96,191 research outputs found
A Comparison of Algorithms for the Construction of SZ Cluster Catalogues
We evaluate the construction methodology of an all-sky catalogue of galaxy
clusters detected through the Sunyaev-Zel'dovich (SZ) effect. We perform an
extensive comparison of twelve algorithms applied to the same detailed
simulations of the millimeter and submillimeter sky based on a Planck-like
case. We present the results of this "SZ Challenge" in terms of catalogue
completeness, purity, astrometric and photometric reconstruction. Our results
provide a comparison of a representative sample of SZ detection algorithms and
highlight important issues in their application. In our study case, we show
that the exact expected number of clusters remains uncertain (about a thousand
cluster candidates at |b|> 20 deg with 90% purity) and that it depends on the
SZ model and on the detailed sky simulations, and on algorithmic implementation
of the detection methods. We also estimate the astrometric precision of the
cluster candidates which is found of the order of ~2 arcmins on average, and
the photometric uncertainty of order ~30%, depending on flux.Comment: Accepted for publication in A&A: 14 pages, 7 figures. Detailed
figures added in Appendi
Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks
Modelling, Measuring and Compensating Color Weak Vision
We use methods from Riemann geometry to investigate transformations between
the color spaces of color-normal and color weak observers. The two main
applications are the simulation of the perception of a color weak observer for
a color normal observer and the compensation of color images in a way that a
color weak observer has approximately the same perception as a color normal
observer. The metrics in the color spaces of interest are characterized with
the help of ellipsoids defined by the just-noticable-differences between color
which are measured with the help of color-matching experiments. The constructed
mappings are isometries of Riemann spaces that preserve the perceived
color-differences for both observers. Among the two approaches to build such an
isometry, we introduce normal coordinates in Riemann spaces as a tool to
construct a global color-weak compensation map. Compared to previously used
methods this method is free from approximation errors due to local
linearizations and it avoids the problem of shifting locations of the origin of
the local coordinate system. We analyse the variations of the Riemann metrics
for different observers obtained from new color matching experiments and
describe three variations of the basic method. The performance of the methods
is evaluated with the help of semantic differential (SD) tests.Comment: Full resolution color pictures are available from the author
Neural network ensembles: Evaluation of aggregation algorithms
Ensembles of artificial neural networks show improved generalization
capabilities that outperform those of single networks. However, for aggregation
to be effective, the individual networks must be as accurate and diverse as
possible. An important problem is, then, how to tune the aggregate members in
order to have an optimal compromise between these two conflicting conditions.
We present here an extensive evaluation of several algorithms for ensemble
construction, including new proposals and comparing them with standard methods
in the literature. We also discuss a potential problem with sequential
aggregation algorithms: the non-frequent but damaging selection through their
heuristics of particularly bad ensemble members. We introduce modified
algorithms that cope with this problem by allowing individual weighting of
aggregate members. Our algorithms and their weighted modifications are
favorably tested against other methods in the literature, producing a sensible
improvement in performance on most of the standard statistical databases used
as benchmarks.Comment: 35 pages, 2 figures, In press AI Journa
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