Les communications de type machine-à-machine M2M sont considérées comme des formes de communication de données qui ne requièrent pas nécessairement d'interaction humaine. Cependant, ce type de communication n'est pas efficace dans les réseaux cellulaires, en raison de leurs caractéristiques spécifiques, telles que. L'objectif de cette thèse est de proposer des mécanismes et d'optimiser les techniques de la couche d'accès radio LTE pour les communications M2M. Pour l'accès au canal de liaison montante, nous proposons deux méthodes afin d'améliorer la performance d'accès aléatoire en terme de latence et de consommation énergétique: une méthode d'agrégation de paquets et une autre de transmission multiple pendant l'intervalle de temps de transmission. Afin de réduire encore plus le temps de latence de liaison montante et permettre une connexion d'un grand nombre de machines au réseau, nous proposons une nouvelle méthode d'accès basée sur la contention CBA pour éviter d'une part la signalisation redondante pour accéder au canal et d'autre part la latence de l'ordonnanceur. Pour la réception de liaison descendante, nous proposons deux méthodes pour analyser les performances du mécanisme de réception discontinu DRX pour les applications M2M: la première se base sur une distribution de Poisson, la suivante sur une distribution Pareto pour le trafic sporadique. Avec les modèles proposés, le facteur d'économie d’énergie et la latence pour transiter du mode sommeil au mode actif peuvent être estimés avec précision pour un choix donné de paramètres DRX, permettant ainsi de sélectionner ceux permettant d'atteindre le compromis optimal. / Machine type communications is seen as a form of data communication, among devices and/or from devices to a set of servers, that do not necessarily require human interaction. However, it is challenging to accommodate MTC in LTE as a result of its specific characteristics and requirements. The aim of this thesis is to propose mechanisms and optimize the access layer techniques for MTC in LTE. For uplink access, we propose two methods to improve the performance of random access in terms of latency: a packet aggregation method and a Transmission Time Interval bundling scheme. To further reduce the uplink latency and enable massive number of connected device, we propose a new contention based access method (CBA) to bypass both the redundant signaling in the random access procedure and also the latency of regular scheduling. For downlink reception, we propose two methods to analyze the performance of discontinuous reception DRX mode for MTC applications: the first with the Poisson distribution and the second with the Pareto distribution for sporadic traffic. With the proposed models, the power saving factor and wake up latency can be accurately estimated for a given choice of DRX parameters, thus allowing to select the ones presenting the optimal tradeoff.

Word embeddings is a useful method that has shown enormous success in various NLP tasks, not only in open domain but also in biomedical domain. The biomedical domain provides various domain specific resources and tools that can be exploited to improve performance of these word embeddings. However, most of the research related to word embeddings in biomedical domain focuses on analysis of model architecture, hyper-parameters and input text. In this paper, we use SemMedDB to design new sentences called `Semantic Sentences'. Then we use these sentences in addition to biomedical text as inputs to the word embedding model. This approach aims at introducing biomedical semantic types defined by UMLS, into the vector space of word embeddings. The semantically rich word embeddings presented here rivals state of the art biomedical word embedding in both semantic similarity and relatedness metrics up to 11%. We also demonstrate how these semantic types in word embeddings can be utilized.

