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showing 10 items of 124 documents

Jutekliskās uztveres tematizācija Pētera Handkes stāstā „Senviktuāras kalna mācība”

2015

Bakalaura darbs ir veltīts tēmai „Jutekliskās uztveres tematizācija Pētera Handkes stāstā Senviktuāra kalna mācība”. Darba mērķis ir izmeklēt un aplūkot Pētera Handkes jutekliskās uztveres atspoguļojumu stāstā Senviktuāra kalna mācība. Darba uzdevums ir izpētīt rakstnieka Pētera Handkes personību un viņa slaveno tetraloģiju, it īpaši tetraloģijas otro daļu. Darbs ir sadalīts trijās nodaļās. Pirmajā nodaļā tiek apskatīts un visparēji raksturots jēdziens „mūsdienu literatūra”, kā arī tiek apskatīta tā laika politiskā un sabiedriskā situācija Vācijā un Austrijā. Papildus tiek apskatīta mūsdienu rakstnieku paaudze un Pēters Handke tiek prezentēts kā mūsdienu vācu rakstnieku paaudzes populārs pā…

Erläuterung des Begriffs Gegenwartsliteratur. Charakterisierung der zeitgenössischen Literatur und des Epochenhitergrundes.ValodniecībaBekannte Tetralogie Peter Handkes mit dem Titel Langsame Heimkehr (1979 - 1981); Darstellung der Handlung der drei Erzählungen Langsame Heimkehr Die Lehre der Sainte-Victoire und Kindergeschichte und dem dramatischen Gedicht Über die Dörfer.Thema der Bakkalaureusarbeit lautet „Thematisierung der sinnlichen Wahrnehmung in Peter Handkes Erzählung Die Lehre der Sainte-Victoire“.Übersicht über die Schriftstellergeneration der Gegenwart. Peter Handke als einer der bekanntesten zeitgenössischen deutschsprachigen Autoren; seine Biographie und literariche Entwicklung.Untersuchungsobjekt für diese Arbeit – das 1980 mit einem Vorwort von Karl Krolow herausgegebene Buch von Peter Handke „Die Lehre der Sainte-Victoire“.
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On the interpretability and computational reliability of frequency-domain Granger causality

2017

This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, s…

FOS: Computer and information sciences0301 basic medicineTheoretical computer scienceImmunology and Microbiology (all)Computer scienceTime series analysiMathematics - Statistics TheoryStatistics Theory (math.ST)Statistics - ApplicationsGeneral Biochemistry Genetics and Molecular BiologyMethodology (stat.ME)Causality (physics)03 medical and health sciences0302 clinical medicinegranger causalityGranger causalityCorrespondenceFOS: MathematicsApplications (stat.AP)Physiological oscillationGeneral Pharmacology Toxicology and PharmaceuticsTime seriessignal processingStatistical Methodologies & Health Informaticsfrequency-domain connectivityReliability (statistics)Statistics - MethodologyInterpretabilityGranger-Geweke causalityBiochemistry Genetics and Molecular Biology (all)Interpretation (logic)General Immunology and Microbiologybrain connectivityGeneral MedicineArticlesvector autoregressive models030104 developmental biologyMathematics and StatisticsWildcardVector autoregressive modelPharmacology Toxicology and Pharmaceutics (all)Frequency domaintime series analysisspectral decompositionSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaBrain connectivity; Directed coherence; Frequency-domain connectivity; Granger-Geweke causality; Physiological oscillations; Spectral decomposition; Time series analysis; Vector autoregressive models; Biochemistry Genetics and Molecular Biology (all); Immunology and Microbiology (all); Pharmacology Toxicology and Pharmaceutics (all)directed coherence030217 neurology & neurosurgeryphysiological oscillations
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Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

2017

Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by prevalently redundant or sy…

