Asymptotic power law of moments in a random multiplicative process with weak additive noise

It is well known that a random multiplicative process with weak additive noise generates a power-law probability distribution. It has recently been recognized that this process exhibits another type of power law: the moment of the stochastic variable scales as a function of the additive noise strength. We clarify the mechanism for this power-law behavior of moments by treating a simple Langevin-type model both approximately and exactly, and argue this mechanism is universal. We also discuss the relevance of our findings to noisy on-off intermittency and to singular spatio-temporal chaos recently observed in systems of non-locally coupled elements.Comment: 11 pages, 9 figures, submitted to Phys. Rev.

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Pleiotropy of FRIGIDA enhances the potential for multivariate adaptation.

An evolutionary response to selection requires genetic variation; however, even if it exists, then the genetic details of the variation can constrain adaptation. In the simplest case, unlinked loci and uncorrelated phenotypes respond directly to multivariate selection and permit unrestricted paths to adaptive peaks. By contrast, 'antagonistic' pleiotropic loci may constrain adaptation by affecting variation of many traits and limiting the direction of trait correlations to vectors that are not favoured by selection. However, certain pleiotropic configurations may improve the conditions for adaptive evolution. Here, we present evidence that the Arabidopsis thaliana gene FRI (FRIGIDA) exhibits 'adaptive' pleiotropy, producing trait correlations along an axis that results in two adaptive strategies. Derived, low expression FRI alleles confer a 'drought escape' strategy owing to fast growth, low water use efficiency and early flowering. By contrast, a dehydration avoidance strategy is conferred by the ancestral phenotype of late flowering, slow growth and efficient water use during photosynthesis. The dehydration avoidant phenotype was recovered when genotypes with null FRI alleles were transformed with functional alleles. Our findings indicate that the well-documented effects of FRI on phenology result from differences in physiology, not only a simple developmental switch

Bounding biomass in the Fisher equation

The FKPP equation with a variable growth rate and advection by an incompressible velocity field is considered as a model for plankton dispersed by ocean currents. If the average growth rate is negative then the model has a survival-extinction transition; the location of this transition in the parameter space is constrained using variational arguments and delimited by simulations. The statistical steady state reached when the system is in the survival region of parameter space is characterized by integral constraints and upper and lower bounds on the biomass and productivity that follow from variational arguments and direct inequalities. In the limit of zero-decorrelation time the velocity field is shown to act as Fickian diffusion with an eddy diffusivity much larger than the molecular diffusivity and this allows a one-dimensional model to predict the biomass, productivity and extinction transitions. All results are illustrated with a simple growth and stirring model.Comment: 32 Pages, 13 Figure

Determining appropriate approaches for using data in feature selection

Feature selection is increasingly important in data analysis and machine learning in big data era. However, how to use the data in feature selection, i.e. using either ALL or PART of a dataset, has become a serious and tricky issue. Whilst the conventional practice of using all the data in feature selection may lead to selection bias, using part of the data may, on the other hand, lead to underestimating the relevant features under some conditions. This paper investigates these two strategies systematically in terms of reliability and effectiveness, and then determines their suitability for datasets with different characteristics. The reliability is measured by the Average Tanimoto Index and the Inter-method Average Tanimoto Index, and the effectiveness is measured by the mean generalisation accuracy of classification. The computational experiments are carried out on ten real-world benchmark datasets and fourteen synthetic datasets. The synthetic datasets are generated with a pre-set number of relevant features and varied numbers of irrelevant features and instances, and added with different levels of noise. The results indicate that the PART approach is more effective in reducing the bias when the size of a dataset is small but starts to lose its advantage as the dataset size increases

Balancing employee needs, project requirements and organisational priorities in team deployment

The 'people and performance' model asserts that performance is a sum of employee ability, motivation and opportunity (AMO). Despite extensive evidence of this people-performance link within manufacturing and many service sectors, studies within the construction industry are limited. Thus, a recent research project set out to explore the team deployment strategies of a large construction company with the view of establishing how a balance could be achieved between organisational strategic priorities, operational project requirements and individual employee needs and preferences. The findings suggested that project priorities often took precedence over the delivery of the strategic intentions of the organisation in meeting employees' individual needs. This approach is not sustainable in the long term because of the negative implications that such a policy had in relation to employee stress and staff turnover. It is suggested that a resourcing structure that takes into account the multiple facets of AMO may provide a more effective approach for balancing organisational strategic priorities, operational project requirements and individual employee needs and preferences more appropriately in the future