Search results for "Computer Science::Machine Learning"

showing 10 items of 32 documents

Nonlinear Pulse Shaping in Optical Fibres with a Neural Network

2020

We use a supervised machine-learning model based on a neural network to solve the direct and inverse problems relating to the shaping of optical pulses that occurs upon nonlinear propagation in optical fibres.

Computer Science::Machine Learning[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics]Optical fiberArtificial neural networkComputer science02 engineering and technologyInverse problem01 natural sciencesPulse shapinglaw.invention010309 opticsNonlinear system020210 optoelectronics & photonicslaw0103 physical sciences0202 electrical engineering electronic engineering information engineeringElectronic engineeringComputingMilieux_MISCELLANEOUS
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Average Performance Analysis of the Stochastic Gradient Method for Online PCA

2019

International audience; This paper studies the complexity of the stochastic gradient algorithm for PCA when the data are observed in a streaming setting. We also propose an online approach for selecting the learning rate. Simulation experiments confirm the practical relevance of the plain stochastic gradient approach and that drastic improvements can be achieved by learning the learning rate.

Computer Science::Machine Learning[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]Computer science0502 economics and business05 social sciencesMathematicsofComputing_NUMERICALANALYSISRelevance (information retrieval)050207 economics010501 environmental sciencesStochastic gradient method01 natural sciencesAlgorithm0105 earth and related environmental sciences
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On the duality between mechanistic learners and what it is they learn

1993

All previous work in inductive inference and theoretical machine learning has taken the perspective of looking for a learning algorithm that successfully learns a collection of functions. In this work, we consider the perspective of starting with a set of functions, and considering the collection of learning algorithms that are successful at learning the given functions. Some strong dualities are revealed.

Computer Science::Machine Learningbusiness.industryPerspective (graphical)Duality (mathematics)Multi-task learningInductive reasoningMachine learningcomputer.software_genreRecursive functionsStrong dualityArtificial intelligenceSet (psychology)businesscomputerMathematics
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Quantum autoencoders via quantum adders with genetic algorithms

2017

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between quantum autoencoders and quantum adders, which approximately add two unknown quantum states supported in different quantum systems. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoe…

FOS: Computer and information sciencesComputer Science::Machine Learning0301 basic medicineComputer Science - Machine LearningAdderPhysics and Astronomy (miscellaneous)Quantum machine learningField (physics)Computer scienceMaterials Science (miscellaneous)Computer Science::Neural and Evolutionary ComputationFOS: Physical sciencesData_CODINGANDINFORMATIONTHEORYTopology01 natural sciencesMachine Learning (cs.LG)Statistics::Machine Learning03 medical and health sciencesQuantum state0103 physical sciencesNeural and Evolutionary Computing (cs.NE)Electrical and Electronic Engineering010306 general physicsQuantumQuantum PhysicsArtificial neural networkComputer Science - Neural and Evolutionary ComputingTheoryofComputation_GENERALAutoencoderAtomic and Molecular Physics and OpticsQuantum technology030104 developmental biologyComputerSystemsOrganization_MISCELLANEOUSQuantum Physics (quant-ph)
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Probabilistic and team PFIN-type learning: General properties

2008

We consider the probability hierarchy for Popperian FINite learning and study the general properties of this hierarchy. We prove that the probability hierarchy is decidable, i.e. there exists an algorithm that receives p_1 and p_2 and answers whether PFIN-type learning with the probability of success p_1 is equivalent to PFIN-type learning with the probability of success p_2. To prove our result, we analyze the topological structure of the probability hierarchy. We prove that it is well-ordered in descending ordering and order-equivalent to ordinal epsilon_0. This shows that the structure of the hierarchy is very complicated. Using similar methods, we also prove that, for PFIN-type learning…

FOS: Computer and information sciencesComputer Science::Machine LearningTheoretical computer scienceComputer Networks and CommunicationsExistential quantificationStructure (category theory)DecidabilityType (model theory)Learning in the limitTheoretical Computer ScienceMachine Learning (cs.LG)Probability of successFinite limitsMathematicsOrdinalsDiscrete mathematicsHierarchybusiness.industryApplied MathematicsAlgorithmic learning theoryProbabilistic logicF.1.1 I.2.6Inductive inferenceInductive reasoningDecidabilityComputer Science - LearningTeam learningComputational Theory and MathematicsArtificial intelligencebusinessJournal of Computer and System Sciences
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The dyon charge in noncommutative gauge theories

2007

We present an explicit classical dyon solution for the noncommutative version of the Yang-Mills-Higgs model (in the Prasad-Sommerfield limit) with a tehta term. We show that the relation between classical electric and magnetic charges also holds in noncommutative space. Extending the Noether approach to the case of a noncommutative gauge theory, we analyze the effect of CP violation at the quantum level, induced both by the theta term and by noncommutativity and we prove that the Witten effect formula for the dyon charge remains the same as in ordinary space.

