VictorJuiz/thesis huggingface dataset:
Essays on Fundamental-Driven Quantitative Financial Models
Keywords: Commodities
Fundamental-Driven Models
Hedging
Investments
Pricing Models

Fundamental and quantitative analysis are the two traditional approaches to analysis and assessment of financial products. Fundamental analysis measures intrinsic value by incorporating economic and financial indicators while quantitative approaches utilize black box methodology for valuations. In this work, we set out to develop fundamental-driven quantitative models, incorporating the underlying fundamental factors with flexibility and superiority of quantitative methods. We focus our attention on commodity markets as they represent a growing field of investment with implications for consumers, producers, and national security. We develop toolkits for model calibration and back-testing to facilitate the model development process. Utilizing the toolkits, three of the most popular commodity models were applied to crude oil prices and hedging of over-the-counter crude oil futures options. It was shown that model and parameter uncertainty have significant impact on hedging profit and loss distributions, demonstrating the need for a model that could match the price dynamics better and provide better convergence to a set of global optimal parameters. As such, the economic theory of ``price elasticity of normalized excess supply'' was developed. The economic model demonstrated superior explanatory power for crude oil, copper, nickel, zinc, lead, and tin over the period of 1995-2017. Furthermore, a system of stochastic differential equations (SDEs) model based on the economic theory was developed with analytical solutions and the model was used to value commodity derivatives and forecast commodity prices. Application of the SDEs model on crude oil demonstrated superior forecasting power compared to market participants' and other models. Furthermore, the SDEs model was applied to copper prices and then utilized within a real option analysis model to value a copper mining project, demonstrating one possible application of the theory and model.
An Investigation of Using Random Dot Patterns to Achieve X-Ray Vision For Near-Field Applications of Stereoscopic Video Based Augmented Reality Displays
Keywords: Augmented Reality
Depth Perception
Human Factors
Random Dot Pattern
Stereoscopic
X-ray Vision

As one of the most interesting applications of Augmented Reality, ‘X-ray vision’ involves the presentation of computer generated objects as if they lie behind a real object surface. Achieving this notion presents several challenges, including perceptual ambiguity about the ordinal and absolute depth of the virtual object relative to the real object’s surface and maintaining sufficient information about the virtual and real object. This thesis investigates how random dots on an object’s surface can facilitate seeing a virtual object as behind the surface with stereoscopic displays. Using a psychophysical method, Experiment 1 demonstrated the potential of this approach to improve ordinal depth judgements. Experiment 2 investigated the effect of dot size and dot density on transparency ratings of the real surface while preserving surface details. Paired Comparison results revealed an advantage of the proposed method in comparison with the ‘no pattern’ condition for the transparency ratings. Surface detail preservation was also descriptively shown to decrease with increasing dot density and dot size. Experiment 3 explored the impact of variations in image sharpness, dot size and dot density of the random dot pattern, and the depth of the virtual object, on the accuracy and difficulty of absolute depth judgements about the virtual object. Compared to the ‘no-pattern’ condition, the random dot patterns improved the accuracy of depth judgments. In estimating the depth of the virtual object when random dot patterns were used, no main effect of dot size was found. However, interactions suggested higher dot densities lead to smaller errors. Moreover, subjective difficulty ratings in performing depth judgements with sharper patterns may indicate that the random dot patterns support the use of convergence as a beneficial depth cue. The implications of these findings for the design of ‘X-ray’ displays for near-field (including medical) stereoscopic AR are discussed.
Monitoring the carbon cycle: Evaluation of terrestrial biosphere models and anthropogenic greenhouse gas emissions with atmospheric observations
Keywords: atmospheric CO2
flux inversion
remote sounding
terrestrial biosphere models
urban greenhouse gas emissions

