Increasing the accuracy of Nitrogen Dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics.

Nitrogen Dioxide (NO2) is known to act as an environmental trigger for many respiratory illnesses. As a pollutant it is difficult to map accurately, as concentrations can vary greatly over small distances. In this study three geostatistical techniques were compared, producing maps of NO2 concentrations in the United Kingdom (UK). The primary data source for each technique was NO2 point data, generated from background automatic monitoring and background diffusion tubes, which are analysed by different laboratories on behalf of local councils and authorities in the UK. The techniques used were simple kriging (SK), ordinary kriging (OK) and simple kriging with a locally varying mean (SKlm). SK and OK make use of the primary variable only. SKlm differs in that it utilises additional data to inform prediction, and hence potentially reduces uncertainty. The secondary data source was Oxides of Nitrogen (NOx) derived from dispersion modelling outputs, at 1km x 1km resolution for the UK. These data were used to define the locally varying mean in SKlm, using two regression approaches: (i) global regression (GR) and (ii) geographically weighted regression (GWR). Based upon summary statistics and cross-validation prediction errors, SKlm using GWR derived local means produced the most accurate predictions. Therefore, using GWR to inform SKlm was beneficial in this study.

Explaining Social Class Inequalities in Smoking: The Role of Education, Self-Efficacy, and Deprivation

Rates of smoking have decreased dramatically in most Northern European countries over the last 50 years or so, but manual working class groups are substantially more likely to smoke daily than are the professional and managerial classes. This article examines three hypotheses about the processes producing these inequalities. The first argues that social class inequalities reflect differences across education groups in knowledge of the risks of smoking. The second suggests that the living conditions of lower social class groups leads to the development of lower self-efficacy and a lower propensity to quit smoking. The third states that smoking has a functional use among poorer individuals. This article draws upon data from the Republic of Ireland to assess these hypotheses. Our analysis provides some support for the first hypothesis in that education independently reduces the odds of a manual class person smoking relative to a non-manual by 12 per cent. The second hypothesis is not supported by the data. The third hypothesis gains the most support: measures of disadvantage and deprivation account for almost one-third of the class differential in smoking. The results suggest that smoking cessation policy should reflect the importance of social and economic context in quitting behaviour.

Class inequalities in educational attainment among the adult population in the Republic of Ireland

Substantial increases in participation rates at secondary and third level in recent years have often been assumed to be associated with increased equality of opportunity. However, there is little evidence from elsewhere that expansion per se, except when it takes the form of saturation of the demand from higher classes, leads to a reduction in class inequalities. In exploring the factors that contribute to trends over time, or to a distinctive position in comparison with other countries, we have drawn on the recent literature to argue that the crucial factors are those which affect decisions to continue in education. We have also operated on the assumption that students and their parents rationally consider the costs and benefits associated with educational choices. The most recent evidence relating to the adult population provides no support for the existence of any trend towards equality of educational opportunity. It is, rather consistent with the class reproduction perspective that stresses the ability of privileged classes to maintain their advantages.

Optimization of Weighted Finite State Transducer for Speech Recognition

There is considerable interest in creating embedded, speech recognition hardware using the weighted finite state transducer (WFST) technique but there are performance and memory usage challenges. Two system optimization techniques are presented to address this; one approach improves token propagation by removing the WFST epsilon input arcs; another one-pass, adaptive pruning algorithm gives a dramatic reduction in active nodes to be computed. Results for memory and bandwidth are given for a 5,000 word vocabulary giving a better practical performance than conventional WFST; this is then exploited in an adaptive pruning algorithm that reduces the active nodes from 30,000 down to 4,000 with only a 2 percent sacrifice in speech recognition accuracy; these optimizations lead to a more simplified design with deterministic performance.

