First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

Modelling wind direction from satellite scatterometer data

Most of the common techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we apply two novel techniques to the problem of extracting the distribution of wind vector directions from radar scatterometer data gathered by a remote-sensing satellite.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.

GTM-based data visualisation with incomplete data

We analyse how the Generative Topographic Mapping (GTM) can be modified to cope with missing values in the training data. Our approach is based on an Expectation -Maximisation (EM) method which estimates the parameters of the mixture components and at the same time deals with the missing values. We incorporate this algorithm into a hierarchical GTM. We verify the method on a toy data set (using a single GTM) and a realistic data set (using a hierarchical GTM). The results show our algorithm can help to construct informative visualisation plots, even when some of the training points are corrupted with missing values.

Negative data in DEA:a directional distance approach applied to bank branches

This paper is drawn from the use of data envelopment analysis (DEA) in helping a Portuguese bank to manage the performance of its branches. The bank wanted to set targets for the branches on such variables as growth in number of clients, growth in funds deposited and so on. Such variables can take positive and negative values but apart from some exceptions, traditional DEA models have hitherto been restricted to non-negative data. We report on the development of a model to handle unrestricted data in a DEA framework and illustrate the use of this model on data from the bank concerned.

Fixed or variable noise in contrast discrimination? The jury's still out

The ability to distinguish one visual stimulus from another slightly different one depends on the variability of their internal representations. In a recent paper on human visual-contrast discrimination, Kontsevich et al (2002 Vision Research 42 1771 - 1784) re-considered the long-standing question whether the internal noise that limits discrimination is fixed (contrast-invariant) or variable (contrast-dependent). They tested discrimination performance for 3 cycles deg-1 gratings over a wide range of incremental contrast levels at three masking contrasts, and showed that a simple model with an expansive response function and response-dependent noise could fit the data very well. Their conclusion - that noise in visual-discrimination tasks increases markedly with contrast - has profound implications for our understanding and modelling of vision. Here, however, we re-analyse their data, and report that a standard gain-control model with a compressive response function and fixed additive noise can also fit the data remarkably well. Thus these experimental data do not allow us to decide between the two models. The question remains open. [Supported by EPSRC grant GR/S74515/01]

Why organizational and community diversity matter:the emergence of incivility and organizational outcomes

Integrating sociological and psychological perspectives, this research considers the value of organizational ethnic diversity as a function of community diversity. Employee and patient surveys, census data, and performance indexes relevant to 142 hospitals in the United Kingdom suggest that intraorganizational ethnic diversity is associated with reduced civility toward patients. However, the degree to which organizational demography was representative of community demography was positively related to civility experienced by patients and ultimately enhanced organizational performance. These findings underscore the understudied effects of community context and imply that intergroup biases manifested in incivility toward out-group members hinder organizational performance.

Firm growth and barriers to growth among small firms in India

Empirical work on micro and small firms focuses on developed countries, while existing work on developing countries is all too often based on small samples taken from ad hoc questionnaires. The census data we analyze here are fairly representative of small business structure in India. Consistent with findings from prior research on developed countries, size and age have a negative impact on firm growth in the majority of specifications. Enterprises managed by women have lower expected growth rates. Proprietary firms face lower growth on the whole, especially if they are young firms. Exporting has a positive effect on firm growth, especially for young firms and for female-owned firms. Although some small firms are able to convert know-how into commercial success, we find that many others are unable to translate it into superior growth.

UK social housing and housing market in England:a statistical review and trends

Around 80% of the 63 million people in the UK live in urban areas where demand for affordable housing is highest. Supply of new dwellings is a long way short of demand and with an average annual replacement rate of 0.5% more than 80% of the existing residential housing stock will still be in use by 2050. A high proportion of owner-occupiers, a weak private rental sector and lack of sustainable financing models render England’s housing market one of the least responsive in the developed world. As an exploratory research the purpose of this paper is to examine the provision of social housing in the United Kingdom with a particular focus on England, and to set out implications for housing associations delivering sustainable community development. The paper is based on an analysis of historical data series (Census data), current macro-economic data and population projections to 2033. The paper identifies a chronic undersupply of affordable housing in England which is likely to be exacerbated by demographic development, changes in household composition and reduced availability of finance to develop new homes. Based on the housing market trends analysed in this paper opportunities are identified for policy makers to remove barriers to the delivery of new affordable homes and for social housing providers to evolve their business models by taking a wider role in sustainable community development.

Making sense of microposts (#MSM2013) concept extraction challenge

Microposts are small fragments of social media content that have been published using a lightweight paradigm (e.g. Tweets, Facebook likes, foursquare check-ins). Microposts have been used for a variety of applications (e.g., sentiment analysis, opinion mining, trend analysis), by gleaning useful information, often using third-party concept extraction tools. There has been very large uptake of such tools in the last few years, along with the creation and adoption of new methods for concept extraction. However, the evaluation of such efforts has been largely consigned to document corpora (e.g. news articles), questioning the suitability of concept extraction tools and methods for Micropost data. This report describes the Making Sense of Microposts Workshop (#MSM2013) Concept Extraction Challenge, hosted in conjunction with the 2013 World Wide Web conference (WWW'13). The Challenge dataset comprised a manually annotated training corpus of Microposts and an unlabelled test corpus. Participants were set the task of engineering a concept extraction system for a defined set of concepts. Out of a total of 22 complete submissions 13 were accepted for presentation at the workshop; the submissions covered methods ranging from sequence mining algorithms for attribute extraction to part-of-speech tagging for Micropost cleaning and rule-based and discriminative models for token classification. In this report we describe the evaluation process and explain the performance of different approaches in different contexts.

Anticholinergic burden in older adults with intellectual disability; relationships with multimorbidity and adverse effects

Background: Anticholinergic medications may be associated with adverse clinical outcomes, including acute impairments in cognition and anticholinergic side effects, the risk of adverse outcomes increasing with increasing anticholinergic exposure. Older people with intellectual disability may be at increased risk of exposure to anticholinergic medicines due to their higher prevalence of comorbidities. We sought to determine anticholinergic burden in ageing people with intellectual disability. Methods: Medication data (self-report/proxy-report) was drawn from Wave 1 of the Intellectual Disability Supplement to the Irish Longitudinal Study on Ageing (IDS-TILDA), a study on the ageing of 753nationally representative people with an IDC40 years randomly selected from the National Intellectual Disability Database. Each individual’s cumulative exposure to anticholinergic medications was calculated using the Anticholinergic Cognitive Burden Scale (ACB) amended by a multi-disciplinary group with independent advice to account for the range of medicines in use in this population. Results: Overall, 70.1 % (527) reported taking medications with possible or definite anticholinergic properties (ACBC1), with a mean (±SD) ACB score of 4.5 (±3.0) (maximum 16). Of those reporting anticholinergic exposure (n=527), 41.3 % (217) reported an ACB score o fC5. Antipsychotics accounted for 36.4 % of the total cumulative ACB score followed by anticholinergics (16 %) and antidepressants (10.8 %). The most frequently reported medicine with anticholinergic activity was carbamazepine 16.8 % (127). The most frequently reported medicine with high anticholinergic activity (ACB 3) was olanzapine13.4 % (101). There was a significant association between higher anti-cholinergic exposure and multimorbidity, particularly mental health morbidity, and some anticholinergic adverse effects such as constipation and day-time drowsiness but not self-rated health. Conclusion: Using simple cumulative measures proved an effective means to capture total burden and helped establish that anticholinergic exposure in the study population was high. The finding highlights the need for comprehensive reviews of medications.