An adaptive spline method for smoothing is proposed which combines features from both regression spline and smoothing spline approaches One of its advantages is the ability to vary the amount of smoothing in response to the inhomogeneous curvature of true functions at di erent locations This method can be applied to many multivariate function estimation problems which is illustrated in this pap...

Smoothing algorithms allow to reduce artifacts from mesh generation, but often degrade accuracy. Thus, we present a method that identifies staircase artifacts which result from image inhomogeneities and binary segmentation in medical image data for subsequent removal by adaptive mesh smoothing. This paper makes the following specific contributions: caps, which are flat regions, resulting from s...

In this paper we present a novel surface reconstruction method for particle-based fluid simulators such as Smoothed Particle Hydrodynamics. In particle-based simulations, fluid surfaces are usually defined as a level set of an implicit function. We formulate the implicit function as a sum of anisotropic smoothing kernels, and the direction of anisotropy at a particle is determined by performing...

Whenever a new mesh smoothing algorithm is introduced in the literature, initial experimental analysis is often performed on relatively simple geometric domains where the meshes need little or no element size grading. Here, we present a comparative study of a large number of well-known smoothing algorithms on triangulations of complex geometric domains. Our study reveals the limitations of some...

In the language modeling approach to information retrieval, Dirichlet prior smoothing frequently outperforms Jelinek-Mercer smoothing. Both Dirichlet prior and Jelinek-Mercer are forms of linear interpolated smoothing. The only difference between them is that Dirichlet prior determines the amount of smoothing based on a document’s length. Our hypothesis was that Dirichlet prior’s performance ad...

We prove a convergence estimate for the Algebraic Multigrid Method with prolongations deened by aggregation using zero energy modes, followed by a smoothing. The method input is the problem matrix and a matrix of the zero energy modes. The estimate depends only polylogarithmically on the mesh size, and requires only a weak approximation property for the aggregates, which can be a-priori veriied...

In this paper, we propose a Big-? smoothing method for solving the P 0 matrix linear complementarity problem. We study the trajectory deened by the augmented smoothing equations and global convergence of the method under an assumption that the original P 0 matrix linear complementarity problem has a solution. The method has been tested on the P 0 matrix linear complementarity problem with unbou...

Article history: Received 15 November 2009 Received in revised form 28 March 2010 Accepted 22 May 2010 Available online 27 May 2010

We present an algorithm for learning the intrinsic value of a batted ball in baseball. This work addresses the fundamental problem of separating the value of a batted ball at contact from factors such as the defense, weather, and ballpark that can affect its observed outcome. The algorithm uses a Bayesian model to construct a continuous mapping from a vector of batted ball parameters to an intr...