检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Fan, Annan Huang, Guang-Yao Liang, Shi-Dong School of Physics Sun Yat-Sen University Guangzhou510275 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China State Key Laboratory of Optoelectronic Material and Technology Guangdong Province Key Laboratory of Display Material and Technology Sun Yat-Sen University Guangzhou510275 China

We propose a pair of the complex Berry curvatures associated with the non-Hermitian Hamiltonian and its Hermitian adjoint to reveal new physics in non-Hermitian systems. We give the complex Berry curvature and Berry phase for the two-dimensional non-Hermitian Dirac model. The imaginary part of the complex Berry phase induces the quantum Hall susceptance such that the quantum Hall conductance is generalized to quantum Hall admittance for non-Hermitian systems, which implies that the non-Hermiticity of systems could induce an intrinsic quantum capacitance or inductance of systems depending on the non-Hermitian parameters. We analyze the complex energy band structures of the two-dimensional non-Hermitian Dirac model and demonstrate the point and line gaps and their closings as the exceptional degeneracy of the energy bands in the parameter space, which are associated with topological phases. In the continuum limit, we obtain the complex Berry phase in the parameter space. © 2021, CC BY-NC-ND.

关键词： Fruits

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Privacy-Preserving Decision Protocols Based on Quantum Oblivious key Distribution

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computers, Materials & Continua 2020年第9期64卷 1915-1928页

作者： Kejia Zhang Chunguang Ma Zhiwei Sun Xue Zhang Baomin Zhou Yukun Wang School of Computer Science and Technology Harbin Engineering UniversityHarbin150001China School of Mathematical Science Heilongjiang UniversityHarbin150080China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China Center for Quantum Computing Peng Cheng LaboratoryShenzhen518055China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Shenzhen PolytechnicShenzhen518055China Department of Electrical and Computer Engineering National University of Singapore117583Singapore

Oblivious key transfer(OKT)is a fundamental problem in the field of secure multi-party *** makes the provider send a secret key sequence to the user obliviously,i.e.,the user may only get almost one bit key in the sequence which is unknown to the ***,a number of works have sought to establish the corresponding quantum oblivious key transfer model and rename it as quantum oblivious key distribution(QOKD)from the well-known expression of quantum key distribution(QKD).In this paper,a new QOKD model is firstly proposed for the provider and user with limited quantum capabilities,where both of them just perform computational basis measurement for single *** we show that the privacy for both of them can be protected,since the probability of getting other’s raw-key bits without being detected is exponentially ***,we give the solutions to some special decision problems such as set-member decision and point-inclusion by announcing the improved shifting strategies followed ***,the further discussions and applications of our ideas have been presented.

关键词： Quantum cryptography quantum computing privacy-preserving quantum oblivious key distribution set-member decision point-inclusion decision

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution

arXiv

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arXiv 2024年

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code will be publicly released. © 2024, CC BY.

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Characterization of state-Preparation Uncertainty in Quantum key Distribution

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Physical Review Applied 2023年第1期19卷 014048-014048页

作者： Anqi Huang Akihiro Mizutani Hoi-Kwong Lo Vadim Makarov Kiyoshi Tamaki Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 People’s Republic of China Graduate School of Engineering Science Osaka University Toyonaka Osaka 560-8531 Japan Department of Electrical & Computer Engineering and Department of Physics Centre for Quantum Information and Quantum Control (CQIQC) University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia Faculty of Engineering University of Toyama Gofuku 3190 Toyama 930-8555 Japan

To achieve secure quantum key distribution, all imperfections in the source unit must be incorporated in a security proof and measured in the lab. Here we perform a proof-of-principle demonstration of the experimental techniques for characterizing the source phase and intensity fluctuation in commercial quantum key distribution systems. When we apply the measured phase-fluctuation intervals to the security proof that takes into account fluctuations in the state preparation, it predicts a key distribution distance of over 100km of fiber. The measured intensity fluctuation intervals are, however, so large that the proof predicts zero key, indicating a source improvement may be needed. Our characterization methods pave the way for a future certification standard.

