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A Sweep-Line Method for Büchi Automata-based Model Checking

100%

Evangelista S. , Kristensen L. M.

Fundamenta Informaticae

2014

tom Vol. 131, nr 1

27--53

The sweep-line method allows explicit state model checkers to delete states from memory on-the-fly during state space exploration, thereby lowering the memory demands of the verification procedure. The sweep-line method is based on a least-progress-first search order that prohibits the immediate use of standard on-the-fly Büchi automata-based model checking algorithms that rely on a depth-first search order in the search for an acceptance cycle. This paper proposes and experimentally evaluates an algorithm for Büchi automata-based model checking compatible with the search order and deletion of states prescribed by the sweep-line method.

On Tight Separation for Blum Measures Applied to Turing Machine Buffer Complexity

100%

Šíma J. , Žák S.

Fundamenta Informaticae

2017

tom Vol. 152, nr 4

397--409

We formulate a very general tight diagonalization method for the Blum complexity measures satisfying two additional axioms related to our diagonalizer machine. We apply this method to two new, mutually related, distance and buffer complexities of Turing machine computations which are important nontrivial examples of natural Blum complexity measures different from time and space. In particular, these measures capture how many times the worktape head needs to move a certain distance during the computation which corresponds to the number of necessary block uploads into a buffer cache memory. We start this study by proving a tight separation which shows that a very small increase in the distance or buffer complexity bound (roughly from f(n) to f(n + 1)) brings provably more computational power to both deterministic and nondeterministic Turing machines even for unary languages. We also obtain hierarchies of the distance and buffer complexity classes.

An Orientation-space Super Sampling Technique for Six-dimensional Diffraction Contrast Tomography

86%

Viganò N. , Batenburg K. J. , Ludwig W.

Fundamenta Informaticae

2016

tom Vol. 146, nr 2

219--230

Diffraction contrast tomography (DCT) is an X-ray full-field imaging technique that allows for the non-destructive three-dimensional investigation of polycrystalline materials and the determination of the physical and morphological properties of their crystallographic domains, called grains. This task is considered more and more challenging with the increasing intra-granular deformation, also known as orientation-spread. The recent introduction of a sixdimensional reconstruction framework in DCT (6D-DCT) has proven to be able to address the intra-granular crystal orientation for moderately deformed materials. The approach used in 6D-DCT, which is an extended sampling of the six-dimensional combined position-orientation space, has a linear scaling between the number of sampled orientations, which determine the orientation-space resolution of the problem, and computer memory usage. As a result, the reconstruction of more deformed materials is limited by their high resource requirements from a memory and computational point of view, which can easily become too demanding for the currently available computer technologies. In this article we propose a super-sampling method for the orientation-space representation of the six-dimensional DCT framework that enables the reconstruction of more deformed cases by reducing the impact on system memory, at the expense of longer reconstruction times. The use of super-sampling can further improve the quality and accuracy of the reconstructions, especially in cases where memory restrictions force us to adapt to inadequate (undersampled) orientation-space sampling.