Navigating Faulty Logic Noise Characteristics in Fault-Tolerant Quantum Computation

The full potential of quantum computers will be attainable when large devices can employ quantum error correction and attain fault-tolerance. Great strides have been made, with experimental demonstrations of pivotal components for quantum error correction on a variety of platforms, substantiating that the foundations for fault-tolerant logic are realisable. This thesis explores the kinds of noise we expect to find at the logical level while employing schemes for fault-tolerance in this emerging period and how we may optimise such schemes. We study how a noise characteristic called bias is transformed in the implementation of a fault-tolerant gadget for the T-gate. We show that noise bias is amplified at the logical level due to error correction, which is furthermore affected by the noise-transforming properties of the injection gadget, which separately induces logical phase bias. We experimentally characterise the noise on a trapped-ion quantum computer. We target a logical CNOT with this method such that we are able to: understand and mitigate control layer imperfections, account for the distinction in noise characteristics between individual CNOT and transversal CNOTs, and make predictions on logical level performance if these transversal CNOTs are used as a logical CNOT for the Steane code. This is achieved by leveraging information about correlated noise across the device from the physical characterisation of gates, demonstrating that such diagnostic information can be learned scalably. Finally, noting the transversal gate capabilities of recent architectures, we optimise fault-tolerant circuits for preparing magic states in that setting. We describe an algorithm that recompiles and synthesises such circuits for minimal T-depth and low CNOT depth, while maintaining the total qubit count. We apply this algorithm to fault-tolerant circuits for |CCZ>, |CS> and |T> and show how we can reduce overheads for preparing magic states

Saudi Families-Teachers Partnerships and Attitudes Towards Families' Involvement in Early Childhood Inclusive Education

This study describes the families' involvement in their child's learning in inclusive early childhood settings in Riyadh, Saudi Arabia. It aims to examine the family-school partnerships level, and attitudes of those surrounding a child regarding family involvement, including the role of both parents in their child's learning within these environments, as well as the attitudes of teachers and providers. Bronfenbrenner’s bio-ecological systems theory (1979, 2009) has been employed to support the current study conceptualisation as it provides an analytical lens considering multiple environments and relations (e.g., preschool and family), which have direct influence on children. Subsequently, certain factors that could underpin participants’ perspectives on family involvement were examined. These included age, education level, teaching experience, and number in family. The study analysed surveys from 265 participants to initially explore attitudes towards family involvement. Following this, semi-structured interviews were developed and conducted with 17 participants, to gain a deeper understanding and explanation of the enablers and barriers participants face regarding family involvement in their child's learning. In the quantitative phase of this study, parents had the most positive attitudes towards family involvement. At the same time, the number of children per family emerged as a key element affecting parental involvement. Conversely, interviews suggested that a lower educational level among parents may adversely impact their involvement in their children's education. Furthermore, participants overwhelmingly focused on the difficulty of involving fathers in Saudi early childhood education, citing traditional and preschool structural reasons. The study provides an original notion of the barriers to fathers' involvement, and suggestions to facilitate this issue in the Saudi context

Exploring Self-Supervised Learning for Speech Emotion Recognition: Feature Analysis, Dimensional Enhancement and Emotion Classification

Speech Emotion Recognition (SER) aims to identify emotional states from speech signals by analyzing acoustic properties that reflect affective expression. Traditional SER approaches often rely on handcrafted acoustic features such as Mel-Frequency Cepstral Coefficients (MFCCs) and prosodic descriptors, which may lack the capacity to capture context-sensitive or subtle emotional variations. Recent advancements in self-supervised learning (SSL) have enabled the development of models trained on large-scale unlabeled speech data, producing general-purpose speech embeddings that enhance emotion recognition without task-specific fine-tuning. This thesis investigates the effectiveness of SSL-derived acoustic embeddings in both dimensional and categorical SER tasks, with a particular focus on dimensional SER (DSER). The study addresses three key objectives: (1) systematically compare traditional handcrafted features with SSL embeddings across three benchmark datasets for DSER; (2) enhance temporal modeling of emotional dynamics using transformer-based encoders with a two-step sequence reduction strategy; and (3) explore strategies to improve categorical SER (CSER) by leveraging DSER outputs through integration, regression-informed mapping, and multi-task learning (MTL). Empirical results demonstrate that pre-trained SSL models such as WavLM and UniSpeech-SAT outperform traditional baselines in DSER, with the greatest improvements observed for valence, followed by dominance and arousal. Transformer-based architectures with sequence reduction further enhance valence prediction. Integrating DSER into CSER frameworks yields consistent performance gains, particularly via MTL and SSL-enhanced mappings. This work contributes to building more generalizable, flexible, and context-aware SER systems

