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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Peng Zhang Qing Gao Jinhu Lv Daoyi Dong School of Automation Science and Electrical Engineering Beihang University Beijing China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Engineering and Information Technology University of New South Wales Canberra Australia

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

In order to reduce the computational cost for online calculation of the quantum filter, in this paper, a linear quantum projection filtering scheme is developed by projecting the time evolution trajectory of quantum system state onto a lower-dimensional linear submanifold spanned by a set of basis matrices. The lower-dimensional submanifold is obtained through an extended singular value decomposition algorithm. Compared with the existing works on quantum projection filtering, the proposed approach requires no prior knowledge on the structure of the submanifold, and thus can be used to deal with general open quantum systems. Simulation results demonstrate that the proposed linear projection filter is able to approximate the high-dimensional quantum filter within acceptable accuracy.

关键词： Filtering theory Optical filters Nonlinear filters Maximum likelihood detection Information filters Quantum computing Mathematical model

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Formation-containment control for general linear multi-agent systems with time-varying delays and switching topologies

Formation-containment control for general linear multi-agent...

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International Conference on Control and Automation (ICCA)

作者： Shiyu Zhou Yongzhao Hua Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing P.R. China University of Bristol Bristol United Kingdom Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P.R. China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

Formation-containment control problems for general linear multi-agent systems with time-varying delays and switching topologies are studied. The leaders are required to accomplish a given time-varying formation and the followers are allowed to enter the convex envelope spanned by those of the leaders simultaneously. Firstly, formation-containment protocols based on distributed state observer with switching interaction topologies and time-varying delays are presented for leaders and followers respectively, where an edge-based state observer is presented for each follower to estimate the whole states of all the leaders under the influences of switching interaction topologies and time-varying delays. Then, formation- containment problems are transformed into asymptotic stability problems. Furthermore, an algorithm to determine the gain matrices in the protocols is given based on linear matrix inequality technique and common Lyapunov-Krasovskii stability theory. Sufficient conditions for multi-agent systems to achieve formation-containment under the designed protocol are proposed. Finally, numerical simulations are provided to demonstrate theoretical results.

关键词： Switches Delays Control systems Topology Protocols Symmetric matrices Time-varying systems

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Current switching of the antiferromagnetic Néel vector in Pd/CoO/MgO(001)

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Physical Review B 2022年第21期106卷 214405-214405页

作者： M. Yang Q. Li T. Wang B. Hong C. Klewe Z. Li X. Huang P. Shafer F. Zhang C. Hwang W. S. Yan R. Ramesh W. S. Zhao Y. Z. Wu Xixiang Zhang Z. Q. Qiu Institute of Physical Science and Information Technology Anhui University Hefei Anhui 230601 China National Synchrotron Radiation Laboratory University of Science and Technology of China Hefei Anhui 230029 China Department of Physics University of California Berkeley California 94720 USA Fert Beijing Research Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Materials Science and Engineering University of California Berkeley California 94720 USA Korea Research Institute of Standards and Science Yuseong Daejeon 305-340 Korea Department of Physics State Key Laboratory of Surface Physics Fudan University Shanghai 200433 China Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia

Recently, the electrical switching of antiferromagnetic (AFM) order has been intensively investigated because of its application potential in data storage technology. Herein, we report the current switching of the AFM Néel vector in epitaxial Pd/CoO films as a function of temperature. Using combined measurements of Hall resistance (HR) and x-ray magnetic linear dichroism (XMLD) below and above the AFM Néel temperature, we unambiguously identified both magnetic and nonmagnetic contributions to the current-induced HR change. Through magnetic field-induced HR measurements, we quantitatively determined the percentage of current-induced CoO spin switching. Further, we showed that the thermal effect dominated the CoO magnetic switching more in samples with a thinner Pd layer and that samples with a thicker CoO layer required higher thermal activation for current-induced magnetic switching. These results provide a clear and comprehensive picture of current-induced AFM spin switching across the AFM Néel temperature.

关键词： Hall effect Magnetism Spintronics X-ray magnetic linear dichroism

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TCIM: Triangle Counting Acceleration With Processing-In-MRAM Architecture

TCIM: Triangle Counting Acceleration With Processing-In-MRAM...

