Innovation Labs: Leveraging Openness for Radical Innovation?

A growing range of public, private and civic organisations, from Unicef through Nesta to NHS, now run units known as “innovation labs”. The hopeful assumption they share is that labs, by building on openness among other features, can generate promising solutions to grand challenges of systemic nature. Despite their seeming proliferation and popularisation, the underlying innovation principles embodied by labs have, however, received scant academic attention. This is a missed opportunity, because innovation labs appear to leverage openness for radical innovation in an unusual fashion. Indeed, in this exploratory paper we draw on original interview data and online self-descriptions to illustrate that, beyond convening “uncommon partners” across organisational boundaries, labs apply the principle of openness throughout the innovation process, including the experimentation and development phases. While the emergence of labs clearly forms part of a broader trend towards openness, we show how it transcends established conceptualisations of open innovation (Chesbrough et al., 2006), open science (David, 1998) or open government (Janssen et al., 2012)

MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining

We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in many pattern mining tasks. The MCERA allows us to compute upper bounds to the maximum deviation of sample means from their expectations, thus it can be used to find both statistically-significant functions (i.e., patterns) when the available data is seen as a sample from an unknown distribution, and approximations of collections of high-expectation functions (e.g., frequent patterns) when the available data is a small sample from a large dataset. This feature is a strong improvement over previously proposed solutions that could only achieve one of the two. MCRapper uses upper bounds to the discrepancy of the functions to efficiently explore and prune the search space, a technique borrowed from pattern mining itself. To show the practical use of MCRapper, we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining. TFP-R gives guarantees on the probability of including any false positives (precision) and exhibits higher statistical power (recall) than existing methods offering the same guarantees. We evaluate MCRapper and TFP-R and show that they outperform the state-of-the-art for their respective tasks

A Markov Chain Approach To Reconstruction Of Long Haplotypes

Haplotypes are important for association based gene mapping, but there are no practical laboratory methods for obtaining them directly from DNA samples. We propose simple Markov models for reconstruction of haplotypes for a given sample of multilocus genotypes. The models are aimed specifically for long marker maps, where linkage disequilibrium between markers may vary and be relatively weak. Such maps are ultimately used in chromosome or genome-wide association studies. Haplotyp

New approaches to model and study social networks

We describe and develop three recent novelties in network research which are particularly useful for studying social systems. The first one concerns the discovery of some basic dynamical laws that enable the emergence of the fundamental features observed in social networks, namely the nontrivial clustering properties, the existence of positive degree correlations and the subdivision into communities. To reproduce all these features we describe a simple model of mobile colliding agents, whose collisions define the connections between the agents which are the nodes in the underlying network, and develop some analytical considerations. The second point addresses the particular feature of clustering and its relationship with global network measures, namely with the distribution of the size of cycles in the network. Since in social bipartite networks it is not possible to measure the clustering from standard procedures, we propose an alternative clustering coefficient that can be used to extract an improved normalized cycle distribution in any network. Finally, the third point addresses dynamical processes occurring on networks, namely when studying the propagation of information in them. In particular, we focus on the particular features of gossip propagation which impose some restrictions in the propagation rules. To this end we introduce a quantity, the spread factor, which measures the average maximal fraction of nearest neighbors which get in contact with the gossip, and find the striking result that there is an optimal non-trivial number of friends for which the spread factor is minimized, decreasing the danger of being gossiped.Comment: 16 Pages, 9 figure

Daycare attendance and respiratory tract infections: A prospective birth cohort study

Objective: We explored the burden of respiratory tract infections (RTIs) in young children with regard to day-care initiation.Design: Longitudinal prospective birth cohort study.Setting and methods: We recruited 1827 children for follow-up until the age of 24 months collecting diary data on RTIs and daycare. Children with continuous daycare type and complete data were divided into groups of centre-based daycare (n=299), family day care (FDC) (n=245) and home care (n=350). Using repeated measures variance analyses, we analysed days per month with symptoms of respiratory tract infection, antibiotic treatments and parental absence from work for a period of 6 months prior to and 9 months after the start of daycare.Results: We documented a significant effect of time and type of daycare, as well as a significant interaction between them for all outcome measures. There was a rise in mean days with symptoms from 3.79 (95% CI 3.04 to 4.53) during the month preceding centre-based daycare to 10.57 (95% CI 9.35 to 11.79) at 2 months after the start of centre-based daycare, with a subsequent decrease within the following 9 months. Similar patterns with a rise and decline were observed in the use of antibiotics and parental absences. The start of FDC had weaker effects. Our findings were not changed when taking into account confounding factors.Conclusions: Our study shows the rapid increase in respiratory infections after start of daycare and a relatively fast decline in the course of time with continued daycare. It is important to support families around the beginning of daycare.</p