Search results for "FOS: Computer and information sciences"

showing 10 items of 857 documents

Computation of the topological type of a real Riemann surface

2012

We present an algorithm for the computation of the topological type of a real compact Riemann surface associated to an algebraic curve, i.e., its genus and the properties of the set of fixed points of the anti-holomorphic involution $\tau$, namely, the number of its connected components, and whether this set divides the surface into one or two connected components. This is achieved by transforming an arbitrary canonical homology basis to a homology basis where the $\mathcal{A}$-cycles are invariant under the anti-holomorphic involution $\tau$.

Computational Geometry (cs.CG)FOS: Computer and information sciencesreal Riemann surface[MATH.MATH-AG] Mathematics [math]/Algebraic Geometry [math.AG]homology basis[ MATH.MATH-AG ] Mathematics [math]/Algebraic Geometry [math.AG]Mathematics - Algebraic Geometryreal algebraic curveholomorphic differentialsFOS: MathematicsComputer Science - Computational Geometryreal ovals[MATH.MATH-AG]Mathematics [math]/Algebraic Geometry [math.AG]Algebraic Geometry (math.AG)
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A Survey and Taxonomy of Urban Traffic Management: Towards Vehicular Networks

2014

Urban Traffic Management (UTM) topics have been tackled since long time, mainly by civil engineers and by city planners. The introduction of new communication technologies - such as cellular systems, satellite positioning systems and inter-vehicle communications - has significantly changed the way researchers deal with UTM issues. In this survey, we provide a review and a classification of how UTM has been addressed in the literature. We start from the recent achievements of "classical" approaches to urban traffic estimation and optimization, including methods based on the analysis of data collected by fixed sensors (e.g., cameras and radars), as well as methods based on information provide…

Computer Science - Networking and Internet ArchitectureNetworking and Internet Architecture (cs.NI)FOS: Computer and information sciencesComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS
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Energy Efficient Resource Allocation and User Scheduling for Collaborative Mobile Clouds with Hybrid Receivers

2014

In this paper, we study the resource allocation and user scheduling algorithm for minimizing the energy cost of data transmission in the context of OFDMA collaborative mobile cloud (CMC) with simultaneous wireless information and power transfer (SWIPT) receivers. The CMC, which consists of several collaborating MTs offers one potential solution for downlink content distribution and for the energy consumption reduction at the terminal side. Meanwhile, as RF signal can carry both information and energy simultaneously, the induced SWIPT has gained much attention for energy efficiency design of mobile nodes. Previous work on the design of CMC system mainly focused on the cloud formulation or en…

Computer Science - Networking and Internet ArchitectureNetworking and Internet Architecture (cs.NI)FOS: Computer and information sciencescollaborative mobile cloudscontent distributiongreen communicationsuser schedulingsubchannel allocationresursointipower allocationuser cooperation
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MultivariateApart: Generalized partial fractions

2021

We present a package to perform partial fraction decompositions of multivariate rational functions. The algorithm allows to systematically avoid spurious denominator factors and is capable of producing unique results also when being applied to terms of a sum separately. The package is designed to work in Mathematica, but also provides interfaces to the Form and Singular computer algebra systems.

Computer Science - Symbolic ComputationHigh Energy Physics - TheoryFOS: Computer and information sciencesPolynomialComputer scienceFOS: Physical sciencesGeneral Physics and AstronomyRational functionSymbolic Computation (cs.SC)Partial fraction decomposition01 natural sciencesGröbner basisHigh Energy Physics - Phenomenology (hep-ph)ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION0103 physical sciences010306 general physicsSpurious relationshipcomputer.programming_language010308 nuclear & particles physicsFunction (mathematics)Symbolic computationAlgebraHigh Energy Physics - PhenomenologyHigh Energy Physics - Theory (hep-th)Hardware and ArchitectureComputer Science::Mathematical SoftwareWolfram LanguagecomputerComputer Physics Communications
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Introduction to the GiNaC Framework for Symbolic Computation within the C++ Programming Language

2002

AbstractThe traditional split into a low level language and a high level language in the design of computer algebra systems may become obsolete with the advent of more versatile computer languages. We describe GiNaC, a special-purpose system that deliberately denies the need for such a distinction. It is entirely written in C++and the user can interact with it directly in that language. It was designed to provide efficient handling of multivariate polynomials, algebras and special functions that are needed for loop calculations in theoretical quantum field theory. It also bears some potential to become a more general purpose symbolic package.

Computer Science - Symbolic ComputationI.1.3FOS: Computer and information sciencesFor loopTheoretical computer scienceAlgebra and Number TheoryFOS: Physical sciencesI.1.1; I.1.3Symbolic Computation (cs.SC)Computational Physics (physics.comp-ph)Symbolic computationI.1.1High Energy Physics - PhenomenologyComputational MathematicsHigh Energy Physics - Phenomenology (hep-ph)General purposeHigh-level programming languageSpecial functionsFourth-generation programming languagePhysics - Computational PhysicsC programming languageLow-level programming languageMathematicsJournal of Symbolic Computation
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Percolation on correlated random networks

2011

We consider a class of random, weighted networks, obtained through a redefinition of patterns in an Hopfield-like model and, by performing percolation processes, we get information about topology and resilience properties of the networks themselves. Given the weighted nature of the graphs, different kinds of bond percolation can be studied: stochastic (deleting links randomly) and deterministic (deleting links based on rank weights), each mimicking a different physical process. The evolution of the network is accordingly different, as evidenced by the behavior of the largest component size and of the distribution of cluster sizes. In particular, we can derive that weak ties are crucial in o…

