With the prevalence of data-hungry deep learning approaches in Artificial Intelligent (AI) as the de facto standard, now more than ever there is a need for labelled data. However, while there have been interesting recent discussions on the definition of readiness levels of data, the same type of scrutiny on annotations is still missing in general: we do not know how or when the annotations were collected or what their inherent biases are. Additionally, there are now forms of annotation beyond standard static sets of labels that call for a formalisation and redefinition of the annotation concept (e.g., rewards in reinforcement learning or directed links in causality). During this Fellowship we will design and establish the protocols for transparent annotations that empowers the data curator to report on the process, the practitioner to automatically evaluate the value of annotations and the users to provide the most informative and actionable feedback. This Fellowship will address all these through a holistic human-centric research agenda, bridging gaps in fundamental research and public engagement with AI. The Fellowship aims to lay the foundations for a two-way approach to annotations, where the paradigm is shifted from annotations simply being a resource to them becoming a means for AI systems and humans to interact. The bigger picture is that, with annotations seen as an interface between both entities, we will be in a much better position to guide the relation of trust in between learning systems and users, where users translate their preferences into the learning systems' objective functions. This approach will help produce a much needed transformation in how potentially sensitive aspects of AI become a step closer to being reliable and trustworthy.

Over the past decade, deep learning methods have had an enormous impact on the academic and industrial worlds, opening new multi-billion markets ranging from driver-less cars to speech recognition and machine translation. Deep learning has been an emerging technology for decades; it took an orchestrated scientific and engineering effort as well as harnessing of the increasing computational power and large datasets to achieve an overarching technological and societal impact. Most of the successful deep learning methods such as Deep Convolutional Neural Networks (DCNNs) are based on classical signal/image processing models that limit their applicability to data with underlying Euclidean grid-like structure, e.g., 2D/3D images or audio signals. Non-Euclidean (graph-or manifold-structured) data are becoming increasingly abundant; prominent examples include 3D objects (represented as meshes or point clouds) in CV and graphics, as well as social networks, graphs of molecules, and interactomes. Until recently, this has been a significant obstacle precluding the adoption of ML tools in some of the most promising fields. To bridge the gap between Euclidean (e.g., images, videos & speech) and non-Euclidean (e.g., graph and manifolds) ML umbrella terms have recently been coined, such as ''Geometric Deep Learning'' (GDL). Such methods have gained a keen interest in the ML community the past couple of years since graphs can model very abstract systems of relations or interactions, and thus potentially applied across the board. Recent successful examples of the application of non-Euclidean deep learning are as diverse as semantic segmentation on meshes and point clouds, drug-design and event classification in particle physics. Nevertheless, the focus is mainly on discriminative approaches (e.g., classification and segmentation problems) and limited progress has been made towards generative methodologies (i.e., unsupervised methodologies that model the distribution of data) on non-Euclidean spaces. The drawback of discriminative methodologies is that they require a massive amount of labelled, mainly manually, data, which is very expensive, or even impossible to find in many settings. On the other hand, generative approaches can operate in unsupervised scenarios and can even be used to produce data that can be utilised to train discriminative approaches. Currently, available generative frameworks have been developed primarily for Euclidean data (e.g., images, videos) and are not suitable for the non-Euclidean setting. GNoMON aims at bridging this gap by developing a mathematically principled framework for designing and implementing Generative Models for non-Euclidean domains such as graphs or manifolds. We will explore challenging problems in 3D CV and graphics. Nevertheless, the developed techniques will be designed in such a way to be general so that can aid the research in many other fields.

