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Reneging On The Promise: The Institutionalization Of The Equity Administrator In Public And Private Schools (george Floyd And Beyond)
No full textThis study examines the role of Equity Administrators (EAs) as equity leaders within schools and their capacity to drive systemic change. It explores how transformative leadership practices either facilitate or hinder EAs\u27 work and how these practices can be implemented in their daily responsibilities. While the presence of Equity leaders in schools has increased, limited research exists on how EAs engage with transformative leadership and its impact on fostering lasting change. This study underscores the need for further research into the EAs’ role in promoting school improvement, particularly in the post-2020 period, when many schools established equity-focused positions following the murder of George Floyd and the COVID-19 pandemic. Using a qualitative case study approach, this research investigates how EAs perceive their roles and align them with Transformative Leadership Theory (TLT). The study addresses key questions, including how current EA practices align with TLT, the factors that facilitate or disrupt their work, and the primary obstacles they face. By examining the experiences of EAs across several school districts within the same county, this research identifies barriers and successes in implementing equity initiatives. The study aims to contribute to the literature on how transformative leadership can be institutionalized and sustained in schools, fostering systemic change that benefits marginalized communities. Four themes emerged from the data: “The Four Ls”—lack of mentorship, loneliness of leadership, limited reach and focus, and the aspirational goal of liberation. These themes highlight critical challenges that influence EAs’ self-efficacy and offer valuable insights for EAs\u27 supervisors in fostering collaboration to reshape the EA role
Handling Emerging Sources Of Non-Determinism: Proportional Control With Quantum Noise, And Lyapunov-Based Economic Model Predictive Control To Address Stability, Profitability, And Dynamics Of Nonlinear Systems Under Cyberattack
No full textThe fundamental objective of process control is to establish safe process operating conditions while ensuring minimal economic loss. One of the fundamental challenges of achieving this objective is non-determinism from various sources that can create unexpected process behavior. A traditional goal in industrial process control is the rejection of disturbances, which are time-varying process inputs that are not directly modified by the control system. Other sources of uncertainty can include measurement inaccuracies/noise and changes in process dynamics as a plant operates. Many techniques have been developed to ensure notions of stabilization and safety despite these sources of stochasticity for both linear and nonlinear dynamic systems. In recent decades, manufacturers have sought to push toward more efficient and digitalized operations within a framework called “smart manufacturing” or “Industry 4.0”. This framework involves an increased utilization of cyber-physical systems, which are process systems integrated with computers, communication, and networking components to improve process transparency and efficiency. Additionally, it can be accompanied by new sources of uncertainty that require a thorough characterization of safety risks and mitigation techniques. One such source of non-determinism, introduced using automation and networked systems, is a cyberattack on control systems in which a malicious actor can manipulate sensor readings or actuator outputs and potentially mask their actions. Furthermore, control being implemented on computing platforms is influenced by technological developments in the computing domain and must also be vetted from a cyber-physical systems safety perspective. For example, advances in quantum computation prompt whether this emerging computing paradigm (currently affected by hardware-related quantum “noise”) could ever be evaluated for control applications, or if the hardware limitation that introduces non-determinism as noise will render it unsafe for control applications.
Motivated by the need for a rigorous characterization of safety in manufacturing systems with new sources of non-determinism derived from digitalization and emerging technologies, our work has developed fundamental advances in control systems theory and practice toward evaluating closed-loop stability under control system cyberattacks and quantum noise in control action computation. This dissertation presents our work in these areas. First, we will present cyberattack detection schemes for nonlinear systems that provide guarantees on the time available before safety risks are potentially significant after an undetected attack on the control systems. Safety guarantees are made possible by integrating the detection policy with an optimization-based control strategy for the process called Lyapunov-based economic model predictive control (LEMPC). Second, we will utilize simulation studies to evaluate and analyze the safety associated with a linear single-input/single-output system operated by a control system that incorporates quantum computation with depolarizing error (a type of quantum noise). We will discuss the role of control system parameters and system characteristics in mitigating the effects of noise on the steady-state tracking objective
Adequacy, Equity, And Agency: A Study Of Professional Development Through Teacher Perceptions And Experiences In Urban Public Schools
