On Jan. 13, banks in the European Union will become the leaders of the so-called open banking movement, allowing access to customer account data for any third-party service provider their customers approve via a dedicated communication interface. The move is the fruit of the second Payment Services Directive (PSD2), which the European Commission says will "facilitate innovation, competition and efficiency," give consumers more and better choice in the EU retail payment market, and introduce higher security standards for online payments. In the U.S., however, open banking is largely ad hoc and more of a workaround. A few large banks, such as Wells Fargo and JPMorgan Chase, have made bilateral agreements with data aggregators and accounting software providers. The rest have mostly opted out.

Modern AI is based on machine learning which creates models by learning from data. Data used in many applications such as health and human behaviour is private and needs protection. New privacy-aware machine learning methods have been developed recently based on the concept of differential privacy. They guarantee that the published model or result can reveal only limited information on each data subject. "Previously you needed one party with unrestricted access to all the data. Our new method enables learning accurate models for example using data on user devices without the need to reveal private information to any outsider", Assistant Professor Antti Honkela of the University of Helsinki says.

On Friday, June 24, 2016, the world watched in horror as Britain voted to commit economic suicide as a nation. On November 8, 2016, America will vote. Will it also commit economic and political suicide? Increasing inequality is building up great stress in the world economic system. The disenfranchised masses are expressing their anger, including in irrational ways such as the Brexit vote.

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data). It proposes an instance-based approach whose principle consists in determining the minimal changes needed to alter a prediction: given a data point whose classification must be explained, the proposed method consists in identifying a close neighbour classified differently, where the closeness definition integrates a sparsity constraint. This principle is implemented using observation generation in the Growing Spheres algorithm. Experimental results on two datasets illustrate the relevance of the proposed approach that can be used to gain knowledge about the classifier.

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2 FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of ~7% of a typical body dimension, and orientation error of 12 degrees, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.

In this work we establish the first linear convergence result for the stochastic heavy ball method. The method performs SGD steps with a fixed stepsize, amended by a heavy ball momentum term. In the analysis, we focus on minimizing the expected loss and not on finite-sum minimization, which is typically a much harder problem. While in the analysis we constrain ourselves to quadratic loss, the overall objective is not necessarily strongly convex.

Singleton arc consistency is an important type of local consistency which has been recently shown to solve all constraint satisfaction problems (CSPs) over constraint languages of bounded width. We aim to characterise all classes of CSPs defined by a forbidden pattern that are solved by singleton arc consistency and closed under removing constraints. We identify five new patterns whose absence ensures solvability by singleton arc consistency, four of which are provably maximal and three of which generalise 2-SAT. Combined with simple counter-examples for other patterns, we make significant progress towards a complete classification.

If 2017 left you breathless, exhausted by unexpected headlines, then brace yourself. The coming year may bring even more turbulent change, according to the CEOs, academics, economists and other bold thinkers we consulted for our annual peek at the year ahead.

A simple birth control method for men is about to begin testing, with researchers hoping a new topical gel will give men an alternative to contraceptive method outside of vasectomies and condoms. Clinical testing is set to begin in April as the National Institute of Child Health and Human Development, a division of the National Institutes of Health (NIH), will conduct the largest U.S. effort to test a hormonal birth control for men. The gel itself can simply be rubbed onto the user's skin, although the researchers suggest that it not be placed directly on one's genitals. The same team of researchers conducted a 2012 trial that combined the application of two gels and the the number of sperm in the test subjects' semen dropped to less than 1 million per millilitre, MIT Technology Review reports. That number is far below the average 15 to 200 million sperm per millilitre that would actually decrease chances of fertility. The six-month study showed the gel method to be effective in reducing fertility.

There have long been fears about the time children spend on their phone, and new research has revealed youngsters are now spending nearly five hours a day in front of a screen. In 2000, young people spent two hours and 59 minutes doing screen-based activities - mainly watching TV and playing computer and video games, the study found. In 2015, this had risen to four hours and 45 minutes as children increasingly use their technology while doing other things such as socialising and studying. However, new Oxford University research has revealed that as digital past-times have become intertwined with daily life, children have adapted their behaviours to include their devices. Much like adults, they are able to multi-task and also do all the things that they would do anyway. Gender differences - Although boys and girls spend similar amounts of time using devices, boys spend significantly more time playing videogames compared to girls', spending 50 mins per day, compared girls' 9.