17,936 research outputs found

    Grand Fujii-Fujii-Nakamoto operator inequality dealing with operator order and operator chaotic order

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    In this paper, we shall prove that a grand Fujii-Fujii-Nakamoto operator inequality implies operator order and operator chaotic order under different conditions

    Further development of positive semidefinite solutions of the operator equation βˆ‘j=1nAnβˆ’jXAjβˆ’1=B\sum_{j=1}^{n}A^{n-j}XA^{j-1}=B

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    In \cite{Positive semidefinite solutions}, T. Furuta discusses the existence of positive semidefinite solutions of the operator equation βˆ‘j=1nAnβˆ’jXAjβˆ’1=B\sum_{j=1}^{n}A^{n-j}XA^{j-1}=B. In this paper, we shall apply Grand Furuta inequality to study the operator equation. A generalized special type of BB is obtained due to \cite{Positive semidefinite solutions}

    H\"older continuity of the integrated density of states for Extended Harper's Model with Liouville frequency

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    In this paper, we study the non-self-dual extended Harper's model with a Liouville frequency. Based on the work of \cite{SY}, we show that the integrated density of states (IDS for short) of the model is 12\frac{1}{2}-Ho¨\ddot{\text{o}}lder continuous. As an application, we also obtain the Carleson homogeneity of the spectrum.Comment: 1 figur

    A sufficient condition on operator order for strictly positive operators

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    Let A1A_{1}, A2A_{2}, ......, AkA_{k} be strictly positive operators on a Hilbert space. This note is to show a sufficient condition of Akβ‰₯Akβˆ’1β‰₯β‰₯A3β‰₯A2β‰₯A1A_{k}\geq A_{k-1}\geq\geq A_{3}\geq A_{2}\geq A_{1}, which extends the related result before

    A "boundedness implies convergence" principle and its applications to collapsing estimates in K\"ahler geometry

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    We establish a general "boundedness implies convergence" principle for a family of evolving Riemannian metrics. We then apply this principle to collapsing Calabi-Yau metrics and normalized K\"ahler-Ricci flows on torus fibered minimal models to obtain convergence results.Comment: All comments are welcome

    Thresholded Multiscale Gaussian Processes with Application to Bayesian Feature Selection for Massive Neuroimaging Data

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    Motivated by the needs of selecting important features for massive neuroimaging data, we propose a spatially varying coefficient model (SVCMs) with sparsity and piecewise smoothness imposed on the coefficient functions. A new class of nonparametric priors is developed based on thresholded multiresolution Gaussian processes (TMGP). We show that the TMGP has a large support on a space of sparse and piecewise smooth functions, leading to posterior consistency in coefficient function estimation and feature selection. Also, we develop a method for prior specifications of thresholding parameters in TMGPs and discuss their theoretical properties. Efficient posterior computation algorithms are developed by adopting a kernel convolution approach, where a modified square exponential kernel is chosen taking the advantage that the analytical form of the eigen decomposition is available. Based on simulation studies, we demonstrate that our methods can achieve better performance in estimating the spatially varying coefficient. Also, the proposed model has been applied to an analysis of resting state functional magnetic resonance imaging (Rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) study, it provides biologically meaningful results.Comment: 37 pages, 7 figure

    Convergence of Laplacian Spectra from Point Clouds

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    The spectral structure of the Laplacian-Beltrami operator (LBO) on manifolds has been widely used in many applications, include spectral clustering, dimensionality reduction, mesh smoothing, compression and editing, shape segmentation, matching and parameterization, and so on. Typically, the underlying Riemannian manifold is unknown and often given by a set of sample points. The spectral structure of the LBO is estimated from some discrete Laplace operator constructed from this set of sample points. In our previous papers, we proposed the point integral method to discretize the LBO from point clouds, which is also capable to solve the eigenproblem. Then one fundmental issue is the convergence of the eigensystem of the discrete Laplacian to that of the LBO. In this paper, for compact manifolds isometrically embedded in Euclidean spaces possibly with boundary, we show that the eigenvalues and the eigenvectors obtained by the point integral method converges to the eigenvalues and the eigenfunctions of the LBO with the Neumann boundary, and in addition, we give an estimate of the convergence rate. This result provides a solid mathematical foundation for the point integral method in the computation of Laplacian spectra from point clouds

    Convergence of the Point Integral method for the Poisson equation with Dirichlet boundary on point cloud

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    The Poisson equation on manifolds plays an fundamental role in many applications. Recently, we proposed a novel numerical method called the Point Integral method (PIM) to solve the Poisson equations on manifolds from point clouds. In this paper, we prove the convergence of the point integral method for solving the Poisson equation with the Dirichlet boundary condition

    Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data

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    In this paper we propose a generalized Gaussian process concurrent regression model for functional data where the functional response variable has a binomial, Poisson or other non-Gaussian distribution from an exponential family while the covariates are mixed functional and scalar variables. The proposed model offers a nonparametric generalized concurrent regression method for functional data with multi-dimensional covariates, and provides a natural framework on modeling common mean structure and covariance structure simultaneously for repeatedly observed functional data. The mean structure provides an overall information about the observations, while the covariance structure can be used to catch up the characteristic of each individual batch. The prior specification of covariance kernel enables us to accommodate a wide class of nonlinear models. The definition of the model, the inference and the implementation as well as its asymptotic properties are discussed. Several numerical examples with different non-Gaussian response variables are presented. Some technical details and more numerical examples as well as an extension of the model are provided as supplementary materials

    Low temperature properties of one-dimensional SU(4) Hubbard-like model at low concentration

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    On the basis of Bethe ansatz solution of one dimensional SU(4) Hubbard-like model, we study its thermodynamics properties by means of Yang-Yang thermodynamics Bethe ansatz. The Land\'e gg factor is taken into account so as to describe electrons with orbital degeneracy. The free energy at low temperature is given and the specific heat both in strong coupling and weak coupling limits are obtained.Comment: 11 page
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