关键词： Fluctuations & noise Optical quantum information processing Quantum cryptography Quantum engineering Quantum state engineering

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performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP

arXiv

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arXiv 2024年

作者： Ismail, Muhammad Abbas, Farkhanda Shah, Shahid Munir Aljawarneh, Mahmoud Dhomeja, Lachhman Das Abbas, Fazila Shoaib, Muhammad Alrefaei, Abdulwahed Fahad Albeshr, Mohammed Fahad Department of Computer Science Karakoram International University Gilgit15100 Pakistan School of Computer Science China University of Geosciences Wuhan430074 China Department of Computing Faculty of Engineering Science and Technology Hamdard University Karachi74600 Pakistan Faculty of Information Technology Applied Science Private University Amman11937 Jordan Department of Information Technology Faculty of Engineering and Technology University of Sindh Jamshoro76080 Pakistan Institute of Soil and Environmental Sciences University of Agriculture Faisalabad Faisalabad38000 Pakistan State Key Laboratory of Hydraulic Engineering Simulation and Safety School of Civil Engineering Tianjin University Tianjin300072 China Department of Zoology College of Science King Saud University Riyadh11451 Saudi Arabia

Environmental monitoring and predictive modeling of Water Quality Index (WQI) through the assessment of the water quality is an important area of research. In order to predict WQI this study utilizes four popular Deep Learning models i.e. Artificial Neural Network (ANN), Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP). Based on the Area Under the Operating Characteristic Curve (AUC), the per- formance of the employed models is evaluated and the results are compared. Comparative analysis showed that TCN achieved the highest AUC score i.e. 0.94 as compared to the other employed models i.e. MLP: 0.94,’LSTM’: 0.77, and’ANN’: 0.93. This comparative study provides insights into the suitability of various Deep Learning models for predicting WQI, contributing to advancements in environmental monitoring and management practices. © 2024, CC BY.

关键词： Environmental monitoring

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Evidence for particle acceleration approaching PeV energies in the W51 complex

