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Forty pneumonia and 40 fracture cases were matched by patient age and gender with normal cases. Pediatric cases constituted 25% of this sample. All 160 examinations were laser-digitized at a spot size of 210 microns, a contrast resolution of 12 bits, and a spatial resolution of 2.35 line pairs per millimeter. The digitized images were transmitted 5 miles(More)
Jackendoff (2002) posed four challenges that linguistic combinatoriality and rules of language present to theories of brain function. The essence of these problems is the question of how to neurally instantiate the rapid construction and transformation of the compositional structures that are typically taken to be the domain of symbolic processing. He(More)
We provide an overview of Vector Symbolic Architectures (VSA), a class of structured associative memory models that offers a number of desirable features for artificial general intelligence. By directly encoding structure using familiar , computationally efficient algorithms, VSA bypasses many of the problems that have consumed unnecessary effort and(More)
This paper describes a rapid technique: communal analysis suspicion scoring (CASS), for generating numeric suspicion scores on streaming credit applications based on implicit links to each other, over both time and space. CASS includes pair-wise communal scoring of identifier attributes for applications, definition of categories of suspiciousness for(More)
We present a fully distributed connectionist architecture supporting lateral inhibition / winner-takes all competition. All items (individuals, relations, and structures) are represented by high-dimensional distributed vectors, and (multi)sets of items as the sum of such vectors. The architecture uses a neurally plausible permutation circuit to support a(More)
Analogy-making is a key function of human cognition. Therefore, the development of computational models of analogy that automatically learn from examples can lead to significant advances in cognitive systems. Analogies require complex, relational representations of learned structures, which is challenging for both symbolic and neurally inspired models.(More)