152 research outputs found
Handwritten digit recognition by bio-inspired hierarchical networks
The human brain processes information showing learning and prediction
abilities but the underlying neuronal mechanisms still remain unknown.
Recently, many studies prove that neuronal networks are able of both
generalizations and associations of sensory inputs. In this paper, following a
set of neurophysiological evidences, we propose a learning framework with a
strong biological plausibility that mimics prominent functions of cortical
circuitries. We developed the Inductive Conceptual Network (ICN), that is a
hierarchical bio-inspired network, able to learn invariant patterns by
Variable-order Markov Models implemented in its nodes. The outputs of the
top-most node of ICN hierarchy, representing the highest input generalization,
allow for automatic classification of inputs. We found that the ICN clusterized
MNIST images with an error of 5.73% and USPS images with an error of 12.56%
EM Training of Hidden Markov Models for Shape Recognition Using Cyclic Strings
Shape descriptions and the corresponding matching techniques must be robust to noise and invariant to transformations for their use in recognition tasks. Most transformations are relatively easy to handle when contours are represented by strings. However, starting point invariance is difficult to achieve. One interesting possibility is the use of cyclic strings, which are strings with no starting and final points. Here we present the use of Hidden Markov Models for modelling cyclic strings and their training using Expectation Maximization. Experimental results show that our proposal outperforms other methods in the literature
Statistical mechanics of transcription-factor binding site discovery using Hidden Markov Models
Hidden Markov Models (HMMs) are a commonly used tool for inference of
transcription factor (TF) binding sites from DNA sequence data. We exploit the
mathematical equivalence between HMMs for TF binding and the "inverse"
statistical mechanics of hard rods in a one-dimensional disordered potential to
investigate learning in HMMs. We derive analytic expressions for the Fisher
information, a commonly employed measure of confidence in learned parameters,
in the biologically relevant limit where the density of binding sites is low.
We then use techniques from statistical mechanics to derive a scaling principle
relating the specificity (binding energy) of a TF to the minimum amount of
training data necessary to learn it.Comment: 25 pages, 2 figures, 1 table V2 - typos fixed and new references
adde
Determination of polarized parton distribution functions and their uncertainties
We investigate the polarized parton distribution functions (PDFs) and their
uncertainties by using the world data on the spin asymmetry A_1. The
uncertainties of the polarized PDFs are estimated by the Hessian method. The up
and down valence-quark distributions are determined well. However, the
antiquark distributions have large uncertainties at this stage, and it is
particularly difficult to fix the gluon distribution. The \chi^2 analysis
produces a positively polarized gluon distribution, but even \Delta g(x)=0
could be allowed according to our uncertainty estimation. In comparison with
the previous AAC (Asymmetry Analysis Collaboration) parameterization in 2000,
accurate SLAC-E155 proton data are added to the analysis. We find that the E155
data improve the determination of the polarized PDFs, especially the polarized
antiquark distributions. In addition, the gluon-distribution uncertainties are
reduced due to the correlation with the antiquark distributions. We also show
the global analysis results with the condition \Delta g(x)=0 at the initial
scale, Q^2=1 GeV^2, for clarifying the error correlation effects with the gluon
distribution.Comment: 9 pages, 15 eps figures, REVTeX, FORTRAN package is available at the
web site http://www-hs.phys.saga-u.ac.jp/aac.html. Replaced 3 eps figures in
Fig.
