Astronauts are having to climb into space to fix a computer in an emergency move. Two inhabitants of the International Space Station (ISS) will have to venture out on a spacewalk to repair a computer part – a data relay box – that broke over the weekend. The broken computer means that the lab is floating over the Earth relying only on its second computer, potentially putting the astronauts on board at risk. From the International Space Station, Expedition 42 Flight Engineer Terry W. Virts took this photograph of the Gulf of Mexico and U.S. Gulf Coast at sunset This image of an area on the surface of Mars, approximately 1.5 by 3 kilometers in size, shows frosted gullies on a south-facing slope within a crater. The image was taken by Nasa's HiRISE camera, which is mounted on its Mars Reconaissance Orbiter The Soyuz TMA-15M rocket launches from the Baikonur Cosmodrome in Kazakhstan on Monday, Nov. 24, 2014, carrying three new astronauts to the International Space Station.

The majority of people in the UK are unaware of just how closely the government can monitor their online activities, a new report claims. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The ...

Since December of 2015, Americans have been required to register any drone that weighs more than two sticks of butter with the Federal Aviation Administration (FAA). It's a minor hassle and costs a little bit of money and seems like a reasonable idea considering how many people are flying sizeable drones nowadays. However, there was one particular group that really didn't appreciate the new ruling: model aircraft enthusiasts. One of them sued the FAA in February of 2016, and a federal court in Washington, D.C. ruled in favor of hobbyists, meaning that the FAA can no longer require you (or anyone else) to register their personal drones. Here's the important bit of the ruling (you can read the whole thing here): In short, the 2012 FAA Modernization and Reform Act provides that the FAA "may not promulgate any rule or regulation regarding a model aircraft," yet the FAA's 2015 Registration Rule is a "rule or regulation regarding a model aircraft."

Artificial intelligence and machine learning are often typecast as the futuristic underpinnings for a robot-ruled world -- but in reality, federal agencies are already using more practical applications of the technologies today to improve the way they serve Americans and achieve their missions. The space agency's Jet Propulsion Lab wants to leverage the power of the cloud, machine learning and artificial intelligence to open space voyage to all Americans --whether they're standing on the surface of Mars or in the comfort of their homes. "The idea with this is we're all going to be the future explorers," JPL IT Chief Technology and Innovation Officer Tom Soderstrom said at a recent conference. "Your children are the ones who are one day going to walk on Mars, whether it is virtually through augmented reality or physically as astronauts." Indeed, NASA partnered with Amazon Web Services using its automatic speech recognition and natural language understanding service Lex, which are the same deep-learning technologies that drive the Amazon's Alexa, to develop NASA Mars: an app that allows humans to ask questions about Mars and engage them with NASA's missions.

Cyber attacks have increased in frequency and severity, and financial institutions are particularly interesting targets to cyber criminals. Join this presentation to learn the latest cybersecurity threats and challenges plaguing the financial industry, and the policies and solutions your organization needs to have in place to protect against them. Viewers will learn: • Current trends in Cyber attacks • FFIEC Cyber Assessment Toolkit • NIST Cybersecurity Framework principles • Security Metrics • Oversight of third parties • How to measure cybersecurity preparedness • Automated approaches to integrate Security into DevOps About the Presenter: Ulf Mattsson is the Chief Technology Officer of Security Solutions at Atlantic BT, and earlier at Compliance Engineering. Ulf was the Chief Technology Officer and a founder of Protegrity, He invented the Protegrity Vaultless Tokenization, Data Type Preservation (DTP2) and created the initial architecture of Protegrity's database security technology. Prior to Protegrity, Ulf worked 20 years at IBM in software development and in IBM's Research organization, in the areas of IT Architecture and Security, and received a US Green Card of class'EB 11 – Individual of Extraordinary Ability' after endorsement by IBM.

In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process "grounding"). The approach is particularly well-suited for extracting large numbers of concepts from text. We apply the new model to address an important problem in healthcare of understanding what medical concepts are discussed in clinical text. Using a publicly available dataset derived from Intensive Care Units, we learn to label a patient's diagnoses and procedures from their discharge summary. Our evaluation shows a clear advantage to using our proposed architecture over a variety of strong baselines.

