Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : DREAM (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
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 : 1855600.0 €

DREAM, Drafting and Enacting the Revolutions in the Arab Mediterranean, seeks to write the history of the revolutions in the Arab Mediterranean since the independences. It aims to write a transnational history of often forgotten struggles, recall facts and original forms of resistance. We know very few about the revolts that occurred in this period, and even less about the memory that they left in the societies, the way these memories circulated. This rediscovery of revolutions in the shadows must be done through the collection of original material, specifically “poor archives” of the ordinary and the production of Archives – through a combination of classical interviews and innovative methods that involve researchers, archivists, artists and the actors themselves. The objective is to write a history that focuses on emotions and paths of revolts, telling us more about the link between all dimensions of human lives in these territories (religion, gender, social positions) and the articulation of these dimensions in the revolutionary projects. DREAM aims to write a history that doesn’t produce heroes or big figures, doesn’t discuss success or failure, but tries to understand the motivations and the potentialities that were at stake in different episodes and moments, during the uprisings and in between them. It aims to explore the historical signification and the concrete aspects of the call for dignity (Karama/sharaf) in a space that, after liberating itself from the colonial domination, was trapped into the illusion of a common faith (being it the Arab nation or the Islamic umma) and the concrete oppression of authoritarian regimes. This period needs urgently to be explored and history, with its modern tools and patterns, can embrace and trace the particular conditions in which Arab people lived for more than six decades, and specifically the frames of their dreams and projections.

Mots-clés : arab world,social history,revolutions
Projet ERC : DYBBUK (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Haifa — Pays : Israël — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 278320.0 €

DYBBUK will uncover and explore the popular theatre that made up the daily cultural reality of the Jewish masses at the turn of the 20th century. It will crack open the textual body of remaining musical and dramatic manuscripts, to the complete performances of this popular theatre. Amid a massive migration movement at the turn of the 20th century, the Yiddish popular theatre prospered, producing a staggering volume of entertainment consumed by millions of Jews. Most of this mass-appealing output was delegitimized by Jewish intellectuals as Shund (trash), a term that encompasses primitive, plagiarized, vulgar, and worthless art. Consequently, a valuable component of modern popular culture has been overlooked. DYBBUK will restore this neglected yet highly influential corpus of Yiddish popular theatre and make it available for our appreciation. Revising canonical cultural paradigms, DYBBUK recognizes delegitimized artistic ‘lowbrow’ theatre as a vital component in the fashioning of modernist culture. Focusing on the unexplored theatrical corpus of the two most prolific Shund authors: Moyshe Hurwitz (1844-1910) and Joseph Lateiner (1853-1935), this project offers an integrative typological, embodied, and theoretical analysis of theatrical themes, forms, and practices. The Yiddish popular theatre played a crucial role in transporting cultural styles, ideas, and products. To untangle and understand the complexity and scope of this theatre, this project devises a groundbreaking analytical and embodied toolkit for studying theatre history. We will incorporate into our analysis resources of various media, and we will reenact a forgotten Shund performance in a practice-based research. In doing so, DYBBUK will transform our understanding of popular theatre, revealing it as a preeminent ethnographic and historical source for examining the sensual and experiential dimensions of theatre, and the theatrical and cultural exchange of minorities with mainstream culture.

Mots-clés : jewish culture,european and american popular culture,popular theatre,modernism,jewish folklore,practice based theatre research,theatre history and historiography,yiddish theatre,yiddish language and culture,jewish immigration,performance analysis,music theatre
Projet ERC : SCALAR (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Twente — Pays : Pays-Bas — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 466182.34 €

Damage associated with climate change is a core benchmark in science and policy. Macro Integrated Assessment Models estimating damages are criticized for neglecting risk distribution, adaptation dynamics and the possible collapse of regional economies. Micro-level social science studies contain substantial knowledge on individual behavior, decisions under risk and autonomous climate adaptation, and go beyond monetary losses by focusing on resilience. This knowledge can ameliorate theoretical and empirical flaws in current macro assessments, if adequate scaling up methods were to exist. SCALAR aims to bridge the gap between micro and macro research traditions by modeling the behavioral aspects of autonomous adaptation processes of heterogeneous agents, and integrating them into macro level climate policy models. The project focuses on floods. Its innovative nature allows to revisit the classic micro-macro aggregation problem through a unique combination of: 1) New behavioral data on climate adaptation decisions collected in multiple survey waves using mobile applications, going beyond a snapshot to uncover evolving decision processes; 2) Advances in agent-based modeling to scale up adaptation decisions of heterogeneous households and firms to a regional economy while including land use and hazard data; 3) Cutting-edge ways of integrating micro-simulation models with traditional macro models to synergize the two approaches for developing new theory- and data-grounded macro damage assessments. SCALAR will drive a major breakthrough in integrating behavioral aspects of human decision-making into macro climate policy models. It will enable the quantitative exploration of cross-scale damage cascades, the identification of thresholds over which autonomous adaptation impacts the macro level, and the tracing of the emergence of socio-economic resilience as climate change unfolds. The methodological advancements will have impact far beyond the domain of climate adaptation.

