检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Ye, Chen Xie, Xiangnan Lv, Wenxing Huang, Ke Yang, Allen Jian Jiang, Sicong Liu, Xue Zhu, Dapeng Qiu, Xuepeng Tong, Mingyu Zhou, Tong Hsu, Chuang-Han Chang, Guoqing Lin, Hsin Li, Peisen Yang, Kesong Wang, Zhenyu Jiang, Tian Wang, Xiao Renshaw Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Physics Laboratory Industrial Training Center Shenzhen Polytechnic Guangdong Shenzhen518055 China Department of NanoEngineering Program of Chemical Engineering University of California La Jolla San DiegoCA92093-0448 United States Program of Materials Science and Engineering University of California La Jolla San DiegoCA92093-0418 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Insitute of Physics Academia Sinica Taipei115229 Taiwan College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences PLA China Beijing100010 China Beijing Academy of Quantum Information Sciences Beijing100084 China Beijing Institute for Advanced Study National University of Defense Technology Changsha410073 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here we report an observation of nonreciprocal transport, i.e. current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators. © 2022, CC BY.

关键词： Magnetism

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Voltage Manipulation of Desired Magnetization Orientation in Multiferroic Nanomagnets

SSRN

引用

SSRN 2020年

作者： Liu, Jiahao Yang, Xiaokuo Hong, Dunhua Li, Cheng Xu, Nuo Yang, Binbin Fang, Liang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Basic Sciences Air Force Engineering University Xi’an710051 China School of Physics and Electronic Science Zhunyi Normal University Zhunyi563006 China

We report the pure voltage manipulation of the desired magnetization orientation of the multiferroic nanomagnet: through integrating spintronics and multiferroics, we investigate magnetostrictive elliptical two-step-shaped nanomagnets. A pair of electrodes is fabricated on the piezoelectric substrate to generate localized stress by applying voltage. Through the inverse magnetoelectric coupling, slightly inclined stress induces the magnetization of the nanomagnet to desired orientation at room temperature. Our method liberates the electrical manipulation of multiferroic nanomagnets from the strict requirements of the voltage waveform. The results can be effectively applied to electrical induced information storage and logic writing of low- energy-consumption spintronic devices. © 2020, The Authors. All rights reserved.

关键词： Nanomagnetics

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New metric improving Bayesian calibration of a multistage approach studying hadron and inclusive jet suppression

引用

Physical Review C 2024年第6期109卷 064903-064903页

作者： W. Fan G. Vujanovic S. A. Bass A. Angerami R. Arora S. Cao Y. Chen T. Dai L. Du R. Ehlers H. Elfner R. J. Fries C. Gale Y. He M. Heffernan U. Heinz B. V. Jacak P. M. Jacobs S. Jeon Y. Ji L. Kasper M. Kordell, II A. Kumar J. Latessa Y.-J. Lee R. Lemmon D. Liyanage A. Lopez M. Luzum A. Majumder S. Mak A. Mankolli C. Martin H. Mehryar T. Mengel J. Mulligan C. Nattrass J. Norman J.-F. Paquet C. Parker J. H. Putschke G. Roland B. Schenke L. Schwiebert A. Sengupta C. Shen C. Sirimanna D. Soeder R. A. Soltz I. Soudi M. Strickland Y. Tachibana J. Velkovska X.-N. Wang W. Zhao Department of Physics Duke University Durham North Carolina 27708 USA Department of Physics and Astronomy Department of Physics Lawrence Livermore National Laboratory Livermore California 94550 USA Research Computing Group University Technology Solutions The University of Texas at San Antonio San Antonio Texas 78249 USA Institute of Frontier and Interdisciplinary Science Shandong University Qingdao Shandong 266237 China Laboratory for Nuclear Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics McGill University Montréal QC H3A 2T8 Canada Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Physics Division Oak Ridge National Laboratory Oak Ridge Tennessee 37830 USA GSI Helmholtzzentrum für Schwerionenforschung 64291 Darmstadt Germany Institute for Theoretical Physics Goethe University 60438 Frankfurt am Main Germany Frankfurt Institute for Advanced Studies 60438 Frankfurt am Main Germany Cyclotron Institute Texas A&M University College Station Texas 77843 USA Department of Physics and Astronomy Texas A&M University College Station Texas 77843 USA Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou 510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou 510006 China Department of Physics The Ohio State University Columbus Ohio 43210 USA Department of Physics University of California Berkeley California 94270 USA Nuclear Science Division Lawrence Berkeley National Laboratory Berkeley California 94270 USA Department of Statistical Science Duke University Durham North Carolina 27708 USA Department of Physics and Astronomy Vanderbilt University Nashville Tennessee 37235 USA Department of Computer Sci

We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution at high virtuality, and (linearized) Boltzmann transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high-pT charged hadrons, D mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient q̂. To facilitate this undertaking, we develop a quantitative metric for validating the Bayesian workflow, which is used to analyze the sensitivity of various model parameters to individual observables. The usefulness of this new metric in improving Bayesian model emulation is shown to be highly beneficial for future such analyses.

