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arXiv 2021年

作者： Zheng, Zimu Chen, Qiong Hu, Chuang Wang, Dan Liu, Fangming The National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The Edge Cloud Innovation Lab. Technical Innovation Department Cloud BU Huawei Technologies Co. Ltd. Shenzhen China The Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Hong Kong

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes heavy computation burden to the resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify task importance. We then show that task allocation with task importance for MTL (TATIM) is a variant of NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study which bridges model and practice via a new architecture and main components design within AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4% energy consumption compared with the state-of-the-art when solving TATIM. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy utilization

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A new SAT Encoding Scheme for Exactly-one Constraints

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Journal of Physics: Conference Series 2019年第1期1288卷

作者： Jiatong Cai Yating Su Xixi Yang Jionghao Min Yong Lai College of Software Jilin University Changchun P.R. China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun P.R. China

Exactly-one constraints have comprehensive applications for the fields of artificial intelligence and operations research. For many encoded SAT problems generated by the existing encoding schemes of exactly-one constraints, the state-of-the-art knowledge compilers cannot complete compilation. In this paper, we propose a new encoding scheme of exactly-one constraints. We introduce two-dimensional auxiliary variables (represented as a matrix) to denote the constraint that exactly one of some variables can be assigned as true. The clauses generated by our scheme is significantly less than those generated by three other existing encoding schemes. The experimental results on the exact cover problems show that the encoded CNF formulas generated by our scheme requires less compilation time, compared with the other three coding schemes.

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Social-enhanced recommendation using graph-based contrastive learning

Social-enhanced recommendation using graph-based contrastive...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Peng Xue Qian Gao Jun Fan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models.

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Dynamic radiomics: A new methodology to extract quantitative time-related features from tomographic images

arXiv

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arXiv 2020年

作者： Che, Fengying Shi, Ruichuan Li, Shuqin Wu, Jian Li, Haoran Chen, Weixing Zhang, Hao Qu, Hui Li, Zhi Cui, Xiaoyu College of Medicine and Biological Information Engineering Northeastern University Liaoning Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Liaoning Shenyang China Department of Medical Oncology The First Affiliated Hospital of China Medical University Liaoning110001 China Shenzhen College of Advanced Technology University of the Chinese Academy of Sciences Beijing100049 China Department of Breast Surgery Liaoning Cancer Hospital and Institute Cancer Hospital of China Medical University Liaoning Shenyang110042 China

The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics. This study proposes a new dynamic radiomics feature extraction workflow that uses time-dependent tomographic images of the same patient, focuses on the changes in image features over time, and then quantifies them as new dynamic features for diagnostic or prognostic evaluation. We first define the mathematical paradigm of dynamic radiomics and introduce three specific methods that can describe the transformation process of features over time. Three different clinical problems are used to validate the performance of the proposed dynamic feature with conventional 2D and 3D static *** results show that compared with static features, dynamic feature extraction can achieve higher robustness and accuracy for time-dependent tomographic images. We also found that the dynamic features that influence different clinical problems are also quite different. Copyright © 2020, The Authors. All rights reserved.

关键词： Tomography

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Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception

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Brain Science Advances 2023年第2期9卷 95-113页

作者： Zhongtian Guan Meng Lin Qiong Wu Jinglong Wu Kewei Chen Hongbin Han Dehua Chui Xu Zhang Chunlin Li School of Biomedical Engineering Capital Medical UniversityBeijing 100069China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Capital Medical UniversityBeijing 100069China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical UniversityBeijing 100069China Peking University First Hospital Beijing 100034China Biomedical Engineering Laboratory Graduate School of Natural Science and TechnologyOkayama University3-1-1 Tsushima-nakaOkayamaJapan Key Laboratory of Biomimetic Robots and Systems Ministry of EducationBeijing Institute of TechnologyBeijing 100081China Computational Image Analysis Banner Alzheimer’s Institute and Banner Good Samaritan Medical CentrePET CentrePhoenixAZ 85006USA Radiology Peking University Third HospitalBeijing 100191China Neuroscience Research Institute/Peking University Third Hospital Beijing 100191China

Top-down attention mechanisms require the selection of specificobjects or locations;however,the brain mechanism involved when attention is allocated across different modalities is not well *** aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlyingdivided and selective spatial attention.A concurrent audiovisual stimulus was used,and subjects were prompted to focus on a visual,auditory and audiovisual stimulus in a Posner *** results confirmed the better performance of selectiveattention compared to devided *** found differences in the activation level of the frontoparietal network,visual/auditorycortex,the putamen and the salience network under differentattention *** further used Granger causality(GC)toexplore effective connectivity differences between *** in GC connectivity between visual and auditory selective tasksreflected the visual dominance effect under spatial *** addition,our results supported the role of the putamen inredistributing attention and the functional separation of the *** summary,we explored the audiovisual top-down allocation of attention and observed the differences in neuralmechanisms under endogenous attention modes,which revealedthe differences in cross-modal expression in visual and auditory attention under attentional modulation.

