High resolution, high capacity, spatial specificity in perceptual learning.

Research of perceptual learning has received significant interest due to findings that training on perceptual tasks can yield learning effects that are specific to the stimulus features of that task. However, recent studies have demonstrated that while training a single stimulus at a single location can yield a high-degree of stimulus specificity, training multiple features, or at multiple locations can reveal a broad transfer of learning to untrained features or stimulus locations. We devised a high resolution, high capacity, perceptual learning procedure with the goal of testing whether spatial specificity can be found in cases where observers are highly trained to discriminate stimuli in many different locations in the visual field. We found a surprising degree of location specific learning, where performance was significantly better when target stimuli were presented at 1 of the 24 trained locations compared to when they were placed in 1 of the 12 untrained locations. This result is particularly impressive given that untrained locations were within a couple degrees of visual angle of those that were trained. Given the large number of trained locations, the fact that the trained and untrained locations were interspersed, and the high-degree of spatial precision of the learning, we suggest that these results are difficult to account for using attention or decision strategies and instead suggest that learning may have taken place for each location separately in retinotopically organized visual cortex

Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller

School Reopenings, Mobility, and COVID-19 Spread: Evidence from Texas

This paper examines the effect of fall 2020 school reopenings in Texas on county-level COVID19 cases and fatalities. Previous evidence suggests that schools can be reopened safely if community spread is low and public health guidelines are followed. However, in Texas, reopenings often occurred alongside high community spread and at near capacity, likely making it difficult to meet social distancing recommendations. Using event-study models and hand-collected instruction modality and start dates for all school districts, we find robust evidence that reopening Texas schools gradually but substantially accelerated the community spread of COVID-19. Results from our preferred specification imply that school reopenings led to at least 43,000 additional COVID-19 cases and 800 additional fatalities within the first two months. We then use SafeGraph mobility data to provide evidence that spillovers to adults’ behaviors contributed to these large effects. Median time spent outside the home on a typical weekday increased substantially in neighborhoods with large numbers of school-age children, suggesting a return to in-person work or increased outside-of-home leisure activities among parents

Exploring Future Teachers’ Awareness, Competence, Confidence, and Attitudes Regarding Teaching Online: Incorporating Blended/Online Experience into the Teaching and Learning in Higher Education Course for Graduate Students

Dalhousie University’s Centre for Learning and Teaching offers a Certificate in University Teaching and Learning, which includes a 12-week course entitled Teaching and Learning in Higher Education. This course provides the certificate’s theory component and has evolved to reflect the changing needs of future educators. One significant change is the development of a blended course model that incorporates graded online facilitation, prompted by the recognition that teaching assistants and faculty are increasingly required to teach online or blended (i.e., combining face-to-face and online) courses. This study invited graduate students enrolled in the course to participate in pre- and post-facilitation questionnaires that assessed their awareness, competence, confidence, and attitudes towards online and blended learning. Students recognized the value of the online component for future teaching expertise and experienced increased awareness, competence, and confidence regarding teaching online. However, preference for face-to-face teaching and student learning did not change.  Le Centre for Learning and Teaching de l’Université Dalhousie offre un certificat en pédagogie de l’enseignement universitaire, lequel comprend un cours de douze semaines intitulé « Teaching and Learning in Higher Education ». Au fil du temps, ce cours théorique a évolué pour s’adapter aux besoins des futurs pédagogues. En outre, parce que les assistants à l’enseignement et les membres de la faculté sont sollicités de plus en plus fréquemment pour donner des cours en ligne ou hybrides (combinant l’enseignement en classe et à distance), on a intégré un modèle de cours hybride facilitant la correction de travaux. C’est ainsi qu’on a invité des étudiants des cycles supérieurs à remplir un questionnaire avant de suivre le cours et après l’avoir suivi. Ce questionnaire évaluait leurs connaissances, compétences et attitudes par rapport à l’enseignement en ligne et hybride. Les étudiants ont reconnu l’importance de l’enseignement en ligne dans leur formation. Ils ont démontré une plus grande connaissance des approches d’enseignement et ont rapporté avoir une meilleure confiance en eux relativement à l’enseignement en ligne. Néanmoins, leur préférence pour l’enseignement en classe n’a pas changé.   &nbsp

Comparison of Methods to Determine Maritime Safety Zones for LNG Terminals

PresentationA Maritime Safety Zone is defined by the U.S. Coast Guard as a water area, shore area, or water and shore area combined to which, for safety or environmental purposes, access is limited to authorized persons, vehicles, or vessels. A Maritime Safety Zone is established to prevent interference with safe navigation and tug maneuvers, to exclude third party access in order to reduce ignition probability, to aid in emergency preparation, and to protect the public from being exposed to potential harm. USCG also defines a Maritime Security Zone for protection against intentional threats, and this is usually larger than the Safety Zone. The USCG requires that a Maritime Safety Zone be defined before a Letter of Recommendation is given. While the Canada Marine Act only suggests that a port authority may establish Traffic Control Zones, the USCG suggests using the Sandia study as guidance for determining Maritime Safety and Security Zones. Though there are guidance documents, no standard method is currently available for determining site specific Maritime Safety Zones applicable to accidental spills that could occur at the waterfront configuration of the terminal. A common methodology would enable Safety Zones to be more properly defined and create safer waterways. Two types of approaches have been proposed by DNV GL in this paper to determine the Safety Zones surrounding LNG terminals from accidental spills. A deterministic approach is based on a single maximum credible event among a set of representative scenarios that have been modeled. This approach may conservatively produce a very large hazard distance depending on the consequence of the maximum credible scenario. A probabilistic approach is a risk based approach which associates the consequence (the thermal radiation intensities and the flammable vapor dispersion distances) with the likelihood of having such a consequence. A probabilistic approach provides a more realistic basis for making informed decisions

Metadata Deep Dive: Results from a Detailed Quality Assessment of NASA's Earth Observation Metadata

No abstract availabl

Did Social-Distancing Measures in Kentucky Help to Flatten the COVID-19 Curve?

In the absence of a vaccine or more effective treatment options, containing the spread of novel coronavirus disease 2019 (COVID-19) must rely on non-pharmaceutical interventions. All U.S. states adopted social-distancing measures in March and April of 2020, though they varied in both timing and scope. Kentucky began by closing public schools and restaurant dining rooms on March 16th before progressing to closing other non-essential businesses and eventually issuing a “Healthy at Home” order with restrictions similar to the shelter-in-place (SIPO) orders adopted by other states. We aim to quantify the impact of these measures on COVID-19 case growth in the state. An event-study model allows us to link adoption of social distancing measures across the Midwest and South to the growth rate of cases, allowing for effects to emerge gradually to account for the lag between infection and positive test result. We then use the results to predict how the number of cases would have evolved in Kentucky in the absence of these policy measures – in other words, if the state had relied on voluntary social distancing alone. We estimate that, by April 25, Kentucky would have had 44,482 confirmed COVID-19 cases without social distancing restrictions, as opposed to the 3,857 actually observed