The origins of large-scale spatial patterns in biology have been an important source of theoretical speculation since the pioneering work by Turing (1952) on the chemical basis of morphogenesis. Knowing how these patterns emerge and their functional role is important to our understanding of the evolution of biocomplexity and the role played by self organization. However, so far, conclusive evidence for local activation-long-range inhibition mechanisms in real biological systems has been elusive. Here a welldefined experimental and theoretical analysis of the pattern formation dynamics exhibited by clustering behavior in ant colonies is presented. These experiments and a simple mathematical model show that these colonies do indeed use this type of mechanism. All microscopic variables have been measured and provide the first evidence, to our knowledge, for this type of self-organized behavior in complex biological systems, supporting early conjectures about its role in the organization of insect societies.M any biological systems display large-scale features involving some characteristic scale that is much larger than the size of its individual components (1). These structures are observed in a broad range of systems and scales, from animal coats (2), shell patterns (3, 4), and neural structures (5) to the spatial distribution of individuals in ecosystems (6). In many cases, they reflect functionality and adaptation and in all of them, they provide clues for the underlying rules that generate them. In most cases, it is clear that the information available to individual units is gathered from a local neighborhood much smaller than the resulting structures, suggesting some type of amplification mechanism that relies on collective behavior.The first theoretical explanation of these types of structures was suggested in 1952 by Alan Turing (1,7,8). The basic mechanism at work involves local amplification of fluctuations (activation) and long-range inhibition and actually falls within a general class of mechanisms (9-12). These mechanisms have been identified in physical (13) and chemical (14) systems, in ecosystems (2, 6, 10, 15-18) and morphogenesis (2-5, 11, 19-26). In the slime mold (27, 28), the evidence is also strong. Critics have argued that a proof requires the identification and measurement of the microscopic mechanisms at work, and this is obviously a rather difficult task in biology.In this context, it was early suggested that social insects might actually use these types of mechanisms to build their nests (29,30) and produce a wide variety of spatiotemporal structures (31-34). Here we use social insects and their behavioral patterns of organization as our reference system. We follow a standard approach, using a well-defined and controlled experimental setup in which the whole set of parameters can be measured and therefore all of the microscopic rules can be identified. We show that the formation of cemeteries in ants (35-38) falls within the family of local activation-long range inhibition (LALI) processes...

An Active Appearance Model (AAM) is a variable shape and appearance model built from annotated training images. It has been largely used to synthesize or fit face images. Person-independent face AAM fitting is a challenging open issue. For standard AAMs, fitting a face image for an individual which is not in the training set is often limited in accuracy, thereby restricting the range of application. As a first contribution, we show that the limitation mainly comes from the inability of the AAM appearance counterpart to generalize, i.e. to accurately generate previously unseen visual data. As a second contribution, we propose an efficient person-independent face fitting framework based on what we call multi-level segmented AAMs. Each segment encodes a physically meaningful part of the face, such as an eye. A coarse-to-fine fitting strategy with a gradually increasing number of segments is used in order to ensure a large convergence basin. Fitting accuracy is assessed by comparison with manual labelling statistics constructed from multiple data annotations. Experimental results support the claim that standard AAMs are well-adapted to person-specific fitting while segmented AAMs outperform the classical AAMs in a personindependent context in terms of accuracy, and ability to generate new faces.