Computers Math

It’s hard to tell the difference between a newborn boy and girl based solely on temperament characteristics such as the baby’s propensity to display fear, smile or laugh. But once babies reach around a year old that begins to change.
A new study in PLOS ONE used machine learning to analyze temperament data on 4,438 babies in an attempt to classify the infants by gender and age.
The results indicate it is far easier for computer algorithms to determine the age of a baby than it is for them to decipher a baby’s gender based off temperament data during the infant’s first 48 weeks of life.
However, once the babies passed 48 weeks of age, gender classification improved for the multiple algorithms considered, suggesting gender differences in infancy become more accentuated around this time.
“It is at least suggestive of a picture where temperament begins to differentiate by gender in a more powerful way around age one,” said Maria Gartstein, lead author of the study and a professor of psychology at Washington State University.
Previous research has investigated age and gender-based temperament differences in babies, but few if any studies have looked at the two variables together. More

Fruit flies synchronize the movements of their heads and bodies to stabilize their vision and fly effectively, according to Penn State researchers who utilized virtual-reality flight simulators. The finding appears to hold true in primates and other animals, the researchers say, indicating that animals evolved to move their eyes and bodies independently to conserve energy and improve performance. This understanding could inform the design of advanced mobile robots, according to principal investigator Jean-Michel Mongeau, assistant professor of mechanical engineering.
The researchers published their results yesterday, May 3, in The Proceedings of the National Academy of Sciences.
“We discovered that when controlling gaze, fruit flies minimize energy expenditure and increase flight performance,” Mongeau said. “And, using that coordination information, we developed a mathematical model that accurately predicts similar synchronization in [other] visually active animals.”
Researchers used high-speed cameras to record a fruit fly surrounded by LED video screens upon which the researchers projected footage of what a fly would see while in flight, creating an immersive virtual-reality experience and causing the fly to move as if freely flying.
“When a fly moves, it coordinates its head, wings and body to fly through the air, evade predators or look for food,” Mongeau said. “We were interested in studying how flies coordinate these movements, and we did so by simulating flight in virtual reality.”
Responding to both slow and fast visual motion in the virtual-reality flight simulator, the fly moved its head and body at different rates. The researchers took measurements and tracked the fly’s head movements to determine the direction of its gaze, since its eyes are fixed to its head and cannot move independently. More

In a world of materials that normally expand upon heating, one that shrinks along one 3D axis while expanding along another stands out. That’s especially true when the unusual shrinkage is linked to a property important for thermoelectric devices, which convert heat to electricity or electricity to heat.
In a paper just published in the journal Advanced Materials, a team of scientists from Northwestern University and the U.S. Department of Energy’s Brookhaven National Laboratory describe the previously hidden sub-nanoscale origins of both the unusual shrinkage and the exceptional thermoelectric properties in this material, silver gallium telluride (AgGaTe2). The discovery reveals a quantum mechanical twist on what drives the emergence of these properties — and opens up a completely new direction for searching for new high-performance thermoelectrics.
“Thermoelectric materials will be transformational in green and sustainable energy technologies for heat energy harvesting and cooling — but only if their performance can be improved,” said Hongyao Xie, a postdoctoral researcher at Northwestern and first author on the paper. “We want to find the underlying design principles that will allow us to optimize the performance of these materials,” Xie said.
Thermoelectric devices are currently used in limited, niche applications, including NASA’s Mars rover, where heat released by the radioactive decay of plutonium is converted into electricity. Future applications might include materials controlled by voltage to achieve very stable temperatures critical for operation of high-tech optical detectors and lasers.
The main barrier to wider adoption is the need for materials with just the right cocktail of properties, including good electrical conductivity but resistance to the flow of heat.
“The trouble is, these desirable properties tend to compete,” said Mercouri Kanadzidis, the Northwestern professor who initiated this study. “In most materials, electronic conductivity and thermal conductivity are coupled and both are either high or low. Very few materials have the special high-low combination.”
Under certain conditions, silver gallium telluride appears to have just the right stuff — highly mobile conducting electrons and ultra-low thermal conductivity. In fact, its thermal conductivity is significantly lower than theoretical calculations and comparisons with similar materials such as copper gallium telluride would suggest. More

Imagine a computer that can think as fast as the human brain while using very little energy. That’s the goal of scientists seeking to discover or develop materials that can send and process signals as easily as the brain’s neurons and synapses. Identifying quantum materials with an intrinsic ability to switch between two distinct forms (or more) may hold the key to these futuristic sounding “neuromorphic” computing technologies.
In a paper just published in the journal Physical Review X, Yimei Zhu, a physicist at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, and his collaborators describe surprising new details about vanadium dioxide, one of the most promising neuromorphic materials. Using data collected by a unique “stroboscopic camera,” the team captured the hidden trajectory of atomic motion as this material transitions from an insulator to a metal in response to a pulse of light. Their findings could help guide the rational design of high-speed and energy-efficient neuromorphic devices.
“One way to reduce energy consumption in artificial neurons and synapses for brain-inspired computing is to exploit the pronounced non-linear properties of quantum materials,” said Zhu. “The principal idea behind this energy efficiency is that, in quantum materials, a small electrical stimulus may produce a large response that can be electrical, mechanical, optical, or magnetic through a change of material state.”
“Vanadium dioxide is one of the rare, amazing materials that has emerged as a promising candidate for neuro-mimetic bio-inspired devices,” he said. It exhibits an insulator-metal transition near room temperature in which a small voltage or current can produce a large change in resistivity with switching that can mimic the behavior of both neurons (nerve cells) and synapses (the connections between them).
“It goes from completely insulating, like rubber, to a very good metal conductor, with a resistivity change of 10,000 times or more,” Zhu said.
Those two very different physical states, intrinsic in the same material, could be encoded for cognitive computing. More

