Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : HUNTING (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Wageningen University & Research — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : WUR
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1376377.06 €

Data-driven political campaigns are on the rise. Concerns have been voiced that practices like online political microtargeting techniques are harmful for democracy. These concerns grew after the unexpected outcome of the US presidential elections in 2016, the Brexit vote in the UK, and several recent elections in Europe. However, it is unclear if data-driven campaigns using online microtargeting techniques are an actual threat to democracy. The project will focus on the consequences of data-driven targeting and digital persuasion. In light of ongoing political and societal turmoil, investigating how citizens may be persuaded in a changing media landscape has never been of more importance. The overarching objective of this project is to identify the conditions and the extent to which data-driven online political microtargeting affect citizens’ attitudes and opinions, and eventually voting behavior. The project addresses three research questions: To what extent and under which conditions does data-driven political targeting have a beneficial impact, and under which conditions a harmful impact on democracy? And how can the beneficial effects trump the harmful effects? The project is novel as it systematically analyses the impact of data-driving campaigning, providing a theoretical dual-processing model, while using a mixture of research methods and a comparative perspective. As a whole, the project will offer a deeper understanding of the global impact of online data-driven targeting techniques during elections in several countries.

Mots-clés : political communication,political science,microtargeting,data driven campaigns,elections,effects,persuasion,social media
Projet ERC : InPhoTime (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
Entité participante : Czech Academy of Sciences — Pays : Rép. Tchèque — Rôle : PI
Acronyme de l'entité : AVČR;CAS
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

Daylength measuring devices such as the photoperiodic timer enable animals to anticipate and thus survive adverse seasons. This ability has contributed to the great success of insects living in temperate regions. Yet the basis of photoperiodic sensing remains elusive, because of the lack of suitable genetic models expressing photoperiod-dependent seasonal phenotypes. We have developed the linden bug, Pyrrhocoris apterus, into a genetically tractable model with a robust, photoperiod-dependent reproductive arrest (diapause). With the available tools, this insect has become ideal for deciphering the regulation of seasonality. The project has 3 clear and ambitious objectives: 1). Our goal is to define the molecular and anatomical bases of the photoperiodic timer. To achieve this, we propose to identify photoperiodic timer genes, genes regulating input to the timer, and early output markers, through an RNA interference screen(s). To define the molecular mechanism of the timer, we will employ genome editing to precisely alter properties of the key players. 2). Next, we will combine techniques of neuronal backfilling, in-vivo fluorescent reporters, and microsurgery to define the photoperiodic timer anatomically and to examine its spatial relationship to the circadian clock in the insect brain. 3). We will exploit the great natural geographic variability of photoperiodic timing in P. apterus to explore its genetic basis. Genetic variants correlating with phenotypic differences will be causally tested by genome editing within the original genetic backgrounds. Both the established and the innovative strategies provide a complementary approach to the first molecular characterization of the seasonal photoperiodic timer in insects. The proposed research aspires to explain mechanisms underlying the critical physiological adaptation to changing seasons. Deciphering mechanisms underpinning widespread adaptation might bring general implications for environment-friendly pest control.

Mots-clés : chronobiology,diapause,circadian clock,seasonality
Projet ERC : INFLUENCE (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Liverpool — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Decision-theoretic sequential decision making (SDM) is concerned with endowing an intelligent agent with the capability to choose actions that optimize task performance. SDM techniques have the potential to revolutionize many aspects of society and recent successes, e.g., agents that play Atari games and beat a world champion in the game of Go, have sparked renewed interest in this field. However, despite these successes, fundamental problems of scalability prevents these methods from addressing other problems with hundreds or thousands of state variables. For instance, there is no principled way of computing an optimal or near-optimal traffic light control plan for an intersection that takes into account the current state of traffic in an entire city. I will develop one in this project. To achieve this, I will develop a new class of influence-based SDM methods that overcome scalability issues for such problems by using novel ways of abstraction. Considered from a decentralized system perspective, the intersection’s local problem is manageable, but the influence that the rest of the network exerts on it is complex. The key idea is that by using (deep) machine learning methods, we can learn sufficiently accurate representations of such influence to facilitate near-optimal decisions. This project will construct a theoretical framework for such approximate influence representations and SDM methods that use them. Scalability of these methods will be demonstrated by rigorous empirical evaluation on two simulated challenge domains: traffic lights control in an entire city, and robotic order picking in a large-scale autonomous warehouse. If successful, INFLUENCE will produce a range of influence-based SDM algorithms that can, in a principled manner, deal with a broad range of very large complex problems consisting of hundreds or thousands of variables, thus making an important step towards realizing the promise of autonomous agent technology.
Projet ERC : Gen-Epix (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : University of Edinburgh — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499717.0 €

