Computer simulations of copper and gold nanowires and single-wall nanowires

Copper and gold nanowires under tension evolve to form linear atomic chains (LACs), and the study and understanding of this evolution is an important subject for the development of nanocontacts. Here we study the differences and similarities between copper and gold nanowires (NWs) under stress along the [111] crystallographic direction until their rupture using tight-binding molecular dynamics. In both metals, the first significant rearrangement occurs due to one inside atom that goes to the NW` surface. In an attempt to better understand this effect, for both metals we also consider hollow NW`s where the inside atoms were excluded after the initial relaxation to create single-wall NW`s (SWNWs). The dynamical evolution of these SWNWs provides insight on the formation of the constriction that evolves to form LACs. Studying the calculated forces supported by the NW`s we show that SWNWs can sustain larger forces before the first major rearrangement in the copper and gold when compared to the original NW`s.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.