Abstract: Due to the dynamic nature of Mobile Ad Hoc Network (MANET) the Quality of Service (QoS) requires several improvements. In this paper, we propose a novel hierarchical routing protocol based on clustering approach inspired on the heuristic Max-Min D-Cluster that we have improved

to lightwave. It uses a returnable-time design in which a distributed subnet of time servers operating in a selforganizing, hierarchical-master-slave configuration synchronizes local clocks within the subnet and to national time standards via wire or radio. The servers can also redistribute reference time via

Abstract We present an approach to support the evolution of online, distributed, reusable, and extendable ontologies based on the RDF data model. The approach works on the basis of atomic changes, basically additions or deletions of statements to or from an RDF graph. Such atomic changes are aggregated to more complex changes, resulting in a hierarchy of changes, thus facilitating the human reviewing process on various levels of detail. These derived compound changes may be annotated with meta-information and classified as ontology evolution patterns. The introduced ontology evolution patterns in conjunction with appropriate data migration algorithms enable the automatic migration of instance data in distributed environments. 1

... In this paper we present two general types of Gibbs samplers that can be used to fit posteriors of Bayesian hierarchical models based on stick-breaking priors. The first type of Gibbs sampler, referred to as a Polya urn Gibbs sampler, is a generalized version of a widely used Gibbs sampling

A preliminary unedited version of this paper was incorrectly published as part of Volume

Swarm is a multi-agent software platform for the simulation of complex adaptive systems. In the Swarm system the basic unit of simulation is the swarm, a collection of agents executing a schedule of actions. Swarm supports hierarchical modeling approaches whereby agents can be composed of swarms

on hierarchically structured data. Since in many cases changes must be computed from old and new versions of the data, we define the hierarchical change detection problem as the problem of finding a “minimum-cost edit script ” that transforms one data tree to another, and we present efficient algorithms

, the structure of the hierarchical model provides both Bayesian and likelihood meth-ods for selecting the Lasso parameter. Slight modifications lead to Bayesian versions of other Lasso-related estimation methods like bridge regression and a robust variant.

Conventional algorithms for computing large one-dimensional fast Fourier transforms (FFTs), even those algorithms recently developed for vector and parallel computers, are largely unsuitable for systems with external or hierarchical memory. The principal reason for this is the fact that most FFT

This paper defines a hierarchical version of the maximum flow problem. In this model, the capacities increase over time and the resulting solution is a sequence of flows that build on each other incrementally. Thus far, hierarchical problems considered in the literature have been built on NP