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.

Mobile search and the cloud: The benefits of offloading

Abstract: This paper presents the benefits and drawbacks of mobile desktop search coupled with cloud-assisted operations, such as operation offloading, cloud storage, and cloud-assisted search. The energy trade-off when offloading a task is analyzed and measured in several different scenarios. An example case of offloading indexing is presented. The problem of cloud-assisted mobile desktop search is introduced and a possible solution outlined. This paper argues that the synergy between mobile platforms and cloud computing is under-utilized and should be explored further, particularly in the search and synchronization use case. Our measurements support offloading (parts of) search related tasks to a cloud service.

Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation

Abstract: Computationally modeling changes in binding free energies upon mutation (interface ΔΔG) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using “backrub” to generate an ensemble of models, then applying torsion minimization, side chain repacking and averaging across this ensemble to estimate interface ΔΔG values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting non-linear reweighting model improved agreement with experimentally determined interface DDG values, but also highlights the necessity of future energy function improvements.

Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability

Abstract: Bayesian network structure learning is the well-known computationally hard problem of nding a directed acyclic graph structure that optimally describes given data. A learned structure can then be used for probabilistic inference. While exact inference in Bayesian networks is in general NP-hard, it is tractable in networks with low treewidth. This provides good motivations for developing algorithms for the NPhard problem of learning optimal bounded treewidth Bayesian networks (BTW-BNSL). In this work, we develop a novel score-based approach to BTW-BNSL, based on casting BTW-BNSL as weighted partial Maximum satisability. We demonstrate empirically that the approach scales notably better than a recent exact dynamic programming algorithm for BTW-BNSL.

FP-Bonsai: The Art of Growing and Pruning Small FP-Trees

Abstract: In the context of mining frequent itemsets, numerous strategies have been proposed to push several types of constraints within the most well known algorithms. In this paper, we integrate the recently proposed ExAnte data reduction technique within the FP-growth algorithm. Together, they result in a very efficient frequent itemset mining algorithm that effectively exploits monotone constraints.