Secure circuits embed hardware primitives that provide security properties: Physical Unclonable Functions (PUFs) or attack sensors, for example. These only fulfil their role when powered, which makes a new class of attacks that would be carried out when the targeted circuit is powered off particularly worrying. The aim of our project is precisely to verify the feasibility of laser attacks on powered-off devices and to propose suitable countermeasures to protect against these attacks. In order to carry out this work, we first plan to design in-house and then have an external service provider manufacture a test circuit with carefully selected elementary blocks and simple security primitives for characterisation, testing and modelling purposes. We then plan to carry out laser injection campaigns on this circuit, but also on other circuits already available from the project partners. These experimental campaigns can therefore start at the beginning of the project. This first stage will lead to the development of a fault model, describing the observed faults as exhaustively as possible, at different levels of abstraction: physical, logical and functional. Once we understand the effects of laser attacks on powered-off devices, we plan to apply the resulting fault model to two classical examples of safety primitives. For the PUF, the aim will be to disprove the unclonability property, by experimentally modifying the statistical distribution of the identifiers generated by the PUF. This could go as far as gaining precise control of individual bits of the response obtained. The second application will be the deactivation of an attack sensor before its use, by exposing it to laser radiation when it is powered off. The aim here is to render the sensor non-functional once it is powered. Finally, we plan to illustrate the developed fault model by applying it to two existing systems, resulting from previous ANR projects, and which use the security primitives described above. Thus, we will first target the intellectual property protection system of the SALWARE project, protecting IP cores against illegal copying. This system is based on the intrinsic identification of the different instances of an IP core using a PUF, and the possibility of cloning the PUF would make it possible to illegally activate several components from a single legal activation. The second target device is an integrated substrate current sensor, known as BBICS, from the ANR LIESSE project. The objective here is to raise the detection threshold of the sensor to make it insensitive to the currents induced by a laser attack carried out later. Finally, once this original threat has been clearly identified and validated, we will propose countermeasures that are adapted and suitably designed.

In a rapidly changing world, we need operational tools to predict and manage responses of biodiversity. To date, although it is clear from both theoretical and empirical work that adaptation can influence the persistence of populations on short time scales, biodiversity scenarios are conspicuously lacking an evolutionary component. One major limitation to the implementation of scenarios including adaptation dynamics is that our knowledge of evolutionary potential and constraints is still too imperfect. In this project, we propose to improve our understanding of adaptive mechanisms in wild populations by integrating theoretical and empirical approaches in wild bird populations at different spatial and taxonomic scales. Using state of the art molecular and quantitative genetics tools in combination with demographic analysis, we will use several populations / species of birds studied in the long-term to identify i) forces of selection acting on natural populations, and especially forces driven by climate change, ii) environmental factors affecting dispersal rates, with a special interest for habitat structure and fragmentation, iii) ecological and phylogenetic factors shaping genetic architecture and affecting its stability, and iv) which regions of the genome show signatures of selection and are therefore likely to be partially responsible for adaptation to different environments. Using a comparative approach among populations and species will allow investigating evolutionary processes at different time and space scales and hence link micro and macroevolutionary patterns. These results will be included in predictive niche models that will assess to which extent the inclusion of rapid evolution and stability of evolutionary potential are affecting predictions from biodiversity scenarios. Hence our approach should provide new tools at the interplay of ecology and evolutionary biology to quantify to what extent model projections neglecting the adaptive component might bias estimates of species extinction risks which are key parameters for policymakers. Moreover, we will put great emphasis on communicating the importance of the biodiversity/evolution interface by (i) collaborating with policy-makers working on biodiversity within the Food and Agricultural Organisation of the United Nations and by (ii) strengthening citizen science through the organisation of exhibitions and conferences in a leading natural history museum (Museum d’Histoire Naturelle, Paris). All in all, results from this project will provide an integrative picture of factors affecting responses to global change improving fundamental knowledge at the interface of ecology and evolution but also including a resolutely operational dimension.

The aim of the muteSWARM project is to propose novel control techniques in aerial swarm robotics that are not based on radio communication (i.e. muted). The key strategy will consist for a robot, as animals do in the wild, to sense the close neighbors and its environment by means of its perception and not by direct communication and even without labeling of the drones detected. We propose to investigate hearing here in particular, which is little explored in robotics. Multi-rotor robots are noisy and the sound UAVs produce is relevant information for their localization by neighbors, without needing to design and embed extra sound transmitter devices. The acoustic localization of an aerial robot will be explored, particularly when using the natural sound produced by the robot itself. Here, it would be a premiere maintaining a swarm of UAVs in formation without radio communication, only based on the acoustic signature of neighbors to determine the inter-UAV distances. In addition and to go even further in bio-inspired flying robotics, it is also planned to make the swarm navigation more robust by applying multimodal sensing: not only acoustic localization will be studied, but also a combination of acoustic sensing and low-resolution visual alternatives. In this context, AI-enhanced swarm formation control algorithms will be developed. The project is intended to be frugal, seeking to design a low-tech and low-cost UAV swarm, with lower-resolution perception, low resource requirements, and low energy consumption. It is also relevant to point out that the hoped-for gain in terms of parsimony, scalability, and resiliency, could apply to other robots or swarms of robots, not only to UAVs. Besides, algorithms will be made as generic as feasible so that they can suit any system. The targeted experimental validation scenario will be to maintain a flight formation in a mixed indoor and outdoor environment, where the full potential of the multimodal proposal will be exploited (in the framework of the French national research infrastructure Robotex 2.0 and Equipex+ TIRREX). The acoustic mode will offset the blinding effects when UAVs fly from inside to outside, while conversely, the visual mode could be helpful for the reverberation effects when the robots return indoors.

