Computers Math

Last year, a team of biologists and computer scientists from Tufts University and the University of Vermont (UVM) created novel, tiny self-healing biological machines from frog cells called “Xenobots” that could move around, push a payload, and even exhibit collective behavior in the presence of a swarm of other Xenobots.
Get ready for Xenobots 2.0.
The same team has now created life forms that self-assemble a body from single cells, do not require muscle cells to move, and even demonstrate the capability of recordable memory. The new generation Xenobots also move faster, navigate different environments, and have longer lifespans than the first edition, and they still have the ability to work together in groups and heal themselves if damaged. The results of the new research were published today in Science Robotics.
Compared to Xenobots 1.0, in which the millimeter-sized automatons were constructed in a “top down” approach by manual placement of tissue and surgical shaping of frog skin and cardiac cells to produce motion, the next version of Xenobots takes a “bottom up” approach. The biologists at Tufts took stem cells from embryos of the African frog Xenopus laevis (hence the name “Xenobots”) and allowed them to self-assemble and grow into spheroids, where some of the cells after a few days differentiated to produce cilia — tiny hair-like projections that move back and forth or rotate in a specific way. Instead of using manually sculpted cardiac cells whose natural rhythmic contractions allowed the original Xenobots to scuttle around, cilia give the new spheroidal bots “legs” to move them rapidly across a surface. In a frog, or human for that matter, cilia would normally be found on mucous surfaces, like in the lungs, to help push out pathogens and other foreign material. On the Xenobots, they are repurposed to provide rapid locomotion.
“We are witnessing the remarkable plasticity of cellular collectives, which build a rudimentary new ‘body’ that is quite distinct from their default — in this case, a frog — despite having a completely normal genome,” said Michael Levin, Distinguished Professor of Biology and director of the Allen Discovery Center at Tufts University, and corresponding author of the study. “In a frog embryo, cells cooperate to create a tadpole. Here, removed from that context, we see that cells can re-purpose their genetically encoded hardware, like cilia, for new functions such as locomotion. It is amazing that cells can spontaneously take on new roles and create new body plans and behaviors without long periods of evolutionary selection for those features.”
“In a way, the Xenobots are constructed much like a traditional robot. Only we use cells and tissues rather than artificial components to build the shape and create predictable behavior.” said senior scientist Doug Blackiston, who co-first authored the study with research technician Emma Lederer. “On the biology end, this approach is helping us understand how cells communicate as they interact with one another during development, and how we might better control those interactions.”
While the Tufts scientists created the physical organisms, scientists at UVM were busy running computer simulations that modeled different shapes of the Xenobots to see if they might exhibit different behaviors, both individually and in groups. Using the Deep Green supercomputer cluster at UVM’s Vermont Advanced Computing Core, the team, led by computer scientists and robotics experts Josh Bongard and under hundreds of thousands of random environmental conditions using an evolutionary algorithm. These simulations were used to identify Xenobots most able to work together in swarms to gather large piles of debris in a field of particles. More

The qubit is the building block of quantum computing, analogous to the bit in classical computers. To perform error-free calculations, quantum computers of the future are likely to need at least millions of qubits. The latest study, published in the journal PRX Quantum, suggests that these computers could be made with industrial-grade silicon chips using existing manufacturing processes, instead of adopting new manufacturing processes or even newly discovered particles.
For the study, researchers were able to isolate and measure the quantum state of a single electron (the qubit) in a silicon transistor manufactured using a ‘CMOS’ technology similar to that used to make chips in computer processors.
Furthermore, the spin of the electron was found to remain stable for a period of up to nine seconds. The next step is to use a similar manufacturing technology to show how an array of qubits can interact to perform quantum logic operations.
Professor John Morton (London Centre for Nanotechnology at UCL), co-founder of Quantum Motion, said: “We’re hacking the process of creating qubits, so the same kind of technology that makes the chip in a smartphone can be used to build quantum computers.
“It has taken 70 years for transistor development to reach where we are today in computing and we can’t spend another 70 years trying to invent new manufacturing processes to build quantum computers. We need millions of qubits and an ultra-scalable architecture for building them, our discovery gives us a blueprint to shortcut our way to industrial scale quantum chip production.”
The experiments were performed by PhD student Virginia Ciriano Tejel (London Centre for Nanotechnology at UCL) and colleagues working in a low-temperature laboratory. During operation, the chips are kept in a refrigerated state, cooled to a fraction of a degree above absolute zero (?273 degrees Celsius).
Ms Ciriano Tejel said: “Every physics student learns in textbooks that electrons behave like tiny magnets with weird quantum properties, but nothing prepares you for the feeling of wonder in the lab, being able to watch this ‘spin’ of a single electron with your own eyes, sometimes pointing up, sometimes down. It’s thrilling to be a scientist trying to understand the world and at the same time be part of the development of quantum computers.”
A quantum computer harnesses laws of physics that are normally seen only at the atomic and subatomic level (for instance, that particles can be in two places simultaneously). Quantum computers could be more powerful than today’s super computers and capable of performing complex calculations that are otherwise practically impossible.
While the applications of quantum computing differ from traditional computers, they will enable us to be more accurate and faster in hugely challenging areas such as drug development and tackling climate change, as well as more everyday problems that have huge numbers of variables — just as in nature — such as transport and logistics.
Story Source:
Materials provided by University College London. Note: Content may be edited for style and length. More

