After-action Analysis of the Magic Maggiore Workshop on Expert Support and Reachback

The European Commission’s Joint Research Centre (JRC) in collaboration with the Global Initiative to Combat Nuclear Terrorism (GICNT) organized a two and a half-day workshop on expert support and reachback entitled Magic Maggiore at the JRC Ispra, Italy in 28-30 March 2017. Through a series of presentations, case studies, panel discussions, and a demonstration exercise, Magic Maggiore helped raise awareness and build commitment towards technical reachback. Furthermore, the workshop presented best practices to address key challenges, and identified areas for future work in this field. The workshop included a real-time detection and reachback exercise of a hypothetical nuclear security incident, put on between the JRC (Ispra) and France (Paris). The demonstration focused on core components of alarm adjudication and information exchange between front line officers, a national reachback centre, and an advanced centralised reachback centre located in Paris. A list of concrete post-workshop activities has been generated. The purpose of the list is to pave the way for the identification of the next steps towards development of European capabilities for nuclear security and in more general, for CBRNE security. Reachback is necessary for alarm adjudication to provide timely information for a balanced response. Information sharing between competent authorities is of vital importance for nuclear security. Due to the variety of responsibilities, Technical, Scientific and Operational support needs to be defined. The Member States should consider developing joint protocols on data structures and data handling to ease the information flow and so the response time.JRC.E.2-Technology Innovation in Securit

List-mode data acquisition based on digital electronics - State-of-the-art report

This report deals with digital radiation detection systems employing list-mode data collection, which improves data analysis capabilities. Future data acquisition systems shall also ultimately enable the movement of detection data from first responders electronically to analysis centres rather than the costly and time consuming process of moving experts and/or samples. This new technology is especially useful in crisis events, when time and resources are sparse and increased analysis capacity is required. In order to utilise the opportunities opened by these new technologies, the systems have to be interoperable, so that the data from each type of detector can easily be analysed by different analysis centres. Successful interoperability of the systems requires that European and/or international standards are devised for the digitised data format. The basis of such a format is a list of registered events detailing an estimate of the energy of the detected radiation, along with an accurate time-stamp for recorded events (and optionally other parameters describing each event).JRC.G.5-Security technology assessmen

Kinetic analysis of the growth of bone marrow mononuclear phagocytes in long term cultures

Contains fulltext : 4427.pdf (publisher's version ) (Open Access

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