VictorJuiz/thesis huggingface dataset:
A Computational Model of the Acquisition of Mental State Verbs
Keywords: Bayesian models
Cognitive modeling
Computational Linguistics
Mental state verbs
verb acquisition

Children first use verbs to refer to inner mental states, such as thoughts and beliefs, only towards their 4th birthday. Presenting this ability serves as important evidence of the stage of cognitive development of a child since it entails an ability to conceptualize mental states. This developmental milestone is necessary for children to engage in social interaction since appropriate interaction relies on directing the interaction and anticipate actions surrounding them given the mental states of others. Thus far, psycholinguistic models have been widely focused on the development of cognitive and linguistic skills required to enable the expression of mental states by children. However, while computational models have proved to be a useful tool in studying the developmental trajectory of similar language acquisition phenomena, no computational model to our knowledge has been used to analyze the linguistic development required for the expression of mental states. In this thesis, I present a computational model used to analyze the way linguistic development plays a role in learning to express mental state verbs. The computational model in this thesis offers an integrated framework that simultaneously models several of the cognitive and linguistic factors in the acquisition of mental state verbs. The cognitive factor simulates the difficulty in attending to mental states, while the linguistic properties represent the typical semantic and syntactic properties of use of mental state verbs, e.g., reference to mental states using a sentential complement syntactic structure. The experimental work in this thesis replicates psycholinguistic observations from the child acquisition of mental state verbs. The results of these experiments shed light on the facilitating role of certain linguistic properties of mental state verbs in learning to verbally refer to mental meanings. Importantly, I achieve these results within the context of naturalistic language input that mimics the complexity and diversity of use of mental state verbs and verbs from additional semantic classes in child-directed speech. Finally, I present a novel extension to an existing computational model of verb argument structure learning that enables the simultaneous and incremental learning of verb classes. The novel model offers a probabilistic framework to analyze monotonically growing verb classes in comparison to previously offered batch models that limit the capabilities of the computational simulation. Moreover, this model gives support to the importance of an additional linguistic property of mental state verb, i.e., the role of their use with syntactic structures other than the well-studied co-occurrence with sentential complement syntax in child-directed speech. Finally, I show the contribution of this novel computational component in comparison with previous computational models in a wider context of argument structure learning.
Essays on the Economics of Media Coverage of Climate Change
Keywords: climate change
media bias
media economics

For the majority of people, media coverage is the primary source of information about important issues of the day. I explore media coverage, particularly in relation to climate change, in the three chapters of this thesis. The first chapter analyzes the divergence between the views of the scientific community and the public through the lens of media capture. I develop an objective measure of the tone of climate change coverage by creating an index which uses phrase frequency analysis to compare newspaper text with the UNâ s IPCC reports and the Heartland Instituteâ s skeptical response. The index captures the extent to which newspaper text aligns with each report, classifying text that shares more language with the IPCC report as being more environmental. Using the index and panel data on advertising spending, I implement an instrumental variables strategy and find that advertising from carbon-emitting industries increases the prevalence of skepticism and lowers the quantity of coverage of climate change in US newspapers. The second chapter proposes a model that explores the issue of media bias. The model complements the literature that discusses demand-driven (Gentzkow and Shapiro, 2006) and supply-driven (Baron, 2006) bias by showing that biased coverage can arise when neither media outlets nor consumers are biased. Instead, the bias in the model I present evolves from the premise that information is costly for media outlets to acquire. The third chapter presents an empirical analysis of competition among newspapers. Newspapers compete by taking a homogeneous input -- the news that is available to report on -- and producing a differentiated output: the coverage they provide. I study the mechanics of this process by analyzing coverage of climate change in British newspapers during the period 2006-2015. Using Latent Dirichlet Allocation to classify newspaper articles by topic, I compare the prevalence of topics across newspapers. Newspapers tend to cover the similar topics at a given time, but are able to differentiate themselves based on which topics they cover most. If a newspaper usually provides extensive coverage of a topic related to climate change, it will increase coverage more in response to a news story involving that topic.
Institutions and Stakeholder Influence on CEO Compensation and Corporate Social Responsibility
Keywords: CEO Compensation
Corporate Social Responsibility
Institutions
Stakeholders

