597 research outputs found
Thermodynamic Properties of the SU(2) Chiral Quark-Loop Soliton
We consider a chiral one-loop hedgehog soliton of the bosonized SU(2)
Nambu & Jona-Lasinio model which is embedded in a hot medium of constituent
quarks. Energy and radius of the soliton are determined in self-consistent
mean-field approximation. Quasi-classical corrections to the soliton energy are
derived by means of the pushing and cranking approaches. The corresponding
inertial parameters are evaluated. It is shown that the inertial mass is
equivalent to the total internal energy of the soliton. Corrected nucleon and
isobar masses are calculated in dependence on temperature and density
of the medium. As a result of the self-consistently determined internal
structure of the soliton the scaling between constituent quark mass, soliton
mass and radius is noticeably disturbed.Comment: 34 pages, 7 Postscript figures, uses psfig.st
Supervised learning of short and high-dimensional temporal sequences for life science measurements
The analysis of physiological processes over time are often given by
spectrometric or gene expression profiles over time with only few time points
but a large number of measured variables. The analysis of such temporal
sequences is challenging and only few methods have been proposed. The
information can be encoded time independent, by means of classical expression
differences for a single time point or in expression profiles over time.
Available methods are limited to unsupervised and semi-supervised settings. The
predictive variables can be identified only by means of wrapper or
post-processing techniques. This is complicated due to the small number of
samples for such studies. Here, we present a supervised learning approach,
termed Supervised Topographic Mapping Through Time (SGTM-TT). It learns a
supervised mapping of the temporal sequences onto a low dimensional grid. We
utilize a hidden markov model (HMM) to account for the time domain and
relevance learning to identify the relevant feature dimensions most predictive
over time. The learned mapping can be used to visualize the temporal sequences
and to predict the class of a new sequence. The relevance learning permits the
identification of discriminating masses or gen expressions and prunes
dimensions which are unnecessary for the classification task or encode mainly
noise. In this way we obtain a very efficient learning system for temporal
sequences. The results indicate that using simultaneous supervised learning and
metric adaptation significantly improves the prediction accuracy for
synthetically and real life data in comparison to the standard techniques. The
discriminating features, identified by relevance learning, compare favorably
with the results of alternative methods. Our method permits the visualization
of the data on a low dimensional grid, highlighting the observed temporal
structure
Mathematical Foundations of the Self Organized Neighbor Embedding ({SONE}) for Dimension Reduction and Visualization
Abstract. In this paper we propose the generalization of the recently introduced Neighbor Embedding Exploratory Observation Machine (NE-XOM) for dimension reduction and visualization. We provide a general mathematical framework called Self Organized Neighbor Embedding (SONE).Ittreatsthecomponents, likedatasimilarity measures andneighborhood functions, independently and easily changeable. And it enables the utilization of different divergences, based on the theory of Fréchet derivatives. In this way we propose a new dimension reduction and visualization algorithm, which can be easily adapted to the user specific request and the actual problem.
Towards a Semantic Gas Source Localization under Uncertainty
Towards a Semantic Gas Source Localization under Uncertainty.Communications in Computer and Information Science book series (CCIS, volume 855), doi:10.1007/978-3-319-91479-4_42This work addresses the problem of efficiently and coherently
locating a gas source in a domestic environment with a mobile
robot, meaning efficiently the coverage of the shortest distance as possible
and coherently the consideration of different gas sources explaining
the gas presence. The main contribution is the exploitation, for the
first time, of semantic relationships between the gases detected and the
objects present in the environment to face this challenging issue. Our
proposal also takes into account both the uncertainty inherent in the
gas classification and object recognition processes. These uncertainties
are combined through a probabilistic Bayesian framework to provide a
priority-ordered list of (previously observed) objects to check. Moreover
the proximity of the different candidates to the current robot location
is also considered by a cost function, which output is used for planning
the robot inspection path. We have conducted an initial demonstration
of the suitability of our gas source localization approach by simulating
this task within domestic environments for a variable number of objects,
and comparing it with an greedy approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
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