Reliable projections of climate change will require terrestrial biosphere models (TBMs) that produce robust projections of changes in the exchange of CO2 between the atmosphere and terrestrial biosphere. In this thesis, atmospheric CO2 observations are used to evaluate TBMs. First, the sensitivity of several observing systems to surface fluxes of CO2 is characterized. This analysis identifies the spatiotemporal scales over which atmospheric CO2 observations provide significant constraints on net ecosystem exchange (NEE) fluxes. Second, constraints from atmospheric CO2 and solar-induced fluorescence (SIF) observations are combined to evaluate the seasonality of NEE, gross primary productivity (GPP) and ecosystem respiration (Re) fluxes over the northern mid-latitudes for a set of TBMs. It is shown that model-based seasonal cycles of Re exhibit systematic differences from optimized Re constrained by atmospheric CO2 and SIF measurements, with the models overestimating Re during June-July and underestimating Re during the fall. Further analysis suggests that the differences could be due to seasonal variations in the carbon use efficiency and to seasonal variations in the leaf litter and fine root carbon pool. Finally, the ability of TBMs to simulate interannual variability (IAV) in NEE is evaluated. IAV in NEE produced by a set of TBMs and CO2 flux inversions is compared to proxies of IAV in the carbon cycle, including temperature anomalies, SIF anomalies, and the Palmer drought index. It is shown that CO2 flux inversions that assimilate observations from the Greenhouse Gases Observing Satellite (GOSAT) out-perform most TBMs in recovering NEE anomalies driven by climate anomalies, suggesting that GOSAT CO2 flux inversions can be used to evaluate NEE anomalies produced by TBMs on large scales. This thesis also describes the installation of an open-path Fourier transform infrared spectroscopy (OP-FTIR) system in downtown Toronto. This system provides continuous observations of CO2, CO, CH4 and N2O, which, in combination with other observing stations, will provide valuable top-down constraints on GHG emissions from Toronto. An initial evaluation of this instrument is performed and comparisons of the observed gases with meteorological observations and CO2, CO, and CH4 measurements at a nearby site are presented.
Architectures and Tools for Efficient Reconfigurable Computing
Keywords: CGRA
FPGA

Recent decades have seen large growth in the silicon industry with transistor scaling and transistor count approximately doubling every two years. With the continued growth of transistors-per-chip and increasing power density, dark silicon challenges have risen. Reconfigurable computing poses a possible solution to some of the challenges through improving performance and energy efficiency by tailoring the hardware to the application. Field-Programmable Gate Arrays (FPGAs) are a platform for realizing reconfigurable computing and have had traction for over a decade. Their recent introduction into mainstream data-centres bodes well for the field. Another type of reconfigurable architecture, Coarse Grained Reconfigurable Arrays (CGRAs), poses one other platform for computing. Being more specialized than FPGAs, CGRAs’ main selling point is increased efficiency versus FPGAs, at the cost of platform flexibility. This dissertation looks at efficient computing first from the perspective of FPGAs, developing new architectures and new CAD tools, making for a more efficient FPGA. The proposed FPGA architecture consists of a hybrid multiplexer / look-up-table logic block that has reduced area with respect to traditional architectures. Then, with the prospect that CGRA architectures hold, we develop an open-source framework, CGRA-ME, for the modelling and exploration of CGRAs. This unifying software framework incorporates, architecture description through a custom language, architecture modelling, application mapping, and RTL generation, and allows further development of CGRA architectures and related CAD tools throughout the research community. Within the CGRA-ME framework, a new architecture-agnostic application mapper formulated in an integer linear program was also developed for generic CGRAs. Through this dissertation, we have made headway towards more efficient reconfigurable architectures through architecture design and related CAD and are optimistic that these contributions will have positive impact on further research and industrial application of reconfigurable architectures.
The upper small intestinal microbiota regulates nutrient sensing pathways to influence glucose homeostasis
Keywords: Diabetes
Gastrointestinal tract
Microbiota
Nutrient sensing
Obesity

