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.

The flip-the-state transition operator for restricted Boltzmann machines

Abstract: Most learning and sampling algorithms for restricted Boltzmann machines (RMBs) rely on Markov chain Monte Carlo (MCMC) methods using Gibbs sampling. The most prominent examples are Contrastive Divergence learning (CD) and its variants as well as Parallel Tempering (PT). The performance of these methods strongly depends on the mixing properties of the Gibbs chain. We propose a Metropolis-type MCMC algorithm relying on a transition operator maximizing the probability of state changes. It is shown that the operator induces an irreducible, aperiodic, and hence properly converging Markov chain, also for the typically used periodic update schemes. The transition operator can replace Gibbs sampling in RBM learning algorithms without producing computational overhead. It is shown empirically that this leads to faster mixing and in turn to more accurate learning.

Burst statistics in an early biofilm quorum sensing model: the role of spatial colony-growth heterogeneity

Abstract: Quorum-sensing bacteria in a growing colony of cells send out signalling molecules (so-called “autoinducers”) and themselves sense the autoinducer concentration in their vicinity. Once—due to increased local cell density inside a “cluster” of the growing colony—the concentration of autoinducers exceeds a threshold value, cells in this clusters get “induced” into a communal, multi-cell biofilm-forming mode in a cluster-wide burst event. We analyse quantitatively the influence of spatial disorder, the local heterogeneity of the spatial distribution of cells in the colony, and additional physical parameters such as the autoinducer signal range on the induction dynamics of the cell colony. Spatial inhomogeneity with higher local cell concentrations in clusters leads to earlier but more localised induction events, while homogeneous distributions lead to comparatively delayed but more concerted induction of the cell colony, and, thus, a behaviour close to the mean-field dynamics. We quantify the induction dynamics with quantifiers such as the time series of induction events and burst sizes, the grouping into induction families, and the mean autoinducer concentration levels. Consequences for different scenarios of biofilm growth are discussed, providing possible cues for biofilm control in both health care and biotechnology.

Consumer Attitudes Towards Energy Consumption of Mobile Phones and Services

Abstract: We investigated consumer attitudes towards energy consumption of mobile phones and mobile services with a questionnaire study among Finnish university students (N = 150). Our questions covered topics like how battery consumption affected phone and application selection and configuration, what effect different battery levels had on user behavior, what kind of recharging policies were used, how satisfied users were with energy consumption feedback, and what kind of wishes for improvements they had. Battery consumption is clearly important for users when buying mobile phones and choosing applications to use. Importance of energy awareness, energy-driven customization, and energy as a decision criterion stood out as other key themes among the replies.

Maximizing the Diversity of Exposure in a Social Network

Abstract: Social-media platforms have created new ways for citizens to stay informed and participate in public debates However, to enable a healthy environment for information sharing, social deliberation, and opinion formation, citizens need to be exposed to sufficiently diverse viewpoints that challenge their assumptions, instead of being trapped inside filter bubbles In this paper, we take a step in this direction and propose a novel approach to maximize the diversity of exposure in a social network We formulate the problem in the context of information propagation, as a task of recommending a small number of news articles to selected users We propose a realistic setting where we take into account content and user leanings, and the probability of further sharing an article This setting allows us to capture the balance between maximizing the spread of information and ensuring the exposure of users to diverse viewpoints The resulting problem can be cast as maximizing a monotone and submodular function subject to a matroid constraint on the allocation of articles to users It is a challenging generalization of the influence maximization problem Yet, we are able to devise scalable approximation algorithms by introducing a novel extension to the notion of random reverse-reachable sets We experimentally demonstrate the efficiency and scalability of our algorithm on several real-world datasets

A hybrid approach to optimistic file system directory tree synchronization

Abstract: There are two main approaches to optimistic file system synchronization: distributed file systems and file synchronizers. The former type is characterized by a log-based approach that depends on access to file system internals, the latter by a state-based approach that utilizes the standard file system interface, which limits the efficiency of change detection.We propose a hybrid approach that 1) defines a minor extension to the semantics of the file system interface that enables efficient state-based file system change detection and 2) employs selectively instantiated XML documents to make the use of state-based algorithms for optimistic synchronization feasible on large file systems.The hybrid approach is simple, well-suited for current file system architectures, and allows us to leverage existing state-based reconciliation algorithms. An initial implementation shows our approach to be feasible, lightweight, and interoperable and to have satisfactory performance.