Applying machine learning to catalogue matching in astrophysics

We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study, we take two partially matched catalogues where one of the two catalogues has a large positional uncertainty. The two catalogues we used here were taken from the H I Parkes All Sky Survey (HIPASS) and SuperCOSMOS optical survey. Previous work had matched 44 per cent (1887 objects) of HIPASS to the SuperCOSMOS catalogue. A supervised learning algorithm was then applied to construct a model of the matched portion of our catalogue. Validation of the model shows that we achieved a good classification performance (99.12 per cent correct). Applying this model to the unmatched portion of the catalogue found 1209 new matches. This increases the catalogue size from 1887 matched objects to 3096. The combination of these procedures yields a catalogue that is 72 per cent matched.

ADSL transceivers applying DSM and their nonstationary noise robustness

Dynamic spectrum management (DSM) comprises a new set of techniques for multiuser power allocation and/or detection in digital subscriber line (DSL) networks. At the Alcatel Research and Innovation Labs, we have recently developed a DSM test bed, which allows the performance of DSM algorithms to be evaluated in practice. With this test bed, we have evaluated the performance of a DSM level-1 algorithm known as iterative water-filling in an ADSL scenario. This paper describes the results of, on the one hand, the performance gains achieved with iterative water-filling, and, on the other hand, the nonstationary noise robustness of DSM-enabled ADSL modems. It will be shown that DSM trades off nonstationary noise robustness for performance improvements. A new bit swap procedure is then introduced to increase the noise robustness when applying DSM.

Revising the theoretical structure of personality and applying it to the world of work

Most modern models of personality are hierarchical, perhaps as a result of their development by means of exploratory factor analysis. Based on new ideas about the structure of personality and how it divides into biologically based and sociocognitively based components (as proposed by Carver, Cloninger, EUiot and Thrash, and ReveUe), I develop a series of rules that show how scales of personality may be linked from those that are most distal to those which are most proximal. I use SEM to confirm the proposed structure in scales of the Temperament Character Inventory (TCI) and the Eysenck Personality Profiler. Good fit is achieved and all proposed paths are significant. The model is then used to predict work performance, deviance and job satisfacdon.