The United States said Wednesday it suspected Iranian involvement in the alleged hijacking of a ship in the Gulf of Oman as it vowed to work with Britain to respond to an earlier deadly attack it blamed on Tehran. Oman said that the Asphalt Princess, an asphalt and bitumen tanker, was involved in "a hijacking incident in international waters" and that it deployed aircraft and naval ships. The United States and Britain said that the murky incident in the Gulf of Oman concluded after one day, with the alleged hijackers leaving the Panamanian-flagged vessel. "We believe that these personnel were Iranian, but we're not in a position to confirm this at this time," State Department spokesman Ned Price told reporters in Washington. "Iran has undertaken a pattern of belligerence in terms of proxy attacks in the region and of course, these maritime attacks," Price said, while adding that circumstances in the latest incident were "still emerging".

CORVALLIS, Ore. – Cassie the robot, invented at Oregon State University and produced by OSU spinout company Agility Robotics, has made history by traversing 5 kilometers, completing the route in just over 53 minutes. Cassie was developed under the direction of robotics professor Jonathan Hurst with a 16-month, $1 million grant from the Defense Advanced Research Projects Agency, or DARPA. Since Cassie's introduction in 2017, in collaboration with artificial intelligence professor Alan Fern OSU students funded by the National Science Foundation and the DARPA Machine Common Sense program have been exploring machine learning options for the robot. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State's campus untethered and on a single battery charge. "The Dynamic Robotics Laboratory students in the OSU College of Engineering combined expertise from biomechanics and existing robot control approaches with new machine learning tools," said Hurst, who co-founded Agility in 2017.

Astronaut Thomas Pesquet watched from the International Space Station as Russia's Pirs module was discarded on June 26 and raced towards its death in Earth's atmosphere. The stunning video shows Pirs break up into a'shooting star' and slowly disappearing into a sea of ominous clouds hanging over our planet. 'Atmospheric reentry without a heat shield results in a nice fireball,' Pesquet wrote in a Facebook post, which also included a French description. 'You clearly see smaller pieces of melting metal floating away and adding to the fireworks.' Although the video was speed up, Pesquet and a few other crew members watched Pirs break up above the clouds for six minutes.

Science is a catalyst for human progress. But a publishing monopoly and funding monopsony have inhibited research. We intend to improve incentives in science by developing smart research contracts. These will collectively reward scientific activities, including proposals, papers, replications, datasets, analyses, annotations, editorials, and more. Peer-to-peer review networks will be designed to help evaluate proposals and publications. Long term, these smart contracts help accelerate research by minimizing science friction, ensuring science quality, and maximizing science variance. Email bits@atoms.org or follow @atoms_org to help us build a flourishing research economy. Papers are the fundamental asset of the research economy: they serve as proof of work that valuable research has been completed. Funding agencies and research institutions evaluate scientists based on their publications. Principal investigators (PIs) attract prospective students and collaborators via papers. Investors and companies use scientific literature to conduct due diligence on research ranging from basic discoveries to clinical studies. Thus, the evaluation and dissemination of papers are vital to this research economy. Publishers are the sole arbiters of papers today. They assign a value -- denominated in "prestige" -- by accepting a paper into the appropriate journal based on selectivity and domain. To evaluate papers, journals typically outsource it to two or three PIs, who often outsource it further to their students. Reviewers are unpaid for this peer review work, as it is an expected part of their scientific duties. Peer review is believed to be necessary because of the industrialization of science. Research papers and proposals have become too specialized and too numerous, making it difficult to assess merit prima facie. As a result, scientific incentives have become distorted in two major ways: prestige capture and reviewer misalignment. Over half of all research papers in 2013 were published by five companies, who have used their centuries of brand equity to build an economic moat. This results in prestige capture, which akin to regulatory capture, causes public and scientific interest to be directed towards the regulators of prestige.