Mots-clés : climate change adaptation,flood risk,agent based modeling,coupled human natural systems,coupled social ecological systems,resilience,model integration
Projet ERC : KeepOnLearning (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Acronyme de l'entité : KUL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499065.0 €

Data is key for modern AI solutions, especially deep learning. Unfortunately, the data-driven nature of deep learning that makes it so powerful when dealing with complex and high-dimensional data, is also at the core of its main weakness: a model is only as good as the data it builds on. In this project, we want to tackle some strong limitations inherent to the standard machine learning paradigm, which makes restrictive assumptions that are problematic in many real-world (“in the wild”) conditions. By addressing these, we want to make a fundamental step towards more powerful deep learning systems that can learn continuously and know how to adapt as new data becomes available, in the context of computer vision. Traditional deep learning relies on the training data being representative for data encountered during system deployment. This is perfect when working with stationary datasets. Yet in practice, data distributions are often non-stationary, i.e., they change over time. This can have a multitude of reasons – think of social trends, seasonal or geographic variations. This calls for a new generation of deep learning methods, able to adapt to new conditions by continuously updating the models based on new training data becoming available. Learning from non-stationary streaming data is, however, still a major challenge requiring fundamental research. In this project, we build on our earlier expertise in continual learning, to realize this ambitious goal. If successful, this will lead to machine learning systems that keep on learning over time, systematically improving their skills and never getting outdated. It also may lower the threshold for applying machine learning, as it reduces the need for a skilled data scientist carefully preparing the data beforehand. As a practical application, we plan to showcase our work’s feasibility, scalability and flexibility in the context of automatic generation of audio descriptions of videos for the visually impaired.

Mots-clés : continual learning,computer vision
Projet ERC : DATAFIED LIVING (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : University of Copenhagen — Pays : Danemark — Rôle : PI
Acronyme de l'entité : UCPH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1426201.0 €

Datafied living develops a communication-centric conceptual and empirical research agenda on the relationship between infrastructures for tracking of the self and forms of agency and meaning in datafied living. It focuses on the mundane experiences and implications of voluntary, pushed, coerced and imposed forms of self-tracking that unfold as we practice ourselves and pursue the good life through digital media. Despite a general, growing scholarly and public concern about the power that big tech companies have amassed in shaping societal developments and individual life possibilities through datafication, we lack knowledge about how exactly datafication is experienced, what it means to ordinary people, and how it shapes diverse contexts of everyday life. Such knowledge is crucial to intervene in tech development and its regulation in support for human flourishing: in facilitating good, safe and meaningful datafied living for all. Datafied Living is groundbreaking in five respects: 1) by framing datafication as conditioned on communication, I develop a novel communicative framework that cuts across domains of application and significantly advances both communication theory and datafication research; 2) I cover uncharted empirical territory of “datafication as viewed from below”, unifying data-driven and classic methods in an ambitious, context-sensitive methodology to study infrastructures and experiences of self-tracking and tracking of the self by others across personal, work and institutional life in the welfare state of Denmark, one of the most digitized and datafied countries in the world; 3) I unify and synthesize infrastructural analyses and experiential perspectives to elicit agentic potentials of mundane self-tracking across contexts; 4) I bring a much needed European, and context-sensitive perspective to an otherwise US-dominated debate; 5) I offer a well-tested methodology, and software, to scale the study of datafied living to cross-national comparison.