关键词： top-down mechanism divided spatial attention selective spatial attention fMRI Granger causality

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Adaptive Fuzzy Tracking Control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems

arXiv

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arXiv 2022年

作者： Mao, Bing Wu, Xiaoqun Liu, Hui Xu, Yuhua Lü, Jinhu The School of Mathematics and Statistics Wuhan University Hubei430072 China Hubei Key Laboratory of Computational Science Wuhan University Hubei430072 China Research Center of Complex Network Wuhan University Hubei430072 China The School of Automation Huazhong University of Science and Technology Hubei430074 China The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei430074 China The School of Statistics and Data Science Nanjing Audit University Jiangsu211815 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functions are designed, and a novel error transformation function is introduced to remove the initial value constraints and solve the singularity problem in existing works. Secondly, based on dynamic surface control methods, controllers with or without approximating structures are established to guarantee that the tracking error achieves prescribed transient performance and converges into a prescribed bounded set within prescribed time. In particular, the settling time and initial value of the prescribed performance function are completely independent of initial conditions of the tracking error and system parameters, which improves existing results. Moreover, with a novel Lyapunov-like energy function, not only the differential explosion problem frequently occurring in backstepping techniques is solved, but the drawback of the semi-global boundedness of tracking error induced by dynamic surface control can be overcome. The validity and effectiveness of the main results are verified by numerical simulations on practical examples. Copyright © 2022, The Authors. All rights reserved.

关键词： Fuzzy control

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LI-FPN: Depression and Anxiety Detection from Learning and Imitation

LI-FPN: Depression and Anxiety Detection from Learning and I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid Network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

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data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Research Square

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Research Square 2021年

作者： Fan, Chaodong Hou, Bo Qu, Xilong Xiao, Leyi Cheng, Fanyong Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology School of Information Technology and Management Hunan University of Finance and Economics Changsha410205 China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Anhui Polytechnic University Wuhu241000 China Foshan Green Intelligent Manufacturing Research Institute Xiangtan University Guangdong 528311 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore School of Automation and Electronic Information Xiangtan University Xiangtan411105 China

The economic operation optimization of microgrid is an important research topic in the power system. Therefore, this paper proposes a surrogate model particle swarm optimization algorithm based on the global-local search mechanism. Firstly, aiming at the problem that the statistical information of Kriging model is difficult to guarantee the prediction accuracy, the dynamic transformation is carried out to enhance the robustness of the model;secondly, the global-local search mechanism is introduced to make the algorithm fully explore the fitness landscape near the Kriging model after quickly locating the current optimal particle position, so as to achieve the balance of convergence quality and convergence efficiency. The proposed method has been tested on numerous benchmark test functions from two test suites, and the results show that the proposed algorithm has more advantages than other comparison algorithms in optimization accuracy. Finally, simulations are carried out in two operating modes of microgrid islanding and grid-connected, which has verified the effectiveness of the proposed method. © 2021, CC BY.

关键词： Particle swarm optimization (PSO)

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Tianhao Chen Qian Gao Jun Fan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach.

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Cooperator-driven and defector-driven punishments: How do they influence cooperation?

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Physical Review E 2019年第5期100卷 052304-052304页

作者： Pengbi Cui (崔鹏碧) Zhi-Xi Wu Tao Zhou Xiaojie Chen School of Astronautics Northwestern Polytechnical University Xi'an 710072 People's Republic of China National Key Laboratory of Aerospace Flight Dynamics Xi'an 710072 People's Republic of China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou Gansu 730000 China and Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education Lanzhou University Lanzhou Gansu 730000 China Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China Chengdu 610073 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu 611731 China

Economic studies have shown that there are two types of regulation schemes which can be considered as a vital part of today's global economy: self-regulation enforced by self-regulation organizations to govern industry practices and government regulation which is considered as another scheme to sustain corporate adherence. An outstanding problem of particular interest is to understand quantitatively the role of these regulation schemes in evolutionary dynamics. Typically, punishment usually occurs for enforcement of regulations. Taking into account both types of punishments to influence the regulations, we develop a game model where six evolutionary situations with corresponding combinations of strategies are considered. Furthermore, a semianalytical method is developed to allow us to give accurate estimations of the boundaries between the phases of full defection and nondefection. We find that, associated with the evolutionary dynamics, for an infinite well-mixed population, the mix of both punishments performs better than one punishment alone in promoting public cooperation, but for a networked population the cooperator-driven punishment turns out to be a better choice. We also reveal the monotonic facilitating effects of the synergy effect, punishment fine, and feedback sensitivity on the public cooperation for an infinite well-mixed population. Conversely, for a networked population an optimal intermediate range of feedback sensitivity is needed to best promote punishers' populations. Overall, a networked structure is overall more favorable for punishers and further for public cooperation, because of both network reciprocity and mutualism between punishers and cooperators who do not punish defectors. We provide physical understandings of the observed phenomena, through a detailed statistical analysis of frequencies of different strategies and spatial pattern formations in different evolution situations. These results provide valuable insights into how to select

关键词： Collective behavior in networks Competition Fitness Population dynamics Collective dynamics Evolutionary games Lattices Mean field theory Monte Carlo methods

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