[article] Perustieteiden korkeakoulu / SCI

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Browsing [article] Perustieteiden korkeakoulu / SCI by Department "Tietojenkäsittelytieteen laitos"

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  • How Penalty Leads to Improvement: a Measurement Study of Wireless Backoff
    (2014) Kuptsov, Dmitriy; Nechaev, Boris; Lukyanenko, Andrey; Gurtov, Andrei
     School of Science | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Despite much theoretical work, different modifications of backoff protocols in 802.11 networkslack empirical evidence demonstrating their real-life performance. To fill the gap we have set out to experiment with performance of exponential backoff by varying its backoff factor. Despite the satisfactory results for throughput, we have witnessed poor fairness manifesting in severe capture effect. The design of standard backoff protocol allows already successful nodes to remain successful, giving little chance to those nodes that failed to capture the channel in the beginning. With this at hand, we ask a conceptual question: Can one improve the performance of wireless backoff by introducing a mechanism of self-penalty, when overly successful nodes are penalized with big contention windows? Our real-life measurements using commodity hardware demonstrate that in many settings such mechanism not only allows to achieve better throughput, but also assures nearly perfect fairness. We further corroborate these results with simulations and an analytical model. Finally, we present a backoff factor selection protocol which can beimplemented in access points to enable deployment of the penalty backoff protocol to consumer devices.
  • Searching for functional gene modules with interaction component models
    (2010) Parkkinen, Juuso; Kaski, Samuel
     School of Science | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Background:Functional gene modules and protein complexes are being sought from combinations of gene expression and protein-protein interaction data with various clustering-type methods. Central features missing from most of these methods are handling of uncertainty in both protein interaction and gene expression measurements, and in particular capability of modeling overlapping clusters. It would make sense to assume that proteins may play different roles in different functional modules, and the roles are evidenced in their interactions. Results:We formulate a generative probabilistic model for protein-protein interaction links and introduce two ways for including gene expression data into the model. The model finds interaction components, which can be interpreted as overlapping clusters or functional modules. We demonstrate the performance on two data sets of yeast Saccharomyces cerevisiae. Our methods outperform a representative set of earlier models in the task of finding biologically relevant modules having enriched functional classes. Conclusions:Combining protein interaction and gene expression data with a probabilistic generative model improves discovery of modules compared to approaches based on either data source alone. With a fairly simple model we can find biologically relevant modules better than with alternative methods, and in addition the modules may be inherently overlapping in the sense that different interactions may belong to different modules.
  • Supplementary Materials - Self-Supervised MRI Tissue Segmentation by Discriminative Clustering
    (2013) Goncalves, Nicolau; Nikila, Janne; Vigario, Ricardo
     School of Science | J Muu elektroninen julkaisu