Spontaneous Analogy by Piggybacking on a Perceptual System

Most computational models of analogy assume they are given a delineated source domain and often a specified target domain. These systems do not address how analogs can be isolated from large domains and spontaneously retrieved from long-term memory, a process we call spontaneous analogy. We present a system that represents relational structures as feature bags. Using this representation, our system leverages perceptual algorithms to automatically create an ontology of relational structures and to efficiently retrieve analogs for new relational structures from long-term memory. We provide a demonstration of our approach that takes a set of unsegmented stories, constructs an ontology of analogical schemas (corresponding to plot devices), and uses this ontology to efficiently find analogs within new stories, yielding significant time-savings over linear analog retrieval at a small accuracy cost.Comment: Proceedings of the 35th Meeting of the Cognitive Science Society, 201

Nanoscale clusters in the high performance thermoelectric AgPbmSbTem+2

The local structure of the AgPbmSbTem+2 series of thermoelectric materials has been studied using the atomic pair distribution function (PDF) method. Three candidate-models were attempted for the structure of this class of materials using either a one-phase or a two-phase modeling procedure. Combining modeling the PDF with HRTEM data we show that AgPbmSbTem+2 contains nanoscale inclusions with composition close to AgPb3SbTe5 randomly embedded in a PbTe matrix.Comment: 7 pages, 5 figures, 2 tables, submitted to PR