Milky Way’s Bulge Holds Oldest Stars

Pointing the Australian National Univ.’s SkyMapper telescope towards the center of the galaxy, astronomers surveyed the dense bulge of the Milky Way. They were searching for celestial bodies that might hold clues to the galaxy’s start.

“We found what we think are the oldest stars in the galaxy and potentially the oldest objects ever discovered,” said Louise Howes, a PhD student at the university’s Research School of Astronomy & Astrophysics and the lead author of a recent study published in Nature.

“Pretty much the galaxy formed around them,” she said of the nine stars discovered and analyzed for the study

According to Howes, the stars formed during a time astronomers call the Epoch of Reionization. According to the Massachusetts Institute of Technology, the epoch defines a period when the universe went from being a predominantly neutral intergalactic medium to an ionized one. Multiple luminous sources, which may have been stars, galaxies, quasars or a combination of the three, ignited, giving light to the universe.

Google Releases Newest Machine Learning System to Everyone

Google’s internal deep learning infrastructure DistBelief, developed in 2011, has helped the technology company advance its capabilities. It helped improve speech recognition in the Google app by 25%, assisted the image search option in Google Photos and helped in a myriad of the company’s experiments.

“Machine learning is the secret sauce for the products of tomorrow,” said Greg Corrado, a senior research scientist with Google. “It no longer makes sense to have separate tools for researchers of machine learning and people who are developing real products. There should really be one set of tools that researchers can use to try out their crazy ideas, and if those ideas work, they can move them directly into products without having to rewrite code.”

This week Google announced the open source release of its software TensorFlow, the technology company’s second-generation machine learning system. 

“Part of the point of TensorFlow is to allow collaboration and communication between researchers,” Corrado said.

Exoplanet Boasts Winds Traveling at 5,400 MPH

Barring winds associated with tornados, the strongest wind gust recorded occurred on April 10, 1996 at Barrow Island, Australia. According to the World Meteorological Association, the record shattering wind gust was a result of Cyclone Olivia. The speed was 408 km/h, or around 253 mph. 

But beyond Earth, beyond the reaches of the solar system, an exoplanet is ravaged by winds seven times the speed of sound.

“Whilst we have previously known of wind on exoplanets, we have never before been able to directly measure and map a weather system,” said Tom Louden, of the university’s astrophysics group.

Part of a group called “Hot Jupiters,” HD 189733b is 10% larger than Jupiter but 180 times closer to its host star. Its surface temperature is 1,200 C.

DNA sequencing improved by slowing down

EPFL scientists have developed a method that improves the accuracy of DNA sequencing up to a thousand times. The method, which uses nanopores to read individual nucleotides, paves the way for better – and cheaper – DNA sequencing.

DNA sequencing is a technique that can determine exact sequence of a DNA molecule. One of the most critical biological and medical tools available today, it lies at the core of genome analysis. Reading the exact make-up of genes, scientists can detect mutations, or even identify different organisms. A powerful DNA sequencing method uses tiny, nano-sized pores that read DNA as it passes through. However, “nanopore sequencing” is prone to high inaccuracy because DNA usually passes through very fast. EPFL scientists have now discovered a viscous liquid that slows down the process up to a thousand times, vastly improving the method’s resolution and accuracy. The breakthrough is published in Nature Nanotechnology.

Tracking nanowalkers with light

Nanotechnology is taking its first steps. Researchers from the Max Planck Institute for Intelligent Systems in Stuttgart have developed a gold nanocylinder equipped with discrete DNA strands as ‘feet’ that can walk across a DNA origami platform. They are able to trace the movements of the nanowalker, which is smaller than the optical resolution limit, by exciting plasmons in the gold nanocylinder. Plasmons are collective oscillations of numerous electrons. The excitation changes the ray of light, thus allowing the researchers to actually observe the nanowalker. Their main objective is to use such mobile plasmonic nanoobjects to study how miniscule particles interact with light.

