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Distributed machine learning is becoming increasingly popular for large scale data mining on large scale cluster. To mitigate the interference of straggler machines, recent distributed machine learning systems support flexible model consistency, which allows worker using a local stale model to compute model update without waiting for the newest model, while limiting the asynchronous step in a certain...
In order to process very large graphs, existing graph processing systems, such as Pregel and Giraph, usually partition and distribute the graph computation on large number of nodes (i.e., workers). However, due to the heterogeneity of computing clusters (e.g., nodes with various bandwidth or CPU resource), blindly increasing the number of workers for a job may even degrade the overall performance...
Accurate noise model is essential for dose reduction in iterative reconstruction (IR) in computed tomography, and has been studied extensively in literature. It is also important to understand how the noise model can be used for noise reduction at low counts without sacrificing spatial resolution and image quality. In this work we present a method to reduce data noise caused by a low photon count...
The heterogeneous nature of Peer-to-Peer (P2P) networks can be exploited to optimize a wide range of applications. But this requires an accurate characterization of peer heterogeneity, which is difficult due to huge population and disparate properties of individuals. To overcome this barrier, we conduct a thorough inspection of peer heterogeneity in terms of individual churn and resource capacity...
In this paper, we propose an multilateral multi-issue negotiation model based on hybrid genetic algorithm. This model dynamically estimates the opponent's preference according to the change of its offer in the process of negotiation. Meanwhile the hybrid genetic algorithm is used to optimize the process of negotiation optimization. Experimental analysis shows that this model is efficient and agents...
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