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Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations
, 1988
"... We devise new numerical algorithms, called PSC algorithms, for following fronts propagating with curvaturedependent speed. The speed may be an arbitrary function of curvature, and the front also can be passively advected by an underlying flow. These algorithms approximate the equations of motion, w ..."
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Cited by 1183 (60 self)
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in the moving fronts. The algorithms handle topological merging and breaking naturally, work in any number of space dimensions, and do not require that the moving surface be written as a function. The methods can be also used for more general HamiltonJacobitype problems. We demonstrate our algorithms
NONLINEAR SCHRÖDINGER EQUATIONS IN ONE AND TWO SPACE DIMENSIONS
, 2003
"... Long range scattering for nonlinear Schrödinger equations in one and two space dimensions by ..."
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Long range scattering for nonlinear Schrödinger equations in one and two space dimensions by
Search for Extra SpaceDimensions at the LHC
, 2002
"... On behalf on the ATLAS and CMS collaborations The introduction of extra space dimensions in the theory could be an elegant way to solve the hierarchy problem. There could even be one energy scale at which all interactions could unify. The limits coming from our knowledge of the gravitation at low di ..."
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On behalf on the ATLAS and CMS collaborations The introduction of extra space dimensions in the theory could be an elegant way to solve the hierarchy problem. There could even be one energy scale at which all interactions could unify. The limits coming from our knowledge of the gravitation at low
Dynamics of chaotic inflation with variable space dimension
, 2008
"... Within the framework of a model Universe with variable space dimension, we study chaotic inflation with the potential m 2 φ 2 /2, and calculate the dynamical solutions of the inflaton field, variable space dimension, scale factor, and their interdependence during the inflationary epoch. We show that ..."
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Within the framework of a model Universe with variable space dimension, we study chaotic inflation with the potential m 2 φ 2 /2, and calculate the dynamical solutions of the inflaton field, variable space dimension, scale factor, and their interdependence during the inflationary epoch. We show
Nearestneighbor searching and metric space dimensions
 In NearestNeighbor Methods for Learning and Vision: Theory and Practice
, 2006
"... Given a set S of n sites (points), and a distance measure d, the nearest neighbor searching problem is to build a data structure so that given a query point q, the site nearest to q can be found quickly. This paper gives a data structure for this problem; the data structure is built using the distan ..."
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Cited by 107 (0 self)
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the distance function as a “black box”. The structure is able to speed up nearest neighbor searching in a variety of settings, for example: points in lowdimensional or structured Euclidean space, strings under Hamming and edit distance, and bit vector data from an OCR application. The data structures
Large N field theories, string theory and gravity
, 2001
"... We review the holographic correspondence between field theories and string/M theory, focusing on the relation between compactifications of string/M theory on Antide Sitter spaces and conformal field theories. We review the background for this correspondence and discuss its motivations and the evide ..."
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Cited by 1443 (45 self)
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We review the holographic correspondence between field theories and string/M theory, focusing on the relation between compactifications of string/M theory on Antide Sitter spaces and conformal field theories. We review the background for this correspondence and discuss its motivations
SupportVector Networks
 Machine Learning
, 1995
"... The supportvector network is a new learning machine for twogroup classification problems. The machine conceptually implements the following idea: input vectors are nonlinearly mapped to a very highdimension feature space. In this feature space a linear decision surface is constructed. Special pr ..."
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Cited by 3703 (35 self)
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The supportvector network is a new learning machine for twogroup classification problems. The machine conceptually implements the following idea: input vectors are nonlinearly mapped to a very highdimension feature space. In this feature space a linear decision surface is constructed. Special
The many faces of Publish/Subscribe
, 2003
"... This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many v ..."
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Cited by 743 (23 self)
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This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many
Data cube: A relational aggregation operator generalizing groupby, crosstab, and subtotals
, 1996
"... Abstract. Data analysis applications typically aggregate data across many dimensions looking for anomalies or unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zerodimensional or onedimensional aggregates. Applications need the Ndimensional generalization of these op ..."
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Cited by 860 (11 self)
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in more complex nonprocedural data analysis programs. The cube operator treats each of the N aggregation attributes as a dimension of Nspace. The aggregate of a particular set of attribute values is a point in this space. The set of points forms an Ndimensional cube. Superaggregates are computed
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
 International Journal of Computer Vision
, 2001
"... In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a se ..."
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Cited by 1313 (81 self)
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set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space
Results 11  20
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