检索结果-内蒙古大学图书馆

An Enigmatic PeVatron in an Area around HII Region G35.6−0.5

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, B.Q. 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. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, 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 Liang, E.W. Liang, Y.F. Lin, S.J. Liu, 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. Luo, Q. Luo, Y. 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. Ng, K.C.Y. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Key Laboratory of Particle Astrophysics & 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 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany 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 Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China South-Western Institute for Astronomy Research Yunnan University Yunnan Kunming650091 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Key Laboratory of Radio Astronomy and Technology National Astronomical Obse

Identifying Galactic PeVatrons (PeV particle accelerators) from the ultra-high-energy (UHE, >100 TeV) γ-ray sources plays a crucial role in revealing the origin of Galactic cosmic rays. The UHE source 1LHAASO J1857+0203u is suggested to be associated with HESS J1858+020, which may be attributed to the possible PeVatron candidate supernova remnant (SNR) G35.6−0.4 or HII region G35.6−0.5. We perform detailed analysis on the very-high-energy and UHE γ-ray emissions towards this region with data from the Large High Altitude Air Shower Observatory (LHAASO). 1LHAASO J1857+0203u is detected with a significance of 11.6σ above 100 TeV, indicating the presence of a PeVatron. It has an extension of ∼ 0.18◦ with a power-law (PL) spectral index of ∼2.5 in 1-25 TeV and a point-like emission with a PL spectral index of ∼3.2 above 25 TeV. Using the archival CO and HI data, we identify some molecular and atomic clouds that may be associated with the TeV γ-ray emissions. Our modelling indicates that the TeV γ-ray emissions are unlikely to arise from the clouds illuminated by the protons that escaped from SNR G35.6−0.4. In the scenario that HII region G35.6−0.5 could accelerate particles to the UHE band, the observed GeV-TeV γ-ray emission could be well explained by a hadronic model with a PL spectral index of ∼2.0 and cutoff energy of ∼450 TeV. However, an evolved pulsar wind nebula origin cannot be ruled out. © 2024, CC BY.

关键词： Cosmic rays

Calculation of heavy meson light-cone distribution amplitudes from lattice QCD

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Han, Xue-Ying Hua, Jun Ji, Xiangdong Lü, Cai-Dian Schäfer, Andreas Su, Yushan Wang, Wei Xu, Ji Yang, Yibo Zhang, Jian-Hui Zhang, Qi-An Zhao, Shuai Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China Maryland Center for Fundamental Physics Department of Physics University of Maryland 4296 Stadium Dr. College ParkMD20742 United States Institut für Theoretische Physik University Regensburg RegensburgD-93040 Germany School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute of Modern Physics Chinese Academy of Sciences Guangdong Province Huizhou516000 China School of Nuclear Science and Technology Lanzhou University Lanzhou730000 China School of Physics and Microelectronics Zhengzhou University Henan Zhengzhou450001 China CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou310024 China International Centre for Theoretical Physics Asia-Pacific Hangzhou Beijing China School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China School of Physics Beihang University Beijing102206 China School of Science Tianjin University Tianjin300072 China

We develop an approach for calculating heavy quark effective theory (HQET) light-cone distribution amplitudes (LCDAs) by employing a sequential effective theory methodology. The theoretical foundation of the framework is established, elucidating how the quasi distribution amplitudes (quasi DAs) with three scales can be utilized to compute HQET LCDAs. We provide theoretical support for this approach by demonstrating the rationale behind devising a hierarchical ordering for the three involved scales, discussing the factorization at each step, clarifying the underlying reason for obtaining HQET LCDAs in the final phase, and addressing potential theoretical challenges. The lattice QCD simulation aspect is explored in detail, and the computations of quasi DAs are presented. We employ three fitting strategies to handle contributions from excited states and extract the bare matrix elements. For renormalization purposes, we apply hybrid renormalization schemes at short and long distance separations. To mitigate long-distance perturbations, we perform an extrapolation in λ = z · Pz and assess the stability against various parameters. After two-step matching, our results for HQET LCDAs are found in agreement with existing model parametrizations. The potential phenomenological implications of the results are discussed, shedding light on how these findings could impact our understanding of the strong interaction dynamics and physics beyond the standard model. It should be noted, however, that systematic uncertainties have not been accounted for yet. © 2024, CC BY.

关键词： Bosons

Light-Tunable Ferromagnetism in Atomically Thin Fe3GeTe2 Driven by Femtosecond Laser Pulse

学校读者我要写书评

暂无评论

Physical Review Letters 2020年第26期125卷 267205-267205页

作者： Bo Liu Shanshan Liu Long Yang Zhendong Chen Enze Zhang Zihan Li Jing Wu Xuezhong Ruan Faxian Xiu Wenqing Liu Liang He Rong Zhang Yongbing Xu National Laboratory of Solid State Microstructures and Jiangsu Provincial Key Laboratory of Advanced Photonic and Electronic Materials School of Electronic Science and Engineering Nanjing University Nanjing 210093 People’s Republic of China State Key Laboratory of Surface Physics and Department of Physics Fudan University Shanghai 200433 People’s Republic of China York-Nanjing Joint Center in Spintronics Department of Electronic Engineering and Department of Physics The University of York York YO10 5DD United Kingdom Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 People’s Republic of China Department of Electronic Engineering Royal Holloway University of London Egham Surrey TW20 0EX United Kingdom

