Search results for "NETWORKS"

showing 10 items of 3260 documents

Correlation based networks of equity returns sampled at different time horizons

2006

We investigate the planar maximally filtered graphs of the portfolio of the 300 most capitalized stocks traded at the New York Stock Exchange during the time period 2001-2003. Topological properties such as the average length of shortest paths, the betweenness and the degree are computed on different planar maximally filtered graphs generated by sampling the returns at different time horizons ranging from 5 min up to one trading day. This analysis confirms that the selected stocks compose a hierarchical system progressively structuring as the sampling time horizon increases. Finally, a cluster formation, associated to economic sectors, is quantitatively investigated.

Physics - Physics and Societynetworks of equity different time horizonsStatistical Finance (q-fin.ST)Equity (finance)Quantitative Finance - Statistical FinanceFOS: Physical sciencesRangingPhysics and Society (physics.soc-ph)Condensed Matter PhysicsElectronic Optical and Magnetic MaterialsCorrelationFOS: Economics and businessBetweenness centralityStock exchangePhysics - Data Analysis Statistics and ProbabilityStatisticsHierarchical control systemPortfolioSampling timeData Analysis Statistics and Probability (physics.data-an)Mathematics
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Correlation filtering in financial time series

2005

We apply a method to filter relevant information from the correlation coefficient matrix by extracting a network of relevant interactions. This method succeeds to generate networks with the same hierarchical structure of the Minimum Spanning Tree but containing a larger amount of links resulting in a richer network topology allowing loops and cliques. In Tumminello et al. \cite{TumminielloPNAS05}, we have shown that this method, applied to a financial portfolio of 100 stocks in the USA equity markets, is pretty efficient in filtering relevant information about the clustering of the system and its hierarchical structure both on the whole system and within each cluster. In particular, we have…

Physics - Physics and SocietynetworksFOS: Physical sciencesPhysics and Society (physics.soc-ph)econophysiccomplex system
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Non-Markovian dynamics from band edge effects and static disorder

2017

It was recently shown [S. Lorenzo et al., Sci. Rep. 7, 42729 (2017)] that the presence of static disorder in a bosonic bath - whose normal modes thus become all Anderson-localised - leads to non-Markovianity in the emission of an atom weakly coupled to it (a process which in absence of disorder is fully Markovian). Here, we extend the above analysis beyond the weak-coupling regime for a finite-band bath so as to account for band edge effects. We study the interplay of these with static disorder in the emergence of non-Markovian behaviour in terms of a suitable non-Markovianity measure.

Physics and Astronomy (miscellaneous)Anderson localizactionMarkov processNon-MarkovianityFOS: Physical sciencesEdge (geometry)01 natural sciencesMeasure (mathematics)Static disorderCondensed Matter::Disordered Systems and Neural NetworksSettore FIS/03 - Fisica Della Materia010305 fluids & plasmassymbols.namesakeNormal modeQuantum mechanicsAtom (measure theory)0103 physical sciencesband edge mode010306 general physicsband edge modesPhysicsQuantum PhysicsDynamics (mechanics)disordersymbolsQuantum Physics (quant-ph)Anderson localizaction; band edge modes; disorder; Non-Markovianity; Physics and Astronomy (miscellaneous)
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Study of ordered hadron chains with the ATLAS detector

2017

The analysis of the momentum difference between charged hadrons in high-energy proton-proton collisions is performed in order to study coherent particle production. The observed correlation pattern agrees with a model of a helical QCD string fragmenting into a chain of ground-state hadrons. A threshold momentum difference in the production of adjacent pairs of charged hadrons is observed, in agreement with model predictions. The presence of low-mass hadron chains also explains the emergence of charge-combination-dependent two-particle correlations commonly attributed to Bose-Einstein interference. The data sample consists of 190 μb-1 of minimum-bias events collected with proton-proton colli…

Physics and Astronomy (miscellaneous)Atlas detectorHadronNuclear Theory01 natural sciencesangular correlation [charged particle]High Energy Physics - ExperimentSubatomär fysikHigh Energy Physics - Experiment (hep-ex)correlation: Bose-EinsteinSubatomic Physicsscattering [p p][PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]difference [momentum]Nuclear ExperimentQCQuantum chromodynamicsPhysicsLarge Hadron ColliderAtlas (topology)Settore FIS/01 - Fisica SperimentaleMonte Carlo [numerical calculations]ATLASCERN LHC Coll7000 GeV-cmsComputingMethodologies_DOCUMENTANDTEXTPROCESSINGangular distribution: measuredLHCcolliding beams [p p]numerical calculations: Monte Carlomeasured [angular distribution]Particle Physics - ExperimentCoherence (physics)correlation: two-particleParticle physicsp p: scatteringCiências Naturais::Ciências Físicas530 Physics:Ciências Físicas [Ciências Naturais]ground state [hadron]interferencequantum chromodynamics: stringFOS: Physical sciences530Nuclear physicsNational Graphene InstituteBose-Einstein [correlation][ PHYS.HEXP ] Physics [physics]/High Energy Physics - Experiment [hep-ex]0103 physical sciencesddc:530High Energy Physics010306 general physicstwo-particle [correlation]Ciencias ExactasScience & TechnologyATLAS detector010308 nuclear & particles physicshep-exmomentum: differenceHigh Energy Physics::PhenomenologyFísicacoherencestring [quantum chromodynamics]hadron: ground stateQCD stringResearchInstitutes_Networks_Beacons/national_graphene_instituteExperimental High Energy Physicsproton-proton collisionsHigh Energy Physics::Experimentcharged particle: angular correlationp p: colliding beamsexperimental resultsPhysical Review D
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Fingerprint classification based on deep learning approaches: Experimental findings and comparisons

