Let fn → f0 be a convergent sequence of rational maps, preserving critical relations, and f0 be geometrically finite with parabolic points. It is known that for some unlucky choices of sequences fn,the Julia sets J(fn) and their Hausdorff dimensions may fail to converge as n → ∞. Our main result here is to prove the convergence of J(fn) and H.dim J(fn) for generic sequences fn. The same conclusion was obtained earlier, with stronger hypotheses on the sequence fn, by Bodart-Zinsmeister and then by McMullen. We characterize those choices of fn by means of flows of appropriate polynomial vector fields (following Douady-Estrada-Sentenac). We first prove an independent result about the (s-dimensional) length of separatrices of such flows, and then use it to estimate tails of Poincar´e series. This, together with existing techniques,provides the desired control of conformal densities and Hausdorff dimensions. Our method may be applied to other problems relatedto parabolic perturbations.

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Partial differential equations (PDE) on manifolds arise in many areas, including mathematics and many applied fields. Due to the complicated geometrical structure of the manifold, it is difficult to get efficient numerical method to solve PDE on manifold. In the paper, we propose a method called point integral method (PIM) to solve the Poisson-type equations from point clouds. Among different kinds of PDEs, the Poisson-type equations including the standard Poisson equation and the related eigenproblem of the Laplace-Beltrami operator are one of the most important. In PIM, the key idea is to derive the integral equations which approximates the Poisson-type equations and contains no derivatives but only the values of the unknown function. This feature makes the integral equation easy to be discretized from point cloud. In the paper, we explain the derivation of the integral equations, describe the point integral method and its implementation, and present the numerical experiments to demonstrate the convergence of PIM.

We study an efficient dynamic blind source separation algorithm of convolutive sound mixtures based on updating statistical information in the frequency domain, and minimizing the support of time domain demixing filters by a weighted least square method. The permutation and scaling indeterminacies of separation, and concatenations of signals in adjacent time frames are resolved with optimization of l 1 l norm on cross-correlation coefficients at multiple time lags. The algorithm is a direct method without iterations, and is adaptive to the environment. Computations on recorded benchmark mixtures of speech and music signals show excellent performance. The method in general separates a foreground source from a background of sounds as often encountered in realistic situations.