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MULTIMODE METAMATERIAL RING RESONATOR AS AN ENTANGLING BUS FOR ARTIFICIAL ATOMS
Get PDFCircuit quantum electrodynamics (cQED) systems with superconducting qubits coupled to linear microwave resonators are a prominent platform for realizing scalable quantum information processors. Combining cQED architectures with multimode resonators leads to a broad set of applications for performing analog quantum simulation, implementing dense quantum memory, and generating multimode entangled states between physically distant qubits. Microwave resonators in cQED are typically formed from distributed transmission lines that exhibit conventional dispersion with harmonic mode spacing; in such systems, usually only a single resonant mode can be strongly coupled to a qubit. Superconducting metamaterial resonators comprised of lumped circuit elements can be designed to produce a left-handed dispersion that results in a dense mode structure in the typical frequency range for operating superconducting qubits, thus allowing for a qubit to couple strongly to multiple modes simultaneously. Forming these metamaterial structures into a ring with qubits coupled at certain points around the ring results in a multi-mode bus with a compact physical footprint. In this thesis, we present a review of the design and fabrication of superconducting left-handed metamaterial ring resonators. We show, through low temperature measurements, that when we couple two flux-tunable transmon qubits to such a ring resonator, the system shows extreme versatility in coupling parameters due to the unique wave structure of the modes in the ring. We measure and model the interactions between the qubits and the ring resonator modes, as well as the inter qubit entangling interactions mediated by the multimode system. We describe how this platform could be used to implement two-qubit gates and generate entanglement between physically distant qubits
The Use of Instructional Materials in Elementary Science Classrooms
Get PDFThis mixed methods study explored how elementary teachers reported using a commercially published instructional unit to plan and deliver science instruction in their elementary classrooms. Of particular focus is what elements of the program teachers eliminated during planning and instruction, what outside materials teachers added to instruction, what modifications they made to the materials, and their rationales for these changes to the prescribed program. This study consisted of two phases. The first was a survey of elementary science teachers who taught the Smithsonian Science for the Classroom units. Data collected from this phase informed the subsequent interview phase. I used deductive coding to analyze the data and a descriptive narrative format to report the findings and implications of this study to answer the following questions: How do teachers report using instructional materials in elementary science during the planning and delivery of instruction? What modifications, if any, do teachers report making to instructional materials? What rationales do teachers report for their modifications? Two clear themes emerged from the study. First, teachers eliminated lessons and shortened tasks to fit instruction into the minutes designated for teaching science. Time constraints or the perception by teachers that the tasks would be too difficult for their students were typical rationales given for making any changes to the materials. Implications for teachers, administrators, professional development providers, and policymakers were discussed
Three Essays on Representation, Participation, and Conflict in Environmental Justice Councils
No full textIn this dissertation, representation, participation, and conflict are examined in state-mandated environmental justice councils. A novel dataset is constructed from council meeting minutes to explore patterns across these key concepts at the individual-level, meeting-level, and council level over time. The first essay examines how meeting-level factors – such as participant diversity, attributes of the meeting, and context – associate with meeting-level participation measured as different types of communication. This essay builds on participation research in collaborative governance, exploring participation through the lens of communication. Leveraging multiple OLS regressions, associations between meeting-level factors and participation through communication are explored. The results help pull apart the dynamics of two-way communication in public participation, identifying unique patterns of cross-sector information exchange as well as meeting-level mechanisms for engaging stakeholders. These results add nuance to how participation is understood in collaborative fora. The findings of this study are applied to the broader field of collaborative governance. The second essay explores how an actor’s interpersonal relationships (i.e., conflict and reciprocity) and goal advancement (i.e., supporting or opposing a council’s annual objectives) associates with the actor’s participation measured as attendance over time. This essay adds to recent work exploring collaborative governance evolution by exploring the factors influencing sustained engagement in the collaboration at the actor-level. Manual and computational text analysis approaches, along with a Stochastic Actor-Oriented Model (SAOM or colloquially SIENA models), are applied to individual-level data across eight years of meetings. The results identify positive and negative interpersonal interactions are associated with changes in individual attendance, whereas only comments opposing a council’s annual objectives are associated with increases in individual attendance. This suggests ‘who remains at the table’ to make policy decisions is influenced by iterative, individual experiences. The third essay tracks the stated goals of a collaboration over time to explore patterns of participation across meetings to better understand how diverse stakeholders engage over time. In this essay, I leverage Emerson and Nabatchi’s collaboration dynamics to explore the change in participation between a collaboration’s decision to address a policy issue (i.e., shared motivation) and a collaboration’s decision in how the issue should be addressed (i.e., capacity for joint action). Separable temporal exponential-family random graph models (i.e., STERGMs) are used to evaluate the pre- and post-trends in representation, which are then clustered into eight representation patterns using a K-Means Cluster Analysis. By framing policy issue advancement as an event study, I argue the results of this paper suggest there are multiple pre-post trajectories in the council, offering insight into the complex nature of goal identification and action in the collaborative setting. The discussion works to link these results to patterns identified in theory
