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Conference on Lasers and Electro-Optics (CLEO)

作者： Tian Dong Tianxiang Dai Ye Chen Yueyang Liu Hancheng Liu Yiting Wang Anqi Ma Haifeng Hu Lihua Xu Le Zhao Weiguo Chu Chao Peng Jianwei Wang Yang Li Department of Precision Instrument State Key Laboratory of Precision Measurement Technology and Instrument Tsinghua University Beijing China State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing China Department of Electronics and Frontiers Science Center for Nano-optoelectronics State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Nanofabrication Laboratory National Center for Nanoscience and Technology Beijing China

We report flexible zero-index waveguides and devices whose loss is two orders of magnitude lower than the state of the art, enabling phase-error-free high-dense photonic integrated circuits for classical and quantum i... 详细信息

ISBN: (纸本)9798350369311

关键词： Optical losses Quantum computing Photonic integrated circuits Waveguide lasers Information processing Lasers and electrooptics Electro-optical waveguides

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Anger makes fake news viral online

arXiv

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arXiv 2020年

作者： Chuai, Yuwei Zhao, Jichang School of Economics and Management Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing China

Fake news that manipulates political elections, strikes financial systems and even incites riots is more viral than real news online, resulting in unstable societies and buffeted democracy. The easier contagion of fake news online can be causally explained by the greater anger it carries. Offline questionnaires reveal that anger leads to more incentivized audiences in terms of anxiety management and information sharing and accordingly makes fake news more contagious than real news online. Our results suggest that the digital contagion of emotions, in particular anger, should be comprehensively considered in profiling the spread of online information. Cures such as tagging anger in social media could be implemented to slow or prevent the contagion of fake news at the source. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Collective firing patterns of neuronal networks with short-term synaptic plasticity

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Physical Review E 2021年第2期103卷 022312-022312页

作者： Chong-Yang Wang Ji-Qiang Zhang Zhi-Xi Wu Jian-Yue Guan Lanzhou Center for Theoretical Physics and Key Laboratory of Theoretical Physics of Gansu Province Lanzhou University Lanzhou Gansu 730000 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China School of Physics and Electronic-Electrical Engineering Ningxia University Yinchuan 750021 China

We investigate the occurrence of synchronous population activities in a neuronal network composed of both excitatory and inhibitory neurons and equipped with short-term synaptic plasticity. The collective firing patterns with different macroscopic properties emerge visually with the change of system parameters, and most long-time collective evolution also shows periodic-like characteristics. We systematically discuss the pattern-formation dynamics on a microscopic level and find a lot of hidden features of the population activities. The bursty phase with power-law distributed avalanches is observed in which the population activity can be either entire or local periodic-like. In the purely spike-to-spike synchronous regime, the periodic-like phase emerges from the synchronous chaos after the backward period-doubling transition. The local periodic-like population activity and the synchronous chaotic activity show substantial trial-to-trial variability, which is unfavorable for neural code, while they are contrary to the stable periodic-like phases. We also show that the inhibitory neurons can promote the generation of cluster firing behavior and strong bursty collective firing activity by depressing the activities of postsynaptic neurons partially or wholly.

关键词： Neuronal dynamics Population dynamics Synapses Synchronization Collective dynamics Neuronal avalanches Neuronal networks Integrate-and-fire model

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Recent advances in distributed adaptive consensus control of uncertain nonlinear multi-agent systems

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Journal of Control and Decision 2020年第1期7卷 44-63页

作者： Wei Wang Jiang Long Changyun Wen Jiangshuai Huang School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191People’s Republic of China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191People’s Republic of China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Automation Chongqing UniversityChongqingPeople’s Republic of China

So far,distributed adaptive consensus problems for uncertain nonlinear multi-agent systems have not yet been extensively *** with centralised adaptive control,some new challenges need to be well addressed,for examples,(i)how to reach asymptotically consensus tracking with directed topology condition,by using totally distributed adaptive control strategies;(ii)how to ensure globally uniform boundedness of closed-loop systems while achieving leaderless consensus with semi-positive definite Laplacian matrix;(iii)how to maintain system performance while effectively reducing the communication burden among connected *** paper is mainly devoted to report some recent advances in distributed adaptive consensus ***,some interesting research topics which are worthy of further investigation will also be discussed.

