Computers Math

In 2018, industry and academic researchers revealed a potentially devastating hardware flaw that made computers and other devices worldwide vulnerable to attack.
Researchers named the vulnerability Spectre because the flaw was built into modern computer processors that get their speed from a technique called “speculative execution,” in which the processor predicts instructions it might end up executing and preps by following the predicted path to pull the instructions from memory. A Spectre attack tricks the processor into executing instructions along the wrong path. Even though the processor recovers and correctly completes its task, hackers can access confidential data while the processor is heading the wrong way.
Since Spectre was discovered, the world’s most talented computer scientists from industry and academia have worked on software patches and hardware defenses, confident they’ve been able to protect the most vulnerable points in the speculative execution process without slowing down computing speeds too much.
They will have to go back to the drawing board.
A team of University of Virginia School of Engineering computer science researchers has uncovered a line of attack that breaks all Spectre defenses, meaning that billions of computers and other devices across the globe are just as vulnerable today as they were when Spectre was first announced. The team reported its discovery to international chip makers in April and will present the new challenge at a worldwide computing architecture conference in June.
The researchers, led by Ashish Venkat, William Wulf Career Enhancement Assistant Professor of Computer Science at UVA Engineering, found a whole new way for hackers to exploit something called a “micro-op cache,” which speeds up computing by storing simple commands and allowing the processor to fetch them quickly and early in the speculative execution process. Micro-op caches have been built into Intel computers manufactured since 2011. More

Researchers have developed a brain-like computing device that is capable of learning by association.
Similar to how famed physiologist Ivan Pavlov conditioned dogs to associate a bell with food, researchers at Northwestern University and the University of Hong Kong successfully conditioned their circuit to associate light with pressure.
The research will be published April 30 in the journal Nature Communications.
The device’s secret lies within its novel organic, electrochemical “synaptic transistors,” which simultaneously process and store information just like the human brain. The researchers demonstrated that the transistor can mimic the short-term and long-term plasticity of synapses in the human brain, building on memories to learn over time.
With its brain-like ability, the novel transistor and circuit could potentially overcome the limitations of traditional computing, including their energy-sapping hardware and limited ability to perform multiple tasks at the same time. The brain-like device also has higher fault tolerance, continuing to operate smoothly even when some components fail.
“Although the modern computer is outstanding, the human brain can easily outperform it in some complex and unstructured tasks, such as pattern recognition, motor control and multisensory integration,” said Northwestern’s Jonathan Rivnay, a senior author of the study. “This is thanks to the plasticity of the synapse, which is the basic building block of the brain’s computational power. These synapses enable the brain to work in a highly parallel, fault tolerant and energy-efficient manner. In our work, we demonstrate an organic, plastic transistor that mimics key functions of a biological synapse.”
Rivnay is an assistant professor of biomedical engineering at Northwestern’s McCormick School of Engineering. He co-led the study with Paddy Chan, an associate professor of mechanical engineering at the University of Hong Kong. Xudong Ji, a postdoctoral researcher in Rivnay’s group, is the paper’s first author. More

Tomorrow’s cutting-edge technology will need electronics that can tolerate extreme conditions. That’s why a group of researchers led by Michigan State University’s Jason Nicholas is building stronger circuits today.
Nicholas and his team have developed more heat resilient silver circuitry with an assist from nickel. The team described the work, which was funded by the U.S. Department of Energy Solid Oxide Fuel Cell Program, on April 15 in the journal Scripta Materialia.
The types of devices that the MSU team is working to benefit — next-generation fuel cells, high-temperature semiconductors and solid oxide electrolysis cells — could have applications in the auto, energy and aerospace industries.
Although you can’t buy these devices off the shelf now, researchers are currently building them in labs to test in the real world, and even on other planets.
For example, NASA developed a solid oxide electrolysis cell that enabled the Mars 2020 Perseverance Rover to make oxygen from gas in the Martian atmosphere on April 22. NASA hopes this prototype will one day lead to equipment that allows astronauts to create rocket fuel and breathable air while on Mars.
To help such prototypes become commercial products, though, they’ll need to maintain their performance at high temperatures over long periods of time, said Nicholas, an associate professor in the College of Engineering. More

