VictorJuiz/thesis huggingface dataset:
Aerial Robots for Wireless Coverage, Traffic Monitoring, and Transport Applications: A Path Planning and Fleet Management Perspective
Keywords: Aerial Robots
Drone Delivery
Fleet Management
Path Planning
Traffic Management

Unmanned aerial vehicles (UAVs) have recently attracted significant attention in a wide range of commercial and civilian applications. Wireless coverage, traffic monitoring, and transport are among the emerging applications of UAVs for future smart cities. This study focuses on developing efficient path planning and fleet management algorithms for the UAVs in the mentioned applications. We begin by modeling a UAV-assisted communication network where the UAVs are used as the aerial base stations. We solve the trajectory optimization and resource allocation problem for the UAVs to improve the coverage of ground users. We extend this study to the Internet of Things (IoT) networks where UAVs are utilized to collect data of IoT devices. We propose an efficient algorithm that generates multi-channel representations from the data collection problem and uses deep reinforcement learning (RL) to solve it. Next, we study the path planning problem under connectivity constraints, which is important for applications such as traffic monitoring and drone delivery. We propose a model-aided path planning algorithm that uses federated learning to create a model of the connectivity in the environment. Using this model and rapidly exploring random trees, our algorithm optimizes the UAV path to guarantee connectivity. We extend our study to a traffic monitoring system where a team of UAVs monitors the traffic condition in a road network. To address the stochastic nature of traffic events, we define an uncertainty metric for the road network. We develop a scalable algorithm based on distributed RL to solve the multi-UAV traffic monitoring problem. Our goal is to minimize the network’s average uncertainty even in scenarios where the UAVs’ observations are partial, and the information exchange between the UAVs and the traffic management center is limited. Finally, we study the dynamic UAV-assignment problem for a drone delivery system. We adopt a queuing theoretic approach to model the customer-service nature of the problem and use deep RL to relocate the UAVs in the system. Our RL-based method guarantees a probabilistic upper bound on the queue length of the packages waiting in each package distribution center. Moreover, it outperforms state-of-the-art schemes for different arrival classes.
Ambiguity Aversion in Commodity Markets
Keywords: Ambiguity Aversion
Commodity Markets
Model Uncertainty
Stochastic Control

This thesis explores the impact of model uncertainty on commodity market models. The commodity markets include various idiosyncratic uncertainties that are not present in other markets, and this thesis is the first to provide a thorough and detailed analysis of their effects. We assume that agents acknowledge the possibility of model misspecification, adopt the framework of ambiguity aversion to protect themselves, and utilize a robust indifference pricing framework for valuation and hedging. Most exotic financial options written on commodities are valued by discretizing continuous-time and state models. Thus, in the first part of this thesis, we study the impact of ambiguity aversion in discrete-time and state using trinomial trees. We develop a general mathematical framework for incorporating model uncertainty, compare it with existing methodologies, and look at practical examples, including the valuation of swing options with model uncertainty. Next, we consider the continuous-time formulation of model uncertainty by investigating option pricing with both stochastic volatility and Levy jumps. We provide analytical characterization of ambiguity, robust prices, and resulting hedging strategies and provide perturbative approximations when analytical methods fail. A key insight is that the effect of ambiguity aversion is the strongest at-the-money and the weakest deep-in- or deep out-of-the-money. Finally, we study electricity interconnectors from the perspective of statistical arbitrage with model uncertainty. We provide closed-form optimal strategies when there is no ambiguity and obtain perturbative approximations of the robust optimal strategies. We illustrate the efficacy of the resulting strategies on simulated data. We propose a calibration algorithm on jump-diffusion data to bridge the abstract mathematical theory and real-world dynamics.
An Exploration of How Age and Aging Shape Caregiving Experiences in Oldest-old Spousal Caregivers of People with Dementia
Keywords: Aging
Caregivers
Dementia
Oldest-old

