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  1. Abstract There has been considerable recent interest in the high-pressure behavior of silicon carbide, a potential major constituent of carbon-rich exoplanets. In this work, the atomic-level structure of SiC was determined through in situ X-ray diffraction under laser-driven ramp compression up to 1.5 TPa; stresses more than seven times greater than previous static and shock data. Here we show that the B1-type structure persists over this stress range and we have constrained its equation of state (EOS). Using this data we have determined the first experimentally based mass-radius curves for a hypothetical pure SiC planet. Interior structure models are constructed for planets consisting of a SiC-rich mantle and iron-rich core. Carbide planets are found to be ~10% less dense than corresponding terrestrial planets.
    Free, publicly-accessible full text available December 1, 2023
  2. ABSTRACT

    We present the results of high-resolution adaptive optics imaging observations of four kinematically identified recoiling supermassive black hole (rSMBH) candidates. Ellipse fitting was carried out to measure the spatial offset between the active galactic nucleus (AGN) and the centre of the host galaxy. Two rSMBH candidates (J1713 + 3523 and J2054 + 0049) are found to be offset AGN. However, the Very Long Baseline Array 1.5 GHz continuum imaging observation and spectral decomposition of the [O iii]5007 line suggest that J1713 + 3523 is a dual AGN and its spatial offset is not due to a recoil event. The spatial offset between the AGN and the centre of the host galaxy in J2054 + 0049 is 0.06 ± 0.01 arcsec (201 ± 22 pc). Spectral decomposition of J2054 + 0049 also suggests that it could be a dual AGN system and the measured spatial offset may not be due to a recoil event.

  3. We present our experimental results on generating photon pairs entangled in a transverse-mode Bell state in few-mode optical fiber by controlling the transverse mode of the pump to selectively excite spontaneous four-wave mixing processes.

  4. The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These unvaccinated pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations. Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. In addition, we used a statistically equivalent Synthetic Population to study the effect of combined demographics (eg, people of a particular race and age), which is not possible using US Census datamore »alone. We validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups. Our results will be presented at IAAI-22, but given the critical nature of the pandemic, we offer this extended version of that paper for more timely consideration of our approach and to cover additional findings.« less
  5. While formal coursework remains one of the most common strategies for developing ethics knowledge and competence among engineering students, ethical situations also surface in many other settings. In our own research on engineering student perceptions of ethics and social responsibility, we found that many engineering interns and co-ops reported encountering ethical issues or dilemmas in the workplace. To further illuminate such encounters, this paper aims to: 1) identify and describe real-world ethical issues encountered by engineering students in workplace settings, and 2) investigate what students learned from these experiences. We address these objectives by reporting select results from an ongoing qualitative analysis of 33 interviews with undergraduate students in their fourth year of college. We more specifically present a series of illustrative cases drawn from four of the interviews, selected because the participants described specific work situations in considerable detail and the cases represent a wide variety of ethical concerns. The purpose for sharing these cases is threefold. First, we note some specific lessons that our subjects learned (or failed to learn) through the selected cases. Second, we argue that the workplace is a particularly rich setting for learning about professional ethics. Third, we make recommendations for better scaffolding andmore »supporting student learning in workplace settings. We expect this paper will be of particular interest to engineering ethics scholars studying where and how students learn about ethics, instructors looking for ways to enhance and extend ethics learning, and students preparing for internship, co-op, and/or full-time job roles.« less
  6. Sosnovsky, S. ; Brusilovsky, P ; Baraniuk, R. G. ; Lan, A. S. (Ed.)
    As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty and augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions.
  7. Free, publicly-accessible full text available February 1, 2023