The mapping between surface and lexical levels is reduced to a single function call and two simple conversions from text to binary, and vice versa.
This data set had binary random-dot stereograms (40% dot density) that included transparent surfaces.
In this paper, we will focus on recursive trees and binary search trees as underlying classes of trees.
Our refined approaches will also exhibit some universality; we will demonstrate this by showing that they straightforwardly apply to random binary search trees as well.
The binary operator seq evaluates to its second argument if and only if its first argument converges, otherwise the whole seq expression diverges.
These binary variables are coded positive if the symptom score is greater than or equal to 0.5, and zero otherwise.
We now describe a model for the evolution of binary sequences on a tree.
Although it correctly predicts poor performance on object relatives, it wrongly predicts consistent role reversal, namely, a below-chance performance in binary sentence-picture matching.
