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Saudi Women started removing their Burqa and they called that its our freedom and right, what you say?

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Saudi Women started removing their Burqa || They called its our freedom Saudi Women have now wants to change their life style and they wants to remove Burqa from their life. Most of them a...

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SMART PEN................

Now, smart pen that vibrates when you make spelling error.

Lernstift is a regular pen with real ink, but inside is a special motion sensor and a small battery-powered Linux computer with a WiFi chip.

Together those parts allow the pen to recognize specific movements, letter shapes and know a wide assortment of words. If it senses bad letter formation or messy handwriting, it will vibrate.

Users can choose between two functions:
Calligraphy Mode - pointing out flaws of form and legibility
or
Orthography Mode - recognizing words and comparing the word to a language database.

Uncertainty principle certainly big enough to see

Physicists have performed a rare practical demonstration that shows a principle of quantum physics at work on an object large enough to be seen by the naked eye.

The landmark study shows the so-called Heisenberg uncertainty principle at work in an experiment involving laser light bouncing between a pair of mirrors.

The Heisenberg principle is an overarching law of quantum mechanics which sets a limit to the accuracy with which certain pairs of a particle's properties, such as its position and momentum, can be measured. The more accurately you measure one of those properties, the less precisely the other can be known.

Although the principle is often thought of in conceptual terms, the latest study, reported in today's issue of Science, shows how it hindered measurement in a practical way.

The experiment by researchers from the University of Colorado is one of the few approaches that demonstrate the uncertainty principle in action, writes Gerard Milburn from the University of Queensland (UQ) in an accompanying editorial in Science.

The researchers set up an experimental system with two small mirrors facing each other in a chamber. The mirrors were carefully positioned so that a beam of laser light scientists shone into the chamber bounced between the mirrors at a resonant frequency.

Between the mirrors, the scientists positioned a thin refractive membrane attached to the apparatus in a way that allowed it to move back and forth. They then cooled the set-up to far below 0°C, to minimise the amount of natural quantum vibration within the membrane.

As they sent the laser bouncing between the mirrors, some photons were reflected by the membrane, while others passed through it. Each time the membrane reflected a photon, the membrane vibrated, effectively changing the resonant frequency of the light bouncing between the mirrors.

"When you play a guitar, you can put a little piece of steel on the strings, and moving the steel up and down changes the length of the string, and that changes the frequency.That's what this moving membrane is doing," explains Milburn, from UQ's Centre for Engineered Quantum Systems.

Whether individual photons passed through the membrane without disturbing it, or "bounced" off its surface and made it move was a random occurrence, creating an effect known as radiation pressure shot noise.
Measuring uncertainty

The next element in the experimental set up was a detector placed outside the pair of mirrors, which could detect photons that passed through the mirrors. In theory, scientists could measure the movement of the membrane by detecting changes in the small amount of light hitting the sensors.

But when the level of light in the chamber was low, the number of photons that passed through the mirrors to the detectors was also random.

"If the motion of the membrane is pretty small, those ordinary fluctuations in intensity just coming from the granular nature of light will mask the movement of the mirror," explains Milburn.

"The way to fix that is to turn the intensity of the laser up higher and higher, so the photon flux is enormous and the tiny changes in the membrane position can be seen above the random shot noise on the detector."

But turning up the intensity of the laser created another limit. The 'kicks' that photons were giving to the membrane inside became too large, again losing accuracy in the measurement.

"Balancing those two demands is precisely what leads to the Heisenberg uncertainty principle in this experiment," says Milburn.

"In trying to monitor the position of this mirror as carefully as they possibly can, they have to keep cranking up the power so they get really good signal to noise ratio. But then, as the mirror vibrates, the intensity changes by a lot, and the net effect is that this random force gets bigger."

In other words, trying to measure one property of the photons more precisely limited the scientists' ability to measure the other property.

"This new optomechanical technology allows them to get right down to the quantum ground state of system where they are at the Heisenberg limit at the very beginning, so they can see this trade-off happening right away," says Milburn.

