Computationally efficient sequential learning algorithms for direct link resource-allocating networks

Computionally efficient sequential learning algorithms are developed for direct-link resource-allocating networks (DRANs). These are achieved by decomposing existing recursive training algorithms on a layer by layer and neuron by neuron basis. This allows network weights to be updated in an efficient parallel manner and facilitates the implementation of minimal update extensions that yield a significant reduction in computation load per iteration compared to existing sequential learning methods employed in resource-allocation network (RAN) and minimal RAN (MRAN) approaches. The new algorithms, which also incorporate a pruning strategy to control network growth, are evaluated on three different system identification benchmark problems and shown to outperform existing methods both in terms of training error convergence and computational efficiency. (c) 2005 Elsevier B.V. All rights reserved.

The Effect of Human RIghts on Criminal Evidentiary Processes: Convergence, Divergence or Realignment?

This article examines the contribution which the European Court of Human Rights has made to the development of common evidentiary processes across the common law and civil law systems of criminal procedure in Europe. It is argued that the continuing use of terms such as 'adversarial' and 'inquisitorial' to describe models of criminal proof and procedure has obscured the genuinely transformative nature of the Court's jurisprudence. It is shown that over a number of years the Court has been steadily developing a new model of proof that is better characterised as 'participatory' than as 'adversarial' or 'inquisitorial'. Instead of leading towards a convergence of existing 'adversarial' and 'inquisitorial' models of proof, this is more likely to lead towards a realignment of existing processes of proof which nonetheless allows plenty of scope for diverse application in different institutional and cultural settings.

Contingent screening for down syndrome is an efficient alternative to non-disclosure sequential screening

Objective To present a first and second trimester Down syndrome screening strategy, whereby second-trimester marker determination is contingent on the first-trimester results. Unlike non-disclosure sequential screening (the Integrated test), which requires all women to have markers in both trimesters, this allows a large proportion of the women to complete screening in the first trimester. Methods Two first-trimester risk cut-offs defined three types of results: positive and referred for early diagnosis; negative with screening complete; and intermediate, needing second-trimester markers. Multivariate Gaussian modelling with Monte Carlo simulation was used to estimate the false-positive rate for a fixed 85% detection rate. The false-positive rate was evaluated for various early detection rates and early test completion rates. Model parameters were taken from the SURUSS trial. Results Completion of screening in the first trimester for 75% of women resulted in a 30% early detection rate and a 55% second trimester detected rate (net 85%) with a false-positive rate only 0.1% above that achievable by the Integrated test. The screen-positive rate was 0.1% in the first trimester and 4.7% for those continuing to be tested in the second trimester. If the early detection rate were to be increased to 45% or the early completion rate were to be increased to 80%, there would be a further 0.1% increase in the false-positive rate. Conclusion Contingent screening can achieve results comparable with the Integrated test but with earlier completion of screening for most women. Both strategies need to be evaluated in large-scale prospective studies particularly in relation to psychological impact and practicability.

Heterogeneity, convergence, and autocorrelations

This paper is a contribution to the literature on the explanatory power and calibration of heterogeneous asset pricing models. We set out a new stochastic market-fraction asset pricing model of fundamentalists and trend followers under a market maker. Our model explains key features of financial market behaviour such as market dominance, convergence to the fundamental price and under- and over-reaction. We use the dynamics of the underlying deterministic system to characterize these features and statistical properties, including convergence of the limiting distribution and autocorrelation structure. We confirm these properties using Monte Carlo simulations.