Computers Math

If you’ve ever played the claw game at an arcade, you know how hard it is to grab and hold onto objects using robotics grippers. Imagine how much more nerve-wracking that game would be if, instead of plush stuffed animals, you were trying to grab a fragile piece of endangered coral or a priceless artifact from a sunken ship.
Most of today’s robotic grippers rely on embedded sensors, complex feedback loops, or advanced machine learning algorithms, combined with the skill of the operator, to grasp fragile or irregularly shaped objects. But researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have demonstrated an easier way.
Taking inspiration from nature, they designed a new type of soft, robotic gripper that uses a collection of thin tentacles to entangle and ensnare objects, similar to how jellyfish collect stunned prey. Alone, individual tentacles, or filaments, are weak. But together, the collection of filaments can grasp and securely hold heavy and oddly shaped objects. The gripper relies on simple inflation to wrap around objects and doesn’t require sensing, planning, or feedback control.
The research was published in the Proceedings of the National Academy of Sciences (PNAS).
“With this research, we wanted to reimagine how we interact with objects,” said Kaitlyn Becker, former graduate student and postdoctoral fellow at SEAS and first author of the paper. “By taking advantage of the natural compliance of soft robotics and enhancing it with a compliant structure, we designed a gripper that is greater than the sum of its parts and a grasping strategy that can adapt to a range of complex objects with minimal planning and perception.”
Becker is currently an Assistant Professor of Mechanical Engineering at MIT. More

A new study released in the American Cancer Society journal Cancer reconsiders guidelines for when to start screening with mammograms if a woman has a mother, sister, or daughter who was diagnosed with breast cancer.
Women with a first-degree family relative diagnosed with breast cancer, who are otherwise at average risk, are often advised to get screened 10 years earlier than the relative’s diagnosis age. However, there is little evidence to support the long-standing recommendation.
UC Davis Comprehensive Cancer Center researcher Diana Miglioretti joined Danielle Durham, with the Department of Radiology at University of North Carolina at Chapel Hill, and five other researchers on the study. They analyzed data from the Breast Cancer Surveillance Consortium on screening mammograms conducted from 1996-2016 to evaluate when screenings should begin for women with a family history of breast cancer.
More than 300,000 women were included in the national study. Researchers compared cumulative 5-year breast cancer incidence among women with and without a first-degree family history of breast cancer by relative’s age at diagnosis and screening age.
“The study concluded that a woman with a relative diagnosed at or before age 45 may wish to consider, in consultation with her doctor, initiating screening 5-8 years earlier than their relative’s diagnosis age, rather than a decade earlier. That puts them at a risk that is equal to that of an average-risk woman who is age 50, which is the most recommended age for starting mammograms,” said Durham.
BRCA gene mutation carriers may benefit from starting screenings earlier. Women ages 30-39 with more than one first-degree relative diagnosed with breast cancer may wish to consider genetic counseling.
Increasing the age for initiating screening could reduce the potential harms of starting breast cancer screenings too early. These include increased radiation exposure and false positive results that require women to return to the clinic for diagnostic imaging and possibly invasive procedures, but do not result in a breast cancer diagnosis. The earlier a woman starts receiving mammograms, the more screenings they will undergo over their lifetime — and that increases the chances of experiencing these harms.
“Mammography also may not perform as well in younger women because they are more likely to have dense breasts which increase the difficulty of finding cancer on the images and results in more false-positives,” Miglioretti said.
The other authors on this study include Linn A. Abraham, Kaiser Permanente Washington Health Research Institute; Megan C. Roberts, UNC Eshelman School of Pharmacy; Carly P. Khan, Patient-Centered Outcomes Research Institute; Robert A. Smith, American Cancer Society and Karla Kerlikowske, UCSF Health. Miglioretti is an affiliate investigator with UC Davis Center for Healthcare Policy and Research and Kaiser Permanente Washington Health Research.
