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Andrew N Norris - One of the best experts on this subject based on the ideXlab platform.

  • acoustic cloaking theory
    Proceedings of The Royal Society A: Mathematical Physical and Engineering Sciences, 2008
    Co-Authors: Andrew N Norris
    Abstract:

    An acoustic cloak is a compact region enclosing an object, such that sound incident from all directions passes through and around the cloak as though the object was not present. A theory of acoustic cloaking is developed using the transformation or change-of-variables method for mapping the cloaked region to a point with vanishing scattering strength. We show that the acoustical parameters in the cloak must be anisotropic: either the mass density or the mechanical stiffness or both. If the stiffness is isotropic, corresponding to a fluid with a single bulk modulus, then the inertial density must be infinite at the inner surface of the cloak. This requires an infinitely massive cloak. We show that perfect cloaking can be achieved with finite mass through the use of anisotropic stiffness. The Generic Class of anisotropic material required is known as a pentamode material (PM). If the transformation deformation gradient is symmetric then the PM parameters are explicit, otherwise its properties depend on a stress-like tensor that satisfies a static equilibrium equation. For a given transformation mapping, the material composition of the cloak is not uniquely defined, but the phase speed and wave velocity of the pseudo-acoustic waves in the cloak are unique. Examples are given from two and three dimensions.

  • Acoustic cloaking theory
    Proceedings of the Royal Society A: Mathematical Physical and Engineering Sciences, 2008
    Co-Authors: Andrew N Norris
    Abstract:

    An acoustic cloak envelopes an object so that sound incident from all directions passes through and around the cloak as though the object were not present. A theory of acoustic cloaking is developed using the transformation or change-of-variables method for mapping the cloaked region to a point with vanishing scattering strength. We show that the acoustical parameters in the cloak must be anisotropic: either the mass density or the mechanical stiffness or both. If the stiffness is isotropic, corresponding to a fluid with a single bulk modulus, then the inertial density must be infinite at the inner surface of the cloak. This requires an infinitely massive cloak. We show that perfect cloaking can be achieved with finite mass through the use of anisotropic stiffness. The Generic Class of anisotropic material required is known as a pentamode material. If the transformation deformation gradient is symmetric then the pentamode material parameters are explicit, otherwise its properties depend on a stress like tensor which satisfies a static equilibrium equation. For a given transformation mapping the material composition of the cloak is not uniquely defined, but the phase and wave speeds of the pseudo-acoustic waves in the cloak are unique. Examples are given from 2D and 3D.

Sáenz Manuel - One of the best experts on this subject based on the ideXlab platform.

  • Strong replica symmetry for high-dimensional disordered log-concave Gibbs measures
    2020
    Co-Authors: Barbier Jean, Panchenko Dmitry, Sáenz Manuel
    Abstract:

    We consider a Generic Class of log-concave, possibly random, (Gibbs) measures. Using a new type of perturbation we prove concentration of an infinite family of order parameters called multioverlaps. These completely parametrise the quenched Gibbs measure of the system, so that their self-averaging behavior implies a simple representation of asymptotic Gibbs measures, as well as decoupling of the variables at hand in a strong sense. Our concentration results may prove themselves useful in several contexts. In particular in machine learning and high-dimensional inference, log-concave measures appear in convex empirical risk minimisation, maximum a-posteriori inference or M-estimation. We believe that our results may be applicable in establishing some type of "replica symmetric formulas" for the free energy, inference or generalisation error in such settings

  • Strong replica symmetry for high-dimensional disordered log-concave Gibbs measures
    2020
    Co-Authors: Barbier Jean, Panchenko Dmitry, Sáenz Manuel
    Abstract:

    We consider a Generic Class of log-concave, possibly random, (Gibbs) measures. We prove the concentration of an infinite family of order parameters called multioverlaps. Because they completely parametrise the quenched Gibbs measure of the system, this implies a simple representation of the asymptotic Gibbs measures, as well as the decoupling of the variables in a strong sense. These results may prove themselves useful in several contexts. In particular in machine learning and high-dimensional inference, log-concave measures appear in convex empirical risk minimisation, maximum a-posteriori inference or M-estimation. We believe that they may be applicable in establishing some type of "replica symmetric formulas" for the free energy, inference or generalisation error in such settings

Fangfang Wang - One of the best experts on this subject based on the ideXlab platform.

