Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : SPARKs (Horizon 2020, appel 2018, Proof of concept grants)
Entité participante : Institut national de recherche en sciences et technologies du numérique — Pays : France — Rôle : PI
Acronyme de l'entité : Inria
Type d'entité : Recherche
Financement ERC de l'entité : 149533.0 €

Spreadsheet applications (such as Microsoft Excel + VBA) are heavily used in a wide range of fields including engineering, finance, management, statistics and health. However, they do not ensure robustness properties, thus spreadsheet errors are hard to avoid, common and potentially very costly. According to estimates, the annual cost of spreadsheet errors is around 7 billion dollars. For instance, in 2013, a series of basic spreadsheet errors at JPMorgan incurred 6 billion dollars trading losses. To avoid such problems, spreadsheet users need better support from rigorous tools, since the development of industrial spreadsheets typically involves multiple tabs, formulas, macros and data read from external sources (e.g., the internet). However, as of today, spreadsheet environment offer almost no verification support, and third party tools do not allow to reason correctly over macros, and future uses of existing formulas. The MemCAD ERC StG project opened the way to novel formal analysis techniques for spreadsheet applications. Indeed, the MemCAD project led to the design of powerful abstraction techniques to reason over complex data-structures such as tables as found in spreadsheets. During this project, we have implemented AiXL, a prototype that was able to analyze large public benchmarks. It uncovered defects that are beyond the scope of other approaches. We propose to leverage these results into a toolbox able to safely verify, optimize and maintain spreadsheets, so as to reduce the likelihood of errors. The envisioned toolbox relies on automatic and conservative semantic static analysis, so that it will report all occurrences of certain classes of errors. It will have an open architecture with plugins, and will easily extend to user specific properties. This works will take place in a partnership with MatrixLEAD, a startup created based on the results of the ERC MemCAD project. MatrixLEAD will provide industrial use-cases and commercialization plans.

Mots-clés : spreadsheet,software reliability,verification
Projet ERC : MODSTABVAR (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1201370.0 €

Stable varieties, originally introduced by Kollár and Shepherd-Barron, are higher dimensional generalizations of the algebro-geometric notion of stable curves from many perspectives. Their partially conjectural moduli space classifies smooth projective varieties of general type up to birational equivalence, and it also provides a projective compactification for this classifying space. The latter is essential for applying algebraic geometry to the moduli space itself. Furthermore, over the complex numbers, stable varieties can be also defined surprisingly as the projective varieties admitting a negative curvature (singular) Kähler-Einstein metric by the work of Berman and Guenancia, or as the canonically polarized K-stable varieties by Odaka. The fundamental objective of the project is to construct the coarse moduli space of stable surfaces with fixed volume over the integers (possibly excluding finitely many primes, not depending on the volume). In particular this involves showing the Minimal Model Program for 3-folds that are projective over a 1 dimensional mixed characteristic base. The main motivations are applications to the general algebraic geometry and arithmetic of higher dimensional varieties. The above fundamental goal is also an incarnation of Grothendieck's philosophy that algebraic geometry statements should be proved in a relative setting. This was implemented right at the beginning for stable curves, but it has not been possible to attain for stable varieties of higher dimensions, due to the lack of technology. Hence, the project aims to establish new technology in mixed and positive characteristic geometry based on recent developments, such as modern Minimal Model Program, the vanishings given by balanced big Cohen-Macaulay algebras (the existence of which was shown by André using Scholze's perfectoid theory), trace method for lifting sections, p-torsion cohomology killing via alterations (by Bhatt), torsor method on singular varieties, etc.

