Prediction of inertial particle focusing in curved microfluidic ducts. This project aims to develop mathematical models to predict migration of particles suspended in flow through curved microfluidic ducts and their focusing by size to different regions in the cross-section of the duct. New knowledge in mathematics and engineering will be generated through models that capture the two-way force balance between fluid and particles and by a novel use of asymptotics for computational efficiency. Exp ....Prediction of inertial particle focusing in curved microfluidic ducts. This project aims to develop mathematical models to predict migration of particles suspended in flow through curved microfluidic ducts and their focusing by size to different regions in the cross-section of the duct. New knowledge in mathematics and engineering will be generated through models that capture the two-way force balance between fluid and particles and by a novel use of asymptotics for computational efficiency. Expected outcomes are understanding of the physics that drives particle migration and the parameters that may be used to control particle focusing. This will benefit design and operation of microfluidic devices for particle sorting as required for "liquid biopsy", the isolation of cancer cells in a routine blood sample.Read moreRead less
Mathematical modelling of information flow in social networks. This proposal aims to develop new mathematical and statistical methods to understand information flow in social networks. By using novel information theoretic techniques, it will create new methods to characterise social information flow in social networks. These tools will allow derivation of fundamental limits of predictability for AI methods applied to digital data. New mathematics of information flow will produce insights into so ....Mathematical modelling of information flow in social networks. This proposal aims to develop new mathematical and statistical methods to understand information flow in social networks. By using novel information theoretic techniques, it will create new methods to characterise social information flow in social networks. These tools will allow derivation of fundamental limits of predictability for AI methods applied to digital data. New mathematics of information flow will produce insights into social influence in online social networks. Benefits include: better understanding of how echo chambers may form in social networks, predictive models for how misinformation can spread online such as during an emergency, and a framework for intercomparison of AI methods applied to digital data on individuals. Read moreRead less
Price-Setting Rules and Allocative Efficiency in Oligopolies. This project aims to investigate under which circumstances restrictions on how often firms can change prices increase competition in an oligopoly and bring down prices. For this, we propose the use of laboratory experiments with a novel design followed by field experiments and a real price-data analysis for external validation. This study will result in both the advancement of theory describing how firms compete in dynamic oligopolies ....Price-Setting Rules and Allocative Efficiency in Oligopolies. This project aims to investigate under which circumstances restrictions on how often firms can change prices increase competition in an oligopoly and bring down prices. For this, we propose the use of laboratory experiments with a novel design followed by field experiments and a real price-data analysis for external validation. This study will result in both the advancement of theory describing how firms compete in dynamic oligopolies and practical policy advice on how price setting rules can be used to improve consumer welfare. This project has the potential to generate sizable benefits to Australian consumers, as the resulting policy advice would be applicable to large markets such as those for petrol, groceries and online retail.Read moreRead less
Immersive analytics: interactive data analysis using surfaces and spaces. This project aims to explore the potential for new immersive display and interaction technologies to greatly enhance the field of visual data analytics. Humans struggle to understand the masses of complex data they now accumulate. Visual data analytics offers a solution. The project expects to provide practical and theoretical frameworks for immersive data analysis and valuable intellectual property on the first practical ....Immersive analytics: interactive data analysis using surfaces and spaces. This project aims to explore the potential for new immersive display and interaction technologies to greatly enhance the field of visual data analytics. Humans struggle to understand the masses of complex data they now accumulate. Visual data analytics offers a solution. The project expects to provide practical and theoretical frameworks for immersive data analysis and valuable intellectual property on the first practical tools for immersive data analytics. This will provide significant benefits, such as allowing those across government and industry to make more informed decisions from data.Read moreRead less
Mathematics to underpin and drive novel inertial microfluidic technologies. Particles suspended in flow through microfluidic ducts migrate under inertial and drag forcing to different regions in the cross-section depending on particle size, duct geometry and control parameters, enabling isolation of, for example, cancer cells/microplastics from a blood/water sample. Device design needs mathematical models yielding understanding of the particle dynamics, and tools for determining geometry and con ....Mathematics to underpin and drive novel inertial microfluidic technologies. Particles suspended in flow through microfluidic ducts migrate under inertial and drag forcing to different regions in the cross-section depending on particle size, duct geometry and control parameters, enabling isolation of, for example, cancer cells/microplastics from a blood/water sample. Device design needs mathematical models yielding understanding of the particle dynamics, and tools for determining geometry and control parameters. Particle boundary conditions strongly influence the inertial lift and drag forces that drive particle motion. This project will develop these mathematical tools for boundary conditions applicable to both passive and active particles, so driving development of novel devices for existing and new applications.Read moreRead less
