Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : COMPROP (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1980112.0 €

Social media can have an impressive impact on civic engagement and political discourse. Yet increasingly we find political actors using digital media and automated scripts for social control. Computational propaganda—through bots, botnets, and algorithms—has become one of the most concerning impacts of technology innovation. Unfortunately, bot identification and impact analysis are among the most difficult research challenges facing the social and computer sciences. COMPROP objectives are to advance a) rigorous social and computer science on bot use, b) critical theory on digital manipulation and political outcomes, c) our understanding of how social media propaganda impacts social movement organization and vitality. This project will innovate through i) “real-time” social and information science actively disseminated to journalists, researchers, policy experts and the interested public, ii) the first detailed data set of political bot activity, iii) deepened expertise through cultivation of a regional expert network able to detect bots and their impact in Europe. COMPROP will achieve this through multi-method and reflexive work packages: 1) international qualitative fieldwork with teams of bot makers and computer scientists working to detect bots; 2a) construction of an original event data set of incidents of political bot use and 2b) treatment of the data set with fuzzy set and traditional statistics; 3) computational theory for detecting political bots and 4) a sustained dissemination strategy. This project will employ state-of-the-art “network ethnography” techniques, use the latest fuzzy set / qualitative comparative statistics, and advance computational theory on bot detection via cutting-edge algorithmic work enhanced by new crowd-sourcing techniques. Political bots are already being deployed over social networks in Europe. COMPROP will put the best methods in social and computer science to work on the size of the problem and the possible solutions.

Mots-clés : political communication,socio computational systems
Projet ERC : BOTFIND (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149921.0 €

Social media platforms have become tools for manipulating public opinion during elections. In particular, “bots” are algorithms that automate rapid, widespread interactions with citizens, often with deleterious effects on public knowledge of science, social inequality, and public policy options. Through the ERC COMPROP Consolidator award, researchers have demonstrated that even simple bots (i) effectively keep negative messages and fake news in circulation longer (ii) target journalists and civil society groups and (iii) operate with little oversight from social media firms. Such algorithms have negative consequences both for public trust in technology innovation and for the quality of public deliberation in Europe’s democracies. ERC researchers have been able to identify highly automated, politically-manipulative social media accounts post hoc. This project will allow researchers to take what we have learned and produce an online tool that allows people to evaluate suspicious social media accounts. Most social media platforms are slow to address troll and bot activity, so this innovative tool will put ERC research into public service in Europe—and around the world.

Mots-clés : algorithms,bots,social media,civic engagement,trustworthy computing,automation,computational propaganda
Projet ERC : RELEVANCE (Horizon 2020, appel 2019, Synergy grants)
Entité participante : KU Leuven — Pays : Belgique — Rôle : CO-PI
Acronyme de l'entité : KUL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2806896.0 €

Social species, and specifically human and nonhuman primates, rely heavily on conspecifics for survival. Considerable time is spent watching each other’s behavior because this is often the most relevant source of information for preparing adaptive social responses. The project RELEVANCE aims to understand how the brain evolved special structures to process highly relevant social stimuli like bodies and to reveal how social vision sustains adaptive behaviour. This requires a novel way of thinking about biological information processing, currently among the brains’ most distinctive and least understood characteristic that accounts for the biggest difference between brains and computers. The project will develop a mechanistic and computational understanding of the visual processing of bodies and interactions and show how this processing sustains higher abilities such as understanding intention, action and emotion. Relevance will accomplish this by integrating advanced methods from multiple disciplines: psychophysics and high-field functional imaging in combination with virtual reality and neural stimulation in humans; electrophysiology with optogenetics and laminar recordings in monkeys. Crosstalk between human and monkey methods will establish homologies between the species, revealing cornerstones of the theory. In a radical departure from current practice, we will develop novel deep neural network models that unify the data. These models will not only capture detailed mechanisms of neural processing of complex social stimuli and its dynamics, but also reproduce the modulation of brain activity during active behavior. RELEVANCE will reveal novel ways of understanding and diagnosing social communication deficits in neuropsychiatry, and suggest novel hypotheses about their genetic basis. It will motivate novel principles and architectures for processing of socially relevant information in computer and robotic systems.

Mots-clés : social vision,body representation,high level vision,neural modeling
Projet ERC : CBTC (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Haifa — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1495091.0 €

Social-science explanations for rising wage inequality have reached a dead end. Most economists argue that computerization has been primarily responsible, while on the other side of the argument are sociologists and political scientists who stress the role of political forces in the evolution process of wages. I would like to use my knowledge and experience to come up with an original theory on the complex dynamics between technology and politics in order to solve two unsettled questions regarding the role of computerization in rising wage inequality: First, how can computerization, which diffused simultaneously in rich countries, explain the divergent inequality trends in Europe and the United States? Second, what are the mechanisms behind the well-known observed positive correlation between computers and earnings? To answer the first question, I develop a new institutional agenda stating that politics, broadly defined, mitigates the effects of technological change on wages by stimulating norms of fair pay and equity. To answer the second question, I propose a truly novel perspective that conceptualizes the earnings advantage that derives from computerization around access to and control of information on the production process. Capitalizing on this new perspective, I develop a new approach to measuring computerization to capture the form of workers’ interaction with computers at work, and build a research strategy for analysing the effect of computerization on wages across countries and workplaces, and over time. This research project challenges the common understanding of technology’s role in producing economic inequality, and would thereby significantly impact all of the abovementioned disciplines, which are debating over the upswing in wage inequality, as well as public policy, which discusses what should be done to confront the resurgence of income inequality.

