A multi-scale theory for solid-granular transition due to fragmentation. The prediction of rock fragmentation and fragment sizes during its phase transition from solid (rock mass) to granular (ore fragments) is the most crucial problem in a cave mining operation. Current practice relies on empirical tools without fundamentals of fracture, and hence cannot reliably predict the fragmentation process and fragment sizes. This can lead to huge economic loss due to damage to extraction points, hold-up ....A multi-scale theory for solid-granular transition due to fragmentation. The prediction of rock fragmentation and fragment sizes during its phase transition from solid (rock mass) to granular (ore fragments) is the most crucial problem in a cave mining operation. Current practice relies on empirical tools without fundamentals of fracture, and hence cannot reliably predict the fragmentation process and fragment sizes. This can lead to huge economic loss due to damage to extraction points, hold-ups for safety precautions, and mine closures. The project will develop a new theory and models to describe this solid-granular transition, and computational tools for simulations of cave mining operations. The expected benefits and outcomes include safer operations, and better control of production schedule and budgeting.Read moreRead less
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less
Contemporary stress and tectonics of Australia. This project will conduct a detailed examination of the state and controls on present-day tectonic stress in Australia. Tectonic stresses are a primary control on deformation in the Earth and this project has direct applications for earthquake hazard assessment, mine stability, production of petroleum and geothermal energy, and carbon dioxide sequestration.
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less
Nature's mechanisms for leaching and remobilising metals. This project aims to understand the chemical and physical processes that govern reactive transport and metal scavenging in rocky environments. Much of Australia's mineral wealth is the result of the interaction of warm fluids with rocks deep in the Earth over geological timescales. The formation of ore deposits is governed by the physical chemistry of mineral dissolution and crystallisation, and by fluid flow through porous rocks and frac ....Nature's mechanisms for leaching and remobilising metals. This project aims to understand the chemical and physical processes that govern reactive transport and metal scavenging in rocky environments. Much of Australia's mineral wealth is the result of the interaction of warm fluids with rocks deep in the Earth over geological timescales. The formation of ore deposits is governed by the physical chemistry of mineral dissolution and crystallisation, and by fluid flow through porous rocks and fractures. This project integrates innovation in geology, chemistry, and mineral engineering, and will deliver mineral-scale reaction models that will increase efficiency of in-situ mining and leaching technologies. Knowledge generated can be applied to improve mineral exploration, mining, and processing, contributing to unlocking billions of dollars’ worth of resources tied up in low grade, mineralogically complex ores.Read moreRead less
Molecular Structure and Transport Properties of Hydrothermal Fluids under Extreme Conditions: Near-Critical, High Salinity, High Pressure and High Volatile Contents. The experimental capabilities, theoretical understanding, and numerical modeling methods developed in this project have broad implication for supporting both well-established (mineral exploration and ore processing) and emerging (geothermal energy; geosequestration) industries of core significance for the future of Australia's econo ....Molecular Structure and Transport Properties of Hydrothermal Fluids under Extreme Conditions: Near-Critical, High Salinity, High Pressure and High Volatile Contents. The experimental capabilities, theoretical understanding, and numerical modeling methods developed in this project have broad implication for supporting both well-established (mineral exploration and ore processing) and emerging (geothermal energy; geosequestration) industries of core significance for the future of Australia's economy. This project also provides access to unique technology developed overseas; this technology will be adapted for the unique challenges faced by Australia, and made available to the broader scientific community via the Australian Synchrotron.Read moreRead less
Efficient causal discovery from observational data. Discovering cause-effect relationships is the ultimate goal for many applications. Randomised control trial is the gold standard for discovering causal relationships. However, conducting such trials is impossible in many cases due to cost and/or ethical concerns. In contrast, a large amount of data has been accumulated in all areas. It is desirable to infer causal relationships from data directly and automatically. This project aims to develop ....Efficient causal discovery from observational data. Discovering cause-effect relationships is the ultimate goal for many applications. Randomised control trial is the gold standard for discovering causal relationships. However, conducting such trials is impossible in many cases due to cost and/or ethical concerns. In contrast, a large amount of data has been accumulated in all areas. It is desirable to infer causal relationships from data directly and automatically. This project aims to develop fast and scalable data mining methods for identifying causal relationships from large and/or high dimensional data sets. The developed methods will mainly be evaluated in real world biological applications. The research outcomes will be useful in many areas for causal reasoning and decision making.Read moreRead less
Fairness aware data mining for discrimination free decision-making. This project aims to develop data mining methods to detect algorithmic discriminations and to build fair decision models. It expects to provide techniques for regulatory organisations to detect discriminations in algorithmic decisions, and for various companies and organisations to build fair decision systems. Expected outcomes are novel and accurate methods for discrimination detection, practical and versatile techniques for fa ....Fairness aware data mining for discrimination free decision-making. This project aims to develop data mining methods to detect algorithmic discriminations and to build fair decision models. It expects to provide techniques for regulatory organisations to detect discriminations in algorithmic decisions, and for various companies and organisations to build fair decision systems. Expected outcomes are novel and accurate methods for discrimination detection, practical and versatile techniques for fair decision model building, and improved understanding of the relationships between privacy preservation and discrimination prevention to enable new techniques to achieve both goals. The developed techniques enable society to tackle ethical challenges in the big data era where many decisions are analytics based. Read moreRead less
Developing novel data mining methods to reveal complex group relationships from heterogeneous data. This project aims to develop novel and effective data mining methods that will enable us to unravel the relationships between multiple, rather than individual, components of complex systems (such as genes, gene regulators and cancer), which is crucial to understanding how such systems work. Potential applications for such methods are extensive.
Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are compl ....Online Learning for Large Scale Structured Data in Complex Situations. Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are complete and noise-free. These weaknesses limit its utility, because real data such as those that must be analysed in processing social networks, fraud detection do not satisfy the restrictions. The aim of this project is to develop theoretical and practical advances in OL that overcome the existing weaknesses.Read moreRead less