Granular materials are ubiquitous in nature and in various industries, such as chemicals, pharmaceuticals, food and ceramics. Their thermomechanical behaviours are governed by the interactions between solid particles, as well as between particles and the surrounding media (gas or liquid). Although granular materials have been investigated extensively, there are still some unsolved challenging issues concerning the thermomechanical behaviours, including heat generation (i.e. self-heating) and transfer, and thermal effects on material properties and process performance. Furthermore, the unique thermomechanical attributes have led to emerging applications with granular materials, such as additive manufacturing, powder coating, high quality composites, insulation and efficient thermal processing for energy conservation, but there is a lack of mechanistic understanding of thermomechanical behaviour of granular materials in these emerging applications. MATHEGRAM will hence deliver a timely, concerted research and training programme to address these challenges, bringing together a multi-disciplinary and inter-sectorial consortium consisting of 6 leading academic institutes, 4 non-academic beneficiaries and 6 partner organisations from 8 EU member states. Our vision is to develop robust new numerical models and novel experimental techniques that can predict and characterise heat generation and transfer, as well as thermal effects in granular materials. The enhanced mechanistic understanding of granular materials will enable them to be used in diverse industries, while also achieving energy conservation and CO2 emission reduction. We will also train a cohort of 15 ESRs with balanced gender, who will be the next generation scientific and technological leaders with competency and the research and transferable skills to work effectively across disciplinary and sectoral boundaries.

The goal of COCOBOTS is to develop conversational assistants and cobots capable of interacting with human coworkers in sophisticated ways. One crucial such way is through the development of a natural language programming toolkit that will allow human users to teach new actions to conversational cobots and construct joint actions with them through natural conversation in an interactive way. Programming through conversation would allow a human user without sophisticated programming skills or access to massive amounts of training data to program an assistant or cobot on the spot in the way that we teach other humans, without having to rely on an expert programmer to intervene. One could try out an idea with the robot and then modify it just as one would do with another human when teaching or developing a joint action. Such a toolkit would open up a wide range of new markets for companies, such as LINAGORA, who specialize in the development of conversational assistants or cobots that need to perform actions such as alerting workers on an assembly line to malfunctioning equipment or physically intervening to fix that equipment. It would also bring increased value to companies, such as Airbus, that seek to boost their manufacturing output by adding cobots to assembly lines or maintenance tasks. For the moment, the utility of conversational assistants or cobots is limited to carrying out commands and performing actions that are pre-defined via hard-coding or, in the case of robots, learned through demonstration or manual manipulation. A natural language programming toolkit would give a user without programming expertise the power to adapt their assistant to their needs via on the spot training. To demonstrate the efficacy of our toolkit, COCOBOTS will develop a proof of concept featuring a simulated assembly cobot that is able to learn new concepts associated with manufacturing, such as a torx, and new actions, such as how to build a certain kind of bridge, by stringing together atomic actions as instructed by a human user. Bringing together conversational models with the capacity of cobots to physically interact in their environments will be crucial for testing our approach, as we think that the capacity to understand situated conversation (and in fact, any conversation at all) is greatly enhanced by physical interaction with the outside world. Observing a robot's interaction with objects in a physical environment or its ability to string together primitive actions based on multimodal, conversational instructions, will also provide clear criteria to evaluate our approach and show that following a program specified via natural language is more effective than hard coding, demonstration, or manual manipulation of a robot. To develop the model of multimodal dialogue needed to make cobots truly conversational, we will build on a solid foundation of expertise of COCOBOTS members in semantic grounding (ANITI/CerCo, Airbus), dialogue models (University of Potsdam, LINAGORA, ANITI/IRIT), conversational assistants (LINAGORA) and robotics (ANITI/LAAS, Airbus). The novelty of our approach will lie in bringing together work on semantic and conversational grounding, which is generally pursued by separate communities, to develop a hybrid model that exploits the way that these processes influence each other. Our approach will require us to overcome three major challenges. First, we will need to bring the compositionality of referential meaning to bear on the semantic grounding of complex expressions using a hybrid AI approach. Second, we will need to account for the different ways that the nonlinguistic environment can ground and contributed to discourse meaning. Third, we will need to develop a model of situation discourse that provides a symbolic skeleton that we can then flesh out with subsymbolic pairing of nonlinguistic content with linguistic expressions.

The ability to observe the internals of an execution of a computer-based system is a fundamental requirement for ultimately ensuring correctness and safe behaviour. Within COEMS (Continuous Observation of Embedded Multicore Systems) a novel observer platform with supporting verification methods for software systems is created. COEMS tackles the issues of detection and identification of non-deterministic software failures caused by race conditions and access to inconsistent data. It gives insight to the system’s actual behaviour without affecting it allowing new verification methods. An efficient real-time access and analysis as a critical element for operating safe systems will be developed and validated by COEMS. Moreover, a cross-layer programming approach supporting failure detection will be proposed. COEMS aims at shortening the development cycle by considerably increased test efficiency and effectivity, by increased debug efficiency (especially for non-deterministically occurring failures) and by supporting performance optimization. COEMS improves the reliability of delivered systems, enabling software developers to identify, understand, and remove software defects before release, as well as improving efficiency of software for multi/many-core computing systems in terms of performance, real-time behaviour, and energy consumption. The two Global Players Thales Group and Airbus Group, both active in safety-critical domains, will validate the COEMS approach by suitable demonstrators, i.e. testing and debugging of real-world multicore applications. In addition to these two domains, we will address the domains of safety-critical medical applications, automation and automotive industry, as well as the Internet of Things. Technologically, COEMS will provide the world-wide first comprehensive online observation approach that is non-intrusive allowing improved testing and debugging. Altogether, COEMS will define a new state-of-the-art for software systems development.

Terrestrial demands on space missions are increasing rapidly in terms of complexity, technology and velocity. Next to navigation (GPS, GALILEO), science (investigation of space and the universe) and exploration (ISS, Mars), two types of space missions are very important for Europe: Earth Observation (EO, for the sustainability of nature and mankind) and Telecommunication (TC, for business and global connectivity). Each mission requires partly unique technologies, which are produced by only very few global suppliers. If these technologies are not available from within Europe, there is a danger that non-dependent missions may not be performed, created and tailored with a consequent loss of sovereignty in political decisions and a loss of market shares. One of these so-called “Critical Technologies” is the “Large Deployable Reflector (LDR)”. Packed in stowed configurations, these reflectors can be accommodated on satellites, which then still comply with the limited launcher fairing volumes. By enlarging the size of the reflector it is possible to offer higher sensitivity and resolution, e.g. for radar missions (EO & science) and implement stronger communication links for e.g. higher data throughput (TC). Within the upcoming eight years the demand for such reflectors will increase worldwide, whereas the Consortium targets a certain market share with its “Large European Antenna (LEA)”. The proposed H2020 project would now enable the combination of the technologies previously developed by the consortium members and the joining of further European entities to fill the remaining gaps and form one strong and complete European team. Through obtaining an EC-grant for LEA, each building block will be upgraded with innovation, adapted to a scenario and qualified to meet one common target, namely: 1st European PFM (including reflector and arm) reaching TRL 8 to be ready for integration by the end of 2020 and for flight in 2021.