Monthly Archives: April 2016

Wearable fitness tracker

The wearable device industry is estimated to grow to more than $30 billion by 2020. These sensors, often worn as bracelets or clips, count the number of steps we take each day; the number of hours we sleep; and monitor our blood pressure, heart rate, pulse and blood sugar levels.

The list of biophysical functions these devices can measure is growing rapidly. “But nobody has yet figured out a way to translate the information gathered by these devices into measures of health and longevity, let alone monetize this information — until now,” says S. Jay Olshansky, professor of epidemiology and biostatistics at the University of Illinois at Chicago School of Public Health and chief scientist at Lapetus Solutions, who is lead author on the paper. The researchers report that for the first time, the trillions of data points collected by wearable sensors can now be translated into empirically-verified measures of health risks and longevity — measures that have significant financial value to third parties like mortgage lenders, life insurance companies, marketers and researchers.

In the study, Olshansky and colleagues use the number of steps taken daily — a measure collected by almost all wearable sensors — and show how, using scientifically verified formulas, the step data can be translated into measures of health risk. By combining step count with age, sex, height, weight, walking speed, stride length, steps per mile and calories burned per step, they can derive the reduction in risk of death and expected gain in life expectancy and healthy-life expectancy if that same level of physical activity — in this case walking — is continued.

“In effect, we can take the data collected by your Fitbit and translate that into scientifically verified measures of health risk,” Olshansky said. “For example, we know that a 65-year-old, 5-foot-6-inch male weighing 175 pounds will reduce his risk of death by 33 percent if he regularly walks at a pace of 4 miles per hour,” Olshansky said. “The fact that it significantly reduces this man’s risk of death is valuable to the person walking, and also valuable to companies interested in interacting with someone with his level of daily physical activity.”

In the new health-data economy, your health information, once processed into longevity and health risk, will have a market.

“Imagine getting paid to upload your wearable sensor information to a new health data cloud,” Olshansky said. “Not only would researchers and companies be interested, but your own physician could access the data at your next physical to see, in effect, how you’d ‘driven’ your body since your last visit. “That information would provide a much better, more accurate picture of your overall health than the snapshot you get from blood and urine collected on the day of your once-a-year checkup.”

Computer may someday save us billions

Now, an entirely new type of computer that blends optical and electrical processing, reported Oct. 20 in the journal Science, could get around this impending processing constraint and solve those problems. If it can be scaled up, this non-traditional computer could save costs by finding more optimal solutions to problems that have an incredibly high number of possible solutions.

“This is a machine that’s in a sense the first in its class, and the idea is that it opens up a sub-field of research in the area of non-traditional computing machines,” said Peter McMahon, postdoctoral scholar in applied physics and co-author of the paper. “There are many, many questions that this development raises and we expect that over the next few years, several groups are going to be investigating this class of machine and looking into how this approach will pan out.”

The traveling salesman problem

There is a special type of problem — called a combinatorial optimization problem — that traditional computers find difficult to solve, even approximately. An example is what’s known as the “traveling salesman” problem, wherein a salesman has to visit a specific set of cities, each only once, and return to the first city, and the salesman wants to take the most efficient route possible. This problem may seem simple but the number of possible routes increases extremely rapidly as cities are added, and this underlies why the problem is difficult to solve.

“Those problems are challenging for standard computers, even supercomputers, because as the size grows, at some point, it takes the age of the universe to search through all the possible solutions,” said Alireza Marandi, a former postdoctoral scholar at Stanford and co-author of the study. “This is true even with a supercomputer because the growth in possibilities is so fast.”

It may be tempting to simply give up on the traveling salesman, but solving such hard optimization problems could have enormous impact in a wide range of areas. Examples include finding the optimal path for delivery trucks, minimizing interference in wireless networks, and determining how proteins fold. Even small improvements in some of these areas could result in massive monetary savings, which is why some scientists have spent their careers creating algorithms that produce very good approximate solutions to this type of problem.

An Ising machine

The Stanford team has built what’s called an Ising machine, named for a mathematical model of magnetism. The machine acts like a reprogrammable network of artificial magnets where each magnet only points up or down and, like a real magnetic system, it is expected to tend toward operating at low energy.