LEARNING IN RELATION: THE EXPERIENCES OF DISABLED GENDERQUEER YOUTH LEARNING IN NATURE
No full textSummer camps span the United States, with approximately 26 million children attending overnight or “stay away” summer camps when not in school (ACA as cited in Gay, 2022). Twenty-six million children attending summer camps is approximately half the number of children attending public school during the school year, which was approximately 49.4 million in 2021 (NCES, 2023). Arguably, summer camp should be for all kids (Gay, 2022), but these numbers represent that not all kids have access to summer camp. Camp is not school, but that does not mean the knowledge located and curated outside of schools is not beneficial to the larger educational system–politically, culturally, and socially. Kids learn at camp; they just do not “go to school” to learn. At school, kids are schooled. Kids at camp do not learn algebraic expressions or write essays demonstrating their reading comprehension skills. Even though kids are away from home, living in nature, belonging to a community, and filling their day with activities in nature, they still learn. These are the very reasons kids learn at camp. Drawing upon the foundations of educational and qualitative research, I merge ethnography and phenomenology to analyze informal learning in nature through the experiences of disabled, genderqueer youth. Through merging methods, I detail how I used critical ethnography and feminist phenomenology to inform the research design, process, and aspects of collection and analysis. From a macro-level analysis, I deductively coded data holistically to derive the meaning behind the phenomenon of learning at an inclusive summer camp. Findings include examinations of disability discourses, disabled, genderqueer youth learning in brave spaces, and a network of community created to support learning. Informal learning in nature occurs in relation to the more-than-human world and, more importantly, within community, can be transformative through experiences of healing for disabled, genderqueer youth
Representation of People with Disabilities in Legislatures: A Proposed Model of Approval Representation
No full textThis research examines the effect of the right to legislative representation for people with disabilities in the fields of Political Science and Law through a combination of doctrinal analysis and interviews with 12 individuals with disabilities. These individuals are disability rights advocates, politicians, and leaders of disability organizations from Kenya, Uganda, Rwanda, Zimbabwe, and Egypt, which are the only jurisdictions that have constitutionally recognized descriptive representation of people with disabilities in their legislatures. The research investigates the legal issues and challenges that people with disabilities face in achieving descriptive representation in their legislatures. Additionally, the research identifies gaps and deficiencies in the current Descriptive Representation model developed by the field of Political Science, which has failed to ensure comprehensive parliamentary inclusion for people with disabilities. In response to these findings, a refined model called Approval Representation is introduced. This model capitalizes on the strengths of Descriptive Representation while addressing its weaknesses, with the ultimate goal of achieving parliamentary inclusion for people with disabilities. The overarching goal of this project is, therefore, to elevate the right to descriptive representation for disabled people, a group right recognized under international human rights law, and to ensure equitable descriptive representation for persons with disabilities in their respective parliaments
Exploring the Roles of Food Parenting Practices, Dietary Self-Efficacy, and Food Insecurity on Fruit and Vegetable Consumption Among College Students
Get PDFThe current study explores the links between food parenting practices during childhood, specifically restriction and pressure to eat, and fruit and vegetable consumption among college students. It further investigates whether dietary self-efficacy mediates this relationship and how food insecurity moderates the mediated pathway. Drawing upon Bronfenbrenner\u27s ecological systems theory and Bandura\u27s social cognitive theory, the study hypothesizes that dietary self-efficacy acts as a mediator in the relationship between childhood food parenting practices and current fruit and vegetable consumption among college students. Additionally, it examines the moderating role of food insecurity on this mediated pathway. Participants were recruited through Cornell\u27s SONA system and the Prolific online platform to complete a survey assessing their childhood food parenting experiences, current dietary self-efficacy, fruit and vegetable consumption, and food insecurity status. A total of 278 actively enrolled college students between the ages of 18-29 completed the anonymous online survey. The data were analyzed using bivariate Pearson correlations, ordinary least squares regression analyses, and moderated mediation analyses. The findings reveal that dietary self-efficacy significantly mediates the relationship between food parenting practices, particularly parental restriction, and fruit and vegetable consumption among college students. Contrary to initial hypotheses, pressure to eat did not significantly predict fruit or vegetable consumption nor was it associated with dietary self-efficacy. Also contrary to initial hypotheses, food insecurity was not found to moderate the pathways between dietary self-efficacy and fruit consumption, nor vegetable consumption. This study contributes to the understanding of how early-life food parenting practices influence dietary behaviors among college students, highlighting the importance of dietary self-efficacy. The findings suggest that enhancing dietary self-efficacy could be a key strategy in promoting healthier eating behaviors among college students. However, additional research is needed to explore the complexity of food insecurity among college students and its potential impact on eating behaviors
The Role Of Acculturation Strategies In Relation To Honor And Sexuality Attitudes, Sexism, Conservatism, And Religiosity Among Turkish Immigrants Living In The U.S.
