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检索条件"任意字段=International Workshop on Multilevel Optimization - Algorithms and Applications"

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Rule-based parallel query optimization for OQL using a parallelism extraction technique

Rule-based parallel query optimization for OQL using a paral...

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international workshop on Database and Expert Systems applications

作者： S.F. Mendes P.R. Falcone Sampaio Departmento de Computacao Campus do Pici Universidade Federal do Ceara Fortaleza Brazil Computer Science Department University of Manchester Institute of Science and Technology UK

This work deals with query optimization far parallel object-oriented databases using rule-based optimizers. The solution enacted departs from an existing non-parallel database query optimizer and extends it with a parallelism extraction module that modifies the original query optimizer to generate query plans for a parallel OODBMS architecture. The ideas are applied in the design of a parallel optimizer for OQL. The implementation approach shows that the parallelism extraction technique combined with a rule-based optimizer generator tool can help to produce a parallel optimizer from a non-parallel one with minimum effort.

关键词： Query processing Algebra Design optimization Resource management Parallel architectures Parallel processing Computer science Read only memory Data models Parallel algorithms

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The 1st international workshop on Federated Learning with Graph Data (FedGraph) 22

The 1st International Workshop on Federated Learning with Gr...

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31st ACM international Conference on Information and Knowledge Management (CIKM)

作者： Yang, Carl Li, Xiaoxiao Baracaldo, Nathalie Shah, Neil He, Chaoyang Lyu, Lingjuan Sun, Lichao Avestimehr, Salman Emory Univ Atlanta GA 30322 USA Univ British Columbia Vancouver BC Canada IBM Res San Jose CA USA Snap Res Seattle WA USA FedML Inc Los Angeles CA USA Sony AI Tokyo Japan Lehigh Univ Bethlehem PA 18015 USA Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781450392365

The field of graph data mining, one of the most important AI research areas, has been revolutionized by graph neural networks (GNNs), which benefit from training on real-world graph data with millions to billions of nodes and links. Unfortunately, the training data and process of GNNs involving graphs beyond millions of nodes are extremely costly on a centralized server, if not impossible. Moreover, due to the increasing concerns about data privacy, emerging data from realistic applications are naturally fragmented, forming distributed private graphs of multiple "data silos", among which direct transferring of data is forbidden. The nascent field of federated learning (FL), which aims to enable individual clients to jointly train their models while keeping their local data decentralized and completely private, is a promising paradigm for large-scale distributed and private training of GNNs. FedGraph2022 aims to bring together researchers from different backgrounds with a common interest in how to extend current FL algorithms to operate with graph data models such as GNNs. FL is an extremely hot topic of large commercial interest and has been intensively explored for machine learning with visual and textual data. The exploration from graph mining researchers and industrial practitioners is timely catching up just recently. There are many unexplored challenges and opportunities, which urges the establishment of an organized and open community to collaboratively advance the science behind it. The prospective participants of this workshop will include researchers and practitioners from both graph mining and federated learning communities, whose interests include, but are not limited to: graph analysis and mining, heterogeneous network modeling, complex data mining, large-scale machine learning, distributed systems, optimization, meta-learning, reinforcement learning, privacy, robustness, explainability, fairness, ethics, and trustworthiness.

关键词： graph mining distributed learning data privacy federated learning graph neural networks

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A stock price forecasting application using neural networks with multi-optimizer

A stock price forecasting application using neural networks ...

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IEEE international workshop on Computational Intelligence & applications (IWCIA)

作者： Chukiat Worasucheep Department of Mathematics King Mongkut's University of Technology Thonburi (KMUTT) Bangkok Thailand

ISBN: (纸本)9781509027767

This paper proposes an application prototype for forecasting of stock prices using feed-forward neural network with back propagation, Particle Swarm optimization and Differential Evolution. The prototype provides a convenient graphical user interface that allows choosing stocks, period of data, percentage of training set, technical indicators for model inputs and other algorithmic parameters. Multithreading is provided for efficient running and the downloaded historical data and forecasted output can be save for future use. An experiment was performed to investigate the performance of the three algorithms as well as the effects of number of hidden nodes of the neural networks.

关键词： Forecasting Prototypes Algorithm design and analysis Graphical user interfaces Training Neural networks Particle swarm optimization

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Interactive system design and end-to-end optimization in sensor networks

Interactive system design and end-to-end optimization in sen...

