12,858 research outputs found
The contribution of statistical physics to evolutionary biology
Evolutionary biology shares many concepts with statistical physics: both deal
with populations, whether of molecules or organisms, and both seek to simplify
evolution in very many dimensions. Often, methodologies have undergone parallel
and independent development, as with stochastic methods in population genetics.
We discuss aspects of population genetics that have embraced methods from
physics: amongst others, non-equilibrium statistical mechanics, travelling
waves, and Monte-Carlo methods have been used to study polygenic evolution,
rates of adaptation, and range expansions. These applications indicate that
evolutionary biology can further benefit from interactions with other areas of
statistical physics, for example, by following the distribution of paths taken
by a population through time.Comment: 18 pages, 3 figures, glossary. Accepted in Trend in Ecology and
Evolution (to appear in print in August 2011
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
Consciousness, cognition, and the hierarchy of context: extending the global neuronal workspace model
We adapt an information theory analysis of interacting cognitive biological and social modules to the problem of the global neuronal workspace, the new standard neuroscience paradigm for consciousness. Tunable punctuation emerges in a natural way, suggesting the possibility of fitting appropriate phase transition power law, and away from transition, generalized Onsager relation expressions, to observational data on conscious reaction. The development can be extended in a straightforward manner to include psychosocial stress, culture, or other cognitive modules which constitute a structured, embedding hierarchy of contextual constraints acting at a slower rate than neuronal function itself. This produces a 'biopsychosociocultural' model of individual consciousness that, while otherwise quite close to the standard treatment, meets compelling philosophical and other objections to brain-only descriptions
Effects of Dynamic Goals on Agent Performance
Autonomous systems are increasingly being used for complex tasks in dynamic environments. Robust automation needs to be able to establish its current goal and determine when the goal has changed. In human-machine teams autonomous goal detection is an important component of maintaining shared situational awareness between both parties. This research investigates how different categories of goals affect autonomous change detection in a dynamic environment. In order to accomplish this goal, a set of autonomous agents were developed to perform within an environment with multiple possible goals. The agents perform the environmental task while monitoring for goal changes. The experiment tests the agents over a range of goal changes to determine how detection performance is affected by the different categories of goals. Results show that detection is highly dependent on what goal is being switch to and from. The point similarity between goals is the most significant factor in evaluating the change detection time. An additional experiment improved upon the goal agent and demonstrated the importance of having the proper perception mechanics for feedback within the environment
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