On the measurement of ecological novelty: scale-eating pupfish are separated by 168 my from other scale-eating fishes.

The colonization of new adaptive zones is widely recognized as one of the hallmarks of adaptive radiation. However, the adoption of novel resources during this process is rarely distinguished from phenotypic change because morphology is a common proxy for ecology. How can we quantify ecological novelty independent of phenotype? Our study is split into two parts: we first document a remarkable example of ecological novelty, scale-eating (lepidophagy), within a rapidly-evolving adaptive radiation of Cyprinodon pupfishes on San Salvador Island, Bahamas. This specialized predatory niche is known in several other fish groups, but is not found elsewhere among the 1,500 species of atherinomorphs. Second, we quantify this ecological novelty by measuring the time-calibrated phylogenetic distance in years to the most closely-related species with convergent ecology. We find that scale-eating pupfish are separated by 168 million years of evolution from the nearest scale-eating fish. We apply this approach to a variety of examples and highlight the frequent decoupling of ecological novelty from phenotypic divergence. We observe that novel ecology is not always tightly correlated with rates of phenotypic or species diversification, particularly within recent adaptive radiations, necessitating the use of additional measures of ecological novelty independent of phenotype

Matching, Ethnicity and Identity. Reflections on the practice and realities of ethnic matching in adoption.

Ethnicity and adoption have taken centre stage in the Coalition Government's focus on child care social policy in the UK. The current political perspective is one of promoting the placement of children of minority ethnic heritage with white families, in order to avoid delay in adoption where no families of a similar ethnic heritage are available. John Wainwright and Julie Ridley reflect on the contemporary debate by discussing the findings from a commissioned service evaluation of an adoption agency that specialised in recruiting families of black, Asian and dual heritage, and placing children of black and minority ethnic (BME) heritage. This service evaluation provides evidence that focusing on recruiting BME individuals and families and matching them with children of similar heritage can be effective. The evaluation utilised mixed methods, including interviews with staff in the service, prospective and current adopters, and statistical information that informed an understanding of the type of ethnic matches made. Comparison was also made with a general adoption service within the commissioning agency using the same data collection methods

Themes, Issues and Practice Dilemmas in Ethnically Matched Adoption Placements

The issue of ethnicity and adoption in children’s social work services is a controversial and complex area of child care practice. Ethnic matching has been regarded as a successful way to place children and provide BME (Black and minority ethnic) children with a stable and settled placement. Crucially, it is argued that ethnically matched placements encourage and nurture a positive black identity within BME children, which is perceived as central to their well-being. Using the findings from a service evaluation in the UK, this paper explores themes and issues, as well as policy, and practice dilemmas inherent in the practice of ethnic matching in adoption. Research has rarely examined this process in any depth, nor has it prioritised the views and experiences of adopters from BME communities. The field is dominated by the discourse surrounding trans ethnic adoption (the placement of BME children with white adopters). Qualitative and quantitative methods were used to evaluate practice in a specialist BME adoption service and to compare this to a mainstream adoption service within the same organisation. Results indicated the specialist focus was important. However, it also posed conceptual and practice dilemmas. The increasingly complex nature of ethnicity in modern society underlines the importance of services engaging directly with the challenging fluidity of ethnic and cultural identity

Randomized Smoothing for Stochastic Optimization

We analyze convergence rates of stochastic optimization procedures for non-smooth convex optimization problems. By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergence rates of stochastic optimization procedures, both in expectation and with high probability, that have optimal dependence on the variance of the gradient estimates. To the best of our knowledge, these are the first variance-based rates for non-smooth optimization. We give several applications of our results to statistical estimation problems, and provide experimental results that demonstrate the effectiveness of the proposed algorithms. We also describe how a combination of our algorithm with recent work on decentralized optimization yields a distributed stochastic optimization algorithm that is order-optimal.Comment: 39 pages, 3 figure

Stonehenge remodelled

We are pleased to present the latest account of the sequence of burial and construction at the site of Stonehenge, deduced by its most recent excavators and anchored in time by the application of Bayesian radiocarbon modelling. Five prehistoric stages are proposed, of varied duration, and related by our authors to neighbouring monuments in the Stonehenge environs. While it may never be possible to produce a definitive chronology for this most complex of monuments, the comprehensive and integrated achievement owed to these researchers has brought us much closer to that goal. It is from this firm platform that Stonehenge can begin its new era of communication with the public at large

Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization

Relative to the large literature on upper bounds on complexity of convex optimization, lesser attention has been paid to the fundamental hardness of these problems. Given the extensive use of convex optimization in machine learning and statistics, gaining an understanding of these complexity-theoretic issues is important. In this paper, we study the complexity of stochastic convex optimization in an oracle model of computation. We improve upon known results and obtain tight minimax complexity estimates for various function classes