VictorJuiz/thesis huggingface dataset:
Efficient Structured Prediction for Visual Scene Understanding
Keywords: Free Space
Markov Random Field
Object Detection
Scene Understanding
Segmentation

This thesis focuses on three topics in visual scene understanding, sorted from low level to high level: unsupervised segmentation, collision-free space detection and holistic scene understanding. All of them are modeled within the framework of probabilistic graphical models. We tackle the problem of unsupervised segmentation in the form of superpixels, with the emphasis on speed and accuracy. To this end, a real-time coarse-to-fine topologically preserving segmentation algorithm [188] is proposed. In this algorithm, we define the problem as a markov random field (MRF) which preserves the boundary in the image and the topology of superpixels. We propose a coarse to fine optimization technique that speeds up the inference dramatically without sacrificing the accuracy for both static images and stereo images. This thesis next tackles the problem of the drivable collision-free space from a monocular video [189]. In contrast to previous approaches that use stereo cameras or LIDAR, we show a method to solve this problem using a single camera. The free space estimation is reduced to an inference problem on a 1D graph, where each node represents a column in the image and its label denotes a position that separates the free space from obstacles. We exploit both spatial and temporal features to define potential functions on the 1D graph, whose parameters are learned through structured SVM. The inference on the 1D graph can be efficiently and exactly solved by dynamic programming. Lastly this thesis interprets the scene by performing scene classification, object detection and semantic segmentation. Rather than solving these problems independently, the proposed method [187] is based on a holistic model which jointly reasons about three individual problems as a whole within a conditional random field (CRF). Learning and inference in the model are efficient as we reason at the segment level instead of pixel level, and introduce auxiliary variables that allow us to decompose the inherent high-order potentials into pairwise potentials between a few variables with small number of states (at most the number of classes). The experimental results show that the joint model has better performance than solving each problem independently.
Illuminating Mergers of Carbon-Oxygen White Dwarfs and Their Possible Link to Thermonuclear Supernovae
Keywords: Computer simulations
Hydrodynamics
Stellar evolution
Supernovae
White dwarfs

The merger of two carbon-oxygen white dwarfs (CO WDs) can either create a more massive WD, lead to collapse into a neutron star, or explode spectacularly as a thermonuclear, or Type Ia, supernova (SN Ia). It has traditionally been believed that SNe Ia result only from mergers at or above the Chandrasekhar mass (MCh), as these can subsequently become dense enough to trigger runaway fusion. Recently, however, it has been proposed that the merged product, or "remnant", might instead subsequently ignite fusion from high temperatures. This opens the possibility of SNe Ia arising even from sub-MCh mergers. To investigate this, I conducted a series of hydrodynamic simulations of the merging process. I first performed simulations spanning the range of possible mergers using the smoothed-particle hydrodynamics code Gasoline, finding that remnant configurations are roughly homologous for mergers of WDs with the same difference in mass ∆M. In particular, "similar-mass" mergers with ∆M
Towards the Development of Phospholipid-Encapsulated Gold Nanorod Chains for Enhanced Raman Scattering and the Improvement of Student Scientific Communication Skills in Undergraduate Classroom and Laboratory Settings
Keywords: Nanoplasmonics
Nanorods
Phospholipid Encapsulation
Undergraduate Education
Writing Initatives

Depending on functionalization, nanospecies can serve effectively as Surface Enhanced Raman Scattering (SERS) probes. Their efficacy can be improved by tuning their shape and concentrating their electric field profile into smaller regions. This tuning is seen particularly well in nanorods, which concentrate such fields near the rod tips or ends, in ‘hotspots’. These hotspots can be constructively enhanced through the self-assembly of the nanospecies in question, further increasing the enhancement of Raman-active species within. When considering their potential application as SERS probes, both the separation of end-to-end assembled nanorods (gap size), as well as the degree of assembly (chain length), are factors that must be optimized to obtain maximal signal. This thesis reports on the development of robust and variable methods for assembling gold nanorod species in an end-to-end configuration, and for investigating their effectiveness as SERS probes. Using both polymers and short charged ligands with control over their locations of attachment to gold nanorods, nanorod assembly could be initiated in a longitudinal direction either through changing the solvent system or through the introduction of bridging ligands. Exploitation of the inter-rod gap ‘hotspot’ region allowed for significant Raman enhancement of species located in said gap. Using phospholipids to encapsulate the assembling nanorod allowed for significant control over the proportions of species, in terms of length of the nanorod chain. This control allowed for further optimization of the Raman signals from species of interest. This thesis also details the investigation, over a period of several academic years, of the success of the Writing Instruction and Training (WIT) program, an initiative to iteratively improve student written communication skills as they related to scientific chemical communication. In courses ranging from first to third year, undergraduates were provided with opportunities to explore their perceptions of scientific communication and receive extensive and formative feedback on all aspects of their submitted work, with an eye towards fostering improvement. Examining the impact both qualitatively and quantitatively revealed that such a program was highly successful in gradually building upon and expanding student knowledge of the traditions, styles, concepts and progressions of ideas so central to chemical communication.
Peer Similarity and Use of Online and In-person Supports Amongst Adult Children Caregivers: A Mixed Method Study
Keywords: Caregiving
Internet
Online
Peer Support
Social Support
Twitter

