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Content available Lazy shortest path computation in dynamic graphs
We address the problem of single-source shortest path computation in digraphs with non-negative edge weights subjected to frequent edge weight decreases such that only some shortest paths are requested in-between updates. We optimise a recent semidynamic algorithm for weight decreases previously reported to be the fastest one in various conditions, resulting in important time savings that we demonstrate for the problem of incremental path map construction in usersteered image segmentation. Moreover, we extend the idea of lazy shortest path computation to digraphs subjected to both edge weight increases and decreases, comparing favourably to the fastest recent state-of-the-art algorithm.
This paper presents an approach to the phonetic interpretation of multilinear feature representations of speech utterances combining linguistic knowledge and efficient computational techniques. Multilinear feature representations are processed as intervals and the linguistic knowledge used by the system takes the form of feature implication rules (constraints) represented as subsumption hierarchies which are used to validate each interval. In the case of noisy or underspecified data, the linguistic constraints can be used to enrich the representations. Experiments are also presented to show that the system is logically correct and does not introduce errors in the data, and that it deals with underspecified and noisy input.
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