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Design Automation Conference

作者： Xueyan Wang Jianlei Yang Yinglin Zhao Yingjie Qi Meichen Liu Xingzhou Cheng Xiaotao Jia Xiaoming Chen Gang Qu Weisheng Zhao Fert Beijing Research Institute Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China Department of Electrical and Computer Engineering University of Maryland College Park MD USA

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and FPGA. However, these approaches suffer from the bandwidth bottleneck because TC calculation involves a large amount of data transfers. In this paper, we propose to overcome this challenge by designing a TC accelerator utilizing the emerging processing-in-MRAM (PIM) architecture. The true innovation behind our approach is a novel method to perform TC with bitwise logic operations (such as AND), instead of the traditional approaches such as matrix computations. This enables the efficient in-memory implementations of TC computation, which we demonstrate in this paper with computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) arrays. Furthermore, we develop customized graph slicing and mapping techniques to speed up the computation and reduce the energy consumption. We use a device-to-architecture co-simulation framework to validate our proposed TC accelerator. The results show that our data mapping strategy could reduce 99.99% of the computation and 72% of the memory WRITE operations. Compared with the existing GPU or FPGA accelerators, our in-memory accelerator achieves speedups of 9× and 23.4×, respectively, and a 20.6× energy efficiency improvement over the FPGA accelerator.

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An Amplitudes-Perturbation data Augmentation Method in Convolutional Neural Networks for EEG Decoding 5

An Amplitudes-Perturbation Data Augmentation Method in Convo...

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5th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2018

作者： Zhang, Xian-Rui Lei, Meng-Ying Li, Yang Department of Automation Sciences and Electrical Engineering Beihang University Beijing China Department of Automation Sciences and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Advanced Innovation Center for Big Date-based Precision Medicine Beihang University Beijing China

ISBN: (纸本)9781538670880

brain-Computer Interface (BCI)system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information. Electroencephalography (EEG)is one of most commonly used brain signals and EEG recognition is an important part of BCI system. Recently, convolutional neural networks (ConvNet)in deep learning are becoming the new cutting edge tools to tackle the problem of EEG recognition. However, training an effective deep learning model requires a big number of data, which limits the application of EEG datasets with a small number of samples. In order to solve the issue of data insufficiency in deep learning for EEG decoding, we propose a novel data augmentation method that add perturbations to amplitudes of EEG signals after transform them to frequency domain. In experiments, we explore the performance of signal recognition with the state-of-the-art models before and after data augmentation on BCI Competition IV dataset 2a and our local dataset. The results show that our data augmentation technique can improve the accuracy of EEG recognition effectively. © 2018 IEEE.

关键词： Electroencephalography

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Magnetic skyrmions for future potential memory and logic applications: Alternative information carriers

Magnetic skyrmions for future potential memory and logic app...

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2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018

作者： Kang, Wang Chen, Xing Zhu, Daoqian Li, Sai Huang, Yangqi Zhang, Youguang Zhao, Weisheng Fert Beijing Research Institute Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9783981926316

Magnetic skyrmions are swirling topological configurations, which are mostly induced by chiral interactions between atomic spins in non-centrosymmetric magnetic bulks or in thin films with broken inversion symmetry. They hold promise as information carriers in future ultra-dense, low-power memory and logic devices owing to the nanocale size and extremely low spin-polarized currents needed to move them. To date, an intense research effort has led to the identification, creation/annihilation, motion and manipulation of skyrmions at room temperature. Meanwhile, a rich variety of skyrmion-based device concepts and prototypes have been proposed, indicating the considerable potential of magnetic skyrmions in future electronic applications. However, current studies mainly focus on physical or principle investigations, whereas the electrical design methodology, implementation and evaluations are still lacking. In this paper, we will bring the readers in the 'design, automation and test (DAT) society' the current status and outlook of skyrmions in relation to future potential racetrack memory and neuromorphic computing applications. Most importantly, we also want to evoke the effort from the DAT society to address the challenges, e.g., all-electrical manipulation of skyrmions at room temperature, for the research and development of practical skyrmion-based electronics. © 2018 EDAA.

关键词： Logic devices

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RWNE: A scalable random-walk-based network embedding framework with personalized higher-order proximity preserved

arXiv

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arXiv 2019年

作者： Li, Jianxin Ji, Cheng Peng, Hao He, Yu Song, Yangqiu Zhang, Xinmiao Peng, Fanzhang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China Department CSE HKUST Hong Kong Beihang University China

Higher-order proximity preserved network embedding has attracted increasing attention. In particular, due to the superior scalability, random-walk-based network embedding has also been well developed, which could efficiently explore higher-order neighborhoods via multi-hop random walks. However, despite the success of current random-walk-based methods, most of them are usually not expressive enough to preserve the personalized higher-order proximity and lack a straightforward objective to theoretically articulate what and how network proximity is preserved. In this paper, to address the above issues, we present a general scalable random-walk-based network embedding framework, in which random walk is explicitly incorporated into a sound objective designed theoretically to preserve arbitrary higher-order proximity. Further, we introduce the random walk with restart process into the framework to naturally and effectively achieve personalized-weighted preservation of proximities of different orders. We conduct extensive experiments on several real-world networks and demonstrate that our proposed method consistently and substantially outperforms the state-of-the-art network embedding methods. © 2019, CC BY.