Decoding of the genome during development and differentiation depends on sequence-specific DNA binding proteins that regulate transcription. The activity of transcription factors is constrained, however, by chromatin structure and by modification of histones and DNA, known collectively as the “epigenome”. Diseased states, particularly cancers, are often accompanied by epigenomic disturbances that contribute to aetiology, but despite much research the molecular determinants of chromatin and DNA marking remain poorly understood. A widespread view is that the epigenome responds to developmental decisions or environmental impacts that are memorised by the epigenetic machinery. Complementary to this “memory” hypothesis, there is evidence that the epigenome can directly reflect the underlying DNA sequence. We aim to explore genetic determinants of the epigenome based on our over-arching hypothesis that chromatin structure is influenced by the interaction of DNA binding proteins with short, frequent base sequence motifs. Prototypes for this scenario are proteins that bind to the two base pair sequence CpG. These proteins accumulate at CpG islands (CGIs), which are platforms for gene regulation, where they recruit multi-protein complexes that lay down epigenetic marks. By identifying and characterising novel DNA-binding proteins that sense global properties of the DNA sequence (e.g. base composition), we will address several major unanswered questions about genome regulation, including the origin of global DNA methylation patterns and the causal basis of higher order chromosome structures. Our research programme will advance genome biology and shed light on the role of epigenetic signalling in development. In particular it will explore the extent to which the epigenome is “hard-wired” by genes, with important implications for health.

Mots-clés : epigenetics,dna base composition,dna methylation
Projet ERC : BUILDUP (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Groningen — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : RUG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1902235.0 €

Deep galaxy surveys are the most valuable asset to understand the history of our Universe. They are key to test galaxy formation models which, based on the Cold Dark Matter framework, are successful at reproducing general aspects of galaxy evolution with cosmic time. However, important discrepancies still exist between models and observations, most notably at high redshifts. This Project will reconstruct the history of galaxy buildup from the first billion years of cosmic time through the peak activity epoch of the Universe, which occurred 10 billion years ago, providing a fundamental constraint for galaxy formation models. I am leading the largest ultra-deep galaxy survey that will ever be conducted with the Spitzer Space Telescope. In this Project, I will exploit my new Spitzer program to do a groundbreaking study of galaxy buildup in the young Universe, paving the way for further galaxy evolution studies with the forthcoming James Webb Space Telescope (JWST). My main objectives are: 1) quantifying galaxy stellar mass assembly beyond the peak activity epoch, through the study of the galaxy stellar mass function up to z~7; 2) measuring, for the first time, galaxy clustering with stellar mass information up to such high redshifts; 3) linking galaxy growth to dust-obscured star formation using Spitzer and new APEX/AMKID sub-millimetre data; 4) unveiling the first steps of galaxy buildup at z>7 with JWST; 5) optimizing the official JWST Mid Infrared Instrument (MIRI) data reduction pipeline for the analysis of deep galaxy surveys. The delivery of an optimized MIRI pipeline is an important added value to the scientific outcome of this Project, which will benefit the general Astronomical community. This is the right time for this Project to make a maximum impact. We are now in a turning point for IR Astronomy, and this opportunity should not be missed. This Project will have a long-lasting legacy, bridging current and next generations of IR galaxy surveys.