Sedentary lifestyles, excessive food intake and genetic predisposition all contribute to the development of Type 2 Diabetes, which is characterized by a dysregulation in glucose homeostasis. The microbiota within the gastrointestinal tract has been suggested to play a role in glucose regulation. However, the majority of work to date has focused on the distal intestinal microbiota despite the fact that the upper small intestine has immense glucoregulatory function and harbors a significant microbial community. Indeed, nutrients that enter the upper small intestine activate negative feedback pathways to maintain glucose homeostasis. Interestingly, some nutrient sensing mechanisms are impaired in response to a high fat diet (HFD). However, whether HFD-induced changes in the gut microbiota alter these selective pathways to impair glucose regulation, remains unknown. Furthermore, the anti-diabetic therapy metformin alters the distal gut microbiota and modifies small intestinal nutrient sensing. However, whether changes in the upper small intestinal microbiota and/or nutrient sensing mediate the antidiabetic action of metformin is currently unknown. Through Study 1 and 2 we demonstrate that upper small intestinal lipids and glucose activate nutrient sensing pathways to regulate glucose homeostasis in healthy rodents, while HFD-feeding impairs these pathways in parrallel to reductions in key nutrient sensing proteins. In addition, in conditions of HFD-feeding we see a reduction in the abundance of the bacterial genus Lactobacillus in the upper small intestine, while metformin treatment restores Lactobacillus abundance to healthy conditions. Interestingly, restoration of Lactobacillus abundance via administration of Lactobacillus probiotics, or transplantation of upper small intestinal microbiota from healthy or metformin-treated donor rats, improves nutrient sensing and glucose homeostasis via upregulation of nutrient sensor expression in the upper small intestinal mucosa. This work highlights a novel role for the upper small intestinal microbiota in glucose regulation and will lay the groundwork for the development of gut microbiota-targeted antidiabetic therapy.
DESIGN PRINCIPLES FOR LAB-IN-A-PHOTONIC-CRYSTAL BIOSENSORS
Keywords: biosensor
label-free sensing
lab-on-a-chip
photonics crystal

A new class of photonic crystal biosensors, geared towards rapid biomedical diagnostics at the point-of-care, is proposed. These Lab-in-a-Photonic-Crystal devices employ cascaded transmission of light through a photonic band-gap material by leveraging the interaction of surface modes at entry and exit termini with waveguide modes in centrally-embedded line-defects. A rich spectral signature, composed of conventional resonance-shifts complemented by transmission-level modulation, enables these devices to discriminate between combinations of various bio-markers, attached as thin layers to the surface and line-defect artifacts. An initial two-dimensional conceptual paradigm is proposed, and then developed into a full-fledged, fabrication-feasible, three-dimensional design. The final design consists of a high-index nano-pillar array forming a two-dimensional, fluid-infiltrated photonic crystal with two surface gratings and a central waveguide, whose optical modes are trapped by index-guiding in the third dimension. The nano-pillars are housed in a glass trough, while being open to fluid at the top, allowing easy addition of bio-fluid samples onto the device, and helping prevent clogs due to macroscopic suspended impurities. The design is further optimized, both optically and structurally, by the introduction of a thin-layer of high-index backing material between the nano-pillars and the glass trough substrate, reducing nano-pillar height by half of the unbacked-design height. The system is capable of detecting all eight possible combinations of up to three distinct bio-markers, with the possibility to increase the number of sensed bio-markers through the use of multi-mode waveguides (and potentially, multi-mode surfaces). Using index-guided bulk modes of the three-dimensional structure, a mechanism for calibrating the device under impurity-induced variations in fluid refractive index is proposed. The Lab-in-a-Photonic-Crystal device is suitable for the detection of multiple diseases, or multiple stages of a given disease, in a single optical measurement. Its functionality, as well as robustness to imperfect fabrication, has been validated using Finite-Difference Time-Domain solutions to Maxwell’s Equations.
Probing topological insulators and itinerant magnets at the nano- and atomic-scale with Andreev reflection spectroscopy
Keywords: Itinerant antiferromagnet
STM
Superconductivity
Topological Insulator
XPS
YBCO

