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SSRN 2023年

作者： Li, Hongqing Jiang, Yunqing Zhang, Xiaoqiang Zhang, Fan Xiao, Yongguang Tang, Minghua Zhao, Zhenyang Xu, Yong Liu, Fengguang Zhao, Weisheng School of Materials Science and Engineering Xiangtan University Hunan Xiangtan411105 China School of Integrated Circuit Science and Engineering Hefei Innovation Research Insititute Beihang University Beijing100191 China Anhui High Reliability Chips Engineering Laboratory Hefei230013 China Shandong Dongyi Photoelectric Instruments Co. Ltd Yantai264670 China Shandong Dongyi Institute of Optoelectronic Technology for Industry Yantai264670 China Beijing Advanced Innovation Center for Big Data and Brain Computing China

Terahertz (THz) wave as an important electromagnetic wave has been increasingly applied in wireless communication and imaging sensing technology. It has been an increasingly practical application. Ultra-high data rates in communication technology push toward a broadband, controllable polarization, and multiple transmission channels signal sources. Here, we propose a programmable polarization spintronics terahertz source (PP-STS), in which contains a controllable polarization spintronic THz emitter and a microcontroller unit. The PP-STS not only can generate THz waves with wide frequency bandwidth, but also has the function of switching the orthogonal linear polarization state of the generated THz waves flexibly. By coding the microcontroller unit, the designed PP-SPS can directly program the orthogonal linear polarization states as a series of information to transmit. To achieve the interconversions between digital codes and the THz polarization information, an optimum matching THz polarization beam splitter (THz-PBS) is also designed via simulations and experiments. The THz-PBS, as a decoder, can accurately split the transverse electric (TE) and transverse magnetic (TM) modes into two THz detectors. At last, a coding and decoding prototype system of spintronic THz polarization is built by integrating the PP-STS, the THz-PBS and two THz detectors. The send and receive information with high speed and accuracy are verified in this prototype system as well. We believe that our work takes THz applications for multiplexing communications one step further and sheds light on commercializing the PP-STS with exciting manufacturing lines for efficient and economical THz polarization technology. © 2023, The Authors. All rights reserved.

关键词： Microcontrollers

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Spintronic Terahertz Polarization Programmable System for Information Encoding

SSRN

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SSRN 2023年

关键词： Microcontrollers

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Spintronic Terahertz Polarization Programmable System for Information Encoding

SSRN

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SSRN 2023年

作者： Li, Hongqing Jiang, Yunqing Zhang, Xiaoqiang Zhang, Fan Xiao, Yongguang Tang, Minghua Zhao, Zhenyang Xu, Yong Liu, Fengguang Zhao, Weisheng Beijing Advanced Innovation Center for Big Data and Brain Computing China School of Materials Science and Engineering Xiangtan University Hunan Xiangtan411105 China School of Integrated Circuit Science and Engineering Hefei Innovation Research Insititute Beihang University Beijing100191 China Anhui High Reliability Chips Engineering Laboratory Hefei230013 China Shandong Dongyi Photoelectric Instruments Co. Ltd Yantai264670 China Shandong Dongyi Institute of Optoelectronic Technology for Industry Yantai264670 China

关键词： Microcontrollers

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Spintronic Terahertz Polarization Programmable System for Information Encoding

SSRN

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SSRN 2023年

关键词： Microcontrollers

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Robust Optimization for Quantum Reinforcement Learning Control using Partial Observations

arXiv

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arXiv 2022年

作者： Jiang, Chen Pan, Yu Wu, Zheng-Guang Gao, Qing Dong, Daoyi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and quantum approximate optimization algorithm. It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing. © 2022, CC BY.

关键词： Robust control

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BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation 23rd

BEFD: Boundary Enhancement and Feature Denoising for Vessel ...

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23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Mo Yu, Fei Zhao, Jie Zhang, Li Li, Quanzheng Center for Data Science Peking University Beijing100871 China Center for Data Science in Health and Medicine Peking University Beijing100871 China Laboratory for Biomedical Image Analysis Beijing Institute of Big Data Research Beijing100871 China Center for Advanced Medical Computing and Analysis MGH/BWH Center for Clinical Data Science Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA02115 United States

ISBN: (纸本)9783030597214

Blood vessel segmentation is crucial for many diagnostic and research applications. In recent years, CNN-based models have leaded to breakthroughs in the task of segmentation, however, such methods usually lose high-frequency information like object boundaries and subtle structures, which are vital to vessel segmentation. To tackle this issue, we propose Boundary Enhancement and Feature Denoising (BEFD) module to facilitate the network ability of extracting boundary information in semantic segmentation, which can be integrated into arbitrary encoder-decoder architecture in an end-to-end way. By introducing Sobel edge detector, the network is able to acquire additional edge prior, thus enhancing boundary in an unsupervised manner for medical image segmentation. In addition, we also utilize a denoising block to reduce the noise hidden in the low-level features. Experimental results on retinal vessel dataset and angiocarpy dataset demonstrate the superior performance of the new BEFD module. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Nonreciprocal coherent coupling of nanomagnets by exchange spin waves

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Nano Research 2021年第7期14卷 2133-2138页

