TSuNAMi: Time Series Network Animal Modelling. Our proposal is motivated by and based upon the successful representation of time series as a network (or graph). We construct an abstract representation of a system from measurements of its changing behaviour over time. Properties of that structure (the network) then allow us to infer diagnostic information of the system. Specifically, we propose to apply this to livestock welfare during transport. By measuring the biological and environment condi ....TSuNAMi: Time Series Network Animal Modelling. Our proposal is motivated by and based upon the successful representation of time series as a network (or graph). We construct an abstract representation of a system from measurements of its changing behaviour over time. Properties of that structure (the network) then allow us to infer diagnostic information of the system. Specifically, we propose to apply this to livestock welfare during transport. By measuring the biological and environment condition of the animal we construct a network representation of that system. Geometric features of that network can then be used to infer health or duress of the subject. This proposal will develop the generic mathematical machinery to connect geometric features of the network with system behaviour. Read moreRead less
Metaphotonics and metasurfaces for disruptive sensing technologies. This project aims to address a big challenge in nanophotonics by developing revolutionary methods for efficient chiral sensing of molecules without the need for spectrometry, frequency scanning, or moving mechanical parts, and to enhance chiroptical signals a hundredfold with the help of metasurface structures. Resonant metasurfaces are arrays of engineered dielectric nanoparticles with extraordinary characteristics, and they wo ....Metaphotonics and metasurfaces for disruptive sensing technologies. This project aims to address a big challenge in nanophotonics by developing revolutionary methods for efficient chiral sensing of molecules without the need for spectrometry, frequency scanning, or moving mechanical parts, and to enhance chiroptical signals a hundredfold with the help of metasurface structures. Resonant metasurfaces are arrays of engineered dielectric nanoparticles with extraordinary characteristics, and they would allow to overcome current limitations of chiral sensing analytical tools. Detecting chiral molecules in low concentrations is crucially important to many fields of biology, chemistry, and pharmacy, as well as to the food and cosmetics industries, constituting a market of tens of billions of dollars.Read moreRead less
High-resolution multiscale modelling of pandemics: COVID-19 and beyond. The project aims to develop high-resolution computational models for pandemic mitigation and control, focussing on the novel coronavirus and its emerging variants, and leveraging demographic, genomic and epidemiological data. It expects to rigorously compare multi-scale effects of complex vaccination and social distancing strategies and quantify optimal responses under the COVID-19 induced uncertainty. The intended outcomes ....High-resolution multiscale modelling of pandemics: COVID-19 and beyond. The project aims to develop high-resolution computational models for pandemic mitigation and control, focussing on the novel coronavirus and its emerging variants, and leveraging demographic, genomic and epidemiological data. It expects to rigorously compare multi-scale effects of complex vaccination and social distancing strategies and quantify optimal responses under the COVID-19 induced uncertainty. The intended outcomes include computational models of how the most infectious viral variants emerge and spread in presence of interventions, how to predict the outbreaks, and which are the most vulnerable communities. This should make a significant economic and social impact, improving population health while maintaining a resilient economy.Read moreRead less
Quantifying emergence and dynamics of foodborne epidemics in Australia. The project aims to greatly improve the accuracy and scope of computational epidemiological models predicting emergence and evolution of foodborne diseases in Australia. It expects to reveal key pathways for both biological evolution of microorganisms, and their spread though food supply chains and human interactions. The intended outcomes include discovering how the most dominant strains of foodborne infection emerge and se ....Quantifying emergence and dynamics of foodborne epidemics in Australia. The project aims to greatly improve the accuracy and scope of computational epidemiological models predicting emergence and evolution of foodborne diseases in Australia. It expects to reveal key pathways for both biological evolution of microorganisms, and their spread though food supply chains and human interactions. The intended outcomes include discovering how the most dominant strains of foodborne infection emerge and self-organise in complex networks, how to predict and contain the epidemics closer to their source, and which are the most vulnerable groups and communities. This should make a significant economic and social impact, improving health of the population, while also safeguarding national and international supply chains.Read moreRead less