Get PDFThe purpose of this research was to understand the complex relationships between acculturation strategies and attitudes towards sexuality (particularly the specific Turkish-related concept of “namus”) in the Turkish acculturating community. Studies exploring the acculturation process of other immigrant groups in the U.S. have revealed a generally liberalizing impact of different acculturation processes on gender role and sexuality attitudes of immigrants. However, there has been very little work specifically focusing on the Turkish immigrant community, particularly in the American context. Turkey is one of the countries known to have a culture of honor, where family honor (namus) is tied to a woman’s chastity, which has significant implications for women’s well-being in the Turkish context. The current study explored acculturation in an immigrant Turkish community in the U.S. and investigated how their acculturation strategies relate to their religiosity, honor and sexuality attitudes, sexism, and conservatism from the scope of Berry’s framework (1997, 2005). Data was collected from an acculturating Turkish community living in a mid-sized city through snowball sampling, resulting in 87 participants who completed the questionnaires. The findings from factor analysis revealed that namus emerged as a component of more broad sexuality attitudes. Regression analyses demonstrated that these broader attitudes were predicted by acculturation strategies and religiosity of the participants. Specifically, immigrants who endorsed integration acculturation strategy more held more liberal sexuality and namus attitudes, while immigrants who endorsed separation strategy more held more conservative sexuality and namus attitudes. Most importantly, the association of a higher degree of separation with conservative sexuality attitudes was mediated via the participants’ strong religious adherence. The present study demonstrates the complex mechanisms through which religiosity plays a role in the maintenance of conservative sexuality attitudes. Consequently, this study has important implications for intervention at the individual and societal levels, regarding healthy adaptation of immigrants and eradication of namus- and sexuality-related oppression of immigrant women across cultures
Bridging the Gap between Detection Algorithms and Real-World Challenges
No full textIn recent years, the remarkable progress in the fields of computer vision and machine learning has unleashed a wave of groundbreaking applications, transforming the way we interact with technology on a daily basis. From smartphones to complex hardware systems, these advancements have become integral components of our lives, offering unprecedented capabilities and convenience. At the core of computer vision lies the creation of advanced detection models. These models are crafted to autonomously recognize and scrutinize a diverse range of visual elements, encompassing objects, faces, and anomalies within images and videos. The significance of detection models extends across various applications, spanning from surveillance and security to healthcare and autonomous vehicles. They serve as the fundamental building blocks for comprehending and engaging with the visual realm, forming the basis for numerous innovative technologies and services. This thesis explores the development and application of detection algorithms across multiple domains, addressing real-world challenges and making significant contributions to the field. We begin by focusing on pedestrian detection, proposing novel methodologies that incorporate saliency features and thermal imaging. By leveraging saliency information, our approach enhances the accuracy and robustness of pedestrian detection algorithms, enabling reliable performance even in complex scenes with occlusions or low visibility. Additionally, we harness the power of thermal cameras, which provide valuable thermal signatures, to further improve pedestrian detection performance in challenging scenarios such as nighttime or adverse weather conditions. Next, we turn our attention to the development and reliability assessment of an occupancy sensor system tailored for residential buildings. First, we present an AI-based detection algorithm that we developed by using around 24K images. For this approach, we gathered data for various corner case scenarios, including individuals reclining on couches, individuals resting in beds while being covered by a blanket at various amounts, individuals donning hats and/or sunglasses, diverse indoor camera perspectives, and more. This data was acquired from online sources and supplemented by our own data collection efforts. On the hardware front, we have designed a stand-alone, battery-powered residential occupancy detection system. This system offers a cost-effective, high-precision solution for addressing the limitations of existing occupancy detection approaches. These limitations include (i) not being able to detect stationary occupants; (ii) not being able to classify the source of the motion (such as a pet); (iii) not allowing for embedded or onboard computation, and requiring external or cloud-based processing, especially depending on camera resolution and employed algorithms, (iv) being sensitive to lighting changes, and thus prone to missed detections or false alarms; (v) requiring adjustment of settings for different scenarios, and thus complicating self-commissioning; (vi) not providing high-enough accuracy, especially in the corner cases mentioned above; (vii) being costly; and (viii) not being battery-powered, which limits the ease of use and installation. The system is designed and built to function efficiently on standard alkaline batteries, operating autonomously without