Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Stream Learning for Multilingual Knowledge Transfer (SELMA)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Countries involved: Germany,Germany,Latvia,France,Portugal
Call: Big Data technologies and extreme-scale analytics (H2020-ICT-2020-1, 2020)
Start date: 2021-01-01
End date: 2023-12-31
Duration (months): 36.0
Total project cost (EUR): 3452506.25
EU funding (EUR): 3452506.25
Participants: 5
Project status: SIGNED

SELMA builds a continuous deep learning multilingual media platform using extreme analytics. Large amounts of multilingual text and speech data are available in the internet, but the potential to fully take advantage of this data has remained largely untapped. Recent advances in deep learning and transfer learning have opened the door to new possibilities – in particular integrating knowledge from these large unannotated datasets into plugable models for tackling machine learning tasks. The aim of the Stream Learning for Multilingual Knowledge Transfer (SELMA) is to address three tasks: ingest large amounts of data and continuously train machine learning models for several natural language tasks; monitor these data streams using such models to improve multilingual Media Monitoring (use case 1); and improve the task of multilingual News Content Production (use case 2), thereby closing the loop between content monitoring and production. SELMA has eight goals: 1. Enable processing of massive video and text data streams in a distributed and scalable fashion 2. Develop new methods for training unsupervised deep learning language models in 30 languages 3. Enable knowledge transfer across tasks and languages, supporting low-resourced languages 4. Develop novel data analytics methods and visualizations to facilitate the media monitoring decision-making process 5. Develop an open-source platform to optimize multilingual content production in 30 languages 6. Fine-tune deep learning models from user feedback, reducing recurring errors 7. Ensure a sustainable exploitation of the SELMA platform 8. Encourage active user involvement in the platform. Achieving these aims requires advancing the state of the art in multiple technologies (transfer learning, language modelling, speech recognition, machine translation, summarization, speech synthesis, named entity linking, learning from user feedback), while building upon previous project results and existing services.

Keywords: stream learning,learning from user feedback,extreme analytics,summarization,multilingualism,natural language understanding,speech to text,machine translation,named entity recognition
Co-targeting androgen receptor signalling and DNA damage repair for precision therapy in advanced prostate cancer (AR-DDR)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Spain
Call: Individual Fellowships (H2020-MSCA-IF-2018, 2018)
Start date: 2019-05-01
End date: 2021-07-21
Duration (months): 24.0
Total project cost (EUR): 172932.48
EU funding (EUR): 172932.48
Participants: 1
Project status: CLOSED

Prostate cancer is the 2nd most common cancer in the western world. The advanced stage, metastatic castration-resistant prostate cancer (mCRPC), is a lethal disease. Understanding inter-patient genomic heterogeneity renders the opportunity to advance towards personalised patient care. Prostate cancer is a disease primarily driven by the androgen receptor (AR) pathway; however, the applicant prior work contributed to identifying 1) that up to 25% of mCRPC harbour defects in DNA damage repair (DDR) genes, and 2) that some of these mCPRC patients with DDR defects are sensitive to targeted treatment with PARP inhibitors. In the proposed research plan, we aim to exploit the cross-regulation between AR and DDR pathways to optimize precise therapeutic options for mCRPC patients. To achieve the objectives, the applicant will use models generated at the host through CRISRP/Cas9 to pursue functional studies and characterise how defects in ATM impact DDR function and sensitivity to inhibitors of PARP, ATR and DNA-PK. We hypothesize such sensitivity would be modulated by co-targeting of the AR pathway and by second events such as TP53 loss-of-function. ChIP-Seq assays will be pursued to identify genes co-regulated by the androgen receptor and PARP-1, to identify potential synthetic vulnerabilities. Then, mCRPC patient’s biopsies acquired in clinical practice from patients receiving AR-targeting therapies will be used to study how AR inhibition modulate transcriptional regulation of DDR pathways, to inform the optimal design of combination therapies. These data would be correlated with genomics and immunofluorescence tests of homologous recombination function, in order to refine patient stratification in the clinic. The proposed research will be conducted in parallel to a personalised training and career development plans, designed for the applicant to achieve a position of academic independence as physician-scientist before the end of the fellowship at the host institute.

Keywords: prostate cancer,dna repair,biomarkers,tp53,parp
Cellular and molecular mechanisms of metabolic immune activation triggering non-alcoholic steatohepatitis (NASH) and HCC (HepatoMetaboPath)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Physiology in Health, Disease and Ageing
Panel description: Organ and tissue physiology, comparative physiology, physiology of ageing, pathophysiology, interorgan and tissue communication, endocrinology, nutrition, metabolism, interaction with the microbiome, non-communicable diseases including cancer (and except disorders of the nervous system and immunity-related diseases)
Countries involved: Germany,Germany
Call: ERC Consolidator Grant (ERC-2015-CoG, 2015)
Start date: 2016-07-01
End date: 2021-06-30
Duration (months): 60.0
Total project cost (EUR): 1995860.0
EU funding (EUR): 1508710.0
Participants: 2
Project status: CLOSED

Overweight and metabolic syndrome are reaching pandemic dimensions in industrialized countries and are rising in developing countries. Clinically these diseases can manifest in non-alcoholic fatty liver disease (NAFLD), the most frequent liver disease world-wide. A significant number of NAFLD patients develop non-alcoholic steatohepatitis (NASH), fibrosis and hepatocellular carcinoma (HCC), making NASH-driven HCC the most rapidly increasing cancer in the USA, with a similar trend in Europe. While HCC is the second most common cause of cancer related death, the mechanisms triggering NASH and subsequent HCC are poorly understood and efficacious therapies are lacking. My group has strong expertise in inflammation-driven HCC (e.g. by Hepatitis B, C viruses). Recently, we have established a mouse model of NASH-driven HCC recapitulating human pathology in the context of metabolic syndrome. We demonstrated for the first time that CD8+ T- and natural killer T (NKT)-cells become activated during metabolic syndrome, cross-talk with hepatocytes and alter hepatic lipid metabolism causing NASH and HCC. We found an identical profile of CD8+T and NKT-cell activation in human NASH underlining the clinical relevance of our model. As the mechanisms of immune cell activation in NASH and transition to HCC remain unknown, this research proposal aims to (1) Identify the priming cell types in metabolic CD8+ T-, NKT-cell activation and the molecular mechanisms of immune cell-hepatocyte crosstalk. (2) Determine the role of antigen recognition and danger- or pathogen-associated molecular patterns in NASH/HCC. (3) Identify the environmental and genetic determinants of NASH to HCC transition. Our findings will enhance the understanding of NASH and HCC development by identifying the underlying mechanisms of immune cell activation. We will identify genetic changes facilitating NASH to HCC transition and whether metabolic normalization of former NASH patients suffices to significantly reduce HCC.

Keywords: obesity,nash,metabolic syndrome,immune cell activation
Digital Do It Yourself (DiDIY)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Countries involved: Italy,Italy,Greece,Netherlands,United Kingdom,United Kingdom
Call: Human-centric Digital Age (H2020-ICT-2014-1, 2014)
Start date: 2015-01-01
End date: 2017-06-30
Duration (months): 30.0
Total project cost (EUR): 2081767.5
EU funding (EUR): 2081767.5
Participants: 7
Project status: CLOSED

Digital do it yourself (DiDIY) is a new socio-technological phenomenon in which the widespread availability of digital devices supporting the convergence of physical and informational components and the growing accessibility of knowledge and data through open online communities presage scenarios in which the distinction between users and producers of physical artefacts is fuzzy and new opportunities and threats emerge. DiDIY-related technologies and social practices amplify the creativity and skills of individuals who affordably develop digitally self-made objects, e.g., unique-by-design objects designed by 3D modelling software and generated by 3D printers or networked smart objects equipped with microcontrollers dealing with context information via sensors and actuators. Two network effects catalyse DiDIY: what is custom produced by an individual could be the outcome of contributions from a world-wide community of developers sharing their interest towards open innovation, thus operating as knowledge multiplier; what is made available here and now by a smart object could be the aggregation of signals from a set of sources in the network, thus operating as information extender. In this (presumed) industrial revolution the project aims at: * setting a conceptual framework to explore the impact of DiDIY, * producing information, models and guidelines to support education and policy making on DiDIY that, while enabled by technology, should be driven by social and cultural strategies. The project will study how DiDIY is * reshaping organization and work, education and research, * impacting on social and legal systems * changing creative design and ethics. The development of a systemic interpretation is the challenge for the multidisciplinary project team, which will collaboratively explore a complex phenomenon with implications on identity, privacy, reputation, responsibility and safety and will offer a roadmap fostering a DiDIY-based human-centric European development.