Background: Due to current demographics, adult children are increasingly caring for an elderly parent, which can threaten their health. Social support from similar peers can mitigate caregiving-related health declines. Studies have only explored ‘peer similarity’ within the intervention context, limiting our understanding of its role in naturally occurring support. Many carers are turning to the Internet for convenient and efficient access to peer support. Literature to date has largely focused on Internet-based interventions, overlooking carers’ everyday engagement in peer support across communication modalities. Objective: To explore: 1) the relationship between peer similarity and perceived support; and 2) carers’ experiences with online and in-person peer support. Methods: A mixed method study guided by the Interactional Cognitive Model of Social Support. Twitter was used as an innovative recruitment avenue similar to snowball sampling. A web-based survey measured peer similarity and perceived support. Interviews explored carers’ perspectives on peer similarity and experiences with online and in-person peer support. A hierarchical regression analyzed quantitative data and interviews were thematically analyzed. Results: Seventy-one adult children carers (ACCs) participated, with 15 completing qualitative interviews. The mean age of ACCs was 51 and 90% were daughters. Thirty-eight percent of carers were recruited through Twitter and found to be demographically comparable to those recruited through other strategies. ACCs mobilized their existing networks for support and used a blend of communication modalities to meet practical and relational support needs. Type of support received was consistent across online and in-person modalities. Peer similarity positively and significantly influenced perceived support. ‘Shared caregiving experience’ was described as the most important aspect of similarity since it was multi-faceted and optimized support and relationship quality. Conclusion: Interventions for ACCs should emulate their naturally pragmatic support-seeking style. ‘Shared caregiving experience’ and its various dimensions should be comprehensively investigated to inform measurement instruments and peer-matching interventions.
Living Arrangements and Intergenerational Support Among Immigrant and Canadian-born Seniors
Keywords: grandchild care
immigrant seniors
Immigration
Intergenerational relations
intergenerational support
living arrangements

In the context of population aging, growing scholarly attention has been paid to intergenerational relations between seniors and their adult children. However, with increasing proportions of the senior population being foreign born, scholars of family gerontology and immigrant scholars have not joined forces to study intergenerational relations in later-life immigrant families. This dissertation addresses this gap in the literature by examining nativity differences in three aspects of intergenerational relations: living arrangements, intergenerational economic support, and grandchild care. Using pooled data from Canadian censuses and the National Household Survey (NHS), the first analytical chapter finds that, from 1981 to 2011, the higher propensity for immigrant seniors to coreside with their adult children compared to Canadian-born seniors continued to increase. A regression decomposition approach further reveals that the most important factor underlying these temporal trends is shifting immigrant source regions towards countries where intergenerational coresidence is more of a norm. Focusing on coresidential households, the second analytical chapter finds diverse patterns of intergenerational economic support based on nativity status of both elderly parents and adult children and on race. These patterns are explained by compositional differences in demographic and socioeconomic characteristics of adult children more than by those characteristics of elderly parents. Finally, the third analytical chapter examines nativity differences in occasional and regular grandchild care using the General Social Survey (GSS) Cycle 21 and Cycle 25. Immigrant grandfathers with immigrant adult children are less likely to assist with any grandchild care compared to Canadian-born grandfathers, but no nativity difference is found among grandmothers. In addition, compared to those in Canadian-born families, immigrant adult children have lower odds of regularly using child care by grandparents versus using parental care only. Implications of these findings are discussed in relation to the importance of adding a family perspective to studies on immigrant seniors and the importance of ascertaining the underlying factors of nativity differences in intergenerational relations.
Design and Development of a Precision Miniature Multi-DOF Motor Based on Galfenol
Keywords: Galfenol
Impact Drive Mechanism
Miniature Multi-DOF Motor
Nonlinear Magnetization
Precision Positioning

