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TechRxiv 2021年

作者： Song, Xiangyu Aryal, Sunil Ting, Kai Ming Liu, Zhen He, Bin Changchun130033 China The University of Chinese Academy of Sciences Beijing100049 China The School of Information Technology Deakin University 75 Pigdons Rd Waurn Ponds VIC3216 Australia National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Big Data Research Center and Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods. © 2021, CC BY.

关键词： Anomaly detection

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Pc(4457) → Pc(4312)π/γ in the molecular picture

arXiv

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arXiv 2021年

作者： Ling, Xi-Zhe Lu, Jun-Xu Liu, Ming-Zhu Geng, Li-Sheng School of Physics Beihang University Beijing102206 China School of Space and Environment Beihang University Beijing102206 China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing102206 China School of Physics and Microelectronics Zhengzhou University Henan Zhengzhou450001 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and Engineering Beihang University Beijing100191 China

The three pentaquark states, Pc(4312), Pc(4440), and Pc(4457), discovered by the LHCb Collaboration in 2019, can be nicely arranged into a multiplet of D¯ (∗)Σc(∗) of seven molecules dictated by heavy quark spin symmetry. In this work we employ the effective Lagrangian approach to investigate the two decay modes of Pc(4457), Pc(4457) → Pc(4312)π and Pc(4457) → Pc(4312)γ, via the triangle mechanism, assuming that Pc(4457) and Pc(4312) are D¯ ∗Σc and D¯ Σc bound states but the spin of Pc(4457) can be either 1/2 or 3/2. Our results show that the spin of Pc(4457) can not be discriminated through these two decay modes. The decay widths of Pc(4457) → Pc(4312)π and Pc(4457) → Pc(4312)γ are estimated to be of order of 100 keV and 1 keV, respectively. The ratio of the partial decay widths of Pc(4457) → Pc(4312)π to Pc(4457) → Pc(4312)γ is similar to the ratio of D∗ → Dπ to D∗ → Dγ, which could be used to check the molecular nature of Pc(4457) and Pc(4312) if they can be observed in the future. © 2021, CC BY.

关键词： Lagrange multipliers

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SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Yuan, Zhenlong Yang, Zhidong Cai, Yujun Wu, Kuangxin Liu, Mufan Zhang, Dapeng Jiang, Hao Li, Zhaoxin Wang, Zhaoqi Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China School of Electrical Engineering and Computer Science Australia Hunan Police Academy Information Technology Department Changsha410100 China Shanghai Jiao Tong University Cooperative MediaNet Innovation Center Shanghai200240 China Lanzhou University DSLAB School of Information Science and Engineering 730000 China Ministry of Agriculture and Rural Affairs Agricultural Information Institute Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big Data 100081 China

Recently, patch-deformation methods have exhibited significant effectiveness in multi-view stereo owing to the deformable and expandable patches in reconstructing textureless areas. However, existing approaches neglect to address the problem of deformation instability caused by easily overlooked edge-skipping, potentially leading to matching distortions, thus leaving room for further improvement. To fill this gap, we propose SED-MVS, which adopts panoptic segmentation and multi-trajectory diffusion strategy for segmentation-driven and edge-aligned patch deformation. Specifically, to prevent unanticipated edge-skipping, we first employ SAM2 for panoptic segmentation as depth-edge guidance to guide patch deformation, followed by multi-trajectory diffusion strategy to ensure patches are comprehensively aligned with depth edges. Moreover, to avoid potential inaccuracy of random initialization, we combine both sparse points from LoFTR and monocular depth map from DepthAnything V2 to restore reliable and realistic depth map for initialization and supervised guidance. Finally, we integrate the segmentation image with the monocular depth map to exploit inter-instance occlusion relationship, then further regard them as occlusion map to implement two distinct edge constraint, thereby facilitating occlusion-aware patch deformation. Extensive results on ETH3D, Tanks & Temples, BlendedMVS, Strecha and DL3DV-10K datasets validate the state-of-the-art performance and robust generalization capability of our proposed method. © 1991-2012 IEEE.

关键词： Image segmentation

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Traffic Intersection Detection Using Floating Car data

Traffic Intersection Detection Using Floating Car Data

引用

IEEE International Conference on big data Analysis (ICBDA)

作者： Rong Hu Ye Xia Chih-Yu Hsu Hanlin Chen Weihui Xu Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou China School of Ecological Environment and Urban Construction Fujian University of Technology Fuzhou China School of Information Science and Engineering Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications National Demonstration Center Fuzhou China

ISBN: (数字)9781728141114

ISBN: (纸本)9781728141121

The autonomous vehicle navigation system and the intelligent traffic system need real-time routing planning in traffic engineering and management. Urban road intersections play bottlenecks of urban roads, contributing a great deal to the loss of travel time due to traffic interference, management and control. The detection of the traffic intersection is important for obtaining real-time road regulation information. This paper proposes a method to detect the traffic intersection based on floating car data that is typically timestamped geo-localization, direction and speed data directly collected by moving vehicles. The angle of the direction difference can be used as the features for detecting the traffic intersection. The identification of the traffic intersections algorithm is developed based on the data density clustering algorithm. The floating car data is collected by about 15,000 cars using an onboard with the Global Positioning System receiver and sensors that providing positions (latitude, longitude) instantaneous speed, drive direction and time etc. The assessment results show the approach has excellent performance with high accuracy for traffic intersection detection.

