Frameworks of Many into the Creation of One [FMOC] is a choreographic work that delves into the concept of identity: Who am I? Who are you? Who are we? What is the narrative that binds us? This piece offers an exploration of our interactions with the world around us as we navigate the ongoing process of self-discovery. FMCO illuminates the intersectionality of the individual within a community, examining the diverse ways in which we express ourselves in relation to our environment. IDENTITY: Individual Differences Expressed and Negotiated through Environmental Information. The work delves into the idea that identity is shaped by the transfer of information within one's environment. Through the mediums of storytelling, dance, and multimedia, FMCO offers the audience an immersive experience of frameworks and concepts that influence both their own identities and the identity of Alecea Housworth. The piece invites viewers to contemplate the dynamic interplay between individualism and the influences of one's surroundings. This paper delves into the intricate interplay between individuality and community, shedding light on the complexity inherent in the human experience. Exploring the frameworks being race, gender, religion, and gender, which impact experiences that shape the development of one's sense of self. Through the lens of dance, this study examines how individuals construct and embody their identities, offering a nuanced understanding of self-conception through communal engagement.

In today’s world, artificial intelligence (AI) is increasingly becoming a part of our daily lives. For this integration to be successful, it’s essential that AI systems can effectively interact with humans. This means making the AI system’s behavior more understandable to users and allowing users to customize the system’s behavior to match their preferences. However, there are significant challenges associated with achieving this goal. One major challenge is that modern AI systems, which have shown great success, often make decisions based on learned representations. These representations, often acquired through deep learning techniques, are typically inscrutable to the users inhibiting explainability and customizability of the system. Additionally, since each user may have unique preferences and expertise, the interaction process must be tailored to each individual. This thesis addresses these challenges that arise in human-AI interaction scenarios, especially in cases where the AI system is tasked with solving sequential decision-making problems. This is achieved by introducing a framework that uses a symbolic interface to facilitate communication between humans and AI agents. This shared vocabulary acts as a bridge, enabling the AI agent to provide explanations in terms that are easy for humans to understand and allowing users to express their preferences using this common language. To address the need for personalization, the framework provides mechanisms that allow users to expand this shared vocabulary, enabling them to express their unique preferences effectively. Moreover, the AI systems are designed to take into account the user’s background knowledge when generating explanations tailored to their specific needs.

In game development, interaction frameworks provide generic functionality for users to engage with virtual worlds and are required to enable this on nonstandard hardware such as extended reality platforms. Currently, no publicly accessible frameworks exist that implement interactive world objects in XR settings, prompting the question: if one were to be made, how and why would it be usable? This thesis explores the properties that make an XR-enabled object interaction framework usable by game developers and game designers. This thesis introduces the basic form of such a framework and the design of a set of user studies centered around this framework’s utilization in a game development workflow. User feedback is gathered for the study’s results, and is evaluated around user perception of framework expressiveness, extensibility, and ease of use. The results of the study found that users primarily judged usability through comparisons to real-world equivalents, utilization of conventional systems, object interactivity, clarity of framework components, usability of framework toolkits and these are discussed in relation to existing research on virtual object interaction.

Mexican-origin families (MO) have been more negatively impacted by the COVID-19 pandemic compared to White European-Americans. Latinx youth also reported increased concern about somatic symptoms (i.e. bodily symptoms) during the pandemic compared to non-Latinx peers. Current research on the pandemic indicates that cohesive and supportive families fare better than families within high-conflict households. However, no research has directly examined parent-child conflict during the pandemic in relation to youth somatic symptoms. Previous studies considered cultural proxies as predictors of somatic symptoms in Latinx children, the specific influence of prominent cultural values like familism remains less explored. Familism, emphasizing family well-being, is considered protective for Latinx youth, but evidence suggests it may pose risks in high parent-child conflict households. Utilizing Qualtrics panel, I collected data from 301 MO parents reporting on a target child (Mage= 11.4 years, SD= 3.7; 50.2% female) between March and June 2022. Approximately 40% of parents completed the survey in Spanish. Parental familism dimensions (support, family as referent, and obligations) were assessed as well as youth somatic symptoms. Changes in parent-child conflict and changes in social and family contacts due to the pandemic were also measured. Logistic regression models revealed that greater increases in conflict significantly predicted the presence of youth somatic symptoms (OR = 0.52, 95% CI= [0.27, 1.00]. Unexpectedly, total familism did not significantly moderate the relation between change in parent-child conflict and the presence of somatic symptoms (RR = 1.16, 95% CI= [0.99, 1.36]. However, post hoc analyses revealed that parental familism support was the only dimension of familism that was directly associated with the count of child somatic symptoms (RR= 0.81, 95% Confidence Interval [CI]= 0.71, 0.93). Parental familism as a referent significantly moderated the relation between change in parent-child conflict and the count of somatic symptoms (RR= 1.19, 95% CI = [1.01, 1.41]), such that higher familism values strengthened the positive relationship of change in parent-child conflict and higher count of somatic symptoms. Findings emphasize the need for future longitudinal research, considering medical conditions, to understand how parental familism values, particularly support, may buffer against somatic symptoms for Latinx youth.

