Innovating innovation policy: the emergence of ‘Responsible Research and Innovation’

Within the policy structures of the European Union (EU), the concept of ‘Responsible Research and Innovation’ (RRI) appears to have a very specific emergence point, via a workshop for invited experts hosted by the European Commission's Directorate-General for Research and Innovation in May 2011. Through a textual analysis of EU documents, this paper will explore the processes through which RRI has been incorporated into Horizon 2020 as a policy framework for the European Research Area which promises that technological innovation will be shaped towards social goods. It concludes by discussing some of the tensions between RRI and policies emanating from other EU institutions, highlighting areas which may impede RRI's progress towards its goals

Placing the Czech shadow banking sector under the light

The size of the shadow banking sector (SBS) has more than doubled in the Czech Republic over the last decade. This places a potential burden on policy makers. On the one hand, the SBS complements regular banking by expanding access to credit and investments, enabling better risk sharing and maturity transformation, and supporting market liquidity. On the other hand, SBS activities can put the stability of the financial system at risk and amplify its procyclicality by exacerbating the buildup of leverage and asset price bubbles. We implement a FAVAR model of the Czech economy to determine the impact of macroeconomic factors on the SBS. We find that the SBS: (i) is sensitive to changes in market interest rates and term spread; (ii) exhibits great procyclicality; (iii) can act as a complement to regular banking and satisfy some additional demand for credit. We also define some potential risks of continued growth of the SBS, linked to our empirical evidence.Web of Science29128

A visual analysis of the usage efficiency of library books

The monographic collections in academic libraries have undergone a period of tremendous growth in volume, in subject diversity, and in formats during the recent several decades. Readers may find it difficult to prioritize which book(s) should be borrowed for a specific purpose. The log data of book loan record may serve as a visible indicator for the more sought-after books by the readers. This paper describes our experimental efforts in works in a university library setting. The visual analysis is thought to provide an effective way to extract the book usage information, which may yield new insights into a host of other related technical as well as user behavior issues. Initial experiment has demonstrated that the proposed approach as articulated in this article can actually benefit end-users as well as library collection development personnel in their endeavor of book selections with effective measure.</p

Monte Carlo Methods for Top-k Personalized PageRank Lists and Name Disambiguation

We study a problem of quick detection of top-k Personalized PageRank lists. This problem has a number of important applications such as finding local cuts in large graphs, estimation of similarity distance and name disambiguation. In particular, we apply our results to construct efficient algorithms for the person name disambiguation problem. We argue that when finding top-k Personalized PageRank lists two observations are important. Firstly, it is crucial that we detect fast the top-k most important neighbours of a node, while the exact order in the top-k list as well as the exact values of PageRank are by far not so crucial. Secondly, a little number of wrong elements in top-k lists do not really degrade the quality of top-k lists, but it can lead to significant computational saving. Based on these two key observations we propose Monte Carlo methods for fast detection of top-k Personalized PageRank lists. We provide performance evaluation of the proposed methods and supply stopping criteria. Then, we apply the methods to the person name disambiguation problem. The developed algorithm for the person name disambiguation problem has achieved the second place in the WePS 2010 competition

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dirichlet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-specific contexts results in the nDP mixture over content variables. We provide a Polya-urn view of the model and an efficient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.Comment: Full version of ICML 201