Search results for "Hierarchical"

showing 10 items of 260 documents

Addressing Manufacturing Challenges with Cost-Efficient Fault Tolerant Routing

2010

The high-performance computing domain is enriching with the inclusion of Networks-on-chip (NoCs) as a key component of many-core (CMPs or MPSoCs) architectures. NoCs face the communication scalability challenge while meeting tight power, area and latency constraints. Designers must address new challenges that were not present before. Defective components, the enhancement of application-level parallelism or power-aware techniques may break topology regularity, thus, efficient routing becomes a challenge.In this paper, uLBDR (Universal Logic-Based Distributed Routing) is proposed as an efficient logic-based mechanism that adapts to any irregular topology derived from 2D meshes, being an alter…

010302 applied physicsStatic routingDynamic Source Routingnetwork on chip; routing; manufacturing faultComputer sciencebusiness.industryRouting tableDistributed computingPolicy-based routing02 engineering and technology01 natural sciences020202 computer hardware & architecturenetwork on chipRouting domainLink-state routing protocolrouting0103 physical sciencesMultipath routing0202 electrical engineering electronic engineering information engineeringmanufacturing faultbusinessHierarchical routingComputer network
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Using latent variable models to identify large networks of species‐to‐species associations at different spatial scales

2015

Summary We present a hierarchical latent variable model that partitions variation in species occurrences and co-occurrences simultaneously at multiple spatial scales. We illustrate how the parameterized model can be used to predict the occurrences of a species by using as predictors not only the environmental covariates, but also the occurrences of all other species, at all spatial scales. We leverage recent progress in Bayesian latent variable models to implement a computationally effective algorithm that enables one to consider large communities and extensive sampling schemes. We exemplify the framework with a community of 98 fungal species sampled in c. 22 500 dead wood units in 230 plot…

0106 biological sciences010604 marine biology & hydrobiologyEcological ModelingBayesian probabilityCo-occurrenceLatent variable15. Life on land010603 evolutionary biology01 natural sciencesHierarchical database modelStatisticsCovariateEconometricsLeverage (statistics)Latent variable modelEcology Evolution Behavior and SystematicsPartial correlationMathematicsMethods in Ecology and Evolution
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A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distri…

2019

Global climate change (GCC) may be causing distribution range shifts in many organisms worldwide. Multiple efforts are currently focused on the development of models to better predict distribution range shifts due to GCC. We addressed this issue by including intraspecific genetic structure and spatial autocorrelation (SAC) of data in distribution range models. Both factors reflect the joint effect of ecoevolutionary processes on the geographical heterogeneity of populations. We used a collection of 301 georeferenced accessions of the annual plant Arabidopsis thaliana in its Iberian Peninsula range, where the species shows strong geographical genetic structure. We developed spatial and nonsp…

0106 biological sciences0301 basic medicineHierarchical Bayesian modelsArabidopsis thalianaRange (biology)Bayesian probabilitySpecies distributionArabidopsisGenetic admixtureBiologyResidual010603 evolutionary biology01 natural sciences03 medical and health sciencesAfrica NorthernStatisticsGeneticsSpatial analysisEcology Evolution Behavior and SystematicsSpatial AnalysisPortugalPlant DispersalGenetic heterogeneityGlobal climate changePhylogeographyGenetics Population030104 developmental biologySpainGenetic structureGeographic genetic structureMaxentPeptidesSpatial autocorrelationBiotechnology
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Elaboración de modelos de la distribución de la tórtola europea (Streptopelia turtur L. 1758) en el sureste de la península ibérica

2021

The European turtle dove population and breeding range has declined sharply in Spain. This study reanalyses data from the Atlas of Breeding Birds in Alicante (SE Spain), aiming to identify the main variables related to its occurrence and abundance. We used hierarchical partitioning analysis to identify important environmental variables associated with natural vegetation, farming, hydrological web, anthropic presence, climate, and topography. Analysis combining the most explicative variables of each group identified the mixture of pines and scrubland in the semiarid areas and the length of unpaved roads as the most important variables with a positive effect on occurrence, while herbaceous cr…

0106 biological sciencesDistribution (economics)Mediterranean010603 evolutionary biology01 natural sciences010605 ornithologylaw.inventionPeninsulalawCambio de hábitatIntensificación de la agriculturaTurtle (robot)Análisis de partición jerárquicaMediterráneoNature and Landscape Conservationgeographygeography.geographical_feature_categorybiologybusiness.industryAgriculture intensificationPopulation declineEcologíabiology.organism_classificationStreptopelia turturFisheryHabitat changeQL1-991Hierarchical partitioning analysisDisminución de la poblaciónAnimal Science and ZoologyPinarbusinessZoologyDoveSouth easternPinewoodAnimal Biodiversity and Conservation
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Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach

