VictorJuiz/thesis huggingface dataset:
Gibbs Pruning: A Framework for Structured and Unstructured Neural Network Pruning
Keywords: artificial intelligence
edge intelligence
machine learning
neural network compression
neural network pruning
neural networks

Gibbs pruning is a novel framework for expressing and designing neural network pruning methods. Combining approaches from statistical physics and stochastic regularization methods, it can train and prune a network simultaneously in such a way that the learned weights and pruning mask are well-adapted for each other. It can be used for structured or unstructured pruning and we propose a number of specific methods for each. We compare our proposed methods to a number of contemporary neural network pruning methods and find that Gibbs pruning outperforms them. In particular, we achieve a new state-of-the-art result for pruning ResNet-56 with the CIFAR-10 dataset.
Prediction of Overall and Differential Treatment Response for Depression using Personality Facets: Evaluating Multivariable Prediction Models using Multi-Study Data
Keywords: depression
machine learning
personality
personalized medicine
treatment

The potential utility of self-reported personality in the context of a ‘personalized medicines’ approach to the treatment of Major Depressive Disorder (MDD) remains unclear. Specifically, although cross-sectional relations between the Five Factor Model (FFM) of personality and MDD are well-established, treatment-related studies have had several limitations (e.g., focused on FFM domains, less focused on optimal treatment selection). Using multi-study data with similar methodologies, the present study was concerned with exploring whether FFM facets can be used to robustly predict overall treatment response (OTR) and differential treatment response (DTR). Results suggested that various FFM facets were able to best predict OTR in HRSD scores, while pre-treatment depression was able to best predict OTR in BDI-II scores. Results did not support the use of any of the candidate variables for DTR in BDI-II scores, while a variety of models with different predictor combinations were supported for DTR in HRSD scores.
Patient and Public Co-design of Smart Technologies for Health Care: A Meta-narrative Review
Keywords: Co-design
Data
Health Care
Internet-of-Things
Participatory Design
Smart Technology

This thesis reports on how patient and public co-design of smart technologies for health care has been conceptualized in the academic literature. Drawing on perspectives from critical data studies, it analyzed findings to generate insights into the circumstances under which co-design might create a positive future with these technologies as they become more deeply embedded in health care. Three meta-narratives animating the literature on co-design of smart technologies for health care were identified: co-design as usable design, co-design as responsible design, and co-design as design by society. The study concludes with the presentation of a series of ‘myths and misconceptions’ related to co-design of smart technologies for health care. It suggests that these myths serve particular interests over time, and that alternative formulations might serve as a starting point for illuminating strategies that enable co-design to engage with the broader range of normative issues raised by digital capitalism.
Fusion of ECG and PPG Signals in Apply to Spoof Detection and Biometric Authentication
Keywords: Authentication
Biometric detection
Electrocardiogram (ECG)
Fusion
Photoplethysmogram (PPG)
Spoof detection

This thesis proposes a novel spoof detection method in biometric systems upon fusing ECG and PPG characteristics. The suggested model is constituted by two main phases: First, authentication of each of the ECG and PPG signals; Second, validation of the correlation between ECG-PPG pair. During the first phase, each ECG and PPG is processed separately to distinguish natural or artificial signals. For this purpose, a set of signal processing, feature extraction, and machine learning methods have been utilized. If the authenticity of the input signal is confirmed, the second phase will be implemented to evaluate the general characteristics of the two signals pair at different timestamps through calculating the correlation features between signals’ spectro-temporal modulations. A Convolutional Neural Network (CNN) generates detection results to determine whether the two signals belong to the same identity. Experiment results indicate that the proposed methodology detect spoof attacks with average accuracy of 98.9\%.
Non-invasive Detection and Classification of Prostate Cancer using Plasma Cell-free DNA Methylomes
Keywords: Cell-Free DNA (cfDNA)
Methylation
Prostate Cancer

