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Sex and Sexual Misbehaviours: The Portrayal of Sex in Clerical Writings of the Thirteenth and Fourteenth Centuries
Get PDFThis thesis will argue that texts written by clerics in the thirteenth and fourteenth centuries discussing sex heavily encouraged celibacy by warning people away from sexual misbehaviours and highlighting the negative consequences that could occur if one were to follow their lust. Although these texts were, for the most part, originally written with a clerical audience in mind, this research highlights the widening of an audience beyond Clerical, leading to different interpretations of these writings and on some occasions a disconnect in ideas about sex between the writings done by clerics and the lay audience that reads them. Firstly, I will argue that texts drawing inspiration from Greek and Roman authors highlight the tragic consequences that befall someone who allows love and lust to overpower their reason, strongly advising their readers to stay away from these feelings and to remain celibate. Fabliaux used humour to show their readers similar consequences of having affairs and committing sexual misbehaviours through characters such as the promiscuous priest and examples of couples having sex on holy grounds. Statutes and canon law are shown to have great concern for sexual sins such as fornication and adultery, discussing these sins in an obviously negative light, and confession manuals reinforce the consequences of priests and other figures not enforcing and respecting the goal of chastity. Finally, visitation reports and ecclesiastical court records show concerns for sexual sins, also depicting illicit and extramarital sex in a particularly negative manner, but also highlight an important disconnect between these texts written by clerics and lay people’s sexual practices, such as the frequency of lay people having sex outside of marriage, which shows a tendency to ignore the rules of chastity before marriage
Exploring Zoom as a Platform for Language Learning: An Interactionist Approach
Get PDFVideo Conferencing tools like Zoom have provided new avenues for authentic learner interaction in second language (L2) learning, supporting research that highlights the role of interaction in facilitating L2 acquisition (e.g., Putri et al., 2021; Swain, 1985). For instance, according to Long’s (1996 et seq.) Interactionist Approach, it is hypothesized that learners acquire language most effectively when they engage in meaningful communication and negotiate meaning with others. Zoom aligns with this approach by providing authentic virtual interaction opportunities (Zhao & Lai, 2023).
This study draws upon Cardoso’s (2022) chronological framework for examining technological tools for L2 learning. Specifically, it focuses on stage 2 of the framework, which involves assessing the pedagogical potential of an existing technology. Following Long’s (1996) Interactive Approach, the study explored how Zoom’s features can be leveraged to promote interactions by providing learners with access to the L2 input (e.g., listening) and promoting opportunities for output (e.g., speaking) and negotiation of meaning (e.g., to solve a communication breakdown). Our analysis suggests that most Zoom features fulfil the criteria set forth by the Interaction Approach. For instance, Breakout Rooms and Polls/Quizzes have the potential to increase student engagement and provide immediate feedback. Students can also collaborate in Video Conferencing and Chats within Breakout Rooms to engage in meaningful exchanges that drive L2 learning. Our discussion of these analyses highlights Zoom’s potential as a versatile platform for language learning, offering significant benefits for both synchronous and asynchronous L2 learning
“Just Because Children Are ‘Digital Natives’ Doesn’t Mean Parents Are Obsolete”: Exploring the Domestic Context of Youth Smartphone Mediation
Get PDFOver the past year, issues surrounding “children and screens” have intensified both in Québec and internationally. While this topic is often addressed in the media and political discourse as a monolithic issue, this thesis focuses on a specific aspect: how francophone parents in Québec