引用

science Bulletin 2024年第18期69卷 2833-2841页

作者： LHAASO Collaboration Zhen Cao F.Aharonian Axikegu Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi W.Bian A.V.Bukevich Q.Cao W.Y.Cao Zhe Cao J.Chang J.F.Chang A.M.Chen E.S.Chen H.X.Chen Liang Chen Lin Chen Long Chen M.J.Chen M.L.Chen Q.H.Chen S.Chen S.H.Chen S.Z.Chen T.L.Chen Y.Chen N.Cheng Y.D.Cheng M.Y.Cui S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu X.Q.Dong K.K.Duan J.H.Fan Y.Z.Fan J.Fang J.H.Fang K.Fang C.F.Feng H.Feng L.Feng S.H.Feng X.T.Feng Y.Feng Y.L.Feng S.Gabici B.Gao C.D.Gao Q.Gao W.Gao W.K.Gao M.M.Ge L.S.Geng G.Giacinti G.H.Gong Q.B.Gou M.H.Gu F.L.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han M.Hasan H.H.He H.N.He J.Y.He Y.He Y.K.Hor B.W.Hou C.Hou X.Hou H.B.Hu Q.Hu S.C.Hu D.H.Huang T.Q.Huang W.J.Huang X.T.Huang X.Y.Huang Y.Huang X.L.Ji H.Y.Jia K.Jia K.Jiang X.W.Jiang Z.J.Jiang M.Jin M.M.Kang I.Karpikov D.Kuleshov K.Kurinov B.B.Li C.M.Li Cheng Li Cong Li D.Li F.Li H.B.Li H.C.Li Jian Li Jie Li K.Li S.D.Li W.L.Li W.L.Li X.R.Li Xin Li Y.Z.Li Zhe Li Zhuo Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu D.B.Liu H.Liu H.D.Liu J.Liu J.L.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.Liu Y.N.Liu Q.Luo Y.Luo H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao Z.Min W.Mitthumsiri H.J.Mu Y.C.Nan A.Neronov L.J.Ou P.Pattarakijwanich Z.Y.Pei J.C.Qi M.Y.Qi B.Q.Qiao J.J.Qin A.Raza D.Ruffolo A.Sáiz M.Saeed D.Semikoz L.Shao O.Shchegolev X.D.Sheng F.W.Shu H.C.Song Yu.V.Stenkin V.Stepanov Y.Su D.X.Sun Q.N.Sun X.N.Sun Z.B.Sun J.Takata P.H.T.Tam Q.W.Tang R.Tang Z.B.Tang W.W.Tian C.Wang C.B.Wang G.W.Wang H.G.Wang H.H.Wang J.C.Wang Kai Wang Kai Wang L.P.Wang L.Y.Wang P.H.Wang R.Wang W.Wang X.G.Wang X.Y.Wang Y.Wang Y.D.Wang Y.J.Wang Z.H.Wang Z.X.Wang Zhen Wang Zheng Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu Q.W.Wu S.Wu X.F.Wu Y.S.Wu S.Q.Xi J.Xia G.M.Xiang D.X.Xiao G.Xiao Y.L.Xin Y.Xing D.R.Xiong Z.Xiong D.L.Xu R.F.Xu R.X.Xu W.L.Xu L.Xue D.H.Yan J.Z.Yan T.Yan C.W.Yang C.Y.Yang F.Yang F.F.Yang L.L.Yang M.J.Yang R.Z.Yang W.X.Yang Y.H.Yao Z.G.Yao L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.Yue H.D.Zeng T.X.Zeng W.Zeng M.Zha B.B.Zhang F.Zhang H.Zhang 不详 Key Laboratory of Particle Astrophysics&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 ChengduChina Dublin Institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-Planck-Institut for Nuclear Physics P.O.Box 103980Heidelberg 69029Germany School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031China School of Astronomy and Space Science Nanjing UniversityNanjing 210023China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Institute for Nuclear Research of Russian Academy of Sciences Moscow 117312Russia Hebei Normal University Shijiazhuang 050024China University of Science and Technology of China Hefei 230026China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311121China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China School of Physics and Astronomy Yunnan UniversityKunming 650091China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Physics and Astronomy(Zhuhai)&School of Physics(Guangzhou)&Sino-French Institute of Nuclear Engineering and Technology(Zhuhai) Sun Yat-sen UniversityZhuhai 519000&Guangzhou 510275GuangdongChina Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237China APC UniversitéParis Cité

Theγ-ray emission from the W51 complex is widely acknowledged to be attributed to the interaction between the cosmic rays(CRs)accelerated by the shock of supernova remnant(SNR)W51C and the dense molecular clouds in the adjacent star-forming region,***,the maximum acceleration capability of W51C for CRs remains *** on observations conducted with the Large high Altitude Air Shower Observatory(LHAASO),we report a significant detection ofγrays emanating from the W51 complex,with energies from 2 to 200 *** LHAASO measurements,for the first time,extend theγ-ray emission from the W51 complex beyond 100 TeV and reveal a significant spectrum bending at tens of *** combining the"π^(0)-decay bump"featured data from Fermi-LAT,the broadbandγ-ray spectrum of the W51 region can be well-characterized by a simple pp-collision *** observed spectral bending feature suggests an exponential cutoff at~400 TeV or a power-law break at~200 TeV in the CR proton spectrum,most likely providing the first evidence of SNRs serving as CR accelerators approaching the PeV ***,two young star clusters within W51B could also be theoretically viable to produce the most energeticγrays observed by *** findings strongly support the presence of extreme CR accelerators within the W51 complex and provide new insights into the origin of Galactic CRs.

