ERC FUNDED PROJECTS

Designers draw extensively to externalize their ideas and communicate with others. However, drawings are currently not directly interpretable by computers. To test their ideas against physical reality, designers have to create 3D models suitable for simulation and 3D printing. However, the visceral and approximate nature of drawing clashes with the tediousness and rigidity of 3D modeling. As a result, designers only model finalized concepts, and have no feedback on feasibility during creative exploration. Our ambition is to bring the power of 3D engineering tools to the creative phase of design by automatically estimating 3D models from drawings. However, this problem is ill-posed: a point in the drawing can lie anywhere in depth. Existing solutions are limited to simple shapes, or require user input to “explain” to the computer how to interpret the drawing. Our originality is to exploit professional drawing techniques that designers developed to communicate shape most efficiently. Each technique provides geometric constraints that help viewers understand drawings, and that we shall leverage for 3D reconstruction. Our first challenge is to formalize common drawing techniques and derive how they constrain 3D shape. Our second challenge is to identify which techniques are used in a drawing. We cast this problem as the joint optimization of discrete variables indicating which constraints apply, and continuous variables representing the 3D model that best satisfies these constraints. But evaluating all constraint configurations is impractical. To solve this inverse problem, we will first develop forward algorithms that synthesize drawings from 3D models. Our idea is to use this synthetic data to train machine learning algorithms that predict the likelihood that constraints apply in a given drawing. In addition to tackling the long-standing problem of single-image 3D reconstruction, our research will significantly tighten design and engineering for rapid prototyping.

1 482 761 €

Start date: 2017-02-01, End date: 2022-01-31

This project will apply two distinctly demographic concepts to research questions that go far beyond demography. The wellbeing indicators proposed here will be based on life table methods and the recently operationalized concept of Demographic Metabolism – modelling social change through the replacement of generations – will be used to get a quantitative analytical handle on the temporal dynamics of improving human wellbeing. The project will theoretically develop, empirically estimate, test and forecast indicators of human wellbeing that are based on life table methods and hence reflect the basic – but often overlooked fact – that being alive is a necessary prerequisite for enjoying any quality of life. But since mere survival is not sufficient as an ultimate goal for most people the person years lived at each age will be weighted with four different dimensions of empowerment: health, literacy, happiness and being out of poverty. These are four dimensions of an indicator tentatively called ELY (Empowered Life Years). ELY will also serve as the explanandum of a global level econometric estimation of the determinants of wellbeing considering human, manufactured and natural capitals as well as knowledge and institutions. The global level analysis is complemented by a set of strategically chosen in-depth systems-analytical case studies in Namibia/Western Cape, Nepal, Costa Rica and historical Finland modelling the population-development-environment (PDE) interactions including feed-backs e.g. from environmental degradation to wellbeing and taking the trends of ELY in different sub-populations as sustainability criteria. They will also include stake holder involvement and science-policy interactions. This innovative inter-disciplinary cross-fertilisation can potentially make an important contribution to the current discussions about operationalizing the criteria and end goal of sustainable development and developing better human wellbeing based metrics of progress.

1 819 250 €

Start date: 2017-11-01, End date: 2022-10-31

There is mounting evidence that firms are becoming more fragmented; production is less often made “in-house”. Firms buy inputs from abroad. Tasks are often split in parts. Some are offshored, others are subcontracted. Hence, firms buy services from other, local or international, firms. But they also supply inputs to other firms. Technical change, the internet, and globalization, all facilitate this transformation. In order to better understand how firms thrive in the new global environment, the proposed research aims to construct a networks view of the firm. Fragmentation offers new opportunities: firms may specialize in what they make best, hence creating a business network of customers and suppliers. Networks are also useful to secure provision of fragmented tasks. The firms’ suppliers of goods and services – accountants, logisticians, consultants… -- may well be related to the firm through its workers’ social networks: family ties, boardroom relations… These social networks should be useful when times are tough -- board members could help find financing in banks where their schoolmates have a job – or when times are unusually good -- employees could help in spotting the right hires among their former co-workers. The proposed research will focus on how firms social and business networks help firms to be resilient in the face of shocks. Resilience will be measured using the firms’ and workers’ outcomes – value-added, wages, employment, or occupations. The research will have a theoretical component using general equilibrium models with heterogeneous firms, an empirical component with unique data sources from at least two countries (France, Sweden), and an “econometric theory” component which will seek to develop techniques for the study of many-to-one matches in the presence of networks. The research will speak to the labor economics community but also to the international trade community, the management community, as well as the econometrics community.

1 753 288 €

Start date: 2017-09-01, End date: 2022-08-31

We live in an intimate symbiosis with our gut microbiota, which provides us services such as vitamin production, breakdown of dietary compounds, and immune training. Sequencing-based approaches that have been applied to catalogue the gut microbiota have revealed intriguing discoveries associating the microbiome with diet and disease. The next outstanding challenge is to unravel the many activities and interactions that define gut microbiota function. The gut microbiota is a diverse community of cooperating and competing microbes. These interactions form a network that links organisms with each other and their environment. Interactions in such a “functional network” are based partially, though not exclusively, on food webs. Certain “keystone species”, such as Rumonicoccus bromii, are thought to play a major role in these networks. Though some evidence exists for the presence of keystone species, their identity and activity remains largely unknown. As keystone species are vital to networks they are ideal targets for manipulating the gut microbiota to improve metabolic health and protect against enteropathogen infection. Given the complexity of the gut microbiota, networks can only be elucidated directly in the native community. This project aims to identify functional networks and keystone species in the human gut using novel approaches that are uniquely and ideally suited for studying microbial activity in complex communities. Using state-of-the-art methods such as stable isotope labeling, Raman microspectroscopy, and secondary ion mass spectrometry (NanoSIMS) we will illuminate functional networks in situ. This will allow us to identify what factors shape gut microbiota activity, reveal important food webs, and ultimately use network knowledge to target the microbiota with prebiotic/probiotic treatments rationally designed to promote health.

1 498 279 €

Start date: 2017-04-01, End date: 2022-03-31