Humans interact with tens of objects daily, at home (e.g. cooking/cleaning) or outdoors (e.g. ticket machines/shopping bags), during working (e.g. assembly/machinery) or leisure hours (e.g. playing/sports), individually or collaboratively. When observing people interacting with objects, our vision assisted by the sense of hearing is the main tool to perceive these interactions. Let's take the example of boiling water from a kettle. We observe the actor press a button, wait and hear the water boil and the kettle's light go off before water is used for, say, preparing tea. The perception process is formed from understanding intentional interactions (called ideomotor actions) as well as reactive actions to dynamic stimuli in the environment (referred to as sensormotor actions). As observers, we understand and can ultimately replicate such interactions using our sensory input, along with our underlying complex cognitive processes of event perception. Evidence in behavioural sciences demonstrates that these human cognitive processes are highly modularised, and these modules collaborate to achieve our outstanding human-level perception. However, current approaches in artificial intelligence are lacking in their modularity and accordingly their capabilities. To achieve human-level perception of object interactions, including online perception when the interaction results in mistakes (e.g. water is spilled) or risks (e.g. boiling water is spilled), this fellowship focuses on informing computer vision and machine learning models, including deep learning architectures, from well-studied cognitive behavioural frameworks. Deep learning architectures have achieved superior performance, compared to their hand-crafted predecessors, on video-level classification, however their performance on fine-grained understanding within the video remains modest. Current models are easily fooled by similar motions or incomplete actions, as shown by recent research. This fellowship focuses on empowering these models through modularisation, a principle proven since the 50s in Fodor's Modularity of the Mind, and frequently studied by cognitive psychologists in controlled lab environments. Modularity of high-level perception, along with the power of deep learning architectures, will bring a new understanding to videos analysis previously unexplored. The targeted perception, of daily and rare object interactions, will lay the foundations for applications including assistive technologies using wearable computing, and robot imitation learning. We will work closely with three industrial partners to pave potential knowledge transfer paths to applications. Additionally, the fellowship will actively engage international researchers through workshops, benchmarks and public challenges on large datasets, to encourage other researchers to address problems related to fine-grained perception in video understanding.

The UK's carbon targets, as defined by the Climate Change Act of 2008, specify an emissions reduction of 80% by 2050, which the government has recently revised down to 'net zero' for the same year. In 2017, 17% of the UK's carbon emissions were associated with non-electric use in the residential sector (64.1 Mt CO2), the majority of which were associated with natural gas space heating, cooking and domestic hot water. The UK must therefore decarbonise residential heat to be able to meet its climate change targets, but, in combination with electric vehicles (EVs), this could lead to a 200-300% increase in the UK's annual electricity demand. In terms of deployment at scale, Air Source Heat Pumps (ASHP) operating either in isolation or as a hybrid gas system appear a key technology as they are not site specific and are applicable to both new build housing and retrofit. The UK's low voltage (LV) electricity network will not however, be able to operate with unconstrained electrical heating or EV charging loads. Both loads must be deferrable or scheduled in a manner to support the electricity network and maintain substations and feeders within limits. Household electric heating has the potential to operate as a significant deferrable load which LATENT is seeking to understand and harness. This can provide benefits across scales, namely to the UK (energy security and carbon targets), DNO (Distributed Network Operator as grid support), heat pump suppliers (by demonstrating added grid value), householders (in terms of bill reduction and avoidance of peaking dynamic tariffs) and electricity suppliers by applying aggregation techniques to minimise energy service costs. The key aim of LATENT therefore, is to be able to predict the impact of customers with electrical heating (predominantly ASHP) operating with 3rd party deferrable heating control on the LV network at the feeder / substation level. 3rd party control in this context would be through the energy service supplier, with whom, unlike the DNO, a household has an existing financial contract relationship. LATENT will inform industry of the potential of 3rd party control of deferrable heat through a rigorous field experiment, and, in doing so, accelerate the transition to decarbonised household heating. LATENT will determine the influence of householder personality trait (OCEAN traits: either positive / negative as Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) alongside more traditional Census metrics such as educational attainment, house type etc to deliver a multi-variate regression model to describe deferrable heat reduction at the household level. A substation or feeder can then be analysed in terms of its household type mix (10% C+ detached, 30% E- flat etc) to produce a composite substation level, deferrable heat reduction estimate. This model will be realised through field trials with LATENT's industrial partner, Igloo Energy. Igloo have a customer base with smart heating systems and ASHP which support remote 3rd party control. LATENT will test (i) householder's stated acceptance to deferral of heating (in terms of temperature drop and duration) through focus groups and surveys, (ii) actual acceptance of heat deferral through heating season field trials, and (iii) operation of a commercial deferrable heat tariff with a sample of Igloo's customer base.