regulate their children’s smartphone use within the household. The primary aim is to explore the strategies employed by parents to mediate smartphone usage, including the specific approaches they use and the goals behind these strategies. This research draws on interviews with parents of first-time smartphone users who have recently integrated smartphones into their households, as well as a Google search analysis of queries related to parental concerns about smartphone regulation. The findings reveal the multifaceted nature of parental mediation, highlighting the importance of digital media literacy in modern parenting. Gendered patterns also emerged, with mothers more likely to engage in online research, reflecting the emotional labor associated with parenting and concerns over children’s media consumption. Through this study, I contribute to a nuanced understanding of how parents navigate the complexities of digital regulation within the québécois family
Novel Deep Learning Techniques for the Detection and Classification of Neurodegenerative Diseases using Resting State Electroencephalography
Get PDFNeurodegenerative diseases are debilitating conditions that progressively deteriorate the life quality of those affected. Compared with traditional neuroimaging modalities, such as Magnetic Resonance Imaging, Electroencephalography (EEG) can provide a more cost-effective and accessible alternative to help underprivileged populations obtain an early diagnosis of their condition, which is paramount for effective patient care. Resting-state EEG (rs-EEG), which records signals while a subject is at rest, offers an alternative to the commonly used task-based experiments for easier-to-adopt data acquisition protocols. While deep learning techniques have been shown to be effective for automatically classifying most EEG signals, they struggle with modeling the longrange temporal dependencies, complex spatial relationships, and the lack of time-locked events in rs-EEG. Aiming to address these issues, we first propose an explainable Graph Neural Network technique for rs-EEG-based Parkinson’s disease detection. Our method uses structured global convolutions to model long-range dependencies and novel multi-head graph structure learning to capture the complex spatial relationships in EEG data. We also propose a head-wise gradient-weighted graph attention explainer to obtain rich connectivity insights. Our second major contribution leverages recent innovations in state space modeling techniques to classify individuals with dementia, and we explore spectral and spatial approaches for learning relationships between EEG channels for the designated task. Additionally, we probe our model’s outputs with explainability techniques and demonstrate that our model learns physiologically relevant features. This thesis puts forth novel deep-learning methods that show promise in addressing challenges in neurodegenerative disease classification using rs-EEG
Detection and Identification of Covert and Replay Attacks in Cyber-physical Systems Using Model-Based and Data-Driven Based Methods
Get PDFCyber-physical systems (CPSs) have revolutionized various domains, including smart grids, manufacturing, transportation systems, and autonomous vehicles such as Unmanned Aerial Vehicles (UAVs). While CPS configurations offer numerous advantages, they are particularly vulnerable to stealthy cyber-attacks due to their specific structure, in which the command and control center is far from the plant and operation side, making security a critical concern. Among these attacks, covert and replay attacks pose significant challenges for detection, particularly in UAV applications. This research focuses on detecting such stealthy attacks using two approaches: a model-based scenario where the mathematical model of the system is available and a data-driven scenario where only data is accessible, making it more practical for high-fidelity systems.
In the model-based approach, the research develops a coding design to enhance the detection functionality and expand its scope to meet security requirements. This framework strengthens the system’s ability to counter stealthy attacks effectively. On the other hand, for scenarios where only data is available and inherently vulnerable to cyber-attacks, an effective algorithm is designed
to detect and identify covert and replay attacks by assigning appropriate labels. This algorithm leverages neural network (NN) models for training and evaluation, ensuring high accuracy and
proficiency in attack detection.
To optimize computational efficiency, the algorithm employs feature selection techniques during the data preprocessing stage, minimizing the reliance on complex NN models. This not only
reduces computational resource consumption but also enhances the accuracy of the detection model. The effectiveness of the proposed methodologies is validated through simulations on a 6-degree-of-freedom quadrotor, a critical application highly susceptible to cyber-attacks. The results demonstrate the efficiency and reliability of the contributions in detecting and mitigating stealthy cyberattacks in CPS configurations.
