"Aye, but It Were Wasted on Thee": Cricket, British Asians, Ethnic Identities, and the 'Magical Recovery of Community'

People in sport tend to possess rather jaded perceptions of its colour-blindness and thus, they are reluctant to confront the fact that, quite often racism is endemic. Yorkshire cricket in particular, has faced frequent accusations from minority ethnic communities of inveterate and institutionalised racism and territorial defensiveness. Drawing upon semi-structured interviews conducted with amateur white and British Asian cricketers, this paper examines the construction of regional identities in Yorkshire at a time when traditional myths and invented traditions of Yorkshire and 'Yorkshireness' are being deconstructed. This is conceptualised through a reading of John Clarke's 'magical recovery of community'. Although cricket has been multiracial for decades, I argue that some people's position as insiders is more straightforward than others. I present evidence to suggest that, regardless of being committed to Yorkshire and their 'Yorkshireness', white Yorkshire people may never fully accept British Asians as 'one of us'. Ideologically and practically, white Yorkshire people are engaged in constructing British Asians as anathema to Yorkshire culture. The paper concludes by advocating that, for sports cultures to be truly egalitarian, the ideology of sport itself has to change. True equality will only ever be achieved within a de-racialised discourse that not only accepts difference, but embraces it.British Asians; Community; Cricket; Identity; Racism; Symbolic Boundaries

The Riemannian Geometry of Deep Generative Models

Deep generative models learn a mapping from a low dimensional latent space to a high-dimensional data space. Under certain regularity conditions, these models parameterize nonlinear manifolds in the data space. In this paper, we investigate the Riemannian geometry of these generated manifolds. First, we develop efficient algorithms for computing geodesic curves, which provide an intrinsic notion of distance between points on the manifold. Second, we develop an algorithm for parallel translation of a tangent vector along a path on the manifold. We show how parallel translation can be used to generate analogies, i.e., to transport a change in one data point into a semantically similar change of another data point. Our experiments on real image data show that the manifolds learned by deep generative models, while nonlinear, are surprisingly close to zero curvature. The practical implication is that linear paths in the latent space closely approximate geodesics on the generated manifold. However, further investigation into this phenomenon is warranted, to identify if there are other architectures or datasets where curvature plays a more prominent role. We believe that exploring the Riemannian geometry of deep generative models, using the tools developed in this paper, will be an important step in understanding the high-dimensional, nonlinear spaces these models learn.Comment: 9 page

Hierarchical Graphical Models for Multigroup Shape Analysis using Expectation Maximization with Sampling in Kendall's Shape Space

This paper proposes a novel framework for multi-group shape analysis relying on a hierarchical graphical statistical model on shapes within a population.The framework represents individual shapes as point setsmodulo translation, rotation, and scale, following the notion in Kendall shape space.While individual shapes are derived from their group shape model, each group shape model is derived from a single population shape model. The hierarchical model follows the natural organization of population data and the top level in the hierarchy provides a common frame of reference for multigroup shape analysis, e.g. classification and hypothesis testing. Unlike typical shape-modeling approaches, the proposed model is a generative model that defines a joint distribution of object-boundary data and the shape-model variables. Furthermore, it naturally enforces optimal correspondences during the process of model fitting and thereby subsumes the so-called correspondence problem. The proposed inference scheme employs an expectation maximization (EM) algorithm that treats the individual and group shape variables as hidden random variables and integrates them out before estimating the parameters (population mean and variance and the group variances). The underpinning of the EM algorithm is the sampling of pointsets, in Kendall shape space, from their posterior distribution, for which we exploit a highly-efficient scheme based on Hamiltonian Monte Carlo simulation. Experiments in this paper use the fitted hierarchical model to perform (1) hypothesis testing for comparison between pairs of groups using permutation testing and (2) classification for image retrieval. The paper validates the proposed framework on simulated data and demonstrates results on real data.Comment: 9 pages, 7 figures, International Conference on Machine Learning 201

Computing hulls in positive definite space

posterP(n): a Riemannian manifold Definition: symmetric positive-definite (n) (n) matrices Applications: Diffusion Tensor MRI (DT-MRI) Flow through voxel modeled in P(3) Elasticity Tensors Modeled by elements of P(6) Machine Learning Used in kernels Convex Hulls Data on P(n): Want to analyze this data Centerpoints, clustering, shape Convex hull (CH) is a useful data analysis tool Describes shape of the data Can use max CH peeling depth to find a centerpoint A framework for analyzing shape in spaces where CH is difficult to work with (ball hull) An approximation to the ball hull (""-ball hull) A way to measure width as a side benefit (extent) Horofunctions provide a good way to analyze manifolds like thi

