检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Feng, Chun-Mei Yang, Zhanyuan Fu, Huazhu Xu, Yong Yang, Jian Shao, Ling Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China School of Automation Engineering University of Electronic Science and Technology of China 611731 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjiang University of Science and Technology Nanjiang210094 China Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration has long been the subject of research. This is commonly achieved by obtaining multiple undersampled images, simultaneously, through parallel imaging. In this paper, we propose the Dual-Octave Network (DONet), which is capable of learning multi-scale spatialfrequency features from both the real and imaginary components of MR data, for parallel fast MR image reconstruction. More specifically, our DONet consists of a series of Dual-Octave convolutions (Dual-OctConv), which are connected in a dense manner for better reuse of features. In each Dual-OctConv, the input feature maps and convolutional kernels are first split into two components (i.e., real and imaginary), and then divided into four groups according to their spatial frequencies. Then, our Dual-OctConv conducts intra-group information updating and inter-group information exchange to aggregate the contextual information across different groups. Our framework provides three appealing benefits: (i) It encourages information interaction and fusion between the real and imaginary components at various spatial frequencies to achieve richer representational capacity. (ii) The dense connections between the real and imaginary groups in each Dual-OctConv make the propagation of features more efficient by feature reuse. (iii) DONet enlarges the receptive field by learning multiple spatial-frequency features of both the real and imaginary components. Extensive experiments on two popular datasets (i.e., clinical knee and fastMRI), under different undersampling patterns and acceleration factors, demonstrate the superiority of our model in accelerated parallel MR image reconstruction. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A

arXiv

引用

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, 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. 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. 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. 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, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. 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. 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. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, D.X. Sun, Q.N. Sun, X.N. Sun, Z.B. Takata, J. 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 Chengdu610000 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 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 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 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 National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Physics and Astronomy Guangdong Zhuhai519000 China School of Physics Guangdong Guangzhou510275 China Sino-French Institute of Nuclear Engineering and Technolo

We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3−30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at 3.67±0.05±0.15 PeV. Below the knee, the spectral index is found to be −2.7413±0.0004± 0.0050, while above the knee, it is −3.128 ± 0.005 ± 0.027, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of −0.1200±0.0003±0.0341. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components. © 2024, CC BY.

关键词： Cosmic rays

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Exact distributed quantum algorithm for generalized Simon’s problem

arXiv

引用

arXiv 2023年

作者： Li, Hao Qiu, Daowen Luo, Le Mateus, Paulo Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University 510006 China QUDOOR Technologies Inc. Zhuhai China School of Physics and Astronomy Sun Yat-sen University Zhuhai519082 China Instituto de Telecomunicações Departamento de Matemática Instituto Superior Técnico Av. Rovisco Pais Lisbon1049-001 Portugal

Simon’s problem is one of the most important problems demonstrating the power of quantum algorithms, as it greatly inspired the proposal of Shor’s algorithm. The generalized Simon’s problem is a natural extension of Simon’s problem, and also a special hidden subgroup problem: Given a function f : {0, 1}n → {0, 1}m, with the property that for any x, y ∈ {0, 1}n, there is some unknown hidden subgroup S ≤ 2n such that f(x) = f(y) iff x ⊕ y ∈ S, where |S| = 2k for some 0 ≤ k ≤ n (m ≥ n − k). The goal of generalized Simon’s problem is to find the hidden subgroup S. In this paper, we present two key contributions. Firstly, we characterize the structure of the generalized Simon’s problem in distributed scenario and introduce a corresponding distributed quantum algorithm. Secondly, we refine the algorithm to ensure exactness due to the application of quantum amplitude amplification technique. Our algorithm offers exponential acceleration compared to the distributed classical algorithm. When contrasted with the centralized quantum algorithm for the generalized Simon’s problem, our algorithm’s oracle requires fewer qubits, thus making it easier to be physically implemented. Particularly, the exact distributed quantum algorithm we develop for the generalized Simon’s problem outperforms the best previously proposed distributed quantum algorithm for Simon’s problem in terms of generalizability and exactness. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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Contrastive Learning for Robust Android Malware Familial Classification

arXiv

引用

arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

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Real-time Efficient Facial Landmark Detection Algorithms

Real-time Efficient Facial Landmark Detection Algorithms

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2020年第三届国际人工智能和模式识别会议

作者： Hanying Xiong Tongwei Lu Hongzhi Zhang Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan Institute of Technology School of Information Management and Statistics Hubei University of Economics

