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Group Norms Moderate the Association between Individual-Level Characteristics and Peer Perceived Gender Typicality
Get PDFA multilevel analysis conducted with a cross-sectional sample of 324 pre -adolescents (N= 324) in grades five and six (Mage = 11.5) of girls (N= 170) and boys from three mixed-gender primary school schools in Montreal, Quebec, Canada and two mixed-gender schools in Barranquilla, Colombia (N= 174) was used to examine the effect of group norms on peer perceived gender typicality. Group level variables including descriptive same-gender and other-gender group norms for each of the social behaviors. SES, culture, and gender were examined as moderators of the association between four forms of gendered social behaviors, specifically care, justice, physical aggression, and relational aggression, and children’s perceptions of their peers as being typical members of their cis gender group categories. Multilevel modeling indicated distinct patterns of effects for the level 2 group variables on the intercept and slope for each of the four social behaviors. For the intercept, same-gender and other-gender group means were positively associated with gender typicality for three (care, justice and relational aggression) of the four behaviors. For the slope – gender typicality was high when other-gender group means for physical aggression, relational aggression and care were low. Only relational aggression was found to be a significant predictor at level 1. SES and place effects also found. The findings show that perceptions of gender typicality are contextually determined
A Comparative Analysis of Game Asset Creation Using Conventional and AI Methods
Get PDFThe rapidly evolving Artificial Intelligence (AI) field is having a significant impact on many industries, as well as individuals. ChatGPT and image generating applications are undoubtedly among the most well-known and widely utilized AI tools. It is interesting to explore how these AI tools may be used in video games, specifically for asset creation, and how they compare to more traditional methods.
This thesis investigates the creation of video game assets using traditional techniques and AI tools such as ChatGPT and Midjourney. A group of 34 participants, each with varying levels of experience in different techniques and asset production, were given the task of creating basic game components using both traditional and AI tools. The goal was to rank both methods according to the overall rating, the satisfaction of the end result, and the ease of use to determine which one is preferred. The sample scene was a simple 2D platformer game, similar to Mario, with which most people are familiar and most likely played at some point. The participants were involved in both the creation process of game assets, as well as their evaluation, which brings a new perspective on the matter. It was discovered that, on average, AI tools are rated higher and are simpler to use than traditional approaches. Participants were highly satisfied with the results. However, the content created in such a way may not be as creative or as tailored for the specific needs as content made by people. Using these technologies makes it difficult to maintain a consistent style or create exactly what the person envisions, as compared to producing them manually, when the artist has complete control over every step and detail. Both methodologies have value, and depending on the project's goals and available resources, one may be preferred over the other. A hybrid method, which combines AI efficiency with artist creativity, may be the best option
Modular forms modulo p
Get PDFThe aim of this work is to define modular forms modulo p and to present a result about their structure adopting two perspectives. First we deal with the case of the full modular group and we see modular forms in the classical sense through their q-expansions. In
order to generalize their characterization to level N we approach another, more intrinsic, definition of modular forms which arises from the geometry of elliptic curves. Following the work of Katz they are either functions on classes of elliptic curves with additional
data, or sections of line bundles over the modular curve. The only modular form whose q-expansion is 1 is the Hasse invariant A. Multiplication by A does not change q-expansions and naturally determines a filtration on the graded algebra of modular forms. The main theorem analyzes the properties of such a filtration through the behaviour of the operator θ whose construction represents the core of this thesis
The Certification Effect: Stamp of Approval or Just Another Anomaly?
