Search results for "Statistical physics"

showing 10 items of 1402 documents

Transient dynamics of pulse-driven memristors in the presence of a stable fixed point

2019

Abstract Some memristors are quite interesting from the point of view of dynamical systems. When driven by narrow pulses of alternating polarities, their dynamics has a stable fixed point, which may be useful for future applications. We study the transient dynamics of two types of memristors characterized by a stable fixed point using a time-averaged evolution equation. Time-averaged trajectories of the Biolek window function memristor and resistor-threshold type memristor circuit (an effective memristor) are determined analytically, and the times of relaxation to the stable fixed point are found. Our analytical results are in perfect agreement with the results of numerical simulations.

FOS: Computer and information sciencesDynamical systems theoryFOS: Physical sciencesComputer Science - Emerging TechnologiesMemristorFixed point01 natural sciencesWindow function010305 fluids & plasmaslaw.inventionMemristive systemComputer Science::Hardware ArchitectureComputer Science::Emerging TechnologieslawStablefixed pointMesoscale and Nanoscale Physics (cond-mat.mes-hall)0103 physical sciencesAttractorStatistical physics010306 general physicsPhysicsCondensed Matter - Mesoscale and Nanoscale PhysicsAttractorMemristorResistance switching memoryCondensed Matter PhysicsAtomic and Molecular Physics and OpticsElectronic Optical and Magnetic MaterialsPulse (physics)Emerging Technologies (cs.ET)Relaxation (physics)Transient (oscillation)Physica E-Low-Dimensional Systems & Nanostructures
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Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

2017

Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by prevalently redundant or sy…

FOS: Computer and information sciencesInformation transferComputer scienceGaussianSocial SciencesGeneral Physics and AstronomyInformation theory01 natural sciences010305 fluids & plasmasState spaceStatistical physicslcsh:Scienceinformation theorymultiscale entropylcsh:QC1-999Interaction informationMathematics and Statisticssymbolsinformation dynamicsInformation dynamics; Information transfer; Multiscale entropy; Multivariate time series analysis; Redundancy and synergy; State space models; Vector autoregressive models; Physics and Astronomy (all)information dynamics; information transfer; multiscale entropy; multivariate time series analysis; redundancy and synergy; state space models; vector autoregressive modelsMultivariate time series analysiMathematics - Statistics Theorylcsh:AstrophysicsStatistics Theory (math.ST)Statistics - ApplicationsMethodology (stat.ME)symbols.namesakePhysics and Astronomy (all)0103 physical scienceslcsh:QB460-466FOS: Mathematicsinformation transferRelevance (information retrieval)Applications (stat.AP)Transfer Entropy010306 general physicsGaussian processStatistics - MethodologyState space modelstate space modelsmultivariate time series analysisredundancy and synergyvector autoregressive modelsInformation dynamicVector autoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaTransfer entropylcsh:Qlcsh:PhysicsEntropy
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Multiscale analysis of information dynamics for linear multivariate processes.

2016

In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving aver…

FOS: Computer and information sciencesInformation transferMultivariate statisticsMultivariate analysisComputer scienceComputer Science - Information Theory0206 medical engineeringStochastic ProcesseBiomedical EngineeringFOS: Physical sciencesInformation Storage and RetrievalHealth Informatics02 engineering and technology01 natural sciencesEntropy (classical thermodynamics)Moving average0103 physical sciencesEntropy (information theory)Computer SimulationStatistical physicsEntropy (energy dispersal)Time series010306 general physicsEntropy (arrow of time)Multivariate Analysi1707Stochastic ProcessesEntropy (statistical thermodynamics)Stochastic processInformation Theory (cs.IT)Probability and statisticsModels Theoretical020601 biomedical engineeringComplex dynamicsAutoregressive modelPhysics - Data Analysis Statistics and ProbabilitySignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaMultivariate AnalysisData Analysis Statistics and Probability (physics.data-an)Entropy (order and disorder)Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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Epidemic spreading and aging in temporal networks with memory

2018

Time-varying network topologies can deeply influence dynamical processes mediated by them. Memory effects in the pattern of interactions among individuals are also known to affect how diffusive and spreading phenomena take place. In this paper we analyze the combined effect of these two ingredients on epidemic dynamics on networks. We study the susceptible-infected-susceptible (SIS) and the susceptible-infected-removed (SIR) models on the recently introduced activity-driven networks with memory. By means of an activity-based mean-field approach we derive, in the long time limit, analytical predictions for the epidemic threshold as a function of the parameters describing the distribution of …