Mots-clés : rising wage inequality,class politics,politics of production
Projet ERC : SuccessfulGovernance (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Utrecht University — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2019022.5 €

Societies cannot survive and thrive if they are not governed well. The public’s business – e.g. security and safety; health and well-being – needs to be managed effectively. Achieving this in the current era of connectivity, transparency, accountability and assertive, skeptical and empowered citizens deeply challenges the institutions of government, which were largely designed for a drastically different era. We urgently need to learn how we can govern societies successfully under the new circumstances. However, in both the popular and academic discourse, the focus is on the frailty and fallibility of our government institutions. We excel in explaining how policies fail, reforms falter, public money is wasted, public leaders are distrusted, and public institutions eroded. So much so that robust knowledge about the practices that produce good governance is hard to come by. And yet good governance is all around us, allowing us to learn from successes as well. This proposal seeks to address the imbalance. It offers a constructive, yet rigorous and systematic investigation of ‘success’ in 21st century governance. An innovative combination of theoretical perspectives, comparative approaches, and mixed methods is developed to answer five questions: 1. How is success in public governance defined and assessed by those who engage in it and those who experience it? 2. Why are some public policies enduringly successful? 3. Why are some public organizations enduringly successful? 4. Why are some interactive, collaborative governance initiatives enduringly successful? 5. How do these successful examples jointly contribute towards understanding the principles of a theory for governance success? The inquiry will increase our insight into the pivotal yet ill-understood phenomenon of governance that ‘works’. It will enrich the field with new methodologies, provoking the discipline to reconsider the emphasis on failure and undertake the systematic study of success.

Mots-clés : public management,governance,success,performance,legitimacy,endurance,social construction,group deliberation
Projet ERC : PUBLICGOOD (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Vrije Universiteit Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : VU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Societies that contain widespread cooperation can solve problems of public good provision and resource conservation, yet many societies fail to display the cooperation necessary to solve these problems. A puzzle facing the social sciences is understanding the origin of cross-societal variation in cooperation. Strikingly, multiple disciplines propose the same, not yet established, explanation: ecological conditions, such as subsistence, environmental hazards, and relational mobility, determine how people are interdependent (i.e. how actions affect own and others’ outcomes), and interdependence can be the mechanism through which diverse ecologies shape a culture of cooperation. For example, rice versus wheat production plausibly has led to more versus less dependence on others, which then led to different cultures (e.g. values, beliefs, and norms) that affect strategies of when and how people cooperate. I use a multi-discipline, multi-method approach to answer three questions about whether ecologies indeed create different interdependence, and how this leads to variation in culture and cooperation. Do ecologies create different kinds of interdependence? I measure the interdependence and cooperation people experience across different ecologies in 10 contemporary small-scale societies, among rice and wheat farmers in China, and in over 200 societies documented in the ethnographic record. Can interdependence cause differences in culture and cooperation? I use agent-based models and experiments to study how variation in interdependence can cause different norms of cooperation. Does variation in interdependence relate to culture and cooperation? I apply experience sampling to measure interdependence and cooperation in daily life across 35 societies that vary in culture. The ground-breaking innovation of this project is establishing interdependence as a common mechanism through which diverse features of the ecology shape cross-societal differences in culture and cooperation

Mots-clés : cooperation,interdependence,culture,ecology
Projet ERC : MAGIC (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : National University of Ireland — Pays : Irlande — Rôle : PI
Acronyme de l'entité : NUI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999085.0 €

Soft magnetoactive materials can change their properties and undergo extremely large deformations when excited by magnetic stimuli. These reconfigurable soft materials hold great potential for a large variety of applications from sensing devices to energy harvesting, noise and vibration mitigation, and soft robotics. However, these materials operate at high magnetic fields, thus, limiting potential application of the technology. A promising approach to significantly enhance the magnetomechanical performance, and reduce the required magnetic field, is to design soft magnetoactive composites through architectured microstructures. Highly ordered microstructures are an origin for multiscale magnetomechanical instabilities and possible failure of the materials. In this research proposal, we directly address this crucial aspect for MAE-based technology. Moreover, we declare an ambitious goal: Turning failure into functionalities. Our strategy is to take the risk of operating MAEs in the unstable regime with predesigned instability developments. This novel MAE design concept will capitalize on controllable cascade microstructure transformations while attempting to avoid catastrophic failure. If successful, this concept will open a new avenue in design of morphing magnetoactive materials with new functionalities and superior performance. To achieve this ambitious goal, we will develop multiscale theoretical and computational frameworks to reveal and to predict the behavior of possible advantageous microstructures in the extreme regimes. If successful, we will fill the gap in magnetomechanical multiscale instability phenomena, and will significantly advance the frontier of knowledge about the reconfigurable soft matter. We will probe our ideas experimentally, and will fabricate the revealed advantageous materials with engineered microstructures and properties. We envision revealing the fundamental multiphysics mechanisms of the multiscale magnetomechanical instabilities.

