Applying Formal Methods to Gossiping Networks with mCRL and Groove

In this paper we explore the practical possibilities of using formal methods to analyze gossiping networks. In particular, we use mCRL and Groove to model the peer sampling service, and analyze it through a series of model transformations to CTMCs and finally MRMs. Our tools compute the expected value of various network quality indicators, such as average path lengths, over all possible system runs. Both transient and steady state analysis are supported. We compare our results with the simulation and emulation results found in [10]

Optically activated ZnO/Sio2/Si cantilever beams

The photomechanical effect induced by periodically varying sub-bandgap illumination in thin ZnO films deposited on oxidized Si has been demonstrated for the first time. The efficiency of this effect is at least one order of magnitude higher as compared to the photothermal activation of Si. Thus it can be considered as a powerful optical drive for resonant sensors. A phenomenological model of the mechanisms involved in the process is proposed. The optomechanical effect can also be used as a complementary method in determination of the surface state parameters of ZnO films

Research methodology of grazing

Throughout Europe, grass is the main feed for dairy cattle. This report presents the main results of the first meeting of the European Grassland Federation (EGF) Working Group Grazing in Kiel on 29 August 2010. The theme of the meeting was "Research methodology of grazing". There were three sessions: - setting the scene; - modelling of grazing; and - field measurements

R package hiphop: parentage assignment using bi-allelic genetic markers. Statistical software

Can be used for paternity and maternity assignment and outperforms conventional methods where closely related individuals occur in the pool of possible parents. The method compares the genotypes of offspring with any combination of potentials parents and scores the number of mismatches of these individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). It elaborates on a prior exclusion method based on the Homozygous Opposite Test (HOT; Huisman 2017 ) by introducing the additional exclusion criterion HIPHOP (Homozygous Identical Parents, Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential parents are excluded if they have more mismatches than can be expected due to genotyping error and mutation, and thereby one can identify the true genetic parents and detect situations where one (or both) of the true parents is not sampled. Package 'hiphop' can deal with (a) the case where there is contextual information about parentage of the mother (i.e. a female has been seen to be involved in reproductive tasks such as nest building), but paternity is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, because there is no contextual information on which female laid eggs and which male fertilized them (e.g. polygynandrous mating system where multiple females and males deposit young in a common nest, or organisms with external fertilisation that breed in aggregations)

Arbeid op hightechbedrijf en lagekostenbedrijf

De laatste twee jaar realiseerden de bedrijven de werkzaamheden in een gemiddelde bedrijfstijd van 50 uur per week

Deep learning for inferring cause of data anomalies

Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify 'channels' which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.Comment: Presented at ACAT 2017 conference, Seattle, US