关键词： Distributed adaptive control consensus nonlinear multi-agent systems uncertainties

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Intelligent safety monitoring and early warning system for construction site 20

Intelligent safety monitoring and early warning system for c...

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2nd World Symposium on Software Engineering, WSSE 2020

作者： Hu, Zheyuan Cai, Jun Wang, Huiwei Zhang, Dehao Liu, Yang Li, Xin Li, Yanlong Zhao, Fengyan Zhang, Hongjun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Shanxi zhen'An Pumped Storage Co. Ltd Shanxi711500 China Beijing Nelda Technology Co. Ltd. Beijing100194 China

ISBN: (纸本)9781450387873

Faced with the complex environment and difficult construction of infrastructure projects, designing an intelligent safety monitoring and early warning system for construction sites can effectively detect existing violations and reduce the probability of accidents. Existing violation detection methods for construction sites mainly include hand-crafted feature extraction and deep neural network. However, the method of extracting features is usually difficult to design and the architecture of the deep learning-based method is simple, which might lead to poor detection performance in extreme cases (Insufficient light, small detection object, occlusion, etc.) and cannot be used in actual detection environment. Therefore, we improve the existing target detection algorithm by adding image preprocessing module, multi-scale feature fusion module, and repulsion loss term. We also use the KCF algorithm to continuously track targets to identify specific violations. On this basis, we develop an intelligent safety monitoring and early warning system to classify the detected violations and send the information to the responsible person in time, which significantly improves the management capacity at the construction site. Through a series of experiments, we compared the impact of different modules on detection accuracy. The results show that our model has a significant improvement compared to existing methods on our dataset, especially in harsh environments. © 2020 ACM.

关键词： Object detection

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Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces

arXiv

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arXiv 2022年

作者： Sun, Li Ye, Junda Peng, Hao Wang, Feiyang Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Computer Science University of Illinois ChicagoIL United States

Continual graph learning routinely finds its role in a variety of real-world applications where the graph data with different tasks come sequentially. Despite the success of prior works, it still faces great challenges. On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence. On the other hand, continual learners in the literature rely on abundant labels, but labeling graph in practice is particularly hard especially for the continuously emerging graphs on-the-fly. To address the aforementioned challenges, we propose to explore a challenging yet practical problem, the self-supervised continual graph learning in adaptive Riemannian spaces. In this paper, we propose a novel self-supervised Riemannian Graph Continual Learner (RieGrace). In RieGrace, we first design an Adaptive Riemannian GCN (AdaRGCN), a unified GCN coupled with a neural curvature adapter, so that Riemannian space is shaped by the learnt curvature adaptive to each graph. Then, we present a Label-free Lorentz Distillation approach, in which we create teacher-student AdaRGCN for the graph sequence. The student successively performs intra-distillation from itself and inter-distillation from the teacher so as to consolidate knowledge without catastrophic forgetting. In particular, we propose a theoretically grounded Generalized Lorentz Projection for the contrastive distillation in Riemannian space. Extensive experiments on the benchmark datasets show the superiority of RieGrace, and additionally, we investigate on how curvature changes over the graph sequence. © 2022, CC BY-NC-SA.

关键词： Distillation

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A Camera–Radar Fusion Method Based on Edge computing

A Camera–Radar Fusion Method Based on Edge Computing

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IEEE International Conference on Edge computing (EDGE)

作者： Yanjin Fu Daxin Tian Xunting Duan Jianshan Zhou Ping Lang Chunmian Lin Xin You Beijing Advanced Innovation Center for Big Data and Brain Computing School of Transportation Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728182544

ISBN: (纸本)9781728182551

Multi-access edge computing provides a low-latency and high-performance network environment for the Internet of Vehicle services by migrating computing and storage resources to the edge of networks and supports the deployment of more regional-specific vehicle-to-everything services. In this paper, we propose a fusion perception method for roadside cameras and millimeter wave radar as an application of edge computing. First, we used YOLOv3 to process camera data and DBSCAN clustering method to process radar data to obtain the position, speed, and category of detections. Next, we used joint calibration to spatially synchronize camera and radar data and a direct update method to temporally synchronize camera and radar data. Furthermore, we used the Munkres algorithm to associate camera and radar detections, and Kalman filter to track the proposed fusion method's perception results. Finally, we conducted simulation experiments to evaluate the proposed method. The simulation results demonstrate the effectiveness of our algorithm.

