Results 31 to 40 of about 667,125 (317)

Distilling Gaussian States with Gaussian Operations is Impossible [PDF]

open access: yesPhysical Review Letters, 2002
We show that no distillation protocol for Gaussian quantum states exists that relies on (i) arbitrary local unitary operations that preserve the Gaussian character of the state and (ii) homodyne detection together with classical communication and postprocessing by means of local Gaussian unitary operations on two symmetric identically prepared copies ...
Eisert, J, Scheel, S, Plenio, MB
openaire   +4 more sources

Hierarchical Gaussian process mixtures for regression [PDF]

open access: yes, 2005
As a result of their good performance in practice and their desirable analytical properties, Gaussian process regression models are becoming increasingly of interest in statistics, engineering and other fields.
Titterington, D.M.   +2 more
core   +1 more source

Conformations of Steroid Hormones: Infrared and Vibrational Circular Dichroism Spectroscopy

open access: yesMolecules, 2023
Steroid hormone molecules may exhibit very different functionalities based on the associated functional groups and their 3D arrangements in space, i.e., absolute configurations and conformations.
Yanqing Yang   +6 more
doaj   +1 more source

Analyses and Modeling Impulse Noise Generated by Household Appliances

open access: yesAdvances in Electrical and Electronic Engineering, 2014
This paper describes analysis of impulse noise generated by small household appliances. Furthermore we propose a new model of impulse noise based on the averaged power spectrum and the random phase generation with various phase distributions.
Jaroslav Krejci, Tomas Zeman
doaj   +1 more source

Gaussian equilibration

open access: yesPhysical Review E, 2013
A finite quantum system evolving unitarily equilibrates in a probabilistic fashion. In the general many-body setting the time-fluctuations of an observable \mathcal{A} are typically exponentially small in the system size. We consider here quasi-free Fermi systems where the Hamiltonian and observables are quadratic in the Fermi operators. We first prove
Venuti, Lorenzo Campos, Zanardi, Paolo
openaire   +4 more sources

A Probabilistic Perspective on Gaussian Filtering and Smoothing [PDF]

open access: yes, 2010
15.07.13 KB. Ok to add report to Spiral.We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of ...
Deisenroth, MP, Ohlsson, H
core   +1 more source

Mach-Zehnder Interferometer Sensor Curvature Demodulation Method Based on the Orthogonal Decomposition of Spectral Curves

open access: yesIEEE Access, 2020
This paper proposes a curvature demodulation of Mach-Zehnder interferometer (MZI) sensors based on complete spectral information. An MZI sensor composed of two waist-enlarged single mode fibers (SMFs) was fabricated.
Mingyao Liu   +9 more
doaj   +1 more source

A simple proof of distance bounds for Gaussian rough paths [PDF]

open access: yes, 2013
We derive explicit distance bounds for Stratonovich iterated integrals along two Gaussian processes (also known as signatures of Gaussian rough paths) based on the regularity assumption of their covariance functions.
Riedel, Sebastian, Xu, Weijun
core   +1 more source

Multiplying a Gaussian matrix by a Gaussian vector [PDF]

open access: yesStatistics & Probability Letters, 2017
We provide a new and simple characterization of the multivariate generalized Laplace distribution. In particular, this result implies that the product of a Gaussian matrix with independent and identically distributed columns by an independent isotropic Gaussian vector follows a symmetric multivariate generalized Laplace distribution.
openaire   +4 more sources

Invariances for Gaussian models [PDF]

open access: yes, 2015
At the heart of a statistical analysis, we are interested in drawing conclusions about random variables and the laws they follow. For this we require a sample, therefore our approach is best described as learning from data.
Adametz, David
core   +1 more source

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