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.

Gaussian process test for high-throughput sequencing time series : application to experimental evolution

Abstract: Motivation: Recent advances in high-throughput sequencing (HTS) have made it possible to monitor genomes in great detail. New experiments not only use HTS to measure genomic features at one time point but also monitor them changing over time with the aim of identifying significant changes in their abundance. In population genetics, for example, allele frequencies are monitored over time to detect significant frequency changes that indicate selection pressures. Previous attempts at analyzing data from HTS experiments have been limited as they could not simultaneously include data at intermediate time points, replicate experiments and sources of uncertainty specific to HTS such as sequencing depth. Results: We present the beta-binomial Gaussian process model for ranking features with significant non-random variation in abundance over time. The features are assumed to represent proportions, such as proportion of an alternative allele in a population. We use the beta-binomial model to capture the uncertainty arising from finite sequencing depth and combine it with a Gaussian process model over the time series. In simulations that mimic the features of experimental evolution data, the proposed method clearly outperforms classical testing in average precision of finding selected alleles. We also present simulations exploring different experimental design choices and results on real data from Drosophila experimental evolution experiment in temperature adaptation. Availability and implementation: R software implementing the test is available at https://github.com/handetopa/BBGP. Contact: if.otlaa@apot.ednah, ta.ca.inudemtev@sanoj.senga, ta.ca.inudemtev@loisok.nilorac, if.tiih@aleknoh.ittna Supplementary information: Supplementary data are available at Bioinformatics online.

The Design of the Sixth Answer Set Programming Competition - - Report -.

Abstract: Answer Set Programming (ASP) is a well-known paradigm of declarative programming with roots in logic programming and non-monotonic reasoning. Similar to other closely-related problem-solving technologies, such as SAT/SMT, QBF, Planning and Scheduling, advances in ASP solving are assessed in competition events. In this paper, we report about the design of the Sixth ASP Competition, which is jointly organized by the University of Calabria (Italy), Aalto University (Finland), and the University of Genova (Italy), in affiliation with the 13th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2015). This edition maintains some of the design decisions introduced in the last event, e.g., the design of tracks, the scoring scheme, and the adherence to a fixed modeling language in order to push the adoption of the Open image in new window standard. On the other hand, it features also some novelties, like a benchmarks selection stage to classify instances according to their expected hardness, and a “marathon” track where the best performing systems are given more time for solving hard benchmarks.

Tightening bounds for Bayesian network structure learning

Abstract: A recent breadth-first branch and bound algorithm (BFBnB) for learning Bayesian network structures (Malone et al. 2011) uses two bounds to prune the search space for better efficiency; one is a lower bound calculated from pattern database heuristics, and the other is an upper bound obtained by a hill climbing search. Whenever the lower bound of a search path exceeds the upper bound, the path is guaranteed to lead to suboptimal solutions and is discarded immediately. This paper introduces methods for tightening the bounds. The lower bound is tightened by using more informed variable groupings when creating the pattern databases, and the upper bound is tightened using an anytime learning algorithm. Empirical results show that these bounds improve the efficiency of Bayesian network learning by two to three orders of magnitude.

The number of Latin squares of order 11

Abstract: Constructive and nonconstructive techniques are employed to enumerate Latin squares and related objects. It is established that there are (i) 2036029552582883134196099 main classes of Latin squares of order 11; (ii) 6108088657705958932053657 isomorphism classes of one-factorizations of $K_{11,11}$; (iii) 12216177315369229261482540 isotopy classes of Latin squares of order 11; (iv) 1478157455158044452849321016 isomorphism classes of loops of order 11; and (v) 19464657391668924966791023043937578299025 isomorphism classes of quasigroups of order 11. The enumeration is constructive for the 1151666641 main classes with an autoparatopy group of order at least 3.

From Isolation to Involvement: Adapting Machine Creativity Software to Support Human-Computer Co-Creation

Abstract: This paper investigates how to transform machine creativity systems into interactive tools that support human-computer co-creation. We use three case studies to identify common issues in this transformation, under the perspective of User-Centered Design. We also analyse the interactivity and creative behavior of the three platforms in terms of Wiggins’ formalization of creativity as a search. We arrive at the conclusion that adapting creative software for supporting human-computer cocreation requires redesigning some major aspects of the software, which guides our on-going project of building an interactive poetry composition tool.