Embodied Active Learning of Relational State Abstractions for Bilevel Planning

State abstraction is an effective technique for planning in robotics environments with continuous states and actions, long task horizons, and sparse feedback. In object-oriented environments, predicates are a particularly useful form of state abstraction because of their compatibility with symbolic planners and their capacity for relational generalization. However, to plan with predicates, the agent must be able to interpret them in continuous environment states (i.e., ground the symbols). Manually programming predicate interpretations can be difficult, so we would instead like to learn them from data. We propose an embodied active learning paradigm where the agent learns predicate interpretations through online interaction with an expert. For example, after taking actions in a block stacking environment, the agent may ask the expert: "Is On(block1, block2) true?" From this experience, the agent learns to plan: it learns neural predicate interpretations, symbolic planning operators, and neural samplers that can be used for bilevel planning. During exploration, the agent plans to learn: it uses its current models to select actions towards generating informative expert queries. We learn predicate interpretations as ensembles of neural networks and use their entropy to measure the informativeness of potential queries. We evaluate this approach in three robotic environments and find that it consistently outperforms six baselines while exhibiting sample efficiency in two key metrics: number of environment interactions, and number of queries to the expert. Code: https://tinyurl.com/active-predicatesComment: Conference on Lifelong Learning Agents (CoLLAs) 202

Factors influencing individuals' decision-making during high-risk short-notice disasters: the case study of the August 21st, 2011 Goderich, Ontario tornado

The hazards literature has identified many factors as being influential in the decision making process during high risk, short-notice disasters. Risk perception and previous disaster experience are commonly identified as two of the more influential factors in this complex process. However, few studies adequately address the complex role(s) that these factors play in self-protective decision-making during successive high-risk events. In particular, the role of previous disaster experience during subsequent events is still a matter of considerable discussion and inconsistent findings. This thesis examines two events that occurred in August, 2011 in Goderich, Ontario: an F-3 tornado that struck the community on August 21st and a tornado warning that was posted for the region three days later on August 24th. This case study provided the opportunity to examine the roles of risk perception and previous disaster experience in the decision-making process during successive high-risk events. Semi-structured interviews (n=35) and close-ended questionnaires (n=268) were conducted to learn about the ways that individuals obtained and understood risk information, and to explore whether and how such information guided protective behaviors during the two events. The interviews were analyzed using thematic coding to identify response patterns, and the questionnaires were analyzed using IBM SPSS software. It was found that a sizable portion of the sample population took protective actions on August 24th in ways that were inconsistent with their actions on August 21st. Also, a significant portion of respondents chose not to take any form of protective action on August 24th despite having previously experienced the damaging tornado. The findings of this research suggest that the significance of previous disaster experience in the decision-making process is highly variable and context-dependent. A second significant research finding involves the impact of the tornado on the place attachments of Goderich residents. It was found that the disaster had significant impacts, both positive and negative, on participants' sense of place. These findings have implications for both short- and long-term disaster recovery

Public Attention to Environmental Hazards

Although public attention has been noted as being influential within the hazard-response cycle, it has received almost no consideration within the risk and hazards literature. This is surprising, as attention is often noted in other disciplines for bridging the gap between information and action, and therefore public attention is highly relevant to the study of risk communication and response. It is prudent, therefore, to draw insights on public attention from other disciplines and bring them to bear on challenges pertaining to the human dimensions of environmental hazards. This dissertation presents original research that investigates this important issue. The first manuscript examines the use of Facebook after a significant tornado event that occurred in southern Ontario, Canada in August 2011. The results of this research underscore the usefulness of Facebook and Facebook groups for information seeking, decision support, and misinformation management. The second manuscript investigates the ways that Twitter was used by different actors groups (e.g., weather professionals, weather enthusiasts, news media, first responders, and citizens) during a second tornado-warning storm that affected southern Ontario, Canada in September 2016. The results of this research underscore the fact that Twitter is a powerful platform for the interpretation of both official and unofficial weather information. This interpretation is an iterative process that occurs both individually and collectively—a process that is often referred to as sense-making. The results of the second manuscript also highlight the fact that activity on Twitter can be indicative of professional, rather than “public”, attention to severe weather. The final manuscript draws on theoretical and empirical insights from research across numerous disciplines in order to frame the concept of public attention. Next, theoretical insights from the existing literature on public attention were taken together with empirical insights gained from the two original research projects, in order to develop a conceptual model of public attention. This model shows the process of attention creation from the initial point of exposure, to the iterative and collaborative process of sense-making, to an outcome (i.e., perception, decision, or action). The results of this dissertation emphasize the usefulness of public attention as a lens through which social scientists and other researchers can explore human behaviour when confronted with uncertainty—a topic that is of interest across the social sciences

