Computers Math

Researchers are a step closer to realizing a new kind of memory that works according to the principles of spintronics which is analogous to, but different from, electronics. Their unique gallium arsenide-based ferromagnetic semiconductor can act as memory by quickly switching its magnetic state in the presence of an induced current at low power. Previously, such current-induced magnetization switching was unstable and drew a lot of power, but this new material both suppresses the instability and lowers the power consumption too.
The field of quantum computing often gets covered in the technical press; however, another emerging field along similar lines tends to get overlooked, and that is spintronics. In a nutshell, spintronic devices could replace some electronic devices and offer greater performance at far low power levels. Electronic devices use the motion of electrons for power and communication. Whereas spintronic devices use a transferable property of stationary electrons, their angular momentum, or spin. It’s a bit like having a line of people pass on a message from one to the other rather than have the person at one end run to the other. Spintronics reduces the effort needed to perform computational or memory functions.
Spintronic-based memory devices are likely to become common as they have a useful feature in that they are nonvolatile, meaning that once they are in a certain state, they maintain that state even without power. Conventional computer memory, such as DRAM and SRAM made of ordinary semiconductors, loses its state when it’s powered off. At the core of experimental spintronic memory devices are magnetic materials that can be magnetized in opposite directions to represent the familiar binary states of 1 or 0, and this switching of states can occur very, very quickly. However, there has been a long and arduous search for the best materials for this job, as magnetizing spintronic materials are no simple matter.
“Magnetizing a material is analogous to rotating a mechanical device,” said Associate Professor Shinobu Ohya from the Center for Spintronics Research Network at the University of Tokyo. “There are rotational forces at play in rotating systems called torques; similarly there are torques, called spin-orbit torques, in spintronic systems, albeit they are quantum-mechanical rather than classical. Among spin-orbit torques, ‘anti-damping torque’ assists the magnetization switching, whereas ‘field-like torque’ can resist it, raising the level of the current required to perform the switch. We wished to suppress this.”
Ohya and his team experimented with different materials and various forms of those materials. At small scales, anti-damping torque and field-like torque can act very differently depending on physical parameters such as current direction and thickness. The researchers found that with thin films of a gallium arsenide-based ferromagnetic semiconductor just 15 nanometers thick, about one-seven-thousandth the thickness of a dollar bill, the undesirable field-like torque became suppressed. This means the magnetization switching occurred with the lowest current ever recorded for this kind of process.

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People borrowing money for the first time should only be given small amounts until they have proved their competence, a new study says.
The paper argues that new borrowers — especially young people and those of an “impulsive” disposition — need protection to prevent them falling into long-term debt.
It says lenders should have a duty of care, requiring them to consider age, experience and personality traits, which can be detected by psychometric tests.
The study, by Professor Stephen Lea of the University of Exeter, reviews evidence on the psychology of debt, and makes recommendations to help reduce debt problems.
“I argue that — similar to obtaining a driving licence — people should have to demonstrate their competence before taking out debts that could have long-term negative consequences,” Professor Lea said.
“Some people are particularly susceptible to debt problems.

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“This includes those of an impulsive disposition, but it particularly applies to young people — and debts contracted early in life can have long-term ill effects.
“Accordingly, steps need to be taken to protect people at this vulnerable life stage.
“Although this would involve a restriction of the financial freedom of people who are legally adults, the evidence suggests that access to credit should be controlled more carefully.”
Speaking about rules relating to people of an “impulsive” disposition, Professor Lea said: “Lenders might well resist such regulations, but in fact financial advisors are already required to assess risk preference when advising people on investments.
“This shows that such a measure can be brought in without too much difficulty or expense to those who have to implement it.”
Professor Lea acknowledges that debt is heavily influenced by economic inequality, and that no psychological factor can prevent debt if excessive socio-economic disadvantage is not addressed.

