La ballerine illettrée:transformations of the feminine in Mallarmé's sance writings

Stéphane Mallarmé's texts on dance criticism occupy a prominent place in dance literature. They have also been of major interest to the Mallarme specialists who have seen in them the key to a greater understanding of the whole of his work. In this article I determine how his dance writings can be interpreted in the context of the whole of the œuvre, including for example the women's magazine he wrote and produced, La Dernière Mode. I establish to what extent the female presence in Mallarmé's poetry tends towards metaphors, women's bodies absent or negated by the gaze of the poet. As the poet responds to the stimulus of the dance, he divests the female dancer of her womanly characteristics, recasting her in his own imagination both as feminine principle and incarnation of 'l'Idée', and also as mythical being, 'au-delà de toute vie possible', a composite of other female characters inhabiting his œuvre, constantly poised between presence and absence, stillness and movement, a locus for a precarious balance which is endlessly re-enacted in h.is own writing practice.

Using XML and XSLT for flexible elicitation of mental-health risk knowledge

Current tools for assessing risks associated with mental-health problems require assessors to make high-level judgements based on clinical experience. This paper describes how new technologies can enhance qualitative research methods to identify lower-level cues underlying these judgements, which can be collected by people without a specialist mental-health background. Content analysis of interviews with 46 multidisciplinary mental-health experts exposed the cues and their interrelationships, which were represented by a mind map using software that stores maps as XML. All 46 mind maps were integrated into a single XML knowledge structure and analysed by a Lisp program to generate quantitative information about the numbers of experts associated with each part of it. The knowledge was refined by the experts, using software developed in Flash to record their collective views within the XML itself. These views specified how the XML should be transformed by XSLT, a technology for rendering XML, which resulted in a validated hierarchical knowledge structure associating patient cues with risks. Changing knowledge elicitation requirements were accommodated by flexible transformations of XML data using XSLT, which also facilitated generation of multiple data-gathering tools suiting different assessment circumstances and levels of mental-health knowledge. © 2007 Informa UK Ltd All rights reserved.

The impact of ageing and gender on visual mental imagery processes:a study of performance on tasks from the Complete Visual Mental Imagery Battery (CVMIB)

In this study we aim to evaluate the impact of ageing and gender on different visual mental imagery processes. Two hundred and fifty-one participants (130 women and 121 men; age range = 18–77 years) were given an extensive neuropsychological battery including tasks probing the generation, maintenance, inspection, and transformation of visual mental images (Complete Visual Mental Imagery Battery, CVMIB). Our results show that all mental imagery processes with the exception of the maintenance are affected by ageing, suggesting that other deficits, such as working memory deficits, could account for this effect. However, the analysis of the transformation process, investigated in terms of mental rotation and mental folding skills, shows a steeper decline in mental rotation, suggesting that age could affect rigid transformations of objects and spare non-rigid transformations. Our study also adds to previous ones in showing gender differences favoring men across the lifespan in the transformation process, and, interestingly, it shows a steeper decline in men than in women in inspecting mental images, which could partially account for the mixed results about the effect of ageing on this specific process. We also discuss the possibility to introduce the CVMIB in clinical assessment in the context of theoretical models of mental imagery.

Gravimetric and density profiling using the combination of surface acoustic waves and neutron reflectivity

A new approach is described herein, where neutron reflectivity measurements that probe changes in the density profile of thin films as they absorb material from the gas phase have been combined with a Love wave based gravimetric assay that measures the mass of absorbed material. This combination of techniques not only determines the spatial distribution of absorbed molecules, but also reveals the amount of void space within the thin film (a quantity that can be difficult to assess using neutron reflectivity measurements alone). The uptake of organic solvent vapours into spun cast films of polystyrene has been used as a model system with a view to this method having the potential for extension to the study of other systems. These could include, for example, humidity sensors, hydrogel swelling, biomolecule adsorption or transformations of electroactive and chemically reactive thin films. This is the first ever demonstration of combined neutron reflectivity and Love wave-based gravimetry and the experimental caveats, limitations and scope of the method are explored and discussed in detail.

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.

Robust automatic mapping algorithms in a network monitoring scenario

Automatically generating maps of a measured variable of interest can be problematic. In this work we focus on the monitoring network context where observations are collected and reported by a network of sensors, and are then transformed into interpolated maps for use in decision making. Using traditional geostatistical methods, estimating the covariance structure of data collected in an emergency situation can be difficult. Variogram determination, whether by method-of-moment estimators or by maximum likelihood, is very sensitive to extreme values. Even when a monitoring network is in a routine mode of operation, sensors can sporadically malfunction and report extreme values. If this extreme data destabilises the model, causing the covariance structure of the observed data to be incorrectly estimated, the generated maps will be of little value, and the uncertainty estimates in particular will be misleading. Marchant and Lark [2007] propose a REML estimator for the covariance, which is shown to work on small data sets with a manual selection of the damping parameter in the robust likelihood. We show how this can be extended to allow treatment of large data sets together with an automated approach to all parameter estimation. The projected process kriging framework of Ingram et al. [2007] is extended to allow the use of robust likelihood functions, including the two component Gaussian and the Huber function. We show how our algorithm is further refined to reduce the computational complexity while at the same time minimising any loss of information. To show the benefits of this method, we use data collected from radiation monitoring networks across Europe. We compare our results to those obtained from traditional kriging methodologies and include comparisons with Box-Cox transformations of the data. We discuss the issue of whether to treat or ignore extreme values, making the distinction between the robust methods which ignore outliers and transformation methods which treat them as part of the (transformed) process. Using a case study, based on an extreme radiological events over a large area, we show how radiation data collected from monitoring networks can be analysed automatically and then used to generate reliable maps to inform decision making. We show the limitations of the methods and discuss potential extensions to remedy these.

