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How Do Novice Programmers Interact with ChatGPT while Solving Code -Tracing Problems?
Full text linkLarge Language Models (LLMs) like ChatGPT are generative AI tools that can answer questions and provide solutions to problems in various domains. To date, little is known about how students interact with LLM tools while problem solving. We recruited novice programmers (N = 21) and gave them a brief video-based lesson on the fundamentals. We then asked them to use ChatGPT version 4.0 (1) to learn about one additional construct, namely, for loops, and (2) to solve four problems that required specifying the output of a program with a for loop, with access to ChatGPT. Participants verbalized their thoughts as they worked, and these were transcribed and analyzed. We used a qualitative approach to identify how students reasoned and what strategies they employed while solving problems. We found substantially more independent problem solving than expected, given prior reports on students’ overreliance on LLMs
Beyond Boundaries: Rethinking The Mosque A Design for Montreal’s North African Community of Le Petit Maghreb
No full textThis thesis is focused on the design of a mosque in Montreal engaging with questions of boundaries, thresholds and limits. In doing so, it explores how various constraints —ranging from zoning laws, politics, and orientation to cultural norms and gender segregation— affect its design and functionality. With my interest being on the city of Montreal and it’s so-called multicultural landscape, I seek to develop a comprehensive understanding of the mosque’s role in both reflecting and interacting with the North African Maghrebi community of “Le Petit Maghreb” in the Villeray—Saint-Michel—Parc-Extension borough. Through a combination of spatial mapping and collages, analysis of regulatory frameworks, and a proposed speculative design, I intend to provide a response to how mosques can be designed to overcome challenges related to visibility, safety, and inclusivity, fostering a sense of belonging, welcoming and identity within Montreal’s urban fabric
Intelligent Cooperative Sensing for Connected and Autonomous Vehicles
Full text linkConnected and autonomous vehicles (CAVs) enhance driving safety and efficiency through onboard sensors and vehicle-to-everything (V2X) communications. However, the limited sensing capabilities of individual vehicles and constraints in computational and communication resources pose significant challenges for reliable autonomous driving. This thesis addresses these challenges by developing innovative methods for intelligent cooperative sensing in CAVs, focusing on three key aspects: optimal sensor deployment, efficient data transmission, and multi-level information fusion. First, we propose an enhanced 3D sensor deployment method using Decision Transformers (DT) to optimize the placement of roadside sensors while considering traffic patterns and CAV sensing requirements. The method incorporates a bi-level optimization framework with reward redistribution to effectively handle long-delayed rewards, significantly improving sensing coverage and detection accuracy in complex road environments. Second, we introduce an intelligence-guided rewardless reinforcement learning (IRL) framework to enhance sensing information processing and transmission. Unlike conventional reinforcement learning approaches that rely on explicit reward functions, our method leverages intelligence as a metric to guide the learning process, enabling improved adaptability in diverse driving environments. The framework considers heterogeneous sensing requirements and resource constraints while ensuring reliable sensing assistance through efficient resource utilization. Third, we develop an improved DT-based cooperative sensing framework that intelligently integrates multi-source and multi-level sensing information. The framework employs a novel reward shaping mechanism and bi-level optimization to effectively handle long-delayed rewards and adapt to dynamic driving scenarios. This approach enables efficient fusion of sensor data at various abstraction levels while optimizing resource allocation. Comprehensive simulations and experiments demonstrate the effectiveness of our proposed methods. The enhanced 3D sensor deployment method achieves up to 20\% improvement in sensing coverage with fewer sensors compared to existing approaches. The IRL framework shows significant enhancements in detection accuracy and latency reduction across various traffic scenarios. The improved DT-based fusion approach demonstrates superior performance in sensing accuracy and resource utilization. These results validate the practical applicability of our solutions in real-world CAV environments, contributing to the advancement of reliable and efficient autonomous driving systems
Analysis Of Longitudinal Data With Nonignorable Missing Responses And Measurement Errors In Covariates
Full text linkThis thesis presents a comprehensive exploration of linear mixed models that incorporate measurement errors in specific covariates for longitudinal data with non-ignorable and non-monotone missing responses. The primary objective is to estimate mean response parameters and variance components utilizing a combined methodology of Regression Calibration (RC) and Monte Carlo EM. The investigation begins with an in-depth examination of the RC method applied to linear mixed models, particularly its adaptation to longitudinal data with covariates affected by measurement errors. A thorough simulation study is conducted, involving various mean response functions, revealing that the RC method produces unbiased and efficient estimators in scenarios where the true underlying model includes covariates with measurement errors. Subsequently, linear mixed models are introduced for longitudinal data with non-ignorable missing responses. The thesis proposes a semi-parametric Monte Carlo EM algorithm for the simultaneous estimation of regression parameters and variance components in linear mixed models with non-ignorable and non-monotone missing responses, combining the Monte Carlo EM algorithm of Ibrahim et al. and the RC method. The simulation study demonstrates the effectiveness of the proposed method, even in the presence of a substantial proportion of non-ignorable missing responses. The thesis concludes with an application of the proposed RC-MCEM method to actual longitudinal data from the Canadian Community Health Survey (CCHS), focusing on individuals with alcoholism and exploring how measurement errors in certain covariates can introduce biases in parameter estimation. The complexity of the problem is further elucidated when missingness occurs in the response variable
Breaking free: insights into auxin and ethylene control of abscission zone development in Arabidopsis thaliana
Full text linkAbscission allows plants to shed organs through specialized structures called abscission zones (AZ). In Arabidopsis thaliana, pollination triggers cell separation at the base of floral organs, where AZ cells form distinct separation and lignified layers. While auxin inhibits and ethylene promotes abscission, their structural effects on the AZ remained unclear. My research shows that AZ layers initiate one position before organ detachment. Exogenous auxin disrupts AZ structures and halts abscission, while inhibiting auxin transport accelerates layer formation. Post-pollination, lignin deposition follows a gradual auxin decline in AZ cells. Ethylene primarily regulates separation layer timing, with ethylene-insensitive mutants exhibiting delayed AZ maturation and ethylene gas triggering premature layer formation in flower buds. These findings suggest early ethylene responsiveness in AZ cells. Understanding hormonal control of AZ development offers critical insights for agriculture, where controlling organ shedding can reduce yield loss, optimize harvest timing, and improve fruit and flower retention in crops