Optical coherence tomography (OCT) imaging from different camera devices causes challenging domain shifts and can cause a severe drop in accuracy for machine learning models. In this work, we introduce a minimal noise adaptation method based on a singular value decomposition (SVDNA) to overcome the domain gap between target domains from three different device manufacturers in retinal OCT imaging. Our method utilizes the difference in noise structure to successfully bridge the domain gap between different OCT devices and transfer the style from unlabeled target domain images to source images for which manual annotations are available. We demonstrate how this method, despite its simplicity, compares or even outperforms state-of-the-art unsupervised domain adaptation methods for semantic segmentation on a public OCT dataset. SVDNA can be integrated with just a few lines of code into the augmentation pipeline of any network which is in contrast to many state-of-the-art domain adaptation methods which often need to change the underlying model architecture or train a separate style transfer model.

Cirrus is a product that uses AI and deep learning to determine the optimal price to charge to flights travelers. The animation shows the progress from determining different prices to the actual flight. It extends the style into modern, dynamic elements, reflecting the image of the app.

Big data has always been a part of high-performance computing and the science it supports, but new open-source technologies are now being applied to a wider range of scientific and business problems. We've spent time recently testing some of the big data toolkits. Two of the largest drivers of big data applications have been mobile applications and the Internet-of-Things (IoT). Smartphones contain a remarkable array of sensors and are very interesting as mobile devices for sensing and recording a user's environment. The increasing prevalence of low-power sensors monitoring air and water quality, traffic density and so on gives an opportunity to take better socio-economic decisions with better data.