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![]() Plenaries From ozone measurements to pensionsBarbara D'Ambrogi-Ola, Ilmarinen Mutual Pension Insurance Company Once upon a time a young mathematician was enchanted by aurora borealis…. Number theory meets digital televisionCamilla Hollanti, Aalto University In this talk I will
explain how digital TV broadcasting can benefit from algebraic number
theory via using so-called space-time codes. We will see how to
translate purely practical questions into equivalent purely number
theoretic questions Luzin's condition (N)Pekka Koskela, University of Jyväskylä For a model of elastic deformations to be realistic, one must among other things rule out creation of matter. Games, analytics, business, academia – how do they mix together?Ville Suur-Uski, Supercell What’s the value of a scientific education at Supercell, a mobile games company? Mathematics in Patient MonitoringKimmo Uutela, GE Healthcare GE Healthcare is a
multinational medical device manufacturer. GE Healthcare Finland is
developing patient monitors for hospitals. The patient monitor measures
signals from the patient and calculates indicators that help the
caregivers in their work. The monitor uses signal processing, estimation
methods and statistical methods to provide useful and reliable
information for clinical decision making. The presentation includes
examples of development tasks where mathematical methods have been used. Reasons behind cost overruns – systematic bias, selection bias, or both?Eeva Vilkkumaa, Aalto University Projects that are selected for implementation based on uncertain cost estimates often end up costing considerably more than estimated. For instance, cost overruns have occurred in 90% of large transportation infrastructure projects worldwide with an average overrun of 28%. Cost overruns are typically attributed to a systematic downward bias in the projects’ cost estimates. However, even if the cost estimates among project proposals are unbiased but only those projects with the lowest estimates are implemented, cost overruns are to be expected due to selection bias. We develop a model for systematic bias and selection bias in project selection, and show how the relative magnitudes of these biases can be determined using an Expectation Maximization algorithm. Furthermore, we show how the model can be used to mitigate cost overruns resulting from both biases. |
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