A total of £84m in government funding to support robotics technology research and the development of smart energy systems has been announced by the government as part of a package of fresh support for...

Physicists on the MINERvA neutrino experiments at the Department of Energy's Fermilab faced a conundrum. Their particle detector was swamping them with images. The machine then takes a digital photo of all of the new particles' movements. As the relevant interactions occur very rarely, having a huge amount of data should have been a good thing. But there were simply too many pictures for the scientists to be able to analyze them as thoroughly as they would have liked to.

Welcome to California Inc., the weekly newsletter of the L.A. Times Business Section. One story stood out for me: Uber says it will introduce flying taxis in Los Angeles by 2020. "We're trying to work with cities in the early days who are interested in partnering to make it happen, while knowing that there will be pitfalls along the way," says Jeff Holden, Uber's chief product officer. Solar panels: President Trump will be presented with a plan Monday to impose restrictions and tariffs on imports of the most popular photovoltaic generating panels used in the booming U.S. residential and utility-scale solar markets. The U.S. International Trade Commission, an independent agency, has proposed the action after two international solar-panel producers with U.S. plants complained that they needed protection from low-cost imports.

I'm a big fan of Amazon's Echo line of smart speakers when it comes to smart home control, and the second-generation Echo is a big improvement over the first. But the Alexa-powered Fabriq Chorus delivers more bang for the buck and it sounds better, too. Where the Echo requires an AC outlet to operate, the Chorus features a 2200mAh rechargeable battery that Fabriq says can deliver up to 6 hours of performance, so you can take the speaker with you. The Chorus also comes with a contact-based charging cradle, so you don't need to bother with plugging and unplugging a USB cable to run the speaker--or charge its battery. The 6.3-inch tall cylindrical speaker is wrapped in wooly fabric (you can choose from four designs), with a thin LED ring around its base.

Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.

Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predictor fitted using a set of solution samples from which it learns to generate subsequent basis functions at a lower computational cost than solving the local problems. The computational advantage of this approach is realized for uncertainty quantification tasks where a large number of realizations has to be evaluated. We attribute the ability to learn these basis functions to the modularity of the local problems and the redundancy of the permeability patches between samples. The proposed method is evaluated on elliptic problems yielding very promising results.

With President Trump's visit to Beijing this week, there's been a lot of nervous talk about China's growing wealth and trade. The country will soon overtake the US to become the world's largest economy, and its government is aggressively pushing innovation in everything from solar power to artificial intelligence to electric cars. But one of China's most important industries is also one of its oldest: rice. It's unclear how much rice the POTUS enjoyed during his visit, but some seems likely--not only given his reported love of the stuff, but also its role in Chinese culture. People began cultivating the grain in China nearly 10,000 years ago. Today, rice is its biggest crop, with farmers growing roughly 200 million tons--more than any other country, and nearly a third of the world's supply--every year.

There's a chart I came across earlier this year, and not only does it tell an extremely important story about automation, but it also tells a story about the state of the automation discussion itself. It even reveals how we can expect both automation and the discussion around automation to continue unfolding in the years ahead. The chart is a plot of oil rigs in the United States compared to the number of workers the oil industry employs, and it's an important part of a puzzle that needs to be pieced together before it's too late. What should be immediately apparent is that as the number of oil rigs declined due to falling oil prices, so did the number of workers the oil industry employed. But when the number of oil rigs began to rebound, the number of workers employed didn't. That observation itself should be extremely interesting to anyone debating whether technological unemployment exists or not, but there's even more to glean from this chart.

There are many different types of sites that provide a wealth of free, freemium and paid data that can help audience developers and journalists with their reporting and storytelling efforts, The team at State of Digital Publishing would like to acknowledge these, as derived from manual searches and recognition from our existing audience. Kaggle's a site that allows users to discover machine learning while writing and sharing cloud-based code. Relying primarily on the enthusiasm of its sizable community, the site hosts dataset competitions for cash prizes and as a result it has massive amounts of data compiled into it. Whether you're looking for historical data from the New York Stock Exchange, an overview of candy production trends in the US, or cutting edge code, this site is chockful of information. It's impossible to be on the Internet for long without running into a Wikipedia article.

During a recent four-day trip to Marib with a group of Western journalists and researchers, I saw a town struggling for a sense of normalcy -- and even progress -- despite the collapsed country around it. The trip was organized by the Sana Center for Strategic Studies, a research institute focused on Yemen, and led by Farea al-Muslimi, an energetic young Yemeni scholar, who said he worried that the international community was forgetting about Yemen, to the peril of both. "We can't stop the war in Yemen right now, but at least we can cause more conversation about it," he said. "We want to bring the world to Yemen and bring Yemen to the world." Marib's unlikely success is partly a symptom of the near complete shattering of the Yemeni state, which has left regions to fend for themselves in providing life's basics for their people.