Inter-Regional Transfer Trade Flows in the English Football League

This paper examines the pattern and extent of inter-regional transfers of professional footballers between clubs in the English football league system over the period 1990-1991 to 1999-2000. Our study emlploys a variant form of an established trade flow model and, in presenting and confirming tbe pattern and extent of inter-regional transfers, it indicates the wider applications of the conventional trade model.

Varieties of Health Care Devolution: “Systems or Federacies”? LEQS Discussion Paper No. 130/2018 February 2018

Some European countries have devolved health care services to subnational units. This is especially the case in unitary states that are organised as a national health service, where choice is not ‘built into’ the health care system. We argue that there are different models of devolving authority to subnational jurisdictions which have repercussions for regional health care inequalities and the amount of policy interdependence across regions. We examine broad trends in two institutional models of devolution: a ‘federacy model’, where only a few territories obtain health care responsibilities (such as in the United Kingdom), and a ‘systems model’, where the whole health system is devolved to a full set of subnational units (such as in Spain). This paper briefly discusses the impact of these two models of devolution on the regional diversity of the health system. Our findings suggest that a ‘systems model’ of decentralisation, unlike a ‘federacy model’, gives rise to significant policy interdependence. Another finding indicates that geographical dispersion of health care activity is larger in the ‘federacy model’

Influence of phytophagous behaviour on prey consumption by Macrolophus pygmaeus

Omnivorous Heteroptera constitute an important component of predatory guilds with high potential for biological control. Understanding the relative effects of plant feeding on the suppression of prey by omnivores could be an important element for improving biological control strategies. In the current paper, the effects of different plant food sources on the predation rate of the omnivorous predator Macrolophus pygmaeus (Hemiptera: Miridae) were examined. In all the experiments, second instar nymphs of the aphid Myzus persicae (Homoptera: Aphididae) were used as prey at different densities. First, we evaluated the rate at which the predator preyed on M. persicae at various prey densities on pepper and eggplant leaves. Then, using eggplant flowers or pollen as additional food sources, we estimated predator efficiency for three different prey densities. The predation rate was not affected by the type of plant leaf used. However, the results showed that the predation rate of M. pygmaeus was significantly reduced when flowers or pollen were provided at high prey densities. The importance of these results in understanding the influence of phytophagy on predation rates of omnivorous predators is discussed

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics