Aurelia Butler

A team of scientists at Los Alamos National Laboratory proposes that modulated quantum metasurfaces can control all properties of photonic qubits, a breakthrough that could impact the fields of quantum information, communications, sensing and imaging, as well as energy and momentum harvesting. The results of their study were released yesterday in the journal Physical Review Letters, published by the American Physical Society.
“People have studied classical metasurfaces for a long time,” says Diego Dalvit, who works in the Condensed Matter and Complex Systems group at the Laboratory’s Theoretical Division. “But we came up with this new idea, which was to modulate in time and space the optical properties of a quantum metasurface that allow us to manipulate, on-demand, all degrees of freedom of a single photon, which is the most elementary unit of light.”
Metasurfaces are ultrathin structures that can manipulate light in ways not usually seen in nature. In this case, the team developed a metasurface that looked like an array of rotated crosses, which they can then manipulate with lasers or electrical pulses. They then proposed to shoot a single photon through the metasurface, where the photon splits into a superposition of many colors, paths, and spinning states that are all intertwined, generating so-called quantum entanglement — meaning the single photon is capable of inheriting all these different properties at once.
“When the metasurface is modulated with laser or electrical pulses, one can control the frequency of the refracted single photon, alter its angle of trajectory, the direction of its electric field, as well as its twist,” says Abul Azad from the Center for Integrated Nanotechnologies at the Laboratory’s Materials Physics and Applications Division.
By manipulating these properties, this technology could be used to encode information in photons traveling within a quantum network, everything from banks, quantum computers, and between Earth and satellites. Encoding photons is particularly desirable in the field of cryptography because “eavesdroppers” are unable to view a photon without changing its fundamental physics, which if done would then alert the sender and receiver that the information has been compromised.
The researchers are also working on how to pull photons from a vacuum by modulating the quantum metasurface.
“The quantum vacuum is not empty but full of fleeting virtual photons. With the modulated quantum metasurface one is able to efficiently extract and convert virtual photons into real photon pairs,” says Wilton Kort-Kamp, who works in the Theoretical Division at the Lab’s Condensed Matter and Complex Systems group.
Harnessing photons that exist in the vacuum and shooting them in one direction should create propulsion in the opposite direction. Similarly, stirring the vacuum should create rotational motion from the twisted photons. Structured quantum light could then one day be used to generate mechanical thrust, using only tiny amounts of energy to drive the metasurface.
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Materials provided by DOE/Los Alamos National Laboratory. Note: Content may be edited for style and length. More

Northwestern University researchers have developed a new framework using machine learning that improves the accuracy of interatomic potentials — the guiding rules describing how atoms interact — in new materials design. The findings could lead to more accurate predictions of how new materials transfer heat, deform, and fail at the atomic scale.
Designing new nanomaterials is an important aspect of developing next-generation devices used in electronics, sensors, energy harvesting and storage, optical detectors, and structural materials. To design these materials, researchers create interatomic potentials through atomistic modeling, a computational approach that predicts how these materials behave by accounting for their properties at the smallest level. The process to establish materials’ interatomic potential — called parameterization — has required significant chemical and physical intuition, leading to less accurate prediction of new materials design.
The researchers’ platform minimizes user intervention by employing multi-objective genetic algorithm optimization and statistical analysis techniques, and screens promising interatomic potentials and parameter sets.
“The computational algorithms we developed provide analysts with a methodology to assess and avoid traditional shortcomings,” said Horacio Espinosa, James N. and Nancy J. Farley Professor in Manufacturing and Entrepreneurship and professor of mechanical engineering and (by courtesy) biomedical engineering and civil and environmental engineering, who led the research. “They also provide the means to tailor the parameterization to applications of interest.”
The findings were published in a study titled “Parametrization of Interatomic Potentials for Accurate Large Deformation Pathways Using Multi-Objective Genetic Algorithms and Statistical Analyses: A Case Study on Two-Dimensional Materials” on July 21 in Nature Partner Journals — Computational Materials.
Xu Zhang and Hoang Nguyen, both students in Northwestern Engineering’s Theoretical and Applied Mechanics (TAM) graduate program, were co-first authors of the study. Other co-authors included Jeffrey T. Paci of the University of Victoria, Canada, Subramanian Sankaranarayanan of Argonne National Laboratory, and Jose Mendoza of Michigan State University. More

To be useful, drones need to be quick. Because of their limited battery life they must complete whatever task they have — searching for survivors on a disaster site, inspecting a building, delivering cargo — in the shortest possible time. And they may have to do it by going through a series of waypoints like windows, rooms, or specific locations to inspect, adopting the best trajectory and the right acceleration or deceleration at each segment.