FOS: Computer and information sciencesPhysics - Physics and SocietyComputer scienceAnalytical predictionsEpidemic dynamicsFOS: Physical sciencesPhysics and Society (physics.soc-ph)Network topology01 natural sciences010305 fluids & plasmasNetworks and Complex Systems0103 physical sciencesQuantitative Biology::Populations and EvolutionStatistical physicsLimit (mathematics)010306 general physicsQuantitative Biology - Populations and EvolutionEpidemic controlSocial and Information Networks (cs.SI)Populations and Evolution (q-bio.PE)Computer Science - Social and Information NetworksFunction (mathematics)Computer Science::Social and Information NetworksArticlesDynamic modelsEpidemic thresholdEpidemic spreadingFOS: Biological sciencesMean field approachPhysical Review. E
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Classical and Quantum Annealing in the Median of Three Satisfiability

2011

We determine the classical and quantum complexities of a specific ensemble of three-satisfiability problems with a unique satisfying assignment for up to N = 100 and 80 variables, respectively. In the classical limit, we employ generalized ensemble techniques and measure the time that a Markovian Monte Carlo process spends in searching classical ground states. In the quantum limit, we determine the maximum finite correlation length along a quantum adiabatic trajectory determined by the linear sweep of the adiabatic control parameter in the Hamiltonian composed of the problem Hamiltonian and the constant transverse field Hamiltonian. In the median of our ensemble, both complexities diverge e…

FOS: Computer and information sciencesPolynomialComputational complexity theoryQuantum dynamicsFOS: Physical sciencesComputational Complexity (cs.CC)Classical limitClassical capacityQuantum mechanicsddc:530Statistical physicsALGORITHMAmplitude damping channelQuantumQuantum fluctuationCondensed Matter - Statistical MechanicsMathematicsPhysicsQuantum PhysicsStatistical Mechanics (cond-mat.stat-mech)Stochastic processQuantum annealingAdiabatic quantum computationAtomic and Molecular Physics and OpticsSatisfiabilityJComputer Science - Computational ComplexityComputerSystemsOrganization_MISCELLANEOUSQuantum algorithmPHASE-TRANSITIONSQuantum dissipationQuantum Physics (quant-ph)
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Simulation-based marginal likelihood for cluster strong lensing cosmology

2015

Comparisons between observed and predicted strong lensing properties of galaxy clusters have been routinely used to claim either tension or consistency with $\Lambda$CDM cosmology. However, standard approaches to such cosmological tests are unable to quantify the preference for one cosmology over another. We advocate approximating the relevant Bayes factor using a marginal likelihood that is based on the following summary statistic: the posterior probability distribution function for the parameters of the scaling relation between Einstein radii and cluster mass, $\alpha$ and $\beta$. We demonstrate, for the first time, a method of estimating the marginal likelihood using the X-ray selected …

FOS: Computer and information sciencesSTATISTICAL [METHODS]Cold dark matterCosmology and Nongalactic Astrophysics (astro-ph.CO)NUMERICAL [METHODS]Ciencias FísicasPosterior probabilityFOS: Physical sciencesAstrophysics::Cosmology and Extragalactic Astrophysics01 natural sciencesStatistics - ApplicationsCosmologymethods: numerical//purl.org/becyt/ford/1 [https]cosmology: theory0103 physical sciencesCluster (physics)Applications (stat.AP)Statistical physics010303 astronomy & astrophysicsInstrumentation and Methods for Astrophysics (astro-ph.IM)Galaxy clusterPhysicsmethods: statisticalgravitational lensing: strong; methods: numerical; methods: statistical; galaxies: clusters: general; cosmology: theory010308 nuclear & particles physicsgravitational lensing: strongAstronomy and AstrophysicsBayes factor//purl.org/becyt/ford/1.3 [https]STRONG [GRAVITATIONAL LENSING]RedshiftMarginal likelihoodAstronomíaTHEORY [COSMOLOGY]Space and Planetary Sciencegalaxies: clusters: generalPhysics - Data Analysis Statistics and ProbabilityCLUSTERS: GENERAL [GALAXIES]Astrophysics - Instrumentation and Methods for AstrophysicsData Analysis Statistics and Probability (physics.data-an)CIENCIAS NATURALES Y EXACTASAstrophysics - Cosmology and Nongalactic Astrophysics
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Microstructure reconstruction using entropic descriptors

