Computers Math

Simon Fraser University researchers have designed a remarkably fast engine that taps into a new kind of fuel — information.
The development of this engine, which converts the random jiggling of a microscopic particle into stored energy, is outlined in research published this week in the Proceedings of the National Academy of Sciences (PNAS) and could lead to significant advances in the speed and cost of computers and bio-nanotechnologies.
SFU physics professor and senior author John Bechhoefer says researchers’ understanding of how to rapidly and efficiently convert information into “work” may inform the design and creation of real-world information engines.
“We wanted to find out how fast an information engine can go and how much energy it can extract, so we made one,” says Bechhoefer, whose experimental group collaborated with theorists led by SFU physics professor David Sivak.
Engines of this type were first proposed over 150 years ago but actually making them has only recently become possible.
“By systematically studying this engine, and choosing the right system characteristics, we have pushed its capabilities over ten times farther than other similar implementations, thus making it the current best-in-class,” says Sivak.
The information engine designed by SFU researchers consists of a microscopic particle immersed in water and attached to a spring which, itself, is fixed to a movable stage. Researchers then observe the particle bouncing up and down due to thermal motion.
“When we see an upward bounce, we move the stage up in response,” explains lead author and PhD student Tushar Saha. “When we see a downward bounce, we wait. This ends up lifting the entire system using only information about the particle’s position.”
Repeating this procedure, they raise the particle “a great height, and thus store a significant amount of gravitational energy,” without having to directly pull on the particle.
Saha further explains that, “in the lab, we implement this engine with an instrument known as an optical trap, which uses a laser to create a force on the particle that mimics that of the spring and stage.”
Joseph Lucero, a Master of Science student adds, “in our theoretical analysis, we find an interesting trade-off between the particle mass and the average time for the particle to bounce up. While heavier particles can store more gravitational energy, they generally also take longer to move up.”
“Guided by this insight, we picked the particle mass and other engine properties to maximize how fast the engine extracts energy, outperforming previous designs and achieving power comparable to molecular machinery in living cells, and speeds comparable to fast-swimming bacteria,” says postdoctoral fellow Jannik Ehrich.
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From the branching pattern of leaf veins to the variety of interconnected pathways that spread the coronavirus, nature thrives on networks — grids that link the different components of complex systems. Networks underlie such real-life problems as determining the most efficient route for a trucking company to deliver life-saving drugs and calculating the smallest number of mutations required to transform one string of DNA into another.
Instead of relying on software to tackle these computationally intensive puzzles, researchers at the National Institute of Standards and Technology (NIST) took an unconventional approach. They created a design for an electronic hardware system that directly replicates the architecture of many types of networks.
The researchers demonstrated that their proposed hardware system, using a computational technique known as race logic, can solve a variety of complex puzzles both rapidly and with a minimum expenditure of energy. Race logic requires less power and solves network problems more rapidly than competing general- purposed computers.
The scientists, who include Advait Madhavan of NIST and the University of Maryland in College Park and Matthew Daniels and Mark Stiles of NIST, describe their work in Volume 17, Issue 3, May 2021 of the ACM Journal on Emerging Technologies in Computing Systems.
A key feature of race logic is that it encodes information differently from a standard computer. Digital information is typically encoded and processed using values of computer bits — a “1” if a logic statement is true and a “0” if it’s false. When a bit flips its value, say from 0 to 1, it means that a particular logic operation has been performed in order to solve a mathematical problem.
In contrast, race logic encodes and processes information by representing it as time signals — the time at which a particular group of computer bits transitions, or flips, from 0 to 1. Large numbers of bit flips are the primary cause of the large power consumption in standard computers. In this respect, race logic offers an advantage because signals encoded in time involve only a few carefully orchestrated bit flips to process information, requiring much less power than signals encoded as 0s or 1s. More

You enter a room and quickly scan the crowd to gain a sense of who’s there — how many men versus women. How reliable is your estimate?
Not very, according to new research from Duke University.
In an experimental study, researchers found that participants consistently erred in estimating the proportion of men and women in a group. And participants erred in a particular way: They overestimated whichever group was in the minority.
“Our attention is drawn to outliers,” said Mel W. Khaw, a postdoctoral research associate at Duke and the study’s lead author. “We tend to overestimate people who stand out in a crowd.”
For the study, which appears online in the journal Cognition, researchers recruited 48 observers ages 18-28. Participants were presented with a grid of 12 faces and were given just one second to glance at the grid. Study participants were then asked to estimate the number of men and women in the grid.
Participants accurately assessed homogenous groups — groups containing all men or all women. But if a group contained fewer women, say, participants overestimated the number of women present. More

