Bayesian Inference of Recursive Sequences of Group Activities from Tracks

We present a probabilistic generative model for inferring a description of coordinated, recursively structured group activities at multiple levels of temporal granularity based on observations of individuals' trajectories. The model accommodates: (1) hierarchically structured groups, (2) activities that are temporally and compositionally recursive, (3) component roles assigning different subactivity dynamics to subgroups of participants, and (4) a nonparametric Gaussian Process model of trajectories. We present an MCMC sampling framework for performing joint inference over recursive activity descriptions and assignment of trajectories to groups, integrating out continuous parameters. We demonstrate the model's expressive power in several simulated and complex real-world scenarios from the VIRAT and UCLA Aerial Event video data sets.Comment: 10 pages, 6 figures, in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 201

Evaluating an online support package delivered within a disability unemployment service: study protocol for a randomised controlled feasibility study

Background Mental health problems such as anxiety and depression are known to be higher in those who are unemployed. Cognitive behavioural therapy (CBT) is a recognised support for people with such problems and can improve the ability of people to get back to work.<p></p> Methods/design Participants with symptoms of low mood will be recruited from the disability employment service, Remploy. Participants will receive either immediate or delayed access to an online CBT-based life skills intervention, the “Living Life” package. The primary end point will be at 3 months when the delayed group will be offered the intervention. This feasibility study will test the trial design and assess recruitment, retention, acceptability and adherence, as well as providing efficacy data.<p></p> Discussion The study will inform the design and sample size for a future full randomised controlled trial (RCT) which will be carried out to determine the effectiveness of the online package in improving mood and employment status.<p></p&gt

Metazoans of redoxcline sediments in Mediterranean deep-sea hypersaline anoxic basins

Background: The deep-sea hypersaline anoxic basins (DHABs) of the Mediterranean (water depth similar to 3500 m) are some of the most extreme oceanic habitats known. Brines of DHABs are nearly saturated with salt, leading many to suspect they are uninhabitable for eukaryotes. While diverse bacterial and protistan communities are reported from some DHAB haloclines and brines, loriciferans are the only metazoan reported to inhabit the anoxic DHAB brines. Our goal was to further investigate metazoan communities in DHAB haloclines and brines. Results: We report observations from sediments of three DHAB (Urania, Discovery, L'Atalante) haloclines, comparing these to observations from sediments underlying normoxic waters of typical Mediterranean salinity. Due to technical difficulties, sampling of the brines was not possible. Morphotype analysis indicates nematodes are the most abundant taxon; crustaceans, loriciferans and bryozoans were also noted. Among nematodes, Daptonema was the most abundant genus; three morphotypes were noted with a degree of endemicity. The majority of rRNA sequences were from planktonic taxa, suggesting that at least some individual metazoans were preserved and inactive. Nematode abundance data, in some cases determined from direct counts of sediments incubated in situ with CellTracker (TM) Green, was patchy but generally indicates the highest abundances in either normoxic control samples or in upper halocline samples; nematodes were absent or very rare in lower halocline samples. Ultrastructural analysis indicates the nematodes in L'Atalante normoxic control sediments were fit, while specimens from L'Atalante upper halocline were healthy or had only recently died and those from the lower halocline had no identifiable organelles. Loriciferans, which were only rarely encountered, were found in both normoxic control samples as well as in Discovery and L'Atalante haloclines. It is not clear how a metazoan taxon could remain viable under this wide range of conditions. Conclusions: We document a community of living nematodes in normoxic, normal saline deep-sea Mediterranean sediments and in the upper halocline portions of the DHABs. Occurrences of nematodes in mid-halocline and lower halocline samples did not provide compelling evidence of a living community in those zones. The possibility of a viable metazoan community in brines of DHABs is not supported by our data at this time

Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection

This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from dense sensor data collected from 30 participants. The ADLs considered are related to preparing and drinking (i) tea, and (ii) coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal and sequential aspects of the actions that are part of each ADL and that vary between participants. The average and standard deviation for the duration and number of steps of each activity are calculated to define the average time and steps and a range within which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) is used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity in terms of time and steps. Analysis shows that CDF can provide precise and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute or consist of many steps. Finally, this approach could be used to train machine learning algorithms for abnormal behaviour detection.status: publishe