Prostate cancer is the second most commonly diagnosed cancer in men worldwide. Diagnosis and staging of the disease heavily relies on prostate specific antigen (PSA) which suffers from low specificity and can lead to unnecessary biopsies and result in overtreatment, undertreatment, or unnecessary treatment of indolent disease. Therefore, novel non-invasive biomarkers with improved accuracy for prostate cancer diagnosis and classification are required. The findings from our study have identified 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) plasma cell-free DNA methylation patterns that can distinguish localized prostate cancer patients from healthy subjects, and metastatic prostate cancer patients from localized prostate cancer patients. Potential diagnostic differentially methylated regions (DMRs) have been identified which correlate to downregulated gene expression, and metastatic DMRs and differentially hydroxymethylated gene bodies (DhMGs) have also been identified. We also found metastatic patients with high hyper and low hypo DMRs encounter worse progression-free survival and overall survival. Therefore, the findings in this study provide the groundwork for identifying accurate non-invasive markers for prostate cancer detection and classification.
An Exploration of the Emotional and Mental Challenges Children Experience following Sport-related Concussions
Keywords: Emotional Challenges
Mental Health
Recovery Outcomes
Rehabilitation
Sport-Related Concussion

Recent consensus statements on sport-related concussions (SRCs) highlight the need for further research investigating emotional challenges following SRC in children (Graham et al., 2014; McCrory et al., 2013). The current study used an exploratory retrospective approach to gain a better understanding of the emotional and mental experiences post-concussion. Twenty-one athletes ages 15-24 years participated in semi-structured telephone interviews regarding their concussion and recovery. Thematic content analysis was conducted to identify themes among responses. Results indicated three overarching domains with underlying themes and subthemes within: 1) Acute Challenges Post-Concussion (difficulty accepting unknowns, self-image and mattering, school, missing out and isolation, feelings of hopelessness), 2) Coping with Acute Challenges Post-Concussion (support, previous concussions, prioritizing mental health) and 3) Take Aways Post-Concussion (learning about injury, self-growth, long lasting impacts on overall health). The current results suggest that child athletes face numerous emotional challenges post-concussion and better interventions and support are critical to help athletes overcome these challenges.
Inverse Optimization for Clinical Pathway Concordance
Keywords: cancer
clinical pathway
concordance
inverse optimization
network flow
survival analysis

In this thesis, we propose the first data-driven inverse optimization approach to measuring the concordance of patient care pathways with clinical guidelines. Our methodological approach considers a patient's journey as a walk in a directed graph where the weights of the arcs are derived by solving an inverse shortest path problem. The inverse optimization model uses two sources of information to find the arc weights: reference pathways derived from the clinical guidelines (primary) and data from real-world patient-related activity from patients with both positive and negative clinical outcomes (secondary). Our approach can be applied to complex applications requiring context sensitive weights. We present case studies for colon and breast cancer patient pathways and validate the association of our metric with patient survival. Lastly, we demonstrate the application of our metric in identifying broad patterns of discordant behavior within a patient cohort.
A Comparison of Material Quantities Estimates to Onsite Material Use for Bridge Infrastructure Projects
Keywords: Concrete
Construction
Detailed Design
Highway bridges
Material estimates
Material quantity variability

Material estimates play a crucial role in predicting project cost, project duration, and embodied CO2e emissions for construction projects. Several factors that occur during the implementation stage introduce discrepancies in material quantity estimates, which misinform critical decisions that affect project delivery. There is, however, a limited understanding of the variability in material estimates for construction projects, and its impacts on other estimating processes. This thesis compares construction stage quantities to detailed design estimates for eighteen Canadian-based bridges to quantify the variability in material quantities and to determine the driving factors. Results show a 3%-85%, 8%-23%, 5%-19%, and 11%-17% increase in concrete, rebar, structural steel, and asphalt quantities between estimates and onsite use. The results of this thesis inform our understanding of design estimates and their interpretation. Adjusting for the discrepancy between estimates and onsite measurements and targeting the driving factors will reduce environmental impacts, minimize cost overruns and limit delays.
The Impact of Learning Sequence on Performance in a Rule-plus-exceptions Categorization Task
Keywords: Category learning
Computational modelling
Hippocampus