Obsessive-compulsive disorder (OCD) in children and adolescents is associated with impaired education and worse general health later in life. Access to specialist treatment is often limited. According to a study from Centre for Psychiatry Research at Karolinska Institutet in Sweden and Region Stockholm, internet-delivered cognitive behavioural therapy (CBT) can be as effective as conventional CBT. The study, published in the journal JAMA, can help make treatment for OCD more widely accessible.
Obsessive-compulsive disorder (OCD) is a potentially serious mental disorder that normally debuts in childhood.
Symptoms include intrusive thoughts that trigger anxiety (obsessions), and associated repetitive behaviours (compulsions), which are distressing and time consuming.
Early diagnosis and treatment are essential to minimise the long-term medical and socioeconomic consequences of the disorder, including suicide risk.
The psychological treatment of OCD requires highly trained therapists and access to this kind of competence is currently limited to a handful of specialist centres across Sweden.
Earlier research has shown that while CBT helps a majority of young people who receive it, several years can pass between the onset of symptoms and receipt of treatment. More

As the use of artificial intelligence (AI) in health applications grows, health providers are looking for ways to improve patients’ experience with their machine doctors.
Researchers from Penn State and University of California, Santa Barbara (UCSB) found that people may be less likely to take health advice from an AI doctor when the robot knows their name and medical history. On the other hand, patients want to be on a first-name basis with their human doctors.
When the AI doctor used the first name of the patients and referred to their medical history in the conversation, study participants were more likely to consider an AI health chatbot intrusive and also less likely to heed the AI’s medical advice, the researchers added. However, they expected human doctors to differentiate them from other patients and were less likely to comply when a human doctor failed to remember their information.
The findings offer further evidence that machines walk a fine line in serving as doctors, said S. Shyam Sundar, James P. Jimirro Professor of Media Effects in the Donald P. Bellisario College of Communications and co-director of the Media Effects Research Laboratory at Penn State.
“Machines don’t have the ability to feel and experience, so when they ask patients how they are feeling, it’s really just data to them,” said Sundar, who is also an affiliate of Penn State’s Institute for Computational and Data Sciences (ICDS). “It’s possibly a reason why people in the past have been resistant to medical AI.”
Machines do have advantages as medical providers, said Joseph B. Walther, distinguished professor in communication and the Mark and Susan Bertelsen Presidential Chair in Technology and Society at UCSB. He said that, like a family doctor who has treated a patient for a long time, computer systems could — hypothetically — know a patient’s complete medical history. In comparison, seeing a new doctor or a specialist who knows only your latest lab tests might be a more common experience, said Walther, who is also director of the Center for Information Technology and Society at UCSB. More

People are more persuaded by the actual messages contained in social media posts than they are by how many others viewed the posts, a new study suggests.
Researchers found that when people watched YouTube videos either for or against e-cigarette use, their level of persuasion wasn’t directly affected by whether the video said it was viewed by more than a million people versus by fewer than 20.
What mattered for persuasion was viewers’ perception of the message as truthful and believable.
“There wasn’t a bandwagon effect in which people were persuaded by a video just because a lot of other people watched it,” said Hyunyi Cho, lead author of the study and professor of communication at The Ohio State University.
“The message itself was most important for persuasion.”
The study will appear in the June 2021 issue of the journal Media Psychology. More

