VictorJuiz/thesis huggingface dataset:
Errors in Moral Forecasting: The Importance of Affect for the Relationship Between Moral Behaviours and Moral Forecasts
Keywords: affect
decision-making
emotion
moral forecasting
morality

In recent years, the field of moral psychology has been heavily dominated by studies on moral judgment that have relied on hypothetical moral dilemmas. But how do individuals' responses to such dilemmas map on to real-life moral behaviour? The aim of this dissertation was to explore this association, as well as to explore the role that affective experience plays in the relationship between peoples' actual and forecasted moral behaviours. The results of five experiments suggest that people might act more morally than they would predict and that affective experience plays in important role in motivating moral behaviours, as well as accurate forecasts. In Studies 1 and 2, participants acted more morally than they predicted they would in a series of moral dilemmas including a one-shot Dictator Game (Study 1), as well as in a math task where they had the chance to cheat on a math test through either commission or omission (Study 2). In Study 3, participants who actually had the chance to cheat on this same math task displayed significantly more autonomic nervous system arousal than participants forecasting their behaviour in this same dilemma. Critically, the moral underestimation effect in this study was mediated by autonomic arousal. In Study 4, I found that inducing affective salience by providing false somatic feedback indicative of arousal increased forecasting accuracy among participants for the same math task dilemma. Finally, in Study 5, I found that individual differences in emotional awareness moderate moral forecasting accuracy in this same moral dilemma, such that individuals low in emotional awareness exhibit exacerbated forecasting errors. This research suggests that the affective arousal present during real-life moral dilemmas may not be fully engaged during moral forecasting, and that this may account for the moral forecasting errors that individuals make. Given that the majority of moral psychologists rely on hypothetical scenarios and self-report to study the nature of morality, I propose that investigating the way in which affective experience interacts with and shapes individuals' moral forecasts is an important and meaningful pursuit.
Evolvable Mathematical Models: A New Artificial Intelligence Paradigm
Keywords: Artificial Intelligence
Artificial Life
Evolutionary Computation
Evolutionary Robotics
Genetic Programming

We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, equations can be constructed that approximate any analytic function. These Evolvable Mathematical Models (EMMs) are tested and compared to their Artificial Neural Network (ANN) counterparts on two benchmarking tasks: the double-pole balancing without velocity information benchmark and the challenging discrete Double-T Maze experiments with homing. The results from these experiments show that EMMs are capable of solving tasks typically solved by ANNs, and that they have the ability to produce agents that demonstrate learning behaviours. To further explore the capabilities of EMMs, as well as to investigate the evolutionary origins of communication, we develop
Toward a Typology of Semantic Transparency: The Case of French Compounds
Keywords: compounds
french
morphology
semantics
semantic transparency

This thesis proposes an extension to existing models of semantic transparency in compounding by incorporating previously unexplored features into the concept. Although the term semantic transparency is widely used in research on multi-word lexemes, the concept is often viewed as simply a matter of semantic compositionality, which is to say that transparency is determined solely by the meaning of individual constituents. While this approach offers a number of advantages, it is nevertheless insufficient on two accounts: one, it groups together a number of compositional compounds that are otherwise semantically distinct (e.g. ice breaker ~ ice cube ~ ice age), and two, it offers no clear means to order or rank partially compositional compounds (e.g. firefly ~ butterfly ~ barfly). This thesis therefore argues for a more holistic approach to semantic transparency, one that views the phenomenon as both scalar and multi-faceted, the result of which is a more granular model capable of further distinguishing between several different compound types.The typology of semantic transparency of compounds proposed in this work consists of four basic factors supported by a dataset consisting of more than 1,000 French NN and N Ă  N compounds extracted from Wiktionary. The first factor, headedness, touches on features pertaining to a compound's semantic head, for which concepts such as canonical position and strong/weak centricity are advanced. Second, compositionality is formalized according to a four-way configuration based on how individual constituents contribute meaning to the whole. The third factor, semantic homogeneity, relates to the degree of shared meaning between analogically similar compounds. Finally, the implicit semantic relations within compounds are explored. Consequently, fifteen basic associations are proposed and evaluated using the compound data collected from Wiktionary. Together, these four factors yield a typology involving sixteen possible transparency profiles, each of which is ordered according to the relative weight of its semantic features. It is believed that this typology offers a far richer conceptual space within which to further the discussion of semantic transparency as it pertains to complex constructions such as compounds.
Language Attitudes towards Canadian French and English, 1691-1902: The Emergence of the Canadian Voices
Keywords: Canadian History
English Language
Folk Linguistics
French Language
Language Attitudes
Stereotypes