The study was supported through funding by the Cancer Prevention Fellowship Program, Division of Cancer Prevention and the National Cancer Institute (NCI) at the National Institutes of Health. Data collection by the Breast Cancer Surveillance Consortium was funded by the NCI (grant numbers P01CA154292, U54CA163303 and PCS-1504-30370).
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Materials provided by University of California – Davis Health. Original written by Stephanie Winn. Note: Content may be edited for style and length. More

Quantum computers are powerful computational devices that rely on quantum mechanics, or the science of how particles like electrons and atoms interact with the world around them. These devices could potentially be used to solve certain kinds of computational problems in a much shorter amount of time. Scientists have long hoped that quantum computing could be the next great advance in computing; however, existing limitations have prevented the technology from hitting its true potential. For these computers to work, the basic unit of information integral to their operation, known as quantum bits, or qubits, need to be stable and fast.
Qubits are represented both by simple binary quantum states and by various physical implementations. One promising candidate is a trapped electron that levitates in a vacuum. However, controlling the quantum states, especially the vibrational motions, of trapped electrons can be difficult.
In a paper published in Physical Review Research, researchers identified possible solutions to some of the limitations of qubits for quantum computing. They looked at two different hybrid quantum systems: an electron-superconducting circuit and an electron-ion coupled system. Both systems were able to control the temperature and the movement of the electron.
“We found a way to cool down and measure the motion of an electron levitated in a vacuum, or a trapped electron, both in the quantum regime,” said Assistant Professor Alto Osada at the Komaba Institute for Science at the University of Tokyo. “With the feasibility of quantum-level control of the motion of trapped electrons, the trapped electron becomes more promising and attractive for quantum-technology applications, such as quantum computing.”
The proposed systems that the researchers focused on included an electron trapped in a vacuum called a Paul trap interacting with superconducting circuits and a trapped ion. Because ions are positively charged and electrons are negatively charged, when they are trapped together, they move toward each other because of a phenomenon called Coulomb attraction. Because the electron has such a light mass, the interactions between the electron and circuit and the electron and the ion were particularly strong. They also found that they were able to control the temperature of the electron using microwave fields and optical lasers.
Another important metric that the researchers used to measure the success of their calculations was the phonon mode of the electron. Phonon refers to a unit of energy that characterizes a vibration, or, in this case, the oscillation of the trapped electron. The desirable result was a single-phonon readout and ground-state cooling. Ground-state cooling refers to the frozen state of the electron. Researchers were able to accomplish these through their two hybrid systems they analyzed. “Highly efficient and high-fidelity quantum operations are available in the trapped-electron system,” said Osada. “This novel system manifests itself as a new playground for the development of quantum technologies.”
Looking ahead, researchers note that additional experimental research will need to be done to see if their methods can be implemented and applied to quantum computing. For example, they plan to demonstrate their idea with a proof-of-concept experiment. “We are planning to examine our schemes using electrons trapped in a microwave cavity,” said Osada. “Through this research, we will be able to get another step closer toward precise quantum operations and toward the implementation of quantum computation.”
The JST ERATO MQM project, JSPS KAKENHI and JST SPRING supported this research.
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We often conceive of learning through the lens of cramming for an exam or teaching a dog to sit, but humans and other mammals aren’t the only entities capable of adapting to their environment — schools of fish, robots, and even our genes can learn new behaviors, explain Jan De Houwer and Sean Hughes (Ghent University) in a new Perspectives on Psychological Science article. Embracing a broader definition of learning that includes any behavioral adaption developed in response to regular features of an environment could help researchers collaborate across the fields of psychology, computer science, sociology, and genetics, De Houwer explained in an interview.
“Most people think of learning as some kind of mechanism for the storage of new information, but this makes it very difficult to compare learning in different systems because different systems probably use different mechanisms for storing information,” De Houwer said. “We define learning as changes in the way a system responds to its environment — that is, as learned behavior.”
