This paper proposes Deep Hyperalignment (DHA) as a regularized, deep extension, scalable Hyperalignment (HA) method, which is well-suited for applying functional alignment to fMRI datasets with nonlinearity, high-dimensionality (broad ROI), and a large number of subjects. Unlink previous methods, DHA is not limited by a restricted fixed kernel function. Further, it uses a parametric approach, rank-$m$ Singular Value Decomposition (SVD), and stochastic gradient descent for optimization. Therefore, DHA has a suitable time complexity for large datasets, and DHA does not require the training data when it computes the functional alignment for a new subject. Experimental studies on multi-subject fMRI analysis confirm that the DHA method achieves superior performance to other state-of-the-art HA algorithms.

We are living in a world facing unprecedented global challenges. The Earth has been in a state of relative stability for the last 10,000 years, enabling civilisations to thrive. In a short space of time, however, this stability has been put at risk. Scientists at the Stockholm Environment Institute have identified that four out of the Earth's nine'Planetary Boundaries' have been crossed, namely climate, biodiversity, land-system change and biogeochemical cycles. Risks will only heighten as population swells to a projected 9 billion by 2050 increasing food, materials and energy needs.

Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results. The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs.

Deutsche Post DHL Group, the world's largest mail and logistics company, and ZF, a top automotive parts supplier, plan to deploy a fleet of autonomous delivery trucks based on the new chips, starting in 2019, NVIDIA said. The third generation of NVIDIA's Drive PX automotive line, code-named Pegasus, are chips the size of car license plates with datacenter-class processing power. They can handle 320 trillion operations per second, representing roughly a 13-fold increase over the calculating power of the current PX 2 class. This dramatic improvement is a pre-condition for developing and testing future autonomous cars, experts said. "NVIDIA is one step ahead. But you can be sure you can expect (rival chipmakers) Intel, NXP and Bosch not to be too far behind," said Luca De Ambroggi, principal automotive electronics analyst with industry market research firm IHS Markit.

Consumer demands keep businesses constantly chasing faster, more convenient and more impressive ways to improve our everyday lives. Driverless vehicles are becoming our closest touchpoint to the potential future that we could experience, with many businesses, such as Amazon and Google, testing ways to allow cars to drive themselves across entire countries. Of course, blue-sky thinking is all well and good but what are the real-world implications of autonomous vehicles and how do we get there? NVIDIA thinks they are close to opening up the world to the possibility of smart driving. At GTC Europe 2017 in Munich, Germany, NVIDIA today has announced the Drive PX Pegasus, a new entry to the Drive PX family of computing modules for self-driving cars.

French startup Regaind says its tools will help give meaning to photos. Apple has reportedly acquired a computer vision startup that could help organize that mess of a photo library on your iPhone. France-based Regaind makes software that will "extract game-changing insights from your images," according to its website. Some of its offerings, which are powered by artificial intelligence, include recognizing duplicate shots, selecting the best image from a set of similar shots, and facial recognition. TechCrunch on Friday cited "multiple sources" saying Apple has already acquired the company, but the terms of the deal remain unknown.

Google may have taught its AI to dream up filthy images based off of real-world examples, but one researcher has built an AI capable of painting entirely new worlds completely from scratch. The image at the top of this story isn't real, it's a fake world painted by an AI. Created by Qifeng Chen from Stanford University and Intel, the AI essentially paints by numbers what it believes a street view should look like. By using thousands of reference images, learning where a "car", "road" or "sign" should be placed, it can then paint a depiction of the real world – one that technically doesn't exist at all. Trained with 3,000 images of German streets, the AI is able to recognise what a "car" is, drawing one from memory.

Pakistani officials have long struggled to impose security in the Pashtun tribal heartland. The area stretches for hundreds of kilometers, including rugged mountainous terrain, and has been a hotbed of arms and heroin smuggling for decades. U.S drone strikes have also targeted militants from al Qaeda and other groups in the region.

Crucial time and tremendous amounts of resources are lost every day in the world's healthcare systems. Misdiagnoses cost unnecessary additional tests, result in delayed treatment plans and diminished survival or remission rates from what would have transpired had it been caught and identified correctly earlier. Trials, treatments and research completed in silos so there's no leveraging the insights across the country or the world. Some healthcare and technology innovators are collaborating and trying to change our current reality by experimenting with artificial intelligence (AI) and machine learning. Computers and the algorithms they run can scrub colossal amounts of data--much faster and more accurately than human scientists or medical professionals--to unearth patterns and predictions to enhance disease diagnosis, inform treatment plans and enhance public health and safety.

Tiny mobile robots are learning to work with insects in the hope the creatures' sensitive antennae and ability to squeeze into small spaces can be put to use serving humans. With a soft electronic whirr, a rather unusual looking ant trundles along behind a column of its arthropod comrades as they march off to fetch some food. While the little insects begin ferrying tiny globules of sugar back home, their mechanical companion bustles forward to effortlessly pick up the entire container and carry it back to the nest. It is a dramatic demonstration of how robots can be introduced and accepted into insect societies. But the research, which is being conducted as part of the EU-funded CyBioSys project, could be an important step towards using robots to subtly control, or work alongside, animals or humans.