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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yangtao Zheng Di Huang Songtao Liu Yunhong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach.

关键词： Feature extraction Subspace constraints Object detection Detectors Task analysis Semantics Prototypes

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Matrix Classifier On Dynamic Functional Connectivity For Mci Identification

Matrix Classifier On Dynamic Functional Connectivity For Mci...

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IEEE International Conference on Image Processing

作者： Lei Zhou Liang Zhang Xiao Bai Jun Zhou School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Software Development Environment Beihang University Beijing School of Information and Communication Technology Griffith University Nathan

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

One of the most popular method for Alzheimer's disease (AD) diagnosis is exploring the Brain functional connectivity (FC) from resting-state functional magnetic resonance imaging (RS-fMRI). To early prevent AD, it is crucial to distinguish AD and and its preclinical stage, mild cognitive impairment (MCI) and early MCI (eMCI). In many existing works, dynamic functional connectivity (dFC) which contains rich spatiotemporal information has been exploited for the MCI and eMCI identification. However, most of these dFC based methods only consider the correlation between discrete brain status while ignore the valuable spatiotemporal information contained in dFC. To overcome this limitation, we propose a matrix classifier based method on the dFC signal for MCI and eMCI identification. Specifically, we first represent the dFC correlations by matrix features which contain rich spatiotemporal information and then learn the support matrix machines (SMM) to classify AD and its preclinical stage. Experiments on 600 real people data provide by the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed matrix classifier based method outperforms other FC and dFC based methods for both normal controls (NC)/MCI identification and NC/eMCI identification.

关键词： Feature extraction Correlation Dementia Microsoft Windows Spatiotemporal phenomena Training

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On PID control for synchronization of complex dynamical network with delayed nodes

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science China(Technological sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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Cross-domain object detection through coarse-to-fine feature adaptation

arXiv

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arXiv 2020年

作者： Zheng, Yangtao Huang, Di Liu, Songtao Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing100191 China

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarseto-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Interpretable Spatiotemporal Deep Learning Model for Traffic Flow Prediction Based on Potential Energy Fields

Interpretable Spatiotemporal Deep Learning Model for Traffic...

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IEEE International Conference on Data Mining (ICDM)

作者： Jiahao Ji Jingyuan Wang Zhe Jiang Jingtian Ma Hu Zhang School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China MOE Engineering Research Center of ACAT Beihang University Beijing China Department of Computer Science The University of Alabama Tuscaloosa Alabama USA

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Traffic flow prediction is of great importance in traffic management and public safety, but is challenging due to the complex spatial-temporal dependencies as well as temporal dynamics. Existing work either focuses on traditional statistical models, which have limited prediction accuracy, or relies on black-box deep learning models, which have superior prediction accuracy but are hard to interpret. In contrast, we propose a novel interpretable spatiotemporal deep learning model for traffic flow prediction. Our main idea is to model the physics of traffic flow through a number of latent Spatio-Temporal Potential Energy Fields (ST-PEFs), similar to water flow driven by the gravity field. We develop a Wind field Decomposition (WD) algorithm to decompose traffic flow into poly-tree components so that ST-PEFs can be established. We then design a spatiotemporal deep learning model for the ST-PEFs, which consists of a temporal component (modeling the temporal correlation) and a spatial component (modeling the spatial dependencies). To the best of our knowledge, this is the first work that make traffic flow prediction based on ST-PEFs. Experimental results on real-world traffic datasets show the effectiveness of our model compared to the existing methods. A case study confirms our model interpretability.

关键词： Deep learning Potential energy Predictive models Prediction algorithms Data models Spatiotemporal phenomena Safety

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BioRel: A Large-Scale Dataset for Biomedical Relation Extraction

BioRel: A Large-Scale Dataset for Biomedical Relation Extrac...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Rui Xing Tengwei Song Jie Luo State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781728118680

Valuable biomedical knowledge usually exists in the form of electronic publications and literature, which is growing at an enormous rate. Relation extraction plays a critical role in discovering such knowledge and transform them into structural form. Previous relation extraction datasets in biomedical domain are mainly human-annotated, whose scales are usually limited due to their lab.r-intensive and time-consuming nature. In this paper, we present BioRel, a large-scale dataset constructed by using Unified Medical Language System (UMLS) as knowledge base and Medline as corpus. Entities in sentences of Medline are identified and linked to UMLS by Metamap. Relation lab.l for each sentence is recognized using distant supervision. We adapt both state-of-the-art deep learning and statistical machine learning methods as baseline models and conduct comprehensive experiments on BioRel. Experimental results show that BioRel is suitable for training and evaluating relation extraction models for both deep learning and statistical methods by providing both reasonable baseline performance and many remaining challenges.

关键词： distant supervision relation extraction dataset Medline MEDLINE Dataset Unified Medical Language System biomedical Machine Learning Adjudication

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Parallel Evolutionary Algorithm in Scheduling Work Packages to Minimize Duration of software Project Management 17th

Parallel Evolutionary Algorithm in Scheduling Work Packages ...

