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QMUL

Queen Mary University of London
Country: United Kingdom
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2,108 Projects, page 1 of 422
  • Funder: UKRI Project Code: EP/G007845/1
    Funder Contribution: 785,724 GBP

    Several years ago, an exciting demonstration showed how text from a rolled papyrus sample could be read non-invasively, i.e., without unrolling it. This was performed using X-ray microtomography (XMT or micro-CT) with advanced visualisation and modelling techniques and held the promise to historians and archivists of gleaning information from hitherto inaccessible texts in delicate and damaged documents. But although it caused quite a stir at the time, against the harsh realities of X-ray photon statistics and the characteristics of real materials, that promise failed to deliver and today the promise has receded. The stumbling block, hindering progression beyond proof of concept, is the conceptually simple step of increasing the XMT scanner performance (particularly contrast ratio) beyond the reach of commercial or even research XMT systems. What is conceptually a small step actually requires a technological leap in imaging methodology, involving entirely new modalities in data acquisition, reconstruction and processing. With a unique track record in this specialist area of development, we have the capability of delivering the necessary imaging hardware and analysis software to revive this exciting application and make the dream of 'reading the unreadable' a reality.Just as the term high definition in television refers to the screen dimension in terms of pixel numbers rather than physical size, so in XMT we use this term to describe the imaged volume dimensions in voxels, irrespective of resolution. The problem is that as the definition increases, so the detector elements must become smaller and the contrast ratio in the projections must increase. The combination of these two requirements gives a fourth order relationship between definition and required X-ray exposure. Furthermore, increasing the definition increases the susceptibility to systematic errors that give rise to ring and other image artefacts, creating problems even for synchrotron XMT systems (limiting attainable contrast resolution even with ring artefact reduction techniques). The QMUL group is already leading the way with high definition XMT scanners which are currently the only ones to employ moving time-delay integration cameras for ring artefact elimination and field size extension. We thus already have the ability to resolve ink in historical parchment samples much larger than the test samples previously demonstrated.The Cardiff group have been analysing and imaging historical artefacts in conjunction with the major archives in the UK; building excellent collaborations /and trust- between fundamental scientists and experts in conservation and curation. Their research has focussed on the degradation of parchment, which leads to fragility and ultimately to the need for virtual unrolling with the proposed facility. The Cardiff team is already working with experts in high performance computing on code parallelisation for X-ray diffraction analysis. They will develop a suite of 3D image processing tools that can be used to extract information from a wide variety of document types with varying degrees of damage and deformation. This will cover anisotropic noise filtering to aid identification of the ink containing layer of the medium, surface identification, volume rendering (visualisation of the parchment surface and mapping to two dimensions) and 2D image processing to improve text clarity. As this software is being developed, the QMUL group will develop an advanced equiangular TDI XMT scanner with extended field size and ultra-high contrast resolution, capable of imaging whole documents. The archivists will work with us to identify historical documents amenable to this scanning method and at the culmination of this project, all the groups will work closely together to produce the first real world results. This will be a milestone in historical information recovery that will foster interest throughout the world.

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  • Funder: UKRI Project Code: EP/L50483X/1
    Funder Contribution: 1,158,860 GBP

    Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

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  • Funder: UKRI Project Code: EP/T004738/1
    Funder Contribution: 199,605 GBP

    The financial sector is challenged by the digital revolution, utmost competitiveness, and serious risks along with advancing regulatory and accounting requirements, which leads to highly complex computational problems. A prominent example is the computation of the capital reserve where counterparty credit risks have to be incorporated as a direct regulatory response to the causes of the last financial crisis. This is of fundamental importance for the financial system. To develop adequate computational methods for option pricing and risk management we develop a new approach combining deep learning with well-understood numerical techniques, which have already proven highly trustworthy for complex real-world problems in engineering, where risk control is taken very seriously. To be more precise, we develop an offline-online method for parameter-dependent partial differential equations. Offline, the complex problem is processed with the help of machine learning to prepare an efficient solver. Online, this solver can be called in real-time for all parameters. This way, we will bring together the efficiency of deep learning with the reliability and economic interpretability of numerical algorithms. Thus we will contribute to the applicability of machine learning in the financial sector, where an intuitive understanding of the results is as crucial as their liability. High-dimensionality is intrinsic in finance and resulting computational problems are traditionally solved by Monte Carlo simulations. For urgent tasks such as real-time risk monitoring, uncertainty quantification and credit value adjustments, this approach is computationally too expensive. Lacking appropriate computational tools, ad hoc simplifications are the current market standard, yielding uncontrollable operational risk. The new combination of model order reduction for parametric (nonlinear) partial differential equations with deep learning allows us to break the curse of dimensionality. While classical discretisations are infeasible, deep neural networks allow for efficient approximations of high-dimensional functions. While with machine learning, results are efficiently evaluated, but difficult to interpret, we gain economic interpretability. The performance of the novel techniques will be evaluated and compared and their capabilities will be shown on realistic examples.

