ERC FUNDED PROJECTS

"Children learn any language that they grow up with, adapting to any of the ca. 7000 languages of the world, no matter how divergent or complex their structures are. What cognitive processes make this extreme flexibility possible? This is one of the most burning questions in cognitive science and the ACQDIV project aims at answering it by testing and refining the following leading hypothesis: Language acquisition is flexible and adaptive to any kind of language because it relies on a small set of universal cognitive processes that variably target different structures at different times during acquisition in every language. The project aims at establishing the precise set of processes and at determining the conditions of variation across maximally diverse languages. This project focuses on three processes: (i) distributional learning, (ii) generalization-based learning and (iii) interaction-based learning. To investigate these processes I will work with a sample of five clusters of languages including longitudinal data of two languages each. The clusters were determined by a clustering algorithm seeking the structurally most divergent languages in a typological database. The languages are: Cluster 1: Slavey and Cree, Cluster 2: Indonesian and Yucatec, Cluster 3: Inuktitut and Chintang, Cluster 4: Sesotho and Russian, Cluster 5: Japanese and Turkish. For all languages, corpora are available, except for Slavey where fieldwork is planned. The leading hypothesis will be tested against the acquisition of aspect and negation in each language of the sample and also against the two structures in each language that are most salient and challenging in them (e. g. complex morphology in Chintang). The acquisition processes also depend on statistical patterns in the input children receive. I will examine these patterns across the sample with respect to repetitiveness effects, applying data-mining methods and systematically comparing child-directed and child-surrounding speech."

1 998 438 €

Start date: 2014-09-01, End date: 2019-08-31

Drones are disrupting industries, such as agriculture, package delivery, inspection, and search and rescue. However, they are still either controlled by a human pilot or heavily rely on GPS for navigating autonomously. The alternative to GPS are onboard sensors, such as cameras: from the raw data, a local 3D map of the environment is built, which is then used to plan a safe trajectory to the goal. While the underlying algorithms are well understood, we are still far from having autonomous drones that can navigate through complex environments as good as human pilots. State-of-the-art perception and control algorithms are mature but not robust: coping with unreliable state estimation, low-latency perception, real-time planning in dynamic environments, and tight coupling of perception and action under severe resource constraints are all still unsolved research problems. Another issue is that, because battery energy density is increasing at a very slow rate, drones need to navigate faster in order to accomplish more within their limited flight time. To obtain more agile robots, we need faster sensors and low-latency processing. The goal of this project is to develop novel scientific methods that would allow me to demonstrate autonomous, vision-based, agile quadrotor navigation in unknown, GPS-denied, and cluttered environments with possibly moving obstacles, which can be as effective in terms of maneuverability and agility as those of professional drone pilots. The outcome would not only be beneficial for disaster response scenarios, but also for other scenarios, such as aerial delivery or inspection. To achieve this ambitious goal, I will first develop robust, low-latency, multimodal perception algorithms that combine the advantages of standard cameras with event cameras. Then, I will develop novel methods that unify perception and state estimation together with planning and control to enable agile maneuvers through cluttered, unknown, and dynamic environments.

2 000 000 €

Start date: 2020-09-01, End date: 2025-08-31

Carbon dioxide capture and storage is a technology to mitigate climate change by removing CO2 from flue gas streams or the atmosphere and storing it in geological formations. While CO2 removal from natural gas by amine scrubbing is implemented on the large scale, the cost of such process is currently prohibitively expensive. Inexpensive alkali earth metal oxides (MgO and CaO) feature high theoretical CO2 uptakes, but suffer from poor cyclic stability and slow kinetics. Yet, the key objective of recent research on alkali earth metal oxide based CO2 sorbents has been the processing of inexpensive, naturally occurring CO2 sorbents, notably limestone and dolomite, to stabilize their modest CO2 uptake and to establish re-activation methods through engineering approaches. While this research demonstrated a landmark Megawatt (MW) scale viability of the process, our fundamental understanding of the underlying CO2 capture, regeneration and deactivation pathways did not improve. The latter knowledge is, however, vital for the rational design of improved, yet practical CaO and MgO sorbents. Hence this proposal is concerned with obtaining an understanding of the underlying mechanisms that control the ability of an alkali metal oxide to capture a large quantity of CO2 with a high rate, to regenerate and to operate with high cyclic stability. Achieving these aims relies on the ability to fabricate model structures and to characterize in great detail their surface chemistry, morphology, chemical composition and changes therein under reactive conditions. This makes the development of operando and in situ characterization tools an essential prerequisite. Advances in these areas shall allow achieving the overall goal of this project, viz. to formulate a roadmap to fabricate improved CO2 sorbents through their precisely engineered structure, composition and morphology.

1 994 900 €

Start date: 2019-06-01, End date: 2024-05-31

ARTIVISM aims at exploring new artistic forms of political expression under difficult, precarious and/or oppressive conditions. It asks how social actors create belonging and multiple forms of resistance when they use art in activism or activism in art. What kind of alliances do these two forms of social practices generate in super-diverse places, in times of crisis and in precarious situations? Thus, ARTIVISM seeks to understand how social actors engage artistically in order to bring about social, economic and political change. Going beyond former research in urban and migration studies, and beyond the anthropology of art, ARTIVISM focuses on a broad range of artistic tools, styles and means of expression, namely festive events and parades, cartoons and comics and street art. By articulating performance studies, street anthropology and the sociology of celebration with migration and diversity studies, the project challenges former concepts, which took stable social groups for granted and reified them with ethnic lenses. The applied methodology considerably renews the field by bringing together event-, actor- and condition-centred approaches and a multi-sensory framework. Besides its multidisciplinary design, the ground-breaking nature of ARTIVISM lies in the application of the core concepts of performativity and liminality, as well as in an examination of the way to advance and refine these concepts and to create new analytical tools to respond to recent social phenomena. We have developed and tested innovative methods that respond to a postmodern type of fluid and temporary social action: audio-visual ethnography, urban event ethnography, street ethnography, field-crossing, and sensory ethnography (apprenticeship). Therefore, ARTIVISM develops new methods and theories in order to introduce a multi-faceted trans-disciplinary approach to the study of an emerging field of social transformations that is of challenging significance to the social sciences.

1 999 287 €

Start date: 2016-09-01, End date: 2022-02-28