This article examines the concept of void with the question of whether the concept forms a sign. A void in this article is defined as a spatio-temporal empty space existing in a representation. The aim of the analysis is to consider the hidden nature of signs, which cannot be highlighted only through an analysis of typical signs within social convention. As a semiotic tool to conduct the discussion, the notion of a zero sign of Lévi-Strauss is introduced. The article considers (...) all possible zeros that can exist within the Saussurian sign model and introduces three cases: an omitted sign, an ambiguous signified, and an ambiguous sign. After examining the features of each kind, the concept of void is considered in these terms, with the conclusion that it belongs to the ambiguous sign case. Analysis of void with respect to the zero sign highlights the nature of signs as being reflexive, holistic and speculative. Because the signifier is barely delimited, it influences the surrounding sign systems, including the one existing at the meta-level. An ambiguous sign is deemed to allow for concept sharing among people even when the signifier and the signified remain ambiguous. (shrink)

This article argues how one problem of computing lies in realizing a significant instance given a class or type. Analysis of a case study on digital narrative suggests two general processes for instantiating significant instances: interaction and optimization. The article then explains how the problem of universals needs to be deconstructed when trying to understand what type of entities significant instances are and what the process for obtaining them is.

Long-range correlation, a property of time series exhibiting long-term memory, is mainly studied in the statistical physics domain and has been reported to exist in natural language. Using a state-of-the-art method for such analysis, long-range correlation is first shown to occur in long CHILDES data sets. To understand why, Bayesian generative models of language, originally proposed in the cognitive scientific domain, are investigated. Among representative models, the Simon model was found to exhibit surprisingly good long-range correlation, but {\em not} the (...) Pitman-Yor model. Since the Simon model is known not to correctly reflect the vocabulary growth of natural language, a simple new model is devised as a conjunct of the Simon and Pitman-Yor models, such that long-range correlation holds with a correct vocabulary growth rate. The investigation overall suggests that uniform sampling is one cause of long-range correlation and could thus have a relation with actual linguistic processes. (shrink)