Search results for "Tree"

showing 10 items of 1841 documents

Winter feeding leads to a shifted phenology in the browntail moth Euproctis chrysorrhoea on the evergreen strawberry tree Arbutus unedo

2010

1 The browntail moth Euproctis chrysorrhoea is a highly polyphagous univoltine forest pest. Although its young larvae usually overwinter in diapause from early autumn to the beginning of spring, winter larval feeding has been reported when this species feeds on the evergreen woody shrub strawberry tree Arbutus unedo. 2 The present study investigated life-history traits of four populations of E. chrysorrhoea feeding on A. unedo, including phenology of the different life stages, larval feeding activity and diapause incidence. By modelling the relationship between larval size and host plant leaf persistence, elevation and mean annual temperature, we also studied larval development in ten popul…

Euproctisbiologyved/biologyPhenologyfungived/biology.organism_classification_rank.speciesVoltinismForestryDiapauseEvergreenbiology.organism_classificationShrubHorticultureStrawberry treeInsect ScienceBotanyAgronomy and Crop ScienceArbutus unedo
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Safety analysis in the LBE-Cooled XADS plant through an integrated use of HAZOP, FAULT TREE and thermalhydraulic transient analyses

2004

A detailed description of possible accidental scenarios, with their frequency of occurrence and their consequences, taking into account both internal and external causes that might contribute to them, can be attained by using the most important risk analysis methodologies (HAZard and OPerability studies, HAZOP; Event Tree, ET; Fault Tree, FT; etc.). As it is known, the Fault Tree methodology is aimed at the evaluation of the frequency of an undesired accidental event (Top Event, TE), and is used to describe this one as a combination of primary events identified, for example, by a HAZOP analysis [1]. However, the classic HAZOP analysis, while allowing to perform an exhaustive study of the ex…

Event treeHazard (logic)Fault tree analysisOperabilityComputer scienceHazard and operability studyRisk analysis (business)Event (computing)Transient (oscillation)Reliability engineering
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Topology testing of phylogenies using least squares methods

2006

[Background] The least squares (LS) method for constructing confidence sets of trees is closely related to LS tree building methods, in which the goodness of fit of the distances measured on the tree (patristic distances) to the observed distances between taxa is the criterion used for selecting the best topology. The generalized LS (GLS) method for topology testing is often frustrated by the computational difficulties in calculating the covariance matrix and its inverse, which in practice requires approximations. The weighted LS (WLS) allows for a more efficient albeit approximate calculation of the test statistic by ignoring the covariances between the distances.

EvolutionInverseHepacivirusBiologyTopologyDNA MitochondrialLeast squares methodsLeast squaresEvolution MolecularGoodness of fit:CIENCIAS DE LA VIDA::Genética ::Ingeniería genética [UNESCO]Test statisticQH359-425AnimalsHumansLeast-Squares AnalysisPhylogenyEcology Evolution Behavior and SystematicsStatisticPhylogenetic treeCovariance matrixUNESCO::CIENCIAS DE LA VIDA::Genética ::Ingeniería genéticaMethodology ArticlePhylogenies; Least squares methodsClassificationHepatitis CTree (graph theory)Sea UrchinsPhylogenies
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Deep-Time Phylogenetic Clustering of Extinctions in an Evolutionarily Dynamic Clade (Early Jurassic Ammonites)

2012

7 pages; International audience; Conservation biologists and palaeontologists are increasingly investigating the phylogenetic distribution of extinctions and its evolutionary consequences. However, the dearth of palaeontological studies on that subject and the lack of methodological consensus hamper our understanding of that major evolutionary phenomenon. Here we address this issue by (i) reviewing the approaches used to quantify the phylogenetic selectivity of extinctions and extinction risks; (ii) investigating with a high-resolution dataset whether extinctions and survivals were phylogenetically clustered among early Pliensbachian (Early Jurassic) ammonites; (iii) exploring the phylogene…

