A novel supersolid phase is predicted for an ensemble of Rydberg atoms in the dipole-blockade regime, interacting via a repulsive dipolar potential softened at short distances. Using exact numerical techniques, we study the low-temperature phase diagram of this system, and observe an intriguing phase consisting of a crystal of mesoscopic superfluid droplets. At low temperature, phase coherence throughout the whole system, and the ensuing bulk superfluidity, are established through tunnelling of identical particles between neighboring droplets.

Contemporary fire regimes of Canadian forests have been well documented based on forest fire records between the late 1950s to 1990s. Due to known limitations of fire datasets, an analysis of changes in fire-regime characteristics could not be easily undertaken. This paper presents fire-regime trends nationally and within two zonation systems, the homogeneous fire-regime zones and ecozones, for two time periods, 1959–2015 and 1980–2015. Nationally, trends in both area burned and number of large fires (≥200 ha) have increased significantly since 1959, which might be due to increases in lightning-caused fires. Human-caused fires, in contrast, have shown a decline. Results suggest that large fires have been getting larger over the last 57 years and that the fire season has been starting approximately one week earlier and ending one week later. At the regional level, trends in fire regimes are variable across the country, with fewer significant trends. Area burned, number of large fires, and lightning-caused fires are increasing in most of western Canada, whereas human-caused fires are either stable or declining throughout the country. Overall, Canadian forests appear to have been engaged in a trajectory towards more active fire regimes over the last half century.

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date, and then review the use of ML in wildfire science as broadly categorized into six problem domains, including: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, we identfied 300 relevant publications up to the end of 2019, where the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML methods to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods, such as deep learning, requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high quality, and freely available wildfire data for use by practitioners of ML methods.