Search results for "Gaussia"
showing 10 items of 653 documents
Symmetric logarithmic derivative of Fermionic Gaussian states
2018
In this article we derive a closed form expression for the symmetric logarithmic derivative of Fermionic Gaussian states. This provides a direct way of computing the quantum Fisher Information for Fermionic Gaussian states. Applications ranges from quantum Metrology with thermal states and non-equilibrium steady states with Fermionic many-body systems.
Hardware-efficient matrix inversion algorithm for complex adaptive systems
2012
This work shows an FPGA implementation for the matrix inversion algebra operation. Usually, large matrix dimension is required for real-time signal processing applications, especially in case of complex adaptive systems. A hardware efficient matrix inversion procedure is described using QR decomposition of the original matrix and modified Gram-Schmidt method. This works attempts a direct VHDL description using few predefined packages and fixed point arithmetic for better optimization. New proposals for intermediate calculations are described, leading to efficient logic occupation together with better performance and accuracy in the vector space algebra. Results show that, for a relatively s…
Stochastic resonance in a trapping overdamped monostable system.
2009
The response of a trapping overdamped monostable system to a harmonic perturbation is analyzed, in the context of stochastic resonance phenomenon. We consider the dynamics of a Brownian particle moving in a piecewise linear potential with a white Gaussian noise source. Based on linear-response theory and Laplace transform technique, analytical expressions of signal-to-noise ratio (SNR) and signal power amplification (SPA) are obtained. We find that the SNR is a nonmonotonic function of the noise intensity, while the SPA is monotonic. Theoretical results are compared with numerical simulations.
Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields
2022
In this paper, we propose a structured additive regression (STAR) model for modeling the occurrence of a disease in fields or nurseries. The methodological approach involves a Gaussian field (GF) affected by a spatial process represented by an approximation to a Gaussian Markov random field (GMRF). This modeling allows the building of maps with prediction probabilities regarding the presence of a disease in plants using Bayesian kriging. The advantage of this modeling is its computational benefit when compared with known spatial hierarchical models and with the Bayesian inference based on Markov chain Monte Carlo (MCMC) methods. Inference through the use of the integrated nested Laplace app…
Separate regression modelling of the Gaussian and Exponential components of an EMG response from respiratory physiology.
2014
If Y1 \sim N(\mu ;\sigma^2) and Y2 \sim Exp(\nu), with Y1 independent of Y2, then their sum Y = Y1 +Y2 follows an Exponentially Modified Gaussian (EMG) distribution. In many applications it is of interest to model the two components separately, in order to investigate their (possibly) different important predictors. We show how this can be done through a GAMLSS with EMG response, and apply this separate regression modelling strategy to a dataset on lung function variables from the SAPALDIA cohort study.
Dynamics of myoglobin in confinement: An elastic and quasi-elastic neutron scattering study
2008
In order to clarify the role of hard confinement on protein dynamics, elastic and quasi-elastic neutron scattering experiments have been performed on ferric horse myoglobin in two different systems: the protein embedded in a porous silica matrix, and the corresponding hydrated protein powder. Elastic data have been analysed using two different models (dynamical heterogeneity and anharmonic double-well potential) that take into account deviations of elastic intensity from Gaussian behaviour. The profile of quasi-elastic spectra has been approximated by a combination of Lorentzian and Gaussian components. Comparison between the data relative to the two different samples indicates that geometr…
Gamma-convergence of Gaussian fractional perimeter
2021
Abstract We prove the Γ-convergence of the renormalised Gaussian fractional s-perimeter to the Gaussian perimeter as s → 1 - {s\to 1^{-}} . Our definition of fractional perimeter comes from that of the fractional powers of Ornstein–Uhlenbeck operator given via Bochner subordination formula. As a typical feature of the Gaussian setting, the constant appearing in front of the Γ-limit does not depend on the dimension.
Model selection for penalized Gaussian Graphical Models
2013
High-dimensional data refers to the case in which the number of parameters is of one or more order greater than the sample size. Penalized Gaussian graphical models can be used to estimate the conditional independence graph in high-dimensional setting. In this setting, the crucial issue is to select the tuning parameter which regulates the sparsity of the graph. In this paper, we focus on estimating the "best" tuning parameter. We propose to select this tuning parameter by minimizing an information criterion based on the generalized information criterion and to use a stability selection approach in order to obtain a more stable graph. The performance of our method is compared with the state…
Hierarchical Bayesian models for analysing fish biomass data. An application to Parapenaeus longirostris biomass data
2022
The Mediterranean International Trawl Survey (MEDITS) programme provides spatially referenced ecological data. We adopted a hierarchical Bayesian model to analyse Parapenaeus longirostris biomass data. The model comprises three parts, each of which identifies: the variability due to the explanatory variables, the variability due to the spatial domain (seen as a Gaussian Process) and the irregular component modelled as white noise. The estimated parameters show that some seabed characteristics affect biomass quantity and that the estimated behaviour of the Gaussian Process changes over different groups of years.
GAUSSIAN EFFECTIVE POTENTIAL AND ANTIFERROMAGNETISM IN THE HUBBARD MODEL
2012
The Gaussian Effective Potential (GEP) is shown to be a useful variational tool for the study of the magnetic properties of strongly correlated electronic systems. The GEP is derived for a single band Hubbard model on a two-dimensional bi-partite square lattice in the strong coupling regime. At half-filling the antiferromagnetic order parameter emerges as the minimum of the effective potential with an accuracy which improves over RPA calculations and is very close to that achieved by Monte Carlo simulations. Extensions to other magnetic systems are discussed.