Here's What You Need to Know: AI technology is fast evolving. The national security establishment is racing to adopt artificial intelligence in nearly every aspect of operations, from processing payroll to processing disparate battlefield information into a cohesive whole, such as in the Pentagon's Joint All Domain Command and Control effort to network otherwise separated operational "nodes" to one another in warfare to optimize and streamline attack. However, training AI systems to recognize the things they are meant to recognize requires vast, even seemingly limitless volumes of annotated data. As promising AI is, an AI system is only as effective as its training data. At the moment, there seem to be few barriers to AI and its promise for the future, yet an actual AI-system is only as effective as its database.

Venus is nearly the same size and mass as Earth. Its terrain is rocky and compacted, like ours. Once, it might have had oceans. NASA is eager to send a mission there. Unfortunately, its surface is 800 degrees Fahrenheit, with pressure so crushing it'd turn you into paste.

GIDE was designed to increase access to real-time information that can help leaders prepare for enemy action and hopefully deter it, rather than react to conflict once it has started. The US military is testing the use of cutting-edge data gathering tools combined with artificial intelligence to predict enemies' next moves up to days in advance. Speaking at a press conference, the commander of the US Northern Command (NORTHCOM) Glen VanHerck revealed that trials have been ongoing to improve the military's use of data when making key strategic decisions, with the third part of an initiative called the Global Information Dominance Experiment (GIDE) showing promising results. GIDE was designed to increase access to real-time information that can help leaders prepare for enemy action and hopefully deter it, rather than react to conflict once it has started. SEE: Attacks on critical infrastructure are dangerous.

I highly recommend reading the McKinsey Global Institute's new report, "Reskilling China: Transforming The World's Largest Workforce Into Lifelong Learners", which focuses on the country's biggest employment challenge, re-training its workforce and the adoption of practices such as lifelong learning to address the growing digital transformation of its productive fabric. How to transform the country that has become the factory of the world, where manual assembly was the cheapest due to its low labor costs, into an artificial intelligence giant, with the largest public blockchain infrastructure in the world, a digital currency in an advanced stage of development that will see an end to cash payments, along with the world's largest 5G network? Xi Jinping's state capitalism is transforming the Asian giant: the possibility of drawing up and maintaining long-term strategies thanks to political stability is driving change at an unprecedented rate, that includes autonomous driving and digital healthcare, advanced retail or even livestock farming. No matter where you look: the modernization and robotization of Chinese assembly factories has led to enormous reductions in the size of their workforces, which, moreover, immediately correspond not only to an increase in their production capacity, but also to a drastic reduction in the number of errors. And the COVID-19 pandemic, far from slowing the process, has accelerated it even further.

We present a Python tool to generate a standard dataset from solar images that allows for user-defined selection criteria and a range of pre-processing steps. Our Python tool works with all image products from both the Solar and Heliospheric Observatory (SoHO) and Solar Dynamics Observatory (SDO) missions. We discuss a dataset produced from the SoHO mission's multi-spectral images which is free of missing or corrupt data as well as planetary transits in coronagraph images, and is temporally synced making it ready for input to a machine learning system. Machine-learning-ready images are a valuable resource for the community because they can be used, for example, for forecasting space weather parameters. We illustrate the use of this data with a 3-5 day-ahead forecast of the north-south component of the interplanetary magnetic field (IMF) observed at Lagrange point one (L1). For this use case, we apply a deep convolutional neural network (CNN) to a subset of the full SoHO dataset and compare with baseline results from a Gaussian Naive Bayes classifier.

In this paper, we present a Stochastic Deep Neural Network-based Model Reference Adaptive Control. Building on our work "Deep Model Reference Adaptive Control", we extend the controller capability by using Bayesian deep neural networks (DNN) to represent uncertainties and model non-linearities. Stochastic Deep Model Reference Adaptive Control uses a Lyapunov-based method to adapt the output-layer weights of the DNN model in real-time, while a data-driven supervised learning algorithm is used to update the inner-layers parameters. This asynchronous network update ensures boundedness and guaranteed tracking performance with a learning-based real-time feedback controller. A Bayesian approach to DNN learning helped avoid over-fitting the data and provide confidence intervals over the predictions. The controller's stochastic nature also ensured "Induced Persistency of excitation," leading to convergence of the overall system signal.