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  • Funder: UKRI Project Code: BB/M007863/1
    Funder Contribution: 419,630 GBP

    With advances in diet and health care there has been an increase in the general age of the population. With increases in age comes an increase in age-related disease including a decline in mental ability; As animals age their memory and ability to learn new things as well as to adapt to changes in their surroundings declines. However, this physical and mental deterioration is not uniform; there are many who continue with good health well into their 80s and 90s whilst others suffer increasingly debilitating mental decline and disease. There are many factors that influence healthy ageing including diet and general life style but it is also influenced by ones initial mental ability and genetic makeup. Here we aim to identify genes that influence mental ability and age-related deterioration in memory and learning using zebrafish as the experimental system. Zebrafish have become an established system for the identification of genes affecting human development and disease as many genes and the cell biological processes the genes regulate are conserved between fish and humans. Zebrafish are an ideal system in which to search for genes affecting mental ageing as, in addition to conservation of genetic structure and neural processes, zebrafish show gradual ageing over a 2 year period and are capable of performing behavioural tasks similar to those used in mammals and humans to assess mental ability and mental decline. Above all, it is relatively easy to generate lines of fish carrying genetic mutations and many loss of function lines that can be used in our studies already exist. Here we take advantage of the availability of lines of zebrafish that carry mutations disrupting the function of individual genes to identify genes that affect memory and learning and age-related deterioration of these processes. We shall assess individuals from 50-75 different families of fish that carry mutations in specific genes for performance in behavioral tasks commonly used to assess working memory and response times (delayed matching to sample, 5 choice serial reaction time), at 2 different ages: young (6 months) and old (2 years). Fish that perform very well or very badly in these tasks when compared to a normal population are kept and an additional 20-40 siblings from the family assessed in the same tasks. When all mutant members within a family show the same behaviour, the mutation is taken as affecting a gene that controls an aspect of memory and learning. Analysis of performance at 6 months will identify genes affecting learning, memory and response times as measures of mental ability per se. By comparing responses at 2 years with those at 6 months we will be able to identify genes affecting the rate of change in memory and response times i.e the conservation of mental processes. The outcome of this study will be the elucidation of genes and molecular processes influencing mental ability and maintenance of mental ability in old age. It is important to gain insight into the genetics of age-related mental deterioration as it helps understanding of how environmental factors may interact with genetics to influence the rate of mental decline in old age. This knowledge helps us develop strategies to minimize the impact of ageing on society. Moreover, understanding age-related changes in mental ability sets a background against which it is possible to assess the effects of pathological disease states such as dementia.

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  • Funder: UKRI Project Code: EP/K036106/1
    Funder Contribution: 21,707 GBP

    Variable selection in statistical modelling is concerned with the question of choosing the most relevant explanatory variables from a potentially large set of candidates. The goal is to achieve a good fit between a model and data without including too many variables. The main objective of this project is to adapt methods from the topological theory of persistent homology in order to develop new variable selection techniques. The proposal combines model description from the point of view of computational algebra with statistical methods such as the Lasso technique and Bayesian model selection. Partners from network modeling, climate modeling and from robust engineering and design will provide data which will be used in case studies to assess the performance of the new methods. Also, the project will scope the use of software for combining topological and statistical algorithms, which will enable researchers to combine the different approaches without having to do extensive programming. Methodological results from this project have the potential to be applied to the analysis of large data sets in areas where the detection of interactions between explanatory variables is crucial.

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