Mots-clés : galaxy surveys,infrared galaxies,high redshifts
Projet ERC : RED (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Technical University of Darmstadt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999814.0 €

Deep learning approaches, mostly in the form of convolutional neural networks (CNNs), have taken the field of computer vision by storm. While the progress in recent years has been astounding, it would be dangerous to believe that important problems in computer vision are close to being solved. Many canonical deep networks for vision tasks ranging from image understanding to 3D reconstruction or motion estimation perform incredibly well "on dataset", i.e.~in the very setting in which they have been trained. The generalization to novel, related scenarios is still lacking, however. Moreover, large amounts of labeled data are required for training, which are not available in all potential application areas. In addition, the majority of deep networks in computer vision show deficiencies in terms of explainability. That is, the role of network components is often opaque and most deep networks in vision do not output reliable quantifications of the uncertainty of the prediction, limiting the comprehension by users. In this project, we aim to significantly advance deep networks in computer vision toward improved robustness and explainability. To that end, we will investigate structured network architectures, probabilistic methods, and hybrid generative/discriminative models, all with the goal of increasing robustness and gaining explainability. This is accompanied by research on how to assess robustness and aspects of explainability via appropriate datasets and metrics. While we aim to develop a toolbox that is as independent of specific tasks as possible, the work program is grounded in concrete vision problems to monitor progress. We specifically consider the challenges of 3D scene analysis from images and video, including tasks such as panoptic segmentation, 3D reconstruction, and motion estimation. We expect the project to have significant impact in applications of computer vision where robustness is key, data is limited, and user trust is paramount.

Mots-clés : computer vision,deep learning,robustness,explainability,3d scene analysis,image and video understanding,structured networks,uncertainty estimation,bayesian methods,hybrid models,datasets and metrics
Projet ERC : OptEIon (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Zürcher Fachhochschule — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ZFH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1980735.0 €

Defects in semiconductor materials commonly deteriorate the performance of optoelectronic devices such as solar cells and light-emitting diodes. In the recently emerged and highly successful hybrid metal halide perovskite, some lattice defects are even mobile leading to mixed ionic-electronic conductivity. This and other outstanding properties (tunable bandgap, lower dimensional embodiments, solution processability) make the perovskite a very interesting material for research and application. At the same time, it suffers from various degradation processes, linked to these poorly understood ionic defects. The major questions are: Where and what are these defects? How are they formed and how can we control their movement? OptEIon will provide answers to these questions. Based on my expertise in the device physics and experience in perovskites I will proceed as follows: First, I will characterize the transient response of devices with different perovskite materials, different stoichiometry, partial pressure of constituents, temperature, etc. to find clear evidence for the nature of the mobile defects and their diffusion constant. Second, I will employ nano-scale characterization on cross sections of working devices to measure location and time evolution of defects causing recombination losses in solar cells. In addition to established measurement techniques, I will use tip-enhanced (near field) spectroscopic techniques, which can provide super-resolution imaging. Third, I will apply device simulation to examine the measurement results. I will furthermore evaluate how machine learning in combination with our physical model could be implemented to help analyse device data. Fourth, I will exploit the results by fabricating demonstrator memristor arrays that can be controlled by light. The outcome will be more efficient and stable solar cells and novel optoelectronic devices such as memristors, which are supposed to herald a new era of neuromorphic computing.

Mots-clés : perovskite,defects,ionic electronic conductivity
Projet ERC : INTRACYTE (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Ghent University — Pays : Belgique — Rôle : PI
Acronyme de l'entité : UGent
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Delivering macromolecular compounds into living cells in vitro or ex vivo is a common requirement for both fundamental biological research as well as biomedical applications. Examples include the intracellular delivery of gene editing molecules to unravel molecular pathways or to create engineered therapeutic cells. Irrespective of the application, the common challenge is to combine efficient intracellular delivery with flexibility, high throughput and low cytotoxicity. Photoporation, which combines laser irradiation with light-sensitive nanomaterials, is an upcoming physical intracellular delivery technology that offers these benefits. Over several years of research in the principal investigator’s group, photoporation has proven its worth to deliver a variety of molecules into a broad range of cell types, including primary hard-to-transfect cells. Offering high delivery efficiency, low cytotoxicity, single cell precision and minimal hands-on time, it holds great promise to become the next generation of intracellular delivery technologies. Having developed prototype devices which already have been validated by several independent partners, the time has come to fully explore the technology's market potential. The first aim of the INTRACYTE project is to further confirm interest from the academic and industrial R&D market by installing the technology at selected international Key Opinion Leaders (KOLs) for an extensive validation in variety of topics. The second aim is to explore the cell therapy market by initiating discussions with interested companies in order to fine-tune the technology’s value proposition and to define technical requirements for use in a cell production facility. Thirdly, upscaling of nanomaterial production will be investigated, apart from the design of a low-cost bench-top photoporation instrument. Together this should lay the foundation bring this exciting new technology to the market within the next 12 months.

