Lifeboat Foundation

A state-of-the-art brain-machine interface created by UC San Francisco neuroscientists can generate natural-sounding synthetic speech by using brain activity to control a virtual vocal tract—an anatomically detailed computer simulation including the lips, jaw, tongue, and larynx. The study was conducted in research participants with intact speech, but the technology could one day restore the voices of people who have lost the ability to speak due to paralysis and other forms of neurological damage.

Stroke, traumatic brain injury, and neurodegenerative diseases such as Parkinson’s disease, multiple sclerosis, and amyotrophic lateral sclerosis (ALS, or Lou Gehrig’s disease) often result in an irreversible loss of the ability to speak. Some people with severe speech disabilities learn to spell out their thoughts letter-by-letter using assistive devices that track very small eye or facial muscle movements. However, producing text or synthesized speech with such devices is laborious, error-prone, and painfully slow, typically permitting a maximum of 10 words per minute, compared to the 100–150 words per minute of natural speech.

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I suspect that if you asked an engineer at Intel about quantum computing, they probably wouldn’t want to know about it unless the chips could be fabricated using standard fabrication technology. Using standard processes means using electrons as the basis for quantum computing.

Electrons are lovely in many respects, but they are rather extroverted. It doesn’t matter what you do, they will run off and play with the neighbors. The constantly interacting electron does not look after its quantum state, so quantum information is rapidly lost, making processing really difficult. This makes the achievement of a quantum non-demolition measurement in an electron system rather remarkable.

Researchers from the University of Maryland have for the first time measured an effect that was predicted more than 40 years ago, called the Casimir torque.

When placed together in a vacuum less than the diameter of a bacterium (one micron) apart, two pieces of metal attract each other. This is called the Casimir effect. The Casimir torque—a related phenomenon that is caused by the same quantum electromagnetic effects that attract the materials—pushes the materials into a spin. Because it is such a tiny effect, the Casimir torque has been difficult to study. The research team, which includes members from UMD’s departments of electrical and computer engineering and physics and Institute for Research in Electronics and Applied Physics, has built an apparatus to measure the decades-old prediction of this phenomenon and published their results in the December 20th issue of the journal Nature.

“This is an interesting situation where industry is using something because it works, but the mechanism is not well-understood,” said Jeremy Munday, the leader of the research. “For LCD displays, for example, we know how to create twisted liquid crystals, but we don’t really know why they twist. Our study proves that the Casimir torque is a crucial component of liquid crystal alignment. It is the first to quantify the contribution of the Casimir effect, but is not the first to prove that it contributes.”

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A new brain-computer interface translates neurological signals into complete sentences.

Researchers from Osaka University have developed a technique for improving accuracy of laser beam shaping and wavefront obtained by conventional methods with no additional cost by optimizing virtual phase grating. The results of their research were published in Scientific Reports.

A high quality square flattop beam is in demand for various fields, such as uniform laser processing and medicine, as well as ultrahigh intensity laser applications for accelerators and nuclear fusion. Beam shape is key to realizing the laser’s potential abilities and effects. However, since beam shape and wavefront vary by laser, beam shaping is essential for producing the desired shapes to respond to various needs.

Static and adaptive beam shaping methods have been developed for various applications. With Diffractive Optical Element (DOE) as a static method, edge steepness and flatness are low and wavefront becomes deformed after shaping. (Figure 1 (a)) In addition, computer-generated hologram (CGH) as a typical adaptive method has the same difficulties.

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A KAIST research team developed a microfluidic-based drug screening chip that identifies synergistic interactions between two antibiotics in eight hours. This chip can be a cell-based drug screening platform for exploring critical pharmacological patterns of antibiotic interactions, along with potential applications in screening other cell-type agents and guidance for clinical therapies.

Antibiotic susceptibility testing, which determines types and doses of antibiotics that can effectively inhibit bacterial growth, has become more critical in recent years with the emergence of antibiotic-resistant pathogenic bacteria strains.

To overcome the antibiotic-resistant bacteria, combinatory therapy using two or more kinds of antibiotics has been gaining considerable attention. However, the major problem is that this therapy is not always effective; occasionally, unfavorable antibiotic pairs may worsen results, leading to suppressed antimicrobial effects. Therefore, combinatory testing is a crucial preliminary process to find suitable antibiotic pairs and their concentration range against unknown pathogens, but the conventional testing methods are inconvenient for concentration dilution and sample preparation, and they take more than 24 hours to produce results.

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The multiplication of integers is a problem that has kept mathematicians busy since Antiquity. The “Babylonian” method we learn at school requires us to multiply each digit of the first number by each digit of the second one. But when both numbers have a billion digits each, that means a billion times a billion or 10¹⁸ operations.

At a rate of a billion operations per second, it would take a computer a little over 30 years to finish the job. In 1971, the mathematicians Schönhage and Strassen discovered a quicker way, cutting calculation time down to about 30 seconds on a modern laptop. In their article, they also predicted that another algorithm—yet to be found—could do an even faster job. Joris van der Hoeven, a CNRS researcher from the École Polytechnique Computer Science Laboratory LIX, and David Harvey from the University of New South Wales (Australia) have found that algorithm.

They present their work in a new article that is available to the scientific community through the online HAL archive. But one problem raised by Schönhage et Strassen remains to be solved: proving that no quicker method exists. This poses a new challenge for theoretical computer science.

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Quantum computers can’t selectively forget information. A new algorithm for multiplication shows a way around that problem.

A team of researchers at NTT Corporation has developed a way to use light-based computer hardware that allows it to to compete with silicon. In their paper published in the journal Nature Photonics, the group describes their research, the devices they created and how well they worked.

Computer scientists have known for some time that the era of increasing computer speed by modifying silicon-based computer parts is coming to an end. To that end, many have turned to quantum computing as the way to speed up computers—but to date, such efforts have not led to useful machines and there is no guarantee they ever will. Because of that, others in the computer business are looking for other options, such as using light to move data around inside of computers instead of electrons. Currently, light is generally only used to carry data long distances. In this new effort, the researchers report that they have developed computing devices based partially on light that performed as well as electron-based hardware.

The idea of using only light as a data medium in computer hardware is still a long way off—instead, engineers are focusing on using light in areas where it seems feasible and electrons everywhere else. Because of that computer devices must be able to convert between the two mediums, a problem that until now has prevented such devices from being built. Prior efforts have required too much power to be feasible and the conversion process has been too slow. To get around both problems, the researchers developed a new kind of photonic crystal that was able to diffuse light in a way that allowed it to follow a designated path on demand and to also be absorbed when needed to be used for generating current. The crystal was also able to work in reverse.

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