SZTAKI

Graph-theoretical models are natural tools for the description of road networks, circuits, communication networks, and abstract relations between objects, hence algorithmic graph problems appear in a wide range of computer science applications. As most of these problems are computationally hard in their full generality, research in graph algorithms, approximability, and parameterized complexity usually aims at identifying restricted variants and special cases, which are at the same time sufficiently general to be of practical relevance and sufficiently restricted to admit efficient algorithmic solutions. The goal of the project is to put the search for tractable algorithmic graph problems into a systematic and methodological framework: instead of focusing on specific sporadic problems, we intend to obtain a unified algorithmic understanding by mapping the entire complexity landscape of a particular problem domain. Completely classifying the complexity of each and every algorithmic problem appearing in a given formal framework would necessarily reveal every possible algorithmic insight relevant to the formal setting, with the potential of discovering novel algorithmic techniques of practical interest. This approach has been enormously successful in the complexity classifications of Constraint Satisfaction Problems (CSPs), but comparatively very little work has been done in the context of graphs. The systematic investigation of hard algorithmic graph problems deserves the same level of attention as the dichotomy program of CSPs, and graph problems have similarly rich complexity landscapes and unification results waiting to be discovered. The project will demonstrate that such a complete classification is feasible for a wide range of graph problems coming from areas such as finding patterns, routing, and survivable network design, and novel algorithmic results and new levels of algorithmic understanding can be achieved even for classic and well-studied problems.

The Sun is an enigmatic star that produces the most powerful explosive events in our solar system - solar flares and coronal mass ejections. Studying these phenomena can provide a unique opportunity to develop a deeper understanding of fundamental processes on the Sun, and critically, to better forecast space weather. The Active Region Classification and Flare Forecasting (ARCAFF) project will develop a beyond state-of-the-art flare forecasting system utilising end-to-end deep learning (DL) models to significantly improve upon traditional flare forecasting capabilities. ARCAFF will increase the accuracy and timeliness of current operational flare forecast products and create new time series flare forecasts. Furthermore, ARCAFF forecasts will include forecast uncertainties, another major improvement over current systems. The large amount of available space-based solar observations are an ideal candidate for this type of analysis, given DL effectiveness in modelling complex relationships. DL has already been successfully developed and deployed in weather forecasting, financial services, and health care domains, but has not been fully exploited in the solar physics domain. Solar flare forecasts from ARCAFF will be benchmarked against current systems using international community standards, and will demonstrate ARCAFF’s superior forecasting capabilities. The datasets, codes and DNNS developed for ARCAFF will be made openly available to support further research efforts and encourage their re-use. ARCAFF is relevant to the work program as it will exploit currently available data space weather data to train DL models to improve forecast accuracy. DL itself is an innovation enabling technology and analysis of the DL models will improve scientific understanding of solar flares. Through the creation of new forecast products it will develop and mature new concepts for both scientific and monitoring purposes, following the best-practices of meteorological services.

EXCELL foresees the collaboration of academics from 4 EU countries (HU, DE, UK, BE) in the multidisciplinary topic of Big Data Applications for Cyber-Physical Systems in Production and Logistics Networks. The main scientific and innovation focus of EXCELL was devised both from world-wide tendencies and local requirements (expressed by the regional S3), departing from the present competences of the cooperating partners (SZTAKI, Fraunhofer-FIT, Aston, and KU Leuven). Main project actions focus on: • Knowledge acceleration through transnational secondments and training sessions in 8 selected Priority Research Fields (PRFs) and 6 Complementary Skills, • Supporting exploitation of research results and user-driven innovation, • Augmenting scientific impact actions through key publications and active participation in the scientific discourse, • Dissemination and sustainable innovation. The 8 PRFs are: 1.Cyber-Physical Systems and Human System Interaction (responsible Fraunhofer), 2.Business-based Internet of Things and Services (resp. Fraunhofer), 3.Next-generation Authentication and Authorization Solutions (resp. KU Leuven), 4.Enhanced Context-aware Services (resp. KU Leuven), 5.Cognitive modelling and social networking approaches (resp. ASTON), 6.Data mining (resp. ASTON), 7.Data interoperability (resp. SZTAKI), 8.Tracking and tracing (resp. SZTAKI). The Complementary Skills include: Technology transfer and results deployment to industry, Emotional intelligence, Motivating others, Personal impact, Stress management for managers, and Hands-on experience by using and contributing to software tools. EXCELL activities are aligned towards 4 major types of impact: 1. Scientific impact, 2. Industry-driven innovation and technology transfer, 3. Sustainability and exploitation, 4. General and societal engagement, enabling strategic growth in both cross-sector and cross-regional cooperation, and sustainable scientific capabilities of all staff/institutions involved.

Flight Phase Adaptive Aero-Servo-Elastic Aircraft Design Methods (FliPASED) opens a complete new dimension for the integrated aircraft design. Coupling between aeroelasticity, gust response, flight control methods, instrumentation and certification aspects is not exploited in current aircraft design. A common set of models, coupled with joint requirements enable a multidisciplinary-optimized design for the entire aircraft, leading to more optimized overall performance. The concept of exploiting coupling between disciplines will take advantage of tools developed by the partners in former projects. The main objectives of the proposal aim at tightly coupled multi-objective optimization of advanced, active controlled wing designs through the integration of a collaborative design tool chain. More than 10% fuel efficiency improvement, and 20% reduction in peak amplitude of the gust response, as well as a 50% reduction of number of distinct models used during the development and certification process are set as project goals. Through the integration of all discipline tools from aerodynamics, structural design, aeroelastic simulation and control design in one integrated tool chain an active, condition optimized wing design becomes feasible, enabling enhanced performance at lower weight and cost. The project will raise the efficiency of a currently separately existing development toolchains, by advanced multidisciplinary and collaborative capabilities for whole aircraft along its life cycle. It will develop methods and tools for very accurate flexible-mode modelling and flexible aircraft control synthesis, in the context of reliable implementation of the avionics system, taking into consideration the fault detection and reconfiguration. The accuracy of developed tools and methods will be validated on a safe and affordable experimental platform, and results will be shared along with design requirements and standardized interfaces in an open source approach.

The objective of the proposal is to establish the Centre of Excellence in Production Informatics and Control (EPIC CoE) as a leading, internationally acknowledged focus point in the field of cyber-physical production systems representing excellence in RDI. The focus is devised both from world-wide tendencies and regional S3 strategies. EPIC CoE will be constituted and run through the cooperation of the Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI), two faculties of the Budapest University of Technology and Economics (BME) and four institutions of the Fraunhofer Gesellschaft (FhG) under the coordination of the National Research, Development and Innovation Office (NKFIH), as a governmental actor. The EPIC project will lead to: 1. The upgrade of MTA SZTAKI as existing Centre of Excellence (CoE) of the EU. 2. The establishment of a new legal entity (EPIC Ltd) of the academic partners relying on the very positive experiences gained by the Fraunhofer-SZTAKI Project Centre for Production Management and Informatics (PMI), a joint initiative of FhG and the Hungarian Academy of Sciences (MTA), started in 2010. (The cooperating FhG institutions have been the FhG IPA and Fraunhofer Austria). 3. The extension of the FhG-SZTAKI cooperation with two faculties of BME, and with FhG IPT and the FhG IPK. 4. The close cooperation of academia, university and industry, with a special emphasis on SMEs. Due to direct, institutionalized interactions with FhG, not only the scientific capabilities of the Hungarian partners but also their ability to transfer scientific results to industry-relevant applications will be enhanced, and in a sustainable way, providing a “high speed lift” to innovation culture and performance in Hungary and the CEE region. The large expectations towards the EPIC CoE are manifested by 42 Letters of Support received from industrial companies and clusters.