Horizon will tackle the challenge of harnessing the power of ubiquitous computing for the digital economy in a way that is acceptable to our society and increases the quality of life for all. This will involve establishing a world-leading and sustainable centre of excellence for research and knowledge transfer for the ubiquitous digital economy. Horizon will conduct a five-year programme of research into the key scientific challenges involved in the widespread adoption of ubiquitous computing; collaborate with users to create, demonstrate and study next generation services; deliver a knowledge transfer programme that ensures that the results of our research are fully connected to the digital economy; train a new generation of researchers to meet the demands of industry for skilled interdisciplinary staff; engage with policy makers and the wider public in order to address societal concerns; and provide a focal point for international, national and regional research in this area.Horizon will exploit the distinctive nature of hub funding to develop a unique approach to this challenge. Our Collaborative Research Programme will be driven by the overarching concept of a lifelong contextual footprint, the idea that each of us throughout our lifetimes will lay down a digital trail that captures our patterns of interaction with digital services. Our research will explore the major infrastructural, human and business challenges associated with this concept, adopting a unique multidisciplinary approach that integrates insights from computer science, psychology, sociology, business, economics and the arts and humanities. We will collaborate with over 30 users from different sectors of the Digital Economy in order to create, deploy and study a series of next generation services 'in the wild' so as to drive our underlying research. We will initially focus on the creative industries and transportation sectors, but subsequently extend our focus to additional sectors in partnership with other hubs and major initiatives. In parallel, our Transformation Programme will drive knowledge transfer and long-term economic impact through partnership management, public engagement, international outreach, incubation of new ventures, the transfer of people, and training for 24 associated PhD students, funded by the University.Our team draws on leading groups at Nottingham spanning computer science, engineering, business, psychology and sociology, complemented by expertise at two spokes: distributed systems and communications at Cambridge, and mathematical modelling and advertising at Reading. A series of further mini-spokes will enable us to introduce other key individuals through hub fellowships.These multiple disciplines and partners will be brought together in a new centre at Nottingham where they will be able to engage with a critical-mass cohort of research staff and students to explore innovative and challenging new projects. The Hub will be directed by Professor Derek McAuley who brings extensive experience of working in academia, directing major industrial research laboratories, and also launching spin-out companies. He will be supported by Professor Tom Rodden, an EPSRC Senior Research Fellow who previously directed the Equator IRC. The net result will be a unique partnership between EPSRC, industry, the public, and the University, with the latter committing 16M of its own funds to match the 12M requested from EPSRC.

The last decade has seen a significant shift in the way computers are designed. Up to the turn of the millennium advances in performance were achieved by making a single processor, which could execute a single program at a time, go faster, usually by increasing the frequency of its clock signal. But shortly after the turn of the millennium it became clear that this approach was running into a brick wall - the faster clock meant the processor got hotter, and the amount of heat that can be dissipated in a silicon chip before it fails is limited; that limit was approaching rapidly! Quite suddenly several high-profile projects were cancelled and the industry found a new approach to higher performance. Instead of making one processor go ever faster, the number of processor cores could be increased. Multi-core processors had arrived: first dual core, then quad-core, and so on. As microchip manufacturing capability continues to increase the number of transistors that can be integrated on a single chip, the number of cores continues to rise, and now multi-core is giving way to many-core systems - processors with 10s of cores, running 10s of programs at the same time. This all seems fine at the hardware level - more transistors means more cores - but this change from one to many programs running at the same time has caused many difficulties for the programmers who develop applications for these new systems. Writing a program that runs on a single core is much better understood than writing a program that is actually 10s of programs running at the same time, interacting with each other in complex and hard-to-predict ways. To make life for the programmer even harder, with many-core systems it is often best not to make all the cores identical; instead, heterogeneous many-core systems offer the promise of much higher efficiency with specialised cores handling specialised parts of the overall program, but this is even harder for the programmer to manage. The Programme of projects we plan to undertake will bring the most advanced techniques in computer science to bear on this complex problem, focussing particularly on how we can optimise the hardware and software configurations together to address the important application domain of 3D scene understanding. This will enable a future smart phone fitted with a camera to scan a scene and not only to store the picture it sees, but also to understand that the scene includes a house, a tree, and a moving car. In the course of addressing this application we expect to learn a lot about optimising many-core systems that will have wider applicability too, and the prospect of making future electronic products more efficient, more capable, and more useful.

In 2003. over 44,000 people in the UK were diagnosed with breast cancer. This is now the commonest cancer occurring in the UK. The lifetime risk of developing breast cancer is 1 in 9, and while most of the women who get breast cancer are past their menopause, almost 8,000 diagnosed each year are under 50 years old. Improving outcomes is a key challenge in treatment. High throughput genomic methods, such as expression profiling of frozen tissue samples using microarrays, have resulted in the discovery of many novel gene signatures that are correlated with clinical outcomes in cancer treatment. It is essential that these potential biomarkers are validated on large numbers of independent samples prior to clinical use. Tissue microarrays (TMA) created from paraffin-embedded tumour samples from large clinical trials are the ideal reagent for these validation experiments. However, the analysis and scoring of antibody-based markers on TMAs that may contain thousands of patient samples presents major challenges for pathology reporting and image handling. Our initial pilot PathGrid study (Oct 2007 to Oct 2008) is using a range of techniques which have been developed in astronomy to both analyse imaging data and to handle and manipulate the resulting data products. These have been applied to the challenges involved in analysing the TMA image data taken from the SEARCH study population-based study of breast cancer. We have utilised astronomy 'Virtual Observatory' components specifically adapted from those developed within the AstroGrid Virtual Observatory eScience programme (http://www.astrogrid.org), to facilitate secure data transport, resource discovery through appropriate metadata, data acquisition, ingression to a database system, and secure distributed access to those data and information resources. Image analysis has been applied to the input TMA data utilising a range of algorithms originally developed for diverse astronomical use cases. The resulting data products will in turn be interfaced (though work planned here) to the clinical trials systems developed through the CancerGrid system (see http://www.cancergrid.org). In our initial test case we have automated the scoring of Estrogen Receptor (ER) assessments. ER is an important regulator of mammary growth, but is also a key prognostic and therapeutic target in breast cancer. Assessing ER status at time of diagnosis of breast cancer, determines which treatment programmes should be followed by patients. In particular, those patients who have ER-positive breast cancer will be offered estrogen antagonist therapies such as tamoxifen. At CR-UK, breast cancer studies utilising genomics tools are underway to validate existing and new prognostic and/or predictive markers. TMAs have been created from a large population-based clinical trial (SEARCH; part of the Anglia Breast Cancer study) for analysis with a range of candidate markers. Immunohistochemistry is used to assess the level of nuclear ER expression. The focus of our initial pilot has been on the algorithm development and validation. For the miniPIPSS programme the work will move to increasing the utility of the analysis system by running all processing operations in a pipeline. This automation, the operational basis of our Pathgrid system, has been implemented in prototype by making use of the application-grid infrastructural components from AstroGrid This miniPIPSS project will facilitate the further interchange of ideas and technologies between the physical and medical sciences, and provide methods for the handling and processing of clinical image data in an open and extensible manner The interaction with Oracle represents the initial stage of a longer term partnership, aiming to further develop these analysis techniques such that they are available to he wider medical research community - and further ahead for use in a clinical environment.