Fairness in Natural Language Processing. Natural language processing (NLP) has achieved spectacular commercial successes in recent years, and has been deployed across an ever-increasing breadth of devices and application areas. At the same time, there has been stark evidence to indicate that naively-trained models amplify biases in training data, and perform inconsistently across text relating to different demographic groupings of individuals. This project aims to systematically quantify the ext ....Fairness in Natural Language Processing. Natural language processing (NLP) has achieved spectacular commercial successes in recent years, and has been deployed across an ever-increasing breadth of devices and application areas. At the same time, there has been stark evidence to indicate that naively-trained models amplify biases in training data, and perform inconsistently across text relating to different demographic groupings of individuals. This project aims to systematically quantify the extent of such biases, and develop models that are both more socially equitable, as well as less prone to expose private data in the learned representations. In doing so, it will make NLP more accessible to new populations of users, and remove socio-technological barriers to NLP uptake.Read moreRead less
Algal direct-air CO2 capture through interfacial enzyme immobilisation . Capturing CO2 directly from the atmosphere is challenging due to inherently slow mass transfer kinetics. This project aims to overcome this using an enzyme that can rapidly solubilise CO2 from air into water, to produce algae. By engineering the enzyme immobilisation at the air-water interface, this project will activate and protect the enzymes, increasing their lifespan and reducing costs. By understanding mass transfer an ....Algal direct-air CO2 capture through interfacial enzyme immobilisation . Capturing CO2 directly from the atmosphere is challenging due to inherently slow mass transfer kinetics. This project aims to overcome this using an enzyme that can rapidly solubilise CO2 from air into water, to produce algae. By engineering the enzyme immobilisation at the air-water interface, this project will activate and protect the enzymes, increasing their lifespan and reducing costs. By understanding mass transfer and enzyme activity in the interfacial immobilisation media, floating enzyme rafts can be developed for deployment over expansive areas, facilitating large-scale conversion of atmospheric CO2 into algae-derived fuels, feeds and chemicals.Read moreRead less
Leveraging 3D computer vision for camera-based precise geo-localisation. This project aims to develop advanced 3D computer vision and image processing technology that can turn regular cameras into high-precision location-sensing devices. Spatial Location is a fundamental type of information of our physical world. Determining the precise location of people, vehicle, and mobile devices is essential for many critical applications. Outcomes of the project will enable a wide range of novel applicatio ....Leveraging 3D computer vision for camera-based precise geo-localisation. This project aims to develop advanced 3D computer vision and image processing technology that can turn regular cameras into high-precision location-sensing devices. Spatial Location is a fundamental type of information of our physical world. Determining the precise location of people, vehicle, and mobile devices is essential for many critical applications. Outcomes of the project will enable a wide range of novel applications of significant social, environmental and economic value, such as Location-Aware Service, Environment Monitoring, Augmented Reality, Autonomous Vehicle, and Rapid Emergency Response. The project will enhance Australia's international competitive advantage in forefront of ICT research and technology innovation.Read moreRead less
Deep visual understanding: learning to see in an unruly world. Deep Learning has achieved incredible success at an astonishing variety of Computer Vision tasks recently. This project aims to convey this success into the challenging domain of high-level image-based reasoning. It will extend deep learning to achieve flexible semantic reasoning about the content of images based on information gleaned from the huge volumes of data available on the Internet. The project expects to overcome one of the ....Deep visual understanding: learning to see in an unruly world. Deep Learning has achieved incredible success at an astonishing variety of Computer Vision tasks recently. This project aims to convey this success into the challenging domain of high-level image-based reasoning. It will extend deep learning to achieve flexible semantic reasoning about the content of images based on information gleaned from the huge volumes of data available on the Internet. The project expects to overcome one of the primary limitations of deep learning and will greatly increase its practical application to a range of industrial, cultural or health settings.Read moreRead less
Automated assessment of data quality in biological knowledge resources. This project aims to develop methods for identifying poor quality data in biological databases. Research in biomedicine is underpinned by massive databases of biological data. Data quality is largely managed through manual curation, but automated methods to assess quality are critically needed. This project expects to develop a suite of computational tools for assessing biological data quality, utilising an innovative approa ....Automated assessment of data quality in biological knowledge resources. This project aims to develop methods for identifying poor quality data in biological databases. Research in biomedicine is underpinned by massive databases of biological data. Data quality is largely managed through manual curation, but automated methods to assess quality are critically needed. This project expects to develop a suite of computational tools for assessing biological data quality, utilising an innovative approach based on network analysis of database record connectivity. These tools will enable quantifying data quality at scale. Researchers, evidence-based decision-makers in biomedicine, and the analytical or predictive tools that use this data will make more reliable inferences and decisions.Read moreRead less
