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  1. Since its inception in 2015, the National Science Foundation Revolutionizing Engineering Departments (RED) program has supported engineering and computer science educators as they work to transform the preparation of undergraduate students. As part of the program, members of RED teams connect with one another as a community of practice (CoP). More than just a collection of individuals who possess a shared interest, a CoP is defined by several distinct features: members of the CoP are practitioners; they develop a shared repertoire of resources that represent their shared practice; and they develop their community over time as a result of shared interaction. In our work with RED teams, we have identified aspects of their interactions that suggest that they operate as a CoP and gain benefits from their engagements. We see the RED CoP as instrumental to their success as change makers and an example of how CoPs can contribute to implementing change in other academic contexts. 
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  2. Transforming academic organizations to be more equitable and inclusive requires a range of change agents working together and engaging in diversity, equity, and inclusion (DEI) initiatives. Central to this DEI work is learning how to create change. Yet, change agents do not always know at the outset what resources are necessary to enact change; they often acquire the necessary resources and skills over time. This research paper investigates how change agents participating in a community of practice (CoP) across academic institutions learn about and mobilize resources to transform engineering education. This analysis of resource mobilization mechanisms comes from research with the National Science Foundation (NSF) Revolutionizing Engineering Departments (RED) grant recipient teams. To date, 26 teams have been funded through the RED mechanism to create revolutionary organizational and cultural changes within their departments with the goal of improving equity, inclusion, and educational outcomes. Projects vary in how they define and the degree to which they focus on equity. We find that resource mobilization practices in the CoP center and strengthen DEI values in two main ways. Firstly, participants learn about and gain access to resources that are explicitly DEIrelated: they mobilize resources to advance equity at the institutional level as an outcome of the projects and collaborate on additional projects to embed DEI into the process of change-making itself, starting from the initial stages of writing a proposal. Secondly, the way participants engage with each other, and approach change goals puts equity and inclusion into practice: participants identify and tackle structural barriers to change through DEI-aligned behaviors, from addressing how institutional circumstances create resistance to DEI, to developing a shared vision for systemic change that is inclusive and collaborative. 
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  3. Atluri, V. ; Di Pietro, R. ; Jensen, C.D. ; Meng, W. (Ed.)
    System auditing is a powerful tool that provides insight into the nature of suspicious events in computing systems, allowing machine operators to detect and subsequently investigate security incidents. While auditing has proven invaluable to the security of traditional computers, existing audit frameworks are rarely designed with consideration for Real-Time Systems (RTS). The transparency provided by system auditing would be of tremendous benefit in a variety of security-critical RTS domains, (e.g., autonomous vehicles); however, if audit mechanisms are not carefully integrated into RTS, auditing can be rendered ineffectual and violate the real-world temporal requirements of the RTS. In this paper, we demonstrate how to adapt commodity audit frameworks to RTS. Using Linux Audit as a case study, we first demonstrate that the volume of audit events generated by commodity frameworks is unsustainable within the temporal and resource constraints of real-time (RT) applications. To address this, we present Ellipsis, a set of kernel-based reduction techniques that leverage the periodic repetitive nature of RT applications to aggressively reduce the costs of system-level auditing. Ellipsis generates succinct descriptions of RT applications’ expected activity while retaining a detailed record of unexpected activities, enabling analysis of suspicious activity while meeting temporal constraints. Our evaluation of Ellipsis, using ArduPilot (an open-source autopilot application suite) demonstrates up to 93% reduction in audit log generation. 
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    The star-forming activity in the H ii region RCW 42 is investigated using multiple wavebands, from near-infrared to radio wavelengths. Located at a distance of 5.8 kpc, this southern region has a bolometric luminosity of 1.8 × 106 L⊙. The ionized gas emission has been imaged at low radio frequencies of 610 and 1280 MHz using the Giant Metrewave Radio Telescope, India, and shows a large expanse of the H ii region, spanning 20 × 15 pc2. The average electron number density in the region is estimated to be ∼70 cm−3, which suggests an average ionization fraction of the cloud to be 11 % . An extended green object EGO G274.0649-01.1460 and several young stellar objects have been identified in the region using data from the 2MASS and Spitzer surveys. The dust emission from the associated molecular cloud is probed using Herschel Space Telescope, which reveals the presence of five clumps, C1-C5, in this region. Two millimetre emission cores of masses 380 and 390 M⊙ towards the radio emission peak have been identified towards C1 from the ALMA map at 1.4 mm. The clumps are investigated for their evolutionary stages based on association with various star-formation tracers, and we find that all the clumps are in active/evolved stage.

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  5. Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained on observed outcomes (e.g. whether it has rained or not, or whether a patient has died or not), because the ground-truth probabilities of the events of interest are typically unknown. The problem is therefore analogous to binary classification, with the difference that the objective is to estimate probabilities rather than predicting the specific outcome. This work investigates probability estimation from high-dimensional data using deep neural networks. There exist several methods to improve the probabilities generated by these models but they mostly focus on model (epistemic) uncertainty. For problems with inherent uncertainty, it is challenging to evaluate performance without access to ground-truth probabilities. To address this, we build a synthetic dataset to study and compare different computable metrics. We evaluate existing methods on the synthetic data as well as on three real-world probability estimation tasks, all of which involve inherent uncertainty: precipitation forecasting from radar images, predicting cancer patient survival from histopathology images, and predicting car crashes from dashcam videos. We also give a theoretical analysis of a model for high-dimensional probability estimation which reproduces several of the phenomena evinced in our experiments. Finally, we propose a new method for probability estimation using neural networks, which modifies the training process to promote output probabilities that are consistent with empirical probabilities computed from the data. The method outperforms existing approaches on most metrics on the simulated as well as real-world data. 
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  6. Principal investigators and project teams funded by the National Science Foundation are familiar with the requirement to discuss the impact of their research. Whether the discussion appears in a new proposal, or as part of annual or final reporting, describing the impacts of a project is key to demonstrating the value of the work itself. PIs and project teams may not, however, consider the ways in which their reporting on impacts can help them disseminate their work to stakeholders and propagate their innovations to other researchers. Impact statements can also be useful to NSF program officers who are often in the position of informing about and advocating for the projects under their management. Consequently, our work to support the NSF Revolutionizing Engineering Departments (RED) program helps project teams develop more coherent and persuasive impact statements. These impact statements lay the foundation for teams to persuasively disseminate their work. As part of our work to support the NSF Revolutionizing Engineering Departments (RED) program, we have developed an impacts tutorial that helps proposal and report writers capture what is impactful about their projects and to communicate that impact to multiple audiences (e.g., the NSF program officer, stakeholders for the project, etc.). We piloted the tutorial during the 2019 RED Consortium Meeting to the 21 RED teams in attendance. The tutorial began with a clear statement of the purpose of impact statements generally that was included in a printed workbook distributed to all attendees. From that starting point, groups made up of representatives from different RED teams worked to draft responses to the NSF Annual Report question prompts that address impacts. Initial feedback from NSF about this session have been positive and indicate improvements in reporting by RED teams. During our poster presentation at ASEE, we will introduce this method of writing impact statements, share elements of the workbook, and help attendees apply the method to their own NSF reporting. 
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