Along with the development of robotics, medical and precision machining industry, novel miniature actuators that can achieve large stroke and high precision positioning has attracted tremendous attentions. There are various mechanisms that can be used to transform energy in magnetic, electrical, and thermal forms into mechanical deformation. The impact drive mechanism is distinguished from the other mechanisms because of the geometry simplicity that renders the mechanism a great advantage in miniature actuators/motors. Conventionally, the impact drive actuators/motors employ the piezoelectric material as the driving element which material suffers significantly from the brittle nature. The objective of this thesis is to develop a novel multi-degree-of-freedom in-plane motor employing the impact drive mechanism to achieve high precision and large stroke motion. Because of the excellent robustness, an iron and gallium alloy, Galfenol, has been used as the driving element. This thesis can be divided into three parts. The first part presents a generalized dynamic model of Galfenol for longitudinal magnetostrictive actuators using the energy approach. Methods, limiting the detrimental effect of eddy-current, have been validated. It is found that the Galfenol has an excellent ability of being employed in miniature resonant actuators/motors. The second part exploits a miniature U-shape Galfenol element as the driving element to develop the miniature motor. Because of the special design of Galfenol, the swing motion of the motor arm is achieved which leads to a strengthened performance. The design can not only achieve a maximum linear motion velocity of 4.03mm/s and a maximum carrying weight of 300g, but also a maximum angular velocity for rotation of 2.65rpm. The third part introduces the feedback control of the proposed motor for achieving the large stroke and high precision positioning with the amplitude modulation and the frequency modulation been explored. Employing the nonlinear amplitude modulation, a positioning accuracy of 500nm is achieved with a finest step resolution of 300nm. Also, this design is capable of achieving a positioning accuracy of 100um in the large stroke motion mode. It is anticipated that this work will provide a preferable choice for broader applications in aforementioned fields.
The Recognition and Mobility of DNA-Double Strand Breaks
Keywords: budding yeast
chromatin mobility
DNA double-strand break
genome stability
telomere

DNA double-strand breaks (DSBs) pose a threat to cell survival and genomic integrity, and remarkable mechanisms exist to deal with these breaks. A single DSB activates a signalling response that profoundly impacts cell physiology, not least through the engagement of DSB repair pathways and the arrest of cell division. Here I study these processes in the budding yeast Saccharomyces cerevisiae and investigate two central themes to this response. First, I examine how the natural ends of chromosomes, telomeres, are differentiated from DSBs by generating DNA ends with increasing telomeric character. I discover a striking transition in the activity of the telomerase inhibitor Pif1 at these ends and propose that this is the dividing line between DSBs and telomeres. Second, I investigate a phenomenon whereby a DSB increases the mobility of chromosomes within the nucleus. This increase in mobility is dependent on the Mec1 kinase and is proposed to promote repair by homologous recombination. I identify that the Mec1-dependent phosphorylation of Cep3, a kinetochore component, is required to stimulate chromatin mobility following DNA breakage and provide a new model for how a DSB affects the constraints on chromosomes. Unexpectedly, I find that increased mobility is not required for DSB repair and instead propose that Cep3 helps arrest the cell cycle in response to a DSB. Finally, my investigation into Cep3 phosphorylation provides new insight into the role of chromatin remodelers and the histone variant H2A.Z in DSB-induced chromatin mobility.
Molecular Mechanisms of Tumor Dendritic Cell Dysfunction in Cancer
Keywords: Cancer
Dendritic Cells
Signalling
TLR2
Tumor Microenvironment

Dendritic cells (DCs) dysfunction in cancer is a well-established phenomenon that is considered one of the key mechanisms of immune evasion. Defects in tumor DCs are caused primarily by tumor-derived factors present in the tumor milieu. However, the mechanisms and the proximal signals that drive this process remain elusive. This thesis explores the molecular mechanisms that drive tumor DC dysfunction. In the first sections of this thesis, I showed the critical importance of tumor-derived toll-like receptor (TLR)-2 ligands in the generation of immunosuppressive Gr-1+ IL-10-producing human and mouse DCs. TLR2 activation induced two parallel synergistic process that converged to activate STAT3: stimulation of autocrine IL-6 and IL-10, and upregulation of their respective cell surface receptors. These processes imprint the capacity of tumor DC in the milieu to respond to lower concentrations of tumor associated cytokines, therefore lowering the STAT3 activation threshold. I identified versican as a soluble tumor-derived factor that ligates with TLR2 to induce DC dysfunction. In the last section of the thesis, I explored the possibility of TLR2 blockade to enhance the efficacy of tumor immunotherapy. TLR2 blockade improved intra-tumor DC immunogenicity. These functional properties was associated with better anti-tumor immune responses elicited by a GM-CSF producing whole cell tumor vaccine (GVAX), and higher rates of proliferation and expansion of adoptively transferred cytotoxic T cells (CTLs). Overall, the findings presented in this thesis provide a basis of understanding the molecular mechanisms of DC dysfunction in cancer and highlight TLR2 as a relevant therapeutic target to improve cancer immunotherapy.
Phase Partitioning during the Formation of Secondary Organic Aerosol
Keywords: aqueous phase
atmospheric aerosol particles
phase partitioning
salt effect
Setschenow coefficients
SOA