Condensed Matter Physics; Statistical and Nonlinear Physics; Statistics and ProbabilityStatistics and ProbabilitySocial and Information Networks (cs.SI)FOS: Computer and information sciencesRandom graphDiscrete mathematicsPhysics - Physics and SocietyStatistical Mechanics (cond-mat.stat-mech)Interdependent networksFOS: Physical sciencesComputer Science - Social and Information NetworksStatistical and Nonlinear PhysicsPercolation thresholdPhysics and Society (physics.soc-ph)Complex networkCondensed Matter PhysicsGiant componentPercolationContinuum percolation theoryStatistical physicsCondensed Matter - Statistical MechanicsClustering coefficientMathematicsPhysical Review E
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Multiscale Information Storage of Linear Long-Range Correlated Stochastic Processes

2019

Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric approach which allows one to compute information storage across multiple timescales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). Our analysis is performed in the popular framework of multiscale entropy, whereby a time series is first "coarse grained" at the chosen timescale through low-pass filtering and downsampling, and then …

Conditional entropyFOS: Computer and information sciencesComputer scienceStochastic processDynamical system01 natural sciencesMeasure (mathematics)010305 fluids & plasmasMethodology (stat.ME)Multiscale Entropy Information Theory ComplexityAutoregressive model0103 physical sciencesState space010306 general physicsRepresentation (mathematics)AlgorithmStatistics - MethodologyParametric statistics
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Criminal networks analysis in missing data scenarios through graph distances.

2021

Data collected in criminal investigations may suffer from: (i) incompleteness, due to the covert nature of criminal organisations; (ii) incorrectness, caused by either unintentional data collection errors and intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyse nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data and to determine which network type is most affected by it. The networks are firstly pruned following two specific methods: …

Data AnalysisFOS: Computer and information sciencesComputer and Information SciencesScienceIntelligenceSocial SciencesTransportationCriminologyCivil EngineeringSocial NetworkingComputer Science - Computers and SocietyLaw EnforcementSociologyComputers and Society (cs.CY)PsychologyHumansComputer NetworksSocial and Information Networks (cs.SI)Algorithms; Humans; Terrorism; Criminals; Data Analysis; Social NetworkingSettore INF/01 - InformaticaQCognitive PsychologyRBiology and Life SciencesEigenvaluesComputer Science - Social and Information NetworksCriminalsTransportation InfrastructurePoliceRoadsProfessionsAlgebraLinear AlgebraPeople and PlacesPhysical SciencesEngineering and TechnologyCognitive ScienceMedicineLaw and Legal SciencesPopulation GroupingsTerrorismCrimeCriminal Justice SystemMathematicsNetwork AnalysisAlgorithmsResearch ArticleNeurosciencePLoS ONE
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Analyzing big datasets of genomic sequences: fast and scalable collection of k-mer statistics

2019

Abstract Background Distributed approaches based on the MapReduce programming paradigm have started to be proposed in the Bioinformatics domain, due to the large amount of data produced by the next-generation sequencing techniques. However, the use of MapReduce and related Big Data technologies and frameworks (e.g., Apache Hadoop and Spark) does not necessarily produce satisfactory results, in terms of both efficiency and effectiveness. We discuss how the development of distributed and Big Data management technologies has affected the analysis of large datasets of biological sequences. Moreover, we show how the choice of different parameter configurations and the careful engineering of the …

Data AnalysisFOS: Computer and information sciencesTime FactorsTime FactorComputer scienceStatistics as TopicBig dataApache Spark; distributed computing; performance evaluation; k-mer countinglcsh:Computer applications to medicine. Medical informaticsBiochemistryDomain (software engineering)Databases03 medical and health sciences0302 clinical medicineStructural BiologyComputer clusterStatisticsSpark (mathematics)Molecular Biologylcsh:QH301-705.5030304 developmental biology0303 health sciencesGenomeSettore INF/01 - InformaticaBase SequenceNucleic AcidApache Sparkbusiness.industryResearchApache Spark; Distributed computing; k-mer counting; Performance evaluation; Algorithms; Base Sequence; Software; Time Factors; Data Analysis; Databases Nucleic Acid; Genome; Statistics as TopicApplied Mathematicsk-mer countingDistributed computingComputer Science ApplicationsAlgorithmData AnalysiComputer Science - Distributed Parallel and Cluster Computinglcsh:Biology (General)030220 oncology & carcinogenesisScalabilityPerformance evaluationlcsh:R858-859.7Algorithm designDistributed Parallel and Cluster Computing (cs.DC)Databases Nucleic AcidbusinessAlgorithmsSoftware
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Distributed Real-Time Sentiment Analysis for Big Data Social Streams

2014

Big data trend has enforced the data-centric systems to have continuous fast data streams. In recent years, real-time analytics on stream data has formed into a new research field, which aims to answer queries about "what-is-happening-now" with a negligible delay. The real challenge with real-time stream data processing is that it is impossible to store instances of data, and therefore online analytical algorithms are utilized. To perform real-time analytics, pre-processing of data should be performed in a way that only a short summary of stream is stored in main memory. In addition, due to high speed of arrival, average processing time for each instance of data should be in such a way that…

Data streamFOS: Computer and information sciencesComputer Science - Computation and LanguageComputer sciencebusiness.industryData stream miningSentiment analysisBig dataMachine Learning (stat.ML)Databases (cs.DB)Data structurecomputer.software_genreField (computer science)Computer Science - Information RetrievalTree (data structure)Computer Science - DatabasesComputer Science - Distributed Parallel and Cluster ComputingAnalyticsStatistics - Machine LearningData miningDistributed Parallel and Cluster Computing (cs.DC)businesscomputerComputation and Language (cs.CL)Information Retrieval (cs.IR)
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