Search results for "tractor"

showing 10 items of 219 documents

Backbone Extraction of Weighted Modular Complex Networks based on their Component Structure

2023

This work introduces a generic backbone extraction framework exploiting the mesoscopic network structure. Indeed, numerous real-world networks are made of dense groups of nodes called communities, multi-core or local components. To deal with these groups' heterogeneity, we propose to extract the backbones independently from their various components and fuse them. Experimental investigations on real-world networks demonstrate the effectiveness of the proposed approach compared to the classical techniques' agnostic of the mesoscopic structure of real-world networks.

Component Structurecommunity-aware backbone extractorBackbone extraction[INFO] Computer Science [cs]Multi-Core StructureCommunity structure
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Graphical Structure of Attraction Basins of Hidden Chaotic Attractors : The Rabinovich-Fabrikant System

2019

The attraction basin of hidden attractors does not intersect with small neighborhoods of any equilibrium point. To the best of our knowledge this property has not been explored using realtime interactive three-dimensions graphics. Aided by advanced computer graphic analysis, in this paper, we explore this characteristic of a particular nonlinear system with very rich and unusual dynamics, the Rabinovich–Fabrikant system. It is shown that there exists a neighborhood of one of the unstable equilibria within which the initial conditions do not lead to the considered hidden chaotic attractor, but to one of the stable equilibria or are divergent. The trajectories starting from any neighborhood o…

Computer Science::Computer Science and Game Theorykaaosteoriadata visualisationvisualisointihidden chaotic attractortietokonegrafiikkaRabinovich-Fabrikant system
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Boolean Networks: A Primer

2021

Abstract Autism Spectrum Disorders (ASDs) stand out as a relevant example where omics-data approaches have been extensively and successfully employed. For instance, an outstanding outcome of the Autism Genome Project relies in the identification of biomarkers and the mapping of biological processes potentially implicated in ASDs’ pathogenesis. Several of these mapped processes are related to molecular and cellular events (e.g., synaptogenesis and synapse function, axon growth and guidance, etc.) that are required for the development of a correct neuronal connectivity. Interestingly, these data are consistent with results of brain imaging studies of some patients. Despite these remarkable pr…

Computer scienceIn silicoAttractor Autism spectrum disorders (ASDs) Axon guidance Basin of attraction Boolean network BoolNet Computational model Copy number variants (CNVs) Growth cone In silico mutagenesis Mutations Neurodevelopmental disorders Systems biologyGenome projectComputational biologyGene mutationmedicine.diseasePhenotypeEndophenotypemental disordersmedicineAutismIdentification (biology)Function (biology)
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Prediction of Hidden Oscillations Existence in Nonlinear Dynamical Systems: Analytics and Simulation

2013

From a computational point of view, in nonlinear dynamical systems, attractors can be regarded as self-excited and hidden attractors. Self-excited attractors can be localized numerically by a standard computational procedure, in which after a transient process a trajectory, starting from a point of unstable manifold in a neighborhood of equilibrium, reaches a state of oscillation, therefore one can easily identify it. In contrast, for a hidden attractor, a basin of attraction does not intersect neighborhoods of equilibria. While classical attractors are self-excited, attractors can therefore be obtained numerically by the standard computational procedure, for localization of hidden attracto…

Computer scienceOscillationbusiness.industryProcess (computing)State (functional analysis)Machine learningcomputer.software_genreManifoldNonlinear Sciences::Chaotic DynamicsAttractorTrajectoryPoint (geometry)Transient (oscillation)Artificial intelligenceStatistical physicsbusinesscomputer
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New systems for extracting 3-D shape information from images

1993

Neural architectures may offer an adequate way to deal with early vision since they are able to learn shape features or classify unknown shapes, generalising the features of a few meaningful examples, with a low computational cost after the training phase. Two different neural approaches are proposed by the authors: the first one consists of a cascaded architecture made up by a first stage named BWE (Boundary Webs Extractor) which is aimed to extract a brightness gradient map from the image, followed by a backpropagation network that estimates the geometric parameters of the object parts present in the perceived scene. The second approach is based on the extraction of the boundary webs map …

Computer sciencebusiness.industryBoundary (topology)Pattern recognitionObject (computer science)BackpropagationExtractorImage (mathematics)SuperquadricsComputer visionArtificial intelligenceD-ShapeBrightness gradientbusiness
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Line reconstruction using prior knowledge in single non-central view

