Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : REDIAL (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Cambridge — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. Cambridge
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1495036.0 €

In just a few short years, breakthroughs from the field of deep learning have transformed how computers perform a wide-variety of tasks such as recognizing a face, driving a car or translating a language. Not only has deep learning become an everyday tool, it is also the most promising direction for tackling a number of still open problems in machine learning and artificial intelligence. However, routine deep learning activities (such as training a model) exert severe resource demands (e.g., memory, compute, energy) that are currently slowing the advancement of the field, and preventing full global participation in this research to only the largest of companies. The goal of REDIAL is to solve core technical challenges that span the areas of machine learning and system research which collectively can enable a radical jump in the efficiency of deep learning. It aims to address both the challenge of high training costs and time, as well as the barrier to deploying models on constrained devices (like wearables, sensors) that currently require new efficiency techniques to be invented each time a deep learning innovation occurs. To accomplish this REDIAL takes two complementary approaches. First, it seeks to build a theoretical understanding of current approaches to deep learning efficiency, a desperately needed step given current over reliance on empirical observations. Second, it aims to develop new architectures and methods for training and inference that tackle core efficiency bottlenecks, such as: dependencies preventing parallelization and excessive on-chip data movement; while also opening new opportunities including the greater adoption of analog processing within accelerators. REDIAL aims to change the way the world trains its models, and deploys them to constrained devices, by producing a series of new deep architectures and algorithms with properties that promote high efficiency that can serve as a foundation for new machine learning innovation.
Projet ERC : REDIAL (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

In just a few short years, breakthroughs from the field of deep learning have transformed how computers perform a wide-variety of tasks such as recognizing a face, driving a car or translating a language. Not only has deep learning become an everyday tool, it is also the most promising direction for tackling a number of still open problems in machine learning and artificial intelligence. However, routine deep learning activities (such as training a model) exert severe resource demands (e.g., memory, compute, energy) that are currently slowing the advancement of the field, and preventing full global participation in this research to only the largest of companies. The goal of REDIAL is to solve core technical challenges that span the areas of machine learning and system research which collectively can enable a radical jump in the efficiency of deep learning. It aims to address both the challenge of high training costs and time, as well as the barrier to deploying models on constrained devices (like wearables, sensors) that currently require new efficiency techniques to be invented each time a deep learning innovation occurs. To accomplish this REDIAL takes two complementary approaches. First, it seeks to build a theoretical understanding of current approaches to deep learning efficiency, a desperately needed step given current over reliance on empirical observations. Second, it aims to develop new architectures and methods for training and inference that tackle core efficiency bottlenecks, such as: dependencies preventing parallelization and excessive on-chip data movement; while also opening new opportunities including the greater adoption of analog processing within accelerators. REDIAL aims to change the way the world trains its models, and deploys them to constrained devices, by producing a series of new deep architectures and algorithms with properties that promote high efficiency that can serve as a foundation for new machine learning innovation.
Projet ERC : HIPPO (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : University of Cologne — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1327574.25 €

In light of rapidly increasing prices of housing, gentrification and increasing regional inequalities within and across cities in many countries, I study the effectiveness of various public policies to counter these challenges. In particular, I will study the efficiency and distributional effects of seven different, commonly used policy instruments directly or indirectly targeted at the housing market: residential property taxes, commercial property taxes, real-estate transaction taxes, capital gains transaction taxes, local public spending, social housing programs and rent control. The overarching research question of the project is how do different public housing policies shape local housing markets and affect regional inequality. Analyzing the effects of these different instruments provides a comprehensive overview of available policy options. I analyze the policy effects combining state-of-the-art theoretical models with clean empirical evidence. The theoretical predictions about the different policy effects are based on widely used local labor market models, which I tailor and extend to the specific institutional context and specific research questions at hand. Guided by the resulting theoretical predictions, I exploit rich, micro-level data on housing markets, local labor markets, and local policy instruments in various European countries (Finland, Spain and Germany) and the United States. I selected the specific countries based on the availability of suitable data and, importantly, institutional features that allow exploiting quasi-experimental variation to identify the causal effects of the different policy instruments. I combine the empirical evidence with the theoretical insights to eventually derive the efficiency costs and redistributive effects of the respective policies. These results provide guidance for policymakers when addressing the challenges induced by the current housing crisis.

