Five-year predictors of physical activity decline among adults in low-income communities: a prospective study

BACKGROUND: Obesity in North America is now endemic, and increased understanding of the determinants of physical inactivity is critical. This analysis identified predictors of declines in physical activity over 5 years among adults in low-income, inner-city neighbourhoods. METHODS: Data on leisure time physical activity were collected in telephone interviews in 1992 and 1997 from 765 adults (47% of baseline respondents), as part of the evaluation of a community-based cardiovascular disease risk reduction program. RESULTS: One-third of 527 participants who were physically active at baseline, were inactive in 1997. Predictors of becoming inactive included female sex (OR = 1.63 95% CI (1.09, 2.43)), older age (1.02 (1.01, 1.04)), higher BMI (1.57 (1.03, 2.40)), poor self-rated health (1.39 (1.05, 1.84)), lower self-efficacy for physical activity (1.46 (1.00, 2.14)), and not using a neighborhood facility for physical activity (1.61 (1.02, 2.14)). CONCLUSION: These results highlight the fact that a variety of variables play a role in determining activity level, from demographic variables such as age and sex, to psychosocial and environmental variables. In addition, these results highlight the important role that other health-related variables may play in predicting physical activity level, in particular the observed association between baseline BMI and the increased risk of becoming inactive over time. Lastly, these results demonstrate the need for multi-component interventions in low-income communities, which target a range of issues, from psychosocial factors, to features of the physical environment

The Cryogenic Refractive Indices of S-FTM16, a Unique Optical Glass for Near-Infrared Instruments

The Ohara glass S-FTM16 is of considerable interest for near-infrared optical designs because it transmits well through the K band and because negative S-FTM16 elements can be used to accurately achromatize positive calcium fluoride elements in refractive collimators and cameras. Glass manufacturers have sophisticated equipment to measure the refractive index at room temperature, but cannot typically measure the refractive index at cryogenic temperatures. Near-infrared optics, however, are operated at cryogenic temperatures to reduce thermal background. Thus we need to know the temperature dependence of S-FTM16's refractive index. We report here our measurements of the thermal dependence of S-FTM16's refractive index between room temperature and ~77 K. Within our measurement errors we find no evidence for a wavelength dependence or a nonlinear temperature term so our series of measurements can be reduced to a single number. We find that Delta n_{abs} / Delta T = -2.4x10^{-6} K^{-1} between 298 K and ~77 K and in the wavelength range 0.6 micron to 2.6 micron. We estimate that the systematic error (which dominates the measurement error) in our measurement is 10%, sufficiently low for most purposes. We also find the integrated linear thermal expansion of S-FTM16 between 298 K and 77 K is -0.00167 m m^{-1}.Comment: 8 pages, including 9 figures. Uses emulateapj.cls. Accepted for publication in PAS

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Balancing employee needs, project requirements and organisational priorities in team deployment

The 'people and performance' model asserts that performance is a sum of employee ability, motivation and opportunity (AMO). Despite extensive evidence of this people-performance link within manufacturing and many service sectors, studies within the construction industry are limited. Thus, a recent research project set out to explore the team deployment strategies of a large construction company with the view of establishing how a balance could be achieved between organisational strategic priorities, operational project requirements and individual employee needs and preferences. The findings suggested that project priorities often took precedence over the delivery of the strategic intentions of the organisation in meeting employees' individual needs. This approach is not sustainable in the long term because of the negative implications that such a policy had in relation to employee stress and staff turnover. It is suggested that a resourcing structure that takes into account the multiple facets of AMO may provide a more effective approach for balancing organisational strategic priorities, operational project requirements and individual employee needs and preferences more appropriately in the future

CNN Architectures for Large-Scale Audio Classification

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.Comment: Accepted for publication at ICASSP 2017 Changes: Added definitions of mAP, AUC, and d-prime. Updated mAP/AUC/d-prime numbers for Audio Set based on changes of latest Audio Set revision. Changed wording to fit 4 page limit with new addition

Anisotropy in the Cosmic Microwave Background at Degree Angular Scales: Python V Results

Observations of the microwave sky using the Python telescope in its fifth season of operation at the Amundsen-Scott South Pole Station in Antarctica are presented. The system consists of a 0.75 m off-axis telescope instrumented with a HEMT amplifier-based radiometer having continuum sensitivity from 37-45 GHz in two frequency bands. With a 0.91 deg x 1.02 deg beam the instrument fully sampled 598 deg^2 of sky, including fields measured during the previous four seasons of Python observations. Interpreting the observed fluctuations as anisotropy in the cosmic microwave background, we place constraints on the angular power spectrum of fluctuations in eight multipole bands up to l ~ 260. The observed spectrum is consistent with both the COBE experiment and previous Python results. There is no significant contamination from known foregrounds. The results show a discernible rise in the angular power spectrum from large (l ~ 40) to small (l ~ 200) angular scales. The shape of the observed power spectrum is not a simple linear rise but has a sharply increasing slope starting at l ~ 150.Comment: 5 page

Finding rare objects and building pure samples: Probabilistic quasar classification from low resolution Gaia spectra

We develop and demonstrate a probabilistic method for classifying rare objects in surveys with the particular goal of building very pure samples. It works by modifying the output probabilities from a classifier so as to accommodate our expectation (priors) concerning the relative frequencies of different classes of objects. We demonstrate our method using the Discrete Source Classifier, a supervised classifier currently based on Support Vector Machines, which we are developing in preparation for the Gaia data analysis. DSC classifies objects using their very low resolution optical spectra. We look in detail at the problem of quasar classification, because identification of a pure quasar sample is necessary to define the Gaia astrometric reference frame. By varying a posterior probability threshold in DSC we can trade off sample completeness and contamination. We show, using our simulated data, that it is possible to achieve a pure sample of quasars (upper limit on contamination of 1 in 40,000) with a completeness of 65% at magnitudes of G=18.5, and 50% at G=20.0, even when quasars have a frequency of only 1 in every 2000 objects. The star sample completeness is simultaneously 99% with a contamination of 0.7%. Including parallax and proper motion in the classifier barely changes the results. We further show that not accounting for class priors in the target population leads to serious misclassifications and poor predictions for sample completeness and contamination. (Truncated)Comment: MNRAS accepte

Reactive halogen chemistry in volcanic plumes

Bromine monoxide (BrO) and sulphur dioxide (SO2) abundances as a function of the distance from the source were measured by ground-based scattered-light Multi AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) in the volcanic plumes of Mt. Etna on Sicily, Italy in August-October 2004 and May 2005 and Villarica in Chile in November 2004. BrO and SO2 spatial distributions in a cross section of Mt. Etna’s plume were also determined by Imaging DOAS. We observed an increase in the BrO/SO2 ratio in the plume from below the detection limit near the vent to about 4.5 x 10-4 at 19 km (Mt. Etna) and to about 1.3 x 10-4 at 3 km (Villarica) distance, respectively. Additional attempts were undertaken to evaluate the compositions of individual vents on Mt. Etna. Furthermore, we detected the halogen species ClO and OClO. This is the first time that OClO could be detected in a volcanic plume. Using calculated thermodynamic equilibrium compositions as input data for a one–dimensional photochemical model, we could reproduce the observed BrO and SO2 vertical columns in the plume and their ratio as function of distance from the volcano as well as vertical BrO and SO2 profiles across the plume with current knowledge of multiphase halogen chemistry, but only when we assumed the existence of an ”effective source region”, where volcanic volatiles and ambient air are mixed at about 600°C (in the proportions of 60% and 40%, respectively