Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Development and application of Novel, Integrated Tools for monitoring and managing Catchments (INTCATCH)

Framework: Horizon 2020 — Societal Challenges
Programme: Climate action, environment,  resource efficiency and raw materials
Theme: Enabling the transition towards a green economy and society through eco-innovation
Action type: Innovation actions
Countries involved: Austria,Germany,Spain,Italy,Italy,Italy,Germany,Greece,Norway,United Kingdom,United Kingdom,United Kingdom,United Kingdom,United Kingdom
Call: Demonstration/pilot activities (H2020-WATER-2015-two-stage, 2015)
Start date: 2016-06-01
End date: 2020-01-31
Duration (months): 44.0
Total project cost (EUR): 8770935.0
EU funding (EUR): 7570335.0
Participants: 20
Project status: CLOSED

INTCATCH will instigate a paradigm shift in the monitoring and management of surface water quality that is fit for global waters in the period 2020-2050. INTCATCH will do this by developing efficient, user-friendly water monitoring strategies and systems based on innovative technologies that will provide real time data for important parameters, moving towards SMART Rivers. The business model will transform water governance by facilitating sustainable water quality management by community groups and NGOs using a clouds data linked to a decision support system and eco-innovative technologies. The INTCATCH project will use demonstration activities to showcase eco-innovative autonomous and radio controlled boats, sensors, DNA test kits and run-off treatment technologies. Actions which develop and evaluate these in a range of catchments will address the important innovation barriers to uptake, notably, a lack of knowledge of new technologies and their capabilities, identified by the European Innovation Plan (EIP) on water. By conceptually moving the laboratory to the ‘field’, the monitoring techniques that will be developed aim to supersede the inefficient, time dependent, costly and labour-intensive routine sampling and analysis procedures currently deployed to understand the quality of receiving waters. It will compliment routine monitoring that is required for baseline datasets, but also enable cost-effective impact and management investigations. INTCATCH will incentivise stakeholder innovation in monitoring and will facilitate new financing for innovation through its innovative franchise business model and empowerment of community groups and NGOs. The market ambition is that the INTCATCH business will facilitate an eco-innovative approach to deliver good quality water bodies across Europe and beyond. This will support green growth, increase resilience to climate change and capture greater market-share for Europe’s innovative industries.

Keywords: robotic boat runoff treatment
Intra-operative microscope for tumor margin assessment (HistologTM Scanner)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Switzerland
Call: SME instrument (H2020-SMEInst-2018-2020-2, 2018)
Start date: 2018-09-01
End date: 2021-08-31
Duration (months): 36.0
Total project cost (EUR): 3310125.0
EU funding (EUR): 2317087.5
Participants: 1
Project status: CLOSED

Breast Cancer is the most common cancer type and main cause of cancer related death in women worldwide. One in eight women in the EU and US will develop breast cancer in their lifetime. Breast-Conserving Surgery (BCS) also called lumpectomy is a standard of care for early breast cancer. Unlike mastectomy, in which the entire breast and lymph nodes are removed, lumpectomy involves only removal of the cancerous tissue and a margin of healthy tissue. Government initiatives to increase public awareness for early breast cancer detection are drastically increasing the number of patients eligible for BCS. Now, BCS is the most performed breast cancer surgery and accounts today for about 70% of all breast cancer surgeries. In counterpart, BCS brings about a risk for the patient to need to undergo an additional surgery in case that not all the cancerous tissue is removed. Re-operation rates (RoR) in BCS are evaluated at 30% on average. Re-operation in BCS is costly for hospitals (€20.000 per re-operation), inconvenient for patients and delays initiation of adjuvant therapies. There is a clinical need for improved intra-operative margin assessment (IMA) methods to assess breast cancer tumor specimen margins in real time in the operating room to decrease RoR. HistologTM Digital Solution allows surgeons real-time, accurate, affordable, whole margin breast tumor assessment through the highest resolution at cellular and subcellular level by using a user-friendly graphical interface supported by a differentiating Automating Reading CAD tool without the need of a pathologist. HistologTM Digital Solution is fully compatible with the gold standard post-operational H&E assessment since it does not destroy specimen and can be integrated with patient information systems. SamanTree Medical’s goal is to make of HistologTM Digital Solution the standard for IMA of breast cancer tumor margins, and decrease the RoR from the current 30% to less than 3%.