关键词： Simulation Image edge detection Radar detection Millimeter wave radar Cameras Synchronization Edge computing

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Comprehensive Characteristic Decomposition of Parametric Polynomial Systems 21

Comprehensive Characteristic Decomposition of Parametric Pol...

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46th International Symposium on Symbolic and Algebraic Computation, ISSAC 2021

作者： Dong, Rina Lu, Dong Mou, Chenqi Wang, Dongming Chongqing Key Laboratory of Automated Reasoning and Cognition Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Mathematical Sciences Beihang University Beijing100191 China LMIB-School of Mathematical Sciences Beihang University Beijing100191 China Centre National de la Recherche Scientifique Paris cedex 1675794 France

ISBN: (纸本)9781450383820

This paper presents an algorithm that decomposes an arbitrary set F of multivariate polynomials involving parameters into finitely many sets Gi of (lexicographical) Gröbner bases G ij such that associated with each Gi there is a system Ai of parametric constraints, all the Ai's partition the parameter space, all the Gij's in each Gi remain Gröbner bases under specialization of the parameters satisfying the constraints in Ai, and for each i the Gröbner bases Gij together with their corresponding W-characteristic sets form a normal characteristic decomposition of F. The sets of Gröbner bases computed by the algorithm provide a comprehensive characteristic decomposition of F that is structure-invariant under the associated constraints and possesses many algebraic and geometric properties, including most of the notable properties on comprehensive Gröbner systems and comprehensive triangular decomposition. Some of these properties are highlighted in the paper and advantages of the proposed algorithm are discussed briefly from the aspects of methodological simplicity and computational performance using illustrative examples and preliminary experiments. © 2021 ACM.

关键词： Polynomials

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Fusion Strategy of Multi-sensor Based Object Detection for Self-driving Vehicles

Fusion Strategy of Multi-sensor Based Object Detection for S...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Yanqi Li Jianwei Niu Zhenchao Ouyang Hangzhou Innovation Institute Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Beijing China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

Lidar and optical camera are common sensors in the sensor layer of autopilot system. Lidar can use depth data to obtain accurate relative distance and contour information of obstacles, which is not easily affected by external light conditions. Optical camera can obtain rich object/environment semantic information through high-resolution image, which is relatively mature in technology. The two different sensors are highly complementary, and previous studies show that the fusion of laser point cloud and image data can greatly improve the efficiency of object detection in out door environment. In this paper, a deep convolutional neural network detection model based on Lidar and image information features layered fusion is studied. We try different fusion depth at the CNN model to seek the best solution according to the detection performance. The experimental results on the KITTI dataset show that the detection accuracy of the fusion based on YOLOv3 is 1.08% higher than original model. Another small scale experiment with our own self-driving platform on local area also show the final fusion model can achieve better detection accuracy in real road condition.

关键词： Feature extraction Three-dimensional displays Object detection Laser radar Cameras Sensor phenomena and characterization

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On Functions of Markov Random Fields

arXiv

引用

arXiv 2020年

作者： Geiger, Bernhard C. Al-Bashabsheh, Ali Know-Center GmbH Graz8010 Austria Beijing Advanced Inst. on Big Data and Brain Computing Beijing China

We derive two sufficient conditions for a function of a Markov random field (MRF) on a given graph to be a MRF on the same graph. The first condition is information-theoretic and parallels a recent information-theoretic characterization of lumpability of Markov chains. The second condition, which is easier to check, is based on the potential functions of the corresponding Gibbs field. We illustrate our sufficient conditions at the hand of several examples and discuss implications for practical applications of MRFs. As a side result, we give a partial characterization of functions of MRFs that are information-preserving. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetorheological fluids

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