On the Reconstruction Theorem of Holonomlc Modules In the Gevrey Classes
Publications of the Research Institute for Mathematical Sciences, 1991 The aim of this paper is to extend a remarkable result due to \textit{T. Kashiwara} and \textit{M. Kawai} [ibid. 17, 813-979 (1981; Zbl 0505.58033)]which asserts that if \({\mathfrak M}\) is a holonomic \({\mathcal E}_ X\) module then there exists an holonomic \({\mathcal E}_ X\) module \({\mathfrak M}_{\text{reg}}\) with regular singularities such ...
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Double exponential quadrature for fractional diffusion. [PDF]
Numer Math (Heidelb), 2023 Rieder A.
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A parametrised version of Moser's modifying terms theorem [PDF]
A sharpened version of Moser's `modifying terms' KAM theorem is derived, and it is shown how this theorem can be used to investigate the persistence of invariant tori in general situations, including those where some of the Floquet exponents of the ...
Wagener, F.O.O.
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Transposon accumulation at xenobiotic gene family loci in aphids. [PDF]
Genome Res, 2023 Baril T, Pym A, Bass C, Hayward A.
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Gevrey estimates of the resolvent and sub-exponential time-decay of solutions
, 2017 In this article, we study a class of non-selfadjoint Schr{\"o}dinger operators H which are perturbation of some model operator H 0 satisfying a weighted coercive assumption.
Wang, Xue Ping
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Gevrey Regularity for Solution of the Spatially Homogeneous Landau Equation
, 2009 In this paper, we study the Gevrey class regularity for solutions of the spatially homogeneous Landau equations in the hard potential case and the Maxwellian molecules ...
Chen, Hua, Li, Weixi, Xu, Chao-Jiang
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Using Technology to Identify Children With Autism Through Motor Abnormalities. [PDF]
Front Psychol, 2021 Simeoli R, Milano N, Rega A, Marocco D.
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Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries. [PDF]
PLoS One, 2022 Peng HY +10 more
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Reproducibility of Gene Expression Signatures in Diffuse Large B-Cell Lymphoma. [PDF]
Cancers (Basel), 2022 Plaça JR +12 more
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Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency. [PDF]
Phys Med Biol, 2022 Barragán-Montero A +12 more
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