In early years of computational pathology, the algorithms were mainly focussed on segmentation and identification of objects such as nuclei, glands, ducts, vessels, and other patterns which are of interest to pathologists in every day clinical practice. The concept was to assist pathologists in identifying patterns which are difficult to eyeball over the huge landscape of cancer tissue in a whole slide image (WSI). The advent of modern CPath algorithms based on deep learning (DL) found that there are hidden features which humans usually ignore due to inattentional blindness. Therefore, CPath has moved beyond identification and classification of individual patterns within a whole slide image (WSI) towards WSI-level or case-level diagnosis, mutation and therapeutic response prediction discovering new morphological patterns, tissue phenotypes, even surpassing pathologist performance in some cases. On the other hand, DL algorithms are usually considered to be a black box due to lack of interpretability of the learnt features which makes it difficult to understand the biology of different diseases. One of the reasons is our inability to analyse huge landscapes in the tumour microenvironment (TME) where the WSIs are divided into small patches before analysis due to hardware limitations and complex DL architectures required for the analysis of images from different stains and modalities. The challenge is the gigapixel size of the WSIs containing the landscape of cancer which on one hand compels exploration but at the same time faces technological challenges. Due to tumour heterogeneity these small patches are usually not representative of the WSIs. Therefore, we need to develop techniques which can analyse WSIs without dividing them into smaller patches keeping the spatial information intact. This not only allows to overcome tumour heterogeneity limitations but helps in identifying heterogenous regions and embedded spatial relationships linked to patient outcome and other clinical variables. These techniques should be able to overcome the practical limitations of the hardware, invariant to the input stains and should be able to help with interpretability and biological understanding of the TME. The algorithmic limitations are currently being tackled by WSI-level weakly supervised labels or compressed representations. These approaches have some major drawbacks e.g., these approaches discard the essential spatial information required to incorporate cell-to-cell interactions in clinically significant regions during compression and are focussed mostly on identification or classification of disease into sub-categories where the DL model is treated as a black box. Analysis of TME at the cellular level is important to understand mechanisms in cancer where tumour heterogeneity plays a significant role. Multiplexed Immunofluorescence (MxIF) images provide additional data to subtype individual cells on the same tissue section which is not currently possible with existing brightfield approaches. There have been recent advances in whole slide image fluorescence imaging which allow scanning of WSIs with multiple markers. Therefore, we need stain and modality agnostic approaches which can analyse WSIs without losing spatial information at the cellular level so the rich data can be mined for better understanding of cancer. We propose to build on existing technology and utilise the extracted information to understand TME interactions at the whole slide image level. In this project, we will develop stain agnostic techniques to analyse and identify patterns in whole slide images (WSIs) by creating HistoMaps which can be directly related to biologically meaningful and clinically relevant parameters i.e., mutations, survival and response to therapy linking histology landscapes to clinical variables for better understanding of cancer helping oncologists to make informed decisions on therapeutic interventions and assisting pharma to develop new targets.

Imaging methods are used to obtain visual representations of objects that are otherwise invisible to the naked eye. The physical principles in which imaging methods are based are common across disciplines and, hence, can be adapted. Here I propose to lead an inter-disciplinary project that will focus on obtaining images of medical and geophysical targets that are traditionally difficult to image with ultrasound or seismic waves, such as the brain. Rapid brain imaging is central to the diagnosis and treatment of stroke and other acute neurological conditions, but existing methods for imaging the brain (mainly X-rays and magnetic resonance imaging) require large, immobile, high-power instruments that are near-impossible to deploy outside specialised environments. I will create a device that can be applied to any patient, at any time and in any place by exploiting advances that have already revolutionised imaging in geophysics and using ultrasound waves transmitted across the head. In particular, I will adapt an imaging algorithm known as full-waveform inversion to transform the recorded ultrasound data into the first highly detailed image of an adult brain with ultrasound, and with a much higher resolution than those obtained with conventional ultrasound. To achieve this goal, I will design a safe and suitable device for its application to healthy volunteers, and I will use the recorded data and full-waveform inversion conveniently adapted. This will require solving several technical aspects, such as accounting for involuntary movement due to breathing, obtaining the characteristics of the skull from the data and accelerating the computations on graphics processing units. The success of this project would represent a major breakthrough in brain imaging and would be particularly relevant to improve the survival rate and wellbeing of patients with acute stroke, which is the second-largest cause of death and acquired adult disability. Then, I will study the capability of ultrasound full-waveform inversion for breast cancer detection, in particular for patients with dense breasts in which traditional mammography fails, and for bone imaging - in particular for detecting osteoporosis and fractures. To achieve these goals, I will develop and validate in the laboratory new full-waveform inversion algorithms to recover multiple characteristics of biological tissues and I will use low-frequency ultrasound that easily penetrates bone. Next, I will investigate the potential of full-waveform inversion of ultrahigh-frequency seismic data, a particular type of seismic waves that travel small distances but can interact with small objects, in order to characterise the first 100 meters of the subsurface in offshore wind farms. This new approach will be particularly useful to characterise vast areas of the subsurface and locate adequate regions for the installation of wind turbines to reduce maintenance costs. Finally, I will evaluate different strategies to obtain subsurface images over time with full-waveform inversion of seismic data at carbon dioxide storage sites, which play a crucial role in reducing the carbon footprint. This will help engineers better understand how carbon dioxide reservoirs evolve and how to make them safer and more efficient.