The robotic equivalent of a Swiss army knife - MIT News Office Reconfigurable robot a step toward something that can become almost anything.

Spending a day in someone else’s shoes can help us to learn what makes them tick. Now the same approach is being used to develop a better understanding between humans and robots, to enable them to work together as a team.

Robots are increasingly being used in the manufacturing industry to perform tasks that bring them into closer contact with humans. But while a great deal of work is being done to ensure robots and humans can operate safely side-by-side, more effort is needed to make robots smart enough to work effectively with people, says Julie Shah, an assistant professor of aeronautics and astronautics at MIT and head of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL).

“People aren’t robots, they don’t do things the same way every single time,” Shah says. “And so there is a mismatch between the way we program robots to perform tasks in exactly the same way each time and what we need them to do if they are going to work in concert with people.”

Most existing research into making robots better team players is based on the concept of interactive reward, in which a human trainer gives a positive or negative response each time a robot performs a task.

However, human studies carried out by the military have shown that simply telling people they have done well or badly at a task is a very inefficient method of encouraging them to work well as a team.

So Shah and PhD student Stefanos Nikolaidis began to investigate whether techniques that have been shown to work well in training people could also be applied to mixed teams of humans and robots. One such technique, known as cross-training, sees team members swap roles with each other on given days. “This allows people to form a better idea of how their role affects their partner and how their partner’s role affects them,” Shah says.

In a paper to be presented at the International Conference on Human-Robot Interaction in Tokyo in March, Shah and Nikolaidis will present the results of experiments they carried out with a mixed group of humans and robots, demonstrating that cross-training is an extremely effective team-building tool.

To allow robots to take part in the cross-training experiments, the pair first had to design a new algorithm to allow the devices to learn from their role-swapping experiences. So they modified existing reinforcement-learning algorithms to allow the robots to take in not only information from positive and negative rewards, but also information gained through demonstration. In this way, by watching their human counterparts switch roles to carry out their work, the robots were able to learn how the humans wanted them to perform the same task.

Each human-robot team then carried out a simulated task in a virtual environment, with half of the teams using the conventional interactive reward approach, and half using the cross-training technique of switching roles halfway through the session. Once the teams had completed this virtual training session, they were asked to carry out the task in the real world, but this time sticking to their own designated roles.

Shah and Nikolaidis found that the period in which human and robot were working at the same time — known as concurrent motion — increased by 71 percent in teams that had taken part in cross-training, compared to the interactive reward teams. They also found that the amount of time the humans spent doing nothing — while waiting for the robot to complete a stage of the task, for example — decreased by 41 percent.

What’s more, when the pair studied the robots themselves, they found that the learning algorithms recorded a much lower level of uncertainty about what their human teammate was likely to do next — a measure known as the entropy level — if they had been through cross-training.

Finally, when responding to a questionnaire after the experiment, human participants in cross-training were far more likely to say the robot had carried out the task according to their preferences than those in the reward-only group, and reported greater levels of trust in their robotic teammate. “This is the first evidence that human-robot teamwork is improved when a human and robot train together by switching roles, in a manner similar to effective human team training practices,” Nikolaidis says.

Shah believes this improvement in team performance could be due to the greater involvement of both parties in the cross-training process. “When the person trains the robot through reward it is one-way: The person says ‘good robot’ or the person says ‘bad robot,’ and it’s a very one-way passage of information,” Shah says. “But when you switch roles the person is better able to adapt to the robot’s capabilities and learn what it is likely to do, and so we think that it is adaptation on the person’s side that results in a better team performance.”

The work shows that strategies that are successful in improving interaction among humans can often do the same for humans and robots, says Kerstin Dautenhahn, a professor of artificial intelligence at the University of Hertfordshire in the U.K. “People easily attribute human characteristics to a robot and treat it socially, so it is not entirely surprising that this transfer from the human-human domain to the human-robot domain not only made the teamwork more efficient, but also enhanced the experience for the participants, in terms of trusting the robot,” Dautenhahn says.

