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., Pan, S., Aggarwal, C., & Salehi, M. (2022). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys.
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Conventional x-ray imaging is firmly established as an invaluable tool in medicine, security, research and manufacturing. However, conventional methods extract only a fraction of the sample
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Project Background & Motivation Active learning (AL) mitigates the heavy annotation costs of deep learning by strategically querying the most informative unlabeled samples. However, traditional AL
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Understanding factors related to student retention and experience in physics and astrophysics major units. Using quantitative (surveys) and qualitative data (interviews with students) this project
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the optical-to-radio wavelength range, from major surveys and space telescopes (e.g: Gaia, SDSS, JWST, Hubble, Roman, Rubin-LSST). These are analysed using advanced machine learning and data-driven methods. My
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, interviews, digital methods) public attitudes and social dynamics (survey research, experiments, mixed methods) lived experience, reporting, and community/institutional responses (interviews, ethnography, case
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use imaging surveys at X-ray, optical, infrared and radio wavelengths to measure the emission from stars, active galactic nuclei, warm dust, atomic hydrogen and relativistic electrons. Spectroscopic
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from mobile devices and classify them into different categories or types of ringtones. The activities of the project include gathering a diverse dataset of audio samples representing various types
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best configuration of chart parameters, such as sample size, sampling interval, and control limits, to minimize detection time for process shifts while controlling false alarm rates. It explores both
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respond to issues of climate change and the likely regular occurrence of bushfires such as those of the Australian 2019-2020 summer. We will survey a variety of people from various backgrounds in order to