Oldest-old spousal caregivers (CGs) of people with dementia are a fast growing, highly vulnerable, yet poorly understood population. As oldest-old individuals, these CGs have a high likelihood of experiencing aging-related changes (e.g., frailty, multimorbidities) that result in unique caregiving experiences and support needs. Specialized interventions, sensitive to CGs' age- and aging-related experiences and needs, may be required to provide adequate support to this group. To date, this group has received limited attention in the literature. The purpose of my dissertation was to elucidate how age and aging shape caregiving experiences in oldest-old spousal CGs of people with dementia. I conducted two studies. In the first, I used a meta-study methodology to explore existing qualitative research on the experiences of this caregiving subgroup. The meta-study revealed a critical lack of research exploring issues of age and aging in the caregiving role, and illuminated numerous limitations to be considered in future research. In the second study, I used a narrative gerontology approach to explore issues of age and aging from CGs' perspectives. Data were analyzed to address two separate objectives. The first analysis explored CGs' perceptions of how their experiences are shaped by age and aging and caregiving. Results illustrated how age and aging can shape CGs' experiences through a multitude of age- and aging-related factors such as physical decline, changes in social networks, life experiences, socio-cultural norms, and stereotypes. The second analysis explored how CGs manage age-related caregiving challenges. CGs recounted the use of a myriad of strategies to master their caregiving goals and to reduce stress, and reported on the facilitators and barriers to using different strategies. Together the studies illustrated many ways in which age and aging matter in caregiving. This research can ultimately help inform the tailoring of caregiving supports to the unique age- and aging-related experiences and needs of oldest-old spousal CGs of people with dementia.
High Precision Real World Perception for Autonomous Driving
Keywords: Autonomous Driving
Computer Vision
Machine Learning

The captivating hopes for a future with autonomous vehicles promises to free us from one of the most laborious, monotonous, and dangerous of our daily tasks. This thesis presents several works that I have undertaken to improve the perception capabilities of autonomous vehicles, with a particular attention to high precision. The first part of this thesis describes a technique to exploit the rich, high resolution camera information and the unambiguous 3D measurements of LiDAR to detect lane boundaries in 3D in real time with centimeter-level accuracy. We show that our approach significantly outperforms the baselines, particularly at longer distances. Next, we leverage the implicit boundary parameterization used in the previous work in a multi-task neural network for panoptic segmentation in the image space, with the goal of categorizing each pixel in an image into various semantic classes and delineating different instances of countable objects. We compare with the state-of-the-art technique and show that our approach can produce more accurate object boundaries. Finally, we bring temporal information into the segmentation task. Here, we leverage the full history of observations and reasoning results encoded into a non-parametric memory structure and intelligently incorporate new information at each instant in time to perceive small, safety critical objects such as construction elements in 3D.
A Rhetorical Approach to Moral Argumentation
Keywords: Argumentation
Democratic
Growth
Language
Moral
Philosophy

I argue in this dissertation that our North American educational institutions are obligated to formally attend to the inherent pervasiveness and complexities of rhetoric in preparing students to be fully-participating members of democratic society. The definition of rhetoric relevant here involves the use of language and stylistic elements to convince an audience of something. I advocate for rhetoric in two senses. I believe that our students, and societal members generally, should be knowledgeable of what makes rhetoric effective. I also claim that a form of “productive” rhetoric, not to be confused with solely “effective” rhetoric, is best suited for moral argumentation, deliberation and public debate. The first sense involves ‘knowledge of’ while the second concerns appropriate contextual ‘use’. A number of prototypical examples of productive rhetoric are offered as well as a depiction of what is and what is not conceived as productive, growth-oriented usage. The emotive, motivational and contextual elements of a rhetorical approach to moral argumentation need to be given more attention. Rhetoric holds potential to be an invaluable resource in moral argument, decision-making processes and education. Furthermore, it has the capacity to uniquely cause participants to cohere or connect. It can facilitate a shared affective foundation upon which common ends might be pursued. Not only does rhetoric hold this potential, but it is always already imbued with certain moral qualities. Directed by this claim, I survey the relationship between rhetoric and moral argumentation in the three major normative philosophical traditions: consequentialism, aretaic virtue ethics, and deontic discourse ethics. I also defend Rortyan and Davidsonian conceptions of rhetorical communication. The various traditions drawn from in this thesis all point to certain features of and opportunities for rhetoric in relation to possible growth among participants in moral discourse and education. While rhetoric is usually used to urge one conceptual framework over another, I champion its use to bridge frameworks. Certain forms of productive rhetoric canreasonably be expected to open up dialogue in an effort to create innovative solutions, mutual understandings, and sustainable agreements.
Community Disturbance and Host-parasite Interactions at the Wildlife-domestic Interface in a Neotropical Biodiversity Hotspot
Keywords: mammal community
population density
tropical forest
wildlife disease