This research provides a framework for improving the security of CPSs in both model-based and data-driven scenarios, contributing to safer and more resilient systems in critical applications
Characterization of Monomer GAPDH S-Loop Motion and NAD+ Binding using Molecular Dynamics Simulations
No full textGlyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) is a highly abundant enzyme in cells, performing numerous functions in various oligomeric forms. The stable cytoplasmic homo-tetramer plays the primary (glycolytic) role. Here, the focus is the unstable, nuclear monomer (subunit), implicated in DNA replication, repair, and packing into chromosomes. Insight into enzyme function, via temporal sampling of conformational changes and transitions
between binding states, is difficult to achieve using experiment. Functional loop (S-Loop) motion and cofactor (NAD+) unbinding of the Human Placental subunit structure (PDB Entry 1U8F) were studied using classical, all-atom Molecular Dynamics (MD) simulations. Conformational trajectories, generated using conventional MD simulations, revealed that S-Loop motion consists of pivoting (mainly) and internal deformation, and that this motion becomes more concerted (increased magnitude and directionality) when NAD+ is bound to the subunit. Ligand unbinding trajectories, generated using steered and umbrella sampling MD simulations, revealed that NAD+ unbinding is unfavourable, but still feasible if the adenine ribose is pulled away from the subunit while its S-Loop is pivoted away from the NAD+ domain, and entails among other things, Asp-35 unbinding, as well as Lys-194 binding and subsequent unbinding. The present work reinforces the vital role of bound NAD+ in bringing order to subunit S-Loop motion, which in turn is essential for maintaining inter-subunit interactions in the GAPDH tetramer
The relationship between accentedness and perceived friendliness, intelligence, and employability: A Montreal-based investigation
Get PDFThe present study investigates and characterizes potential relationships between accentedness and three language attitude traits: friendliness, intelligence, and employability, in a comparison of L1 English and L1 non-English individuals. Using a direct approach method and situated in the broader English as a Lingua Franca (ELF) context, this research is intended to provide insights for the second language speaker of English regarding the perception of their accented speech. Insights into such perceptions are of high concern to the second language speaker, whose accented speech output is inherently linked with positive or negative judgments by listeners. These judgments are prevalent, subjective, and significantly impact outcomes of opportunity among second language speakers. Twelve-item questionnaires were issued to the sample population, and their responses collated and analyzed for statistical significance. The findings indicate a difference in mean ratings of the measures of friendliness, intelligence, and employability between English and non-English L1 raters, though at a significance level precluding rejection of the null hypothesis. However, significant correlations were observed between ratings of friendliness, intelligence, and employability, and between ratings of accentedness and intelligence. These findings suggest that participants perceived more highly accented speech as less intelligent. Furthermore, ratings of friendliness, intelligence, and employability were closely interrelated across participants. Additional research is suggested to evaluate these relationships, oriented around achieving a wider and more representative population sample, and further investigation of the friendliness, intelligence, and employability constructs for sub-dimensionality
Combining Environmental Factors and Species Co-occurrence Patterns to Predict Species Abundance and Community Biomass: Method Development and Validation in Ontario Lakes
Get PDFPredicting species abundance and community biomass is vital for ecosystem management, particularly in freshwater lakes, where this information guides conservation efforts, resource management, and biodiversity assessments. These metrics provide crucial insights into population dynamics, ecosystem productivity, and ecological balance. However, traditional models often rely on abiotic factors or limited species presence-absence data, missing the complex interspecies relationships that shape community structure and ecosystem function. This thesis aims to enhance predictive models of species abundance and biomass by incorporating community-level data, using latent variables derived from species co-occurrence and environmental variables. Latent variables are unobserved or hidden variables that are inferred from observed data. By integrating both biotic and abiotic factors, this approach enhances the accuracy of ecological predictions, offering more reliable tools for ecosystem management and conservation efforts. The three chapters build upon one another, progressively expanding the scope of the models and their applications. Chapter 2 lays the groundwork using simulated data to refine single-species abundance models, exploring how different levels of information (true environmental drivers versus latent variables based on species co-occurrence) affect model accuracy. This simulation framework is essential for understanding the robustness of the models before their application to real-world data. Chapter 3 extends this work by applying the developed framework to empirical data from lakes, focusing on sport fish species. It examines the role of latent variables and various fish assemblages in improving abundance predictions and explores how lake-specific characteristics influence model performance. This real-world application allows for a deeper understanding of how the framework operates under natural conditions, particularly in aquatic ecosystems. Finally, Chapter 4 uses the abundance predictions from Chapter 3 to develop a stacked model for predicting community biomass. It compares the stacked model’s effectiveness to a community model across varying spatial scales and species richness levels. By transitioning from single-species models to multi-species and ultimately biomass prediction, the chapters are sequentially linked, each addressing a broader ecological question while refining and testing the models at different levels of complexity. This cohesive approach enhances both the predictive accuracy of species abundance and the practical applications of these models for ecosystem management.