The ionising output and gas content of galaxies

Molecular gas is the primary fuel for star formation in galaxies. Gas within giant molecular clouds collapses to form the next generation of stars. This star formation is critical in determining both how galaxies appear, via their starlight, as well as their evolution. Nowhere is this more evident than for the first generation of star-forming galaxies. These galaxies were fuelled by pristine gas, driving intense star formation, giving rise to massive stars. The starlight from these galaxies bathed the Universe with ultraviolet light, reionising the neutral hydrogen in the intergalactic medium. In this thesis we study analogues of the first galaxies, targeting Lyman continuum emission in z ∼ 3 galaxies. We determine significant individual escape fractions of ionising photons for a large sample of galaxies for the first time. However, a stack of non-detections provides a stringent upper limit on the escape fraction of < 0.5%, with no clear difference in the properties of detections and non-detections, suggesting a dichotomy in the way that Lyman continuum photons escape their host galaxy. We then use the xCOLD GASS survey to determine the molecular gas content of the local Universe more accurately than before. We calculate the ratio of molecular to atomic gas and investigate how this varies with stellar mass. Finally, we build a modular analytical model based upon empirical scaling relations. We model the SFR-M∗ plane and scaling relations between SFR and gas masses. We successfully use our model to predict the SFR distribution function, matching empirically derived results. We also use our model to predict the molecular and atomic gas mass functions, demonstrating the potential of this simple model based on observations. This thesis provides insight into the contribution of the first galaxies to reionisation, the molecular gas content of the local Universe and how scaling relations can be used to predict galaxy properties

The Effects of Parcels and Latent Variable Scores on the Detection of Interactions in Structural Equation Modeling

Numerous theories in the behavioral and organizational sciences involve the regression of an outcome variable on component terms and their product to evaluate interaction effects. There are numerous statistical difficulties with this multiple regression approach. The most serious is measurement error, requiring the use of structural equation modeling. Jöreskog and Yang (1996) described a nonlinear structural equation modeling procedure that incorporates mean structures in the covariance analysis. They demonstrated that only one indicator for the product term is necessary for model identification. Unfortunately, the Jöreskog-Yang procedure leads to biased estimates of the product coefficient. In this dissertation, I propose that (1) the proper use of item parcels can reduce bias in estimates, and (2) that using a relatively new technique of analysis (creation of latent variable scores) can also be fruitful in removing measurement error and improving the estimation of product terms. Two studies investigated these proposals. In Study 1, archival data were analyzed using the proposed techniques. The interaction hypothesis tested by the various techniques is that a competitive climate influences perceptions of coworker support, and that this relationship is moderated by (interacts with) a person\u27s level of trait competitiveness. Study 2 involved a Monte Carlo investigation of methods for estimating an interaction effect. The Monte Carlo research included design factors for (a) effect size, (b) parceling strategy, and (c) method of analysis. Study 1 demonstrated that method of analysis and parceling strategy affected the detection of the moderator effect of competition on two types of coworker support (instrumental and affective). Variability in the t-tests and effect size indices lend credibility for the need for the Monte-Carlo investigation. Study 2 demonstrated that (1) there is greater variability in the estimation of the interaction effect with the Jöreskog-Yang method than the latent variable scores method, (2) parceling strategy has the most influence on the interaction effect in the Jöreskog-Yang method, and this effect is dependent upon which strategy is used, and (3) the latent variable score method is superior to the Jöreskog-Yang method with respect to statistical decision making (i.e., fewer Type II errors). Practical implications and future research directions are considered

Combination of PCA and DWT features from hyperspectral images for skin tumor detection

In this paper, the detection of skin tumors using hyperspectral fluorescence images of poultry carcasses is investigated. Skin tumors are not always visually obvious. The visual region of the spectrum may be too limited to meet all the requirements so that the tumors may be accurately classified so the multiple bands from hyperspectral imaging may be of some use. Each of the hyperspectral fluorescence images will consist of 65 spectral bands ranging from 425 nm to 711 nm. Multiple detection schemes will utilized to provide adequate classification rates. Principal component analysis PCA) and discrete wavelet transforms (DWT) are utilized to transform the data from the spectral space to a feature space. A small number of features are selected to provide dimensionality reduction without a significant loss of information. A support vector machine (SVM) classifier is used to determine if a pixel falls in a normal skin or tumorous skin categories. To provide additional classifier accuracy, an algorithm based on the average intensity of the pixel signal, is used to combine the two classifiers. The accuracy of the three classifiers was tested using 11 hyperspectral fluorescence images with a combined total of 38 tumors. The PCA-SVM classifier provided a tumor detection rate of 86.8% with 17 false positives and 5 missed tumors. The DWT-SVM classifier provided a tumor detection rate of only 42.1 % with 18 false positives and 12 missed tumors. The classifier that selected the best method showed that the PCA/DWT-SVM classifier provided a classification rate of 94. 7% with 6 false positives and only one missed tumor

DECOLONISING DEVELOPMENT TO END HUNGER IN RURAL PERU

This paper focuses on the issue of hunger and food insecurity in the Peruvian Andes. It provides a critique of the development industry and argues that the responses it utilises often draw on colonial structures that reproduce oppression. The development industry tends to be driven by the idea that the remedy to hunger and poverty exists in embracing globalisation and marketisation. However, interventions rooted in capitalism can actually exacerbate rather than resolve these issues.Rather than relying on imported solutions from the global North, this essay discusses a process of cultural affirmation and the reassertion of traditional knowledge in the Peruvian Andes. It canvases the decolonising work of a Peruvian Non-Government Organisation (NGO) known as El Proyecto Andino de Tecnologías Campesinas (PRATEC) and argues that contextualised responses that affirm Indigenous knowledge and encourage the co-production of knowledge rather than the wholesale importation of foreign solutions can reduce food insecurity at a local level