Aurelia Butler

Jigang Wang offered a quick walk-around of a new sort of microscope that can help researchers understand, and ultimately develop, the inner workings of quantum computing.
Wang, an Iowa State University professor of physics and astronomy who’s also affiliated with the U.S. Department of Energy’s Ames National Laboratory, described how the instrument works in extreme scales of space, time and energy — billionths of a meter, quadrillionths of a second and trillions of electromagnetic waves per second.
Wang pointed out and explained the control systems, the laser source, the maze of mirrors that make an optical path for light pulsing at trillions of cycles per second, the superconducting magnet that surrounds the sample space, the custom-made atomic force microscope, the bright yellow cryostat that lowers sample temperatures down to the temperature of liquid helium, about -450 degrees Fahrenheit.
Wang calls the instrument a Cryogenic Magneto-Terahertz Scanning Near-field Optical Microscope. (That’s cm-SNOM for short.) It’s based at the Ames National Laboratory’s Sensitive Instrument Facility just northwest of Iowa State’s campus.
It took five years and $2 million — $1.3 million from the W.M. Keck Foundation of Los Angeles and $700,000 from Iowa State and Ames National Laboratory — to build the instrument. It has been gathering data and contributing to experiments for less than a year.
“No one has it,” Wang said of the extreme-scale nanoscope. “It’s the first in the world.”
It can focus down to about 20 nanometers, or 20 billionths of a meter, while operating below liquid-helium temperatures and in strong, Tesla magnetic fields. That’s small enough to get a read on the superconducting properties of materials in these extreme environments. More

The human genome is three billion letters of code, and each person has millions of variations. While no human can realistically sift through all that code, computers can. Artificial intelligence (AI) programs can find patterns in the genome related to disease much faster than humans can. They also spot things that humans miss. Someday, AI-powered genome readers may even be able to predict the incidence of diseases from cancer to the common cold. Unfortunately, AI’s recent popularity surge has led to a bottleneck in innovation.
“It’s like the Wild West right now. Everyone’s just doing whatever the hell they want,” says Cold Spring Harbor Laboratory (CSHL) Assistant Professor Peter Koo. Just like Frankenstein’s monster was a mix of different parts, AI researchers are constantly building new algorithms from various sources. And it’s difficult to judge whether their creations will be good or bad. After all, how can scientists judge “good” and “bad” when dealing with computations that are beyond human capabilities?
That’s where GOPHER, the Koo lab’s newest invention, comes in. GOPHER (short for GenOmic Profile-model compreHensive EvaluatoR) is a new method that helps researchers identify the most efficient AI programs to analyze the genome. “We created a framework where you can compare the algorithms more systematically,” explains Ziqi Tang, a graduate student in Koo’s laboratory.
GOPHER judges AI programs on several criteria: how well they learn the biology of our genome, how accurately they predict important patterns and features, their ability to handle background noise, and how interpretable their decisions are. “AI are these powerful algorithms that are solving questions for us,” says Tang. But, she notes: “One of the major issues with them is that we don’t know how they came up with these answers.”
GOPHER helped Koo and his team dig up the parts of AI algorithms that drive reliability, performance, and accuracy. The findings help define the key building blocks for constructing the most efficient AI algorithms going forward. “We hope this will help people in the future who are new to the field,” says Shushan Toneyan, another graduate student at the Koo lab.
Imagine feeling unwell and being able to determine exactly what’s wrong at the push of a button. AI could someday turn this science-fiction trope into a feature of every doctor’s office. Similar to video-streaming algorithms that learn users’ preferences based on their viewing history, AI programs may identify unique features of our genome that lead to individualized medicine and treatments. The Koo team hopes GOPHER will help optimize such AI algorithms so that we can trust they’re learning the right things for the right reasons. Toneyan says: “If the algorithm is making predictions for the wrong reasons, they’re not going to be helpful.”
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Materials provided by Cold Spring Harbor Laboratory. Original written by Luis Sandoval. Note: Content may be edited for style and length. More

Gamers who buy ‘loot boxes’ are up to two times more likely to gamble, shows new research published today in the peer-reviewed journal Addiction Research & Theory.
They are also more likely to have a gambling problem compared with the gamers who don’t purchase these ‘virtual’ treasure chests, according to the findings based on more than 1,600 adults in Canada.
The authors say the results cast doubt on the theory that psychological factors create the link between gambling and loot boxes — banned by some countries including Belgium and discussed for legislation in many others worldwide.
Their study demonstrates that the association between these video game features and gambling exists even when childhood neglect, depression and other known risk factors for gambling are taken into account.
The authors say their findings have potential implications for policymakers and for healthcare. They are calling for more research into the benefit of harm minimization features, with some online platforms having already implemented these — such as telling players the odds of winning when they buy a loot box.
“Findings indicate that loot box purchasing represents an important marker of risk for gambling and problem gambling among people who play video games,” says Sophie Coelho, a PhD student at York University, Toronto. More