Prédire l’abondance des espèces et la biomasse des communautés est essentiel à la gestion des écosystèmes, particulièrement dans les lacs d’eau douce, où ces informations guident les efforts de conservation, la gestion des ressources, et les évaluations de la biodiversité. Ces métriques fournissent des connaissances cruciales sur la dynamique des populations, la productivité des écosystèmes et l’équilibre écologique. Cependant, les modèles traditionnels s’appuient souvent sur des facteurs abiotiques ou sur des données limitées sur présence-absence d’espèces, omettant les relations interspécifiques complexes qui façonnent la structure de la communauté et le fonctionnement de l’écosystème. Cette thèse vise à améliorer les modèles prédictifs d'abondance des espèces et de biomasse en incorporant des données à l'échelle de la communauté, et en utilisant des variables latentes dérivées de la cooccurrence des espèces et des variables environnementales. Les variables latentes sont des variables non observées ou cachées qui sont déduites à partir de données observées. En intégrant à la fois les facteurs biotiques et abiotiques, cette approche améliore la précision des prévisions écologiques, offrant des outils plus fiables pour la gestion des écosystèmes et les efforts de conservation. Les trois chapitres sont construits de manière séquentielle, élargissant progressivement la portée des modèles et leurs applications. Le Chapitre 2 pose les bases en utilisant des données simulées pour affiner les modèles d’abondance mono-espèce, en explorant comment différents niveaux d’information (véritables moteurs environnementaux par rapport aux variables latentes basées sur la cooccurrence des espèces) affectent la précision du modèle. Ce cadre de simulation est essentiel pour comprendre la robustesse des modèles avant leur application à des données réelles. Le Chapitre 3 étend ce travail en appliquant le cadre développé à des données empiriques de lacs, en se concentrant sur les espèces de poissons de sport. Il examine le rôle des variables latentes et de divers assemblages de poissons dans l’amélioration des prévisions d’abondance et explore comment les caractéristiques spécifiques des lacs influencent les performances du modèle. Cette application concrète permet de mieux comprendre le fonctionnement du cadre de modélisation dans des conditions naturelles, notamment dans les écosystèmes aquatiques. Enfin, le Chapitre 4 utilise les prévisions d’abondance du chapitre 3 pour développer un modèle empilé permettant de prédire la biomasse de la communauté. Il compare l’efficacité du modèle empilé à un modèle communautaire à travers différentes échelles spatiales et niveaux de richesse en espèces. En passant des modèles mono-espèces aux modèles multi-espèces et finalement à la prédiction de la biomasse, les chapitres sont liés séquentiellement, chacun abordant une question écologique plus large tout en affinant et en testant les modèles à différents niveaux de complexité. Cette approche cohésive améliore à la fois la précision prédictive de l’abondance des espèces et les applications pratiques de ces modèles pour la gestion des écosystèmes
Foucault’s Late Theory: Politics and Thought
Get PDFRecent literature has claimed that Michel Foucault’s late work amounts to a rejection of politics and an embracing of a neoliberal logic and an individualizing ethics. This thesis is situated in a response to this recent literature which reads in Foucault’s late work several political commitments and avenues for investigation that remained unfinished. Specifically, this thesis examines two understandings of politics in the work of Foucault: politics as civil war and politics as singular thought. The introduction situates this thesis’s content in the context of the crisis of Marxism in the 1960s through to the 1980s, as Marxist conceptions and practices of politics began to meet their limits. The first chapter of this thesis traces the development of an initial concept of politics as civil war in Foucault’s work over the course of several years utilizing a method of reading from Étienne Balibar’s examination of the same concept in the work of Karl Marx. The second chapter of this thesis provides a comparative reading of the work of Sylvain Lazarus and the later work of Michel Foucault to illuminate their differing concepts of singularity and the irreducibility of politics. Reading Lazarus’s critique of Foucault’s conception of singularity presented in The Order of Things and an exchange between Foucault and a group known as the Cercle d’Épistémologie points the way to Foucault’s late work on spirituality and the transformable subject and a new theory of politics as singular thought founded on a political spirituality and a rupture of the everyday
Detecting Textual and Visual Dark Patterns Using a Large Language Model in E-Commerce
Get PDFThis research explores the textual and visual detection of dark patterns in e-commerce websites using Large Language Models and image recognition. It builds on Arunesh Mathur’s taxonomy from the Dark Patterns at Scale paper, published in 2019. The study has two main outcomes: First, the development of an open-source Chrome plugin to identify dark patterns on websites, and second, the analysis of a dataset of websites using a multimodal approach for dark pattern detection on a dataset of 256 e-commerce websites. This analysis reveals current manipulative trends across various dark pattern categories and offers insights for designers advocating for increased awareness to counter certain long-standing manipulative practices in e-commerce