The theory is that, if the connections among a network of magnets can be programmed to represent the problem at hand, once they settle on the optimal, low-energy directions they should face, the solution can be derived from their final state. In the case of the traveling salesman, each artificial magnet in the Ising machine represents the position of a city in a particular path.

Rather than using magnets on a grid, the Stanford team used a special kind of laser system, known as a degenerate optical parametric oscillator, that, when turned on, will represent an upward- or downward-pointing “spin.” Pulses of the laser represent a city’s position in a path the salesman could take. In an earlier version of this machine (published two years ago), the team members extracted a small portion of each pulse, delayed it and added a controlled amount of that portion to the subsequent pulses. In traveling salesman terms, this is how they program the machine with the connections and distances between the cities. The pulse-to-pulse couplings constitute the programming of the problem. Then the machine is turned on to try to find a solution, which can be obtained by measuring the final output phases of the pulses.

The problem in this previous approach was connecting large numbers of pulses in arbitrarily complex ways. It was doable but required an added controllable optical delay for each pulse, which was costly and difficult to implement.

Scaling up

The latest Stanford Ising machine shows that a drastically more affordable and practical version could be made by replacing the controllable optical delays with a digital electronic circuit. The circuit emulates the optical connections among the pulses in order to program the problem and the laser system still solves it.

Nearly all of the materials used to make this machine are off-the-shelf elements that are already used for telecommunications. That, in combination with the simplicity of the programming, makes it easy to scale up. Stanford’s machine is currently able to solve 100-variable problems with any arbitrary set of connections between variables, and it has been tested on thousands of scenarios.

Nano diamonds may be boost for quantum computing

Currently, computers use binary logic, in which each binary unit — or bit — is in one of two states: 1 or 0. Quantum computing makes use of superposition and entanglement, allowing the creation of quantum bits — or qubits — which can have a vast number of possible states. Quantum computing has the potential to significantly increase computing power and speed.

A number of options have been explored for creating quantum computing systems, including the use of diamonds that have “nitrogen-vacancy” centers. That’s where this research comes in.

Normally, diamond has a very specific crystalline structure, consisting of repeated diamond tetrahedrons, or cubes. Each cube contains five carbon atoms. The NC State research team has developed a new technique for creating diamond tetrahedrons that have two carbon atoms; one vacancy, where an atom is missing; one carbon-13 atom (a stable carbon isotope that has six protons and seven neutrons); and one nitrogen atom. This is called the NV center. Each NV-doped nanodiamond contains thousands of atoms, but has only one NV center; the remainder of the tetrahedrons in the nanodiamond are made solely of carbon.

It’s an atomically small distinction, but it makes a big difference.

“That little dot, the NV center, turns the nanodiamond into a qubit,” says Jay Narayan, the John C. Fan Distinguished Chair Professor of Materials Science and Engineering at NC State and lead author of a paper describing the work. “Each NV center has two transitions: NV0 and NV-. We can go back and forth between these two states using electric current or laser. These nanodiamonds could serve as the basic building blocks of a quantum computer.”

To create these NV-doped nanodiamonds, the researchers start with a substrate, such as such as sapphire, glass or a plastic polymer. The substrate is then coated with amorphous carbon — elemental carbon that, unlike graphite or diamond, does not have a regular, well-defined crystalline structure. While depositing the film of amorphous carbon, the researchers bombard it with nitrogen ions and carbon-13 ions. The carbon is then hit with a laser pulse that raises the temperature of the carbon to approximately 4,000 Kelvin (or around 3,727 degrees Celsius) and is then rapidly quenched. The operation is completed within a millionth of a second and takes place at one atmosphere — the same pressure as the surrounding air. By using different substrates and changing the duration of the laser pulse, the researchers can control how quickly the carbon cools, which allows them to create the nanodiamond structures.

“Our approach reduces impurities; controls the size of the NV-doped nanodiamond; allows us to place the nanodiamonds with a fair amount of precision; and directly incorporates carbon-13 into the material, which is necessary for creating the entanglement required in quantum computing,” Narayan says. “All of the nanodiamonds are exactly aligned through the paradigm of domain matching epitaxy, which is a significant advance over existing techniques for creating NV-doped nanodiamonds.”