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He also says the current Covid-19 pandemic is likely to increase debt problems.
His recommendations include tackling poverty (reducing the “decades-long drift towards greater inequality in almost all countries”) and intensifying regulation of high-cost lenders.
Recommending better financial education of children, Professor Lea said: “Many people are shockingly bad at assessing credit deals.
“What seems to be needed is fluency in seeing, without effortful calculation, what is or is not a good deal when borrowing money.”
The paper calls for policies to improve people’s awareness of their credit position, and says debtors should be advised to seek independent advice before dealing with lenders to whom they owe money.
He concludes: “If all these recommendations were adopted overnight, the problems of debt in society would not go away.
“Credit enhances consumer choice and is a necessary function in a modern economy, and so long is credit is available, some people will get into difficulties with debt.
“But, as is the case with poverty itself, neither the extent nor the level of debt is fixed.
“Appropriate policies, such as those proposed here, could reduce both.”

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Modern microscopes used for biological imaging are expensive, are located in specialized laboratories and require highly qualified staff. To research novel, creative approaches to address urgent scientific issues — for example in the fight against infectious diseases such as Covid-19 — is thus primarily reserved for scientists at well-equipped research institutions in rich countries. A young research team from the Leibniz Institute of Photonic Technology (Leibniz IPHT) in Jena, the Friedrich Schiller University and Jena University Hospital wants to change this: The researchers have developed an optical toolbox to build microscopes for a few hundred euros that deliver high-resolution images comparable to commercial microscopes that cost a hundred to a thousand times more. With open-source blueprints, components from the 3D printer and smartphone camera, the UC2 (You. See. Too.) modular system can be combined specifically in the way the research question requires — from long-term observation of living organisms in the incubator to a toolbox for optics education.
The basic building block of the UC2 system is a simple 3D printable cube with an edge length of 5 centimeters, which can host a variety of components such as lenses, LEDs or cameras. Several such cubes are plugged on a magnetic raster base plate. Cleverly arranged, the modules thus result in a powerful optical instrument. An optical concept according to which focal planes of adjacent lenses coincide is the basis for most of the complex optical setups such as modern microscopes. With the UC2 toolbox, the research team of PhD students at the lab of Prof. Dr. Rainer Heintzmann, Leibniz IPHT and Friedrich Schiller University Jena, shows how this inherently modular process can be understood intuitively in hands-on-experiments. In this way, UC2 also provides users without technical training with an optical tool that they can use, modify and expand — depending on what they are researching.
Monitor pathogens — and then recycle the contaminated microscope
Helge Ewers, Professor of Biochemistry at the Free University of Berlin and the Charité, is investigating pathogens usind the UC2 toolbox. “The UC2 system allows us to produce a high-quality microscope at low cost, with which we can observe living cells in an incubator,” he states. UC2 thus opens up areas of application for biomedical research for which conventional microscopes are not suitable. “Commercial microscopes that can be used to examine pathogens over a longer period of time cost hundreds or thousands of times more than our UC2 setup,” says Benedict Diederich, PhD student at Leibniz-IPHT, who developed the optical toolbox there together with René Lachmann. “You can hardly get them into a contaminated laboratory from which you may not be able to remove them because they cannot be cleaned easily.” The UC2 microscope made of plastic, on the other hand, can be easily burned or recycled after its successful use in the biological safety laboratory. For a study at Jena University Hospital, the UC2 team observed the differentiation of monocytes into macrophages in the incubator over a period of one week in order to gain insights into how the innate immune system fights off pathogens in the body.
Building according to the Lego principle: From the idea to the prototype
Building according to the Lego principle — this not only awakens the users’ inner play instinct, observes the UC2 team, but it also opens up new possibilities for researchers to design an instrument precisely tailored to their research question. “With our method, it is possible to quickly assemble the right tool to map specific cells,” explains Benedict Diederich. “If, for example, a red wavelength is required as excitation, you simply install the appropriate laser and change the filter. If an inverted microscope is needed, you stack the cubes accordingly. With the UC2 system, elements can be combined depending on the required resolution, stability, duration or microscopy method and tested directly in the “rapid prototyping” process.
The Vision: Open Science
The researchers publish construction plans and software on the freely accessible online repository GitHub, so that the open-source community worldwide can access, rebuild, modify and expand the presented systems. “With the feedback from users, we improve the system step by step and add ever new creative solutions,” reports René Lachmann. The first users have already started to expand the system for themselves and their purposes. “We are eager to see when we can present the first user solutions.”
The aim behind this is to enable open science. Thanks to the detailed documentation, researchers can reproduce and further develop experiments anywhere in the world, even beyond well-equipped laboratories. “Change in Paradigm: Science for a Dime” is what Benedict Diederich calls this vision: to herald a paradigm shift in which the scientific process is as open and transparent as possible, freely accessible to all, where researchers share their knowledge with each other and incorporate it into their work.
UC2 experiment box brings science to schools
In order to get especially young people interested in optics, the research team has developed a sophisticated tool set for educational purposes in schools and universities. With “The Box” UC2 introduces a kit that enables users to learn about and try out optical concepts and microscopy methods. “The components can be combined to form a projector or a telescope; you can build a spectrometer or a smartphone microscope,” explains Barbora Maršíková, who developed experiments and a series of ready-to-use documentations that the UC2 team already tested in several workshops in and around Jena as well as in the US, in Great Britain and Norway. In Jena, the young researchers have already used the UC2 toolbox in several schools and e.g. supported pupils to build a fluorescence microscope to detect microplastics. “We have combined UC2 with our smartphone. This enabled us to build our own fluorescence microscope cost-effectively without any major optical knowledge and to develop a comparably simple method for detecting plastic particles in cosmetics,” reports Emilia Walther from the Montessori School in Jena, who together with her group is pursuing an innovative interdisciplinary learning approach.
“We want to make modern microscopy techniques accessible to a broad public,” says Benedict Diederich, “and build up an open and creative microscopy community.” This build-it-yourself approach to teaching has a huge potential, especially at times of the Corona pandemics, when access to teaching material at home is severely limited. More