Mots-clés : soft materials,instabilities,magnetoactive materials,microstructures,3d printing
Projet ERC : chemech (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : École supérieure de physique et de chimie industrielles de la ville de Paris — Pays : France — Rôle : Partner
Acronyme de l'entité : ESPCI PARIS-PSL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 574920.0 €

Soft materials are irreplaceable in engineering applications where large reversible deformations are needed, and in life sciences to mimic ever more closely or replace a variety of living tissues. While mechanical strength may not be essential for all applications, excessive brittleness is a strong limitation. Yet predicting if a soft material will be tough or brittle from its molecular composition or structure relies on empirical concepts due to the lack of proper tools to detect the damage occurring to the material before it breaks. Taking advantage of the recent advances in materials science and mechanochemistry, we propose a ground-breaking method to investigate the mechanisms of fracture of tough soft materials. To achieve this objective we will use a series of model materials containing a variable population of internal sacrificial bonds that break before the material fails macroscopically, and use a combination of advanced characterization techniques and molecular probes to map stress, strain, bond breakage and structure in a region ~100 µm in size ahead of the propagating crack. By using mechanoluminescent and mechanophore molecules incorporated in the model material in selected positions, confocal laser microscopy, digital image correlation and small-angle X-ray scattering we will gain an unprecedented molecular understanding of where and when bonds break as the material fails and the crack propagates, and will then be able to establish a direct relation between the architecture of soft polymer networks and their fracture energy, leading to a new molecular and multi-scale vision of macroscopic fracture of soft materials. Such advances will be invaluable to guide materials chemists to design and develop better and more finely tuned soft but tough and sometimes self-healing materials to replace living tissues (in bio engineering) and make lightweight tough and flexible parts for energy efficient transport.

Mots-clés : fracture,mechanical properties,polymer physics
Projet ERC : FlowBot (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Dutch Research Council — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : NWO
Type d'entité : Recherche
Financement ERC de l'entité : 1498636.0 €

Soft robots have the potential to be more robust, adaptable, and safer for human interaction than traditional rigid robots. State-of-the-art developments push these soft robotic systems towards applications such as rehabilitation and diagnostic devices, exoskeletons for gait assistance, and grippers that can handle delicate objects. However, despite these exciting developments, there are two major challenges: (1) existing electronic control, intelligence and power of soft robots is too bulky to be embedded, and (2) the power efficiency of soft robots is extremely low. As a result, most existing soft robots still rely on tethers to deliver the required power and control. To increase the application perspective of soft robots, I aim to ‘cut the tethers’, with the ultimate goal to realise fully autonomous and smart soft robots that can interact with humans and complex environments. To do so, I propose to replace electronics with embedded fluidic circuits in a three step approach. First, I aim to achieve intelligent behaviour in soft robots by embedding smart fluidic circuits, that have integrated fluidic sensors to allow for feedback with the environment. Then, I will close the fluidic circuit to be able to reuse elastic energy that is currently lost after each actuation cycle, to dramatically increase the efficiency of soft robots. Finally, I will embed a reliable pressure source based on chemical fuel regulated by soft fluidic circuits. Taken together, I propose to develop new concepts to effectively power and control the behaviour of soft robots, and to achieve this I will develop new experimental standards, computational approaches, and generalise design principles. As a results, this research will pave the way for the first fully autonomous soft robots that are capable of operating for longer periods of time.

Mots-clés : embodied intelligence,soft robotics,non linear mechanics,optimisation,instabilities,control
Projet ERC : CoVeCe (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1407413.0 €

Software and hardware bugs cost hundreds of millions of euros every year to companies and administrations. Formal methods like verification provide automatic means of finding some of these bugs. Certification, using proof assistants like Coq or Isabelle/HOL, make it possible to guarantee the absence of bugs (up to a certain point). These two kinds of tools are crucial in order to design safer programs and machines. Unfortunately, state-of-the art tools are not yet satisfactory. Verification tools often face state-explosion problems and require more efficient algorithms; certification tools need more automation: they currently require too much time and expertise, even for basic tasks that could be handled easily through verification. In recent work with Bonchi, we have shown that an extremely simple idea from concurrency theory could give rise to algorithms that are often exponentially faster than the algorithms currently used in verification tools. My claim is that this idea could scale to richer models, revolutionising existing verification tools and providing algorithms for problems whose decidability is still open. Moreover, the expected simplicity of those algorithms will make it possible to implement them inside certification tools such as Coq, to provide powerful automation techniques based on verification techniques. In the end, we will thus provide efficient and certified verification tools going beyond the state-of-the-art, but also the ability to use such tools inside the Coq proof assistant, to alleviate the cost of certification tasks.

Mots-clés : automata,coq proof assistant,coalgebra,model checking,proof theory,kleene algebra,linear logic