Algorithm outperforms professional pilots
The best human drone pilots are very good at doing this and have so far always outperformed autonomous systems in drone racing. Now, a research group at the University of Zurich (UZH) has created an algorithm that can find the quickest trajectory to guide a quadrotor — a drone with four propellers — through a series of waypoints on a circuit. “Our drone beat the fastest lap of two world-class human pilots on an experimental race track,” says Davide Scaramuzza, who heads the Robotics and Perception Group at UZH and the Rescue Robotics Grand Challenge of the NCCR Robotics, which funded the research.
“The novelty of the algorithm is that it is the first to generate time-optimal trajectories that fully consider the drones’ limitations,” says Scaramuzza. Previous works relied on simplifications of either the quadrotor system or the description of the flight path, and thus they were sub-optimal. “The key idea is, rather than assigning sections of the flight path to specific waypoints, that our algorithm just tells the drone to pass through all waypoints, but not how or when to do that,” adds Philipp Foehn, PhD student and first author of the paper.
External cameras provide position information in real-time
The researchers had the algorithm and two human pilots fly the same quadrotor through a race circuit. They employed external cameras to precisely capture the motion of the drones and — in the case of the autonomous drone — to give real-time information to the algorithm on where the drone was at any moment. To ensure a fair comparison, the human pilots were given the opportunity to train on the circuit before the race. But the algorithm won: all its laps were faster than the human ones, and the performance was more consistent. This is not surprising, because once the algorithm has found the best trajectory it can reproduce it faithfully many times, unlike human pilots.
Before commercial applications, the algorithm will need to become less computationally demanding, as it now takes up to an hour for the computer to calculate the time-optimal trajectory for the drone. Also, at the moment, the drone relies on external cameras to compute where it was at any moment. In future work, the scientists want to use onboard cameras. But the demonstration that an autonomous drone can in principle fly faster than human pilots is promising. “This algorithm can have huge applications in package delivery with drones, inspection, search and rescue, and more,” says Scaramuzza.
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Materials provided by University of Zurich. Note: Content may be edited for style and length. More

As the Summer Olympics draw near, the world will shift its focus to photo finishes and races determined by mere fractions of a second. Obtaining such split-second measurements relies on faultlessly rounding a raw time recorded by a stopwatch or electronic timing system to a submitted time.
Researchers at the University of Surrey found certain stopwatches commit rounding errors when converting raw times to final submitted times. In American Journal of Physics, by AIP Publishing, David Faux and Janet Godolphin outline a series of computer simulations based on procedures for converting raw race times for display.
Faux was inspired when he encountered the issue firsthand while volunteering at a swim meet. While helping input times into the computer, he noticed a large portion of times they inputted were rounded to either the closest half-second or full second.
“Later, when the frequencies of the digit pairs were plotted, a distinct pattern emerged,” he said. “We discovered that the distribution of digit pairs was statistically inconsistent with the hypothesis that each digit pair was equally likely, as one would expect from stopwatches.”
Stopwatches and electronic timing systems use quartz oscillators to measure time intervals, with each oscillation calculated as 0.0001 seconds. These times are then processed for display to 0.01 seconds, for example, to the public at a sporting venue.
Faux and Godolphin set to work simulating roughly 3 million race times corresponding to swimmers of all ages and abilities. As expected, the raw times indicated each fraction of a second had the same chance of being a race time. For example, there was 1% chance a race time ended in either 0.55 seconds or 0.6 seconds.
When they processed raw times through the standard display routine, the uniform distribution disappeared. Most times were correctly displayed.
Where rounding errors occurred, they usually resulted in changes of one one-hundredth of a second. One raw time of 28.3194 was converted to a displayed time of 28.21.
“The question we really need to answer is whether rounding errors are uncorrected in electronic timing systems used in sporting events worldwide,” Faux said. “We have so far been unable to unearth the actual algorithm that is used to translate a count of quartz oscillations to a display.”
The researchers collected more than 30,000 race times from swimming competitions and will investigate if anomalous timing patterns appear in the collection, which would suggest the potential for rounding errors in major sporting events.
The article “The floating point: Rounding error in timing devices” is authored by David A. Faux and Janet Godolphin. The article appears in American Journal of Physics.
