Search results for "equation"

showing 10 items of 4219 documents

Unbiased Estimators and Multilevel Monte Carlo

2018

Multilevel Monte Carlo (MLMC) and unbiased estimators recently proposed by McLeish (Monte Carlo Methods Appl., 2011) and Rhee and Glynn (Oper. Res., 2015) are closely related. This connection is elaborated by presenting a new general class of unbiased estimators, which admits previous debiasing schemes as special cases. New lower variance estimators are proposed, which are stratified versions of earlier unbiased schemes. Under general conditions, essentially when MLMC admits the canonical square root Monte Carlo error rate, the proposed new schemes are shown to be asymptotically as efficient as MLMC, both in terms of variance and cost. The experiments demonstrate that the variance reduction…

FOS: Computer and information sciencesMonte Carlo methodWord error rate010103 numerical & computational mathematicsstochastic differential equationManagement Science and Operations ResearchStatistics - Computation01 natural sciences010104 statistics & probabilityStochastic differential equationstratificationSquare rootFOS: MathematicsApplied mathematics0101 mathematicsComputation (stat.CO)stokastiset prosessitMathematicsProbability (math.PR)ta111EstimatorVariance (accounting)unbiased estimatorsComputer Science ApplicationsMonte Carlo -menetelmät65C05 (Primary) 65C30 (Secondary)efficiencykerrostuneisuusVariance reductionunbiasemultilevel Monte CarlodifferentiaaliyhtälötMathematics - ProbabilityOperations Research
researchProduct

Probabilistic Memristive Networks: Application of a Master Equation to Networks of Binary ReRAM cells

2020

Abstract The possibility of using non-deterministic circuit components has been gaining significant attention in recent years. The modeling and simulation of their circuits require novel approaches, as now the state of a circuit at an arbitrary moment in time cannot be predicted deterministically. Generally, these circuits should be described in terms of probabilities, the circuit variables should be calculated on average, and correlation functions should be used to explore interrelations among the variables. In this paper, we use, for the first time, a master equation to analyze the networks composed of probabilistic binary memristors. Analytical solutions of the master equation for the ca…

FOS: Computer and information sciencesProbabilistic computingComputer scienceGeneral MathematicsGeneral Physics and AstronomyBinary numberFOS: Physical sciencesComputer Science - Emerging TechnologiesMemristorTopologylaw.inventionModeling and simulationComputer Science::Hardware ArchitectureComputer Science::Emerging TechnologieslawMaster equationMesoscale and Nanoscale Physics (cond-mat.mes-hall)Probabilistic logicElectronic circuitCondensed Matter - Materials ScienceCondensed Matter - Mesoscale and Nanoscale PhysicsApplied MathematicsProbabilistic logicMaterials Science (cond-mat.mtrl-sci)Statistical and Nonlinear PhysicsMoment (mathematics)Emerging Technologies (cs.ET)State (computer science)NetworksMemristors
researchProduct

Quadratic speedup for finding marked vertices by quantum walks

2020

A quantum walk algorithm can detect the presence of a marked vertex on a graph quadratically faster than the corresponding random walk algorithm (Szegedy, FOCS 2004). However, quantum algorithms that actually find a marked element quadratically faster than a classical random walk were only known for the special case when the marked set consists of just a single vertex, or in the case of some specific graphs. We present a new quantum algorithm for finding a marked vertex in any graph, with any set of marked vertices, that is (up to a log factor) quadratically faster than the corresponding classical random walk.

FOS: Computer and information sciencesQuadratic growthQuantum PhysicsQuantum algorithmsSpeedupMarkov chainMarkov chainsProbability (math.PR)FOS: Physical sciencesRandom walkVertex (geometry)CombinatoricsQuadratic equationSearch by random walkQuantum searchComputer Science - Data Structures and AlgorithmsFOS: MathematicsData Structures and Algorithms (cs.DS)Quantum walkQuantum algorithmQuantum Physics (quant-ph)Mathematics - ProbabilityMathematicsQuantum walks
researchProduct

A novel exact representation of stationary colored Gaussian processes (fractional differential approach)

2010

A novel representation of functions, called generalized Taylor form, is applied to the filtering of white noise processes. It is shown that every Gaussian colored noise can be expressed as the output of a set of linear fractional stochastic differential equations whose solution is a weighted sum of fractional Brownian motions. The exact form of the weighting coefficients is given and it is shown that it is related to the fractional moments of the target spectral density of the colored noise.

