Colossal Black Hole Equal to 17 Billion Suns Discovered - May Overturn Existing Models.
“A group of astronomers led by Remco van den Bosch from the Max Planck Institute for Astronomy (MPIA) have discovered a black hole that could shake the foundations of current models of galaxy evolution. The Hubble image above shows the small, flattened disk galaxy NGC 1277, which contains one of the biggest central super-massive black holes ever found in its center. With the mass of 17 billion Suns, the black hole weighs in at an extraordinary 14% of the total galaxy mass —a mass much greater than current models predict — in particular in relation to the mass of its host galaxy. This could be the most massive black hole found to date. Astronomers would have expected a black hole of this size inside blob-like (“elliptical”) galaxies ten times larger. Instead, this black hole sits inside a fairly small disk galaxy.
If the additional candidates are confirmed, astronomers will need to rethink fundamentally their models of galaxy evolution. In particular, they will need to look at the early universe: The galaxy hosting the new black hole appears to have formed more than 8 billion years ago, and does not appear to have changed much since then. Whatever created this giant black hole must have happened a long time ago.
To the best of our astronomical knowledge, almost every galaxy should contain in its central region what is called a supermassive black hole: a black hole with a mass between that of hundreds of thousands and billions of Suns. The best-studied super-massive black hole sits in the center of our home galaxy, the Milky Way, with a mass of about four million Suns.
NGC 1277 is embedded in the nearby Perseus galaxy cluster, at a distance of 250 million light-years from Earth. All the ellipticals and round yellow galaxies in the image below are galaxies located in this cluster. Compared to all the other galaxies around it, NGC 1277 is a relatively compact. (Second Image Credit: David W. Hogg, Michael Blanton, and the SDSS Collaboration)”
(via abcstarstuff)
Source: dailygalaxy.com
Reading, Writing and Playing Games May Help Aging Brains Stay Healthy
Mental activities like reading and writing can preserve structural integrity in the brains of older people, according to a new study presented at the annual meeting of the Radiological Society of North America (RSNA).
While previous research has shown an association between late-life cognitive activity and better mental acuity, the new study from Konstantinos Arfanakis, Ph.D., and colleagues from Rush University Medical Center and Illinois Institute of Technology in Chicago studied what effect late-life cognitive activity might have on the brain’s white matter, which is composed of nerve fibers, or axons, that transmit information throughout the brain.
“Reading the newspaper, writing letters, visiting a library, attending a play or playing games, such as chess or checkers, are all simple activities that can contribute to a healthier brain,” Dr. Arfanakis said.
The researchers used a magnetic resonance imaging (MRI) method known as diffusion tensor imaging (DTI) to generate data on diffusion anisotropy, a measure of how water molecules move through the brain. In white matter, diffusion anisotropy exploits the fact that water moves more easily in a direction parallel to the brain’s axons, and less easily perpendicular to the axons, because it is impeded by structures such as axonal membranes and myelin. “This difference in the diffusion rates along different directions increases diffusion anisotropy values,” Dr. Arfanakis said. “Diffusion anisotropy is higher when more diffusion is happening in one direction compared to others.”
The anisotropy values in white matter drop, however, with aging, injury and disease.
“In healthy white matter tissue, water can’t move as much in directions perpendicular to the nerve fibers,” Dr. Arfanakis said. “But if, for example, you have lower neuronal density or less myelin, then the water has more freedom to move perpendicular to the fibers, so you would have reduced diffusion anisotropy. Lower diffusion anisotropy values are consistent with aging.”
(Image credit: Flickr.com, Courtesy of Luis de Bethencourt)
La constellation d’Orion au-dessus de la pyramide maya d’El Castillo également connue sous le nom de “temple de Kulkukan”. Superbe photo de Stéphane Guisard.
Scientists See Promise for Artificial Intelligence in Deep-Learning Programs
Using an artificial intelligence technique inspired by theories about how the brain recognizes patterns, technology companies are reporting startling gains in fields as diverse as computer vision, speech recognition and the identification of promising new molecules for designing drugs.
The advances have led to widespread enthusiasm among researchers who design software to perform human activities like seeing, listening and thinking. They offer the promise of machines that converse with humans and perform tasks like driving cars and working in factories, raising the specter of automated robots that could replace human workers.
The technology, called deep learning, has already been put to use in services like Apple’s Siri virtual personal assistant, which is based on Nuance Communications’ speech recognition service, and in Google’s Street View, which uses machine vision to identify specific addresses.
But what is new in recent months is the growing speed and accuracy of deep-learning programs, often called artificial neural networks or just “neural nets” for their resemblance to the neural connections in the brain.
“There has been a number of stunning new results with deep-learning methods,” said Yann LeCun, a computer scientist at New York University who did pioneering research in handwriting recognition at Bell Laboratories. “The kind of jump we are seeing in the accuracy of these systems is very rare indeed.
Say hello to Roboray, Samung’s latest humanoid robot (more info + video)
Calligraphy robot uses a Motion Copy System to reproduce detailed brushwork
A week ago I posted the news of a robotic system that can record the brushwork input of calligraphy and technically reproduce it as well as the human artist. Now, DigInfo have a video demonstration of the technology in action:
A research group at Keio University, led by Seiichiro Katsura, has developed the Motion Copy System. This system can identify and store detailed brush strokes, based on information about movement in calligraphy. This enables a robot to faithfully reproduce the detailed brush strokes.
This system stores calligraphy movements by using a brush where the handle and tip are separate. The two parts are connected, with the head as the master system and the tip as the slave system. Characters can be written by handling the device in the same way as an ordinary brush.
Unlike conventional motion capture systems, a feature of this one is, it can record and reproduce the force applied to the brush as well as the sensation when you touch something. Until now, passing on traditional skills has depended on intuition and experience. It’s hoped that this new system will enable skills to be learned more efficiently.
More at DigInfo here
