Coleman's work with low-dimensional nanostructures, as well as Nicolosi's in the field of battery development, bring 2D materials to the fore. Plummeting production costs mean that such 2D materials are emerging with a wide range of applications. Thomas Swann, for example, produces materials in commercial quantities, though the research in Ireland would largely relate to smaller measurements.

LONDON: Understanding the hierarchical structure of biological networks like human brain -- a network of neurons -- could be useful in creating more complex, intelligent computational brains in the fields of artificial intelligence and robotics, says a study. Like large businesses, many biological networks are hierarchically organised, such as gene, protein, neural, and metabolic networks. This means they have separate units that can each be repeatedly divided into smaller and smaller subunits. Apple to sell solar energy now Apple is now planning to sell excess solar energy produced at its solar farms in Cupertino and Nevada. To understand as to why biological networks evolve to be hierarchical, researchers from the University of Wyoming and the French Institute for Research in Computer Science and Automation (INRIA) simulated the evolution of computational brain models, known as artificial neural networks, both with and without a cost for network connections.

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with an example of providing energy estimates to prospective tenants. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

With the general availability of the "Knights Landing" Xeon Phi many core processors from Intel last month, some of the largest supercomputing labs on the planet are getting their first taste of what the future style of high performance computing could look like for the rest of us. We are not suggesting that the Xeon Phi processor will be the only compute engine that will be deployed to run traditional simulation and modeling applications as well as data analytics, graph processing, and deep learning algorithms. But we are suggesting that this style of compute engine – it is more than a processor since it includes high bandwidth memory and fabric interconnect adapters on a single package – is what the future looks like. And that goes for Knights family processors and co-processors as well as the "Pascal" and "Volta" accelerators made by Nvidia, the Sparc64-XIfx and ARM chips that will be used in the used in the Post-K system in Japan made by Fujitsu, the Matrix2000 DSP accelerator being created by China for one of its pre-exascale systems, or the CPU-GPU hybrids based on its "Zen" Opterons that AMD is cooking up for supercomputing systems in the United States and, with licensing partners, in China. During the recent ISC16 supercomputing conference in Frankfurt, Germany, Intel gathered up the executives in charge of some of the largest supercomputing facilities on the planet who are also – not coincidentally, but absolutely intentionally – also early adopters of the Knights Landing Xeon Phi and, in some cases, the Omni-Path interconnect that is a kicker to Intel's True Scale InfiniBand networking.

The DURUS robot can walk more than a mile in man's shoes. A pair of size 13, Adidas sneakers, to be specific. Engineers at the Georgia Institute of Technology have tackled what they describe as a deceptively difficult challenge: develop a battery-powered robot that mimics the subtle complexity of the human footstep. Aaron Ames, an associate professor of automation and mechatronics, said their feat represents a stride in robotic efficiency and mobility and could allow for robots to function more seamlessly in environments meant for humans. "What drives me a lot is the cool factor, to be honest, but that's my professor hat," Ames said.

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.

Is your garage door opening right now? Is your washing machine running? A growing number of products attempt to give consumers data on the sources of their household energy use--crucial data for home efficiency efforts and utility peak-hour conservation programs. But Sense, a startup in Cambridge, Massachusetts, is the first to offer a consumer product that reads incoming household power levels a million times per second--enough to tease out telltale clues to which specific appliances, even low-wattage ones, are operating in real time. "It's at the cutting edge of what I have seen people attempting in this area," says Michael Baker, a vice president at SBW, an energy efficiency consultancy in Seattle.

Many problems in science and engineering can be formulated as a sequential decision-making problem under uncertainty. A common scenario in such problems that occurs in many different fields, such as online marketing, inventory control, health informatics, and computational finance, is to find a good or an optimal strategy/policy, given a batch of data generated by the current strategy of the company (hospital, investor). Although there are many techniques to find a good policy given a batch of data, only a few of them guarantee that the obtained policy will perform well, when it is deployed. Since deploying an untested policy can be risky for the business, the product (hospital, investment) manager does not usually allow it to happen, unless we provide her/him with some performance guarantees of the obtained strategy, in comparison to the baseline policy (e.g., the policy that is currently in use). In this paper, we focus on the model-based approach to this fundamental problem in the context of infinite-horizon discounted Markov decision processes (MDPs). In this approach, we use the batch of data and build a model or a simulator that approximates the true behavior of the dynamical system, together with an error function that captures the accuracy of the model at each state of the system. Our goal is to compute a safe policy, i.e., a policy that is guaranteed to perform at least as well as the baseline strategy, using the simulator and error function. Most of the work on this topic has been in the model-free setting, where safe policies are computed directly from the batch of data, without building an explicit model of the system [12, 13]. Another class of model-free algorithms are those that use a batch of data generated by the current policy and return a policy that is guaranteed to perform better.

It used to be that one great technology defined an era. The steam engine, for example, served as the catalyst for the rise of the industrial age. Nowadays, however, a number of amazing technical advances and inventions are contending for bragging rights as the leading technology of our times. I would argue that one is particularly worthy of such boasting: machine learning. Although it has been in slow and steady development for years and has been used in a few enterprise applications, it has recently burst onto the scene in response to the explosion of data in today's increasingly connected digital world.

With the JIMU robot, UBTECH Robotics, a Schenzen-based company that has been around for eight years, is stepping into a space that's quickly getting cluttered with motorized toys. The company already has a lineup that includes industrial bots in China and commercial humanoids like Alpha 2, but now they're using their existing infrastructure to build affordable, programmable robots for kids, eight and older, with DIY inclinations. They might also find a home in schools that are looking to adopt coding in their curriculum. The MeeBot kit, which is available exclusively at Apple stores for 130 starting today, comes with interlocking parts that include colorful blocks, connectors, motors and a rechargeable lithium-ion battery. A platinum grey control box with a U-shaped line across the front doubles as a smiling face.