2021

In this work, we discuss the use of a methodological approach for modelling spatial relationships among species by means of a Bayesian spatial coregionalized model. Inference and prediction is performed using the integrated nested Laplace approximation methodology to reduce the computational burden. We illustrate the performance of the coregionalized model in species interaction scenarios using both simulated and real data. The simulation demonstrates the better predictive performance of the coregionalized model with respect to the univariate models. The case study focus on the spatial distribution of a prey species, the European anchovy (Engraulis encrasicolus), and one of its predator spe…

0106 biological sciencesGeneral MathematicsSpecies distributionBayesian probabilityspeciescoregionalized modelsBayesian hierarchical models010603 evolutionary biology01 natural sciences010104 statistics & probabilitymodelsEngraulisHakeAnchovyStatisticsComputer Science (miscellaneous)INLAdistributionEuropean anchovyPesqueríasCentro Oceanográfico de Murcia0101 mathematicsEngineering (miscellaneous)SPDEfishspecies interactionbiologymathematicslcsh:MathematicsUnivariateMerluccius merlucciusbiology.organism_classificationlcsh:QA1-939fisheriesEnvironmental sciencepredation
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Bayesian spatio-temporal approach to identifying fish nurseries by validating persistence areas

2015

Spatial and temporal closures of fish nursery areas to fishing have recently been recognized as useful tools for efficient fisheries management, as they preserve the reproductive potential of populations and increase the recruitment of target species. In order to identify and locate potential nursery areas for spatio-temporal closures, a solid understanding of species− environment relationships is needed, as well as spatial identification of fish nurseries through the application of robust analyses. One way to achieve knowledge of fish nurseries is to analyse the persistence of recruitment hotspots. In this study, we propose the comparison of different spatiotemporal model structures to ass…

0106 biological sciencesMediterranean climatehttp://aims.fao.org/aos/agrovoc/c_28840[SDV]Life Sciences [q-bio]01 natural sciencesMediterranean seaAbundance (ecology)Ecosystem approachEcologybiologyEcologyU10 - Informatique mathématiques et statistiquesinteraction élevage environnementmodèle de distributionMerluccius merlucciushttp://aims.fao.org/aos/agrovoc/c_41529zone de pêcheNursery areasSpatio temporal analysisanalyse bayésienneGeographyGestion des pêchesgestion spatialealevinageFisheries managementFishinganalyse spatiotemporellegestion des ressources naturellesAquatic Science010603 evolutionary biologyhttp://aims.fao.org/aos/agrovoc/c_24026étude comparativeHakeMerluccius merluccius14. Life underwaterhttp://aims.fao.org/aos/agrovoc/c_4699Ecology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_12399Distribution patternapproche ecosystémiqueÉcologie marinehttp://aims.fao.org/aos/agrovoc/c_4609010604 marine biology & hydrobiologybiology.organism_classificationBiologie marineFisheryThéorie bayésiennehttp://aims.fao.org/aos/agrovoc/c_9000115M40 - Écologie aquatiqueBayesian hierarchical modellingMarine protected areaSpatial fisheries managementNursery areas;Distribution pattern;Ecosystem approach;Spatial fisheries management;Spatio temporal analysis;Bayesian hierarchical modelling;Merluccius merluccius
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Dealing with physical barriers in bottlenose dolphin (Tursiops truncatus) distribution

2019

Abstract Worldwide, cetacean species have started to be protected, but they are still very vulnerable to accidental damage from an expanding range of human activities at sea. To properly manage these potential threats we need a detailed understanding of the seasonal distributions of these highly mobile populations. To achieve this goal, a growing effort has been underway to develop species distribution models (SDMs) that correctly describe and predict preferred species areas. However, accuracy is not always easy to achieve when physical barriers, such as islands, are present. Indeed, SDMs assume, if only implicitly, that the spatial effect is stationary, and that correlation is only depende…

0106 biological sciencesRange (biology)Bayesian probabilitySpecies distributionDistribution (economics)Sede Central IEO010603 evolutionary biology01 natural sciencesINLAPesqueríasArchipelago de La MaddalenaSPDEgeographyCetaceansgeography.geographical_feature_categorybiologybusiness.industry010604 marine biology & hydrobiologyEcological ModelingEnvironmental resource managementBottlenose dolphinbiology.organism_classificationPhysical BarrierHabitatArchipelagoHierarchical Bayesian spatial modelsbusinessEcological Modelling
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Fishery-dependent and -independent data lead to consistent estimations of essential habitats

