1,413 research outputs found
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes
Information-theoretic principles for learning and acting have been proposed
to solve particular classes of Markov Decision Problems. Mathematically, such
approaches are governed by a variational free energy principle and allow
solving MDP planning problems with information-processing constraints expressed
in terms of a Kullback-Leibler divergence with respect to a reference
distribution. Here we consider a generalization of such MDP planners by taking
model uncertainty into account. As model uncertainty can also be formalized as
an information-processing constraint, we can derive a unified solution from a
single generalized variational principle. We provide a generalized value
iteration scheme together with a convergence proof. As limit cases, this
generalized scheme includes standard value iteration with a known model,
Bayesian MDP planning, and robust planning. We demonstrate the benefits of this
approach in a grid world simulation.Comment: 16 pages, 3 figure
A Framework for Generalising the Newton Method and Other Iterative Methods from Euclidean Space to Manifolds
The Newton iteration is a popular method for minimising a cost function on
Euclidean space. Various generalisations to cost functions defined on manifolds
appear in the literature. In each case, the convergence rate of the generalised
Newton iteration needed establishing from first principles. The present paper
presents a framework for generalising iterative methods from Euclidean space to
manifolds that ensures local convergence rates are preserved. It applies to any
(memoryless) iterative method computing a coordinate independent property of a
function (such as a zero or a local minimum). All possible Newton methods on
manifolds are believed to come under this framework. Changes of coordinates,
and not any Riemannian structure, are shown to play a natural role in lifting
the Newton method to a manifold. The framework also gives new insight into the
design of Newton methods in general.Comment: 36 page
Thermodynamics as a theory of decision-making with information processing costs
Perfectly rational decision-makers maximize expected utility, but crucially
ignore the resource costs incurred when determining optimal actions. Here we
propose an information-theoretic formalization of bounded rational
decision-making where decision-makers trade off expected utility and
information processing costs. Such bounded rational decision-makers can be
thought of as thermodynamic machines that undergo physical state changes when
they compute. Their behavior is governed by a free energy functional that
trades off changes in internal energy-as a proxy for utility-and entropic
changes representing computational costs induced by changing states. As a
result, the bounded rational decision-making problem can be rephrased in terms
of well-known concepts from statistical physics. In the limit when
computational costs are ignored, the maximum expected utility principle is
recovered. We discuss the relation to satisficing decision-making procedures as
well as links to existing theoretical frameworks and human decision-making
experiments that describe deviations from expected utility theory. Since most
of the mathematical machinery can be borrowed from statistical physics, the
main contribution is to axiomatically derive and interpret the thermodynamic
free energy as a model of bounded rational decision-making.Comment: 26 pages, 5 figures, (under revision since February 2012
Dyslexia detection from EEG signals using SSA component correlation and Convolutional Neural Networks
Objective dyslexia diagnosis is not a straighforward task since it is
traditionally performed by means of the intepretation of different behavioural
tests. Moreover, these tests are only applicable to readers. This way, early
diagnosis requires the use of specific tasks not only related to reading. Thus,
the use of Electroencephalography (EEG) constitutes an alternative for an
objective and early diagnosis that can be used with pre-readers. In this way,
the extraction of relevant features in EEG signals results crucial for
classification. However, the identification of the most relevant features is
not straighforward, and predefined statistics in the time or frequency domain
are not always discriminant enough. On the other hand, classical processing of
EEG signals based on extracting EEG bands frequency descriptors, usually make
some assumptions on the raw signals that could cause indormation loosing. In
this work we propose an alternative for analysis in the frequency domain based
on Singluar Spectrum Analysis (SSA) to split the raw signal into components
representing different oscillatory modes. Moreover, correlation matrices
obtained for each component among EEG channels are classfied using a
Convolutional Neural network.Comment: 11 pages, 7 figures. Submitted to conferenc
Identifying Variability in Process Performance Indicators
The performance perspective of business processes is concerned
with the definition of performance requirements usually specified
as a set of Process Performance Indicators (PPIs). Like other business
process perspectives such as control-flow or data, there are cases in which
PPIs are subject to variability. However, although the modelling of business
process variability (BPV) has evolved significantly, there are very
few contributions addressing the variability in the performance perspective
of business processes. Modelling PPI variants with tools and techniques
non-suitable for variability may generate redundant models, thus
making it difficult its maintenance and future adaptations, also increasing
