Computers Math

They may be tiny weapons, but Brigham Young University’s holography research group has figured out how to create lightsabers — green for Yoda and red for Darth Vader, naturally — with actual luminous beams rising from them.
Inspired by the displays of science fiction, the researchers have also engineered battles between equally small versions of the Starship Enterprise and a Klingon Battle Cruiser that incorporate photon torpedoes launching and striking the enemy vessel that you can see with the naked eye.
“What you’re seeing in the scenes we create is real; there is nothing computer generated about them,” said lead researcher Dan Smalley, a professor of electrical engineering at BYU. “This is not like the movies, where the lightsabers or the photon torpedoes never really existed in physical space. These are real, and if you look at them from any angle, you will see them existing in that space.”
It’s the latest work from Smalley and his team of researchers who garnered national and international attention three years ago when they figured out how to draw screenless, free-floating objects in space. Called optical trap displays, they’re created by trapping a single particle in the air with a laser beam and then moving that particle around, leaving behind a laser-illuminated path that floats in midair; like a “a 3D printer for light.”
The research group’s new project, funded by a National Science Foundation CAREER grant, goes to the next level and produces simple animations in thin air. The development paves the way for an immersive experience where people can interact with holographic-like virtual objects that co-exist in their immediate space.
“Most 3D displays require you to look at a screen, but our technology allows us to create images floating in space — and they’re physical; not some mirage,” Smalley said. “This technology can make it possible to create vibrant animated content that orbits around or crawls on or explodes out of every day physical objects.”
To demonstrate that principle, the team has created virtual stick figures that walk in thin air. They were able to demonstrate the interaction between their virtual images and humans by having a student place a finger in the middle of the volumetric display and then film the same stick finger walking along and jumping off that finger.
Smalley and Rogers detail these and other recent breakthroughs in a new paper published in Nature Scientific Reports this month. The work overcomes a limiting factor to optical trap displays: wherein this technology lacks the ability to show virtual images, Smalley and Rogers show it is possible to simulate virtual images by employing a time-varying perspective projection backdrop.
“We can play some fancy tricks with motion parallax and we can make the display look a lot bigger than it physically is,” Rogers said. “This methodology would allow us to create the illusion of a much deeper display up to theoretically an infinite size display.”
Video: https://www.youtube.com/watch?v=N12i_FaHvOU&list=TLGGbyUMLSISdIswNzA1MjAyMQ&t=1s
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Materials provided by Brigham Young University. Original written by Todd Hollingshead. Note: Content may be edited for style and length. More

Early on in the COVID-19 pandemic, health officials seized on contact tracing as the most effective way to anticipate the virus’s migration from the initial, densely populated hot spots and try to curb its spread. Months later, infections were nonetheless recorded in similar patterns in nearly every region of the country, both urban and rural.
A team of environmental engineers, alerted by the unusual wealth of data published regularly by county health agencies throughout the pandemic, began researching new methods to describe what was happening on the ground in a way that does not require obtaining information on individuals’ movements or contacts. Funding for their effort came through a National Research Foundation RAPID research grant (CBET 2028271).
In a paper published May 6 in the Proceedings of the National Academy of Sciences, they presented their results: a model that predicts where the disease will spread from an outbreak, in what patterns and how quickly.
“Our model should be helpful to policymakers because it predicts disease spread without getting into granular details, such as personal travel information, which can be tricky to obtain from a privacy standpoint and difficult to gather in terms of resources,” explained Xiaolong Geng, a research assistant professor of environmental engineering at NJIT who built the model and is one of the paper’s authors.
“We did not think a high level of intrusion would work in the United States so we sought an alternative way to map the spread,” noted Gabriel Katul, the Theodore S. Coile Distinguished Professor of Hydrology and Micrometeorology at Duke University and a co-author.
Their numerical scheme mapped the classic SIR epidemic model (computations based on a division of the population into groups of susceptible, infectious and recovered people) onto the population agglomeration template. Their calculations closely approximated the multiphase COVID-19 epidemics recorded in each U.S. state. More

A Rice University laboratory has adapted its laser-induced graphene technique to make high-resolution, micron-scale patterns of the conductive material for consumer electronics and other applications.
