Creating a sustainable, healthy, and equitable food system. This project aims to develop a whole-of-food system approach that will result in a more healthy, sustainable, and equitable food environment. A multi-disciplinary approach, based on the US Vermont Farm to Plate initiative, will bring together key stakeholders to collectively increase availability and access to locally sourced food, increase consumer awareness of sustainable food choices, accompanied with a retail “Love Local” campaign. ....Creating a sustainable, healthy, and equitable food system. This project aims to develop a whole-of-food system approach that will result in a more healthy, sustainable, and equitable food environment. A multi-disciplinary approach, based on the US Vermont Farm to Plate initiative, will bring together key stakeholders to collectively increase availability and access to locally sourced food, increase consumer awareness of sustainable food choices, accompanied with a retail “Love Local” campaign. Knowledge created by this research will inform policy and legislative reforms that will empower local governments and communities to respond to food system challenges. This case study in regional NSW will demonstrate the effectiveness of a framework that can be upscaled to other areas and countries.Read moreRead less
Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi ....Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.Read moreRead less
Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected ....Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected outcomes include new-generation theories and methods for the unsupervised learning of complex interactions in real-life big data, which are anticipated to enable the intrinsic processing of big data complexities and substantially enhance Australia’s leadership in frontier data science research and applications. Read moreRead less
Molecular and immunological approaches to managing Australia's seafood allergy epidemic. Seafood is an increasingly important cause of food allergy. Novel insight into the functions of why and how proteins from seafood develop to potent allergens will lead to the development of better diagnostics and therapeutics. This will assist patients to better manage their serious food allergy.
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less
Engineering Artificial Intelligence: A Spatial Representation and Reasoning Perspective. Spatial information is important in areas of national interest such as mining and exploration, environmental monitoring and planning, emergency response, and defence. Mission control centres, for instance, receive different forms of spatial data from satellites, radar, or people on the ground. They have to process the input data and make intelligent decisions in a very limited time. Intelligent systems that ....Engineering Artificial Intelligence: A Spatial Representation and Reasoning Perspective. Spatial information is important in areas of national interest such as mining and exploration, environmental monitoring and planning, emergency response, and defence. Mission control centres, for instance, receive different forms of spatial data from satellites, radar, or people on the ground. They have to process the input data and make intelligent decisions in a very limited time. Intelligent systems that are able to assist with processing different forms of spatial data efficiently and that offer reliable decision support are essential for improving the quality and reliability of such applications. This research enables future intelligent systems with these capabilities. This will directly benefit applications in areas of national interest.Read moreRead less
Declarative Networks: Towards Robust and Explainable Deep Learning. The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory a ....Declarative Networks: Towards Robust and Explainable Deep Learning. The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory and algorithms that allow physical and mathematical models to be embedded within a deep learning framework, providing performance guarantees and interpretability. This would likely benefit machine learning based products that can understand the world and interact with humans naturally through vision and language.Read moreRead less
Unlocking Viral Contribution to Terrestrial Nitrogen Cycling. This project aims to investigate how soil viruses steer key nitrogen cycling microorganisms and processes, by utilising emerging approaches of viromes, DNA-stable-isotope probing, and Raman-spectroscopy-based single-cell-sorting technology. This project expects to generate new knowledge in harnessing the potential of soil viruses to improve fertiliser nitrogen use efficiency through manipulating the biological pathways of nitrogen los ....Unlocking Viral Contribution to Terrestrial Nitrogen Cycling. This project aims to investigate how soil viruses steer key nitrogen cycling microorganisms and processes, by utilising emerging approaches of viromes, DNA-stable-isotope probing, and Raman-spectroscopy-based single-cell-sorting technology. This project expects to generate new knowledge in harnessing the potential of soil viruses to improve fertiliser nitrogen use efficiency through manipulating the biological pathways of nitrogen losses from agricultural ecosystems. Expected outcomes of this project include novel and comprehensive evidence for the roles of soil viruses in controlling terrestrial nitrogen cycling processes. This should provide significant benefits to Australian agriculture and environmental management.Read moreRead less
Fatigue Life Prediction of Nano-filler Modified Composites. The proposed project aims to study the behaviour and the failure mechanisms of polymer nanocomposites under cyclic loading. The outcomes of the project will make original contributions to our knowledge base on such materials. The mechanics modelling and statistical analysis of the prediction of fatigue life will provide a sound physical basis and a useful tool for any future improvement and optimisation of the composites to achieve bett ....Fatigue Life Prediction of Nano-filler Modified Composites. The proposed project aims to study the behaviour and the failure mechanisms of polymer nanocomposites under cyclic loading. The outcomes of the project will make original contributions to our knowledge base on such materials. The mechanics modelling and statistical analysis of the prediction of fatigue life will provide a sound physical basis and a useful tool for any future improvement and optimisation of the composites to achieve better reliability and integrity in their intended applications. This study will bring economic benefits to the end-users of advanced material technology including the Australian materials industries. Read moreRead less
Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to vi ....Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to video surveillance applications, which can enhance Australia’s homeland security.Read moreRead less