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1. Development of an Undergraduate Quantum Engineering Degree

Author

Andrew S. Dzurak, Julien Epps, Arne Laucht, Robert Malaney, Andrea Morello, Hendra I. Nurdin, Jarryd J. Pla, Andre Saraiva, and Chih Hwan Yang

Subjects
Degree, education, engineering, quantum, undergraduate, Atomic physics. Constitution and properties of matter, QC170-197, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Quantum computing, communications, sensing, and simulations are radically transformative technologies, with great potential to impact industries and economies. Worldwide, national governments, industries, and universities are moving to create a new class of workforce—the Quantum Engineers. Demand for such engineers is predicted to be in the tens of thousands within a five-year timescale, far exceeding the rate at which the world’s universities can produce Ph.D. graduates in the discipline. How best to train this next generation of engineers is currently a matter of debate. Quantum mechanics—long a pillar of traditional physics undergraduate degrees—must now be merged with traditional engineering offerings. This article discusses the history, development, and the first year of operation of the world’s first undergraduate degree in quantum engineering to be grown out of an engineering curriculum. The main purpose of this article is to inform the wider discussion, now being held by many institutions worldwide, on how best to formally educate the Quantum Engineer.

Published
2022
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4. Robust Sounds of Activities of Daily Living Classification in Two-Channel Audio-Based Telemonitoring

Author

David Maunder, Julien Epps, Eliathamby Ambikairajah, and Branko Celler

Subjects
Medicine
Abstract

Despite recent advances in the area of home telemonitoring, the challenge of automatically detecting the sound signatures of activities of daily living of an elderly patient using nonintrusive and reliable methods remains. This paper investigates the classification of eight typical sounds of daily life from arbitrarily positioned two-microphone sensors under realistic noisy conditions. In particular, the role of several source separation and sound activity detection methods is considered. Evaluations on a new four-microphone database collected under four realistic noise conditions reveal that effective sound activity detection can produce significant gains in classification accuracy and that further gains can be made using source separation methods based on independent component analysis. Encouragingly, the results show that recognition accuracies in the range 70%–100% can be consistently obtained using different microphone-pair positions, under all but the most severe noise conditions.

Published
2013
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50. Ordinal Logistic Regression With Partial Proportional Odds for Depression Prediction

Author

Sadari Jayawardena, Julien Epps, and Eliathamby Ambikairajah

Subjects
Human-Computer Interaction, Ordinal data, Ranking, Mean squared error, Model selection, Statistics, Ordered logit, Logistic regression, Spearman's rank correlation coefficient, Ordinal regression, Software, Mathematics
Abstract

Like many psychological scales, depression scales are ordinal in nature. Depression prediction from behavioural signals has so far been posed either as classification or regression problems. However, these naive approaches have fundamental issues because they are not focused on ranking, unlike ordinal regression, which is the most appropriate approach. Ordinal regression to date has comparatively few methods when compared with other branches in machine learning, and its usage is limited to specific research domains. Ordinal logistic regression (OLR) is one such method, which is an extension for ordinal data of the well-known logistic regression, but is not familiar in speech processing, affective computing or depression prediction. The primary aim of this study is to investigate proportionality structures and model selection for the design of ordinal regression systems within the logistic regression framework. A new greedy based algorithm for partial proportional odds model selection (GREP) is proposed that allows the parsimonious design of effective ordinal logistic regression models, which avoids an exhaustive search and outperforms model selection using the Brant test. Evaluations on the DAIC-WOZ and AViD depression corpora show that OLR models exploiting GREP can outperform two competitive baseline systems (GSR and CNN), in terms of both RMSE and Spearman correlation.

Published
2023

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