Keywords: microscopy,histopathology,breast cancer,lumpectomy
Functional Blood Analysis for Clinical Application in Sepsis (BASIC)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Germany
Call: ERC-Proof of Concept (ERC-2017-PoC, 2017)
Start date: 2018-02-01
End date: 2019-07-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

We want to prepare the commercialisation of a novel clinical diagnostic test for the status of the immune system – the mechanical haemogram – with the purpose of improving the therapy management of severe infections, in particular in case of sepsis. Sepsis is ranked the third most common cause of death worldwide, and represents one of the most challenging problems even in modern health care systems. Our test is based on the analysis of single cell mechanical properties of white blood cells found in peripheral blood. Those properties serve as functional biomarker for the activation status of the immune system. With this test, we aim to provide an early detection of sepsis and direct monitoring of treatment effects for an efficient therapy management. The goal of the proposed project is to create and evaluate a business case for the successful implementation of this in vitro diagnostic test for clinical application in sepsis management.

Keywords: biomarker,sepsis,haemogram,rt dc,zellmechanik,cell mechanics
The World's First Safe and Efficacious Human Blood Substitute: HbMP-700, a Haemoglobin based oxygen carrier, based on bovine blood. (HbMP-700)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Treating and managing disease
Action type: SME instrument
Countries involved: Germany
Call: Supporting innovative SMEs in the healthcare biotechnology sector (H2020-SMEINST-1-2016-2017, 2016)
Start date: 2017-01-01
End date: 2017-04-30
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

CC-Ery GmbH develops HbMP-700, the world’s first substitute for human blood as a source for red blood cell (RBC) transfusions. 85 Mio. RBC transfusions are used p.a., representing a 10-15nb€ market. They are used to compensate for blood loss during surgery, treat patients with haematological malignancies, as well as victims of accidents, natural disasters, and military conflicts. The only source for RBC transfusions today are voluntary human blood donors. This leads to frequent supply shortages in the EU and other major healthcare markets. Donor blood must match patient blood. It can only be stored a few weeks, adding to the critical supply situation. Developing countries face a more severe situation as they cannot afford the standard of care of the developed world. In addition, human blood bears the risk of contamination with infections. HbMP-700 is extracted from bovine blood – a nearly unlimited, low cost supply source. It is transformed by a patented purification and manufacturing method to a lyophilized powder that is as effective as RBC transfusions from human blood, but bears no risk of infection, can be stored for months, and can be given to patients of all blood types. Bovine blood from one major EU slaughterhouse is sufficient to produce enough HbMP-700 to supply global demand for RBC transfusions. CC-Ery implemented the manufacturing method on a laboratory scale and is currently conducting preclinical trials, so far with excellent results. The objective of the SME Phase I project is to develop a business plan for the market introduction of HbMP-700. For this, a technological, regulatory, and economic feasibility analysis will be conducted. In Phase II, it is planned to build up a GMP (Good Manufacturing Practice) compliant manufacturing process and subsequently conduct a GCP (Good Clinical Practice) safety and proof of concept trial in humans. Total cost to market is estimated to be € 25-35 Mio. Peak sales are estimated to be more than € 1 bn. annually.

Keywords: haemoglobin based oxygen carrier,human blood substitute,manufacturing scale up,proof of concept,haematology,transfusion medicine,red blood cells
Flexible ATCO Endorsement and LDACS Complement (PJ33-W3 FALCO)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: Single European Sky Air Traffic Management Research
Action type: Research and Innovation actions
Partnership: SESAR (Art-187)
Countries involved: Austria,Germany,Spain,Ireland,Italy,Germany,Sweden,Switzerland,Belgium,Croatia,Hungary,Norway,Belgium,France,France,Czechia,Netherlands,Denmark,United Kingdom
Call: Increased flexibility in the allocation of ATCO resources (H2020-SESAR-2020-2, 2020)
Start date: 2021-01-01
End date: 2023-06-30
Duration (months): 30.0
Total project cost (EUR): 8350466.26
EU funding (EUR): 5315206.77
Participants: 29
Project status: SIGNED

