where d = thickness of the film, ,o = resistivity of the film, = resistivity of the bulk material having the same structure and approximately the same number of defects as the films, l0 = mean free path of the charge carriers. The values of l0 calculated by the two methods were found to agree satisfactorily. Figs. 7 and 8 show the variation of the Hall mobility (/̂ H) with film thickness and sub...

Antonín Kučera?1 and Javier Esparza??2 1 Faculty of Informatics, Masaryk University, Botanická 68a, 60200 Brno, Czech Republic, [email protected] 2 Institut für Informatik, Technische Universität München, Arcisstr. 21, D-80290 München, Germany, [email protected] Abstract. We study the following problem: Given a transition system T and its quotient T= under an equivalence , which are the sets L, L...

Nevertheless the normalized rows of Pn(ω) in general do not converge: suppose for instance that all the matrices in M are positive but do not have the same positive normalized left-eigenvector, let Lk such that LkMk = ρkLk. For any positive matrix M , the normalized rows of MM0 n converge to L0 and the ones of MM1 n to L1. Consequently we can choose the sequence (nk)k∈N sufficiently increasing ...

Power consumption is an important design issue of current embedded systems. It has been shown that instruction cache accounts for a significant portion of the power dissipation of the whole chip. Several studies have proposed to add a cache (L0 cache) that is very small relative to the conventional L1 cache on chip for power optimization since a smaller cache has lower load capacitance. However...

Recently, there has been focus on penalized loglikelihood covariance estimation for sparse inverse covariance (precision) matrices. The penalty is responsible for inducing sparsity, and a very common choice is the convex l1 norm. However, the best estimator performance is not always achieved with this penalty. The most natural sparsity promoting “norm” is the non-convex l0 penalty but its lack ...

Images decomposition attracts much attention in recent years. Many variational methods were proposed. The most famous variational decomposition model is OSV model because of its concise expression. However, the OSV model suffers from many drawbacks. One is that the discretization of the EularLagrange equation is hard because the 4th order term was introduced. The other drawback is that the effe...

In this paper we consider l0 regularized convex cone programming problems. In particular, we first propose an iterative hard thresholding (IHT) method and its variant for solving l0 regularized box constrained convex programming. We show that the sequence generated by these methods converges to a local minimizer. Also, we establish the iteration complexity of the IHT method for finding an -loca...

We consider a symmetric minimal relation L0 generated by an integral equation with operators measures. obtain form of generalized resolvents and give description boundary value problems associated to resolvents.