Assessing Physical Activity During a High Altitude Trek in Peru

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Solving Navigational Uncertainty Using Grid Cells on Robots

To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate their location and orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the path integration process is subject to the accumulation of error, while landmark calibration is limited by perceptual ambiguity. It remains unclear how animals form coherent spatial representations in the presence of such uncertainty. Navigation research using robots has determined that uncertainty can be effectively addressed by maintaining multiple probabilistic estimates of a robot's pose. Here we show how conjunctive grid cells in dorsocaudal medial entorhinal cortex (dMEC) may maintain multiple estimates of pose using a brain-based robot navigation system known as RatSLAM. Based both on rodent spatially-responsive cells and functional engineering principles, the cells at the core of the RatSLAM computational model have similar characteristics to rodent grid cells, which we demonstrate by replicating the seminal Moser experiments. We apply the RatSLAM model to a new experimental paradigm designed to examine the responses of a robot or animal in the presence of perceptual ambiguity. Our computational approach enables us to observe short-term population coding of multiple location hypotheses, a phenomenon which would not be easily observable in rodent recordings. We present behavioral and neural evidence demonstrating that the conjunctive grid cells maintain and propagate multiple estimates of pose, enabling the correct pose estimate to be resolved over time even without uniquely identifying cues. While recent research has focused on the grid-like firing characteristics, accuracy and representational capacity of grid cells, our results identify a possible critical and unique role for conjunctive grid cells in filtering sensory uncertainty. We anticipate our study to be a starting point for animal experiments that test navigation in perceptually ambiguous environments

Investigation of lunar surface chemical contamination by LEM descent engine and associated equipment Final report

Lunar surface contamination from LEM rocket exhaust - methods of minimizing sample contaminatio

Identifying artificially deformed crania

In this paper we report on a new discriminant function for the identification of artificially deformed crania. Development of the function, based on a sample of deformed and undeformed crania from the Philippines, required visual classification of the sample into deformed and undeformed groups. Working from the observation that deformed crania display flattened frontal and occipital regions, the sample was seriated based on degree of flattening; classification was based on the results of this seriation. The discriminant function, calculated using curvature indices, required only six simple measurements: arc and chord measurements for the frontal (glabella to bregma), parietals (bregma to lambda) and occipital (lambda to opisthion). The function was designed to be conservative, in that a deformed cranium may be classified as undeformed, but the opposite should not occur. Our function classified the undeformed crania with 100% accuracy and deformed crania with 76.9% accuracy, for a total of 91.9% agreement with visual classification. In order to evaluate whether the function is applicable for samples from outside the Philippines, a double blind test was conducted with a large sample of deformed and undeformed crania from a broad geographical and temporal range. For this sample, the function agreed with visual classification in 89.7% of cases; 98.8% of undeformed crania were correctly classified, while deformed crania were identified with 73.7% accuracy. These results demonstrate the utility of the new discriminant function for the classification of artificially deformed crania from diverse contexts. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57385/1/910_ftp.pd

Distributed expertise: Qualitative study of a British network of multidisciplinary teams supporting parents of children with chronic kidney disease

© 2014 The Authors. Background: Long-term childhood conditions are often managed by hospital-based multidisciplinary teams (MDTs) of professionals with discipline specific expertise of a condition, in partnership with parents. However, little evidence exists on professional-parent interactions in this context. An exploration of professionals' accounts of the way they individually and collectively teach parents to manage their child's clinical care at home is, therefore, important for meeting parents' needs, informing policy and educating novice professionals. Using chronic kidney disease as an exemplar this paper reports on one aspect of a study of interactions between professionals and parents in a network of 12 children's kidney units in Britain. Methods: We conducted semi-structured, qualitative interviews with a convenience sample of 112 professionals (clinical-psychologists, dietitians, doctors, nurses, pharmacists, play-workers, therapists and social workers), exploring accounts of their parent-educative activity. We analysed data using framework and the concept of distributed expertise. Results: Four themes emerged that related to the way expertise was distributed within and across teams: (i) recognizing each other's' expertise, (ii) sharing expertise within the MDT, (iii) language interpretation, and (iv) acting as brokers. Two different professional identifications were also seen to co-exist within MDTs, with participants using the term 'we' both as the intra-professional 'we' (relating to the professional identity) when describing expertise within a disciplinary group (for example: 'As dietitians we aim to give tailored advice to optimize children's growth'), and the inter-professional 'we' (a 'team-identification'), when discussing expertise within the team (for example: 'We work as a team and make sure we're all happy with every aspect of their training before they go home'). Conclusions: This study highlights the dual identifications implicit in 'being professional' in this context (to the team and to one's profession) as well as the unique role that each member of a team contributes to children's care. Our methodology and results have the potential to be transferred to teams managing other conditions

