Computers Math

Researchers at the National Institute of Standards and Technology (NIST) have revived and improved a once-reliable technique to identify and count defects in transistors, the building blocks of modern electronic devices such as smartphones and computers. Over the past decade, transistor components have become so small in high-performance computer chips that the popular method, known as charge pumping, could no longer count defects accurately. NIST’s new and improved method is sensitive enough for the most modern, minuscule technology, and can provide an accurate assessment of defects that could otherwise impair the performance of transistors and limit the reliability of the chips in which they reside.
The new, modified charge pumping technique can detect single defects as small as the diameter of a hydrogen atom (one-tenth of a billionth of a meter) and can indicate where they’re located in the transistor. Researchers could also use the new capability to detect and manipulate a property in each electron known as quantum spin. The ability to manipulate individual spins has applications in both basic research and quantum engineering and computing.
Transistors act as electrical switches. In the on position, which represents the “1” of binary digital information, a designated amount of current flows from one side of a semiconductor to the other. In the off position, representing the “0” of binary logic, current ceases to flow.
Defects in a transistor can interfere with the reliable flow of current and significantly degrade the performance of transistors. These defects could be broken chemical bonds in the transistor material. Or they could be atomic impurities that trap electrons in the material. Scientists have devised several ways to categorize defects and minimize their impact, tailored to the structure of the transistor under study.
In the traditional design known as the metal oxide semiconductor field effect transistor (MOSFET), a metal electrode called the gate sits atop a thin insulating layer of silicon dioxide. Below the insulating layer lies the interface region that separates the insulating layer and the main body of the semiconductor. In a typical transistor, current travels through a narrow channel, only one billionth of a meter thick, that extends from the source, which lies on one side of the gate, to a “drain” on the other side. The gate controls the amount of current in the channel.
Charge pumping is a two-step process in which the examiner alternately pulses the gate with a positive test voltage, then a negative one. (The transistor does not act as an on/off switch during this testing mode.) In traditional charge pumping, the alternating voltage pulses are applied at a single, set frequency. More

The opening lines of the great Chinese historical novel Romance of the Three Kingdoms condense its complex and spectacular stories into a coherent pattern, that is, power blocs divide and unite cyclically in turbulent battle years.
A good philosophy or theorem has general implications. Now, published in the journal Nature Synthesis, scientists from Australia, New Zealand, and the US reported a new type of solidification patterns that resembles the plots in the Chinese classic, but this time appearing on the surface of solidifying liquid metals.
The team dissolved a small amount of metals such as silver (Ag) in low-melting-point solvent metals such as gallium (Ga), and investigated how the metallic components interact and separate to form patterns when the metallic liquid mixtures (alloys) solidify.
The researchers found that a single silver-gallium system can produce distinct patterns such as particles or bundle-like structures of a Ag2Ga compound.
The individual Ag2Ga structures that build the patterns are small, with micrometre or nanometre thicknesses, tens or hundreds of times less than a human hair. More

Recommendation algorithms can make a customer’s online shopping experience quicker and more efficient by suggesting complementary products whenever the shopper adds a product to their basket. Did the customer buy peanut butter? The algorithm recommends several brands of jelly to add next.
These algorithms typically work by associating purchased items with items other shoppers have frequently purchased alongside them. If the shopper’s habits, tastes, or interests closely resemble those of previous customers, such recommendations might save time, jog the memory, and be a welcome addition to the shopping experience.
But what if the shopper is buying peanut butter to stuff a dog toy or bait a mousetrap? What if the shopper prefers honey or bananas with their peanut butter? The recommendation algorithm will offer less useful suggestions, costing the retailer a sale and potentially annoying the customer.
New research led by Negin Entezari, who recently received a doctoral degree in computer science at UC Riverside, Instacart collaborators, and her doctoral advisor Vagelis Papalexakis, brings a methodology called tensor decomposition — used by scientists to find patterns in massive volumes of data — into the world of commerce to recommend complementary products more carefully tailored to customer preferences.
Tensors can be pictured as multi-dimensional cubes and are used to model and analyze data with many different components, called multi-aspect data. Data closely related to other data can be connected in a cube arrangement and related to other cubes to uncover patterns in the data.
“Tensors can be used to represent customers’ shopping behaviors,” said Entezari. “Each mode of a 3-mode tensor can capture one aspect of a transaction. Customers form one mode of the tensor and the second and third mode captures product-to-product interactions by considering products co-purchased in a single transaction.”
For example, three hypothetical shoppers — A, B, and C — make the following purchases: More

