JuxtaLearn DELIVERABLE Report D3.5 Service scenario documentation

The purpose of Deliverable 3.5 is to provide guidelines to creating juxtaposed performance, particularly to advise non-drama teachers on what to do and how to manage performance in stage 3 of the JuxtaLearn process. Building on pedagogies of threshold concepts and juxtaposed learning, it explains the performance steps, orchestrating learning through participative video making and story making with peers. It provides guidance for teachers, offering resources that include suggested lesson plans and example timings. Thus, in the absence of a shared touchable with JuxtaLearn software, it suggests a practical additional and alternative to using the software of WP4 touch table

Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG.

Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the Hierarchical Event Descriptor (HED) system for systematically describing both laboratory and real-world events. HED version 2, first described here, provides the semantic capability of describing a variety of subject and environmental states. HED descriptions can include stimulus presentation events on screen or in virtual worlds, experimental or spontaneous events occurring in the real world environment, and events experienced via one or multiple sensory modalities. Furthermore, HED 2 can distinguish between the mere presence of an object and its actual (or putative) perception by a subject. Although the HED framework has implicit ontological and linked data representations, the user-interface for HED annotation is more intuitive than traditional ontological annotation. We believe that hiding the formal representations allows for a more user-friendly interface, making consistent, detailed tagging of experimental, and real-world events possible for research users. HED is extensible while retaining the advantages of having an enforced common core vocabulary. We have developed a collection of tools to support HED tag assignment and validation; these are available at hedtags.org. A plug-in for EEGLAB (sccn.ucsd.edu/eeglab), CTAGGER, is also available to speed the process of tagging existing studies

A low pre-infall mass for the Carina dwarf galaxy from disequilibrium modelling

Dark matter only simulations of galaxy formation predict many more subhalos around a Milky Way like galaxy than the number of observed satellites. Proposed solutions require the satellites to inhabit dark matter halos with masses between one to ten billion solar masses at the time they fell into the Milky Way. Here we use a modelling approach, independent of cosmological simulations, to obtain a preinfall mass of 360 (+380,-230) million solar masses for one of the Milky Way's satellites: Carina. This determination of a low halo mass for Carina can be accommodated within the standard model only if galaxy formation becomes stochastic in halos below ten billion solar masses. Otherwise Carina, the eighth most luminous Milky Way dwarf, would be expected to inhabit a significantly more massive halo. The implication of this is that a population of "dark dwarfs" should orbit the Milky Way: halos devoid of stars and yet more massive than many of their visible counterparts.Comment: 5 pages, 3 figures, 1 table, and supplementary material availabl

Driving NEMO Towards Exascale: Introduction of a New Software Layer in the NEMO Stack Software

This paper addresses scientific challenges related to high level implementation strategies that leads NEMO to effectively use of the opportunities of exascale systems. We consider two software modules as proof-of-concept: the Sea Surface Height equation solver and the Variational Data Assimilation system, which are components of the NEMO ocean model (OPA). Advantages rising from the introduction of consolidated scientific libraries in NEMO are highlighted: such advantages concern both the "software quality" improvement (see the software quality parameters like robustness, portability, resilence, etc.) and time reduction of software development

Main memory in HPC: do we need more, or could we live with less?

An important aspect of High-Performance Computing (HPC) system design is the choice of main memory capacity. This choice becomes increasingly important now that 3D-stacked memories are entering the market. Compared with conventional Dual In-line Memory Modules (DIMMs), 3D memory chiplets provide better performance and energy efficiency but lower memory capacities. Therefore, the adoption of 3D-stacked memories in the HPC domain depends on whether we can find use cases that require much less memory than is available now. This study analyzes the memory capacity requirements of important HPC benchmarks and applications. We find that the High-Performance Conjugate Gradients (HPCG) benchmark could be an important success story for 3D-stacked memories in HPC, but High-Performance Linpack (HPL) is likely to be constrained by 3D memory capacity. The study also emphasizes that the analysis of memory footprints of production HPC applications is complex and that it requires an understanding of application scalability and target category, i.e., whether the users target capability or capacity computing. The results show that most of the HPC applications under study have per-core memory footprints in the range of hundreds of megabytes, but we also detect applications and use cases that require gigabytes per core. Overall, the study identifies the HPC applications and use cases with memory footprints that could be provided by 3D-stacked memory chiplets, making a first step toward adoption of this novel technology in the HPC domain.This work was supported by the Collaboration Agreement between Samsung Electronics Co., Ltd. and BSC, Spanish Government through Severo Ochoa programme (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272). This work has also received funding from the European Union’s Horizon 2020 research and innovation programme under ExaNoDe project (grant agreement No 671578). Darko Zivanovic holds the Severo Ochoa grant (SVP-2014-068501) of the Ministry of Economy and Competitiveness of Spain. The authors thank Harald Servat from BSC and Vladimir Marjanovi´c from High Performance Computing Center Stuttgart for their technical support.Postprint (published version