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University College Cork
Country: Ireland
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21 Projects, page 1 of 5
  • Funder: UKRI Project Code: EP/W004488/1
    Funder Contribution: 317,779 GBP
    Partners: NTU, UCC, Braingrade

    Drugs are horrible. While many psychiatric and neurological conditions are treated with pharmaceutical drugs, side effects remain severe. As one sufferer of epilepsy commented: "If the side effects of anti-epileptic drugs would be considered on their own, they would be an own disease category". Drugs, once passing through the blood-brain-barrier, affect the entire brain and its effect is not just limited to the regions that are linked to brain network disorders. Even worse, often the drugs don't work. For a third of epilepsy patients, drugs are ineffective in reducing the number of seizures, and for diseases such as dementia, there are no drugs with obvious benefits in humans. This situation is unlikely to improve as, for example, dementia clinical trials have failed, and more and more pharmaceutical companies are quitting brain disorder drug development altogether. Brain stimulation of distinct regions of the brain offers a potential route to new treatments. However, the effects are often inconsistent between patients and, in particular for older subjects, invasive approaches using implants are seen as risky by clinicians. Therefore, for neuromodulation to replace drugs in the future, interventions need to be personalised and non-invasive. The solution to replace pharmaceutical drugs for many brain disorders before 2050 is the proposed development of an approach that is (a) non-invasive through using focused ultrasound stimulation and (b) personalised through using computer models to predict which network nodes or edges should be targeted and which stimulation protocol should be used. Enabled by this proof of concept, computer simulations can test thousands of different stimulation approaches before deciding on an optimal approach for an individual patient. This is crucial, as trial-and-error in human patients is not an option. For deep-brain stimulation in Parkinson's disease, the fine-tuning of the stimulation algorithm for each patient takes several weeks. Whereas this is tuning the protocol alone, the situation will be more challenging when also the target structure needs to be determined. Phase 1 will show whether predicting stimulation outcomes is feasible and will therefore de-risk the healthcare implementation in phase 2.

  • Funder: UKRI Project Code: EP/R025193/1
    Funder Contribution: 2,808,150 GBP
    Partners: UCC, University of Cambridge, Aixtron Ltd, Compound Semiconductor Centre

    This proposal aims to bring to the UK an amazing microscope which will provide new and powerful capability in understanding the properties of light emitting materials and devices. These materials are key to many technologies, not only technologies that utilise the light emission from materials directly (such as energy efficient light bulbs based on light emitting diodes) but also a range of other devices which utilise the same family of materials such as solar cells and electronic devices for power conversion. Some of these technologies are in current use, but their efficiency and performance can be enhanced by achieving a better understanding of the relevant materials. Other target technologies are further from the market, but may represent the building blocks of our future security and prosperity. For example, the new microscope will provide information about light sources which emit one and only one fundamental particle of light (photon) on demand. Such "quantum light sources" are a potential building block for quantum computers and for quantum cryptography schemes which represent the ultimate in secure data transfer. How will the new microscope allow us to advance the development of all these technologies? It is based on a scanning electron microscope, which utilises an electron beam incident on a sample surface to achieve resolutions almost three orders of magnitude better than can be achieved using a standard light microscope. It thus accesses the nanometre scale, which is vital to addressing modern day electronic devices. Standard electron microscopy accesses the topography of a surface, but the incoming electron beam also excites some of the electrons within the material under examination into states with a higher energy. When these electrons relax back down to their usual low energy state, light may be given out, and the colour and intensity of that light is incredibly informative about the properties of the material under examination. This light emission can be mapped on a scale of ~10 nanometres so that nanoscale structures ranging from defects to deliberately engineered quantum objects can be addressed. This technique is known as cathodoluminescence, and has been in use for many years. The new capability of our proposed system is that it will map not only the colour and intensity of the light emission, but also allow us to measure the timescales on which an electron relaxes back down to its low energy state. We use the phrase "in the blink of an eye" to describe something that happens extraordinarily quickly. A real eye blink takes at least 100 milliseconds, whereas the relevant timescales for the electron to return to its low energy state could be almost 10 billion times quicker than this! The new microscope will be able to measure processes occurring on this time scale, by addressing how long after an electron pulse excites the material a photon is emitted. It will even be able to distinguish between photons with different wavelengths (or colours) being emitted on different time scales. Crucially, coupling this time-resolved capability with the ability to vary the temperature, we will be able to infer not only the time scales on which electrons relax to low energy sites emitting a photon, but also the time scales by which electrons reduce their energy by other, non-light-emitting routes. These non-light-emitting processes are what limit the efficiency of light emitting diodes, for example. Overall, across a broad range of materials, we will build up an understanding of how electrons interact with nanoscale structure to define a material's electrical and optical properties and hence what factors limit or improve the performance of devices. The proposed system will be the most advanced in the world, and will give UK researchers working on these hugely important photonic and electronic technologies a global advantage in developing new materials, devices and ultimately products.