Category learning is key to successfully navigating an information-rich world. However, many categories contain exceptions that violate a general rule. Previous research has implicated hippocampus (HC) in rule-plus-exception (RPE) category learning, but limited work has explored hippocampal sensitivity to changes in learning sequence. Here we examine how delaying the introduction of exception items in an RPE category learning task impacts learning outcomes and explore HC’s role in this manipulation. Behavioural results indicate that exception categorization improves when exceptions are introduced later in learning, after exposure to rule-following stimuli. Simulations of this task using a computational model of HC replicate these behavioural findings. These results provide novel computational evidence for HC’s sensitivity to the sequence of learning and further support its proposed role in RPE learning. Our findings serve as a starting point for further exploration of the role of hippocampal subfields and white matter pathways in learning.
Advances in Heap Leach Pad Surface Moisture Mapping using Unmanned Aerial Vehicle Technology and Aerial Remote Sensing Imagery
Keywords: Heap leach pad monitoring
Heap leaching
Moisture mapping
Remote sensing
Unmanned aerial vehicle

As easily accessible high-grade mineral reserves are depleting, heap leaching (HL) is gaining an increased interest in the mining industry due to its economic feasibility for processing low-grade ores. For HL operations, monitoring heap leach pad (HLP) surface moisture distribution is essential to ensure optimal leaching conditions and to achieve a high metal recovery. Conventional monitoring methods rely on manual sampling and naked-eye observation by technical staff, which are labour-intensive and expose personnel to hazardous leaching reagents frequently. To complement the conventional approaches, the use of unmanned aerial vehicles (UAVs) combined with aerial imaging techniques can acquire representative data depicting the moisture status across the HLP surface. This thesis presents a practical framework for HLP surface moisture monitoring, consisting of UAV-based data collection and advanced data analytics to generate HLP surface moisture maps, which provide direct visualization of the surface moisture distribution and are effective tools to streamline the HLP monitoring process.
The Pharmacogenetics of Opioid Treatment for Post-operative Pain Management
Keywords: Analgesia
Dentistry
Genetics
Opioid
Pain
Pharmacogenetic

Opioids are among the most commonly prescribed medications, internationally. Current evidence suggests that variability in opioid analgesia can be attributed to genetic factors. The association of ten opioid-related genetic variants and opioid analgesia was investigated in a sample of 72 healthy participants experiencing pain after the removal of wisdom teeth. Percent change in pain severity and pain interference was assessed over a seven-day post-operative period using a pain questionnaire. Data were analyzed using non-parametric tests. In this cohort, 58% were female, 42% were male, and the mean age was 23 years of age. Participants were prescribed codeine (47%), tramadol (3%), oxycodone (1%), morphine (36%), or hydromorphone (13%). The OPRD1 gene T921C variant (p=0.02), the ABCB1 gene C3435T variant (p=0.01), and CYP2D6 metaboliser status (p=0.01) were associated with opioid analgesia. These findings suggest involvement of these variants in opioid response, which may be helpful in optimizing individualized therapy for post-operative pain.
Examining the Effects of Concussion on the Autonomic Nervous System using 24-hour Heart Rate Variability
Keywords: Autonomic nervous system
Concussion
Heart rate variability

Sport-related concussions (SRC) cause various physiological alterations; however, the research examining the use of heart rate variability (HRV) in SRC is limited. The purpose of this thesis was to identify potential autonomic nervous system perturbations caused by SRC. This prospective cohort study examined 24-hour HRV of athletes with concussion compared to athletes with musculoskeletal injuries and healthy athletes. No significant between-group differences were observed at both the acute and 1-month post-injury time points. Both female and male athletes with concussion had significantly lower values of HRV measures at 1-month post-injury when compared to the acute time point. However, male athletes with musculoskeletal injuries displayed an increase in HRV measures at 1-month post-injury compared to the acute time point whereas no significant findings were observed for female athletes. These findings suggest that although there were no significant between-group differences, it appears that injury groups had differing HRV responses over time.
Nudging Eco-driving Behaviour using Motive Substitution
Keywords: Design for Behaviour Change
Eco-Driving
Gameful Design
Motive Substitution
Nudge
Pro-Environmental Behaviour