关键词： Automobiles Trajectory Roads Clustering algorithms Turning Real-time systems Urban areas

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AdarGCN: Adaptive aggregation GCN for few-shot learning

arXiv

引用

arXiv 2020年

作者： Zhang, Jianhong Zhang, Manli Lu, Zhiwu Xiang, Tao Wen, Jirong Beijing Key Laboratory of Big Data Management and Analysis Methods School of Information Renmin University of China Beijing100872 China Department of Electrical and Electronic Engineering University of Surrey Guildford SurreyGU2 7XH United Kingdom

Existing few-shot learning (FSL) methods assume that there exist sufficient training samples from source classes for knowledge transfer to target classes with few training samples. However, this assumption is often invalid, especially when it comes to fine-grained recognition. In this work, we define a new FSL setting termed few-shot few-shot learning (FSFSL), under which both the source and target classes have limited training samples. To overcome the source class data scarcity problem, a natural option is to crawl images from the web with class names as search keywords. However, the crawled images are inevitably corrupted by large amount of noise (irrelevant images) and thus may harm the performance. To address this problem, we propose a graph convolutional network (GCN)-based label denoising (LDN) method to remove the irrelevant images. Further, with the cleaned web images as well as the original clean training images, we propose a GCN-based FSL method. For both the LDN and FSL tasks, a novel adaptive aggregation GCN (AdarGCN) model is proposed, which differs from existing GCN models in that adaptive aggregation is performed based on a multi-head multi-level aggregation module. With AdarGCN, how much and how far information carried by each graph node is propagated in the graph structure can be determined automatically, therefore alleviating the effects of both noisy and outlying training samples. Extensive experiments show the superior performance of our AdarGCN under both the new FSFSL and the conventional FSL settings. Copyright © 2020, The Authors. All rights reserved.

关键词： Sampling

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Nonlinear Longitudinal Control for Connected Vehicle Platoon Considering the Acceleration Difference

Nonlinear Longitudinal Control for Connected Vehicle Platoon...

引用

Chinese Control Conference (CCC)

作者： Ao Jiao Yongfu Li Shuyou Yu Zhongwei Cui Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing Chongqing University of Posts and Telecommunications Chongqing China Department of Control Science & Engineering Jilin University Changchun China School of Mathematics and Big Data Guizhou Education University Guiyang China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

This study proposes a distributed nonlinear control algorithm for the heterogeneous connected vehicle (CV) platoon. In particular, in order to capture the nonlinear behavior of vehicle such as stop-and-go phenomena, the effects of the car-following interactions are considered. Besides, the vehicle dynamics of heterogeneous CVs is characterized by using the third-order vehicle dynamics model, and the controller is designed by considering the acceleration difference. In addition, the stability and consensus of the proposed control algorithm is analyzed by using Routh-Hurwitz stability criterion, hence, the negative velocity, negative spacing error and unreasonable acceleration/deceleration can be avoided based on the proposed control algorithm. Finally, the effectiveness and robustness of proposed algorithm is verified by conducting multiple numerical simulation experiments in presence of external disturbances and multiple real vehicle confirmatory experiments.

关键词： Acceleration Topology Vehicle dynamics Heuristic algorithms Stability analysis Convergence Numerical stability

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Robust Bidirectional Generative Network For Generalized Zero-Shot Learning

Robust Bidirectional Generative Network For Generalized Zero...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yun Xing Sheng Huang Luwen Huangfu Feiyu Chen Yongxin Ge Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society School of Big Data and Software Engineering at Chongqing University Chongqing P.R.China Fowler College of Business at San Diego State University San Diego USA

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

In this work, we propose a novel generative approach named Robust Bidirectional Generative Network (RBGN) based on Conditional Generative Adversarial Network (CGAN) for Generalized Zero-shot Learning (GZSL). RBGN employs the adversarial attack to train a more rigorous discriminator, thus enhancing the generalizability and robustness of the feature generator under minimax strategy. Moreover, RBGN decodes the generated visual features back to their semantic representations to further improve the representational ability of generated visual features and alleviate the hubness problem. The experimental results of GZSL on four datasets, i.e. CUB, SUN, AWA1, AWA2, demonstrate that our model achieves competitive performance compared to state-of-the-art approaches and owns better generalizability to the unseen classes over conventional generative GZSL models. Further robustness analysis also validates the strong robustness of our model to the different types of semantic disturbance.

