"We've had nothing for so long that we don't know what to ask for", New Deal for Communities and the regeneration of socially excluded terrain

This paper explores New Labour’s desire to refurbish the physical and social fabric of excluded neighbourhoods through its New Deal for Communities (NDC) programme. It begins by examining three key concepts that embody and underpin this policy intervention – community, agency and exclusion and proceeds by contrasting these conceptual dimensions with a set of discordant, intra-neighbourhood processes of conflict, contestation and division, identified by recently conducted fieldwork in an NDC area. I argue that such processes produce a complex social terrain that is inhabited by social agents with a diverse range of needs, values and experiences, before discussing how this challenges and de-stabilises NDC’s aspiration to ‘promote’ community, change individual behaviour and tackle exclusion effectively. The paper concludes by questioning whether New Labour’s desire to implement a ‘community’ project, shaped by theoretical precepts, constrains NDC’s ability to deliver lasting change to excluded areas

Henry A. Wallace to John D. Feerick

Letter from Vice President Henry A. Wallace to Dean John D. Feerick, regarding his scholarly article on presidential inability.https://ir.lawnet.fordham.edu/twentyfifth_amendment_correspondence/1009/thumbnail.jp

Play it Again, Uncle Sam

Tashima, currently a federal judge, relates his experience in a Japanese American internment camp at Poston AZ during WWII. The unjust internment was in part a failure of the federal courts to protect the constitutional rights of American citizens

Analysis of reinforcement learning strategies for predation in a mimic-model prey environment

In this paper we propose a mathematical learning model for a stochastic automaton simulating the behaviour of a predator operating in a random environment occupied by two types of prey: palatable mimics and unpalatable models. Specifically, a well known linear reinforcement learning algorithm is used to update the probabilities of the two actions, eat prey or ignore prey, at every random encounter. Each action elicits a probabilistic response from the environment that can be either favorable or unfavourable. We analyse both fixed and varying stochastic responses for the system. The basic approach of mimicry is defined and a short review of relevant previous approaches in the literature is given. Finally, the conditions for continuous predator performance improvement are explicitly formulated and precise definitions of predatory efficiency and mimicry efficiency are also provided

Background of the SOP sup 2

Two areas of interest are elaborated: Saturn mission design history, and Saturn's place in NASA's program plans. The first area provides a view of how changing concepts and techniques can affect mission design and science return. The second puts Saturn in perspective with regard to its role in NASA's overall planetary program