The behaviour change technique of motive substitution may be a more effective method of motivating drivers to adopt an energy efficient driving style than existing strategies. Previous research has tested dashboard displays that inform, score, and advise drivers, but do little to overcome attitudinal barriers, intention-action gaps, and apathy. An animated sloshing coffee-cup display applies motive substitution by providing an alternate gameful experience that is intuitive, engaging, and congruous with the goal of low-acceleration driving. This intervention incorporates principles from cognitive and social psychology, human factors engineering, and behavioural economics to influence driver behaviour while managing distraction risks. The intuitive elements of the display were tested in an online survey with drivers reporting a significant preference for the coffee-cup display over an acceleration dial gauge. A driving simulator study was developed to test the behavioural impacts of a functional prototype using measures of acceleration, fuel consumption, and eye glance durations.
Characterizing Atrial Structural Remodeling in Atrial Fibrillation Patients without Fibrotic Atrial Myopathy
Keywords: Atrial
Atrial Fibrillation
Cardiology
Electrophysiology
Fibrosis
Structural Remodeling

Bi-atrial structural remodeling in AF patients with no LA FAM has not been studied. We hypothesized that AF patients with no LA FAM will exhibit atrial structural remodeling that is less extensive than patients with LA FAM. Prospectively enrolled patients were divided into three groups: (1) no LA FAM (
Generative Design to Reduce Embodied GHG Emissions of High-rise Buildings
Keywords: design
embodied
emissions
generative
GHG
LCA

Although countries have reduced their total greenhouse gas emissions by improving energy and transportation policies, the contribution of the building sector has been widely overlooked. Embodied emissions (EE) are particularly important since they are released upfront rather than over the lifespan of buildings, making them critical for accomplishing the 2030 Canadian emission reduction targets. Accordingly, this study developed a tool to reduce EE at the conceptual stage of high-rise residential buildings. The tool also incorporates goals and constraints that are inherent to conceptual building design, such as maximizing site use, views, and complying with building codes. In a case study, it was able to achieve a 7% reduction in EE when compared to a sub-optimal solution. This research elucidated the potential of using generative design in early-stage design, proposed novel systems for the generation and evaluation of design alternatives, and delivered GenGHG, a ready-to-use, open-source tool for conceptual building design.
An Assessment of the Use of Autonomous Ground Vehicles for Last-mile Parcel Delivery
Keywords: Automation
Autonomous Vehicles
E-Commerce
Last-Mile
Parcel Delivery
Vehicle Routing Problem

Last-mile parcel delivery is a particularly costly element of the freight supply chain. The high cost of last-mile delivery can be attributed to the complexities associated with business to consumer e-commerce and current labour-intensive delivery methods. This thesis quanties the cost savings associated with implementing an automated last-mile delivery system. A literature review focused on vehicle routing problems and their applications to automated delivery systems is presented. Parcel demand data are provided by a large courier company operating in Canada. These data are described with gures and summary statistics. A novel synchronized split-delivery vehicle routing problem is formulated, which ensures delivery vehicles arrive at their destinations at the same time as all others if deliveries are split between vehicles. The model is applied to the sample data to compare cost of operating an automated system with the current manual system. Finally, recommendations to the data provider on implementing such a system are made.
A Systems-based Approach to Characterizing Intraoperative Factors Leading to Uncertainty in Laparoscopic General Surgery
Keywords: Framework analysis
Human factors
Laparoscopic surgery
Patient safety
Systems factors
Uncertainty

