Matternet has long used Switzerland as a testing ground for its delivery drone technology, and now it's ramping things up a notch. The company has revealed plans to launch the first permanent autonomous drone delivery network in Switzerland, where its flying robot couriers will shuttle blood and pathology samples between hospital facilities. The trick is the Matternet Station you see above: when a drone lands, the Station locks it into place and swaps out both the battery and the cargo (loaded into boxes by humans, who scan QR codes for access). Stations even have their own mechanisms to manage drone traffic if the skies are busy. And the automation isn't just for the sake of cleverness -- it might be crucial to saving lives.

In the smart grid, the intent is to use flexibility in demand, both to balance demand and supply as well as to resolve potential congestion. A first prominent example of such flexible demand is the charging of electric vehicles, which do not necessarily need to be charged as soon as they are plugged in. The problem of optimally scheduling the charging demand of electric vehicles within the constraints of the electricity infrastructure is called the charge scheduling problem. The models of the charging speed, horizon, and charging demand determine the computational complexity of the charge scheduling problem. For about 20 variants, we show, using a dynamic programming approach, that the problem is either in P or weakly NP-hard. We also show that about 10 variants of the problem are strongly NP-hard, presenting a potentially significant obstacle to their use in practical situations of scale.

In many information systems, rankings are widely used to represent the preferences over a set of items or candidates, ranging from information retrieval, recommender to decision making systems [1], [2], [3], [4], [5], [6], in order to improve quality of the services provided by the systems. For example, in search engine, the list of the terms suggested by a search engine after a user's few keystrokes is a typical ranking and such ranking service, widely adopted nowadays, has great impact on user's search experience; it is also recognized that the list of search results is a ranking after a query is issued. A ranking is an ordered sequence of items, in which an item with higher ranking score is more preferred than the items with lower ranking scores. The consensus of rankings is the degree to which the rankings agree according to certain common patterns. The consensus measure, can be used in many information systems, in order to uncover how close or related the rankings are. For example, in the group decision making, a group of experts express their preferences over a set of candidates by using rankings and the measure of the degree of consensus is very useful for reaching consensus [2]. In many information system with large volume of items, such as search engines, it is hard to clearly define what ground truth is, which make it more difficult to evaluate and compare the rankings returned from the systems. The consensus measure of rankings, as a tool for understanding how related or close the rankings are, will help engineers and researchers to discern what aspects of a ranking system need to be improved and to detect outliers [7], [8].

In recent years, the number of papers on Alzheimer's disease classification has increased dramatically, generating interesting methodological ideas on the use machine learning and feature extraction methods. However, practical impact is much more limited and, eventually, one could not tell which of these approaches are the most efficient. While over 90% of these works make use of ADNI an objective comparison between approaches is impossible due to variations in the subjects included, image pre-processing, performance metrics and cross-validation procedures. In this paper, we propose a framework for reproducible classification experiments using multimodal MRI and PET data from ADNI. The core components are: 1) code to automatically convert the full ADNI database into BIDS format; 2) a modular architecture based on Nipype in order to easily plugin different classification and feature extraction tools; 3) feature extraction pipelines for MRI and PET data; 4) baseline classification approaches for unimodal and multimodal features. This provides a flexible framework for benchmarking different feature extraction and classification tools in a reproducible manner. Data management tools for obtaining the lists of subjects in AD, MCI converter, MCI nonconverters, CN classes are also provided. We demonstrate its use on all (1519) baseline T1 MR images and all (1102) baseline FDG PET images from ADNI 1, GO and 2 with SPM-based feature extraction pipelines and three different classification techniques (linear SVM, anatomically regularized SVM and multiple kernel learning SVM).

We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces. We evaluate the performance of this approach in a simulation-based autonomous driving scenario. Our results resemble the intuitive relation between the reward function and readings of distance sensors mounted at different poses on the car. We also show that, after a few learning rounds, our simulated agent generates collision-free motions and performs human-like lane change behaviour.

If you're interested in drone deliveries, it's likely because you want your internet shopping dropped at your door within an hour of clicking "buy." And while companies like Amazon are working to make that happen, complicated logistics and thorny regulations mean it's likely to be years before you start hearing the whir of rotors on your front porch. Yet drones are already proving their worth with more urgent, medical, missions. The latest of these comes from Silicon Valley startup Matternet, which has been testing an autonomous drone network over Switzerland, shuttling blood and other medical samples between hospitals and testing facilities. "We have a vision of a distributed network, not hub and spoke, but true peer-to-peer," says Matternet CEO Andreas Raptopoulos.

FARMERS can now zap their crops with a handheld scanner to instantly determine nutritional content, which could prove crucial in mitigating the effects of climate change on food quality. It also brings similar consumer gadgets a step closer – so we can find out what is in our food for ourselves. The device, called GrainSense, analyses wheat, oats, rye and barley by scanning a sample with various frequencies of near-infrared light. The amount of each type of light that is absorbed allows it to precisely determine the levels of protein, moisture, oil and carbohydrate in the grain. This technique has been used for decades in the lab, but this is the first time it has been available instantly on a handheld device.

The term "industrial Internet of Things" has a more muted-sounding promise of driving operational efficiencies through automation, connectivity and analytics. But the focus of IIoT -- on industry at large -- is broader. Here, we take a comprehensive view, rounding up 20 IIoT leaders and pioneers, drawing on the feedback from industry analysts and consultants. The focus here is not on vendors offering, say, a cloud-based platform for monitoring industrial machines but on the companies that themselves are using IIoT technology to drive their business forward. For the sake of this feature, we focus on organizations that use connected technology in tandem with cloud-based analytics to drive efficiencies and launch new business models.

Facebook has conceded that technology companies could do more to counter online extremism after Theresa May and the French president, Emmanuel Macron, proposed fining firms that move too slowly to remove extremist content being shared by terrorist groups. The social media giant told a meeting between political leaders and its own executives as well as others from Google and Microsoft at the United Nations general assembly in New York that it is now employing thousands of content reviewers around the globe and a staff of 150 people dedicated to countering terrorism on its platform in a bid to remove more extremist content. But, along with Google, it warned that using emerging artificial intelligence technologies to spot dangerous material was not yet foolproof. Facebook sources said the company accepted the industry could do more and said it was committed to building more technology to help address these issues. But it said it was already accelerating its efforts, in particular by using artificial intelligence to flag up extremist content and sharing this data with rival firms.

New research from Capgemini's Digital Transformation Institute shows that four out of five companies implementing AI have created new jobs as a result of AI technology Paris, September 7, 2017 – Capgemini, a global leader in consulting, technology and outsourcing services, has today announced the findings of "Turning AI into concrete value: the successful implementers' toolkit", a study of nearly 1,000 organizations with revenues of more than $500m that are implementing artificial intelligence (AI), either as a pilot or at scale[1]. The research both counters fears that AI will cause massive job losses in the short term, as 83% of firms surveyed say AI has generated new roles in their organizations, and highlights the growth opportunity presented by AI: three-quarters of firms have seen a 10% uplift in sales, directly tied to AI implementation. The report, which surveyed executives from nine countries and across seven sectors, found that four out of five companies (83%) have created new jobs as a result of AI technology. Specifically, organizations are producing jobs at a senior level, with two in three jobs being created at the grade of a manager or above. Furthermore, among organizations that have implemented AI at scale, more than 3 in 5 (63%) said that AI has not destroyed any jobs in their organization.