Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths. Though some approaches have advantages over others, the resulting networks are often qualitatively similar. We find that, in dynamically-routed networks trained to classify images, layers and branches become specialized to process distinct categories of images. Additionally, given a fixed computational budget, dynamically-routed networks tend to perform better than comparable statically-routed networks.Comment: ICML 2017. Code at https://github.com/MasonMcGill/multipath-nn Video abstract at https://youtu.be/NHQsDaycwy

HMM-guided frame querying for bandwidth-constrained video search

We design an agent to search for frames of interest in video stored on a remote server, under bandwidth constraints. Using a convolutional neural network to score individual frames and a hidden Markov model to propagate predictions across frames, our agent accurately identifies temporal regions of interest based on sparse, strategically sampled frames. On a subset of the ImageNet-VID dataset, we demonstrate that using a hidden Markov model to interpolate between frame scores allows requests of 98% of frames to be omitted, without compromising frame-of-interest classification accuracy

HMM-guided frame querying for bandwidth-constrained video search

We design an agent to search for frames of interest in video stored on a remote server, under bandwidth constraints. Using a convolutional neural network to score individual frames and a hidden Markov model to propagate predictions across frames, our agent accurately identifies temporal regions of interest based on sparse, strategically sampled frames. On a subset of the ImageNet-VID dataset, we demonstrate that using a hidden Markov model to interpolate between frame scores allows requests of 98% of frames to be omitted, without compromising frame-of-interest classification accuracy

Developing transferable management skills through Action Learning

There has been increasing criticism of the relevance of the Master of Business Administration (MBA) in developing skills and competencies. Action learning, devised to address problem-solving in the workplace, offers a potential response to such criticism. This paper offers an insight into one university’s attempt to integrate action learning into the curriculum. Sixty-five part-time students were questioned at two points in their final year about their action learning experience and the enhancement of relevant skills and competencies. Results showed a mixed picture. Strong confirmation of the importance of selected skills and competencies contrasted with weaker agreement about the extent to which these were developed by action learning. There was, nonetheless, a firm belief in the positive impact on the learning process. The paper concludes that action learning is not a panacea but has an important role in a repertoire of educational approaches to develop relevant skills and competencies

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Spatially structured environmental filtering of collembolan traits in late successional salt marsh vegetation

Both the environment and the spatial configuration of habitat patches are important factors that shape community composition and affect species diversity patterns. Species have traits that allow them to respond to their environment. Our current knowledge on environment to species traits relationships is limited in spite of its potential importance for understanding community assembly and ecosystem function. The aim of our study was to examine the relative roles of environmental and spatial variables for the small-scale variation in Collembola (springtail) communities in a Dutch salt marsh. We used a trait-based approach in combination with spatial statistics and variance partitioning, between environmental and spatial variables, to examine the important ecological factors that drive community composition. Turnover of trait diversity across space was lower than for species diversity. Most of the variation in community composition was explained by small-scale spatial variation in topography, on a scale of 4-6 m, most likely because it determines the effect of inundation, which restricts where habitat generalists can persist. There were only small pure spatial effects on species and trait diversity, indicating that biotic interactions or dispersal limitation probably were less important for structuring the community at this scale. Our results suggest that for springtails, life form (i.e. whether they live in the soil or litter or on the surface/in vegetation) is an important and useful trait to understand community assembly. Hence, using traits in addition to species identity when analysing environment-organism relationships results in a better understanding of the factors affecting community composition