  • hybrid garch a Generic Class of models for volatility predictions using high frequency data
    Statistica Sinica, 2015
    Co-Authors: Xilong Chen, Eric Ghysels, Fangfang Wang
    Abstract:

    We propose a general GARCH framework that allows the predict volatility using returns sampled at a higher frequency than the prediction horizon. We call the Class of models High FrequencY Data-Based PRojectIon-Driven GARCH, or HYBRID-GARCH models, as the volatility dynamics are driven by what we call HYBRID processes. The HYBRID processes can involve data sampled at any frequency.

  • hybrid garch a Generic Class of models for volatility predictions using mixed frequency data
    Social Science Research Network, 2011
    Co-Authors: Xilong Chen, Eric Ghysels, Fangfang Wang
    Abstract:

    We propose a general GARCH framework that allows the predict volatility using returns sampled at a higher frequency than the prediction horizon. We call the Class of models High FrequencY Data-Based PRojectIon-Driven GARCH, or HYBRID-GARCH models, as the volatility dynamics are driven by what we call HYBRID processes. The HYBRID processes can involve data sampled at any frequency. As far as empirical specifications go, we obtain some powerful findings that deviate substantially from the existing literature. Models featuring intra-daily asymmetries (news impact curves applied to intra-daily returns) dominate symmetric models up to weekly horizons, while the reverse is true for longer horizons. Models using daily realized volatility are less preferred than HYBRID involving intra-daily weighting scheme even for longer horizons.

Barbier Jean - One of the best experts on this subject based on the ideXlab platform.

  • Strong replica symmetry for high-dimensional disordered log-concave Gibbs measures
    2020
    Co-Authors: Barbier Jean, Panchenko Dmitry, Sáenz Manuel
    Abstract:

    We consider a Generic Class of log-concave, possibly random, (Gibbs) measures. Using a new type of perturbation we prove concentration of an infinite family of order parameters called multioverlaps. These completely parametrise the quenched Gibbs measure of the system, so that their self-averaging behavior implies a simple representation of asymptotic Gibbs measures, as well as decoupling of the variables at hand in a strong sense. Our concentration results may prove themselves useful in several contexts. In particular in machine learning and high-dimensional inference, log-concave measures appear in convex empirical risk minimisation, maximum a-posteriori inference or M-estimation. We believe that our results may be applicable in establishing some type of "replica symmetric formulas" for the free energy, inference or generalisation error in such settings

  • Strong replica symmetry for high-dimensional disordered log-concave Gibbs measures
    2020
    Co-Authors: Barbier Jean, Panchenko Dmitry, Sáenz Manuel
    Abstract:

    We consider a Generic Class of log-concave, possibly random, (Gibbs) measures. We prove the concentration of an infinite family of order parameters called multioverlaps. Because they completely parametrise the quenched Gibbs measure of the system, this implies a simple representation of the asymptotic Gibbs measures, as well as the decoupling of the variables in a strong sense. These results may prove themselves useful in several contexts. In particular in machine learning and high-dimensional inference, log-concave measures appear in convex empirical risk minimisation, maximum a-posteriori inference or M-estimation. We believe that they may be applicable in establishing some type of "replica symmetric formulas" for the free energy, inference or generalisation error in such settings

Gary B Lamont - One of the best experts on this subject based on the ideXlab platform.

  • on measuring multiobjective evolutionary algorithm performance
    Congress on Evolutionary Computation, 2000
    Co-Authors: D A Van Veldhuizen, Gary B Lamont
    Abstract:

    Solving optimization problems with multiple (often conflicting) objectives is generally a quite difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this Generic Class. During the past decade a multiplicity of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define and execute a quantitative MOEA performance comparison methodology. Almost all comparisons cited in the current literature visually compare algorithmic results, resulting in only relative conclusions. Our methodology gives a basis for absolute conclusions regarding MOEA performance. Selected results from its execution with four MOEAs are presented and described.