Ilya Gluhovsky

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Probabilistic Boolean networks (PBNs) have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steady-state (long-run) behaviour of a PBN by analysing the corresponding Markov chain. This allows one to(More)
This article presents a technique for taking a sparse set of cache simulation data and fitting a multivariate model to fill in the missing points over a broad region of cache configurations. We extend previous work by its applicability to multiple miss rate components and its ability to model a wide range of cache parameters, including size, associativity(More)
Cache miss rates are an important subset of system model inputs. Cache miss rate models are used for broad design space exploration in which many cache configurations cannot be simulated directly due to limitations of trace collection setups or available resources. Often it is not practical to simulate large caches. Large processor counts and consequent(More)
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We consider a median pyramidal transform for denoising applications. Traditional techniques of pyramidal denoising are similar to those in wavelet-based methods. In order to remove noise, they use the thresholding of transform coefficients. We propose to model the structure of the transform coefficients as a Markov random field. The goal of modeling(More)