Mots-clés : housing markets,public policy,property taxaxtion,regional inequality,social housing,rent control,wealth inequality
Projet ERC : HIPPO (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Leibniz Association — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : LG
Type d'entité : Recherche
Financement ERC de l'entité : 151063.75 €

In light of rapidly increasing prices of housing, gentrification and increasing regional inequalities within and across cities in many countries, I study the effectiveness of various public policies to counter these challenges. In particular, I will study the efficiency and distributional effects of seven different, commonly used policy instruments directly or indirectly targeted at the housing market: residential property taxes, commercial property taxes, real-estate transaction taxes, capital gains transaction taxes, local public spending, social housing programs and rent control. The overarching research question of the project is how do different public housing policies shape local housing markets and affect regional inequality. Analyzing the effects of these different instruments provides a comprehensive overview of available policy options. I analyze the policy effects combining state-of-the-art theoretical models with clean empirical evidence. The theoretical predictions about the different policy effects are based on widely used local labor market models, which I tailor and extend to the specific institutional context and specific research questions at hand. Guided by the resulting theoretical predictions, I exploit rich, micro-level data on housing markets, local labor markets, and local policy instruments in various European countries (Finland, Spain and Germany) and the United States. I selected the specific countries based on the availability of suitable data and, importantly, institutional features that allow exploiting quasi-experimental variation to identify the causal effects of the different policy instruments. I combine the empirical evidence with the theoretical insights to eventually derive the efficiency costs and redistributive effects of the respective policies. These results provide guidance for policymakers when addressing the challenges induced by the current housing crisis.

Mots-clés : housing markets,public policy,property taxaxtion,regional inequality,social housing,rent control,wealth inequality
Projet ERC : CEC (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Ruhr University Bochum — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : RUB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1358687.0 €

In light of the proceeding globalization, societies becoIn light of the proceeding globalization, societies become more and more diverse regarding people’s ethnic and religious backgrounds, their habits, ideals, and beliefs. While this increasing diversity offers great opportunities for Europe and the world, it also comes with great challenges. The human tendency to derogate out-groups and minority groups poses a particular threat to peace and cooperation in diversifying societies. Most available psychological explanations for the formation of negative attitudes and stereotypes towards others rely on people’s self-serving motivations, and therefore suffer from circularity. This leaves our understanding of some of societies’ greatest challenges largely incomplete. Based on the research that I have conducted over the past years, I propose a theoretical framework that provides novel explanations for the formation of negative attitudes and stereotypes about out-groups and minorities. Accordingly, these phenomena arise as natural by-products of innocent cognitive processes and the structure of the external information ecology. This cognitive-ecological framework argues that people associate out-groups and minority groups with their distinct attributes which differentiate them from in-groups and majorities. In the external information ecology, however, distinct attributes have a high probability of being negative, and therefore out-groups and minorities suffer a general evaluative disadvantage. In addition, the framework can explain why most existing stereotypes are negative. In the domain of news reporting, it can explain why news recipients form the erroneous impression that minorities are particularly dangerous, and why politician’s and journalists’ attempt to counteract often backfire. The ERC Starting Grant would enable me to empirically test my framework, and if successful, it will renew our understanding of some of the most pressing societal challenges of our time.
Projet ERC : NterAct (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : University of Bergen — Pays : Norvège — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999273.0 €