This thesis of three (stand-alone) chapters centres around the premise that institutions and stakeholders influence corporate policy, in particular with regards to CEO Compensation, and Corporate Social Responsibility (CSR). Chapter 1 focuses on the implicit regulation that unions may pose to corporate governance, and finds that during the financial crisis (2008-2011), union density at corporate level was positively related to non-equity components of CEO pay such as salary and pension contributions with no significant effects on equity components such as options and restricted stock units. These findings have some precedence in the literature but they also go counter to more prominent theoretical predictions and empirical research which posit that unions should in fact reduce CEO compensation. Chapter 2 looks at the influence of unions on CSR profiles and, through the lenses of stakeholder and neo-institutional theories, finds that unions generally tend to gear companies towards more internal rather than external CSR. However, the observed effect of unions on the internal-external CSR profile of companies is not linear. At low levels of unionization, there is a substitution effect where companies substitute internal for external CSR, but at higher levels of unionization, both elements complement and reinforce each other. Drawing from the employment relations literature, I propose that the complementarity perhaps points to a labour-management relationship which is based on more equally-shared power, trust, and loyalty. Chapter 3 looks at the causal impact of regulatory changes on Corporate Social Performance. Using difference-in-difference techniques, I find that forcing companies to disclose CSR practices makes them improve their social performance. I attribute this finding to firms facing higher social pressures and firms being soft targets for activists given that their CSR practices become public information. Further, I find that the social and governance elements of CSR improve more than the environment aspect. This finding bodes well with stakeholder identification and salience theory, which posits that stakeholders who have power and whose claims are more legitimate and urgent will be given more attention by management. All three chapters reinforce the hypothesis that institutional pressure and stakeholder pressure have an impact on Corporate policy. The data used in this thesis come from new and proprietary sources, and cover three different countries (Canada, the UK, and India), adding to the expansion of the management literature.
Towards Unspecific Detection of Pathogenic Agents in Environmental Waters
Keywords: Biosensors
Electrochemical Biosensors
Environmental Waters
Pathogen Detection
Toll-Like Receptors

The need for effective and efficient techniques for detection of microorganisms in water samples is as strong as ever because the health risk posed by consumption of pathogen contaminated water remains to be a serious threat to the population. The techniques which are currently and commonly used for water-quality monitoring rely on lengthy and time-consuming culturing procedures which do not allow for rapid decision making process. PCR-based methods, DNA sequencing-based methods and Immunofluorescence tests are recently being introduced by different researchers as promising pathogen detection techniques however, these techniques are complex, expensive and can also be time-consuming. One of the research streams focused on for the purpose of pathogen detection in water is biosensor technology. One of the integral properties of biosensors is their high specificity towards their analyte of interest. Although for many applications such a high specificity is a necessity, for some applications such as pathogen detection in water, it can be restrictive and will be considered a limitation. As such a highly specific biosensor will only detect one pathogenic agent and miss the other possible pathogens. In this project, the possibility of using Toll-Like Receptors (TLRs); immunoproteins from higher organisms, as bio-recognition elements in biosensors for detection of a broad class of pathogens in water was evaluated. Toll-Like Receptors recognize and bind to the molecular patterns of pathogens and upon this binding the immune response against that pathogen is initiated. Using TLR3-modified Au sensors, detection and quantification of polyinosinic-polycytidylic acid (Ploy (I:C)); a mimic of dsRNA; the molecular signature of viruses was achieved with a detection limit of 0.18 Âľg mL-1. Also the detection and quantification of lipopolysaccharide (LPS); the molecular signature of Gram-negative bacteria, was possible using TLR4-modified Au sensors. However, TLR4-modified Au sensors were not able to discriminate the LPS samples from E. coli and Salmonella as a result of the unspecific approach to detection (detection of Gram-negative bacteria as a broad class of pathogens and not any specific bacterium). The limit of detection for the quantification was 1.3Ă 10-4 EU mL-1 and 1.5Ă 10-4 EU mL-1 for LPS from E. coli and LPS from Salmonella, respectively. The opportunities, challenges and drawbacks of using TLRs as bio-recognition elements for biosensors for pathogen detection in water also were discussed and a new research work based on multiplexing TLR sensors and pattern recognition was suggested for future work.
Post-transcriptional Regulatory Mechanisms Controlling Development of Murine Cerebral Cortical Precursors
Keywords: brain development
neurogenesis
post-transcriptional regulation
RNA-binding proteins
stem cells

