14 research outputs found
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Properties of kinetic transition networks for atomic clusters and glassy solids
A database of minima and transition states corresponds to a network where the minima represent nodes and the transition states correspond to edges between the pairs of minima they connect via steepest-descent paths. Here we construct networks for small clusters bound by the Morse potential for a selection of physically relevant parameters, in two and three dimensions. The properties of these unweighted and undirected networks are analysed to examine two features: whether they are small-world, where the shortest path between nodes involves only a small number or edges; and whether they are scale-free, having a degree distribution that follows a power law. Small-world character is present, but statistical tests show that a power law is not a good fit, so the networks are not scale-free. These results for clusters are compared with the corresponding properties for the molecular and atomic structural glass formers ortho-terphenyl and binary Lennard-Jones. These glassy systems do not show small-world properties, suggesting that such behaviour is linked to the structure-seeking landscapes of the Morse clusters.JM acknowledges the support of a Sackler Studentship from the University of Cambridge. Part of this work was performed while DM was a member of the Department of Chemistry at the University of Cambridge, financially supported by the Engineering and Physical Sciences Research Council and the European Research Council
A cross-curricular physical activity intervention to combat cardiovascular disease risk factors in 11-14 year olds: 'Activity Knowledge Circuit'
Background: Cardiovascular disease is the leading cause of mortality worldwide. Risk factors associated with cardiovascular disease have been shown to track from childhood through to adulthood. Previous school-based physical activity interventions have demonstrated modest improvements to cardiovascular disease risk factors by implementing extra-curricular activities or improving current physical education curriculum. Few have attempted to increase physical activity in class-room taught curriculum subjects. This study will outline a school-based cross-curricular physical activity intervention to combat cardiovascular disease risk factors in 11-14 year old children. Method/Design: A South Wales Valley school of low socio-economic status has been selected to take part. Participants from year eight (12-13 years) are to be assigned to an intervention group, with maturation-matched participants from years seven (11-12 years) and nine (13-14 years) assigned to a control group. A cross-curricular physical activity intervention will be implemented to increase activity by two hours a week for 18 weeks. Participants will briskly walk 3200 m twice weekly during curriculum lessons (60 minutes duration). With the exception of physical education, all curriculum subjects will participate, with each subject delivering four intervention lessons. The intervention will be performed outdoors and on school premises. An indoor course of equal distance will be used during adverse weather conditions. Cardiovascular disease risk factors will be measured pre- and post-intervention for intervention and control groups. These will take place during physical education lessons and will include measures of stature, mass, waist, hip, and neck circumferences, together with skinfold measure's taken at four sites. Blood pressure will be measured, and fitness status assessed via the 20 m multi-stage fitness test. Questionnaires will be used to determine activity behaviour (physical activity questionnaire for adolescence), diet (seven day food diary) and maturation status. Fasting blood variables will include total cholesterol, lowdensity lipoprotein cholesterol, high density lipoprotein cholesterol, triglycerides, insulin, glucose, high-sensitivity C-reactive protein, interleukin-6, adiponectin, and fibrinogen. Motivational variables and psychological well-being will be assessed by questionnaire. Discussion: Our study may prove to be a cost effective strategy to increase school time physical activity to combat cardiovascular disease risk factors in children.</p
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Characterising the area under the curve loss function landscape
Abstract
One of the most common metrics to evaluate neural network classifiers is the area under the receiver operating characteristic curve (AUC). However, optimisation of the AUC as the loss function during network training is not a standard procedure. Here we compare minimising the cross-entropy (CE) loss and optimising the AUC directly. In particular, we analyse the loss function landscape (LFL) of approximate AUC (appAUC) loss functions to discover the organisation of this solution space. We discuss various surrogates for AUC approximation and show their differences. We find that the characteristics of the appAUC landscape are significantly different from the CE landscape. The approximate AUC loss function improves testing AUC, and the appAUC landscape has substantially more minima, but these minima are less robust, with larger average Hessian eigenvalues. We provide a theoretical foundation to explain these results. To generalise our results, we lastly provide an overview of how the LFL can help to guide loss function analysis and selection.EPSRC
Downing College, Cambridge
Interdisciplinary Institute for Artificial Intelligence at 3iA Cote d'Azu
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On the capacity and superposition of minima in neural network loss function landscapes
Abstract
Minima of the loss function landscape (LFL) of a neural network are locally optimal sets of weights that extract and process information from the input data to make outcome predictions. In underparameterised networks, the capacity of the weights may be insufficient to fit all the relevant information. We demonstrate that different local minima specialise in certain aspects of the learning problem, and process the input information differently. This effect can be exploited using a meta-network in which the predictive power from multiple minima of the LFL is combined to produce a better classifier. With this approach, we can increase the area under the receiver operating characteristic curve by around
20
%
for a complex learning problem. We propose a theoretical basis for combining minima and show how a meta-network can be trained to select the representative that is used for classification of a specific data item. Finally, we present an analysis of symmetry-equivalent solutions to machine learning problems, which provides a systematic means to improve the efficiency of this approach.</jats:p