This dissertation examines the origins and development of attitudes (in the guise of beliefs and stereotypes) towards Canadian French and Canadian English during the 18th and 19th centuries, as expressed primarily by foreign travellers to North America. By conducting a comparative study of these two languages, a bridge is built between French Canadian studies on Canadian French, and Anglophone Canadian studies on Canadian English, two fields which have historically been distinct and separate.The time period under study (1691-1902) is marked by major political and social changes (e.g., the English Conquest, the Confederation), all of which had major, lasting effects on the development and status of both languages. In order to study the evolution of language attitudes during this period, I used content analysis on the metalinguistic commentary found in a wide variety of texts, including travel journals, scholarly and newspaper articles, monographs, and prescriptive texts, written in French and English.My analysis shows that British commentators were the most critical of both languages (and peoples), and relied almost entirely on beliefs and stereotypes rather than empirical evidence to support their claims. As colonial peoples in the process of renegotiating their linguistic and cultural identities, French and British Canadians were strongly affected by these beliefs, particularly following Confederation. From this point forward, we see evidence for what I have termed the "Emergence of the Canadian Voices" that is, Canadians' rejection of foreign opinion about their language and culture, and a move towards an endogenous linguistic norm, which was supported by the increasing use of linguistic studies on both languages. Examining how these "Canadian Voices" developed provides an important insight into the politically charged discourse about the French and English languages in the late 19th century, and is essential for understanding the complex and fiercely debated language question in Canada today.
Striatal Mechanisms Mediating Parkinsonism and L-DOPA Induced Dyskinesia: Contribution of the Direct - and Indirect - Output Pathways
Keywords: Direct pathway
Indirect pathway
L-DOPA-induced dyskinesia
Parkinson's disease
Striatum
Synaptic plasticity

L-DOPA (L-3,4-dihydroxyphenylanalnine) therapy is indispensible in the treatment of Parkinson's disease (PD) motor symptoms, but the use of these drugs is plagued by the development of major side effects termed L-DOPA-induced dyskinesia (LID). Smooth motor performance results from the balanced influence of these pathways on the neural activity of the basal ganglia output structures. The prevailing view of PD, and LID etiology is tightly associated with a putative interference in the democracy between these two pathways. Due to the lack of appropriate animal models, to date, this theory has not been directly proven. To circumvent these issues models of PD and LID were developed in bacterial artificial chromosome (BAC) mice, which selectively tag neurons of the direct- and indirect-pathways with enhanced green fluorescence protein (eGFP). By using these models is was possible to characterize how chronically perturbed striatal dopamine transmission drives an imbalance in through changes in short- and long-term properties of direct and indirect corticostriatal synapses. Following striatal dopamine depletion, indirect pathway neurons exhibited abnormal short-term changes, showing decreased levels of paired pulse facilitation, enhanced sensitivity to presynaptic stimulation and an increase in the relative contribution of NMDAR to the EPSC but no change in spontaneous synaptic activity. While, in the direct pathway, firing threshold was reduced, spike frequency adaptation developed and the frequency of spontaneous activity was also reduced. In addition, changes in the properties of NMDAR kinetics suggest that these receptors were desensitized. Long-term abnormalities in the direct and indirect striatal output pathways were examined in models of parkinsonism and dyskinesia. Thus in parkinsonism the indirect pathway exhibits only long-term potentiation (LTP), providing a basis for its over-activity, while the direct pathway, exhibits only long-term depression (LTD) will become underactive. By contrast, in the dyskinetic state, the direct pathway exhibits only LTP, becoming generally overactive, and the indirect pathway exhibits only LTD. These findings explain some of the positive effects and motor complications elicited by L-DOPA and offer firm observations on the role of the direct- and indirect-pathway in basal ganglia circuitry abnormalities in LID and PD.
Deformable Dose Reconstruction to Optimize the Planning and Delivery of Liver Cancer Radiotherapy
Keywords: Deformable image registration
Dosimetry
Finite element analysis
Liver cancer
Radiation oncology