The overall objective of PJ.33 is to increase (cost) efficiency of the air traffic management (ATM) in two different ways: On one hand, the goal is to increase air traffic control (ATC) capacity by investigating and validating technical and procedural means based on the previous work done in PJ.10, enabling the re-organization of the endorsement of air traffic controllers working in lower and upper area control service (explicitly excluding approach control service), which will also lead to a reduction in personnel costs and an increase in ATC cost efficiency. This will be achieved by introducing new endorsement rules which are more based on traffic complexity, sector classes and the level of skills, experience and training a controller has received on a specific class of working environment and supporting system. On the other hand, the efficiency of air-ground communication will be increased (e.g. maximum information throughput per minute) by investigating the technical enabler to broadly introduce the voice capability supported by L-Band Digital Aeronautical Communication System (LDACS) with the ultimate goal to replace analogue voice communication. Furthermore, the future air-ground terrestrial datalink (LDACS) will increase connectivity between air and ground that is essentially to support the future concepts, according to the Master Plan principles of service-orientation and scalability, and it will support also the resilience of aircraft navigation.

Keywords: air traffic management atm,controller endorsement,l band digital aeronautical communication system ldacs
Hardware-Assisted Decoupled Access Execution on the Digital Market: The EDRA Framework (EDRA)

Framework: Horizon 2020 — Excellent Science
Programme: Future and Emerging Technologies (FET)
Theme: EIC Pathfinder
Destination: Novel ideas for radically new technologies
Action type: Coordination and support actions
Countries involved: Greece
Call: FET Innovation Launchpad (H2020-FETOPEN-2018-2019-2020-03, 2018)
Start date: 2019-05-01
End date: 2020-10-31
Duration (months): 18.0
Total project cost (EUR): 100000.0
EU funding (EUR): 100000.0
Participants: 1
Project status: CLOSED

The FET project “EXTRA” (Exploiting eXascale Technology with Reconfigurable Architectures) aimed at devising efficient ways to deploy ultra-efficient heterogeneous compute nodes for future exascale High Performance Computing (HPC) applications. One major outcome was the development of a framework for mapping applications to reconfigurable hardware, relying on the concept of Decoupled Access – Execute (DAE) approach. This project focuses on the commercialization of the EXTRA framework for cloud HPC platforms. More specifically, it targets the deployment of Virtual Machines (VMs) that integrate the EXTRA DAE Reconfigurable Architecture (EDRA) on custom hardware within the cloud infrastructure of one of the largest cloud service providers, the Amazon Web Services marketplace. End-users will be able to automatically map their applications to “EDRA-enhanced” VMs, and directly deploy them onto Amazon’s cloud infrastructure for optimal performance and minimal cost. Towards a successful exploitation outcome, the project will put effort on (a) applying the required software- and hardware-level modifications on the current EXTRA framework to comply with Amazon’s infrastructure, (b) devising a business strategy to effectively address various user groups, and (c) disseminating the benefits of this solution to large public events and summits.

Keywords: decoupled access execute,fpgas,cloud reconfigurable architectures
Learning Generative 3D Scene Models for Training and Validating Intelligent Systems (LEGO-3D)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Computer science and informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Germany
Call: ERC Starting Grant (ERC-2019-STG, 2019)
Start date: 2020-10-01
End date: 2025-09-30
Duration (months): 60.0
Total project cost (EUR): 1467500.0
EU funding (EUR): 1467500.0
Participants: 1
Project status: SIGNED

Recently, the field of computer vision has witnessed a major transformation away from expert designed shallow models towards more generic deep representation learning. However, collecting labeled data for training deep models is costly and existing simulators with artist-designed scenes do not provide the required variety and fidelity. Project LEGO-3D will tackle this problem by developing probabilistic models capable of synthesizing 3D scenes jointly with photo-realistic 2D projections from arbitrary viewpoints and with full control over the scene elements. Our key insight is that data augmentation, while hard in 2D, becomes considerably easier in 3D as physical properties such as viewpoint invariances and occlusion relationships are captured by construction. Thus, our goal is to learn the entire 3D-to-2D simulation pipeline. In particular, we will focus on the following problems: (A) We will devise algorithms for automatic decomposition of real and synthetic scenes into latent 3D primitive representations capturing geometry, material, light and motion. (B) We will develop novel probabilistic generative models which are able to synthesize large-scale 3D environments based on the primitives extracted in project (A). In particular, we will develop unconditional, conditioned and spatio-temporal scene generation networks. (C) We will combine differentiable and neural rendering techniques with deep learning based image synthesis, yielding high-fidelity 2D renderings of the 3D representations generated in project (B) while capturing ambiguities and uncertainties. Project LEGO-3D will significantly impact a large number of application areas. Examples include vision systems which require access to large amounts of annotated data, safety-critical applications such as autonomous cars that rely on efficient ways for training and validation, as well as the entertainment industry which seeks to automate the creation and manipulation of 3D content.