Laser-induced graphene (LIG), introduced in 2014 by Rice chemist James Tour, involves burning away everything that isn’t carbon from polymers or other materials, leaving the carbon atoms to reconfigure themselves into films of characteristic hexagonal graphene.
The process employs a commercial laser that “writes” graphene patterns into surfaces that to date have included wood, paper and even food.
The new iteration writes fine patterns of graphene into photoresist polymers, light-sensitive materials used in photolithography and photoengraving.
Baking the film increases its carbon content, and subsequent lasing solidifies the robust graphene pattern, after which unlased photoresist is washed away.
Details of the PR-LIG process appear in the American Chemical Society journal ACS Nano.
“This process permits the use of graphene wires and devices in a more conventional silicon-like process technology,” Tour said. “It should allow a transition into mainline electronics platforms.”
The Rice lab produced lines of LIG about 10 microns wide and hundreds of nanometers thick, comparable to that now achieved by more cumbersome processes that involve lasers attached to scanning electron microscopes, according to the researchers.
Achieving lines of LIG small enough for circuitry prompted the lab to optimize its process, according to graduate student Jacob Beckham, lead author of the paper.
“The breakthrough was a careful control of the process parameters,” Beckham said. “Small lines of photoresist absorb laser light depending on their geometry and thickness, so optimizing the laser power and other parameters allowed us to get good conversion at very high resolution.”
Because the positive photoresist is a liquid before being spun onto a substrate for lasing, it’s a simple matter to dope the raw material with metals or other additives to customize it for applications, Tour said.
Potential applications include on-chip microsupercapacitors, functional nanocomposites and microfluidic arrays.
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The uncertainty principle, first introduced by Werner Heisenberg in the late 1920’s, is a fundamental concept of quantum mechanics. In the quantum world, particles like the electrons that power all electrical product can also behave like waves. As a result, particles cannot have a well-defined position and momentum simultaneously. For instance, measuring the momentum of a particle leads to a disturbance of position, and therefore the position cannot be precisely defined.
In recent research, published in Science, a team led by Prof. Mika Sillanpää at Aalto University in Finland has shown that there is a way to get around the uncertainty principle. The team included Dr. Matt Woolley from the University of New South Wales in Australia, who developed the theoretical model for the experiment.
Instead of elementary particles, the team carried out the experiments using much larger objects: two vibrating drumheads one-fifth of the width of a human hair. The drumheads were carefully coerced into behaving quantum mechanically.
“In our work, the drumheads exhibit a collective quantum motion. The drums vibrate in an opposite phase to each other, such that when one of them is in an end position of the vibration cycle, the other is in the opposite position at the same time. In this situation, the quantum uncertainty of the drums’ motion is cancelled if the two drums are treated as one quantum-mechanical entity,” explains the lead author of the study, Dr. Laure Mercier de Lepinay.
This means that the researchers were able to simultaneously measure the position and the momentum of the two drumheads — which should not be possible according to the Heisenberg uncertainty principle. Breaking the rule allows them to be able to characterize extremely weak forces driving the drumheads.
“One of the drums responds to all the forces of the other drum in the opposing way, kind of with a negative mass,” Sillanpää says.
Furthermore, the researchers also exploited this result to provide the most solid evidence to date that such large objects can exhibit what is known as quantum entanglement. Entangled objects cannot be described independently of each other, even though they may have an arbitrarily large spatial separation. Entanglement allows pairs of objects to behave in ways that contradict classical physics, and is the key resource behind emerging quantum technologies. A quantum computer can, for example, carry out the types of calculations needed to invent new medicines much faster than any supercomputer ever could.
In macroscopic objects, quantum effects like entanglement are very fragile, and are destroyed easily by any disturbances from their surrounding environment. Therefore, the experiments were carried out at a very low temperature, only a hundredth a degree above absolute zero at -273 degrees.
In the future, the research group will use these ideas in laboratory tests aiming at probing the interplay of quantum mechanics and gravity. The vibrating drumheads may also serve as interfaces for connecting nodes of large-scale, distributed quantum networks.
