Composing textual modelling languages in practice

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 6th International Workshop on Multi-Paradigm Modeling, http://dx.doi.org/10.1145/2508443.2508449Complex systems require descriptions using multiple modelling languages, or languages able to express different concerns, like timing or data dependencies. In this paper, we propose techniques for the modular definition and composition of languages, including their abstract, concrete syntax and semantics. These techniques are based on (meta-)model templates, where interface elements and requirements for their connection can be established. We illustrate the ideas using the MetaDepth textual meta-modelling tool

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation

The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving transformations like depth-rotations. Current computational models of object recognition, including recent deep learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations. While simulations of these models recapitulate the ventral stream's progression from early view-specific to late view-tolerant representations, they fail to generate the most salient property of the intermediate representation for faces found in the brain: mirror-symmetric tuning of the neural population to head orientation. Here we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules can provide approximate invariance at the top level of the network. While most of the learning rules do not yield mirror-symmetry in the mid-level representations, we characterize a specific biologically-plausible Hebb-type learning rule that is guaranteed to generate mirror-symmetric tuning to faces tuning at intermediate levels of the architecture

Buckling Testing and Analysis of Honeycomb Sandwich Panel Arc Segments of a Full-Scale Fairing Barrel

Four honeycomb sandwich panel types, representing 1/16th arc segments of a 10-m diameter barrel section of the Heavy Lift Launch Vehicle (HLLV), were manufactured and tested under the NASA Composites for Exploration program and the NASA Constellation Ares V program. Two configurations were chosen for the panels: 6-ply facesheets with 1.125 in. honeycomb core and 8-ply facesheets with 1.000 in. honeycomb core. Additionally, two separate carbon fiber/epoxy material systems were chosen for the facesheets: in-autoclave IM7/977-3 and out-of-autoclave T40-800b/5320-1. Smaller 3- by 5-ft panels were cut from the 1/16th barrel sections. These panels were tested under compressive loading at the NASA Langley Research Center (LaRC). Furthermore, linear eigenvalue and geometrically nonlinear finite element analyses were performed to predict the compressive response of each 3- by 5-ft panel. This manuscript summarizes the experimental and analytical modeling efforts pertaining to the panels composed of 6-ply, IM7/977-3 facesheets (referred to as Panels B-1 and B-2). To improve the robustness of the geometrically nonlinear finite element model, measured surface imperfections were included in the geometry of the model. Both the linear and nonlinear models yield good qualitative and quantitative predictions. Additionally, it was correctly predicted that the panel would fail in buckling prior to failing in strength. Furthermore, several imperfection studies were performed to investigate the influence of geometric imperfections, fiber angle misalignments, and three-dimensional (3-D) effects on the compressive response of the panel

CD40-mediated amplification of local immunity by epithelial cells is impaired by HPV

The interaction between the transmembrane glycoprotein surface receptor CD40 expressed by skin epithelial cells (ECs) and its T-cell-expressed ligand CD154 was suggested to exacerbate inflammatory skin diseases. However, the full spectrum of CD40-mediated effects by ECs underlying this observation is unknown. Therefore, changes in gene expression after CD40 ligation of ECs were studied by microarrays. CD40-mediated activation for 2 hours stimulated the expression of a coordinated network of immune-involved genes strongly interconnected by IL8 and TNF, whereas after 24 hours anti-proliferative and anti-apoptotic genes were upregulated. CD40 ligation was associated with the production of chemokines and the attraction of lymphocytes and myeloid cells from peripheral blood mononuclear cells (PBMCs). Thus, CD40-mediated activation of ECs resulted in a highly coordinated response of genes required for the local development and sustainment of adaptive immune responses. The importance of this process was confirmed by a study on the effects of human papilloma virus (HPV) infection to the EC's response to CD40 ligation. HPV infection clearly attenuated the magnitude of the response to CD40 ligation and the EC's capacity to attract PBMCs. The fact that HPV attenuates CD40 signaling in ECs indicates the importance of the CD40-CD154 immune pathway in boosting cellular immunity within epithelia

A syndrome of altered cardiovascular, craniofacial, neurocognitive and skeletal development caused by mutations in TGFBR1 or TGFBR2

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The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

A multi-paradigm modelling approach for the engineering of modelling languages

The main problem statement in this Ph.D. is in the domain of modelling language engi-neering: How can we engineer domain-specific modelling languages (DSMLs) in a more structured way? We look at this problem from a tool-builder’s point of view. This means that our solution is a tool that supports language engineers in modelling DSMLs in a more struc-tured way. This problem is a very broad one. We have chosen some sub-topics for which the research questions can be described as: In the case of evolution of a DSML, how can we provide support for (semi-)automatic co-evolution of related artefacts such as instances and transformations? Since the cre-ation of the DSML is part of the software development cycle, it is also subject to evolu-tion. We investigate how we can automate co-evolution on models that are instances of the language, and transformations defined for this language. Automatically co-evolving the latter turns out to be very hard, as the language definition does not contain a forma