4,908 research outputs found
Dynamic quantum clustering: a method for visual exploration of structures in data
A given set of data-points in some feature space may be associated with a
Schrodinger equation whose potential is determined by the data. This is known
to lead to good clustering solutions. Here we extend this approach into a
full-fledged dynamical scheme using a time-dependent Schrodinger equation.
Moreover, we approximate this Hamiltonian formalism by a truncated calculation
within a set of Gaussian wave functions (coherent states) centered around the
original points. This allows for analytic evaluation of the time evolution of
all such states, opening up the possibility of exploration of relationships
among data-points through observation of varying dynamical-distances among
points and convergence of points into clusters. This formalism may be further
supplemented by preprocessing, such as dimensional reduction through singular
value decomposition or feature filtering.Comment: 15 pages, 9 figure
Identification of network modules by optimization of ratio association
We introduce a novel method for identifying the modular structures of a
network based on the maximization of an objective function: the ratio
association. This cost function arises when the communities detection problem
is described in the probabilistic autoencoder frame. An analogy with kernel
k-means methods allows to develop an efficient optimization algorithm, based on
the deterministic annealing scheme. The performance of the proposed method is
shown on a real data set and on simulated networks
Brownian motion in a non-homogeneous force field and photonic force microscope
The Photonic Force Microscope (PFM) is an opto-mechanical technique based on
an optical trap that can be assumed to probe forces in microscopic systems.
This technique has been used to measure forces in the range of pico- and
femto-Newton, assessing the mechanical properties of biomolecules as well as of
other microscopic systems. For a correct use of the PFM, the force field to
measure has to be invariable (homogeneous) on the scale of the Brownian motion
of the trapped probe. This condition implicates that the force field must be
conservative, excluding the possibility of a rotational component. However,
there are cases where these assumptions are not fulfilled Here, we show how to
improve the PFM technique in order to be able to deal with these cases. We
introduce the theory of this enhanced PFM and we propose a concrete analysis
workflow to reconstruct the force field from the experimental time-series of
the probe position. Furthermore, we experimentally verify some particularly
important cases, namely the case of a conservative or rotational force-field
Research and applications: Artificial intelligence
The program is reported for developing techniques in artificial intelligence and their application to the control of mobile automatons for carrying out tasks autonomously. Visual scene analysis, short-term problem solving, and long-term problem solving are discussed along with the PDP-15 simulator, LISP-FORTRAN-MACRO interface, resolution strategies, and cost effectiveness
Fractal Descriptors in the Fourier Domain Applied to Color Texture Analysis
The present work proposes the development of a novel method to provide
descriptors for colored texture images. The method consists in two steps. In
the first, we apply a linear transform in the color space of the image aiming
at highlighting spatial structuring relations among the color of pixels. In a
second moment, we apply a multiscale approach to the calculus of fractal
dimension based on Fourier transform. From this multiscale operation, we
extract the descriptors used to discriminate the texture represented in digital
images. The accuracy of the method is verified in the classification of two
color texture datasets, by comparing the performance of the proposed technique
to other classical and state-of-the-art methods for color texture analysis. The
results showed an advantage of almost 3% of the proposed technique over the
second best approach.Comment: Chaos, Volume 21, Issue 4, 201
Phase transitions in optimal unsupervised learning
We determine the optimal performance of learning the orientation of the
symmetry axis of a set of P = alpha N points that are uniformly distributed in
all the directions but one on the N-dimensional sphere. The components along
the symmetry breaking direction, of unitary vector B, are sampled from a
mixture of two gaussians of variable separation and width. The typical optimal
performance is measured through the overlap Ropt=B.J* where J* is the optimal
guess of the symmetry breaking direction. Within this general scenario, the
learning curves Ropt(alpha) may present first order transitions if the clusters
are narrow enough. Close to these transitions, high performance states can be
obtained through the minimization of the corresponding optimal potential,
although these solutions are metastable, and therefore not learnable, within
the usual bayesian scenario.Comment: 9 pages, 8 figures, submitted to PRE, This new version of the paper
contains one new section, Bayesian versus optimal solutions, where we explain
in detail the results supporting our claim that bayesian learning may not be
optimal. Figures 4 of the first submission was difficult to understand. We
replaced it by two new figures (Figs. 4 and 5 in this new version) containing
more detail
Microstructure identification via detrended fluctuation analysis of ultrasound signals
We describe an algorithm for simulating ultrasound propagation in random
one-dimensional media, mimicking different microstructures by choosing physical
properties such as domain sizes and mass densities from probability
distributions. By combining a detrended fluctuation analysis (DFA) of the
simulated ultrasound signals with tools from the pattern-recognition
literature, we build a Gaussian classifier which is able to associate each
ultrasound signal with its corresponding microstructure with a very high
success rate. Furthermore, we also show that DFA data can be used to train a
multilayer perceptron which estimates numerical values of physical properties
associated with distinct microstructures.Comment: Submitted to Phys. Rev.
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