Book Review: The economics of poverty by Martin Ravallion

In a post for From Poverty to Power, Oxfam inequality number cruncher Deborah Hardoon reviews The Economics of Poverty by Martin Ravallion

Semantic Models for Machine Learning

In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature selection techniques in machine learning applications. The applied approaches obtain new feature directions on data, such that machine learning applications would show an increase in performance. We review three principle methods that are used throughout the thesis. Firstly Canonical Correlation Analysis (CCA), which is a method of correlating linear relationships between two multidimensional variables. CCA can be seen as using complex labels as a way of guiding feature selection towards the underlying semantics. CCA makes use of two views of the same semantic object to extract a representation of the semantics. Secondly Partial Least Squares (PLS), a method similar to CCA. It selects feature directions that are useful for the task at hand, though PLS only uses one view of an object and the label as the corresponding pair. PLS could be thought of as a method that looks for directions that are good for distinguishing the different labels. The third method is the Fisher kernel. A method that aims to extract more information of a generative model than simply by their output probabilities. The aim is to analyse how the Fisher score depends on the model and which aspects of the model are important in determining the Fisher score. We focus our theoretical investigation primarily on CCA and its kernel variant. Providing a theoretical analysis of the method's stability using Rademacher complexity, hence deriving the error bound for new data. We conclude the thesis by applying the described approaches to problems in the various fields of image, text, music application and medical analysis, describing several novel applications on relevant real-world data. The aim of the thesis is to provide a theoretical understanding of semantic models, while also providing a good application foundation on how these models can be practically used

Two view learning: SVM-2K, theory and practice

Kernel methods make it relatively easy to define complex highdimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. When two views of the same phenomenon are available kernel Canonical Correlation Analysis (KCCA) has been shown to be an effective preprocessing step that can improve the performance of classification algorithms such as the Support Vector Machine (SVM). This paper takes this observation to its logical conclusion and proposes a method that combines this two stage learning (KCCA followed by SVM) into a single optimisation termed SVM-2K. We present both experimental and theoretical analysis of the approach showing encouraging results and insights

Ranking algorithms for implicit feedback

This report presents novel algorithms to use eye movements as an implicit relevance feedback in order to improve the performance of the searches. The algorithms are evaluated on "Transport Rank Five" Dataset which were previously collected in Task 8.3. We demonstrated that simple linear combination or tensor product of eye movement and image features can improve the retrieval accuracy

Analysing recent time trends in coronary heart disease and type 2 diabetes in the UK

Coronary heart disease (CHD) mortality rates have fallen since the 1960s in the UK. The prevalence of type 2 diabetes (T2DM), in contrast, has increased markedly in recent decades. Few attempts have been made to examine the reasons for these striking, divergent time trends. The CHD mortality and T2DM prevalence trends likely reflect in part contemporaneous trends in incidence of these conditions. The broad aim of this thesis is therefore to analyse recent trends in CHD and T2DM incidence in the UK, in relation to trends in aetiological exposures and treatment use, and in relation to each other. This epidemiological research involves statistical analysis of pre-collected data from different UK-based observational data sources, each used according to their strengths: the British Regional Heart Study cohort, The Health Improvement Network primary care database, and the Whitehall II cohort. The principal findings are that favourable time trends in major modifiable aetiological exposures (smoking, blood pressure and HDL and non-HDL cholesterol) may explain half of a 62% decline in major CHD incidence in men over 25 years. Findings for women are similar. Much of the blood pressure decline, and a third of the non-HDL cholesterol decline was associated with increased preventive medication use. Conversely, unfavourable rising adiposity levels limited the scale of the decline in major CHD incidence, and explain an estimated one quarter of a rise in T2DM incidence since the 1980s. Major CHD incidence declined faster among those with T2DM, than without, corresponding to an attenuation of excess risk of CHD associated with T2DM. By highlighting what can be achieved in terms of reducing CHD, while showing the adverse impact of rising obesity levels, the results provide evidence to help inform future efforts to reduce CHD further and curb the rise in T2DM, in the UK and in other locations

Revision rates after primary hip and knee replacement in England between 2003 and 2006

<b>Background</b>: Hip and knee replacement are some of the most frequently performed surgical procedures in the world. Resurfacing of the hip and unicondylar knee replacement are increasingly being used. There is relatively little evidence on their performance. To study performance of joint replacement in England, we investigated revision rates in the first 3 y after hip or knee replacement according to prosthesis type. <b>Methods and Findings</b>: We linked records of the National Joint Registry for England and Wales and the Hospital Episode Statistics for patients with a primary hip or knee replacement in the National Health Service in England between April 2003 and September 2006. Hospital Episode Statistics records of succeeding admissions were used to identify revisions for any reason. 76,576 patients with a primary hip replacement and 80,697 with a primary knee replacement were included (51% of all primary hip and knee replacements done in the English National Health Service). In hip patients, 3-y revision rates were 0.9% (95% confidence interval [CI] 0.8%–1.1%) with cemented, 2.0% (1.7%–2.3%) with cementless, 1.5% (1.1%–2.0% CI) with “hybrid” prostheses, and 2.6% (2.1%–3.1%) with hip resurfacing (p < 0.0001). Revision rates after hip resurfacing were increased especially in women. In knee patients, 3-y revision rates were 1.4% (1.2%–1.5% CI) with cemented, 1.5% (1.1%–2.1% CI) with cementless, and 2.8% (1.8%–4.5% CI) with unicondylar prostheses (p < 0.0001). Revision rates after knee replacement strongly decreased with age. <b>Interpretation</b>: Overall, about one in 75 patients needed a revision of their prosthesis within 3 y. On the basis of our data, consideration should be given to using hip resurfacing only in male patients and unicondylar knee replacement only in elderly patients