Women in Britain’s First Muslim Mosques:Hidden from History, but Not Without Influence

Two of the earliest Muslim communities in Britain evolved around the first mosques in Liverpool and Woking (both—1889). The history of these early British Muslims is being recovered but little is known about the women (usually converts) in these communities. This article will draw upon original findings from archival research, to examine ‘leadership’ that women in these communities undertook and their influence in shaping their nascent British Muslim communities. The practical, theological and philosophical negotiations around gender roles, female leadership, and veiling and the social contexts within which they took place are examined. By uncovering historical responses to issues that remain topical in British Muslim communities, this article provides historical grounding for contemporary debates about female Muslim leadership in British Muslim communities

Learning from experience leading to engagement: for a Europe of religion and belief diversity

The Religious Diversity and Anti-Discrimination Training Program provides a remarkable opportunity for participants of all walks of life to share opinions, concerns and needs of a variety of very real and practical issues such as the role of religion in education, accommodating religious practice in the work place, adapting social services to religio-cultural needs and limitations, engaging minorities in community development, negotiating the use of public space, gender relations, etc. Not only do participants report that the training influences their own roles in local decision-making, but the issues which they raise can be very informative for policy-makers. This Policy Brief, based upon feedback gathered systematically from participants and trainers, provides new insights and ideas to European policy-makers on emerging issues and possible interventions that need to be considered.CEJ

A memoryless, stochastic mechanism of timing of phases of behavior by a neural network controller

For a sensorimotor network to generate adaptive behavior in the environment, the phases of the behavior must be appropriately timed. When the behavior is driven simply by the sensory stimuli from the environment, these can supply the timing. But when the behavior is driven by an internal "goal" that ignores and perhaps even opposes the immediate sensory stimuli, the timing must be generated internally by the network. We have modeled a realistic behavioral scenario that requires such internal timing.

When the sea slug Aplysia feeds, it incrementally ingests long strips of seaweed, driven by ingestive stimuli emanating from the seaweed. But if, having ingested a strip, the animal fails to break the strip off the substrate, it must incrementally egest the entire strip again. To do this, it must ignore the inherent ingestiveness of the seaweed and generate the opposite, egestive behavior, driven by an internal egestive goal, for a length of time that is appropriate for the length of the strip to be egested.

In previous work, we found that a differential-equation model of the Aplysia feeding network, with dynamics like those experimentally observed, performed this task extremely well. In this model, the goal-driven egestion was appropriately timed by a slowly decaying dynamical transient that "remembered" the time elapsed since the beginning of the egestion.

We have now used genetic algorithms to evolve very simple artificial neural network controllers that perform the task equally well. But these networks time the egestion by a completely different mechanism. Their dynamics are characterized by discrete ingestive and egestive attractors, to which they switch in response to ingestive and egestive stimuli. However, the switch in behavior follows the switch in stimulus only with a considerable delay, during which the network continues to generate the old behavior. Existing always near an attractor, the network has no long-term memory. Instead, the switch in behavior finally occurs when a sufficiently high local stimulus density appears in the stochastic stimulus input stream. This complex event occurs rarely. To perform the task, the evolution of the network tunes its connection weights so that the switch requires a density that occurs, on average, about as often as the time that is required to egest the typical length of seaweed strip with which the network is evolved. Thus a simple memoryless network, aware of only the local stimulus, is nevertheless able to organize behavior over arbitrarily long time scales

Multi-level Memory for Task Oriented Dialogs

Recent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the structure of the knowledge base, such as the use of triples to represent knowledge, and combines dialog utterances (context) as well as knowledge base (KB) results as part of the same memory. This causes an explosion in the memory size, and makes the reasoning over memory harder. In addition, such a memory design forces hierarchical properties of the data to be fit into a triple structure of memory. This requires the memory reader to infer relationships across otherwise connected attributes. In this paper we relax the strong assumptions made by existing architectures and separate memories used for modeling dialog context and KB results. Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results. The multi-level memory first addresses queries, followed by results and finally each key-value pair within a result. We conduct detailed experiments on three publicly available task oriented dialog data sets and we find that our method conclusively outperforms current state-of-the-art models. We report a 15-25% increase in both entity F1 and BLEU scores.Comment: Accepted as full paper at NAACL 201