Nanomachines – i.e. mechanical devices with dimensions of nanometers – could one day carry out specific tasks in fields such as medicine, information processing, chemistry or scientific research, according to nanotechnology experts. Yet miniature machines that are thousands of times smaller than the diameter of a human hair pose significant challenges for scientists: firstly, the individual constituents merely consist of a small number of atoms; it is barely possible to handle such components, let alone assemble them in a precise manner. Moreover, the machines would then need to be supplied with energy. And ultimately, the researchers cannot simply check to see if their device is in fact working. The microscopy techniques necessary for such observation are complex and require for example vacuum chambers, in which the devices would be destroyed. At the Max Planck Institute for Intelligent Systems in Stuttgart, a team of researchers including Chao Zhou and Xiaoyang Duan, headed by Laura Na Liu has now created a nanowalker that they can observe with the help of a nanooptical effect.

A (nano) wrench in the works

Hold up your two hands. They are identical in structure, but mirror opposites. No matter how hard you try, they can’t be superimposed onto each other. Or, as chemists would say, they have “chirality,” from the Greek word for hand. A molecule that is chiral comes in two identical, but opposite, forms—just like a left and right hand.

University of Vermont chemist Severin Schneebeli has invented a new way to use chirality to make a wrench. A nanoscale wrench. His team’s discovery allows them to precisely control nanoscale shapes and holds promise as a highly accurate and fast method of creating customized molecules.

This use of “chirality-assisted synthesis” is a fundamentally new approach to control the shape of large molecules — one of the foundational needs for making a new generation of complex synthetic materials, including polymers and medicines.

Tiny wires could provide a big energy boost

Wearable electronic devices for health and fitness monitoring are a rapidly growing area of consumer electronics; one of their biggest limitations is the capacity of their tiny batteries to deliver enough power to transmit data. Now, researchers at MIT and in Canada have found a promising new approach to delivering the short but intense bursts of power needed by such small devices.

The key is a new approach to making supercapacitors — devices that can store and release electrical power in such bursts, which are needed for brief transmissions of data from wearable devices such as heart-rate monitors, computers, or smartphones, the researchers say. They may also be useful for other applications where high power is needed in small volumes, such as autonomous microrobots.

The new approach uses yarns, made from nanowires of the element niobium, as the electrodes in tiny supercapacitors (which are essentially pairs of electrically conducting fibers with an insulator between). The concept is described in a paper in the journal ACS Applied Materials and Interfaces by MIT professor of mechanical engineering Ian W. Hunter, doctoral student Seyed M. Mirvakili, and three others at the University of British Columbia.

Automating big data analysis

Big data analysis consists of searching for buried patterns that have some kind of predictive power. But choosing which “features” of the data to analyze usually requires some human intuition. In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.

Massachusetts Institute of Technology (MIT) researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too. To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets. Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615.

In two of the three competitions, the predictions made by the Data Science Machine were 94% and 96% as accurate as the winning submissions. In the third, the figure was a more modest 87%. But where the teams of humans typically labored over their prediction algorithms for months, the Data Science Machine took somewhere between two and 12 hrs to produce each of its entries.

New report on energy-efficient computing

A report that resulted from a workshop jointly funded by the Semiconductor Research Corporation (SRC) and National Science Foundation (NSF) outlines key factors limiting progress in computing--particularly related to energy consumption--and novel research that could overcome these barriers.

The findings and recommendations in the report are in alignment with the nanotechnology-inspired Grand Challenge for Future Computing announced by the White House Office of Science and Technology Policy. The Grand Challenge calls for new approaches to produce computing systems capable of operating with the efficiency of the human brain. It also aligns with the National S

Energy efficiency is vital to improving performance at all levels. These levels range from devices and transistors to large information technology systems, and from small sensors at the edge of the Internet of Things to large data centers in cloud and supercomputing systems.