A sound parameterization of the gas-particle partitioning process is essential for understanding and quantifying secondary organic aerosol (SOA) formation. This thesis aimed to improve the understanding and description of phase partitioning during SOA formation through a combination of both laboratory and modeling studies. Partitioning of organic compounds between gas and particle phase is influenced by the presence of a large quantity of inorganic salts in aerosol, which is known as the salt effect. The salt effects of atmospherically relevant inorganic salts for a large number of organic compounds with various functional groups were measured in this study. The results revealed the importance of both salt species and organic compound identities on the salt effect, with the former as the dominant determinant. Models in predicting salt effect were calibrated and evaluated using the experimental data. Salt effect in mixtures was also investigated, which assists the understanding of salt effect in mixture salt solutions, including aerosols. A new approach for predicting gas-particle partitioning during SOA formation based on quantum chemical calculations was presented, which considers the partitioning species explicitly and captures the dynamic aspects of the aerosol formation processes. The role of different atmospheric parameters and chemical properties (organic loading, liquid water content, salinity, chemical ageing, etc.) was investigated and compared. Performance of the model was found to be comparable to the best currently used group contribution methods. SOA formation from constant emission and oxidation of precursor compounds was simulated to resemble the realistic scenario in the ambient atmosphere. The differential yield that describes the amount of SOA formed from a certain amount of added oxidation products was introduced, which is more relevant for SOA formation in the ambient atmosphere. The necessity of considering kinetic processes in addition to the thermodynamic equilibrium process was also discussed.
Bayesian Applications in Financial Econometrics
Keywords: Bayesian Econometrics
Bayesian Nonparametrics
Financial Econometrics
Time Series

This thesis consists of three chapters in Bayesian financial econometrics. The three chapters apply both Bayesian nonparametric and parametric methods to financial market and macroeconomic time series. Chapter 1 extends popular discrete time short-rate models to include Markov switching of infinite dimension. This is a Bayesian nonparametric model that allows for changes in the unknown conditional distribution over time. Applied to weekly U.S. data we find significant parameter change over time and strong evidence of non-Gaussian conditional distributions. Our new model with an hierarchical prior provides significant improvements in density forecasts as well as point forecasts. We find evidence of recurring regimes as well as structural breaks in the empirical application. Chapter 2 studies the joint dynamic behaviour between stock market returns and real economic growth rates. Their relationship is an important empirical question in finance and macroeconomics. This chapter investigates their linkage by proposing a vector autoregressive infinite hidden Markov model. Our model has two advantages over the existing approaches in the literatures. In contrast to Markov switching models with fixed states, our model will learn the number of states from the data rather than fixing it a priori. The vector autoregressive setting in our model allows the joint time series of stock market returns and real growth rates to share the same unobserved state variable. Compared to existing models, our model shows significant improvements in out-of-sample density forecast accuracy. This paper demonstrates the predictive power of stock market returns for future growth rates are better captured by the unobserved states variables, rather than the lagged stock market returns. Chapter 3 studies the predictive power of oil price information for forecasting the U.S. industrial production. Oil price information is divided into two distinct categories: nominal oil price changes and oil price shocks using four definitions proposed in the literature. Previous work has documented lack of predictive relationship of oil price changes but significant predictive power of oil price shocks for U.S. economic growth. However, existing studies focused only on predicting the mean of the economic growth using classical point forecast techniques. As a contribution to the existing literature, we propose a new forecasting model for the analyzed relationship where oil price shocks have the additional flexibility to influence both the mean and the variance of U.S. industrial production. Our forecast is well performed using the Bayesian predictive likelihood as opposed to classical point estimate. We show that the new model for oil price shocks outperforms existing models in terms of forecasting ability. We further confirm previous findings regarding the lack of predictive power of nominal oil price changes.