Support, School Climate and Teacher Wellbeing in the Wake of COVID-19: A National Study Using NTSP Data

Research indicates a significant number of teachers planned to leave the profession following the COVID-19 pandemic, with national surveys in 2021 revealing about 25% considering departure. Stress, low pay, remote teaching demands, and lack of support contributed to attrition, impacting school climate, which influences productivity, collaboration, and job satisfaction, and is inversely related to turnover. The shift to remote learning also demanded new skills and exposed technological disparities. This quantitative study examined the pandemic\u27s impact on teachers\u27 perceptions of school climate, support, resources, job satisfaction, burnout, and intent to leave using the 2020–2021 National Teacher and Principal Survey. Factor analyses revealed two school climate constructs: administrative tasks/outcomes and shared belief systems. While a weak positive relationship existed between resources/support and school climate, organizational climate positively correlated with job satisfaction and negatively with burnout and intent to leave. Similarly, staff collaboration/cohesiveness negatively correlated with burnout/intent to leave and positively with job satisfaction. These findings emphasize the importance of positive administrative interactions, fair policies, and shared values in supporting teacher well-being and reducing turnover in the post-pandemic educational context.Advisor: Jiangang Xi

Relationship of Estrous Cycle to Herpes Simplex Virus Type 2 Susceptibility in Female Mice

In CBA/NJ mice, splenic natural killer (NK) cell activity varies with stages of estrous. Susceptibility of ICR mice to intravaginal inoculation of herpes simplex virus type 2 (HSV-2) decreases with age. Susceptibility of female ICR and CBA/NJ mice to HSV-2 inoculated intravaginally and intraperitoneally was examined during the estrous cycle. In cycling ICR mice, greatest susceptibility to intravaginal inoculation was observed during diestrous and the least during metestrous. CBA/NJ mice were most susceptible to intravaginal inoculation of HSV-2 during diestrous. ICR mice were ovariectomized to mimic diestrous and found to be highly susceptible to intravaginal inoculation at all virus doses. No difference in susceptibility among phases of the estrous cycle was seen following intraperitoneal inoculation

Explainable Model-Agnostic Similarity and Confidence in Face Verification

Recently, face recognition systems have demonstrated remarkable performances and thus gained a vital role in our daily life. They already surpass human face verification accountability in many scenarios. However, they lack explanations for their predictions. Compared to human operators, typical face recognition network system generate only binary decisions without further explanation and insights into those decisions. This work focuses on explanations for face recognition systems, vital for developers and operators. First, we introduce a confidence score for those systems based on facial feature distances between two input images and the distribution of distances across a dataset. Secondly, we establish a novel visualization approach to obtain more meaningful predictions from a face recognition system, which maps the distance deviation based on a systematic occlusion of images. The result is blended with the original images and highlights similar and dissimilar facial regions. Lastly, we calculate confidence scores and explanation maps for several state-of-the-art face verification datasets and release the results on a web platform. We optimize the platform for a user-friendly interaction and hope to further improve the understanding of machine learning decisions. The source code is available on GitHub, and the web platform is publicly available at http://explainable-face-verification.ey.r.appspot.com

Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration

Object detectors are at the heart of many semi- and fully autonomous decision systems and are poised to become even more indispensable. They are, however, still lacking in accessibility and can sometimes produce unreliable predictions. Especially concerning in this regard are the -- essentially hand-crafted -- non-maximum suppression algorithms that lead to an obfuscated prediction process and biased confidence estimates. We show that we can eliminate classic NMS-style post-processing by using IoU-aware calibration. IoU-aware calibration is a conditional Beta calibration; this makes it parallelizable with no hyper-parameters. Instead of arbitrary cutoffs or discounts, it implicitly accounts for the likelihood of each detection being a duplicate and adjusts the confidence score accordingly, resulting in empirically based precision estimates for each detection. Our extensive experiments on diverse detection architectures show that the proposed IoU-aware calibration can successfully model duplicate detections and improve calibration. Compared to the standard sequential NMS and calibration approach, our joint modeling can deliver performance gains over the best NMS-based alternative while producing consistently better-calibrated confidence predictions with less complexity. The \hyperlink{https://github.com/Blueblue4/IoU-AwareCalibration}{code} for all our experiments is publicly available