2025
Proposal for many-body quantum chaos detection
Das A, Cianci C, Cabral D, Zarate-Herrada D, Pinney P, Pilatowsky-Cameo S, Matsoukas-Roubeas A, Batista V, del Campo A, Torres-Herrera E, Santos L. Proposal for many-body quantum chaos detection. Physical Review Research 2025, 7: 013181. DOI: 10.1103/physrevresearch.7.013181.Peer-Reviewed Original ResearchSpectral form factorCorrelation holeForm factorsQuantum chaosTwo-point spectral correlation functionAnalysis of level statisticsMany-body quantum systemsSpin autocorrelation functionQuantum systemsRandom matrix theoryQuench dynamicsQuantum computationCorrelation functionLevel statisticsSpectral correlation functionPhysical quantitiesMatrix theoryChaos detectionHolesAutocorrelation functionSpectral correlationSurvival probabilityChaosQuantumSpin
2022
Expectations for Horizon-Scale Supermassive Black Hole Population Studies with the ngEHT
Pesce D, Palumbo D, Ricarte A, Broderick A, Johnson M, Nagar N, Natarajan P, Gómez J. Expectations for Horizon-Scale Supermassive Black Hole Population Studies with the ngEHT. Galaxies 2022, 10: 109. DOI: 10.3390/galaxies10060109.Peer-Reviewed Original ResearchSupermassive black holesBlack hole massBlack holesEvent Horizon Telescope collaborationHole massEvent Horizon TelescopeBlack hole spinBlack hole shadowSMBH accretionEmission structurePrior theoretical studiesHole spinPhysical quantitiesSimple geometric modelPhysical parametersGeometric modelEntire skySource sizeTheoretical studyFlux densityHolesTelescopeSpinParametersEstimatesReconsider phase reconstruction in signals with dynamic periodicity from the modern signal processing perspective
Alian A, Lo Y, Shelley K, Wu H. Reconsider phase reconstruction in signals with dynamic periodicity from the modern signal processing perspective. Foundations Of Data Science 2022, 4: 355-393. DOI: 10.3934/fods.2022010.Peer-Reviewed Original ResearchAdaptive non-harmonic modelVector-valued functionsFundamental physical quantitiesSignal processing perspectiveFunction modelMultiple oscillatory componentsNon-sinusoidal oscillationTime-varying frequencyPhysical quantitiesSame physiological systemsOscillatory time seriesFilter schemeModern signalOscillatory componentsReal-world databasesBiomedical signalsUnified modelFundamental phasesPhase reconstructionTime seriesAnalysis toolsTheoretical supportTime-frequency analysis toolModelProcessing perspective
2017
Reconstruction of normal forms by learning informed observation geometries from data
Yair O, Talmon R, Coifman RR, Kevrekidis IG. Reconstruction of normal forms by learning informed observation geometries from data. Proceedings Of The National Academy Of Sciences Of The United States Of America 2017, 114: e7865-e7874. PMID: 28831006, PMCID: PMC5617245, DOI: 10.1073/pnas.1620045114.Peer-Reviewed Original ResearchNormal formNonlinear differential equationsDynamical systems theoryAppropriate normal formFundamental physical quantitiesDifferential equationsDynamical regimesState variablesPhysical quantitiesPhysical lawsSystems theoryGeometry learningEmpirical observationsObservation geometryHeart of scienceDynamicsPrior knowledgeEquationsRealizationLawParametersGeometryTheoryExplicit referenceForm
2009
Nanoforce and Imaging
Le Grimellec C, Milhiet P, Perez E, Pincet F, Aimé J, Emiliani V, Thoumine O, Lionnet T, Croquette V, Allemand J, Bensimon D. Nanoforce and Imaging. 2009, 375-475. DOI: 10.1007/978-3-540-88633-4_8.Peer-Reviewed Original ResearchScanning tunneling microscopeDevelopment of nanobiotechnologyLocal probe microscopyIntermolecular interaction forcesInteraction forcesTunneling microscopeSubnanometric resolutionProbe microscopyAtomic resolutionElectron transferPhoton transferSample surfaceCrystal samplesMolecular scaleSingle moleculesSurface propertiesNanometric orderBiological samplesPhysical quantitiesPhysiological environmentComplex biological structuresStructural biologyEukaryotic cellsAFMPlasma membrane
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