Turku Centre for Computer Science

About: Turku Centre for Computer Science is a facility organization based out in Turku, Finland. It is known for research contribution in the topics: Decidability & Word (group theory). The organization has 382 authors who have published 1027 publications receiving 19560 citations.

Constructions with Countable Subshifts of Finite Type

Abstract: We present constructions of countable two-dimensional subshifts of finite type SFTs with interesting properties. Our main focus is on properties of the topological derivatives and subpattern posets of these objects. We present a countable SFT whose iterated derivatives are maximally complex from the computational point of view, constructions of countable SFTs with high Cantor-Bendixson ranks, a countable SFT whose subpattern poset contains an infinite descending chain and a countable SFT whose subpattern poset contains all finite posets. When possible, we make these constructions deterministic, and ensure the sets of rows are very simple as one-dimensional subshifts.

Coordination Among Mobile Objects

Abstract: When designing distributed object-based systems one is often faced with the problem of modelling the movement of objects from site to site in a distributed network. In order to model such an activity, some supervising or coordination mechanisms are needed, to insure correctness of both movement and communication in the network. In this paper we propose distributed object-based action systems as a basis for (coordinated) mobile computing. In order to model mobility and coordination we extend the action systems and OO-action systems formalisms with so called mobile objects and coordinator objects. The mobile objects move in some domain whereas the coordinator objects control the actions of the mobile objects within their respective domains. We show the applicability of the proposed framework with a small though nontrivial example.

A continuation approach to mode-finding of multivariate Gaussian mixtures and kernel density estimates

Abstract: Gaussian mixtures (i.e. linear combinations of multivariate Gaussian probability densities) appear in numerous applications due to their universal ability to approximate multimodal probability distributions. Finding the modes (maxima) of a Gaussian mixture is a fundamental problem arising in many practical applications such as machine learning and digital image processing. In this paper, we propose a computationally efficient method for finding a significant mode of the Gaussian mixture. Such a mode represents an area of large probability, and it often coincides with the global mode of the mixture. The proposed method uses a Gaussian convolution in order to remove undesired local maxima of the Gaussian mixture and preserve its underlying structure. The transformation between the maximizers of the smoothed Gaussian mixture and the original one is formulated as a differential equation. A robust trust region method for tracing the solution curve of this differential equation is described. Our formulation also allows mixtures with negative weights or even negative values, which occur in some applications related to machine learning or quantum mechanics. The applicability of the method to mode-finding of Gaussian kernel density estimates obtained from experimental data is illustrated. Finally, some numerical results are given to demonstrate the ability of the method to find significant modes of Gaussian mixtures and kernel density estimates.