2016

AbstractSpecies mapping is an essential tool for conservation programmes as it provides clear pictures of the distribution of marine resources. However, in fishery ecology, the amount of objective scientific information is limited and data may not always be directly comparable. Information about the distribution of marine species can be derived from two main sources: fishery-independent data (scientific surveys at sea) and fishery-dependent data (collection and sampling by observers in commercial vessels). The aim of this paper is to compare whether these two different sources produce similar, complementary, or different results. We compare them in the specific context of identifying the Es…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28840Biodiversité et Ecologiehabitatmodélisation spatialehttp://aims.fao.org/aos/agrovoc/c_38371OceanographyGaleus melastomus01 natural sciencesRessource halieutiquehttp://aims.fao.org/aos/agrovoc/c_38127Scyliorhinus caniculamodèle hiérarchiqueSpatial statisticsEcologymodèle de distributionSampling (statistics)Contrast (statistics)Cross-validationModélisation et simulationGeographyHabitatGestion des pêchesModeling and Simulationhttp://aims.fao.org/aos/agrovoc/c_10566http://aims.fao.org/aos/agrovoc/c_3456http://aims.fao.org/aos/agrovoc/c_38117survey designMarine conservationSpecies Distribution ModelsEcology (disciplines)Bayesian probabilityEtmopterus spinaxenquête statistiqueDonnée sur les pêchesmodèle spatiotemporelSede Central IEOContext (language use)Aquatic ScienceDistribution des populationsBayesian hierarchical models010603 evolutionary biologyhttp://aims.fao.org/aos/agrovoc/c_24026elasmobranchsBiodiversity and Ecologyélasmobrancheétude comparativeBayesian hierarchical models;Cross-validation;Species Distribution Models;Spatial statistics;INLA;elasmobranchs ; survey designINLA14. Life underwaterspecies distribution modelsEcology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_6113collecte des donnéesÉcologie marinehttp://aims.fao.org/aos/agrovoc/c_29788http://aims.fao.org/aos/agrovoc/c_4609010604 marine biology & hydrobiologyGestion et conservation des pêchescross validation[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulationmodèle bayésienFisheryM01 - Pêche et aquaculture - Considérations généraleshttp://aims.fao.org/aos/agrovoc/c_2a75d27eThéorie bayésienneM40 - Écologie aquatiqueSpatial ecologyhttp://aims.fao.org/aos/agrovoc/c_2942[SDE.BE]Environmental Sciences/Biodiversity and Ecologyvalidation croiséeElasmobranchii
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Modelling sensitive elasmobranchs habitat

2013

Basic information on the distribution and habitat preferences of ecologically important species is essential for their management and protection. In the Mediterranean Sea there is increasing concern over elasmobranch species because their biological (ecological) characteristics make them highly vulnerable to fishing pressure. Their removal could affect the structure and function of marine ecosystems, inducing changes in trophic interactions at the community level due to the selective elimination of predators or prey species, competitors and species replacement. In this study Bayesian hierarchical spatial models are used to map the sensitive habitats of the three most caught elasmobranch spe…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28840Etmopterus spinaxhabitatAquatic ScienceDistribution des populationshttp://aims.fao.org/aos/agrovoc/c_38371OceanographyGaleus melastomus010603 evolutionary biology01 natural sciencesElasmobranch habitatPredationMediterranean seahttp://aims.fao.org/aos/agrovoc/c_38127http://aims.fao.org/aos/agrovoc/c_3041Scyliorhinus caniculaMediterranean SeaVulnerable speciesMarine ecosystem14. Life underwaterhttp://aims.fao.org/aos/agrovoc/c_4699Ecology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_12399Trophic levelhttp://aims.fao.org/aos/agrovoc/c_6113biologyEcologyU10 - Informatique mathématiques et statistiques010604 marine biology & hydrobiologyScyliorhinus caniculabiology.organism_classificationBiologie marinetechnique de prévisionBayesian hierarchical spatial modelSpecies distribution modelingFisheryHabitatThéorie bayésienneGaleus melastomusM40 - Écologie aquatiquehttp://aims.fao.org/aos/agrovoc/c_10566http://aims.fao.org/aos/agrovoc/c_3456http://aims.fao.org/aos/agrovoc/c_38117Elasmobranchii
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VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS

2014

International audience; The paper proposes a variable selection method for pro-teomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (…

0209 industrial biotechnologybusiness.industryComputer scienceInstrumental variablePosterior probabilityBayesian probabilityPattern recognitionFeature selection02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingLogistic regression01 natural sciences010104 statistics & probability020901 industrial engineering & automationCohortProbability distributionBayesian hierarchical modelingArtificial intelligence0101 mathematicsbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSelection (genetic algorithm)[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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