Ridesourcing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesourcing activities taking advantages of the sharing economy can greatly impair the healthy development of this emerging industry. As the first step to regulate on-demand ride services and eliminate black market, we design a method to detect ridesourcing cars from a pool of cars based on their trajectories. Since licensed ridesourcing car traces are not openly available and may be completely missing in some cities due to legal issues, we turn to transferring knowledge from public transport open data, i.e, taxis and buses, to ridesourcing detection among ordinary vehicles. We propose a two-stage transfer learning framework. In Stage 1, we take taxi and bus data as input to learn a random forest (RF) classifier using trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we use the RF to label all the candidate cars. In Stage 2, leveraging the subset of high confident labels from the previous stage as input, we further learn a convolutional neural network (CNN) classifier for ridesourcing detection, and iteratively refine RF and CNN, as well as the feature set, via a co-training process. Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus traces show that our transfer learning framework, with no need of a pre-labeled ridesourcing dataset, can achieve similar accuracy as the supervised learning methods.

Adversarial examples are maliciously perturbed inputs designed to mislead machine learning (ML) models at test-time. They often transfer: the same adversarial example fools more than one model. In this work, we propose novel methods for estimating the previously unknown dimensionality of the space of adversarial inputs. We find that adversarial examples span a contiguous subspace of large (~25) dimensionality. Adversarial subspaces with higher dimensionality are more likely to intersect. We find that for two different models, a significant fraction of their subspaces is shared, thus enabling transferability. In the first quantitative analysis of the similarity of different models' decision boundaries, we show that these boundaries are actually close in arbitrary directions, whether adversarial or benign. We conclude by formally studying the limits of transferability. We derive (1) sufficient conditions on the data distribution that imply transferability for simple model classes and (2) examples of scenarios in which transfer does not occur. These findings indicate that it may be possible to design defenses against transfer-based attacks, even for models that are vulnerable to direct attacks.

The goal of this paper is to improve learning for multivariate processes whose structure is dependent on some known graph topology; especially when the number of available samples is much smaller than the number of variables. Typically, the graph information is incorporated into the learning process via a smoothness assumption postulating that the values supported on well-connected vertices exhibit small variations. We argue that smoothness is not enough. To capture the behavior of complex interconnected systems, such as transportation and biological networks, it is important to train expressive models, being able to reproduce a wide range of graph and temporal behaviors. Motivated by this need, this paper puts forth a novel definition of time-vertex wide-sense stationarity, or joint stationarity for short. We believe that the proposed definition is natural, at it intimately relates to existing definitions of stationarity in the time and vertex domains. We use joint stationarity to regularize learning and to reduce computational complexity in both estimation and recovery tasks. In particular, we show that for any jointly stationary process: (a) one can learn the covariance structure from O(1) samples, and (b) can solve MMSE recovery problems, such as interpolation, denoising, forecasting, in complexity that is linear to the edges and timesteps. Experiments with three datasets suggest that joint stationarity can yield significant accuracy improvements in the reconstruction effort of under-sampled problems, even when the graph is only approximately known or the process is only close to stationary.

US Special Forces battling ISIS have taken delivery of hundreds of'kamikaze drones' that can be launched from bazooka-like launchers. Earlier this year leaders with U.S. Special Operations Command, or SOCOM, requested 325 'Miniature Aerial Missile Systems,' or LMAMS. Known as Switchblades, they are'miniature flying lethal missiles' that feature inbuilt GPS and even object recognition cameras to ensure they hit their targets. Known as Switchblades, they are'miniature flying lethal missiles' that feature inbuilt GPS and cameras to ensure they hit their targets, and military bosses have unveiled a new'hacker lab' for weapons designed to blow up even bigger targets The weapons led military bosses to set up a new'hacker lab' for weapons designed to blow up even bigger targets. 'The threat is really changing -- this explosion of commercial technology, of super-empowered commercial technology, of each individual technology path on an accelerated schedule,' James'Hondo' Geurts, who leads SOCOM's acquisitions, technology and logistics efforts, said Tuesday at a National Defense Industry Association event, according to Defense One. AeroVironment's combat proven Switchblade offers special operations forces'a back-packable, rapidly deployable, loitering precision strike munition for use against beyond-line-of-sight (BLOS) targets' the firm says.