Academic Entitlement in the Context of Learning Styles

This study explores the linkages between students’ sense of entitlement and their approaches to learning, based on survey research at a large public university in Canada.  Through literature review and pilot testing, a questionnaire instrument was developed that measures four constructs:  academic entitlement, deep learning, surface learning and strategic learning.  Survey responses (n=1=2116) suggest that students approach learning in mixed ways, and that approaches to learning intersect with students’ sense of entitlement in complex ways.  Overall, students’ scores on the sense of entitlement scale were found to be moderate, challenging some of the assertions about today’s students that have been made in the popular press.&nbsp

The E3 ubiquitin ligase component, Cereblon, is an evolutionarily conserved regulator of Wnt signaling

Immunomodulatory drugs (IMiDs) are important for the treatment of multiple myeloma and myelodysplastic syndrome. Binding of IMiDs to Cereblon (CRBN), the substrate receptor of the CRL4CRBN E3 ubiquitin ligase, induces cancer cell death by targeting key neo-substrates for degradation. Despite this clinical significance, the physiological regulation of CRBN remains largely unknown. Herein we demonstrate that Wnt, the extracellular ligand of an essential signal transduction pathway, promotes the CRBN-dependent degradation of a subset of proteins. These substrates include Casein kinase 1α (CK1α), a negative regulator of Wnt signaling that functions as a key component of the β-Catenin destruction complex. Wnt stimulation induces the interaction of CRBN with CK1α and its resultant ubiquitination, and in contrast with previous reports does so in the absence of an IMiD. Mechanistically, the destruction complex is critical in maintaining CK1α stability in the absence of Wnt, and in recruiting CRBN to target CK1α for degradation in response to Wnt. CRBN is required for physiological Wnt signaling, as modulation of CRBN in zebrafish and Drosophila yields Wnt-driven phenotypes. These studies demonstrate an IMiD-independent, Wnt-driven mechanism of CRBN regulation and provide a means of controlling Wnt pathway activity by CRBN, with relevance for development and disease

Review of research to inform California's climate scoping plan: Agriculture and working lands

Agriculture in California contributes 8% of the state's greenhouse gas (GHG) emissions. To inform the state's policy and program strategy to meet climate targets, we review recent research on practices that can reduce emissions, sequester carbon and provide other co-benefits to producers and the environment across agriculture and rangeland systems. Importantly, the research reviewed here was conducted in California and addresses practices in our specific agricultural, socioeconomic and biophysical environment. Farmland conversion and the dairy and intensive livestock sector are the largest contributors to GHG emissions and offer the greatest opportunities for avoided emissions. We also identify a range of other opportunities including soil and nutrient management, integrated and diversified farming systems, rangeland management, and biomass-based energy generation. Additional research to replicate and quantify the emissions reduction or carbon sequestration potential of these practices will strengthen the evidence base for California climate policy

Recent Progress and Next Steps for the MATHUSLA LLP Detector

We report on recent progress and next steps in the design of the proposed MATHUSLA Long Lived Particle (LLP) detector for the HL-LHC as part of the Snowmass 2021 process. Our understanding of backgrounds has greatly improved, aided by detailed simulation studies, and significant R&D has been performed on designing the scintillator detectors and understanding their performance. The collaboration is on track to complete a Technical Design Report, and there are many opportunities for interested new members to contribute towards the goal of designing and constructing MATHUSLA in time for HL-LHC collisions, which would increase the sensitivity to a large variety of highly motivated LLP signals by orders of magnitude.Comment: Contribution to Snowmass 2021 (EF09, EF10, IF6, IF9), 18 pages, 12 figures. v2: included additional endorser

Editorial: The Contingency Perspective to Crisis Management

Security and Global Affair