Get PDFThe relation between analyst publications and their effects on the underlying stocks is a well-researched field of finance. However, none go so far as to identify the fundamental effect of a firm receiving its first recommendation from an analyst beyond standard recommendation
effects. We examine a sample of 6168 analyst initiation of coverage events, separated into a positive subsample and negative subsample to determine the abnormal returns generated in the two days after, one-month after, and one year after event windows using an event study methodology. In addition, we compare these abnormal returns to those generated by a respective control subsample of mid-caps to calculate the certification effect. Finally, we study the relationship the different event window certification effects have with firm-level variables. We
find that the abnormal returns generated by the small-caps are consistently different than those generated by the respective control subsample of mid-caps, and that the certification effects are
significantly non-zero values. We also find that the short certification effect has no relationship with firm level variables, while mid and long certification effects have a relationship with Board Size. We fail to establish a relationship between the mid and long certification effects and the change of stock volatility of the underlying firm. We conclude that while the certification effect does exist in the short-term, its effect is lost in the long-term as new events take place and confound the results
Robust Graph Convolutional Networks for Adversarial Resilience and Anomaly Detection
Get PDFAdversarial attacks and anomaly detection are closely related, both focusing on identifying irregularities that deviate from normal patterns across various data types, including graph-structured data. Adversarial attacks on graphs pose a significant threat to graph convolutional networks (GCNs), involving intentional manipulation of graph data to mislead GCNs into making incorrect predictions. Standard GCNs, while powerful, often exhibit vulnerabilities to adversarial attacks that can significantly degrade their performance in anomaly detection tasks. These networks also have inherent limitations, such as their inability to effectively consider higher-order neighbour information, restricting their capacity to capture the full context of a node within the graph. To address these challenges, this thesis introduces an iterative graph filtering framework, which builds upon the graph signal processing concept of iteratively solving graph filtering using the fixed-point iterative method. The proposed framework is designed to enhance resilience against adversarial attacks while improving anomaly detection capabilities. The thesis makes two main contributions: a flexible spectral modulation filter that selectively attenuates high-frequency components of graph signals; and a robust aggregation mechanism that efficiently captures information from higher-order node neighbors, expanding the networks receptive field without increasing computational complexity. Extensive experiments are conducted on benchmark datasets to evaluate the effectiveness of the proposed methods. The results demonstrate significant improvements in anomaly detection accuracy and adversarial robustness compared to strong baselines. This highlights the potential of the proposed framework for reliable graph-based downstream tasks, paving the way for robust GCNs that can handle the complexities and adversarial threats inherent in real-world applications
Cyber-Security of Over-Actuated Cyber-Physical Systems: A Study on Dynamic Positioning Systems
Get PDFOver-actuated systems are crucial for advanced control functionalities in various industries, with Dynamic Positioning (DP) systems serving as a prime example. DP systems, commonly found in marine vessels and offshore platforms, maintain fixed positions using multiple thrusters. As Cyber-Physical Systems (CPS), DP systems integrate computation, communication, and physical components to achieve control objectives. However, their reliance on communication networks makes them vulnerable to False Data Injection (FDI) cyber-attacks, where adversaries can compromise signals sent from the controller to the thrusters. This thesis presents methods for secure estimation, attack reconstruction, isolation, and compensation within a centralized thrust allocation framework. Key contributions include achieving these goals without relying on strict input-output matrix conditions, and allowing thrusters to remain operational even when affected by FDI attacks. Furthermore, the thesis addresses the challenge of reducing and shifting the attack surface, particularly in over-actuated systems, where numerous communication links increase vulnerability. To mitigate this, the allocation scheme is transformed from a centralized to a decentralized framework. In this approach, control signals are sent to a randomly chosen thruster, periodically switching to prevent successful attacks. Thrusters then coordinate through a consensus network to realize the control commands, while the consensus protocol is resilient under attacks on the communication channels between the thrusters. The decentralized protocol is effective in both closed-loop and open-loop operations. Finally, the estimation and compensation techniques developed earlier in the thesis are also applied to enhance the resilience of the decentralized architecture
Transition to School for Children with Autism Spectrum Disorder: An Exploration of Teachers' Practices and Parental Experiences
