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- Per Austrin, Petteri Kaski, Mikko Koivisto, Jussi Määttä
- ICALP
- 2013

The technique of Schroeppel and Shamir (SICOMP, 1981) has long been the most efficient way to trade space against time for the Subset Sum problem. In the random-instance setting, however, improved tradeoffs exist. In particular, the recently discovered dissection method of Dinur et al. (CRYPTO 2012) yields a significantly improved space–time tradeoff curve… (More)

- Jussi Määttä, Samuli Siltanen, Teemu Roos
- 2014 5th European Workshop on Visual Information…
- 2014

A novel image denoising approach based on iterated median filtering is proposed. It is well suited for removing white noise and produces visually pleasing smooth surfaces while preserving edges and without producing artifacts. The denoised image is the fixed point of a nonlinear operator and can be obtained as the limit of a convergent sequence. We show… (More)

- Jussi Määttä, Teemu Roos
- 2015

The Predictive Least Squares (PLS) model selection criterion is known to be consistent in the context of linear regression. For small sample sizes, however, it can exhibit erratic behavior. We show that this shortcoming can be amended by incorporating a Student’s t-distribution into PLS. The resulting criterion is shown to be asymptotically equivalent to… (More)

- Jussi Määttä, Teemu Roos
- PloS one
- 2016

The maximum parsimony (MP) method for inferring phylogenies is widely used, but little is known about its limitations in non-asymptotic situations. This study employs large-scale computations with simulated phylogenetic data to estimate the probability that MP succeeds in finding the true phylogeny for up to twelve taxa and 256 characters. The set of… (More)

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