Do Proto-Jovian Planets Drive Outflows?

We discuss the possibility that gaseous giant planets drive strong outflows during early phases of their formation. We consider the range of parameters appropriate for magneto-centrifugally driven stellar and disk outflow models and find that if the proto-Jovian planet or accretion disk had a magnetic field of >~ 10 Gauss and moderate mass inflow rates through the disk of less than 10^-7 M_J/yr that it is possible to drive an outflow. Estimates based both on scaling from empirical laws observed in proto-stellar outflows and the magneto-centrigugal disk and stellar+disk wind models suggest that winds with mass outflow rates of 10^-8 M_J/yr and velocities of order ~ 20 km/s could be driven from proto-Jovian planets. Prospects for detection and some implications for the formation of the solar system are briefly discussed.Comment: AAS Latex, accepted for Ap

BIO-INSPIRED SONAR IN COMPLEX ENVIRONMENTS: ATTENTIVE TRACKING AND VIEW RECOGNITION

Bats are known for their unique ability to sense the world through echolocation. This allows them to perceive the world in a way that few animals do, but not without some difficulties. This dissertation explores two such tasks using a bio-inspired sonar system: tracking a target object in cluttered environments, and echo view recognition. The use of echolocation for navigating in dense, cluttered environments can be a challenge due to the need for rapid sampling of nearby objects in the face of delayed echoes from distant objects. If long-delay echoes from a distant object are received after the next pulse is sent out, these “aliased” echoes appear as close-range phantom objects. This dissertation presents three reactive strategies for a high pulse-rate sonar system to combat aliased echoes: (1) changing the interpulse interval to move the aliased echoes away in time from the tracked target, (2) changing positions to create a geometry without aliasing, and (3) a phase-based, transmission beam-shaping strategy to illuminate the target and not the aliasing object. While this task relates to immediate sensing needs and lower level motor loops, view recognition is involved in higher level navigation and planning. Neurons in the mammalian brain (specifically in the hippocampus formation) named “place cells” are thought to reflect this recognition of place and are involved in implementing a spatial map that can be used for path planning and memory recall. We propose hypothetical “echo view cells” that could contribute (along with odometry) to the creation of place cell representations actually observed in bats. We strive to recognize views over extended regions that are many body lengths in size, reducing the number of places to be remembered for a map. We have successfully demonstrated some of this spatial invariance by training feed-forward neural networks (traditional neural networks and spiking neural networks) to recognize 66 distinct places in a laboratory environment over a limited range of translations and rotations. We further show how the echo view cells respond in between known places and how the population of cell outputs can be combined over time for continuity

Material Properties Measurements for Selected Materials

Hugoniot equation of state measurements were made on Coconino sandstone, Vacaville basalt, Kaibab limestone, Mono Crater, pumice and Zelux (a polycarbonate resin) for pressures to 2 Mb. A single data point was obtained for fused quartz at 1.6 Mb. In addition to the hugoniot studies, the uniaxial compressive stress behavior of Vacaville basalt and Zelux was investigated at strain rates from about 10(exp -5)/sec to 10(exp 3)/second. The data presented include the stress - strain relations as a function of strain rate for these two materials

A qualitative investigation of the impact of coronavirus disease 2019 (COVID-19) on emergency physicians\u27 emotional experiences and coping strategies

Study objective Throughout the coronavirus disease 2019 (COVID-19) pandemic, emergency physicians in the United States have faced unprecedented challenges, risks, and uncertainty while caring for patients in an already vulnerable healthcare system. As such, the pandemic has exacerbated high levels of negative emotions and burnout among emergency physicians, but little systematic qualitative work has documented these phenomena. The purpose of this qualitative investigation was to study emergency physicians’ emotional experiences in response to COVID-19 and the coping strategies that they employed to navigate the pandemic. Methods From September 2020 to February 2021, we conducted semistructured interviews with 26 emergency physicians recruited from 2 early COVID-19 epicenters: New York City and the Metro Boston region. Interviews, coding, and analyses were conducted using a grounded theory approach. Results Emergency physicians reported heightened anxiety, empathy, sadness, frustration, and anger during the pandemic. Physicians frequently attributed feelings of anxiety to medical uncertainty around the COVID-19 virus, personal risk of contracting the virus and transmitting it to family members, the emergency environment, and resource availability. Emergency physicians also discussed the emotional effects of policies prohibiting patients’ family members from entering the emergency department (ED), both on themselves and patients. Sources of physician anger and frustration included changing policies and rules, hospital leadership and administration, and pay cuts. Some physicians described an evolving, ongoing coping process in response to the pandemic, and most identified collective discussion and processing within the emergency medicine community as an effective coping strategy. Conclusions Our findings underscore the need to investigate the effects of physicians’ pandemic-related emotional stress and burnout on patient care. Evidence-based interventions to support emergency physicians in coping with pandemic-related trauma are needed

Longitudinal associations between conflict monitoring and emergent academic skills: An event‐related potentials study

Identifying the links between specific cognitive functions and emergent academic skills can help determine pathways to support both early academic performance and later academic achievement. Here, we investigated the longitudinal associations between a key aspect of cognitive control, conflict monitoring, and emergent academic skills from preschool through first grade, in a large sample of socioeconomically diverse children (N = 261). We recorded event‐related potentials (ERPs) during a Go/No‐Go task. The neural index of conflict monitoring, ΔN2, was defined as larger N2 mean amplitudes for No‐Go versus Go trials. ΔN2 was observed over the right hemisphere across time points and showed developmental stability. Cross‐lagged panel models revealed prospective links from ΔN2 to later math performance, but not reading performance. Specifically, larger ΔN2 at preschool predicted higher kindergarten math performance, and larger ΔN2 at kindergarten predicted higher first‐grade math performance, above and beyond the behavioral performance in the Go/No‐Go task. Early academic skills did not predict later ΔN2. These findings provided electrophysiological evidence for the contribution of conflict monitoring abilities to emergent math skills. In addition, our findings suggested that neural indices of cognitive control can provide additional information in predicting emergent math skills, above and beyond behavioral task performance.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149228/1/dev21809.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149228/2/dev21809_am.pd

Statistical relational learning with soft quantifiers

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results