Techno’s Sexual Counter-Space: Ecstasy and Electronics as Technologies of White Sex

This article aims to lift the veil on white sexuality by studying how young people ‘perform’ this within the Rotterdam techno scene. It relies on previous work that has highlighted that white sexuality is, like whiteness itself, rarely recognized, let alone referred to as white. This is also true of the sexuality practised by young people in the techno world. Our extensive observations and in-depth interviews conducted for this study identified that both ravers and cultural studies scholars construct an image of techno as a sexual ‘counter-space’ in which erotic agency can be experienced away from the confines of traditional hook-up sex. This space, they argue, is produced by the affective powers of ecstasy and electronics, which help young ravers to have a heightened sense of control over their sexu

Neurobiology of rodent self-grooming and its value for translational neuroscience

Self-grooming is a complex innate behaviour with an evolutionarily conserved sequencing pattern and is one of the most frequently performed behavioural activities in rodents. In this Review, we discuss the neurobiology of rodent self-grooming, and we highlight studies of rodent models of neuropsychiatric disorders-including models of autism spectrum disorder and obsessive compulsive disorder-that have assessed self-grooming phenotypes. We suggest that rodent self-grooming may be a useful measure of repetitive behaviour in such models, and therefore of value to translational psychiatry. Assessment of rodent self-grooming may also be useful for understanding the neural circuits that are involved in complex sequential patterns of action.National Institutes of Health (U.S.) (Grant NS025529)National Institutes of Health (U.S.) (Grant HD028341)National Institutes of Health (U.S.) (Grant MH060379

Photography and sustainability in historical perspective

Photography exists since the early 1800's. This book gives an overview of sustainability of photography during the past 200 years. Both the (chemistry of the) image carrier and photo camera's are treated. While the former has shown consistent improvements, the latter has displayed rapid deterioration in time. Some ideas about a hypothetical "sustainable camera" are presented, and an appendix illustrates some highlights in 19th century photography, indicating that most of the present applications of photography have in fact found their roots in the early 19th centur

Neuropharmacology of brain-stimulation-evoked aggression.

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Hybrid connection and host clustering for community detection in spatial-temporal network data

Network data clustering and sequential data mining are largefields of research, but how to combine them to analyze spatial-temporalnetwork data remains a technical challenge. This study investigates anovel combination of two sequential similarity methods (Dynamic TimeWarping and N-grams with Cosine distances), with two state-of-the-artunsupervised network clustering algorithms (Hierarchical Density-basedClustering and Stochastic Block Models). A popular way to combine suchmethods is to first cluster the sequential network data, resulting in connection types. The hosts in the network can then be clustered conditionedon these types. In contrast, our approach clusters nodes and edges in onego, i.e., without giving the output of a first clustering step as input for asecond step. We achieve this by implementing sequential distances as covariates for host clustering. While being fully unsupervised, our methodoutperforms many existing approaches. To the best of our knowledge, theonly approaches with comparable performance require manual filteringof connections and feature engineering steps. In contrast, our method isapplied to raw network traffic. We apply our pipeline to the problem ofdetecting infected hosts (network nodes) from logs of unlabelled networktraffic (sequential data). On data from the Stratosphere IPS project (CTUMalware-Capture-Botnet-91), which includes malicious (Conficker botnet) as well as benign hosts, we show that our method perfectly detectsperipheral, benign, and malicious hosts in different clusters. We replicate our results in the well-known ISOT dataset (Storm, Waledac, Zeusbotnets) with comparable performance: conjointly, 99.97% of nodes werecategorized correctlyCyber Securit