关键词： Semantics Visualization Robustness Generators Training Decoding Gallium nitride

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Budgeted Persuasion on User Opinions via Varying Susceptibility

Budgeted Persuasion on User Opinions via Varying Susceptibil...

引用

IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Wenyi Tang Xinrui Xu Guangchun Luo Zaobo He Kaiming Zhan School of Computer Science and Engineering University of Electronic Science and Technology of China School of Information and Software Engineering University of Electronic Science and Technology of China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Miami University Sichuan Jiuyuan Yinhai Software Co. Ltd

ISBN: (数字)9781728198293

ISBN: (纸本)9781728198309

Nowadays, the social network becomes an indispensable part of people's daily life, meanwhile offers an unprecedentedly convenient access for purposive individuals to influence the opinions of network users. Current studies present a subtle persuasion approach that finds a number of key users meanwhile varies their susceptibility extent to impact the public opinion. Such persuasion is significantly critical for public security, as it could facilitate both the spreading and dispelling of malicious rumors. However, the major body of these studies enclose impractical assumptions, such that persuaders have an unlimited budget, or the costs of varying different users' susceptibilities are the same, thus rendering these works unsuitable for realistic scenarios. Therefore, this work originally proposes a more practical and generalized problem of persuasion, where varying the susceptibilities of different users holds different costs. The analysis of its non-convexity, non-submodularity and complexity shows that solving the proposed problem is nontrivial, thus inspiring us to provide an intuitive greedy algorithm. Furthermore, we design an accelerated algorithm based on the community property, which reduces the time consumption more than one order of magnitude. The acceleration is based on the intuition that the impact of a user within a proper community could be a good estimation of the impact in the whole network, while the computation of the former one is much more efficient. The relationship between two algorithms is fully analyzed, which shows the community-based algorithm can degenerate to the intuitive greedy algorithm under a specific setting. Finally, comprehensive evaluations on real-world datasets show the superiority of proposed algorithms on both effectiveness and efficiency.

关键词： Greedy algorithms Social networking (online) Heuristic algorithms Search problems Rendering (computer graphics) Acceleration Security

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Multispectral Pan-sharpening via Dual-Channel Convolutional Network with Convolutional LSTM Based Hierarchical Spatial-Spectral Feature Fusion

arXiv

引用

arXiv 2020年

作者： Wang, Dong Bai, Yunpeng Li, Ying School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xian China School of Computing and Information Systems University of Melbourne VIC3010 Australia

Multispectral pan-sharpening aims at producing a high resolution (HR) multispectral (MS) image in both spatial and spectral domains by fusing a panchromatic (PAN) image and a corresponding MS image. In this paper, we propose a novel dual-channel network (DCNet) framework for MS pan-sharpening. In our DCNet, the dual-channel backbone involves a spatial channel to capture spatial information with a 2D CNN, and a spectral channel to extract spectral information with a 3D CNN. This heterogeneous 2D/3D CNN architecture can minimize causing spectral information distortion, which typically happens in conventional 2D CNN models. In order to fully integrate the spatial and spectral features captured from different levels, we introduce a multi-level fusion strategy. Specifically, a spatial-spectral CLSTM (S2-CLSTM) module is proposed for fusing the hierarchical spatial and spectral features, which can effectively capture correlations among multi-level features. The S2-CLSTM module attaches two fusion ways: the intra-level fusion via bi-directional lateral connections and inter-level fusion via the cell state in the S2-CLSTM. Finally, the ideal HR-MS image is recovered by a reconstruction module. Extensive experiments have been conducted at both simulated lower scale and the original scale of real-world datasets. Compared with the state-of-the-art methods, the proposed DCNet achieves superior or competitive performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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A resource usage prediction-based energy-aware scheduling algorithm for instance-intensive cloud workflows 14th

A resource usage prediction-based energy-aware scheduling al...

引用

14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Wang, Zhibin Wen, Yiping Zhang, Yu Chen, Jinjun Cao, Buqing School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan China Swinburne Data Science Research Institute Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9783030129804

The applications of instance-intensive workflow are widely used in e-commerce, advanced manufacturing, etc. However, existing studies normally do not consider the problem of reducing energy consumption by utilizing the characters of instance-intensive workflow applications. This paper presents a resource usage Prediction-based Energy-Aware scheduling algorithm, named PEA. Technically, this method improves the energy efficiency of instance-intensive cloud workflow by predicting resources utilization and the strategies of batch processing and load balancing. The efficiency and effectiveness of the proposed algorithm are validated by extensive experiments. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Scheduling algorithms

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