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    30819 research outputs found

    They’re in a Better Place Now: Navigating Collection Transfers

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    Jasmine Smith: Rehoming the Reading Eagle; Bethany J. Antos: Being the Better Place: Providing Reference in Transferred Collections; Sandra Glascock: Should it Stay or Should it Go?: Using Reappraisal and Deaccessioning as Collection Management Tools.Session 9: Although transferring a collection of materials from one institution to another can be a big undertaking, rehoming it may enhance the accessibility and visibility of the materials, provide a better storage environment, or confer other advantages for one or both institutions. This presentation will address both the practical and philosophical sides of inter-institutional collection transfers. In addition to discussing the logistics and documentation of collection transfers, presenters will talk about why a particular collection move was proposed and accepted, and how it affected staff and researchers. This session will not discuss transfers that are part of scheduled records management activities

    Navigating college search and choice: How immigrant capital paves a path to postsecondary education for first-generation Students of Color

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    Immigrant youth represent one of the fastest growing and most diverse groups in the U.S. K-16 system. Though immigrant youth generally report high educational aspirations, they face multiple interrelated obstacles to postsecondary enrollment. Despite barriers, data indicate that immigrants are going to college and in some cases are enrolling at a rate higher than their non-immigrant counterparts. Previous research highlights multiple forms of capital, including community cultural wealth (Yosso, 2005), that immigrants who share a racial or ethnic background leverage to access higher education. However, few studies have examined the extent to which immigrants, across race and ethnicity, engage similar resources to navigate the college choice process. This study sheds light on the pre-college experiences of a racially diverse sample of 1.5-generation immigrants who, at the time of this study, were first-year students at a 4-year institution.The following research questions guided this study: (a) How do low-income immigrant students of color engage in the college search and choice process? (b) How do various forms of capital and community resources shape students’ college choice process. Through semistructured interviews, 10 Asian, Black, and Latinx immigrants shared detailed accounts of their family background, migration, and transition to U.S. schools; development of college aspirations; and college search, application, and decision-making experiences. Participants also discussed the tools and resources they used, individuals who assisted them, and how they made sense of their experiences, significant moments, and turning points in their journey. Findings reveal multiple forms of capital that developed within participants’ immigrant families: capital that fostered an early predisposition toward college and enabled participants to navigate a complex college application process, during the COVID-19 pandemic, to ultimately gain admission to multiple postsecondary institutions. Findings from this study suggest immigrant capital as a unifying concept capturing skills, assets, and perspectives immigrants use to achieve their educational goals. Findings also have implications for future research, policy, and practice


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    This work focuses on the design of a capacitance-to-digital converter (CDC) for high-speed high-resolution readout of capacitive sensors. Most previously reported CDCs show a tradeoff in resolution and conversion speed; In this work a two-step successive approximation register (SAR) CDC is proposed to improve resolution and conversion speed over state-of-the-art. First, the coarse conversion stage performs a capacitive offset compensation down to within 10fF. The fine conversion stage converts the amplified residue voltage with a resolution of 200aF. These bits are communicated off chip on an I2C bus. The effective number of bits (ENOB) is compared under different measurement conditions. The circuit achieves 9.8 ENOB with a 28 µs conversion time. When overclocked, the circuit achieves 8.2 ENOB with a 14 µs conversion time. This equates to an overall figure of merit (ENOB throughput) of 350 kbits/s and 585 kbits/s, respectively, which is among the highest values reported in the literature. The interface circuit design is described, simulated, and measured to characterize performance

    "These Songs will Save our Language": Reclaiming Kiowa Language and Music through Kiowa Sound Resurgence

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    This dissertation examines the intersection of Indigenous language reclamation and music, primarily among the Kiowa Tribe. Through multi-sited ethnographic fieldwork, interviews, music/language analysis, and participatory action research, I show how music plays a key role in the resurgence of Kiowa language and identity. I begin in Washington, D.C. by revealing how Kiowas (and other Indigenous Peoples) strategically use their own modes of storytelling and music making to resist the imposition of settler colonial narratives. Indigenous performers reclaim stories about their language initiatives and challenge problematic congressional language planning and policy. The dissertation then moves towards Oklahoma and examines the language efforts of a community-based institution: the Kiowa Language and Culture Revitalization Program (KLCRP). I show how KLCRP used Kiowa Christian hymns—which are performed in the Kiowa language and musical style— as a pedagogical approach to revive and strengthen forms of Kiowa sound and audibility, including speech, music making, storytelling, and listening. I frame the recovery of these practices as Kiowa sound resurgence. I explore the multiple ways in which Kiowas engaged in Kiowa sound resurgence through traditional and non-traditional pedagogies before and during the COVID-19 pandemic. This dissertation contributes to interdisciplinary dialogues in ethnomusicology, Native American and Indigenous studies, and linguistic anthropology on Indigenous language reclamation and music scholarship. The case study of Kiowa sound resurgence illuminates how Kiowas creatively reclaim, revive, and resurge sound through Kiowa ways of knowing, doing, and being. The findings of this dissertation have relevance to both academia and Indigenous communities who are actively engaging in efforts of cultural reclamation and resurgence

