Energy is one of the most important issues of the twenty-first century, because our future supply is currently threatened by progressively decreasing fossil fuel reserves, political instability and environmental problems resulting in pollution and global warming. Renewable hydrogen, H2, is widely considered as a potential future fuel, but its cheap and efficient production is still a major unresolved practical issue. The sun provides our planet with a continuous flow of electromagnetic and carbon-free energy and it is the only energy source, which is capable of sustaining human kind's long-term energy demand. The aim of this EPSRC-funded project is the development of an efficient bio-inspired H2 production catalyst from abundant and inexpensive raw materials and its coupling to light-harvesting complexes to capture energy provided by the sun to power H2 production from water - the storage of solar energy in the chemical bond of H2.Selective and economical chemical catalysts are needed for the central chemical interconversion of energy, water and H2 if there is to be a real prospect of promoting H2 as a sustainable fuel. Commonly employed precious metal catalysts (e.g. platinum) cannot be used for H2 production in the post-fossil fuel era, because of (i) limited resources and high cost, (ii) poor reaction selectivity (e.g. energy is wasted on unwanted side-reactions), and (iii) poisoning (catalyst-killing) by trace amounts of common chemicals, e.g. carbon monoxide. Microbial life forms handle the challenging task of H2 production using bio-catalysts (hydrogenases) to drive the selective and reversible production of H2 from water at fast rates under the safe conditions of room temperature and neutral pH. The catalytic reaction centre (active site) of hydrogenases contains an iron or nickel-iron metal centre surrounded typically by cysteine, carbon monoxide and cyanide ligands. Thus, the active site of a hydrogenase is an interesting biological motif to mimic in order to build H2 production catalysts from abundant and inexpensive raw materials. This adventurous work on solar H2 production has the prospect of being a fundamental step towards large-scale water photolysis for a sustainable hydrogen economy. International (France, USA) and national (Manchester) academic as well as industrial (Evonik Industries) collaborators with expertise in enzyme biology, spectroscopy, solar cells, nanoparticles, and neutron diffraction will support this project under my guidance. In addition, this work on bio-inspired/biomimetic H2 production catalysts will also deal with wastewater treatment, the synthesis of fine chemicals, and might give us insight into how living organisms convert water into H2 on a molecular level, and reveal how the reverse reaction works: the generation of energy from H2, which is important for fuel cell applications.

The basic theme of this research is to use random walks and interacting particle systems to improve network exploration and structure. Our aim is to study algorithmic problems in Computer Science modeled by particles (agents, messages, robots) moving more or less randomly on a large network. We suppose such particles may be single or numerous, of various types, and that they may able to interact with each other and with the network. We assume the particles have a purpose either in relation to the network, such as searching the network or modifying its structure; or in relation to other particles, such as passing messages to each other. Many large networks can be found in modern society, and obtaining information from these networks is an important problem. Examples of such networks include the World Wide Web, and social networks such as Twitter and FaceBook. These networks are very large, change over time and are essentially unknowable or do not need to be known in detail. They are highly interlinked (e.g. URL's embedded in Twitter and FaceBook) and can be viewed as part of a larger whole. New social networks appear frequently, and the influence of these networks on social, economic and political aspects of everyday life is substantial. Searching, sampling and indexing the content of such networks is a major application area a substantial user of computer time, and likely to become more so in the future. The evolving use of these networks is changing social and economic behavior. Improving the ability to search such networks is of value to us all. Random walks are a simple method of network exploration, and as such, are particularly suitable for searching massive networks. A random walk traverses a network by moving from its current position to a neighboring vertex chosen at random. Decisions about where to go next can be taken locally, and only limited resources are needed for the search. The exploration is carried out in a fully distributed manner, and can adapt easily to dynamic networks. The long run behavior of the walk acts as a distributed voting mechanism, in which the number of visits to a vertex is proportional to its popularity or accessability. Suppose we could alter the behavior of the random walk to reduce the search time. How can this be done, and at what cost? Speeding up random walks, to reduce search time, is a fundamental question in the theory of computing. The price of this speed up, is normally some extra work which is performed locally by the walk, or undertaken by the vertices of the graph. Possible ways of speeding up random walks we have identified include biassed transitions, use of previous history and local exploration around current position. One way to reduce search time is to use several random walks which search simultaneously. In the simplest model the walks are oblivious of each other and do not interact in any way. Search time should be reduced, but at the expense of using additional walks. Suppose we could also allow the random walks to interact with each other, or with the underlying network? How should this interaction be designed, in order to speed up search, and what other applications might it have? Historically, interacting particle systems have only been analyzed on infinite networks, and even then not with computer science applications in mind. Recently, we began to make progress in this direction, and found that many related problems such as distributed voting, to elect a leader for example, could be understood in the framework we developed. Potential applications of interacting particle systems are many and include: Gossiping and broadcasting among agents moving on a network, Models of epidemics spreading between particles and the graph, Distributed search with intelligent robots, Software agents moving in an intranet. Models of voting and social consensus. Good agents chasing bad agents on a network.