As interaction on online Web-based platforms is becoming an essential part of people's everyday lives and data-driven AI algorithms are starting to exert a massive influence on society, we are experiencing significant tensions in user perspectives regarding how these algorithms are used on the Web. These tensions result in a breakdown of trust: users do not know when to trust the outcomes of algorithmic processes and, consequently, the platforms that use them. As trust is a key component of the Digital Economy where algorithmic decisions affect citizens' everyday lives, this is a significant issue that requires addressing. ReEnTrust explores new technological opportunities for platforms to regain user trust and aims to identify how this may be achieved in ways that are user-driven and responsible. Focusing on AI algorithms and large scale platforms used by the general public, our research questions include: What are user expectations and requirements regarding the rebuilding of trust in algorithmic systems, once that trust has been lost? Is it possible to create technological solutions that rebuild trust by embedding values in recommendation, prediction, and information filtering algorithms and allowing for a productive debate on algorithm design between all stakeholders? To what extent can user trust be regained through technological solutions and what further trust rebuilding mechanisms might be necessary and appropriate, including policy, regulation, and education? The project will develop an experimental online tool that allows users to evaluate and critique algorithms used by online platforms, and to engage in dialogue and collective reflection with all relevant stakeholders in order to jointly recover from algorithmic behaviour that has caused loss of trust. For this purpose, we will develop novel, advanced AI-driven mediation support techniques that allow all parties to explain their views, and suggest possible compromise solutions. Extensive engagement with users, stakeholders, and platform service providers in the process of developing this online tool will result in an improved understanding of what makes AI algorithms trustable. We will also develop policy recommendations and requirements for technological solutions plus assessment criteria for the inclusion of trust relationships in the development of algorithmically mediated systems and a methodology for deriving a "trust index" for online platforms that allows users to assess the trustability of platforms easily. The project is led by the University of Oxford in collaboration with the Universities of Edinburgh and Nottingham. Edinburgh develops novel computational techniques to evaluate and critique the values embedded in algorithms, and a prototypical AI-supported platform that enables users to exchange opinions regarding algorithm failures and to jointly agree on how to "fix" the algorithms in question to rebuild trust. The Oxford and Nottingham teams develop methodologies that support the user-centred and responsible development of these tools. This involves studying the processes of trust breakdown and rebuilding in online platforms, and developing a Responsible Research and Innovation approach to understanding trustability and trust rebuilding in practice. A carefully selected set of industrial and other non-academic partners ensures ReEnTrust work is grounded in real-world examples and experiences, and that it embeds balanced, fair representation of all stakeholder groups. ReEnTrust will advance the state of the art in terms of trust rebuilding technologies for algorithm-driven online platforms by developing the first AI-supported mediation and conflict resolution techniques and a comprehensive user-centred design and Responsible Research and Innovation framework that will promote a shared responsibility approach to the use of algorithms in society, thereby contributing to a flourishing Digital Economy.

