Verifying correctness and robustness of programs and systems is a major challenge in a society which relies more and more on safety-critical systems controlled by embedded software. This issue is even more critical when the computations involve floating-point number arithmetic, an arithmetic known for its quite unusual behaviors, and which is increasingly used in embedded software. Note for example the "catastrophic cancellation" phenomenon where most of the significant digits of a result are cancelled or, numerical sequences whose limit is very different over the real numbers and over the floating-point numbers. A more important problem arises when we want to analyse the relationship between floating-point computations and an "idealized" computation that would be carried out with real numbers, the reference in the design of the program. The point is that for some input values, the control flow over the real numbers can go through one conditional branch while it goes through another one over the floating-point numbers. Certifying that a program, despite some control flow divergences, computes what it is actually expected to compute with a minimum error is the subject of the robustness or continuity analysis. Providing a set of techniques and tools for verifying the accuracy, correctness and robustness for critical embedded software is a major challenge. The aim of this project is to address this challenge by exploring new methods based on a tight collaboration between abstract interpretation (IA) and constraint programming (CP). In other words, the goal is to push the limits of these two techniques for improving accuracy analysis, to enable a more complete verification of programs using floating point computations, and thus, to make critical decisions more robust. The cornerstone of this project is the combination of the two approaches to increase the accuracy of the proof of robustness by using PPC techniques, and, where appropriate, to generate non-robust test cases. The goal is to benefit from the strengths of both techniques: PPC provides powerful but computationally expensive algorithms to reduce domains with an arbitrary given precision whereas AI does not provide fine control over domain precision, but has developed many abstract domains that quickly capture program invariants of various forms. Incorporating some PPC mechanisms (search tree, heuristics) in abstract domains would enable, in the presence of false alarms, to refine the abstract domain by using a better accuracy. The first problem to solve is to set the theoretical foundations of an analyser based on two substantially different paradigms. Once the interactions between PPC and IA are well formalized, the next issue is to handle constraints of general forms and potentially non-linear abstract domains. Last but not least, an important issue concerns the robustness analysis of more general systems than programs, like hybrid systems which are modeling control command programs. Research results will be evaluated on realistic benchmarks coming from industrial companies, in order to determine their benefits and relevance. For the explored approaches, using realistic examples is a key point since the proposed techniques often only behave in an acceptable manner on a given sub classes of problems (if we consider the worst-case computational complexity all these problems are intractable). That's why many solutions are closely connected to the target problems.

The mammalian circadian timing system rhythmically controls most aspects of behaviour and physiology over the 24 h. The underlying basic component of this system is a molecular clock present in virtually every cell and controlled by a genetic network. Self-sustained oscillations of these circadian clocks are synchronised by external or internal time cues and in turn coordinate key cellular processes such as signalling, cell cycle, and metabolism. While the molecular makeup of circadian clock is relatively well known, we are still far from fully understanding how the clock mechanism is integrated with other important processes to ensure optimal temporal coordination at the molecular, cellular and physiological levels. This is a critical issue because circadian misalignment or disruption as observed for example in workers exposed to rotating shift work compromise health and wellbeing. Indeed, experimental and clinical evidence increasingly supports the hypothesis that poor circadian coordination is a risk factor for major pathologies such as cancer, cardiovascular, metabolic, inflammatory and sleep disorders. In addition, tolerability and efficacy of treatments is strongly influenced by the time of administration because the circadian system also controls key drug pharmacology determinants. Accordingly, the concept of chronotherapy aims at integrating circadian timing and pharmacology in order to improve the therapeutic index of drugs through appropriate timing of delivery. The network and the dynamic nature of the circadian clock mechanism makes it difficult to investigate and understand its behaviour when coupled with input and output pathways or other genetic or biochemical networks, using experimental approaches exclusively. We have in two previous projects successfully combined experimental and mathematical modelling approaches to (i) provide the proof of principle that circadian data based modelling can predict optimal timing of irinotecan delivery leading to improved tolerability in a preclinical setting and (ii) demonstrate the consequences of the coupling between the clock and the cell cycle on the dynamical behaviour of the system in proliferating cells. Although such systems biology approach is potentially powerful, current modelling approaches still suffer from several limitations because they were not initially developed for genetic networks involving chronometric time while parameter estimation remains challenging. The HyClock project gathers a multidisciplinary team of experts in computer sciences, mathematical modelling, chronobiology and chronopharmacology to develop novel formal methods and hybrid modelling frameworks and apply them to the analysis and understanding of circadian clock function in mammals. This novel modelling strategy will be first used to predict and analyse how the coupled circadian clock-cell cycle network responds to physiological synchronisation in healthy cells with consequences on proliferation. Second, we will investigate in vivo using experimental design guided by these hybrid modelling approach how we can reinforce circadian timing system coordination of the host through synchronisation, in order to improve the tolerability to treatments using the widely used anticancer targeted agent everolimus (mTOR inhibitor) and cytostatic chemotherapeutic agent irinotecan (Top1 inhibitor) as model drugs. HyClock is expected to provide a general and innovative approach as well as invaluable tools and information for both the modelling of biological time and for the forthcoming personalization of chronotherapy.

