From creation to its degradation, the RNA molecule is the action field of many binding proteins with different roles in regulation and RNA metabolism. Since these proteins are involved in a large number of processes, a variety of diseases are related to abnormalities occurring within the binding mechanisms. One of the experimental methods for detecting the binding sites of these proteins is PAR-CLIP built on the next generation sequencing technology. Due to its size and intrinsic noise, PAR-CLIP data analysis requires appropriate pre-processing and thorough statistical analysis. The present work has two main goals. First, to develop a modular pipeline for preprocessing PAR-CLIP data and extracting necessary signals for further analysis. Second, to devise a novel statistical model in order to carry out inference about presence of protein binding sites based on the signals extracted in the pre-processing step.

Six children between the ages of seven and eleven and their parents were interviewed to gather perceptions of the Clip Chart Management System. The Clip Chart system is a behavior system used in many primary classrooms where teachers move students’ clothespins up and down a chart in response to students’ behavior. Some findings from this qualitative case study are: students experienced feelings of shame and embarrassment, students compared their clips to their peers’ clips, and students labeled other students as “bad.” Some parents appreciated the consistency of behavior monitoring, but other parents felt that the system was ineffective and contributed to their child’s negative feelings about school. The findings of this study suggest the need for much more research if this method is continued to be implemented in schools.

This thesis explores the application of Contrastive Language-Image Pre-Training (CLIP), a vision-language model, in an automated video surveillance system for anomaly detection. The ability of CLIP to perform zero-shot learning, coupled with its robustness against minor image alterations due to its lack of reliance on pixel-level image analysis, makes it a suitable candidate for this application. The study investigates the performance of CLIP in tandem with various anomaly detection algorithms within a visual surveillance system. A custom dataset was created for video anomaly detection, encompassing two distinct views and two varying levels of anomaly difficulty. One view offers a more zoomed-in perspective, while the other provides a wider perspective. This was conducted to evaluate the capacity of CLIP to manage objects that occupy either a larger or smaller portion of the entire scene. Several different anomaly detection methods were tested with varying levels of supervision, including unsupervised, one-class classification, and weakly- supervised algorithms, which were compared against each other. To create better separation between the CLIP embeddings, a metric learning model was trained and then used to transform the CLIP embeddings to a new embedding space. The study found that CLIP performs effectively when anomalies take up a larger part of the image, such as in the zoomed-in view where some of the One- Class-Classification (OCC) and weakly supervised methods demonstrated superior performance. When anomalies take up a significantly smaller part of the image in the wider view, CLIP has difficulty distinguishing anomalies from normal scenes even using the transformed CLIP embeddings. For the wider view the results showed on better performance for the OCC and weakly supervised methods.

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Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

This study explores the incorporation of film and other audiovisual media in the instruction of French as a foreign language. The primary focus is on understanding the diverse approaches adopted by two teachers in the southern region of Sweden. Through interviews, these educators share insights into their goals when integrating films into language learning and the specific methodologies employed to attain these objectives. Additionally, the study incorporates a questionnaire to gather student perspectives on the efficacy and impact of using films within the classroom setting.

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<p dir="ltr">Land cover classification has always been a crucial topic in the remote sensing domain. Utilizing data collected by unmanned aerial vehicles and satellites, researchers can detect land degradation, monitor environmental changes, and provide insights for urban planning. Recent advancements in large multi-modal models have enabled open-vocabulary classification, which is particularly beneficial in this field. Becuase of the pre-training method, these models can perform zero-shot inference on unseen data, significantly reducing the costs associated with data collection and model training. This open-vocabulary feature of large-scale vision-language pre-training aligns well with the requirements of land cover classification, where benchmark datasets in the remote sensing domain comprise various categories, and transferring results from one dataset to another through supervised learning methods is challenging.</p><p dir="ltr">In this thesis, the author explored the performance of zero-shot CLIP and linear probe CLIP to assess the feasibility of using the CLIP model for land cover classification tasks. Further, the author fine-tuned CLIP by creating hierarchical label sets for the datasets, leading to better zero-shot classification results and improving overall accuracy by 2.5%. Regarding data engineering, the author examined the performance of zero-shot CLIP and linear probe CLIP across different categories and proposed a categorization method for land cover datasets. In summary, this work evaluated CLIP's overall performance on land cover datasets of varying spatial resolutions and proposed a hierarchical classification method to enhance its zero-shot performance. The thesis also offers a practical approach for modifying current dataset categorizations to better align with the model.</p>

Prediction of the clip reactions of a standing seam roof in a metal building under dynamic loading is of great interest because currently static uplift tests are the standard for determining the design load capacity. The use of a static test to replicate a dynamic loading leads to a great amount of debate because clearly a standing seam roof visually behaves very different under the two different types of loads. This leads to the question of whether a static test accurately replicates the magnitude of loads that the roof clips would feel under a dynamic wind load. This study uses a magnetic suspension uplift loading for the simulation of wind tunnel data in comparison with the ASTM E-1592 ?Standard Test Method for Structural Performance of Sheet Metal Roof and Siding Systems by Uniform Static Air Pressure Difference? test to determine clip reactions. An approximate finite element model is also utilized to verify the validity of the experimentally acquired clip reactions to form another comparison.