Mots-clés : datafied living,mundane self tracking,infrastructure,personal data,communication theory
Projet ERC : ALiEN (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Pompeu Fabra University — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UPF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2489541.0 €

Deep neural networks (DNNs) are specialized computational models lacking a standard interface. If a complex task requires different DNNs, an ad-hoc connection must be laboriously designed. Inspired by human language, ALiEN wants to replace such ad-hoc interfaces with generic communication protocols optimized for ease of learning by DNNs that might have different architectures and functions. ALiEN “languages” are not hand-crafted: they emerge by training DNNs to share information through communication, offering the scalability and robustness to noise that is an asset of learned systems. A first set of experiments will study, in tightly controlled settings, the impact of input, training community size and communication channel on the expressiveness and ease of acquisition of emergent protocols. The emergence of general protocols will be encouraged by a training environment characterized by varied inputs and interaction among numerous DNNs. The best emerged protocols will also be taught in a supervised way to new DNNs, with the final aim of establishing a “universal” DNN language. Next, I will explore how emergent protocols can help interfacing out-of-the box, state-of-the-art DNNs, only requiring the addition of light input and output layers to existing pre-trained models. Finally, a simplified home automation use case will demonstrate the usefulness of emergent protocols in a scenario that features some of the complexities to be expected in real-life applications. The project will also thoroughly analyze the emerging protocols, with the concurrent aims of i) identifying and favoring features that make them more expressive and easier to learn; ii) enhancing interpretability; and iii) gathering scientific insights into communication emergence in a non-human “species”. All in all, ALiEN will take a first bold step towards enabling autonomous DNN interaction, and thus genuinely adaptive AI systems.

Mots-clés : deep neural networks,emergent communication,generalization,deep neural network analysis
Projet ERC : WAPITI (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : Partner
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 395000.0 €

Deep water formed around the Antarctic continent drives the world ocean circulation. 50-70% of this deep water is formed within only about 10% of the Antarctic circumpolar band: the Weddell Sea. Subtle changes in the circulation of the Weddell Sea can lead to major changes in floating ice-shelves, with critical implications for global sea-level, the production of deep water and the global ocean overturning circulation. Despite these critical climate implications, the Antarctic shelf circulation remains poorly understood. I propose an ambitious project at the crossroads of experimental and numerical oceanography. By drawing on the strengths of each discipline I will explore the regional water-mass pathways in the Weddell Sea: an unchartered cornerstone for understanding the polar ocean circulation and its links to global climate. A key issue facing climate scientists will be addressed: “What sets the tridimensional water-mass structure and pathways in the Weddell Sea and modulates the flow of deep waters between the Antarctica ice-shelves and the global ocean circulation?” To address this question I propose to investigate several key aspects of the Weddell Sea system: the dynamical forcing of the Weddell gyre and its response to atmospheric variability; the forcing and the circulation on the continental shelf and its interaction with the gyre; and the time-scale and mixing associated with bottom water sinking along the continental shelf. WAPITI approaches these objectives through a series of innovations, including (i) an ambitious field experiment to investigate the shelf circulation and processes, (ii) a powerful conceptual framework applied for the first time to a realistic eddy-resolving model of the Weddell gyre, and (iii) a novel instrument that will be developed to directly observe the sinking of deep water into the abyssal ocean for the first time. Collectively, the project will contribute a new insight into global climate feedbacks.

Mots-clés : southern ocean,overturning circulation,polar oceanography
Projet ERC : GLOWING (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Delft University of Technology — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499460.0 €

Delay of laminar-turbulent flow transition on aircraft wings can potentially reduce aerodynamic drag by up to 15%, reducing emissions and fuel consumption considerably. The main cause of laminar-turbulent transition on commonly used swept wings is the development of crossflow (CF) instabilities. Despite their importance, our fundamental understanding of CF instabilities is limited due to inability of current measurement techniques to capture their complex and multi-scale spatio-temporal features. This severely limits our ability to delay CF transition, which is further impeded by the lack of simple, robust and efficient control concepts. In this proposal I will achieve unprecedented spatio-temporal measurements of CF instabilities and develop a novel active flow control system that can successfully delay transition on swept wings. To achieve these goals, I bring forth a unique combination of cutting-edge technologies, such as tomographic particle image velocimetry, advanced plasma-based actuators and linear/non-linear stability and control theory. Spatio-temporal volumetric velocity measurements of CF instabilities will be achieved at three important stages of their life, namely inception, growth and breakdown, providing breakthrough insights into the underlying physics of swept wing transition and turbulence production. The results will be used to postulate and validate linear and non-linear stability and control theory models and provide top benchmarks for high-fidelity CFD. The unprecedented wealth of information, enabled through these advances, will be used to design and demonstrate the first synergetic plasma-based laminar flow control system. This system will feature minimum-thickness plasma actuators, able to suppress the growth of CF instabilities and achieve and sustain considerable transition delay at high Reynolds numbers. These advances will finally enable robust and efficient laminar flow on future air transport.

Mots-clés : boundary layer transition,crossflow instability,flow control,plasma actuators,flow diagnostics,hydrodynamic stability
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 Konstanz — Pays : Allemagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 422067.5 €

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