Mots-clés : stable varieties,moduli space,birational geometry,minimal model program,mixed characteristic,positive characteristic
Projet ERC : INCEPT (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Consorzio per l'AREA di Ricerca Scientifica e Tecnologica di Trieste — Pays : Italie — Rôle : Partner
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

Standard time domain experiments measure the time evolution of the reflected/transmitted mean number of photons in the probe pulses. The evolution of the response of a material is typically averaged over the illuminated area as well as over many pump and probe measurements repeated stroboscopically. The aim of this project is to extend time domain optical spectroscopy beyond mean photon number measurements by performing a full Time Resolved Quantum State Reconstruction (TRQSR) of the probe pulses as a function of the pump and probe delay. The nature of the light matter interaction and the transient light-induced states of matter will be imprinted into the probe quantum state after the interaction with the material and can be uncovered with unprecedented detail with this new approach to time domain studies. TRQSR will be implemented by combining pump and probe experiments resolving single light pulses with balanced homodyne detection quantum tomography in the pulsed regime. We will apply and exploit the unique capabilities of TRQSR to address two different unresolved problems in condensed matter. Firstly, we will investigate the coherent and squeezed nature of low energy photo-induced vibrational states. We will use TRQSR with probe pulses shorter than the phonon timescale to interrogate the time evolution of the vibrational state induced by the pump pulse. Secondly, we will address inhomogeneities in photo-induced phase transformations. With TRQSR we can perform time domain measurements with a very small photon number per pulse which will give information on the interaction between the material (as prepared by the pump pulse) and individual photons. In this limit, TRQSR will allow us to retrieve rich statistics. While the average will deliver the information of a standard pump and probe experiment, higher order moments will give information on the time evolution of spatial inhomogenieties in the transient state.
Projet ERC : CSF (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 1616050.0 €

Star Formation is a hierarchical process from the build-up of large clouds to the assembly of stars. This ERC project aims at studying the multi-scale processes of this conversion from diffuse gas to stars. The fundamental data are provided by two PI-led large observing programs at two of the most advanced radio and mm interferometers, the Very Large Array (VLA) and the Plateau de Bure Interferometer (PdBI). This combined approach is designed to address outstanding key questions in the field of cloud and star formation. The 225 hours THOR program at the VLA investigates the cloud formation and conversion of atomic to molecular gas, as well as feedback processes back to the interstellar medium (ISM). To reach these goals, THOR observes the Milky Way in atomic HI, molecular OH, and cm continuum and radio recombination lines tracing the ionized components of the ISM. At smaller spatial scales, the 300 hours PdBI project CORE will study the fragmentation of the gas clumps to form clusters, the formation of accretion disks and outflows, and the gas infall rates as proxy for the accretion rates. Therefore, assisted by Atacama Large Millimeter Array (ALMA) observations, we will investigate a sample of high-mass star-forming regions at the highest spatial resolution of 0.2'' to dissect the physical processes during the assembly of the highest mass stars. The core aim of this ERC project is to study all spatial scales from molecular clouds to individual massive cores. This will be a decisive step in the understanding of the conversion of gas and dust into stars. Through his decade-long expertise and leadership in star/cloud formation, the PI is uniquely positioned to advance the field significantly. The surveys at the heart of the program are extremely computing and work intensive accumulating data in the 100 TByte regime. This proposals asks for a strong core group of three postdocs and one PhD student to conduct the project and fully exploit the scientific results.
Projet ERC : Print-SENSE (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Trinity College Dublin — Pays : Irlande — Rôle : PI
Acronyme de l'entité : TCD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Strain gauges are devices that are used to measure a variety of forces. The broad nature of these devices means that they have applications in industries ranging from construction and aerospace to medical and automotive fields. Our project looks to develop a new approach to strain sensing devices by utilizing the unique properties of nanomaterial based composites. Through the expertise gained through the previously awarded ERC FUTURE-PRINT grant we look to develop the next generation of commercial strain sensing technology. The invention described is an ink blend consisting of a polymer (PDMS) and a nanomaterial (graphene) deposited to form a flexible, polymer-nanomaterial composite film with unique electro-mechanical properties suitable for high performance strain sensing applications This ink can be deposited using a variety of printing methods (screen printing, aerosol and mechanical deposition) resulting, once the ink dries, in the formation of a flexible, polymer-nanomaterial composite film which is extremely sensitive to any external forces - ~ 50 x more sensitive than standard industry strain gauges. We have started the patent process to establish the technology and underlying processes as the IP of the inventors and Trinity College Dublin. This IP would form the basis of an eventual spin-out launched by inventors should the application to the ERC PoC be successful. The objectives necessary to achieve this are the identification of an appropriate market based on the unique selling points of our technology, developing a prototype to be tested in real world applications, engage industry and take the further necessary steps towards commercialization. To accomplish these objectives, we have established an experienced team of researchers, IP/tech transfer and commercialization specialists with a prior track record of working effectively and efficiently together in achieving the outlined objectives in the area of research commercialization.

