766 research outputs found
One-loop conformal anomaly in an implicit momentum space regularization framework
In this paper we consider matter fields in a gravitational background in
order to compute the breaking of the conformal current at one-loop order.
Standard perturbative calculations of conformal symmetry breaking expressed by
the non-zero trace of the energy-momentum tensor have shown that some violating
terms are regularization dependent, which may suggest the existence of spurious
breaking terms in the anomaly. Therefore, we perform the calculation in a
momentum space regularization framework in which regularization dependent terms
are judiciously parametrized. We compare our results with those obtained in the
literature and conclude that there is an unavoidable arbitrariness in the
anomalous term .Comment: in European Physical Journal C, 201
Existence results for a fourth order partial differential equation arising in condensed matter physics
We study a higher order parabolic partial differential equation that arises
in the context of condensed matter physics. It is a fourth order semilinear
equation whose nonlinearity is the determinant of the Hessian matrix of the
solution. We consider this model in a bounded domain of the real plane and
study its stationary solutions both when the geometry of this domain is
arbitrary and when it is the unit ball and the solution is radially symmetric.
We also consider the initial-boundary value problem for the full parabolic
equation. We summarize our results on existence of solutions in these cases and
propose an open problem related to the existence of self-similar solutions.Comment: To appear in Mathematica Bohemic
Evolução da produção e do mercado mundial do feijão.
Realizou-se uma avaliação do mercado internacional de feijão, considerando produção, exportação e importação, mostrando o comportamento e oscilações dos principais países
On Optimal Regularization Parameters via Bilevel Learning
Variational regularization is commonly used to solve linear inverse problems,
and involves augmenting a data fidelity by a regularizer. The regularizer is
used to promote a priori information, and is weighted by a regularization
parameter. Selection of an appropriate regularization parameter is critical,
with various choices leading to very different reconstructions. Existing
strategies such as the discrepancy principle and L-curve can be used to
determine a suitable parameter value, but in recent years a supervised machine
learning approach called bilevel learning has been employed. Bilevel learning
is a powerful framework to determine optimal parameters, and involves solving a
nested optimisation problem. While previous strategies enjoy various
theoretical results, the well-posedness of bilevel learning in this setting is
still a developing field. One necessary property is positivity of the
determined regularization parameter. In this work, we provide a new condition
that better characterises positivity of optimal regularization parameters than
the existing theory. Numerical results verify and explore this new condition
for both small and large dimensional problems.Comment: 26 pages, 6 figure
Brazil nut almonds: nutritional and market aspects.
The aim of this study was to estimate the annual growth rate in production and exportation of Brazil nut almonds. Presents information related to the composition of different fatty acids from brazil nut almonds and the human daily intake needs, as well as an analysis of its production, its growth rate during the last 20 years and its export. and the human daily intake needs
Sharing Positive Affective States Amongst Rodents
Group living is thought to benefit from the ability to empathize with others. Much attention has been paid to empathy for the pain of others as an inhibitor of aggression. Empathizing with the positive affect of others has received less attention although it could promote helping by making it vicariously rewarding. Here, we review this latter, nascent literature to show that three components of the ability to empathize with positive emotions are already present in rodents, namely, the ability to perceive, share, and prefer actions that promote positive emotional states of conspecifics. While it has often been argued that empathy evolved as a motivation to care for others, we argue that these tendencies may have selfish benefits that could have stabilized their evolution: approaching others in a positive state can provide information about the source of valuable resources; becoming calmer and optimistic around animals in a calm or positive mood can help adapt to the socially sensed safety level in the environment; and preferring actions also benefiting others can optimize foraging, reduce aggression, and trigger reciprocity. Together, these findings illustrate an emerging field shedding light on the emotional world of rodents and on the biology and evolution of our ability to cooperate in groups.</p
Consumo, valor nutritivo e evolução da produção e de mercado mundial de feijão.
Apresentado Painel na 6º Semana de Ensino, Pesquisa e Extensão Universidade Federal de Santa Catarina
On Optimal Regularization Parameters via Bilevel Learning
Variational regularization is commonly used to solve linear inverse problems, and involves augmenting a data fidelity by a regularizer. The regularizer is used to promote a priori information and is weighted by a regularization parameter. Selection of an appropriate regularization parameter is critical, with various choices leading to very different reconstructions. Classical strategies used to determine a suitable parameter value include the discrepancy principle and the L-curve criterion, and in recent years a supervised machine learning approach called bilevel learning has been employed. Bilevel learning is a powerful framework to determine optimal parameters and involves solving a nested optimization problem. While previous strategies enjoy various theoretical results, the well-posedness of bilevel learning inthis setting is still an open question. In particular, a necessary property is positivity of the determined regularization parameter. In this chapter, we provide a new condition that better characterizes positivity of optimal regularization parameters than the existing theory. Numerical results verify and explore this new condition for both small and high-dimensional problems
Nature versus Nurture: The curved spine of the galaxy cluster X-ray luminosity -- temperature relation
The physical processes that define the spine of the galaxy cluster X-ray
luminosity -- temperature (L-T) relation are investigated using a large
hydrodynamical simulation of the Universe. This simulation models the same
volume and phases as the Millennium Simulation and has a linear extent of 500
h^{-1} Mpc. We demonstrate that mergers typically boost a cluster along but
also slightly below the L-T relation. Due to this boost we expect that all of
the very brightest clusters will be near the peak of a merger. Objects from
near the top of the L-T relation tend to have assembled much of their mass
earlier than an average halo of similar final mass. Conversely, objects from
the bottom of the relation are often experiencing an ongoing or recent merger.Comment: 8 pages, 7 figures, submitted to MNRA
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