Tropical ecosystems worldwide are threatened by land-use change. The resulting increased interaction between wildlife, domestic species, and humans can lead to parasite spillover and emerging diseases that can significantly impact wildlife. Measuring spillover risk presents multiple challenges, including identifying drivers of species interaction, quantifying host densities and overlap, and estimating parasite prevalence. I studied anthropogenic pressures and the associated risk of parasite spillover in the Osa peninsula, an important biodiversity hotspot in southern Costa Rica. In Chapter 1, I analyzed how anthropogenic disturbance, land protection, and habitat quality shape the vertebrate community. In collaboration with multiple institutions, I conducted a region-wide camera-trap survey and analyzed the occurrence of terrestrial mammals and ground birds using a joint species distribution model. Overall disturbance had negative impacts on occupancy of large predators and herbivores. Mesopredators like ocelots (Leopardus pardalis) were only marginally affected by disturbance and could therefore be exposed to parasites of domestic species. In Chapter 2, I used camera trap data, and estimation frameworks for marked and unmarked species, to determine ocelot density, a key determinant of epidemiological dynamics. I found important design incompatibilities between frameworks regarding camera distribution, which should be considered for density comparisons. The estimated density (34 individuals/100 km2 ) suggests a healthy population. However, ocelots frequently use trails, exposing them further to parasites of domestic species, particularly dogs. In Chapter 3, I analyzed the spillover risk of soil transmitted helminths from domestic hosts to wild felids using microscopy, molecular diagnostic, and mathematical modelling tools. I found high prevalence of hookworms (Ancylostoma caninum) in dog scats (74%). I could not identify a related parasite found in feline scat. The hookworm prevalence observed in ocelots (25%), however, was higher than the prediction from a mathematical model (5%), suggesting there is no regular transmission of helminths across species. Human encroachment on natural habitats will continue to represent a risk of emerging disease for vulnerable wildlife, domestic animals, and humans, particularly in highly biodiverse systems like the Osa. Parasite monitoring therefore must become an integral part of conservation studies at the wildlife-domestic interface. These urgent efforts will necessarily require multidisciplinary, local-based approaches.
Avicenna on Emotions: Their Nature and Significance for Knowledge and Morality
Keywords: Aristotle
Avicenna
Emotions
Ibn Sina
Physiology
Psychology

This dissertation examines the view of emotions found in the writings of the philosopher and medical doctor Avicenna (Ibn Sīna, d. 1098 CE). Avicenna is known to have influenced the course of cognitive psychology in the Muslim East and the Latin West, yet little has been written on his affective psychology. Over the course of five chapters, I examine his accounts of animal and human emotions, their physiology, and their relation to voluntary action and moral dispositions. Avicenna is notable for the degree to which he highlights uniquely human emotions, while simultaneously giving attention to the complexity of emotions able to be felt by animals. He also stands out for the way that he integrates his knowledge of physiology into his analysis of philosophical questions. Emotions, according to Avicenna, are acts of the animal motive faculty, caused by cognitions of varying complexity. For example, uniquely human emotions are caused by cognitions of which only humans are capable, but they are fundamentally actualizations of the same motive faculty shared by animals and humans alike. Emotions are also to be understood as inclinations (Ar: inbiʿāthāt/nizāʿāt), meaning that they incline us towards various objects and courses of action, but do not on their own initiate any type of movement. This dissertation includes a full translation of Avicenna’s section on emotion in his Healing: Rhetoric.
Drainage Reorganization and Landscape Evolution at the Puna Plateau Margin, NW Argentina
Keywords: Andes
Drainage reorganization
Fluvial erosion
Tectonic geomorphology