High Energy Physics - TheoryComputer Science::Machine LearningCiencias FísicasGeneral Physics and AstronomyFOS: Physical sciencesSpace (mathematics)Computer Science::Digital LibrariesStatistics::Machine Learningsymbols.namesakeGeneral Relativity and Quantum CosmologyHigh Energy Physics::TheoryMathematics::Quantum AlgebraGauge theoryLimit (mathematics)Ciencias ExactasMathematical physicsPhysicsnoncommutative gauge theoryMathematics::Operator AlgebrasHigh Energy Physics::PhenomenologyFísicaCharge (physics)Noncommutative geometryDyonHigh Energy Physics - Theory (hep-th)Computer Science::Mathematical SoftwaresymbolsCP violationNoether's theorem
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Conformal equivalence of visual metrics in pseudoconvex domains

2017

We refine estimates introduced by Balogh and Bonk, to show that the boundary extensions of isometries between smooth strongly pseudoconvex domains in $\C^n$ are conformal with respect to the sub-Riemannian metric induced by the Levi form. As a corollary we obtain an alternative proof of a result of Fefferman on smooth extensions of biholomorphic mappings between pseudoconvex domains. The proofs are inspired by Mostow's proof of his rigidity theorem and are based on the asymptotic hyperbolic character of the Kobayashi or Bergman metrics and on the Bonk-Schramm hyperbolic fillings.

Mathematics - Differential GeometryComputer Science::Machine LearningPure mathematicsGeneral Mathematics32T15 32Q45 32H40 53C23 53C17Rigidity (psychology)Conformal mapMathematical proofComputer Science::Digital Libraries01 natural sciencesdifferentiaaligeometriaStatistics::Machine LearningCorollaryMathematics - Metric Geometry0103 physical sciencesFOS: MathematicsMathematics::Metric GeometryComplex Variables (math.CV)0101 mathematicsEquivalence (formal languages)kompleksifunktiotMathematicsMathematics - Complex VariablesMathematics::Complex Variables010102 general mathematicsMetric Geometry (math.MG)16. Peace & justiceDifferential Geometry (math.DG)Bounded functionComputer Science::Mathematical Software010307 mathematical physicsMathematische Annalen
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First observation of a baryonic Bc+ decay

2014

A baryonic decay of the $B_c^+$ meson, $B_c^+\to J/\psi p\overline{p}\pi^+$, is observed for the first time, with a significance of $7.3$ standard deviations, in $pp$ collision data collected with the LHCb detector and corresponding to an integrated luminosity of $3.0$ fb$^{-1}$ taken at center-of-mass energies of $7$ and $8$ $\mathrm{TeV}$. With the $B_c^+\to J/\psi \pi^+$ decay as normalization channel, the ratio of branching fractions is measured to be \begin{equation*} \frac{\mathcal{B}(B_c^+\to J/\psi p\overline{p}\pi^+)}{\mathcal{B}(B_c^+\to J/\psi \pi^+)} = 0.143^{\,+\,0.039}_{\,-\,0.034}\,(\mathrm{stat})\pm0.013\,(\mathrm{syst}). \end{equation*} The mass of the $B_c^+$ meson is dete…

Nuclear TheoryAnalytical chemistryGeneral Physics and Astronomy01 natural sciencesSettore FIS/04 - Fisica Nucleare e SubnucleareHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]TOOLFactorizationNuclear ExperimentQCPhysicsPhysicsParticle physics12.39.StPhysical SciencesComputer Science::Mathematical SoftwareFísica nuclearLHCMESONParticle Physics - ExperimentComputer Science::Machine LearningMeson530 Physics14.40.NdPhysics MultidisciplinaryFOS: Physical sciencesPhysics InstituteLHCb - Abteilung HofmannAstrophysics::Cosmology and Extragalactic AstrophysicsComputer Science::Digital LibrariesNONuclear physicsPhysics and Astronomy (all)Hadronic decays of bottom meson0103 physical sciencesPi010306 general physicsScience & Technology010308 nuclear & particles physicshep-exHigh Energy Physics::Phenomenologymeson; toolBaryonLHCb13.25.HwBottom mesons (|B|>0)High Energy Physics::ExperimentFísica de partículesExperiments
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JUNO sensitivity to low energy atmospheric neutrino spectra