  • Funder: CHIST-ERA Project Code: CHIST-ERA-19-XAI-008
    Partners: Complutense University of Madrid, RGU, BT FRANCE (SAS), UCC

    A right to obtain an explanation of the decision reached by a machine learning (ML) model is now an EU regulation. Different stakeholders (e.g. patients, clinicians, developers, auditors, etc.) may have different background knowledge, competencies and goals, thus requiring different kinds of explanations. Fortunately, there is a growing armoury of ways of interpreting ML models and explaining decisions. Let us use the phrase ‘explanation strategy’ to refer collectively to interpretable models, methods for visualisation, and algorithms for explaining the predictions of models that have been built by Machine Learning (ML). As these explanation strategies mature, practitioners will gain experience that helps them know which strategies to use in different circumstances. Whilst existing XAI libraries provide interfaces to a limited number of explanation strategies, these efforts remain disconnected and provide no easy route to reusability at scale. Our aim goes well beyond the development of a library. We aim to transform the XAI landscape through an open platform that can assist a spectrum of users (knowledge engineers, domain experts, novice users) in the selection and application of appropriate explanation strategies given an AI problem-solving task. The iSee Project will show how end-users of AI can capture, share and re-use their explanation experiences with other users who have similar explanation needs. We hypothesise that episodes of explanation strategy experience can be captured and reused in similar future task settings. Our idea is to create a unifying platform, underpinned by case-based reasoning (CBR), in which successful experiences of applying an explanation strategy to an ML task can be captured as cases and retained in a case base for future reuse. Our cases will encode knowledge about the decisions made by a user and the effectiveness of the strategy, so that our CBR system can recommend how best to explain ML predictions to other users in similar circumstances. We recognise that explanation strategies can be foundational, of the kind found in the research literature, and these can seed the case base. However, user needs are often multi-faceted. We will show how new cases that capture composite strategies can be composed from foundational ones, by extending the CBR technique of constructive reuse. Our proposal describes how we will develop an ontology for describing a library of explanation strategies; develop metrics to evaluate their acceptability and suitability and use these in a case representation that we will develop to capture experiences of using explanation strategies. Cases record the objective and subjective experience of different users of different ML explanation strategies, so that they can be shared and re-used. We include a number of high-impact use cases, where we work with real-world users to co-design the representations and algorithms described above, and to evaluate and validate our approach. These use cases will also seed the case base. The target outcome in the CHIST-ERA call that it most directly addresses is the following: “Developing a means to measure the effectiveness of explainable systems for different stakeholders (objective benchmarks and evaluation strategies for research in this domain).” But it underpins this by providing a platform that enables these measures of effectiveness to be recorded and that guides stakeholders in the future deployment of explanation strategies. Additionally, also drawing form the CHIST-ERA call, we will argue that our proposal fosters explanation strategy performance evaluation and experiment reproducibility exhibits international collaboration is based on co-creation of representations and evaluation criteria with our partners develops a framework that promotes explanation strategy re-use; and meets the best research standards in terms of open access to software and published results.

  • Funder: UKRI Project Code: EP/T018178/1
    Funder Contribution: 492,025 GBP
    Partners: UCC, University of Copenhagen, Hadley Centre, University of Exeter

    In climate modelling, there is international consensus on the need for action to mitigate the effects of climate change due to anthropogenic forcing. The United Nations Framework Convention on Climate Change (UNFCCC) has the objective to "stabilize greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system", and to do so "within a time-frame sufficient to allow ecosystems to adapt naturally to climate change....". Policy discussions generally focus on the first part of this objective, which is about defining a "dangerous" level of global warming which must be avoided - most recently through the 2015 Paris agreement which aims to hold the increase in the global average temperature to well below 2C. This proposal aims to develop methodologies that address the second part of this objective, namely to understand how changes to the system may interact with time-frame of response for the system. The presence of sudden changes in past climate (as evidenced in paleoclimate records) and nonlinear feedbacks between components has highlighted the likelihood that parts of the climate systems may "tip" from one state to another - for example, runaway ice-loss due to the positive albedo feedback, or major changes in ocean heat transport patterns. Slowly changing forcing may already lead to relatively fast changes in climate state, and passing through threshold value of a forcing parameter (for instance, in atmospheric greenhouse gas concentration) has been implicated in tipping points in the past. These are questions about nonautonomous dynamical systems, i.e. dynamical systems that evolve in time but where parameters also change with time. Although there has been a concerted attempt to gain a theoretical underpinning of nonautonomous dynamical systems in recent years, these results can be hard to apply or not particularly useful in specific applications: this is because most results in this area are either for very general, or for very specific systems. There are especially few applicable tools for cases where a change to the system (forcing/input) occurs on timescale similar to those within the system. This proposal aims to rectify this problem by developing such tools. We intend to develop new methods to understand "typical behaviour" in terms of local pullback attractors of Milnor type and their instabilities to forcing (that may be non-stationary), guided by applications in climate modelling. Particular problems we will focus on are the response of global mean temperature and ocean circulation by greenhouse gases to anthropogenic forcing, especially where the forcing timescale coincides with ocean circulation timescales.

  • Funder: UKRI Project Code: EP/H006907/1
    Funder Contribution: 197,819 GBP
    Partners: RAS, Universität Innsbruck, University of London, JGU, UCC

    The fundamental principle behind research and development in the nanosciences is the need to manipulate the fundamental structure and behaviour of materials on the atomic and molecular scale. The aim in this proposed collaborative project is to investigate the integration of optical nanofibres as interface tools into novel nanoscale environments, including ion traps, cold atom traps and optical tweezers, for the manipulation and control of single nanoparticles. Such systems could have wide-ranging applications from biomedical instrumentation to quantum information technologies.