Optical photons are ideal carriers of quantum information. But to work together in a quantum computer or network, they need to have the same color — or frequency — and bandwidth. Changing a photon’s frequency requires altering its energy, which is particularly challenging on integrated photonic chips.
Recently, researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) developed an integrated electro-optic modulator that can efficiently change the frequency and bandwidth of single photons. The device could be used for more advanced quantum computing and quantum networks.
The research is published in Light: Science & Applications.
Converting a photon from one color to another is usually done by sending the photon into a crystal with a strong laser shining through it, a process that tends to be inefficient and noisy. Phase modulation, in which photon wave’s oscillation is accelerated or slowed down to change the photon’s frequency, offers a more efficient method, but the device required for such a process, an electro-optic phase modulator, has proven difficult to integrate on a chip.
One material may be uniquely suited for such an application — thin-film lithium niobate.
“In our work, we adopted a new modulator design on thin-film lithium niobate that significantly improved the device performance,” said Marko Lončar, the Tiantsai Lin Professor of Electrical Engineering at SEAS and senior author of the study. “With this integrated modulator, we achieved record-high terahertz frequency shifts of single photons.”
The team also used the same modulator as a “time lens” — a magnifying glass that bends light in time instead of space — to change the spectral shape of a photon from fat to skinny. More

Water that simply will not freeze, no matter how cold it gets — a research group involving the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) has discovered a quantum state that could be described in this way. Experts from the Institute of Solid State Physics at the University of Tokyo in Japan, Johns Hopkins University in the United States, and the Max Planck Institute for the Physics of Complex Systems (MPI-PKS) in Dresden, Germany, managed to cool a special material to near absolute zero temperature. They found that a central property of atoms — their alignment — did not “freeze,” as usual, but remained in a “liquid” state. The new quantum material could serve as a model system to develop novel, highly sensitive quantum sensors. The team has presented its findings in the journal Nature Physics.
On first sight, quantum materials do not look different from normal substances — but they sure do their own thing: Inside, the electrons interact with unusual intensity, both with each other and with the atoms of the crystal lattice. This intimate interaction results in powerful quantum effects that not only act on the microscopic, but also on the macroscopic scale. Thanks to these effects, quantum materials exhibit remarkable properties. For example, they can conduct electricity completely loss-free at low temperatures. Often, even slight changes in temperature, pressure, or electrical voltage are enough to drastically change the behavior of the material.
In principle, magnets can also be regarded as quantum materials; after all, magnetism is based on the intrinsic spin of the electrons in the material. “In some ways, these spins can behave like a liquid,” explains Prof. Jochen Wosnitza from the Dresden High Field Magnetic Laboratory (HLD) at HZDR. “As temperatures drop, these disordered spins can then freeze, much like water freezes into ice.” For example, certain kind of magnets, so-called ferromagnets, are non-magnetic above their “freezing,” or more precisely ordering point. Only when they drop below it can they become permanent magnets.
High-purity material
The international team intended to create a quantum state in which the atomic alignment that is associated with the spins did not order, even at ultracold temperatures — similar to a liquid that will not solidify, even in extreme cold. To achieve this state, the research group used a special material — a compound of the elements, praseodymium, zirconium, and oxygen. They assumed that in this material, the properties of the crystal lattice would enable the electron spins to interact with their orbitals around the atoms in a special way.
“The prerequisite, however, was to have crystals of extreme purity and quality,” Prof. Satoru Nakatsuji of the University of Tokyo explains. It took several attempts, but eventually the team was able to produce crystals pure enough for their experiment: In a cryostat, a kind of super thermos flask, the experts gradually cooled their sample down to 20 millikelvin — just one fiftieth of a degree above absolute zero. To see how the sample responded to this cooling process and inside the magnetic field, they measured how much it changed in length. In another experiment, the group recorded how the crystal reacted to ultrasound waves being directly sent through it.
An intimate interplay
The result: “Had the spins ordered, it should have caused an abrupt change in the behavior of the crystal, such as a sudden change in length,” Dr. Sergei Zherlitsyn, HLD’s expert in ultrasound investigations, describes. “Yet, as we observed, nothing happened! There were no sudden changes in either length or in its response to ultrasound waves.” The conclusion: The pronounced interplay of spins and orbitals had prevented ordering, which is why the atoms remained in their liquid quantum state — the first time such a quantum state had been observed. Further investigations in magnetic fields confirmed this assumption.
This basic research result could also have practical implications one day: “At some point we might be able to use the new quantum state to develop highly sensitive quantum sensors,” Jochen Wosnitza speculates. “To do this, however, we still have to figure out how to generate excitations in this state systematically.” Quantum sensing is considered a promising technology of the future. Because their quantum nature makes them extremely sensitive to external stimuli, quantum sensors can register magnetic fields or temperatures with far greater precision than conventional sensors. More