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作者： Grythe, Knut Lie, Arne Balasingham, Ilangko SINTEF ICT Dept. of Communication Systems Norway Rikshospitalet University Hospital Dept. of Electronics and Telecommunications Norwegian University of Science and Technology Norway

ISBN: (纸本)9781424452538

This paper proposes an application oriented four layered quality of service (QoS) stack for wireless sensor networks (WSN), including a supporting system architecture for interacting with WSNs using the MPEG-21 multimedia framework in an medical application setting. The application of the WSNs is signal processing algorithms reporting elaborated observed sensor data to external users. The QoS model supports the tradeoff between end-users expected QoS combined with optimized energy consumption. The MPEG-21 standard is chosen as the media resource framework due to the fact that future hospitals will have networks comprising WSNs supporting an always increasing amount of both low rate medical sensor data as well as high rate audiovisual content. Furthermore due to the heterogeneous set of user terminals and network resources, media adaptation will become important to provide reliable and robust quality of service. In this paper, we argue for an extension of the MPEG-21 terminology for use in biomedical wireless sensor networks and incorporate the requirements needed for medical applications. © 2009 IEEE.

关键词： Quality of service

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Research on a Scale-Free Network with Tunable Clustering Coefficient

Research on a Scale-Free Network with Tunable Clustering Coe...

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international workshop on Intelligent Systems and applications, ISA

作者： Qin Yang Lulong Lan Jianhui Zhang National Digital Switching System Engineering Technological Research and Development Center Zhengzhou Henan China

Network topology plays an important role in design and optimization of routing protocols and network algorithms. Based on the characteristics of real networks and local world phenomenon, this paper proposes a evolving scale-free network with tunable clustering coefficient(SFTC) by introducing triad formation, which dynamically simulates the adding and deleting of nodes or edges, and the evolvements inside or outside the local world. The evaluation of C++ and Matlab not only support the analytical calculation of model's degree distribution, but also indicates that the clustering coefficient can be tuned to be a preferable value and the SFTC model totally reproduces the properties of real network.

关键词： Mathematical model Network topology IP networks Algorithm design and analysis Design optimization Routing protocols Switching systems Design engineering Systems engineering and theory Research and development

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Computation on parametric curves with applications in localization and grasping

Computation on parametric curves with applications in locali...

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5th international workshop on the Algorithmic Foundations of Robotics, WAFR 2002

作者： Jia, Yan-Bin Iowa State University Ames IA 50011-1040 United States

ISBN: (纸本)3540404767

This paper investigates efficient curve processing that originates from the localization and grasping of 2D curved objects. The first algorithm locates the boundary section of an object traced out by a rolling finger based on the length and total curvature information obtained with the finger's tactile sensor. The algorithm slides an imaginary segment along the object boundary by alternatively marching its two endpoints forward, stretching or contracting the segment if necessary. The second algorithm computes all pairs of antipodal points on an object. Two fingers placed at such a pair of points can achieve a force-closure grasp in the presence of friction. Dissecting the boundary into segments everywhere convex or everywhere concave, the algorithm marches simultaneously on every two segments with opposing normals and alternates marching with numerical bisection recursively. It builds on a procedure that constructs common tangents of two curves with quadratic local convergence. Completeness (up to numerical resolution) of the above algorithms is established by applying curvature-based analyses. Dissection and the coupling of marching with bisection introduced in this paper are potentially applicable to optimization problems involving curves and curved surfaces. © 2004 Springer-Verlag.

关键词： Curve fitting

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Three Sub-Swarm Particle Swarm optimization Algorithm

Three Sub-Swarm Particle Swarm Optimization Algorithm

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international workshop on Intelligent Systems and applications, ISA

作者： Ming-Ming Bai Hui Sun Lie-Yang Wu Ze-Tao Jiang Wen-Huan Wu Dept. of Comput. Sci. & Technol. NanChang Inst. of Technol. Nanchang Nanchang Hangkong University Nanchang Hangkong University Nanchang Jiangxi CN

In order to overcome the drawback of the standard PSO, such as being subject to falling into local optimization, an improved PSO algorithm based on three sub-swarms exchange is proposed. Firstly the method divides the whole swarm into three sub-swarms which evolve jointly according to three different models, that is, one evolves with the standard PSO model, and the second with social only model and the third with cognition only model respectively. When the community evolution achieves the equilibrium state, we exchange some particles between the three different sub-swarms, which can increase the information exchange between the sub-swarms, improve the population diversity and reduce the possibility of getting local extreme value. The results of simulation show that the proposed algorithm in the paper has the better optimization performance than the standard PSO.