Low-temperature Andreev reflection (AR) spectroscopy measurements were performed on topological insulators, various compounds with atomically-smooth surfaces, a half-metallic manganite, and itinerant antiferromagnets. The purpose of these measurements was to expand the study of AR to include the various phenomena present in these materials. To understand the superconducting proximity effect that occurs across the c-axis interface of non-ideal three-dimensional topological insulators, we performed point-contact AR spectroscopy on Bi2X3 (X=Se,Te) single crystals with Nb tips at 4.2 K. Robust AR spectra were observed and analyzed with the Blonder-Tinkham-Klapwijk theory, taking into account tip-induced spin-orbit coupling, Fermi-surface mismatch, and the co-presence of bulk band and topological surface states at the Fermi level. Spectral analysis based on realistic band structures indicates that the superconductivity that can be proximity-induced into Bi2X3 is predominantly non-topological. The spatial resolution of AR measurement was increased from the nano to the atomic scale by exploiting the resonant nature of d-wave AR for near-nodal junctions in the strong-barrier regime. Atomic-scale measurements of d-wave AR were achieved using YBa2Cu3O7−δ (YBCO) single crystals as superconducting tips at 4.2 K, to form non-contact junctions on graphite, 2H-NbSe2, and Bi2Se3. Differential conductance spectroscopy showed pronounced zero-bias peaks, characteristic of d-wave Andreev resonance, and whose heights were dependent on the spin-polarization of paramagnetic nickelate and half-metallic ferromagnetic manganite thin films. These results demonstrate that d-wave AR can potentially be used to study a variety of spin-based phenomena at the atomic scale. To study how AR is affected by itinerant antiferromagnetism, we performed d-wave AR spectroscopy with YBCO to probe TiAu and variously-oxidized Nb (NbOx) samples. X-ray photoelectron spectroscopy was also used on the latter to measure their surface oxide composition. For NbOx, samples with a greater degree of oxidation exhibited greater suppression of d-wave AR features. For both TiAu and NbOx, low-impedance tip-sample junctions suppressed AR more than high-impedance junctions. AR suppression was also observed above the Néel temperature in both compounds, implying that spin fluctuations can suppress d-wave AR. Our data demonstrate that d-wave AR is suppressed by itinerant antiferromagnetism, and help identify suitable surface treatments of Nb when used in superconducting devices.
Projection schemes for high-dimensional problems in stochastic structural dynamics
Keywords: a priori error estimation
curse of dimensionality
functional ANOVA decomposition
polynomial chaos expansions
stochastic ordinary differential equations
stochastic structural dynamics

The focus of the present thesis is to formulate efficient schemes to solve high-dimensional stochastic ordinary differential equations (SODEs) encountered in stochastic structural dynamics. Most of the methods for the parametric uncertainty analysis of SODEs suffer from the curse of dimensionality. To alleviate it, we investigate a few different methods. Firstly, we formulate a Generalized Spectral Decomposition (GSD) method for linear SODEs. It is a stochastic Galerkin method proposed to alleviate the curse of dimensionality faced by the classical gPC-based stochastic Galerkin projection scheme. Numerical studies suggest that it is not straightforward to scale the GSD method to large-scale problems since it is a sequential iterative scheme and there is no clear guidance on how to optimize the number of expansion terms. Secondly, we propose an anchored ANOVA Petrov-Galerkin (AAPG) scheme for linear SODEs. The main idea of the AAPG scheme is to approximate the dynamic response using a Hoeffding functional ANOVA decomposition along with appropriate constraints to ensure the uniqueness of the decomposition. We show that when the test functions in the weighted residual form are chosen appropriately, the original high-dimensional stochastic problem can be decoupled into a sequence of low-dimensional stochastic subproblems that can be solved independently of each other. We also extend the AAPG scheme to nonlinear SODEs and reach the conclusion that the AAPG scheme holds significant potential to alleviate the curse of dimensionality for linear and nonlinear SODEs, as confirmed by numerical studies. Finally, we carry out a theoretical analysis of the AAPG scheme focusing on a priori error estimation and computational cost. This analysis motivates the formulation of an adaptive version of the AAPG scheme that enables additional improvements in computational efficiency by reducing the number of subproblems to be solved. Numerical studies on test problems with up to 80 stochastic degrees-of-freedom suggest that the adaptive AAPG can provide a similar level of accuracy as the original scheme while providing a substantial reduction in computational cost.
Pedagogy of the Deceased: The Cemetery as a Classroom for Community Development and Hope
Keywords: Cemeteries
Community Development
Critical pedagogy
Death education
Hope
Paulo Freire