Much like Darwin’s theory of evolution, De Houwer and Hughes’ functional definition of learning focuses on how systems adapt to their environment, regardless of the mechanisms through which those adaptions may occur. The “system” in question could be an individual organism, a part of an organism such as a gene or the spinal cord, or a community of organisms. In fact, De Houwer added, evolution itself could be conceived of as a form of learning in which a species of animal is seen as a system that adapts to its environment.
“Because our definition of learning is ‘mechanism-free,’ it allows for interactions between scientists who study learning in different systems,” De Houwer said. “It breaks the barriers between different sciences and allows for an exchange of ideas that is bound to promote the study of learning in general.”
In addition to supporting comparisons between learning in different kinds of systems, this definition can also help researchers take a closer look at how these systems may influence each other’s learning, De Houwer and Hughes write. A corn plant may learn to become more drought resistant, for example, because its genes have an epigenetic response to dehydration that prompts its cells to retain more water, ultimately influencing the learned behavior of the entire plant.
Learning can also occur at the group level, such as in a school of fish, because of the learning of some but not all members in that group, De Houwer added. A fish at the head of a school may learn to avoid a shipwreck after repeatedly finding sharks there, for example, whereas fish at the back of the school may perform a similar behavior by simply continuing to follow the fish ahead of them without learning about the shipwreck.
This analysis can also be applied to the study of robots and artificial intelligence. Though each can be studied separately, the ability of a robot to learn how to navigate obstacles also depends on how its algorithm responds to the environment, the researchers explain.
It is important to note, however, that a system cannot be described as learning just because its behavior has changed in response to the environment. A system can only be said to have learned something if it changes the way it responds to a stimulus as the result of regularities in its environment, such as repeated exposure to a stimulus or the co-occurrence of stimuli, De Houwer said. Learning researchers examine the conditions under which regularities in the environment change behavior, he continued.
Developing a precise definition of learning can help scientists communicate existing findings and promote new interdisciplinary research, De Houwer and Hughes conclude.
“Definitions are tools at the service of better science,” they write. “Our definition allows scientists to share knowledge and thereby explore new ways of studying learning in different systems.”
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While most of us are never without our smartphones, robots may also soon become indispensable companions. It certainly seems so based on the recent experiments conducted by researchers in Japan, who developed a hand-held soft robot that can improve the experience of patients while undergoing potentially unpleasant medical procedures, such as injections.
During the campaign to encourage vaccination against COVID-19, public health officials recognized that some people are simply afraid of needles, which contributed to reduced vaccination rates. While the problems of patient anxiety and pain during medical procedures have been well studied, there remains a need to test and implement solutions to help patients.
In a recently published study in Scientific Reports, researchers at the University of Tsukuba have developed a wearable soft robot for patients to use during treatments, in an attempt to ease their pain. On being subjected to a moderate heat stimulus, the study participants who wore the robot experienced less pain than in the tests in which they did not wear the robot. “Our results suggest that the use of wearable soft robots may reduce fear as well as alleviate the perception of pain during medical treatments, including vaccinations,” says senior author Professor Fumihide Tanaka.
The soft, fur-covered robot the scientists called Reliebo was designed to be attached to the participant’s hand; it contained small airbags that could inflate in response to hand movements. The researchers tested its effectiveness under various conditions based on the clenching of the participant’s hand, while applying the painful thermal stimulus to the other arm that was not being used to hold the robot. The researchers also measured the levels of oxytocin and cortisol (which are biomarkers for stress) from the patients’ saliva samples. Additionally, subjective pain ratings were recorded using an assessment scale, and a survey test was conducted to evaluate the patients’ fear of injections and psychological state before and after the experiments.
The researchers found that holding the robot helped relieve the experience for patients regardless of the experimental conditions used, and speculated that the feelings of well-being that can be created by human touch may have also been activated by the robot. “It is well known that interpersonal touch can reduce pain and fear, and we believe that this effect can be achieved even with nonliving soft robots,” states Professor Tanaka. This may be useful when actual human contact is not feasible, such as during pandemics. Future versions of the robot might use a controlled gaze or even AR (augmented reality) technologies to help build a connection with the patient or distract them from pain perception in various situations.