The complex neural circuitry of the mammalian nervous system arises from a small pool of neural precursors that, during development, sequentially gives rise to neurons, astrocytes and oligodendrocytes. Several molecular mechanisms and environmental stimuli regulate the expansion of the neural precursor pool and the subsequent generation of differentiated progeny. In this thesis, I sought to investigate whether post-transcriptional regulation plays a role in the development of mammalian neural precursors. In the first part of this thesis, I show that the double stranded RNA binding protein Staufen2 is part of a repressive complex, with Pumilio2 and DDX1, that prevents early differentiation of neural precursors into neurons by repressing the translation of neurogenic mRNAs such as prox1. In the second part of the thesis I show that Smaug2, a translational repressor, also prevents early generation of neurons by repressing nanos1 mRNA, which encodes another RNA-binding protein. I also show that nanos1 repression occurs by its inclusion in a P-body like granule with 4E-T, another known repressor of mRNA translation. In the last chapter I identify mRNAs associating with Smaug2 and I suggest that Smaug2 may have additional functions that are independent of nanos1 regulation and that unlike nanos1, which is regulated together by Smaug2 and 4E-T, the majority of Smaug2 mRNAs are regulated independently from 4E-T. Together, these studies suggest that several RNA-binding proteins are crucial regulators of cortical development and that they perform this role by forming several, largely independent, repressive RNA-protein complexes. I suggest that these repressive complexes prime neural precursors to generate progeny at the appropriate time by repressing proneurogenic mRNAs until the appropriate developmental cue.
Exploring Patient-centred Primary Care in Family Health Teams
Keywords: co-location
Family Health Team
interprofessional team
patient-centred care
primary health care
workflow

Background: Family Health Teams (FHTs) are primary care models established to provide comprehensive, patient-centred care to Ontarians through an interprofessional team approach. However, little is known about how patient-centred interprofessional team care is operationalized in a FHT, specifically regarding the workflow processes employed across diverse clinical contexts. Objectives: To (1) define patient-centred care in an interprofessional primary health care team context; (2) to identify factors that act as either enablers or barriers to operationalizing patient-centred care; and (3) propose a framework of patient-centred interprofessional team care. Design: Qualitative approach Method: Semi-structured interviews were conducted with 16 patient and 24 provider participants from three Family Health Teams in Ontario, Canada. Participants were asked to describe experiences defining aspects of patient-centred care and how contextual factors influenced their experiences. Interview questions were guided by the Patient Centred Clinical Method (Stewart et al. 2003). Transcripts were coded by two independent investigators using a content analysis approach, followed by discussion to reach consensus. Data analysis was guided by the qualitative analysis framework defined by Miles and Huberman (1994): data reduction, data display and conclusion drawing/verification. Results: Patient-centred care is provided by the “wider provider”, defined by participants as a patient’s interprofessional primary health care team. The wider provider is formed through referrals and may adjust in response to patient needs. Members of the wider provider leverage information collected by one another to address scope appropriate patient needs. Participants described contextual factors (such as co-location status of providers and access to electronic patient records); and individual factors (such as physician referral practices) that act as either intentional or unintentional workflow “blocks” on the functioning of the “wider provider”. These “blocks” encourage alternate patterns of workflow, or “workarounds”, which impact both the patient and provider experience. Conclusion: In an interprofessional primary health care team context, a patient-centred experience is defined by factors beyond just the patient’s relationship and interaction with an individual provider. This study presents a novel framework of patient-centred care in an interprofessional primary health care team context.
Optimized Time-dependent Congestion Pricing System for Large Networks: Integrating Distributed Optimization, Departure Time Choice, and Dynamic Traffic Assignment in the Greater Toronto Area
Keywords: Congestion Pricing
departure time choice
Distributed genetic optimization
dynamic traffic assignment simulation
large-scale network
schedule-delay cost

Congestion pricing is one of the most widely contemplated methods to manage traffic congestion. The purpose of congestion pricing is to manage traffic demand generation and supply allocation by charging fees (i.e., tolling) for the use of certain roads in order to distribute traffic demand more evenly over time and space. This study presents a system for large-scale optimal time-varying congestion pricing policy determination and evaluation. The proposed system integrates a theoretical model of dynamic congestion pricing, a distributed optimization algorithm, a departure time choice model, and a dynamic traffic assignment (DTA) simulation platform, creating a unified optimal (location- and time-specific) congestion pricing system. The system determines and evaluates the impact of optimal tolling on road traffic congestion (supply side) and travellers’ behavioural choices, including departure time and route choices (demand side). For the system’s large-scale nature and the consequent computational challenges, the optimization algorithm is executed concurrently on a parallel cluster. The system is applied to simulation-based case studies of tolling major highways in the Greater Toronto Area (GTA) while capturing the regional effects of tolling. The models are developed and calibrated using regional household travel survey data that reflect travellers’ heterogeneity. The DTA model is calibrated using actual traffic counts from the Ontario Ministry of Transportation and the City of Toronto. The main results indicate that: (1) more benefits are attained from variable tolling due to departure time rescheduling as opposed to mostly re-routing only in the case of flat tolling, (2) widespread spatial and temporal re-distributions of traffic are observed across the regional network in response to tolling significant – yet limited – highways in the region, (3) optimal variable pricing mirrors congestion patterns and induces departure time re-scheduling and rerouting patterns, resulting in improved average travel times and schedule delays at all scales, (4) tolled routes have different sensitivities to identical toll changes, (5) the start times of longer trips are more sensitive (elastic) to variable distance-based tolling policies compared to shorter trips, (6) optimal tolls intended to manage traffic demand are significantly lower than those intended to maximize toll revenues, (7) toll payers benefit from tolling even before toll revenues are spent, and (8) the optimal tolling policies determined offer a win-win solution in which travel times are improved while also raising funds to invest in sustainable transportation infrastructure.
Embedded Networks on Chip for Field-programmable Gate Arrays
Keywords: CAD
FPGA
NoC