The field of soft robotics has exploded in the past decade, as ever more researchers seek to make real the potential of these pliant, flexible automata in a variety of realms, including search and rescue, exploration and medicine.
For all the excitement surrounding these new machines, however, UC Santa Barbara mechanical engineering professor Elliot Hawkes wants to ensure that soft robotics research is more than just a flash in the pan. “Some new, rapidly growing fields never take root, while others become thriving disciplines,” Hawkes said.
To help guarantee the longevity of soft robotics research, Hawkes, whose own robots have garnered interest for their bioinspired and novel locomotion and for the new possibilities they present, offers an approach that moves the field forward. His viewpoint, written with colleagues Carmel Majidi from Carnegie Mellon University and Michael T. Tolley of UC San Diego, is published in the journal Science Robotics.
“We were looking at publication data for soft robotics and noticed a phase of explosive growth over the last decade,” Hawkes said. “We became curious about trends like this in new fields, and how new fields take root.”
The first decade of widespread soft robotics research, according to the group, “was characterized by defining, inspiring and exploring,” as roboticists took to heart what it meant to create a soft robot, from materials systems to novel ways of navigating through and interacting with the environment.
However, the researchers argue, “for soft robotics to become a thriving, impactful field in the next decade, every study must make a meaningful contribution.” According to Hawkes, the long-term duration of a rapidly growing field is often a matter of whether the initial exploratory research matures. More

The Mayo Clinic data scientists who developed highly accurate computer modeling to predict trends for COVID-19 cases nationwide have new research that shows how important a high rate of vaccination is to reducing case numbers and controlling the pandemic.
Vaccination is making a striking difference in Minnesota and keeping the current level of positive cases from becoming an emergency that overwhelms ICUs and leads to more illness and death, according to a study published in Mayo Clinic Proceedings. The study, entitled “Quantifying the Importance of COVID-19 Vaccination to Our Future Outlook,” outlines how Mayo’s COVID-19 predictive modeling can assess future trends based on the pace of vaccination, and how vaccination trends are crucial to the future course of the pandemic.
The Mayo researchers estimate that a peak of more than 800 patients would be in hospital ICUs in Minnesota this spring if no vaccines had been developed. The projections take into account new variants of the SARS-CoV-2 virus as well as current public health measures and masking standards.
The predicted ICU census levels would be more than double the number of Minnesota COVID-19 patients who were hospitalized in ICUs on Dec. 1, at the height of the most recent surge last year.
“It is difficult to untangle how much of this elevated rate of spread right now is due to new variants as opposed to changes in social behavior,” the authors say, but “regardless of the reason, the absence of vaccinations in the current environment would have been likely to result in by far the largest surge to date.”
If Minnesota had achieved vaccination of 75% of the population by early April, the study estimates that the 7-day average of cases per 100,000 residents, the number of COVID-19 patients hospitalized and the number in ICUs would plummet by early July. “According to the model, this level of vaccination would completely suppress the growth (even in the face of the recent elevated spread rate) and immediately drive cases and hospitalizations down to very low levels,” the authors say.
The Mayo Clinic study was led by Curtis Storlie, Ph.D., and Sean Dowdy, M.D., whose team developed the computer model for forecasting COVID-19’s impact on hospital usage that has helped guide Mayo’s response to the pandemic. Mayo Clinic’s predictive modeling also has been shared with Minnesota public health leadership to help inform critical decisions over the past year.
Mayo Clinic’s forecasting of COVID-19 trends nationally is available online at the Mayo Clinic COVID-19 Resource Center (https://www.mayoclinic.org/coronavirus-covid-19). The Coronavirus Map tracking tool has county-by-county information on COVID-19 cases and trends nationwide.
When the pandemic emerged last year, Mayo Clinic data scientists developed predictive modeling to assess when and where COVID-19 hot spots would occur. The model accurately predicted the timing and magnitude of COVID-19 case and hospitalization surges, which enabled Mayo Clinic to prepare and assure it could provide the best care while keeping patients and staff safe.
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Materials provided by Mayo Clinic. Original written by Jay Furst. Note: Content may be edited for style and length. More

