In this chapter we describe a model of decision-making skills under time stress, a training strategy based on that model, and experimental tests of the training strategy. A prime example of the kind of decision making 155

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This document defines extensions of the RDF data model and of the SPARQL query language that capture an alternative approach to represent statement-level metadata. While this alternative approach is backwards compatible with RDF reification as defined by the RDF standard, the approach aims to address usability and data management shortcomings of RDF reification. One of the great advantages of the proposed approach is that it clarifies a means to (i) understand sparse matrices, the property graph model, hypergraphs, and other data structures with an emphasis on link attributes, (ii) map such data onto RDF, and (iii) query such data using SPARQL. Further, the proposal greatly expands both the freedom that database designers enjoy when creating physical indexing schemes and query plans for graph data annotated with link attributes and the interoperability of those database solutions.

High performance graph analytics are critical for a long list of application domains. In recent years, the rapid advancement of many-core processors, in particular graphical processing units (GPUs), has sparked a broad interest in developing high performance parallel graph programs on these architectures. However, the SIMT architecture used in GPUs places particular constraints on both the design and implementation of the algorithms and data structures, making the development of such programs difficult and time-consuming.We present MapGraph, a high performance parallel graph programming framework that delivers up to 3 billion Traversed Edges Per Second (TEPS) on a GPU. MapGraph provides a high-level abstraction that makes it easy to write graph programs and obtain good parallel speedups on GPUs. To deliver high performance, MapGraph dynamically chooses among different scheduling strategies depending on the size of the frontier and the size of the adjacency lists for the vertices in the frontier. In addition, a Structure Of Arrays (SOA) pattern is used to ensure coalesced memory access. Our experiments show that, for many graph analytics algorithms, an implementation, with our abstraction, is up to two orders of magnitude faster than a parallel CPU implementation and is comparable to state-of-the-art, manually optimized GPU implementations. In addition, with our abstraction, new graph analytics can be developed with relatively little effort.

The focus of this research was on the intersection of cognitive and perceptual aspects of human target recognition performance, and on potential enhancements of the human-ATR interface. Three series of experiments were conducted with active duty Army pilots. Each study attempted to lay a scienti fic basis, and to test a practical methodology, for a promising ATR design application. The studies address the following issues in ATR-human interface design: (1) effective displays of target classification conclusions to support rapid verification and application to the mission (2) effective displays of target imagery to support rapid and accurate user verification of ATR conclusions, and (3) effective support for decision making processes that allocate user attention, decide where and how long to verify ATR conclusions, and determine which targets to engage. Our results suggest that: (1) ATR conclusions should be labeled at different levels of specificity for different types of vehicles; (2) enhancement of vehicle profile and selected vehicle details can improve speed and accuracy of visual recognition; and (3) engagement decision making is improved by techniques for quickly guiding user attention to images classified as high-confidence enemies, high confidence friends, or significant and uncertain.