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The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement le ...
A multi-domain finite element formulation has been applied to determine the semi-solid permeability of an equiaxed granular structure. The granular model was constructed from a Voronoï tessellation algorithm. The liquid-solid interface is represented impli ...
In this paper, we extend the theory of sampling signals with finite rate of innovation (FRI) to a specific class of two-dimensional curves, which are defined implicitly as the zeros of a mask function. Here the mask function has a parametric representation ...
Institute of Electrical and Electronics Engineers2014
A method for certifying exact input trackability for constrained discrete time linear systems is introduced in this paper. A signal is assumed to be drawn from a reference set and the system must track this signal with a linear combination of its inputs. U ...
The imposition of federal conservatorships on September 6, 2008, at the Federal National Mortgage Association and the Federal Home Loan Mortgage Corporation—commonly known as Fannie Mae and Freddie Mac—was one of the most dramatic events of the financial c ...
In the field of image segmentation, most of level-set-based active contour approaches are based on a discrete representation of the associated implicit function. We present in this paper a different formulation where the level-set is modelled as a continuo ...
Perceptual learning is an implicit form of learning which induces long-lasting perceptual enhancements. Perceptual learning shows intriguing characteristics. For example, a minimal number of trials per session is needed for learning and the interleaved pre ...
The most classic approach to the dynamics of an n-dimensional mechanical system constrained by d independent holonomic constraints is to pick explicitly a new set of (n - d) curvilinear coordinatesparametrizingthe manifold of configurations satisfying the ...
In the field of image segmentation, most level-set-based active-contour approaches take advantage of a discrete representation of the associated implicit function. We present in this paper a different formulation where the implicit function is modeled as a ...
This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of ma ...