Search results for "Neural"

showing 10 items of 2783 documents

First measurement of the Sivers asymmetry for gluons using SIDIS data

2017

The Sivers function describes the correlation between the transverse spin of a nucleon and the transverse motion of its partons. It was extracted from measurements of the azimuthal asymmetry of hadrons produced in semi-inclusive deep inelastic scattering of leptons off transversely polarised nucleon targets, and it turned out to be non-zero for quarks. In this letter the evaluation of the Sivers asymmetry for gluons in the same process is presented. The analysis method is based on a Monte Carlo simulation that includes three hard processes: photon-gluon fusion, QCD Compton scattering and leading-order virtual-photon absorption process. The Sivers asymmetries of the three processes are simul…

hadron: angular distributionmuon+: polarized beamNuclear TheoryPartonmuon+ deuteron: deep inelastic scatteringhadron: transverse momentumtransverse momentum dependence01 natural sciencesCOMPASSHigh Energy Physics - ExperimentSubatomär fysikSivers functionHigh Energy Physics - Experiment (hep-ex)High Energy Physics - Phenomenology (hep-ph)photon gluon: fusionSubatomic Physics[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]partonNuclear Experimentmedia_commonQuantum chromodynamicsPhysicsgluon: distribution functiondeep inelastic scattering: semi-inclusive reactionhigher-order: 0polarized target: transversehep-phDeep inelastic scattering; Gluon; PDF; Sivers; TMD; Nuclear and High Energy Physicslcsh:QC1-999High Energy Physics - PhenomenologySivereffect: CollinsNucleonCompton scatteringnumerical calculations: Monte Carlospin: asymmetryParticle Physics - ExperimentDeep inelastic scatteringQuarkParticle physicsNuclear and High Energy Physicsdata analysis methoddeuteron: polarized targethadron: asymmetryangular distribution: asymmetryneural networkmedia_common.quotation_subjectpolarization: longitudinalFOS: Physical sciencesAsymmetryPDFGluonNuclear physics[ PHYS.HEXP ] Physics [physics]/High Energy Physics - Experiment [hep-ex]0103 physical sciencesquantum chromodynamicsSivers010306 general physicsParticle Physics - Phenomenology010308 nuclear & particles physicshep-ex160 GeV/cHigh Energy Physics::PhenomenologyTMDnucleon: spin: transverseCERN SPSDeep inelastic scatteringGluonmuon+ p: deep inelastic scatteringcorrelation[PHYS.HPHE]Physics [physics]/High Energy Physics - Phenomenology [hep-ph][ PHYS.HPHE ] Physics [physics]/High Energy Physics - Phenomenology [hep-ph]High Energy Physics::Experimentabsorptionlcsh:PhysicsLeptonexperimental results
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Most hippocampal CA1 pyramidal cells in rabbits increase firing during awake sharp-wave ripples and some do so in response to external stimulation an…

2020

Hippocampus forms neural representations of real-life events including multimodal information of spatial and temporal context. These representations, i.e. organized sequences of neuronal firing are repeated during following rest and sleep, especially when so-called sharp-wave ripples (SPW-Rs) characterize hippocampal local-field potentials. This SPW-R –related replay is thought to underlie memory consolidation. Here, we set out to explore how hippocampal CA1 pyramidal cells respond to the conditioned stimulus during trace eyeblink conditioning and how these responses manifest during SPW-Rs in awake adult female New Zealand White rabbits. Based on reports in rodents, we expected SPW-Rs to ta…

hippocampusPhysiologyConditioning Classicalclassical conditioningHippocampusStimulationHippocampal formation03 medical and health sciences0302 clinical medicinemedicineAnimalspyramidisoluthippokampusTheta RhythmCA1 Region Hippocampalmuisti (kognitio)030304 developmental biologypyramidal cell0303 health sciencesBehavior AnimalBlinkingChemistrymusculoskeletal neural and ocular physiologyGeneral NeuroscienceCa1 pyramidal neuronPyramidal CellsClassical conditioningneurotieteetBrain Wavessharp-wave ripplehermosolutehdollistuminenmedicine.anatomical_structurenervous systemEyeblink conditioningthetaFemaleElectrocorticographyRabbitsPyramidal cellNeuroscienceSharp wave030217 neurology & neurosurgeryJournal of neurophysiology
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Time Unification on Local Binary Patterns Three Orthogonal Planes for Facial Expression Recognition

2019

International audience; Machine learning has known a tremendous growth within the last years, and lately, thanks to that, some computer vision algorithms started to access what is difficult or even impossible to perceive by the human eye. While deep learning based computer vision algorithms have made themselves more and more present in the recent years, more classical feature extraction methods, such as the ones based on Local Binary Patterns (LBP), still present a non negligible interest, especially when dealing with small datasets. Furthermore, this operator has proven to be quite useful for facial emotions and human gestures recognition in general. Micro-Expression (ME) classification is…

human eyeHistogramsgeometryUnificationComputer scienceLocal binary patternsoptimisationFeature extraction02 engineering and technologyhuman gestures recognitionFacial recognition systemcomputer visionVideos[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]time unification method03 medical and health sciences0302 clinical medicineMathematical modelLBPemotion recognition0202 electrical engineering electronic engineering information engineeringfacial emotionsfacial expression recognitionlocal binary patternsFace recognitionContextual image classificationArtificial neural networkbusiness.industryDeep learningdeep learning[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Pattern recognitionComputational modelingmicroexpression classificationInterpolationorthogonal planesneural netsmachine learning[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Micro expressionFeature extraction020201 artificial intelligence & image processinglearning (artificial intelligence)Artificial intelligencebusiness030217 neurology & neurosurgeryGestureimage classification
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L’influenza dei sistemi per il condizionamento dell’aria sul consumo di energia elettrica del settore residenziale: un modello neurale