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Heart disease can take a number of forms, but some types of heart disease, such as asymptomatic low ejection fraction, can be hard to recognize, especially in the early stages when treatment would be most effective. The ECG AI-Guided Screening for Low Ejection Fraction, or EAGLE, trial set out to determine whether an artificial intelligence (AI) screening tool developed to detect low ejection fraction using data from an EKG could improve the diagnosis of this condition in routine practice. Study findings are published in Nature Medicine.
Systolic low ejection fraction is defined as the heart’s inability to contract strongly enough with each beat to pump at least 50% of the blood from its chamber. An echocardiogram can readily diagnose low ejection fraction, but this time-consuming imaging test requires more resources than a 12-lead EKG, which is fast, inexpensive and readily available. The AI-enabled EKG algorithm was tested and developed through a convolutional neural network and validated in subsequent studies.
The EAGLE trial took place in 45 medical institutions in Minnesota and Wisconsin, including rural clinics, and community and academic medical centers. In all, 348 primary care clinicians from 120 medical care teams were randomly assigned to usual care or intervention. The intervention group was alerted to a positive screening result for low ejection fraction via the electronic health record, prompting them to order an echocardiogram to confirm.
“The AI-enabled EKG facilitated the diagnosis of patients with low ejection fraction in a real-world setting by identifying people who previously would have slipped through the cracks,” says Peter Noseworthy, M.D., a Mayo Clinic cardiac electrophysiologist. Dr. Noseworthy is senior author on the study.
In eight months, 22,641 adult patients had an EKG under the care of the clinicians in the trial. The AI found positive results in 6% of the patients. The proportion of patients who received an echocardiogram was similar overall, but among patients with a positive screening result, a higher percentage of intervention patients received an echocardiogram.
“The AI intervention increased the diagnosis of low ejection fraction overall by 32% relative to usual care. Among patients with a positive AI result, the relative increase of diagnosis was 43%,” says Xiaoxi Yao, Ph.D., a health outcomes researcher in cardiovascular diseases at Mayo Clinic and first author on the study. “To put it in absolute terms, for every 1,000 patients screened, the AI screening yielded five new diagnoses of low ejection fraction over usual care.”
“With EAGLE, the information was readily available in the electronic health record, and care teams could see the results and decide how to use that information,” says Dr. Noseworthy. “The takeaway is that we are likely to see more AI use in the practice of medicine as time goes on. It’s up to us to figure how to use this in a way that improves care and health outcomes but does not overburden front-line clinicians.”
Also, the EAGLE trial used a positive deviance approach to evaluate the top five care team users and the top five nonusers of the AI screening information. Dr. Yao says this cycle of learning and feedback from physicians will demonstrate ways of improving adaptation and application of AI technology in the practice.
EAGLE is one of the first large-scale trials to demonstrate value of AI in routine practice. The low ejection fraction algorithm, which has received Food and Drug Administration breakthrough designation, is one of several algorithms developed by Mayo and licensed to Anumana Inc., a new company focusing on unlocking hidden biomedical knowledge to enable early detection as well as accelerate treatment of heart disease. The low ejection fraction algorithm was also previously licensed to Eko Devices Inc., specifically for hand-held devices that are externally applied to the chest.
The EAGLE trial was funded by Mayo Clinic’s Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, in collaboration with the departments of Cardiovascular Medicine and Family Medicine, and the Division of Community Internal Medicine.
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Materials provided by Mayo Clinic. Original written by Terri Malloy. Note: Content may be edited for style and length. More

Like conductors of a spooky symphony, researchers at the National Institute of Standards and Technology (NIST) have “entangled” two small mechanical drums and precisely measured their linked quantum properties. Entangled pairs like this might someday perform computations and transmit data in large-scale quantum networks.
The NIST team used microwave pulses to entice the two tiny aluminum drums into a quantum version of the Lindy Hop, with one partner bopping in a cool and calm pattern while the other was jiggling a bit more. Researchers analyzed radar-like signals to verify that the two drums’ steps formed an entangled pattern — a duet that would be impossible in the everyday classical world.
What’s new is not so much the dance itself but the researchers’ ability to measure the drumbeats, rising and falling by just one-quadrillionth of a meter, and verify their fragile entanglement by detecting subtle statistical relationships between their motions.
The research is described in the May 7 issue of Science.