Whether parents prefer a conformance-oriented or independence-oriented supplemental education program for their children depends on political ideology, according to a study of more than 8,500 American parents by a research team from Rice University and the University of Texas at San Antonio.
“Conservative parents have a higher need for structure, which drives their preference for conformance-oriented programs,” said study co-author Vikas Mittal, a professor of marketing at Rice’s Jones Graduate School of Business. “Many parents are surprised to learn that their political identity can affect the educational choices they make for their children.”
Supplemental education programs include private tutoring, test preparation support and educational books and materials as well as online educational support services. The global market for private tutoring services is forecasted to reach $260.7 billion by 2024, and the U.S. market for tutoring is reported to be more than $8.9 billion a year. According to the Bureau of Labor Statistics, there are more than 100,000 businesses in the private education services industry. Supplemental education program brands are among the top 500 franchises in Entrepreneur magazine’s 2020 rankings, and they include popular providers such as Kumon (ranked No. 12), Mathnasium (No. 29) and Huntington Learning Center (No. 39).
For over five decades, education psychologists have utilized two pedagogical orientations — conformance orientation and independence orientation. A conformance orientation is more standardized and guided, emphasizing lecture-based content delivery, knowledge and memorization, frequent use of homework assignments, standardized examinations with relative evaluation and classroom attendance discipline and rules. In contrast, an independence orientation features discussion-based seminars and student-led presentations, an emphasis on ideas rather than facts, use of multimodal interaction instead of books, and highly variable and unstructured class routines. The two approaches do not differ in terms of topics covered in the curriculum or the specific qualities to be imparted to students.
The research team asked parents about their preferences for different programs framed as conformance- or independence-oriented. In five studies of more than 8,500 parents, conservative parents preferred education programs that were framed as conformance-oriented, while liberal parents preferred independence-oriented education programs. This differential preference emerged for different measures of parents’ political identity: their party affiliation, self-reported political leaning and whether they watch Fox or CNN/MSNBC for news.
“By understanding the underlying motivations behind parents’ preferences, educational programs’ appeal to parents can be substantially enhanced,” Mittal said. “Supplemental tutoring will be a major expenditure and investment for parents grappling with their child’s academic performance in the post-pandemic era. Informal conversations show parents gearing up to supplement school-based education with tutoring. Despite this, very little research exists about the factors that affect parents’ preference for and utilization of supplemental education.”
Mittal cautioned that these results do not speak to ultimate student performance. “This study only speaks to parents’ preferences but does not study ultimate student achievement,” he said.
Story Source:
Materials provided by Rice University. Note: Content may be edited for style and length. More

