Handling massive datasets poses a significant challenge in modern data analysis, particularly within epidemiology and medicine. In this study, we introduce a novel approach using sequential ensemble learning to effect...
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Handling massive datasets poses a significant challenge in modern data analysis, particularly within epidemiology and medicine. In this study, we introduce a novel approach using sequential ensemble learning to effectively analyze extensive datasets. Our method prioritizes efficiency from both statistical and computational perspectives, addressing challenges such as data communication and privacy, as discussed in federated learning literature. To demonstrate the efficacy of our approach, we present compelling real-world examples using COVID-19 data alongside simulation studies.
Cloud computing is rapidly emerging as the preferred computing environment, largely because of the manifold advantages that this paradigm offers in terms of scale, flexibility and economics of computing. While the ben...
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ISBN:
(纸本)9781467368308
Cloud computing is rapidly emerging as the preferred computing environment, largely because of the manifold advantages that this paradigm offers in terms of scale, flexibility and economics of computing. While the benefits of cloud computing in comparison to traditional standalone computing environments are compelling, the shift in paradigm introduces unprecedented and varied challenges in data security, recovery, user privacy, access control, trust, notwithstanding legal issues. This paper lists the various challenges that are unique to cloud computing environments and also presents a comparative analysis of solutions and standards that have been presented towards mitigation of security risks in cloud computing.
Striving for better simulation results, transport planners want to simulate larger domains with increased levels of detail. Achieving fast execution times for these complex traffic simulations requires the parallel co...
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Striving for better simulation results, transport planners want to simulate larger domains with increased levels of detail. Achieving fast execution times for these complex traffic simulations requires the parallel computing power of modern hardware. This paper presents an architectural update to the MATSim traffic simulation framework, introducing a prototype that adapts the existing traffic flow model to a distributed parallel algorithm. The prototype is capable of scaling across multiple compute nodes, utilizing the parallel computing power of modern hardware. Benchmarking reveals a 119-fold improvement in execution speed over the current implementation, and a 43 times speedup when compared to single-core performance. The prototype can simulate 24 h of large-scale traffic in just 3.5 s. Based on these results, we advocate for integrating a distributed simulation approach into MATSim and outline steps for further optimizing the prototype for large-scale applications.
The one of important issues in artificial intelligence (AI) research is the development of AI for games because of its difficulty. To promote the research on video games AI, there have been several game AI competition...
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The one of important issues in artificial intelligence (AI) research is the development of AI for games because of its difficulty. To promote the research on video games AI, there have been several game AI competitions. However, some games with physics engine (geometry friends or Angry Birds) have no support on the prediction of future events using simulation. It makes much difficult to build AI for the games with physics. As a result, AI creator should spend much time to optimize the parameters of their program by trial and errors. In this paper, we report our approach to build AI for Angry Birds (Plan A+, 3rd rank in 2014 Angry Birds AI competition and the first entry achieved 1 million points in benchmarking test). In our controller, we adopt multiple strategies to increase generalization ability and hybrid optimization techniques (greedy search from human's manually tuned parameters) with parallel machines.
The ever growing number of computation-intensive applications calls for the interoperation of distributed infrastructures such as Clouds, Grids and private clusters. The European SHIWA and ER-flow projects have been i...
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The ever growing number of computation-intensive applications calls for the interoperation of distributed infrastructures such as Clouds, Grids and private clusters. The European SHIWA and ER-flow projects have been initiated to enable the combination of heterogeneous scientific workflows, and to execute them in a large-scale system consisting of multiple distributed computing Infrastructures including Grids and Clouds. One of the resource management challenges of these projects called parameter study job scheduling. A parameter study job of a workflow has a large number of input files to be consumed by independent job instances. In this paper we propose a meta-brokering solution for these workflows to be applied in science gateways. To cope with the high uncertainty and unpredictable load of the utilized infrastructures, we rely on resource priority services. These tools are capable of determining and dynamically updating priorities of the available infrastructures to be selected for job instances.
