Programming with heterogeneous structures: Manipulating XML data using bondi

Manipulating semistructured data, such as XML, does not fit well within conventional programming languages. A typical manipulation requires finding all occurrences of a structure matching a structured search pattern, whose context may be different in different places, and both aspects cause difficulty. If a special-purpose query language is used to manipulate XML, an interface to a more general programming environment is required, and this interface typically creates runtime overhead for type conversion. However, adding XML manipulation to a general-purpose programming language has proven difficult because of problems associated with expressiveness and typing. We show an alternative approach that handles many kinds of patterns within an existing strongly-typed general-purpose programming language called bondi. The key ideas are to express complex search patterns as structures of simple patterns, pass these complex patterns as parameters to generic data-processing functions and traverse heterogeneous data structures by a generalized form of pattern matching. These ideas are made possible by the language's support for pattern calculus, whose typing on structures and patterns enables path and pattern polymorphism. With this approach, adding a new kind of pattern is just a matter of programming, not language design. Copyright © 2006, Australian Computer Society, Inc

Programming Heterogeneous Parallel Machines Using Refactoring and Monte-Carlo Tree Search

Funding: This work was supported by the EU Horizon 2020 project, TeamPlay, Grant Number 779882, and UK EPSRC Discovery, Grant Number EP/P020631/1.This paper presents a new technique for introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a combination of algorithmic skeletons (such as farms and pipelines), Monte–Carlo tree search for deriving mappings of tasks to available hardware resources, and refactoring tool support for applying the patterns and mappings in an easy and effective way. Using our approach, we demonstrate easily obtainable, significant and scalable speedups on a number of case studies showing speedups of up to 41 over the sequential code on a 24-core machine with one GPU. We also demonstrate that the speedups obtained by mappings derived by the MCTS algorithm are within 5–15% of the best-obtained manual parallelisation.Publisher PDFPeer reviewe

Angular and Current-Target Correlations in Deep Inelastic Scattering at HERA

Correlations between charged particles in deep inelastic ep scattering have been studied in the Breit frame with the ZEUS detector at HERA using an integrated luminosity of 6.4 pb-1. Short-range correlations are analysed in terms of the angular separation between current-region particles within a cone centred around the virtual photon axis. Long-range correlations between the current and target regions have also been measured. The data support predictions for the scaling behaviour of the angular correlations at high Q2 and for anti-correlations between the current and target regions over a large range in Q2 and in the Bjorken scaling variable x. Analytic QCD calculations and Monte Carlo models correctly describe the trends of the data at high Q2, but show quantitative discrepancies. The data show differences between the correlations in deep inelastic scattering and e+e- annihilation.Comment: 26 pages including 10 figures (submitted to Eur. J. Phys. C

Angular and Current-target Correlations in Deep Inelastic Scattering at HERA

Correlations between charged particles in deep inelastic e+ p scattering have been studied in the Breit frame with the ZEUS detector at HERA using an integrated luminosity of 6.4pb-1. Short-range correlations are analysed in terms of the angular separation between current-region particles within a cone centred around the virtual photon axis. Long-range correlations between the current and target regions have also been measured. The data support predictions for the scaling behaviour of the angular correlations at high Q2 and for anti-correlations between the current and target regions over a large range in Q2 and in the Bjorken scaling variable x. Analytic QCD calculations and Monte Carlo models correctly describe the trends of the data at high Q2, but show quantitative discrepancies. The data show differences between the correlations in deep inelastic scattering and e+e- annihilation

Discovering structure in Islamist postings using systemic nets

© 2016 IEEE. Textual analytics based on representations of documents as bags of words has been extremely successful. However, analysis that requires deeper insight into language, into author properties, or into the contexts in which documents were created requires a richer representation. Systemic nets are one such representation. The jihadist groups AQAP, ISIS, and the Taliban have all produced English magazines designed to influence Western sympathizers. Using a model of jihadi language, we construct a systemic functional net for these magazines, and contrast the structures revealed by clustering using words versus clustering using the choices implicit in systemic functional nets. We then show that the systemic functional net derived from the magazines is consistent with the structure present in two Islamist forums, and therefore reveals two different mindsets, one that is political and another that is religious, that seem widely held within the relevant communities

Comparing SVD and SDAE for Analysis of Islamist Forum Postings

© 2015 IEEE. We analyze postings in the Turn to Islam forum using techniques based on singular value decomposition (SVD) and the deep learning technique of stacked denoising autoencoders (SDAE). Models based on frequent words and jihadist language intensity are used, and the results compared. Our main conclusion is that SDAE approaches, while clearly discovering structure in document-word matrices, do not yet provide a natural interpretation strategy, limiting their practical usefulness. In contrast, SVD approaches provide interpretable models, primarily because of the coupling between document and word variation patterns

Integrative visual data mining of biomedical data: Investigating cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia

This chapter presents an integrative visual data mining approach towards biomedical data. This approach and supporting methodology are presented at a high level. They combine in a consistent manner a set of visualisation and data mining techniques that operate over an integrated data set of several diverse components, including medical (clinical) data, patient outcome and interview data, corresponding gene expression and SNP data, domain ontologies and health management data. The practical application of the methodology and the specific data mining techniques engaged are demonstrated on two case studies focused on the biological mechanisms of two different types of diseases: Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia, respectively. The common between the cases is the structure of the data sets. © 2008 Springer-Verlag Berlin Heidelberg