Learning the meso-scale organization of complex networks. This project aims to model and learn the organization of online social networks. We will combine mathematical models, inference, and domain knowledge from computational social sciences to obtain interpretable descriptions of the role groups of users play in the network. The expected outcomes are new mathematical models and computational methods that learn from data how to best decompose a complex network into building blocks and their int ....Learning the meso-scale organization of complex networks. This project aims to model and learn the organization of online social networks. We will combine mathematical models, inference, and domain knowledge from computational social sciences to obtain interpretable descriptions of the role groups of users play in the network. The expected outcomes are new mathematical models and computational methods that learn from data how to best decompose a complex network into building blocks and their interactions, linking connectivity to function. This should provide benefits to industries and policy makers interested in how information spreads in social media, including the critical questions of understanding the mechanisms contributing to political polarization and fragmentation.Read moreRead less
CD1C-LIPID-REACTIVE T CELLS. The immune system patrols our body examining molecules such as proteins and lipids that signal whether or not everything is ok. While protein recognition by the immune system is well understood, our knowledge of the fundamental features of lipid detection is poor. This project will investigate the detection of lipid molecules that are presented to the immune system in association with a molecule known as CD1c. The aims are to understand: 1. The cells that respond to ....CD1C-LIPID-REACTIVE T CELLS. The immune system patrols our body examining molecules such as proteins and lipids that signal whether or not everything is ok. While protein recognition by the immune system is well understood, our knowledge of the fundamental features of lipid detection is poor. This project will investigate the detection of lipid molecules that are presented to the immune system in association with a molecule known as CD1c. The aims are to understand: 1. The cells that respond to these lipids; 2. The cellular receptors that bind to these lipids; 3. The types of lipids involved in this process. This work is essential for us to understand lipid-based immunology which is critical if we ultimately wish to harness this to improve human health.Read moreRead less
Variable Structure Complex Network Systems with Smart Grid Applications. This project aims to establish a breakthrough theory and technology to help deliver reliability and security of complex network systems, which are subject to structure changes, against faults and cyberattacks. Expected outcomes include a new theory that lays the foundation for understanding such systems, innovative algorithms and tools for their design, and a practical software platform used for ensuring reliability and sec ....Variable Structure Complex Network Systems with Smart Grid Applications. This project aims to establish a breakthrough theory and technology to help deliver reliability and security of complex network systems, which are subject to structure changes, against faults and cyberattacks. Expected outcomes include a new theory that lays the foundation for understanding such systems, innovative algorithms and tools for their design, and a practical software platform used for ensuring reliability and security of such systems. It will be applied directly to critical infrastructure such as the national power grid to help maintain lifeline resilience and achieve economic benefits. It will also provide an opportunity to train the next generation engineers in this cutting-edge technology for Australia.Read moreRead less
Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algori ....Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algorithms for applied researchers. This project benefits not only advanced manufacturing by finding optimal stopping time for wood panel compression, but also superior forecasting for mortality in demography, climate data in environmental science, asset returns in finance, and electricity consumption in economics. Read moreRead less
Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility ....Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility services delivery and lower customer utility bills. Project benefits include enabling utilities to better manage and plan resources in the information age, while empowering customers with real-time water end-use data and behaviour changing consumption recommendations.Read moreRead less
Antigen selection mechanisms control T cell immunity against bacteria. CD4+ T (T helper) cells are required to control many important bacterial infections. This Project aims to identify the key targets of CD4+ T cells responding to a model bacterial infection, and to correlate potential antigen effectiveness with native expression, antigen presentation, and the function of antigen-specific CD4+ T cells over time. Our validated experimental 'pipeline' has unprecedented potential to define potent ....Antigen selection mechanisms control T cell immunity against bacteria. CD4+ T (T helper) cells are required to control many important bacterial infections. This Project aims to identify the key targets of CD4+ T cells responding to a model bacterial infection, and to correlate potential antigen effectiveness with native expression, antigen presentation, and the function of antigen-specific CD4+ T cells over time. Our validated experimental 'pipeline' has unprecedented potential to define potent CD4+ T cell antigens within the thousands of proteins expressed by a bacterial pathogen. Our unbiased analysis may help establish the rules that define effective antigenicity. Our work will improve the understanding of bacterial immunity, and inform future design of T-cell based vaccines in the agricultural sector.Read moreRead less