Investigating the controls of fluvial erosion and how these controls are recorded in topography is key for understanding tectonic-climate coupling and the landscape evolution of orogenic plateaus. However, these investigations are limited by systematic assumptions that may bias apparent controls of fluvial erosion and neglecting how rivers respond through adjusting planform patterns. This thesis explores these complications at the Puna Plateau margin and broken foreland of NW Argentina and (1) assesses how spatial variability and interactions among potential tectonic, climatic, and lithologic controls drive fluvial erosion rates, (2) identifies planform drainage reorganization and its controls, and (3) determines how planform reorganization may influence the preservation and destruction of orogenic plateau margins. This thesis systematically analyzes how spatial variability and independent variable interactions influence 10Be-derived catchment-wide fluvial erosion rates through a multivariate Bayesian regression model. Within-catchment spatial variability of vegetation produces the strongest relationship; this suggests that catchment-wide fluvial erosion rates in NW Argentina are likely dominated by rapid erosion in steep, high elevation, and unvegetated portions of a single catchment. These results highlight the utility of considering summary statistics other than the catchment-wide mean when conducting quantitative analyses of fluvial erosion rate controls. Using a combination of geomorphic, morphometric, and structural evidence, this thesis identifies drainage reorganization across the Puna margin. Stream captures and progressive divide migration forced the Puna margin into the plateau interior, until it largely stabilized at a position predicted by tectonically-controlled base-level differences across the divide. Locations of ongoing divide migration coincide with rapidly-uplifting thrust faults. This thesis additionally synthesizes published thermochronologic and sedimentologic evidence to construct a chronology of range uplift and drainage reorganization in the broken foreland. Unlike thin- and thick-skinned wedges, fault-parallel longitudinal drainages in the broken foreland are long-lived, and are controlled by the unique tectonic setting of the region, in which shortening is accommodated by out-of-sequence uplift along steep inherited faults. This thesis concludes that tectonics, particularly through tectonically-controlled base-levels and inherited structures, controls patterns of drainage reorganization and therefore plays a key role in shaping the topography of orogenic plateaus and their associated forelands.
Addressing the Data Annotation Bottleneck in Breast Digital Pathology
Keywords: digital histopathology
medical image analysis
self-supervised learning
unsupervised learning
weakly supervised learning
whole-slide images

Early diagnosis and targeted therapies are priorities in the treatment of cancer. Advancements such as whole-slide scanning of a tissue sample can help experts assess cancer more precisely; however, analysis of a tissue specimen is expensive and time-consuming. Machine learning can alleviate the load for the expert by providing an objective assessment complementary to the pathologist, or by highlighting possible regions of interest on the slide to reduce the examination time. The number of labeled images in machine learning tasks is found to be positively correlated with the task performance; however, labeled data is scarce and expensive. The problem is exacerbated in medical image analysis tasks, where an expert must exhaustively annotate data to cover both common and rare cases of disease. In this thesis, I propose techniques to either expedite the labeling step, or to reduce the requirements for labeled data. I start by addressing the generalization gap, where an automatic diagnostic tool (e.g., breast cancer detection) trained using data from a specific scanner or a hospital performs poorly on data from another hospital for the same task. My aim is to achieve similar performance on the unseen data by identifying features that are common in both the training and the unseen data. Later, I propose a technique to train a segmentation network by leveraging easier-to-label image-level patches combined with a few pixel-level segmentation annotations. Thirdly, I utilize a learning paradigm called self-supervision which is used to learn salient features from unlabeled images without any supervision. These learned features are later used to train for tasks such as classification, regression, and segmentation. The experiments show that the accuracy can be improved by up to 30% on various tasks, including breast cancer, prostate cancer, and colorectal tissue classification tasks. Finally, I outline unique challenges associated with whole-slide images (WSIs), and find techniques that achieve satisfactory performance on smaller images may not translate to good performance in a more clinically relevant workflow involving WSIs. I propose alternative methods to overcome this discrepancy, and verify the efficacy of the proposed technique in a clinically challenging breast cancer task.
Hölder Continuity of Tangent Cones and Non-branching in RCD(K,N) Spaces
Keywords: Geometric analysis
Metric geometry
Riemannian geometry

