Search results for "Hessian"

showing 10 items of 31 documents

Electron-density critical points analysis and catastrophe theory to forecast structure instability in periodic solids

2018

The critical points analysis of electron density,i.e. ρ(x), fromab initiocalculations is used in combination with the catastrophe theory to show a correlation between ρ(x) topology and the appearance of instability that may lead to transformations of crystal structures, as a function of pressure/temperature. In particular, this study focuses on the evolution of coalescing non-degenerate critical points,i.e. such that ∇ρ(xc) = 0 and λ1, λ2, λ3≠ 0 [λ being the eigenvalues of the Hessian of ρ(x) atxc], towards degenerate critical points,i.e. ∇ρ(xc) = 0 and at least one λ equal to zero. The catastrophe theory formalism provides a mathematical tool to model ρ(x) in the neighbourhood ofxcand allo…

Hessian matrixElectron densitycatastrophe theory010504 meteorology & atmospheric sciencesCondensed Matter Physic010502 geochemistry & geophysics01 natural sciencesBiochemistryInstabilityInorganic Chemistrysymbols.namesakeStructural BiologyAb initio quantum chemistry methodsGeneral Materials Sciencephase/state transitions in crystalPhysical and Theoretical Chemistryphase/state transitions in crystalsEigenvalues and eigenvectors0105 earth and related environmental sciencesPhysicsab initio calculationelectron-density critical pointCondensed matter physicsab initio calculationsDegenerate energy levelsCondensed Matter PhysicsGibbs free energyelectron-density critical points catastrophe theory phase/state transitions in crystals ab initio calculations.symbolsMaterials Science (all)Catastrophe theoryelectron-density critical points
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Interactive simulation of one-dimensional flexible parts

2006

Computer simulations play an ever growing role for the development of automotive products. Assembly simulation, as well as many other processes, are used systematically even before the first physical prototype of a vehicle is built in order to check whether particular components can be assembled easily or whether another part is in the way. Usually, this kind of simulation is limited to rigid bodies. However, a vehicle contains a multitude of flexible parts of various types: cables, hoses, carpets, seat surfaces, insulations, weatherstrips... Since most of the problems using these simulations concern one-dimensional components and since an intuitive tool for cable routing is still needed, w…

Hessian matrixEngineeringBending (metalworking)Computer scienceCoordinate systemStructure (category theory)Automotive industryMechanical engineeringVirtual realityTopologyIndustrial and Manufacturing EngineeringContact forcelaw.inventionsymbols.namesakeSoftwarelawCartesian coordinate systemQuaternionSimulationOrientation (computer vision)business.industryTorsion (mechanics)Frame rateComputer Graphics and Computer-Aided DesignComputer Science ApplicationssymbolsRouting (electronic design automation)businessProceedings of the 2006 ACM symposium on Solid and physical modeling
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On the Symmetry of Solutions to a k-Hessian Type Equation

2013

Abstract In this note we prove that if u is a negative solution to a nonlinear elliptic equation involving a Hessian operator, and u is zero on the boundary of a ball, then u is radially symmetric and increasing along the radii.

Hessian matrixGeneral Mathematics010102 general mathematicsCharacteristic equationStatistical and Nonlinear Physics01 natural sciencesSymmetry (physics)010101 applied mathematicsExplicit symmetry breakingType equationsymbols.namesakeSymmetrySettore MAT/05 - Analisi Matematicasymbols0101 mathematicsHessian equationsMathematical physicsMathematics
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AUTOMATIC DETECTION OF SMALL SPHERICAL LESIONS USING MULTISCALE APPROACH IN 3D MEDICAL IMAGES

2013

International audience; Automated detection of small, low level shapes such as circular/spherical objects in images is a challenging computer vision problem. For many applications, especially microbleed detection in Alzheimer's disease, an automatic pre-screening scheme is required to identify potential seeds with high sensitivity and reasonable specificity. A new method is proposed to detect spherical objects in 3D medical images within the multi-scale Laplacian of Gaussian framework. The major contributions are (1) breaking down 3D sphere detection into 1D line profile detection along each coordinate dimension, (2) identifying center of structures by normalizing the line response profile …

