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Institution

Helsinki Institute for Information Technology

FacilityEspoo, Finland
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.


Papers
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Journal ArticleDOI
TL;DR: The results suggest that alterations at 2p16 combined with other markers could be useful in diagnosing asbestos-related lung cancer.
Abstract: Five to seven percent of lung tumours are estimated to occur because of occupational asbestos exposure. Using cDNA microarrays, we have earlier detected asbestos exposure-related genomic regions in lung cancer. The region at 2p was one of those that differed most between asbestos-exposed and non-exposed patients. Now, we evaluated genomic alterations at 2p22.1-p16.1 as a possible marker for asbestos exposure. Lung tumours from 205 patients with pulmonary asbestos fibre counts from 0 to 570 million fibres per gram of dry lung, were studied by fluorescence in situ hybridisation (FISH) for DNA copy number alterations (CNA). The prevalence of loss at 2p16, shown by three different FISH probes, was significantly increased in lung tumours of asbestos-exposed patients compared with non-exposed (P=0.05). In addition, a low copy number loss at 2p16 associated significantly with high-level asbestos exposure (P=0.02). Furthermore, 27 of the tumours were studied for allelic imbalances (AI) at 2p22.1–p16.1 using 14 microsatellite markers and also AI at 2p16 was related to asbestos exposure (P=0.003). Our results suggest that alterations at 2p16 combined with other markers could be useful in diagnosing asbestos-related lung cancer.

28 citations

Proceedings ArticleDOI
17 Nov 2009
TL;DR: A multipath scheduler is designed and implemented that distributes the incoming traffic among multiple available paths and confirms effectiveness and TCP-friendliness of multipath transfer for a range of path bandwidths and in the presence of cross-traffic.
Abstract: Multi-interface mobile devices and multihomed residential Internet connections are becoming commonplace. However, standard transport protocols TCP and SCTP are unable to take advantage of several available paths so that the application using a single transport connection would receive the aggregate bandwidth of all paths. Multihoming and advanced security features make the Host Identity Protocol a good candidate to provide multipath data delivery. In this paper, we design and implement a multipath scheduler that distributes the incoming traffic among multiple available paths. Using Fastest Path First scheduling, packets from a single TCP connection could be spread to multiple paths with no reordering. Our simulations confirm effectiveness and TCP-friendliness of multipath transfer for a range of path bandwidths and in the presence of cross-traffic.1

28 citations

Proceedings ArticleDOI
15 Aug 2005
TL;DR: It is proposed that the governments in the rich countries should in fact learn from developing countries and have a more proactive policy approach to open source software.
Abstract: This article starts by considering the global framework of current open source migration. We show that the fight against software piracy is most likely speeding up the adoption especially in the developing countries. The situation is somewhat different in those parts of the world, which have lower piracy rates. There, political lobbying seems to offer the major push for open source software. This brings us to study the actual open source software adoption in the Finland, which is both the home of Linux and also one of the most advances information societies with little piracy. The outcome is rather surprising - the Finnish government is currently ignoring open source. The results we have got from our a survey to all Finnish municipalities and from additional expanded interviews shows that there is currently high demand and growing interest for open source solutions within the Finnish municipalities but the government (by ignoring the issue) and the private sector (being mainly committed to proprietary solutions) are not able to fill the needs. We propose that the governments in the rich countries should in fact learn from developing countries and have a more proactive policy approach to open source software.

28 citations

Journal ArticleDOI
TL;DR: This work considers how the parameters of a prior model should be estimated from observations of uncorrupted signals, and obtains an objective function that approximates the error occurred in signal restoration due to an imperfect prior model.
Abstract: In signal restoration by Bayesian inference, one typically uses a parametric model of the prior distribution of the signal. Here, we consider how the parameters of a prior model should be estimated from observations of uncorrupted signals. A lot of recent work has implicitly assumed that maximum likelihood estimation is the optimal estimation method. Our results imply that this is not the case. We first obtain an objective function that approximates the error occurred in signal restoration due to an imperfect prior model. Next, we show that in an important special case (small gaussian noise), the error is the same as the score-matching objective function, which was previously proposed as an alternative for likelihood based on purely computational considerations. Our analysis thus shows that score matching combines computational simplicity with statistical optimality in signal restoration, providing a viable alternative to maximum likelihood methods. We also show how the method leads to a new intuitive and geometric interpretation of structure inherent in probability distributions.

28 citations

Posted Content
TL;DR: In this paper, a Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks is presented, which draws samples from the posterior distribution of partial orders on the nodes.
Abstract: We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior distribution of partial orders on the nodes; for each sampled partial order, the conditional probabilities of interest are computed exactly. We give both analytical and empirical results that suggest the superiority of the new method compared to previous methods, which sample either directed acyclic graphs or linear orders on the nodes.

27 citations


Authors

Showing all 632 results

NameH-indexPapersCitations
Dimitri P. Bertsekas9433285939
Olli Kallioniemi9035342021
Heikki Mannila7229526500
Jukka Corander6641117220
Jaakko Kangasjärvi6214617096
Aapo Hyvärinen6130144146
Samuel Kaski5852214180
Nadarajah Asokan5832711947
Aristides Gionis5829219300
Hannu Toivonen5619219316
Nicola Zamboni5312811397
Jorma Rissanen5215122720
Tero Aittokallio522718689
Juha Veijola5226119588
Juho Hamari5117616631
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
20224
202185
202097
2019140
2018127