FOS: Computer and information sciencesInformation transferComputer scienceGaussianSocial SciencesGeneral Physics and AstronomyInformation theory01 natural sciences010305 fluids & plasmasState spaceStatistical physicslcsh:Scienceinformation theorymultiscale entropylcsh:QC1-999Interaction informationMathematics and Statisticssymbolsinformation dynamicsInformation dynamics; Information transfer; Multiscale entropy; Multivariate time series analysis; Redundancy and synergy; State space models; Vector autoregressive models; Physics and Astronomy (all)information dynamics; information transfer; multiscale entropy; multivariate time series analysis; redundancy and synergy; state space models; vector autoregressive modelsMultivariate time series analysiMathematics - Statistics Theorylcsh:AstrophysicsStatistics Theory (math.ST)Statistics - ApplicationsMethodology (stat.ME)symbols.namesakePhysics and Astronomy (all)0103 physical scienceslcsh:QB460-466FOS: Mathematicsinformation transferRelevance (information retrieval)Applications (stat.AP)Transfer Entropy010306 general physicsGaussian processStatistics - MethodologyState space modelstate space modelsmultivariate time series analysisredundancy and synergyvector autoregressive modelsInformation dynamicVector autoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaTransfer entropylcsh:Qlcsh:PhysicsEntropy
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Local Granger causality

2021

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes. We exploit such equivalence and calculate exactly the 'local Granger causality', i.e. the profile of the information transfer at each discrete time point in Gaussian processes; in this frame Granger causality is the average of its local version. Our approach offers a robust and computationally fast method to follow the information transfer along the time history of linear stochastic processes, as well as of nonlinear …

FOS: Computer and information sciencesInformation transferGaussianFOS: Physical sciencestechniques; information theory; granger causalityMachine Learning (stat.ML)Quantitative Biology - Quantitative Methods01 natural sciences010305 fluids & plasmasVector autoregressionsymbols.namesakegranger causalityGranger causalityStatistics - Machine Learning0103 physical sciencesApplied mathematicstime serie010306 general physicsQuantitative Methods (q-bio.QM)Mathematicsinformation theoryStochastic processDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksComputational Physics (physics.comp-ph)Discrete time and continuous timeAutoregressive modelFOS: Biological sciencesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticasymbolsTransfer entropytechniquesPhysics - Computational Physics
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An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian Regions

2020

Abstract The availability of intensive care beds during the COVID‐19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short‐term prediction of COVID‐19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area‐specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave‐last‐out rationale. The approach has been set up and validated during t…

FOS: Computer and information sciencesStatistics and ProbabilityTime FactorsOccupancyCoronavirus disease 2019 (COVID-19)Computer science01 natural sciencesGeneralized linear mixed modelSARS‐CoV‐2law.inventionclustered data; COVID-19; generalized linear mixed model; integer autoregressive; integer autoregressive model; panel data; SARS-CoV-2; weighted ensembleMethodology (stat.ME)panel data010104 statistics & probability03 medical and health sciences0302 clinical medicinelawCOVID‐19Intensive careEconometricsHumansclustered data030212 general & internal medicine0101 mathematicsPandemicsStatistics - MethodologySARS-CoV-2Reproducibility of ResultsCOVID-19General Medicineweighted ensembleIntensive care unitResearch PapersTerm (time)integer autoregressiveIntensive Care UnitsAutoregressive modelItalyNonlinear Dynamicsgeneralized linear mixed modelinteger autoregressive modelclustered data; COVID-19; generalized linear mixed model; integer autoregressive; integer autoregressive model; panel data; SARS-CoV-2; weighted ensemble; COVID-19; Humans; Intensive Care Units; Italy; Nonlinear Dynamics; Pandemics; Reproducibility of Results; Time Factors; ForecastingStatistics Probability and UncertaintySettore SECS-S/01Settore SECS-S/01 - StatisticaPanel dataResearch PaperForecasting
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Multiscale partial information decomposition of dynamic processes with short and long-range correlations: theory and application to cardiovascular co…

2022

Abstract Objective. In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations. Approach. We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H), as well as the decomposition into unique, redundant and s…

FOS: Computer and information sciencesmultivariate time seriesPhysiologyEntropyRespirationBiomedical EngineeringBiophysicsheart rate variabilitytransfer entropyredundancy and synergyBlood PressureHeartQuantitative Biology - Quantitative MethodsCardiovascular SystemMethodology (stat.ME)Heart RatePhysiology (medical)FOS: Biological sciencesCardiovascular controlSettore ING-INF/06 - Bioingegneria Elettronica E Informaticavector autoregressive fractionally integrated (VARFI) modelsHumansQuantitative Methods (q-bio.QM)Statistics - MethodologyPhysiological measurement
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Trading Nokia: The roles of the Helsinki vs the New York stock exchanges