Keywords: digital do it yourself,open innovation,open online communities,open source hardware and software,individual and social creativity,ethics of digital manufacturing,personal fabrication
Mass-customization of implants: the business model (Mass-Customization)

Framework: Horizon 2020 — Societal Challenges
Programme: Europe in a changing world - inclusive, innovative and reflective societies
Action type: SME instrument
Countries involved: Lithuania
Call: SME business model innovation (H2020-SMEINST-1-2015, 2015)
Start date: 2016-03-01
End date: 2016-08-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

The key objectives of the innovation project are: 1. Develop software systems for the technology platform managing and control to enable transition from the singular CMI & PSI production to their mass customization to make design, production and validation processes much more efficient in terms of production costs and lead terms. 2. Develop a technology platform for the cardinal new implant supply chain, based on direct communication between surgeons and personalized implant manufacturers. It includes development of instrumental 3D software tools supporting participation of surgeons in product configuration workflow, design and validation processes and software systems for all the processes management and control. Specific objectives for the feasibility study are: 1. Analysis of technological viability of mass customization of medical implants business model concept; 2. Analysis of economic viability of the business model concept; 3. Freedom to operate analysis; 4. Market and competition environment analysis; 5. Analysis of the product supply and distribution systems (include hidden interests analysis); 6. Preparation of the elaborated business plan.

Keywords: custom made implants,custom made orthopedic implants,patient specific single use surgical instruments,mass customization of implants
RoBot for Autonomous unDerGround trenchless opERations, mapping and navigation (BADGER)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Partnership: Robotics SPARC (cPPP)
Countries involved: Germany,Italy,Spain,Spain,Greece,Greece,United Kingdom
Call: Advanced robot capabilities research and take-up (H2020-ICT-2016-1, 2016)
Start date: 2017-01-01
End date: 2020-06-30
Duration (months): 42.0
Total project cost (EUR): 3698003.75
EU funding (EUR): 3698003.5
Participants: 7
Project status: CLOSED

The goal of the proposed project is the design and development of the BADGER autonomous underground robotic system that can drill, manoeuvre, localise, map and navigate in the underground space, and which will be equipped with tools for constructing horizontal and vertical networks of stable bores and pipelines. The proposed robotic system will enable the execution of tasks that cut across different application domains of high societal and economic impact including trenchless constructions, cabling and pipe installations, geotechnical investigations, large-scale irrigation installations, search and rescue operations, remote science and exploration, and defence applications. For this purpose, BADGER will deliver a highly innovative robotic system by integrating research into all required novel technical advances. BADGER will integrate innovative mechatronic concepts with robust industrial drilling tools to yield advanced manoeuvrability and motion capability; will integrate perception, localisation and mapping techniques in order to sense map and interpret the surrounding underground environment; the system will merge collected underground data with legacy digital maps to plan and track the motion of the robot with respect to physical landmarks. The robotic system actions and reactions will be governed by the cognition component which makes decisions on task execution, path planning and motion planning. Finally, the robotic system will be capable to manage and intelligently combine the massive data gathered during underground operation so as to continuously improve its perception and cognition abilities whilst also providing human users the means to store, process and analyse this data, thus enabling the efficient off-line planning and on-line remote monitoring and control of the overall operation process.

Keywords: underground robotics,trenchless technologies,ground penetrating radar,navigation and guidance,environment perception,digital fabrication,gis mapping,slam,robot cognition,path planning
A multidisciplinary approach for the stratification of patients with carotid artery disease (TAXINOMISIS)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Understanding health, wellbeing and disease
Action type: Research and Innovation actions
Countries involved: Germany,Italy,Spain,Italy,Serbia,Greece,Switzerland,Belgium,Finland,Serbia,Finland,Greece,Netherlands,United Kingdom
Call: New concepts in patient stratification (H2020-SC1-2017-Two-Stage-RTD, 2017)
Start date: 2018-01-01
End date: 2023-12-31
Duration (months): 72.0
Total project cost (EUR): 5999402.5
EU funding (EUR): 5999402.5
Participants: 18
Project status: SIGNED

Carotid artery disease, the primary trigger of ischaemic cerebrovascular events including stroke, causes major morbidity, mortality and healthcare costs worldwide. Still, treatment is based on criteria established in the 90s that do not take into account the molecular evolution we have witnessed since, nor the introduction of new medication, leading to remarkably high unnecessary surgical treatment while missing most patients at risk. TAXINOMISIS will provide novel disease mechanism-based stratification for carotid artery disease patients to address the needs for stratified and personalised therapeutic interventions in the current era. This will be achieved through (1) the dissection of mechanisms mediating carotid artery disease, and identification of susceptibility and protection factors of plaque erosion and/or rupture using longitudinal cohorts and multi-omics, (2) the definition of distinct disease phenotypes and endotypes, and generation of molecular fingerprints of high versus low-risk states through systems medicine, (3) the development of a multilevel risk prediction model of the symptomatic plaque incorporating new biomarkers and advanced imaging, implemented in a software, to assist patient stratification and clinical decision making, (4) the development of novel pharmacogenomics solutions based on lab-on-a-chip technology to support personalized treatment, (5) the evaluation of the new risk prediction model and lab-on-a-chip device in a prospective observational clinical study, and (6) the assessment of regulatory, cost-effectiveness and ethical issues towards the implementation and commercialization of the programme’s outcomes. TAXINOMISIS has therefore the potential to rationally change the current state-of-the-art in the stratification of patients with carotid artery disease by reducing unnecessary operations, refining medical treatment and opening up new avenues for therapeutic intervention, while strengthening the European biotechnology sector.

