Genetics and competing strategies in a threshold model for mail processing

Multi-agent algorithms inspired by the division of labour in social insects are applied to a problem of distributed mail retrieval in which agents must visit mail producing cities and choose between mail types under certain constraints.The efficiency (i.e. the average amount of mail retrieved per time step), and the flexibility (i.e. the capability of the agents to react to changes in the environment) are investigated both in static and dynamic environments. New rules for mail selection and specialisation are introduced and are shown to exhibit improved efficiency and flexibility compared to existing ones. We employ a genetic algorithm which allows the various rules to evolve and compete. Apart from obtaining optimised parameters for the various rules for any environment, we also observe extinction and speciation. From a more theoretical point of view, in order to avoid finite size effects, most results are obtained for large population sizes. However, we do analyse the influence of population size on the performance. Furthermore, we critically analyse the causes of efficiency loss, derive the exact dynamics of the model in the large system limit under certain conditions, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities.

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

Description and critical appraisal of principal components analysis (PCA) methodology applied to pulsed-field gel electrophoresis profiles of methicillin-resistant Staphylococcus aureus isolates

Principal components analysis (PCA) has been described for over 50 years; however, it is rarely applied to the analysis of epidemiological data. In this study PCA was critically appraised in its ability to reveal relationships between pulsed-field gel electrophoresis (PFGE) profiles of methicillin- resistant Staphylococcus aureus (MRSA) in comparison to the more commonly employed cluster analysis and representation by dendrograms. The PFGE type following SmaI chromosomal digest was determined for 44 multidrug-resistant hospital-acquired methicillin-resistant S. aureus (MR-HA-MRSA) isolates, two multidrug-resistant community-acquired MRSA (MR-CA-MRSA), 50 hospital-acquired MRSA (HA-MRSA) isolates (from the University Hospital Birmingham, NHS Trust, UK) and 34 community-acquired MRSA (CA-MRSA) isolates (from general practitioners in Birmingham, UK). Strain relatedness was determined using Dice band-matching with UPGMA clustering and PCA. The results indicated that PCA revealed relationships between MRSA strains, which were more strongly correlated with known epidemiology, most likely because, unlike cluster analysis, PCA does not have the constraint of generating a hierarchic classification. In addition, PCA provides the opportunity for further analysis to identify key polymorphic bands within complex genotypic profiles, which is not always possible with dendrograms. Here we provide a detailed description of a PCA method for the analysis of PFGE profiles to complement further the epidemiological study of infectious disease. © 2005 Elsevier B.V. All rights reserved.

Stock repurchases and treasury share sales: Do they stabilize price and enhance liquidity?

The article studies the impact of a firm’s trading in its own shares on the volatility and market liquidity of the firm’s stock in the Italian stock market. In the study, both stock repurchases and treasury share sales executed on the open market are defined as trading in own shares. The study finds that Italian firms can reduce the volatility of their stock and boost market liquidity by trading their own shares.

Development of a questionnaire to assess the relative subjective benefits of presbyopia correction

Purpose To develop a standardized questionnaire of near visual function and satisfaction to complement visual function evaluations of presbyopic corrections. Setting Eye Clinic, School of Life and Health Sciences, Aston University, Midland Eye Institute and Solihull Hospital, Birmingham, United Kingdom. Design Questionnaire development. Methods A preliminary 26-item questionnaire of previously used near visual function items was completed by patients with monofocal intraocular lenses (IOLs), multifocal IOLs, accommodating IOLs, multifocal contact lenses, or varifocal spectacles. Rasch analysis was used for item reduction, after which internal and test–retest reliabilities were determined. Construct validity was determined by correlating the resulting Near Activity Visual Questionnaire (NAVQ) scores with near visual acuity and critical print size (CPS), which was measured using the Minnesota Low Vision Reading Test chart. Discrimination ability was assessed through receiver-operating characteristic (ROC) curve analysis. Results One hundred fifty patients completed the questionnaire. Item reduction resulted in a 10-item NAVQ with excellent separation (2.92), internal consistency (Cronbach a = 0.95), and test–retest reliability (intraclass correlation coefficient = 0.72). Correlations of questionnaire scores with near visual acuity (r = 0.32) and CPS (r = 0.27) provided evidence of validity, and discrimination ability was excellent (area under ROC curve = 0.91). Conclusion Results show the NAVQ is a reliable, valid instrument that can be incorporated into the evaluation of presbyopic corrections.

Admissible monetary aggregates for the Euro area

We use the Fleissig and Whitney (2003) weak separability test to determine admissible levels of monetary aggregation for the Euro area. We find that the Euro area monetary assets in M2 and M3 are weakly separable and construct admissible Divisia monetary aggregates for these assets. We evaluate the Divisia aggregates as indicator variables, building on Nelson (2002), Reimers (2002), and Stracca (2004). Specifically, we show that real growth of the admissible Divisia aggregates enter the Euro area IS curve positively and significantly for the period from 1980 to 2005. Out of sample, we show that Divisia M2 and M3 appear to contain useful information for forecasting Euro area inflation.

The virtual pedagogy initiative

Contemporary Higher Education Institutions must adapt to address government funded calls for expansion and widened participation. The adoption of e-learning strategies, such as the use of the podcasts, can facilitate flexible learning around the needs and expectations of students. In this article we outline a number of e-learning developments at Aston University collectively referred to as the Virtual Pedagogy Initiative. Each of the strands, podcasts, vodcasts, mobile telephony and the campus wide remote broadcasts, are described pedagogically as well as technically. Where possible data highlighting the student response and experience are included. The article begins with the contention that contemporary undergraduates may be qualitatively different and can considered „digital natives?.