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.

Likelihood-free inference via classification

Abstract: Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference. A likelihood-free inference framework has emerged where the parameters are identified by finding values that yield simulated data resembling the observed data. While widely applicable, a major difficulty in this framework is how to measure the discrepancy between the simulated and observed data. Transforming the original problem into a problem of classifying the data into simulated versus observed, we find that classification accuracy can be used to assess the discrepancy. The complete arsenal of classification methods becomes thereby available for inference of intractable generative models. We validate our approach using theory and simulations for both point estimation and Bayesian inference, and demonstrate its use on real data by inferring an individual-based epidemiological model for bacterial infections in child care centers.

Surviving task interruptions: Investigating the implications of long-term working memory theory

Abstract: Typically, we have several tasks at hand, some of which are in interrupted state while others are being carried out. Most of the time, such interruptions are not disruptive to task performance. Based on the theory of Long-Term Working Memory (LTWM; Ericsson, K.A., Kintsch, W., 1995. Long-term working memory. Psychological Review, 102, 211-245), we posit that unless there are enough mental skills and resources to encode task representations to retrieval structures in long-term memory, the resulting memory traces will not enable reinstating the information, which can lead to memory losses. However, once encoded to LTWM, they are virtually safeguarded. Implications of the theory were tested in a series of experiments in which the reading of an expository text was interrupted by a 30-s interactive task, after which the reading was continued. The results convey the remarkably robust nature of skilled memory-when LTWM encoding speed is fast enough for the task-processing imposed by the interface, interruptions have no effect on memory, regardless of their pacing, intensity, or difficulty. In the final experiment where presentation time in the main task was notably speeded up to match the limits of encoding speed, interruptions did hamper memory. Based on the results and the theory, we argue that auditory rehearsal or time-based retrieval cues were not utilized in surviving interruptions and that they are in general weaker strategies for surviving interruptions in complex cognitive tasks. We conclude the paper by suggesting three ways to support interruption tolerance by the means of task and interface design: (1) actively facilitating the development of memory skills, (2) matching encoding speed to task processing demands, and (3) supporting encoding-retrieval symmetry.

ContextContacts: re-designing SmartPhone's contact book to support mobile awareness and collaboration

Abstract: Acontextuality of the mobile phone often leads to a caller's uncertainty over a callee's current state, which in turn often hampers mobile collaboration. We are interested in re-designing a Smartphone's contact book to provide cues of the current situations of others. ContextContacts presents several meaningful, automatically communicated situation cues of trusted others. Its interaction design follows social psychological findings on how people make social attributions based on impoverished cues, on how self-disclosure of cues is progressively and interactionally managed, and on how mobility affects interaction through cues. We argue how our design choices support mobile communication decisions and group coordinations by promoting awareness. As a result, the design is very minimal and integrated, in an "unremarkable" manner, to previously learned usage patterns with the phone. First laboratory and field evaluations indicate important boundary conditions for and promising avenues toward more useful and enjoyable mobile awareness applications.

The State-of-the-Art of Set Visualization

Abstract: Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net.

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.