Is there a role for workplaces in reducing employees' driving to work? Findings from a cross-sectional survey from inner-west Sydney, Australia

<p>Abstract</p> <p>Background</p> <p>The role of workplaces in promoting active travel (walking, cycling or using public transport) is relatively unexplored. This study explores the potential for workplaces to reduce employees' driving to work in order to inform the development of workplace interventions for promoting active travel.</p> <p>Methods</p> <p>An analysis of a cross-sectional survey was conducted using data from parents/guardians whose children participated in the Central Sydney Walk to School Program in inner-west Sydney, Australia. A total of 888 parents/guardians who were employed and worked outside home were included in this analysis. The role of the workplace in regards to active travel was assessed by asking the respondents' level of agreement to eight statements including workplace encouragement of active travel, flexible working hours, public transport availability, convenient parking, shower and change rooms for employees and whether they lived or worked in a safe place. Self-reported main mode of journey to work and demographic data were collected through a self-administrated survey. Binary logistic regression modelling was used to ascertain independent predictors of driving to work.</p> <p>Results</p> <p>Sixty nine per cent of respondents travelled to work by car, and 19% agreed with the statement, "My workplace encourages its employees to go to and from work by public transport, cycling and/or walking (active travel)." The survey respondents with a workplace encouraging active travel to work were significantly less likely to drive to work (49%) than those without this encouragement (73%) with an adjusted odds ratio (AOR) of 0.41 (95% CI 0.23-0.73, P = 0.002). Having convenient public transport close to the workplace or home was also an important factor that could discourage employees from driving to work with AOR 0.17 (95% CI 0.09-0.31, P < 0.0001) and AOR 0.50 (95% CI 0.28-0.90, P = 0.02) respectively. In contrast, convenient parking near the workplace significantly increased the likelihood of respondents driving to work (AOR 4.6, 95% CI 2.8-7.4, P < 0.0001).</p> <p>Conclusions</p> <p>There is a significant inverse association between the perception of workplace encouragement for active travel and driving to work. Increases in the number of workplaces that encourage their employees to commute to work via active travel could potentially lead to fewer employees driving to work. In order to make active travel more appealing than driving to work, workplace interventions should consider developing supportive workplace policies and environments.</p

Multi-domain neural network language generation for spoken dialogue systems

Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains. Therefore, it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation. In this paper, we propose a procedure to train multi-domain, Recurrent Neural Network-based (RNN) language generators via multiple adaptation steps. In this procedure, a model is first trained on counterfeited data synthesised from an out-of-domain dataset, and then fine tuned on a small set of in-domain utterances with a discriminative objective function. Corpus-based evaluation results show that the proposed procedure can achieve competitive performance in terms of BLEU score and slot error rate while significantly reducing the data needed to train generators in new, unseen domains. In subjective testing, human judges confirm that the procedure greatly improves generator performance when only a small amount of data is available in the domain.Toshiba Research Europe Ltd.This is the accepted manuscript. It is currently embargoed pending publication

Dialogue manager domain adaptation using Gaussian process reinforcement learning

Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a computer interface can facilitate swift, human-like acquisition of information. In recent years, speech interfaces have become ever more popular, as is evident from the rise of personal assistants such as Siri, Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning methods have been applied to dialogue modelling and the results achieved for limited-domain applications are comparable to or outperform traditional approaches. Methods based on Gaussian processes are particularly effective as they enable good models to be estimated from limited training data. Furthermore, they provide an explicit estimate of the uncertainty which is particularly useful for reinforcement learning. This article explores the additional steps that are necessary to extend these methods to model multiple dialogue domains. We show that Gaussian process reinforcement learning is an elegant framework that naturally supports a range of methods, including prior knowledge, Bayesian committee machines and multi-agent learning, for facilitating extensible and adaptable dialogue systems.Engineering and Physical Sciences Research Council (Grant ID: EP/M018946/1 ”Open Domain Statistical Spoken Dialogue Systems”

Anyonic interferometry and protected memories in atomic spin lattices

Strongly correlated quantum systems can exhibit exotic behavior called topological order which is characterized by non-local correlations that depend on the system topology. Such systems can exhibit remarkable phenomena such as quasi-particles with anyonic statistics and have been proposed as candidates for naturally fault-tolerant quantum computation. Despite these remarkable properties, anyons have never been observed in nature directly. Here we describe how to unambiguously detect and characterize such states in recently proposed spin lattice realizations using ultra-cold atoms or molecules trapped in an optical lattice. We propose an experimentally feasible technique to access non-local degrees of freedom by performing global operations on trapped spins mediated by an optical cavity mode. We show how to reliably read and write topologically protected quantum memory using an atomic or photonic qubit. Furthermore, our technique can be used to probe statistics and dynamics of anyonic excitations.Comment: 14 pages, 6 figure

Electro-osmotic consolidation of soil with variable compressibility, hydraulic conductivity and electro-osmosis conductivity

