Fairness in Water Quality: A Descriptive Approach

Muscle strength is important for firefighters work capacity. Laboratory tests used for measurements of muscle strength, however, are complicated, expensive and time consuming. The aims of the present study were to investigate correlations between physical capacity within commonly occurring and physically demanding firefighting work tasks and both laboratory and field tests in full time (N = 8) and part-time (N = 10) male firefighters and civilian men (N = 8) and women (N = 12), and also to give recommendations as to which field tests might be useful for evaluating firefighters' physical work capacity. Laboratory tests of isokinetic maximal (IM) and endurance (IE) muscle power and dynamic balance, field tests including maximal and endurance muscle performance, and simulated firefighting work tasks were performed. Correlations with work capacity were analyzed with Spearman's rank correlation coefficient (rs). The highest significant (p<0.01) correlations with laboratory and field tests were for Cutting: IE trunk extension (rs = 0.72) and maximal hand grip strength (rs = 0.67), for Stairs: IE shoulder flexion (rs = −0.81) and barbell shoulder press (rs = −0.77), for Pulling: IE shoulder extension (rs= −0.82) and bench press (rs = −0.85), for Demolition: IE knee extension (rs = 0.75) and bench press (rs = 0.83), for Rescue: IE shoulder flexion (rs = −0.83) and bench press (rs = −0.82), and for the Terrain work task: IE trunk flexion (rs = −0.58) and upright barbell row (rs = −0.70). In conclusion, field tests may be used instead of laboratory tests. Maximal hand grip strength, bench press, chin ups, dips, upright barbell row, standing broad jump, and barbell shoulder press were strongly correlated (rs≥0.7) with work capacity and are therefore recommended for evaluating firefighters work capacity

Right for the Right Reason: Training Agnostic Networks

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio

Fifty years of Hoare's Logic

We present a history of Hoare's logic.Comment: 79 pages. To appear in Formal Aspects of Computin