FOS: Computer and information sciencesStatistics and ProbabilityDifferential equationFOS: Physical sciencesGeneral Physics and AstronomyStatistics - ComputationStochastic differential equationsymbols.namesakeSpectral MomentsApplied mathematicsStationary processeGaussian processCondensed Matter - Statistical MechanicsComputation (stat.CO)Mathematical PhysicsMathematicsGeneralized functionStatistical Mechanics (cond-mat.stat-mech)Statistical and Nonlinear PhysicsMathematical Physics (math-ph)White noiseClosed and exact differential formsColors of noiseGaussian noiseFractional CalculuModeling and SimulationsymbolsSettore ICAR/08 - Scienza Delle Costruzioni
researchProduct

Nowcasting COVID‐19 incidence indicators during the Italian first outbreak

2020

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replica…

FOS: Computer and information sciencesStatistics and ProbabilityNowcastingEpidemiologyComputer scienceCOVID-19 growth curves Richards’ equation SARS-CoV-2COVID-19; growth curves; Richards' equation; SARS-CoV-2; Disease Outbreaks; Humans; Incidence; Italy; SARS-CoV-2; COVID-19growth curvesStatistics - Applications01 natural sciencesSARS‐CoV‐2Disease Outbreaks010104 statistics & probability03 medical and health sciences0302 clinical medicineCOVID‐19StatisticsHumansApplications (stat.AP)030212 general & internal medicine0101 mathematicsResearch ArticlesParametric statisticsrichards' equationExternal variableDisease OutbreakSARS-CoV-2Estimation theorycovid-19; richards' equation; sars-cov-2; growth curvesIncidenceIncidence (epidemiology)COVID-19OutbreakRegression analysisReplicatesars-cov-2Richards' equationItalycovid-19Settore SECS-S/01Settore SECS-S/01 - StatisticaResearch Articlegrowth curveHuman
researchProduct

Dirac equation as a quantum walk over the honeycomb and triangular lattices

2018

A discrete-time Quantum Walk (QW) is essentially an operator driving the evolution of a single particle on the lattice, through local unitaries. Some QWs admit a continuum limit, leading to well-known physics partial differential equations, such as the Dirac equation. We show that these simulation results need not rely on the grid: the Dirac equation in $(2+1)$--dimensions can also be simulated, through local unitaries, on the honeycomb or the triangular lattice. The former is of interest in the study of graphene-like materials. The latter, we argue, opens the door for a generalization of the Dirac equation to arbitrary discrete surfaces.

FOS: Computer and information sciences[ INFO ] Computer Science [cs]Differential equationFOS: Physical sciencestriangulation01 natural sciences010305 fluids & plasmassymbols.namesakeHigh Energy Physics - Lattice[PHYS.QPHY]Physics [physics]/Quantum Physics [quant-ph]Lattice (order)Mesoscale and Nanoscale Physics (cond-mat.mes-hall)0103 physical sciences[ PHYS.PHYS.PHYS-GEN-PH ] Physics [physics]/Physics [physics]/General Physics [physics.gen-ph]unitaritysurface[INFO]Computer Science [cs]Quantum walkHexagonal latticeDirac equationcontinuum limit010306 general physicsQuantumComputingMilieux_MISCELLANEOUSlatticeMathematical physicsPhysicsQuantum PhysicsPartial differential equationCondensed Matter - Mesoscale and Nanoscale PhysicsUnitarity[PHYS.HLAT]Physics [physics]/High Energy Physics - Lattice [hep-lat]High Energy Physics - Lattice (hep-lat)[ PHYS.HLAT ] Physics [physics]/High Energy Physics - Lattice [hep-lat]differential equations[PHYS.PHYS.PHYS-GEN-PH]Physics [physics]/Physics [physics]/General Physics [physics.gen-ph]Computer Science - Distributed Parallel and Cluster ComputingDirac equationsymbolsDistributed Parallel and Cluster Computing (cs.DC)Quantum Physics (quant-ph)Physical Review A
researchProduct

Study design in causal models

2012

The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing data mechanism together with the causal structure and allow the direct application of causal calculus in the estimation of the causal effects. The flow of the study is visualized by ordering the nodes of the causal diagram in two dimensions by their causal order and the time of the observation. Conclusions whether a causal or observational relationship can be estimated from the collect…

