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DEEP Learning For Large Deep Imaging Programs
In future large surveys like LSST or Euclid, high precision photometric redshift measurements and light curve classification will play a central role. We propose to revisit the current methods used to analyze these data by developing deep learning techniques that will directly use the multi-band images at the pixel level to measure a redshift or to perform a classification.
Inferring Folding Pattern Specific U-Bundle Atlases
The core structure of the short-range cortical connections is a set of U-shaped fiber bundles that circumvent the cortical folds. They have never been mapped at a large scale in the human brain, probably because of the large variability of the cortical folding patterns. We propose to leverage the emergence of a dictionary of the most frequent folding patterns to infer a specific U-bundle atlas for each such pattern.
natural Deep Eutectic solvents foR Microalgae bIorefinery in Cosmetic
The DERMIC project aims to study the potential of new solvents from renewable resources, deep eutectic solvents, for the development of microalgae biorefinery schemes. The goal being the valorization of polar and apolar metabolites of 3 biomasses, by proposing in a reduced number of steps, a maximum of ready-to-use ingredients for the cosmetic industries.
Parc national d'équipements innovants pour l'étude spatiale et temporelle de la Zone Critique des Bassins Versants
membres non inclus dans le consortium initial. En 2021, le meeting annuel de CRITEX a été couplé à celui [...] en support de l’équipement. 2021 a vu la signature et le financement du projet PIA3 [...] ateliers. Évènement fort de l’année 2021, l’école d’été d’OZCAR du coté de Barcelonnette
Agency for mathematics in interaction with enterprise and society
Mars 2021. - 1 SEME a été organisée : Troyes du 29 novembre au 3 décembre 2021. [...] Depuis le début du projet et jusqu'à fin 2021, 157 projets de recherche avec une entreprise ont [...] Détails des principaux programmes pour l'année 2021 : - 12 PEPS ont été attribués
Science and Engineering for Advanced Materials and devices
sélectionnés en 2021 (3). le début des projets de thèse cofinancés sélectionnés en 2021 impliquant [...] projets émergents sélectionnés en 2018 (1) et en 2021 (2). la continuité de projets de thèse [...] thèse cofinancés sélectionnés en 2020 (3) et 2021 (4), un projet de thèse hors AAP, entre deux laboratoires
Rational design of chemosensory compounds targeting smell, taste and emotion
Rational design of chemosensory compounds targeting smells, tastes and emotions
Knowledge Delta based improvement and continuous evaluation of retrieval engines
To propose a framework to handke continuous evluation of search engines.
Exploring the dynamics of X-linked gene expression in female B cells: implication in humoral immunity to influenza virus
The response initiated by TLR7 activation in innate immune cells and B cells is an essential line of defense against RNA viruses. We propose that ICX escape from TLR7 plays a major role in innate and adaptive immunity against certain pathogens such as RNA viruses, as well as in protective immunity in vaccination against influenza virus.
Learning Reasoning, Memory and Behavior
We will focus on methodological contributions (models and algorithms) for training virtual and real agents to learn to solve complex tasks autonomously, targeting terrestrial mobile robots, typically service robots; industrial cobotics; autonomous vehicles; UAVs; humanoid robots. In particular, intelligent agents require high-level reasoning capabilities, situation awareness, and the capacity of robustly taking the right decisions at the right moments. The required behavior policies are complex, since they involve high-dimensional input spaces and state spaces, partially observed problems, as well as highly non-linear and entangled interdependencies. Learning them crucially depends on the algorithm’s capacity of learning compact, structured and semantically meaningful memory representations, which are able to capture short and long range regularities in the task and the environment. A second key requirement is the ability to learn these representations with a minimal amount of human interventions and annotations, as the manual design of complex representations is up to impossible. This requires the efficient usage of raw data through the discovery of regularities by different means: supervised, unsupervised or self-supervised learning, through reward or intrinsic motivation etc.