Beyond ‘witnessing’: children’s experiences of coercive control in domestic violence and abuse

Children’s experiences and voices are underrepresented in academic literature and professional practice around domestic violence and abuse. The project ‘Understanding Agency and Resistance Strategies’ addresses this absence, through direct engagement with children. We present an analysis from interviews with 21 children in the United Kingdom (12 girls and 9 boys, aged 8-18 years), about their experiences of domestic violence and abuse, and their responses to this violence. These interviews were analysed using interpretive interactionism. Three themes from this analysis are presented: a) ‘Children’s experiences of abusive control’, which explores children’s awareness of controlling behaviour by the adult perpetrator, their experience of that control, and its impact on them; b) ‘Constraint’, which explores how children experience the constraint associated with coercive control in situations of domestic violence, and c) ‘Children as agents’ which explores children’s strategies for managing controlling behaviour in their home and in family relationships. The paper argues that, in situations where violence and abuse occurs between adult intimate partners, children are significantly impacted, and can be reasonably described as victims of abusive control. Recognising children as direct victims of domestic violence and abuse would produce significant changes in the way professionals respond to them, by 1) recognising children’s experience of the impact of domestic violence and abuse; 2) recognising children’s agency, undermining the perception of them as passive ‘witnesses’ or ‘collateral damage’ in adult abusive encounters; and 3) strengthening professional responses to them as direct victims, not as passive witnesses to violence

Improved Laboratory Transition Probabilities for Neutral Chromium and Re-determination of the Chromium Abundance for the Sun and Three Stars

Branching fraction measurements from Fourier transform spectra in conjunction with published radiative lifetimes are used to determine transition probabilities for 263 lines of neutral chromium. These laboratory values are employed to derive a new photospheric abundance for the Sun: log $\epsilon$(Cr I)$_{\odot}$ = 5.64$\pm$0.01 ($\sigma = 0.07$). These Cr I solar abundances do not exhibit any trends with line strength nor with excitation energy and there were no obvious indications of departures from LTE. In addition, oscillator strengths for singly-ionized chromium recently reported by the FERRUM Project are used to determine: log $\epsilon$(Cr II)$_{\odot}$ = 5.77$\pm$0.03 ($\sigma = 0.13$). Transition probability data are also applied to the spectra of three stars: HD 75732 (metal-rich dwarf), HD 140283 (metal-poor subgiant), and CS 22892-052 (metal-poor giant). In all of the selected stars, Cr I is found to be underabundant with respect to Cr II. The possible causes for this abundance discrepancy and apparent ionization imbalance are discussed.Comment: 44 pages, 6 figure

Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

Visual place recognition (VPR) is a robot’s ability to determine whether a place was visited before using visual data. While conventional handcrafted methods for VPR fail under extreme environmental appearance changes, those based on convolutional neural networks (CNNs) achieve state-of-the-art performance but result in heavy runtime processes and model sizes that demand a large amount of memory. Hence, CNN-based approaches are unsuitable for resource-constrained platforms, such as small robots and drones. In this article, we take a multistep approach of decreasing the precision of model parameters, combining it with network depth reduction and fewer neurons in the classifier stage to propose a new class of highly compact models that drastically reduces the memory requirements and computational effort while maintaining state-of-the-art VPR performance. To the best of our knowledge, this is the first attempt to propose binary neural networks for solving the VPR problem effectively under changing conditions and with significantly reduced resource requirements. Our best-performing binary neural network, dubbed FloppyNet, achieves comparable VPR performance when considered against its full-precision and deeper counterparts while consuming 99% less memory and increasing the inference speed by seven times

Road Edge Extraction Using a Plan-View Image Transformation

A new technique to extract road edges in the road-following algorithm for autonomous road vehicle navi-gation is described. It is based on finding road edges on a subsampled plan-view of a portion of the road ahead of the vehicle. The method is illustrated in the real-time identification of road edges using a fast vertical edge de-tector and link operator applied to the transformed plan view. Location of both road edges at 20 frames per sec-ond is demonstrated. Research on autonomous navigation of robot vehicles has been increasing in the last few years 1'2>3. Part of this research consists of identifying the road from a digitised image received by a camera positioned on the robot ve-hicle. The problem of identifying roads to drive an autonomou