Mots-clés : drug delivery,cell therapy,transfection,intracellular delivery,biophotonics,valorization,spin off
Projet ERC : PRODEMINFO (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : Graz University of Technology — Pays : Autriche — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 76056.25 €

Democracy is under threat. Populist movements—mainly but not exclusively on the political right—have pitted "the people'' against a presumed "elite'' that is variously constructed to include the judiciary, mainstream media, politicians, experts, and academics. PRODEMINFO builds on the realization that factual inaccuracy can be a political asset rather than a liability: Populist politicians may state obvious falsehoods to signal disregard for the "establishment" norm of honesty, thus identifying themselves as authentic champions of "the people." The rebranding of inaccuracy as authenticity reflects a profound shift in the underlying ontology of truth: Whereas liberal democracy is tied to a realist ontology of truth and a shared body of accepted knowledge, the populist ontology of truth is entirely constructivist and subjective. Understanding, documenting, and then addressing this ontological shift is crucial because it drastically changes the way in which one might confront misinformation—there is little point in correcting falsehoods if facts are not considered a relevant attribute of public discourse. PRODEMINFO will build tools to protect democracy in Europe by drawing a strategic arc from the threat arising from the insurgent populist ontology of truth to the development of protective solutions that can be rolled out at scale online. The objectives of PRODEMINFO are (1) to understand contemporary misinformation in Europe and how it connects to people's tacit ontology of truth; (2) to develop countermessages based on inoculation theory that are sensitive to people's different ontologies (e.g., addressing authenticity rather than accuracy of political speech); and (3) to test such countermessages at scale on social media. PRODEMINFO will pursue these objectives by combining controlled behavioural and cognitive experimentation with "big data'' analyses of social media, simulation modelling of behaviour online, and text modelling of large corpora.

Mots-clés : misinformation and its correction,populism,democracy
Projet ERC : PRODEMINFO (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : University of Bristol — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1454825.79 €

Democracy is under threat. Populist movements—mainly but not exclusively on the political right—have pitted "the people'' against a presumed "elite'' that is variously constructed to include the judiciary, mainstream media, politicians, experts, and academics. PRODEMINFO builds on the realization that factual inaccuracy can be a political asset rather than a liability: Populist politicians may state obvious falsehoods to signal disregard for the "establishment" norm of honesty, thus identifying themselves as authentic champions of "the people." The rebranding of inaccuracy as authenticity reflects a profound shift in the underlying ontology of truth: Whereas liberal democracy is tied to a realist ontology of truth and a shared body of accepted knowledge, the populist ontology of truth is entirely constructivist and subjective. Understanding, documenting, and then addressing this ontological shift is crucial because it drastically changes the way in which one might confront misinformation—there is little point in correcting falsehoods if facts are not considered a relevant attribute of public discourse. PRODEMINFO will build tools to protect democracy in Europe by drawing a strategic arc from the threat arising from the insurgent populist ontology of truth to the development of protective solutions that can be rolled out at scale online. The objectives of PRODEMINFO are (1) to understand contemporary misinformation in Europe and how it connects to people's tacit ontology of truth; (2) to develop countermessages based on inoculation theory that are sensitive to people's different ontologies (e.g., addressing authenticity rather than accuracy of political speech); and (3) to test such countermessages at scale on social media. PRODEMINFO will pursue these objectives by combining controlled behavioural and cognitive experimentation with "big data'' analyses of social media, simulation modelling of behaviour online, and text modelling of large corpora.

Mots-clés : misinformation and its correction,populism,democracy