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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Li Xu, Hua Wang, Ziteng Wang, Chengming Jiang, Yu State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China Meituan Inc. Block F&G Wangjing International R&D Park No.6 Wang Jing East Rd Chaoyang District Beijing100102 China

ISBN: (纸本)9781577358800

Graph convolutional neural network (GCN) based methods have achieved noticeable performance in solving mixed integer programming problems (MIPs). However, the generalization of existing work is limited due to the problem structure. This paper proposes a self-paced learning (SPL) based GCN network (SPGCN) with curriculum learning (CL) to make the utmost of samples. SPGCN employs a GCN model to imitate the branching variable selection during the branch and bound process, while the training process is conducted in a self-paced fashion. Specifically, SPGCN contains a loss-based automatic difficulty measurer, where the training loss of the sample represents the difficulty level. In each iteration, a dynamic training dataset is constructed according to the difficulty level for GCN model training. Experiments on four NP-hard datasets verify that CL can lead to generalization improvement and convergence speedup in solving MIPs, where SPL performs better than predefined CL methods. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Integer programming

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STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 226-238页

作者： Zebing Wei Hongxia Zhao Zhishuai Li Xiaojie Bu Yuanyuan Chen Xiqiao Zhang Yisheng Lv Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the Shandong Key Laboratory of Smart Transportation(Preparation) Shandong Jiao Tong UniversityShanghai 200240China the College of Science China University of PetroleumQingdao 266580China the Department of Transportation Engineering School of Transportation Science and TechnologyHarbin Institute of TechnologyHarbin 150090China IEEE

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the *** existing frameworks typically utilize separate modules for spatial and temporal correlations ***,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some ***,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between *** overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies ***,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution *** each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting ***,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven ***,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global *** projects the embedding input repeatedly with multiple different ***,the predicted values are generated by stacking several multi-stream *** experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA

关键词： Deep learning graph neural network(GNN) multistream spatial-temporal feature extraction temporal graph traffic prediction

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Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 146-159页

作者： Yinlong Zhang Wei Liang Mingze Yuan Hongsheng He Jindong Tan Zhibo Pang Key Laboratory of Networked Control Systems Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Computing Wichita State UniversityWichita 67260 USA Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeTN 37996 USA Department of Automation Technology ABB Corporate Research SwedenVasteras 72178Sweden Department of Intelligent Systems Royal Institute of Technology(KTH)Stockholm 11428Sweden

Reliable and accurate calibration for camera,inertial measurement unit(IMU)and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment ***,traditional calibrations suffer inaccuracy and *** address these problems,this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying *** our method,the spatial relationship is geometrically correlated between the sensing units and robotic *** decoupled estimations on rotation and translation could reduce the coupled errors during the ***,the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and *** calibration has been evaluated on our developed *** the experiments,the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7°and 0.01 m,*** comparisons with state-of-the-art results prove our calibration consistency,accuracy and effectiveness.

关键词： Calibration inertial measurement unit(IMU) monocular camera robotic arm spiral moving trajectory

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Electrically tunable planar liquid-crystal singlets for simultaneous spectrometry and imaging

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Light(science & Applications) 2024年第11期13卷 2589-2598页

作者： Zhou Zhou Yiheng Zhang Yingxin Xie Tian Huang Zile Li Peng Chen Yan-qing Lu Shaohua Yu Shuang Zhang Guoxing Zheng Electronic Information School and School of MicroelectronicsWuhan UniversityWuhan 430072China NUS Graduate School National University of SingaporeSingapore 119077Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and Manipulationand College of Engineering and Applied SciencesNanjing UniversityNanjing 210093China Peng Cheng Laboratory Shenzhen 518055China New Cornerstone Science Laboratory Department of PhysicsUniversity of Hong KongHong KongChina Department of Electrical and Electronic Engineering University of Hong KongHong KongChina Wuhan Institute of Quantum Technology Wuhan 430206China

Conventional hyperspectral cameras cascade lenses and spectrometers to acquire the spectral datacube,which forms the fundamental framework for hyperspectral ***,this cascading framework involves tradeoffs among spectral and imaging performances when the system is driven toward ***,we propose a spectral singlet lens that unifies optical imaging and computational spectrometry functions,enabling the creation of minimalist,miniaturized and high-performance hyperspectral *** a paradigm,we capitalize on planar liquid crystal optics to implement the proposed framework,with each liquid-crystal unit cell acting as both phase modulator and electrically tunable spectral *** with various targets show that the resulting millimeter-scale hyperspectral camera exhibits both high spectral fidelity(>95%)and high spatial resolutions(~1.7 times the diffraction limit).The proposed“two-in-one”framework can resolve the conflicts between spectral and imaging resolutions,which paves a practical pathway for advancing hyperspectral imaging systems toward miniaturization and portable applications.

关键词： crystal planar singlet

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Root Pose Decomposition Towards Generic Non-rigid 3D Reconstruction with Monocular Videos

arXiv

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arXiv 2023年

作者： Wang, Yikai Dong, Yinpeng Sun, Fuchun Yang, Xiao State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China RealAI China

This work focuses on the 3D reconstruction of non-rigid objects based on monocular RGB video sequences. Concretely, we aim at building high-fidelity models for generic object categories and casually captured scenes. To this end, we do not assume known root poses of objects, and do not utilize category-specific templates or dense pose priors. The key idea of our method, Root Pose Decomposition (RPD), is to maintain a per-frame root pose transformation, meanwhile building a dense field with local transformations to rectify the root pose. The optimization of local transformations is performed by point registration to the canonical space. We also adapt RPD to multi-object scenarios with object occlusions and individual differences. As a result, RPD allows non-rigid 3D reconstruction for complicated scenarios containing objects with large deformations, complex motion patterns, occlusions, and scale diversities of different individuals. Such a pipeline potentially scales to diverse sets of objects in the wild. We experimentally show that RPD surpasses state-of-the-art methods on the challenging DAVIS, OVIS, and AMA datasets. We provide video results in https://***. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Quorum sensing-based interactions among drugs,microbes,and diseases

