Targeting Mr Average: Participation, gender equity and school sport partnerships

The School Sport Partnership Programme (SSPP) is one strand of the national strategy for physical education and school sport in England, the physical education and school sport Club Links Strategy (PESSCL). The SSPP aims to make links between school physical education (PE) and out of school sports participation, and has a particular remit to raise the participation levels of several identified under-represented groups, of which girls and young women are one. National evaluations of the SSPP show that it is beginning to have positive impacts on young people's activity levels by increasing the range and provision of extra curricular activities (Office for Standards in Education (OFSTED), 2003, 2004, 2005; Loughborough Partnership, 2005, 2006). This paper contributes to the developing picture of the phased implementation of the programme by providing qualitative insights into the work of one school sport partnership with a particular focus on gender equity. The paper explores the ways in which gender equity issues have been explicitly addressed within the 'official texts' of the SSPP; how these have shifted over time and how teachers are responding to and making sense of these in their daily practice. Using participation observation, interview and questionnaire data, the paper explores how the coordinators are addressing the challenge of increasing the participation of girls and young women. The paper draws on Walby's (2000) conceptualisation of different kinds of feminist praxis to highlight the limitations of the coordinators' work. Two key themes from the data and their implications are addressed: the dominance of competitive sport practices and the PE professionals' views of targeting as a strategy for increasing the participation of under-represented groups. The paper concludes that coordinators work within an equality or difference discourse with little evidence of the transformative praxis needed for the programme to be truly inclusive. © 2008 Taylor & Francis

Study of resonance light scattering for remote optical probing

Enhanced scattering and fluorescence processes in the visible and UV were investigated which will enable improved remote measurements of gas properties. The theoretical relationship between scattering and fluorescence from an isolated molecule in the approach to resonance is examined through analysis of the time dependence of re-emitted light following excitation of pulsed incident light. Quantitative estimates are developed for the relative and absolute intensities of fluorescence and resonance scattering. New results are obtained for depolarization of scattering excited by light at wavelengths within a dissociative continuum. The experimental work was performed in two separate facilities. One of these utilizes argon and krypton lasers, single moded by a tilted etalon, and a 3/4 meter double monochromator. This facility was used to determine properties of the re-emission from NO2, I2 and O3 excited by visible light. The second facility involves a narrow-line dye laser, and a 3/4 meter single monochromator. The dye laser produces pulsed light with 5 nsec pulse duration and 0.005 nm spectral width

A recurrent neural network with ever changing synapses

A recurrent neural network with noisy input is studied analytically, on the basis of a Discrete Time Master Equation. The latter is derived from a biologically realizable learning rule for the weights of the connections. In a numerical study it is found that the fixed points of the dynamics of the net are time dependent, implying that the representation in the brain of a fixed piece of information (e.g., a word to be recognized) is not fixed in time.Comment: 17 pages, LaTeX, 4 figure

Hierarchical Self-Programming in Recurrent Neural Networks

We study self-programming in recurrent neural networks where both neurons (the `processors') and synaptic interactions (`the programme') evolve in time simultaneously, according to specific coupled stochastic equations. The interactions are divided into a hierarchy of $L$ groups with adiabatically separated and monotonically increasing time-scales, representing sub-routines of the system programme of decreasing volatility. We solve this model in equilibrium, assuming ergodicity at every level, and find as our replica-symmetric solution a formalism with a structure similar but not identical to Parisi's $L$-step replica symmetry breaking scheme. Apart from differences in details of the equations (due to the fact that here interactions, rather than spins, are grouped into clusters with different time-scales), in the present model the block sizes $m_i$ of the emerging ultrametric solution are not restricted to the interval $[0,1]$, but are independent control parameters, defined in terms of the noise strengths of the various levels in the hierarchy, which can take any value in [0,\infty\ket. This is shown to lead to extremely rich phase diagrams, with an abundance of first-order transitions especially when the level of stochasticity in the interaction dynamics is chosen to be low.Comment: 53 pages, 19 figures. Submitted to J. Phys.

The XY Spin-Glass with Slow Dynamic Couplings

We investigate an XY spin-glass model in which both spins and couplings evolve in time: the spins change rapidly according to Glauber-type rules, whereas the couplings evolve slowly with a dynamics involving spin correlations and Gaussian disorder. For large times the model can be solved using replica theory. In contrast to the XY-model with static disordered couplings, solving the present model requires two levels of replicas, one for the spins and one for the couplings. Relevant order parameters are defined and a phase diagram is obtained upon making the replica-symmetric Ansatz. The system exhibits two different spin-glass phases, with distinct de Almeida-Thouless lines, marking continuous replica-symmetry breaking: one describing freezing of the spins only, and one describing freezing of both spins and couplings.Comment: 7 pages, Latex, 3 eps figure

The challenges of intersectionality: Researching difference in physical education

Researching the intersection of class, race, gender, sexuality and disability raises many issues for educational research. Indeed, Maynard (2002, 33) has recently argued that ‘difference is one of the most significant, yet unresolved, issues for feminist and social thinking at the beginning of the twentieth century’. This paper reviews some of the key imperatives of working with ‘intersectional theory’ and explores the extent to these debates are informing research around difference in education and Physical Education (PE). The first part of the paper highlights some key issues in theorising and researching intersectionality before moving on to consider how difference has been addressed within PE. The paper then considers three ongoing challenges of intersectionality – bodies and embodiment, politics and practice and empirical research. The paper argues for a continued focus on the specific context of PE within education for its contribution to these questions

Stochastic learning in a neural network with adapting synapses

We consider a neural network with adapting synapses whose dynamics can be analitically computed. The model is made of $N$ neurons and each of them is connected to $K$ input neurons chosen at random in the network. The synapses are $n$-states variables which evolve in time according to Stochastic Learning rules; a parallel stochastic dynamics is assumed for neurons. Since the network maintains the same dynamics whether it is engaged in computation or in learning new memories, a very low probability of synaptic transitions is assumed. In the limit $N\to\infty$ with $K$ large and finite, the correlations of neurons and synapses can be neglected and the dynamics can be analitically calculated by flow equations for the macroscopic parameters of the system.Comment: 25 pages, LaTeX fil

To Learn or Not to Learn Features for Deformable Registration?

Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In this paper, we examine the question on how features learnt using various Deep Learning (DL) frameworks can be used for deformable registration and whether this feature learning is necessary or not. We investigate the use of features learned by different DL methods in the current state-of-the-art discrete registration framework and analyze its performance on 2 publicly available datasets. We draw insights into the type of DL framework useful for feature learning and the impact, if any, of the complexity of different DL models and brain parcellation methods on the performance of discrete registration. Our results indicate that the registration performance with DL features and SSC are comparable and stable across datasets whereas this does not hold for low level features.Comment: 9 pages, 4 figure