Motivation: Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional data sets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to determine the two parameters fuzzifier and cluster number. Wrong parameter values may either lead to the inclusion of purely ...

The all nearest smaller values problem is deened as follows. Let A = (a 1 ; a 2 ; : : :; a n) be n elements drawn from a totally ordered domain. For each a i , 1 i n, nd the two nearest elements in A that are smaller than a i (if such exist): the left nearest smaller element a j (with j < i) and the right nearest smaller element a k (with k > i). We give an O(loglog n) time optimal parallel alg...

The random-cluster model has been widely studied as a unifying framework for random graphs, spin systems and random spanning trees, but its dynamics have so far largely resisted analysis. In this work we study a natural non-local Markov chain known as the Chayes-Machta dynamics for the mean-field case of the random-cluster model, and identify a critical regime (λs, λS) of the model parameter λ ...

Many computer vision problems are naturally formulated as random fields, specifically MRFs or CRFs. The introduction of graph cuts has enabled efficient and optimal inference in associative random fields, greatly advancing applications such as segmentation, stereo reconstruction and many others. However, while fast inference is now widespread, parameter learning in random fields has remained an...

the present study deals with the analysis of the effects of radiative heat transfer of micropolar fluid flow over a porous and stretching sheet in the presence of magnetic field. the dynamic viscosity and thermal conductivity coefficient have formulated by temperature-dependent relations to obtain more exact results. the flow is supposed two-dimensional, incompressible, steady and laminar and t...

This paper introduces a fast image mosaicing technique that does not require costly search on image domain (e.g., pixel-to-pixel correspondence search on the image domain) and the iterative optimization (e.g., gradient-based optimization, iterative optimization, and random optimization) of geometric transformation parameter. The proposed technique is organized in a two-step manner. At both step...

We consider quantum systems with a chaotic classical limit that depend on an external parameter, and study correlations between the spectra at different parameter values. In particular, we consider the parametric spectral form factor K(τ, x) which depends on a scaled parameter difference x. For parameter variations that do not change the symmetry of the system we show by using semiclassical per...

This paper considers the problem of estimating the phase parameters of a linear FM signal which is modulated by a random process and is embedded in additive noise. In particular, we consider the use of cyclic moments and derive variance expressions for the phase parameter estimates for all values of the lag parameter of the second order cyclic moment, . It is seen that the accuracy of the phase...

This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric imputation method for the missing values, which then leads to imputed estimating equations for the finite...