Некоторые вопросы моделирования центробежных насосов
This work presents a novel object tracking approach, where the motion model is learned from sets of frame-wise detections with unknown associations. We employ a higher-order Markov model on position space instead of a first-order Markov model on a high-dimensional state-space of object dynamics. Compared to the latter, our approach allows the use of marginal rather than joint distributions, which results in a significant reduction of computation complexity. Densities are represented using a grid-based approach, where the rectangular windows are replaced with estimated smooth Parzen windows sampled at the grid points. This method performs as accurately as particle filter methods with the additional advantage that the prediction and update steps can be learned from empirical data. Our method is compared against standard techniques on image sequences obtained from an RC car following scenario. We show that our approach performs best in most of the sequences. Other potential applications are surveillance from cheap or uncalibrated cameras and image sequence analysis.DIPLEC
Three Dimensional Monocular Human Motion Analysis in End-Effector Space
Abstract. In this paper, we present a novel approach to three dimen-sional human motion estimation from monocular video data. We employ a particle filter to perform the motion estimation. The novelty of the method lies in the choice of state space for the particle filter. Using a non-linear inverse kinematics solver allows us to perform the filtering in end-effector space. This effectively reduces the dimensionality of the state space while still allowing for the estimation of a large set of motions. Preliminary experiments with the strategy show good results compared to a full-pose tracker.
MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications
Copyright © Springer 2013. The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-013-9399-zHidden Markov models (HMMs) are flexible, well established models useful in a diverse range of applications.
However, one potential limitation of such models lies in their inability to explicitly structure the holding times of each hidden state. Hidden semi-Markov models (HSMMs) are more useful in the latter respect as they incorporate additional temporal structure by explicit modelling of the holding times. However, HSMMs have generally received less attention in the literature, mainly due to their intensive computational requirements. Here a Bayesian implementation of HSMMs is presented. Recursive algorithms are proposed in conjunction with Metropolis-Hastings in such a way as to avoid sampling from the distribution of the hidden state sequence in the MCMC sampler. This provides a computationally tractable estimation framework for HSMMs avoiding the limitations associated with the conventional EM algorithm regarding model flexibility. Performance of the proposed implementation is demonstrated through simulation experiments as well as an illustrative application relating to recurrent failures in a network of underground water pipes where random effects are also included into the HSMM to allow for pipe heterogeneity
Fitting the integrated Spectral Energy Distributions of Galaxies
Fitting the spectral energy distributions (SEDs) of galaxies is an almost
universally used technique that has matured significantly in the last decade.
Model predictions and fitting procedures have improved significantly over this
time, attempting to keep up with the vastly increased volume and quality of
available data. We review here the field of SED fitting, describing the
modelling of ultraviolet to infrared galaxy SEDs, the creation of
multiwavelength data sets, and the methods used to fit model SEDs to observed
galaxy data sets. We touch upon the achievements and challenges in the major
ingredients of SED fitting, with a special emphasis on describing the interplay
between the quality of the available data, the quality of the available models,
and the best fitting technique to use in order to obtain a realistic
measurement as well as realistic uncertainties. We conclude that SED fitting
can be used effectively to derive a range of physical properties of galaxies,
such as redshift, stellar masses, star formation rates, dust masses, and
metallicities, with care taken not to over-interpret the available data. Yet
there still exist many issues such as estimating the age of the oldest stars in
a galaxy, finer details ofdust properties and dust-star geometry, and the
influences of poorly understood, luminous stellar types and phases. The
challenge for the coming years will be to improve both the models and the
observational data sets to resolve these uncertainties. The present review will
be made available on an interactive, moderated web page (sedfitting.org), where
the community can access and change the text. The intention is to expand the
text and keep it up to date over the coming years.Comment: 54 pages, 26 figures, Accepted for publication in Astrophysics &
Space Scienc
An Observational Overview of Solar Flares
We present an overview of solar flares and associated phenomena, drawing upon
a wide range of observational data primarily from the RHESSI era. Following an
introductory discussion and overview of the status of observational
capabilities, the article is split into topical sections which deal with
different areas of flare phenomena (footpoints and ribbons, coronal sources,
relationship to coronal mass ejections) and their interconnections. We also
discuss flare soft X-ray spectroscopy and the energetics of the process. The
emphasis is to describe the observations from multiple points of view, while
bearing in mind the models that link them to each other and to theory. The
present theoretical and observational understanding of solar flares is far from
complete, so we conclude with a brief discussion of models, and a list of
missing but important observations.Comment: This is an article for a monograph on the physics of solar flares,
inspired by RHESSI observations. The individual articles are to appear in
Space Science Reviews (2011
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