As social media platforms like Instagram, Snapchat, TikTok and others continue to grow in popularity, adolescents are spending more of their time online navigating a complex virtual world.
New research suggests that these increased hours spent online may be associated with cyberbullying behaviors. According to a study by the University of Georgia, higher social media addiction scores, more hours spent online, and identifying as male significantly predicted cyberbullying perpetration in adolescents.
“There are some people who engage in cyberbullying online because of the anonymity and the fact that there’s no retaliation,” said Amanda Giordano, principal investigator of the study and associate professor in the UGA Mary Frances Early College of Education. “You have these adolescents who are still in the midst of cognitive development, but we’re giving them technology that has a worldwide audience and then expecting them to make good choices.”
Cyberbullying can take on many forms, including personal attacks, harassment or discriminatory behavior, spreading defamatory information, misrepresenting oneself online, spreading private information, social exclusion and cyberstalking.
The study surveyed adolescents ranging in age from 13-19 years old. Of the 428 people surveyed, 214 (50%) identified as female, 210 (49.1%) as male, and four (0.9%) as other.
Exploring social media addiction
When adolescents are online, they adapt to a different set of social norms than when they’re interacting with their peers in person. Oftentimes, they are more aggressive or critical on social media because of the anonymity they have online and their ability to avoid retaliation. Additionally, cyberbullies may feel less remorse or empathy when engaging in these behaviors because they can’t see the direct impact of their actions. More

Math’s top prize, the Fields Medal, has succeeded in making mathematics more inclusive but still rewards elitism, according to a Dartmouth study.
Published in Nature’s Humanities and Social Sciences Communications, the study analyzed the effectiveness of the Fields Medal to make math at its highest level more representative across nations and identities. The result provides a visual, data-driven history of international migration and social networks among math elites, particularly since World War II.
“With so much recent discussion on equality in academia, we came to this study recognizing that math has a reputation of being egalitarian,” says Herbert Chang, a research affiliate in Dartmouth’s Fu Lab and lead author of the paper. “Our results provide a complex and rich story about the world of math especially since the establishment of the Fields Medal.”
The Fields Medal, widely considered the Nobel Prize of mathematics, is awarded every four years to mathematicians under the age of 40. It was first presented in 1936 to honor young mathematicians from groups that were typically underrepresented in top math circles.
According to the Dartmouth mathematicians, the prize has received criticism over its history for rewarding existing power structures rather than making math more inclusive and equitable at the elite level. Against this criticism, the study set out to explore how well the award has lived up to its original promise.
The analysis shows that the Fields Medal has elevated mathematicians of marginalized nationalities, but that the there is also “self-reinforcing behavior,” mostly through mentoring relationships among math elites. More

A team of researchers at the Technical University of Munich (TUM) has developed a new early warning system for vehicles that uses artificial intelligence to learn from thousands of real traffic situations. A study of the system was carried out in cooperation with the BMW Group. The results show that, if used in today’s self-driving vehicles, it can warn seven seconds in advance against potentially critical situations that the cars cannot handle alone — with over 85% accuracy.
To make self-driving cars safe in the future, development efforts often rely on sophisticated models aimed at giving cars the ability to analyze the behavior of all traffic participants. But what happens if the models are not yet capable of handling some complex or unforeseen situations?
A team working with Prof. Eckehard Steinbach, who holds the Chair of Media Technology and is a member of the Board of Directors of the Munich School of Robotics and Machine Intelligence (MSRM) at TUM, is taking a new approach. Thanks to artificial intelligence (AI), their system can learn from past situations where self-driving test vehicles were pushed to their limits in real-world road traffic. Those are situations where a human driver takes over — either because the car signals the need for intervention or because the driver decides to intervene for safety reasons.
Pattern recognition through RNN
The technology uses sensors and cameras to capture surrounding conditions and records status data for the vehicle such as the steering wheel angle, road conditions, weather, visibility and speed. The AI system, based on a recurrent neural network (RNN), learns to recognize patterns with the data. If the system spots a pattern in a new driving situation that the control system was unable to handle in the past, the driver will be warned in advance of a possible critical situation.
“To make vehicles more autonomous, many existing methods study what the cars now understand about traffic and then try to improve the models used by them. The big advantage of our technology: we completely ignore what the car thinks. Instead we limit ourselves to the data based on what actually happens and look for patterns,” says Steinbach. “In this way, the AI discovers potentially critical situations that models may not be capable of recognizing, or have yet to discover. Our system therefore offers a safety function that knows when and where the cars have weaknesses.”
Warnings up to seven seconds in advance
The team of researchers tested the technology with the BMW Group and its autonomous development vehicles on public roads and analyzed around 2500 situations where the driver had to intervene. The study showed that the AI is already capable of predicting potentially critical situations with better than 85 percent accuracy — up to seven seconds before they occur.
Collecting data with no extra effort
For the technology to function, large quantities of data are needed. After all, the AI can only recognize and predict experiences at the limits of the system if the situations were seen before. With the large number of development vehicles on the road, the data was practically generated by itself, says Christopher Kuhn, one of the authors of the study: “Every time a potentially critical situation comes up on a test drive, we end up with a new training example.” The central storage of the data makes it possible for every vehicle to learn from all of the data recorded across the entire fleet.
Story Source:
Materials provided by Technical University of Munich (TUM). Note: Content may be edited for style and length. More