As individuals and as communities, how do we learn to recognize that death contributes to life? The purpose of my research is to explore how matters pertaining to death and mortality can teach us to connect everyday personal experiences with issues of injustice, violence, and ecological crisis. Working with Freirean critical pedagogy in conjunction with key insights from the North American death education movement, my research lays groundwork for an innovative “pedagogy of the deceased” whereby humanizing praxis (as per critical pedagogy) can be integrated with death-inclusionary praxis (as per death education). By way of contextualizing and embodying this integrated pedagogy of the deceased, my research includes a place-based component (based in Waterloo, Ontario) involving a group of eleven people who participate in a series of Cemetery Café meetings in two local cemeteries. While recognizing the very wide array of funerary traditions (cremation, burial, sky burial, etc.) including many traditions in which the significance of mortality does not depend on any particular site, my research focuses on the cemetery as one particular place in which community-based education can be usefully facilitated. The Cemetery Café includes discussions, embodied learning activities, individual reflection, and group brainstorming about how the cemetery can foster community development and hope. Datasets include one-to-one interviews, group-generated flipchart papers, photographs, individual homework assignments, and my own researcher field notes. Following an integrative-interpretive approach, I first analyze the “who” of the pedagogy of the deceased by asking who gets to participate in this pedagogy. Secondly, I analyze the cemetery as a classroom, including consideration of how the cemetery can be a place for embodied learning. Thirdly, I analyze what kind of consciousness is cultivated within this pedagogy, and I explore the relationship between human mortality and critical consciousness. In line with the aforementioned analytical work, I conclude by looking at (1) indigenizing the cemetery and our view of mortality, (2) establishing teaching cemeteries (along the lines of teaching hospitals), (3) advancing an ecological approach to death and cemeteries through such practices as green burial, and (4) working and hoping for an end to violence.
Genetics and Epigenetics of Suicidal Behavior
Keywords: CpG
DNA methylation
Epigenetics
Schizophrenia
single-nucleotide polymorphism
Suicidal behavior

According to the latest World Health Organization report (2012), suicide was the second leading cause of death among people. Approximately 90% of suicide victims or suicide attempters are diagnosed with a psychiatric disorder. Schizophrenia patients have been shown to be at an increased risk for suicide. Although many risk factors have been identified, few of these factors have a significant clinical impact on predicting suicide. Whether genetic and epigenetic alterations are potential risk factors for suicidal behavior in schizophrenia has yet to be investigated. A well-characterized sample of schizophrenia patients with a distinct suicide attempt history was recruited to evaluate the influence of genetic and epigenetic alterations as potential risk factors for suicidal behavior. Sociocultural and clinical variables were also examined. Selected genes were genotyped to test whether independently or combined, specific clinical variables and DNA variants can accurately predict suicidal behavior. Our study found no evidence that genetic or epigenetic factors have a significant influence on the risk for suicidal behavior in schizophrenia.