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We study the problem of finding optimal strategies in Markov decision processes with lexicographic ω-regular objectives, which are ordered collections of ordinary ω-regular objectives. The goal is to compute strategies that maximise the probability of satisfaction of the first 𝜔-regular objective; subject to that, the strategy should also maximise the probability of satisfaction of the second ω-regular objective; then the third and so forth. For instance, one may want to guarantee critical requirements first, functional ones second and only then focus on the non-functional ones. We show how to harness the classic off-the-shelf model-free reinforcement learning techniques to solve this problem and evaluate their performance on four case studies. 

We study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs). The state of a (discrete-time) BMCs is a collection of entities of various types that, while spawning other entities, generate a payoff. In comparison with BMCs, where the evolution of a each entity of the same type follows the same probabilistic pattern, BMDPs allow an external controller to pick from a range of options. This permits us to study the best/worst behaviour of the system. We generalise model-free reinforcement learning techniques to compute an optimal control strategy of an unknown BMDP in the limit. We present results of an implementation that demonstrate the practicality of the approach. 

As climate change impacts intensify, communities in rural Alaska are undergoing and adapting to changes to infrastructure from increased permafrost thawing, flooding, and coastal erosion. Climate change adaptation, defined as a process, action, or outcome in a system to better adjust to actual or expected climate change impacts, is needed to address significant structural failures and safety concerns. Despite the recognition of the need for support from stakeholders and adaptation of infrastructure, the level of adaptation activity remains limited and inconsistent across regions and communities in rural Alaska. We address this need by identifying drivers and barriers of adaptation based on stakeholder perspectives (N=25). Stakeholders included people who work for government agencies, non-profits, engineering firms, or academic institutions in rural Alaska. Results show that strong community leadership and flexibility of funding conditions were drivers to adaptation of infrastructure. Further, results show that the high cost of technology and infrastructure and lack of access to and stipulations on funding were barriers to adaptation of infrastructure. These drivers and barriers emphasize the importance of adaptation processes that effectively accommodate the unique contexts of addressing impacts in rural Alaska. Results demonstrate the need for national adaptation funding and policy that encourages local decision-making power. Specifically, results outline the need for adaptation funding and policy that supports the collaboration of Alaska based institutions and rural Alaska communities in adaptation. 

Summer computing camps for high school students are rapidly becoming a staple at High Performance Computing (HPC) centers and Computer Science departments around the country. Developing complexity in education in these camps remains a challenge. Here, we present a report about the implementation of such a program. The Summer Computing Academy (SCA) at is a weeklong cybertraining1 program offered to high school students by High Performance Research Computing (HPRC) at Texas A&M University (Texas A&M; TAMU). The Summer Computing Academy effectively uses cloud computing paradigms, artificial intelligence technologies coupled with Raspberry Pi micro-controllers and sensors to demonstrate “computational thinking”. The program is steeped in well- reviewed pedagogy; the refinement of the educational methods based on constant assessment is a critical factor that has contributed to its success. The hands-on exercises included in the program have received rave reviews from parents and students alike. The camp program is financially self-sufficient and has successfully broadened participation of underrepresented groups in computing by including diverse groups of students. Modules from the SCA program may be implemented at other institutions with relative ease and promote cybertraining efforts nationwide. 

The Arctic is experiencing intensified impacts from climate change, resulting in unprecedented rates of change, especially for Indigenous communities. Alaska Natives are experiencing transformations in housing, food security, economic stability, and cultural practices as a result of the biophysical changes such as thawing permafrost and coastal erosion. In response, communities are prioritizing adaptation. Although Indigenous communities have been adapting for hundreds of years, adaptation strategies, or actions that seek to moderate harm through the adjustment to actual or expected climate change effects, are not well documented. Housing adaptation strategies are especially understudied, which include any adaptation strategy that is in response to or in preparation for a biophysical change affecting housing. Housing adaptation strategies in response to climate change are primarily focused on physical dimensions (e.g., retrofitting homes, constructing sea wall). Nevertheless, adaptations to changes in biophysical systems are closely interlinked to sociocultural systems, which are often neglected in adaptation discourse. Analyzing existing strategies through the lens of community values captures the sociocultural aspects of adaptation and is critical for sustainable adaptation. This paper presents a research design that addresses these gaps in adaptation discourse by asking: How are community values represented in housing adaptation strategies in response to climate change? This research will employ interviews, focus groups, and observations in partnership with two Alaska Native communities in Oscarville, Alaska and Point Lay, Alaska using community based participatory research methods (CBPR). Understanding the role of community values in housing adaptation is essential for developing sustainable adaptation plans, engineering designs, and future research studies. Further, employing CBPR methodologies in the context of adaptation, grounds identified strategies and resulting plans in community experience. As a result, future findings will not only contribute to the intellectual understanding of adaptation processes and theory, but also facilitate actions in response to climate change. 

A search for pair production of squarks or gluinos decaying via sleptons or weak bosons is reported. The search targets a final state with exactly two leptons with same-sign electric charge or at least three leptons without any charge requirement. The analysed data set corresponds to an integrated luminosity of 139 fb⁻¹of proton-proton collisions collected at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Multiple signal regions are defined, targeting several SUSY simplified models yielding the desired final states. A single control region is used to constrain the normalisation of theWZ+ jets background. No significant excess of events over the Standard Model expectation is observed. The results are interpreted in the context of several supersymmetric models featuring R-parity conservation or R-parity violation, yielding exclusion limits surpassing those from previous searches. In models considering gluino (squark) pair production, gluino (squark) masses up to 2.2 (1.7) TeV are excluded at 95% confidence level.

 

A search for supersymmetry targeting the direct production of winos and higgsinos is conducted in final states with either two leptons (eorμ) with the same electric charge, or three leptons. The analysis uses 139 fb⁻¹ofppcollision data at$$ \sqrt{s} $$$\sqrt{s}$= 13 TeV collected with the ATLAS detector during Run 2 of the Large Hadron Collider. No significant excess over the Standard Model expectation is observed. Simplified and complete models with and withoutR-parity conservation are considered. In topologies with intermediate states including eitherWhorWZpairs, wino masses up to 525 GeV and 250 GeV are excluded, respectively, for a bino of vanishing mass. Higgsino masses smaller than 440 GeV are excluded in a naturalR-parity-violating model with bilinear terms. Upper limits on the production cross section of generic events beyond the Standard Model as low as 40 ab are obtained in signal regions optimised for these models and also for anR-parity-violating scenario with baryon-number-violating higgsino decays into top quarks and jets. The analysis significantly improves sensitivity to supersymmetric models and other processes beyond the Standard Model that may contribute to the considered final states.

 

Search for a new pseudoscalar a-boson decaying to muons in events with additional top quark pairs.