Adaptation to climate change is a core sustainability challenge across the Global South. Development and government organizations conceptualize and govern climate adaptation by creating national and sub-national action plans and implementing projects. This dissertation confronts the inherent tensions that arise when formal planned adaptation interventions encounter the complex, often messy realities of the implementation context. In doing so, this research examines how planned adaptation—with its incentives, provisioned resources, prescribed behaviors, and expectations of commitment from target beneficiaries —interacts with individuals and communities already balancing diverse risks while pursuing their livelihood aspirations. Two broad questions guide this dissertation: 1) how is adaptation envisioned by planners and practitioners? and, 2) how do project beneficiaries engage with, and experience planned adaptation interventions? The research employs an exploratory and inductive qualitative research design. Using Foucault’s lens of governmentality, this research utilises document analysis to examine how the first wave of Indian adaptation projects envision goals, conceptualize problems, delineate roles, and frame expectations of intended beneficiaries. Next, using a case study of an adaptation project implemented in Uttarakhand, India, the study examines the motivations and associated trade-offs behind the engagement and disengagement of the intended beneficiaries: smallholder farmers. Insights from gender-differentiated focus group discussions guide this analysis. Both inquiries are supplemented with findings from semi-structured interviews with Indian adaptation experts and project implementers. The analysis finds that: 1) project reports construct identities of the climate vulnerable beneficiary, implicitly assigning roles and transferring responsibilities for sustaining adaptation efforts beyond project timelines, 2) project participants are not default beneficiaries, but instead exercise agency in decision-making by either opting-in or opting-out of planned initiatives, and 3) the implicit and explicit costs of engaging in planned adaptation interventions are substantial, encompassing significant contributions of time, physical labor, and active participation during and post the project period. This dissertation challenges existing notions of whom planned adaptation serves, and to what end, offering new insights into its design and effectiveness. Furthermore, this research suggests that for planned adaptation to be sustainable, a concerted effort to align with evolving needs, aspirations and livelihood shifts of those on the frontlines of climate change is essential.

Recent advancements in computer vision models have largely been driven by supervised training on labeled data. However, the process of labeling datasets remains both costly and time-intensive. This dissertation delves into enhancing the performance of deep neural networks when faced with limited or no labeling information. I address this challenge through four primary methodologies: domain adaptation, self-supervision, input regularization, and label regularization. In situations where labeled data is unavailable but a similar dataset exists, domain adaptation emerges as a valuable strategy for transferring knowledge from the labeled dataset to the target dataset. This dissertation introduces three innovative domain adaptation methods that operate at pixel, feature, and output levels.Another approach to tackle the absence of labels involves a novel self-supervision technique tailored to train Vision Transformers in extracting rich features. The third and fourth approaches focus on scenarios where only a limited amount of labeled data is available. In such cases, I present novel regularization techniques designed to mitigate overfitting by modifying the input data and the target labels, respectively.

3D perception poses a significant challenge in Intelligent Transportation Systems (ITS) due to occlusion and limited field of view. The necessity for real-time processing and alignment with existing traffic infrastructure compounds these limitations. To counter these issues, this work introduces a novel multi-camera Bird-Eye View (BEV) occupancy detection framework. This approach leverages multi-camera setups to overcome occlusion and field-of-view limitations while employing BEV occupancy to simplify the 3D perception task, ensuring critical information is retained. A noble dataset for BEV Occupancy detection, encompassing diverse scenes and varying camera configurations, was created using the CARLA simulator. Subsequent extensive evaluation of various Multiview occupancy detection models showcased the critical roles of scene diversity and occupancy grid resolution in enhancing model performance. A structured framework that complements the generated data is proposed for data collection in the real world. The trained model is validated against real-world conditions to ensure its practical application, demonstrating the influence of robust dataset design in refining ITS perception systems. This contributes to significant advancements in traffic management, safety, and operational efficiency.

