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated by a pathologist-in-the-loop pipeline with the help of AI models and checked by the label review board. Among 532 registrations, 28 teams submitted the results in the test phase with over 1,000 submissions. Finally, the first place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919). According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS. Cutmix data augmentation has been widely adopted to generate more reliable samples. With the success of this challenge, we believe that WSSS approaches with patch-level annotations can be a complement to the traditional pixel annotations while reducing the annotation efforts. The entire dataset has been released to encourage more researches on computational pathology in LUAD and more novel WSSS techniques. © 2022, CC BY.

关键词： Semantic Segmentation

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