Multimaterial fibers that integrate metal, glass and semiconductors could be useful for applications such as biomedicine, smart textiles and robotics. But because the fibers are composed of the same materials along their lengths, it is difficult to position functional elements, such as electrodes or sensors, at specific locations. Now, researchers reporting in ACS Central Science have developed a method to pattern hundreds-of-meters-long multimaterial fibers with embedded functional elements.

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Youngbin Lee, Polina Anikeeva and colleagues developed a thiol-epoxy/thiol-ene polymer that could be combined with other materials, heated and drawn from a macroscale model into fibers that were coated with the polymer. When exposed to ultraviolet light, the polymer, which is photosensitive, crosslinked into a network that was insoluble to common solvents, such as acetone. By placing “masks” at specific locations along the fiber in a process known as photolithography, the researchers could protect the underlying areas from UV light. Then, they removed the masks and treated the fiber with acetone. The polymer in the areas that had been covered dissolved to expose the underlying materials.
As a proof of concept, the researchers made patterns along fibers that exposed an electrically conducting filament underneath the thiol-epoxy/thiol-ene coating. The remaining polymer acted as an insulator along the length of the fiber. In this way, electrodes or other microdevices could be placed in customizable patterns along multimaterial fibers, the researchers say.

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Journal Reference:
Youngbin Lee, Andres Canales, Gabriel Loke, Mehmet Kanik, Yoel Fink, Polina Anikeeva. Selectively Micro-Patternable Fibers via In-Fiber Photolithography. ACS Central Science, 2020; DOI: 10.1021/acscentsci.0c01188

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American Chemical Society. “Patterning method could pave the way for new fiber-based devices, smart textiles.” ScienceDaily. ScienceDaily, 25 November 2020. .
American Chemical Society. (2020, November 25). Patterning method could pave the way for new fiber-based devices, smart textiles. ScienceDaily. Retrieved November 25, 2020 from www.sciencedaily.com/releases/2020/11/201125091506.htm
American Chemical Society. “Patterning method could pave the way for new fiber-based devices, smart textiles.” ScienceDaily. www.sciencedaily.com/releases/2020/11/201125091506.htm (accessed November 25, 2020). More