New Paradigms for Robust Fitting: Kernelisation and Polyhedral Search. Outliers inevitably exist in visual data due to imperfect data acquisition or preprocessing. To enable computer vision applications that can perform reliably, robust fitting algorithms are necessary to counter the biasing influence of outliers. However, current robust algorithms are unsatisfactory: they are unreliable (due to using randomisation) or too computationally costly (due to using exhaustive search). This project wil ....New Paradigms for Robust Fitting: Kernelisation and Polyhedral Search. Outliers inevitably exist in visual data due to imperfect data acquisition or preprocessing. To enable computer vision applications that can perform reliably, robust fitting algorithms are necessary to counter the biasing influence of outliers. However, current robust algorithms are unsatisfactory: they are unreliable (due to using randomisation) or too computationally costly (due to using exhaustive search). This project will develop new robust algorithms to mitigate these shortcomings. It will do so by investigating two new paradigms of kernelisation and polyhedral search, which offer unprecedented theoretical insights into the problem. The outcomes will contribute towards computer vision applications that are more practical and reliable.Read moreRead less
Understanding the mechanisms that inhibit and promote biofilm expansion. Yeasts have been used for biotechnology throughout recorded history. They are important human pathogens, and major experimental models of eukaryotic cells. Although yeasts are some of the most studied organisms in biology, their modes of colony biofilm formation are not fully understood. Methods to investigate the environmental and genetic processes that drive colony biofilm formation will be developed in this proposed pro .... Understanding the mechanisms that inhibit and promote biofilm expansion. Yeasts have been used for biotechnology throughout recorded history. They are important human pathogens, and major experimental models of eukaryotic cells. Although yeasts are some of the most studied organisms in biology, their modes of colony biofilm formation are not fully understood. Methods to investigate the environmental and genetic processes that drive colony biofilm formation will be developed in this proposed project. They will provide a deeper understanding of the mechanisms that inhibit and promote biofilm formation, and colonial morphology in the different modes of growth of Saccharomyces cerevisiae, with implications for this and other biofilm-forming yeasts of biotechnological or medical importance.Read moreRead less
An advanced framework for multi-agent strategic interactions. Communication security protocols and computer algorithms are expressible in terms of strategic interactions between competing agents, which can be analyzed in a game theory setting. This project will exploit the recent advances in extending this game theory framework to multidimensional spaces, thereby strengthening the theoretical foundations. This will provide new insights into the working of algorithms, potentially improving futur ....An advanced framework for multi-agent strategic interactions. Communication security protocols and computer algorithms are expressible in terms of strategic interactions between competing agents, which can be analyzed in a game theory setting. This project will exploit the recent advances in extending this game theory framework to multidimensional spaces, thereby strengthening the theoretical foundations. This will provide new insights into the working of algorithms, potentially improving future secure key distribution. Multi-agent interactions in higher dimensional spaces are considered intractable using traditional matrix methods and this project will build on our exciting new breakthrough showing that such interactions are tractable using geometric multivectors.Read moreRead less
Pattern formation of precursor films: a new mathematical model. This project aims to develop a new mathematical model to predict the pattern formation of a new class of permanent lubricants. Ionic liquids are conductive and do not evaporate, creating a unique opportunity to develop such coatings. These thin films form patterns where the pattern type (patches, stripes or holes) depends on the liquid/surface interaction. Only some patterns result in good lubrication; current limited understanding ....Pattern formation of precursor films: a new mathematical model. This project aims to develop a new mathematical model to predict the pattern formation of a new class of permanent lubricants. Ionic liquids are conductive and do not evaporate, creating a unique opportunity to develop such coatings. These thin films form patterns where the pattern type (patches, stripes or holes) depends on the liquid/surface interaction. Only some patterns result in good lubrication; current limited understanding of the pattern formation process hampers selection of a good lubricant for a chosen material. Current mathematical approaches are computationally expensive and time consuming. The new model expected from this project would provide a cheap, fast and reliable alternative for screening suitable liquid/surface pairs.Read moreRead less
Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently ....Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.Read moreRead less