Sure, you know your mobile phone is essential, but exactly how much can you rely on it? A few days ago in Japan, Google threw down a gauntlet: how far can you get in a foreign country, where you can't be sure of finding an English speaker, where the words, even the alphabet, are unfamiliar, and where the address system is notoriously tricky? So there I was, in Tokyo, charged with solving a series of puzzles using a smartphone and nothing more. First, I had to get myself from bustling Tokyo (where English speakers are plentiful and, because they are Japanese, endlessly helpful) to the distant city of Kanazawa. I had a JR train pass, which is the best way to get around Japan for a foreigner and which offers fantastic value, though you must buy it before you arrive in the country.

Under normal circumstances, this tweet wouldn't be so surprising: Lamar Smith, the chair of the committee since 2013, is a well-known climate-change denier. But these are not normal times. The tweet is best interpreted as something new: a warning shot. It's a sign of things to come--a declaration of the Trump Administration's intent to sideline science. In a 1946 essay, George Orwell wrote that "to see what is in front of one's nose needs a constant struggle."

We present a machine learning approach to the inversion of Fredholm integrals of the first kind. The approach provides a natural regularization in cases where the inverse of the Fredholm kernel is ill-conditioned. It also provides an efficient and stable treatment of constraints. The key observation is that the stability of the forward problem permits the construction of a large database of outputs for physically meaningful inputs. We apply machine learning to this database to generate a regression function of controlled complexity, which returns approximate solutions for previously unseen inputs; the approximate solutions are then projected onto the subspace of functions satisfying relevant constraints. We also derive and present uncertainty estimates. We illustrate the approach by applying it to the analytical continuation problem of quantum many-body physics, which involves reconstructing the frequency dependence of physical excitation spectra from data obtained at specific points in the complex frequency plane. Under standard error metrics the method performs as well or better than the Maximum Entropy method for low input noise and is substantially more robust to increased input noise. We expect the methodology to be similarly effective for any problem involving a formally ill-conditioned inversion, provided that the forward problem can be efficiently solved.

The year Jojo and I started eighth grade, the power plant officially cut electricity to two hours a day. We'd already been through years of brownouts, of flickering lights, blinking monitors, older ag drones without artificial neural networks rebooting in their stations and randomly launching to spray the fields again or overfeed the chickens. So when Public Works & Electric issued a message to all our devices telling us about its irregular hours of operation, no one was surprised. The message was full of obfuscating language, but anyone with a tide chart could spot the correlation. Anyone driving down the causeway to the airport, past the power plant, could see through its chain-link fence the turbines standing silent, tense as raised shoulders; the grounds swamped in seawater, the ebbing tide dragging out an iridescent Rorschach of petroleum.

Renewable power sources such as wind now provide about one-third of Germany's electricity. The rows of towering wind turbines and legions of glistening solar panels spread across Germany's landscape are striking emblems of the country's shift to non-nuclear, low-carbon power. But although Germany is the world's poster child for renewable energy, its grids cannot yet cope with the erratic nature of wind and solar power. In June, German meteorologists, engineers and utility firms began to test whether big data and machine learning can make these power sources more grid-friendly. "To operate the grid more efficiently and keep fossil reserves at a minimum, operators need to have a better idea of how much wind and solar power to expect at any given time," says Malte Siefert, a physicist at the Fraunhofer Institute for Wind Energy and Energy System Technology in Kassel, Germany, and a leader on the project, called EWeLiNE.

Coalition formation typically involves the coming together of multiple, heterogeneous, agents to achieve both their individual and collective goals. In this paper, we focus on a special case of coalition formation known as Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the agents constrains the formation of coalitions. We focus on this type of problem given that in many real-world applications, agents may be connected by a communication network or only trust certain peers in their social network. We propose a novel representation of this problem based on the concept of edge contraction, which allows us to model the search space induced by the GCCF problem as a rooted tree. Then, we propose an anytime solution algorithm (CFSS), which is particularly efficient when applied to a general class of characteristic functions called $m+a$ functions. Moreover, we show how CFSS can be efficiently parallelised to solve GCCF using a non-redundant partition of the search space. We benchmark CFSS on both synthetic and realistic scenarios, using a real-world dataset consisting of the energy consumption of a large number of households in the UK. Our results show that, in the best case, the serial version of CFSS is 4 orders of magnitude faster than the state of the art, while the parallel version is 9.44 times faster than the serial version on a 12-core machine. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems of agents (i.e., with more than 2700 agents).

Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the inter-seasonal variations in renewable generation of a future grid scenario necessitates the use of computationally intensive time-series analysis. In this paper, we propose a planning framework for fast stability scanning of future grid scenarios using a novel feature selection algorithm and a novel self-adaptive PSO-k-means clustering algorithm. To achieve the computational speed-up, the stability analysis is performed only on small number of representative cluster centroids instead of on the full set of operating conditions. As a case study, we perform small-signal stability and steady-state voltage stability scanning of a simplified model of the Australian National Electricity Market with significant penetration of renewable generation. The simulation results show the effectiveness of the proposed approach. Compared to an exhaustive time series scanning, the proposed framework reduced the computational burden up to ten times, with an acceptable level of accuracy.

Digital-related investment in industrial production is growing fast. Through 2020, enterprises expect to pump $907 billion annually into digital technologies on the industrial floor, according to PwC's Industry 4.0 research. That investment is expected to increase revenues by $493 billion annually and reduce costs by $421 billion each year. But where and how those dividends will be unearthed is only now coming into view. PwC sees enterprises following a path that spans prediction, prescription, optimization, and new business models.

Across the globe, energy systems are changing and creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, National Grid is facing shrinking margins, looming capacity shortages and unpredictable peaks and troughs in energy supply caused by increasing levels of renewable penetration. At the Reinventing Energy Summit, Michael Bironneau, Head of Technology Development at Open Energi, will explore how the same machine learning techniques that have let machines defeat chess and Go masters, can also be leveraged to orchestrate massive amounts of flexible demand-side capacity – from industrial equipment, co-generation and battery storage systems – towards the one goal of creating a smarter grid; one that is cleaner, cheaper, more secure and more efficient. For World Cities Day 2016, I asked Michael a few questions to learn more about utilising data science in energy, creating a smarter grid, political challenges, and more. A smarter grid is one where we can integrate renewable energy efficiently without having to keep polluting power stations online to manage intermittency.

Japan's space program, JAXA, soars this weekend after its robotic cargo spacecraft began its four-day journey to the International Space Station (ISS) on Friday. Onboard the spacecraft, called Kounotori (after the Japanese word for "white stork"), are more than four tons of cargo, including JAXA's massive debris clearing space whip and a new array of lithium ion batteries for the space station's solar arrays. Friday's cargo launch is particularly important after the failure of a Russian Progress cargo launch earlier this month. Several other cargo launches have met similar fates over the past two years. "Spaceflight's not an easy thing," said NASA astronaut Peggy Whitson in an interview aboard the ISS.