MaaS and Sustainability: How MaaS may deliver sustainable goals in an urban context

Mobility-as-a-Service (MaaS) has gained attention as an innovative approach to enhancing mobility services and promoting sustainable travel by reducing private car dependency. Despite its potential, MaaS faces challenges in encouraging sustainable travel behaviour and achieving commercial scalability. This thesis addresses these challenges through two studies focusing on environmental sustainability and commercial viability. The first study analyses MaaS users’ mode choice behaviour using revealed preference data from the Sydney MaaS trial. A joint choice model was developed to examine how different MaaS products influence sustainable travel behaviour change in terms of mode selection. Findings suggest that well-designed MaaS bundles, particularly those with financial incentives and multiple mobility options, can reduce private vehicle use and encourage shared and public transport. However, the study also highlights the need for financial incentives and the potential for unintended travel behaviour changes. The second study explores the commercial potential of expanding MaaS into a multiservice platform integrating non-mobility services to achieve scalability. Semi-structured interviews with stakeholders identified services for the Australian market, including entertainment discounts, delivery services, media subscriptions, and point rewards schemes. A stated choice experiment was conducted to evaluate user preferences for multiservice offers. Discrete choice modelling revealed that pay-asyou- go multiservice options are preferred, with demand for non-mobility services varying by market segment. Willingness-to-pay estimates provide insights into the feasibility of integrating these services into MaaS. This thesis offers policy implications for MaaS design and contributes with original empirical evidence on sustainable travel behaviour under MaaS and public preferences for multiservices, providing insights into achieving commercial scalability

Seasonality & Sensitivity Of Microbial Decomposition in Semi-Arid Grasslands

This dataset captures seasonal soil microbial decomposition dynamics in semi-arid grasslands of northern NSW, Australia, under regenerative grazing and landscape rehydration practices. It includes Tea Bag Index metrics (TBI_k, TBI_S), soil moisture and temperature readings, and remote sensing indicators (ET, LST, EVI), highlighting the influence of hydrological conditions on decomposition and carbon stability

Skew-product graph of groups and their toolkits

The K-theory associated with a C*-algebra plays a fundamental role in the classification and structural understanding of C*-algebras. This thesis investigates the C*-algebras associated with graphs of groups, a rich mathematical structure first systematically developed by Bass and Serre in their foundational work on group actions on trees. We adapt and extend the skew-product construction for directed graphs to the graph of groups setting. Specifically, given a cocycle labelling the edges of a graph of groups by a discrete group, a definition of skew-product graphs of groups is provided. The main theoretical contribution demonstrates that there is a natural connection between the skew-product graph of groups C*-algebra and the crossed product by the induced coaction. In addition, this definition of skew-product graphs of groups is shown to be consistent with the existing definition of skew-product graphs, in terms of the directed graph associated to graphs of groups E_G. Finally, using the existing isomorphism between graphs of groups C*-algebras and its fibred product groupoid algebra, the isomorphism between the skew-product graph of groups C*-algebra and the crossed product by the induced coaction is extended to the crossed product fibred product groupoid algebra by coaction. This thesis also includes a survey of K-theory for C*-algebras, including the K-theory of graph algebras. The last chapter also acts as a literature review of the recent developments in K-theory for both graph of groups C*-algebras and graph of groups actions on multitrees

Standardised Outcomes in Nephrology – Chronic Kidney Disease (SONG-CKD): Establishing a core outcome set in chronic kidney disease