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This thesis focuses on a leading approach for handling model uncertainty: model averaging. I examine the performance of model averaging compared to conventional econometric methods and to more recent machine learning algorithms, and demonstrate how model averaging can be applied to empirical problems in economics. It comprises of three chapters. Chapter 1 evaluates the relative performance of frequentist model averaging (FMA) to individual models, model selection, and three popular machine learning algorithms – bagging, boosting, and the post-lasso – in terms of their mean squared error (MSE). I find that model averaging performs well compared to these other methods in Monte Carlo simulations in the presence of model uncertainty. Additionally, using the National Longitudinal Survey, I use each method to estimate returns to education to demonstrate how easily model averaging can be adopted by empirical economists, with a novel emphasis on the set of candidate models that are averaged. This chapter makes three contributions: focusing on FMA rather than the more popular Bayesian model averaging; examining FMA compared to machine learning algorithms; and providing an illustrative application of FMA to empirical labour economics. Chapter 2 expands on Chapter 1 by investigating different approaches for constructing a set of candidate models to be used in model averaging – an important, yet often over- looked step. Ideally, the candidate model set should balance model complexity, breadth, and computational efficiency. Three promising approaches – model screening, recursive partitioning-based algorithms, and methods that average over nonparametric models – are discussed and their relative performance in terms of MSE is assessed via simulations. Additionally, certain heuristics necessary for empirical researchers to employ the recommended approach for constructing the candidate model set in their own work are described in detail. Chapter 3 applies the methods discussed in depth in earlier chapters to currently timely microdata. I use model selection, model averaging, and the lasso along with data from the Canadian Labour Force Survey to determine which method is best suited for assessing the impacts of the COVID-19 pandemic on the employment of parents with young children in Canada. I compare each model and method using classification metrics, including correct classification rates and receiver operating characteristic curves. I find that the models selected by model selection and model averaging and the lasso model perform better in terms of classification compared to the simpler parametric model specifications that have recently appeared in the literature, which suggests that empirical researchers should consider statistical methods for the choice of model rather than relying on ad hoc selection. Additionally, I estimate the marginal effect of sex on the probability of being employed and find that the results differ in magnitude across models in an economically important way, as these results could affect policies for post-pandemic recovery. / Thesis / Doctor of Philosophy (PhD) / This thesis focuses on model averaging, a leading approach for handling model uncertainty, which is the likelihood that one’s econometric model is incorrectly specified. I examine the performance of model averaging compared to conventional econometric methods and to more recent machine learning algorithms in simulations and applied settings, and show how easily model averaging can be applied to empirical problems in economics. This thesis makes a number of contributions to the literature. First, I focus on frequentist model averaging instead of Bayesian model averaging, which has been studied more extensively. Second, I use model averaging in empirical problems, such as estimating the returns to education and using model averaging with COVID-19 data. Third, I compare model averaging to machine learning, which is becoming more widely used in economics. Finally, I focus attention on different approaches for constructing the set of candidate models for model averaging, an important yet often overlooked step.

5-year survival rate of patients with metastasized non-small cell lung cancer (NSCLC) who received chemotherapy was less than 5% (Kathryn C. Arbour, 2019). Our ability to provide survival status of a patient i.e. Alive or death at any time in future is important from at least two standpoints: a) from clinical standpoint it enables clinicians to provide optimal delivery of healthcare and b) from personal standpoint by providing patient’s family with opportunities to plan their life ahead and potentially cope with emotional aspect of loss of life. / Thesis / Master of Applied Science (MASc)

With the rapid development of image hardware, outdoor computer vision systems, for instance, surveillance cameras, have been extensively utilized for various applications. These systems typically equip a protective glass layer installed in front of the camera. How- ever, during inclement weather conditions, images captured through such glass often suffer from obstructions adhering to its surface, such as raindrops or dust particles. Consequently, this leads to a degradation in image quality, which significantly affects the performance of the system. Existing obstruction removal algorithms attempt to resolve these issues using deep learning techniques with synthetic data, which may not achieve a good visual result for complex real-world situations. To solve this, some studies employ real-world data. How- ever, they tend to focus on a singular type of obstruction, such as raindrops. This thesis addresses the more challenging task of restoring images taken through glass surfaces, which are impacted by various adherent obstructions such as dirt, raindrops, muddy raindrops, and other small foreign particles commonly found in real-life scenar- ios, including stone fragments and leaf particles. This work introduces an encoder-decoder network that incorporates auxiliary learning and an attention mechanism. During the test- ing phase, the auxiliary branch updates the shared internal hyperparameters of the model, enabling it to restore images from not limited to known categories of obstructions from the training dataset, but also unseen ones. To better accommodate real-world situations, this work presents a dataset comprising real-world adherent obstruction pairs, which cov- ers a large variety of common obstructions along with their corresponding clean ground truth images. Experimental results indicate that the proposed technique outperforms many existing methods in both quantitative and qualitative assessments. / Thesis / Master of Applied Science (MASc)