Aurelia Butler

When fighting the spread of misinformation, social media platforms typically place most users in the passenger seat. Platforms often use machine-learning algorithms or human fact-checkers to flag false or misinforming content for users.
“Just because this is the status quo doesn’t mean it is the correct way or the only way to do it,” says Farnaz Jahanbakhsh, a graduate student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).
She and her collaborators conducted a study in which they put that power into the hands of social media users instead.
They first surveyed people to learn how they avoid or filter misinformation on social media. Using their findings, the researchers developed a prototype platform that enables users to assess the accuracy of content, indicate which users they trust to assess accuracy, and filter posts that appear in their feed based on those assessments.
Through a field study, they found that users were able to effectively assess misinforming posts without receiving any prior training. Moreover, users valued the ability to assess posts and view assessments in a structured way. The researchers also saw that participants used content filters differently — for instance, some blocked all misinforming content while others used filters to seek out such articles.
This work shows that a decentralized approach to moderation can lead to higher content reliability on social media, says Jahanbakhsh. This approach is also more efficient and scalable than centralized moderation schemes, and may appeal to users who mistrust platforms, she adds. More

New wearable electronics paired with artificial intelligence could transform screening for health problems.
Flexible, wearable electronics are making their way into everyday use, and their full potential is still to be realized. Soon, this technology could be used for precision medical sensors attached to the skin, designed to perform health monitoring and diagnosis. It would be like having a high-tech medical center at your instant beck and call.
Such a skin-like device is being developed in a project between the U.S. Department of Energy’s (DOE) Argonne National Laboratory and the University of Chicago’s Pritzker School of Molecular Engineering (PME). Leading the project is Sihong Wang, assistant professor in UChicago PME with a joint appointment in Argonne’s Nanoscience and Technology division.
Worn routinely, future wearable electronics could potentially detect possible emerging health problems — such as heart disease, cancer or multiple sclerosis — even before obvious symptoms appear. The device could also do a personalized analysis of the tracked health data while minimizing the need for its wireless transmission. “The diagnosis for the same health measurements could differ depending on the person’s age, medical history and other factors,” Wang said. “Such a diagnosis, with health information being continuously gathered over an extended period, is very data intensive.”
Such a device would need to collect and process a vast amount of data, well above what even the best smartwatches can do today. And it would have to do this data crunching with very low power consumption in a very tiny space.
To address that need, the team called upon neuromorphic computing. This AI technology mimics operation of the brain by training on past data sets and learning from experience. Its advantages include compatibility with stretchable material, lower energy consumption and faster speed than other types of AI. More

Researchers of Delft University of Technology, Vrije Universiteit Amsterdam and VSL have developed an alternative positioning system that is more robust and accurate than GPS, especially in urban settings. The working prototype that demonstrated this new mobile network infrastructure achieved an accuracy of 10 centimeter. This new technology is important for the implementation of a range of location-based applications, including automated vehicles, quantum communication and next-generation mobile communication systems. The results were published in Nature today.
A lot of our vital infrastructure relies on global navigation satellite systems such as the US GPS and EU Galileo. Yet these systems that rely on satellites have their limitations and vulnerabilities. Their radio signals are weak when received on Earth, and accurate positioning is no longer possible if the radio signals are reflected or blocked by buildings. “This can make GPS unreliable in urban settings, for instance” says Christiaan Tiberius of Delft University of Technology and coordinator of the project, “which is a problem if we ever want to use automated vehicles. Also, citizens and our authorities actually depend on GPS for many location-based applications and navigation devices. Furthermore, so far we had no back-up system.”
The aim of the project entitled SuperGPS was to develop an alternative positioning system that makes use of the mobile telecommunication network instead of satellites and that could be more robust and accurate than GPS. ‘We realized that with a few cutting-edge innovations, the telecommunication network could be transformed into a very accurate alternative positioning system that is independent of GPS,’ says Jeroen Koelemeij of Vrije Universiteit Amsterdam. “We have succeeded and have successfully developed a system that can provide connectivity just like existing mobile and Wi-Fi networks do, as well as accurate positioning and time distribution like GPS.”
An atomic clock
One of these innovations is to connect the mobile network to a very accurate atomic clock, so that it can broadcast perfectly timed messages for positioning, just like GPS satellites do with the help of the atomic clocks they carry on board. These connections are made through the existing fiber-optic network. “We had already been investigating techniques to distribute the national time produced by our atomic clocks to users elsewhere through the telecommunication network,” says Erik Dierikx of VSL. “With these techniques we can turn the network into a nationwide distributed atomic clock — with many new applications such as very accurate positioning through mobile networks. With the hybrid optical-wireless system that we have demonstrated now, in principle anyone can have wireless access to the national time produced at VSL. It basically forms an extremely accurate radio clock that is good to one billionth of a second.”
Furthermore, the system employs radio signals with a bandwidth much larger than commonly used. “Buildings reflect radio signals, which can confuse navigation devices. The large bandwidth of our system helps sorting out these confusing signal reflections, and enables higher positioning accuracy,” Gerard Janssen of Delft University of Technology explains. “At the same time, bandwidth within the radio spectrum is scarce and therefore expensive. We circumvent this by using a number of related small bandwidth radio signals spread over a large virtual bandwidth. This has the advantage that only a small fraction of the virtual bandwidth is actually used and the signals can be very similar to those of mobile phones.”
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Materials provided by Delft University of Technology. Note: Content may be edited for style and length. More

