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Trento, Italy

Agency: Cordis | Branch: H2020 | Program: RIA | Phase: INFRAIA-1-2014-2015 | Award Amount: 10.00M | Year: 2016

ENSAR2 is the integrating activity for European nuclear scientists who are performing research in three of the major subfields defined by NuPECC: Nuclear Structure and Dynamics, Nuclear Astrophysics and Nuclear Physics Tools and Applications. It proposes an optimised ensemble of Networking (NAs), Joint Research (JRAs) and Transnational Access Activities (TAs), which will ensure qualitative and quantitative improvement of the access provided by the current ten infrastructures, which are at the core of this proposal. The novel and innovative developments that will be achieved by the RTD activities will also assure state-of-the-art technology needed for the new large-scale projects. Our community of nuclear scientists profits from the diverse range of world-class research infrastructures all over Europe that can supply different ion beams and energies and, with ELI-NP, high-intensity gamma-ray beams up to 20 MeV. We have made great effort to make the most efficient use of these facilities by developing the most advanced and novel equipment needed to pursue their excellent scientific programmes and applying state-of-the-art developments to other fields and to benefit humanity (e.g. archaeology, medical imaging). Together with multidisciplinary and application-oriented research at the facilities, these activities ensure a high-level socio-economic impact. To enhance the access to these facilities, the community has defined a number of JRAs, using as main criterion scientific and technical promise. These activities deal with novel and innovative technologies to improve the operation of the facilities. The NAs of ENSAR2 have been set-up with specific actions to strengthen the communities coherence around certain resarch topics and to ensure a broad dissemination of results and stimulate multidisciplinary, application-oriented research and innovation at the Research Infrastructures.

Agency: Cordis | Branch: H2020 | Program: RIA | Phase: ICT-19-2015 | Award Amount: 3.74M | Year: 2016

REPLICATE will assemble a world-class team of creative thinkers from research and private sectors to: - Fuel the creative industries growing demand for high-quality content by developing a user-centric, mobile-based, 3D-acquisition tool to transform the real-world into new forms of creative-assets by recruiting and encouraging the involvement of everyone. - Establish rich and flexible forms of reusable content through the development of semantic decomposition tools that can guide users through the process of unlocking sub-elements of objects and easily add lifelike properties to complex objects. - Introduce and stimulate the creative industries to new ways of content creation, access and reusability through a carefully devised crowd-sourcing strategy, fuelled by 3 Creativity Incubators. Enhance the human creative process through the integration of novel Mixed-Reality (MR) user experiences, enabling experimental solutions like 3D/4D storyboarding in unconstrained environments and the ad-hoc expression of ideas by disassembling and reassembling objects in a co-creative workspace. REPLICATE will benefit many stakeholder groups: Citizens will be empowered to generate 3D and be encouraged to solve disambiguations during reconstruction and decomposition. Creative people without prior 3D expertise will get icon-driven tools to experiment and play with 3D. Creativity professionals will be able to add richness and semantic-awareness to 3D models by harnessing human experience and state-of-the-art object detection. Researchers in the humanities will be able to take advantage of the creativity platform, usability and usage outcomes to further their research into co-creativity.

Agency: Cordis | Branch: H2020 | Program: RIA | Phase: ICT-17-2014 | Award Amount: 4.00M | Year: 2015

