Browsing Mathematics by Title
Now showing items 674-693 of 1478
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$L^p$-bounds for pseudo-differential operators on compact Lie groups
Given a compact Lie group $G$, in this paper we establish $L^p$-bounds for pseudo-differential operators in $L^p(G)$. The criteria here are given in terms of the concept of matrix symbols defined on the non-commutative ... -
$L^p$-bounds for pseudo-differential operators on graded Lie groups
In this work we obtain sharp $L^p$-estimates for pseudo-differential operators on arbitrary graded Lie groups. The results are presented within the setting of the global symbolic calculus on graded Lie groups by using the ... -
A Large Deviation Perspective on Ratio Observables in Reset Processes: Robustness of Rate Functions
(Springer Science and Business Media LLC, 2020-03)We study large deviations of a ratio observable in discrete-time reset processes. The ratio takes the form of a current divided by the number of reset steps and as such it is not extensive in time. A large deviation rate ... -
Large deviation theory of percolation on multiplex networks
(arXiv, 2019)Recently increasing attention has been addressed to the fluctuations observed in percolation defined in single and multiplex networks. These fluctuations are extremely important to characterize the robustness of real finite ... -
Large Fluctuations in Locational Marginal Prices
(Royal Society, The, 2020)This paper investigates large fluctuations of Locational Marginal Prices (LMPs) in wholesale energy markets caused by volatile renewable generation profiles. Specifically, we study events of the form ℙ(LMP∉∏ni=1[α−i,α+i]), ... -
Large subgroups in finite groups
(2018-03) -
Lasso and elastic nets by orthants
(2023-07-21) -
Lasso for hierarchical polynomial models
(2020-01-21) -
Late-time structure of the Bunch-Davies de Sitter wavefunction
(2015-11-30)We examine the late time behavior of the Bunch-Davies wavefunction for interacting light fields in a de Sitter background. We use perturbative techniques developed in the framework of AdS/CFT, and analytically continue to ... -
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification.
(2020-07-24)In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized ... -
Learning process in public goods games
(2015-07-01) -
Learning Regularization Parameter-Maps for Variational Image Reconstruction Using Deep Neural Networks and Algorithm Unrolling
(Society for Industrial & Applied Mathematics (SIAM), 2023-12-31)We introduce a method for the fast estimation of data-adapted, spatially and temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variation (TV) minimization. The ...