A new technique, named DPOL MUM, is proposed for the segmentation of multi-frequency polarimetric SAR images. By following a Maximum Likelihood approach, an optimal split-merge test is derived, which fully exploits the characteristic block-diagonal structure of the data covariance matrix. This test is demonstrated to yield a reduced fluctuation error than the extension of the polarimetric test ...

We report for the first time exact ground-states deduced for the D = 2 dimensional generic periodic Anderson model at finite U , the Hamiltonian of the model not containing direct hopping terms for f -electrons (t = 0). The deduced itinerant phase presents non-Fermi liquid properties in the normal phase, emerges for real hybridization matrix elements, and not requires anisotropic unit cell. In ...

In this paper we present a complete perturbation analysis for the Hamiltonian Schurform of a Hamiltonian matrix under similarity transformations with unitary symplectic matrices.Both linear asymptotic and non-linear perturbation bounds are presented. The same analysis isalso carried out for two less condensed block-Schur forms. It suggests that the block forms areless sensitive ...

In this work we analyse a method to construct a numerically efficient and computationally cheap sparse approximations of some of the matrix blocks arising in the block-factorised preconditioners for matrices with a two-by-two block structure. The matrices arise from finite element discretizations of partial differential equations. We consider scalar elliptic problems, however the approach is ap...

fifty years ago warren and root have introduced the shape factor. this fundamental parameter for modeling of naturally fractured reservoirs has been discussed stormily ever since. different definitions for shape factor have been suggested which all of them are heuristically based. recently, heinemann and mittermeir mathematically derived - based on the dual-continuum theorem assuming pseudo-ste...

The stochastic block model and its variants have been a popular tool for analyzing large network data with community structures. In this paper we develop an efficient network cross-validation (NCV) approach to determine the number of communities, as well as to choose between the regular stochastic block model and the degree corrected block model. The proposed NCV method is based on a block-wise...