The precise delivery of radiation to liver cancer patients results in improved control with higher tumor doses and minimized normal tissues doses. A margin of normal tissue around the tumor requires irradiation however to account for treatment delivery uncertainties. Daily image-guidance allows targeting of the liver, a surrogate for the tumor, to reduce geometric errors. However poor direct tumor visualization, anatomical deformation and breathing motion introduce uncertainties between the planned dose, calculated on a single pre-treatment computed tomography image, and the dose that is delivered. A novel deformable image registration algorithm based on tissue biomechanics was applied to previous liver cancer patients to track targets and surrounding organs during radiotherapy. Modeling these daily anatomic variations permitted dose accumulation, thereby improving calculations of the delivered doses. The accuracy of the algorithm to track dose was validated using imaging from a deformable, 3-dimensional dosimeter able to optically track absorbed dose. Reconstructing the delivered dose revealed that 70% of patients had substantial deviations from the initial planned dose. An alternative image-guidance technique using respiratory-correlated imaging was simulated, which reduced both the residual tumor targeting errors and the magnitude of the delivered dose deviations. A planning and delivery strategy for liver radiotherapy was then developed that minimizes the impact of breathing motion, and applied a margin to account for the impact of liver deformation during treatment. This margin is 38% smaller on average than the margin used clinically, and permitted an average dose-escalation to liver tumors of 9% for the same risk of toxicity. Simulating the delivered dose with deformable dose reconstruction demonstrated the plans with smaller margins were robust as 90% of patients' tumors received the intended dose. This strategy can be readily implemented with widely available technologies and thus can potentially improve local control for liver cancer patients receiving radiotherapy.
Insights into the Mechanism of Drug-induced Agranulocytosis: A Study of the Immune Changes Induced by Clozapine and Amodiaquine
Keywords: Adverse drug reactions
Agranulocytosis
Amodiaquine
Clozapine
Toxicology

Drug-induced agranulocytosis is an idiosyncratic adverse drug reaction, and although its mechanism is not well understood, it is suspected to involve both metabolic (reactive metabolites) and immune components. Numerous drugs that cause agranulocytosis are metabolized to reactive species by neutrophils that, in turn, covalently bind to the cells; a process hypothesized to be dependent on neutrophil myeloperoxidase. However, despite extensive in vitro studies, in vivo evidence to support this is lacking. Therefore, the first goal of these studies was to characterize the involvement of myeloperoxidase in the covalent binding of amodiaquine and clozapine to neutrophils. In myeloperoxidase knockout mice treated with amodiaquine, covalent binding to neutrophils was found to decrease 2-fold compared to control. Interestingly, lack of the enzyme did not completely abolish binding, and lymphocytes were also found to extensively bind the drug. These studies confirm that myeloperoxidase is responsible for some of the covalent binding to neutrophils, but that other pathways not yet defined in neutrophils, as well as other cells, are also capable of oxidizing amodiaquine. The second goal of this work was to investigate the immune component associated with drug-induced agranulocytosis by studying the response of neutrophils and the overall immune system to clozapine. Due to difficulties accessing patients, these studies were pursued by first designing a rat model of early clozapine treatment. Using this model, the response of neutrophils to clozapine was found to reflect that of G-CSF-mediated stimulation of the bone marrow. A proinflammatory response to clozapine was detected in the rats, characterized by neutrophilia and elevated levels of the acute phase protein -1-acid glycoprotein, which led us to investigate the ability of clozapine to activate inflammasomes. These studies led to the proposal of a novel hypothesis, specifically, that the proinflammatory response to clozapine at the start of treatment is mediated by inflammasome-generated IL-1.
Clinical Supervision and Learning in Acute Care Environments: A Multifaceted Relationship
Keywords: Clinical learning
Clinical supervision
Critical care

ContextClinical supervision is intended to maintain a balance between a safe and timely provision of care to the patients and the creation of learning opportunities for the trainees. Many changes have recently affected the health care and medical education systems. The effects of these changes on current supervisory practices and on clinical learning are only partially understood. The objective of this program of research was therefore to explore the complex relationships between clinical supervision and learning in today's acute care environments.DesignTwo studies were combined: a mixed methods, simulation-based study, followed by an observational, naturalistic study. For the simulation study, 53 residents were randomized to one of three levels of clinical supervision and completed acute simulated scenarios. This study explored the impact of the level of clinical supervision on quantitative outcomes related to patient care and trainee learning, and on the learning processes involved in acute resuscitation. The second study, based on 350 hours of participant observation in real acute care environments, explored three aspects of the learning interactions among supervisors and trainees.ResultsThe simulation study indicated a benefit of direct supervision for patient care. Direct supervision had a neutral effect on learning, despite the creation of distinct learning opportunities. In real acute clinical environments, learning interactions between clinical supervisors and trainees facilitated trainee involvement in patient care and allowed supervisors to adjust the level of supervision. Factors related to the clinical case, the participants, and the broader context interacted to affect the occurrence of these learning interactions. In addition, the specific clinical situations in which participants interacted affected the nature and the educational potential of the learning interactions. ConclusionThis program of research revealed that direct supervision could improve patient care without compromising trainee learning. In-person interactions between clinical supervisors and trainees were commonly used to create learning opportunities in today's acute clinical environments. Clinical supervisors and trainees displayed different types of learning interactions in response to different clinical circumstances.
Parts and Methods in Synthetic Biology: An Investigation of Light Induced Gene Regulation in E. coli and the Design and Characterization of a Dual Mode Promoter in S. cerevisiae
Keywords: Dual mode promoter
Feedback gene network
Gene regulation
Light induced
promoter design
Synthetic biology

