We give a new algorithm to construct optimal alphabetic ternary trees, where every internal node has at most three children. This algorithm generalizes the classic Hu-Tucker algorithm, though the overall computational complexity has yet to be determined.

We describe a modiication of the Hu{Tucker algorithm for constructing an optimal alphabetic tree that runs in O(n) time for several classes of inputs. These classes can be described in simple terms and can be detected in linear time. We also give simple conditions and a linear algorithm for determining, in some cases, if two adjacent nodes will be combined in the optimal alphabetic tree.

Word and pseudoword reading are related abilities that are fundamental to reading development in alphabetic orthographies. They are respectively assumed to index children’s orthographic representations of words as acquired through the underlying ‘self-teaching mechanism’ of alphabetic pseudoword decoding. However, little is known about the concurrent growth trajectories of these skills in the e...

We show how to build an alphabetic minimax tree for a sequence W = w1, . . . , wn of real weights in O(nd log logn) time, where d is the number of distinct integers ⌈wi⌉. We apply this algorithm to building an alphabetic prefix code given a sample.

We prove that the Büchi topology, the automatic topology, the alphabetic topology and the strong alphabetic topology are Polish, and provide consequences of this. 1998 ACM Subject Classification F.1.1 Models of Computation, F.1.3 Complexity Measures and Classes, F.4.1 Mathematical Logic, F.4.3 Formal Languages

In this paper, we survey current evidence on cognitive precursors of reading in different orthographies by reviewing studies with a cross-linguistic research design. Graphic symbol knowledge, phonological awareness, morphological and rapid automatized naming were found to be associated acquisition all investigated. However, apart from naming, association is mostly interactive, meaning that youn...