关键词： UHE c-ray Cosmic rays SNR W51C Star clusters

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Optimizing AoI in RIS-aided Wireless Powered Backscatter-Active Communication Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Hu, Huimin Zhang, Ruichen Ding, Zhiguo Jiang, Fan Jiang, Ruihong Niyato, Dusit Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an710121 China Nanyang Technological University College of Computing and Data Science Singapore Singapore Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

In this paper, we investigate the average age of information (AoI) in wireless powered backscatter-active communication networks (WPBACN) assisted by a reconfigurable intelligent surface (RIS). To minimize the average AoI, we jointly optimize the phase shift of the RIS, the backscatter-active communication time allocation factor, and the transmitted power of IoT devices. Since the optimization problem is composed of coupled variables, the optimization variables are implicit in the objective function and the problem is non-convex, which makes it challenging to solve. To address this challenge, we decompose the problem into an RIS phase shift optimization subproblem and a joint power and time allocation subproblem. Simulation results indicate that the proposed scheme has a significant performance advantage in reducing the average AoI compared to the scheme using random RIS phase shifts and the scheme without RIS and supports the challenging quality of service required by real-time information update applications. © 1967-2012 IEEE.

关键词： age of information Reconfigurable intelligent surface WPBACN

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The all-particle spectrum and mean logarithmic mass of cosmic rays in the knee region by LHAASO-KM2A 38

The all-particle spectrum and mean logarithmic mass of cosmi...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Zhang, Hengying He, Huihai Feng, Cunfeng Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin 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.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Key Laboratory of Particle Astrophysics 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 Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 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 School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University 519000 Zhuhai Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Facu

The kilometer-square array (KM2A) of the Large high Altitude Air Shower Observatory (LHAASO, located at 4410 m above sea level with an atmospheric depth of 600 g/cm2) can simultaneously measure air shower sizes of both electromagnetic particles and muon contents with high precision for cosmic rays with energies in the knee region. The energy is reconstructed by combining parameters of muons and electromagnetic particles, which is weakly dependent on the mass composition of cosmic rays. We study the measurement method of the all particle spectrum and mean logarithmic mass through the simulation data, and discuss the corresponding systematic errors. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Systematic errors

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Electron–phonon coupling in topological insulator Bi_2Se_3 thin films with different substrates

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Chinese Optics Letters 2019年第2期17卷 21-24页

作者： Tian Jiang Runlin Miao Jie Zhao Zhongjie Xu Tong Zhou Ke Wei Jie You Xin Zheng Zhenyu Wang Xiang’ai Cheng College of Advanced Interdisciplinary Studies National University of Defense Technology State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Interdisciplinary Center of Quantum Information National University of Defense Technology National Institute of Defense Technology Innovation Academy of Military Sciences PLA China

Broadband transient reflectivity traces were measured for Bi_2 Se_3 thin films with various substrates via a 400 nm pump–white-light-probe setup. We have verified the existence of a second Dirac surface state in Bi_2 Se_3 and qualitatively located it by properly analyzing the traces acquired at different probe wavelengths. Referring to the band structure of Bi_2 Se_3, the relaxation mechanisms for photo-excited electrons with different energies are also revealed and studied. Our results show a second rise of the transient reflection signal at the time scale of several picoseconds. The types of substrate can also significantly affect the dynamics of the rising signal. This phenomenon is attributed to the effect of lattice heating and coherent phonon processes. The mechanism study in this work will benefit the fabrication of high-performance photonic devices based on topological insulators.

关键词： phonon coupling insulator films

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Xdn: towards efficient inference of residual neural networks on cambricon chips 2nd

Xdn: towards efficient inference of residual neural networks...

引用

2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019

作者： Li, Guangli Wang, Xueying Ma, Xiu Liu, Lei Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China College of Computer Science and Technology Jilin University Changchun China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun China

ISBN: (纸本)9783030495558

In this paper, we present XDN, an optimization and inference engine for accelerating residual neural networks on Cambricon chips. We leverage a channel pruning method to compress the weights of ResNet-50. By exploring the optimization opportunities in computational graphs, we propose a layer fusion strategy, which dramatically decreases the number of scalar computation layers, such as Batch Normalization, Scale. Furthermore, we design an efficient implementation of XDN, including data preprocessing, hyper-parameter auto-tuning, etc. The experimental results show that the ResNet-50 model can achieve significant speedup without accuracy loss by using our XDN engine. © Springer Nature Switzerland AG 2020.

关键词： Engines

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