The global median prevalence of chronic kidney disease (CKD) is estimated to be 9.5%. Patients with CKD have an increased risk of progression to kidney failure requiring kidney replacement therapy in the form of dialysis or kidney transplant, life-threatening comorbidities, and impaired quality of life. Advances in care and outcomes may be limited, in part, by problems with the selection and reporting of outcomes in trials in CKD. The aim of the Standardised Outcomes in Nephrology – Chronic Kidney Disease (SONG-CKD) project is to establish a core outcome set for trials in adults with chronic kidney disease not yet requiring kidney replacement therapy. The core outcomes set will be based on the shared priorities of patients, caregivers, and health professionals. This will help to ensure that trials include outcomes that are of critical importance to all stakeholders to support shared decisionmaking. The SONG-CKD projects included in this these are: focus groups with nominal group technique to identify and rank patient-important outcomes; interviews with clinicians in caring for patients with CKD; an international two-round Delphi survey to develop consensus among patients and caregivers to identify, rank, and describe reasons for their choice of outcomes; and two stakeholder workshops (in English and Spanish) to discuss and endorse the proposed core outcomes. The thesis also includes the report of the SONG-CKD Life participation workshop as this outcome was identified as a core patient-reported outcome. The SONG-CKD core outcome set will ultimately improve the evidence base for shared decisionmaking regarding treatment among patients, caregivers, and health professionals

Home-based, tailored intervention to reduce rate of falls after stroke (FAST): a randomised trial. Data Set

DATA SET for FAST trial. FAST was a two-armed, randomised trial which recruited ambulatory stroke survivors from three states in Australia who were within 5 years of stroke and had been discharged from formal rehabilitation to the community. Between August 2019 and December 2023, 370 people with stroke were enrolled. Primary outcome was rate of falls over 12 months. Secondary outcomes were: proportion of participants experiencing a fall, community participation, self-efficacy, balance, mobility, physical activity, ADL, depression and health-related quality of life.A prospective, multistate, Phase III randomised trial with concealed allocation, blinded measurement and intention-to-treat analysis. Primary outcome was rate of falls over 12 months. Secondary outcomes were: proportion of participants experiencing a fall

Event-based Satellite Docking

Dataset to accompany Le Gentil et al. "Mixing Data-driven and Geometric Models for Satellite Docking Port State Estimation using an RGB or Event Camera", IEEE International Conference on Robotics and Automation (ICRA) 2025. In-orbit automated servicing is a promising path towards lowering the cost of satellite operations and reducing the amount of orbital debris. For this purpose, we present a pipeline for automated satellite docking port detection and state estimation using monocular vision data from standard RGB sensing or an event camera. Rather than taking snapshots of the environment, an event camera has independent pixels that asynchronously respond to light changes, offering advantages such as high dynamic range, low power consumption and latency, etc. This work focuses on satellite-agnostic operations (only a geometric knowledge of the actual port is required) using the recently released Lockheed Martin Mission Augmentation Port (LM-MAP) as the target. By leveraging shallow data-driven techniques to preprocess the incoming data to highlight the LM-MAP's reflective navigational aids and then using basic geometric models for state estimation, we present a lightweight and data-efficient pipeline that can be used independently with either RGB or event cameras. We demonstrate the soundness of the pipeline and perform a quantitative comparison of the two modalities based on data collected with a photometrically accurate test bench that includes a robotic arm to simulate the target satellite's uncontrolled motion

Multivariate Volatility Measures and Models with Applications

This thesis explores methodologies for modelling and estimating correlation and covariance dynamics, presenting advancements in statistical approaches and their applications across multiple domains. We provide a comprehensive literature review of existing methodologies for modelling covariance matrices, focusing on their advantages, limitations, and practical implications, which highlights the need for efficient estimators and dynamic modelling techniques to address challenges such as heteroskedasticity, non-positive definiteness, and dynamic correlation structures. With our proposed range-based correlation matrix measures, we extend the two-stage multivariate Conditional Autoregressive Range Model (MCARR)-return models to directly model covariance matrix series using the Wishart distribution. Through simulation studies, we compare two approaches: modelling the covariance matrices and modelling the variances and correlation matrices. Correlation matrix modelling demonstrates better performance, guided by specific priors and stationary conditions