Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Middleware for energy-awareness in mobile devices

Abstract: The growing complexity of mobile applications coupled with slow progress in the development of batteries has led to the requirement of energy-awareness in mobile devices. Nevertheless, no general solution exists for supporting energy-awareness across various mobile platforms and application domains. To address the above mentioned problems, we propose a middleware framework which utilizes the concept of application classification, and power estimation to accomplish application-specific power management, as well as providing basic support for active power management and fundamental services for energy-aware applications. To this end, we have implemented a basic prototype reflecting the functionalities of our framework, and evaluated it using mobile YouTube.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

Abstract: Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: if.iknisleh@aksnohcic.anna or if.iknisleh@nenirip.ittam Supplementary information: Supplementary data are available at Bioinformatics online.

The Boolean Column and Column-Row Matrix Decompositions

Abstract: Matrix decompositions are used for many data mining purposes. One of these purposes is to find a concise but interpretable representation of a given data matrix. Different decomposition formulations have been proposed for this task, many of which assume a certain property of the input data (e.g., nonnegativity) and aim at preserving that property in the decomposition.

Partial Order MCMC for Structure Discovery in Bayesian Networks

Abstract: We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior distribution of partial orders on the nodes; for each sampled partial order, the conditional probabilities of interest are computed exactly. We give both analytical and empirical results that suggest the superiority of the new method compared to previous methods, which sample either directed acyclic graphs or linear orders on the nodes.

Subset Selection in Linear Regression using Sequentially Normalized Least Squares: Asymptotic Theory

Abstract: This article examines the recently proposed sequentially normalized least squares criterion for the linear regression subset selection problem. A simplified formula for computation of the criterion is presented, and an expression for its asymptotic form is derived without the assumption of normally distributed errors. Asymptotic consistency is proved in two senses: (i) in the usual sense, where the sample size tends to infinity, and (ii) in a non-standard sense, where the sample size is fixed and the noise variance tends to zero.