Using smart technology to monitor the health, reproductivity, location, and environmental conditions of cattle can help with food safety and supply chain efficiency, but this monitoring adds energy cost to an already highly emissive industry. To combat this, researchers publishing in the journal iScience on December 1 have designed a wearable smart device for cows that captures the kinetic energy created by even their smallest movements and uses it to power smart ranch technology.
“On a ranch, monitoring environmental and health information of cattle can help prevent diseases and improve the efficiency of pasture breeding and management,” says co-author Zutao Zhang, an energy researcher at Southwest Jiaotong University in China. “This information can include oxygen concentration, air temperature and humidity, amount of exercise, reproductive cycles, disease, and milk production.”
The team’s smart ranch design involves cows wearing small sensory devices around their ankles and necks that are powered by everything cows do as they go about their regular ranch activities. “There is a tremendous amount of kinetic energy that can be harvested in cattle’s daily movements, such as walking, running, and even neck movement,” says co-author Yajia Pan, also an energy researcher at Southwest Jiaotong University.Once captured, the energy is stored in a lithium battery and used to power the device.
“Our kinetic energy harvester specially harvests the kinetic energy of weak motion,” says Zhang. The team’s design is unique because it contains a motion enhancement mechanism that uses magnets and a pendulum to amplify small movements the cows make.
Zhang hopes that implementing smart technology in ranches will be part of a larger effort to improve the world’s food systems. “With the development of 5G technology and the Internet of Things, the operation of the entire industrial chain of the food system is more intelligent and transparent,” he says.
Zhang and his colleagues also tested the devices on humans and found that a light jog was enough to power temperature measurement in the device. The researchers see future applications in sports monitoring, healthcare, smart home, and the construction of human wireless sensor networks.
“Kinetic energy is everywhere in the environment — leaves swaying in the wind, the movement of people and animals, the undulation of waves, the rotation of the earth — these phenomena all contain a lot of kinetic energy,” says Zhang, “We shouldn’t let this energy go to waste.”
This work was supported by the National Natural Foundation of China, Science and Technology Projects of Sichuan, and Science and Technology Projects of Chengdu.
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Materials provided by Cell Press. Note: Content may be edited for style and length. More

The gap between human creativity and artificial intelligence seems to be narrowing. Previous studies have compared AI-generated versus human-written poems and whether people can distinguish between them.
Now, a study led by Yoshiyuki Ueda at Kyoto University Institute for the Future of Human and Society, has shown AI’s potential in creating literary art such as haiku — the shortest poetic form in the world — rivaling that of humans without human help.
Ueda’s team compared AI-generated haiku without human intervention, also known as human out of the loop, or HOTL, with a contrasting method known as human in the loop, or HITL.
The project involved 385 participants, each of whom evaluated 40 haiku poems — 20 each of HITL and HOTL — plus 40 composed entirely by professional haiku writers.
“It was interesting that the evaluators found it challenging to distinguish between the haiku penned by humans and those generated by AI,” remarks Ueda.
From the results, HITL haiku received the most praise for their poetic qualities, whereas HOTL and human-only verses had similar scores.
“In addition, a phenomenon called algorithm aversion was observed among our evaluators. They were supposed to be unbiased but instead became influenced by a kind of reverse psychology,” explains the author.
“In other words, they tended to unconsciously give lower scores to those they felt were AI-generated.”
Ueda points out that his research has put a spotlight on algorithm aversion as a new approach to AI art.
“Our results suggest that the ability of AI in the field of haiku creation has taken a leap forward, entering the realm of collaborating with humans to produce more creative works. Realizing the existence of algorithmic aversion will lead people to re-evaluate their appreciation of AI art.”
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Materials provided by Kyoto University. Note: Content may be edited for style and length. More

Cambridge researchers have developed a method for measuring overall fitness accurately on wearable devices — and more robustly than current consumer smartwatches and fitness monitors — without the wearer needing to exercise.
Normally, tests to accurately measure VO2max — a key measurement of overall fitness and an important predictor of heart disease and mortality risk — require expensive laboratory equipment and are mostly limited to elite athletes. The new method uses machine learning to predict VO2max — the capacity of the body to carry out aerobic work — during everyday activity, without the need for contextual information such as GPS measurements.
In what is by far the largest study of its kind, the researchers gathered activity data from more than 11,000 participants in the Fenland Study using wearable sensors, with a subset of participants tested again seven years later. The researchers used the data to develop a model to predict VO2max, which was then validated against a third group who carried out a standard lab-based exercise test. The model showed a high degree of accuracy compared to lab-based tests, and outperforms other approaches.
Some smartwatches and fitness monitors currently on the market claim to provide an estimate of VO2max, but since the algorithms powering these predictions aren’t published and are subject to change at any time, it’s unclear whether the predictions are accurate, or whether an exercise regime is having any effect on an individual’s VO2max over time.
The Cambridge-developed model is robust, transparent and provides accurate predictions based on heart rate and accelerometer data only. Since the model can also detect fitness changes over time, it could also be useful in estimating fitness levels for entire populations and identifying the effects of lifestyle trends. The results are reported in the journal npj Digital Medicine.
A measurement of VO2max is considered the ‘gold standard’ of fitness tests. Professional athletes, for example, test their VO2max by measuring their oxygen consumption while they exercise to the point of exhaustion. There are other ways of measuring fitness in the laboratory, like heart rate response to exercise tests, but these require equipment like a treadmill or exercise bike. Additionally, strenuous exercise can be a risk to some individuals. More