Keywords: carotid artery disease,stroke,stratification,omics,computational modeling,pharmacogenomics
Fifth Generation Communication Automotive Research and innovation (5GCAR)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Partnership: 5G PPP (cPPP)
Countries involved: Germany,Germany,Germany,Spain,Spain,Germany,Sweden,Finland,Poland,Sweden,France,France,France,United Kingdom
Call: 5G PPP Research and Validation of critical technologies and systems (H2020-ICT-2016-2, 2016)
Start date: 2017-06-01
End date: 2019-07-31
Duration (months): 26.0
Total project cost (EUR): 7995413.75
EU funding (EUR): 7995413.75
Participants: 18
Project status: CLOSED

There are two ongoing industrial trends, one in the mobile communications industry and one in the automotive industry, which are becoming interwoven and will jointly provide new capabilities and functionality for upcoming intelligent transport systems and future driving. The automotive industry is on a path where vehicles are continuously becoming more aware of their environment, due to a permanent increase in various types of integrated sensors; at the same time the amount of automation in vehicles increases, which – with some intermediate steps – will eventually culminate in fully-automated driving without human intervention. Along this path, the amount of interactions increases, both in-between vehicles, as well as between vehicles and an increasingly intelligent road infrastructure. As a consequence, the significance and reliance on capable communication systems for vehicle-to-anything (V2X) communication is becoming a key asset that combined with sensor-based technologies will enhance the performance of automated driving and increase further traffic safety. On the other hand, the mobile communications industry has over the last 25 years connected more than 5 billion people and mobile phones have become part of our daily living. The next step in wireless connectivity is to connect all kinds of devices that can benefit from being connected, with a total of 28 billion connected devices predicted until 2021. It will support the transformation of industries on their journey of digitization. In this step, mobile communications has the ambition to explicitly target the communication needs of vertical industry with corresponding requirements being set for the standardization of 5G until 2020. 5GCAR brings together a strong consortium from the automotive industry and the mobile communications industry, to develop innovation at the intersection of those industrial sectors in order to support a fast, and successful path towards safer and more efficient future driving.

Keywords: 5g v2x,ullc,5g demonstrator,5g sidelink communication,automated driving,ultra reliable low latency communication,vehicle to anything communication,intelligent transport systems
Reliable Data-Driven Decision Making in Cyber-Physical Systems (RADDICS)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Computer science and informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Switzerland
Call: ERC Consolidator Grant (ERC-2018-COG, 2018)
Start date: 2019-01-01
End date: 2023-12-31
Duration (months): 60.0
Total project cost (EUR): 1996500.0
EU funding (EUR): 1996500.0
Participants: 1
Project status: SIGNED

This ERC project pushes the boundary of reliable data-driven decision making in cyber-physical systems (CPS), by bridging reinforcement learning (RL), nonparametric estimation and robust optimization. RL is a powerful abstraction of decision making under uncertainty and has witnessed dramatic recent breakthroughs. Most of these successes have been in games such as Go - well specified, closed environments that - given enough computing power - can be extensively simulated and explored. In real-world CPS, however, accurate simulations are rarely available, and exploration in these applications is a highly dangerous proposition. We strive to rethink Reinforcement Learning from the perspective of reliability and robustness required by real-world applications. We build on our recent breakthrough result on safe Bayesian optimization (SAFE-OPT): The approach allows - for the first time - to identify provably near-optimal policies in episodic RL tasks, while guaranteeing under some regularity assumptions that with high probability no unsafe states are visited - even if the set of safe parameter values is a priori unknown. While extremely promising, this result has several fundamental limitations, which we seek to overcome in this ERC project. To this end we will (1) go beyond low-dimensional Gaussian process models and towards much richer deep Bayesian models; (2) go beyond episodic tasks, by explicitly reasoning about the dynamics and employing ideas from robust control theory and (3) tackle bootstrapping of safe initial policies by bridging simulations and real-world experiments via multi-fidelity Bayesian optimization, and by pursuing safe active imitation learning. Our research is motivated by three real-world CPS applications, which we pursue in interdisciplinary collaboration: Safe exploration of and with robotic platforms; tuning the energy efficiency of photovoltaic powerplants and safely optimizing the performance of a Free Electron Laser.

Keywords: reinforcement learning,algorithmic decision making,bayesian optimization,exploration,cyber physical systems