关键词： Jet quenching Jets & heavy flavor physics Quark-gluon plasma Relativistic heavy-ion collisions Bayesian methods

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A Fragile Watermarking Algorithm for Speech Authentication by Modifying Least Significant Digits

A Fragile Watermarking Algorithm for Speech Authentication b...

引用

International Conference on computers, Communications, and Systems (ICCCS)

作者： Qing Qian Yunhe Cui School of Information International Joint Research Center for Data Science and High-Performance Computing Guizhou University of Finance and Economics Guiyang China School of Computer Science and Technology Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

Speech content is often faced with malicious forgery and tampering in transit. Therefore, we introduce a novel fragile watermarking algorithm by modifying the least significant digits for speech content authentication and tampering recovery. In this letter, the proposed authentication scheme consists of two processes: the first one is watermark generation and embedding, the second one is content authentication and recovery. In the former process, the compressed speech signal, frame number and check information are generated based on speech content to form a watermark. Subsequently, the generated watermark is embedded into the least significant digits of speech sampling points using the proposed watermarking algorithm. In the latter process, the embedded watermark is utilized to locate the tampered area and recover that tampered content synchronously. Experimental results show that the proposed watermarking algorithm is fragile to common signal processing. Additionally, it has favorable inaudibility, accurate location ability and high recovery quality.

关键词： Watermarking Authentication Speech coding Signal to noise ratio Speech processing

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Fuzzy Association Rule Mining Algorithm Based on Load Classifier 6th

Fuzzy Association Rule Mining Algorithm Based on Load Classi...

引用

6th International Conference on Data science, ICDS 2019

作者： Chen, Jing Zheng, Hui Li, Peng Zhang, Zhenjiang Li, Huawei Liu, Wei School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks NanjingJiangsu Province210003 China Baotou Teachers’ College of Inner Mongolia University of Science and Technology BaotouInner Mongolia014030 China School of Software Engineering Beijing Jiaotong University Beijing100044 China Institute of Computing Technology Chinese Academy of Science Beijing100044 China

ISBN: (纸本)9789811528095

In this paper, the fuzzy association algorithm based on Load Classifier is proposed to study the fuzzy association rules of numerical data flow. A method of dynamic partitioning of data blocks by load classifier for data stream is proposed, and the membership function of design optimization is proposed. The FP-Growth algorithm is used to realize the parallelization processing of fuzzy association rules. First, based on the load balancing classifier, variable window is proposed to divide the original data stream. Second, the continuous data preprocessing is performed and is converted into fuzzy interval data by the improved membership function. Finally through simulation experiments of the Load Classifier, compared with the four algorithms, the data processing time is similar after convergence, and the data processing time of SDBA (Spark Dynamic Block Adjustment Spark) is lower than 25 ms. © Springer Nature Singapore Pte Ltd 2020.

关键词： Association rules

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An Adaptive Multi-objective Salp Swarm Algorithm for Efficient Demand Side Management

An Adaptive Multi-objective Salp Swarm Algorithm for Efficie...

引用

IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Zezheng Zhao Chunqiu Xia Lian Chi Xiaomin Chang Wei Li Ting Yang Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Business School Nanjing University of Information Science and Technology China Key Laboratory of Smart Grid of Ministry of Education School of Electrical and Information Engineering Tianjin University China

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

With the continuous growth in population and energy demands more attention has been paid to energy consumption issues in residential environments. At the user-end, the home energy management system (HEMS) has been proposed as a cost-effective solution to reduce the electricity cost in households, while maintaining users' comfort and reducing the pressure on energy providers. However, it is a challenge to design a cost-effective scheduling strategies for HEMS which takes many objectives into consideration while potentially benefiting both users and providers. In our work, we propose a new approach named adaptive multi-objective salp swarm algorithm (AMSSA) based on traditional multi-objective salp swarm algorithm (MSSA) to realise a multi-objective optimisation approach for the power scheduling problem. AMSSA not only fulfils the trade-off among users' comfort, electricity cost and peak to average ratio (PAR), but also enhances the convergence speed for the overall optimisation process. Moreover, we also set up a testbed by using smart appliances and implemented our design on an edge-based energy management system. The experiment results demonstrated a reduction in both electricity cost (47.55%) and PAR (45.73%), compared with the case without a scheduling scheme.

