Search results for " Informatica"

showing 10 items of 978 documents

Redundancy and synergy in interactions among basic cardiovascular oscillations

2020

The cardiovascular control system comprises a complex network of various control mechanisms operating on many time scales resulting in complex and mutually interconnected output signals (e.g. heart rate, systolic and diastolic blood pressures). The analysis of these interconnections could noninvasively provide an information on the regulatory mechanisms involved in cardiovascular control and thus could be potentially applied to better characterize cardiovascular dysregulation in pathological conditions. Our study demonstrates that the strength of interactions among signals changes with the time scale and as a response to changed autonomic state (orthostasis compared to supine rest). Novel i…

cardiovascular oscillationsComputer scienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaComplex networkCardiovascular controlNeuroscience
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Exploring Heterogeneity with Category and Cluster Analyses for Mixed Data

2023

Precision medicine aims to overcome the traditional one-model-fits-the-whole-population approach that is unable to detect heterogeneous disease patterns and make accurate personalized predictions. Heterogeneity is particularly relevant for patients with complications of type 2 diabetes, including diabetic kidney disease (DKD). We focus on a DKD longitudinal dataset, aiming to find specific subgroups of patients with characteristics that have a close response to the therapeutic treatment. We develop an approach based on some particular concepts of category theory and cluster analysis to explore individualized modelings and achieving insights onto disease evolution. This paper exploits the vi…

category theorySettore INF/01 - Informaticaprecision medicinecluster analysiDKD diseasedistance
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Information Decomposition in Bivariate Systems: Theory and Application to Cardiorespiratory Dynamics

2015

In the framework of information dynamics, the temporal evolution of coupled systems can be studied by decomposing the predictive information about an assigned target system into amounts quantifying the information stored inside the system and the information transferred to it. While information storage and transfer are computed through the known self-entropy (SE) and transfer entropy (TE), an alternative decomposition evidences the so-called cross entropy (CE) and conditional SE (cSE), quantifying the cross information and internal information of the target system, respectively. This study presents a thorough evaluation of SE, TE, CE and cSE as quantities related to the causal statistical s…

causalityInformation dynamicsTransfer entropyDynamical systems theoryComputationGeneral Physics and Astronomylcsh:AstrophysicsBivariate analysisMultivariate autoregressive processeMachine learningcomputer.software_genreMultivariate autoregressive processesCardiorespiratory interactionsPhysics and Astronomy (all)Systems theoryDynamical systemslcsh:QB460-466Decomposition (computer science)Statistical physicslcsh:ScienceCardiorespiratory interactions; Causality; Dynamical systems; Heart rate variability; Information dynamics; Multivariate autoregressive processes; Transfer entropyHeart rate variabilityMathematicsCardiorespiratory interactions; Causality; Dynamical systems; Heart rate variability; Information dynamics; Multivariate autoregressive processes; Transfer entropy; Physics and Astronomy (all)business.industryCardiorespiratory interactionheart rate variabilitytransfer entropyDynamical systemcardiorespiratory interactionsdynamical systemslcsh:QC1-999CausalityInformation dynamicCross entropySettore ING-INF/06 - Bioingegneria Elettronica E Informaticamultivariate autoregressive processesBenchmark (computing)lcsh:QTransfer entropyArtificial intelligenceinformation dynamicsbusinesscomputerlcsh:PhysicsEntropy
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Fiberless, Multi-Channel fNIRS-EEG System Based on Silicon Photomultipliers: Towards Sensitive and Ecological Mapping of Brain Activity and Neurovasc…

2020

Portable neuroimaging technologies can be employed for long-term monitoring of neurophysiological and neuropathological states. Functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are highly suited for such a purpose. Their multimodal integration allows the evaluation of hemodynamic and electrical brain activity together with neurovascular coupling. An innovative fNIRS-EEG system is here presented. The system integrated a novel continuous-wave fNIRS component and a modified commercial EEG device. fNIRS probing relied on fiberless technology based on light emitting diodes and silicon photomultipliers (SiPMs). SiPMs are sensitive semiconductor detectors, whose large…

clinical brain monitoringBrain activity and meditationComputer scienceneurovascular couplingElectroencephalographylcsh:Chemical technologySettore ING-INF/01 - Elettronica01 natural sciencesBiochemistryArticleAnalytical Chemistry010309 optics03 medical and health sciences0302 clinical medicineSilicon photomultiplierNeuroimagingInterference (communication)Component (UML)0103 physical sciencesmedicineHumanslcsh:TP1-1185electroencephalography (EEG)Electrical and Electronic EngineeringSpectroscopyInstrumentationBrain MappingSpectroscopy Near-Infraredmedicine.diagnostic_testEcologyHemodynamicsmultimodal neuroimagingBrainMultimodal neuroimagingElectroencephalographyNeurophysiologyAtomic and Molecular Physics and Opticsmedicine.anatomical_structureFPGA Brain Oxygenation Map clinical brain monitoringScalpSettore ING-INF/06 - Bioingegneria Elettronica E Informaticasilicon photomultipliers.Neurovascular couplingsilicon photomultipliers030217 neurology & neurosurgeryfunctional near infrared spectroscopy (fNIRS)Sensors
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The Three Steps of Clustering in the Post-Genomic Era: A Synopsis