Machine learning automatically identifies

A new study shows that computer technology known as machine learning is up to 93 percent accurate in correctly classifying a suicidal person and 85 percent accurate in identifying a person who is suicidal, has a mental illness but is not suicidal, or neither. These results provide strong evidence for using advanced technology as a decision-support tool to help clinicians and caregivers identify and prevent suicidal behavior, says John Pestian, PhD, professor in the divisions of Biomedical Informatics and Psychiatry at Cincinnati Children’s Hospital Medical Center and the study’s lead author.

“These computational approaches provide novel opportunities to apply technological innovations in suicide care and prevention, and it surely is needed,” says Dr. Pestian. “When you look around health care facilities, you see tremendous support from technology, but not so much for those who care for mental illness. Only now are our algorithms capable of supporting those caregivers. This methodology easily can be extended to schools, shelters, youth clubs, juvenile justice centers, and community centers, where earlier identification may help to reduce suicide attempts and deaths.”

The study is published in the journal Suicide and Life-Threatening Behavior, a leading journal for suicide research.

Dr. Pestian and his colleagues enrolled 379 patients in the study between Oct. 2013 and March 2015 from emergency departments and inpatient and outpatient centers at three sites. Those enrolled included patients who were suicidal, were diagnosed as mentally ill and not suicidal, or neither — serving as a control group.

Each patient completed standardized behavioral rating scales and participated in a semi-structured interview answering five open-ended questions to stimulate conversation, such as “Do you have hope?” “Are you angry?” and “Does it hurt emotionally?”

Hardware to fight computer viruses

“The impact will potentially be felt in all computing domains, from mobile to clouds,” said Dmitry Ponomarev, professor of computer science at Binghamton University, State University of New York. Ponomarev is the principal investigator of a project titled “Practical Hardware-Assisted Always-On Malware Detection.”

More than 317 million pieces of new malware — computer viruses, spyware, and other malicious programs — were created in 2014 alone, according to work done by Internet security teams at Symantec and Verizon. Malware is growing in complexity, with crimes such as digital extortion (a hacker steals files or locks a computer and demands a ransom for decryption keys) becoming large avenues of cyber attack.

“This project holds the promise of significantly impacting an area of critical national need to help secure systems against the expanding threats of malware,” said Ponomarev. “[It is] a new approach to improve the effectiveness of malware detection and to allow systems to be protected continuously without requiring the large resource investment needed by software monitors.”

Countering threats has traditionally been left solely to software programs, but Binghamton researchers want to modify a computer’s central processing unit (CPU) chip — essentially, the machine’s brain — by adding logic to check for anomalies while running a program like Microsoft Word. If an anomaly is spotted, the hardware will alert more robust software programs to check out the problem. The hardware won’t be right about suspicious activity 100 percent of the time, but since the hardware is acting as a lookout at a post that has never been monitored before, it will improve the overall effectiveness and efficiency of malware detection.

“The modified microprocessor will have the ability to detect malware as programs execute by analyzing the execution statistics over a window of execution,” said Ponomarev. “Since the hardware detector is not 100-percent accurate, the alarm will trigger the execution of a heavy-weight software detector to carefully inspect suspicious programs. The software detector will make the final decision. The hardware guides the operation of the software; without the hardware the software will be too slow to work on all programs all the time.”

The modified CPU will use low complexity machine learning — the ability to learn without being explicitly programmed — to classify malware from normal programs, which is Yu’s primary area of expertise.

“The detector is, essentially, like a canary in a coal mine to warn software programs when there is a problem,” said Ponomarev. “The hardware detector is fast, but is less flexible and comprehensive. The hardware detector’s role is to find suspicious behavior and better direct the efforts of the software.”

Much of the work — including exploration of the trade-offs of design complexity, detection accuracy, performance and power consumption — will be done in collaboration with former Binghamton professor Nael Abu-Ghazaleh, who moved on to the University of California-Riverside in 2014.