ERC FUNDED PROJECTS

The Virtual Element Method (VEM) is a novel technology for the discretization of partial differential equations (PDEs), that shares the same variational background as the Finite Element Method. First but not only, the VEM responds to the strongly increasing interest in using general polyhedral and polygonal meshes in the approximation of PDEs without the limit of using tetrahedral or hexahedral grids. By avoiding the explicit integration of the shape functions that span the discrete space and introducing an innovative construction of the stiffness matrixes, the VEM acquires very interesting properties and advantages with respect to more standard Galerkin methods, yet still keeping the same coding complexity. For instance, the VEM easily allows for polygonal/polyhedral meshes (even non-conforming) with non-convex elements and possibly with curved faces; it allows for discrete spaces of arbitrary C^k regularity on unstructured meshes. The main scope of the project is to address the recent theoretical challenges posed by VEM and to assess whether this promising technology can achieve a breakthrough in applications. First, the theoretical and computational foundations of VEM will be made stronger. A deeper theoretical insight, supported by a wider numerical experience on benchmark problems, will be developed to gain a better understanding of the method's potentials and set the foundations for more applicative purposes. Second, we will focus our attention on two tough and up-to-date problems of practical interest: large deformation elasticity (where VEM can yield a dramatically more efficient handling of material inclusions, meshing of the domain and grid adaptivity, plus a much stronger robustness with respect to large grid distortions) and the cardiac bidomain model (where VEM can lead to a more accurate domain approximation through MRI data, a flexible refinement/de-refinement procedure along the propagation front, to an exact satisfaction of conservation laws).

980 634 €

Start date: 2016-07-01, End date: 2021-06-30

Next-generation sequencing technology has dramatically reduced the cost and time of reading the DNA. Huge investments are targeted to sequencing the DNA of large populations, and repositories of well-curated sequence data are being collected. Answers to fundamental biomedical problems are hidden in these data, e.g. how cancer arises, how driving mutations occur, how much cancer is dependent on environment. But genomic computing has not comparatively evolved. Bioinformatics has been driven by specific needs and distracted from a foundational approach; hundreds of methods solve individual problems, but miss the broad perspective. The objective of GeCo is to rethink genomic computing through the lens of basic data management. We will first design the data model, using few general abstractions that guarantee interoperability between existing data formats. Next, we will design a new-generation query language inspired by classic relational algebra and extended with orthogonal, domain-specific abstractions for genomics. Query processing will trace metadata and computation steps, opening doors to the seamless integration of descriptive statistics and high-level data analysis (e.g., DNA region clustering and extraction of regulatory networks). Genomic computing is a “big data” problem, therefore we will also achieve computational efficiency by using parallel computing on both clusters and public clouds; the choice of a suitable data model and of computational abstractions will boost performance in a principled way. The resulting technology will be applicable to individual and federated repositories, and will be exploited for providing integrated access to curated data, made available by large consortia, through user-friendly search services. Our most far-fetching vision is to move towards an Internet of Genomes exploiting data indexing and crawling. The PI’s background in distributed data, data modelling, query processing and search will drive a radical paradigm shift.

2 500 000 €

Start date: 2016-09-01, End date: 2021-08-31

The transmission of microeconomic and macroeconomic shocks to firms' price and demand in product markets is the cornerstone of a large volume of macroeconomic literature. Product market frictions, by reducing the ability of demand to relocate across different suppliers, affect firms' incentives when setting prices, and therefore the pass-through of shocks to both demand and prices. In this project we plan to study the implications of product market frictions for firm level price and demand dynamics, as well as for macroeconomic dynamics. The aim is to integrate micro and macro economics, both theoretically and empirically, to a greater extent than is currently done in the literature. We will apply our tools to two main areas of interest. First, we will study how product market frictions affect the optimal pricing decision of firms, and the relocation of consumers across different suppliers. We will provide novel empirical microeconomic evidence on the relationship between price and consumer dynamics. We will build a rich but yet tractable model where product market frictions give rise to firm pricing with customer markets. The aim is to use observable statistics from the micro data to estimate the key parameters of the model and quantify the relevance of the product market frictions for firm pricing and demand dynamics. Second, we will explore the importance of product market frictions for macroeconomic dynamics. We will apply our quantified model of price and consumer dynamics to areas of macroeconomics where we expect our methodology and empirical analysis to be more relevant, both because of the types of questions addressed and because of a direct relationship with the mechanism. In particular we will focus on the role of product market frictions for business cycle fluctuations and international trade.

1 192 000 €

Start date: 2016-02-01, End date: 2020-01-31

Nearly all activities in which the public sector is involved, from defense to transportation, from education to healthcare, require the public sector to procure works or goods from private contractors. Thus, it is crucial that the procedures through which procurement occurs be designed to avoid waste and enhance social welfare. Preventing corruption and ensuring contractor compliance with their obligations constitute primary design goals. Nevertheless, very limited evidence exists as to how different awarding methods are susceptible to corruption, and how contractors’ past reputation should be used to award new tenders. This research proposal describes three empirical projects that will advance the frontier of our understanding of the roles of corruption and reputation in procurement. Component 1 focuses on the use of reputation in contract procurement. It analyzes the evidence produced by the introduction of a vendor rating system to: i) determine whether the new system induced contractors to improve their performance, ii) determine whether performance improvements caused higher procurement costs, and iii) evaluate concerns on corruption and entry of new bidders. Component 2 focuses on corruption in public procurement. It analyzes evidence on the presence of networks of firms engaged in criminal activities in public procurement to determine: i) the extent of the phenomenon, (ii) the functioning of different awarding rules against corruption, and iii) the use of tests to detect corruption. Component 3 focuses on healthcare procurement regulations. It analyzes evidence on the public procurement of medical devices to accomplish: i) a descriptive analysis of the procurement practices across the EU, ii) an assessment of whether discretionary awarding rules are used to foster corruption or to reward contractors with better reputation, and iii) an evaluation of these procurement practices in terms of patients’ welfare.

1 046 850 €

Start date: 2016-02-01, End date: 2021-01-31