关键词： Sociology Peak to average power ratio Sensor systems Scheduling Energy management systems Statistics Optimization

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performance of LHAASO-WCDA and observation of the Crab Nebula as a standard candle

引用

Chinese Physics C 2021年第8期45卷 166-181页

作者： F.Aharonian Q.An Axikegu L.X.Bai Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi H.Cai J.T.Cai Z.Cao Z.Cao J.Chang J.F.Chang X.C.Chang B.M.Chen J.Chen L.Chen L.Chen L.Chen M.J.Chen M.L.Chen Q.H.Chen S.H.Chen S.Z.Chen T.L.Chen X.L.Chen Y.Chen N.Cheng Y.D.Cheng S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu D.della Volpe B.D'Ettorre Piazzoli X.J.Dong J.H.Fan Y.Z.Fan Z.X.Fan J.Fang K.Fang C.F.Feng L.Feng S.H.Feng Y.L.Feng B.Gao C.D.Gao Q.Gao W.Gao M.M.Ge L.S.Geng G.H.Gong Q.B.Gou M.H.Gu J.G.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han H.H.He H.N.He J.C.He S.L.He X.B.He Y.He M.Heller Y.K.Hor C.Hou X.Hou H.B.Hu S.Hu S.C.Hu X.J.Hu D.H.Huang Q.L.Huang W.H.Huang X.T.Huang Z.C.Huang F.Ji X.L.Ji H.Y.Jia K.Jiang Z.J.Jiang C.Jin D.Kuleshov K.Levochkin B.B.Li C.Li C.Li F.Li H.B.Li H.C.Li H.Y.Li J.Li K.Li W.L.Li X.Li X.Li X.R.Li Y.Li Y.Z.Li Z.Li Z.Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu H.Liu H.D.Liu J.Liu J.L.Liu J.S.Liu J.Y.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.N.Liu Z.X.Liu W.J.Long R.Lu H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao A.Masood W.Mitthumsiri T.Montaruli Y.C.Nan B.Y.Pang P.Pattarakijwanich Z.Y.Pei M.Y.Qi B.Q.Qiao D.Ruffolo V.Rulev A.Saiz L.Shao O.Shchegolev X.D.Sheng J.R.Shi H.C.Song Yu.V.Stenkin V.Stepanov Q.N.Sun X.N.Sun Z.B.Sun P.H.T.Tam Z.B.Tang W.W.Tian B.D.Wang C.Wang H.Wang H.G.Wang J.C.Wang J.S.Wang L.P.Wang L.Y.Wang R.N.Wang W.Wang W.Wang X.G.Wang X.J.Wang X.Y.Wang Y.D.Wang Y.J.Wang Y.P.Wang Z.Wang Z.Wang Z.H.Wang Z.X.Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu S.Wu W.X.Wu X.F.Wu S.Q.Xi J.Xia J.J.Xia G.M.Xiang G.Xiao H.B.Xiao G.G.Xin Y.L.Xin Y.Xing D.L.Xu R.X.Xu L.Xue D.H.Yan C.W.Yang F.F.Yang J.Y.Yang L.L.Yang M.J.Yang R.Z.Yang S.B.Yang Y.H.Yao Z.G.Yao Y.M.Ye L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.D.Zeng T.X.Zeng W.Zeng Z.K.Zeng M.Zha X.X.Zhai B.B.Zhang H.M.Zhang H.Y.Zhang J.L.Zhang J.W.Zhang L.Zhang L.Zhang L.X.Zhang P.F.Zhang P.P.Zhang R.Zhang S.R.Zhang S.S.Zhang X.Zhang X.P.Zhang Y.Zhang Y.Zhang Y.F.Zhang Y.L.Zhang B.Zhao J.Zhao L.Zhao L.Z.Zhao S.P.Zhao F.Zheng Y.Zheng B.Zhou H.Zhou J.N.Zhou Key Laboratory of Particle Astrophyics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China TIANFU Cosmic Ray Research Center ChengduSichuanChina State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Hefei 230026China Department of Engineering Physics Tsinghua UniversityBeijing 100084China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China National Space Science Center Chinese Academy of SciencesBeijing 100190China School of Physics Peking UniversityBeijing 100871China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China School of Physics and Astronomy&School of Physics(Guangzhou) Sun Yat-sen UniversityZhuhai 519082China School of Physical Science and Technology Guangxi UniversityNanning 530004China Hebei Normal University Shijiazhuang 050024China School of Physics and Microelectronics Zhengzhou UniversityZhengzhou 450001China School of Astronomy and Space Science Nanjing UniversityNanjing 210023China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031China College of Physics Sichuan UniversityChengdu 610065China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000China School of Physics and Technology Wuhan UniversityWuhan 430072China School of Physics and Astronomy Yunnan University

