359 research outputs found
Lower and upper bounds for the first eigenvalue of nonlocal diffusion problems in the whole space
We find lower and upper bounds for the first eigenvalue of a nonlocal
diffusion operator of the form T(u) = - \int_{\rr^d} K(x,y) (u(y)-u(x)) \,
dy. Here we consider a kernel where
is a bounded, nonnegative function supported in the unit ball and means a
diffeomorphism on \rr^d. A simple example being a linear function .
The upper and lower bounds that we obtain are given in terms of the Jacobian of
and the integral of . Indeed, in the linear case we
obtain an explicit expression for the first eigenvalue in the whole \rr^d and
it is positive when the the determinant of the matrix is different from
one. As an application of our results, we observe that, when the first
eigenvalue is positive, there is an exponential decay for the solutions to the
associated evolution problem. As a tool to obtain the result, we also study the
behaviour of the principal eigenvalue of the nonlocal Dirichlet problem in the
ball and prove that it converges to the first eigenvalue in the whole
space as
A splitting method for the augmented Burgers equation
In this paper we consider a splitting method for the augmented Burgers equation and prove that it is of ļ¬rst order. We also analyze the large-time behavior of the approximated solution by obtaining the ļ¬rst term in the asymptotic expansion. We prove that, when time increases, these solutions be have as the self-similar solutions of the viscous Burgers equation
Uniqueness results for an ODE related to a generalized Ginzburg-Landau model for liquid crystals
We study a singular nonlinear ordinary differential equation on intervals {[}0, R) with R <= +infinity, motivated by the Ginzburg-Landau models in superconductivity and Landau-de Gennes models in liquid crystals. We prove existence and uniqueness of positive solutions under general assumptions on the nonlinearity. Further uniqueness results for sign-changing solutions are obtained for a physically relevant class of nonlinearities. Moreover, we prove a number of fine qualitative properties of the solution that are important for the study of energetic stability
Symmetry and Multiplicity of Solutions in a Two-Dimensional Landauāde Gennes Model for Liquid Crystals
We consider a variational two-dimensional Landauāde Gennes model in the
theory of nematic liquid crystals in a disk of radius R. We prove that under a
symmetric boundary condition carrying a topological defect of degree k
2 for some given even non-zero integer k, there are exactly two minimizers for all large enough
R. We show that the minimizers do not inherit the full symmetry structure of the
energy functional and the boundary data. We further show that there are at least
five symmetric critical points
Genome-wide expression patterns in physiological cardiac hypertrophy
Abstract
Background
Physiological left ventricular hypertrophy (LVH) involves complex cardiac remodeling that occurs as an adaptive response to chronic exercise. A stark clinical contrast exists between physiological LVH and pathological cardiac remodeling in response to diseases such as hypertension, but little is known about the precise molecular mechanisms driving physiological adaptation.
Results
In this study, the first large-scale analysis of publicly available genome-wide expression data of several in vivo murine models of physiological LVH was carried out using network analysis. On evaluating 3 million gene co-expression patterns across 141 relevant microarray experiments, it was found that physiological adaptation is an evolutionarily conserved processes involving preservation of the function of cytochrome c oxidase, induction of autophagy compatible with cell survival, and coordinated regulation of angiogenesis.
Conclusion
This analysis not only identifies known biological pathways involved in physiological LVH, but also offers novel insights into the molecular basis of this phenotype by identifying key networks of co-expressed genes, as well as their topological and functional properties, using relevant high-quality microarray experiments and network inference.
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Detecting Inspiring Content on Social Media
Inspiration moves a person to see new possibilities and transforms the way
they perceive their own potential. Inspiration has received little attention in
psychology, and has not been researched before in the NLP community. To the
best of our knowledge, this work is the first to study inspiration through
machine learning methods. We aim to automatically detect inspiring content from
social media data. To this end, we analyze social media posts to tease out what
makes a post inspiring and what topics are inspiring. We release a dataset of
5,800 inspiring and 5,800 non-inspiring English-language public post unique ids
collected from a dump of Reddit public posts made available by a third party
and use linguistic heuristics to automatically detect which social media
English-language posts are inspiring.Comment: accepted at ACII 202
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