Leveraging Mathematical Subject Information to Enhance Bibliometric Data

The field of mathematics is known to be especially challenging from a bibliometric point of view. Its bibliographic metrics are especially sensitive to distortions and are heavily influenced by the subject and its popularity. Therefore, quantitative methods are prone to misrepresentations, and need to take subject information into account. In this paper we investigate how the mathematical bibliography of the abstracting and reviewing service Zentralblatt MATH (zbMATH) could further benefit from the inclusion of mathematical subject information MSC2010. Furthermore, the mappings of MSC2010 to Linked Open Data resources have been upgraded and extended to also benefit from semantic information provided by DBpedia

Visualisation of Large-Scale Call-Centre Data

The contact centre industry employs 4% of the entire United King-dom and United States’ working population and generates gigabytes of operational data that require analysis, to provide insight and to improve efficiency. This thesis is the result of a collaboration with QPC Limited who provide data collection and analysis products for call centres. They provided a large data-set featuring almost 5 million calls to be analysed. This thesis utilises novel visualisation techniques to create tools for the exploration of the large, complex call centre data-set and to facilitate unique observations into the data.A survey of information visualisation books is presented, provid-ing a thorough background of the field. Following this, a feature-rich application that visualises large call centre data sets using scatterplots that support millions of points is presented. The application utilises both the CPU and GPU acceleration for processing and filtering and is exhibited with millions of call events.This is expanded upon with the use of glyphs to depict agent behaviour in a call centre. A technique is developed to cluster over-lapping glyphs into a single parent glyph dependant on zoom level and a customizable distance metric. This hierarchical glyph repre-sents the mean value of all child agent glyphs, removing overlap and reducing visual clutter. A novel technique for visualising individually tailored glyphs using a Graphics Processing Unit is also presented, and demonstrated rendering over 100,000 glyphs at interactive frame rates. An open-source code example is provided for reproducibility.Finally, a novel interaction and layout method is introduced for improving the scalability of chord diagrams to visualise call transfers. An exploration of sketch-based methods for showing multiple links and direction is made, and a sketch-based brushing technique for filtering is proposed. Feedback from domain experts in the call centre industry is reported for all applications developed

Predictive decoding of neural data

In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder\u27s sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose modeling assumptions, it removes potential bias in the analysis of neural data due to otherwise possible forward model misspecification. The proposed methodology has been formalized into a free and open source Python framework for statistical learning based data analysis. This framework, PyMVPA, is described in Chapter 5

Experimental and modelling study of the alkali-silica-reaction in concrete

The alkali-silica reaction (ASR) is a durability issue of concrete. The amorphous silica of aggregates reacts with the alkalies present in the cement paste pore solution to form a hydrophilic gel which swells in the presence of moisture. Many mass concrete structures are affected and understanding of the reaction and its development is crucial, notably for dam owners and managers. Although some parameters affecting the reaction are well understood, such as temperature, others which depend on the concrete mix design, such as aggregate sizes and particle size distribution (PSD) and external parameters such as the applied load have an effect on the development of the reaction which is not as well understood. To advance the understanding of ASR an experimental programme was put into place to explore some of these factors. In parallel, a modelling platform was designed and implemented to allow the simulation of the reaction at the material microstructure level. The expansion of affected mortars and concretes had been linked to the damage state of the aggregates by Ben Haha. We could model this effect and reproduce the effect of changing the aggregate sizes. Simple kinetics were implemented in the model with two factors were required to account for changes in the cure conditions and sample sizes. The expansion due to the reaction has been reported to be anisotropic in the literature with respect to the direction of casting. We could demonstrate this effect in two independent set of experiments. The overall shape of the expansion curve was found to be related to the fracture of the aggregates and the interactions between them rather than changes in the rate of the chemical reaction. The effect of restraining stress was found to more complex than previously reported in the literature, as it notably affects the direction of propagation of microcracks in the aggregates and paste. This leads to an acceleration of the damage and expansion for loads above about 5MPa threshold