Despite the tremendous interest of the scientific community on photovoltaic solar cells based on hybrid perovskites, many physical phenomena are still not fully understood and subject of controversy, such as the role of electrode/perovskite contact quality. The HYPERSOL project intends to introduce new solutions to improve the interface quality of hybrid perovskites solar cells in order to pin the quasi Fermi levels at Ec and Ev so as to extend the open circuit voltage at the thermodynamic limit for a single junction. Because the Voc in such devices is currently between 1 and 1.1V, we can expect an increase of about 30% in the PCE compared to current devices. To this aim, new dopants and customized self-assembled monolayers will be synthesized and introduced into state-of-the-art devices. Advanced characterization techniques will be used to construct a physical model allowing a complete description of the physics of hybrid perovskites solar cells and their optimization.

In a society where identity theft is a criminogenic phenomenon that threatens all sectors of activity, with considerable economic impact, people identification is of great importance. The objective of this project is to develop innovative secure printing solutions for visual authentication of physical ID documents to prevent their counterfeiting and forgery. SLICID aims at developing a competitive solution based on a combination of academic and industrial expertise. This solution is based on the elaboration of a specific wrinkled multilayer stack of inorganic materials inserted inside the ID document. The functionalities of this stack will be to provide highly contrasted and angle-controlled colors after laser processing. SLICID relies on different breakthroughs and innovations: (1) laser texturing of an inorganic multilayer on a PC sheet to get specific scattering patterns; (2) obtaining highly saturated colors, written by laser, from embedded metallic nanoparticles in an optical interference coating; (3) laser printing of multiplexed images to display different images independently in the different scattering directions; (4) elaboration of numerical tools to simulate the electromagnetic response of nonplanar disordered ensembles of nanoparticles in nonplanar layered environments. The proposed technology allows complete customization that will define a new level of security for ID documents. To reach this goal, SLICID gathers academics and companies with complementary expertise. It will benefit from the know-how and expertise of LabHC in color management, laser-matter interaction, plasmonics and image processing, of HID in card elaboration, laser processing and security documents, of Institut Fresnel in laser processing, multiphysical modeling and scattering measurements, of ILM in electromagnetic modeling and color rendering, and of IREIS in thin film deposition and simulations for optical applications.

Imagine you have to answer the following questions: how to build a computer-aided diagnosis tool for neurological disorders from images acquired from different medical imaging devices? that could identify which emotion is feeling a person from her face and her voice? How could these tools be still operational even when some data of a type is missing and/or poor quality? These questions are at the core of some problems addressed by the Institut de Neurosciences de la Timone (INT), where people have expertise in brain imaging based medical diagnosis, and Picxel, a SME centered on affective computing. The Laboratoire d'Informatique de Paris 6 (LIP6), the Laboration Hubert Curien (LaHC), and the Laboratoire d'Informatique Fondamentale de Marseille (LIF, head of the PI) are the other partners that are teaming up with INT and Picxel: in this project, they provide their renowned knowledge in machine learning, wherein they have developed, theoretical, algorithmic, and practical contributions. The five partners will closely work together to propose original and innovative advances in machine learning with a constant concern to articulate theoretical and applicative findings. The above questions pose the problem of (a) building a classifier capable of predicting the class (i.e. a diagnosis, or an emotion) of some object, (b) that of taking advantage of the few modalities or *views* used to depict the objects to classify and, possibly (c) that of building relevant representations that take advantages of these views. This is precisely what the present project aims at: the development of a well-founded machine learning framework for learning in the presence of what we have dubbed *interacting views*, and which is *the* notion we will take time to uncover and formalize. To address the issues of multiview learning, we propose to structure as follows. On the one hand, we will devote time to establish when and how classical (i.e. monoview-based) learnability results carry over to the multiview setting (WP1); this may require us to brush up on our understanding of different notions and accompanying measures of interacting views. On the other hand, possibly building upon the results just mentioned, we will build new dedicated multiview learning algorithms, according to the following lines of research: a) we will investigate the problem of learning (compact) multiview representations (WP2), then b) we will create new algorithms by leveraging some recent works on transfer learning -- multitasks and domain adaptation -- to the multiview setting (WP3), and, c) we will address the scalability of our algorithms to real-life conditions, such as large-dimension datasets and missing views (WP4). Finally, the performances of our learning algorithms will be assessed on benchmark datasets, both synthetic and real, that we will collect and make available for the machine learning community (WP5). Beyond the mere evaluation of our algorithms, these dataset will be disseminated to promote reproducible research, to identify the most suitable algorithms in a multiview setting, and to make the machine learning community aware of the exciting problems of multiview learning for affective computing and brain-image analysis.

The project consists in studying the arguments used by the Christian Churches (both Catholic and Protestant) to defend religious truths in contemporary Western societies, from the Enlightenment to the 21st century. This study mainly focuses on France, Italy, Germany, the United Kingdom and Spain, without refraining from occasionally taking advantage of US examples. The use of the word apologetics for describing a general system of response to the challenges of the times is quite recent (mid-nineteenth century/mid-twentieth century), and it allows to identify a specific period of reconfiguration of the relations between society and religion. The appearance of the word apologetics is largely prepared upstream, since Christian apologies changed at the turn of the 17th and 18th centuries, and its rarefaction and its exclusion from the ecclesiastical field since the mid-20th century is then the manifestation of a new reconfiguration. The understanding of these scansions allows to grasp, according a long term historical analysis, the evolutions or the tensions of the believing discourse in its relations to modernity. Such an investigation first requires to approach ecclesial discourse from within. This approach is based on an abundant body of sources. However, the project mainly aims at using this corpus as a mirror of the changes experienced by contemporary Western societies: essentially "reactive", that is to say first of all defensive, apologetics is primarily a confrontation with all the difficulties that arise from the progressive transformation of the status of beliefs in contemporary societies. From this point of observation, it is possible to note that the difficulties, the blocking points (the opposition with philosophy, the “exact” or “social” sciences, the evolution of mores, etc.), allow to draw up an inventory of the characteristics of modernity in its relations with the Christian religion.

This research intends to study the response of different organizations to the Covid-19 pandemic, comparing the period from March to May 2020 with the period beginning in October 2020. It will focus on three sets of organizations: 1) Government and central administrations, 2) regional and local institutions, 3) the (socio-)health sector. The interviews conducted will be organized around three thematic entries that will help us to better understand the relationships between the different actors and organizations: 1) protection and prevention measures (masks, lock-down, curfew, isolation and physical distancing) ; 2) the organization of tests and screening (availability and choice of tests, contact cases, applications); 3) the management of patients and populations at risk (in hospital, at home, respiratory equipment, treatments, transportation). This research aims to uncover and analyse the capacities of these organisations to transform themselves or not in a period of uncertainty, by favoring an approach centered on collective action (to analyse the forms of cooperation or conflict that arise during crisis management) and a cognitive approach (which looks at the way in which actors make sense of the crisis and legitimize their actions). By comparing two periods, we will seek to see whether the capacities for cooperation differ according to whether the situation is marked by a high degree of uncertainty, urgency and extraordinary functioning; or, on the contrary, a better knowledge of the risks, less time pressure and a return to ordinary functioning. The goal will be to produce, in addition to fundamental knowledge about organizations in crisis situations, an analysis shared with the actors involved in the management of the crisis in these different organizations, with a view to collective learning.