While the growing prevalence of robots in industry and daily life necessitatesknowing how to operate them safely and effectively, the steep learning curve of programming languages and formal AI education is a barrier for most beginner users. This thesis presents an interactive platform which leverages a block based programming interface with natural language instructions to teach robotics programming to novice users. An integrated robot simulator allows users to view the execution of their high-level plan, with the hierarchical low level planning abstracted away from them. Users are provided human-understandable explanations of their planning failures and hints using LLMs to enhance the learning process. The results obtained from a user study conducted with students having minimal programming experience show that JEDAI-Ed is successful in teaching robotic planning to users, as well as increasing their curiosity about AI in general.

Introduction: There is growing evidence to suggest that the integration of genomics into the palliative oncology setting is not only critical for the identification of individuals who may be at increased familial risk; but that it is also a key component of providing family centered care - a concept at the heart of both genetic counseling and palliative care alike. Barriers to this integration have been well described, and some strides have been made in describing the current genetic practices and policies within palliative oncology. However, most of this research has been in the form of broad literature reviews performed outside of the United States. Methods: To better describe regional genetics-palliative practices, an online, qualitative survey was distributed to both cancer genetic counselors and palliative oncology providers at four of the US Mayo Clinic locations. The survey was used to illuminate the current processes, policies, and relationships between the groups; as well as to identify potential improvements. Results: Responses were received from 15 MD/DOs, 9 PAs, 16 NPs, 27 RNs, 4 GCs, and 8 “Others,” of which 54.4% worked primarily in hematology & oncology, 35.4% in palliative care, and 6.3% in genetic counseling. 89% of palliative care providers and 62% of oncology providers reported never or only once yearly referring patients for genetic counseling; citing that they were (1) unaware of genetic counseling resources or referral processes, (2) uncertain on how genetic testing would influence patients’ medical management, and (3) felt it was out of their scope of practice. Similarly, each genetic counselor that responded reported never or once yearly receiving referrals from palliative care. Conclusion: While additional, larger studies are required to most accurately represent the practices of genetic counselors and palliative oncology providers at the Mayo Clinic, this study suggests that providers across all specialties surveyed would find additional resources for referring patients to both palliative care and genetic counseling to be useful. Thus, future efforts could be directed towards educating palliative oncology providers on the role of genetic counselors, as well as educating genetic counselors on the role of palliative care.

This dissertation presents a comprehensive study on the advancement of astrophysical radio, microwave, and terahertz instrumentation/simulations with three pivotal components.First, theoretical simulations of high metallicity galaxies are conducted using the supercomputing resources of Purdue University and NASA. These simulations model the evolution of a gaseous cloud akin to a nascent galaxy, incorporating variables such as kinetic energy, mass, radiation fields, magnetic fields, and turbulence. The objective is to scrutinize the spatial distribution of various isotopic elements in galaxies with unusually high metallicities and measure the effects of magnetic fields on their structural distribution. Next, I proceed with an investigation of the technology used for reading out Microwave Kinetic Inductance Detectors (MKIDs) and their dynamic range limitations tied to the current method of FPGA-based readout firmware. In response, I introduce an innovative algorithm that employs PID controllers and phase-locked loops for tracking the natural frequencies of resonator pixels, thereby eliminating the need for costly mid-observation frequency recalibrations which currently hinder the widespread use of MKID arrays. Finally, I unveil the novel Spectroscopic Lock-in Firmware (SpLiF) algorithm designed to address the pernicious low-frequency noise plaguing emergent quantum-limited detection technologies. The SpLiF algorithm harmonizes the mathematical principles of lock-in amplification with the capabilities of a Fast Fourier Transform to protect spectral information from pink noise and other low-frequency noise contributors inherent to most detection systems. The efficacy of the SpLiF algorithm is substantiated through rigorous mathematical formulation, software simulations, firmware simulations, and benchtop lab results.