In this study we explored the use of coherence and Granger causality (GC) to separate patients in minimally conscious state (MCS) from patients with severe neurocognitive disorders (SND) that show signs of awareness. We studied 16 patients, 7 MCS and 9 SND with age between 18 and 49 years. Three minutes of ongoing electroencephalographic (EEG) activity was obtained at rest from 19 standard scalp locations, while subjects were alert but kept their eyes closed. GC was formulated in terms of linear autoregressive models that predict the evolution of several EEG time series, each representing the activity of one channel. The entire network of causally connected brain areas can be summarized as a graph of incompletely connected nodes. The 19 channels were grouped into five gross anatomical regions, frontal, left and right temporal, central, and parieto-occipital, while data analysis was performed separately in each of the five classical EEG frequency bands, namely delta, theta, alpha, beta, and gamma. Our results showed that the SND group consistently formed a larger number of connections compared to the MCS group in all frequency bands. Additionally, the number of connections in the delta band (0.1-4 Hz) between the left temporal and parieto-occipital areas was significantly different (P < 0.1%) in the two groups. Furthermore, in the beta band (12-18 Hz), the input to the frontal areas from all other cortical areas was also significantly different (P < 0.1%) in the two groups. Finally, classification of the subjects into distinct groups using as features the number of connections within and between regions in all frequency bands resulted in 100% classification accuracy of all subjects. The results of this study suggest that analysis of brain connectivity networks based on GC can be a highly accurate approach for classifying subjects affected by severe traumatic brain injury.

The OpenSHMEM community would like to announce a new effort to standardize SHMEM, a communications library that uses one-sided communication and utilizes a partitioned global address space.OpenSHMEM is an effort to bring together a variety of SHMEM and SHMEM-like implementations into an open standard using a community-driven model. By creating an open-source specification and reference implementation of OpenSHMEM, there will be a wider availability of a PGAS library model on current and future architectures. In addition, the availability of an OpenSHMEM model will enable the development of performance and validation tools.We propose an OpenSHMEM specification to help tie together a number of divergent implementations of SHMEM that are currently available.To support an existing and growing user community, we will develop the OpenSHMEM web presence, including a community wiki and training material, and face-to-face interaction, including workshops and conference participation.

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Heterogeneous systems are becoming increasingly prevalent. In order to exploit the rich compute resources of such systems, robust programming models are needed for application developers to seamlessly migrate legacy code from today's systems to tomorrow's. Over the past decade and more, directives have been established as one of the promising paths to tackle programmatic challenges on emerging systems. This work focuses on applying and demonstrating OpenMP offloading directives on five proxy applications. We observe that the performance varies widely from one compiler to the other; a crucial aspect of our work is reporting best practices to application developers who use OpenMP offloading compilers. While some issues can be worked around by the developer, there are other issues that must be reported to the compiler vendors. By restructuring OpenMP offloading directives, we gain an 18x speedup for the su3 proxy application on NERSC's Cori system when using the Clang compiler, and a 15.7x speedup by switching max reductions to add reductions in the laplace mini-app when using the Cray-llvm compiler on Cori.

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