Below we showcase several projects in which SIH has delivered verbal advice. See all projects.
Wheat yield prediction with uncertainty estimates
Predicting crop yield using a range of proximal and remote sensor measurements is area of active research. Such predictions are important for optimisation of crop management (e.g. nitrogen application) and robust associated uncertainty estimates help to improve this process and understand its limitations. We wrote code implementing a Bayesian regression model with spatially correlated residuals for application to wheat crop yield forecasting using a range of sensor data. We used this to generate predictive maps of wheat yield with robust uncertainty bounds.
Grant application support for cardiovascular AI impact
We assisted the Westmead Applied Research Centre to apply for the Google Impact AI Challenge, and are involved in the ongoing design and engineering to make use of the $1M prize. WARC called on our expertise in data science and natural language processing to revise their application and to support their case in interview for the award. Our involvement ensured that the proposal amounted to a realistic, operationalised application of artificial intelligence technologies.
Identifying ram mating behaviour
Monitoring livestock has historically been labour intensive. The advent of on-animal sensors means this monitoring can be conducted remotely, continuously, and accurately. The ability to identify the precise time when sheep are mating using ram-mounted accelerometer data would unlock unprecedented information on the reproductive performance of these animals. We fit a classifier model to data from collar accelerometers labelled by videoing rams in the presence of ewes in oestrus. We then wrote code to detect change points in new acceleration data and to predict the occurence of mating events.
Video Tracking Predator-Prey Interactions in Fish.
By video-tracking the interaction between prey mosquitofish, Gambusia holbrooki, and their predator, jade perch, Scortum barcoo, under controlled conditions, we provide some of the first fine-scale characterisation of how prey adapt their behaviour according to their continuous assessment of risk based on both predator behaviour and angular distance to the predator’s mouth. When these predators were inactive and posed less of an immediate threat, prey were often found within the attack cone of the predator showing reductions in speed and acceleration, characteristic of predator-inspection behaviour. However, when predators became active, prey swam faster with greater acceleration and were closer together within the attack cone of predators. Most importantly, this study provides evidence that prey do not adopt a uniform response to the presence of a predator. Instead, we demonstrate that prey are capable of rapidly and dynamically updating their assessment of risk and showing fine-scale adjustments to their behaviour.
Paper: “Fine-scale behavioural adjustments of prey on a continuum of risk”. M.I.A. Kent, J.E. Herbert-Read, G.D. McDonald, A.J. Wood, A.J.W. Ward. Proceedings of the Royal Society B. 2019
Predicting Crime using a Spatial-Demographic Framework
Responding to domestic violence related assaults dominate much of the NSW Police’s resources. We try to understand the relationships that drive social-demographic change and cause the occurrence of crime using a complex modelling framework. The social-demographic-crime network and its inter-dependencies were modelled using a Bayesian vector autoregression model. We built a collaboration with BOCSAR, the crime database of all offences in NSW over the last 20 years, and sourced demographic data for multiple census years. The results of this study will help inform policy decision-making by government and police.
Research Environment for Ancient Documents (READS) efficiency
Research Environment for Ancient Documents (READ) is an integrated Open Source web platform for epigraphical and manuscript research. It allows digital images of texts to be annotated, and for multiple annotations to be maintained for critical analysis.
The READ research and development team consulted with SIH because they had trouble getting their software to perform well with long texts. SIH Research Engineers helped them to identify parts of the system that were slower than was reasonable. Their software engineers were able to then resolve these bottlenecks, enabling the READ system to be more widely adopted in archaeology, history and manuscript studies.