Search results for "Linear"
showing 10 items of 7165 documents
Determinants of spatial intensity of stop locations on cruise passengers tracking data
2021
This paper aims at analyzing the spatial intensity in the distribution of stop locations of cruise passengers during their visit at the destination through a stochastic point process modelling approach on a linear network. Data collected through the integration of GPS tracking technology and questionnaire-based survey on cruise passengers visiting the city of Palermo are used, to identify the main determinants which characterize their stop locations pattern. The spatial intensity of stop locations is estimated through a Gibbs point process model, taking into account for both individual-related variables, contextual-level information, and for spatial interaction among stop points. The Berman…
Hawkes processes on networks for crime data
2022
Motivated by the analysis of crime data in Bucaramanga (Colombia), we propose a spatio-temporal Hawkes point process model adapted to events living on linear networks. We first consider a non-parametric modelling strategy, for both the background and the triggering components, and then we include a parametric estimation of the background based on covariates, and a non-parametric one of the triggering effects. Our network model outperforms a planar version, improving the fitting of the self-exciting point process model.
Special factors and the combinatorics of suffix and factor automata
2011
AbstractThe suffix automaton (resp. factor automaton) of a finite word w is the minimal deterministic automaton recognizing the set of suffixes (resp. factors) of w. We study the relationships between the structure of the suffix and factor automata and classical combinatorial parameters related to the special factors of w. We derive formulae for the number of states of these automata. We also characterize the languages LSA and LFA of words having respectively suffix automaton and factor automaton with the minimal possible number of states.
Salient Pixels and Dimensionality Reduction for Display of Multi/Hyperspectral Images
2012
International audience; Dimensionality Reduction (DR) of spectral images is a common approach to different purposes such as visualization, noise removal or compression. Most methods such as PCA or band selection use either the entire population of pixels or a uniformly sampled subset in order to compute a projection matrix. By doing so, spatial information is not accurately handled and all the objects contained in the scene are given the same emphasis. Nonetheless, it is possible to focus the DR on the separation of specific Objects of Interest (OoI), simply by neglecting all the others. In PCA for instance, instead of using the variance of the scene in each spectral channel, we show that i…
On the convergent parts of high order spectral moments of stationary structural responses
1986
The paper deals with the evaluation of the convergent parts of the high spectral moments of linear systems subjected to stationary random input. An adequate physical meaning of these quantities in both the time and frequency domains is presented. Recurrence formulas to obtain the high convergent cross spectral moments of any order are given in the case of white noise input.
Analytic evaluation of spectral moments
1988
In this paper an analytic procedure that drastically reduces the computational effort in evaluating the spectral moments of the response of multi-degree-of-freedom systems is presented. It is shown that the cross-spectral moments of any order of two oscillators subjected to a filtered stochastic process can be obtained in a recursive manner as a linear combination of the spectral moment of each oscillator up to the third order separately taken. A numerical procedure is also presented in order to evaluate such first few spectral moments.
Analytical evaluation of structural response for stationary multicorrelated input
1990
Abstract An analytical procedure is presented which can drastically reduce computational effort in the evaluation of the spectral moments of an elastic linear multi-degree-of-freedom system subjected to a stationary multicorrelated input process. The reduction in computer time is possible since the cross-spectral moments of two oscillators can be obtained in recursive manner as a linear combination of the spectral moment of each oscillator taken separately, which is evaluated by means of a very fast numerical technique.
Spectral moments of the edge adjacency matrix in molecular graphs. 3. Molecules containing cycles
1998
A substructural approach to quantitative structure−property relationships based on the spectral moments of the edge adjacency matrix is extended to molecules containing cycles. Spectral moments are expressed as linear combinations of structural fragments of any kind of nonweighted graphs. The boiling points of a series of 80 cycloalkanes was well-described by the present approach. The predictive power of the model was proved by using a test set of another 26 compounds. An equation that expresses the contribution of the different fragments of the molecules to the boiling point was obtained.
Covariant Operator Formalism for Quantized Superfields
1988
The Takahashi-Umezawa method of deriving the free covariant quantization relations from the linear equations of motion is extended to superfields. The Cauchy problem for free superfields is solved, and an expression for the time independent scalar product is given. For the case of interacting fields, we give the general Kallen-Lehmann spectral representation for the two-point superfield Green functions and, after the introduction of the asymptotic condition for superfields, we give the superfield extension of the Yang-Feldman equation. The case of the D = 2 real scalar superfield and the case of the D = 4 chiral superfield are discussed in detail.
The Tan 2Θ Theorem in fluid dynamics
2017
We show that the generalized Reynolds number (in fluid dynamics) introduced by Ladyzhenskaya is closely related to the rotation of the positive spectral subspace of the Stokes block-operator in the underlying Hilbert space. We also explicitly evaluate the bottom of the negative spectrum of the Stokes operator and prove a sharp inequality relating the distance from the bottom of its spectrum to the origin and the length of the first positive gap.