2021

Biometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs…

Physics and Astronomy (miscellaneous)BiometricsComputer scienceGeneral Mathematicsfingerprint featuresfingerprint classification; deep learning; convolutional neural networks; fingerprint featuresConvolutional neural networks Deep learning Fingerprint classification Fingerprint featuresImage processing02 engineering and technologyConvolutional neural networkField (computer science)fingerprint classification020204 information systemsconvolutional neural networksQA1-9390202 electrical engineering electronic engineering information engineeringComputer Science (miscellaneous)Reliability (statistics)business.industryDeep learningFingerprint (computing)deep learningPattern recognitionIdentification (information)Chemistry (miscellaneous)Convolutional neural networks; Deep learning; Fingerprint classification; Fingerprint features020201 artificial intelligence & image processingArtificial intelligencebusinessMathematics
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Stringlike Cooperative Motion in a Supercooled Liquid

1998

Extensive molecular dynamics simulations are performed on a glass-forming Lennard-Jones mixture to determine the nature of the cooperative motions occurring in this model fragile liquid. We observe stringlike cooperative molecular motion (``strings'') at temperatures well above the glass transition. The mean length of the strings increases upon cooling, and the string length distribution is found to be nearly exponential.

Physics010304 chemical physicsCondensed matter physicsMathematical modelGeneral Physics and AstronomyCondensed Matter::Disordered Systems and Neural Networks01 natural sciences3. Good healthExponential functionCondensed Matter::Soft Condensed MatterMolecular dynamics0103 physical sciencesQuasiparticleRelaxation (physics)Dynamical heterogeneity010306 general physicsGlass transitionSupercoolingPhysical Review Letters
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The phase diagram of the multi-dimensional Anderson localization via analytic determination of Lyapunov exponents

2004

The method proposed by the present authors to deal analytically with the problem of Anderson localization via disorder [J.Phys.: Condens. Matter {\bf 14} (2002) 13777] is generalized for higher spatial dimensions D. In this way the generalized Lyapunov exponents for diagonal correlators of the wave function, $$, can be calculated analytically and exactly. This permits to determine the phase diagram of the system. For all dimensions $D > 2$ one finds intervals in the energy and the disorder where extended and localized states coexist: the metal-insulator transition should thus be interpreted as a first-order transition. The qualitative differences permit to group the systems into two classes…

PhysicsAnderson localizationGroup (mathematics)DiagonalFOS: Physical sciencesLyapunov exponentFunction (mathematics)Disordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksCondensed Matter PhysicsElectronic Optical and Magnetic Materialssymbols.namesakePercolationsymbolsCritical dimensionMathematical physicsPhase diagram
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Spatial multifractal properties of wave packets in the Anderson model of localization.

1993

The multifractal properties of electronic wave functions in disordered samples are investigated. In a given energy range all eigenstates are determined for the same disorder configuration in the Anderson model of localization. It is shown that the singularity spectrum and the generalized dimensions change only slowly with energy, aside from statistical fluctuations. More important, the wave packet constructed by linear combination of the eigenstates shows quantitatively the same multifractal properties. Consequences for the transport properties of electronic states in disordered systems are discussed.

PhysicsAnderson localizationQuantum mechanicsWave packetMultifractal systemElectronic structureStatistical physicsStatistical fluctuationsSingularity spectrumWave functionCondensed Matter::Disordered Systems and Neural NetworksAnderson impurity modelPhysical review. B, Condensed matter
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The relaxation dynamics of a viscous silica melt: II The intermediate scattering functions

2001

We use molecular dynamics computer simulations to study the relaxation dynamics of a viscous melt of silica. The coherent and incoherent intermediate scattering functions, F_d(q,t) and F_s(q,t), show a crossover from a nearly exponential decay at high temperatures to a two-step relaxation at low temperatures. Close to the critical temperature of mode-coupling theory (MCT) the correlators obey in the alpha-regime the time temperature superposition principle (TTSP) and show a weak stretching. We determine the wave-vector dependence of the stretching parameter and find that for F_d(q,t) it shows oscillations which are in phase with the static structure factor. The temperature dependence of the…

PhysicsArrhenius equationCondensed matter physicsStatistical Mechanics (cond-mat.stat-mech)ScatteringThermodynamicsFOS: Physical sciencesDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksFick's laws of diffusionPower lawsymbols.namesakeTime–temperature superpositionsymbolsRelaxation (physics)Exponential decayStructure factorCondensed Matter - Statistical Mechanics
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Evidence against a glass transition in the 10-state short range Potts glass

2002

We present the results of Monte Carlo simulations of two different 10-state Potts glasses with random nearest neighbor interactions on a simple cubic lattice. In the first model the interactions come from a \pm J distribution and in the second model from a Gaussian one, and in both cases the first two moments of the distribution are chosen to be equal to J_0=-1 and Delta J=1. At low temperatures the spin autocorrelation function for the \pm J model relaxes in several steps whereas the one for the Gaussian model shows only one. In both systems the relaxation time increases like an Arrhenius law. Unlike the infinite range model, there are only very weak finite size effects and there is no evi…

PhysicsArrhenius equationStatistical Mechanics (cond-mat.stat-mech)GaussianMonte Carlo methodAutocorrelationFOS: Physical sciencesGeneral Physics and AstronomyDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural Networksk-nearest neighbors algorithmsymbols.namesakesymbolsStatistical physicsGlass transitionGaussian network modelCondensed Matter - Statistical MechanicsSpin-½
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