Voice and resistance: coalminers' struggles to represent their health and safety interests in Australia and New Zealand 1871–1925

The activism of coalmining unions in Australia, the UK, the USA and elsewhere securing improvements in safety including better legislation in the 19th and 20th centuries, has been widely researched and acknowledged. However, a relatively neglected aspect of this history was a campaign to secure worker inspectors (check-inspectors). These began in coalmining a century before similar measures were introduced for workers more generally as part of overhauling occupational health and safety laws in the 1970s/1980s. We document this struggle for mine safety in Australia and New Zealand, and the activities of check-inspectors in the period to 1925. Notwithstanding strong opposition from coal-owners and conservative governments, check-inspectors played an important role in safeguarding coalminers and improving the regulatory oversight of coalmines. Check-inspectors not only gave coalminers a ‘voice’ in OHS, but they also provided an exemplar of the value and legitimacy of worker’s ‘knowledge activism’. This system remains. Furthermore, the struggle is relevant to understanding contemporary debates about collective worker involvement in occupational health and safety

The Merging History of Massive Black Holes

We investigate a hierarchical structure formation scenario describing the evolution of a Super Massive Black Holes (SMBHs) population. The seeds of the local SMBHs are assumed to be 'pregalactic' black holes, remnants of the first POPIII stars. As these pregalactic holes become incorporated through a series of mergers into larger and larger halos, they sink to the center owing to dynamical friction, accrete a fraction of the gas in the merger remnant to become supermassive, form a binary system, and eventually coalesce. A simple model in which the damage done to a stellar cusps by decaying BH pairs is cumulative is able to reproduce the observed scaling relation between galaxy luminosity and core size. An accretion model connecting quasar activity with major mergers and the observed BH mass-velocity dispersion correlation reproduces remarkably well the observed luminosity function of optically-selected quasars in the redshift range 1<z<5. We finally asses the potential observability of the gravitational wave background generated by the cosmic evolution of SMBH binaries by the planned space-born interferometer LISA.Comment: 4 pages, 2 figures, Contribute to "Multiwavelength Cosmology", Mykonos, Greece, June 17-20, 200

A Hybrid N-body--Coagulation Code for Planet Formation

We describe a hybrid algorithm to calculate the formation of planets from an initial ensemble of planetesimals. The algorithm uses a coagulation code to treat the growth of planetesimals into oligarchs and explicit N-body calculations to follow the evolution of oligarchs into planets. To validate the N-body portion of the algorithm, we use a battery of tests in planetary dynamics. Several complete calculations of terrestrial planet formation with the hybrid code yield good agreement with previously published calculations. These results demonstrate that the hybrid code provides an accurate treatment of the evolution of planetesimals into planets.Comment: Astronomical Journal, accepted; 33 pages + 11 figure

Real Lives II: findings from the All-Ireland Gay Men’s Sex Surveys, 2005 and 2006

All Ireland Gay Men's Sex Survey (Vital Statistics) Duration: March 2000 - September 2010 Sigma Research has been working with Ireland's Gay Health Network (GHN) since 2000. GHN is an umbrella organisation working towards gay men's health and HIV prevention. GHN instigated a community-based, self-completion survey to take place across The Republic of Ireland and Northern Ireland during the summer of 2000 and commissioned Sigma Research to work with them. This large-scale community research project was the third such survey among gay men in Ireland, and built on previous findings. After the development and piloting of the survey, recruitment commenced at Dublin Pride in June 2000 and continued throughout the summer at similar events in Belfast, Derry, Galway, Limerick and Waterford. Recruitment in bars and clubs took place in Dublin and Cork, and social groups in more rural area were sent copies of the questionnaire and a request to distribute them to their members. 1,290 questionnaires were returned by gay men (81%), bisexual men (11%) and other homosexually active men living in Ireland. 19% of all respondents lived in Northern Ireland. A full survey report, including implications for HIV prevention planning is available to download. Since 2003 Gay Health Network members - particularly The Gay Men's Health Service (Health Services Executive) and the Rainbow Project, Northern Ireland - have collaborated with our online UK version of the Gay Men’s Sex Survey (Vital Statistics) by promoting it to men in Ireland via community websites and postcards distributed on the gay scene

An intelligent assistant for exploratory data analysis

In this paper we present an account of the main features of SNOUT, an intelligent assistant for exploratory data analysis (EDA) of social science survey data that incorporates a range of data mining techniques. EDA has much in common with existing data mining techniques: its main objective is to help an investigator reach an understanding of the important relationships ina data set rather than simply develop predictive models for selectd variables. Brief descriptions of a number of novel techniques developed for use in SNOUT are presented. These include heuristic variable level inference and classification, automatic category formation, the use of similarity trees to identify groups of related variables, interactive decision tree construction and model selection using a genetic algorithm

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

Exploitation of Dynamic Communication Patterns through Static Analysis

Abstract not provide

Collisional Dark Matter and the Origin of Massive Black Holes

If the cosmological dark matter is primarily in the form of an elementary particle which has cross section and mass for self-interaction having a ratio similar to that of ordinary nuclear matter, then seed black holes (formed in stellar collapse) will grow in a Hubble time, due to accretion of the dark matter, to a mass range 10^6 - 10^9 solar masses. Furthermore, the dependence of the final black hole mass on the galaxy velocity dispersion will be approximately as observed and the growth rate will show a time dependence consistent with observations. Other astrophysical consequences of collisional dark matter and tests of the idea are noted.Comment: 7 pages, no figures, LaTeX2e, Accepted for publication in Phys. Rev. Lett. Changed conten

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming

Towards an automated classification of spreadsheets

Many spreadsheets in the wild do not have documentation nor categorization associated with them. This makes difficult to apply spreadsheet research that targets specific spreadsheet domains such as financial or database.We introduce with this paper a methodology to automatically classify spreadsheets into different domains. We exploit existing data mining classification algorithms using spreadsheet-specific features. The algorithms were trained and validated with cross-validation using the EUSES corpus, with an up to 89% accuracy. The best algorithm was applied to the larger Enron corpus in order to get some insight from it and to demonstrate the usefulness of this work