The recent discovery of intrinsic ferromagnetism in two-dimensional (2D) van der Waals (vdW) crystals has opened up a new arena for spintronics, raising an opportunity of achieving tunable intrinsic 2D vdW magnetism. Here, we show that the magnetization and the magnetic anisotropy energy (MAE) of few-layered Fe3GeTe2 (FGT) is strongly modulated by a femtosecond laser pulse. Upon increasing the femtosecond laser excitation intensity, the saturation magnetization increases in an approximately linear way and the coercivity determined by the MAE decreases monotonically, showing unambiguously the effect of the laser pulse on magnetic ordering. This effect observed at room temperature reveals the emergence of light-driven room-temperature (300 K) ferromagnetism in 2D vdW FGT, as its intrinsic Curie temperature TC is ∼200 K. The light-tunable ferromagnetism is attributed to the changes in the electronic structure due to the optical doping effect. Our findings pave a novel way to optically tune 2D vdW magnetism and enhance the TC up to room temperature, promoting spintronic applications at or above room temperature.

关键词： Ferromagnetism Light-induced magnetic effects Magnetic anisotropy Optical generation of spin carriers

Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling 5th

学校读者我要写书评

暂无评论

Mobile APP User Attribute Prediction by Heterogeneous Inform...

5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications, DependSys 2019

作者： Zhang, Hekai Gong, Jibing Teng, Zhiyong Wang, Dan Wang, Hongfei Du, Linfeng Bhuiyan, Zakirul Alam School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Lab for Computer Virtual Technology and System Integration Yanshan University Qinhuangdao066004 China School of Computer Science and Engineering Beihang University Beijing100191 China Shenyuan Honors College Beihang University Beijing100191 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214000 China Department of Computer and Information Sciences Fordham University New York United States

ISBN: (纸本)9789811513039

User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior analysis and other aspects. Although many scholars have made achievements in user attribute prediction and other related fields, there are still two main problems that impede further improvement on the accuracy of classification: (1) Traditional machine learning classification merely takes each object as a single individual, ignoring the relationship between them;(2) At present, the popular Heterogeneous Path-Mine Information Network only considers whether the user has a relationship with the attributes of other nodes, rather than the degree of correlation of the attributes. It employs a linear regression model to fit the weight of meta-path, which is highly sensitive to outliers. To solve the above two problems, this paper advances the HetPathMine model and puts forward TPathMine model. With applying the number of clicks of attributes under each node to express the user’s emotional preference information, optimizations of the solution of meta-path weight are also presented. Based on meta-path in heterogeneous information networks, the new model integrates all relationships among objects into isomorphic relationships of classified objects. Matrix is used to realize the knowledge dissemination of category knowledge among isomorphic objects. The experimental results show that: (1) the prediction of user attributes based on heterogeneous information networks can achieve higher accuracy than traditional machine learning classification methods;(2) TPathMine model based on the number of clicks is more accurate in classifying users of different age groups, and the weight of each meta-path is consistent with human intuition or the real world situation. © 2019, Spr

关键词： Forecasting

Cooperative bi-path metric for few-shot learning

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Wang, Zeyuan Zhao, Yifan Li, Jia Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing100191 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518066 China

Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples. Most existing methods only pay attention to the relationship between labeled and unlabeled samples of novel classes, which do not make full use of information within base classes. In this paper, we make two contributions to investigate the few-shot classification problem. First, we report a simple and effective baseline trained on base classes in the way of traditional supervised learning, which can achieve comparable results to the state of the art. Second, based on the baseline, we propose a cooperative bi-path metric for classification, which leverages the correlations between base classes and novel classes to further improve the accuracy. Experiments on two widely used benchmarks show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

Enhanced phase estimation with coherently boosted two-mode squeezed beams and its application to optical gyroscopes

学校读者我要写书评

暂无评论

Physical Review A 2020年第2期102卷 022614-022614页

作者： Xiao-Qi Xiao Elisha S. Matekole Jiankang Zhao Guihua Zeng Jonathan P. Dowling Hwang Lee Department of Communication Engineering Shanghai Dianji University Shanghai 200240 China Hearne Institute for Theoretical Physics and Department of Physics and Astronomy Louisiana State University Baton Rouge Louisiana 70803 USA State Key Laboratory of Advanced Optical Communication Systems and Networks Institute of Sensing and Navigation Department of Electronic Engineering Shanghai Jiaotong University Shanghai 200030 China National Institute of Information and Communications Technology Tokyo 184-8795 Japan NYU-ECNU Institute of Physics at NYU Shanghai Shanghai 200062 China CAS-Alibaba Quantum Computing Laboratory USTC Shanghai 201315 China

Quantum techniques, developed in recent decades, provide new approaches to achieving high-precision measurements beyond the classical bounds. In this paper, we theoretically demonstrate a metrology method for improving the sensitivity of the interferometric optical gyroscope, robust against the loss, by using coherent-light stimulated two-mode squeezed beams as the light source. The detection protocol is based on a simple intensity measurement, and the quantum noise is far below the shot-noise limit. The enhancement factors for different coherent light fields are analyzed in detail. Additionally, the influence of loss during the propagation in the optical path is studied, and the conditions for achieving sub-shot-noise measurement sensitivity are obtained. We also find that the phase sensitivity of the proposed gyroscope scheme becomes closer to the quantum Cramér-Rao bound with increasing of the photon number of the coherent beams.

