With ever increasing data rates produced by modern radio telescopes like LOFAR and future telescopes like the SKA, many data processing steps are overwhelmed by the amount of data that needs to be handled using limited compute resources. Calibration is one such operation that dominates the overall data processing computational cost, nonetheless, it is an essential operation to reach many science goals. Calibration algorithms do exist that scale well with the number of stations of an array and the number of directions being calibrated. However, the remaining bottleneck is the raw data volume, which scales with the number of baselines, and which is proportional to the square of the number of stations. We propose a 'stochastic' calibration strategy where we only read in a mini-batch of data for obtaining calibration solutions, as opposed to reading the full batch of data being calibrated. Nonetheless, we obtain solutions that are valid for the full batch of data. Normally, data need to be averaged before calibration is performed to accommodate the data in size-limited compute memory. Stochastic calibration overcomes the need for data averaging before any calibration can be performed, and offers many advantages including: enabling the mitigation of faint radio frequency interference; better removal of strong celestial sources from the data; and better detection and spatial localization of fast radio transients.

Modeling how human moves on the space is useful for policy-making in transportation, public safety, and public health. The human movements can be viewed as a dynamic process that human transits between states (e.g., locations) over time. In the human world where both intelligent agents like humans or vehicles with human drivers play an important role, the states of agents mostly describe human activities, and the state transition is influenced by both the human decisions and physical constraints from the real-world system (e.g., agents need to spend time to move over a certain distance). Therefore, the modeling of state transition should include the modeling of the agent's decision process and the physical system dynamics. In this paper, we propose to model state transition in human movement through learning decision model and integrating system dynamics. In experiments on real-world datasets, we demonstrate that the proposed method can achieve superior performance against the state-of-the-art methods in predicting the next state and generating long-term future states.

A new algorithm for deciding the satisfiability of polynomial formulas over the reals is proposed. The key point of the algorithm is a new projection operator, called sample-cell projection operator, custom-made for Conflict-Driven Clause Learning (CDCL)-style search. Although the new operator is also a CAD (Cylindrical Algebraic Decomposition)-like projection operator which computes the cell (not necessarily cylindrical) containing a given sample such that each polynomial from the problem is sign-invariant on the cell, it is of singly exponential time complexity. The sample-cell projection operator can efficiently guide CDCL-style search away from conflicting states. Experiments show the effectiveness of the new algorithm.

With a forceful buzz, Pete Bitar's home-made personal aircraft takes to the skies above Silicon Valley, his aluminium pilot chair glinting in the morning sunlight above four spinning propellers. Dubbed the Verticycle, it wobbles to a height of about three metres before tipping sideways and plunging back to the runway with a loud crash. Fortunately, Bitar is piloting the vehicle remotely today from a wireless controller nearby. The craft's battery packs were damaged in another crash the week before, and replacements couldn't generate enough thrust to lift him and the …

Space scientists have successfully predicted satellite-killing radiation storms two days before they strike – beating out the previous model that alerted experts only one day in advance. The new model, called PreMevE 2.0, uses machine-learning to improve forecasts by incorporating upstream solar wind speeds from the Van Allen belts. The technology compiles existing data sets to'learn' patterns and predict future storms so satellite operators can take protective measures, including temporarily shutting down part of or even the whole satellite to avoid damage. The model's creators have also noted that it can be used to capture earthquake patterns on earth in order to predict when these natural disasters will strike. This new model, called PreMevE 2.0, uses machine learning to improve forecasts by incorporating upstream solar wind speeds from the Van Allen belts PreMevE 2.0 was developed by space scientists at Los Alamos National Laboratory, who are working in a NASA and National Oceanic and atmospheric Administration (NOAA).

Facebook has updated its Messenger app to prioritise Stories and kill off the business and ad-focused'Discover' tab. The update to both iOS and Android gives greater prominence to Stories – a feature copied from Instagram – at the expense of Discover, which was designed to provide users with easy access to businesses, chatbots and games. Facebook announced plans to phase out the Discover tab back in August 2019. 'Simply put, we want to make it more seamless for people to reach out to businesses on Messenger in places where they're already looking to connect,' it said in a blog post at the time. 'Businesses will continue to appear in the app through the search feature and advertising surfaces, making it easy for people to connect with them.'

Google's parent company, Alphabet, wants to throw the power of its AI behind a mission to monitor sea life around the ocean. According to a blog post, a project called Tidal - part of Alphabet's'X' division that develops'moonshot' projects - is creating a computer vision system that uses AI to monitor thousands of fish. The goal, says the company, is to help glean understanding of how over-fishing and other human impacts of fish populations affect sea life across the globe. With that information, Alphabet hopes that people will not just understand what happens beneath the ocean's surface, but help fix the problems that plague ocean life and habitats. 'One of the biggest barriers to protecting the ocean -- and our future -- is that we don't know much about what's going on under the water.

Controversial facial recognition company, Clearview AI, is reportedly developing surveillance cameras and augmented reality glasses despite mounting public scrutiny over the company's ethics. According to documents obtained by Buzzfeed News, Clearview AI is exploring the possibility of making surveillance cameras that use computer vision software to identify subjects by cross-referencing a database. Its database of photos has been the subject of controversy after it was found to be scraping pictures from Facebook and Instagram without people's consent. Those pictures were used to train its facial recognition algorithm. The company has also partnered with at least 600 law enforcement agencies across the US.

On behalf of the entire community, we are proud to announce Kubeflow 1.0, our first major release. Kubeflow was open sourced at Kubecon USA in December 2017, and during the last two years the Kubeflow Project has grown beyond our wildest expectations. There are now hundreds of contributors from over 30 participating organizations. Kubeflow's goal is to make it easy for machine learning (ML) engineers and data scientists to leverage cloud assets (public or on-premise) for ML workloads. You can use Kubeflow on any Kubernetes-conformant cluster.

Just look at the reports on AI. When I asked one expert last week how many of these documents are out there, she said at least 120. The von der Leyen Commission's much anticipated White Paper dropped on February 19th. It follows the statement on AI in her own political guidelines, and the previous Commission's Communication on Artificial Intelligence for Europe from last spring. And it's accompanied by the Commission's Report on the safety and liability implications of Artificial Intelligence, the Internet of Things and robotics.