We study the asymptotics of the Poisson kernel and Green's functions of the fractional conformal Laplacian for conformal infinities of asymptotically hyperbolic manifolds. We derive sharp expansions of the Poisson kernel and Green's functions of the conformal Laplacian near their singularities. Our expansions of the Green's functions answer the first part of the conjecture of KimMussoWei[
In this paper, we propose a new class of operator factorization methods to discretize the integral fractional Laplacian
 Publication Date:
 NSFPAR ID:
 10336766
 Journal Name:
 Discrete & Continuous Dynamical Systems  S
 Volume:
 15
 Issue:
 4
 Page Range or eLocationID:
 851
 ISSN:
 19371632
 Sponsoring Org:
 National Science Foundation
More Like this

21 ] in the case of locally flat conformal infinities of PoincareEinstein manifolds and together with the Poisson kernel asymptotic is used also in our paper [25 ] to show solvability of the fractional Yamabe problem in that case. Our asymptotics of the Green's functions on the general case of conformal infinities of asymptotically hyperbolic space is used also in [29 ] to show solvability of the fractional Yamabe problem for conformal infinities of dimension and fractional parameter in\begin{document}$ 3 $\end{document} corresponding to a global case left by previous works.\begin{document}$ (\frac{1}{2}, 1) $\end{document} 
This paper introduces a novel generative encoder (GE) framework for generative imaging and image processing tasks like image reconstruction, compression, denoising, inpainting, deblurring, and superresolution. GE unifies the generative capacity of GANs and the stability of AEs in an optimization framework instead of stacking GANs and AEs into a single network or combining their loss functions as in existing literature. GE provides a novel approach to visualizing relationships between latent spaces and the data space. The GE framework is made up of a pretraining phase and a solving phase. In the former, a GAN with generator
capturing the data distribution of a given image set, and an AE network with encoder\begin{document}$ G $\end{document} that compresses images following the estimated distribution by\begin{document}$ E $\end{document} are trained separately, resulting in two latent representations of the data, denoted as the generative and encoding latent space respectively. In the solving phase, given noisy image\begin{document}$ G $\end{document} , where\begin{document}$ x = \mathcal{P}(x^*) $\end{document} is the target unknown image,\begin{document}$ x^* $\end{document} is an operator adding an addictive, or multiplicative, or convolutional noise, or equivalently given such an image\begin{document}$ \mathcal{P} $\end{document} more »\begin{document}$ x $\end{document} and the image
is recovered in a generative way via\begin{document}$ x^* $\end{document} , where\begin{document}$ \hat{x}: = G(z^*)\approx x^* $\end{document} is a hyperparameter. The unification of the two spaces allows improved performance against corresponding GAN and AE networks while visualizing interesting properties in each latent space.\begin{document}$ \lambda>0 $\end{document} 
Consider the linear transport equation in 1D under an external confining potential
:\begin{document}$ \Phi $\end{document} For
(with\begin{document}$ \Phi = \frac {x^2}2 + \frac { \varepsilon x^4}2 $\end{document} small), we prove phase mixing and quantitative decay estimates for\begin{document}$ \varepsilon >0 $\end{document} , with an inverse polynomial decay rate\begin{document}$ {\partial}_t \varphi : =  \Delta^{1} \int_{ \mathbb{R}} {\partial}_t f \, \mathrm{d} v $\end{document} . In the proof, we develop a commuting vector field approach, suitably adapted to this setting. We will explain why we hope this is relevant for the nonlinear stability of the zero solution for the Vlasov–Poisson system in\begin{document}$ O({\langle} t{\rangle}^{2}) $\end{document} D under the external potential\begin{document}$ 1 $\end{document} .\begin{document}$ \Phi $\end{document} 
We establish an instantaneous smoothing property for decaying solutions on the halfline
of certain degenerate Hilbert spacevalued evolution equations arising in kinetic theory, including in particular the steady Boltzmann equation. Our results answer the two main open problems posed by Pogan and Zumbrun in their treatment of\begin{document}$ (0, +\infty) $\end{document} stable manifolds of such equations, showing that\begin{document}$ H^1 $\end{document} solutions that remain sufficiently small in\begin{document}$ L^2_{loc} $\end{document} (i) decay exponentially, and (ii) are\begin{document}$ L^\infty $\end{document} for\begin{document}$ C^\infty $\end{document} , hence lie eventually in the\begin{document}$ t>0 $\end{document} stable manifold constructed by Pogan and Zumbrun.\begin{document}$ H^1 $\end{document} 
We consider the wellknown LiebLiniger (LL) model for
bosons interacting pairwise on the line via the\begin{document}$ N $\end{document} potential in the meanfield scaling regime. Assuming suitable asymptotic factorization of the initial wave functions and convergence of the microscopic energy per particle, we show that the timedependent reduced density matrices of the system converge in trace norm to the pure states given by the solution to the onedimensional cubic nonlinear Schrödinger equation (NLS) with an explict rate of convergence. In contrast to previous work [\begin{document}$ \delta $\end{document} 3 ] relying on the formalism of second quantization and coherent states and without an explicit rate, our proof is based on the counting method of Pickl [65 ,66 ,67 ] and Knowles and Pickl [44 ]. To overcome difficulties stemming from the singularity of the potential, we introduce a new shortrange approximation argument that exploits the Hölder continuity of the\begin{document}$ \delta $\end{document} body wave function in a single particle variable. By further exploiting the\begin{document}$ N $\end{document} subcritical wellposedness theory for the 1D cubic NLS, we can prove meanfield convergence when the limiting solution to the NLS has finitemore »\begin{document}$ L^2 $\end{document}