How predictable is evolution? Do species respond in the same way to similar environmental pressures? The question of repeatability is crucial in evolutionary biology because it can inform on the mechanisms generating and shaping diversity and can shed light on how organisms might react in the face of future changes. We propose to investigate the repeatability of the genomic and phenotypic bases of speciation, the ultimate process underlying biodiversity. To this end, we take advantage of the exceptional potential for natural replication offered by a ‘suture zone’, a narrow region where multiple racial hybrid zones of butterfly species coincide at a strong environmental gradient between the Amazon and the Andes. For eight of these species, we will: (1) quantify divergence for ecologically important traits (wing colour pattern, sexual pheromones, microhabitat, hostplants) and measure the contribution of these traits to reproductive isolation, using experimental approaches; (2) quantify genome-wide patterns of divergence and the genetic architecture of reproductive isolation, based on genomic scans; (3) identify the genetic bases of trait variation, using genetic mapping (crosses and association mapping) and genome-wide gene annotation on two de novo assembled reference genomes; (4) analyse the relationships between divergence and reproductive isolation in a comparative framework and compare our findings with those obtained in other Lepidoptera, particularly mimetic butterflies Our unique combination of cutting-edge methods in a comparative framework will provide important advances in our understanding of speciation by revealing general patterns and singularities in the speciation process. The project will also provide a large-scale test of existing models of speciation and will contribute to identifying the genomic regions underlying key phenotypic traits.

The goal of this project is to develop a new generation of deep learning based methods that can create accurate digital 3D replicas of clothed people from a single color image captured with a consumer grade camera. These replicas can take the form of an explicit colored 3D reconstruction of the subject, or an implicit function encoding their 3D shape and appearance, that can be subsequently tessellated into an explicit 3D model or rendered from novel view-points. This research has promissing applications in human 3D content generation and immersive video-conferencing. We propose original solutions to the existing methods' limitations, namely their memory and computational inefficiency, lack of generalization and heavy dependence on expensive full 3D supervision.

Billion of connected devices are announced in 2020. This could cause a major revolution in ambiant intelligence if we can formulate appropriate architectures to process the massive data that will be produced or required by these devices. These last years, the question of the most adequate architecture for intelligent systems based on connected devices was intensively studied. Most of the proposed solutions were based on centralized models. Here, connected devices send data to a central platform (typically a cloud) where are implemented the services that process the data. Once the processing is done, results are sent, if needed, to the referred connected device. There exists several applications where the centralized models were successfully applied. Despite these successes, Internet of Things experts (Gartner, IBM etc.) say that these solutions are not sustanaible. Indeed, it is hard to manage network contention, confidentiality or real-time processing in centralized architectures. One of the most promising alternatives to these models consist of realizing the processing required by a set of connected devices by the devices themselves. The challenge then is to coordinate the set of devices in an environment for the realization of the required computations. Doing so, we build what we call cloud of things. Such systems are nowadays possible because of the increasing computing power of connected devices. In addition, clouds of things have interesting advantages on confidentiality, reactivity and energy consumption. The goal of the GRECO project is to develop a reference resource manager for cloud of things. The manager should act at the IaaS, PaaS and SaaS layer of the cloud. One of the principal challenges here will consist in handling the execution context of the environment in which the cloud of things operate. Indeed, unlike classical resource managers, connected devices imply to consider new types of networks, execution supports, sensors and new constraints like human interactions. The great mobility and variability of these contexts complexify the modeling of the quality of service. To face this challenge, we intend to innovate in designing scheduling and data management systems that will use machine learning techniques to automatically adapt their behavior to the execution context. Adaptation here requires a modeling of the recurrent cloud of things usages, the modeling of the physical cloud architecture and its dynamic. The GRECO project is built upon a collaboration between an enterprise (Qarnot Computing) and two French research institutes: the «Laboratoire d'Informatique de Grenoble (LIG)» and the «Institut National de recherche en informatique et automatique (Inria)». In the project, the LIG will bring its expertise in the design of schedulers for large systems. Inria will contribute in the design of a data management system for massive data. Qarnot will offer the expertise it gained in designing a resource manager for its network of digital heaters. This network will also be used for the validation of the project. However, the proposed solutions should be interoperable: they must be usable to build other systems like Edge/Extreme edge computing systems.

Repeated adaptation in related lineages to similar environmental conditions could result from natural selection acting independently in each lineage, or from adaptive introgression between lineages during periods of range overlap. Here we focus on a complex of butterfly species distributed along the altitudinal gradient, and with different histories of altitudinal adaptation, to understand how populations adapt to higher elevation. We will analyse genomes of butterflies in contact zones to identify introgressions and rearrangements between taxa, i.e. regions more or less permeable to gene flow, and associate them with adaptive phenotypic variation. By using hybrid taxa originating from ancient hybridization, we will untangle the effects of genome-wide differentiation due to allopatry and demography from those of selection on genes involved in local adaptation and reproductive isolation. We will reveal to what extent these genes were exchanged between lineages. To understand how the key traits conferring altitudinal adaptation are shared by introgression among alpine lineages or act as barriers to gene flow, we will use admixed populations in contact zones, taking advantage of a natural recombination experiment allowing the segregation of the phenotypic traits characterising each taxon through many generations of recombination. This will allow linking traits and candidate genes with adaptation to changes in climatic and biotic conditions with altitude. This system offers an excellent opportunity to decipher the processes involved in adaptation to new conditions along the altitudinal gradient, and identify the key traits and candidate genes involved. This project will mobilise forces from thres distinct labs with expertise in bioinformatics, population genomics, ecology, and experimental approaches.trogressions with adaptation to changes in climatic and biotic conditions with altitude.