This work was supported by JSPS KAKENHI Grant Number 20K21800 and 22K19784.
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Efficient pricing will be crucial to minimize energy costs for private operators who provide on-the-highway wireless charging for electric cars — and for consumers who will use this service, according to new Cornell University research in Applied Energy.
Employing dynamic pricing strategies in the marketplace could save consumers as much as 6% off the retail electricity price, according to the new paper, which envisions future wireless charging highways that allay so-called “range anxiety” about low batteries on longer trips.
“Electrifying transportation is great, since you can eliminate carbon emissions,” said senior author Oliver Gao, the Howard Simpson Professor of Civil and Environmental Engineering in Cornell Engineering. “You can energize your car while driving in the charging lane. But if you’re managing a charging highway that can provide energy to cars, you’re buying and selling electricity on an industrial scale. We’re trying to suggest a smart business strategy.”
Electricity prices can change drastically within a day, according to Gao, who is a faculty fellow at the Cornell Atkinson Center for Sustainability.
An efficient bidding strategy is crucial to minimizing the energy cost for operators of wireless charging roads. The primary goal of the new research is to design a competitive, price-sensitive demand bidding strategy for wireless charging road owners — who have electricity storage capacity.
The paper, “Bidding Strategy for Wireless Charging Roads with Energy Storage in Real-Time Electricity Markets,” designs an efficient, price-sensitive way for a wireless charging road to participate in a real-time electricity market. The research suggests an algorithm to predict the real-time electricity load on a charging highway, in order to evaluate a price forecast and electricity availability.
The proposed bidding strategy not only reduces the energy cost for operating a wireless charging road but helps to alleviate electricity load pressure on a power network.
“Our paper comes from the perspective of running a gas station,” Gao said. “If you’re running a charging highway — or if you get the contract to run a charging highway — you’re buying electricity and you’re selling electricity. It’s dynamic. You either buy an hour ahead of time and then you sell it one hour later or you bid on electricity, you submit your bid, buy it and then you sell it.”
Today, it takes a lot of time to charge an electric vehicle and — due to battery limitations — a car’s range may only be a few hundred miles. With wireless charging lanes, a driver may simply move into the charging lane, much like today’s high-occupancy vehicles move into special lanes to avoid dense traffic.
Lowering energy costs imposes less pressure on the existing power grid, according to the paper. “These two merits can bring broad benefits to our society,” Gao said. “Cost reduction in operating wireless charging roads is likely to attract more investment in constructing these roads and lower the corresponding charging price — promoting overall electric vehicle adoption.
“The alleviation of required pressure on power grid is great news to the power industry,” Gao said, “which already suffers significant strain on the existing infrastructure.”
In addition to Gao, the other authors are Jie Shi, lead, former Cornell postdoctoral researcher, and Nanpeng Yu, University of California, Riverside. The research was funded by Cornell University.
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Materials provided by Cornell University. Original written by Blaine Friedlander, courtesy of the Cornell Chronicle. Note: Content may be edited for style and length. More

Passive smartphone monitoring of people’s walking activity can be used to construct population-level models of health and mortality risk, according to a new study publishing October 20thin the open access journal PLOS Digital Health by Bruce Schatz of University of Illinois at Urbana-Champaign, USA, and colleagues.
Previous studies have used measures of physical fitness, including walk tests and self-reported walk pace, to predict individual mortality risk. These metrics focus on quality rather than quantity of movement; measuring an individual’s gait speed has become a standard practice for certain clinical settings, for example. The rise of passive smartphone activity monitoring opens the possibility for population-level analyses using similar metrics.
