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Robotic search often involves teleoperating vehicles into unknown environments. In such scenarios, prior knowledge of target location or environmental map may be a viable resource to tap into and control other autonomous robots in the vicinity towards an improved search performance. In this paper, we test the hypothesis that despite having the same skill, prior knowledge of target or environment affects teleoperator actions, and such knowledge can therefore be inferred through robot movement. To investigate whether prior knowledge can improve human-robot team performance, we next evaluate an adaptive mutual-information blending strategy that admits a time-dependent weighting for steering autonomous robots. Human-subject experiments show that several features including distance travelled by the teleoperated robot, time spent staying still, speed, and turn rate, all depend on the level of prior knowledge and that absence of prior knowledge increased workload. Building on these results, we identified distance travelled and time spent staying still as movement cues that can be used to robustly infer prior knowledge. Simulations where an autonomous robot accompanied a human teleoperated robot revealed that whereas time to find the target was similar across all information-based search strategies, adaptive strategies that acted on movement cues found the target sooner more often than a single human teleoperator compared to non-adaptive strategies. This gain is diluted with number of robots, likely due to the limited size of the search environment. Results from this work set the stage for developing knowledge-aware control algorithms for autonomous robots in collaborative human-robot teams. 

Abstract The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain—phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible–exposed–infectious–removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time. 

In an emergency evacuation, people almost always come in close proximity as they quickly leave a built environment under a potential threat. With COVID19, this situation presents yet another challenge: that of getting unintentionally exposed to an infected individual. To assess the epidemiological consequences of an emergency evacuation, we expanded a popular pedestrian dynamic model to enable social distancing during a normal exit and analyze the effect of possible transmission through respiratory droplets and aerosol. Computer simulations point to a troubling outcome, whereby the benefits of a quick exit could be outweighed by the risk of infection. 

Social animals exhibit collective behavior whereby they negotiate to reach an agreement, such as the coordination of group motion. Bats are unique among most social animals, since they use active sensory echolocation by emitting ultrasonic waves and sensing echoes to navigate. Bats’ use of active sensing may result in acoustic interference from peers, driving different behavior when they fly together rather than alone. The present study explores quantitative methods that can be used to understand whether bats flying in pairs move independently of each other or interact. The study used field data from bats in flight and is based on the assumption that interactions between two bats are evidenced in their flight patterns. To quantify pairwise interaction, we defined the strength of coupling using model-free methods from dynamical systems and information theory. We used a control condition to eliminate similarities in flight path due to environmental geometry. Our research question is whether these data-driven methods identify directed coupling between bats from their flight paths and, if so, whether the results are consistent between methods. Results demonstrate evidence of information exchange between flying bat pairs, and, in particular, we find significant evidence of rear-to-front coupling in bats’ turning behavior when they fly in the absence of obstacles. 

Abstract Our efforts as a society to combat the ongoing COVID‐19 pandemic are continuously challenged by the emergence of new variants. These variants can be more infectious than existing strains and many of them are also more resistant to available vaccines. The appearance of these new variants cause new surges of infections, exacerbated by infrastructural difficulties, such as shortages of medical personnel or test kits. In this work, a high‐resolution computational framework for modeling the simultaneous spread of two COVID‐19 variants: a widely spread base variant and a new one, is established. The computational framework consists of a detailed database of a representative U.S. town and a high‐resolution agent‐based model that uses the Omicron variant as the base variant and offers flexibility in the incorporation of new variants. The results suggest that the spread of new variants can be contained with highly efficacious tests and mild loss of vaccine protection. However, the aggressiveness of the ongoing Omicron variant and the current waning vaccine immunity point to an endemic phase of COVID‐19, in which multiple variants will coexist and residents continue to suffer from infections. 

Abstract The potential waning of the vaccination immunity to COVID‐19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near‐complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID‐19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional vaccine dose, the booster shot, is being administered worldwide. A projected study with an outlook of 6 months explores the interplay between the rate at which boosters are distributed and the extent to which testing practices are implemented, using a highly granular agent‐based model tuned on a medium‐sized US town. Theoretical projections indicate that the administration of boosters at the rate at which the vaccine is currently administered could yield a severe resurgence of the pandemic. Projections suggest that the peak levels of mid‐spring 2021 in the vaccination rate may prevent such a scenario to occur, although exact agreement between observations and projections should not be expected due to the continuously evolving nature of the pandemic. This study highlights the importance of testing, especially to detect asymptomatic individuals in the near future, as the release of the booster reaches full speed.