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
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
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
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
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
Identifying climate-resilient wheat for a warmer, high CO2 world. This project aims to reveal which plant traits help maintain or increase crop yield under the CO2 and temperature conditions predicted for the next few decades, thus providing early insights for generating climate-resilient wheat. Wheat production is vital to global food security, but its yield decreases 5-6% per 1 degree Celsius of warming. Elevated CO2 may offset yield losses, but reduces grain protein and nutrients. As the firs ....Identifying climate-resilient wheat for a warmer, high CO2 world. This project aims to reveal which plant traits help maintain or increase crop yield under the CO2 and temperature conditions predicted for the next few decades, thus providing early insights for generating climate-resilient wheat. Wheat production is vital to global food security, but its yield decreases 5-6% per 1 degree Celsius of warming. Elevated CO2 may offset yield losses, but reduces grain protein and nutrients. As the first study to evaluate Australian wheat performance under the dual pressures of elevated CO2 and temperature, the project will deliver important fundamental knowledge on wheat productivity and quality resilience traits, novel inputs for future breeding programs, and help enable food security in a changing climate.Read moreRead less
Nanobionic sensors for Real-Time Plant Health Monitoring. This project aims to develop nanosensors to detect and monitor plant health in real-time by measuring stress molecules. The project will create new knowledge on functional materials with unique optical, electronic and thermal properties as well as their bio-nano interactions with plants. The expected outcomes of the project will provide insight into 1) how localised nanosensors target organelles in living plants to 2) generate signals tha ....Nanobionic sensors for Real-Time Plant Health Monitoring. This project aims to develop nanosensors to detect and monitor plant health in real-time by measuring stress molecules. The project will create new knowledge on functional materials with unique optical, electronic and thermal properties as well as their bio-nano interactions with plants. The expected outcomes of the project will provide insight into 1) how localised nanosensors target organelles in living plants to 2) generate signals that can be picked up by portable devices to 3) report on plant health. Functional nanosensors will enable smart farming, precision agriculture and contribute to future agronomic research, further strengthening Australia’s position as an international leader in nanobiotechnology.Read moreRead less
Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts ....Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts existing knowledge to new domains, and provide explanations to the graph learning system. The research results should provide benefit to governments and businesses in many critical applications, such as bioassay activity prediction, credit assessment, and drug discovery and vaccine development in response to the pandemic.Read moreRead less
Exploiting Context in Multilingual Understanding and Generation. Automatic translation technologies produce incoherent and incorrect outputs in critical areas, such as health, finance, and law. This is due to translating sentences independently, without regard to the global extra-sentential context and rich linguistic structures inherent in the wider document context. This project aims to exploit global linguistic structures, capitalising on recent advances in deep neural networks, in order to g ....Exploiting Context in Multilingual Understanding and Generation. Automatic translation technologies produce incoherent and incorrect outputs in critical areas, such as health, finance, and law. This is due to translating sentences independently, without regard to the global extra-sentential context and rich linguistic structures inherent in the wider document context. This project aims to exploit global linguistic structures, capitalising on recent advances in deep neural networks, in order to generate coherent and faithful text. Expected outcome include next-generation computational technologies for language understanding and generation. This should significantly benefit document-based language technologies and increase their applications in a range of cultural, industrial, and health settings.Read moreRead less