2008

hvac rete neurale consumi elettrici
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Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

2021

Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weath…

hybrid neural networkSVDP::Landbruks- og Fiskerifag: 900::Landbruksfag: 910farm-scale crop yield prediction; deep learning; hybrid neural network; convolutional neural network; recurrent neural network; Sentinel-2 satellite remote sensing datadeep learningconvolutional neural networkSentinel-2 satellite remote sensing datarecurrent neural networkAgriculturefarm-scale crop yield predictionAgronomy and Crop ScienceAgronomy
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A method for anomaly detection in hyperspectral images, using deep convolutional autoencoders

2017

Menetelmä poikkeavuuksien havaitsemiseen hyperspektrikuvista käyttäen syviä konvolutiivisia autoenkoodereita. Poikkeavuuksien havaitseminen kuvista, erityisesti hyperspektraalisista kuvista, on hankalaa. Kun ongelmaan yhdistetään ennalta tuntematon data ja poikkeavuudet, muodostuu ongelma vielä laajemmaksi. Spektraalisten poikkeavuuksien havaitsemiseen on kehitetty useita eri menetelmiä, mutta spatiaalisten poikkeavuuksien havaitseminen on huomattavasti hankalempaa. Tässä työssä esitellään uudenkaltainen menetelmä sekä spatiaalisten että spektraalisten poikkeavuuksien samanaikaiseen havaitsemiseen. Menetelmä on suunniteltu erityisesti spektraaliselle datalle, mutta soveltuu myös perinteisil…

hyperspectral imagesautoencoderautoenkooderithdbscanSCAEconvolutional neural networkdeep learninghavaitseminenneuroverkotanomaly detectionconvolutional autoencodermachine learningkoneoppiminenpoikkeavuuskonvoluutioälytekniikkaCAEhyperspektrikuvatautoenkooderi
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FPI Based Hyperspectral Imager for the Complex Surfaces : Calibration, Illumination and Applications

2022

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pé…

ihoconvolutional neural networkphotometric stereoneuroverkotinterferometrydiagnostiikkacalibrationoptical modellingLED illuminationihosyöpähyperspectralFPIoptical biopsykoneoppiminenskin surface modelbiomedical imagingdermatological applicationihotaudithyperspektrikuvantaminen
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Humanoid Cognitive Robots That Learn by Imitating: Implications for Consciousness Studies.

2018

While the concept of a conscious machine is intriguing, producing such a machine remains controversial and challenging. Here we describe how our work on creating a humanoid cognitive robot that learns to perform tasks via imitation learning relates to this issue. Our discussion is divided into three parts. First, we summarize our previously-detailed framework for advancing the understanding of the nature of phenomenal consciousness. This framework is based on identifying computational correlates of consciousness. Second, we describe a cognitive robotic system that we recently developed that learns to perform tasks by imitating human-provided demonstrations. This humanoid robot uses cause-ef…

imitation learningartificial consciousnessComputer sciencemedia_common.quotation_subjectlcsh:Mechanical engineering and machinerymachine consciousnessArtificial consciousnesscognitive phenomenology050105 experimental psychologylcsh:QA75.5-76.95working memory03 medical and health sciences0302 clinical medicineArtificial Intelligence0501 psychology and cognitive scienceslcsh:TJ1-1570cognitive robotsmedia_commonOriginal ResearchCognitive scienceRobotics and AIWorking memory05 social sciencesCognitioncomputational explanatory gapComputer Science Applicationsneural network gating mechanismsRobotCausal reasoninglcsh:Electronic computers. Computer scienceConsciousnessNeurocognitive030217 neurology & neurosurgeryHumanoid robotFrontiers in robotics and AI
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Role of TNF-α receptor 2 (TNFR2) in the regulation of adult neural stem cells

2017

Adult stem cells are tissue-resident undifferentiated and self-renewing cells responsible for tissue homeostasis. Under physiological conditions, stem cells co-exist in a reversible cell cycle-arrested state known as quiescence and an activated proliferative state, which results in the production of cell progeny through asymmetric division. Different types of injuries can activate stem cells to produce progeny that contributes to tissue repair, but the molecular triggers and regulators of this activation are barely known. Living organisms are constantly exposed to a variety of internal and external stimuli and some of them can be classified as danger signals. Upon detection, a complex respo…

inflamaciónUNESCO::CIENCIAS DE LA VIDAquiescenciacélulas madre neurales:CIENCIAS DE LA VIDA [UNESCO]
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L'intelligenza. Teorie e modelli. II° Edizione

2007

Rassegna ampia e dettagliata delle più significative teorie dell'intelligenza, così come si sono sviluppate dalla seconda metà dell'Ottocento fino ad oggi, dai contributi teorici dei positivisti inglesi alle ricerche più recenti, lagate allo sviluppo dei modelli computazionali e simulativi.

intelligenza intelligenza artificiale reti neurali
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