""Description 'Mercenaries', 'cheats', 'destroying the soul of (English) football', 'destroying the link between football clubs and their supporters': foreign football players have been accused of being at the origin of all the ills of... more
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'Mercenaries', 'cheats', 'destroying the soul of (English) football', 'destroying the link between football clubs and their supporters': foreign football players have been accused of being at the origin of all the ills of contemporary football. How true is this? Foreign players and football supporters: The Old Firm, Arsenal, Paris Saint-Germain is the first academic book to look at supporters' reactions to the increase in the number of foreign players in the very clubs they support week in week out. It shows that football supporters identify with their club through a variety of means, which may change or be replaced with others, and provides the most comprehensive view on football supporters' attachment to their club in the European Union, following the increase in European legislation. Divided into three case studies on Glasgow (Celtic and Rangers), Paris Saint-Germain and Arsenal in London, the book adopts a multidisciplinary approach to chart the evolution of the link between supporters and club between 1995 and today. It is based on extensive research through the press of three nations, as well as interviews with officials and supporters. It provides an excellent read for students and researchers in Sports Studies, Politics, European Studies, French Studies and other Social Sciences, or to anyone interested in one of the most original institutions of contemporary western societies: mass spectator sports.
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Background: The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from... more
Background: The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep. Purpose: To investigate the validity of developing personalized machine learning models, trained and tested on individual-level actigraphy data, for improved prediction of sleep-wake states and reliable estimation of nightly sleep parameters. Participants and methods: We used a dataset including 54 participants and systematically trained and tested 5 different personalized machine learning models as well as their generalized counterparts. We evaluated model performance compared to concurrent PSG through extensive machine learning experiments and statistical analyses. Results: Our experiments show the superiority of personalized models over their generalized counterparts in estimating PSG-derived sleep parameters. Personalized models of regularized logistic regression, random forest, adaptive boosting, and extreme gradient boosting achieve estimates of total sleep time, wake after sleep onset, sleep efficiency, and number of awakenings that are closer to those obtained by PSG, in absolute difference, than the same estimates from their generalized counterparts. We further show that the difference between estimates of sleep parameters obtained by personalized models and those of PSG is statistically non-significant. Conclusion: Personalized machine learning models of sleep-wake states outperform their generalized counterparts in terms of estimating sleep parameters and are indistinguishable from PSG labeled sleep-wake states. Personalized machine learning models can be used in actigraphy studies of sleep health and potentially screening for some sleep disorders.
Humans regularly produce new utterances that are understood by other members of the same language community1. Linguistic theories account for this ability through the use of syntactic rules (or generative grammars) that describe the... more
Humans regularly produce new utterances that are understood by other members of the same language community1. Linguistic theories account for this ability through the use of syntactic rules (or generative grammars) that describe the acceptable structure of utterances2. The recursive, hierarchical embedding of language units (for example, words or phrases within shorter sentences) that is part of the ability to construct new utterances minimally requires a ‘context-free’ grammar2,3 that is more complex than the ‘finite-state’ grammars thought sufficient to specify the structure of all non-human communication signals. Recent hypotheses make the central claim that the capacity for syntactic recursion forms the computational core of a uniquely human language faculty4,5.Here we show that European starlings (Sturnus vulgaris) accurately recognize acoustic patterns defined by a recursive, self-embedding, context-free grammar. They are also able to classify new patterns defined by the grammar and reliably exclude agrammatical patterns. Thus, the capacity to classify sequences from recursive, centre-embedded grammars is not uniquely human. This finding opens a new range of complex syntactic processing mechanisms to physiological investigation.