作者： Hanchen Wang Jilei Chen Tao Yu Chuanpu Liu Chenyang Guo Song Liu Ka Shen Hao Jia Tao Liu Jianyu Zhang Marco A.Cabero Z Qiuming Song Sa Tu Mingzhong Wu Xiufeng Han Ke Xia Dapeng Yu Gerrit E.W.Bauer Haiming Yu Fert Beijing Institute School of Integrated Circuit Science and EngineeringBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Max Planck Institute for the Structure and Dynamics of Matter 22761 HamburgGermany Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100190China Shenzhen Institute for Quantum Science and Engineering(SIQSE) and Department of PhysicsSouthern University of Science and Technology(SUSTech)Shenzhen 518055China Department of Physics Beijing Normal UniversityBeijing 100875China Department of Physics Colorado State UniversityFort CollinsColorado 80523USA Kavli Institute of Nanoscience Delft University of Technology2628 CJ DelftThe Netherlands Institute for Materials Research WPI-AIMR and CSNRTohoku UniversitySendai 980-8577Japan Zernike Institute for Advanced Materials University of GroningenGroningenNijenborgh 49747 AG GroningenThe Netherlands

Nanomagnets are widely used to store information in non-volatile spintronic *** waves can transfer information with low-power consumption as their propagations are independent of charge ***,to dynamically couple two distant nanomagnets via spin waves remains a major challenge for *** we experimentally demonstrate coherent coupling of two distant Co nanowires by fast propagating spin waves in an yttrium iron garnet thin film with sub-50 nm *** in two nanomagnets are unidirectionally phase-locked with phase shifts controlled by magnon spin torque and spin-wave *** coupled system is finally formulated by an analytical theory in terms of an effective non-Hermitian *** results are attractive for analog neuromorphic computing that requires unidirectional information transmission.

关键词： spintronics nanomagnets spin waves coherent coupling nonreciprocity

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System architecture for high-performance permissioned blockchains

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Frontiers of Computer Science 2019年第6期13卷 1151-1165页

作者： Libo FENG Hui ZHANG Wei-Tek TSAI Simeng SUN State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China Digital Society&Blockchain Laboratory School of Computer ScienceBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA

Blockchain(BC),as an emerging distributed database technology with advanced security and reliability,has attracted much attention from experts who devoted to efinance,intellectual property protection,the internet of things(IoT)and so ***,the inefficient transaction processing speed,which hinders the BC’s widespread,has not been well tackled *** this paper,we propose a novel architecture,called Dual-Channel Parallel Broadcast model(DCPB),which could address such a problem to a greater extent by using three methods which are dual communication channels,parallel pipeline processing and block broadcast *** the dual-channel model,one channel processes transactions,and the other engages in the execution of *** parallel pipeline processing allows the system to operate *** block generation strategy improves the efficiency and speed of *** experiments have been applied to BeihangChain,a simplified prototype for BC system,illustrates that its transaction processing speed could be improved to 16K transaction per second which could well support many real-world scenarios such as BC-based energy trading system and Micro-film copyright trading system in CCTV.

关键词： blockchain concurrency performance dualchannel model parallel pipeline consensus algorithm

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Identifying topologies and system parameters of uncertaintime-varying delayed complex networks

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Science China(Technological Sciences) 2019年第1期62卷 94-105页

作者： WANG Xiong GU HaiBo WANG QianYao Lü JinHu LSC Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Node dynamics and network topologies play vital roles in determining the network features and network dynamical *** it is of great theoretical significance and practical value to recover the topology structures and system parameters of uncertain complex networks with available information. This paper presents an adaptive anticipatory synchronization-based approach to identify the unknown system parameters and network topological structures of uncertain time-varying delayed complex networks in the presence of noise. Moreover, during the identification process, our proposed scheme guarantees anticipatory synchronization between the uncertain drive and constructed auxiliary response network simultaneously. Particularly, our method can be extended to several special cases. Furthermore, numerical simulations are provided to verify the effectiveness and applicability of our method for reconstructing network topologies and node parameters. We hope our method can provide basic insight into future research on addressing reconstruction issues of uncertain realistic and large-scale complex networks.

关键词： system parameters and network topologies identification anticipatory synchronization uncertain time-varying delayed complex networks noise-perturbed complex networks

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Representation learning of reconstructed graphs using random walk graph convolutional network

arXiv

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arXiv 2021年

作者： Li, Xing Wei, Wei Feng, Xiangnan Zheng, Zhiming School of Mathematical Science Beihang University Beijing China Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education China Peng Cheng Laboratory ShenzhenGuangdong China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education China

Graphs are often used to organize data because of their simple topological structure, and therefore play a key role in machine learning. And it turns out that the low-dimensional embedded representation obtained by graph representation learning are extremely useful in various typical tasks, such as node classification, content recommendation and link prediction. However, the existing methods mostly start from the microstructure (i.e., the edges) in the graph, ignoring the mesoscopic structure (high-order local structure). Here, we propose wGCN – a novel framework that utilizes random walk to obtain the node-specific mesoscopic structures of the graph, and utilizes these mesoscopic structures to reconstruct the graph And organize the characteristic information of the nodes. Our method can effectively generate node embeddings for previously unseen data, which has been proven in a series of experiments conducted on citation networks and social networks (our method has advantages over baseline methods). We believe that combining high-order local structural information can more efficiently explore the potential of the network, which will greatly improve the learning efficiency of graph neural network and promote the establishment of new learning models. © 2021, CC BY.

关键词： Graph neural networks

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