Lattice Field Theory (LFT) provides the tools to study the fundamental forces of nature using numerical simulations. The traditional realm of application of LFT has been Quantum Chromodynamics (QCD), the theory describing the strong nuclear force within the Standard Model (SM) of particle physics. These calculations now include electromagnetic effects and achieve sub percent accuracy. Other applications span a wide range of topics, from theories beyond the Standard Model, to low-dimensional strongly coupled fermionic models, to new cosmological paradigms. At the core of this scientific endeavour lies the ability to perform sophisticated and demanding numerical simulations. The Exascale era of High Performance Computing therefore looks like a time of great opportunities. The UK LFT community has been at the forefront of the field for more than three decades and has developed a broad portfolio of research areas, with synergetic connections to High-Performance Computing, leading to significant progress in algorithms and code performance. Highlights of successes include: influencing the design of new hardware (Blue Gene systems); developing algorithms (Hybrid Monte Carlo) that are used widely by many other communities; maximising the benefits from new technologies (lattice QCD practitioners were amongst the first users of new platforms, including GPUs for scientific computing); applying LFT techniques to new problems in Artificial Intelligence. The research programme in LFT, and its impact, can be expanded in a transformative way with the advent of pre-Exascale and Exascale systems, but only if key challenges are addressed. As the number of floating point operations per second increases, the communications between computing nodes are lagging behind, and this imbalance will severely affect future LFT simulations across the board. These challenges are common to all LFT codebases, and more generally to other communities that are large users of HPC resources. The bottlenecks on new architectures need to be carefully identified, and software that minimises the communications must be designed in order to make the best usage of forthcoming large computers. As we are entering an era of heterogeneous architectures, the design of new software must clearly isolate the algorithmic progress from the details of the implementation on disparate hardware, so that our software can be deployed efficiently on forthcoming machines with limited effort. The goal of the EXA-LAT project is to develop a common set of best practices, KPIs and figures of merit that can be used by the whole LFT community in the near future and will inform the design and procurement of future systems. Besides the participation of the LFT community, numerous vendors and computing centres have joined the project, together with scholars from 'neighbouring' disciplines. Thereby we aim to create a national and international focal point that will foster the activity of scholars, industrial partners and Research Sotfware Engineers (RSEs). This synergetic environment will host training events for academics, RSEs and students, which will contribute to the creation of a skilled work force immersed in a network that comprises the leading vendors in the subject. EXA-LAT will set the foundations for a long-term effort by the LFT community to fully benefit of Exascale facilities and transfer some of the skills that characterise our scientific work to a wider group of users across disciplines.

