Digital, memory and mixed-signal test engineering education: five centres of competence in Europe

The launching of the EuNICE-Test project was announced two years ago at the first DELTA Conference. This project is now completed and the present paper describes the project actions and outcomes. The original idea was to build a long-lasting European Network for test engineering education using both test resource mutualisation and remote experiments. This objective is fully fulfilled and we have now, in Europe, five centres of competence able to deliver high-level and high-specialized training courses in the field of test engineering using a high-performing industrial ATE. All the centres propose training courses on digital testing, three of them propose mixed-signal trainings and three of them propose memory trainings. Taking into account the demand in test engineering, the network is planned to continue in a stand alone mode after project end. Nevertheless a new European proposal with several new partners and new test lessons is under construction

Is there more than one linkage between Social Network and Inequality?

The paper aims to analyse how income inequality affects social networks strength in fourteen European Countries. We introduce some new evidences by using the ECHP for testing the networks-inequality nexus and being able to construct directly inequality indices from the microdata as well their decomposition. In particular, we focus on two main point: firstly, we analyse how total income inequality could be related to social network; secondly, we introduce the "clustered network" definition, by decomposing total income inequality based on the education level. We test the existence of a pluralism linkage between Social Network and Inequality and many results confirm that the linkage is neither unambiguous nor unidirectional. We introduce and stress some important issue. First, we use dierent levels of social network: narrow, wide and anonymous; second, we use different inequality indexes (different sensitiveness to changes at different part of the income distribution); third, the ambiguous linkage could be explained on one hand by the positive role of emulation and reciprocity behaviors and on the other hand by negative ones of the envy, amoral familism and keeping up with the Joneses mechanisms. Finally, we stress the different roles of within and between components of inequality. Our idea is that higher income inequality - related to the changing education premia - could affect social network formation among individuals through two different channels: higher inequality among dierent educated ind ividuals could raise (clustered networks), while higher inequality among similars could halt the social networks.Social Network ; Inequality ; Clustered Network ; Envy ; Emulation

The marketing of high-tech innovation: research and teaching as a multidisciplinary communication task

Economically successful high-tech innovation is one of the driving forces for global welfare. Like innovation half-life, break-even time to market or technology acceptance, effective multidisciplinary communication between engineering and marketing is a critical success factor. This paper aims to show the requirements of multidisciplinary communication in B2B marketing of high-tech innovation and methodical approaches in research and academic education: 1. Requirements in high-tech innovation marketing as an ongoing dialogue between technology, finance and marketing. 2. Experimental method of marketing test beds for innovative high-tech start-ups based on a multidisciplinary approach. 3. Results of a multidisciplinary education scheme conducted by three universities that cooperate in high-tech innovation marketing by setting up workshops in pharmacy and health, agricultural and bio products, and information and communication technology (ICT). 4. Requirements of a multidisciplinary network spanning the triangle of science – education – business. This paper was funded by the European Territorial Cooperation Frame Program for Cross-Border Cooperation, SR-AUT 2007-2013, project code N00092, Cross-Border Hi-Tech Center

Societal education and the education divide in European identity, 1992-2015

The fact that highly educated individuals are significantly more likely to self-identify as Europeans than those with lower levels of educational attainment is one of the most robust findings in the scholarship on individual Europeanization. Previous work also shows that this cleavage in supranational identification varies cross-nationally and over time. We contribute to the existing literature by examining the country-level, socio-structural conditions that influence the education cleavage. Focusing on how the educational environment influences identity formation, we test two divergent predictions of how societal education—i.e. the average national level of educational attainment—shapes the cleavage between individuals of differing education levels with respect to their self-identification as European. According to Welzel’s (2013) ‘cross-fertilization approach’, societal education should widen the education divide. By contrast, our alternative ‘cross-attenuating approach’ posits that societal education should instead help to close it. Using a cross-national time-series dataset that includes 28 EU member states and 28 Eurobarometers covering 1992–2015, as well as between–within multilevel models, we find a significantly narrower education cleavage in countries where societal education increased the most during the period of our study. This result provides strong support for the cross-attenuating approach presented here. We theorize that societal education helps to narrow the individual-level education cleavage through a discursive and a network mechanism