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science China(Life sciences) 2023年第1期66卷 137-151页

作者： Shengbo Wu Shujuan Yang Manman Wang Nan Song Jie Feng Hao Wu Aidong Yang Chunjiang Liu Yanni Li Fei Guo Jianjun Qiao School of Chemical Engineering and Technology Tianjin UniversityTianjin 300072China School of Computer Science and Engineering Central South UniversityChangsha 410083China State Key Laboratory of Chemical Engineering Tianjin UniversityTianjin 300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) Tianjin 300072China Key Laboratory of Systems Bioengineering Ministry of Education(Tianjin University)Tianjin 300072China School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin 300350China Department of Engineering Science University of OxfordOxford OX13PJUK Institute of Shaoxing Tianjin UniversityShaoxing 312300China

Many diseases and health conditions are closely related to various microbes,which participate in complex interactions with diverse drugs;nonetheless,the detailed targets of such drugs remain to be *** existing studies have reported causal associations among drugs,gut microbes,or diseases,calling for a workflow to reveal their intricate *** this study,we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease(QSDMD)database(http://***/),which includes diverse interactions for more than 8,000 drugs,163 microbes,and 42 common *** interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental ***,we have constructed a QS-based drug-receptor interaction network,proposed a systematic framework including various drug-receptor-microbe-disease connections,and mapped a paradigmatic circular interaction network based on the QS-DMD,which can provide the underlying QS-based mechanisms for the reported causal *** QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse *** work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

关键词： quorum sensing drugs microbe-disease association microbe-host interaction gut microbiota personalized medicine

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Dependency-aware Task Offloading and Resource Pricing in Vehicular Edge Computing: A Stackelberg Game Approach 22

Dependency-aware Task Offloading and Resource Pricing in Veh...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhao, Liang Huang, Shuai Cao, Yuxiang Zhou, Huan Leung, Victor C. M. China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology Yichang China Northwestern Polytechnical University School of Computer Science Xi'an China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798331509712

Vehicular Edge Computing (VEC) allows vehicles to offload their delay-sensitive tasks to nearby Road Side Units (RSUs) for processing, which improves network quality of service (QoS). However, the self-interested SDN controller is unwilling to ask RSUs to provide free computing resources for vehicles. At the same time, complicated dependencies between vehicular subtasks may cause non-ideal task delay and energy consumption. In order to solve these problems, this paper proposes a Stackelberg game-based Dependency-aware task Offloading and resource Pricing framework (SDOP). Specifically, we first model a vehicular edge network that partially offloads dependency-aware tasks. Then, we depict the interaction between the SDN controller and vehicles as a Stackelberg game, with the goal of maximizing the utility of both parties. Next, we present a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the problem. Finally, numerous simulations are performed, and the results show that compared with other baseline schemes, the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios. © 2024 IEEE.

关键词： Quality of service

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Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for Engineering Problems

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Journal of Bionic Engineering 2024年第2期21卷 1055-1091页

作者： Yupeng Li Dong Zhao Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang College of Computer Science and Technology Changchun Normal UniversityChangchun130032JilinChina School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuChina Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China School of Computer Science and Engineering Southeast UniversityNanjing210096China

In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more *** novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this *** them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization ***,it tends to stagnate prematurely to local optima in solving some specific ***,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called *** this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of *** objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with *** final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real ***,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

关键词： Meta-heuristic algorithms Engineering optimization Spherical evolution algorithm Global optimization

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NeuroSparse: An Unsupervised Framework for Inferring Brain Connectivity in Autism Diagnosis

NeuroSparse: An Unsupervised Framework for Inferring Brain C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Shuqi Zhang, Lijuan Lan, Qing Ma, Qiujie Huang, Huan Miao, Jiaqing Mou, Shiyu Luo, Cheng Yao, Dezhong Tan, Ying Southwest Minzu University Key Laboratory for Computer Systems of State Ethnic Affairs Commission Chengdu China Moe Key Lab for Neuroinformation University of Electronic Science and Technology of China Clinical Hospital Chengdu Brain Science Institute Chengdu China University of Malaya Faculty of Engineering Department of Biomedical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350386226

Autism spectrum disorder (ASD) is a mental disorder that severely affects social interaction and communication skills. A timely and accurate diagnosis is crucial for effective intervention. However, as an objective diagnostic tool, the existence of spurious connections and noise within the functional connectivity (FC) matrix in the brain contribute to the complexity of ASD diagnosis. In this study, we propose an unsupervised sparse graph structure learning framework, called NeuroSparse, for reasoning regarding the topological connectivity relationships between brain regions. First, the framework fuses spatiotemporal information from static and dynamic FC to provide a comprehensive representation of complex brain networks. Next, the context and neighborhood information of nodes are captured as their local and global embeddings, and the latent representations of node embeddings are learned, which are then decoded to infer the connectivity relationships among the regions of interest. Finally, we introduce three innovative loss functions to regularize and optimize the sparse representation generation process. The experimental results showed that, after learning using NeuroSparse, the FC matrix achieved state-of-the-art performance in ASD diagnosis using only a simple multilayer perceptron, indicating its wide-ranging application potential. The code is available at https://***/yangshuqigit/NeuroSparse. © 2024 IEEE.

关键词： Unsupervised learning

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