22,364 research outputs found
A Package for the Automated Classification of Periodic Variable Stars
We present a machine learning package for the classification of periodic
variable stars. Our package is intended to be general: it can classify any
single band optical light curve comprising at least a few tens of observations
covering durations from weeks to years, with arbitrary time sampling. We use
light curves of periodic variable stars taken from OGLE and EROS-2 to train the
model. To make our classifier relatively survey-independent, it is trained on
16 features extracted from the light curves (e.g. period, skewness, Fourier
amplitude ratio). The model classifies light curves into one of seven
superclasses - Delta Scuti, RR Lyrae, Cepheid, Type II Cepheid, eclipsing
binary, long-period variable, non-variable - as well as subclasses of these,
such as ab, c, d, and e types for RR Lyraes. When trained to give only
superclasses, our model achieves 0.98 for both recall and precision as measured
on an independent validation dataset (on a scale of 0 to 1). When trained to
give subclasses, it achieves 0.81 for both recall and precision. In order to
assess classification performance of the subclass model, we applied it to the
MACHO, LINEAR, and ASAS periodic variables, which gave recall/precision of
0.92/0.98, 0.89/0.96, and 0.84/0.88, respectively. We also applied the subclass
model to Hipparcos periodic variable stars of many other variability types that
do not exist in our training set, in order to examine how much those types
degrade the classification performance of our target classes. In addition, we
investigate how the performance varies with the number of data points and
duration of observations. We find that recall and precision do not vary
significantly if the number of data points is larger than 80 and the duration
is more than a few weeks. The classifier software of the subclass model is
available from the GitHub repository (https://goo.gl/xmFO6Q).Comment: 16 pages, 11 figures, accepted for publication in A&
Inventive Process as a Recombinant Search over Complex Landscape: Evidence from the Disk Drive Industry
Invention, Recombinant search, Complexity, NK Model, Simulation, Interdependence
Dimension Reduction by Mutual Information Discriminant Analysis
In the past few decades, researchers have proposed many discriminant analysis
(DA) algorithms for the study of high-dimensional data in a variety of
problems. Most DA algorithms for feature extraction are based on
transformations that simultaneously maximize the between-class scatter and
minimize the withinclass scatter matrices. This paper presents a novel DA
algorithm for feature extraction using mutual information (MI). However, it is
not always easy to obtain an accurate estimation for high-dimensional MI. In
this paper, we propose an efficient method for feature extraction that is based
on one-dimensional MI estimations. We will refer to this algorithm as mutual
information discriminant analysis (MIDA). The performance of this proposed
method was evaluated using UCI databases. The results indicate that MIDA
provides robust performance over different data sets with different
characteristics and that MIDA always performs better than, or at least
comparable to, the best performing algorithms.Comment: 13pages, 3 tables, International Journal of Artificial Intelligence &
Application
Bott Periodicity and Realizations of Chiral Symmetry in Arbitrary Dimensions
We compute the chiral symmetries of the Lagrangian for confining
"vector-like" gauge theories with massless fermions in -dimensional
Minkowski space and, under a few reasonable assumptions, determine the form of
the quadratic fermion condensates which arise through spontaneous breaking of
these symmetries. We find that for each type (complex, real, or pseudoreal) of
representation of the gauge group carried by the fermions, the chiral
symmetries of the Lagrangian, as well as the residual symmetries after
dynamical breaking, exactly follow the pattern of Bott periodicity as the
dimension changes. The consequences of this for the topological features of the
low-energy effective theory are considered.Comment: v2: Small additions and clarifications. To appear in Physics Letters
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