Sixteen- and 24-month-olds' use of mutual exclusivity as a default assumption in second-label learning.

The significance of the mutual exclusivity assumption for early word learning has been questioned on 2 accounts: (1) Children learn second labels for objects, which violates the assumption, and (2) evidence documenting use of mutual exclusivity comes mostly from older children. This article addresses both concerns. Use of mutual exclusivity predicts not that learning 2nd labels is impossible but that it is harder than learning 1st labels. To test this, very young children were taught novel labels for objects they either could or could not already name. In Study 1, contrary to predictions, 24-mo-olds learned 1st and 2nd labels equally well. But in Study 2, when children had an additional word to learn, they had trouble learning second (but not first) labels. Similarly, in Study 3, 16-mo-olds who were taught only one new word had trouble learning second labels. Thus, from 16 mo on, mutual exclusivity helps children interpret novel words. Yet, when their processing capacity is not overly taxed, 24-mo-olds can override this default assumption. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Censored life-data analysis using linguistic variables

Parameter estimates for censored life data using ordinary censoring methods (noninformative censoring) can be biased when the cause of censoring for a unit is related to its final life (informative censoring). An algorithm is presented for obtaining estimated lifetimes for informatively censored units. It is based on linguistic variable concepts from fuzzy set theory. These estimates are combined with the lifetimes for actual field units to form a complete sample for parameter estimation purposes     

An approach to large-scale nonlinear programming

Due to changing matrix elements, many of the computational benefits embodied in sparse matrix theory and implemented in commercial LP codes for maintaining a sparse matrix inverse updates are lost for NLP. This paper reports on the results of investigating the use of structural decomposition in large, sparse NLP problems using the GRG algorithm. The approach is to partition the basis matrix into block lower triangular (BLT) form. At each step of the GRG algorithm, all operations are based upon the smaller diagonal subsets of variables.This approach led to the development of an algorithm to dynamically order a square matrix into block, lower triangular form after a column replacement. The method is fast, showing computational time reductions of up to a factor of 10 over performing the ordering on the complete occurrence matrix, while requiring a minimal amount of computer memory. Only one subset of the occurrence matrix followed by only the condensed occurrence matrix is required in computer memory to order the modified matrix. The algorithm is applicable to any numerical method which requires structural modification of a matrix by changing the structure of one column.An experimental GRG computer code, called GRGLSS, was developed for testing the technique. Examples demonstrate significantly faster computation speeds in the feasibility phase of the GRG algorithm.