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Backchanneling behaviors on a robot, such as nodding, can make talking to a robot feel more natural and engaging by giving a sense that the robot is actively listening. For backchanneling to be effective, it is important that the timing of such cues is appropriate given the humans’ conversational behaviors. Recent progress has shown that these behaviors can be learned from datasets of human-human conversations. However, recent data-driven methods tend to overfit to the human speakers that are seen in training data and fail to generalize well to previously unseen speakers. In this paper, we explore the use of data augmentation for effective nodding behavior in a robot. We show that, by augmenting the input speech and visual features, we can produce data-driven models that are more robust to unseen features without collecting additional data. We analyze the efficacy of data-driven backchanneling in a realistic human-robot conversational setting with a user study, showing that users perceived the data-driven model to be better at listening as compared to rule-based and random baselines. 

Sarcocystis neuronawas recognised as an important cause of mortality in southern sea otters (Enhydra lutris nereis) after an outbreak in April 2004 and has since been detected in many marine mammal species in the Northeast Pacific Ocean. Risk ofS. neuronaexposure in sea otters is associated with consumption of clams and soft-sediment prey and is temporally associated with runoff events. We examined the spatial distribution ofS. neuronaexposure risk based on serum antibody testing and assessed risk factors for exposure in animals from California, Washington, British Columbia and Alaska. Significant spatial clustering of seropositive animals was observed in California and Washington, compared with British Columbia and Alaska. Adult males were at greatest risk for exposure toS. neurona, and there were strong associations with terrestrial features (wetlands, cropland, high human housing-unit density). In California, habitats containing soft sediment exhibited greater risk than hard substrate or kelp beds. Consuming a diet rich in clams was also associated with increased exposure risk. These findings suggest a transmission pathway analogous to that described forToxoplasma gondii, with infectious stages traveling in freshwater runoff and being concentrated in particular locations by marine habitat features, ocean physical processes, and invertebrate bioconcentration.

 

Sea otter (Enhydra lutris) populations in southwest Alaska declined substantially between about 1990 and the most recent set of surveys in 2015. Here we report changes in the distribution and abundance of sea otters, and covarying patterns in reproduction, mortality, body size and condition, diet and foraging behavior, food availability, health profiles, and exposure to environmental contaminants over this 25‐yr period. The population decline, which resulted in densities on the order of 5% of environmental carrying capacity, ranged from Attu Island in the west to about Castle Cape (on the south side of the Alaska Peninsula) in the east. Remaining sea otters moved closer to shore and into shallow, protected habitats. Reproductive rates appeared unchanged with the decline. Although the demographic cause of the decline was clearly elevated mortality, stranded carcasses were rare or absent. The net rate of energy gain by foraging sea otters, body length and condition, and prey biomass density, all increased after the decline and varied inversely with sea otter population density beyond the area of decline. Sea otters within the area of decline showed no increases in health anomalies, disease, contaminant exposure, or abnormal gene transcription patterns as compared to animals outside the area of decline. These collective findings are inconsistent with nutritional limitation, disease, or environmental contaminants, and consistent with predation (or possibly some other density‐independent factor) as the reason for the sea otter population decline. Our approach and analyses provide a broad conceptual template for thinking about and assessing the causes of wildlife population declines.

 

Despite its recent invasion into the marine realm, the sea otter (Enhydra lutris) has evolved a suite of adaptations for life in cold coastal waters, including limb modifications and dense insulating fur. This uniquely dense coat led to the near-extinction of sea otters during the 18th–20th century fur trade and an extreme population bottleneck. We used the de novo genome of the southern sea otter (E. l. nereis) to reconstruct its evolutionary history, identify genes influencing aquatic adaptation, and detect signals of population bottlenecks. We compared the genome of the southern sea otter with the tropical freshwater-living giant otter (Pteronura brasiliensis) to assess common and divergent genomic trends between otter species, and with the closely related northern sea otter (E. l. kenyoni) to uncover population-level trends. We found signals of positive selection in genes related to aquatic adaptations, particularly limb development and polygenic selection on genes related to hair follicle development. We found extensive pseudogenization of olfactory receptor genes in both the sea otter and giant otter lineages, consistent with patterns of sensory gene loss in other aquatic mammals. At the population level, the southern sea otter and the northern sea otter showed extremely low genomic diversity, signals of recent inbreeding, and demographic histories marked by population declines. These declines may predate the fur trade and appear to have resulted in an increase in putatively deleterious variants that could impact the future recovery of the sea otter.

 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.