Researchers at North Carolina State University have created a ring-shaped soft robot capable of crawling across surfaces when exposed to elevated temperatures or infrared light. The researchers have demonstrated that these “ringbots” are capable of pulling a small payload across the surface — in ambient air or under water, as well as passing through a gap that is narrower than its ring size.
The ringbots are made of liquid crystal elastomers in the shape of looped ribbon, resembling a bracelet. When you place the ringbot on a surface that is at least 55 degrees Celsius (131 degrees Fahrenheit), which is hotter than the ambient air, the portion of the ribbon touching the surface contracts, while the portion of the ribbon exposed to the air does not. This induces a rolling motion in the ribbon. Video of the ringbots can be found here: https://youtu.be/yL5gVAjh1mQ.
Similarly, when researchers shine infrared light on the ringbot, the portion of the ribbon exposed to the light contracts, while the portion shielded from the light does not. This also induces a rolling motion in the ribbon.
In practical terms, this means that the crawling ringbot moves from the bottom up when placed on a hot surface. But when exposed to infrared light, the movement begins from the top down.
One of the things that drives this continuous motion is the fact that the ringbots are bistable, meaning that there are two shapes when it is at rest. If the ribbon begins to twist, it will either snap back to its original shape, or snap forward into the other bistable state.
Picture a rubber bracelet shaped like a ribbon. If you fold two ends of the bracelet forward a little bit, then let go, it will snap back to its original shape. But if you fold the ends over far enough, it will snap over — essentially folding the bracelet inside out. More

Inspired by living systems, researchers at Aalto University have developed a new material that changes its electrical behaviour based on previous experience, effectively giving it a basic form of adaptive memory. Such adaptive materials could play a vital role in the next generation of medical and environmental sensors, as well as in soft robots or active surfaces.
Responsive materials have become common in a range of applications, from glasses that darken in sunlight to drug delivery systems. But existing materials always react in the same way — their response to a change doesn’t depend on their history, nor do they adapt based on their past.
This is fundamentally different from living systems, which dynamically adapt their behaviour based on previous conditions. ‘One of the next big challenges in material science is to develop truly smart materials inspired by living organisms. We wanted to develop a material that would adjust its behaviour based on its history,’ says Bo Peng, an Academy Research Fellow at Aalto University who was one of the senior authors of this study.
The researchers synthesised micrometre-sized magnetic beads which were then stimulated by a magnetic field. When the magnet was on, the beads stacked up to form pillars. The strength of the magnetic field affects the shape of the pillars, which in turn affects how well they conduct electricity.
‘With this system, we coupled the magnetic field stimulus and the electrical response. Interestingly, we found that the electrical conductivity depends on whether we varied the magnetic field rapidly or slowly. That means that the electrical response depends on the history of the magnetic field. The electrical behaviour was also different if the magnetic field was increasing or decreasing. The response showed bistability, which is an elementary form of memory. The material behaves as though it has a memory of the magnetic field,’ explains Peng.
Basic learning
The system’s memory also allows it to behave in a way that resembles rudimentary learning. Although learning in living organisms is enormously complex, its most basic element in animals is a change in the response of connections between neurons, known as synapses. Depending on how frequently they are stimulated, synapses in a neuron will become harder or easier to activate. This change, known as short-term synaptic plasticity, makes the connection between a pair of neurons stronger or weaker depending on their recent history.
The researchers were able to accomplish something similar with their magnetic beads, even though the mechanism is totally differently. When they exposed the beads to a quickly pulsing magnetic field, the material became better at conducting electricity, whereas slower pulsing made it conduct poorly.
‘This is reminiscent of short term-synaptic plasticity,’ says Aalto’s Distinguished Professor Olli Ikkala. ‘Our material functions a bit like a synapse. What we’ve demonstrated paves the way for the next generation of life-inspired materials, which will draw on biological process of adaptation, memory and learning.’
‘In the future, there could be even more materials that are algorithmically inspired by life-like properties, though they won’t involve the full complexity of biological systems. Such materials will be central to the next generation of soft robots and for medical and environmental monitoring,’ adds Ikkala.
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Materials provided by Aalto University. Note: Content may be edited for style and length. More

