Artificial intelligence (AI) is transforming our societies, but the more it proliferates, the higher the customer demands for functionality and efficiency (most notably energy). Thus, as time progresses the limitations of statistical learning-based AI that has underpinned most AI work so far are beginning to naturally become more exposed. Tasks such as variable binding and manipulation, inductive reasoning and 1-shot learning, at which statistical learning is not as strong, suggest solutions in the sphere of abstract symbol processing AI. The commonly referenced 'next wave of AI' that is capable of such exploits (towards "strong AI") is likely to make extensive use of symbol processing capabilities and simultaneously demand a bespoke set of hardware solutions. The proposed project primarily addresses the issue of developing general-purpose (platform-level) hardware for precisely symbolic AI. The proposed project seeks to develop a memory module that features: a) an internal structure and b) in-memory computing capabilities that render it particularly suitable for symbolic processing-based artificial intelligence (AI) systems. Ultimately the project seeks to deliver: 1) Two microchip iterations prototyping the memory system. 2) A software environment (infrastructure) for easy programming and operation of the resulting microchips (includes simulation capabilities for proof-of-concept tests). 3) A demonstration of the memory cell operating together with a symbolic processor as an aggregate system. 4) A functioning set of starter applications illustrating the capabilities of the design. The overall effort is driven by a philosophy of co-optimising the memory across the entire trio of fundamental device components, symbolic AI mechanics and hardware design facets. Specifically: functionality in the proposed memory system will be pursued by: a) Designing a resistive RAM-based (ReRAM) memory unit where operation of the ReRAM devices and ReRAM tech specifications themselves are subservient to the specific operational goals of the memory system. b) Adapting the mathematical machinery of the system in order to map functional operations to hardware-friendly machine-code level operations: the stress is on hardware-friendliness, not mathematical elegance. This will be inextricably linked to the design of the memory's instruction set. c) Designing an architecture that runs the symbolic memory efficiently by using memory allocation techniques that maximise locality and making extensive use of power-gating. Simultaneously, implementation of a solid software stack infrastructure will enable efficient and fast prototyping and hypothesis testing. The cornerstone of the targeted project impact is to lay the foundations for launching an industrial-scale design effort towards hardware for symbolic AI. Hence the bulk of the effort is in chip design (prototype-based de-risking of the idea) and toolchain development (impact acceleration by lowering barriers to user uptake). Simultaneously, it is expected that the project will play a significant role in enhancing interest in symbol-level AI and very crucially, inducing interest in connecting symbolic AI with statistical learning one; thereby significant impact on knowledge is achieved. Finally, the increased in the capabilities of AI, as well as the transparency of decision-making (typically readily expressible via formal expressions or even in pseudo-natural language) offered by the symbolic approach promise to make a significant impact in enhancing acceptance of AI by society, providing a solid scientific foundation for certification processes (AI trust - broadening the scope of applications that accept an AI solution). With hardware available for this task, significant impact on productivity and quality of life is to be achieved. The project is self-contained and is designed to launch a much broader, sustainable effort, headed by the PI in this field.