Differential network is an essential tool to reveal structural changes across two populations. However, existing single-machine methods for estimating differential network face computational and storage limitations wh...
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Differential network is an essential tool to reveal structural changes across two populations. However, existing single-machine methods for estimating differential network face computational and storage limitations when dealing with large-scale data sets. To address this issue, this paper develops a distributed estimation algorithm, which divides the estimation of differential network into several small-scale node-wise regression tasks and reduces local estimation bias through a debiasing technique. After aggregating debiased estimators, a global estimator is constructed efficiently. Theoretical analysis shows that the proposed distributed estimator can achieve global estimation consistency under mild conditions, with a convergence rate comparable to that of the single-machine method, while also facilitating support set recovery. Finally, we provide extensive numerical experiments to demonstrate the superior performance of our estimator compared to several baselines.
This paper describes the operational mechanism of the Java RMI. It elaborated with RMI to achieve distributed computing steps and methods. Finally, referring to the steps, the paper gives a distributed computing examp...
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This paper describes the operational mechanism of the Java RMI. It elaborated with RMI to achieve distributed computing steps and methods. Finally, referring to the steps, the paper gives a distributed computing examples.
distributed systems are dynamic systems where software and hardware together deliver information processing services to allow modelling, interaction, reasoning, analysis and control of the external environment. The in...
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distributed systems are dynamic systems where software and hardware together deliver information processing services to allow modelling, interaction, reasoning, analysis and control of the external environment. The intent of the distributed computation is to execute computational workflows using computing resources. The software contains the algorithms that specify the tasks while the hardware provides the required resources to execute the algorithms. The initial structure is defined by the association of software with hardware and the dynamic structure is defined by their temporal evolution. The meta-knowledge of the intent of the algorithm, the association of specific algorithm to a specific device, their temporal evolution and exception handling when the computation deviates from the intent is outside the software and hardware design and is expressed in non-functional requirements. In this paper, we describe an architecture to capture the meta-knowledge in meta-containers and enforce the intent of the computation while the computation is in progress.
The main activities of the Laboratory of Information Technologies (LIT) of the Joint Institute for Nuclear Research (JINR) are addressed with emphasis on the development of distributed computing. The contribution of t...
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ISBN:
(纸本)9781479989171
The main activities of the Laboratory of Information Technologies (LIT) of the Joint Institute for Nuclear Research (JINR) are addressed with emphasis on the development of distributed computing. The contribution of the LIT staff to the development of computational models for experiments done at the Large Hadron Collider (LHC) at CERN is briefly described. A brief overview is given of projects devoted to the development of distributed computations with LIT involvement in Russia, CERN, the USA, China, JINR Member States. Particular attention is devoted to the creation of a CMS Tier1 center in JINR and the development of computational models for the mega-project NICA. The report ends with the LIT work on the integration of the HPC, grid, cloud, and BigData technologies in large scale international projects.
As the computational complexity of the problem and/or the number of objectives increases, a large population has to be evaluated at each generation of algorithm, and this process needs more computational resources, or...
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ISBN:
(纸本)9781479974931
As the computational complexity of the problem and/or the number of objectives increases, a large population has to be evaluated at each generation of algorithm, and this process needs more computational resources, or requires more time for the same computational resource. However, distributing the tasks into different processors (or cores) is a good solution in speeding up the process overall. In this study, a novel and pragmatic distributed computing approach for multiobjective evolutionary optimization algorithm is proposed. Instead of dividing the objective space into pre-defined cone-domination principles, as proposed in an earlier study, a distribution of reference points initialized on a hyper-plane spanning the entire objective space is assigned to different processors and the R-NSGA-II procedure is invoked to find respective partial efficient fronts. Our results show that the proposed distributed computing approach reduces the overall computational effort compared to that needed with a single-processor method.
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