2016

International audience; Line projections in non-central systems contain more geometric information than in central systems. The four degrees of freedom of the 3D line are mapped to the line-image and the 3D line can be theoretically recovered from 4 projecting rays (i.e. line-image points) from a single non-central view. In practice, extraction of line-images is consid- erably more difficult and the resulting reconstruction is imprecise and sensitive to noise. In this paper we present a minimal solution to recover the geometry of the 3D line from only three line-image points when the line is parallel to a given plane. A second minimal solution allows to recover the 3D line from two points w…

Computer sciencebusiness.industryPlane (geometry)Pipeline (computing)[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]02 engineering and technologyDegrees of freedom (mechanics)[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]01 natural sciencesExtractor[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]0103 physical sciencesLine (geometry)Vertical direction0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionNoise (video)Artificial intelligence010306 general physicsbusinessPlucker
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Hidden attractors on one path : Glukhovsky-Dolzhansky, Lorenz, and Rabinovich systems

2017

In this report, by the numerical continuation method we visualize and connect hidden chaotic sets in the Glukhovsky-Dolzhansky, Lorenz and Rabinovich systems using a certain path in the parameter space of a Lorenz-like system.

Computer sciencechaosChaoticFOS: Physical sciencesPhysics::Data Analysis; Statistics and ProbabilityParameter space01 natural sciences010305 fluids & plasmasRabinovich systemLorenz system0103 physical sciencesAttractorGlukhovsky–Dolzhansky systemApplied mathematics010301 acousticsEngineering (miscellaneous)kaaosteoriaApplied Mathematicsta111Lorenz-like systemNonlinear Sciences - Chaotic DynamicsNonlinear Sciences::Chaotic DynamicsNumerical continuationModeling and SimulationPath (graph theory)numeerinen analyysiChaotic Dynamics (nlin.CD)hidden attractorInternational Journal of Bifurcation and Chaos
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The fabric attractor

1997

Abstract The nature of fabric accumulation in high strain zones such as ductile shear zones depends on the nature and orientation of flow eigenvectors or apophyses. Some flow apophyses can act as ‘attractors’ of material lines or principal finite strain axes. This paper explains the nature of such attractors and discusses their significance and orientation in different monoclinic flow types. In ductile shear zones, strain values are high enough to show the effect of attractors in deformed rocks clearly. The concept of attractors can be used in deformation modelling, and can help in understanding the accumulation of deformation fabrics in homogeneous and inhomogeneous flow, e.g. around boudi…

Condensed Matter::Materials ScienceFlow (mathematics)Strain (chemistry)Finite strain theoryOrientation (geometry)AttractorGeologyGeotechnical engineeringGeometryDeformation (engineering)Shear zoneEigenvalues and eigenvectorsGeologyJournal of Structural Geology
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Determination of disproportionate tenders in public procurement

2013

[EN] Public procurement is one of the fundamental pillars of the construction sector and the understanding of its statutory regulation is one of the keys to success. The government regulates the concept of disproportionate tenders to avoid deals that are too low, something which could jeopardise the execution of the work or cause problems during implementation such as conflicting prices, project modifications and delays. The criteria for determining the disproportionality of the offers are numerous and each contracting authority determines which to use in each process by carrying out a comparative analysis. The results show that the formulas of disproportionality based on a percentage of th…

Construction managementFinanceGovernmentCall for bidsActuarial sciencebusiness.industryContract managementContractor selectionTenderingProcurementWork (electrical)Statutory lawSpainEconomicsBid pricebusinessPROYECTOS DE INGENIERIAConstruction managementContract Management
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Sensitivity analysis of consumption cycles

2018

We study the special case of a nonlinear stochastic consumption model taking the form of a 2-dimensional, non-invertible map with an additive stochastic component. Applying the concept of the stochastic sensitivity function and the related technique of confidence domains, we establish the conditions under which the system's complex consumption attractor is likely to become observable. It is shown that the level of noise intensities beyond which the complex consumption attractor is likely to be observed depends on the weight given to past consumption in an individual's preference adjustment.

Consumption (economics)Applied Mathematics05 social sciencesGeneral Physics and AstronomyStatistical and Nonlinear PhysicsObservable01 natural sciences010305 fluids & plasmasNoiseNonlinear system0502 economics and business0103 physical sciencesAttractorEconometricsSensitivity (control systems)050207 economicsSpecial casePreference (economics)Mathematical PhysicsMathematicsChaos: An Interdisciplinary Journal of Nonlinear Science
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