Unmanned aerial vehicles (UAVs), also called drones, have proved to be a very efficient means to address surveillance and inspection applications in both the military and civilian domains. Among the numerous missions that can be achieved with drones, some of them only require to hover or fly in a narrow area. They are best addressed with rotary-wings drones, alike the well-known quadcopter. Other missions require covering long distances or staying in the air for a long time. In this case fixed-wing aircraft is preferred due to its good energy efficiency. In some cases, both hovering capacity and energy efficiency in cruising flight are needed. Examples are numerous and include surveillance missions in a large area with take-off from a narrow zone (ship, natural terrain, cluttered environments, etc), or inspection of several structures far apart from each other. Aircraft dedicated to such applications involve the association of propellers with wing(s), and are sometimes referred to in the literature as tilt-rotor, convertible, hybrid, or compound aircraft, depending on their mechanical structure. For simplicity, all these aircraft will be denoted in the sequel as "Convertible UAVs". The project concerns more specifically Convertible MAVs (Mini Aerial Vehicles), i.e. with Maximum Take-Off Weight less than 30 kg (according to UVS international classification). The main objective of this proposal is to design autopilots for Convertibles MAVs ensuring energy efficient and robust flight capacities. Several issues have to be addressed: _ Convertible MAVs are redundant systems that can exploit both rotary wings degrees of freedom (rotors' angular velocities) and fixed wings degrees of freedom (flaps, ailerons, etc) for control. At low flight speed the control essentially boils down to the control of a multi-rotor system. At high speed one may view the vehicle as an airplane and make use of associated control approaches. At intermediate speeds, in particular during transition between low and high speed, other methods must be developed. Controlling such "transitions" is a major difficulty for convertible UAVs. _ Due to the system's redundant actuation, there exist many possible control modes with different energy efficiency levels. Achieving energy efficient flight in all flight phases is essential to justify the utility of this type of structure. _ Convertible MAVs can be subjected to fast and strong variations of aerodynamic forces acting on the vehicle, due to the presence of wings. Typical cases include transition between stationary and cruising flights, adverse wind gusts perturbations, or aggressive flight maneuvers. Ensuring a large flight envelope is necessary to guarantee flight safety. _Flight dynamics models valid in the entire flight domain are difficult to obtain. Main issues include aerodynamic modelling at low Reynolds numbers or large angles of attack, stall modelling, and propellers/wings aerodynamic interactions. For all these reasons, designing an efficient and robust autopilot for a convertible vehicle remains a challenge. This project aims at providing solutions to this problem. The project relies on two pillars: advanced control and estimation techniques, and extensive field tests. From a theoretical point of view, the project will build on the design of nonlinear control laws and observers. A key issue is the real-time estimation of the main aerodynamic efforts which, as explained above, can be difficult to model precisely. From an experimental point of view, an essential aspect is to register the maximum quantity of flight data so as to provide solid ground for modelling and evaluation. This justifies the development of prototypes with dedicated sensor suites. Therefore, the project includes a strong effort on the development and instrumentation of prototypes.