Mots-clés : strain sensors,graphene,nancomposites,2d materials,commercialization,medical diagnostics,spin out
Projet ERC : AV-SMP (Horizon 2020, appel 2017, 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 : 164612.0 €

String is among the most fundamental and commonly used data types in virtually all modern programming languages, especially with the rapidly growing popularity of scripting languages (e.g. JavaScript and Python). Programs written in such languages tend to perform heavy string manipulations, which are complex to reason about and could easily lead to programming mistakes. In some cases, such mistakes could have serious consequences, e.g., in the case of client-side web applications, cross-site scripting (XSS) attacks that could lead to a security breach by a malicious user. The central objective of the proposed project is to develop novel verification algorithms for analysing the correctness (esp. with respect to safety and termination properties) of programs with string variables, and transform them into robust verification tools. To meet this key objective, we will make fundamental breakthroughs on both theoretical and tool implementation challenges. On the theoretical side, we address two important problems: (1) design expressive constraint languages over strings (in combination with other data types like integers) that permit decidability with good complexity, and (2) design generic semi-algorithms for verifying string programs that have strong theoretical performance guarantee. On the implementation side, we will address the challenging problem of designing novel implementation methods that can substantially speed up the basic string analysis procedures in practice. Finally, as a proof of concept, we will apply our technologies to two key application domains: (1) automatic detection of XSS vulnerabilities in web applications, and (2) automatic grading systems for a programming course. The project will not only make fundamental theoretical contributions — potentially solving long-standing open problems in the area — but also yield powerful methods that can be used in various applications.

Mots-clés : automata
Projet ERC : NOGOPROOF (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : University of Zurich — Pays : Suisse — Rôle : PI
Acronyme de l'entité : UZH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Stroke is the leading cause of adult disability and represents a major health problem in the EU and the Western world. Despite available acute care treatment and rehabilitation therapies, more than half of the patients remain disabled and there is a large unmet medical need accordingly. Regeneration of interrupted nerve fiber tracts and plastic “hardware” changes in the adult mammalian central nervous system are extremely restricted, a phenomenon which represents a key reason for the low degree of recovery following CNS injuries including stroke. The molecular impediments that form the basis of this phenomenon are neurite growth inhibitory proteins expressed in central nervous system myelin, in particular the membrane protein Nogo-A which was discovered by our group. Importantly, antibodies directed against Nogo-A have been shown to induce axonal regeneration, enhance plastic circuit rearrangements and mediate significant improvements in functional recovery in rodent and non-human primate models of stroke and spinal cord injury. As recently shown in the ERC advanced grant supported the project ‘Nogorise’, almost full functional recovery was observed in rats with large strokes when anti-Nogo-A antibody treatment was followed by intensive rehabilitative training. As currently no therapy for human stroke patients beyond acute care and rehabilitation exists, there is a substantial market opportunity for the anti- Nogo-A therapy. The proposed activities within the present project aim at further establishing preclinical proof-of-concept for an anti-Nogo-A administration paradigm translatable to human stroke patients and development of human antibodies targeting Nogo-A. The expected data package will allow the critical transition from preclinical to clinical development and support the development of a comprehensive business strategy.

