Representing miners in arrangements for health and safety in coalmines: a study of current practice

This article explores the operation of regulatory provisions for worker occupational health and safety (OHS) representation in coalmining in Australia. Using data on inspections, combined with qualitative interviews, it looks at what occurs in a generally hostile labour relations climate and what supports or constrains representation in this scenario. It finds evidence of the engagement of worker representatives with serious risks in coalmining. By using the various means with which they are provided by regulatory measures, they make a significant contribution to the operation of the regulatory strategy of enforced self-regulation of OHS management. They are successful in doing so despite the unsupportive climate of labour relations in which they are frequently situated. However, the study poses questions concerning the fit of this approach with increasingly dominant versions of OHS management pursued by large and globally active corporations and discusses some implications of this for policy and further study

Schizophrenic performance on speeded classification tasks

The response times of ten process schizophrenics, ten reactive schizophrenics and twenty non-hospitalised normal controls were compared on four speeded classification card-sorting tasks; control, filtering, grouping and condensation, which varied the ways in which visual stimuli could be assigned to one of two response classes. Results suggest that on tasks, such as filtering, which require the ignoring of irrelevant stimulus attributes for successful classification of stimuli, schizophrenic response times are slightly longer, but on tasks such as grouping and condensation which progressively increase the response selection demand aspects of visual information processing, schizophrenic response times are progressively increased by a much greater degree than are those of normals. The process and reactive groups did not differ on any measure

Towards the re-construction of a clinical psychologist and a reflexive body of practice

SIGLEAvailable from British Library Document Supply Centre-DSC:DX192991 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A study of the role of workers' representatives in health and safety arrangements in coal mines in Queensland

Coal mining is a dangerous industry which demands a strong emphasis on ensuring the protection of miners' health, safety and well being. This study investigates the role and effectiveness of health & safety reps in Queensland Coal Mines and was undertaken by the Cardiff Work Environment Research Centre at Cardiff University in Wales, UK. The aim of the study is to contribute further knowledge of the effectiveness of the role of worker reps in managing those risks. Early mining disasters resulting in the deaths of many mineworkers highlighted the need for unions to take a proactive role in ensuring their own safety. Originally known as check inspectors, they were first appointed by unions in the Hunter Valley in the 1870s, and in Queensland they gained statutory recognition in 1915. The study, which can be downloaded as the full report or in summary form, is specifically focused on the experience of representative participation in Queensland coal mines

Evolution of Binary Supermassive Black Holes via Chain Regularization

A chain regularization method is combined with special purpose computer hardware to study the evolution of massive black hole binaries at the centers of galaxies. Preliminary results with up to N=260,000 particles are presented. The decay rate of the binary is shown to decrease with increasing N, as expected on the basis of theoretical arguments. The eccentricity of the binary remains small.Comment: 8 pages. To appear in "Nonlinear Dynamics in Astronomy and Physics, A Workshop Dedicated to the Memory of Professor Henry E. Kandrup", ed. J. R. Buchler, S. T. Gottesman and M. E. Maho

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]Comment: 27 pages, 12 figures, to be published in ApJ, uses emulateapj.cl

Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units

We present ARU, an Adaptive Recurrent Unit for streaming adaptation of deep globally trained time-series forecasting models. The ARU combines the advantages of learning complex data transformations across multiple time series from deep global models, with per-series localization offered by closed-form linear models. Unlike existing methods of adaptation that are either memory-intensive or non-responsive after training, ARUs require only fixed sized state and adapt to streaming data via an easy RNN-like update operation. The core principle driving ARU is simple --- maintain sufficient statistics of conditional Gaussian distributions and use them to compute local parameters in closed form. Our contribution is in embedding such local linear models in globally trained deep models while allowing end-to-end training on the one hand, and easy RNN-like updates on the other. Across several datasets we show that ARU is more effective than recently proposed local adaptation methods that tax the global network to compute local parameters.Comment: 9 pages, 4 figure