Hessian matrixGround truthOrientation (computer vision)business.industry02 engineering and technologyTranslation (geometry)Blob detectionObject detection030218 nuclear medicine & medical imagingScale space03 medical and health sciencessymbols.namesake0302 clinical medicine[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Line (geometry)[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringsymbols020201 artificial intelligence & image processingComputer visionArtificial intelligencebusinessMathematics
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Comparison results for Hessian equations via symmetrization

2007

where the λ’s are the eigenvalues of the Hessian matrix D2u of u and Sk is the kth elementary symmetric function. For example, for k = 1, S1(Du) = 1u, while, for k = n, Sn(D 2u) = detD2u. Equations involving these operators, and some more general equations of the form F(λ1, . . . , λn) = f in , (1.2) have been widely studied by many authors, who restrict their considerations to convenient cones of solutions with respect to which the operator in (1.2) is elliptic. Following [25] we define the cone 0k of ellipticity for (1.1) to be the connected component containing the positive cone 0 = {λ ∈ R : λi > 0 ∀i = 1, . . . , n} of the set where Sk is positive. Thus 0k is an open, convex, symmetric…

Hessian matrixHessian equationsymmetrizationHessian operatorApplied MathematicsGeneral Mathematicscomparison resultHessian equationCombinatoricssymbols.namesakeOperator (computer programming)Cone (topology)Settore MAT/05 - Analisi MatematicaVertex (curve)symbolsSymmetrizationElementary symmetric polynomialMoser type inequalitiesAlgorithmEigenvalues and eigenvectorsMathematics
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Non-quadratic improved Hessian PDF reweighting and application to CMS dijet measurements at 5.02 TeV

2019

Hessian PDF reweighting, or "profiling", has become a widely used way to study the impact of a new data set on parton distribution functions (PDFs) with Hessian error sets. The available implementations of this method have resorted to a perfectly quadratic approximation of the initial $\chi^2$ function before inclusion of the new data. We demonstrate how one can take into account the first non-quadratic components of the original fit in the reweighting, provided that the necessary information is available. We then apply this method to the CMS measurement of dijet pseudorapidity spectra in proton-proton (pp) and proton-lead (pPb) collisions at 5.02 TeV. The measured pp dijet spectra disagree…

Hessian matrixHessian matrixParticle physicsPhysics and Astronomy (miscellaneous)parton distribution functionsNuclear TheoryFOS: Physical scienceslcsh:AstrophysicsPartonApproxhiukkasfysiikka114 Physical sciences01 natural sciencesNuclear Theory (nucl-th)symbols.namesakeQuadratic equationHigh Energy Physics - Phenomenology (hep-ph)lcsh:QB460-4660103 physical scienceslcsh:Nuclear and particle physics. Atomic energy. Radioactivity010306 general physicsNuclear ExperimentEngineering (miscellaneous)Physicsproton–proton collisions010308 nuclear & particles physicsFunction (mathematics)GluonHigh Energy Physics - PhenomenologyDistribution functionproton-heavy ion collisionsPARTON DISTRIBUTIONSPseudorapiditysymbolslcsh:QC770-798High Energy Physics::Experimentydinfysiikka
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A Sequential Quadratic Programming Method for Volatility Estimation in Option Pricing

2006

Our goal is to identify the volatility function in Dupire's equation from given option prices. Following an optimal control approach in a Lagrangian framework, we propose a globalized sequential quadratic programming (SQP) algorithm with a modified Hessian - to ensure that every SQP step is a descent direction - and implement a line search strategy. In each level of the SQP method a linear-quadratic optimal control problem with box constraints is solved by a primal-dual active set strategy. This guarantees L^1 constraints for the volatility, in particular assuring its positivity. The proposed algorithm is founded on a thorough first- and second-order optimality analysis. We prove the existe…

Hessian matrixMathematical optimizationLine searchComputer scienceMathematicsofComputing_NUMERICALANALYSISOptimal controlsymbols.namesakeValuation of optionsLagrange multipliersymbolsDescent directionVolatility (finance)Dupire equation parameter identification optimal control optimality conditions SQP method primal-dual active set strategySequential quadratic programming
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Hessian PDF reweighting meets the Bayesian methods