Approximating differentiable relationships between delay embedded dynamical systems with radical basis functions

This thesis is about the study of relationships between experimental dynamical systems. The basic approach is to fit radial basis function maps between time delay embeddings of manifolds. We have shown that under certain conditions these maps are generically diffeomorphisms, and can be analysed to determine whether or not the manifolds in question are diffeomorphically related to each other. If not, a study of the distribution of errors may provide information about the lack of equivalence between the two. The method has applications wherever two or more sensors are used to measure a single system, or where a single sensor can respond on more than one time scale: their respective time series can be tested to determine whether or not they are coupled, and to what degree. One application which we have explored is the determination of a minimum embedding dimension for dynamical system reconstruction. In this special case the diffeomorphism in question is closely related to the predictor for the time series itself. Linear transformations of delay embedded manifolds can also be shown to have nonlinear inverses under the right conditions, and we have used radial basis functions to approximate these inverse maps in a variety of contexts. This method is particularly useful when the linear transformation corresponds to the delay embedding of a finite impulse response filtered time series. One application of fitting an inverse to this linear map is the detection of periodic orbits in chaotic attractors, using suitably tuned filters. This method has also been used to separate signals with known bandwidths from deterministic noise, by tuning a filter to stop the signal and then recovering the chaos with the nonlinear inverse. The method may have applications to the cancellation of noise generated by mechanical or electrical systems. In the course of this research a sophisticated piece of software has been developed. The program allows the construction of a hierarchy of delay embeddings from scalar and multi-valued time series. The embedded objects can be analysed graphically, and radial basis function maps can be fitted between them asynchronously, in parallel, on a multi-processor machine. In addition to a graphical user interface, the program can be driven by a batch mode command language, incorporating the concept of parallel and sequential instruction groups and enabling complex sequences of experiments to be performed in parallel in a resource-efficient manner.

Basic elements for microfiber photonics : micro/nanofibers and microfiber coil resonators

The basic functional element of microfiber photonics is a microfiber coil resonator (MCR), which potentially can perform filtering, time delay, and nonlinear transformations of electromagnetic waves, as well as sensing of the ambient medium. The first experimental demonstration of an MCR has been recently performed by researchers of the OFS Laboratories (Optical Fiber Communication Conference 2007, Postdeadline paper PDP46). This paper follows up on the later publication presenting a brief introduction to the theory, transmission properties and applications of optical micro/nanofibers and MCRs. Fabrication of MCRs in air and in liquid is reported. For the MCR immersed in liquid, the Q-factor exceeding 60 000 is achieved. © 2008 IEEE.

Basic elements for microfiber photonics:micro/nanofibers and microfiber coil resonators

The basic functional element of microfiber photonics is a microfiber coil resonator (MCR), which potentially can perform filtering, time delay, and nonlinear transformations of electromagnetic waves, as well as sensing of the ambient medium. The first experimental demonstration of an MCR has been recently performed by researchers of the OFS Laboratories (Optical Fiber Communication Conference 2007, Postdeadline paper PDP46). This paper follows up on the later publication presenting a brief introduction to the theory, transmission properties and applications of optical micro/nanofibers and MCRs. Fabrication of MCRs in air and in liquid is reported. For the MCR immersed in liquid, the Q-factor exceeding 60 000 is achieved. © 2008 IEEE.

Still entrenched in the conflict/cooperation dichotomy? EU–Russia relations and the Ukraine crisis

The article highlights that the traditional conflict/cooperation dichotomy which characterised the dynamic of European Union (EU)–Russia relation during the post-Cold War period has remained stable throughout the Ukraine crisis. It identifies a pattern of continuity rather than change in the main characteristics of the traditional conflict/cooperation dichotomy: the post-Cold War order on the European continent, values and worldviews, perceptions of self and other, and policies towards each other and post-Soviet space. Secondly, in tune with neoclassical realism the article aims to account for the relative persistence of the conflict/cooperation dichotomy. It argues that the dynamic of EU–Russia relations remained rather stable due to the fact that neither the EU nor Russian foreign policy has undergone major transformations (of both power, scope and organisation) that would provide incentive or constrains for a complete overhaul of the conflict/cooperation dichotomy. Moreover, the article claims that the relative stability of world politics since the start of the Ukraine crisis has not given any the EU and Russia incentives – or constrained them – to seek to change the overall dynamic of their relationship.

In-cavity transformations for new nonlinear regimes of pulse generation in mode-locked fibre lasers

We review recent progress in the research on nonlinear mechanisms of pulse generation in passively mode-locked fibre lasers. These include parabolic self-similar pulse mode-locking, a mode-locking regime featuring pulses with a triangular distribution of the intensity, and spectral compression arising from nonlinear pulse propagation. We also report on the possibility of achieving various regimes of advanced temporal waveform generation in a mode-locked fibre laser by inclusion of a spectral filter into the laser cavity.