Unsupervised pattern recognition algorithms support the existence of three gamma-ray burst classes; Class I (long, large fluence bursts of intermediate spectral hardness), Class II (short, small fluence, hard bursts), and Class III (soft bursts of intermediate durations and fluences). The algorithms surprisingly assign larger membership to Class III than to either of the other two classes. A known systematic bias has been previously used to explain the existence of Class III in terms of Class I; this bias allows the fluences and durations of some bursts to be underestimated (Hakkila et al., ApJ 538, 165, 2000). We show that this bias primarily affects only the longest bursts and cannot explain the bulk of the Class III properties. We resolve the question of Class III existence by demonstrating how samples obtained using standard trigger mechanisms fail to preserve the duration characteristics of small peak flux bursts. Sample incompleteness is thus primarily responsible for the existence of Class III. In order to avoid this incompleteness, we show how a new dual timescale peak flux can be defined in terms of peak flux and fluence. The dual timescale peak flux preserves the duration distribution of faint bursts and correlates better with spectral hardness

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We present a database of spectral lags and internal luminosity function (ILF ) measurements for gamma-ray bursts (GRBs) in the BATSE catalog. Measurements were made using 64 ms count rate data and are defined for various combinations of the four broadband BATSE energy channels. We discuss the processes used for measuring lags and ILF characteristics. We discuss the statistical and systematic uncertainties in measuring these attributes, as well as the role of temporal resolution in measuring lags and/or ILFs-these are particularly noticeable for GRBs belonging to the Short class. Correlative and clustering properties of the lag and ILF are examined, including the ability of these attributes to predict GRB time history morphologies. We conclude that the ILF and lag have great potential for studying GRB physics when used with other burst attributes.

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Abstract. To-date, the application of high-performance computing resources to Semantic Web data has largely focused on commodity hardware and distributed memory platforms. In this paper we make the case that more specialized hardware can offer superior scaling and close to an order of magnitude improvement in performance. In particular we examine the Cray XMT. Its key characteristics, a large, global sharedmemory, and processors with a memory-latency tolerant design, offer an environment conducive to programming for the Semantic Web and have engendered results that far surpass current state of the art. We examine three fundamental pieces requisite for a fully functioning semantic database: dictionary encoding, RDFS inference, and query processing. We show scaling up to 512 processors (the largest configuration we had available), and the ability to process 20 billion triples completely in-memory.