Researchers from Boston University and University of Virginia published a new paper in the Journal of Marketing that examines how consumers respond to AI recommenders when focused on the functional and practical aspects of a product (its utilitarian value) versus the experiential and sensory aspects of a product (its hedonic value).
The study, forthcoming in the the Journal of Marketing, is titled “Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The ‘Word-of-Machine’ Effect” and is authored by Chiara Longoni and Luca Cian.
More and more companies are leveraging technological advances in AI, machine learning, and natural language processing to provide recommendations to consumers. As these companies evaluate AI-based assistance, one critical question must be asked: When do consumers trust the “word of machine,” and when do they resist it?
A new Journal of Marketing study explores reasons behind the preference of recommendation source (AI vs. human). The key factor in deciding how to incorporate AI recommenders is whether consumers are focused on the functional and practical aspects of a product (its utilitarian value) or on the experiential and sensory aspects of a product (its hedonic value).
Relying on data from over 3,000 study participants, the research team provides evidence supporting a word-of-machine effect, defined as the phenomenon by which the trade-offs between utilitarian and hedonic aspects of a product determine the preference for, or resistance to, AI recommenders. The word-of-machine effect stems from a widespread belief that AI systems are more competent than humans at dispensing advice when functional and practical qualities (utilitarian) are desired and less competent when the desired qualities are experiential and sensory-based (hedonic). Consequently, the importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, while the importance or salience of hedonic attributes determine resistance to AI recommenders over human ones.
The researchers tested the word-of-machine effect using experiments designed to assess people’s tendency to choose products based on consumption experiences and recommendation source. Longoni explains that “We found that when presented with instructions to choose products based solely on utilitarian/functional attributes, more participants chose AI-recommended products. When asked to only consider hedonic/experiential attributes, a higher percentage of participants chose human recommenders.”
When utilitarian features are most important, the word-of-machine effect was more distinct. In one study, participants were asked to imagine buying a winter coat and rate how important utilitarian/functional attributes (e.g., breathability) and hedonic/experiential attributes (e.g., fabric type) were in their decision making. The more utilitarian/functional features were highly rated, the greater the preference for AI over human assistance, and the more hedonic/experiential features were highly rated, the greater the preference for human over AI assistance.
Another study indicated that when consumers wanted recommendations matched to their unique preferences, they resisted AI recommenders and preferred human recommenders regardless of hedonic or utilitarian preferences. These results suggest that companies whose customers are known to be satisfied with “one size fits all” recommendations (i.e., not in need of a high level of customization) may rely on AI-systems. However, companies whose customers are known to desire personalized recommendations should rely on humans.
Although there is a clear correlation between utilitarian attributes and consumer trust in AI recommenders, companies selling products that promise more sensorial experiences (e.g., fragrances, food, wine) may still use AI to engage customers. In fact, people embrace AI’s recommendations as long as AI works in partnership with humans. When AI plays an assistive role, “augmenting” human intelligence rather than replacing it, the AI-human hybrid recommender performs as well as a human-only assistant.
Overall, the word-of-machine effect has important implications as the development and adoption of AI, machine learning, and natural language processing challenges managers and policy-makers to harness these transformative technologies. As Cian says, “The digital marketplace is crowded and consumer attention span is short. Understanding the conditions under which consumers trust, and do not trust, AI advice will give companies a competitive advantage in this space.”

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Materials provided by American Marketing Association. Original written by Matt Weingarden. Note: Content may be edited for style and length. More