In mammalian cells, N-terminal (Nt) acetylation is one of the most abundant protein modifications. It is catalysed by N-terminal acetyltransferases (NATs) and mostly occurs co-translationally. However, in contrast to the defined co-translational NATs, post-translational NATs which have crucial regulatory roles are mostly unexplored. Distinct peptide hormones regulate appetite, metabolism, sexual behaviour and pain, and their biological activity is critically modulated by post-translational Nt-acetylation. However, the identity of the NAT responsible for this modification, ‘HormNat’, is unknown, thus the molecular and physiological ramifications of this regulatory circuit remain elusive. Another example is actin, a key regulator of cell motility and cell division. The actin N-terminus is crucial for actin function and in mammals actin is modified by an unknown post-translational NAT, ‘ActNat’. Hence, the objectives of this project are to identify these human NATs acting post-translationally, and to investigate their molecular mechanisms, regulation and impact. We will identify the novel NATs by a combination of classical and newly developed in-house tools like in vitro acetylation assays, unique bisubstrate analogues, Nt-acetylation specific antibodies, and targeted mass spectrometry. Interestingly, Nt-acetylation is considered irreversible, but there is reason to believe that specific substrates are Nt-deacetylated. Elucidation of post-translational NATs and the reversible nature of Nt-acetylation would represent a new era in the field of protein and peptide regulation and identify key cellular and organismal switches.

Mots-clés : acetylation,acetyltransferase,peptide hormone,cytoskeleton,actin,pomc
Projet ERC : Totipotency (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
Entité participante : Friedrich Miescher Institute — Pays : Suisse — Rôle : PI
Acronyme de l'entité : FMI
Type d'entité : Recherche
Financement ERC de l'entité : 2859560.0 €

In mammals, fusion of two highly differentiated gametes gives rise to a totipotent zygote capable of developing into a whole organism. It coincides with translation and degradation of maternally provided transcripts, initiation of global transcription called zygotic genome activation (ZGA), and “epigenetic reprogramming” of germline chromatin states into an embryonic state. The molecular mechanisms underlying this exquisite reprogramming of cell fate are barely understood. This research program has the ambitious goal to identify and characterize in a comprehensive way the transcription factors and chromatin regulators which initiate and regulate ZGA in a parental specific manner in early mouse embryos. We will utilize novel and highly sensitive genomic approaches to measure nascent transcription and determine open and modified chromatin landscapes in oocytes and early embryos, wild-type and conditionally deficient for major epigenetic modifiers. We will apply computational approaches to identify candidate TFs and histone modifiers controlling ZGA. We will use molecular and developmental biology approaches, combined with sensitive quantitative live-imaging, to interrogate the function of TFs and their binding sites for ZGA. We will further investigate the significance of possible paternal inheritance of nucleosomes at CpG islands for gene regulation during ZGA and later development by depleting nucleosomes from mature sperm by using sophisticated conditional deficiency and gain-of-function mouse models. By transferring nuclei of immature spermatid and mature sperm into oocytes, we will interrogate the relevance of nucleosome eviction during spermatogenesis, as a possibly truly epigenetic reprogramming process, for defining embryonic competence. ERC funding would represent a crucial contribution to dissecting the molecular mechanisms underlying acquisition of totipotency in mouse embryos and may impact on the use of Assisted Reproductive Technologies in human med

Mots-clés : chromatin,imaging
Projet ERC : LEIT (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Institut Català de Nanociència i Nanotecnologia — Pays : Espagne — Rôle : Partner
Acronyme de l'entité : ICN2;ICN2-CERCA
Type d'entité : Recherche
Financement ERC de l'entité : 915187.5 €

In nanoelectronic circuits, interconnects use more energy than microprocessors, a situation clearly undesirable for e.g. autonomous Internet of Things applications based on charge and other information tokens. Overcoming this issue and minimising overall power consumption will be of paramount importance as we move towards Beyond-CMOS circuits. A novel approach is required. In LEIT I propose to investigate phonons as information carriers with typical ultralow energies of a fraction of a meV. As quanta of lattice vibrations, the high interactivity of phonons presents two key challenges: phonon-phonon scattering and losses in waveguides caused by interaction with e.g. lattice defects. I propose to overcome this by engineering phonon-phonon scattering in custom-designed phononic crystal-based structures moving towards narrow frequencies and non-interacting phonons at room temperature. These structures will exhibit a unique combination of features to allow phonon filtering, reflection and confinement, as well as transmission from one element (source) to another (modulator and waveguides), all of which will serve to direct and guide the phonon waves. Phonon losses will be minimised even eradicated by using topological phononic waveguides to transmit phonons over micrometre distances. The technological platforms will be made from silicon (Si) and Si-compatible materials, also incorporating transition metal dichalcogenides in order to reach the higher frequencies. In LEIT I will draw on my extensive experimental research on phonons in semiconductor nanostructures, Si membranes and phononic crystals to demonstrate the viability of acoustic phonons as low-energy information carriers. By doing so I will lay the scientific and technological foundations of a new phononics-based approach to information processing, offering a means of transmitting information that is extremely low-power and lossless, while also compact and integrable with Si-technologies.

