Does word similarity across languages help or hinder bilingual speakers? This project aims to understand in more detail how bilinguals can accurately speak in both their languages. Speaking is a complex skill, particularly if you have two languages to choose from, which will be true for over half of Australia’s population by 2025. This project aims to investigate the factors that influence speech production in both monolinguals and bilinguals including those with language impairment, and develop ....Does word similarity across languages help or hinder bilingual speakers? This project aims to understand in more detail how bilinguals can accurately speak in both their languages. Speaking is a complex skill, particularly if you have two languages to choose from, which will be true for over half of Australia’s population by 2025. This project aims to investigate the factors that influence speech production in both monolinguals and bilinguals including those with language impairment, and develop a better bilingual theory. The benefit of this new theory will be to provide a clear basis for diagnosis and treatment for children in bilingual households who have problems learning to speak, and for bilingual people with language problems after a stroke or dementia.Read moreRead less
Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Using visual science to reduce the dangers of night driving. This project aims to develop novel tests of visual function relevant to the modern night driving environment. Night driving is challenging for all drivers and has been linked to poor visibility under low light conditions. This project will characterise the visual challenges of the modern night driving environment, develop visual tests that incorporate the dynamic light levels typical of night-time roads and assess the association of th ....Using visual science to reduce the dangers of night driving. This project aims to develop novel tests of visual function relevant to the modern night driving environment. Night driving is challenging for all drivers and has been linked to poor visibility under low light conditions. This project will characterise the visual challenges of the modern night driving environment, develop visual tests that incorporate the dynamic light levels typical of night-time roads and assess the association of these tests with night driving performance. The outcomes will contribute new knowledge regarding dynamic visual processing and the ageing visual system and will inform vision testing, potential interventions to improve visual function for night driving and reduce the dangers of night driving.Read moreRead less
Listen and learn - statistical learning and the adapting auditory brain. This project aims to explore the link between rapid neural adaptation - a form of learning referred to as statistical learning - and human listening performance in noisy environments. The project aims to generate a new understanding of mechanisms that contribute to listeners' abilities to understand speech in noise, and to complex communication disorders such as dyslexia. Expected outcomes will include increased capacity to ....Listen and learn - statistical learning and the adapting auditory brain. This project aims to explore the link between rapid neural adaptation - a form of learning referred to as statistical learning - and human listening performance in noisy environments. The project aims to generate a new understanding of mechanisms that contribute to listeners' abilities to understand speech in noise, and to complex communication disorders such as dyslexia. Expected outcomes will include increased capacity to investigate a broad range of cognitive and communication functions. Benefits will include potential technologies and algorithms to assist listening (in devices such as hearing aids), language development and reading.Read moreRead less
Neural plasticity in older adult human vision. This project aims to expand our understanding of age related changes in brain function, specifically plasticity. The project will increase knowledge of the role of an inhibitory neurotransmitter GABA in visual plasticity. Expected outcomes include new knowledge regarding the regulation of brain function in adulthood, enabling future research and planning for societal benefit to older Australia.
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
Fine-grained Human Action Recognition with Deep Graph Neural Networks. This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and e ....Fine-grained Human Action Recognition with Deep Graph Neural Networks. This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and enabling techniques for more objective human action analysis in many domains such as sports and health. This should provide significant benefits to any application domain involving big and complex spatial-temporal data for finer analytics and better knowledge discovery.Read moreRead less
Shape4D: Modelling the Spatiotemporal Deformation Patterns in 3D Shapes. This research will develop new mathematical methods and algorithms that will enable the use of population-level longitudinal studies to model the spatial and temporal deformation patterns in 3D biological objects. Using novel geometric and deep learning techniques, it will create new methods that will allow the characterization of how the 3D shape of objects deforms with ageing, disease progression and interaction with thei ....Shape4D: Modelling the Spatiotemporal Deformation Patterns in 3D Shapes. This research will develop new mathematical methods and algorithms that will enable the use of population-level longitudinal studies to model the spatial and temporal deformation patterns in 3D biological objects. Using novel geometric and deep learning techniques, it will create new methods that will allow the characterization of how the 3D shape of objects deforms with ageing, disease progression and interaction with their environment, and the simulation of spatiotemporal deformations in anatomical organs. Benefits include a better understanding of growth processes, predictive models of how degenerative diseases progress and a computational framework that will assist in designing proper mitigation and intervention strategies.Read moreRead less
Tensor and Hypergraph Methods in Fitting Visual Data. This proposal will put an important class of clustering (extracting data that should fit a geometric model) on a more solid theoretical foundation. This will lead to better understanding of how to certify outcomes, efficiency, reliability etc. The type of clustering under consideration is relevant to many problems in machine learning and computer vision, as well as data mining and a wide variety of other settings.
Mid-Career Industry Fellowships - Grant ID: IM230100157
Funder
Australian Research Council
Funding Amount
$788,572.00
Summary
Improving Australian iron ore comminution for green steel production. Decarbonisation of the iron ore and steel industry will involve the design of new mineral processing approaches to make the Australian iron ore amenable to green steel production. Energy-efficient ore crushing for optimal ore grades production is key to the development and economics of green steel.
This fellowship project, with embedded industry experts, aims at better understanding the fragmentation mechanics of Pilbara iron ....Improving Australian iron ore comminution for green steel production. Decarbonisation of the iron ore and steel industry will involve the design of new mineral processing approaches to make the Australian iron ore amenable to green steel production. Energy-efficient ore crushing for optimal ore grades production is key to the development and economics of green steel.
This fellowship project, with embedded industry experts, aims at better understanding the fragmentation mechanics of Pilbara iron ore. It will exploit micro-computed tomography coupled with advanced mechanical testing to offer transformative characterisation methods of ore comminution. The project outcomes will help develop new technologies and optimal production paths to realise a higher-grade iron ore needed for a decarbonised steel industry.
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