Magic squares belong to the imagination of humanity for a long time. The oldest known magic square comes from China and is over 2000 years old. One of the most famous magic squares can be found in Albrecht Dürer’s copper engraving Melencolia I. Another one is on the facade of the Sagrada Família in Barcelona. A magic square is a square of numbers such that every column and every row sums to the same number. For example, in the magic square of the Sagrada Família every row and column sums to 33.
If the magic square can contain real numbers, and every row and column sums to 1, then it is called a doubly stochastic matrix. One particular example would be a matrix that has 0’s everywhere except for one 1 in every column and every row. This is called a permutation matrix. A famous theorem says that every doubly stochastic matrix can be obtained as a convex combination of permutation matrices. In words, this means that permutation matrices “contain all the secrets” of doubly stochastic matrices — more precisely, that the latter can be fully characterized in terms of the former.
In a new paper in the Journal of Mathematical Physics, Tim Netzer and Tom Drescher from the Department of Mathematics and Gemma De las Cuevas from the Department of Theoretical Physics have introduced the notion of the quantum magic square, which is a magic square but instead of numbers one puts in matrices. This is a non-commutative, and thus quantum, generalization of a magic square. The authors show that quantum magic squares cannot be as easily characterized as their “classical” cousins. More precisely, quantum magic squares are not convex combinations of quantum permutation matrices. “They are richer and more complicated to understand,” explains Tom Drescher. “This is the general theme when generalizations to the non-commutative case are studied.”

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Scientists have developed a lung-on-chip model to study how the body responds to early tuberculosis (TB) infection, according to findings published today in eLife.
TB is a disease caused by the bacterium Mycobacterium tuberculosis (M. tuberculosis) and most often affects the lungs. The model reveals that respiratory system cells, called alveolar epithelial cells, play an essential role in controlling early TB infection. They do this by producing a substance called surfactant — a mixture of molecules (lipids and proteins) that reduce the surface tension where air and liquid meet in the lung.
These findings add to our understanding of what happens during early TB infection, and may explain in part why those who smoke or have compromised surfactant functionality have a higher risk of contracting primary or recurrent infection.
TB is one of the world’s top infectious killers and affects people of all ages. While it mostly affects adults, there are currently no effective vaccines available to this group. This is partly due to challenges with studying the early stages of infection, which take place when just one or two M. tuberculosis bacteria are deposited deep inside the lung.
“We created the lung-on-chip model as a way of studying some of these early events,” explains lead author Vivek Thacker, a postdoctoral researcher at the McKinney Lab, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland. “Previous studies have shown that components of surfactant produced by alveolar epithelial cells can impair bacterial growth, but that the alveolar epithelial cells themselves can allow intracellular bacterial growth. The roles of these cells in early infection are therefore not completely understood.
“We used our model to observe where the sites of first contact are, how M. tuberculosis grows in alveolar epithelial cells compared to bacteria-killing cells called macrophages, and how the production of surfactant affects growth, all while maintaining these cells at the air-liquid interface found in the lung.”
The team used their lung-on-chip model to recreate a deficiency in surfactant produced by alveolar epithelial cells and then see how the lung cells respond to early TB infection. The technology is optically transparent, meaning they could use an imaging technique called time-lapse microscopy to follow the growth of single M. tuberculosis bacteria in either macrophages or alveolar epithelial cells over multiple days.
Their studies revealed that a lack of surfactant results in uncontrolled and rapid bacterial growth in both macrophages and alveolar epithelial cells. On the other hand, the presence of surfactant significantly reduces this growth in both cells and, in some cases, prevents it altogether.
“Our work shines a light on the early events that take place during TB infection and provides a model for scientists to build on for future research into other respiratory infections,” says senior author John McKinney, Head of the Laboratory of Microbiology and Microtechnology at EPFL. “It also paves the way for experiments that increase the complexity of our model to help understand why some TB lesions progress while others heal, which can occur at the same time in the same patient. This knowledge could one day be harnessed to develop effective new interventions against TB and other diseases.”
The authors add that they are currently using a human lung-on-chip model to study how our lungs may respond to a low-dose infection and inoculation of SARS-CoV-2, the virus that causes COVID-19.

