Search results for "Data analysis."

showing 10 items of 377 documents

Recensione a Ferruccio Biolcati-Rinaldi, Cristiano Vezzoni, L’analisi secondaria nella ricerca sociale, STUDI DI SOCIOLOGIA, Il Mulino, Itinerari, Bo…

2014

Settore SPS/07 - Sociologia Generalericerca sociale data analysis analisi secondaria
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XMM-Newton large programme on SN1006 - II. Thermal emission

2016

Based on the XMM-Newton large program on SN1006 and our newly developed spatially resolved spectroscopy tools (Paper~I), we study the thermal emission from ISM and ejecta of SN1006 by analyzing the spectra extracted from 583 tessellated regions dominated by thermal emission. With some key improvements in spectral analysis as compared to Paper~I, we obtain much better spectral fitting results with less residuals. The spatial distributions of the thermal and ionization states of the ISM and ejecta show different features, which are consistent with a scenario that the ISM (ejecta) is heated and ionized by the forward (reverse) shock propagating outward (inward). Different elements have differe…

Shock wave010504 meteorology & atmospheric sciences[ PHYS.ASTR ] Physics [physics]/Astrophysics [astro-ph]FOS: Physical sciencesCosmic rayAstrophysicsMethods: Data analysi01 natural sciencesSpectral linecosmic raysIonization0103 physical sciencesEjectaSupernova remnant010303 astronomy & astrophysics0105 earth and related environmental sciencesLine (formation)ISM: supernova remnantsacceleration of particlesHigh Energy Astrophysical Phenomena (astro-ph.HE)PhysicsAstronomyAstronomy and Astrophysicsshock wavesAstronomy and AstrophysicAcceleration of particlemethods: data analysisCosmic rayX-rays: ISMInterstellar mediumISM: Supernova remnant13. Climate actionShock waveSpace and Planetary ScienceAstrophysics - High Energy Astrophysical Phenomena[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]Supernova remnants; Methods: Data analysis; Shock waves; X-rays: ISM; Astronomy and Astrophysics; Space and Planetary Science [Acceleration of particles; Cosmic rays; ISM]
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Toward a Collective Agenda on AI for Earth Science Data Analysis

2021

In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer. Thanks to both the massive availability of observational data, improved simulations, and algorithmic advances, these disciplines have found common objectives and challenges to advance the modeling and understanding of the Earth system. Despite such great opportunities, we also observed a worrying tendency to remain in disciplinary comfort zones applying recent advances from artificial intelligence on well resolved remote sensing problems. Here we take a position on research directions where we think the interface between these fields will have the most impact and be…

Signal Processing (eess.SP)FOS: Computer and information sciences010504 meteorology & atmospheric sciencesGeneral Computer Science530 PhysicsInterface (Java)Computer Vision and Pattern Recognition (cs.CV)Earth sciencedata analysisComputer Science - Computer Vision and Pattern Recognition0211 other engineering and technologiesearth observation02 engineering and technology01 natural sciencesEnvironmental scienceData modelingFOS: Electrical engineering electronic engineering information engineeringClimate science1700 General Computer ScienceElectrical Engineering and Systems Science - Signal ProcessingElectrical and Electronic EngineeringInstrumentation021101 geological & geomatics engineering0105 earth and related environmental sciences11476 Digital Society Initiative3105 Instrumentation2208 Electrical and Electronic Engineering1900 General Earth and Planetary SciencesDeep learninginterpretable AIRemote sensingartificial intelligencehybrid modelsEarth system scienceAIRemote sensing (archaeology)10231 Institute for Computational ScienceGeneral Earth and Planetary SciencesPotential gameDisciplineIEEE Geoscience and Remote Sensing Magazine
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Rapid parameter estimation of discrete decaying signals using autoencoder networks

2021

Machine learning: science and technology 2(4), 045024 (2021). doi:10.1088/2632-2153/ac1eea

