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We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in q-space. By estimating the signal on a grid, we can do synthetic diffusion spectrum imaging: reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier transform. We also propose an alternative reconstruction method guaranteeing a nonnegative EAP that integrates to unity. The reconstruction is validated on data simulated from two Gaussians at various crossing angles. Moreover, we demonstrate on non-uniformly sampled in vivo data that the method is far superior to linear interpolation, and allows a drastic undersampling of the data with only a minor loss of accuracy. We envision the method as a potential replacement for standard diffusion spectrum imaging, in particular when acquistion time is limited.

We develop correlated random measures, random measures where the atom weights can exhibit a flexible pattern of dependence, and use them to develop powerful hierarchical Bayesian nonparametric models. Hierarchical Bayesian nonparametric models are usually built from completely random measures, a Poisson-process based construction in which the atom weights are independent. Completely random measures imply strong independence assumptions in the corresponding hierarchical model, and these assumptions are often misplaced in real-world settings. Correlated random measures address this limitation. They model correlation within the measure by using a Gaussian process in concert with the Poisson process. With correlated random measures, for example, we can develop a latent feature model for which we can infer both the properties of the latent features and their dependency pattern. We develop several other examples as well. We study a correlated random measure model of pairwise count data. We derive an efficient variational inference algorithm and show improved predictive performance on large data sets of documents, web clicks, and electronic health records.

Communities of people are often modelled as social networks consisting of individual actors whose roles in the community correspond to the network patterns present around their corresponding nodes. Examples of such roles for individual actors in social networks are people bridging two communities, central people through which a large part of communication passes, and outliers. In social network analysis, recognising user roles is helpful to gain deeper understanding of the underlying communities. For large online social networks, the only scalable way to achieve this is through automatic labelling of nodes, i.e. using machine learning. If, in a community, persons are already annotated with roles (by whatever method), this can be exploited to train a classifier to detect person roles in case new people appear in the community.

Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that transmits alerts when relevant video sequences are detected. Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort. To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.

If you were dreaming of having your next grande no-whip soy latte delivered by drone, you can forget about it. Project Wing's wings were clipped by Google parent Alphabet as it tightens budgets across the board, Bloomberg reported Tuesday, quoting people familiar with the decision. Bloomberg said the decision to end the proposed venture with Starbucks followed the departure of project leader Dave Vos, who has not been replaced. Hiring also was frozen, and some people were urged to seek employment elsewhere in the company, Bloomberg reported. The Alphabet decision comes as other companies are ramping up drone programs despite a lack of Federal Aviation Administration approval for deliveries outside test zones.

This was the title of a very popular book published in 2012, featuring several job interview questions (brain teasers) asked by Google's hiring managers to candidates. They apparently dropped all these questions, as they found out that they were not good indicators of career success. I had one phone interview with Google long ago, and was rejected right away. The interviewer was just focused on very technical details, and spent all her time arguing about Lasso regression, and was clearly looking for a specialist, dismissing people with a broad range of skills and non-standard approach to solving tech problems. Big companies do not value things like intuition, innovation, vision or a disruptive mindset (despite claiming the contrary), and for good reasons.

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Scientists at Oxford University have developed a machine that can lip-read better than humans. The artificial intelligence system - LipNet - watches video of a person speaking and matches the text to the movement of their mouths with 93% accuracy, the researchers said. Automating the process could help millions, they suggested. But experts said the system needed to be tested in real-life situations. Lip-reading is a notoriously tricky business with professionals only able to decipher what someone is saying up to 60% of the time.

Everywhere you look, now there is some form of artificial intelligence appearing. Whether it's to make a process more efficient or whether it's to keep humans safe and away from danger, robots are creeping in at every chance they get, and this is expected to carry on for quite some years to come. Now, a new center has been launched in Cambridge, England that will look to continue the study of AI more closely along with the implications that come with these marvelous machines. The Centre for the Future of Intelligence (CFI) has one aim: "to work together to ensure that we humans make the best of the opportunities of artificial intelligence as it develops over coming decades." It's a collaboration between four top universities which are Cambridge, Oxford, Imperial, and Berkeley, and has the full backing and support of the Leverhulme Trust.