“If you analyze the position and momentum data for the two drums independently, they each simply look hot,” NIST physicist John Teufel said. “But looking at them together, we can see that what looks like random motion of one drum is highly correlated with the other, in a way that is only possible through quantum entanglement.”
Quantum mechanics was originally conceived as the rulebook for light and matter at atomic scales. However, in recent years researchers have shown that the same rules can apply to increasingly larger objects such as the drums. Their back-and-forth motion makes them a type of system known as a mechanical oscillator. Such systems were entangled for the first time at NIST about a decade ago, and in that case the mechanical elements were single atoms. More

When you save an image to your smartphone, those data are written onto tiny transistors that are electrically switched on or off in a pattern of “bits” to represent and encode that image. Most transistors today are made from silicon, an element that scientists have managed to switch at ever-smaller scales, enabling billions of bits, and therefore large libraries of images and other files, to be packed onto a single memory chip.
But growing demand for data, and the means to store them, is driving scientists to search beyond silicon for materials that can push memory devices to higher densities, speeds, and security.
Now MIT physicists have shown preliminary evidence that data might be stored as faster, denser, and more secure bits made from antiferromagnets.
Antiferromagnetic, or AFM materials are the lesser-known cousins to ferromagnets, or conventional magnetic materials. Where the electrons in ferromagnets spin in synchrony — a property that allows a compass needle to point north, collectively following the Earth’s magnetic field — electrons in an antiferromagnet prefer the opposite spin to their neighbor, in an “antialignment” that effectively quenches magnetization even at the smallest scales.
The absence of net magnetization in an antiferromagnet makes it impervious to any external magnetic field. If they were made into memory devices, antiferromagnetic bits could protect any encoded data from being magnetically erased. They could also be made into smaller transistors and packed in greater numbers per chip than traditional silicon.
Now the MIT team has found that by doping extra electrons into an antiferromagnetic material, they can turn its collective antialigned arrangement on and off, in a controllable way. They found this magnetic transition is reversible, and sufficiently sharp, similar to switching a transistor’s state from 0 to 1. The results, published today in Physical Review Letters, demonstrate a potential new pathway to use antiferromagnets as a digital switch. More

With state legislatures nationwide preparing for the once-a-decade redrawing of voting districts, a research team has developed a better computational method to help identify improper gerrymandering designed to favor specific candidates or political parties.
In an article in the Harvard Data Science Review, the researchers describe the improved mathematical methodology of an open source tool called GerryChain (https://github.com/mggg/GerryChain). The tool can help observers detect gerrymandering in a voting district plan by creating a pool, or ensemble, of alternate maps that also meet legal voting criteria. This map ensemble can show if the proposed plan is an extreme outlier — one that is very unusual from the norm of plans generated without bias, and therefore, likely to be drawn with partisan goals in mind.
An earlier version of GerryChain was used to analyze maps proposed to remedy the Virginia House of Delegates districts that a federal court ruled in 2018 were unconstitutional racial gerrymanders. The updated tool will likely play a role in the upcoming redistricting using new census data.
“We wanted to build an open-source software tool and make that available to people interested in reform, especially in states where there are skewed baselines,” said Daryl DeFord, assistant mathematics professor at Washington State University and a co-lead author on the paper. “It can be an impactful way for people to get involved in this process, particularly going into this year’s redistricting cycle where there are going to be a lot of opportunities for pointing out less than optimal behavior.”
The GerryChain tool, first created by a team led by DeFord as a part of the 2018 Voting Rights Data Institute, has already been downloaded 20,000 times. The new paper, authored by Deford along with Moon Duchin of Tufts University and Justin Solomon of the Massachusetts Institute of Technology, focuses on how the mathematical and computational models implemented in GerryChain can be used to put proposed voting districting plans into context by creating large samples of alternative valid plans for comparison. These alternate plans are often used when a voting plan is challenged in court as being unfair as well as to analyze potential impacts of redistricting reform.
For instance, the enacted 2010 House of Delegates plan in Virginia had 12 voting districts with a Black voting age population at or above 55%. By comparing that plan against an ensemble of alternate plans that all fit the legal criteria, advocates showed that map was an extreme outlier of what was possible. In other words, it was likely drawn intentionally to “pack” some districts with a Black voter population to “crack” other districts, breaking the influence of those voters. More