In present study, the non-linear variations of soil compressibility, hydraulic and electro-osmosis conductivities were analyzed through laboratory experiments, and incorporated in a one-dimensional model. The analytical solutions for excess pore water pressure and degree of consolidation were derived, and numerical simulations were performed to verify its effectiveness. The results indicated that both the non-linear variations of hydraulic and electro-osmosis conductivities showed remarkable impacts on the excess pore water pressure and degree of consolidation, especially for soils with relative high compressibility. A further comparison with previous analytical solutions indicated that more accurate predictions could be obtained with the proposed analytical solutions. (C) 2016 Elsevier Ltd. All rights reserved

Should physical activity recommendations be ethnicity-specific? Evidence from a cross-sectional study of south Asian and European men

Background Expert bodies and health organisations recommend that adults undertake at least 150 min.week−1 of moderate-intensity physical activity (MPA). However, the underpinning data largely emanate from studies of populations of European descent. It is unclear whether this level of activity is appropriate for other ethnic groups, particularly South Asians, who have increased cardio-metabolic disease risk compared to Europeans. The aim of this study was to explore the level of MPA required in South Asians to confer a similar cardio-metabolic risk profile to that observed in Europeans undertaking the currently recommended MPA level of 150 min.week−1.&lt;p&gt;&lt;/p&gt; Methods Seventy-five South Asian and 83 European men, aged 40–70, without cardiovascular disease or diabetes had fasted blood taken, blood pressure measured, physical activity assessed objectively (using accelerometry), and anthropometric measures made. Factor analysis was used to summarise measured risk biomarkers into underlying latent ‘factors’ for glycaemia, insulin resistance, lipid metabolism, blood pressure, and overall cardio-metabolic risk. Age-adjusted regression models were used to determine the equivalent level of MPA (in bouts of ≥10 minutes) in South Asians needed to elicit the same value in each factor as Europeans undertaking 150 min.week−1 MPA.&lt;p&gt;&lt;/p&gt; Findings For all factors, except blood pressure, equivalent MPA values in South Asians were significantly higher than 150 min.week−1; the equivalent MPA value for the overall cardio-metabolic risk factor was 266 (95% CI 185-347) min.week−1.&lt;p&gt;&lt;/p&gt; Conclusions South Asian men may need to undertake greater levels of MPA than Europeans to exhibit a similar cardio-metabolic risk profile, suggesting that a conceptual case can be made for ethnicity-specific physical activity guidance. Further study is needed to extend these findings to women and to replicate them prospectively in a larger cohort.&lt;p&gt;&lt;/p&gt

Women’s self-rated attraction to male faces does not correspond with physiological arousal

Data Availability Statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.Peer reviewedPublisher PD

The inflammatory microenvironment in colorectal neoplasia

Peer reviewedPublisher PD

Exploiting likely-positive and unlabeled data to improve the identification of protein-protein interaction articles

<p>Abstract</p> <p>Background</p> <p>Experimentally verified protein-protein interactions (PPI) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be made faster by ranking newly-published articles' relevance to PPI, a task which we approach here by designing a machine-learning-based PPI classifier. All classifiers require labeled data, and the more labeled data available, the more reliable they become. Although many PPI databases with large numbers of labeled articles are available, incorporating these databases into the base training data may actually reduce classification performance since the supplementary databases may not annotate exactly the same PPI types as the base training data. Our first goal in this paper is to find a method of selecting likely positive data from such supplementary databases. Only extracting likely positive data, however, will bias the classification model unless sufficient negative data is also added. Unfortunately, negative data is very hard to obtain because there are no resources that compile such information. Therefore, our second aim is to select such negative data from unlabeled PubMed data. Thirdly, we explore how to exploit these likely positive and negative data. And lastly, we look at the somewhat unrelated question of which term-weighting scheme is most effective for identifying PPI-related articles.</p> <p>Results</p> <p>To evaluate the performance of our PPI text classifier, we conducted experiments based on the BioCreAtIvE-II IAS dataset. Our results show that adding likely-labeled data generally increases AUC by 3~6%, indicating better ranking ability. Our experiments also show that our newly-proposed term-weighting scheme has the highest AUC among all common weighting schemes. Our final model achieves an F-measure and AUC 2.9% and 5.0% higher than those of the top-ranking system in the IAS challenge.</p> <p>Conclusion</p> <p>Our experiments demonstrate the effectiveness of integrating unlabeled and likely labeled data to augment a PPI text classification system. Our mixed model is suitable for ranking purposes whereas our hierarchical model is better for filtering. In addition, our results indicate that supervised weighting schemes outperform unsupervised ones. Our newly-proposed weighting scheme, TFBRF, which considers documents that do not contain the target word, avoids some of the biases found in traditional weighting schemes. Our experiment results show TFBRF to be the most effective among several other top weighting schemes.</p