This thesis is concerned with the study of the structure theory of metric measure spaces (X, d, m) satisfying the synthetic lower Ricci curvature bound condition RCD(K, N). We prove that such a space is non-branching and that tangent cones from the same sequence of rescalings are Hölder continuous along the interior of every geodesic in X. More precisely, we show that the geometry of balls of small radius centred in the interior of any geodesic changes in at most a Hölder continuous way along the geodesic in pointed Gromov-Hausdorff distance. This improves a result in the Ricci limit setting by Colding-Naber where the existence of at least one geodesic with such properties between any two points is shown. As in the Ricci limit case, this implies that the regular set of an RCD(K, N) space has m-a.e. constant dimension, a result recently established by Brué-Semola, and is m-a.e convex. It also implies that the top dimension regular set is weakly convex and, therefore, connected. In proving the main theorems, we develop in the RCD(K, N) setting the expected second order interpolation formula for the distance function along the Regular Lagrangian flow of some vector field using its covariant derivative.
Relating Ionic Composition to the Photophysics and Photochemistry of Natural Organic Matter in Aqueous Environments
Keywords: Aqueous Photochemistry
Environmental Chemistry
Interfacial Chemistry
Metal Complexes
Natural Organic Matter
Volatile Organic Compounds

Photochemical reactions in the natural aquatic environment are important drivers of chemistry, including degradation of pollutants and formation of volatile species at the air-water interface. Several species found in this environment may contribute to photochemistry. One of the most important species is natural organic matter (NOM), a complex and ubiquitous component of aquatic environments. NOM contains chromophores that absorb light and may undergo intersystem crossing to form the excited triplet state, which can act as a photosensitizer or react, forming reactive intermediates, such as singlet oxygen and hydroxyl radical. In this thesis, several NOM samples were used to investigate the differences in NOM photophysics and photochemistry in seawater compared to freshwater. Systematic investigations, using seawater components such as halides and metal ions, determined how water composition affects the photophysics of NOM through fluorescence. The changes in photophysics were then linked to changes in NOM photochemistry via the formation of reactive intermediates, such as the excited triplet and singlet oxygen, or photosensitization mechanisms. The first investigation indicated that the presence of magnesium changed the photophysics of NOM through enhanced NOM fluorescence, linked to a change in NOM photochemistry through decreased formation kinetics of the excited triplet state and singlet oxygen. The study hypothesized that the decrease in the formation kinetics of singlet oxygen was due to changes in the conformation of NOM due to NOM-magnesium complexes. Fluorescence anisotropy was used in the second study to determine that NOM-metal complexes did not change the conformation of NOM, but did influence the photophysics of NOM. Finally, the difference in photochemistry occurring in seawater versus freshwater was investigated at the air-water interface by exploring the formation kinetics of gas-phase products from seawater and freshwater. These investigations determined that differences in water composition affected the number, concentrations, and growth rates of species formed. The studies presented in this thesis illustrate that water composition plays a significant role in altering the photophysical and photochemical mechanisms of NOM, with greater implications towards changes in aqueous photochemical processes.