SPH (smoothed particle hydrodynamics), and Lagrangian approaches to hydrodynamics in general, are a powerful approach to hydrodynamics problems. In this scheme, the fluid is represented by a large number of particles, moving with the flow. The scheme does not require a predefined grid making it very suitable for tracking flows with moving boundaries, particularly flows with free surfaces, and problems that involve flows with physically active elements or large dynamic range. The range of applications of the method is growing rapidly and is being adopted by a rapidly growing range of commercial companies including Airbus, Unilever, Shell, EDF, Michelin and Renault. The widespread use of SPH, and its potential for adoption across a wide range of science domains, make it a priority use case for the Excalibur project. Massively parallel simulations with billion to hundreds of billions of particles have the potential for revolutionising our understanding of the Universe and will empower engineering applications of unprecedented scale, ranging from the end-to-end simulation of transients (such as a bird strike) in jet engines to the simulation of tsunami waves over-running a series of defensive walls. The working group will identify a path to the exascale computing challenge. The group has expertise across both Engineering and Astrophysics allowing us to develop an approach that satisfies the needs of a wide community. The group will start from two recent codes that already highlight the key issues and will act as the working group's starting point. - SWIFT (SPH with Interdependent Fine-grained Tasking) implements a cutting-edge approach to task-based parallelism. Breaking the problem into a series of inter-dependent tasks allows for great flexibility in scheduling, and allows communication tasks to be entirely overlapped with communication. The code uses a timestep hierarchy to focus computational effort where is most need in response to the problems. - DualSPHysics draws its speed from effective use of GPU accelerators to execute the SPH operations on large groups of identical particles. This allows the code to gain from exceptional parallel execution. The challenge is to effectively connect multiple GPUs across large numbers of inter-connected computing nodes. The working group will build on these codes to identify the optimal approach to massively parallel execution on exa-scale systems. The project will benefit from close connections to the Excalibur Hardware Pilot working group in Durham, driving the co-design of code and hardware. The particular challenges that we will address are: - Optimal algorithms for Exascale performance. In particular, we will address the best approaches to the adaptive time-stepping and out-of-time integration, and adaptive domain decomposition. The first allows different spatial regions to be integrated forward in time optimally, the second allows the regions to be optimally distributed over the hardware. - Modularisation and Separation of Concerns. Future codes need to be flexible and modularised, so that a separation can be achieved between integration routines, task scheduling and physics modules. This will make the code future-proof and easy to adapt to new science domain requirements and computing hardware. - CPU/GPU performance optimisation. Next generation hardware will require specific (and possibly novel) techniques to be developed to optimally advance particles in the SPH scheme. We will build on the programming expertise gain in DualSPHysics to allow efficient GPU use across multiple nodes. - Communication performance optimisation. Separated computational regions need to exchange information at their boundaries. This can be done asynchronously, so that the time-lag of communication does not slow computation. While this has been demonstrated on current systems, the scale of Excalibur will overload current subsystems, and a new solution is needed.

Wearable neurotechnology utilization is expected to increase dramatically in the coming years, with applications in enabling movement-independent control and communication, rehabilitation, treating disease and improving health, recreation and sport among others. There are multiple driving forces:- continued advances in underlying science and technology; increasing demand for solutions to repair the nervous system; increase in the ageing population worldwide producing a need for solutions to age-related, neurodegenerative disorders, and "assistive" brain-computer interface (BCI) technologies; and commercial demand for nonmedical BCIs. There is a significant opportunity for the UK to lead in the development of AI-enabled neurotechnology R&D. There are a number of key challenges to be addressed, mainly associated with the complexity of signals measured from the brain. AI has the potential to revolutionise the neurotechnology industry and neurotechnology presents an excellent challenge for AI. This fellowship will build on the award-winning AI and neurotechnology research of the fellow and offer real potential for impact through established clinical partnerships and in the neurotechnology industry. The objective of this project is to build on award-winning AI and neurotechnology R&D to address key shortcomings of neurotechnology that limit its widespread use and adoption using a range of key neural network technologies in a state-of-the-art framework for processing neural signals developed by the proposed fellow. The AI technologies developed for neurotechnology will be applied across sectors to demonstrate translational AI through engagement with at least 10 companies across at least 5 sectors during the fellowship, to demonstrate societal and economic benefit and interdisciplinary and translational AI skills development. The project has multiple industry, clinical and academic partners and is expected to produce world-leading AI technologies and propel the fellow to world-leading status in developing AI for neurotechnology which will impact widely. A major focus of the project is ensuring the expectations of the fellow role are met. This includes:- -Ensuring the processes and resources are in place to build a world-leading profile by the end of the fellowship; -Focusing on planning research of the team as new results emerge and hypothesis are tested, to refine and develop a high-quality programme of ambitious, novel and creative research, in AI-enabled Neurotechnology. Specific focus will be ensuring meticulous planning, execution and follow-up to produce world-leading results; -Continuing to perform my leadership role as director of the ISRC and leader of the data analytics theme, expanding the team and actively seek to develop into a position of higher leadership of the research agenda at Ulster, and in the national and international research community; -Focusing on strengthening relationships and collaborations with colleagues in industry and academia, and maximising the potential for flexible career paths for researchers within the team -Acting as an ambassador and advocate for AI, science and ED&I including by continuing to actively provide opinions and engaging with questions around AI and ethics, and responsible research and innovation (RRI). A focus will be embedding this throughout the activities of the fellowship but across the region and internationally; -Seeking to engage with and influence the strategic direction of the UK AI research and innovation landscape through engagement with their peers, policymakers, and other stakeholders including the public through. -Ensuring that the fundamental research is developed to have a high likelihood of impact on UK society/economy through trials across a range of patient groups to develop the evidence base and transfer of intellectual property to products, in particular through NeuroCONCISE Ltd, a main project partner.