Violation of Bell's inequalities with preamplified homodyne detection

We show that the use of probabilistic noiseless amplification in entangled coherent state-based schemes for the test of quantum nonlocality provides substantial advantages. The threshold amplitude to falsify a Bell-CHSH nonlocality test, in fact, is significantly reduced when amplification is embedded into the test itself. Such a beneficial effect holds also in the presence of detection inefficiency. Our study helps in affirming noiseless amplification as a valuable tool for coherent information processing and the generation of strongly nonclassical states of bosonic systems.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we present AGWAN (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the AGWAN model to real-world graphs and for generating random graphs from the model. Using the Enron “who communicates with whom” social graph, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to the structure of real-world graphs.

Who Feels Inferior? A Test of the Status Anxiety Hypothesis of Social Inequalities in Health

The empirical association between income inequality, population health and other social problems is now well established and the research literature suggests that the relationship is not artefactual. Debate is still ongoing as to the cause of this association. Wilkinson, Marmot and colleagues have argued for some time that the relationship stems from the psycho-social effects of status comparisons. Here, income inequality is a marker of a wider status hierarchy that provokes an emotional stress response in individuals that is harmful to health and well-being. We label this the ‘status anxiety hypothesis’. If true, this would imply a structured relationship between income inequality at the societal level, individual income rank and anxiety relating to social status. This paper sets out strong and weak forms of the hypothesis and then presents three predictions concerning the structuring of ‘status anxiety’ at the individual level given different levels of national income inequality and varying individual income. We then test these predictions using data from a cross-national survey of over 34,000 individuals carried out in 2007 in 31 European countries. Respondents from low inequality countries reported less status anxiety than those in higher inequality countries at all points on the income rank curve. This is an important precondition of support for the status anxiety hypothesis and may be seen as providing support for the weaker version of the hypothesis. However, we do not find evidence to support the stronger version of the hypothesis which requires the negative effect of income rank on status anxiety to be exacerbated by increasing income inequality.

Robust visual tracking using structurally random projection and weighted least squares

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we presentAgwan (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the Agwanmodel to real-world graphs and for generating random graphs from the model. Using real-world directed and undirected graphs as input, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to graph structure.

Addressing inequalities in physical activity participation: Implications for public health policy and practice

OBJECTIVE: To investigate the characteristics of those doing no moderate-vigorous physical activity (MVPA) (0days/week), some MVPA (1-4days/week) and sufficient MVPA (≥5days/week) to meet the guidelines in order to effectively develop and target PA interventions to address inequalities in participation.

METHOD: A population survey (2010/2011) of 4653 UK adults provided data on PA and socio-demographic characteristics. An ordered logit model investigated the covariates of 1) participating in no PA, 2) participating in some PA, and 3) meeting the PA guidelines. Model predictions were derived for stereotypical subgroups to highlight important policy and practice implications.

RESULTS: Mean age of participants was 45years old (95% CI 44.51, 45.58) and 42% were male. Probability forecasting showed that males older than 55years of age (probability=0.20; 95% CI 0.11, 0.28), and both males (probability=0.31; 95% CI 0.17, 0.45) and females (probability=0.38; 95% CI 0.27, 0.50) who report poor health are significantly more likely to do no PA.

CONCLUSIONS: Understanding the characteristics of those doing no MVPA and some MVPA could help develop population-level interventions targeting those most in need. Findings suggest that interventions are needed to target older adults, particularly males, and those who report poor health.

Environmental Inequalities and Democratic Citizenship: Linking Normative Theory with Empirical Research

The aim of this paper is to link empirical findings concerning environmental inequalities with different normative yard-sticks for assessing whether these inequalities should be deemed unjust, or not. We argue that such an inquiry must necessarily take into account some caveats regarding both empirical research and normative theory. We suggest that empirical results must be contextualised by establishing geographies of risk. As a normative yard-stick we propose a moderately demanding social-egalitarian account of justice and democratic citizenship, which we take to be best suited to identify unjust as well as legitimate instances of socio-environmental inequality.