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Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

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data poisoning attacks on neighborhood-based recommender systems

arXiv

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arXiv 2019年

作者： Chen, Liang Xu, Yangjun Xie, Fenfang Huang, Min Zheng, Zibin school of data and computer science Sun Yat-Sen University Guangzhou China school of data and computer science Sun Yat-Sen University Guangzhou China National Engineering Research Center of Digital Life Sun Yat-sen University Guangzhou China College of software South China University of Technology Guangzhou China

Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighbourhood-based collaborative filtering is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning attacks on their performance. Can the neighbourhood-based recommender systems be easily fooled? To this end, we shed light on the robustness of neighbourhood-based recommender systems and propose a novel data poisoning attack framework encoding the purpose of attack and constraint against them. We firstly illustrate how to calculate the optimal data poisoning attack, namely UNAttack. We inject a few well-designed fake users into the recommender systems such that target items will be recommended to as many normal users as possible. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and the transferability of our proposed method. Besides, some interesting phenomenons can be found. For example, 1) neighbourhood-based recommender systems with Euclidean Distance-based similarity have strong robustness. 2) the fake users can be transferred to attack the state-of-the-art collaborative filtering recommender systems such as Neural Collaborative Filtering and Bayesian Personalized Ranking Matrix Factorization. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

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Efficient Block Pruning Based on Kernel and Feature Stablization

Efficient Block Pruning Based on Kernel and Feature Stabliza...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Sheng Xu Hanlin Chen Kexin Liu Jinhu Lii Baochang Zhang School of Automatic Science and Electrical Engineering Beihang University China State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

With the development of computer vision research, the architecture of convolutional neural network becomes more and more complex to reach the state-of-the-art performance. Is the complexity of the model necessarily proportional to its accuracy? To answer this, the compression of the network has attracted much attention in the academy and industry. Existing network pruning methods mostly rely on the scoring mechanism of complexity or diversity of kernels to compress the network, and then build the network model after removing the kernels by tuning or training on the input data. These methods are cumbersome and depend on a well-trained pre-trained model. In this paper, we propose an end-to-end block pruning method based on kernel and feature stability by pruning blocks efficiently. To accomplish this, we firstly introduce a mask to scale the output of the blocks, and the L1 regularization term to monitor the mask update. Second, we introduce the Center Loss to guarantee that the feature does not deviate greatly during learning. To converge fast, we introduce fast iterative shrinkage-thresholding algorithm (FISTA) to optimize the mask, by which a more fast and reliable pruning process is achieved. We implement experiments on different datasets, including CIFAR-10 and ImageNet ILSVRC2012. All the experiments have achieved the state-of-the-art accuracy.

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A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

arXiv

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arXiv 2021年

作者： Li, Zekun Zhao, Wei Shi, Feng Qi, Lei Xie, Xingzhi Wei, Ying Ding, Zhongxiang Gao, Yang Wu, Shangjie Liu, Jun Shi, Yinghuan Shen, Dinggang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University Nanjing210046 China School of Computer Science and Artificial Intelligence Southeast University Nanjing210018 China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai201807 China School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of Department of Radiology Second Xiangya Hospital Central South University Hunan410011 China Department of Radiology Quality Control Center Changsha410011 China Department of Radiology Hangzhou First People's Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China Department of Pulmonary and Critical Care Medicine Second Xiangya Hospital Central South University Changsha410011 China

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues - weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works. Copyright © 2021, The Authors. All rights reserved.

关键词： Computerized tomography

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Optimization of Extraction Process of Valeriana officinalis L. Root Essential Oil and Study on Its Anti-Free Radical Activity

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IOP Conference Series: Earth and Environmental science 2020年第1期526卷

作者： Tianlin Miao Hongjuan Zheng Xue Wang Ying Lan Yuehua Zhang Yujun Song Lan Li School of Life & Science Jiamusi University Jiamusi City Heilongjiang Province 154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province 543000 China

Taking Valerianrhizome as raw material and the yield of volatile oil of Valerian as an index, the extraction process of valerian volatile oil was analyzed by microwave assisted steam distillation, and optimized by Box-Behnken response surface test. The effects of scavenging capacity of Li, DPPH and hydroxyl radicals on their antioxidant capacity were studied. The results showed that the optimal process conditions of microwave extraction of volatile oil of Valerian were 350W microwave power, extraction time 25min, liquid-to-material ratio 8.8mL: 1.0g, and the yield of volatile oil of Valerian extracted under this condition was 5.88%. Microwave-extracted volatile oil of Valerian has strong scavenging ability and high reducing power to DPPH free radicals and hydroxyl free radicals. Compared with steam distillation, it has obvious differences, which can provide reference for the development of antioxidant products of Valerian oil.

