In this paper we investigate an error formula for bivariate ideal *** shall call it the "normal" error formula which derives from the "good" error formula raised by Carl de *** prov...
In this paper we investigate an error formula for bivariate ideal *** shall call it the "normal" error formula which derives from the "good" error formula raised by Carl de *** prove that a lexicographic order reduced Gr?bner basis admits such an error *** 2010,Boris Shekhtman proves the ideal projector P* defined by kerP* =(x2-y,xy,y2)does not have a "good" error *** an example,we will show such a P* has a "normal" error formula.
This paper deals with a novel local arc length estimator for curves in gray-scale *** method first estimates a cubic spline curve fit for the boundary points using the gray-level information of the nearby pixels,and t...
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This paper deals with a novel local arc length estimator for curves in gray-scale *** method first estimates a cubic spline curve fit for the boundary points using the gray-level information of the nearby pixels,and then computes the sum of the spline segments’*** this model,the second derivatives and y coordinates at the knots are required in the computation;the spline polynomial coefficients need not be computed *** provide the algorithm pseudo code for estimation and preprocessing,both taking linear *** shows that the proposed model gains a smaller relative error than other state-of-the-art methods.
XML documents cluster analysis is a hot research topic. Researchers proposed a number of methods to cluster XML document collections. Boosting is successful well-known methods for improving the quality of clustering. ...
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Hidden Naive Bayes (HNB) has demonstrated remarkable progress in classification accuracy, accurate class probability estimation and ranking. Since HNB is based on one-dependence estimators to get the approximate value...
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The cloud platform provides abundant resources and services for users. More and more mobile users began to use the cloud services. They have higher real-time demands on service. The size of traditional virtual machine...
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The cloud platform provides abundant resources and services for users. More and more mobile users began to use the cloud services. They have higher real-time demands on service. The size of traditional virtual machine (VM) operating system is basically large. It will take many resources in deployment and communication process, and always affect the real-time performance of system. To reduce communication overhead and improve deployment speed of VMs, this paper proposes an approach of customized VM image with LFS. LFS can reduce the size of VM image efficiently and enable flexible customization of the VM image by incremental customization. The experimental results show us that the size of VM image generated by the proposed method is smaller than the one generated by kernel tailoring technology in system overhead. Meanwhile it is also faster in running speed.
In this paper, a modified particle swarm optimization(MPSO) algorithm is proposed to solve the reliability redundancy optimization problem. This algorithm modifies the strategy of generating new position of particles....
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In this paper, a modified particle swarm optimization(MPSO) algorithm is proposed to solve the reliability redundancy optimization problem. This algorithm modifies the strategy of generating new position of particles. For each generation solution, the flight velocity of particles is removed. Whereas the new position of each particle is generated by using difference strategy. Moreover, an adaptive parameter is used to ensure diversity of feasible solutions. Experimental results on four benchmark problems demonstrate that the proposed MPSO has better robustness, effectiveness and efficiency than other algorithms reported in literatures for solving the reliability redundancy optimization problem.
Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived...
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Essential proteins are crucial to cellular survival and development. Traditionally, essential proteins are identified by knock-out experiments, which are expensive and often fatal to the target organisms. Regarding th...
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Essential proteins are crucial to cellular survival and development. Traditionally, essential proteins are identified by knock-out experiments, which are expensive and often fatal to the target organisms. Regarding this, an important approach to essential protein identification is through computational prediction. In this research, we present a novel computational method, Integrated Edge Weights (IEW), to innovatively predict proteins' essentiality based on essential protein-protein interactions. The experimental results on all three organisms: Saccharomyces cere-visiae (Yeast), Escherichia coli (E. coli), and Caenorhabditis ele-gans (C. elegans) show that IEW achieves better performance than the state-of-the-art methods in terms of precision-recall. Furthermore, we have demonstrated that the highly-ranked protein-protein interactions predicted by our approach tend to be biologically significant in Yeast, E. coli, and C. elegans protein-protein interaction (PPI) networks.
This paper finds the most expressive segments of a shape category called similar and discriminative parts, which can distinguish the learned shape class from other groups. The proposed model chooses a computationally ...
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