Curvature-driven smoothing: a learning algorithm for feed-forward networks

The performance of feed-forward neural networks in real applications can be often be improved significantly if use is made of a-priori information. For interpolation problems this prior knowledge frequently includes smoothness requirements on the network mapping, and can be imposed by the addition to the error function of suitable regularization terms. The new error function, however, now depends on the derivatives of the network mapping, and so the standard back-propagation algorithm cannot be applied. In this paper, we derive a computationally efficient learning algorithm, for a feed-forward network of arbitrary topology, which can be used to minimize the new error function. Networks having a single hidden layer, for which the learning algorithm simplifies, are treated as a special case.

Regularization and complexity control in feed-forward networks

In this paper we consider four alternative approaches to complexity control in feed-forward networks based respectively on architecture selection, regularization, early stopping, and training with noise. We show that there are close similarities between these approaches and we argue that, for most practical applications, the technique of regularization should be the method of choice.

Topographic mappings and feed-forward neural networks

This thesis is a study of the generation of topographic mappings - dimension reducing transformations of data that preserve some element of geometric structure - with feed-forward neural networks. As an alternative to established methods, a transformational variant of Sammon's method is proposed, where the projection is effected by a radial basis function neural network. This approach is related to the statistical field of multidimensional scaling, and from that the concept of a 'subjective metric' is defined, which permits the exploitation of additional prior knowledge concerning the data in the mapping process. This then enables the generation of more appropriate feature spaces for the purposes of enhanced visualisation or subsequent classification. A comparison with established methods for feature extraction is given for data taken from the 1992 Research Assessment Exercise for higher educational institutions in the United Kingdom. This is a difficult high-dimensional dataset, and illustrates well the benefit of the new topographic technique. A generalisation of the proposed model is considered for implementation of the classical multidimensional scaling (¸mds}) routine. This is related to Oja's principal subspace neural network, whose learning rule is shown to descend the error surface of the proposed ¸mds model. Some of the technical issues concerning the design and training of topographic neural networks are investigated. It is shown that neural network models can be less sensitive to entrapment in the sub-optimal global minima that badly affect the standard Sammon algorithm, and tend to exhibit good generalisation as a result of implicit weight decay in the training process. It is further argued that for ideal structure retention, the network transformation should be perfectly smooth for all inter-data directions in input space. Finally, there is a critique of optimisation techniques for topographic mappings, and a new training algorithm is proposed. A convergence proof is given, and the method is shown to produce lower-error mappings more rapidly than previous algorithms.

Is the clustering of neurofibrillary tangles in Alzheimer's patients related to the cells of origin of specific cortico-cortical projections?

The spatial pattern of cellular neurofibrillary tangles (NFT) was studied in the supra- and infragranular layers of various cortical regions in cases of Alzheimer's disease (AD). The objective was to test the hypothesis that NFT formation was associated with the cells of origin of specific cortico-cortical projections. The novel feature of the study was that pattern analysis enabled the dimension and spacing of NFT clusters along the cortical ribbon to be estimated. In the majority of brain regions studied, NFT occurred in clusters of neurons which were regularly spaced along the cortical strip. This pattern is consistent with the predicted distribution of the cells of origin of specific cortico-cortico projections. Mean NFT cluster size varied from 250 to > 12800 microns in different cortical tissues suggesting either variation in the size of the cell clusters or a dynamic process in the development of NFT in relation to these cell clusters. The formation of NFT in cell clusters which may give rise to the feed-forward and feed-back cortico-cortical projections suggests a possible route of spread of NFT pathology in AD between cortical regions and from the cortex to subcortical areas.

Novel real-time homodyne coherent receiver using a feed-forward based carrier extraction scheme for phase modulated signals

We report a novel real-time homodyne coherent receiver based on a DPSK optical-electrical-optical (OEO) regenerator used to extract a carrier from carrier-less phase modulated signals based on feed-forward based modulation stripping. The performance of this non-DSP based coherent receiver was evaluated for 10.66Gbit/s BPSK signals. Self-homodyne coherent detection and homodyne detection with an injection-locked local oscillator laser was demonstrated. The performance was evaluated by measuring the electrical signal-to-noise (SNR) and recording the eye diagrams. Using injection-locking for the LO improves the performance and enables homodyne detection with optical injection-locking to operate with carrier-less BPSK signals without the need for polarization multiplexed pilot-tones.

Novel synchronous DPSK optical regenerator based on a feed-forward based carrier extraction scheme

We experimentally demonstrate a novel synchronous 10.66Gbit/s DPSK OEO regenerator which uses a feed-forward carrier extraction scheme with an injection-locked laser to synchronize the regenerated signal wavelength to the incoming signal wavelength. After injection-locking, a low-cost DFB laser used at the regenerator exhibited the same linewidth characteristics as the narrow line-width transmitter laser. The phase regeneration properties of the regenerator were evaluated by emulating random Gaussian phase noise applied to the DPSK signal before the regenerator using a phase modulator driven by an arbitrary waveform generator. The overall performance was evaluated in terms of electrical eye-diagrams, BER measurements, and constellation diagrams.

Full-field feed-forward equalizer with adaptive system optimization

We numerically investigate the combination of full-field detection and feed-forward equalizer (FFE) for adaptive chromatic dispersion compensation up to 2160 km in a 10 Gbit/s on-off keyed optical transmission system. The technique, with respect to earlier reports, incorporates several important implementation modules, including the algorithm for adaptive equalization of the gain imbalance between the two receiver chains, compensation of phase misalignment of the asymmetric Mach-Zehnder interferometer, and simplified implementation of field calculation. We also show that in addition to enabling fast adaptation and simplification of field calculation, full-field FFE exhibits enhanced tolerance to the sampling phase misalignment and reduced sampling rate when compared to the full-field implementation using a dispersive transmission line.

An analysis of food demand in Peru:implications for food-feed competition

In their policy proposals on how best to stimulate economic growth, economists have been increasingly emphasizing free markets. It is, however, possible that free-market-led economic growth can lead to increased income inequity which can further increase poverty. One of the more interesting but thus far insufficiently explored mechanisms for the latter is food–feed competition. Using Peruvian Living Standard Survey (PLSS) data for 1985–86 and 1990, the paper examines the demand patterns of households and concludes that the empirical evidence is in agreement with the hypotheses underlying the theory of food–feed competition.

A 'sexy space' for women? Heterosexual women's experiences of a male strip show venue

The rise of male strip shows marketed towards heterosexual women has called into question the idea that only men can 'gaze' or be 'sexual scrutinizers' in public leisure spaces. This paper details the findings of an ethnographic study of a male strip event 'Cheeky's',1 located in the Midlands, England. Utilising observation, informal interviews and photography, the paper describes the physical environment and the atmosphere of the event, and analyses interview data with female customers. The paper questions what spaces such as Cheeky's mean for female sexualities, sexual roles and desires. The findings are twofold. On the one hand the club was a relatively novel sexual space, as some women spectators experienced it as an 'empowering' space in which they could be 'sexually aggressive'; on the other hand, the character of the club was actively and adeptly manufactured by the Master of Ceremonies (MC), and male dancers, so as to encourage - and even to coercively elicit - extrovert behaviour from women customers. Despite shifting normative gendered expectations of women's sexual behaviour to some extent, ultimately the club structured women's sexual experiences around traditional heterosexist lines. The club did not encourage women's autonomous sexual expression, and many women claimed they had not found it a very 'sexy space'. © 2011 Taylor & Francis.