In the new study, researchers studied 100,000 participants in the UK Biobank national cohort who wore activity monitors with motion sensors for 1 week. While the wrist sensor is worn differently than how smartphone sensors are carried, their motion sensors can both be used to extract information on walking intensity from short bursts of walking — a daily living version of a walk test.
The team was able to successfully validate predictive models of mortality risk using only 6 minutes per day of steady walking collected by the sensor, combined with traditional demographic characteristics. The equivalent of gait speed calculated from this passively collected data was a predictor of 5-year mortality independent of age and sex (pooled C-index 0.72). The predictive models used only walking intensity to simulate smartphone monitors.
“Our results show passive measures with motion sensors can achieve similar accuracy to active measures of gait speed and walk pace,” the authors say. “Our scalable methods offer a feasible pathway towards national screening for health risk.”
Schatz adds, “I have spent a decade using cheap phones for clinical models of health status. These have now been tested on the largest national cohort to predict life expectancy at population scale.”
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Spectrometers are widely used throughout industry and research to detect and analyse light. Spectrometers measure the spectrum of light — its strength at different wavelengths, like the colours in a rainbow — and are an essential tool for identifying and analysing specimens and materials. Integrated on-chip spectrometers would be of great benefit to a variety of technologies, including quality inspection platforms, security sensors, biomedical analysers, healthcare systems, environmental monitoring tools, and space telescopes.
An international research team led by researchers at Aalto University has developed high-sensitivity spectrometers with high wavelength accuracy, high spectral resolution, and broad operation bandwidth, using only a single microchip-sized detector. The research behind this new ultra-miniaturised spectrometer was published today in the journal Science.
‘Our single-detector spectrometer is an all-in-one device. We designed this optoelectronic-lab-on-a-chip with artificial intelligence replacing conventional hardware, such as optical and mechanical components. Therefore, our computational spectrometer does not require separate bulky components or array designs to disperse and filter light. It can achieve a high resolution comparable to benchtop systems but in a much smaller package,’ says Postdoctoral Researcher Hoon Hahn Yoon.
‘With our spectrometer, we can measure light intensity at each wavelength beyond the visible spectrum using a device at our fingertips. The device is entirely electrically controllable, so it has enormous potential for scalability and integration. Integrating it directly into portable devices such as smartphones and drones could advance our daily lives. Imagine that the next generation of our smartphone cameras could be fitted with hyperspectral cameras that outperform colour cameras,’ he adds.
Shrinking computational spectrometers is essential for their use in chips and implantable applications. Professor Zhipei Sun, the head of the research team, says, ‘Conventional spectrometers are bulky because they need optical and mechanical components, so their on-chip applications are limited. There is an emerging demand in this field to improve the performance and usability of spectrometers. From this point of view, miniaturised spectrometers are very important to offer high performance and new functions in all fields of science and industry.’
Professor Pertti Hakonen adds that ‘Finland and Aalto have invested in photonics research in recent years. For example, there has been great support from the Academy of Finland’s Centre of Excellence on quantum technology, Flagship on Photonics Research and Innovation, InstituteQ, and the Otanano Infrastructure. Our new spectrometer is a clear demonstration of the success of these collaborative efforts. I believe that with further improvements in resolution and efficiency, these spectrometers could provide new tools for quantum information processing.’
In addition to Postdoctoral Researcher Hoon Hahn Yoon and Professors Zhipei Sun and Pertti Hakonen, the key Aalto members linked to the work included Postdoctoral Researchers Henry A. Fernandez and Faisal Ahmed, Doctoral Researchers Fedor Nigmatulin, Xiaoqi Cui, Md Gius Uddin, and Professor Harri Lipsanen. Professor Ethan D. Minot, from Oregon State University, joined this work as a visiting scholar at Aalto University for one year. The international research team led by Aalto university also included Professors Weiwei Cai (Shanghai Jiao Tong University), Zongyin Yang (Zhejiang University), Hanxiao Cui (Sichuan University), Kwanpyo Kim (Yonsei University), and Tawfique Hasan (University of Cambridge).
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