Creating an entrepreneurial region: exploring the entrepreneurial capacity of the East Midlands

This paper explores the notion of the entrepreneurial region and, in particular, the relevance and appropriateness of this concept to the East Midlands. An outline framework is developed that depicts aspects and dimensions of an entrepreneurial region. This is then applied to the East MIdlands to gauge how entrepreneurial the region is

Mobile Virtual Realities and Portable Magic Circles

Hybrid reality games such as Pokémon GO enable new approaches to embodied space that problematise traditional understandings of play. More recently, smartphones have again become involved in the provision of a new kind of relationship with space: the space of virtual reality. It is the intention of this exploratory chapter to examine mobile virtual reality as part of the continuum of mobile media in the context of two related themes: (1) physical distraction and (2) embodied space. The chapter will consider how this reassessment might provide new understandings of play’s connection to the ordinary space of daily life before expanding upon these issues within the broader context of the “smartphone movement” and concluding with suggested directions for future research within the field

Forty years on: Uta Frith's contribution to research on autism and dyslexia, 1966–2006

Uta Frith has made a major contribution to our understanding of developmental disorders, especially autism and dyslexia. She has studied the cognitive and neurobiological bases of both disorders and demonstrated distinctive impairments in social cognition and central coherence in autism, and in phonological processing in dyslexia. In this enterprise she has encouraged psychologists to work in a theoretical framework that distinguishes between observed behaviour and the underlying cognitive and neurobiological processes that mediate that behaviour

A Duet for one.

This paper considers communication in terms of inference about the behaviour of others (and our own behaviour). It is based on the premise that our sensations are largely generated by other agents like ourselves. This means, we are trying to infer how our sensations are caused by others, while they are trying to infer our behaviour: for example, in the dialogue between two speakers. We suggest that the infinite regress induced by modelling another agent - who is modelling you - can be finessed if you both possess the same model. In other words, the sensations caused by others and oneself are generated by the same process. This leads to a view of communication based upon a narrative that is shared by agents who are exchanging sensory signals. Crucially, this narrative transcends agency - and simply involves intermittently attending to and attenuating sensory input. Attending to sensations enables the shared narrative to predict the sensations generated by another (i.e. to listen), while attenuating sensory input enables one to articulate the narrative (i.e. to speak). This produces a reciprocal exchange of sensory signals that, formally, induces a generalised synchrony between internal (neuronal) brain states generating predictions in both agents. We develop the arguments behind this perspective, using an active (Bayesian) inference framework and offer some simulations (of birdsong) as proof of principle

Gentle Masking of Low-Complexity Sequences Improves Homology Search

Detection of sequences that are homologous, i.e. descended from a common ancestor, is a fundamental task in computational biology. This task is confounded by low-complexity tracts (such as atatatatatat), which arise frequently and independently, causing strong similarities that are not homologies. There has been much research on identifying low-complexity tracts, but little research on how to treat them during homology search. We propose to find homologies by aligning sequences with “gentle” masking of low-complexity tracts. Gentle masking means that the match score involving a masked letter is , where is the unmasked score. Gentle masking slightly but noticeably improves the sensitivity of homology search (compared to “harsh” masking), without harming specificity. We show examples in three useful homology search problems: detection of NUMTs (nuclear copies of mitochondrial DNA), recruitment of metagenomic DNA reads to reference genomes, and pseudogene detection. Gentle masking is currently the best way to treat low-complexity tracts during homology search

Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i