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Northwestern University researchers have developed a new artificial intelligence (A.I.) platform that detects COVID-19 by analyzing X-ray images of the lungs.
Called DeepCOVID-XR, the machine-learning algorithm outperformed a team of specialized thoracic radiologists — spotting COVID-19 in X-rays about 10 times faster and 1-6% more accurately.
The researchers believe physicians could use the A.I. system to rapidly screen patients who are admitted into hospitals for reasons other than COVID-19. Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner.
The study’s authors also believe the algorithm could potentially flag patients for isolation and testing who are not otherwise under investigation for COVID-19.
The study will be published on Nov. 24 in the journal Radiology.
“We are not aiming to replace actual testing,” said Northwestern’s Aggelos Katsaggelos, an A.I. expert and senior author of the study. “X-rays are routine, safe and inexpensive. It would take seconds for our system to screen a patient and determine if that patient needs to be isolated.”
“It could take hours or days to receive results from a COVID-19 test,” said Dr. Ramsey Wehbe, a cardiologist and postdoctoral fellow in A.I. at the Northwestern Medicine Bluhm Cardiovascular Institute. “A.I. doesn’t confirm whether or not someone has the virus. But if we can flag a patient with this algorithm, we could speed up triage before the test results come back.”

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Katsaggelos is the Joseph Cummings Professor of Electrical and Computer Engineering in Northwestern’s McCormick School of Engineering. He also has courtesy appointments in computer science and radiology. Wehbe is a postdoctoral fellow at Bluhm Cardiovascular Institute at Northwestern Memorial Hospital.
A trained eye
For many patients with COVID-19, chest X-rays display similar patterns. Instead of clear, healthy lungs, their lungs appear patchy and hazy.
“Many patients with COVID-19 have characteristic findings on their chest images,” Wehbe said. “These include ‘bilateral consolidations.’ The lungs are filled with fluid and inflamed, particularly along the lower lobes and periphery.”
The problem is that pneumonia, heart failure and other illnesses in the lungs can look similar on X-rays. It takes a trained eye to tell the difference between COVID-19 and something less contagious.

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Katsaggelos’ laboratory specializes in using A.I. for medical imaging. He and Wehbe had already been working together on cardiology imaging projects and wondered if they could develop a new system to help fight the pandemic.
“When the pandemic started to ramp up in Chicago, we asked each other if there was anything we could do,” Wehbe said. “We were working on medical imaging projects using cardiac echo and nuclear imaging. We felt like we could pivot and apply our joint expertise to help in the fight against COVID-19.”
A.I. vs. human
To develop, train and test the new algorithm, the researchers used 17,002 chest X-ray images — the largest published clinical dataset of chest X-rays from the COVID-19 era used to train an A.I. system. Of those images, 5,445 came from COVID-19-positive patients from sites across the Northwestern Memorial Healthcare System.
The team then tested DeepCOVID-XR against five experienced cardiothoracic fellowship-trained radiologists on 300 random test images from Lake Forest Hospital. Each radiologist took approximately two-and-a-half to three-and-a-half hours to examine this set of images, whereas the A.I. system took about 18 minutes.
The radiologists’ accuracy ranged from 76-81%. DeepCOVID-XR performed slightly better at 82% accuracy.
“These are experts who are sub-specialty trained in reading chest imaging,” Wehbe said. “Whereas the majority of chest X-rays are read by general radiologists or initially interpreted by non-radiologists, such as the treating clinician. A lot of times decisions are made based off that initial interpretation.”
“Radiologists are expensive and not always available,” Katsaggelos said. “X-rays are inexpensive and already a common element of routine care. This could potentially save money and time — especially because timing is so critical when working with COVID-19.”
Limits to diagnosis
Of course, not all COVID-19 patients show any sign of illness, including on their chest X-rays. Especially early in the virus’ progression, patients likely will not yet have manifestations on their lungs.
“In those cases, the A.I. system will not flag the patient as positive,” Wehbe said. “But neither would a radiologist. Clearly there is a limit to radiologic diagnosis of COVID-19, which is why we wouldn’t use this to replace testing.”
The Northwestern researchers have made the algorithm publicly available with hopes that others can continue to train it with new data. Right now, DeepCOVID-XR is still in the research phase, but could potentially be used in the clinical setting in the future.
Study coauthors include Jiayue Sheng, Shinjan Dutta, Siyuan Chai, Amil Dravid, Semih Barutcu and Yunan Wu — all members of Katsaggelos’ lab — and Drs. Donald Cantrell, Nicholas Xiao, Bradly Allen, Gregory MacNealy, Hatice Savas, Rishi Agrawal and Nishant Parekh — all radiologists at Northwestern Medicine. More