Industrial Transformation Training Centres - Grant ID: IC190100031
Funder
Australian Research Council
Funding Amount
$3,973,202.00
Summary
ARC Training Centre in Data Analytics for Resources and Environments (DARE). Understanding the cumulative impact of actions regarding the use of our resources has important long-term consequences for Australia’s economic, societal and environmental health. Yet despite the importance of these cumulative impacts, and the availability of data, many decisions and policies are based on limited amounts of data and rudimentary data analysis, with little appreciation of the critical role that understand ....ARC Training Centre in Data Analytics for Resources and Environments (DARE). Understanding the cumulative impact of actions regarding the use of our resources has important long-term consequences for Australia’s economic, societal and environmental health. Yet despite the importance of these cumulative impacts, and the availability of data, many decisions and policies are based on limited amounts of data and rudimentary data analysis, with little appreciation of the critical role that understanding and quantifying uncertainty plays in the process. The aim of Data Analytics in Resources and Environment (DARE) is to develop and deliver the data science skills and tools for Australia’s resource industries to make the best possible evidence-based decisions in exploiting and stewarding the nation’s natural resources.Read moreRead less
Tracking towards a complete model of skilled reading comprehension. This project aims to promote the development of the first complete computational model of reading comprehension. Many computational models of sub-components of reading have been developed, but none fully explain the complex co-ordination of perceptual, attentional and cognitive processes required for successful comprehension. The project intends to use eye tracking studies to test and refine Über-Reader, a new computational mode ....Tracking towards a complete model of skilled reading comprehension. This project aims to promote the development of the first complete computational model of reading comprehension. Many computational models of sub-components of reading have been developed, but none fully explain the complex co-ordination of perceptual, attentional and cognitive processes required for successful comprehension. The project intends to use eye tracking studies to test and refine Über-Reader, a new computational model that aims to provide a complete account of the memory systems and cognitive processes involved in reading comprehension and how they differ with reading skill. The outcomes will advance understanding of the causes of success and failure in reading and contribute to diagnosing and remediating reading difficulties.Read moreRead less
Tracking the Flow of Perceptual Information Through Decision Networks. The choices we make define our lives. Despite exciting progress in neuroscience, we still don’t know how the inner workings of the brain give rise to simple decisions. This project brings together experts from diverse domains of computational neuroscience to investigate how our brains turn perceptual information into action. Together, we will develop new methods to track information flow through the brain during the decision ....Tracking the Flow of Perceptual Information Through Decision Networks. The choices we make define our lives. Despite exciting progress in neuroscience, we still don’t know how the inner workings of the brain give rise to simple decisions. This project brings together experts from diverse domains of computational neuroscience to investigate how our brains turn perceptual information into action. Together, we will develop new methods to track information flow through the brain during the decision making process. By doing so, we will develop a world-leading model of how the brain makes decisions, and also provide the broader scientific community with a set of exciting new tools for studying information processing in the brain.Read moreRead less
Making sense of ambiguity: brain system interactions and visual uncertainty. This project aims to identify and characterise the interactions between brain regions underlying a fundamental process in visual perception: interpreting sensory input that is unclear or ambiguous. It will use two complementary neuroimaging techniques and cutting-edge analysis methods. The intended outcomes include new insights into a fundamental but poorly characterised aspect of brain function: how brain regions inter ....Making sense of ambiguity: brain system interactions and visual uncertainty. This project aims to identify and characterise the interactions between brain regions underlying a fundamental process in visual perception: interpreting sensory input that is unclear or ambiguous. It will use two complementary neuroimaging techniques and cutting-edge analysis methods. The intended outcomes include new insights into a fundamental but poorly characterised aspect of brain function: how brain regions interact, and advanced analysis methods with wide application. Expected benefits include important advances in knowledge that lay foundations for future study of neural disorders, international collaboration, and new methods placing Australia at the forefront of the international effort to understand the human brain. Read moreRead less
Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the bui ....Making Meta-learning Generalised . This project aims to develop novel machine learning techniques, termed generalised meta-learning, to make machines better utilise past experience to solve new tasks with few data. It expects to reduce the undesirable dependence of current machine learning on labelled data and significantly expand its application scope. Expected outcomes of the project consist of new theoretical results on meta-learning and a set of innovative algorithms that can support the building of next generation of computer vision systems to work in open and dynamic environments. This should be able to produce solid benefits to the science, society, and economy of Australian via the application of these advanced intelligent systems.Read moreRead less