Get PDFThe transition to primary school is a significant milestone that introduces new demands and challenges for children, families, and teachers alike. Children with disabilities, including ASD, are particularly vulnerable, facing distinct and heightened challenges during this critical period. The present study aims to: (1) examine the implementation of recommended transition practices for children with ASD by general education teachers, assessing their perceived importance and primary concerns; (2) explore the experiences and perceptions of parents caring for children with ASD during the transition to school process; (3) investigate the interplay between teacher involvement in the transition process and parental concerns; and (4) identify the impact of parental competence on active involvement during the transition to school for children with ASD. Nine general education kindergarten teachers (n = 9) and four parents of children with ASD (n = 4) were recruited. A mixed methods approach (semi-structured interviews, questionnaires, and scales) was used to address the research questions. Findings revealed significant concerns and barriers in the transition process, including the need for better communication, support services, and individualized transition practices. Despite high parental competence, disparities between teacher involvement and parental concerns underscore the urgency for improved collaboration. This research offers valuable insights for parents, educators, schools, and policymakers striving to enhance the educational outcomes and well-being of children with special needs
Assessing the Impact of Climate-Related Risks on Canadian Real Estate Investment Trusts: Insights and Implications for Investors
Get PDFThis study aims to analyze the impact of physical climate risks on the Canadian real estate market. Building upon the framework established by Duprey et al. (2021), we define and utilize a Multi-hazard Exposure Average Index (MHE) to measure the intensity and frequency of natural disaster exposure for each of the 1658 Forward Sortation Areas (FSA) in Canada. We examine the effects of the Average MHE on the operating and equity performance of Canadian Real Estate Investment Trusts (REITs). Our findings reveal that REITs with properties facing heightened exposure to climate change physical risks report lower rental revenues and operating expenses. Additionally, our analysis indicates no significant relationship between the exposure of property portfolios to physical climate risks and abnormal stock returns, suggesting that the effects of climate risks are already integrated into market valuations. We further develop our study by exploring the interactions between the MHE Average Index and the main property types within REIT portfolios, where we observe statistically significant effects. This paper contributes to the understanding of how environmental factors are reshaping the financial dynamics of Canadian real estate investments, highlighting the importance of considering climate risks in investment decisions and property management
Prismatic Dieudonné Theory for Truncated Barsotti-Tate Groups
Get PDFThe aim of this thesis is to classify truncated Barsotti-Tate groups over p-torsion free quasi-syntomic rings via a semilinear category which is a prismatic analogue of the category truncated displays introduced by Lau-Zink. This rests crucially on the classification of p-divisible groups over quasi-syntomic rings due to Anschutz-Le Bras and an argument of Beilinson which was used by Kisin to deduce a similar classification of truncated Barsotti-Tate groups over rings of integers of p-adic fields in terms of certain Breuil-Kisin modules
Examining the movement patterns of the European common blue butterfly (Polyommatus icarus): A study on short-term and long-term movement of an invasive species
Get PDFUnderstanding how individuals interact with biotic and abiotic factors in their habitats, and how these interactions influence movement through landscapes is a key step in understanding dispersal events. The world is experiencing increased introduction of non-native species into new regions, however without an understanding of how species use the space they occupy, it is not possible to understand how dispersal events occur. We studied the short- and long-term movement patterns of a non-native butterfly species in Montreal, Canada: the European common blue butterfly, Polyommatus icarus, to determine whether the movement patterns of these butterflies over multiple days can be predicted by individual movement behaviours measured over short time periods. We asked what factors can predict short-term movements in females of this species, and whether these short-term movements can be used to predict long-term movements. We examined short-term movement by following individuals over short time periods and compared these movements to long-term movements observed via a mark-release-recapture study. In doing so, we found that flowering, host-plant species presence, increased vegetation height, and road edges can predict short-term movement, and that the short-term movement model produced can be used to accurately predict long-term movements when they are less than 100 m. These findings suggest that an understanding of ground cover characteristics is important in being able to predict the dispersal of most individuals, but that further work will be required to accurately predict long-distance dispersal events, which are what appear to be driving range expansion of P. icarus in North America