Search results for "statistical [error]"

showing 10 items of 36 documents

Impact of self-steepening on incoherent dispersive spectral shocks and collapse-like spectral singularities

2014

International audience; Incoherent dispersive shock waves and collapselike singularities have been recently predicted to occur in the spectral evolution of an incoherent optical wave that propagates in a noninstantaneous nonlinear medium. Here we extend this work by considering the generalized nonlinear Schrödinger equation. We show that self-steepening significantly affects these incoherent spectral singularities: (i) It leads to a delay in the development of incoherent dispersive shocks, and (ii) it arrests the incoherent collapse singularity. Furthermore, we show that the spectral collapselike behavior can be exploited to achieve a significant enhancement (by two orders of magnitudes) of…

Shock wavespecklesIncoherent scatterDegree of coherencespeckles steepening shock waves01 natural sciencesNO010305 fluids & plasmasSingularity[ MATH.MATH-AP ] Mathematics [math]/Analysis of PDEs [math.AP][NLIN.NLIN-PS]Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]Quantum mechanicsNonlinear medium0103 physical sciences[MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP][ NLIN.NLIN-PS ] Nonlinear Sciences [physics]/Pattern Formation and Solitons [nlin.PS]010306 general physicsPhysicsstatistical opticsshock wavesAtomic and Molecular Physics and Optics[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]Nonlinear systemQuantum electrodynamicsGravitational singularitysteepening[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Coherence (physics)
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Deep Gaussian processes for biogeophysical parameter retrieval and model inversion

2020

Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have provided useful and informative so…

Signal Processing (eess.SP)FOS: Computer and information sciencesComputer Science - Machine LearningEarth observation010504 meteorology & atmospheric sciencesIASIComputer science0211 other engineering and technologiesExtrapolation02 engineering and technologyDeep Gaussian Processes01 natural sciencesArticleMachine Learning (cs.LG)symbols.namesakeCopernicus programmeSentinelsMachine learningRadiative transferFOS: Electrical engineering electronic engineering information engineeringElectrical Engineering and Systems Science - Signal ProcessingComputers in Earth SciencesModel inversionStatistical retrievalEngineering (miscellaneous)Gaussian processChlorophyll contentMoisture021101 geological & geomatics engineering0105 earth and related environmental sciencesbusiness.industryInorganic suspended matterTemperatureInversion (meteorology)Statistical modelAtomic and Molecular Physics and OpticsComputer Science ApplicationsInfrared sounderNonlinear systemsymbolsGlobal Positioning SystemColoured dissolved matterbusinessAlgorithm
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Classical Statistical Mechanics

2003

Some aspects of statistical mechanics that are particularly important for computer simulation approaches are recalled. Using Ising and classical Heisenberg models as examples, various statistical ensembles and appropriate thermodynamic potentials are introduced, and concepts such as Legendre transformations between ensembles and the thermodynamic integration method to obtain the entropy are mentioned. Probability distributions characterizing statistical fluctuations are discussed, fluctuation relations for response functions are derived, and the behavior of these quantities at first and second order phase transitions are described qualitatively. Also the general consequences of phase coexis…

Statistical ensembleEntropy (statistical thermodynamics)Thermodynamic limitStatistical physicsStatistical mechanicsStatistical fluctuationsQuantum statistical mechanicsAnalytical dynamicsThermodynamic potentialMathematics
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Transitions between imperfectly ordered crystalline structures: A phase switch Monte Carlo study

2012

A model for two-dimensional colloids confined laterally by ``structured boundaries'' (i.e., ones that impose a periodicity along the slit) is studied by Monte Carlo simulations. When the distance $D$ between the confining walls is reduced at constant particle number from an initial value ${D}_{0}$, for which a crystalline structure commensurate with the imposed periodicity fits, to smaller values, a succession of phase transitions to imperfectly ordered structures occur. These structures have a reduced number of rows parallel to the boundaries (from $n$ to $n\ensuremath{-}1$ to $n\ensuremath{-}2$, etc.) and are accompanied by an almost periodic strain pattern, due to ``soliton staircases'' …

Statistical ensemblePhase transitionMathematical optimizationStatistical Mechanics (cond-mat.stat-mech)Monte Carlo methodPhase (waves)Thermodynamic integrationFOS: Physical sciencesStatistical mechanicsOrders of magnitude (time)Statistical physicsEnergy (signal processing)Condensed Matter - Statistical MechanicsMathematics
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Statistical Properties of Statistical Ensembles of Stock Returns

1999

We select n stocks traded in the New York Stock Exchange and we form a statistical ensemble of daily stock returns for each of the k trading days of our database from the stock price time series. We analyze each ensemble of stock returns by extracting its first four central moments. We observe that these moments are fluctuating in time and are stochastic processes themselves. We characterize the statistical properties of central moments by investigating their probability density function and temporal correlation properties.

