ERC FUNDED PROJECTS

The fundamental 3D-BioMat project aims at providing a biomineralization model to explain the formation of microscopic calcareous single-crystals produced by living organisms. Although these crystals present a wide variety of shapes, associated to various organic materials, the observation of a nanoscale granular structure common to almost all calcareous crystallizing organisms, associated to an extended crystalline coherence, underlies a generic biomineralization and assembly process. A key to building realistic scenarios of biomineralization is to reveal the crystalline architecture, at the mesoscale, (i. e., over a few granules), which none of the existing nano-characterization tools is able to provide. 3D-BioMat is based on the recognized PI’s expertise in the field of synchrotron coherent x-ray diffraction microscopy. It will extend the PI’s disruptive pioneering microscopy formalism, towards an innovative high-throughput approach able at giving access to the 3D mesoscale image of the crystalline properties (crystal-line coherence, crystal plane tilts and strains) with the required flexibility, nanoscale resolution, and non-invasiveness. This achievement will be used to timely reveal the generics of the mesoscale crystalline structure through the pioneering explorations of a vast variety of crystalline biominerals produced by the famous Pinctada mar-garitifera oyster shell, and thereby build a realistic biomineralization scenario. The inferred biomineralization pathways, including both physico-chemical pathways and biological controls, will ultimately be validated by comparing the mesoscale structures produced by biomimetic samples with the biogenic ones. Beyond deciphering one of the most intriguing questions of material nanosciences, 3D-BioMat may contribute to new climate models, pave the way for new routes in material synthesis and supply answers to the pearl-culture calcification problems.

1 966 429 €

Start date: 2017-03-01, End date: 2022-08-31

"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

The vision of creating autonomous materials constituted of microscale motile units promises to disrupt a broad range of technologies but is still far beyond our reach. Inspired by nature, these materials are active, i.e. they convert available energy into functions, and adaptive, i.e. they respond to stimuli by reconfiguring via internal feedback and signalling schemes. In order to progress, we need to rethink the way in which we design, fabricate and control synthetic active units, aka active colloids or artificial microswimmers. I propose an innovative approach that combines colloidal synthesis, assembly and actuation with nanofabrication and the implementation of feedback to realize a new class of active colloids. Borrowing ideas from soft-robotic systems, we aim to realize and study “cyber-free” artificial microswimmers, which, in addition to on-board energy conversion, present internal degrees of freedom allowing for sensing, feedback and communication pathways ultimately to be regulated without external intervention. In particular, we will: 1) Numerically and experimentally implement feedback schemes to regulate single-particle motility and collective behaviour based on control theory. 2) Use a unique combination of capillary assembly and two-photon nanolithography to create shape-shifting active colloids that autonomously regulate their motility based on stimuli orthogonal to their propulsion schemes. 3) Create “transmitting” and “receiving” active colloids, sending and sensing chemical signals (pH changes), to regulate their motility. By introducing strong coupling between particles, and with stimuli beyond classical colloidal interactions, this proposal will enable a forward leap in the study of the emergent physics of active systems, as required to realize the vision of autonomous materials and microscale devices.

1 997 718 €

Start date: 2021-05-01, End date: 2026-04-30

Present-day financial markets are turning algorithmic, as market orders are increasingly being executed by fully automated computer algorithms, without any direct human intervention. Although algorithmic finance seems to fundamentally reshape the central dynamics in financial markets, and even though it prompts core sociological questions, it has not yet received any systematic attention. In a pioneering contribution to economic sociology and social studies of finance, ALGOFINANCE aims to understand how and with what consequences the turn to algorithms is changing financial markets. The overall concept and central contributions of ALGOFINANCE are the following: (1) on an intra-firm level, the project examines how the shift to algorithmic finance reshapes the ways in which trading firms operate, and does so by systematically and empirically investigating the reconfiguration of organizational structures and employee subjectivity; (2) on an inter-algorithmic level, it offers a ground-breaking methodology (agent-based modelling informed by qualitative data) to grasp how trading algorithms interact with one another in a fully digital space; and (3) on the level of market sociality, it proposes a novel theorization of how intra-firm and inter-algorithmic dynamics can be conceived of as introducing a particular form of sociality that is characteristic to algorithmic finance: a form of sociality-as-association heuristically analyzed as imitation. None of these three levels have received systematic attention in the state-of-the-art literature. Addressing them will significantly advance the understanding of present-day algorithmic finance in economic sociology. By contributing novel empirical, methodological, and theoretical understandings of the functioning and consequences of algorithms, ALGOFINANCE will pave the way for other research into digital sociology and the broader algorithmization of society.

1 590 036 €

Start date: 2017-05-01, End date: 2021-04-30