Microsoft has developed a vibrating watch that alleviates the hand tremors of sufferers of Parkinson's disease. The'Emma Watch', named after the Parkinson's sufferer it was designed for, was showcased at Microsoft's Build 2017 conference in Seattle yesterday. The prototype device works by sending tiny vibrations into the wearer's wrist, which disrupts the feedback loop between the brain and the hand that causes the tremors. Microsoft has developed a vibrating watch (pictured) that alleviates the hand tremors of sufferers of Parkinson's disease Microsoft's Emma Watch works via a combination of motion sensors and artificial intelligence. This allows the watch to sense and react to symptoms like tremors, stiffness and instability, among others, according to Microsoft.

The UK's National Health Service (NHS) and Intel are working together to make cancer detection more efficient through artificial intelligence. Last week, the University of Warwick, University Hospitals Coventry & Warwickshire NHS Trust (UHCW) alongside Intel said a new collaboration between the groups will push forward the classification of cancer cells "more efficiently and accurately through ground-breaking artificial intelligence." A team of scientists, hosted by the University of Warwick's Tissue Image Analytics (TIA) laboratory and led by Professor Nasir Rajpoot are currently creating a digital repository of known tumor and immune cells based on thousands of human tissue cells. This database of cancer information will then be used by algorithms to recognize these cells automatically. While some types of cancer are more aggressive than others, time is almost always an issue.

This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose cross-label suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the cross-label suppression, we don't resort to frequently-used $\ell_0$-norm or $\ell_1$-norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets including face recognition, object categorization, scene classification, texture recognition and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency.

Cancer cells are to be detected and classified more efficiently and accurately, using ground-breaking artificial intelligence – thanks to a new collaboration between the University of Warwick, Intel Corporation, the Alan Turing Institute and University Hospitals Coventry & Warwickshire NHS Trust (UHCW). Scientists at the University of Warwick's Tissue Image Analytics (TIA) Laboratory--led by Professor Nasir Rajpoot from the Department of Computer Science--are creating a large, digital repository of a variety of tumour and immune cells found in thousands of human tissue samples, and are developing algorithms to recognize these cells automatically. "We are very excited about working with Intel under the auspices of the strategic relationship between Intel and the Alan Turing Institute," said Professor Rajpoot, who is also an Honorary Scientist at University Hospitals Coventry & Warwickshire NHS Trust (UHCW). "The collaboration will enable us to benefit from world-class computer science expertise at Intel with the aim of optimising our digital pathology image analysis software pipeline and deploying some of the latest cutting-edge technologies developed in our lab for computer-assisted diagnosis and grading of cancer." The digital pathology imaging solution aims to enable pathologists to increase their accuracy and reliability in analysing cancerous tissue specimens over what can be achieved with existing methods.

We consider the nonlinear Kalman filtering problem using Kullback-Leibler (KL) and $\alpha$-divergence measures as optimization criteria. Unlike linear Kalman filters, nonlinear Kalman filters do not have closed form Gaussian posteriors because of a lack of conjugacy due to the nonlinearity in the likelihood. In this paper we propose novel algorithms to optimize the forward and reverse forms of the KL divergence, as well as the alpha-divergence which contains these two as limiting cases. Unlike previous approaches, our algorithms do not make approximations to the divergences being optimized, but use Monte Carlo integration techniques to derive unbiased algorithms for direct optimization. We assess performance on radar and sensor tracking, and options pricing problems, showing general improvement over the UKF and EKF, as well as competitive performance with particle filtering.

Editor's note: This is a followup to the recently published part 1. You may want to check it out before moving forward. In the last article we went through some basics of image-processing using OpenCV and basics of DICOM image. In this article we will talk about basics of deep learning from the lens of Convolutional Neural Nets. In the next article we will use Kaggle's lung cancer data-set, review the key items to look for in a lung cancer DICOM image and use Kera's to develop a model to predict lung cancer.

We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of our approach by producing photo-realistic cell images using our generative model. The conditional nature of the model provides the ability to predict the localization of unobserved structures given cell and nuclear morphology.

Can a machine learn aesthetics in a way a human would? Could it then look at a set of photos, and draw on those same aesthetics to reproduce a different set? It's a big question because it has long-term implications for how AI is going to develop. Is it just what you "like"? How does it all work?

While we've previously seen researchers train artificial intelligence algorithms to transform your crappy doodles into atrocious cat monsters, this hardly shows the true potential of the technology – though it certainly is a fun way to get people interested. But now the researchers behind the AI model powering the doodle-to-cat-monster tool are back with another impressive image manipulation implementation that lets you turn horses into zebras, apples into oranges, winters into summers and so much more. We've teamed up with Product Hunt to offer you the chance to win an all expense paid trip to TNW Conference 2017! In a new paper, Jun-Yan Zhu and Taesung Park from the University of California Berkeley lay out a new model that essentially allows you to transform images in a'cycle consistent' way, meaning that any changes to the original image are expected to ultimately remain fully reversible. For example, the algorithm could be used to turn a zebra into a horse and vice versa.

While we've previously seen researchers train artificial intelligence algorithms to transform your crappy doodles into atrocious cat monsters, this hardly shows the true potential of the technology – though it certainly is a fun way to get people interested. But now the researchers behind the AI model powering the doodle-to-cat-monster tool are back with another impressive image manipulation implementation that lets you turn horses into zebras, apples into oranges, winters into summers and so much more. TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017. In a new paper, Jun-Yan Zhu and Taesung Park from the University of California Berkeley lay out a new model that essentially allows you to transform images in a'cycle consistent' way, meaning that any changes to the original image are expected to ultimately remain fully reversible.