Lightweight models, high accuracy and real-time performance are essential for facial landmark detection algorithms. Considering these three aspects, this paper proposes a real-time and efficient face landmark algorithm. First, mobilenetV2 is used as the backbone network. Next, the traditional convolution operation is replaced with deeply separable convolution, and the shallow and deep feature maps are merged to enhance the context connection. Then multi-scale fusion output is used in the output layer to enhance the detection efficiency of small-sized faces. Finally, the Euler angle weights are introduced into the loss function, and the 14 key points in the average face model are compared with the predicted key points. During the training process, this paper proposes rotated the 300 W and AFLW datasets in multi-angle to occlude the dataset and enhance the generalization ability of the model. The experimental results show that the proposed algorithm in this paper can achieve real-time and efficient facial landmark detection.

关键词： Facial landmark Convolution neural network Multi-scale feature modules Euler angle

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Preparing Quantum States by Measurement-feedback Control with Bayesian Optimization

arXiv

引用

arXiv 2022年

作者： Wu, Yadong Yao, Juan Zhang, Pengfei Department of Physics Fudan University Shanghai200438 China Fudan University Shanghai200438 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Shanghai Qi Zhi Institute AI Tower Xuhui District Shanghai200232 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China International Quantum Academy Guangdong Shenzhen518048 China Guangdong Provincial Key Laboratory ofQuantum Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Walter Burke Institute for Theoretical Physics Institute for Quantum Information and Matter California Institute of Technology PasadenaCA91125 United States

Preparation of quantum states is of vital importance for performing quantum computations and quantum simulations. In this work, we propose a general framework for preparing ground states of many-body systems by combining the measurement-feedback control process (MFCP) and the machine learning method. Using the Bayesian optimization (BO) strategy, the efficiency of determining the measurement and feedback operators in the MFCP is demonstrated. Taking the one dimensional Bose-Hubbard model as an example, we show that BO can generate optimal parameters, although constrained by the operator basis, which can drive the system to the low energy state with high probability in typical quantum trajectories. Copyright © 2022, The Authors. All rights reserved.

关键词： Feedback control

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A Robust Deep Learning-Based DOA Super-Resolution Estimation Method With Single Snapshot

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IEEE Transactions on Vehicular technology 2025年

作者： Luo, Tao Chen, Peng Yang, Zihan Zheng, Le Zhang, Yudong Liu, Jun Southeast University State Key Laboratory of Millimeter Waves Nanjing210096 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China University of Leicester School of Computing and Mathematical Sciences LeicesterLE1 7RH United Kingdom Army Engineering University of PLA National Key Laboratory on Electromagnetic Environment Effects and Electro-optical Engineering Nanjing210007 China

At present, deep learning (DL) has attracted widespread attention and has been widely applied in different areas of wireless communication, and signal processing. Due to its excellent feature extraction capability, it has been extensively used in the direction of arrival (DOA) estimation. In this paper, a robust network for super-resolution single-snapshot DOA estimation called DeepDOA is proposed. Different from other network-based DOA algorithms, DeepDOA takes the normalized real and imaginary parts of the received signal as inputs. Meanwhile, the network output is a vector instead of a spatial spectrum, and the corresponding spatial spectrum can be obtained by a simple transformation, which makes the spectrum smoother. Moreover, a sparse constraint is first introduced into the loss function, which improves the accuracy of the algorithm. Numerical simulations are conducted to verify its excellent performance. Finally, a frequency-modulated continuous wave (FMCW) radar location system is built to validate the proposed algorithm. © 1967-2012 IEEE.

关键词： Frequency modulation

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Attribute Prototype Network for Any-Shot Learning

arXiv

引用

arXiv 2022年

作者： Xu, Wenjia Xian, Yongqin Wang, Jiuniu Schiele, Bernt Akata, Zeynep State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Computer Vision Laboratory ETH Zurich Switzerland University of Tübingen Tübingen Germany Max Planck Institute for Intelligent Systems Tübingen Germany City University of Hong Kong Hong Kong Hong Kong Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences China

