Search results for "INTEGRATION"

showing 10 items of 1465 documents

Single neuron binding properties and the magical number 7

2008

When we observe a scene, we can almost instantly recognize a familiar object or can quickly distinguish among objects differing by apparently minor details. Individual neurons in the medial temporal lobe of humans have been shown to be crucial for the recognition process, and they are selectively activated by different views of known individuals or objects. However, how single neurons could implement such a sparse and explicit code is unknown and almost impossible to investigate experimentally. Hippocampal CA1 pyramidal neurons could be instrumental in this process. Here, in an extensive series of simulations with realistic morphologies and active properties, we demonstrate how n radial (ob…

Computer scienceCognitive NeuroscienceModels NeurologicalHippocampusCA1 pyramidal neuronHippocampusTemporal lobesynaptic integrationmedicineCode (cryptography)Humansoblique dendritesNeuronsbinding proceSettore INF/01 - InformaticahippocampuProcess (computing)Oblique casefood and beveragesObject (computer science)computational modelmedicine.anatomical_structureMemory Short-TermNeuronNeural codingNeuroscience
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A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior.

2011

The neural mechanisms underlying schizophrenic behavior are unknown and very difficult to investigate experimentally, although a few experimental and modeling studies suggested possible causes for some of the typical psychotic symptoms related to this disease. The brain region most involved in these processes seems to be the hippocampus, because of its critical role in establishing memories for objects or events in the context in which they occur. In particular, a hypofunction of the N-methyl-D-aspartate (NMDA) component of the synaptic input on the distal dendrites of CA1 pyramidal neurons has been suggested to play an important role for the emergence of schizophrenic behavior. Modeling st…

Computer scienceCognitive Neurosciencemedia_common.quotation_subjectSchizophrenia Realistic model CA1 Hippocampus Object recognition Synaptic integrationCentral nervous systemModels NeurologicalCa1 neuronHippocampusHippocampal formationSynapse03 medical and health sciences0302 clinical medicineArtificial IntelligencePerceptionmedicineAnimalsHumansInvariant (mathematics)CA1 Region Hippocampal030304 developmental biologymedia_common0303 health sciencesRecallArtificial neural networkPyramidal NeuronSynaptic integrationPyramidal CellsCognitive neuroscience of visual object recognitionDendritesmedicine.diseasemedicine.anatomical_structurenervous systemSchizophreniaSynapsesSchizophreniaNMDA receptorNeuronNerve NetNeuroscience030217 neurology & neurosurgeryNeural networks : the official journal of the International Neural Network Society
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UNIVERSITY IS ARCHITECTURE FOR THE RESEARCH EVALUATION SUPPORT

2017

The measuring of research results can be used in different ways e.g. for assignment of research grants and afterwards for evaluation of project’s results. It can be used also for recruiting or promoting research institutions’ staff. Because of a wide usage of such measurement, the selection of appropriate measures is important. At the same time there does not exist a common view which metrics should be used in this field, moreover many existing metrics that are widely used are often misleading due to different reasons, e.g. computed from incomplete or faulty data, the metric’s computation formula may be invalid or the computation results can be interpreted wrongly. To produce a good framewo…

Computer scienceData qualityInformation systemScopusSelection (linguistics)research evaluation; research metrics; data integration; information system; data qualityMetric (unit)Architecturecomputer.software_genrecomputerData scienceField (computer science)Data integrationEnvironment. Technology. Resources.
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Adaptive Population Importance Samplers: A General Perspective

2016

Importance sampling (IS) is a well-known Monte Carlo method, widely used to approximate a distribution of interest using a random measure composed of a set of weighted samples generated from another proposal density. Since the performance of the algorithm depends on the mismatch between the target and the proposal densities, a set of proposals is often iteratively adapted in order to reduce the variance of the resulting estimator. In this paper, we review several well-known adaptive population importance samplers, providing a unified common framework and classifying them according to the nature of their estimation and adaptive procedures. Furthermore, we interpret the underlying motivation …

Computer scienceMatemáticasMonte Carlo methodPopulation02 engineering and technologyMachine learningcomputer.software_genre01 natural sciences010104 statistics & probability[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineering0101 mathematicseducationComputingMilieux_MISCELLANEOUSeducation.field_of_studybusiness.industryEstimator020206 networking & telecommunicationsStatistical classificationRandom measureMonte Carlo integrationData miningArtificial intelligencebusinessParticle filtercomputer[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingImportance sampling
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Analysis of block random rocking on nonlinear flexible foundation

2020

Abstract In this paper the rocking response of a rigid block randomly excited at its foundation is examined. A nonlinear flexible foundation model is considered accounting for the possibility of uplifting in the case of strong excitation. Specifically, based on an appropriate nonlinear impact force model, the foundation is treated as a bed of continuously distributed springs in parallel with nonlinear dampers. The statistics of the rocking response is examined by an analytical procedure which involves a combination of static condensation and stochastic linearization methods. In this manner, repeated numerical integration of the highly nonlinear differential equations of motion is circumvent…

