18,651 research outputs found
Quantization and Fractional Quantization of Currents in Periodically Driven Stochastic Systems I: Average Currents
This article studies Markovian stochastic motion of a particle on a graph
with finite number of nodes and periodically time-dependent transition rates
that satisfy the detailed balance condition at any time. We show that under
general conditions, the currents in the system on average become quantized or
fractionally quantized for adiabatic driving at sufficiently low temperature.
We develop the quantitative theory of this quantization and interpret it in
terms of topological invariants. By implementing the celebrated Kirchhoff
theorem we derive a general and explicit formula for the average generated
current that plays a role of an efficient tool for treating the current
quantization effects.Comment: 22 pages, 7 figure
A six-parameter space to describe galaxy diversification
Galaxy diversification proceeds by transforming events like accretion,
interaction or mergers. These explain the formation and evolution of galaxies
that can now be described with many observables. Multivariate analyses are the
obvious tools to tackle the datasets and understand the differences between
different kinds of objects. However, depending on the method used,
redundancies, incompatibilities or subjective choices of the parameters can
void the usefulness of such analyses. The behaviour of the available parameters
should be analysed before an objective reduction of dimensionality and
subsequent clustering analyses can be undertaken, especially in an evolutionary
context. We study a sample of 424 early-type galaxies described by 25
parameters, ten of which are Lick indices, to identify the most structuring
parameters and determine an evolutionary classification of these objects. Four
independent statistical methods are used to investigate the discriminant
properties of the observables and the partitioning of the 424 galaxies:
Principal Component Analysis, K-means cluster analysis, Minimum Contradiction
Analysis and Cladistics. (abridged)Comment: Accepted for publicationin A\&
Inducing safer oblique trees without costs
Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the
distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification.
Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety.
This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming
How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging
We present the results of applying new object classification techniques to
difference images in the context of the Nearby Supernova Factory supernova
search. Most current supernova searches subtract reference images from new
images, identify objects in these difference images, and apply simple threshold
cuts on parameters such as statistical significance, shape, and motion to
reject objects such as cosmic rays, asteroids, and subtraction artifacts.
Although most static objects subtract cleanly, even a very low false positive
detection rate can lead to hundreds of non-supernova candidates which must be
vetted by human inspection before triggering additional followup. In comparison
to simple threshold cuts, more sophisticated methods such as Boosted Decision
Trees, Random Forests, and Support Vector Machines provide dramatically better
object discrimination. At the Nearby Supernova Factory, we reduced the number
of non-supernova candidates by a factor of 10 while increasing our supernova
identification efficiency. Methods such as these will be crucial for
maintaining a reasonable false positive rate in the automated transient alert
pipelines of upcoming projects such as PanSTARRS and LSST.Comment: 25 pages; 6 figures; submitted to Ap
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