Researchers at the Department of Energy’s Oak Ridge National Laboratory are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.
“There are so many potential materials, some of which we cannot study at all with conventional tools, that need more efficient and systematic approaches to design and synthesize,” said Maxim Ziatdinov of ORNL’s Computational Sciences and Engineering Division and the CNMS. “We can use smart automation to access unexplored materials as well as create a shareable, reproducible path to discoveries that have not previously been possible.”
The approach, published in Nature Machine Intelligence, combines physics and machine learning to automate microscopy experiments designed to study materials’ functional properties at the nanoscale.
Functional materials are responsive to stimuli such as heat or electricity and are engineered to support both everyday and emerging technologies, ranging from computers and solar cells to artificial muscles and shape-memory materials. Their unique properties are tied to atomic structures and microstructures that can be observed with advanced microscopy. However, the challenge has been to develop efficient ways to locate regions of interest where these properties emerge and can be investigated.
Scanning probe microscopy is an essential tool for exploring the structure-property relationships in functional materials. Instruments scan the surface of materials with an atomically sharp probe to map out the structure at the nanometer scale — the length of one billionth of a meter. They can also detect responses to a range of stimuli, providing insights into fundamental mechanisms of polarization switching, electrochemical reactivity, plastic deformation or quantum phenomena. Today’s microscopes can perform a point-by-point scan of a nanometer square grid, but the process can be painstakingly slow, with measurements collected over days for a single material.
“The interesting physical phenomena are often only manifested in a small number of spatial locations and tied to specific but unknown structural elements. While we typically have an idea of what will be the characteristic features of physical phenomena we aim to discover, pinpointing these regions of interest efficiently is a major bottleneck,” said former ORNL CNMS scientist and lead author Sergei Kalinin, now at the University of Tennessee, Knoxville. “Our goal is to teach microscopes to seek regions with interesting physics actively and in a manner much more efficient than performing a grid search.”
Scientists have turned to machine learning and artificial intelligence to overcome this challenge, but conventional algorithms require large, human-coded datasets and may not save time in the end. More

