We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 billion simulated LHC events corresponding to 10\, fb^{-1}10fb−1 of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

Despite ‘orphan drug' legislation, bringing new medicines for rare diseases to market and securing funding for their provision is sometimes both costly and problematic, even in the case of medicines for very rare ‘ultra orphan' oncological indications. In this paper difficulties surrounding the introduction of a new treatment for osteosarcoma exemplify the challenges that innovators can face. The implications of current policy debate on ‘value-based' medicines pricing in Europe, North America and elsewhere are also explored in the context of sustaining research into and facilitating cancer patient access to medicines for low-prevalence indications. Tensions exist between utilitarian strategies aimed at optimising the welfare of the majority in the society and minority-interest-focused approaches to equitable care provision. Current regulatory and pricing strategies should be revisited with the objective of facilitating fair and timely drug supply to patients without sacrificing safety or overall affordability. Failures effectively to tackle the problems considered here could undermine public interests in developing better therapies for cancer patients.

Of the 109 specialist female physical education students who answered a detailed questionnaire on menstruation and the contraceptive pill in relation to exercise, 91 (83.5%) reported that they suffered menstrual problems. These included stomach ache, depression, abdominal cramps and backache. Over two-thirds of the students considered that these problems adversely influenced their physical performance. However, whether they had a mainly physiological or psychological effect is not clear. Many of the students with menstrual problems thought that exercise had a beneficial effect and helped alleviate their discomfort. A small number of students reported problems such as amenorrhoea and reduced menses possibly due to excessive training.Just under half the students in the investigation took the contraceptive pill, and though as many students taking the pill complained of menstrual problems as those not taking it, they reported less problems and to a lesser degree. Most students claimed that taking the contraceptive pill had no effect upon their performance. REVIEW OF LITERATUREVarious studies of the effects of menstruation on performance suggest that co-ordination is decreased before and during menstruation and is increased immediately after menstruation (Erdelyi, 1962;Shangold, 1980) whereas anaerobic and aerobic work capacity are unaffected during the menstrual cycle (Stephenson et al, 1980). However, the most common effects on sports performance often result from changes in the balance between oestrogen and progesterone before and during the menstrual period. These menstrual problems are known as the "dysmenorrhoea" syndrome.

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The benefits and barriers to a career in academic medicine