Mots-clés : phonon signal transmission,phonons in si,si membranes,phononic crystals,low energy phonon transport,phononic interconnects,topological phonon waveguides,surface acoustic waves,brillouin scattering,ir imaging
Projet ERC : FEAR-SAP (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : Ludwig-Maximilians-Universität München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 916786.68 €

In natural and agricultural habitats, plants grow in organismal communities and therefore have to compete for limited resources. Competition between different crop plants and between crops and weeds leads to losses of potential agricultural product and requires heavy use of fertilizer and herbicides, with negative effects for the environment and human health. Plants have evolved various strategies to outcompete their neighbours and to secure their access to resources; one of them is the release of toxic chemical compounds into the soil that interfere with the growth of neighbouring plants. Many of today’s major crops, such as wheat, rye and maize, produce phytotoxins. Conversely, crop species also suffer from chemical attack by other plants growing in their vicinity. Although many of the chemical compounds applied in this biochemical warfare have been identified, we know little about how they act in the target plant; neither do we understand how some plant species are able to tolerate this chemical attack. FEAR-SAP studies the genetic architecture that underlies biochemical plant-plant interference and the evolution of weed resistance to crop-released phytotoxins. To this end it employs a comprehensive array of molecular genetics, genomics and metagenomics analyses, unprecedented in the research on plant-plant competition. The aims of FEAR-SAP are to uncover the molecular targets of plant-derived phytotoxins and to identify the genetic components that are essential for tolerance to these substances. Moreover, FEAR-SAP investigates how the microbial community that is associated with the plant might enhance efficiency of the donor and/or mediate tolerance of the target plant. Ultimately, we will use this information to explore intelligent engineering of more refined and competitive crops, which will be at the foundation of efficient and ecologically responsible weed control and improved crop rotation strategies.

Mots-clés : plant plant competition,allelopathy,microbiome,population genomics,chromatin
Projet ERC : TRIPOD (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : Partner
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 383093.88 €

In order to meet its long-term climate targets, the European Union has decided to reduce its power sector carbon emissions by 93-99% by 2050. This means that Europe aims to transition to a largely, or fully, renewable power system. This is however not the only energy policy aim: besides a transition to renewables, further aims include an energy efficiency increase and demand reduction; liberalisation of the power markets and exposure of renewables to competition; and europeanisation of renewable energy policy, power grids, and the creation of a European internal power market. Here, I investigate whether and how these policies interact and affect the chances for and costs of a transition to a renewable power system; how the other aims constrain the options for renewables; and how policy conflicts can be resolved. Current policy discourse treats these policy aims either as independent or synergistic. My hypothesis is that they are not at all independent and that pursuing the aims of demand reduction, liberalisation and europeanisation strongly influences the transition to renewables, and that the aims are partially antagonistic, implying a need for trade-offs. The purpose of my research is to test these hypotheses and explore the policy synergies or antagonisms, by investigating yet under-researched aspects of the interactions. These include how reaching the other aims influences the transition dynamics; how key actors may alter their behaviour due to such other developments; and how reaching another policy aim impacts the stability of a fully renewable power system. I adopt an interdisciplinary approach, drawing on transition research, engineering, political science and economics, with each perspective adding a piece of the puzzle. The answers will contribute to both the disciplinary and the policy-driven renewable energy research, and provide insights to help policy-makers define less conflicting policies, thus supporting the European transition to renewables.

Mots-clés : renewable electricity,policy interactions