关键词： Quantum metrology

Simulation of Passenger Evacuation of High-speed Train Cars 9

学校读者我要写书评

暂无评论

Simulation of Passenger Evacuation of High-speed Train Cars

9th International Conference on Fire Science and Fire Protection engineering, ICFSFPE 2019

作者： Huang, Shenshi Wei, Ruichao Shi, Kai Hu, Jinxing Lu, Shouxiang Lian, Liping Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen China Smart City Research Institute of China Electronics Technology Group Corporation Shenzhen China State Key Laboratory of Fire Science University of Science and Technology of China Hefei China School of Urban Planning and Design Peking University Shenzhen China

ISBN: (纸本)9781728153223

This study is to evaluate the passenger evacuation of the high-speed train. Geometry was built up based on current Chinese train type CRH380B, and scenarios were designed according to the current emergency response plans. Simulation runs were performed by Pathfinder and uncertainty of evacuation time was obtained. It is observed that the passenger flow rates in train coaches are generally stable. Passenger transfer time and evacuation time are approximately proportional to passenger attendance rate. However, in some complicated scenarios, it is found that the process of passenger transfer affects the evacuation results a lot. Reasonable passenger transfer scale can maximize the effectiveness of emergency plans. This study provides an evaluation method on the topic of train car evacuation, and future work can be carried out to optimize the design of emergency response. © 2019 IEEE.

关键词： Railroad cars

Quantum Deep Learning for Mutant COVID-19 Strain Prediction

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Jin, Yu-Xin Hu, Jun-Jie Li, Qi Luo, Zhi-Cheng Zhang, Fang-Yan Tang, Hao Qian, Kun Jin, Xian-Min TuringQ Co. Ltd. Shanghai 200240 Singapore School Of Physics And Astronomy State Key Laboratory Of Advanced Optical Communication Systems And Networks Shanghai Jiao Tong University Shanghai200240 China CAS Center For Excellence Synergetic Innovation Center In Quantum Information And Quantum Physics University Of Science And Technology Of China Anhui Hefei230026 China School Of Mathematical Sciences Nankai University Tianjin300071 China Department Of Information And Intelligence Development Zhongshan Hospital Fudan University Shanghai200032 China Biomedical Engineering Imperial College London LondonSW7 2BX United Kingdom Biological Science Carnegie Mellon University PittsburghPA15213 United States

New COVID-19 epidemic strains like Delta and Omicron with increased transmissibility and pathogenicity emerge and spread across the whole world rapidly while causing high mortality during the pandemic period. Early prediction of possible variants (especially spike protein) of COVID-19 epidemic strains based on available mutated SARS-CoV-2 RNA sequences may lead to early prevention and treatment. Here, combining the advantage of quantum and quantum-inspired algorithms with the wide application of deep learning, we propose a development tool named DeepQuantum, and use this software to realize the goal of predicting spike protein variation structure of COVID-19 epidemic strains. In addition, this hybrid quantum-classical model for the first time achieves quantum-inspired blur convolution similar to classical depthwise convolution and also successfully applies quantum progressive training with quantum circuits, both of which guarantee that our model is the quantum counterpart of the famous style-based GAN. The results state that the fidelities of random generating spike protein variation structure are always beyond 96% for Delta, 94% for Omicron. The training loss curve is more stable and converges better with multiple loss functions compared with the corresponding classical algorithm. At last, evidences that quantum-inspired algorithms promote the classical deep learning and hybrid models effectively predict the mutant strains are strong. © 2022, CC BY.

关键词： Convolution

A review-driven neural model for sequential recommendation

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Li, Chenliang Niu, Xichuan Luo, Xiangyang Chen, Zhenzhong Quan, Cong School of Cyber Science and Engineering Wuhan University School of Remote Sensing and Information Engineering Wuhan University State Key Lab of Mathematical Engineering and Advanced Computing Zhengzhou School of Computer Science Wuhan University

Writing review for a purchased item is a unique channel to express a user's opinion in ECommerce. Recently, many deep learning based solutions have been proposed by exploiting user reviews for rating prediction. In contrast, there has been few attempt to enlist the semantic signals covered by user reviews for the task of collaborative filtering. In this paper, we propose a novel review-driven neural sequential recommendation model (named RNS) by considering users' intrinsic preference (long-term) and sequential patterns (short-term). In detail, RNS is devised to encode each user or item with the aspect-aware representations extracted from the reviews. Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both unionlevel and individual-level. Extensive experiments on three real-world datasets of different domains demonstrate that RNS obtains significant performance improvement over uptodate state-of-the-art sequential recommendation models. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems