In quantum matter, vortices are topological excitations characterized by quantized circulation of the velocity field. They can be found in contexts as diverse as superconductors, Bose-Einstein condensates, laser beams, and even in the recently detected gravitational waves emitted during the merging of two spinning black holes. Quantum vortices are often modeled as funnel-like holes around which the quantum uid exhibits a swirling ow. In this perspective, vortex cores are nothing more than empty regions where the superuid density goes to zero, and their motion is governed by first-order differential equations. In the last few years, this simple view has been challenged and it is now increasingly clear that, in many real systems, vortex cores are not that empty. In these cases, the hole in the superuid is lled by particles or excitations which dress the vortices and provide them with an effective inertial mass. This feature opens the door to exciting new scenarios where inertial effects compete with the usual inter-vortex interactions. In this way, well-established results about vortex dynamics, binding-unbinding phase transitions, and robustness of superuidity are challenged. The project “Vortexons” will provide a complete description of the physics disclosed by quantum vortices with massive cores, addressing these crucial open issues from both the theoretical and the experimental sides. The resulting theory aims to be not only the gold standard in all those phenomena where quantum matter features massive topological excitations, but also the necessary foundation for the development of new high-performance superconductors. In this perspective, our theory will thus possibly be the seed of major breakthroughs having a disruptive impact on society, economy, and environmental policies, such as higher-resolution magnetic resonance scanners, low-power microprocessors, and high-speed transportation.

This project develops a simulation strategy aiming to create a 3D virtual fire lab that can model radiative transfer in open landscape scale vegetation fire. The end objective is to help improving fire monitoring Earth Observation (EO) products. It builds on an initial system designed by the Experience Researcher (ER) during a previous European Space Agency project, and the coupling of models of fire spread, atmospherics dynamics and radiative transfer developed by the hosts. It takes opportunity of (a) the wide experience of the beneficiary host organization in fire monitoring and modeling, (b) recent efforts conducted by host partners to improve atmospheric representation in radiative transfer model and fire effects in atmospheric model, and (c) the ER’s experience in the fire remote sensing community. Fire disturbance is parameterized in large scale atmospheric modeling (e.g. forecast model) via emission inventory based on EO products. A well-established approach is to use the Fire Radiative Power product (FRP) to estimate total fire energy emission and infer the associated fuel mass consumption and trace gas emission. So far, the conversion from emissive radiative energy to mass consumption is based on a linear relationship that has only been demonstrated for small scale fire and little evidence are currently present to validate it in the context of large-scale fire scenario. The simulation strategy proposed here aims to setup a tool able to study energy transfer in large-scale fires that will help us understand the roles of the flames and the plume to eventually evaluate their sensitivity in the fire emission FRP retrievals. While project results have potential high application in the atmospheric community, the training organized with the host and the two partners will provide a wide range of expertise in atmospheric dynamics, radiative transfer, image processing, fire modeling, data science that will enrich ER's career prospective.

There is a fast-growing interest in extending the capabilities of computing systems to perform human-like tasks in an intelligent way. These technologies are usually referred to as cognitive computing. We envision a next revolution in computing in the forthcoming years that will be driven by deploying many “intelligent” devices around us in all kind of environments (work, entertainment, transportation, health care, etc.) backed up by “intelligent” servers in the cloud. These cognitive computing systems will provide new user experiences by delivering new services or improving the operational efficiency of existing ones, and altogether will enrich our lives and our economy. A key characteristic of cognitive computing systems will be their capability to process in real time large amounts of data coming from audio and vision devices, and other type of sensors. This will demand a very high computing power but at the same time an extremely low energy consumption. This very challenging energy-efficiency requirement is a sine qua non to success not only for mobile and wearable systems, where power dissipation and cost budgets are very low, but also for large data centers where energy consumption is a main component of the total cost of ownership. Current processor architectures (including general-purpose cores and GPUs) are not a good fit for this type of systems since they keep the same basic organization as early computers, which were mainly optimized for “number crunching”. CoCoUnit will take a disruptive direction by investigating unconventional architectures that can offer orders of magnitude better efficiency in terms of performance per energy and cost for cognitive computing tasks. The ultimate goal of this project is to devise a novel processing unit that will be integrated with the existing units of a processor (general-purpose cores and GPUs) and altogether will be able to deliver cognitive computing user experiences with extremely high energy-efficiency.

INTERACT will develop new Interactive Learning Algorithms (ILA), motivated by applications in Natural Language Understanding (NLU). The main assumptions behind supervised approaches are unrealistic because most NLU applications have unique information needs, and large collections of annotated data are necessary to achieve good performance. INTERACT follows a collaborative machine learning paradigm that breaks the distinction between annotation and training. We focus on compositional latent-state models (CLSMs) because natural language is rich, complex and compositional. To reduce the amount of human feedback necessary for learning CLSMs we must eliminate annotation redundancy. We argue that to achieve this in the context of CLSMs we must combine: (1) An optimal human feedback strategy, with (2) inducing a latent structure of parts in the compositional domain. Annotation effort will be minimized because the method will only request representative feedback from each latent class. INTERACT marries representation learning (i.e. of parts) and active learning for CLSMs. Our approach goes beyond classical active learning where the ILA asks labels for samples chosen from a pool of unlabeled data. We empower the ILA with the ability to ask for labels for any complete or partial structure in the domain, i.e. the ILA will be able to generate samples. We work under the framework of spectral learning of weighted automata and grammars and use ideas from query learning. A key idea is reducing the problem of interactive learning of CLSMs to a form of interactive low-rank matrix completion. Our concrete goals are: (1) Develop ILAs for CLSMs based on spectral learning techniques; and (2) Investigate optimal strategies to leverage human feedback, taking into account what is optimal for the ILA and what is easy for the teacher. We will experiment with NLU tasks of increasing complexity, from sequence and tree classification to parsing problems where the outputs are trees.

The objective of this proposal is to develop fundamental and influential research into combinatorial designs. These objects have fascinated pure mathematicians for over 200 years and have also found many applications, for example in biological experiment design or in the design of strong error correcting codes in order to transmit data securely. The majority of research in design theory has focused on the existence and construction of designs. However recent breakthroughs have opened up exciting new perspectives, allowing for a much deeper understanding of these objects. The aim of this project is to develop these new directions by adopting a probabilistic stance and studying random designs. Our key objectives explore the existence and statistics of global structures in large designs and the longstanding problem of efficient algorithms for random sampling of designs. Through this, the project will foster connections between areas of pure mathematics, in particular extremal and probabilistic combinatorics, and the field of randomized algorithms in theoretical computer science. In order to achieve the proposal's objectives, the researchers will build upon a range of powerful novel methods, drawing on the expertise of the experienced researcher in absorption techniques and spanning structures and that of the host in rainbow structures and Markov chains. This will foster an exchange of knowledge between the two parties and greatly enhance the research potential of the experienced researcher. This project thus provides a pivotal opportunity for the development of his career as a young scientist, enabling him to push this exciting branch of design theory forward, broaden his knowledge and cement himself as a prominent researcher in several disciplines.