For the past two years, the COVID-19 pandemic has exerted pressure on the hospital system, with consequences for patients’ care pathways. To support hospital planning strategies, it is important to anticipate COVID-19 health care demand and to continue to improve predictive models.
In this study published in the Proceedings of the National Academy of Sciences, scientists from the Mathematical Modeling of Infectious Diseases Unit at the Institut Pasteur identified the most relevant predictive variables for anticipating hospital demand and proposed using an ensemble model based on the average of the predictions of several individual models.
The scientists began by evaluating the performance of 12 individual models and 19 predictive variables, or “predictors,” such as epidemiological data (for example the number of cases) and meteorological or mobility data (for example the use of public transport). The scientists showed that the models incorporating these early predictive variables performed better. The average prediction error was halved for 14-day-ahead predictions. “These early variables detect changes in epidemic dynamics more quickly,” explains Simon Cauchemez, Head of the Mathematical Modeling of Infectious Diseases Unit at the Institut Pasteur and last author of the study. “The models that performed best used at least one epidemiological predictor and one mobility predictor,” he continues. The addition of a meteorological variable also improved forecasts but with a more limited impact.
The scientists then built an ensemble model, taking the average of several individual models, and tested the model retrospectively using epidemiological data from March to July 2021. This approach is already used in climate forecasting. “Our study shows that it is preferable to develop an ensemble model, as this reduces the risk of the predicted trajectory being overly influenced by the assumptions of a specific model,” explains Juliette Paireau, a research engineer in the Mathematical Modeling of Infectious Diseases Unit at the Institut Pasteur and joint first author of the study.
This ensemble model has been used to monitor the epidemic in France since January 15, 2021.
The study demonstrates an approach that can be used to better anticipate hospital demand for COVID-19 patients by combining different prediction models based on early predictors.
The full results of the study can be found on the Modeling page : https://modelisation-covid19.pasteur.fr/realtime-analysis/hospital/
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Researchers at Children’s Hospital of Philadelphia (CHOP) have developed a new tool to help researchers interpret the clinical significance of somatic mutations in cancer. The tool, known as CancerVar, incorporates machine learning frameworks to go beyond merely identifying somatic cancer mutations and interpret the potential significance of those mutations in terms of cancer diagnosis, prognosis, and targetability. A paper describing CancerVar was published today in Science Advances.
“CancerVar will not replace human interpretation in a clinical setting, but it will significantly reduce the manual work of human reviewers in classifying variants identified through sequencing and drafting clinical reports in the practice of precision oncology,” said Kai Wang, PhD, Professor of Pathology and Laboratory Medicine at CHOP and senior author of the paper. “CancerVar documents and harmonizes various types of clinical evidence including drug information, publications, and pathways for somatic mutations in detail. By providing standardized, reproducible, and precise output for interpreting somatic variants, CancerVar can help researchers and clinicians prioritize mutations of concern.”
“Somatic variant classification and interpretation are the most time-consuming steps of tumor genomic profiling,” said Marilyn M. Li, MD, Professor of Pathology and Laboratory Medicine, Director of Cancer Genomic Diagnostics and co-author of the paper. “CancerVar provides a powerful tool that automates these two critical steps. Clinical implementation of this tool will significantly improve test turnaround time and performance consistency, making the tests more impactful and affordable to all pediatric cancer patients.”
The growth of next-generation sequencing (NGS) and precision medicine has led to the identification of millions of somatic cancer variants. To better understand whether those mutations are related to or impact the clinical course of disease, researchers have established several databases that catalogue these variants. However, those databases did not provide standardized interpretations of somatic variants, so in 2017, the Association for Molecular Pathology (AMP), American Society of Clinical Oncology (ASCO), and College of American Pathologists (CAP) jointly proposed standards and guidelines for interpreting, reporting, and scoring somatic variants.
Yet even with these guidelines, the AMP/ASCO/CAP classification scheme did not specify how to implement these standards, so different knowledge bases were providing different results. To solve this problem, the CHOP researchers, including CHOP data scientist and co-senior author of the paper Yunyun Zhou, PhD, developed CancerVar, an improved somatic variant interpretation tool using command-line software called Python with an accompanying web server. With a user-friendly web server, CancerVar includes clinical evidence for 13 million somatic cancer variants from 1,911 cancer census genes that were mined through existing studies and databases.
In addition to including millions of somatic mutations, whether of known significance or not, the tool uses deep learning to improve clinical interpretation of those mutations. Users can query clinical interpretations for variants using information such as the chromosome position or protein change and interactively fine-tune how specific scoring features are weighted, based on prior knowledge or additional user-specified criteria. The CancerVar web server generates automated descriptive interpretations, such as whether the mutation is relevant for diagnosis or prognosis or to an ongoing clinical trial.
“This tool shows how we can use computational tools to automate human generated guidelines, and also how machine learning can guide decision making,” Wang said. “Future research should explore applying this framework to other areas of pathology as well.”
The research was supported by the National Institutes of Health (NIH)/National Library of Medicine (NLM)/ National Human Genome Research Institute (NHGRI) (grant number LM012895), NIH/National Institute of General Medical Sciences (NIGMS) (grant number GM120609 and GM132713), CHOP Pathology diagnostic innovation fund, and the CHOP Research Institute.
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A team from the Tulane University School of Science and Engineering has developed a new family of two-dimensional materials that researchers say has promising applications, including in advanced electronics and high-capacity batteries.
Led by Michael Naguib, an assistant professor in the Department of Physics and Engineering Physics, the study has been published in the journal Advanced Materials.
“Two-dimensional materials are nanomaterials with thickness in the nanometer size (nanometer is one millionth of a millimeter) and lateral dimensions thousands of times the thickness,” Naguib said. “Their flatness offers unique set of properties compared to bulk materials.”
The name of the new family of 2D materials is transition metal carbo-chalcogenides, or TMCC. It combines the characteristics of two families of 2D materials — transition metal carbides and transition metal dichalcogenides.
Naguib, the Ken & Ruth Arnold Early Career Professor in Science and Engineering, said the latter is a large family of materials that has been explored extensively and found to be very promising, especially for electrochemical energy storage and conversion. But he said one of the challenges in utilizing them is their low electrical conductivity and stability.
On the other hand, he said, transition metal carbides are excellent electrical conductors with much more powerful conductivity. Merging the two families into one is anticipated to have great potential for many applications such as batteries and supercapacitors, catalysis, sensors and electronics.
“Instead of stacking the two different materials like Lego building blocks with many problematic interfaces, here we develop a new 2D material that has the combination of both compositions without any interface,” he said.
“We used an electrochemical-assisted exfoliation process by inserting lithium ions in-between the layers of bulk transition metals carbo-chalcogenides followed by agitation in water,” said Ahmad Majed, the first author of the article and a doctoral candidate in Materials Physics and Engineering at Tulane working in Naguib’s group.
Unlike other exotic nanomaterials, Majed said, the process of making these 2D TMCC nanomaterials is simple and scalable.
In addition to Naguib and Majed, the team includes Jiang Wei, an associate professor in physics and engineering physics; Jianwei Sun, an assistant professor in physics and engineering physics; PhD candidates Kaitlyn Prenger, Manish Kothakonda and Fei Wang at Tulane; and Dr Eric N. Tseng and professor Per O.A. Persson of Linkoping University in Sweden.
This study was supported by Naguib’s National Science Foundation Career Award that he received less than a year ago.
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