United States

We all critically depend on and use digital systems that sense and control physical processes and environments. Electricity, gas, water, and other utilities require the continuous operation of both national and local infrastructures to deliver their services. Industrial processes, for example for chemical manufacturing, production of materials such as cement, steel, aluminium or fertilizers, and manufacturing chains for car production or pharmaceuticals similarly lie at this intersection of the digital and the physical. This intersection also applies in other CPS such as robots, autonomous cars, and drones. All such systems are exposed to malicious threats and have been the target of cyber-attacks by different threat actors ranging from disgruntled employees to hacktivists, terrorists, organised crime and nation states. The increasing fragility and vulnerability of our cyber-enabled society is rapidly approaching intolerable limits. As these systems become larger and more complex interruption of service in any of these infrastructures can cause significant cascading effects with safety, economic and societal impacts. Because we critically depend on the operation of such systems, disruption to their operations must be minimised even when they are under attack and have been partially compromised. Because they operate in a physical environment, the safety of such systems must be preserved at all times to avoid physical damage and even threat to life. Therefore, ensuring the resilience of such systems, their survivability and continued operation when exposed to malicious threats requires the integration of methods and processes from security analysis, safety analysis, system design and operation that have traditionally been done separately and that each involve specialist skills and a significant amount of human effort. This is not only costly, but also error prone and delays response to security events. The full integration and automation of such methodologies will be a challenge for many years to come. However, RESICS aims to significantly advance the state-of-the-art and deliver novel contributions that facilitate: a) risk analysis for such systems in the face of adversarial threats taking into account the impact of security events across the cascading inter-dependencies; b) characterising attacks that can have an impact on the safety of the system, identifying the paths that make such attacks possible; c) identifying countermeasures that can be applied to mitigate threats and contain the impact of attacks; and d) ensuring that such countermeasures can be applied whilst preserving the system's safety and operational constraints and maximising its availability. These contributions will be evaluated across several test beds, digital twins, a cyber range and a number of use-cases across different industry sectors. They will deliver increased automation, lower the skill requirements involved in the analysis and in mitigating threats and improve response times to security incidents. To achieve these goals RESICS will combine model-driven and empirical approaches across both security and safety analysis, adopting a systems-thinking approach which emphasises Security, Safety and Resilience as emerging properties of the system. RESICS leverages preliminary results in the integration of safety and security methodologies with the application of formal methods and the combination of model-based and empirical approaches to the analysis of inter-dependencies in ICSs and CPSs.

The overall aim of the project is the development of automated tools for automatic spatio-temporal analysis and understanding of human subtle facial behaviour from 4D facial information (i.e. 3D high-quality video recordings of facial behaviour). Two exemplar applications related to security issues will be specifically addressed in this proposal: (a) person verification (i.e. using facial behaviour as a biometric trait), and (b) deception indication. The importance of non-obtrusive person verification and deception indication is undisputable - every day, thousands of people go through airport security checkpoints, border crossing checkpoints, and other security screening points. Automated, unobtrusive monitoring and assessing of deceptive behaviour will form a valuable tool for end users, such as police, justice and prison services. This is in particular important as currently only informal interpretations for detecting deceptive behaviour are used. In addition, the development of alternative methods for person verification that are not based on physical traits only but on behavioural, easily observable traits like facial expressions, would be of great value for the development of multimodal biometric system. Such multi-modal biometric systems will be of great interested to government agencies such as the Home Office or the UK Border agency. For automatic deception indication we propose to develop methodologies for detecting 4D micro-expressions and their dynamics being typical of deceptive behaviour as reported by research in psychology. For automatic person identification we propose to increase the robustness of static face- image-based verification systems by including facial dynamics as an additional biometric trait. The underlying motivation is that the dynamic 4D facial behaviour is very difficult to imitate and , hence, it has natural resilience against spoof attacks. The project focuses on 3D video recordings rather than on 2D video recordings of facial behaviour due to two main reasons: (1) increased robustness to changes in head-pose, and (2) ability to spot subtle changes in the depth of facial surface such as jaw clench and tremor appearance on the cheeks, which are typical of deceptive behaviour and cannot be spotted in 2D images. The research on 3D facial dynamics is now made possible by the tremendous advance of sensors and devices for the acquisition of 3D face video recordings. The core of the project will deal with both the development of 4D-FAB research platform containing tools for human subtle facial behaviour analysis in 4D videos and the development of annotated data repository consisting of two parts: (1) annotated 4D recordings of deceptive and truthful behavior, and (2) annotated 4D recordings of subjects uttering a sentence, deliberately displaying certain facial actions and expressions, and spontaneously displaying certain facial actions and expressions. The work plan is oriented around this central goal of developing 4D-FAB technology and is carried out in 3 work packages described in the proposal. A team of 3 Research Associates (RAs), led by the PIs, and having the background in computer vision and machine learning, will develop 4D-FAB technology. The team will be closely assisted by 6 members of the Advisory Board: Prof. Burgoon, University of Arizona, advising on psychology of deception and credibility Prof. Cohn, Pittsburgh University / Carnegie Mellon University, advising on face perception and facial behaviometrics Prof. Nunamaker, Director of BORDERS, US Nat'l Center for Border Security and Immigration, advising on making 4D-FAB useful for end users in security domain Dr Hampson, Head of Science & Technology, OSCT, Home Office, advising on making 4D-FAB useful for end users Dr Cohen, Director of United Technologies Research Centre Ireland, advising on making 4D-FAB useful for end users Dr Urquhart, CEO of Dimensional Imaging, advising on 4D recording setup design