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Content available remote Handling Non-determinism in Spiking Neural P Systems : Algorithms and Simulations
EN
Spiking Neural P system is a computing model inspired on how the neurons in a living being are interconnected and exchange information. As a model in embrane computing, it is a non-deterministic and massively-parallel system. The latter makes GPU a good candidate for accelerating the simulation of these models. A matrix representation for systems with and without delay have been previously designed, and algorithms for simulating them with deterministic systems was also developed. So far, non-determinism has been problematic for the design of parallel simulators. In this work, an algorithm for simulating non-deterministic spiking neural P system with delays is presented. In order to study how the simulations get accelerated on a GPU, this algorithm was implemented in CUDA and used to simulate non-uniform and uniform solutions to the Subset Sum problem as a case study. The analysis is completed with a comparison of time and space resources in the GPU of such simulations.
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EN
In this paper we introduce distinct types of Tribonacci quaternions. We describe dependences between them and we give some their properties also related to a matrix representation.
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Content available remote Tolerance Soft Set Relation on a Soft Set and its Matrix Applications
EN
In this paper the tolerance soft set relation on a soft set is defined and some examples are given with their matrix representations. Also, pre-class and tolerance class concepts for a given tolerance soft set relation are introduced and some examples related to these definitions are illustrated. Some theoretical results are proved such as every pre-class contained by a tolerance class and intersection of two pre-classes is a pre-class as well. Moreover, a method to find out the tolerance classes and pre-classes by using matrix representation of a tolerance soft set relation is explained with examples.
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