relying on cloud-based or external computing resources. It comprises energy-efficient, Artificial Intelligence (AI)-enabled IoT platforms, each equipped with multi-modal sensors for motion, audio, and video data processing. These platforms autonomously analyze sensor data locally, transmitting only binary occupancy status results to a central platform. We conducted extensive real-world testing, covering various challenging scenarios, including individuals in various postures (e.g., lying down, seated), scenes featuring pets (e.g., cats), as well as very low-light and no-light conditions. Integrated laboratory tests demonstrated exceptional accuracy. For daytime tests that lasted over 111 hours, 100% accuracy with zero false positives is achieved. Similarly, for the no-light tests conducted over 10 hours, the accuracy exceeded 99%. Further real-life testing was carried out in three different apartments, totaling approximately 412 hours, with an average accuracy of 99.37%. An extensive evaluation of the platform was conducted to gauge its reliability and performance. Inspired by established methodologies in assessing occupancy sensor systems, the critical importance of thorough evaluation in enhancing the reliability of occupancy detection systems is recognized. The platform, designed for accurate occupancy detection under various conditions, underwent rigorous testing and analysis. This evaluation not only contributes to understanding the system\u27s performance intricacies but also aligns with broader goals of advancing reliable occupancy detection methods, thereby fostering energy efficiency and automation in diverse applications. After proposing ML-based approaches for thermal camera and visible range images, this thesis presents another advanced detection algorithm for another sensor modality, more specifically magnetic resonance imaging (MRI) scans. In the realm of healthcare, our research aims to address the early detection of Alzheimer\u27s disease using MRI scans. By developing attention networks and applying machine learning techniques, we strive to identify preclinical stages of Alzheimer\u27s disease. This early detection is crucial for timely intervention and treatment, potentially leading to better patient outcomes. Leveraging the rich information captured in brain MRI scans, our methodology effectively exploits the attention mechanism to highlight subtle abnormalities and patterns indicative of preclinical stage Alzheimer\u27s disease. Through extensive experiments and comparisons with existing approaches, we demonstrate the efficacy and potential of our proposed methodology for early detection and monitoring of Alzheimer\u27s disease, contributing to the growing body of research in neuroimaging and disease detection. This work is the first one that uses a transformer network that detects this disease in its very early stage when all the indicators, including assessments by doctors, show that the patient is healthy, with no symptoms. Collectively, this thesis delves into the intricacies of detection algorithms and their practical applications in multi-domain imagery, ranging from thermal images to visible-range images to MRI scans. Through our research in pedestrian detection, occupancy detection, occupancy sensor reliability assessment and Alzheimer\u27s disease detection, we strive to bridge the gap between detection algorithms and real-world challenges. Our studies contribute to the field of computer vision and machine learning, offering promising solutions for critical and challenging problems in diverse domains
(Crip)ping Art Therapy: Imagining Alternatives to Ableism in Mental Health Treatment
Get PDFThis paper explores how the lived experiences of disabled art therapists can inform, through a qualitative and arts-based research approach, a theory and possible applications for improving accessibility within the field of art therapy for both disabled persons seeking intervention and for disabled professionals working in the field. The student researcher collected data through interviews with three self-identifying disabled art therapists or art therapists in training. The interviews were transcribed, coded and analyzed to develop a definition of accessibility, along with a grounded theory on how accessibility can be improved within the field of art therapy. The study explored possible approaches in practice for working with disabled persons, as well as possible interventions to challenge ableism for disabled art therapists within professional settings. Art therapists can use the definitions and approaches presented here as a way to enhance their cultural humility and practice in working with disabled bodyminds
Beyond the Brush: How Women Artists Navigate Communication and Creativity Amidst the Rise of AI
Get PDFWomen artists who use physical paints, canvases, charcoal, and pencils to create their work represent a marginalized group in the art world who may be perceived as distanced or removed from artificial intelligence (AI). While AI, art, feminist aesthetics, and media representation are each areas of rich research, they have not yet been brought together. As art itself is reconsidered alongside the rise of AI, this study conducted 20 in-depth, semi-structured qualitative interviews with women artists to understand the implications of choosing to incorporate AI into their work, changes to their artistic processes, adjustments to their communications about their work, as well as the reasons why they may not interact with technology. The interviews captured five themes analyzed using diffusion of innovations as a theoretical framework. These themes paint a more nuanced, and at times surprising, picture of how AI impacts the artistic process and communications of women artists