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Proceedings of Cambridge 2012: Innovation and Impact - Openly Collaborating to Enhance Education
The Liability Threshold Model for Censored Twin Data
Family studies provide an important tool for understanding etiology of
diseases, with the key aim of discovering evidence of family aggregation and to
determine if such aggregation can be attributed to genetic components.
Heritability and concordance estimates are routinely calculated in twin studies
of diseases, as a way of quantifying such genetic contribution. The endpoint in
these studies are typically defined as occurrence of a disease versus death
without the disease. However, a large fraction of the subjects may still be
alive at the time of follow-up without having experienced the disease thus
still being at risk. Ignoring this right-censoring can lead to severely biased
estimates. We propose to extend the classical liability threshold model with
inverse probability of censoring weighting of complete observations. This leads
to a flexible way of modeling twin concordance and obtaining consistent
estimates of heritability. We apply the method in simulations and to data from
the population based Danish twin cohort where we describe the dependence in
prostate cancer occurrence in twins
A flexible architecture for privacy-aware trust management
In service-oriented systems a constellation of services cooperate, sharing potentially sensitive information and responsibilities. Cooperation is only possible if the different participants trust each other. As trust may depend on many different factors, in a flexible framework for Trust Management (TM) trust must be computed by combining different types of information. In this paper we describe the TAS3 TM framework which integrates independent TM systems into a single trust decision point. The TM framework supports intricate combinations whilst still remaining easily extensible. It also provides a unified trust evaluation interface to the (authorization framework of the) services. We demonstrate the flexibility of the approach by integrating three distinct TM paradigms: reputation-based TM, credential-based TM, and Key Performance Indicator TM. Finally, we discuss privacy concerns in TM systems and the directions to be taken for the definition of a privacy-friendly TM architecture.\u
VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene
High-definition (HD) map serves as the essential infrastructure of autonomous
driving. In this work, we build up a systematic vectorized map annotation
framework (termed VMA) for efficiently generating HD map of large-scale driving
scene. We design a divide-and-conquer annotation scheme to solve the spatial
extensibility problem of HD map generation, and abstract map elements with a
variety of geometric patterns as unified point sequence representation, which
can be extended to most map elements in the driving scene. VMA is highly
efficient and extensible, requiring negligible human effort, and flexible in
terms of spatial scale and element type. We quantitatively and qualitatively
validate the annotation performance on real-world urban and highway scenes, as
well as NYC Planimetric Database. VMA can significantly improve map generation
efficiency and require little human effort. On average VMA takes 160min for
annotating a scene with a range of hundreds of meters, and reduces 52.3% of the
human cost, showing great application value
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