Expressive Rendering is a recent branch of Computer Graphics that offers promising novel styles, and is increasingly used in many application domains such as video games or movie production. At the present time, only expert artists are able to create compelling animations, and still, this is an extremely time-consuming process, with many constraints that strongly limit creativity. The reason is that current models are not sophisticated enough to provide intuitive manipulations and versatile styles. The motivation behind this project is to overcome these limitations both for 2D and 3D animation systems.

Brain-Computer Interfaces (BCI) are communication systems that enable their users to send commands to computers through brain activity only, this activity being measured and processed by the BCI (usually using ElectroEncephaloGraphy – EEG). Making computer control possible without any physical activity, BCI have promised to revolutionize many application areas, notably assistive technologies for paralyzed users (e.g., for wheelchair control) and human-computer interaction (HCI). Despite this promising potential, BCI are still barely used outside laboratories, due to a poor reliability. For instance, current BCI based on only 2 imagined movements correctly recognize less than 80% of the users’ mental commands, on average, while between 10 to 30% of BCI users (depending on the BCI type) cannot control a BCI at all. Designing a reliable BCI requires to consider it as a co-adaptive system, with its users learning to produce distinct brain activity patterns that the machine learns to recognize using signal processing. Indeed, BCI control is a skill that the user has to learn. Most research efforts so far have been dedicated to signal processing or human-computer interaction techniques, i.e., on the computer side. Unfortunately BCI user training is as essential but 1) is only scarcely studied and 2) standard approaches are only based on heuristics, without satisfying human learning principles. Thus, currently poor BCI reliability is probably due to a large extent to highly suboptimal user training. Therefore, to obtain a much higher reliability for BCI we need a major rethinking of their fundamentals in algorithmics (signal processing, machine learning) and user training (feedback and training tasks). In particular, we propose to create a new generation of BCI that apply human learning principles to ensure the users can learn high quality control skills which will go much beyond those obtained with currently available systems, hence making Brain-Computer Interfaces reliable and trustable. To do so, we will first work on understanding and modeling BCI skill acquisition from a neurophysiological point of view. In other words, we first aim at identifying what are the EEG features defining good EEG patterns (that are successfully recognized by the BCI), and how they evolve with training. Then, we will propose new EEG signal processing tools to quantify such training-related EEG features in real-time. This will enable us to identify objectives to reach with BCI training and a way to quantify and guide the user’s progress during training. Afterwards, we will combine these new EEG features and BCI training models with recommendations and principles from human learning and education psychology to propose new and relevant feedback and training tasks to radically improve BCI training. In particular, we will propose adaptive and adapted training tasks and provide the users with an explanatory feedback (indicating what is good or bad about the EEG patterns performed) based on our new training-related EEG features. Finally we will extensively evaluate and validate our new BCI, first on healthy users, then on a few motor-impaired ones. Overall, we target a new BCI design leading to a fast acquisition of reliable BCI control skills. Such a reliable BCI could actually positively change HCI as BCI have promised but failed to do so far.

The code FluidBox is a non-specialized platform for compressible fluids flows simulation. The targeted fluids can be of very different nature and are characterized by their state. A version of this software is at present downloadable under open source license CeCILL-C in the INRIA’s GForge (https: // to gforge.inria.fr / projects / fluidbox/). Possible applications’ domains concern the aerodynamics, the multi-fluid flows and multi-phases, turbulent. This code has been developed for more than ten years and requires henceforth a more professional management if we want to have a more important transfer. The purpose of this project, which will end in a platform of calculation named RealFluids and whose code FluidBox constitutes a part of the scientific base, is to create a professional version of FluidBox by including software management tools, better definition of input/output modules, a better management of different mesh formats, a human-machine interface, a base of not-regression, almost absent things at present. It will thus be a question of facilitating a better management of the code (creation and validation of not-regressive tests, portability of the code, the facilitation of management of modules, to allow easier encapsulation of the code in the other physical or mathematical models, the numerical stability and the control of floating-point errors, etc.), to envisage the distribution of a new open source version (the choice of the license will be made at the right moment) but also a transfer towards one or more SMEs. FluidBox interested, indeed, in past the company Glaizer Innovation and more recently the company AXS and Valeol, the latter situated in the suburb of Bordeaux and making pales of wind turbines.

The ability to detect and respond to the presence of pathogens is essential for survival and development of every living organism. In the two most famous branches of the eukaryotic tree complex immune responses have been described. Detection of a pathogen usually initiates a cell death reaction restricted to the infected cell by the inflammatory reaction in animals and the hypersensitive response in plants. In both cases the innate immune response is initiated when Pathogen Associated Molecular Patterns (PAMP) resulting from the presence of the pathogen are recognized by dedicated Pattern Recognition Receptors (PRR). Interestingly, PRRs in plants and animals share a common architecture associating a central nucleotide binding domain (NB) to an N terminal effecter domain and a C terminal protein protein interaction domain made of Leucine Rich Repeats (LRR). This organisation, characteristic of the STAND class of proteins, results from convergent evolution. In both plants and animals, mutations associated to PRRs can lead to auto-immune diseases resulting in the activation of defence mechanisms in absence of any pathogen. Surprisingly no immune system has been described in the fungal branch of the eukaryotic tree despite fungi being closely related to animals. In filamentous fungi a conspecific non self recognition process known as Vegetative Incompatibility (VI) is initiated after the somatic fusion of cells originating from genetically different strains. This recognition ensured by het genes triggers a cell death reaction restricted to the fusion and surrounding cells that is now well described. het genes have been cloned in the model species Podospora anserina. Alleles of het-c encoding for a glycolipid transfer protein (GLTP) can be incompatible with alleles from the NWD gene family encoding for STAND proteins. NWD family members have the ability to permanently generate a considerable number of variants differing by their protein-protein interaction domain (WD domain). We have recently hypothesized that the P. anserina NWD family members actually encode for PRRs and that the VI reaction is a pathological manifestation of the fungal immune response as a by-product of the pathogen driven diversification of the NWD genes. In addition we have identified a fungal species, Epicoccum nigrum that triggers a very strong reaction when confronted to WT P. anserina. On confrontation plates, E. nigrum filaments grow within P anserina territory, and in return, P. anserina hyphae appear to coil around and kill the E. nigrum cells. This P. anserina reaction is appears less efficient in P. anserina mutant strains that cannot proceed through the VI reaction, including suppressor mutants and the strain deleted for the GLTP encoding gene het-c does not react to the presence of this fungal species. We thus hypothesize that the HET-C protein and the NWD PRR receptors are involved in recognition of heterospecific non-self and induction of an appropriate response. Preliminary results indicate that this response includes many features of the VI reaction. The present project aims at characterizing the molecular components of the heterospecific non-self recognition machinery using the P. anserina/E. nigrum interaction. What recognizes what and how? We will investigate the role of the different molecular actors of the recognition process with a particular emphasis on the HET-C and NWD proteins. We will also try to confirm that the P. anserina response to the pathogen is comparable to the VI reaction. Finally, we will extend the description of fungal immune response to additional fungal host/Pathogen systems.