Publisher Summary This chapter provides an account of different neural network architectures for pattern recognition. A neural network consists of several simple processing elements called neurons. Each neuron is connected to some other neurons and possibly to the input nodes. Neural networks provide a simple computing paradigm to perform complex recognition tasks in real time. The chapter categorizes neural networks into three types: single-layer networks, multilayer feedforward networks, and feedback networks. It discusses the gradient descent and the relaxation method as the two underlying mathematical themes for deriving learning algorithms. A lot of research activity is centered on learning algorithms because of their fundamental importance in neural networks. The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue. It closes with the discussion of performance and implementation issues.

Neural Networks for Pattern Recognition

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

/pdf/climate-change-2007-the-physical-science-basis-1x93ttz9jt.pdf

Climate Change 2007: The Physical Science Basis.

Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.

Statistical physics of social dynamics

We study the dynamics of co-evolution of producers and customers described by bit-strings representing individual traits. Individual ''size-like'' properties are controlled by binary encounters which outcome depends upon a recognition process. Depending upon the parameter set-up, mutual selection of producers and customers results in different types of attractors, either an exclusive niches regime or a competition regime.

Hiking the hypercube: producers and consumers

Immunology1 is not a new field of research: the first vaccinations against smallpox were done by the end of the seventeenth century by Jenner. But the practice of vaccination, although much more intricate than described in elementary textbooks, remained largely empirical. The important successes of experimental methods derived from immunology in molecular biology never had much counterpart from the theoretical point of view. Only a few models of infection, based on population dynamics, have been proposed. Until quite recently most immunologists considered that clinical conditions could be explained by the presence or absence of some specific macromolecules or cell types. Such simple approaches have in fact been sufficient to handle efficiently a number of clinical and experimental problems. It is only quite recently that the self /non-self recognition problem led N. Jerne2 to formulate his network hypothesis, and that people realized that the consequences of the hypothesis could only be checked through some effort in theoretical modeling3, 4. Before presenting our contribution let us review the important characteristics of the immune response that are indispensable to the understanding of our model.

Gérard Weisbuch

Papers

Hiking the hypercube: producers and consumers

Problems in Theoretical Immunology