Mots-clés : stroke,neural plasticity,regeneration,anti nogo a antibody therapy,preclinical proof of concept
Projet ERC : SoftReVision (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : Heinrich Heine University Düsseldorf — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HHU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Stroke is the third leading cause of death in Europe. In 2015, 613.148 people died of stroke, producing suffering in the patients and an economic burden of 45 billion euros. Most suffering and thereby the economic burden could be reduced if rehabilitation would be early and intense enough, thus resulting in an autonomous mobility in patients. Current rehabilitation procedures for post-stroke symptoms do not lead to quick and long-term recovery. With SoftRevision, a rehabilitation software shall be offered that runs together with a commercially available VR equipment and is easily suitable for bedside usage. Importantly, this software shall combine diagnostic and rehabilitation such that training procedures can be individually tailored to the needs of each patient. The rehabilitation procedure will be based on knowledge obtained in the associated ERC starting grant moreSense. In order to evaluate the efficacy of the software three studies with patients will be conducted. In these studies the rehabilitation success of the software will be quantified in comparison to existing procedures. The project will be carried out in cooperation with a rehabilitation clinic and a virtual reality software company. For the development of SoftRevision a software developer will be hired. The software developer will create the product based on the ideas of the PI. In addition, the developer will generate a graphical user interface that allows a “plug-and-play” version of the software. This shall make the software accessible for patients without previous technological experience. The rehabilitation clinic hosts 1000 patients, thus providing ample opportunity to validate and optimize SoftRevision. The virtual reality company shall market the product and offer long-term technical support for users and provide updates. In conclusion, with SoftReVision the team aims to produce an innovative tool for diagnostics and rehabilitation of post-stroke symptoms.

Mots-clés : software for rehabilitation,stroke,perceptual deficit
Projet ERC : SAVEBRAIN (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Fraunhofer Society — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : FHG
Type d'entité : Recherche
Financement ERC de l'entité : 78097.5 €

Stroke represents currently one of the largest – if not the largest - unmet medical need: It is the second leading cause of death and the leading cause of disability. In contrast to this high demand for effective treatments, there is only one drug available, a blood-clot dissolving agent. However, this drug is marginally effective has over 30 contra-indications, and bears an extremely high risk of causing fatal bleeding that 85% of all stroke patients are not treated with it. The needed innovation would be a drug which is broadly applicable, i.e. has no contraindication, can be given in all forms of stroke and already in the ambulance, is safe, i.e. bears no risk to cause secondary bleeding, is effective, i.e. reduces the brain infarct, increases survival and improves neurological outcomes for patients, has a different mechanism of action, i.e. is directly neuroprotective. Our therapeutic principle, discovered in the course of the ERC-ADG RadMed fulfils all above criteria and is also commercially an innovative approach because we reduce the risk by developing only a single compound by here combining three compounds that target the same disease mechanism but at different points. We thereby expect to dramatically reduce the likelihood risk of failure in our post-proof of concept (PoC) clinical development and commercialisation process. We here propose to conduct the final step, the PoC, before a first-in-man study in stroke. Our PoC plan has three major objectives: To complete an IPR FTPO and Foreground analysis, to chose the optimal compounds and complete phase III pre-clinical to provide PoC for a first-in-man trial in stroke, to then allow the work-out of a complete business plan including competitive analysis, development strategy, and commercial/business development strategy.

Mots-clés : drug development,spin off company,out licencing
Projet ERC : LensEra (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Portsmouth — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1794707.0 €

Strong gravitational lensing is on the cusp of a new era. Strong lensing occurs when the mass of a galaxy deforms space time so much that multiple images of a single background source is observed. Strong lensing is a powerful cosmological probe, but it is hamstrung by sample size: currently we only know of a few hundred systems. I am leading the work on a new telescope, the Large Synoptic Survey Telescope, which in its first year will observe more of the optical Universe than all previous telescopes combined. This revolutionary dataset will transform strong lensing into a statistical science by enabling LensEra to discover 30,000 new lenses and exploit them for cosmology. The first objective of LensEra uses machine learning, citizen science and automated lens modelling to build a discovery engine to find 30,000 new lenses in the Large Synoptic Survey Telescope data, including 600 lenses with multiple background sources, and 300 lensed supernovae. LensEra will then confirm these using the 4MOST multi-object spectroscopic survey and the robotic Liverpool Telescope. The second objective of LensEra develops 3 new tests of cosmology using rare subsets of the lens population: measuring the expansion of the Universe with samples of lensed supernovae; measuring the equation of state of dark energy with lenses with multiple background sources; and, testing the validity of General Relativity with lensing combined with stellar kinematics. The new sample from the first objective will allow LensEra to launch strong lensing cosmology into a new statistical age. Combining detailed analysis of ~200 golden lenses (the best LSST lensed supernovae and brightest double source plane lenses) with an automated modelling of the full sample will enable precise and accurate cosmological inference.

Mots-clés : strong gravitational lensing