Evolutionary ProcessesEcological MetricsCombined uselcsh:MedicineBiologyForms of EvolutionExtinction BiologicalPhylogeneticsPhyletic PatternsAnimalsCluster AnalysisEvolutionary SystematicsCladelcsh:ScienceBiologyDeep timeSpecies ExtinctionPhylogeny[ SDU.STU.PG ] Sciences of the Universe [physics]/Earth Sciences/PaleontologyAmmoniteEvolutionary BiologyMultidisciplinaryExtinctionModels StatisticalPhylogenetic treeEcologyEcologyFossilslcsh:RPaleontologysocial sciencesBiological Evolutionlanguage.human_languagehumanities[ SDV.BID.EVO ] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE]CephalopodaPhylogenetic PatternExtinction RisklanguageEarth SciencesMacroevolutionlcsh:QPaleoecologyPaleobiologyResearch ArticlePLoS ONE
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PACo: a novel procrustes application to cophylogenetic analysis.

2013

We present Procrustean Approach to Cophylogeny (PACo), a novel statistical tool to test for congruence between phylogenetic trees, or between phylogenetic distance matrices of associated taxa. Unlike previous tests, PACo evaluates the dependence of one phylogeny upon the other. This makes it especially appropriate to test the classical coevolutionary model that assumes that parasites that spend part of their life in or on their hosts track the phylogeny of their hosts. The new method does not require fully resolved phylogenies and allows for multiple host-parasite associations. PACo produces a Procrustes superimposition plot enabling a graphical assessment of the fit of the parasite phyloge…

Evolutionary ProcessesParàsitsZoologylcsh:MedicineBiologia Models matemàticsAnimal PhylogeneticsBiostatisticsBiologyForms of EvolutionStatistical powerPlot (graphics)Host-Parasite InteractionsEvolution MolecularCongruence (geometry)StatisticsAnimalsEvolutionary SystematicsComputer SimulationParasiteslcsh:ScienceBiologyPhylogenyStatisticEvolutionary BiologyMultidisciplinaryPhylogenetic treeStatisticslcsh:RConfidence intervalPhylogeneticsParasitologylcsh:QZoologyJackknife resamplingMathematicsSoftwareResearch ArticleCoevolutionType I and type II errorsPLoS ONE
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An Augmented Reality (AR) CAD System at Construction Sites

2011

Augmented Reality (AR) technologies allow computer-generated content to be superimposed over a live camera view of the real world. Although AR is still a very promising technology, currently only a few commercial applications for industrial purposes exploit the potential of adding contextual content to real scenarios. Most of AR applications are oriented to fields such as education or entertainment, where the requirements in terms of repeatability, fault tolerance, reliability and safety are low. Different visualization devices, tracking methods and interaction techniques are described in the literature, establishing a classification between Indoor and Outdoor AR systems. On the one hand, t…

ExploitHuman–computer interactionComputer scienceFace (geometry)Reliability (computer networking)Scale (chemistry)Augmented realityFault toleranceVariation (game tree)Visualization
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Adaptive learning of compressible strings

2020

Suppose an oracle knows a string $S$ that is unknown to us and that we want to determine. The oracle can answer queries of the form "Is $s$ a substring of $S$?". In 1995, Skiena and Sundaram showed that, in the worst case, any algorithm needs to ask the oracle $\sigma n/4 -O(n)$ queries in order to be able to reconstruct the hidden string, where $\sigma$ is the size of the alphabet of $S$ and $n$ its length, and gave an algorithm that spends $(\sigma-1)n+O(\sigma \sqrt{n})$ queries to reconstruct $S$. The main contribution of our paper is to improve the above upper-bound in the context where the string is compressible. We first present a universal algorithm that, given a (computable) compre…

