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.

Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison

Abstract: Complete transcriptional regulatory network inference is a huge challenge because of the complexity of the network and sparsity of available data. One approach to make it more manageable is to focus on the inference of context-specific networks involving a few interacting transcription factors (TFs) and all of their target genes.

Grounding the innovation of future technologies

Abstract: Mobile and ubiquitous technologies can potentially change the role of information and communication technology in human lives. Empirical, human-centered approaches are emerging as an alternative to technology-driven approaches in the innovation of these technologies. Three necessary empirical stages, intertwined with analytical ones and with each informing and grounding the succeeding stages, are analyzed. First, needfinding is utilized to discover societal and individual demands for technology. Second, observational and experimental studies examine the social and cognitive preconditions for interaction. From these two steps, a hypothesis is formulated regarding how technology will change existing practices. Finally, this hypothesis, embodied in the design of a prototype, is tested in a field trial. Four design cases illustrate the value of empirical grounding.

A New Frontier in Digital Content Policy: Case Studies in the Regulation of Virtual Goods and Artificial Scarcity

Abstract: Millions of people around the world are spending billions of euros per year on virtual items, characters and currencies in online games, social networking sites, and other digital hangouts. In this paper, we examine this shift in consumer behavior and business models from a public policy perspective. We present three case studies to examine the key policy issues that virtual goods are giving rise to, and analyze some of the regulatory responses that have been effected so far: judicial protection of the possession of virtual goods in Finland and the Netherlands, statutory regulation of virtual goods trade in Korea, and application of consumer protection law to virtual goods sales in Finland. As with the debate over copyright, the first big content policy debate of the digital era, this new digital policy debate tends to pit individual consumers and entrepreneurs against the interests of publishers and established public policy. However, the roles are curiously reversed: it is not the publishers but the consumers who demand that pieces of digital content be respected as property, and turn to courts to enforce their view. While copyright and virtual goods both aim to impose artificial scarcity on non-rivalrous matter, copyright is designed to provide economic incentives to producers, while in virtual goods scarcity provides benefits to consumers directly.

Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic Systems

Abstract: We consider approximate maximum likelihood parameter estimation in nonlinear state-space models. We discuss both direct optimization of the likelihood and expectation--maximization (EM). For EM, we also give closed-form expressions for the maximization step in a class of models that are linear in parameters and have additive noise. To obtain approximations to the filtering and smoothing distributions needed in the likelihood-maximization methods, we focus on using Gaussian filtering and smoothing algorithms that employ sigma-points to approximate the required integrals. We discuss different sigma-point schemes based on the third, fifth, seventh, and ninth order unscented transforms and the Gauss--Hermite quadrature rule. We compare the performance of the methods in two simulated experiments: a univariate nonlinear growth model as well as tracking of a maneuvering target. In the experiments, we also compare against approximate likelihood estimates obtained by particle filtering and extended Kalman filtering based methods. The experiments suggest that the higher-order unscented transforms may in some cases provide more accurate estimates