A European Digital Single Market free of barriers, including language barriers, is a stated EU objective to be achieved by 2020. The findings of the META-NET Language White Papers show that currently only 3 of the EU-27 languages enjoy moderate to good support by our machine translation technologies, with either weak (at best fragmentary) or no support for the vast majority of the EU-27 languages. This lack is a key obstacle impeding the free flow of people, information and trade in the European Digital Single Market. Many of the languages not supported by our current technologies show common traits: they are morphologically complex, with free and diverse word order. Often there are not enough training resources and/or processing tools. Together this results in drastic drops in translation quality. The combined challenges of linguistic phenomena and resource scenarios have created a large and under-explored grey area in the language technology map of European languages. Combining support from key stakeholders, QT21 addresses this grey area developing (1) substantially improved statistical and machine-learning based translation models for challenging languages and resource scenarios, (2) improved evaluation and continuous learning from mistakes, guided by a systematic analysis of quality barriers, informed by human translators, (3) all with a strong focus on scalability, to ensure that learning and decoding with these models is efficient and that reliance on data (annotated or not) is minimised. To continuously measure progress, and to provide a platform for sharing and collaboration (QT21 internally and beyond), the project revolves around a series of Shared Tasks, for maximum impact co-organised with WMT. To support early technology transfer, QT21 proposes a Technology Bridge linking ICT-17(a) and (b) projects and opening up the possibility of showing technical feasibility of early research outputs in near to operational environments.

Agency: Cordis | Branch: H2020 | Program: RIA | Phase: FETPROACT-1-2014 | Award Amount: 4.21M | Year: 2015

We propose visionary research to develop modeling, computational, and ICT tools needed to predict and influence disease spread and other contagion phenomena in complex social systems. To achieve non-incremental advances we will combine large scale, realistic, data-driven models with participatory data-collection and advanced methods for Big Data analysis. In particular we will go beyond the one-dimensional focus of current approaches tackling one aspect of the problem at a time. We will interconnect contagion progression (e.g. epidemics) with social adaptation, the economic impact and other systemic aspects that will finally allow a complete analysis of the inherent systemic risk. We will develop models dealing with multiple time and length scales simultaneously, leading to the definition of new, layered computational approaches. Towards policy impact and social response we will work to close the loop between models, data, behavior and perception and develop new concepts for the explanation, visualization and interaction with data and models both on individual and on collective level. We will cast the fundamental advances into an integrated system building on widely accepted open ICT technologies that will be used and useful beyond the project. As a tangible ICT outcome directed at facilitating the uptake and impact of the project, we will implement Interactive Social Exploratories defined as interactive environments which act as a front-end to a set of parameterizable and adjustable models, data analysis techniques, visualization methods and data collection frameworks. In summary, we aim to (1) produce fundamental theoretical, methodological and technological advances (2) mold them into a broadly usable ICT platform that will be a catalyst for producing, delivering, and embedding scientific evidence into the policy and societal processes and (3) evaluate the system empirically with policy makers and citizens focusing on the concrete problem of epidemic spreading.

Agency: Cordis | Branch: H2020 | Program: RIA | Phase: EURO-6-2015 | Award Amount: 3.63M | Year: 2016

A seamless interaction with the public administration (PA) is crucial to make the daily activities of companies and citizens more effective and efficient, saving time and money in the management of administrative processes. In particular, online public services have an enormous potential for reducing the administrative burden of companies and citizens, as well as for creating saving opportunities for the PA. This potential is however far from being fully exploited. Online services made available by the PA typically rely on standardized processes, copied from their offline counterparts and designed only from the public sector organizations own perspective. This results in online services that fail to adapt to the specific needs of citizens and companies. With SIMPATICO, we address the issues above by proposing a novel approach for the delivery of personalized online services that, combining emerging technologies for language processing and machine learning with the wisdom of the crowd, makes interactions with the PA easier, more efficient and more effective. SIMPATICO combines top-down knowledge of the PA with bottom-up contributions coming from the community. These contributions can be of different types, ranging from the qualified expertise of civil servants and professionals to problems and doubts raised by citizens and companies that find online services difficult to use. Our approach is able to take into account both explicit information sources coming from citizens, professionals and civil servants, and implicit ones, extracted from user logs and past user interactions. SIMPATICOs learning by doing approach will use this information and match it with user profiles to continuously adapt and improve interactions with the public services. All the collected information on public services and procedures will be made available within Citizenpedia, a collective knowledge database released as a new public domain resource.

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