13,316 research outputs found
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&
Two novel evolutionary formulations of the graph coloring problem
We introduce two novel evolutionary formulations of the problem of coloring
the nodes of a graph. The first formulation is based on the relationship that
exists between a graph's chromatic number and its acyclic orientations. It
views such orientations as individuals and evolves them with the aid of
evolutionary operators that are very heavily based on the structure of the
graph and its acyclic orientations. The second formulation, unlike the first
one, does not tackle one graph at a time, but rather aims at evolving a
`program' to color all graphs belonging to a class whose members all have the
same number of nodes and other common attributes. The heuristics that result
from these formulations have been tested on some of the Second DIMACS
Implementation Challenge benchmark graphs, and have been found to be
competitive when compared to the several other heuristics that have also been
tested on those graphs.Comment: To appear in Journal of Combinatorial Optimizatio
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
V-like formations in flocks of artificial birds
We consider flocks of artificial birds and study the emergence of V-like
formations during flight. We introduce a small set of fully distributed
positioning rules to guide the birds' movements and demonstrate, by means of
simulations, that they tend to lead to stabilization into several of the
well-known V-like formations that have been observed in nature. We also provide
quantitative indicators that we believe are closely related to achieving V-like
formations, and study their behavior over a large set of independent
simulations
Mass Generation from Lie Algebra Extensions
Applied to the electroweak interactions, the theory of Lie algebra extensions
suggests a mechanism by which the boson masses are generated without resource
to spontaneous symmetry breaking. It starts from a gauge theory without any
additional scalar field. All the couplings predicted by the Weinberg-Salam
theory are present, and a few others which are nevertheless consistent within
the model.Comment: 11 pages; revtex; title and PACS have been changed; comments included
in the manuscrip
Fast and secure key distribution using mesoscopic coherent states of light
This work shows how two parties A and B can securely share sequences of
random bits at optical speeds. A and B possess true-random physical sources and
exchange random bits by using a random sequence received to cipher the
following one to be sent. A starting shared secret key is used and the method
can be described as an unlimited one-time-pad extender. It is demonstrated that
the minimum probability of error in signal determination by the eavesdropper
can be set arbitrarily close to the pure guessing level. Being based on the
-ry encryption protocol this method also allows for optical amplification
without security degradation, offering practical advantages over the BB84
protocol for key distribution.Comment: 11 pages and 4 figures. This version updates the one published in PRA
68, 052307 (2003). Minor changes were made in the text and one section on
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