Any-shot image classification allows to recognize novel classes with only a few or even zero samples. For the task of zero-shot learning, visual attributes have been shown to play an important role, while in the few-shot regime, the effect of attributes is underexplored. To better transfer attribute-based knowledge from seen to unseen classes, we argue that an image representation with integrated attribute localization ability would be beneficial for any-shot, i.e. zero-shot and few-shot, image classification tasks. To this end, we propose a novel representation learning framework that jointly learns discriminative global and local features using only class-level attributes. While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features. Furthermore, we introduce a zoom-in module that localizes and crops the informative regions to encourage the network to learn informative features explicitly. We show that our locality augmented image representations achieve a new state-of-the-art on challenging benchmarks, i.e. CUB, AWA2, and SUN. As an additional benefit, our model points to the visual evidence of the attributes in an image, confirming the improved attribute localization ability of our image representation. The attribute localization is evaluated quantitatively with ground truth part annotations, qualitatively with visualizations, and through welldesigned user studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Image representation

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MSIsensor-pro: Fast, Accurate, and Matched-normal-sample-free Detection of Microsatellite Instability

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Genomics, Proteomics & Bioinformatics 2020年第1期18卷 65-71页

作者： Peng Jia Xiaofei Yang Li Guo Bowen Liu Jiadong Lin Hao Liang Jianyong Sun Chengsheng Zhang Kai Ye School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Laboratory for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China Leiden Institute of Advanced Computer Science Leiden UniversityLeiden 2311 ZENetherlands School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’an 710049China Precision Medicine Center the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Jackson Laboratory for Genomic Medicine FarmingtonCT 06032USA Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China

Microsatellite instability(MSI)is a key biomarker for cancer therapy and *** experimental assays are laborious and time-consuming,and next-generation sequencingbased computational methods do not work on leukemia samples,paraffin-embedded samples,or patient-derived xenografts/organoids,due to the requirement of matched normal ***,we developed MSIsensor-pro,an open-source single sample MSI scoring method for research and clinical ***-pro introduces a multinomial distribution model to quantify polymerase slippages for each tumor sample and a discriminative site selection method to enable MSI detection without matched normal *** demonstrate that MSIsensor-pro is an ultrafast,accurate,and robust MSI calling *** samples with various sequencing depths and tumor purities,MSIsensor-pro significantly outperformed the current leading methods in both accuracy and computational ***-pro is available at https://***/xjtu-omics/msisensor-pro and free for non-commercial use,while a commercial license is provided upon request.

关键词： Microsatellite Polymerase slippage Multinomial distribution Microsatellite instability Tumor

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Surface Charging Phenomena on HVDC Spacers for Compressed SF_(6) Insulation and Charge Tailoring Strategies

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CSEE Journal of Power and Energy Systems 2020年第1期6卷 83-99页

作者： Chuanyang Li Yuan Xu Chuanjie Lin Geng Chen Youping Tu Yao Zhou Zhipeng Lei Tao Han Simone Vincenzo Suraci Jian Wang Weidong Liu M.Tariq Nazir Shun He Andrea Cavallini Giovanni Mazzanti Davide Fabiani Jinliang He Electrical Insulation Research Center Institute of Materials ScienceElectrical and Computer EngineeringUniversity of ConnecticutStorrsCT 06269USA. China Electric Power Research Institute Beijing 100192China Department of Electrical Electronicand Information Engineering“Guglielmo Marconi”University of BolognaViale Risorgimento 2Bologna40136Italy State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Beijing Area Major Laboratory of High Voltage and Electromagnetic Compatibility North China Electric Power UniversityBeijing 102206China Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control National and Provincial Joint EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Smart Grid of Education Ministry School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China School of Mechanical and Manufacturing Engineering University of New South Wales SydneyNSW 2052Australia Electric Power Research Institute Yunnan Electric Power Grid Co.ltd.KunmingYunnan 650217China

Surface flashover of spacers is a key factor limiting the application of HVDC GIS/GIL,while the charge accumula-tion on the surface of the spacer could have a potential adverse effect on the surface flashover *** paper discusses the laws regarding distribution patterns of surface charges and the related *** field-dependent property is discussed in detail to comprehensively illustrate the charge transport mecha-nism and explain the research differences regarding different surface charge patterns obtained by previous *** addition,the main surface charge control methods for epoxy resin are summarized and *** potential research directions of charge control methods and key points in manufacturing of spacers used in HVDC GIS/GIL are also explored.

关键词： GIL HVDC insulator SF6 surface charge accumulation surface flashover

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