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Integrating the pressure-state-response model with the extension catastrophe progression for flood risk and resilience assessment

Environmental and Sustainability Indicators

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Environmental and Sustainability Indicators 2025年 27卷

作者： Shahab S. Band Sultan Noman Qasem Sajad Eslami Zulkefli Mansor Brij B. Gupta Hao-Ting Pai Meghdad Biyari Amir Mosavi Farzaneh Sajedi Hosseini Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia School of Business Stevens Institute of Technology Hoboken 07030 NJ USA Research Center for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Bandar Baru Bangi Selangor Darul Ehsan Malaysia Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan School of Cybersecurity Korea University Seoul South Korea Symbiosis Centre for Information Technology Symbiosis International University Pune India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan John von Neumann Faculty of Informatics Obuda University 1034 Budapest Hungary Ludovika University of Public Service Budapest Hungary Abylkas Saginov Karaganda Technical University Karaganda Kazakhstan Reclamation of Arid and Mountainous Regions Department Faculty of Natural Resources University of Tehran Karaj Iran

Despite the devastating effects of floods, the concept of resilience is still not fully considered in the assessment and management of flood risk. To study how resilience can lower the risk of floods and further enhance disaster response, this research aims to close this knowledge gap. With a focus on the Kashkan watershed in Iran, the study combines the extended catastrophe progression method with the pressure-state-response model. Three catastrophe models, namely the cusp, swallowtail, and butterfly, are applied. According to the findings, southern regions, i.e., Pol-Dokhtar city, have the highest risk of floods and the lowest resilience. Resilience and flood risk have a complementary relationship, according to the analysis, and resilience is a helpful metric for risk assessment. The results emphasize the necessity to incorporate resilience-focused pre-disruption planning and post-disaster recovery into flood risk management strategy. This work offers a foundation to incorporate resilience into future flood policies and strategies.

关键词： Flood Risk Resilience Pressure-state-response model Extension catastrophe progression method

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Prediction of the hand function part of the Fugl-Meyer scale after stroke using an automatic quantitative assessment system

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Shugeng Chen Xiaolei Lin Jianghong Fu Yeye Qian Zihang Chen Zhanbo Huang Qiang Liu Xiaofeng Lu Jie Jia Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Contribution: Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Writing - original draft Writing - review & editing School of Data Science Fudan University Shanghai China Contribution: Data curation Formal analysis Methodology Writing - original draft Writing - review & editing Contribution: Data curation Investigation Writing - review & editing Peking University Shenzhen Hospital Shenzhen Guangdong China Contribution: Data curation Formal analysis Investigation Methodology Writing - review & editing Contribution: Writing - original draft Writing - review & editing School of Communication and Information Engineering Shanghai University Shanghai China Contribution: Conceptualization Funding acquisition Resources Software Supervision Writing - review & editing National Clinical Research Center for Aging and Medicine Huashan Hospital Fudan University Shanghai China National Center for Neurological Disorders Shanghai China Xiaofeng Lu and Jie Jia. Contribution: Conceptualization Funding acquisition Investigation Resources Supervision Validation Writing - review & editing

Hand function assessment is an essential component of the process of stroke rehabilitation because of the high incidence of hand motor dysfunction. In terms of the manual evaluation of hand function, the Fugl-Meyer scale is a recommended scale with high reliability and validity. However, the need for accurate assessments and increasing developments in technology has led to the promotion of automatic quantitative assessment systems for the hand. In this study, we collected quantitative data on hand function with an automatic system and the upper limb Fugl-Meyer assessment (FMA) from 79 people with stroke. We developed decision tree (DT) and gradient-boosted decision tree (GBDT) predictive models for the Fugl-Meyer score using features extracted from the Hand Automatic Quantitative Assessment System (HAQAS). Predictive performances were compared between these models regarding the predictive accuracy and Cohen's kappa. There were high correlations between features automatically collected by the HAQAS and the Fugl-Meyer scale in all the sub-items, with the maximal correlations all being over 0.5, indicating the high validity of the HAQAS in automatic FMA prediction. Hand functions were more highly correlated (average correlation coefficient 0.90) with HAQAS features than wrist functions (average correlation coefficient 0.54), and the GBDT achieved higher predictive accuracies and agreement than the DT algorithm. We conclude that the HAQAS is feasible for stroke patients with hand dysfunction and convenient for clinicians and therapists. This study was registered in the Chinese Clinical Trial Registry (ChiCTR1800019098).

