Inhaled particle counts on bicycle commute routes of low and high proximity to motorised traffic

Frequent exposure to ultrafine particles (UFP) is associated with detrimental effects on cardiopulmonary function and health. UFP dose and therefore the associated health risk are a factor of exposure frequency, duration, and magnitude of (therefore also proximity to) a UFP emission source. Bicycle commuters using on-road routes during peak traffic times are sharing a microenvironment with high levels of motorised traffic, a major UFP emission source. Inhaled particle counts were measured along popular pre-identified bicycle commute route alterations of low (LOW) and high (HIGH) motorised traffic to the same inner-city destination at peak commute traffic times. During commute, real-time particle number concentration (PNC; mostly in the UFP range) and particle diameter (PD), heart and respiratory rate, geographical location, and meteorological variables were measured. To determine inhaled particle counts, ventilation rate was calculated from heart-rate-ventilation associations, produced from periodic exercise testing. Total mean PNC of LOW (compared to HIGH) was reduced (1.56 x e4 ± 0.38 x e4 versus 3.06 x e4 ± 0.53 x e4 ppcc; p = 0.012). Total estimated ventilation rate did not vary significantly between LOW and HIGH (43 ± 5 versus 46 ± 9 L•min; p = 0.136); however, due to total mean PNC, accumulated inhaled particle counts were 48% lower in LOW, compared to HIGH (7.6 x e8 ± 1.5 x e8 versus 14.6 x e8 ± 1.8 x e8; p = 0.003). For bicycle commuting at peak morning commute times, inhaled particle counts and therefore cardiopulmonary health risk may be substantially reduced by decreasing exposure to motorised traffic, which should be considered by both bicycle commuters and urban planners

Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales

The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user's musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.Comment: Author pre-print version, 20 pages, 6 figures, 4 table

Efficient Grounding of Abstract Spatial Concepts for Natural Language Interaction with Robot Manipulators

Our goal is to develop models that allow a robot to understand natural language instructions in the context of its world representation. Contemporary models learn possible correspondences between parsed instructions and candidate groundings that include objects, regions and motion constraints. However, these models cannot reason about abstract concepts expressed in an instruction like, “pick up the middle block in the row of five blocks”. In this work, we introduce a probabilistic model that incorporates an expressive space of abstract spatial concepts as well as notions of cardinality and ordinality. The graph is structured according to the parse structure of language and introduces a factorisation over abstract concepts correlated with concrete constituents. Inference in the model is posed as an approximate search procedure that leverages partitioning of the joint in terms of concrete and abstract factors. The algorithm first estimates a set of probable concrete constituents that constrains the search procedure to a reduced space of abstract concepts, pruning away improbable portions of the exponentiallylarge search space. Empirical evaluation demonstrates accurate grounding of abstract concepts embedded in complex natural language instructions commanding a robot manipulator. The proposed inference method leads to significant efficiency gains compared to the baseline, with minimal trade-off in accuracy.United States. Army Research Laboratory. Robotics Consortium (Collaborative Technology Alliance Program)National Science Foundation (U.S.) (Grant No.1427547

The substantial first impact of bottom fishing on rare biodiversity hotspots: a dilemma for evidence-based conservation

The present study set out to describe the impact of a single first passage of two types of bottom fishing gear on rare protected shellfish beds formed by the horse mussel Modiolus modiolus. From the available evidence it was clear that one of the study sites had been trawled and the other had been scallop-dredged. Divers collected high-resolution video imagery of epifauna from quadrats at the two study sites and directed infaunal samples at one. The total number of epifaunal organisms was substantially less after a single pass of a trawl (90%) or scallop dredge (59%) as was the diversity of the community and the total number of Modiolus modiolus. A year later no recovery was evident at the site where the trawl had passed and substantially less infauna were recorded in the trawl track. The relative severity of the impact of the two types of fishing gear is unexpected and may reflect differences in community between the two sites or the undisturbed status of the habitats compared to previous studies. As a ‘priority habitat’ the nature of the impacts described on M. modiolus communities are important to the development of indicators of ‘Good Environmental Status’ under the European Union Marine Strategy Framework Directive.Conservation managers are under pressure to support decisions with good quality evidence. Elsewhere indirect studies have shown declines of M. modiolus communities in fishing grounds but, given the protected status of the habitat, premeditated demonstration of direct impact is unethical and / or illegal in Marine Protected Areas. The present study therefore provides a unique opportunity to directly investigate the scale of the impact from bottom fishing gear whilst also to reflect on the paradox of evidence – based conservation management

Factorised spatial representation learning: application in semi-supervised myocardial segmentation

The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical information, as well as properties related to the specific imaging setting. Anatomical information is required to perform further analysis, whereas imaging information is key to disentangle scanner variability and potential artefacts. The ability to factorise these would allow for training algorithms only on the relevant information according to the task. To date, such factorisation has not been attempted. In this paper, we propose a methodology of latent space factorisation relying on the cycle-consistency principle. As an example application, we consider cardiac MR segmentation, where we separate information related to the myocardium from other features related to imaging and surrounding substructures. We demonstrate the proposed method's utility in a semi-supervised setting: we use very few labelled images together with many unlabelled images to train a myocardium segmentation neural network. Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI. Code will be made available at https://github.com/agis85/spatial_factorisation.Comment: Accepted in MICCAI 201