FOS: Computer and information sciencesCentroid decompositionGeneral Computer ScienceString compressionAdaptive learningKolmogorov complexityContext (language use)Data_CODINGANDINFORMATIONTHEORYString reconstructionTheoretical Computer ScienceCombinatoricsString reconstruction; String learning; Adaptive learning; Kolmogorov complexity; String compression; Lempel-Ziv; Centroid decomposition; Suffix treeSuffix treeIntegerComputer Science - Data Structures and AlgorithmsOrder (group theory)Data Structures and Algorithms (cs.DS)Adaptive learning; Centroid decomposition; Kolmogorov complexity; Lempel-Ziv; String compression; String learning; String reconstruction; Suffix treeTime complexityComputer Science::DatabasesMathematicsLempel-ZivSettore INF/01 - InformaticaLinear spaceString (computer science)SubstringBounded functionString learningTheoretical Computer Science
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Uncommon Suffix Tries

2011

Common assumptions on the source producing the words inserted in a suffix trie with $n$ leaves lead to a $\log n$ height and saturation level. We provide an example of a suffix trie whose height increases faster than a power of $n$ and another one whose saturation level is negligible with respect to $\log n$. Both are built from VLMC (Variable Length Markov Chain) probabilistic sources; they are easily extended to families of sources having the same properties. The first example corresponds to a ''logarithmic infinite comb'' and enjoys a non uniform polynomial mixing. The second one corresponds to a ''factorial infinite comb'' for which mixing is uniform and exponential.

FOS: Computer and information sciencesCompressed suffix arrayPolynomialLogarithmGeneral MathematicsSuffix treevariable length Markov chain[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]Generalized suffix treeprobabilistic source0102 computer and information sciences02 engineering and technologysuffix trie01 natural scienceslaw.inventionCombinatoricslawComputer Science - Data Structures and AlgorithmsTrieFOS: Mathematics0202 electrical engineering electronic engineering information engineeringData Structures and Algorithms (cs.DS)Mixing (physics)[ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS]MathematicsDiscrete mathematicsApplied MathematicsProbability (math.PR)020206 networking & telecommunicationssuffix trie.Computer Graphics and Computer-Aided Design[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]010201 computation theory & mathematicsmixing properties60J05 37E05Suffix[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - ProbabilitySoftware
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Towards Responsible AI for Financial Transactions

2020

Author's accepted manuscript. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The application of AI in finance is increasingly dependent on the principles of responsible AI. These principles-explainability, fairness, privacy, accountability, transparency and soundness form the basis for trust in future AI systems. In this empirical study, we address the first p…

FOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceComputer Science - Artificial IntelligenceDecision tree02 engineering and technologyMachine learningcomputer.software_genreMachine Learning (cs.LG)Empirical research020204 information systems0202 electrical engineering electronic engineering information engineeringRobustness (economics)Categorical variableVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Soundnessbusiness.industryDocument clusteringTransparency (behavior)ComputingMethodologies_PATTERNRECOGNITIONArtificial Intelligence (cs.AI)Financial transaction020201 artificial intelligence & image processingArtificial intelligencebusinesscomputer
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Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization With Medical Applications

2019

Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead in accuracy, this leap comes at the sacrifice of interpretability. To address this accuracy-interpretability challenge, we here introduce, for the first time, a text categorization approach that leverages the recently introduced Tsetlin Machine. In all brevity, we represent the terms of a text as propositional variables. From these, we capture categories using simple propositional formulae, such as: if "rash" and "reaction" and "penicillin" then Allergy. The Tsetlin Machine learns these formulae from a labelled tex…

FOS: Computer and information sciencesComputer Science - Machine LearningGeneral Computer ScienceComputer sciencetext categorizationNatural language understandingDecision treeMachine Learning (stat.ML)02 engineering and technologyVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Annen informasjonsteknologi: 559Machine learningcomputer.software_genresupervised learningMachine Learning (cs.LG)Naive Bayes classifierText miningStatistics - Machine Learning0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceTsetlin machinehealth informaticsInterpretabilityPropositional variableClassification algorithmsArtificial neural networkbusiness.industryDeep learning020208 electrical & electronic engineeringGeneral EngineeringRandom forestSupport vector machinemachine learningCategorization020201 artificial intelligence & image processingArtificial intelligencelcsh:Electrical engineering. Electronics. Nuclear engineeringbusinessPrecision and recallcomputerlcsh:TK1-9971
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