2011

Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. Following Handl et al., it can be summarized as a three step process: (a) choice of a distance function; (b) choice of a clustering algorithm; (c) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Unfortunately, the high dimensionality of the data and their noisy nature makes cluster analysis of genomic data particul…

cluster validation indicesSettore INF/01 - InformaticaProcess (engineering)Computer sciencebusiness.industryGenomic datadistance functionMachine learningcomputer.software_genreObject (computer science)ClusteringCluster algorithmPredictive powerRelevance (information retrieval)Artificial intelligenceHigh dimensionalitybusinessCluster analysiscomputer
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BALANCE PROPERTIES AND DISTRIBUTION OF SQUARES IN CIRCULAR WORDS

2010

We study balance properties of circular words over alphabets of size greater than two. We give some new characterizations of balanced words connected to the Kawasaki-Ising model and to the notion of derivative of a word. Moreover we consider two different generalizations of the notion of balance, and we find some relations between them. Some of our results can be generalized to non periodic infinite words as well.

combinatoria delle parole parole circolari parole bilanciateCombinatoricsCombinatorics on wordsSettore INF/01 - InformaticaComputer Science (miscellaneous)Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Computer Science::Formal Languages and Automata TheoryMathematicsInternational Journal of Foundations of Computer Science
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Social network analysis approaches to study crime

2022

Social Network Analysis (SNA) studies groups of individuals and can be applied in a lot of areas such us organizational studies, psychology, economics, information science and criminology. One of the most important results of SNA has been the definition of a set of centrality measures (e.g., degree, closeness, betweenness, or clustering coefficient) which can be used to identify the most influential people with respect to their network of relationships. The main problem with computing centrality metrics on social networks is the typical big size of the data. From the computational point of view, SNA represents social networks as graphs composed of a set of nodes connected by another set of …

complex networkSettore INF/01 - Informaticagraph theorynetwork scienceSocial network analysicentralitycriminal networkmultilayer network
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CHANGING PERSPECTIVE ON PERCEPTION PHYSIOLOGY: CAN YOU REALLY SEE WHAT IS HAPPENING?

2018

Perception is a complex, neural mechanism that requires organization and interpretation of input meaning and it has been a key topic in medicine, neuroscience and philosophy for centuries. Gestalt psychology proposed that the underlying mechanism is a constructive process that depends on both input of stimuli and the sensory-motor state of the agent. The Bayesian Brain hypothesis reframed it as probabilistic inference of previous beliefs, which are revised to accommodate new information. The Predictive Coding Theory proposes that this process is implemented through a top-down cascade of cortical predictions of lower level input and the concurrent propagation of a bottom-up prediction error …

computational modelSettore ING-INF/06 - Bioingegneria Elettronica E Informaticaperception neurological cognition modelsperceptionSettore BIO/09 - Fisiologianeural model
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Implicit perception simplicity and explicit perception complexity in sensorimotor communication: Comment on "The body talks: Sensorimotor communicati…

2019

[No abstract available]

comunication sensory-motor computational models embodied cognition theory of mindSettore M-PSI/02 - Psicobiologia E Psicologia Fisiologicaddc:150Settore ING-INF/06 - Bioingegneria Elettronica E InformaticaSettore BIO/09 - Fisiologia
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Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological N…

2020

The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state&ndash

conditional transfer entropyInformation transferlinear predictionDynamical systems theoryComputer scienceState–space modelsGeneral Physics and Astronomylcsh:AstrophysicsNetwork topologycomputer.software_genrenetwork physiology01 natural sciencesArticle03 medical and health sciences0302 clinical medicinepenalized regression techniquelcsh:QB460-4660103 physical sciencesEntropy (information theory)Statistics::Methodologylcsh:Science010306 general physicspartial information decompositionmultivariate time series analysisinformation dynamics; partial information decomposition; entropy; conditional transfer entropy; network physiology; multivariate time series analysis; State–space models; vector autoregressive model; penalized regression techniques; linear predictionState–space modellcsh:QC1-999multivariate time series analysiInformation dynamicData pointpenalized regression techniquesAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaParametric modelOrdinary least squaresvector autoregressive modellcsh:QData mininginformation dynamicsentropycomputerlcsh:Physics030217 neurology & neurosurgery
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