possibility of errors in its managing. In this paper we present different
cases of PPI variability detected as result of the analysis of several
processes where BPV is present. Based on an existent metamodel used
for defining PPIs over BPs, we propose its formal extension that allows
the definition of PPI variability according to the cases identified.Ministerio de EconomÃa y Competitividad TIN2015-70560-RJunta de AndalucÃa P12-TIC-1867Junta de AndalucÃa P10-TIC-590
Neurogenesis Drives Stimulus Decorrelation in a Model of the Olfactory Bulb
The reshaping and decorrelation of similar activity patterns by neuronal
networks can enhance their discriminability, storage, and retrieval. How can
such networks learn to decorrelate new complex patterns, as they arise in the
olfactory system? Using a computational network model for the dominant neural
populations of the olfactory bulb we show that fundamental aspects of the adult
neurogenesis observed in the olfactory bulb -- the persistent addition of new
inhibitory granule cells to the network, their activity-dependent survival, and
the reciprocal character of their synapses with the principal mitral cells --
are sufficient to restructure the network and to alter its encoding of odor
stimuli adaptively so as to reduce the correlations between the bulbar
representations of similar stimuli. The decorrelation is quite robust with
respect to various types of perturbations of the reciprocity. The model
parsimoniously captures the experimentally observed role of neurogenesis in
perceptual learning and the enhanced response of young granule cells to novel
stimuli. Moreover, it makes specific predictions for the type of odor
enrichment that should be effective in enhancing the ability of animals to
discriminate similar odor mixtures
Extracellular Hsp72 concentration relates to a minimum endogenous criteria during acute exercise-heat exposure
Extracellular heat-shock protein 72 (eHsp72) concentration increases during exercise-heat stress when conditions elicit physiological strain. Differences in severity of environmental and exercise stimuli have elicited varied response to stress. The present study aimed to quantify the extent of increased eHsp72 with increased exogenous heat stress, and determine related endogenous markers of strain in an exercise-heat model. Ten males cycled for 90 min at 50% O2peak in three conditions (TEMP, 20°C/63% RH; HOT, 30.2°C/51%RH; VHOT, 40.0°C/37%RH). Plasma was analysed for eHsp72 pre, immediately post and 24-h post each trial utilising a commercially available ELISA. Increased eHsp72 concentration was observed post VHOT trial (+172.4%) (P<0.05), but not TEMP (-1.9%) or HOT (+25.7%) conditions. eHsp72 returned to baseline values within 24hrs in all conditions. Changes were observed in rectal temperature (Trec), rate of Trec increase, area under the curve for Trec of 38.5°C and 39.0°C, duration Trec ≥ 38.5°C and ≥ 39.0°C, and change in muscle temperature, between VHOT, and TEMP and HOT, but not between TEMP and HOT. Each condition also elicited significantly increasing physiological strain, described by sweat rate, heart rate, physiological strain index, rating of perceived exertion and thermal sensation. Stepwise multiple regression reported rate of Trec increase and change in Trec to be predictors of increased eHsp72 concentration. Data suggests eHsp72 concentration increases once systemic temperature and sympathetic activity exceeds a minimum endogenous criteria elicited during VHOT conditions and is likely to be modulated by large, rapid changes in core temperature
Spatial heterogeneity of habitat suitability for Rift Valley fever occurrence in Tanzania: an ecological niche modelling approach
Despite the long history of Rift Valley fever (RVF) in Tanzania, extent of its suitable habitat in the country remains unclear. In this study we investigated potential effects of temperature, precipitation, elevation, soil type, livestock density, rainfall pattern, proximity to wild animals, protected areas and forest on the habitat suitability for RVF occurrence in Tanzania. Presence-only records of 193 RVF outbreak locations from 1930 to 2007 together with potential predictor variables were used to model and map the suitable habitats for RVF occurrence using ecological niche modelling. Ground-truthing of the model outputs was conducted by comparing the levels of RVF virus specific antibodies in cattle, sheep and goats sampled from locations in Tanzania that presented different predicted habitat suitability values. Habitat suitability values for RVF occurrence were higher in the northern and central-eastern regions of Tanzania than the rest of the regions in the country. Soil type and precipitation of the wettest quarter contributed equally to habitat suitability (32.4% each), followed by livestock density (25.9%) and rainfall pattern (9.3%). Ground-truthing of model outputs revealed that the odds of an animal being seropositive for RVFV when sampled from areas predicted to be most suitable for RVF occurrence were twice the odds of an animal sampled from areas least suitable for RVF occurrence (95% CI: 1.43, 2.76, p < 0.001). The regions in the northern and central-eastern Tanzania were more suitable for RVF occurrence than the rest of the regions in the country. The modelled suitable habitat is characterised by impermeable soils, moderate precipitation in the wettest quarter, high livestock density and a bimodal rainfall pattern. The findings of this study should provide guidance for the design of appropriate RVF surveillance, prevention and control strategies which target areas with these characteristics
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