Imagine you have to answer the following questions: how to build a computer-aided diagnosis tool for neurological disorders from images acquired from different medical imaging devices? that could identify which emotion is feeling a person from her face and her voice? How could these tools be still operational even when some data of a type is missing and/or poor quality? These questions are at the core of some problems addressed by the Institut de Neurosciences de la Timone (INT), where people have expertise in brain imaging based medical diagnosis, and Picxel, a SME centered on affective computing. The Laboratoire d'Informatique de Paris 6 (LIP6), the Laboration Hubert Curien (LaHC), and the Laboratoire d'Informatique Fondamentale de Marseille (LIF, head of the PI) are the other partners that are teaming up with INT and Picxel: in this project, they provide their renowned knowledge in machine learning, wherein they have developed, theoretical, algorithmic, and practical contributions. The five partners will closely work together to propose original and innovative advances in machine learning with a constant concern to articulate theoretical and applicative findings. The above questions pose the problem of (a) building a classifier capable of predicting the class (i.e. a diagnosis, or an emotion) of some object, (b) that of taking advantage of the few modalities or *views* used to depict the objects to classify and, possibly (c) that of building relevant representations that take advantages of these views. This is precisely what the present project aims at: the development of a well-founded machine learning framework for learning in the presence of what we have dubbed *interacting views*, and which is *the* notion we will take time to uncover and formalize. To address the issues of multiview learning, we propose to structure as follows. On the one hand, we will devote time to establish when and how classical (i.e. monoview-based) learnability results carry over to the multiview setting (WP1); this may require us to brush up on our understanding of different notions and accompanying measures of interacting views. On the other hand, possibly building upon the results just mentioned, we will build new dedicated multiview learning algorithms, according to the following lines of research: a) we will investigate the problem of learning (compact) multiview representations (WP2), then b) we will create new algorithms by leveraging some recent works on transfer learning -- multitasks and domain adaptation -- to the multiview setting (WP3), and, c) we will address the scalability of our algorithms to real-life conditions, such as large-dimension datasets and missing views (WP4). Finally, the performances of our learning algorithms will be assessed on benchmark datasets, both synthetic and real, that we will collect and make available for the machine learning community (WP5). Beyond the mere evaluation of our algorithms, these dataset will be disseminated to promote reproducible research, to identify the most suitable algorithms in a multiview setting, and to make the machine learning community aware of the exciting problems of multiview learning for affective computing and brain-image analysis.

Interfacial rheology is key to control the stability of multiphasic assemblies like foams or emulsions as it governs the local interfacial flows between the plateau borders and the thin films. The stability and the foam behavior under drainage for example depend on both elastic and surface viscous moduli. Interfacial rheology also plays a major role in the rheology of suspensions of deformable particles of living fluids like blood. Indeed, the interactions between Red Blood Cells are governed by the mechanical response to the hydrodynamic flow which results from many-body interactions. The shear membrane viscosity has an essential contribution. In many applications in painting or in bioengineering such as encapsulation, self-healing is a sought property to ensure a sufficient life-time of interfaces, much like macromolecular re-assembly, which has its signature in the viscoelastic moduli and the constitutive law governing interfacial rheology. Unfortunately, intrinsic difficulties to measure these properties is blocking progress. This difficulty comes from the quasi-impossibility to quantify independently each parameter as shear and dilatational strains are often concomitant. A good agreement between the different techniques available is only found for scarce cases of surfactants. In the case of microcapsules and their thin biopolymer membranes, the dilatational viscosity and elasticity are nearly always ignored. Gathering a multidisciplinary consortium of three laboratories in physical-chemistry / soft matter (LPS), rheology / fluid mechanics (LRP) and High Performance Computing / mechanics (M2P2), 2DVISC will develop a versatile toolbox to measure the viscoelastic moduli characterizing the interfacial rheology of bubbles, droplets and microcapsules. It means the surface tension, the Marangoni modulus and both shear and dilatational surface viscosities in the case of bubbles and droplets and the shear and dilatational surface elastic moduli and both surface viscosities in the case of microcapsules. The principle is not to control purely shear or dilatational strains (or deformations) but to apply different simple linear flows, each one being characterized by two known components of shear and elongation rates to deform bubbles, droplets and microcapsules using (milli-)microfluidic tools. The overall deformation, orientation and the associated characteristic times depend on the viscoelastic moduli. A careful comparison of the dynamics of deformation and orientation with theoretical expressions determined in the limit of quasi-spherical shapes and advanced numerical models in the linear and nonlinear regimes allow to extract the interfacial surface moduli by inverse analysis. Several flow configurations will be investigated to demonstrate the self-consistency of the method. These parameters will be compared to standard independent measurements to validate the method. Finally, in the case of droplets and microcapsules, the method will be integrated in the microfluidic Four-Roll Mill to provide a unique toolbox. Full interfacial characterization will become possible using a single device.