Nutrients are essential for phytoplankton growth in the sea. Phytoplankton take up nutrients from the surrounding water and the extent of their growth is largely dependent on nutrient availability. Nitrate and phosphate are vital nutrients and their presence in light-rich surface waters can sustain algal blooms. Phytoplankton draw down carbon dioxide from the atmosphere and in shelf seas can lock this carbon up in the sediments. In this project we will investigate the idea that the seasonal supply of nutrients to shelf seas is controlled by physical mixing processes at the shelf edge. This will be carried out using new sensors capable of determining nitrate and phosphate in-situ which will be deployed on underwater gliders for up to 3 weeks at a time. Underwater gliders are autonomous vehicles that, after launching from close to the coast, can "fly" to a sampling site and send back data via a satellite link. We will use four glider missions over a seasonal timescale (June & Sept 2013, Jan & April 2014) in order to gain a fundamental insight into the annual supply of nutrients to the highly productive shelf seas around the Malin Shelf. Alongside the nutrient sensors on the gliders will be three other sensors, a conductivity and temperature sensor, an oxygen sensor and a sensor which can determine chlorophyll a which is used as a proxy for phytoplankton biomass in the water. For each mission we will use two gliders to simultaneously characterise both the on- and off-shelf concentration gradients. These glider missions will be integrated into a large NERC-funded consortium, FASTNEt, which aims to identify the physical processes that facilitate the exchange of water across the edge of the north-west European shelf and understand how they evolve across the seasons. FASTNEt will be deploying long-term, shelf-edge current meter moorings and another underwater autonomous vehicle, the Autosub Long Range (ALR) in the same location. The use of sensors deployed on gliders allow us to increase the spatial and temporal coverage of data that would not be possible from traditional shipboard measurements, that cannot be carried out in stormy conditions. The combination of in-depth knowledge of the physical processes occurring at the Malin Shelf along with accurate high resolution nutrient data from the sensors means that we will be able to investigate what controls nutrient supply to the photic zone and how this impacts on the phytoplankton growth. Additionally, we will be trialling new deployment methodologies for these underwater gliders - using fast day-boats to take them as far offshore as possible from a coastal base. Our data will be freely available and we will share our findings with end-users (such as the UK government's Department for Environment, Food and Rural Affairs who are responsible for fisheries, and the UK Met Office) throughout the project.

Nerve agents are amongst the most deadly chemicals known to man and continue to pose a significant societal threat. Nerve agents function by inhibiting chemicals in the brain that affect the nervous system. Thus, nerve agents cause seizures in the brain which can lead to severe brain damage and even death. Several drugs are used as initial treatments for nerve agent poisoning, but these are sometimes ineffective and can themselves be harmful. In addition, the greater the delay between exposure to the nerve agent and the provision of treatment, for example on the battlefield, the lower the likelihood of the effectiveness of these treatments. Consequently, new and better treatment options are needed to protect against the effects of nerve agents. Current methods used for testing new drug effectiveness for the treatment of nerve agent poisoning are largely reliant on the use of rodents. Such experiments are slow and costly, and usually involve severe procedures, such as the surgical implantation of electrodes in the brain and exposure to nerve agents. There is a requirement to develop higher throughput methods for identifying novel treatments, that are also more ethically favourable than those currently available. The non-protected 4-days post-fertilisation (dpf) larval zebrafish could prove invaluable as they have been shown to be responsive to a range of seizure-inducing drugs and can be tested quickly and easily in large numbers. Scientists from the universities of Portsmouth and Exeter will build on previous NC3Rs-funded work to transfer a non-protected larval zebrafish seizure model to Dstl, where the methods can be used to identify novel treatments for nerve agent poisoning. Dr Parker is a zebrafish behavioural expert, and will develop behavioural measures of seizures in 4dpf larvae. Many of the protocols were developed during Dr Parker's work on an NC3Rs project grant at Queen Mary, London (PI Caroline Brennan). Dr Winter is an expert in examining the brain during seizures in zebrafish using advanced imaging techniques, some of which have been developed during an ongoing NC3Rs studentship. His team will focus on developing approaches assessing seizure activity in the 4dpf zebrafish brain to understand model relevance for predicting effects in mammals. The end users at Dstl will utilise this approach for the identification and development of novel treatments for nerve agent poisoning. Dstl colleagues will promote the wider uptake of this approach, and the zebrafish as a model for assessing chemical toxicity, within the international defence research community. This approach could replace a significant number of rodents used in testing novel treatments against nerve agent toxicity, thus reducing overall rodent use by an estimated 75%. Limited rodent experiments would remain only for confirmatory purposes. Our approach could therefore prevent the yearly global use of at least 1500 rodents in these severe protocols. In addition to the direct replacement of rodents, the data generated in non- protected zebrafish larvae can also be used to refine remaining rodent studies to ensure that appropriate non-toxic doses are used. Refinement will also result from the identification and ruling out of any putative treatments with undesirable properties prior to escalation to rodent models. Dstl actively participates in a number of international research collaborations including bilateral arrangements with European countries and an important multinational agreement between the Australia, Canada, the UK and the USA, the CBR Memorandum of Understanding (MOU). Dr Kearn will use these arrangements and his position as UK lead for a predictive toxicology task under the CBR MOU to share data and methodologies from this project, champion its outputs and influence other Nations' programmes to encourage uptake of this technology.