    Noble Metal Ion-Directed Assembly of 2D Materials for Heterostructured Catalysts and Metallic Micro-Texturing

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    Assembling 2D-material (2DM) nanosheets into micro- and macro-architectures with augmented functionalities requires effective strategies to overcome nanosheet restacking. Conventional assembly approaches involve external binders and/or functionalization, which inevitably sacrifice 2DM's nanoscale properties. Noble metal ions (NMI) are promising ionic crosslinkers, which can simultaneously assemble 2DM nanosheets and induce synergistic properties. Herein, a collection of NMI–2DM complexes are screened and categorized into two sub-groups. Based on the zeta potentials, two assembly approaches are developed to obtain 1) NMI-crosslinked 2DM hydrogels/aerogels for heterostructured catalysts and 2) NMI–2DM inks for templated synthesis. First, tetraammineplatinum(II) nitrate (TPtN) serves as an efficient ionic crosslinker to agglomerate various 2DM dispersions. By utilizing micro-textured assembly platforms, various TPtN–2DM hydrogels are fabricated in a scalable fashion. Afterward, these hydrogels are lyophilized and thermally reduced to synthesize Pt-decorated 2DM aerogels (Pt@2DM). The Pt@2DM heterostructures demonstrate high, substrate-dependent catalytic activities and promote different reaction pathways in the hydrogenation of 3-nitrostyrene. Second, PtCl4 can be incorporated into 2DM dispersions at high NMI molarities to prepare a series of PtCl4–2DM inks with high colloidal stability. By adopting the PtCl4–graphene oxide ink, various Pt micro-structures with replicated topographies are synthesized with accurate control of grain sizes and porosities.https://doi.org/10.1002/adfm.20221522


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    Clinical evidence suggests that experiencing pregnancy increases a woman’s risk of knee osteoarthritis, a painful and mobility limiting disease that results from cartilage deterioration. While understanding the underlying causes and the association with pregnancy is complex, the mechanical load on cartilage during walking appears to be important to the initiation and progression of the disease, especially if walking mechanics are abnormal. Pregnancy involves various changes in mechanical factors like mass, center of mass, and joint laxity which are known to progressively change walking mechanics throughout gestation. However, it is unknown if mechanical changes associated with pregnancy, which may be substantial in magnitude but may be limited in duration, can explain the osteoarthritis risk since osteoarthritis is diagnosed later in life. Given that women typically experience pregnancy early in their lifetime and will need healthy knees for decades after they become mothers, this research aimed to model the mechanical consequences of pregnancy on knee joint loading and knee joint health over the lifetime. Specifically, this dissertation sought to (i) determine how pregnancy influences variables like resultant knee joint kinetics, which more directly indicate the load on cartilage over a range of walking speeds (ii) estimate the impact of pregnancy on internal knee joint forces and tibiofemoral cartilage load during walking and (iii) evaluate the isolated effect of altered loading experienced during pregnancy on cartilage degeneration and the risk of knee osteoarthritis throughout a woman's lifetime. Results suggest that (i) 3D knee joint moments over a range of walking speeds are greater in pregnant vs. non-pregnant individuals and knee adduction moments are altered as pregnant women walk faster. Similarly, pregnant women experience greater total knee joint loading and greater medial knee joint loading which results in additional and altered peak strain on knee cartilage with greater walking speed. Finally, the elevated and altered compressive load experienced over one or more pregnancies resulted in a greater cartilage failure probability, with differential effects when women experience multiple pregnancies later in their lifetime. These findings support the notion that the mechanical factors associated with pregnancy significantly alter knee joint loading and mechanical changes may, in part, contribute to the known association between pregnancy and risk for knee osteoarthritis risk over a woman’s lifetime. Further, present-day American mothers who are conceiving at later stages of life compared to previous generations may be more susceptible to knee osteoarthritis. Future investigations are needed to explore effects postpartum and for populations beyond healthy, active pregnant women. Further research could also investigate if biomechanical adjustments could be used as potential interventions to lessen knee joint loading and potentially decrease the risk of knee osteoarthritis among this population

