Event models for tumor classification with SAGE gene expression data

Serial Analysis of Gene Expression (SAGE) is a relatively new method for monitoring gene expression levels and is expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. A promising application of SAGE gene expression data is classification of tumors. In this paper, we build three event models (the multivariate Bernoulli model, the multinomial model and the normalized multinomial model) for SAGE data classification. Both binary classification and multicategory classification are investigated. Experiments on two SAGE datasets show that the multivariate Bernoulli model performs well with small feature sizes, but the multinomial performs better at large feature sizes, while the normalized multinomial performs well with medium feature sizes. The multinomial achieves the highest overall accuracy.

The AGILE policy expression language for autonomic systems

This paper presents the AGILE policy expression language. The language enables powerful expression of self-managing behaviours and facilitates policy-based autonomic computing in which the policies themselves can be adapted dynamically and automatically. The language is generic so as to be deployable across a wide spectrum of application domains, and is very flexible through the use of simple yet expressive syntax and semantics. The development of AGILE is motivated by the need for adaptive policy mechanisms that are easy to deploy into legacy code and can be used by non autonomics-expert practitioners to embed self-managing behaviours with low cost and risk. A library implementation of the policy language is described. The implementation extends the state of the art in policy-based autonomics through innovations which include support for multiple policy versions of a given policy type, multiple configuration templates, and higher-level ‘meta-policies’ to dynamically select between differently configured business-logic policy instances and templates. Two dissimilar example deployment scenarios are examined.

On "fitting in": the effects of social context and gender on big 5 trait expression.

[This is a summary of an Oral Presentation] The study explored the expression of Big 5 personality traits in three different social contexts (with parents friends and work colleagues) to test the prediction that personality is socially variable due to the motivation to ‘fit in’. The questionnaire-based method produced results that support this hypothesis; all Big 5 traits were significantly variable across contexts with Conscientiousness the least variable and Extraversion possessiveness. The results indicated that females reported being more distressed than males and older respondents reported being less distressed then younger respondents. The findings from this study contribute to the literature on online infidelity in terms of understanding differences in the way it is perceived.