Ampli aims at generating huge virtual worlds using machine learning and inverse procedural methods.

https://projet.liris.cnrs.fr/ampli/

This project concerns privacy-preserving machine learning approaches forpersonal heath data.

La finalité du projet est de développer et combiner des approches en « apprentissage machine » (Machine Learning) et en sciences des données (Data Analytics, Data Science), avec un objectif d’apprentissage de modèles complexes dans deux domaines scientifiques importants et porteurs d'enjeux globaux : la compréhension et la modélisation des mécanismes fondamentaux du climat, et la compréhension quantitative de mécanismes clefs en sciences sociales.

In numerous fields (e.g., material sciences, medical imaging), non-invasive acquisition devices such as magnetic resonance, X-ray tomography or microtomography are required for observation, measurements or diagnostic aids. These acquisition devices usually generate volumic data, i.e., 3D images, composed of regularly spaced data in a cuboidal domain. 3D volumes come from the segmentation of such 3D images. They can also be generated from scientific modelling because numerous simulation schemes rely on the regularity of the data support.

The main objective of ESMARTCITY is the improvement of the innovation capacity of the cities in the MED region by creating innovative ecosystems, involving Quadruple Helix actors. To this end, the project aims to pilot the ideas of the Smart City, using digital technologies and energy efficiency technologies to provide better services to the citizens with less environmental impact.

The Internet of Things connects physical devices offering sensing or actuating with their vicinity. The ever-growing capabilities of devices allow to imagine new architectures including them as first class citizens. New added-value applications can then be envisioned in smart agriculture, smart buildings, smart cities, energy and water management, e-health and ageing well...