Various theorems for the preservation of set-theoretic axioms under forcing are proved, regarding both forcing axioms and axioms true in the Levy-Collapse. These show in particular that certain applications of forcing axioms require to add generic countable sequences high up in the set-theoretic hierarchy even before collapsing everything down to א1. Later we give applications, among them the c...

In order to create a Grid infrastructure using ARC-enabled computing resources, information description and aggregation services need to be deployed. ARIS is coupled to a computing resource and collects information about it. EGIIS keeps a list of ARIS instances, and eventually, of other EGIIS instances lower down in hierarchy. Top-level EGIIS instances thus serve as an entry point to the Grid, ...

This paper describes TreeGNG, a top-down unsupervised learning method that produces hierarchical classification schemes. TreeGNG is an extension to the Growing Neural Gas algorithm that maintains a time history of the learned topological mapping. TreeGNG is able to correct poor decisions made during the early phases of the construction of the tree, and provides the novel ability to influence th...

There is a growing understanding that both top-down and bottom-up signals underlie perception. But it is not known how these signals integrate with each other and how this depends on the perceived stimuli's predictability. 'Predictive coding' theories describe this integration in terms of how well top-down predictions fit with bottom-up sensory input. Identifying neural markers for such signal ...

Perception and cortical responses are not only driven "bottom-up" by the external stimulus but are altered by internal constraints such as expectancy or the current behavioral goal. To investigate neurophysiological mechanisms of such top-down effects, we analyzed the temporal interactions of neurons on different levels of the cortical hierarchy during perception of stimuli with varying behavio...

The top-down method is efficient and commonly used in hierarchical text classification. Its main drawback is the error propagation from the higher to the lower nodes. To address this issue we propose an efficient incremental reranking model of the top-down classifier decisions. We build a multiclassifier for each hierarchy node, constituted by the latter and its children. Then we generate sever...

The visualisation of taxonomic hierarchies has evolved from indented lists of names to techniques that can display thousands of nodes and onto hundreds of thousands of nodes over multiple taxonomies. However, challenges remain within multiple hierarchy visualisation, and for taxonomic hierarchy visualisation in particular. Firstly, at present, there is no support for handling specific taxonomic...

A hierarchical key assignment scheme is a method to assign some private information and encryption keys to a set of classes in a partially ordered hierarchy, in such a way that the private information of a higher class can be used to derive the keys of all classes lower down in the hierarchy. In this paper we design and analyze hierarchical key assignment schemes which are provablysecure and su...

Decision trees have provided a classical mechanism for progressively narrowing down a search from a large group of possibilities to a single alternative. The structuring of a decision tree is based on a heuristic that maximizes the value of the information gained at each level in the hierarchy. Decision trees are effective when an agent needs to reach the goal of complete diagnosis as quickly a...

Many significant real-world classification tasks involve a large number of categories which are arranged in a hierarchical structure; for example, classifying documents into subject categories under the library of congress scheme, or classifying world-wide-web documents into topic hierarchies. We investigate the potential benefits of using a given hierarchy over base classes to learn accurate m...