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A new wearable brain-machine interface (BMI) system could improve the quality of life for people with motor dysfunction or paralysis, even those struggling with locked-in syndrome — when a person is fully conscious but unable to move or communicate.
A multi-institutional, international team of researchers led by the lab of Woon-Hong Yeo at the Georgia Institute of Technology combined wireless soft scalp electronics and virtual reality in a BMI system that allows the user to imagine an action and wirelessly control a wheelchair or robotic arm.
The team, which included researchers from the University of Kent (United Kingdom) and Yonsei University (Republic of Korea), describes the new motor imagery-based BMI system this month in the journal Advanced Science.
“The major advantage of this system to the user, compared to what currently exists, is that it is soft and comfortable to wear, and doesn’t have any wires,” said Yeo, associate professor on the George W. Woodruff School of Mechanical Engineering.
BMI systems are a rehabilitation technology that analyzes a person’s brain signals and translates that neural activity into commands, turning intentions into actions. The most common non-invasive method for acquiring those signals is ElectroEncephaloGraphy, EEG, which typically requires a cumbersome electrode skull cap and a tangled web of wires.
These devices generally rely heavily on gels and pastes to help maintain skin contact, require extensive set-up times, are generally inconvenient and uncomfortable to use. The devices also often suffer from poor signal acquisition due to material degradation or motion artifacts — the ancillary “noise” which may be caused by something like teeth grinding or eye blinking. This noise shows up in brain-data and must be filtered out. More

In order to win the battle against COVID-19, studies to develop vaccines, drugs, devices and re-purposed drugs are urgently needed. Randomized clinical trials are used to provide evidence of safety and efficacy as well as to better understand this novel and evolving virus. As of July 15, more than 6,180 COVID-19 clinical trials have been registered through ClinicalTrials.gov, the national registry and database for privately and publicly funded clinical studies conducted around the world. Knowing which ones are likely to succeed is imperative.
Researchers from Florida Atlantic University’s College of Engineering and Computer Science are the first to model COVID-19 completion versus cessation in clinical trials using machine learning algorithms and ensemble learning. The study, published in PLOS ONE, provides the most extensive set of features for clinical trial reports, including features to model trial administration, study information and design, eligibility, keywords, drugs and other features.
This research shows that computational methods can deliver effective models to understand the difference between completed vs. ceased COVID-19 trials. In addition, these models also can predict COVID-19 trial status with satisfactory accuracy.
Because COVID-19 is a relatively novel disease, very few trials have been formally terminated. Therefore, for the study, researchers considered three types of trials as cessation trials: terminated, withdrawn, and suspended. These trials represent research efforts that have been stopped/halted for particular reasons and represent research efforts and resources that were not successful.
“The main purpose of our research was to predict whether a COVID-19 clinical trial will be completed or terminated, withdrawn or suspended. Clinical trials involve a great deal of resources and time including planning and recruiting human subjects,” said Xingquan “Hill” Zhu, Ph.D., senior author and a professor in the Department of Computer and Electrical Engineering and Computer Science, who conducted the research with first author Magdalyn “Maggie” Elkin, a second-year Ph.D. student in computer science who also works full-time. “If we can predict the likelihood of whether a trial might be terminated or not down the road, it will help stakeholders better plan their resources and procedures. Eventually, such computational approaches may help our society save time and sources to combat the global COVID-19 pandemic.”
For the study, Zhu and Elkin collected 4,441 COVID-19 trials from ClinicalTrials.gov to build a testbed. They designed four types of features (statistics features, keyword features, drug features and embedding features) to characterize clinical trial administration, eligibility, study information, criteria, drug types, study keywords, as well as embedding features commonly used in state-of-the-art machine learning. In total, 693 dimensional features were created to represent each clinical trial. For comparison purposes, researchers used four models: Neural Network; Random Forest; XGBoost; and Logistic Regression.
Feature selection and ranking showed that keyword features derived from the MeSH (medical subject headings) terms of the clinical trial reports, were the most informative for COVID-19 trial prediction, followed by drug features, statistics features and embedding features. Although drug features and study keywords were the most informative features, all four types of features are essential for accurate trial prediction.
By using ensemble learning and sampling, the model used in this study achieved more than 0.87 areas under the curve (AUC) scores and more than 0.81 balanced accuracy for prediction, indicating high efficacy of using computational methods for COVID-19 clinical trial prediction. Results also showed single models with balanced accuracy as high as 70 percent and an F1-score of 50.49 percent, suggesting that modeling clinical trials is best when segregating research areas or diseases.