The Bionics+ NetworkPlus will represent the spectrum of research, clinical and industrial communities across bionic technologies within the EPSRC Grand Challenge theme of Frontiers of Physical Intervention. It will invigorate and support a cohesive, open and active network with the mission of creating a mutually supportive environment. It will lead to the co-creation of user-centred bionic solutions that are fit for purpose. These advances will have a global impact, consolidating the world-leading position of the UK. The founding tranche will focus on ambitious and transformative research, new collaborative and translational activities, and the formulation of a longer-term strategy. Within this context, as a community, we will explore and identify areas of opportunity and value, driven by Bionics users' needs, complementary to existing activity and strengths. The network will instigate and support early-stage research in these priority areas, alongside providing an outward-facing representation and engagement of the UK Bionics community. Further, we aim to contribute in an advisory capacity to public bodies, UK industry and government policy. At the time of the application, we have obtained a positive commitment from circa 70 groups including bionic users, academic partners from universities in England, Scotland, Wales and Northern Ireland and a few international partners; partners in medical devices, orthotics and prosthetics industry, both large corporates and small-medium size companies; and many clinicians, surgeons and aligned health experts from relevant NHS clinics and the private sector.

Towards an Equitable Social VR Social Virtual Reality (SVR) constructs a digital parallel to the physical world, enabling remote social engagement mediated by modern immersive Virtual Reality (VR) technology. This social engagement is not strictly limited to conventional social interaction, but has also recently expanded to include activities such as remote participation in training, work, and service delivery. This digital parallel world offers significant opportunities for greater inclusion of individuals who are currently marginalised by the physical world, thereby widening access to the Digital Economy. SVR is a rapidly emerging technology and its pace of adoption has accelerated in the global pandemic. However, to date, there has been limited research examining the accessibility and inclusion requirements of SVR for users who currently face digital access barriers due to a disability or age-related capability loss. As a society, we sit at a critical juncture where concepts of inclusion and accessibility can be embedded into SVR while the technology is still in its formative stage. Towards an Equitable Social VR addresses the need to ensure that SVR platforms are accessible and inclusive for people with disabilities and older people, thus allowing for the potential of the platforms in contributing to the quality of life of these population groups to be realised in full. The project will undertake a programme of R&D with the aim of delivering the SVR Inclusion Framework: a collection of formalised guidance and tools serving to facilitate equal participation in SVR for disabled and older users. The project will take into account the whole spectrum of capability loss manifestations, including vision, hearing, mobility, dexterity, and neurodiversity aspects of cognition (learning difficulties) and mental health, as well as the co-occurrence of capability loss.

Optimisation -- the problem of identifying a satisficing solution among a vast set of candidates -- is not only a fundamental problem in Artificial Intelligence and Computer Science, but essential to the competitiveness of UK businesses. Real-world optimisation problems are often tackled using evolutionary algorithms, which are optimisation techniques inspired by Darwin's principles of natural selection. Optimisation with classical evolutionary algorithms has a fundamental problem. These algorithms depend on a user-provided fitness function to rank candidate solutions. However, for real world problems, the quality of candidate solutions often depend on complex adversarial effects such as competitors which are difficult for the user to foresee, and thus rarely reflected in the fitness function. Solutions obtained by an evolutionary algorithm using an idealised fitness function, will therefore not necessarily perform well when deployed in a complex and adversarial real-world setting. So-called co-evolutionary algorithms can potentially solve this problem. They simulate a competition between two populations, the "prey" which attempt to discover good solutions, and the "predators" which attempt to find flaws in these. This idea greatly circumvents the need for the user to provide a fitness function which foresees all ways solutions can fail. However, due to limited understanding of their working principles, co-evolutionary algorithms are plagued by a number of pathological behaviours, including loss of gradient, relative over-generalisation, and mediocre objective stasis. The causes and potential remedies for these pathological behaviours are poorly understood, currently limiting the usefulness of these algorithms. The project has been designed to bring a break-through in the theoretical understanding of co-evolutionary algorithms. We will develop the first mathematically rigorous theory which can predict when a co-evolutionary algorithm reaches a solution efficiently, and when pathological behaviour occurs. This theory has the potential to make co-evolutionary algorithms a reliable optimisation method for complex real-world problems.