Signal Processing (eess.SP)FOS: Computer and information sciencesAccuracy and precisionComputer Science - Machine LearningComputer scienceddc:621.3FOS: Physical sciences01 natural sciencesSignalMachine Learning (cs.LG)010309 opticsExponential growthArtificial Intelligence0103 physical sciencesFOS: Electrical engineering electronic engineering information engineeringLimit (mathematics)Neural and Evolutionary Computing (cs.NE)Electrical Engineering and Systems Science - Signal Processing010306 general physicsSignal processingArtificial neural networkEstimation theoryComputer Science - Neural and Evolutionary ComputingAutoencoder621.3Human-Computer InteractionPhysics - Data Analysis Statistics and ProbabilityAlgorithmSoftwareData Analysis Statistics and Probability (physics.data-an)Machine Learning: Science and Technology
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Synergistic integration of optical and microwave satellite data for crop yield estimation

2019

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or des…

Signal Processing (eess.SP)FOS: Computer and information sciencesEarth observationCoefficient of determinationTeledetecció010504 meteorology & atmospheric sciencesEnhanced vegetation index0208 environmental biotechnologyFOS: Physical sciencesSoil Science02 engineering and technologyStatistics - Applications01 natural sciencesArticleModerate resolution imaging spectroradiometer (MODIS)Robustness (computer science)Machine learningLinear regressionFOS: Electrical engineering electronic engineering information engineeringFeature (machine learning)Kernel ridge regressionCrop yield estimationVegetation optical depthApplications (stat.AP)Electrical Engineering and Systems Science - Signal ProcessingComputers in Earth Sciences0105 earth and related environmental sciencesRemote sensingMathematics2. Zero hungerCrop yieldProcessos estocàsticsGeologyEnhanced vegetation indexAgro-ecosystems020801 environmental engineeringPhysics - Data Analysis Statistics and ProbabilityMetric (mathematics)Soil moisture active passive (SMAP)Data Analysis Statistics and Probability (physics.data-an)Imatges Processament
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unitas: the universal tool for annotation of small RNAs

2017

AbstractBackgroundNext generation sequencing is a key technique in small RNA biology research that has led to the discovery of functionally different classes of small non-coding RNAs in the past years. However, reliable annotation of the extensive amounts of small non-coding RNA data produced by high-throughput sequencing is time-consuming and requires robust bioinformatics expertise. Moreover, existing tools have a number of shortcomings including a lack of sensitivity under certain conditions, limited number of supported species or detectable sub-classes of small RNAs.ResultsHere we introduce unitas, an out-of-the-box ready software for complete annotation of small RNA sequence datasets, …

Small RNAtRNA-derived fragments (tRFs)Computational biologypiRNABiologyDNA sequencing570 Life sciencesAnnotationEnsemblHumansRNA-seq data analysismiRNAGeneticsbusiness.industryphasiRNARNAHigh-Throughput Nucleotide SequencingUsabilityMolecular Sequence AnnotationNon-coding RNAKey (cryptography)RNA Small UntranslatedSmall non-coding RNAsbusinessSoftwareHeLa Cells570 Biowissenschaften
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A Comparison of Dyadic and Social Network Assessments of Peer Influence.

2021

The present study compares two methods for assessing peer influence: the longitudinal actor–partner interdependence model (L-APIM) and the longitudinal social network analysis (L-SNA) Model. The data were drawn from 1,995 (49% girls and 51% boys) third grade students ( Mage= 9.68 years). From this sample, L-APIM ( n = 206 indistinguishable dyads and n = 187 distinguishable dyads) and L-SNA ( n = 1,024 total network members) subsamples were created. Students completed peer nominations and objective assessments of mathematical reasoning in the spring of the third and fourth grades. Patterns of statistical significance differed across analyses. Stable distinguishable and indistinguishable L-AP…