FOS: Computer and information sciencesdesignstructural equation modelG.362A01 62-09 62F99 62D05 62P10 62K99 68T30graphical modelMachine Learning (stat.ML)G.2.2Statistics - ApplicationsG.3; G.2.2Methodology (stat.ME)missing dataStatistics - Machine LearningkausaliteettiApplications (stat.AP)epidemiologiaStatistics - Methodology
researchProduct

Nonlinear quantum Langevin equations for bosonic modes in solid-state systems

2017

Based on the experimental evidence that impurities contribute to the dissipation properties of solid-state open quantum systems, we provide here a description in terms of nonlinear quantum Langevin equations of the role played by two-level systems in the dynamics of a bosonic degree of freedom. Our starting point is represented by the description of the system/environment coupling in terms of coupling to two separate reservoirs, modelling the interaction with external bosonic modes and two level systems, respectively. Furthermore, we show how this model represents a specific example of a class of open quantum systems that can be described by nonlinear quantum Langevin equations. Our analysi…

FOS: Physical sciences02 engineering and technology01 natural sciencesOpen quantum systemQuantum mechanics0103 physical sciencesMesoscale and Nanoscale Physics (cond-mat.mes-hall)Point (geometry)010306 general physicsQuantumOptomechanicsParametric statisticsPhysicsQuantum PhysicsCondensed Matter - Mesoscale and Nanoscale Physicsta114Dissipation021001 nanoscience & nanotechnologyNonlinear systemCoupling (physics)solid-state systemsClassical mechanics0210 nano-technologyQuantum Physics (quant-ph)Langevin equationsPhysics - OpticsOptics (physics.optics)Physical Review A
researchProduct

Acoustic Su-Schrieffer-Heeger lattice: Direct mapping of acoustic waveguides to the Su-Schrieffer-Heeger model

2021

Topological physics strongly relies on prototypical lattice model with particular symmetries. We report here on a theoretical and experimental work on acoustic waveguides that is directly mapped to the one-dimensional Su-Schrieffer-Heeger chiral model. Starting from the continuous two dimensional wave equation we use a combination of monomadal approximation and the condition of equal length tube segments to arrive at the wanted discrete equations. It is shown that open or closed boundary conditions topological leads automatically to the existence of edge modes. We illustrate by graphical construction how the edge modes appear naturally owing to a quarter-wavelength condition and the conserv…

FOS: Physical sciences02 engineering and technologyPhysics - Classical PhysicsEdge (geometry)[SPI.MAT] Engineering Sciences [physics]/Materials01 natural sciences[PHYS] Physics [physics][SPI.MAT]Engineering Sciences [physics]/Materials[SPI]Engineering Sciences [physics]Simple (abstract algebra)Robustness (computer science)0103 physical sciencesMesoscale and Nanoscale Physics (cond-mat.mes-hall)Boundary value problem010306 general physicsElectronic band structurePhysics[PHYS]Physics [physics]Condensed Matter - Mesoscale and Nanoscale PhysicsClassical Physics (physics.class-ph)021001 nanoscience & nanotechnologyWave equationstatesLattice (module)Classical mechanicsHomogeneous space0210 nano-technology
researchProduct

Time-dependent transport in Aharonov–Bohm interferometers

2010

A numerical approach is employed to explain transport characteristics in realistic, quantum Hall based Aharonov-Bohm interferometers. First, the spatial distribution of incompressible strips, and thus the current channels, are obtained applying a self-consistent Thomas-Fermi method to a realistic heterostructure under quantized Hall conditions. Second, the time-dependent Schr\"odinger equation is solved for electrons injected in the current channels. Distinctive Aharonov-Bohm oscillations are found as a function of the magnetic flux. The oscillation amplitude strongly depends on the mutual distance between the transport channels and on their width. At an optimal distance the amplitude and t…

FOS: Physical sciencesGeneral Physics and AstronomyFlux02 engineering and technologySTRIPSElectronQuantum Hall effect01 natural sciencesSchrödinger equationlaw.inventionCondensed Matter - Strongly Correlated Electronssymbols.namesakelawMesoscale and Nanoscale Physics (cond-mat.mes-hall)0103 physical sciences010306 general physicsPhysicsStrongly Correlated Electrons (cond-mat.str-el)Condensed Matter - Mesoscale and Nanoscale PhysicsCondensed Matter::Mesoscopic Systems and Quantum Hall Effect021001 nanoscience & nanotechnologyMagnetic fluxMagnetic fieldAmplitudeQuantum electrodynamicssymbols0210 nano-technologyNew Journal of Physics
researchProduct