2014

We discuss the Hessian PDF reweighting - a technique intended to estimate the effects that new measurements have on a set of PDFs. The method stems straightforwardly from considering new data in a usual $\chi^2$-fit and it naturally incorporates also non-zero values for the tolerance, $\Delta\chi^2>1$. In comparison to the contemporary Bayesian reweighting techniques, there is no need to generate large ensembles of PDF Monte-Carlo replicas, and the observables need to be evaluated only with the central and the error sets of the original PDFs. In spite of the apparently rather different methodologies, we find that the Hessian and the Bayesian techniques are actually equivalent if the $\Delta…

Hessian matrixNuclear TheoryComputer scienceBayesian probabilityFOS: Physical sciencesObservableExponential functionStatistics::ComputationSet (abstract data type)Nuclear Theory (nucl-th)High Energy Physics - Phenomenologysymbols.namesakeHigh Energy Physics - Phenomenology (hep-ph)Simple (abstract algebra)symbolsApplied mathematicsLikelihood functionNuclear theory
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Impact of dijet and D-meson data from 5.02 TeV p+Pb collisions on nuclear PDFs

2020

We discuss the new constraints on gluon parton distribution function (PDF) in lead nucleus, derivable with the Hessian PDF reweighting method from the 5.02 TeV p+Pb measurements of dijet (CMS) and $D^0$-meson (LHCb) nuclear modification ratios. The impact is found to be significant, placing stringent constraints in the mid- and previously unconstrained small-$x$ regions. The CMS dijet data confirm the existence of gluon anti-shadowing and the onset of small-$x$ shadowing, as well as reduce the gluon PDF uncertainties in the larger-$x$ region. The gluon constraints from the LHCb $D^0$ data, reaching down to $x \sim 10^{-5}$ and derived in a NLO perturbative QCD approach, provide a remarkable…

Hessian matrixNuclear and High Energy PhysicsParticle physicsdijet productionNuclear TheoryFOS: Physical sciencesnuclear parton distribution functionPartonopen heavy flavour114 Physical sciences7. Clean energy01 natural sciencessymbols.namesakeHigh Energy Physics - Phenomenology (hep-ph)0103 physical sciencesD meson010306 general physicsNuclear ExperimentPhysics010308 nuclear & particles physicsHigh Energy Physics::PhenomenologyPerturbative QCDGluonUniversality (dynamical systems)proton–nucleus collisionHigh Energy Physics - PhenomenologyDistribution functionDGLAPsymbolsHigh Energy Physics::Experiment
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Can we fit nuclear PDFs with the high-x CLAS data?

2020

AbstractNuclear parton distribution functions (nuclear PDFs) are non-perturbative objects that encode the partonic behaviour of bound nucleons. To avoid potential higher-twist contributions, the data probing the high-x end of nuclear PDFs are sometimes left out from the global extractions despite their potential to constrain the fit parameters. In the present work we focus on the kinematic corner covered by the new high-x data measured by the CLAS/JLab collaboration. By using the Hessian re-weighting technique, we are able to quantitatively test the compatibility of these data with globally analyzed nuclear PDFs and explore the expected impact on the valence-quark distributions at high x. W…

Hessian matrixParticle physicsPhysics and Astronomy (miscellaneous)EMC effectNuclear TheoryFOS: Physical sciencesPartonlcsh:Astrophysicshiukkasfysiikka01 natural sciences114 Physical sciencessymbols.namesakeHigh Energy Physics - Phenomenology (hep-ph)0103 physical scienceslcsh:QB460-466lcsh:Nuclear and particle physics. Atomic energy. Radioactivity010306 general physicsNuclear ExperimentEngineering (miscellaneous)Physics010308 nuclear & particles physicsddc:530530 Physiknuclear parton distribution functions (nuclear PDFs)High Energy Physics - PhenomenologyDistribution functionsymbolslcsh:QC770-798Nucleonydinfysiikka
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