A novel technique for studying vortices in quantum fluids has been developed by Lancaster physicists.
Andrew Guthrie, Sergey Kafanov, Theo Noble, Yuri Pashkin, George Pickett and Viktor Tsepelin, in collaboration with scientists from Moscow State University, used tiny mechanical resonators to detect individual quantum vortices in superfluid helium.
Their work is published in the current volume of Nature Communications.
This research into quantum turbulence is simpler than turbulence in the real world, which is observed in everyday phenomena such as surf, fast flowing rivers, billowing storm clouds, or chimney smoke. Despite the fact it is so commonplace and is found at every level, from the galaxies to the subatomic, it is still not fully understood.
Physicists know the fundamental Navier-Stokes Equations which govern the flow of fluids such as air and water, but despite centuries of trying, the mathematical equations still cannot be solved.
Quantum turbulence may provide the clues to an answer.
Turbulence in quantum fluids is much simpler than its “messy” classical counterpart, and being made up of identical singly-quantised vortices, can be thought of as providing an “atomic theory” of the phenomenon.
Unhelpfully, turbulence in quantum systems, for example in superfluid helium 4, takes place on microscopic scales, and so far scientists have not had tools with sufficient precision to probe eddies this small.
But now the Lancaster team, working at temperature of a few thousandths of a degree above absolute zero, has harnessed nanoscience to allow the detection of single quantum vortices (with core sizes on a par with atomic diameters) by using a nanoscale “guitar string “in the superfluid.
How the team does it is to trap a single vortex along the length of the “string” (a bar of around 100 nanometres across). The resonant frequency of the bar changes when a vortex is trapped, and thus the capture and release rate of vortices can be followed, opening a window into the turbulent structure.
Dr Sergey Kafanov who initiated this research said: “The devices developed have many other uses, one of which is to ping the end of a partially trapped vortex to study the nanoscale oscillations of the vortex core. Hopefully the studies will add to our insight into turbulence and may provide clues on how to solve these stubborn equations.”
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As more private data is stored and shared digitally, researchers are exploring new ways to protect data against attacks from bad actors. Current silicon technology exploits microscopic differences between computing components to create secure keys, but artificial intelligence (AI) techniques can be used to predict these keys and gain access to data. Now, Penn State researchers have designed a way to make the encrypted keys harder to crack.
Led by Saptarshi Das, assistant professor of engineering science and mechanics, the researchers used graphene — a layer of carbon one atom thick — to develop a novel low-power, scalable, reconfigurable hardware security device with significant resilience to AI attacks. They published their findings in Nature Electronics today (May 10).
“There has been more and more breaching of private data recently,” Das said. “We developed a new hardware security device that could eventually be implemented to protect these data across industries and sectors.”
The device, called a physically unclonable function (PUF), is the first demonstration of a graphene-based PUF, according to the researchers. The physical and electrical properties of graphene, as well as the fabrication process, make the novel PUF more energy-efficient, scalable, and secure against AI attacks that pose a threat to silicon PUFs.
The team first fabricated nearly 2,000 identical graphene transistors, which switch current on and off in a circuit. Despite their structural similarity, the transistors’ electrical conductivity varied due to the inherent randomness arising from the production process. While such variation is typically a drawback for electronic devices, it’s a desirable quality for a PUF not shared by silicon-based devices.
After the graphene transistors were implemented into PUFs, the researchers modeled their characteristics to create a simulation of 64 million graphene-based PUFs. To test the PUFs’ security, Das and his team used machine learning, a method that allows AI to study a system and find new patterns. The researchers trained the AI with the graphene PUF simulation data, testing to see if the AI could use this training to make predictions about the encrypted data and reveal system insecurities.
“Neural networks are very good at developing a model from a huge amount of data, even if humans are unable to,” Das said. “We found that AI could not develop a model, and it was not possible for the encryption process to be learned.”
This resistance to machine learning attacks makes the PUF more secure because potential hackers could not use breached data to reverse engineer a device for future exploitation, Das said. Even if the key could be predicted, the graphene PUF could generate a new key through a reconfiguration process requiring no additional hardware or replacement of components.
“Normally, once a system’s security has been compromised, it is permanently compromised,” said Akhil Dodda, an engineering science and mechanics graduate student conducting research under Das’s mentorship. “We developed a scheme where such a compromised system could be reconfigured and used again, adding tamper resistance as another security feature.”
With these features, as well as the capacity to operate across a wide range of temperatures, the graphene-based PUF could be used in a variety of applications. Further research can open pathways for its use in flexible and printable electronics, household devices and more.
Paper co-authors include Dodda, Shiva Subbulakshmi Radhakrishnan, Thomas Schranghamer and Drew Buzzell from Penn State; and Parijat Sengupta from Purdue University. Das is also affiliated with the Penn State Department of Materials Science and Engineering and the Materials Research Institute.
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