11,416 research outputs found
Error detection and control for nonlinear shell analysis
A problem-adaptive solution procedure for improving the reliability of finite element solutions to geometrically nonlinear shell-type problem is presented. The strategy incorporates automatic error detection and control and includes an iterative procedure which utilizes the solution at the same load step on a more refined model. Representative nonlinear shell problem are solved
Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model
Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense
An empirical study of neighbourhood decay in Kohonen\u27s self organizing map
In this paper, empirical results are presented which suggest that size and rate of decay of region size plays a much more significant role in the learning, and especially the development, of topographic feature maps. Using these results as a basis, a scheme for decaying region size during SOM training is proposed. The proposed technique provides near optimal training time. This scheme avoids the need for sophisticated learning gain decay schemes, and precludes the need for a priori knowledge of likely training times. This scheme also has some potential uses for continuous learning
A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market
We propose a dynamic network model where two mechanisms control the
probability of a link between two nodes: (i) the existence or absence of this
link in the past, and (ii) node-specific latent variables (dynamic fitnesses)
describing the propensity of each node to create links. Assuming a Markov
dynamics for both mechanisms, we propose an Expectation-Maximization algorithm
for model estimation and inference of the latent variables. The estimated
parameters and fitnesses can be used to forecast the presence of a link in the
future. We apply our methodology to the e-MID interbank network for which the
two linkage mechanisms are associated with two different trading behaviors in
the process of network formation, namely preferential trading and trading
driven by node-specific characteristics. The empirical results allow to
recognise preferential lending in the interbank market and indicate how a
method that does not account for time-varying network topologies tends to
overestimate preferential linkage.Comment: 19 pages, 6 figure
Double Whammy - How ICT Projects are Fooled by Randomness and Screwed by Political Intent
The cost-benefit analysis formulates the holy trinity of objectives of
project management - cost, schedule, and benefits. As our previous research has
shown, ICT projects deviate from their initial cost estimate by more than 10%
in 8 out of 10 cases. Academic research has argued that Optimism Bias and Black
Swan Blindness cause forecasts to fall short of actual costs. Firstly, optimism
bias has been linked to effects of deception and delusion, which is caused by
taking the inside-view and ignoring distributional information when making
decisions. Secondly, we argued before that Black Swan Blindness makes
decision-makers ignore outlying events even if decisions and judgements are
based on the outside view. Using a sample of 1,471 ICT projects with a total
value of USD 241 billion - we answer the question: Can we show the different
effects of Normal Performance, Delusion, and Deception? We calculated the
cumulative distribution function (CDF) of (actual-forecast)/forecast. Our
results show that the CDF changes at two tipping points - the first one
transforms an exponential function into a Gaussian bell curve. The second
tipping point transforms the bell curve into a power law distribution with the
power of 2. We argue that these results show that project performance up to the
first tipping point is politically motivated and project performance above the
second tipping point indicates that project managers and decision-makers are
fooled by random outliers, because they are blind to thick tails. We then show
that Black Swan ICT projects are a significant source of uncertainty to an
organisation and that management needs to be aware of
Deformable face ensemble alignment with robust grouped-L1 anchors
Many methods exist at the moment for deformable face fitting. A drawback to nearly all these approaches is that they are (i) noisy in terms of landmark positions, and (ii) the noise is biased across frames (i.e. the misalignment is toward common directions across all frames). In this paper we propose a grouped -norm anchored method for simultaneously aligning an ensemble of deformable face images stemming from the same subject, given noisy heterogeneous landmark estimates. Impressive alignment performance improvement and refinement is obtained using very weak initialization as "anchors"
Active Learning Strategies for Technology Assisted Sensitivity Review
Government documents must be reviewed to identify and protect any sensitive information, such as personal information, before the documents can be released to the public. However, in the era of digital government documents, such as e-mail, traditional sensitivity review procedures are no longer practical, for example due to the volume of documents to be reviewed. Therefore, there is a need for new technology assisted review protocols to integrate automatic sensitivity classification into the sensitivity review process. Moreover, to effectively assist sensitivity review, such assistive technologies must incorporate reviewer feedback to enable sensitivity classifiers to quickly learn and adapt to the sensitivities within a collection, when the types of sensitivity are not known a priori. In this work, we present a thorough evaluation of active learning strategies for sensitivity review. Moreover, we present an active learning strategy that integrates reviewer feedback, from sensitive text annotations, to identify features of sensitivity that enable us to learn an effective sensitivity classifier (0.7 Balanced Accuracy) using significantly less reviewer effort, according to the sign test (p < 0.01 ). Moreover, this approach results in a 51% reduction in the number of documents required to be reviewed to achieve the same level of classification accuracy, compared to when the approach is deployed without annotation features
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