This is an exploratory study that analyzes recordings captured by the OR Black Box® to characterize system triggers of uncertainty and their impact on intraoperative adverse events in laparoscopic general surgery. Seventy surgical recordings were analyzed over an 8-month data collection period. Using the Framework Analysis method, a 3-part taxonomy was developed to assess the predictability of uncertainty (planned vs. unplanned), system triggers of uncertainty (environment, interpersonal, patient, personal, task, or tools/technology) and responses to uncertainty (discussing, engaging, guiding, repeating, pausing, and seeking advice). Planned uncertainty arose mostly from personal triggers wherein staff responded with guidance and seeking advice. Unplanned uncertainty arose from all system triggers and was resolved with discussion between team members. Altogether, this study provides a structured model that differentiates between the underlying system triggers of uncertainty and supports the need for the development of targeted interventions to properly identify and manage uncertainty.
Low Power Analog Front End Design for 112 Gbps PAM-4 SERDES Receiver
Keywords: analog
CTLE
FinFET
front-end
PAM-4
SERDES

This thesis proposes an analog front end (AFE) design of a 112 Gbps PAM-4 SERDES receiver in 16 nm FinFET technology. It consists of a front-end termination block and a CMOS inverter-based continuous-time linear equalizer (CTLE). The high transconductance of the CMOS inverter as an amplifier provides a low power equalization solution. The front end-termination network is exploited to achieve certain passive equalization. The common-mode feedback loop (CMFB) improves the common-mode rejection ratio (CMRR) of the CTLE and it is a low power biasing solution in comparison to a self-biased diode-connected inverter load. The tunability in the CMOS tristate inverter cell enables power-scalability in the design. This also helps to track PVT variations. Using the CTLE as the only means of active equalization for a long reach channel (backplane) with 30 dB attenuation at 28GHz Nyquist frequency, this AFE design achieves 17dB of equalization consuming 10 mW power with a 0.8 V supply voltage in post layout extracted results.
Altered Hippocampal Structure In Trigeminal Neuralgia
Keywords: Chronic Neuropathic Pain
Facial Pain
Hippocampus
Structural MR analyses
Trigeminal Neuralgia

The hippocampus has long been studied for its crucial role in learning and memory. Recent findings suggest that the hippocampus also plays a role in shaping pain experience. This thesis aims to investigate hippocampal alterations in chronic neuropathic pain, using trigeminal neuralgia (TN) as a model. TN is a facial neuropathic pain syndrome that is highly debilitating but is amenable to surgical intervention. In this thesis, structural magnetic resonance images (MRI) were used to measure grey matter volume in the hippocampus. Study 1 which includes 21 TN patients, demonstrated that the hippocampus is smaller in TN subjects and hippocampal subfields are selectively affected. Study 2 which includes 61 TN patients with MRI before and after surgery, shown that the hippocampal subfield abnormalities normalized after pain resolution following surgical interventions. This body of work highlights that the hippocampus can be dynamically altered in chronic pain conditions.
Characterizing the Effects of Metformin on Brain Development following Cranial Irradiation
Keywords: late effects
metformin
mri
neurodevelopment
neurogenesis
radiation

Cranial radiation therapy (CRT) is a mainstay of treatment for primary pediatric CNS tumours and high-risk leukemia. Although CRT improves long-term survival, it has been shown to disrupt normal brain development and result in debilitating cognitive impairments in cancer survivors. Animal studies suggest that there is potential for promotion of brain recovery after injury using metformin, a first-line treatment for managing type-2 diabetes. Our aim was to evaluate whether metformin can restore neuroanatomical development in a pediatric mouse model of CRT using in vivo multiple-mouse MRI. Our results show that metformin administration starting on the day of irradiation exacerbated radiation-induced injury, whereas delayed treatment improved neuroanatomical outcomes. We also found sex-dependence in both the effects of CRT and of metformin administration. Taken together, this study provides insight into the benefits and difficulties of metformin as a strategy to improve brain development after CRT.