2004

We use the Autoregressive Conditional Duration (ACD) framework of Engle and Russell (1998) to study the effect of trading volume on price duration (ie the time lapse between consecutive price changes) of a stock listed both in the domestic and the foreign market. As a case study we use the example of Nokia's share, which is actively traded both in the Helsinki Stock Exchange and the New York Stock Exchange (NYSE). We find asymmetry in the volume-price duration relationship between the two markets. In the NYSE the negative relationship is much stronger and exists both during and outside common trading hours. Outside common trading hours no such relationship is significant in Helsinki. Based …

Financial economicsAutoregressive conditional durationcross-listing; Autoregressive Conditional Duration; market microstructurecomputer.software_genreCommercejel:G14Cross listingNegative relationshipStock exchangejel:G19BusinessAlgorithmic tradingcomputerStock (geology)Foreign market
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A Novel Approach to Propagation Pattern Analysis in Intracardiac Atrial Fibrillation Signals

2010

The purpose of this study is to investigate propagation patterns in intracardiac signals recorded during atrial fibrillation (AF) using an approach based on partial directed coherence (PDC), which evaluates directional coupling between multiple signals in the frequency domain. The PDC is evaluated at the dominant frequency of AF signals and tested for significance using a surrogate data procedure specifically designed to assess causality. For significantly coupled sites, the approach allows also to estimate the delay in propagation. The methods potential is illustrated with two simulation scenarios based on a detailed ionic model of the human atrial myocyte as well as with real data recordi…

Frequency analysiComputer scienceBiomedical EngineeringElectrogramAction PotentialsIntracardiac injectionPattern Recognition AutomatedSurrogate datalaw.inventionHeart Conduction SystemlawAtrial FibrillationmedicineHumansCoherence (signal processing)Computer SimulationDiagnosis Computer-AssistedSimulationFrequency analysisbusiness.industryBody Surface Potential MappingPartial directed coherenceModels CardiovascularPropagation patternAtrial fibrillationPattern recognitionAtrial arrhythmiamedicine.diseaseInformation engineeringMappingFrequency domainSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityMultivariate autoregressive modelingArtificial intelligencebusinessSimulationAnnals of Biomedical Engineering
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A framework for assessing frequency domain causality in physiological time series with instantaneous effects.

2013

We present an approach for the quantification of directional relations in multiple time series exhibiting significant zero-lag interactions. To overcome the limitations of the traditional multivariate autoregressive (MVAR) modelling of multiple series, we introduce an extended MVAR (eMVAR) framework allowing either exclusive consideration of time-lagged effects according to the classic notion of Granger causality, or consideration of combined instantaneous and lagged effects according to an extended causality definition. The spectral representation of the eMVAR model is exploited to derive novel frequency domain causality measures that generalize to the case of instantaneous effects the kno…

General MathematicsGeneral Physics and AstronomyModels BiologicalCausality (physics)Physics and Astronomy (all)Engineering (all)Granger causalityEconometricsMathematics (all)Coherence (signal processing)AnimalsHumansComputer SimulationDirected coherenceMathematicsMultivariate autoregressive modelModels StatisticalSeries (mathematics)Partial directed coherenceGeneral EngineeringSystem identificationAC powerAutoregressive modelFrequency domainSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityDirected coherence; Granger causality; Multivariate autoregressive models; Partial directed coherence; Mathematics (all); Engineering (all); Physics and Astronomy (all)AlgorithmsPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences
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Extending the spectral decomposition of Granger causality to include instantaneous influences: application to the control mechanisms of heart rate va…

2021

Assessing Granger causality (GC) intended as the influence, in terms of reduction of variance of surprise, that a driver variable exerts on a given target, requires a suitable treatment of ‘instantaneous’ effects, i.e. influences due to interactions whose time scale is much faster than the time resolution of the measurements, due to unobserved confounders or insufficient sampling rate that cannot be increased because the mechanism of generation of the variable is inherently slow (e.g. the heartbeat). We exploit a recently proposed framework for the estimation of causal influences in the spectral domain and include instantaneous interactions in the modelling, thus obtaining (i) a novel index…

General MathematicsGeneral Physics and AstronomyVector autoregressionMatrix decompositionCausality (physics)granger causalityGranger causalityHeart RateEconometricsvector autoregressionMedicine and Health SciencesHeart rate variabilitycardiorespiratory systemComputer SimulationTime seriesMathematicsinformation theoryGeneral Engineeringheart rate variabilityVariance (accounting)BaroreflexScience Generalspectral analysisCausalityVariable (computer science)Mathematics and Statisticstime series analysisAlgorithmsPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences
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