Computer scienceMonte Carlo methodAerospace Engineering020101 civil engineeringOcean Engineering02 engineering and technology0201 civil engineeringDamper0203 mechanical engineeringLinearizationCivil and Structural EngineeringBlock (data storage)Mechanical EngineeringMathematical analysisNonlinear flexible foundationStatistical and Nonlinear PhysicsFilter (signal processing)Condensed Matter PhysicsNumerical integrationNonlinear systemRocking motion020303 mechanical engineering & transportsNuclear Energy and EngineeringImpactRandom base excitationSettore ICAR/08 - Scienza Delle CostruzioniProbabilistic Engineering Mechanics
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CLUSTER MONTE CARLO ALGORITHMS IN STATISTICAL MECHANICS

1992

The cluster Monte Carlo method, where variables are updated in groups, is very efficient at second order phase transitions. Much better results can be obtained with less computer time. This article reviews the method of Swendsen and Wang and some of its applications.

Computer scienceMonte Carlo methodGeneral Physics and AstronomyStatistical and Nonlinear PhysicsComputer Science ApplicationsHybrid Monte CarloComputational Theory and MathematicsDynamic Monte Carlo methodMonte Carlo integrationMonte Carlo method in statistical physicsStatistical physicsQuasi-Monte Carlo methodParallel temperingAlgorithmMathematical PhysicsMonte Carlo molecular modelingInternational Journal of Modern Physics C
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Group Metropolis Sampling

2017

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…

Computer scienceMonte Carlo methodMarkov processSlice samplingProbability density function02 engineering and technologyMultiple-try MetropolisBayesian inferenceMachine learningcomputer.software_genre01 natural sciencesHybrid Monte Carlo010104 statistics & probabilitysymbols.namesake[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing0202 electrical engineering electronic engineering information engineering0101 mathematicsComputingMilieux_MISCELLANEOUSMarkov chainbusiness.industryRejection samplingSampling (statistics)020206 networking & telecommunicationsMarkov chain Monte CarloMetropolis–Hastings algorithmsymbolsMonte Carlo method in statistical physicsMonte Carlo integrationArtificial intelligencebusinessParticle filter[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingcomputerAlgorithmImportance samplingMonte Carlo molecular modeling
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Fusion of experimental data

1997

Abstract The integration of information from various sensory systems is one of the most difficult challenges in understanding both perception and cognition. For example, the problem of auditory-visual integration is a correspondence problem between perceived auditory and visual scenes. Two main questions arise when designing data analysis systems: what is the useful information to be integrated?, and what are the integration rules? The problem of integrating information becomes relevant whenever: (a) the same kind of data are detected by spatially distributed sensors; (b) heterogeneous data are detected by different sensors; (c) heterogeneous distributed data are involved. General problems …

Computer sciencePerceptionmedia_common.quotation_subjectExperimental dataAstronomy and AstrophysicsCognitionSatelliteData miningcomputer.software_genreCorrespondence problemcomputermedia_commonInformation integration
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Updated design and integration of the ancillary circuits for the European Test Blanket Systems

2019

The validation of the key technologies relevant for a DEMO Breeding Blanket is one of the main objectives of the design and operation of the Test Blanket Systems (TBS) in ITER. In compliance with the main features and technical requirements of the parent breeding blanket concepts, the European TBM Project is developing the HCLL (Helium Cooled Lithium Lead) and HCPB (Helium Cooled Pebble Bed)-TBS, focusing in this phase on the design life cycle and on R&D activities in support of the design. The TBS ancillary systems are mainly circuits devoted to the removal of thermal power and to the extraction and recovery of the tritium generated in the Test Blanket Modules. They are: • The Helium C…

Computer scienceThermo-mechanical analysiTest Blanket ModuleBlanketCAD integration; Test Blanket Module; Thermo-hydraulic analysis; Thermo-mechanical analysis; Tritium technologies01 natural sciences7. Clean energy010305 fluids & plasmasTritium technologiesConceptual design0103 physical sciencesGeneral Materials Science010306 general physicsSettore ING-IND/19 - Impianti NucleariCivil and Structural EngineeringElectronic circuitThermo-hydraulic analysisThermo-mechanical analysisMechanical EngineeringCAD integrationCoolantTest (assessment)Nuclear Energy and EngineeringPhysical spaceSystems engineeringThermo-hydraulic analysiFusion Engineering and Design
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Towards MKDA: A Knowledge Discovery Assistant for Researches in Medicine

2007

Nowadays doctors are generating a huge amount of raw data. These data, analyzed with data mining techniques, could be sources of new knowledge. Unluckily such tasks need skilled data analysts, and not so much researchers in Medicine are also data mining experts. In this paper we present a web based system for knowledge discovery assistance in Medicine able to advice a medical researcher in this kind of tasks. The user must define only the experiment specifications in a formal language we have defined. The system GUI helps users in their composition. Then the system plans a Knowledge Discovery Process (KDP) on the basis of rules in a knowledge base. Finally the system executes the KDP and pr…

Computer sciencebusiness.industryKnowledge engineeringOpen Knowledge Base Connectivitycomputer.software_genreData scienceKnowledge-based systemsKnowledge extractionKnowledge baseKnowledge integrationSoftware miningDomain knowledgeWeb servicebusinesscomputer
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