Cellular processes are astonishing and complex but precise in regulation with great modularity which makes us wonder "how do they work?" or if it is possible to redesign them for our specific need. Synthetic biology is a field of research that combines molecular biology and engineering aspect to understand cellular processes as well as to redesign "biological devices" (e.g. feedback networks) or "parts"(e.g. promoters) for constructing controlled biological system. For this, synthetic biology needs resources such as libraries of biological parts and methods to develop those parts, and to validate them in targeted organisms. Here, one of the projects was to validate and develop an in vivo system for light-responsive transcriptional gene regulation. We implemented a blue light-responsive reporter system with conventional E. coli promoter design methods to validate DNA binding activity of six in vitro characterized variants of fusion photoactive yellow protein (PYP). The in vivo system design involved placing blue light induced PYP binding DNA sequences such that PYP would inhibit access of RNA polymerase to the promoter upon light exposure. Comparison of results of in vivo DNA binding activity via fluorescence of these six fusion PYP with the Kd value of in vitro DNA binding activity showed weak correlation. Development of this kind of in vivo screening method would narrow down the search for better light responsive DNA binding fusion PYP variants. The other project in this work was to develop a "dual-mode" promoter to enrich the synthetic biology library, with the motivation for the design arising from our ongoing effort to design and implement a feedback controller in S. cerevisiae. To design this promoter we started with the minimal cytochrome C promoter in yeast to incorporate five steroid hormone responsive elements and one lac operator site, respectively upstream and downstream of the TATA box. This allows activation through the testosterone-responsive androgen receptor, and repression through the LacI repressor. Exposure to varying concentrations of testosterone and IPTG demonstrated the functionality of the promoter's activating and repressing modes, and the ability to combine these effects to tune the promoter's output curve over a wide range.
Exploring Deep Learning Methods for Discovering Features in Speech Signals
Keywords: Deep Learning
Deep Neural Networks
DNN-HMM hybrid
Hidden Markov Models
Speech Recognition

This thesis makes three main contributions to the area of speech recognition with Deep Neural Network - Hidden Markov Models (DNN-HMMs). Firstly, we explore the effectiveness of features learnt from speech databases using Deep Learning for speech recognition. This contrasts with prior works that have largely confined themselves to using traditional features such as Mel Cepstral Coefficients and Mel log filter banks for speech recognition. We start by showing that features learnt on raw signals using Gaussian-ReLU Restricted Boltzmann Machines can achieve accuracy close to that achieved with the best traditional features. These features are, however, learnt using a generative model that ignores domain knowledge. We develop methods to discover features that are endowed with meaningful semantics that are relevant to the domain using capsules. To this end, we extend previous work on transforming autoencoders and propose a new autoencoder with a domain-specific decoder to learn capsules from speech databases. We show that capsule instantiation parameters can be combined with Mel log filter banks to produce improvements in phone recognition on TIMIT. On WSJ the word error rate does not improve, even though we get strong gains in classification accuracy. We speculate this may be because of the mismatched objectives of word error rate over an utterance and frame error rate on the sub-phonetic class for a frame. Secondly, we develop a method for data augmentation in speech datasets. Such methods result in strong gains in object recognition, but have largely been ignored in speech recognition. Our data augmentation encourages the learning of invariance to vocal tract length of speakers. The method is shown to improve the phone error rate on TIMIT and the word error rate on a 14 hour subset of WSJ. Lastly, we develop a method for learning and using a longer range model of targets, conditioned on the input. This method predicts the labels for multiple frames together and uses a geometric average of these predictions during decoding. It produces state of the art results on phone recognition with TIMIT and also produces significant gains on WSJ.