Based on a complete Heyting algebra, we modify the definition oflattice-valued fuzzifying convergence space using fuzzy inclusionorder and construct in this way a Cartesian-closed category, calledthe category of $L-$ordered fuzzifying convergence spaces, in whichthe category of $L-$fuzzifying topological spaces can be embedded.In addition, two new categories are introduced, which are called the...

We built 3-D and 1-D look up tables (LUTs) to transform a user’s desired device-independent colors (CIELab) to the device-dependent color space (RGB). We considered experimental adaptive neighborhood and estimation methods for building the 3-D and 1-D LUTs. Methods of finding neighborhoods include: smallest enclosing neighborhood (SEN), smallest enclosing inclusive neighborhood (SENR), natural ...

The k nearest neighbor classification (k-NN) is a very simple and popular method for classification. However, it suffers from a major drawback, it assumes constant local class posterior probability. It is also highly dependent on and sensitive to the choice of the number of neighbors k. In addition, it severely lacks the desired probabilistic formulation. In this article, we propose a Bayesian ...

Defining the neighborhood of an initial set of nodes is an important task in network analysis. For example, we show that the neighborhood of an initial set of brain cancer related genes is highly enriched with other cancer genes as well. It remains an active area of research to define a biologically meaningful concept of neighborhood in gene or protein networks. In gene networks, genes with hig...

This paper analyzes if a multi-scale representation of geographical context based on statistical aggregates computed for individualized neighborhoods can lead to improved estimates of neighborhood effect. Our study group consists of individuals born in 1980 that have lived in Sweden since 1995 and we analyze the effect of neighborhood context at age 15 on educational outcome at age 30 controlli...

Novel topological measures for static multiprocessor interconnection networks, disconnectivity, looseness', and cost-effectiveness, are developed. These and other measures are employed for a comparative analysis of such networks. The goal of this analysis is to predict network effectiveness, without resorting to execution benchmark techniques. In particular, we compare the hypereube and perfect...

Various kinds of k-nearest neighbor (KNN) based classification methods are the bases of many well-established and high-performance pattern-recognition techniques, but both of them are vulnerable to their parameter choice. Essentially, the challenge is to detect the neighborhood of various data sets, while utterly ignorant of the data characteristic. This article introduces a new supervised clas...

The k-local hyperplane distance nearest neighbors classification (HKNN) builds a non-linear decision surface with maximal local margin in the input space, with invariance inferred from the local neighborhood rather than the prior knowledge, so that it performs very well in many applications. However, it still cannot be comparable with human being in classification on the noisy, the sparse, and ...

Nearest neighbor searching is an important geometric subproblem in vector quanti-zation. Existing studies have shown that the diiculty of solving this problem eeciently grows rapidly with dimension. Indeed, existing approaches on unstructured codebooks in dimension 16 are little better than brute-force search. We show that if one is willing to relax the requirement of nding the true nearest nei...

OBJECTIVE To assess the association between proximity to a needle exchange program (NEP) and experience of violence in an inner city neighborhood. METHODS A cross-sectional survey of residents of Harlem, New York City provided data for this study. We used geocoding to obtain the distance between respondents and the nearest NEP. Respondents reported violence they were aware of or had personall...