The survey of sensory biological solutions to the physics problems offers an inspiring wealthy source for the design of artificial or bio-inspired sensory systems. A better understanding of sensory coding may have fruitful applications, in the long term for people with low or impaired vision, while enactive vision is targeted in the short term. Furthermore, the whole corpus of produced knowledge is going to help better understanding how the brain works. A Chilean and French multidisciplinary group of research teams with expertise in sensory biology; mathematical modeling, computational neuroscience and computer vision proposes to associate their complementarities to address: The integration of non-standard behaviors from retinal neural sensors, dynamically rich, sparse and robust observed in natural conditions, into neural coding models and their translation into real, highly non-linear, bio-engineering artificial solutions. An interdisciplinary platform for translation from neuroscience into bioengineering will seek convergence from (i) experimental and (ii) analysis/models, with a fine articulation between biological inspired computation and brain neural (coding / decoding) signal processing. As a corollary, tackling modern problems in Neuroscience requires sophisticated electronic and computational equipment and provides for electronic or computer engineers and biologists in Chile and France the opportunity of new sectors of development; we thus expect as an outcome, industrial solutions, with high-level in design and implementation, performing beyond the present state of the art on degraded natural image sequences. At a concrete level, this project is going to provide new experimental facts about non-standard retinal cell behavior in natural scenarios, the application of high-level statistical methods and tools to the design of innovative visual operators, an open-source software implementing the previous concepts at the numerical level and an experimentation platform to benchmark the obtained results. Research topics: -1- Dynamics of non-standard behavior of retinal cells in front of natural image sequences. -2- Identifying non-linear mapping from natural images to non-standard sensor behavior. -3- Statistical analysis of neural coding response in vertebrate retina, the framework of statistical physics. -4- Computer design and numerical development of a non-standard bio-inspired early-vision front-end - 5- Study of the integration of these new dynamic sensory modules in a visual architecture and experimental study of their performances in the case of degraded visual sources This proposal fills the need of fostering a joint collaborative scientific and technological progress, for which France and Chile have a long tradition of cultural and scientific exchanges. However the challenge of doing science together is still pending. Until this year, all the available collaborative projects from CONICYT-CNRS-INRIA-INSERM-ECOS were for short visits and student exchanges. We see now an fantastic opportunity for doing "real" joint scientific project, and we also foresee that the area of STIC will have a tremendous impact on Education, S&T generation and transfer to a variety of areas like biomedicine, robotics and artificial intelligence.

The Hybride research project aims at developing new methods and tools for supporting knowledge discovery from textual data by combining methods from Natural Language Processing (NLP) and Knowledge Discovery in Databases (KDD). A key idea is to design an interacting and convergent process where NLP methods are used for guiding text mining and KDD methods are used for analysing textual documents. NLP methods are mainly based on text analysis, and extraction of general and temporal information, while KDD methods are based on pattern mining, e.g. itemsets and sequences, formal concept analysis and variations, and graph mining. For example, NLP methods applied to some texts locate ``textual information'' that can be used by KDD methods as constraints for focusing the mining of textual data. By contrast, KDD methods can extract itemsets or sequences that can be used for guiding information extraction from texts and text analysis. This combination of NLP and KDD methods for common objectives, can be viewed as a ``virtuous circle'', i.e. a sequence of complex operations from NLP and KDD that reinforces itself through a feedback loop. Experimental and validation parts associated with the Hybride project are provided by an application to the documentation of rare diseases in the context of Orphanet. The fundamental aspects of the \acro project can be understood through the main steps of the knowledge discovery loop with a NLP/KDD perspective: (i) data preparation, (ii) data mining, (iii) interpretation and validation of the results, (iv) knowledge construction. At each step, new methods have to be designed for achieving this interrelated NLP/KDD loop. The consortium has gained a rather good experience on NLP and KDD, but efforts are still necessary for adapting the classical KDD loop to become an actual NLP/KDD loop. There is a need to solve interaction problems at each steps of the NLP/KDD loop where interaction amounts for one process to prepare the application of the second. Finally, a system integrates the operations involved within the whole loop, in the context of Orphanet for text analysis and production of new documentation on rare diseases. The implementation of such a system combines various interrelated aspects, namely natural language processing, knowledge discovery, data mining, and knowledge engineering. This original combination still remains a challenge in computer science.

There is nowadays a huge quantity of information that flows through untrusted channels. And this will still increase dramatically in the future. Mobile sensor networks are deployed everywhere and control more and more social activites: car driving, health care, traffic control, ... Mobile and ad hoc networks, iphone and more generally mobile terminals are increasingly used in everyday life. Chips are used to store sensitive data and are supposed to secure critical transactions: they are embedded in electronic passports, cash cards, ... Securing the communications is therefore an important challenge. The research community in computer and information sciences has deployed a lot of efforts in trying to secure the communications. There is also a lot of efforts, in trying to increase our confidence by providing with ``security proofs''. Such proofs require a formal model for the protocols, for the security properties and for the attacker capabilities. Until 2001, different research communities worked independently, using different formal models. This includes for instance a logical (or symbolic) model and analysis, whose successes can be illustrated by the numerous man-in-the-middle attacks that have been found on security protocols. A second approach is computational, inheriting from complexity theory; it assumes for instance that the attacker is any randomized polynomial time Turing machine. This is the favorite model of cryptographers. A third approach is more pragmatic and tries to prove/find attacks on actual implementations of the protocols (this has been more successful in finding attacks than in proving protocols secure). The main goal of the ProSe project is to provide with security proofs at several levels: symbolic, computational, and implementation. In a nutshell, the aim of the project is to develop automatic protocol verification tools in the symbolic model, in the computational model, and in an implementation model. We will rely on the partner's experience in the areas of symbolic security proofs and their relationships with computational proofs.