Background: Opioid overdoses have killed almost 20,000 Canadians since 2016. To address this, Canada has established supervised consumption sites where people can use drugs in the presence of trained staff and get access to pharmacological treatments such as methadone. However, there is very little research on whether supervised consumption clients use methadone, or whether their use of methadone prevents opioid overdose. Methods: A secondary data analysis of information collected from one supervised consumption site was undertaken in order to explore relationships between client self-reported methadone dosage and subsequent observed same-day opioid overdose. Results: Statistical analysis showed no correlation between methadone usage and reduced chance of opioid overdose. However, the most common dosage of methadone reported (30mg/day) was far below the minimum therapeutic dose of methadone. Conclusion: Clients of supervised consumption sites often report being prescribed methadone, but not at a dose high enough to reduce opioid overdose.

An event is something that happens at a time and location. Events include major life events such as graduating college or getting married, and also simple day-to-day activities such as commuting to work or eating lunch. Most work on event extraction detects events and the entities involved in events. For example, cooking events will usually involve a cook, some utensils and appliances, and a final product. In this work, we target the task of determining whether events result in their expected outcomes. Specifically, we target cooking and baking events, and characterize event outcomes into two categories. First, we distinguish whether something edible resulted from the event. Second, if something edible resulted, we distinguish between perfect, partial and alternative outcomes. The main contributions of this thesis are a corpus of 4,000 tweets annotated with event outcome information and experimental results showing that the task can be automated. The corpus includes tweets that have only text as well as tweets that have text and an image.

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This paper explores the discourse surrounding the introduction of supervised injection facilities (SIF) in New York City following their recent introduction in November 2021.  The contemporary debate surrounding SIF in New York is more diverse than may be assumed, even within a seemingly liberal city in the United States, as there are many competing perspectives which make broad categorizations of the discourse difficult. Nevertheless, supporters of SIF have continued to emphasize the efficacy of this program and its potential to prevent overdoses in a largely uniform manner.  In contrast, critics of SIF in NYC have demonstrated a multiplicity of oppositional narratives, which take the forms of traditional abstinence perspectives, localism/not in my backyard rhetoric, law and order beliefs, and social justice evaluations that question the equality of such programs. Nearly all sentiments regarding SIF are founded in the individual perceptions of addiction, specifically whether the individual views it as a disease or a moral failing.        The United States is slowly continuing to adopt more dynamic approaches to substance abuse and move away from the punitive policies established through the War on Drugs strategies advanced throughout the previous decade.  This pilot program can be viewed as a continuation of existing harm reduction policies such as syringe exchange programs.  While the introduction of SIF signifies a substantial evolution of the existing harm reduction policies and provides the basis for national expansion of the program, the current socio-political environment does not prove conducive to its evolution.  Overall, this study explores the diverse range of narratives surrounding SIF, their informing ideology, and attempts to situate these opinions within their broader sociological and historical backgrounds, providing the basis for further research regarding this subject.

In this dissertation, an ensemble non-uniform spectral feature selection and a kernel density decision fusion framework are proposed for the classification of hyperspectral data using a support vector machine classifier. Hyperspectral data has more number of bands and they are always highly correlated. To utilize the complete potential, a feature selection step is necessary. In an ensemble situation, there are mainly two challenges: (1) Creating diverse set of classifiers in order to achieve a higher classification accuracy when compared to a single classifier. This can either be achieved by having different classifiers or by having different subsets of features for each classifier in the ensemble. (2) Designing a robust decision fusion stage to fully utilize the decision produced by individual classifiers. This dissertation tests the efficacy of the proposed approach to classify hyperspectral data from different applications. Since these datasets have a small number of training samples with larger number of highly correlated features, conventional feature selection approaches such as random feature selection cannot utilize the variability in the correlation level between bands to achieve diverse subsets for classification. In contrast, the approach proposed in this dissertation utilizes the variability in the correlation between bands by dividing the spectrum into groups and selecting bands from each group according to its size. The intelligent decision fusion proposed in this approach uses the probability density of training classes to produce a final class label. The experimental results demonstrate the validity of the proposed framework that results in improvements in the overall, user, and producer accuracies compared to other state-of-the-art techniques. The experiments demonstrate the ability of the proposed approach to produce more diverse feature selection over conventional approaches.

A data analytic materials informatics methodology is proposed after applying different data mining techniques on some datasets of particular domain in order to discover and model certain patterns, trends and behavior related to that domain. In essence, it is proposed to develop an information mining tool for vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites as a case study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF weight fraction) and testing temperature were utilized as inputs and the storage modulus, loss modulus, and tan delta were selected as outputs or responses. The data mining and knowledge discovery algorithms and techniques included self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature had the most significant effect on the output responses followed by VGCNF weight fraction. A clustering technique, i.e., fuzzy C-means (FCM) algorithm, was also applied to discover certain patterns in nanocomposite behavior after using principal component analysis (PCA) as a dimensionality reduction technique. Particularly, these techniques were able to separate the nanocomposite specimens into different clusters based on temperature and tan delta features as well as to place the neat VE specimens in separate clusters. In addition, an artificial neural network (ANN) model was used to explore the VGCNF/VE dataset. The ANN was able to predict/model the VGCNF/VE responses with minimal mean square error (MSE) using the resubstitution and 3olds cross validation (CV) techniques. Furthermore, the proposed methodology was employed to acquire new information and mechanical and physical patterns and trends about not only viscoelastic VGCNF/VE nanocomposites, but also about flexural and impact strengths properties for VGCNF/ VE nanocomposites. Formulation and processing factors (curing environment, use or absence of dispersing agent, mixing method, VGCNF fiber loading, VGCNF type, high shear mixing time, sonication time) and testing temperature were utilized as inputs and the true ultimate strength, true yield strength, engineering elastic modulus, engineering ultimate strength, flexural modulus, flexural strength, storage modulus, loss modulus, and tan delta were selected as outputs. This work highlights the significance and utility of data mining and knowledge discovery techniques in the context of materials informatics.

This dissertation develops new techniques employing the Low-rank and Sparse Representation approaches to improve the performance of state-of-the-art algorithms in hyperspectral image analysis. The contributions of this dissertation are outlined as follows. 1) Low-rank and sparse representation approaches, i.e., low-rank representation (LRR) and low-rank subspace representation (LRSR), are proposed for hyperspectral image analysis, including target and anomaly detection, estimation of the number of signal subspaces, supervised and unsupervised classification. 2) In supervised target and unsupervised anomaly detection, the performance can be improved by using the LRR sparse matrix. To further increase detection accuracy, data is partitioned into several highly-correlated groups. Target detection is performed in each group, and the final result is generated from the fusion of the output of each detector. 3) In the estimation of the number of signal subspaces, the LRSR low-rank matrix is used in conjunction with direct rank calculation and soft-thresholding. Compared to the state-of-the-art algorithms, the LRSR approach delivers the most accurate and consistent results across different datasets. 4) In supervised and unsupervised classification, the use of LRR and LRSR low-rank matrices can improve classification accuracy where the improvement of the latter is more significant. The investigation on state-of-the-art classifiers demonstrate that, as a pre-preprocessing step, the LRR and LRSR produce low-rank matrices with fewer outliers or trivial spectral variations, thereby enhancing class separability.