1,161 research outputs found
Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It
We empirically show that Bayesian inference can be inconsistent under
misspecification in simple linear regression problems, both in a model
averaging/selection and in a Bayesian ridge regression setting. We use the
standard linear model, which assumes homoskedasticity, whereas the data are
heteroskedastic, and observe that the posterior puts its mass on ever more
high-dimensional models as the sample size increases. To remedy the problem, we
equip the likelihood in Bayes' theorem with an exponent called the learning
rate, and we propose the Safe Bayesian method to learn the learning rate from
the data. SafeBayes tends to select small learning rates as soon the standard
posterior is not `cumulatively concentrated', and its results on our data are
quite encouraging.Comment: 70 pages, 20 figure
Adoption of alternative transport technologies in the construction industry
This research examines how the construction industry adopts alternative transport technologies. This paper presents the general characteristics of the adopter and what his perceptions are towards innovative transport technologies. The study focused on four rates of innovation, related tot alternative transport technologies. The results show that 83% of the respondents choose innovation over no innovation; more than half of the respondents choose an innovation that can be characterized as “architectural”. Further, the perceived benefits of the innovation characteristics for an incremental innovation are higher then the perceived benefits for an architectural or radical innovation. Finally, from the ventures that chose to innovate, smaller companies prefer an architectural - more challenging - innovation rather then an incremental innovation
Dynamic Tardos Traitor Tracing Schemes
We construct binary dynamic traitor tracing schemes, where the number of
watermark bits needed to trace and disconnect any coalition of pirates is
quadratic in the number of pirates, and logarithmic in the total number of
users and the error probability. Our results improve upon results of Tassa, and
our schemes have several other advantages, such as being able to generate all
codewords in advance, a simple accusation method, and flexibility when the
feedback from the pirate network is delayed.Comment: 13 pages, 5 figure
Real-time complexity constrained encoding
Complex software appliances can be deployed on hardware with limited available computational resources. This computational boundary puts an additional constraint on software applications. This can be an issue for real-time applications with a fixed time constraint such as low delay video encoding. In the context of High Efficiency Video Coding (HEVC), a limited number of publications have focused on controlling the complexity of an HEVC video encoder. In this paper, a technique is proposed to control complexity by deciding between 2Nx2N merge mode and full encoding, at different Coding Unit (CU) depths. The technique is demonstrated in two encoders. The results demonstrate fast convergence to a given complexity threshold, and a limited loss in rate-distortion performance (on average 2.84% Bjontegaard delta rate for 40% complexity reduction)
Bootstrapped CNNs for Building Segmentation on RGB-D Aerial Imagery
Detection of buildings and other objects from aerial images has various
applications in urban planning and map making. Automated building detection
from aerial imagery is a challenging task, as it is prone to varying lighting
conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are
robust against some of these variations, although they fail to distinguish easy
and difficult examples. We train a detection algorithm from RGB-D images to
obtain a segmented mask by using the CNN architecture DenseNet.First, we
improve the performance of the model by applying a statistical re-sampling
technique called Bootstrapping and demonstrate that more informative examples
are retained. Second, the proposed method outperforms the non-bootstrapped
version by utilizing only one-sixth of the original training data and it
obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.Comment: Published at ISPRS Annals of the Photogrammetry, Remote Sensing and
Spatial Information Science
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