No full textThis research was undertaken to address notable gaps in the existing literature concerning the professional development experiences of educators, with a particular focus on the dimensions of agency and empowerment. The findings are intended to aid researchers and policymakers in designing professional development (PD) programs that are more attuned to the perspectives and experiences of teachers. Employing a qualitative phenomenological narrative inquiry approach, the study sought to investigate the professional development experiences of middle and high school teachers in urban districts, both within and outside the state. Grounded in the concept of teacher agency, the research questions and methodology were directed at uncovering teachers\u27 needs, expectations, experiences, and perceptions regarding professional development, especially those elements that either facilitate or obstruct empowerment and agency in a PD context. Data were gathered through qualitative techniques, including surveys and semi-structured interviews conducted via video conferencing. The analysis identified several significant themes: (a) experiences with formal professional development, highlighting obstacles to PD and factors that promote professional growth, (b) preferred conditions that support teacher agency in PD; (c) teachers\u27 needs; (d) experiences with external professional development, and (e) beliefs and experiences related to teacher agency within the PD framework. The implications of this study could lead to meaningful improvements in professional development practices and initiatives in both formal and informal environments, thereby fostering a culture of professional development that respects teachers as professionals, values their experiences, and acknowledges their potential for agency
Algorithms For Efficient Feature-Based Visual Simultaneous Localization And Mapping In Dynamic Environments
No full textIn dynamic environments, the presence of moving objects presents significant challenges for visual SLAM systems, leading to inconsistent trajectory estimation and mapping inaccuracies. Traditional SLAM systems are primarily designed for static environments, where scene elements remain stationary. However, in real-world scenarios, dynamic objects such as pedestrians, vehicles, and other moving entities frequently occlude environmental features, causing disruptions in scene continuity and reducing mapping accuracy. These challenges are further exacerbated by the unpredictable nature of dynamic objects, leading to frequent occlusions, motion blur, and inconsistent feature matching. Addressing these issues requires advanced dynamic object handling mechanisms that can effectively segment, track, and inpaint occluded regions, ensuring seamless scene reconstruction and robust trajectory estimation.
We propose DynaGaussian-SLAM, a novel dense visual SLAM system designed for highly dynamic environments by integrating dynamic object handling, inpainting, and memory-based optimization. Building upon the foundations of REDO-SLAM and GAN-SLAM, our approach enhances robustness in dynamic scenes while maintaining computational efficiency. REDO-SLAM’s motion-aware dynamic object segmentation, leveraging a combination of optical flow and Deep Reinforcement Learning (DRL), enables efficient mask prediction, while GAN-SLAM’s flow-guided video inpainting recovers occluded backgrounds to improve scene consistency.
REDO-SLAM\u27s motion-aware dynamic object segmentation technique efficiently detects and segments dynamic objects by analyzing motion patterns using optical flow. This is further enhanced with DRL, enabling adaptive mask prediction that can generalize to various dynamic scenarios. By integrating this segmentation framework, DynaGaussian-SLAM effectively tracks and segments moving objects in real-time, ensuring consistent trajectory estimation even in highly dynamic environments.
Building on GAN-SLAM\u27s flow-guided video inpainting, DynaGaussian-SLAM restores occluded backgrounds, ensuring seamless scene reconstruction. GAN-SLAM utilizes optical flow to guide the inpainting process, maintaining temporal consistency and generating realistic textures for occluded regions. This method significantly enhances scene continuity by accurately reconstructing background elements hidden by dynamic objects. By leveraging this approach, DynaGaussian-SLAM not only improves visual fidelity but also enhances mapping accuracy by maintaining consistent environmental features, even in the presence of moving objects. To further optimize computational efficiency, we introduce a hierarchical memory-based transformer that leverages both long-term and short-term memory to minimize redundant computations. Long-term memory stores keyframe information, preventing repeated model inference for previously observed scenes, while short-term memory retains adjacent frame details for rapid data retrieval. This dual-memory architecture ensures efficient data processing, reducing latency and enhancing real-time performance. By strategically managing memory usage, DynaGaussian-SLAM achieves faster computation speeds while maintaining high accuracy in trajectory estimation.
One of the key innovations in DynaGaussian-SLAM is the adaptive fusion of dynamic object segmentation and inpainting, creating a cohesive pipeline for consistent scene reconstruction. Unlike conventional SLAM systems that treat segmentation and inpainting as separate modules, DynaGaussian-SLAM dynamically fuses these processes to achieve seamless scene consistency. This adaptive fusion is guided by flow information, allowing real-time adjustments in segmentation masks and inpainting regions based on object motion and occlusions. This integrated approach ensures that the reconstructed scene remains coherent, enhancing mapping accuracy and robustness in dynamic environments.
