Monday, April 28, 2008

First Superheavy Element Found In Nature

KentuckyFC writes "The first naturally occurring superheavy element has been found. An international team of scientists found several nuclei of unbibium in a sample of the naturally occurring heavy metal thorium. Unbibium has an atomic number of 122 and an atomic weight of 292. In general, very heavy elements tend to be unstable but scientists have long predicted that even heavier nuclei would be stable. The group that found unbibium in thorium say it has a half life in excess of 100 million years and an abundance of about 10^(-12) relative to thorium, which itself is about as abundant as lead."
I'd also like it known that my spell checker did not know 'unbibium' before today, but it is now one word closer to encompassing all human knowledge.

First superheavy element found in nature

The hunt for superheavy elements has focused banging various heavy nuclei together and hoping they’ll stick. In this way, physicists have extended the periodic table by manufacturing elements 111, 112, 114, 116 and 118, albeit for vanishingly small instants. Although none of these elements is particularly long lived, they don’t have progressively shorter lives and this is taken as evidence that islands of nuclear stability exist out there and that someday we’ll find stable superheavy elements.

But if these superheavy nuclei are stable, why don’t we find them already on Earth? Turns out we do; they’ve been here all along. The news today is that a group led by Amnon Marinov at the Hebrew University of Jerusalem has found the first naturally occuring superheavy nuclei by sifting through a large pile of the heavy metal thorium.

What they did was fire one thorium nucleus after another through a mass spectrometer to see how heavy each was. Thorium has an atomic number of 90 and occurs mainly in two isotopes with atomic weights of 230 and 232. All these showed up in the measurements along with a various molecular oxides and hydrides that form for technical reasons.

But something else showed up too. An element with a weight of 292 and an atomic number of around 122. That’s an extraordinary claim and quite rightly the team has been diligent in attempting to exclude alternative explanations such as th epresence of exotic molecules formed from impurities in the thorium sample or from the hydrocarbon in oil used in the vacuum pumping equipment). But these have all been ruled out, say Marinov and his buddies.

What they’re left with is the discovery of the first superheavy element, probably number 122.

What do we know about 122? Marinov and co say it has a half life in excess of 100 million years and occurs with an abundance of between 1 and 10 x10^-12, relative to thorium, which is a fairly common element (about as abundant as lead).

Theorists have mapped out the superheavy periodic table and 122 would be a member of the superheavy actinide group. It even has a name: eka-thorium or unbibium. Welcome to our world!

This may well open the flood gates to other similar discoveries. Uranium is the obvious next place to look for superheavy actinides. I’d bet good money that Marinov and his pals are eyeballing the stuff as I write.

Ref: Evidence for a Long-lived superheavy Nucleus with Atomic Mass Number A = 292 and Atomic Number Z @ 122 in Natural Th

A Google Prototype for a Precision Image Search

SAN FRANCISCO — Google researchers say they have a software technology intended to do for digital images on the Web what the company’s original PageRank software did for searches of Web pages.
On Thursday at the International World Wide Web Conference in Beijing, two Google scientists presented a paper describing what the researchers call VisualRank, an algorithm for blending image-recognition software methods with techniques for weighting and ranking images that look most similar.

Although image search has become popular on commercial search engines, results are usually generated today by using cues from the text that is associated with each image.

Despite decades of effort, image analysis remains a largely unsolved problem in computer science, the researchers said. For example, while progress has been made in automatic face detection in images, finding other objects such as mountains or tea pots, which are instantly recognizable to humans, has lagged.

“We wanted to incorporate all of the stuff that is happening in computer vision and put it in a Web framework,” said Shumeet Baluja, a senior staff researcher at Google, who made the presentation with Yushi Jing, another Google researcher. The company’s expertise in creating vast graphs that weigh “nodes,” or Web pages, based on their “authority” can be applied to images that are the most representative of a particular query, he said.

The research paper, “PageRank for Product Image Search,” is focused on a subset of the images that the giant search engine has cataloged because of the tremendous computing costs required to analyze and compare digital images. To do this for all of the images indexed by the search engine would be impractical, the researchers said. Google does not disclose how many images it has cataloged, but it asserts that its Google Image Search is the “most comprehensive image search on the Web.”

The company said that in its research it had concentrated on the 2000 most popular product queries on Google’s product search, words such as iPod, Xbox and Zune. It then sorted the top 10 images both from its ranking system and the standard Google Image Search results. With a team of 150 Google employees, it created a scoring system for image “relevance.” The researchers said the retrieval returned 83 percent less irrelevant images.
Google is not the first into the visual product search category. Riya, a Silicon Valley start-up, introduced in 2006. The service, which refers users to shopping sites, makes it possible for a Web shopper to select a particular visual attribute, such as a certain style of brown shoes or a style of buckle, and then be presented with similar products available from competing Web merchants.

Rather than relying on a text query, the service focuses on the ability to match shapes or objects that might be hard to describe in writing, said Munjal Shah, the chief executive of Riya.
“I think what they’re trying to accomplish is largely impossible,” he said. “Our belief is, there is not large-scale solutions.”

Mr. Shah said there had been a number of technology demonstrations by Google Labs researchers, such as a project in 2005 that used machine learning techniques to recognize the gender of a person in an image. However, the company has been slow to deploy its research, he said.