Topic of Centre: This i4Nano CDT will accelerate the discovery cycle of functional nanotechnologies and materials, effectively bridging from ground-breaking fundamental science toward industrial device integration, and to drive technological innovation via an interdisciplinary approach. A key overarching theme is understanding and control of the nano-interfaces connecting complex architectures, which is essential for going beyond simple model systems and key to major advances in emerging scientific grand challenges across vital areas of Energy, Health, Manufacturing (particularly considering sustainability), ICT/Internet of things, and Quantum. We focus on the science of nano-interfaces across multiple time scales and material systems (organic-inorganic, bio-nonbio interfaces, gas-liquid-solid, crystalline-amorphous), to control nano-interfaces in a scalable manner across different size scales, and to integrate them into functional systems using engineering approaches, combining interfaces, integration, innovation, and interdisciplinarity (hence 'i4Nano'). The vast range of knowledge, tools and techniques necessary for this underpins the requirement for high-quality broad-based PhD training that effectively links scientific depth and application breadth. National Need: Most breakthrough nanoscience as well as successful translation to innovative technology relies on scientists bridging boundaries between disciplines, but this is hindered by the constrained subject focus of undergraduate courses across the UK. Our recent industry-academia nano-roadmapping event attended by numerous industrial partners strongly emphasised the need for broadly-trained interdisciplinary nanoscience acolytes who are highly valuable across their businesses, acting as transformers and integrators of new knowledge, crucial for the UK. They consistently emphasise there is a clear national need to produce this cadre of interdisciplinary nanoscientists to maintain the UK's international academic leadership, to feed entrepreneurial activity, and to capitalise industrially in the UK by driving innovations in health, energy, ICT and Quantum Technologies. Training Approach: The vision of this i4Nano CDT is to deliver bespoke training in key areas of nano to translate exploratory nanoscience into impactful technologies, and stimulate new interactions that support this vision. We have already demonstrated an ability to attract world-class postgraduates and build high-calibre cohorts of independent young Nano scientists through a distinctive PhD nursery in our current CDT, with cohorts co-housed and jointly mentored in the initial year of intense interdisciplinary training through formal courses, practicals and project work. This programme encourages young researchers to move outside their core disciplines, and is crucial for them to go beyond fragmented graduate training normally experienced. Interactions between cohorts from different years and different CDTs, as well as interactions with >200 other PhD researchers across Cambridge, widens their horizons, making them suited to breaking disciplinary barriers and building an integrated approach to research. The 1st year of this CDT course provides high-quality advanced-level training prior to final selection of preferred PhD research projects. Student progression will depend on passing examinable components assessed both by exams and coursework, providing a formal MRes qualification. Components of the first year training include lectures and practicals on key scientific topics, mini/midi projects, science communication and innovation/scale-up training, and also training for understanding societal and ethical dimensions of Nanoscience. Activities in the later years include conferences, pilot projects, further innovation and scale up training, leadership and team-building weekends, and ED&I and Responsible Innovation workshops

Web Science is the science of the World Wide Web and its impact, both positive and negative, on society. The Web is a socio-technical mixture of the people, organizations, browsers, policies, applications, standards, data centres, shopping baskets and social network status updates that have come to shape our everyday lives and global futures. Web Science offers the insights necessary to understand the flow of data and knowledge around the globe, and the social and technical processes that can turn gigabytes and terabytes of raw data and into valuable new applications or evidence-based policy. Web Science helps us appreciate the threats to our online identities but also the opportunities of allowing our personal digital avatars to participate in new kinds of online businesses, online politics and online social engagements. Web Science offers a basis for innovating new personal practices and new social formations, and the ability to predict the consequences for the UK's digitally connected citizens. With an integrated understanding of these research areas, Web Science doctoral graduates will be able to innovate in the shaping of Web growth and Web policy, positioned to lead UK industry and government to reap the maximum economic and social value from its emerging digital economy. The Centre will recruit 13 excellent candidates annually from a variety of science, engineering, social science and humanities backgrounds. It will provide a cohort-based, 4-year doctoral programme with an initial training year that combines foundational aspects of Web Science research with technical aspects of the Web's architecture, an intensive training in interdisciplinarity and a grounding in innovation. A student-centred process of PhD research selection will begin at the end of the first semester with students starting to negotiate a potential project topic and multidisciplinary supervisor team with members of the Supervisor Forum. The CDT will offer a thorough programme of postgraduate research and professional training in co-ordination with the University Research and Graduate School. Complementary cohort-specific training will be offered to support and enhance the opportunities offered by the CDT (e.g. more intensive team building courses or communication training to prepare for specific industry events). The cohort experience is maintained throughout the PhD with frequent team-based events including collaborations with industry partners and international research exchanges. The Web Science CDT will use a multidisciplinary training approach that has successfully cut across traditional disciplinary silos in research practice, institutional structure and University administration. Its novel cohort-based training environment creates a socially cohesive and self-supporting group of students that successfully integrate their diverse disciplinary expertise in collaborative teams. Its programme of cross-cohort activities encourages mentorship, thus making the CDT self-sustaining and allowing it to amplify the research leadership of the supervisory staff. The net effect of these cohort benefits is to allow each student to undertake more challenges and to achieve more excellent training outcomes than possible in an individual training regime.