2021

Atmospheric neutrinos are one of the most relevant natural neutrino sources that can be exploited to infer properties about cosmic rays and neutrino oscillations. The Jiangmen Underground Neutrino Observatory (JUNO) experiment, a 20 kton liquid scintillator detector with excellent energy resolution is currently under construction in China. JUNO will be able to detect several atmospheric neutrinos per day given the large volume. A study on the JUNO detection and reconstruction capabilities of atmospheric $\nu_e$ and $\nu_\mu$ fluxes is presented in this paper. In this study, a sample of atmospheric neutrino Monte Carlo events has been generated, starting from theoretical models, and then pro…

Physics and Astronomy (miscellaneous)Physics::Instrumentation and Detectorsscintillation counter: liquidenergy resolutionAtmospheric neutrinoQC770-798Astrophysics7. Clean energy01 natural sciencesneutrino: fluxHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)particle source [neutrino]neutrinoneutrino: atmosphere[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Cherenkovneutrino/e: particle identificationenergy: low [neutrino]Jiangmen Underground Neutrino ObservatoryPhysicsJUNOphotomultiplierliquid [scintillation counter]primary [neutrino]neutrino: energy spectrumDetectoroscillation [neutrino]neutrinosMonte Carlo [numerical calculations]atmosphere [neutrino]QB460-466observatorycosmic radiationComputer Science::Mathematical Softwareproposed experimentNeutrinonumerical calculations: Monte CarloComputer Science::Machine LearningParticle physicsdata analysis methodAstrophysics::High Energy Astrophysical PhenomenaFOS: Physical sciencesCosmic rayScintillatorComputer Science::Digital LibrariesNOStatistics::Machine LearningPE2_2neutrino: primaryneutrino: spectrumNuclear and particle physics. Atomic energy. Radioactivity0103 physical sciencesddc:530structure010306 general physicsNeutrino oscillationEngineering (miscellaneous)Cherenkov radiationparticle identification [neutrino/mu]Scintillationneutrino/mu: particle identificationflavordetectorparticle identification [neutrino/e]010308 nuclear & particles physicsneutrino: energy: lowHigh Energy Physics::Phenomenologyspectrum [neutrino]resolutionenergy spectrum [neutrino]flux [neutrino]neutrino: particle source13. Climate actionHigh Energy Physics::Experimentneutrino: oscillationneutrino detector
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Deformed quons and bi-coherent states

2017

We discuss how a q-mutation relation can be deformed replacing a pair of conjugate operators with two other and unrelated operators, as it is done in the construction of pseudo-fermions, pseudo-bosons and truncated pseudo-bosons. This deformation involves interesting mathematical problems and suggests possible applications to pseudo-hermitian quantum mechanics. We construct bi-coherent states associated to $\D$-pseudo-quons, and we show that they share many of their properties with ordinary coherent states. In particular, we find conditions for these states to exist, to be eigenstates of suitable annihilation operators and to give rise to a resolution of the identity. Two examples are discu…

Pseudo-bosonComputer Science::Machine LearningSimilarity (geometry)Mathematical problemGeneral MathematicsFOS: Physical sciencesGeneral Physics and AstronomyComputer Science::Digital Libraries01 natural sciencesPhysics and Astronomy (all)Statistics::Machine LearningTheoretical physicsIdentity (mathematics)Engineering (all)Quon0103 physical sciencesMathematics (all)0101 mathematics010306 general physicsSettore MAT/07 - Fisica MatematicaEigenvalues and eigenvectorsMathematical PhysicsPhysicsQuantum PhysicsAnnihilation010102 general mathematicsGeneral EngineeringMathematical Physics (math-ph)Bounded functionComputer Science::Mathematical SoftwareCoherent statesQuantum Physics (quant-ph)Coherent stateResolution (algebra)
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