NFTs, or nonfungible tokens, created using blockchain technology, first made a splash in the art world as a platform to buy and sell digital art backed by a digital contract. But could NFT digital contracts be useful in other marketplaces? A global, multidisciplinary team of scholars in ethics, law and informatics led by bioethicists at Baylor College of Medicine wrote one of the first commentaries on how this new emerging technology could be repurposed for the healthcare industry.
In a new publication in the journal Science, the researchers propose that the tool could help patients gain more control over their personal health information. NFT digital contracts could provide an opportunity for patients to specify who can access their personal health information and to track how it is shared.
“Our personal health information is completely outside of our control in terms of what happens to it once it is digitalized into an electronic health record and how it gets commercialized and exchanged from there,” said Dr. Kristin Kostick-Quenet, first author of the paper and assistant professor at the Center for Medical Ethics and Health Policy at Baylor. “NFTs could be used to democratize health data and help individuals regain control and participate more in decisions about who can see and use their health information.”
“In the era of big data, health information is its own currency; it has become commodified and profitable,” said Dr. Amy McGuire, senior author of the paper and Leon Jaworski Professor of Biomedical Ethics and director of the Center for Medical Ethics and Health Policy at Baylor. “Using NFTs for health data is the perfect storm between a huge market place that’s evolving and the popularity of cryptocurrency, but there are also many ethical, legal and social implications to consider.”
The researchers point out that NFTs are still vulnerable to data security flaws, privacy issues, and disputes over intellectual property rights. Further, the complexity of NFTs may prevent the average citizen from capitalizing on their potential. The researchers believe it is important to consider potential benefits and challenges as NFTs emerge as a potential avenue to transform the world of health data.
“Federal regulations already give patients the right to connect an app of their choice to their doctor’s electronic health record and download their data in a computable format,” said Dr. Kenneth Mandl, co-author of the paper, director of the Computational Health Informatics Program at Boston Children’s Hospital and Donald A.B. Lindberg Professor of Pediatrics and Biomedical Informatics at Harvard Medical School. “It’s intriguing to contemplate whether NFTs or NFT-like technology could enable intentional sharing of those data under smart contracts in the future.”
Dr. Timo Minssen, I. Glenn Cohen, Dr. Urs Gasser and Dr. Isaac Kohane also contributed to this publication. They are from the following institutions: Boston Children’s Hospital, Harvard Medical School, Harvard Law School, University of Copenhagen and Technical University of Munich. See the publication for a full list of funding for these researchers.
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Materials provided by Baylor College of Medicine. Note: Content may be edited for style and length. More

Damage to a part of the brain that processes visual information — the inferotemporal (IT) cortex — can be devastating, especially for adults. Those affected may lose the ability to read (a disorder known as alexia), or recognize faces (prosopagnosia) or objects (agnosia), and there is currently not much doctors can do.
A more accurate model of the visual system may help neuroscientists and clinicians develop better treatments for these conditions. Carnegie Mellon University researchers have developed a computational model that allows them to simulate the spatial organization or topography of the IT and learn more about how neighboring clusters of brain tissue are organized and interact. This could also help them understand how damage to that area affects the ability to recognize faces, objects and scenes.
The researchers — Nicholas Blauch, a Ph.D. student in the Program in Neural Computation, and his advisors David C. Plaut and Marlene Behrmann, both professors in the Department of Psychology and the Neuroscience Institute at CMU — described the model in the Jan. 18 issue of the Proceedings of the National Academy of Sciences.
Blauch said the paper may help cognitive neuroscientists answer longstanding questions about how different parts of the brain work together.
“We have been wondering for a long time if we should be thinking of the network of regions in the brain that responds to faces as a separate entity just for recognizing faces, or if we should think of it as part of a broader neural architecture for object recognition,” Blauch said. “We’re trying to come at this problem using a computational model that assumes this simpler, general organization, and seeing whether this model can then account for the specialization we see in the brain through learning to perform tasks.”
To do so, the researchers developed a deep learning model endowed with additional features of biological brain connectivity, hypothesizing that the model could reveal the spatial organization, or topography of the IT. More