Advancing the visualisation and quantification of nephrons with MRI. . This project aims to characterise key components of nephrons, the glomeruli and tubules, using magnetic resonance imaging without contrast agents, in combination with Deep Learning and super-resolution techniques. Nephrons, the basic functional unit of the kidney, are critical to the maintenance of the body’s homeostasis. Their number and architecture are critical determinants of kidney function. The expected outcomes are inn ....Advancing the visualisation and quantification of nephrons with MRI. . This project aims to characterise key components of nephrons, the glomeruli and tubules, using magnetic resonance imaging without contrast agents, in combination with Deep Learning and super-resolution techniques. Nephrons, the basic functional unit of the kidney, are critical to the maintenance of the body’s homeostasis. Their number and architecture are critical determinants of kidney function. The expected outcomes are innovative semi-automated nephron visualisation and quantitation tools that enable efficient renal phenotyping. Techniques tailored to widely accessible preclinical research scanners are expected to accelerate research into genetic and environmental factors affecting kidney microstructure in embryonic and post-natal life.Read moreRead less
Innovative approach to a fair tax system for Multinationals and Governments. Multinationals (MNCs) tax avoidance has become a national blight and a global problem impacting tax fairness, transparency and economic efficiency. This project aims to find the optimal solution for the tax avoidance problem for both MNCs and governments via effective cost-benefit analysis through the design of a cutting-edge interdisciplinary machine-learning technique. Expected outcomes will include profound breakthro ....Innovative approach to a fair tax system for Multinationals and Governments. Multinationals (MNCs) tax avoidance has become a national blight and a global problem impacting tax fairness, transparency and economic efficiency. This project aims to find the optimal solution for the tax avoidance problem for both MNCs and governments via effective cost-benefit analysis through the design of a cutting-edge interdisciplinary machine-learning technique. Expected outcomes will include profound breakthroughs for enhancing economic growth via tax policy reform in Australia but also globally through cross-country tax avoidance comparison. The benefits will be instrumental in reforming fiscal and investment policies that are highly critical for improving economic welfare and capital inflows in Australia.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190100626
Funder
Australian Research Council
Funding Amount
$393,000.00
Summary
Towards data-efficient future action prediction in the wild. This project aims to build state-of-the-art deep learning models to predict future actions in videos. The project expects to produce the next great step for machine intelligence, the potential to explore a handful of labelled examples to better understand, interpret and infer human actions. Expected outcomes of this project lay theoretical foundations for learning future action prediction in the wild scenario and build the next generat ....Towards data-efficient future action prediction in the wild. This project aims to build state-of-the-art deep learning models to predict future actions in videos. The project expects to produce the next great step for machine intelligence, the potential to explore a handful of labelled examples to better understand, interpret and infer human actions. Expected outcomes of this project lay theoretical foundations for learning future action prediction in the wild scenario and build the next generation of intelligent systems to accommodate limited supervision. This should benefit science, society, and the economy nationally through the applications of autonomous vehicles, sensor technologies, and cybersecurity.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100203
Funder
Australian Research Council
Funding Amount
$348,575.00
Summary
Deep space-time models for modelling complex environmental phenomena. This project aims to adapt deep-learning models, used in areas of artificial intelligence such as image tagging and automatic text translation, to improve our understanding of the environment. The project expects to develop new theory for deep-learning models to learn from measurement data and numerical-model output about environmental phenomena that evolve in space and time, such as ice sheets and the atmosphere. Expected out ....Deep space-time models for modelling complex environmental phenomena. This project aims to adapt deep-learning models, used in areas of artificial intelligence such as image tagging and automatic text translation, to improve our understanding of the environment. The project expects to develop new theory for deep-learning models to learn from measurement data and numerical-model output about environmental phenomena that evolve in space and time, such as ice sheets and the atmosphere. Expected outcomes include the ability to provide reliable predictions and quantification of uncertainty on environmental concerns of national importance, such as sea-level rise. Key benefits include improved risk management and mitigation, for example through financial incentives or infrastructure planning.Read moreRead less
How people learn inhibitory associations. This project aims to combine insights from associative and cognitive theories to investigate how people acquire, represent and generalise knowledge about inhibitory, or preventative, relationships. The project intends to use novel methods to assess the inhibitory causal structures inferred by individual participants, expected to include direct outcome prevention, modulation of a causal relationship, and configural learning. This project should expand our ....How people learn inhibitory associations. This project aims to combine insights from associative and cognitive theories to investigate how people acquire, represent and generalise knowledge about inhibitory, or preventative, relationships. The project intends to use novel methods to assess the inhibitory causal structures inferred by individual participants, expected to include direct outcome prevention, modulation of a causal relationship, and configural learning. This project should expand our understanding of the mechanisms of human associative learning. The project should benefit and inform clinical interventions based on identifying and normalising maladaptive learned associations.Read moreRead less