Although robotic devices are used in everything from assembly lines to medicine, engineers have a hard time accounting for the friction that occurs when those robots grip objects — particularly in wet environments. Researchers have now discovered a new law of physics that accounts for this type of friction, which should advance a wide range of robotic technologies.
“Our work here opens the door to creating more reliable and functional haptic and robotic devices in applications such as telesurgery and manufacturing,” says Lilian Hsiao, an assistant professor of chemical and biomolecular engineering at North Carolina State University and corresponding author of a paper on the work.
At issue is something called elastohydrodynamic lubrication (EHL) friction, which is the friction that occurs when two solid surfaces come into contact with a thin layer of fluid between them. This would include the friction that occurs when you rub your fingertips together, with the fluid being the thin layer of naturally occurring oil on your skin. But it could also apply to a robotic claw lifting an object that has been coated with oil, or to a surgical device that is being used inside the human body.
One reason friction is important is because it helps us hold things without dropping them.
“Understanding friction is intuitive for humans — even when we’re handling soapy dishes,” Hsiao says. “But it is extremely difficult to account for EHL friction when developing materials that controls grasping capabilities in robots.”
To develop materials that help control EHL friction, engineers would need a framework that can be applied uniformly to a wide variety of patterns, materials and dynamic operating conditions. And that is exactly what the researchers have discovered.
“This law can be used to account for EHL friction, and can be applied to many different soft systems — as long as the surfaces of the objects are patterned,” Hsiao says.
In this context, surface patterns could be anything from the slightly raised surfaces on the tips of our fingers to grooves in the surface of a robotic tool.
The new physical principle, developed jointly by Hsiao and her graduate student Yunhu Peng, makes use of four equations to account for all of the physical forces at play in understanding EHL friction. In the paper, the research team demonstrated the law in three systems: human fingers; a bio-inspired robotic fingertip; and a tool called a tribo-rheometer, which is used to measure frictional forces. Peng is first author of the paper.
“These results are very useful in robotic hands that have more nuanced controls for reliably handling manufacturing processes,” Hsiao says. “And it has obvious applications in the realm of telesurgery, in which surgeons remotely control robotic devices to perform surgical procedures. We view this as a fundamental advancement for understanding touch and for controlling touch in synthetic systems.”
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The burden of student loans in the U.S. continues to grow unabatedly, currently accounting for a total of $1.7 trillion in household debt among nearly 45 million borrowers. “The introduction of income-based repayment over the past decade has made student loans rather complicated products,” Paolo Guasoni of Dublin City University said. As borrowers navigate this complex process, they face long-term consequences; people with student debt are less likely to own homes or become entrepreneurs, and generally postpone their enrollment in graduate or professional studies. Though legislative reform is necessary to combat this problem on a grand scale, individual borrowers can take steps to repay their loans with minimal long-term costs.
In a paper that published in April in the SIAM Journal on Financial Mathematics, Guasoni — along with Yu-Jui Huang and Saeed Khalili (both of the University of Colorado, Boulder) — developed a strategy for minimizing the overall cost of repaying student loans. “In the literature, we found mostly empirical studies discussing what borrowers are doing,” Huang said. “But what we wanted to know was rather, how should a borrower repay to minimize debt burden?”
Students become responsible for repaying their loans a few months after they graduate or unenroll, and must contend with the loan growing at a national fixed interest rate. One option for borrowers is to repay their balances in full by a fixed maturity — the date at which a loan’s final payment is due. Another is to enroll in an income-based scheme, in which monthly payments are only due if the borrower has an income above a certain subsistence threshold. If payments are required, they are proportional to the amount the borrower makes above that threshold. After roughly 20 to 25 years, any remaining balance is forgiven but taxed as ordinary income. “The tension is between postponing payments until forgiveness and letting interest swell the loan balance over time,” Guasoni said. The tax cost of delaying payments increases exponentially with longer timeframes until forgiveness, potentially offsetting the supposed savings.
The intuitive approach for many borrowers may be to pay off small loans as quickly as possible, since even minimum payments would extinguish the balance by the end of its term, making forgiveness irrelevant. Similarly, one may wish to minimize the payments for a large loan through an income-based scheme, especially if the loan will be forgiven in a few years anyway. However, the situation is not always as simple as it seems. “The counterintuitive part is that, if your loan is large and forgiveness is far away, it may be better to maximize payments over the first few years to keep the loan balance from exploding,” Huang said. “Then you can switch to income-based repayment and take advantage of forgiveness.”
To investigate what is truly the optimal way to pay back a student loan, the authors created a mathematical model of a borrower who took out a federal student loan — the most common type of student loan — with a constant interest rate. The model assumes that the borrower is able to repay the loan under its original term and even possibly make additional payments; otherwise, they would have no choice but to enroll in an income-based scheme. Quickly paying off the loan leads to lower costs from compounding interest. However, the borrower’s motivation to do so is contradicted by the possibility of the remaining balance being forgiven and taxed in the future, which encourages them to delay payment until the forgiveness date.
The mathematical model revealed several possible approaches for a borrower who wishes to minimize the overall cost of their loan. “The optimal strategy is to either (i) repay the loan as quickly as possible [if the initial balance is sufficiently low], or (ii) maximize payments up to a critical horizon (possibly now) and then minimize them through income-based repayment,” Guasoni said. The critical horizon occurs when the benefits of forgiveness begin to outweigh the compounding costs of interest on the loan balance. For large loans with a high interest rate — which are common for professional degrees — the savings from the strategy of high initial payments followed by enrollment in an income-based scheme can be substantial, for those that are able to afford such a plan.
The authors provided an example of a dental school graduate with a balance of $300,000 in Direct PLUS loans that carry an interest rate of 7.08 percent (according to the American Dental Education Association, 83 percent of dental school graduates have student loan debt, with an average balance of $292,169). This graduate has a starting salary of $100,000 that will grow four percent annually, and is able to repay at most 30 percent of the income that they make above the subsistence level. If they kept up such maximal payments, they would repay the loan in less than 20 years with a total cost of $512,000.
The example graduate could also immediately enroll in income-based repayment, paying only 10 percent of the income that they make above subsistence. After 25 years, their balance would equal $1,053,000 due to compounding interest. This balance would be forgiven and taxed as income at a 40 percent rate, yielding a total cost of $524,000. Alternatively, the graduate could use the authors’ suggested strategy and repay 30 percent of their income above subsistence for around nine years, then switch to the income-based repayment scheme. The remaining balance to be forgiven after 25 years would then be $462,000, leading to a total cost from payments and tax of $490,000 — the lowest of all the strategies. The reduction in the balance through multiple years of high payments curbs the balance’s ensuing growth during the period of minimum payments.
Future research could further explore the more complicated factors of student debt repayment. The authors’ model is deterministic — it does not account for the fact that the interest rates could potentially change in the future. However, interest rates can increase or decrease, which may compel borrowers to refinance or delay payments. Further work is necessary to determine the influence of such changes on optimal debt repayment.
This research illuminated the way in which borrowers’ choices in their loan repayments can have a sizable impact on overall costs, especially given compounding interest. “If you have student loans, you should consider your specific options carefully and see what the total cost would be under different strategies,” Guasoni said. Huang agreed, noting that their proposed strategy may be especially beneficial for the large loans that are often held by law and dental school graduates. “Each loan is slightly different,” he said. “Our model does not capture every possible detail, but it helps to focus the attention on two possibilities: quickest full repayment or enrollment in an income-based scheme, possibly after a period of high payments.” A careful, mathematical consideration of the approach to loan repayment can help borrowers make decisions that will benefit them in the years to come. More

