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38,342
Global wellposedness in Sobolev space implies global existence for weighted L 2 initial data for L 2 critical NLS, preprint
"... The L 2critical defocusing nonlinear Schrödinger initial value problem on R d is known to be locally wellposed for initial data in L 2. Hamiltonian conservation and the pseudoconformal transformation show that global wellposedness holds for initial data u0 in Sobolev H 1 and for data in the weigh ..."
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Cited by 7 (1 self)
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The L 2critical defocusing nonlinear Schrödinger initial value problem on R d is known to be locally wellposed for initial data in L 2. Hamiltonian conservation and the pseudoconformal transformation show that global wellposedness holds for initial data u0 in Sobolev H 1 and for data
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
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Cited by 2182 (27 self)
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The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements
A theory for multiresolution signal decomposition : the wavelet representation
 IEEE Transaction on Pattern Analysis and Machine Intelligence
, 1989
"... AbstractMultiresolution representations are very effective for analyzing the information content of images. We study the properties of the operator which approximates a signal at a given resolution. We show that the difference of information between the approximation of a signal at the resolutions ..."
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Cited by 3538 (12 self)
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2 ’ + ’ and 2jcan be extracted by decomposing this signal on a wavelet orthonormal basis of L*(R”). In LL(R), a wavelet orthonormal basis is a family of functions ( @ w (2’ ~n)),,,“jEZt, which is built by dilating and translating a unique function t+r (xl. This decomposition defines an orthogonal
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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of the leaves were represented by a noisyor: ? (Child= OIParents) = eBoL; B,Paren t; where 110 represents the "leak" term. The QMRDT network The QMRDT is a bipartite network whose structure is the same as that shown in figure 2 but the size is much larger. There are approximately 600 diseases
Climate and atmospheric history of the past 420,000 years from the Vostok ice core,
 Antarctica. Nature
, 1999
"... Antarctica has allowed the extension of the ice record of atmospheric composition and climate to the past four glacialinterglacial cycles. The succession of changes through each climate cycle and termination was similar, and atmospheric and climate properties oscillated between stable bounds. Inte ..."
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Cited by 716 (15 self)
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temperature, and of atmospheric greenhousegas concentrations, with respect to global ice volume and Greenland airtemperature changes during glacial terminations. The ice record The data are shown in Figs 1, 2 and 3 (see Supplementary Information for the numerical data). They include the deuterium content
Data Compression Using Adaptive Coding and Partial String Matching
 IEEE TRANSACTIONS ON COMMUNICATIONS
, 1984
"... The recently developed technique of arithmetic coding, in conjunction with a Markov model of the source, is a powerful method of data compression in situations where a linear treatment is inappropriate. Adaptive coding allows the model to be constructed dynamically by both encoder and decoder during ..."
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Cited by 442 (20 self)
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The recently developed technique of arithmetic coding, in conjunction with a Markov model of the source, is a powerful method of data compression in situations where a linear treatment is inappropriate. Adaptive coding allows the model to be constructed dynamically by both encoder and decoder
Efficient sparse coding algorithms
 In NIPS
, 2007
"... Sparse coding provides a class of algorithms for finding succinct representations of stimuli; given only unlabeled input data, it discovers basis functions that capture higherlevel features in the data. However, finding sparse codes remains a very difficult computational problem. In this paper, we ..."
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Cited by 445 (14 self)
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present efficient sparse coding algorithms that are based on iteratively solving two convex optimization problems: an L1regularized least squares problem and an L2constrained least squares problem. We propose novel algorithms to solve both of these optimization problems. Our algorithms result in a
ReTiling Polygonal Surfaces
 Computer Graphics
, 1992
"... This paper presents an automatic method of creating surface models at several levels of detail from an original polygonal description of a given object. Representing models at various levels of detail is important for achieving high frame rates in interactive graphics applications and also for speed ..."
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Cited by 445 (3 self)
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surface that is faithful to both the geometry and the topology of the original surface. Themain contributions of this paper are: 1) a robust method of connecting together new vertices over a surface, 2) a way of using an estimate of surface curvature to distribute more new vertices at regions of higher
Unsupervised learning of finite mixture models
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2002
"... This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective ªunsupervisedº is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectationmaximization (EM) alg ..."
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Cited by 418 (22 self)
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This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective ªunsupervisedº is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectationmaximization (EM
Statistical shape influence in geodesic active contours
 In Proc. 2000 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Hilton Head, SC
, 2000
"... A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a probability distribution over the variances of a set of training shapes. The segmentation process embeds an initial curve as the zero l ..."
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Cited by 396 (4 self)
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shape information and the image information. We then evolve the surface globally, towards the MAP estimate, and locally, based on image gradients and curvature. Results are demonstrated on synthetic data and medical imagery, in 2D and 3D. 1
Results 1  10
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38,342