Combining new synthetic biology tools to boost crop CO2 capture and growth. A solution for improving crop yield is to enhance the carbon dioxide fixation properties of the enzyme Rubisco whose inefficient activity often limits plant growth. This project makes use of new synthetic biology capabilities to artificially evolve Rubisco in the laboratory and select for new versions with improved performance. These beneficial changes will be introduced into crop Rubisco using targeted gene editing appr ....Combining new synthetic biology tools to boost crop CO2 capture and growth. A solution for improving crop yield is to enhance the carbon dioxide fixation properties of the enzyme Rubisco whose inefficient activity often limits plant growth. This project makes use of new synthetic biology capabilities to artificially evolve Rubisco in the laboratory and select for new versions with improved performance. These beneficial changes will be introduced into crop Rubisco using targeted gene editing approaches and the improvements in photosynthesis, growth and yield evaluated. This information will aid complimentary biotechnological efforts seeking to supercharge photosynthesis and help deliver the second Green Revolution needed to meet the improvement required in future agriculture productivity and resource use.Read moreRead less
New biocatalysts for selective chemical oxidations under extreme conditions. This project will identify and design new enzyme biocatalysts which function under extreme conditions such as elevated temperature and high concentrations of peroxides. These enzymes will be sourced from microorganisms which are located in extreme biological environments e.g. hot springs (the so-called extremophiles). The expected outcome of this project are the identification of robust enzymes which can catalyse select ....New biocatalysts for selective chemical oxidations under extreme conditions. This project will identify and design new enzyme biocatalysts which function under extreme conditions such as elevated temperature and high concentrations of peroxides. These enzymes will be sourced from microorganisms which are located in extreme biological environments e.g. hot springs (the so-called extremophiles). The expected outcome of this project are the identification of robust enzymes which can catalyse selective oxidation reactions in complex organic molecules, such as steroids. The new biocatalysts developed in this project will have significant benefit in the development of new routes to access bespoke molecules of value in fine chemical synthesis and drug development.
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Deep analytics of non-occurring but important behaviours. This project aims to build a systematic theory for the deep analytics of complex and important occurring and non-occurring behaviours. Behaviours that should occur but do not take place, called non-occurring behaviours (NOB), are widely evident but easily overlooked, such as missed important medical treatments. While often occurring behaviours are focused, such NOB may be associated with significant effects such as a threat to health. Thi ....Deep analytics of non-occurring but important behaviours. This project aims to build a systematic theory for the deep analytics of complex and important occurring and non-occurring behaviours. Behaviours that should occur but do not take place, called non-occurring behaviours (NOB), are widely evident but easily overlooked, such as missed important medical treatments. While often occurring behaviours are focused, such NOB may be associated with significant effects such as a threat to health. This project expects to fill the knowledge gaps in representing, analysing and evaluating NOB complexities and impact, with significant benefits for the evidence-based detection, prediction and risk management of covert NOB applications and their important effects.Read moreRead less
Constraint-based Reasoning for Multi-agent Pathfinding. Automation is a transformative technology for logistics -- using robots to manipulate inventory allows warehouses to be more efficient, and larger-scale, than ever before. But doing this in practice requires efficient, reliable methods for coordinating ever-larger fleets of robots. These problems are extremely difficult, and current approaches either scale poorly or give weak or no guarantees on solution quality. The project will develop t ....Constraint-based Reasoning for Multi-agent Pathfinding. Automation is a transformative technology for logistics -- using robots to manipulate inventory allows warehouses to be more efficient, and larger-scale, than ever before. But doing this in practice requires efficient, reliable methods for coordinating ever-larger fleets of robots. These problems are extremely difficult, and current approaches either scale poorly or give weak or no guarantees on solution quality. The project will develop transformative approaches to multi-agent pathfinding which can handle industrial size problems, and handle all of the complications that arise in practical applications. This will deliver improved cost-effectiveness and productivity to automated warehouse logistics and other agent coordination problems.Read moreRead less
Time Series Classification for Complex Dynamic Global Problems. This project aims to increase understanding of complex dynamic processes by creating new ways of analysing large quantities of data collected over time. These new approaches will be specifically designed to greatly improve the understanding obtained from time varying data for trillions of global earth observation data points in an application-agnostic way that is applicable to many tasks. The outcomes are expected to advance the the ....Time Series Classification for Complex Dynamic Global Problems. This project aims to increase understanding of complex dynamic processes by creating new ways of analysing large quantities of data collected over time. These new approaches will be specifically designed to greatly improve the understanding obtained from time varying data for trillions of global earth observation data points in an application-agnostic way that is applicable to many tasks. The outcomes are expected to advance the theory and practice of time-series data analysis and transform the analysis of complex dynamics. This should support innovation in industry, commerce, government and research and magnify benefit from many data investments including the $1 billion Australian governments invest annually in satellite imaging.Read moreRead less