关键词： Particle swarm optimization Convergence Iterative algorithms Acceleration Sun Computer science Cognition Cultural differences Birds Testing

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multilevel Load Balancing Algorithm for Domestic Heterogeneous Manycore Architecture

Multilevel Load Balancing Algorithm for Domestic Heterogeneo...

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international Symposium on Parallel and Distributed Processing with applications, ISPA

作者： Yi Ma Xin Chen Heng Guo Fang Li Xin Liu Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Load imbalance often occurs in particle-in-cell simulations on parallel computing, which seriously affects the efficiency of applications. Due to the characteristics of multilevel parallelism and communication asymmetry of compute nodes in domestic heterogeneous manycore architecture, the impact of load imbalance is more prominent. The paper proposes a multilevel load-balancing algorithm for domestic heterogeneous manycore architecture. Inside the supernode, computing tasks are redivided based on manycore acceleration. Between the supernodes, a greedy-based communication mode is designed to minimize communication across supernodes. The experimental results show that the proposed algorithm achieves almost ideal dynamic load balance, and improves the performance of the evaporation module in two-phase flow simulation by 10.9-19.7 times for the 50 million-sized grid.

关键词： Heuristic algorithms Computational modeling Computer architecture Parallel processing Load management Nonhomogeneous media Supercomputers Evaporation optimization Load modeling

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Determination of microscopic parameters of piezoceramic materials under electrical loading using genetic algorithm

Determination of microscopic parameters of piezoceramic mate...

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IEEE international Symposium on applications of Ferroelectrics (ISAF)

作者： J. Bustillo M. Domenjoud J. Fortineau G. Gautier M. Lethiecq GREMAN UMR 7347 University of Tours CEA Tours France

The purpose of this study is the characterization of microscopic parameters of piezoceramic materials, such as spontaneous polarization and spontaneous strain. In previous works, a model has been developed by bridging characteristics of microscopic domain distribution into the macroscopic behavior. It reproduces longitudinal strain and electrical displacement as a function of uniaxial electrical loading, according to material parameters and applied electrical field. optimization is performed between theoretical and experimental hysteresis curves. This method is based on a genetic algorithm procedure in order to ensure robust convergence even if the system is multimodal. Materials used in this study are PLZT8/65/35 and PZT-5A. After optimization, experimental curves are well fitted to theoretical curves and a good agreement has been shown between retrieved parameters and values reported in literature. This validates domain wall modeling and genetic algorithm as an efficient way to characterize piezoceramic materials under harsh operating conditions.

关键词： Genetic algorithms Materials optimization Strain Loading Convergence Piezoelectric polarization

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A Hybrid Particle Swarm optimization Algorithm for Multimodal Function optimization

A Hybrid Particle Swarm Optimization Algorithm for Multimoda...

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international workshop on Intelligent Systems and applications, ISA

作者： Jirong Gu Lin Lin Hui Wang College of Geography and Resources Sciences Sichuan Normal University China Department of Computer Science Yili Normal School Yining China State Key Laboratory of Software Engineering Wuhan University of China China

Particle swarm optimization (PSO) has shown its good performance on numerical function problems. However, on some multimodal functions the PSO easily suffers from premature convergence because of the rapid decline in velocity. In this paper, a hybrid PSO algorithm, called HPSO, is proposed, which employs a modified velocity model to guarantee a non-zero velocity. In addition, a Cauchy mutation operator conducted on the global best particle is used for improving the global search ability of PSO. Experimental studies on a suite of multimodal functions with many local minima show that the HPSO outperforms the standard PSO, CEP, Gaussian swarm with Gaussian mutation (GPSO+GJ) and Gaussian swarm with Cauchy mutation (GPSO+CJ) on most test functions.

关键词： Particle swarm optimization Genetic mutations Evolutionary computation Equations Software algorithms Educational institutions Geography Computer science Software engineering Convergence

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