Keywords: computer vision,machine learning,generative models,primitive representations,differentiable rendering,data augmentation,simulation
High-Performance Indexing for Emerging GPU-Coupled Databases (CoupledDB)

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: Information Science and Engineering
Countries involved: Norway
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2017-05-01
End date: 2019-04-30
Duration (months): 24.0
Total project cost (EUR): 208400.4
EU funding (EUR): 208400.4
Participants: 1
Project status: CLOSED

Index structures are foundational to the performance of database systems and large-scale simulations. Even small advances in indexing can therefore have widespread, sweeping impact on both industry competitiveness and scientific productivity. The confluence of several hardware trends is setting the stage for disruptive innovation in database indexing: deescalating costs of memory make it feasible to organise most of the "hot", frequently accessed data in memory rather than on disk; and increasingly commonplace accelerators such as graphics processing units (GPUs) offer large-scale parallelism with a lower energy footprint. Thus, in-memory indexing that exploits GPUs could be much cheaper, faster, and greener. However, effectively incorporating GPUs into computation is a principal research challenge. To idle the powerful multicore system in favour of exclusively using the GPU connected to it, as done currently, is to squander valuable resources. On the other hand, the GPU has a vastly different computational model, so cannot straight-forwardly leverage multicore techniques. The challenges in handling this dichotomy, in fact, will cross-cut many research areas as the heterogeneity in the compute ecosystem becomes ubiquitous in parallel processing. Building on preliminary results that suggest common data structures processed by architecture-specific algorithms can support heterogeneity, this action will design indexes for the coupled multicore-GPU database systems that will soon be ubiquitous. The indexes will enable more responsive simulations of complex objects such as neurons and vehicle trajectories and support the recent proliferation of mobile-generated data. Moreover, through the action, the researcher will transfer technical parallel programming skills to the host, while the host will transfer expertise about new data types to the researcher. The project results will contribute to Europe's positioning at the forefront of heterogeneous parallel processing.

Keywords: databases,parallel algorithms,portable algorithms,co processing,modern heterogeneous hardware,clustering,multicore,numa,simd,word representations,spatio temporal keyword indexing
Unequal Democracies (UneqDems)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: The Social World and Its Diversity
Panel description: Sociology, social psychology, social anthropology, education sciences, communication studies
Countries involved: Switzerland
Call: ERC Advanced Grant (ERC-2016-ADG, 2016)
Start date: 2017-09-01
End date: 2023-02-28
Duration (months): 66.0
Total project cost (EUR): 2497525.0
EU funding (EUR): 2497525.0
Participants: 1
Project status: SIGNED

The proposed research program explores the implications of rising income inequality for the political process in advanced democracies and for the policies produced by competition among political parties and organized interests. The program posits that the political implications of inequality operates through two channels: inequality influences what citizens want from government, but it also affects political participation and influence and hence, by extension, government responsiveness to the preferences of different citizens. Students of the politics of inequality have tended to focus on only one channel, to the neglect of the other. The fundamental objective of the proposed research program is to develop a unified framework that draws on both research traditions and, in so doing, addresses lacunae in each. Another objective is to explore how the political consequences of low-end inequality (growing separation of the poor from the middle class) differ from the political consequences of high-end inequality (the growing concentration of income at the very top of the income distribution). The core questions that animate the research program are “macro” questions, pertaining processes and outcomes that are observed at the country level (or, in other words, the political-system level), but these questions will be addressed, in part, through analyses of individual attitudes, preferences and behavior. The latter analyses will involve a couple of original surveys, including a survey of attitudes towards the rich, as well as the use of existing national and cross-national survey data. With respect to macro-level comparisons, the research program will emphasize changes over time: changes in the structure of inequality as well as the level of inequality, changes in preferences and coalitions among citizens and organized interests and, finally, changes in income (or class) bias in democratic representation.

Keywords: democratic representation,public opinion,electoral participation