We apply a discrete network approximation to the problem of the effective conductivity of the high contrast, highly packed composites. The inclusions are irregularly (randomly) distributed in the hosting medium, so that a significant fraction of them may not participate in the conducting spanning cluster. For this class of inclusion distributions we derive a discrete network approximation and o...

The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, called Mini-Clustering (MC) extends the partition-based approximation offered by mini-bucket elimination, to tree de-compositions. The beneet of this extension is that all single variable beliefs are computed (approximately) at once, using a two-phase message-passing process along the cluster tree. ...

We introduce a discrete network approximation to the problem of the effective conductivity of the high contrast, highly packed composites in three dimensions. The inclusions are irregularly (randomly) distributed in a hosting medium, so that a significant fraction of them may not participate in the conducting spanning cluster. For this class of spacial arrays of inclusions we derive a discrete ...

Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of the basic cluster ensemble problem, notably including cluster ensembles with missing values, as well as row-distributed or column-distributed cluster ensembles. Existing cluster ensemble algorithms are applicable only to...

An explicitly correlated version of the distinguishable-cluster approximation is presented and extensively benchmarked. It is shown that the usual F12-type explicitly correlated approaches are applicable to distinguishable-cluster theory with single and double excitations, and the results show a significant improvement compared to coupled-cluster theory with singles and doubles for closed and o...

Similar document search is the problem of retrieving documents that resemble a given document. In this paper, we describe a cluster-based retrieval scheme that approximates the classic nearest neighbor search scheme, by identifying the clusters that are closest to the input document and restricting attention to these clusters only. Cluster signatures play an important role in the effectiveness ...

The coupled cluster method is applied to a strongly correlated lattice Hamiltonian, and the coupled cluster linear response method is extended to the calculation of electronic spectra by finding an approximation to a resolvent operator which describes the spectral response of the coupled cluster solution to excitation operators. In this spectral coupled cluster method, the ground and excited st...

BACKGROUND Small number of clusters and large variation of cluster sizes commonly exist in cluster-randomized trials (CRTs) and are often the critical factors affecting the validity and efficiency of statistical analyses. F tests are commonly used in the generalized linear mixed model (GLMM) to test intervention effects in CRTs. The most challenging issue for the approximate Wald F test is the ...

This paper presents a simple method of approximation for calculating the scattering cross section for a random orientated convex particle illuminated by a non polarized electromagnetic wave. This method is proved efficient for sphere and spheroids as the scattering efficiency is smaller than one and as the material is optically either soft or hard. Introduction Since the nineteenth century, phy...