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\The omission of Native Peoples’ existence, experiences, and perspectives is systematic and widespread across numerous societal domains, referred to as Native omission. In mainstream media, for example, less than 0.5% of representations are of contemporary Native Peoples. We theorize that Native omission is a tool furthering settler colonial goals to oppress and eventually erase Native Peoples. To make this case, we will review both experimental and national survey studies that unpack how Native omission shapes psychological processes among non‐Native and Native individuals and contribute to discrimination, oppression, and disparities facing Native Peoples. We then discuss ways in which Native Peoples are actively resisting Native omission. Finally, we provide a series of policy recommendations to address Native omission and promote Native equity. By making visible the pernicious consequences of omission for Native Peoples, we chart a path for creating a more equitable future. 

Security failures in software arising from failures to practice secure programming are commonplace. Improving this situation requires that practitioners have a clear understanding of the foundational concepts in secure programming to serve as a basis for building new knowledge and responding to new challenges. We developed a Secure Programing Concept Inventory (SPCI) to measure students' understanding of foundational concepts in secure programming. The SPCI consists of thirty-five multiple choice items targeting ten concept areas of secure programming. The SPCI was developed by establishing the content domain of secure programming, developing a pool of test items, multiple rounds of testing and refining the items, and finally testing and inventory reduction to produce the final scale. Scale development began by identifying the core concepts in secure programming. A Delphi study was conducted with thirty practitioners from industry, academia, and government to establish the foundational concepts of secure programming and develop a concept map. To build a set of misconceptions in secure programming, the researchers conducted interviews with students and instructors in the field. These interviews were analyzed using content analysis. This resulted in a taxonomy of misconceptions in secure programming covering ten concept areas. An item pool of multiple-choice questions was developed. The item pool of 225 was administered to a population of 690 students across four institutions. Item discrimination and item difficulty scores were calculated, and the best performing items were mapped to the misconception categories to create subscales for each concept area resulting in a validated 35 item scale. 

Anthropogenic sulfate aerosols are estimated to have offset sixty percent of greenhouse-gas-induced warming in the Arctic, a region warming four times faster than the rest of the world. However, sulfate radiative forcing estimates remain uncertain because the relative contributions from anthropogenic versus natural sources to total sulfate aerosols are unknown. Here we measure sulfur isotopes of sulfate in a Summit, Greenland ice core from 1850 to 2006 CE to quantify the contribution of anthropogenic sulfur emissions to ice core sulfate. We use a Keeling Plot to determine the anthropogenic sulfur isotopic signature (δ34Santhro = +2.9 ± 0.3 ‰), and compare this result to a compilation of sulfur isotope measurements of oil and coal. Using δ34Santhro, we quantify anthropogenic sulfate concentration separated from natural sulfate. Anthropogenic sulfate concentration increases to 68 ± 7% of non-sea-salt sulfate (65.1 ± 20.2 µg kg-1) during peak anthropogenic emissions from 1960 to 1990 and decreases to 45 ± 11% of non-sea-salt sulfate (25.4 ± 12.8 µg kg-1) from 1996 to 2006. These observations provide the first long-term record of anthropogenic sulfate distinguished from natural sources (e.g., volcanoes, dimethyl sulfide), and can be used to evaluate model characterization of anthropogenic sulfate aerosol fraction and radiative forcing over the industrial era. 

SecTutor is a tutoring system that uses adaptive testing to select instructional modules that allow users to pursue secure programming knowledge at their own pace. This project aims to combat one of the most significant cybersecurity challenges we have today: individuals’ failure to practice defensive, secure, and robust programming. To alleviate this, we introduce SecTutor, an adaptive online tutoring system, to help developers understand the foundational concepts behind secure programming. SecTutor allows learners to pursue knowledge at their own pace and according to their own interests, based on assessments that identify and structure educational modules based on their current level of understanding. 

SecTutor is a tutoring system that uses adaptive testing to select instructional modules that allow users to pursue secure programming knowledge at their own pace. This project aims to combat one of the most significant cybersecurity challenges we have today: individuals’ failure to practice defensive, secure, and robust programming. To alleviate this, we introduce SecTutor, an adaptive online tutoring system, to help developers understand the foundational concepts behind secure programming. SecTutor allows learners to pursue knowledge at their own pace and according to their own interests, based on assessments that identify and structure educational modules based on their current level of understanding. 

Computing students are not receiving enough education and practice in secure programming. A key part of being able to successfully implement secure programming practices is the development of secure programming self-efficacy. This paper examines the development of a scale to measure secure programming self-efficacy among students participating in a secure programming clinic (SPC). The results show that the secure programming self-efficacy scale is a reliable and useful measure that correlates satisfactorily with related measures of programming expertise. This measure can be used in secure programming courses and other learning environments to assess students’ secure programming efficacy. 

Abstract Tidal disruption events (TDEs) are among the brightest transients in the optical, ultraviolet, and X-ray sky. These flares are set into motion when a star is torn apart by the tidal field of a massive black hole, triggering a chain of events which is – so far – incompletely understood. However, the disruption process has been studied extensively for almost half a century, and unlike the later stages of a TDE, our understanding of the disruption itself is reasonably well converged. In this Chapter, we review both analytical and numerical models for stellar tidal disruption. Starting with relatively simple, order-of-magnitude physics, we review models of increasing sophistication, the semi-analytic “affine formalism,” hydrodynamic simulations of the disruption of polytropic stars, and the most recent hydrodynamic results concerning the disruption of realistic stellar models. Our review surveys the immediate aftermath of disruption in both typical and more unusual TDEs, exploring how the fate of the tidal debris changes if one considers non-main sequence stars, deeply penetrating tidal encounters, binary star systems, and sub-parabolic orbits. The stellar tidal disruption process provides the initial conditions needed to model the formation of accretion flows around quiescent massive black holes, and in some cases may also lead to directly observable emission, for example via shock breakout, gravitational waves or runaway nuclear fusion in deeply plunging TDEs. 

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.