Multi-layer radial basis function networks: an extension to the radial basis function

This paper presents the initial research carried out into a new neural network called the multilayer radial basis function network (MRBF). The network extends the radial basis function (RBF) in a similar way to that in which the multilayer perceptron extends the perceptron. It is hoped that by connecting RBFs together in a layered fashion, an equivalent increase in ability can be gained, as is gained from using MLPs instead of single perceptrons. The results of a practical comparison between individual RBFs and MRBF's are also given.

An introduction to radial basis functions for system identification: a comparison with other neural network methods

A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RBFs are firstly considered in detail themselves and are subsequently compared with a multi-layered perceptron (MLP), in terms of performance and usage.

A model for exchange rates with crawling bands: an application to the Colombian peso

This paper builds upon previous research on currency bands, and provides a model for the Colombian peso. Stochastic differential equations are combined with information related to the Colombian currency band to estimate competing models of the behaviour of the Colombian peso within the limits of the currency band. The resulting moments of the density function for the simulated returns describe adequately most of the characteristics of the sample returns data. The factor included to account for the intra-marginal intervention performed to drive the rate towards the Central Parity accounts only for 6.5% of the daily change, which supports the argument that intervention, if performed by the Central Bank, it is not directed to push the currency towards the limits. Moreover, the credibility of the Colombian Central Bank, Banco de la República’s ability to defend the band seems low.

Testing for non-stationarity and cointegration allowing for the possibility of a structural break: an application to EuroSterling interest rates

In this paper we examine the order of integration of EuroSterling interest rates by employing techniques that can allow for a structural break under the null and/or alternative hypothesis of the unit-root tests. In light of these results, we investigate the cointegrating relationship implied by the single, linear expectations hypothesis of the term structure of interest rates employing two techniques, one of which allows for the possibility of a break in the mean of the cointegrating relationship. The aim of the paper is to investigate whether or not the interest rate series can be viewed as I(1) processes and furthermore, to consider whether there has been a structural break in the series. We also determine whether, if we allow for a break in the cointegration analysis, the results are consistent with those obtained when a break is not allowed for. The main results reported in this paper support the conjecture that the ‘short’ Euro-currency rates are characterised as I(1) series that exhibit a structural break on or near Black Wednesday, 16 September 1992, whereas the ‘long’ rates are I(1) series that do not support the presence of a structural break. The evidence from the cointegration analysis suggests that tests of the expectations hypothesis based on data sets that include the ERM crisis period, or a period that includes a structural break, might be problematic if the structural break is not explicitly taken into account in the testing framework.

Adjuvant-free immunization with hemagglutinin-Fc fusion proteins as an approach to influenza vaccines

The hemagglutinins (HAs) of human H1 and H3 influenza viruses and avian H5 influenza virus were produced as recombinant fusion proteins with the human immunoglobulin Fc domain. Recombinant HA-human immunoglobulin Fc domain (HA-HuFc) proteins were secreted from baculovirus-infected insect cells as glycosylated oligomer HAs of the anticipated molecular mass, agglutinated red blood cells, were purified on protein A, and were used to immunize mice in the absence of adjuvant. Immunogenicity was demonstrated for all subtypes, with the serum samples demonstrating subtype-specific hemagglutination inhibition, epitope specificity similar to that seen with virus infection, and neutralization. HuFc-tagged HAs are potential candidates for gene-to-vaccine approaches to influenza vaccination.

Inertia in Japanese organizations: knowledge management routines and failure to innovate

This paper argues that features of Japanese organizations, previously held to be the foundations of innovation, change and flexibility, can equally be significant barriers to change, innovation and adaptation in turbulent economic environments. This paper draws on two in-depth case studies of Japanese organizations. It shows how, in both cases, these firms displayed specific weaknesses in the ways in which they integrate and bundle knowledge, in particular around their research and development (R&D) functions. Despite the adoption of strategies of technological innovation and internationalization, the data suggest that the pursuit of both strategies is beset by barriers of inertia. Embedded internal network connections and knowledge-sharing routines between central R&D and other divisions are inappropriate for the revised strategy. Existing external connections, with preferred suppliers and customers within keiretsu structures, and close relationships with existing R&D partners retard these firms' strategic flexibility. With a limited variety of latent routines, knowledge, capabilities and agency to draw on when needed, these firms have limited organizational responsiveness and high levels of path-dependency.

Genetic predisposition to type 2 diabetes is associated with impaired insulin secretion but does not modify insulin resistance or secretion in response to an intervention to lower dietary saturated fat

Genome-wide association studies have identified SNPs reproducibly associated with type 2 diabetes (T2D). We examined the effect of genetic predisposition to T2D on insulin sensitivity and secretion using detailed phenotyping in overweight individuals with no diagnosis of T2D. Furthermore, we investigated whether this genetic predisposition modifies the responses in beta-cell function and insulin sensitivity to a 24-week dietary intervention. We genotyped 25 T2D-associated SNPs in 377 white participants from the RISCK study. Participants underwent an IVGTT prior to and following a dietary intervention that aimed to lower saturated fat intake by replacement with monounsaturated fat or carbohydrate. We composed a genetic predisposition score (T2D-GPS) by summing the T2D risk-increasing alleles of the 25 SNPs and tested for association with insulin secretion and sensitivity at baseline, and with the change in response to the dietary intervention. At baseline, a higher T2D-GPS was associated with lower acute insulin secretion (AIRg 4% lower/risk allele, P = 0.006) and lower insulin secretion for a given level of insulin sensitivity, assessed by the disposition index (DI 5% lower/risk allele, P = 0.002), but not with insulin sensitivity (Si). T2D-GPS did not modify changes in insulin secretion, insulin sensitivity or the disposition index in response to the dietary interventions to lower saturated fat. Participants genetically predisposed to T2D have an impaired ability to compensate for peripheral insulin resistance with insulin secretion at baseline, but this does not modify the response to a reduction in dietary saturated fat through iso-energetic replacement with carbohydrate or monounsaturated fat.

On discrimination algorithms for ill-posed problems with an application to magnetic tomography

In this paper we explore classification techniques for ill-posed problems. Two classes are linearly separable in some Hilbert space X if they can be separated by a hyperplane. We investigate stable separability, i.e. the case where we have a positive distance between two separating hyperplanes. When the data in the space Y is generated by a compact operator A applied to the system states ∈ X, we will show that in general we do not obtain stable separability in Y even if the problem in X is stably separable. In particular, we show this for the case where a nonlinear classification is generated from a non-convergent family of linear classes in X. We apply our results to the problem of quality control of fuel cells where we classify fuel cells according to their efficiency. We can potentially classify a fuel cell using either some external measured magnetic field or some internal current. However we cannot measure the current directly since we cannot access the fuel cell in operation. The first possibility is to apply discrimination techniques directly to the measured magnetic fields. The second approach first reconstructs currents and then carries out the classification on the current distributions. We show that both approaches need regularization and that the regularized classifications are not equivalent in general. Finally, we investigate a widely used linear classification algorithm Fisher's linear discriminant with respect to its ill-posedness when applied to data generated via a compact integral operator. We show that the method cannot stay stable when the number of measurement points becomes large.

Bayesian estimation of the spatial Durbin error model with an application to voter turnout in the 2004 presidential election

The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, we formulate and estimate the previously unutilized spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from an empirical perspective. Formal Bayesian model comparison techniques are employed to compare the normal linear model, the spatially lagged X model (SLX), the spatial Durbin model, and the spatial Durbin error model. The results overwhelmingly support the spatial Durbin error model as the appropriate empirical model.