Chancellery Research - Research Publications

Permanent URI for this collection

http://hdl.handle.net/11343/377

Filters

2023 - 2024 8
Reset filters

Settings
Statistics
Citations
Start date: 2021 × Author: Smith-Miles, Kate ×

Search Results

Now showing 1 - 8 of 8
  • Item
    No Preview Available
    On the impact of initialisation strategies on Maximum Flow algorithm performance
    Alipour, H ; Munoz, MA ; Smith-Miles, K (PERGAMON-ELSEVIER SCIENCE LTD, 2024-03)
  • Item
    No Preview Available
    Generating Dynamic Kernels via Transformers for Lane Detection
    Chen, Z ; Liu, Y ; Gong, M ; Du, B ; Qian, G ; Smith-Miles, K (IEEE, 2023-01-01)
  • Item
    No Preview Available
    Instance space analysis for 2D bin packing mathematical models
    Liu, C ; Smith-Miles, K ; Wauters, T ; Costa, AM (ELSEVIER, 2024-06-01)
  • Item
    No Preview Available
    Instance Space Analysis of Search-Based Software Testing
    Neelofar, N ; Smith-Miles, K ; Munoz, MA ; Aleti, A (IEEE COMPUTER SOC, 2023-04-01)
  • Item
    No Preview Available
    Multi-objective optimization in real-time operation of rainwater harvesting systems
    Zhen, Y ; Smith-Miles, K ; Fletcher, TD ; Burns, MJ ; Coleman, RA (ELSEVIER, 2023)
  • Item
    No Preview Available
    Bayesian coarsening: rapid tuning of polymer model parameters
    Weeratunge, H ; Robe, D ; Menzel, A ; Phillips, AW ; Kirley, M ; Smith-Miles, K ; Hajizadeh, E (Springer, 2023-10)
    Abstract A protocol based on Bayesian optimization is demonstrated for determining model parameters in a coarse-grained polymer simulation. This process takes as input the microscopic distribution functions and temperature-dependent density for a targeted polymer system. The process then iteratively considers coarse-grained simulations to sample the space of model parameters, aiming to minimize the discrepancy between the new simulations and the target. Successive samples are chosen using Bayesian optimization. Such a protocol can be employed to systematically coarse-grained expensive high-resolution simulations to extend accessible length and time scales to make contact with rheological experiments. The Bayesian coarsening protocol is compared to a previous machine-learned parameterization technique which required a high volume of training data. The Bayesian coarsening process is found to precisely and efficiently discover appropriate model parameters, in spite of rough and noisy fitness landscapes, due to the natural balance of exploration and exploitation in Bayesian optimization.
  • Item
    No Preview Available
    Optimal selection of benchmarking datasets for unbiased machine learning algorithm evaluation
    Pereira, JLJ ; Smith-Miles, K ; Munoz, MA ; Lorena, AC (SPRINGER, 2024-03)
  • Item
    Thumbnail Image
    Empirical evidence of climate change and urbanization impacts on warming stream temperatures
    Grey, V ; Smith-Miles, K ; Fletcher, TD ; Hatt, B ; Coleman, R (Elsevier, 2023-10)
    Climate change and urbanization threaten streams and the biodiversity that rely upon them worldwide. Emissions of greenhouse gases are causing air and sea surface temperatures to increase, and even small areas of urbanization are degrading stream biodiversity, water quality and hydrology. However, empirical evidence of how increasing air temperatures and urbanization together affect stream temperatures over time and their relative influence on stream temperatures is limited. This study quantifies changes in stream temperatures in a region in South-East Australia with an urban-agricultural-forest landcover gradient and where increasing air temperatures have been observed. Using Random Forest models we identify air temperature and urbanization drive increasing stream temperatures and that their combined effects are larger than their individual effects occurring alone. Furthermore, we identify potential mitigation measures useful for waterway managers and policy makers. The results show that both local and global solutions are needed to reduce future increases to stream temperature.