Physicists from the University of Sussex have developed an extremely thin, large-area semiconductor surface source of terahertz, composed of just a few atomic layers and compatible with existing electronic platforms.
Terahertz sources emit brief light pulses oscillating at ‘trillion of times per second’. At this scale, they are too fast to be handled by standard electronics, and, until recently, too slow to be handled by optical technologies. This has great significance for the evolution of ultra-fast communication devices above the 300GHz limit — such as that required for 6G mobile phone technology — something that is still fundamentally beyond the limit of current electronics.
Researchers in the Emergent Photonics (EPic) Lab at Sussex, led by the Director of the Emergent Photonics (EPic) Lab Professor Marco Peccianti, are leaders in surface terahertz emission technology having achieved the brightest and thinnest surface semiconductor sources demonstrated so far. The emission region of their new development, a semiconductor source of terahertz, is 10 times thinner than previously achieved, with comparable or even better performances.
The thin layers can be placed on top of existing objects and devices, meaning they are able to place a terahertz source in places that would have been inconceivable otherwise, including everyday object such as a teapot or even a work of art — opening up huge potential for anti-counterfeiting and ‘the internet of things’ — as well as previously incompatible electronics, such as a next generation mobile phone.
Dr Juan S. Totero Gongora, Leverhulme Early Career Fellow at the University of Sussex, said:
“From a physics perspective, our results provide a long-sought answer that dates back to the first demonstration of terahertz sources based on two-colour lasers. More

Superconductivity is known to be easily destroyed by strong magnetic fields. NIMS, Osaka University and Hokkaido University have jointly discovered that a superconductor with atomic-scale thickness can retain its superconductivity even when a strong magnetic field is applied to it. The team has also identified a new mechanism behind this phenomenon. These results may facilitate the development of superconducting materials resistant to magnetic fields and topological superconductors composed of superconducting and magnetic materials.
Superconductivity has been used in various technologies, such as magnetic resonance imaging (MRI) and highly sensitive magnetic sensors. Topological superconductors, a special type of superconductor, have been attracting great attention in recent years. They are able to retain quantum information for a long time and can be used in combination with magnetic materials to form qubits that may enable quantum computers to perform very complex calculations. However, superconductivity is easily destroyed by strong magnetic fields or magnetic materials in close proximity. It is therefore desirable to develop a topological superconducting material resistant to magnetic fields.
The research team recently fabricated crystalline films of indium, a common superconducting material, with atomic-scale thickness. The team then discovered a new mechanism that prevents the superconductivity of these films from being destroyed by a strong magnetic field. When a magnetic field is applied to a superconducting material, the magnetic field interacts with electron spins. It causes the electronic energy of the material to change and destroys its superconductivity. However, when a superconducting material is thinned to a two-dimensional atomic layer, the spin and the momentum of the electrons in the layer are coupled, causing the electron spins to frequently rotate. This offsets the effect of the changes in electronic energy induced by the magnetic field and thus preserves superconductivity. This mechanism can enhance the critical magnetic field — the maximum magnetic field strength above which superconductivity disappears — up to 16-20 Tesla, which is approximately triple the generally accepted theoretical value. It is expected to have a wide range of applications as it was observed for an ordinary superconducting material and does not require either special crystalline structures or strong electronic correlations.
Based on these results, we plan to develop superconducting thin films capable of resisting even stronger magnetic fields. We also intend to create a hybrid device composed of superconducting and magnetic materials that is needed for the development of topological superconductors: a vital component in next-generation quantum computers.
Story Source:
Materials provided by National Institute for Materials Science, Japan. Note: Content may be edited for style and length. More