Search results for "Gaussia"

Flux of a Vector Field

2012

In this chapter we concentrate on aspects of vector calculus. A common physical application of this theory is the fluid flow problem of calculating the amount of fluid passing through a permeable surface. The abstract generalization of this leads us to the flux of a vector field through a regular 2-surface in \(\mathbb{R}^3\). More precisely, let the vector field F in \(\mathbb{R}^3\) represent the velocity vector field of a fluid. We immerse a permeable surface S in that fluid, and we are interested in the amount of fluid flow across the surface S per unit time. This is the flux integral of the vector field F across the surface S

Discrete-Gauss states and the generation of focussing dark beams

2014

Discrete-Gauss states are a new class of gaussian solutions of the free Schr\"odinger equation owning discrete rotational symmetry. They are obtained by acting with a discrete deformation operator onto Laguerre-Gauss modes. We present a general analytical construction of these states and show the necessary and sufficient condition for them to host embedded dark beams structures. We unveil the intimate connection between discrete rotational symmetry, orbital angular momentum, and the generation of focussing dark beams. The distinguishing features of focussing dark beams are discussed. The potential applications of Discrete-Gauss states in advanced optical trapping and quantum information pro…

A polymer chain trapped between two parallel repulsive walls: A Monte-Carlo test of scaling behavior

1998

An off-lattice bead-spring model of a polymer chain trapped between two parallel walls a distance D apart is studied by Monte-Carlo methods, using chain lengths N in the range $$32 \le N \le 512$$ and distances D from 4 to 32 (in units of the maximum spring extension). The scaling behavior of the coil linear dimensions parallel to the plates and of the force on the walls is studied and discussed with the help of current theoretical predictions. Also the density profiles of the monomers across the slit are obtained and it is shown that the predicted variation with the distance z from a wall, $$\rho (z) \propto {z^{1/\nu }}$$ , is obtained only when one introduces an extrapolation length λ in…

Optimal estimation of losses at the ultimate quantum limit with non-Gaussian states

2009

We address the estimation of the loss parameter of a bosonic channel probed by arbitrary signals. Unlike the optimal Gaussian probes, which can attain the ultimate bound on precision asymptotically either for very small or very large losses, we prove that Fock states at any fixed photon number saturate the bound unconditionally for any value of the loss. In the relevant regime of low-energy probes, we demonstrate that superpositions of the first low-lying Fock states yield an absolute improvement over any Gaussian probe. Such few-photon states can be recast quite generally as truncations of de-Gaussified photon-subtracted states.

Analytical design of 160 Gbits/s densely dispersion-managed optical fiber transmission systems using Gaussian and raised cosine RZ ansätze

2004

We present an easy and efficient analytical method to design 160 Gbits/s densely dispersion-managed optical fiber transmission systems using Gaussian and raised cosine RZ ansatze.

New approaches based on modified Gaussian models for the prediction of chromatographic peaks

2012

Abstract The description of skewed chromatographic peaks has been discussed extensively and many functions have been proposed. Among these, the Polynomially Modified Gaussian (PMG) models interpret the deviations from ideality as a change in the standard deviation with time. This approach has shown a high accuracy in the fitting to tailing and fronting peaks. However, it has the drawback of the uncontrolled growth of the predicted signal outside the elution region, which departs from the experimental baseline. To solve this problem, the Parabolic-Lorentzian Modified Gaussian (PLMG) model was developed. This combines a parabola that describes the variance change in the peak region, and a Lor…

Dynamic factorial graphical models for dynamic networks

2014

Dynamic networks models describe a growing number of important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they are sparse. For example, the molec- ular structure of genes make interactions with other components a highly-structured and, therefore, a sparse process. Penalized Gaussian graphical models have been used to estimate sparse networks. H…

Noisy dynamics in long and short Josephson junctions

The study of nonlinear dynamics in long Josephson junctions and the features of a particular kind of junction realized using a graphene layer, are the main topics of this research work. The superconducting state of a Josephson junction is a metastable state, and the switching to the resistive state is directly related to characteristic macroscopic quantities, such as the current the voltage across the junction, and the magnetic field through it. Noise sources can affect the mean lifetime of this superconducting metastable state, so that noise induced effects on the transient dynamics of these systems should be taken into account. The long Josephson junctions are investigated in the sine-Gor…

Special Functions for the Study of Economic Dynamics: The Case of the Lucas-Uzawa Model

2004

The special functions are intensively used in mathematical physics to solve differential systems. We argue that they should be most useful in economic dynamics, notably in the assessment of the transition dynamics of endogenous growth models. We illustrate our argument on the Lucas-Uzawa model, which we solve by the means of Gaussian hypergeometric functions. We show how the use of Gaussian hypergeometric functions allows for an explicit representation of the equilibrium dynamics of the variables in level. In contrast to the preexisting approaches, our method is global and does not rely on dimension reduction.

Local Granger causality

2021

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes. We exploit such equivalence and calculate exactly the 'local Granger causality', i.e. the profile of the information transfer at each discrete time point in Gaussian processes; in this frame Granger causality is the average of its local version. Our approach offers a robust and computationally fast method to follow the information transfer along the time history of linear stochastic processes, as well as of nonlinear …