Statistical ensemblePhysics::Physics and SocietyStatistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)Stochastic processFinancial economicsQuantitative Finance - Statistical FinanceFOS: Physical sciencesProbability density functionTemporal correlationStock priceFOS: Economics and businessStock exchangeComputer Science::Computational Engineering Finance and ScienceEconomicsEconometricsGeneral Economics Econometrics and FinanceFinanceStock (geology)Condensed Matter - Statistical Mechanics
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Sub-threshold signal processing in arrays of non-identical nanostructures

2011

Weak input signals are routinely processed by molecular-scaled biological networks composed of non-identical units that operate correctly in a noisy environment. In order to show that artificial nanostructures can mimic this behavior, we explore theoretically noise-assisted signal processing in arrays of metallic nanoparticles functionalized with organic ligands that act as tunneling junctions connecting the nanoparticle to the external electrodes. The electronic transfer through the nanostructure is based on the Coulomb blockade and tunneling effects. Because of the fabrication uncertainties, these nanostructures are expected to show a high variability in their physical characteristics and…

Statistical ensembleSignal processingMaterials scienceMechanical EngineeringThermal fluctuationsCoulomb blockadeSignal Processing Computer-AssistedBioengineeringNanotechnologyElectrochemical TechniquesEquipment DesignGeneral ChemistryNanostructuresModels ChemicalMechanics of MaterialsNanotechnologyGeneral Materials ScienceKinetic Monte CarloElectrical and Electronic EngineeringBiological systemElectrodesParallel arrayElectronic circuitVoltageNanotechnology
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Variety and volatility in financial markets

2000

We study the price dynamics of stocks traded in a financial market by considering the statistical properties both of a single time series and of an ensemble of stocks traded simultaneously. We use the $n$ stocks traded in the New York Stock Exchange to form a statistical ensemble of daily stock returns. For each trading day of our database, we study the ensemble return distribution. We find that a typical ensemble return distribution exists in most of the trading days with the exception of crash and rally days and of the days subsequent to these extreme events. We analyze each ensemble return distribution by extracting its first two central moments. We observe that these moments are fluctua…

Statistical ensembleStatistical Finance (q-fin.ST)Statistical Mechanics (cond-mat.stat-mech)Stochastic processFinancial marketQuantitative Finance - Statistical FinanceFOS: Physical sciencesProbability density functionRelative strengthFOS: Economics and businessStock exchangeEconometricsVolatility (finance)Condensed Matter - Statistical MechanicsStock (geology)MathematicsPhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics
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The distribution of velocities in an ensemble of accelerated particles on a surface

2016

An ensemble of particles diffusing with acceleration on a surface is considered as a 2D billiard system. The process of the finite-time diffusion of particles is studied using the balance equation. The probability distribution functions of the velocity and lifetime of particles are obtained analytically and by means of numerical simulations. A thermodynamic interpretation of the process is discussed. The effective temperature and entropy obey the relationship for an ideal gas.

Statistics and ProbabilityPhysicsIsothermal–isobaric ensembleStatistical and Nonlinear Physics02 engineering and technologyMechanicsEffective temperature021001 nanoscience & nanotechnology01 natural sciencesIdeal gas0103 physical sciencesOpen statistical ensembleBalance equationProbability distributionStatistical physicsStatistics Probability and UncertaintyDynamical billiards010306 general physics0210 nano-technologyJournal of Statistical Mechanics: Theory and Experiment
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Determination of the LEP centre-of-mass energy from Zγ events

1999

Radiative returns to the Z resonance (Zgamma events) are used to determine the LEP2 centre-of-mass energy from the data collected with the ALEPH detector in 1997. The average centre-of-mass energy is measured to be: E_CM = 182.50 +- 0.19(stat) +- 0.08(syst) GeV in good agreement with the precise determination by the LEP energy working group of 182.652 +- 0.050 GeV. If applied to the measurement of the W mass, its precision translates into a systematic error on M_W which is smaller than the statistical error achieved from the corresponding dataset.

Systematic errorPhysicsNuclear and High Energy PhysicsParticle physicsAleph[PHYS.HEXP] Physics [physics]/High Energy Physics - Experiment [hep-ex]010308 nuclear & particles physicsElectron–positron annihilationDetectorFOS: Physical sciences01 natural sciencesResonance (particle physics)High Energy Physics - ExperimentNuclear physicsHigh Energy Physics - Experiment (hep-ex)0103 physical sciences[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Radiative transferStatistical errorHigh Energy Physics::Experiment010306 general physicsParticle Physics - ExperimentEnergy (signal processing)
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A Representation of Relational Systems

2003

In this paper elements of a theory of multistructures are formulated. The theory of multistructures is used to define a binary representation of relational systems.

Theoretical computer scienceRelational calculusRelational databaseCodd's theoremComputer scienceStatistical relational learningRelational modelConjunctive queryDomain relational calculusRelational Model/Tasmania
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