Our objective is to design in this three-year project an original “lab-on-fiber” tool for remote, label-free “in vivo” molecular analysis that could be dedicated in the future to endoscopic diagnosis. While micro-arrays are currently used “in vitro” for multi-parametric detections of biomarkers, concerning “in vivo” molecular detection, no multi-parametric label-free approach has been developed so far. Nonetheless this tool could be very useful and bring significant progress in the medical field especially in diagnosis technique like endoscopy. This tool could efficiently complete this imaging medical analysis. Indeed, the diagnosis would be facilitated by the real time detection, quantification, and localization of specific pathologies’ biomarkers. This tool-associated gain could highly benefit to the patients and significantly reduce the overall cost of diagnosis. To achieve this project our approach is based on functionalized nanotextured optical fiber bundles. When appropriately designed and covered by a gold layer those nanostructured fibers exhibit interesting plasmonic properties. We wish to take advantage of those optical properties to perform remote biological analysis. For this, we plan to design an array of different biological probes at the nanotextured bundle end face to confer specific bio-sensing properties. The so formed device will be used “in vivo” for biological targets multiplexed specific detection in a remote mode. This project is highly interdisciplinary as it combines skills at the forefront of optics, electrochemistry, surface chemistry, biochemistry, nano- and micro-technologies applied to the biological field. The work will be done by four complementary research groups (ISM / NSYSA, SPRAM / CREAB, LAAS / NBS, IAB/ AICD). The main steps of the project are schematically presented below. The fibers bundles will be etched and metalized in ISM (Bordeaux). This group is specialized in the development of new analytical nanosystems for biological applications by combining mainly optical and electrochemical techniques at the nanometer scale. LAAS (Toulouse) will provide patterning tools for the localized biofunctionalization of fibers surfaces by means of e.g. micromachined cantilevers thanks to their skills in micro- and nanotechnologies. SPRAM (Grenoble) will develop a dedicated optical setup in order to characterize the sensitivity of the designed probe. SPRAM has an expertise in the field of surface plasmon resonance (SPR) bio-chip functionalization and instrumentation development. IAB (Grenoble) studies the immune system and its digressions in chronic pathologies associated to inflammations. They will provide the required knowledge to qualify the bundles in model tissue before validating the tool efficiency for “in vivo” multiplexed molecular detection in an endoscopic and minimally-invasive way in a mouse model.

In living organisms, gene expression is finely regulated by the joint action of regulatory proteins. Among these proteins, transcription factors (TFs hereafter) play a key role since they bind specific sequences in the promoters of genes to initiate their regulation. TFs can combine to form complexes and regulate the expression of new genes or to alter the direction or level of regulation of genes already targeted by one of the two TFs alone. The complexes thus diversify the repertoire and regulatory levels of genes targeted by the transcription factors. Little is known about the extent of this phenomenon, the number of complexes, the identity of the partners and the way they bind to DNA. This project proposes to develop a bioinformatics model to predict the existence of protein complexes formed by transcription factors and likely to regulate gene expression in the plant Arabidopsis thaliana. In a second phase, the project will explore the predictions of the model to verify the existence of the predicted complexes, and to characterize their DNA binding mode and their target genes. The discovery of new complexes will be done by developing a model that integrates clues scattered in different types of genomic data. These clues are (i) the common binding of the TFs on promoter regions, the motifs and combinations of DNA motifs bound by the TFs on these bound regions, (i) the co-expression of the TFs, (iii) the target genes common to both TFs, and (iv) the co-evolution of amino acid residues between the two TFs forming a complex. The model will be obtained by machine learning on these data: the model will be built and its parameters adjusted to optimize the predictions against a set of TFs known to form complexes. Newly predicted interactions, in particular those between transcription factors studied in our lab and new partners, will be explored in detail to understand how these complexes form (interaction surface), how they bind DNA and to know which genes and functions they regulate. The results of the model will be represented in the form of an interaction network for all Arabidopsis thaliana TFs.. This network will be made available to the community so that biologists can in turn explore the potential partners of their favorite TFs. In the medium term, the model could be applied to other plant species such as rice and maize, two species characterized by extensive genomic data. This approach represents a considerable time saving compared to the genetic method and works even in the case where several TFs play a redundant role.