Our experimental evaluation on the TUM and KITTI datasets demonstrates that DynaGaussian-SLAM outperforms state-of-the-art dynamic SLAM systems in terms of robustness, scalability, and computational efficiency. The system achieves higher accuracy in dynamic object segmentation and trajectory estimation while maintaining real-time performance. Comparative analysis with REDO-SLAM and GAN-SLAM shows that DynaGaussian-SLAM improves mapping consistency and reduces processing latency. Furthermore, the hierarchical memory-based transformer enhances computational efficiency, resulting in lower energy consumption and faster processing speeds
From Bow To Llms: Advancing Work Attitudes And Perceptions Measurement From Organizational Text
No full textThis research investigated advances in natural language processing (NLP) techniques for measuring work attitudes and perceptions from organizational text. Traditional Bag-of-Words (BOW) approaches, while widely used, fail to capture semantic nuances and contextual meanings critical for accurate theme identification. This study compared BOW with three alternative approaches: Fully Fine-Tuned Standard Transformer-Based Theme Score Algorithm (FAST), Partially Fine-Tuned Standard Transformer-Based Theme Score Algorithm (PAST), and Generative Automated Theme Scoring (GATS) with zero-shot and few-shot configurations. Using a dataset of approximately 5,000 employee narratives across 21 work-related constructs, models were evaluated against subject matter expert (SME) ratings. Results demonstrated that FAST significantly outperformed BOW across multiple metrics, while PAST revealed diminishing returns as labeled data increased. Construct complexity moderated this relationship, with less complex constructs requiring fewer examples to reach optimal performance. Zero-shot GATS demonstrated strong performance without task-specific training, while few-shot GATS with 3-12 examples yielded minimal additional improvements. These findings advance organizational science by providing empirically validated approaches for efficiently analyzing qualitative employee data at scale. Practically, organizations can strategically allocate resources when implementing these methods, selecting appropriate approaches based on available data, computational resources, and construct complexity. This research contributes to more accurate, efficient, and accessible measurement of work attitudes and perceptions from narrative data
No Evidence of Selection or Influence on Political Attitudes: Findings From the Swiss StudentLife Study
No full textThis ID:dp001234805 study examines whether selection, deselection, and influence processes contribute to political homogeneity in young adults. Political similarity in newly formed friendships and that in enduring friendships are distinguished. Using two cohorts from the Swiss StudentLife Study (N = 921; M age at baseline = 20.1, SD = 3.6; 18.4% female; 29.0% non-Swiss) and applying stochastic actororiented models, the coevolution of friendships, defined as an outgoing friendship nomination, and political attitudes were examined. The results provided no evidence that political attitudes significantly affect the formation or maintenance of friendships, nor do influence mechanisms shape political homogeneity. No evidence was found that relationship duration strengthened the influence process. These findings suggest that in contexts of relatively low political engagement and polarization, such as among Swiss students, political attitudes are not a significant factor in the formation or maintenance of social relationships, and peers do not affect each other’s political attitudes
The Impact Of Knowing Family History On Psychosocial Functioning In Young Adults: Considerations For Immigrant Status And Perceived Parenting
No full textIntergenerational storytelling about family history can shape personal narratives and foster resilience (Driessnack, 2017; Fivush, 2008). Development of the intergenerational self may be particularly relevant during specific life stages (e.g., emerging adulthood) or for those experiencing unique stressors (e.g., adjustment through immigration). The aim of the present study was to examine how knowledge of family history would impact psychosocial functioning in emerging adults. It was predicted that a positive relation would be identified between knowledge of family history and trait hope, flourishing, and other components of positive youth development, respectively. It was also predicted that a negative relation would be identified between knowledge of family history and depression, anxiety, and stress, respectively. In addition, the study explored the mediating role of perceived quality of relationship with parents and moderating role of immigrant generational status (i.e., first-, second-, and continuing-generation), predicting that the relationships would be strongest for first-generation immigrant participants. Participants included a national sample of emerging adults (N = 275; ages 18-29 years old), who were recruited through the online Prolific platform. Participants were asked to complete a survey including measures of all variables in the study. A mixed methods approach, with a cross-sectional, convergent parallel design was used to assess quantitative measures and qualitative questions. Quantitative measures included demographic characteristics (e.g., age, gender, race, ethnicity, immigrant generational status, adverse childhood experiences, living conditions checklist, attitudes towards mental health problems); knowledge of family history, including follow-up qualitative questions; psychological symptoms (i.e., depression, anxiety, stress); trait hope; flourishing; five components of positive youth development; and perception of quality of relationship with parents, including follow-up qualitative questions. Quantitative data was analyzed with standard and hierarchical linear regressions to test the hypothesized relation among variables. Qualitative data were analyzed from a phenomenological research approach as well as through quantitative analysis and integration after coding. Results followed predicted and unpredicted patterns, revealing mixed findings between immigrant generational status groups. Future directions and implications for supporting emerging adults, at large, and immigrant young adults, specifically, are discussed