0000000000984617

AUTHOR

Roberto Prevete

showing 4 related works from this author

A system for the automatic measurement of the nuchal translucency thickness from ultrasound video stream of the foetus

2013

Nowadays the measurement of the nuchal translucency thickness is being used as part of routine ultrasound scanning during the end of the first trimester of pregnancy, for the screening of chromosomal defects, as trisomy 21. Currently, the measurement is being performed manually by physicians. The measurement can take a long time for being accomplished, needs to be performed by highly skilled operators, and is prone to errors. Semi-automated methods requires that the user manually selects a region of the image containing the nuchal translucency, procedure that is somewhat time consuming. In this paper we present a complete system prototype that is able to perform the measurement of the nucha…

medicine.medical_specialtyHighly skilledRoutine ultrasoundSettore INF/01 - Informaticaultrasoundbusiness.industryUltrasoundautomatic measurementSurgeryFirst trimesterNuchal translucencyComing outMedicineComputer visionArtificial intelligencetranslucency thicknebusinessnuchal translucency thickness
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Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality

2015

A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments perfo…

Cognitive NeuroscienceEntropyFOS: Physical sciencesOverfittingcomputer.software_genreMachine learningGranger causalityArtificial IntelligenceMedicine and Health SciencesEntropy (information theory)Non-uniform embeddingComputer SimulationMathematicsArtificial neural networkbusiness.industryProbability and statisticsModels TheoreticalNeural Networks (Computer)ClassificationNeural networkAlgorithmCausalityPhysics - Data Analysis Statistics and ProbabilitySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityEmbeddingA priori and a posterioriTransfer entropyNeural Networks ComputerArtificial intelligenceData miningbusinesscomputerAlgorithmsNeural networksData Analysis Statistics and Probability (physics.data-an)
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The role of synergies within generative models of action execution and recognition: A computational perspective

2015

Controlling the body – given its huge number of degrees of freedom – poses severe computational challenges. Mounting evidence suggests that the brain alleviates this problem by exploiting “synergies”, or patterns of muscle activities (and/or movement dynamics and kinematics) that can be combined to control action, rather than controlling individual muscles of joints [1–10]. D’Ausilio et al. [11] explain how this view of motor organization based on synergies can profoundly change the way we interpret studies of action recognition in humans and monkeys, and in particular the controversy on the “granularity” of the mirror neuron system (MNs): whether it encodes either (lower) kinematic aspects…

Computer sciencebusiness.industryDegrees of freedomProbabilistic logicGeneral Physics and AstronomyInferenceMotor control[SCCO.COMP]Cognitive science/Computer scienceRoboticsGenerative model[SCCO]Cognitive scienceAction (philosophy)Artificial Intelligence[SCCO.PSYC]Cognitive science/PsychologyArtificial intelligenceGeneral Agricultural and Biological SciencesbusinessMirror neuronComputingMilieux_MISCELLANEOUS
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The role of synergies within generative models of action execution and recognition: A computational perspective. Comment on "Grasping synergies: A mo…

2015

Controlling the body – given its huge number of degrees of freedom – poses severe computational challenges. Mounting evidence suggests that the brain alleviates this problem by exploiting “synergies”, or patterns of muscle activities (and/or movement dynamics and kinematics) that can be combined to control action, rather than controlling individual muscles of joints [1–10]. D’Ausilio et al. [11] explain how this view of motor organization based on synergies can profoundly change the way we interpret studies of action recognition in humans and monkeys, and in particular the controversy on the “granularity” of the mirror neuron system (MNs): whether it encodes either (lower) kinematic aspects…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazionisynergiesMirror NeuronHand Strengthgenerative modelsAnimalArtificial IntelligenceMotor ActivityHuman
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