An efficient randomised sphere cover classifier

This paper describes an efficient randomised sphere cover classifier(aRSC), that reduces the training data set size without loss of accuracy when compared to nearest neighbour classifiers. The motivation for developing this algorithm is the desire to have a non-deterministic, fast, instance-based classifier that performs well in isolation but is also ideal for use with ensembles. We use 24 benchmark datasets from UCI repository and six gene expression datasets for evaluation. The first set of experiments demonstrate the basic benefits of sphere covering. The second set of experiments demonstrate that when we set the a parameter through cross validation, the resulting aRSC algorithm outperforms several well known classifiers when compared using the Friedman rank sum test. Thirdly, we test the usefulness of aRSC when used with three feature filtering filters on six gene expression datasets. Finally, we highlight the benefits of pruning with a bias/variance decompositio

Student Teaching and Learning Consultants: developing conversations about teaching and learning

This paper outlines a model for students and staff working in partnership to enhance teaching and learning and describes the role of the Student Teaching and Learning Consultant. The background, structure and process of the scheme are presented. Training activities were designed to develop students’ confidence in their perspectives, in order to enable them to act as partners in dialogue; such dialogue was to be focused on discussing teaching and learning practice rather than solving problems and offering solutions. One of the Student Consultants reflects on her experience of taking part, including her view of how the Student Consultant role differs from that of a Course Representative in terms of their work with staff

Binary Shapelet Transform for Multiclass Time Series Classification

Shapelets have recently been proposed as a new primitive for time series classification. Shapelets are subseries of series that best split the data into its classes. In the original research, shapelets were found recursively within a decision tree through enumeration of the search space. Subsequent research indicated that using shapelets as the basis for transforming datasets leads to more accurate classifiers. Both these approaches evaluate how well a shapelet splits all the classes. However, often a shapelet is most useful in distinguishing between members of the class of the series it was drawn from against all others. To assess this conjecture, we evaluate a one vs all encoding scheme. This technique simplifies the quality assessment calculations, speeds up the execution through facilitating more frequent early abandon and increases accuracy for multi-class problems. We also propose an alternative shapelet evaluation scheme which we demonstrate significantly speeds up the full search

Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes

Bone age assessment is a task performed daily in hospitals worldwide. This involves a clinician estimating the age of a patient from a radiograph of the non-dominant hand. Our approach to automated bone age assessment is to modularise the algorithm into the following three stages: segment and verify hand outline; segment and verify bones; use the bone outlines to construct models of age. In this paper we address the final question: given outlines of bones, can we learn how to predict the bone age of the patient? We examine two alternative approaches. Firstly, we attempt to train classifiers on individual bones to predict the bone stage categories commonly used in bone ageing. Secondly, we construct regression models to directly predict patient age. We demonstrate that models built on summary features of the bone outline perform better than those built using the one dimensional representation of the outline, and also do at least as well as other automated systems. We show that models constructed on just three bones are as accurate at predicting age as expert human assessors using the standard technique. We also demonstrate the utility of the model by quantifying the importance of ethnicity and sex on age development. Our conclusion is that the feature based system of separating the image processing from the age modelling is the best approach for automated bone ageing, since it offers flexibility and transparency and produces accurate estimate

An Experimental Evaluation of Nearest Neighbour Time Series Classification

Data mining research into time series classification (TSC) has focussed on alternative distance measures for nearest neighbour classifiers. It is standard practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a straw man for comparison. As part of a wider investigation into elastic distance measures for TSC~\cite{lines14elastic}, we perform a series of experiments to test whether this standard practice is valid. Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to standard classifiers, examine whether the performance of 1-NN Euclidean approaches that of 1-NN DTW as the number of cases increases, assess whether there is any benefit of setting $k$ for $k$-NN through cross validation whether it is worth setting the warping path for DTW through cross validation and finally is it better to use a window or weighting for DTW. Based on experiments on 77 problems, we conclude that 1-NN with Euclidean distance is fairly easy to beat but 1-NN with DTW is not, if window size is set through cross validation

Читая папирусы. Создавая древнюю историю

Автор демонстрирует методы сбора исторических данных из разрозненных и нередко плохо сохранившихся источников для воссоздания картины общественной, экономической и культурной жизни многоязычного и многонационального античного мира. В процессе анализа современных методов работы с древними текстами он также исследует альтернативные пути освоения этих документов.Настоящая монография издана в серии "Approaching the Ancient World". Приложения: список цитируемых современных работ (с. 130-136), тематическая библиография (с. 137-141), общий указатель (с. 142-145)

Photovoltaic technologies

Photovoltaics is already a billion dollar industry. It is experiencing rapid growth as concerns over fuel supplies and carbon emissions mean that governments and individuals are increasingly prepared to ignore its current high costs. It will become truly mainstream when its costs are comparable to other energy sources. At the moment, it is around four times too expensive for competitive commercial production. Three generations of photovoltaics have been envisaged that will take solar power into the mainstream. Currently, photovoltaic production is 90% first-generation and is based on silicon wafers. These devices are reliable and durable, but half of the cost is the silicon wafer and efficiencies are limited to around 20%. A second generation of solar cells would use cheap semiconductor thin films deposited on low-cost substrates to produce devices of slightly lower efficiency. A number of thin-film device technologies account for around 5–6% of the current market. As second-generation technology reduces the cost of active material, the substrate will eventually be the cost limit and higher efficiency will be needed to maintain the cost-reduction trend. Third-generation devices will use new technologies to produce high-efficiency devices. Advances in nanotechnology, photonics, optical metamaterials, plasmonics and semiconducting polymer sciences offer the prospect of cost-competitive photovoltaics. It is reasonable to expect that cost reductions, a move to second-generation technologies and the implementation of new technologies and third-generation concepts can lead to fully cost- competitive solar energy in 10–15 years