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16th National Conference on software and Applications, NASAC 2017 and 17th National Conference on software and Applications, NASAC 2018

作者： Hu, Jinghui Wang, Xu Ren, Jian Liu, Chao AVIC Manufacturing Technology Institute Beijing100024 China Aeronautical Key Laboratory for Digital Manufacturing Technology Beijing100024 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9789811503092

software project management problem mainly includes resources allocation and work packages scheduling. This paper presents an approach to Search Based software Project Management based on parallel implementation of evolutionary algorithm on GPU. We redesigned evolutionary algorithm to cater for the purpose of parallel programming. Our approach aims to parallelize the genetic operators including: crossover, mutation and evaluation in the evolution process to achieve faster execution. To evaluate our approach, we conducted a "proof of concept" empirical study, using data from three real-world software projects. Both sequential and parallel version of a conventional single objective evolutionary algorithm are implemented. The sequential version is based on common programming approach using C++, and the parallel version is based on GPGPU programming approach using CUDA. Results indicate that even a relatively cheap graphic card (GeForce GTX 970) can speed up the optimization process significantly. We believe that deploy parallel evolutionary algorithm based on GPU may fit many applications for other software project management problems, since software projects often have complex inter-related work packages and resources, and are typically characterized by large scale problems which optimization process ought to be accelerated by parallelism. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Application programs

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Rely-Guarantee Reasoning about Messaging System for Autonomous Vehicles

Rely-Guarantee Reasoning about Messaging System for Autonomo...

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Theoretical Aspects of software engineering (TASE)

作者： Wenjing Xu Yongwang Zhao Dianfu Ma YuXin Zhang Qian Xiao State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang Univerisity Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Jilin China School of Computer Science and Technology Guizhou University Guiyang China

Messaging system as a communicating infrastructure is a safety-critical component of autonomous vehicles. For the purpose of safety certification of Level 4 autonomous driving systems, its necessary to provide a formally verified specification of messaging systems. This paper presents a realistic case study using the PiCore rely-guarantee framework to formally verify the DGPS (Differential Global Positioning System) of UISEE autonomous driving systems. We first create an axiom model of messaging systems by extending PiCore, which is reusable for concrete applications. The model supports dynamic configuration of message buffers as well as the automatic generation of the rely and guarantee conditions for compositional reasoning. It is instantiated when developing the formal specification of DGPS. Then, we use the rely-guarantee proof system of PiCore to verify the functional correctness and invariant of DGPS. The specification and its proof provide a strong evidence for the ongoing safety certification of DGPS.

关键词： Cognition Safety Formal specifications Vehicle dynamics Autonomous vehicles Certification Global Positioning System

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Vehicle re-identification with location and time stamps 32

Vehicle re-identification with location and time stamps

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32nd IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2019

作者： Lv, Kai Du, Heming Hou, Yunzhong Deng, Weijian Sheng, Hao Jiao, Jianbin Zheng, Liang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University University of Chinese Academy of Sciences Australian National University

ISBN: (纸本)9781728125060

This paper focuses on the problem of vehicle re-identification (Re-ID). In our attempt, we propose a re-identification framework by exploiting vehicle location and time stamps. The location and time information have the potential to cover the shortage of appearance-based feature representations. First, we introduce an ensemble technique to combine the informative cues of multiple Re-ID models effectively. To further improve the accuracy, we then build up a system to acquire the vehicle location and time stamps. Specifically, we utilize the detected results to obtain the needed information. With the help of the proposed system, we can remove irrelevant images from a given ranking list. Our system finished 3rd place in the 2019 AI-City challenge for city-scale multi-camera vehicle re-identification. © 2019 IEEE computer Society. All rights reserved.

关键词： Vehicles

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DDLB: A dynamic and distributed load balancing strategy 21

DDLB: A dynamic and distributed load balancing strategy

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Liu, Bingqi Chang, Jiulong Xiao, Limin Qin, Guangjun Wei, Bing Huo, Zhisheng State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China Smart City College Beijing Union University Beijing100101 China

ISBN: (纸本)9781728120584

In view of the problem of load imbalance for distributed file systems, the current solutions either adopt centralized algorithm and implement load balancing by increasing the number of files as the replica, or lack considerations for network transmission and network bandwidth. Therefore, the current solutions are relatively inefficient in distributed file system. To solve this problem, this paper proposes DDLB, a dynamic and distributed load balancing algorithm which is completely based on distributed load balancing architecture. DDLB can not only monitor the IO load on the data server dynamically and real time, but also use the load prediction model, which based on weighted mean, to reduce the impact of network delay and collection delay on load decision-making. In order to minimize the frequency of remote load collection and decrease network bandwidth occupation, DDLB adopts the remote distributed load collection method based on threshold and the cooling collection mechanism. Moreover, dynamic replica management mechanism of DDLB takes data blocks instead of files as the basic unit for replica replication, and reduces the IO load of the data server by adding replicas of hotspot data blocks. For proving the effectiveness of DDLB, this paper conducts a comprehensive evaluation in the HDFS cluster environment. The results show that compared with the traditional load balancing algorithm, DDLB can effectively balance the IO load between data servers in the distributed file system and improve the data access performance of IO-intensive applications. © 2019 IEEE.

关键词： Decision making

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