Induced subgraphs play a central role in both structural and algorithmic graph theory. A graph H is an induced subgraph of a graph G if one can delete vertices of G to obtain H. This is the strongest notion of subgraph, hence being H-free (that is not containing H as an induced subgraph) is not a very restrictive requirement. Weaker notions of containment, like for instance minors, are now well understood, and the next achievement in Graph Theory should certainly be the understanding of forbidden induced structures. We focus in this proposal on the following very general question: Given a (possibly infinite) family F of graphs, what properties does a F-free graph have? This is the key question of many important and longstanding problems, because many crucial graph classes are defined in terms of forbidden induced subgraphs. This field is now quickly growing, and new techniques and tools have been recently developed. Our first goal is to establish bounds on some classical graph parameters for F-free graphs, such as the clique number, the stability number and the chromatic number. A second goal is to design efficient algorithms to recognize F-free graphs and to determine or approximate some parameters for those graphs. We also plan to study similar questions for oriented graphs. For this purpose, we plan to use and develop various proof techniques, some of these being recently discovered, such as the structural description of graph classes, the regularity lemma, graph limits, flag algebras, VC-dimension, discharging method as well as computer-assisted proofs.

VISIIR is a project aiming at exploring new methods for semantic image annotation. This topic is extensively studied for more than a decade now due to its large number of applications in areas as diverse as Information Retrieval, Computer Vision, Image Processing, and Artificial Intelligence. Semantic annotation refers to the ability of predicting a semantic concept based on the visual content of the image. Filling the semantic gap between visual data and concepts is the main goal followed by researchers in the field. In supervised learning, a large amount of labeled data is mandatory to provide effective semantic annotation tools. In interactive Image Retrieval Systems (CBIR), the annotation requires to formulate the user query with an example, i.e. an image. User feedback interaction is commonly used to interactively refine a query concept by asking the user whether some selected images are relevant or not. To be effective, one major challenge in interactive CBIR is to minimize the required number of feedback loops to grasp the semantic query of the user. The VISIIR project aims at exploring new methods for providing powerful semantic annotation systems. The originality of the proposal is three-fold: • Eye-tracker-driven system. A major specificity of the project is to use the latest eye-tracker techniques for validating and improving the vision and learning models developed by the academic partners. • New paradigm for visual representation and learning. We introduce a novel learning scheme combining supervised and interactive methods. • Web filtering for food annotation. The new methods developed in the project will be validated in a specific web application dedicated to retrieve food classes. In terms of methodology, the first lock for semantic annotation relies on the representation of visual content. In order to make one step further compared to current state-of-the-art methods, we want to develop new bio-inspired representations. One key idea is to provide a hybrid representation, combining visual saliency models and unsupervised deep networks. In the second part of VISIIR, we design new interactive learning schemes. We exploit the additional source of information provided by the eye-tracker to boost the learning quality (i.e. the active learning convergence), at two different levels. Firstly, eye-tracker features are used in conjunction to user’s annotation to jointly optimize the classification function and the visual representations learned off-line in task 1. In addition to this gaze analysis purpose, we propose to use the eye-tracker to control the learning process and develop new type of interaction. Typically, eye-tracking statistics will act as user feedback. Finally, one strong axis of VISIIR is the rigorous evaluation of the proposed semantic annotation methods in a specific web filtering application dedicated to food retrieval. A complete database will be provided through the project with the goal of finding images of recipes. This fine-grained classification task will be used as a use case to validate the visual representations and interactive learning methods of task 1-2. A methodological aspect addressed in this task is the scalability of the interactive search when applied to the huge amount of images harvested from the web. We want to tackle this scalability lock by marrying efficient hashing structures for indexing and search with exploration techniques. To carry out VISIIR, the complementary required skills will be provided by the consortium partners. UPMC will bring skills in image classification and statistical learning, I3S on CBIR and scalability, L3I on visual saliency and attentional models, and Tobii on Eye-tracker technology.