16,857 research outputs found
Closed Expressions for Lie Algebra Invariants and Finite Transformations
A simple procedure to obtain complete, closed expressions for Lie algebra
invariants is presented. The invariants are ultimately polynomials in the group
parameters. The construction of finite group elements require the use of
projectors, whose coefficients are invariant polynomials. The detailed general
forms of these projectors are given. Closed expressions for finite Lorentz
transformations, both homogeneous and inhomogeneous, as well as for Galilei
transformations, are found as examples.Comment: 34 pages, ps file, no figure
Influence of disordered porous media in the anomalous properties of a simple water model
The thermodynamic, dynamic and structural behavior of a water-like system
confined in a matrix is analyzed for increasing confining geometries. The
liquid is modeled by a two dimensional associating lattice gas model that
exhibits density and diffusion anomalies, in similarity to the anomalies
present in liquid water. The matrix is a triangular lattice in which fixed
obstacles impose restrictions to the occupation of the particles. We show that
obstacules shortens all lines, including the phase coexistence, the critical
and the anomalous lines. The inclusion of a very dense matrix not only suppress
the anomalies but also the liquid-liquid critical point
Network conduciveness with application to the graph-coloring and independent-set optimization transitions
We introduce the notion of a network's conduciveness, a probabilistically
interpretable measure of how the network's structure allows it to be conducive
to roaming agents, in certain conditions, from one portion of the network to
another. We exemplify its use through an application to the two problems in
combinatorial optimization that, given an undirected graph, ask that its
so-called chromatic and independence numbers be found. Though NP-hard, when
solved on sequences of expanding random graphs there appear marked transitions
at which optimal solutions can be obtained substantially more easily than right
before them. We demonstrate that these phenomena can be understood by resorting
to the network that represents the solution space of the problems for each
graph and examining its conduciveness between the non-optimal solutions and the
optimal ones. At the said transitions, this network becomes strikingly more
conducive in the direction of the optimal solutions than it was just before
them, while at the same time becoming less conducive in the opposite direction.
We believe that, besides becoming useful also in other areas in which network
theory has a role to play, network conduciveness may become instrumental in
helping clarify further issues related to NP-hardness that remain poorly
understood
On the use of machine learning algorithms in the measurement of stellar magnetic fields
Regression methods based in Machine Learning Algorithms (MLA) have become an
important tool for data analysis in many different disciplines.
In this work, we use MLA in an astrophysical context; our goal is to measure
the mean longitudinal magnetic field in stars (H_ eff) from polarized spectra
of high resolution, through the inversion of the so-called multi-line profiles.
Using synthetic data, we tested the performance of our technique considering
different noise levels: In an ideal scenario of noise-free multi-line profiles,
the inversion results are excellent; however, the accuracy of the inversions
diminish considerably when noise is taken into account. In consequence, we
propose a data pre-process in order to reduce the noise impact, which consists
in a denoising profile process combined with an iterative inversion
methodology.
Applying this data pre-process, we have found a considerable improvement of
the inversions results, allowing to estimate the errors associated to the
measurements of stellar magnetic fields at different noise levels.
We have successfully applied our data analysis technique to two different
stars, attaining by first time the measurement of H_eff from multi-line
profiles beyond the condition of line autosimilarity assumed by other
techniques.Comment: Accepted for publication in A&
Avaliação do desempenho do gado mestiço leiteiro da Embrapa Pecuária Sudeste.
bitstream/CPPSE/16901/1/Boletim06.pd
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