MIT engineers have created genetic circuits in bacterial cells that not only perform logic functions, but also remember the results, which are encoded in the cell’s DNA and passed on for dozens of generations.

The circuits, described in the Feb. 10 online edition of Nature Biotechnology, could be used as long-term environmental sensors, efficient controls for biomanufacturing, or to program stem cells to differentiate into other cell types.

“Almost all of the previous work in synthetic biology that we’re aware of has either focused on logic components or on memory modules that just encode memory. We think complex computation will involve combining both logic and memory, and that’s why we built this particular framework to do so,” says Timothy Lu, an MIT assistant professor of electrical engineering and computer science and biological engineering and senior author of the Nature Biotechnology paper.

Lead author of the paper is MIT postdoc Piro Siuti. Undergraduate John Yazbek is also an author.

More than logic

Synthetic biologists use interchangeable genetic parts to design circuits that perform a specific function, such as detecting a chemical in the environment. In that type of circuit, the target chemical would generate a specific response, such as production of green fluorescent protein (GFP).

Circuits can also be designed for any type of Boolean logic function, such as AND gates and OR gates. Using those kinds of gates, circuits can detect multiple inputs. In most of the previously engineered cellular logic circuits, the end product is generated only as long as the original stimuli are present: Once they disappear, the circuit shuts off until another stimulus comes along.

Lu and his colleagues set out to design a circuit that would be irreversibly altered by the original stimulus, creating a permanent memory of the event. To do this, they drew on memory circuits that Lu and colleagues designed in 2009. Those circuits depend on enzymes known as recombinases, which can cut out stretches of DNA, flip them, or insert them. Sequential activation of those enzymes allows the circuits to count events happening inside a cell.

Lu designed the new circuits so that the memory function is built into the logic gate itself. With a typical cellular AND gate, the two necessary inputs activate proteins that together turn on expression of an output gene. However, in the new circuits, the inputs stably alter regions of DNA that control GFP production. These regions, known as promoters, recruit the cellular proteins responsible for transcribing the GFP gene into messenger RNA, which then directs protein assembly.

For example, in one circuit described in the paper, two DNA sequences called terminators are interposed between the promoter and the output gene (GFP, in this case). Each of these terminators inhibits the transcription of the output gene and can be flipped by a different recombinase enzyme, making the terminator inactive.

Each of the circuit’s two inputs turns on production of one of the recombinase enzymes needed to flip a terminator. In the absence of either input, GFP production is blocked. If both are present, both terminators are flipped, resulting in their inactivation and subsequent production of GFP.

Once the DNA terminator sequences are flipped, they can’t return to their original state — the memory of the logic gate activation is permanently stored in the DNA sequence. The sequence also gets passed on for at least 90 generations. Scientists wanting to read the cell’s history can either measure its GFP output, which will stay on continuously, or if the cell has died, they can retrieve the memory by sequencing its DNA.

Using this design strategy, the researchers can create all two-input logic gates and implement sequential logic systems. “It’s really easy to swap things in and out,” says Lu, who is also a member of MIT’s Synthetic Biology Center. “If you start off with a standard parts library, you can use a one-step reaction to assemble any kind of function that you want.”

Long-term memory

Such circuits could also be used to create a type of circuit known as a digital-to-analog converter. This kind of circuit takes digital inputs — for example, the presence or absence of single chemicals — and converts them to an analog output, which can be a range of values, such as continuous levels of gene expression.

For example, if the cell has two circuits, each of which expresses GFP at different levels when they are activated by their specific input, those inputs can produce four different analog output levels. Moreover, by measuring how much GFP is produced, the researchers can figure out which of the inputs were present.

That type of circuit could offer better control over the production of cells that generate biofuels, drugs or other useful compounds. Instead of creating circuits that are always on, or using promoters that need continuous inputs to control their output levels, scientists could transiently program the circuit to produce at a certain level. The cells and their progeny would always remember that level, without needing any more information.