Periodicity-enhanced (meta) materials and surfaces are artificial structures that possess properties not found in naturally-occurring materials and surfaces. The periodicity stems from the regular spacing of inclusions in a host matrix or roughness on a surface. Inclusions range from solid cylinders in air such as encountered in 'sonic crystals' to a grid framework in a poroelastic material such as an air-filled foam used for sound absorption. Roughness elements can be of various shapes and profiles ranging from identical rectangular grooves to arrays with fractal profiles. Without further modification, periodicity-enhanced materials stop the passage of some incident wavelengths (or frequencies) and enhance the transmission of others. By modifying the roughness of a surface, the interference between waves travelling directly from a source to a receiver above the surface and waves reflected from the surface can be controlled. The proposal is concerned with ways of extending the frequency range over which the periodicity-enhanced materials and surfaces reduce the transmission of sound and vibration. The methods to be investigated include use of locally resonant inclusions or roughness elements, use of multiple resonances, exploitiation of interactions and overlaps between resonances periodicity-related transmission loss and spatial variation of periodicity and other characteristics thereby producing graded systems and roughness profiles. The work will provide a basis for the design of more efficient sound and vibration absorbing devices that are lightweight yet offer high transmission loss and vibration damping properties. The resulting surface designs will include alternatives to conventional noise barriers, while allowing access and preserving line of sight, and cost-effective methods for protecting buildings against ground-borne vibrations.

Nutrients are essential for phytoplankton growth in the sea. Phytoplankton take up nutrients from the surrounding water and the extent of their growth is largely dependent on nutrient availability. Nitrate and phosphate are vital nutrients and their presence in light-rich surface waters can sustain algal blooms. Phytoplankton draw down carbon dioxide from the atmosphere and in shelf seas can lock this carbon up in the sediments. In this project we will investigate the idea that the seasonal supply of nutrients to shelf seas is controlled by physical mixing processes at the shelf edge. This will be carried out using new sensors capable of determining nitrate and phosphate in-situ which will be deployed on underwater gliders for up to 3 weeks at a time. Underwater gliders are autonomous vehicles that, after launching from close to the coast, can "fly" to a sampling site and send back data via a satellite link. We will use four glider missions over a seasonal timescale (June & Sept 2013, Jan & April 2014) in order to gain a fundamental insight into the annual supply of nutrients to the highly productive shelf seas around the Malin Shelf. Alongside the nutrient sensors on the gliders will be three other sensors, a conductivity and temperature sensor, an oxygen sensor and a sensor which can determine chlorophyll a which is used as a proxy for phytoplankton biomass in the water. For each mission we will use two gliders to simultaneously characterise both the on- and off-shelf concentration gradients. These glider missions will be integrated into a large NERC-funded consortium, FASTNEt, which aims to identify the physical processes that facilitate the exchange of water across the edge of the north-west European shelf and understand how they evolve across the seasons. FASTNEt will be deploying long-term, shelf-edge current meter moorings and another underwater autonomous vehicle, the Autosub Long Range (ALR) in the same location. The use of sensors deployed on gliders allow us to increase the spatial and temporal coverage of data that would not be possible from traditional shipboard measurements, that cannot be carried out in stormy conditions. The combination of in-depth knowledge of the physical processes occurring at the Malin Shelf along with accurate high resolution nutrient data from the sensors means that we will be able to investigate what controls nutrient supply to the photic zone and how this impacts on the phytoplankton growth. Additionally, we will be trialling new deployment methodologies for these underwater gliders - using fast day-boats to take them as far offshore as possible from a coastal base. Our data will be freely available and we will share our findings with end-users (such as the UK government's Department for Environment, Food and Rural Affairs who are responsible for fisheries, and the UK Met Office) throughout the project.