Crater lake cichlids individually specialize along the benthic-limnetic axis

A common pattern of adaptive diversification in freshwater fishes is the repeated evolution of elongated open water (limnetic) species and high-bodied shore (benthic) species from generalist ancestors. Studies on phenotype-diet correlations have suggested that population-wide individual specialization occurs at an early evolutionary and ecological stage of divergence and niche partitioning. This variable restricted niche use across individuals can provide the raw material for earliest stages of sympatric divergence. We investigated variation in morphology and diet as well as their correlations along the benthic-limnetic axis in an extremely young Midas cichlid species, Amphilophus tolteca, endemic to the Nicaraguan crater lake Asososca Managua. We found that A. tolteca varied continuously in ecologically relevant traits such as body shape and lower pharyngeal jaw morphology. The correlation of these phenotypes with niche suggested that individuals are specialized along the benthic-limnetic axis. No genetic differentiation within the crater lake was detected based on genotypes from 13 microsatellite loci. Overall, we found that individual specialization in this young crater lake species encompasses the limnetic- as well as the benthic macro-habitat. Yet there is no evidence for any diversification within the species, making this a candidate system for studying what might be the early stages preceding sympatric divergence

Second-order Democratic Aggregation

Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classification, and scene understanding. In this paper, we study a class of orderless aggregation functions designed to minimize interference or equalize contributions in the context of second-order features and we show that they can be computed just as efficiently as their first-order counterparts and they have favorable properties over aggregation by summation. Another line of work has shown that matrix power normalization after aggregation can significantly improve the generalization of second-order representations. We show that matrix power normalization implicitly equalizes contributions during aggregation thus establishing a connection between matrix normalization techniques and prior work on minimizing interference. Based on the analysis we present {\gamma}-democratic aggregators that interpolate between sum ({\gamma}=1) and democratic pooling ({\gamma}=0) outperforming both on several classification tasks. Moreover, unlike power normalization, the {\gamma}-democratic aggregations can be computed in a low dimensional space by sketching that allows the use of very high-dimensional second-order features. This results in a state-of-the-art performance on several datasets

The Bayesian Spatial Bradley--Terry Model: Urban Deprivation Modeling in Tanzania

Identifying the most deprived regions of any country or city is key if policy makers are to design successful interventions. However, locating areas with the greatest need is often surprisingly challenging in developing countries. Due to the logistical challenges of traditional household surveying, official statistics can be slow to be updated; estimates that exist can be coarse, a consequence of prohibitive costs and poor infrastructures; and mass urbanisation can render manually surveyed figures rapidly out-of-date. Comparative judgement models, such as the Bradley--Terry model, offer a promising solution. Leveraging local knowledge, elicited via comparisons of different areas' affluence, such models can both simplify logistics and circumvent biases inherent to house-hold surveys. Yet widespread adoption remains limited, due to the large amount of data existing approaches still require. We address this via development of a novel Bayesian Spatial Bradley--Terry model, which substantially decreases the amount of data comparisons required for effective inference. This model integrates a network representation of the city or country, along with assumptions of spatial smoothness that allow deprivation in one area to be informed by neighbouring areas. We demonstrate the practical effectiveness of this method, through a novel comparative judgement data set collected in Dar es Salaam, Tanzania.Comment: 23 pages, 7 figures, to be published in the journal of the Royal Statistical Society: Series

Proposing a rational resilience credo for use with athletes

© 2016 Association for Applied Sport Psychology. While the reported use of Rational Emotive Behavior Therapy (REBT) is growing in sport, little is written about specific tools used by practitioners when applying REBT with athletes. The Athlete Rational Resilience Credo (ARRC) adapts Windy Dryden's (2007) original Rational Resilience Credo for application with athletes. The ARRC promotes rational beliefs in athletes, which are important for resilient responding to adverse events. The ARRC is presented in full, followed by some explanation as to its purposes, critical practitioner reflections, and guidance for its use in sport

Defining the concept of ‘tick repellency’ in veterinary medicine

Although widely used, the term repellency needs to be employed with care when applied to ticks and other periodic or permanent ectoparasites. Repellency has classically been used to describe the effects of a substance that causes a flying arthropod to make oriented movements away from its source. However, for crawling arthropods such as ticks, the term commonly subsumes a range of effects that include arthropod irritation and consequent avoiding or leaving the host, failing to attach, to bite, or to feed. The objective of the present article is to highlight the need for clarity, to propose consensus descriptions and methods for the evaluation of various effects on ticks caused by chemical substances

Classical and quantum ergodicity on orbifolds

We extend to orbifolds classical results on quantum ergodicity due to Shnirelman, Colin de Verdi\`ere and Zelditch, proving that, for any positive, first-order self-adjoint elliptic pseudodifferential operator P on a compact orbifold X with positive principal symbol p, ergodicity of the Hamiltonian flow of p implies quantum ergodicity for the operator P. We also prove ergodicity of the geodesic flow on a compact Riemannian orbifold of negative sectional curvature.Comment: 14 page

Returning children home from care: What can be learned from local authority data?

International Human Rights and child rights conventions as well as U.K. wide legislation and guidance require that children in care should be returned home to one or both parents wherever possible. Reunification with parents is the most common route out of care, but rates of re‐entry are often higher than for other exit routes. This study used 8 years of administrative data (on 2,208 care entrants), collected by one large English local authority, to examine how many children were returned home and to explore factors associated with stable reunification (not re‐entering care for at least 2 years). One‐third of children (36%) had been reunified, with adolescent entrants being the most likely age group to return home. Three quarters (75%) of reunified children had a stable reunification. In a fully adjusted regression model, age at entry, being on a care order prior to return home, staying longer in care, being of minority ethnicity, and having fewer placements in care were all significant in predicting chances of stable reunification. The results underline the importance of properly resourcing reunification services. The methods demonstrate the value to local authorities of analysing their own data longitudinally to understand the care pathways for children they look after

A geometric approach to visualization of variability in functional data

We propose a new method for the construction and visualization of boxplot-type displays for functional data. We use a recent functional data analysis framework, based on a representation of functions called square-root slope functions, to decompose observed variation in functional data into three main components: amplitude, phase, and vertical translation. We then construct separate displays for each component, using the geometry and metric of each representation space, based on a novel definition of the median, the two quartiles, and extreme observations. The outlyingness of functional data is a very complex concept. Thus, we propose to identify outliers based on any of the three main components after decomposition. We provide a variety of visualization tools for the proposed boxplot-type displays including surface plots. We evaluate the proposed method using extensive simulations and then focus our attention on three real data applications including exploratory data analysis of sea surface temperature functions, electrocardiogram functions and growth curves