Used as environmental sensors, such circuits could also provide very precise long-term memory. “You could have different digital signals you wanted to sense, and just have one analog output that summarizes everything that was happening inside,” Lu says.

This platform could also allow scientists to more accurately control the fate of stem cells as they develop into other cell types. Lu is now working on engineering cells to follow sequential development steps, depending on what kinds of inputs they receive from the environment.

Michael Jewett, an assistant professor of chemical and biological engineering at Northwestern University, says the new design represents a “huge advancement in DNA-encoded memory storage.”

“I anticipate that the innovations reported here will help to inspire larger synthetic biology efforts that push the limits of engineered biological systems,” says Jewett, who was not involved in the research.

The research was funded by the Office of Naval Research and the Defense Advanced Research Projects Agency.

Scientists Create 'Building Block' of Quantum Networks

A proof-of-concept device that could pave the way for on-chip optical quantum networks has been created by a group of researchers from the US.

Presenting the device February 8, in the Institute of Physics and German Physical Society's New Journal of Physics, it has been described as the "building block of future quantum networks."

In an optical quantum network, information is carried between points by photons -- the basic unit of light. There is a huge potential for this type of network in the field of quantum computing and could enable computers that are millions of times faster at solving certain problems than what we are used to today.

This new device, which combines a single nitrogen-vacancy centre in diamond with an optical resonator and an optical waveguide, could potentially become the memory or the processing element of such a network.

A nitrogen-vacancy centre is a defect in the lattice structure of diamond where one of the carbon atoms is replaced by a nitrogen atom and the nearest neighbour carbon atom is missing. The nitrogen-vacancy centre has the property of photoluminescence, whereby a substance absorbs photons from a source and then subsequently emits photons.

The emitted photons are special in that they are correlated, or entangled, with the nitrogen-vacancy centre that they came from, which as the researchers state is crucial for future experiments that will look to examine this correlation. You cannot get these correlated photons from a normal light source.

In this device, the photons are produced from a nitrogen-vacancy centre within a diamond microring resonator. The nitrogen-vacancy centre is located inside the diamond resonator as it is more likely to emit photons than when it is located in the waveguide or just in plain diamond. Moreover, the photons emitted in the resonator are easier to couple into an on-chip waveguide.

The cotton bud-shaped waveguide sends the photons out into a desired direction through gratings at either end.

"One of the holy grails in quantum photonics is to develop networks where optical quantum emitters are interconnected via photons," said lead author of the study Andrei Faraon.

"In this work we take the first step and demonstrate that photons -- the information carriers -- from a single nitrogen-vacancy centre can be coupled to an optical resonator and then further coupled to a photonic waveguide. We hope that multiple devices of this kind will be interconnected in a photonic network on a chip."

The study, undertaken by researchers from the California Institute of Technology, Hewlett Packard Laboratories and University of Washington, tested the device by cooling it to temperatures below 10K and shining a green laser onto the nitrogen vacancy to evoke photoluminescence.

The entire device was etched in a diamond membrane that was around 300 nanometres thick.

Astronomers used 150 blazars (green dots) to detect photons from the universe’s first stars. The map shows the sky in gamma rays, with the Milky Way shown in orange.
Credit: NASA, DOE, Fermi LAT Collaboration

Astronomers spot leftover light from ancient stars http://ow.ly/eYxjz

The ancient arthropod Fuxianhuia protensa (nearly intact specimen shown) lived in the Cambrian period about 520 million years ago. The preserved brain tissue in one F. protensa fossil (inset) suggests arthropods evolved complex nervous systems early in their history. Credit: Main: X. Ma/Natural History Museum, inset: N. Strausfeld/Univ. of Arizona

Early arthropod had a fancy brain: http://www.sciencenews.org/view/generic/id/345680/description/Early_arthropod_had_a_fancy_brain