There is an emerging trend in robotics : rather than by a few bulky robots, certain tasks can be performed by tiny robots, each with very limited capabilities. This trend is similar to the less recent trend about sensor networks, with sensors that are also of limited capabilities but deployed in huge number. This is the emergence of systems where elementary bricks are simple and cheap though can provide relatively complex collective behavior. From an algorithmic point of view, one need to consider a new computing paradigm « Moving and computing »: the study and design of systems where the computational entities themselves are capable of movement within the spatial universe they inhabit. The field has applications in areas as diverse as autonomous robots moving in a terrain, software agents moving in a network, autonomous intelligent vehicles, wireless mobile ad-hoc networks, and networks of mobile sensors; where the computational objectives are exploration, coordination and cooperation. When considering the design of algorithms for mobile robots in a geometric environment, the modeling of the environment, i.e., the way the mobile entities have access to it, is crucial. The entities can have access to only limited aspects of the environment : e.g. where it can move. Indeed, in this setting, the robot main responsibility is to compute where to go next. Such a modeling implies the study of the graph of the possible locations, linked by the elementary moves. Such a graph does not have an arbitrary structure but inherit some combinatorial properties from its geometric context. In this framework, using the mobile agent model, from classical distributed computing, is very much relevant. The specificity of our study is that the graphs under consideration are of geometric type (e.g. the visibility graph of a polygon). Moreover, it is known that adding more sensing capabilities will yield more efficient algorithms. A natural investigation is therefore to characterize what are the weakest kind of sensors, i.e., the kind of geometric information, that enable to solve efficiently problems such as exploration, map construction, rendezvous, ... In contrast with the previous situation, in the case of mobile sensors, the computation is more how to react to moves and changes in the topology that are not directly under control. However, similarly, by using geometric properties, it is possible to improve the efficiency of algorithms, compare to algorithms using only combinatorial graphs properties. Hence, solving tasks such as broadcast, computing/repairing local structures, benefits from a deep understanding of the relationship generated by the actual topology of the sensor network, especially the possible dynamic evolutions of such networks. It is also possible to show that the ``mobile agent" model is also relevant in this context, because it enables to use a simple computational structure within a complex data structure. A distant goal for our research, that will be of importance in the near future, is to investigate thoroughly the interactions and evolutions of mobile agents in dynamic networks. More generally, our objective is to dramatically improve the models and algorithms for distributed robot computing. Such a study implies to get a better understanding of the relations between local, global and geometric properties shared by those problems and environments. We will benefit from tools and known results from geometric graph theory, discrete algebraic topology, computational geometry and timed systems. Last, part of the validation of this project's results will be done with « LED's CHAT » (http://leds-chat.net/): a modular light and sensor system developed at LIF, and currently subject to a technology transfer program with the aim of commercializing the technology to actual light applications.

ECOSAFE project is focused on the control and safety issues raised by massive implementation of alternative fuel like hydrogen, especially in confined systems such as internal geometry of fuel cell stack. Understanding the conditions for propagation of a flame in such slender geometries requires the systematic study of the coupling processes between the flame shape, the flow, the thermal dissipation at the walls and the acoustics. For that purpose, laboratory experiments will be performed but pose a tremendous challenge in terms of visualizations. Complementary numerical simulations using an novel Reactive Lattice-Boltzmann Model will be a determining step for the project.

The main goal of the Aggreg project is to develop efficient algorithms for answering aggregate queries for databases and data streams of various kinds. Aggregate queries are central for computing statistics on data collections: Rdf stores, NoSql databases, streams of data trees in Xml or Json format, uncertain databases, relational databases, and datawarehouses. Considering that counting is the basis of aggregate queries the principal difficulty here is to overcome the inherent computational hardness of many counting problems, which precludes general and efficient solutions. Instead, we propose to: study the complexity of expressive fragments of the class of aggregate queries, search for efficient algorithms on tractable fragments, identify which parameters can be fixed in order to obtain tractability, find general algorithms that are gracefully degrading, and also efficient approximation algorithms. We apply methods from algebra, automata, probability, algorithmics and complexity theory.