Modern field-programmable gate arrays (FPGAs) have a large capacity and a myriad of embedded blocks for computation, memory and I/O interfacing. This allows the implementation of ever-larger applications; however, the increase in application size comes with an inevitable increase in complexity, making it a challenge to implement on-chip communication. Today, it is a designer's burden to create a customized communication circuit to interconnect an application, using the fine-grained FPGA fabric that has single-bit control over every wire segment and logic cell. Instead, we propose embedding a network-on-chip (NoC) to implement system-level communication on FPGAs. A prefabricated NoC improves communication efficiency, eases timing closure, and abstracts system-level communication on FPGAs, separating an application's behaviour and communication which makes the design of complex FPGA applications easier and faster. This thesis presents a complete embedded NoC solution, including the NoC architecture and interface, rules to guide its use with FPGA design styles, application case studies to showcase its advantages, and a computer-aided design (CAD) system to automatically interconnect applications using an embedded NoC. We compare NoC components when implemented hard versus soft, then build on this component-level analysis to architect embedded NoCs and integrate them into the FPGA fabric; these NoCs are on average 20-23x smaller and 5-6x faster than soft NoCs. We design custom interfaces between the embedded NoC and the FPGA fabric to transport data efficiently without compromising on the FPGA's configurability, and then we enumerate the necessary conditions to implement FPGA-compatible communication styles using our NoC. Next, our application case study with image compression shows that an embedded NoC improves frequency by 10-80% and reduces the utilization of scarce long wires by 40%. Additionally, we leverage our embedded NoC to create an Ethernet switch that has ~5x more bandwidth and ~3x lower area compared to other FPGA-based switches. Finally, we create a CAD system (LYNX) that automatically connects an application using an embedded NoC. We compare our LYNX + embedded NoC interconnection solution to a commercial CAD tool that generates a custom soft bus and show that we improve both the efficiency and performance of most systems.
Development and Modeling of Functionally Graded Porous Structures and Composites
Keywords: finite element analysis
functionally graded materials
microstructure homogenization
modeling
porous composites
viscoelasticity

Functionally graded (FG) materials provide the solution to spatial and temporal control of material properties with smooth transition at different length scales of the material structure. Tailoring material properties is becoming a necessity to the evolving products that have certain design requirements. Smooth transition at different length scales is important to eliminate abrupt change of material properties and microstructure which avoids stress jumps and other structural problems like delamination. Two challenges are associated with FG materials, i) fabrication procedures with correlation to the microstructure and mechanical properties, and ii) the numerical treatment of FG materials that accounts for the microstructural details and gradient. This research addresses both challenges with enough depth to design FG porous polymeric materials. The fabrication process focused on developing FG porous structures and stitched composites. Different processing parameters were successfully correlated to the microstructure and showed the potential to produce structures with desired features. The microstructure was further correlated to mechanical properties like creep compliance and impact energy absorption. This motivated the development of numerical homogenization procedures that help in conducting numerical experiments with FG structures. A statistical based homogenization model was developed, which accounted for the microstructure gradient. The model was implemented to higher order plate theory with stretching terms, and provided accurate results compared to experimental data. To alleviate the dependency on experimental data which was the drawback of the statistical model, a purely numerical procedure was developed which is based on 3D reconstruction of the microstructure to statistically reduce the system but preserve the same main features of the parent structure. The model was in good agreement with experimental results. This makes the homogenization procedure independent in providing details about mechanical properties for an assumed graded porous structure or composite. Combined with the processing-microstructure graphs, the numerical tool can be used in an inverse-homogenization procedure to make graded porous structures with desired mechanical properties.