    Localized Photoactuation of Polymer Pens for Nanolithography

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    Localized actuation is an important goal of nanotechnology broadly impacting applications such as programmable materials, soft robotics, and nanolithography. Despite significant recent advances, actuation with high temporal and spatial resolution remains challenging to achieve. Herein, we demonstrate strongly localized photoactuation of polymer pens made of polydimethylsiloxane (PDMS) and surface-functionalized short carbon nanotubes based on a fundamental understanding of the nanocomposite chemistry and device innovations in directing intense light with digital micromirrors to microscale domains. We show that local illumination can drive a small group of pens (3 × 3 over 170 µm × 170 µm) within a massively two-dimensional array to attain an out-of-plane motion by more than 7 µm for active molecular printing. The observed effect marks a striking three-order-of-magnitude improvement over the state of the art and suggests new opportunities for active actuation.https://doi.org/10.3390/molecules2803117

    Sirius: A Self-Localization System for Resource-Constrained IoT Sensors

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    Low-power sensor networks are transforming large-scale sensing in precision farming, livestock tracking, climate-monitoring and surveying. Accurate and robust localization in such low-power sensor nodes has never been as crucial as it is today. This paper presents, Sirius, a self-localization system using a single receiver for low-power IoT nodes. Traditionally, systems have relied on antenna arrays and tight synchronization to estimate angle-of-arrival (AoA) and time-of-flight with known access points. While these techniques work well for regular mobile systems, low-power IoT nodes lack the resources to support these complex systems. Sirius explores the use of gain-pattern reconfigurable antennas with passive envelope detector-based radios to perform AoA estimation without requiring any kind of synchronization. It shows a technique to embed direction specific codes to the received signals which are transparent to regular communication channel but carry AoA information with them. Sirius embeds these direction-specific codes by using reconfigurable antennas and fluctuating the gain pattern of the antenna. Our prototype demonstrates a median error of 7 degrees in AoA estimation and 2.5 meters in localization, which is similar to state-of-the-art antenna array-based systems. Sirius opens up new possibilities for low-power IoT nodes.https://doi.org/10.1145/3581791.359686

    The Limitations of Deep Learning Methods in Realistic Adversarial Settings

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    The study of adversarial examples has evolved from a niche phenomenon to a well-established branch of machine learning (ML). In the conventional view of an adversarial attack, the adversary takes an input sample, e.g., an image of a dog, and applies a deliberate transformation to this input, e.g., a rotation. This then causes the victim model to abruptly change its prediction, e.g., the rotated image is classified as a cat. Most prior work has adapted this view across different applications and provided powerful attack algorithms as well as defensive strategies to improve robustness. The progress in this domain has been influential for both research and practice and it has produced a perception of better security. Yet, security literature tells us that adversaries often do not follow a specific threat model and adversarial pressure can exist in unprecedented ways. In this dissertation, I will start from the threats studied in security literature to highlight the limitations of the conventional view and extend it to capture realistic adversarial scenarios. First, I will discuss how adversaries can pursue goals other than hurting the predictive performance of the victim. In particular, an adversary can wield adversarial examples to perform denial-of-service against emerging ML systems that rely on input-adaptiveness for efficient predictions. Our attack algorithm, DeepSloth, can transform the inputs to offset the computational benefits of these systems. Moreover, an existing conventional defense is ineffective against DeepSloth and poses a trade-off between efficiency and security. Second, I will show how the conventional view leads to a false sense of security for anomalous input detection methods. These methods build modern statistical tools around deep neural networks and have shown to be successful in detecting conventional adversarial examples. As a general-purpose analogue of blending attacks in security literature, we introduce the Statistical Indistinguishability Attack (SIA). SIA bypasses a range of published detection methods by producing anomalous samples that are statistically similar to normal samples. Third, and finally, I will focus on malware detection with ML, a domain where adversaries gain leverage over ML naturally without deliberately perturbing inputs like in the conventional view. Security vendors often rely on ML for automating malware detection due to the large volume of new malware. A standard approach for detection is collecting runtime behaviors of programs in controlled environments (sandboxes) and feeding them to an ML model. I have first observed that a model trained using this approach performs poorly when it is deployed on program behaviors from realistic, uncontrolled environments, which gives malware authors an advantage in causing harm. We attribute this deterioration to distribution shift and investigate possible improvements by adapting modern ML techniques, such as distributionally robust optimization. Overall, my dissertation work has reinforced the importance of considering comprehensive threat models and applications with well-documented adversaries for properly assessing the security risks of ML

    Global Climate Change and UNESCO World Heritage

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    This article considers the fiftieth anniversary of the 1972 United Nations Educational, Scientific and Cultural Organization’s (UNESCO) World Heritage Convention in light of climate change, offering a state of the field review of climate responses for World Heritage sites (WHS). Opening with a brief review of UNESCO World Heritage activities around climate change, we then detail the primary impacts and risks that climate change pose for WHS and the reporting and monitoring systems in place to document and track these impacts. Looking forward, we examine the most promising pathways for World Heritage to advance in the domains of climate mitigation, adaptation, climate communication, and climate action.https://doi.org/10.1017/S094073912200026


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