When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned.
As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper publishing in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time.
“The brains of living beings can continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan,” said Shriram Ramanathan, a professor in Purdue University’s School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.
Unlike the brain, which constantly forms new connections between neurons to enable learning, the circuits on a computer chip don’t change. A circuit that a machine has been using for years isn’t any different than the circuit that was originally built for the machine in a factory.
This is a problem for making AI more portable, such as for autonomous vehicles or robots in space that would have to make decisions on their own in isolated environments. If AI could be embedded directly into hardware rather than just running on software as AI typically does, these machines would be able to operate more efficiently.
In this study, Ramanathan and his team built a new piece of hardware that can be reprogrammed on demand through electrical pulses. Ramanathan believes that this adaptability would allow the device to take on all of the functions that are necessary to build a brain-inspired computer. More

National Institute for Materials Science, Japan. “Observation of quantum transport at room temperature in a 2.8-nanometer CNT transistor: Semiconductor nanochannels created within metallic CNTS by thermally and mechanically altering the helical structure.” ScienceDaily. ScienceDaily, 3 February 2022. .
National Institute for Materials Science, Japan. (2022, February 3). Observation of quantum transport at room temperature in a 2.8-nanometer CNT transistor: Semiconductor nanochannels created within metallic CNTS by thermally and mechanically altering the helical structure. ScienceDaily. Retrieved February 4, 2022 from www.sciencedaily.com/releases/2022/02/220203123008.htm
National Institute for Materials Science, Japan. “Observation of quantum transport at room temperature in a 2.8-nanometer CNT transistor: Semiconductor nanochannels created within metallic CNTS by thermally and mechanically altering the helical structure.” ScienceDaily. www.sciencedaily.com/releases/2022/02/220203123008.htm (accessed February 4, 2022). More

Social media sites continue to amplify misinformation and conspiracy theories. To address this concern, an interdisciplinary team of computer scientists, physicists and social scientists led by the University of South Florida (USF) has found a solution to ensure social media users are exposed to more reliable news sources.
In their study published in the journal Nature Human Behaviour, the researchers focused on the recommendation algorithm that is used by social media platforms to prioritize content displayed to users. Rather than measuring engagement based on the number of users and pageviews, the researchers looked at what content gets amplified on a newsfeed, focusing on a news source’s reliability score and the political diversity of their audience.
“Low-quality content is engaging because it conforms to what we already know and like, regardless of whether it is accurate or not,” said Giovanni Luca Ciampaglia, assistant professor of computer science and engineering at USF. “As a result, misinformation and conspiracy theories often go viral within like-minded audiences. The algorithm ends up picking the wrong signal and keeps promoting it further. To break this cycle, one should look for content that is engaging, but for a diverse audience, not for a like-minded one.”
In collaboration with researchers at Indiana University and Dartmouth College, the team created a new algorithm using data on the web traffic and self-reported partisanship of 6,890 individuals who reflect the diversity of the United States in sex, race and political affiliation. The data was provided by online polling company YouGov. They also reviewed the reliability scores of 3,765 news sources based on the NewGuard Reliability Index, which rates news sources on several journalistic criteria, such as editorial responsibility, accountability and financial transparency.
They found that incorporating the partisan diversity of a news audience can increase the reliability of recommended sources while still providing users with relevant recommendations. Since the algorithm isn’t exclusively based on engagement or popularity, it is still able to promote reliable sources, regardless of their partisanship.
“This is especially welcome news for social media platforms, especially since they have been reluctant of introducing changes to their algorithms for fear of criticism about partisan bias,” said co-author Filippo Menczer, distinguished Luddy professor of informatics and computer science at Indiana University.
Researchers say that platforms would easily be able to include audience diversity into their own recommendation algorithms because diversity measures can be derived from engagement data, and platforms already log this type of data whenever users click “like” or share something on a newsfeed. Ciampaglia and his colleagues propose social media platforms adopt this new strategy in order to help prevent the spread of misinformation.
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Materials provided by University of South Florida (USF Innovation). Note: Content may be edited for style and length. More