Social Psychologysocial network analysisLogical reasoningmedia_common.quotation_subjectkoululaisetsosiaalinen vuorovaikutusArticleEducationDevelopmental Neurosciencesosiaaliset verkostot0502 economics and businesssocial contextDevelopmental and Educational PsychologyPeer influencedyadic data analysismatemaattiset taidotvertaisoppiminen0501 psychology and cognitive sciencesLife-span and Life-course StudiesDyadic data analysismedia_commonpeer influenceSocial networkbusiness.industry4. Educationlongitudinal methods05 social sciencesSocial network analysis (criminology)Social environmentalakoululaisetsosiaaliset suhteetFriendshippeer relationshipsbusinessPsychologySocial psychology050203 business & managementSocial Sciences (miscellaneous)vertaissuhteet050104 developmental & child psychologyNetwork analysisInternational journal of behavioral development
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JEM–X science analysis software

2003

The science analysis of the data from JEM-X on INTEGRAL is performed through a number of levels including corrections, good time selection, imaging and source finding, spectrum and light-curve extraction. These levels consist of individual executables and the running of the complete analysis is controlled by a script where parameters for detailed settings are introduced. The end products are FITS files with a format compatible with standard analysis packages such as XSPEC. Martinez Nuñez, Silvia, Silvia.Martinez@uv.es

Software ; X-ray data analysis ; INTEGRAL ; Satellite ; JEM-X010504 meteorology & atmospheric sciencesAstrophysicsUNESCO::ASTRONOMÍA Y ASTROFÍSICA01 natural sciencesSoftware0103 physical sciencesAnalysis software010303 astronomy & astrophysicsSelection (genetic algorithm)0105 earth and related environmental sciencesPhysicsX-ray data analysisINTEGRALbusiness.industryAstronomy and Astrophysicscomputer.file_format:ASTRONOMÍA Y ASTROFÍSICA::Cosmología y cosmogonia [UNESCO]Computer engineeringSatelliteSpace and Planetary ScienceJEM-XSatelliteExecutableUNESCO::ASTRONOMÍA Y ASTROFÍSICA::Cosmología y cosmogoniabusinesscomputerSoftware:ASTRONOMÍA Y ASTROFÍSICA [UNESCO]Astronomy & Astrophysics
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A machine learning algorithm for direct detection of axion-like particle domain walls

2021

The Global Network of Optical Magnetometers for Exotic physics searches (GNOME) conducts an experimental search for certain forms of dark matter based on their spatiotemporal signatures imprinted on a global array of synchronized atomic magnetometers. The experiment described here looks for a gradient coupling of axion-like particles (ALPs) with proton spins as a signature of locally dense dark matter objects such as domain walls. In this work, stochastic optimization with machine learning is proposed for use in a search for ALP domain walls based on GNOME data. The validity and reliability of this method were verified using binary classification. The projected sensitivity of this new analy…

Space and Planetary SciencePhysics - Data Analysis Statistics and ProbabilityFOS: Physical sciencesddc:530Astronomy and AstrophysicsAstrophysics - Instrumentation and Methods for AstrophysicsInstrumentation and Methods for Astrophysics (astro-ph.IM)Data Analysis Statistics and Probability (physics.data-an)Physics::Geophysics
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TheINTEGRALspectrometer SPI: performance of point-source data analysis

2005

The performance of the SPI point-source data analysis system is assessed using a combination of simulations and of observations gathered during the first year of INTEGRAL operations. External error estimates are derived by comparing source positions and fluxes obtained from independent analyses. When the source detection significance provided by the SPIROS imaging reconstruction program increases from ∼10 to ∼100, the errors decrease as the inverse of the detection significance, with values from ∼10 to ∼1 arcmin in positions, and from ∼10 to ∼1 per cent in relative flux. These errors are dominated by Poisson counting noise. Our error estimates are consistent with those provided by the SPIRO…

Statistical noisePoint sourceInstrumentationdata analysis -gamma raysPoisson distribution01 natural sciencesNoise (electronics)[PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]symbols.namesakeSignal-to-noise ratioOptics0103 physical sciencesSpurious relationship010303 astronomy & astrophysicsinstrumentationPhysics[SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph]010308 nuclear & particles physicsbusiness.industryAstronomy and AstrophysicsComputational physicsobservationsSpace and Planetary SciencesymbolsDeconvolutionbusinessmiscellaneous -methodsMonthly Notices of the Royal Astronomical Society
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