关键词： automatic assessment Fugl-Meyer scale hand function stroke prediction

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Joint long-term cache allocation and short-term content delivery in green cloud small cell networks

arXiv

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arXiv 2019年

作者： Wu, Xiongwei Li, Qiang Li, Xiuhua Leung, Victor C.M. Ching, P.C. Dept. of Electronic Engineering Chinese University of Hong Kong Shatin Hong Kong School of Info. & Comm. Eng. University of Electronic Science and Technology of China Chengdu China School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Dept. of Electrical & Computer Engineering University of British Columbia Vancouver Canada

Recent years have witnessed an exponential growth of mobile data traffic, which may lead to a serious traffic burn on the wireless networks and considerable power consumption. Network densification and edge caching are effective approaches to addressing these challenges. In this study, we investigate joint long-term cache allocation and short-term content delivery in cloud small cell networks (C-SCNs), where multiple smallcell BSs (SBSs) are connected to the central processor via fronthaul and can store popular contents so as to reduce the duplicated transmissions in networks. Accordingly, a long-term power minimization problem is formulated by jointly optimizing multicast beamforming, BS clustering, and cache allocation under quality of service (QoS) and storage constraints. The resultant mixed timescale design problem is an anticausal problem because the optimal cache allocation depends on the future file requests. To handle it, a two-stage optimization scheme is proposed by utilizing historical knowledge of users' requests and channel state information. Specifically, the online content delivery design is tackled with a penalty-based approach, and the periodic cache updating is optimized with a distributed alternating method. Simulation results indicate that the proposed scheme significantly outperforms conventional schemes and performs extremely close to a genie-aided lower bound in the low caching region. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel state information

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Multiple layers uneven clustering algorithm based on residual energy for wireless sensor networks

Multiple layers uneven clustering algorithm based on residua...

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作者： Liu, Jing Su, Shoubao Lu, Yuhua Dong, Jun School of Computer Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Key Laboratory of Data Science and Smart Software Nanjing211169 China

Since the node energy is limited, wireless sensor networks need efficient routing algorithms to reduce energy consumption. The authors proposed a multiple layer uneven clustering algorithm. This algorithm is used for single-hop network. Nodes submit data to the cluster heads, and then all cluster heads submit data to the sink node by single hop. The authors divide the whole network into several layers with different reign. The inner diameter and outer diameter of each layer are calculated by a sink node to meet the aim of balancing energy consumption. The algorithm chooses cluster heads according to the nodes’ residual energy in each layer. According to the reign of each layer, from the perspective of reducing energy consumption, the authors limit the minimum distance between two cluster heads, so that the cluster heads can be distributed evenly in one layer. Simulation results show that the algorithm can promote the lifetime of networks. © 2018 Institution of engineering and Technology. All rights reserved.

关键词： Clustering algorithms

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Assessing Mental Stress Based on Smartphone Sensing data: An Empirical Study

Assessing Mental Stress Based on Smartphone Sensing Data: An...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Feng Wang Yasha Wang Jiangtao Wang Haoyi Xiong Junfeng Zhao Daqing Zhang Key Laboratory of High Confdence Software Technologies Ministry of Education Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Peking University Information Technology Institute Tianjin Binhai China National Engineering Research Center of Software Engineering Beijing China Big Data Lab Baidu Research Beijing

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mental stress is a critical factor affecting one's physical and mental well-being. At the early stage, the effect of stress is often underestimated, while it usually leads to serious issue Lateran. Therefore, it is crucial to detect stress before it evolves into severe problems. Traditional stress detection methods are based on either questionnaires or professional devices, which are time-consuming, costly and intrusive. With the popularity of smartphones embedded with a rich set of sensors, which can capture people's context, such as movement, sound, location and so on, it is an alternative way to access people's behavior by smartphones. Through an empirical study, this paper proposes an automatic and non-intrusive stress detection framework based on smartphone sensing data. First, we construct various discriminative features from multi-modality phone sensing data, in which both absolute and relative features are considered to make the model more personalized. Then, to tackle the challenge of label insufficiency, we further develop a co-training based method for stress level classification. Finally, we evaluate our model based on an open dataset, and the experimental results verify its advantages over other baselines.

关键词： Sensors Stress Feature extraction Psychology Mobile handsets data models Biomedical monitoring

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