The Milky Way Tomography With SDSS. III. Stellar Kinematics

We study Milky Way kinematics using a sample of 18.8 million main-sequence stars with r 20 degrees). We find that in the region defined by 1 kpc < Z < 5 kpc and 3 kpc < R < 13 kpc, the rotational velocity for disk stars smoothly decreases, and all three components of the velocity dispersion increase, with distance from the Galactic plane. In contrast, the velocity ellipsoid for halo stars is aligned with a spherical coordinate system and appears to be spatially invariant within the probed volume. The velocity distribution of nearby (Z < 1 kpc) K/M stars is complex, and cannot be described by a standard Schwarzschild ellipsoid. For stars in a distance-limited subsample of stars (< 100 pc), we detect a multi-modal velocity distribution consistent with that seen by HIPPARCOS. This strong non-Gaussianity significantly affects the measurements of the velocity-ellipsoid tilt and vertex deviation when using the Schwarzschild approximation. We develop and test a simple descriptive model for the overall kinematic behavior that captures these features over most of the probed volume, and can be used to search for substructure in kinematic and metallicity space. We use this model to predict further improvements in kinematic mapping of the Galaxy expected from Gaia and the Large Synoptic Survey Telescope.NSF AST-615991, AST-0707901, AST-0551161, AST-02-38683, AST-06-07634, AST-0807444, PHY05-51164NASA NAG5-13057, NAG5-13147, NNXO-8AH83GPhysics Frontier Center/Joint Institute for Nuclear Astrophysics (JINA) PHY 08-22648U.S. National Science FoundationMarie Curie Research Training Network ELSA (European Leadership in Space Astrometry) MRTN-CT-2006-033481Fermi Research Alliance, LLC, United States Department of Energy DE-AC02-07CH11359Alfred P. Sloan FoundationParticipating InstitutionsJapanese MonbukagakushoMax Planck SocietyHigher Education Funding Council for EnglandMcDonald Observator

Needs and challenges facing parents/caregivers of children with autism spectrum disorder: the Southeast European Autism Network survey

The Southeast European Autism Network (SEAN) was established to enhance understanding of diagnosis, needs and services for children with Autism Spectrum Disorder (ASD) and their caregivers in Southeast Europe. Toward this end, a survey was conducted in 2015/2016 with the main aim to understand the diagnostics, services and experiences of families/caregivers affected by ASD in the Southeast European region, including the Republic of Macedonia. The survey was performed using a questionnaire specially designed by the members of the SEAN network, which included the following four domains: demographic/family characteristics, index child characteristics, service encounters and parent/caregiver perceptions. In this article we present the findings from 60 parents/caregivers of children with ASD in the Republic of Macedonia. Although the average ages at first concern (20.1 months, SD 7.0) and at ASD diagnosis (35.3 months, SD 14.3) were comparable to those in US and Western European countries, important needs and challenges need to be addressed related to early diagnosis, interventions and inclusive educational practices. In this study we conducted descriptive analyses and non parametric Spearman correlation analyses to examine whether current age of the child was associated significantly with time-to diagnosis. We also conducted linear regression analysis and the t-test to measure the effects of diagnosis, parental education, time to diagnosis, and current age of the child on impact of the disorder and level of difficulties reported by the parents

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Sh(k)) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms.The authors acknowledge Basque Government (Eusko Jaurlaritza) grant (IT1045-16) - 2016-2021 for consolidated research groups. This work was supported by the "Collaborative Project in Genomic Data Integration (CICLOGEN)" PI17/01826 funded by the Carlos III Health Institute, as part of the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER). This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and "Drug Discovery Galician Network" Ref. ED431G/01 and the "Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. CR Munteanu acknowledges the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research

Desperately Seeking Selznick: Cooptation and the Dark Side of Public Management in Networks

Most literature on public-sector networks focuses on how to build and manage systems and ignores the political problems that networks can create for organizations. This article argues that individual network nodes can work to bias the organization's actions in ways that benefit the organization's more advantaged clientele. The argument is supported by an analysis of performance data from 500 organizations over a five-year period. A classic theoretical point is supported in a systematic empirical investigation. While networks can greatly benefit the organization, they have a dark side that managers and scholars need to consider more seriously