High-velocity features: A ubiquitous property of Type Ia supernovae

Evidence of high-velocity features (HVFs) such as those seen in the near-maximum spectra of some Type Ia supernovae (SNe Ia; e. g., SN 2000cx) has been searched for in the available SN Ia spectra observed earlier than 1 week before B maximum. Recent observational efforts have doubled the number of SNe Ia with very early spectra. Remarkably, all SNe Ia with early data ( seven in our Research Training Network sample and 10 from other programs) show signs of such features, to a greater or lesser degree, in Ca II IR and some also in the Si II lambda 6355 line. HVFs may be interpreted as abundance or density enhancements. Abundance enhancements would imply an outer region dominated by Si and Ca. Density enhancements may result from the sweeping up of circumstellar material (CSM) by the highest velocity SN ejecta. In this scenario, the high incidence of HVFs suggests that a thick disk and/or a high-density companion wind surrounds the exploding white dwarf, as may be the case in single degenerate systems. Large-scale angular fluctuations in the radial density and abundance distribution may also be responsible: this could originate in the explosion and would suggest a deflagration as the more likely explosion mechanism. CSM interaction and surface fluctuations may coexist, possibly leaving different signatures on the spectrum. In some SNe, the HVFs are narrowly confined in velocity, suggesting the ejection of blobs of burned material.

Does the Child Health Computing System adequately identify children with cerebral palsy?

Background This paper assesses the usefulness of the Child Health Computing System as a source of information about children with cerebral palsy.

Methods A comparative survey of information held on the Child Health Computing System (CHCS) and the Northern Ireland Cerebral Palsy Register (NICPR) in one Health and Social Services Board in Northern Ireland was carried out. The sample comprised children with cerebral palsy aged 5–9 years.

Results Of the 135 cases recorded on the NICPR, 47 per cent were not found on the CHCS; the majority of these children had no computer record of any medical diagnosis. Of the 82 cases recorded on the CHCS, 10(12 per cent) were not found on the NICPR; five of these cases (6 per cent) were found on follow–up not to have CP.

Conclusions Unless improvements are made in case ascertainment, case validation and recording activities, the evidence suggests that the CHCS will not be able to provide the same quality of information for needs assessment and surveillance of very low birthweight infants in relation to cerebral palsy as is provided by a specialist case register.

A Capabilities-Aware Framework for Using Computational Accelerators in Data-Intensive Computing

Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.