关键词： Network embeddings

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Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks

Pairwise Learning for Name Disambiguation in Large-Scale Het...

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IEEE International Conference on data Mining (ICDM)

作者： Qingyun Sun Hao Peng Jianxin Li Senzhang Wang Xiangyun Dong Liangxuan Zhao Philip S. Yu Lifang He Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Shenyuan Honors College Beihang University Beijing China Nanjing University of Aeronautics and Astronautics Nanjing China Beihang University China University of Illinois at Chicago Chicago USA Lehigh University Bethlehem PA USA

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Name disambiguation aims to identify unique authors with the same name. Existing name disambiguation methods always exploit author attributes to enhance disambiguation results. However, some discriminative author attributes (e.g., email and affiliation) may change because of graduation or job-hopping, which will result in the separation of the same author's papers in digital libraries. Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network. Inspired by this idea, we introduce Multi-view Attention-based Pairwise Recurrent Neural Network (MA-PairRNN) to solve the name disambiguation problem. We divided papers into small blocks based on discriminative author attributes and blocks of the same author will be merged according to pairwise classification results of MA-PairRNN. MA-PairRNN combines heterogeneous graph embedding learning and pairwise similarity learning into a framework. In addition to attribute and structure information, MA-PairRNN also exploits semantic information by meta-path and generates node representation in an inductive way, which is scalable to large graphs. Furthermore, a semantic-level attention mechanism is adopted to fuse multiple meta-path based representations. A Pseudo-Siamese network consisting of two RNNs takes two paper sequences in publication time order as input and outputs their similarity. Results on two real-world datasets demonstrate that our framework has a significant and consistent improvement of performance on the name disambiguation task. It was also demonstrated that MA-PairRNN can perform well with a small amount of training data and have better generalization ability across different research areas.

关键词： Recurrent neural networks Fuses Semantics Training data Libraries Electronic mail Task analysis

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TCIM: Triangle counting acceleration with processing-in-MRAM architecture

arXiv

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arXiv 2020年

作者： Wang, Xueyan Yang, Jianlei Zhao, Yinglin Qi, Yingjie Liu, Meichen Cheng, Xingzhou Jia, Xiaotao Chen, Xiaoming Qu, Gang Zhao, Weisheng Fert Beijing Research Institute School of Microelectronics Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering University of Maryland College ParkMD United States

关键词： data transfer

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Spatio-temporal propagation of COVID-19 pandemics

arXiv

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arXiv 2020年

作者： Gross, Bnaya Zheng, Zhiguo Liu, Shiyan Chen, Xiaoqi Sela, Alon Li, Jianxin Li, Daqing Havlin, Shlomo Department of Physics Bar-Ilan University Ramat-Gan52900 Israel School of Reliability and Systems Engineering Beihang University Beijing100191 China Department of Industrial Engineering Ariel University Ariel Israel Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China State Key Laboratory of Software Development Environment Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China

The new coronavirus known as COVID-19 is rapidly spreading since December 2019. Without any vaccination or medicine, the means of controlling it are limited to quarantine and social distancing. Here we study the spatio-temporal propagation of the COVID-19 virus in China and compare it to other global locations. Our results suggest that the disease propagation is highly related to population migration from Hubei resembling a Lévy flight which is characteristic of human mobility and thus could be controlled by efficient quarantines. Since quarantine is usually applied on a city level, more insight can be obtained in analyzing the epidemic in cities. Our results suggest that the disease spread in a city in China is characterized by two-stages process. At early times, at order of few days, the infection rate in the city is close to constant probably due to the lack of means to detect infected individuals before infection signs are observed and at later times it decays approximately exponentially due to quarantines. These two stages can explain the significant differences between the propagation in China and in other world-wide locations. While most cities in China control the disease which resulted in the decaying stage, in other world-wide countries the situation is still becoming worse probably due to less social interactions control and overloaded health systems which reflects in the death and recovery rates. Copyright © 2020, The Authors. All rights reserved.

关键词： Population dynamics

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