Albert Einstein is famous for his theory of relativity, and GPS navigation and nuclear energy would be impossible without the equation e=mc2.
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Take a microscopic look at the world’s tiniest computer, which is smaller than a grain of salt. (via Seeker)
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Exclusive: 71 million people stand to benefit from reduced price treatment for virus which can lead to liver cirrhosis, cancer and death.
Thu 12 Apr 2018 01.00 EDT Last modified on Thu 12 Apr 2018 19.55 EDT.
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It may sound like a futuristic device out of a spy novel, a computer the size of a pinhead, but according to new research from the University of New Hampshire, it might be a reality sooner than once thought. Researchers have discovered that using an easily made combination of materials might be the way to offer a more stable environment for smaller and safer data storage, ultimately leading to miniature computers.
“We’re really optimistic about the possibilities,” said Jiadong Zang, assistant professor of physics. “There is a push in the computer industry toward smaller and more powerful storage, yet current combinations of materials can create volatile situations, where data can be lost once the device is turned off. Our research points to this new combination as a much safer option. We’re excited that our findings might have the potential to change the landscape of information technology.”
In their study, recently published in the journal Science Advances, the researchers outline their proposed combination which would allow for a more stable perpendicular anisotropic energy (PMA), the key driving component in a computer’s RAM (random-access memory) or data storage. The material would be made up of ultrathin films, known as Fe monolayers, grown on top of non-magnetic substances, in this case X nitride substrate, where X could be boron, gallium, aluminum or indium. According to the research, this combination showed anisotropic energy would increase by fifty times, from 1 meV to 50 meV, allowing for larger amounts of data to be stored in smaller environments. There is a provisional patent pending which has been filed by UNHInnovation, which advocates for, manages, and promotes UNH’s intellectual property.
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Researchers proposed implementing the residential energy scheduling algorithm by training three action dependent heuristic dynamic programming (ADHDP) networks, each one based on a weather type of sunny, partly cloudy, or cloudy. ADHDP networks are considered ‘smart,’ as their response can change based on different conditions.
“In the future, we expect to have various types of power supplies to every household including the grid, windmills, solar panels and biogenerators. The issues here are the varying nature of these power sources, which do not generate electricity at a stable rate,” said Derong Liu, a professor with the School of Automation at the Guangdong University of Technology in China and an author on the paper. “For example, power generated from windmills and solar panels depends on the weather, and they vary a lot compared to the more stable power supplied by the grid. In order to improve these power sources, we need much smarter algorithms in managing/scheduling them.”
The details were published on the January 10th issue of IEEE/CAA Journal of Automatica Sinica, a joint bimonthly publication of the IEEE and the Chinese Association of Automation.
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People are remarkably good at focusing their attention on a particular person in a noisy environment, mentally “muting” all other voices and sounds. Known as the cocktail party effect, this capability comes natural to us humans. However, automatic speech separation — separating an audio signal into its individual speech sources — while a well-studied problem, remains a significant challenge for computers.
In “Looking to Listen at the Cocktail Party”, we present a deep learning audio-visual model for isolating a single speech signal from a mixture of sounds such as other voices and background noise. In this work, we are able to computationally produce videos in which speech of specific people is enhanced while all other sounds are suppressed. Our method works on ordinary videos with a single audio track, and all that is required from the user is to select the face of the person in the video they want to hear, or to have such a person be selected algorithmically based on context. We believe this capability can have a wide range of applications, from speech enhancement and recognition in videos, through video conferencing, to improved hearing aids, especially in situations where there are multiple people speaking.
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