29,449 research outputs found
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
Distributed signal processing for wireless sensor networks enables that
different devices cooperate to solve different signal processing tasks. A
crucial first step is to answer the question: who observes what? Recently,
several distributed algorithms have been proposed, which frame the
signal/object labelling problem in terms of cluster analysis after extracting
source-specific features, however, the number of clusters is assumed to be
known. We propose a new method called Gravitational Clustering (GC) to
adaptively estimate the time-varying number of clusters based on a set of
feature vectors. The key idea is to exploit the physical principle of
gravitational force between mass units: streaming-in feature vectors are
considered as mass units of fixed position in the feature space, around which
mobile mass units are injected at each time instant. The cluster enumeration
exploits the fact that the highest attraction on the mobile mass units is
exerted by regions with a high density of feature vectors, i.e., gravitational
clusters. By sharing estimates among neighboring nodes via a
diffusion-adaptation scheme, cooperative and distributed cluster enumeration is
achieved. Numerical experiments concerning robustness against outliers,
convergence and computational complexity are conducted. The application in a
distributed cooperative multi-view camera network illustrates the applicability
to real-world problems.Comment: 12 pages, 9 figure
Nature-Inspired Learning Models
Intelligent learning mechanisms found in natural world are still unsurpassed in their learning performance and eficiency of dealing with uncertain information coming in a variety of forms, yet remain under continuous challenge
from human driven artificial intelligence methods. This work intends to demonstrate how the phenomena observed in physical world can be directly used to guide artificial learning models. An inspiration for the new
learning methods has been found in the mechanics of physical fields found in both micro and macro scale.
Exploiting the analogies between data and particles subjected to gravity, electrostatic and gas particle fields, new algorithms have been developed and applied to classification and clustering while the properties of the
field further reused in regression and visualisation of classification and classifier fusion. The paper covers extensive pictorial examples and visual interpretations of the presented techniques along with some testing over
the well-known real and artificial datasets, compared when possible to the traditional methods
Torsion Axial Vector and Yvon-Takabayashi Angle: Zitterbewegung, Chirality and all that
We consider propagating torsion as a completion of gravitation in order to
describe the dynamics of curved-twisted space-times filled with Dirac spinorial
fields; we discuss interesting relationships of the torsion axial vector and
the curvature tensor with the Yvon-Takabayashi angle and the module of the
spinor field, that is the two degrees of freedom of the spinor field itself: in
particular, we shall discuss in what way the torsion axial vector could be seen
as the potential of a specific interaction of the Yvon-Takabayashi angle, and
therefore as a force between the two chiral projections of the spinor field
itself. Chiral interactions of the components of a spinor may render effects of
zitterbewegung, as well as effective mass terms and other related features: we
shall briefly sketch some of the analogies and differences with the similar but
not identical situation given by the Yukawa interaction occurring in the Higgs
sector of the standard model. We will provide some overall considerations about
general consequences for contemporary physics, consequences that have never
been discussed before, so far as we are aware, in the present physics
literature.Comment: 8 page
On the dissolution of star clusters in the Galactic centre. I. Circular orbits
We present N-body simulations of dissolving star clusters close to galactic
centres. For this purpose, we developed a new N-body program called nbody6gc
based on Aarseth's series of N-body codes. We describe the algorithm in detail.
We report about the density wave phenomenon in the tidal arms which has been
recently explained by Kuepper et al. (2008). Standing waves develop in the
tidal arms. The wave knots or clumps develop at the position, where the
emerging tidal arm hits the potential wall of the effective potential and is
reflected. The escaping stars move through the wave knots further into the
tidal arms. We show the consistency of the positions of the wave knots with the
theory in Just et al. (2009). We also demonstrate a simple method to study the
properties of tidal arms. By solving many eigenvalue problems along the tidal
arms, we construct numerically a 1D coordinate system whose direction is always
along a principal axis of the local tensor of inertia. Along this coordinate
system, physical quantities can be evaluated. The half-mass or dissolution
times of our models are almost independent of the particle number which
indicates that two-body relaxation is not the dominant mechanism leading to the
dissolution. This may be a typical situation for many young star clusters. We
propose a classification scheme which sheds light on the dissolution mechanism.Comment: 18 pages, 20 figures; accepted by MNRA
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