Scientists in China have developed a small, flexible device that can convert heat emitted from human skin to electrical power. In their research, presented April 29 in the journal Cell Reports Physical Science, the team showed that the device could power an LED light in real time when worn on a wristband. The findings suggest that body temperature could someday power wearable electronics such as fitness trackers.
The device is a thermoelectric generator (TEG) that uses temperature gradients to generate power. In this design, researchers use the difference between the warmer body temperature and the relatively cooler ambient environment to generate power.
“This is a field with great potential,” says corresponding author Qian Zhang of Harbin Institute of Technology, Shenzhen. “TEGs can recover energy that’s lost as waste heat and thus improve the rate of power utilization.”
Unlike traditional generators that use the energy of motion to produce power, thermoelectric generators have no moving parts, making them essentially maintenance free. These generators are installed on machines located in remote areas and on board space probes to supply energy.
Zhang and her colleagues have been working on designing thermoelectric generators for years. With wearable devices becoming increasingly popular in recent years, the team wanted to explore whether these reliable generators could replace traditional battery in these devices, including fitness trackers, smart watches, and biosensors.
“Don’t underestimate the temperature differences between our body and the environment — it’s small, but our experiment shows it can still generate power,” she says.
Conventional TEGs are usually rigid and can only withstand fewer than 200 instances of bending. Although the flexible kinds can meet the bending requirement, their performance tends to be inadequate. To overcome this limitation and make the device more adaptable to wearables, researchers attached the core electrical components to a stretchable and more adhesive polyurethane material. Tests showed that the device survived at least 10,000 instances of repeated bending without significant changes in performance.
In addition, commercially available TEGs rely heavily on rare metal bismuth that does not naturally occur in large quantities. The new design partially replaced it with a magnesium-based material, which can substantially lower the costs in large-scale production.
Researchers designed a prototype of a self-powered electronic system. They connected an LED to a TEG band measuring 4.5 in long and 1.1 in wide. Then, the team wrapped the TEG band around the wrist of someone whose body temperature measured at 92.9?F in ambient environmental conditions. With a temperature difference, the generator harvested heat given off from the skin and successfully lit up the LED.
“Our prototype already has good performance if it’s introduced to the market,” says corresponding author Feng Cao of Harbin Institute of Technology, Shenzhen. He adds that with the proper voltage converter, the system can power electronics such as smart watches and pulse sensors.
Looking forward, the team plans to further improve the design so the device can absorb heat more efficiently.
“There’s an increasing demand for greener energy, and TEGs fit right in, for they can turn wasted heat into power,” Cao says. “While, for example, solar energy can only be generated when there’s sun, TEGs can produce power in many scenarios — as long as there’s a temperature difference.”
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