Computability

About: Computability is a research topic. Over the lifetime, 2829 publications have been published within this topic receiving 85162 citations.

Coherent randomness tests and computing the $K$-trivial sets

Abstract: We show that a Martin-Lof random set for which the effective version of the Lebesgue density theorem fails computes every K-trivial set. Combined with a recent result by Day and Miller, this gives a positive solution to the ML-covering problem (Question 4.6 in Randomness and computability: Open questions. Bull. Symbolic Logic, 12(3):390–410, 2006). On the other hand, we settle stronger variants of the covering problem in the negative. We show that any witness for the solution of the covering problem, namely an incomplete random set which computes all K-trivial sets, must be very close to being Turing complete. For example, such a random set must be LR-hard. Similarly, not every K-trivial set is computed by the two halves of a random set. The work passes through a notion of randomness which characterises computing K-trivial sets by random sets. This gives a “smart” K-trivial set, all randoms from whom this set is computed have to compute all K-trivial sets.

Asynchronous Spiking Neural P Systems with Anti-Spikes

Abstract: Spiking neural P systems with anti-spikes (ASN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes and inhibitory spikes. ASN P systems working in the synchronous manner with standard spiking rules have been proved to be Turing completeness, do what Turing machine can do. In this work, we consider the computing power of ASN P systems working in the asynchronous manner with standard rules. As expected, the non-synchronization will decrease the computability of the systems. Specifically, asynchronous ASN P systems with standard rules can only characterize the semilinear sets of natural numbers. But, by using weighted synapses, asynchronous ASN P systems can achieve the equivalence with Turing machine again. It implies that weighted synapses has some "programming capacity" in the sense of achieving computing power. The obtained results have a nice interpretation: the loss in power entailed by removing the synchronization from ASN P systems can be compensated by using weighted synapses among connected neurons.

High-Dimensional Potential Energy Surfaces for Molecular Simulations

Abstract: An overview of computational methods to describe high-dimensional potential energy surfaces suitable for atomistic simulations is given. Particular emphasis is put on accuracy, computability, transferability and extensibility of the methods discussed. They include empirical force fields, representations based on reproducing kernels, using permutationally invariant polynomials, and neural network-learned representations and combinations thereof. Future directions and potential improvements are discussed primarily from a practical, application-oriented perspective.