#sweet!

Electrons which act like slow-pouring honey have been observed for the first time in graphene, prompting a new approach to fundamental physics.

Electrons are known to move through metals like bullets being reflected only by imperfections, but in graphene they move like in a very viscous liquid, University of Manchester researchers have found.

The possibility of a highly viscous flow of electrons in metals was predicted several decades ago but despite numerous efforts never observed, until now as reported in the journal Science.

Fujitsu Laboratories today announced that it has developed deep learning technology that can analyze time-series data with a high degree of accuracy. Demonstrating promise for Internet-of-Things applications, time-series data can also be subject to severe volatility, making it difficult for people to discern patterns in the data. Deep learning technology, which is attracting attention as a breakthrough in the advance of artificial intelligence, has achieved extremely high recognition accuracy with images and speech, but the types of data to which it can be applied is still limited. In particular, it has been difficult to accurately and automatically classify volatile time-series data–such as that taken from IoT devices–of which people have difficulty discerning patterns.

Now Fujitsu Laboratories has developed an approach to deep learning that uses advanced mathematical techniques to extract geometric features from time-series data, enabling highly accurate classification of volatile time-series. In benchmark tests held at UC Irvine Machine Learning Repository that classified time-series data captured from gyroscopes in wearable devices, the new technology was found to achieve roughly 85% accuracy, about a 25% improvement over existing technology. This technology will be used in Fujitsu’s Human Centric AI Zinrai artificial intelligence technology. Details of this technology will be presented at the Fujitsu North America Technology Forum (NAFT 2016), which will be held on Tuesday, February 16, in Santa Clara, California.

Background

In recent years, in the field of machine learning, which is a central technology in artificial intelligence, deep learning technology has been attracting attention as a way to automatically extract feature values needed to interpret and assess phenomena without rules being taught manually. Especially in the IoT era, massive volumes of time-series data are being accumulated from devices. By applying deep learning to this data and classifying it with a high degree of accuracy, further analyses can be performed, holding the prospect that it will lead to the creation of new value and the opening of new business areas.

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Imagine if your clothing could, on demand, release just enough heat to keep you warm and cozy, allowing you to dial back on your thermostat settings and stay comfortable in a cooler room. Or, picture a car windshield that stores the sun’s energy and then releases it as a burst of heat to melt away a layer of ice.

According to a team of researchers at MIT, both scenarios may be possible before long, thanks to a new material that can store solar energy during the day and release it later as heat, whenever it’s needed. This transparent polymer film could be applied to many different surfaces, such as window glass or clothing.

Although the sun is a virtually inexhaustible source of energy, it’s only available about half the time we need it—during daylight. For the sun to become a major power provider for human needs, there has to be an efficient way to save it up for use during nighttime and stormy days. Most such efforts have focused on storing and recovering solar energy in the form of electricity, but the new finding could provide a highly efficient method for storing the sun’s energy through a chemical reaction and releasing it later as heat.

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