The first Water Cherenkov detector of the LHAASO experiment(WCDA-1)has been operating since April *** data for the first year have been analyzed to test its performance by observing the Crab Nebula as a standard *** WCDA-1 achieves a sensitivity of 65 mCU per year,with a statistical threshold of 5 *** accomplish this,a 97.7%cosmic-ray background rejection rate around 1 TeV and 99.8%around 6 TeV with an ap proximate photon acceptance of 50%is achieved after applying an algorithm to separate gamma-induced *** angular resolution is measured using the Crab Nebula as a point source to be approximately 0.45°at 1 TeV and better than 0.2°above 6 TeV,with a pointing accuracy better than 0.05°.These values all match the design *** energy resolution is found to be 33%for gamma rays around 6 *** spectral energy distribution of the Crab Nebula in the range from 500 GeV to 15.8 TeV is measured and found to be in agreement with the results from other TeV gamma ray observatories.

关键词： LHAASO-WCDA Crab Nebula angular resolution spectral energy distribution

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Ultra-bright multiplexed energy-time entangled photon generation from lithium niobate on insulator chip

arXiv

引用

arXiv 2020年

作者： Xue, Guang-Tai Niu, Yun-Fei Liu, Xiaoyue Duan, Jia-Chen Chen, Wenjun Pan, Ying Jia, Kunpeng Wang, Xiaohan Liu, Hua-Ying Zhang, Yong Xu, Ping Zhao, Gang Cai, Xinlun Gong, Yan-Xiao Hu, Xiaopeng Xie, Zhenda Zhu, Shining National Laboratory of Solid State Microstructures School of Physics School of Electronic Science and Engineering College of Engineering and Applied Sciences Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China State Key Laboratory of Optoelectronic Materials and Technologies School of Physics and Engineering Sun Yat-sen University Guangzhou510275 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computing National University of Defense Technology Changsha410073 China

high-flux entangled photon source is the key resource for quantum optical study and application. Here it is realized in a lithium niobate on isolator (LNOI) chip, with 2.79×1011 Hz/mW photon pair rate and 1.53×109 Hz/nm/mW spectral brightness. These data are boosted by over two orders of magnitude compared to existing technologies. A 130-nm broad bandwidth is engineered for 8-channel multiplexed energy-time entanglement. Harnessed by high-extinction frequency correlation and Franson interferences up to 99.17% visibility, such energy-time entanglement multiplexing further enhances high-flux data rate, and warrants broad applications in quantum information processing on a chip. Copyright © 2020, The Authors. All rights reserved.

关键词： Photons

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Identification of proton and gamma in LHAASO-KM2A simulation data with deep learning algorithms 37

Identification of proton and gamma in LHAASO-KM2A simulation...

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Zhang, F. Zhu, F.R. Liu, S.M. Hao, Y.C. He, C. Hou, J. Li, Z. Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D'Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. School of Physical Science and Technology Southwest Jiaotong University Chengdu611756 China Graduate School of Tangshan Southwest Jiaotong University Tangshan063000 China School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Beijing100049 China Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Dipartimento di Fisica Università di Napoli "Federico II"

Identification of proton and gamma plays an essential role in ultra-high energy gamma-ray astronomy with LHAASO-KM2A. In this work, two neural networks (deep neural networks (DNN) and graph neural networks (GNN)) are applied to distinguish proton and gamma in the LHAASOKM2A simulation data. The receiver operating characteristic (ROC) curves are used to evaluate the quality of the model. Both KM2A-DNN and KM2A-GNN models give higher Area Under Curve (AUC) scores than the traditional baseline model. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

引用

arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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