“Clinical trials that have stopped for various reasons are costly and often represent a tremendous loss of resources. As future outbreaks of COVID-19 are likely even after the current pandemic has declined, it is critical to optimize efficient research efforts,” said Stella Batalama, Ph.D., dean, College of Engineering and Computer Science. “Machine learning and AI driven computational approaches have been developed for COVID-19 health care applications, and deep learning techniques have been applied to medical imaging processing in order to predict outbreak, track virus spread and for COVID-19 diagnosis and treatment. The new approach developed by professor Zhu and Maggie will be helpful to design computational approaches to predict whether or not a COVID-19 clinical trial will be completed so that stakeholders can leverage the predictions to plan resources, reduce costs, and minimize the time of the clinical study.”
The study was funded by the National Science Foundation awarded to Zhu.
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Materials provided by Florida Atlantic University. Original written by Gisele Galoustian. Note: Content may be edited for style and length. More

One of the major challenges in modern cancer therapy is the adaptive response of cancer cells to targeted therapies: initially, these therapies are very often effective, then adaptive resistance occurs, allowing the tumor cells to proliferate again. Although this adaptive response is theoretically reversible, such a reversal is hampered by numerous molecular mechanisms that allow the cancer cells to adapt to the treatment. The analysis of these mechanisms is limited by the complexity of cause and effect relationships that are extremely difficult to observe in vivo in tumor samples. In order to overcome this challenge, a team from the University of Geneva (UNIGE) and the University Hospitals of Geneva (HUG), Switzerland, has used information theory for the first time, in order to objectify in vivo the molecular regulations at play in the mechanisms of the adaptive response and their modulation by a therapeutic combination. These results are published in the journal Neoplasia.
Adaptive response limits the efficiency of targeted therapies used to fight the development of tumors: after an effective treatment phase that reduces the tumor size, an adaptation to the used molecule occurs that allows the tumor cells to proliferate again. “We now know that this resistance to treatment has a large reversible component that does not involve mutations, which are an irreversible process,” explains Rastine Merat, a researcher in the Department of Pathology and Immunology at the UNIGE Faculty of Medicine, the head of the Onco-Dermatology Unit at the HUG and the principal investigator of the study.
Research confronted with the complexity of biological regulations
In order to prevent resistance to targeted therapies, scientists need to understand the molecular mechanisms of the adaptive response. “These mechanisms may involve variations in gene expression, for example,” explains Rastine Merat. It is then necessary to modify or prevent these variations by means of a therapeutic combination that blocks the consequences or even prevents them. One challenge remains: the description of these mechanisms and their modulation under the effect of a therapeutic combination is very often carried out on isolated cultured cells and not validated in tumor tissue in the body. “This is essentially due to the difficulty of objectifying these mechanisms, which may occur in a transient manner and only in a minority of cells in tumor tissues, and above all which involve non-linear cause and effect relationships,” explains the Geneva researcher.
Applying information theory to tumors
To counter these difficulties, the UNIGE and HUG team came up with the idea of using information theory, more specifically by quantifying mutual information. This approach has previously been used in biology, mainly to quantify cell signaling and understand genetic regulation networks. “This statistical method makes it possible to link two parameters involved in a mechanism by measuring the reduction in the uncertainty of one of the parameters when the value of the other parameter is known,” simplifies Rastine Merat.
Practically, the scientists proceed step by step: they take biopsies of tumors (in this case melanomas) in a mouse model at different stages of their development during therapy. Using immunohistochemical analyses — i.e. tumor sections — they measure, using an automated approach, the expression of proteins involved in the mechanism at play in the adaptive response. “The proposed mathematical approach is easily applicable to routine techniques such as immunohistochemistry and makes it possible to validate in vivo the relevance of the mechanisms under study, even if they occur in a minority of cells and in a transient manner,” the Geneva researcher explains. Thus, scientists can not only validate in the organism the molecular mechanisms they are studying, but also the impact of innovative therapeutic combinations that result from the understanding of these mechanisms. “Similarly, we could use this approach in therapeutic trials as a predictive marker of response to therapeutic combinations that seek to prevent adaptive resistance,” he continues.
A method suitable for all types of cancer
“This method, developed in a melanoma model, could be applied to other types of cancer for which the same issues of adaptive resistance to targeted therapies occur and for which combination therapy approaches based on an understanding of the mechanisms involved are under development,” concludes Rastine Merat.
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Materials provided by Université de Genève. Note: Content may be edited for style and length. More