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98,155
The PoissonDirichlet distribution and the scaleinvariant Poisson process
 COMBIN. PROBAB. COMPUT
, 1999
"... We show that the Poisson–Dirichlet distribution is the distribution of points in a scaleinvariant Poisson process, conditioned on the event that the sum T of the locations of the points in (0,1] is 1. This extends to a similar result, rescaling the locations by T, and conditioning on the event that ..."
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Cited by 13 (4 self)
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We show that the Poisson–Dirichlet distribution is the distribution of points in a scaleinvariant Poisson process, conditioned on the event that the sum T of the locations of the points in (0,1] is 1. This extends to a similar result, rescaling the locations by T, and conditioning on the event
WideArea Traffic: The Failure of Poisson Modeling
 IEEE/ACM TRANSACTIONS ON NETWORKING
, 1995
"... Network arrivals are often modeled as Poisson processes for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. We evaluate 24 widearea traces, investigating a number of widearea TCP arrival processes (session and con ..."
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Cited by 1775 (24 self)
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Network arrivals are often modeled as Poisson processes for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. We evaluate 24 widearea traces, investigating a number of widearea TCP arrival processes (session
The twoparameter PoissonDirichlet distribution derived from a stable subordinator.
, 1995
"... The twoparameter PoissonDirichlet distribution, denoted pd(ff; `), is a distribution on the set of decreasing positive sequences with sum 1. The usual PoissonDirichlet distribution with a single parameter `, introduced by Kingman, is pd(0; `). Known properties of pd(0; `), including the Markov ..."
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Cited by 356 (33 self)
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The twoparameter PoissonDirichlet distribution, denoted pd(ff; `), is a distribution on the set of decreasing positive sequences with sum 1. The usual PoissonDirichlet distribution with a single parameter `, introduced by Kingman, is pd(0; `). Known properties of pd(0; `), including the Markov
Sampling signals with finite rate of innovation
 IEEE Transactions on Signal Processing
, 2002
"... Abstract—Consider classes of signals that have a finite number of degrees of freedom per unit of time and call this number the rate of innovation. Examples of signals with a finite rate of innovation include streams of Diracs (e.g., the Poisson process), nonuniform splines, and piecewise polynomials ..."
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Cited by 350 (67 self)
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Abstract—Consider classes of signals that have a finite number of degrees of freedom per unit of time and call this number the rate of innovation. Examples of signals with a finite rate of innovation include streams of Diracs (e.g., the Poisson process), nonuniform splines, and piecewise
Interactive Digital Photomontage
 ACM TRANS. GRAPH
, 2004
"... We describe an interactive, computerassisted framework for combining parts of a set of photographs into a single composite picture, a process we call "digital photomontage." Our framework makes use of two techniques primarily: graphcut optimization, to choose good seams within the consti ..."
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Cited by 304 (17 self)
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the constituent images so that they can be combined as seamlessly as possible; and gradientdomain fusion, a process based on Poisson equations, to further reduce any remaining visible artifacts in the composite. Also central to the framework is a suite of interactive tools that allow the user to specify a
A finitevolume, incompressible Navier–Stokes model for studies of the ocean on parallel computers.
 J. Geophys. Res.,
, 1997
"... Abstract. The numerical implementation of an ocean model based on the incompressible Navier Stokes equations which is designed for studies of the ocean circulation on horizontal scales less than the depth of the ocean right up to global scale is described. A "pressure correction" method i ..."
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Cited by 293 (32 self)
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is used which is solved as a Poisson equation for the pressure field with Neumann boundary conditions in a geometry as complicated as that of the ocean basins. A major objective of the study is to make this inversion, and hence nonhydrostatic ocean modeling, efficient on parallel computers. The pressure
A Markovmodulated characterization of packetized voice and data traffic and related statistical multiplexer performance
 IEEE J. ON SELECTED AREAS IN COMMUN
, 1986
"... We study the performance of a statistical multiplexer whose inputs consist of a superposition of packetized voice sources and data. The performance analysis predicts voice packet delay distributions, which usually have a stringent requirement, as well as data packet delay distributions. The superpos ..."
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Cited by 288 (4 self)
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. The superposition is approximated by a correlated Markov modulated Poisson process (MMPP), which is chosen such that several of its statistical characteristics identically match those of the superposition. Matrix analytic methods are then used to evaluate system performance measures. In particular, we obtain
Scaleinvariant random spatial networks
"... Realworld road networks have an approximate scaleinvariance property; can one devise mathematical models of random networks whosedistributionsareexactlyinvariantunderEuclideanscaling? This requires working in the continuum plane. We introduce an axiomatization of a class of processes we call scale ..."
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Cited by 7 (5 self)
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Realworld road networks have an approximate scaleinvariance property; can one devise mathematical models of random networks whosedistributionsareexactlyinvariantunderEuclideanscaling? This requires working in the continuum plane. We introduce an axiomatization of a class of processes we call
Slow Sodium Inactivation and ScaleInvariant
, 2011
"... In neuroscience, a vast body of worked is aimed at crafting single neuron models that reproduce the firing characteristics of neurons in vivo. Recently, there has been much interest in understanding the behaviour of these single neurons over long time scales, from hours to days. Experiments have sho ..."
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shown that, on such time scales, nerve cells exhibit dynamics that are not captured in any neuron model to date. In particular, the neuronal excitability exhibits scaleinvariant statistics. In this work we propose a neuron model that exhibits this behaviour, by augmenting the canonical sodium
Results 1  10
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98,155