A study published in Nature Medicine reports the ability of a smartwatch ECG to accurately detect heart failure in nonclinical environments. Researchers at Mayo Clinic applied artificial intelligence (AI) to Apple Watch ECG recordings to identify patients with a weak heart pump. Participants in the study recorded their smartwatch ECGs remotely whenever they wanted, from wherever they were. Periodically, they uploaded the ECGs to their electronic health records automatically and securely via a smartphone app developed by Mayo Clinic’s Center for Digital Health.
“Currently, we diagnose ventricular dysfunction — a weak heart pump — through an echocardiogram, CT scan or an MRI, but these are expensive, time consuming and at times inaccessible. The ability to diagnose a weak heart pump remotely, from an ECG that a person records using a consumer device, such as a smartwatch, allows a timely identification of this potentially life-threatening disease at massive scale,” says Paul Friedman, M.D., chair of the Department of Cardiovascular Medicine at Mayo Clinic in Rochester. Dr. Friedman is the senior author of the study.
People with a weak heart pump might not have symptoms, but this common form of heart disease affects about 2% of the population and 9% of people over 60. When the heart cannot pump enough oxygen-rich blood, symptoms may develop, including shortness of breath, a rapid heart rate and swelling in the legs. Early diagnosis is important because once identified, there are numerous treatments to improve quality of life and decrease the risks of heart failure and death.
Mayo researchers interpreted Apple Watch single-lead ECGs by modifying an earlier algorithm developed for 12-lead ECGs that is proven to detect a weak heart pump. The 12-lead algorithm for low ventricular ejection fraction is licensed to Anumana Inc., an AI-driven health technology company, co-created by nference and Mayo Clinic.
While the data are early, the modified AI algorithm using single-lead ECG data had an area under the curve of 0.88 to detect a weak heart pump. By comparison, this measure of accuracy is as good as or slightly better than a medical treadmill diagnostic test.
“These data are encouraging because they show that digital tools allow convenient, inexpensive, scalable screening for important conditions. Through technology, we can remotely gather useful information about a patient’s heart in an accessible way that can meet the needs of people where they are,” says Zachi Attia, Ph.D., the lead AI scientist in the Department of Cardiovascular Medicine at Mayo Clinic. Dr. Attia is first author of the study.
“Building the capability to ingest data from wearable consumer electronics and provide analytic capabilities to prevent disease or improve health remotely in the manner demonstrated by this study can revolutionize health care. Solutions like this not only enable prediction and prevention of problems, but also will eventually help diminish health disparities and the burden on health systems and clinicians,” says Bradley Leibovich, M.D., the medical director for the Mayo Clinic Center for Digital Health, and co-author on the study.
All 2,454 study participants were Mayo Clinic patients from across the U.S. and 11 countries. They downloaded an app created by the Mayo Clinic Center for Digital Health to securely upload their Apple Watch ECGs to their electronic health records. Participants logged more than 125,000 previous and new Apple Watch ECGs to their electronic health records between August 2021 and February 2022. Clinicians had access to view all the ECG data on an AI dashboard built into the electronic health record, including the day and time it was recorded.
Approximately 420 participants had an echocardiogram — a standard test using sound waves to produce images of the heart — within 30 days of logging an Apple Watch ECG in the app. Of those, 16 patients had low ejection fraction confirmed by the echocardiogram, which provided a comparison for accuracy.
This study was funded by Mayo Clinic with no technical or financial support from Apple. Drs. Attia and Friedman, along with others, are co-inventors of the low ejection fraction algorithm licensed to Anumana and may benefit from its commercialization.
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Materials provided by Mayo Clinic. Original written by Terri Malloy. Note: Content may be edited for style and length. More

A new statistical method provides a more efficient way to uncover biologically meaningful changes in genomic data that span multiple conditions — such as cell types or tissues.
Whole genome studies produce enormous amounts of data, ranging from millions of individual DNA sequences to information about where and how many of the thousands of genes are expressed to the location of functional elements across the genome. Because of the amount and complexity of the data, comparing different biological conditions or across studies performed by separate laboratories can be statistically challenging.
“The difficulty when you have multiple conditions is how to analyze the data together in a way that can be both statistically powerful and computationally efficient,” said Qunhua Li, associate professor of statistics at Penn State. “Existing methods are computationally expensive or produce results that are difficult to interpret biologically. We developed a method called CLIMB that improves on existing methods, is computationally efficient, and produces biologically interpretable results. We test the method on three types of genomic data collected from hematopoietic cells — related to blood stem cells — but the method could also be used in analyses of other ‘omic’ data.”
The researchers describe the CLIMB (Composite LIkelihood eMpirical Bayes) method in a paper appearing online Nov. 12 in the journal Nature Communications.
“In experiments where there is so much information but from relatively few individuals, it helps to be able to use information as efficiently as possible,” said Hillary Koch, a graduate student at Penn State at the time of the research and now a senior statistician at Moderna. “There are statistical advantages to be able to look at everything together and even to use information from related experiments. CLIMB allows us to do just that.”
The CLIMB method uses principles from two traditional techniques to analyze data across multiple conditions. One technique uses a series of pairwise comparisons between conditions but becomes increasingly challenging to interpret as additional conditions are added. More