2009

A multi-scale approach to the inverse reconstruction of a pattern's microstructure is reported. Instead of a correlation function, a pair of entropic descriptors (EDs) is proposed for stochastic optimization method. The first of them measures a spatial inhomogeneity, for a binary pattern, or compositional one, for a greyscale image. The second one quantifies a spatial or compositional statistical complexity. The EDs reveal structural information that is dissimilar, at least in part, to that given by correlation functions at almost all of discrete length scales. The method is tested on a few digitized binary and greyscale images. In each of the cases, the persuasive reconstruction of the mic…

FOS: Computer and information sciencesStatistical Mechanics (cond-mat.stat-mech)General MathematicsComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionGeneral EngineeringGeneral Physics and AstronomyBinary numberInverseFOS: Physical sciencesBinary patternGrayscaleImage (mathematics)CorrelationCorrelation function (statistical mechanics)Computer Science::Computer Vision and Pattern RecognitionStochastic optimizationStatistical physicsCondensed Matter - Statistical MechanicsMathematics
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Conditional particle filters with diffuse initial distributions

2020

Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which are common in statistical applications. We propose a simple but generally applicable auxiliary variable method, which can be used together with the CPF in order to perform efficient inference with diffuse initial distributions. The method only requires simulatable Markov transitions that are reversible with respect to the initial distribution, which can be improper. We focus in particular on random-walk type transitions which are reversible with respect to a uniform init…

FOS: Computer and information sciencesStatistics and ProbabilityComputer scienceGaussianBayesian inferenceMarkovin ketjut02 engineering and technology01 natural sciencesStatistics - ComputationArticleTheoretical Computer ScienceMethodology (stat.ME)010104 statistics & probabilitysymbols.namesakeAdaptive Markov chain Monte Carlotilastotiede0202 electrical engineering electronic engineering information engineeringStatistical physics0101 mathematicsDiffuse initialisationHidden Markov modelComputation (stat.CO)Statistics - MethodologyState space modelHidden Markov modelbayesian inferenceMarkov chaindiffuse initialisationbayesilainen menetelmäconditional particle filtersmoothingmatemaattiset menetelmät020206 networking & telecommunicationsConditional particle filterCovariancecompartment modelRandom walkCompartment modelstate space modelComputational Theory and MathematicsAutoregressive modelsymbolsStatistics Probability and UncertaintyParticle filterSmoothingSmoothing
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Fractal surfaces from simple arithmetic operations

2015

Fractal surfaces ('patchwork quilts') are shown to arise under most general circumstances involving simple bitwise operations between real numbers. A theory is presented for all deterministic bitwise operations on a finite alphabet. It is shown that these models give rise to a roughness exponent $H$ that shapes the resulting spatial patterns, larger values of the exponent leading to coarser surfaces.

FOS: Computer and information sciencesStatistics and ProbabilityDiscrete mathematicsOther Computer Science (cs.OH)Condensed Matter Physics01 natural sciences010305 fluids & plasmasSelf-affinityFractalSimple (abstract algebra)Computer Science - Other Computer Science0103 physical sciencesRoughness exponentExponentStatistical physicsAlphabet010306 general physicsBitwise operationReal numberMathematics
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Coherent Quantum Tomography

2016

We discuss a quantum mechanical indirect measurement method to recover a position dependent Hamilton matrix from time evolution of coherent quantum mechanical states through an object. A mathematical formulation of this inverse problem leads to weighted X-ray transforms where the weight is a matrix. We show that such X-ray transforms are injective with very rough weights. Consequently, we can solve our quantum mechanical inverse problem in several settings, but many physically relevant problems we pose also remain open. We discuss the physical background of the proposed imaging method in detail. We give a rigorous mathematical treatment of a neutrino tomography method that has been previous…

FOS: Physical sciences01 natural sciencesMatrix (mathematics)neutrino physics0103 physical sciencesClassical Analysis and ODEs (math.CA)FOS: MathematicsStatistical physics0101 mathematics010306 general physicsQuantumMathematical PhysicsMathematicsQuantum Physicsinverse problemsgeophysicsApplied Mathematicsta111quantum mechanics010102 general mathematicsMathematical analysisTime evolutionweighted ray transformsMathematical Physics (math-ph)81Q99 81V99 86A22 44A12Inverse problemQuantum tomographyInjective functionComputational MathematicsMathematics - Classical Analysis and ODEsTomographyNeutrinoQuantum Physics (quant-ph)AnalysisSIAM Journal on Mathematical Analysis
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